diff --git a/.readthedocs.yml b/.readthedocs.yml index 0afd6623..8c9396f3 100644 --- a/.readthedocs.yml +++ b/.readthedocs.yml @@ -10,7 +10,7 @@ sphinx: configuration: docs/conf.py # fail_on_warning might generate hard to fix error, in this case it can be # disabled but this also means those errors will fail silently, choose wisely. - fail_on_warning: false + fail_on_warning: true build: os: ubuntu-22.04 @@ -18,12 +18,13 @@ build: python: "mambaforge-4.10" jobs: post_install: - - pip install .[docs,gis] + - pip install .[docs] pre_build: - - sphinx-apidoc -o docs/apidoc --private --module-first ravenpy + - env SPHINX_APIDOC_OPTIONS="members,undoc-members,show-inheritance,noindex" sphinx-apidoc -o docs/apidoc --private --module-first ravenpy - sphinx-build -b linkcheck docs/ _build/linkcheck || true -formats: all +formats: + - pdf conda: environment: environment-rtd.yml diff --git a/Makefile b/Makefile index 7a8d1994..a175b310 100644 --- a/Makefile +++ b/Makefile @@ -73,10 +73,13 @@ coverage: ## check code coverage quickly with the default Python autodoc: clean-docs ## create sphinx-apidoc files: sphinx-apidoc -o docs/apidoc --private --module-first ravenpy +autodoc-custom-index: clean-docs ## create sphinx-apidoc files but with special index handling for indices and indicators + env SPHINX_APIDOC_OPTIONS="members,undoc-members,show-inheritance,noindex" sphinx-apidoc -o docs/apidoc --private --module-first ravenpy + linkcheck: autodoc ## run checks over all external links found throughout the documentation $(MAKE) -C docs linkcheck -docs: linkcheck ## generate Sphinx HTML documentation, including API docs +docs: autodoc-custom-index ## generate Sphinx HTML documentation, including API docs $(MAKE) -C docs html ifndef READTHEDOCS $(BROWSER) docs/_build/html/index.html diff --git a/docs/conf.py b/docs/conf.py index 3336e06a..c2d17437 100755 --- a/docs/conf.py +++ b/docs/conf.py @@ -41,12 +41,12 @@ "sphinx.ext.todo", "sphinx.ext.viewcode", "IPython.sphinxext.ipython_console_highlighting", - #"nbsphinx", + # "nbsphinx", "myst_nb", "sphinx_click", "sphinx_codeautolink", "sphinx_copybutton", - #"sphinxcontrib.autodoc_pydantic", + # "sphinxcontrib.autodoc_pydantic", ] linkcheck_ignore = [ @@ -64,7 +64,8 @@ autosummary_generate = True nb_execution_mode = "cache" -nb_execution_allow_errors = True +nb_execution_allow_errors = False +nb_execution_excludepatterns = ["notebooks/*.ipynb", "notebooks/paper/*.ipynb"] # nbsphinx_execute = "auto" # nbsphinx_timeout = 1 @@ -145,7 +146,7 @@ # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This patterns also effect to html_static_path and html_extra_path -exclude_patterns = ["_build", "Thumbs.db", ".DS_Store", ".jupyter_cache", "jupyter_execute"] +exclude_patterns = ["_build", "Thumbs.db", ".DS_Store", ".jupyter_cache", "jupyter_execute", "notebooks/paper"] # The name of the Pygments (syntax highlighting) style to use. pygments_style = "sphinx" @@ -154,7 +155,10 @@ todo_include_todos = False # Suppress "WARNING: unknown mimetype for ..." when building EPUB. -suppress_warnings = ["epub.unknown_project_files"] +suppress_warnings = [ + "epub.unknown_project_files", + "mystnb.unknown_mime_type" +] # -- Options for HTML output ------------------------------------------- diff --git a/docs/configuration.md b/docs/configuration.md index 48b7baa7..bb2075ca 100644 --- a/docs/configuration.md +++ b/docs/configuration.md @@ -94,6 +94,8 @@ Config( Of course, setting an option with an invalid value will raise a `ValidationError`. For example, the error message below suggests there is a typo in the routing method, it should read `ROUTE_DIFFUSIVE_WAVE`: ```{code-cell} ipython3 +:tags: ["raises-exception"] + Config(Routing="ROUTE_DIFFUSIVE") ``` diff --git a/docs/installation.rst b/docs/installation.rst index 871e14d0..ba264ef3 100644 --- a/docs/installation.rst +++ b/docs/installation.rst @@ -33,12 +33,14 @@ Python Installation (pip) ------------------------- .. warning:: + In order to compile the Raven model (provided by the `raven-hydro` package, a C++ compiler (`GCC`, `Clang`, `MSVC`, etc.) and either `GNU Make` (Linux/macOS) or `Ninja` (Windows) must be exposed on the `$PATH`. .. warning:: + The Raven model also requires that NetCDF4 libraries are installed on the system, exposed on the `$PATH`, and discoverable using the `FindNetCDF.cmake` helper script bundled with `raven-hydro`. - On Linux, this can be provided by the `libnetcdf-dev` system library; On macOS by the `netcdf` homebrew package; And on Windows by using UNIDATA's [pre-built binaries](https://docs.unidata.ucar.edu/netcdf-c/current/winbin.html). + On Linux, this can be provided by the `libnetcdf-dev` system library; On macOS by the `netcdf` homebrew package; And on Windows by using UNIDATA's `pre-built binaries `_. In order to perform this from Ubuntu/Debian: diff --git a/docs/notebooks/02_Extract_geographical_watershed_properties.ipynb b/docs/notebooks/02_Extract_geographical_watershed_properties.ipynb index c1979cb8..3fde2888 100644 --- a/docs/notebooks/02_Extract_geographical_watershed_properties.ipynb +++ b/docs/notebooks/02_Extract_geographical_watershed_properties.ipynb @@ -22,22 +22,22 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 1, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "# We need to import a few packages required to do the work\n", "import os\n", - "import xml.etree.ElementTree as et\n", "\n", + "os.environ[\"USE_PYGEOS\"] = \"0\"\n", "import geopandas as gpd\n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import rasterio\n", - "import requests\n", "import rioxarray as rio\n", - "import xarray as xr\n", "from birdy import WPSClient\n", "\n", "from ravenpy.utilities.testdata import get_file\n", @@ -62,9 +62,112 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 2, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# The contour can be generated using notebook \"01_Delineating watersheds, where it would be placed\n", "# in the same folder as the notebooks and available in your workspace. The contour could then be accessed\n", @@ -93,8 +196,10 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 3, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "shape_resp = wps.shape_properties(shape=feature_url)" @@ -109,13 +214,54 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 4, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': 'USGS_HydroBASINS_lake_na_lev12.96044',\n", + " 'COAST': 0,\n", + " 'DIST_MAIN': 141.3,\n", + " 'DIST_SINK': 141.3,\n", + " 'ENDO': 0,\n", + " 'HYBAS_ID': 7120319552,\n", + " 'LAKE': 0,\n", + " 'NEXT_DOWN': 7120319551,\n", + " 'NEXT_SINK': 7120034330,\n", + " 'ORDER': 1,\n", + " 'PFAF_ID': 724083033100,\n", + " 'SIDE': 'R',\n", + " 'SORT': 96044,\n", + " 'SUB_AREA': 56.2,\n", + " 'UP_AREA': 73072.4,\n", + " 'area': 55919453.46888515,\n", + " 'centroid': [-71.41715786806483, 48.47239495054429],\n", + " 'perimeter': 45133.04400352313,\n", + " 'gravelius': 1.7025827715870572}" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "{'area': 55.91945346888515,\n", + " 'longitude': -71.41715786806483,\n", + " 'latitude': 48.47239495054429,\n", + " 'gravelius': 1.7025827715870572,\n", + " 'perimeter': 45133.04400352313}" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "[\n", - " properties,\n", - "] = shape_resp.get(asobj=True)\n", + "(properties,) = shape_resp.get(asobj=True)\n", "prop = properties[0]\n", "display(prop)\n", "\n", @@ -143,15 +289,17 @@ "\n", "## Land-use information\n", "\n", - "Now we extract the land-use properties of the watershed using the `nalcms_zonal_stats` process. As mentioned, it uses a dataset from the [North American Land Change Monitoring System](http://www.cec.org/north-american-environmental-atlas), and retrieve properties over the given region.\n", + "Now we extract the land-use properties of the watershed using the `nalcms_zonal_stats` process. As mentioned, it uses a dataset from the [North American Land Change Monitoring System](http://www.cec.org/north-american-environmental-atlas), and retrieve properties over the given region. \n", "\n", - "With the `nalcms_zonal_stats_raster` process, we also return the grid with variable accessors (`gdal`, `rasterio`, or `rioxarray`) depending on what libraries are available in our runtime environment (The following examples show `rioxarray`-like access)." + "With the `nalcms_zonal_stats_raster` process, we also return the raster grid itself. Note that this is a high-resolution dataset, and to avoid taxing the system's resource, requests are limited to areas under 100,000kmĀ². " ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 5, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "stats_resp = wps.nalcms_zonal_stats_raster(\n", @@ -163,22 +311,48 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Here we will get the raster data and show it as a grid. We're first reading the data in memory, then opening it as an `xarray.Dataset` using `rasterio`/`rioxarray`. Note that `pymetalink` needs to be installed for this to work." + "Here we will get the raster data and show it as a grid. Here the `birdy` client automatically transforms the returned `geotiff` file to a `DataArray` using either `gdal`, `rasterio`, or `rioxarray`, depending on what libraries are available in our runtime environment. Note that `pymetalink` needs to be installed for this to work. " ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 6, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading to /tmp/tmp8gyvvqhc/subset_1.tiff.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "from rasterio.io import MemoryFile\n", - "\n", "features, statistics, raster = stats_resp.get(asobj=True)\n", - "with MemoryFile(raster[0]) as memfile:\n", - " with memfile.open(driver=\"GTiff\") as dataset:\n", - " grid = rio.open_rasterio(dataset)\n", - " grid.plot()" + "grid = raster[0]\n", + "grid.plot()" ] }, { @@ -190,9 +364,64 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 7, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'Land use ratios'" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "{'Ocean': 0.0,\n", + " 'Forest': 0.9095753879046077,\n", + " 'Shrubs': 0.004920532612284039,\n", + " 'Grass': 0.005753721840562167,\n", + " 'Wetland': 0.0009589536400936945,\n", + " 'Crops': 0.045605319834619795,\n", + " 'Urban': 0.02361226831837261,\n", + " 'Water': 0.009573815849459998,\n", + " 'SnowIce': 0.0}" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'Land use percentages'" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "{'Ocean': '0.0 %',\n", + " 'Forest': '90.96 %',\n", + " 'Shrubs': '0.49 %',\n", + " 'Grass': '0.58 %',\n", + " 'Wetland': '0.1 %',\n", + " 'Crops': '4.56 %',\n", + " 'Urban': '2.36 %',\n", + " 'Water': '0.96 %',\n", + " 'SnowIce': '0.0 %'}" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "lu = statistics[0]\n", "total = sum(lu.values())\n", @@ -214,9 +443,40 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 8, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The land-use categories available are: [ 1 5 6 8 10 14 15 16 17 18 127]\n", + "The number of occurrences of each land-use category is: [18949 9163 29747 313 72 61 2901 294 1502 609 51649]\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "unique, counts = np.unique(grid, return_counts=True)\n", "print(\"The land-use categories available are: \" + str(unique))\n", @@ -279,9 +539,22 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 9, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import cartopy.crs as ccrs\n", "\n", @@ -311,8 +584,10 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 10, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "terrain_resp = wps.terrain_analysis(\n", @@ -322,11 +597,25 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 11, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'elevation': 165.37033101757254,\n", + " 'slope': 3.8477161303214786,\n", + " 'aspect': 5.4659402646877995}" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "properties, dem0 = terrain_resp.get(asobj=True)\n", + "properties, dem = terrain_resp.get(asobj=True)\n", "\n", "elevation = properties[0][\"elevation\"]\n", "slope = properties[0][\"slope\"]\n", @@ -338,15 +627,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 12, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "crs = ccrs.LambertConformal(\n", " central_latitude=49, central_longitude=-95, standard_parallels=(49, 77)\n", ")\n", "\n", - "dem = rio.open_rasterio(rasterio.MemoryFile(dem0).open())\n", "dem.name = \"Elevation\"\n", "dem.attrs[\"units\"] = \"m\"\n", "ax = plt.subplot(projection=crs)\n", @@ -356,9 +657,45 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 13, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'https://pavics.ouranos.ca/wpsoutputs/raven/274bbfe2-feed-11ed-8c70-0242ac130010/input.json'" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'https://pavics.ouranos.ca/wpsoutputs/raven/274bbfe2-feed-11ed-8c70-0242ac130010/clipped_vftry25z.tiff'" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "array([[-32768, -32768, 86, ..., -32768, -32768, -32768],\n", + " [ 120, 103, 86, ..., -32768, -32768, -32768],\n", + " [ 120, 104, 93, ..., -32768, -32768, -32768],\n", + " ...,\n", + " [-32768, -32768, -32768, ..., -32768, -32768, -32768],\n", + " [-32768, -32768, -32768, ..., -32768, -32768, -32768],\n", + " [-32768, -32768, -32768, ..., -32768, -32768, -32768]], dtype=int16)" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# We can also access the files directly via their URLs:\n", "properties, dem = terrain_resp.get(asobj=False)\n", @@ -379,9 +716,37 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 14, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'area': 55.91945346888515,\n", + " 'longitude': -71.41715786806483,\n", + " 'latitude': 48.47239495054429,\n", + " 'gravelius': 1.7025827715870572,\n", + " 'perimeter': 45133.04400352313,\n", + " 'Ocean': 0.0,\n", + " 'Forest': 0.9095753879046077,\n", + " 'Shrubs': 0.004920532612284039,\n", + " 'Grass': 0.005753721840562167,\n", + " 'Wetland': 0.0009589536400936945,\n", + " 'Crops': 0.045605319834619795,\n", + " 'Urban': 0.02361226831837261,\n", + " 'Water': 0.009573815849459998,\n", + " 'SnowIce': 0.0,\n", + " 'elevation': 165.37033101757254,\n", + " 'slope': 3.8477161303214786,\n", + " 'aspect': 5.4659402646877995}" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "all_properties = {**shape_info, **land_use, **terrain}\n", "display(all_properties)" @@ -404,7 +769,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.9" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/docs/notebooks/03_Extracting_forcing_data.ipynb b/docs/notebooks/03_Extracting_forcing_data.ipynb index af93b14e..c4b88675 100644 --- a/docs/notebooks/03_Extracting_forcing_data.ipynb +++ b/docs/notebooks/03_Extracting_forcing_data.