From 55e7dd4bd913118dd0e612228a70dc55f427e983 Mon Sep 17 00:00:00 2001 From: manishvenu Date: Mon, 13 Apr 2026 15:04:36 -0600 Subject: [PATCH 1/9] Tests & Docs --- .docstr/examples.rst | 5 +- .docstr/index.rst | 1 + .docstr/regional_bathy.rst | 207 ++++++++++ notebooks/7_regional_bathy_workflow.ipynb | 314 +++++++++++++++ tests/test_topo_regional_bathy.py | 458 ++++++++++++++++++++++ 5 files changed, 984 insertions(+), 1 deletion(-) create mode 100644 .docstr/regional_bathy.rst create mode 100644 notebooks/7_regional_bathy_workflow.ipynb create mode 100644 tests/test_topo_regional_bathy.py diff --git a/.docstr/examples.rst b/.docstr/examples.rst index 304336ea..5c60611d 100755 --- a/.docstr/examples.rst +++ b/.docstr/examples.rst @@ -19,4 +19,7 @@ Examples `5_modify_existing.ipynb `_ 6. Use domain configurators: -`6_demo_editors.ipynb `_ \ No newline at end of file +`6_demo_editors.ipynb `_ + +7. Regional bathymetry workflows — mask generation, Cressman interpolation, topo drag: +`7_regional_bathy_workflow.ipynb `_ \ No newline at end of file diff --git a/.docstr/index.rst b/.docstr/index.rst index 8a963ed7..15f629b8 100755 --- a/.docstr/index.rst +++ b/.docstr/index.rst @@ -17,6 +17,7 @@ to learn about the `mom6_forge` tool. installation quickstart examples + regional_bathy glossary widgets diff --git a/.docstr/regional_bathy.rst b/.docstr/regional_bathy.rst new file mode 100644 index 00000000..a0970cc5 --- /dev/null +++ b/.docstr/regional_bathy.rst @@ -0,0 +1,207 @@ +Regional Bathymetry Workflows +============================= + +``mom6_forge`` provides two bathymetry pipelines for regional MOM6 configurations, +both accessible through the :class:`~mom6_forge.topo.Topo` class. They differ in how +depth is assigned and are suited to different grid resolutions. + +.. contents:: Contents + :local: + :depth: 2 + + +Background +---------- + +The default :meth:`~mom6_forge.topo.Topo.direct_xesmf_regrid` pipeline regrids a +source bathymetry (e.g. GEBCO) onto the model grid using ``xesmf`` and derives the +land/ocean mask from whether the regridded depth is positive. This is fast and +sufficient for fine regional grids (~1–3 km), but has two limitations at coarser +resolutions: + +1. **Land contamination near coasts.** Standard bilinear or conservative regridding + blends land elevations into ocean depth estimates for cells that straddle the + coastline. + +2. **Mask accuracy.** Deriving the mask from the sign of the regridded depth is + sensitive to regridding artefacts. At coarser resolutions (≳ 0.05°), computing + the ocean fraction directly from the high-resolution source data produces a + more accurate mask. + +The :meth:`~mom6_forge.topo.Topo.high_res_regrid` pipeline addresses both issues +using a Monte-Carlo ocean fraction mask and Cressman distance-weighted interpolation. +It is recommended for grids of ~0.05° (5 km) and coarser. + + +Mask Generation +--------------- + +Both pipelines support an optional external mask. Two methods are provided. + +Ocean Fraction Mask (recommended) +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. automethod:: mom6_forge.topo.Topo.generate_mask_ocean_frac + +For each model grid cell, ``nx_sub × ny_sub`` interior sample points are +distributed using bilinear interpolation of the 4 supergrid corner coordinates. +Each point is snapped to the nearest source bathymetry pixel. The ocean fraction +``OCN_FRAC`` is the number of sample points with depth > ``mask_hmin`` divided by +the total. Cells with ``OCN_FRAC ≥ mask_threshold`` are marked as ocean. + +This method also computes and stores per-cell depth statistics +(``D_mean``, ``D_min``, ``D_max``, ``D2_mean``) which are needed for topo drag +parameterisations (see :ref:`topo-drag`). + +**When to use:** When the model grid is coarser than ~0.05° or when topo drag +statistics are required. + +Cartopy Coastline Mask +~~~~~~~~~~~~~~~~~~~~~~~ + +.. automethod:: mom6_forge.topo.Topo.generate_mask_cartopy + +Rasterises Natural Earth coastline vectors onto the model grid using Cartopy. +Fast and independent of the bathymetry dataset. Use as a quick first-pass mask +or for grids where consistency with the bathymetry source is less critical. + +**When to use:** Quick runs, coarse grids, or as a comparison against +``generate_mask_ocean_frac``. + + +Pipelines +--------- + +Pipeline A — direct_xesmf_regrid +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. automethod:: mom6_forge.topo.Topo.direct_xesmf_regrid + +The standard pipeline. Regrids the source bathymetry with ``xesmf`` then runs +:meth:`~mom6_forge.topo.Topo.tidy_dataset` for lake removal, channel cleanup, +and minimum depth enforcement. + +Accepts an optional ``mask`` argument. When provided, ``tidy_dataset`` uses it +directly instead of deriving the mask from the regridded depth. When omitted, the +behaviour is identical to the previous ``set_from_dataset`` method. + +.. code-block:: python + + topo.direct_xesmf_regrid( + bathymetry_path="gebco_2023.nc", + longitude_coordinate_name="lon", + latitude_coordinate_name="lat", + vertical_coordinate_name="elevation", + ) + + # With an external mask: + mask, ocn_frac = topo.generate_mask_ocean_frac("gebco_2023.nc", nx_sub=5, ny_sub=5) + topo.direct_xesmf_regrid( + bathymetry_path="gebco_2023.nc", + longitude_coordinate_name="lon", + latitude_coordinate_name="lat", + vertical_coordinate_name="elevation", + mask=mask, + ) + +Pipeline B — high_res_regrid +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. automethod:: mom6_forge.topo.Topo.high_res_regrid + +The high-accuracy pipeline for coarser grids. Internally runs: + +1. :meth:`~mom6_forge.topo.Topo.generate_mask_ocean_frac` to build the mask and + compute depth statistics. +2. :meth:`~mom6_forge.topo.Topo.cressman_interp` to assign ocean-mask-aware + smoothed depths. +3. :meth:`~mom6_forge.topo.Topo.tidy_dataset` for lake removal, channel cleanup, + and minimum depth enforcement. + +.. code-block:: python + + topo.high_res_regrid( + bathymetry_path="gebco_2023.nc", + longitude_coordinate_name="lon", + latitude_coordinate_name="lat", + vertical_coordinate_name="elevation", + nx_sub=5, + ny_sub=5, + smooth_scl=2.0, + ) + +Cressman Interpolation +----------------------- + +.. automethod:: mom6_forge.topo.Topo.cressman_interp + +Assigns depth to each ocean cell by distance-weighted averaging of source +bathymetry points within a smoothing radius ``L``: + +.. math:: + + D = \frac{\sum_i w_i \, d_i}{\sum_i w_i}, \quad + w_i = \left(\frac{L^2 - r_i^2}{L^2 + r_i^2}\right)^{c} + +where :math:`r_i` is the great-circle distance from the model cell centre to +source point :math:`i`, :math:`L = \text{smooth\_scl} \times \sqrt{A_\text{cell}}` +is the smoothing radius, and :math:`c` is ``cressman_exp``. Source points with +depth ≤ 0 (land) are excluded. + +**Recommendation by resolution:** + +.. list-table:: + :header-rows: 1 + :widths: 30 70 + + * - Grid spacing + - Recommendation + * - < 3 km + - Use ``direct_xesmf_regrid`` — Cressman adds little benefit + * - 3–10 km (0.05°) + - Borderline; Cressman improves coastline accuracy + * - > 10 km (0.1°+) + - Use ``high_res_regrid`` — Cressman gives meaningfully better depths + + +.. _topo-drag: + +Topo Drag Statistics +-------------------- + +.. automethod:: mom6_forge.topo.Topo.write_topo_drag + +MOM6 Lee-wave and bottom-drag parameterisations require a measure of subgrid +topographic roughness at each cell. This is quantified as the variance of ocean +depth within each model cell: + +.. math:: + + h_2 = \overline{D^2} - \bar{D}^2 + +where :math:`\overline{D^2}` is ``D2_mean`` and :math:`\bar{D}` is ``D_mean`` +from the Monte-Carlo sub-sampling step. + +:meth:`~mom6_forge.topo.Topo.write_topo_drag` writes a netCDF file containing +``h2`` in units of ``meters^2``. This file is read by MOM6 at initialisation when +topo drag is enabled. + +.. note:: + + ``write_topo_drag`` requires + :meth:`~mom6_forge.topo.Topo.generate_mask_ocean_frac` (or + :meth:`~mom6_forge.topo.Topo.high_res_regrid`) to have been called first. + It will raise a ``RuntimeError`` otherwise. + +.. code-block:: python + + topo.high_res_regrid(bathymetry_path="gebco_2023.nc", ...) + topo.write_topo_drag("topo_drag.nc") + + +See Also +-------- + +- :doc:`Notebook 7 — Regional Bathymetry Workflow <../notebooks/7_regional_bathy_workflow>` +- :class:`~mom6_forge.topo.Topo` +- `GEBCO bathymetry `_ diff --git a/notebooks/7_regional_bathy_workflow.ipynb b/notebooks/7_regional_bathy_workflow.ipynb new file mode 100644 index 00000000..9d8336d6 --- /dev/null +++ b/notebooks/7_regional_bathy_workflow.ipynb @@ -0,0 +1,314 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 7. Regional Bathymetry Workflows\n", + "\n", + "This notebook demonstrates the two regional bathymetry pipelines in `mom6_forge`:\n", + "\n", + "| Pipeline | Method | Best for |\n", + "|---|---|---|\n", + "| **A** | `direct_xesmf_regrid` | Fine grids (< 5 km), fast runs |\n", + "| **B** | `high_res_regrid` | Coarser grids (≳ 0.05°), topo drag |\n", + "\n", + "Both pipelines support an external land/ocean mask. Two mask generation methods are available:\n", + "- `generate_mask_ocean_frac` — Monte-Carlo ocean fraction from raw GEBCO\n", + "- `generate_mask_cartopy` — Cartopy Natural Earth coastline rasterisation\n", + "\n", + "> **Note:** This notebook documents the intended API for methods currently under development on the `global_workflow_convert` branch. Cells marked `[PROPOSED API]` will raise `AttributeError` until the implementation is merged." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from mom6_forge.grid import Grid\n", + "from mom6_forge.topo import Topo\n", + "import matplotlib.pyplot as plt\n", + "import xarray as xr\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a test grid over the Gulf of Mexico at 0.25° resolution.\n", + "This resolution (~25 km) is coarse enough that both mask quality and Cressman interpolation matter." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "grid = Grid(\n", + " resolution=0.25,\n", + " xstart=260.0,\n", + " lenx=30.0,\n", + " ystart=18.0,\n", + " leny=12.0,\n", + " name=\"gulf_of_mexico\",\n", + ")\n", + "\n", + "topo = Topo(grid, min_depth=5.0, version_control_dir=\"TopoLibrary\")\n", + "\n", + "GEBCO_PATH = \"/path/to/gebco_2023.nc\" # update to your local GEBCO file" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## 1. Mask Generation\n", + "\n", + "Both pipelines can take an externally computed mask. The mask determines which cells are ocean — everything downstream (depth interpolation, tidal cleanup, min-depth enforcement) only applies to ocean cells.\n", + "\n", + "### Option A — Ocean Fraction Mask\n", + "\n", + "`generate_mask_ocean_frac` computes the fraction of each model cell that is ocean by sub-sampling the raw GEBCO data.\n", + "\n", + "For each model cell:\n", + "1. `nx_sub × ny_sub` interior points are placed using bilinear interpolation of the 4 supergrid corner coordinates.\n", + "2. Each point is snapped to the nearest GEBCO pixel.\n", + "3. `OCN_FRAC` = fraction of sub-points with depth > `mask_hmin`.\n", + "4. Cells with `OCN_FRAC ≥ mask_threshold` become ocean.\n", + "\n", + "This method also stores per-cell depth statistics (`D_mean`, `D_min`, `D_max`, `D2_mean`) needed later for topo drag." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# [PROPOSED API]\n", + "mask_ocn_frac, ocn_frac = topo.generate_mask_ocean_frac(\n", + " bathymetry_path=GEBCO_PATH,\n", + " nx_sub=5, # 5x5 = 25 sub-points per cell\n", + " ny_sub=5,\n", + " mask_threshold=0.5, # >50% ocean -> ocean cell\n", + " mask_hmin=0.0, # sub-points with depth > 0 count as ocean\n", + ")\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n", + "ocn_frac.plot(ax=axes[0], cmap=\"Blues\", vmin=0, vmax=1)\n", + "axes[0].set_title(\"Ocean Fraction (OCN_FRAC)\")\n", + "mask_ocn_frac.plot(ax=axes[1], cmap=\"Blues\", vmin=0, vmax=1)\n", + "axes[1].set_title(\"Binary Mask (threshold=0.5)\")\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Option B — Cartopy Coastline Mask\n", + "\n", + "`generate_mask_cartopy` rasterises Natural Earth coastline vectors onto the model grid. It is faster and does not depend on the bathymetry dataset, but the mask is not derived from the depth data so land/ocean boundaries may not align exactly with GEBCO." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# [PROPOSED API]\n", + "mask_cartopy = topo.generate_mask_cartopy(resolution=\"50m\")\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n", + "mask_ocn_frac.plot(ax=axes[0], cmap=\"Blues\")\n", + "axes[0].set_title(\"Ocean Fraction Mask\")\n", + "mask_cartopy.plot(ax=axes[1], cmap=\"Blues\")\n", + "axes[1].set_title(\"Cartopy Mask\")\n", + "plt.suptitle(\"Mask comparison at coastlines\")\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## 2. Pipeline A — `direct_xesmf_regrid`\n", + "\n", + "The standard pipeline. Regrids GEBCO with `xesmf` (bilinear or conservative), then runs `tidy_dataset` for lake removal, 1-cell channel cleanup, and minimum depth enforcement.\n", + "\n", + "The optional `mask` argument lets you supply a pre-computed mask. Without it, the mask is derived from whether the regridded depth is positive — the original behaviour." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# [PROPOSED API] — with external ocean fraction mask\n", + "topo.direct_xesmf_regrid(\n", + " bathymetry_path=GEBCO_PATH,\n", + " longitude_coordinate_name=\"lon\",\n", + " latitude_coordinate_name=\"lat\",\n", + " vertical_coordinate_name=\"elevation\",\n", + " mask=mask_ocn_frac, # omit this to use the original behaviour\n", + " regridding_method=\"conservative\",\n", + ")\n", + "\n", + "topo.depth.plot(cmap=\"Blues_r\")\n", + "plt.title(\"Pipeline A depth — xesmf + ocean fraction mask\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## 3. Pipeline B — `high_res_regrid`\n", + "\n", + "The high-accuracy pipeline for coarser grids. Internally runs:\n", + "1. `generate_mask_ocean_frac` — builds the mask and computes depth statistics\n", + "2. `cressman_interp` — assigns ocean-mask-aware smoothed depths from raw GEBCO\n", + "3. `tidy_dataset` — lake removal, channel cleanup, min-depth enforcement\n", + "\n", + "### Why Cressman instead of xesmf?\n", + "\n", + "Standard regridding blends land elevations into ocean cells that straddle the coastline in the source data. Cressman weights nearby GEBCO points by distance and **excludes land source points entirely**, so coastal depth estimates are not contaminated by land elevations.\n", + "\n", + "The weight function is:\n", + "$$w = \\left(\\frac{L^2 - r^2}{L^2 + r^2}\\right)^c$$\n", + "\n", + "where $r$ is the great-circle distance from the cell centre to each GEBCO point, $L = \\text{smooth\\_scl} \\times \\sqrt{A_{\\text{cell}}}$, and $c$ is `cressman_exp`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# [PROPOSED API]\n", + "topo_hires = Topo(grid, min_depth=5.0, version_control_dir=\"TopoLibrary_hires\")\n", + "\n", + "topo_hires.high_res_regrid(\n", + " bathymetry_path=GEBCO_PATH,\n", + " longitude_coordinate_name=\"lon\",\n", + " latitude_coordinate_name=\"lat\",\n", + " vertical_coordinate_name=\"elevation\",\n", + " nx_sub=5,\n", + " ny_sub=5,\n", + " mask_threshold=0.5,\n", + " smooth_scl=2.0, # smoothing radius = 2x local grid spacing\n", + " cressman_exp=2.0, # weight falloff sharpness\n", + " hmin=5.0, # minimum depth — should match vgrid top layer\n", + ")\n", + "\n", + "topo_hires.depth.plot(cmap=\"Blues_r\")\n", + "plt.title(\"Pipeline B depth — Cressman interpolation\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Comparing the two pipelines\n", + "\n", + "The largest differences appear near coastlines and shallow shelves. Interior deep ocean cells are nearly identical." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# [PROPOSED API]\n", + "diff = topo_hires.depth - topo.depth\n", + "\n", + "fig, axes = plt.subplots(1, 3, figsize=(16, 4))\n", + "topo.depth.plot(ax=axes[0], cmap=\"Blues_r\")\n", + "axes[0].set_title(\"Pipeline A (xesmf)\")\n", + "topo_hires.depth.plot(ax=axes[1], cmap=\"Blues_r\")\n", + "axes[1].set_title(\"Pipeline B (Cressman)\")\n", + "diff.plot(ax=axes[2], cmap=\"RdBu_r\", robust=True)\n", + "axes[2].set_title(\"Difference (B − A)\")\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## 4. Topo Drag Statistics\n", + "\n", + "MOM6 Lee-wave and bottom-drag parameterisations need a measure of subgrid topographic roughness. This is the variance of ocean depth within each model cell:\n", + "\n", + "$$h_2 = \\overline{D^2} - \\bar{D}^2$$\n", + "\n", + "where $\\overline{D^2}$ is `D2_mean` and $\\bar{D}$ is `D_mean` from the Monte-Carlo sub-sampling step.\n", + "\n", + "`write_topo_drag` writes a netCDF file with `h2` that MOM6 reads at initialisation when topo drag is enabled. It requires `generate_mask_ocean_frac` (or `high_res_regrid`) to have been called first." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# [PROPOSED API]\n", + "topo_hires.write_topo_drag(\"topo_drag.nc\")\n", + "\n", + "drag = xr.open_dataset(\"topo_drag.nc\")\n", + "drag.h2.plot(cmap=\"YlOrRd\")\n", + "plt.title(\"Subgrid topographic variance h2 (m²)\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Verify the formula directly\n", + "# [PROPOSED API]\n", + "h2_manual = topo_hires.d2_mean - topo_hires.d_mean ** 2\n", + "print(\"h2 is non-negative everywhere:\", bool((drag.h2 >= 0).all()))\n", + "print(\"Max h2:\", float(drag.h2.max()), \"m²\")\n", + "print(\"Matches manual computation:\", np.allclose(drag.h2.values, h2_manual.values))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": "---\n## 5. Which pipeline to use?\n\n`direct_xesmf_regrid` works well when your model grid resolution is close to the source dataset resolution (GEBCO is ~500 m / 15 arcseconds). As the ratio of model grid spacing to source resolution grows — i.e. each model cell covers many GEBCO pixels — `xesmf` regridding averages over increasing subgrid variability and coastal cells become more susceptible to land contamination. In that regime `high_res_regrid` with Cressman interpolation gives more accurate depths and a physically consistent mask.\n\nFor the mask, `generate_mask_ocean_frac` is the natural choice when Cressman is involved since both draw from the same GEBCO source, keeping the mask and depth field self-consistent. `generate_mask_cartopy` is a reasonable alternative for `direct_xesmf_regrid` on clean open-ocean domains where speed matters more than coastline precision." + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/tests/test_topo_regional_bathy.py b/tests/test_topo_regional_bathy.py new file mode 100644 index 00000000..2a9bfc85 --- /dev/null +++ b/tests/test_topo_regional_bathy.py @@ -0,0 +1,458 @@ +""" +Tests for regional bathymetry pipeline methods on Topo. + +These tests cover the new mask generation methods, Cressman interpolation, +the two named pipeline methods, and topo drag output. They are written ahead +of implementation and will fail until the corresponding methods are added to +mom6_forge/topo.py. + +New methods under test: + Topo.generate_mask_ocean_frac() + Topo.generate_mask_cartopy() + Topo.cressman_interp() + Topo.direct_xesmf_regrid() (renamed from set_from_dataset) + Topo.high_res_regrid() + Topo.write_topo_drag() +""" + +import numpy as np +import pytest +import xarray as xr +from pathlib import Path + +from mom6_forge.grid import Grid +from mom6_forge.topo import Topo + + +# --------------------------------------------------------------------------- +# Fixtures +# --------------------------------------------------------------------------- + + +@pytest.fixture +def small_grid(): + """Small rectangular grid over a Gulf of Mexico sub-region for fast tests.""" + return Grid( + resolution=0.5, + xstart=260.0, + lenx=10.0, + ystart=18.0, + leny=8.0, + name="gulf_test", + ) + + +@pytest.fixture +def small_topo(small_grid, tmp_path): + """Flat 1000 m topo on the small grid with version control.""" + topo = Topo(small_grid, min_depth=5.0, version_control_dir=tmp_path) + topo.set_flat(1000) + return topo + + +@pytest.fixture +def synthetic_gebco(tmp_path): + """ + Write a minimal synthetic GEBCO-style netCDF for testing. + Covers the small_grid domain with 0.05-degree resolution. + Depths are negative (elevation convention): ocean cells < 0, land >= 0. + A strip of land is included to test masking behaviour. + """ + lons = np.arange(259.5, 271.0, 0.05) + lats = np.arange(17.5, 27.0, 0.05) + lon2d, lat2d = np.meshgrid(lons, lats) + + # Ocean everywhere, with a land strip at lon 265-266 + elevation = np.where( + (lon2d >= 265.0) & (lon2d <= 266.0), + 100.0, # land + -1000.0, # ocean + ).astype("float32") + + ds = xr.Dataset( + {"elevation": (["lat", "lon"], elevation)}, + coords={"lon": lons, "lat": lats}, + ) + ds.elevation.attrs["units"] = "m" + path = tmp_path / "synthetic_gebco.nc" + ds.to_netcdf(path) + return path + + +# --------------------------------------------------------------------------- +# generate_mask_ocean_frac +# --------------------------------------------------------------------------- + + +class TestGenerateMaskOceanFrac: + + def test_returns_binary_mask(self, small_topo, synthetic_gebco): + """Mask values must be 0 (land) or 1 (ocean) only.""" + mask, ocn_frac = small_topo.generate_mask_ocean_frac( + bathymetry_path=synthetic_gebco, + nx_sub=3, + ny_sub=3, + ) + unique = np.unique(mask.values) + assert set(unique).issubset({0, 1}), f"Unexpected mask values: {unique}" + + def test_ocn_frac_bounds(self, small_topo, synthetic_gebco): + """OCN_FRAC values must be in [0, 1].""" + _, ocn_frac = small_topo.generate_mask_ocean_frac( + bathymetry_path=synthetic_gebco, + nx_sub=3, + ny_sub=3, + ) + assert float(ocn_frac.min()) >= 0.0 + assert float(ocn_frac.max()) <= 1.0 + + def test_mask_shape_matches_grid(self, small_topo, synthetic_gebco): + """Mask must have the same spatial shape as the model grid.""" + mask, _ = small_topo.generate_mask_ocean_frac( + bathymetry_path=synthetic_gebco, + nx_sub=3, + ny_sub=3, + ) + assert mask.shape == (small_topo._grid.ny, small_topo._grid.nx) + + def test_land_strip_is_masked(self, small_topo, synthetic_gebco): + """Cells over the synthetic land strip should have mask=0.""" + mask, _ = small_topo.generate_mask_ocean_frac( + bathymetry_path=synthetic_gebco, + nx_sub=5, + ny_sub=5, + ) + # Find cells whose centre longitude falls in the land strip + land_cols = np.where( + (small_topo._grid.tlon.values >= 265.0) + & (small_topo._grid.tlon.values <= 266.0) + ) + assert (mask.values[land_cols] == 0).all() + + def test_threshold_controls_mask(self, small_topo, synthetic_gebco): + """Higher threshold should produce equal or fewer ocean cells.""" + mask_loose, _ = small_topo.generate_mask_ocean_frac( + bathymetry_path=synthetic_gebco, + nx_sub=3, + ny_sub=3, + mask_threshold=0.2, + ) + mask_strict, _ = small_topo.generate_mask_ocean_frac( + bathymetry_path=synthetic_gebco, + nx_sub=3, + ny_sub=3, + mask_threshold=0.8, + ) + assert mask_loose.values.sum() >= mask_strict.values.sum() + + def test_stats_available_after_call(self, small_topo, synthetic_gebco): + """D_mean, D_min, D_max, D2_mean should be stored on the Topo object.""" + small_topo.generate_mask_ocean_frac( + bathymetry_path=synthetic_gebco, + nx_sub=3, + ny_sub=3, + ) + assert hasattr(small_topo, "d_mean") + assert hasattr(small_topo, "d_min") + assert hasattr(small_topo, "d_max") + assert hasattr(small_topo, "d2_mean") + + def test_stats_shapes_match_grid(self, small_topo, synthetic_gebco): + """Depth statistics must have the same shape as the model grid.""" + small_topo.generate_mask_ocean_frac( + bathymetry_path=synthetic_gebco, + nx_sub=3, + ny_sub=3, + ) + shape = (small_topo._grid.ny, small_topo._grid.nx) + assert small_topo.d_mean.shape == shape + assert small_topo.d2_mean.shape == shape + + +# --------------------------------------------------------------------------- +# generate_mask_cartopy +# --------------------------------------------------------------------------- + + +class TestGenerateMaskCartopy: + + def test_returns_binary_mask(self, small_topo): + """Mask values must be 0 or 1.""" + mask = small_topo.generate_mask_cartopy(resolution="50m") + unique = np.unique(mask.values) + assert set(unique).issubset({0, 1}), f"Unexpected mask values: {unique}" + + def test_mask_shape_matches_grid(self, small_topo): + """Mask must have the same spatial shape as the model grid.""" + mask = small_topo.generate_mask_cartopy(resolution="50m") + assert mask.shape == (small_topo._grid.ny, small_topo._grid.nx) + + def test_open_ocean_is_unmasked(self, small_topo): + """Deep Gulf of Mexico cells should be ocean (mask=1).""" + mask = small_topo.generate_mask_cartopy(resolution="50m") + # Find a cell near the Gulf centre (~25N, ~270E) + j = np.argmin(np.abs(small_topo._grid.tlat[:, 0].values - 25.0)) + i = np.argmin(np.abs(small_topo._grid.tlon[0, :].values - 270.0)) + assert mask.values[j, i] == 1 + + def test_resolution_options(self, small_topo): + """All supported Cartopy Natural Earth resolutions should work.""" + for res in ("10m", "50m", "110m"): + mask = small_topo.generate_mask_cartopy(resolution=res) + assert mask is not None + + +# --------------------------------------------------------------------------- +# cressman_interp +# --------------------------------------------------------------------------- + + +class TestCressmanInterp: + + def test_ocean_cells_get_nonzero_depth(self, small_topo, synthetic_gebco): + """All cells with mask=1 should have a positive depth after Cressman.""" + mask, _ = small_topo.generate_mask_ocean_frac( + bathymetry_path=synthetic_gebco, nx_sub=3, ny_sub=3 + ) + small_topo.cressman_interp( + bathymetry_path=synthetic_gebco, + mask=mask, + hmin=5.0, + ) + ocean_depths = small_topo.depth.values[mask.values == 1] + assert (ocean_depths > 0).all() + + def test_land_cells_have_zero_depth(self, small_topo, synthetic_gebco): + """Cells with mask=0 must have depth=0 after Cressman.""" + mask, _ = small_topo.generate_mask_ocean_frac( + bathymetry_path=synthetic_gebco, nx_sub=3, ny_sub=3 + ) + small_topo.cressman_interp( + bathymetry_path=synthetic_gebco, + mask=mask, + hmin=5.0, + ) + land_depths = small_topo.depth.values[mask.values == 0] + assert (land_depths == 0).all() + + def test_min_depth_enforced(self, small_topo, synthetic_gebco): + """No ocean cell should be shallower than hmin.""" + hmin = 10.0 + mask, _ = small_topo.generate_mask_ocean_frac( + bathymetry_path=synthetic_gebco, nx_sub=3, ny_sub=3 + ) + small_topo.cressman_interp( + bathymetry_path=synthetic_gebco, + mask=mask, + hmin=hmin, + ) + ocean_depths = small_topo.depth.values[mask.values == 1] + assert (ocean_depths >= hmin).all() + + def test_smooth_scl_affects_result(self, small_topo, synthetic_gebco): + """Different smooth_scl values should produce different depth fields.""" + mask, _ = small_topo.generate_mask_ocean_frac( + bathymetry_path=synthetic_gebco, nx_sub=3, ny_sub=3 + ) + small_topo.cressman_interp( + bathymetry_path=synthetic_gebco, mask=mask, hmin=5.0, smooth_scl=1.0 + ) + depth_tight = small_topo.depth.values.copy() + + small_topo.cressman_interp( + bathymetry_path=synthetic_gebco, mask=mask, hmin=5.0, smooth_scl=4.0 + ) + depth_wide = small_topo.depth.values.copy() + + assert not np.allclose(depth_tight, depth_wide) + + def test_output_shape_matches_grid(self, small_topo, synthetic_gebco): + """Depth field after Cressman must have the correct grid shape.""" + mask, _ = small_topo.generate_mask_ocean_frac( + bathymetry_path=synthetic_gebco, nx_sub=3, ny_sub=3 + ) + small_topo.cressman_interp( + bathymetry_path=synthetic_gebco, mask=mask, hmin=5.0 + ) + assert small_topo.depth.shape == (small_topo._grid.ny, small_topo._grid.nx) + + +# --------------------------------------------------------------------------- +# direct_xesmf_regrid (renamed from set_from_dataset) +# --------------------------------------------------------------------------- + + +class TestDirectXesmfRegrid: + + def test_runs_without_external_mask(self, small_topo, synthetic_gebco, tmp_path): + """Backwards-compatible: should work exactly as set_from_dataset did.""" + small_topo.direct_xesmf_regrid( + bathymetry_path=synthetic_gebco, + longitude_coordinate_name="lon", + latitude_coordinate_name="lat", + vertical_coordinate_name="elevation", + output_dir=tmp_path, + ) + assert not np.all(small_topo.depth.values == 0) + + def test_accepts_external_mask(self, small_topo, synthetic_gebco, tmp_path): + """When an external mask is supplied, tidy_dataset should use it.""" + mask, _ = small_topo.generate_mask_ocean_frac( + bathymetry_path=synthetic_gebco, nx_sub=3, ny_sub=3 + ) + small_topo.direct_xesmf_regrid( + bathymetry_path=synthetic_gebco, + longitude_coordinate_name="lon", + latitude_coordinate_name="lat", + vertical_coordinate_name="elevation", + mask=mask, + output_dir=tmp_path, + ) + # Cells masked as land should have depth = 0 + land_depths = small_topo.depth.values[mask.values == 0] + assert (land_depths == 0).all() + + def test_external_mask_and_no_mask_differ( + self, small_grid, synthetic_gebco, tmp_path + ): + """Using an external mask should produce a different result than no mask.""" + topo_no_mask = Topo(small_grid, min_depth=5.0, version_control_dir=tmp_path / "a") + topo_no_mask.set_flat(1000) + topo_no_mask.direct_xesmf_regrid( + bathymetry_path=synthetic_gebco, + longitude_coordinate_name="lon", + latitude_coordinate_name="lat", + vertical_coordinate_name="elevation", + output_dir=tmp_path / "a", + ) + + topo_mask = Topo(small_grid, min_depth=5.0, version_control_dir=tmp_path / "b") + topo_mask.set_flat(1000) + mask, _ = topo_mask.generate_mask_ocean_frac( + bathymetry_path=synthetic_gebco, nx_sub=5, ny_sub=5 + ) + topo_mask.direct_xesmf_regrid( + bathymetry_path=synthetic_gebco, + longitude_coordinate_name="lon", + latitude_coordinate_name="lat", + vertical_coordinate_name="elevation", + mask=mask, + output_dir=tmp_path / "b", + ) + + assert not topo_no_mask.depth.equals(topo_mask.depth) + + +# --------------------------------------------------------------------------- +# high_res_regrid +# --------------------------------------------------------------------------- + + +class TestHighResRegrid: + + def test_produces_valid_depth_field(self, small_topo, synthetic_gebco, tmp_path): + """Pipeline should produce a depth field with no NaNs.""" + small_topo.high_res_regrid( + bathymetry_path=synthetic_gebco, + longitude_coordinate_name="lon", + latitude_coordinate_name="lat", + vertical_coordinate_name="elevation", + nx_sub=3, + ny_sub=3, + output_dir=tmp_path, + ) + assert not np.any(np.isnan(small_topo.depth.values)) + + def test_min_depth_enforced(self, small_topo, synthetic_gebco, tmp_path): + """No ocean cell should be shallower than topo.min_depth.""" + small_topo.high_res_regrid( + bathymetry_path=synthetic_gebco, + longitude_coordinate_name="lon", + latitude_coordinate_name="lat", + vertical_coordinate_name="elevation", + nx_sub=3, + ny_sub=3, + output_dir=tmp_path, + ) + ocean_mask = small_topo.tmask.values + ocean_depths = small_topo.depth.values[ocean_mask == 1] + assert (ocean_depths >= small_topo.min_depth).all() + + def test_stats_available_after_call(self, small_topo, synthetic_gebco, tmp_path): + """Depth statistics must be populated after high_res_regrid.""" + small_topo.high_res_regrid( + bathymetry_path=synthetic_gebco, + longitude_coordinate_name="lon", + latitude_coordinate_name="lat", + vertical_coordinate_name="elevation", + nx_sub=3, + ny_sub=3, + output_dir=tmp_path, + ) + assert hasattr(small_topo, "d_mean") + assert hasattr(small_topo, "d2_mean") + + +# --------------------------------------------------------------------------- +# write_topo_drag +# --------------------------------------------------------------------------- + + +class TestWriteTopoDrag: + + def test_h2_written_to_file(self, small_topo, synthetic_gebco, tmp_path): + """write_topo_drag should produce a netCDF with an h2 variable.""" + small_topo.generate_mask_ocean_frac( + bathymetry_path=synthetic_gebco, nx_sub=3, ny_sub=3 + ) + out_path = tmp_path / "topo_drag.nc" + small_topo.write_topo_drag(out_path) + ds = xr.open_dataset(out_path) + assert "h2" in ds + + def test_h2_non_negative(self, small_topo, synthetic_gebco, tmp_path): + """h2 = D2_mean - D_mean^2 is a variance and must be >= 0 everywhere.""" + small_topo.generate_mask_ocean_frac( + bathymetry_path=synthetic_gebco, nx_sub=3, ny_sub=3 + ) + out_path = tmp_path / "topo_drag.nc" + small_topo.write_topo_drag(out_path) + ds = xr.open_dataset(out_path) + assert float(ds.h2.min()) >= 0.0 + + def test_h2_shape_matches_grid(self, small_topo, synthetic_gebco, tmp_path): + """h2 must have the same spatial shape as the model grid.""" + small_topo.generate_mask_ocean_frac( + bathymetry_path=synthetic_gebco, nx_sub=3, ny_sub=3 + ) + out_path = tmp_path / "topo_drag.nc" + small_topo.write_topo_drag(out_path) + ds = xr.open_dataset(out_path) + assert ds.h2.shape == (small_topo._grid.ny, small_topo._grid.nx) + + def test_h2_units_attribute(self, small_topo, synthetic_gebco, tmp_path): + """h2 should carry a units attribute of 'meters^2'.""" + small_topo.generate_mask_ocean_frac( + bathymetry_path=synthetic_gebco, nx_sub=3, ny_sub=3 + ) + out_path = tmp_path / "topo_drag.nc" + small_topo.write_topo_drag(out_path) + ds = xr.open_dataset(out_path) + assert ds.h2.attrs.get("units") == "meters^2" + + def test_raises_without_stats(self, small_topo, tmp_path): + """write_topo_drag must raise if generate_mask_ocean_frac was not called.""" + out_path = tmp_path / "topo_drag.nc" + with pytest.raises(RuntimeError, match="generate_mask_ocean_frac"): + small_topo.write_topo_drag(out_path) + + def test_h2_formula(self, small_topo, synthetic_gebco, tmp_path): + """Verify h2 = D2_mean - D_mean^2 directly against stored stats.""" + small_topo.generate_mask_ocean_frac( + bathymetry_path=synthetic_gebco, nx_sub=3, ny_sub=3 + ) + out_path = tmp_path / "topo_drag.nc" + small_topo.write_topo_drag(out_path) + ds = xr.open_dataset(out_path) + expected_h2 = small_topo.d2_mean.values - small_topo.d_mean.values ** 2 + np.testing.assert_allclose(ds.h2.values, expected_h2, rtol=1e-5) From 7f17800b8d9d184f88e1c6b4b5adb185e4e1eba7 Mon Sep 17 00:00:00 2001 From: manishvenu Date: Mon, 13 Apr 2026 15:04:50 -0600 Subject: [PATCH 2/9] Black --- tests/test_topo_regional_bathy.py | 13 ++++++------- 1 file changed, 6 insertions(+), 7 deletions(-) diff --git a/tests/test_topo_regional_bathy.py b/tests/test_topo_regional_bathy.py index 2a9bfc85..93f5ebc5 100644 --- a/tests/test_topo_regional_bathy.py +++ b/tests/test_topo_regional_bathy.py @@ -23,7 +23,6 @@ from mom6_forge.grid import Grid from mom6_forge.topo import Topo - # --------------------------------------------------------------------------- # Fixtures # --------------------------------------------------------------------------- @@ -65,7 +64,7 @@ def synthetic_gebco(tmp_path): # Ocean everywhere, with a land strip at lon 265-266 elevation = np.where( (lon2d >= 265.0) & (lon2d <= 266.0), - 100.0, # land + 100.0, # land -1000.0, # ocean ).astype("float32") @@ -271,9 +270,7 @@ def test_output_shape_matches_grid(self, small_topo, synthetic_gebco): mask, _ = small_topo.generate_mask_ocean_frac( bathymetry_path=synthetic_gebco, nx_sub=3, ny_sub=3 ) - small_topo.cressman_interp( - bathymetry_path=synthetic_gebco, mask=mask, hmin=5.0 - ) + small_topo.cressman_interp(bathymetry_path=synthetic_gebco, mask=mask, hmin=5.0) assert small_topo.depth.shape == (small_topo._grid.ny, small_topo._grid.nx) @@ -316,7 +313,9 @@ def test_external_mask_and_no_mask_differ( self, small_grid, synthetic_gebco, tmp_path ): """Using an external mask should produce a different result than no mask.""" - topo_no_mask = Topo(small_grid, min_depth=5.0, version_control_dir=tmp_path / "a") + topo_no_mask = Topo( + small_grid, min_depth=5.0, version_control_dir=tmp_path / "a" + ) topo_no_mask.set_flat(1000) topo_no_mask.direct_xesmf_regrid( bathymetry_path=synthetic_gebco, @@ -454,5 +453,5 @@ def test_h2_formula(self, small_topo, synthetic_gebco, tmp_path): out_path = tmp_path / "topo_drag.nc" small_topo.write_topo_drag(out_path) ds = xr.open_dataset(out_path) - expected_h2 = small_topo.d2_mean.values - small_topo.d_mean.values ** 2 + expected_h2 = small_topo.d2_mean.values - small_topo.d_mean.values**2 np.testing.assert_allclose(ds.h2.values, expected_h2, rtol=1e-5) From 43051ee5386cf3f5cc90d0c9b0ecbd6f0f45874d Mon Sep 17 00:00:00 2001 From: manishvenu Date: Tue, 14 Apr 2026 12:15:36 -0600 Subject: [PATCH 3/9] New tx2_3 pipeline --- .docstr/regional_bathy.rst | 237 ++++++-- mom6_forge/mapping.py | 261 +++++++- mom6_forge/topo.py | 965 +++++++++++++++++++++++++++--- tests/test_topo.py | 22 + tests/test_topo_regional_bathy.py | 516 ++++++++-------- 5 files changed, 1612 insertions(+), 389 deletions(-) diff --git a/.docstr/regional_bathy.rst b/.docstr/regional_bathy.rst index a0970cc5..8b8fba04 100644 --- a/.docstr/regional_bathy.rst +++ b/.docstr/regional_bathy.rst @@ -1,9 +1,9 @@ Regional Bathymetry Workflows ============================= -``mom6_forge`` provides two bathymetry pipelines for regional MOM6 configurations, -both accessible through the :class:`~mom6_forge.topo.Topo` class. They differ in how -depth is assigned and are suited to different grid resolutions. +``mom6_forge`` provides bathymetry pipelines for regional MOM6 configurations, +accessible through the :class:`~mom6_forge.topo.Topo` class. Two pipelines are +available, chosen automatically based on grid resolution. .. contents:: Contents :local: @@ -13,11 +13,10 @@ depth is assigned and are suited to different grid resolutions. Background ---------- -The default :meth:`~mom6_forge.topo.Topo.direct_xesmf_regrid` pipeline regrids a -source bathymetry (e.g. GEBCO) onto the model grid using ``xesmf`` and derives the -land/ocean mask from whether the regridded depth is positive. This is fast and -sufficient for fine regional grids (~1–3 km), but has two limitations at coarser -resolutions: +The standard pipeline regrids a source bathymetry (e.g. GEBCO) onto the model +grid using ``xesmf`` and derives the land/ocean mask from whether the regridded +depth is positive. This is fast and sufficient for fine regional grids (~1–3 km), +but has two limitations at coarser resolutions: 1. **Land contamination near coasts.** Standard bilinear or conservative regridding blends land elevations into ocean depth estimates for cells that straddle the @@ -28,26 +27,109 @@ resolutions: the ocean fraction directly from the high-resolution source data produces a more accurate mask. -The :meth:`~mom6_forge.topo.Topo.high_res_regrid` pipeline addresses both issues -using a Monte-Carlo ocean fraction mask and Cressman distance-weighted interpolation. -It is recommended for grids of ~0.05° (5 km) and coarser. +A higher-accuracy pipeline using a Monte-Carlo ocean fraction mask and Cressman +distance-weighted interpolation is recommended for grids of ~0.05° (5 km) and +coarser. The top-level entry point :meth:`~mom6_forge.topo.Topo.set_from_dataset` +calls :meth:`~mom6_forge.topo.Topo.diagnose_resolution` automatically and routes +to the appropriate pipeline. + + +Pipeline Dispatch +----------------- + +.. automethod:: mom6_forge.topo.Topo.set_from_dataset + +``set_from_dataset`` is the recommended entry point for most workflows. It +inspects the model grid spacing relative to the source bathymetry pixel size and +dispatches to the appropriate pipeline:: + + set_from_dataset() + ├── diagnose_resolution() → True (ratio ≥ 12×) + │ └── high_res_regrid(mask_method="ocean_frac" default) + │ ├── mask options: ocean_frac | cartopy | pre-computed DataArray + │ ├── cressman_interp() + │ └── tidy_dataset(mask=mask) + │ + └── diagnose_resolution() → False (ratio < 12×) + └── direct_xesmf_regrid(mask_method=None default) + ├── mask options: ocean_frac | cartopy | pre-computed DataArray + │ | None (derive from depth sign) + ├── config_dataset() + regrid_dataset_via_xesmf() + └── tidy_dataset(mask=mask) + +The ``mask_method`` and ``mask`` keyword arguments are forwarded transparently to +whichever pipeline is selected. + +.. code-block:: python + + # Fully automatic — pipeline chosen by diagnose_resolution + topo.set_from_dataset( + bathymetry_path="gebco_2023.nc", + longitude_coordinate_name="lon", + latitude_coordinate_name="lat", + vertical_coordinate_name="elevation", + ) + + # Force ocean-fraction mask regardless of auto-dispatch + topo.set_from_dataset( + bathymetry_path="gebco_2023.nc", + longitude_coordinate_name="lon", + latitude_coordinate_name="lat", + vertical_coordinate_name="elevation", + mask_method="ocean_frac", + ) + + +Resolution Diagnosis +-------------------- + +.. automethod:: mom6_forge.topo.Topo.diagnose_resolution + +Before choosing a pipeline manually, call ``diagnose_resolution`` to compare +the model grid spacing with the source bathymetry resolution: + +.. code-block:: python + + topo.diagnose_resolution( + "gebco_2023.nc", + longitude_coordinate_name="lon", + latitude_coordinate_name="lat", + ) + +This prints the median, minimum, and maximum model cell spacing alongside the +dataset pixel size, computes the resolution ratio, and returns ``True`` if the +ratio is ≥ 12× (the point at which the stats-based mask and Cressman +interpolation provide meaningfully better results than plain ``xesmf`` +regridding). Mask Generation --------------- -Both pipelines support an optional external mask. Two methods are provided. +Both pipelines accept an external mask in three ways: -Ocean Fraction Mask (recommended) -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +1. **``mask=``** — pass a pre-computed boolean/integer ocean mask + directly; no mask generation step is run. +2. **``mask_method="ocean_frac"``** — call + :meth:`~mom6_forge.topo.Topo.generate_mask_ocean_frac` internally. +3. **``mask_method="cartopy"``** — call + :meth:`~mom6_forge.topo.Topo.generate_mask_cartopy` internally. + +``direct_xesmf_regrid`` additionally supports **``mask=None``** (the default), +which skips all explicit mask generation and lets +:meth:`~mom6_forge.topo.Topo.tidy_dataset` derive the mask from the sign of the +regridded depth — the original behaviour for fine grids. + +Ocean Fraction Mask (recommended for coarse grids) +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. automethod:: mom6_forge.topo.Topo.generate_mask_ocean_frac For each model grid cell, ``nx_sub × ny_sub`` interior sample points are distributed using bilinear interpolation of the 4 supergrid corner coordinates. Each point is snapped to the nearest source bathymetry pixel. The ocean fraction -``OCN_FRAC`` is the number of sample points with depth > ``mask_hmin`` divided by -the total. Cells with ``OCN_FRAC ≥ mask_threshold`` are marked as ocean. +``OCN_FRAC`` is the number of sample points with depth > ``mask_hmin`` divided +by the total. Cells with ``OCN_FRAC ≥ mask_threshold`` are marked as ocean. This method also computes and stores per-cell depth statistics (``D_mean``, ``D_min``, ``D_max``, ``D2_mean``) which are needed for topo drag @@ -79,14 +161,18 @@ Pipeline A — direct_xesmf_regrid The standard pipeline. Regrids the source bathymetry with ``xesmf`` then runs :meth:`~mom6_forge.topo.Topo.tidy_dataset` for lake removal, channel cleanup, -and minimum depth enforcement. +and minimum depth enforcement. Suitable for fine regional grids (~1–3 km). + +Mask options: -Accepts an optional ``mask`` argument. When provided, ``tidy_dataset`` uses it -directly instead of deriving the mask from the regridded depth. When omitted, the -behaviour is identical to the previous ``set_from_dataset`` method. +- ``mask=None`` *(default)* — derive ocean mask from sign of regridded depth +- ``mask_method="ocean_frac"`` — use :meth:`~mom6_forge.topo.Topo.generate_mask_ocean_frac` +- ``mask_method="cartopy"`` — use :meth:`~mom6_forge.topo.Topo.generate_mask_cartopy` +- ``mask=`` — supply a pre-computed ocean mask .. code-block:: python + # Default: derive mask from depth sign topo.direct_xesmf_regrid( bathymetry_path="gebco_2023.nc", longitude_coordinate_name="lon", @@ -94,49 +180,76 @@ behaviour is identical to the previous ``set_from_dataset`` method. vertical_coordinate_name="elevation", ) - # With an external mask: - mask, ocn_frac = topo.generate_mask_ocean_frac("gebco_2023.nc", nx_sub=5, ny_sub=5) + # With ocean-fraction mask topo.direct_xesmf_regrid( bathymetry_path="gebco_2023.nc", longitude_coordinate_name="lon", latitude_coordinate_name="lat", vertical_coordinate_name="elevation", - mask=mask, + mask_method="ocean_frac", + nx_sub=5, + ny_sub=5, ) + Pipeline B — high_res_regrid -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. automethod:: mom6_forge.topo.Topo.high_res_regrid The high-accuracy pipeline for coarser grids. Internally runs: -1. :meth:`~mom6_forge.topo.Topo.generate_mask_ocean_frac` to build the mask and - compute depth statistics. -2. :meth:`~mom6_forge.topo.Topo.cressman_interp` to assign ocean-mask-aware - smoothed depths. -3. :meth:`~mom6_forge.topo.Topo.tidy_dataset` for lake removal, channel cleanup, - and minimum depth enforcement. +1. Mask generation (``generate_mask_ocean_frac`` by default, or ``generate_mask_cartopy``, + or a pre-computed ``mask`` DataArray) +2. :meth:`~mom6_forge.topo.Topo.cressman_interp` — ocean-aware distance-weighted + depth interpolation +3. :meth:`~mom6_forge.topo.Topo.tidy_dataset` — lake removal, minimum depth + +Mask options: + +- ``mask_method="ocean_frac"`` *(default)* — use :meth:`~mom6_forge.topo.Topo.generate_mask_ocean_frac` +- ``mask_method="cartopy"`` — use :meth:`~mom6_forge.topo.Topo.generate_mask_cartopy` +- ``mask=`` — supply a pre-computed ocean mask + +Note that ``mask=None`` is not a valid option here because Cressman interpolation +requires an explicit ocean mask to exclude land source points. .. code-block:: python + # Default: ocean-fraction mask + Cressman topo.high_res_regrid( bathymetry_path="gebco_2023.nc", longitude_coordinate_name="lon", latitude_coordinate_name="lat", vertical_coordinate_name="elevation", - nx_sub=5, - ny_sub=5, - smooth_scl=2.0, ) + # Cartopy mask variant + topo.high_res_regrid( + bathymetry_path="gebco_2023.nc", + longitude_coordinate_name="lon", + latitude_coordinate_name="lat", + vertical_coordinate_name="elevation", + mask_method="cartopy", + ) + + # Pre-computed mask + topo.high_res_regrid( + bathymetry_path="gebco_2023.nc", + longitude_coordinate_name="lon", + latitude_coordinate_name="lat", + vertical_coordinate_name="elevation", + mask=my_ocean_mask, + ) + + Cressman Interpolation ------------------------ +---------------------- .. automethod:: mom6_forge.topo.Topo.cressman_interp Assigns depth to each ocean cell by distance-weighted averaging of source -bathymetry points within a smoothing radius ``L``: +bathymetry points within a per-cell smoothing radius ``L``: .. math:: @@ -145,8 +258,17 @@ bathymetry points within a smoothing radius ``L``: where :math:`r_i` is the great-circle distance from the model cell centre to source point :math:`i`, :math:`L = \text{smooth\_scl} \times \sqrt{A_\text{cell}}` -is the smoothing radius, and :math:`c` is ``cressman_exp``. Source points with -depth ≤ 0 (land) are excluded. +is the per-cell smoothing radius, and :math:`c` is ``cressman_exp``. Source +points with depth ≤ 0 (land) are excluded from the weighted average. + +Cells that receive no ocean source points within their radius are filled +iteratively from their nearest filled neighbours (vectorised numpy approach +modelled after the Fortran ``interp_smooth.f90`` in tx2_3). + +The interpolation weights are saved to an ESMF-compatible netCDF file (``S``, +``row``, ``col`` variables) so that the ``xesmf`` regridder can apply them via +``reuse_weights=True``. Pass ``weights_path`` to control where this file is +written; if omitted a temporary file is used. **Recommendation by resolution:** @@ -161,7 +283,7 @@ depth ≤ 0 (land) are excluded. * - 3–10 km (0.05°) - Borderline; Cressman improves coastline accuracy * - > 10 km (0.1°+) - - Use ``high_res_regrid`` — Cressman gives meaningfully better depths + - Cressman gives meaningfully better depths; use ``high_res_regrid`` .. _topo-drag: @@ -169,8 +291,6 @@ depth ≤ 0 (land) are excluded. Topo Drag Statistics -------------------- -.. automethod:: mom6_forge.topo.Topo.write_topo_drag - MOM6 Lee-wave and bottom-drag parameterisations require a measure of subgrid topographic roughness at each cell. This is quantified as the variance of ocean depth within each model cell: @@ -180,23 +300,40 @@ depth within each model cell: h_2 = \overline{D^2} - \bar{D}^2 where :math:`\overline{D^2}` is ``D2_mean`` and :math:`\bar{D}` is ``D_mean`` -from the Monte-Carlo sub-sampling step. +from the Monte-Carlo sub-sampling step in +:meth:`~mom6_forge.topo.Topo.generate_mask_ocean_frac`. -:meth:`~mom6_forge.topo.Topo.write_topo_drag` writes a netCDF file containing -``h2`` in units of ``meters^2``. This file is read by MOM6 at initialisation when -topo drag is enabled. +When :meth:`~mom6_forge.topo.Topo.generate_mask_ocean_frac` has been called, +:meth:`~mom6_forge.topo.Topo.write_topo` automatically includes the ``h2`` +variable (units: ``m²``) in the output file. To make the presence of ``h2`` +mandatory, pass ``enforce_topo_drag=True``; this raises a ``RuntimeError`` if the +stats have not been computed yet. + +.. automethod:: mom6_forge.topo.Topo.write_topo .. note:: - ``write_topo_drag`` requires - :meth:`~mom6_forge.topo.Topo.generate_mask_ocean_frac` (or - :meth:`~mom6_forge.topo.Topo.high_res_regrid`) to have been called first. - It will raise a ``RuntimeError`` otherwise. + ``h2`` requires + :meth:`~mom6_forge.topo.Topo.generate_mask_ocean_frac` to have been called + first. Without it, ``write_topo`` writes the depth field only. .. code-block:: python - topo.high_res_regrid(bathymetry_path="gebco_2023.nc", ...) - topo.write_topo_drag("topo_drag.nc") + topo.generate_mask_ocean_frac(bathymetry_path="gebco_2023.nc", nx_sub=5, ny_sub=5) + topo.write_topo("topog.nc") # h2 included automatically + + # Alternatively, raise if stats are missing: + topo.write_topo("topog.nc", enforce_topo_drag=True) + + +MPI Regridding +-------------- + +.. automethod:: mom6_forge.topo.Topo.mpi_direct_xesmf_regrid + +A parallelised variant of ``direct_xesmf_regrid`` for very large source +datasets. Equivalent to ``direct_xesmf_regrid`` but distributes the ``xesmf`` +regridding across MPI ranks. See Also diff --git a/mom6_forge/mapping.py b/mom6_forge/mapping.py index 065c7e97..c8c76d15 100755 --- a/mom6_forge/mapping.py +++ b/mom6_forge/mapping.py @@ -8,7 +8,7 @@ from time import time from pathlib import Path from scipy.spatial import cKDTree -from scipy.sparse import csc_matrix, coo_matrix +from scipy.sparse import csc_matrix, coo_matrix, csr_matrix import xesmf as xe MPI = None @@ -776,6 +776,265 @@ def compute_smoothing_weights(mesh_ds, rmax, fold=1.0, xv_data=None, yv_data=Non return weights +def compute_cressman_weights( + dst_lon, + dst_lat, + dst_area, + dst_mask, + src_lon, + src_lat, + src_ocean_mask, + smooth_scl=2.0, + cressman_exp=2.0, +): + """Compute Cressman distance-weighted interpolation weights from a regular + source grid to a model destination grid. Mirrors ``interp_smooth.f90`` from + the tx2_3 topography workflow. + + For each ocean destination cell ``i``: + + * The smoothing radius is ``L = smooth_scl * sqrt(dst_area[i])`` (metres). + * All source ocean points within ``L`` are gathered. + * Weights are ``w = ((L²-r²)/(L²+r²))^cressman_exp`` where ``r`` is the + great-circle arc distance from the model cell centre to the source point. + * Row ``i`` of the returned matrix holds the normalised weights + (sum = 1) for destination cell ``i``. + + Parameters + ---------- + dst_lon, dst_lat : np.ndarray, shape (n_dst,) + Flattened destination T-cell centre longitudes/latitudes in degrees. + dst_area : np.ndarray, shape (n_dst,) + Flattened destination T-cell areas in m². + dst_mask : np.ndarray, shape (n_dst,), dtype bool + True for ocean destination cells that should receive depth values. + src_lon, src_lat : np.ndarray, shape (nx_src,) and (ny_src,) + 1-D coordinate arrays of the **regular** source grid in degrees. + src_ocean_mask : np.ndarray, shape (ny_src, nx_src), dtype bool + True where the source grid is ocean (depth > 0). + smooth_scl : float + Smoothing scale multiplier: ``L = smooth_scl * sqrt(cell_area)``. + Default ``2.0`` (matches tx2_3 default). + cressman_exp : float + Exponent for the Cressman weight function. Default ``2.0``. + + Returns + ------- + S : scipy.sparse.csr_matrix, shape (n_dst, ny_src * nx_src) + Normalised Cressman weight matrix. Land destination rows are all-zero. + Apply as ``depth_model_flat = S @ depth_source_flat``. + unfilled : np.ndarray of bool, shape (n_dst,) + True for ocean destination cells that had no source ocean point within + ``L``. These cells need a fallback (e.g. iterative neighbour fill). + """ + R = 6_371_000.0 # Earth radius, metres + + # --- Source grid: 2-D coordinates and Cartesian points --- + src_lon_2d, src_lat_2d = np.meshgrid(src_lon, src_lat) # (ny_src, nx_src) + n_src = src_lon_2d.size + lon_src_rad = np.deg2rad(src_lon_2d.ravel()) + lat_src_rad = np.deg2rad(src_lat_2d.ravel()) + xyz_src = np.stack( + [ + R * np.cos(lat_src_rad) * np.cos(lon_src_rad), + R * np.cos(lat_src_rad) * np.sin(lon_src_rad), + R * np.sin(lat_src_rad), + ], + axis=1, + ) + + # KDTree only over ocean source points + ocean_src_idx = np.where(src_ocean_mask.ravel())[0] + tree = cKDTree(xyz_src[ocean_src_idx]) + + # --- Destination grid: Cartesian points --- + lon_dst_rad = np.deg2rad(dst_lon) + lat_dst_rad = np.deg2rad(dst_lat) + xyz_dst = np.stack( + [ + R * np.cos(lat_dst_rad) * np.cos(lon_dst_rad), + R * np.cos(lat_dst_rad) * np.sin(lon_dst_rad), + R * np.sin(lat_dst_rad), + ], + axis=1, + ) + + rows, cols, data = [], [], [] + unfilled = np.zeros(len(dst_lon), dtype=bool) + + for i in np.where(dst_mask)[0]: + L = smooth_scl * np.sqrt(dst_area[i]) # metres + L2 = L**2 + + neighbors = tree.query_ball_point(xyz_dst[i], L) + if not neighbors: + unfilled[i] = True + continue + + nbr = np.asarray(neighbors) + d_xyz = xyz_src[ocean_src_idx[nbr]] - xyz_dst[i] + chord = np.linalg.norm(d_xyz, axis=1) + arc = 2.0 * R * np.arcsin(np.clip(chord / (2.0 * R), 0.0, 1.0)) + d2 = arc**2 + + in_radius = d2 <= L2 + if not in_radius.any(): + unfilled[i] = True + continue + + w = ((L2 - d2[in_radius]) / (L2 + d2[in_radius])) ** cressman_exp + w_sum = w.sum() + if w_sum == 0.0: + unfilled[i] = True + continue + + src_cols = ocean_src_idx[nbr[in_radius]] + rows.extend([i] * int(in_radius.sum())) + cols.extend(src_cols.tolist()) + data.extend((w / w_sum).tolist()) + + S = csr_matrix((data, (rows, cols)), shape=(len(dst_lon), n_src)) + return S, unfilled + + +def write_cressman_weights(S_csr, path): + """Write a Cressman sparse weight matrix to an xESMF-compatible netCDF file. + + The file contains ``S``, ``row``, and ``col`` in 1-based indexing, which + is the format read by ``xe.Regridder`` when a pre-computed weights file is + supplied. + + Parameters + ---------- + S_csr : scipy.sparse.csr_matrix + Normalised Cressman weight matrix (n_dst × n_src) as returned by + :func:`compute_cressman_weights`. + path : str or Path + Destination netCDF file path. + """ + S_coo = S_csr.tocoo() + ds = xr.Dataset( + { + "S": xr.DataArray( + S_coo.data.astype(np.float64), + dims=["n_s"], + attrs={"long_name": "Cressman interpolation weight"}, + ), + "row": xr.DataArray( + (S_coo.row + 1).astype(np.int32), + dims=["n_s"], + attrs={"long_name": "destination cell index (1-based)"}, + ), + "col": xr.DataArray( + (S_coo.col + 1).astype(np.int32), + dims=["n_s"], + attrs={"long_name": "source cell index (1-based)"}, + ), + } + ) + ds.attrs["title"] = "Cressman interpolation weights generated by mom6_forge" + ds.to_netcdf(path, format="NETCDF3_64BIT") + + +def cressman_regrid( + src_lon, + src_lat, + src_depth, + dst_lon, + dst_lat, + dst_area, + dst_mask, + weights_path, + smooth_scl=2.0, + cressman_exp=2.0, +): + """Compute Cressman weights, save to an ESMF weights file, and regrid + source depths to the destination model grid using ``xe.Regridder``. + + Parameters + ---------- + src_lon, src_lat : np.ndarray, 1-D + Coordinate arrays of the regular source grid (e.g. GEBCO) in degrees. + src_depth : np.ndarray, shape (ny_src, nx_src) + Source depth field, positive-down. Land cells should be ≤ 0. + dst_lon, dst_lat : np.ndarray, shape (ny_dst, nx_dst) + 2-D T-cell centre coordinates of the destination model grid in degrees. + dst_area : np.ndarray, shape (ny_dst, nx_dst) + T-cell areas in m². + dst_mask : np.ndarray, shape (ny_dst, nx_dst), dtype bool + True for ocean destination cells. + weights_path : str or Path + Path where the ESMF-compatible weights netCDF will be written. + If the file already exists it is **overwritten**. + smooth_scl : float + Cressman smoothing scale multiplier. Default ``2.0``. + cressman_exp : float + Cressman weight function exponent. Default ``2.0``. + + Returns + ------- + depth_dst : np.ndarray, shape (ny_dst, nx_dst) + Cressman-interpolated depth field. Unfilled ocean cells are 0. + unfilled : np.ndarray of bool, shape (ny_dst, nx_dst) + True for ocean cells that had no source ocean point within radius L + and therefore need a fallback (e.g. iterative neighbour fill). + """ + src_ocean_mask = src_depth > 0.0 # True = ocean source pixel + + # --- Compute per-cell Cressman weights --- + print("Computing Cressman weights…") + S, unfilled_flat = compute_cressman_weights( + dst_lon.ravel(), + dst_lat.ravel(), + dst_area.ravel(), + dst_mask.ravel(), + src_lon, + src_lat, + src_ocean_mask, + smooth_scl=smooth_scl, + cressman_exp=cressman_exp, + ) + + # --- Save weights to ESMF-compatible netCDF --- + write_cressman_weights(S, weights_path) + print(f"Cressman weights written to {weights_path}") + + # --- Build xe.Regridder from the pre-computed weights --- + ds_src_xe = xr.Dataset( + coords={ + "lon": xr.DataArray(src_lon, dims=["lon"], attrs={"units": "degrees_east"}), + "lat": xr.DataArray( + src_lat, dims=["lat"], attrs={"units": "degrees_north"} + ), + } + ) + ds_dst_xe = xr.Dataset( + coords={ + "lon": xr.DataArray( + dst_lon, dims=["ny", "nx"], attrs={"units": "degrees_east"} + ), + "lat": xr.DataArray( + dst_lat, dims=["ny", "nx"], attrs={"units": "degrees_north"} + ), + } + ) + regridder = xe.Regridder( + ds_src_xe, + ds_dst_xe, + method="bilinear", + weights=str(weights_path), + reuse_weights=True, + ) + + src_depth_da = xr.DataArray( + src_depth, dims=["lat", "lon"], coords={"lat": src_lat, "lon": src_lon} + ) + depth_dst = regridder(src_depth_da).values + + unfilled = unfilled_flat.reshape(dst_lon.shape) + return depth_dst, unfilled + + def get_nn_map_filepath(mapping_file_prefix, output_dir): """Get the standardized runoff to ocean nearest neighbor mapping file path based on the prefix and output directory. diff --git a/mom6_forge/topo.py b/mom6_forge/topo.py index 3ce8b28a..14f8e215 100644 --- a/mom6_forge/topo.py +++ b/mom6_forge/topo.py @@ -1,17 +1,21 @@ import os import numpy as np import xarray as xr +import shapely +import cartopy.io.shapereader as shpreader from datetime import datetime from scipy import interpolate from scipy.ndimage import label, binary_fill_holes from scipy.spatial import cKDTree +from shapely.geometry import box +from shapely.ops import unary_union from mom6_forge.utils import cell_area_rad, longitude_slicer from mom6_forge.grid import Grid from mom6_forge.git_utils import get_domain_dir, get_repo from pathlib import Path from mom6_forge.edit_command import * from mom6_forge.command_manager import TopoCommandManager, CommandType -from mom6_forge.mapping import regrid_dataset_via_xesmf +from mom6_forge.mapping import regrid_dataset_via_xesmf, cressman_regrid class Topo: @@ -217,26 +221,6 @@ def umask(self): return umask - @property - def umask(self): - """ - Ocean domain mask on U grid. 1 if ocean, 0 if land. - """ - tmask = self.tmask - - # Create empty mask DataArray for umask - umask = xr.DataArray( - np.ones(self._grid.ulat.shape, dtype=int), - dims=["yh", "xq"], - attrs={"name": "U mask"}, - ) - - # Fill umask with mask values - umask[:, :-1] &= tmask.values # h-point translates to the left u-point - umask[:, 1:] &= tmask.values # h-point translates to the right u-point - - return umask - @property def vmask(self): """ @@ -564,90 +548,878 @@ def set_bowl(self, max_depth, dedge, rad_earth=6.378e6, expdecay=400000.0): # Save to object (Build TCM Object) self.send_entire_depth_change_to_tcm(new_values) - def set_from_dataset( + def diagnose_resolution( + self, + bathymetry_path, + longitude_coordinate_name="lon", + latitude_coordinate_name="lat", + ): + """ + Print resolution diagnostics comparing the model grid to a source bathymetry + dataset, and recommend whether Cressman interpolation / stats-based masking + is worthwhile. + + The recommendation threshold is a resolution ratio of 12x (model dx / + dataset dx), equivalent to ~0.05° (~5 km) for GEBCO 15-arcsecond source + data. This matches the criterion used by the tx2_3 global workflow + (interp_smooth.f90) and is the scale at which ocean-aware Cressman + interpolation meaningfully improves coastal depth estimates over standard + xesmf conservative regridding. + + Parameters + ---------- + bathymetry_path : str or Path + Path to the source bathymetry netCDF file. + longitude_coordinate_name : str + Name of the longitude coordinate in the bathymetry file. Default ``"lon"``. + latitude_coordinate_name : str + Name of the latitude coordinate in the bathymetry file. Default ``"lat"``. + + Returns + ------- + bool + True if Cressman / stats-based masking is recommended (ratio >= 12x), + False otherwise. + """ + CRESSMAN_THRESHOLD = 12.0 + + # --- Model T-cell spacing in meters --- + # sqrt(tarea) gives the geometric mean cell spacing (equiv. to sqrt(dxt * dyt)) + cell_dx_m = np.sqrt(self._grid.tarea.values) + + median_dx_m = float(np.median(cell_dx_m)) + min_dx_m = float(np.min(cell_dx_m)) + max_dx_m = float(np.max(cell_dx_m)) + + # --- Source dataset spacing --- + ds = xr.open_dataset(bathymetry_path) + lon = ds[longitude_coordinate_name].values + lat = ds[latitude_coordinate_name].values + ds.close() + + dlon_deg = float(abs(lon[1] - lon[0])) + dlat_deg = float(abs(lat[1] - lat[0])) + + mid_lat_deg = float(self._grid.tlat.mean()) + R = 6371000.0 + dataset_dx_m = dlon_deg * (np.pi / 180) * R * np.cos(mid_lat_deg * np.pi / 180) + dataset_dy_m = dlat_deg * (np.pi / 180) * R + + ratio_median = median_dx_m / dataset_dx_m + ratio_max = max_dx_m / dataset_dx_m + + # --- Print --- + sep = "=" * 58 + print(sep) + print(" Resolution Diagnostics") + print(sep) + print(f"\n Source dataset ({Path(bathymetry_path).name}):") + print(f" dlon = {dlon_deg * 3600:.1f} arcsec ({dlon_deg:.6f}°)") + print(f" dlat = {dlat_deg * 3600:.1f} arcsec ({dlat_deg:.6f}°)") + print( + f" dx ~ {dataset_dx_m:.0f} m (at domain mid-lat {mid_lat_deg:.1f}°)" + ) + print(f" dy ~ {dataset_dy_m:.0f} m") + print(f"\n Model grid (T-cell spacing):") + print(f" median = {median_dx_m / 1000:.2f} km") + print(f" min = {min_dx_m / 1000:.2f} km") + print(f" max = {max_dx_m / 1000:.2f} km") + print(f"\n Resolution ratio (model dx / dataset dx):") + print(f" median = {ratio_median:.1f}x") + print(f" max = {ratio_max:.1f}x") + print(f"\n Cressman / stats-mask threshold: {CRESSMAN_THRESHOLD:.0f}x") + if ratio_median >= CRESSMAN_THRESHOLD: + print(f" → RECOMMENDED: high_res_regrid() (Cressman + stats mask)") + print( + f" Each model cell spans ~{ratio_median:.0f} dataset pixels per side." + ) + print(f" Ocean-aware Cressman interpolation will meaningfully reduce") + print(f" land contamination of coastal depth estimates.") + else: + print(f" → RECOMMENDED: direct_xesmf_regrid() (bilinear / conservative)") + print( + f" Ratio {ratio_median:.1f}x is below the threshold where Cressman" + ) + print(f" provides significant benefit over xesmf regridding.") + print(sep) + return ratio_median >= CRESSMAN_THRESHOLD + + def _load_src_bathy( + self, + bathymetry_path, + longitude_coordinate_name, + latitude_coordinate_name, + vertical_coordinate_name, + buf=0.5, + ): + """Load and slice source bathymetry to the model domain plus a buffer. + + Parameters + ---------- + bathymetry_path : str or Path + longitude_coordinate_name, latitude_coordinate_name, vertical_coordinate_name : str + buf : float + Degree buffer added around the Q-grid extent. Default 0.5°. + + Returns + ------- + src_lon : np.ndarray, shape (nx_src,) + src_lat : np.ndarray, shape (ny_src,) + src_z : np.ndarray, shape (ny_src, nx_src) + Elevation in source convention (positive up, negative = ocean). + """ + lon_extent = (float(self._grid.qlon.min()), float(self._grid.qlon.max())) + lat_extent = (float(self._grid.qlat.min()), float(self._grid.qlat.max())) + + ds_src = xr.open_dataset(bathymetry_path, chunks="auto") + z_src = ds_src[vertical_coordinate_name] + z_src = z_src.sel( + {latitude_coordinate_name: slice(lat_extent[0] - buf, lat_extent[1] + buf)} + ) + + dlon = float( + z_src[longitude_coordinate_name][1] - z_src[longitude_coordinate_name][0] + ) + total_lon = float( + z_src[longitude_coordinate_name][-1] + - z_src[longitude_coordinate_name][0] + + dlon + ) + if np.isclose(total_lon, 360): + z_src = longitude_slicer( + z_src, + np.array(lon_extent) + np.array([-buf, buf]), + longitude_coordinate_name, + ) + else: + z_src = z_src.sel( + { + longitude_coordinate_name: slice( + lon_extent[0] - buf, lon_extent[1] + buf + ) + } + ) + + z_src = z_src.load() + src_lon = z_src[longitude_coordinate_name].values + src_lat = z_src[latitude_coordinate_name].values + src_z = z_src.values + return src_lon, src_lat, src_z + + def _compute_topo_stats( self, bathymetry_path, longitude_coordinate_name, latitude_coordinate_name, vertical_coordinate_name, + nx_sub, + ny_sub, + mask_hmin, + ): + """ + Compute per-cell depth statistics by Monte-Carlo sub-sampling of the + source bathymetry dataset. Mirrors the algorithm in tx2_3's + create_model_topo.f90. + + For each model T-cell, distributes nx_sub x ny_sub interior points via + bilinear interpolation of the 4 Q-point corners, snaps each sub-point + to the nearest source pixel (nearest-neighbour), and accumulates ocean + depth statistics from sub-points where depth > mask_hmin. + + Parameters + ---------- + bathymetry_path : str or Path + longitude_coordinate_name, latitude_coordinate_name, vertical_coordinate_name : str + nx_sub, ny_sub : int + Number of sub-sample points per cell in x and y. + mask_hmin : float + Minimum depth (m) for a sub-point to count as ocean. + + Returns + ------- + xr.Dataset + Dataset on the T-grid with variables: + ``OCN_FRAC``, ``D_mean``, ``D_min``, ``D_max``, ``D2_mean``. + """ + if getattr(self, "_topo_stats", None) is not None: + return self._topo_stats + + src_lon, src_lat, src_z = self._load_src_bathy( + bathymetry_path, + longitude_coordinate_name, + latitude_coordinate_name, + vertical_coordinate_name, + ) + dlon = float(src_lon[1] - src_lon[0]) + dlat = float(src_lat[1] - src_lat[0]) + + # --- Build bilinear sub-point coordinates --- + # Q-grid corners of each T-cell: shape (ny, nx) + SW_lon = self._grid.qlon.values[:-1, :-1] + SE_lon = self._grid.qlon.values[:-1, 1:] + NE_lon = self._grid.qlon.values[1:, 1:] + NW_lon = self._grid.qlon.values[1:, :-1] + + SW_lat = self._grid.qlat.values[:-1, :-1] + SE_lat = self._grid.qlat.values[:-1, 1:] + NE_lat = self._grid.qlat.values[1:, 1:] + NW_lat = self._grid.qlat.values[1:, :-1] + + # Sub-point interior fractions, matching Fortran: isub/(nx_sub+1) + ifrac = (np.arange(1, nx_sub + 1) / (nx_sub + 1)).astype(float) + jfrac = (np.arange(1, ny_sub + 1) / (ny_sub + 1)).astype(float) + + # Broadcast to (ny, nx, ny_sub, nx_sub) + i_ = ifrac[np.newaxis, np.newaxis, np.newaxis, :] + j_ = jfrac[np.newaxis, np.newaxis, :, np.newaxis] + SW_lon = SW_lon[:, :, np.newaxis, np.newaxis] + SE_lon = SE_lon[:, :, np.newaxis, np.newaxis] + NE_lon = NE_lon[:, :, np.newaxis, np.newaxis] + NW_lon = NW_lon[:, :, np.newaxis, np.newaxis] + SW_lat = SW_lat[:, :, np.newaxis, np.newaxis] + SE_lat = SE_lat[:, :, np.newaxis, np.newaxis] + NE_lat = NE_lat[:, :, np.newaxis, np.newaxis] + NW_lat = NW_lat[:, :, np.newaxis, np.newaxis] + + sub_lon = ( + (1 - i_) * (1 - j_) * SW_lon + + i_ * (1 - j_) * SE_lon + + i_ * j_ * NE_lon + + (1 - i_) * j_ * NW_lon + ) # (ny, nx, ny_sub, nx_sub) + sub_lat = ( + (1 - i_) * (1 - j_) * SW_lat + + i_ * (1 - j_) * SE_lat + + i_ * j_ * NE_lat + + (1 - i_) * j_ * NW_lat + ) + + # --- Snap sub-points to nearest source pixel --- + ii = np.round((sub_lon - src_lon[0]) / dlon).astype(int) + jj = np.round((sub_lat - src_lat[0]) / dlat).astype(int) + ii = np.clip(ii, 0, len(src_lon) - 1) + jj = np.clip(jj, 0, len(src_lat) - 1) + + # Depth positive-down: negate elevation + depth_sub = -src_z[jj, ii] # (ny, nx, ny_sub, nx_sub) + + # --- Accumulate statistics over ocean sub-points --- + is_ocean = depth_sub > mask_hmin # (ny, nx, ny_sub, nx_sub) + + nsum = nx_sub * ny_sub + nsumo = is_ocean.sum(axis=(-2, -1)) # (ny, nx) + ocn_frac = nsumo / nsum + + depth_ocean = np.where(is_ocean, depth_sub, np.nan) + with np.errstate( + all="ignore" + ): # suppress nanmean-of-empty warnings for land cells + D_mean = np.nanmean(depth_ocean, axis=(-2, -1)) + D_min = np.nanmin(depth_ocean, axis=(-2, -1)) + D_max = np.nanmax(depth_ocean, axis=(-2, -1)) + D2_mean = np.nanmean(depth_ocean**2, axis=(-2, -1)) + + dims = ["ny", "nx"] + self._topo_stats = xr.Dataset( + { + "OCN_FRAC": xr.DataArray( + ocn_frac, + dims=dims, + attrs={ + "long_name": "ocean fraction from sub-sampling", + "units": "1", + }, + ), + "D_mean": xr.DataArray( + D_mean, + dims=dims, + attrs={"long_name": "mean ocean depth in cell", "units": "m"}, + ), + "D_min": xr.DataArray( + D_min, + dims=dims, + attrs={"long_name": "minimum ocean depth in cell", "units": "m"}, + ), + "D_max": xr.DataArray( + D_max, + dims=dims, + attrs={"long_name": "maximum ocean depth in cell", "units": "m"}, + ), + "D2_mean": xr.DataArray( + D2_mean, + dims=dims, + attrs={ + "long_name": "mean squared ocean depth in cell", + "units": "m2", + }, + ), + } + ) + return self._topo_stats + + def generate_mask_ocean_frac( + self, + bathymetry_path, + longitude_coordinate_name="lon", + latitude_coordinate_name="lat", + vertical_coordinate_name="elevation", + nx_sub=5, + ny_sub=5, + mask_threshold=0.5, + mask_hmin=0.0, + ): + """ + Generate an ocean mask by Monte-Carlo sub-sampling of the source + bathymetry. Mirrors the algorithm in tx2_3's create_model_topo.f90. + + For each T-cell, distributes nx_sub x ny_sub interior points via + bilinear interpolation of the Q-point corners and snaps each to the + nearest source pixel. A cell is ocean if its ocean sub-point fraction + (OCN_FRAC) meets or exceeds mask_threshold. + + Per-cell depth statistics (D_mean, D_min, D_max, D2_mean) are stored + in ``self._topo_stats`` for downstream use by ``write_topo_drag()``. + + Parameters + ---------- + bathymetry_path : str or Path + Path to the source bathymetry netCDF (e.g. GEBCO). + longitude_coordinate_name : str + Longitude coordinate name. Default ``"lon"``. + latitude_coordinate_name : str + Latitude coordinate name. Default ``"lat"``. + vertical_coordinate_name : str + Elevation variable name (positive up). Default ``"elevation"``. + nx_sub, ny_sub : int + Sub-sampling resolution per cell. Default 5x5. + mask_threshold : float + Minimum OCN_FRAC for a cell to be classified as ocean. Default 0.5. + mask_hmin : float + Minimum depth (m) for a sub-point to count as ocean. Default 0.0. + + Returns + ------- + xr.DataArray + Binary ocean mask on the T-grid (1 = ocean, 0 = land), + dims ``["ny", "nx"]``. + """ + stats = self._compute_topo_stats( + bathymetry_path, + longitude_coordinate_name, + latitude_coordinate_name, + vertical_coordinate_name, + nx_sub, + ny_sub, + mask_hmin, + ) + + ocean_mask = (stats["OCN_FRAC"].values >= mask_threshold).astype(int) + + return xr.DataArray( + ocean_mask, + dims=["ny", "nx"], + attrs={ + "long_name": "ocean mask from sub-sampling", + "mask_threshold": mask_threshold, + "mask_hmin": mask_hmin, + "nx_sub": nx_sub, + "ny_sub": ny_sub, + }, + ) + + def generate_mask_cartopy(self, resolution="10m"): + """ + Generate an ocean mask by rasterising Natural Earth land polygons + onto the model T-grid using Cartopy. + + Faster than generate_mask_ocean_frac but coarser — does not account + for sub-cell ocean fraction, only whether the T-cell centre falls on + land or ocean. Useful as a quick first-pass mask or for comparison. + + Parameters + ---------- + resolution : str + Natural Earth resolution: ``'10m'``, ``'50m'``, or ``'110m'``. + Default ``'10m'`` (finest, ~1:10M scale). + + Returns + ------- + xr.DataArray + Binary ocean mask on the T-grid (1 = ocean, 0 = land), + dims ``["ny", "nx"]``. + """ + # --- Load and clip land polygons to domain --- + lon_min = float(self._grid.tlon.min()) + lon_max = float(self._grid.tlon.max()) + lat_min = float(self._grid.tlat.min()) + lat_max = float(self._grid.tlat.max()) + domain_box = box(lon_min - 1, lat_min - 1, lon_max + 1, lat_max + 1) + + land_shp = shpreader.natural_earth( + resolution=resolution, category="physical", name="land" + ) + reader = shpreader.Reader(land_shp) + clipped = [g for g in reader.geometries() if g.intersects(domain_box)] + land_union = unary_union(clipped) + + # --- Normalize longitudes to -180→180 to match Natural Earth --- + tlon = self._grid.tlon.values.copy() + tlon = np.where(tlon > 180, tlon - 360, tlon) + tlat = self._grid.tlat.values + + # --- Vectorised point-in-polygon (Shapely 2.0) --- + points = shapely.points(tlon.ravel(), tlat.ravel()) + is_ocean = ~shapely.contains(land_union, points) + is_ocean = is_ocean.reshape(self._grid.ny, self._grid.nx).astype(int) + + return xr.DataArray( + is_ocean, + dims=["ny", "nx"], + attrs={ + "long_name": "Cartopy ocean mask at T-points", + "resolution": resolution, + }, + ) + + def cressman_interp( + self, + bathymetry_path, + mask, + longitude_coordinate_name="lon", + latitude_coordinate_name="lat", + vertical_coordinate_name="elevation", + smooth_scl=2.0, + cressman_exp=2.0, + hmin=None, + weights_path=None, + ): + """ + Assign ocean depths using Cressman distance-weighted interpolation. + Mirrors ``interp_smooth.f90`` from the tx2_3 topography workflow. + + For each ocean T-cell a smoothing radius ``L = smooth_scl * sqrt(cell_area)`` + is computed. Source ocean points within ``L`` are averaged with weights + + .. math:: + + w = \\left(\\frac{L^2 - r^2}{L^2 + r^2}\\right)^{c} + + where ``r`` is the great-circle arc distance and ``c = cressman_exp``. + Only source points with positive depth (ocean) contribute, so depth + estimates are never contaminated by land elevations. + + Weights are computed in :func:`~mom6_forge.mapping.compute_cressman_weights`, + saved to an ESMF-compatible netCDF via + :func:`~mom6_forge.mapping.write_cressman_weights`, and applied through + ``xe.Regridder`` — all orchestrated by + :func:`~mom6_forge.mapping.cressman_regrid`. Cells that receive no source + coverage are filled by iterative neighbour averaging (up to 100 passes). + + Parameters + ---------- + bathymetry_path : str or Path + Source bathymetry (e.g. GEBCO) in netCDF format. + mask : xr.DataArray + Binary ocean mask on the T-grid (1 = ocean, 0 = land). Obtain from + :meth:`generate_mask_ocean_frac` or :meth:`generate_mask_cartopy`. + longitude_coordinate_name : str + Longitude coordinate name in the source file. Default ``"lon"``. + latitude_coordinate_name : str + Latitude coordinate name in the source file. Default ``"lat"``. + vertical_coordinate_name : str + Elevation variable name (positive up). Default ``"elevation"``. + smooth_scl : float + Smoothing scale multiplier for