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "pycharm": { "is_executing": true @@ -20,14 +20,13 @@ }, "outputs": [], "source": [ - "# Boilerplate packages required. Do not modify!\n", "import datetime as dt\n", "import tempfile\n", "from pathlib import Path\n", "\n", - "import fsspec\n", + "import fsspec # noqa\n", "import intake\n", - "import s3fs\n", + "import s3fs # noqa\n", "import xarray as xr\n", "from clisops.core import subset\n", "\n", @@ -48,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -83,27 +82,529 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Get the ERA5 data from the Wasabi/Amazon S3 server.\n", "catalog_name = \"https://raw.githubusercontent.com/hydrocloudservices/catalogs/main/catalogs/atmosphere.yaml\"\n", "cat = intake.open_catalog(catalog_name)\n", - "ds = cat.era5_reanalysis_single_levels.to_dask()\n", - "\n", - "\"\"\"\n", - "Get the ERA5 data. We will rechunk it to a single chunck to make it compatible with other codes on the platform, especially bias-correction.\n", - "We are also taking the daily min and max temperatures as well as the daily total precipitation.\n", - "\"\"\"\n", + "ds = cat.era5_reanalysis_single_levels.to_dask()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get the ERA5 data. We will rechunk it to a single chunk to make it compatible with other codes on the platform, especially bias-correction.\n", + "We are also taking the daily min and max temperatures as well as the daily total precipitation." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.DataArray 'tp' (time: 367, latitude: 1, longitude: 1)>\n",
+       "dask.array<rechunk-merge, shape=(367, 1, 1), dtype=float32, chunksize=(367, 1, 1), chunktype=numpy.ndarray>\n",
+       "Coordinates:\n",
+       "  * latitude   (latitude) float64 48.5\n",
+       "  * longitude  (longitude) float64 -71.5\n",
+       "  * time       (time) datetime64[ns] 1985-12-31 1986-01-01 ... 1987-01-01\n",
+       "Attributes:\n",
+       "    long_name:     Total precipitation\n",
+       "    units:         m\n",
+       "    grid_mapping:  crs
" + ], + "text/plain": [ + "\n", + "dask.array\n", + "Coordinates:\n", + " * latitude (latitude) float64 48.5\n", + " * longitude (longitude) float64 -71.5\n", + " * time (time) datetime64[ns] 1985-12-31 1986-01-01 ... 1987-01-01\n", + "Attributes:\n", + " long_name: Total precipitation\n", + " units: m\n", + " grid_mapping: crs" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ "# We will add a wrapper to ensure that the following operations will preserve the original data attributes, such as units and variable names.\n", "with xr.set_options(keep_attrs=True):\n", " ERA5_reference = subset.subset_shape(\n", " ds.sel(time=slice(reference_start_day, reference_stop_day)), basin_contour\n", " )\n", - " ERA5_tmin = ERA5_reference[\"t2m\"].resample(time=\"1D\").min().chunk(-1, -1, -1)\n", - " ERA5_tmax = ERA5_reference[\"t2m\"].resample(time=\"1D\").max().chunk(-1, -1, -1)\n", - " ERA5_pr = ERA5_reference[\"tp\"].resample(time=\"1D\").sum().chunk(-1, -1, -1)" + " ERA5_tas = ERA5_reference[\"t2m\"].resample(time=\"1D\")\n", + " ERA5_tmin = ERA5_tas.min().chunk(-1, -1, -1)\n", + " ERA5_tmax = ERA5_tas.max().chunk(-1, -1, -1)\n", + " ERA5_pr = ERA5_reference[\"tp\"].resample(time=\"1D\").sum().chunk(-1, -1, -1)\n", + "\n", + "ERA5_pr" ] }, { @@ -117,19 +618,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tmin units: K\n", + "Tmax units: K\n", + "Precipitation units: m\n" + ] + } + ], "source": [ - "print(\"INFORMATION FOR TMIN\")\n", - "display(ERA5_tmin)\n", - "print(\"\")\n", - "print(\"INFORMATION FOR TMAX\")\n", - "display(ERA5_tmax)\n", - "print(\"\")\n", - "print(\"INFORMATION FOR PRECIPITATION\")\n", - "display(ERA5_pr)\n", - "print(\"\")" + "print(f\"Tmin units: {ERA5_tmin.units}\")\n", + "print(f\"Tmax units: {ERA5_tmax.units}\")\n", + "print(f\"Precipitation units: {ERA5_pr.units}\")" ] }, { @@ -143,7 +648,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -167,19 +672,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tmin units: degC\n", + "Tmax units: degC\n", + "Precipitation units: mm\n" + ] + } + ], "source": [ - "print(\"INFORMATION FOR TMIN\")\n", - "display(ERA5_tmin)\n", - "print(\"\")\n", - "print(\"INFORMATION FOR TMAX\")\n", - "display(ERA5_tmax)\n", - "print(\"\")\n", - "print(\"INFORMATION FOR PRECIPITATION\")\n", - "display(ERA5_pr)\n", - "print(\"\")" + "print(f\"Tmin units: {ERA5_tmin.units}\")\n", + "print(f\"Tmax units: {ERA5_tmax.units}\")\n", + "print(f\"Precipitation units: {ERA5_pr.units}\")" ] }, { @@ -191,7 +700,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -212,9 +721,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Check and see if the precipitation makes sense:\n", "ERA5_pr.pr.plot()" @@ -224,13 +754,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Here we will write the files to disk in a temporary folder since the root folder containing these notebooks is read-only. \n", + "### Here we will write the files to disk in a temporary folder since the root folder containing these notebooks is read-only.\n", "You can change the path here to your own preferred path in your writable workspace. Alternatively, if you copy this notebook to your writable-workspace as shown in the introduction documentation, you can save just the filename (no absolute path) and the file will appear \"beside\" the notebooks, ready to be read by the next series of notebooks." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -239,9 +769,17 @@ " tmp = Path(tempfile.mkdtemp())\n", " ERA5_tmin.to_netcdf(tmp / \"ERA5_tmin.nc\")\n", " ERA5_tmax.to_netcdf(tmp / \"ERA5_tmax.nc\")\n", - " ERA5_pr.to_netcdf(tmp / \"ERA5_pr.nc\")\n", - "\n", - " # We can also prepare a single file that merges all three variables into one netcdf file:\n", + " ERA5_pr.to_netcdf(tmp / \"ERA5_pr.nc\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# We can also prepare a single file that merges all three variables into one netcdf file:\n", + "with xr.set_options(keep_attrs=True):\n", " xr.merge([ERA5_tmin, ERA5_tmax, ERA5_pr]).to_netcdf(tmp / \"ERA5_weather_data.nc\")" ] }, @@ -251,7 +789,7 @@ "source": [ "We now have daily precipitation and minimum/maximum temperatures to drive our Raven Model, which we will do in the next notebook!\n", "\n", - "Note that our dataset generated here is very short (1 year) but the same dataset for the period 1980-12-31 to 1991-01-01 has been pre-generated and stored on the server for efficiency." + "Note that our dataset generated here is very short (1 year) but the same dataset for the period 1980-12-31 to 1991-01-01 has been pre-generated and stored on the server for efficiency.\n" ] } ], @@ -271,7 +809,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.9" + "version": "3.9.16" }, "nbdime-conflicts": { "local_diff": [ diff --git a/docs/notebooks/04_Emulating_hydrological_models.ipynb b/docs/notebooks/04_Emulating_hydrological_models.ipynb index 535fbb5b..8602101e 100644 --- a/docs/notebooks/04_Emulating_hydrological_models.ipynb +++ b/docs/notebooks/04_Emulating_hydrological_models.ipynb @@ -51,8 +51,6 @@ "import tempfile\n", "from pathlib import Path\n", "\n", - "import xarray as xr\n", - "\n", "from ravenpy.config import commands as rc\n", "from ravenpy.utilities.testdata import get_file" ] @@ -74,7 +72,6 @@ "source": [ "# Define the hydrological response unit. We can use the information from the tutorial notebook #02! Here we are using\n", "# arbitrary data for a test catchment.\n", - "hru = {}\n", "hru = dict(\n", " area=4250.6,\n", " elevation=843.0,\n", @@ -265,7 +262,7 @@ "# we are providing. We will generate the list now and pass it later to Ravenpy as an argument to the model.\n", "data_type = [\"TEMP_MAX\", \"TEMP_MIN\", \"PRECIP\"]\n", "\n", - "# Setup the gauge using the second method, i.e., using a single file that contains all meteorological inputs. As\n", + "# Set up the gauge using the second method, i.e., using a single file that contains all meteorological inputs. As\n", "# you can see, a single gauge is added, but it contains all the information we need.\n", "default_emulator_config[\"Gauge\"] = [\n", " rc.Gauge.from_nc(\n", diff --git a/docs/notebooks/05_Advanced_RavenPy_configuration.ipynb b/docs/notebooks/05_Advanced_RavenPy_configuration.ipynb index b14f9d2b..1523359e 100644 --- a/docs/notebooks/05_Advanced_RavenPy_configuration.ipynb +++ b/docs/notebooks/05_Advanced_RavenPy_configuration.ipynb @@ -79,7 +79,6 @@ "source": [ "from ravenpy import OutputReader\n", "from ravenpy.ravenpy import run\n", - "from ravenpy.utilities.nb_graphs import hydrographs\n", "\n", "run_name = \"raven-gr4j-salmon\" # As can be seen in the config above, this is the name of the .rvX files.\n", "configdir = config[\n", @@ -104,7 +103,7 @@ "# Read the output files at the output_path\n", "\n", "outputs = OutputReader(run_name=None, path=outputs_path) # Get the outputs\n", - "# Note. We setup the run_name to None, because we didnt rename the output files. If you gave a different name to your file\n", + "# Note. We set up the run_name to None, because we didn't rename the output files. If you gave a different name to your file\n", "# compared to the one above, you should change the run_name value to this new name. It's important though that you keep the end\n", "# of the filename the same\n", "\n", @@ -211,7 +210,6 @@ "import datetime as dt\n", "\n", "import matplotlib.pyplot as plt\n", - "import xarray as xr\n", "\n", "from ravenpy import Emulator\n", "from ravenpy.config import commands as rc\n", diff --git a/docs/notebooks/06_Raven_calibration.ipynb b/docs/notebooks/06_Raven_calibration.ipynb index a9f1aabe..be86b701 100644 --- a/docs/notebooks/06_Raven_calibration.ipynb +++ b/docs/notebooks/06_Raven_calibration.ipynb @@ -28,7 +28,6 @@ "outputs": [], "source": [ "import datetime as dt\n", - "import tempfile\n", "\n", "import spotpy\n", "\n", @@ -56,12 +55,12 @@ "from ravenpy.utilities.testdata import get_file\n", "\n", "# We get the netCDF for testing on a server. You can replace the getfile method by a string containing the path to your own netCDF\n", - "nc_files = get_file(\n", + "nc_file = get_file(\n", " \"raven-gr4j-cemaneige/Salmon-River-Near-Prince-George_meteo_daily.nc\"\n", ")\n", "\n", - "# Display the datasets that we will be using\n", - "print(nc_files)" + "# Display the dataset that we will be using\n", + "print(nc_file)" ] }, { @@ -111,10 +110,10 @@ "\n", "# We need to create the desired model with its parameters the same way as in the Notebook 04_Emulating_hydrological_models.\n", "model_config = GR4JCN(\n", - " ObservationData=[rc.ObservationData.from_nc(nc_files, alt_names=\"qobs\")],\n", + " ObservationData=[rc.ObservationData.from_nc(nc_file, alt_names=\"qobs\")],\n", " Gauge=[\n", " rc.Gauge.from_nc(\n", - " nc_files,\n", + " nc_file,\n", " alt_names=alt_names,\n", " data_kwds={\"ALL\": {\"elevation\": hru[\"elevation\"]}},\n", " )\n", @@ -185,7 +184,7 @@ "# be sure of all the configuration above before executing with a high number of model evaluations.\n", "model_evaluations = 10\n", "\n", - "# Setup the spotpy sampler with the method, the setup configuration, a run name and other options. Please refer to\n", + "# Set up the spotpy sampler with the method, the setup configuration, a run name and other options. Please refer to\n", "# the spotpy documentation for more options. We recommend sticking to this format for efficiency of most applications.\n", "sampler = spotpy.algorithms.dds(\n", " spot_setup, dbname=\"RAVEN_model_run\", dbformat=\"ram\", save_sim=False\n", diff --git a/docs/notebooks/07_Making_and_using_hotstart_files.ipynb b/docs/notebooks/07_Making_and_using_hotstart_files.ipynb index 6dbc70df..e5091917 100644 --- a/docs/notebooks/07_Making_and_using_hotstart_files.ipynb +++ b/docs/notebooks/07_Making_and_using_hotstart_files.ipynb @@ -15,11 +15,11 @@ "\n", "Hydrological models have state variables that describe the snow pack, soil moisture, underground reservoirs, etc. Typically, those cannot be measured empirically, so one way to estimate those values is to run the model for a period before the period we are actually interested in, and save the state variables at the end of this *warm-up* simulation.\n", "\n", - "This notebook shows how to save those state variables and use them to configure another Raven simulation. These *states* are configured by the `:HRUStateVariableTable` and `:BasinStateVariables` commands, but `ravenpy` has a convenience function `set_solution` to update those directly from the `solution.rvc` simulation output. \n", + "This notebook shows how to save those state variables and use them to configure another Raven simulation. These *states* are configured by the `:HRUStateVariableTable` and `:BasinStateVariables` commands, but `ravenpy` has a convenience function `set_solution` to update those directly from the `solution.rvc` simulation output.\n", "\n", - "In the following, we run the model on two years then save the final states. Next, we use those final states to configure the initial state of a second simulation over the next two years. If everything is done correctly, these two series should be identical to a simulation over the full four years. \n", + "In the following, we run the model on two years then save the final states. Next, we use those final states to configure the initial state of a second simulation over the next two years. If everything is done correctly, these two series should be identical to a simulation over the full four years.\n", "\n", - "## Model configuration \n", + "## Model configuration\n", "\n", "At this point the following blocks of code should be quite familiar! If not, please go back to notebook \"04 - Emulating hydrological models\" to understand what is happening.\n" ] @@ -34,7 +34,6 @@ "import datetime as dt\n", "import warnings\n", "\n", - "import xarray as xr\n", "from matplotlib import pyplot as plt\n", "\n", "from ravenpy import Emulator, RavenWarning\n", @@ -42,7 +41,6 @@ "\n", "# Import the GR4JCN model\n", "from ravenpy.config.emulators import GR4JCN\n", - "from ravenpy.ravenpy import run\n", "from ravenpy.utilities.testdata import get_file" ] }, @@ -58,7 +56,6 @@ "end_date = dt.datetime(1988, 1, 1)\n", "\n", "# Define HRU\n", - "hru = {}\n", "hru = dict(\n", " area=4250.6,\n", " elevation=843.0,\n", @@ -126,7 +123,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Now let's run the model for the next two years, setting the initial conditions to the final states of the first simulation. \n", + "### Now let's run the model for the next two years, setting the initial conditions to the final states of the first simulation.\n", "\n", "The path to the `solution.rvc` file can be found in `out1.files[\"solution\"]`.\n", "The content itself can be displayed with `out1.solution`" @@ -160,7 +157,7 @@ "source": [ "## Compare with simulation over entire period\n", "\n", - "Now in theory, those two simulations should be identical to one simulation over the whole period of four years, let's confirm this. " + "Now in theory, those two simulations should be identical to one simulation over the whole period of four years, let's confirm this." ] }, { diff --git a/docs/notebooks/08_Getting_and_bias_correcting_CMIP6_data.ipynb b/docs/notebooks/08_Getting_and_bias_correcting_CMIP6_data.ipynb index bb10d09c..aa8e7332 100644 --- a/docs/notebooks/08_Getting_and_bias_correcting_CMIP6_data.ipynb +++ b/docs/notebooks/08_Getting_and_bias_correcting_CMIP6_data.ipynb @@ -13,7 +13,7 @@ "source": [ "## Applying bias correction on climate model data to perform climate change impact studies on hydrology\n", "\n", - "This notebook will guide you on how to conduct bias correction of climate model outputs that will be fed as inputs to the hydrological model `Raven` to perform climate change impact studies on hydrology. \n", + "This notebook will guide you on how to conduct bias correction of climate model outputs that will be fed as inputs to the hydrological model `Raven` to perform climate change impact studies on hydrology.\n", "\n", "## Geographic data\n", "In this tutorial, we will be using the shapefile or GeoJSON file for watershed contours as generated in previous notebooks. The file can be uploaded to your workspace here and used directly in the cells below. In this notebook, we present a quick demonstration of the bias-correction approach on a small and predetermined dataset, but you can use your own basin according to your needs." @@ -21,8 +21,25 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 1, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import warnings\n", + "\n", + "from numba.core.errors import NumbaDeprecationWarning\n", + "\n", + "warnings.simplefilter(\"ignore\", category=NumbaDeprecationWarning)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "import datetime as dt\n", @@ -62,8 +79,10 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 3, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "# We get the basin contour for testing on a server. You can replace the getfile method by a string containing the path\n", @@ -133,9 +152,92 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 4, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "

pangeo-cmip6 catalog with 7674 dataset(s) from 514818 asset(s):

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" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Prepare the filesystem that allows reading data. Data is read on the Google Cloud Services, which host a copy of the CMIP6 (and other) data.\n", "fsCMIP = gcsfs.GCSFileSystem(token=\"anon\", access=\"read_only\")\n", @@ -158,9 +260,109 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 5, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['CMCC-CM2-HR4',\n", + " 'EC-Earth3P-HR',\n", + " 'HadGEM3-GC31-MM',\n", + " 'HadGEM3-GC31-HM',\n", + " 'HadGEM3-GC31-LM',\n", + " 'EC-Earth3P',\n", + " 'ECMWF-IFS-HR',\n", + " 'ECMWF-IFS-LR',\n", + " 'HadGEM3-GC31-LL',\n", + " 'CMCC-CM2-VHR4',\n", + " 'GFDL-CM4',\n", + " 'GFDL-AM4',\n", + " 'IPSL-CM6A-LR',\n", + " 'E3SM-1-0',\n", + " 'CNRM-CM6-1',\n", + " 'GFDL-ESM4',\n", + " 'GFDL-ESM2M',\n", + " 'GFDL-CM4C192',\n", + " 'GFDL-OM4p5B',\n", + " 'GISS-E2-1-G',\n", + " 'GISS-E2-1-H',\n", + " 'CNRM-ESM2-1',\n", + " 'BCC-CSM2-MR',\n", + " 'BCC-ESM1',\n", + " 'MIROC6',\n", + " 'AWI-CM-1-1-MR',\n", + " 'EC-Earth3-LR',\n", + " 'IPSL-CM6A-ATM-HR',\n", + " 'CESM2',\n", + " 'CESM2-WACCM',\n", + " 'CNRM-CM6-1-HR',\n", + " 'MRI-ESM2-0',\n", + " 'SAM0-UNICON',\n", + " 'GISS-E2-1-G-CC',\n", + " 'UKESM1-0-LL',\n", + " 'EC-Earth3',\n", + " 'EC-Earth3-Veg',\n", + " 'FGOALS-f3-L',\n", + " 'CanESM5',\n", + " 'CanESM5-CanOE',\n", + " 'INM-CM4-8',\n", + " 'INM-CM5-0',\n", + " 'NESM3',\n", + " 'MPI-ESM-1-2-HAM',\n", + " 'CAMS-CSM1-0',\n", + " 'MPI-ESM1-2-LR',\n", + " 'MPI-ESM1-2-HR',\n", + " 'MRI-AGCM3-2-H',\n", + " 'MRI-AGCM3-2-S',\n", + " 'MCM-UA-1-0',\n", + " 'INM-CM5-H',\n", + " 'KACE-1-0-G',\n", + " 'NorESM2-LM',\n", + " 'FGOALS-f3-H',\n", + " 'FGOALS-g3',\n", + " 'MIROC-ES2L',\n", + " 'FIO-ESM-2-0',\n", + " 'NorCPM1',\n", + " 'NorESM1-F',\n", + " 'MPI-ESM1-2-XR',\n", + " 'CESM1-1-CAM5-CMIP5',\n", + " 'E3SM-1-1',\n", + " 'KIOST-ESM',\n", + " 'ACCESS-CM2',\n", + " 'NorESM2-MM',\n", + " 'ACCESS-ESM1-5',\n", + " 'IITM-ESM',\n", + " 'GISS-E2-2-G',\n", + " 'CESM2-FV2',\n", + " 'GISS-E2-2-H',\n", + " 'CESM2-WACCM-FV2',\n", + " 'CIESM',\n", + " 'E3SM-1-1-ECA',\n", + " 'TaiESM1',\n", + " 'AWI-ESM-1-1-LR',\n", + " 'EC-Earth3-Veg-LR',\n", + " 'CMCC-ESM2',\n", + " 'CMCC-CM2-SR5',\n", + " 'EC-Earth3-AerChem',\n", + " 'IPSL-CM6A-LR-INCA',\n", + " 'IPSL-CM5A2-INCA',\n", + " 'BCC-CSM2-HR',\n", + " 'EC-Earth3P-VHR',\n", + " 'CESM1-WACCM-SC',\n", + " 'CAS-ESM2-0',\n", + " 'EC-Earth3-CC',\n", + " 'MIROC-ES2H',\n", + " 'ICON-ESM-LR']" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Get the list of models. Replace \"source_id\" with any of the catalog categories (table_id, activity_id, variable_id, etc.)\n", "list(col.df.source_id.unique())" @@ -175,7 +377,7 @@ "Now, we can be more selective about what we want to get from the CMIP6 project data:\n", "\n", "- source_id: The climate model, in this case 'MIROC6'\n", - "- experiment_id: The forcing scenario. Here we will use 'historical' (for the historical period) and for future data we could use any of the SSP simulations, such as 'ssp585' or 'ssp245'. \n", + "- experiment_id: The forcing scenario. Here we will use 'historical' (for the historical period) and for future data we could use any of the SSP simulations, such as 'ssp585' or 'ssp245'.\n", "- table_id: The timestep of the model simulation. Here we will use 'day' for daily data, but some models have monthly and 3-hourly data, for example.\n", "- variable_id: The codename for the variable of interest. Here we will want 'tasmin', 'tasmax', and 'pr' for minimum temperature, maximum temperature and total precipitation, respectively.\n", "- member_id: The code identifying the model member. Some models are run multiple times with varying initial conditions to represent natural variability. Here we will only focus on the first member 'r1i1p1f1'.\n", @@ -188,9 +390,79 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 6, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " activity_id institution_id source_id experiment_id member_id table_id \\\n", + "0 CMIP MIROC MIROC6 historical r1i1p1f1 day \n", + "\n", + " variable_id grid_label zstore \\\n", + "0 tasmin gn gs://cmip6/CMIP6/CMIP/MIROC/MIROC6/historical/... \n", + "\n", + " dcpp_init_year version \n", + "0 NaN 20191016 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Build a query dictionary for all of our requests, for tasmin.\n", "query = dict(\n", @@ -218,8 +490,10 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 7, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "# Get the object locator object\n", @@ -231,7 +505,7 @@ "metadata": {}, "source": [ "The final step is to open the dataset with xarray by using the 'open_zarr()' function. The following block performs multiple operations to get the data that we want:\n", - " \n", + "\n", "- It opens the data using xarray\n", "- It extracts only the times that we need for the reference/historical period\n", "- It then subsets it spatially by getting only the points within the catchment boundaries. If your catchments is too small and this fails, try with a larger basin or apply a buffer around your boundaries.\n", @@ -244,9 +518,1446 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 8, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "hwloc/linux: Ignoring PCI device with non-16bit domain.\n", + "Pass --enable-32bits-pci-domain to configure to support such devices\n", + "(warning: it would break the library ABI, don't enable unless really needed).\n" + ] + }, + { + "data": { + "text/html": [ + "
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<xarray.DataArray 'tasmin' (time: 3653, geom: 1)>\n",
+       "dask.array<rechunk-merge, shape=(3653, 1), dtype=float32, chunksize=(3653, 1), chunktype=numpy.ndarray>\n",
+       "Coordinates: (12/19)\n",
+       "  * time       (time) datetime64[ns] 1980-12-31T12:00:00 ... 1990-12-31T12:00:00\n",
+       "    lon        (geom) float64 dask.array<chunksize=(1,), meta=np.ndarray>\n",
+       "    lat        (geom) float64 dask.array<chunksize=(1,), meta=np.ndarray>\n",
+       "  * geom       (geom) int64 0\n",
+       "    COAST      (geom) int64 dask.array<chunksize=(1,), meta=np.ndarray>\n",
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+       "    ...         ...\n",
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+       "Attributes:\n",
+       "    cell_measures:  area: areacella\n",
+       "    cell_methods:   area: mean time: minimum\n",
+       "    comment:        minimum near-surface (usually, 2 meter) air temperature (...\n",
+       "    long_name:      Daily Minimum Near-Surface Air Temperature\n",
+       "    original_name:  T2\n",
+       "    standard_name:  air_temperature\n",
+       "    units:          K
" + ], + "text/plain": [ + "\n", + "dask.array\n", + "Coordinates: (12/19)\n", + " * time (time) datetime64[ns] 1980-12-31T12:00:00 ... 1990-12-31T12:00:00\n", + " lon (geom) float64 dask.array\n", + " lat (geom) float64 dask.array\n", + " * geom (geom) int64 0\n", + " COAST (geom) int64 dask.array\n", + " DIST_MAIN (geom) float64 dask.array\n", + " ... ...\n", + " PFAF_ID (geom) int64 dask.array\n", + " SIDE (geom) object dask.array\n", + " SORT (geom) int64 dask.array\n", + " SUB_AREA (geom) float64 dask.array\n", + " UP_AREA (geom) float64 dask.array\n", + " id (geom) object dask.array\n", + "Attributes:\n", + " cell_measures: area: areacella\n", + " cell_methods: area: mean time: minimum\n", + " comment: minimum near-surface (usually, 2 meter) air temperature (...\n", + " long_name: Daily Minimum Near-Surface Air Temperature\n", + " original_name: T2\n", + " standard_name: air_temperature\n", + " units: K" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Get the CMIP6 data from Google Cloud and read it in memory using xarray. This is done via \"lazy loading\" and is not actually reading the data in memory\n", "# yet, but is keeping track of what it will need to get, eventually.\n", @@ -276,36 +1987,40 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can see that we have a single chunk of 10 years of tasmin data, as expected! However, you might also have noticed that there is no metadata, such as units and variable properties left in the data array. We can fix that by wrapping the code in a block that forces xarray to keep the metadata. \n", + "We can see that we have a single chunk of 10 years of tasmin data, as expected! However, you might also have noticed that there is no metadata, such as units and variable properties left in the data array. We can fix that by wrapping the code in a block that forces xarray to keep the metadata.\n", "\n", - "Also, since we will need to use this block of code for each variable, it might become tedious. Therefore, to simplify the code, we can combine everything into a single line, like this:\n", - " \n", - " " + "Also, since we will need to use this block of code for each variable, it might become tedious. Therefore, to simplify the code, we can combine everything into a function." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 9, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "with xr.set_options(keep_attrs=True):\n", - " ds = xr.open_zarr(mapper, consolidated=True)\n", + "def extract_and_average(mapper, start, end, geometry):\n", + " with xr.set_options(keep_attrs=True):\n", + " ds = xr.open_zarr(mapper, consolidated=True)\n", "\n", - " # Convert to numpy.datetime64 object to be compatbile\n", - " if type(ds.time[0].values) is not type(np.datetime64(\"1980-01-01\")):\n", - " ds = ds.convert_calendar(\"standard\")\n", + " # Convert to numpy.datetime64 object to be compatbile\n", + " if type(ds.time[0].values) is not type(np.datetime64(\"1980-01-01\")):\n", + " ds = ds.convert_calendar(\"standard\")\n", "\n", - " historical_tasmin = (\n", - " average.average_shape(\n", - " ds.sel(time=slice(reference_start_day, reference_end_day)),\n", - " basin_contour,\n", - " )\n", - " .reset_coords(\"height\", drop=True)[\"tasmin\"]\n", - " .chunk(-1)\n", - " )\n", + " # Compute average over region\n", + " out = average.average_shape(ds.sel(time=slice(start, end)), geometry)\n", "\n", - "display(historical_tasmin)" + " # Convert geometry variables into attributes\n", + " attrs = {\n", + " key: out[key].values.item()\n", + " for key in out.coords\n", + " if key not in [\"time\", \"time_bnds\", \"lon\", \"lat\"]\n", + " }\n", + " out = out.isel(geom=0).reset_coords(attrs.keys(), drop=True)\n", + " out.attrs.update(attrs)\n", + "\n", + " return out.chunk(-1)" ] }, { @@ -317,9 +2032,24 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 10, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "historical tasmin\n", + "historical tasmax\n", + "historical pr\n", + "ssp585 tasmin\n", + "ssp585 tasmax\n", + "ssp585 pr\n" + ] + } + ], "source": [ "# We will add a wrapper to ensure that the following operations will preserve the original data attributes, such as units and variable names.