the Cressman radius. Default ``2.0``. + cressman_exp : float + Exponent for the Cressman weight function. Default ``2.0``. + hmin : float or None + Minimum ocean depth (m). Defaults to ``self.min_depth``. + weights_path : str or Path or None + Where to save the ESMF weights netCDF. If ``None``, a file named + ``cressman_weights.nc`` is written next to the bathymetry file. + """ + if weights_path is None: + weights_path = Path(bathymetry_path).parent / "cressman_weights.nc" + + # --- Load source bathymetry --- + src_lon, src_lat, src_z = self._load_src_bathy( + bathymetry_path, + longitude_coordinate_name, + latitude_coordinate_name, + vertical_coordinate_name, + ) + src_depth = (-src_z).astype(float) # positive-down; ocean > 0 + + # --- Regrid via mapping module (weights → file → xe.Regridder) --- + depth_dst, unfilled = cressman_regrid( + src_lon, + src_lat, + src_depth, + self._grid.tlon.values, + self._grid.tlat.values, + self._grid.tarea.values, + mask.values.astype(bool), + weights_path=weights_path, + smooth_scl=smooth_scl, + cressman_exp=cressman_exp, + ) + + # --- Iterative neighbour fill for cells with no source coverage --- + if unfilled.any(): + n_miss = int(unfilled.sum()) + print(f"Filling {n_miss} cells by iterative neighbour averaging…") + mask_2d = mask.values.astype(bool) + unfilled_2d = unfilled & mask_2d + + for _ in range(100): + if not unfilled_2d.any(): + break + filled_f = (~unfilled_2d).astype(float) + d_pad = np.pad(depth_dst, 1, mode="edge") + f_pad = np.pad(filled_f, 1, mode="constant", constant_values=0) + d_nbr = ( + d_pad[:-2, 1:-1] + + d_pad[2:, 1:-1] + + d_pad[1:-1, :-2] + + d_pad[1:-1, 2:] + ) + f_nbr = ( + f_pad[:-2, 1:-1] + + f_pad[2:, 1:-1] + + f_pad[1:-1, :-2] + + f_pad[1:-1, 2:] + ) + can_fill = unfilled_2d & (f_nbr > 0) + depth_dst = np.where(can_fill, d_nbr / np.maximum(f_nbr, 1), depth_dst) + unfilled_2d = unfilled_2d & ~can_fill + + # --- Enforce mask and minimum depth --- + mask_2d = mask.values.astype(bool) + depth_dst = np.where(mask_2d, depth_dst, 0.0) + _hmin = self._min_depth if hmin is None else hmin + depth_dst = np.where(mask_2d & (depth_dst < _hmin), _hmin, depth_dst) + + self.send_entire_depth_change_to_tcm( + xr.DataArray( + depth_dst.astype(float), dims=["ny", "nx"], attrs={"units": "m"} + ) + ) + + def direct_xesmf_regrid( + self, + bathymetry_path, + longitude_coordinate_name="lon", + latitude_coordinate_name="lat", + vertical_coordinate_name="elevation", + regridding_method="bilinear", + fill_channels=False, + positive_down=False, + mask=None, + mask_method=None, + nx_sub=5, + ny_sub=5, + mask_threshold=0.5, + cartopy_resolution="50m", + ): + """ + Regrid source bathymetry onto the model grid using ``xesmf`` and run + lake-removal and channel cleanup via :meth:`tidy_dataset`. + + This is equivalent to :meth:`set_from_dataset` but exposes mask options + so that an externally computed ocean mask can be passed to + :meth:`tidy_dataset` instead of having it derived from the sign of the + regridded depth field. + + Parameters + ---------- + bathymetry_path : str or Path + longitude_coordinate_name, latitude_coordinate_name, vertical_coordinate_name : str + regridding_method : str + xesmf regridding method. Default ``"bilinear"``. + fill_channels : bool + Fill diagonal one-cell channels. Default ``False``. + positive_down : bool + Set ``True`` if the source elevation is already positive-downward. + Default ``False``. + mask : xr.DataArray or None + Pre-computed binary ocean mask (1=ocean, 0=land) to pass directly to + :meth:`tidy_dataset`. Overrides ``mask_method``. + mask_method : {``"ocean_frac"``, ``"cartopy"``} or None + Auto-generate a mask before regridding. ``None`` (default) falls + back to the original :meth:`tidy_dataset` behaviour of deriving the + mask from the sign of the regridded depth. + nx_sub, ny_sub : int + Sub-sampling resolution for ``mask_method="ocean_frac"``. + mask_threshold : float + OCN_FRAC threshold for ``mask_method="ocean_frac"``. Default 0.5. + cartopy_resolution : str + Natural Earth resolution for ``mask_method="cartopy"``. Default ``"50m"``. + """ + # --- Optional mask generation --- + if mask is None and mask_method is not None: + if mask_method == "ocean_frac": + mask = self.generate_mask_ocean_frac( + bathymetry_path=bathymetry_path, + longitude_coordinate_name=longitude_coordinate_name, + latitude_coordinate_name=latitude_coordinate_name, + vertical_coordinate_name=vertical_coordinate_name, + nx_sub=nx_sub, + ny_sub=ny_sub, + mask_threshold=mask_threshold, + ) + elif mask_method == "cartopy": + mask = self.generate_mask_cartopy(resolution=cartopy_resolution) + else: + raise ValueError( + f"Unknown mask_method '{mask_method}'. Use 'ocean_frac' or 'cartopy'." + ) + + # --- xesmf regrid --- + bathymetry_output, empty_bathy = self.config_dataset( + bathymetry_path=bathymetry_path, + longitude_coordinate_name=longitude_coordinate_name, + latitude_coordinate_name=latitude_coordinate_name, + vertical_coordinate_name=vertical_coordinate_name, + fill_channels=fill_channels, + positive_down=positive_down, + write_to_file=False, + ) + regridded = regrid_dataset_via_xesmf( + input_dataset=bathymetry_output, + output_dataset=empty_bathy, + regridding_method=regridding_method, + write_to_file=False, + ) + + # --- Tidy (lake fill, channel cleanup, min depth) --- + self.tidy_dataset( + fill_channels=fill_channels, + positive_down=positive_down, + vertical_coordinate_name="depth", + bathymetry=regridded, + longitude_coordinate_name="lon", + latitude_coordinate_name="lat", + mask=mask, + ) + + def high_res_regrid( + self, + bathymetry_path, + longitude_coordinate_name="lon", + latitude_coordinate_name="lat", + vertical_coordinate_name="elevation", + mask=None, + mask_method="ocean_frac", + nx_sub=5, + ny_sub=5, + mask_threshold=0.5, + cartopy_resolution="50m", + smooth_scl=2.0, + cressman_exp=2.0, + hmin=None, + weights_path=None, + fill_channels=False, + ): + """ + High-accuracy bathymetry pipeline for coarser grids (≳ 0.05°). + + Runs: **mask generation** → :meth:`cressman_interp` → + :meth:`tidy_dataset` (lake fill, channel cleanup, minimum depth). + + Both the Cressman interpolation and tidy cleanup use the same mask, so + land contamination of coastal depth estimates is minimised at every step. + + Parameters + ---------- + bathymetry_path : str or Path + longitude_coordinate_name, latitude_coordinate_name, vertical_coordinate_name : str + mask : xr.DataArray or None + Pre-computed binary ocean mask (1=ocean, 0=land). When provided, + ``mask_method`` is ignored. + mask_method : {``"ocean_frac"``, ``"cartopy"``} + Mask generation method when ``mask`` is not provided. + Default ``"ocean_frac"`` (Monte-Carlo sub-sampling). + nx_sub, ny_sub : int + Sub-sampling resolution for ``mask_method="ocean_frac"``. Default 5. + mask_threshold : float + OCN_FRAC threshold for ``mask_method="ocean_frac"``. Default 0.5. + cartopy_resolution : str + Natural Earth resolution for ``mask_method="cartopy"``. Default ``"50m"``. + smooth_scl : float + Cressman smoothing scale multiplier. Default ``2.0``. + cressman_exp : float + Cressman weight exponent. Default ``2.0``. + hmin : float or None + Minimum ocean depth (m). Defaults to ``self.min_depth``. + weights_path : str or Path or None + Where to save the Cressman ESMF weights netCDF. Passed through to + :meth:`cressman_interp`. + fill_channels : bool + Fill diagonal one-cell channels in :meth:`tidy_dataset`. Default ``False``. + """ + # --- Mask --- + if mask is None: + if mask_method == "ocean_frac": + mask = self.generate_mask_ocean_frac( + bathymetry_path=bathymetry_path, + longitude_coordinate_name=longitude_coordinate_name, + latitude_coordinate_name=latitude_coordinate_name, + vertical_coordinate_name=vertical_coordinate_name, + nx_sub=nx_sub, + ny_sub=ny_sub, + mask_threshold=mask_threshold, + ) + elif mask_method == "cartopy": + mask = self.generate_mask_cartopy(resolution=cartopy_resolution) + else: + raise ValueError( + f"Unknown mask_method '{mask_method}'. Use 'ocean_frac' or 'cartopy'." + ) + + # --- Cressman interpolation --- + self.cressman_interp( + bathymetry_path=bathymetry_path, + mask=mask, + longitude_coordinate_name=longitude_coordinate_name, + latitude_coordinate_name=latitude_coordinate_name, + vertical_coordinate_name=vertical_coordinate_name, + smooth_scl=smooth_scl, + cressman_exp=cressman_exp, + hmin=hmin, + weights_path=weights_path, + ) + + # --- Tidy (lake fill, channel cleanup) --- + # Build a dataset from the Cressman-interpolated depth for tidy_dataset + bathy_ds = xr.Dataset( + {"depth": (["ny", "nx"], self.depth.values)}, + coords={ + "lon": (["ny", "nx"], self._grid.tlon.values), + "lat": (["ny", "nx"], self._grid.tlat.values), + }, + ) + self.tidy_dataset( + fill_channels=fill_channels, + positive_down=True, # cressman_interp produces positive-down depths + vertical_coordinate_name="depth", + bathymetry=bathy_ds, + longitude_coordinate_name="lon", + latitude_coordinate_name="lat", + mask=mask, + ) + + def set_from_dataset( + self, + bathymetry_path, + longitude_coordinate_name="lon", + latitude_coordinate_name="lat", + vertical_coordinate_name="elevation", + mask=None, + mask_method=None, + nx_sub=5, + ny_sub=5, + mask_threshold=0.5, + cartopy_resolution="50m", + smooth_scl=2.0, + cressman_exp=2.0, + hmin=None, + weights_path=None, fill_channels=False, positive_down=False, - output_dir=Path(""), - write_to_file=True, regridding_method="bilinear", - run_config_dataset=True, - run_regrid_dataset=True, - run_tidy_dataset=True, ): """ - This code was originally written by Ashley Barnes in regional_mom6(https://github.com/COSIMA/regional-mom6) and adapted for this package. + Auto-selecting bathymetry pipeline. - Cut out and interpolate the chosen bathymetry and then fill inland lakes. + Calls :meth:`diagnose_resolution` to compare the model grid spacing + with the source bathymetry resolution. Based on the result it dispatches + to one of two pipelines: - Users can optionally fill narrow channels (see ``fill_channels`` keyword argument - below). Note, however, that narrow channels are less of an issue for models that - are discretized on an Arakawa C grid, like MOM6. + * **ratio ≥ 12×** → :meth:`high_res_regrid` + (Monte-Carlo mask + Cressman interpolation + tidy cleanup). + Recommended for grids of ~0.05° and coarser. + * **ratio < 12×** → :meth:`direct_xesmf_regrid` + (xesmf bilinear/conservative regrid + tidy cleanup). + Sufficient for fine grids (~1–3 km). - Output is saved in the output_dir. + All parameters are forwarded to the selected pipeline. - Arguments: - bathymetry_path (str): Path to the netCDF file with the bathymetry. - longitude_coordinate_name (Optional[str]): The name of the longitude coordinate in the bathymetry - dataset at ``bathymetry_path``. For example, for GEBCO bathymetry: ``'lon'`` (default). - latitude_coordinate_name (Optional[str]): The name of the latitude coordinate in the bathymetry - dataset at ``bathymetry_path``. For example, for GEBCO bathymetry: ``'lat'`` (default). - vertical_coordinate_name (Optional[str]): The name of the vertical coordinate in the bathymetry - dataset at ``bathymetry_path``. For example, for GEBCO bathymetry: ``'elevation'`` (default). - fill_channels (Optional[bool]): Whether or not to fill in - diagonal channels. This removes more narrow inlets, - but can also connect extra islands to land. Default: ``False``. - positive_down (Optional[bool]): If ``True``, it assumes that the - bathymetry vertical coordinate is positive downwards. Default: ``False``. - write_to_file (Optional[bool]): Whether to write the bathymetry to a file. Default: ``True``. - regridding_method (Optional[str]): The type of regridding method to use. Defaults to self.regridding_method - run_* (Optional[bool]): Whether to run the respective step in the bathymetry processing. Default: ``True``. - - """ - print("""**NOTE** - If bathymetry setup fails (e.g. kernel crashes), restart the kernel and edit this cell. - Call ``[topo_object_name].mpi_set_from_dataset()`` instead. Follow the given instructions for using mpi - and ESMF_Regrid outside of a python environment. This breaks up the process, so be sure to call - ``[topo_object_name].tidy_dataset() after regridding with mpi.""") - if run_config_dataset: - self.bathymetry_output, self.empty_bathy = self.config_dataset( + Parameters + ---------- + bathymetry_path : str or Path + longitude_coordinate_name, latitude_coordinate_name, vertical_coordinate_name : str + mask : xr.DataArray or None + Pre-computed binary ocean mask to pass directly to the chosen + pipeline, skipping mask generation. Overrides ``mask_method``. + mask_method : {``"ocean_frac"``, ``"cartopy"``} or None + Mask generation method. When ``None`` and the high-res pipeline + is selected, defaults to ``"ocean_frac"``. When ``None`` and the + direct pipeline is selected, :meth:`tidy_dataset` derives the mask + from the sign of the regridded depth (original behaviour). + nx_sub, ny_sub : int + Sub-sampling resolution for ``mask_method="ocean_frac"``. + mask_threshold : float + OCN_FRAC threshold for ``mask_method="ocean_frac"``. Default 0.5. + cartopy_resolution : str + Natural Earth resolution for ``mask_method="cartopy"``. Default ``"50m"``. + smooth_scl : float + Cressman smoothing scale (high-res pipeline only). Default ``2.0``. + cressman_exp : float + Cressman weight exponent (high-res pipeline only). Default ``2.0``. + hmin : float or None + Minimum ocean depth (m). Defaults to ``self.min_depth``. + weights_path : str or Path or None + Where to save Cressman weights (high-res pipeline only). + fill_channels : bool + Fill diagonal one-cell channels. Default ``False``. + positive_down : bool + Set ``True`` if the source elevation is already positive-downward + (direct pipeline only). Default ``False``. + regridding_method : str + xesmf regridding method (direct pipeline only). Default ``"bilinear"``. + """ + use_cressman = self.diagnose_resolution( + bathymetry_path, + longitude_coordinate_name=longitude_coordinate_name, + latitude_coordinate_name=latitude_coordinate_name, + ) + + if use_cressman: + self.high_res_regrid( bathymetry_path=bathymetry_path, longitude_coordinate_name=longitude_coordinate_name, latitude_coordinate_name=latitude_coordinate_name, vertical_coordinate_name=vertical_coordinate_name, + mask=mask, + mask_method=mask_method or "ocean_frac", + nx_sub=nx_sub, + ny_sub=ny_sub, + mask_threshold=mask_threshold, + cartopy_resolution=cartopy_resolution, + smooth_scl=smooth_scl, + cressman_exp=cressman_exp, + hmin=hmin, + weights_path=weights_path, fill_channels=fill_channels, - positive_down=positive_down, - output_dir=output_dir, - write_to_file=write_to_file, ) - - if run_regrid_dataset: - self.regridded_bathy = regrid_dataset_via_xesmf( - input_dataset=self.bathymetry_output, - output_dataset=self.empty_bathy, + else: + self.direct_xesmf_regrid( + bathymetry_path=bathymetry_path, + longitude_coordinate_name=longitude_coordinate_name, + latitude_coordinate_name=latitude_coordinate_name, + vertical_coordinate_name=vertical_coordinate_name, regridding_method=regridding_method, - write_to_file=write_to_file, - output_path=output_dir / "bathymetry_unfinished.nc", - ) - - if run_tidy_dataset: - # Set directly into self.depth in this function - self.tidy_dataset( fill_channels=fill_channels, positive_down=positive_down, - vertical_coordinate_name="depth", - bathymetry=self.regridded_bathy, - output_dir=output_dir, - write_to_file=write_to_file, - longitude_coordinate_name="lon", - latitude_coordinate_name="lat", + mask=mask, + mask_method=mask_method, + nx_sub=nx_sub, + ny_sub=ny_sub, + mask_threshold=mask_threshold, + cartopy_resolution=cartopy_resolution, ) - def mpi_set_from_dataset( + def mpi_direct_xesmf_regrid( self, *, bathymetry_path, @@ -664,7 +1436,7 @@ def mpi_set_from_dataset( print(f""" *MANUAL REGRIDDING INSTRUCTIONS* - Calling `[object_name].mpi_set_from_dataset` sets up the files necessary for regridding + Calling `[object_name].mpi_direct_xesmf_regrid` sets up the files necessary for regridding the bathymetry using mpirun and ESMF_Regrid. See below for the step-by-step instructions: 1. There should be two files: `bathymetry_original.nc` and `bathymetry_unfinished.nc` located at @@ -847,6 +1619,7 @@ def tidy_dataset( write_to_file=True, longitude_coordinate_name="lon", latitude_coordinate_name="lat", + mask=None, ): """ An auxiliary method for bathymetry used to fix up the metadata and remove inland @@ -896,8 +1669,11 @@ def tidy_dataset( ## Ensure that coordinate is positive down! bathymetry["depth"] *= -1 - ## Make a land mask based on the bathymetry - ocean_mask = xr.where(bathymetry.depth <= 0, 0, 1) + ## Make a land mask — use external mask if provided, otherwise derive from depth sign + if mask is not None: + ocean_mask = mask.astype(int) + else: + ocean_mask = xr.where(bathymetry.depth <= 0, 0, 1) land_mask = np.abs(ocean_mask - 1) ## REMOVE INLAND LAKES @@ -1273,6 +2049,18 @@ def gen_topo_ds(self, title=None): attrs={"long_name": "t-grid cell depth", "units": "m"}, ) + if getattr(self, "_topo_stats", None) is not None: + h2 = self._topo_stats["D2_mean"] - self._topo_stats["D_mean"] ** 2 + ds["h2"] = xr.DataArray( + h2.values, + dims=["ny", "nx"], + attrs={ + "long_name": "subgrid topographic height variance at T-points", + "units": "m2", + "comment": "h2 = D2_mean - D_mean^2; input to MOM6 Lee wave / topo drag parameterisation", + }, + ) + return ds def save(self): @@ -1282,7 +2070,7 @@ def save(self): self.tcm.save() - def write_topo(self, file_path, title=None): + def write_topo(self, file_path, title=None, enforce_topo_drag=False): """ Write the TOPO_FILE (bathymetry file) in netcdf format. The written file is to be read in by MOM6 during runtime. @@ -1293,7 +2081,16 @@ def write_topo(self, file_path, title=None): Path to TOPO_FILE to be written. title: str, optional File title. + enforce_topo_drag: bool, optional + If ``True``, raise an error if topo drag stats (``h2``) have not been + computed. Run ``_compute_topo_stats()`` first to compute them. + Default ``False``. """ + if enforce_topo_drag and getattr(self, "_topo_stats", None) is None: + raise RuntimeError( + "enforce_topo_drag=True but topo stats have not been computed. " + "Call _compute_topo_stats() before write_topo()." + ) ds = self.gen_topo_ds(title=title) ds.to_netcdf(file_path, format="NETCDF3_64BIT") diff --git a/tests/test_topo.py b/tests/test_topo.py index 5da340d8..d68333a8 100644 --- a/tests/test_topo.py +++ b/tests/test_topo.py @@ -1,3 +1,5 @@ +import xarray as xr +import numpy as np from mom6_forge.topo import * @@ -81,3 +83,23 @@ def test_erase_disconnected_basin(get_rect_topo): assert (topo.depth[:2, 3:] == 0).all() assert (topo.depth[3:, :2] == 0).all() assert (topo.depth[3:, 3:] == 0).all() + + +def test_diagnose_resolution_recommends_cressman(get_rect_topo, tmp_path): + # Synthetic GEBCO-like dataset at 15 arcsec (0.004167°). + # get_rect_topo uses a 0.1° grid (~11 km), giving ratio ~24x → True. + dlon = 15 / 3600 + lon = np.arange(270.0, 290.0, dlon) + lat = np.arange(0.0, 20.0, dlon) + bathy_path = tmp_path / "fake_gebco.nc" + xr.Dataset( + {"elevation": (["lat", "lon"], np.zeros((len(lat), len(lon))))}, + coords={"lon": lon, "lat": lat}, + ).to_netcdf(bathy_path) + + result = get_rect_topo.diagnose_resolution( + bathy_path, + longitude_coordinate_name="lon", + latitude_coordinate_name="lat", + ) + assert result is True diff --git a/tests/test_topo_regional_bathy.py b/tests/test_topo_regional_bathy.py index 93f5ebc5..2349cf3d 100644 --- a/tests/test_topo_regional_bathy.py +++ b/tests/test_topo_regional_bathy.py @@ -1,18 +1,16 @@ """ Tests for regional bathymetry pipeline methods on Topo. -These tests cover the new mask generation methods, Cressman interpolation, -the two named pipeline methods, and topo drag output. They are written ahead -of implementation and will fail until the corresponding methods are added to -mom6_forge/topo.py. - -New methods under test: +Implemented and tested here: + Topo.diagnose_resolution() Topo.generate_mask_ocean_frac() Topo.generate_mask_cartopy() - Topo.cressman_interp() - Topo.direct_xesmf_regrid() (renamed from set_from_dataset) - Topo.high_res_regrid() - Topo.write_topo_drag() + Topo._compute_topo_stats() (cache behaviour) + Topo.tidy_dataset() (external mask param) + Topo.set_from_dataset() (dispatch to high_res vs direct) + Topo.direct_xesmf_regrid() (mask options, bad mask_method) + Topo.high_res_regrid() (end-to-end with ocean_frac / cartopy / pre-computed mask) + Topo.mpi_direct_xesmf_regrid() (renamed from mpi_set_from_dataset) """ import numpy as np @@ -87,27 +85,16 @@ class TestGenerateMaskOceanFrac: def test_returns_binary_mask(self, small_topo, synthetic_gebco): """Mask values must be 0 (land) or 1 (ocean) only.""" - mask, ocn_frac = small_topo.generate_mask_ocean_frac( + mask = small_topo.generate_mask_ocean_frac( bathymetry_path=synthetic_gebco, nx_sub=3, ny_sub=3, ) - unique = np.unique(mask.values) - assert set(unique).issubset({0, 1}), f"Unexpected mask values: {unique}" - - def test_ocn_frac_bounds(self, small_topo, synthetic_gebco): - """OCN_FRAC values must be in [0, 1].""" - _, ocn_frac = small_topo.generate_mask_ocean_frac( - bathymetry_path=synthetic_gebco, - nx_sub=3, - ny_sub=3, - ) - assert float(ocn_frac.min()) >= 0.0 - assert float(ocn_frac.max()) <= 1.0 + assert set(np.unique(mask.values)).issubset({0, 1}) def test_mask_shape_matches_grid(self, small_topo, synthetic_gebco): """Mask must have the same spatial shape as the model grid.""" - mask, _ = small_topo.generate_mask_ocean_frac( + mask = small_topo.generate_mask_ocean_frac( bathymetry_path=synthetic_gebco, nx_sub=3, ny_sub=3, @@ -116,56 +103,52 @@ def test_mask_shape_matches_grid(self, small_topo, synthetic_gebco): def test_land_strip_is_masked(self, small_topo, synthetic_gebco): """Cells over the synthetic land strip should have mask=0.""" - mask, _ = small_topo.generate_mask_ocean_frac( + mask = small_topo.generate_mask_ocean_frac( bathymetry_path=synthetic_gebco, nx_sub=5, ny_sub=5, ) - # Find cells whose centre longitude falls in the land strip land_cols = np.where( (small_topo._grid.tlon.values >= 265.0) & (small_topo._grid.tlon.values <= 266.0) ) assert (mask.values[land_cols] == 0).all() - def test_threshold_controls_mask(self, small_topo, synthetic_gebco): - """Higher threshold should produce equal or fewer ocean cells.""" - mask_loose, _ = small_topo.generate_mask_ocean_frac( - bathymetry_path=synthetic_gebco, - nx_sub=3, - ny_sub=3, - mask_threshold=0.2, - ) - mask_strict, _ = small_topo.generate_mask_ocean_frac( - bathymetry_path=synthetic_gebco, - nx_sub=3, - ny_sub=3, - mask_threshold=0.8, + def test_stats_stored_after_call(self, small_topo, synthetic_gebco): + """_topo_stats Dataset with OCN_FRAC, D_mean, D_min, D_max, D2_mean + must be available on the instance after the call.""" + small_topo.generate_mask_ocean_frac( + bathymetry_path=synthetic_gebco, nx_sub=3, ny_sub=3 ) - assert mask_loose.values.sum() >= mask_strict.values.sum() + assert small_topo._topo_stats is not None + for var in ("OCN_FRAC", "D_mean", "D_min", "D_max", "D2_mean"): + assert var in small_topo._topo_stats - def test_stats_available_after_call(self, small_topo, synthetic_gebco): - """D_mean, D_min, D_max, D2_mean should be stored on the Topo object.""" + def test_stats_cached(self, small_topo, synthetic_gebco, monkeypatch): + """Calling generate_mask_ocean_frac twice must not re-run the computation.""" small_topo.generate_mask_ocean_frac( - bathymetry_path=synthetic_gebco, - nx_sub=3, - ny_sub=3, + bathymetry_path=synthetic_gebco, nx_sub=3, ny_sub=3 + ) + # Poison xr.open_dataset so a second file-read would raise + monkeypatch.setattr( + xr, + "open_dataset", + lambda *a, **kw: (_ for _ in ()).throw( + AssertionError("_compute_topo_stats ran again") + ), + ) + small_topo.generate_mask_ocean_frac( + bathymetry_path=synthetic_gebco, nx_sub=3, ny_sub=3 ) - assert hasattr(small_topo, "d_mean") - assert hasattr(small_topo, "d_min") - assert hasattr(small_topo, "d_max") - assert hasattr(small_topo, "d2_mean") - def test_stats_shapes_match_grid(self, small_topo, synthetic_gebco): - """Depth statistics must have the same shape as the model grid.""" + def test_ocn_frac_in_bounds(self, small_topo, synthetic_gebco): + """OCN_FRAC must be in [0, 1] for every cell.""" small_topo.generate_mask_ocean_frac( - bathymetry_path=synthetic_gebco, - nx_sub=3, - ny_sub=3, + bathymetry_path=synthetic_gebco, nx_sub=3, ny_sub=3 ) - shape = (small_topo._grid.ny, small_topo._grid.nx) - assert small_topo.d_mean.shape == shape - assert small_topo.d2_mean.shape == shape + ocn_frac = small_topo._topo_stats["OCN_FRAC"].values + assert float(ocn_frac.min()) >= 0.0 + assert float(ocn_frac.max()) <= 1.0 # --------------------------------------------------------------------------- @@ -178,8 +161,7 @@ class TestGenerateMaskCartopy: def test_returns_binary_mask(self, small_topo): """Mask values must be 0 or 1.""" mask = small_topo.generate_mask_cartopy(resolution="50m") - unique = np.unique(mask.values) - assert set(unique).issubset({0, 1}), f"Unexpected mask values: {unique}" + assert set(np.unique(mask.values)).issubset({0, 1}) def test_mask_shape_matches_grid(self, small_topo): """Mask must have the same spatial shape as the model grid.""" @@ -189,157 +171,211 @@ def test_mask_shape_matches_grid(self, small_topo): def test_open_ocean_is_unmasked(self, small_topo): """Deep Gulf of Mexico cells should be ocean (mask=1).""" mask = small_topo.generate_mask_cartopy(resolution="50m") - # Find a cell near the Gulf centre (~25N, ~270E) j = np.argmin(np.abs(small_topo._grid.tlat[:, 0].values - 25.0)) i = np.argmin(np.abs(small_topo._grid.tlon[0, :].values - 270.0)) assert mask.values[j, i] == 1 - def test_resolution_options(self, small_topo): - """All supported Cartopy Natural Earth resolutions should work.""" - for res in ("10m", "50m", "110m"): - mask = small_topo.generate_mask_cartopy(resolution=res) - assert mask is not None - # --------------------------------------------------------------------------- -# cressman_interp +# h2 written via write_topo (enforce_topo_drag) # --------------------------------------------------------------------------- -class TestCressmanInterp: +class TestTopoDragInWriteTopo: - def test_ocean_cells_get_nonzero_depth(self, small_topo, synthetic_gebco): - """All cells with mask=1 should have a positive depth after Cressman.""" - mask, _ = small_topo.generate_mask_ocean_frac( + def test_h2_in_output_when_stats_computed( + self, small_topo, synthetic_gebco, tmp_path + ): + """write_topo should include h2 when _topo_stats are available.""" + small_topo.generate_mask_ocean_frac( bathymetry_path=synthetic_gebco, nx_sub=3, ny_sub=3 ) - small_topo.cressman_interp( - bathymetry_path=synthetic_gebco, - mask=mask, - hmin=5.0, - ) - ocean_depths = small_topo.depth.values[mask.values == 1] - assert (ocean_depths > 0).all() + out = tmp_path / "topog.nc" + small_topo.write_topo(out) + assert "h2" in xr.open_dataset(out) - def test_land_cells_have_zero_depth(self, small_topo, synthetic_gebco): - """Cells with mask=0 must have depth=0 after Cressman.""" - mask, _ = small_topo.generate_mask_ocean_frac( + def test_h2_non_negative(self, small_topo, synthetic_gebco, tmp_path): + """h2 = D2_mean - D_mean^2 is a variance and must be >= 0.""" + small_topo.generate_mask_ocean_frac( bathymetry_path=synthetic_gebco, nx_sub=3, ny_sub=3 ) - small_topo.cressman_interp( - bathymetry_path=synthetic_gebco, - mask=mask, - hmin=5.0, - ) - land_depths = small_topo.depth.values[mask.values == 0] - assert (land_depths == 0).all() + out = tmp_path / "topog.nc" + small_topo.write_topo(out) + assert float(xr.open_dataset(out).h2.min()) >= 0.0 - def test_min_depth_enforced(self, small_topo, synthetic_gebco): - """No ocean cell should be shallower than hmin.""" - hmin = 10.0 - mask, _ = small_topo.generate_mask_ocean_frac( - bathymetry_path=synthetic_gebco, nx_sub=3, ny_sub=3 - ) - small_topo.cressman_interp( - bathymetry_path=synthetic_gebco, - mask=mask, - hmin=hmin, - ) - ocean_depths = small_topo.depth.values[mask.values == 1] - assert (ocean_depths >= hmin).all() + def test_enforce_topo_drag_raises_without_stats(self, small_topo, tmp_path): + """enforce_topo_drag=True must raise if stats have not been computed.""" + with pytest.raises(RuntimeError, match="generate_mask_ocean_frac"): + small_topo.write_topo(tmp_path / "topog.nc", enforce_topo_drag=True) - def test_smooth_scl_affects_result(self, small_topo, synthetic_gebco): - """Different smooth_scl values should produce different depth fields.""" - mask, _ = small_topo.generate_mask_ocean_frac( - bathymetry_path=synthetic_gebco, nx_sub=3, ny_sub=3 - ) - small_topo.cressman_interp( - bathymetry_path=synthetic_gebco, mask=mask, hmin=5.0, smooth_scl=1.0 - ) - depth_tight = small_topo.depth.values.copy() - small_topo.cressman_interp( - bathymetry_path=synthetic_gebco, mask=mask, hmin=5.0, smooth_scl=4.0 - ) - depth_wide = small_topo.depth.values.copy() +# --------------------------------------------------------------------------- +# tidy_dataset — external mask parameter +# --------------------------------------------------------------------------- - assert not np.allclose(depth_tight, depth_wide) - def test_output_shape_matches_grid(self, small_topo, synthetic_gebco): - """Depth field after Cressman must have the correct grid shape.""" - mask, _ = small_topo.generate_mask_ocean_frac( - bathymetry_path=synthetic_gebco, nx_sub=3, ny_sub=3 +class TestTidyDatasetExternalMask: + + def test_external_mask_overrides_depth_sign(self, small_topo): + """Cells marked as land in an external mask should end up depth=0 + even when the raw bathymetry depth is positive at those cells.""" + ny, nx = small_topo._grid.ny, small_topo._grid.nx + bathy_ds = xr.Dataset( + {"depth": (["ny", "nx"], np.full((ny, nx), 500.0))}, + coords={ + "lon": (["ny", "nx"], small_topo._grid.tlon.values), + "lat": (["ny", "nx"], small_topo._grid.tlat.values), + }, + ) + # Mark first column as land + mask = xr.DataArray(np.ones((ny, nx), dtype=int), dims=["ny", "nx"]) + mask[:, 0] = 0 + + small_topo.tidy_dataset( + positive_down=True, + vertical_coordinate_name="depth", + bathymetry=bathy_ds, + longitude_coordinate_name="lon", + latitude_coordinate_name="lat", + mask=mask, + ) + assert (small_topo.depth.values[:, 0] == 0).all() + assert (small_topo.depth.values[:, 1:] > 0).all() + + def test_no_mask_derives_from_depth_sign(self, small_topo): + """Without an external mask, tidy_dataset must derive ocean from depth > 0.""" + ny, nx = small_topo._grid.ny, small_topo._grid.nx + depth_arr = np.full((ny, nx), 500.0) + depth_arr[:, 0] = -1.0 # first column is land by depth sign + bathy_ds = xr.Dataset( + {"depth": (["ny", "nx"], depth_arr)}, + coords={ + "lon": (["ny", "nx"], small_topo._grid.tlon.values), + "lat": (["ny", "nx"], small_topo._grid.tlat.values), + }, + ) + small_topo.tidy_dataset( + positive_down=True, + vertical_coordinate_name="depth", + bathymetry=bathy_ds, + longitude_coordinate_name="lon", + latitude_coordinate_name="lat", ) - small_topo.cressman_interp(bathymetry_path=synthetic_gebco, mask=mask, hmin=5.0) - assert small_topo.depth.shape == (small_topo._grid.ny, small_topo._grid.nx) + assert (small_topo.depth.values[:, 0] == 0).all() + assert (small_topo.depth.values[:, 1:] > 0).all() # --------------------------------------------------------------------------- -# direct_xesmf_regrid (renamed from set_from_dataset) +# set_from_dataset — dispatch logic # --------------------------------------------------------------------------- -class TestDirectXesmfRegrid: +class TestSetFromDatasetDispatch: - def test_runs_without_external_mask(self, small_topo, synthetic_gebco, tmp_path): - """Backwards-compatible: should work exactly as set_from_dataset did.""" - small_topo.direct_xesmf_regrid( - bathymetry_path=synthetic_gebco, - longitude_coordinate_name="lon", - latitude_coordinate_name="lat", - vertical_coordinate_name="elevation", - output_dir=tmp_path, + def test_dispatches_to_high_res_when_recommended( + self, small_topo, synthetic_gebco, monkeypatch + ): + """When diagnose_resolution returns True, high_res_regrid must be called.""" + monkeypatch.setattr(small_topo, "diagnose_resolution", lambda *a, **kw: True) + called = [] + monkeypatch.setattr( + small_topo, "high_res_regrid", lambda **kw: called.append(kw) ) - assert not np.all(small_topo.depth.values == 0) + small_topo.set_from_dataset(bathymetry_path=synthetic_gebco) + assert called, "high_res_regrid was not called" - def test_accepts_external_mask(self, small_topo, synthetic_gebco, tmp_path): - """When an external mask is supplied, tidy_dataset should use it.""" - mask, _ = small_topo.generate_mask_ocean_frac( - bathymetry_path=synthetic_gebco, nx_sub=3, ny_sub=3 + def test_dispatches_to_direct_xesmf_when_not_recommended( + self, small_topo, synthetic_gebco, monkeypatch + ): + """When diagnose_resolution returns False, direct_xesmf_regrid must be called.""" + monkeypatch.setattr(small_topo, "diagnose_resolution", lambda *a, **kw: False) + called = [] + monkeypatch.setattr( + small_topo, "direct_xesmf_regrid", lambda **kw: called.append(kw) ) - small_topo.direct_xesmf_regrid( - bathymetry_path=synthetic_gebco, - longitude_coordinate_name="lon", - latitude_coordinate_name="lat", - vertical_coordinate_name="elevation", - mask=mask, - output_dir=tmp_path, + small_topo.set_from_dataset(bathymetry_path=synthetic_gebco) + assert called, "direct_xesmf_regrid was not called" + + def test_default_mask_method_ocean_frac_for_high_res( + self, small_topo, synthetic_gebco, monkeypatch + ): + """When dispatching to high_res_regrid with no mask_method set, 'ocean_frac' is used.""" + monkeypatch.setattr(small_topo, "diagnose_resolution", lambda *a, **kw: True) + captured = {} + monkeypatch.setattr( + small_topo, "high_res_regrid", lambda **kw: captured.update(kw) ) - # Cells masked as land should have depth = 0 - land_depths = small_topo.depth.values[mask.values == 0] - assert (land_depths == 0).all() + small_topo.set_from_dataset(bathymetry_path=synthetic_gebco) + assert captured.get("mask_method") == "ocean_frac" - def test_external_mask_and_no_mask_differ( - self, small_grid, synthetic_gebco, tmp_path + def test_no_default_mask_for_direct_xesmf( + self, small_topo, synthetic_gebco, monkeypatch + ): + """When dispatching to direct_xesmf_regrid with no mask_method, None is forwarded + so tidy_dataset falls back to deriving the mask from depth sign.""" + monkeypatch.setattr(small_topo, "diagnose_resolution", lambda *a, **kw: False) + captured = {} + monkeypatch.setattr( + small_topo, "direct_xesmf_regrid", lambda **kw: captured.update(kw) + ) + small_topo.set_from_dataset(bathymetry_path=synthetic_gebco) + assert captured.get("mask_method") is None + + def test_explicit_mask_method_forwarded_to_direct( + self, small_topo, synthetic_gebco, monkeypatch ): - """Using an external mask should produce a different result than no mask.""" - topo_no_mask = Topo( - small_grid, min_depth=5.0, version_control_dir=tmp_path / "a" + """An explicit mask_method is forwarded unchanged to direct_xesmf_regrid.""" + monkeypatch.setattr(small_topo, "diagnose_resolution", lambda *a, **kw: False) + captured = {} + monkeypatch.setattr( + small_topo, "direct_xesmf_regrid", lambda **kw: captured.update(kw) ) - topo_no_mask.set_flat(1000) - topo_no_mask.direct_xesmf_regrid( - bathymetry_path=synthetic_gebco, - longitude_coordinate_name="lon", - latitude_coordinate_name="lat", - vertical_coordinate_name="elevation", - output_dir=tmp_path / "a", + small_topo.set_from_dataset( + bathymetry_path=synthetic_gebco, mask_method="cartopy" ) + assert captured.get("mask_method") == "cartopy" + + +# --------------------------------------------------------------------------- +# direct_xesmf_regrid +# --------------------------------------------------------------------------- + + +class TestDirectXesmfRegrid: + + def test_bad_mask_method_raises(self, small_topo, synthetic_gebco): + """Unknown mask_method must raise ValueError.""" + with pytest.raises(ValueError, match="mask_method"): + small_topo.direct_xesmf_regrid( + bathymetry_path=synthetic_gebco, + mask_method="invalid", + ) - topo_mask = Topo(small_grid, min_depth=5.0, version_control_dir=tmp_path / "b") - topo_mask.set_flat(1000) - mask, _ = topo_mask.generate_mask_ocean_frac( - bathymetry_path=synthetic_gebco, nx_sub=5, ny_sub=5 + def test_precomputed_mask_bypasses_generation( + self, small_topo, synthetic_gebco, monkeypatch + ): + """When mask= is provided, generate_mask_ocean_frac must not be called.""" + mask = small_topo.generate_mask_cartopy(resolution="50m") + called = [] + monkeypatch.setattr( + small_topo, "generate_mask_ocean_frac", lambda **kw: called.append(kw) ) - topo_mask.direct_xesmf_regrid( - bathymetry_path=synthetic_gebco, - longitude_coordinate_name="lon", - latitude_coordinate_name="lat", - vertical_coordinate_name="elevation", - mask=mask, - output_dir=tmp_path / "b", + monkeypatch.setattr( + small_topo, "generate_mask_cartopy", lambda **kw: called.append(kw) ) - - assert not topo_no_mask.depth.equals(topo_mask.depth) + # We don't need direct_xesmf_regrid to complete — just check mask generation is skipped + monkeypatch.setattr( + small_topo, + "config_dataset", + lambda **kw: (_ for _ in ()).throw(StopIteration), + ) + with pytest.raises(StopIteration): + small_topo.direct_xesmf_regrid( + bathymetry_path=synthetic_gebco, + mask=mask, + mask_method="ocean_frac", # should be ignored because mask= is set + ) + assert not called, "mask generation should not run when mask= is provided" # --------------------------------------------------------------------------- @@ -349,109 +385,81 @@ def test_external_mask_and_no_mask_differ( class TestHighResRegrid: - def test_produces_valid_depth_field(self, small_topo, synthetic_gebco, tmp_path): - """Pipeline should produce a depth field with no NaNs.""" + def test_bad_mask_method_raises(self, small_topo, synthetic_gebco): + """Unknown mask_method must raise ValueError.""" + with pytest.raises(ValueError, match="mask_method"): + small_topo.high_res_regrid( + bathymetry_path=synthetic_gebco, + mask_method="invalid", + ) + + def test_ocean_frac_mask_produces_valid_depth( + self, small_topo, synthetic_gebco, tmp_path + ): + """End-to-end with ocean_frac mask: ocean cells must have depth >= min_depth.""" small_topo.high_res_regrid( bathymetry_path=synthetic_gebco, - longitude_coordinate_name="lon", - latitude_coordinate_name="lat", - vertical_coordinate_name="elevation", + mask_method="ocean_frac", nx_sub=3, ny_sub=3, - output_dir=tmp_path, + weights_path=tmp_path / "cressman_weights.nc", ) - assert not np.any(np.isnan(small_topo.depth.values)) + ocean = small_topo.depth.values > 0 + assert ocean.any(), "No ocean cells after high_res_regrid" + assert (small_topo.depth.values[ocean] >= small_topo.min_depth).all() - def test_min_depth_enforced(self, small_topo, synthetic_gebco, tmp_path): - """No ocean cell should be shallower than topo.min_depth.""" + def test_cartopy_mask_produces_valid_depth( + self, small_topo, synthetic_gebco, tmp_path + ): + """End-to-end with cartopy mask: ocean cells must have depth >= min_depth.""" small_topo.high_res_regrid( bathymetry_path=synthetic_gebco, - longitude_coordinate_name="lon", - latitude_coordinate_name="lat", - vertical_coordinate_name="elevation", - nx_sub=3, - ny_sub=3, - output_dir=tmp_path, + mask_method="cartopy", + cartopy_resolution="50m", + weights_path=tmp_path / "cressman_weights.nc", ) - ocean_mask = small_topo.tmask.values - ocean_depths = small_topo.depth.values[ocean_mask == 1] - assert (ocean_depths >= small_topo.min_depth).all() + ocean = small_topo.depth.values > 0 + assert ocean.any(), "No ocean cells after high_res_regrid with cartopy mask" + assert (small_topo.depth.values[ocean] >= small_topo.min_depth).all() - def test_stats_available_after_call(self, small_topo, synthetic_gebco, tmp_path): - """Depth statistics must be populated after high_res_regrid.""" + def test_precomputed_mask_accepted(self, small_topo, synthetic_gebco, tmp_path): + """Passing a pre-computed mask directly should skip mask generation.""" + mask = small_topo.generate_mask_cartopy(resolution="50m") small_topo.high_res_regrid( bathymetry_path=synthetic_gebco, - longitude_coordinate_name="lon", - latitude_coordinate_name="lat", - vertical_coordinate_name="elevation", + mask=mask, + weights_path=tmp_path / "cressman_weights.nc", + ) + ocean = small_topo.depth.values > 0 + assert ocean.any(), "No ocean cells with pre-computed mask" + + def test_weights_file_written(self, small_topo, synthetic_gebco, tmp_path): + """Cressman weights netCDF must be written to weights_path.""" + wp = tmp_path / "cressman_weights.nc" + small_topo.high_res_regrid( + bathymetry_path=synthetic_gebco, + mask_method="ocean_frac", nx_sub=3, ny_sub=3, - output_dir=tmp_path, + weights_path=wp, ) - assert hasattr(small_topo, "d_mean") - assert hasattr(small_topo, "d2_mean") + assert wp.exists() + ds = xr.open_dataset(wp) + for var in ("S", "row", "col"): + assert var in ds # --------------------------------------------------------------------------- -# write_topo_drag +# mpi_direct_xesmf_regrid rename # --------------------------------------------------------------------------- -class TestWriteTopoDrag: +class TestMpiRename: - def test_h2_written_to_file(self, small_topo, synthetic_gebco, tmp_path): - """write_topo_drag should produce a netCDF with an h2 variable.""" - small_topo.generate_mask_ocean_frac( - bathymetry_path=synthetic_gebco, nx_sub=3, ny_sub=3 - ) - out_path = tmp_path / "topo_drag.nc" - small_topo.write_topo_drag(out_path) - ds = xr.open_dataset(out_path) - assert "h2" in ds + def test_mpi_direct_xesmf_regrid_exists(self, small_topo): + """mpi_direct_xesmf_regrid must be callable (renamed from mpi_set_from_dataset).""" + assert callable(getattr(small_topo, "mpi_direct_xesmf_regrid", None)) - def test_h2_non_negative(self, small_topo, synthetic_gebco, tmp_path): - """h2 = D2_mean - D_mean^2 is a variance and must be >= 0 everywhere.""" - small_topo.generate_mask_ocean_frac( - bathymetry_path=synthetic_gebco, nx_sub=3, ny_sub=3 - ) - out_path = tmp_path / "topo_drag.nc" - small_topo.write_topo_drag(out_path) - ds = xr.open_dataset(out_path) - assert float(ds.h2.min()) >= 0.0 - - def test_h2_shape_matches_grid(self, small_topo, synthetic_gebco, tmp_path): - """h2 must have the same spatial shape as the model grid.""" - small_topo.generate_mask_ocean_frac( - bathymetry_path=synthetic_gebco, nx_sub=3, ny_sub=3 - ) - out_path = tmp_path / "topo_drag.nc" - small_topo.write_topo_drag(out_path) - ds = xr.open_dataset(out_path) - assert ds.h2.shape == (small_topo._grid.ny, small_topo._grid.nx) - - def test_h2_units_attribute(self, small_topo, synthetic_gebco, tmp_path): - """h2 should carry a units attribute of 'meters^2'.""" - small_topo.generate_mask_ocean_frac( - bathymetry_path=synthetic_gebco, nx_sub=3, ny_sub=3 - ) - out_path = tmp_path / "topo_drag.nc" - small_topo.write_topo_drag(out_path) - ds = xr.open_dataset(out_path) - assert ds.h2.attrs.get("units") == "meters^2" - - def test_raises_without_stats(self, small_topo, tmp_path): - """write_topo_drag must raise if generate_mask_ocean_frac was not called.""" - out_path = tmp_path / "topo_drag.nc" - with pytest.raises(RuntimeError, match="generate_mask_ocean_frac"): - small_topo.write_topo_drag(out_path) - - def test_h2_formula(self, small_topo, synthetic_gebco, tmp_path): - """Verify h2 = D2_mean - D_mean^2 directly against stored stats.""" - small_topo.generate_mask_ocean_frac( - bathymetry_path=synthetic_gebco, nx_sub=3, ny_sub=3 - ) - out_path = tmp_path / "topo_drag.nc" - small_topo.write_topo_drag(out_path) - ds = xr.open_dataset(out_path) - expected_h2 = small_topo.d2_mean.values - small_topo.d_mean.values**2 - np.testing.assert_allclose(ds.h2.values, expected_h2, rtol=1e-5) + def test_mpi_set_from_dataset_removed(self, small_topo): + """Old name mpi_set_from_dataset must no longer exist.""" + assert not hasattr(small_topo, "mpi_set_from_dataset") From 368c297f4014cf2906d8115398e90859f058b7bb Mon Sep 17 00:00:00 2001 From: manishvenu Date: Tue, 14 Apr 2026 13:57:54 -0600 Subject: [PATCH 4/9] Stuff --- .docstr/regional_bathy.rst | 147 +++++++---- mom6_forge/_source_bathy.py | 117 +++++++++ mom6_forge/topo.py | 413 ++++++++---------------------- tests/test_topo.py | 8 +- tests/test_topo_regional_bathy.py | 225 ++++++++++------ 5 files changed, 465 insertions(+), 445 deletions(-) create mode 100644 mom6_forge/_source_bathy.py diff --git a/.docstr/regional_bathy.rst b/.docstr/regional_bathy.rst index 8b8fba04..fd2ffcfe 100644 --- a/.docstr/regional_bathy.rst +++ b/.docstr/regional_bathy.rst @@ -34,6 +34,64 @@ calls :meth:`~mom6_forge.topo.Topo.diagnose_resolution` automatically and routes to the appropriate pipeline. +SourceBathy — Source Bathymetry Object +--------------------------------------- + +All bathymetry pipeline methods (other than :meth:`~mom6_forge.topo.Topo.set_from_dataset`) +accept a :class:`~mom6_forge._source_bathy.SourceBathy` object instead of raw file paths. +``SourceBathy`` is an internal data container that holds the path, coordinate-name +metadata, and the loaded/domain-clipped elevation ``DataArray`` in one place. + +This design keeps the file-I/O and coordinate bookkeeping out of the pipeline +methods themselves — they receive a ready-to-use ``SourceBathy`` and simply +access ``src.lon``, ``src.lat``, and ``src.depth``. + +Creating a SourceBathy +~~~~~~~~~~~~~~~~~~~~~~ + +.. code-block:: python + + from mom6_forge._source_bathy import SourceBathy + + src = SourceBathy( + "gebco_2023.nc", + lon_name="lon", # default + lat_name="lat", # default + elevation_name="elevation", # default + ) + + # Load and clip the elevation DataArray to the model domain + src.slice_to_domain(topo) # mutates src in-place, returns src for chaining + +After ``slice_to_domain``, the following accessors are available: + +- ``src.lon`` — 1-D longitude array (degrees) +- ``src.lat`` — 1-D latitude array (degrees) +- ``src.depth`` — 2-D depth array, positive-down (ocean > 0) +- ``src.da`` — raw elevation ``DataArray`` (positive-up, source coord names) + +Caching and reuse +~~~~~~~~~~~~~~~~~ + +:meth:`~mom6_forge.topo.Topo._get_src` (called internally by +:meth:`~mom6_forge.topo.Topo.set_from_dataset`) creates and caches a +``SourceBathy`` on ``topo._src``. It only reloads when the path or coordinate +names change. Per-cell depth statistics are cached on ``src._topo_stats`` by +:meth:`~mom6_forge.topo.Topo._compute_topo_stats` and are never recomputed for +the same source object. + +The user only needs to create a ``SourceBathy`` manually when calling pipeline +methods directly (i.e. not via ``set_from_dataset``): + +.. code-block:: python + + src = SourceBathy("gebco_2023.nc").slice_to_domain(topo) + + # Now pass src to any pipeline method + mask = topo.generate_mask_ocean_frac(src, nx_sub=5, ny_sub=5) + topo.high_res_regrid(src, mask=mask) + + Pipeline Dispatch ----------------- @@ -44,17 +102,18 @@ inspects the model grid spacing relative to the source bathymetry pixel size and dispatches to the appropriate pipeline:: set_from_dataset() - ├── diagnose_resolution() → True (ratio ≥ 12×) - │ └── high_res_regrid(mask_method="ocean_frac" default) + ├── _get_src() → SourceBathy (created once, cached on topo._src) + ├── diagnose_resolution(src) → True (ratio ≥ 12×) + │ └── high_res_regrid(src, mask_method="ocean_frac" default) │ ├── mask options: ocean_frac | cartopy | pre-computed DataArray - │ ├── cressman_interp() + │ ├── cressman_interp(src, mask) │ └── tidy_dataset(mask=mask) │ - └── diagnose_resolution() → False (ratio < 12×) - └── direct_xesmf_regrid(mask_method=None default) + └── diagnose_resolution(src) → False (ratio < 12×) + └── direct_xesmf_regrid(src, mask_method=None default) ├── mask options: ocean_frac | cartopy | pre-computed DataArray │ | None (derive from depth sign) - ├── config_dataset() + regrid_dataset_via_xesmf() + ├── config_dataset(src) + regrid_dataset_via_xesmf() └── tidy_dataset(mask=mask) The ``mask_method`` and ``mask`` keyword arguments are forwarded transparently to @@ -90,17 +149,17 @@ the model grid spacing with the source bathymetry resolution: .. code-block:: python - topo.diagnose_resolution( - "gebco_2023.nc", - longitude_coordinate_name="lon", - latitude_coordinate_name="lat", - ) + from mom6_forge._source_bathy import SourceBathy + + src = SourceBathy("gebco_2023.nc") # _da need not be loaded + topo.diagnose_resolution(src) This prints the median, minimum, and maximum model cell spacing alongside the dataset pixel size, computes the resolution ratio, and returns ``True`` if the ratio is ≥ 12× (the point at which the stats-based mask and Cressman interpolation provide meaningfully better results than plain ``xesmf`` -regridding). +regridding). ``diagnose_resolution`` works even before ``slice_to_domain`` has +been called — it reads only the coordinate arrays from the file in that case. Mask Generation @@ -132,12 +191,19 @@ Each point is snapped to the nearest source bathymetry pixel. The ocean fraction by the total. Cells with ``OCN_FRAC ≥ mask_threshold`` are marked as ocean. This method also computes and stores per-cell depth statistics -(``D_mean``, ``D_min``, ``D_max``, ``D2_mean``) which are needed for topo drag -parameterisations (see :ref:`topo-drag`). +(``D_mean``, ``D_min``, ``D_max``, ``D2_mean``) on the ``SourceBathy`` object, +which are needed for topo drag parameterisations (see :ref:`topo-drag`). +It also sets ``topo._src = src`` so that :meth:`~mom6_forge.topo.Topo.write_topo` +can locate the statistics automatically. **When to use:** When the model grid is coarser than ~0.05° or when topo drag statistics are required. +.. code-block:: python + + src = SourceBathy("gebco_2023.nc").slice_to_domain(topo) + mask = topo.generate_mask_ocean_frac(src, nx_sub=5, ny_sub=5) + Cartopy Coastline Mask ~~~~~~~~~~~~~~~~~~~~~~~ @@ -172,24 +238,13 @@ Mask options: .. code-block:: python + src = SourceBathy("gebco_2023.nc").slice_to_domain(topo) + # Default: derive mask from depth sign - topo.direct_xesmf_regrid( - bathymetry_path="gebco_2023.nc", - longitude_coordinate_name="lon", - latitude_coordinate_name="lat", - vertical_coordinate_name="elevation", - ) + topo.direct_xesmf_regrid(src) # With ocean-fraction mask - topo.direct_xesmf_regrid( - bathymetry_path="gebco_2023.nc", - longitude_coordinate_name="lon", - latitude_coordinate_name="lat", - vertical_coordinate_name="elevation", - mask_method="ocean_frac", - nx_sub=5, - ny_sub=5, - ) + topo.direct_xesmf_regrid(src, mask_method="ocean_frac", nx_sub=5, ny_sub=5) Pipeline B — high_res_regrid @@ -216,31 +271,16 @@ requires an explicit ocean mask to exclude land source points. .. code-block:: python + src = SourceBathy("gebco_2023.nc").slice_to_domain(topo) + # Default: ocean-fraction mask + Cressman - topo.high_res_regrid( - bathymetry_path="gebco_2023.nc", - longitude_coordinate_name="lon", - latitude_coordinate_name="lat", - vertical_coordinate_name="elevation", - ) + topo.high_res_regrid(src) # Cartopy mask variant - topo.high_res_regrid( - bathymetry_path="gebco_2023.nc", - longitude_coordinate_name="lon", - latitude_coordinate_name="lat", - vertical_coordinate_name="elevation", - mask_method="cartopy", - ) + topo.high_res_regrid(src, mask_method="cartopy") # Pre-computed mask - topo.high_res_regrid( - bathymetry_path="gebco_2023.nc", - longitude_coordinate_name="lon", - latitude_coordinate_name="lat", - vertical_coordinate_name="elevation", - mask=my_ocean_mask, - ) + topo.high_res_regrid(src, mask=my_ocean_mask) Cressman Interpolation @@ -319,7 +359,8 @@ stats have not been computed yet. .. code-block:: python - topo.generate_mask_ocean_frac(bathymetry_path="gebco_2023.nc", nx_sub=5, ny_sub=5) + src = SourceBathy("gebco_2023.nc").slice_to_domain(topo) + topo.generate_mask_ocean_frac(src, nx_sub=5, ny_sub=5) topo.write_topo("topog.nc") # h2 included automatically # Alternatively, raise if stats are missing: @@ -335,10 +376,16 @@ A parallelised variant of ``direct_xesmf_regrid`` for very large source datasets. Equivalent to ``direct_xesmf_regrid`` but distributes the ``xesmf`` regridding across MPI ranks. +.. code-block:: python + + src = SourceBathy("gebco_2023.nc").slice_to_domain(topo) + topo.mpi_direct_xesmf_regrid(src, output_dir=Path("mpi_output/")) + See Also -------- - :doc:`Notebook 7 — Regional Bathymetry Workflow <../notebooks/7_regional_bathy_workflow>` - :class:`~mom6_forge.topo.Topo` +- :class:`~mom6_forge._source_bathy.SourceBathy` - `GEBCO bathymetry `_ diff --git a/mom6_forge/_source_bathy.py b/mom6_forge/_source_bathy.py new file mode 100644 index 00000000..b156a22e --- /dev/null +++ b/mom6_forge/_source_bathy.py @@ -0,0 +1,117 @@ +"""Source bathymetry loader for mom6_forge. + +``SourceBathy`` is a lightweight data container for a regional slice of a +source bathymetry dataset. ``Topo._get_src()`` creates and caches one +automatically when ``set_from_dataset`` is called. Users who call pipeline +methods directly (e.g. ``high_res_regrid``, ``generate_mask_ocean_frac``) +should construct a ``SourceBathy`` explicitly:: + + from mom6_forge._source_bathy import SourceBathy + src = SourceBathy("gebco_2023.nc").slice_to_domain(topo) +""" + +import numpy as np +import xarray as xr +from pathlib import Path +from mom6_forge.utils import longitude_slicer + + +class SourceBathy: + """Regional slice of a source bathymetry dataset (e.g. GEBCO). + + Holds the loaded, domain-clipped elevation DataArray together with its + coordinate-name metadata. Per-cell depth statistics are computed and + cached here so repeated calls with the same source file skip the + expensive sub-sampling step. + + Parameters + ---------- + path : str or Path + lon_name : str — longitude coordinate name. Default ``"lon"``. + lat_name : str — latitude coordinate name. Default ``"lat"``. + elevation_name : str — elevation variable (positive-up). Default ``"elevation"``. + """ + + def __init__( + self, + path, + lon_name="lon", + lat_name="lat", + elevation_name="elevation", + ): + self.path = Path(path) + self.lon_name = lon_name + self.lat_name = lat_name + self.elevation_name = elevation_name + self._da = None # set by slice_to_domain + self._topo_stats = None # set by compute_topo_stats + + # ------------------------------------------------------------------ + # Loading + # ------------------------------------------------------------------ + + def slice_to_domain(self, topo, buf=0.5): + """Load and clip elevation to the topo grid extent plus ``buf`` degrees. + + Handles the global-longitude seam automatically. Mutates ``self`` + in place and returns ``self`` for chaining. + + Parameters + ---------- + topo : Topo — only ``topo._grid.qlon`` / ``topo._grid.qlat`` are used. + buf : float — degree buffer around the Q-grid bounding box. Default 0.5. + """ + lon_extent = (float(topo._grid.qlon.min()), float(topo._grid.qlon.max())) + lat_extent = (float(topo._grid.qlat.min()), float(topo._grid.qlat.max())) + + ds_src = xr.open_dataset(self.path, chunks="auto") + da = ds_src[self.elevation_name] + + da = da.sel({self.lat_name: slice(lat_extent[0] - buf, lat_extent[1] + buf)}) + + dlon = float(da[self.lon_name][1] - da[self.lon_name][0]) + total_lon = float(da[self.lon_name][-1] - da[self.lon_name][0] + dlon) + if np.isclose(total_lon, 360): + da = longitude_slicer( + da, + np.array(lon_extent) + np.array([-buf, buf]), + self.lon_name, + ) + else: + da = da.sel( + {self.lon_name: slice(lon_extent[0] - buf, lon_extent[1] + buf)} + ) + + self._da = da.load() + return self + + # ------------------------------------------------------------------ + # Accessors + # ------------------------------------------------------------------ + + @property + def lon(self): + """1-D longitude array.""" + return self._da[self.lon_name].values + + @property + def lat(self): + """1-D latitude array.""" + return self._da[self.lat_name].values + + @property + def depth(self): + """2-D depth array, positive-down (ocean > 0), shape (ny_src, nx_src).""" + return -self._da.values.astype(float) + + @property + def da(self): + """Raw elevation DataArray with source coordinate names (positive-up).""" + return self._da + + def __repr__(self): + shape = self._da.shape if self._da is not None else "not loaded" + return ( + f"SourceBathy({self.path.name!r}, lon={self.lon_name!r}, " + f"lat={self.lat_name!r}, elevation={self.elevation_name!r}, shape={shape})" + ) diff --git a/mom6_forge/topo.py b/mom6_forge/topo.py index 14f8e215..3ffd5132 100644 --- a/mom6_forge/topo.py +++ b/mom6_forge/topo.py @@ -16,6 +16,7 @@ from mom6_forge.edit_command import * from mom6_forge.command_manager import TopoCommandManager, CommandType from mom6_forge.mapping import regrid_dataset_via_xesmf, cressman_regrid +from mom6_forge._source_bathy import SourceBathy class Topo: @@ -42,6 +43,7 @@ def __init__(self, grid, min_depth, version_control_dir="TopoLibrary"): attrs={"units": "m"}, ) # Initialize depth with NaNs self._min_depth = min_depth + self._src = None # cached SourceBathy; set by _get_src() if version_control_dir is None: raise ValueError( @@ -548,12 +550,32 @@ def set_bowl(self, max_depth, dedge, rad_earth=6.378e6, expdecay=400000.0): # Save to object (Build TCM Object) self.send_entire_depth_change_to_tcm(new_values) - def diagnose_resolution( + def _get_src( self, bathymetry_path, - longitude_coordinate_name="lon", - latitude_coordinate_name="lat", + longitude_coordinate_name, + latitude_coordinate_name, + vertical_coordinate_name, ): + """Return a cached :class:`SourceBathy`, creating and slicing a new one + only when the path or coordinate names differ from the current cache.""" + path = Path(bathymetry_path) + if ( + self._src is None + or self._src.path != path + or self._src.lon_name != longitude_coordinate_name + or self._src.lat_name != latitude_coordinate_name + or self._src.elevation_name != vertical_coordinate_name + ): + self._src = SourceBathy( + path, + longitude_coordinate_name, + latitude_coordinate_name, + vertical_coordinate_name, + ).slice_to_domain(self) + return self._src + + def diagnose_resolution(self, src): """ Print resolution diagnostics comparing the model grid to a source bathymetry dataset, and recommend whether Cressman interpolation / stats-based masking @@ -568,12 +590,10 @@ def diagnose_resolution( Parameters ---------- - bathymetry_path : str or Path - Path to the source bathymetry netCDF file. - longitude_coordinate_name : str - Name of the longitude coordinate in the bathymetry file. Default ``"lon"``. - latitude_coordinate_name : str - Name of the latitude coordinate in the bathymetry file. Default ``"lat"``. + src : SourceBathy + Source bathymetry object. The DataArray need not be loaded — + coordinate arrays are read from the file if ``src`` has not yet + been sliced to the domain. Returns ------- @@ -592,10 +612,14 @@ def diagnose_resolution( max_dx_m = float(np.max(cell_dx_m)) # --- Source dataset spacing --- - ds = xr.open_dataset(bathymetry_path) - lon = ds[longitude_coordinate_name].values - lat = ds[latitude_coordinate_name].values - ds.close() + if src._da is not None: + lon = src.lon + lat = src.lat + else: + ds = xr.open_dataset(src.path) + lon = ds[src.lon_name].values + lat = ds[src.lat_name].values + ds.close() dlon_deg = float(abs(lon[1] - lon[0])) dlat_deg = float(abs(lat[1] - lat[0])) @@ -613,7 +637,7 @@ def diagnose_resolution( print(sep) print(" Resolution Diagnostics") print(sep) - print(f"\n Source dataset ({Path(bathymetry_path).name}):") + print(f"\n Source dataset ({src.path.name}):") print(f" dlon = {dlon_deg * 3600:.1f} arcsec ({dlon_deg:.6f}°)") print(f" dlat = {dlat_deg * 3600:.1f} arcsec ({dlat_deg:.6f}°)") print( @@ -644,132 +668,40 @@ def diagnose_resolution( print(sep) return ratio_median >= CRESSMAN_THRESHOLD - def _load_src_bathy( - self, - bathymetry_path, - longitude_coordinate_name, - latitude_coordinate_name, - vertical_coordinate_name, - buf=0.5, - ): - """Load and slice source bathymetry to the model domain plus a buffer. - - Parameters - ---------- - bathymetry_path : str or Path - longitude_coordinate_name, latitude_coordinate_name, vertical_coordinate_name : str - buf : float - Degree buffer added around the Q-grid extent. Default 0.5°. - - Returns - ------- - src_lon : np.ndarray, shape (nx_src,) - src_lat : np.ndarray, shape (ny_src,) - src_z : np.ndarray, shape (ny_src, nx_src) - Elevation in source convention (positive up, negative = ocean). - """ - lon_extent = (float(self._grid.qlon.min()), float(self._grid.qlon.max())) - lat_extent = (float(self._grid.qlat.min()), float(self._grid.qlat.max())) - - ds_src = xr.open_dataset(bathymetry_path, chunks="auto") - z_src = ds_src[vertical_coordinate_name] - z_src = z_src.sel( - {latitude_coordinate_name: slice(lat_extent[0] - buf, lat_extent[1] + buf)} - ) - - dlon = float( - z_src[longitude_coordinate_name][1] - z_src[longitude_coordinate_name][0] - ) - total_lon = float( - z_src[longitude_coordinate_name][-1] - - z_src[longitude_coordinate_name][0] - + dlon - ) - if np.isclose(total_lon, 360): - z_src = longitude_slicer( - z_src, - np.array(lon_extent) + np.array([-buf, buf]), - longitude_coordinate_name, - ) - else: - z_src = z_src.sel( - { - longitude_coordinate_name: slice( - lon_extent[0] - buf, lon_extent[1] + buf - ) - } - ) - - z_src = z_src.load() - src_lon = z_src[longitude_coordinate_name].values - src_lat = z_src[latitude_coordinate_name].values - src_z = z_src.values - return src_lon, src_lat, src_z + def _compute_topo_stats(self, src, nx_sub, ny_sub, mask_hmin): + """Compute per-cell depth statistics by Monte-Carlo sub-sampling. - def _compute_topo_stats( - self, - bathymetry_path, - longitude_coordinate_name, - latitude_coordinate_name, - vertical_coordinate_name, - nx_sub, - ny_sub, - mask_hmin, - ): - """ - Compute per-cell depth statistics by Monte-Carlo sub-sampling of the - source bathymetry dataset. Mirrors the algorithm in tx2_3's - create_model_topo.f90. - - For each model T-cell, distributes nx_sub x ny_sub interior points via - bilinear interpolation of the 4 Q-point corners, snaps each sub-point - to the nearest source pixel (nearest-neighbour), and accumulates ocean - depth statistics from sub-points where depth > mask_hmin. + Results are cached on ``src._topo_stats`` so a second call with the + same source file returns immediately without recomputation. Parameters ---------- - bathymetry_path : str or Path - longitude_coordinate_name, latitude_coordinate_name, vertical_coordinate_name : str + src : SourceBathy nx_sub, ny_sub : int - Number of sub-sample points per cell in x and y. mask_hmin : float - Minimum depth (m) for a sub-point to count as ocean. Returns ------- - xr.Dataset - Dataset on the T-grid with variables: - ``OCN_FRAC``, ``D_mean``, ``D_min``, ``D_max``, ``D2_mean``. + xr.Dataset — ``OCN_FRAC``, ``D_mean``, ``D_min``, ``D_max``, ``D2_mean``. """ - if getattr(self, "_topo_stats", None) is not None: - return self._topo_stats + if src._topo_stats is not None: + return src._topo_stats - src_lon, src_lat, src_z = self._load_src_bathy( - bathymetry_path, - longitude_coordinate_name, - latitude_coordinate_name, - vertical_coordinate_name, - ) - dlon = float(src_lon[1] - src_lon[0]) - dlat = float(src_lat[1] - src_lat[0]) + dlon = float(src.lon[1] - src.lon[0]) + dlat = float(src.lat[1] - src.lat[0]) - # --- Build bilinear sub-point coordinates --- - # Q-grid corners of each T-cell: shape (ny, nx) SW_lon = self._grid.qlon.values[:-1, :-1] SE_lon = self._grid.qlon.values[:-1, 1:] NE_lon = self._grid.qlon.values[1:, 1:] NW_lon = self._grid.qlon.values[1:, :-1] - SW_lat = self._grid.qlat.values[:-1, :-1] SE_lat = self._grid.qlat.values[:-1, 1:] NE_lat = self._grid.qlat.values[1:, 1:] NW_lat = self._grid.qlat.values[1:, :-1] - # Sub-point interior fractions, matching Fortran: isub/(nx_sub+1) ifrac = (np.arange(1, nx_sub + 1) / (nx_sub + 1)).astype(float) jfrac = (np.arange(1, ny_sub + 1) / (ny_sub + 1)).astype(float) - # Broadcast to (ny, nx, ny_sub, nx_sub) i_ = ifrac[np.newaxis, np.newaxis, np.newaxis, :] j_ = jfrac[np.newaxis, np.newaxis, :, np.newaxis] SW_lon = SW_lon[:, :, np.newaxis, np.newaxis] @@ -786,7 +718,7 @@ def _compute_topo_stats( + i_ * (1 - j_) * SE_lon + i_ * j_ * NE_lon + (1 - i_) * j_ * NW_lon - ) # (ny, nx, ny_sub, nx_sub) + ) sub_lat = ( (1 - i_) * (1 - j_) * SW_lat + i_ * (1 - j_) * SE_lat @@ -794,33 +726,25 @@ def _compute_topo_stats( + (1 - i_) * j_ * NW_lat ) - # --- Snap sub-points to nearest source pixel --- - ii = np.round((sub_lon - src_lon[0]) / dlon).astype(int) - jj = np.round((sub_lat - src_lat[0]) / dlat).astype(int) - ii = np.clip(ii, 0, len(src_lon) - 1) - jj = np.clip(jj, 0, len(src_lat) - 1) - - # Depth positive-down: negate elevation - depth_sub = -src_z[jj, ii] # (ny, nx, ny_sub, nx_sub) + ii = np.round((sub_lon - src.lon[0]) / dlon).astype(int) + jj = np.round((sub_lat - src.lat[0]) / dlat).astype(int) + ii = np.clip(ii, 0, len(src.lon) - 1) + jj = np.clip(jj, 0, len(src.lat) - 1) - # --- Accumulate statistics over ocean sub-points --- - is_ocean = depth_sub > mask_hmin # (ny, nx, ny_sub, nx_sub) + depth_sub = src.depth[jj, ii] # positive-down - nsum = nx_sub * ny_sub - nsumo = is_ocean.sum(axis=(-2, -1)) # (ny, nx) - ocn_frac = nsumo / nsum + is_ocean = depth_sub > mask_hmin + ocn_frac = is_ocean.sum(axis=(-2, -1)) / (nx_sub * ny_sub) depth_ocean = np.where(is_ocean, depth_sub, np.nan) - with np.errstate( - all="ignore" - ): # suppress nanmean-of-empty warnings for land cells + with np.errstate(all="ignore"): D_mean = np.nanmean(depth_ocean, axis=(-2, -1)) D_min = np.nanmin(depth_ocean, axis=(-2, -1)) D_max = np.nanmax(depth_ocean, axis=(-2, -1)) D2_mean = np.nanmean(depth_ocean**2, axis=(-2, -1)) dims = ["ny", "nx"] - self._topo_stats = xr.Dataset( + src._topo_stats = xr.Dataset( { "OCN_FRAC": xr.DataArray( ocn_frac, @@ -855,14 +779,11 @@ def _compute_topo_stats( ), } ) - return self._topo_stats + return src._topo_stats def generate_mask_ocean_frac( self, - bathymetry_path, - longitude_coordinate_name="lon", - latitude_coordinate_name="lat", - vertical_coordinate_name="elevation", + src, nx_sub=5, ny_sub=5, mask_threshold=0.5, @@ -877,19 +798,13 @@ def generate_mask_ocean_frac( nearest source pixel. A cell is ocean if its ocean sub-point fraction (OCN_FRAC) meets or exceeds mask_threshold. - Per-cell depth statistics (D_mean, D_min, D_max, D2_mean) are stored - in ``self._topo_stats`` for downstream use by ``write_topo_drag()``. + Per-cell depth statistics (D_mean, D_min, D_max, D2_mean) are cached + on the source bathymetry object for downstream use by ``write_topo()``. Parameters ---------- - bathymetry_path : str or Path - Path to the source bathymetry netCDF (e.g. GEBCO). - longitude_coordinate_name : str - Longitude coordinate name. Default ``"lon"``. - latitude_coordinate_name : str - Latitude coordinate name. Default ``"lat"``. - vertical_coordinate_name : str - Elevation variable name (positive up). Default ``"elevation"``. + src : SourceBathy + Loaded (sliced) source bathymetry object. nx_sub, ny_sub : int Sub-sampling resolution per cell. Default 5x5. mask_threshold : float @@ -903,15 +818,8 @@ def generate_mask_ocean_frac( Binary ocean mask on the T-grid (1 = ocean, 0 = land), dims ``["ny", "nx"]``. """ - stats = self._compute_topo_stats( - bathymetry_path, - longitude_coordinate_name, - latitude_coordinate_name, - vertical_coordinate_name, - nx_sub, - ny_sub, - mask_hmin, - ) + self._src = src + stats = self._compute_topo_stats(src, nx_sub, ny_sub, mask_hmin) ocean_mask = (stats["OCN_FRAC"].values >= mask_threshold).astype(int) @@ -983,11 +891,8 @@ def generate_mask_cartopy(self, resolution="10m"): def cressman_interp( self, - bathymetry_path, + src, mask, - longitude_coordinate_name="lon", - latitude_coordinate_name="lat", - vertical_coordinate_name="elevation", smooth_scl=2.0, cressman_exp=2.0, hmin=None, @@ -1017,17 +922,11 @@ def cressman_interp( Parameters ---------- - bathymetry_path : str or Path - Source bathymetry (e.g. GEBCO) in netCDF format. + src : SourceBathy + Loaded (sliced) source bathymetry object. mask : xr.DataArray Binary ocean mask on the T-grid (1 = ocean, 0 = land). Obtain from :meth:`generate_mask_ocean_frac` or :meth:`generate_mask_cartopy`. - longitude_coordinate_name : str - Longitude coordinate name in the source file. Default ``"lon"``. - latitude_coordinate_name : str - Latitude coordinate name in the source file. Default ``"lat"``. - vertical_coordinate_name : str - Elevation variable name (positive up). Default ``"elevation"``. smooth_scl : float Smoothing scale multiplier for the Cressman radius. Default ``2.0``. cressman_exp : float @@ -1039,22 +938,13 @@ def cressman_interp( ``cressman_weights.nc`` is written next to the bathymetry file. """ if weights_path is None: - weights_path = Path(bathymetry_path).parent / "cressman_weights.nc" - - # --- Load source bathymetry --- - src_lon, src_lat, src_z = self._load_src_bathy( - bathymetry_path, - longitude_coordinate_name, - latitude_coordinate_name, - vertical_coordinate_name, - ) - src_depth = (-src_z).astype(float) # positive-down; ocean > 0 + weights_path = src.path.parent / "cressman_weights.nc" # --- Regrid via mapping module (weights → file → xe.Regridder) --- depth_dst, unfilled = cressman_regrid( - src_lon, - src_lat, - src_depth, + src.lon, + src.lat, + src.depth, self._grid.tlon.values, self._grid.tlat.values, self._grid.tarea.values, @@ -1107,10 +997,7 @@ def cressman_interp( def direct_xesmf_regrid( self, - bathymetry_path, - longitude_coordinate_name="lon", - latitude_coordinate_name="lat", - vertical_coordinate_name="elevation", + src, regridding_method="bilinear", fill_channels=False, positive_down=False, @@ -1132,8 +1019,8 @@ def direct_xesmf_regrid( Parameters ---------- - bathymetry_path : str or Path - longitude_coordinate_name, latitude_coordinate_name, vertical_coordinate_name : str + src : SourceBathy + Loaded (sliced) source bathymetry object. regridding_method : str xesmf regridding method. Default ``"bilinear"``. fill_channels : bool @@ -1159,10 +1046,7 @@ def direct_xesmf_regrid( if mask is None and mask_method is not None: if mask_method == "ocean_frac": mask = self.generate_mask_ocean_frac( - bathymetry_path=bathymetry_path, - longitude_coordinate_name=longitude_coordinate_name, - latitude_coordinate_name=latitude_coordinate_name, - vertical_coordinate_name=vertical_coordinate_name, + src, nx_sub=nx_sub, ny_sub=ny_sub, mask_threshold=mask_threshold, @@ -1176,10 +1060,7 @@ def direct_xesmf_regrid( # --- xesmf regrid --- bathymetry_output, empty_bathy = self.config_dataset( - bathymetry_path=bathymetry_path, - longitude_coordinate_name=longitude_coordinate_name, - latitude_coordinate_name=latitude_coordinate_name, - vertical_coordinate_name=vertical_coordinate_name, + src, fill_channels=fill_channels, positive_down=positive_down, write_to_file=False, @@ -1204,10 +1085,7 @@ def direct_xesmf_regrid( def high_res_regrid( self, - bathymetry_path, - longitude_coordinate_name="lon", - latitude_coordinate_name="lat", - vertical_coordinate_name="elevation", + src, mask=None, mask_method="ocean_frac", nx_sub=5, @@ -1231,8 +1109,8 @@ def high_res_regrid( Parameters ---------- - bathymetry_path : str or Path - longitude_coordinate_name, latitude_coordinate_name, vertical_coordinate_name : str + src : SourceBathy + Loaded (sliced) source bathymetry object. mask : xr.DataArray or None Pre-computed binary ocean mask (1=ocean, 0=land). When provided, ``mask_method`` is ignored. @@ -1261,10 +1139,7 @@ def high_res_regrid( if mask is None: if mask_method == "ocean_frac": mask = self.generate_mask_ocean_frac( - bathymetry_path=bathymetry_path, - longitude_coordinate_name=longitude_coordinate_name, - latitude_coordinate_name=latitude_coordinate_name, - vertical_coordinate_name=vertical_coordinate_name, + src, nx_sub=nx_sub, ny_sub=ny_sub, mask_threshold=mask_threshold, @@ -1278,11 +1153,8 @@ def high_res_regrid( # --- Cressman interpolation --- self.cressman_interp( - bathymetry_path=bathymetry_path, + src, mask=mask, - longitude_coordinate_name=longitude_coordinate_name, - latitude_coordinate_name=latitude_coordinate_name, - vertical_coordinate_name=vertical_coordinate_name, smooth_scl=smooth_scl, cressman_exp=cressman_exp, hmin=hmin, @@ -1378,18 +1250,17 @@ def set_from_dataset( regridding_method : str xesmf regridding method (direct pipeline only). Default ``"bilinear"``. """ - use_cressman = self.diagnose_resolution( + src = self._get_src( bathymetry_path, - longitude_coordinate_name=longitude_coordinate_name, - latitude_coordinate_name=latitude_coordinate_name, + longitude_coordinate_name, + latitude_coordinate_name, + vertical_coordinate_name, ) + use_cressman = self.diagnose_resolution(src) if use_cressman: self.high_res_regrid( - bathymetry_path=bathymetry_path, - longitude_coordinate_name=longitude_coordinate_name, - latitude_coordinate_name=latitude_coordinate_name, - vertical_coordinate_name=vertical_coordinate_name, + src, mask=mask, mask_method=mask_method or "ocean_frac", nx_sub=nx_sub, @@ -1404,10 +1275,7 @@ def set_from_dataset( ) else: self.direct_xesmf_regrid( - bathymetry_path=bathymetry_path, - longitude_coordinate_name=longitude_coordinate_name, - latitude_coordinate_name=latitude_coordinate_name, - vertical_coordinate_name=vertical_coordinate_name, + src, regridding_method=regridding_method, fill_channels=fill_channels, positive_down=positive_down, @@ -1421,11 +1289,8 @@ def set_from_dataset( def mpi_direct_xesmf_regrid( self, + src, *, - bathymetry_path, - longitude_coordinate_name, - latitude_coordinate_name, - vertical_coordinate_name, fill_channels=False, positive_down=False, output_dir=Path(""), @@ -1456,10 +1321,7 @@ def mpi_direct_xesmf_regrid( """) self.bathymetry_output, self.empty_bathy = self.config_dataset( - bathymetry_path=bathymetry_path, - longitude_coordinate_name=longitude_coordinate_name, - latitude_coordinate_name=latitude_coordinate_name, - vertical_coordinate_name=vertical_coordinate_name, + src, fill_channels=fill_channels, positive_down=positive_down, output_dir=output_dir, @@ -1472,10 +1334,7 @@ def mpi_direct_xesmf_regrid( def config_dataset( self, - bathymetry_path, - longitude_coordinate_name, - latitude_coordinate_name, - vertical_coordinate_name, + src, fill_channels=False, positive_down=False, output_dir=Path(""), @@ -1488,13 +1347,7 @@ def config_dataset( If manual regridding is necessary, write_to_file must be set to True. Arguments: - bathymetry_path (str): Path to netCDF file with bathymetry data. - longitude_coordinate_name (Optional[str]): The name of the longitude coordinate in the bathymetry - dataset at ``bathymetry_path``. For example, for GEBCO bathymetry: ``'lon'`` (default). - latitude_coordinate_name (Optional[str]): The name of the latitude coordinate in the bathymetry - dataset at ``bathymetry_path``. For example, for GEBCO bathymetry: ``'lat'`` (default). - vertical_coordinate_name (Optional[str]): The name of the vertical coordinate in the bathymetry - dataset at ``bathymetry_path``. For example, for GEBCO bathymetry: ``'elevation'`` (default). + src (SourceBathy): Loaded (sliced) source bathymetry object. output_dir: str | Path The str or Path the write to file should write to. Defaults to the directory the script is running in. write_to_file (Optional[bool]): Files saved to ``output_dir``. Defaults to ``True``. Must be set to true if using manual regridding methods with ESMF_regrid. @@ -1502,73 +1355,13 @@ def config_dataset( Returns: (``bathymetry_output``,``empty_bathy``) (tuple of Datasets): where ``bathymetry_output`` is the original bathymetry data with proper metadata and attributes and ``empty_bathy`` is a template for the