\n", "with xr.set_options(keep_attrs=True):\n", @@ -347,23 +2077,9 @@ " mapper = fsCMIP.get_mapper(col_subset.df.zstore[0])\n", " ds = xr.open_zarr(mapper, consolidated=True)\n", "\n", - " # Convert to numpy.datetime64 object to be compatbile\n", - " if type(ds.time[0].values) is not type(np.datetime64(\"1980-01-01\")):\n", - " ds = ds.convert_calendar(\"standard\")\n", - "\n", - " # special case for precipitation, which does not have the \"height\" variable that we need to discard as for tasmax and tasmin.\n", - " if variable == \"pr\":\n", - " out[exp][variable] = average.average_shape(\n", - " ds.sel(time=slice(period_start, period_end))[variable],\n", - " basin_contour,\n", - " ).chunk(-1)\n", - " else:\n", - " out[exp][variable] = average.average_shape(\n", - " ds.sel(time=slice(period_start, period_end)).reset_coords(\n", - " \"height\", drop=True\n", - " )[variable],\n", - " basin_contour,\n", - " ).chunk(-1)\n", + " out[exp][variable] = extract_and_average(\n", + " mapper, period_start, period_end, basin_contour\n", + " )[variable]\n", "\n", "# We can now extract the variables that we will need later:\n", "historical_tasmax = out[\"historical\"][\"tasmax\"]\n", @@ -387,7 +2103,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": { "tags": [] }, @@ -417,19 +2133,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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N9YaHhyM8PNzXXSIiIiKDkNwHSC5hYWGIi4vDmjVrXF5fs2YNevfu7fY9vXr1qrH86tWr0bNnT4SGhuL666/Hvn37sHv3bufPww8/jLvvvhu7d+82ZKuO32O/JSLy0bZjBfg+NVvrMMhgRCVAY8aMwcWLF0WvdNy4cbhw4YKo9c6ZMwfz5s1DRkYGRo8ejaysLMTHxzvX88wzzziXj4+Px6lTpzBmzBhkZGRg3rx5mDt3Ll599VUAQEREBLp16+byc9VVV6FRo0bo1q0bwsLCRO8DKccE3qsjIvk8NecPvPbDXhw4W6R1KGQgohKgzz//HGVlZaJXOnPmTBQWFnpcbvDgwUhISMCECRPQo0cPbNq0CUlJSWjbti0AICcnx2VMoNjYWCQlJWHjxo3o0aMHPvjgA0yfPh2DBg0SHRsRka8sNjtWH8hFYZlZ61CoipzCcq1DIAMR1QdIEAR06tRJ9MzvUlqLRo4ciZEjR7r924IFC2q81rdvX+zatUv0+t2tg7Ql8L4XGdyM9cfw+bqjuC6qEVaN/ovW4RCRF0QlQPPnz5e8YjkGSiQi0qNf95wFABw+V6JxJETkLVEJ0LPPPqt0HBRA2AeIiJTAoUBICs2eAiP5MbEgUgdv4hIZHxMgP8K+NUREROIwASJNMWkjI2JbK5HxMQEi1en9Pn1hmRlPfbUdP6Sd1joUIiJSCBMgomoS1h7FtuPn8er/9mgdChERKUTyXGAXL17Exx9/jHXr1iEvL88515bDiRMnZAuO/JNQ5a6XHjtuF5dbtA6BiIgUJjkBGj58OJKTkzFkyBC0atVK9OCIRO6wDxB5UlRmwWvL9uDRm67B/d1aah0O6RgvRySF5ATo999/x4oVK3DHHXcoEQ8FAFZSJMVna49g1YFzWHXgHE5+/JDW4QDgY/BE/kByH6AmTZqgadOmSsRCPtLj7SQiX+WXVmgdAhH5IckJ0AcffIB3331X0uSoRET+hF81iIxP8i2wTz/9FMePH0dUVBTatWuH0NBQl79LmaiUSOC9BDIgHrZExic5ARowYIACYVAg4bdnIiLSmuQE6L333lMiDpKBEZ+oYodoMiIetkTGJzkBckhLS0NGRgZMJhO6dOmCm266Sc64yI/pPU1jZ3LyRO/HcKDiuUtSSE6A8vLy8MQTT2Djxo246qqrIAgCioqKcPfdd2PJkiVo0aKFEnGSn9JjHyAjtqQREZE0kp8Ce+mll1BcXIwDBw7gwoUL+PPPP7F//34UFxdj1KhRSsRIfobf0YiISGuSW4BWrlyJtWvXonPnzs7XunTpgpkzZ6Jfv36yBkfSsPlXHv5ajmVmK9JO/Ynb2zdDaDCnASSiwCa5FrTb7TUefQeA0NDQGvOCERmRv94CG7EwDUPm7sC0NUe0DoWISHOSE6C//vWvePnll3H27Fnna2fOnMHo0aNxzz33yBoc+akqDSz+mWro05ZjBQCAxTuyNI6EiEh7khOgGTNmoKSkBO3atcO1116LDh06IDY2FiUlJfjvf/+rRIxEqvLXW2AkHx4hRMYnuQ9QTEwMdu3ahTVr1uDQoUMQBAFdunTBvffeq0R8RKrz11tgJB8eITrFzJQk8HocoPvuuw/33XefnLEQERERqUJUAjR9+nSMGDECERERmD59ep3L8lF47Rix5ULQ4UBAvAVGpG+CIMDEYeTJR6ISoM8++wxPP/00IiIi8Nlnn9W6nMlkYgJERESKmbnhGOZvzcTyF+5Am2b1Xf6mdkp0vrQCv+3NwYAerRFZv+bT0aRvohKgzMxMt7+TvrDlgoj83ZRVhwEAH6/MQOLTcS5/U7s9+bmvU7EnuxAbDudhwdBbVd46+UryU2ATJkxAWVlZjdcvXbqECRMmyBIUkRiCIMBu198tNCIKDHuyCwEAGw/naxsIeUVyAjR+/HiUlpbWeL2srAzjx4+XJSjyb1VbqrxNXwRBwKOJ2/DQf7cwCSIiIskkPwVWW+ezPXv2oGnTprIEReTJRbMNuy9/+8otLkf0VfW0DYiINMdOACSF6ASoSZMmMJlMMJlM6NSpk0sSZLPZUFpaivj4eEWCJP8ix9Nqenx6zCh4kSB/wD6P5CvRCVBCQgIEQcBzzz2H8ePHIzIy0vm3sLAwtGvXDr169VIkSKK68GlYaZg6EhFJSICeffZZAEBsbCx69+7tdkJUIqm8bchR8iLOhIrImDg2EEkhuQ9Q3759nb9funQJFovF5e+NGzf2PSryTgCd+1UTJ7mbwnl3jciYeGucpJD8FFhZWRlefPFFXH311WjYsCGaNGni8kNERESkd5IToP/85z9Yv349EhMTER4ejjlz5mD8+PGIjo7GwoULlYiRxOKXH1n4eyu6n+8eEZEokm+B/frrr1i4cCHuuusuPPfcc+jTpw86dOiAtm3b4ttvv8XTTz+tRJzkp7x+IqzqLTCZr+j+3oru57une+dLK9CkfhiCgpiK+sRN8bEPEEkhuQXowoULiI2NBVDZ3+fChQsAgDvvvBObNm2SNzoiEVjlkdq87Wuy/cR5xH24FiO/3SVzRFSXS2ab1iGQDklOgNq3b4+TJ08CALp06YLvv/8eQGXL0FVXXSVnbOSvZGiCkGMsodrwSyQp5atNJwAAKw/kahxJ4PhszRF0fnclNhzO0zoU0hnJCdDQoUOxZ88eAMC4ceOcfYFGjx6N//znP7IHSKQ2f78FxvzOd7zVok/uPpXP1x0FALz38wF1gyHdk9wHaPTo0c7f7777bhw6dAipqam49tprceONN8oaHAUAOZINXouIfLIr60/8lH4GY/tdh8h6xh3jzc+/u5DMJLcALVy4EBUVFc7/t2nTBgMHDkTnzp35FJjWAigRULKVhl/uKdAMTNyGhSmnMCkpQ+tQiFTj1S2woqKiGq+XlJRg6NChsgRF5EnV/IdzAknDb8na0XvZH88v1ToE0QLhrDdb7Xhj2V6s2JujdSh+SXICVNts8KdPn3aZH4yIiEhN/pYULU3NxpKd2fj3d3xqUAmi+wDddNNNztng77nnHoSEXHmrzWZDZmYm7r//fkWCJP+l92/E/sjfLhJa8PYxeL2Xvb8/AGA0+SUVnhcir4lOgAYMGAAA2L17N/r374+GDRs6/+aYDX7QoEGyB0jkTtULEPvsEBGRVKIToPfeew8A0K5dOwwePBgRERGyBJCYmIgpU6YgJycHXbt2RUJCAvr06VPr8snJyRgzZgwOHDiA6OhovPbaa4iPj3f+ffny5Zg4cSKOHTsGi8WCjh07YuzYsRgyZIgs8eqaQb69yR0m8x9Sm7ePwRvkFDUsfhkiKST3AXr22WdlS36WLl2KV155BW+99RbS09PRp08fPPDAA8jKynK7fGZmJh588EH06dMH6enpePPNNzFq1CgsW7bMuUzTpk3x1ltvISUlBXv37sXQoUMxdOhQrFq1SpaYSR94ISEtcdZxfeLHQlKIagFq2rQpjhw5gubNm6NJkyZ1fvtxTI0hxrRp0zBs2DAMHz4cAJCQkIBVq1Zh1qxZmDRpUo3lZ8+ejTZt2iAhIQEA0LlzZ6SmpmLq1KnO22933XWXy3tefvllfP3119iyZQv69+8vOjZSh7cVFis6MpIVe3MwZ8sJlFUYY0qG9YfOYdqaI5j2eA90imqkdThucTBK8pWoBOizzz5Do0aVJ4Ej+fCV2WxGWloa3njjDZfX+/Xrh23btrl9T0pKCvr16+fyWv/+/TF37lxYLBaEhroO4CUIAtavX4/Dhw9j8uTJtcZSUVHhMrZRcXGx1N3RhwCqD6pOhcFciNQm9eJrlKd4HOfScwtSAQAvLErDurF3aRaPVMyJSApRCdCzzz7r9ndfFBQUwGazISoqyuX1qKgo5Oa6nycnNzfX7fJWqxUFBQVo1aoVAKCoqAitW7dGRUUFgoODkZiYiPvuu6/WWCZNmoTx48f7uEdEFCgC5RZYcblV6xAkmbslEze0jsRV9cO0DoUMQPJUGEDlY+8//vgjMjIyYDKZ0LlzZzzyyCMuj8aLVf2bVG3jDNW1fPXXGzVqhN27d6O0tBTr1q3DmDFj0L59+xq3xxzGjRuHMWPGOP9fXFyMmJgYqbtCagqM6w8RSbDxcD5eWbobC4beqnUoZACSM5b9+/fjkUceQW5uLq677joAwJEjR9CiRQv88ssvuOGGG0Stp3nz5ggODq7R2pOXl1ejlcehZcuWbpcPCQlBs2bNnK8FBQWhQ4cOAIAePXogIyMDkyZNqjUBCg8PR3h4uKi4SV7ezupe9V1yfxlnKzqRcW08nK91CGQQkp8CGz58OLp27YrTp09j165d2LVrF7Kzs9G9e3eMGDFC9HrCwsIQFxeHNWvWuLy+Zs0a9O7d2+17evXqVWP51atXo2fPnjX6/1QlCIJLHx8yPiXvQLBxiYh0IUButWpFcgvQnj17kJqaiiZNmjhfa9KkCT766CPccsstktY1ZswYDBkyBD179kSvXr3w5ZdfIisryzmuz7hx43DmzBnnJKvx8fGYMWMGxowZg+effx4pKSmYO3cuFi9e7FznpEmT0LNnT1x77bUwm81ISkrCwoULMWvWLKm7SgqR45z2tuWIiPwDW2rJV5IToOuuuw7nzp1D165dXV7Py8tz3nYSa/DgwTh//jwmTJiAnJwcdOvWDUlJSWjbti0AICcnx2VMoNjYWCQlJWH06NGYOXMmoqOjMX36dJcRqC9evIiRI0fi9OnTqFevHq6//nosWrQIgwcPlrqrZBByJ0NqVqzf/ZGFCqsNQ++IVXGrRO4FSuduw+BjbYqSnABNnDgRo0aNwvvvv4/bb78dALB9+3ZMmDABkydPdnmEvHHjxh7XN3LkSIwcOdLt3xYsWFDjtb59+2LXrtofKf3www/x4YcfetyuP9h7uhCjFqdrHYZPAnkcoAqrDW/+uA8A8Pcbo9G8Ifuhqe30n2X4fO1RDO/THte1FD/eDceg0Q8mbeQtyQnQ3/72NwDA448/7qwEHAfg3//+d+f/TSYTbDZjDPplVMO/TkVegE6W5w9Vns1+ZS/KLTxXtDBiYRoO5hTj5z1nceTDB0S/z18vukzsKJBIToA2bNigRBwk0uaj+ci+cAlP3dYGl8yBe9F0uQDp8Fp0scKK+VszcX+3VuhwdUOPy6t5PeVF7opDuZUt1marXeNI9MFfEzvD4uehKMkJUN++fZWIg0QaMncHAKBba8+3F/2Z3uuFySsPYWHKKUxdfQQnP37I7TImjbpx8iJHRCQyAdq7dy+6deuGoKAg7N27t85lu3fvLktgVLezheV+8RiEv16Kd2X96XEZPsmmPZPJpP9smtxyNGT69cfH1lpFiUqAevTogdzcXFx99dXo0aMHTCaT22+R7PdDYsh94ffn+o+UxdYwosAlKgHKzMxEixYtnL+THgR2xe0P1y2tboEREZHIBMgxLk/134m0IrUVaVnaaRw5V4I3HrhelU7Aek7Q2An6Ct4Cc2XEkjBizKLx2FSU5KkwJk2ahHnz5tV4fd68eZg8ebIsQVHg8PYWhMtDYCJWMfZ/e/DFphPYdvy8V9tTAvMQ0jt/uv6yzx1VJzkB+uKLL3D99dfXeL1r166YPXu2LEGRONWvn7yeelZYZlFlO2KSG60uLuz3QkTkRQKUm5uLVq1a1Xi9RYsWyMnJkSUoIk+8vYTzWyCReHUl8lnny5Cw9ggKy8zqBeQDQ/a5YzOxoiSPAxQTE4OtW7ciNtZ17qKtW7ciOjpatsCoboH+Jb5qK4Yekxoxnw/rNjKyR2ZuwZ9lFhw8W4wvn+mp+vYdpw9bNMlbkhOg4cOH45VXXoHFYsFf//pXAMC6devw2muvYezYsbIHSOIZsRrwei4wecPQHEeCJqP58/Lt5B0nL2iyfX+rA9xicqcoyQnQa6+9hgsXLmDkyJEwmyubPiMiIvD6669j3LhxsgdI/kfuc5p1hP/6fV8OVuzlrXW1VD+X/Onc0mNLMWlLcgJkMpkwefJkvPPOO8jIyEC9evXQsWNHhIdzJmtSjz9VzABvh9XmhW93aR0C6ZTzFpimUZCRSe4E7ZCbm4sLFy7g2muvRXh4OO/DaoC3MqTT62EqZ1yrDuRi5oZjtZ6TejxXBUHAWz/uw8wNx1Tdrhxn0CMztuDj3w/JsCb90fPTjFIZshM0KUpyAnT+/Hncc8896NSpEx588EHnk1/Dhw9nHyDShNz1r9x55e7sQlWTjv/7Jg1TVh3GH5na9M3wxv4zxfj2jyxMWXVY61Ak23O6CLOTj2sdBvkjfslVlOQEaPTo0QgNDUVWVhbq16/vfH3w4MFYuXKlrMFR7dxdTgPrVPFyAEWZoxBjwMytWLT9VM1Yqg7mqEBk+SUVbl/XY8vhJQvnEDQqrQ4nPR7HsjNK85pBSU6AVq9ejcmTJ+Oaa65xeb1jx444dapmJU8UaL5IPo4DZ4tdXvv2jyyNoiE1ZV8ow4z1R1Gk0oCbStPz9dfRqqrnGEnfJHeCvnjxokvLj0NBQQE7QpNqXKfCEF8DKv2d0W4XMMmL/iDsn+AfBszcivMXzThwthiz/hmndTiSeZNL6DEB+Sn9DP4sM2PoHbGeF6aAJbkF6C9/+QsWLlzo/L/JZILdbseUKVNw9913yxoc+T+1K0+lN6enEaoD4Q6B3py/WDk0SMoJ/cw556/qugX2ytLdGP/rQZw6f1HFiMhoJLcATZkyBXfddRdSU1NhNpvx2muv4cCBA7hw4QK2bt2qRIwkkcVmx74zRejeOhIhwV4/6KcYOS71OvzSqTt6/GZO+lZjfkEJSXRRmQUrD+Tg/m6tEFkvVNa46lLXl4fiS1bV4lAEv8UoSvLVsUuXLti7dy9uvfVW3Hfffbh48SIGDhyI9PR0XHvttUrESG4IQu3nxvu/HMDAxG0Y/+tBdYPSiJQLvRxPYxWUViDl+Hmf18WB2bTH64srb45IRxm+uHgXXl+2D6MWp8saU0DT6FtMRk4x3vxxH/KKyzXZvloktQBZLBb069cPX3zxBcaPH69UTOQjR4fbb7afwgcDuol6z4z1R5G0LxdL/u92NI5Q79ubEf3lkw0oM9vw5ZA49OvaUutwamX0i/t/1x1VfBuciqVuUspn89ECAEDykXyFonFl8MNb1x74fDOAyglvFw2/TeNolCOpBSg0NBT79+8PjMcPA8zU1UdwMKcYC7edVHW73raCeHvhkuPYLTNXPrK94bB8FT1vV9X06ZojWocQeLw4ELU6dnnKKO9QbrHnhQxM8i2wZ555BnPnzlUiFtIBs82/qxU9joKslADaVa8/V2/zYX4F1I9AOs7V5u9lK7kTtNlsxpw5c7BmzRr07NkTDRo0cPn7tGnTZAuOqDb+0H9Gq8rF3y7e+04XYeiCnXjt/uvweM8YVbZp/KOvFtUyQj039us4NDIIyQnQ/v37cfPNNwMAjhxxbaLmrTH1CBAUqQD4CUqh78tgbaejvqOWbuR3aSgorcBrP+xVLQHyW7Vk5WcKLwEAWl9Vr8bf9Fzt6zk20p7kBGjDhg1KxEEByutOqIL737UWSLfY9OKSWf1pNALluioIQLnFhjs+Xg8AWPz87WgQHozu11zlsoxe6Tk20p7kBKiq7OxsmEymGtNiENXFf5IE+S6D/lIiWqiw2FXfZiB9XoVVpvV48qvtAIATEx/UKpwrJJ5+/nDbnOQluRO01WrFO++8g8jISLRr1w5t27ZFZGQk3n77bVgs/jH/DZEaWB3Lo8KqfgIUSNwlDro4dnURhH/z9yKW3AL04osv4scff8Qnn3yCXr16AQBSUlLw/vvvo6CgALNnz5Y9SFKPUe6ZKz2Tuj8zyEcsms1vWhT1xyj1QW2qxs/59qg6yQnQ4sWLsWTJEjzwwAPO17p37442bdrgiSeeYAKkEnd1vhE7oat96RJzrfSlojTCpdgIMUrhy1Ff+VlLLxFP2zTemXiF1SatRa3okkYt/5cLua5zmrmxZyXlFoxbvg8P3xit64FdlSD5FlhERATatWtX4/V27dohLCxMjphIpOoJj//0rfGMrT6e8RuvcjwdfUY9OvecLkKX91Y5/x9AVUrAmrH+GH7bm4MR36TJvu6Scgtsdv0eRJIToH//+9/44IMPUFFR4XytoqICH330EV588UVZgyMSwx8qaSWS10BJEgNjL9VjrtanysjnV9XviIFyPkiVV1JR6998qZfOFl7CDe+vxqBZ2wAAy9JO46mvtqOwzOz1OuUm+RZYeno61q1bh2uuuQY33ngjAGDPnj0wm8245557MHDgQOeyy5cvly9SUoVRWg2MXCk7BFKLnb/xl1tg+88UYdmu01qH4RVHXcXERp9W7M0BAOzOLgQAjP3fHgBAwtqjeP/hrlqF5UJyAnTVVVdh0KBBLq/FxHDwMRLPdQwfz5XXL3vOon3zBujWOlLBqLyh74q3tmTWKBdnsbTYH31/8uL97b9bPC6j132Vmvg4zoc/L5qR/WeZy1hGpJ7icv08LS45AZo/f74ScZBEeq2U5JZ68gJGLU4HAJz8+CG3y0gpC6W/LXrbqHMotwTtWzSUNxjyTKHsSY/n58e/H8LVjcLx3J2xot9T62jiBm69vH3SOlRY7fj+/3rh1timWofjt2p9JkdHh47kPkCkH/72Td6do3mlWodQB/k+gZHf7pJlPWIuTDqqf7QXIIVxOLcEs5OPY8JvByW9T895jre36x3jRiUfyZMzHL+kxMevp0OKCVAAOZxbgtN/ltW5jAGfpDcsJSqCVQfOKbBWdRm5dcFBzGl0scJao8OxUkorrKpsRwtiD5fqrb9+cJiRj5gA+ZG6xgEqKK1A/4RNuHOyvuZy87YOktqPKFBsP3Fe6xCMxc0pY7ba8fgXKZiy6pCim+763ir0+WS9otuQg7vzi2cc+QMmQAHiZMFFt68fyyvFf9cddf6fDUC+UaqP0Z7sQrz2wx7klZRLet8by/bisVnbXAa30/tnrGo+62Zbv+/PwY7MC5i54bjimz9XXPsjyFQ3qa3VRnnCldTj02SopB25Wj3unZYsy3rU5m2iYdTGokdmbgUA5JdUYP7QW0W/b8nObADAjpMXFInLH1lsBj1IFKDnW+KOc5mflrHoqcVeVAI0ffp00SscNWqU18GQNFIqJz1XZO6UW2w4cq5EVMKin9NJecfz3bfkueMyCFwgFZIUbs4LbyvolOP+d/vRn48bP9412Uj5/M1WO/JLK9D6qnrKBSQzUQnQZ599JmplJpOJCZCG5Mis1U6Uagt5+Nep2HKsAJ1bNVY3IBUpfXEx6sXLoGHjn3P/0DoERbg7jvRwbBntS52/GzBzKw7mFGPZC70R17aJ1uGIIioByszMVDoOIhdbjhUAADJyit3+XQ8VsLtLteJJjZfpAa8VytPznEdy4+jLVN3By3X1T+lnENe2iSEm52YnaDI8KUmHPhIndRig/nFLT30ESP/0crycKy7HoFnb8PPuM6LfU2a2InHjMRzP1/N4Z9J4So718WlV8ioBOn36NBITE/HGG29gzJgxLj9SJSYmIjY2FhEREYiLi8PmzZvrXD45ORlxcXGIiIhA+/btMXv2bJe/f/XVV+jTpw+aNGmCJk2a4N5778WOHTskx0X6po+TSP0Mg0+y+KfMgosot9i0DsNQfD0T5M6bJvx2EGmn/sTLS3aLfs8nKw/jk5WHcc+n7h9G0bqe00tyqRTJT4GtW7cODz/8MGJjY3H48GF069YNJ0+ehCAIuPnmmyWta+nSpXjllVeQmJiIO+64A1988QUeeOABHDx4EG3atKmxfGZmJh588EE8//zzWLRoEbZu3YqRI0eiRYsWzvnJNm7ciCeffBK9e/dGREQEPvnkE/Tr1w8HDhxA69atpe6uzrlWAXI0OarfbOnfJ1id1OwDZKC8Sc0jQg/FsvPkBfxjdgpimzfAhlfv0jocUfRwXdRBCC6KL0mf4yr1FJ/O1JLkFqBx48Zh7Nix2L9/PyIiIrBs2TJkZ2ejb9+++Mc//iFpXdOmTcOwYcMwfPhwdO7cGQkJCYiJicGsWbPcLj979my0adMGCQkJ6Ny5M4YPH47nnnsOU6dOdS7z7bffYuTIkejRoweuv/56fPXVV7Db7Vi3bl2tcVRUVKC4uNjlx//ooaqXj+s3E3mrQvE5oN6qYDKiX3afBVDZCkTSGfks9JRIGrnWrnUqMB19YJIToIyMDDz77LMAgJCQEFy6dAkNGzbEhAkTMHnyZNHrMZvNSEtLQ79+/Vxe79evH7Zt2+b2PSkpKTWW79+/P1JTU2GxuM++y8rKYLFY0LRp7ZPeTZo0CZGRkc4fzm4fGMxWOxbvyELWedfpQaScoOeKy3X7+LNR+wARkXHpKcHxRHIC1KBBA1RUVI5eGh0djePHr4yWWlBQIHo9BQUFsNlsiIqKcnk9KioKubm5bt+Tm5vrdnmr1Vrrtt944w20bt0a9957b62xjBs3DkVFRc6f7Oxs0fuhJn+/H+stSZ2gq/z+1eYTGLd8H/4yxdvpQUy4beI6PPnVdmw+ml/nkoVlFpSZrXjw882YvFLZKRaMTuvDnGeZK60/j9r43AeIn7QijFSqkvsA3X777di6dSu6dOmChx56CGPHjsW+ffuwfPly3H777ZIDqN7nRBCEOvuhuFve3esA8Mknn2Dx4sXYuHEjIiIial1neHg4wsPDpYStOb1WSmK4zuPl5TpkiKO2ebO8aTnZdvw8+nRsUevfBQhYlnYaB3OKcTCnGK/ff71XFbD3j8GzOYjkY+T6x0i0Lmatt680yQnQtGnTUFpa+cje+++/j9LSUixduhQdOnQQPWAiADRv3hzBwcE1Wnvy8vJqtPI4tGzZ0u3yISEhaNasmcvrU6dOxcSJE7F27Vp0795ddFx6Vr3S8eeRoI3G0wXBLmg7xcKRcyWabVsqrb+Z81TxTOvPqCojJWOZBRdRWm7FDddEah2K4mq75ujp45KcALVv3975e/369ZGYmOjVhsPCwhAXF4c1a9bg0Ucfdb6+Zs0aPPLII27f06tXL/z6668ur61evRo9e/ZEaGio87UpU6bgww8/xKpVq9CzZ0+v4gtURkmUXFqRtIui5iu1BCNXsXrbklNaYZUpAm0VlplxVf0wRbehpwqa/MvdUzcCAHa8eQ+ublz7XQkHg1THhiW5D1D79u1x/nzNWweFhYUuyZEYY8aMwZw5czBv3jxkZGRg9OjRyMrKQnx8PIDKvjnPPPOMc/n4+HicOnUKY8aMQUZGBubNm4e5c+fi1VdfdS7zySef4O2338a8efPQrl075ObmIjc319lqRUT6Vtc3+h4T1tR669IbSiX8RhgFVyx3rT1GanVxqLEfGu5D1oWyGq9tPVbgF6OJG+nYkJwAnTx5EjZbzQG7KioqcOaM+BEwAWDw4MFISEjAhAkT0KNHD2zatAlJSUlo27YtACAnJwdZWVnO5WNjY5GUlISNGzeiR48e+OCDDzB9+nTnGEBA5cCKZrMZjz32GFq1auX8qfqovFEZ6LgSzft98nY2eDlL8cpFTq3RT6Xceqh6Cfaj6zFmbTzueSGRlKqs/f2BBV3tXR3B6P24r3qYPD3nD8zdckK7YNzR1QctP9G3wH755Rfn76tWrUJk5JV7mDabDevWrUO7du0kBzBy5EiMHDnS7d8WLFhQ47W+ffti165dta7v5MmTkmMwIqn34HVeD/jECNcaLSaUNECxkEr0ngh4Q8w+VT3H1HwQoMxsRf2w2i+vtZ2by9LOoOiSBb/vz8X3/9fL0Odw7eMA6WevRCdAAwYMAFDZtOsYB8ghNDQU7dq1w6effiprcOSq+oHj7elc15N2fFpICmknshynvRafz5zNJ7B81xksGn4bmjZQtv+N2rxNDHRUh4titHiN7mRBGbpEN/bqvTM3VLZwJqw9giYK93cLdKITILvdDqDyNtTOnTvRvHlzxYIiZQmC8b8RKnnrYslO78eBUvrpGG9vgfniwxUZAICZG47hnb91kWmt8tHjtd2v+gC5bb3UY6lLo+UeiDk6LpntaFJf8VAUYJxjQ/JTYJmZmUrEQQGk6kVcjnpUzqRD2qjOVaoxjyEIumlb8/baXGFVZ7JOI1xbPZWhPyQIddHT3tV1/ptM4paTm1zbUqPOUPtY1dOx49Vs8MnJyfj73/+ODh06oGPHjnj44Yc9zuJOvvPlwKn6jVRPB6C3vN0HT+/LK6nwcs3SafU5+Nu1WYvEUukyrLDaMPzrnfh620lZ1udLg5SRDxcjH+t6GGtJrmuOXklOgBYtWoR7770X9evXx6hRo/Diiy+iXr16uOeee/Ddd98pEWNAG710N56dt0O1LN0Ax6xb5RZ5Wiek7b/4z0RPFfH5i2atQ6iT5hW/DJuXUvm7298f0k5jbUYe3vvlgO/BQF/Hn1aM2L9RjY9N7kTF47Gmo2NR8i2wjz76CJ988glGjx7tfO3ll1/GtGnT8MEHH+Cpp56SNcBAJggCfkyvHFrgWF4p2jVvINt6jf5cWNWTbMrKw1h3KA/fPX8bel+rTd80Rzh6vdAYsfJXg7flovQXhdJyfQxcWVBagWFf76zxuh6Oc8dnV1csev9Cp4NiDGiSW4BOnDiBv//97zVef/jhh9k/SEECas6hVf3k1vm57pYc3/bXHcoDAHz4W4bP66La6eGi54/cJWF6unCfyL9Y80UeC7Lw975ieic5AYqJicG6detqvL5u3TrExMTIEhRVkvPcqFqf+uspdzCnGFnna46wWht35SutOVjKbY6a5e5N5edti4Xmt5Uk8FQsekgOPMV44aIZaaf+BADkFF1SIaK6uXQGDtCLbvVzQOtyGLN0Nw7lus7Rd7jqnH06+JgcZWSzC5LLSw/nqSeiE6DnnnsOJSUlGDt2LEaNGoUXXngB33zzDRYtWoT4+Hi8/PLLLlNSkPzUGG3YAMcsgNorr79M2eDhjXX/Wa3997bylfQYfJWd8adbYEa5fg+atQ3F5RaM/X6P1qHIXmZGSqj1anm655kT9HDWlpmtuOPj9fj3d7UPQFyVp2NNT8eO6D5AX3/9NT7++GO88MILaNmyJT799FN8//33AIDOnTtj6dKltU5iSvqi9QVE6+3Lp+rj/B6SU8H1Mfgbx6/GmPs6KRSXY5tXfpfj25haH5sRDg+x5VlUZnE775PH9evi0lc7PZ3DYkNRs0w9JwEi1+NzJL5bl5GH3OJyJO3LlfQ+fR/BlUQnQFUr+EcffdRlBndShh4OfikEQUBhmQVNPIwWbBOqJg5ebsu7tymutriqv15cbsX7vx5ULRY9XbB8pYemdaOVp+stMO3KUBAErDqQi67RkYhpashR/kgkI5wikvoAGeG5fn/hblbgGp2gddYf5J2f9+OmD9Zg7cFzdS734W/KXvgdzFY7Tp1304GzDtIOcf8/H8xWu+rb9NyaplIgKtHTLQGlJe3LRfyiXejzyQYIgoBpa45g9QFpLQsOvl6OZL8tKKHF1Z9rDiMdz5Ieg+/UqZPHJOjChQs+BUSVUw7M3HAM/4vvpfq2falUFm3PAgBMXX0Y93aJqnW5DYfzvd/IZWIqr398kYI92YVY+NytotfrdVKp83Pe28/1sdnbnL/rfR/lIqYCV/q7oNzrl78PkHd2ZF4ZaX39oTxMX3cUAHDy44e8j0Xkzil9Ya7eylZ3LJ7p4XTzaSDE2taphx27TFICNH78eJdZ4EkZU1YdBgCMl/EWiZST01/syS4EACxNvTK3V12V4JFzJUg+kidhC/oeCFGObe49XeT8ffGOLEwaeIPvK/WRHhqi5X1CUwc7JJH3nfivyC0ulycYnVDiHDfekeGZnq4/khKgJ554AldffbVSsZCPjHiyqHIy1LKN6slQv882+b6pOnZI7fO+6v7JdWyUma2oHyZ5/FRJPA4kq6MKVAsbDufhzJ+X8M/b24p+j+ucWPqgduKn9PaUmKhYL5+VvxJdk7H/j7EZ8VtmXYx0n9mfWKwCUHcfd0MxYrU2dH7lyMw9Yq5Ct9bGapFXu7jrqvfkrkGk1LE7T/7pcRmtxynylpHCFt0J2qgfhqFVK3KXTtBQpvKWI1EyyqHi+76Kn2C2+mPwtS0jJyU+BzUSTyMMhKgHuUWebyFZbXacL63QTR8gJY6eutap1xngJ688pGAk8vHpuDHAiSq6BchuV/9pEKqb14+QGyRBqZOEfahaMbkmkb4WhHEKUq66yM3DibL7Zc9ZRIRIHqRel7Q+1x5N3IZ9Z4oweZC8fbfk2C9fj0m9XV+1/qyVInuHfB3Vm8rezCdZ+XLgiD2I1a5U9HQyiJHnoeOmL3uj5PgsclXOdhVq+Xd+2q/4NvTE3TkgV5eDfWcqO7H/vPvsle35wWTIYplcWmmNVdfohdRT3kilzARIxzyNIFu9jhRw5cmnuihdEahym0TxLbj3/MJUt6/LNfKrnM6XVsi+Tn/9lqsUb/MYudMTf/7c9NISrrcWKbXY7QJST3nu06RHTIB0zJvHRJfvOq1AJMYmZ0W3p8pj4XKT+xpVdaRpuSrnQPgW7e54+e6PLDRtEIb7u7X0eV3VqfGAgtyfmxzr832vpa1B8afAdNLPSm6e6o7Xl+3F/9KuXHcc5WCEfNA/brQHiOonWPUDTPSjlXo5swxP2imuZbHLlbgF4rFzIr8Ub/64D/GL0rQORT8UOg6KLlnw4OebkbjxmHZBeMkfzw0xiW7V5AcA8ksrUFBH67OeyokJkIHp6DhyIeUAV6r5Oqfokvv3ebe52qKQdVEln7RcsTdHlvVU7wP08+4zuG3iWlG3Xo3qwkWz1+/1+haYgl+f9VpvAMC8LZk4mFOMT1Ye1joUyQL1Flh1m47ko+eHa1XpL+grJkAGUvVw8uVi6XhnSbnFp3iU4msi8E3KSfSatL7K+twvd7ZQvpFoHd+U6gpd6frxzR/3YcG2k4puo/r+vbxkN84VV2Dkt7sU3a4SrDY7ysw2RbchCPr6xisXOXbJXcJgtol/2rjcYr8ci7hoqi8n+21BuW+BGfy40WIeQamYABlYjVtgJvEV06SkDNzw/mq5QwIAHM0rhUVCRVbdH5me55Orq/L6cEWGx/cfyytFZoG0iVJ9IeZz8aW+M1vt+O6PLB/WIE5tMVp9GCbjp/QzePyLFK/f763lu86ovk2tGOViKuVLwo/pnj8/Tw+SyCkQ+sfJQU+lxARIY7/uOYudJ6VPIOvLPGGCIOCLTSe8fr8YW44WeP3ewjLlW6Y2HJIy51dt9NPmrVbla1dgIKBXlu6WfZ1i5CvwlFx1JpPnWyNuH4NXKB65GCWhqtpvy99Gw1eDEp+z2WrHz7vP1NlPSC1MgDR05FwJXlqcjn/MFvftt+qtodIKq9uxQnwe21imG9k2hUfMq/NWU43hAZSKxf0Ai26XFFGT+FLZqFW56+G+vl0AUo6fR7FKt3B9OSX0eAtML/HIdsz62/74ueQj+Xh5yW48mrhV61CYAGnp9J9XmmdXHciV/P7qF1U9TbB3/mKFIq0FvlK88ldxl2cnH8ei7afU2yDUGQnak01H8vHkV9sxMHGb1qHIQp3H4OVenw4OBB/J/9h63SvkdFKusi+4f1BFTUyANFS14vu/b9KwYGumYrOJu8wGrcJ5+PqyfXju652il7fbBczaeBx/nDjv87b1+E1MgIj5wkR8wo7P7kzhJXz8+yG8rfKoyWJb9iqsNszccAwHzxYrFsuxvFJRy50tvIR/zd+B5CP5isVSF29akAJ18mlln37TrlXaq/XJuzrVGeEYZgKkI+//ehAbJVTSej/ANh72vC82u4CV+3Mxb2smJq88hMFfbhe1bimVg7v5v9T+BiumcpRSgZaWW70Pxifigvxq0wlMWXUYD07frHA8NVVYbbBW6YQ/bvk+bDycj2fn7RD1ftlbSzyOEm68S50sF3uN74At2HYS5RZlngL0l8ae2nZj2urDmL81s873pmfpf3RoJkA6kywiafCZBo9qF5VZcDy/5jf2JTuzEb8oTdSTW9/vzEbWefWe6pCbHGXrLucVBEG1i6jYin3/GeVafupSbrHhpglr8NdPk52vnfNiRHW5fLHpOM4U1t3UL7YlyyduvgTISWzLoJaJgbuW4cU7lH9y0kHJfS8ut6h2i+1Efimmrz/m8UGc32Qae0xJTIC0JPGKaORvFT0+WI17Pk3GiWpJUPJh8U9jvbZsL/4yZQOAuu+n19UwptXtMT1OReCPjp4rRZnZ5vL4s5Ytpd+KGJpg+4maT4H6EvL50gqMW75X0cEpqx99C1NOKrKd5btOI+2U9KdkxSq+JF9Lqvwth65rHP71TrfDi+zIvIDu76/G68v2yhyBe6UVWrU+y48JkM74UvHVVdFrPSuy41yuPsaPGhcnlwEkNZoIVq7k1d161HzSqLbN6LHflRKM0JH17Z/2Y/GObDwyU7mnbKqXQ/XWgGVp7uckrHq6uztiqh5H6Vl/Ysz3ezBolvpjRMll4+E8DJn7h8sDL95am5GHlftrPiwzfd1RAMD3qXXPA3kot9irFnR/PreZABmJ/uveGrx9Us3jeuv4m5jRfeU+qeW4MBrg2oqJSZ5vVUqx/4xyk8s6yDnpphE+o6MebqmpsQ9j/7fH522fEnmxFrtOd19O5PxC5O44+9f8ndh8tAD/+d9eWbZ0yU2fJTH7cL60AvcnbHa2oEtRYwRtA5wDYjEB8nP3TkvG+78ccHlNyhg6vtqVVSjvCr3g0gn68u9iK766x5oRX1hy1Rlu+wB5ua4Nh/IwZ/OVATHFDF0vpmO7FH/77xZZ16c0tep+X05Dd8k4b5nKL2HtEZf/Vy3hC2VmvPbDlSSwtrkJ6/Lb3hz8d72YSWE983pEbD8/bJgAaUjuhkV36zuWVyr7/FBzt2Tin3P+EPUEhRHmgwEqB9Zz5191PjkkoXYQ5H0M3jWp866WGrpgJz5ckYG0UxeweEcWOr39O1bu13/HRT3Qc0KhRmRytAJ4c/vbl2Ey5G71TVh71PWFKmUy/tcDLrekLDblPhWlW2R4C4wUIaUCKLpkwdc+dDR0GQfIw7I2u4DP1x7FtuPup7P44LeD2HKsQNQTFAIE/Jh+pSKQrYXJy5O+tu0/+ZX7x++ltmDVVhnpeQySnKJyjFu+DwAQv8h4k5p6Imerpmp9gHwaflq+MNTmabfdTRukx2T0RL7rPIO+zI2oteqxe1PaehwUFwBCtA6AXNWWbb/43S6cK/Y8d4och9lP6Wfw2eXm3ZMfP+Tyt4tVngAQ09dmw6E8fLW57vEilHelVLy5fm06ko+/dGohcgu1/F3w3OLn7bVVn1WLcdX1OTj+pOdvxe6mK/GXfhtyPzShVrlYbHbFkmdfV+upJb/qXH3eJjLT1x/1vJAG2AKkM7Wd32KSH7F1cp2PkKPu+8U/1PJ0R22qjwdz5k/P98IP5ngeQ0bNb33PzNtRa2uYFHLMTF3b8eFLefjLxbE2vl4zq34DNmpZqT24oxhqp5DuO0Eru34Hq4K3wHz1u5snyxyq79NPu894lch9kazs5NveYgJkYO4qdqUG23Ow+tiUOV1Epz7HY51K8Db6XafqHtW0tj5EVXnqiyUmNvd9gES80Y/5ekx68kSV0ckTN8rTKdUTnzpBy7w+8p2/nKJ7Tyv/1KaamABpSM1KSWwfIJPJpOvmfQcpF301EgSPjx4rH4LXlI6t6rQUH/x2EO/9rM78ZY5vqnIezzU6vuqQGse7Ny2On6w8hG/qmLzX21tEPu2vit8e9FwH1MbTuWOEcbHqwj5AGlJzgFqxx+kXyccRFKT/BMhbSu9ZbcXsrl9GjfdKqEyqXoAECLpuBUrPLsQt7ZqipNyCuVsq+4O9+NeOaNEoXOPI/JOnWz1aHCtWmx2JG4+7vFa9/hu6YKfswyy4bE/DL3ZKJgpKdgdw259Msa2pjy1AfkbKbRR3zhaV47SIfjpGpeTJ681YH3rjbVJe1/sOXB7s0F7lYRKxc0f5QhAqH5vep8Bgi0r3QfPpITC3o4XLG6/U1YlZXEzys/WY733xfHUsrxT3Tkuu8bqev4R4y9PtZbH77G4ARz1gAuRHjHDrSi6SboEpF4aLzIKLnheqg7dxCiLGGKr7/cqW0Pu/HkRBqYhO/AoY/KX74Q38mbuP06zxY9juYvLUMdhdkrmtWl+7jYfzRF9c5UpaX/thj+QJbAUolyCpnXj5U6LHBEhnpKQwUjpBu3SaVbER09PEearPU6nR2Stmsw9N34z5W9UZMqCozP0I10p9HGKe/jMSo33ZqP40pq/kOIvqHmVdnH/N34n/1DLthhje7IeY4T/0zG4XcMnLfaj+ZcnouRATIA3JXYmeKSzDzpN1P60EAK//oM6swQAUuf0AqH/iiRl/ZMHWk7hz8nqvt5F94VKNSSVrxuH+damtODdOWC1peSPyukVNzIjcSt8C86Fu8HQsaDIZsodtuotZbBnoYbodoO7jTU+tJoO/TEHnd1f60Cqro53xkeYJUGJiImJjYxEREYG4uDhs3ry5zuWTk5MRFxeHiIgItG/fHrNnz3b5+4EDBzBo0CC0a9cOJpMJCQkJCkbvG7ctOD7kRNtPXBA1hs4GBTsa6pFaTyqsPnhO8W+H7h6Dl5NdAI7llSizclKFGke7EZ/+cZdQqbUbehqt2vElefWBcxpHoj1NE6ClS5filVdewVtvvYX09HT06dMHDzzwALKy3E+xkJmZiQcffBB9+vRBeno63nzzTYwaNQrLli1zLlNWVob27dvj448/RsuWLdXaFVKZEStgpfjeB8j1/88tSPUpHncyRCTmclPyGDHaLTCtefooAuF0VioJUrPoqreEG70e1jQBmjZtGoYNG4bhw4ejc+fOSEhIQExMDGbNmuV2+dmzZ6NNmzZISEhA586dMXz4cDz33HOYOnWqc5lbbrkFU6ZMwRNPPIHwcHGP2VZUVKC4uNjlRw1qVqF6+gbiL3Zn+9egYA6n//R9xOrqx9sby/fJ0ufDk4vmuvucycVoT4HJTZVWpmpbKbok//GzO7vQMBM2y83dMVzXOE1AZcJj8JzHhWYJkNlsRlpaGvr16+fyer9+/bBt2za370lJSamxfP/+/ZGamgqLxfuTY9KkSYiMjHT+xMTEeL0uf/TLnrPo/9kmyU8+iKFGJ2ihlt99pUWrhju+jgP03i8HXP5vF3z/Zufu7ecvmn1apxhixlsymo9W1N0vrDrP/W18iUYZYkJaImLy5bq3UXMrW44VYHSVua58Wr9WBSvjZt/5SdogpTo8lCTRLAEqKCiAzWZDVFSUy+tRUVHIzXU/N0lubq7b5a1WKwoKvB8fYty4cSgqKnL+ZGdne70uX0mZ7E+tZvhRi9Nx+FyJbBWFHIx+4umJu2/Wvg5I5+7ziQhVvrpxfdrRuKqe2VInE1alBUiGbeglEVuxL0fxbQiCWp+LhMFUvYxHJx+bLDTvBO3unmJdSUBt9yB9mSU4PDwcjRs3dvlRhQ5Hgq6LVmO5UN0EAbLXSodyfesI7e54C1KhuU9vFxm5nC28JHq7KowxKZnnPkDKB63pSNCKrvvK2u/4eD0mrzwk6n1lZqvh+/D4SrMEqHnz5ggODq7R2pOXl1ejlcehZcuWbpcPCQlBs2bNFIuVKl30MKaPmnydC8zo573S8Uu9leTm+Zqar1R5adCsbYr0vbDYlZ+5XYtjp/fH6zF19WFZ1iVH+It9vB0FeL79bbSO5nqoUs4WlWNWtSlHajMx6RD+75s0Seuvvo9Gr0c1S4DCwsIQFxeHNWvWuLy+Zs0a9O7d2+17evXqVWP51atXo2fPnggNDVUsVqX4eoJnXfC9s6oURj/YAeCblLo7+RlRZQOQvj4ct0lnlRjPFF7ChsN5sm/34yRx337dKSqz4MPfDuLA2St9u1bur3l7RI2SdpcczNwg7sKmRoSOOd3Ecjs/mZ/crhRFpzu4+mBgPwqv6WSoY8aMwZAhQ9CzZ0/06tULX375JbKyshAfHw+gsm/OmTNnsHDhQgBAfHw8ZsyYgTFjxuD5559HSkoK5s6di8WLFzvXaTabcfDgQefvZ86cwe7du9GwYUN06NBB/Z00uOpzNumnydS7OByztqs+ArWClPhM7D7eR3H3bru9+v/lj/vwuSu37qQmheN/O4Dlu864vBa/aFeN5QRB0HXrhKfD4c+LZmw4lIfIesb70ugLvX1JkIuWU2EYvUw1TYAGDx6M8+fPY8KECcjJyUG3bt2QlJSEtm3bAgBycnJcxgSKjY1FUlISRo8ejZkzZyI6OhrTp0/HoEGDnMucPXsWN910k/P/U6dOxdSpU9G3b19s3LhRtX3zFyv3u++QbiTVT9GC0gq/aM2qSu798TU3cZeUVa8s9fYRHDwr7qk+JVvcHp6xBbfFNkXHqxt5vQ5PkT351XbVJzz2ZhwgtS+uiRuPYfGOLPwQ3xtRjSNkXbdc+1JY5t2TlL70ka1OP1+CfadpAgQAI0eOxMiRI93+bcGCBTVe69u3L3btqvmtzKFdu3aG+YCkzOWllQseTjhBEPDLnrPoGh3p1fqzL6g/R1TPD9eqvk25Va1QlTjafX2c3N27DXJaeqTkfuw9XYS9p4vwyaDuim1D7eRHLwpKzPh595la//7Jyso+Vglrj2LSwBskr9/TcbFGhttNE5MOYcBNrbF81xn8I+4aNGsYLvv5b6lj4lx/OYcdNE+AqBqdZUAFJXU/+bVyfy5eXrJbnWCqkNYJ2thn7ZRV3vdr8ZYS4wB5s4zSMXi1Xt21Xbky4vHudlC+Kv31apu8V4pLFpuoukqJW7OCALy0OF2WdQ2dvxMHzhZjw6E8LP2/XqLeI2UMt7RTf9b5d5dx1Yx3qLnQ/DH4QKazXMetz9cdrfPvu08XqhOIxqasOox8D8mgUqp2fnU3F9imI/k4V1Iu6zbFXAPqKg+3fYB0VFvmFpVjyirvnqqaszkTVpvC++JD5aDLx+A9/P2zNTXrmaqTnN44YTUKFRgJWi1yfiSOTvp/ZF6oXLeI82piUobo9RvhuiQXtgAFCB1de2Shxe5MklCJKCWnqObtixe/k+ebZVV1JSu7sv5Ei4bh+Nt/t9S6jLtKWU8X5km/e/9Zeps4qcWILUCzkz0/4bZXpS9bnrrL1Fa8xit1YgKkoXlbpT1KSpXu+Hg9HureSvXtqjGVgycCKufr+Sm99r4Mcm2nNgMT3U9V44meWoDKzDatQ1CMfkr5CjGd4j2vQ65o6lZXArR4R5bLk4ZiqZ2Uvv/LAbz/cFevtl9Xh2lfp93RGyZAGrHa7Fh1wFhjMOjluD9TeAlfbjohenm5Tlg9XMAFQfp8Pd6YtfE4WvrwJIz7wSerPwWmfXnqVSDdhhBr2/HzWoeAccv3aR2CKAu2nXRJgKSoKwFcl5GHRdt9HwRTL9gHSCNqV/2BfLGRa9+rj4nk76pPklqXGlPUuCnz6sWnZSdov04wdHiY6v2pwKrDfSzeIf9ckEruqprFmFPk2tdQT5+hN5gA6YyeB1jTb2Tq0EMLkFHoffoRfx4BV0fFbBjxi6RNCeGWng5wHwRSPc8EiEQToI9maKlkuwUm/9RVfstdmaudQBq51bO2fhienua5WGFFqU7m7Ltt4lrnmDLuBzr0L1rtT12nleNJTSmxWSW0dBv5HAOYAGnGiF8WSiusWH9I/vmbjMJmxA9NI+5vgbH8fOWp79uczfp5sOJccQX6f7ap1r8H0uGgxb5OX3cUt3y0FnO3ZEpq1Xniy+2KxaQ3TIA0UlvmrNQcVYFU2SiFF3DxxNwCU7o0tx4zXmulr/70cqoEpZwouFj5i8FOHbtdwCWzDR+tOIjUkxe0Dscr09YcAQB88NtBxbZh9CqRCZBGajtweJGVn1xlqsQIsf5KD51el+6UvzOrWgKhH4aeb59sPJKHxI3H8NXmTDw2O0XrcEghTIB05otk8Y93S6HfqsY4eAtMAhF9gJQeG2Vthv92dDYad8mOnk+nnSf/xNFz4qePALTbHx0Xo0fJR/I13T7HAQoQRhwdVm9YhOKxD5A2lLqFHmhmbfQ8MjX5nnx9svIQ+nZqIUss3mACpAFBqLy/rCZZHvM0qO0n5LmHz+u3eO7KSotJc41KSiJTdMmCRxO34oFuLZULyEf+cO6kZ9U9SahmNCzcbu+t8un9Wh8XvAWmgSFzd+CmD9aous1zxcpM5Kn1AaymANpVn7krqyIDT2aptq+3nRS97KLtp3Ai/6LLpLlGYKRW6dyicjzqYQoYrfo0GacUa9I6diZAGthyrEDrEGRjtgbO4DhGqrC1plZZfbRCuSdctLTndJHoZY0wQvkaNwNP7j9TrEEk3jl1/qLWIfglretUJkDkE/Y5IHfUqta+0tG4N+TeqfMX8dqyvTVeX3kg183S+hQU5Lmi0/Mo/uQeEyDySSA1irATb+2qls03KSeRnlWoXTCkK6f/vKR1CD4Tkf/o+rF+co8JEBH5zDFJYn5JBd75WfwkquT/ft+fo3UIPqttapKqtLqlZ+TvZVrHzgRIZWcLjf9tKFCdPF+mdQi6J+XpRi0qv/G/HtC830GgWbQ9S+sQfMabW8rQutWMCZDKPlpR92SGRhNIt4W06PC9/4z4zrB6oPc+YfO3nkTKCf+dIoP9UJQRpOMDW+skwhdaXz6YAKnsolkfMzWTMYz/1X9vJ2lVcRdf4jlI0ug4/yEfMAFSmT+dR2Vmq+YZPOmLES4UgTwoKHln5X7jPLHmjl6raa3jYgKkMjGd6Yxi5objhm5+Jfn50/FN5HAi39jjAOn1rNS6Px4TIJXp9UD0FluAyOFwbonfHd9Gw/xTGXotV5tdEPX0mV6raa2vH0yAVKbXE8lbej2xSH1fbDouqbOo1pWfP8ovUWbKm0Cn13r7lo/Wah2CoTEBUp1OzyQvZfHRcHIQpF0o3vxxn3KxBJCqRf7LnrOaxUHqu3DRLGq5wjJ9zsOn9XcgzgavMr1+k/CWP81rRr5Znn4GFTbxQwWUWwJnHjkyNrZWKkPrPkBMgFTmZ/kPKeSS2Ybj+aWGq3hX7DX+qL9E1QXSeGdq0rpUmQCpzN9agEgZnd9dqXUIRHTZGY7g75fYB0hlHKmVPHniyxStQyCiKrSa58vfad2wxgRIZWwBIk+2n7igdQhERIrTehw5JkAqW33wnNYhEBERaY4tQAHGZte62xcREZH2mAAREZFkwxemYvPRfK3DIDIsJkBERAY1ZO4OrUMg8prW4wAxAVKR1h82ERGRXmh9RWQCpCLmP0T6pdfpAjzhk6VE3mECpCKOJkqkX7nF5VqHQBRQtL4kMgFSER8AIyK5WWysWMiYOA5QAGELEBHJ7fN1R7UOgcgrWl8SmQCpSOsPm4iISC8uVlg13T4TIBWxBYiIiKiSjY/BBw4mQERERJWsGvdfYwKkIqY/RERElawaPxmkeQKUmJiI2NhYREREIC4uDps3b65z+eTkZMTFxSEiIgLt27fH7NmzayyzbNkydOnSBeHh4ejSpQt+/PFHpcKXRLBrHQEREREBGidAS5cuxSuvvIK33noL6enp6NOnDx544AFkZWW5XT4zMxMPPvgg+vTpg/T0dLz55psYNWoUli1b5lwmJSUFgwcPxpAhQ7Bnzx4MGTIEjz/+OP744w+1dqtWvAVGRESkDyZBw/kZbrvtNtx8882YNWuW87XOnTtjwIABmDRpUo3lX3/9dfzyyy/IyMhwvhYfH489e/YgJSUFADB48GAUFxfj999/dy5z//33o0mTJli8eLGouIqLixEZGYmioiI0btzY292rYXd2IQbM3Crb+oiIiIwqOMiE4xMflHWdUq7fmrUAmc1mpKWloV+/fi6v9+vXD9u2bXP7npSUlBrL9+/fH6mpqbBYLHUuU9s6AaCiogLFxcUuP0rIvlCmyHqJiCgwyJ0waCmuTRNNt69ZAlRQUACbzYaoqCiX16OiopCbm+v2Pbm5uW6Xt1qtKCgoqHOZ2tYJAJMmTUJkZKTzJyYmxptd8ui+LlFo26y+IuuWWzuN42x9VT2Eh3g+PP95exvFY+nTsbmi64/vey2ui2qEv3Vvpeh2AOCjR7t5/d6hd7Rz/n5ru6Z49KbWmh8nYrRoFA4AaBQRgq7RjdHh6oYaR+TZzW2uQlhIkGHqC3ceuqH24/nRm1oruu0PB3TDf/pfh4jQmnXI9S0b4T/9r3P+v2F4COL7XgsA+Ffvds7Xx9zXCf+L76VonFKNua8TgoNM2D++v/O1+L7XIjwkCDFN6wEArr58vGsltnkDt6/fGHMVXrv/OpfXisu1nX9Ps1tgZ8+eRevWrbFt2zb06nXlIPvoo4/wzTff4NChQzXe06lTJwwdOhTjxo1zvrZ161bceeedyMnJQcuWLREWFoavv/4aTz75pHOZb7/9FsOGDUN5ufu5fioqKlBRUeH8f3FxMWJiYmS/BQYAtsu93u2CgNDgINjtAqx2AQIECAIQERoMq80OuwCYbXY0DA+BzS7AYrMjLDgIdkFAcJAJpRVWhIUEwW4HgoIqB1kMvfz3CqsdNpuA8NAgBAeZYLHZYbEJCAkyISwkCFabgNBgEyqsdtgFAQ3DQ1BusSM8JAhlFhsahocAqJy93lRtpkVH/EGmyqk97IIAm11AeEgQCsssiAgNRkRoEMw2O0KDglButSHIZMIlsw0CgODL62sQHuycGsR8ed9MJsBstSM0uDJu0+W/hYcEoeiSBZH1QlF0yYKG4SEw2+wIMpkQERqMcosN4SFBsNory9RstSPscvJktdkRUqWcK6yV+2e1C5fLzASbXYBNEFBusSOyXigstsoYSsotCA0OQkRoMITL5VphtQMCEH65Yg0OMjnLu0F4MIJMJgQFmWC9HF+51YbwkGBY7XaUm+3Oz0QQKt8bZIJLGdvtAsw2OyJCg2GzCwgyoXKbAMJDglBhtSMkyITgIBNKKqyw2QQEXf6MHZ+bo+zsl7dhsdkvf2aVn78gCLhksSEkKMjl83M8keH43WKzIyQoCFa7HeEhwQgOMsF+uaxCg2teWCw2O8xWOwTA+Xk6lrPZK7djMgEmVE7fIEBwHgMhQSaUW2wICQ5CWJX3hAabIFz+u+Mzc/zNLlTG4igPQahcTkDl47UCBGfc1dntleVWtcxNJuCS+crxb7EJqBdWeXyFBJlgttlhtQuICAlGmdmK+mEhKK2wIiI0CEEmU+X+BgUhKMgEs9XuLDcAuGSxoUFYMMotdtQLq3x/kKlyn0Iux2Eywbm8Yx8dZe5YZ1hIkLM+MJkqy9lxTJhtdphgggAB9UKDUVxuda7D8WXCUW6NIkJhttpRUm5B0wZhuGi2ISIkCAIq65Iys9V5nlhsdtQPC0G5xYZ6ocGwXl5HWHCQM36L3Y5gk8l5LFc9ru2XP/cKa+W5bLMLCAkOcp5nglB5bpqtdtgur9dRLwj2ynPNLggIMlWeq/XDrpRphcWOkGCTs550lGlYlf01ASiz2GC6fA7Uu/x+R1wW25XlHarWIVWPGavjmBQAi90OQag8X0oqrIgICXbWz45zqszsWmYWmx3Bl4/l0KAg1AsLht0uoMxiQ1hwZdlcstjQIDwYoUFBsNjtsNsrp4yo/ByvHB9V62fH51z9+DZb7QgywVlfhocEobTCikYRoc79dNS7DcJDcLHCWvnZhgQhPCQY5svXCMf5XFJhRWhQkLP8G0WEoKyiMt7KuqbyvWarHbbLn4Nw+TytGnu5xYbiS5XHXoibusQXUm6Bhci6ZQmaN2+O4ODgGi0zeXl5NVpwHFq2bOl2+ZCQEDRr1qzOZWpbJwCEh4cjPFydrNlxkAbjckURZEJYtQracUA4TsDgIBOCgyoPnqDL73McwDXWD1ONi1PN/7tuB4CzUnBU/gBqJD9V4weAYJNje5X/b9IgzPk3x8FeP6xyfRGhVw7+6qpWNNVjjbi831fVD3P5t2rsjnWHBptqrM+xnKOcHX9zLFu5jAkhVWJ2xFC1jE2Xky13++EuGXBs17H/wUHBLhVAbYKCTM59dpR11W1W/b1xLcdAdY5jx8FkMjnjco35yu+hwVeOo7AqDcVBQSbnMVhdaHCQ27KojMHkcuy4K4q6jhFHTGJ5WJUz+XH87ijzqp+R41dHXFWPubCQyuOwaciVY76qsJAgl3JznFeO88xd+VfnKC9HrO7qg9riA4DIenUfH2EhQWjWMNwlvit/c5xnV9bf4PIyVesrx+/hQbUXeFC14zjk8ufoOFZMJpPL8VZ9v2pTPywE9asUv2O9VeNzlGH1/asaV1hIzeOqevLjWN6xbpPJdZ+rn4uO0KuXmbt9CgoyucTnOEaAusu1av1cPcmvfsxUPTaq1mtX6sMgl3ir/92h6n464oysX/W8qFkHA6YaiUZtdanaNLsFFhYWhri4OKxZs8bl9TVr1qB3795u39OrV68ay69evRo9e/ZEaGhoncvUtk4iIiIKPJq1AAHAmDFjMGTIEPTs2RO9evXCl19+iaysLMTHxwMAxo0bhzNnzmDhwoUAKp/4mjFjBsaMGYPnn38eKSkpmDt3rsvTXS+//DL+8pe/YPLkyXjkkUfw888/Y+3atdiyZYsm+0hERET6o2kCNHjwYJw/fx4TJkxATk4OunXrhqSkJLRt2xYAkJOT4zImUGxsLJKSkjB69GjMnDkT0dHRmD59OgYNGuRcpnfv3liyZAnefvttvPPOO7j22muxdOlS3HbbbarvHxEREemTpuMA6ZVS4wARERGRcgwxDhARERGRVpgAERERUcBhAkREREQBhwkQERERBRwmQERERBRwmAARERFRwGECRERERAGHCRAREREFHCZAREREFHA0nQpDrxyDYxcXF2scCREREYnluG6LmeSCCZAbJSUlAICYmBiNIyEiIiKpSkpKEBkZWecynAvMDbvdjrNnz6JRo0YwmUxah6O54uJixMTEIDs7m3OjKYjlrA6WszpYzuphWV8hCAJKSkoQHR2NoKC6e/mwBciNoKAgXHPNNVqHoTuNGzcO+JNLDSxndbCc1cFyVg/LupKnlh8HdoImIiKigMMEiIiIiAIOEyDyKDw8HO+99x7Cw8O1DsWvsZzVwXJWB8tZPSxr77ATNBEREQUctgARERFRwGECRERERAGHCRAREREFHCZAREREFHCYABEREVHAYQJEREREAYcJEJEKysvLtQ4hYJw+fRo5OTkAxM0ITd6xWq3O31nO6mA5y4sJUICyWCyYP38+fvzxRxw6dEjrcPySIAgQBAEvvfQSHnroIVy4cEHrkPyaxWLB//3f/6F379745ptvAICTGSvAbDbjjTfewMiRI/Hee+/h0qVLLGcFmM1mTJ48GTNmzEBycjIAHs9yYwIUgL744gtERUVh3rx5eOWVVzBo0CB8//33AAC73a5xdP7DZDKhsLAQP/zwAzZs2OAsY5JfdnY27rjjDuzbtw//+9//8OSTTzoTUJLPTz/9hLZt22LHjh2IiIjAlClTMGLECJa1zH7//XdER0fjp59+wvz58/Hoo4/i7bffZkuyzJgABRCr1YqEhATMnDkTM2bMwObNm/Hrr7/innvuwSeffAK73Y6gIB4Scjpw4ADuuecevP/++3jrrbeQnZ2tdUh+afXq1YiMjMTWrVtx2223wWQywWq18huzjCoqKvDll1/iueeew/r16zF9+nQsXrwYy5cvR0VFBctaRvPnz8djjz2GlJQUrF27Fl9++SWmTp2K2bNn49KlS1qH5zd4tQsgZrMZJSUleOyxx/DEE08AALp3746uXbsiJCQE+fn5GkfoPxzfhkNDQ3Hs2DGMHj0ajRo1wqRJkzSOzL/Y7XYIgoDU1FTceOONKCwsxOOPP4777rsPt956K0aMGIHc3Fytw/QLe/fuxcaNG3HPPfc4X8vNzcWIESPYciyjzMxMbNu2DX369AEANGnSBI899hhGjBiBRYsWYcuWLRpH6D+YAPm51atXY8+ePQCA+vXrY8iQIXj33XcRFBTkvEg3adIEpaWluPrqq7UM1dAc5ey4EDi+DaempqJjx45o1KgRPvzwQ8ydOxepqamYPXs2jh8/rmXIhlW1rIOCgmAymbB//34AQEJCAgBgxowZiI+Px6+//or33nsPZ86cAcBOpFI4ytlmswEAbrnlFjRt2hQzZszA77//jv/85z8YOXIk1q9fj44dO2LWrFnOL1EsZ/GOHz/uUl5t27aFxWJBcXExADhbfN577z1cvHgRv//+O0pLSzWJ1e8I5Jfmz58vtGzZUrjhhhuERo0aCS+88IKQk5Pj/LvNZnP+PnToUOGf//ynIAiCYDabVY/VyKqX88iRI4WzZ886/z5jxgzhpZdecv6/U6dOgslkEu68807h2LFjWoRsWO7KOisrSxAEQZg2bZoQHBwsdOrUSdi5c6fLe7p27Sr8+uuvWoVtOO7KOTs7WxAEQdi4caMwcuRI4dZbbxU6dOggrFu3Tjh8+LDw4YcfCh07dhS+/vprjaM3jrlz5wpt2rQR4uLihNtuu0345ptvBKvVKgiCIIwYMULo3r27c1lHvfzxxx8LMTExwp9//qlFyH6HCZAfmjNnjtChQwdh8eLFQn5+vvDtt98KDRs2FHbv3u2ynNVqFSwWi3DzzTcLX3zxRY31VE2SqCYx5fziiy8Ks2bNEo4cOSJ07dpVaNKkiRAUFCQsWrRIw8iNx11ZN2jQwFnWu3btEm666SahXbt2wpkzZ1ze27p1a2HWrFlahG04nspZEATBYrEI/fr1q5HsdO3aVXjzzTfVDtmQEhISnOW8ZcsW4d133xVMJpOQmJgo2O124ddffxU6deokJCQkCIIgCOXl5YIgCEJ+fr5Qr149YfPmzVqG7zd4C8yPCIIAm82G9evXo1evXnjiiSfQvHlzPPXUU4iOjq6xfHBwMAoKCpCXl4e+ffsCAHbv3o1//etfAMAO0bXwVM6O219WqxXBwcF4+eWX0bVrV/zlL3/B0aNH8fzzz+O9997D+fPnNd4T/aurrFu3bu28ddCxY0c89dRTyM7OxtatW53vz8/PR9OmTdGoUSOtdsEQPJVzVfn5+di5cyfuuusuAIDNZkNRURHq1auHBg0aaBC9sZSVlWHFihV4+umn8cQTT6B3794YP3487rzzTkycOBGrV6/Gfffdh/79++PTTz/F2bNnER4eDgBIT09HixYteDzLhFc4P2IymRAcHIyDBw8iPDwc586dAwCMGjUKJpMJP/30E7Zv3+7yFMHatWsRGxuL6OhoDBs2DLfeeisKCwudnUupJk/l/OOPPyIlJQUhISHo3r07/vWvfyElJQWJiYlo1qwZ3n77bZw9exZ79+7VeE/0z1NZ//zzz0hJSUFERARefPFF/O1vf8PYsWPx/vvvY/fu3Rg3bhxCQkLw17/+VeM90TexdUdZWRmaNWuGmJgYxMfHY+/evTh9+jTGjh2Lixcv4pFHHtF4T/QvJCQEaWlpuO666wBUPl0HAFdffTXsdju+/fZbWCwWvPTSS2jTpg0eeughfPvttzh27BjmzZuHTp06oWPHjlrugv/QsvmJfPP9998Lw4cPFxISEoS9e/c6X1+yZInQtm1boV+/fkKzZs2E66+/XpgwYYJw9913CzfeeKPw0UcfOZcdPHiwEBwcLDRq1Ejo2bOnkJGRocWu6Jo35dytWzfh008/FQRBcN7Xr6qwsFC1+I3Em7Lu3r27MHHiREEQKvtKjBo1SoiLixOuu+46oW/fvuxr5Ya3dcfHH38sCIIgbNq0SWjRooXQqVMn4ZprrhHuvvtu4ejRo1rtjm7VVs5PPvmkcP311wunT58WBEEQFi1aJNx9993C8OHDhQ4dOgh79uwRBEEQcnNzhfvvv1/o3Lmz0Lp1a+GOO+4QMjMztdgVv8QEyIAKCgqExx57TGjZsqUQHx8v3HnnnUJ0dLQwf/585zJ5eXnClClThL59+wrFxcXO159//nnh0UcfFfLy8gRBEIQnnnhCaNeunbBixQq1d0P35CjngoICDSI3HjnK+ty5c87XSktLmfi44Ws5DxgwwHlMnzp1StixY4ewY8cOtXdD99yVc6tWrYSFCxcKgiAIR44cEdq3by+0b99eiI6OFurXry8sW7ZMEARBCAkJcamPy8vLhZycHGHfvn2a7Is/C9G6BYqk27BhA7KyspCamuq8Pz9gwABMmDABkZGRePTRR9GkSROkpaXhvvvuQ6NGjWA2mxEWFoZGjRph/fr1aNiwIQBgwoQJbE6thRzlXK9ePY33whjkPKYBoEGDBrj22mu12h3dkqOcIyIiAABt2rRBmzZttNwd3aqtnN999100atQIAwYMQHJyMg4ePIjc3Fw8+eSTCA0NRX5+Ptq0aYOysjLnusLDw9GyZUu0bNlSq93xW+wDZEDfffcdrrnmGrRu3do5HsSjjz6KkydPYubMmcjLy0NISAjOnz+P1NRUAEBYWBjOnTuHI0eO4IknnnBWYkx+aidHOdevX1/LXTAMlrU65ChndnT2rLZyPnXqFGbMmIH8/Hxcc801uPfee/HMM88gNDQUQGXiFBYWhjvvvFPL8AMGEyCd27RpE1atWuUy83LHjh1x4MABAHB+6z106BD++te/ory8HD/99BMAYNy4cVixYgXuuOMOjBw5Ej179kRxcTFGjBjBYeurUaqcqSaWtTpYzurwpZyDgoKQn5+PQ4cOYcaMGRg9ejQGDhyI5s2b8yEUNWh9D47cy8/PF5555hnBZDIJN954o0vHt+PHjwstWrQQ+vbtK0yePFno1auXEBsbK6xbt0648cYbhbffftu57I8//ii8/vrrwlNPPSV8//33GuyJvrGc1cOyVgfLWR2+lPM777zjXDYtLU0YMGCAEBsbK3zzzTca7EngYgKkQxaLRUhMTBT69+8vLFmyRKhfv74wadIk52BYgiAIW7ZsEZ5//nnh5ptvFl588UUhPz9fEARBGDJkiDBo0CCtQjcUlrN6WNbqYDmrQ+5y3rVrl6rxUyUmQDq1fft25/D948ePF1q0aCGkp6fXWK6iosL5+7lz54Ru3boJH374oSAIHMlZDJazeljW6mA5q0OOcrZYLKrESu4xAdIpu93u8v/o6GhhxIgRzsdSq/790qVLgtlsFhITE4WbbrrJZbwJqhvLWT0sa3WwnNXBcjY+JkA65/j28P333wshISHC6tWrXf5++vRpITExUejZs6fQtGlT4bvvvtMiTMNjOauHZa0OlrM6WM7GZRIEdjU3it69e6NBgwb49ttvcfXVVyM/Px8tWrTA4sWLcfbsWYwdO1brEP0Cy1k9LGt1sJzVwXI2FiZABmC1WhESEoIDBw7gxhtvxLRp03D8+HFs2bIFX3/9Nbp166Z1iH6B5awelrU6WM7qYDkblLYNUCTVLbfcIphMJqFt27bCypUrtQ7Hb7Gc1cOyVgfLWR0sZ+PgQIgGcfz4cdxwww04cOAAvvrqK5w8eRL9+/fXOiy/w3JWD8taHSxndbCcjYcJkEEEBwdj0KBBKCgowLBhw7QOx2+xnNXDslYHy1kdLGfjYR8gIiIiCjhsASIiIqKAwwSIiIiIAg4TICIiIgo4TICIiIgo4DABIiIiooDDBIiIiIgCDhMgIiIiCjhMgIjIb2zcuBEmkwmFhYVah0JEOseBEInIsO666y706NEDCQkJAACz2YwLFy4gKioKJpNJ2+CISNdCtA6AiEguYWFhaNmypdZhEJEB8BYYERnSv/71LyQnJ+Pzzz+HyWSCyWTCggULXG6BLViwAFdddRV+++03XHfddahfvz4ee+wxXLx4EV9//TXatWuHJk2a4KWXXoLNZnOu22w247XXXkPr1q3RoEED3Hbbbdi4caM2O0pEimALEBEZ0ueff44jR46gW7dumDBhAgDgwIEDNZYrKyvD9OnTsWTJEpSUlGDgwIEYOHAgrrrqKiQlJeHEiRMYNGgQ7rzzTgwePBgAMHToUJw8eRJLlixBdHQ0fvzxR9x///3Yt28fOnbsqOp+EpEymAARkSFFRkYiLCwM9evXd972OnToUI3lLBYLZs2ahWuvvRYA8Nhjj+Gbb77BuXPn0LBhQ3Tp0gV33303NmzYgMGDB+P48eNYvHgxTp8+jejoaADAq6++ipUrV2L+/PmYOHGiejtJRIphAkREfq1+/frO5AcAoqKi0K5dOzRs2NDltby8PADArl27IAgCOnXq5LKeiooKNGvWTJ2giUhxTICIyK+Fhoa6/N9kMrl9zW63AwDsdjuCg4ORlpaG4OBgl+WqJk1EZGxMgIjIsMLCwlw6L8vhpptugs1mQ15eHvr06SPruolIP/gUGBEZVrt27fDHH3/g5MmTKCgocLbi+KJTp054+umn8cwzz2D58uXIzMzEzp07MXnyZCQlJckQNRHpARMgIjKsV199FcHBwejSpQtatGiBrKwsWdY7f/58PPPMMxg7diyuu+46PPzww/jjjz8QExMjy/qJSHscCZqIiIgCDluAiIiIKOAwASIiIqKAwwSIiIiIAg4TICIiIgo4TICIiIgo4DABIiIiooDDBIiIiIgCDhMgIiIiCjhMgIiIiCjgMAEiIiKigMMEiIiIiALO/wOhHmg18M8+cAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "ERA5_pr.plot()" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 13, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "# Here we need to make sure that our units are all in the correct format. You can play around with the tools we've seen thus far to explore the units\n", @@ -458,19 +2197,38 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The model is now going to be trained to find correction factors between the reference dataset (observations) and historical dataset (climate model outputs for the same time period). The correction factors obtained are then applied to both reference and future climate outputs to correct them. This step is called the bias correction. In this test-case, we apply a method named `detrended quantile mapping`. \n", + "The model is now going to be trained to find correction factors between the reference dataset (observations) and historical dataset (climate model outputs for the same time period). The correction factors obtained are then applied to both reference and future climate outputs to correct them. This step is called the bias correction. In this test-case, we apply a method named `detrended quantile mapping`.\n", "\n", "Here we use the `xclim` utilities to bias-correct CMIP6 GCM data using ERA5 reanalysis data as the reference. See `xclim` documentation for more options! (https://xclim.readthedocs.io/en/stable/notebooks/sdba.html)\n", "\n", - "## WARNING: \n", - "This following block of code will take a while to run, and some warning messages will appear during the process (related to longitude wrapping and other information on calendar types). Unless an error message appears, the code should run just fine!" + "> **Warning**\n", + "> This following block of code will take a while to run, and some warning messages will appear during the process (related to longitude wrapping and other information on calendar types). Unless an error message appears, the code should run just fine!" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 14, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/envs/birdy/lib/python3.9/site-packages/xclim/sdba/adjustment.py:117: UserWarning: Strange results could be returned when using dayofyear grouping on data defined in the proleptic_gregorian calendar \n", + " warn(\n", + "/opt/conda/envs/birdy/lib/python3.9/site-packages/xclim/sdba/adjustment.py:117: UserWarning: Strange results could be returned when using dayofyear grouping on data defined in the proleptic_gregorian calendar \n", + " warn(\n", + "/opt/conda/envs/birdy/lib/python3.9/site-packages/xclim/sdba/adjustment.py:117: UserWarning: Strange results could be returned when using dayofyear grouping on data defined in the proleptic_gregorian calendar \n", + " warn(\n", + "/opt/conda/envs/birdy/lib/python3.9/site-packages/xclim/sdba/adjustment.py:117: UserWarning: Strange results could be returned when using dayofyear grouping on data defined in the proleptic_gregorian calendar \n", + " warn(\n", + "/opt/conda/envs/birdy/lib/python3.9/site-packages/xclim/sdba/adjustment.py:117: UserWarning: Strange results could be returned when using dayofyear grouping on data defined in the proleptic_gregorian calendar \n", + " warn(\n" + ] + } + ], "source": [ "# Use xclim utilities (sbda) to give information on the type of window used for the bias correction.\n", "group_month_window = sdba.utils.Grouper(\"time.dayofyear\", window=15)\n", @@ -530,8 +2288,10 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 15, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "# Convert the reference corrected data into netCDF file. We will then apply a special code to remove a dimension in the dataset to make it applicable to the RAVEN models.\n", @@ -544,8 +2304,8 @@ ")\n", "\n", "# Write to temporary folder\n", - "fn_tmp_ref = tmp / \"reference_dataset_tmp.nc\"\n", - "ref_dataset.to_netcdf(fn_tmp_ref)\n", + "fn_ref = tmp / \"reference_dataset.nc\"\n", + "ref_dataset.to_netcdf(fn_ref)\n", "\n", "# Convert the future corrected data into netCDF file\n", "fut_dataset = xr.merge(\n", @@ -556,41 +2316,74 @@ " ]\n", ")\n", "\n", - "fn_tmp_fut = tmp / \"future_dataset_tmp.nc\"\n", - "fut_dataset.to_netcdf(fn_tmp_fut)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### We will now do as we did with Notebook 03 - Extracting forcing data: We will save the data to a temporary location due to this folder being read-only. \n", - "If you copy this Notebook to your writable workspace, you can save the file to that workspace directly and use it in other notebooks. For now, we will keep things simple and provide pre-computed files for the next notebooks." + "fn_fut = tmp / \"future_dataset.nc\"\n", + "fut_dataset.to_netcdf(fn_fut)" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 16, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# Here we will re-open the written file, remove a dimension and re-write the file. This is the easiest way\n", - "# to solve the problem although there probably exists a better way.\n", - "ref_dataset = xr.open_dataset(fn_tmp_ref)\n", - "ref_dataset.isel(geom=0).squeeze().to_netcdf(tmp / \"reference_dataset.nc\")\n", - "\n", - "fut_dataset = xr.open_dataset(fn_tmp_fut)\n", - "fut_dataset.isel(geom=0).squeeze().to_netcdf(tmp / \"future_dataset.nc\")" + "# Show the corrected future precipitation.\n", + "corrected_fut_precip.plot()" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 17, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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DpUqVzM5T3NSpU1GhQgXDv9DQUCdET0RERGrh0QlTIY1GY/S3EKLEtOIszTN27FikpqYa/iUkJMgWKxEREamPRydM1atXB4ASNUVJSUmGWqfq1asjLy8PycnJZucpzt/fH+XLlzf6R0RERJ7LoxOm2rVro3r16ti2bZthWl5eHnbt2oV27doBAFq2bAlfX1+jeW7cuIGzZ88a5iEiIqLSze2fksvIyEB0dLTh79jYWJw8eRKVK1fGgw8+iBEjRmDKlCmoV68e6tWrhylTpiAwMBB9+vQBAFSoUAHvvPMORo8ejSpVqqBy5cr4+OOP0bRpU8NTc0RERFS6uX3CdPToUXTs2NHw96hRowAA/fv3x/Lly/Hpp58iOzsbH374IZKTk9G6dWts3boVQUFBhmVmz54NHx8fvP7668jOzkanTp2wfPlyeHt7u/z7EBERkfpohOBLlxyVlpaGChUqIDU1lf2ZiFRg7fGrGPXrKQBA3LTuCkdDRGplz/Xbo/swEREREcmBCRMRERGRFUyYiIiIiKxgwkRERERkBRMmIiIiIiuYMBGRx7Hy5iMiIrsxYSIij8PBUohIbkyYiIiIiKxgwkRERERkBRMmIiIiIiuYMBERERFZwYSJiIiIyAomTERERERWMGEiIiIisoIJExF5HA5cSURyY8JEREREZAUTJiLyOBzpm4jkxoSJiIiIyAomTERERERWMGEiIiIisoIJExEREZEVTJiIiIiIrGDCRERERGQFEyYi8jgcuJKI5MaEiYiIiMgKJkxEREREVjBhIiIiIrKCCRMReRy+GoWI5MaEiYiIiMgKJkxEREREVjBhIiIiIrKCCRMRERGRFUyYiMjjcOBKIpIbEyYiIiIiK5gwEREREVnBhImIiIjICiZMRERERFYwYSIij8ORvolIbkyYiIiIiKxgwkRERERkBRMmIiIiIiuYMBGRx+HAlUQkNyZMRERERFYwYSIiIiKyggkTERERkRVMmIiIiIisYMJEREREZAUTJiLyOBzpm4jkxoSJiIiIyAomTERERERWMGEiIiIisoIJE+HKnUws3h2DrDyt0qEQyYIjfROR3HykLpiSkoLDhw8jKSkJer3e6LN+/fo5HBi5TqeZu6DVC1xLyUZ4j8ZKh0NERKQ6khKmf/75B3379kVmZiaCgoKgKXI7p9FomDC5Ga2+4JGiw7F3FY6EiIhInSQ1yY0ePRoDBw5Eeno6UlJSkJycbPh39666LrparRaff/45ateujYCAANSpUwdffPGFUa2YEALh4eEICQlBQEAAOnTogMjISAWjJiIiIjWRlDBdu3YNw4YNQ2BgoNzxyG769OlYuHAhvvvuO0RFRWHGjBn4+uuv8e233xrmmTFjBmbNmoXvvvsOR44cQfXq1dGlSxekp6crGDkRERGphaSEqVu3bjh69KjcsTjFgQMH8NJLL6F79+4ICwvDa6+9hq5duxriF0Jgzpw5GDduHHr27IkmTZpgxYoVyMrKwurVqxWOnoiIiNRAUh+m7t2745NPPsG5c+fQtGlT+Pr6Gn3eo0cPWYKTw5NPPomFCxfi4sWLqF+/Pk6dOoW9e/dizpw5AIDY2FgkJiaia9euhmX8/f3Rvn177N+/H4MGDVIociIiIlILSQnTe++9BwD44osvSnym0Wig0+kci0pGY8aMQWpqKho2bAhvb2/odDp89dVXePPNNwEAiYmJAIDg4GCj5YKDg3HlyhWT68zNzUVubq7h77S0NCdFT0RS8NUoRCQ3SU1yer3e7D81JUsAsGbNGqxatQqrV6/G8ePHsWLFCnzzzTdYsWKF0XyaYgO3CCFKTCs0depUVKhQwfAvNDTUafETERGR8jx+4MpPPvkEn332Gd544w00bdoUb731FkaOHImpU6cCAKpXrw7gfk1ToaSkpBK1ToXGjh2L1NRUw7+EhATnfgkisgsHriQiuUlOmHbt2oUXX3wRDz30EOrVq4cePXpgz549csYmi6ysLHh5GX9Nb29vw7ACtWvXRvXq1bFt2zbD53l5edi1axfatWtncp3+/v4oX7680T8iIiLyXJISplWrVqFz584IDAzEsGHD8NFHHyEgIACdOnVS3ZNlL774Ir766its2LABcXFxWLduHWbNmoVXXnkFQEFT3IgRIzBlyhSsW7cOZ8+exYABAxAYGIg+ffooHD0RERGpgaRO31999RVmzJiBkSNHGqYNHz4cs2bNwpdffqmqROPbb7/F+PHj8eGHHyIpKQkhISEYNGgQJkyYYJjn008/RXZ2Nj788EMkJyejdevW2Lp1K4KCghSMnIiIiNRCI4T9z5P4+/sjMjISDz30kNH06OhoNGnSBDk5ObIF6A7S0tJQoUIFpKamumXzXNhnGwAAjWqUx8bhTykcDZHj1p24ipFrTgEA4qZ1VzgaIlIre67fkprkQkNDsX379hLTt2/fzifGiIiIyONIapIbPXo0hg0bhpMnT6Jdu3bQaDTYu3cvli9fjrlz58odIxEAID0nH78cTsDzzWqgZsUApcMhIqJSRFLC9MEHH6B69eqYOXMmfv31VwDAww8/jDVr1uCll16SNUCiQhP/isTaE9fww+4YHP28s9LhEBFRKSIpYQKAV155xfCkGZEr7Im+DQC4nZFrZU4iIiJ5efzAlURERESOsrmGqXLlyrh48SIeeOABVKpUyexrQwDg7t27sgRHREREpAY2J0yzZ882jEs0e/ZsiwkTERERkSexOWHq37+/4b8HDBjgjFiILGKKTkRESpHUh8nb2xtJSUklpt+5cwfe3t4OB0Vkit0jrBIREclEUsJkbnDw3Nxc+Pn5ORQQERERkdrYNazAvHnzABS8sHbJkiUoV66c4TOdTofdu3ejYcOG8kZIREREpDC7EqbZs2cDKKhhWrhwoVHzm5+fH8LCwrBw4UJ5IyS6h32YiIhIKXYlTLGxsQCAjh07Yu3atahUqZJTgiJl8MFHIiIi0ySN9B0RESF3HKQCZrqmERERlXqSX41y9epV/P3334iPj0deXp7RZ7NmzXI4MCIiIiK1kJQwbd++HT169EDt2rVx4cIFNGnSBHFxcRBC4NFHH5U7RiIiIiJFSRpWYOzYsRg9ejTOnj2LMmXK4I8//kBCQgLat2+PXr16yR0jERERkaIkJUxRUVGGkb99fHyQnZ2NcuXK4YsvvsD06dNlDZCIiIhIaZISprJlyyI3NxcAEBISgsuXLxs+u337tjyREREREamEpD5Mbdq0wb59+9CoUSN0794do0ePxpkzZ7B27Vq0adNG7hiJAHDYAyIiUo6khGnWrFnIyMgAAISHhyMjIwNr1qzBQw89ZBjcktwPExIiIiLTJCVMderUMfx3YGAg5s+fL1tAROZwnCgiIlKKpD5MderUwZ07d0pMT0lJMUqmyL0wISFPoeGLdIhIZpISpri4OOh0uhLTc3Nzce3aNYeDIjKFTYZERKQUu5rk/v77b8N/b9myBRUqVDD8rdPpsH37doSFhckWHBGRFAKsLiUiedmVML388ssAAI1GYxiHqZCvry/CwsIwc+ZM2YIjIiIiUgO7Eia9Xg8AqF27No4cOYIHHnjAKUERERERqYmkp+RiY2PljoPIKnbkJSIipdicMM2bNw/vv/8+ypQpg3nz5lmcd9iwYQ4HRq6n9k7V7JdCRERKsTlhmj17Nvr27YsyZcpYHJxSo9EwYSIiIiKPYnPCVLQZjk1ynonjMBEREZkmaRymooQQELzSkguwDxPZivsKEclNcsK0dOlSNGnSBGXKlEGZMmXQpEkTLFmyRM7YiIiIiFRB0lNy48ePx+zZszF06FC0bdsWAHDgwAGMHDkScXFxmDx5sqxBEhGRe9LpBYb8fByNQspjWKd6SodDJJmkhGnBggVYvHgx3nzzTcO0Hj16oFmzZhg6dCgTJiJSFJ+oVI89l25hc2QiNkcmMmEityapSU6n06FVq1Ylprds2RJardbhoEgZah9WgIjcT06+XukQiGQhKWH6z3/+gwULFpSYvmjRIvTt29fhoIiIiIjURFKTHFDQ6Xvr1q1o06YNAODgwYNISEhAv379MGrUKMN8s2bNcjxKIiIiIgVJSpjOnj2LRx99FABw+fJlAEDVqlVRtWpVnD171jCfhm08RERE5AEkJUwRERFyx0EqoPbhtJh/E7kjlZ9YiGwkuUnOHoW1UbbSaDT4+++/UbNmTSdFRO5I7QkdERF5LpsTpp49e2L58uUoX748evbsaXHetWvXGv198uRJjB49GuXKlbO6HSEEpk2bhtzcXFtDIyIywpG+iUhuNidMFSpUMPRJqlChgt0b+uSTT1CtWjWb5p05c6bd6yciIjVi8kqeweaEadmyZSb/2xaxsbGoWrWqzfOfO3cOISEhdm2DHKf2PkJqj4+IiDyXpHGYYmNjcenSpRLTL126hLi4uBLTa9WqZdcTc6GhofD29pYSGhERR/pWFf4W5BkkdfoeMGAABg4ciHr1jIe5P3ToEJYsWYKdO3daXD4nJwenT59GUlIS9HrjUWB79OghJSQiIiIip5GUMJ04cQJPPPFEielt2rTBRx99ZHHZzZs3o1+/frh9+3aJzzQaDXQ6nZSQiIiIiJxGUpOcRqNBenp6iempqalWE56PPvoIvXr1wo0bN6DX643+MVkiIiIiNZKUMD311FOYOnWqUYKj0+kwdepUPPnkkxaXTUpKwqhRoxAcHCxl0+REHOeIiOTHpzXIM0hqkpsxYwaefvppNGjQAE899RQAYM+ePUhLS8OOHTssLvvaa69h586dqFu3rpRNExGRW+GdGHkGSQlTo0aNcPr0aXz33Xc4deoUAgIC0K9fP3z00UeoXLmyxWW/++479OrVC3v27EHTpk3h6+tr9PmwYcOkhEREZMCBK4lIbpJfjRISEoIpU6bYvdzq1auxZcsWBAQEYOfOnUbDDWg0GiZMClL7OEcqD4+IiDyYpD5MQEET3H/+8x+0a9cO165dAwCsXLkSe/futbjc559/ji+++AKpqamIi4tDbGys4V9MTIzUcIiIiIicRlLC9Mcff6Bbt24ICAjA8ePHDe99S09Pt1rrlJeXh969e8PLS3KuRqUUe0IQEanb0r2xWHkgTukwnEJS1jJ58mQsXLgQixcvNuqD1K5dOxw/ftzisv3798eaNWukbJaIiIhU6k5GLr5cfw7j/4pETr7nDRMkqQ/ThQsX8PTTT5eYXr58eaSkpFhcVqfTYcaMGdiyZQuaNWtWotP3rFmzpIRk0bVr1zBmzBhs2rQJ2dnZqF+/PpYuXYqWLVsCAIQQmDRpEhYtWoTk5GS0bt0a33//PRo3bix7LCQd+zCRrfhqFCLXyy6SJGn1nncMSkqYatSogejoaISFhRlN37t3L+rUqWNx2TNnzqBFixYAgLNnzxp9Zs/75myVnJyMJ554Ah07dsSmTZtQrVo1XL58GRUrVjTMM2PGDMyaNQvLly9H/fr1MXnyZHTp0gUXLlxAUFCQ7DGpFcdhIiIiMk1SwjRo0CAMHz4cP/74IzQaDa5fv44DBw7g448/xoQJEywuGxERISlQqaZPn47Q0FAsW7bMMK1ooieEwJw5czBu3Dj07NkTALBixQoEBwdj9erVGDRokEvjJSIicnfCA+/AJfVh+vTTT/Hyyy+jY8eOyMjIwNNPP413330XgwYNsvouOVf7+++/0apVK/Tq1QvVqlVDixYtsHjxYsPnsbGxSExMRNeuXQ3T/P390b59e+zfv1+JkImIiNyOM1qJ1MTuGiadToe9e/di9OjRGDduHM6dOwe9Xo9GjRqhXLlyVpfPycnBt99+i4iICCQlJUGv1xt9bq3TuL1iYmKwYMECjBo1Cv/9739x+PBhDBs2DP7+/ujXrx8SExMBoMSrWoKDg3HlyhWT68zNzTU8GQgAaWlpssasFLXv655+MJJ8OHAlket5Yq1SUXYnTN7e3ujWrRuioqJQuXJltGrVyq7lBw4ciG3btuG1117D448/7vSLoF6vR6tWrQzDHbRo0QKRkZFYsGAB+vXrZ5iveBxCCLOxTZ06FZMmTXJe0GSSpx+MRJ6Ihy15Ckl9mJo2bYqYmBjUrl3b7mU3bNiAjRs34oknnpCyabvVqFEDjRo1Mpr28MMP448//gAAVK9eHQCQmJiIGjVqGOZJSkoy+4LgsWPHYtSoUYa/09LSEBoaKnfoREREbsPTWwEk9WH66quv8PHHH2P9+vW4ceMG0tLSjP5ZUrNmTZc+efbEE0/gwoULRtMuXryIWrVqAQBq166N6tWrY9u2bYbP8/LysGvXLrRr187kOv39/VG+fHmjf0REVJKHX0OpFJFUw/Tss88CAHr06GGUURY2Y+l05gesmjlzJsaMGYOFCxcakhZnGjlyJNq1a4cpU6bg9ddfx+HDh7Fo0SIsWrQIQEFGPGLECEyZMgX16tVDvXr1MGXKFAQGBqJPnz5Oj49s5+l3L0REnsITW2IlJUyODA3QqlUr5OTkoE6dOggMDCwxcOXdu3clr9uUxx57DOvWrcPYsWPxxRdfoHbt2pgzZw769u1rmOfTTz9FdnY2PvzwQ8PAlVu3bi1VYzARERE5wtNvaSUlTO3bt5e8wTfffBPXrl3DlClTEBwc7JJagxdeeAEvvPCC2c81Gg3Cw8MRHh7u9FjUjJ0zyVNwpG/14Hml9Cj6U3ti8iQpYQIKRtBeunQpoqKioNFo8PDDD+Ptt99G5cqVLS63f/9+HDhwAI888ojUTRMREZGKeWKeLKnT965duxAWFoZ58+YhOTkZd+/exbx581C7dm3s2rXL4rINGzZEdna2pGDJudhFiIiIpPL0S4ikGqYhQ4agd+/eWLBgAby9vQEUDGj54YcfYsiQISXeEVfUtGnTMHr0aHz11Vdo2rRpiT5MfOKMiBzFgSuJSG6SEqbLly/jjz/+MCRLQMGAlqNGjcJPP/1kcdnCJ+w6depkNN2WJ+yIiMi9sOa69Cj6W3ti3zVJCdOjjz6KqKgoNGjQwGh6VFQUmjdvbnFZV798l4iIlOOJF04yzdN/a0kJ07BhwzB8+HBER0ejTZs2AICDBw/i+++/x7Rp03D69GnDvM2aNTNa1pEn7IiIiEj9PLFmUVLC9OabbwIoGL/I1Gcajcaoie306dNo0qQJvLxs62MeGRmJBg0awMdH8kN8RERE5EJskjMhNjbWrvlbtGiBxMREVK1a1ab527Zti5MnT6JOnTpSwiMiIiKSlaSEydZXmnTv3h1LliyBEALjx49HYGCgTcvl5eVJCYuIiIjIKZza5rV7925kZ2fj6aefLvECXEvatm2LgIAAJ0ZGRJ6MI30TqYNOL7D70i088n8VUbmsn9LhOMQlnYR27tzpis0QERGRiqw+dAXj/4pE9fJlcPC/nawvoGKSRvomIlIzDlxJpA6bziYCABLTchSOxHFMmIiIiMhhRjcqHtgqzoSJiIiIyAomTERERERW2JwwPfroo0hOTgYAfPHFF8jKyrK6zH//+19UrlzZaNru3buh1WpLzKvVarF7925bwyEiIjfggS0zZAdPGsDS5oQpKioKmZmZAIBJkyYhIyPD6jJjx45FxYoVjaZ17NgRd+/eLTFvamoqOnbsaGs45CQ303Lw33VncD4xTelQiIiIVMPmYQWaN2+Ot99+G08++SSEEPjmm29Qrlw5k/NOmDDB7HoKX5lS3J07d1C2bFlbwyEnGfq/EzgcexerD8Ujblp3pcMx4onvJiLydDxsSzdPOm/bnDAtX74cEydOxPr166HRaLBp0yaT73rTaDQmE6aePXsaPh8wYAD8/f0NnxW+b65du3ZSvgPJKOo6a5aIiMh+Ru+S88DGWJsTpgYNGuCXX34BAHh5eWH79u2oVq2azRuqUKECgIIapqCgIKORvP38/NCmTRu89957Nq+PSh9PagsnInKUEAIxtzNRu0pZeHl5UFWOSkka6Vuv19u9zLJlywAAYWFh+Pjjj9n8RkRUCvA+x3kW74nBlI3n8ebjD2Jqz6ZKh2OkcEwmT7rRlTyswOXLlzF06FB07twZXbp0wbBhw3D58mWry02cOJHJEkniSW3hRESOmrn1IgDgf4fjFY6kJE9skpOUMG3ZsgWNGjXC4cOH0axZMzRp0gSHDh1C48aNsW3bNovL3rx5E2+99RZCQkLg4+MDb29vo39ERETkGTzpRldSk9xnn32GkSNHYtq0aSWmjxkzBl26dDG77IABAxAfH4/x48ejRo0aJp+YIyJSilanR0auFhUD3fvN6kRK8qSmuEKSEqaoqCj8+uuvJaYPHDgQc+bMsbjs3r17sWfPHjRv3lzKpomInOqFb/fifGI69nzaEaGVA5UOx+3xlrj08PTfWlKTXNWqVXHy5MkS00+ePGn1ybnQ0FAIT0w9yelYGUmucD4xHQCw+d5b1pVw5U4mtDr7H65RI57tyVNIqmF677338P777yMmJgbt2rWDRqPB3r17MX36dIwePdrisnPmzMFnn32GH374AWFhYVI2T6UU82wqDTaeuYEPfz6Ojg2qYtnbjysdDpEkhadrTzpvS0qYxo8fj6CgIMycORNjx44FAISEhCA8PBzDhg2zuGzv3r2RlZWFunXrIjAwEL6+vkafm3ptChFRabF0bywAIOLCLYUjIaKiJCVMGo0GI0eOxMiRI5GeXlB9HRQUVGK+ffv2oVWrVkajelvr40RERESewZO6UkhKmIoylSgVeu6553Dy5EnUqVPHMK1///6ObpJKKU868IiIPJkn9lV2OGGyxFSBxcdbHmDrwQcfdFY4RESqV/y8eTDmDjQAWtepokxARLby8JtapyZMpoSFhVkce0mn07kwGiIi9crI1eKNRQcBAOe/fBZlfDm4L6mYiUolT6pocnnCdOLECaO/8/PzceLECcyaNQtfffWVq8MhIlKVojeUyZl5hv/W6j3oykMezxP3VpcnTI888kiJaa1atUJISAi+/vpr9OzZ09Uh0T3sIyS/zFwtftgdg+ebVkfD6uWVDofcTNEkyZsHKKmdh++ikl++awt7XntSv359HDlyxInRkDWeVHWqFjM2n8e87Zfw7Jw9SodCbqjo4JXMl8gdedJ+6/JO32lpaSXmuXHjBsLDw1GvXj1nhkPkcqevpSodArmxfB3vYsg9eeINuKSEKTs7G0IIBAYWvGfpypUrWLduHRo1aoSuXbsa5isco6moihUrlqh5EkIgNDQUv/zyi5RwiIg8kq5Ik5y7XoDcNW6SgJ2+S3rppZfQs2dPDB48GCkpKWjdujV8fX1x+/ZtzJo1Cx988IHZZSMiIoz+9vLyQtWqVfHQQw/Bx8flXarIjWg8vYGcqJh8vWe8T47IE0jKUI4fP47Zs2cDAH7//XcEBwfjxIkT+OOPPzBhwgSLCVP79u2lRUqlnvDI5y6IzPOEu3NP6sNCtvPE87WkhCkrK8swwvfWrVvRs2dPeHl5oU2bNrhy5YrV5S9fvow5c+YgKioKGo0GDz/8MIYPH466detKCYeICEBB8/6kf87h3PU06zOrlCeOkEy2Ox6fjJSsPDzTMFjpUGThSQmzpKfkHnroIfz5559ISEjAli1bDP2WkpKSUL685Uent2zZgkaNGuHw4cNo1qwZmjRpgkOHDqFx48bYtm2blHCIiAAAh2LvYvn+OByO40u8yT31nL8fA5cfRcLdLKVDoWIk1TBNmDABffr0wciRI9GpUye0bdsWQEFtU4sWLSwu+9lnn2HkyJGYNm1aieljxoxBly5dpIREMlD7nQD7MJE1adn5SofgMHPDsbhrEwcrzKS5kZqD0MqBSoch3b3f3ZN+f0k1TK+99hri4+Nx9OhRbN682TC9U6dOhr5N5kRFReGdd94pMX3gwIE4d+6clHBIJp60YxMRuTN3bJp1v4jtI3ngyurVq6NFixbw8vJCWloa/vzzTwQFBaFhw4YWl6tatSpOnjxZYvrJkydRrVo1qeEQEXkEd7xQEpUGkprkXn/9dTz99NP46KOPkJ2djVatWiEuLg5CCPzyyy949dVXzS773nvv4f3330dMTAzatWsHjUaDvXv3Yvr06Rg9erTkL0JExFRDfdTe1E/O4YnHoqSEaffu3Rg3bhwAYN26dRBCICUlBStWrMDkyZMtJkzjx49HUFAQZs6cibFjxwIAQkJCEB4ejmHDhkkJh4jILZ1KSMGxK8kY0C4MXl4FmYVxHyZPvOxQaeJJCbOkJrnU1FRUrlwZALB582a8+uqrCAwMRPfu3XHp0iWLy2o0GowcORJXr15FamoqUlNTcfXqVQwfPtyud88RERXnbmeQl77fhy/Wn8Nfp65ZnVfplrpNZ27graWHcCs9167llI6blOVJv7+khCk0NBQHDhxAZmYmNm/ebBhWIDk5GWXKlLF5PUFBQYbxnIiISquLNzMM/23ch0k9KeAHPx/Hnku3MWVjlNKheJwjcXcx59+LRi9bdsc8o+iu60mJUiFJTXIjRoxA3759Ua5cOTz44IPo0KEDgIKmuqZNm1pc9s6dO5gwYQIiIiKQlJQEfbGh/+/e5fgpRERqdTczT+kQPE6vhQcAAFXK+SscCVkiKWH68MMP8fjjjyMhIQFdunSBl1dBRVWdOnUwefJki8v+5z//weXLl/HOO+8gODiYzXAqovafQu3xEcnPA2/TyazYW5lKh0AWSH7bbatWrdCsWTPExsaibt268PHxQffu3a0ut3fvXuzduxePPPKI1E0TuQ1PrJYm5+JNJHkCdx1o1RJJfZiysrLwzjvvIDAwEI0bN0Z8fDwAYNiwYSVG8C6uYcOGyM7OlrJZcjK1X9zVHh+RHDgOEwE836mRpIRp7NixOHXqFHbu3GnUybtz585Ys2aNxWXnz5+PcePGYdeuXbhz5w7S0tKM/hF5ElYWkFx4/XSeBTsvo8PXEUhKz1E6FLfmibVKRUlqkvvzzz+xZs0atGnTxqj6uFGjRrh8+bLFZStWrIjU1FQ888wzRtOFENBoNNDpdFJColKAyQdZ49mna3KW6ZvPAwC+3R6NL19uonA0nsETa8gkJUy3bt0y+RqTzMxMq+3vffv2hZ+fH1avXs1O306QladFgK83y1UlPPGkQeSptHoesGSepCa5xx57DBs2bDD8XXhxXrx4Mdq2bWtx2bNnz2LZsmXo3bs3OnTogPbt2xv9c7apU6dCo9FgxIgRhmlCCISHhyMkJAQBAQHo0KEDIiMjnR6L3OLvZKHRhC0YuPyI0qEQKcITbhN4s0OkTpJqmKZOnYpnn30W586dg1arxdy5cxEZGYkDBw5g165dFpdt1aoVEhIS0KBBA0kBO+LIkSNYtGgRmjVrZjR9xowZmDVrFpYvX4769etj8uTJ6NKlCy5cuOBWA2uuOVrQ+T7iwi1Jy/M8LT+WKdnL0zp9u1e/FvXE6l7lVpJ7R2+apBqmdu3aYd++fcjKykLdunWxdetWBAcH48CBA2jZsqXFZYcOHYrhw4dj+fLlOHbsGE6fPm30z1kyMjLQt29fLF68GJUqVTJMF0Jgzpw5GDduHHr27IkmTZpgxYoVyMrKwurVq50Wjxpk5WlLTFPzTs7cg0ozT0ukiNyN5HGYmjZtihUrVti9XO/evQEAAwcONEzTaDRO7/Q9ZMgQdO/eHZ07dzYaXDM2NhaJiYmG17sAgL+/P9q3b4/9+/dj0KBBJdaVm5uL3Nz771Nyx6f75vx7EXP+vYSl/VspHYrN3PFywWuca7G4LUvPyUfC3Ww0Cinvsm1q3OpWx51iVR9PP99JTpj0ej2io6NNvt7k6aefNrtcbGys1E1K9ssvv+D48eM4cqRk357ExEQAQHBwsNH04OBgXLlyxeT6pk6dikmTJskfqAvN+bfgJckT/jLuq8XTBZmTnpOPoDK+Sofh8Yr2YTp2JVnWdXeZtRuJaTlY9U5rPFnvAVnXTVSUJ9aISkqYDh48iD59+uDKlSslCsVaLVGtWrWkbFKyhIQEDB8+HFu3brX4YuDiHS0La7xMGTt2LEaNGmX4Oy0tDaGhofIErCAP3L8VV3QXmvRPJIQAwns0Vi4giWZtu4h52y9hft9H8XzTGkqHY5YnJPxFz6lTNp6Xdd2JaQXjDG06e4MJk0kqOgnaGUrrKf9i0Vut8EhoRaeEQxITpsGDB6NVq1bYsGEDatSoIempjnPnziE+Ph55ecYvcuzRo4eUkMw6duwYkpKSjPpW6XQ67N69G9999x0uXLgAoKCmqUaN+xeCpKSkErVOhfz9/eHvz5ckupq7XwyX7YsDAIzoXA8VA/2UDcZO87YX1kieVXXCRLZRUVpAMrmZlovBq47hwNhOSofisSQlTJcuXcLvv/+Ohx56yO5lY2Ji8Morr+DMmTOGvkvA/RoeufswderUCWfOnDGa9vbbb6Nhw4YYM2YM6tSpg+rVq2Pbtm1o0aIFACAvLw+7du3C9OnTZY3FHfBEKi9TtXY6jvXiNJ5csu763dzraS/3vi1T0zhSnthiIekpudatWyM6OlrSBocPH47atWvj5s2bCAwMRGRkJHbv3o1WrVph586dktZpSVBQEJo0aWL0r2zZsqhSpQqaNGliGJNpypQpWLduHc6ePYsBAwYgMDAQffr0kT0eIo6zQ0TOoHSS4oE5khFJNUxDhw7F6NGjkZiYiKZNm8LX17gjaPFxjoo6cOAAduzYgapVq8LLywteXl548sknMXXqVAwbNgwnTpyQEpJDPv30U2RnZ+PDDz9EcnIyWrduja1bt7rVGExy0Gjc/f5KfZgbEbkT9VzypUWinvg9kaSE6dVXXwUgbWgAnU6HcuXKAQAeeOABXL9+HQ0aNECtWrUM/YmcrXhNlkajQXh4OMLDw12yfXv9dfIapm48jx/easkOfUQWMD+1jStrItxrWAFluVfzZekjKWFyZGiAJk2a4PTp06hTpw5at26NGTNmwM/PD4sWLUKdOnUkr9eTDf/lJADgg1XHsF/mDn2e+OinmrB4XYvFTXJKz8mHr7cXyvh6u3zbTDPVR1LCVKFCBVSsWNHkZ9b6Nn3++efIzMwEAEyePBkvvPACnnrqKVSpUgVr1qwxzHf16lWEhITAy0tSNyuPlKdz/uWAFxzn44mQpHDX5Nu9ak3uH51ZeVo0Dd+KAF9vRH35rIu2fn/77lRqpYWkhOn555/Hjh07SoxrdOHCBXTq1AlXr141u2y3bt0M/12nTh2cO3cOd+/eRaVKlYw6wzZq1AgnT55krZMR+Q+homXuridkNWMfJvflXhd6ksf93/zSzQwAQHa+c94+4QxqOoerKRa5SKq+qVSpEl5++WVotfffRRYVFYUOHToY+jfZo3LlyiYHjiTXU/P1nU+XERGpl6dftyUlTH/88QcyMzPRp08fCCFw9uxZdOjQAW+++Sbmzp0rd4zkRJ6+gyvNVPEy73MeOYuWnZWJqChJCVOZMmWwfv16XLp0Cb169UKnTp3Qr18/zJo1S+74qIjSntswuSNruIeQJQt3XcbYtWcsnEvUkyS70+kuI1eLs9dSjWL2xCZtm/swpaWlGf2t0WiwZs0adO7cGa+++irGjx9vmKd8ede9Cbs0ccbuV7SZS6PhBYdItWQ9OEvnkT5tU8G7+V5v9X9o8WAlE3O4d7koFX232btxLSUbX73SRKEIXMPmhKlixYom+5AIIbBw4UL88MMPNo3DZCv2V6Hi3HGfMBUym3qIlJWd5z4duQHLL4NXg2sp2QCADadvKByJc9mcMEVERDgzjhLY/OIaxctZvYeke+Ju7Frcf8kW7nRY3krPRfd5e/DKozUx9rmHXb79lKw8fLcjGj0f/T80CrHcemTUJOdOhWwjmxOm9u3bOzOOEs6dO4eQkBCXblPtmEQSWabEESKEwMmEFDSoHoRAP0kjtRCVUNgHaMmeGCSl5+KHXTGKJEwT/orE36euY8neWMRN6+7y7auJpE7fy5Ytw2+//VZi+m+//YYVK1Y4HBQAhIaGwtvb9aOrqpmz+zARWafO/UWvF7I3s9jaaXXNkQS8Mn8/3lx0UNbtOxPvvdyHPT+VM26qI6+n2r59t6q7s5+khGnatGl44IEHSkyvVq0apkyZ4nBQpAwh3Kuq2h2YzEfVmXPYyL49JDtPh+PxydDrnbtnvbn4IB6esBlJ6blO3Y4pa44mAABOXbX9wiKFu16M1JicqTEmtbLnptr4KbnC//ecwpaUMF25cgW1a9cuMb1WrVqIj493OCgyzRkHOZv5nKu0F+9bSw+h5/z9WHnwilO3cyj2LgBg0xn5Op2yc77nUutFXK1xUQFJCVO1atVw+vTpEtNPnTqFKlWqOBwUKYeXCJLT0SvJAID/HXbNjZQSLcw8Zixjq7/t3P0Gy1T4nnTjISlheuONNzBs2DBERERAp9NBp9Nhx44dGD58ON544w25Y6R7nFEbVHwcptIoM1eLu5l5SofhJtS9k7j7BYdcQ637SdFzsJQYFf9aRk/JKR6N7CQ90jF58mRcuXIFnTp1go9PwSr0ej369evHPkzkdhpP3AIAODWxKyoE+Mq6bs9LQj3vJGgOm0fInd3NzMOVO5lmBugkKSQlTH5+flizZg2+/PJLnDp1CgEBAWjatClq1aold3xUhL2n723nbmLH+ZuY+GJjlPE1/cRh8bsANV8inJ17XLqZjlZhlWVdp8mbLDUXMtnNVU+aynnD7oE3/7JTZogK+dbVdup25Gr1WP1ea7SrW/IhLVvZs3ebusnwpBsPhwYNqV+/PurXry9XLGSNnfvdez8dBQCEVSmLQe3rOiEg13L2Yec5hzXJwZP6XihJjcmZCkOSXa5WDwDYdfGWQwmTPUw9JedJbE6YRo0ahS+//BJly5bFqFGjLM7Ll/Cqy800849aF7875iWCLJO2h7jqoul5TaDu6XxiGrLydHhUpc1B5vrX/O9wAkZ2qY9qQWUUPxdKOWTUWKPtSTceNidMJ06cQH5+vuG/zeFAiET38XBwLTXWZpRGz87ZAwA4Mq6zwpGYZmk3GbfuLBb3a2U0bdu5m5i59QLmvNEcDauXrpfL8xx2n6R3ybn6vXJUwBnXgqJ3Wmq/2Dj7uHXOOFcmpil9y+cQd47dOdzxeuKqfTAxNcftLrjxd7JKTCvs3jDk5+PYPrqDiyNyH3o3up5IIWlYAVKGKx7TNLeFs9dSsTUy0enbJ8/k3kmiOrhjCcr1u2flafHGogNYvDtGlvVJlZGrVXT7UhT9BTafvYFpm87bNfK+o01qnnTsS+r0nZmZiWnTpmH79u1ISkqCXq83+jwmRtmdmmxn6zhML3y7FwCwYdiTaBxSwdlhkWqpu7rA3WozSgM57vNWH4rHwZi7OBhzF+89XUeGoKQt5uz+OI7eFFtbfvCq4wCA5qEV8WyT6g5ty+T2ZV+jukhKmN59913s2rULb731FmrUqMF+Sx7E2i8ZcyuTCZMdTB0anlhVrRayPnpv4+lf7tOfPV/hZEIKIs4n4cOOdeHv47kvK89S6MXKrlb0WurMFoVb6TlOW/d96ixjR0hKmDZt2oQNGzbgiSeekDsessDZfZhKO3vLQqcX8PayfLVk8ZIzvfz9PgCAv68XPuzwkM3LuWq/lGs7PI7cg6nfyZOekpPUh6lSpUqoXFneAf7IOjWcNFQQgip8veU8moZvQeztTKVDsSryeirC/45Ecil4/YvpGr2Se+1fJ6/hYMwdy+tS6EQvZauXbmbIHoe9tkYmou+Sg0hMdW7txUvf70NOvrw1TmrhjBtYU+vM1wmT0/V6gbPXUqHV6Ut8ZtO2JC3lPiQlTF9++SUmTJiArKySTxOQ80itRrbUZFC8OdXTd3i5fB9xGVl5OszadhFAQdPItZRshaMyrfu8vVi+Pw4T/o5ULAY11WhcvJmO4b+cxBuLDjo/IBdRQ03x+yuPYV/0HUz466zs6y56mjqVkIK/T113aH1Si8vZvU9Mv7zWseVN+WL9OfRZfKjE9O8jovHCt3vx8W+n7m/fjgBOJaTcj0UUxqT8vikXm5vkWrRoYXRxjY6ORnBwMMLCwuDra/z+rePHj8sXITlMBedStyC1mKKTMgxNI3HTujttO446fyNNoS2ri9yJrZw1Udl5OrP7h6Wk6M+T1zHnjRayxeGI5Kz7NZly7evFv7rOjqe83EnRPUkU+3+5HTBRwzp/52UA6tqf1MTmhOnll192YhiklOInYVW3Nqs0uLPXUpUOQfXc8fLm6jvjRbsvY8rG8y7dprty9FSg1pvIFQeu2DW/LbU