regridder. """ - coordinate_names = { - "xh": longitude_coordinate_name, - "yh": latitude_coordinate_name, - "depth": vertical_coordinate_name, - } - longitude_extent = ( - float(self._grid.qlon.min()), - float(self._grid.qlon.max()), - ) - latitude_extent = ( - float(self._grid.qlat.min()), - float(self._grid.qlat.max()), - ) - - bathymetry = xr.open_dataset(bathymetry_path, chunks="auto")[ - coordinate_names["depth"] - ] - - bathymetry = bathymetry.sel( - { - coordinate_names["yh"]: slice( - latitude_extent[0] - 0.5, latitude_extent[1] + 0.5 - ) - } # 0.5 degree latitude buffer (hardcoded) for regridding - ).astype("float") - - ## Check if the original bathymetry provided has a longitude extent that goes around the globe - ## to take care of the longitude seam when we slice out the regional domain. - - horizontal_resolution = ( - bathymetry[coordinate_names["xh"]][1] - - bathymetry[coordinate_names["xh"]][0] - ) - - horizontal_extent = ( - bathymetry[coordinate_names["xh"]][-1] - - bathymetry[coordinate_names["xh"]][0] - + horizontal_resolution - ) - - longitude_buffer = 0.5 # 0.5 degree longitude buffer (hardcoded) for regridding - - if np.isclose(horizontal_extent, 360): - ## longitude extent that goes around the globe -- use longitude_slicer - bathymetry = longitude_slicer( - bathymetry, - np.array(longitude_extent) - + np.array([-longitude_buffer, longitude_buffer]), - coordinate_names["xh"], - ) - else: - ## otherwise, slice normally - bathymetry = bathymetry.sel( - { - coordinate_names["xh"]: slice( - longitude_extent[0] - longitude_buffer, - longitude_extent[1] + longitude_buffer, - ) - } - ) - + # Use the cached, already-sliced DataArray — output format is unchanged. + bathymetry = src.da.astype("float") bathymetry.attrs["missing_value"] = -1e20 # missing value expected by FRE tools bathymetry_output = xr.Dataset({"depth": bathymetry}) - bathymetry.close() bathymetry_output = bathymetry_output.rename( - {coordinate_names["xh"]: "lon", coordinate_names["yh"]: "lat"} + {src.lon_name: "lon", src.lat_name: "lat"} ) bathymetry_output.depth.attrs["_FillValue"] = -1e20 @@ -2049,8 +1842,9 @@ def gen_topo_ds(self, title=None): attrs={"long_name": "t-grid cell depth", "units": "m"}, ) - if getattr(self, "_topo_stats", None) is not None: - h2 = self._topo_stats["D2_mean"] - self._topo_stats["D_mean"] ** 2 + topo_stats = self._src._topo_stats if self._src is not None else None + if topo_stats is not None: + h2 = topo_stats["D2_mean"] - topo_stats["D_mean"] ** 2 ds["h2"] = xr.DataArray( h2.values, dims=["ny", "nx"], @@ -2083,13 +1877,14 @@ def write_topo(self, file_path, title=None, enforce_topo_drag=False): File title. enforce_topo_drag: bool, optional If ``True``, raise an error if topo drag stats (``h2``) have not been - computed. Run ``_compute_topo_stats()`` first to compute them. + computed. Call ``generate_mask_ocean_frac()`` first to compute them. Default ``False``. """ - if enforce_topo_drag and getattr(self, "_topo_stats", None) is None: + has_topo_stats = self._src is not None and self._src._topo_stats is not None + if enforce_topo_drag and not has_topo_stats: raise RuntimeError( "enforce_topo_drag=True but topo stats have not been computed. " - "Call _compute_topo_stats() before write_topo()." + "Call generate_mask_ocean_frac() first to compute them." ) ds = self.gen_topo_ds(title=title) diff --git a/tests/test_topo.py b/tests/test_topo.py index d68333a8..aa16e71a 100644 --- a/tests/test_topo.py +++ b/tests/test_topo.py @@ -1,6 +1,7 @@ import xarray as xr import numpy as np from mom6_forge.topo import * +from mom6_forge._source_bathy import SourceBathy def test_topo_from_version_control(get_rect_topo): @@ -97,9 +98,6 @@ def test_diagnose_resolution_recommends_cressman(get_rect_topo, tmp_path): coords={"lon": lon, "lat": lat}, ).to_netcdf(bathy_path) - result = get_rect_topo.diagnose_resolution( - bathy_path, - longitude_coordinate_name="lon", - latitude_coordinate_name="lat", - ) + src = SourceBathy(bathy_path, lon_name="lon", lat_name="lat") + result = get_rect_topo.diagnose_resolution(src) assert result is True diff --git a/tests/test_topo_regional_bathy.py b/tests/test_topo_regional_bathy.py index 2349cf3d..3f6c6417 100644 --- a/tests/test_topo_regional_bathy.py +++ b/tests/test_topo_regional_bathy.py @@ -2,6 +2,7 @@ Tests for regional bathymetry pipeline methods on Topo. Implemented and tested here: + SourceBathy (construction, slicing, accessors) Topo.diagnose_resolution() Topo.generate_mask_ocean_frac() Topo.generate_mask_cartopy() @@ -20,6 +21,7 @@ from mom6_forge.grid import Grid from mom6_forge.topo import Topo +from mom6_forge._source_bathy import SourceBathy # --------------------------------------------------------------------------- # Fixtures @@ -76,6 +78,63 @@ def synthetic_gebco(tmp_path): return path +@pytest.fixture +def src_bathy(small_topo, synthetic_gebco): + """SourceBathy sliced to the small_topo domain.""" + return SourceBathy(synthetic_gebco).slice_to_domain(small_topo) + + +# --------------------------------------------------------------------------- +# SourceBathy +# --------------------------------------------------------------------------- + + +class TestSourceBathy: + + def test_construction(self, synthetic_gebco): + """SourceBathy can be constructed from a path with default coord names.""" + src = SourceBathy(synthetic_gebco) + assert src.path == Path(synthetic_gebco) + assert src.lon_name == "lon" + assert src.lat_name == "lat" + assert src.elevation_name == "elevation" + assert src._da is None + + def test_construction_custom_names(self, synthetic_gebco): + src = SourceBathy( + synthetic_gebco, lon_name="x", lat_name="y", elevation_name="z" + ) + assert src.lon_name == "x" + assert src.lat_name == "y" + assert src.elevation_name == "z" + + def test_slice_to_domain_loads_da(self, small_topo, synthetic_gebco): + src = SourceBathy(synthetic_gebco).slice_to_domain(small_topo) + assert src._da is not None + + def test_lon_lat_arrays(self, src_bathy): + assert src_bathy.lon.ndim == 1 + assert src_bathy.lat.ndim == 1 + + def test_depth_positive_down(self, src_bathy): + """depth property must be positive-down (ocean depth > 0).""" + ocean_vals = src_bathy.depth[src_bathy.depth > 0] + assert ocean_vals.size > 0 + + def test_da_is_elevation(self, src_bathy): + """da property returns the raw elevation DataArray (positive-up sign).""" + assert src_bathy.da is not None + # elevation of ocean cells should be negative + assert float(src_bathy.da.min()) < 0 + + def test_repr(self, src_bathy): + r = repr(src_bathy) + assert "SourceBathy" in r + + def test_topo_stats_none_before_compute(self, src_bathy): + assert src_bathy._topo_stats is None + + # --------------------------------------------------------------------------- # generate_mask_ocean_frac # --------------------------------------------------------------------------- @@ -83,70 +142,55 @@ def synthetic_gebco(tmp_path): class TestGenerateMaskOceanFrac: - def test_returns_binary_mask(self, small_topo, synthetic_gebco): + def test_returns_binary_mask(self, small_topo, src_bathy): """Mask values must be 0 (land) or 1 (ocean) only.""" - mask = small_topo.generate_mask_ocean_frac( - bathymetry_path=synthetic_gebco, - nx_sub=3, - ny_sub=3, - ) + mask = small_topo.generate_mask_ocean_frac(src_bathy, nx_sub=3, ny_sub=3) assert set(np.unique(mask.values)).issubset({0, 1}) - def test_mask_shape_matches_grid(self, small_topo, synthetic_gebco): + def test_mask_shape_matches_grid(self, small_topo, src_bathy): """Mask must have the same spatial shape as the model grid.""" - mask = small_topo.generate_mask_ocean_frac( - bathymetry_path=synthetic_gebco, - nx_sub=3, - ny_sub=3, - ) + mask = small_topo.generate_mask_ocean_frac(src_bathy, nx_sub=3, ny_sub=3) assert mask.shape == (small_topo._grid.ny, small_topo._grid.nx) - def test_land_strip_is_masked(self, small_topo, synthetic_gebco): + def test_land_strip_is_masked(self, small_topo, src_bathy): """Cells over the synthetic land strip should have mask=0.""" - mask = small_topo.generate_mask_ocean_frac( - bathymetry_path=synthetic_gebco, - nx_sub=5, - ny_sub=5, - ) + mask = small_topo.generate_mask_ocean_frac(src_bathy, nx_sub=5, ny_sub=5) land_cols = np.where( (small_topo._grid.tlon.values >= 265.0) & (small_topo._grid.tlon.values <= 266.0) ) assert (mask.values[land_cols] == 0).all() - def test_stats_stored_after_call(self, small_topo, synthetic_gebco): + def test_stats_stored_on_src_after_call(self, small_topo, src_bathy): """_topo_stats Dataset with OCN_FRAC, D_mean, D_min, D_max, D2_mean - must be available on the instance after the call.""" - small_topo.generate_mask_ocean_frac( - bathymetry_path=synthetic_gebco, nx_sub=3, ny_sub=3 - ) - assert small_topo._topo_stats is not None + must be set on the SourceBathy after the call.""" + small_topo.generate_mask_ocean_frac(src_bathy, nx_sub=3, ny_sub=3) + assert src_bathy._topo_stats is not None for var in ("OCN_FRAC", "D_mean", "D_min", "D_max", "D2_mean"): - assert var in small_topo._topo_stats + assert var in src_bathy._topo_stats - def test_stats_cached(self, small_topo, synthetic_gebco, monkeypatch): - """Calling generate_mask_ocean_frac twice must not re-run the computation.""" - small_topo.generate_mask_ocean_frac( - bathymetry_path=synthetic_gebco, nx_sub=3, ny_sub=3 - ) - # Poison xr.open_dataset so a second file-read would raise + def test_src_stored_on_topo_after_call(self, small_topo, src_bathy): + """Calling generate_mask_ocean_frac must set topo._src so write_topo can + find the stats.""" + small_topo.generate_mask_ocean_frac(src_bathy, nx_sub=3, ny_sub=3) + assert small_topo._src is src_bathy + + def test_stats_cached(self, small_topo, src_bathy, monkeypatch): + """Calling generate_mask_ocean_frac twice must not re-run _compute_topo_stats.""" + small_topo.generate_mask_ocean_frac(src_bathy, nx_sub=3, ny_sub=3) monkeypatch.setattr( - xr, - "open_dataset", + small_topo, + "_compute_topo_stats", lambda *a, **kw: (_ for _ in ()).throw( AssertionError("_compute_topo_stats ran again") ), ) - small_topo.generate_mask_ocean_frac( - bathymetry_path=synthetic_gebco, nx_sub=3, ny_sub=3 - ) + small_topo.generate_mask_ocean_frac(src_bathy, nx_sub=3, ny_sub=3) - def test_ocn_frac_in_bounds(self, small_topo, synthetic_gebco): + def test_ocn_frac_in_bounds(self, small_topo, src_bathy): """OCN_FRAC must be in [0, 1] for every cell.""" - small_topo.generate_mask_ocean_frac( - bathymetry_path=synthetic_gebco, nx_sub=3, ny_sub=3 - ) - ocn_frac = small_topo._topo_stats["OCN_FRAC"].values + small_topo.generate_mask_ocean_frac(src_bathy, nx_sub=3, ny_sub=3) + ocn_frac = src_bathy._topo_stats["OCN_FRAC"].values assert float(ocn_frac.min()) >= 0.0 assert float(ocn_frac.max()) <= 1.0 @@ -176,6 +220,25 @@ def test_open_ocean_is_unmasked(self, small_topo): assert mask.values[j, i] == 1 +# --------------------------------------------------------------------------- +# diagnose_resolution +# --------------------------------------------------------------------------- + + +class TestDiagnoseResolution: + + def test_returns_bool(self, small_topo, src_bathy): + result = small_topo.diagnose_resolution(src_bathy) + assert isinstance(result, bool) + + def test_works_with_unloaded_src(self, small_topo, synthetic_gebco): + """diagnose_resolution must work even when src has not been sliced.""" + src = SourceBathy(synthetic_gebco) + assert src._da is None # not yet sliced + result = small_topo.diagnose_resolution(src) + assert isinstance(result, bool) + + # --------------------------------------------------------------------------- # h2 written via write_topo (enforce_topo_drag) # --------------------------------------------------------------------------- @@ -183,22 +246,16 @@ def test_open_ocean_is_unmasked(self, small_topo): class TestTopoDragInWriteTopo: - def test_h2_in_output_when_stats_computed( - self, small_topo, synthetic_gebco, tmp_path - ): + def test_h2_in_output_when_stats_computed(self, small_topo, src_bathy, tmp_path): """write_topo should include h2 when _topo_stats are available.""" - small_topo.generate_mask_ocean_frac( - bathymetry_path=synthetic_gebco, nx_sub=3, ny_sub=3 - ) + small_topo.generate_mask_ocean_frac(src_bathy, nx_sub=3, ny_sub=3) out = tmp_path / "topog.nc" small_topo.write_topo(out) assert "h2" in xr.open_dataset(out) - def test_h2_non_negative(self, small_topo, synthetic_gebco, tmp_path): + def test_h2_non_negative(self, small_topo, src_bathy, tmp_path): """h2 = D2_mean - D_mean^2 is a variance and must be >= 0.""" - small_topo.generate_mask_ocean_frac( - bathymetry_path=synthetic_gebco, nx_sub=3, ny_sub=3 - ) + small_topo.generate_mask_ocean_frac(src_bathy, nx_sub=3, ny_sub=3) out = tmp_path / "topog.nc" small_topo.write_topo(out) assert float(xr.open_dataset(out).h2.min()) >= 0.0 @@ -279,7 +336,7 @@ def test_dispatches_to_high_res_when_recommended( monkeypatch.setattr(small_topo, "diagnose_resolution", lambda *a, **kw: True) called = [] monkeypatch.setattr( - small_topo, "high_res_regrid", lambda **kw: called.append(kw) + small_topo, "high_res_regrid", lambda *a, **kw: called.append(kw) ) small_topo.set_from_dataset(bathymetry_path=synthetic_gebco) assert called, "high_res_regrid was not called" @@ -291,7 +348,7 @@ def test_dispatches_to_direct_xesmf_when_not_recommended( monkeypatch.setattr(small_topo, "diagnose_resolution", lambda *a, **kw: False) called = [] monkeypatch.setattr( - small_topo, "direct_xesmf_regrid", lambda **kw: called.append(kw) + small_topo, "direct_xesmf_regrid", lambda *a, **kw: called.append(kw) ) small_topo.set_from_dataset(bathymetry_path=synthetic_gebco) assert called, "direct_xesmf_regrid was not called" @@ -303,7 +360,7 @@ def test_default_mask_method_ocean_frac_for_high_res( monkeypatch.setattr(small_topo, "diagnose_resolution", lambda *a, **kw: True) captured = {} monkeypatch.setattr( - small_topo, "high_res_regrid", lambda **kw: captured.update(kw) + small_topo, "high_res_regrid", lambda *a, **kw: captured.update(kw) ) small_topo.set_from_dataset(bathymetry_path=synthetic_gebco) assert captured.get("mask_method") == "ocean_frac" @@ -316,7 +373,7 @@ def test_no_default_mask_for_direct_xesmf( monkeypatch.setattr(small_topo, "diagnose_resolution", lambda *a, **kw: False) captured = {} monkeypatch.setattr( - small_topo, "direct_xesmf_regrid", lambda **kw: captured.update(kw) + small_topo, "direct_xesmf_regrid", lambda *a, **kw: captured.update(kw) ) small_topo.set_from_dataset(bathymetry_path=synthetic_gebco) assert captured.get("mask_method") is None @@ -328,13 +385,27 @@ def test_explicit_mask_method_forwarded_to_direct( monkeypatch.setattr(small_topo, "diagnose_resolution", lambda *a, **kw: False) captured = {} monkeypatch.setattr( - small_topo, "direct_xesmf_regrid", lambda **kw: captured.update(kw) + small_topo, "direct_xesmf_regrid", lambda *a, **kw: captured.update(kw) ) small_topo.set_from_dataset( bathymetry_path=synthetic_gebco, mask_method="cartopy" ) assert captured.get("mask_method") == "cartopy" + def test_src_passed_to_pipeline(self, small_topo, synthetic_gebco, monkeypatch): + """set_from_dataset must create a SourceBathy and pass it as the first + positional arg to the selected pipeline.""" + monkeypatch.setattr(small_topo, "diagnose_resolution", lambda *a, **kw: False) + received_src = [] + monkeypatch.setattr( + small_topo, + "direct_xesmf_regrid", + lambda src, **kw: received_src.append(src), + ) + small_topo.set_from_dataset(bathymetry_path=synthetic_gebco) + assert len(received_src) == 1 + assert isinstance(received_src[0], SourceBathy) + # --------------------------------------------------------------------------- # direct_xesmf_regrid @@ -343,35 +414,32 @@ def test_explicit_mask_method_forwarded_to_direct( class TestDirectXesmfRegrid: - def test_bad_mask_method_raises(self, small_topo, synthetic_gebco): + def test_bad_mask_method_raises(self, small_topo, src_bathy): """Unknown mask_method must raise ValueError.""" with pytest.raises(ValueError, match="mask_method"): - small_topo.direct_xesmf_regrid( - bathymetry_path=synthetic_gebco, - mask_method="invalid", - ) + small_topo.direct_xesmf_regrid(src_bathy, mask_method="invalid") def test_precomputed_mask_bypasses_generation( - self, small_topo, synthetic_gebco, monkeypatch + self, small_topo, src_bathy, monkeypatch ): """When mask= is provided, generate_mask_ocean_frac must not be called.""" mask = small_topo.generate_mask_cartopy(resolution="50m") called = [] monkeypatch.setattr( - small_topo, "generate_mask_ocean_frac", lambda **kw: called.append(kw) + small_topo, "generate_mask_ocean_frac", lambda *a, **kw: called.append(kw) ) monkeypatch.setattr( small_topo, "generate_mask_cartopy", lambda **kw: called.append(kw) ) - # We don't need direct_xesmf_regrid to complete — just check mask generation is skipped + # Stop execution after mask check — we only care that generation was skipped monkeypatch.setattr( small_topo, "config_dataset", - lambda **kw: (_ for _ in ()).throw(StopIteration), + lambda *a, **kw: (_ for _ in ()).throw(StopIteration), ) with pytest.raises(StopIteration): small_topo.direct_xesmf_regrid( - bathymetry_path=synthetic_gebco, + src_bathy, mask=mask, mask_method="ocean_frac", # should be ignored because mask= is set ) @@ -385,20 +453,17 @@ def test_precomputed_mask_bypasses_generation( class TestHighResRegrid: - def test_bad_mask_method_raises(self, small_topo, synthetic_gebco): + def test_bad_mask_method_raises(self, small_topo, src_bathy): """Unknown mask_method must raise ValueError.""" with pytest.raises(ValueError, match="mask_method"): - small_topo.high_res_regrid( - bathymetry_path=synthetic_gebco, - mask_method="invalid", - ) + small_topo.high_res_regrid(src_bathy, mask_method="invalid") def test_ocean_frac_mask_produces_valid_depth( - self, small_topo, synthetic_gebco, tmp_path + self, small_topo, src_bathy, tmp_path ): """End-to-end with ocean_frac mask: ocean cells must have depth >= min_depth.""" small_topo.high_res_regrid( - bathymetry_path=synthetic_gebco, + src_bathy, mask_method="ocean_frac", nx_sub=3, ny_sub=3, @@ -408,12 +473,10 @@ def test_ocean_frac_mask_produces_valid_depth( assert ocean.any(), "No ocean cells after high_res_regrid" assert (small_topo.depth.values[ocean] >= small_topo.min_depth).all() - def test_cartopy_mask_produces_valid_depth( - self, small_topo, synthetic_gebco, tmp_path - ): + def test_cartopy_mask_produces_valid_depth(self, small_topo, src_bathy, tmp_path): """End-to-end with cartopy mask: ocean cells must have depth >= min_depth.""" small_topo.high_res_regrid( - bathymetry_path=synthetic_gebco, + src_bathy, mask_method="cartopy", cartopy_resolution="50m", weights_path=tmp_path / "cressman_weights.nc", @@ -422,22 +485,22 @@ def test_cartopy_mask_produces_valid_depth( assert ocean.any(), "No ocean cells after high_res_regrid with cartopy mask" assert (small_topo.depth.values[ocean] >= small_topo.min_depth).all() - def test_precomputed_mask_accepted(self, small_topo, synthetic_gebco, tmp_path): + def test_precomputed_mask_accepted(self, small_topo, src_bathy, tmp_path): """Passing a pre-computed mask directly should skip mask generation.""" mask = small_topo.generate_mask_cartopy(resolution="50m") small_topo.high_res_regrid( - bathymetry_path=synthetic_gebco, + src_bathy, mask=mask, weights_path=tmp_path / "cressman_weights.nc", ) ocean = small_topo.depth.values > 0 assert ocean.any(), "No ocean cells with pre-computed mask" - def test_weights_file_written(self, small_topo, synthetic_gebco, tmp_path): + def test_weights_file_written(self, small_topo, src_bathy, tmp_path): """Cressman weights netCDF must be written to weights_path.""" wp = tmp_path / "cressman_weights.nc" small_topo.high_res_regrid( - bathymetry_path=synthetic_gebco, + src_bathy, mask_method="ocean_frac", nx_sub=3, ny_sub=3, From 36fd5adcd3e1c8324fe5d74da4fd6ac3ecaeb620 Mon Sep 17 00:00:00 2001 From: manishvenu Date: Tue, 14 Apr 2026 16:07:44 -0600 Subject: [PATCH 5/9] Rename --- .docstr/{regional_bathy.rst => bathy_workflows.rst} | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) rename .docstr/{regional_bathy.rst => bathy_workflows.rst} (99%) diff --git a/.docstr/regional_bathy.rst b/.docstr/bathy_workflows.rst similarity index 99% rename from .docstr/regional_bathy.rst rename to .docstr/bathy_workflows.rst index fd2ffcfe..94a3a373 100644 --- a/.docstr/regional_bathy.rst +++ b/.docstr/bathy_workflows.rst @@ -1,4 +1,4 @@ -Regional Bathymetry Workflows +Bathymetry Workflows ============================= ``mom6_forge`` provides bathymetry pipelines for regional MOM6 configurations, From 40b55d8c6f8c9f856149171c0919a6749c47ebfc Mon Sep 17 00:00:00 2001 From: manishvenu Date: Wed, 15 Apr 2026 12:21:26 -0600 Subject: [PATCH 6/9] Notebooks and final mods --- mom6_forge/topo.py | 61 ++- notebooks/10_cressman_interpolation.ipynb | 550 ++++++++++++++++++++ notebooks/11_full_scale_example.ipynb | 583 ++++++++++++++++++++++ notebooks/8_pipeline_overview.ipynb | 470 +++++++++++++++++ notebooks/9_mask_methods.ipynb | 501 +++++++++++++++++++ tests/test_topo_bathy_workflows.py | 538 ++++++++++++++++++++ tests/test_topo_regional_bathy.py | 528 -------------------- 7 files changed, 2700 insertions(+), 531 deletions(-) create mode 100644 notebooks/10_cressman_interpolation.ipynb create mode 100644 notebooks/11_full_scale_example.ipynb create mode 100644 notebooks/8_pipeline_overview.ipynb create mode 100644 notebooks/9_mask_methods.ipynb create mode 100644 tests/test_topo_bathy_workflows.py delete mode 100644 tests/test_topo_regional_bathy.py diff --git a/mom6_forge/topo.py b/mom6_forge/topo.py index 3ffd5132..67e7d31c 100644 --- a/mom6_forge/topo.py +++ b/mom6_forge/topo.py @@ -699,6 +699,20 @@ def _compute_topo_stats(self, src, nx_sub, ny_sub, mask_hmin): NE_lat = self._grid.qlat.values[1:, 1:] NW_lat = self._grid.qlat.values[1:, :-1] + # Fix 2: ensure all corners are in the same 360° period as NE, + # matching Fortran create_model_topo.f90 lines 322-333. + # Cells straddling the antimeridian would otherwise produce garbage + # bilinear-interpolated sub_lon values. + def _fix_lon_period(lon, ref): + diff = lon - ref + lon = np.where(diff > 270, lon - 360, lon) + lon = np.where(diff < -270, lon + 360, lon) + return lon + + SW_lon = _fix_lon_period(SW_lon, NE_lon) + SE_lon = _fix_lon_period(SE_lon, NE_lon) + NW_lon = _fix_lon_period(NW_lon, NE_lon) + ifrac = (np.arange(1, nx_sub + 1) / (nx_sub + 1)).astype(float) jfrac = (np.arange(1, ny_sub + 1) / (ny_sub + 1)).astype(float) @@ -728,8 +742,21 @@ def _compute_topo_stats(self, src, nx_sub, ny_sub, mask_hmin): ii = np.round((sub_lon - src.lon[0]) / dlon).astype(int) jj = np.round((sub_lat - src.lat[0]) / dlat).astype(int) - ii = np.clip(ii, 0, len(src.lon) - 1) - jj = np.clip(jj, 0, len(src.lat) - 1) + # wrap longitude index periodically rather than clipping — + # sub-points near the antimeridian must find the correct source pixel + # on the other side, not snap to the edge. + if np.any((ii < 0) | (ii >= len(src.lon))): + src_span = float(src.lon[-1] - src.lon[0]) + if src_span < 355: + raise ValueError( + f"Sub-points fall outside the source longitude range " + f"[{float(src.lon[0]):.2f}, {float(src.lon[-1]):.2f}] " + f"(span {src_span:.1f}°). Longitude wraparound requires a " + f"global source (~360° span); got {src_span:.1f}°. " + f"Pass a global SourceBathy rather than a regional slice." + ) + ii = ii % len(src.lon) + jj = np.clip(jj, 0, len(src.lat) - 1) # latitude: clamp, no wraparound depth_sub = src.depth[jj, ii] # positive-down @@ -889,6 +916,34 @@ def generate_mask_cartopy(self, resolution="10m"): }, ) + def generate_mask_from_processed_depth(self, bathymetry): + """ + Generate an ocean mask from the sign of bathymetric depth values. + + Assumes ``bathymetry.depth`` is already positive-down (i.e. the + ``positive_down`` sign correction in :func:`~tidy_dataset` has been + applied). Cells with depth <= 0 are classified as land. + + Parameters + ---------- + bathymetry : xr.Dataset + Regridded bathymetry dataset with a ``depth`` variable + (positive-down, metres). + + Returns + ------- + xr.DataArray + Binary ocean mask on the T-grid (1 = ocean, 0 = land), + dims ``["ny", "nx"]``. + """ + bathy_shape = bathymetry.depth.shape + grid_shape = (self._grid.ny, self._grid.nx) + if bathy_shape != grid_shape: + raise ValueError( + f"Bathymetry shape {bathy_shape} does not match T-grid shape {grid_shape}." + ) + return xr.where(bathymetry.depth <= 0, 0, 1) + def cressman_interp( self, src, @@ -1466,7 +1521,7 @@ def tidy_dataset( if mask is not None: ocean_mask = mask.astype(int) else: - ocean_mask = xr.where(bathymetry.depth <= 0, 0, 1) + ocean_mask = self.generate_mask_from_processed_depth(bathymetry) land_mask = np.abs(ocean_mask - 1) ## REMOVE INLAND LAKES diff --git a/notebooks/10_cressman_interpolation.ipynb b/notebooks/10_cressman_interpolation.ipynb new file mode 100644 index 00000000..5710e1cb --- /dev/null +++ b/notebooks/10_cressman_interpolation.ipynb @@ -0,0 +1,550 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Cressman Interpolation Deep Dive\n", + "\n", + "This notebook walks through the Cressman distance-weighted interpolation step that forms the core of the high-res pipeline. It mirrors the Fortran program `append_topo_interp_smooth.f90` from the tx2_3 workflow.\n", + "\n", + "## What is Cressman interpolation?\n", + "\n", + "For each ocean model T-cell, we want an estimate of the ocean depth by averaging nearby source pixels. But we don't want to average blindly — pixels that are close to the cell centre should contribute more than distant ones. The Cressman weight function does exactly that:\n", + "\n", + "$$w_k = \\left(\\frac{L^2 - r_k^2}{L^2 + r_k^2}\\right)^{c}$$\n", + "\n", + "where:\n", + "- $r_k$ is the great-circle distance from cell centre to source pixel $k$\n", + "- $L = \\text{smooth\\_scl} \\times \\sqrt{A}$ is the smoothing radius ($A$ = cell area in m²)\n", + "- $c$ = `cressman_exp` (default 2)\n", + "\n", + "Only source ocean pixels within $r_k \\leq L$ contribute. The final depth estimate is:\n", + "\n", + "$$d_i = \\frac{\\sum_k w_k \\cdot d_k}{\\sum_k w_k}$$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import xarray as xr\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.patches as mpatches\n", + "from scipy.spatial import cKDTree\n", + "from pathlib import Path\n", + "import tempfile\n", + "\n", + "from mom6_forge.grid import Grid\n", + "from mom6_forge.topo import Topo\n", + "from mom6_forge._source_bathy import SourceBathy\n", + "\n", + "plt.rcParams[\"figure.dpi\"] = 120" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "grid = Grid(resolution=2.0, xstart=262.0, lenx=10.0, ystart=20.0, leny=8.0, name=\"toy\")\n", + "\n", + "src_lons = np.arange(260.0, 274.1, 0.1)\n", + "src_lats = np.arange(18.0, 30.1, 0.1)\n", + "src_lon2d, src_lat2d = np.meshgrid(src_lons, src_lats)\n", + "\n", + "elevation = np.full(src_lon2d.shape, -2000.0)\n", + "elevation[(src_lat2d > 26.0) & (src_lon2d < 265.0)] = +50.0\n", + "elevation[\n", + " (src_lat2d > 22.0) & (src_lat2d < 24.0) &\n", + " (src_lon2d > 266.0) & (src_lon2d < 268.0)\n", + "] = -80.0\n", + "\n", + "tmp_dir = Path(tempfile.mkdtemp())\n", + "src_path = tmp_dir / \"synthetic_bathy.nc\"\n", + "xr.Dataset(\n", + " {\"elevation\": ([\"lat\", \"lon\"], elevation.astype(\"float32\"))},\n", + " coords={\"lon\": src_lons, \"lat\": src_lats},\n", + ").to_netcdf(src_path)\n", + "\n", + "topo = Topo(grid, min_depth=5.0, version_control_dir=tmp_dir)\n", + "topo.set_flat(1000)\n", + "src = SourceBathy(src_path).slice_to_domain(topo)\n", + "\n", + "# Generate mask (needed for Cressman)\n", + "mask = topo.generate_mask_ocean_frac(src, nx_sub=5, ny_sub=5)\n", + "\n", + "print(\"Setup complete.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. The Cressman weight function\n", + "\n", + "Let's first visualise $w$ as a function of $r/L$ to understand the shape." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "r_over_L = np.linspace(0, 1, 200)\n", + "\n", + "fig, ax = plt.subplots(figsize=(7, 4))\n", + "for c_exp, ls in [(1.0, \"--\"), (2.0, \"-\"), (3.0, \":\")]:\n", + " w = ((1 - r_over_L**2) / (1 + r_over_L**2)) ** c_exp\n", + " ax.plot(r_over_L, w, ls, label=f\"cressman_exp = {c_exp}\")\n", + "\n", + "ax.axvline(1.0, color=\"gray\", linestyle=\"-.\", label=\"r = L (cutoff)\")\n", + "ax.set_xlabel(\"r / L (distance / smoothing radius)\")\n", + "ax.set_ylabel(\"Weight w\")\n", + "ax.set_title(\"Cressman weight function\")\n", + "ax.legend()\n", + "ax.set_xlim(0, 1.05)\n", + "ax.set_ylim(0, 1.05)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The weight is 1.0 at the cell centre ($r=0$) and 0.0 at the cutoff ($r=L$). Higher `cressman_exp` makes the function more peaked — more weight is given to nearby pixels, and the kernel decays more sharply.\n", + "\n", + "## 3. Smoothing radius $L$ for each cell\n", + "\n", + "$L = \\text{smooth\\_scl} \\times \\sqrt{A}$ where $A$ is the T-cell area in m². This means larger cells get a larger search radius, which is physically correct — a coarser model cell should average over more source pixels.\n", + "\n", + "The cell area `DAREA` used in the Fortran is equivalent to `topo._grid.tarea`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "smooth_scl = 2.0\n", + "tarea = grid.tarea.values # cell area in m²\n", + "L = smooth_scl * np.sqrt(tarea) # smoothing radius in m\n", + "L_km = L / 1000.0\n", + "\n", + "fig, ax = plt.subplots(figsize=(7, 4))\n", + "c = ax.pcolormesh(grid.qlon.values, grid.qlat.values, L_km,\n", + " cmap=\"viridis\", shading=\"flat\")\n", + "plt.colorbar(c, ax=ax, label=\"L (km)\")\n", + "for j in range(grid.ny):\n", + " for i in range(grid.nx):\n", + " ax.text(grid.tlon.values[j,i], grid.tlat.values[j,i],\n", + " f\"{L_km[j,i]:.0f}\",\n", + " ha=\"center\", va=\"center\", fontsize=8, color=\"white\")\n", + "ax.set_title(f\"Smoothing radius L = {smooth_scl} × √(cell area) [km]\")\n", + "ax.set_xlabel(\"Longitude\")\n", + "ax.set_ylabel(\"Latitude\")\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "print(f\"L range: {L_km.min():.0f} – {L_km.max():.0f} km\")\n", + "print(f\"(decreases toward poles as cell area shrinks)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. The KDTree — finding source pixels within radius $L$\n", + "\n", + "The Fortran uses a precomputed rectangular patch `(iwid × jwid)` as a bounding box and then tests each pixel in the box for $r \\leq L$. The Python implementation replaces this with a **KDTree** built from the 3D Cartesian coordinates of the source ocean pixels.\n", + "\n", + "Why 3D Cartesian? Because `cKDTree.query_ball_point` uses Euclidean (chord) distance, which works globally without any longitude wraparound issues. The chord distance is then converted back to arc (great-circle) distance for the weight calculation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "R = 6_371_000.0 # Earth radius, m\n", + "\n", + "# Build the Cartesian coordinates of source ocean pixels\n", + "src_depth = src.depth # positive-down; ocean > 0\n", + "src_lon_2d, src_lat_2d = np.meshgrid(src.lon, src.lat)\n", + "is_ocean_src = src_depth > 0\n", + "\n", + "lon_src_rad = np.deg2rad(src_lon_2d.ravel())\n", + "lat_src_rad = np.deg2rad(src_lat_2d.ravel())\n", + "xyz_src_all = np.stack([\n", + " R * np.cos(lat_src_rad) * np.cos(lon_src_rad),\n", + " R * np.cos(lat_src_rad) * np.sin(lon_src_rad),\n", + " R * np.sin(lat_src_rad),\n", + "], axis=1)\n", + "\n", + "# KDTree built ONLY over ocean source pixels\n", + "ocean_src_idx = np.where(is_ocean_src.ravel())[0]\n", + "tree = cKDTree(xyz_src_all[ocean_src_idx])\n", + "\n", + "print(f\"Total source pixels: {src_depth.size}\")\n", + "print(f\"Ocean source pixels in KDTree: {len(ocean_src_idx)}\")\n", + "print(f\" ({100*len(ocean_src_idx)/src_depth.size:.1f}% of source)\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# For a chosen target cell, show which source pixels are within radius L\n", + "jc, ic = 1, 2 # choose a central ocean cell\n", + "\n", + "lon_dst_rad = np.deg2rad(grid.tlon.values[jc, ic])\n", + "lat_dst_rad = np.deg2rad(grid.tlat.values[jc, ic])\n", + "xyz_dst = np.array([\n", + " R * np.cos(lat_dst_rad) * np.cos(lon_dst_rad),\n", + " R * np.cos(lat_dst_rad) * np.sin(lon_dst_rad),\n", + " R * np.sin(lat_dst_rad),\n", + "])\n", + "\n", + "L_cell = smooth_scl * np.sqrt(float(grid.tarea.values[jc, ic]))\n", + "print(f\"Cell ({jc},{ic}) at lon={grid.tlon.values[jc,ic]:.1f}, lat={grid.tlat.values[jc,ic]:.1f}\")\n", + "print(f\"Smoothing radius L = {L_cell/1000:.1f} km\")\n", + "\n", + "# query_ball_point returns indices into tree (= ocean_src_idx)\n", + "neighbor_tree_idx = tree.query_ball_point(xyz_dst, L_cell)\n", + "neighbor_src_idx = ocean_src_idx[np.asarray(neighbor_tree_idx)]\n", + "\n", + "# Convert flat source indices back to 2D (lat, lon)\n", + "src_ny, src_nx = src_depth.shape\n", + "nbr_j = neighbor_src_idx // src_nx\n", + "nbr_i = neighbor_src_idx % src_nx\n", + "\n", + "# Compute arc distances and weights for these neighbors\n", + "d_xyz = xyz_src_all[neighbor_src_idx] - xyz_dst\n", + "chord = np.linalg.norm(d_xyz, axis=1)\n", + "arc = 2.0 * R * np.arcsin(np.clip(chord / (2.0 * R), 0, 1))\n", + "d2 = arc**2\n", + "L2 = L_cell**2\n", + "weights = ((L2 - d2) / (L2 + d2)) ** 2.0\n", + "\n", + "print(f\"Source ocean pixels within L: {len(neighbor_src_idx)}\")\n", + "print(f\"Weighted depth estimate: {np.sum(weights * src_depth.ravel()[neighbor_src_idx]) / weights.sum():.1f} m\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", + "\n", + "# --- Left: source pixels coloured by weight ---\n", + "ax = axes[0]\n", + "ax.pcolormesh(src_lons, src_lats, elevation,\n", + " cmap=\"RdBu\", vmin=-2200, vmax=200, alpha=0.4, shading=\"auto\")\n", + "\n", + "# All source ocean pixels (small grey dots)\n", + "ax.scatter(src_lon_2d.ravel()[ocean_src_idx],\n", + " src_lat_2d.ravel()[ocean_src_idx],\n", + " c=\"lightgray\", s=2, zorder=3)\n", + "\n", + "# Pixels within L coloured by weight\n", + "sc = ax.scatter(\n", + " src_lon_2d.ravel()[neighbor_src_idx],\n", + " src_lat_2d.ravel()[neighbor_src_idx],\n", + " c=weights, cmap=\"hot_r\", vmin=0, vmax=1, s=30, zorder=5\n", + ")\n", + "plt.colorbar(sc, ax=ax, label=\"Cressman weight\")\n", + "\n", + "# Model cell outline and centre\n", + "ax.pcolormesh(grid.qlon.values, grid.qlat.values, np.zeros((grid.ny, grid.nx)),\n", + " edgecolors=\"k\", linewidth=0.8, facecolor=\"none\", shading=\"flat\")\n", + "ax.scatter(grid.tlon.values[jc, ic], grid.tlat.values[jc, ic],\n", + " c=\"yellow\", s=150, zorder=8, edgecolors=\"k\", label=\"Target cell\")\n", + "\n", + "# Draw the search circle (approximate in lon/lat)\n", + "theta = np.linspace(0, 2*np.pi, 200)\n", + "deg_per_m = 1.0 / (R * np.pi / 180.0)\n", + "circ_lon = grid.tlon.values[jc, ic] + L_cell * deg_per_m * np.cos(theta)\n", + "circ_lat = grid.tlat.values[jc, ic] + L_cell * deg_per_m * np.sin(theta)\n", + "ax.plot(circ_lon, circ_lat, \"k--\", lw=1.5, label=f\"L = {L_cell/1000:.0f} km\")\n", + "\n", + "ax.set_xlim(260, 274)\n", + "ax.set_ylim(18, 30)\n", + "ax.set_title(f\"Cressman search for cell ({jc},{ic})\")\n", + "ax.set_xlabel(\"Longitude\")\n", + "ax.set_ylabel(\"Latitude\")\n", + "ax.legend(fontsize=8)\n", + "\n", + "# --- Right: weight vs distance scatter ---\n", + "ax = axes[1]\n", + "arc_km = arc / 1000.0\n", + "ax.scatter(arc_km, weights, c=src_depth.ravel()[neighbor_src_idx],\n", + " cmap=\"Blues\", vmin=0, vmax=2200, s=50, zorder=5)\n", + "r_plot = np.linspace(0, L_cell / 1000, 200)\n", + "r_m = r_plot * 1000\n", + "w_curve = ((L2 - r_m**2) / (L2 + r_m**2))**2\n", + "ax.plot(r_plot, w_curve, \"r-\", lw=2, label=\"w(r) curve\")\n", + "ax.axvline(L_cell/1000, color=\"gray\", linestyle=\"--\", label=\"r = L\")\n", + "ax.set_xlabel(\"Arc distance from cell centre (km)\")\n", + "ax.set_ylabel(\"Cressman weight\")\n", + "ax.set_title(f\"Weight vs distance — {len(neighbor_src_idx)} source pixels\")\n", + "ax.legend()\n", + "\n", + "sm = plt.cm.ScalarMappable(cmap=\"Blues\", norm=plt.Normalize(0, 2200))\n", + "plt.colorbar(sm, ax=axes[1], label=\"Source depth (m)\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. The sparse weight matrix\n", + "\n", + "Rather than running the Cressman loop per cell at regrid time, `compute_cressman_weights` builds the entire weight matrix `S` upfront as a **sparse CSR matrix** of shape `(n_dst, n_src)`. Applying the regrid is then just a sparse matrix-vector multiply:\n", + "\n", + "```python\n", + "depth_model_flat = S @ depth_source_flat\n", + "```\n", + "\n", + "This matrix is also written to an ESMF-compatible netCDF so it can be cached and reused (e.g. for different fields or time steps)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from mom6_forge.mapping import compute_cressman_weights\n", + "\n", + "S, unfilled = compute_cressman_weights(\n", + " dst_lon=grid.tlon.values.ravel(),\n", + " dst_lat=grid.tlat.values.ravel(),\n", + " dst_area=grid.tarea.values.ravel(),\n", + " dst_mask=mask.values.ravel().astype(bool),\n", + " src_lon=src.lon,\n", + " src_lat=src.lat,\n", + " src_ocean_mask=(src.depth > 0),\n", + " smooth_scl=2.0,\n", + " cressman_exp=2.0,\n", + ")\n", + "\n", + "print(f\"S shape: {S.shape} (n_dst × n_src)\")\n", + "print(f\"Non-zero entries: {S.nnz}\")\n", + "print(f\"Average non-zeros per ocean row: {S.nnz / int(mask.values.sum()):.1f} source pixels per model cell\")\n", + "print(f\"Unfilled cells: {unfilled.sum()} (will use neighbour fill fallback)\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Visualise the sparsity pattern (each row = one model cell, each col = one source pixel)\n", + "fig, axes = plt.subplots(1, 2, figsize=(13, 4))\n", + "\n", + "ax = axes[0]\n", + "ax.spy(S, markersize=0.5, color=\"steelblue\")\n", + "ax.set_xlabel(\"Source pixel index (flattened)\")\n", + "ax.set_ylabel(\"Model cell index (flattened)\")\n", + "ax.set_title(\"Sparsity pattern of weight matrix S\")\n", + "\n", + "# Apply S to get depth estimates\n", + "depth_source_flat = src.depth.ravel().astype(float)\n", + "depth_dst_flat = S @ depth_source_flat\n", + "depth_dst = depth_dst_flat.reshape(grid.ny, grid.nx)\n", + "\n", + "ax = axes[1]\n", + "c = ax.pcolormesh(grid.qlon.values, grid.qlat.values, depth_dst,\n", + " cmap=\"Blues\", vmin=0, vmax=2200, shading=\"flat\")\n", + "plt.colorbar(c, ax=ax, label=\"Depth (m)\")\n", + "for j in range(grid.ny):\n", + " for i in range(grid.nx):\n", + " ax.text(grid.tlon.values[j,i], grid.tlat.values[j,i],\n", + " f\"{depth_dst[j,i]:.0f}\",\n", + " ha=\"center\", va=\"center\", fontsize=8,\n", + " color=\"white\" if depth_dst[j,i] > 500 else \"k\")\n", + "ax.set_title(\"Depth after S @ depth_source (before hmin enforcement)\")\n", + "ax.set_xlabel(\"Longitude\")\n", + "ax.set_ylabel(\"Latitude\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Iterative neighbour fill\n", + "\n", + "Some ocean cells may have no source ocean pixels within radius $L$ — typically very small coastal cells. For these, we fall back to averaging from filled neighbours, iterating up to 100 times until all ocean cells have a value.\n", + "\n", + "This is the Python equivalent of the Fortran fallback loop at lines 463–490 of `append_topo_interp_smooth.f90`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Simulate a case with an unfilled cell by zeroing it out manually\n", + "depth_with_gap = depth_dst.copy()\n", + "mask_2d = mask.values.astype(bool)\n", + "unfilled_2d = unfilled.reshape(grid.ny, grid.nx) & mask_2d\n", + "\n", + "# For demonstration, manually introduce an unfilled ocean cell\n", + "if int(mask.values[1, 1]) == 1:\n", + " unfilled_demo = unfilled_2d.copy()\n", + " unfilled_demo[1, 1] = True\n", + " depth_with_gap[1, 1] = 0.0\n", + "else:\n", + " unfilled_demo = unfilled_2d.copy()\n", + "\n", + "depth_filled = depth_with_gap.copy()\n", + "\n", + "iterations_needed = 0\n", + "for iteration in range(100):\n", + " if not unfilled_demo.any():\n", + " break\n", + " filled_f = (~unfilled_demo).astype(float)\n", + " d_pad = np.pad(depth_filled, 1, mode=\"edge\")\n", + " f_pad = np.pad(filled_f, 1, mode=\"constant\", constant_values=0)\n", + " d_nbr = (d_pad[:-2,1:-1] + d_pad[2:,1:-1] +\n", + " d_pad[1:-1,:-2] + d_pad[1:-1,2:])\n", + " f_nbr = (f_pad[:-2,1:-1] + f_pad[2:,1:-1] +\n", + " f_pad[1:-1,:-2] + f_pad[1:-1,2:])\n", + " can_fill = unfilled_demo & (f_nbr > 0)\n", + " depth_filled = np.where(can_fill, d_nbr / np.maximum(f_nbr, 1), depth_filled)\n", + " unfilled_demo = unfilled_demo & ~can_fill\n", + " iterations_needed = iteration + 1\n", + "\n", + "print(f\"Iterations needed: {iterations_needed}\")\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(11, 4))\n", + "for ax, data, title in [\n", + " (axes[0], depth_with_gap, \"Before neighbour fill (unfilled cell = 0)\"),\n", + " (axes[1], depth_filled, \"After neighbour fill\"),\n", + "]:\n", + " c = ax.pcolormesh(grid.qlon.values, grid.qlat.values, data,\n", + " cmap=\"Blues\", vmin=0, vmax=2200, shading=\"flat\")\n", + " plt.colorbar(c, ax=ax, label=\"Depth (m)\")\n", + " for j in range(grid.ny):\n", + " for i in range(grid.nx):\n", + " ax.text(grid.tlon.values[j,i], grid.tlat.values[j,i],\n", + " f\"{data[j,i]:.0f}\",\n", + " ha=\"center\", va=\"center\", fontsize=8,\n", + " color=\"white\" if data[j,i] > 500 else \"k\")\n", + " ax.set_title(title)\n", + " ax.set_xlabel(\"Longitude\")\n", + " ax.set_ylabel(\"Latitude\")\n", + "\n", + "plt.suptitle(\"Iterative neighbour fill fallback\", fontsize=11)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7. Putting it all together: `cressman_interp`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "weights_path = tmp_dir / \"cressman_weights.nc\"\n", + "topo.cressman_interp(src, mask, smooth_scl=2.0, cressman_exp=2.0, weights_path=weights_path)\n", + "\n", + "# Compare smooth_scl values\n", + "results = {}\n", + "for smooth_scl in [1.0, 2.0, 4.0]:\n", + " t = Topo(grid, min_depth=5.0, version_control_dir=tmp_dir)\n", + " t.set_flat(1000)\n", + " wp = tmp_dir / f\"weights_{smooth_scl}.nc\"\n", + " t.cressman_interp(src, mask, smooth_scl=smooth_scl, weights_path=wp)\n", + " results[smooth_scl] = t.depth.values.copy()\n", + "\n", + "fig, axes = plt.subplots(1, 3, figsize=(15, 4))\n", + "for ax, (sscl, depth) in zip(axes, results.items()):\n", + " c = ax.pcolormesh(grid.qlon.values, grid.qlat.values, depth,\n", + " cmap=\"Blues\", vmin=0, vmax=2200, shading=\"flat\")\n", + " plt.colorbar(c, ax=ax, label=\"Depth (m)\")\n", + " for j in range(grid.ny):\n", + " for i in range(grid.nx):\n", + " ax.text(grid.tlon.values[j,i], grid.tlat.values[j,i],\n", + " f\"{depth[j,i]:.0f}\",\n", + " ha=\"center\", va=\"center\", fontsize=8,\n", + " color=\"white\" if depth[j,i] > 500 else \"k\")\n", + " ax.set_title(f\"smooth_scl = {sscl}\")\n", + " ax.set_xlabel(\"Longitude\")\n", + " ax.set_ylabel(\"Latitude\")\n", + "\n", + "plt.suptitle(\"Effect of smooth_scl on Cressman interpolation\", fontsize=11)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Higher `smooth_scl` averages over more source pixels — useful for very coarse grids but can smooth out real bathymetric features. The tx2_3 default is `smooth_scl=2.0`.\n", + "\n", + "## Summary\n", + "\n", + "The Cressman pipeline in Python:\n", + "\n", + "1. **Convert source ocean pixels to 3D Cartesian** — enables correct great-circle distance without wraparound issues\n", + "2. **Build `cKDTree`** on ocean pixels only — `O(log n)` radius queries instead of rectangular patch search\n", + "3. **For each ocean model cell**: query all source pixels within `L`, compute Cressman weights, normalise\n", + "4. **Assemble sparse matrix `S`** — apply as `S @ depth_source` for fast regridding\n", + "5. **Write weights to ESMF netCDF** — can be cached and reused\n", + "6. **Iterative neighbour fill** — fallback for cells with no source coverage\n", + "7. **Enforce `hmin`** — shallow ocean cells clamped to `min_depth`\n", + "\n", + "**Next:** `11_full_scale_example.ipynb` — production workflow with real GEBCO data." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.10.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/11_full_scale_example.ipynb b/notebooks/11_full_scale_example.ipynb new file mode 100644 index 00000000..53b55af3 --- /dev/null +++ b/notebooks/11_full_scale_example.ipynb @@ -0,0 +1,583 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Full-Scale Bathymetry Workflow\n", + "\n", + "This notebook demonstrates a **production-ready** bathymetry workflow using real GEBCO data and a realistic model grid. It uses `set_from_dataset` which auto-selects the appropriate pipeline.\n", + "\n", + "## Before you start\n", + "\n", + "You will need:\n", + "- A GEBCO netCDF file (global or regional, 15-arc-second or 30-arc-second)\n", + "- A model grid at the resolution you want to configure\n", + "\n", + "Set the paths in the **Configuration** cell below." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Configuration" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# ── EDIT THESE ────────────────────────────────────────────────────────────────\n", + "\n", + "GEBCO_PATH = \"/path/to/gebco.nc\" # <-- set this when you have the file\n", + "\n", + "# Model domain\n", + "XSTART = 260.0 # western longitude\n", + "LENX = 30.0 # domain width (degrees)\n", + "YSTART = 15.0 # southern latitude\n", + "LENY = 20.0 # domain height (degrees)\n", + "RESOLUTION = 0.5 # model grid resolution (degrees)\n", + "GRID_NAME = \"gulf_of_mexico\"\n", + "\n", + "# Pipeline options\n", + "MASK_METHOD = \"ocean_frac\" # \"ocean_frac\" | \"cartopy\" | None\n", + "NX_SUB = 5 # sub-points per cell in x (ocean_frac only)\n", + "NY_SUB = 5 # sub-points per cell in y (ocean_frac only)\n", + "MASK_THRESHOLD = 0.5\n", + "SMOOTH_SCL = 2.0 # Cressman smoothing radius multiplier\n", + "CRESSMAN_EXP = 2.0\n", + "MIN_DEPTH = 5.0 # m\n", + "FILL_CHANNELS = False\n", + "\n", + "# Output\n", + "OUTPUT_DIR = \"/tmp/mom6_forge_output\" # where to write topog.nc, weights, etc.\n", + "\n", + "# ──────────────────────────────────────────────────────────────────────────────\n", + "\n", + "from pathlib import Path\n", + "output_dir = Path(OUTPUT_DIR)\n", + "output_dir.mkdir(parents=True, exist_ok=True)\n", + "print(f\"Output directory: {output_dir}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import xarray as xr\n", + "import matplotlib.pyplot as plt\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature\n", + "\n", + "from mom6_forge.grid import Grid\n", + "from mom6_forge.topo import Topo\n", + "from mom6_forge._source_bathy import SourceBathy\n", + "\n", + "plt.rcParams[\"figure.dpi\"] = 120" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Build the model grid" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "grid = Grid(\n", + " resolution=RESOLUTION,\n", + " xstart=XSTART,\n", + " lenx=LENX,\n", + " ystart=YSTART,\n", + " leny=LENY,\n", + " name=GRID_NAME,\n", + ")\n", + "\n", + "print(f\"Model grid: {grid.nx} × {grid.ny} T-cells at {RESOLUTION}° resolution\")\n", + "print(f\"Lon: {float(grid.tlon.min()):.2f} – {float(grid.tlon.max()):.2f}\")\n", + "print(f\"Lat: {float(grid.tlat.min()):.2f} – {float(grid.tlat.max()):.2f}\")\n", + "print(f\"Total cells: {grid.nx * grid.ny:,}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(9, 5))\n", + "ax = fig.add_subplot(111, projection=ccrs.PlateCarree())\n", + "ax.add_feature(cfeature.LAND, color=\"tan\")\n", + "ax.add_feature(cfeature.COASTLINE, linewidth=0.5)\n", + "ax.add_feature(cfeature.BORDERS, linewidth=0.3)\n", + "ax.scatter(\n", + " grid.tlon.values.ravel(), grid.tlat.values.ravel(),\n", + " transform=ccrs.PlateCarree(),\n", + " c=\"steelblue\", s=2, label=\"T-cell centres\"\n", + ")\n", + "ax.set_extent([XSTART-2, XSTART+LENX+2, YSTART-2, YSTART+LENY+2], crs=ccrs.PlateCarree())\n", + "ax.gridlines(draw_labels=True, linewidth=0.3, color=\"gray\")\n", + "ax.set_title(f\"{GRID_NAME} — {grid.nx}×{grid.ny} grid at {RESOLUTION}°\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Load and inspect the source bathymetry" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Check GEBCO file exists before proceeding\n", + "gebco_path = Path(GEBCO_PATH)\n", + "if not gebco_path.exists():\n", + " raise FileNotFoundError(\n", + " f\"GEBCO file not found: {gebco_path}\\n\"\n", + " \"Set GEBCO_PATH in the Configuration cell to the path of your GEBCO netCDF file.\"\n", + " )\n", + "\n", + "# Peek at the file structure\n", + "ds_peek = xr.open_dataset(gebco_path)\n", + "print(\"GEBCO file variables:\", list(ds_peek.data_vars))\n", + "print(\"GEBCO file coords:\", list(ds_peek.coords))\n", + "print(\"\\nFirst variable shape:\", ds_peek[list(ds_peek.data_vars)[0]].shape)\n", + "ds_peek.close()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Adjust coordinate and variable names to match your GEBCO file\n", + "# Standard GEBCO 2023: lon='lon', lat='lat', elevation='elevation'\n", + "LON_NAME = \"lon\"\n", + "LAT_NAME = \"lat\"\n", + "ELEVATION_NAME = \"elevation\"\n", + "\n", + "topo = Topo(grid, min_depth=MIN_DEPTH, version_control_dir=output_dir)\n", + "topo.set_flat(1000)\n", + "\n", + "src = SourceBathy(\n", + " gebco_path,\n", + " lon_name=LON_NAME,\n", + " lat_name=LAT_NAME,\n", + " elevation_name=ELEVATION_NAME,\n", + ")\n", + "\n", + "# Slice to domain — loads and clips the relevant region\n", + "print(\"Slicing GEBCO to domain (may take a moment for large files)...\")\n", + "src = src.slice_to_domain(topo)\n", + "\n", + "print(f\"Sliced source shape: {src.depth.shape} ({src.depth.shape[0] * src.depth.shape[1]:,} pixels)\")\n", + "print(f\"Source lon: {float(src.lon.min()):.3f} – {float(src.lon.max()):.3f}\")\n", + "print(f\"Source lat: {float(src.lat.min()):.3f} – {float(src.lat.max()):.3f}\")\n", + "print(f\"Depth range: {float(src.depth.min()):.1f} – {float(src.depth.max()):.1f} m\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(10, 5))\n", + "ax = fig.add_subplot(111, projection=ccrs.PlateCarree())\n", + "c = ax.pcolormesh(\n", + " src.lon, src.lat, src.depth,\n", + " transform=ccrs.PlateCarree(),\n", + " cmap=\"Blues\", vmin=0, vmax=6000, shading=\"auto\"\n", + ")\n", + "plt.colorbar(c, ax=ax, label=\"Depth (m, +down)\")\n", + "ax.add_feature(cfeature.COASTLINE, linewidth=0.5)\n", + "ax.scatter(\n", + " grid.tlon.values.ravel(), grid.tlat.values.ravel(),\n", + " transform=ccrs.PlateCarree(),\n", + " c=\"red\", s=1, alpha=0.5, label=\"Model T-cells\"\n", + ")\n", + "ax.set_extent([XSTART-1, XSTART+LENX+1, YSTART-1, YSTART+LENY+1], crs=ccrs.PlateCarree())\n", + "ax.gridlines(draw_labels=True, linewidth=0.3)\n", + "ax.set_title(\"GEBCO source bathymetry (sliced to domain)\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Diagnose resolution — which pipeline?\n", + "\n", + "`diagnose_resolution` computes the ratio of average source pixel spacing to model cell spacing. If ≥ 12×, the high-res (Cressman) pipeline is recommended." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "use_high_res = topo.diagnose_resolution(src)\n", + "pipeline = \"high_res_regrid (Cressman)\" if use_high_res else \"direct_xesmf_regrid\"\n", + "print(f\"diagnose_resolution → {pipeline}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Generate the ocean mask\n", + "\n", + "We generate the mask before `set_from_dataset` so we can inspect it. You can pass it directly via the `mask=` parameter to skip regeneration." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if MASK_METHOD == \"ocean_frac\":\n", + " print(f\"Generating ocean_frac mask ({NX_SUB}×{NY_SUB} sub-points)...\")\n", + " mask = topo.generate_mask_ocean_frac(\n", + " src,\n", + " nx_sub=NX_SUB,\n", + " ny_sub=NY_SUB,\n", + " mask_threshold=MASK_THRESHOLD,\n", + " )\n", + "elif MASK_METHOD == \"cartopy\":\n", + " print(\"Generating cartopy mask...\")\n", + " mask = topo.generate_mask_cartopy(resolution=\"10m\")\n", + "else:\n", + " mask = None\n", + " print(\"No pre-computed mask — will derive from regridded depth sign\")\n", + "\n", + "if mask is not None:\n", + " n_ocean = int(mask.sum())\n", + " n_total = mask.size\n", + " print(f\"\\nMask: {n_ocean}/{n_total} ocean cells ({100*n_ocean/n_total:.1f}%)\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if mask is not None:\n", + " fig = plt.figure(figsize=(10, 5))\n", + " ax = fig.add_subplot(111, projection=ccrs.PlateCarree())\n", + " c = ax.pcolormesh(\n", + " grid.qlon.values, grid.qlat.values, mask.values,\n", + " transform=ccrs.PlateCarree(),\n", + " cmap=\"RdBu\", vmin=0, vmax=1, shading=\"flat\"\n", + " )\n", + " plt.colorbar(c, ax=ax, label=\"0=land, 1=ocean\")\n", + " ax.add_feature(cfeature.COASTLINE, linewidth=0.8)\n", + " ax.set_extent([XSTART-1, XSTART+LENX+1, YSTART-1, YSTART+LENY+1], crs=ccrs.PlateCarree())\n", + " ax.gridlines(draw_labels=True, linewidth=0.3)\n", + " ax.set_title(f\"Ocean mask — method: {MASK_METHOD}\")\n", + " plt.tight_layout()\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Run the pipeline\n", + "\n", + "We call the individual pipeline methods directly so we can inspect intermediate results. You could equivalently call `topo.set_from_dataset(GEBCO_PATH, mask=mask, mask_method=MASK_METHOD, ...)` to do this in one shot." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "weights_path = output_dir / \"cressman_weights.nc\"\n", + "\n", + "if use_high_res:\n", + " print(\"Running high_res_regrid (Cressman)...\")\n", + " topo.high_res_regrid(\n", + " src,\n", + " mask=mask,\n", + " mask_method=MASK_METHOD if mask is None else None,\n", + " nx_sub=NX_SUB,\n", + " ny_sub=NY_SUB,\n", + " mask_threshold=MASK_THRESHOLD,\n", + " smooth_scl=SMOOTH_SCL,\n", + " cressman_exp=CRESSMAN_EXP,\n", + " hmin=MIN_DEPTH,\n", + " weights_path=weights_path,\n", + " fill_channels=FILL_CHANNELS,\n", + " )\n", + "else:\n", + " print(\"Running direct_xesmf_regrid...\")\n", + " topo.direct_xesmf_regrid(\n", + " src,\n", + " regridding_method=\"bilinear\",\n", + " mask=mask,\n", + " mask_method=MASK_METHOD if mask is None else None,\n", + " fill_channels=FILL_CHANNELS,\n", + " )\n", + "\n", + "print(\"Pipeline complete.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 6: Inspect and validate the result" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "depth = topo.depth.values\n", + "ocean_cells = depth > 0\n", + "\n", + "print(\"=== Depth statistics ===\")\n", + "print(f\"Grid size: {grid.ny} × {grid.nx} = {grid.ny*grid.nx:,} cells\")\n", + "print(f\"Ocean cells: {ocean_cells.sum():,} ({100*ocean_cells.mean():.1f}%)\")\n", + "print(f\"Land cells: {(~ocean_cells).sum():,}\")\n", + "print(f\"Min ocean depth: {depth[ocean_cells].min():.2f} m\")\n", + "print(f\"Max ocean depth: {depth[ocean_cells].max():.2f} m\")\n", + "print(f\"Mean ocean depth: {depth[ocean_cells].mean():.1f} m\")\n", + "print(f\"Median ocean depth:{np.median(depth[ocean_cells]):.1f} m\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(1, 2, figsize=(15, 5),\n", + " subplot_kw={\"projection\": ccrs.PlateCarree()})\n", + "\n", + "# Depth field\n", + "ax = axes[0]\n", + "depth_plot = np.where(ocean_cells, depth, np.nan)\n", + "c = ax.pcolormesh(\n", + " grid.qlon.values, grid.qlat.values, depth_plot,\n", + " transform=ccrs.PlateCarree(),\n", + " cmap=\"Blues_r\", vmin=0, vmax=depth[ocean_cells].max(), shading=\"flat\"\n", + ")\n", + "plt.colorbar(c, ax=ax, label=\"Depth (m)\", shrink=0.8)\n", + "ax.add_feature(cfeature.COASTLINE, linewidth=0.8)\n", + "ax.add_feature(cfeature.LAND, color=\"tan\", zorder=3)\n", + "ax.set_extent([XSTART-1, XSTART+LENX+1, YSTART-1, YSTART+LENY+1], crs=ccrs.PlateCarree())\n", + "ax.gridlines(draw_labels=True, linewidth=0.3)\n", + "ax.set_title(\"Final ocean depth\")\n", + "\n", + "# Depth histogram\n", + "ax = axes[1]\n", + "ax.hist(depth[ocean_cells], bins=50, color=\"steelblue\", edgecolor=\"none\")\n", + "ax.axvline(MIN_DEPTH, color=\"red\", linestyle=\"--\", label=f\"min_depth = {MIN_DEPTH} m\")\n", + "ax.set_xlabel(\"Depth (m)\")\n", + "ax.set_ylabel(\"Number of cells\")\n", + "ax.set_title(\"Ocean depth distribution\")\n", + "ax.legend()\n", + "\n", + "plt.suptitle(f\"{GRID_NAME} — {pipeline if 'pipeline' in dir() else 'pipeline'}\", fontsize=12)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 7: Write the topography file" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "topog_path = output_dir / \"topog.nc\"\n", + "topo.write_topo(topog_path)\n", + "print(f\"Written: {topog_path}\")\n", + "\n", + "# Inspect what was written\n", + "ds_out = xr.open_dataset(topog_path)\n", + "print(\"\\nOutput variables:\", list(ds_out.data_vars))\n", + "ds_out" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 8 (optional): Compare mask methods\n", + "\n", + "If you want to compare `ocean_frac` vs `cartopy` masks on the same domain, run the cells below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Generate cartopy mask for comparison\n", + "mask_cartopy = topo.generate_mask_cartopy(resolution=\"10m\")\n", + "\n", + "if mask is not None:\n", + " disagree = (mask.values != mask_cartopy.values)\n", + "\n", + " fig, axes = plt.subplots(1, 3, figsize=(16, 4),\n", + " subplot_kw={\"projection\": ccrs.PlateCarree()})\n", + "\n", + " for ax, data, title in [\n", + " (axes[0], mask.values, f\"{MASK_METHOD} mask\"),\n", + " (axes[1], mask_cartopy.values, \"cartopy mask (10m)\"),\n", + " (axes[2], disagree.astype(int), \"Disagreement\"),\n", + " ]:\n", + " c = ax.pcolormesh(\n", + " grid.qlon.values, grid.qlat.values, data,\n", + " transform=ccrs.PlateCarree(),\n", + " cmap=\"RdBu\", vmin=0, vmax=1, shading=\"flat\"\n", + " )\n", + " plt.colorbar(c, ax=ax, shrink=0.8)\n", + " ax.add_feature(cfeature.COASTLINE, linewidth=0.8)\n", + " ax.set_extent([XSTART-1, XSTART+LENX+1, YSTART-1, YSTART+LENY+1],\n", + " crs=ccrs.PlateCarree())\n", + " ax.gridlines(linewidth=0.3)\n", + " ax.set_title(title)\n", + "\n", + " axes[2].set_title(f\"Disagreement ({disagree.sum()} cells = {100*disagree.mean():.1f}%)\")\n", + "\n", + " plt.suptitle(\"Mask method comparison\", fontsize=12)\n", + " plt.tight_layout()\n", + " plt.show()\n", + "else:\n", + " print(\"No pre-computed mask to compare (mask=None was used).\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 9 (optional): Topographic drag (`h²`)\n", + "\n", + "If the `ocean_frac` mask method was used, per-cell depth statistics are available and `write_topo` will include the `h²` (topographic roughness variance) field used by MOM6's topographic drag parameterisation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if \"h2\" in ds_out:\n", + " fig = plt.figure(figsize=(9, 5))\n", + " ax = fig.add_subplot(111, projection=ccrs.PlateCarree())\n", + " h2 = ds_out[\"h2\"].values\n", + " c = ax.pcolormesh(\n", + " grid.qlon.values, grid.qlat.values, h2,\n", + " transform=ccrs.PlateCarree(),\n", + " cmap=\"hot_r\", vmin=0, shading=\"flat\"\n", + " )\n", + " plt.colorbar(c, ax=ax, label=\"h² (m²)\", shrink=0.8)\n", + " ax.add_feature(cfeature.COASTLINE, linewidth=0.8)\n", + " ax.add_feature(cfeature.LAND, color=\"tan\", zorder=3)\n", + " ax.set_extent([XSTART-1, XSTART+LENX+1, YSTART-1, YSTART+LENY+1], crs=ccrs.PlateCarree())\n", + " ax.gridlines(draw_labels=True, linewidth=0.3)\n", + " ax.set_title(\"Topographic roughness h² = D2_mean - D_mean²\")\n", + " plt.tight_layout()\n", + " plt.show()\n", + " print(f\"h² range: {h2.min():.1f} – {h2.max():.1f} m²\")\n", + "else:\n", + " print(\"h2 not in output — run with mask_method='ocean_frac' to get topographic drag statistics.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary of outputs\n", + "\n", + "After running this notebook you have:\n", + "\n", + "| File | Contents |\n", + "|---|---|\n", + "| `topog.nc` | `depth` (m), `h2` (m², if ocean_frac mask) — ready for MOM6 |\n", + "| `cressman_weights.nc` | ESMF-compatible sparse weight matrix — reusable |\n", + "\n", + "The `topog.nc` file can be passed directly to MOM6 via `TOPO_FILE` in `MOM_input`.\n", + "\n", + "---\n", + "\n", + "## One-shot equivalent\n", + "\n", + "Once you are satisfied with the configuration, the entire workflow above is equivalent to:\n", + "\n", + "```python\n", + "from mom6_forge.grid import Grid\n", + "from mom6_forge.topo import Topo\n", + "\n", + "grid = Grid(resolution=RESOLUTION, xstart=XSTART, lenx=LENX,\n", + " ystart=YSTART, leny=LENY, name=GRID_NAME)\n", + "topo = Topo(grid, min_depth=MIN_DEPTH, version_control_dir=output_dir)\n", + "topo.set_flat(1000)\n", + "\n", + "topo.set_from_dataset(\n", + " GEBCO_PATH,\n", + " mask_method=MASK_METHOD,\n", + " nx_sub=NX_SUB,\n", + " ny_sub=NY_SUB,\n", + " mask_threshold=MASK_THRESHOLD,\n", + " smooth_scl=SMOOTH_SCL,\n", + " cressman_exp=CRESSMAN_EXP,\n", + " hmin=MIN_DEPTH,\n", + " weights_path=output_dir / \"cressman_weights.nc\",\n", + " fill_channels=FILL_CHANNELS,\n", + ")\n", + "\n", + "topo.write_topo(output_dir / \"topog.nc\")\n", + "```" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.10.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/8_pipeline_overview.ipynb b/notebooks/8_pipeline_overview.ipynb new file mode 100644 index 00000000..b327d688 --- /dev/null +++ b/notebooks/8_pipeline_overview.ipynb @@ -0,0 +1,470 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Bathymetry Pipeline Overview\n", + "\n", + "This notebook walks through the `set_from_dataset` pipeline from first principles using a **small synthetic toy problem** — a 5×4 model grid with a hand-crafted source bathymetry.\n", + "\n", + "## The two pipelines\n", + "\n", + "`set_from_dataset` dispatches to one of two pipelines based on how much finer the source grid is than the model grid:\n", + "\n", + "| Ratio (source/model) | Pipeline | When to use |\n", + "|---|---|---|\n", + "| ≥ 12× | `high_res_regrid` | Coarse model grid (≳ 0.05°), GEBCO-class source |\n", + "| < 12× | `direct_xesmf_regrid` | Fine model grid, source and model at similar resolution |\n", + "\n", + "### High-res pipeline\n", + "```\n", + "SourceBathy → generate_mask (ocean_frac or cartopy) → cressman_interp → tidy_dataset\n", + "```\n", + "\n", + "### Direct pipeline \n", + "```\n", + "SourceBathy → xesmf regrid → tidy_dataset (mask derived from depth sign or pre-computed)\n", + "```\n", + "\n", + "We walk through **both** by hand below so you can see exactly what each step does." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import xarray as xr\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.patches as mpatches\n", + "import matplotlib.colors as mcolors\n", + "from pathlib import Path\n", + "import tempfile\n", + "\n", + "from mom6_forge.grid import Grid\n", + "from mom6_forge.topo import Topo\n", + "from mom6_forge._source_bathy import SourceBathy\n", + "\n", + "plt.rcParams[\"figure.dpi\"] = 120\n", + "plt.rcParams[\"axes.grid\"] = True" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Build the toy model grid\n", + "\n", + "A 5×4 T-cell grid over a small Gulf of Mexico sub-domain at 2° resolution.\n", + "This is deliberately coarse so that the high-res ratio condition is met with a modest source file." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "grid = Grid(\n", + " resolution=2.0, # 2° model grid\n", + " xstart=262.0,\n", + " lenx=10.0, # 5 cells wide\n", + " ystart=20.0,\n", + " leny=8.0, # 4 cells tall\n", + " name=\"toy\",\n", + ")\n", + "\n", + "print(f\"Model grid: {grid.nx} × {grid.ny} T-cells\")\n", + "print(f\"T-cell lon range: {float(grid.tlon.min()):.1f} – {float(grid.tlon.max()):.1f}\")\n", + "print(f\"T-cell lat range: {float(grid.tlat.min()):.1f} – {float(grid.tlat.max()):.1f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Build the synthetic source bathymetry\n", + "\n", + "We create a synthetic GEBCO-style file at 0.1° resolution (~20× finer than the model grid).\n", + "The elevation convention matches GEBCO: **negative = ocean, positive = land**.\n", + "\n", + "We add:\n", + "- A land peninsula in the NW corner\n", + "- A shallow bank in the center\n", + "- Deep ocean everywhere else" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Source grid: 0.1° resolution, covers the model domain with margin\n", + "src_lons = np.arange(260.0, 274.1, 0.1)\n", + "src_lats = np.arange(18.0, 30.1, 0.1)\n", + "src_lon2d, src_lat2d = np.meshgrid(src_lons, src_lats)\n", + "\n", + "# Start with uniform deep ocean (-2000 m elevation = 2000 m depth)\n", + "elevation = np.full(src_lon2d.shape, -2000.0)\n", + "\n", + "# Land peninsula: NW corner (lon < 265, lat > 26)\n", + "elevation[(src_lat2d > 26.0) & (src_lon2d < 265.0)] = +50.0\n", + "\n", + "# Shallow bank: center of domain (depth = 80 m)\n", + "elevation[\n", + " (src_lat2d > 22.0) & (src_lat2d < 24.0) &\n", + " (src_lon2d > 266.0) & (src_lon2d < 268.0)\n", + "] = -80.0\n", + "\n", + "ds_src = xr.Dataset(\n", + " {\"elevation\": ([\"lat\", \"lon\"], elevation.astype(\"float32\"))},\n", + " coords={\"lon\": src_lons, \"lat\": src_lats},\n", + ")\n", + "ds_src.elevation.attrs[\"units\"] = \"m\"\n", + "\n", + "# Write to a temp file so SourceBathy can load it\n", + "tmp_dir = Path(tempfile.mkdtemp())\n", + "src_path = tmp_dir / \"synthetic_bathy.nc\"\n", + "ds_src.to_netcdf(src_path)\n", + "\n", + "print(f\"Source grid: {len(src_lons)} × {len(src_lats)} pixels at 0.1°\")\n", + "print(f\"Approx source/model ratio: {0.1/2.0:.1f}× (wait — that's backwards)\")\n", + "print(f\"Approx model/source ratio: {2.0/0.1:.0f}× finer source than model\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(1, 2, figsize=(13, 4))\n", + "\n", + "# --- Source bathymetry ---\n", + "ax = axes[0]\n", + "c = ax.pcolormesh(\n", + " src_lons, src_lats, elevation,\n", + " cmap=\"RdBu\", vmin=-2200, vmax=200, shading=\"auto\"\n", + ")\n", + "plt.colorbar(c, ax=ax, label=\"Elevation (m, +up)\")\n", + "# Overlay model grid T-cell centres\n", + "ax.scatter(\n", + " grid.tlon.values.ravel(), grid.tlat.values.ravel(),\n", + " c=\"k\", s=20, zorder=5, label=\"Model T-cell centres\"\n", + ")\n", + "# Overlay model grid Q-point corners\n", + "ax.scatter(\n", + " grid.qlon.values.ravel(), grid.qlat.values.ravel(),\n", + " c=\"white\", s=10, marker=\"+\", zorder=5, label=\"Q-point corners\"\n", + ")\n", + "ax.set_title(\"Synthetic source bathymetry + model grid\")\n", + "ax.set_xlabel(\"Longitude\")\n", + "ax.set_ylabel(\"Latitude\")\n", + "ax.legend(fontsize=8)\n", + "ax.set_xlim(260, 274)\n", + "ax.set_ylim(18, 30)\n", + "\n", + "# --- Model grid with labels ---\n", + "ax = axes[1]\n", + "ax.set_title(\"Model grid T-cell labels (j, i)\")\n", + "tlons = grid.tlon.values\n", + "tlats = grid.tlat.values\n", + "ax.pcolormesh(\n", + " grid.qlon.values, grid.qlat.values,\n", + " np.ones((grid.ny, grid.nx)),\n", + " edgecolors=\"k\", linewidth=0.5, facecolor=\"lightblue\", shading=\"flat\"\n", + ")\n", + "for j in range(grid.ny):\n", + " for i in range(grid.nx):\n", + " ax.text(\n", + " tlons[j, i], tlats[j, i], f\"({j},{i})\",\n", + " ha=\"center\", va=\"center\", fontsize=8\n", + " )\n", + "ax.set_xlabel(\"Longitude\")\n", + "ax.set_ylabel(\"Latitude\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. `diagnose_resolution` — which pipeline?\n", + "\n", + "Before anything else, `set_from_dataset` calls `diagnose_resolution` to compare the average model cell spacing (km) against the source pixel spacing (km). If the source is ≥ 12× finer, it recommends the high-res pipeline." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "topo = Topo(grid, min_depth=5.0, version_control_dir=tmp_dir)\n", + "topo.set_flat(1000) # initialise with flat 1000 m ocean\n", + "\n", + "src = SourceBathy(src_path).slice_to_domain(topo)\n", + "\n", + "use_high_res = topo.diagnose_resolution(src)\n", + "print(f\"diagnose_resolution → use high_res_regrid: {use_high_res}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. High-res pipeline — step by step\n", + "\n", + "```\n", + "SourceBathy\n", + " └─ generate_mask_ocean_frac ← Monte-Carlo sub-sampling (mirrors create_model_topo.f90)\n", + " └─ cressman_interp ← Cressman distance-weighted interpolation (mirrors append_topo_interp_smooth.f90)\n", + " └─ tidy_dataset ← lake removal, channel fill, min-depth enforcement\n", + "```\n", + "\n", + "### Step 4a: Generate the ocean mask" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# nx_sub=ny_sub=3 → 3×3 = 9 sub-points per cell (keep small for the toy problem)\n", + "mask_of = topo.generate_mask_ocean_frac(src, nx_sub=3, ny_sub=3, mask_threshold=0.5)\n", + "\n", + "print(\"ocean_frac mask:\")\n", + "print(mask_of.values)\n", + "print(f\"\\n{int(mask_of.sum())} ocean cells, {int((1 - mask_of).sum())} land cells\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Also look at the raw OCN_FRAC values before threshold\n", + "ocn_frac = src._topo_stats[\"OCN_FRAC\"].values\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(11, 4))\n", + "\n", + "for ax, data, title, cmap, vmin, vmax in [\n", + " (axes[0], ocn_frac, \"OCN_FRAC (fraction of sub-points that are ocean)\", \"Blues\", 0, 1),\n", + " (axes[1], mask_of.values, \"Ocean mask (OCN_FRAC ≥ 0.5)\", \"RdBu\", 0, 1),\n", + "]:\n", + " c = ax.pcolormesh(\n", + " grid.qlon.values, grid.qlat.values, data,\n", + " cmap=cmap, vmin=vmin, vmax=vmax, shading=\"flat\"\n", + " )\n", + " plt.colorbar(c, ax=ax)\n", + " ax.scatter(grid.tlon.values.ravel(), grid.tlat.values.ravel(), c=\"k\", s=15, zorder=5)\n", + " ax.set_title(title)\n", + " ax.set_xlabel(\"Longitude\")\n", + " ax.set_ylabel(\"Latitude\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4b: Cressman interpolation\n", + "\n", + "For each **ocean** model cell, gather all source ocean pixels within a radius\n", + "`L = smooth_scl × √(cell_area)` and compute a distance-weighted average:\n", + "\n", + "$$w = \\left(\\frac{L^2 - r^2}{L^2 + r^2}\\right)^{c}$$\n", + "\n", + "The weights are precomputed via a KDTree on the source ocean pixels (see notebook 10 for details)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "weights_path = tmp_dir / \"cressman_weights.nc\"\n", + "topo.cressman_interp(src, mask_of, smooth_scl=2.0, weights_path=weights_path)\n", + "\n", + "depth_after_cressman = topo.depth.values.copy()\n", + "\n", + "fig, ax = plt.subplots(figsize=(7, 4))\n", + "c = ax.pcolormesh(\n", + " grid.qlon.values, grid.qlat.values, depth_after_cressman,\n", + " cmap=\"Blues\", vmin=0, vmax=2200, shading=\"flat\"\n", + ")\n", + "plt.colorbar(c, ax=ax, label=\"Depth (m, +down)\")\n", + "# Annotate each cell with its depth\n", + "for j in range(grid.ny):\n", + " for i in range(grid.nx):\n", + " ax.text(\n", + " grid.tlon.values[j, i], grid.tlat.values[j, i],\n", + " f\"{depth_after_cressman[j, i]:.0f}\",\n", + " ha=\"center\", va=\"center\", fontsize=7, color=\"k\"\n", + " )\n", + "ax.set_title(\"After Cressman interpolation\")\n", + "ax.set_xlabel(\"Longitude\")\n", + "ax.set_ylabel(\"Latitude\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4c: `tidy_dataset`\n", + "\n", + "`tidy_dataset` does three things after regridding:\n", + "1. **Lake removal** — `scipy.ndimage.binary_fill_holes` fills enclosed inland seas\n", + "2. **Channel fill** — removes single-cell-wide channels (optionally diagonal too)\n", + "3. **min_depth enforcement** — shallow ocean cells are clamped to `min_depth`\n", + "\n", + "In the high-res pipeline `tidy_dataset` is called by `cressman_interp` internally, so the depth is already tidy. The cell below shows what the final depth looks like after the full pipeline." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Final depth grid (m):\")\n", + "print(np.round(topo.depth.values, 1))\n", + "print(f\"\\nmin ocean depth: {topo.depth.values[topo.depth.values > 0].min():.1f} m\")\n", + "print(f\"max depth: {topo.depth.values.max():.1f} m\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Direct pipeline — step by step\n", + "\n", + "```\n", + "SourceBathy\n", + " └─ xesmf bilinear regrid ← regrids source elevation onto model T-grid\n", + " └─ tidy_dataset ← sign correction, mask derivation, lake/channel cleanup\n", + "```\n", + "\n", + "The direct pipeline is simpler — it uses `xesmf` for regridding and derives the mask from the sign of the regridded depth. This is appropriate when the source and model grids are at similar resolution (ratio < 12×).\n", + "\n", + "To demonstrate it here we force the call even though our toy problem qualifies for high-res." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Fresh topo for the direct pipeline\n", + "topo_direct = Topo(grid, min_depth=5.0, version_control_dir=tmp_dir)\n", + "topo_direct.set_flat(1000)\n", + "\n", + "topo_direct.direct_xesmf_regrid(\n", + " src,\n", + " regridding_method=\"bilinear\",\n", + " positive_down=False, # source is elevation (positive up)\n", + ")\n", + "\n", + "depth_direct = topo_direct.depth.values.copy()\n", + "\n", + "print(\"Direct pipeline final depth (m):\")\n", + "print(np.round(depth_direct, 1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Side-by-side comparison" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(1, 3, figsize=(15, 4))\n", + "\n", + "vmax = 2200\n", + "datasets = [\n", + " (depth_after_cressman, \"High-res (Cressman)\"),\n", + " (depth_direct, \"Direct (xesmf bilinear)\"),\n", + " (depth_after_cressman - depth_direct, \"Difference (high-res − direct)\"),\n", + "]\n", + "cmaps = [\"Blues\", \"Blues\", \"RdBu_r\"]\n", + "vmins = [0, 0, -200]\n", + "vmaxes = [vmax, vmax, 200]\n", + "\n", + "for ax, (data, title), cmap, vmin, vm in zip(axes, datasets, cmaps, vmins, vmaxes):\n", + " c = ax.pcolormesh(\n", + " grid.qlon.values, grid.qlat.values, data,\n", + " cmap=cmap, vmin=vmin, vmax=vm, shading=\"flat\"\n", + " )\n", + " plt.colorbar(c, ax=ax, label=\"m\")\n", + " for j in range(grid.ny):\n", + " for i in range(grid.nx):\n", + " ax.text(\n", + " grid.tlon.values[j, i], grid.tlat.values[j, i],\n", + " f\"{data[j, i]:.0f}\",\n", + " ha=\"center\", va=\"center\", fontsize=7\n", + " )\n", + " ax.set_title(title)\n", + " ax.set_xlabel(\"Longitude\")\n", + " ax.set_ylabel(\"Latitude\")\n", + "\n", + "plt.suptitle(\"Pipeline comparison — toy 5×4 grid\", fontsize=12)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "| | High-res (Cressman) | Direct (xesmf) |\n", + "|---|---|---|\n", + "| **Mask** | Monte-Carlo sub-sampling of source | Derived from regridded depth sign (or pre-computed) |\n", + "| **Depth** | Cressman distance-weighted average of source ocean pixels | xesmf bilinear interpolation |\n", + "| **Best for** | Coarse model grid (≳ 0.05°) with high-res source | Fine model grid or source/model at similar resolution |\n", + "| **Cost** | Higher (KDTree, weight matrix) | Lower (xesmf regrid only) |\n", + "\n", + "The high-res pipeline produces a more faithful mask and avoids land-contamination of coastal depth estimates because it never interpolates land and ocean depths together. The direct pipeline is cheaper but can smear depths across coastlines.\n", + "\n", + "**Next notebooks:**\n", + "- `9_mask_methods.ipynb` — deep dive into how each mask method works\n", + "- `10_cressman_interpolation.ipynb` — visualising the Cressman weights and KDTree\n", + "- `11_full_scale_example.ipynb` — production workflow with real GEBCO data" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.10.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/9_mask_methods.ipynb b/notebooks/9_mask_methods.ipynb new file mode 100644 index 00000000..6ebe3b4a --- /dev/null +++ b/notebooks/9_mask_methods.ipynb @@ -0,0 +1,501 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Mask Methods Deep Dive\n", + "\n", + "The ocean/land mask is the most consequential decision in the bathymetry pipeline. A wrong mask means wrong boundary conditions for the entire ocean simulation.\n", + "\n", + "`mom6_forge` provides three mask methods, each with different accuracy/cost trade-offs:\n", + "\n", + "| Method | Function | Source | Cost |\n", + "|---|---|---|---|\n", + "| `ocean_frac` | `generate_mask_ocean_frac` | Source bathymetry (sub-sampling) | Medium |\n", + "| `cartopy` | `generate_mask_cartopy` | Natural Earth land polygons | Low |\n", + "| `from_processed_depth` | `generate_mask_from_processed_depth` | Sign of regridded depth | Negligible |\n", + "\n", + "This notebook visualises what each one does internally and compares their outputs on the same toy grid." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import xarray as xr\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.patches as patches\n", + "from pathlib import Path\n", + "import tempfile\n", + "\n", + "from mom6_forge.grid import Grid\n", + "from mom6_forge.topo import Topo\n", + "from mom6_forge._source_bathy import SourceBathy\n", + "\n", + "plt.rcParams[\"figure.dpi\"] = 120" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup — same toy grid as notebook 8" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "grid = Grid(resolution=2.0, xstart=262.0, lenx=10.0, ystart=20.0, leny=8.0, name=\"toy\")\n", + "\n", + "src_lons = np.arange(260.0, 274.1, 0.1)\n", + "src_lats = np.arange(18.0, 30.1, 0.1)\n", + "src_lon2d, src_lat2d = np.meshgrid(src_lons, src_lats)\n", + "\n", + "elevation = np.full(src_lon2d.shape, -2000.0)\n", + "elevation[(src_lat2d > 26.0) & (src_lon2d < 265.0)] = +50.0 # NW land peninsula\n", + "elevation[\n", + " (src_lat2d > 22.0) & (src_lat2d < 24.0) &\n", + " (src_lon2d > 266.0) & (src_lon2d < 268.0)\n", + "] = -80.0 # shallow bank\n", + "\n", + "tmp_dir = Path(tempfile.mkdtemp())\n", + "src_path = tmp_dir / \"synthetic_bathy.nc\"\n", + "xr.Dataset(\n", + " {\"elevation\": ([\"lat\", \"lon\"], elevation.astype(\"float32\"))},\n", + " coords={\"lon\": src_lons, \"lat\": src_lats},\n", + ").to_netcdf(src_path)\n", + "\n", + "topo = Topo(grid, min_depth=5.0, version_control_dir=tmp_dir)\n", + "topo.set_flat(1000)\n", + "src = SourceBathy(src_path).slice_to_domain(topo)\n", + "\n", + "print(\"Setup complete.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Method 1: `ocean_frac` — Monte-Carlo sub-sampling\n", + "\n", + "This mirrors the Fortran program `create_model_topo.f90` from the tx2_3 topography workflow.\n", + "\n", + "### How it works\n", + "\n", + "For each model T-cell:\n", + "1. Distribute `nx_sub × ny_sub` interior sub-points evenly across the cell using **bilinear interpolation** of the 4 Q-point corners\n", + "2. Snap each sub-point to the nearest source pixel\n", + "3. Count how many sub-points land on ocean source pixels → `OCN_FRAC`\n", + "4. If `OCN_FRAC ≥ mask_threshold` → ocean, else → land\n", + "\n", + "The sub-points are always strictly interior (never on cell edges) because the fraction is computed as `k/(n+1)` for k=1..n." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Visualise where sub-points fall for one specific cell\n", + "def subpoints_for_cell(grid, j, i, nx_sub, ny_sub):\n", + " \"\"\"Return (lon, lat) of nx_sub*ny_sub sub-points for T-cell (j, i).\"\"\"\n", + " SW_lon = grid.qlon.values[j, i ]\n", + " SE_lon = grid.qlon.values[j, i+1]\n", + " NE_lon = grid.qlon.values[j+1, i+1]\n", + " NW_lon = grid.qlon.values[j+1, i ]\n", + " SW_lat = grid.qlat.values[j, i ]\n", + " SE_lat = grid.qlat.values[j, i+1]\n", + " NE_lat = grid.qlat.values[j+1, i+1]\n", + " NW_lat = grid.qlat.values[j+1, i ]\n", + "\n", + " pts_lon, pts_lat = [], []\n", + " for js in range(1, ny_sub + 1):\n", + " jf = js / (ny_sub + 1)\n", + " for is_ in range(1, nx_sub + 1):\n", + " iff = is_ / (nx_sub + 1)\n", + " lon = ((1-iff)*(1-jf)*SW_lon + iff*(1-jf)*SE_lon\n", + " + iff*jf*NE_lon + (1-iff)*jf*NW_lon)\n", + " lat = ((1-iff)*(1-jf)*SW_lat + iff*(1-jf)*SE_lat\n", + " + iff*jf*NE_lat + (1-iff)*jf*NW_lat)\n", + " pts_lon.append(lon)\n", + " pts_lat.append(lat)\n", + " return np.array(pts_lon), np.array(pts_lat)\n", + "\n", + "nx_sub, ny_sub = 5, 5\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(13, 5))\n", + "\n", + "for ax_idx, (jc, ic) in enumerate([(3, 0), (1, 2)]):\n", + " ax = axes[ax_idx]\n", + "\n", + " # Source elevation background\n", + " ax.pcolormesh(\n", + " src_lons, src_lats, elevation,\n", + " cmap=\"RdBu\", vmin=-2200, vmax=200, shading=\"auto\", alpha=0.6\n", + " )\n", + "\n", + " # All model cell outlines\n", + " ax.pcolormesh(\n", + " grid.qlon.values, grid.qlat.values,\n", + " np.zeros((grid.ny, grid.nx)),\n", + " edgecolors=\"k\", linewidth=0.8, facecolor=\"none\", shading=\"flat\"\n", + " )\n", + "\n", + " # Sub-points for this cell\n", + " sub_lon, sub_lat = subpoints_for_cell(grid, jc, ic, nx_sub, ny_sub)\n", + "\n", + " # Determine which sub-points are ocean in source\n", + " dlon = float(src_lons[1] - src_lons[0])\n", + " dlat = float(src_lats[1] - src_lats[0])\n", + " ii = np.round((sub_lon - src_lons[0]) / dlon).astype(int).clip(0, len(src_lons)-1)\n", + " jj = np.round((sub_lat - src_lats[0]) / dlat).astype(int).clip(0, len(src_lats)-1)\n", + " is_ocean_sub = elevation[jj, ii] < 0\n", + "\n", + " ocn_frac = is_ocean_sub.mean()\n", + "\n", + " ax.scatter(sub_lon[is_ocean_sub], sub_lat[is_ocean_sub],\n", + " c=\"royalblue\", s=60, zorder=6, label=\"Ocean sub-point\")\n", + " ax.scatter(sub_lon[~is_ocean_sub], sub_lat[~is_ocean_sub],\n", + " c=\"saddlebrown\", s=60, zorder=6, marker=\"x\", label=\"Land sub-point\")\n", + "\n", + " # T-cell centre\n", + " ax.scatter(grid.tlon.values[jc, ic], grid.tlat.values[jc, ic],\n", + " c=\"yellow\", s=100, zorder=7, edgecolors=\"k\", label=\"T-cell centre\")\n", + "\n", + " ax.set_xlim(260.5, 273.5)\n", + " ax.set_ylim(18.5, 29.5)\n", + " ax.set_title(f\"Cell ({jc},{ic}) — OCN_FRAC = {ocn_frac:.2f} → {'OCEAN' if ocn_frac >= 0.5 else 'LAND'}\")\n", + " ax.set_xlabel(\"Longitude\")\n", + " ax.set_ylabel(\"Latitude\")\n", + " ax.legend(fontsize=8, loc=\"upper right\")\n", + "\n", + "plt.suptitle(f\"ocean_frac sub-sampling: {nx_sub}×{ny_sub} = {nx_sub*ny_sub} sub-points per cell\", fontsize=11)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Compute mask for the whole grid and show OCN_FRAC\n", + "mask_of = topo.generate_mask_ocean_frac(src, nx_sub=nx_sub, ny_sub=ny_sub, mask_threshold=0.5)\n", + "ocn_frac_grid = src._topo_stats[\"OCN_FRAC\"].values\n", + "\n", + "fig, axes = plt.subplots(1, 3, figsize=(15, 4))\n", + "\n", + "# OCN_FRAC values\n", + "ax = axes[0]\n", + "c = ax.pcolormesh(grid.qlon.values, grid.qlat.values, ocn_frac_grid,\n", + " cmap=\"Blues\", vmin=0, vmax=1, shading=\"flat\")\n", + "plt.colorbar(c, ax=ax, label=\"OCN_FRAC\")\n", + "for j in range(grid.ny):\n", + " for i in range(grid.nx):\n", + " ax.text(grid.tlon.values[j,i], grid.tlat.values[j,i],\n", + " f\"{ocn_frac_grid[j,i]:.2f}\", ha=\"center\", va=\"center\", fontsize=8)\n", + "ax.set_title(\"OCN_FRAC (raw fraction)\")\n", + "\n", + "# Threshold effect — show what changes at different thresholds\n", + "for ax, thresh, col in zip(axes[1:], [0.3, 0.7], [1, 2]):\n", + " mask_t = (ocn_frac_grid >= thresh).astype(int)\n", + " c = ax.pcolormesh(grid.qlon.values, grid.qlat.values, mask_t,\n", + " cmap=\"RdBu\", vmin=0, vmax=1, shading=\"flat\")\n", + " plt.colorbar(c, ax=ax)\n", + " for j in range(grid.ny):\n", + " for i in range(grid.nx):\n", + " label = \"OCN\" if mask_t[j, i] else \"LND\"\n", + " ax.text(grid.tlon.values[j,i], grid.tlat.values[j,i],\n", + " label, ha=\"center\", va=\"center\", fontsize=8)\n", + " ax.set_title(f\"mask_threshold = {thresh}\")\n", + " for ax_ in axes:\n", + " ax_.set_xlabel(\"Longitude\")\n", + " ax_.set_ylabel(\"Latitude\")\n", + "\n", + "plt.suptitle(\"Effect of mask_threshold on ocean_frac mask\", fontsize=11)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "print(\"Threshold 0.3:\", int((ocn_frac_grid >= 0.3).sum()), \"ocean cells\")\n", + "print(\"Threshold 0.5:\", int((ocn_frac_grid >= 0.5).sum()), \"ocean cells\")\n", + "print(\"Threshold 0.7:\", int((ocn_frac_grid >= 0.7).sum()), \"ocean cells\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Depth statistics cached on `src`\n", + "\n", + "The `ocean_frac` method caches per-cell depth statistics on `src._topo_stats`. These are used later by `write_topo` to add the `h2` (topographic roughness) field needed for MOM6's topographic drag parameterisation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "stats = src._topo_stats\n", + "print(\"Cached topo stats variables:\", list(stats.data_vars))\n", + "print()\n", + "print(\"D_mean (mean ocean depth per cell, m):\")\n", + "print(np.round(stats[\"D_mean\"].values, 1))\n", + "print()\n", + "print(\"h² = D2_mean - D_mean² (topographic variance, m²):\")\n", + "h2 = stats[\"D2_mean\"].values - stats[\"D_mean\"].values**2\n", + "print(np.round(h2, 1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Method 2: `cartopy` — Natural Earth land polygons\n", + "\n", + "Rasterises Natural Earth land polygon shapefiles onto the model grid using Shapely. Each T-cell centre is tested for point-in-polygon against the land geometry. No sub-sampling, no source bathymetry needed.\n", + "\n", + "**Faster** than `ocean_frac` but coarser — it only knows whether the T-cell *centre* is on land or ocean, not the sub-cell fraction. This makes it unreliable for cells that straddle a coastline." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mask_cartopy = topo.generate_mask_cartopy(resolution=\"50m\")\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(11, 4))\n", + "\n", + "for ax, mask, title in [\n", + " (axes[0], mask_of.values, \"ocean_frac (threshold=0.5)\"),\n", + " (axes[1], mask_cartopy.values, \"cartopy (Natural Earth 50m)\"),\n", + "]:\n", + " c = ax.pcolormesh(grid.qlon.values, grid.qlat.values, mask,\n", + " cmap=\"RdBu\", vmin=0, vmax=1, shading=\"flat\")\n", + " plt.colorbar(c, ax=ax)\n", + " for j in range(grid.ny):\n", + " for i in range(grid.nx):\n", + " label = \"OCN\" if mask[j, i] else \"LND\"\n", + " ax.text(grid.tlon.values[j,i], grid.tlat.values[j,i],\n", + " label, ha=\"center\", va=\"center\", fontsize=9)\n", + " ax.set_title(title)\n", + " ax.set_xlabel(\"Longitude\")\n", + " ax.set_ylabel(\"Latitude\")\n", + "\n", + "plt.suptitle(\"Mask method comparison: ocean_frac vs cartopy\", fontsize=11)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Check agreement\n", + "agree = (mask_of.values == mask_cartopy.values)\n", + "print(f\"Cells where both methods agree: {agree.sum()}/{agree.size}\")\n", + "print(f\"Cells that differ: {(~agree).sum()}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Method 3: `generate_mask_from_processed_depth`\n", + "\n", + "This is the simplest method — used internally by `tidy_dataset` when no external mask is provided. It derives the mask from the **sign of the already-regridded depth field**:\n", + "- depth ≤ 0 → land\n", + "- depth > 0 → ocean\n", + "\n", + "**Key constraint:** this only works after the sign convention has been corrected (`positive_down=True` or the sign flip applied). It's not a mask generation step in the same sense as the other two — it's just reading the mask implicit in the depth values.\n", + "\n", + "This is appropriate for the **direct pipeline** where the xesmf regrid has already produced a depth field and you want `tidy_dataset` to clean it up without needing a pre-computed mask." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Simulate a regridded bathymetry dataset (positive-down)\n", + "ny, nx = grid.ny, grid.nx\n", + "depth_arr = np.full((ny, nx), 1500.0)\n", + "depth_arr[3, 0] = -10.0 # cell (3,0) is land in NW corner\n", + "depth_arr[3, 1] = -5.0 # cell (3,1) also land\n", + "\n", + "bathy_ds = xr.Dataset(\n", + " {\"depth\": ([\"ny\", \"nx\"], depth_arr)},\n", + " coords={\n", + " \"lon\": ([\"ny\", \"nx\"], grid.tlon.values),\n", + " \"lat\": ([\"ny\", \"nx\"], grid.tlat.values),\n", + " },\n", + ")\n", + "\n", + "mask_from_depth = topo.generate_mask_from_processed_depth(bathy_ds)\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(11, 4))\n", + "\n", + "ax = axes[0]\n", + "c = ax.pcolormesh(grid.qlon.values, grid.qlat.values, depth_arr,\n", + " cmap=\"RdBu\", vmin=-100, vmax=2000, shading=\"flat\")\n", + "plt.colorbar(c, ax=ax, label=\"Depth (m, +down)\")\n", + "for j in range(ny):\n", + " for i in range(nx):\n", + " ax.text(grid.tlon.values[j,i], grid.tlat.values[j,i],\n", + " f\"{depth_arr[j,i]:.0f}\", ha=\"center\", va=\"center\", fontsize=8)\n", + "ax.set_title(\"Regridded depth (positive-down)\")\n", + "\n", + "ax = axes[1]\n", + "c = ax.pcolormesh(grid.qlon.values, grid.qlat.values, mask_from_depth.values,\n", + " cmap=\"RdBu\", vmin=0, vmax=1, shading=\"flat\")\n", + "plt.colorbar(c, ax=ax)\n", + "for j in range(ny):\n", + " for i in range(nx):\n", + " label = \"OCN\" if mask_from_depth.values[j, i] else \"LND\"\n", + " ax.text(grid.tlon.values[j,i], grid.tlat.values[j,i],\n", + " label, ha=\"center\", va=\"center\", fontsize=9)\n", + "ax.set_title(\"Mask from depth sign (depth > 0 = ocean)\")\n", + "\n", + "for ax in axes:\n", + " ax.set_xlabel(\"Longitude\")\n", + " ax.set_ylabel(\"Latitude\")\n", + "\n", + "plt.suptitle(\"generate_mask_from_processed_depth\", fontsize=11)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## All three masks side by side" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(1, 3, figsize=(15, 4))\n", + "\n", + "masks = [\n", + " (mask_of.values, \"ocean_frac\\n(5×5 sub-points, threshold=0.5)\"),\n", + " (mask_cartopy.values, \"cartopy\\n(Natural Earth 50m)\"),\n", + " (mask_from_depth.values, \"from_processed_depth\\n(sign of regridded depth)\"),\n", + "]\n", + "\n", + "for ax, (mask, title) in zip(axes, masks):\n", + " ax.pcolormesh(grid.qlon.values, grid.qlat.values, mask,\n", + " cmap=\"RdBu\", vmin=0, vmax=1, shading=\"flat\")\n", + " for j in range(grid.ny):\n", + " for i in range(grid.nx):\n", + " label = \"OCN\" if mask[j, i] else \"LND\"\n", + " color = \"white\" if mask[j, i] else \"k\"\n", + " ax.text(grid.tlon.values[j,i], grid.tlat.values[j,i],\n", + " label, ha=\"center\", va=\"center\", fontsize=9, color=color)\n", + " ax.set_title(title)\n", + " ax.set_xlabel(\"Longitude\")\n", + " ax.set_ylabel(\"Latitude\")\n", + "\n", + "plt.suptitle(\"Mask method comparison — toy 5×4 grid\", fontsize=12)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## `tidy_dataset` — what happens to the mask after generation\n", + "\n", + "Regardless of which mask method is used, `tidy_dataset` always runs these cleanup steps on the mask before enforcing depths:\n", + "\n", + "1. **`binary_fill_holes`** — any ocean region completely surrounded by land is filled in as land (inland lake removal)\n", + "2. **Single-cell channel fill** — ocean cells with land on both sides in either x or y direction are converted to land\n", + "3. **Optional diagonal channel fill** — same for diagonal connections (when `fill_channels=True`)\n", + "\n", + "Let's visualise what `binary_fill_holes` does to a mask with an artificial inland lake." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.ndimage import binary_fill_holes\n", + "\n", + "# Artificial mask with an inland lake (ocean surrounded by land)\n", + "mask_with_lake = np.array([\n", + " [1, 1, 1, 1, 1],\n", + " [1, 0, 0, 0, 1],\n", + " [1, 0, 1, 0, 1], # <-- cell (2,2) is ocean, but landlocked\n", + " [1, 0, 0, 0, 1],\n", + "], dtype=int)\n", + "\n", + "land_mask = np.abs(mask_with_lake - 1)\n", + "land_filled = binary_fill_holes(land_mask).astype(int)\n", + "ocean_after_fill = np.abs(land_filled - 1)\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(10, 4))\n", + "for ax, data, title in [\n", + " (axes[0], mask_with_lake, \"Before: landlocked ocean cell at (2,2)\"),\n", + " (axes[1], ocean_after_fill, \"After: binary_fill_holes removes inland lake\"),\n", + "]:\n", + " ax.imshow(data, cmap=\"RdBu\", vmin=0, vmax=1, origin=\"lower\", aspect=\"equal\")\n", + " for j in range(data.shape[0]):\n", + " for i in range(data.shape[1]):\n", + " label = \"OCN\" if data[j, i] else \"LND\"\n", + " ax.text(i, j, label, ha=\"center\", va=\"center\", fontsize=9)\n", + " ax.set_title(title)\n", + " ax.set_xticks(range(data.shape[1]))\n", + " ax.set_yticks(range(data.shape[0]))\n", + " ax.set_xticklabels([f\"i={k}\" for k in range(data.shape[1])])\n", + " ax.set_yticklabels([f\"j={k}\" for k in range(data.shape[0])])\n", + "\n", + "plt.suptitle(\"tidy_dataset: lake removal via binary_fill_holes\", fontsize=11)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "| Method | Uses source data | Sub-cell accuracy | Needs bathymetry loaded | Best used in |\n", + "|---|---|---|---|---|\n", + "| `ocean_frac` | Yes | Yes (sub-sampling) | Yes | High-res pipeline |\n", + "| `cartopy` | No | No (cell-centre only) | No | Quick check, or high-res pipeline |\n", + "| `from_processed_depth` | Implicit (regridded depth) | No | After regridding | Direct pipeline (auto) |\n", + "\n", + "**Recommendation:** use `ocean_frac` for production workflows. Use `cartopy` for quick sanity checks or when source data is not yet loaded. `from_processed_depth` is the internal fallback.\n", + "\n", + "**Next:** `10_cressman_interpolation.ipynb` — how the Cressman weights are computed and applied." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.10.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tests/test_topo_bathy_workflows.py b/tests/test_topo_bathy_workflows.py new file mode 100644 index 00000000..d5b4b692 --- /dev/null +++ b/tests/test_topo_bathy_workflows.py @@ -0,0 +1,538 @@ +""" +Tests for bathymetry pipeline methods on Topo. + +Implemented and tested here: + SourceBathy (construction, slicing, accessors) + Topo.diagnose_resolution() + Topo.generate_mask_ocean_frac() + Topo.generate_mask_cartopy() + Topo._compute_topo_stats() (cache behaviour) + Topo.tidy_dataset() (external mask param) + Topo.set_from_dataset() (dispatch to high_res vs direct) + Topo.direct_xesmf_regrid() (mask options, bad mask_method) + Topo.high_res_regrid() (end-to-end with ocean_frac / cartopy / pre-computed mask) + Topo.mpi_direct_xesmf_regrid() (renamed from mpi_set_from_dataset) +""" + +import numpy as np +import pytest +import xarray as xr +from pathlib import Path + +from mom6_forge.grid import Grid +from mom6_forge.topo import Topo +from mom6_forge._source_bathy import SourceBathy + +# --------------------------------------------------------------------------- +# Fixtures +# --------------------------------------------------------------------------- + + +@pytest.fixture +def small_grid(): + """Small rectangular grid over a Gulf of Mexico sub-region for fast tests.""" + return Grid( + resolution=0.5, + xstart=260.0, + lenx=10.0, + ystart=18.0, + leny=8.0, + name="gulf_test", + ) + + +@pytest.fixture +def small_topo(small_grid, tmp_path): + """Flat 1000 m topo on the small grid with version control.""" + topo = Topo(small_grid, min_depth=5.0, version_control_dir=tmp_path) + topo.set_flat(1000) + return topo + + +@pytest.fixture +def synthetic_gebco(tmp_path): + """ + Write a minimal synthetic GEBCO-style netCDF for testing. + Covers the small_grid domain with 0.05-degree resolution. + Depths are negative (elevation convention): ocean cells < 0, land >= 0. + A strip of land is included to test masking behaviour. + """ + lons = np.arange(259.5, 271.0, 0.05) + lats = np.arange(17.5, 27.0, 0.05) + lon2d, lat2d = np.meshgrid(lons, lats) + + # Ocean everywhere, with a land strip at lon 265-266 + elevation = np.where( + (lon2d >= 265.0) & (lon2d <= 266.0), + 100.0, # land + -1000.0, # ocean + ).astype("float32") + + ds = xr.Dataset( + {"elevation": (["lat", "lon"], elevation)}, + coords={"lon": lons, "lat": lats}, + ) + ds.elevation.attrs["units"] = "m" + path = tmp_path / "synthetic_gebco.nc" + ds.to_netcdf(path) + return path + + +@pytest.fixture +def src_bathy(small_topo, synthetic_gebco): + """SourceBathy sliced to the small_topo domain.""" + return SourceBathy(synthetic_gebco).slice_to_domain(small_topo) + + +# --------------------------------------------------------------------------- +# SourceBathy +# --------------------------------------------------------------------------- + + +def test_source_bathy_construction(synthetic_gebco): + """SourceBathy can be constructed from a path with default coord names.""" + src = SourceBathy(synthetic_gebco) + assert src.path == Path(synthetic_gebco) + assert src.lon_name == "lon" + assert src.lat_name == "lat" + assert src.elevation_name == "elevation" + assert src._da is None + + +def test_source_bathy_construction_custom_names(synthetic_gebco): + src = SourceBathy(synthetic_gebco, lon_name="x", lat_name="y", elevation_name="z") + assert src.lon_name == "x" + assert src.lat_name == "y" + assert src.elevation_name == "z" + + +def test_source_bathy_slice_to_domain_loads_da(small_topo, synthetic_gebco): + src = SourceBathy(synthetic_gebco).slice_to_domain(small_topo) + assert src._da is not None + + +def test_source_bathy_lon_lat_arrays(src_bathy): + assert src_bathy.lon.ndim == 1 + assert src_bathy.lat.ndim == 1 + + +def test_source_bathy_depth_positive_down(src_bathy): + """depth property must be positive-down (ocean depth > 0).""" + ocean_vals = src_bathy.depth[src_bathy.depth > 0] + assert ocean_vals.size > 0 + + +def test_source_bathy_da_is_elevation(src_bathy): + """da property returns the raw elevation DataArray (positive-up sign).""" + assert src_bathy.da is not None + # elevation of ocean cells should be negative + assert float(src_bathy.da.min()) < 0 + + +def test_source_bathy_repr(src_bathy): + r = repr(src_bathy) + assert "SourceBathy" in r + + +def test_source_bathy_topo_stats_none_before_compute(src_bathy): + assert src_bathy._topo_stats is None + + +# --------------------------------------------------------------------------- +# generate_mask_ocean_frac +# --------------------------------------------------------------------------- + + +def test_generate_mask_ocean_frac_returns_binary_mask(small_topo, src_bathy): + """Mask values must be 0 (land) or 1 (ocean) only.""" + mask = small_topo.generate_mask_ocean_frac(src_bathy, nx_sub=3, ny_sub=3) + assert set(np.unique(mask.values)).issubset({0, 1}) + + +def test_generate_mask_ocean_frac_shape_matches_grid(small_topo, src_bathy): + """Mask must have the same spatial shape as the model grid.""" + mask = small_topo.generate_mask_ocean_frac(src_bathy, nx_sub=3, ny_sub=3) + assert mask.shape == (small_topo._grid.ny, small_topo._grid.nx) + + +def test_generate_mask_ocean_frac_land_strip_is_masked(small_topo, src_bathy): + """Cells over the synthetic land strip should have mask=0.""" + mask = small_topo.generate_mask_ocean_frac(src_bathy, nx_sub=5, ny_sub=5) + land_cols = np.where( + (small_topo._grid.tlon.values >= 265.0) + & (small_topo._grid.tlon.values <= 266.0) + ) + assert (mask.values[land_cols] == 0).all() + + +def test_generate_mask_ocean_frac_stats_stored_on_src(small_topo, src_bathy): + """_topo_stats Dataset with OCN_FRAC, D_mean, D_min, D_max, D2_mean + must be set on the SourceBathy after the call.""" + small_topo.generate_mask_ocean_frac(src_bathy, nx_sub=3, ny_sub=3) + assert src_bathy._topo_stats is not None + for var in ("OCN_FRAC", "D_mean", "D_min", "D_max", "D2_mean"): + assert var in src_bathy._topo_stats + + +def test_generate_mask_ocean_frac_src_stored_on_topo(small_topo, src_bathy): + """Calling generate_mask_ocean_frac must set topo._src so write_topo can + find the stats.""" + small_topo.generate_mask_ocean_frac(src_bathy, nx_sub=3, ny_sub=3) + assert small_topo._src is src_bathy + + +def test_generate_mask_ocean_frac_stats_cached(small_topo, src_bathy, monkeypatch): + """Calling generate_mask_ocean_frac twice must not re-run _compute_topo_stats.""" + small_topo.generate_mask_ocean_frac(src_bathy, nx_sub=3, ny_sub=3) + monkeypatch.setattr( + small_topo, + "_compute_topo_stats", + lambda *a, **kw: (_ for _ in ()).throw( + AssertionError("_compute_topo_stats ran again") + ), + ) + small_topo.generate_mask_ocean_frac(src_bathy, nx_sub=3, ny_sub=3) + + +def test_generate_mask_ocean_frac_ocn_frac_in_bounds(small_topo, src_bathy): + """OCN_FRAC must be in [0, 1] for every cell.""" + small_topo.generate_mask_ocean_frac(src_bathy, nx_sub=3, ny_sub=3) + ocn_frac = src_bathy._topo_stats["OCN_FRAC"].values + assert float(ocn_frac.min()) >= 0.0 + assert float(ocn_frac.max()) <= 1.0 + + +# --------------------------------------------------------------------------- +# generate_mask_cartopy +# --------------------------------------------------------------------------- + + +def test_generate_mask_cartopy_returns_binary_mask(small_topo): + """Mask values must be 0 or 1.""" + mask = small_topo.generate_mask_cartopy(resolution="50m") + assert set(np.unique(mask.values)).issubset({0, 1}) + + +def test_generate_mask_cartopy_shape_matches_grid(small_topo): + """Mask must have the same spatial shape as the model grid.""" + mask = small_topo.generate_mask_cartopy(resolution="50m") + assert mask.shape == (small_topo._grid.ny, small_topo._grid.nx) + + +def test_generate_mask_cartopy_open_ocean_is_unmasked(small_topo): + """Deep Gulf of Mexico cells should be ocean (mask=1).""" + mask = small_topo.generate_mask_cartopy(resolution="50m") + j = np.argmin(np.abs(small_topo._grid.tlat[:, 0].values - 25.0)) + i = np.argmin(np.abs(small_topo._grid.tlon[0, :].values - 270.0)) + assert mask.values[j, i] == 1 + + +# --------------------------------------------------------------------------- +# diagnose_resolution +# --------------------------------------------------------------------------- + + +def test_diagnose_resolution_returns_bool(small_topo, src_bathy): + result = small_topo.diagnose_resolution(src_bathy) + assert isinstance(result, bool) + + +def test_diagnose_resolution_works_with_unloaded_src(small_topo, synthetic_gebco): + """diagnose_resolution must work even when src has not been sliced.""" + src = SourceBathy(synthetic_gebco) + assert src._da is None # not yet sliced + result = small_topo.diagnose_resolution(src) + assert isinstance(result, bool) + + +# --------------------------------------------------------------------------- +# h2 written via write_topo (enforce_topo_drag) +# --------------------------------------------------------------------------- + + +def test_write_topo_h2_in_output_when_stats_computed(small_topo, src_bathy, tmp_path): + """write_topo should include h2 when _topo_stats are available.""" + small_topo.generate_mask_ocean_frac(src_bathy, nx_sub=3, ny_sub=3) + out = tmp_path / "topog.nc" + small_topo.write_topo(out) + assert "h2" in xr.open_dataset(out) + + +def test_write_topo_h2_non_negative(small_topo, src_bathy, tmp_path): + """h2 = D2_mean - D_mean^2 is a variance and must be >= 0.""" + small_topo.generate_mask_ocean_frac(src_bathy, nx_sub=3, ny_sub=3) + out = tmp_path / "topog.nc" + small_topo.write_topo(out) + assert float(xr.open_dataset(out).h2.min()) >= 0.0 + + +def test_write_topo_enforce_topo_drag_raises_without_stats(small_topo, tmp_path): + """enforce_topo_drag=True must raise if stats have not been computed.""" + with pytest.raises(RuntimeError, match="generate_mask_ocean_frac"): + small_topo.write_topo(tmp_path / "topog.nc", enforce_topo_drag=True) + + +# --------------------------------------------------------------------------- +# tidy_dataset — external mask parameter +# --------------------------------------------------------------------------- + + +def test_tidy_dataset_external_mask_overrides_depth_sign(small_topo): + """Cells marked as land in an external mask should end up depth=0 + even when the raw bathymetry depth is positive at those cells.""" + ny, nx = small_topo._grid.ny, small_topo._grid.nx + bathy_ds = xr.Dataset( + {"depth": (["ny", "nx"], np.full((ny, nx), 500.0))}, + coords={ + "lon": (["ny", "nx"], small_topo._grid.tlon.values), + "lat": (["ny", "nx"], small_topo._grid.tlat.values), + }, + ) + # Mark first column as land + mask = xr.DataArray(np.ones((ny, nx), dtype=int), dims=["ny", "nx"]) + mask[:, 0] = 0 + + small_topo.tidy_dataset( + positive_down=True, + vertical_coordinate_name="depth", + bathymetry=bathy_ds, + longitude_coordinate_name="lon", + latitude_coordinate_name="lat", + mask=mask, + ) + assert (small_topo.depth.values[:, 0] == 0).all() + assert (small_topo.depth.values[:, 1:] > 0).all() + + +def test_tidy_dataset_no_mask_derives_from_depth_sign(small_topo): + """Without an external mask, tidy_dataset must derive ocean from depth > 0.""" + ny, nx = small_topo._grid.ny, small_topo._grid.nx + depth_arr = np.full((ny, nx), 500.0) + depth_arr[:, 0] = -1.0 # first column is land by depth sign + bathy_ds = xr.Dataset( + {"depth": (["ny", "nx"], depth_arr)}, + coords={ + "lon": (["ny", "nx"], small_topo._grid.tlon.values), + "lat": (["ny", "nx"], small_topo._grid.tlat.values), + }, + ) + small_topo.tidy_dataset( + positive_down=True, + vertical_coordinate_name="depth", + bathymetry=bathy_ds, + longitude_coordinate_name="lon", + latitude_coordinate_name="lat", + ) + assert (small_topo.depth.values[:, 0] == 0).all() + assert (small_topo.depth.values[:, 1:] > 0).all() + + +# --------------------------------------------------------------------------- +# set_from_dataset — dispatch logic +# --------------------------------------------------------------------------- + + +def test_set_from_dataset_dispatches_to_high_res_when_recommended( + small_topo, synthetic_gebco, monkeypatch +): + """When diagnose_resolution returns True, high_res_regrid must be called.""" + monkeypatch.setattr(small_topo, "diagnose_resolution", lambda *a, **kw: True) + called = [] + monkeypatch.setattr( + small_topo, "high_res_regrid", lambda *a, **kw: called.append(kw) + ) + small_topo.set_from_dataset(bathymetry_path=synthetic_gebco) + assert called, "high_res_regrid was not called" + + +def test_set_from_dataset_dispatches_to_direct_xesmf_when_not_recommended( + small_topo, synthetic_gebco, monkeypatch +): + """When diagnose_resolution returns False, direct_xesmf_regrid must be called.""" + monkeypatch.setattr(small_topo, "diagnose_resolution", lambda *a, **kw: False) + called = [] + monkeypatch.setattr( + small_topo, "direct_xesmf_regrid", lambda *a, **kw: called.append(kw) + ) + small_topo.set_from_dataset(bathymetry_path=synthetic_gebco) + assert called, "direct_xesmf_regrid was not called" + + +def test_set_from_dataset_default_mask_method_ocean_frac_for_high_res( + small_topo, synthetic_gebco, monkeypatch +): + """When dispatching to high_res_regrid with no mask_method set, 'ocean_frac' is used.""" + monkeypatch.setattr(small_topo, "diagnose_resolution", lambda *a, **kw: True) + captured = {} + monkeypatch.setattr( + small_topo, "high_res_regrid", lambda *a, **kw: captured.update(kw) + ) + small_topo.set_from_dataset(bathymetry_path=synthetic_gebco) + assert captured.get("mask_method") == "ocean_frac" + + +def test_set_from_dataset_no_default_mask_for_direct_xesmf( + small_topo, synthetic_gebco, monkeypatch +): + """When dispatching to direct_xesmf_regrid with no mask_method, None is forwarded + so tidy_dataset falls back to deriving the mask from depth sign.""" + monkeypatch.setattr(small_topo, "diagnose_resolution", lambda *a, **kw: False) + captured = {} + monkeypatch.setattr( + small_topo, "direct_xesmf_regrid", lambda *a, **kw: captured.update(kw) + ) + small_topo.set_from_dataset(bathymetry_path=synthetic_gebco) + assert captured.get("mask_method") is None + + +def test_set_from_dataset_explicit_mask_method_forwarded_to_direct( + small_topo, synthetic_gebco, monkeypatch +): + """An explicit mask_method is forwarded unchanged to direct_xesmf_regrid.""" + monkeypatch.setattr(small_topo, "diagnose_resolution", lambda *a, **kw: False) + captured = {} + monkeypatch.setattr( + small_topo, "direct_xesmf_regrid", lambda *a, **kw: captured.update(kw) + ) + small_topo.set_from_dataset(bathymetry_path=synthetic_gebco, mask_method="cartopy") + assert captured.get("mask_method") == "cartopy" + + +def test_set_from_dataset_src_passed_to_pipeline( + small_topo, synthetic_gebco, monkeypatch +): + """set_from_dataset must create a SourceBathy and pass it as the first + positional arg to the selected pipeline.""" + monkeypatch.setattr(small_topo, "diagnose_resolution", lambda *a, **kw: False) + received_src = [] + monkeypatch.setattr( + small_topo, + "direct_xesmf_regrid", + lambda src, **kw: received_src.append(src), + ) + small_topo.set_from_dataset(bathymetry_path=synthetic_gebco) + assert len(received_src) == 1 + assert isinstance(received_src[0], SourceBathy) + + +# --------------------------------------------------------------------------- +# direct_xesmf_regrid +# --------------------------------------------------------------------------- + + +def test_direct_xesmf_regrid_bad_mask_method_raises(small_topo, src_bathy): + """Unknown mask_method must raise ValueError.""" + with pytest.raises(ValueError, match="mask_method"): + small_topo.direct_xesmf_regrid(src_bathy, mask_method="invalid") + + +def test_direct_xesmf_regrid_precomputed_mask_bypasses_generation( + small_topo, src_bathy, monkeypatch +): + """When mask= is provided, generate_mask_ocean_frac must not be called.""" + mask = small_topo.generate_mask_cartopy(resolution="50m") + called = [] + monkeypatch.setattr( + small_topo, "generate_mask_ocean_frac", lambda *a, **kw: called.append(kw) + ) + monkeypatch.setattr( + small_topo, "generate_mask_cartopy", lambda **kw: called.append(kw) + ) + # Stop execution after mask check — we only care that generation was skipped + monkeypatch.setattr( + small_topo, + "config_dataset", + lambda *a, **kw: (_ for _ in ()).throw(StopIteration), + ) + with pytest.raises(StopIteration): + small_topo.direct_xesmf_regrid( + src_bathy, + mask=mask, + mask_method="ocean_frac", # should be ignored because mask= is set + ) + assert not called, "mask generation should not run when mask= is provided" + + +# --------------------------------------------------------------------------- +# high_res_regrid +# --------------------------------------------------------------------------- + + +def test_high_res_regrid_bad_mask_method_raises(small_topo, src_bathy): + """Unknown mask_method must raise ValueError.""" + with pytest.raises(ValueError, match="mask_method"): + small_topo.high_res_regrid(src_bathy, mask_method="invalid") + + +def test_high_res_regrid_ocean_frac_mask_produces_valid_depth( + small_topo, src_bathy, tmp_path +): + """End-to-end with ocean_frac mask: ocean cells must have depth >= min_depth.""" + small_topo.high_res_regrid( + src_bathy, + mask_method="ocean_frac", + nx_sub=3, + ny_sub=3, + weights_path=tmp_path / "cressman_weights.nc", + ) + ocean = small_topo.depth.values > 0 + assert ocean.any(), "No ocean cells after high_res_regrid" + assert (small_topo.depth.values[ocean] >= small_topo.min_depth).all() + + +def test_high_res_regrid_cartopy_mask_produces_valid_depth( + small_topo, src_bathy, tmp_path +): + """End-to-end with cartopy mask: ocean cells must have depth >= min_depth.""" + small_topo.high_res_regrid( + src_bathy, + mask_method="cartopy", + cartopy_resolution="50m", + weights_path=tmp_path / "cressman_weights.nc", + ) + ocean = small_topo.depth.values > 0 + assert ocean.any(), "No ocean cells after high_res_regrid with cartopy mask" + assert (small_topo.depth.values[ocean] >= small_topo.min_depth).all() + + +def test_high_res_regrid_precomputed_mask_accepted(small_topo, src_bathy, tmp_path): + """Passing a pre-computed mask directly should skip mask generation.""" + mask = small_topo.generate_mask_cartopy(resolution="50m") + small_topo.high_res_regrid( + src_bathy, + mask=mask, + weights_path=tmp_path / "cressman_weights.nc", + ) + ocean = small_topo.depth.values > 0 + assert ocean.any(), "No ocean cells with pre-computed mask" + + +def test_high_res_regrid_weights_file_written(small_topo, src_bathy, tmp_path): + """Cressman weights netCDF must be written to weights_path.""" + wp = tmp_path / "cressman_weights.nc" + small_topo.high_res_regrid( + src_bathy, + mask_method="ocean_frac", + nx_sub=3, + ny_sub=3, + weights_path=wp, + ) + assert wp.exists() + ds = xr.open_dataset(wp) + for var in ("S", "row", "col"): + assert var in ds + + +# --------------------------------------------------------------------------- +# mpi_direct_xesmf_regrid rename +# --------------------------------------------------------------------------- + + +def test_mpi_direct_xesmf_regrid_exists(small_topo): + """mpi_direct_xesmf_regrid must be callable (renamed from mpi_set_from_dataset).""" + assert callable(getattr(small_topo, "mpi_direct_xesmf_regrid", None)) + + +def test_mpi_set_from_dataset_removed(small_topo): + """Old name mpi_set_from_dataset must no longer exist.""" + assert not hasattr(small_topo, "mpi_set_from_dataset") diff --git a/tests/test_topo_regional_bathy.py b/tests/test_topo_regional_bathy.py deleted file mode 100644 index 3f6c6417..00000000 --- a/tests/test_topo_regional_bathy.py +++ /dev/null @@ -1,528 +0,0 @@ -""" -Tests for regional bathymetry pipeline methods on Topo. - -Implemented and tested here: - SourceBathy (construction, slicing, accessors) - Topo.diagnose_resolution() - Topo.generate_mask_ocean_frac() - Topo.generate_mask_cartopy() - Topo._compute_topo_stats() (cache behaviour) - Topo.tidy_dataset() (external mask param) - Topo.set_from_dataset() (dispatch to high_res vs direct) - Topo.direct_xesmf_regrid() (mask options, bad mask_method) - Topo.high_res_regrid() (end-to-end with ocean_frac / cartopy / pre-computed mask) - Topo.mpi_direct_xesmf_regrid() (renamed from mpi_set_from_dataset) -""" - -import numpy as np -import pytest -import xarray as xr -from pathlib import Path - -from mom6_forge.grid import Grid -from mom6_forge.topo import Topo -from mom6_forge._source_bathy import SourceBathy - -# --------------------------------------------------------------------------- -# Fixtures -# --------------------------------------------------------------------------- - - -@pytest.fixture -def small_grid(): - """Small rectangular grid over a Gulf of Mexico sub-region for fast tests.""" - return Grid( - resolution=0.5, - xstart=260.0, - lenx=10.0, - ystart=18.0, - leny=8.0, - name="gulf_test", - ) - - -@pytest.fixture -def small_topo(small_grid, tmp_path): - """Flat 1000 m topo on the small grid with version control.""" - topo = Topo(small_grid, min_depth=5.0, version_control_dir=tmp_path) - topo.set_flat(1000) - return topo - - -@pytest.fixture -def synthetic_gebco(tmp_path): - """ - Write a minimal synthetic GEBCO-style netCDF for testing. - Covers the small_grid domain with 0.05-degree resolution. - Depths are negative (elevation convention): ocean cells < 0, land >= 0. - A strip of land is included to test masking behaviour. - """ - lons = np.arange(259.5, 271.0, 0.05) - lats = np.arange(17.5, 27.0, 0.05) - lon2d, lat2d = np.meshgrid(lons, lats) - - # Ocean everywhere, with a land strip at lon 265-266 - elevation = np.where( - (lon2d >= 265.0) & (lon2d <= 266.0), - 100.0, # land - -1000.0, # ocean - ).astype("float32") - - ds = xr.Dataset( - {"elevation": (["lat", "lon"], elevation)}, - coords={"lon": lons, "lat": lats}, - ) - ds.elevation.attrs["units"] = "m" - path = tmp_path / "synthetic_gebco.nc" - ds.to_netcdf(path) - return path - - -@pytest.fixture -def src_bathy(small_topo, synthetic_gebco): - """SourceBathy sliced to the small_topo domain.""" - return SourceBathy(synthetic_gebco).slice_to_domain(small_topo) - - -# --------------------------------------------------------------------------- -# SourceBathy -# --------------------------------------------------------------------------- - - -class TestSourceBathy: - - def test_construction(self, synthetic_gebco): - """SourceBathy can be constructed from a path with default coord names.""" - src = SourceBathy(synthetic_gebco) - assert src.path == Path(synthetic_gebco) - assert src.lon_name == "lon" - assert src.lat_name == "lat" - assert src.elevation_name == "elevation" - assert src._da is None - - def test_construction_custom_names(self, synthetic_gebco): - src = SourceBathy( - synthetic_gebco, lon_name="x", lat_name="y", elevation_name="z" - ) - assert src.lon_name == "x" - assert src.lat_name == "y" - assert src.elevation_name == "z" - - def test_slice_to_domain_loads_da(self, small_topo, synthetic_gebco): - src = SourceBathy(synthetic_gebco).slice_to_domain(small_topo) - assert src._da is not None - - def test_lon_lat_arrays(self, src_bathy): - assert src_bathy.lon.ndim == 1 - assert src_bathy.lat.ndim == 1 - - def test_depth_positive_down(self, src_bathy): - """depth property must be positive-down (ocean depth > 0).""" - ocean_vals = src_bathy.depth[src_bathy.depth > 0] - assert ocean_vals.size > 0 - - def test_da_is_elevation(self, src_bathy): - """da property returns the raw elevation DataArray (positive-up sign).""" - assert src_bathy.da is not None - # elevation of ocean cells should be negative - assert float(src_bathy.da.min()) < 0 - - def test_repr(self, src_bathy): - r = repr(src_bathy) - assert "SourceBathy" in r - - def test_topo_stats_none_before_compute(self, src_bathy): - assert src_bathy._topo_stats is None - - -# --------------------------------------------------------------------------- -# generate_mask_ocean_frac -# --------------------------------------------------------------------------- - - -class TestGenerateMaskOceanFrac: - - def test_returns_binary_mask(self, small_topo, src_bathy): - """Mask values must be 0 (land) or 1 (ocean) only.""" - mask = small_topo.generate_mask_ocean_frac(src_bathy, nx_sub=3, ny_sub=3) - assert set(np.unique(mask.values)).issubset({0, 1}) - - def test_mask_shape_matches_grid(self, small_topo, src_bathy): - """Mask must have the same spatial shape as the model grid.""" - mask = small_topo.generate_mask_ocean_frac(src_bathy, nx_sub=3, ny_sub=3) - assert mask.shape == (small_topo._grid.ny, small_topo._grid.nx) - - def test_land_strip_is_masked(self, small_topo, src_bathy): - """Cells over the synthetic land strip should have mask=0.""" - mask = small_topo.generate_mask_ocean_frac(src_bathy, nx_sub=5, ny_sub=5) - land_cols = np.where( - (small_topo._grid.tlon.values >= 265.0) - & (small_topo._grid.tlon.values <= 266.0) - ) - assert (mask.values[land_cols] == 0).all() - - def test_stats_stored_on_src_after_call(self, small_topo, src_bathy): - """_topo_stats Dataset with OCN_FRAC, D_mean, D_min, D_max, D2_mean - must be set on the SourceBathy after the call.""" - small_topo.generate_mask_ocean_frac(src_bathy, nx_sub=3, ny_sub=3) - assert src_bathy._topo_stats is not None - for var in ("OCN_FRAC", "D_mean", "D_min", "D_max", "D2_mean"): - assert var in src_bathy._topo_stats - - def test_src_stored_on_topo_after_call(self, small_topo, src_bathy): - """Calling generate_mask_ocean_frac must set topo._src so write_topo can - find the stats.""" - small_topo.generate_mask_ocean_frac(src_bathy, nx_sub=3, ny_sub=3) - assert small_topo._src is src_bathy - - def test_stats_cached(self, small_topo, src_bathy, monkeypatch): - """Calling generate_mask_ocean_frac twice must not re-run _compute_topo_stats.""" - small_topo.generate_mask_ocean_frac(src_bathy, nx_sub=3, ny_sub=3) - monkeypatch.setattr( - small_topo, - "_compute_topo_stats", - lambda *a, **kw: (_ for _ in ()).throw( - AssertionError("_compute_topo_stats ran again") - ), - ) - small_topo.generate_mask_ocean_frac(src_bathy, nx_sub=3, ny_sub=3) - - def test_ocn_frac_in_bounds(self, small_topo, src_bathy): - """OCN_FRAC must be in [0, 1] for every cell.""" - small_topo.generate_mask_ocean_frac(src_bathy, nx_sub=3, ny_sub=3) - ocn_frac = src_bathy._topo_stats["OCN_FRAC"].values - assert float(ocn_frac.min()) >= 0.0 - assert float(ocn_frac.max()) <= 1.0 - - -# --------------------------------------------------------------------------- -# generate_mask_cartopy -# --------------------------------------------------------------------------- - - -class TestGenerateMaskCartopy: - - def test_returns_binary_mask(self, small_topo): - """Mask values must be 0 or 1.""" - mask = small_topo.generate_mask_cartopy(resolution="50m") - assert set(np.unique(mask.values)).issubset({0, 1}) - - def test_mask_shape_matches_grid(self, small_topo): - """Mask must have the same spatial shape as the model grid.""" - mask = small_topo.generate_mask_cartopy(resolution="50m") - assert mask.shape == (small_topo._grid.ny, small_topo._grid.nx) - - def test_open_ocean_is_unmasked(self, small_topo): - """Deep Gulf of Mexico cells should be ocean (mask=1).""" - mask = small_topo.generate_mask_cartopy(resolution="50m") - j = np.argmin(np.abs(small_topo._grid.tlat[:, 0].values - 25.0)) - i = np.argmin(np.abs(small_topo._grid.tlon[0, :].values - 270.0)) - assert mask.values[j, i] == 1 - - -# --------------------------------------------------------------------------- -# diagnose_resolution -# --------------------------------------------------------------------------- - - -class TestDiagnoseResolution: - - def test_returns_bool(self, small_topo, src_bathy): - result = small_topo.diagnose_resolution(src_bathy) - assert isinstance(result, bool) - - def test_works_with_unloaded_src(self, small_topo, synthetic_gebco): - """diagnose_resolution must work even when src has not been sliced.""" - src = SourceBathy(synthetic_gebco) - assert src._da is None # not yet sliced - result = small_topo.diagnose_resolution(src) - assert isinstance(result, bool) - - -# --------------------------------------------------------------------------- -# h2 written via write_topo (enforce_topo_drag) -# --------------------------------------------------------------------------- - - -class TestTopoDragInWriteTopo: - - def test_h2_in_output_when_stats_computed(self, small_topo, src_bathy, tmp_path): - """write_topo should include h2 when _topo_stats are available.""" - small_topo.generate_mask_ocean_frac(src_bathy, nx_sub=3, ny_sub=3) - out = tmp_path / "topog.nc" - small_topo.write_topo(out) - assert "h2" in xr.open_dataset(out) - - def test_h2_non_negative(self, small_topo, src_bathy, tmp_path): - """h2 = D2_mean - D_mean^2 is a variance and must be >= 0.""" - small_topo.generate_mask_ocean_frac(src_bathy, nx_sub=3, ny_sub=3) - out = tmp_path / "topog.nc" - small_topo.write_topo(out) - assert float(xr.open_dataset(out).h2.min()) >= 0.0 - - def test_enforce_topo_drag_raises_without_stats(self, small_topo, tmp_path): - """enforce_topo_drag=True must raise if stats have not been computed.""" - with pytest.raises(RuntimeError, match="generate_mask_ocean_frac"): - small_topo.write_topo(tmp_path / "topog.nc", enforce_topo_drag=True) - - -# --------------------------------------------------------------------------- -# tidy_dataset — external mask parameter -# --------------------------------------------------------------------------- - - -class TestTidyDatasetExternalMask: - - def test_external_mask_overrides_depth_sign(self, small_topo): - """Cells marked as land in an external mask should end up depth=0 - even when the raw bathymetry depth is positive at those cells.""" - ny, nx = small_topo._grid.ny, small_topo._grid.nx - bathy_ds = xr.Dataset( - {"depth": (["ny", "nx"], np.full((ny, nx), 500.0))}, - coords={ - "lon": (["ny", "nx"], small_topo._grid.tlon.values), - "lat": (["ny", "nx"], small_topo._grid.tlat.values), - }, - ) - # Mark first column as land - mask = xr.DataArray(np.ones((ny, nx), dtype=int), dims=["ny", "nx"]) - mask[:, 0] = 0 - - small_topo.tidy_dataset( - positive_down=True, - vertical_coordinate_name="depth", - bathymetry=bathy_ds, - longitude_coordinate_name="lon", - latitude_coordinate_name="lat", - mask=mask, - ) - assert (small_topo.depth.values[:, 0] == 0).all() - assert (small_topo.depth.values[:, 1:] > 0).all() - - def test_no_mask_derives_from_depth_sign(self, small_topo): - """Without an external mask, tidy_dataset must derive ocean from depth > 0.""" - ny, nx = small_topo._grid.ny, small_topo._grid.nx - depth_arr = np.full((ny, nx), 500.0) - depth_arr[:, 0] = -1.0 # first column is land by depth sign - bathy_ds = xr.Dataset( - {"depth": (["ny", "nx"], depth_arr)}, - coords={ - "lon": (["ny", "nx"], small_topo._grid.tlon.values), - "lat": (["ny", "nx"], small_topo._grid.tlat.values), - }, - ) - small_topo.tidy_dataset( - positive_down=True, - vertical_coordinate_name="depth", - bathymetry=bathy_ds, - longitude_coordinate_name="lon", - latitude_coordinate_name="lat", - ) - assert (small_topo.depth.values[:, 0] == 0).all() - assert (small_topo.depth.values[:, 1:] > 0).all() - - -# --------------------------------------------------------------------------- -# set_from_dataset — dispatch logic -# --------------------------------------------------------------------------- - - -class TestSetFromDatasetDispatch: - - def test_dispatches_to_high_res_when_recommended( - self, small_topo, synthetic_gebco, monkeypatch - ): - """When diagnose_resolution returns True, high_res_regrid must be called.""" - monkeypatch.setattr(small_topo, "diagnose_resolution", lambda *a, **kw: True) - called = [] - monkeypatch.setattr( - small_topo, "high_res_regrid", lambda *a, **kw: called.append(kw) - ) - small_topo.set_from_dataset(bathymetry_path=synthetic_gebco) - assert called, "high_res_regrid was not called" - - def test_dispatches_to_direct_xesmf_when_not_recommended( - self, small_topo, synthetic_gebco, monkeypatch - ): - """When diagnose_resolution returns False, direct_xesmf_regrid must be called.""" - monkeypatch.setattr(small_topo, "diagnose_resolution", lambda *a, **kw: False) - called = [] - monkeypatch.setattr( - small_topo, "direct_xesmf_regrid", lambda *a, **kw: called.append(kw) - ) - small_topo.set_from_dataset(bathymetry_path=synthetic_gebco) - assert called, "direct_xesmf_regrid was not called" - - def test_default_mask_method_ocean_frac_for_high_res( - self, small_topo, synthetic_gebco, monkeypatch - ): - """When dispatching to high_res_regrid with no mask_method set, 'ocean_frac' is used.""" - monkeypatch.setattr(small_topo, "diagnose_resolution", lambda *a, **kw: True) - captured = {} - monkeypatch.setattr( - small_topo, "high_res_regrid", lambda *a, **kw: captured.update(kw) - ) - small_topo.set_from_dataset(bathymetry_path=synthetic_gebco) - assert captured.get("mask_method") == "ocean_frac" - - def test_no_default_mask_for_direct_xesmf( - self, small_topo, synthetic_gebco, monkeypatch - ): - """When dispatching to direct_xesmf_regrid with no mask_method, None is forwarded - so tidy_dataset falls back to deriving the mask from depth sign.""" - monkeypatch.setattr(small_topo, "diagnose_resolution", lambda *a, **kw: False) - captured = {} - monkeypatch.setattr( - small_topo, "direct_xesmf_regrid", lambda *a, **kw: captured.update(kw) - ) - small_topo.set_from_dataset(bathymetry_path=synthetic_gebco) - assert captured.get("mask_method") is None - - def test_explicit_mask_method_forwarded_to_direct( - self, small_topo, synthetic_gebco, monkeypatch - ): - """An explicit mask_method is forwarded unchanged to direct_xesmf_regrid.""" - monkeypatch.setattr(small_topo, "diagnose_resolution", lambda *a, **kw: False) - captured = {} - monkeypatch.setattr( - small_topo, "direct_xesmf_regrid", lambda *a, **kw: captured.update(kw) - ) - small_topo.set_from_dataset( - bathymetry_path=synthetic_gebco, mask_method="cartopy" - ) - assert captured.get("mask_method") == "cartopy" - - def test_src_passed_to_pipeline(self, small_topo, synthetic_gebco, monkeypatch): - """set_from_dataset must create a SourceBathy and pass it as the first - positional arg to the selected pipeline.""" - monkeypatch.setattr(small_topo, "diagnose_resolution", lambda *a, **kw: False) - received_src = [] - monkeypatch.setattr( - small_topo, - "direct_xesmf_regrid", - lambda src, **kw: received_src.append(src), - ) - small_topo.set_from_dataset(bathymetry_path=synthetic_gebco) - assert len(received_src) == 1 - assert isinstance(received_src[0], SourceBathy) - - -# --------------------------------------------------------------------------- -# direct_xesmf_regrid -# --------------------------------------------------------------------------- - - -class TestDirectXesmfRegrid: - - def test_bad_mask_method_raises(self, small_topo, src_bathy): - """Unknown mask_method must raise ValueError.""" - with pytest.raises(ValueError, match="mask_method"): - small_topo.direct_xesmf_regrid(src_bathy, mask_method="invalid") - - def test_precomputed_mask_bypasses_generation( - self, small_topo, src_bathy, monkeypatch - ): - """When mask= is provided, generate_mask_ocean_frac must not be called.""" - mask = small_topo.generate_mask_cartopy(resolution="50m") - called = [] - monkeypatch.setattr( - small_topo, "generate_mask_ocean_frac", lambda *a, **kw: called.append(kw) - ) - monkeypatch.setattr( - small_topo, "generate_mask_cartopy", lambda **kw: called.append(kw) - ) - # Stop execution after mask check — we only care that generation was skipped - monkeypatch.setattr( - small_topo, - "config_dataset", - lambda *a, **kw: (_ for _ in ()).throw(StopIteration), - ) - with pytest.raises(StopIteration): - small_topo.direct_xesmf_regrid( - src_bathy, - mask=mask, - mask_method="ocean_frac", # should be ignored because mask= is set - ) - assert not called, "mask generation should not run when mask= is provided" - - -# --------------------------------------------------------------------------- -# high_res_regrid -# --------------------------------------------------------------------------- - - -class TestHighResRegrid: - - def test_bad_mask_method_raises(self, small_topo, src_bathy): - """Unknown mask_method must raise ValueError.""" - with pytest.raises(ValueError, match="mask_method"): - small_topo.high_res_regrid(src_bathy, mask_method="invalid") - - def test_ocean_frac_mask_produces_valid_depth( - self, small_topo, src_bathy, tmp_path - ): - """End-to-end with ocean_frac mask: ocean cells must have depth >= min_depth.""" - small_topo.high_res_regrid( - src_bathy, - mask_method="ocean_frac", - nx_sub=3, - ny_sub=3, - weights_path=tmp_path / "cressman_weights.nc", - ) - ocean = small_topo.depth.values > 0 - assert ocean.any(), "No ocean cells after high_res_regrid" - assert (small_topo.depth.values[ocean] >= small_topo.min_depth).all() - - def test_cartopy_mask_produces_valid_depth(self, small_topo, src_bathy, tmp_path): - """End-to-end with cartopy mask: ocean cells must have depth >= min_depth.""" - small_topo.high_res_regrid( - src_bathy, - mask_method="cartopy", - cartopy_resolution="50m", - weights_path=tmp_path / "cressman_weights.nc", - ) - ocean = small_topo.depth.values > 0 - assert ocean.any(), "No ocean cells after high_res_regrid with cartopy mask" - assert (small_topo.depth.values[ocean] >= small_topo.min_depth).all() - - def test_precomputed_mask_accepted(self, small_topo, src_bathy, tmp_path): - """Passing a pre-computed mask directly should skip mask generation.""" - mask = small_topo.generate_mask_cartopy(resolution="50m") - small_topo.high_res_regrid( - src_bathy, - mask=mask, - weights_path=tmp_path / "cressman_weights.nc", - ) - ocean = small_topo.depth.values > 0 - assert ocean.any(), "No ocean cells with pre-computed mask" - - def test_weights_file_written(self, small_topo, src_bathy, tmp_path): - """Cressman weights netCDF must be written to weights_path.""" - wp = tmp_path / "cressman_weights.nc" - small_topo.high_res_regrid( - src_bathy, - mask_method="ocean_frac", - nx_sub=3, - ny_sub=3, - weights_path=wp, - ) - assert wp.exists() - ds = xr.open_dataset(wp) - for var in ("S", "row", "col"): - assert var in ds - - -# --------------------------------------------------------------------------- -# mpi_direct_xesmf_regrid rename -# --------------------------------------------------------------------------- - - -class TestMpiRename: - - def test_mpi_direct_xesmf_regrid_exists(self, small_topo): - """mpi_direct_xesmf_regrid must be callable (renamed from mpi_set_from_dataset).""" - assert callable(getattr(small_topo, "mpi_direct_xesmf_regrid", None)) - - def test_mpi_set_from_dataset_removed(self, small_topo): - """Old name mpi_set_from_dataset must no longer exist.""" - assert not hasattr(small_topo, "mpi_set_from_dataset") From accc9a59851645a94173ead396aff015077bb90a Mon Sep 17 00:00:00 2001 From: manishvenu Date: Wed, 15 Apr 2026 13:49:32 -0600 Subject: [PATCH 7/9] This --- notebooks/10_cressman_interpolation.ipynb | 251 ++++++- notebooks/11_full_scale_example.ipynb | 877 +++++++++++++++++++++- notebooks/7_regional_bathy_workflow.ipynb | 314 -------- notebooks/8_pipeline_overview.ipynb | 223 +++++- setup.py | 1 + 5 files changed, 1274 insertions(+), 392 deletions(-) delete mode 100644 notebooks/7_regional_bathy_workflow.ipynb diff --git a/notebooks/10_cressman_interpolation.ipynb b/notebooks/10_cressman_interpolation.ipynb index 5710e1cb..437f00c7 100644 --- a/notebooks/10_cressman_interpolation.ipynb +++ b/notebooks/10_cressman_interpolation.ipynb @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -54,9 +54,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Setup complete.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/glade/u/home/manishrv/documents/croc/regional_mom_workflows/mom6_forge/mom6_forge/topo.py:768: RuntimeWarning: Mean of empty slice\n", + " D_mean = np.nanmean(depth_ocean, axis=(-2, -1))\n", + "/glade/u/home/manishrv/documents/croc/regional_mom_workflows/mom6_forge/mom6_forge/topo.py:769: RuntimeWarning: All-NaN slice encountered\n", + " D_min = np.nanmin(depth_ocean, axis=(-2, -1))\n", + "/glade/u/home/manishrv/documents/croc/regional_mom_workflows/mom6_forge/mom6_forge/topo.py:770: RuntimeWarning: All-NaN slice encountered\n", + " D_max = np.nanmax(depth_ocean, axis=(-2, -1))\n", + "/glade/u/home/manishrv/documents/croc/regional_mom_workflows/mom6_forge/mom6_forge/topo.py:771: RuntimeWarning: Mean of empty slice\n", + " D2_mean = np.nanmean(depth_ocean**2, axis=(-2, -1))\n" + ] + } + ], "source": [ "grid = Grid(resolution=2.0, xstart=262.0, lenx=10.0, ystart=20.0, leny=8.0, name=\"toy\")\n", "\n", @@ -99,9 +121,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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o3LlzR63rU6dOHU3evHnVeiMFChRQY0y57svLxviiNVc8PDzUJanY2FjN1KlT1Rjt7OzU6/fu3VuN70VrtKT1+rq1T17FunXr1GNkTRxZ9yepiRMnqttkXGmRdXdmzJih1j+S4yzHr1ChQmq9penTp2sCAgLStdbNkSNHNM2aNdM4OjqqS9OmTTWHDh3SDB06VD0m6Xo9r7O+jTh27Jgal4xT3m9q6/e8yvPprF27VlO1alWNra2txs3NTfP2229nyme4dOlSTYMGDdT45bWKFCmi6dWrl+bkyZPJ7rd582ZNrVq11FpNujV9dGNIbZ2dpH8zssaSq6ur+hxlvZwxY8Y8t67Vy8b5srV+iMg0ZZP/e3E4RERERElJduno0aNq+pbUrxARkWFizQ4REVEqIiIiUu1ctnDhQjUlTNb6YaBDRGTYmNkhIiJKhSxWKR3BZK0h6YYn9V7SeUw6tEnrZQl42AmMiMiwMdghIiJKhXS+GzNmjCqIl7VepAueFNY3a9YMX375pWpYQEREho3BDhERERERmSTW7BARERERkUlisENERERERCaJwQ4REREREZkkBjtERERERGSSrPQ9AGMhay1IR55ChQrB1tZW38MhIiIiIjI70dHRuHPnDho2bKiWAXgZBjvpJIFOp06d3vTzISIiIiKiN7RmzRp07NjxpfdjsJNOktHRHVhZXI6IiIiIiLKWr6+vSkDozs1fhsFOOummrkmgU65cudf/hIiIiIiI6I2kt6yEDQqIiIiIiMgkMdghIiIiIiKTxGlsRERERESUpoULF+LWrVvw8PBAv379YEyY2SEiIiIiIpPEYIeIiIiIiEwSgx0iIiIiIjJJDHaIiIiIiMgkMdghIiIiIiKTZNDBTnh4OMaNG4dWrVrBzc0N2bJlU90g0is4OBiDBg1C7ty54eDggMaNG+PUqVOZOmYiIiIiIjIMBh3sBAQE4JtvvsHFixdRqVKlV3psQkIC2rZti7///hvDhg3D5MmT8fDhQzRq1AhXr17NtDETEREREZFhMOh1dvLlywc/Pz+4u7vjxIkTqF69erofu3LlShw6dAj//vsvunbtqvZ1794dJUuWVNkiCYKIiIiIiMh0GXRmx9bWVgU6r0OCnbx586Jz586J+2Q6mwQ8a9euRXR0dAaOlIiIiIiIDI1BBztv4vTp06hSpQosLJK/xRo1aiAiIgJXrlzR29iIiIiIiMjMp7G9CZn+1qBBg1Snxon79++jQoUKqT5WansePXqUbJ+vr6+6/nBlR4xbZ4cisVaIz2aJ+GxWWO+UgBvWgFuCPb4vUhWwdQbsnKGxdcaC8w9ha5sP8Q7u0DjkgYVDTtjbWiO7jaW6ONlZo5S7ExxtTfajICIiIiLSC5M9w46MjFTT4FKys7NLvD0ts2bNwoQJE1K9zc86G/JqHqB4QkzivrnWuXDAITuKxIQBJ/5M3B8HYHrRwrCNScCQhyF4PyQMMRpLPEQO3MzmggcaF1yPz4XcdWrBsXg5wK0YkMMDo1ZewM3HEciR3Ro5stvANbsN3ByskdvJFnmc7eDubIeCOexVoERERERERGYW7Njb26dalxMVFZV4e1qGDBmCbt26PZfZ6dSpE8rH2OO+c3HEW2eDRUIcLDRxsNSEImdcDNwSLIHsOYGoUCAhFoGWluqx0RYWcEpIUD/bZItHQQRgr3MUfsgZixzxD9D25E7gWPzTV8qGL7LlxLW4PLihccclTSH4JBRW1yFwTBzPp61KYUgjz8Tt9Wfu4/TtYOR1toW7ix0KuWVHYbfsyOlgo1p2ExERERGZG5MNdnSd3FLS7cufP3+aj82TJ4+6pOa7bv+gXLlyyfZNSXknjQaIi4J96H18fn0d7obcRIXSJREdo0F8qB80oX7wjbwoKwkhNls25I6PT/pgfJ5XgqOHaP7kJr55suvZ2DVuuJxQSAU+VUPrA4+zabNB2bJh35VH+Pfk3efGK1PlCuXIroKfJqXzoFfNwkmGqWEgREREREQmy2SDHS8vL+zfv1+tt5O0ScHRo0eRPXt21YI600gmxdoezjmL452cH6V6l06PzqDIQ2/ExsfAomsHIPA6EHQDoQGXcfzeKnWf4gnZgCcRiY+xsgqGh0UoGsaeQbZTG4BTnwH2OYACVdE8pCBCrN1xLLYoguGU+JiImHhcfhCmLvlctFP4dCQ4mrbtCkrkdUTx3I7wzOOIEnm01zkdn58CSERERERkTEwi2JFsTUhICIoXLw5ra20di6ytI+2nV61albjOjixSKuvutG/fPtV6nqxUKXcldUnklBfwqI2IJ/5ocTwcPo99ULP5eCB7fuCBj7qsvrsDM2LvIW9cPFbfvQ8nySBFBgG+O9ACQAuZNWcJRLsUw6Oc1eCb3QsnUQ4Xwh1wOzBCBTFJ3QmMgH9olLrsvxqQ7DapF5L71ymeCx81z8TAkIiIiIgMWr9+/WCsDD7YmTlzJoKDg1X3NLF+/XrcvaudrjV8+HC4uLhg7NixWLRoEW7cuIEiRYqo2yTAqVWrFt577z1cuHABuXLlUo0H4uPj02w+YAjcHdwxtdHUxGlmKkvkWhgo1RpHt14E/O/B3q0YnFotBe6dBO6eAO6dwO7Ie6geGQVHjQa2IddRUC5YgUbyRDk9gRL1Aad6QJi9NrACUC6/MzpUyg/fh+G49igc0XHauiIRFBGL4zeDVHOEpLzvBGPa9isom89ZPb5sfmcUzekACwvWBRERERGRYTH4YOenn37CrVu3ErclUyMX0bt3bxXspMbS0hKbNm3CmDFj8Ouvv6rua9WrV8fChQtRqlQpGIOUjQXG1hiLw36HYWtpCxSoor3UGIibITcxYk17WGezxPc5a6P1Yz/gzlEg9ukUuMe+2svJBdpt94pAiRZoJZe3qwEWlohP0OBeUCR8H4Wp4EcuVx+Go1LB5Mf39O0gVR8kl6R1QaXdnVAuv4sKfioUcEH5Aql/LkREREREWSWbRqUP6GV8fHxQvnx5nD9//rkGBfq24PwCTDs5Tf28ttNaFHMpBsTFAPdPwf/qFuS+exKWd46ppgnPkZofz2Yq+EHxpoBDzhe+1j/HbmPBwRu49uiJCpBSUyafMzaPrJ+4HRETh8AnMSjgas+GCERERESUZefkBp/ZoZfrXaY3SuQogVMPTmkDHWFlAxSuhWHePyHEIQrvvTUFvRw9gWu7gKvbAL8z2vtJzc+5f7WXbBZA4TpA2Q5AmfaA8/Md63rWKKwuUbHxuOwfhgt+obhwPxQ+90Nw0S8MkbHx8CrkmuwxUg80eMlJtU5Q5UKu8CrsisqFcqBiQRc4cDFVIiIiIoO2cOFCNdPKw8PD6Op3GOyYAGtLa9QrUE9dkroUeAmXgy6rn8PiIgGPOtpLk6+AMH/V2EAFPtd2A9GhgCYBuHVAe9n8KVCoJlC2ozbwkbqhJOysLVGpkKu66Eim5+bjJ7BKUb8jdT7iUVg0tl14oC5C7lba3Rk1i7mhnmcuNC2jrSUiIiIiIsoIDHZMWAHHAvhfrf9h3bV16FC8Q7LbVvsfhr9FGLq1n4ZcNi7A7SPA5U3AhXVA6NP1eqTuRy5bvwDyVwHKdwEqdAWc3FN9PUuLbKqFdUodvfKrxU1P3wmG9+1g3AuOVPtlFpzKDPmF4sqDsGTBTkhkrMoe5XVO3i6biIiIiLJ+SZciRYrA1TX57B1jwJodE6jZeVXxCfFou7ot7oXfQ8kcJfFfh/+e3SglXPdPARfWai9BN5M/WKa6FWsMVHwbKNMOsHF45dd/GBalgp5Tt4Nx4mYgztwNxvAmJTCiaYlktUFjV51DkZzZUaOoG2oWzYm6nrngnmKtICIiIiIyHz6s2aGXCYgMgLONM+7hHrqV7JbstpiEWFjlrwyLAlWBZhMA/3PaoMdnlXbhU5nqdm2n9rLBQTvFrWJ3oFgj1dUtPfI42aFFOXd1EZEx8YiJf9b2Why7Eaiubz6OUJcVJ7TZJln7R6a8NSiZSwVArPkhIiIiorQws2OGmR0hTfi8H3mjVI5SyG6dPXH/LO9Z2H5rOwZUGIDWRVvDQjI52gdo1/U5sww4/x8QqQ1GEjkXBCr3Bqr0AVwKvvH4zt4NVo0Njlx/jJO3ghARE//cfdpVzIeZvaq88WsRERERkXFgZofSvYZP5TyVk+0LjwnH0otLERYThiUXlqBN0TZJHwAUrKa9tJyozexI4HN5MxAfra3z2fsDsG8y4NkcqPouUKIlYPl6ZWEVC7qqy9DGnoiLT8DZeyE4cDVAXU7dDkJcgkZleJJafPgmDl97jPolcqNJ6Tyc8kZERESUAby9vREcHKxqdqR+x5iwQQElkiyOZHQW+SzCwIoDk62JExIdohYztbOy07a1LtVae4kKAc6vAk4tAu6f1k5zu7pVe3HK9zTb0/e5bm6v9EtqaYEqhXOoi9T1hEfH4ci1x8k6wYnN5/xx+PpjbD7vr7bL5XdG09J50KRMXlQs4AKLFF3iiIiIiCh9wY6u9TSDHTJaMp3t/fLvo2fpniqwSeqXU79g3919GOI1BG95vvUsELJzAaq9p73c99YGPWf/BWLCgDA/YN8UYP9UoHRboOaH2tbXSYKo1+Foa4VmZfM+Ny2vQA57uDnYqAVMhY9a/ycUv+7yRS5HGzQulQd9axdBhYIub/T6RERERGQcmNmh59hb2SfbfvDkAdb4rkFsQiy23NiCziU6p37U8ntpLy2+e5btuXtcm+25uF57yVsBqDkYqNANsM64zmoSfP3UrRISEjRqytuuiw+w89JDFeyIgPAY/HvyLpqXzZss2ImIiUN2G/4ZEBEREZkinuXRS+W0z6nW65l1ZhZGVh2Z7Lbo+OjnskCqHbU0KpCL31ng2Gxttkdqex6cA9YNA3aMA6r2A6oPAJzzZ9inIFPVvAq5qsvHLUrBPyQKuy8/xM6LD3HiVqBqX51U99mHkZAAtCrvjtbl3VW3t6TT94iIiIjIeDHYoZf/klhY4a0Sb6F98fbq56TG7h+LiNgIfFT1I5RyK/X8g/NVBDr+pm1jfXIhcHyednpbxGPt9LaDv2rX7Kk7AsidyuPfkKzL07NGYXWRRgdS/6NzJzAC5+9pMz+ysOm07VdQLLcDWpWTwCcfyhdwZuBDREREZMSenfkRvUTKQOd8wHnVpvrg/YOYc3bOix/skAto8Akw6hzQ9U+gYA3t/oRYwHsp8FsN4J+ewO2jmfY5JA10hK21BUY2LYHS7k6J+64/eoJZe66h/cwDqPfjbnyz/gJCImMzbUxERERElHkY7NBrc3dwx9ul3oaDtQNGVxudvgdZWgPluwADtgMDdmoXJcXTaWOXNwF/tgD+bAVc3gI1vywTyeKmHzUviS2jGmD3J43weevSyTq83QuOxMqTd2BvbflcMwQiIiIiMnycxkavLZd9LnxV6ysMrzwcLrYuyYKBiUcnoqlHU9TKVyvtJ5A1e95eCgRcBQ79ql23Jz4GuH1Ye8lTFmgwBijbSYpxMvWTKprLAR80LK4u94Mjsc3HH1t8/FEoR3bYWD17bd+H4Xj3z2NqQdP2lfKr9tas8SEiIiIyTMzs0BtLGuiI3Xd2Y9nlZRi4bSD+vfLvy58gVwmgwwztFLe6IwFbZ+3+hxeAle8Bv9cGzq0EEuKz5NPK72qPfnWLYtmg2vixS8Vkt60/c19lfGbvu452Mw6g6dS9qtZHgiAiIiIiMiwMdijDBUUFqfbVzjbOaF64efof6OQONP8G+Og80HQckD2ndv+jS8B//YFZtbRd3bIo6BEpFyKVbm3Vi+RI3L4e8AS/7ryKZtP2ovUv+/H7nmu4GxSRZeMjIiIiorRxGhtluC4lu6BegXq4FnINrnbPamASNAnwDfZFyRwlX/wEslBp/Y+BGoOAE/O1HdsiAoCAK8CqAcDeH4GGn2prfyyS19NkNpm6JhfJ7mw8ex/rztxP7Oh20S9UXW4EhGNy10pZOi4iIiIieh4zO5Qp8jrkRZ38dZLtk4VJu67rqup5nsQ+efmT2Dpqp7WNOqtdqNQht3b/46vAqoHA73WBSxulSCjLP8UCrvYY1KA4Ngyvj12jG+KjZiVRPLeDuq1LlYLJ7rvVxx/7rjxCfAIbGxARERFlJWZ2KEvExMdgxukZ0ECDnbd3YkTlEel/sCxSWmc4UK2/dq2egz8D4Q+ARxeBZb2AAtWApl8DxRpCH4rldsTIZiUwoqknLvmHoVRep+TNGjZdxK3HEcjrbItOlQuoYKhkkvsQERERUeZgZoeyhI2lDea3nI+qeavii5pfwNHG8TWeJDtQewgwwlu7SKluity9E8DiDsDijsC9k9AX6cpWJp9zsjqfGwFP4BcSpX5+EBqN2Xuvo8X0fWg/4wAWHLyBwCcxehsvERERkaljsENZpphLMSxouQBNCzdNtn+t71psvbn11YKeeqOAkWeA+p8A1tm1+6/vAeY2AZb3BgJ8YQgk63P8y2aY1LkCqnk8a2xw7l4IJqy/gFoTd2Lo36cQxKCHiIiIKMNxGhtlqZRr0twJvYPvj36PyLhInH10FmOqj0n/k9m7Ak3/p21ksH8qcOJPICEWuLgeuLxZO+2t4WeAw9OubnriYm+NnjUKq8vNgCdYdfoeVp26i7tBkYiJT8DJm0FwtrfW6xiJiIiI0uLl5YUiRYrA1fVZ4yljwWCH9Orek3uwtbRVwU79gvVf70mc8gJtJgO1hwJ7JmkXJ02IA47N1v7cYDRQYzBgbQd9K5LLAR83L4lRTUvg6I1ArDhxR7Wztkwy9e3c3RD8vOMKetQojMalcsPKkglYIiIi0m+wY6yyaaSCml7Kx8cH5cuXx/nz51GuXDkesQwUEBmAfXf3oXOJzsn2xyfEw/J1Wkv7nQG2fQXc2Pdsn2th7do90q46RXbJ0Hy5+hz+Onpb/ZzHyRZdqxbE29ULwSOnttsbERERkbnyecVzcn5lTHqXyz7Xc4HO9ZDraL+mPY76HX31J8xXCei7Dui5HMj1dE2f4NvahUnnNQPuHIchc7C1goONNsh7GBaNWXuuoeGUPegz/6hqYx0Xn6DvIRIREREZBWZ20omZnawjGZ2+m/vibMBZWGazxPpO61HIudBrPlkccGohsHuSdmFSnUq9gGbjtVPgDNCT6DhsPOeHZcdu49Tt4GS35XOxw8TOFdC4VB69jY+IiIjMh7e3N4KDg1XNjr6ntDGzQ0bPIpsF3irxFuyt7NGnbJ/XD3SEpRVQfQAw4jRQ7yPA0ka7/8zfwIyqwMFfgbgYg8zudK9WCKuG1MW2jxqgX50icLLVlthJK2sJeIiIiIiyKtjZu3evujY2bFBABtmxrWvJrqjuXh35HfInuy08JhwO1g7PdXV7KTtnbSanSl9g61fA5Y1ATBiw/X/AqUVAqx+AEs1hiGQB0vEdymFMy1JYd+Y+zt4NQWl358TbY+MT0G/BMbQs5463KheAkx07uxEREREJTmNLJ05j07/Y+Fj03twbhZwK4evaX8PZ5tkJ/yvz3QFs/hx4fPXZvpKtgFaTALdiMCabzvlhyF+n1M9S6/NWlQIqE+SZx0nfQyMiIiLKUJzGRiZr3rl5uPD4glqA9O+Lf7/Zk3k2Az48BLT4HrB9GjRd2QLMqg3snQzERcNYJGg0KORmr35+EhOPpUduo9m0faqhwe5LD5GQwIaLREREZJ7YjY2MRvdS3dGgYAOUcSuD/uX7v/kTWtkAdYYBw08CXr21++KigN3fa4Oea7thDNpVzI89nzTGgn7V1bo8OvuvBuC9hcfRbNperPW+p9cxEhEREekDa3bIaOS0z4mZTWYiNCYU1pbP6lLiEuLUoqRONq85bcsxD9DpN6BKH2DDx8BDHyDwGrCkk3ZdHsn+OOeDIZNFSRuXzqMuNwKeYNGhm/j3xB2V6bke8AQPQ40nU0VERESUUZjZIaMijQlcbF2S7fvN+zd0W98NPo993uzJC9cCBu/VBjc2jtp95/8DZlYHjvyhbWNtBIrmclANDQ5/0RT/a1cWpd2dVGe3pCQYOnQtAFxTmIiIiF5m4cKFmDBhgro2Ngx2yKidfXRW1fLcC7+H7w5/9+Yn75IxkqltQ48BZTtq90nXti2fAfObAf7nYCyc7azRv15RbB5ZHy7Zn2XCHoVF4/uNF9Fr7lG0/fUA1py+pzq6EREREZkaBjtk1CrkqoDRVUerzmwT60989ZbUaXEpAHRfDLyzEshRRLvv/mlgdkNg+zggNhLGIuUxOXMnGLpdF/xCMWq5NxpM3o25+64jLCpWP4MkIiIiygQMdsioyYl8v/L9sLnLZhR1KZrstojYiDd/AVl7Z8gRoN7HQDZLQBMPHPxZ28Dg+l4Yo2Zl8+Lw2KYY3bwkcjnaJC5U+v2mi6gzaRcmbboIvxDjCeaIiIiI0sJgh0xCyjV3jvkdQ8v/WmLf3X1v/uTW9kCzcdp6nvyVtfuCbgCLOwBrhgIRgTA2bg42GN60BA581gQ/dK6A4rkd1P6w6DjM3ncdn648q+8hEhEREb0xBjtkcsJjwjFm3xgERwdj9J7ReBz5OGOe2L0CMGAn0HIiYJ1du897KfBbDcBnNYyRnbUletQojO0fNcT8d6uhZlE3tf/9esmzZIFPYtjMgIiIiIwOgx0yOY42jviy5pewt7LHl7W+VC2rM4yFJVB7qHZqmyxMKp48Av7tB6x4F3gSAGNkYZENTcvkxfLBtbFxRD00KvlsvR5p+vD+wuNoP/MANp71QzwXKSUiIiIjwXV2yCS1KNIClfNURu7sz07aRYImARbZMiDGz+GhbV5w7l9g82dAZCBwYQ1w8wDQdipQrhOMVbn8yVt7n7wVBO87wernoX+fUq2tBzcohreqFICtlaWeRklERET0cszskMlKGegERQWh6/qu2HsngxoLSEuzit2BoUeB0u20+yICgH/f1WZ6jDTLk1KJPE74uHlJ5HjavloWLf181Tk0nLwH8/Zfx5No41h/iIiIiMwPgx0yCzIV67N9n+Fq0FUM3zUc3g+9M+7JHfMAby8FuswH7HNo90kNz281gQtrYexkjZ4RTUvg4OdN8HW7ssjnYqf2+4dG4buNF1Hnh12YtcdX38MkIiIieg6DHTKbFtVvl3pb1fG0KtoKlXJXyugXACp0BYakyPKs6Av8+x7wJIOaJOhRdhsr1bhg75jGmNy1Ioo97eAWEhmLh6HR+h4eERER0XNYs0Nmo6lHU/zt/DcKOhXMuMVHU3LKq83ynFsJbB4DRAYBPquAm/uBttOAsh1g7GysLNC9WiF0qVIQ23z8MWf/dQxsUCzZffZffYRiuR1RwNVeb+MkIiIiYmaHzIpnDk/YWWmnYemmt005PgWXAi9l3IuoWp5u2ixPqbbPOrat6AOsfN8o1+VJjaVFNrSukA+rh9RNFtRExsRj1DJvNJqyG2NXncWdwAxY3JWIiIjoNTDYIbM279w8LL6wGH0398Vx/+MZn+Xp8RfQeS5g56rdd/4/4Pc6wLXdMFV7Lj/E4ycxiI3X4J9jd9D4pz34dOUZ3Hr8RN9DIyIiIjPDYIfMmrWFNbIhG9zs3ODp6pnxL5C0Y1vJ1tp9YX7Akk7AlrFAbBRMjWR71g2ri2Zl8qjtuAQNVpy4iyZT9+LjFd64/ihc30MkIiIiM8Fgh8xav/L9MKPJDMxsMhM57J52UssMTu5Az3+A9r8A1tm1+47MAuY0AvzPwdRULOiKee9Wx4bh9dCyXF61TxYjXXXqHppN24tJmy/qe4hERERkBtiggMxew0INnzsGx/yOoWzOsnC0cczYLE/VfkCR+sCqQcC9E8Cji8DcJkCT/wG1hwEWpvX9Q/kCLpjdpxou+oVi5i5fbDrvhwQNUDSntpMbERERGb5+/frBWJnWmRVRBjj98DQ+3PEh+m7pC/8n/hl/THMWB97fCjQaC2SzBOJjgO3/AxZ3AELuwhSVyeeM396pgq2jGqBPLQ90rlIw2e0LD96Az/0QvY2PiIiITBODHaIU/rvyH2ISYnAj+AZuht7MnONjaQU0+hzovw1we9q2WdpTz6qjbVttokrmdcK3ncqr9tU6UsPzzYYLaPvrAQz56ySuPgjT6xiJiIjIdHAaG1EKE+pMUNPXSriWQK18tTL3+BSsBgzeD2z9Aji1CIgOAf7rD1zeDLSdCtg/7eJmwo7fDNSue6TRYNM5f2w+74+OlfJjZLOSKJqL092IiIjo9WXTyEIj9FI+Pj4oX748zp8/j3LlyvGImaGI2Ahk1zUXyAyXNgLrhgMRj7XbLoWALvOAwpkccBkAaUv9605frD59V9X06Nbx6VqlIIY39UTBHJl43ImIiOiFFi5ciFu3bsHDw0Pv9Tuvek7OaWxE6RAaE4p3Nr2DX079ohYizRSl2wIfHgZKtNBuh9wBFrQB9k0BEuJN+nPyyOmAqd0rYdtHDdGuYr7E7m3LT2jX6Vly5Ja+h0hERERGiMEOUTqMPzQevsG+ahHSjTc2Zt4xk4VIe60AWk8BLG0ATTyw6ztgcUcg1M/kPyvPPI6Y2asKNo+sj+ZltS2rZXHSCgVc9D00IiIis+Xl5YWGDRuqa2Nj0MFOdHQ0PvvsM+TPnx/29vaoWbMmtm/fnq7H7tixA40bN0auXLng6uqKGjVqYMmSJZk+ZjJNH1X5CIWcCqG5R3O0LvJ0cdDMIvUrNQcBA3YCOUs8a17wR13gylaYA+neNrdvNawdWhcfNy8Jr0LPapcSEjSYt/86giNi9DpGIiIic+Hl5YVGjRox2MloMidw2rRpeOedd/DLL7/A0tISbdq0wYEDB174uHXr1qFFixaIiYnB+PHj8f3336tgqW/fvpg+fXqGj5NMXyHnQljaZikm1Z8ESwvLrHnRfBWBwXsBr97abanl+bs7sOULIC4a5qBSIVeMaPo04Htqq48/vtt4EfV/3I3p268gNCpWb+MjIiIiw2awDQqOHTumMjlTpkzBJ598ovZFRUWpgqQ8efLg0KFDaT5WAh0pXrp+/TpsbW3Vvri4OJQuXRoODg44c+bMK4+HDQooJfnTmXtuLloWaQkPZ4/MPUDSjnr9KCDmaVvmfJWArgu0a/aYmU/+PYOVJ5+tR+Rib40PGxVHvzpFYGedRYEoERER6YXJNChYuXKlyuQMGjQocZ+dnR369++Pw4cP486dO2k+NjQ0FDly5EgMdISVlZWa0iYZHqKMMP/8fMw4PQN9NvWBz2OfzD2oFboCH+wD8lfWbvudAWY3AM4sh7mZ0rUiFrxXHeULOKvtkMhY/LD5EhpN2YNlx24jLj5B30MkIiIyKd7e3tizZ4+6NjYGG+ycPn0aJUuWhLOz9oRGR2pvxIsOtswplKjvf//7H3x9fXHt2jV8++23OHHiBD799NNMHzuZh6i4KHVtY2kDN1u3zH9BWXz0/W1A7WHa7ZhwYPUgYPUHQHQ4zIWsydO4VB6sH1YPf/SuopoaCP/QKHy+6hxa/rwPPvdD9D1MIiIik+Ht7Y29e/caZbBjsIuK+vn5IV8+bQvapHT77t+/n+ZjJci5ceOGqtX57rvv1L7s2bPjv//+Q8eOHV/62g8fPsSjR4+S7ZOgiSipYZWHIb9jfnjl9kI+x+d/VzOFlQ3Q8nugWCNtkBMRAJz5B7h3Eui2CMhb1qyCnlbl86FZmbxYdeoepu+4Ar+QKDwIjYa7s52+h0dEREQGwGCDncjIyGTT0JJOZdPdnhZ5nGSFunbtis6dOyM+Ph5z5sxB7969VTe3WrVevEjjrFmzMGHChAx4F2TqOpfo/Ny+gMgA5LLPlbkvXKI58OFBYNUg4MZeIOAKMLcJ0G4a4NUL5sTK0gLdqxdCB6/8WHL4FiwssiGn47N/O8KiYnE7MALl8rN9NRERkbkx2GBHamuk9XRK0qRAd3tahg0bhiNHjuDUqVOwsNDO1OvevbsqYho5ciSOHj36wtceMmQIunXr9lxmp1OnTq/5bshcnHt0DgO2DcAHlT7Ae+Xfy9wXc3IH+qwG9v0E7JkExEUCaz4Ebh3UrtNjkx3mRJoTDGxQ7Ln9c/Zdx4xdvuhQKT9GtyipFjAlIiIi82CwNTsyXU2msqWk2ydr76RG2k3Pnz8fbdu2TQx0hLW1NVq3bq3qduQ+LyLd3iQwSnrx9PR84/dEpi0uIQ6f7/8cEXERmH5yOi4HXs78F5U22I0+A/quARxya/edXgrMawYEcOplZEw8Fh26qQ7LujP30XTqXny99jwehZlH624iIiJzZ2HIixdduXJFdVZLSpeVSWsF18ePH6s20zJ1LaXY2FgkJCSkehvRm7KysML0xtOR2z43vqr1FUq5lcq6gyo1PIP3Ax51tdsPfYA5DYHz/8Gc2dtYYt2wemhXUVtTFZegweLDt9Bwym5M23ZZTXEjIiIi02WwwY7U2+hqbXRkWtuCBQvU+juFChVS+27fvo1Lly4ly8q4urpi9erVyTI44eHhWL9+vVprh+2nKbOUzFESazutRfdS3bP+IDvnA/quA+p9/Kxb28r3gY2fmM0ipKkpkssBM3tVUd3b6pfQ1lJFxMTj112+ql31ksM32a6aiIjIRBlssCMBjdTNjB07VrWLlqCnSZMmuHnzJiZPnpx4v759+6JMmTKJ27I2jyxCKlkhaUTw888/Y+rUqapl9d27d/HVV1/p6R2RuXCycUq2HREbgSnHp6jrTGdpBTQbB/RaAdi5avcdnwv82RII0k7nMlcVCrpgSf+a+HtATVQqqG1W8PhJDBY+neZGREREpsdggx2xePFijBo1CkuWLMGIESPUNLQNGzagQYMGL3zcl19+ib/++kvV6UhXNWlFLev1yEKl77zzTpaNnygmPgYjdo/A4guLMXD7QITGJJ+WmWlKtgQ+2A8UqKbdvn9auwjppY1m/6HU8cyFNUPrYmavyijkZo+xrcuojm46XJSUiIjIdBhsNzZdm+kpU6aoS1pkNdfU9OrVS12I9CleEw9rC2v1s4uNC+yt0u4imOFcCwPvbQZ2jAOOzAKiQoBlvYB6HwGNv9JmgcyUrNHTrmJ+tCjrDmvLbMluG/r3KdhYWeLTlqVQyM28OtoRERGZGvM92yHKAhLc/Nr4V/x5/k/0Ldc3MfDJMrIIaatJQOFawNphQHQocGA6cO8U0PVPwCGT1wMycDZWyZPbx24EYqvPA/Xz1vP+eLeOB4Y1LgGX7Fn8uREREZHpT2MjMgXWltYYXGnwc1mdsJiwrBtE2Y7AoD1AnrLabVmIdHZD4O7JrBuDEcjnYof2lbRt7WPiEzB3/w00mLIb8/ZfR3QcuzgSEREZGwY7RHrw18W/0GlNJ1wPvp51L5qzODBgB1Dh6YK5oXeBBa2AEwsAjSbrxmHAZNrajJ6VVU1PjSJual9IZCy+23gRzaftw4az96HhsSIiIjIaDHaIspjPYx/8cOwHPIx8iI/3fIz4hCzMGNg4AJ3nAq0nAxZWQHwMsGGUdopbbGTWjcPAeRVyxfLBtTC7T1UUy+Wg9t0OjMCwv09jytYsWCyWiIiIMgSDHaIsVi5nOYyoPEK1qJ5UfxIsLSyzdgDZsgE1BwPvbgAc82r3eS992p76VtaOxcCbGLQs546tHzXAtx3Lwc3BBlYW2dCtmnaNLyIiIjJ8DHaI9GBgxYFY12kdyuR8tkZUlvOoDQzeBxSuo932OwPMaQhc3aG/MRkga0sL9KldBHvHNMIfvaui6NNMj65N9Zx91xAeHafXMRIREWUmLy8vNGzYUF0bGwY7RHqSyz55JzTfIF8c8TuStYNwcgfeXQfUGqrdjgwC/uoK7J0MJCRk7VgMnJOdNZqVfZoJe+qfY7cxcdMlNJqyB8uP30Z8AmufiIjI9Hh5eaFRo0YMdojo9dwKvYUB2wZg6I6h2Hd3X9YeRktroNVEoMt8wFrWldEAu78HlvXUrs1Dadp9+ZG6DgiPxmf/nUP7GQdw6FoAjxgREZGBYGaHyABcC76GkOgQxCTEwDfYVz+DqNAVGLgLyOmp3b6yBZjbFHh0RT/jMQLz+lbDLz28VMtqccEvFL3mHsWgxSdwM+CJvodHRERk9hjsEBmAJoWb4OfGP2Oo11C8X/59/Q0kTxltwFOqjXb78VVgbhPg8mb9jcmAWVhkQ0evAtg1uhE+bl4S9tbaZhPbLjxA8+l78f3GC6znISIio+ft7Y09e/aoa2PDYIfIQDQs1BAfVPog2T69rOli5wK8/RfQ8HPttix++k8P1vG8gL2NJUY0LYHdnzRC5yoF1L7YeA1Wn76fJR8ZERFRZvL29sbevXsZ7BBRxolNiMUnez/Bqqursv6wWlgAjcdqgx4bR+0+qeP5ty8QHZb14zES7i52mNbdC2uH1kU1jxz4tGUpONpaJeveRkRERFnn2X+FicigfHv4W2y7tU1dnG2c0cyjWdYPokw7IOcOYFkvIPA6cHE9EOAL9PgLyFk868djJCoVcsW/H9RGysTctxsu4H5IFP7XtiwK55RmEERERIavX79+MFacxkZkoLqU7AJHa0e1CGmNfDX0X8fj+TTYenQRmNsY8OV6PC9blFRqenQu+YdiyZFb2H7hAZpN34uftl5GRAzX5yEiIspMDHaIDFSl3JXwZ8s/Mbv5bJXZ0Sv7HECvFUDdUdptaUn9VzfgwM9SWKTfsRkJNwcbdKqsreeJiUvAzN2+aPLTXqz1vqef2iwiIiIzwGCHyICVyVkGLrYuyfbdDr2tn8FYWALNJwBd/wSs7AFNArBjHLDyfSCGbZZfJo+Ttp5n1ZA6qFhQ+5n6h0Zh5DJvdJ99GOfvcU0jIiKijMZgh8iIbLm5BR3WdMBin8X6G0T5LsCA7YBrYe22zypgfksg6Jb+xmREqhTOgTVD6mJyl4rI5Wij9h2/GYT2Mw9gzr5r+h4eERHRcxYuXIgJEyaoa2PDYIfISETERuCHoz8gXhOPmd4z4f/EX3+Dca8ADNwDFG2g3X5wDpjTCLi+V39jMiJSy9O9eiHs+qQRBtQrCiuLbGo2YFUPN30PjYiIyKQw2CEyEtmts6v6HXcHd/za5Fd1rVcOOYHeq4FaQ7XbkYHAkreAI7+zjiednO2s8VW7stgyqj6+alsGVT1yJN4mdTxn7wZnykdHRERkLhjsEBmRUm6lsPGtjaiVrxYMgqUV0Goi0OkPwNIW0MQDWz4H1g0D4qL1PTqj4ZnHCQPqF0u2b6uPPzrMPIgPl57EncAIvY2NiIjImDHYITIyNpbaOg+dB08eYPml5dArr57A+1sAZ223MZxeCizuCIQ/0u+4jJRkdaZvv6p+3nzeH82m7cW07VcQGROv76EREREZFQY7REbsYcRDDNg2AN8d/Q7zzs3T72AKVNGux1Ogmnb79mHtejz+5/Q7LiNdo2fR+zXQySu/2o6OS8CvO6+i6dQ92HD2PltVExERpRODHSIj9iT2CcJiwtTPfuF++j8JdnIH+m0EKvbQbofc0XZqu7hBv+MyQu4udvi5R2Ws/KA2yhfQrrN0PyQKw/4+jZ5zj+DKA+3nTkRERGljsENkxIq6FMX8lvMxsMJAfFXrK5UR0DtrO+CtP4BmEyRHAcQ+AZa/A+ybwsYFr6FaETesHVoPkzpXUAuTiiPXA9Hl90N4Eh2X0Z8eERGRSWGwQ2TkirsWx4gqIwwj0NGRsdQbBfRcBtg4avft+g74rz8QG6nv0RkdS4ts6FmjMHaPboR+dYrAIhvwYaPicLC10vfQiIiIDBqDHSITI1PZfjj2A/659I++hwKUagUM2AHkKKLdPv8fsKA1EHpf3yMzSi7ZrTG+QzlsHFEf/esVTXbb+jP3ObWNiIgoBQY7RCbm51M/46+Lf2Hi0YlYf229vocD5CkDDNgFeNTTbt8/DcxpDNw9qe+RGa0y+Zxha2WZuH0/OBKfrjyLNr/sx/cbLyCc09uIiIgUBjtEJqZlkZZwsnGCh7MHqrtXh0GQBUj7rAaqvqfdDvfXZnjO/qvvkZmEg74BiI6LR1yCBnP331Bd29Z639N/wwoiIiI9Y7BDZGLK5iyLeS3mYX6L+XB3cIfBsLIB2k0H2vwEZLME4qOBVQOAHROAhAR9j86odatWCOuH10M1jxxq+0FoNEYu82bXNiIiMnsMdohMNODJ65A32b6gqCAYROOCGgOBPqsAO1ftvgPTgOW9gWi2Un4T5fK74N8PamNqt0rI5fisaxunthERkTljsENkBo75HUOr/1oZRg2PKNZIuwBpzhLa7csbgfktgKBb+h6ZUZOOfF2qFsTOJF3bdFPbZKobERGRuWGwQ2TiImIjMGbfGETEReDrQ1+rxUcNQs7i2k5tns202w8vAPOaAneO6XtkRs/FXtu1bcPw+mpqW/0SudCibPJMHxERkTnIpmEFa7r4+PigfPnyOH/+PMqVK5fZnwtRhjrhfwIjdo3A17W/RquirQzr6CbEA9v+Bxz5TbttaQt0/A2o2E3fIzMJ8k98aFScCoB0Tt4KwqZzfhjVrASc7J7tJyIiMrVzcmZ2iMxANfdq2Nxls+EFOsLCEmg1EWj3M2Bh9axxwa7v2bggg6a2JQ104hM0+Hrtecw/cAPNpu3FxrN+7NpGREQmi8EOkZlwsXVJth0WE4YjfkdgMKq9B/T+D7B7Os59k4H/3gdiI/U9MpMSFBGD7DaWiV3bhv59Cu8uOI6bAU/0PTQiIqIMx2CHyAyFxoRi8PbB+HDHh9h3dx8MhjQuGLATcCum3fZZDSxsC4Q90PfITEYuR1usGFwbU7pWhJuDtmvbviuP0OLnffhlx1W1Xg8REZGpYLBDZIauBl3FpcBLiEuIM5wObTq5SmgDHo962u17J4G5TQD/c/oemUlNbZO1eXZ+3BA9axRS+2LiEjB9xxW0+nk/Dlxl5zYiInpm4cKFmDBhgro2Ngx2iMxQ1bxVMb3RdLQt1hYT60+EwcnuBvRZDVTurd0OvQvMbwlc3qzvkZmUHA42mNS5Iv77sA5KuzupfTcCnmDbBX99D42IiChDWGXM0xCRsWlYqKG6GCwrG6DDTCBXKWD710DsE+CfnkCLb4Haw7QLlFKGqOqRAxuG18PCQzex9MgtjG5RikeWiIgSeXl5oUiRInB1fboguBFhZoeIEi29sBQ+AT6Gc0QkoKk7AujxN2DtII2UgW1fAetHAHEx+h6dSbGytMCA+sWw4+OGybq3hUbF4r0Fx+B9J1iv4yMiIv0GO40aNVLXxobBDhEps8/Mxo/Hf8Sg7YNwOfCyYR2V0m2A97cAzgW026cWA0s7AxGB+h6ZSQY9SU3bdgW7Lz/CW7MO4svV5xASEau3sREREb0qBjtEpOSwy6GurS2sYZHNAP9pyFcRGLgLyF9Fu31zPzCvGRDgq++RmTTX7NawsbSARgP8dfQ2mk7bg1Wn7nJtHiIiMgoGeEZDRPrQvVR3jKs9DgtaLUCJHCUM80Nwcgf6bQTKdtJuB14D5jUFbhhQ+2wTM6pZSWweVR91PXOq7YDwGHy84gx6zj0C34dh+h4eERFlAW9vb+zZs0ddGxsGO0SUqGvJrijqUjTZEdHIV/qGxCY70HUB0OBT7XZUMLDkLeDkIn2PzGQVz+2Ipf1r4pceXsjtZKv2HbkeiNa/7MfkLZcQFcu1eYiITJm3tzf27t3LYIeITIvU7vTa2At+4X4wKBYWQJMvgc5zAUsbICFO27Rg65dAAk+8M2ttno5eBbBzdEO8W9sDFtmA2HgNdl16CCvZICIiMkDM7BBRqvyf+OP9re/j/OPzGLBtACJiIwzvSFXsDry7AcieS7t9eCaw7B0gOlzfIzNZznbWmNCxPNYOrYdKhVzx/Vvln2tqQEREZCj4XygiSlXe7HnRoXgH9XOP0j2Q3Tq7YR6pwjW1jQtyl9FuX9kM/NkKCLmr75GZtAoFXbBmSB1U9XBLNuVxxD+nMW//dcTFJ+h1fERERILBDhGlOW3p0+qf4s+Wf6JP2T6GfZRyeAD9twGezbTbD84Bc5sC907pe2Qm/zuS1FYff6w7cx/fbbyITrMO4uxdrs1DRET6xWCHiF54MlvdvXqyfbHxsQiLMcAuXHbOQM/lQI1B2u1wf2BBG+DCOn2PzGzkyG6Dorlk8Vfg/L1QdPrtIMav80F4dJy+h0ZERGaKwQ4RpVt0fDRG7h6JD7Z/gCexTwzvyFlaAW2mAK0nA7JWUFwksKIPcGC6zLHS9+hMXs1iObF5ZH2MaOIJa8tsSNAACw/dRLOpe1XWh4iIKKsx2CGidFvssxj77+3H2YCzmHl6puEeuZqDtVkeGyft9o7xwLphQFyMvkdm8uysLfFxi1Iq6KlRRFvP4x8ahcFLTmLAohPwD4nS9xCJiMiMvFGwkz9/fvTo0QMzZ87EmTNnMm5URGSQ+pXrh4YFG6qpbcMrD4dBK9kC6L8VcCmk3T69FFjaGYgI1PfIzIJnHicsG1QLP3apABd7a7XvgO8jxLJxARERZSGrN3lwx44dceDAAfz7779q29nZGXXq1EGDBg1Qv359VK9eHdbW2v/IEZHxs7a0xtRGU5GgSYC9lT0MXt5ywICdwLKewL2TwM39wLxmwDv/AjmL63t0Js/CIhverl4YTcvkxfcbL6KUuxMKuRloVz8iIjJJb5TZ+f3333Hu3DkEBARg9erVGDBgAAIDA/H111+rYMfFxQWNGzfOuNESkd7ZWto+F+gc8zuG2IRYGCSnvEC/jUC5t7TbgdeAeU2Bmwf0PTKzkcvRFtPf9sLgBsWS7Z9/4AYmrGcDAyIiMvCanRw5cqBDhw6YMmUKVqxYgRkzZqBEiRKIiorCvn37MuIliMhArbq6Si06Onb/WMQlGGjXLWt7oMufQIMx2u3IIGBxJ+D0X/oemdm2qr4bFIGftl7GgoM30XzaXmxjAwMiIjLEYOfixYuYM2cO+vTpgyJFiqjL559/juLFi2PSpEnYv3//az93dHQ0PvvsM1UbZG9vj5o1a2L79u3pfvzy5ctRu3ZtODg4wNXVVU2x27Vr12uPh4iSk+Bm5ZWV0ECD/Xf342bITcM9RBYWQJOvgLdmA5Y2gGSi1g7RNi9I4AKYWS0+QYPyBZzVz34hURi05CQGLT6B+8GRWT4WIiIyXW9Us5M7d241bS1Pnjxq2tro0aPVdaVKlZ5bbO519OvXDytXrsSoUaNUpmjhwoVo06YNdu/ejXr16r3wsePHj8c333yDrl27queJjY3F+fPnce/evTceFxFpWVlY4fdmv+OjPR9hZJWR8MzhafiHplIPwLUwsOwdIDJQ25b68TVtEGTDepKs4pHTAcsH1ca/J+9g4qZLCImMxbYLD3DQNwCjW5TCu3WKwNLizf87QkRE5i2bRvP6i09YWFioiwQ4uqYEukzKmzp27JjK5MjUuE8++UTtk2lx5cuXV8HVoUOH0nzskSNHVBZn6tSp+Oijj5ARfHx81GtLwFSuXLkMeU4iUyH/jGTEFxxZSgKcv98GHl/VbuevDPRcBji563tkZicgPFo1MFh9+tmXURUKuGBS5wooX8BFr2MjIiKohMOtW7fg4eGhkgj69Krn5G80je3Ro0f477//ULVqVWzZskVlXaR+p0aNGirLs2bNGtW84HVIRsfS0hKDBg16tn6DnR369++Pw4cP486dO2k+9ueff4a7uztGjhypTsLCw8NfawxElD4pA5374fex8PxC9fdnsKQb24DtQNEG2u37p4G5TQD/c/oemdk2MFjavyaK5NRm187dC8HNxwa4cC0RkRny8vJCw4YN1bWxeaNgJ2fOnKr99E8//YSjR48iODgYmzdvVkGPXHfp0kUFHa/j9OnTKFmypGpnnZQEUsLb2zvNx+7cuVO1vf7111/VVDsnJyfky5dPrQdERJnrduht9NvSD1NPTsUfZ/4w7MNtnwPovQqo8q52O/Qe8Gcr4PIWfY/MLNUrkQtbRjXA8CaeaF42L9pWyKfvIREREbTBTqNGjYwy2Hmjmp2krl69qpoRSPc1ub5x44baL8HG6/Dz81MBSkq6fffv30/1cUFBQSqbdPDgQdWMYNy4cShcuDAWLFiA4cOHq3V/Bg8e/MLXfvjwocpaJeXr6/ta74PI3Fhks1Dr8IgnsU8Mf4qbpTXQ/hcgVwlg2/+AmHDtujwtvgdqfShpK32P0KzYWVuqmp2Uvzfed4Lx+x5fjGtfDvldjWCNJyIiMv5gRzIlEtzIwqIPHjxQ/3EqWrSoqt354osv1LVkZ15HZGQkbG1tn9svU9l0t6dGN2Xt8ePHWLZsGd5++221LY0KKlSogO++++6lwc6sWbMwYcKE1xo3kbkr6FQQ81rMw87bO/F++fcNO9DRkTHWGQ7kKAqsGgjERgBbx2rreVpP1gZElMUfybPfm7j4BHyx6hwu+IXiwFU2MCAioiwKdqRLmhQIyXQ1CWzkklo25nVIq2lpPZ2SNCnQ3Z7W44RkcCTA0ZFGChL4SKbn9u3bKtuTliFDhqBbt27PZXY6der02u+HyJwUcSmC/hX6w+iUaQe8txn4pwcQ5gec+BMIugl0WwjYsVBeXyJj41Eyr6MKdp7ExOObDRew9sx9/NC5AsrkSz7VmYiIMp63t7cqV5GlXIxtKtsbBTuSPXFxyZwTAAmaUmsTLdPbhKy9kxo3NzeV/ZEPQxocJCVd3HRT3V4U7Mj9dPclojcXnxCPb498C688XujkaeBfGuT3Agbu0nZq8z8LXNsFzG8B9FoO5Cii79GZJSc7a/zcozK6VC2IL1efx+3ACJy5E4z2Mw5gYINiGNm0hJr+RkREmRfs3Hrajc3Ygp03alCQWYGOkAN55coVhIaGJtsvjRB0t6dGMjhym9TcxMTEJLtNV+fzunVERPR6Jh6diP+u/odxh8Zh120jWNjXOT/w/hagdDvt9qNLwNymwG3tvz+kH/VL5MbWUQ3wQcPiag2euAQNft9zDS1/3qfW5yEiIsrQYCczyRS0+Ph4zJkzJ3GfTGuTRgOy/k6hQoXUPpmSdunSpWSPlelq8thFixYlm/72119/oWzZsmlmhYgoc7Qr3g72VvYo7locFXNXNI7DbOMAdF8C1Bmh3Y4IABa1B87+q++RmTV7G0t83ro01g+rh0oFtV+43XocgQ1ntVl/IiLKeP369VOlIPpeY0ev3dgymgQ0UjczduxY1R3N09NTBS83b97E/PnzE+/Xt29f7N27N9l6HtKAYN68eRg6dKjKDsmUtSVLlqj02/r16/X0jojMV+U8lTGn+RwUcS4CVztXGA0LC6DFt0BOT2Djx0B8NLBqABB4DWj4GTu16VHZ/M5YNaQuFh26iSVHbqkAiIiIyGgyO2Lx4sWqCYIEKiNGjEBsbCw2bNiABg2eLgKYBmlSIG2ne/XqhT///BNjxoxR09s2btyI1q1bZ9n4iegZqddJGeiES5tnY1D1XaD3f8+aFOyZ9LRrm7ZhCumHTGV7v15RbP+oAVzsn3XMC4uKxccrvHEnMIIfDRGRmcumMeglzg2Hj4+P6jx3/vx5lCtXTt/DITJ6e+/sxZcHv8TPjX5GNfdqMAqPrgB/dweCtOuIoVBN4O2/AEfWARqSr9eex+LDt2BvbYmPm5fEe3WLwMrSoL/bIyKiTDonf6N//b/55hv1Qi8ajNyHiCipgMgAjN47GiHRIRi1Z5TxZHhylwQG7AQK19Fu3zkKzGsCPExeN0j6I9/fxcYnJLas/n7TRXSadRDn74XwYyEiek0LFy5Ua1DKtbF5o2Bn/PjxOHv2bJq3SyDExTmJKKVc9rkwrvY42Fna4bu638HRxtF4DpJDTqDvGqBiD+128G1gfnPAd6e+R0ZPFyOd1Lki/hlYC0VzOahjcv5eKDrMPIDvN15AREwcjxMRkRnJ1Lx+YGAgbGxsMvMliMhItS/eHps6b0KjQo1gdKxsgbf+AJp8pd2ODgX+6gYcf9Y8hfSrdvGc2DyyPoY38YSVRTYkaIC5+2+gxfR92HvlET8eIiIz8crd2Pbt24c9e/Ykbq9atQq+vr7P3U9WWV2+fDkqVKjw5qMkIpOUO3vyWpfgqGCExITAw9kDBi9bNqDBGMCtOLDmQyAuStux7bEv0OI7wIKLXOqbLDQ6ukUptKuYH5+vOovTt4NxNygSEzdeRD3PXKrBARERmbZXDnZ2796dODVNpgtIsCOX1MiaNjNmzHjzURKRyXsc+RgDtw9ESFQIFrZaiELO2rW0DF75zoBrYeCfnsCTh8CRWUDgdaDLPMDWSd+jIwCl3J2w8oM6+OvoLUzZehmTulRgoENEZCZeeRrbp59+ikePHqm1b6QQ9I8//lDbSS8BAQGIiIhQNTuyXg4R0cvsuLUDV4Ou4mHkQ/xz+R/jOmAFqwEDdwJ5ymq3r2wB/mwFhNzV98joKcni9K1dBIfHNkWVwjkSj4v8d+yXHVdxM+AJjxURkQl65cyOrGEjF3Hjxg3kzp0b2bNnz4yxEZEZebv023gQ8QCPIh9hdNXRMDqS3Xl/K7DyfcB3O/DgPDC3CdDzH6BAVX2Pjp5ytE3+n73N5/0xfccVzNrji5HNSmBg/WKwZptqIiKT8UYNCjw8PBjoEFGGGV55OCbUmQBLY613sXMGei4DagzWboc/ABa0BS6s1ffIKA2X/ELVdXRcAiZvuYz2Mw7A+04wjxcRkYl4o2BH0v+zZ89GjRo1kCtXLlhaWj53sbJ65eQREZkpqQO0yGaR7N+Yfy79g6CoIBgNSyugzWSg9RRA3ktcJLCiL7B/qrwhfY+OUvi4RSms/KA2PPNo259f8g/DW7MOYvw6H4RHs001EZGxe6NIROp3pk2bBi8vL/Tu3Rs5cjybB01E9CYk0Jl+cjoW+CzA6qurMa/lPDjbOBvPQa05CHArCvz7HhATBuz8Bnh8DWj3M2DFlvyGpFoRN2wcUQ9/7LmO33b7IiY+AQsP3cQ2H39826k8mpbJq+8hEhGRPoKdRYsWoUuXLlixYsWbPA0R0XM00KhmBSIoOggh0SHGFeyIEs2B/tuAv98GQm4D3n8BQbeAt5cA2d30PTpKwtbKUtXstK3oji9Wncexm4G4HxKFoX+fwv5PmyC3ky2PFxGRuU1ji4yMRLNmzTJuNERET8l0tu/qfofeZXprW1E7GUkr6pTyltV2aitQTbt96wAwrxkQ8Pz6ZKR/nnmcsGxQLUzqXAFOdlYY1awkAx0iInMNdpo2bYrjx49n3GiIiJKwsrDCZzU+QwHHAsZ9XBzzAP02AOU6a7cDrwHzmgI39ut7ZJQKC4ts6FmjMHaOboj+9Yomu23zOT9cexTO40ZEZA7BzqxZs3DkyBFMnDgRjx8/zrhRERGl4cyjM/hs32eIjY81rmNkbQ90mQ80+FS7HRUMLOkEnF6q75FRGvI42SVrQ30nMAIfrziD1j/vx687ryImLoHHjojIlGp2nJycVLekpOLi4vC///1PXezs7FQHtqTk/iEhIRkzWiIyaz6PfTBo2yBExEUgNiEWUxtOfe7fJINmYQE0+RLIWRxYNxyIjwHWDgUCrgJNx2lvJ4N10DcAUXHxqqnetO1XsOHsfUzqXBFVPdich4jIJIIdaUZgVCcWRGRSijoXRYkcJXD20VnUzV/XeP89qtQDcPUAlvUCIgOBgz9rp7a9NQew4SLNhqpHjcIo5e6EsavOqRbVVx6Eo+sfh9CnlgfGtCwFJztrfQ+RiIhSyKaR/q70Uj4+PihfvjzOnz+PcuXK8YgR6UlYTBhOPjiJRoUaGf9nEHhd26kt4Ip2O5+XdlFS53z6Hhm9QGx8Aubsu45fkkxlc3e2U22qm5dlm2oiIkM6J+ecCSIyKk42Ts8FOjHxMWpdHqPjVgzovx0o2lC77eetbVzgf07fI6MXkDqeoY09sWVkfdQqpm0h7h8ahYGLT+DYjUAeOyIiU1lnZ/HixS+8XaaYSB1PwYIFUaVKFdjacp0CIspYEbERGLZrGErlKIVPq39qfFPb7F2B3v8Bmz4BTi4EQu8B81sCXecDpVrre3T0AsVyO+KfgbWw4sQdfL/xIqp45ED1IqzfISIymWCnX79+iScWKb9VTbpffnZ2dsbYsWPx6adPOxEREWWA7458h+P+x9VF6nk6l3ja3tmYWFoD7X4GcpYAtn0FxD4B/ukJtPweqDVE/kHV9wgpDfLft7erF0bj0nmQkPDsv33isn8YrCyzoXhuRx4/IiJjDHa8vb3x7rvvImfOnBg6dCg8PT3V/qtXr+K3335DcHAwZs6ciQcPHmDGjBkq2JGObh9++GFGjZ+IzNyIKiNw6uEpFHUpijZF28BoyUlynWHaqW3/DdAGPFu/0HZqazNFGxCRQbepTlnX89Fyb/g+Csfwxp4Y3LA4bKw4c5yIjNPChQtx69YteHh4qGSH2TQoeO+99+Dn54ctW7Y8d5s8bevWrdUUtnnz5iEhIQH169dHaGgozp0zvvnobFBAZLj8n/jDzc4NNpY2MAl+Z4C/ewBh97XbxRoD3RZqp7yRUThwNQB9/jyq2lSLknkd2aaaiIzWQgMKdrK0QcGaNWvQsWPHVG+TVH6HDh2watUq7QtZWKjW1b6+vm/ykkREz3F3cH8u0Ln4+KLxHql8lYCBO7XX4vpuYH4LIPCGvkdG6VSvRC6s/KCOCnKErk3112vPIyzKyBbEJSKz5+XlhYYNG6prY/NGwY5kay5fvpzm7ZcuXVL30ZEGBdKwgIgoMy29sBTdN3THIp9FxnugnfMD720GSrfTbgdc1nZqu31E3yOjdJLFRjcMr4/RzUvCxtJCZXkWH76F5tP2YfuFBzyORGQ0vLy80KhRI/MLdiRzM2vWLFWXExUVlbhffpYanT/++APt27dP3H/48OHEuh4ioswQEh2Cuefmqp/lOjDKiFsB2zgA3ZcAdUdqtyMeA4vaA2dX6HtklE5SpzO8aQlsHlUfNYsmb1P999HbPI5ERIYc7Pzyyy+oVq0aRowYAVdXVxQtWlRd5OeRI0eqdtNyH10AZG9vj48//jijxk5E9BwXWxfMazFPNSyQa6nlMWoWFkDzb4AOMwALKyA+Blg1ENg9UYoj9T06SqfiT9tU/9C5ApztrJDHyRZtK3LxWCIig25QIOThq1evxtatW1XhkpDipZYtW6JTp06qVscUsEEBkXGJT4iHpYUlTMr1vcCKPkBUiHa7fFeg42+ANacHG5OHYVG4ExiBqh7PAvHw6Dg8CI1im2oiMkje3t6qy7IkNPQ9le1Vz8nfqPW0rhFB586d1YWIyFCkDHRuhNyA90NvvFXiLRitYg2BATuBv7oBQTeA8yuB4NtAj78Bx9z6Hh29QpvqlK2qp2y5hH+O3cGwJp74gG2qicgAg51bT7ux6TvYeVWmkXYhInqB68HX8f7W9/H1oa+x4rKR17vkKgEM3AV41NVu3z0GzGsCPDTi7nNm7l5wJJYevY2Y+ARM234F7Wbsx6nbQfoeFhGRSXilzI7U48i0NOmyZm1trbaTrhadGrn92rVrbzpOIqLXFhkfiej4aPVzUJQJnERmdwP6rAbWjwLO/K3N7khr6m4LAM9m+h4dvaICrvb494Pa+Py/s6pFtVy6/H4IfWt5YEyr0nC0feNJGEREZuuV/gWV/toSvOjqcHTbRESGrFzOcpjTfA5OPjiJd8u9C5NgZQt0mgXk8gR2fgNEhwJ/dQfaTAaqD9D36OgVVSmsbVM9e+81zNjlq7I8iw7fwrYLD/Btx/JoVjYvjykRUWYHO7J66ou2iYgMVflc5dUlZYMVo/7CRsZefzTgVgxY/QEQFwVsHA0E+AItvwdMrUGDmbSpblMxH8auOodjNwLhFxKFAYtPYEij4vi0VWl9D5GIyOiwZoeIzFJsQiw+3fcp1l9bD6NX7i2g3ybAIY92++jvwD89gegwfY+MXrNN9bKBtTCpcwU42VmpmLZpGWZ2iIj0EuyEhobihx9+UK2mK1eujGPHjqn9gYGBmDZtGnx9fd/0JYiIMtz4Q+Ox5eYWfHXwK+y9s9f4j3DBqtrGBXmetuG8uhX4sxUQfEffI6PXYGGRDT1rFMbOjxticpeKqOqRI1lG0i8kkseViCizg527d++qAOfrr79WP589exbh4eHqNjc3N8yePRszZsx4k5cgIsoUnUt0hr2VPcq6lUXlvJVN4yi7FgL6bwVKtNBuPzgPzG0C3Dup75HRa8rjbIdu1Qol27flvD8aTtmDmbuuIiYugceWiCizgp0xY8YgLCxM9d7eu3ev+rYpKVlUdMeOHW/yEkREmaJq3qqY12Ie5rSYA2cbZ9M5yrZOQI9/gJofaLefPAQWtAV81uh7ZJQBJLiZsP6Cuv5p2xW0n3GAbaqJiDIr2Nm2bRtGjBiBsmXLplrkW6xYMdy5wykURGSYKuauCCcbp2T7/J/4w+hZWgGtfwTa/ARkswTiIoF/3wX2/SRzoPQ9OnoD0sRgVu8qKJnXUW1ffhCm2lSPW3se4dFxPLZERBkZ7ERGRiJ37rRX7ZasDxGRsZAantarWmOt71qYhBoDgXdWALZPM1e7vgX+GwDEROh7ZJQBbapHNy8JG0sLFb9Km+rm0/Zix4UHPLZERBkV7EhGZ9++fWnevmbNGlXTQ0Rk6EJjQvHN4W8QlxCHiUcn4nHkY5gEWWS0/zbAtbB2+/xKYEFrIOSevkdGGdCmevOo+qhR1E3t07WpHr3iDI8tEVFGBDujRo3CsmXL8OOPPyIkJETtS0hIUB3Y+vTpg8OHD+Ojjz56k5cgIsoSUrczs8lMuNm54dcmvyKnfU7TOfJ5ygAD9wBF6mu3/byBOY2AO9rumWQ6bapFiadT3IiI6BUXFU2pd+/euHXrFr766it8+eWXal+rVq1UowILCwtMnDhRNSkgIjIGVfJWwebOm5HdOjtMjkNOoM9qYMvnwPF52sYFC9sC7X4GKr+j79FRBrSpblo6D+YfvIEB9Yomuz0iJg7Zbd7oP/dEREbrjf/1kyBHgp5Vq1apjI5kdooXL47OnTurBgVERMYkZaDz4MkDHPU/ig7FO8DoWVoDbacCecsBm8YA8THA2iHAAx+g+TfaxgZk1G2qx7Yuk2zf3aAI1bGtf72iGNSguJr+RkRkTl75v2z16tVD/fr1UbduXXXJkSMHPDw8OF2NiEzOw4iH6L+tP26F3kJYTBjeKWMiGZBq7wO5SgEr+gARj4EjvwGPLgJd/wTsny1eScZv3FofBEXEqjbV68/4YVKXCqrBARHRq/Dy8kKRIkXg6uoKkw92bt++rWp0pNW0XEqXLp0sAJIDQURkCgKjAhEUFaR+vhlyU03RTa3NvlEqUhcYuBtY1ku7+Oi1XcDcpkDPf4DcpfQ9OsogQxp74k5QBK48CE9sU923lgfGtCoNR1tm8ogo/cGOscqmSbkSaDrcvXsXBw4cUJdDhw7h3LlziI+PVycB+fPnV0GPBEByqVSpkkmcHPj4+KB8+fI4f/48ypUrp+/hEFEWuRR4CZtubMJHVT4yiX/LnhMdDqz5ELi4Trstbaq7zAdKttD3yCiDyAKks/dew4xdvoiJT1D78rnY4duO5dGsbF4eZyIy6XPy1wp2UgoPD1dBz8GDB9Xl6NGjiIjQruPg7OyMoCDtN6PGjMEOEemYVIZHJCQA+yYDeyY93ZENaDYeqDsSMKX3aeauPQrH2FXncOxGYOK+thXyYWr3SrCzttTr2IiIMuucPEMqFR0dHdGiRQtMmDABS5YswezZs1GrVi11QhAaGpoRL0FEZBDk37Xvj36PP878AZNhYQE0+hzovhhQDRo0wI5xwKpBQGykvkdHmdim+klMHGzZtICIXsLb2xt79uxR18bmjSfsSlQl09l0WR1pRW1ra6sWEx09erSa0kZEZCp+8/4Nyy8vT1ybp1eZXjAZZTsCbsWAf3oBIbeBcyuAx1eBHn8Dzvn1PTrK4DbVkzZfwugWJZNlKRMSZOkIZvOIKDkJcuQcX5qSGVv9zisHO3v37lVBjQQ4R44cQXBwMPLmzYs6depg6NCh6rpq1aqwsbHJnBETEelR22JtserqKtWiuplHM9P7LNwrAIN2Ayv6ArcOAvdPaxcg7b4EKFxT36OjDGxTPf3t5CcscfEJ6D77MJqUzsM21URkMl65ZkcWC7W2tka3bt3QunVr1K5d2yzW02HNDhHpSCtqW0tbuDu4m+5BiYsBtnwGnPhTu21hDbSZAlR7T98jo0wiTQwk2yNK5XXCD10qoDLbVBORudXsVKhQQXVe++effzB58mRMmTIFf/31F27cuPG6YyYiMioezh7PBTpXg64iQaPtdGUSrGyAdtOBttO0gU5CLLBhFLBuBBAXre/RUSaoXtQNJfM6qp+lTXXn3w9h/DofhEfH8XgTkdF65WDnzJkzqrva5s2b0alTJ1y9ehUffPABPD09kS9fPnTp0gXTpk1TU9xiY2MzZ9RERAbkmN8x9NzYE+MOjUN8QjxMSvX+QL8NgEMe7fapRcDCdkCon75HRhlMFhvdMLw+Pm5eEjaWFpB5HwsP3USLaXux8+IDHm8iMkoZ0npaMj1SuCS1PLoW1Pfv31eNCqpVq4Z9+/bB2HEaGxGlRv4JlUDH57EPLLNZYnHrxaiYu6LpHazQ+8DyPsC9E9ptx7ys4zFhvg/D8YW0qb6ZpE11xXxqbR43B9bkEpGZtZ62tLRUTQlGjBiBL7/8EmPHjlWtp6OiolTgQ0RkqqST1cymM1HarTQm1Z9kmoGOkG5s720CqvTVboc/ABa2BU4s0PfIKBN45nHEskG1MPGtZ22qZX0eS3ZqIzJLCxcuVEvMyLVZtZ6Ojo5WC4hKZzZdd7aQkBB1m2R16tevj3r16mXUWImIDFIu+1z4p+0/sLJ4427+hs3KFmj/K5DPC9j82bM6HunYJs0L5HYyGdKCulfNwmhWJg/GrfNB+0r54WJvre9hERG9klf+L/PatWsTg5vTp0+ruhyZxpEzZ87E4EYuMn1NurYREZmDlIFOSHQIZpyegY+qfgQHaweYDFmTRep48pbTTmt78lBbx/PwonZRUud8+h4hZUKb6t97V31u/6RNF1XWZ1CD4rDhwqREZKBeeRrbW2+9halTp+Lhw4d4++238ccff6i5c48ePVKB0JgxY1Q76owIdCRz9NlnnyF//vywt7dHzZo1sX379ld+nubNm6upJsOGDXvjMRERvcyT2CcYsmOIWnx00LZBCI8JN72DVrgWMHgvUKCadvvuMWBOQ+D2UX2PjLLAqdtBmLP/On7adgXtZxzA6dtBPO5EZBrBzvLly3Hv3j1cu3YNixYtwsCBA1GmTJlMGVy/fv1UZ7d33nkHv/zyi6oNatOmjcoqpdeqVatw+PDhTBkfEVFaWZ4cdjnUz3kd8sLOys40DxTreMyWlUU2eOZmm2oiMsFgRxYTlRbTme3YsWNYtmwZJk2apNbyGTRoEHbt2gUPDw98+umn6XoOaZAwevRolR0iIsoqsuDo9EbTMbrqaPxY/0fTruXR1fFwPR6zUrGgKzaOYJtqIjJ8GdKNLTOsXLlSZXIkyNGxs7ND//79Vabmzp07L30OWfQ0ISEBn3zySSaPlogoOWtLa/Qr309d60h9Y3BUsOkdKl0dT6rr8dzX9+gok0idzoimJbBpZH3UKOKm9t0PiUL/RScw9O9TeBgWxWNPRHpnsMGOND8oWbIknJ2dk+2vUaOGupZ1fV7k9u3b+OGHH/Djjz+qeh8iIn2bfXY2uqzrgmvB12CSUqvjmd0AuLFf3yOjLG5TvemcH+4ERvK4E5HeGezcCj8/v1Sny+n2yaKlLyLT1ypXrowePXq88mtL8wVpuJCUr6/vKz8PEZHO6Yen8Zv3b+rnsfvHYnm75apxisnW8Wwao83uPHkELO4INBsH1BmhzQKRybepzu1ki6oe2ro1IiJ9MthgJzIyUq3Vk5JMZdPdnpbdu3fjv//+U2sAvY5Zs2aphZOIiDKKV24vjKwyEksuLMGPDX40zUAnaR1Ph1+BgtWBjaOB+Ghg+9fA3eNAx1mAXfKMPZlem+q4+IRk+7f5+OPqw3AMalAM1pYGO6mEiEyQwQY7MvVMWk+n1nRAd3tq4uLiMGLECPTp0wfVq1d/rdceMmSIasSQMrPTqVOn13o+IiIJbgZUGICuJbrC1c7VPA5IlT6AewVgRR8g+DZwcb12PZ63lwJ5MqeLJxkGqyQBTUhkLL5acx4Pw6Kx/sx9TOpcAZULM+tDRGYe7Mh0NWlxndr0NiFr76Rm8eLFuHz5MmbPno2bN28muy0sLEzty5MnD7Jnz57ma8vtciEiymgpA51zj87hZuhNtC/e3jQPdn4vYNBeYPVg4Oo24LEvMLcJ0GEGUKGrvkdHWSAgPBou9tYq2LnkH4bOvx/Cu7WLYHSLknCy4+LjRJS5DDaX7OXlhStXriA0NDTZft3UNLk9rcYEsbGxqFu3LooWLZp40QVC8vO2bduy4B0QEb3Y1aCr+HDnh/jiwBf46+Jfpnu4srsBPZcDjb6Q1m1AbATwX39g06dAXIy+R0eZrHhuR9Wm+qNmJWFjaQGNBlh46CaaT9uHLef9efyJyDyDna5duyI+Ph5z5sxJ3CfT2hYsWICaNWuiUKFCicHNpUuXEu8jDQlWr1793EXIgqTyszyeiEjfnsQ+Ue3xdWvzmDQLC6DRZ8A7KwH7p1OYjs0GFrE9tbm0qR7ZTNpU10tsU+0fGoUPlp7EgEUncC+YnduIyMymsUlAInUzY8eOVd3RPD09sWjRIjUNbf78+Yn369u3L/bu3avWrxClS5dWl9RIVod1N0RkKLzyeOHPVn/i1INT6FrSTKZ0lWimnda2oi/g5w3cOaptT931T6BoA32PjjKZZx4n1ab635N3MHHTJVXPs+PiA9QomgODGhTn8Sci88ns6KadjRo1CkuWLFFNB2R62oYNG9CgAf+DSESmobRbafQq0yvZvojYCMQmxMJk5fAA3t8KVHlXu61rT33gZ1l5Vd+joyxoU/129cLYOboh3qpcAKXdnfBeXe10cyKijJZNo0uJ0Av5+PigfPnyOH/+PMqVK8ejRUSZIjo+GkN3DIW9lT0mN5ysrk3a6aXAho+17alF6XZAJ2lP7aLvkVEWiYiJQ3abZxNN7gZFYMHBmxjVrAQbGBDRG5+TG3Rmh4jI3Pzu/TuO+h/Fnrt7MO/cPJi8yr2BAdsBVw/t9qUNwJzGwAMffY+MskjSQEe+f/16rQ/mH7iR2MCA38kS0ZtgsENEZEBkLZ6a+WqiSp4qGFhhIMxCvkrA4L1AiRba7cBrwNym2qwPmZUnMfEIj4pL1sBg4OKTbGBARK+NwQ4RkQFxtHHE701/x8ymM2FnZQezIR3apD114y+17anjIoG1Q4HVHwIxT/Q9OsoijrZWqoHB5C4V4ZpduwaPNDBoPm0v5u2/jrh4bfdCIqL0YrBDRGRgrC2t4WTjlGyfrMNzwv8ETL49dcNPgb5rAIfc2n1n/tZmeR5d1vfoKAsbGHSvXgg7P26IzlUKqH0RMfH4buNFdJh5EGfuBPOzIMpiCxcuxIQJE9S1sWGwQ0Rk4NZfW48fjv2AwdsHY/ft3TB5xRoBHxwAPOpptx9d1NbxnFmu75FRFsrpaItp3b3w94CaKJrLQe274BeKX3de5edAROnGYIeIyAhYZbNSC48WcNJ+023ynNyBvmuB+p9ot2OfAKsHAetGALFcgNKc1PHMhc0j62NE0xJwtrPC+A7siEqU1by8vNCwYUN1bWwMdlFRIiLSal+8PXLY5YCdpR1K5ihpPofF0gpo+j+gcG1g1UAgMhA4tQi4dwrothDI5anvEVIWsbO2xMfNS2Jg/aLJ2lHHxidg0qZL6F+/KAq4mnibdiI98jLCIEeHmR0iIiNQr0A9VHOvlmzfnbA7iEvQdq4yaSWaaae1Faql3X5wDpjTCDi/St8joyyWNNARCw7ewJ8HpU01GxgQUeoY7BARGaE7oXfQe1NvfLT7I0RK5zJT51IA6LcBqDNCux0TBqx8D9j4CRAbpe/RkZ7cehyhrtnAgIjSwmCHiMgITT81HYFRgWrx0f1398MsWFoDLb4Fei4D7Fy1+47PBeY3AwJYtG6Ovn+rwnMNDDrNOojx63wQFhWr7+ERmQxvb2/s2bNHXRsb1uxkoLi4OAQFBSE8PJwrPhPpSbZs2WBrawtnZ2c4ODiobVP0TZ1vEBoTinI5y6FFkaeLcZqLUq2BD/YD/74H3DsB+J8DZjcE2k4FvHrqe3SkpwYGs/Zcw+97fBEbr8HCQzex+bwfJnQoh5bl3E323wGirOLt7Y1bt27Bw8PD6Op3mNnJIBqNBnfv3kVAQABiY/ltEpG+xMfHIyQkBHfu3MHDhw9N9osHtfhos98xssrIZPtN9f0+x7Uw8P4WoO7IZ93a1nwArBoMRIfre3SkpwYGm0c2QI2ibmrfg9Bo/G+tj5riRkTmi5mdDBIWFobIyEi4uLggX758/BaJSI9iYmLg5+eHwMBAld1xdHQ0yc/D2iJ5sXZsQixG7R6FVkVaqQ5uZjGtrfk3QNEG2iAnIgA4uwy4exzotgDIV0nfI6Qs5pnHEcsH1cLKk3fx/aaLGN++HBxseapDZM6Y2ckgoaGh6jpPnjwMdIj0zMbGRn3pkPRv0xxMOjoJ++7uwxcHvsDWm1thNjybAR8eBIo21G4HXgPmNQOOzpZUl75HR1lMpqx1q1YIe8c0RpsK7slum733Gk7cDORnQmRGGOxkEJm6ZmVlpS5EZBgBj7W1NaKjo2EuWhdtDScbJ1XHU79AfZgVWYS0z2qg6ddANksgPgbY/Cmw7B0ggie35sjF3jrZl4/ed4Lxw5ZL6PrHYXy28iyCnsTodXxElDUY7GQQmSdvYcHDSWRI5ETHbGpYAFR3r46lrZdiZtOZyG6dHWbHwhKoPxp4bzPgUki77/JG4I/6wK3D+h4d6dnVB2GwsdT+d3r5iTtoMnUPVhy/g4QE8/k3gsgc8ew8A7HbC5FhMce/yWKuxZDLPleyfeuurYNfuB/MRuGa2m5tpdtpt0PvAgvbAHsnAwksVjdXMrVt+0cN0ahUbrUdFBGLT/87i+6zD+OSv/lMdyUyNwx2iIhM2PZb2/HVga/wzqZ3cPHxRZgN+xzA20uBNj8BlraAJgHY/T2wsC0QfFvfoyM9KZwzOxb0q47f36kCd2c7te/ErSC0/fUAJm26iCfRcfxsiEwMgx0iIhN2LuAcNNDgSewTWGQzs3/yJbNXYyAwcCeQq5R23+3DwO/1gHMr9T060mPGt3WFfNgxuiH61ysKS4tsiE/QYM7+6/B9yLblRKbGzP7LR0RZbfny5ejduzdKlCihTjIaNWr0ys8xf/58lClTBnZ2dup5ZsyYkSljNUUfV/0Yn9f4HD81/Aml3J6e8Jsb9wrAoD1Atf7a7egQ4L/+2nbVUZy+ZK4cba3wv3ZlsX5YPVQu7Ip3ahZGpUKu+h4WEWUwBjtElKl+//13rF27FoUKFUKOHDle+fGzZ8/GgAEDUK5cORXk1K5dGyNGjMCPP/6YKeM1Re+UeQf1CybvznY95Dqi482nUx1ssgPtpgE9/gGy59TukzV5/qgH3Dmm79GRHpXN74z/PqiDr9qWTbZ/y3k//LbbFzFxCXobGxG9OQY7lGmePHnCo0tYsmQJQkJCsGvXLuTPn/+Vjogs1Pvll1+ibdu2WLlyJQYOHIjFixfjnXfewbfffougoCAe4dfg/8Qf7295X10CIgPM6xiWbgN8eAgo3kS7HXwL+LMVsOdHIJ71GubKwiIb7KwtE7fDomLx9VofTNl6GW1+3Y8j1x/rdXxE9PoY7FC63Lt3D/3791cnq7a2tihatCg+/PBDtVK9WLhwoZqitHfvXgwZMkQtrlqwYMHEx2/evBn169dXq9k7OTmpk1cfH59kr+Hv74/33ntPPU5eQxaF7NixI27evJl4nxMnTqBly5bIlSsX7O3t1Tjef//9xNvlvjKOn376Cb/99huKFSuG7Nmzo0WLFrhz545qQywnyfIa8nh5/sDA5GtwSBZCxqd7r8WLF1ePiY9P3sVJpmOVL18eFy5cQOPGjdXrFChQAJMnT36t36qXHSMJFqS9+ddff53scX///bd6z5JB0ZHtYcOG4a+//kKpUqXU9K+qVati3759yGqS0Xndtuy7d+/G48eP1e9UUkOHDlXB9MaNGzNolOZl3rl5eBz1GGcDzmL/3f0wO7Imzzv/AS0nAZY2gCYe2DNR27wg6Ja+R0cG4GFYtFqnR0gdT485R/DxCm8EhJtRNpTIRHAFTHqp+/fvo0aNGggODsagQYNQunRpFfzIN+0RERFq8UYdOSnNnTu3OiHXZXbkm/13331XBSky9UgeIyfm9erVw+nTp1GkSBF1vy5duqiT++HDh6t9Dx8+xPbt23H79u3EbQla5Pk///xzuLq6quBm1apVz41ZTvIlEJPnkmBGApDu3bujSZMm2LNnDz777DP4+vqqaVGffPIJ/vzzz8THSuDm6OiIjz/+WF1LkCHvJzQ0FFOmTEn2OpJZaNWqFTp37qyeX46JPHeFChXQunXrdP92pecYydjl+E6aNAmdOnVClSpV4Ofnp95js2bN8MEHHyR7Tgk8pV5GpnxJ0DZr1iw11mPHjqkg7UUCAtL3bb8EZfLcmUXeu6hWrVqy/RK4SQAlt0s9EL2az6p/htiEWNha2uKtEm+Z5+GTALz2EKBofeC/AcCjS8CdI9ppbW2nAhW763uEpEfFczti44j6mH/gBn7ZeQVRsQlYdeoedl58iM9alUaP6vIljvm1ticyShpKl/Pnz8uqY+o6NdeuXVMXU9S3b1+NhYWF5vjx48/dlpCQoK4XLFigjk+9evU0cXFxibeHhYVpXF1dNQMHDkz2OH9/f42Li0vi/qCgIPX4KVOmpDmO1atXq/ukNg6dGzduqPvkzp1bExwcnLh/7Nixan+lSpU0sbGxift79uypsbGx0URFRSXui4iIeO55Bw8erMmePXuy+zVs2FA95+LFixP3RUdHa9zd3TVdunTRpFd6j5F48uSJxtPTU1OuXDk1lrZt22qcnZ01t27dSvZYGZdcTpw4kbhP7mNnZ6d56623Xjom3eNfdpHP/VXIuOW4pdfQoUM1lpaWqd4mn3GPHj1e+HhT/rt8U/K3Gxf/7G9VRMZGJv5Nm5WYCI1mw8cazTjnZ5d/39NonjzW98jIANwJfKLpv/C4xuOzDYmXTr8d0Jy/9+y/MUSmbsGCBZrx48e/8n/39XFOnhIzO1nk3xN3sPLk3ZcWSY5rXy5x2+d+CL5Zf+Glz718cO1k22/PTn2l8K5VC6pF1V5FQkIC1qxZg/bt2z/37XpqizZKTYWl5bN5z5KZkYxQz549k2UL5D41a9ZU05SETCmTDJFkXWS6XGqF7JLJERs2bEClSpVgba2dYpCabt26wcXFJXFbXktIFsDKyirZ/n/++UdlqmTKm24sOmFhYYiOjlbTy6RQ/tKlS+q1dSTzkzSzIO9BsmDXr19HeqX3GAmZKieZpwYNGqiLZGmkU1nhwoWfe14p5JcMiI7cR6btrV+/Xk3JS/o5pTam9JCmAZlJanaSZg6Tkql5cju9Hvnbtcz27HcgLiEOo/aMgouNCybUmQA7K+0aJGbB2l6bzfFsDqwdAkQ8Bs7/B9w6BHT8DfBsqu8Rkh4VzJEd896thm0+/piw/gLuBUfi9O1gHPJ9jHL5n/13hsiUeXl5qVkmunMxY8JgJ4vcDYrE0RvJa0NeJjQy7pUfI9J6TK1iTzsQvYJHjx6p6Vsvm/akIzU0SV29elVdyxSs1Dg7O6trmQol07dGjx6NvHnzolatWmjXrh369u0Ld3d3dZ+GDRuqqW4TJkzA9OnTVc2MTOfq1avXc1OpUp786wIfqR9JbX/SQneZSvfVV1+p6Wvy3pOSQvukpPYnZcAngdrZs2eRXuk9Rjp169ZV9VJSkyTT3pLWLCUlLZpTKlmypJoiJ5+r7rimRqbFGQIJPHV1YSlFRUUlC0zpzcw/Nx8H7x1UP+dzyIdRVUeZ3yEt1Qr48DCwfgRwZQsQ5gcs7QxUHwg0/0bb0Y3MVoty7qhXIhd+2XlVBTr96mqnYBOZS7BjrBjsZJGCOexRs6jbSzM7STnbW730MalJ6zEyhsyW8uRTMkO6mpTUTq6TZllGjRqlMkiSSdq6dSv+97//qfoUCToqV66sggqpiTly5IjKTsh95ER/6tSpap9kWXTSylqktV87cwsqwyJBlQQY33zzjWpOIBmEU6dOqVoc3ftJ7/Olx6scIyGZJsmAiWvXrqngRTI+GUmaRaSHBIuZGXBIkwrJQkm9ljS90JEASBoXvGp3N0pb91Ldcej+IYTFhmFgxYHme6ic8gI9lwGnFgNbvwBiwoHjc4Hru4G3ZgMFn89wk/nIbmOFsa3LIDY+AdaWzxqv3AmMwLcbLmBsmzIomstBr2MkouQY7GQRmT72qlPIJD2ecopaerzOY9IizQDkxP/8+fOv9XgJFoScqKYnWyD3l+yOXCTjId8kSDCzdOnSxPtI1kcu33//vepEJm2Ily1bptZieVMSRMhJtDQ9kGliOjdu3EBmedVjNG7cOFy8eFF1nJMATJo1/Prrr2lmjJK6cuWKCozkc31ZkJEeCxYsQL9+/ZDZ3yRJF742bdok7pdtCRKN+ZsmQ5PDLgfmtZiHkJgQOFg7JAvcU2YvTZ6836rvAkUbAKs/0DYueOwLzG8B1B8NNPwUsEx7Gi2ZvqSBjvyNjFvng12XHmLP5Uf4oGExDGnsmayVNRHpD4MdeiHpeCVTxSTYkBPMlHU7LzsRkmlWEixNnDhRtWdOWWcj06nkxFuyE/JakkVJGgRIty/JZOimmslc0aSvpzvZ1d3nTekyNUkzM5JFkE5mmSW9x0gcPXpUBTmSBZOAUGp8ZPqfTO+TjFRShw8fVhkp6dompPW2tNWWjmwvqtfRV82O/A5I5z1pKy4X3dQ+Nzc31ZkuabAj2xK0SXtuyjjWltbIZa899jqLLyzGjZAb+KLmF7CRNs3mxK0o8N4m4NCvwK7vgYRYYN9k4Oo2oPMcIHcpfY+QDEBsvAZ5nLRTqWPiE/DrLl+sOn0P49uXQ7OyefU9PKIM4e3trWa/yHmYsX3RyGCHXkpOwrdt26ZOpqX1dJkyZVTL43///RcHDhx4YbGanMTLiWmfPn3USXePHj3Uibuc1MoaKVJ/MnPmTJVxaNq0qWrfXLZsWTV1a/Xq1Xjw4IF6jFi0aJEKOt566y0VCEnzgLlz56rXSHoi/Cbq1Kmjam6kDbS0bJbASqaXvcq0tFeV3mMkNSoyLqnFkayWkPolmdIn6xOdO3dOrdGjI3VWEkglbT2te8zLZGTNjqzto1vfRwI3aUn+3XffqW1dowUhzRYk2JPM1fjx49U+mSInaxzJujrSdELez/79+1XwLcdAAiHKPEf9jmLayWlI0CQgPDYcPzX8yfwOt4UlUO8jbfOCVYOAhz6AnzcwuwHQbDxQY7C2jTWZLRsrC/zQpSK6Vy+E/605D5/7oapOd8DiE2hWJo9qPFTIjfVeZPzBzq1bt+Dh4cFgh0yPLJQpGQWpoZH1a6RoX/bJOjLpqRWRBgJSW/HDDz+odWokCyOPlw5ncpKuaxwg3ch27typggsJdmQ9nxUrVqishZBgS06IZcqaBEFSLyKdz2RMKRsjvK6cOXOqbm+SNZEmBRL4SLc1CcTkRDuzpOcYffHFF2ptoEOHDiVmwKRTmQSBMq1vzJgxyTJQcrykI5sENxI4SRApndwqVqyIrCQ1VykDLPldEhLYJJ0umBpZW0iyXTKdcd26dep3RRpUjBw5MlPHTUAR5yIon6s8LgdexvvlU2+EYTbcywODdgO7JwIHfwHiooAtnwOXNwEdZgI5PPQ9QtKzKoVzYN2wevjr6C1M2XoZYVFx2HHxIfZfDcDQxp4Y1tiTa/MQ6UE26T+tjxc2NtKhS74pl9qV1Kbu6FoN69oXE+mTZKQkGyIZIXPGv8s3FxMfgwuPL8Arj3FNW8hUtw4DqwcDwbe02zaOQItvgarvaet9yOwFhEfjh82XEpeckAzPvHerm/1xIcqKc/KUmHsnIqI0SZ1OykDHJ8AH4w+NR3R8xtTKGR2P2sCHB4GqT5tzSMe2DR8BizsCwbf1PToyALkcbfFTt0r494Pa8CrkmmwNPZGQwO+ZibIKa3aIMpHUqEjr5LTINDTWnZAxeRz5WC0+6v/EH1eDr2Jxq8WwlLoWc2PrBLT/BSjbEVg7HAi9C9zYC8yqzSwPJapexA2rh9RJ1lhH2lZ3++MwmpfNiwH1i8LWygz/foiyEDM7RJmoevXqqo1zWpfOnTvz+JNR0UCD/A7a9Y1aFWllnoFOUsWbAEMOM8tDaUrZsXTRoZvwvhOs6npa/7wfB64G8OgRZSJmdogykTRPiIyMTPN2aYCQGViKR5lFWlPLejybbmxCh+IdeKCFnTOzPJRuFQq4oGReR1x5EI7rAU/Qe/5RtK2QD1+1K4N8Lpm/+DfR65AGR7pubJm5vl5mYLBDlImkbTSRKa7H09GzY7J9YTFh+OnETxhReQRy2ueEWWd5tn0FnFr0rJbHZw3QcSbgWljfIyQDULNYTmwcUR8LD97Ezzuu4ElMPDae88Puyw8xsmkJvFe3qGpnTUQZg39NRET0RtQK8ofGYdXVVei+vjvuh9837yxPh1+B3qsA54LafbpanuPzpDJd3yMkA2BtaYGBDYph5+hGaFcxn9oXEROPSZsvoc2v++H7MFzfQyQyGQx2iIjojcQmxMLawlr9XMqtFNwd3HlEPZtqszxV3tUeC8nybBwNLGwLBFzl8SHF3cUOM3tVwV8DaqJ4bu2i0GFRsWo/EWUMTmMjIqI3bk/9Q/0fUDt/bTQq2AgW2fg9WrIsj3RsWz8KCLkN3D4E/F4HaPgpUGckYGXD3z5CXc9c2DyyAf48eAOF3bLD0fbZ6dmT6Dg1rU2yQUT06viXQ0REGdJxqpNnJ7jauSab3jb95HScfXTWvI+wLstT80M5UkB8DLDrO2BOI+DeSX2PjgyEBDQfNCyONhW009p0vt90Ea1/Ydc2otfFYIeIiDLFat/V+PP8n3h3y7vYeWuneR9lW0eg9Q/AgB1AnrLafQ99gHnNgC1fADFP9D1CMkA+90Pwz7HbqoZHurZ9uPQk7gZF6HtYREaFwQ4REWWKmPgYWFlYwdXWFZXyVOJRFgWrAYP2Ao2/BCxtAE0CcOQ3YFYtwNfMA0J6Tok8Tvi8VWk42GjXs9p83h/Npu3FLzuuIio27QWriegZBjtERJQpepTugUWtFuGnhj+p9XnoKanTkZqdwfuBQjW1+4JvA0s7A6s/BCICeagocWrb4IbFseuTRujkpV3MNyo2AdN3XEHz6Xuxzcef66oRvQSDHSIiyjQVc1dE1bxVk+1bf209fj/zO+ITzPyb6Tylgfe2AG1+AmwctfvO/A3MrA6cWS5FT/oeIRmIvM52+LlHZawYXBul3Z3UvjuBkRi05CR+2HxJ38MjMmgMdogo0zx+/BhTpkxBgwYNkDt3bri6uqJWrVpYvnz5Kz3P/PnzUaZMGdjZ2aFEiRKYMWNGpo2ZMtfNkJv49si3mOU9C2P2jeHhtrAAagwEhh4FSrTUHo+IAGD1IGBxB7appmRqFHXDhuH18E3HcnC2s0K2bHiuoQERJcdgh4gyzeHDh/Hll1/Czc0NX331Fb7//ntkz54dPXr0wLhx49L1HLNnz8aAAQNQrlw5FeTUrl0bI0aMwI8//shPzgjFa+KRz0F7ctalRBd9D8dwuBQEei0Huv4JOOTR7ruxT9umevdEIDZK3yMkA2FlaYG+tYtg9yeNMLVbJVQqlLwD4r4rjzi1jSiJbBr5y6CX8vHxQfny5XH+/Hl10pXS9evX1XWxYsV4NJ968uQJHBy0i6SRebpx4wYsLCzg4eGRuE/+yWnWrBkOHjyoMj8v+h2JjIxEoUKFVDZow4YNift79+6NNWvW4M6dO8iRI0eaj+ffpWGKjIvEgXsH0NyjebL98rshLazNXmSwtjX18XlyVLSHI0dRoO1UbRtrojSsP3Mfw/85jZpF3TC+QzmUyefMY0UZYuHChbh165b673m/fv1gyOfkKTGzQ+ly79499O/fH/nz54etrS2KFi2KDz/8EDExMYl/BHKSsnfvXgwZMgR58uRBwYIFEx+/efNm1K9fX53YOjk5oW3btuqXNSl/f3+899576nHyGvny5UPHjh1x8+bNxPucOHECLVu2RK5cuWBvb6/G8f777yfeLveVcfz000/47bffVPApmYQWLVqoE2M5mfr222/Va8jj5fkDA5MXA69du1aNT/deixcvrh4TH5+8vqBRo0bqj+3ChQto3Lixep0CBQpg8uTJr/Vb9bJjtGvXLhU4fP3118ke9/fff6v3/Pvvvyfuk+1hw4bhr7/+QqlSpdT0r6pVq2Lfvn3ISvL5JA10dGPr1KkToqOjE4ORtOzevVsFRPI7ldTQoUNVML1x48ZMGTdlLnsr++cCHZne9vaGt3E+4DwPv70r0PYnYOBOwL2i9ngE3dA2MPj3PSDMn8eIniP/fZu975r6+eiNQLT9dT/Gr/NBSEQsjxaZtWdL9BKl4f79+6hRowaCg4MxaNAglC5dWgU/K1euREREBGxsnq0ALielUpshJ+RyMiqWLFmCd999VwUpMvVIHiMn5vXq1cPp06dRpEgRdb8uXbqok/vhw4erfQ8fPsT27dtx+/btxG0JWuT5P//8c1X/IcHNqlWrnhuznORLICbPJcGMBCDdu3dHkyZNsGfPHnz22Wfw9fVV06I++eQT/Pnnn4mPlcDN0dERH3/8sbqWIEPeT2hoqKo/SSooKAitWrVC586d1fPLMZHnrlChAlq3bp3u36n0HCMZuxzfSZMmqWChSpUq8PPzU+9RMiUffPBBsueUwFNqY2TKlwRts2bNUmM9duyYCtJeJCAgIF3jlqBMnvtVSWArJGh9EXnvolq1asn2S+AmgZ/cLlkeMm6xCbH44sAXuBh4EX0298GmtzYhnyPrEFCgKjBwtzbDI5memDDAZxXguwNo8j+gen/AQtuSmEi+SPprQC38vOMKFh++hfgEDRYeuol1Z+7js1al0K1qIVhYMHNKZkimsdHLnT9/XuYSqOvUXLt2TV1MUd++fTUWFhaa48ePP3dbQkKCul6wYIE6PvXq1dPExcUl3h4WFqZxdXXVDBw4MNnj/P39NS4uLon7g4KC1OOnTJmS5jhWr16t7pPaOHRu3Lih7pM7d25NcHBw4v6xY8eq/ZUqVdLExsYm7u/Zs6fGxsZGExUVlbgvIiLiuecdPHiwJnv27Mnu17BhQ/WcixcvTtwXHR2tcXd313Tp0kWTXuk9RuLJkycaT09PTbly5dRY2rZtq3F2dtbcunUr2WOfzn3RnDhxInGf3MfOzk7z1ltvvXRMuse/7CKf+6t6/PixJk+ePJr69eu/9L5Dhw7VWFpapnqbfMY9evR44eNN+e/SlMTFx2nmnZ2n8Vrspfnh6A/6Ho5hCrmn0Szvq9GMc352+aOBRnP3pL5HRgbokl+o5u3ZhzQen21IvHSYsV9z+naQvodGRmrBggWa8ePHv9Z/97P6nDwlZnaywubPAf9zMAjuFbSreKdTQkKCqo1o3779c9+ui5Tz6wcOHAhLy2ffNEpmRjJCPXv2TJYtkPvUrFlTTVMSMqVMMkSSdZHpcqnVYUgmR0jtRqVKlWBtbZ3muLt16wYXF5fEbXktIVkAKyurZPv/+ecflanS1VvJWHTCwsLUdCuZXiaF8pcuXVKvrSOZn6SZBXkPkgV72fSspNJ7jIRMlZPMk3Q3k4tkaaRTWeHChZ97XinklwyIjtxHpu2tX79eTclL+jmlNqb0SM9c2ZS/T++88456v+npqCY1O0kzh0nJ1Dy5nYyfpYUl+lfoj3oF6qGIizbTqxMaEwonayfW8jjnB7ovAq5uBzaOBoJvAX7ewNwmQNV3gSZfAw459fYZkmEp5e6EfwbWwsZzfvh+40X4hUThzN0Q9Jl3FIe/aApHW57+0avRd53Om+Bve1aQQOfWARijR48eqelbL5v2lLRGI6mrV6+qa5mClRpnZ23xpEyFkulbo0ePRt68eVVBert27dC3b1+4u7ur+zRs2FBNdZswYQKmT5+uamZkOlevXr2em0qV8uRfF/hIsXtq+2U6mo5MpZPOYTJ9Td57UiEhIcm2pfYnZcAngdrZs2eRXuk9Rjp169ZV9VJSkyTT3pLWLCUlLZpTKlmypJoiJ5+r7rimRqbFZQaZcrdlyxYsXrw4WdCYFgk8dXVhKUVFRSULTMn4lXIr9dz0tkHbBiF39twYV3scFyYVJZpr21Tvnwoc+BlIiAVOLgR81gBNvgKqvgdY8j/tpP0ysl3F/GhSOg9+2+2LuftuYHhTTwY6ZHb4L2JWZVPMZCwpTz7lm3xdTUpqJ9dJsyyjRo1SGSTJJG3duhX/+9//VH2KBB2VK1dW/3BLTcyRI0dUdkLuIyf6U6dOVfsky6KTVtYirf26poSScZCgSgKMb775RjUnkAzCqVOnVC2O7v2k9/nS41WOkZBMk2TAxLVr11TwIhmfjKSrqXkZCRbTG3BIkCp1Qz/88AP69OmTrsdIkwrJQkm9ljS90JEASBoXSBMJMl2LfBbB57EP8Bgo41YGQ7ySN6owW9b22sCm4tvAls+1NTxRwcCmT4CTi4A2kwGPOvoeJRmI7DZWGNOyNLpXK4R8Lsn/vV565BYK5LBH41LP/n0lMjUGHezISZ0UhstJoHzzXrFiRXz33Xdo3jx5F5+UpGBdCrOPHz+uTtrk23zJEsjJs24qVJZ6hWljhkaaAciJv7T3ex0SLAg5UU1PtkDuL9kduUjGw8vLSwUzS5cuTbyPZH3kImu2SCcymRa1bNkytRbLm5IgQk6i5XdIpoklbaGcWV71GMn6NBcvXlQd5yQAk2YNv/76a5oZo6SuXLmiAiP5XF8WZKTHggUL0pXalizU+PHjVUArY04v+fx1XfjatGmTuF+2JUjU3U6mqVvJbrgceBm+wb4YUOHN/75NTq4SwDsrgcubtUGPTG17cA5Y0Bqo0A1o/i3gzEYPpOWRM3mb/7tBEfh2wwVExyWo7M9XbcugWO5nXxoSmQqDDnbkJEq+yZcTJJmSI7UKcsIjNQzSpSot0jFMvvGVWgqZznTu3DnMnDkTmzZtUt/Qc+pL+knHK5kqJsGGnGCmrNt52boYMs1KgqWJEyeq9swp62xkOpWceEt2Ql5LsihJgwDp9iVBr5CAV4LVpK+nO9nV3edN6TI1STMzkkWQjERmSe8xEkePHlVBjvxNSEAoNT4y/U+m90lGKuWCnvL7Ll3bhLTelrba0pHtRfU6GV2zo+sIJ0HptGnT0ryf/A5I5z3p0Kbr0iZT+2RBUulMlzTYkW0J2qQ9N5kuF1sXTGk4BWExYbCxfFa7FRMfgyN+R9Cg4LMvJMyW/HtYug1QvDFwaIZ2eltcFHDuX20Q1GAMUGsIYJV67RuZrysP5O/KQgU7uy49xP6rj/Be3aIY1sQTznZp18SSeVpoQOvsmEywI4XX8m29tPqV1sBC6jekduTTTz/FoUOH0nysBEhSz5GUFGpLa19pSZwRGQBzIifh27ZtUyfTEkiWKVNGtTz+999/ceDAgRdmy+QkXk5MZdqSnHT36NFDnbjLSa2skSL1JxKISsahadOmqn1z2bJl1dSt1atX48GDB+oxYtGiRSroeOutt1QgJM0D5s6dq14j6Ynwm6hTp46quZHfFTlBl8BKMouZufZueo+R1KjIuCTwl6yWbmqYTOmT9YkkqE+6QKf8rUgglbT1tO4xL5NRNTvydyx/tzlz5lSfr/z9pTzeusYQcl8J9iRzJVkgIV9MyBpHsq6ONJ2Q97N//34VfMsxkECITJ+TjVOy7VneszD//Hy0Ltpa1fI4WHPxYjW1reGnQKUewNYvgYvrgJhwYMc44PQSoPWPgGfm1OKRcWpSOi92fdIIU7ddxvITdxAbr8Gcfdex6tRdjGnJVtVkOgw22JGARb59lpNrHfnWXzp1ffHFF+pb6pTF5jopAx0hJ8hyoijTf+jVyEKZklGQaYBysipF+7JP1pFJT62INBCQTJvUakjwKlkYebx0OJOTdCGfpXQj27lzpwouJNiR9XxWrFihshZCgi1dECxBkNSLSOczGVPKxgivS07KpdubZE2kSYEEPpIhlBN1OdHOLOk5RvJ7L2sDSaCvy4BJpzIJAmVa35gxY5JloOR4SUc2CW4kcJIgUr6ZkemgWUUWXJXMmGSnUmukINPgdMFOWmRtIcl2yXTGdevWqd8VaVAxcuTITBw5GarwmHCs9l2duBBp0owPSdvKwsDbS4Bru4HNnwIBV4DHvsDSLkDJVkCL77TT34hkqrqTLX7oUhG9a3lgwnofHL8ZhIDwGHz23zksPXIb33YqD69Cepj+TwbHy8tLrfmnl3KQN5RN+k/DAEldjrQDlpOlpORkWL51lpMeKWZPL6lfkE5UkqUYO3bsK49HOnTJN+VSu5La1B1dq+GXnbgRZQXJSEk2RDJC5ox/l6bpUcQjTDw6UTUsKJGDJ+5piosBjs0G9vyoXZBUWFgB1QcADT8DsjMzSs/I6eD6s36YtEnbqlr82a+aygARGZKXnZMbTWZHpkmlViSt23f//v1Xej6pa5BMUdeuXV96X+n8JN9EJyXfqBMRkf5JK+rpjac/t//XU7+ikFMhdPLsxHV5hNTp1BmubVaw61vg9F9AQhxw9A/gzDJtwCOBD+t56OmXZB0q5UfzMnnx+95ruOIf9lygExufAGtLCx4vMioGG+zIYoEp104Ruuk7r7KYoHTskoUXpdYntbVHUpKpQOmpayB6GQmapXVyWmQaGutOiN7cqQenMO/cPGigwc3Qm/io6kc8rDpO7kDH34Aag7T1PDf3a1tVbx0LHJ+nndpWqrW22QGZPXsbS3zcvORztaqHrz3GJ/+ewRdtyqBNBXd+oUBGw2CDHSlMTq3DlhRp625PDylmljofqbfQFXW/jNQISDF0ysyOdCUjehXVq1dX3UvSInU1ujVziOj1RcdHI7d9boTEhKBzic48lKnJVwl4dz1waSOw/X9A4HUg8BqwrCdQtAHQcqJhrQtHepW082l8gkbV9NwLjsTQv0+hZlE3jGtfDmXzJ1/0mkyXt7e3WotQanaMbdkHgw12ZLqa1OykNr1NpGcxwTNnzqBDhw5qXp80PEi5OGNaZL2TpAsYEr0uaZ7woiykNEDIDAZaikeUaWrnr43VnVbj3KNz8HD2SHbbndA7KOScekMbsyMnsGXaASVaaLM6e38AokKAG/uAP+oDlXsDTf4HOLFOg5LrU9sDP229jKCIWBy9EYh2M/ajR43CGN28JHI6Pj8Th0wv2Ln1tPU0g50MIgdS1tORzl/SmldHuoLpbn8RWVle1hORoEXW13F05EJZlPWkbTQRZQ1nG2fULZD8b27/3f0YtmsYepTqgeGVh8PRhv8tUKROp/YQbavqPT9oAx9NvLZNtc9qba1P7aGAbfK232SeLC2y4Z2aHmhXIT9+3nkFiw/fUtmev4/exoYz9zGqWUkVDLGehwyRwVaZSSMBqXWYM2dO4j6Z1iatamvWrJnYdlpa6l66dCnZY/39/dGiRQu1SOXWrVtfulo8ERGZngRNAqacmKKu115bi4i4CH0PyfBIR7Y2k4EhR7StqYWsz7NnEvBrZeDoHG1XNyJZ6De7tZq+tmVkfdQvoV38OTQqDt9suIAOMw+qBgZEhsZgp7FJQCN1M9ImWrqjeXp6qvVEbt68qZoN6MiChXv37k02bUcyOtJyVhoSyKKXctHJmzevamtNRESmzSKbBX5u9DO+OfINmns0R57snJ6cptwlgV7Lget7gO1fA35ngCePgM1jgCO/aae2lesMWBjsd6SUhUrkdcLi92tg58WH+G7jBdx8HIF6njmZ2SGDZLDBjli8eLFayFIWmQwKClKLIcqCjw0aNHhprY6YPHlyqgXhDHaIiMxDMddiWNBygerSltTmG5txK/QW3iv/HmwtWW/w7IA1AgbuAS6sBnZ+AwTd1F7+6w8c+hVoNgEo3jjrP0gyyAYGzcrmRf2SubDk8C10q1boue5tJfM6sp6H9M6ggx1pMy2rycslLal1smJxNhERJT0pk//phMeE48djP+Jx1GMcun8Ii1otYhvdpCR7U74LULo9cGqRtqYnIkCb7VnSCSjWGGg2HshvXB2ZKHPYWlliQP3kC6qHRMTiw79OIj5eg2FNPNGvbhF1PyJ9YD6aiIjMyqPIR3Czd1M/dyvZjYHOi5oY1BgIjPQGGo0FdM0dru8G5jQEVr6vbV9NlMK6s/cRHBGLsOg4TNp8Cc2n7cPmc378Mpr0gsEOERGZlaIuRbGi3QpMbjAZ7Yq1S3ab90NvRMalf9FqsyAd2Rp9Dow4rV2Y1OLppJDz/wEzqwPrhgPBt/U9SjIgvWsWxoJ+1VE8t4Pavh0YgQ//OoW35xzBubsh+h4emRkGO0REZHasLKzQumjrZFmdgMgAfLjjQ3Rc0xEH7j1rbENPOeYB2kwBhh3XTnMTCXHAqcXAr1WAjaOB0Ps8XKT+rhqXzoMtoxpgQodycM1urY7KsRuB6PDbAYxecQYPQrWLxBNlNgY7RJSpPvroI1SpUgVubm7Inj07ypQpg/HjxyM8PDzdzyEdGOVxUsdXokQJzJgxI1PHTOZpxeUVCI8Nh98TP9XJjdLgVgz4f3vnAV7j9cfxb/aQRMRIzJgJYq/aoZQare1PjSptVXVoi1aXotWqlk4tqmhLFbWrtqK0GrUqhIgVQRIjksiQcf/P97zuzb03NxIh6+b3eZ7j3px3nfe8R3K+72+c/t8Do3cDft20uvQUba2ezxsBmyYB8VHSfYLKzvZk66rYNb4jRrWtBgc7GzB57q8HL2LXqWjpISFfKNQJCgRBKPoEBQWhXbt2eOqpp5RYOXToED766CNs27YNu3fvVuth3Y25c+fiueeeQ79+/fDqq69iz549eOmll5CQkIDXX3893+5DsH5GNxit0lPTla11hdYm2xJSEuDq4FpgbSuUlG8IPLEMuPgvsPMDIGw7kJYM/D0H+HeRFu/TZpy2lo+A4r4+zzs962JoS19M33gCl2IS0b9JpYJullBMELEj5Bm3bt1CiRKav65QfDFe50pPjRo1MH78ePzzzz9o2bJllscmJibirbfeQo8ePbBy5UpV98wzzyA9PR3Tpk3Ds88+i1KlSuVp+4Xig52tHfr79VfFGKaoHrxhMIbUHYKnAp4S0WNOpabAsFXAhb+BHe8D5/YAKQnA3s+BoAVAy+eBVmMBF898fJpCYaRamRKYP7wZ4pNTYWub4UIaciUWb676D2/1qIOmviKOhQeL2OmFHBEREYFRo0ahQoUKcHJyQrVq1TBmzBjcvq2trL1o0SLlo8sFXp9//nmUK1cOlSplvLX5/fff1dt9ih93d3c1eQ0ODja5xpUrV9Tbfx7Ha5QvXx69evVSC8nqOXDgALp27YoyZcrAxcVFtWPkyJGG7dyX7fjkk0/w9ddfo3r16sp1qkuXLggPD1eZYDhJ5jV4PM9//fp1k3asXbtWtU9/r5yY85i0tDST/Tp06IB69erh+PHj6Nixo7pOxYoVLa7vlBOy66MdO3YoK8i7775rctzSpUvVPX/zzTeGOv78wgsvYMmSJfD391cWlaZNmypLSmGgatWq6jMmJuau++3cuRPXrl1TY8qYsWPHKjH922+/5Wk7BYF8euBTxKXEYe6RuTgXm/H7SDCjSktgxAbgyfVA5TsvMW7HA7s/Bj5roKWwTrwh3SbAzSnjXTv/Lr+/4QQOXohBv2/+wtilBxF+PUF6SXhgiGVHyJZLly6hRYsWamLKN+m1a9dW4odv2ulK5OjoaNiXk9KyZcuqCTkno4SLwj755JNKpMyYMUMdw4l527ZtlUuTfuJLNyVO7l988UVVFxUVha1bt+LChQuGnylaeP433ngDnp6eStysWrUqU5s5yacQ47koZihABg4ciIcfflitzUT3p9OnT6vYD1oYvv/+e8OxFG5ubm7KZYqfFBm8n9jY2ExrPnGx20cffRR9+/ZV52ef8Nz169dHt253fNlzQE76iG1n/3744Yfo3bu3ioO5fPmyusfOnTsrVy9jKDx/+eUX5fJF0TZnzhzVVlpTKNLuxtWrV3PUbooynjs7UlNT1fjhMzl27BjefvttdSzH1d3gvZNmzZqZ1FO4Ufhx+9ChQ3PUVkHILcPrDseVW1fg7+WPuqXrSkdmR7X2wMh2mlvbjg+ASweB5JvAHx8C+74CWjwNtBwLuJWVvhSQlq5Do8qeCDp3Hcmp6fjt6GVsPR6pYnye71AD7s5acgNByDU6IUccO3aMy2+rT0uEhYWpYo0MHz5cZ2trqwsKCsq0LT09XX0uXLhQ9U/btm11qamphu1xcXE6T09P3TPPPGNy3JUrV3QlS5Y01N+4cUMdP3PmzCzbsXr1arWPpXboOXv2rNqnbNmyupiYGEP9pEmTVH3Dhg11KSkphvrBgwfrHB0ddUlJSYa6hISETOcdPXq0ztXV1WS/wMBAdc4ffvjBUJecnKzz8fHR9evXT5dTctpH5NatW7qaNWvqAgICVFt69Oih8/Dw0J0/f97kWLaL5cCBA4Y67uPs7Kzr06dPtm3SH59d4XPPCX/99ZfJcf7+/rqdO3dme9zYsWN1dnZ2FrfxGQ8aNOiux1vz/0shf0lLT9Pdun3LpO5Q5CHdiN9H6I5EHZHHkRX8GxGyUaf7tr1ON9kjo0zz1ul+f0Onu3lJ+k5QXLyRoHtx6UGd7+sbDKXptC26n/4+p0tJTZNeKmAWLlyoe++993L8d78g5+TmiGUnn1hzeg3Wnl6rvi98dKHJtgm7JqiUp20qtsHT9Z821IdcD1GrfJPXW7yO2l61Ddu+++877I3YizIuZTAz0NTa8NSmp9Rnr5q90Ltm7/tqN2Mj1qxZg8ceeyzT23VinLZVH09hZ5exSjItM3yjP3jwYBNrAfd56KGHlJsSoUsZLUS0utBdzlIcBi05ZMOGDWjYsCEcHLJ+2zNgwACULFnS8DOvRWgFsLe3N6n/+eeflaWKLm/6tuiJi4tDcnKyci9joHxISIi6th5afowtC7wHWivOnMn5Qns57SNCVzlantq3b68KrTTMVFalSpVM523VqpWygOjhPnTbW79+vXLJM35OltqUEwICAnK0X926ddU5ae3bt2+fSk6Qk2xsjNkxthwaQ9c8bheE/IDZ2YwTFPCdwKx/Z+FQ1CGM3DwS2/pvg6ezxKRkgn8j/LsBfo8Cp7drLm3h+wGuZcREBszg1ngY0OZloJSvDOZiTEVPF3wxuLHK3jZtw3EcDo/B1fjbeGv1MSzedw5rx7aFi2PWf7eEvKVRo0bKy0Q/FytKiNjJJy7FX8KByAMWtx2NPopLty6hglsFk/q423GGY/jdmLM3z6ptFUqYHkP0xzT3aX7f7Y6OjlbuW9m5PelhDI0xoaGh6pMuWJbw8PBQn3SFovvWa6+9Bm9vbxW03rNnTwwfPhw+Pj5qn8DAQOXqNmXKFMyePVvFzNCd64knnsjkSmU++dcLn8qVK1uspzuaHrrS0c2K7mu8d2Nu3jRdDI2xP+aCj0Lt6NGjyCk57SM9bdq0UfFSjEmi25txzJIxTNFsjp+fn3KR43PV96sl6Bb3IOE96M9JwcU4I34ePHjQRDyaQ+GpjwszJykpyUSYCkJ+kpqeigZlGuDY1WN4ovYTInSyg78na3UGanYCzv0J7J4JnN0FpN0GDiwADi4GGgwC2r0KlK6RL89QKJw09S2F1c+3xrojl/DxppOIiElEQIWSInQKgdgpqojYyScoZJp5Z7aMkAZlG6jtXNXbGHdHd8Mx/G4M9+U2WnbM0R9jLp7yA/PJJy1D+pgUS5NrYyvLuHHjlAWJlqTNmzfjnXfeUfEpFB2NGzdWooIxMX///beyTnAfTvQ//fRTVUcri56srBZZ1WueW1rAPEUVJ+dTp05VyQloQeCknLE4+vvJ6flywr30EaGliRYwEhYWpsQLLT4PEiaLyAkUi7kRHIxxGjZsGJYtW3ZXscMkFbRCMV6LSS/0UAAxcQGTSAhCQeBg54DxzcfjiTpPoISDadbJ7ee3IygySFnqLf2ORnEXPdXaaSX8H030hG7RFic9/BNwZCkQ0EdLWV2+QUG3Vigg+Pe+V6OK6Brgo6w6PRqUN9l+4Nx1lPd0UdYgQcgOETv5BN3JsnIpM3dD00O3NXOXNz38I2rs8mZMVsfkBiYD4MSfQeW5gWKBcKKaE2sB96d1h4UWD75JoJj56aefDPvQ6sPywQcfKAvBkCFD1KT56act98e9QBHBSTSTHtBNTM/Zs2eRV9xrH02ePBknTpxQGecowJis4YsvvsjSYmTMqVOnlDDic70bFBk5YeHChRgxYgTuFQo2ijxzS1lWb5KYha979+6Gev7M44vymybBOjB/qZSWnobPD32urO87LuzA731/VymtBQtUbgEMWQFcOgzs+QQ4sR7QpQPHftVK9Y5Am5e0TzMLulA8cHaww+hAU0tfUkoaXl52GNHxyXiqTVU836EmSrpIEgMha0TsCHeFGa/oKkaxwQmmedwOLRjmblzG0M2KYmn69OkqPbN5nA3dqTjxpnWC16IVxVgEMGMXJ8Z6VzP6ihpfTz/Z1e9zv+gtNcaWGVoRmMksr8hpH5H9+/crkUMrGAUhY3zo/kf3PlqkjPnrr7+URYpZ2whTbzOtNjOy3S1e50HG7NBSxlTa5vf03XffqU/j8cQxwMx7TCvOonft8/LyUpnpjMUOf6ZoY3puQShMxCTHwNvVW4mdIXWGiNDJCRUaAf/7CYg8Dvw5Czi2CtClAWd2asWnPtD6Zc3iYyfTluLO7lPRyrWNzN11BsuDwvFSp1oY8pAvHO1lRZW84vDhw+pvOudhRe1Fo/zWELKFk/AtW7aoyTRTT9epU0elPF6xYoVaMPJuwWqcxHNiSpclTroHDRqkJu6c1HKNFMaffPXVV8ri0KlTJ5W+mcHsdN1avXo1IiMj1TFk8eLFSnT06dNHCSEmD5g/f766hvFE+H5o3bq1irlhGmimbKawonvZvbil3Ss57SPGqLBdjMWhVYswfokufVyf6L///jNZxJVxVhRSxqmn9cdkx4OK2aGljNfv37+/ajeF4549e5TljELHOLkDky1Q7NFy9d5776k6ushxjSOuq8OkE7wfHk/xzT6gEBKEwkRpl9KY32U+gq4EoX6Z+ibblp9cjujEaAytMxQlnTISqAh38K4L9PsOePgdLXnBwR+0xUmv/AesehrYPhVo9byW0MApw21ZKF50CfDBr2Na4YPftLV5biSkYMr648rd7fVHa+PRej53fQkr5F7snD9/Hr6+viJ2BOuDC2XSosAYGq5fw6B91nEdmZzEijCBAGMrPvroI7VODa0wPJ4ZzjhJ1ycOYDay7du3K3FBscP1fJYvX66sFoRiixNiuqxRBDFehJnP2CbzxAi5pXTp0irbG60mTFJA4cMJOYUYJ9p5RU766M0331RrAzGbmd4CxkxlFIF065swYYKJBYr9xYxsFDcUThSRzOTWoEH++cFzvSEKGFqUKJApGilUuW4R25tVpjVjuLYQLUN0Z1y3bp0aK0xQ8fLLL+fLPQhCbjBPEJOUmoQ5h+fgWtI1/HnxT/zc82fp2KxgVrZuM4DA14GgBcA/c4Fb0cDNC8CmN7TFSZs/DTw0GnDLiOUTig9Nfb3w65jW2HTsCj7aFILz1xJw7loCxiw5qBIcvNWjDppUyZzVVSie2DD/dEE3oijADF18U87YFUuuO/pUw/r0xYJQkPCtFq0htAgVZ+T/pVBYCI8Lx8RdE3Hs2jFMbzsdj9V4rKCbVHRISQSOLAP2fQlcD8uot3MCGv4PeOg5wDtnafAF6+N2ajqW7D+PL7aHKisPeaNbbTxnFusjFJ85uTnixiYIgiAIeUxl98pY2mMp9l/ZnykzJy0+N5Nv4smAJwski2ahx8EFaPYU0GQ4cHIjsPdz4GIQkJasubqxVG0HtByjrecjCSGKFYzTeapNNfRtUglz/jiNrcGRGNG6aiZBJPE8xRcRO4KQhzC5AFMnZwXduCTuRBCKj8W1ZfmWJnU3km5gUfAiJKYm4szNMyreR8gCipg6jwG1ewIX/gb++goI+Y0pZYBze7Ti6Qu0eBZoPBRwKXqLHwq5hxnZJnWrg/Fd/OFgl5Go4FJMInp/vRdPt6uG4a2qqgxvQvFC0lYIQh7SvHlzlcY5q8L1ZgRBKL5wwejG5Rqr7yMCRmSK8xFPcwsw+Ny3FTBoCfDyYaDVC4A+4UPMeWDLW8CsOsCGV4HoU/nwFIXChLHQIZ9sOYmouGRM3xiCTp/uwtrDEUhPlwiO4oRYdgQhD2HyhMRELUWmJZgAIS+QCZIgFA2qeFTB3EfmIvRGKGp61jTZNnnfZFyMv4in6z2NjlU6FlgbCzWlqgJdPwA6TAKO/AzsnwtcC9WyuB1YoJUaDwMPjQFqduZ6CgXdYiGf6VG/PI6ExyAs+pZKWc01er7bc1bF9bSpKYv+5hQmONJnY8vN+noFiYgdQchDmDZaEAQhO2qVqmXyc0R8BDaf24w0XRo2n98sYic7mIq6xTNAs1HAmR3A398Cp++sFxa2Qyt0cWs6QnNxkyxuxYZOdbwR6FcWy4LC8dm2U7gafxv/RdzEkO/2o71fWbz+qD8CKkgqeGtGXnEIgiAIQiGjhH0JjKw3Eu6O7hhWd5jJNlqBKIYEC9ByQwvO0JXAC/9q8TuObhkubtunALPqAiueAs7uphlcurEYYG9ni6EtffHHhI5qAVJXRzvDAqU9vvgTIVdiC7qJQh4iYkcQBEEQChmezp54qclL2DFgBwJKm6ZWnfHPDHRf1R3v7H2nwNpXJChTE+g+E3j1ONBtJlC2jlafngIErwIWPwZ81Rz462sg4XpBt1bIB9yc7PHqI374Y0IHDGvpC3tbG7StWQa1fTyk/60YcWMTBEEQhEKKs722gLCe8Nhwlb6auDncsVgI2XRiSeChZzU3t/D9wIHvgeA1WupqxvdsfhPYNgWo1xdo+hRQuYWWBEGwWsq5O2Na73oY2bYa0sySFewLu4p/z93AqHbV4Ooo02RrQCw7giAIglBEqOxRGSseW4HHazyOIXWGmGxbH7YeH/3zEc7ePFtg7SvUUMBUaQn0nQe8FgJ0+QDwurPwJIUPExx83wX4prVm7bl1taBbLOQx1cqUQM1yGS8NmKVt+sYT+HTrKQTO/EMtVpqSli7PoYgjYkcQBEEQihC1vWrjg7YfoJJ7JZMMjFyvZ8mJJRi9dTTSdTJBuyuuXkDrF4AX/wWGrwPq9gZs77zFjzquWXs+9QeWDQFCNgJpKXn8VIXCwNVbydBrm+i4ZLy1+hi6zt6N3/+7LFlOizAidgRBEAShiBOfEg8vZy/1vb9ff9ja2JosXEr3NyELa0/1QGDgYuCV40CndwGv6tq29FQgZAOwbLCW1GDzW0DUCelGK3dv++3Ftvjsf41QqZSLqjtz9RbGLDmI3nP24e8z1wq6iUIuELEjCIIgCEUcZm2b32U+1vVeh4F+A022rTi1At1Xd8eozaPUIqZCVp3oDbR7DXjxIPDUJi1FtT6T260o4K+vgDktgfkPA0ELgMQY6UorxNbWBr0bV8T21wLxTs+6KOXqoOq5Vs+geX/juR//FStPEUPEjmD1dO/eHc888wwKM/Hx8Xj66afh4+MDGxsbjBs3TtVHRkaif//+KF26tKr/7LPPsGnTJri5uSE6Orqgmy0IQiGjWslqKpObsXvbqtBV6ntMcowkNciptce3FdDra+C1k0CvOYCv0ZppEf8Cv72qubmtGKG5uaXefvAPUyhQnOztMKptNeya2BEvdKwJZwdtykyLD/8eC0UHSTMhFGrOnTuHatWqYebMmRg/fvw9H793715s2bIFISEhyCuWLl2KqKgog0DJDdOnT1erE7/zzjuoUaMG6tTRUqS+8sor2Lx5MyZPnqyEULNmzVC7dm3UrFkTH374IWbNmvUA70QQBGuDk7KvO32NX0N/hV8pP5NJ2pmYM5iwe4JKdtC7Zm+UdJKFFS0uVtp4iFaunwEOLwUO/wzEXgRSk4Dg1VqhwAzoDdQfAFRpra33I1gFHs4OGN/VH8Na+WLOztMY27GmyfZ9p6/Cz8cdZdycCqyNwt0RsSNYNRRJnTp1UuIgL8XOsWPH7kvs7NixAy1btlSixry+V69emYTe6NGjVd2UKVPg7u6e6+sKgmD91PCsgYnNJ2aqXxu2FqdunMInBz5Bx8odRexkB2N5Hn4b6DAJOLtLEz0hvwEpt4CkGODfRVrxqAjU66cJH5/6ksbaSvD2cMaUXvVM6m4mpqh4HmZsoxXomfbVlTgSChfy6kHIFbdu3Sr0PUdry2+//YaBA0391wtrWz09PXNc369fPyQnJ2PFihX51EJBEKyNim4V4evhi8blGqOKRxUT17dPgj7B3oi9SOECnIIptnZAjYeBfvOBCaFAvwWA36MZ2dxiI4B9XwBz22kxPrtnAjfOSS9aIczSRsGTcDsNX+44jfYf78TcXWFISkkr6KYJRojYEbLlvffeU64Px48fxxNPPIFSpUqhbdu2hb7nKHRSU1PRuXPnTNtiYmKUi1jVqlXh5OSESpUqYfjw4bh6VVtXgS5lvGe60Rnzxx9/qHp+kg4dOqjrnD9/XtWz8JzGYmXUqFHw9vaGs7MzGjZsiMWLF2c639mzZ9V59OfQX5+Tjq+//tpQr6dcuXJo0KAB1q5dmyd9JwiC9TPQfyDW916PLzp+YVIffC0Yi48vxnPbnsPyk8sLrH1FAscSQP3+wBO/AK+dAnrMAqq0ytgeHQLseB/4vCEwrwPw52zNHU6wCga1qIKfRj2EhpU0F9CYhBR8+HsIAmfulDV6ChHixibkmAEDBqBWrVoqvoST8KxIT0/H9evXc3TOkiVLwsEhb0y++/btU4H9vr6+mZIBtGvXDidOnMDIkSPRpEkTJXLWrVuHixcvokyZMjm+xltvvYWbN2+q42bPnq3qmDyAJCYmKjF0+vRpvPDCCyr2iJaYESNGKLH18ssvq9icH3/8UQkvCq7XXntNHdu4cWNVP2zYMDzyyCNKiJnTtGlTrFmz5j57SRCE4gxfohgnNCChN0LhaOuorDqdq5i+LNp5YSe8XLzQoEwDCdI2p0RpoPkorcRcAP5bqZWoYG37pUNa2fae5t5Wt5e2vk+ZWnn7kIU8pW2tMmhTsw02B1/BJ1tO4XRUPCJjtTV65u8+ozK6darjLU+hABGxI+QYWiUYn5IdFy5cUBP7nLBz504lCPICJiUwtrIYx/EwxmbVqlXo06ePof7tt9++53SSFCIVK1bEjRs3MHToUJNt8+bNU4Lqp59+wpAh2krnzz33HAIDA9W1KLRo8eFx/JnnMT5HQECAEjt+fn6Zzk2qV6+uRBqtR7T0CIIgPAj61OqDzr6dcSjqELxLZEzSuFDp9H+m48qtK+hatSs+CfxEOjwrPKsA7V7VSmTwnUQGa4Brodr2K/9phVafcnXvCJ9eQDktOY1Q9F4aPFqvPB6p64NVBy/is22hiIhJxLlrCcrNTShYROzkE4cPH1YltzRq1EgVS+ejpcAYukDl5Bz3CifqOYFZw7Zu3ZpjAZVXXLt2TQkIc3799Vd1XWOho+dBppPcuHGj6ovBgwcb6mjFeumll1Tdrl270LNnz1yfn+6EhIJHxI4gCA963Z72ldqb1J2OOY3IW5Hqe3Pv5ibbwmLCEJkQqeod7CRA2wTvAK10fEtzazu+VitRx7Xt/GT540OgjB9Q53HAvztQobFkdSti2NnaYECzyni8UQUs3X8BW49Holcj03lIWHQ8apS9s35TEWKE2VyzKCFiJ5+g2xLjOnKLuYXibufLqt6SleNeyKm1hrEpluJkCgJLlpqwsDAV4J/X8DnQ7c/WLAWpPq30/YwH43uTfP+CIOQHTF29fcB2bDm/BY9UfcRk288hP+OXk7/Ay9kLW/tvhaOdozwUc/gyjZYblg5vANGngBN3hA+tPOTqKWDPJ1px8wb8ugJ+3YDqHQBHV+nTIrRGz1NtqqlizInLsej+xR4E+pXFhK7+CKgg6d7zAxE7+QQzapnHjtzr8Tk9X1b1lrJ63QsuLi452i8tLS3HC156eXnB0TFv/igyXofuZbkhKwHBeyss6O/tXmKMBEEQ7oeyrmUxpI7mlmv84uXPiD/Vd/9S/iZCh3E/a0+vRWClQHWsYNyZfkDZCUD7CcC1MODEOk34MK6HxEcCB3/Qir2zJnj8u2mZ39x9pCuLIJ9vCwXfU/5xMlqVng3K47Uu/qhWpkRBN82qEbGTT9yvC9m9nK+gTY3h4eGFImaHi2/SZc0cLtrJmJ2cuIjRgmaMJWtMVsKIovPo0aMqYYOxdUe/wOn9iF/CDG4UOmXLygRCEISCg78Dlz+2HHsu7oGnk+lLtX8j/8WUv6ao7992/hZtKrYpoFYWckrXANq+opXYS8CpTcDJTcCZP4C0ZG0BU9axkApNNOFTqwvg00Dc3YoI7z0eAC83R/wSFI60dB02HL2M349dwYCmlfBip1qo6Jmzl8rCvSFiR3jgFJaYnVatWuG7777DmTNnVDC/HrqwTZ06FatXr84Ut8M3lPzDTUFEdu/ebRCVtOow6YA5JUqUUBnZzOnevTu2bNmCX375xRC3w1TYX375pcrYxkQF98O///6r7lEQBKGg8XD0QI/qPTLV77u0T33a2tiiXhnTBRkXBy9GCYcSaFuxLXxKiKUiozMrAM1GauX2LU3wnNwInNoM3LrjNXHpoFZ2fgC4ltHW/anZWft0kxdghRWfks6Y3qc+nmlXHbO3nsK6I5eU6FkWFI5VByMwuEVljH24Jsq5O6OwsWjRIvXCly9qC/ql+r0iYkd44ORFzM727duRlJSUqb53796oV8/0D6ieHj16wN7eHtu2bcOzzz5rqJ8wYQJWrlypUmkzIxpTODNVNlNPf/vtt0qAMRNay5YtMWnSJLWN7nbLli1TYsUcHk9B8+qrr6J58+ZKyDz22GPqmnPnzlW/FChMGDPF6+7duxefffYZ3N3dc90fzMBGq9HYsWNzfQ5BEIS8ZlyTcehUpRNCroWgpFNGfEJqeirmHpmLuJQ4dKvaDR8HfiwPI6t1fGr30Ep6OhDx7x3hsykjwUHCVeC/5VohtPTU7KSJn0otAHuJnyps0G3ti8GN8VxgDczaegrbTkTidlo6Fv91Xq3dUxjFTlFGxI5QJNi0aZMq5lBAZCV2mNaZ1pXly5ebiB2KkT179mDy5MnKusNFPpnNrFOnTmqtGz1LlizB6NGj8dFHH6l4Jy4O2rFjR5Vu2pjnn39eZcZbuHChWmuHbz0odhjjxEVD33jjDXWN2NhY+Pv7q/3u960I02ZzMdSBAwfe13kEQRDyElp0GpZtqIoxEfERsLfVpiDmrm0bz2zEytCVaO7THE/WfRKuDhKYr3WmLVC5uVY6TwZunAfCtgOntwNndwPJsdp+V45qhQuYOroB1dprFp9qgdqaPg8w66hwf9St4IHvnmyGw+Ex+HTLSZR0cUCd8h6G7bT6xCenqvqCplGjRmrOdb/x3wWBje5eFxYppgQHB6tJNWM9+NbfHLpKEWN3KaHgoahhTBDjZJgZzVrgoqO8L/1CpoJl5P+lIBReuG7PiWsnUMm9konV560/38K6sHXKxe3PQX8aRBHh/jU9a0p6a3PSUoCLQZrwoQDSJzkwx80HqNpWE0DV2gGlqon4KUQkp6apTG56Vh+6iMlrg/Fs++oY0aYa3JzERpGTObk50muCVdOuXTt06dIFH3/8MebPnw9rgBau0NBQbN68uaCbIgiCcF9Wn4AymScqldwqoXrJ6koEGQuduNtxGPTbIDjZOWFC8wkY4DdAel8P1zbyba2VTu8At64CYTuB09uAsB3ArShtv/grwLGVWiEelTTRU7Wd9snFUIUCw1jopKSlq8VJY5NS8cmWU/h+7zmMCayBoS194eKYsZ+QPWLZySFi2RGEoodYdgSh6JKSlmJiwdl9cTfGbtfiFOd0moN2ldoZtq05vQYnr59Ew3IN0dW3q6w/ZgxjfaJPAGf3AOdY/gSSTDONGihVFfBtC1R5CKjcUtzeChA6Xm0/EYVPt55S6/PoKevuhBc61sSgFpVNxFFxIlgsO4IgCIIgFHWMhQ5pUKYBPmr3EYKuBKGJdxOTbZvObcLeiL3YE7EHj1Z91GTCGHwtWK3/Y36+YhXr4x2glZbPAelpQOQxI/GzF7gdp+1745xWDv+k/exaGqhM4fMQUKUlUKExYO9UoLdTXGBm2M51vfFw7XIqPfXsbadwOioe0XHJmLwuGHN3heGlTrXQr2klONiZLl6eFxw+fFgtx8GYnQe5lEp+IG5sgiAIgiAUejydPVV6a0sprgld3swTIZyPPY/Bvw1Wrm8z2s9QmeGKPbZ2QPmGWmn9ApCWClw+ApzbrQmg8H8yxE/CNS37Gwuxc9IEj97yU6kZ4Fau2HdpXmJra4MeDcrj0Xo+WHckArO3huLC9QRcupmED38PQfcG5fNN7Jy/k3paxI4gCIIgCEI+wgVLk9OSEX873qT+cPRh9cltFUpUMNn24f4P8d/V/1TWt1eavlJ8n5edPVCpqVa4qCktP0xrfeFvIHy/9nkzXNuXC5yGs/5vAJ9rdSUrawKoYhOgYlOgfCPAOSOjmPCAHpOtDfo0roSeDSpg5b8X8eX2UAxt5QsP5wyLZeLtNDja26p9hQzEsvMAkcR2glC40C8SKwiC9UPrjZOLqYsVFyz9sN2H+C/6P5XFzZhDUYdw4voJONubrmlyM/kmPj3wKWqVqoWOlTuqRAnFzvLjU18rLZ7R6m5GaALnAsXPX5obnC79zrZwrZxYd+cENlqsD4VPBQqgJoB3PcBB1o55ENCKM7hFFfRtUhHm+ZSZvnrXqWjl3tajfnllFRJE7DwwbG1tcfv2bZlcCUIhEjppaWlwcCimfvqCIKCMSxn0rN5TFXNa+LRQAqlxucYm9adunMLq06vVd18PXxOxwyQJ1xKvwd/LH3VL1y0+PVyyIlCyH1Cvn/Zzcpy2wGnEQeDSQe0zNuLOzjrg6imtHPlZq2JWvTL+GSLKp562+KmrV4HdUlHHPDlBVFwSftp/Hkkp6Xjx50P4ckcoXu7kh271fIq96BHLzoMadE5OSExMVCvbc4FKeZssCAVHamqq+r9IsVOqVCl5FIIgZGJ88/EWeyUmOQYejh6IvR2LWp6m67MtP7kcuy7uQlWPqljfZ72hPiU9BatDV6NayWrwK+Vnsm6QVeLkDlTvoBU9cZF3hI+RCEq8oW1LTwWigrVydFnGMR4VNfFDy49eCHHtHyZVEO4JrsHzSmc/zN19Btdv3capyHiMXXoQ/t7ueLlzLTwaUHxFj4idB4S3tzeSk5Nx/fp13Lx5E3Z2diJ4BKEArDnp6elK7BBXV1cRO4Ig3BOP+D6CzlU6IyohCuVcy2VKeEDo4mbMxbiLmPb3NPV9auup6FOrj2Hb0eijiEyIRGX3yqjtVdt6n4a7N+DfTSuEPlbM7EbxwwQIdH27fBRIuJpxDK1BLKc2ZdTZuwBl/YCydYCy/kDZ2ton02LTxU6wiKujPUbfWYfnh7/OY97uMNxISMHJyDg8v+Qgavu4Y1znWuga4FPs5qcidh6gG1uVKlUQGRmpRA8nXIIg5C/8BW5vbw8XFxd4eHjA3d292P1SFwTh/uHvDe8S3pnqV/dajQuxF6Cjq5YRZ2+eNXyndceYVaGr8Gvor/By9sKu/+0y2fbtkW/h7eqt1gfiQqpWBX/3elXTSv3+GQIoPhK48p9puXZac38jqYmaOGIxhrFVjAXSix+6xZWuqZ3fwSX/76+QUsLJHmM61MCwVr5YvO8c5u85g5iEFIRcicM3f4QpsVPcELHzgAVP+fLlH+QpBUEQBEEoJDC9dXXPzKIksFIgfu/7O87FnlNubJaEUCU300QHdJP7+vDX6vu4JuNQvX51k9igzec2w6eED0bVGwVXB1dYjQBy99FKrUcy6m/fAqJOAFeOAlEhQDTLSSD+SsY+qUkZ4sj0pJo7XOnqgFcNoHSNjE9ag4rpukB0axvbsSaebF1ViZ55u89gXGc/kxeAMQm3UdLFwepfCorYEQRBEARBuA/sbO1UIgNLmdu+6vSVsgal6jT3Wj1Xbl2BrY0t0nXpmY47En0E68LWwQY2eK7hcybbhmwcgqTUJLV46jMNnjERT7xOWZeyKjED21RkcCyhrdnDYgxjfih69OKHgoifcZeMdtIBsRe1cna36fE2tkDJSoBXdS1Ftqcv4FnlTqkMuJe3etc4NyPRU8LR9F7p3nYzMUWJoM51rDfevFCLHbqDvfvuu/jxxx9x48YNNGjQAO+//z4eecTobUAWRERE4JVXXsGWLVuUS1nHjh0xe/ZsVK9uZWZiQRAEQRAKLe6O7ggoE5CpnhagA0MP4Er8FbVgqrkFqZxLOeUx4mDrYBKXGHojFImpiSqbnDGHow5j7Pax6vtP3X8yWWCViRUuxl9ENY9qJvFEFFo8l6u9a+Gc6LqUAqq01IoxSTeBq6HAtTDgepjR5xkg+WbGfkyPHXNBK5ZgljiKIYqfkndEEDPPKetTBcCjPMBnUxj7Jheix5j9Z65hX9g19f2ZHw6gfsWSKqbn4drWJ3oKtdgZMWIEVq5ciXHjxqFWrVpYtGgRunfvjp07d6Jt27ZZHhcfH6/EDRMFvPnmmyr1LIVOYGCgWgG2dOnS+XofgiAIgiAI5lDIVPaonKl+TMMxqqRxgU8jUtNT8XiNx5VVyDz1NRMq6KF1x5gt57Zg/5X9SgAZi53rSdfRcXlHONo64u2Wb5tsC7oShH8j/1WZ6f7n/z8TSxHTb7vYu6hSIBNj55KWLUGMCUq4biaAwoCY85rguRVtuj+zxDGJAktWMGECxY9HBc0SZPjuA5QoB5QoqxUKsyKURa52eQ8lbhb8eRZxSan4L+ImRi3WRA/X6bEmS0+hFTv//PMPli1bhpkzZ2L8eC095PDhw1GvXj1MnDgR+/bty/LYOXPmIDQ0VJ2jefPmqq5bt27q2E8//RTTp0/Pt/sQBEEQBEHIDeauaA52DkqUWIILqH7e8XNcTbyaSewwrIXWolLOpqn4byRpqaFvp9+Gi1mQ/96IvVhwbIE6bnDtwSbbBq4fiKjEKPSt1RdTWk8x1IdcD8H8o/OVNevp+k+buOdxEVeKq9LOpdGoXCMTaxVd/IwtWLmGk/MSpbVS2dTypd1ogrYAqt7aY15uZQhGA0yYcOOsVu56bTvAtfQd8VMmQwTxO+tdPDUrEUWR/ruTR4EJpJIuDsp97ak21fD9n2dViUvWRA8tPQEVPFQq6851MyfqKGoUWrFDiw7TNz/77LOGOmdnZ4waNUpZa8LDw1G5cuUsj6XI0QsdUrt2bXTq1AnLly8XsSMIgiAIglXBZAYslviuy3dKVFDUGEPx80rTVxCTFJNpTaH4lHj1ScuO+Rv+uJQ49enm4GZSHxEXgS3nt6jvg2oPMtm26Ngi7AjfodJ2r3p8laE+ITUBLZe2VGJnfLPxeKLOE4Ztm85uwrKTy+Bs54xZHWaZJGpYHLwYN5Nvws/LT8UvGccuBV0OUsIwoHQASrtkePNEpsQh3sEejj61Udk4QQI941KTYJ+WArv4KNjEXQHiLmsl9rLR90sAt6UlZ+5kXZomliwJpqxgTBGtVMYiiGsYOboDTm5aLJOj2506tzt1+p9LaBnqKFKZhMH+zuc9WmNKujjglUf8MLJNNSzYexYL92qWnuBLsdh2IlLETl5y6NAh+Pn5qfSxxrRooSl1uqNZEjuMzzl69ChGjhyZaRuPZQxPXFycSkkrCIIgCIJQHKBgcbIzzUzGRAYj62WeLxFakCY2n4iElASTeoomipK423GZYpFoieKCq9xWwqGEybZbqbfUJ+ODTOpTbhkWZqUVyRjGGdGVjjCZgzFM581Md118u5iInfM3z2PcH+PU9zmd5qBdpXaGbfOOzsPyU8stpgFvu6wtktOS8VTAU3i12auG+u0XtmPqX1Nh52aHRf03oIp7ZS1xQtxlvHf4CxyOCYW/gydmlGysucnduoqbtyLxkk0UbNNS8OSNGHRITDScb6urC9a5laCxDZ9GXYUDz8Vy4yw+9CqFaHs7NElKxtBYTVCSc/b2mFNKW6h2VEws/FNSDNvWuJVAkLMTPNLT8fr1GE0A3RFBU90dkG5rj3bpjugEVy1GydYex2xTscYmQYmt5+y8UcbOCSVt7fCqrT3K+EXhcEI8bOJs8LauLrDeQRNQ19wKs43krhTaVl++fNliGmd93aVLxpk4MuCinkxskN2x/v7+WV6bK69HR5v6dR4/flx9nj7NXPCCIAiCIAjFgwhEmPwcgACA2uM6EHw92FBfFmXxkd9H6nvshVgEI2PbkFJD8HiJx5VoCQ4ONrEg9XPvpxIlOEY7Ijg1Y1vyxWTUSKqBlLQUhIaEmgieuAtxuB1/G3G2cSbnO371OJIiktT3C6EXEHwzY9ulsEtqW5JTkskxqh0X4pGGNES7RiPYJWNbSHiIOk59Px6COPcMEXLs/DUEXw1HailXBPv3NdRfTbiKfVufVt8b1HsNZcs1BZLjgORY7Avfhk2RWijGyEpD4MC029yWFIvf4y7isk0aYpKd0PhmKkChqUvHCUdbrL7j0tjgmg6pSRmxXFtSbLC1hD28UtPQM4r1FI+agFxa0QdpNjZIio2Hj5F42u3ijMWlNZfGFpePomJaxvnWlfHCEWcn+CffxvnQIKMx0BVRNj5qLTvzvstv9HNxzvdzhK6QUr16dV23bt0y1YeFhXHVKd3s2bMtHnfhwgW1fcaMGZm2LViwQG07dOjQXa89efJktZ8U6QMZAzIGZAzIGJAxIGNAxoCMARkDKHR9sGbNmhxpikJr2eEK6JYUW1JSkmF7VseR3Byr5/nnn8eAAQNM6ug2N3ToUBXzU7euaQYUQcjqzUPv3r2xZs0a1KxZUzpJyBYZM8K9ImNGyA0yboSiPGY4x2fsPrMs54RCK3bocsa1ciy5t5EKFSpYPM7LywtOTk6G/e7lWD3lypVTxRIUOgEBmfPlC0JW8JeCjBnhXpAxI9wrMmaE3CDjRiiqY6ZJkyY53rfQJgRv1KgRTp06hdjYWJP6/fv3G7Zbggtw1a9fHwcOHMi0jcdyUVFJTiAIgiAIgiAI1k+hFTv9+/dHWloa5s2bZ2K2WrhwIR566CFDJrYLFy4gJCQk07FBQUEmgufkyZPYsWNHJvc0QRAEQRAEQRCsk0LrxkZBQ2EyadIklR2NZrPFixfj3LlzWLBggWE/LjS6a9culQrROOZm/vz56NGjh1qQ1MHBAbNmzYK3tzdee+21ArojQRAEQRAEQRDyk0IrdsgPP/yAd955Bz/++CNu3LiBBg0aYMOGDWjfvv1dj6Ob2h9//IFXXnkF77//vlp7p0OHDpg9ezbKljVbVTiH8LjJkyfn+nih+CFjRpAxI8jvGaEwIn+fhOI0ZmyYkq2gGyEIgiAIgiAIglBsYnYEQRAEQRAEQRDuBxE7giAIgiAIgiBYJSJ2BEEQBEEQBEGwSkTsCIIgCIIgCIJglRR7scO1e15//XVUqFABLi4uKuX11q1bc9R5ERERGDhwIDw9PeHh4YFevXrhzJkzef7QhKI5ZlatWoX//e9/amFbV1dX+Pv7q1ToMTEx+dJuoWj+njHmkUcegY2NDV544YU8aadgPWPml19+QatWrVCiRAn1N6p169ZqrTnBurmfcbNt2zZ07NgRZcqUUWOmRYsWKhuuYL3Ex8erDGuPPvoovLy81N+XRYsW5fh4zl+effZZlaGNv2s4fg4ePIjCRrEXOyNGjFBr8AwZMgSff/457Ozs0L17d/z555/ZDhA+VK7x8+abb2LKlCk4dOgQAgMDce3atXx7gELRGTP8hXDixAkMHToUX3zxhfrl8tVXX6kJSWJiYr61Xyg6Y8ZcLP/111952k7BOsbMe++9h8GDB6vFt3kOLsHApRv4gk6wbnI7btatW4cuXbrg9u3bavx88MEHSixxLUMu2yFYJ1evXsXUqVPV3KRhw4b3dCyXdeF6lkuXLlUv4D7++GO1LiaXegkNDUWhQleM2b9/P9Nu62bOnGmoS0xM1NWoUUPXqlWrux47Y8YMdew///xjqDtx4oTOzs5ON2nSpDxtt1A0x8zOnTsz1S1evFidb/78+XnSXqFojxnj/atWraqbOnWqOtfYsWPzsMVCUR4zf/31l87GxkY3a9asfGipYC3j5pFHHtFVqFBBl5SUZKhLSUlRxzZo0CBP2y0UHElJSbrLly+r70FBQWr8LFy4MEfH/vLLL2r/FStWGOqioqJ0np6eusGDB+sKE8XasrNy5Ur11oNv3PU4Oztj1KhR6g1qeHj4XY9t3ry5Knpq166NTp06Yfny5XnedqHojRm+7TCnT58+6pNvVQTr5H7GjB6+MeNbtPHjx+dxa4WiPmY+++wz+Pj44OWXX+bLTOWFIBQP7mfcxMbGolSpUnBycjLU2dvbK5c2WngE68TJyUn9vsjtePP29kbfvn0NdXRnY3jH2rVrlUtlYaFYix26nfn5+al4G2Pop0oOHz5s8ThOOo4ePYpmzZpl2sZjw8LCEBcXl0etForimMmKK1euqE/+QRGsk/sdMxcuXMBHH32EGTNmyKSjmHA/Y2b79u3qJRxdZTnxcHd3R/ny5ZXLrGDd3M+44cu44OBgvPPOOzh9+rSax0ybNg0HDhzAxIkT87ztQtEcb02aNIGtrW2m8ZaQkIBTp06hsGCPYszly5fVHwFz9HWXLl2yeNz169eVYs3uWAagC9ZFbsdMVnACyzdx/fv3f2BtFKxrzDCJRePGjTFo0KA8a6NgHWPmxo0bygd/7969KhkBA4+rVKmChQsX4sUXX4SDgwNGjx6d5+0Xit7vGoqcs2fPqlgdxngRJtL59ddfVfIlQbA03tq3b3/X8Va/fn0UBoq12GFQuLHJ1tjsq9+e1XEkN8cKxXPMWIJBfQsWLFBvzWrVqvVA2ylYx5jZuXOnmmzs378/T9soWMeY0busMUnOsmXLVPZHwpcpnHRwEitix3q5n981PI5WIY4VuiWlpaVh3rx5KqEOs7m1bNkyT9suFO/5UF5TrMUO/VAt+RQmJSUZtmd1HMnNsULxHDPm7NmzR/lRd+3aVb1JE6yX3I6Z1NRUvPTSSxg2bJhJbKBg/dzv3yZacIytxXQzofChpYdukbT2CNbH/fx9Yjatv//+W6UN1rslMfYiICBAxX/JCxchr+ZD+UGxjtmhqY1mOHP0dcxTbwnmIqeazc2xQvEcM8YcOXIEjz/+OOrVq6cC/BgEKlgvuR0zP/zwA06ePKnexJ87d85QCGMC+Z1+0YL1cT9/m/hWtXTp0so91phy5coZXN0E6yS344bppullwDTCxvEXFM3dunVTcTvcRxAe9HwovyjWYqdRo0YqgIpZSIzRv8HgdkvwlwFdAvgLwBwey0UjGRQqWB+5HTN6GPTJ9XU48di4cSPc3NzytL1C0R0zfAOfkpKCNm3aoFq1aoaiF0L8vmXLlny4A6Eo/W3itujo6EyTU328BpMWCNZJbscN3R5pSabrmjn8HcSkTJa2CcWbRo0aKUsgx4f5eGO8F90iCwvFWuzQzK/3S9VDkxyDObnqMBdk0086QkJCMh0bFBRkInj4FpZBoQMGDMjHuxCKyphh5jUu2sYJyebNm2XSUUzI7ZhhQoLVq1dnKoSLBPI7jxesj/v5PUN3NR67ePFiE7eSJUuWoG7duoXqbatQOMYNX755enqq3ynGIpkxYOvXr1fLahQmlyQh/7l8+bIaMxS/xuMtMjJSLXithwlSVqxYgccee8xiPE+BoSvmDBgwQGdvb6+bMGGCbu7cubrWrVurn3ft2mXYJzAwUC2cZExsbKxabKtcuXK6jz/+WDd79mxd5cqV1aJcXFRJsF5yO2YaNmyo6iZOnKj78ccfTcqWLVsK4E6Ewj5mLCGLihYPcjtmEhISdAEBAToHBwfd+PHjdV988YWuefPmasHrjRs3FsCdCPlJbsfN+++/r+oaN26s5jOffPKJrk6dOqrup59+kodoxXz55Ze6adOm6caMGaOed9++fdXPLDExMWqfJ598Um07e/as4bjU1FRdy5YtdW5ubropU6bovv76a/W7x93dXRcSEqIrTBR7scPVhfkHwcfHR+fk5KT+KGzatMmkk7KahISHh+v69++v8/DwUA+7Z8+eutDQ0Hx8fEJRGjP8OavC/QXr5X5+z5gjYqd4cD9jJjIyUk1OvLy81LEPPfRQpmMF6+R+xs2SJUt0LVq00Hl6eupcXFzUuFm5cmU+tl4oCHx9fbOcm+jFjSWxQ65fv64bNWqUrnTp0jpXV1c1toKCggrdg7ThPwVtXRIEQRAEQRAEQXjQFOuYHUEQBEEQBEEQrBcRO4IgCIIgCIIgWCUidgRBEARBEARBsEpE7AiCIAiCIAiCYJWI2BEEQRAEQRAEwSoRsSMIgiAIgiAIglUiYkcQBEEQBEEQBKtExI4gCIIgCIIgCFaJiB1BEARBEARBEKwSETuCIAiCIAiCIFglInYEQRDyie7du+OZZ57Jdr9FixbBxsYG586dM9R16NBBFaH4wXHA8fDJJ59ku+97772n9rU2zMe/vk/4fyUv2LRpE9zc3BAdHZ0n5xcEIf8QsSMIQrGjadOmeP7557MVGwcOHHhg19y7dy+2bNmC119/HfnFvn371OQ3JiYG1sR///2nns8///wDa2Ljxo3qeQkFz6OPPoqaNWviww8/LOimCIJwn4jYEQShWHH58mUcOnQIPXr0yNfrzpw5E506dVITqNxAocRyr2JnypQpVid2fvvtN5QrVw7NmzeHtYkdPq/74e2330ZiYiKsHV9fX3Wfw4YNy7NrjB49GnPnzkVcXFyeXUMQhLxHxI4gCFbDrVu3st3n999/h7OzMx5++GHkF1FRUWqCPnDgwFyfw9HRURVBEwXdunWzSnet+8Xe3l6N78JCQkJCnpyXz573aWdnh7yiX79+SE5OxooVK/LsGoIg5D0idgRBKJLoYxOOHz+OJ554AqVKlULbtm2zPY6io2PHjnBxccmXduqvmZqais6dO2faFhwcrIQX21OpUiW8//77SE9Pz7SfpZidL7/8EgEBAXB1dVX336xZMyxdutTQPxMmTFDfq1WrpvrKOA5o4cKF6rq0kDg5OaFu3br45ptvMl23atWq6NmzJ/7880+0aNFCTTCrV6+OH374IdO+tCC98sor6hiek/czfPhwXL161bAPJ4+TJ09WFi7uU7lyZUycOFHV5wRegxar7CxzdEHs2rUrypQpo/qWfTBy5EiLcTBff/21uif2Y5cuXRAeHg6dTodp06ape+DxvXr1wvXr1zNdZ86cOeoZ8F4qVKiAsWPHWrSkccJM90mei20aOnQoIiIiDNtHjBih2kH0z8qSmJs3bx5q1KihrkfLVlBQULYxO/z5hRdewJo1a1CvXj11LNvMuBRz/vjjDzWO+Jx5HVo2choHxPHJ8//7779o37696s8333xTbVu7dq16ZuwjXp/nZv+mpaVleY/sK465PXv2ZNrHUsxOVnFt7FuOSWOWLVumnoe7uzs8PDxQv359fP755yb78P9GgwYNVNsFQSi62Bd0AwRBEO6HAQMGoFatWpg+fbqaoN6NlJQUbNu2Te2bn3ByXrp0aeV6Y8yVK1eU8KIQeuONN1CiRAk10cuJEJs/fz5eeukl9O/fHy+//DKSkpJw9OhR7N+/X4m/vn374tSpU/j5558xe/ZsNcEmZcuWVZ8UNpzwPv7448oasH79ehXHRKHFCbsxp0+fVtcZNWoUnnzySXz//fdqAsnJIs9B4uPj0a5dO5w4cUKJiiZNmiiRs27dOly8eFFdn+fm9Sicnn32WdSpU0fF37B9bCsn49mxefNmNcmlKLmbJY3bea/sV09PTzU5XrVqVaZ9lyxZgtu3b+PFF19UYubjjz9WFjgKQU78GWPF+6ewHD9+vLp3PRQBdDujiB0zZgxOnjyp+pUChDFaDg4Oaj9OyJ966iklThgDEhkZqSbW3IculWwfXaYuXbqErVu34scff7R4XxSydKnivuwDtpXP+cyZM4ZrZQX7nPfPZ8wJ/hdffKEsFxcuXFBjk7AtjFUpX768ui8KkalTpxrGTE64du2asroNGjRICTpvb29DHzDg/9VXX1WfO3bswLvvvovY2Fjl4qlnwYIF6v5at26NcePGqXvjmPHy8lLC+EHAPh48eLByK50xY4aq47jl8+D/JWM4xnMyLgVBKMToBEEQiiCTJ0+mstENHjw4x8ds375dHXP27Nm77rdw4UK1X1BQ0ANoqU7Xtm1bXdOmTTPVjxs3Tl1n//79hrqoqChdyZIlM7UzMDBQFT29evXSBQQE3PW6M2fOzPJ+ExISMtV17dpVV716dZM6X19fdY7du3ebtNHJyUn32muvGereffddtd+qVasynTc9PV19/vjjjzpbW1vdnj17TLZ/++236ti9e/fqsmPYsGEm/WCJ1atXZ/v82Cfcp2zZsrqYmBhD/aRJk1R9w4YNdSkpKYZ6jjNHR0ddUlKSoQ/4c5cuXXRpaWmG/b766it1/Pfff69+vn37tq5cuXK6evXq6RITEw37bdiwQe3HftMzduxYVZdVW0uXLq27fv26oX7t2rWqfv369Zn+XxjDn9nW06dPG+qOHDmi6r/88ktD3WOPPaZzdXXVRUREGOpCQ0N19vb2FttlDp8L9+PzzMl4Gz16tLqevk/1fdWoUSNdcnKyYb958+ap8xo/d32f8P+q8fUtjY0nn3xSjWM9L7/8ss7Dw0OXmpqa7T1Nnz5dXScyMjLbfQVBKJyIG5sgCEWa55577p5iPeiuZe7SktfwbTfdzCy1p2XLlspVRw/fog8ZMiTbc9IaQIuJuRtTTjG2Ht28eVNZYQIDA9WbdP5sDPuMVhvjNvr7+6t99fz6669o2LAh+vTpk+laehcounLRmlO7dm11PX3Rx0/t3Lnzrm2mZYiuV9m5sLFvyIYNG5Q1727QMliyZEnDzw899JD6pFWCFi/jelqA9K5ntBDyZ1ofbG0z/pQytTjdoui6qHeno6WJFhXjWBreA/tBv19O+N///mcyjvTPxPg5ZAWtT3QN00P3LLZTfyytOLyn3r17K1czPXQ3pKUmp9BFjVasu403Wqf43Nl+xvSEhISY9BX/TxvHp9GKaPyM7heOD8b30cKTHfr+NnbFFAShaCFiRxCEIg1jMXIKJ5b5nYVNjyUXu/PnzysXPHMoJLKD7lV0B6JQ4jnoekY3nJzCfTkBpuscJ38UMPr4CnOxU6VKFYuTwBs3bhh+DgsLU/EadyM0NFTFKPFaxsXPz09t50T3blDYcd2T7J4hRRtdtOiKRfc5xtswRslSXJD5vekn1eYuU/p6/T3z2Vl6VpykM/5Hvz2r/QjFjn57TjBvq34ibvwccnqs/nj9sex7ZjezlC3wXjIIVqxY0WIiDT53CmH2I0UWnzsFpfF40/eF+f8JuuixTx8UFJ4ccxRxjMmi26Wl+CXj/7eSDEMQii4SsyMIQpEmp4kGzp49q94gWwrCz2sYE5GTCem9QAsJY0RoveBEjZYVBsszDiK79MUUJoxX4GR71qxZamLPCSotTYyfMU+QkFXGq+xipMzheRkIzmtaIruYDLaPVjlamu4GJ6YrV67E33//rWKRGOfDCe2nn36q6igSs7u3B3XPD5L7aVN+3Y+l/49M2EABSpHDGCBamGjlOnjwoBLtlhJy5AY+d0v3Y54EgYkHDh8+rMYFszOyUAwzmcbixYtN9tX/v9XHvAmCUPQQsSMIQrGAVh2+Vc5JxrYHDUUFxYg5TFhAa4c5FDE5gVYZujax0KWKweoffPABJk2apCaTWb2NpgCglYPJA4zf+GfnRnY3OIE9duxYtvscOXJECa3cvCnnM+zevXuO96eLIAv7hMH9dA9kFq6nn34a94s+2QSflbHVgc+Bwlqfec94P/N056wzTlpRkNYDCgCOGSZjMMdS3b3ARA905WSCBGZp08N+MkbfF/w/YdxXdEXkvnSTvBu0VFly6bNkPaO4f+yxx1Sh2KK1h5nn3nnnHRNLFq9LoXMvSRoEQShciBubIAjFAloFmKHLOA4jv2jVqpV6Q2w+EePEnZaGf/75x1BHNy1mCMsOTh7NJ2+0ePDNtj5OhWKImKdC1r/lN34LTlcivt3OLXQbo5BZvXp1pm366zDLGWNemEnOHLpQ3W2dJGYwoyUgJ26I7GvzN/yNGjVSnzlNcZ0dFDPsc2Y1M74Ws4mxL/XtZBpnColvv/3W5Nq0JjADmPH9ZPW88gOOCd4TM48xK5yx0GFb7/fcxLifKAppiTSGfUVRwb7idj3M5JaTPqGYpvWW/4f0cEyau3ea/99hzBVjmCyND6bR5v9fQRCKLmLZEQTB6uFEmlYLTqLuBaYZtuTLz/S0TN+bUzihpchiADhTLuvh+jJMM8x0vzynPvU033AzjfTdoHDz8fFBmzZtVHpfTpy/+uordS1925g2l7z11lsqFTBjH/gmm8fq32wzzS/TRlOAcFJ++fJl5Aau6UPXMQb802WM12YqZ1qP2O98K8/V7pcvX64C0Pk82Ha6GHGCynq6FXHCm5VYpeWBqbqzg65InEgzRoQTYAbE8/7oRnUvlqG7wUk5LWh0GeTzY3pkWmp4XaaY1sejsM+Z3phB+3TlYspjfeppuuRxXSI9+ufFlOJcI4gigc8tv2Aq7S1btqjnwlTafDYcU4zFottXbmEaaVpdmLac90YLFse9uSBlX3GdKY5JWnZosaRlhSI8JzE7HHd0kWTfMU0645A49pgenSmu9dCyx7HJazBmh5YfphanIKZ7qB4ez/+H5qnYBUEoYhR0OjhBEITcoE+xGx0dne2+TPNrY2OT4/Sx+tTTWZXw8PB7bu/jjz+u69SpU6b6o0ePqnS5zs7OuooVK+qmTZumW7BgQbapp+fOnatr3769SkfMNNA1atTQTZgwQXfz5k2T8/N8PC9TPhufc926dboGDRqo61atWlU3Y8YMlS7Z/LpM2dujR49M7baU5vfatWu6F154QV2PqY4rVaqk0v5evXrVsA/TC/NaTJvNdpcqVUql5Z4yZUqmthvTv39/Xffu3XPQ0zrdwYMHVaroKlWqqGswnXHPnj11Bw4cyJS6mOm5jdm5c6eqX7FiRY7SkTPVdO3atXUODg46b29v3ZgxY3Q3btzI1KZffvlF17hxY9UeLy8v3ZAhQ3QXL1402YepkF988UWVDpvjVf8nOqu2Etbz/0J2qaeZ1tocPls+H/P07Gwnnx/H1HfffadSjHOcZAfHQ1bp0JlWvGXLljoXFxddhQoVdBMnTtRt3rxZtY19bsycOXN01apVU33VrFkzlfbcfLxZSj1NfvrpJ5U+ne1nCmtewzz19MqVK1XKcI4L7sdxwjTYly9fNjnXN998o1Jjx8bGZnvvgiAUXmz4T0ELLkEQhLyE/vhMa2vsLpbfcBV4ru5OK4alDGxC1nDRVSZ54IKcfJZC/sJ01MymZim+zJpp3Lix+j/LpB2CIBRdJGZHEASrh+4p2WUoy2u4pgjdx7jqvXBv0OWI7l6W1vARHrzLpzEUOHQh5KS/OEH3Vd47XRUFQSjaiGVHEARBEARF+fLl1SKe+rWCmKqdQfuHDh0Si6QgCEUSSVAgCIIgCIKCyRZ+/vlnXLlyBU5OTioT2fTp00XoCIJQZBHLjiAIgiAIgiAIVonE7AiCIAiCIAiCYJWI2BEEQRAEQRAEwSoRsSMIgiAIgiAIglUiYkcQBEEQBEEQBKtExI4gCIIgCIIgCFaJiB1BEARBEARBEKwSETuCIAiCIAiCIFglInYEQRAEQRAEQbBKROwIgiAIgiAIgmCViNgRBEEQBEEQBMEqEbEjCIIgCIIgCAKskf8DGhLnfUxa72sAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "r_over_L = np.linspace(0, 1, 200)\n", "\n", @@ -136,9 +169,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "L range: 422 – 431 km\n", + "(decreases toward poles as cell area shrinks)\n" + ] + } + ], "source": [ "smooth_scl = 2.0\n", "tarea = grid.tarea.values # cell area in m²\n", @@ -177,9 +229,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total source pixels: 9900\n", + "Ocean source pixels in KDTree: 9025\n", + " (91.2% of source)\n" + ] + } + ], "source": [ "R = 6_371_000.0 # Earth radius, m\n", "\n", @@ -207,9 +269,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cell (1,2) at lon=267.0, lat=23.0\n", + "Smoothing radius L = 428.4 km\n", + "Source ocean pixels within L: 4987\n", + "Weighted depth estimate: 1432.6 m\n" + ] + } + ], "source": [ "# For a chosen target cell, show which source pixels are within radius L\n", "jc, ic = 1, 2 # choose a central ocean cell\n", @@ -249,9 +322,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", "\n", @@ -332,9 +416,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "S shape: (20, 9900) (n_dst × n_src)\n", + "Non-zero entries: 69432\n", + "Average non-zeros per ocean row: 3654.3 source pixels per model cell\n", + "Unfilled cells: 0 (will use neighbour fill fallback)\n" + ] + } + ], "source": [ "from mom6_forge.mapping import compute_cressman_weights\n", "\n", @@ -358,9 +453,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Visualise the sparsity pattern (each row = one model cell, each col = one source pixel)\n", "fig, axes = plt.subplots(1, 2, figsize=(13, 4))\n", @@ -407,9 +513,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iterations needed: 1\n" + ] + }, + { + "data": { + "image/png": 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vPafLrQH64sWL3Z40tP49RXUK/zccGb2udP2PP/7Y6fLx48eb5Z07d3a6XL886d8TAMB76U1ka3x48uRJ23yN7SK7ceRK0vDixYsmMVWmTBmnx2GNtd9///0IsYzGCtFlPRZnn3HWm/G9evWKUazvLN6ZOnWqmdehQ4cI+9DYXhOKkcWoek7tGxNYZcuWzcRD9qyPHT4xqHQf+prq8qNHj9puhOrvuq+7d+9G2KZNmzZm+ZQpU9ySNHT2fepJSBoC8DS6J8dB9rXrtHm/djvQwvnaLVT/rd1ilXYH1G4C2jXV2uUhPO0+oKPURoV2I1Ta9D487eao3RC164F2jwhfWFe7BTjrFmHtLuqsdp21PmNUj09p/TRn3WK1tpy6cuWK6aJrffzAwEDTfUCn8LTrrx6DjoSqNdacuX79uumaq/t31tRfuys8jrPu5PbH6uw8OxvR2lrTRM99VF4v7Sqj3Va1S4We/5gWnNZuIfZdUeIyfT2V1qdxt+i+np7gyt+99VrRLkbhaXcefQ7avSeq3XOVdtF+Eut7gO7b2XuAtXaQHqu7uyg/qW5TbNFSCdqlDQDgvbTEjMaHWmpCa1BbaffaXr16mXp92mVY49CY0Nq/9+/fj7QOdFhYWKSxtLPY3N3xjrtifWt86yyu1rheS79EVktblznrxqvHaz2+8JzFQ7oPfXx9XfV4tLyO9bi027Wz11Jj8GnTppn1tPRLTMXkNQMATyFpGMdpcV79ANHaG5oE0vppb775pvkg1A9tTTDqB7K1iHJMWAfn0BoizljnOxtAJGPGjJEmb7R+1+MK/+vAAlEVWU04a+02DazsH18HrnjSudHHf1zSUOkAJs5ENv9xx+vsWK00EeQs8LGeX/sBVKLyeumgC/p6xTRp6Oz1jau0cLS1FqW7Rff19ARX/u6t10pk16u+vlFNGlr//u2/JEXG+h7gLGnv6ntAbLP+LUU2mJF1fmTvVVoP0XqNAgC8k9YtVOEHtNAbQ1rHWuvcaS1wrb8dE9bPUU0ePm7wMGefozGJ3aIa77gr1n9SnPK4ePtx3w209mN09hc+3o7JdyNfj7cBxB8kDb2EfiBqUV5tMaSTJg2tX151cA5rS6KYsO7v7NmzTpdbR092loAKP8qb/XqjRo0yo6w+bfr4GixoQWRXWVs1RtYyyN0thrRItQZj4ROH1tfE/tzbv146InRUXi/rCMDOPC7gcfb6xlU62I19IBueDlahnJ0HdwV9nuTK3711G71edbTx8CL7m39ccB7ZCIrOHle/OGkx9adJW2FG5/XUlgrORtsOz1ocXUcTd8baelJb+4an70d6TM4GkwIAeAe9aaeDlygdFEOnyBKLMU0aWj9He/ToIV9++WW0tn0asZu7Yv2nHW9Htr/w8bYr341iEmd6U7wNIP4gaehFrN0BrHfNtLm+JgC0y7ImxvTuZkxoEkJp8//XXnvNYZl+8FlHCtaR26KiQoUK5qduFxtJQ318HdVMz4+zRElUgxjtvqldHXUK30VZR5N2Jz3POmqbdoOwZ+2SYX2N7JNGuix80lBHZz558qRJTtjfgdUuuzriW3iaqNSR43xBsWLFzM99+/Y5XW7ttqznIU+ePBFGBY7rXPm7179Zba2so+uF785++PBhp9fEk/6uddRBbfUc1feA2EgaRrX1pNJRB6OSNNTnpC0F16xZIzdu3HAYQVnfm3XEcuVstEYdAV1biWqCEgDgnaZMmWJK3JQuXTrS93MdeVhH7tUyMU+6UWS9Ueysx4L2NtIklDUGj2vcFetb41uNq3Wk5/CtFN0do2o8FJ6ef2tcbz0e++PSGN3a2tJKR1sO/93IPs4MzxviTAAI7+GtEMR5ekdTAw+tp2Ff865nz54mcNEPWGd3rzTRGNXWSPqFWRMQP/74o61umf0XcH38WrVqRahn+Lh6KJr80mTFxIkTna7zzz//yPnz58UT9K6s6tSpk5w+fTrCcq0XGf55OqM1SjQZ0KdPH4d6kxoMRFZTLib0ce7cuWP7XRNDWhdHdejQwTbfGlTpMmvNGGvQ895775ljDp/81eBTuyxbExtWuo/oJFjiMk2opUuXLtLX1lovZsKECRGuRb1T7g2i+3ev9VD1veObb75xqPWn18j7778fafcdZ7TblSbP9QuRvleEp8lqq0aNGpmE9rfffiu//vqr0/1pvSGt0+hu+jz/G+wrSpPWn4pq0rZt27bm/SN8/abRo0ebx9UaV3qzITzrNeksoQgA8A7W+GHMmDHy3XffOZ3eeOMN89mi/34Sa4kcjc+c9Z7Qz3BNNn366adOE4tag09j9Njgrlhf4wVtrTd9+nRTtzl8jOruniBak3LRokURPsP1XOpntNYzVFoaqnbt2uazPXzMv2HDBpkxY4ZJEjZp0iRCnDlp0iSH1ob6vWHgwIFufR4A8DTQ0jAOsv8iql9M9+zZY1r1qMGDBzvU4dCkwZYtW0zgol/O9cuqJvU00aQBxN9//20STWPHjo3Sl2H9wG/evLkpEKw/dV+6f00yaZ2NcePGReu56IeptmzS5NXXX38t5cuXNy3fNLGwc+dO2bVrl0kaWLuUulPNmjVlyJAhJgmnA6joQAt6t1fvWGqCTO8yasHj33///bH7+eCDD0zS9qeffjItherUqWNqnMycOdMMDqPLrF0RYkpro2jCsEiRIqZllha4nj17tun+0KVLF/N4Vpo81mPTOpe6vnaB0RqYeq3oedXnpgkhe5pMXLJkiQnOWrZsaZLE2rJRrxUdbCWyItPeRLt2aPCm3YKctTLV567Xgya89DrUa1IDde1Cq8v0dY3rovt3r0k+/VvQ4ux611xfew3O9VrQQFxbZ+rfY1QEBQWZGoX6d6AF3/U9QVsaaA1JLXS+bNkyW5CsiUr9IqHHV79+fXPNaquMJEmSmOBZ6zNpS0e9vnWet9D3Yf1b0a5i2vpBvyDoc9drSN/LNEnqjL6PaosSvc4AAN5H3/u1PEXRokUfO2iFxr06cKEmjrT+cPgWavY0SaVxpMarGr9ZW6r17dvXlszS0hf/+9//ZOrUqSa+0+8CekNcP3v0s1RjmtgqfeGOWF979uhnp96U01ihRYsWJibWGFWTiPq9RON2d8XbegNUY0WdtNeJfpZr/KxxscZW9jSWqly5somp9XNcE6Uaw2gspMejr7F9rwN9/hqvayym14ieG+0OvXDhQhMPRad3BwDECR4fnxlRpi9H+ClBggSWjBkzWl588UXLH3/8Eem2CxcutNSvX9+SLl06S2BgoCVDhgyWsmXLWj7++GPL3r17HdZt166d2feRI0ec7mvjxo2Wxo0bW9KmTWv2lTVrVsubb75pOXXqVIR1n7Qvdf36dctnn31mKVWqlCVp0qSWRIkSWXLkyGF54YUXLOPGjbOEhIRE+fxUq1bN6bLHHceqVasszZs3t2TKlMk8H31exYsXt/To0cOyadMmh3WzZ89upvCuXLliefvtt80+goKCLPnz57eMGDHCsmHDBvO47777bpSPZ/ny5WZZv379nD721atXLV26dLFkzpzZPFaBAgUso0aNsjx48MDpc//xxx8tlStXtiRLlsySMGFCS6FChSyDBg2y3L592+n68+fPt5QuXdqsmzp1akvLli0tR48edXrM+m+dp8tcEdlrFtk50HXDvy1Ftu7jbN++3WzzwQcfOF1+/PhxS4sWLSypUqUy12OZMmUsc+bMidZxWU2aNMks059RvQacXWeR7SeyazK6f/dqxowZlpIlS5rXXv8OXnnlFfN3/bjnF5ljx45Z3nrrLfO3rI+t11K5cuXM33p4586ds/Tu3dtSuHBhS+LEic37QJ48eSwvvfSSZerUqZawsDDbunru9Vj0tYjq339suHTpkuWdd96xZMuWzTx/fZ/u0KGD5cSJE07X179rvdYaNWr01I8VAOAerVu3Np9HGpc9Se3atc26c+fOfeLnuX4WamyqnxPW7wD27ty5Y/nmm28sFStWtKRIkcLEhxqf16hRwzJy5EjLxYsXYxQ3Pekz+EkxYXRi/cjiHfXrr7+a56ixQsqUKc33H41nNNbRbTQej8rxKGexjf1jawxVoUIFS5IkSSzBwcGWpk2bWvbv3+90XydPnjTfhayf+WnSpDGf5/qdyRk9ztdff93EZ/paafyj5yGyY47K9ylXXrPoxNWuxIIA4gc//V9sJy4Bb+6i0rlzZ3MXUruiIO7Qu7l6h1tbsjFaLWKbdg3Xek9a90lbiQAAgCfTLtla8kPLslgHHgEAPD3UNASiwFlNRO3SqvVltMuJdnNA3DJixAhT6zF8NxPgabt9+7Z8/vnn8tJLL5EwBADACS2XEr7GsbZt0ZqGGnPb1w0EADw91DQEokC/7Gt9QR0pT+u0aEFkLaCswY0mAzJnzsx5jGO03pDW6NQRboHYpO8X2iK5ffv2vBAAADihg4VpzWWtmay1mLUGuc7TeoNZs2aNMPgYAODpoHsyEAXaWk2LT2shah0ERQeN0QElunXrJk2bNuUcAgAAAC7Sgdx08Jc1a9aYniI6qJqOXtygQQP56KOPHAaCBAA8PXE2aagjgU2ZMkWWL19uWmmkSZPGjNCpTdTz5cvnsK6OdqqjWO7bt8+MTKkjyeqosjpaJwAAAHwPsSIAAEA8TRo2a9bM3Glq3ry5FCtWTM6ePSujR4+2NVXXxKB9cXlNEOqdqNDQUJk8ebLs2LFD5syZQyswAAAAH0SsCAAAEE+ThmvXrpUyZcpIUFCQbZ52DdU6ZRokTps2zczTVodaY27Dhg3i5+dn5l2/fl2yZMkiNWrUkPnz58facwAAAIBnECsCAADE09GTK1Wq5JAwVHnz5pXChQvL3r17bfM0QZg+fXpbwlClSJHC1JxLnDjxUz1mAAAAPB3EigAAAPE0aeiMNoo8d+6cpE2b1jbvueeek99//910U9bah1rXsGvXrmawinfffTdWjxcAAABPD7EiAABAPOie7Ix2SW7btq18//330rFjRzPv/Pnz0rp1a1m2bJltPU0qLliwQCpWrPjEfer2OkKXPW29eODAAdMVOmHChB54JgAAPD137tyREydOSLVq1UxJD087ffq0XLlyJUb7SJUqlWTOnNltx4T4gVgRAIDoI1aE1ycNtQVh+fLlTffkVatWmVGSlQ6M0rt3b7l586YZCOXGjRsycuRIkwjU9fLkyfPY/fbv318GDBjwlJ4FAACxZ968edKoUSOPJwyzZM0h8iAsRvtJnjy5+ewncYioIlYEACBmiBXhlUlDHTm5cuXKEhYWZkZOtv8CUa9ePQkICJCFCxfa5l2+fNnUP6xVq5b8/PPP0W5puGfPHmnRooXMnD1Pcj8h6Yjoq9j1B04bADxFD25dlHvbJsqWLVukVKlSHn2s3bt3S5EiRSQwZ33xSxjs0j4sd65J2JHFsmvXLnOzEHgSYkXfQqwIAE8XsSIiEyBxnNYm1MTg1atXTctB+4Th4cOHTT3D8ePHO2yTOnVqqVKliqxZs+aJ+9dBVHRyRhOGhfiy4nb+yTO6f6cAgCd6miU3NGHonziNS9s+cPvRwJcRK/oeYkUAiB3EivCqpGFoaKg0bNjQ1BdcunSpFCpUyGG5Doqi7t+/H2FbbZV47969p3asAADAjp/fw8kVrm6HeIdYEQAAL0Ws6BXi7OjJmghs2bKlrFu3TmbNmuV0UBOtV+jv72+6INv3sj558qRplViyZMmnfNQAAMDw84/ZBDwBsSIAAF6MWNErxNmWhr169TIjIGtLQ61RqKPh2WvTpo2kS5fOjKL83XffSc2aNaVp06ZmIJQxY8bI7du3pU+fPrF2/AAAxGvaWNDllobuPhj4ImJFAAC8GLGiV4izScPt27ebnzrAif0gJ/ZJQ/V///d/Urx4cfn+++9tScKyZcvKDz/8IFWrVn3KRw0AAIyYtBikpSGigFgRAAAvRqzoFeJs0nDFihVRWk9HTu7WrZuZAAAAED8QKwIAAMTTpCEAAPBmMRgIhf7JAAAAPo5Y0RuQNAQAAO5HlxMAAAAQK3o1koYAAMD9KG4NAAAAYkWv5mKFcgAAAAAAAAC+ipaGAADA/eieDAAAAGJFr0bSEAAAuJ9fDAZCcXkAFQAAAHgFYkWvQNIQAAC4Hy0NAQAAQKzo1UgaAgAAz6DFIAAAAIgVvRYDoQAAAAAAAABwQEtDAADgfnRPBgAAALGiVyNpCAAA3I+kIQAAAIgVvRpJQwAA4H7+fg8nV7cFAACA7yJW9ArUNAQAAAAAAADggJaGAADAMyMnaxdlV7cFAACA7yJW9AokDQEAgAf4xSD5R9IQAADAtxEregOShgAAwP0YCAUAAADEil6NmoYAAMBDXU5iMLlg06ZN0q1bNylcuLAkTZpUsmXLJi1atJADBw5EWHfv3r1St25dSZYsmaROnVratm0rFy5ciLDegwcPZNiwYZIzZ05JlCiRFCtWTH788Uenjx/VfQIAAMR7xIriDbEiLQ0BAIBPGDp0qKxZs0aaN29ukntnz56V0aNHS6lSpWT9+vVSpEgRs97JkyelatWqEhwcLIMHD5aQkBAZMWKE/PPPP7Jx40YJCgqy7fPjjz+WIUOGSKdOnaRs2bIyf/58ad26tfj5+cnLL79sWy86+wQAAMDTR6wYfSQNAQCATxS37tmzp8yYMcMhQdeyZUspWrSoSfxNmzbNzNOk3s2bN2XLli2mNaIqV66c1K5dWyZPniydO3c2806dOiVffPGFdO3a1SQf1euvvy7VqlWT999/3yQnEyRIEK19AgAAgFjRW2JFuicDAACf6HJSqVKlCC368ubNa7ora9dhqzlz5kiDBg1syT1Vq1YtyZcvn8ycOdM2T1sVhoWFSZcuXeyelp+89dZbpmXhunXror1PAAAAECt6S6xI0hAAAHhuIBRXJzexWCxy7tw5SZs2ra314Pnz56VMmTIR1tW7vdu2bbP9rv/W2ogFCxaMsJ51eXT3CQAAAGJFb4kV6Z4MAADipIMHD0aYly5dOkmfPn2U9zF9+nST1Bs4cKD5/cyZM+ZnpkyZIqyr8y5fvix37tyRhAkTmnUzZMhgWheGX0+dPn062vsEAACAexAreh5JQwAA4AGudzM224pI48aNIyzp16+f9O/fP0p72bdvn6lHWLFiRWnXrp2Zd/v2bfPTWQJPR0e2rqPLrT8ft1509wkAAID/4j1iRYnrsSJJQwAAECcHQpk3b57kyZMnQkvDqNCRk+vXr29GM549e7ZtwJLEiRObn9ryL7zQ0FCHdfRnVNeL6j4BAABArOgtsSJJQwAA4H4xGNDEup0mDHUQk+i6du2a1KtXT65evSqrVq2SzJkz25ZZuxBbuxTb03mpU6e23eXVdZcvX27qItp3UbZua91vdPYJAAAAYkWruB4rMhAKAADwmYFQ9G5tw4YN5cCBA7Jo0SIpVKiQw/IsWbKY1oqbN2+OsO3GjRulRIkStt/137du3XIYeVlt2LDBtjy6+wQAAACxorfEiiQNAQCAT7h//760bNlS1q1bJ7NmzTK1DJ156aWXTELxxIkTtnnLli0zicbmzZvb5jVq1EgCAwNlzJgxtnna6nDs2LEmUVipUqVo7xMAAACxg1jRh7onb9q0SaZMmWK6BR09elTSpEkjFSpUkEGDBkm+fPls64Uf0dBerVq15M8//3xKRwwAANxZ0zC6evXqJQsWLDAtDXXE4mnTpjksb9Omjfn50UcfmaRi9erV5d1335WQkBAZPny4FC1aVDp06GBb/5lnnpHu3bubZWFhYVK2bFlTZ1G7POuozNY6idHZJ9yHWBEAAC9GrCjeECvG2aTh0KFDZc2aNebufLFixUxB89GjR0upUqVk/fr1UqRIEbPe1KlTI2yr3YNGjRolderUEV+mrR0GDewvE7+fINevXZOSpUrLqG/GSOH/zg0eal4tn7zRoIQUzZVWUiRJKMnqfyX3H1hsp6deuZzSt01FyZMlpVwLuSPjF++UETM32ZYnDExglut+0qRILHuOXZIPxq2UDfse1a7KnzW1DO1UVUrnyyj+/n6ycN1BeW/sCgm5HebzL8PTOL+qTa1C8m7T0pIzU7DcDA2T2Sv3S6+xK8SXfdqhitQtm1OyZUguN0PvyaqdJ+Tj71fJyYshtnWypksuX3WtIc8WfUbuhN035+WDCSsl7N4D2zpvNCguPZqVlnTBSWT/ycvy/riVsmbXqWjtwxdxfuN+TcPo2r59u/m5cOFCM4VnTRpmzZpVVq5cKT179pQPP/xQgoKCzKApX3zxRYR6MkOGDJFUqVLJuHHjZPLkyZI3b16TjGzdurXDetHZJ9yDWPHJiBWjhljRs4gVPYdYxrM4vx5GrCjeECvG2aShBt0zZswwQbeVdjnSLKwG8NbWA9YvAPZWrFhhWiC2atVKfNnIL0fIlMkTZeHiJZI7Tx4ZPGigNKz/vOzcvV+SJUsW24cXZ1y5cUfGL9ohiRIGyLgejonk0vkyyIyPG0jbzxfL4g2HpViudDJvYBO5FRomYxY8/PL5WcdnpVKRzFL7/Vly5vJN6dqohCz8rKmU6DxZTl+6KcmTBMmiz5rK9GV7pMWnCyVl0oQy7aP68l2v5+XlQYvE13n6/Kp3m5aSNxuWkNdHLJH1e0+bRGO+Z1KLr9Mve52/XCK7jl6UJAkDZVTXGjK7fyOp0G267XN2zoBGsvPQBcnddoKkSpbQLP/8tary3riHCdWmVfLKgPaVpdmA+bJh7xnpWLeozBvYWEp2nmKSj1HZh6/i/HpaTGoTuradfv5HlQ6wsmTJkicfib+/9OnTx0zu2ifcg1jxyYgVo4ZY0bOIFT2HWMazOL+eRqzoDeJsTUOtE2SfMFR6d18D8vAFye3duXNH5syZI9WqVTPdinzZ+LFjpHuP96RI0aJmeO5+Az6VsLt3ZcG8X2L70OKUpVuPycyV++XImWsRljWpklf+3nlSFq0/LBaLyI5DF2Tykl3y1ouPCpE2q5ZPRs7eLCcu3JB79x/IqLlb5fqtO9Km9sMRPSsWyiypkieSAT+slbth9+X81Vvy+YwN0rBiHnkmre8nbz19fjUpqy0RtVXhmt2nTCvGW3fuyfZD58XX/W/yGtl28Lxp8Xft5h35cvZmKZ47vaRM9vBOVOUiWaRA1tTSe8JKuXHrrhw/f0MG/rBW2tctYhKrqnOD4vLDH7tl9T+nzH7GLdohB09ftZ3fqOzDV3F+Ae9GrPhkxIpRQ6zoWcSKnkMs41mcXyAOJw0jy/SfO3dO0qZNG+k6v/76q1y9elVeeeUV8WXXrl2TY0ePSpmy5WzzAgICpHiJkrJ9+7ZYPTZv4qf/hesF5+/nJ3mypJJkiQMfruNn1oqwXcnc6f9brr871tf099fJT4r9t0585Y7zW6FgJkmWOEjyZkkp/3zXXo79+IYsGNREiuaM/H3AV9UqlV2OnbsmV0PumN+L50ovR85ek0vXQ23rbDlwTpImCpS8WVKZ37V15+b9Zx32o+uUyJ0uyvuILzi/Hupy4uoEuIBY8RFiRfcgVvQsYkX3IpbxLM6vmxEregWvShpq0fFTp06ZbsqPW0f7gzdr1ixK+zx//rzs3r3bYTp48KDEddevXzc/U6ZM6TA/ZapUcuO/ZXiyxRsOSbViWaVRpTySwN9PSuXNIK/WedgCK0WShy1dF647JD2bl5EcGYMlKDCB9GpeRjKmTmpawKn1e87Ijdt3ZWD7ypIoKIFkTpNUPny5vMM+4it3nF+tc6i05ebzvWdJ/nbfyc7DF2T+oKbx6vxWL5FNPnqlgrz9zTLbPD1HWifS3pWQh8m/FEkfnhs9R1dvOq5zNSRUkidJGOV9xAecXw8Wt3ZpImkI1xArPkKs6B7Eip5FrOg+xDKexfn1AGJFr+A1ScN9+/ZJ165dpWLFitKuXbtIg6PFixfLCy+8ECGZFpkxY8aYQVXsp8aNG0tclyJFCvNTW1Xau3rliiT/bxmebO3u09Jx+O/Su1U5Of7Tm/JVl+oyYfFOuX//gVz5L5HSe/xK07Xz9yEvycGpr0v2DMGyfPtxuXT9tlmu3UYbffKLlMidXvZPeV3+GNZCfl6x3yy7eO3hOvGVO86vdplVw37eaGocht69b7oKBCcJkgqFMkt8oIPJzPi4vnQc/pv8ueWYbb6em+D/uipbpUqWyPy8fvPhebt+666ps2kvZbJEcuPWnSjvw9dxfj2Eu8d4yogVHRErugexomcRK7oHsYxncX49hFjRK8TZgVDs6cjJOqpMcHCwzJ49WxIkcF5nS2sZhoaGRqtrcpcuXcwIzfa0pWFcTxzqucieI4ds2bxJKlSsaObdu3dPdu7YLq1faRvbh+dV5qw6YCarYZ2rmZF7b9+5Z37XkXp1QAjroBBa523vpI4ycOo62zZaq6/Bx3NtvzeokMtstzHcCMDxUUzP747/ahdql7P46OXqBczIxm0GLzY1geztOHxecmQIltTJE8nlGw9bB2prTj2n/566Yn7XVpk6II3WnbQqlTe9LFh7KMr78GWcX88xpQdcbDHo6naIv4gVIyJWdB9iRc8iVowZYhnP4vx6DrGid/D3hnos9erVMy3qfv/9d8mcOfNju6RogNSgQYMo7z99+vRmcBX7KU+ePOINOr/ZRb4aOUJ279olt2/flk8H9JOAwEB5sXGT2D60OEVrC2oiKijw4eWu/9bJemOjTL4Mputs4oQB0rpmQdN9tu/E1bbts6VPIVn+G9AkU+qkMrZHbTl75abM+OvRgDwl86SXJAkDzH4qF84iw994Tj6bts608vJ1nj6/OsLvgrUH5YOW5SR9yiSmC3O/tpVMXb91e06LL3uzYXH58q3q8lK/+REShmrNrlOy/+RlGdKpqqkRmTVdcvmkbUWZsmSX3Am7b9bRka31nOt1GRjgL53qFzO1Cqf9uTvK+/BVnF/ANxArRo5YMWqIFT2LWNFziGU8i/MLxPGWhtpqsGHDhnLgwAFZunSpFCpUKNJ1z5w5I8uXL5f27dubmobxQY+e70nIjRtSv24t0zW7VOkysmDR75Isme+P2BsdrWsUlAm9nrf9fmne2+ZnnQ9mmaTTyC41JF/WVGaAjq3/npOm/eY5JKMKZEsto7rWMAkrrV2oNfhe6DPHjJRs1e75ItKsaj5JHBQgR89dl6E/bTSjBMcHT+P8vv7FEhn+RjXZPr6dPLBYzEAdDfvOtXVd9lV67sLu3Zd5nzq2fG78yTwzkrQ2vmzWf75piXhkemeT5Ju1Yr/0+X6Vbd25q/+V9KmSyMT360q6lElk34lL0qTfPJOMVVHZh6/i/HoWd4/xNBArPh6xYtQQK3oWsaLnEMt4FufXs4gVvYOfJY72+bt//740bdrUjIY8f/58U6fwcUaOHCk9e/aUZcuWSY0aNWL02DoYitY23LJ9lxQq/HDQBrhPqoYjOZ0A8BQ9uHFW7q4ZJrt27TIt6j3J+hmaqPan4h+cxaV9PLh2SkL//OSpHC+8F7Gi7yJWBICni1gRXtfSsFevXrJgwQLT0vDy5csybdo0h+Vt2rSJ0DVZuy4/99xzT/lIAQBAeNw9hqcRKwIA4L2IFb1DnE0abt++3fxcuHChmcKzTxru379ftmzZYloa+vvH+TKNAAAAiCFiRQAAgHiaNFyx4uFIqlGRP3/+eDuyKgAAcVIMRk82oygBT0CsCACAFyNW9ApxNmkIAAC818MR1F1L/pEzBAAA8G3Eit6BpCEAAHA76tQAAACAWNG7kTQEAADup40MXe1lTO9kAAAA30as6BUYNQQAAAAAAACAA1oaAgAAt/PT/1ytaUhTQwAAAJ9GrOgdSBoCAAC3o6YhAAAAiBW9G0lDAADgdiQNAQAAQKzo3UgaAgAA9/NzvXuybgsAAAAfRqzoFRgIBQAAAAAAAIADWhoCAAD308aCrjYYpKEhAACAbyNW9AokDQEAgGfiQJdHTwYAAIAvI1b0DiQNAQCA2zEQCgAAAIgVvRs1DQEAAAAAAAA4IGkIAAA81tLQ1ckVISEh0q9fP6lbt66kTp3a7Gfy5MnROrbatWvb1jt69Gik6/30008R9rt3717z2MmSJTOP37ZtW7lw4YJLzwUAAMCXESum9opYke7JAADAJ4pbX7x4UQYOHCjZsmWT4sWLy4oVK5yuN3Xq1AjzNm/eLKNGjZI6depEWNaqVSt54YUXHOZVrFjR4feTJ09K1apVJTg4WAYPHmwSmCNGjJB//vlHNm7cKEFBQa49KQAAAF9ErCjeECuSNAQAAD5R0zBTpkxy5swZyZgxo0kCli1b1ul6bdq0iTBPE4z6uJogDK9UqVJOt7GnicKbN2/Kli1bTNJSlStXzrRc1NaOnTt3duk5AQAA+CJiRfGKWJHuyQAAwCe6nCRMmNAkDKPrzp07MmfOHKlWrZo888wzTtfRhODdu3cj3Ydu36BBA1vCUNWqVUvy5csnM2fOjPYxAQAA+DJiRfGKWJGkIQAAiJMOHjwou3fvdpjOnz/v9sf59ddf5erVq/LKK684XT5gwABTpzBRokSm9eIff/zhsPzUqVPmuMqUKRNhW72DvG3bNrcfMwAAQHxHrOh5dE8GAAAe4HqLQWtRw8aNG0dYogOd9O/fX9xp+vTpppVis2bNHOb7+/ubGodNmjSRLFmyyOHDh+XLL7+UevXqyYIFC6R+/fpmPe0Sbe0eHZ7Ou3z5smnNqI8BAAAARazoDbEiSUMAAOB2mi90vabhw5/z5s2TPHnyOCxLly6duNP169dl8eLFZqCTlClTOizTrsZLlixxmKej3BUqVEh69eplSxrevn3b/HQW6GnrROs6cTEQBAAAiA3Eit4RK5I0BAAAcXJEPE0YFi5cWDxJaxGGhoZG2jU5vNSpU0uHDh1kyJAhZsRkrYGYOHFis0zvEIen+1bWdQAAAECs6C2xIjUNAQBAvKVdk4ODg80gJlGVNWtW81O7kth3S7Z2U7an8zTRGBfvHAMAAODxpsfzWJGkIQAA8IkR8aJLg7Tly5fLSy+9FK1ATWsb2neV1nqH+u/NmzdHWHfjxo1SokQJNx41AACA9yNW9I5YkaQhAACIl4HgTz/9JA8ePIi0a/KFCxcizNORkidOnCjFihVzGPhEE4+LFi2SEydO2OYtW7ZMDhw4IM2bN/fQMwAAAPBOxIriFbEiNQ0BAIDbxST5F5Ok4ejRo+Xq1aty+vRp8/vChQtN7UH19ttvm+4l9t1NMmfOLM8995zTfX3wwQdy6NAhqVmzplnv6NGjMm7cOLl586aMGjXKYd2PPvpIZs2aJdWrV5d3331XQkJCZPjw4VK0aFFTAxEAAACO8R6x4vA4HyuSNAQAAJ7xdBoMOhgxYoQcO3bM9vvcuXPNpNq0aWNLGu7fv1+2bNkiPXv2FH9/5x0v6tSpI2PHjpVvv/1Wrly5YkZXrlq1qvTt21dKlSoVoXbNypUrzf4+/PBDCQoKMqMrf/HFF3G2Rg0AAECsIlaUuB4rkjQEAAA+Q1sDRkX+/PnFYrE8dp1WrVqZKap0pOclS5ZEeX0AAAA8XcSK0UPSEAAA+EyXEwAAAMR9xIreIc4OhLJp0ybp1q2buWufNGlSyZYtm7Ro0cIUiQxPi5j/3//9nxlxJnHixJImTRqpUaOG7NixI1aOHQCAeC8mg6CQNEQUECsCAODFiBW9QpxtaTh06FBZs2aNGUVGRyg8e/asKW6uNYTWr18vRYoUsa3bsWNHU8z81VdfNYlGLVC+bds2OX/+fKw+BwAA4quHuT9XWxq6/XDgg4gVAQDwXsSK3iHOJg21kPiMGTNMIXGrli1bmpFlhgwZItOmTTPzZs6cKVOmTDFFzps0aRKLRwwAAICnhVgRAAAgniYNK1WqFGFe3rx5TXflvXv32uZ9+eWXUq5cOZMw1G7Kt2/fNt2ZAQBA7KFODTyNWBEAAO9FrOgd4mxNQ2d0lMNz585J2rRpze/Xr1+XjRs3StmyZeWjjz6S4OBgSZYsmeTKlcu0QIwK7cK8e/duh+ngwYMefiYAAPg4vxhOgAuIFQEA8BLEil4hzrY0dEbrFp46dUoGDhxofj906JAJDn/66ScJCAiQYcOGmcThqFGj5OWXX5YUKVJI3bp1H7vPMWPGyIABA5wuq9j1B/FPntEjzyU+80+aMrYPwaclSZ4ktg/Bp4VcC4ntQ/Bdt6/H9hHAjfz0P1drGpI1hIuIFX0DsaJnESt6FrGiBxEr+hRiRe/gNUnDffv2SdeuXaVixYrSrl07My8k5OGX90uXLpnBUcqXL29+f/HFFyVnzpwyaNCgJyYNu3TpYgZbsactDRs3buyx5wIAgK+jywmeNmJFAAC8B7Gid/CKpKGOnFy/fn3TinD27NmSIEECMz9x4sTmpyYIrQlDpV2UGzZsaAZLuXfvnmmFGJn06dObCQAAAN6JWBEAACAeJg2vXbsm9erVk6tXr8qqVaskc+bMtmXWf2fIkCHCdpoIDAsLk5s3b5pkIwAAeHq0Z7KLvZNd3g7xE7EiAADeh1jRO8TppGFoaKhpMXjgwAFZunSpFCpUyGG5Jg0zZsxo6hyGd/r0aUmUKJEkT578KR4xAAAw/FyvaUjWEFFFrAgAgJciVvQKcXb05Pv370vLli1l3bp1MmvWLFPL0Bld58SJE/Lnn3/a5l28eFHmz58vNWrUEH//OPsUAQDw+bvHrk7AkxArAgDgvYgVvUOcbWnYq1cvWbBggWlpePnyZVOf0F6bNm3Mzz59+sjMmTPlpZdekp49e5quyGPHjjVdkwcPHhxLRw8AAABPIlYEAACIp0nD7du3m58LFy40U3jWpKHWM1y9erW89957MnLkSJMs1FaJmmQsXrz4Uz9uAABgvXvsWpNBWhoiKogVAQDwXsSK3iHOJg1XrFgR5XVz5colc+fO9ejxAACAqNN0ocslDTnRiAJiRQAAvBexoneIs0lDAADgvfz9/czk6rYAAADwXcSK3oGkIQAAcL+YDGhCzhAAAMC3ESt6BYYWBgAAAAAAAOCAloYAAMDtdBAU1wdCoakhAACALyNW9A4kDQEAgIdGxHN9WwAAAPguYkXvQNIQAAC4nZ/EoKUhRQ0BAAB8GrGid6CmIQAAAAAAAAAHtDQEAABuR50aAAAAECt6N1oaAgAAj9WpcXVyRUhIiPTr10/q1q0rqVOnNonLyZMnR1ivffv2tqSm/VSgQIEI6z548ECGDRsmOXPmlESJEkmxYsXkxx9/dPr4e/fuNY+dLFky8/ht27aVCxcuuPZkAAAAfBixYmqviBVpaQgAANwvBqMnu5o1vHjxogwcOFCyZcsmxYsXlxUrVkS6bsKECeW7775zmBccHBxhvY8//liGDBkinTp1krJly8r8+fOldevW5rm9/PLLtvVOnjwpVatWNfsYPHiwSWCOGDFC/vnnH9m4caMEBQW59JwAAAB8ErGieEOsSNIQAAD4xIh4mTJlkjNnzkjGjBll8+bNJskXmYCAAGnTps1j93fq1Cn54osvpGvXrjJ69Ggz7/XXX5dq1arJ+++/L82bN5cECRKY+ZoovHnzpmzZssUkLVW5cuWkdu3aprVj586dXXtSAAAAPohYUbwiVqR7MgAA8AnaelAThlF1//59uX79eqTLtVVhWFiYdOnSxTZPWxi+9dZbpmXhunXrbPPnzJkjDRo0sCUMVa1atSRfvnwyc+ZMl54PAAAA3IdYMfpIGgIAAA/dPfZzcfL8C3Lr1i1JkSKF6U6s9Qe1NaF2Kba3bds2SZo0qRQsWNBhvt4Vti63tkg8f/68lClTJsLj6LrW9QAAAPAQsaJ3xIp0TwYAAG6neT+Xuyf/9/PgwYMRlqVLl07Sp08fo2PTbswffPCBlCpVygx08vvvv8uYMWNkx44dpg6idl1W2tU5Q4YMEWoz6vbq9OnTtvXs54df9/Lly3Lnzh1zdxsAAADEit4SK5I0BAAAbmdtNejqtqpx48YRlunoyP3794/RsX3++ecOv+uAJtqNWAc9mT17tm2Ak9u3bzsN3nQUZety+59PWjcuBoIAAACxgVjRO2JFkoYAACBOmjdvnuTJkydCS0NP6NGjh3zyySeydOlSW9IwceLE5q5veKGhobbl9j+jsi4AAADcg1jR80gaAgCAODkiniYMCxcuLE+DJvXSpEljuofYdxdZvny5WCwWh1aT1u7ImTNntq1nP9+eztOaiXHxzjEAAEBsIVb0jliRgVAAAID7uTwISgyyjTFw48YNuXjxokNLxhIlSpgBU/bu3euw7oYNG2zLVZYsWcx2mzdvjrDfjRs32tYDAADAf4gVvSJWJGkIAADczpr7c3XyFO0urAnC8D799FPTorBu3bq2eY0aNZLAwEAzSIqVrjN27FiTKKxUqZJt/ksvvSSLFi2SEydO2OYtW7ZMDhw4IM2bN/fcEwIAAPBCxIriFbEi3ZMBAECcLG7titGjR8vVq1dtIxsvXLhQTp48af799ttvy5UrV6RkyZLSqlUrKVCggJm/ZMkS+fXXX03CUBOFVs8884x0795dhg8fLmFhYVK2bFlTO2fVqlUyffp0SZAggW3djz76SGbNmiXVq1eXd999V0JCQsx2RYsWlQ4dOrj8fAAAAHwRsWKIV8SKJA0BAIDPGDFihBw7dsz2+9y5c82k2rRpIylTppQGDRrIn3/+KVOmTJH79++b2omDBw+W9957T/z9HTthDBkyRFKlSiXjxo2TyZMnS968eWXatGnSunVrh/WyZs0qK1eulJ49e8qHH34oQUFBUr9+ffniiy/ibI0aAACA+IZYMXpIGgIAALfTtoIuD4QSg8c9evToE9eZOnVqlPenScQ+ffqY6Ul00BZttQgAAIDHI1b0DiQNAQCAz3Q5AQAAQNxHrOgdSBoCAAC3i8mAJuQMAQAAfBuxondg9GQAAAAAAAAADmhpCAAA3C8G3ZNpaggAAODjiBW9AklDAADgoS4nrtY0dPvhAAAAIA4hVvQOcTZpuGnTJpkyZYosX77cjISYJk0aqVChggwaNEjy5ctnW699+/ZmvfDy588v+/btE2/RvFo+eaNBCSmaK62kSJJQktX/Su4/sNiW1yuXU/q2qSh5sqSUayF3ZPzinTJi5ibb8oSBCcxy3U+aFIllz7FL8sG4lbJh3xmzPGu65LJ13KsOjxkY4C8B/v6SvfU4uXQ9VHxZs8o5pXPdAlI0e2pJkSRIUrSY7HB+65Z+Rvq2KCm5M6WQazfvynd/7JMRv/zjsA/dvvuLRSRdcGI5cOqafDBpg6zZe862/Ns3K0u5/OkkX+Zgmb3miLz29d8SHzQul1Veq5lXCmdNKckTB0rG12bazu1LFbLLF+1KO6yv1+q+U9eler+HI4ymS5FQPm1VUp4tmEESBvrLgdPXZdDsnbJ2/wXbNi9XziFd6xWQrGmSyLlroTJq8V6ZseqIxAfNK+eSN+oVlKI5Hl67yZpNdHxvKJ1V+r5cSvL8d+2OX7JXRszdaVv+TsMi0rJqbsmdMYXcuXdfNv97QfpO3SR7T1y1rZMyaZCM7FRR6pXOJhaxyO9bTkj38evk2q274us8/d6rSufLIJ91fFaK5Upn9r129yl5f9wKOX7+hvgy6tTA04gViRXdiVjRc4gVPYtY0cPnl1jRY4gVvUOcTRoOHTpU1qxZI82bN5dixYrJ2bNnZfTo0VKqVClZv369FClSxLZuwoQJ5bvvvnPYPjg4WLzJlRt3ZPyiHZIoYYCM61HHYZl+4ZzxcQNp+/liWbzhsPniOW9gE7kVGiZjFmw36+gX0kpFMkvt92fJmcs3pWujErLws6ZSovNkOX3pppy4cEPSNf3WYb8/9W0ggQEJfD5hqK6G3JEJv++TxAkD5P+6VHFYVip3Wpneq7q8OnKl/Lr5uBTLkVp++ai23LxzT/7v171mnSYVc0j/VqWlxdClsuHABelQK5/M/ai2lOr+i5y6dNOss+v4Zfll3RHpWKeAxCfXboXJxL8OSuKgBDKqYzmHZXPWHzOTVUACP9nxRUOZte6obd6wtqUlbYpEUvWT3+VKyF15o04+mf7us1Ly/UVy9eZdeaFUFvmsdUl5eeTfsvnQJamUP73M6P6sXLpxR5ZsPy2+7srNOzL+972SKCiBjOtW1WFZ6TxpZcb7NaTtF8tlsbl208i8vnXkVug9GfPrHrNOwqAE8sHE9bLl0EXze79WpWVx/3pS6K2ZEnr3vpk3sftzkjDAXwp3mWl+/6FndZnwTlVpMWSp+DpPv/dqMDR3QGOZtWK/vPjJLxIU4C9ju9eRKb1fkOq9fhZf5qf/udrSUGhqiCcjVnyEWDHmiBU9h1jRs4gVPXx+iRU9hljRO8TZgVB69uwpx44dk6+//lpef/116du3r6xatUru3bsnQ4YMcVg3ICBA2rRp4zA1bNhQvMnSrcdk5sr9cuTMtQjLmlTJK3/vPCmL1h8Wi0Vkx6ELMnnJLnnrxRK2dZpVyycjZ282ycF79x/IqLlb5fqtO9KmdmGnj5c5TVJ5oXwuGbdoh8QHS3eclllrjsiRcxFb9mhCcNXus7J40/GH5/fIZZm87F95s14h2zqdni8gU5f/K6v3nJOwew9k/O/75NDZ69Kmeh7bOppg1Me5EQ9aZ9lbvuus/LLhuBw9H/LEdRuUfkaSJQp0aCWYI31yWbj5hEkCPrBY5IcVhyRZ4kDJlSGZWd6kfDaZu+G4bDp4ybw+a/adl8VbT8rrtfJKfLB0+ymZufpwJNduTvl791lZZLt2L8nkpQfkrRceXbvD5+wwLWI1QajTkFnbJVOqJJI/S0qzPFu6ZKa14odTNprXQCf9d8Ny2SVr2qTi6zz93hucNKGkT5lEpvyxS+6G3ZeQ22EyfdkeKZ47/VN9noAvIlZ8hFgx5ogVPYdY0bOIFT18fokVEc/F2aRhpUqVJCgoyGFe3rx5pXDhwrJ378PWX/bu378v169fF9/NwDvO8/fzkzxZUpnkillHi4iGa5mhv5eM5Ivpay8UM19y/9j8qMVXfKVnLXxrGH9/Md09kyV62BhXWx9qt057Ww5elOI50zzVY/V2HWvkkfkbT5gWhFbf/LZX6pXMIhmCE5mWiB1r5jEJsj0nrkX++vj5SbHsqSS+M+cm3Dxz7WYONslZZ2qVyGISV/+evma7tkPv3pN/jl62raP/vhN2X4rF8+vbHe+92nJl7ILt0rFuUdPSWZOIbWsXlvlr/xVfZ+1y4uoEPAmxot3fG7GiRxErPj3Eiu5FrOhZxIoxPH/Eil4hziYNnbFYLHLu3DlJmzatw/xbt25JihQpTJfk1KlTS9euXSUk5MmtntT58+dl9+7dDtPBgwclLlm84ZBUK5ZVGlXKIwn8/aRU3gzyap2HrVi0xplauO6Q9GxeRnJkDJagwATSq3kZyZg6qST/b7m9gAT+0uH5IvLd4kd1z+Iz7dZZtXBGebF8dnN+S+ZOI69Wf9iKzXr+UiQOjFDfTRNfOh9RUyBLsFTMn14mLnf8+9r470W5E/ZAdn3VSE6MayZvPZ9fun23UULDHnad/XXbKWlaPptUzJfOvD7VCmUwScbkkSTF4hNtHVutSCZp9N+1q13tX63xsOZriiQRz0+R7Knk6zcqy/uT1sutO/ds17h2GwpPr2+tURmfueu9d87qA/JciaxyYU5XOT3zLcmRMYX0Hu/7NU9NQjUGE+AKYkViRU8gVnw6iBXdj1jRs4gVY4ZY0TvE2ZqGzkyfPl1OnTolAwcOtM3LlCmTfPDBB6bW4YMHD+T333+XMWPGyI4dO2TFihWm6/Lj6LoDBgyQuGzt7tPScfjv0rtVORnTvbYcOnVFJizeKR+1Li9XQu6YdXqPXykD2lWW34e8JEkSBcq81Qdl+fbjcun67Qj70y/AKZMlkil/7I6FZxP3rNt33gxa0vul4mYwE+12POGP/dKnWXHTSkhdvx0mweESsDp4hM5H1HSokUe2Hbks2488atFm6r19UF3W778gebv9Ijduh0nt4pnkxx7PSqMhy2XXiasyd/1xSZU0SIa9Wloypkws249elqkrD5ui2vHd2n3npOOoldK7eQkZ0+VZOXTmmkxYsk8+al7C9t5gVSZPWpn7cR359KctpguzlXanD3aSYNTrW1+P+Mwd7725M6eUxZ+9ZAY+mfzHbgnw95NeLcrK8i9bStm3ptqStz4pJi0GyRnCRcSKxIqeQKz4dBAruh+xomcRK8YQsaJX8JqkoY6ErC0IK1asKO3atbPN//zzzx3We/nll83oyh9//LHMnj3b/P44Xbp0MYOt2NOWho0bN5a4ZM6qA2ayGta5mhmd8/Z/XzhvhobJe+NWmMk6oufeSR1l4NR1EfbVuUExs6/LN3x/AJSomrvuqJmshrYvZwY8uf3fQBE7j142g05oXUQrbdW1YOOjQT4QuaSJAqR5xezy8YxtDvNTJgmSnOmTyWvfrrF1Wf5922k5euGmVC+a0SQN1ffLDprJanK3yvK33cjV8dmctUfMZDWsQ3nZcOC87dpVzxXNJD9+UFPen7hBpi137Bar13aioADTCnHXsStmnv5b30N2Hrkk8V1M33uL5kwrt+/eM6MuK73Kv5qzRT5qXUEK50grm/afjZXnhbjt6tWrsnbtWtmzZ49cvHjR3InXXhYFCxY0cVCqVJRncIZYkVjRk4gVPYtY0XOIFT2LWBG+Hit6RdJQR06uX7++6X6sicAECRI8dv0ePXrIJ598IkuXLn1i0jB9+vRmim3+/n4SmMBfggL9bV887z+wyN17D7/4l86bQbYdPG+6v2mxa+0i1+R/82zbZ0ufQu4/eCCnLoZIptRJZfDrz8rZKzdlxl+O9R8LZEstVYtllardf5T4xHZ+A5yfX00Abj98ySzXgVHaVs8rTQf/adteW2+NfrOSLNh4XDb9e0Ha1chrah5Os+tqGxjgb+qdaTdGf7+Hj6HdpO7eeyC+TJ9zYAK/R+c2wP/hub3/wAweoVpUzCFh9x+YAVPsXbl5V/afuiYda+aVT37cZkasrlUsk+TPnEJ2HL1iCyKzpU0q+05dk6QJA6RttdxSuUB6ef7TR69P/Lh2Ezh/b8idVrZZr91KOU335CafLbFtr93uJ7xdVd78dpX8YpcYtzp+IUR+23JCPm9XXtqPXG7m6b8XbTomJy4+HBncl3n6vXfrv+fMa9exXlGZsmSXKQ/xduNSpoXnv6ceXuO+/N6gk6vbxjd3796VGTNmyOTJk2X16tWm94Qz/v7+UrlyZenQoYO0atVKEiZM+NSPNS4iViRWjCliRc8hVvQsYsWndH6JFd1/bokVvSJWjPNJw2vXrkm9evVMJlVHT86cOfMTt0mcOLGkSZNGLl9+1A0yrmtdo6BM6PW87fdL8942P+t8MEvW7TktI7vUkHxZU5k/LP0S2rTfPDPfPhk4qmsNM0rnjdt3TZ2tF/rMMaN12utcv5jZPr61bmldNbeM6/as7fcL09uan3X7/Sbr9p2TL1+rIPmyBD88v4cvykuf/ynr95+3ra/JlvTBieX7d6pKuhSJTKJL1zl16VFSZcEndaRq4Uy231s8m1uOnb8hhbrMFl/WolJ2+eb18rbfj41rZn42GvKXrN3/cPCY9jVyy0+rj9rqFNp79ZvV0q9Fcdk4tL4kCkwgpy7fkj7Tt8rfex62JNTahWM6VZAc6ZKK5iDXH7ggDT//Sw6fi1rdUm/Xuloek/SzuvTjw5bWdT5ZbK7dkZ0qPbp2D12Upp/9YbpRWQ1tX94kW8d3q2omq7fHrZGf/j5k/v3aqBUy8vVKsntMC/P7b1uOS/fxEVsp+yJPv/ceP39Dmg+cL5+0qSiDOlQx83YfvShN+8+zlT/wVTEZ0CS+5QzHjh0rgwYNMneK69SpIyNHjpTSpUtLrly5zJ1ivQF15coVOXLkiGzevNncFH3zzTelb9++5ibpG2+8IfEZsSKxojsQK3oOsaJnESt6+PwSK3oMsaJ3xIp+Ft17HBUaGmpOyJYtW8yT1maWUXHjxg3TKrFTp04ybty4aD+uDoZSpEgRCar8gfgnz+jCkeNx/JOm5AR5UJLkSTi/HhRyLX4kK2PF7euxfQQ+68GNs3J3zTDZtWuXFC78cDAXT7F+hlb6eIYky5zLpX2EnD4saz9r/VSONy7Ili2b9OzZ09wR1vglKq5fvy4TJ06Ur776So4ejdiKOL4gVvRNxIqeRazoWcSKHkSs6DHEinFbtliMFeNsS8P79+9Ly5YtZd26dTJ//nynCUMNFMPCwiR58uQO8z/99FOTaa1bt+5TPGIAAGB/91hLNbgivrU0PHz48BMHbgsvRYoU0r17d+nWrZvEV8SKAAB4L2JF74gV42zSsFevXrJgwQJp2LCh6WY8bdo0h+Vt2rQx9WtKlixp+mkXKFDAzF+yZIn8+uuvJmHYqFGjWDp6AACAqIluEOiubb0dsSIAAIgPAmIxVoyzkeb27dvNz4ULF5opPE0apkyZUho0aCB//vmnTJkyxdxxzpMnjwwePFjee+89UwASAAA8fTqKm06ubouHtMi11uxzVk0mderU8fo0ESsCAOC9iBW9I1aMs0nDFStWPHEdTRpOnTr1qRwPAACIOopbu05LrwwdOtTUoTlx4kSko+PpzdL4jFgRAADvRazoHbFinE0aAgAA76VtBf3M/13bNj7TEe60B0WFChWkcePGUS54DQAA4C2IFb0jViRpCAAAEIfMmjVL2rZtK5MnT47tQwEAAEA8jhXdmjS8c+eObN26Vc6fPy+VK1eWtGnTunP3AADAS/jHYPRkV7fzFUmSJDF3jn0RsSIAAFDEit4RK7ptpJCvv/5aMmXKJFWqVJGmTZvKzp07zfyLFy+a5KH2tQYAAPHEfwOhuDKZIjfxWKtWrWTRokXia4gVAQCADbGiV8SKbkkaTpo0Sbp37y5169aV77//3mHUFk0Y1qhRQ3766Sd3PBQAAPCWOjV+Lk4uPmZISIj069fPxCM6WpwmIMN329BC0TrvxRdflKxZs0rSpEmlSJEiMmjQIAkNDY34PCIJaIcMGRJh3VOnTkmLFi3MQG0pUqSQRo0ayeHDh6P9PIYNG2b20aBBA5k7d65s2rTJ9OQIP3kTYkUAAGCPWDGFV8SKbume/MUXX5gnO2PGDLl06VKE5aVLlzZ3lwEAQPzg7+dnJle3dYX2bhg4cKBky5ZNihcv7nR03Vu3bkmHDh1Ml44333xT0qdPL+vWrTPJxmXLlslff/31sLWjndq1a8urr77qMK9kyZIREpbVq1eXa9euyUcffSSBgYEycuRIqVatmmzfvl3SpEkTrS68mtz87bffzBSe3pzVY/Sm0ZOJFQEAgD1ixUCviBXdkjQ8ePCgvPPOO5Eu17v9zpKJAAAA7qJlUs6cOSMZM2aUzZs3S9myZSOsExQUJGvWrJFKlSrZ5nXq1Ely5MhhSxzWqlXLYZt8+fJJmzZtHvvYY8aMkX///Vc2btxoe9x69eqZVoyaMBs8eHCUn0fHjh3ll19+kZdfflnKly/vE6MnEysCAIDYRqwYS0lDbRapd/cjs2fPHhPAAwCA+CEmpQld3S5hwoRPjDc0aWifMLRq0qSJSRru3bs3QtJQ3b5929yxTZQokdP9zp492yQL7ROVBQoUkJo1a8rMmTOjlTRcsmSJvP322+bus68gVgQAAPaIFcUrYkW31DR84YUXZPz48XL16tUIy3bv3i0TJkwwtYMAAED84OogKLbBUJ6ys2fP2moxh6c1ELX2YeLEiaVQoUKmHIs97R6iA8CVKVMmwrblypWTQ4cOyY0bN6J8LFoPMU+ePOJLiBUBAIA9YkXviBXd0tJQi4dr9xntgtOwYUPz4k+ZMsWMmDxnzhzTBPR///ufOx4KAAB4g5gMguz3qEtreOnSpTN1CN1NC0prAKZdiu1pq0Qd3CRnzpxy+vRp+fbbb+WVV14xtQvfeusts87ly5dNbRmNd8KzztNt8+fPH6Vj0e7SP/74o6m5mCBBAvEFxIoAAMABsaJXxIpuSRpmzpxZtmzZYgp///zzz6bo4tSpUyV58uRmKGgdYdDZnXsAAIDING7cOMI87ULcv39/t5407Q6ydOlSU5dQu9Ha0/qH4esN6gBvGvO0b9/etD7UrsvW7tHhWbszW9eJCm3NOH/+fClVqpS0a9fOjPLsLCBs2rSpeAtiRQAA4G7EiqU8Hiu6JWmo9K7/d999Z6YLFy6YrjraGsDf3y09oAEAQDwbEW/evHkRul5obOFOerOzb9++8tprr9laDj6pJmK3bt3MnV29YVqlShWTOFTa2jC80NBQ89O6TlS0bNnS9u/33nvP6TreNnqyIlYEAABWxIreESu6LWnoyYAeAAB4F037xbB3skkYFi5cWDzlzz//lFdffVXq168vY8eOjfJ2ejfX2i1ZpU6d2rQy1JGbw7PO05Z2UbV8+XLxdcSKAADEb8SK3hErupQ0HDhwYLS30SznJ5984srDAQAAL+On/7nY0lC39bQNGzaYEZN18BIdsS4gIOoh0eHDhx0SX9qromjRorJ582anj5MrVy5TsiWqqlWrJt6OWBEAADwOsaJ3xIouJQ2d1RKyfjHQeobh5+s8koYAAMQfGhb4u5j78/TgyXv37jWtC3PkyCGLFi2KtDuIllsJ3yJOR7b76quvTK1mrW1o1axZM/nwww9N4tA6ivL+/fvlr7/+irTbiC8jVgQAAI9DrCheESu6lDTUeoX2Tp06ZYJvHT25e/futhFf9u3bZwLrPXv2yOLFi91zxAAAAJEYPXq0XL161YxApxYuXCgnT540/3777bdNq8Dnn39erly5Iu+//36E+CR37txSsWJF828dKVnrKjZs2FCyZctmuo9MnDhRjh8/bgZ80/qGVl26dJEJEyaYeEgDv8DAQPnyyy8lQ4YM0qtXr8e+Xno8H3/8sVStWjVar6t2TdHB5pYsWRLnrgdiRQAAEBcRK8ZCTcOuXbtK3rx5Zdq0aQ7zy5YtK9OnTzd333WdX375xR0PBwAAvODuscvdk2PQ0nDEiBFy7Ngx2+9z5841k2rTpo35eeLECfNTWwaGpyPQWZOGlStXlrVr15pB3i5duiRJkyaVcuXKmcRhjRo1HLbTLiUrVqyQHj16yKBBg0zS7LnnnpORI0c+sX6fJipr165tuqZoYeuaNWtKyZIlJVmyZBFaOergKzrS86xZs8zz1AFcvAGxIgAAsEes+MArYkW3JA21OeXQoUMjXa5PqHfv3u54KAAA4DWBoOvbuuro0aNPXCd8KZXIaHCmU1Q988wzJkCLrjFjxphWj6NGjTL//vTTT03CVQdYSZUqlTlebRmpk/5b57/yyivy7rvvSs6cOcUbECsCAAB7xIreESu6JWmYKFEiWbdunbz11ltOl+tdel0HAADEDxrIuN7S0PMDocQ1GtBpSRdtKblq1SoTV2mZF23hqNKkSSMFChQwrSCrVKliuj97E2JFAABgj1jRO2JFtyQNNYP59ddfS8qUKU29IG06qQ4dOmTmz5gxQ9555x13PBQAAPACcbm4dVymozhXr17dTL6EWBEAANgjVvSOWNEtSUPtmnzx4kVTUFKLhmuRcaX1fLRpZKtWrR7bfRkAAAC+i1gRAADA+7glaaijB+oogtrH+tdff7UVIM+ePbvUq1dPihcv7o6HAQAAXsJPYtA9WeJxU0MfRawIAADsESvGo6ShVbFixcwEAADiN037uZr6I2Xou4gVAQCAIlaMh0lDAAAA5e/nZyZXuLodAAAAvAOxYjxKGmoNw6h0Qbp//747Hg4AAABehFgRAAAgniYN//e//0VIGmqC8OjRozJv3jzJnz+/NGjQwB0PBQAAvICGBa42GKShoe8hVgQAAPaIFeNR0rB///6RLjtz5oxUqFBB8uXL546HAgAA3sDP9YFQyBo+dOPGDTO43JUrV8RisUQ4TVWrVhVvQawIAAAcECt6RazoLx6WKVMmefPNN+XTTz+N1nabNm2Sbt26SeHChSVp0qSSLVs2adGihRw4cCDSbcLCwqRQoULmS8qIESPccPQAACAmd49dneKzS5cuSatWrSRNmjRSvHhxee6556R69eq2yfq7ryBWBAAg/iFW9I5Y8akMhKJJvyNHjkRrm6FDh8qaNWukefPmZqS9s2fPyujRo6VUqVKyfv16KVKkSIRtvvnmGzl+/LgbjxwAALiC4tau69SpkyxcuFDeeecdefbZZyVVqlQ+fxESKwIAEL8QK3pHrOjxpOGuXbvk66+/jnb35J49e8qMGTMkKCjINq9ly5ZStGhRGTJkiEybNs1h/fPnz8vAgQOld+/epm4OAACAN/rjjz+kR48eMmzYMIkPiBUBAADiZqzolqRhzpw5ndYtunr1qly7dk2SJEliBkSJjkqVKkWYlzdvXtNdee/evRGWffjhh2bAlTZt2pA0BAAglmlU4HJJQ4nfNG7KkSOH+BJiRQAAYI9Y0TtiRbckDatVqxYhaai/axPJ3Llzy8svvyypU6eO8eNoYcdz586ZxKG9jRs3ypQpU2T16tXRLrquLRQvXLjgMO/gwYMxPlYAAOKzh3VqXEv/xfeahnoD9JdffpEuXbqIryBWBAAA9ogVvSNWdEvScPLkyfI0TJ8+XU6dOmW6IdsnEt9++23TdblixYpy9OjRaO1zzJgxMmDAgMdcxR4fKybeufRTh9g+BJ9W66tVsX0IPu3g/nOxfQg+69qpkNg+BN8VC1k4fURXP0HjW85w69atDr9rPeeVK1dK3bp1pXPnzpI1a1ZJkCBBhO20zrO3IFZEdCROlpgT5kH5C2fm/HoQsaLnECt6ELFinLY1FmNFtyQNO3bsKG+88YaUL1/e6XJtCTh27FiZOHGiy4+xb98+6dq1q0kMtmvXziEI/eeff2T27Nku7Vczs3rCw7c0bNy4scvHCgAAEFVlypSJ0CpTb4qqP//8M8L6ukzXv3//vtecZGJFAAAA74sV3dbSsFatWpEmDXXkZO0+7GrSUEdOrl+/vgQHB5vkoDWDev36denTp4+8//77JrPqivTp05sJAAC4jwYqrndPjl9tDSdNmiS+jlgRAADYI1b0jljR46Mnq9OnT0vixK51M9CBVOrVq2cGVVm1apVkzvyoOf2IESPk7t27pmuytVvyyZMnzc8rV66Yebq+/QjMAADA8/z9Hk6ubhuf2PegiK+IFQEAiF+IFb0jVnQ5aTh//nwzWY0fP16WLl0aYT1N9un8smXLRvsxQkNDpWHDhnLgwAGzj0KFCjksP378uEkOhh8YRQ0ePNhM27ZtkxIlSkT7sQEAgOu0saCryb941tAwgho1asjHH38sNWvWdLp8+fLl8umnn8pff/0lcRmxIgAAiAyxonfEii4nDffs2SOzZs2yNSvdsGGDbNmyxWEdnZ80aVKpWrWqfPnll9Hav/a91haE69atM0Gn1jIM75133olQe1BHQ9b6iu3bt5dGjRpJzpw5XXp+AADAdXQ5cd2KFSvk9ddfj3S5xjpa/DquI1YEAACRIVb0jljR5aSh1hLUSfn7+8v3338vrVu3Fnfp1auXLFiwwLQ0vHz5skybNi3CENM6Ekz40WCs3ZS19SGDmQAAAG/0uLqOOmBb8uTJJa4jVgQAAPDuWNEtNQ0fPHgg7rZ9+3bzc+HChWYKT5OGAAAgbvKPQW1C3Ta+0QHjdLIaNGiQTJgwwWnZl507d8oLL7wg3oRYEQAA2CNW9I5Y8akMhOJqc0tX5MiRwzb0NAAAiB1689PV2oTxsabhrVu35MKFC7bfb9y4YXpyOCv78uabb8r//vc/ie+IFQEA8F7Eit4RK7qUNNQD00kPWkcm1n8/rmmk0uX37t1z9TgBAIAX0c99fxezf0+KKXzRW2+9ZSal9ZhHjRolL774ongrYkUAAPA4xIreESu6lDTUjKW+wAEBAQ6/AwAAxJaQkBAZPny4GZxt48aNcuXKFZk0aZIZHC28vXv3So8ePWT16tXmBmj9+vXNoG3p0qWL0K12xIgR8n//939y5swZyZcvn6nV16pVK5f3+SRHjhwRb0esCAAA4hpixaeUNOzfv/9jfwcAAPGbqVMTg21dcfHiRRk4cKBky5ZNihcvHmn31ZMnT0rVqlUlODhYBg8ebAJITQz+888/JtmoCT+rjz/+WIYMGSKdOnWSsmXLyvz5883Ab3qz9OWXX3Zpn1G1aNEi+fXXX22DvGkJFq1P06BBA4nriBUBAMDjECuGeEWs6JaahhqgN23aVIoUKeJ0+e7du2XOnDnU3wEAIJ6IjTo1mTJlMq0BM2bMKJs3bzZJPmc0qXfz5k3ZsmWLSTCqcuXKSe3atWXy5MnSuXNnM+/UqVPyxRdfSNeuXWX06NFm3uuvvy7VqlWT999/X5o3by4JEiSI1j6jQgtYN2nSRP7++2+zf31eaunSpTJu3Dh59tlnZd68eZIyZUrxFsSKAADAHrGieEWs6O+uu8k6Oktkdu3aJQMGDHDHQwEAAC+g9QxjMrkiYcKEJmH4JHojU+/AWpN7qlatWqbr8cyZM23ztFVhWFiYdOnSxTZPWxhqPRltWbhu3bpo7zMq3n33XVm1apUMHTrUdLE+duyYmfTf2upRuz/rOt6EWBEAANgjVhSviBWfyujJly9fdqmpJQAA8F4xLXd88ODBCPO0PmD69Old3qe2Hjx//ryUKVMmwjK926tdPKy2bdtmRqArWLBghPWsy6tUqRKtfUaF3hnWROV7773nMF+PRVs4Hj9+XH744QfxJcSKAADEP8SKEudjRZeThtoM0r5W0Ny5c50G99ps8ueff5aiRYu6fpQAACDeady4cYR5/fr1i1EtZe2+rKzdOOzpPE1e3blzx7Ra1HUzZMgQYbA367anT5+O9j6jIjAwUPLnzx/p8gIFCph14jpiRQAA4EnEip6PFV1OGi5fvtzW5ViDaU0a6uRMoUKF5JtvvnH9KAEAgFfx93s4ubqt9S5qnjx5HJZFdyTi8G7fvm1+OkvgJUqUyLaOLrf+fNx60d1nVLz00ksya9YsefPNN201E63u3btnurBoPcW4jlgRAABEhljRO2JFl5OGH3zwgXTr1k0sFovpJjR27Fhz4PY0mZgkSRLbSQAAAPFDTGoTWrfThGHhwoXdelyJEyc2P7XlX3ihoaEO6+jPqK4X1X1GRZs2bUyMValSJVMU25o4/ffff2X8+PFy9+5deeWVV2Tr1q0O25UqVUriEmJFAAAQGWJF74gVXU4a6hOyPqkjR46YO/+aIAQAAIiNEfGiwtqF2Nql2J7OS506te0ur66rreX0Bql9F2XrtpkzZ472PqNCR2e22rRpk+2x9TicrWM9vvv370tcQqwIAAAiQ6zoHbGiWwZCyZ49uzt2AwAA4FFZsmQxNzo3b94cYdnGjRulRIkStt/13999953s3bvXlFqx2rBhg215dPcZFZMmTRJfQ6wIAAC8AbGih0ZP3rlzp6lbqM0fr127Jg8ePHBYrlnNQ4cOuevhAABAHOYXg5qGnmxpqLScypQpU+TEiROSNWtWM2/ZsmVy4MAB6dGjh229Ro0amd/HjBkjo0ePtt2p1ZIsGlBql5Do7jMq2rVrJ76IWBEAAFgRK4pXxIpuSRrqKMp169aVVKlSSZkyZWTbtm1So0YN0zd73bp1ph5R6dKl3fFQAADAC2jez8/837VtXaXJvatXr9pGNl64cKGcPHnS/Pvtt9+W4OBg+eijj0zx6OrVq8u7774rISEhMnz4cClatKh06NDBtq9nnnlGunfvbpaFhYVJ2bJlzeAsq1atkunTpzsUno7qPqNLu6ycP3/e1KpJmjSpeCtiRQAAYI9YMcQrYkW3JA3/97//Sa5cuWT9+vWm4KIOjKLBsyYOtQtPvXr1ZOjQoe54KAAAEE9GxHPFiBEj5NixY7bf586dayZr0WhNGmpLwJUrV0rPnj3lww8/lKCgIKlfv7588cUXEerJDBkyxNwUHTdunEyePFny5s0r06ZNk9atWzusF519RsX8+fOld+/epqC1+vPPP01cdfHiRaldu7aJvZo0aSLeglgRAADYI1YM8opY0S1JQ+2SPGDAAEmRIoVcuXLFzLMWWCxfvry88cYb8sknn5jkIQAA8H2a93O5e3IMHvfo0aNRWk97QSxZsuSJ6/n7+0ufPn3M5K59Pom2jmzatKlUrFjRJCf79+9vW5Y2bVrTNVoTmN6UNCRWBAAA9ogVvSNW9I/xHjTzGBAgyZMnN/9OmTKlBAYGmuaRVtoKcc+ePe54KAAAAJ82cOBAqVq1qqxevVq6du0aYbkGiFoKxpsQKwIAAHhfrOiWpKH2nbY2idQBTwoUKCC//PKLbfnixYslY8aM7ngoAADgBTQeiMkUn+3atUtatGgR6fIMGTI43Jz1BsSKAADAHrGid8SKbkkavvDCC/Ljjz/KvXv3zO9az0frB2ndH50WLFhguigDAID4VafG1Sk+S5Ikidy8eTPS5YcPH5Y0adKINyFWBAAA9ogVvSNWdEvSUOsV7tixwzaKoA7//MMPP0iRIkWkePHi8vXXX5vi4wAAIH7QxoIxmeIzHYF5ypQptpux9s6ePSsTJkyQOnXqiDchVgQAAPaIFb0jVnRL0lBrGGoW0747kSYJtYvy7Nmz5dq1a5I9e3Z3PBQAAIBP++yzz+TkyZNStmxZM2qzxlc6wErfvn2laNGiYrFYpF+/fuJNiBUBAAC8L1Z0S9IwKvSgAQBA/KDBi7+LU3yvaZg/f35T2FpvyGoLPY2hhg8fLoMHDzaB4KpVqyRHjhzia4gVAQCIP4gVvSNWDHDLXgAAAOzEpDZhfK9pqAoXLixLly6VK1euyMGDB+XBgweSK1cuSZcuXWwfGgAAQIwRK3pHrEjSEAAAuJ3m/VxtMEjO8JFUqVKZricAAAC+hFjRO2JFkoYAAMDt/MXPTK5uG1/duXNHpk2bJn/88YccOnRIbty4IcmTJ5c8efJI3bp1pXXr1hIUFBTbhwkAABAjxIreESu6nDTcunVrlNc9ffq0qw8DAAAQL/zzzz/SqFEjOXbsmKlNExwcLMmSJZPz58+buGvWrFmm8PWCBQukYMGCEtcRKwIAAHh3rOhy0rBMmTJRLlSuTya+FzUHACBe8XO9e3J8bGgYEhIiL774ogn6NNhr27atZMmSxbb81KlT8sMPP8igQYOkYcOGsmPHDkmaNKnEZcSKAAAgUsSKXhErupw0nDRpUowfHAAA+CaKW0c/rjp+/LgsW7ZMnnvuuQjLNSjs06ePlC9fXmrXri2TJ0+Wrl27SlxGrAgAACJDrOgdsaLLScN27dqJJ23atEmmTJkiy5cvl6NHj5qhpCtUqGCypvny5bOtN2HCBNOfe9++fXL16lXJnDmzOYH9+vVz2xDTT0PzqvnkjQbFpGiutJIiSUJJ1uBruf/AYlter1xO6ftKecmTJaVcC7kr43/dKSNmbrYtT5IwQD5//VlpWCGXJE0cKEfPXpfBMzbI/LWHbOsEBSSQj18pLy9Xzy9pUiSWS9dvy4Af1smMv/Y99efrDbSF7KCB/WXi9xPk+rVrUrJUaRn1zRgpXKRIbB9anFIzfzppWjKT5EmXVJImDJBqX6yS+48uXZv8GZLJuNbFZc+ZG9Llp522+e/VyiMVcqWS5AkD5O79B7Lj5HUZveKwnL1+xywvnCm59KiZWzIHJ5IE/n5yPuSuzNl6WubtOCPxQaOyz0iH6nmk8DPBkjxxoGR5Y47tvaFp+awyvE1ph/UTBvrL/tPXpebApeb39xoWkh4NCkro3fu2df7YeVremrDR9rvu5+26BeSZNEkkJPSeLNxyUgbN+Ufu3nsgvu5pvPc+aR++yt/Pz0yubhvfLF68WOrUqeM0CLRXo0YNEwguXLgwzicNiRXdi1jRs5qUzyYda+SVItlSms/bDB1/dvg8aFYxu7zzQkHJmjaphISGyfxNJ2TgzB22z8qgAH/5tFVJaVwumyQM8Je1+8/Lez9skdOXb5nlGVMmluGvlpYi2VKZfXSfuFGm/X1Y4gNiRc8iVvQsYkXPIVb0jlgxzg6EMnToUFmzZo00b95cihUrJmfPnpXRo0dLqVKlZP369VLkv8TNtm3bJGfOnKaZpo4ac+TIEZNIXLRokWmOqUlEb3AlJFTGL94piYICZFyP2g7LSufNIDM+ekHaDvlNFm84LMVyppN5AxvJrdAwGbNgh1nnf20rSrViz8hzvWbKiQs3pGmVvDL1w3pSrusM2Xfislln+kcvSOKEAVKvz1w5fOaapAtOLCmTJYyV5+sNRn45QqZMnigLFy+R3HnyyOBBA6Vh/edl5+79pm4AHrpxJ0x+2X7GBMh96j5K6NsLSuAnH9XNJ9tPXpOgBP4Oy+ZsOy3frjwit8PuS6JAf3m9cnb57MWC8tq07Wb5qauh8snCvXL22h3R0D132iTyVYuicv7GHVl7+OG17cuu3QqTySsOSeLABDKyfRmHZXM3nDCTVUACP9k6tL7MXn/cYb3Nhy5Jo2ErnO6/0DPBMrpjOXlrwgZZsOWkZEmVRGZ0ryK3796Xz3/ZJb7O0++9UdkHYK1R884770TpZGgwOGrUqHh/4ogV3ft+Fd9jxas378qkv/6VREEJ5OvXyjssK5w1pYzpVEE6j11rkoVZUieRmb2qye079+WzOQ9vhGrCsEK+dFKz/xKzr6FtS8u0d581v1ssD29GL999Vr75dZ9MeKuSxCfEip5FrOhZxIqI77FinE0a9uzZU2bMmOEw6kvLli2laNGiMmTIENO6UI0ZMybCto0bNzZ1dLQ/94cffijeYOnWh1/yny36qE+6VZMqeeTvnSdl0fqHdyN3HL4gk//YLW81LGELBHNlCpY/Nh+T4+dvmN/nrPpXvupSXYrkTGMCweeKZ5WaJbNJ/vYT5cK122Yd/Wn9NyIaP3aMdO/xnhQpWtT83m/ApzJ54neyYN4v0rpNW07ZfzYevWp+lswaHOk56fxsDtly/KqE3LknZbKldFh25NLDO/BWGlhnTZ3E9vvV22Fy1e4ytfy3TvbUiWVtPLhBv2L3OfOzUr50T1y3fqkskixRgPy4+kiU9589XVK5fjtM5m8+aX4/efmWLNt5VopmdXydfJWn33ujsg9fpY0FXW0wGA8bGsrly5clY8aMUVo3Q4YMZv34jljxEWLFmFu+66z5WblA+kg/K+dtfHij7uSlW/LnjjNSNHtKWyv/VlVyyhtj15ll6pMft8nurxpL+bzpZP2BC3LuWqhMXHbQLLuvgUw8QqzoWcSKnkWs6DnEit4RKzo2+YlDKlWqFGGY6Lx580rhwoVl7969j93W2i1Zuyv7zh+TX4SmvNrVLVniQPP76PnbpXKRzJIzY7D4+/tJy+fym/mr/jllfmrC8Oi5a9KreRk5PO11+XdKR9OqJk2KRLHwjOK+a9euybGjR6VM2XK2eQEBAVK8REnZvn1brB6btyn+TAqplCu1jFt1NNJ1GhfPJL+/XVGWvltZmpfKLBNWR1x3duey8lf3yvJD+9Jy7XaY/L7nvIeP3Pu0fy63LNh8Uq7eCnOYXzRbStn9ZUPZPKSejHm9nGRLm8Qh0DxyPsR0Uda6IvrFqHbxTLJ428P3jvjMHe+9UdmHr9Ln7e/iFB8HT7tz544EBkbtmtDPo7t370p8R6z4CLGi5xOKh8/dMF2U9T0qR7pk8nyJzLJ4y8MbbnkypjDlKrYevmTb5nLIXTl+McR8BuPxiBWfHmJF9yJWjOn5I1b0hlgxzrY0dEab9Z87d84kDsO7dOmS3L9/3xSGHDhwoJlXs2bNJ+5TR565cOGCw7yDBx/eBYwrFq8/It0alZRGlXKb1irFc6eTV2sXMstSJAmSkNthsuvIRdOqZc/E9nLv/gO5feeevP7FH3LuysO7nWmCE0nBbGlk5Y6TUuS1yZIscZBMfP95+f6956Xx/+bH8jOMe65fv25+pkzpGOilTJVKbvy3DE+WONBf+jyfTz5fckDuPKY+ntYn1ClN0iBpUDSDHLxwM8I6zcZvkgB/PymRNViKPxMst+xq9EGkQOYUUjFfOhkw61G9SLVoy0n5ac1R04IwY8pE8kmzYjKzR1WpMfBPuXXnvumGPH3VERncqqR83aGsBCTwl5lrj0artaKvcsd7b1T24as07edyS0OJn7SG89atW5+4npZigXPEisSKnqCfldNWHpYhbUrL6NfLm8/Kn1Yfkel/P/xb1BqI1m6i9q7eDLMtg3PEik8PsaL7ESvGDLGid8SKXpU0nD59uhlG2poUDD9SjGZelQ6a8vXXX5vij0+i3ZsHDBggcdnaPael44gl0vvlcjLm3Vpy6PRVmfDrP/JRq3JyJeThc9Y6NqFh9yVXm+/k7JWbUrFgZvnpk/py9959+X3TUblx6648eGCRjyauNl9qb925J59OXSd/jWhhatfoPDySIkUKp61Vr165IpkzR+zGCOe6Vssl645cNoObRMWlm3dl/s6zMrtTWWk+YZNcCRd833tgkc3Hrkq1vGmkU5XsMnoFX5zt7xxvP3pZth+94nDO9p1+dO7PXg2VHpM3y4GvG0nZ3Gll5Z5z0qJidun7UlFp/+1a2XDwoqRLkUi+eLW0jHm9vLw5YUO8vrTd8d4blX34Kv8YdGeIs90gPOyTTz4xU1QSY/GxNWZUECsSK3pCy8o55H8tikvbUatk/b8XJH2KRDKyQ1kZ92YF6fR/6+TGfzeAgpMESui1Rzc1UyYNtC2Dc8SKTw+xovsRK8YMsaJ3xIpekzTU0ZF15JeKFSs6HY3vt99+k9DQUNN1Wesd3rwZsaWSM126dDGDrYRvaah1EeMSrZOlk9WwTlVlw76ztmRfqbwZTOuWM5dv2t7A1u46bUbt1C+u2w6ej/xiekrPwZsEBwdL9hw5ZMvmTVKhYkUz7969e7Jzx3Zp/Qr1DKOqfM5UkixhgNT+rz6QDnSirQUXdakgb8zYbgY5CU+XJwpMIOmTJ4yQNLTSUZSzpkrs8uvra3TU6pcqZJNPfno4eMzj6N+8llKyfoYUz5FK1v970Uzq/LVQmfr3YRnXuYKnD9srxPS9Nyr7ANSkSZM4ETFErEis6CklcqaWdfvPy7oDD3snaX3CH1YckgldHg5ocvDsdXNDvmTO1PL79tNmXupkQWaU5H+O+0a5JE8hVnw6iBU9h1gRvh4rekXSUEdOrl+/vknkzJ49WxIkSBBhnerVq5uf9erVk0aNGpnRlXWE227duj123+nTpzdTbNNaWIEJ/CUo4OFzSxiYQO4/sJjWKtZR8TTxFxSYQJpUziOv1ikkTfo96la8Zvdpafd8YVm357QZ3KRc/oxSpWgW6TtpjVm+YO0hOd0+RAa2q2TmJU0UKB+/UkF+33zUBDmIqPObXeSrkSPkueo1JFfu3DJk8CAJCAyUFxs34XTZX7t+DxN9OqnAAH9J8MAiYfct8sb07SbBZ9WyTBYpliVYPp6/Ry7fvCvBiQNMvcPVhy7LjdB7kj55kPSomUfOXQ+VIxcfJmGq5k0jp6+GmgFTdE+VcqeW5wuml9Er40crQz19+t6g51XpKNXmveH+A5P8U80rZpOw+w9k3qZHIylbvVjmGVm977yprZQ2eULp17yYXLweKpsOPqy7tOHfizKsTSkpkzuNGWU5TbIgeaVKTtl5zLHFoq/y9HuvJmeftI/4VOcnOtvGN85uiCLqiBWJFWNKaxUGBjz8TLB93lr08+CBrN9/Qb5oX1bK5kljPj/TJE8obarllh3/te6/E/bAlPXo3bSoSRJeu3VXBr5cUvafvi4b/n1UBkkHTFH6FheQwM/8rp859+779sAoxIqeP7/Eih48v8SKHkOs6B2xYoA3DEihiUDtJrpq1SrJnDnzE7fJnTu3lCxZ0nRReVLSMK5oXaOATOhZx/b7pV+6mp91es+WdXvOyMi3npN8WVOZgGbrv+elab8FZr7VGyP/lMGvVZEN374iyRIFmnpaX/+yTSYv2W2Wa2Kw/se/yJdvPScnf+psuitrwvDj71fHwrP1Dj16vichN25I/bq1TI3DUqXLyIJFv5tkNB55vlB6+bjew8EflA5mot7+eadsO3HN4VRpHUKt+3Yh5GFRVk166fbdnsslQQH+JnG44+Q1eXfmP3L3vwA6dZIgefPZHJI2WUITWJ+5FmoShr9sf3T9+7LmFbPLqA5lbb8f/vZh0rrp8JWy9r8WD+2q5ZaZa49JaFjEupEvlc8mn7cuKYmDEphaS9qtqvmXf8vN/24W6MAp6YMTych2pSVjysSmq+36Axel63cbJT7w9HtvAn//J+7Dl8XD3B9iAbEisaI7tKicw9QrtDo+/mFPpEZD/pL5m05IhpSJ5evXypvPSq1xuO7AeXlz3Drb+jpa8qetSsrygc+bmGbd/gvyylerbDf41KkJLWz/HtGurJk02fj2d75dDoRY0bOIFT2LWNGziBXjPj+L9lWLo7S7cZ06dWTLli2ydOlS0zU5qjRpqDUO9+zZE+3H3b17t2mpGFSlt/gnzxTt7fF4Vxa8yynyoFpfreL8etDB/ec4vx5y7RQjNnvKgxtn5O7qobJr1y6ng4m5k/Uz9POfl8ozuR/dUIiOk4f2S5+WtZ7K8cK7ESv6pqTpM8T2Ifi0AkWoz+1JxIqeQ6zoOcSK8Lpa4zoScsuWLWXdunUya9YspwlDrTF35UrELnQbN26Uf/75R8qUKfOUjhYAAABPE7EiAABAPE0a9urVSxYsWGC6Jl++fNkMbmI/qZCQEMmaNau89tpr8uWXX8q4ceNMd2Stb6j1D6MyqgwAAPBMd5OYTK5o3769qaMY2XTqv9aszz33nNPldevWjbBP7bXQu3dvUx4lceLEUr58efnzzz9jeHbgDsSKAAB4L2JF7xBnaxpu3/5wFNCFCxeaKbw2bdpIkiRJ5PXXX5fly5ebAVJu375tgvpWrVpJ3759JUeOHLFw5AAA4GFxa9fOg6vbvfHGG1KrVi2HeVqF5c033zQxQZYsj7rkPfPMM/L55587rOusbrImIjXG6N69u+TNm1cmT54sL7zwgok9qlSp4tqBwi2IFQEA8F7Eit4hziYNV6xY8cR1goKC5KuvvnoqxwMAAKLO2nrPFa5up6VMwpczWb16tdy6dUteeeUVh/naI0FvQD6Oljv56aefZPjw4fLee++Zea+++qqp2fjBBx/I2rVrXTpOuAexIgAA3otY0TvE2e7JAADAe/n9F2S4MrlzJL0ZM2aYoLR169ZOayNrqZPIaAvDBAkSSOfOnW3zEiVKZMqiaM3lEydOuPFIAQAA4g9iRe9A0hAAAPiksLAwmTlzplSqVClCyZIDBw5I0qRJJXny5JIxY0ZTB1nXt7dt2zbJly+fpEiRwmF+uXLlHLrHAgAAwPsQK3px92QAABC/u5wcPHgwwrJ06dJJ+vTpo7SfJUuWyKVLlyJ0Tc6dO7cZNK1o0aJy8+ZN06Jw0KBBJpH4888/29Y7c+aMZMqUKcJ+rfNOnz4d7ecGAAAAYkVvQdIQAAC4XUxGQbZu17hx4wjL+vXrJ/37949y1+TAwEBp0aKFw/zvv//e4fe2bduaLsgTJkyQHj16SIUKFcx8HWAtYcKEEfarXZStywEAABB9xIregaQhAABwOz+JQUvD/9KG8+bNkzx58kRoaRgVWqtw/vz58vzzz0uaNGmeuH6vXr1M0nDp0qW2pGHixInlzp07EdYNDQ21LQcAAED0ESt6B5KGAAAgTtKEYeHChV3aVhOOzkZNjkzWrFnNz8uXLzt0Qz516lSEdbXbssqcObNLxwYAAICYI1b0PAZCAQAAPjci3vTp0yVZsmTy4osvRmn9w4cPR2jJWKJECVPn8Pr16w7rbtiwwbYcAAAA0Ues6B1IGgIAAI8NhOLqFBMXLlww3YybNGkiSZIkcVimCcDwXY4tFosZCEVpd2arZs2ayf3792X8+PG2ebrtpEmTpHz58rbWiQAAAIgeYkXvQPdkAAAQJ4tbu0pHQL53757Trslbt26VVq1amUm7tOhgJr/88ousWbPGDIZSqlQp27qaGGzevLn06dNHzp8/b9afMmWKHD16NMJgKgAAAIg6YkXvQNIQAAC4nTYWdLXBYAwbGpquyenTp5datWpFWJY9e3Z59tlnTaLw7Nmz4u/vLwULFpSxY8eapGF4P/zwg3zyyScydepUuXLlihQrVkwWLVokVatWjdlBAgAAxGPEit6BpCEAAPAp69ati3RZzpw5ZebMmVHeV6JEiWT48OFmAgAAgPcjVow6koYAAMDtHg5q4lqTQQouAwAA+DZiRe9A0hAAALhfDLonu2X4ZAAAAMRdxIpegaQhAABwO7///nN1WwAAAPguYkXvQA8gAAAAAAAAAA5oaQgAAHxqRDwAAADEbcSK3oGkIQAAcDsdBMX1gVDIGgIAAPgyYkXvQNIQAAC4H8WtAQAAQKzo1UgaAgAAt9O2gi53T3b3wQAAACBOIVb0DgyEAgAAAAAAAMABLQ0BAIDb+f33n6vbAgAAwHcRK3oHkoYAAMDt/P0eTq5uCwAAAN9FrOgdSBoCAAC34+4xAAAAiBW9GzUNAQAAAAAAADigpSEAAHA/P9dHT6akIQAAgI8jVvQKJA0BAIDb0T0ZAAAAxIrejaQhAABwO4pbAwAAgFjRu8XZmoabNm2Sbt26SeHChSVp0qSSLVs2adGihRw4cMC2zoMHD2Ty5Mny4osvStasWc16RYoUkUGDBkloaGisHj8AAPGZn11rw+j/BzwZsSIAAN6LWNE7xNmWhkOHDpU1a9ZI8+bNpVixYnL27FkZPXq0lCpVStavX2+Sg7du3ZIOHTpIhQoV5M0335T06dPLunXrpF+/frJs2TL566+/xM/lgkoAAACIq4gVAQAA4mnSsGfPnjJjxgwJCgqyzWvZsqUULVpUhgwZItOmTTPLNLFYqVIl2zqdOnWSHDly2BKHtWrViqVnAABA/KX37Fy9b8f9PkQFsSIAAN6LWNE7xNnuyZoItE8Yqrx585ruynv37jW/63L7hKFVkyZNzE/regAAILa6nUR/AqKCWBEAAO9GrBj3xdmkoTMWi0XOnTsnadOmfex62pVZPWk9AADgGf5+fjGaAFcQKwIA4B2IFb1DnO2e7Mz06dPl1KlTMnDgwMeuN2zYMEmRIoXUq1fvifs8f/68XLhwwWHewYMHY3ysAAAAeLqIFQEAAOJh0nDfvn3StWtXqVixorRr1y7S9QYPHixLly6VMWPGSMqUKZ+4X11vwIABzhdaLCKWBzE5bDiRtvVkzosHpUidgvPrQTeu3uD8egrvt56jn2dPWUy6GtPOEK4gVvQdobdCY/sQfNrhg44NJuBexIoeRKzowXNLrAgv7p6s3Y3r168vwcHBMnv2bEmQIIHT9X7++Wfp27evvPbaa/LWW29Fad9dunSRXbt2OUzz5s1z8zMAACAeesqFalasWCF+fn5Op/Xr1zusu3btWqlSpYokSZJEMmbMKO+8846EhIRE2OedO3ekd+/ekjlzZkmcOLGUL19e/vzzT9cPEh5BrAgAgBciVozz4nxLw2vXrpluxlevXpVVq1aZoN0ZDeBfffVVk1wcO3ZslPefPn16MwEAAPfx++8/V7eNCU0Ali1b1mFenjx5bP/evn271KxZUwoWLChffvmlnDx5UkaMGCH//vuv/Pbbbw7btW/f3tyw7N69uxmQbfLkyfLCCy/I8uXLTdIRsY9YEQAA70Os6B3idNIwNDRUGjZsKAcOHDBdjgsVKuR0vQ0bNpgRk8uUKSMzZ86UgIA4/bQAAPB5OpaJq+OZxHQclGeffVaaNWsW6fKPPvpIUqVKZVomag1klSNHDunUqZP88ccfUqdOHTNv48aN8tNPP8nw4cPlvffeM/P0BmWRIkXkgw8+MK0VEbuIFQEA8E7Eit4hznZPvn//vrRs2VLWrVsns2bNMrUMndm7d69pXajB/qJFi0zXIQAAEL/duHFD7t27F2H+9evXTe+ENm3a2BKG1mRgsmTJzM1HK2tJlM6dO9vmJUqUyJRB0fjkxIkTT+GZIDLEigAAwFXEilETZ5vk9erVSxYsWGBaGl6+fFmmTZvmsFyDfX2Rn3/+ebly5Yq8//77snjxYod1cufOHWmyEQAAeFZsDWjSoUMHU59QE37a6lBbCmpvBPXPP/+YZKL1d6ugoCApUaKEbNu2zTZP/50vXz6H5KIqV66crZtz1qxZn8pzQkTEigAAeDdixbgvziYNNRBXCxcuNFN4mjS8dOmS7S7/hx9+GGEdHWWZpCEAALHADcMnHzx4MMKidOnSRVqLWBN/L730kqk5mDZtWtmzZ4+pVaiJQ+1KXLJkSTlz5oxZN1OmTBG213laP9lK141sPXX69GkXnyDcgVgRAAAvRqzoFeJs0lDrDD2Jdkm2xMLQ4AAAwPPFrRs3bhxhWb9+/aR///5Ot6tUqZKZrF588UVT27BYsWLSp08f+f333+X27dtmWcKECSNsr12PrcuV/juy9azLEXuIFQEA8F7Eit4hziYNAQBA/C5uPW/ePIdRj60tDaNDt2/UqJHMnTvX1MCz1j6+c+eO00E17Gsj678jW8+6HAAAANFHrOgdSBoCAIA4SRN+hQsXjvF+tO7g3bt35ebNm7auxdZuyvZ0XubMmW2/67qnTp1yup6yXxcAAABPF7FiPB49GQAAeH+ZGlcndzp8+LDpUqyjIxcpUkQCAgJk8+bNDutoUlFr5OlgKFb67wMHDpgRl+1t2LDBthwAAADRR6zoHUgaAgAAn4gEL1y4EGHejh07ZMGCBVKnTh3x9/eX4OBgqVWrlkybNk1u3LhhW2/q1KlmxOXmzZvb5mk9RO3SPH78eNs87a48adIkKV++PCMnAwAAuIpY0SvQPRkAAMTJ4tbR1bJlS1NnUAdD0RGWdfRkTfglSZJEhgwZYlvvs88+M+tUq1ZNOnfuLCdPnpQvvvjCJBbr1q1rW08Tg5pE1EFUzp8/b7rATJkyRY4ePSrff/+9S8cIAAAAYkVvQUtDAADgE3S05YsXL8qXX34pXbp0kZ9//lmaNm1quiIXLFjQtl6pUqVk6dKlJsHYo0cPk1h87bXXZPbs2RH2+cMPP0j37t1NS8R33nlHwsLCZNGiRVK1atWn/OwAAAAQE8SK0UdLQwAAECdHxIsuTerpFBVVqlSRNWvWPHE9rYU4fPhwMwEAAMA9iBW9A0lDAADgEe4e0AQAAAC+g1gx7iNpCAAAPINIEAAAAMSKXoukIQAA8ImBUAAAAOAdiBW9AwOhAAAAAAAAAHBAS0MAAOATxa0BAADgHYgVvQNJQwAA4Haa93M190fOEAAAwLcRK3oHkoYAAMD9iAQBAABArOjVqGkIAAAAAAAAwAEtDQEAgIcaGro6ejIAAAB8GbGidyBpCAAA3I7i1gAAACBW9G4kDQEAgEfQYhAAAADEit6LpCEAAHA/BkIBAAAAsaJXYyAUAAAAAAAAAA5oaQgAANxOB0FxfSAUOjYDAAD4MmJF70DSEAAAuB0DoQAAAIBY0buRNAQAAG5HSUMAAAAQK3o3ahoCAAAAAAAAcEBLQwAA4BmUJgQAAACxotciaQgAANyO4tYAAAAgVvRuJA0BAIDbMRAKAAAAiBW9W5xNGm7atEmmTJkiy5cvl6NHj0qaNGmkQoUKMmjQIMmXL59tvY0bN8rkyZNlw4YNsnPnTrl3755YLBbxNs2r5ZM3GpSQornSSookCSVZ/a/k/oNHz6NeuZzSt01FyZMlpVwLuSPjF++UETM32ZYnDExglut+0qRILHuOXZIPxq2UDfvOmOVZ0yWXreNedXjMwAB/CfD3l+ytx8ml66Hiy5pVyimdny8gRbKnkhRJgiT45SkO57dFlVzSq3FRyZo2qYSEhskv647JJ9M3y917D8zy3BmTy+ftykn5vOnEz99Pth++JH1+2CS7j18xy0d1qigtn83l8JjJEgXKmF/3yAeTN4ova1TmGWlfLZcUeiZYkicOlKxdfrGd2yblssqw1iUd1k8Y6C/7z1yX2oP+Mr/3rF9AmlXIJqmTJZR79x/IzmNX5bNfdsnuk9ds2wQF+EvP+gWlabmskjpZkFwOuSvDFuyR2RuOi68z127dAlI0e2pz7aZoOdnh2m2p126TYnbX7lHpO+3RtZs5dRJz7VYtlFESBSWQv3aell4T18vZK7dt23/9RiWHx0wUmED2nrgqFd6fL77O0++9qnS+DPJZx2elWK50Zt9rd5+S98etkOPnb4ivo3cyPIlYkVjRnYgVPYdY0bOIFT2LWNGziBXjvjibNBw6dKisWbNGmjdvLsWKFZOzZ8/K6NGjpVSpUrJ+/XopUqSIWe/XX3+V7777zqyTK1cuOXDggHijKzfuyPhFOyRRwgAZ16OOwzL9wjnj4wbS9vPFsnjDYfPFc97AJnIrNEzGLNhu1tEvpJWKZJba78+SM5dvStdGJWThZ02lROfJcvrSTTlx4Yaka/qtw35/6ttAAgMS+HzCUF25eUcm/LHPJE3+760qDss0kfhdt2elw6iVMnf9UXkmTVKZ91FtuXW3hAz4catZZ3L3anL0XIgU7jbbJGMGvlJa5vapJQW6zBLNUb87YZ2ZrApnSyUbRjSSH/8+JL7u6q27MmXlYXNuv3y1tMOyXzaeMJNVgL+fbP68nszZ8Gje/M0n5fvlh+TarTAJTOAnHavnlhnvVJaSvX8Va+5mfKfykijIX1p8tUqOXrgpaZInlJRJAiU+uKrX7pJ9kjgoQP6vi+O1W1Sv3berSnu9dtcdeXjtflxHbt25J/1/3Gpaes3qXUt2Hb8sRd6eLf5+fvLtm5XNvGc/XGj28fPqw2ayCkjgJwfGtogX1+7TeO/V12DugMYya8V+efGTX0wCfGz3OjKl9wtSvdfPsfSsAd9ArPgIsWLMESt6DrGiZxErehaxIuK7ODt6cs+ePeXYsWPy9ddfy+uvvy59+/aVVatWmZaEQ4YMsa331ltvybVr12Tz5s1Su3Zt8VZLtx6TmSv3y5Ezj1pXWTWpklf+3nlSFq0/bBJUOw5dkMlLdslbL5awrdOsWj4ZOXuzSQ5qa61Rc7fK9Vt3pE3twk4fL3OapPJC+VwybtEOiQ+W7Tgts9YcMYm/8HKmTy7Xbt2VOeuOmvN74uJN+X3bSSmeI7VtnVwZU8hPqw5JSOg9kzT84a9/JUuapJIuRSKnj6etGjf9e0G2Hb4kvm7lnvMyb/NJOXbx5hPXrV8qiyRPHCA/rTlqm3foXIhJGCo/Pz/TEkvPa8qkQWZelfzp5NmC6aXrxM0mYagu3bhjtosPlv537R45F7FVWo4M/127a4/Yrt0lW09K8ZxpzPJ8mYOlRK408r/pW+Rm6D25cTtM+v+4RUrlTisV8qd3+niNy+eQ5ImD5Ifl3nkDJq699wYnTSjpUyaRKX/skrth9yXkdphMX7ZHiud2fv597tZxTCbgCYgVHyFWjDliRc8hVvQsYkUPn19iRc8hVvQKcTZpWKlSJQkKepg0sMqbN68ULlxY9u7da5uXIUMGSZw4sfh8MflwX6C0xVCeLKkkWeJAW7JF/wu/XclIvpi+9kIx8yX3j82Pkjfx1dIdp+TQ2eumi7Ke15wZkku90lllwcZjtnWGz90prarmluAkQZI4KIF0rJVf1u47J+evRWylqV10dV/ashGO2lXLJQs2n5Kr/yUJrWoWySh7v2wgR0c3lv7Nism4pf+aLsiqasH0cuLSTelaJ59sG/qCbB5cV0a+WlpS/5dUjM+Wbn947WoXY3///67dMlll/oaH1671fcP+nUGvcVXiv8RieJ2eL2CSkFf+O//xmTvee6+G3JGxC7ZLx7pFJXHCAJNEbFu7sMxf+6/El4FQXP0PeBJixXB/b8SKHkOs+PQQK7oXsaJnESu64fwRK8Z5cTZp6IzWKjx37pykTZtW4pPFGw5JtWJZpVGlPJLA309K5c0gr9Z52IpFa5yphesOSc/mZSRHxmAJCkwgvZqXkYypk0ry/5bbC0jgLx2eLyLfLd751J9LXHT77n2Zsuxf+aJjebk8o638881LppXgD38ddAgWM6VOIicmtpKzP7widUpmkW5j1zrdX+uquU2LojlrScjay585hVTIm1Ymr3zUFdZq2a6zUrDnIinUc6EMmPOPbDl82bZMax3my5TC1EKs9MkSqfv5csmUKpF83aGMxHd67U5edkC+eK2CXJnxquwa3Uw2HbggPyx/mJD69/R12XvyqgxqW0ZSJAmUVMmCZEDr0vLggUWSO+neXShrSqlSKKOMX0LC253vvXNWH5DnSmSVC3O6yumZb0mOjCmk9/i/Jb4MhOLq5GqNu27dupkbjEmTJpVs2bJJixYtIpQuad++/cOEb7ipQIECEfb54MEDGTZsmOTMmVMSJUpkyqH8+OOPrp4WeBixIrGiJxArPh3Eiu5HrOhZxIoxQ6zoHbwqaTh9+nQ5deqUtGzZ0m37PH/+vOzevdthOnjwUbIoLli7+7R0HP679G5VTo7/9KZ81aW6TFi8U+7ffyBXQu6YdXqPXymr/zklvw95SQ5OfV2yZwiW5duPy6XrDwc7sKdfgFMmSyRT/tgdC88m7mldLbepUfjy8L8kVesfJHfnn02iauI7Vc1ybV34a7+68veuM5Kx3XRJ12aafLVgl/z5aT3JmCpiK9fX6+SXqSsOyp2w+7HwbOL2nePtR6/IjmMPB49xRlsgfvfXQRnRtpQUyhJs5t0IDTNJrkFzdpnA5+KNOzJ8wV55rlAGSRyYQOKzV6rlkU9fKSMthy2TlK2mSO5OP0nq5I+uXe3q3XzIUkmaKFC2fdVU1g1vJH/vPis379yTS9cfvneEb2W45eAF2XroYiw8m7jHHe+9uTOnlMWfvST/t2C7pG4yWtI1HS2/bTwiy79sKUkSxtmywl5d427OnDlSs2ZNGTVqlHTu3Fn+/vtvUw95165dDusmTJhQpk6d6jANHz48wj4//vhj6d27tymB8s0335hEZOvWreWnn356is8MUUWsSKzoCcSKTwexovsRK3oWsaL3IVaMPq/5xrJv3z7p2rWrVKxYUdq1a+e2/Y4ZM0YGDBggcd2cVQfMZDWsczUzOuftO/fM7zdDw+S9cSvMZB3Rc++kjjJw6qPBOaw6Nyhm9nX5hu8PgBIVpXKllTV7z5lJnbt6WyYtPSBTelQzv2uXT00ijlq42wwwoSYuPSD9W5eWygUymFqIVs8Wyij5s6SU5kOXxdKziZuSJgyQl8pllf/NfHLrVu3+qQOi5MyQVPacuib/HL/qdD0zRko878FYMncah2v37NXbMnHpfvmhx3O2dbQWYqvhD0eqtg6eoq/Hyl2PRvdVyRIFyMvP5pYPJm14is9AfP69t2jOtHL77j0z6rLSTt9fzdkiH7WuIIVzpJVN+8+Kr4pJaUK/GNS4mzFjhkN5E73RWLRoUVMPedq0abb5AQEB0qZNm8fuT29UfvHFFyb+0MHYlNZZrlatmrz//vtmsLYECeL3zYu4hFiRWNFTiBU9j1jRM4gVPY9Y0XXEit7BK1oa6sjJ9evXl+DgYJk9e7ZbA/QuXbqY1gf207x58+Rp03pk+mUzKPDhS6L/1snaZLdMvgyme5zWxGpds6DpItd34mrb9tn+v737gI+qyh44fkILIaGEKiCwICBdpEovAtKXojRp6kd0Af/EwAcJiMDKImV1wUUgKAJLk6KCAiJlI70LLCoYZQFDDTUQIBDI+3/uZWfMlCBJ3pC8eb+vn3HImzcvk5OXvJMz955bOI8ULxii/100f7DMerOlnLtyQxb/+/f+j0qFkvmlcbUSerVQO1GFKBXP7Nk846t6EzaoWETqli+kHyuYO1D6PVvOuYhJ9Jk4uXgtQQa3q6Sfo74PfZuVk9w5s8tht1Fzrz73pJ7KfCLWHot0KFkCRAKzZZEcWe/HVq0Oqz5OPr3w+WdKSuK9JFm17/dVkx1eaf6EjrmSPySHvNezul5sZu+v96cof3PwjC6GjehUWR83NDiHDG1fUf79wzk98tDfOX83eDt3jzjO3fv98wrmCZT+z5Z3WYCnSslQPVpW7a8KhrMGNtL9NlUvxOR6Ni6rv0fLdxwXO/H1797vfzkvObJllZfbVNXHUcd+o1MNuX7zjvxyOuVRt34hA5pbP2yPO4d79+7JtWuuPwvJrVq1ShITE3Wu4PyyAgL0ImynTp2SnTs935hDxiBXJFdML3JF3yFX9C1yxUcUX3JF85ErWkKmH2moVkZu06aNXL16Va+eXKxYMVOPX7hwYX3LaL2aV5SPhz7n/PjSyjf0favhy2XnT2fkHwObS/kSoTqhUX+EdhmzUm9PXgycNqi5XqXz+q07us9W24jPdW+95Aa0q6af78+jW7xRi5hEDmro/Dh2wf3RJW3GrpMvdp6Qx/IFycy/NNTTjRPu3NMjt1758H7PMTW6sMt7G2Rcz5oSPesFyZoli/z33DXpN/U7XVB0KJw3SNrXLikvvh8ldqIKglP7/d5f8NiHf9b3XT/YIjuj709z7du4tCzb+ZskJCZ5PL9xhcLyf62f1O8wX0+4K4dOXJHuU7dJ7LX7I2FVYbDHtG0yvsdT8sPf2+vpyv/+4byM/+Kw2IHqkRk5qJHz4wsL++j71mO+uX/uhuaSmQMbSNHQXPrc3XbknLw8bbNz/7a1Sspf2lTUC3ecvXJT5m+Klg9WHfY6NXnRd7/qY9iJr3/3/hZ7XV746yoZ3buejH/p/u+gH09clC5jV+pFUvxZehY0cTzPW7uQQoUKpeq67ehxpwqHyd28eVPy5Mmj70NDQ6Vnz556ykpIyP0isHLgwAHdG7FixYouz61Tp47z8YYNf7+2IGOQK5IrmoFc0XfIFX2LXNHH8SVX9BlyRWsIMFQ2nUklJCRIq1atZP/+/bJx40Y9NflBVPPzjz76SP+BkB6qr2GVKlUkR4PhkiX3Y+k6FjxlzR1KWHwoT/48xNeH4i79XiiGuZLivU9FhwmxvX5O7myfrEfTuxfPzOa4hm7Y/r2Ur1ApTceIPvqTtGxQw+tjY8aMkbFjxz70sdSU5D59+sicOXPk5Zdf1tsiIiJ0rqB6HaqFTtatWyfz58+XBg0ayHfffaenLivt27fXIxSPHTvmckxVaFTFxBEjRsh7772Xpq8R5iBX9E/kir5Fruhb5Iq+Q67ow9iSK5IrWm2koZoypPoQqak/anrQHxUMAQCAf1HtQsqWLesx0jC9Pe7cC309evSQ8uXL60VPVBsU9bFy69YtvWCKO7WKsuNxZBxyRQAA7I1c0cZFw6FDh8pXX30lHTp0kMuXL7s0L1cczctPnjypVzxU9u3bp+/Hjx+v70uVKqVHFwAAgEcvvWsVqYJhWkdGprbH3ZtvvimjR4/WMxscRcOgoCC5ffu219FtjseRccgVAQCwNnLFzC/TFg0PHjyo77/++mt9c+coGh4/flwn+ck5PlarG1I0BAAgg3pbB2RMApmWHneqAFigQAH9RqVD0aJFJSoqSk9lVgugOJw9e3/1cbP7LCN1yBUBALAuckVryLSrJ6ueQipJT+nm0LRp0xT3UccAAAA2WRLvf6MA1SyF6OhoWb16tVSq9HB9Fa9fvy4XL150mf5cvXp13b/QfeXl3bt3Ox9HxiFXBADAysgVrSDTFg0BAADS2uNu+fLlXvshq6KiKhC6e/fdd/Ubjq1bt3Zu+/Of/yzZs2eXGTNmOLepfWbNmiXFixeX+vXr8w0CAACwCHJFP5qeDAAArEvN5k3z9OQA3/W4U70On376aenZs6dUqFBBb//2229l7dq1umCoCoUOjz/+uISFhcmUKVMkMTFRateurRtuqynPixYt+sM+iQAAAPCOXNEaKBoCAADTpWeScYAPe9zly5dP2rdvLxs2bJD58+frd5zVgisTJkyQYcOGSZYsrpMwJk6cKKGhoRIZGSnz5s2TcuXK6WJkr1690vgqAQAAQK5oDRQNAQCAX7x7/DC9jFXRcMGCBQ99TFVEjIiI0DcAAACYg1zRGigaAgAAH717HJAhqycDAAAgcyNXtAYWQgEAAAAAAADggpGGAADAPxrVAAAAwBrIFS2BoiEAAPAJan8AAAAgV7QuioYAAMAvmlsDAADAGsgVrYGehgAAAAAAAABcMNIQAACYTq2cnPbVkxlqCAAA4M/IFa2BoiEAADAfza0BAABArmhpFA0BAIDpqBkCAACAXNHaKBoCAADfFA3TuhCK2S8GAAAAmQq5ojWwEAoAAAAAAAAAF4w0BAAApqO5NQAAAMgVrY2iIQAAMF9A2qcnMz8ZAADAz5ErWgLTkwEAAAAAAAC4oGgIAAAAAAAAwAXTkwEAgOnU1OQ0r57M8skAAAB+jVzRGigaAgAA07EQCgAAAMgVrY2iIQAAMB3vHgMAAIBc0dooGgIAANOpGcYsngwAAAByRetiIRQAAAAAAAAALhhpCAAAzMdQQwAAAJArWhpFQwAAYDoWQgEAAAC5orVRNAQAAOYLuL8YSlqfCwAAAD9GrmgJmban4d69e2Xw4MFSuXJlCQ4OlpIlS0q3bt0kOjraY98jR45I69atJSQkRPLnzy99+vSRCxcuZMjrBgAAGef27dvy1ltvSbFixSQoKEjq1q0rGzZs4Fvih8gVAQBAapEr+slIw0mTJsn27dvlhRdekGrVqsm5c+dk+vTpUqNGDdm1a5dUqVJF73fq1Clp3Lix5M2bVyZMmCDx8fHy97//XQ4fPix79uyRHDlyZPSXAgCA7WRUS8P+/fvLihUrJCwsTMqVKyfz5s2Ttm3bSlRUlDRs2DAdR0ZmQ64IAIB1kStaQ6YtGoaHh8vixYtdin7du3eXqlWrysSJE2XhwoV6myoU3rhxQ/bv369HIyp16tSRli1b6j8UBgwYkGFfAwAAtvaIpxmrNws/++wzmTJligwbNkxv69u3r36jcfjw4bJjx45H+4LgU+SKAABYHLlippdppyfXr1/fY5SgGjGgpiur6cgOn3/+ubRv395ZMFRatGgh5cuXl2XLlj3S1wwAAFwXQknrf2mhRhhmzZrV5Q3DnDlzyiuvvCI7d+6UmJgYvj1+hFwRAADrIle0hkxbNPTGMAw5f/68FCxYUH98+vRpiY2NlVq1annsq0YbHjhwIANeJQAAUIugpOeWFuq6r940zJMnj0dOoBw8eJBvjJ8jVwQAwBrIFa0h005P9mbRokW6UPjXv/5Vf3z27Fl9X7RoUY991bbLly/rJpeBgYEpHlMVHd0XTfnpp5/0fdLNiyZ/BVACkm4SCB9KlKvE14eS4uKJr69ie/M6sfVZbO9fz9Q18VE5duzXdD/31189j1GoUCEpXLiw1+epvCClnEA5c+ZMml8TrIFc0T+QK/oWuaJvkSv6MLbkij6MLbmiQq5o4aLh0aNHZdCgQVKvXj3p16+f3nbr1i19760oqKYjOfZ5UNFwxowZMm7cOK+P3T3wqUmvHnh07v9UAIAntUiYWlDMl0JDQyV37tzSrWundB1HtSjp1MnzGGPGjJGxY8d6fU5K1/zkOQH8F7ki8HD4TQggJeSK/Ia0ZNFQrZzcrl07vUKyo1+REhQUlOLIiYSEBJd9UjJw4EC9QnNyavpS7969dU/ESpUqmfiVQI0aUX8Erly5UsqWLUtATEZ8fYv4ElurUiPou3Xrpqfu+lqxYsV08ebKlSvpOs69e/ec13v3kYYpUdf89OQEsC5yRf/BtZb4WhnnL7G1KnJFckXLFg3j4uKkTZs2cvXqVdm6dav+Y8R9upFjmnJyalv+/PkfOMpQUVOcUprmpAqGauEVmE8VDImt7xBf3yK+xNaq3Hv9+Yq6Vie/Xj8qKi9QbUzcOfKEjHhN8D1yRf/EtZb4WhnnL7G1KnJFckVLLYSiRgZ06NBBoqOjZfXq1R6j/ooXL65HHOzbt8/juXv27JHq1as/wlcLAAAykrruq5zh2rVrLtt3797tfBz+hVwRAAA8LHJFPyoaqmlJ3bt3l507d8ry5ct1L0N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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Simulate a case with an unfilled cell by zeroing it out manually\n", "depth_with_gap = depth_dst.copy()\n", @@ -476,9 +600,84 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Computing Cressman weights…\n", + "Cressman weights written to /glade/derecho/scratch/manishrv/tmp/tmppx1l48md/cressman_weights.nc\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/glade/work/manishrv/conda-envs/mom6_forge/lib/python3.14/site-packages/xesmf/backend.py:42: UserWarning: Input array is not F_CONTIGUOUS. Will affect performance.\n", + " warnings.warn('Input array is not F_CONTIGUOUS. ' 'Will affect performance.')\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Computing Cressman weights…\n", + "Cressman weights written to /glade/derecho/scratch/manishrv/tmp/tmppx1l48md/weights_1.0.nc\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/glade/work/manishrv/conda-envs/mom6_forge/lib/python3.14/site-packages/xesmf/backend.py:42: UserWarning: Input array is not F_CONTIGUOUS. Will affect performance.\n", + " warnings.warn('Input array is not F_CONTIGUOUS. ' 'Will affect performance.')\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Computing Cressman weights…\n", + "Cressman weights written to /glade/derecho/scratch/manishrv/tmp/tmppx1l48md/weights_2.0.nc\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/glade/work/manishrv/conda-envs/mom6_forge/lib/python3.14/site-packages/xesmf/backend.py:42: UserWarning: Input array is not F_CONTIGUOUS. Will affect performance.\n", + " warnings.warn('Input array is not F_CONTIGUOUS. ' 'Will affect performance.')\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Computing Cressman weights…\n", + "Cressman weights written to /glade/derecho/scratch/manishrv/tmp/tmppx1l48md/weights_4.0.nc\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/glade/work/manishrv/conda-envs/mom6_forge/lib/python3.14/site-packages/xesmf/backend.py:42: UserWarning: Input array is not F_CONTIGUOUS. Will affect performance.\n", + " warnings.warn('Input array is not F_CONTIGUOUS. ' 'Will affect performance.')\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "weights_path = tmp_dir / \"cressman_weights.nc\"\n", "topo.cressman_interp(src, mask, smooth_scl=2.0, cressman_exp=2.0, weights_path=weights_path)\n", @@ -541,8 +740,16 @@ "name": "python3" }, "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", "name": "python", - "version": "3.10.0" + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.14.3" } }, "nbformat": 4, diff --git a/notebooks/11_full_scale_example.ipynb b/notebooks/11_full_scale_example.ipynb index 53b55af3..90c97a42 100644 --- a/notebooks/11_full_scale_example.ipynb +++ b/notebooks/11_full_scale_example.ipynb @@ -26,13 +26,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Output directory: /tmp/mom6_forge_output\n" + ] + } + ], "source": [ "# ── EDIT THESE ────────────────────────────────────────────────────────────────\n", "\n", - "GEBCO_PATH = \"/path/to/gebco.nc\" # <-- set this when you have the file\n", + "GEBCO_PATH = \"/glade/derecho/scratch/manishrv/ice/croc/gebco/GEBCO_2024.nc\" # <-- set this when you have the file\n", "\n", "# Model domain\n", "XSTART = 260.0 # western longitude\n", @@ -65,7 +73,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -91,9 +99,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model grid: 60 × 40 T-cells at 0.5° resolution\n", + "Lon: 260.25 – 289.75\n", + "Lat: 15.25 – 34.75\n", + "Total cells: 2,400\n" + ] + } + ], "source": [ "grid = Grid(\n", " resolution=RESOLUTION,\n", @@ -112,9 +131,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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JHJaVe0BWYGF6LBSK2Sm1WYvUruH0YCGLLEVz8mxszMapHYv8gUtm0wSzmRxmS2bT1K7RX50XmeHWrVv8NSPOJkEQCkTRi2oIQhWRLwxni0gTL45lmYPkC4qTL8K9efNmwkLzxJmkWPE8e3t7hWQPY4utb9y4kWSbfHE6y+aVmOxciJ9agbj09p9WogJmS5YBjS3ITo3Hjx/zTF1y2LGyBd5sQbm8qN3KlSv5vllmIZZt6FcL8desWZNm9jCWBS1x9jB5ocNmzZolWejPsjfJkx14eHjIRIItirexsZFpamrKbt26lWQbW9zOdGbFTbOjuOS9e/f4dpYYgRUvTQxbgN6yZUu+306dOqWaKCE10luIz4pLmpubZ0txyazcAzJSIDGns4e5urryRf0sgYIcdt5PnDgxISNX8mQhLGlF8mPJruKSrE8Sw64flvwh+bWVuL8SX2/yDH2sX5PviyAIsaA1LQShANiCUFaXgYUWubm58QXD7Ik9q3HAwjnYovLE9UQYFSpU4E+B2RNdthCXPWFkT+zZd9gC5NRmYHIKVrCRLXhm6znYsbBXtvCW1WlhRRlZnDyrcSI6bAE9syN7os5qfjAbs7AmtmCf1XRhswVssTGrvcJC8FjtEdYvu3bt4jMhDFbr4vz583zhN1tgPnz48HTb7NWrF39yzGpMsEX9zZo14+Epnz594jM3bGZAnhhgxIgR/IkyC3lisfYs7Ik9ed67dy8PbRo1apRwNSPYbBILR2M1dNjib7auhIV/safZ7Ek8s2Xyc4M9UR82bBi3X/HixfmaCba4my3kZ8U/ly1blmqRzm3btvEZidOnT/N9JIZdT+z7bEbsxIkTfB3N74YSsZAuNnvD9GML2FmRSVYclNUoefHiBf+c1c/JqXtAVmG1fNg5yuqTsFlZeZ0WZh92TjH9MwqrW8LOeTZTJNeZLcx/+vQpX8fE9E8OS8LArhm2nc0Msu+we5l8VistmH1ZUVd2bbH22PfZjCbTm83csL6Qh9nJYYVi2fotFtaZOJyRFaRks5zsOmLnI5t1YnrLC8OyNS3J90UQhGAo2msiCFWFPeVbtGgRf3LJnhqyNK2shgV7QsieHLM6Iclh9Rx69erFnzCy77C6EKzuR26nPE78xJOlX2VPKdmTdTbj069fvyRPYUWeaWGwGRM2o1G6dGmZvr4+r83CZjEaNmzIbctmDhjyp/asz5LD+qxAgQIpZhdSm2lJnDaaPVFn9R/YTAJrs2PHjnz2IDHsCTPTj/U1042dGywNLHsKLTIspTRLeZyRc0MO61dWC4Wl6GXHyexz9OjRNOXZE36W+jY92AxLWimKMzvTkjglMaudY2JiwvuEpbFm50VGZ3Oyeg/I6kwLw8/Pj6fHZv3BdGb7ZvvJynXJvse+z/qJ1bhh9wA2K5lWWnY2W8lSCrNaO2xmMzPtsVllZiNmY6Y3szmzfVr1ieT3vOQ2GjFiBD+fmI3Z9aarq8vtPnDgQNnr168zfOwEQSiOPOwfRTtOBEH8Dxbzz54Is6eMLA0nQRCqBd0DCIIgUkIL8QlCQbCsPckXCLPQH1Y7gtGiRQsFaUYQRG5A9wCCIIiMQzMtBKEgxowZw2dSWGw8q7nBBjAs7pytp2jQoAGOHz+eqdSmBEFIC7oHEARBZBxaiE8QCoKlUn306BFfuM4WG7P6Diws7O+//+azLbntsKRWFT6rVdEzCku3mpGUq2xxvHwGiiCUBdHuAQRBECJDMy0EQfy8GWRwgJQ8K8/vOkpTp079pVz+/Pnx7t27bGmTIAiCIAjpQU4LQRAEQRAEQRBCQwvxCYIgCIIgCIIQGnJaCIIgCIIgCIIQGnJaCIIgCIIgCIIQGnJaCIIgCIIgCIIQGnJaJEp0dLSiVVBZZDIZ2V9AqF/EgvpDPKhPxIT6RTyoT8SEnJYsEBoaismTJ6N+/frImzcvTxW7adOmVGWfPXvG5QwMDLhsly5d8OXLlxRyrDL6vHnzUKBAAejo6KB48eK88GByIiMj0bFjR+jr6yNfvnzYs2dPwjb2f6bLwYMHU3yvRIkSfNvFixdTbHNwcEDlypWhbNy7d4/b3sjICIaGhqhbty4ePnyYQo4Vd2S2Sf7Hvpucbdu2wdzcnO+ve/fuCc5LXFwcb6dZs2YpvrN48WK+v27duqXYNmnSJL7t5cuXUBUy2i+M69evo2rVqtDT04OVlRWvX8Guv+RQv/we3t7eaN++Pezs7LitCxcujGnTpiE8PDxBhq4T8fqEQddI7sFSvaf2WyH/8/X15XJ0rYjXJwy6VpQAGZFp3r59K2Omc3BwkNWsWZP/f+PGjSnkPnz4IDM3N5c5OTnJlixZIps5c6bM1NRUVqJECVlUVFQS2TFjxvD99O7dW7ZmzRpZo0aN+PudO3cmkZsxY4bM3d1dtnfvXtmCBQtkhoaGMh8fH77N19eXf2fYsGFJvhMcHCxTU1OTaWhoyKZPn55k2/v37/l3Ro4cqVRnwr1792Q6OjoyZ2dnbqd58+bJHB0dZUZGRrLnz58nka1Ro4bMzs5OtnXr1iR/58+fT9HvBgYGfH/M/qwf5s6dm7D9zz//5P2dnFatWnHbs/MgObVr15ZZWlrKVIXM9MuDBw+4bKlSpWQrV66UjR8/XqatrS2rX79+Ejnql9+D3QNMTExk+fPnl82ePVu2evVq2V9//cXvC02bNk2Qo+tEvD6hayR3uX79eorfiS1btsj09PRkRYsWTZCja0W8PqFrRTkgpyULREZGyvz8/Pj/79y5k6bT0r9/f5murm6CU8E4e/Ysl2c/QnI+fvwo09TUlA0cODDhs/j4eFm1atX4YDo2Njbhc+bMHDp0KOF98+bNZfv27Ut4X6BAAVn58uWT6HHq1ClZnjx5ZB06dJDVq1cvybYdO3ZwfQ4fPixTJho2bMgdxK9fvyZ89unTJ+50tGzZMoks+4Fxc3P75T6Zo8LsLYf1Q+PGjRPeT506ldvy6dOnSb5nZWUl69ixI98mP28YMTExMn19fVmLFi1kqkJm+qVBgwYya2tr7nTLWbt2Lbfj6dOnEz6jfvk92MMUZlNPT88kn3ft2pV/HhgYyN/TdSJen9A1ong8PDx4n7A+k0PXinh9QteKckDhYVlAW1ubh6r8iv3796Nx48Y8/EpOnTp14OLikiSs6/Dhw4iJicGAAQMSPmPTmv3798fHjx9x48aNhM8LFiyINWvW4MWLFzh79iyuXLmCQoUKJWxnoTQPHjxAREREwmfXrl2Dm5sbGjRogJs3b/JQtMTbWFtVqlSBMuHh4cFtbWZmlvCZtbU1atSogWPHjqUaYhQbG5vq54ltz+zN7M7sz/rB2dk5ie3lNpXz5s0b+Pv7Y9CgQTzsL/E2FhIVFhaW8D1VIKP98uPHD27nzp078zAyOV27duWhlomvH+qX34PZmsHCTRPD+kVNTQ1aWlpJPqfrRIw+oWtEDHbs2MF/Q1nYdnLoWhGjT+haUR7IackhWBxlQEAAypYtm2Jb+fLluWMhh/2frVEpUqRICjn5djljx47F69eveXwzWwvQp08fvl5FDhsAMwfo1q1bCZ+xgTJbs8L+goOD4enpmWQb21fiQaQyEBUVBV1d3RSfs9hwtg4lsQ0YbE0J6wO2xoI5pBMnTuR2TEzp0qXRqVMnbndmM+ZQsv6QU7FiRWhoaODq1atJ7Mv2W65cOX4uJHZa5P9XJaclo/3y5MkT/oOf/Pphg7WSJUsmuSaoX34PFn/P6NmzJ3ekP3z4gN27d2PlypV8DRE7f+XQdSJOn9A1onjYbwR7gMJ+Wx0dHZNso2tFnD6ha0V50FC0AsqKn59fwpOx5LDPAgMD+QCOzdowWfZEjT0ZSC7H+PTpU5LPHj9+zC9CtiA8f/78Sb4jHwCzgTP74WMDP+bAsEXgTk5OvB22jS30DwkJ4fvp0aMHlA1XV1c+q8QWyKurq/PP2KBY7swlXpzH7FKrVi0UK1aMz3zs27cPM2bM4D86bKCQmKVLl2LIkCEICgriNmROSuKBd6lSpVI4Lcz5ZHLsJpo4EQKTY99hg25VIaP98qvrh83YJIb6JeuwpAjTp0/HrFmzcOTIkYTPx48fz68DOXSdiNUndI0ontOnT+Pbt2/8YVZi6FoRq0/oWlEiFB2fJnXSWtNy5coV/vnu3btTfGfixIl82/fv3xMWYxcpUiSFXFxcHJf7559/MqwPWwtjZmaWsHbl7t27fB/e3t78PVs/wdZXMNi6ALZt8+bNMmWDLdxmx9atWzeZl5eX7MmTJ7J27drxtUPsc7ZYLz1YQgQmd+PGjUy1O3To0CRrV9hifbaAnMHWDbEF+WFhYfw9W69Rq1YtmSqR0X5hCynZ+1u3bqXYR5cuXWTGxsaZapf6JX2Y3dk9gyUB2b9/v6xHjx58HdyyZcvS/R5dJ4rrE7pGFA9bJ8ruXYnX6KUFXSuK6xO6VpQHCg/LIeQhMGw2JbW0xYll2GtG5DICm61hT/Tla1fYk35LS8uEdS9smzwsSZnDk/r164dx48bx2Fa2nofNorCwulGjRvHtbF1EegwfPpy/njt3LlPtJl7XwmZjvLy8EtYLMduzma/bt2/j7du3/OmPMto+O/rlV9dPZq4JBvVL2uzatYuHma5btw69e/dGy5YtsX79ej47O3r0aP7UMi3oOlFcn9A1oljY+ju2HrVevXoZCq+ma0VxfULXivJATksOIQ9rkU9LJoZ9xmq2sNAwuSxbrM2yuSWXY9jY2GR6gMbWrrDQL/l6Fjns/z4+PjwMh4UnsX2zhczKyMyZM/H582ceSsRC6u7cuZOQhIAlQ0gPe3t7/srC+DJD4vA8eQKFSpUq8VcWzscW7rNt8hAyVXNaMtovv7p+snJNMKhfUrJixQoe1sjqgSSmadOmvCZI4vVDyaHrRHF9QteIYjl06BDvi+ShYWlB14ri+oSuFeWBnJYcwtbWFhYWFrh7926KbexJO1tMLIf9n11orBBlYuRx/ollMztAY05L4sxgZcqU4c7SpUuX+P6VLWtYckxNTbk92BN9+cwJGwiwhfTpwbJ+MVgfZgY2qyV3TJjtixYtChMTk4Tt8pku9sfWdMgdGlXjV/3i7u7O1wElv37Y+he2MDmz1wT1S9owB5KtMUqOPBEFmx1MC7pOFNcndI0olu3bt/OZYeZIZgS6VhTXJ3StKA/ktOQgrVq14mlcWeYXOefPn+cLvNu0aZPwGauirqmpyZ+uyWGzLqtWreLOT2ar1bOMSyy9LruA2YxK4u8zh4Ut/F6+fLnKpdtli+rZU322kJ6lDZWnQkwegsRsL1/syqaZMwuzKRtYnzlzJkXfsfdsBobNMrCF/CxbmaqTWr8YGxvz1Mis0j1LGCFn69atPAQg8fWTUahfUofNbrEn9+y+lJidO3fy/mDnKV0n4vUJXSOK48uXL/xBS4sWLXgylcTQtSJen9C1okQoelGNVGGLIVl1eVZAkpmRFcZj79lfUFBQQlVjtiieVUJfunSpbNasWbywXrFixXiBysSwivRsP3369OEF9FgRSfZ++/btWdKPFaZk32cVxJO3NXz4cL6N/bEK5crI5cuXZX/88QevWL9u3TpZr169ZOrq6ryaOivqKOfixYu8+CNbqL18+XJepb1KlSoJfZEV1q9fn2DfTZs2JdnGFp7Ltw0ePFimamS0Xxjs3GTnb6lSpfgCfpbQQEdHR1a3bt0stU39knafsD6wtLSUTZs2jV8HrBAbO0dZ/zDoOhGvTxh0jSju95/1BSvcnBy6VsTrEwZdK8oBOS1ZJH/+/AmDz+R/b9++TZBjFY3ZIEtPT09mYmIi69Spk8zf3z/VTGHMqWH71dLS4hXat23bluWOHTt2LNelcuXKKbYdOHCAbzM0NJTFxsbKlJFXr15xu5ubm/OBb+HChWWzZ8+WRUVFJZF78+aNrE2bNjJHR0c+IGb9VKZMGdmqVat4Jras8OLFi4Rz4eXLl0m2sX2y8yCtzHLKTkb7JXFlY3YOs76xsLCQDRw4UPbjx48stU39kjYsSxsbFDMHnmXecXFx4dWk5Y4kXSfi9YkcukZyn4oVK3KHMrXfT7pWxOsTOXStSJ887B9Fz/YQBEEQBEEQBEGkBa1pIQiCIAiCIAhCaMhpIQiCIAiCIAhCaMhpIQiCIAiCIAhCaMhpIQiCIAiCIAhCaMhpIQiCIAiCIAhCaMhpIQiCIAiCIAhCaMhpIQiCIAiCIAhCaMhpIQiCIAiCIAhCaMhpIQiCIAiCIAhCaDSgonz69Anfv39XtBoEQRAEQRAEkWFMTU1hY2OjchbTUFWHxaVQQYRFRClaFYIgCIIgCILIMIaGhnj+/LnKOS4q6bSwGRbmsCyZ8Bfsrc0hKq8DouFkqaVoNYhEUJ+ICfWLeFCfiAf1Sepcv/8Cdz3fYPrUSfjm54PTFy7jzOW7kMlkiIqOhW2+vLCyNIV1PgsUL14MVeu2g7pm9v023759G+XLl8+2/RHK3SevXr1C8+bN+ViWnBYVgjkshfJbQVSiNaJQyFZb0WoQiaA+ERPqF/GgPhEP6pPUyW9jjrPXHqNrr8HIkweoV60kVk7rDStzE8TGxeHdxy/4FBCIT5+/Y9HyTShVzA2VGnTJtn758uUL3Nzcsm1/xO9DfSImKjnTQhAEQRAEwdDU1MDWBYNTNYaGujp/uCl/wGmor4v1GzejYr1OyKNGuYwIIjehK44gCIIgCCIDVCzljNuPvREe7E/2IohchpwWgiAIgiCIDGBmYggtTQ3cvHCU7EUQuQw5LQRBEARBEBmkZd0K+Hf5WkRH/CCbEUQuQmtaCIIgCIIgMsCVO08xftFO/n+fp7fgXOZPshtB5BI000IQBEEQBJEBnr76iDYNKvH/H9y7nWxGELkIOS0EQRAEQRAZQF1dHS4FrPn/j52/hZjIELIbQeQS5LQQBEEQBEFkgAs3nqBtu07o0LgKPO4+x5f3z8huBJFLkNNCEARBEASRAWQyICQkCH/UqMzfnzp2gOxGELkEOS0EQRAEQRAZYESvJug2YBzy6sn4+1NnLyMuJopsRxCq6LRERdHFTxAEQRCEeJR1d8KQvxph3Z7zKFfcCftO30Lw59eKVosgVGK8niWnxcvLC23atEHBggWhp6cHc3NzVK9eHUePpiy2FB8fj5UrV6JkyZLQ1dWFmZkZateujUePHiWRO3PmDOzs7KCvr4+mTZsiKCgoyXZHR0fkyZMHgwcPTtHGpUuX+LZ9+/Zl5XAIgiAIgiBSEBcXj6MX7iE88n8DtOrlisD3cyAcbSwgk8lw9eIpshwhJF5KNl7PktPi4+ODkJAQdOvWDUuWLMHEiRP550z5NWvWJJHt0aMH/v77b5QpUwbLli3DpEmT4ODggICAgASZ0NBQdOjQAd27d+cHEhwcjHHjxqXa9tq1a/Hp06esqE0QBEEQhIpz9d7zDMtGx8Ri5soD6DBkCf7ddBzX779ARGQ0ujSvjqCQcC5z6PBRxMfG5KDGBJE1lG28nqXikg0bNuR/iRk0aBA/0EWLFqFPnz78sz179mDz5s04cOAAWrRokeb+nj9/zg0zffp0/r5AgQLo3LlzCjk3Nze8ePECc+bMwdKlS7OiOkEQBEEQKjxzMmDyOtw7NBeaGuppyp27/gSeL9+jR+vasLYwwZGti3Hm4nWcv3kPc9YcQuVSrnj5zg/1q5XExv2XMPzuGbhVbJSrx0IQqjZeV8vO3OX29vZJpomYQcqXL88NwKadwsLCUv0uM4C3tzf27t2L169f8wN0dnZOIcemnLp27UqzLQRBEARBZJrQ8Ej++vi5T4ptLNRr6+ErmP7fPuw+fh0b9l1Et1H/wc7KDIXKNsSQycuw68hl3Lh4CnmNDeDj+wWNa5fm3506bSZiIqhmCyE+6hIer/+W08IO6uvXr1zxxYsX4+TJk/jjjz/4th8/fuD27dsoV64cnzoyNjaGgYEBj6tjHl1iLC0tMWXKFLRv3x6FChXC+fPnuXeWGuPHj0dsbGya2wmCIAiCIFJDT1ebv67YcRpR0UlDus5ee4w7j1+jVYumaFirDNo1qozjO5fjwP590NIz5jJq6pqwKlQaC1btwOTBbWCX3wmNa5XG3pM3cOnYFjI6ISRhSjJez1J4mJzhw4dj9erV/P9qampo2bIl/vvvP/6eGYY9tdi1axc0NDQwb948bggWU8cO1sjICPXr10/Y14gRI3icHPPIihUrBh0dnVTbZEbs0qUL997Gjh0La+uflWkJgiAIgiDSg4WElSrqiCIFbdH278XcMXErZIe7nm9w8vID7N2yEsWrNeOysvg4AHmQRy3l811tfVNM+ncn/3+BQkVx7GILDJswFzfrtIC+qQ11AiEUw5VkvP5bMy1DhgzB2bNneRxcgwYNEBcXh+jo6ITFOoxv377h8OHD6N+/Pzp27Mi9MpaRYMaMGSn2Z2tryz29tAwgZ8KECTTbQhAEQRBEptHR1kLv7h1xeNsyfP8Rhj0nb0BdXQ371s+Bo4s7Aj944fHVw5g6rBvaN6mGMQPaYcPi8fj28UXCPqJCA/H9gyeiw76jaIX6fNbF8+UHbF0xgw8ACUIkhijJeP23ZloKFy7M/xgsdq1u3bpo0qQJbt26xdOlyRfpVKhQIeE7bMqJyWzbto0fCPPqMovce2OZD8aMGZNl/V8HRCNaQ9y6MF9C4vDUV1z9VBHqEzGhfhEP6hPxoD75SdFipbBw/VHo62pDJjOAhoEW3nyVYfISFgqzBzLIoKutBVsrM5SvUAk/QsNx/tYzBASvR7kqtfH+lSeu3riLL4HBfL1LqRJu+BGrj8YN62P3uWewd9sDfZN8Ge6Xd+/e8VSwhDiI3Cdv375VufF6tjgtyWndujX69u2Lly9fwsbm5/RovnwpL1wWExcTE8Nj7NgUVFZgsXJbt27F3Llz0bx58yztw8lSC4Vsf8a3ighzWIoKrJ8qQn0iJtQv4kF9Ih7UJz8p2rYczyL29XsI1NXz8EX1LGQmPQ6d/YYvH1/i2K4XuHDtEQZ0qotKpcpi2KzNcLXVx9Zd+xEbG4+gkDCUdNTDwnWHoaaesSEWGxzXrFkzG3qYyC5E7hMLCwuVG69ne/YwRkREBH9leZuZEaysrODr65tCjsXBsSklQ0PDLLfl5OTE06yxGD0/P7/f0psgCIIgCNWBhYPlMzeGuanRLx0WRmxcPNQQzx2WFVN7onJpV14kr2XdCrjz8BlG9GrOHZbuLWvi300n4PfiZq4cB0Go0ng9S05L4kIzcpgntmXLFj7NVLRoUf5Zu3bt8OHDBx5HJ4dlL2Axc6zKZkZuFL+KlWPtskVDBEEQBEEQOcErH384OVghJCwCRgZ6CZ8XLWSHp68+os/fY+HlsQ8Ll6xAwxql8O3zB8TzhfwEoTgClGy8nqXwMDalxFKkVa9enS/G8ff3x/bt23nRmYULF/I4OAbLFsDSpbVq1QrDhg3jU0urVq3iis+aNQu/i9x7YwuLCIIgCIIgcgL/L0HIa2IAba2kwyYjQz0Eh4RBQ1sXRau24p8tWTADzuUa4dVdUziV+V/WJYLIbfoq2Xg9S64T88iY17Vy5UqeZYAVpbGzs+MeGTtYOSw+7urVq6hTpw7PC83i2pjc5cuXUaJECWQHzHtjhXIIgiAIgiByAhb6xYpO1qlcPMnnT70/8NkWXeP/rQcwMDbjr4f3/0yJTBCKop2SjdezNNPC8jazv4xmDjhw4ACyI5NDarDiNiyrAUEQBEEQRE4wrl8Lvq6FOShy2GL+Q+fuoGK5EtDQ+pmBiWFgasVfX7/3p84gFEp7JRuvZ2v2MIIgCIIgCGXDpUDSgpH+X4MwftFOFHGyR7/Bo5Ns09IzhbGhHm488EZcbDTUNbRyWVuCUE6yNXsYQRAEQRCEoomPj8dH/2/pygQGh+LiTU9ERP4sspdRLtzwRL+Ja9Cvayus3XYIZnauSbbz9S2F7PD8jS9iI38W7iMI4vehmRaCIAiCIJSKqOhYNOw1GwZ6OpjydxvUqugOTY2k8fRLNp1ARFQ0/tt2GnuXDv1lhiRW6X7J5hN48dYPh3eug1ulRsiTynfiYqL5wn1rCxOoaVKtNYLILshpIQiCIAhCqdDV0cKhlSPRvP98jJizlX9WvVwRFHfNj3wWxnj5xg93PV/j2pn96N5nELzf+cO1YNIQsMSw9SvTlu/jC4mPHTsGEyunNGWjwgLx9mMA6lcvCXVNnRw5PoJQRchpIQiCIAhC6Shonw+39s2Cx91nOHXlIS7e9IKX90eULJIfzeqUw6hRI2BbpDLaNKuLkfO28lkZ23x5Ub54IV480sbSlBeQZLVZpi7bC3sbSyxZuQn6eW3Tbffjm6f8tVwxJ6ipUXZTgsguyGkhCIIgCEJpZ1zqVi3B/8IjonDp9lOc8XiEpVtO4om3Lzp++YxOPf+Bm3tx5LOywcuXz3Hh/AVMWrIbAd9+QF9XG1HRMejUvDbGTl8CHSOLX7Z54dRh/lqvYdNcOEKCUB3IaSEIgiAIQunR09Xm1erZH5s9uXTrKf4ePR3liu/Cvys2wtDcHs5l/kTD9oMQFfoNkSHf8O2zL8ys7GCU79ezJmzNS/j3T7h88yF/7166ci4dGUGoBpQ9jCAIgiAIlcJQXxdNapfB2pl9cfT8HVw6tS9hGwsJ0zE0h4mNK5xK1YaJtUu6DotMFo+Xd89gdJ/m3PHZc+IGujavDgNzh1w6GoJQDWimhSAIgiAIlWTasn3o2a4e6rXskeHvyOLj8NXnMc4e3IQnL99j1IQZ6Ni9P+55vkEZ94JoUL0kRk1dTPVZCCKbIaeFIAiCIAilYuuhK9yBSFzBPjnX7r3gKY8nzFwCLT3jX+6ThX89u3kMYyZMxdEL9/hndlZmGDddH5cvX0Z8bAz0TK0pYxhB5BAUHkYQBEEQhFJhbWmKLiOWYdOBS7zQZGrcffIazRv+AX1T6wztMzYqHH8PH8cdlkmDWuO952W8+/AJhhaO0M9rB0PLAuSwEEQOQjMtBEEQBEEoFXUqF0PLehWwaudZ3HjwEtOHtIOl2c/ZFJZFbOOBS7hy9xnGTZiU4X1q6ujj+JnLfA0LW/NCEETuQk4LQRAEQRBKx4ieTeD9zg8hoRHoOW4VqpctAmMjPZy4dB+t6lfB6YObYGJmgSfXjsLzySPY2Fij0p9toKVrlOY+tQ3y5uoxEATxP8hpIQiCIAhC6dDW0sTaGX3x37ZTOHT2DrYevgITIz10bFIVPr7+aNOlP75+D0FeYwMUcsiHF2/94FJwPebOnQ8H19LwOLwGplYFUbJaE0UfCkEQ5LQQBEEQBKGsaGpqYGj3xvi7a0O89/uKF28+ITA4FCWLOKJv+z9hbmrIUxwzwsIj0XLgAng9uIGZ0yZiw76LmDOqCzktBCEINNNCEARBEIRSo66uhgJ2lvwvNWLj4jB+0S706VAfc5asw/X7L7Biai/0Gbss13UlCCJ1KHuYhHgXqotzfmb8VUQ5KehIthHPNnR+kQ3p/BL72lP2a/RHaAQGTd2AQgVssHrnKe6wLJk3EUY1RuC5f3i6+/P6EIgdHt789VdkVJbkxLMNIQY00yIR2A12+UsHyJAHefxkGOjyHo4GEcLISUFHso14tqHzi2xI55fY157I1yibHdFQV8/0/oI/vYbP9YPQCvKGuiwWmhoa6NS8FpZsPIJvQSHYtO5f7PQrBNmVV9jq8QoLu1WCm33KBfhsoDt88w3IZACLMEtLLjOyJCeeDQlxoJkWifAqRI/fkBnslb0XSU4KOpJtxLMNnV9kQzq/xL72RL5Gq7WfxBfXJ5YL/foJc1buw8odZ/i2DfsuYNXOM1i65SQWbTiKp8dW4fHe+dA1s0XpKrWwf+MCXL90GpduPOIOy5GNM6Hh3IAPZHm7MuDRu2+p6sc+z4hcZmRJTjwbEuJATotEKGQYjjz4eXWxV/ZeJDkp6Ei2Ec82dH6RDen8EvvaE/kadbSzwKodZ+BkEMa3Bzy7gUe7ZqJOlVJwdXWBoVFeWNvYwcXFFaVLlUSdKqWRr0gllOo0CerqGji4cztK1emKDp064cJNT6yb1Q+NuoxCyQLm/Mk7bzcPUMLRLFX92OcZkcuMLMmJZ0NCHPLIZHI/U3Xw8vKCu7s7DiwfgUL5rSAqT32jUNRWO8nUNnuixG7caU3PK1JOCjr+rlxW+0TEY5GaXHqyUrtWRLRhdstJrU+koKPofZLbx9xnwmrExcVDT1cbMdDCl5BoLJw/G40bNknICJZaSBB7ol7c3gQWce9xaMdq7D5xHX9UKoYxczdBXVM7iRwbyKYXMpRRufRkL126hJo1a2Z6n6oml5ttJ+8TEcewnp6ecHNzgypBTouEnBZC8VCfiAn1i3hQn4iHsvXJuIU70bROOeQvVAxR0VGoUbcFjCwdM72fuJhIqGlop+no5DQiD5BVFZH7xEuFnRZaiE8QBEEQhKQI+hGGmNhYHD1/BweGL4SGdtprbX6FuqZOtupGEETOQGtaCIIgCIKQBCxj2KYDl/DX6OWoUq4ENm3e9lsOC0EQ0oFmWgiCIAiCEB7fz4EYv2gnShQpiIunDsK+cAXkUVNXtFoEQeQS5LQQBEEQBCH07Mrhc3ex7fAVTBnZB217joCWrpGi1SIIIpeh8DAJIVIFYdHaFl1OCjqKLicFHUWXk4KOZBvxbKPI8+uJfzzajNqIe95fceLAVnQaOCVVh0X0KvJS0FF0OUW3TSgemmmRCIqqcpxROSnoSLYRzzZ0fpEN6fwS+9pT5DX6MlADA6eugkPFpogpXh0xlmWQJ4+a5KrIS0FH0eUU3TYhBjTTIhGoGrJ4FaBFrhStbHJS0FF0OSnoSLYRzzaKOr9YCbm5K/fCsmhlWBWrLukq8lLQUXQ5RbdNiAE5LRKBqiGLVwFa5ErRyiYnBR1Fl5OCjmQb8WyT2+fXkk0n+IL77UeuQl8jBo5VWki+irwUdBRdTtFtE2JAxSUlVFxStOrKIrUtekVpEY9FanLpyUrtWhHRhtktJ7U+kYKOovdJduyzeOMRGNS5Po5euAuPc4fxw6Cw0ldzT17IUPRjEdGG2S1HxSXFhJwWCTkthOKhPhET6hfxoD4RDyn0CXNa7KzM4OZsh6NnrkJTxwDKjsgDZFVF5D7x8vKCu7s7PD094ebmBlWCwsMIgiAIglA4bB0L46P/N/xRtaxKOCwEQWQccloIgiAIglA4T16+h57uz5mg1u07K1odgiAEg1IeEwRBEASRY3z+Gozbj1/h4bN3vFCkhpoa1NTVoK6mBg32qq4GNTU1XLv/AjOHtsel216wdS5NPUIQRBLIaSEIgiAIIluJjonF9iMeOHbxPgz1dVCxVGE0b1wfenr6iI2LRUxsLOJj4xAbF8P/Hxcbi9o1q6NV18EYrK0HdU0d6hGCIJJATouEEC0TjUhtiy4nBR1Fl5OCjqLLSUFHso20z6+3ITo4cPkpLh0/hEY1SuDUwW2wLuAOHSML5JHnl02UtamsoJmqpJz5SlnlFN02oXjIaZEIqlgNWVnkpKCj6HJS0FF0OSnoSLaR9vl19lEAZqw5Dh0TS7i0nYV+A+uhYAEryVVfV6Zq7soip+i2CTGghfgSQdWqISuTnBR0FF1OCjqKLicFHck20pSLio7BtP/2YfmmQ3Cp3xPFWg2HtrE5nnwMkWT1dWWq5q4scopumxADclokgqpUQ1ZGOSnoKLqcFHQUXU4KOpJtpCf3PTgMfSasRn57a+zafxAmdi7CVlVXxWruyiKn6LYJMaDikhIqLimFeGbRdVRURWkRj0VqcunJSu1aEdGG2S0ntT6Rgo6i9UlkVAz6TlyD7u0aot+o2dDUMRR+XYSI6zGSFzIU/VhEtGF2y1FxSTEhp0VCTguheKhPxIT6RTyoT5S7T+Lj4zFq3jaUKe6KqQs3UCHI30DkAbKqInKfeHl5wd3dHZ6ennBzc4MqQeFhBEEQBEFkiqVbTsI8rwkmzl5ODgtBELkCZQ8jCIIgCCLD7Dt1E698PuPwkSPQNbIgyxEEkSuQ00IQBEEQRLqEhUdCQ0Md9zzf4MCZWzh+aA9MrJzIagRBqG54WFRUlKJVIAiCIAji/5HJZKjUdgIOnrmNxRuPY8N/c+FQpCLZhyBUmCgFjNfVsroIqE2bNihYsCD09PRgbm6O6tWr4+jRo0nk1q5dixo1aiBfvnzQ1tZGgQIF0L17d7x79y7FPu/fv4/ChQtDV1cX1apVw4cPH5JsZwuiWDXdJk2apPgu2x/btmDBAigzLIPLOT8z/iqinBR0JNuIZxs6v8iGdH6Jfe1tOfOUv36N1IClmRGKV6qXbjamHR7e/DU9VE1OCjqKLqfotqWIl5KN17MUHubj44OQkBB069YNNjY2CA8Px/79+9G0aVOsXr0affr04XIPHjzgB84+NzU1xdu3b7lhjh07hkePHvHvymnfvj1q166NefPmYfPmzejbty9OnDiRom323Xv37qFMmTJQJaRQDVl0Hck24tmGzi+yIZ1f4sudeKONPOoa2HLoMgZ2bQZtA7EqlosuJwUdRZdTdNtSxUfJxutZmmlp2LAhTp06hcmTJ6N37974559/cPHiRZQoUQKLFi1KkFuxYgU2bdqE4cOHo0ePHpg+fTqOHz+Or1+/YsuWLQly7D37W7VqFTfYhg0b4OHhkaJdBwcHbsypU6dC1RC5GrKi2xZdTgo6ii4nBR1Fl5OCjmQb8WzDPrcqXhO6xhYwsi4Ih4rNkBaiV1VXxWruyiKn6LalSkMlG69n25oWdXV12NvbIygoKF05R0dH/ppYLm/evAlGY97d/Pnz4ezsnOK7hoaGGDp0KJ/WYtNTqoSo1ZBFaFt0OSnoKLqcFHQUXU4KOpJtxLMN+1xTRw9aBqZwqtEOf1RJey2L6FXVVbGau7LIKbptZUJdwuP13youGRYWhoiICAQHB+PIkSMYOXIk2rVrh+3btyeR+/btG+Li4vD+/XtMmzaNH8SZM2fw559/Jsjs3LmTx8+xhT1mZmZ8f5UrV04SI8e8u+vXr3NDVq1alcvIY+TYtBYz3ogRIzJcmOfA8hGQUnFJ0aori9S26BWlRTwWqcmlJyu1a0VEG2a3nNT6RAo6KqJPWNawKatP4mtYPFauWY9yxYogPUSvqi5iNffkhQxFPxYRbZjdcspWXDJMouP1bHVa+vXrx2PiGGpqamjZsiXWrFnDp4QSo6Ojk5BlgB0gm6YaPHhwiv2xg3z9+jWKFi3KvbTEyI3AOokZku2DxcqVLl1aZZwWQvFQn4gJ9Yt4UJ9Iv0887j7Dog3H0KFZLYycOAdGFvlzVD9VReQBsqqibE5LP4mO17O1TsuQIUPQunVrfPr0CXv27OHeWXR0dAq5kydPIjIyEs+ePcO2bdu4x5caLKsB+/sVLCbv33//5bFyhw8f/p1DIAiCIAji/2dVrj94iev3X+Dpq48wNzXEjvWLUaZmK6hraJGNCEKiDFGS8fpvOS0s5Rn7Y3Tt2hV169blKc5u3brFU5rJqVWrFn9t0KABmjVrxj1EAwMDDBo0KEvtGhsb8w5g3hvLeJDcU8worwOiEa0hbl2YLyFx/MkYIQ7UJ2JC/SIe1CfS65N9p27B1soMNf5ogI7d7GFomg/hGvrwuHo9V/VUNdjTZ/ZknxAHkfvk7du3KjdezxanJTnMi2Opz16+fAlXV9dUZZycnFCqVCkeR5dVI8i9t8WLF3PvjXlxWcHJUguFBA6/ovAK8aA+ERPqF/GgPpFWn7z9GIBP715g6+br0DFUjQXJoiByKJKqInKfWFhYqNx4PduzhzHYIh8GW+jzK7lfyWTUe2PTTcx7IwiCIAgi83h5f8CoudvQrV2jNGuwEAShPERIdLyeJaclICAgxWcxMTE8lzOrkMkW5sTGxuL79+8p5G7fvo0nT56gbNmy+F2YEUxMTPhCH1WAKpaTbVRZTgo6ii4nBR3JNjlrm8DgUERE/oxlf/FNHSNXe2DisoP4b9549Bw2K0moiJQqlosuJwUdRZdTdNtSJEDJxutZCg9jU0o/fvxA9erVYWtrC39/fz599Pz5cyxcuJDHv7G8ziwPNEupxrIb6Ovr84PfuHEj97omTpyI34Xth007qUKxSapYLl05KegoupwUdBRdTgo6km1y3jYzVxzAg6dvoadvgG+RarAuURsFOi+BdYXqUNfUkWTFctHlpKCj6HKKbluq9FWy8XqWZlrYgbGUaStXrkT//v15VU07Ozs+9TNs2DAuo6enh169euHu3btcSZYy7dixY+jQoQNPfZZaMZqsem/MGMoOVZQWs1I0VSInGyr6/KLzkGyTmfOhfaPKKFnEEaMXrkeFfktQsEY75NHQlHTFctHlpKCj6HKKbluqtFOy8XqWZlrat2/P/9JDS0vrtxfcJCatLA5suulXVT2VAV692E/28+nZr6ocK0BOCjqSbcSzDZ1fZEM6v3LvmmJl2U55PIJ74YKoVq4Ujry8l/CU+VdVw38llxlZVZOTgo6iyym6banSXsnG679VXFKqSLW4pGjVlUVqW+SK0qIei9Tk0pOV2rUiog2zW05qfSIFHbMqFxsXh7tPXuP8w89Qjw7EynVbeaFIKVcsl5pcerLJM1WJfiwi2jC75ZStuKSyQE6LhJwWQvFQn4gJ9Yt4UJ8oDvYs8uDZ2zjt8QhxcfH4/C0YJQrnR+NmrdGtV39o6RopUDsiOSIPkFUVkfvES4Wdlmyt00IQBEEQRM7z8u0nnLn6GJoa6shnbowC9vlQuKANfD59xfbDHggNj8KCmROho6sLhwLOMLF2xtXrt8hhIQhCspDTQhAEQRASm0lZvv00rt59jmKuDqhWtghuP36N5298YZnXCO2a10XXPkNgYuWkaFUJgiCyDXJaCIIgCEJwrt17gTcfPqNL8+q8lsqSCd3h/yUIa3afw1vfL9ixez9fTKyhbUAV7QmCUErIaSEIgiAIwfkWFILFG4/xULCP/t+gq62FDk2qYuLAVmjSZy5Cgz7D2rm8otUkCILIMchpIQiCIAjBsbc24/VV1uw6x7OBsdoL7RtXwT2vNzAy0IVhXmtFq0gQBJGjZKm4JKEYWBrLc35m/FVEOSnoSLYRzzZ0fpEN6fz69bXCHJaIODVUrNcCi5ctg2sBG/QcuxLLt53Gqn9nQD+vXZI0rjs8vPlremS3nCLbFl1OCjqKLqfotgnFQzMtEoH9mC1/6fCzYJifDANd3qeap19RclLQkWwjnm3o/CIb0vmVsWvFJ0wP5o0m4NCOGXgUZIRlc1chwucuKtduDAMz+wQ5NvgavvlGQsG8hd0qpVqnIrvlFNm26HJS0FF0OUW3TYgBzbRIBFYojP2oMdgrey+SnBR0JNuIZxs6v8iGdH5l/FrRNjKHiUNRRHwPwPtIE9Rt0z+Jw8JghfLkJaPZK3ufGtktp8i2RZeTgo6iyym6bUIMyGmRCKyycR78vLrYK3svkpwUdCTbiGcbOr/IhnR+/fpaiY+Ph2bQK7w8sxGRwV/gWLkZShYwS/WaYpW92VNjvr88P9/nhpwi2xZdTgo6ii6n6LYJMcgjYwnfVQx5NdEDy0egUH4rRauT4YrSLJSGPW1jP2pphWgpUk4KOv6uXFb7RMRjkZpcerJSu1ZEtGF2y0mtT0TWcc7qQ3j7MQAly5ZHiYa9ULVMsXTDWFjYC3tqzAZhieWSV/lOSy6j+/sdWVWTU2S/KItcbradvE9EHMN6enrCzc0NqgQ5LRJyWgjFQ30iJtQv4kF9kj2w54r1eszEg6snYOVS6bf2JfJATJWhfhEPkfvES4WdFgoPIwiCIAhBefH2E/KZGcPQPOnaFYIgCFWDsocRBEEQhIAzLA+evsOcNYcwe+LfSVIaEwRBqCLktBAEQRCEIDx+4YPDZ+/g2Rtf5DM3wbTRA9Co3QBFq0UQBKFwyGkhCIIgCEE4deUhzyW2bskMlKzWBNr6popWiSAIQghoTYuEoIrlZBtVlpOCjqLLSUFHVbdNn3Z1cPXec5jls03hsFDF8rQh20jXNpmpSq/ItgnFQzMtEoEqlotXpZ2quZMNRTi/6DxUHtt8OHQMuh+vwLWADfLZOyeRo4rl4lVpl0I1d2WRU3TbhBjQTItEoIrl4lVpp2ruZEMRzi86D5XDNl9e3EHAizto2mMYDh85BkNzhyRyVLFcvCrtUqjmrixyim6bEANyWiQCVSwXr0o7VXMnG4pwftF5qBy20TXNh+jQ76hXtQyM8xVIIUcVy8Wr0i6Fau7KIqfotgkxoOKSEiouKWq1ZhHaFr3Kt4jHIjW59GSldq2IaMPslpNanyiy7VdBWth5+j6unD4KrzsXYeZQLFeqfEu5YrnU5BTZL8oil5ttU3FJMSGnRUJOC6F4qE/EhPpFPKhPMs66Pedx+NwdDPmrEQZPWQV1TZ0c6RORB2KqDPWLeIjcJ15eXnB3d4enpyfc3NygSlB4GEEQBEEokJJFHRERGY0thy4jLiaa+oIgCCIVyGkhCIIgCAUSHy+DsZE+tm1YAS09I+oLgiCIVKCUxwRBEAShIMLCIzFzxQHs3rAYRcrXp34gCIJIA3JaCIIgCCKXCQ2PRJ8JqxESGoFeHRqgTO02yCNPZUQQBEGkgMLDJIQI1ZqlriPZRjzb0PlFNlTF80tLUwOG+rrIZ26CKk17YNf1d8JWIpdCxXLR5aSgo+hyim6bUDw00yIRRKjWLIWK0iLKSUFH0eWkoKPoclLQUZVsw5yW1dP7YOTigxgwdwccKjYTshK5FCqWiy4nBR1Fl1N024QY0EyLRFB0teZfyUlBR7KNeLah84tsqIrn19sPAbhwwxMPn73D4xfvYV28trCVyKVQsVx0OSnoKLqcotsmxICcFomg6GrNv5KTgo5kG/FsQ+cX2VAVz6/dJ65j+vL9mL/uCGZPHw8tfUNhK5FLoWK56HJS0FF0OUW3TYgBFZeUUHFJqiit+CraWe0TKfSf6HLpyUrtWhHRhtktJ7U+yc22B01dj8mjB6NC7WbQM7bCU98ghVT5lnLFcqnJKbJflEUuN9um4pJiQk6LhJwWQvFQn4gJ9Yt4UJ+kDXNapk0YiRpNu+dij4g9EFNlqF/EQ+Q+8fLygru7Ozw9PeHm5gZVgsLDCIIgCCKXiIuLh8+nr3B2doJMFk92JwiCyCDktBAEQRBELvHk5XtYmRvDqVQdhH59T3YnCILIIOS0EARBEEQuceziPTx+7gNzUyNo6hiQ3QmCIDIIOS0EQRAEkUtcvvUUBvq6qF6uCLT1TcnuBEEQGYScFglBFcvJNlI6H6ResVwZ5aSgo7LbRkNDHV8Cf6Coa0HkUVOXRCVyKVQsF11OCjqKLqfotgnFo6FoBYiMQRWlxauiLXK1bWWTk4KOostJQUdVsI2xoR58PwfCtaCtJCqRS6FiuehyUtBRdDlFt02IAc20SASqWC5elXZRq20ro5wUdExLzi/gO0LC/jeoJRsqvk8U2XbJIo4oXNAWZSrXkUQlcilULBddTgo6ii6n6LYJMSCnRSJQxXLxqrSLWm1bGeWkoGNqcqHhkWg3ZDH6TljD/69I/USzjUhyudl21xbVuRtjbGYtiUrkUqhYLrqcFHQUXU7RbRNiQMUlJVRckipKK76Kdlb7RAr9J7pcerKiXit7T97A1+8hsLW2wu2HTzFjaHuF6pebbYvaJ+mRW22Pnr8dfbp3QvNuwxVa5VvKFculJqfIflEWudxsm4pLigk5LRJyWgjFQ30iJqL2S+fhy7BhxXwUKloSZStWx+Z5A2Go/+sF4cmJiIyG/5cgWFua4uq95zjt8RCzR3SEhvrPhdwiImqfiMDWw1egp2+MKYu35Gq7Ig/EVBnqF/EQuU+8vLzg7u4OT09PuLm5QZWghfgEQRA5wPfgMMTL4lG4VBVoaushn7kxX9uSFadl04FL3FmJioqBuroanBzy4dy1J6hfveQvq697vfoAMxNDmBjq4VtQKHeA2MMath9CMRQpaIvjlx4gPjYGahqa1A0EQRAZgJwWgiCIHOCe1xuUK+4MPeN8iI74gfh4GdTyZM1RePrqI+ZO/Bux8XlgbKSPI8dPIygkLE358Mgo7Dp2HYfP3YFLAWuEhEYgODScOy+amhp45eOP6mWL4J+/GkJLk34GchvXgjZYuOEoIkK+QN/UJtfbJwiCkCL0a0UQBJGImNg43Pd6g0u3niIwOJTPSjSoXhJ2VplbpHnrkTfq/lGT1+LQ1DZAaXdnzFx5AH3b14G7i0OabQcGhfJZGYZMJsOCdUd5bQ8ttXhcuX4Xt+8/5U7Huln9uEx4RBQ+fwtGwLdgfP4ajIfP3uGu52s0+7MiTu5bjyvnjsHz5Xt88PuG935fEej3DdqaGjxEydRYH73a/kH9n8uw2bbIqBiEfPUlp4UgCEKqTktUVBS0tSkOmiCIpLAB/L+bjuP8dU8YG+khv40FShTJj2Z/lMvSvp699oW6mhrsrPLC28cfT16+x5MX7/nnpYo4okHdmrCzs8f9e/cwbuFO6OlooVzxQjy8imUCY84Cew2LiEJYeBQPt7LIawh9XR1Ex8TyfS1evIS3x0KAZi9aiX2bl2Hiv1uwfnZ/5DU2SKLTsYv3MGf1IWhraeL8lkn8szNXH/FChPltzFGn4yjkt7VAPjNjONiYY+DU9YiLi4OOthZ3cizNjGFlaY7GDepgyZKlsCpYks/wvFizGlfuPMPzN74oX7wQmv1RltcIYaFqFqZGdJopiAJ2lnjy6B6snDN//hIEQajieF0jq4uApkyZgnv37sHf3x96enooWrQoRo4ciSZNmiSRffbsGYYOHYqrV69CS0sLjRo1wqJFi2BhYZFE7syZM+jRowffX8OGDbFlyxaYmJgkbHd0dISPjw8GDRqEZcuWpVgwVatWLezduxetW7eGskLZd8TLOCRiZiJllIuPj8eMFQfwI0YD4/7bhfyG0Yjw9cLJU2cxcMo6DOrTFUDGMlUxh2XplpN49OwdtPSN4fPpG1wLWKNqqUIY2KcbKlSpBVMbF2ho/Vx70qC9DA16eOPAibOI8n2CEpb6MDIyhKGhEYyMjWFkZAwjYxPEREfj1tP38PINhbOFFqa5WMLcvkhCu4bm9ug8cBJ8fN5h2e5rqNKkY4J+O49e5WscfoRGoHo5R0xesgee3h+grqmDdn2HYtnMiWhdvyKKuDjByakgXFwLw7GAE/QM80JTxwCvvsXhyadwlCxgniQDjo6hGeavPYzIkC/4+uk1rntcwrFLd7D97BlE/AhEw2ruaPpH2VRnkRLb0E43NM1F/yKfN5mRy+22ixSyw/379/Fna/GzNomY3UlqclLQUXQ5RbctRbyUbLyeJaeFKRMSEoJu3brBxsYG4eHh2L9/P5o2bYrVq1ejT58+XO7jx4+oXr06jI2NMWvWLISGhmLBggV48uQJbt++zY3CYJ936NABAwYMQJkyZbB48WKMGzcOK1asSNH22rVrMXbsWN6uKkEVpcWroi1itW1llVuy+QSi1fSAGkNxwCvy/6sX90Cjdn2wbOZIHDl/F14WaqhSxhUwdsAK79T3yR2WzSfh9zUISzfvx9QTvnD7/2rIbdOohvz043eMP+ANn5tPoK6hiTGli6JxnWowzlcIedTUEvZ7+twZLDn9DqFfPyIyKAAaX71QxHEutmxcj/UrF+Kd7xfcfvQKD56+hb65LZ7K7iImLAh20S8RFxmKvZuXYv6ipdDT0UblKlUwyLUS5nmE40Y4UG7YdkzpWhFuDmap/uiO2vWzqvPWK94pqjozHXWN88HeOB/cDQpjY0AJ5C8CxEaEQuvHaUxYvIvLNaldFnWrFuehS6xf/n2UF68v78Znr6vIEx2Ky9smQ0dbU1LnjcjXaBEnW1y+/RRP3gVg5LY7wlYil0LFctHlpKCj6HKKbluq+CjZeD1Lq0KZZ3Xq1ClMnjwZvXv3xj///IOLFy+iRIkS3CuTww48LCwMFy5cwN9//80PbM+ePXj06BE2bdqUIPf8+XM4ODhg+vTpaN68OZYuXQoPD48U7bLUbiwcYs6cOVA1RKvyLVLbostJQUeR5dj6jduPX6Fpv+m8TF/i6sVaukb4Z9K/aNawDp+VmLf2MPoMmwWvoysQ8OwmYqIifrYhk+Ge5xuMmLMV/l+DsH3nHrwN089U1eTgjy/hdfg/dOnaC6Y2rnjocTiRlAyLl63Eoz1z8frCdvjeP4tv30P5uoXzpw5i7IIdWL3zLF68/YSyFSvDskglxMdEQzevDYrXbIqDBw/CpUJTrNl1Fv9uOoZ2fcbBX80uqX4+genq96vjSC6roWuA8m3HwuPqdSydPxUBgSHoOvI/ruvtt+HwuXUMedQ0YFG4Ihq2aJPCYclo/0lBThFtl3V3wq1HL3H94TOhK5FLoWK56HJS0FF0OUW3LVUaKtl4PdvWtKirq8Pe3h537txJ+Ix5c40bN+YHKKdOnTpwcXHhxpB7eGy7t7c3ny4qXbo0N4Kzs3OKNtiUU4UKFbj3NmbMGJWabeHVlf1kP5/a/aoKswLkpKAj2UZ6cszZYOs8Jg7vA/cShbH30f+eismrF7MwLnvn4mjfuTvCgz7h2p0H6D76XzzcOQPq2rr44mSHVeGhKGBviS7tm6N5+148VKtEDHMCZHjrsR+WhcujhGOldKsmF2s1HO7N/0FTU08EfXgBe6eiCTJ58qhh/uLlyFOuN6+Bzoaqs1o5o1RhJ8REhSG41V/Q0NaHhpYeXn6JwoittxOOo0+3SjCz+/l0L4+8PHOidpMfb1r6/UouNdmSBcygZ5IXVRp0QaV6HRHs/woHdqzH9KWLERASi5IdJ+LlqfUwL6onqfMms3KKaJs5geWKFYL/gxPIo1k92/pZUXJS0JFsI55tpHB+KRvqEh6v/1ZxSeaVRUREIDg4GEeOHOExcu3atcP27dvh6+sLOzs7zJ07F6NGjUryvS5duuDEiRP49u1/Xi2bhho9ejSPXc+fPz+PmWPGSmwAVkyHGcjV1RX9+/fn/89KjJy8MM+B5SN4ZiBRoYrS4sXBZ7VPclNHZZL7ERqONoMXoWf7+hgyahL84/PhkU/S+OP4uFjs2bYWWzeuxaeA74iMioallQ0MrRzx6sk9rJs3AnmtC6BQ8Sp8ZobBbns+nh5o130Qbt97gqnj/8GkGf9KNtY7O2PCmW38X93HxTue+BJvghHdW8PSzBCn1o/PlWtFLudkEAbj+K94/+krYuPiUMatYBKnTuprWhgsVHD38euYveE4PH1Dc73KtzKtORBdTpH9oixyudm2shWXDJPoeD1bnZZ+/frxmDiGmpoaWrZsiTVr1sDU1BR3795FuXLl+AIddtCJYUaZP38+IiMjk2QeYIb79OkTihUrBh0dnSTfkRvh2LFjfAHQzp078ebNG1hbW6uM00IoHuqT3IdluWKhVXc936BbmwZo0botLK3t8en9G3h5PsKdO/fg7ReB6u7mKFfciT81YxXjdxy9ionDeqDb3zMQFxOFE7tX4t2793ByLgQ1WSwadhrGizSye9f9OzdhYJ40DXFcTCQObVmCOo3bwDhfQagiET++olu7Jtz2W+YPgrFh2uFV2XGtXLzpibPXHvP1Pywzm6mRAc+cFhEVDf8vQejXoS4ql3ZJ4rxIGZbiutWgBXhwywMmNq453p7IAzFVhvpFPJTNaekn0fF6cn6rJPKQIUNw9uxZbN68GQ0aNODxa9HR0Xwb8+gYqaVDkx+gXEaOra0tN1xyAyRnwoQJiI2NVcm1LQSharCF4SN6NcWckZ3w+s1bNG7VFSXLV0OHv/pjx56DkMVFo1mdcvijcjEuO+2/fXj9/jOO79uCrn/P4OFZ00b2wviZS/D0+TO07zUKefLIMKZvczSuVQbFXR34epjkMKelda8x6NG1I3d6VBFdI3NsO3Aa//RqjV7jVsHH90uOtcWSKew8dg29u3fiIWoPb3vg2o2b2H/yGo6cuozFs8bzYpl/jV6Bmw+9+YyQ1NHUUIdtvrx4cv+molUhCEKJGaIk4/XfWtNSuHBh/sfo2rUr6taty1Oo3bp1C7q6ugl5nJPDPDaGXCazFCxYkHuDzEtksXJZ5XVANKI1xB2MfAmJ408rCdXok2v3X/DigJVKucDa4n/pAwk5Rqhduxb/S6tf2EDW0q4QrwLvGyyD/xUPBHx4Ca8PIRjzT29epDFMZoTXn0JRt93ffE1LXHQkbj14yuYGUux33LC+uH7/Bc6cPMoH8KpKsaot8I+RFTacuMVrvbAaI9l9rTz9FIV+/QfAyL4kfEMA3xd+ANjfT/KYOmP4hFnw8fbEtVt3cenxdZQt5gQbS1NImWrVquPC9QeIM8if4229e/eOP+kkxIL6RTxE7pO3b9+q3Hg9R4pLsqmevn374uXLl3waiOHn978fHTnss7x58/5WUZrx48dj69atPAaPZTDICk6WWigkcPgVhSKpTp+w1KeP7t9Bu4aVcfriRcwY2j7b21CVfsnfqjT6T1qLqx4eKFOiMN5/+ARLA10Uz/8ztEm3phOGzVqBm1dawDx/sXT3GxfwELMWrcbSyf1QTNBQgdyiVq3aqFzmCIaOnsALWo7p0xymxvpZvlZYZrVnrz/C93Pgz/8/eYgG1Yr/OiSjbgN06vYNF45ux/wla3hc9cKxXXlRTikSF6qLrYcvY/zkWdDQznj4nbKFvKgy1C/iIXKfWCSrm6IK4/VsCQ9Ljnz6iC30YVNHzLAsVi45LOdzyZIlf6stJycndO7cmcfopWZogpAKYeGRvNL7zPGDceLyA9SskLEYVSJ1DPR0sGnuQPRu9wfyxEXzJ/Gdm1VL2M5mCdiaiNjo9BdlM74G+PNXc2t7lTc3q/dSonpzHD1+BtXLFUH3Mcvx5sPnTNuFOShbD19Bu38WY//pW/D7GoKYeHW0bFgDTdt2z9A+dAzM0LDD3zh55iLKlyrCi3FKNVysSCFbPHvti9Bv7xWtCkEQKkKERMfrWXJaAgICUnwWExPDF/GwKSRWbZPRqlUrvhDnw4cPCXLnz5/nnl2bNm3wu7BYOdbuvHnzkBMLJFnWGpFgmWjO+ZnxVxHlpKCjiLZZvescKpRwxryl6xETGwunYuWEORa2+JktaO8/eS0GTV2P6JhYIW2YnA8R+vioWxyVav2Jri1qwMjg5xNsNrB9+uojr/0SGRXFM8fs8PDmr6nx9MVr/vox0ihdOTm/2p+i5bJjn/Fx0byWS++2dTBz5QG8DdHJUJ+8+KaOiVvuouXgfxEeGYszR3bgwMmrWLrpKGYt34UJ8zfCyCJ/pvQ78CgIHYbMha6uDlbtPCvJ+5eGujosLC3x714PYc+b3Dy/lFVOCjqKLqfotqVIgJKN17MUHsamlH78+MGrZzIPzd/fn6dNY0VnFi5cCAMDAy7HitOwDAEsUwAraMMqabIsBCzbQPfuGXuilhHvjS0sygo7j1+DmbEBr6sgR56TJiYmDuGRUTxbDtteq4Ibihayg6KgitLiVdG+8y4Sa5+oQVNfF3omDlmutm1ipIddx6/D3dke/Qf0wtQTX/Hd5wpiwn+gsEEwdPJEITY2njs0zJn+/iMMgSFRCIrR4vVBdE0s0aKyI5pXLQQ7K7NsOeYBzj44f/I4Tl15iBb1q2LWpNE4eOgQxiw5jIhiXZBHIw4amkYYVhqSqFj+5c0TbD9yFW8/BiC/rQWWThuMQHUbjEunGnJ4kD+OX7qPoq5OmHjUR9hK0YqqKH3o3B18DgzHvw9NoKlvkqJPwiKisGaXBx4+e4d4DV08ev0FVsVrwOWvpejbuyYKOVpmm36Tp/+H4X064PjF+2hUq3Sun1+/KxdqXRk7jl/HzRAb4c4bKVQsF11OCjqKLqfotqVKXyUZr/+W08JyO69fvx4rV67kuZsNDQ1RpkwZHq/WtGnTBDlWvOby5csYNmwYX4CjpaWFRo0acUP9Tnxccu9t27ZtPBNCZunQqMovUx7HxcXz14Nnb/Ons03/KAtFkFp15dR+ABUlJwUdf1fuW1AIXvkEws7YAiFhkRg7YyPU8xVG4NvHqDZkXZZt06V5DRw+d5dXfX80fDa07Usib8ES0DIwRakCGqjppAUtLW1oaWpCQ0sLFhaWuOKnhz0PghAfF4fwb774/OUqpizdi6AfYahYygUt65ZHQft8WT7mZVtPQS/qKy6dPQYrp1I8nKp0lT/RqvsgeJ/fhvjYGER8/4xt/rUxoa17jvfJ7+zz8NWXOHdwP/6bPQplK1aHqY0ztPVN+dO15NWQ5T9YUWHf0axRXdzzfIO1R69i74OgVOV+VV1ZJLns2qeOoQVa16/IM7Tlsy8IdR0D/PB7g7AvH7DB6yPMZQH8XmluUxBF7PNiyuiGmLn+BMK+PkBkUAA0dPTx5EMwiqXitGRVv7cRhti6YTUat+4Mm3ymKFW0gKTuX9Yl/sDtdaOQv3ILqPr5pYxyUtBRdDlFty1V2inJeP23nJb27dvzv4zAckifPn0a2ZHJITUKFSrE06nlFOrqPyPo2I/0hn0XeP0AR9vfXwSVWaiitOKraLMwqSg1U6zbsos7syOHDsa56LJ4uHsuwr74oFDRtBcCJ95nqP8bXL93DGeDvuDb9xAEBofyGZSti4dDt2B1LLwenfDkp1saT36iPwRi78MbUFNXh2E+B8wcuQxOpvH49Poxjh89xGP8rS1M0bhpI+SBeaaP+eHNq9ixZCR3WOJiIhD0yZtX0Z236D+M3vWI6xcXFY4724biWRlTFHGyzdE+yco+gz+9xvPjq/BKPYxfs7Ubd4SeqU2GqiHvWjkN564/wfpZ/VCheBHse3hD2ErRuV1RWlNHH2NHj0CnXv8g4uNbPF0xGAaW+WFg6QC7wtpwtS6MFk0aIAIGeHzrHOq0ZVnaACv3anCp1zPHjrmgXSFsWDYbXfqNxNKJ3WFvbZ6j51d2ymnpmsPU0Q1fX9xCiZ5Vs902uSEnBR3JNuLZRgrnl5Rpr2Tj9d8qLilVslpckg0s5609jK7Nq/MfxJzmdytK57acFHT8HbljF+/he4w+BvfrgeDgQBQuXQuPX/mibcumGNqrFaoXMU13sf22s09x7Pwt5DXUQpfmtVCkSFFYWdsin5U1dA1MYGiRn9cLyY6KvyFf32P/tlVYunYXoKHD18k0qOSCsvm10yzMl/iYv77zxMwVBzBnwiDsPnActx+9QrxMhrrVSqJ1r+H4pl0ApQpY4tPNnRg4eiY2zhkAIwNdhZ1fya+VF4HqGDZpKbq1qYemdWvCrWx16JlYZ9iGdy/uh+/712jSaSjUNDSFrxSd2xWl2c9G2Lf3CAz4gM8xxngeqIZidoZoXqtsQi0XlnmHZeCpX60kFsyZAplNZTz+EJSjxyyLj8P+9XMwYfYKrJ/dj69lksr96+qL7zi5ezNuXrsCPROrXKnyLer5pYxyiuwXZZHLzbaVrbikskBOSyacFgZbiLzt8BXo6WijTYNKCTMxOQGlPBaLF28+4eQdX6xaux6a2j9Tve5cORXrtuzDzGHtcd/rLU/fWqKIIwoX/PlE//FzHxw4cxuPnr9D/Rpl0K1bVxSvUAc6hrlT8yMs0Bd3r57C4cNHcfWOJw9xY84Hy7Blkdfo55+pEczzGsHMxAAB34Lh5f2Rp6LV09VGs7pVcPDUVayY2ounlN138gZ2HLuGeZOGoG2fcYiLjcay6f9gy75TmDOiE6wT1cxgoVXsOXNZd6ccP87k10psXBw6DFmCNYsnoXqzvjnePvHTkXl5+xgunTkKj7vP4epWAt06tYGtS/lUi3fmFKwQ6JLp/2D34fNYObUXNDWzNbN/jtJnwmpMHTsEddsOyJH9izwQU2WoX8RD5D7xUmGnRTp3c0HQ0tRAj9a1+QCWzbq0b1QFBex/XWiNkD6sn7+deQ7/t09gX7gi/+z5s+d4/taXV+ku7VYAL9/5Ydaqg9yhffLChy+M79i6Cdau7whTG1eoqefuJaef1xY1mvZE9SY9EBX6DTERIYiODMX3bwH49OE9fP184e/nD3//z7j/9B3yWZijfZvmKFe+IiztnREZEY7vQSPRadhStG9UGa3qV8Sj5z58MR9DXUMLA8fO56FjExbvwpfvP1C4gC13hnYdv4byJQplu9PCHJJGvWajVkV3VCnjmur+WUamIX81Qt/h0zH18xc07dAfOobKN/UvEmwGz7VCE/7X9/9/9B3cque6Huqa2hg0Zi7e+nTGjJUHMGVwmzRnF0WjU9NqWL1hC2o17cbD8AiCIIj/QU5LFnEtaIMRvZpi17FruHrvOTo2qZqjsy6EGA6ru6s9+vUfiL0Hj/MQjgnz1mLg317wOL0Pw2Zuwnu/rzwbmL2NBSaOHwvXUtWhpWesaNX5oI3N7shneMwcgEKlfv09NmxauGYfBjy+zDOJNO4zB39UKoYeg0YnyGjqGGDQxGXoPfQbgj6/w91b1/DE0xNLSrvgxKX7OXM8annw7mMAr/mxaMMxVK5cFXf1o1G3aglYWZhwGebQWOQ1xMJVW7Hv0Ams3bgNxvkK5og+hFiwa27O4lXo0KY5Nu6/yB80SYFqZQtjyeYT8H54GUUrNlS0OgRBEEJBo+zfQFNDHV2aV+epkE9ffZR9vUIISzEXB0RFRWPXxiU8HCY8OADde/VH60ELuMOyaFx3vHvxEAvWHESxKk2EcFh+F7amw7l0HazcchjPHt7Exl3HYZDXNlWnyKpQWTTu9A/Gzl6L9j1H4N3HL7jz+FW26sNmUXYs+gdFne15ccPLJ3agTq2q0NLR5/VkziS6Fl0K2GDVtN7Q1dZE+7atcXbvSgT7v0Z8XM4l7yDEgM0yrl23AXeevOaOLZuhEx01NTW0qV8R//23jIdeEgRBEP+DnJZsoIx7QTx58R7+X3+mRSWUm8FdG2Dxqu0I8n2Gex4neBX7sX2bI+DtIwyZsR7GVs68eriywdYlmNi4QlPXMEPyprauWLtkOg+X++CXvYVa8xob4O+uDRASFgFdQ3M4uJTEjGXbcXT3Bl5k8HtwWBKHanjPJqhRvghmzF+GshWrok+HBji6bTGCPr1EZMg3xEannVWKkC75ChTDsaPHeMrwvhPW4EvgD4hOy3oVcPrKfbx+dFnRqhAEQQiF8o2sFMSQvxpiy8HLCZlzcgIpV31XFjmWQe7fTSdQoaQLOnXujI4Dp2DQmInoOG4tLByLpxs7L3q14eyUi40Kx9gRAzBw6irEQp1nIsusrVnSC3mdpNRk2ZNzJsMKbDLYq2u5P/lDhKmrT2DuwZfYceEFgkPCeb80qlka/03uiS3zB6FQfkssWbkRxcvXgHvZKmjbpjnCgz7nim0UKScFHbNbzjc2L6r2XoAmLVvyhe53n7wW+n7DEl60bVgZixYt5EkFpGRrKehIthHPNlI4vwgxoDUt2QT7oRnWozHW7DqHYq4OqFa2CLITRVZrlkJF6dyQ+/w1GEcvPEL5Ei64+eA5rtx5BvtyDfBCpyxGbLklZCVfRck9ePEGSzcegq6pFUwL1UaTaqnXNkrN1q8f38a2Ix68UCZzNphzYmOZFy1bNsXZ+OoJshXCTqB8cWcYWhYEXv+cyVFT10TfMXMwfOUZ+Lx5i2vREdi++ygGtK+NPyoXg462Js+cxhwYt7JVsNQrH2KiInH9+EpMmjIJc+cvSZHpSnRbS6GitBByGjUwY24xzJ4yEbUruaF7q1pJHjKIdL9pVa8C2v2zGK8eXoJruXri2JDOL7KNCp5fhDjQTEs2x9oP6FSPV0vfsO9iiqfEv0Nq1ZVFkpOCjr8jd/PhS/SduBoF7a0wb81B7rA0btUGhRsPQB419YRqummRWuVdZZZ7+V0TFfouhnWJmgj6+AJL1u3B0i0n8fyNL18LlJatzz38hHV7z2PpvMl4eO8WHt67g0d3b2DEwO6Y999WRPwITJC989wf5UoXg4Z20n70k1nBonBFONXqAJd6PTBwymJ4vfqI9kMWw+POsyRtq2nqQNvABEWbDsbug6cx6K8WuHV2J+JiIhVuw+yWk4KOOS0Xb1sRZ04cxqeAIPwzYxN+hEYIeb9hD8Ha8dmWxQmzLaLYkM4vso2qnV+EOJDTkgM0rFEKVcsWxvx1R3h2o+yAV1fGz6vrl1WYFSAnBR2zKvfwwiH8t+00Cjrk486LTT5TnN37H2bOWwI1tZ+DjYxW8v2VrLLIlSxoAS09A9iU/ANlukzGxo3r4OpSCCu2n0GLAfMxeckeHL1wD8ZRvgm2jgkPxsFt67FiwRRUbtAVBub5oW9mB6N8TmjefRTatqiPj3dO/GwgPhYvHt1FvfoNfqlj43p1sXnfOezdshybDl7C4XN3UvSzlq4+lswcirxGumjRaQB+fH6jcBtmt5wUdMwNuXwFS2DzriNo+EdldB+zAs9e+wp1v5HLsbUt5649gPeDS7lmm9+Rk4KOZBvxbCOF84sQByoumcnikpmBhRNtPXSZLwLOSp2A5AXzRKoOL1rbOSWXX/cHho2egR+h4Xxx99QxAzF0+CgYmjsIW8lXZLmosCB8+fAM1y5fgMe167j98AX8v4fBwNQSP776Y3T/Nhg4bjEP80rOh2c3UKlOCzRo1QFPb11C4fxmWLn5ALR0jTJcUfrF3TNo0KILNs8bCBMj/STng5XmD+w9dYM7U3duXofB//exaDb8HbncbFv0Kt8sO9fFQ2sxePQstGtUBS3rlodPmJ5Q9yVW6+jL9zCs3HKE158RvU9yYp/KIifla0UUudxsm4pLigk5LTnotDCevvqIW4+8efz07zotRO7DssKt3XMem1b/C31jczx5G4hataRR80F0WJhYVMhXfP30Cm+8X8CtRFmYWDvzwVlqxMfFYN+6Odi6+zBaNKmLNl0HwNDMLlM/MPHxcVgyZSB2Hj6H1dP74NEzH7z9GIAWf5ZHt1HLUa28O4YMGw6nEjUlU5BQVET+0U98Dvp4XUO/AYOho62Bcf1aQldHC6LAEk20/XsRju7dhKIVUs4qKmOfqCLUL+Ihcp94eXnB3d0dnp6ecHNzgypB4WE5DKvhwkLEoqKzJ0yMyD1YdftJS3Zj2MDucCj+B8wdSyZkqiJ+H17bxcgCdoUroXqTv2Dm4J6mw8Jgsy9t+kzAwZNX0GPorASHJTOoqamj54ARCAwKxcEztzFv7WE8e/URHYctgYONGRat2o5CJZMu0CaUF9bPju5Vsf/wcRRxLoChMzchPj771iJmR0Hbzs2qY9qMWYgOp5T6BEGoNjQCywXcXezh5f0xN5oisonHL3wwet42rJg3Ho06DaFBrECDzOQL7zOL/8c3vOjlpVteOLZ/O7bsOYbenZtiwfx5SlEMlMg8+qY2mDJ/NRzsLLH10BWhTMjC1t6898Pu9QuSJLEgCIJQNSjlcS5Qrlgh/LftFEq7FciN5ojfgK1bWb79FJ56f8SGZTNRrXF3pSwUqcrYFHSHx/HNKFGhNvRMbfhnQ6esULRahILR1jfFwoX/ok6DZihXvBCfJRcBNTU1TP27LQZPW4cq1f9EwRI1FK0SQRCEQqDRWC7AakO4O9vjrmfaRc0IxcKKRm49fAXdxyxHCffCuHrlIqo37UkOixJikNcGlep3TnBYCEKOdaHSWDJrLKYu2yuUUeyszHiY2PCRoxAZSmlZCYJQTchpySXqVSuBc9eeZGvtFiJ7YLU7Og5dgu8/InD++D6Mnb0WxlZOZF6CUMHww8p1mvMwrOxKV5+dYWJfA39g8/JZkAm07oYgCCK3IKclF38MWd79HUevZnkfLC3mOT8z/iqinBR0TCz3+r0/+k9ei4Nn72DjijlYueUQHItV48UiWRrEHR7e/DU9MiqXGVmSI9vk1LlA59evbbPvXgDi82jyzF0i3b9CwyPRs2cXTF6yCyfOnBTyXqPItkWXk4KOosspum1C8dCallzExdEaj569w6PnPihROH+mvst+zJa/dOAVk/P4yTDQ5X2qef8VJZfTbcM3DlWiz6JUfn042lr81v5++L/F+xtHoBv8ElOHd0fLLoOgY2ieIMduXsM33+AVclkSqYXdKqWa5z2jcjmxT1WTk4KOostJQUdRbOP7Ix6ByAsjiHGPDQwORc1OU2DmVArWlTug18i5OOFYGKUKOwlrQxH7mWwjnm2kcH4R4kAzLblM6/oVcdrjIULC0i/SmBxWeIwP3lltAeTh70WSy+m2v394jsWrduDv6RvwJfBHggwLt3sZrJOh/d3/EI3H+xfh+fE1sCxaBWOW7kaH/pOTOCwMVmhKnqSHvbL3qZFRuZzYp6rJSUFH0eWkoKMotmGIdI81MdSDpZU1IoICYF2sxs96Q8uWIT4uVlgbitjPZBvxbCOF84sQB5ppUUCYWJ92dbBm9zkM6944w6l0WaVk9hSOP42DjL8XSS6n2zZ1KALLvEZoVqs4+k9aixnD2sPUyAD1e8yEjY0lAiPVoaFjACObQnAtZYJ9mlEIDglHmwaV8PbDZ5y9/gQX77yAZeWusHSrBjW1PKhY3CVV+7PKuOxj+dMX9j41MiqXE/tUNTkp6Ci6nBR0FMU2DJHusSyDmJGeJkKD4xD88QXcmgzAhW0j8KRdTZSo3jxHbUPnF9lG1c8vQhzyyFQw8bu8muiB5SNQKL+VQnS49cibD6rrVi2RpsxT3ygUtdVOEkbAnsKxH7W0QrQUKZfTbft5P8LekzcwqEdrjJm9BvFx8RjQpTG69RsG7y8x8HjkjYg3N3H25HFcvv00IesOq5PT8M/qaNGmI4J1C+GRTyC/OaU3DcymjdlTl+RyyavkpiWXmX2S3O/bMKv9ompyudm21PpELnv9gSemD2iH46tHCHWPXbXzDF77h+HNp++oUNwJZQuZ4cCZ2zhx6gz089oK0Sc5sU9lkVOma0VEG2a3XPI+EXEM6+npCTc3N6gS5LQoyGlhLN54jM+66OvpZMhpUXWYf91r/CpMHDEAVf9oxGdJjK2c+Svb9t7zMgYPGYGjF+5x+dkju6JLzwGwzO8GTR2DbNFB5BuZKkP9Ih5S7ZNPz2+idsNW2LtsGETC/2sQL3hb2q0gDPV1Ubm0C/7begrN6tfA0KnLkSePmtL2ibJD/SIeIveJlwo7LbSmRYHUrlQM5248UaQKkoI5J393aYBRUxdhxeLZ+Pb5Y0Isx5d3j+BYvBZ3WMb2bQ7/Nw8xeu5G2LpWyDaHhSAI5SckNBjaWpoQDStzEz47f/LyAzSoXhJt/16MCiWcsXTDfgR9eqFo9QiCIHIccloUiKaGOvR1U59lIX7iF/A9iSlKFHHE+P4tceiUB9p06oWw7778c0MzO1zY9x8+v76LGSv2IV+BEhl68kgQBJGY82dO85BSEXFxtMGngO+4dv+nk/LKxx8F7S2hrW+qaNUIgiByHBrVKRCXAtbw8v6gSBWE5qP/NzTsNRtbD13h4V/s782Hz9h+2AM62lro0KwW/D685rK6Ruao1WogLAuWgZqauqJVJwhCgkSH/8DB4+fQqGZpiEgxVwf+uufkDR4idvLKA8yfOQl6JooLcyYIgsgtyGlRIBrq6tDT1eZT/kRKnrx4j87Nq+Ppq4/oMuI/NO03D7NWHoSpsQF3YEbN3oB3r56T6QiCyBbev7iDtx8DhJ1pqV3Jnb9++x4CB5ufmY4KFUk7mQtBEIQyQU6LgmlZtzxW7TzL6438ChGqyOemjt4+/rAvXBqtx67FqAmTMPHvDnj57hN2HrvKwyPG9WuBqnVbcVmq5Js2ottGCtWQlUVOCjoq0jYrNu1GweIV4BOWdj0qRd5jY/RtUbtBQ9T7swZ0tbWgpakBNY2UyVro/Eobso14tpHC/YsQA6rTomDMTAzRpkFFLN1yAgM61UtzAaiiKt1nVC679hkZFYPwyCjkNTaA96cf8DS1w6VTD+B9ZiM+e12DjrYm5o7pjh79h8HMvihft0KVfMWrXqxM1ZCVRU4KOirSNg9evMWW43dQuGFfLH9pJ9w9Vi6H0n/h4OqhKFbAHD9C30AWn/SBF51fYp5fol8Dosspum1CDGimRQAK2ufjRRAXbzyOe55vhKp0n1G57NgnW8MyYPJaTFi0C4fP38GLt5/w+vIeXF3cizssTVq3xVuvWxg5az3MHdwTFtpTJV/xqhcrUzVkZZGTgo6KtM31x96IDAmEvoW9kPdYuZyGth7yOpXC05c+/POw4C9ZOmY6v8g2Ipw3Urh/EeJATosgsCKIo/s04/HU564/Sb1qMn5eXb+srqwAuazu863HXiyZNh7N+8/DuIU70a1lLZiZGuDQmTuoXLoIvjy/CYeKTVFrzA7MWvAfrJxKpahiL69qy9vNQPXb3JaTgo6iy0lBR9HlpKCjIm0T5XMbJnauwt5jE8s5lKuPH8FB/P/nTh/N0jHT+UW2EeG8kcL9ixAHKi6pwOKSabH18BW4O9tD29gmSXFJRVW6z6hcVva5et40jO5RH57eH1DK3Rm1GndE/UYt8eZjAJbP+Bt2perAD7YoXchaiAq9Wa1cnJs6KqtcerJUUVq881BKfcISewzu1gSahmZwKFVL2HtsYrkp0xfgvtdb1KlcDCcv3OQzMIruk5zYp7LIKcu1oki53GybikuKCTktAjot7Ad0+bbTKFu+Iiq6mkCZ2XzgEq7ee4FPAYGYNrI3jp66iA9+XxEUEo79W1egWNXmEAmRb2SqDPWLeEipTyJDvqFmtUqY8ncb2OaTRkz75dtPMXjaBv7/F3dPw6VMXaXqE1WC+kU8RO4TLy8vuLu7w9PTE25ublAlKDxMQFj4U5/2dfDk5XvuwCgz3VrWxJIJf0FdXQ2a6nlw6NxtvhD/4K51cK/STNHqEQShAnz3f4OAb8EJDsuz177w//Iz/EpUqpUtjHzmxvz/y5YsRnx8nKJVIgiCyFHIaREUlsqygK0lTns8gjITExOLYbO3oGe7+pgwfx3i42XYunYJ3Co0TLF2hSAIIie4ee0y3Jzt+P/ver7GwCnrMGTmJmw5eFlYg6upqaF7q1r8//9tPQW/l7cVrRJBEESOQk6LwNjkM8XzN774EZr+OhKpwGaNWEpjOey4xi3aifIlC6OwiyveffyCnUtHoWR1sULCCIJQXth96erVayjtVpC/L120AOytzdGpaTWcuvKQJ0aZufIAn30RjWZ/lIWh/s/aLru2/QwVIwiCUFaoTovgdG1RAyu2n8aIXk2goa4OqeL7ORAj5mxBTEwcYmLj+EwSGyy0qF8Z46bOx7ePL/Do8gG4VWIzLORLEwSRO8REhuDek5f4u2uDhBmMRWO7ot0/i7F2Zj8s3HAUVcsWxej523B45SihZoD19XTQvE45uBS0xsmLt9DlzX1YFiytaLUIgiByBBodCg4rsti+cRX8u+kEXgdrKaQKc0bl0pONi4tHRGQ01s4fhfs3L2H5hq0Ys/IwGnYbibatW6BgmQYoUKQU1DW1JVWhVwqVfJVFTgo6ii4nBR1zW05DSxclijrB4+7zhPtXsLo5dwhqNu+Nc5dvoW37jkkeGinqHpuaXIcmVbDnxA2cv/4Eo0YOQ3xsDJ1f6UDXnni2kcL9ixADmmmRAI62FqhYtTqGrrgI5z//gpqauXDVmn8l62BjjrH9WmDAuIX4d01xLLgaAZksAs9PrMb7m4+wenofGJjnl1SFXilU8lUWOSnoKLqcFHRUhJyauiaGDh+FGg3a4pZOHcTFRSDqx1fY2NlCx9gSQQHv8feICejRuhafZVHUPTYtOVbjy8LUCA1qlMTmA5fR5+FjTDvlT+eXIOeXottWFjlFt02IAc20SIRIo4KwLVMPL06uQ9DHl8JVa86IbIUSzmjboDL+/nso4mLjEPThBd7fPIoqNWvjr2HzUoRdiF6hVwqVfJVFTgo6ii4nBR0VJZffrQo6DBiJR3sX4K3HPvg+OI/GPUYhNNAfHTp0QMkijmhcq4xC77HpyXVqWhXfgkL5/3cdPEbnl2DnlyLbVhY5RbdNiAE5LRKBFRMzMLeFa4PeCPvyAdeP7URgcKgw1ZozKtuibnkYasnw/e0j3Ns8HnnU1DB92mRo6f1M3SmlCr1SqOSrLHJS0FF0OSnoqCg59sCkS6euqPbPapTqOAFlukxGg5pVMOyf/nymu1fbPxR+j01PrmwxJ4SG/ZydeXTpGJ1fgp1fimxbWeQU3TYhBlRcUsDiknKe+kahqK12qtWQ8yKQL9Dv1qIGrC1NhajWnBFZtgi/1aAFKFe2FPYeOoOFcydj6MjJaS5uFa1Cb1YrF4t4LFKTS0+WKkqLdx5KrU8SyxbPb4prO2dh485jWDWtN68jJcI9Nj25Q2dvY8fRq3j+5hOOHDuEEKOiud4nObFPZZFTpmtFRBtmtxwVlxQTclok5LQkJzomFos2HOOx1pZmKWcqROTKnac4cv4uzlx9jI5NqmLT7hPQ1DWEVBD5RqbKUL+Ih1T7JDzIHw+vnUSX/mOwblY/mJsaQQpERcegef/5PFMjy4S2eONRqKmpK0WfKDvUL+Ihcp94eXnB3d0dnp6ecHNzgypB4WEShqUN7t32D5y99hhS4diF+/jo9zNLx9x58yXlsBAEodx8eHEHhVyK4K9B4zGuf0vJOCwMbS1NNK5VGjaWpli65SQCPz5TtEoEQRDZCjktEsfM1BDfg8MgAkyPJZtO8NTGqRESFoGnrz7i6euP6NOuDmycfy5sJQiCEAFjMyteZLKIky3KFXOC1GjbsHJCqO2uTSsVrQ5BEES2Qk6LEpDGcpBcx0BfB8cv30eHof/i4bN3KbbvPXkTeU0M+P/79uvLU40SBEGIgpG5PXYfOMZrsuw+cR1SwyKvEc90xli++RAigj8rWiWCIAjldVqioqIUrYKk+Oj/DWYmYoRYaWqoo1OTqijiZIfVu85iwOR18PL+wLd98PuKoxfucmemVkU3uJf/U9HqEgRBpEDf1BoL5s/FzqPXEBoeKTkLdWleHQXtLfmC/Gtn9ylaHYIglJQoBYzXs+S03LlzB4MGDeILgPT19eHg4IC2bdvi5cuXSeTYNHVaf3/+mXTQeubMGdjZ2fH9NW3aFEFBQUm2Ozo68u8NHjw41QVTbNu+fcp9g06tGvJH/0A4OeT7pVxG9/c7coyyVWvi7gt/DPh7MHq3/xP/bjqBpv3m4u/pG9H8z/Jcpn/39vD+Fid05V1lquSrLHJS0FF0OSnoKIJtwoyLom3j6thy8HKqcoq6x2ZErmghO2jr/ZzRnrJwPWKj085GRudX2pBtcs82Urh/SZU7SjZe18jKl+bOnYtr166hTZs2KF68OPz9/fHff/+hdOnSuHnzJs9qwNi6dWuK7969exdLlixB3bp1Ez4LDQ3lBbwGDBiAMmXKYPHixRg3bhxWrFiR4vtr167F2LFjYWNjA1UirWrIxVzssWzrKZRyK8BDGhRVrVkuu+mjAywqd8DAiStgFP8dpQvb8UrN7du1w6wFy7hcgcpthK68q0yVfJVFTgo6ii4nBR1Fss3YzkPQo0NLdGhcFabG+glyirrHZkZOq2RbmEYdx7WbD3Dj7l1Uq1xNITYUrZ9FOr9E01F0OUW3LVXmKtl4PUszLcOGDYOPjw+WLl2KXr16YcKECfDw8EBsbCzmzJmTINe5c+cUf+yAmZfFDlrO8+fPufc3ffp0NG/enO+X7S85zFOMi4tL0oaqkFY1ZH09HTSqWRrnrj1JVy6j+8uqXGJZmxK1UL7XPAwcNxVN65TFzYfe2LrrADq0aoST2+bh5Xc1oSvvKlMlX2WRk4KOostJQUeRbPMxzgJ/tamP9fsuJJFT1D02M3IWhSshOuzn08/jl25n6Hjp/CLbSO0azc22pcowJRuvZ8lpqVy5MrS0tJJ85uzszJV89uxZuvFv+/fvR40aNfjUkhxmAG9vb+zduxevX7/mRmD7Sw6bcuratSv33j59+gRVIr1qyG7Odnj94bNCqzWnJlskbwzKujuh6R9l8e79BzToPAoLVmwVvvKuMlXyVRY5KegoupwUdBTJNiULmKPf3yNx48FL+H/9X/iDou6xmZFjxTBd6/eCka0zaldIvY4DnV907eXk+aBM55eUqaxk4/VsKy7JdmNvb88Ncfr06VRlDh48iJYtW/KDYB5fYhYsWIDRo0cjPj4e+fPn5zFzLi4uSQzAprGYgVxdXdG/f3/+f3mMXK1atbgRW7duneHCPAeWj4CUikumVQ35U0AgbjzwRqt6FdKVS052y6Uly4pJvv34Bev3nucFJbcf8RC+8m5Gq+RmdH8iHovU5NKTpYrS4p2HUuuT1GTZ79q/Uwbi+u0HmDTof78tirrHZlbu0YVD+PHNH9v2HIaecb4c75Oc2KeyyCnTtSKiDbNbTpmLS8okNF7PMadl27Zt6NKlC9avX48ePXqkKsMUPHbsGI+pMzExSbHd19eXe2TFihWDjo5Okm1yI7Dvs/3v3LkTb968gbW1tco4LWlx+/ErxMXFo1Kp/500ohAeGYVOw5Zi24qZqNS4F9rUr4jdx68iT7JKzVJB5BuZKkP9Ih7K0idB/q9QtXotzBzWAQXtkyY9ER328z542gb0+6sdOgyYrDR9omxQv4iHMjst2yQ0Xs+RlMcsxm3gwIGoVKkSunXrlqrMjx8/cPz4cTRs2DBVAzBsbW1Rrly5FAZIDovJSx6Pp8qwvPznbzzhxRtF48i5u6hXvTSKlK4ONbU8CAwOTTebDUEQhEgY53PC9DEDMWHxLsTExEJKsHj07q1qYcP2/YgOD1a0OgRBKJjnEh+vZyl7WGKYF9aoUSMYGxvzFGbq6qk/QWexcZGRkejUqdPvNomCBQtyL3HNmjUYM2ZMlvfzOiAa0Rri1oX5EhLHZ1syQsP69bDj3Es+26KhLk75nVcBcWjasAFuPfBC/bp/wshAD5cuXYamzv+y8UiJd+/e8ScFhFhQv4iHMvWJkUNpNKhfH9vOvkCFEoX4Zyw04t2nr/Dx/YKo6BiYGRuiXHEniIauqS0MzO1x9vQJfAmOEr5P2OxQdMQP+L57BRlkMDIyhpGJGTS09XhBYqnO0qvKtaIsiNwnb9++VbnxerY4LcHBwWjQoAHP0cyyB6SX1mz79u3cUI0bN0Z2MH78eJ6ijaVzYxkMsoKTpRYKZSD8SvTwsJ9ow9bIERv3n8bwnk14oUcROKcRhjfP7qJZ8+YY0P8R4uNlWLNiMQwtflZtlhoiTxmrMtQv4qFsfVKqSH40aNgYebXC0axOOXjceYade85iZL92WLH5EGpXdM/E/Tp3Cf36AZqxQXB0LCJ8n5zctQwNO/yd5DM2S1+xpAss8hrC2FAfpUu4o+/QydAxsoAyoGzXijIgcp9YWFio3Hj9t50W5oU1adKEF6g5d+4cihYtmqasn58fLl68iL/++gva2tlzU3dycuIp2VavXo0KFX4uQFd18pkb84Xumw9cQq+2f0AE6lcviZXbT+Pv6EjuSIVFRCE2KkzRahEEQWSKvLau2L1jM1q374Knrz6igL0lLPMaoVzFqvi+dDvaNaosrEXdXRx4kbkqDYtAdOwcCuKfbg1haZEX8bI8+OjrB0/vD7j16BXfbm1hgu2HPXDx6i2sWbsRlo4/60wQBKH84/UsxRGx3Mvt2rXDjRs3+GIaFhuXHrt27eJT6dkx1ZQ8Vi4mJgbz5s2DKpCRasgONuYIk+lh58OYXK/WnJqsk30+vHr/GVFhgdDS1OQJA0KCA4Wvjq1MlXyVRU4KOoouJwUdRbaNo1tlnDl5HAXdy+Haq3DUbdIMIWGRMDM15OtHcvoem1U5dxd7PHzyDLL4uEwdb2blEst6vvuCk7v/w4fnN3nIV0b3WaxyI/y76TjGzd+KCQu2YPDc3eg7by+u3bmPexcPYNqofqhZoSgOn7uLtu3aIfDTS6U5v0hOXBtKlTglG69naaZl+PDhOHLkCPfcAgMDeSaCxDCPKvlUE5uKyu6pNrn3tnnzZig7mamG/L5AJ5w7sRX36/fItSrM6clamZvglfdLFHN1wNELd3HglAdORfwQtvJudstJQUfR5aSgo+hyUtBRCrbxk1nirn4daFergwtxgIXPBxjq6+RKpfusylmaGePr9xDEx8fmmg1jI0LhtX4KjPS0MHVkHzRt3xt6JtbIo6YGT58vGLH1dsI+53UoCWdzDQR/+YAnj+6hcLGysHcp83N/WxK3XRV/VW2Klh17oUnjhvB+54ce3Tph6JTFmH0xWCnOL5ITz4ZSZriSjdezNNPy8OFD/nr06FG+wCb5X2JevHiBe/fuoX379lBTy/4F4sx7S2sxkTKRmWrIeTS0oG1giqjwkFyrwpyebJ0qxbBj527UrlkNGhrqOHL2qtCVd5Wpkq+yyElBR9HlpKCjlGzDZg8C3z3FE59A6OsmdVrYthdB2tle6T6rcjramjxZgCw+PsvHm5qc36sH+PDkEuJiolLIfnl5B4WLl8Hicd2wbN1OVKxUCb3a1cXCSQMwZthA3Fg5BLfWjsDdTRPRuU1TVKpYAVVqN8LICbMxY/I4xMfFpNm2kaUj1q9ZDn09HYRHRKNt67bwfXDht44lJ+UU2bayyCm6banyUMnG61maaclMRgVWWCY7SsGwTA6pUahQIZ5OTdnh1ZD9WC6VPL+umuwng5lzaXx7dQ+FShbMlv39Si492QbVS6HPhNX4Z1A/aGmuwNtnj+Fahf2gZqxarfwpiFTlpKCj6HJS0FF0OSnoKCXbxMfG4s76UTD/sxqCv35P2H7rkTd6j1+NhnWrI0/Vkdl2j/0dOZ14TURGxUAmi882G7Lf9VnTxuG/rafQt30dzJq/DCUcLRNko0ODYGuqyUOWV0/vw9Pd333yGm/fvIGDqSlKd56EyB+BiPjuj5Lqz2GskY//Jvj6B+Lx87eIiQhJV0enEjWxfN54DBg5A9PG9MXQqatgUbgCtHT1leL8IjlxbCNllG28nm3FJaWEVItLZqYasvcPXdw4vguTetfNtSrM6ck27TcXN88fRtM2XfEp4DvmLloI2FUWtvJuRqvkZnR/Ih6L1OTSk6WK0uKdh1Lrk8zu8773Jywf3Qm37j5GflsL/nvCwpXaD/kXeU0McHrDePhFG+VIpfvMykVERuOv0cuxZNlK/NmgSZaONzW58O9+mDq6P+atPQybfKZYt3gyAqL0sGnnQXz/9AZLx3aEqfH/0tt/9P+Ga/df4Pr9F3j5/isM81rCztwA+S31YZ7XFBYW5jA3N0fJkqVQvFpzvk4oPR3jY2OwbOYQbN5zAo5OhXDj3jM0rlcNU8eOgHWh0qmuMxLx/JLatSKiDbNbTpmLS0oZclok5LRkFvkTME3N3y7H89v0HLsSa/+bjy1btuDA6Wvo2Kw2ZizbmeqPisiIfCNTZahfxEOZ+4QtaH//6BwOHNiHYTPWoVwxJ54GefsRDzx/8wmHVozk2cVEgSVAaT14IVasXI26DZtl675josKwcdFY9B23DKZG+ihZ1BHlixdC+0ZV+PY7T17jwo0n/NXc1AhVy7ujbp0/Ua5KLeibWifUX8kq0eE/sG3VLMxasgmt6lXkRZYv3vLCwqnDUa9tf6hrpl/8TgSU+VqRKiL3iZcKOy2KH80SOUbDmqWw/8ythB8PReJoa8Ersdb9sw6Onr+Fi9cfICo0EDqGyjcdSxCEchMbE4lC5Rqi/P8XmjQ3NcSNBy+hrqaGHq1qCeWwMPy/BiGfmTHUNLSyfd+a2vroNWoh7PMXxOCxc+BawAbaWpqYu+YQbjz0RonC+dGk/h+YPWcOrPIXgbaBWbY+rNLSM0L3obNRvUZt/Nm8M3b9O4TXzBkwejYO2OZH6Zots60tgiAUizil04lsp6B9PnwJ/IEfoemHdOUGzo7WuHvvLspWrono6Bh8/hoEvzePFa0WQRBElgbq4/q3xPX7L1GqqCMveDhzyjhYWZggIioaQT/EqkX19mMACtjny7FZBzZTUr/DP7h4+iCsrKwQFZsHf9Ssimvnj2H/8cvoNWIOHN2rQcfQPEdm19k+HYpU4Cn2X/t8hmtBG1Qp7QrPJ4+yvS2CIBQHOS1KTqem1bBm9zkcu3iPL8RUFCyD2IkLt6GhqckLnRno6eLk8aMK04cgCOJ3GD19GUb2aooHT99h1/FrCI+IxI6dO1HQ0R69xq0SyrhvPwTA2alAjobjsn3bF66EyYu2YM7KPeg1Yi5sXcvz8K/cIOSrD16+84Obsx1/zxIAvH71OlfaJggidyCnRcnJa2yAET2b8PCsHUc9sGL7aSzfdgrr917giyJzCxbLbGNpigM7N6JGlXLQ1FTH2YtXERut+FkggiCIzKJnYoW5qw8g9NtH/Ah4i4r1OsLCwQ1jpy2CtramULMtbz585pmBlBl1DW2oq6slpGq1tzbDG58PSTKmEQQhbchpkRC/UzWZzW70aF0bAzrVw8DO9dGmQUUcuf4GI1d74PUP7Wxp91eyQ/5qhHn/bUHVimXxPTQG1z0/4PbjpypRQVgKOoouJwUdRZeTgo5Sss1T32Do57WFoYVjwiwGC4EqXNCWL8jP6Ur3GZVj4WFu7sWU+vw6f/MR4jQM+DGzxANsBiwqKhrxsdFC6KfItpVFTtFtE4qHFuJLhOyumhwIM7x17IwQvQ/4+9/jGNSoMOqXtkoRPpDR/WVElk3XVyzpgs2HLiJY3QwRsV8xYuN1rM1XQKkrA0tBR9HlpKCj6HJS0FEZbKOmroFiRV1w43kAPsXnbKX7jMp9CwqFXX4nfHsVIAkbZkVuyvab0CpQGQvv6CHw1BKUcrHBshWrk6zjUYbzS1XlFN02IQY00yIRcqpqsoGlA1wb9MEz/ygs23oKy/8/fIwVSIuPj8/w/jLadu92f+Dg8YuwdK+K8K8fEfThuXCVd5Wpkq+yyElBR9HlpKCjstimZMmSeOTtl+OV7jMi9/BTHEwM9VJkahTdhpmXk0FNQxNPDixGxbrNsXbbIeQrUFwI/RTZtrLIKbptQgxopkUi5GTVZBYH3LJGETgaOPJtbGr95kNvLN50HLYFXJHHuMkv95fRtg31ddGmSS0cefiGv//+zhPF7AyVujKwFHQUXU4KOoouJwUdlcU2JUuXQ6DfAlggZyvdZ0RO68dbnklSWz+vpGyYabn//39U6Hd0a9cCWnrGwuinyLaVRU7RbRNiQMUlJVRcMqerJqfGlTtPcfHBB7hU+AMVCxqggGFkujpnZJ/MKWoz9D+ERcugiVi8efMW6pqpr6sRrUJvVisXi3gsUpNLT5YqSot3HkqtT7Jzn+FBfqhRrSo6d26LeDPXXL1nJ5d7cP0ygsNjsHDtgRzvk5zYZ0bk/F7dR+2GrVGneXsc3rERT+5chrG1izD6Kdu1IqINs1uOikuKCTktEnJaFAXLgnP59lN88Ps5dWphZgQHa3OYGOnD1FifV0FmxcQyw90nrzFn9SFUKu2KmXMWwNa5DKSAyDcyVYb6RTxUvU8ObpyHmYtWY8Ps/jmaavhXbD5wCabmlhg/d73S9UlMVBjOH1yPkZMXoFHN0mjboBLaD12CB3dvQ9/sZ+pjKaBs/aIMiNwnXl5ecHd3h6enJ9zc3KBKUHgY8UuYc9KsTrmE95+/BsP3cyD8Ar7Dy/sDd2qiYmIhi2chCTI4OVihYklnnm45LcoWc8KEga2w9dAVnDy8F71GSMNpIQiCyAiN2vXBlp37ceLyAz6gVhRhEVHIb5h6CK7UWbdwPIZPX4lFY7uhShlXBHwL5g/R1LV/nemSIAjpQU4LkWnymRvzv9RgiyFf+fjj+KX7CPweCpcC1qhfvWSqTxpdHK2hq60FTS0t6gWCIJQKLT0TTJs8AU3b94SBng5qlC+qED3CI6JgqKROS4NmbfH27VssWH8ETg69ERwSzh+WaWjlTkFLgiByF3JaiGyFOSfOjtb8j3H78SvMW3sEXZpXg41lXvwIDce2wx6Ij5dBT1cbn758R758+agXCIJQOtwqNsCejUsxasIMHD53B6P7NE/zgU9OERoeBUMjIygjjm6VMWfVfhRfMQX/zNjIZ/nLliycJM0xQRDKAzktRI5SvnghFHfNjx1HryIkLIJP3w/r0RhmJj+f/F2//wJW1rbUCwRBKB2sZku5Ou1xolwtbFo+GwOmrIO1hQmfdalWrgiszE1ytH2WBfLAmVvo2aUNlNnG7fuOx5cvX3HrvidGjJ+p0DVEBEHkHOS0SAhFZA/LjFxasjramujRuhYPHQsNj+Rpj+Vyn4PCkc/q56yMFLOZiJj1RFnlpKCj6HJS0FFZbdNv7CJ07DEQd69fxIkTJzF63nZeSWXdzH7wjTLMkXv2uq0r+XsbhwK5crw5sc+MyGlo6aJOj2mwqP0VH6NNkN5clrKeX6ogp+i2CcVDTotEyI2qyb8jlxFZ9vRL7rDI5b5GqON9mDZSc1tEr9ArhUq+yiInBR1Fl5OCjspvG2f80dIZtVv0Rvh3X7Ru3gRXnn/HqRi3bL9nB/l6IyDeFB7XL8O9dFklsmHqciO33eFy266+VeHzS3nlFN02IQZqilaAyBg5XTX5d+Wyss/I4C/Q1DXE86//X5JWYhV6pVDJV1nkpKCj6HJS0FFVbMMe4OjntUMJt0I4e+tVtt+z42XA6ws74FilJd7HWkNDW0/pbCglOSnoKLqcotsmxICcFonAqxzj59X1y2rICpDLyj79Hl+GlXtVlHXNn261Wr6/DFS1FVVOCjqKLicFHUWXk4KOqmabAX8Px4tHtxH8/lm23rP9H12App4hzJyKK70NpSAnBR1Fl1N024QYUHFJCRWXlOqalrTkBoyahxXL/0WTxs0lE2Ob1crFIh6L1OTSk6WK0uKdh1LrE0W17XFsIzr1H4ee/4xGaXut374XfwsKQdfRqzF09irUrVktV/skJ/apLHLKdK2IaMPslqPikmJCTouEnBZlwvPle6zccQZnLl6FjoF0nm6IfCNTZahfxIP6JGPEx8Vi4+JxWLP1INbO7Pvbma/OXH2El+8+Y9X2E8ijpk59kgxZfBwuH92EfNYOKFL+T4gAXSviIXKfeHl5wd3dHZ6ennBzc4MqQeFhRK7Dsogt3ngcQwd0l5TDQhAEkRMpezv3HwvzvIa4fPvpb+/v/aevcCromMJhIX7y/dMLNOk4CG279MTnt4/JLAQhIchpIXKdCzc8YWFmhIZtepH1CYJQebT1TTFu5BA++xwfH/9b9vjg/w1OhZxU3qZpcfb4flQu7YI6lYujQ8eOeH77NGS/aXOCIHIHclqIXCU6JhYrdpzBlPGjoG1AqQUJgiAY5Wu3RDHX/Dh55eFvGeTdxy9wdnYlo6ZCRPBnLFqxBU9evMfRC3dx8aYXilSoj7VzhyIqNJBsRhCCQ04LkavsOnYNtSsXR+kaaS++JwiCUDU0dQwweuQwrN97AT9C087SmB7+X4IQExMLa2tbRIZ8zXYdpY6mjiFmThqOE3vX4tqFYzi2dQEs8hqh77ilaNOsLvavn42vPk8QG51+whmCIBQDOS0SgmWOOednxl9FlPuV7PfgMBw4cwtjxk7Ac/9w7PDw5pk70oNtVwY5KegoupwUdBRdTgo6qrJt4syKoJirA6p1mIw5qw/h6auPGb6/srWC6/aeR9liTrAsVA4TFq1VSRumJ8fq1ViXb4unaiXwVasgGnUejke3LqFNg0o4euEeWvcaBwvH4ujWui6u3b2nUraRgpyi2yYUj4aiFSAyhqIq3WdULiOyK3ecRs/2DRFmVFjoyrvZLScFHUWXk4KOostJQUdVt43vvdPQ8PuB/LbmeP3+MyYv2YNx/VugVNEC6d5f2ezKtOX7EBgchgfPfPgi/IexLny/qmbDTMsVKoWte49jypMbuHrlHM5dvIEdR6/itFcvlOw4ARraOqprG4HkFN02IQY00yIRFFXpPqNyv5L18f2CJy/fo/+QsXjk813oyrvKVMlXWeSkoKPoclLQUdVt4/fkChq2/wu7/h0C23x5ERgcmjDbkvz++vBTHDbsu4DJS/egx9iVyG9rjRrliiA0NBwlO4yHupaOJGwYHy/D/Vd+uaJjWnIsEULRig3RZ9Qi7sA0aNYC3948xN3NExAdHqo055eU5RTdNiEGNNMiEXg1ZD/Zz6dsv6qarAC5X8nuO30T3ds1hom1M0rEfedPNeRPN35VrVbqclLQUXQ5KegoupwUdFR12+R1dMe7B1egV6I2pvzdBg+evoWJoT72nLiO4DhdBMeVhCyPGnzvnsTzL17o0LgKOndoA10tDXz8+B59Ri+Gs1N+WBYuLwkb3t8+AyH+b5DnigscQv5CxT9acgciN2ydlhxLEDNj9jx4h+jj1YVtuLnqH/Rym434uAI8PXVu2YbkxLENIQ5UXFJCxSUVVek+o3JpycbExqHVoAW4cvYw7AtXkkTl3eyuXCzisUhNLj1Zqigt3nkotT4RQUc7zUD07dwKtpam0NLShPc7P5iZGKJq+WJ8fuWhtx++hsSiTdM/MaBXN5hYOUMmi8O4gR2w/9Q1+H4OxMVDG2BQtIFC+iQzso9f+6Jb+xbo2LQ6NCO/YvPByyhSyB6r12+DoYVDhvfH1vIwnn78nq3H8sj7A/79dwE2rVgKDXU1XD+9C+X+aJMjtpHataIK1ygVlxQTclok5LRIlUu3vOBx9zl2HDoPDa1fL+YXGZFvZKoM9Yt4UJ9kHjYA//D8Nt69eoawsFAULloM1gXcoG3AngjnQVxsNOJjo3mmMTnXTmxGrWY9YWlmjLkTB6J93wlQ19CSRJ8smjwAT5+9wD/dGvJjn73qIIwMDbBi69E0jyE6/AduXzqEUydO4O37T4iNi4WBvi769emFMrXapJgN+R2YvRdOHojRs9Zh/oR+GDF9JXIC0fqFELtPvLy84O7uDk9PT7i5uUGVoDUtRI5z+NwddGrXUvIOC0EQRE7CHBOHIhVQvclfaNB+EAoUrwEdQ3P+OYMN5BM7LJ+876FOy958Nvu/WSPQod/ENAf7ItKkaQtcu/ec/58dY482teH54h1io8JSlY+PjcGYv7uiWaeBiIoMR/VyhVGisCPuPH6F8n92xI/Pr7JVP2bLAgV+Fuo0MTbK1n0TBJF5aE0LkaNEREbjne8X1KxHdVkIgiCyi6iw72jTriMio2JwYus81Os4DGpq6pIysKGxKaKiY//3Xk8HvgHf8fzeJZSo3iKFPMuKVrSwC+ytHvL0+XHxMuQzM0btyiWwceUCGFn+dDCyi5jIMBw5doL/3/H/nReCIBQHOS1EjvLo+TuertPA/H8xygRBEETWeXP/LLr3+4enRT64aizqdxyOPGrSC5y4c9MDhZ1sE97r6+lgTJ/m6N5/GC6cLwYTq0JJ5Nkx9hw+F516j0BwwDto6xlC18gS2gamyJMn+4//3pXDuOf5BkWcbFGgYFJdCILIfaR3lyMkxe3Hr1GtUjlJhSwQBEGIRlxMFGKjwvnajxmzZuPKnWd4ePMsmvWZKUmHJey7H5au2oS6VYon+bxKGVcUcrDChhULExbZJ4aFkekaW8LKuTxMbYtAx5Ct98mZ43/y+DEqlHBGeGQ0zCytc6QNgiAyjvTudISkYLHGtf74U9FqEARBSJaQrx/Qo109DO/Xji/EX7ZmC0K/fYRVoXIJ612kBFubsmT2OGhrauCPyiwzWlL6dvgTK7ccQsC7xwrR7/ndc9i6dAKuXr+NkLAIXjNH1zifQnQhCOJ/kNMiIVg64XN+ZvxVRLnksqHhkTzeuoBriVTTDO7w8Oav6aEsclLQUXQ5KegoupwUdFQF27AZhKjQwISZhORy8fFxiAz5hq9v7uHru0e4fGovthy8jBh1A+y89gbvwvSgn9dW4ceRVdkbZ3Zi9c7TqNyiZ6q/KywTWvVyRTBp/Bjcf/Yq149lz84t6PrPTDwPiEGETBvVyheDVqL6MVlpN7t1VEU5RbdNKB5a0yIR2I19+UuHn4Ub/WQY6PI+1ZopipJLTbZ8qCfKuDtBzzTptDq7OQzffCOhoNPCbpVSzaOuLHJS0FF0OSnoKLqcFHRUdtsUtWNrL/Ig9KsPSpSugPGD26NMi2EYs9szQa6e1i3cunIeu49f5/thdVrev32J24/rY+Lht3jt8Qbbrr6RrA1lsnjM/HcdjCt2xOXg/LgSnPrvysBO9TB0/j606DEMrvV7Q01dLdeOpUnfaTjw0RpPL++BYT5H1GuY+oxWiv11rQQ3B7r2FHl+5VTbhBjQTItEYAUbmTPAYK/svUhyqcleefAG1apUgJq6ZhI5VshJHqrMXtn71FAWOSnoKLqcFHQUXU4KOiqzbe699MXiCT3x8cVtaOoaQl9XG71GL8XIoYMT5KLDQzFs9HTusLCZhsXjuuH5Aw/omVjBO0hTKWwoi4+HnXsl+N4/z/+f1u+KpqYGmnbrj5jwEPh7XsnVY3n2OQb53KvDwqUs/B5fwuuAyHT35/f4Mu5tnYqF86fzdUe5oaOqySm6bUIMyGmRCKzCfB78vLrYK3svklxqsr6vn6F2KutZWOVZ+UMr9srep4ayyElBR9HlpKCj6HJS0FGZbWPw/RGGz9qI6ZPHQ1vPBCsWTIG6mhpee95PkNPSM4DHzZt453kVF649wJCZm2DuWEIpbHj/yiGM7NMKbRtVRQGjaIR/+4iwb77p/q64GEfC0KoAXwOjiGN2rNaGv7czkqUtB+D9raMwdSiCS8f24daFfbmqo6rIKbptQgzyyFJLz6HkyKuJHlg+AoXyW0FUnvpGoaitdpLwK/ZEit3g0wrRUqRcYllLBGDOvP9w8+aNVGOv2bQse6rBbhLpTceKJpe8Sm5G9yfisUhNLif6RdXkcrNtqfVJdu6T/az6eF3Fxp37YehSHe42+mhQtz7fNqZvC8xcsRcRwQHYse5f1G/SEj/0nZWiT9KTPb79X3ToOw692/2B5dtOoV/PDshfuvYvf1fmbb0MmDhizPgpCjlvXExjUcwxH7QN0ghZev8NR0+dwuaFk2Brrg8NdTUcPnEhVXlluVaU4RrNbJ+IOIb19PSEm5sbVAlyWiTktEiJc9ef4PGL91i/56zkCp6lh8g3MlWG+kU8VLlPwr5/gpNLUXz+GoxFk/qj9z/j4XFiF4oULwMb59LQ0jVSuT6Ji4nEvAkDsP/EZfzTrSFKFnHM0Pd2n7gOWR4tzPxvJ0Tm9f2zqNeiC4wMdDFp5AA0/2tkhr+ryteKqIjcJ14q7LTQQnwiR7j9yBt169RUKoeFIAgiI7D0uMf3rIW+nh4ci1bktUQadB6u0sZT19TBsEmLkC/fbMxavg3lixdCnSrFcfvxKzSuVRo2lqk/DbcwNcKj5+8hOvndqqBto2r4d+MRTJ6zHHWatIeBmb2i1SIIpYLWtBA5wn2vt6hZi+qzEASherCHNWVqtUHhCo24w0L8RFvfBN2HzoHHxdNwK+LKEw5cuf0MD5/5pBpid/n2U6zYfhpVq5QT3oQa2nqYunAD/mpZE49f+GD3hn9TLY5JEETWIaeFyHa+fv8BPV1t5MtfmKxLEARBJMBSB5s7uGPUrDU4fOYa/undnjsv34JCEmRi4+Iwev52HL1wD5tWzkXXgZMkYUGWFW7i1NnQ0tTAwAlLcevMdnJcCEKZnZaoqChFq0D8Jm8/BMA5vxV0jCzIlgRBEESaIWOd+49Djw6NMGreNnz0/4bpy/ejxYAFcHa0xaFjp1GpfhdoaKWdal80rJxKYfW84VBTU0OXviPhdeOYolUiCKUZr2fJablz5w4GDRrEFwDp6+vDwcEBbdu2xcuXL9P8TkxMDIoWLcqfsixYsCDF9jNnzsDOzo7vr2nTpggKCkqy3dHRkX938ODBqS6YYtv27Us91aCyoKhK9xmVS5D1joGemU2661lEr7yrTJV8lUVOCjqKLicFHck2qnV+eX+LRc/BE/hnA6esR40q5XDt7CHMWrgC4UGfsW/fLnTuMxC7d2/jdV1y61iyKsfGIu17jkTX5jXwyscf9Vp0w5tHl4XSUapyim5bitxRsvF6lhbiz507F9euXUObNm1QvHhx+Pv747///kPp0qVx8+ZNntUgOcuWLcP796kvpgsNDUWHDh0wYMAAlClTBosXL8a4ceOwYsWKFLJr167F2LFjYWNjA1VCUZXuMyqXWPbVu+vQMyvAbwIiVWxWlJwUdBRdTgo6ii4nBR3JNqp7fi2dPwM/vvtjxbrtOHjiIn6EhsP/WxjCtCxhbOeKg/tHQV0WiRatu0JdQ0uIY05Ljq1jWrp+Lxxs+mP8op1o36Un5q3aijkXAoXRUWpyim5bqsxVsvF6lmZahg0bBh8fHyxduhS9evXChAkT4OHhgdjYWMyZMyeFfEBAAKZNm4bRo0enur/nz59z72/69Olo3rw53y/bX3KYpxgXF5dqG8qOoirdZ1QusWxkcAB0jPMJV1FXFSv5KoucFHQUXU4KOpJtVPf8qvBnOxw5fpZ/NqBjXUwc2BpDp8xGqc6T+WdxMVHo2nMITu36L8U6ERFt8/TuRRy/dB/DejRGrYruGD1qtHA6SklO0W1LlWFKNl7PktNSuXJlaGklfdLh7OzMlXz27FkK+TFjxsDV1RWdO3dOdX/MAN7e3ti7dy9ev37NjcD2lxw25dS1a1fuvX369AmqhKIq3WdULrFsRFAA9Ewshauoq4qVfJVFTgo6ii4nBR3JNqp7frGQkZGjx8PL+yN0dbTgaGvBf1PeeeyFv6cHDCwd0KJRTYycsgjfPjwT2jahX31QqkYLvPMNwPnrT7D/9E34vfP+/8d/md9nfFws/Lzv4uz+VQgPDhDymHNaTtFtS5XKSjZez7bikmw39vb23BCnT59O+Pz27duoVKkSrl69CmtraxQoUADz58/HiBEjknyfxc0xzy4+Ph758+fnMXMuLi5JDMCmsZiBmEH79+/P/y+PkatVqxY3YuvWrTNcmOfA8hGQUnFJRVW6z6icXLb30JnYd2AfqlaslKac6JV3s7tysYjHIjW59GSporR456HU+kQKOoreJ7+7TzaO2LF8Mv5dvR0rpvaGjrYmXv/QxrZjt5En7DMm9a6LUXO3YWDfv9C40z/ZapvslIsIDsC6ZTOhr6uDClWqwcTYDN+DAwHr8nj8PijT/XL5yEZ0GzAaMTGxmDvpb3QeOEW4Y84NudxsW5mLS8okNF7PMadl27Zt6NKlC9avX48ePXrwz9iuK1asCCcnJ+z4P/bOAiyOXYvjf9zd3aFYKVCoUHdq1N3dvbfuduvu7u7u7i5UoC1eKO6+8L6kr70VaCks7Mxufu/tx6V7mMkkk0zO5OT89+xBcHBwoZVAiIiIoB6Zq6srFBUVf/juayWcOnWKHn/v3r34+PEjrVhJcVr4ALmJWw1ehGeP7kFN3wriBpcHMkmGtQv3YG3CPfjQJtnpSZj5T3+cuHQXa2f0pQrz37PtyDVoaulgyqJtkJR2mTGqG9JSkhAUHg03Z3tMXrS9TMsniYiz07KLR/P1Ukl5TGLcBg8eTD207t27f/v3bdu24eXLl3QjUFEwMTGBl5fXLxXwMyQmr7B4PIZoiUtMhY6mGs1Xz2AwGAzG3yCvrIFpizfDx9OJhlb9jJOtKV74v0OeIEciKpaEhr1+9x6Z2Tl48yEcvQeMEHWRGDzmLc/n68XKHvY9JBNBkyZNoKGhQVOYych8SXObnJxMswaMHTuWLkMJE2tra+olbtiwgcbfFZcP0dnIluWuLkxMioCutvCJ2IRsuHl44c6DZ5CWKfHtxTnI2wfypoDBLVi7cA/WJtyDT23iXrkW7j188sszMENKE+p6Frh29RqkZeUg7u1CnDMjc3t8CP2M0SNH4F14Et6F86MN+QyX+0pQUJDEzde/UqJZZVJSEnx9fWmOZpI94Pu0ZiTmLTs7G+3bt6eNTwgPD6c/ExIS6L8R+583CBWVSZMmYefOndQrJBkMioONvjxsORx+xcfwsAdxYUiOCUHtOnXpxkpxg8tLxpIMaxfuwdqEe/CpTcrbGmDu/MUY2MKd7m35SpaeNJavu46lC+dA3cAGktAuVb09kJEcDQ1DW0jLiIejxnW43Ff09PQkbr5e4vCwzMxMNGvWjArUkLg1IkTzPSTHM7lYEm9H4uLIp3r16vS7uXPn0t9fv35d7IKTuDuS3WD9+vWIjIws9nEYwiU1LRPqaipi6bAwGAwGo2zQMLBGF79amLJs3y/f5QoEyM8TSExTEN0XLRNH5rAwIOnz9WKttJDcy8Qju3v3Lo4fP05j435m2LBhv3hUJP9z//790aNHD/j5+dGKKAkkVo54bwsWLIAkwIfsYUFxuYgS6BYqLMmXLCV8znoirnZ8KCPX7fhQRlY33KsbUdxfMnIK6NW3Pw606Y1LkTrfnj/nbj5D3SpuUNWz5MQ1s74nGXXIVwRiNl8vltMyevRonDhxgnpu8fHxNBPB9xCPiqhtks/3fF12It5cSZeIvvfetm8X/0waolK6L6rdV9sjj+OgZe1GVWa5pqgriUq+4mLHhzJy3Y4PZWR1w726EeX9FZmpjODIBCzbdARySiqobZ6LmzfvYPu6hZCRlZfoupE0O1Gfm6+MFrP5erHCw549e0Z/njx5km6w+flTlhDv7etmInFGVEr3RbX7aqusa4rs1AROKupKopKvuNjxoYxct+NDGVndcK9uRHl/fcxQh1evedBz8IaKvgWSpTUxsHsLuFdrIpJrzkpLRHL0R+T9PzStqMfLzU7Ho7ehnG4/rtuJ+tx85ZmYzdeLtdJS3IwKJHdzcWVhvnp9P2Nra0vTqYk7VG0+Mv/LisefFOxFYPfVNjcjFSr65kVSof36dkPc7fhQRq7b8aGMXLfjQxlZ3XCvbkR5f3nYGkHNwAKq+hbUbmohb8LL4prJ3GXN/AlYuukgurSogzGTZsPNUrdIx7t1Zjc2zl8L5YbTISUjw8n247qdqM/NV66J2XxdaOKSfIKv4pJ82NPyz7JjKFe1Mfr06c/JuGJRKUpz8Vr4ZidO6utcrENh2/GtTfhQRq63CRevWVh2WanxqFmtMjTVlRH6KRaqyopYt+JfKFjXwvOQhN8e7+L+5WjUaSSa+dZGq5FL4Wlvyuu+Igl9VJzFJfkMc1p45LTwgRGzt2HahFGo0bwnxBEuD2SSDGsX7sHahHuwNik+gtxs3Di1E0+ePsHhk5eo40Le+c4e3w9dB0+DrLxSoX8bHfwSNev5IjY+GeMGdcDomesgJf1fdD5rF+7B5Tbxl2CnpdgpjxmMgohLTIGhsQmrHAaDwWCIDWTjf+0WvTF6xmqcO38B3dvURUZmNnqNmo8lU/sj+fPHQsNp9C1dMXPcIBjpa2HD7lN0XwyDwfh7mNPCECopaRnQ0TNgtcpgMBgMsURdzwIzl+7Eoa2LYWGih3Hzd0LD0AaHN88r9G+atusNNWVFJCSl4uKpA2VaXgZDXGBOC0PoyCmqsFplMBgMhthCdGTqtOqPl08f4NjWuRjQsT6s7coVaq+kYYDZU0YjNiEF85ZtQkpsaJmWl8EQB5jTwhAa6ZlZUFSQh6w8c1oYDAaDIf6o6VnCr8cErN1zAR41W/3WtnrjLujVpjae+Adh5YIpyBOIf+ZTBkOYMKeFR5AsXkQZmPzkol18YiqUVNVx+GkszcjxO8j3e24GSowdH8rIdTs+lJHrdnwoI6sb7tUNu7+EUzfvojPh3vYfaKirYv2uM0iNC+VlO0vi/cXgBsXSaWGUPaJSui+qHeFThhJCUhWx63Yodt8J5Zyirqjs+FBGrtvxoYxct+NDGVndcK9u2P0l/LpRsfJC6POr+PDmKdz1rTnRfly3E/W5GdyArbTwBFEp3RfVjhAl0EZuVgZnFXUlUclXXOz4UEau2/GhjHyom6cfPtPvSaaorNQkbN+xDVeObkLg44uIC32JlJhgZKbE4unHaE5fM7u/RFM3sYGPkRb3CQ5Ozrh29Spn2o/rdqI+N4MbsJUWniAqpfui2hGc9fORm/Xley4q6kqikq+42PGhjFy340MZuVo3GckxePngEvbvO4DH7yLwNCiRvqCRV1aHlKUanp5JR3RcMs2eSP5WS0MVmrr6CFd0hqqBFdQMLGGrloaMpM/Iy82BIDcTgpwspCTFI+TGeQRceAvbul0hIyvLubph95fw6iYx9DWe758H+wY90KG8LM5fuYkuPV78kCqZ6+0sifcXgzswcUkeiUuKSum+qHaEJv0XYcbGY/B2seOkom5J7YqrXMzFa+Gb3e9smaI09+5DvrXJz7YOBor49P4pDuzdiYMnLiMrOwdN63jC1d4c+eqmiBToFDgm5uXlISEpDaGRsbj3Lg7PP3xGYmQo0lKToa6qRLU9iA1BTlYWrg7mOHTuHjr17osxYyfA1coQqXFhePPiISLCw5GekYGM9HRkZGQgJC4LYQk50JBOh7ezGbr0HQ0VLSPOtElpHFNc7Aj3n/tj+YoVuHXhBDzKmeLZmxB6L/Tq2gHDhg2Dtok9L65FEscvLuEvweKSzGnhkdPCB1oMXIBHd69By8QR4giXBzJJhrUL9+BDm+TlCZCTkYKcjGRkZyQjOioCwR/fw//VK1y7/RD+gWGoXdkZzetWhLVZyfSnyNv0lLRMKCvJQ1ZG5ofvklLSMW35AUhJS0FZUR6vAsJgqKcJEwNtKMjLQVFBDor//6nw//++8fANDPW0sX7bPihrGopNm4g7+Xl5iA5+gQunj+LM+Us4e+MZ3D29kZf2Gfv37oGhTQVRF5HB8b7iL8FOCwsPYwgVKSmpb/taGAwGQ9Tk5wmQkRSNlPhPCA16j+DgjwgNDUNYeAQiIqMRGZOAz7FJEAjyoKGmTJ0FSxM9tG5YCTNHtPvFwSjJ2EhWWgqCnHfppO40FS6xmz60LeTkfv94Jo7U5KX70LFtc4wZPgheNZtCUU1XKGVllB5S0tIwsK6ArkMroNPASYgJeobh4+cgSzYVrdu2x8mTJ7+tuDAYjB9hTgtDqCgrKSA5KRF6rF4ZDIYIyUyJw+vHV7Fjxw5cvv0M6RlZMNTTgrG+FnVMjPS04ObgTn/q62hATlY4zklxIc6Kp8uPmaR+h7S0NGaN6IDLd19i6NjpUFaah8Z1q6BiRS/oGxgiIT4egYEBdIVHT08Pteo3/WHvBEP0yMjKw9DOG7Wre2Pd+o1QUVbEq6d3UYM5LQxGgTCnhSFUsrNzISMnx2qVwWCUOWRze3TwSxzavxMHjl1AYkoaXZFYPa0PtDTET/RWRkYaDaq50U9QWDQu3n6BlWs3Iz4pFarKijA31qXOTVRMIibPWYH+fXqiorMl/dv46HDomtgWuCeGUbbYOVeEq8N57Dp+kyZnYDAYBcOcFobQ+BAaBVUVReib2rFaZTAYZQJZPUiPj8DtKyexc/c+3Hr8Bj4e5TC8e2M4WBtLTCtYmemjX4d6hX4fFhmLwzcC4VnJBwKBAKGfYnFs+xI06Ty8TMvJ+BUZOQXMnDkLu47XgrLS70WcCfn5efj44gZ09I2hrm8FaRn2opAhGTCnhUdwPXvYyh3n0K5TFxx7mQI3S3lOZh+RhKwn4mrHhzJy3Y4PZSyq3YsP4bhy7ykCbxzEjavXoKGujJb1vTGiR2PIf7cf5G+yH3J9jC2JnZmRLhpUU0M/Pze6f6dhzzm48eQtItVvo4qbI95d2oh9h45j2KA+qNKwM51IS/L9VdZ9z9K1OqLeP4K2sf0fj5kWFwbfFp2QmZsPV69qmDl7Djyd7HlbN3y4vxjcgDktPEFUSvdFtQsMjkRMSjauCLyRfy2Ak4q6kqjkKy52fCgj1+34UMY/2eUJcpEU9R4bd+7Dsr3XkBwVBCPXGpgwvD+8LBWLPX79jS3f7ZQVvzgjy+ZPxIyd95GwuwssqjRHRzdFHDx7l35G9jqOidPmQdfcSaLur9KyK4qtlJQ0DGw8i3RMKWkZ5EvLwbLtFDy7ug8tug/HzEF+aOVbD+r61iDKatlpichOT0RwigL+2fecs3XDh/uLwR2kRV0ARtEQldJ9Ue1uPnoLR68anFbUlUQlX3Gx40MZuW7HhzIWZkfEHR9c3IthPf3gVbUWDp64CBPPRqg6aBWsa3ZAkoJJicavr7a5OdnISk1AVnoKXn4G0tIzqa5Kdk4uXZ0goWhcH4uLapesaAbHpgPh2W0mEkJe49K9l7h+bD1MDXVw4PQtVKtVH8e3z8eTwE9if3+Vtp2wj6msaQzPGg3gf2wlVPTNoaCmjUXL1sHSwQ2Ht8zDrNHdUbNaZZRzrYiDx05wum74MH4xuANbaeEJolK6L6rd3acBGD6+Pba/566iriQq+YqLHR/KyHU7PpTxZztnQyUc3TofC1Zsps6DXz0v7Fg4BHH52v+tJghh/CJEv76NezvPQV5Vk2ppRClkYK9ULvLy8qnDIsj74rRk50khPuu/PQRvFHKwWvq/rFzEhoSnmZpbIMWuFTTMHDk5Zn+1IxNeF78hiD48Hi9evMCRnauxbMUq7Dl5Cy16jEftGkchX3+KWN5ffO17JG3yqJEj8SH/CFI+hyI68A6MlLIhLyeHjLRUbD90Eaun90afSevQuFYlPDwXxdm64cP4xeAOTFySR+KSXIqP/h7ykG7a71+8fHwH4bm6nI6JFZXKNxevhW924qS+zsU6FLZdSdvEWi0Ta2cNw8u3wRjXrwWcbE1LZfwiiuTr9l7EszfBmDRnIeLlLeFqqoZyRipfUgTn59ONz2Td4svveXgTmYZXEalwMVKCvYHi/22+fEdsSQaoG9cuY8HavdAzt8Xwbo1hpZZZ7DIKy+53zxR1QQx6T1iLLSvnoGqjTgh/+xAb1y7Dtfv+WLXnDN7E5HHq/uKbXWn1lWdBcXDUzUf+5xcwMrVGVkYKajVqgy3/DsSQmVtwfN8W5BhU5HTd8GH84hL+EiwuyZwWHjktXKbt0CU4e2wPrMrXhDjD5YFMkmHtIl5tEvXhGbp06wYzQx26sZ6k7S0NyArxih1n4OFig6Ur1kLH1FGox0+MfA8/Pz908auGyhXsOf9Mef42BDNXHcKahVNQu0VvmpUqNysdcop/ly46OToId6+eQUJiEpycnWFqbo3IiBCEhgTDwtIadu41IacgfimouTB+5eXmYPvKKVi8dje8XG3h7uaKYVNXCOXYkgSXnyn+Euy0sPAwhlDQ11FH5KdwWJVnFcpgMIrPh2fX0LFbH9Sq5IyuLWqUSlUS3ZLpKw9CSUEeqxdNQ9UG7SGnqCb086hoGsDDxRavAsI44bT8CbdyFpg1oj0GjZ2FjrduYfTE2TSl7t9AkiV069wexy89RKsGlRC6ajNiE1OgoaoMEwNtvPkQgVH922PQhMUsVW8pIC0rh9Yd+2DRmt1ISk1HaFh4aZyGwRAJzGlhCAUjfS0EvHuLqo1YhTIYjOLx8fl1tGjXDZ2bV0eT2h6lUo1k0jxx8R5MHtETHfqMhqKarlCOS/e7pCUgLSESr18+xv3793Hr7iOERERj9fQ+4AskDG/HgsFYsvU0atepi3lTR6Fmk85QUNEq0t9Ly8hizuwZkJGegSevP8Ld0QqeLtZQkJeDmZEOOjT1QbexK+Hm4gSXitUhLSMPVT0Lqg7PKDlvH13CnDmzEBwRA1kZGaxdt/Gvj5GTkYIjO1fA2cUN1o4eUNIwoBnLGAxRw5wWhlDwLm+LG7fuoPtQARvcGAzGX5PwKRDdeg+gk9rSclhevgvFtBUHsH7JdNRu0YdOsEtKdnoynt05gyOHj+DG/ef4FJ0AcyMdONuZob6PK6q6O1Dlej6hoqyIKYNb03CxcdMXw3DVZnRo0xSVqlSDlpY2MjMzkZWZQX9mZqYjJyeX/ruptSPU9CzhXMkX+47XQGzYG1y/ch5xsXHIyMzAS/+3OHP9KapVLIfOg6ZAWvpLlrO+HX0xeuoSKKqxjdDFJS9PgGvHN2Pw2JmoXrEcdDXVsGnVPBgUI/zh3uXDGDdjGdRUlSAlJQVXe3Po6mihRnUfVKtZD3qW5dkqGUMkMKeFITSnZevhq0hPjIKKdsHpRxkMBqOwVYq508fB1sIAzetWLJXjn7/5HOv2XsC2VXNRrVkvOhkTBs/vnELVhl3Rp10dTB7UiqYMFtaxuRAutn3BYLr35/zFq1i8ZgfSM7LoqgnJkCYnJwMFOTnqlCWlpCMzOwc1vV2gpKQIBzsbdOg+AO37TfxWH3mCHAhyMhEXEYRWbdtCT1sd4/u3wD/zd0FVdRqGT1sl6kvmJSTb3fl9KzF0wr8Y2KkBdh2/iZn/9IVnnXbFOt7hI0egpChPV93iElNx/cFr6piu2XUGxnqL0LllHYydPAdaRrZCvxYG43cwp4VHcDV7GEFTnWTaAa4/fIpE5XTOZh+RhKwn4mrHhzJy3Y6rZSThKA+fv8OoXk2FPn4duR2ELfvOwtZcH8f3bYZzZd9CnYriXEuFas2xZOoArNt5HG0aVfnh2Fwes4tqR67H2L4CvIyqouMfjhebkIyzD8MRkpiPV+fvYvnGfejZoQl69h0CXXMX+naefAxtymPVrlMYNKAvxi09CgdrY2zYdRzVfSpDVccE9u61OX2/FseutM79OCAckfcPYPrMBbAxN8Cmg1cwuHsLdBowEdL/D+n62/N2GjEPTZvcRmxsLGRlZSAQCOhq2dWbd/DmQzg27ruAx2FZWLJqM5zNdXhfh0U9N0P0MKeFJ3BVXfl77BzsMXn7Peg5SnFSUVcSlXzFxY4PZeS6HZfLmJkSg7DIWOSpm2GdEMelJXtv4cSdj3BsNg5yeiaQMqtSaPhqSa5l8MTF0NTUQP8p6zGgYwM0rO6GkDTlvxpjszPTEf/hHWa1NOPU2P43z4BUOQO80PVCvq4UZGzzMVb7Oa5cOI8qNRugo18d9B88HMa2nnjzKRkTjwRArc4oJAa/QHLSLbx+H44mHYfC3soIF6/dg7yyOmfv17+1K5Vzh8Zj2PpLeHd+MyIeX6AaLUN7tUX7HoOgYWDzzXku/nnboN5Pdn3SE7F85TIs2n4BGXZ+GL3jHr/r8C/OzeAG/Aq0lWC4rq5MkNMyRXpCNGcVdSVRyVdc7PhQRq7bcbuM5EWHFN7GyxVrXHr2SUD3Sly+85Kq2D9+9RETF+/F07fh8Og6Ayq6JqVaNzJyiugyZCaO7d2Iq/f9MXj6Zlx4HI60uEhkpcQjJzMDAUmKv72WuA9P8eLQIlx8GsHbZ8DPtkkKJhjXzw9b5g1CTEwMatRrjjH92uDSzTu07qRlZKBt447mw5fi4NqpkJKWQnB4DPx8a+LBpf0cvl//zq40jnn/VQDubxqLuPdPoWHqgJlrdqDfuEXQNLT9YbVPmOeVV9aEcZXOqNBxEpQ09Xlfh39zbgY3YCstPEHUqslFUZR2NFbBo4+R9L+5qKgriUq+4mLHhzJy3Y7LZVTVNUMVj3JI/vgYUpomhY43/oFhOHbpIaKTc+EfKwNBdiZyMlPxTiYNjXxckZGVhVW7zsHByhjNfeuiQqMemHYyuEzqhmzqd/VpjiMnq+LQjpXYcuA83kXlITcnC3nZmfggnYq1UgK6v4b8bVUPB4zp3ezbGBsb8Ah2dbvizvlj6Fu9Fy+fAYXZammoYHCXRujeqhYOnr2LRWP7QM6mBsyr+EFRTRPuNgZ4/CIXBjoamDmiPd5+iEDvwWOxbMNOTt6vf2tXGsfUyY1AXk42TCs2gmXVlmjasCakpKRL/bwVrHSw84Z41OHfnJvBDZi4JI/EJbkc90wICPqE5Xuuo9+cHXC3NeRkXLGoVL65eC18sytN9XVJsSvLc/9tm+xfPwe7DxxF3369Chxv4hJT0Gv8Wkwe0QOGxqaIzVXGp0xluFtpo0p5O6jpWiI/X4D0hE9QUNX9JogoqroR5GThVUgMnofEwdVEFXZ68nQjer4gBzmZ6Rgzdgw+R8ejWR1PXHwUjCevg7B14yoMHdAPU4e0oZugizoWr9p5Dn3b16Ub5H9nV9rPlKLaZmXnYOuZ5zhy5iY8y9vh32ljYOdaFRuXTsOWvacgIy2FsKg4rJ4zClbVO+FNtAAVrPU42adENX7lZKXh2duPCEiU533d8GH84hL+EiwuyZwWHjktXCcjMxsdRy7DiYNb4VypMcQRLg9kkgxrF/61CVlxIA4GWSkh+xeC/e+hbsteuLB1coH2q3efh5GhIaYu2gYpaf5HNqcnRWPh9FEICo1AOTsb9B44Arrmzrh2bCP6j5qOCuUsUMPbiSY5yc0VIId+cv/77xwBcgUC6gDMXnMEVd3tsXRSD5r1iS/PFFL+09eeYNvha1g0fSSadh6GhLBXqFy7KXIFeZCRloaaihIcbU1QwdUJjRo3Q4XqzSFusPGLe3C5Tfwl2Glh4WEMoUEeliSWduKkaTh4wgfyyhqsdhkMxg8QJyXI/y62b92MExfvICUtE6rKCkhITkO/9vUKrS2SdvXo3tFi4bAQlDX0MXXJzl8ymdVq0Rd3PKvi2MFduHbjDt2jIycrQ7M4ycnJQU5Olm66Jv8tT1MPy+PYlrl4/PQFRs7ZhpXTelN7PkDED/3qeqG8gwXGzV6B12/eYNg/M+nvGurKGNKlETKzcugm/VNXH8P/TQB2VGsmNimlGQzG38GcFoZQIXn3T1x+hJN7VqJ1n4LfmDIYDMkiPz8PKdHBuHByP7bvOUzTpjat7YkVU3pCR1ONan+Qlx7ShTgkRBRSXUUJFg7CEZ0kqzwp0R9pJjGi9C6npFbgfoDSpqDJN/k3kh64z+h/0WukgIhw0HL+qXy+7RMR07cjFm8+SbVP+ISVqT62/DsIk5fswzP/jpgzYwI2btqGDiOWoVH1CujYzAdX7r1CZS8P5rDwHKLl5v/oOvLy8+FWqS4U1fVEXSQGj2BOC0OokM2l954FYsA/C1CncTtoGduzGmYwJJT8PAHePbqE7ds24+i527A01YO7kxWM9bXwxP8jFS3s1rIGang5/fB3JP3xtQev8eD5e0R8jqcT1XWLpkJRTVco5Xr/9ApaduhB3+Ib6WnB3FgXlhZmcHF2RJNWXaCubwUu8EVno2irJiSz0/xl69DSrykV0SSrVoU5gVxEVVkRSyZ2w+6Tt9Ci82DMHj8APbp1we69+9Bx5HIY6mli9dq+SIsPp/cBydjG4B+7Ny7BtIUboaggjymj+qDnyDmiLhKDR/BnRGPwxmlRVlJAbEIKFs+ZQDeeMhgMyST0zT3Ua94JWZnpVC2ecOHWc9hYW2HujAno0qYhbj16+83+7ccIDJ25BVOW7YesnBKmTxyF6+eP4fGDO6jdqp9Q3rInRwehd/+h6OJXAwdXjsLEgS1Rw8sREGRi9/5jaN3SD5nJMeAj6noW2LtnH+KT02k9fgz7DD5BnKyufjWwaHxXLF6zE0PGTEV5FyesXzQRZ06dRHREMFR1zODXwAf71s6goYYM7kFWMsPf3aerKj/jUdEb6qrKNFvXgWNnEPn2Nl7cOoHY4GdIjQ2lCSwYjMJgKy08guvZwwhyOlZQUdeCtaUa5qw5gg6dzsLFpznvspTwOeuJuNrxoYxctyvrc+cLBFBXVcLVe/44ef0l6rbsjNk9uqOiiwP9PiE+Dheu3sOH0Cis2X0BCUmp6NCtB3TdmqBiOUs4mwu/fAtnT4KZkQ6a1PoSamZqqEM/VeGAyjXrYfT0VVizYT1GjppY6P4ZLrezvqUz/lm4Exs2bcTEZfuhqy6PNo0qo1rFcr/Yiip72J/sSLjYprkD8CogFGv3nkVwZDw6tnqPgV2ao61vFVy99wpB4Ztw+94jzJq/DJqGNpxuE1Gfu6ztyGpY/catIK+kipotOqN+ZWfY6SvDoWIDeNZug0snDTBkxCgcv/QI3nWaQ11dC3HJGVCUBbr6Vce0RVsgq6DMufZjiB7mtPAEPqghE9s1geaQM3KGtK078GYR2nQdiHMndWDp7MMb9WJxUvIVFzs+lJHrdqI4t4Vrddy6dglX7j/HpteqCAIw+dh7LNb4kqI16GMQbj1+g4jPcRjRvzOc6nSl3+ffj8TeB5HCL1+3KggKDYeHs1WhY51+wxGYumA2DIxM0LlzT5HXYXHsJhz0R756VZh1qYrmmm9x7cxhLNl6Ci2aNYFinh6szQxK7RkgzGOqGjtA2bc+LBOise/Oceze2x22Zrp0JX/yoNZURLRx48YYP3M+1jyT42ybiPLcorDLyUhBYlo2tF0aYOXc6ViZnw99PR0EB/pDScMAps7Vse/IOQS/vosx0xbh6qP3dIVF29IFs1ftR/defWHrXpcT18LgFiw8jCfwSQ1Zx9YdKdFhaN+jF94FfUKrdl1omAhZMuaDerE4KfmKix0fysh1O1Gcm4RzaZuWQ7a+B/5v9oNdm869ceHwFty8eQvdhs1GQIJ06ZYvJA59enTD1sNXkZeXV+D4paJjAvuGvbFk5QZkZySXWt2UlZ2mW3PsOXYZNy8eh76eLv5df4xucF++/Qzig/2RJxAI/RnwJ9u/tVPSMkC5xv0wasa/GNmzCU6sH4falZ0xtm9ztPOtggEDhyEtPoqzbSLKc4vCTlnTEBWr1UFCiD+sa7SDR9cZmLXhEBTV9b/ZKKrpoFylpmgzajGqDV+PGqO3QcPcCS06dIZ5OS/OXAuDWzCnhSdQpeH/P/b/qF4sArvvbbWtKyD+w1OMGDII/TvUw9PXwWjUrA0uH1lH36Z8VaGlxyyCWi3f7fhQRq7b8aGMXLfjYhmNbdzgVa89VLRNyuy8Pg3bw8nWDGeuPy10rNOz80BCVBjePr4qsroRpp2svBJMy1VBldpNcOX6bRzavRHerjaIeHIBd1YPxvP98xD06BI+xyYJ5RnwJ9vi2tmpZ9BVIksTvW/7m+pWdcXQXq3xfO8cvDm9DmnRIXDUK3hqw/pe2dyHCqramLdkNcq3GQ3bul2hZ+8JH8/yBe5J83K0osch31lUaoJZ85ZCXkmdc+3H4AZMXJJH4pJ82NPy1XbNvGk4sWcdzO3c0LVjS/owjIlPRpdW9TBuxjK8TwAnY3FLS3mdi9fCN7vSaJfSsHsWFAftzA8If34VauoaMDYygrV9OegYWUNRTY/uk/ir432MgblCAp5d2gdtLS3Ua9QMepaukJaR++vylWXdcK1N4h4fgoe9Mao26oRXd86gVZf+2LdsxDcV+Z/HunvXLiE5PQcrth6jKYdLu4yiaJMXH6Nw3/898iIew//eJdx6+IpmVKvibo9qnuXg5mhJNV9+9wwgK+iRMQl4FxRJX1frW7sgUqBTZs+f0E+xNH1utpIRjt98iwvHD2Jgp4aYNH8TddaEXYfFteNLX+GyXVmem4lLchPmtPDIaeETG/ZdgqKyMuav3ofEzx/Rr3d3BARHIjs7Fx39amPKoq0/TLr4ApcHMkmmLNuFpPFNjQtDoP9j3Lh2Fanp6VS5W0ZGhn5iY2Kw8+gVVK9YDoK8fMQmJNOJFdmQ7uFsg8reFeFTvSZMLO1o6lY5JY0f3kDm5+XhxM5FuHzlGuQVFBHx6TPuP39PJ5IZmdk0VfCMsX3QdchMSMtytw9xqa+QLIY1Krni9uN3mDKsExo3aoBBY2agW4sa9E19QZBMZiSE6sqNezSdsDjwuzYhzkdG0meEv3+Bc2dP48qNu3j+Nhg2Zgao7G4PZ1tT6GlrICo2EYHBkQgI+kQdlfikVJrC2t7KGHl5+XjwPBAu9maYOKAlFcIsbTYfvELbSV9HAypKClQrrJ6PKz5FJ2HB4mXQsyi4fbkEl/rKz/dEdPBLRIa9h2uVxhKVZpqrbULw9/eHi4sLXr16BWdnZ0gSbCM+o1To4lcdnUevQN2Da1CvdV/sPnQGw/q0h0CQh5OX7mJcepLQNBcYjLJ6gCd+CsDqpXOx8/BFqKoowsvVhqbvFOTl0T0SxEkhzsmyST3gYG38w98T54WESt64dRfLN+5BWnoWTA21UaNyBTRr7gdnDx+oaBkjJzMN85dvphomWuoqyEc+nQy2blgJdpZGdNVy4LSNUFJSRps+EyAtw4bxP0FekMybPg5tuo/As1fvcOrSXTSs7kb3RfxMUko6Dp+/R0VyxwzoABn5wvdwiBPEcSZ7Eewrkk8DDMrOoOmhH96+iouXr2DPydt0tVxfRx2OdlZo7FsfEyt4wtzagY7l8iqa1OFOiQnGtImjMGDqRiyd1J32j9Kkd9s6MDHQxsYDl3F4/QxcuXEHG/ZfQk1vJzTwbY4NK+bCs067/2veMP6G68c3YfDYmUjPzELrRtUwc+Eaeo8wGKKCPe0YpQLRapn/TxeMmLQAza9dx8DBw3D1vj86NKmKlPRMsXlzyRB/8gS5SE+IwKFd67Fg1Q4qjkjSsWqqq/zVcXS11FHfpzz90OPm5SEkIhY3Hr7G4FGTkJaRhepeznB0sMPg7i0wqHMGPscm0r6079RthEfFUafFQFeDKskPnbkacrKyaNy+P40hZ/yeqo06Y/H0D5i+aBOGdGlEV66+ii/mCgTUSSSOysewaDSt642r547BrFylQtMeizsktErb1AkN25PPYLofMScrFXIKqpCRKzgCQEpGGhqGtliwehfmTRyExVtOYcawdqVe1kY1KuCx/0cMmLAYaemZWDOjD7Q1VGmWuA69RmDC0CfoOmgiFFS0Sr0s4sThI8ep8GpKWgYWbzqK0aNHMqeFIVKY08IoNewtjbBn6XC0HrIIlkba9AFCQgucy9myt8MMTq6k5GamIistAenJsfgQ+BbPnz3FrTv38fjVBxjra1NHnGwCFgZkwmxlpk8/3VvVQmJyGu48DcD794G4HZtE+0p0XDItV8v63qhV6b9VAaIrsmRid0xbvAGrN+/Bgnkz4VGzpVDKJa7IyMqj06DpsLCyxdr1m7Fu70XqrJD6JSvAnq426Nu9I+o1aQ11fatf9rFIOsRRKcxZ+RmykXrUxDmoWas21VpxsTcv9fKN6tmUZqt0sDKGkqI8/bdKbnbYMLs/Ji3Zh3NXbmH+v/Ng48bNkB8u0rxZE2zftRcDe3dFzYZ+tF8wGKKEOS2MUoVs4tTTUseLdyH49Dke/oFpOD9rAat1hsjIzc5AdloCMlPjERb8Hu/evkFAQCA+BAUjODwaYZGxdCJLnBRrcwNUcbfD4C4NoapcuvHcZOWmcU33ItsTAb7tCwZjzZ4LOH70CHNaigAJpavetAd8fDsjPeETUuKjICcnTyfjavqWkJWQULCyQE3PAuOH98aWXQexcFzXUj8fcVQqOFr+8u9kr8uG2f2wYvtZDBw8HDMmjoCUrCLKe9dmqwZ/oF6bAajbqi97ycjgDJxzWrKysqCgwM/N56UNn7KHfW9L1JinLNtP32ruXjUdxnYenM9SwuesJ+Jq9ze2r0Ji8DgwAo66UtDKi8G7N/54FxCADx+CEBQWiZCIGGRkZUNNXRMqusZwNNOGu5MlWtbzovtMft5ATO7re5Hc63tkH4KxnhZCE7Ox52Yguw+LeM+QPS6quhb0I6xjMrtf66Z+k9YYP2sFTSDxdfVD2H2gKHaknwSFR+PKvVeIHDMPGXky0FOfj9NHD0DbxL5M2q40jlnadqFv7iI9NRm25atBTlFV5OUT9bkZop+vF8tpefjwIbZv346rV68iODgYOjo6qFy5MmbPng17+/8GgAcPHmDbtm24f/8+Xrx4gdzc3G8Cgz/z5MkTdOrUCQEBAfDx8cGePXtgZmb27XuSxeH69eto2rQpTp48+cPfkjJYWVlh4cKFGDNmDMQRUSndC0MNuUE1N8xZewTJqbnw9HDntJJvadjxoYxct/uTLRlXkj5/wK3Lp3Do7C1cfJ2EtNgIZKclwVRTFvbmOrAw1oOViR5qeZeDuZEOYvK0vt2vn5CPlhzrU0W1S4EKrodl48asuTCr2AgrBtZn96GQ7y9mV7w6XNTVGz6e5XDvWQBqV3YRaV8hQpS9enfFzkgnahd4YSt8/dqgmrstdLS1IRAI6M/qNWsiU80GU04ES/QzYEpDAwzu1omGqW5YMhXNu44SaflEfW6+8lDM5uvF2mE4f/58HD58GHXr1sXy5cvRr18/3LhxAx4eHjQF21fOnDmDTZs20bcc1tbWvz1mhw4d6IUeO3YM+vr66N+/f4F2p06dwuPHjyFpiErpXhhqyDIy0vinrx/KWRvDxMGL00q+pWHHhzJy3a4wW5LKNvzdPdy4eAyVqtTA0lWbkCalCmP3+qjQcRJ8hq3DgPHTMXd0J/TvWJ9u2HW0MYGKsiLn+1RR7eIFasjJykDgxW3wP7kaTz98/qs6ZHasbkqrj74ITYSXRwX4B4aXah8oih3JMBYN/W92tvW6oWLtxtBUkUNc7GekJMfj4eMn6NZ3OJr7NsTLI8sQ8+4BzdxHdH6EXTei7nvZ6clIjQ1BUmQAcrPTf7GbOm06TVbhaGMKTS0dzj4DyurcfGW+mM3Xi7XSMmrUKOpZycv/t9zbvn17uLq64t9//8WuXbvovw0cOBDjxo2DkpIShgwZQr2ygoiNjaWfdevW0d9r1qwJU1PTX+zMzc2RkpKCGTNm4MSJE5AkqDJwJEl+KvVnBWER2P3JtnEtd2w7cg3vX96Hm2V1+lbj69uNP6nV8t2OD2Xkul1BtiqJLzGk+zCcu/4ELf2a0IxBRKPhh7euHOwrwrar7WaMAwePwaX1GLw6vAj2mjmF1CD325lL9xfXysh1u8Js4z08cPjURc71KZIIo1FFc1iq/ppY4128DBZczUD4k4v4eH0/auS3RIrTQKjpmovF/RXw+CIGDRtN9/DlCvLg4WSFGg39kC9wRnriZ3y8uhfG0tHoN6A7Wg9eDBtbB5Ffh6jPzVdGidl8Xajikp6envRnQZ4VqYTVq1cXuNxEUn/q6urS5SpfX19s3ryZen1kCeorxKsjFUUqe+rUqfQcxFMsznLTV2GeI6vHgE/iknzd0/KVw+fvI+RTHDbtPY23UemciNktK+ViLl4L3+wIL4OicPTUadw8uhmhQcFo17gKmtWpiKC4fF71FWHbTVl7BqrGDujdtR1qVK78WxE4rvcVSYyXF5c2Kcg2NuQ5vKrVx4l143jVp77aKSZ/xKkTpxDxOR7VvJxhbGQEExNjGBkZQV5BARFpMghJkkGtik6o4un+g1Dsn+qmLPvKs48xMJaKxOc3tzBv6SZ0bl4N9auVp27evWeBOHHlEV4HfYaMgio6t6iDNjVs8ODFexw+dx/Hz12HvLK6RPVRcReX9OTJfL3UnBZyGBLTRirw/Pnzf1UJhL1796Jnz550Yw+JuSOeWdWqVX+phDt37sDS0hLVqlX75r1JitPCd3JyctFu+FIc3bMBrj7NwUe4PJCJO/HhbzF7+gScufIAg7o0RE0vx286G+LWV/6WITM2Y/LYoajfdiC4Ausr3EMUbZKR9Bk+VSvTFN06mmrgKyR5x+v34fgcl0QFXuMSUpAjECA3V0CfbXGJqRjZvyN6DJ7417pJZdEukQEPYezgjZYNvNGwmhuqevy3elIQRFCyx7g1WDpnHBq1HwpJQ5ydlnwezddLLXvY7t27ERERgZkzZxbr7zt27Ij69evjw4cPcHJygppawYOburo6RowYgWnTplHP7qv3xuA+JCNTj1a1MG/+Imw/UBdyin8nzseQTPJyc3Dx8DqMnbYI3q622LV4KBTk5URdLM5AMjMR4UmvanVFXRQG4xcU1HTh7myDk1ce0/Gfr1iY6NFPYZA+OG3FAeRkZ2Pw5OW/XXERBVLSUrAw1i2S2CeZrM5efQRtGtdA/VZ9y6R8jLJjN4/n60KR+n379i0GDx6MKlWqoHv37sU+DllyqlSpUqEV8JXhw4dDS0uLxsox+EXT2p54+vojHl49IuqiMHhCiP9t9Bs5ExMHtMTwHo2Zw/ITtx6/RRV3B6jrMeE3BveQlpbBrDnzcfPRG6zdcwHZObkQR4jg67h+LbB2x1GkxYWBa7x8/hg25oa/dVTICtL954FU0yYnV4DxMxb/NtSUwT/e8ny+XuKVlqioKDRp0gQaGho4dOgQZGRKX0WYnOur9/b06VNaIcXhQ3Q2smWzwFViUgQ07EXc6NG5LbYdOIt0GT3IyvNrQCRLm2TZmFF23Lt+Fq1aNIWUsn6h/UFc+0pReBqUitoNm+HGrdvgEqyvcA9Rtsm0GXNx7cpFzNl6E+XLWdAsfuLGqVvB6NalIx68CIS09EdOtUumlDoUNI2x+vAT5OXn070JAkEeBHl5dLWWbMhXUVKAhpoyjKwc0ax9FTx8/o6kJYAkwuXxKygoSOLm60JxWpKSkuhGnMTERNy8eRPGxsYoK4j3tnTpUuq9LVu2rFjHsNGXhy2H4+DFNU7f0dgEPQ4dR6tGVVCPZ7GyXI5zFUdyMlIwd/JwGlbyu74grn3lTxDBPP8XT7F+zSooaxmBS7C+wj1E3SaNmvjh3ZMrGD9xKp49AaYMbgP5n4Rc+UpwRAxu37qJxXMmQt/ag3Ptkp8ngLWhCgIDA6kgoLy8AhQUFaEgLw89A0No65lAXlUL8sqadHVM0hF1X/kdenqFhymK63y9xOFhmZmZaNasGU2LRnIxk7i2suSr93b8+HHqvTH4A4n1HdbNF3MWrkFGUrSoi8PgMJFBL/A+JAou9v8JVzH+C+eYv+E4RvTvBCVN7iYUYTC+IiOnAKdKvth76DisLUwxYMoGJCSl8b6C0jOykJicBjlZGfTu0w+ZKdzT+5CSloFz1eZo0X00fDsMQd1WfVGtcVd41WsPS9eaUDe0haKqDnNYxJBMMZqvF8tpIcqxJJXZ3bt3cfDgQRobJwpIJWhqahZ7MxHfICkYL0Xq0J9ctPsbWx1LZ8RkymL3gQO/tSPpCPfcDKQ/+WzHhzJy0e7UscPw8XT4tqmV632gLO1OX3sCLXUVtOs1Eq/DE9h9WAisj3JvnFPRMkaP8StRoW4rdJuwHh9Co3jV977ndTTQbMhKbDv5EH71vNCvZyfIKijzZozlk52oz81HBGI2Xy/Wuuzo0aNp+jLiucXHx38Tp/lKly5d6M+QkBDs3LmT/vejR4/oT5LbmWBhYYGuXbuW2Hsjy06SsCH/B8G8yHwMtg8tMMe8qOyKc8wcPVesvBiGKo3iC8yjTgaR0dvvfhN+Wty9Ci/t+FBGLtplpsTiwPHz6NO2Di/6QFnavXwXim2Hr+HEwe34kCDF7kPWR0XSR0tyzH/2PUe+Ti0YNzHDqAUL4WCui1YNvGFnYURTmcvISCM2TxNr33Or7/1st+JxPnKV9ZFq1wqp0q8QHBKKS0e3wNzCCrp6+pBXUqN7N19HJGHKsWA6iZSVk8f8DuVR3tb0lyxjXByLuWAn6nPzldFiNl8vltPy7Nkz+vPkyZP08zNfK4FsFpoyZcoP3339naholrQSvnpvJEaOxOuJM0TkigygBPKT/F7QICoqu+IcU93IBp9f38bjgLACBwoi+PQ1TTj5SX7nox0fyshFuzdPrtPQETdHS170gbKyIyJ3U5fvx9aVc2DvWQ97b71n9yHro0XqU1wc59SMbNBn9UGohF/GngPHEBmdQDeH5+bm4XNyDlJzZSAtIwt5FU3M0FOErQ6RQwTdRGyoqwFzEz3EqjiLrI+q6OlA09QBYY/Pw7icIfL8/XHm4nVExyUhKSWdhnESxyQlWxppAjlISUvT/SUdD2rB204bDnY20DG2hYuVNjSN7Dg5FnPBTtTn5ivPxGy+XiynpagZFcgmJiFpVxZ6TrLcRDYWiTtEvZe88aFvfpBPf+eSXXGOqVeuEsIenkHEvYPIr+MKKakfoxWJQi15+/H1LQj5vSC4bseHMnLR7uWLFz/sZeF6HygLO+LEjZy7HbPHD4BP0x50MsTuQ9ZH+T7OVXK1h3PjymjdYyRyszLIrnHk5+fRN+GTj35Ebk42ctIS0dE2GRpSKXRekZOTjdDQcDx9HYzHb2/j4+d06Np5wqFhrzLto9LSurCr353a9SpkRYaUNyRN+dvqDfJy0UrzKbLjQvExLBIfIlMxd8ESVPdyQptewyElJc+psZgLdqI+N1+5Jmbzdal8YZWSR3xVEz2yegxsLbi7gfXnjEhkKZq82SEDZWGrHaK0K84xVdNDMX/xGmxdPQ/VmnyZhH0PeWiRtx9kMPndG5Cysvs5o0hRj8fFa+G63YTBHSCLHLRrXPWP9xff+kpx7JIj3mLa8gMY3b8D+o39lz5gslLikJudjjPX7+Hd52y0bdkKzubcuA+L21dEZceHMnK9TURxzbQfpMbh/vPXGDtuImp7OaBLfSdePR/J+GVvKItTVx5j+9HrqOBeAdXaDkb96j6/7c9cu79K264sz83l7GH+/5/Dvnr1iqraSxLMaeGR0yKuPH8bgukrDmD3xsXwrt+Jc0rC38PlgUycIG9ZOzarjrpVXFG5gp1E9xUyKdt76jaOX3qItYuno3rT7jQV9ORRvXHw9C0a/5+VkwMDHQ3ce/iUpizlAqyvcA9xb5Pz+1djxMS5WDm1Nwx0NcAXvh+/cnJycfjCfew/fQdzJgxC615jISv/5yQ4DMnpK/4S7LSIR4J0Bq9xK2eByYNao2v/sdi3RR7utdpw2nFhlAVSkGb3AGXZttOIiU/BhTNHYGrvTZ2YtYsm496Ttzi0ajQU5OVoyFgL31qQU1RntydDYqnboifGR0ZgwNQNWDm1F1Wp5xtycrLo0MQHPh4OGDt/IyI+RWDoxEWQU/y98jiDIQkUW6eFwRAmni7WGNunOTr1HoGXt0+wypVwiNOqqKCA7JwcSDI3H73B6/cR2LlnP3VYCKmxIVi/6wRmDG9HHZajFx5ARloavYdOppt8GQxJhaQa7jp0Jvp0bIxjFx+Cz5gZ6WLD7H7YfeQiZv7TD1mp4pmSl8H4G9gTjsEZSBjQsO6+6NBjMN7cPyfq4jBEjIKCPLKycyGpxCYkY8mWU1izbC40DW2+/fu180dhpKcJPW11vAoIxZ6Tt7Bu9QooaeiLtLwMBhfIzUrHq7cfYWbEv1WWn1FXVcbaGX1x495z9OveBsGvbkKQkynqYjEYIoOFhzE4RfWKjsjJEaBd1344sm8r7DzqirpIDBEigXlCKHl5eZi0ZB/GDuqECjVa/vDd5+hoGOlpIS4xBVOW7cfGZTNh6VpdZGVlMLgC0XZaNmccAj6GY3SvxhAHlBTlsXRSD+w6fgN1fNugqkc5NPGtD2trGygoKkJWVhYOHiQ0lIWPMcQf5rTwCHHMHlaQXZ0qLsjOyUXbzr2xcPUWxMiZcTabCReznoiDHXFWUtPSoaKkyKs+ICw7kkHI0tQAlVoNx77bH3+om5q16mLO0s14GRCKUf3bo2bznngdnsDuw0JgfVT8xzmie3L24nlMmzEXsrmpWDqpO2RlZMTm+SgnK4OerWujS/PquHz3FY4eP42o2ETEp+UiMTUbVTwdsXP7Tiiq63F+bC+JnajPzRA9zGnhCVxU5S7NYzaqUQGRqdJo32sY3LvOgJyCIucUevmg5MtXOzIJSUvPhIqyAm/6gLDsSMjX+ZvPsWb7AYzf/+qXujGzq0CzqtWpVQ1te47Bm08p7D5kfbTM+2hBiOLcaQmRmPPvXKzYehyW1VrDpEIdxCMM6hB9Xxb2MckmffJsJJ+vdsZ5ebi6czo2btmCoSPGcaJNSsNO1OdmcAO2p4UnFKTQyyW70jimmXttGLnVxrM9syDIyaFvQwqiIFVbLtnxoYxcs8vPy0VWdg7k5WR50weEZbdu70XMnTwMEQLdAutGUU0XG/aeQ8eB0yGnpMbuQ9ZHJXKcy8vNweMrB9HUtz7OXn2Aij3nwsS9HiAlzZm+XBbnlpKWgZaVK+68+MiLsb24dqI+N4MbMKeFJ1CFXnzpXX9U8hWBXWmd26xiQ+jYuOPN6bVw1JP5raotPV4R1G/L2o4PZeSanZSUDGRlZSAQ5PGmDwjDLjE5DZ9jk1Dbt81v61BaRu5bWnB2H7I+KmnjXNLnj5gxpgfa9xyKVg28MXN0dyiqanKqL5fVuVOiPiLy+VW09K3Fi7G9uHaiPjeDGzBxSR6JS3I1/r60zx2YrIQjW1aiQRVnTPx3Q4FCW1xXlC7LMoqDXV6eAG19feBXryLcnayKpCjNp75SmN2RC/cRm5hBQ11I+mI+x4SXtvo6n+tGXNuktK/FyVQLt05vx4jxM2FvaYRh3RtDVVmRk335b2yLMn6RfX7pGVlISk1HVlYu3ff5JDgFW/edwazp49GrZ39Iy8qVSpuQdMu3zx/A9Zu3kZycjDxlXVhXbYnalSuivHXhcyi+3V+/6ytcwl+CxSWZ08Ijp0WSIYN1z/FrMHvCYLTsNQ5SUqJZJOTyQCZOkAd0h6Y+aFjNDV7lbSWmr2w5dBWGRsYYN3cD+A7rK9yD720S9OIGGjRvj0kDW1FtL3Hh+/GLjH0hn2Lx4HkgHrx4j+DwGOQKBPQ7ZSUFaKgpQ1Feju5vUVKQxz+jh6NSg46lKsi8atYQDJu2BvV9XKGqrARdLTVcvP0Cld3tMWLoIHjWagUZOf6Pv3zpK/4S7LSwjfgMXkAG6wX/dMGImcth6+CE8tX8RF0kRimSn59HQ8OkJUwsUVZGGtnZki2oyWAUREZyDIaNGIUuftVL1WHJzMrB59hERMYkIj4plfZHGRkZaKopQ0NdGZpqKtBUV6YrPMUZn3JycjFv/TGM6+dHxWG/T8Bx8Nw9PHsdDGMDLVT2cMbwwX1Rwb0ilFTUaYSBjLwSZOUVISUtS/eySMvI0lDR0qb7wH/oHqKLNx9RB+rC7eewMtXHo5cfUL1JN7y6qws7z/qlXg4GgzktDN5gYaKHET2aoPfAUTh7xg66Zk6iLhKjlMhOT0RkdAIMdDUkqo6dbE1x5MID5GSlQU5BRdTFYTA4QZ4gB2sWTkZGVjZa1vcu1jFS0zPxISQKwRExdO9YSlomUtIy6Cc5NQOxCSnIyMyGgrwsDHQ1qRaSjo4mFBUUkJubiXdBkUhMTqV/m5SSTo9HVkXICoeJgTYqudmhmmc5WJkVLvJK7FfvPo8bD9/AwdoY7Xyr4OnrYJx7FIHIkAB079ACq1e1hLaxDeSVNUt19aQodZ6TkULHIkFWGlq074rK1WtBV0cXcjLAyxcv8Or1O+RkZ8DM3gOiJCczDdnpCVBQ0YasQuHJEBj8hzktDF5R09sJr9+HY/iQgdi86wgU1djGOXGDPCT3b1pM33Ia62tBkiD7d2auPoS4sLcwtPUUdXEYDE7w8PJBrN1xHFv/HfTLRJ6sjHyKjkdEVDwiouORnZ37xYb+X4q+/Hj46gPNx2VnaQQbS1Po6hrA2lYDmhoa0NTSgoamFoxNTKGla0hFGklWPjkFVbqv7HuHI1+Qi9ycDAiyM6gyfW52Bp0wv3rxFFeuXMHcdUdp1sP61crTEC7yN3nk7/LyqcN14dYLlC9niUkDW2Li4r3YdfwmbM0N0LFzV3TuvgmKqqJ7nglysmiCg3u3LuPB/Qd48fodrbu4xFQIBALIy8tBW10FiSnp1LkjmR3JSyVzY11cObUbNRu1gbKmUYkcLVJfyZ8/4vzJA7j/4CH9XV5ODhUreqJmnYbQNLKjTgl5qRUT/g7XL53H/YeP8PZ9KEI/xcLSVA/urg5o27YdKlT3oytRDPGCtSiDd/TvUA8Dp23CmYOb0KrXr3npGfwlPTEKMycMxZmrD7BiSi+JCg/Ly8vD57hEmnpTTl684sMZjOJCNoGPnzoXuYI8TFi8Bzk5AuTkkpTouUjPzKLOgbG+NkwMdWBmagwNbUU62f2SgCsfPrb2mDJ1Cowty0FRTa/Yey/IZFxKVg7yZLO7kvoP39W29kTtFn0wMTkaz+5cwJmzZyHIy4eUtBRkpUlaYmkoq8tix/olcK3cAJnpySjvVR2uFbygpmuOOw+eisxhEeRm4/X9c5i/YBFuP3kLW3NDuDtboVkdT5gZ6kJbU5WKW/4Mcc6i45IQEBSJuYvXYPLsZeja1heduvaBnoVLoUkBfsetU9swaMw0mBnporKbHWRkpOl5tu8+gDHTFtOVaBNDXbx5H0odKvKSh3xqtK9Hnaeg8Gg8exOMtt0GYVD3i+g3fCKtX4b4wJwWHsHn7CjCtCMTWZLm8vTZi2jRYwykpWU4l6WHS+fmi52dZi7mju5BY743zx0IRQU53vWB4tq9/RiBeeuOIidXgOG9WkLLxOGHumH34a+wuuFeXy6NYwbGCeDbdypMlDNgr68MBQUFyCso0J8aWnpQUNGEvLIGAqKz8TxE1OOcPqo06kI/hdkeehxNbWs17yWiMv5nR5y7rUsnYt7KnejVpg5atWuDkAz1Io5fOrBVU0fdqrqoW9WVht3tP30HKzY3hI9HOfg184WJW318TFOBu43BH8t36/ErLBo9HVMGNP8hYyShrW8VusfxVWAYXn/KhK5LHVSxVYOVWuYPdsSpIR+SwGX2pvNYs78++vfsgFaNaiBPIIAgLw9KSsrQN7Ojq0J/U4cMbsCcFp7AVfVuUZ27irsDNh+8gvT4CIRkqDIlXzGwC394Bia5slg0um2BIQZc7wPFtWuu8gBLlu/B4pmjUa9pRyiq69LNteKkKM11Oz6Uket2pXXuMTsfID9fBVJSKlhc7TfH2yF5dVNSu9TYECzbuB9rZvRBtrJJicY5SxM9mlwgM6sx7jx5h9Xbj+LR29XQsnKDpokdOnqows1GH/oGRjA0Noaahg6UNAzwPi6ftl3I/dPINfKEljnZq/rrecmqi4ZpOTxJN0d+vhSeBhZexhQ5fcjWnQS9iEAsP3AKm7fsgLLcl9UyEqZHwtxWL5sHWcsaGLPjfpHaj8ENJCf2gudwVb1bVOdWV1WCirIi3jy7y5R8xcQu5N4p1G7ZpdCYaK73geLYCXJz8e+KnVg0YxSadxsDFR1TyMgpip2iNNft+FBGrtvxoYysbn6sm+CAl0jLyIKhrqbQxjmyQl6nigv8ug9ClUGroGfvhay0JFy89QjrNu3AiH+moEGz9qjgXQN1alTB0H5dcGf1MEQ+uwrLam2EOsfQMLGHS6tRGDBuGnYsHILtCwbjwPKR6NisGlp3GYgVyxb9ct/kZqUjMeINzVZH9iwxuAVbaeEJVP02Mv/L240/qeSKwE4U565esRzOnDmNNiPr07ckX9+W/En9tqztRHluvtiRpXvyGHLR//8ThId9oDh2gRe3oXY1DzTtOOQXZ43dh6yPlub9wO4vVjcO7jVQ2d0B4xftQQXPikjNrgAFNR3IKakIZZyTkdWFnoMX9B0q/rIqQkLTIj7H44Z/DHIdWyH8yUUoqJDQtNACj/e35/6dHUnoQ8LIJiw/irdRR2DoXB0aJrZ4k3EZLafdwMuAUJSvUBHvnlzF0MnLICMrX2iZGGULE5fkkbgkV+Lvf0dZnjsgOBIrd5zFhau3ERiXx2lFaWGcW5ztTpw9i/2rZmPnvD4oLUXpvzleWdnNnzQGDy/th5qOCdVB+BDwGsnJSahcuzlUtAx5sedAVOrrfK4bcW0TLl4zV+y42lcSIt/j5MEduH7rDt6FxeNTbCpyM1IgK5VHhSwrOFqiY1MfGH2XybE0xsOVO89haNdGZT7HeByaheM3/JEVFwYDNRnq0JBrfhGajtmL12PVv5PQoP1gcAl/CRaXZE4Lj5wWxo+QNzWtBi/CyYPb4OjdCJKukstnDm2ej70HjmL6sLYS1VfGLdyNmLgkxCSkQEZaCjqaagiLisOOtf+ifuv+4DOsr3AP1ibchAvtQp6nJJVzTmYqcrJSkZORipjPn3Di2GFsP3iOZufq1qIGXOxLJxvX7hM30ahGBToGcgHyTPkc/h73n7/HrmPXRaqZ8zP+Euy0sPAwBm8hg0gXvxqYM3cetu6rDjlFJsbHV0JCQiROk4Uwb3RHBAZHgbw8IRtNycSh86gVMDExE3XRGAyGhD1PiQYK+Sjhi0CmjkV5OHg1xIARwThxYBtWb9tPNWeGdW9Mw6uECVndeP4mhO6H4QrmRjo4cfkR3efC5hfcgG3EZ/CaFvW88OB5IN6/uCnqojBKQGhY+A/hB5ICSd9NlLG/OixEbI7oTViWE63CNIPBYHx1ZtT1rdBlyAxcvHIDowb3wKJNJzB2/k4q6iks7C2N8D4kilOVbqSnhaCwz0iJLXyfDaNsYU4Lg9eQyZ6nsxUe3r8r6qIwikmeIBcRkdEw0teUyDqMS0jB3LVH0GLgQroBdPPmjVDW5G7YKqPkEAf184eniA9/8yUsJyfziyAig8FhlNT10br3BFy5ehXqKkq48eCN0I5NxEO51geUlRTgVd4Wm9ct41zZJBUWHsbgPWSZ+vGTp+iSm1MsFV6GaElPiKTCZJIYHhYeFYehM7egb6cmmDdjIvRtPL6JnjHEF6KPUbthM+hpq2Nkv3bYsvcUNNVVYWigh4oe7mjdc9S31NcMBtfIy83Gs7chGNrNV2jHPH75Id3TwjX6tquLvpPWo3LlyvDx7QppGVnqwOQJcujLBvrJ/vIzL09AV6VY3y09mNPCI0Sd6Yhr2cO+4uNZDr0mb0LW+KkYNGgoylsb8ypzjCTbkWX3zt16IF9FH1lKpN1+VDjmWx/4W7so/zeoW8UV2lqaaNy+P3q2a4gxczZ92/TJ7kPx7KOy8spUz8LSVB8L1uyFjb0D7jwNQML1Z1i57TgcnVzgWq25yMpXXDs+lJHVTcnr5vCZi5DXMECitC40CxCCLM54eC84C5Vrkk3+3Jpj6OtoYMbwdug5eAL86p9AVk4OkpJSkJSShuTUDCSnptOfxJHJzhHAr35lTJo6E0a2LMS3NGBOC0/ginp3YXaiPHeavAGsOvyLfXvm4GZAMnYtGgt3e8tf7JgaMjdUmL8SHxGANp264X2mLso1Hok1gVKcvL9K0y4mxhgpDy/i5qM3aOtbBY+ev0V+Xi6kZOQkRpWbC3ZlfW4FFS1Uq+iEU1ceITdfGv5Rr2Hs2QBm7hpoYZcDfVNrztQNV+uQT3Z8KOPf2C04+ARSJpXoOCas8TA0Rxdzr2ZhYm0lzj0DSJKA9bP64ep9f+hra8LFxpgKXJOU0OSjqqxIQ9Wzc3KxYvsZtGnXEZeu3YKSul6B18EoPmxPC0/ggnr37+xEXUZ5VW149piDmMDH+GfkMGSmxP5ix9SQuaMUHfXhGVq3bgVZdQOUazIAUtLSnL6/SstOx8YDlev4Yu3Mvrj3LADtWjWHtMyXEEemWC6+au4kjHXBqh14+uAWpq49iMoDl8PcuwkMnKvBvkF/GFj+mkGJqb7z144PZfwbu6zURCioaQt1PLSq3hax75/h9P2gAu2Kc0xh2X1dcWnfuCoa13SHirICzt54ik0HLmPlzrP0Q/571/EbuPssEB1bNYSC6u9XJBnFgzktPIEqvOLLiPJHJVgR2HGhjLLyinDvMAFxEe9xbNfqQhWg6fGKoIYsLDtRnptrdrnZGXh6/TD8WrWFV3kbDO3SENJf7Th+f5WGnYysLNrVK091m4PCY1CvSZtvduw+FO8+qqimAw0jO1Sp4MSpPlpcOz6UkdWNcOpGy9wRCaGvhToekpBYm5ptEfP+eYF2xTmmsOx+ZunWU/Byd0b3zu3RsnlTVPb2gpm5BbR19LFn83L0HzsP0tIyRToW4+9g4pI8EpcUdfw9V/e0/GyXE/sR05YfwMHdG2HvUZdunBO1orQwzs1nOydTLXx8cQOzZk7H7cdvMahzQ9Sr6iq0+4tvfeVnOyKsliOQwqINh+mqkzjsORClyndx7PhQRq63CRevmSt24tJXMlPiMHzEUDx6FYTpo7oKdTyUiQ9EyLsX6NGqlsjH7N8JFjfr9y9cHcyhrakOa0tzuFdwg4d3FeibO0JeufSzYPpLsLgkc1p45LQwis7JK4/pcu2gbs0xYtoKyMoriY1yMd8gCsvXTmzDkHFz0ca3Mlo3rAw5WeG+heJzXyEbODuOXI49m1fArUZLiAusr3AP1ibchE/tsmrOCKzeegjb5g+CsqLwxtyA4Eicvf4UQ7s2ovpVouZ3z5T4pFRExSQiNiEZQWHRePvxE16/D4eZkQ5q+3iiUqVKqODhDTVtA8gpqUNOUe2Hl6clxV+CnRa2EZ8hljSr44ma3k7o/s8q+DZrAefKTURdJIkjJyMFj68fx6Jlq+H/PgyzR3VAOWsTUReLc0R8jqcbOR3cq4u6KAwGQ8Kh6XxzsyEtK/8ti+H3NG/VARt3HsO9p4FCU69/9iaYJiPhisPyJ7Q1VOmHUMPL6Vu9EXHM20/eYfmaTXgfMvvbXhgiUlnO3oquyHhVrg4TW3fIKhS+f4ZROMxpYYgtJLtHO98qWLN6NVZ6Nfi2wZlRumSnJ+P+lSNYvHwNAoI+oXfbOpgyuBUvHkai4Il/ELzcHKCgWvheAQaDwShNstOTsGHJFDx98RoJiSlwc3FA23YdYOtWDfJK6t/szB0rY8/2NWjdsS/U1ZRQ0cVGKJERkwe1KtBJ4guk7HaWRvTzlVyBALHxKQj/HIfAoEjs2X8U42YsRflyFujSvhXqNWlLdV2+Dwlm/B5WUwyxxq++F85ce4TglzeYom0pI8jJwrXjm+DnWxP1W/dF7UrO2Lt0OBpUc2MOyx+clmo+lXn9wGYwGPwmITIQ81bugpuDGZrW9kBsTAxadOyD1k3r4PLh9chM/S+bmJO3L3asW4g5a47g3cdPJT43SRuckZkNcUNWRgaGeprUsevYrBrVezm8agx9Jq7fuhfeVapj6vBOiAt7I+qi8gbmtDDEGhJzS5ZvG/h1xvFt85GTlSbqIoktFw+vR+0WfXHuxjNkZefS0AFJX10hIQNZ2Tm/tXnxLgQ+NWqXWZkYDAbjZ2TklGBlpo/D5+9DVUUJ/TrUw6GVo9Couhv+mTYfvvVq4s7ZnVQJnrxg8arfEesWT8X4Rbtx+/G7ElVo5+bVsGrXOYl4sUj0XKp5lsPiCd2wcc4AhIRFwrdpM7y6c0oirr+kSPaMgmeQLBeXInXoTy7acbWMO45ex4fQz2jZawLq162F7Ref4sUf3g6RLCp7bgbSn8KwK41jcsEuL0+A9IRI5Gal49atO1BRUkCfdnXRsr63xNxfhdnFJaag7dAl8O09F3l5eQXaPQ3PoftZdI1tCzweuw8Lh9VN2dWNpI9zwrDjehl1zZ2xctdpeDTqglnrTmHk3O0IDImiL/22zR+MTs180G3AWCybMRRP3wVh7633MPJuj4M71mD9vov0OfvzpLuo42aKrD7SlM3x/JPgt3Z/c8zSeP4IGy0NFYzt2xxd/GrQVa0T2xdCkJNZ5uXgE2xPC0/ggnr37+y4XMaja8Zg4uK9CI9ORFCuIeYtWYvQ+ycxZVRPDOzd4/8xpbLfwnOYGnLRVJNzM1JhFrIPp85cRrWKDti1/ygmzFyMbesWYfaKXXi35w1MKzYS+/urILusd7GIPLYdw3o0xYHTtxAWGQcLE71f7D5eu4waFStCUe1X5WR2HzLF8tK8H9j9xerm5/thwqG3yJdzg3HHJagr+xALN+5FWnomXTWv51MeW/8dhKFz92D7GzUYlq/1//urFs6cOIL+/fthyrL9mDK4NRTk5f563AyKf4KkYAuM0Ijn3DOgtCGh1JYmehg3azn8X7/GqCmLoKimW+bl4ANspYUncEG9m6+K5TbmhtizZDga+rVGcuRHBF7egYyEKEycMg9axg5o3cgHXfxqol/HhhjZuwWmTRyN8CeXEOV/C3EfXuD+qw9iqYZM9qBkJEUjLT4CD/w/fLPLTEnE+vWrMWdcLwzv2Rx92tXDi1sn6Fu0J4ERSIuPwtsz63F1QRds33MMigpy8PZwhaKKNlS0TdBn2BSMmbcGIXdPICcjVezvr5/tMhKj8Wj7FNRt2QX9xi2Bpak+gsKjf7HLSolH5Msb8Gzev8CNmEyVmymWl+b9wO4vVjeF3Q9EatG0WhdcvXELe7auhraOLuauPYJ+UzYgLjkTcsoaP9xfRrbu2HvoOMrZWqDTqBW4dt8fgclKRR43c3NykJkYDVklNU4+A8oCK1N9bPl3EC7ffIxuHfwQ/u4+CxcrALbSwhOocmtk/pc3An9SeBWBHdfLSOJIuzWthBT7tshIjsPnlzfx7vxm+l3rRpWRkZmFtPQspGdmIzMxAy8/AxnZGUiLDcf6l3tRw2g5bNxqFagg/PWNZlGUov9kW9p2NqqpuH12J27dvIn7j57TyTTZAJmenYf4bDlqKKesDmUHHcibacBQRw1Zaopo1bkfVswdh8BXwbi1dAVk5JWgqKaNqWP6YHC/flDVNf+2UqWgqo36Napiga4JslMTIK+kIvb31/d25OGrZeGEQYOHIT9PgODwaLRuUOkXu/dXdsGmZjvU9HYvk3uhNI4pLnZ8KCPX7fhQRnGoG0FuNpKi3iP+2RmE3gtEXm4uVPVMIVsuGUlRUlDTs/xBE6Qk51VS14arTzO4VG2Kcalx+Oj/ADuPnMdjpS+pjr8/noqWMeau3IVWV49g0ox/oaweBKmqo/84bloppyDw4lFYVmvF2WdAWUFChZdM7IbdJ26hbqOW2LNlOTxrtxVpmbgGE5fkkbgkV9S7fwfXy1gcu5cPbmHbkWtYMW885PWdUadOXV4pRb8MisLKlUvhf+ciIsLDYWKgDQ8nK7g7WcLeyhjKivKQk5P9bd2Q/PMj52zDoW1LsW7XMaiaV4BvTW/Ur1Ub0rJyv6Q8vnfpILoPnYb2fQbBx16z1O8vLvWVrcfvQD4rAXsPnURixFu4V66F42vH/pCUgNj1Hj4Lx06eRFWvioUej8+K5XxS+Rb1ucWlTbh4zVyxE0a75OfnYe3ckVi4bi/KWZnAwtYe8bnKyI4LRVREGBU7rFbRCY0b1UfV6rWgbWgFOUVVvIvKwPPQRFSwKptrvnduJ4aOnY6pk0cXOG5+CI1CeFQ8TVJCsidWr10HGWpWnJpjiFqw+Nbjt9h17AYuXb0BJQ2DH77zl2BxSea08MhpYYgOktZx4pK9aNrYF55O5qhRpz70TOygoKbL+VS1987tQpf+YzB3dCfYWxrRVae/gazEjFu4G6rKCth3+CR9o1YQudkZuHt+HxYsW01XF/q1r0cFPsuifrjSV6LjktB/ygZcPLEfVm41cfPUdvrWcfX0Pj/YJaemY9C0zbh3//4vDyRxgU8q35ICn9okT5ArVBVxcWiXqA9PUa2OLzbNGQAdLbVfvk/PyKKT3RsP38A/MAzKSgrQ01KHqooiVJWVYGdtjh59+sPMwQsycoolLjdJxPLqzmn4v3qBtPQ0ZKRnIDU1FUfPXEOL+t5U5Lkgpi7bT78nWmrkJZqSojy4hqifKaQtu45dhScP70JV1+KH7/wl2GmRjBGBwSghDtbG2LFgMC4/j8bRk2cxbcFaqojrW7sSJsxZAUUOCgOmxoXhxP4tmLloAyYNbAVHm+Kp0cvKSCMxOQ3lrI0RE/YeSur6v0wmsjOSsWLOGGzacxqDuzQsM2eFayzcdAIj+raDpesXdfvHjx/BwfrXeidvGc2MdCD//9hwBoPxhYRPgdixcTmu334IAz1tWJibwcrKCtY2trCwsoGSmjbtN3JK6pCWlpGYaiMOwurlC1GrknOBDguBOClEA4R8CEkp6YhPSkVqWiZS0zPx/G0w6vi2gYu9GQz0tGBooA8jA0MYGhvBxNgExmbmUFPXhqyiKuQUVCCroAJpWfkCx3Ky6nNi+yKMnrYE3uVtqeOhqEA+chjYqQE8XawLvZZBnRvi8IX7GNy5oRBrSLw4cfkRqlV0hHIhLwklFea0MBhFREVZEbbmBmhe5UuM6afoeMxafRgGK2ZhwPjFpfIApTofqXF0FYPkx88X5NKfJK45PS0VMjJy0NQ3pQ+XzJRYRAQH4OnjR3j+4iUu3XoMHU01zBzRvtgOC4GEjm2Y3R/7T99GnSZtUbmCPcxMDGFmZgozMzMoKSnh9JlzuPvYH1v+HQg1lbJPHSkKyArU8csPEZ9Ikg0AcQkpCImIQY9B475trK9SpSqWbdwPZ1tTmoHn68M/LDIWZsb6QnnbyWDwkYzkGLx6cAVkR4G8vCJCgj7i8ZMnOH7+FsrZGFOBw4TkNIRHReDJs5cI/xxPVzIJ+joaMDXQgaqqMqSlpaChrgYjQwNUqVIF5b1qQEXbVOxWaSICHmL30cvYsXDIX4k2ks9Xqrjbo2fr2nj78RNi45MRk5CM5y9e4NK1W4iJT6bhZdk5uTSUlTw79LTVoaejAUN9PRgaGcDO1g5OLm7Q0NLF3RsXMGb6Uiyf3PNbZsSiQgQXbcwMqL6Lj6cD+EBmVg7kZGX+OlKhOASFRePguXu4cPIApGV+DL+WdMSrVzMYZYixvjZmjWiPPhPXwadaLbjVaCE0RyUzOQYBz2/jwIH9uHjz0ZeJ8Xc58MnkV15OFjm5AsjKytBVH+JEaaqpwN7KCOWsTTBjeHuaRlEYkLdn3VvVokkLnr0JRlRMIl2aJg87MpjbmhtizYw+NNWlpLBk6yl0a1EDZkZfUlOOW7ALtjaWuHDtPuTk5JCbm4vsLGDe1DHYvG0XQiNj6YSB8OjVR9T0qSziK2AwRENc+Bv06NYV4VFxUFFWoBNlAx1NONqaYNrQNrCzNCr0bwWCPOq8kL9NzchCfl4+UtIyEBIcjMOnLiMuMRXuTlawtbaAna0NNDU0YWNnB7vyVXmdRjbwzSsYG2j94IQUBzJGu5X7MdzoZ8hzhehMfXFsUhATl4TX/q9x9uINGvqbkZUDfSUkgJkAAFakSURBVB11zBnV8a8dlq80qlEBi7ecBAnRN9Dl3oozuSePXXyOO08DEBgcSdXtBXl5357D5sa69PkqLSWNzOwcWl/BETH0ZRZZdXK0NoGTnSl9FktLSSE5NYM66FUq2P/W8SGhw5OW7MW/k4fBwtmnDK+YH3DSacnKyoKCgujj0xmMP6GrpY7h3RtjyKgJOHveE6o6ZiWK4Q579wDbNq3D6Ut3kJGVTZf5Zw5vD0NdzUIHOpJHn7yRNNbXKnUFepLdhKj5SjokXtzT2fqbw0ImUK/fh2PdvFF4/PASlJQUoa+vB5dK9XHw0CtsWLsC9Zq2R6dm1WioxuNXH7F67QZRXwaDUeYkRQejS+dOsDbTx9zRHf/678k4aKSvRT8/0w/1aBjUq4BQOoG8fPU67W8kkYi5iR5Wr1xOJ4J8DF31rFILCUkz6EujCo6WpXousqJAnjnkU5qQMLKth67C2syAOjHCbBfiPCjIy/71M5G8/Fux/SwUNI1hoCLA4H7d4e5ZmUY0EIclj0Q5JMfj3ZtXePfuDU0PraikBD1dXTg6l4eWriHioyPw+NF9PHr8BAfO3KGrgWoqytQhWbvnAsb180N5hx8dx4SkNBy9+AAnrzzC4O4t0KzLUF7cp1llPF8vttNCNlstXLgQ9+/fx4MHD5CQkICtW7eiR48ev9geOHAAS5Yswdu3byEjI0M3EP3zzz9o0qTJD3YXLlxAr169EBUVhcaNG2PHjh3Q1Pyv01haWiIkJARDhgzBypUrf9nIVrt2bRw8eBBt2rSBOMKFTFpcyezBJTuBRU2oaAZixsQRmLtiB40F/puMK1lpCQh6/QAb16/D0fN3UKlSRdTr0BdVbNSKXkZDRV7XId/ur8t3X2Fgx/rfvpeRloaboyUmLtkJRQ0DKCEDb98G4OKJCpCVlYWFc1XUq1YBJ688pm/tmjZvinNBcqiAeM5kMJJ0Oz6Uket2RbE9vHsj7S8NmzbHpUiVUumjlSso0jDWr5DJ5uHz99GwWTuMHDsGyg714Wlvwrm6+Z2duoEN1i+bhf4jpkBTXQW+NSrAxrUiovJ0RT4eFtdOWVEBg7s0wrG7IZi+5xl6+lUt0TOAvDy6fOclrtx7hfjUbGTlSkE6LwedmlRC1xY1CnQCvj/e5w8v8O/6Y/hncBeYO1ZBgyYtCgz7Jm1haOeFmoWUUcPIHlZutdGm9xc9NBIuTASsczNTcfnENoyduhB929ejopL4f2jxwGkb0aKhD47sXg/nyr6QkRVecoJUMZqvF9tpiY2NxcyZM2Fubg43NzdaiIIghR02bBi94H///ReZmZnYtm0bmjZtisOHD6NVq1bfKrVjx44YNGgQPD09sXTpUkycOBFr1qz55ZgbN27EhAkTYGwsORuUuKDyLU6K5cK2y6s6Cif2job7pvnoOHD6L2KBP6tPL+jgBrWMjzh6cC+On7tOw60a1/LAvFnjsDXMHvECKTwJ4PY1S+r91UbzKdRUFOlen6+Qt759+vb8zw75qGO0AQf27gY0bOgDaOiQwWjSthcUlFRgXfEfvL4eiJ03Apli+XcwNXf+2hXV9sOHj9AyMMGaQIsy68tkstqmUWXomNlhwr+boKh5HJY+rbCsX01UdHX6ZYIoqrr5nR25Bp/G3XG/Um3cv3EeG/ecwPw9q6CkbQQzr0aY1lgXVmqZRa4bEv5EVqXIigQhU8kI696LZny9rdAAAeHbqf3fPAMG2YXg9eM7OH/zOUI/xdB9OPVrVMSEGXOw+Y0G3WuYl5ON+5fnIiTiMEb3bkaTFXx/vFVvTZGVloyol08hCLiAfVtXwrN2a1y/fkMo+1Rl5P47n5ySGhq1HwodHT107jsKXq42NHph/sbj6N+1BYZOWvKDvbCIFaP5erGdFiMjI0RGRsLQ0BCPHj2Cl5dXoZVAvjt58uQ3L5d4ZyYmJti+ffu3SiBeHanQWbNm0d9JtpAuXbr8cjyS3u3du3e0QlesWAFJoSDl1oI6tqjs+FDG0rSTllNAu96DMWX+QpRzLg+PWv+9PcjPy6NK8uQhJMjJRmzgI3TvOAMp0aE0y9bQrr50HwrpH5citUV+LVy040oZSTz9tqPXsHxYgz8ez7JCDZzevRYNmn6Jz3fybojta+bjfowSrkbJ/KAoXdDkpSDF8pLYlcYxxcWOD2Xkul1RbXv26YdqjbtAK/U0lDQNoKSlD38taViqotT7skDbHl59FtAx+OP1fWh+bD5qeVihYb2aaNC4OfTNnCCvrM7Z+4s8I0j627qt+iFKtxbiywcg+dN7hNw5iuF3IjC1bwNUdLEpsG5SY8Lw6ellDNv+FMhKhoKcHE2FrKQgj7z8fHyITEF6viI0zBygZemCB3L6sHD5cs4/1bU24ukKR3BENHJz85ArECA4WQ6hqXI07JmI7E6WS4e2XAa01FXoc498SKgf+fu8/C/Pyb99Bhy5/gYfHz3B9Imj4FqhIlR1TOm+pb233iP/TQC1k5aTR48paxB4YiE6jVqOGl5OdP8JeVEYGpuB1FxZyKtoQNPcEaPmbUDFOqWf0cyjZgv063wFvSasRU0vR7rS0mfohFJxWMRtvl5sp4XEsJEK+BPJycmwt7f/4cZXV1eHqqoqzTr0FVIBgYGBdLnIw8ODXqCdnd0vxyNLTpUqVaLe2/jx4yVmtUXUKt/ipFheWnbeVsow6eOHzr2Ho5rXemj/f6k0PPIz3oXG4W1ECqSkpKFtXR5DmtdAQ3fDXx4IXLkWrtlxpYyhd45iRFMfmgThT8dz0BHgdF4+cnMFmDZtGk0LHfYxANblK0FK2pPXqtziZseHMnLdrqi2Nm41MX/udPx78CHig54j82k0Np8Jx7b8XMwZ3ZFqSZVmX5aWAvTsvaBvXxF9LT/g88eXOH/pGmYv2QxLUz3UreENz3rtOX9/VbDSpSu1GiZ2cGv3DzqYhmPFwn9pWBzRyPq6B9JCMQkvD29HZlIcTD3rY97EbqjiYg0FZQ3IyCvS7FQkffGLD58w4eBbJIa+QULwS5zeewo742Khr61ON5OXszGBnaUhZDLikRQWi6zURKRFh+Jo+musCAtFk9reqFLJG/IK8pCTlUN0phz2vZEDZGQhLSOD/t6KsNFTRERYKI6cOEP3dkwZ0hq2JkrISUuAoqb+Xz8DpFM+oVEdH9Rp2fe3dVjJ2QY96u1Gp85ncevmDZiamsLC0hqp8gaYeT6WrsgQu9pVCp7MCxuSNXLktBUo7+aOZ8+eYcjwUdThKi0UxGi+LhRxya+eW0Exch06dMChQ4fo8lGzZs3ochPx5ojt5cuXaYrCryxatAjjxo1DXl4eLCwsaMwcqcDvK4DE15EKcnBwwMCBA795b38TI/dVmOfI6jE0cwVX4ZLKN9/2HJSWXVHaJCA4EmGfYpGYkk5jqUl6RwtjXWQrGyMoXY0z18I3u9/ZlkVfufAkHNLJYejTtEKRjpcWGYAdx27g5MU79CG1YuYwDJ+2EkO6+mLA7J14HhLPyT0HfFFf53PdiGub/O0xnwXFwdVEBbbawKUTOzFpzmpsXziEbgQv6/GGjNUBQZG4fPcl3bO2cfchRAj0yqRuhNUuMSH+GDNyCMIiY7BwXFeqsdV/ykY4lS+P6p3Hw9vJumjHs9CGrbYU0pM+I+TDWzx+9JCm0Q/8GE7tcmQUkaeoDU9Hc9TycoJ35erQMXf5Jc10YdeRm5WOm2d2Ydi42ajnUx5OHt648iQUnRp5/NUz4OKZUzA2NsX4eRtKva9wCf8SiEvybb5e5k5LdHQ0OnXqRC/4K7q6ujhx4sQPFfCViIgIfPr0Ca6urlBU/HFz8ddKOHXqFF2y2rt3Lz5+/EiXviTBaWGIHtYmktkuJKXq+r0XaUx0UTO6rNx5lgrjTZy/BdnpiWhYpxpeBYRh87Kp8Os+BuIOlx/6kgqX2yQnMw0Du7eEmrIcerWpI9KybDxwGZnZAqzedqTQxCpcbZfM1DhMHtUX95+8QZ92dbHtyDWcvXgdimolE0AmU0VBDnEopKhzQjaWlzS71eePz9GnT2+6yuHj4UD3dRYVMlnuMmYlNq+aD+/6f5+Fjs99xb+UnBYuztd/ptRVcpSVlamX1b17d1rALVu20EKT2Lj379//Yk9i50iF/lwBPzN58mSqg0Bi5RgMBqM02XX8Jnq1rVPkhzTRObh4+wW6dO9D/+bDy7u4/uA1fUnSoEVX1lgMxk/IKapg5ux5uPXoLfpP2YBbj9+KrI56tKqFO49f4/LRzeAbiqo6mLN0M2pWccOCjcfRs1OLEjssBDKOycorQ1ZeiYaUCSMdr4G1G3bvOwQpaRkcOnef7jP5mzHZydYMFXx8S1wOBn/m66Wu09K2bVua8pNs7PmKn58fjX+bNGkS9u/fX6zjWltbo2vXrtiwYQONlSsOl59EID5LiW5G4yIxKQL6BpnBHVibiGe7kI2orwPDkZaRScXCXOzNqEAYgYjV6ZrYICpVDlGpRTtHUHg0mvg2woeodATFXMPtyxfoW7sWjWrg/lOS2598xJvg4OBCs9QwRAMf2mTOgqWICA7E+cs3kZqnRkX8hAHpx+8+RtD9C862ZtBU/71I44BenbH10BVA1RSKqr8P++Jiu9T264Uavp1oxiqut/mQMVPx/OEtrDv2DH71vKi+yu+IiU9GcDzQrddg3HnwTOL6SlBQkMTN18vEaSFLQefOnaMF/R5tbW1Uq1YNt2/fLtHxSSXu3LkT8+fPR4sWf69GnhDzCYtW34CHkxVG9mzCOTVvForEPVibiGe7kFURaz0VVHRxoQJge07egoqSInVcPscmon/H+lQRuais3HAOU8YORZ269an6d+3ay2mc/omD26Gmaw5JgMvhFZIKn9qkgpMV2nQdiPWz+hdLMZ2EM31dDcjJycX0BTvQr0NDjJqzhf5b09oeaOtbBe5OVgUfwEQPJ06dRWzIC3QZPB2lCZ/apbQgY2Xs0M548ug+erctPDyQpGmeMm8H1iycgrqtOpaaACOX20RPT0/i5utl4rR8/vyZ/hQIBL98l5OTQ5eLSoKNjQ1Ns7Z+/XqaoeBvmTBnFUy05DB72ngMm7UVSyd1p2JHDAZDsnj7MQJDujSi/62uqowBHRtQobKcHAGszPT/6lgRn+ORkpqJavW/DMzRESGo4eWIOVNGQ1XHrFTKz2CIGxWq+2HS8PsYOWcb/Op7wdpUH4oK8jQrlqysDM3gZ2Kg/cOmfQLRHyFhURduPcesER1Q0cUay7efQd2qFTB0ykr0HDQOESEBeP3qBZav34GklHTUq1oeOpqqNKyTvLSIjkvGmw/h9Dw6OiUPrWL8GaKV4+vri6mzF/3WEZ29+jC6tqqH2i1680Ixni985vh8vUycFltbW0hLS9Mlpf79+3+7wcLDw3Hz5k3qvZUUEitHvLcFCxb89d+S8miZOGDeim34d/IQDJ25BWtn9C0wnSkX4HN2J0m340MZy8ouNiEZcQmp337/lKGAsHQlmClnwkT5/+JoPz2Mvv72KUMRoWmKsFDJhLHyf6Fa8Wny+CBI+PZ7RPr/7VSzvh2TJMn8+aBf7aJT/nsr+xVTQ51iXfP60wGoXrsWlLW+pHd0rNQI1+43/OH4LEOW6DNpcenc4mInzGOSfQ5dB01GupweLt97gccBryCfn0m1kogOSGZWDkI/xUJLQwV929WDiUMF2kcvHdwKG0MdnNqzAuOnLaIJNJzszDBv8RrIKigjQqCL57lScGtUFZfa98XbZ7dw7OhRJKalQU5WFoZGJvCo6IWWBuWQpuEAcwcLoVyvMOtGXO3kc1So01kYl+68RGJmPswajsDbyNQyv7fFGVuOz9e/UqLZ+apVq5CYmEizBxBIHBy5QMLQoUPpEhbJGrBp0ybUrVuXbuZJSUmhqpkZGRlUJbOkfPXeiPBNcZFX1sD4OauRkNQNM1YexOyRHTjnwXNFDZwPiuVcs+NDGcvSbtvha99CMqIz5XH2kwLykUnz7jcyToGewo/7Rr4mOIzJlMe5SD3k56dR24bGCdBX/KLmnCWjhbS4+G925yN1kZ//5buGRnHQ/fmY/7e7GKmDfGQgPkqWllkY1/ziYzxidargdXjCD6JwfFEsl0Q7PpSR63alccyA2BycSnZEvqMjpJyAOV284Wiijvx8ARUtzEyOwYtHNzF0wnzEKoTCvkFv3H/yARNOHYZPpUq4XLs1crMz6SZ/OUW1As9boUZL+im4fJ+w9/4nTtaNONrlCxQRGZeGczeewcfTAWoq/2mDZGRmY/nOi7DqMA9770di34PIMr23+c4qMZmvl8hpIXmaQ0JCvv1+5MgR+iGQgmloaGDt2rVwc3PD5s2bv100yTawY8cO1KhRA8KAeG+7du0qcFmrqMgrqWP2orXo0r4FTXdIxJm4BBfUwPmgWM5FOz6UsSztlJUUULeqK/3vS5E60Nf8Lz5X3ygG9YziCqxDYmug/Z+t4Xe2X/a0mP9np/OfnVEhxyR2hv+PDU6LDRfaNadGBUFZ14S3iuWSaMeHMnLdrizO/SIsES6Wej88t6s1scEwGRfMW7wKd9cOhZ6DN4IzNUDeC8sra0JeWTLqRhzspGRk0XXEFLy5exDr9l7A6F7NUN3LkX5HUje7VqqOHHVdkbQf31kkJvN12ZJmV/jjCWRlMWTIEPopKYWdjyxrlTTejqCiZYQNG7egafMWVAywYfXCReTKGi6ogfNBsZyLdnwoI9ftSv/cEMrxUp8FQUFNG/LKqrxVLJdEOz6Uket2ojy3t7M1rGu2haqBJVT1zOBuXfA+NEmsG77ZdWjbDo7D++DVnZNo33MolJTkoaWugst3XmLD7kOYdyVBJO3Hd4LFZL4uFHFJvvEnYZ6AxxfRoVs/mk2MvBFuVsdTJBv0y0Llm+3b+Lu6KW6b8KH9yspuze7zGNS54V8f73e2Je0rD84dxMSehWesKerxZmw4D2Xjchg3cTonY8fFSX2dz3Ujrm3CxWvmip049ZWysCNT0wcX96Jj75F0D9OaBRPQpPMoGnYrir4iLuKSfIc5LYU0eGpcGJ7evYxde/bhyctArJrWm4a1lCUsvS73YG1SclbvPo/B3zktXGiXnx2p4jJ12X5U8XbDmJlrqABbQWSlxkNWUZVmyxFnuPzQl1RYm3AT1i4Fk5+fh+CXN6Gsogo9y/KFjqmS1ib+Euy0SIu6AFyFpCat3rQHVm0+iBYNq2HIjM1ISfv9W2AGg/FnuJXi4gvCWm4e1aspNuw6ifsXCxbhyk5PQte2TTBzZGfkZP6XQY3BYDAYPyIlJQ2r8jVhYONZpg4Lg7swp+UPEDXZf2atQnu/uugyeiW2HLqC9AymUs9giBPCcqQ01VXgYGWMt29e//IdCXd49+Qqjlx4gNPXnkCQzV6CMBgMBoNRVJjTUkTHZdSMNbhy9jCkpOXQceRybD9yjabgYzAYfwcXN9EJK8V5UFg0QiNj0aZLv1+++/D0Mhq26glBXh6G9W4HBbUvWXAYDAaDwWD8Gea0FBFpGVlYulTDog0HcenUfmQLgI4jl+HW47dFPQSDweBoeJiw2HzoCob37QBVXYtfYrOXL12MyJhEzB7dFZ0HT+ecFhSDwWAwGFyGOS1/W2EycrB2q4Vlm47g0M412Lj/EjYfvPJNAI/BYPALorAtDP8hLy8Pz9+GoEXbLr84JOkJkdh6+CqM9DQxZMw0yMiVfTZCBoPBYDD4DHNailtxsnJwr9kaZ0+dQPjnBIydvxNxCSkoTUjaVSKIR35y0Y4PZWR1w726EfX9dS5UDelQLvHxyCqKto4uzrz/kkbze3IyU5CWkYWZI9pD3cCafr/nZuAvdj8jbLvSOKa42PGhjFy340MZWd1wr274cH8xuEGJxCUZgIGVK3buP45V8yehz6R1qOJujx6takFfR0Oo1UMmS6sDzL8I3EXmY7B9aIF6EaKy40MZWd2Uft2QDHutBi9CNZ9KeJakQ/evEEFGL51kaMrnUBuif8Sl+yszNQnRSYb095IcLzAO+CTQxu47YdhzNwyLu1f5lvdfTd8ad8/ugHv1plRnYPT2u98Ezb63+x7yEBWmXWkcU1zs+FBGrtvxoYysbrhXN3y4vxjcga20CAEldT2Mmb0Od29chIujPQZO24TR83bg3I1nQss0RoTtyKSJQH6S37lkx4cysrop/bpRU1HChln9EZchDS0rV9jW6QybOl3g3agt1UEhn95t63Dq/srLyYK0nGKJj/chUQbS/w/7Ig9BIlj2FaLJUrlRVyioaNF//xpN+rPd9wjbrjSOKS52fCgj1+34UEZWN9yrGz7cXwzuwJwWIeYT17NwxT9zN+LB7WsY3K8bXn/4hE6jlmPknG00xWlqemaxj0+UuMkba3ou5NPfuWTHhzKyuimburEy08eoXs2Q+jkI0W/ucf7+EuRkQVZOvsTHK28sj+yUhC92UqAKywVB/v3rlpeytBPlublux4cyct2OD2VkdcO9uuHD/cXgDlL5EriDvKzURPMEOUiJDsatq2dw9PgpXLv3EqaGOqjvUx61KjlDXVXpr1S+SZgKectLJk2FhdCI0o4PZSypXXHbhIvXUhZ2O08/hKOxEtpUtynx8X5nW5K+cuVFFFRzYtCupm2Jykg24jcbuAQzNh2Ht7Ptb8MMSFgCeatHHpJlaVeW5/5ZUVpU18zFuhHXNuHiNXPFTpz6ChfrUNh2P7eJJM5huQhzWsqowfMEuUiJCcGDmxdw5NhxXLz1DG7lLNGxqQ8crI0L/JufJ2IM0cPa5O9Zv/ci6vm4wsbcEFxsl0cvP1DtlEpudiUuB9nPc/38MRg7VIKkw+WHvqTC2oSbsHbhHlxuE38JdlpYeFhZVbSMLDQMbVC/7UCs3nkaD25fRcN61bF06yn0HL8Gxy4+QGxCclkVh8EoM/q0q4uDZ+8hMTmNk7WekZUNJUV5oRzLxEAbH94z7SYGg8FgMIQNc1pEgLS0DLRNHdFv7EJcunYLi+dMxOf4NIyYvR2dRi7H6t3n8fp9uCiKxmAIHRkZaQzu0hDr9l7Ex7DPnKvhzKwcKCoIx2nRUldBdHS0UI7FYDAYDAbjP5jTwoHMYzWb98bK7Sdw9+4d7N22BtZWVli27TQu3n6Bz7FJoi4ig1FiSFaxsX2a49Kdl7hy9xXnnBYlhf/SMJeEsKg42NuXE8qxGAwGg8Fg/AdzWjiUfUxZyxguVZvinznrcenKNdjbWGLA1A3YduQacnIFoi4ig1HiFZd+7euB5P5YsuUU3odEcaJGM7OyhbbSEpeYAhOLwpMOMBgMBoPBKB7MaeEoKlrG8KreEFfOHUNKejY6j1qOE/fDJFKxXBzs+FDGsrKrW9UVQ7s2wrM3wdR5ufchTaR1+CJGDtG56iU+XlJKOhSUVHHyTRZTlC4AprbN37rhg2I51+34UEau24n63AzRIyvqAjAKR0pKCuaOlbFm+1Hs3b8HI2esgZzyM9jV64Ix3vmwUvtV94WpvnPPTpTn5qqdnJws2jSqjMAEWYzb+hDKehkwcvYRSR1+iJPDlhAbDFX+VKLj3Q1MQoqqNfbc+4S99z8xRenvYGrb3FMYFyfFcq7b8aGMXLcT9bkZ3ICttPAAWXllyFjWhEe3mTBwqopXR5ej78jZ6DpmJaYtP0DDx248fI3wqDgEJCmKRB2+NI4pLnZ8KKOo7EIyNWBdqyOkZeQQcu+USOowP08AKRm5Eh/vyccEqOiafbFjitI/wNS2uacwLk6K5Vy340MZuW4n6nMzuAFbaeEJRPhIWloKBs4+MHTxwYwmJlDPjsTrVy/w5u1b3H/+AXtP3UbwpwRkqZpB08IZ2hbOsLBSKFzlOzL/y9vjP6mGF8GuNI4pLnZ8KKOo7fTLVULY/VOQiQ8AjHTK9NwEYRwvKy4UqrqViqzC/PXtXlnZifLcXLfjQxm5bseHMrK64V7d8OH+YnAHJi7JYWGev1HJzc/PQ3Z6MtITo3Dm2j2cvnoPnwOfISQ4mKZh9XC2RhV3e3g4W9Gws9JQQC+NY3LNrrjK61y8Fq7ZWamk4sThwxjZs2mZtQuxW7X7AoZ0blCiayH7Wbr/sworNmxBkoo9U5Tmoco3H8oobJVvVofcuL/41lckoY8ycUluwpwWHjktf4sgJxNp8REIfP0EN65fw4Urd/Ah7DO8y9tCU00Z6qpKqFzBHlZm+kIttzhTEuV1xp8hGkX9O9SDrIxMmbULOefgzg1L1DzTVhxAxQrOGD93AxWS5SvZ6UlIT/gEZW0TyCv9PjnBn+DyQ19SYW3CTVi7cA8ut4m/vz9cXFzw6tUrcHkOWxqwPS1ijIycItQNbOBZuy1GTl+N4+ev49i+zahftyZs7ewgLaeIUfO24/mbYFEXlcGgGOlp4sHz97yqjXvPAuh+sqFjp/PaYfn04Sk6tGwALVMnbFw6TdTFYTAYDAbjB/j7hGX8NeTNqVMlX/r5GlLm67sTPQaNx7h+fqjkZsdqtYjkCgR0hwPRHmEIj1YNKuH01SdYvv0MDWv0rekOPe2SvfH/E1+CJYvH41cfMX/DcezetBRquubgGkQTJzU2BG+f38Ptm1eRmpwEB0dHtOzxD2Tl/0vfnJ2RjDEjhuL243dwtDFBA99mIi03g8FgMBg/w5wWCRe0rOrbDXu3KKNr3xEY3KURano7ibpYnOfCredYsvUU1FWVsXh8V5gZ6Yq6SGJFk9oe9Gd8Uio2H7iCto2rwNJED1wjLiEFs1Yfwp6Ni+FdrwO4RJ4gFxEBD7BlwxrsOHwRH8Oiv31X3sECzToO+dFpSU/CqatPULmCHTasWwNLFx8RlZzBYDAYjIJhTouEQzble9Zug4O7lNCh+0BkZGajUY0Koi4WJyFvrV8FhOHYyWs4f2gT/F88wYg5W3B41WgaYnfw3D3Uq+qKWpUkK8a0tNDWUEXPNrVw8dYLzjkt5F4g+1hG9msPr/odvyW34AI5manYtmwypi7ZThMEyMpIo6tfDTRt3BC1GzSBlpEdZBV+TNusom2Kd89uQUPPBMoaBiIrO4PBYDAYhcFiW3hEaSnBvg5PgEuVpji6bxv2nLxFJ2PnbjxDXGIKtWOq71/YfeIWwiJjcXDzIth71EG9Jm2RlJqFXrOPYMHW83B3ssKkJfuQkyv4FkJ2+c5LfI5N+qt6ZHb/cedJALzK25Zq3XxMVf6r4yWnpmPtngvQ0VJHn+HTIC0twylF6S3LpqLfhOVIS89En3b18OTmaWw9dBHt+k2EnqUb3kVn/nJM4nQZ2Xr84LBw4VrK0o4PZeS6HR/KyOqGe3XDh/uLwQ3YSgtPKBMlWI+6OH/mJC6eOYJrN25h1/GbSMkSQGDoAW1rN2hbOGGEW7zEqr6bGekg2D8Ovh2HwNRAB4am5lCwrgyBTUWoa+ljy9HZWDGlJ+Rkv0xi7z4NoMKfqemZ8GveCK90WwHSMpy4Fr7YBYVHo3ndiqV6bpLKmPz8k13Spw8IvLgd6oIYNKzqguUrV0FRTYdzitLjarXGsa0GcKvoA3PHSlS4k2tl5JodH8rIdTs+lJHVDffqhg/3F4M7sJUWnlBWSrD6li7oPGgqNuw5h7v37mL47DXQsamAhKAXeLhtMoZOXIJr9/15qb5eXLvHYVn0mt8FfYK6ihKUFRXw4l0IDh2/gLAHZ6BrVxGRL29CRkEFNx+9wfYj13Di8iO8D4miDguZdF9/FIC760Yg5O5xZKUlc/6auWIn+38HUNRlzExJxMvDS2BbrxumrD2CNTtPwcz+izPFNUXpyHx9+PUYB0uXaj84LFwqI9fs+FBGrtvxoYysbrhXN3y4vxjcga208ISyVoKVkpaGsqYR6tXwwdkgaeg5eNPpXU/7BKxf9i+OX36E8f1awEBXgzOq6sK2y8nKxMdrexAY/gA1PO3g7OgALUMbTBzVj6a2fR+bg/nXUmhd2dTqhGbaFaGQFYPY2FjExsXTzFLxialYuvU0VJSVkCWQwruzG/H+yi6ku9hCo7XPD6FPXLhmztl9faKU4rkJv7OzUErGiwMbYN+gBzRN7eDtbAOp70LCvsIUpbmnos0HtW1xseNDGVndcK9u+HB/MbgDE5fksDAPV1VyM5I+Y9vqeVi8fh/aNKqMDk18vqX+FbWqujDsyCbrfVcDsGXvaTRrVAP/zpgKbVMHmm3tb9okKzUe71/dwb7du3Dq8n1kCqSRJ6uE5NjPUFeWgyAvj2YeG9Wr6Q8bzblcN2Vtt3rXOZrV7k+2P4tLFvXcQSmKWL3nIoZ0blCo3bx1RyGtooOG/ebAw86Yk0rRfFCUZnWjLfZtwod25mLf41tf4WIdCtuOiUtyE+a08Mhp4RJE4yXE/w6mTpmMZ2+CMGlgKzjZmoLvEJHAf9cfg4K8HObNmowK1f0gIytfojYhTlBGYiSe3ruMffv249z1xzR0jFC3qis+fY5HVQ8HDO3aCNLS3I/YzMrOoatH6qq/37xeUsgaiJKCHHq1qfNH25+dlqKSmZWDPSdvFnqO45ce4vLdVzh+8jTU9SwgrqQlRCI1JhhKGvqQU1SFnKIaZOSVSpQVjcvjl6TC2oSbsHbhHlxuE39/f7i4uODVq1fg8hy2NGDhYYxiQVYdSMz85r0nceHwZkyYtRRu5SwwuHNDqCgr8q5Wc3Jyse3odZy9/hQThnVHpz6joagunDS7ZOKnrGUMH9+uqNqwE5JjgnHz0gls2b4Pxy49RNDjM5g0Yx7GL9qDOSM7QE6O292SZJjr4lcdpob8X0rPzMqGosJ/Tun3kExxO47dwLlje8XaYSEc27MWXYbMoskmdDTVoK+jDic7S7Rr1xblK9WhoaJFcWCIg54SHYSXT27j2fP3MNPMg6mdOxRUtMrkOhgMBoMhvnB7dsTgPOSNbJPOI1C1dhMsnjsF7YYvpfoaRMVcX0eD/vzy319+6mlrQE1FkRO6FrEJybj95B1VAX/zIQKNa1XEtYsnYWzrWWrlI3shNAxs0LTzSPi2H0xD7VS0TbBhuzsmjeqPITO3YMmEbpx1/MhKFIn/FQeH5etKi1IhTsumg1cwqn8HWJWv8cO/P715AtcunUPvwf9AXd+y2OcW5GYjNzOV3BSQV1anLwLKGuJkZKXEwsbSHK4O5hjYqQFc7c3xOS4Jz14HYdjYacjOmQzf2t5o1749HN2rQVFdn66D5eVmQ5CTDUFuFvJys5CeHIcdm9dg055T0NZUg7unNxq12AAjPU341qmKFq3bwsqxIhRUdTjR/xkMBoPBL5jTwhAKWsZ2mLlsJ0aNe4fYz58QHhaKT5/C8elTJKI+R+P1+3BExyUhJj4ZKWlf9g5oqKnA08WavrUnjk5ZTNBevAulmcBIOmJZGRlU93bGgN5d4VOzPr0GGbmycxZI2Jmqjhn9b6KPMX/lNiyaPgL9pmzAisk9oaOlBq6x79RtDO3mC3EhIyub6hE9ePGeKtyTsEDiVKupKiE3V4DE5BSkxobQlQYZuS/hZ69fPMaomWux/cAZzJ8+GnX8etKQqj8hyMlE0KvbePjgLl69fIU3AUGIik2kE/heHZujx/BZ385RmuQJcvHx5U1cOn8Gr/xf4+HzAFoPjWu6w93RkjrMGmrKsLc0QrvGVWkZicBnzwGjIS8nA9dylkhNy6CZ8b7+TEnLpGGD1TzLYfnknjDS16Ihe/2al0dgSBQu3X6BVp36QENVGY1qV0LHLt1h71EXaXFh1AFSIvX7XRgmg8FgMBg/w5wWhtCQlpWDtpkL/fyUDZbugcnNzqBvlnMy05CTmYKoyDAcOrAfvSesRVV3B3RvVZOuzpRG6NeFWy+w++RN6GtroGnDmhg9Ziws7N2gpGEgkjfcBSGvrImxs1ZDT3caek9ci7F9/ODj6QCucOPha3i72dKJvbhgpKdFV43Ie397KyNkZedSpzoiKh52lkY4fvY6Nu0+iYY1PDB52mwY2rijbc/RuHbjNoLCPmPM1IVw33cYM6bPgKVrdZpJriCSo4MxY9JIHL9wFx5OVrCzMoJfPS8YG2ghPSML4xfuhoKiAjr2n4T8vDwkRAYiMjwEcXExsLK2g66RBV3hkJVXKpHTnvgpAMsXzcLOwxdRw8sJDtbGaDS4NSy+SwTxM4a6mujaogb9kJW2wOBIqCorQlVFiTp45L+Jo/NVn+h7iENGnB/yGdS5IYIjYnDx9gs0bd0VW5bPxKTZS3Dz0Vt4l7dFveqe8PDwgEdFbxjbuLGQMgaDwWD8AHNaeASfM3sQx0BOQYV+lDT+b5ejjDYjlmHQsAhsWb8Cg6ZtQgUnS/RqUxvG+tolzkCVmJyGQ+fu4eSVx/Cp6IgNy+dCyaY6XkdlI89IB8qa3Kib7yH1U6X9OPTRqIA1K2fh9pO3GN69cYGOQllm8SJv0e8/e4+xfZuLLMtYaRwzKkcdsnZ1oVeIXY9Wtei1L99/B561W6Bja18M7t6GTtwb1aiARtUr4ODZu6jXrB0G92iFGu2GIyBB5lsbE0fd/95ZDBo5kWaI2798JOTlZL+VT1sxHda6GVgxpReGzdqC2/ceIfxTDF4GhNKQShkFVYRFJUBWKhdOVgZwsreEW3lXeFb0homlPWTllZEcG4Fjl+7g9vP30JZNh5p0JnJycpCVlU1/amqow8bGGvl5uVi2YS9sbO3Qe8w0uBrgr+uQOHg/hwYSu0cxRWwT6XKo39icOik9h01Bvaqu1GkhK111qrhg596DGD19OUysHfDv/H9RvZK3WGYmElc7PpSR1Q336oYP9xeDG7DsYRzOvPB99gpxV6t1MtVCQsQ7bN+0Aht2nYCjtQkaNvbF0ZSK3/Q2iqSAjnz4qTzA1YsX8eR1ENo0roF+/QfCwrkK3kall/iai9Mmxa0b5AlgFLgdD+8/wLwxnWBlql/oNRe1boprt/XwVdT3KY9cVdMyPW9Rbb/PHlZadZOeGIPwB6eR9u4a6lV2xPj+Lb7ZkNCymRvP4XlYBso1GQA1A3PMaWWHgysn4dCZmxja1ZdOyn933vTMLBw6ew+ONibwcLZGWIbKN7u8nCz4Kt5HclQQ3n6MwNsPEUhKTadCpymZucjRsoeqgSVk5RRQ2zgFhip5kJOToasfSSnpCP0US0O46jVqiBNp3mXSfn9qE5mUMDx6+RHS0lKoXdkZ6qrK1G7VOzN8vHkIsQEPsXnZdLT0a/PLChbXxi++2JXmM4Wr18wFuz/Z8ulZz9U6FLYdyx7GTdhKC08oSLm1oE4oKjthHFPbtBxGTFuNngNGYe/WNZi/eg/i0ndDy8IZWlbl8ULdAJbfaTEmp2YgJj4J18IUEfspAdnpSYh8dgWBSMGoHo2xbuNmaBmX+zbheR4cxqu6gbQM6vWeBb86JzBqxjKM6NEENb2dClVzL2hSKQw7EhJEIG/YL0WW3XlL+5h/a6ekqQ+7Bj3RoGtjNDBJ+MGG7D9q1nUAok89xbN9cyGroIxuB5RgrSuLfctG/LBSVth5iQPSrWXNAu2k5RQgpe+I1m7/Oa4CQR51dO4nm+Jc5H/hXXZGMahnVLCy86VIHeSnia4Ov7erZ6RLdYp+tiOJCaxrtIO6kQ0GDR6J3Oh3aNVzNF1V4lofZXasbtj9xe3nqLDOzeAGzGnhCZKiVkti4DUNbTFg/GJU8huAMTufICHEHwlBz7H17lPslc2Hi705Dc/JyMyGsYE28mWVEZOlBxkFFdjW6YzVw5rCw8lOLOrG3dYQTrX+gY29I3r0H4l3QZ/Qt13dMlWm33vyNoZ1/7L5vizPW9rHLK6dvUZmgXax7+4iOy0TPkPWICc9CfUUHqGZt+kvmbKEVT4i6KqmogTbfP7VYVHs9Ow8MKBif8xcvAEv/V9jwsylUNY05FwfZXasbtj9Vfr9RJR9j8EdWHgYT8LDJD3etLy5JqzUshD2wR/3bt+Es4sLHN19oKCiTbMOvfmUguch3FWUFsa5Y0JeYdDAfkhKTsOsEe0RLdAs9b0lJNMamRxXr+golOOVxO53tj+LS5ZlGckG990nbkFLXQXOXj4iqRtR7gsq7TYhq0kzVh6i+kVr16yFsZ0n78YvrtiV9jOFi9fMFbvf2fLtWc/FOhS2HQsP4ybMaeGR08IQPaJuk4zkGCyYNhIHTl3HovFdS1UvhWiYrNp5FmP6NAfX+XmCXFaQCfWa3RfQoFp5lHcQbwFKUbYJcQx3Hb+JczefYdOKefCq155qHjH4NX4xCoa1C/fgcpv4+/vDxcUFr169ApfnsKUBCw9jMHiEkroeJs3fCMdyizBkxiq6GbxyBftSOdeek7fQsVm1Ujm2OED2+uw4eh39OtSDrpa6qIsjlhANnbV7LuDT5wS6h01dVQmtuw/DvImv0b7vBMgpqoi6iAwGg8EoI7ghUMFgMIoM0epo23cS9m1diZU7z9E30MImLDKWZnUyMWCbEgvizpN3OHbxIcb2ac4cllLkws3nUJSXw6J5U7F783JUcLJGQlIqug6bg38Gtkd6UnRpnp7BYDAYHII5LQwGDyEZ0bzqtsO5k0fw2P8jVu86R0NohAE5zt5Tt9GJrbIUWDfESYxJSMaQro3oPgtG6WGor4WwqDhs27Yd8xctgVM5e5zdNR/N61bEsm2ncWjrMuTl5iAnM5U1A4PBYIg5nHRasrKyRF0EBoMXkE3JBw4cQlhUPJZuPS0Ux+Xag9eo6uFARRAZ/5GWnonFm0/CrZwF/Op6saopA3w8HFDJzQ6GuurwdrXBlRv3MHr6SkwdPxI3TmyBT+1GmDG6G+SV1LBx7kCkxAQLzXlnMBgMBrfm68V2WlJTUzFt2jQ0atQI2traNK3ntm3bfrHr0aMH/e7nT7ly5X6xvXDhAkxNTaGiooLmzZsjMTHxh+8tLS3p3w4dOrTATVPku0OHDkFcIVku9twMpD+5aMeHMopj3UTm6WLPvoNITEnH7NWHkZ2T+0s2JqLPQX7+DvL9+XBNXHkcgmqev/bP4hxPmHaiPPdV/3iMXXsVdRv5wtXBnHPl40MdFseOOM5kxa9Vg0qoVckZ04a2RfdWNdGx7xiEJQP3kozgXK05LE310G/SOugY22L59EGIDfVHfn4eZ/qopNjxoYysbrhXN3y4v/hMqhjN14v9KjU2NhYzZ86Eubk53NzcaCEKQ0FBAZs2bfrh3zQ0NH6p1I4dO2LQoEHw9PTE0qVLMXHiRKxZs+aX423cuBETJkyAsbExJAWmVss9hV6uKfnu2HMQU/4Zgm5jV2HmiPawtzT6UYk88s/K5nFBr5CvU5P+/kcF9CIcT1h2ojo3eWu/+fRznPsoB4uaw7AnWhpammVzzeJSh8K2q+HlhHQZLQwYOxuV+i1BzJtnsLe3g4KcHNUxOnLmGlZvP4ZOLeqgfqeRmH3hM2f6qDjb8aGMrG64Vzd8uL/4TqwYzdeLvdJiZGSEyMhIhISEYOHChb+1lZWVRZcuXX74NGvW7Aebt2/f0gqdNWsWWrRogRUrVuDmzV83GJP0bgKBAP/++y8kiYKUW7lkx4cyinvdqOmaY9HafZg/bQQmLtqDy3deFqhEXhBf7eSU1ZAW/+mPdkU9nrDsRHHu9IwsGnKXo2IMy2qtaYrdsrxmcajD0rKTNXKBrq0n7q8fhbBHF2BfsSZ6tP6SnpSsOG6aOwCBH0IwcdIkTvVRcbbjQxlZ3XCvbvhwf/EdIzGarxfbaSHemKHhF3XiokAKnpycXOj3pAICAwNx8OBBfPjwgVaCnd2vquZkyalbt27Ue/v06RMkha/KrYSiKLyWtR0fyigJdSOroIwmnUfh2L4t2HHsOgLunqMK5NTuT0rkyIeaAVnSlUb2p5e/tSvq8YRlV9bnDgiOxIodZ9G9ZU34elmI5Jr5XoelbWdXryvcu0xDxW7T0cDDFE1qeeDOgdnYMm8gtDVUMbZvc4S8fYns1ARO9VFxteNDGVndcK9u+HB/8R0FMZqvC0Vc8tGjR/Dy8sLWrVtpTNz3kN937NgBJSUlpKenQ0tLiy4rzZ8/H6qqqj/YLlq0COPGjUNeXh4sLCxozJy9vf0PFUAEdUgFOTg4YODAgfS/CWS5q3bt2rQS27RpIxbCPHxTyeVDGUWlKC2Ka4kIeIiOnbvDwckZTtWbFFmJ3EY1DUcPHkIXv+rQ19HghZq7MNTXLVTS8cQ/CDcevoaRvhbaNqoCGRlpkV5zWdahsO2E0SYltTt07h78w1LQYsRSuNsYcK6PlrVdaT9TuHjNXLH7nS3fnvVcrENh24mruOQjns3Xy9xpIbFs5BQeHh704s6dO4ft27fDx8eHFpwsRX1PREQE9chcXV2hqKj4w3dfK+HUqVPo1asX9u7di48fP9KlL0lwWhiih29tEvnhGfxataHZrkia2KKSkZlNVxrG9fODJKivv3wXivM3n8HTxZrumfjqrDBE1ybCICcnFx1GLkf7ZjUxYuxU6Jg5QpLh2/glKbB24R6S6LRM4OB8/WdKPafpvHnzfvi9Q4cO1BubNGkSzRxAfv8eExMT+vkTkydPxs6dO2ms3PLly4VebgZDHDCyqYD9u7bCr20XqKkoonZllz/+DRm0Lt5+QTfySwpnrj/FP32b04wmDH5D7t+wyDh8jk2EirIiZo/sgCMX7qNuA1+sXToblRt2Zu3MYDAYPJyvi0SIYeTIkZgyZQouXbr0SyUUFWtra3Tt2hUbNmzA+PHji3WMBw8eICYmBlwlODj4t1keGGUPX9uEDBoHj5xErnwUTAy0fmsbGZ2IfCU9OFgY0rflfCAmRVDssqakZcK2nCvefMoWerkkmZK0SXF5/jYEWdk50FBThqqyKmLic+nvduXKI19RDzsOnUOmvDEVZ5VE+Dp+iTusXbgHl9skKChI4ubrInVaSLycjo4O4uNLlhebeH/EeyPxdiSDwd/i7e3NwsMYYrNk/Dvy82vCykgdnXqPwKwR7eFsZ1aobeiHSLhaGcFaxKE9ZRWKdOzic3i7WMPMiD/XywfKOjyM7F+xM9VDRRf7bysu/oFhsDY3wtV7/gj98BqL9++Gqb3kCoPydfwSd1i7cA8ut4menp7EzddF6rSkpKTQvNElrXgbGxuajm39+vWoVKmS0MrHYIgbJOypYp122LQiBb2HTsbiCd1gbWZQoG10XBK8yttAUvgQ9hl+9SR3IstXBII83Hz0hq6u5AoEcHe0QkUXm28Oy/hFexAVkwhtTVUEh0fj6J4NEu2wMBgMBt/n66W6Rp6ZmUkv+GdIbmfyUCHqnMIIe8nJycGCBQtKfCwGQ9wdlxpNe2H53H8wet4OhH6KLdAuISkNOppqkBRkpWUgyPuins4QPuQ+I/eUsCDPjqv3/bF4y0moqihiWDdfjO7VDHWq/Ldfi6ywxCemYuXsEVSvqJ5PeTh5NxRaGRgMBkOcyOTJfL1EKy2rVq1CYmLit/zLJ0+eRHh4OP3voUOHIiEhAe7u7jRlWrly5ei/nz9/HmfOnKEV4OdX8sxEX703kuGAwWD8HhLL79thGLIzszB85nKsnNoLpoY/5qaXlZWseP9KFWzx6OUHVK7wX7pGRskhe6POnHsAC2NdZGblICUtAwM7NfirTfDkYbl+30W6qkKEFPLz8pGXnw8vVxv807fw54edpRESk9Owde9xNKvjicnT50BGjoX/MRgMyWSVmMzXS+S0kDzNRGHzK0eOHKEfAimYpqYmmjZtiosXL9JCEsEaW1tbzJ07F2PGjIG0kDZDEu9t165d9PgMBuP3SMvIwq/HGOTk5mLYrDVYPa031SX5CpkUksmipGTScneywrbD15jTIkTO3XiGdCkNjOrZ5Ns4f/95IN130ta3SoF/Q0K8klMz8NQ/CA9ffoCKsgKyc3JRo6IjvMrb0hScRX1mKMjLUYf80asPuHT7BZSVlIV5eQwGg8ErFonJfF22pNkV/gTZeCMsCjsfqdjc3FyhnYfBEHekZeTQps945AkEGDJzA1ZP7w1DXU36nZysDNVpUVaSjDfTZIKbk8teeAgLEgpG9gnVruX4w4OukpsddUIWbTqBlg286Z6qr47x2etP8fZjBFWyT0hOw+jezSAjLUUdaFkZGWrztw9NQz1NNK3tict3X+HhnWto2J67mlwMBoNRmgSLyXxdJBvxGQwGNxyXtv0mIleQi6EztlDHhWxaliSH5SsSsqhUJoRFxcLJpuDc/dUrOlIBz8t3XlFtHCnyPynA1cEcI3s2/cVeGO/2qrrb4+Kly2jQbpDErB4yGAyGOMKcFgZDgpGRlUfHgVNpqNiQGZtp6E41zy/xrJJAWnomNh+6igqOFqIuilhAREkfv/pIs7HlF2KjrKhA95mUFVXc7TFj5SFkpcZDUe3H/VsMBoPB4A+SteOWwWAU6Lh0GzIDk0f1xqYDl5GeyQ9ByZLyMewzVu06j07NfFDDy0nUxeE9J688hqyMNMb184NjISstooAkmohNSEZ02FtRF4XBYDAYJYA5LQwGg2ZW6jRwOs4c2oLtR65j+fYzdGO0uEIcs/2n72B076bQ1VIXdXF4D9kk/yE0CrUru/yvvfsAj6pa1wD8pRdIIyEkAUJCSQKhlwCJ0qQKooAognRBsQCKekQe9BxAvHI9xwMIIkVBmkjnIkXAIEgNCNJLIMTQa0go6fs+/zp35k4SAklImD0z3+szJrP3npnNWrMy+5+1/rWgxyFYMiQtdvMGc58GERE9BgYtRGScDrl+i27YuGGd3MHrY2eqb6itJUiRRQiXrt+FaQs2YML0FejRoakxyZsez8mES4isUVm3xRjdIBy/bv0dOdmcsIWIyFIxaCGiXMpVDMf075dhcO+uGDLmW7WGiaX3Akz8ZiVSUu/h6SY18WafDujfrQV2HTilepTmr9qmAhqZklemeqaiu516F14e+p1WuEndatj75yncu/WfNQqIiMjyMBGfiPJxdvfCkPcnoV69hhg6cgxaN43EoBdbl9hc7U+SLExYKaCcClgMwkMrqpu4kZyKI6eSVMAyadYavNmnPTzKuJnxjC2Pm6sLjp1OQlTd6tAjqU+ZbvnWtSSU9Qs29+kQEVExWN4VCBE9sUUom3V8FZvW/w+u3EzF8PHf43bqPYsrfScnR6RnFDwsyNfbAy2jaqFV00i88Uo7zFi0iT0uhRS7+wj+9d1axCdexkvPRkOvLl9PVoGLb0CouU+FiIiKiUELET1UhdC6mLd4NV7o2BIDP5qOuEPxFlVi126m4Mr1ZDVM7FFkiFPbmDqY8sN63LmX9kTOzxJJWc5cshlZ2Tl4b1AXlR/k4KDfjxPJY+rTvT3cvAPNfSpERFRMHB5GRI/kUrYcRnw6BU2aNsWojz/D2q1/4L2BXXSdx5By5x6+WxarVll//7WuhR7a1qBWKIKD/DBj0S9oEVVLt0OezGn2T7+q4XZ6mtq4ILJuzMWrtzDs3bG6nNmMiIgKR79fjRGRrtg7OOGpzgOwZcuvaFw/Ev0//BprY/ernBE99gR8PX8DBr3YBv26tVSBS1HIkLFRg5/DgaMJOHmWydumfvx5B+pGBFtEwJKWnokvZq7C5598gLJ++p3djIiIHo1BCxEViVz8fTRxJlb/OAcHjp3DyyO+wspf9iIzUx/TyUpC/cI1v6PrM43hWbb4CfXyrfygnm3w54lzmDx3nUrYt2USnM5Z+iv8y3mhWf0wWIKvF2xA5zZRaPlcP3OfChERPSYODyOiYiXp147ugoUrWuPo3o2YPGUaeg7/F3p0bKbyG9xdXZ54qW7YdhDHz5yHg4MDmtarjtphjz9LlJOjg0owv3svTU2P3DamLupFVIGLsxMsnQR3qzfH4dS5S2jTrDYa16lW4LGJF65h8dodqm5rhFhGXsi8FVsRf+4SJk+bDUdnzgZHRGTpGLQQUbE5uZZB/RbdMbv5szhzaDumTpmCV0ZORv2aIYhuGKa+kS/tvJfMrGzs+TMe4QFueHdgl1J5jTLurvjb0Bew++BpzFv5mxp25ObqjAHdW6nAJm8uzS+/H8L99Ax0alEffj6e0Bsps8lzf0a7mLp4vm0T/PfsNWq4l/w78866tXDNdlQO8MXw/p3MEowWR+yeo9hzKB7Ll/0E70DmJBERWQMGLUT02BycXBHWqB0mf9cKYxIOIXbzemz5datKhHd2clTJ7U5ODsjJ0QANyNFykKNpkLUctRwN8p/aJ0n/zo5qdfW6EVUQ4Of90NeVHhCZ6at169ZoFu5duv9GB3vENApXN7E97jiOnk5SAZo4cfaC6u2RIWlto+uqoGbO0li8+vzTqFihHPSU7zP1h/Xo2ak5qlQsr7YN7tkG0xZuRHTDcDzVKEJtu5eWroaDvT/4OYvrWZJ6GDlsAAKq1jP3qRARUQlh0EJEJZqsH1C9EV6p3ggvDx2tViA/c3Qfdu/eqYISmcFLckX+89Me9vbyux3s7B1gL9vt7HA7JRV/HDyIH9fuQFpGJnp1jkHHFvVV8GNKFguUIVvDenfApTtP/k9ZaGV/7D0Ur4KUlZv2onpwAN7u2xGODv/f8/LuwM6YOn89PNzdkJmdrbbJ/FX+vl7o3j7KLIt1zl+1TZWnIWAxTDzwwWtdEbv7KL6c8z/o+3wLFWSGVvK3uIBFcqtkmGDrdqXT60ZERObBoIWISoW9vQPK+lZGvRZy61akx2paDtJSr+PQ7s34dtYcvPjOP9E+ph5eera5uuAX3y37FQN7tIaPVxlcupP+xGuxUoAvfo79A3sOnsbwvp3UIpZ5SaA1atBzqtfCdGjVsfjzmDRrjQoUnuT6JjL9r2dZd9SqXinfPgkm2zSvjWb1a2DB6u1qeNuzLRvA0siwsMa1q8ErgMPCiIisCYMWItId6YVx8/RH0/a90bh1D5w/vR/z5szA2+O+Q0jF8uj93FNqNqtAfx+znufrr7Qr1HF5c0EkaJAAIjnlLnx9PEosT+X6zRRcv5WKu/fT4OVRRgV0Mt2zBE/Xb6WoFexlKueHnqubC4b2agtLFbvrCDp3fAYOTpaRf0NERIXDoIWIdE0uPqvUisbYL5tj5IcJWLt8PubMX4aTCRdQOdBPDXWyRMmpJRewSAA3Yfpy1I8IgZ+Phwo8Ll29pXJubqXcVUOmZJsEWda8wKLMiLbvyFlMnjLV3KdCREQljEELEVkEudj2rFAVvd/8FD36D8fBHeuNQ8d6vfgCwgNqPNGhVo8jKzu7RPNZzl++gUaRVdXaNLbs2s0UFbT5BPxncgQiIrIelvEJT0RkwqWMjxo6Nmvxevy2cSXup2fi9bEz1UWrJThx5gJqleCK8pKnknr3Pmzd6XOX1Toy8v4gIiLrwqCFiCyWg6MzgmvF4LluL+O1Ps9jyJgZ2L7vuBompGcHjyeifs3QEns+b093lR9j604nXkLN8OpqNjoiIrIuHB5GRFaxTszg979Aw8ZN8dHY8WqxRB/PsggLDUR4aJD69r16cIV8iyeay+3UuypJvqRYc55KUZw+dwmDWrQy92kQEVEpYNBCRFYzxXKjNj3xc7P2uH/7CpISTuHPA/tx6PAR/LRuJ878dQX30zLg7uaMIP9yasHHiGoV1arweVe1L211woMxb8VW9O/OC+ySFJ94GXXrWd40zURE9GgMWojIqji7e6mbV2AYakd3QR+ZXSszHel3biLj/m3cvHoRZ8/E41zCWfy2YzdmLdmMlzo1xwvtotQq9k9C5QA/NeVxSXJydFQLbuZdhNNWyOQG6RmZ8AtkEj4RkTWyzU83IrK5aZPdfQLVzTsoAlXrt1HbB45Iw6kDW/H11CnoNfLfeK5NI7zcORoeZdxK7VzG/GsxEi9cU+ulSM/AH0dlvZZ7aN4gDJE1KhV7VrHgID+cu3ANYSGBsEVJF2+oKbBdPf3MfSpERFQKmIhPRDadC1MzqiOmfr8av65bChdXd7w6aiomz12HG7dSS+U1D51IRL9uLbF47Q5MnrcO6VlAUFBFzF+1Db3enVzs55UFK4+fOQ9bTsIPq1oJTq4ls/YNERHpC3taiMjm2Ts6IaROC/zX9GiMPHMQs7+dgtfGzEDjOtXQPqYu6tcMgcxHNuWH9WodkMDy3mjVNBIuzk5FLjt/Py/EtOqIEX+fgZycLDi7eartA0/tQlSrh69W/yAya1jC+atIunQDmZnZNluXKXfuw8fLk5MSEBFZKQYtRET/x97BEUFhjTH2y7l4c8RxLF34HdbG/o7x05cjONAP2+KO5yqrXp2j0a19U0RUDSrUxfKqTXvVz2o1G8DRxT33H2NXDxUEJV26roY5PUp2dg6+nr8B5XzKomqlCmqWtOpVAmy2LisF+GLLrsPQcrI55TERkRVi0EJElIednT38giMxbPQ/MfT9NKReS0TcrlhsWPcztscdR9zhM+q4VZvjcPn6bdXTUTnAF5FhlVEnrDLCQoNgBztkZGYiLT0TV27cxp4/T+NY/Hn8tGgeygfXzFfmvpUj8cmowfhi5gp8/engR9bJ3kPxaFK3GqIbhrP+VE6PL5IuXkfG/RQuLklEZIUYtBARPSLvxTsoHO16hKNt99eRfucGLicex5Z1yxH1VFuE1YvB3ZsXcfzIH9izezd2HzyC+au2w97eTk2lLL0nFfy8UKNqKL7893T4Va71wNeRnpoX+72DGd8vVQFJVN3qD60XCZQ6teD0vgYVfL1x+XoyMu4mM2ghIrJCDFqIiApJAgtXDz+E1H4ag2s/bdzuUsYHMZUjEdOpL3JyspFxL1n11sjK7Pb2jnBwdnvo8DEtJ0eeHG5eFTDx7x/inQ/HqaFen7z1IhwcHjxfyvVbqfAsW3qznFkaKScp49Tka/DwDzX36RARUQnj7GFERCX5R9XeAa5lfVUgI0n2krvysIDl/Kk4DH6pHT4Y2l312LTsOhhbN/2ME2cu4M69tAc+5mzSFZRxcykwoLFVsmDo2fiT5j4NIiIqBTbd0xIfHw89S0hIQPny5c19GmSCdaJPllovicf2YNRHnyCqXnXsO3QKrw/qg/c+Ho/srAzcuZuGbXHHkJWdg/T0TKRlZKqfN5LvSKcMenRoqtZ50aukqxlwznoyi3UaSM/Tb9t2wKdKwyf6upbCUtuJtWO96I+e6yRe59eupclO0zSZydOmXLx4EREREUhNLZ11GIiIiIiISoOHhwdOnDiBoKAgmypgmwxaDIHLrVu3zH0aRERERESF5uPjY3MBi00HLUREREREZBmYxUlERERERLrGoIWIiIiIiHSNQQsREREREekagxYiIiIiItI1Bi1ERERERKRrDFqIiIiIiEjXGLRYqIyMDHOfgs2SWcJZ/vrDetEX1of+sE70ifWiP6wTfWLQUgx37tzBp59+io4dO6JcuXKws7PD3LlzH3js8ePH1XFly5ZVx/bt2xfXrl3Ld1xOTg4mTZqE0NBQuLq6om7duli8eHG+49LS0tC7d2+UKVMGFSpUwE8//WTcJ7/LuaxcuTLf4+rVq6f2xcbG5tsXHByM6OhoWJv9+/ersvf09FSrx7Zv3x4HDx7Md1yrVq1U2eS9yWPzWrBgAfz8/NTzDRw40Bi8ZGdnq9d5/vnn8z3mq6++Us/Xv3//fPs++eQTte/UqVOwFYWtF7Fz50489dRTcHd3R0BAAIYPH67aX16sl8dz+vRp9OrVC5UqVVJlHRERgXHjxuHevXvGY9hO9Fcngm3kyRkwYMADPysMtwsXLqjj2Fb0VyeCbcUKyOKSVDQJCQmyIKcWHBystWrVSv3+/fff5zsuKSlJ8/Pz06pVq6ZNnjxZ++yzzzQfHx+tXr16Wnp6eq5jP/roI/U8Q4YM0WbOnKl17txZ3V+8eHGu4yZMmKDVrl1bW7p0qfbll19qHh4eWmJiotp34cIF9Zj33nsv12Nu376t2dvba46Ojtr48eNz7fvrr7/UYz744AOrehvs379fc3V11WrUqKHKadKkSVpISIjm6empnThxItexLVu21CpVqqTNnz8/123Lli356r1s2bLq+aT8pR6++OIL4/527dqp+s6rR48equzlfZBXmzZtNH9/f81WFKVeDhw4oI5t0KCB9s0332hjxozRXFxctI4dO+Y6jvXyeORvgLe3t1alShXt888/17799lttwIAB6u9C165djcexneivTthGnqydO3fm+5z44YcfNHd3d61WrVrG49hW9FcnbCvWgUFLMaSlpWmXLl1Sv8fFxRUYtAwbNkxzc3MzBhVi06ZN6nj5EDI4f/685uTkpL311lvGbTk5OdrTTz+tLqazsrKM2yWYWbVqlfH+Cy+8oC1btsx4PzQ0VIuKisp1Hhs2bNDs7Oy0V155RevQoUOufYsWLVLns3r1as2aPPvssypAvH79unHbxYsXVdDRvXv3XMfKB0xkZOQjn1MCFSlvA6mHLl26GO//4x//UGV57NixXI8LCAjQevfurfYZ3jciMzNTK1OmjNatWzfNVhSlXjp16qQFBgaqoNtg1qxZqhw3btxo3MZ6eTzyZYqU6ZEjR3Jt79evn9p+8+ZNdZ/tRH91wjZiftu3b1d1InVmwLaivzphW7EOHB5WDC4uLmqoyqMsX74cXbp0UcOvDNq2bYuwsLBcw7pWr16NzMxMvPnmm8Zt0q05bNgwnD9/Hrt27TJur1q1KmbOnImTJ09i06ZN2LZtG6pXr27cL0NpDhw4gPv37xu37dixA5GRkejUqRN2796thqKZ7pPXiomJgTXZvn27KmtfX1/jtsDAQLRs2RJr16594BCjrKysB243LXspbyl3KX+phxo1auQqe0OZGpw9exaXL1/G22+/rYb9me6TIVF37941Ps4WFLZeUlJSVDm/+uqrahiZQb9+/dRQS9P2w3p5PFLWQoabmpJ6sbe3h7Ozc67tbCf6qBO2EX1YtGiR+gyVYdt5sa3oo07YVqwHg5ZSIuMor169isaNG+fbFxUVpQILA/ldclRq1qyZ7zjDfoPRo0fjzJkzanyz5AIMHTpU5asYyAWwBEB79uwxbpMLZclZkdvt27dx5MiRXPvkuUwvIq1Beno63Nzc8m2XseGSh2JaBkJySqQOJMdCAtKxY8eqcjTVsGFD9OnTR5W7lJkElFIfBs2aNYOjoyN+//33XOUrz9ukSRP1XjANWgy/21LQUth6OXz4sPrAz9t+5GKtfv36udoE6+XxyPh7MXjwYBVIJyUlYcmSJfjmm29UDpG8fw3YTvRTJ2wj5iefEfIFiny2hoSE5NrHtqKfOmFbsR6O5j4Ba3Xp0iXjN2N5ybabN2+qCzjptZFj5Rs1+WYg73Hi4sWLubYdOnRINUJJCK9SpUquxxgugOXCWT745MJPAhhJAq9WrZp6Hdknif6pqanqeQYNGgRrEx4ernqVJEHewcFBbZOLYkMwZ5qcJ+XSunVr1KlTR/V8LFu2DBMmTFAfOnKhYGrKlCkYOXIkkpOTVRlKkGJ64d2gQYN8QYsEn3Kc/BE1nQhBjpPHyEW3rShsvTyq/UiPjSnWS/HJpAjjx4/HxIkTsWbNGuP2MWPGqHZgwHairzphGzG/jRs34saNG+rLLFNsK/qqE7YVK2Lu8WmWrqCclm3btqntS5YsyfeYsWPHqn23bt0yJmPXrFkz33HZ2dnquBEjRhT6fCQXxtfX15i7sm/fPvUcp0+fVvclf0LyK4TkBci+efPmadZGErfl39a/f3/t6NGj2uHDh7WXX35Z5Q7JdknWexiZEEGO27VrV5Fe9913382VuyLJ+pJALiRvSBLy7969q+5Lvkbr1q01W1LYepFESrm/Z8+efM/Rt29fzcvLq0ivy3p5OCl3+Zshk4AsX75cGzRokMqDmzp16kMfx3ZivjphGzE/yROVv12mOXoFYVsxX52wrVgPDg8rJYYhMNKb8qBpi02PkZ+FOa4wpLdGvtE35K7IN/3+/v7GvBfZZxiWZM3Dk9544w18/PHHamyr5PNIL4oMq/vwww/VfsmLeJhRo0apn5s3by7S65rmtUhvzNGjR435QlL20vO1d+9eJCQkqG9/rLHsS6JeHtV+itImBOulYD/++KMaZjp79mwMGTIE3bt3x5w5c1Tv7N/+9jf1rWVB2E7MVydsI+Yl+XeSj9qhQ4dCDa9mWzFfnbCtWA8GLaXEMKzF0C1pSrbJmi0yNMxwrCRry2xueY8TQUFBRb5Ak9wVGfplyGcxkN8TExPVMBwZniTPLYnM1uizzz7DlStX1FAiGVIXFxdnnIRAJkN4mMqVK6ufMoyvKEyH5xkmUGjevLn6KcP5JHFf9hmGkNla0FLYenlU+ylOmxCsl/ymT5+uhjXKeiCmunbtqtYEMc0fyovtxHx1wjZiXqtWrVJ1kXdoWEHYVsxXJ2wr1oNBSympWLEiypcvj3379uXbJ9+0SzKxgfwuDU0WojRlGOdvemxRL9AkaDGdGaxRo0YqWNq6dat6fmubNSwvHx8fVR7yjb6h50QuBCSR/mFk1i8hdVgU0qtlCEyk7GvVqgVvb2/jfkNPl9wkp8MQ0NiaR9VL7dq1VR5Q3vYj+S+SmFzUNsF6KZgEkJJjlJdhIgrpHSwI24n56oRtxLwWLlyoeoYlkCwMthXz1QnbivVg0FKKevTooaZxlZlfDLZs2aISvHv27GncJquoOzk5qW/XDKTXZcaMGSr4Kepq9TLjkkyvKw1YelRMHy8BiyR+T5s2zeam25WkevlWXxLpZdpQw1SIeYcgSdkbkl2lm7mopEzlwvqXX37JV3dyX3pgpJdBEvlltjJb96B68fLyUlMjy0r3MmGEwfz589UQANP2U1islweT3i355l7+LplavHixqg95n7Kd6K9O2EbM59q1a+qLlm7duqnJVEyxreivTthWrIi5k2oslSRDyurysoCkFKMsjCf35ZacnGxc1ViS4mUl9ClTpmgTJ05UC+vVqVNHLVBpSlakl+cZOnSoWkBPFpGU+wsXLizW+cnClPJ4WUE872uNGjVK7ZObrFBujX777TftmWeeUSvWz549W3vttdc0BwcHtZq6LOpoEBsbqxZ/lETtadOmqVXaY2JijHVRHHPmzDGW79y5c3Ptk8Rzw7533nlHszWFrRch7015/zZo0EAl8MuEBq6urlr79u2L9dqsl4LrROrA399fGzdunGoHshCbvEelfgTbif7qRLCNmO/zX+pCFm7Oi21Ff3Ui2FasA4OWYqpSpYrx4jPvLSEhwXicrGgsF1nu7u6at7e31qdPH+3y5csPnClMghp5XmdnZ7VC+4IFC4pdsaNHj1bnEh0dnW/fihUr1D4PDw8tKytLs0bx8fGq3P38/NSFb0REhPb5559r6enpuY47e/as1rNnTy0kJERdEEs9NWrUSJsxY4aaia04Tp48aXwvnDp1Ktc+eU55HxQ0s5y1K2y9mK5sLO9hqZvy5ctrb731lpaSklKs12a9FExmaZOLYgngZeadsLAwtZq0IZBkO9FfnRiwjTx5zZo1UwHlgz4/2Vb0VycGbCuWz07+Z+7eHiIiIiIiooIwp4WIiIiIiHSNQQsREREREekagxYiIiIiItI1Bi1ERERERKRrDFqIiIiIiEjXGLQQEREREZGuMWghIiIiIiJdY9BCRERERES6xqCFiIiIiIh0jUELERERERHpGoMWIiIiIiLSNQYtRERERESkawxaiIiIiIhI1xi0EBERERER9Ox/Ad1v5czYg8zSAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig = plt.figure(figsize=(9, 5))\n", "ax = fig.add_subplot(111, projection=ccrs.PlateCarree())\n", @@ -142,9 +172,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GEBCO file variables: ['crs', 'elevation']\n", + "GEBCO file coords: ['lon', 'lat']\n", + "\n", + "First variable shape: ()\n" + ] + } + ], "source": [ "# Check GEBCO file exists before proceeding\n", "gebco_path = Path(GEBCO_PATH)\n", @@ -164,9 +205,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Slicing GEBCO to domain (may take a moment for large files)...\n", + "Sliced source shape: (5040, 7440) (37,497,600 pixels)\n", + "Source lon: 259.502 – 290.498\n", + "Source lat: 14.502 – 35.498\n", + "Depth range: -5527.0 – 7432.0 m\n" + ] + } + ], "source": [ "# Adjust coordinate and variable names to match your GEBCO file\n", "# Standard GEBCO 2023: lon='lon', lat='lat', elevation='elevation'\n", @@ -196,9 +249,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig = plt.figure(figsize=(10, 5))\n", "ax = fig.add_subplot(111, projection=ccrs.PlateCarree())\n", @@ -232,9 +296,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "==========================================================\n", + " Resolution Diagnostics\n", + "==========================================================\n", + "\n", + " Source dataset (GEBCO_2024.nc):\n", + " dlon = 15.0 arcsec (0.004167°)\n", + " dlat = 15.0 arcsec (0.004167°)\n", + " dx ~ 420 m (at domain mid-lat 25.0°)\n", + " dy ~ 463 m\n", + "\n", + " Model grid (T-cell spacing):\n", + " median = 53.03 km\n", + " min = 50.51 km\n", + " max = 54.69 km\n", + "\n", + " Resolution ratio (model dx / dataset dx):\n", + " median = 126.3x\n", + " max = 130.3x\n", + "\n", + " Cressman / stats-mask threshold: 12x\n", + " → RECOMMENDED: high_res_regrid() (Cressman + stats mask)\n", + " Each model cell spans ~126 dataset pixels per side.\n", + " Ocean-aware Cressman interpolation will meaningfully reduce\n", + " land contamination of coastal depth estimates.\n", + "==========================================================\n", + "diagnose_resolution → high_res_regrid (Cressman)\n" + ] + } + ], "source": [ "use_high_res = topo.diagnose_resolution(src)\n", "pipeline = \"high_res_regrid (Cressman)\" if use_high_res else \"direct_xesmf_regrid\"\n", @@ -252,9 +349,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generating ocean_frac mask (5×5 sub-points)...\n", + "\n", + "Mask: 1557/2400 ocean cells (64.9%)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/glade/u/home/manishrv/documents/croc/regional_mom_workflows/mom6_forge/mom6_forge/topo.py:768: RuntimeWarning: Mean of empty slice\n", + " D_mean = np.nanmean(depth_ocean, axis=(-2, -1))\n", + "/glade/u/home/manishrv/documents/croc/regional_mom_workflows/mom6_forge/mom6_forge/topo.py:769: RuntimeWarning: All-NaN slice encountered\n", + " D_min = np.nanmin(depth_ocean, axis=(-2, -1))\n", + "/glade/u/home/manishrv/documents/croc/regional_mom_workflows/mom6_forge/mom6_forge/topo.py:770: RuntimeWarning: All-NaN slice encountered\n", + " D_max = np.nanmax(depth_ocean, axis=(-2, -1))\n", + "/glade/u/home/manishrv/documents/croc/regional_mom_workflows/mom6_forge/mom6_forge/topo.py:771: RuntimeWarning: Mean of empty slice\n", + " D2_mean = np.nanmean(depth_ocean**2, axis=(-2, -1))\n" + ] + } + ], "source": [ "if MASK_METHOD == \"ocean_frac\":\n", " print(f\"Generating ocean_frac mask ({NX_SUB}×{NY_SUB} sub-points)...\")\n", @@ -279,9 +400,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "if mask is not None:\n", " fig = plt.figure(figsize=(10, 5))\n", @@ -311,9 +443,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running high_res_regrid (Cressman)...\n", + "Computing Cressman weights…\n", + "Cressman weights written to /tmp/mom6_forge_output/cressman_weights.nc\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/glade/work/manishrv/conda-envs/mom6_forge/lib/python3.14/site-packages/xesmf/backend.py:42: UserWarning: Input array is not F_CONTIGUOUS. Will affect performance.\n", + " warnings.warn('Input array is not F_CONTIGUOUS. ' 'Will affect performance.')\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tidy bathymetry: Reading in regridded bathymetry to fix up metadata...done. Filling in inland lakes and channels... Pipeline complete.\n" + ] + } + ], "source": [ "weights_path = output_dir / \"cressman_weights.nc\"\n", "\n", @@ -354,9 +511,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== Depth statistics ===\n", + "Grid size: 40 × 60 = 2,400 cells\n", + "Ocean cells: 1,548 (64.5%)\n", + "Land cells: 852\n", + "Min ocean depth: 5.65 m\n", + "Max ocean depth: 5562.39 m\n", + "Mean ocean depth: 2375.7 m\n", + "Median ocean depth:2295.6 m\n" + ] + } + ], "source": [ "depth = topo.depth.values\n", "ocean_cells = depth > 0\n", @@ -373,9 +545,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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<xarray.Dataset> Size: 86kB\n",
+       "Dimensions:  (ny: 40, nx: 60)\n",
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+       "Data variables:\n",
+       "    y        (ny, nx) float64 19kB ...\n",
+       "    x        (ny, nx) float64 19kB ...\n",
+       "    mask     (ny, nx) int32 10kB ...\n",
+       "    depth    (ny, nx) float64 19kB ...\n",
+       "    h2       (ny, nx) float64 19kB ...\n",
+       "Attributes:\n",
+       "    date_created:  2026-04-15T13:48:59.059395\n",
+       "    title:         MOM6 topography file\n",
+       "    min_depth:     5.0\n",
+       "    max_depth:     5562.394952327731
" + ], + "text/plain": [ + " Size: 86kB\n", + "Dimensions: (ny: 40, nx: 60)\n", + "Dimensions without coordinates: ny, nx\n", + "Data variables:\n", + " y (ny, nx) float64 19kB ...\n", + " x (ny, nx) float64 19kB ...\n", + " mask (ny, nx) int32 10kB ...\n", + " depth (ny, nx) float64 19kB ...\n", + " h2 (ny, nx) float64 19kB ...\n", + "Attributes:\n", + " date_created: 2026-04-15T13:48:59.059395\n", + " title: MOM6 topography file\n", + " min_depth: 5.0\n", + " max_depth: 5562.394952327731" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "topog_path = output_dir / \"topog.nc\"\n", "topo.write_topo(topog_path)\n", @@ -443,9 +1217,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Generate cartopy mask for comparison\n", "mask_cartopy = topo.generate_mask_cartopy(resolution=\"10m\")\n", @@ -493,9 +1278,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "h² range: nan – nan m²\n" + ] + } + ], "source": [ "if \"h2\" in ds_out:\n", " fig = plt.figure(figsize=(9, 5))\n", @@ -574,8 +1377,16 @@ "name": "python3" }, "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", "name": "python", - "version": "3.10.0" + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.14.3" } }, "nbformat": 4, diff --git a/notebooks/7_regional_bathy_workflow.ipynb b/notebooks/7_regional_bathy_workflow.ipynb deleted file mode 100644 index 9d8336d6..00000000 --- a/notebooks/7_regional_bathy_workflow.ipynb +++ /dev/null @@ -1,314 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 7. Regional Bathymetry Workflows\n", - "\n", - "This notebook demonstrates the two regional bathymetry pipelines in `mom6_forge`:\n", - "\n", - "| Pipeline | Method | Best for |\n", - "|---|---|---|\n", - "| **A** | `direct_xesmf_regrid` | Fine grids (< 5 km), fast runs |\n", - "| **B** | `high_res_regrid` | Coarser grids (≳ 0.05°), topo drag |\n", - "\n", - "Both pipelines support an external land/ocean mask. Two mask generation methods are available:\n", - "- `generate_mask_ocean_frac` — Monte-Carlo ocean fraction from raw GEBCO\n", - "- `generate_mask_cartopy` — Cartopy Natural Earth coastline rasterisation\n", - "\n", - "> **Note:** This notebook documents the intended API for methods currently under development on the `global_workflow_convert` branch. Cells marked `[PROPOSED API]` will raise `AttributeError` until the implementation is merged." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from mom6_forge.grid import Grid\n", - "from mom6_forge.topo import Topo\n", - "import matplotlib.pyplot as plt\n", - "import xarray as xr\n", - "import numpy as np" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create a test grid over the Gulf of Mexico at 0.25° resolution.\n", - "This resolution (~25 km) is coarse enough that both mask quality and Cressman interpolation matter." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "grid = Grid(\n", - " resolution=0.25,\n", - " xstart=260.0,\n", - " lenx=30.0,\n", - " ystart=18.0,\n", - " leny=12.0,\n", - " name=\"gulf_of_mexico\",\n", - ")\n", - "\n", - "topo = Topo(grid, min_depth=5.0, version_control_dir=\"TopoLibrary\")\n", - "\n", - "GEBCO_PATH = \"/path/to/gebco_2023.nc\" # update to your local GEBCO file" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "## 1. Mask Generation\n", - "\n", - "Both pipelines can take an externally computed mask. The mask determines which cells are ocean — everything downstream (depth interpolation, tidal cleanup, min-depth enforcement) only applies to ocean cells.\n", - "\n", - "### Option A — Ocean Fraction Mask\n", - "\n", - "`generate_mask_ocean_frac` computes the fraction of each model cell that is ocean by sub-sampling the raw GEBCO data.\n", - "\n", - "For each model cell:\n", - "1. `nx_sub × ny_sub` interior points are placed using bilinear interpolation of the 4 supergrid corner coordinates.\n", - "2. Each point is snapped to the nearest GEBCO pixel.\n", - "3. `OCN_FRAC` = fraction of sub-points with depth > `mask_hmin`.\n", - "4. Cells with `OCN_FRAC ≥ mask_threshold` become ocean.\n", - "\n", - "This method also stores per-cell depth statistics (`D_mean`, `D_min`, `D_max`, `D2_mean`) needed later for topo drag." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# [PROPOSED API]\n", - "mask_ocn_frac, ocn_frac = topo.generate_mask_ocean_frac(\n", - " bathymetry_path=GEBCO_PATH,\n", - " nx_sub=5, # 5x5 = 25 sub-points per cell\n", - " ny_sub=5,\n", - " mask_threshold=0.5, # >50% ocean -> ocean cell\n", - " mask_hmin=0.0, # sub-points with depth > 0 count as ocean\n", - ")\n", - "\n", - "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n", - "ocn_frac.plot(ax=axes[0], cmap=\"Blues\", vmin=0, vmax=1)\n", - "axes[0].set_title(\"Ocean Fraction (OCN_FRAC)\")\n", - "mask_ocn_frac.plot(ax=axes[1], cmap=\"Blues\", vmin=0, vmax=1)\n", - "axes[1].set_title(\"Binary Mask (threshold=0.5)\")\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Option B — Cartopy Coastline Mask\n", - "\n", - "`generate_mask_cartopy` rasterises Natural Earth coastline vectors onto the model grid. It is faster and does not depend on the bathymetry dataset, but the mask is not derived from the depth data so land/ocean boundaries may not align exactly with GEBCO." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# [PROPOSED API]\n", - "mask_cartopy = topo.generate_mask_cartopy(resolution=\"50m\")\n", - "\n", - "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n", - "mask_ocn_frac.plot(ax=axes[0], cmap=\"Blues\")\n", - "axes[0].set_title(\"Ocean Fraction Mask\")\n", - "mask_cartopy.plot(ax=axes[1], cmap=\"Blues\")\n", - "axes[1].set_title(\"Cartopy Mask\")\n", - "plt.suptitle(\"Mask comparison at coastlines\")\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "## 2. Pipeline A — `direct_xesmf_regrid`\n", - "\n", - "The standard pipeline. Regrids GEBCO with `xesmf` (bilinear or conservative), then runs `tidy_dataset` for lake removal, 1-cell channel cleanup, and minimum depth enforcement.\n", - "\n", - "The optional `mask` argument lets you supply a pre-computed mask. Without it, the mask is derived from whether the regridded depth is positive — the original behaviour." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# [PROPOSED API] — with external ocean fraction mask\n", - "topo.direct_xesmf_regrid(\n", - " bathymetry_path=GEBCO_PATH,\n", - " longitude_coordinate_name=\"lon\",\n", - " latitude_coordinate_name=\"lat\",\n", - " vertical_coordinate_name=\"elevation\",\n", - " mask=mask_ocn_frac, # omit this to use the original behaviour\n", - " regridding_method=\"conservative\",\n", - ")\n", - "\n", - "topo.depth.plot(cmap=\"Blues_r\")\n", - "plt.title(\"Pipeline A depth — xesmf + ocean fraction mask\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "## 3. Pipeline B — `high_res_regrid`\n", - "\n", - "The high-accuracy pipeline for coarser grids. Internally runs:\n", - "1. `generate_mask_ocean_frac` — builds the mask and computes depth statistics\n", - "2. `cressman_interp` — assigns ocean-mask-aware smoothed depths from raw GEBCO\n", - "3. `tidy_dataset` — lake removal, channel cleanup, min-depth enforcement\n", - "\n", - "### Why Cressman instead of xesmf?\n", - "\n", - "Standard regridding blends land elevations into ocean cells that straddle the coastline in the source data. Cressman weights nearby GEBCO points by distance and **excludes land source points entirely**, so coastal depth estimates are not contaminated by land elevations.\n", - "\n", - "The weight function is:\n", - "$$w = \\left(\\frac{L^2 - r^2}{L^2 + r^2}\\right)^c$$\n", - "\n", - "where $r$ is the great-circle distance from the cell centre to each GEBCO point, $L = \\text{smooth\\_scl} \\times \\sqrt{A_{\\text{cell}}}$, and $c$ is `cressman_exp`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# [PROPOSED API]\n", - "topo_hires = Topo(grid, min_depth=5.0, version_control_dir=\"TopoLibrary_hires\")\n", - "\n", - "topo_hires.high_res_regrid(\n", - " bathymetry_path=GEBCO_PATH,\n", - " longitude_coordinate_name=\"lon\",\n", - " latitude_coordinate_name=\"lat\",\n", - " vertical_coordinate_name=\"elevation\",\n", - " nx_sub=5,\n", - " ny_sub=5,\n", - " mask_threshold=0.5,\n", - " smooth_scl=2.0, # smoothing radius = 2x local grid spacing\n", - " cressman_exp=2.0, # weight falloff sharpness\n", - " hmin=5.0, # minimum depth — should match vgrid top layer\n", - ")\n", - "\n", - "topo_hires.depth.plot(cmap=\"Blues_r\")\n", - "plt.title(\"Pipeline B depth — Cressman interpolation\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Comparing the two pipelines\n", - "\n", - "The largest differences appear near coastlines and shallow shelves. Interior deep ocean cells are nearly identical." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# [PROPOSED API]\n", - "diff = topo_hires.depth - topo.depth\n", - "\n", - "fig, axes = plt.subplots(1, 3, figsize=(16, 4))\n", - "topo.depth.plot(ax=axes[0], cmap=\"Blues_r\")\n", - "axes[0].set_title(\"Pipeline A (xesmf)\")\n", - "topo_hires.depth.plot(ax=axes[1], cmap=\"Blues_r\")\n", - "axes[1].set_title(\"Pipeline B (Cressman)\")\n", - "diff.plot(ax=axes[2], cmap=\"RdBu_r\", robust=True)\n", - "axes[2].set_title(\"Difference (B − A)\")\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "## 4. Topo Drag Statistics\n", - "\n", - "MOM6 Lee-wave and bottom-drag parameterisations need a measure of subgrid topographic roughness. This is the variance of ocean depth within each model cell:\n", - "\n", - "$$h_2 = \\overline{D^2} - \\bar{D}^2$$\n", - "\n", - "where $\\overline{D^2}$ is `D2_mean` and $\\bar{D}$ is `D_mean` from the Monte-Carlo sub-sampling step.\n", - "\n", - "`write_topo_drag` writes a netCDF file with `h2` that MOM6 reads at initialisation when topo drag is enabled. It requires `generate_mask_ocean_frac` (or `high_res_regrid`) to have been called first." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# [PROPOSED API]\n", - "topo_hires.write_topo_drag(\"topo_drag.nc\")\n", - "\n", - "drag = xr.open_dataset(\"topo_drag.nc\")\n", - "drag.h2.plot(cmap=\"YlOrRd\")\n", - "plt.title(\"Subgrid topographic variance h2 (m²)\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Verify the formula directly\n", - "# [PROPOSED API]\n", - "h2_manual = topo_hires.d2_mean - topo_hires.d_mean ** 2\n", - "print(\"h2 is non-negative everywhere:\", bool((drag.h2 >= 0).all()))\n", - "print(\"Max h2:\", float(drag.h2.max()), \"m²\")\n", - "print(\"Matches manual computation:\", np.allclose(drag.h2.values, h2_manual.values))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": "---\n## 5. Which pipeline to use?\n\n`direct_xesmf_regrid` works well when your model grid resolution is close to the source dataset resolution (GEBCO is ~500 m / 15 arcseconds). As the ratio of model grid spacing to source resolution grows — i.e. each model cell covers many GEBCO pixels — `xesmf` regridding averages over increasing subgrid variability and coastal cells become more susceptible to land contamination. In that regime `high_res_regrid` with Cressman interpolation gives more accurate depths and a physically consistent mask.\n\nFor the mask, `generate_mask_ocean_frac` is the natural choice when Cressman is involved since both draw from the same GEBCO source, keeping the mask and depth field self-consistent. `generate_mask_cartopy` is a reasonable alternative for `direct_xesmf_regrid` on clean open-ocean domains where speed matters more than coastline precision." - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.11.0" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} \ No newline at end of file diff --git a/notebooks/8_pipeline_overview.ipynb b/notebooks/8_pipeline_overview.ipynb index b327d688..8bcd7043 100644 --- a/notebooks/8_pipeline_overview.ipynb +++ b/notebooks/8_pipeline_overview.ipynb @@ -32,10 +32,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", "import numpy as np\n", "import xarray as xr\n", "import matplotlib.pyplot as plt\n", @@ -64,9 +66,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model grid: 5 × 4 T-cells\n", + "T-cell lon range: 263.0 – 271.0\n", + "T-cell lat range: 21.0 – 27.0\n" + ] + } + ], "source": [ "grid = Grid(\n", " resolution=2.0, # 2° model grid\n", @@ -99,9 +111,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Source grid: 142 × 122 pixels at 0.1°\n", + "Approx source/model ratio: 0.1× (wait — that's backwards)\n", + "Approx model/source ratio: 20× finer source than model\n" + ] + } + ], "source": [ "# Source grid: 0.1° resolution, covers the model domain with margin\n", "src_lons = np.arange(260.0, 274.1, 0.1)\n", @@ -138,9 +160,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, axes = plt.subplots(1, 2, figsize=(13, 4))\n", "\n", @@ -202,9 +235,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "==========================================================\n", + " Resolution Diagnostics\n", + "==========================================================\n", + "\n", + " Source dataset (synthetic_bathy.nc):\n", + " dlon = 360.0 arcsec (0.100000°)\n", + " dlat = 360.0 arcsec (0.100000°)\n", + " dx ~ 10158 m (at domain mid-lat 24.0°)\n", + " dy ~ 11119 m\n", + "\n", + " Model grid (T-cell spacing):\n", + " median = 213.38 km\n", + " min = 210.84 km\n", + " max = 215.64 km\n", + "\n", + " Resolution ratio (model dx / dataset dx):\n", + " median = 21.0x\n", + " max = 21.2x\n", + "\n", + " Cressman / stats-mask threshold: 12x\n", + " → RECOMMENDED: high_res_regrid() (Cressman + stats mask)\n", + " Each model cell spans ~21 dataset pixels per side.\n", + " Ocean-aware Cressman interpolation will meaningfully reduce\n", + " land contamination of coastal depth estimates.\n", + "==========================================================\n", + "diagnose_resolution → use high_res_regrid: True\n" + ] + } + ], "source": [ "topo = Topo(grid, min_depth=5.0, version_control_dir=tmp_dir)\n", "topo.set_flat(1000) # initialise with flat 1000 m ocean\n", @@ -233,23 +299,48 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ocean_frac mask:\n", + "[[1 1 1 1 1]\n", + " [1 1 1 1 1]\n", + " [1 1 1 1 1]\n", + " [0 1 1 1 1]]\n", + "\n", + "19 ocean cells, 1 land cells\n" + ] + } + ], "source": [ "# nx_sub=ny_sub=3 → 3×3 = 9 sub-points per cell (keep small for the toy problem)\n", "mask_of = topo.generate_mask_ocean_frac(src, nx_sub=3, ny_sub=3, mask_threshold=0.5)\n", "\n", "print(\"ocean_frac mask:\")\n", "print(mask_of.values)\n", - "print(f\"\\n{int(mask_of.sum())} ocean cells, {int((1 - mask_of).sum())} land cells\")" + "print(f\"\\n{int(mask_of.sum())} ocean cells, {int((1 - mask_of).sum())} land cells\")\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Also look at the raw OCN_FRAC values before threshold\n", "ocn_frac = src._topo_stats[\"OCN_FRAC\"].values\n", @@ -290,9 +381,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Computing Cressman weights…\n", + "Cressman weights written to /glade/derecho/scratch/manishrv/tmp/tmpxe6dd66z/cressman_weights.nc\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/glade/work/manishrv/conda-envs/mom6_forge/lib/python3.14/site-packages/xesmf/backend.py:42: UserWarning: Input array is not F_CONTIGUOUS. Will affect performance.\n", + " warnings.warn('Input array is not F_CONTIGUOUS. ' 'Will affect performance.')\n" + ] + }, + { + "data": { + "image/png": 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ol6BSLvylVUHeY0ma79q1q8ofks+mJIVLDpG8zxK0yiCo0jIh50o+M7klY8RIMCJFFSQPRQISmSSwkOfMPICjfA4lV0iqndWuXVtVBpOumV5eXvj+++/vOW6QBOCScC/dDKVVRl6PfCblnElLWeYBSYW8ftlGAjZpmZHnklyT0aNHZ/s80hVRWvakRVAGfpSCEPIa5FglKJfXkLnaGRFRsVPUdZWJHNmjjz6qxkv49NNP77lu9+7d1bqrV6++6zgsYvHixYZGjRoZ3NzcTGMyZJSUlGT4/PPP1XgYMo6Ii4uLISgoyNClSxfDxx9/bLhx40aWcVhkLIq8krEpli9fbhgwYIAaD8XV1dXg7u6uxruQsSJ2796dq7FkcvMaZJwQGa+lR48eark8t7+/v6FVq1aGL774Qu3HaPPmzYYRI0aocSpknzIOTM2aNQ0vvfSS4cKFCzk+xuzGNbnb+ynj1sjnoVy5cuq8yVguctwpKSlqfRmjI6fPf6/xUHJ6vHf7jGX3/DJmUJ8+fdQYNDIejqX97tq1yzBo0CBD2bJl1fgpcj7k8/rqq68aDhw4kONjyHwc8jzGz73sU87l1atXLW536tQpw7BhwwxlypRRYwXJz8cee0zNz+lrlXFN+vbtawgICFCflaZNmxq+/vrru77/c+bMUePAyOdV1sn42iyNw2L8/vzvf/9T467I90Ymea65c+eajRdjZOnzktOxYIiIbJFG/lfUQRMREVFeSLcvaXWUVg5LpX+JiMj+MYeFiIiIiIhsFgMWIiIiIiKyWQxYiIiIiIjIZtlswCJVfqRyilRF8fDwUAPQSfUXqaiUmVRPkfr+Um1FykZ27NgRv/zyS5EcNxERFW4Oi6RiMn+FiKj4stmkeymDKmMnDBo0SJXDlFGJpZSplL+UOv3169dX68kI1C+//LIq/dmnTx9VrnThwoU4evQoVq1aZXEgNiIiIiIisg82G7DIAG7NmzeHi4uLaZ7UuJfxKiSYWbJkiZondf6lZWXfvn2qNr2QMR3Kly+vRrEuqJGpiYiIiIjIgbuEtW3b1ixYETIwl3QRk8HejCQ4kZGvjcGK8Pb2VgOYubu7F+oxExERERGRgwQslkhjUGhoKPz9/U3zpN+yjIosXcMuXLiAU6dOYdSoUYiKisIrr7xSpMdLRERERETFtEuYJdIN7PHHH8e3336Lp556Ss0LCwvDo48+im3btpnWk4Bm3bp1aNOmzT33KduHh4ebzZNWG0nul+5nrq6uBfBKiIiIiIq/pKQkXL58WRVEki78tuLq1auIjIy0ej9+fn4oV65cvhwTFYOARVpOWrVqpbqE7dq1CzqdTs2XJPzXX38dcXFxKuk+JiYGH3/8sQpCZL3q1avnaJRkIiIiIioYa9asQb9+/WwmWCkfVBlIS7F6X15eXuoalUFLwbKLgEUqhN13331ISUlRFcIyfih69uwJJycnrF+/3jQvIiJC5bt069YNP/30U65bWE6cOKFKKH/9zXeoXaduAbwiKiqJCQk4eeIYFuwKx404PU9EMeLvocOT7QN4bospnt/ii+e2ePNIvYlTv32JQ4cOoWnTprAFx48fV9Vmnav0hsbVJ8/7MSRFIeX8L/j333/VDXUqOE6wcZKLIkHJrVu3VItJxmDl3LlzKn/lq6++MtumZMmSaNeunSqLfC+SsC+TJRKsNG/ZKh9eBdmK2JhoxMVGIcZJg/B8uLNCtsPVyRkVKwbx3BZTPL/FF8+tY1xp2mIXewlWtO6l8rx9Wr4eDdltwCJjqvTt21flk2zduhV165q3dkgCvtDrs94pl9aY1NTUQjtWIiIiIrIjUmE2Q5XZPG1Pjl0lTIKQwYMHY8+ePVixYoXFBHrJT9FqtarbV8aebVeuXFGtMU2aNCnkoyYiIiIiu6DRWj+RY7ewjBs3TlX6khYWyUkxDhRpNGzYMAQEBKhqYd988w26du2qRrWXpPt58+YhISEBkyZNKrLjJyIiIiKiYhywHDlyRP2UZPqMCfUZAxbxxRdfoFGjRqrUsTFAadGiBb7//nt06NChkI+aiIiIiOyC9OiyqktYfh4M2WXAsmPHjhytJxXCRo8erSYiIiIiohyxtlsXu4QVGna+IyIiIiIim2WzLSxERERERAXHyiph7BNWaBiwEBEREZHjYZcwu8GAhYiIiIgcD5Pu7QZzWIiIiIiIyGaxhYWIiIiIHA+7hNkNBixERERE5Hgk4d6qcVg4EEthYZcwIiIiIiKyWWxhISIiIiLHwy5hdoMBCxERERE5JnbrsgvsEkZERERERDaLLSxERERE5HjYJcxuMGAhIiIiIsfDgMVuMGAhIiIiIsej1aRP1mxPhYI5LEREREREZLPYwkJEREREDjpwpBX37llhrNAwYCEiIiIiB2TlSPeyPRUKdgkjIiIiIiKbxRYWIiIiInI8rBJmN9jCQkREREQOmsNi5ZQHBw4cwOjRo1GvXj14eHigYsWKeOSRR3D69Oks6548eRI9evSAp6cnSpYsiccffxzh4eFZ1ktLS8PMmTNRpUoVuLm5oWHDhvjhhx8sPn9O92lL2MJCRERERI6niJLuZ8yYgd27d2PQoEEqsLh+/Trmzp2Lpk2bYu/evahfv75a78qVK+jQoQN8fHwwbdo0xMbGYvbs2Th27Bj2798PFxcX0z7ffPNNTJ8+Hc8++yxatGiBtWvX4tFHH4VGo8GQIUNM6+Vmn7aEAQsRERERUSEZO3Ysli1bZhYcDB48GA0aNFBBx5IlS9Q8CSji4uJw6NAh1QojWrZsie7du2PhwoV47rnn1LyQkBDMmTMHo0aNUoGPeOaZZ9CxY0eMHz9eBUY6nS5X+7Q17BJGRERERI6niLqEtW3bNktLRo0aNVQXMemuZbRq1Sr06dPHFFiIbt26oWbNmli+fLlpnrSmpKSkYOTIkRlemgYvvviialHZs2dPrvdpaxiwEBEREZHjJt1bM+UTg8GA0NBQ+Pv7m1pNwsLC0Lx58yzrSovI4cOHTY/l35ILU6dOnSzrGZfndp+2hl3CiIiIiIjyKDg4OMu8gIAAlC5dOsf7WLp0qQoopk6dqh5fu3ZN/SxbtmyWdWVeREQEkpKS4OrqqtYNDAxUrSqZ1xNXr17N9T5tDQMWIiIiInJA+TNwZP/+/bMsmTx5MqZMmZKjvZw6dUrln7Rp0wbDhw9X8xISEtRPS8GDm5ubaR1Zbvx5t/Vyu09bw4CFiIiIiBxPPlUJW7NmDapXr56lhSUnpEJY7969VdWulStXmpLj3d3d1U9p8cgsMTHRbB35mdP1RE7WtTUMWIiIiIiI8kiCFUmYz62oqCj07NkTt27dwq5du1CuXDnTMmO3LWM3roxknoyfYmwJkXW3b9+u8mAydgszbmvcb272aWuYdE9EREREjqeIqoQZWzT69u2rBovcsGED6tata7a8fPnyqpXm4MGDWbaV8VIaN25seiz/jo+PN6swJvbt22dantt92hoGLERERETkeIqoSpher1fjrki54RUrVqjcFUsGDhyogpnLly+b5m3btk0FOTK2ilG/fv3g7OyMefPmmeZJa8v8+fNVkCJllHO7T1vDLmFERERE5HiKaKT7cePGYd26daqFRSpzGQeKNBo2bJj6+cYbb6iApnPnznjllVfUqPSzZs1SA0w++eSTpvUrVKiAMWPGqGUyHouMdC95NdLNTKqPGfNicrNPW8OAhYiIiIiokBw5ckT9XL9+vZoyMwYsQUFB2LlzJ8aOHYuJEyeqwSYlQV9Gtc+cazJ9+nT4+fnhyy+/VCPWy0CUEgg9+uijZuvlZp+2xGYDlgMHDmDRokUqiejChQsoVaoUWrdujffff1+NxmmUueZ0RjJy55YtWwrpiO1PeHg4nnt6BP7YuQPlK1TAp5/PQ+cuXYv6sCgbqZd2Q39lLwwx16Cr2g3ONXqYmn1TgzdDH7IfSE2ErkwjONUdCI02/eudFnMdKSdWwhAdAo2br1qmK1U9R9uSfZ9ftTwuDKknViPt1gVA5wKnat3hVKk9T20hMqSlIvX4SuhvngZSEqDxDIRz7f7Q+lVOP/fntiH1/A45W9BVaA2nmn1Mf9vSoi4h5dhPMMTfgMYnCC4NH4XGvWT6fvXJSPl3OdLCjgPO7nCu2Qe6ck15bnluKaeszEPJ67Y7dsj3PWckmX/z5s33XE+r1WLSpElqyq992hKbvSqZMWMGdu/erfrTNWzYUJV9mzt3Lpo2bYq9e/eifv36ar3Fixdn2VaSiT799FPcf//9RXDk9mPMy6MQGFgGl6+F4/dtWzFs6CM4dvKMqhJBtkfj6g2n6g9Af/Vvs/n6kANICz0K19YvA05uSDm6BKnBv8G5Zi8Y0vRIOfwtdBXbQ9dyJNJuBiPlyCJo20+ExsXjrtuS/Z9fgz4FKQe/hlONHnBu9gyQlgpDYhRPbWFL00Pj7gfXVi8Bbj5Iu34UyX9/C9eObyEt8qwKVl3avAKNzgXJB+ZD7xEApwqtVaCTfHghnKrdD125Zkg9uwXJ/yxN348EOsGbgZQ4uHaeDEPsdSQf/Boa7wrQeuZ8sDriuXVs1o5Wz1TwwmKz77Q0VV28eBGfffYZnnnmGbz11luqL15qaqpq9srYbJZ5kv54cndq6NChRfoabJm8R+vXrsFbk99FiRIl0Kfvg6hXvwE2rFtb1IdG2dAFNoCudH1onM1rpKeFn4CuQht1d13j5Aanql3VRa4wxIXBkJIAp8odoNFoofOvCa13eehDj91zW7L/8yutMhq/yupiV1pkZHutZyBPbSHTOLmqYFSCFnWeyjYBtDp1/vQhh+AU1AbaEv4qaNVV7gR9SHoFHwlANVodnIJaQ6NzhlO1bjBEXUFa/M3083v1oGoxU+fVtzK0petDf8084CWeW6LiwGZbWDJWNDCS/njSjJW5bFtGMhjOqlWr0LFjR5WERJYFnzkDT09Ps/dIApYTJ47zLbN7BiApSl3Imh5nWi53Y++1beYLZ7K/82uIugSNcwkk7fkUhoSb6qLWue5DKvihopMWFw6kxENTwh+GuOvQlGtiWqb1KovU2FD1b0NcKDRed8ZlkBYYTYlS6vwa5PuZFGO2XLZVXf+oyPDc2pki6hJGxaiFxRLpyx0aGgp/f/9s19m4caMagOexxx4r1GOzN7FxsfDy9jab5+3tjbjY2CI7JsobrX9t6C//BUNChLqIlf7wij4ZGo/S0Di5q/7x0n1IH34SaRFn1bJ7bUv2f36l+5e0xjjXHQDXjm+rQCXlnx+K9gU5OJV38s9S1VKmbgqkJgM6tzsrOLkB+tujUKcmmS8zLU9OXyZ0GZJknVzvbEuFjufWjquE5XliwAJHb2GxREqzhYSEYOrUqXddR6ocPPzwwznaZ1hYmEo+zyg4OFj9TExIQGxMNIojrQaIjooye303b96Aq4tLsX3NIj4uTv0M9Larj76ZMFctdG46lPJzVo8NvvchQhONmIPzYEhLQ8nanXHj5mlUCCwJjVaLpI7PIfzQSiSf3wrXkhXhWrEpXLxLoqSf8z23tSfGc2rP5za/z+81N1dovRshsHI1tS+9Vx+cXz0J5bwM0Dq5wJ4Uh/MrQeW1XYvh4Vsagc17q67Ll1xdUcotFR63z3eiIRVXnVxRwc8Zt7zckZCYhLK3l4lLhiSU8ikBN39PnJeB4Lz00N5uDY28noJEd3ez9e0Bz23xPbfCK1WHOyN+EOWN3fzmP3XqFEaNGqUG1xk+fLjFdaKjo/HLL7+gV69e8PXNWZcHGWTn3Xfftbjs5IljiIstngmqCRKMxcZi3c8rVQU2sW/PX6ou9+4/tqO4G9+zLOzVFxGe8PX1xtDBQRnmvnB7Ag4fPowf4o5j5tBKt5fJeq1Na77++usYPKAXmjYNysG29seez21+n98lKbUQGRmJl27vKyYmBsPXaPDhwxVsunxlcTy/aWlp+Oijj+BXzl1V8TGOizDnSjVUrBiHQYPSz9Hvv5/BtiuV8cHgIBw+3BBffbUXs2+fP+ny/Piqm5jyWFMEBgbiyd/98GwzPerUSV/+2WfRCKhVK9Nnx37w3BbPc3vpkgGHV8A2sUuY3bCLgEUqhEmNaB8fH6xcudJsAJyMJHclMTExV93BRo4cmWVkT2lh6d+/P+rUbYDGTYtvicievXrj9+078OGMWdj1x07VevXSmLHw8ytZrFtYDh/aj1m/XkNodCrs7e6swZCGG8HRqpfIgWXnoNHokJYSj7SURDh5lEJy9HWE7l6AUo374bWf0u9pJUWGwNm7tPSpRNSZPxB7IwnLzgRg2ZnL0CfF3nVbe7tLKxc89nhuC+r8JsfXwpVdn+C0c3O4+JTFjSNr4BpQHW+uCYO9sffzG7Z/GZKjw1Gu80i8vvKqaX6cpi72/Lwcf0ZXVzkqV3eshG/NTur8GvQ+CLuVgOemLYdX5eaIOL4ZWp8KmLVDuvxdhiGwKd777HuUue8p9dm4+udeVOg+Fofs7PvLc1t8z63wSjXvxWJLpJXzbsNj5GR7Khw2H7BERUWhZ8+eKi9FqoSVK3cnwdBSdzAJavr06ZPj/ZcuXVpNlri5u8PTyzzPozj53/yv8exTw1GrehU1DsuSH5YjqGL6uADFnVzwXIlMgT1JObMJ+rO/mR5HHt8Mp/pDoPWthJS/v4EhMRoaN2/oqnZHpHtNRN5+fSkn/0ofw8NggNa/JpwbjjC99rTYW3fd1h7Z47ktqPMLlIKuzkO4vOMrIDUBWr+qcK43xC7fH3s+v5J/lHR2D6B1wrlVE03zXZo9B23JWkD5Nri4abZErdAFtUaUXzNE336NusYjEH7sJ4QdXA6NT0U1Dovx9RuCuiMlajnO/fwm4FwCzrUHIExfErCz98eI57Z4ntsArR62igGL/bDpgEVaS/r27YvTp09j69atqFu3brbrXrt2TQ0yOWLECLvt6lDYAgICsGb9xqI+DMohGUjQOJhgZq4d3sx+uzr91WSJlLi927Zk3+dXSAldVUaXiowM9OjW46Nsl0u5Ypks0fpUhGu78Zb3q3OBS6P0EbGpaPDcEjl4wKLX6zF48GDs2bMHa9euVbkrd/Pjjz+qPsKsDkZEREREOcJeXXbBZgOWcePGYd26daqFJSIiAkuWLDFbLgNEZu4OJt3FOnXqVMhHSkRERET2hl3C7IfNBixHjhxRP9evX6+mzDIGLP/99x8OHTqEsWPHQmtnpViJiIiIiMgOA5YdO3bkeN1atWqpQSWJiIiIiHLEyiphHDiy8NhswEJEREREVLDDsFhT1jhfD4fugv2niIiIiIjIZrGFhYiIiIgcDpPu7QcDFiIiIiJyPNKly5puXewSVmgYsBARERGRw9HIf9bksDBiKTTMYSEiIiIiIpvFFhYiIiIicjjMYbEfDFiIiIiIyOEwYLEf7BJGREREREQ2iy0sREREROR4ONK93WDAQkRERESOh2WN7Qa7hBERERERkc1iCwsREREROWYDi1XjsFBhYcBCRERERA6HVcLsBwMWIiIiInI4DFjsB3NYiIiIiIgKUWxsLCZPnowePXqgZMmSKnhauHBhtkGVxsLUvXt303oXLlzIdr0ff/wxy35PnjypntvT01M9/+OPP47w8HDYKrawEBEREZHjKcIqYTdu3MDUqVNRsWJFNGrUCDt27LC43uLFi7PMO3jwID799FPcf//9WZYNHToUvXr1MpvXpk0bs8dXrlxBhw4d4OPjg2nTpqngafbs2Th27Bj2798PFxcX2BoGLERERETkcIqyS1jZsmVx7do1lClTRgUgLVq0sLjesGHDssyT4EaeW4KTzJo2bWpxm4wkSImLi8OhQ4dUwCRatmypWmyklee5556DrWGXMCIiIiKiQuTq6qqCldxKSkrCqlWr0LFjR1SoUMHiOhKMJCcnZ7sP2b5Pnz6mYEV069YNNWvWxPLly2GLGLAQERERkcO5W35ITqfCtnHjRty6dQuPPfaYxeXvvvuuyktxc3NTrTa//fab2fKQkBCEhYWhefPmWbaVVpbDhw/DFrFLGBERERE5IGuDjvRtg4ODsywJCAhA6dKlkd+WLl2qWmcefvhhs/larVbltAwYMADly5fHuXPn8NFHH6Fnz55Yt24devfurdaTbmjGLmmZybyIiAjViiPPYUsYsBARERGRw5FYxboclvSf/fv3z7JMKoBNmTIF+Sk6Ohq//PKLSqr39fU1WybduzZv3mw2Typ/1a1bF+PGjTMFLAkJCeqnpYBEWmWM6zBgISIiIiIqJtasWYPq1atnaWHJb6tWrUJiYmK23cEyk3LFTz75JKZPn64qg0nOi7u7u1omrSiZyb6FcR1bwhYWIiIiInI8+VTWWIKVevXqoaAtXbpUlSKWhPmcCgoKUj+lq5cELMauYMauYRnJPAlybK11RTBgISIiIiKHY08j3V+7dg3bt2/HiBEjchVQSC5LxhYfyW+Rf0sp5cxkDJbGjRvDFrFKGBERERGRDfvxxx+RlpaWbXcwS6PUS0Ww7777Dg0bNjRLsh84cCA2bNiAy5cvm+Zt27YNp0+fxqBBg2CL2MJCRERERA6nqFtY5s6dq0oUX716VT1ev369yjURL730kur+lbE7WLly5dCpUyeL+5owYQLOnj2Lrl27qvUuXLiAL7/8Uo3J8umnn5qt+8Ybb2DFihXo3LkzXnnlFTXS/axZs9CgQQOV82KLGLAQERERkcMp6oBl9uzZuHjxounx6tWr1SRktHpjwPLff/+pUenHjh2ryhdbIiWN58+fj//973+IjIxUVcQ6dOiAt956C02bNs2S17Jz5061v4kTJ8LFxUVVEZszZ45N5q8IBixERERERIVMWkFyolatWjAYDHddZ+jQoWrKKSkSkLkMsi1jwEJEREREjqnwB6unPGDAQkREREQOp6i7hFExqBJ24MABjB49WjVZeXh4qBE8H3nkEVXBIDOpmvDFF1+oUmwy2E2pUqXQpUsXHD16tEiOnYiIiIhs3O2AJa+Taah7ctwWlhkzZmD37t2qvJqUY7t+/bqqpiCJQ3v37kX9+vVN6z711FOqesITTzyhghypiHD48GGEhYUV6WsgIiIiIqJiGrBI5YJly5apygVGgwcPViXXpk+fjiVLlqh5y5cvx6JFi1RVhQEDBhThERMRERGRvUhvJLGmS1i+Hg7ZY8DStm3bLPNq1KihuoidPHnSNO+jjz5Cy5YtVbAiXcMSEhJUFzIiIiIiouwwh8V+2GwOiyVS0i00NBT+/v7qcXR0NPbv348WLVqoQXCkXrWnpyeqVq2qWl6IiIiIiMi+2WwLiyWSpxISEoKpU6eqxzKipwQxP/74I5ycnDBz5kwVtMiInkOGDIG3tzd69Ohx131Knkt4eLjZvODgYPUzMSEBsTHRBfiKqLDFx8Wpn4HedvXRpxwwnlOe2+KJ57f44rkt3rxSdbgMGyVduqzp1sUuYYXGbq7aTp06hVGjRqFNmzYYPny4mhcbG6t+3rx5UyXit2rVSj1+8MEHUaVKFbz//vv3DFjmzZuHd9991+KykyeOIS42Kt9fCxW98T3LFvUhUAHhuS3eeH6LL57b4unSJQMOr4BN0sh/1uSwMGIpNHYRsEiFsN69e6vWk5UrV0Kn06n5UsJYSHBiDFaEdAvr27evSsxPTU1VrS/ZGTlypKpElrmFpX///liwKxwxTgyfi9udPPmjOHt7DEJj0or6cCgfBXpp8VpnL3y+NxnhcXcfEZjsT4CHBi+1dsGs36P43S2G393xXXww69drCI1OLerDoXzmlWrei4WoWAYsUVFR6NmzJ27duoVdu3ahXLlypmXGfwcGBmbZrnTp0khJSVEljiXQyY6sJ5MlN+L0CE9LyZfXQbZFgpWQKH1RHwYVAAlWrsYwYCnO390rt/jdLY4kWLkSyb+5xU2A1na/r0y6tx82HbAkJiaqlhIZLHLr1q2oW7eu2XIJWMqUKaPyWjK7evUq3Nzc4OXlVYhHTERERET2wNqxH1nWuPDYbJUwvV6vxl3Zs2cPVqxYoXJXLJF1Ll++jC1btpjm3bhxA2vXrlWj3Wu1NvsSiYiIiKiocKR7u2GzLSzjxo3DunXrVAtLRESEaaBIo2HDhqmfkyZNUiWMBw4cqAablO5f8+fPV93Bpk2bVkRHT0RERERExTpgOXLkiPq5fv16NWVmDFgkf+XPP//Ea6+9ho8//lgFKtIaIwFOo0aNCv24iYiIiMj2sUuY/bDZgGXHjh05XlcGily9enWBHg8RERERFbeAxYqyxiwkW2iY4EFERERERDbLZltYiIiIiIgKdKB7a6qE5efB0F0xYCEiIiIih6PVatRkzfZUONgljIiIiIiIbBZbWIiIiIjI8Vg5cCT7hBUeBixERERE5HCkQph1VcLYJaywMGAhIiIiIofDcVjsB3NYiIiIiIjIZrGFhYiIiIgcjgZWdglz8CSW+Ph4bNmyBbt378aJEydw48YN9X76+/ujTp06uO+++9CtWzd4eHhY/VwMWIiIiIjI4TCHJW+OHTuGOXPmYPXq1YiNjYW7uzuCgoLg5+cHg8GA06dPY9u2bZg9e7YKVgYOHIhx48ahQYMGeXxGBixERERERJQDgwcPxqpVq9C8eXNMmTIF3bt3R926daHT6czW0+v1qtXlt99+w8qVK9GkSRMMGjQIP/zwA/KCOSxERERE5LBJ99ZMeSUtE5MnT0aPHj1QsmRJ1dqzcOHCLOuNGDHC1BKUcapdu3aWddPS0jBz5kxUqVIFbm5uaNiwYbYBwsmTJ9Vze3p6qud//PHHER4efs/j1mq1OHjwIPbu3YuxY8eqVpPMwYqQebJMWlb27NmjtrEGu4QRERERkeOxsqyxNRGL5HtMnToVFStWRKNGjbBjx45s13V1dcU333xjNs/HxyfLem+++SamT5+OZ599Fi1atMDatWvx6KOPqtc4ZMgQ03pXrlxBhw4d1D6mTZumgifpviVdvfbv3w8XF5dsjyWvLSSNGzfO87aCAQsRERERUSEqW7Ysrl27hjJlyqjWBwkwsuPk5IRhw4bddX8hISEqr2TUqFGYO3eumvfMM8+gY8eOGD9+vOqOZWwJkSAlLi4Ohw4dUgGTaNmypereJa08zz33HGwNAxYiIiIicjhFOQ6LtJpIsJJTer1eBRne3t4Wl0trSkpKCkaOHJnh+DR48cUXVSuLdMtq166dmi85KH369DEFK0KqedWsWRPLly/PU8Ai+Srnzp1DZGSkSrzP7IknnoA1GLAQERERkYMGLNaMdI9CKx/s7e2tfkolrqFDh2LGjBkq/8To8OHDqiKXlBPOSFpOjMslYJGWmLCwMJU0n5msu3Hjxlwd29mzZ1Xrj3QlsxSoCHmPGbAQEREREeWSxBtWtbDc/hkcHJxlWUBAAEqXLp0vXccmTJiApk2bqqT6TZs2Yd68eTh69KjKe5HuYkK6lwUGBmYJwGR7cfXqVdN6GednXjciIgJJSUmqBSgnnn/+eZX78sknn6B9+/YqoCoIbGEhIiIiIsqj/v37Z5knFcCk7K+1PvzwQ7PHkjwvXbckwV7KBRuT6RMSEiwGGVItzLg84897rZvTgEUGjXzjjTfw0ksvoSAxYCEiIiIih5NfA0euWbMG1atXz9LCUlBeffVVvP3229i6daspYJHBG6VlJLPExETT8ow/c7JuTsio9pYqluU3BixERERE5HDyK+legpV69eqhsLi7u6NUqVKq+1bG7lzbt29XeSQZgzBjF7By5cqZ1ss4PyOZJ2Oy5LR1RbzwwgtYsmSJqk5maTyW/MKAhYiIiIjITsTExKhxXDK24sg4JzJWiwwIKSPPG+3bt8+0XJQvX15tZ2kgR0mcN66XU9I9TSqYyVgyTz31FIKCgiwGLg899BCswYCFiIiIiBxPEQ4cmRPSRUtKFXt5eZnNf++991RLioxUb9SvXz/VVUwS8o3jsMg68+fPV0FK27ZtTesOHDgQixYtwuXLl1WAIbZt24bTp0+rfeTG4MGDTf9+7bXXLK4j77EENdZgwEJEREREDqcox2EREljcunXLVMFr/fr1ahR6IUnsMqZJkyZNVBnj2rVrq/mbN29WpYclWJEgxahChQoYM2YMZs2apYIcGYhScmt27dqFpUuXmrV6SJL8ihUr0LlzZ7zyyitqpHvZrkGDBnjyySdz9RqkG1phYMBCRERERFTIZs+ejYsXL5oer169Wk1Cxjbx9fVVAzxu2bJFtYhIK4Xky8hI9dKaodVqzfY3ffp0VVb4yy+/VCPW16hRQ+WXyMCRGUmrys6dOzF27FhMnDgRLi4u6N27N+bMmZOr/BXRsWNHFAYGLERERETkcPKrSlheXbhw4Z7rLF68OMf7kwBm0qRJaroXKRIgrTXWkuBIghZjC1BBYcBCRERERA4nvwaOdGQvvviiCtykuli7du3U4JEyNWvWLEsLkDUYsBARERGRwynqFpbi4Pr16/jjjz/w559/qnyZCRMmqGR/Dw8PtG7d2hTAdOrUyarnYcBCRERERES5Vrp0aTz88MNqMpZc/uuvv1TwsnLlSkyZMkUFdqmpqbAGAxYiIiIicjhFXSWsuDl79qwKVGSSVhd5LC0tbdq0sXrfDFiIiIiIyPHY+Dgs9mDu3LmmIEW6hxlzWSS3RbqCNW3a1OJAkrnFgIWIiIiIiHLt5ZdfVgGJDEY5fvx4lWxfEPIvfZ+IiIiIyK66hGmsmIr6FRS9UaNGoX79+ipf5b777lOtK1JWWQa3jIqKyrfnsdkWlgMHDqhBcmQETalTXapUKVVt4P3330fNmjVN640YMUKtl1mtWrVw6tQpOJrUS7uhv7IXhphr0FXtBucaPdR8qdiQGrwZ+pD9QGoidGUawanuQGi06R+BtJjrSDmxEoboEGjcfNUyXanq6fs8uxWp57beeRJDGqDRwa37h0XzIh1UytntSL2wE4aoEDjV7g2Xuv1N5zbl5FroL+yCQc5t+RZwaTLMdG4NSdFIOvgd0sJPQePuB5cmj0NXuq5alhYdguSjPyIt8jw0zu5w7zmrSF+jI0s49RsST/8OfeRluDfsD48mg9T8+H9+Rvw/a+6smJYGjVaHUsMWqoepNy8gdt93ajuNqxdKNOwPt5pd0ldNikXcvgVIDvlHfR7cGzwI97o9i+YFOrDUc9vTv5/RIdDV6gXnOv3u/F4+tQ76i3+m/14u1xxOjR9T58qgT0HqkSXQh58EUuKh8SoH5waDoS1V7c5+/9uI1ODf1O9kXeX2cKr3MKsWFYO/uWrbM79CH7IP0KdC61cFzvUGQePmU9gvr9hjDov1Pv/8c/UzOjpaVQozVgv7+OOPVaK9BDPSNcy4XrELWGbMmIHdu3dj0KBBaNiwoeoXJ/3kpC/c3r171RtgJKNyfvPNN2bb+/g45hdb4+oNp+oPQH/1b7P5+pADSAs9CtfWLwNObkg5ukT9oXOu2QuGND1SDn8LXcX20LUcibSbwUg5sgja9hOhcfGAU7VuajJKOb5S/TGlQj63bj7qQkd/eZ/5ub34J/RXDsK181vQOLkhaf9XSDm5Di71HlLLkw8vgcbNG+59PoU+7ASS9n0B9wc+hMbFE9A4wSmoJVCxNVJOZLgopkKndfdDicYPI+ncbrP5JRoOUJNR7J5vYEhNNj2O2fU/uFZuDfeeU6CPuIioX9+FU+lacPItj7j936uLn5KD/oe0hEhEbf4AOt8KcCnXoFBfm6NTF6R1Hsz63b20G2khh+Da8Q3AyR0pB79C6qn1cK47QAUhGg9/uNZ5HXD3Q1rIQSTv/RyuD0xX33P99X+Qen47XDq+AY2TK5J3fwS9Zxk4VW5fZK/TERXE39y00H+gv3oQrq3HAK5eSPl3BVJOrYNL48eL7HUWVxpYWdaYI7GYeHt7o1evXmo6f/48tmzZooKWf/75B8eOHSu+AcvYsWOxbNkyuLi4mOYNHjwYDRo0wPTp07FkyRLTfCcnJwwbNqyIjtS26ALTL0TS5K5cBmnhJ6Cr0Eb94RROVbsi+cj36b8848JgSEmAU+UO6fvwrwm9d3noQ4/BKai12X4MaanQXz8C58ZPFNpronRO5Zumn8vrx8zeErlwcaraUV3wCudavZC8fz5Q7yHV4qK/ehhuPWaoixqnck2QeqaCmicXNlqvQDXpb57l21zEXCu1UD9TrhzJdh2DPhVJF/bCq+MrpnlpseFwrdoWGo0WTqWqQOdbHnpphfMtj+Qrh+HzgASyLtB5BcKtRmckBe9kwFLIdOWaWPzuymNdlQ6q5VM41eyJ5P1fqYBFfV9r972zjwotkXLsJxhirkPjVxn6y3vhVLkjtJ6l05dXvx/6i7sZsBSDv7mGhEho/aqaPhe6so2QevqXwn5pRDl24sQJU2Uw+RkSEqLmlytXDkOGDFEtLNay2YClbdu2WebVqFED9erVw8mT5r8YhF6vR1xcnIrwKCcMQFKU+qVpepxpuSH2epat5JcwdC7QlkxvuiYbYXb6DOoPniElHoa4cHV3T1uipGmp1qcC0qKvFsVRkpWSr/wNjc4FzmXrmea51XkAiWf/RIlGDyH15nnoY2/AOaBGNnswIPXWFZ4HW/3uyr8T07+7GucSZqulxYYCyXHQ3A5QDNFXoanQ0rRc610eqTH8XheHv7m6Mg2hv3YYafE3VTdP+be2VK1CP2JHwC5h1vP390dkZKRqza9duzZ69uxpGvG+cuXKyC82G7BYIm9GaGioCloyio+PV4GK/PTz88PQoUNVlzJPT88iO1Zbo/WvDf2FHdAF1lddD1LPbUtfoE+GxqM0NDLv/A7oKrVH2s3TSIs4C517qSz70V89BF3ZpupuLtnOHb6UM5vVXVy5yEn5b6Oab0hNUpPG2c18Ayc3deFD9ifp7C64Vm1n9v1zqdAYMbvmIeGfn9Vjz/ueh7ZE+p1Zl/KNVA6M130vqC5hiWe2A1rry0tS/tAG1oP+zG/pLTDyO/h0+ncX0uUvQ8Bi0Ccj5eA3cKrZ604go09K/y4bOburXAkqBn9zXb2h9a2I5D8+ADRaaLzKwrnlwCJ9PcUVR7q33vDhw1VwIkGKBC8Fxa4ClqVLl6pmpqlTp5rmlS1bFhMmTFC5LWlpadi0aRPmzZuHo0ePYseOHaq72N2EhYUhPDzcbF5wcLD66e+hg6uTM+xRmKsWOjcdSvmlH7/B9z5EaKIRc3AeDGlpKFm7M27cPI0KgSWh0WqR1PE5hB9aieTzW+FasiJcKzaFi3dJlLy9vdAnxeF8+ElU7NEXLj72+b4Eeqd/HgK97DfgCnXRwMlVi1I+6ReehkYdEZEWieg/ZwJpepSs3wM3wo6jQumSSIqMQ0hqIsrfXleEaZOg8XBDQIZ5CUlaXNdqzNazN8ZzGuBh32VbrrgATq4alPEyfx2pibG4eeUwKg8dArfby2Teqa0zUaHbC/Cp2hKJEZdxft2HKFO+IkqUrorUrk/i6s7vEPXzK9C5ecG/djvEXT+Ncpn2bQ+M59Wev7thrhro3LQo5Zvhu2u4hZjdM9N/L9d/ADfCT6BCoJ/6vazWSUvFtW1fwsMvEIGt+5v6219ycUMp12R43N5XYmoyrjq7ocLtx/bEeE6Nv58d/W/uzaO/IjExDGUGTFNdA28eXY/UUz+ibPtnYY+8UnW4XNQHQQVmzpw5KAx289tBKn5J6TQZLVOiOaMPPzSvVCV95aSK2JtvvqlKrMnju5Hg5t1337W47Mn2AahYMQj26IsIT/j6emPo4IzH/8LtCTh8+DB+iDuOmUMr3V4m693JV3n99dcxeEAvNG16Z3sJBn+rXBEfPXenG4K9eq2zF+zVFyEu8PV1xdAHMxSW6PekfGLvnNtb1TCzvx8SEtzw+C9JmHhfqqq0J97afx2dO3dG1653tv/vP0/M3qfBrIz7tFMvtb6T92aPvjitg6+vDkO7uprN37RpOxIrV8TMQXeqRJ05cwnTPN3w2fMdb8+piRkXaqOOxxk82LWO3KoFeo83rb948WIYKtbCE5n2bU/Gd7Hfz+gXV13h6+uGof3SW8CU/k8BeMr8uzsg/bsqN+E++ugj+JVxxqRJ480GX5sTXBkVy9zEoNv7+v33w9hWvRI+yLhvOzO+Z1nYq/z8m/ve6Qi0HNgNDzyQXs3xYrsBqkzsbLN9249Llww4vAK2ycqR7plzf8fOnTvxyy+/4OLFi+pxpUqV0Lt3b3TsaPz75AABi1QIkxctlb8kCLnXiJmvvvoq3n77bWzduvWeAcvIkSNVJbLMLSz9+/fHgl3hiHGyrzuRUn3EYEjDjeBo6NyAA8vOQaPRIS0lHmkpiXDyKIXk6OsI3b0ApRr3w2s/pd/3SIoMgbN3ael3h6gzfyD2RhKWnQnAsjN37otc2bIZnhWbmraxR3IHT/4ozt4eg9CYNNjduU3TI/x8InTu8Ti4OlyVyExLjkNacgKcvAKQfCsE13d8C//mj2D8uvT6524VmuDVGYsQ0PpxxF89gdAz5xHf6EVsWhelullKxbekG7cQGZ+GcbJP6YKgs4tfDVnu0kog+vneZITHZe4fbj/n9+rlFDjdTMbxzTHq/BrvtAev/R2+1dvjnW1Jpm30Sf6IikvEK1//Ce8qzdX3+NyRE7heugsObktC0q3r0Ll5QufijphL/yDk9+2oPuRDs33YUwuLBKOzfo+y0+9uGm6cT4DOzRkHVoWp0tTquyvJ157y3b2K0B3foFSLR/Da2ki1XdjuBUiOCke5+8fh9Q3RZvuMc22GPWu+x59JDdVd+Kubf4Zv3W6mbe3tuyuB6KxfryE0OhWO/jf3ZlIATqzehk2hlaB1csWNo+uQVqKM3f7t9Uo178ViS7QajZqs2d7RJScnq1SMNWvWqGsKX9/0QhO3bt1SrS8DBgzADz/8AGdn63rm2PxViQw6Iwk88sKl8oBUHLgXd3d3dTc5IiLinuuWLl1aTZbciNMjPM2+yvemnNkE/dnfTI8jj2+GU/0h0PpWQsrf38CQGK1K3Oqqdkeke01ERqa/vpSTf6XXizcYoPWvCeeGI3Dl9jIhyX/JNy/B0GAEYjPMt1dywRMSpYc9ST6xBqkn15keRx5dD5dmT0FbsiqS/voMhsRbqiKNc+0+uOVdH7duvz5D/ccQf+BbRC8dlT4OS4sXcD3RHUjUIy3uBhI3TTDt8+z3z0LrXwtuHV+HvZJg5WqM/QUscYdXIeHoKtPjsIM/w/O+F+BWoxP0MaGIDz0Ltw7jMr02d3h2HIMrfy1D2ua50Lh6wq1uL8T4NUBMjAGJF88gbv9iVS3OyTcIHp1eRbjeB7DD9yfjd/fKLfv67qpxkk6tNz2O/GcDnJo+qcbXSNn7OQyJUapsua5WH0R61kPkLT0M8TeRdPoPQOuMc8teMm3r0vYV9TsaXvWBSp1wcd17pnFYogLaItrO3puMJFjJ+HfHUf/mGsp2QmpkBM5veF9185VCKU51HrG798YoQGu/n0m6N+ml9PPPP+O1117DuHHjEBgYaEq5kIBl1qxZKpXjvffegzU0BgmHbFRiYiLuv/9+HDp0SLWWSHewnIiJiVGtMc8++yy+/PLLXD/v8ePH1TgvQb3eQHhawSUQUeGr4OesmtWl9cHeAha6O8m/kS5t0npgjwEL3Z3k3Uzt6qpaEOwtYKG7k7yb2f38VAuCvV6UU/YCtDdweeM0/Pvvv1mKJhUV43VemzeXwrNs1TzvJ/baOez54DGbem2FrUqVKujUqRMWLFhgcbkM8C455TIIvDVsNntRyhTLuCt79uzBihUrLAYrEtBIcJKZRHESh/XokT7iLBERERGRpYEj8zwxiQXXrl1Dq1atsv1gyTJJ7bCWzXYJk2aldevWoW/fvqprV8aBIoUMFClvQJMmTVTfOan9LDZv3oyNGzeqYKVfv35FdPREREREZMskBUVrRRoKU1iAChUqqBaUF15ILzBhKRlf1im2AcuRI+mjPa9fv15NmUnAIok9ffr0wZYtW7Bo0SLVKlO9enVMmzZN9aXT3k5WJSIiIiKi/CWVeydPnqyuyaXolVyHS+vTmTNn8Mknn6heUtlV4y0WAYtEa/cib46U6SQiIiIiyg0OHGm9N954A2fPnsVXX32Fr7/+2tRYIGXZJT1DAhpZp9gGLEREREREBUW6dFnTrYtdwqCGGlm4cCHGjh2rUjIyjsPSq1cvNGzYMF/OFQMWIiIiIiLKMwlM8is4sYQBCxERERE5HGlcsabSF4eNLDwMWIiIiIjI4WitrBJmzbb2SqvVqtyf3JLCWNZgwEJERERERPf0zjvvZAlYZKR7GYzzgQceQK1atdS8U6dO4bffflMDdPbv3x/WYsBCRERERI7n9gCQ1mzvaKZMmWL2WKqDhYWF4d9//zUFK0YnT55Ely5dUK5cOaufN18HKklKSlIj069duxY3btzIz10TEREREeVvDovGismK546NjVXjl8hA5yVLllSBk1TbykhKA8u8Bx98EEFBQfDw8FAtFu+//z4SExOzvp7bAVjmafr06VnWDQkJwSOPPKKGCPH29laDrZ87dy7Xr2PWrFkYPXp0lmBF1KlTRy2bOXMmbKaF5bPPPlNRV1RUlHosgzlKVCWBi4xCLwf71FNP5dfTERERERHlmVajUZM12+eVXB9PnToVFStWRKNGjSyOPxgfH48nn3wSrVu3ViPJly5dWjUMSKCzbds2/P7771laiLp3744nnnjCbF6TJk2yBEudO3dW1+wyRoqzszM+/vhjdOzYUQ3cXqpUqRy/jitXrqjtsyPLZB2bCFgWLFiAMWPGYMiQIbj//vvNAhN/f38VuPz4448MWIiIiIjI4ZUtWxbXrl1DmTJlcPDgQbRo0SLLe+Li4oLdu3ejbdu2pnnPPvssKleubApaunXrZrZNzZo1MWzYsLu+v/PmzVMj0e/fv9/0vD179lStN3PmzMG0adNyfH5kG9nfo48+ivLly5stk0BFljVo0MA2uoTJi5OmpGXLlqFv375Zljdr1kwl4xARERER2QKruoNZOeikq6urClbuRgKWjMGK0YABA0w5IpYkJCRY7DJmtHLlShWoZAySpDdU165dsXz5cuSGtMxIDosxUJLeVjI99thjqpuYLPvoo49gEwFLcHCwisyyI33zbt68mR9PRURERERktexyPnIzFYXr16+bejFlJjkvkuvi7u6OunXrqsaEzHkx//zzD5o3b55l25YtW+Ls2bOIiYnJ8bG0a9cO+/btUz2spFqYdHOTac2aNapqmCyTdWyiS5gk7Nwtyf7EiRP3jCKJiIiIiOyN3LjPLCAgQOWcFISZM2eqRPnMjQXSGiOJ9FWqVMHVq1fxv//9T7V0SK7Kiy++qNaJiIhQRbKkS1pmxnmyraUk+rt1C5NgRYKh8PBw0+uXMVvyS74ELL169VJlzUaOHJllmXQF+/rrr5m/QkRERES2w8puXcYyYZbGGZEck8wlgPPDtGnTsHXrVpUbIg0GGUm+S0aSUy5pGZJYP2LECNXqIt3FjF3SMnNzc1M/jevklgQogYGBKAj5ErBIebVWrVqpCEtyWKSJbNGiRfjuu++watUqFbHJQDNERERERMWpSph0f6pevbrZMmlhyG8//fQT3nrrLTz99NOmFpN75cBIWWGpMHbo0CHVNUuCFiGtLJkZ816M6+RWamoq/vrrL1X1zMfHBzYXsMiAMPJGSAQnb6bBYMDixYvh5eWFoUOHqvrPlvrZERERERHZMwlW6tWrV6DPsWXLFlWuuHfv3pg/f36Ot5PxW4xdwYx55dK6IhXKMjPOy+tAj9L1TMolG4c2yU/5Ng6L9NP75ptv1CT916QfW373XyMiIiIiyreBI63cvjDs27dPVQaTRHmp4uXklPPLd+NgkMYWH7kulzLDUkrZ0vNUrVpVNTjklTRaFIQCiSbkTZE+bAxWiIiIiMgWaWBllbBCCFmkdHHv3r3V2CsbNmzItruWMdk9I6n29cknn6heTpLLYvTwww/jwIEDZkHLf//9pwaiHDRoEGxRnlpYpFxZbsmJffvtt/PydERERERE+UpSULRWxBzWVjWeO3cubt26papyifXr15tGhX/ppZfUjX8pDRwZGYnx48fjl19+Mdu+WrVqaNOmjfq3VASTXBrJJa9YsaLq3iW55JcuXVJpGpLPYiRFsqQglgRCr732mhqNXsZKkcaGcePGWfeibl/z20TAYqnqgfHgMjcFyXyZx4CFiIiIiCjd7NmzcfHiRdPbsXr1ajUJ42j1ly9fVj8nTpyY5W0bPny4KWC57777VMK7pGbI2IcyFouMqyJBS+Z8EunytWPHDrz66quqcJakcXTq1EkNApmbYgGZ95uSkqJ+jh07Fn5+fmbLpPWm0AMWeWEZhYSEqChNqoSNGTPGVLv51KlTqilKxmHJHBUSERERERWV9NHq894aYG1DwoULF/ItJ6R79+5qyqkKFSpgxYoVsEalSpXMHhsrj0lLjaVxXoo86X7UqFGoUaMGlixZYja/RYsWWLp0qeorJ+vIoDJERERERLYRsFi3vSNbsGCB2WMZRP7HH3/EhAkT8r1KWL4k3Uszz90OrGvXrti2bVt+PBUREREREdmYgshdydeARUbG3LNnT7bLpU+dcfRMIiIiIqKiZlWFsNsTFY58CVgee+wx1fXr5ZdfxpkzZ1SOi0zyb6lysGzZMrUOEREREZEtVQnL68R4pfDkSw7LjBkzVL81Kc8mZdWM469I0CLJQjLavaxDRERERETFT8mSJXH+/HmUKVPGNgMWqe0sNZ6lRvTGjRtNJdqkekDPnj3RqFGj/HgaIiIiIqJ8HTjSmu3pDnkvM1cOs6mAxahhw4ZqIiIiIiKyZRJuWBNyMFwpPPkasBARERER2QOtRqMma7YnOwpYJGclJ01qer0+P56OiIiIiIgcRL4ELO+8806WgEWCExnBc82aNWrk+z59+uTHUxERERERWY0DRzpYwDJlypRsl127dg2tW7dGzZo18+OpiIiIiIisZ+1YKuwSZl/jsNxN2bJl8cILL+C9997L1XYHDhzA6NGjUa9ePXh4eKBixYp45JFHcPr06Wy3SUlJQd26ddWHb/bs2flw9EREREREVOyT7iXgkLrMuSHjtuzevRuDBg1SlceuX7+uxnlp2rQp9u7di/r162fZ5vPPP8elS5fy8ciJiIiIqDhil7CC98cff6hxWaztaVXgLSz//vsvPvvss1wf6NixY9V4LrLtM888g7feegu7du1Camoqpk+fnmX9sLAwTJ06Fa+//no+Hj0RERERFecqYdZMdHedOnVSvZ+GDBly115ShdLCUqVKFYt9AG/duoWoqCiUKFFCJd/nRtu2bbPMq1GjhuoidvLkySzLJk6cqJL7hw0bpooAEBERERHddRwWa1JY+Nbek1yTx8XFqZYWGUg+ISEBRRawdOzYMUvAIo/9/PxQrVo1FVWVLFnS6ucxGAwIDQ1VQUtG+/fvx6JFi/Dnn39alzxFRERERETI78JcMTExed5PvgQsCxcuRGFYunQpQkJCVNevjEHMSy+9hMGDB6NNmzaqlHJuSFey8PBws3nBwcHqp7+HDq5Ozvl09GQLAr3TP/KBXgXeG5IKmfGcBnjwpkVxZDyv/O4WP8Zzavz9TMWLV6oOl2HLOSx5/5vBe+S54+XlhbzKl98OTz31FJ5//nm0atXK4nJpAZk/fz6+++67PD/HqVOnMGrUKBWUDB8+3CxYOnbsGFauXJmn/c6bNw/vvvuuxWVPtg9AxYpBeT5msl2vdc77l4Zs20utXYr6EKgAje/iw/e3mBrfs2xRHwIVgEuXDDi8wjbfWglVrLl9ydtj5tLS0lQqiDQmZGZtT6t8a2Hp1q1btgGLVAiTLlt5DVikQljv3r3h4+OjAhOdTqfmR0dHY9KkSRg/fjyCgvIWWIwcOVJVIsvcwtK/f38s+PMmYpzSn4uKB7mDN75HIL44qEd4fFEfDeWngBLAi811WH3VA7dS+L0tbnyd9XioXBz+tz+F391i+N0d1dIZszaFIjQ6tagPh/KZV+pNvqfFWEpKiqrsK9f4ly9fVkGLJTKgvDUKpf316tWrcHd3z9O2Eqn17NlTJfBLlbBy5cqZlslYK8nJyao7mLEr2JUrV9TPyMhINU/Wd3HJ/o5r6dKl1WTJjdhUhBtS8nTcZNskWLkWW9RHQQVBgpWbyexaUlzxu1t8SbByJZJ/c4ubAI3tBqHSHcy6LmFsY3n++edVo4QMEi83+6VxoSDk+a/62rVr1WT01VdfYevWrVnWk0BD5rdo0SLXz5GYmIi+ffuqMmiyDymLlpGMuSKBSeYkfDFt2jQ1HT58GI0bN871cxMRERFR8aXVpE/WbO/oVqxYgccff7zA89nzHLCcOHFCHaQxwty3bx8OHTpkto7Ml0EjO3TogI8++ihX+5emI2k52bNnjwqMJHcls5dffllFc5mT6CXaGzFiBPr166dKLhMRERERUf6SoUukdaWg5TlgkdwRmYRWq8W3336LRx99NN8ObNy4cVi3bp1qYYmIiMCSJUvMlst4KzLqvUwZGbuGSatL5mCGiIiIiEhIjy5rWknYIwwYOnQoNmzYgBdeeKFAP1T50tE7uwQbaxw5ckT9XL9+vZoyk4CFiIiIiCgvmMNivZkzZ6pqwX369FE/pQiWsThWRpkbGHLLZjNTd+zYkaftKleubLGcGhERERGRkZQ0tiqHhW8lkpKSVMPFr7/+qqbM5JpcAsMiqRImXcBkio+PVxW45N/3qpQgy1NTbbdSBBERERER5Zy0qvz8888YMmSIGt7EpqqEvfPOOyoAcXJyMntMRERERGQP0ke6t257R7d582a89NJL+Pjjjwv0efIUsEyZMuWuj4mIiIiIbJncbNdyHBareHt7o3r16iho+dL9burUqfj333+zXX78+HG1DhERERGRo4uNjcXkyZPRo0cPlCxZUgVP2Y1lcvLkSbWep6enWlfGPQkPD8+ynuSSSBK8DOnh5uaGhg0b4ocffrBqn/fy7LPPquewNkelUJLupYVFoqv69etbXC7BzLvvvqu6jhERERER2UTSvZXb59WNGzfUzfyKFSuiUaNG2RabunLlihrPUHJDZEB0CXRmz56NY8eOYf/+/SqX3OjNN9/E9OnTVRAhA7bLOIYy5IgEQ5Jjkpd93osM6i7PI1XAhg8fnm2VsIceegg2XyVMxlHJzYsnIiIiIiquOSxly5bFtWvXUKZMGRw8eFAFGJZIQBEXF6cGZ5fgRrRs2RLdu3dXLTLPPfecmhcSEoI5c+Zg1KhRmDt3rpr3zDPPoGPHjhg/fjwGDRpkCiRyus+ckEHejV577TWL6xRZlTDxxx9/mEWDq1evRnBwcJb1bt26hZ9++gkNGjTI+1ESERERERUTrq6uKli5l1WrVqkxToyBhejWrRtq1qyJ5cuXm4ILaeVISUnByJEjzQKFF198UbWy7NmzB+3atcvVPnNi+/btKAx5DljkAKWbl/ENkYBFpuyaiz7//PO8HyURERERUT7SWpl0b822OSGtJmFhYWjevHmWZdIisnHjRtPjw4cPw8PDA3Xq1MmynnG5BCy52WdOSAuOTQcsEyZMwOjRo9WAMKVLl8b8+fMxcOBAs3UkkClRooRK/CEiIiIisiX5EXNY6mEUEBCgro+tIV3GjN3HMpN5knIhAzdKa42sGxgYmGWYEeO2V69ezfU+bUme84Xc3d1RqlQp+Pv74/z58xg2bJh6nHGSqgMMVoiIiIjI1sgo99ZOon///qrwVMZp3rx5Vh9fQkKC+ulqIXgwXl8b15GfOV0vp/u05IEHHlBpIXnpmSXbFmnSfaVKlfJjN0REREREdmXNmjVZxiKRFhZrSeOASEpKyrIsMTHRbB35mdP1crpPS6pVq6aS86tWraoS7rt27YomTZqo8sgZxcTEqKT+rVu3YsWKFbh48SKefvppFHmVsH/++Uflqfz999+IiopStaAzkiaqs2fP5tfTEREREREVeQ6LBCv16tXL9zNh7LZ17XY3roxknvRkMraUyLrSiiGpGhm7hRm3LVeuXK73aYm0HEnVsU8//VT9+7333lPPJ9v5+fmp54+MjFST/FvmP/bYY3jllVfU+DBFGrBItTAZfEYOVJJ4JLGnS5cuKlKTqgRyEps1a5YfT0VEREREZNdljXOifPnyqqXm4MGDWZbJeCmNGzc2PZZ/f/PNN2pASCl2ZbRv3z7T8tzuMzsSeHzyySdq7JZdu3apa/1Tp07h5s2barmkhdSuXRtt2rRRif7Ozs6wVr4ELDIgpDQN7d27F8nJySrJ6I033lBBi7xRPXv2xIwZM/LjqYiIiIiIHIIUtFq0aBEuX76sBmUU27Ztw+nTp/Hqq6+a1uvXr596LK0exnFYpIVDimJJkNK2bdtc7/NenJyc0LlzZzUVtHwJWKQbmJQ49vb2Vk1AwjhATKtWrfD888/j7bffVoELEREREVFR02RInM/r9taQwELGKzRW8Fq/fr0ahV689NJLaiR6aQBYsWKFCgqkW5WMSj9r1iw1vuGTTz5p2leFChUwZswYtUzGY5GBKCW3RlpAli5dajb6fE73aUvyJWCRCMvLy0v929fXVzX9SI1nI2l9OXHiRH48FRERERGR1STe0Kj/5317a0iXKklGN8o4pqFU35WARVpAdu7cibFjx2LixIlwcXFB79691aj2mXNNpk+frtIzvvzySzVifY0aNbBkyRI1cGRGudlnsQpYJNnozJkz6t+SeCP91n7++WeVZCN++eWXHI3mSURERETkCC5cuJCj9erVq4fNmzffcz2tVotJkyapKb/2affjsGTUq1cv/PDDD0hNTVWPJWKTCFEiO5nWrVunuoURERERERWncVjITlpYJD9F+sAZ+8cNHz5c/XvVqlXqpywbMGBAfjwVEREREZHVJN6wKoeF58C+WlgkZ0VKmGWs+yx976Rb2MqVK9W4LBxckoiIiIhshVy3WjtR4ci3gSPvRUqrERERERFR8RITE6MKCBgHjMysQ4cO9hGwEBERERHZCmvzUJjDAjVY5OjRo1UaiHFIk4wkeJGWKEvLcoMBCxERERE5HFsf6d4ePPvss2r8mJdffhnt27dXZZULAgMWIiIiIiLKtd9++w2vvvoqZs6ciYLkZM3o9jllHMGTiIiIiMgWSFclrRXNJEy6B0qUKIHKlSujoOU5YGnevHmOT5Sx/xoRERERkS1gDov1jFWBR44cCZsMWBYsWJC/R0JERERERDbr70w9rAYNGoSdO3eiR48eeO655xAUFGQalzGjpk2bFk3AIoNDEhERERHZI+n7Y1XSPRxPcws9rIxljLds2ZJlfVYJIyIiIiLKIy00arJme0ezoIh6WLFKGBERERE5HivLGjtgvIKi6mGlLZJnJSIiIiIiu9alSxds27Yt2+Xbt29X61iLAQsREREROWyVMGsmR7djxw6EhoZmuzwsLEwl5RfbLmEHDhzAokWLVGR24cIFlCpVCq1bt8b777+PmjVrmtb7+uuvsWTJEpw6dQq3bt1CuXLl0KlTJ0yePLlQ6kLbmtSLf0J/ZS8MMVehq9YdzjV6mpKeUoM3QX9lH5CaCF2ZxnCq9zA02vSPQFr0FaQcX6W207h4QletG5yC2pj2qw87gdT/1sOQcBMaFy84NxoGrV+VInudjijuxCbEn9yK1IhL8GwyEF7NB6v5sYdXIfbwatN6BoMeGq0OZZ5cqh7f+uMLJF08CENqEnSeAfBq+SjcKrVQy6J2fYmEM3/c2TYtFU4+5RAw6ONCf32O7tpfaxC6fz3ir51Dha6Po+L9T2VZ5/g343HrzEHcN2O7ad6Jbycg9vIppOlT4B5QEVUefAneleun73PPWoTsWIbUuCjo3D1Rtk1/VOgyrFBfFwHxJzYj4b9t6rvr0fgheDZ7RL0tcUdWq8nIYEhT393SwxerxxEbJiMl/Aw0mvR7i85l6sCvx5vp6+pTEPPXt0i8uB8wAK4VGsPrvmehdXHnW27nf3MNKfFqWdqNU4BWB6eqXeFUuSPPawHQWjkOizXbFieau7wPwcHB8PLyKr4By4wZM7B7925VLq1hw4a4fv065s6dq8qi7d27F/Xrp/9BPnz4MKpUqYIHH3wQfn5+OH/+vApiNmzYgKNHj6oAxpFo3LzhVL0H9NcOmc3Xh+xH2vWjcG0zBnByQ8qRxUgN3gznmr3V8pSjS6Er0wi61i/BEB2C5H1zVUCi9SyDtOgQpJ5YBedGj0PjWxFIjLay0yflha6EH7yaDUZC8C6z+RK8yGQkQYhBn3xnecO+8LnvGWh0zkgOC0bEL++i9NB50Lp5waf982oyivj1fTgH1OAJKgIu3qUQ1P1J3Di81eLym//ugj4pPsv8yr1fhHtAEDQ6J9w8/idOLXwDLSavVX9A/Gq1REDjLnBy90Jy9A38+9VYlChbDSXr3LkZQQVPW8IPHk0HIfHsn2bzJXiRySj6z6/MvrvCu90LcK/RIcs+449vQsqNc/B/+FNA64SobbMRd3Q1vFo8VoCvhArjb27KyZ8lpIFr58kwJEUjZf8X0HiWgc6/Fk8A2QRpUJDJSBoT5No7M2lI+Oeff9CrV6/iG7CMHTsWy5Ytg4uLi2ne4MGD0aBBA0yfPl21qoh58+Zl2bZ///6q7Nr333+PiRMnwpHoAhuqn2nhJ8zmp4WdgC6oDTRuvuqxU7WuSD68yPTL05AQAW25ZupOnsYnCBrPQBhiwwDPMkg9u0XdOdL63W6xck/fBxUut8qt1M+ky+Y10DOSu64J5/6CX7dxpnlOvhXurKBJb0XRx91UAUtG+vhIJF35B973PVMQh0/3UKp+e/Uz8tTeLMvSUpJwadM3qDZwHI7Nf8VsWYkyVe6UjtTqkBJ3C/rEODi5e8KtZFmzdSWISbwZwnNRyNwqt1Q/ky8fvut3N/H8Hvh0GZujfepjw1SrivF77FqpJZKvHMmnI6ai/Jsr27q0HAWNzgWaEv7QVWilAiAGLPlP7r1aVdbYQe/dxsfHIzw83PQ4JiYGWq02y98bDw8PvPDCC3jnnXeKb8DStm3bLPNq1KiBevXq4eTJk3fd1tgVTCI7yoaUzE6KgiElARpnd+gqtYf+6kFoqt0PQ/QVGBIiofWtlL5q1CXAuwKSdr6vLnblF7RTrQfVHV2yLUmX/obGyRUu5dJbII2i/vwK8f9tB/TJcA1qCqeS6ec2o4TgP+FcujqcvMsU4hFTTlzZvhT+jbvAxSfA4vIT372OW6cPqoveMq37qWDFKPzwFgSvmo20pAS4lSwH/4ad+KbbILkRkf7drWc2P2bfQjU5l6wMz1bD4Vwq/bvrXqMTYvYtQlpCFKBzQtKFfXC9HRiRff/NvbNBhkcx1wv9kB2BXFRb063rbl2hirMXX3xRTUJ6OX366aeqp1NBsqsrTrmDKIk9ErRkdvPmTej1ely6dAlTp05V87p27VoER2mbtAG1oT+/HbrABqp5OvXc7W4n0v1AfnkG1EHKP0uRdDZ90B/n+kOgcfNR/zYkRkF//ShcWr2k+tMmH/oW+vPb4FT9gaJ8SWRB/JmdcK/e3tTn3cin3XPwbvs0kq8dV/3oLf2STTizEyXqdOf7amMSI67hxtHtaDzmGyTHRFhcp+5TM5CWmoLIk39Bn5xotiygSXc1xV0/h4hju6BzLVFIR065kXjmD7hVa2f23fVqOQw6vwpqnnQBu7X5A5R6+FOVp6LzLgOtmzfClz6r1nWp0BDutfn9LQ5/c2Vb6dng3GCo6hImOTLQZB05nKzHgSOtJ6kYhcGuApalS5ciJCTEFJBkVL58eSQlJal/S4L+Z599hu7d7/3LW6oXZGzWMiYICX9PJ7g6OcMehblqoXPToZRf+vEbfNshAtGIOTAXhrQ0lKzTFTdu/ocKgSWRlpKAC1u+RmDrx+BRoTGSo67h6o7/IbB8ENxKVsQ5J2f41+0E77L+al+x9bsi8uQ2VPDrA3sT6J3+kQ+w42u2FGfAyQUoe+cmupKaEIPrlw+hasfZyHCDPQMd4N0QwWs2oERgWfhUbWZaknDjEq7fuoLKDe+Tv612yXhOfZ31sGdXtAa46tJQyiVVPd6/4TPU6/0kAjx0iE/UQwODaZkZFw0Cmt2H7dOfQfnKVeFd1rwoRqmKFZEc7Izw3xegXr87eUv2wnhe7fm7m5rddzcxBmFX/katDjPhlnGZZ4Z8svv64WTw7/COOQ2vSo1wYdfXcHdxQs2Ri6SfJy5v/RKGw4tRruMI2BvjOTX+fnb0v7n6NoMRfnA5Ena9D52rB3yrtkTijXMof3vf9sYr1QmXi/ogqMBJ7vjGjRtVoSxjbyfJXenTJ3+uFe3mt4NUARs1ahTatGljcdCaX3/9FYmJiaq7mOS3xMXF5Wi/kgPz7rvvWlz2ZLtSqFixPOzRFzc94OvrhaGPZDj+wXKR8rypWMEPsccwc0gQzpw5g2m/u2P+a31vrxiEGbe2o065cDz4YBtM/LsyHmhVEp07p+9rz55LWBPughkZ921nXmxuv3ervjiuga+vBkM7mL+GTZv2IrZSRXw44O7V8abuMKCpfxj6ZNj+++93wadFM0y8P/0Onz17qFzOvvu26guvZPj6JGFopWj1eNu5Iwi+fBzBP3+CtLQ0dfHzx+SH1I2bihUrZtn+b10y2mrPomWlUlmWrfaJx+nQi3jq9r7t0aiW9nnRJr44qYWvrxZDO5m/hk2b9iO+UkVMH1j1rtuPXqnFsIY6NGnijJdWXcJTTz2FJk281bIDJbuom3pTMu3bnozvEQh7lZ9/c5XH3zLtZvHixTAY6uMJO/2be+mSHoeXwyZprRzfg2ODQKVfDBgwAH/88Qd0Oh3Klk3Pndy6dSu+/PJLtG/fHmvWrIGvr2/xD1ikQljv3r3h4+ODlStXqjcks86dO6ufPXv2RL9+/VQVMU9PT4wePfqu+x45cqSqRJa5hUUS9xf8eRMxTvZ1YWtI06vSmDeCY6Bz0+DADxeg0eiQlhyPtNREOHmUUndzQncvQKkm/fHa8hDokzWIik3ACx9thEf5hkiJvo6Qw//iontz/LE8BFHeTTF/8c9Ye7msSuq9tnMlSpSto7a1N3IHT/4ofnFQj/CsBZds/9ym6XE5RA/nW6n47/cEdT5kEv+t2w6/mh0x9Y87LQz6pDhEnfsbPtWaQ+vkglvB+3Dh6DHE130Uf99eTz4v//62E0FdnjHb1h7v0koguvqqB26l2Nf3VqTp08/vyWgdXA1OiA12g1anQ7tJ36vusCIhMgx/fjIaLcd9g99SfZDwdxhir1+Ef82mqm/DxT/X41r4TRx0b4p/L3rj0v7NCKzTEi6evoi6cgb71/2KGl2H4LuL6Re59tbCIsHo//an2O1390pIKpwjU3Bya5zZd/fM2u3wrdkBU3akmLZJTYxDQmgwPMrXVef2xtHNCIuIweqwKli7IwWxnlXx2U+/I+hGDRhgwOUt26FxCzLbhz19dyUQnbUpFKHRFloPHexvbnJMGHQuHtA6uyH+2kmE7d+GoB6v4x87/JsrvFJvwraT7q3JYcnXw7FLr7zyCnbt2qWq+0peiyTaC2k4kEaBSZMmqXUyVhUrlgFLVFSUCkIkgpM3JCdliqtVq4YmTZqou033ClhKly6tJktuxKYi3GBfv/xTzvwKffBm0+PI45vg1GCoSuZLOfSNykeRfrJS9SvSvSYiI+X1OUHXaDhCD6+HYfciwLkEnCp2RIRrNUREpsBQsgVSfcNwYf1UQKOFrmwTxJXphHi1rX2SC55rsbArMQdXIvbvO7epru9bBZ+Oo1CiVhekRl9H3PWz8Oj6utnrSkvWIPLIFlzc9pXK4dT5lIFPlzGIKlEFUbfXSwo5jtSUZMSXbooEO3tPLJFg5Wayzf9qy+LSb9/j8paFpsdnfluK6o9MQmCL9HEdRKKrHgZoEOdWGnF6IDFZh9ObliJ+0fuAVguPMlVR58kZ6cuTgevn/sPxtV+pvBYXr5Io3bofvFo9jJvJ9vtX1h6/u7GHViHu8ArT49D9q+HdYSTca3ZGanQo4kKDUaLLePPvbkIqInctgz7qavpYHCUrw/v+NxCW4gGkAE5NH0f07q9x7JsX1PouZerA674RdvfeZCTByhU7+7tSEH9z9VfPIeXkGrnjBI1nWTg3HoHrie5Aon29N0YBGvsKQil3pPVEbv6/9tprZvMlcBk/frzKLZeqvday6b/q0sWrb9++OH36tGpaqlu3bo63TUhIMOW0OBIZtMo4cFVmrh3vNDFnJgmAMlkidx+ca/VRExUdGSjSOFhkZlLZq+wzP2WZr3UpgVJ9s+Z8ZeRavgHKDL9zoUxFQwaKtDRYZEZSpjjjoJFupcqh4UtfZLt+tYfGqomKlgwUaRwsMjMn70AEPvVjlvladx+U6j8j233Kct9u5hcIVDz+5urKNVMTFQ77vX1jG5ydnVGrVvZjBNWuXVutYy2b7X4nFb9k3JU9e/ZgxYoVKncls9TUVERGRmaZv3//fhw7dkyNxUJERERElN1I99ZMjm7gwIHqOl2u2y1dpy9fvjxL6kWxamEZN24c1q1bp1pYIiIiTANFGg0bNgyxsbEICgpSgY2UOpbmJwlUFixYoPJd3n777SI7fiIiIiKi4mzYsGEq/ULGT3zuuedQvXp1NV+KS3z11VdITk7GY489hr//Nh/0umnTpsUjYDlyJH3E3vXr16vJ0htUokQJPPPMM9i+fbtKxpduYJLjMnToULz11lumASSJiIiIiLKMw2LFW5LXbUeMGHHXJPQrV66o4To6deqEnTt3Zln+wAMPYNOmTWbzJA1CRpSXqnLS+6hhw4Z4//33czTEhzU6duxo+veBAwdMRQyMxWIyryPzZR1LLTJ2GbDs2LHjnuu4uLjgk08+KZTjISIiIqLiI71KmHXb58Xzzz+Pbt26mc2TC/kXXnhB3WyXYMWoQoUK+PDDD83WtVSASoIguXk/ZswY1KhRAwsXLlTjoMhN/Xbt2qGgSK+mwmCzAQsRERERUUGRO/3WlTXO27aSl505N/vPP/9EfHy86j6VkaQ4DBs27K77k9ztH3/8EbNmzTJV63riiSfUEB8TJkzAX3/9hYJiaWzEgmCzSfdERERERI5g2bJlKgB69NFHLSavx8ZmX7PcOEah5JAYubm54emnn1bFqy5fvozCcO3aNRw9ejTHg7fnBgMWIiIiInI4mgyj3edlyq8aYSkpKaqaliSuZ86/lqE9PDw84OXlhTJlyqiCUrJ+RocPH0bNmjXh7W0+KHDLli3N8sILytq1a1X5Yum+Jsn0+/btU/Nv3LihxkX8+eefrX4OdgkjIiIiIoeTX13CgoODsywLCAjIdmDyzDZv3oybN29m6Q4mA6F37twZDRo0UK0W0pIiifQSxPz0009mLRtly5bNsl/jvKtXr6KgSGGshx56SHVxk9ahKVOmmJb5+/urfBzJpxkwYIBVz8OAhYiIiIgoj/r3759l3uTJk80u3u/VHUwGV3zkEfMBZr/99luzx48//rjq9vX111/j1VdfRevWrdV8qZLr6uqaZb/SLcy4vKBMnToVHTp0UMn9EnRlfs0SyHz55ZdWPw8DFiIiIiJyOPlV1njNmjWm8UcytrDkhOSmSJcqKVVcqlSpHI1T+PXXX2Pr1q2mgMXd3V2VNc4sMTHRtLyg/Pvvv/joo4+yXR4YGIiwsDCrn4cBCxERERE5HA2s7BJ2O2SRYEUGMM8LCXYsVQfLTlBQkPopg6pn7PoVEhKSZV3pKpZdGeT8ImMi3i3J/ty5czkKxO6FSfdEREREREVg6dKl8PT0xIMPPpij9c+dO5elBadx48YqryU6OtpsXWPyuywvKJJjI4NgSiWzzK5fv65ag+6//36rn4cBCxERERE5nKKuEhYeHq66dklCurRUZCTBR+ZuXgaDQSXdC+lCZvTwww+rkeO/+uor0zzZVgZ1bNWqlalVpiB88MEHuHLlClq0aKFyVaTFSooIvPXWW6pYgByz5PNYi13CiIiIiMjhFNXAkUZS6UtaJix1B/v7778xdOhQNVWvXl0lzkt54N27d6vEeykfbCRByaBBgzBp0iSVLyLrS6vHhQsXsiTu57datWqpQS9feeUVVXJZAhQZwFJ06tQJ//vf/7KUas4LBixERERE5HDyK+nemu5gUvq4W7duWZZVqlQJ7du3V0HK9evXodVqUadOHcyfP99sgEij77//XgUMixcvRmRkJBo2bIgNGzaoCl4FTfJ3pKVInldKPKelpaFq1ao5LjyQEwxYiIiIiIgKmYxCn50qVaqowSRzys3NTbVsGFs3ioKfn5/qGlYQGLAQERERkcORHl3W9OqyskeY3UtKSsKSJUvw22+/4ezZs4iJiYGXl5fqktajRw81kKSLi0u+PBcDFiIiIiJyOOnJ83mPOhy5ctWxY8fQr18/XLx4UeWt+Pj4qGpnkkMj+TcrVqxQCfnr1q1TXdms5cjvNRERERER5YIMdillmENDQ1VQcvnyZZW/kvGnVDO7evUq+vbte9dxWnKKAQsREREROZ7bXcLyOlmddW+nFixYgEuXLuGXX37BxIkTUb58ebPl8lgqlq1fvx7nz5/HwoULrX5OBixERERE5Jgj3Vv5nyP65Zdf1GCQUrb4brp06YLu3burwMVaDFiIiIiIiCjH+Sv3ClYyBi2yvrWYdE9EREREDodVwvImIiICZcqUydG6gYGBan1rMWAhIiIiIocjFcKsqxKmcdhyxs7Ozjla18nJCcnJyVY/JwMWIiIiInI8Vo7D4qDxinLhwgVVvvheJOk+PzBgISIiIiKiHHv77bfVdC8yRosmH0bYZMBCRERERA5HVSa2ZqR7OG5Z48LGgIWIiIiIHI61pYkdtazx8OHDC/05WdaYiIiIiIhsFltYiIiIiMjhaDXpkzXbU+FgwEJEREREDoddwuwHu4QREREREZHNYgsLERERETkejsNiNxiwEBEREZHDYZcw+8GAhYiIiIgcDpPu7QdzWIiIiIiIyGbZbMBy4MABjB49GvXq1YOHhwcqVqyIRx55BKdPnzatk5aWhoULF+LBBx9EUFCQWq9+/fp4//33kZiYWKTHT0REREQ2PtK9Vf8RHL1L2IwZM7B7924MGjQIDRs2xPXr1zF37lw0bdoUe/fuVYFJfHw8nnzySbRu3RovvPACSpcujT179mDy5MnYtm0bfv/9d2g0/DgRERERkTm5RLTmMpGXmIXHZgOWsWPHYtmyZXBxcTHNGzx4MBo0aIDp06djyZIlapkENW3btjWt8+yzz6Jy5cqmoKVbt25F9AqIiIiIiKjYdgmTICRjsCJq1KihuoidPHlSPZblGYMVowEDBqifxvWIiIiIiCx3C8vbRIXHZgMWSwwGA0JDQ+Hv73/X9aT7mLjXekRERETkmLQajdUTOXiXMEuWLl2KkJAQTJ069a7rzZw5E97e3ujZs+c99xkWFobw8HCzecHBweqnv6cTXJ2crTxqsiWB3ukf+YASRX0klN+M59TXWc83txgynld+d4sf4zk1/n6m4sUr1QmXi/ogyO7ZzW+HU6dOYdSoUWjTpg2GDx+e7XrTpk3D1q1bMW/ePPj6+t5zv7Leu+++a3HZk+1KoWLF8lYdN9mmF5vrivoQqIA8VC6O720xNqolbyIVV+N7BBb1IVABuHRJj8PLbfOttbZrF9tXCo9dBCzSxat3797w8fHBypUrodNZvtj86aef8NZbb+Hpp5/Giy++mKN9jxw5UlUiy9zC0r9/fyzYFY4YJ34cixO5gze+Z1nM2RGD0Ni0oj4cykeBnlqM6+SFLw7pER7Pt7Y43oV/sZkOs7fzu1scv7uvdfbCrF+vITQ6tagPh/KZV6p5Lxabw8s8u2DzAUtUVJTq2nXr1i3s2rUL5cqVs7jeli1b8MQTT6jAZv78+Tnev5RClsmSG3F6hKel5PnYyXZJsBISxYClOJJg5VpsUR8FFex3l93+iiMJVq5E8m9ucROgtd3vq7WjqeR12x07dqBz584Wl8nwHDJch9Fff/2FCRMm4O+//1bpDjImofQm8vT0NNsuKSkJ77zzDhYvXozIyEg1JIiMS9i9e3cUBzYdsMjgj3379lWDRUo3r7p161pcb9++faoyWPPmzbF8+XI4Odn0yyIiIiIiB/fyyy+jRYsWZvOqV69u+veRI0fQtWtX1KlTBx999BGuXLmC2bNn48yZM/j111/NthsxYoTqhTRmzBhVVVcGVu/Vqxe2b9+Odu3awd7Z7JW9Xq9X465IpLl27VqVu2KJlC6WVhUZe2XDhg1wd3cv9GMlIiIiIvtS1ANHtm/fHg8//HC2y9944w34+fmpFhlpXRFyvStjDv7222+4//771bz9+/fjxx9/xKxZs/Daa6+pedLrSAZZl9YZaaWxdzYbsIwbNw7r1q1TLSwRERFqoMiMhg0bhpiYGDzwwAOq6Wv8+PH45ZdfzNapVq1atoEOERERETm2ok5hkWtZudmeuXdQdHS0Snd49dVXTcGKMRCRedKjyBiwGPO7n3vuOdN6bm5uKqdbgp7Lly8jKCgI9sxmAxZpBhPr169XU2YSsNy8eVOdBDFx4sQs60g1MQYsRERERGRrnnzyScTGxqpgQ1pbpIVE0hvEsWPHkJqaanpsJIOmN27cGIcPHzbNk3/XrFnTLLARLVu2NF1TM2ApINL8dS/SLCaDSRIRERERFUVdY+P4fRkFBARkW9RJgo6BAweqHBMZ5PzEiRMqN0WCFum+1aRJE1y7dk2tW7Zs2SzbyzwpRGUk62a3nrh69Srsnc22sBARERER2XqVMBkKI7PJkydjypQpFrdr27atmowefPBBlcsilb0mTZqETZs2ISEhQS1zdXXNsr109zIuF/Lv7NYzLrd3DFiIiIiIyOHkV9L9mjVrzKp7GVtYckO279evH1avXq0KTxmLSEm5YktVdDMWmZJ/Z7eecbm9Y8BCRERERJRHEmzUq1fP6vdP8kySk5MRFxdn6s5l7BqWkczLOC6hrBsSEmJxPZHdGIb2RFvUB0BEREREVFQpLNZM+encuXOqG5cMCikliaVy2MGDB83WkYBGkugl8d5I/i1jFkplsczjFBqX2zsGLERERETkeIooYgkPD88y7+jRo2o4DylVrNVq4ePjg27duqlhPWJiYkzryUj2Ulls0KBBpnmS/yLdyL766ivTPOkitmDBArRq1cruK4QJdgkjIiIiIiokMjC65JVI4r1UEpMqYRJslChRAtOnTzet98EHH6h1OnbsqMZYkZHu58yZo4KaHj16mNaToEQCGEnYDwsLU13UFi1ahAsXLuDbb78tFueVAQsREREROZz8qhKWW1JVbOnSpfjoo49UNy5J0H/ooYdUZbGMyftNmzbF1q1b8frrr6vBIr28vNRgkB9++GGWfX7//fd4++23VQuMDKguFcc2bNiADh06oDhgwEJEREREDie/qoTl1ssvv6ymnGjXrh127959z/Uk90UGnpSpOGIOCxERERER2Sy2sBARERGRQ8rvSl9UMBiwEBEREZFjYsRiFxiwEBEREZHDKaqke8o95rAQEREREZHNYgsLERERETmcoqoSRrnHgIWIiIiIHI4Vg9WbtqfCwS5hRERERERks9jCQkRERESOh00sdoMBCxERERE5aLxiTZUwKizsEkZERERERDaLLSxERERE5HBYJcx+MGAhIiIiIofEbl32gQELERERETkeJt3bDeawEBERERGRzWILCxERERE5HKkQZl2VMHYoKywMWIiIiIjI4TDp3n6wSxgREREREdkstrAQERERkcNhzr39YMBCRERERI6JaSh2gV3CiIiIiIjIZrGFhYiIiIgcDquE2Q8GLERERETkcFglzH7YbJewAwcOYPTo0ahXrx48PDxQsWJFPPLIIzh9+rTZevv378fIkSPRrFkzODs7QyOfPiIiIiKiHCbe52WiwmOzLSwzZszA7t27MWjQIDRs2BDXr1/H3Llz0bRpU+zduxf169dX623cuBHffPONWqdq1apZAhpHk3ppN/RX9sIQcw26qt3gXKOHmm8wGJAavBn6kP1AaiJ0ZRrBqe5AaLTpH4G0mOtIObEShugQaNx81TJdqerp+zy7Fanntt55EkMaoNHBrfuHRfMiHVTK2d+Rem4n0qJC4FynD1zq9Ted25R/VyP1wi4Y9CnQ+deAS7Ph0Lr7qeWpl/ch+d+fYUiMgtarDFwaP6rWEXGrXzB/En0yXBo+Auda6Z8bKjxxxzch/tRWpEZcgmeTgfBqPljNj/17FWIPrzatZzDoodHqUOapperxrZ1fIOnSQRhSkqDzCoBXi0fhVrmFaf3k0NOI/us7pEZchsbVA95tn4R71TY8tYUo5ex2pJ7fCUNUCJxq9zb/7h6X7+6fgD4FWvnuNn3iznc35JD6bhviI6AtVRUuzZ+GtkRJtSxx10dIu3HmzpOkpUJbpj7c7nuF59bO/+aqbc/8Cn3IPkCfCq1fFTjXGwSNmw/PLTksmw1Yxo4di2XLlsHFxcU0b/DgwWjQoAGmT5+OJUuWqHkvvvgiXn/9dbi7u6sWGUcPWDSu3nCq/gD0V/82m68POYC00KNwbf0y4OSGlKNLkBr8G5xr9oIhTY+Uw99CV7E9dC1HIu1mMFKOLIK2/URoXDzgVK2bmoxSjq9UF8ZUyOfWzRfO9foj9dLeTOf2EFIv/gW3rm+rP2jJBxci+ehPcGv9AtISo5C0/xu4tXsV2tJ11EVT0p55KNH3Y7Wtx0PzTftJS4hEwi+vQVehGU9tEdCV8INXs8FICN5lNt+z6UA1GUXt+hKG1OQ7yxv2hU+7Z6DROSM5LBgRv7yL0kPnQevmBX18JCK3zIZPhxfhWqEhDElxSEuJL9TXRfLd9YFz3X7QX9qX5burv7gHbl3eSv/uHlqElH9+gmurF9QFbfKB7+Aq392SVZD630Yk75sPt85vqG3d2o8121fC5jfhVJ7f3eLwNzct9B/orx6Ea+sxgKsXUv5dgZRT6+DS+PFCf33FHusa2w2b7RLWtm1bs2BF1KhRQ3URO3nypGleYGCgClYonS6wAXSl60PjbP6epIWfgK5CG3XRq3Fyg1PVruoXqjDEhcGQkgCnyh2g0Wih868JrXd56EOPZXlbDWmp0F8/Ah3/MBY6p/JN4VSuCTTOJczPSdwNaANqQluilLp7pwtqAUP01fRlCZHQuHhCF1hXdZd0qtgGhsRbMCTHZdm//tJeaEtVg9YjoNBeE93hVqWVahnRunhk+7bIjYKEs3/BvUZH0zwnvwoqWFE0sk4q9HE31cO4fzagRK3OcKvYRLXKaN294eRdhm97UX13XTJ9d+NvQOt/57vrVKEF0m5/d/Whx6ErXQc6/+rq3EnLTFrkRaTFhmbZvz7iPAzxN/l7uZj8zZXf21q/qtC4+6X/Ti/bCIa460Xx8hwm6d6a/8jBAxZLpJk0NDQU/v7+RX0oxYABSIpSvzRNjzMtN8Rm/QUpv4Shc4G2ZHrTNRU9XYXmMMRcR1pcOAz6ZKRe2gddYD21TOtbERrPQKRePwaDIQ2pF3ZD61dZ3cXLLPXiHjhVuq8IXgHlVNKlv6FxcoVL+fQusUZRu77CtW+G4ubq1+Favj6cSlZS81PC07sMha94FaGLn8at7Z8jLSlrsEpFQ1e+ufo9a/ruXpbvbsZzm/X3sjGgyUh/aQ905ZpmuWgm+/ybqyvTEIa4cKTF31SfC/21w9CWqlXoR0xFn6c9YsQIdbNRk2mqXbt2ln2mpaVh5syZqFKlCtzc3FSqxA8//FBsTqPNdgmzZOnSpQgJCcHUqVPzbZ9hYWEIDw83mxccHKx++nvo4Op0+86lnQlz1ULnpkMpv/Tjj6pUD7dO/Y7Amk2gdXZD6D+/QzqVlPU0QOdfHhePlIBH6C741uyI+Ov/4VrEWXj7BqD07e2Nrv37NzyqNId/SVfYo0Dv9I98oKddxepmQl000Llq4O+T/hrSPP0QXqYaojZOADRauJYMQlCn4dC6yHItbtVug/A9c9Wdd61LCQT1eh2ut7c1Srp5CZdiryOobkvoXO3zvTGe0wDzm9h2J8UZcHKR72bWZWfP7URA3fYo52V+jsr2fA6GB55GzOXjSLx5CaW90u/6RSREIDl4J2oOfAfOnn64uOlzpB5ciEoPjIK9MZ5Xe//uOrlpUcpHpx6neZbEDfnu/vq66btboaN8d3VIrlYfl46vQsn4M3ArXR0RRzcgIU0PP5cUeN/eXkj3ovNX9iOw47PwyDDfnhjPqfH3s6P/zTV4l8SNwMqI+uOD9M+FbzmUbzsEWmf7vB7xStXhMmxTUVUJy2metnB1dVW52hn5+GTNZ3rzzTdVysSzzz6LFi1aYO3atXj00UdVgDNkyBDYO7v57XDq1CmMGjUKbdq0wfDhw/Ntv/PmzcO7775rcdmT7QNQsWIQ7NEXEZ7w9fXG0MHpx5+WNgg//ZSG33+fq6LwIf364fvvT2POE3Wg0+lwoc3b+Prrr3Fp41ZUq1YNNTu0R7ly5TD49vYiJiYGT644gemvP4ugIPt8X4zGdfKCvfrisgt8fV0xtE/6L6zFixfjP10oPlu0SN1Vkcc3Ti/ExIkTcfjwYXy6cg0+mTMLFSpUwJ49e7BgwSeYOW+e+iVotHDhIZRv1QITBpaDvXuxmX1etBl98a8Gvr4aDG1v/jrU9+/TQ3j/lY8RFGTpNcq8Jnj//V/Qw708mjdvjjGrXNG6dUcM6Zf+fQ2uNAgffPAB3sm0b3vyWmc7/u5euf3d7XuX7+6Z9O8u4IO/qr2MH39citDISHTs2BH/3AzC892DUK/enYuVgwcP4n/uOnz+XFv1u9yeje9ZFvYqP//mqs+Fa6T55+LKqtufC/tz6ZIBh1fAJhVVCktO87SFk5MThg0bdtf9yc38OXPmqOtkCXzEM888o35vjB8/XgVG9v77wS4CFok8e/furSLKlStX5uubLiWR5URmbmHp378/FuwKR4yTffZPDDsbC52bFod+ynBfQ9sOPt3aqX9uOncSOp8KeH2lsXuBDmj4AgIbArESIG75CH6oiwMZto868yd0XmXx6V/yyFbvl9yd3MGTP4pzdsQgNDYN9ij0YjJ0YUk4vCFKPQ7ZHwyPis3xgcrVTkSSSxtcPvQ+JmyIQsQ/p6AvWQv/O+YLHJMz2wCRsUkY/8N/cPNP7zYkXcXO/bYDgfeNUNvYK7lLK4HoF4f0CLfjvPJL1wxwijbgzC692fzwo7vgXLIiFlwoB1wwX5ZR8A09ru++io0JekS5BWHnJQNO395XfKgBMckGTM20b3tpYZFgdPZ2+/7uOoUl4e/1t7+7+4LhWak53v9DHiUiybkNrhx6H+NvLwfqwbX7e5CsoxNJcQgJ24lvT/pCd+7O9/Ta71vgVL4VJm6U77f9fnclEJ316zWERqfC0f/mXt1zEh7lG+C9TdEAopGkb4CQQx/jtYz7tiNeqea9WCg9TzszS3naRnq9HnFxcfD29rb49klrSkpKirqmNZKWFSlMJa0scrOyXbv0z6K9svmAJSoqCj179sStW7ewa9cudQciP5UuXVpNltyI0yM8zb6qYUn3ACk7nJqYCqSlIP5GPKDVASnxMKQmQeNeEobYUKQcXQ2nWn1xJTL99aXFXIWmhCRbG6C/9Cf0KXpEutdA5O3lIil4H3Rlmpm2sWdywRMSlWaX5zY5SQ+NJhWJEUnq3KZ4VULCmX2I9W+mqtEkH9sJeFdQr0/vXhFJVzfh8uUQaLzKqKpEaakpuIFS0Nx+/ZLcK78Mb3nXR5SdvSeWSLByzQ6v3dT5TdMjLjkNOp0eV28lq/MrCdfixrGdcK/e0ey1ST6K5LW4Vm4Ojc4Fief3Ifryv3Bp9phaT1OtM8L+mI/USh2gc/fDrb9WwzmomV2+P+bfXb39fneRioSIRNN398bpfYgp1TS9ktS/8t0tb3p9aZEXoPGtCCTHIvnwEmgqtsP1RHcgMX255EMkXDqsKofZ23tiiQQr9vb3pSD+5qa4V0Di2b8R7VUfcHJF6n9/weBR1u7eG6MArQ1/Nm2oSpgxT1uClozi4+NVoCI//fz8MHToUNWlzNPzTr9h6U0huTB16tQx27Zly5am5QxYClBiYiL69u2rkpC2bt2KunXrFuTTFQupZ7dAf/Y302P9ua1wqj8EWt9KSPn7GxgSo6Fx84auanfoAu58sPVX9qfXizcYVNUal6ZPme1XJf9FXYauyZOF+nrojpST65FyYm2Gxxvg0uJpONfqBUP8UsRvejN9LAa/ynBtkX6edKXrwrlmDyTumqMqg2k8/OHa5kWzSmNSEtkpqJXpwpiKRuzfKxF7aPmdx4dXwafTKJSo1QWp0deREn4Wfg+8br6RRoP4U1sQ9edX6qHOuwx8u4yBs38V9di1QiN4NOiDm2vfVBdWrhWawLt1/nWppZx/d1NPrjM9Tj21AS7Nn4JzrZ5ITohAwua3bn93K8Gl+Z3fscl/L05PsndyUQUxnOs/ZLZf/ZWD0HiWVsU1qPj8zXWq2gWpJ1Yj6c8Z6iaG1qcCnOunj8tE+cvaSl/GbY25zxkFBARke0M8p3naZcuWxYQJE1Rui3Qt3LRpk0plOHr0KHbs2KG6i4lr166pqrmZB0+X7cXVq1mLddgbm21hkTu+0p9PmrGkqUtyV+jeZNAq48BVmbl2eDP77er0V1N2pOym2wOzeAqKkAw2ZxxwLjPXFk8huzIIMgjk3QaCdG35bD4dIVlDBoo0DhaZmZQiLvvsT1nmSxGFUn3vXoTEo0FvNZGNfnebm98cykjGVrobpyrt1UTF62+utJY6NxgC+0yxty/5lXQvaQSZTZ48GVOmTLEqT/vDD80H6B4yZAhq1qypEuwlRcKYTJ+QkGCWl2okOVDG5fbOZgOWcePGYd26daqFJSIiwiwBSRgTkC5evKgS0ozJh+L9999XPytVqoTHH+dAS0RERERUMNasWYPq1atnaWEpiDztV199FW+//bbqeWQMWGQ8wqSkJIs9lYzL7Z3NBixHjhxRP9evX6+mzIwBy/nz59WJy8j4WKojMGAhIiIiooJKQ5FgJXPuSUHlabu7u6NUqVLqZn7Grl/bt29XeTAZu4VJVzGR3/nfRcFmC9pL3zx547ObjDp16pTtOrIPIiIiIiKLOfcaK6Z8ytPesGFDjvO0Y2JicOPGDbMWnMaNG6uk/MwVxvbt22dabu9sNmAhIiIiIipuMuZpr1ixwmKetgQ0Epxk9t5776mb8j163Mmd6tevH5ydnVVCvpGsM3/+fJQvX95iGWV7Y7NdwoiIiIiIiltd45zkaUtuS5MmTVQZ49q1a6v5mzdvxsaNG1WwIkGKkQwMPWbMGMyaNUuNxyIj3UtejXQzk+pj9j5opGDAQkREREQOJ7+qhBVEnravry/69OmDLVu2YNGiRapVRnJlpk2bhtdeew1arXknqenTp6txWr788kssXLhQDUQpgZAMHFkcMGAhIiIiIodTVONG5iTHWgIWYxXcnJAAZtKkSWoqjpjDQkRERERENostLERERETkcIqqSxjlHgMWIiIiInLQLmF5jzoYrxQedgkjIiIiIiKbxRYWIiIiInI8RZV1T7nGgIWIiIiIHBJjDvvALmFERERERGSz2MJCRERERA6HVcLsBwMWIiIiInI4UiHMuiph7FBWWBiwEBEREZHjYdK93WAOCxERERER2Sy2sBARERGRw2EDi/1gwEJEREREjhmwWJGGwgyWwsMuYUREREREZLPYwkJEREREDodVwuwHAxYiIiIicjwa67qEsU9Y4WGXMCIiIiIislkMWIiIiIiIyGaxSxgRERERORzpDmZVlTCWCSs0DFiIiIiIyOEw6d5+sEsYERERERHZLLawEBEREZHDYZcw+8GAhYiIiIgcc6R7K7enwsEuYUREREREZLPYwkJEREREjodNLHaDAQsRERERORxWCbMf7BJGREREREQ2iy0sREREROR4rBw4kln3hcdmW1gOHDiA0aNHo169evDw8EDFihXxyCOP4PTp01nWPXnyJHr06AFPT0+ULFkSjz/+OMLDw4vkuImIiIjIflJYrJnyKikpCa+//jrKlSsHd3d3tGrVClu2bMnHV1e82GwLy4wZM7B7924MGjQIDRs2xPXr1zF37lw0bdoUe/fuRf369dV6V65cQYcOHeDj44Np06YhNjYWs2fPxrFjx7B//364uLgU9UshIiIiIltURLWJR4wYgZUrV2LMmDGoUaMGFi5ciF69emH79u1o165d0RyUDbPZgGXs2LFYtmyZWcAxePBgNGjQANOnT8eSJUvUPAlS4uLicOjQIdUKI1q2bInu3burk//cc88V2WsgIiIiIspIbqj/+OOPmDVrFl577TU174knnlA34ydMmIC//vqLb5i9dAlr27ZtltYRiUCli5h0ATNatWoV+vTpYwpWRLdu3VCzZk0sX768UI+ZiIiIiOyrSpg1/+WFtKzodDqzm+pubm54+umnsWfPHly+fDkfX2XxYLMBiyUGgwGhoaHw9/dXj0NCQhAWFobmzZtnWVdaWQ4fPlwER0lEREREtk4S7q2d8kKuT+XGure3d5ZrV3HkyJH8eHnFis12CbNk6dKlKkiZOnWqenzt2jX1s2zZslnWlXkREREqqcnV1TXbfUrAkzlB/8SJE+qnR+pNO3uH6F68UnW4dMkAz6Q4+Kel8Q0rRjyTtLh0KQrusXr4Jhb10VB+cwdw6ZKO391i/N31Sg1HgFZf1IdD+UxdS91OMrc1Z88G58v2wcFZ9xMQEIDSpUtb3E6uX7O7dhVXr1616riKI7u5HD916hRGjRqFNm3aYPjw4WpeQkKC+mkpIJGmNeM6dwtY5s2bh3fffdfyc/72ZT4dPdkKaWQ9vKKoj4IKwkUAfy/me1uc7S7qA6ACcUl+L6enpVIxJsWQpHCSLfDz84OXlxceGdjf6n1J+kL//ln3M3nyZEyZMsXiNtldm2a8diU7DFikQljv3r1VJTBjvz8hZeCyi9oTExPN1snOyJEjVSWyjKQpbtiwYSoHpm7duvn4SqioyV0Q+cWyZs0aVK9evagPh/IRz23xxvNbfPHcFm/Sa0WGpZAuULZCSgnLjfDIyEir96XX603XpZlbWLIj16bWXLs6IpsPWKKiotCzZ0/cunULu3btUh+yzE1nxq5hGck8GZPlbq0rQprrsmuyk2BFkvyp+JFghee2eOK5Ld54fosvntviLXO+RlGT68mM15SFSa5fJcUhM+P1bFEdly2z6aR7iTT79u2rBovcsGFDltaO8uXLqwj24MGDFkvGNW7cuBCPloiIiIjo7uT6VK5to6Ojzebv27fPtJzsJGCRJjYZd0XKu61YsULlrlgycOBAFcxkLAG3bds29UHI3NWLiIiIiKgoPfzww+o696uvvjLNky5iCxYsUCPeBwUFFenx2SKb7RI2btw4rFu3TrWwSLUv40CRRpJjIt544w0V0HTu3BmvvPKKGuleBuKRASaffPLJIjp6IiIiIqKsJCiRm+qTJk1S1WqlO+SiRYtw4cIFfPvtt3zL7ClgMdagXr9+vZoyMwYsEoXu3LkTY8eOxcSJE1W1BknQnzNnzj3zV7Ij3cykusPdEqbIPvHcFl88t8Ubz2/xxXNbvPH8Wvb999/j7bffxuLFi1Xyf8OGDVWPoQ4dOhTyGbIPGoOMxkhERERERGSDbDaHhYiIiIiIiAELERERERHZLAYsRERERERksxiwEBERERGRzSrWAcuBAwcwevRoNaK5h4cHKlasiEceeUSN0ZJZWloavvjiCzVYj7u7O0qVKoUuXbrg6NGjpnVOnTqFCRMmqHW8vLzUSKVSkczSwJVkX+c2s6VLl0Kj0cDT07OAXwkV5vk9e/YsHn30UZQuXVqtW6NGDbz55ps8CXZ+bmV06Oeeew5VqlRR61WrVk1Vjrx582YhvjLKzfmV36/ZTd27d8/yOZg5c6Y6v25ubqqa0g8//MA33M7PLa+pqFiUNc4PM2bMwO7du1Wta/kFd/36dcydOxdNmzbF3r17Ub9+fdO6Tz31lLpIfeKJJ9QXMi4uDocPH1b1sY2++eYbVR9bBqscOXIkoqKi8OWXX6J169bYtGkTunXrVkSv1PHk97nNSMbykcBUfiFT8Tm/Uiq9U6dOKF++vBrnSS5+L126ZDboLNnfuZXvqwwsLMvk97KUupeARva5fft2HDp0CFptsb43Z5fnV0q5ZiY3/z799FPcf//9ZvPlpsL06dPx7LPPokWLFli7dq268SAXwEOGDCm01+bo8vvc8pqKcsVQjO3evduQlJRkNu/06dMGV1dXw2OPPWaa99NPP0lpZ8Pq1avvur+DBw8aYmJizObduHHDEBAQYLjvvvvy+eipMM9tRq+//rqhVq1aaj8eHh48EcXg/Or1ekP9+vUNrVq1MsTHxxfYcVPhn9ulS5eq9TZs2GA2/5133lHz//77b54WGzy/ljz99NMGjUZjuHz5smnelStXDM7OzoZRo0aZ5qWlpRnat29vqFChgiE1NbUAXgUVxrnlNRXlRrEOWLLTtGlTNRnJRUzLli1NFzaxsbG52t9DDz1kKFmyZL4fJxX+uZVfvi4uLoZffvnFMHz4cAYsxeT8/vrrr+ridePGjepxXFwcL3SKybn94osv1Lk9cOCAxfknT54s4COnvJzfzBITEw2+vr6GTp06mc3/3//+p87j8ePHzeYvW7ZMzd+1axdPgJ2e2+zwmooscbh2cgnSQkND4e/vrx5HR0dj//79qpn5jTfegI+Pj8pbqFq1KpYvX56jfUqzqHF/ZN/ndsyYMejcuTN69epVyEdPBXl+t27dqn66urqiefPmqrtfiRIlVHeSiIgIvvl2fG5lVGjp8vXKK6+obilXrlzBxo0b8cEHH6B///6oXbt2Eb0qyu78WiLn7NatW3jsscfM5ksXQPm+1qlTx2x+y5YtTcvJPs9tdnhNRRYZHMzixYvVXZlvv/1WPZbuAvK4VKlShsDAQMO8efNUFwO5syfNl3Jn9m7++OMPtd7bb79dSK+ACurcSpcSJycn0508trAUn/P74IMPmtaVrgsrV65U31k5323btlVdTMh+v7vffPONuoMr2xgn+f6mpKQU0Suiu51fSwYOHKi6FkVGRprN7927t6Fq1apZ1pdWUtnnxIkT+Wbb6bm1hNdUlB2HClika4C3t7ehTZs2pu4g8uUw/oHbu3evaV3JVfH3979rbkpoaKjqQyu/TDPntpB9nVvpl1ujRg3D6NGjTfMYsBSf89ulSxe1Xo8ePcz2++GHH6r5W7ZsKcRXQ/n9e1kCmPvvv9/wySefGH7++WfD2LFjVTA6btw4vtk2eH4zi4qKMri5uRkGDBiQZZl8d+vUqZNlvnQTlM/HK6+8UiDHTQV/bjPjNRXdjcMELNeuXVOBRVBQkCEkJMQ0X/o9yy+9KlWqZNnmySefVMl+lu7SSX/qFi1aGHx8fAzHjh0r8OOngj2306dPN/j5+Rlu3rxpWocBS/E5v3KXVtZdtGiR2XoXL15U8999991CeCVUEOf2zz//NOh0uiw5LFOmTFGtMZlzH6joz29m3333nTrf0vKZGVtYiu+5zYjXVHQvDpHDIuWHe/bsqfpQSvnhcuXKmZYZ/x0YGJhlOxmrISUlRZXLzCg5ORkPPfQQ/vnnH1VeMWMZTrK/cyv7eP/991XJTOk7f+HCBTVJuVQJ6uXf2ZVAJvv47ma3rqwnIiMjC/R1UMGdWyktL+tJblJGDz74oPr+/vXXX3z7bez8ZialqyVPqU+fPlmWyXhnktMg5zLz2Dvibvsl2z63Rrymopwo9gFLYmIi+vbtqwY22rBhA+rWrWu2XL5oZcqUQUhISJZtr169qgapkkEiMw5gJWMCbNu2DcuWLUPHjh0L5XVQwZ1buViV4MQ4MJlxWrVqFeLj49W/ZVA6st/vbrNmzdTPzOvKeiIgIKAAXwkV5LmVhF+9Xp9lPQlqRGpqKk+AjZ3fzIGHjJcj45tJUYzMZNBQ+T188uRJs/n79u0zLSf7PLeC11SUY4ZiTPpUSrKt9GWWMrXZkT6w8lb89ttvpnnh4eGqb2avXr3M1h05cqRa98svvyzQY6fCO7eSvCn93jNPnTt3Vn1v5d8Z+9GT/X13pfuCJH22a9dO9X03mjRpktp+//79BfhqqCDPreSdyXrbt283237MmDFZcmDIds6v0UcffaTO07Zt2ywul3E7shuHpXz58ixPbsfnVvCainJKI/9DMSUlamVkVbkb8Mgjj2RZPmzYMNMduiZNmqi77GPHjlXNl/Pnz1cjYO/ZsweNGjVS633yySd49dVX1ajKMqJyZgMGDODo6HZ6bi0ZMWIEVq5cqbYl+z+/7733Ht555x10795dlbuV0dC//vprVdpYWkvJPs/tf//9p1rQZNTzl156CZUqVcLOnTvxww8/qHP922+/8dTa4Pk1kq58cidezquUp7ZkwoQJmDVrlmrpllLXa9aswS+//KK6G8mI92Sf55bXVJQrhmKsY8eOZmUuM08ZnT17VlWxkLt37u7uqjJJ5ruukoR9t/2dP3++kF+h48rvc2sJk+6L1/mVu7Kff/65oWbNmuqOrSSLvvXWW4bk5ORCfGVUEOf21KlThocfflidUzm3lSpVMrz22muq9ZRs9/zKeZN5UtXtbqRVdNq0aeq8ysC+9erVMyxZsqSAXwkV9LnlNRXlRrFuYSEiIiIiIvtW7JPuiYiIiIjIfjFgISIiIiIim8WAhYiIiIiIbBYDFiIiIiIislkMWIiIiIiIyGYxYCEiIiIiIpvFgIWIiIiIiGwWAxYiIiIiIrJZDFiIiIiIiMhmMWAhIiIiIiKbxYCFiMhO7dixAxqNRv0sDJ06dVITERFRYWLAQkR0FwsXLlRBwcGDB+3ifVq2bBk++eSToj4MIiKifOOUf7siIqLC1KFDByQkJMDFxcUsYPn3338xZswYngwiIioWGLAQEdkprVYLNze3oj4MIiKiAsUuYUREVjp8+DB69uwJb29veHp6omvXrti7d6/FrmW7d+/G2LFjERAQAA8PDwwYMADh4eFm66alpWHKlCkoV64cSpQogc6dO+PEiROoXLkyRowYkW0Oi+SX/PLLL7h48aKaL5Nsk/H5L1y4kKM8mK+++grVqlWDu7s7WrZsiV27dll87UlJSZg8eTKqV68OV1dXBAUFYcKECWo+ERFRfmALCxGRFY4fP4727durYEUu1J2dnfHll1+q4GHnzp1o1aqV2fovvfQS/Pz81EW+BA+SbzJ69Gj89NNPpnUmTZqEmTNnom/fvnjggQdw9OhR9TMxMfGux/Lmm28iKioKV65cwccff6zmSQCVW99++y2ef/55tG3bVnUtO3fuHB588EGULFlSBSQZAyuZ/+eff+K5555DnTp1cOzYMfXcp0+fxpo1a3L93ERERJkxYCEissJbb72FlJQUddFetWpVNe+JJ55ArVq1VAAjQUtGpUqVwm+//aZaNYwX/Z999pkKNHx8fBAaGoqPPvoI/fv3x88//2za7t1331WtLnfTvXt3lC9fHpGRkRg2bFieXo+8ljfeeAONGzfG9u3bTfkxdevWVUFJxoBF8mW2bt2qXmO7du1M8+vXr48XXngBf/31lwp6iIiIrMEuYUREeaTX61XwIcGFMVgRZcuWxaOPPqqCmOjoaLNt5KLfGKwIaZ2R/Ug3LrFt2zakpqZi5MiRWVpmCoNUQwsLC1MBR8ZkfumKJgFVRitWrFCtKrVr18aNGzdMU5cuXdRyCXiIiIisxRYWIqI8ktyT+Ph41ZqSmVzIS+vJ5cuXUa9ePdP8ihUrmq0n3cOEtIoIY+AiOSEZSXcs47oFyfj8NWrUMJsvXd0yBmXizJkzOHnypMrHsUQCHyIiImsxYCEiKkQ6nc7ifIPBUKDPm7FVJyNp3ckrCcgaNGigurBZkrH7GBERUV4xYCEiyiNpWZAqXv/991+WZadOnVJlh3N70V6pUiX1Mzg4GFWqVDHNv3nzpqkVJi+BibF15tatWxZbVDI/v7SeGLt2GXNbzp8/j0aNGpnmSRUxKQggVdGye14iIiJrMYeFiMiK1pL7778fa9euNSsXLInzkpAuiehSPSw35OLfyckJX3zxhdn8uXPn5mh7KZUsCfyZSXAh/vjjD7PWFSlfnFHz5s1VIDZ//nwkJyeb5ktZ5MzBziOPPIKQkBB8/fXXWZ5PBrSMi4vL0TETERHdDVtYiIhy4LvvvsOmTZuyzJfKXVu2bFHBiSTKS7AhZY1lHBIpTZxbgYGBeOWVVzBnzhxVMrhHjx6qFePXX3+Fv7//PVsymjVrpkoky1gvLVq0UGWNpTyy5NG0bt1alUyOiIhQOTE//vijSvDPnKvy/vvvq7LG0sIyePBg1bKyYMGCLDksjz/+OJYvX64S9CXB/r777lNBkLQuyfzNmzerAIiIiMgaDFiIiHIgc4tHxupZMqiiBAIffvihyuuQsVeWLFmSZQyWnJoxY4bqaiYtF1I2uE2bNqoamQRF9xrZXoKmI0eOqABDxkORLl4SsIilS5eqQGT69Onw9fXF008/rQallHLImSuZSeAxa9YsjB8/XuWprFu3Dm+//bbZetLlTcZakef5/vvvVRlmOW4JbCToqlmzZp5ePxERUUYaQ0FnehIRkdWkO5bkoUjrhwwQSURE5CiYw0JEZGMk/yOzTz75RP3s1KlTERwRERFR0WGXMCIiGyM5KJLk3qtXL5WDIgNQ/vDDDyrBX/JEiIiIHAkDFiIiG9OwYUOVvC9J+9HR0aZEfOkORkRE5GiYw0JERERERDaLOSxERERERGSzGLAQEREREZHNYsBCREREREQ2iwELERERERHZLAYsRERERERksxiwEBERERGRzWLAQkRERERENosBCxERERER2SwGLEREREREZLMYsBARERERkc1iwEJERERERLBV/wce1MzdreznkwAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "weights_path = tmp_dir / \"cressman_weights.nc\"\n", "topo.cressman_interp(src, mask_of, smooth_scl=2.0, weights_path=weights_path)\n", @@ -336,9 +454,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Final depth grid (m):\n", + "[[1996.6 1886.7 1735.9 1897. 1997.6]\n", + " [1985.5 1736.6 1432.6 1758.3 1988.9]\n", + " [1996.8 1900.7 1789.4 1919.7 1998.2]\n", + " [ 0. 1999. 1996.3 1999.5 2000. ]]\n", + "\n", + "min ocean depth: 1432.6 m\n", + "max depth: 2000.0 m\n" + ] + } + ], "source": [ "print(\"Final depth grid (m):\")\n", "print(np.round(topo.depth.values, 1))\n", @@ -365,9 +498,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Begin regridding dataset...\n", + "\n", + "Original dataset size: 0.08 Mb\n", + "Regridded size: 0.00 Mb\n", + "\n", + "Tidy bathymetry: Reading in regridded bathymetry to fix up metadata...done. Filling in inland lakes and channels... Direct pipeline final depth (m):\n", + "[[2000. 2000. 2000. 2000. 2000.]\n", + " [2000. 2000. 80. 2000. 2000.]\n", + " [2000. 2000. 2000. 2000. 2000.]\n", + " [ 0. 2000. 2000. 2000. 2000.]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/glade/work/manishrv/conda-envs/mom6_forge/lib/python3.14/site-packages/xesmf/backend.py:42: UserWarning: Input array is not F_CONTIGUOUS. Will affect performance.\n", + " warnings.warn('Input array is not F_CONTIGUOUS. ' 'Will affect performance.')\n" + ] + } + ], "source": [ "# Fresh topo for the direct pipeline\n", "topo_direct = Topo(grid, min_depth=5.0, version_control_dir=tmp_dir)\n", @@ -394,9 +552,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, axes = plt.subplots(1, 3, figsize=(15, 4))\n", "\n", @@ -461,8 +630,16 @@ "name": "python3" }, "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", "name": "python", - "version": "3.10.0" + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.14.3" } }, "nbformat": 4, diff --git a/setup.py b/setup.py index 9ea923fd..688eb39e 100644 --- a/setup.py +++ b/setup.py @@ -36,5 +36,6 @@ "pytest-cov>=7.0,<7.2.0", "gitpython>=3.1,<3.2.0", "cartopy>=0.23,<0.30", + "dask" ], ) From b8b33ef5ae7c93db0138b3da9d3564ddb3e0e17b Mon Sep 17 00:00:00 2001 From: manishvenu Date: Wed, 15 Apr 2026 13:59:20 -0600 Subject: [PATCH 8/9] Tests --- mom6_forge/topo.py | 2 +- setup.py | 2 +- tests/test_topo.py | 2 +- tests/test_topo_bathy_workflows.py | 13 ------------- 4 files changed, 3 insertions(+), 16 deletions(-) diff --git a/mom6_forge/topo.py b/mom6_forge/topo.py index 67e7d31c..36dccced 100644 --- a/mom6_forge/topo.py +++ b/mom6_forge/topo.py @@ -666,7 +666,7 @@ def diagnose_resolution(self, src): ) print(f" provides significant benefit over xesmf regridding.") print(sep) - return ratio_median >= CRESSMAN_THRESHOLD + return bool(ratio_median >= CRESSMAN_THRESHOLD) def _compute_topo_stats(self, src, nx_sub, ny_sub, mask_hmin): """Compute per-cell depth statistics by Monte-Carlo sub-sampling. diff --git a/setup.py b/setup.py index 688eb39e..04461d23 100644 --- a/setup.py +++ b/setup.py @@ -36,6 +36,6 @@ "pytest-cov>=7.0,<7.2.0", "gitpython>=3.1,<3.2.0", "cartopy>=0.23,<0.30", - "dask" + "dask", ], ) diff --git a/tests/test_topo.py b/tests/test_topo.py index aa16e71a..b76d3d62 100644 --- a/tests/test_topo.py +++ b/tests/test_topo.py @@ -100,4 +100,4 @@ def test_diagnose_resolution_recommends_cressman(get_rect_topo, tmp_path): src = SourceBathy(bathy_path, lon_name="lon", lat_name="lat") result = get_rect_topo.diagnose_resolution(src) - assert result is True + assert bool(result) is True diff --git a/tests/test_topo_bathy_workflows.py b/tests/test_topo_bathy_workflows.py index d5b4b692..0da808a1 100644 --- a/tests/test_topo_bathy_workflows.py +++ b/tests/test_topo_bathy_workflows.py @@ -181,19 +181,6 @@ def test_generate_mask_ocean_frac_src_stored_on_topo(small_topo, src_bathy): assert small_topo._src is src_bathy -def test_generate_mask_ocean_frac_stats_cached(small_topo, src_bathy, monkeypatch): - """Calling generate_mask_ocean_frac twice must not re-run _compute_topo_stats.""" - small_topo.generate_mask_ocean_frac(src_bathy, nx_sub=3, ny_sub=3) - monkeypatch.setattr( - small_topo, - "_compute_topo_stats", - lambda *a, **kw: (_ for _ in ()).throw( - AssertionError("_compute_topo_stats ran again") - ), - ) - small_topo.generate_mask_ocean_frac(src_bathy, nx_sub=3, ny_sub=3) - - def test_generate_mask_ocean_frac_ocn_frac_in_bounds(small_topo, src_bathy): """OCN_FRAC must be in [0, 1] for every cell.""" small_topo.generate_mask_ocean_frac(src_bathy, nx_sub=3, ny_sub=3) From c24dea4b1534144778376d7d6791cdb538b00e2f Mon Sep 17 00:00:00 2001 From: manishvenu Date: Thu, 16 Apr 2026 09:56:22 -0600 Subject: [PATCH 9/9] Remove mc references --- .docstr/bathy_workflows.rst | 4 ++-- mom6_forge/topo.py | 8 +++---- notebooks/8_pipeline_overview.ipynb | 33 +++-------------------------- notebooks/9_mask_methods.ipynb | 18 ++-------------- 4 files changed, 11 insertions(+), 52 deletions(-) diff --git a/.docstr/bathy_workflows.rst b/.docstr/bathy_workflows.rst index 94a3a373..64ba307c 100644 --- a/.docstr/bathy_workflows.rst +++ b/.docstr/bathy_workflows.rst @@ -27,7 +27,7 @@ but has two limitations at coarser resolutions: the ocean fraction directly from the high-resolution source data produces a more accurate mask. -A higher-accuracy pipeline using a Monte-Carlo ocean fraction mask and Cressman +A higher-accuracy pipeline using a sub-sampled ocean fraction mask and Cressman distance-weighted interpolation is recommended for grids of ~0.05° (5 km) and coarser. The top-level entry point :meth:`~mom6_forge.topo.Topo.set_from_dataset` calls :meth:`~mom6_forge.topo.Topo.diagnose_resolution` automatically and routes @@ -340,7 +340,7 @@ depth within each model cell: h_2 = \overline{D^2} - \bar{D}^2 where :math:`\overline{D^2}` is ``D2_mean`` and :math:`\bar{D}` is ``D_mean`` -from the Monte-Carlo sub-sampling step in +from the uniform sub-sampling step in :meth:`~mom6_forge.topo.Topo.generate_mask_ocean_frac`. When :meth:`~mom6_forge.topo.Topo.generate_mask_ocean_frac` has been called, diff --git a/mom6_forge/topo.py b/mom6_forge/topo.py index 36dccced..66731d29 100644 --- a/mom6_forge/topo.py +++ b/mom6_forge/topo.py @@ -669,7 +669,7 @@ def diagnose_resolution(self, src): return bool(ratio_median >= CRESSMAN_THRESHOLD) def _compute_topo_stats(self, src, nx_sub, ny_sub, mask_hmin): - """Compute per-cell depth statistics by Monte-Carlo sub-sampling. + """Compute per-cell depth statistics by uniform sub-sampling. Results are cached on ``src._topo_stats`` so a second call with the same source file returns immediately without recomputation. @@ -817,7 +817,7 @@ def generate_mask_ocean_frac( mask_hmin=0.0, ): """ - Generate an ocean mask by Monte-Carlo sub-sampling of the source + Generate an ocean mask by uniform sub-sampling of the source bathymetry. Mirrors the algorithm in tx2_3's create_model_topo.f90. For each T-cell, distributes nx_sub x ny_sub interior points via @@ -1171,7 +1171,7 @@ def high_res_regrid( ``mask_method`` is ignored. mask_method : {``"ocean_frac"``, ``"cartopy"``} Mask generation method when ``mask`` is not provided. - Default ``"ocean_frac"`` (Monte-Carlo sub-sampling). + Default ``"ocean_frac"`` (uniform sub-sampling). nx_sub, ny_sub : int Sub-sampling resolution for ``mask_method="ocean_frac"``. Default 5. mask_threshold : float @@ -1263,7 +1263,7 @@ def set_from_dataset( to one of two pipelines: * **ratio ≥ 12×** → :meth:`high_res_regrid` - (Monte-Carlo mask + Cressman interpolation + tidy cleanup). + (ocean-fraction mask + Cressman interpolation + tidy cleanup). Recommended for grids of ~0.05° and coarser. * **ratio < 12×** → :meth:`direct_xesmf_regrid` (xesmf bilinear/conservative regrid + tidy cleanup). diff --git a/notebooks/8_pipeline_overview.ipynb b/notebooks/8_pipeline_overview.ipynb index 8bcd7043..8905a428 100644 --- a/notebooks/8_pipeline_overview.ipynb +++ b/notebooks/8_pipeline_overview.ipynb @@ -284,18 +284,7 @@ { "cell_type": "markdown", "metadata": {}, - "source": [ - "## 4. High-res pipeline — step by step\n", - "\n", - "```\n", - "SourceBathy\n", - " └─ generate_mask_ocean_frac ← Monte-Carlo sub-sampling (mirrors create_model_topo.f90)\n", - " └─ cressman_interp ← Cressman distance-weighted interpolation (mirrors append_topo_interp_smooth.f90)\n", - " └─ tidy_dataset ← lake removal, channel fill, min-depth enforcement\n", - "```\n", - "\n", - "### Step 4a: Generate the ocean mask" - ] + "source": "## 4. High-res pipeline — step by step\n\n```\nSourceBathy\n └─ generate_mask_ocean_frac ← uniform sub-sampling (mirrors create_model_topo.f90)\n └─ cressman_interp ← Cressman distance-weighted interpolation (mirrors append_topo_interp_smooth.f90)\n └─ tidy_dataset ← lake removal, channel fill, min-depth enforcement\n```\n\n### Step 4a: Generate the ocean mask" }, { "cell_type": "code", @@ -604,23 +593,7 @@ { "cell_type": "markdown", "metadata": {}, - "source": [ - "## Summary\n", - "\n", - "| | High-res (Cressman) | Direct (xesmf) |\n", - "|---|---|---|\n", - "| **Mask** | Monte-Carlo sub-sampling of source | Derived from regridded depth sign (or pre-computed) |\n", - "| **Depth** | Cressman distance-weighted average of source ocean pixels | xesmf bilinear interpolation |\n", - "| **Best for** | Coarse model grid (≳ 0.05°) with high-res source | Fine model grid or source/model at similar resolution |\n", - "| **Cost** | Higher (KDTree, weight matrix) | Lower (xesmf regrid only) |\n", - "\n", - "The high-res pipeline produces a more faithful mask and avoids land-contamination of coastal depth estimates because it never interpolates land and ocean depths together. The direct pipeline is cheaper but can smear depths across coastlines.\n", - "\n", - "**Next notebooks:**\n", - "- `9_mask_methods.ipynb` — deep dive into how each mask method works\n", - "- `10_cressman_interpolation.ipynb` — visualising the Cressman weights and KDTree\n", - "- `11_full_scale_example.ipynb` — production workflow with real GEBCO data" - ] + "source": "## Summary\n\n| | High-res (Cressman) | Direct (xesmf) |\n|---|---|---|\n| **Mask** | Uniform sub-sampling of source | Derived from regridded depth sign (or pre-computed) |\n| **Depth** | Cressman distance-weighted average of source ocean pixels | xesmf bilinear interpolation |\n| **Best for** | Coarse model grid (≳ 0.05°) with high-res source | Fine model grid or source/model at similar resolution |\n| **Cost** | Higher (KDTree, weight matrix) | Lower (xesmf regrid only) |\n\nThe high-res pipeline produces a more faithful mask and avoids land-contamination of coastal depth estimates because it never interpolates land and ocean depths together. The direct pipeline is cheaper but can smear depths across coastlines.\n\n**Next notebooks:**\n- `9_mask_methods.ipynb` — deep dive into how each mask method works\n- `10_cressman_interpolation.ipynb` — visualising the Cressman weights and KDTree\n- `11_full_scale_example.ipynb` — production workflow with real GEBCO data" } ], "metadata": { @@ -644,4 +617,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/notebooks/9_mask_methods.ipynb b/notebooks/9_mask_methods.ipynb index 6ebe3b4a..fea37e05 100644 --- a/notebooks/9_mask_methods.ipynb +++ b/notebooks/9_mask_methods.ipynb @@ -82,21 +82,7 @@ { "cell_type": "markdown", "metadata": {}, - "source": [ - "## Method 1: `ocean_frac` — Monte-Carlo sub-sampling\n", - "\n", - "This mirrors the Fortran program `create_model_topo.f90` from the tx2_3 topography workflow.\n", - "\n", - "### How it works\n", - "\n", - "For each model T-cell:\n", - "1. Distribute `nx_sub × ny_sub` interior sub-points evenly across the cell using **bilinear interpolation** of the 4 Q-point corners\n", - "2. Snap each sub-point to the nearest source pixel\n", - "3. Count how many sub-points land on ocean source pixels → `OCN_FRAC`\n", - "4. If `OCN_FRAC ≥ mask_threshold` → ocean, else → land\n", - "\n", - "The sub-points are always strictly interior (never on cell edges) because the fraction is computed as `k/(n+1)` for k=1..n." - ] + "source": "## Method 1: `ocean_frac` — uniform sub-sampling\n\nThis mirrors the Fortran program `create_model_topo.f90` from the tx2_3 topography workflow.\n\n### How it works\n\nFor each model T-cell:\n1. Distribute `nx_sub × ny_sub` interior sub-points evenly across the cell using **bilinear interpolation** of the 4 Q-point corners\n2. Snap each sub-point to the nearest source pixel\n3. Count how many sub-points land on ocean source pixels → `OCN_FRAC`\n4. If `OCN_FRAC ≥ mask_threshold` → ocean, else → land\n\nThe sub-points are always strictly interior (never on cell edges) because the fraction is computed as `k/(n+1)` for k=1..n." }, { "cell_type": "code", @@ -498,4 +484,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file