/9nxXT6oNcgabE6aJEyc6Mw6ygVqrkZ0lOTMPl5Iy8FhYJbd9EjMtO1/pEEoVRXYTmbbJZMk8JZ9EtOZWei42n72Bl1rURPkyvtYXkJFaEz/APW+SrJHUh6lOnTq4c6dkdV5KSgrq1JFhjAwySeoOaOmgsjcRcWVfiWdm7sTrPxzAlsib9zbu3O05IyGNkaFTuF4vkC+xE2ZpI+fu6UlP9xTnqqN4zZF4nPPwpuC3lh7C+L8iMfaPM0qHYvaHFULgyh3Hz0Vz/r1o9+CdnnQcSUqY4uLioNOVfEohNzcXV69edTgoUo6a7gqSswpqZ7adu6lwJPKyN+l8ZcF+tJ6y3WOfDCL76fQC0zapv0bqf4cTsOBevxh3Y2sH7POJ6QCccJ6S8WT84744tP96p/VNWtnmnH8vYcZm+/Y7T2rms2scpr///tvw31u2bEGFCvcHMNTpdNi+fTtq164tX3QkC0s1IGp4usYawwHnOTcqdil88uT01VQ8XpvDeZRWRY/UtcevYuEu90xE1MAdzntysjXJsaVUij4JZ3FdHljEdiVMhR2/NRoN+vfvb/SZr68vwsLCMHPmTNmCo2Ik7oD27LilNCchJyttFyhnU+sQFoBrLpSO9mmyJUTFz4Uao/+TjEeefOxKmArfGVe7dm0cOXIEDzzwgFOCItOcUbXpFp2pXTWGj4s2xBOYawnh3M7gbnAElaCG/NWWkfLVxqXnS2H0f/YsIitz31kIgcw8Hcr5S3phiFuS1IcpNjbWpmSpadOmSEhIkLIJkpE75ESWuOzcroKLCDlGzn397PVUfLfjEvK0ljvcu/vxpYQ/jl1Fw/GbEHE+yab5lUjwSuXpwIYvfTMtF3cz8/DJ76fRZOIWHI9PNrOqgpXZ2un77LVU/HHsqqpro52aGsbFxSE/n49Vu4q5HdPS/ld851TvrqreO3m3qKUju/11smA0aY1GgyEdbX9Pmyuo+ckjW84ho++NJP3OiiOImWp9sNfiHP3+Kr4ml6D0L118+4lpOXj0y22Gv+dHWO5LZ2vN/Qvf7gUAPBDkj/b1q9oVo6tIqmEiZVg7yD3paYSiCpM6z/x2pYM7/3ZH4+4qHQIA97rIy03+exLrhal0olLI0Z/d1v1GrxfI1ys/fMmFRPUOQ8GEyY1IPXDseZecWk4Snqw0X/jckb3jzpD8ih8zn/91FidtfFpL7cztX1JugE01Z9m6nlcW7Jf13CR1XWp+6w0TJlI9FR8/AJhkejpr7y1zVfPYf5YcQua9i6uaW4Et9UGRq39KnlZveH+ju5u8/pxi2y76e5gbLsDV+5qabyiZMHkQNfdrcEThAeTsb6fi41Q1JJ88HSxcIYRNF1tnnGzV0kftws10/Lg3VukwFCH7q1FUdLAfjrXc5GvPV7f3a6mpHAqpuWsJEyY3Yu2CIceOpt5d1XOo+YRgjSJPKwmBfj8eRq+FB1T9BI0rZOTJ0zzozH3QGWtWy8/ujNzZ2ldz5ld36roNN7r2vn7LCcHIxOGEKScnx+xnP/zwA4KDgx3dBN2j4v3IqdT+vVVSAeGxsvN12HPpNo5eSUbCXcsDNrrit7iRmo21x68a3u9XGn7/5Mw8pUOQlS3nFLWfd+QghMD1lGzM3nbRedvwoJKUlDDp9Xp8+eWXqFmzJsqVK4eYmBgAwPjx47F06VLDfH369EHZsmXliZRUQc3ZvxRqr7FQe3yuULQI1JCcdJ65C6N+PYVFu2OUDsVlPv3jtFPWa2tzpzs0yUlNDMx9Nbm6IlgcVgYFfePmbr/k4Fbko+ZznqSEafLkyVi+fDlmzJgBPz8/w/SmTZtiyZIlsgVHxqztR3L0YVLB9agEVx1AajxOi8Y0+reTOHdd2UdupV64lCxaubedmVfwEuTdF2/JvGb1OhRzx+Z57TmObD22lTg2Te3qakjYiyoej73lpBcCMbcz7dqGraQmkGo8DxeSlDD99NNPWLRoEfr27Qtvb2/D9GbNmuH8efW/QdtTeVLVZ1Gu+lbS7xDtPKPYc0Ep8t8Jd7PR/ds99m1LZmo+mQHqj08urr5ul5JidYgSD93Yur+bS3rUeLyYCik5Mw9rjsQjPUfZgbAlJUzXrl3DQw+VHPlWr9dzZG+VOxGfjOikdLOfq/D4KTV0eoH90beRVuSkUGIkdjf9gWJvZ2LlgThVV7ebYy1mOWod0rJLx3lT6s8ve5OcDWc6192ouWAbbnTY6U0E++5PRzHmjzP45DfnNA3bStKrURo3bow9e/agVq1aRtN/++03tGjRQpbASH5J6Tl4Zf5+pcOwnxMPdjWdSFbsj8MX68+hYfUgbB7xtNLhyG78X5GoEOiHHo+EOG0btlxY5br2yrnr/HwoXsa12cDO4J35aLtN65R5pZITNxfWItkSY66V9xxaYyo5kYvUVZta7tiVgvfVbY5MdCAix0lKmCZOnIi33noL165dg16vx9q1a3HhwgX89NNPWL9+vdwxko2sHczWnjBSK8NLHM1cDf85dR1/nbyGWb2bo3wZX1eGBsD+u19z55E/T14DAJxPTLc6r1IcvdM/fyPNqQmTM0gdhyknX4db6bkIrRwoc0SeQ6kxrmw5rtTSXcmec4C5mjN3apJbvCcGI7vUVzoMkyQ1yb344otYs2YNNm7cCI1GgwkTJiAqKgr//PMPunTpIneMZCM5+jCp5SRh6g3Y5ppGhv7vBP6NSsJ3O6KdHZZTmfp6ajuhORqPs7+OS8vLyjgznWbuwlMzIhB5PVX2Taut83FRag3NHZuDpVqyx/gJTnNf3abhFaw9bCTzD55176EKNZJUwwQA3bp1Q7du3eSMhdyAqzqW9yzSdGjree6uhLFi1H4K9bSO/KXomoVrKQU1ulvOJqJxSAWFo3GMfbUc6nPueppsNcGutD/6NpZKGN198oYow39bShTlaJIrTce0pIRp3Lhx6NChA5544gkEBrK62V2YOmEUntQLlaJ9vwTp/RrkYer38bSTkbMTQDXWvKjxJ3RqTCrbabPytHh+nvHTpVJrmyztX3Lv2wJAnyWHZFmXOzXJqZmkJrljx47h1VdfRaVKldC2bVuMHTsWmzdvRkZGhtzxkR0s9WHK1+nx65EEF0YjH8MAbjZeDbPytOg0cyfC/460bzv2BiaRO5+kHE5I3Pi7F6dk7Z8z+v5sO3cTcVbG5LGJyrLWlCxpTyCq61vYxu5ziwy7sMp+bqeSlDBt3rwZycnJ2LlzJ1566SWcOHECvXv3RuXKldGmTRu5YyQbWTqBr9gfh1/sTJjWHr+K6ynKdxS398L054nruHwrE8v3xzknoFJM7X2YTG7TyRmqtQuGOyTIuy/ewns/HUWHb3Y6vjILX1jA9a9Z8ZLxip6eY/5dfnI/QSfn2sz3YVLfU3JqJrkPk7e3N9q2bYvKlSujUqVKCAoKwp9//onLly/LGR/JQAPgYIzlN2IXzGd8yI/69RTK+Hrh/JfPOSWuJXtiEHUjHV+/1gxeXvKdHnR2HKlyXEydeYfliScdT+FJv42phyykslYsY5z0mhVz5BydXko/Sak8dXymK3cysXBXDAY9XQdhD5SFEEKxpyXtJamGacGCBXjjjTdQo0YNPPXUU9i6dSueeuopHDt2DLduqet1AVOnTsVjjz2GoKAgVKtWDS+//DIuXLhgNI8QAuHh4QgJCUFAQAA6dOiAyEj7mnM8gam7jZx8aeN85Gn1+Hb7JZy+mmJ2nskbovDH8avYG33bclwedGGyl6uafYQQyNVafzrF0fOalAS16BLOrs1xRm2UGjvuF/+ectaOWFuTnMmZVKXpnCJgoQ+THOu3s/bqP0sP4X+H49Fn8UEs3xeLx776Fxdvmh9MWU0kJUxDhgxBREQERo4ciejoaPzxxx8YNmwYmjVrJnd8Dtu1axeGDBmCgwcPYtu2bdBqtejatSsyM++31c+YMQOzZs3Cd999hyNHjqB69ero0qUL0tPd40csJMdJz9oBZOuJZtm+WMzcdhE9vttndd6sPPPV3LbE5CpJaTmyNCeYO5EoOaxA3yWH0HTiVqRK7O9hKynfx1wSo9cLnEpIQZ6Dg/eRvLWkajleC0n9as6u89DrBXR6+TuKm5xuLqlRIHMsHA/wemoOwv85h9sZefhy/TkL82e5KjSrJCVMa9euRd++ffHLL7+gWrVqaN26NcaMGYNNmzapruP35s2bMWDAADRu3BiPPPIIli1bhvj4eBw7dgxAwQ4zZ84cjBs3Dj179kSTJk2wYsUKZGVlYfXq1QpHb15Ovg7TN5/HsSv3m9rUdCdbdPBFANh/+TbGrj2j+LuAiitaYpZOHuk5+Xh8yna0+HKbiU+dd2p11S+6//Id5On02H7+puV4VNSHacGuy3jp+30Y9r8ThmmO14DZMa8T1qkUU8Wm1wvoJVzQVfd9TT19qvCrUYQQePG7veg0c6fsSVPJbTl3eTmSbX8f86lI19m7Hd+ATCQlTC+//DJmzZqF48eP4+bNmxg/fjxu3ryJl156CVWqVJE7RlmlphYMIle5cmUAQGxsLBITE9G1a1fDPP7+/mjfvj327zf9GpHc3FykpaUZ/XO1xbtjsGDnZby64IDLty1Fn8UF1bCzt10y8akGh2LuoNfC/Yi6UbIs1XACjnfBXY6rmvFvZ+Ri+ubziDXxRJSzy1pSDZOZ6YvvDc6n1OsSFBkI0UmbLL7v6fUCz83dg+fn7XH7AR9N1bwr/ZXydHpEXk9D3J0sXE02fW6Rs9yd2SRnjj3h+/t6m/0sO189A1lK7vR99+5d7Nq1Czt37sTOnTtx9uxZVKlSBe3bt5czPlkJITBq1Cg8+eSTaNKkCQAgMbHgZBscHGw0b3BwMK5cuWJyPVOnTsWkSZOcG6wV0bdK1uTJ0SQn25hCZqbH3zX12LJA70UHAQD9fzyMw+M6yxSFOtlVi+GEM/u4dWewJfImftofh8gvnrVrWbX3zVT6QmiKCkMqoXin29sZubhwr19Jqp0vBlb7PmJNYfz2fg2pNfxKdu6W43iRo0ncXXYZSQlTs2bNcO7cOVSuXBlPP/003nvvPXTo0MGQhKjVRx99hNOnT2Pv3r0lPit+wrDUc3/s2LEYNWqU4e+0tDSEhobKG6wV7rKD2etWRq6JqQVHtZLf2dKJxalPycm4rsI4z1wtqGXNNPEKAmdf7KRcVOw5qTvcJCdhGZc+4eOqTRXZjr0XVUvzZ1rpr+gM9j6A64qku+jNrSOvLbFlIUvHnBzdOHZdVNeDXs4kKWF6//333SJBKmro0KH4+++/sXv3bvzf//2fYXr16tUBFNQ01ahRwzA9KSmpRK1TIX9/f/j7+zs3YAnk2Pnl6vQt5/L2LmPXm9WLrFvpmgBnd/ouXFegv/nD/vdjV/F80xooY6GKXI4YpCqanJhalyvfJn9/m5Y54wIsd2JYfH1GF3S7121+iWbhW+1cm+NMJbS2/Ca2fO+z1+6/J1CJfc9WZvcXJ570TK36iWk7XB2GrGxOmIrWqABATEyMmTmBWbNmSY9IZkIIDB06FOvWrcPOnTtRu3Zto89r166N6tWrY9u2bWjRogUAIC8vD7t27cL06dOVCNkmzrirlfWAl9hmbulE5vSDSo1HrRNi8rFwy73/8h3M3nYRY59/WP4NS2VXR2zTM19ITEcZXy/UqlJWpqDs6PQt54/orD5MxQ5YL6MaJvs2as95RKkUQ65i/HaHqT6Z7sPVzYHFX8XlbmxOmE6cOGF9Jri4etoGQ4YMwerVq/HXX38hKCjI0GepQoUKCAgIgEajwYgRIzBlyhTUq1cP9erVw5QpUxAYGIg+ffooHL19ip6o1p24avfyjp7Y87R6HLuSjEdrVTS/DQfH4XHG/PYqOYaNncvLF4pdbD00t59PclrCpEQH4pTsfHSbU/CkTdy07hbntSc+R77K+cQ0VAr0k74CmZWoYSoywd6HuNT0tK4j1HUlcw7V9PlTSxxW2JwwRUREODMOp1mwYAEAoEOHDkbTly1bhgEDBgAAPv30U2RnZ+PDDz9EcnIyWrduja1btyIoKMjF0drO2sE8cs2pYlNs2yMdOUmE/xOJ1Yfi0fPRmg6spaTCi5i12H4/dhVTeza1ur4zV1Px69EEjOpSH4H+tjU92XpikXvUWnsuPsmZeSjr7wM/M4/oquHkKCUERy/Air/ep1j4CXez8OycPabntcZJV/Hiqy36t73lr4b9LD0nHxvP3EDXRtVlXW/xQ1vqd3VWUmn3b+XE4RUM520PyjwlPyXnLmy5Y9RoNAgPD0d4eLjzA3IiV9/ZaXV6+HjfvzivPhQPAFh7/JrZpMnZEa48cAW+Fsb0AIAXvyvo9J+anY+ve90fbNXW8rPYbCiknyBMLWfrCflqchaenB6BOg+UxY6PO0gLwAXUcDG1RFqnb/vmL9rvRSpHm8+L/w4la5iKzmzfutVwgfz4t1PYEnkTvx69ikVvtSzxuS3XBZXvqrJQy/HoLrWSksZhIhVw0knJ1t12zr8X0XD8ZkRed/zkb409h9KlJNsHTi0+HP/A5UctxGA6Cg3sb4Y2d7I22enbxnVuO1cw4GSMhbfN2xqm2sbdsSecfdF3HFreGax1sHZoZTIpnoCV6PRt11AY8sTkiC2RBcfDsSvJdi9r6fcp/pH02heJC8q8LRX8VG6FCZMHceVTGnP+vQStXuCrDVFW503Juv86EVueBBu88pij4cnO3NN0xb+O3CcgOZMXNVzI3OVO0hae800sJwlq2G/kZukrFX5fV43DZC0Od1UYvrt/j6KYMLkp+5Mj5yRTttwtN//C1OtEzCs+crPhBOaEr2Bz3ySb16fM2UHW4QfkW1XJdVtZ+cytFzDk5+NGr+QouoizbgmEEFi0+zL2XrL8IuhiC9kUkxAC+To9cgwjFjvwLQoHVXRhs5e7J7kmo5fpKymZDEQnlXzXqf1jZrn3b+tqTJg8iBwnNqsnf5vXY3pNUiJMuJuF4/HJNp8MnH0tKXqS0Th5e7Y/un7f5rM3kJSWU2IeWy+yMbcysfNCko1btk/ROHPydfj50BWjV0N8uyMaG87cwJG4Iu9IlLWWzfS6dl68hSkbz+Pt5Udk21ZR7WdEoNmkrUWSJmWVbCYsNnBvkTnkHLjSHRQWhamvYan53dXjMGWZGHjWFFtq0ywu7+APast5x132GSZMbsoZd5hSDnhXnCRibmei5/z9iLtjvn+Os5ntd2Tlb9Prsme7ts9baPCq4+g8a5f9CxYxYJlzEoei3+e7HdEYt+4suswq+XLNXDOvW8jX6XEo5o4sr2Mo6mqyvE/SFe3YLUTBm9nztHrE3MpUaR8ml2/SZeRM+EwMgVnkv1wzJIW78MTv6PFPyZUm5pIXe07Qrh7DyB75OnUcgY7esdm3LVuf3DOeLy3H/CsolB0r7X6ce6ILmr9MvVzTXD+xcevOYm/0bfRp/aC0rYvif0sfBuLUVdMPPGTkavHCt/dfv2T8XdSxD1tj1GdPwStfnlZv96tNbGHL72DLZqUWzRWT79QssXZpK7eDM3/a0b+exIqBjzsUx5I95gfIVgJrmNyU1Nd/WJzPljE5ig/aaCYQJa/JFxJLtu2bE2vhqbKizJVMwVNyNm/Ofu5xfbWZoyfovfeSrMIhLGzaZpH/ztPpcTczz+y8UhRPuO5mGK/fOPlwsAlXU1DL9vsx+wemtVRmcnb6lmuXzdPq0XLyNrT/eqdD6zF1XrPl3ZCmm+SKr9t2er1AZm7BjcyL35Z8n6kcTMVj+Sk5551g4u5kYeDyIw4d85NteKjIlZgweRDLO7/0vdbdOgauPHjFpvnOJ6bjubn2DyBo6f1z7lKDoJRfjiTg9NUUu5aRc/frOns3Rv1afFBX55J7n/hxbyyi7Rg+o9B/150x+5lcj8vbqzABuXQzHYmpBf3uIq+nIjE1B3F3MpGeo8W1lGz5n0Cz9JmTvnz/ZYfReOIWXE3Oclptuc7OYdmdfWq/fMvWG1L3OG+ySc5N2VOrYde89odSKtg+0rfM27V1Pju2q/Rv3OO7fbK+osSaouuKv5tV7DPn14ZuOH1DvpWJgvf9yc1Ss6QzL6pCAElpOegyu6Af286PO6D7vILal60jn3ZJDPa836x4twfjB0As70h77j2B+eeJa3ZEZ565Mvk+Itr2dcgSifyG/HwcmXnmuxUohQmTBzF3wK45kmCxT4s1sj6yrtYj1CrTgTu7Sa60lr079fux9vMnpeca/rsgQXNsh9l18ZZDy5tS8pUf98tcb+eOY2+yW3Sw2TPFOsvLwsJ6dHqBJ6btMPmZ0uMwSfX1lgs2z+uK1gMpT8ltOCPjTYaM2CTnpux5Os2eZEmuw8dcdAJC1U18by46iM1nLR+sRo9cm5nn7LVUzN520eF4bL1YqeVk7Uqp2flKh2C3vdG3MeGvs9JXIFNyXvwYXGWhGduZe5ZGY+Oj7Q5s44v150qu794K83Uln7a01IepePk7s2yslYsdz+TJOsxLacYaJjflrGEFskxUg1o6qOy9W76TkYcnp0fgpeYhdkbnGgdi7uBAzJ0STUb2Nsm9YKFTp63rOnYlGa8u2G/Xdi1Rwzu+bGbuMTknbka+dZpf6/TN552wxXvblfjEX+ztTFy8eb+W53xiGh4o52+0Xmcy+9oho/fZSY9hvZ1NolI35epxmOxhroxdce/q7ITYlVjDREbs7DNot/OJ6biWko35Oy8bpsm5SbmSgjsZuUZ/G12/i/xRMHClc06UQ34+bvO8tpRhYdz2NrGUBuq91BVj4adbfTge4X9H2p3gFH9q8KXv9hn9fT0lB+m5ru9P4tS+UzKddYwfAHH+cfWHhKcjST5MmNyUM2oLzph5i7qlE7C5MJSqzZDrJNty8r9Yf/q61fWaekrOFS8kdsR5O4ZdUIotzZ6ybOfeD+vINtRSczdu3Vks3x+HAzH2dQovHn+uVm+0vw//5YTJ5W5n5GL+zmiTo8rbKl8n8MEq6zcGcu8DQgDxd7JMHteF5WHqZy0+JpSrbz1G/3YKaTn2NUUXDGVhvpME2Y5Ncm5Gq9PjeHwK1p9yXac4ZzyV4w6mbjyPF5rZ13QoBPDeiqNOisgxhgtBkX4jjgzc6EzuWgkmhDB74yELG36qtGx5a4PumBm36oNVx3AkLhkbz9zA+qFPSV5/hgK1V5//WdCPrHer0BKfGY4NG9bjzOZKc2tOzsxDWT/7Lt1KNsnx1SikmNd/OIDXfzjg0iryy7fMj/li7mBIznK/DrmWmDsxagz/c5+1zsi2Vt3bNUK7Lf0E7s3jVWTF9o7bUjykpLQc/HfdGZy7nmbXeuzhLidTAPjpwBV8tNp0jYxamdrNbNlHj8QlAwDOXjP+7Z3xezlrH1hzNME5K3ai9l/vxIvf7pUlWbO0BrW891BNmDC5mePxKS7fZtFrqq2H6LZzN50Si6MsJX+2sjxwpXzsGs3dji0XbVbQOXjSHf3bKaw+FI/n5xkPAHohMd3upgNzHO0bYqnP1s30XLOfmWL6HXb3C3TF/ji71qeUoiWixhrG4lz5FKilJjlL/RVd2en73A37blDMPiVnoViljCYvnXvcFTFhIquk9GFSiqVz/5mrqeg00/aX0l5PvT+gne0DSCpz4NvzlFzRC6TeznfYFt9MlIkT9/H4ZHSbsxvPfLPTvpUX3Y6MxWipEs3cGDxFafUCP+y6jFMJKfhs7Wn5AnMSWfIfC2W26+Itpz7t54grdzKRr9Mr0q9MzUN7mG2SsxCzXDVM7lRDbA37MJHiIs4nOW3di3ZfxouPhCDhbjZe/+GAXcsKAfRZfBA/v9vaeHqRk4wGEga4M3ECib+TVaLvi6N3/mevpaJJzQolpnsXWa9Wrwfg7cBWSsa4I6rg97ydYf6dbb8fu2rxTGruqUQp3lx00Oo8lkr6VEIKTiWkOBaEi93JyEXlsn427UNS9rIFRZ5ydQVb9oEd529i4PKjaF27sizbsufJ0+LOXU+DXgiTx5+r3xpg8V1ydjTlO9O/Uc67BsiJNUxkldEBU+zgkaM6/zcZq36v3DF+d9GUjefRa+EBbD8vrYlw/+U7uJOZZ8cLjG1z4PIdjPr1JJLvdah9f6V9HcWFECWGPihu0MpjJqcXbZKzt4bJll+7jK/108rHv53CqavGCeL2KOc042qdPVaGC/180PpLh/88cQ0tJ/+LKRtte3GpqUPYkRJzRk2LLWtceaBg8M1DsXcVqfkubPbK0+rx/Lw9eOHbvYaX7aqV5ZcQq639QHlMmMiqon1AkrPy7HpTvKvtiy75RN/V5GyHrgDFTyozt5ofwdvWxOrNxQex9vg1fLUxCsmZeXY/6l977Ea0nPyvYTRxU02BpgYhBYp1+pZ4+yiEwH+WHMLtIklb7O2CZNXPR9pp5Z0iTxcW/T6mRmOWm7sMK2DLE2WbziYCABbviTU/k5Uv/JsbdoaW6wJvqQ9TccbDXxT8d472flOWqQdAZtr4BgBrX8eefdb8SN/Ov5nwpLyLCRNZVfSQmrrpvNFbzz3oWLCo6Ill6V7jC5EjJ+r4u1mISizZD+hEfLJNJ5q52y+Z/cxsXEU7fdv7dvN7/38jNQd7o28bfXbz3ng8XjKeIbdEJqL91ztlW58zeOIx8I2FmwJThv1P+ScDi/4OjhyT9jTJWVreUbK+R1LBYQU8CfswqdxNBwaFk4vlalvXxaGUgvff2Tyz3bOYuvt7Zb5tr0QxrNOeE1+Ree1NmGSPxdw67v2/uWZFUpe/T1kf5NVeRrU3Kr2wW4tLDS/mllqLpAGgl+H8oNbfTgrWMKlc6ynblQ5B1U9/uIozT05ynFRNbdWWsX3tfU1K4Tot1SLJsb940klWjfZG38avRwqa3dT8DrRCtuxTciUnF27a3jxuaaRwc5+7muWRvs3befEWmoZvsas8PB1rmMgqywe9+k+2ABwLU1g+YTtaArI8BW4ivDuZeZhqpeOv1PN58VdEOOpEfLK8K7STm+zFsknNzsenf5xGk5oVVFVLbDQ+lI2/il4v4OVl/LyqHF/JlnUUf8hEraQ0ye2+eEuWbR+KvSvLetSANUxUOjjS6RvyPgps1EHbyXegP+yOKTGtaK2SveNGGeaW+SJrbxOkWqS6+Yj211Oyrc9kJ7l2aaOLvJmVHo9PRvMvtmL1oXj8W+QpS2fV2haNKT0nH9dTnddlQq6afb0wf15i64F9mDCpmFKDIBZnqR1bybvTVQev4KPV1l/cKQcZuzCV4OrHd43yNYm7WHZeyUHtjsbddWidxly77/8s8cnP/ssOyxyJa32z9YLSIRgpes7rWSSJNrc3fPTzcaTlaI0eRAFc08yofP9S246RP45fRa7JEerV0WToTpgwqZhadma1DmPz+Z9nsf60819CLISV0c6N+iwo1YfJ9u06cldZGGqXWbtLfPbN1ovIydfJkuq4et83NWK5LU4mpKiqScuU9Jx89Jy/z+Rn9g5nYQuHWr+L/O5ZJpLyEvM7sC3HaYr9VfB30e/gyL7himMgzwVDdngSJkwqppY8xZn9d9xBm6nbsefSbZOfFX06CHDOU2fWzNp6AXP+NT+8QHG21jBl5mrxy+F43DLxvjVzJ9qvNtg2WKInUXun6RX74yy+g1LuhM8ZR4C5GxFz+68ciYCpYsnJ1xtiKV5u7ti8tfGM8284PQkTJhVTTZOchTAuJTn+MltXyDQziKOtio+9VNSlm/fL4PEp25Fp5c7YqDg1jnegnrcj2q75iz8ldyTOdKfMSf9E4rO1Z9BnsfVXixT6/dhVWYcVIMeZa44pJHfCVziAqRT2dk52ZpJibs2r7jXfmi01mUJyRc2ltX2DjDFhUjG1XDQsJW6OnByLqhjoK8t6zFllwyslpPpi/TkH16BcDcX/Dsej10LT79jbdKZgxGh7k2I5LmJbIhMdXgcVcPXe5Ugtq73JthL3lAvvvUfPXN9DuZI4V3w3ldyTuw0mTCqmlp3Z3rF6pJBzdGiyosjP+buF9/hJeW1Kdr4O0TLUOv518rr1mcg2Vo6tX1X0GhTzNUn2TXcmQ5OcDfM6s7l2+mbHO+yrpRXDXXAcJhVTS5t4Ro7l5qzkzDzk2/sW12LkHtdHzfZfNn7fnatzxaIJsKVk2FRNQVJ6Lk4mpFhc/9rj1yTHpoTwvyMdWl7tub618Jbvj3NFGDbZYGefGmde782VW+FhUaIPkzD+f2c7LMP4Rq64GfYkTJhUTC378op7bwE3p8WX22TYisqvOjIa/+dZo79d9c0v3sxAdp7xU2yWdjFz/Rte/t70E1fuSk0JgzOoPaErasf5JJPTzZ8LXd+HSW+oYTIuWO29TEolp22bqPUJaLVikxypQmmqYVLSj/uMO6/L8a4oui873/qj8K6m9qf4bKHEy2N1ZmrNrR0yRZu5HGklcMWRyRom+zBhUrHStC+X5j5MrvzutzNyi53QSU43nDjys1SefGg5a/+NvZ2JVxeYfhjC3LACADBr20WckzimlxJK0zVGDmySUzG19GFyhVJbwyTsv6DtcuAdTxpojPeq0rOLOY3aLzqecGyZ7QzupMLv+M1O87Gg4OlSU6+Vmbfd9vHQrHFFh2zWMNmHCZOKlaZ92dWvB3Fn/X+U/joOAWG0X/GE6TgpTxO6kicfW0qUfHJWHsauPWN9Rqj/HM4WefuwSU7FStO+7FWK90Ql+5hYG2STrFPzo9lSX/niLpTog2fPz+3ImFSuuJnhDZN9SvFlSv3UfCKWW8Jd+d+aTiVpdaVnn3IVNd+lPzd3j0f0YXLXc6EjtcGrJb4Q2h7uWq5KYcKkYtyVSwdXXtBWHrQ8RATZL0/lr5fYc9H0exDdiZoGrrRHjANvQjgSlyxjJKY5OHxeqcOEScWY/JcO/J1Nc5e7373R6k5Izid6cLOcG+wiW1X8mp/S9GCRHJgwqRn3ZSrF3CRfUj1P6PRtbl9whz447688hhwVjs8FqLs5WY2YMKmYrdl/4YtKvTzh+WEbjfn9tNIhyOJw3F189L/jSoehSu5wMXQHnnBaMPckors8tJCvU2fbl7vU4qoFEyYVs3VfHrTyGADPODHaao2KXhjqqCt3spQOQZV2X5I+3hTd5wk1TEdkeG+aktT6GzBfsg8TJhWzd1/+dke0U+IgUsLA5UeVDsEj3ErPVToEh11KylA6BIeotbZUrXGpFRMmFbO3utSRMT+IiMg5eny7V+kQTOIlwz5MmFSM+zIRkfuLU2mze8xt9665czUmTCpmTwVT04lbnBcIERF5HA4YbB8mTCpmzxgZ6blaJ0ZCRERUujFhUjO2yREREakCEyYVY75ERESkDkyYVIxPfBIREakDEyYV43t+iIiI1IEJk4qxhomIiEgdmDAVMX/+fNSuXRtlypRBy5YtsWfPHkXjYb5ERESkDkyY7lmzZg1GjBiBcePG4cSJE3jqqafw3HPPIT4+XrGYAny9Fds2ERGRmngr/MJUJkz3zJo1C++88w7effddPPzww5gzZw5CQ0OxYMECxWKqXNYPFQN9Fds+ERGRWrzYrIai22fCBCAvLw/Hjh1D165djaZ37doV+/fvLzF/bm4u0tLSjP45S2ilQKetm4iIyF2UUbjVhQkTgNu3b0On0yE4ONhoenBwMBITE0vMP3XqVFSoUMHwLzQ01GmxTXyxkdPWbU6PR0JkWU/z0IqyrMfdaMzUGg9qXwd1q5aVtM5AP2VOFEM61lVku8V5e2nQsHqQS7f5xmPOO66l8vVWtkmiuK6Ngq3PpCJNa1ZQOgQsG/CYyelVg/xl24aXBhjRuZ7N84dWDrB53goByrV6DHgiTLFtA4BGCD6Ldf36ddSsWRP79+9H27ZtDdO/+uorrFy5EufPnzeaPzc3F7m5uYa/09LSEBoaitTUVJQvX172+PR6gVytHhoNoNUL+Hl7wddbA41Gg1ytDrlaPcr4eCNfp4e/jxc0Gg2y8rQIKlOwYwtRsHxuvh7lyvggV6tDGR9v5On08NJo4OOlgU4I+HhpkJOvR4CfN4QQyNcV7Bo5Wh0AIPBedq8XBQekl0aDPJ0evt5ehm0Xfpar1RvuBnK1Oghx/+4gT6uHTi+gFwK+3l7w0hR0cPfWaODlpYFeL6C5tw5fby9o9XrkafXw9/FGYRO2l6YgZiEKLiIZuVr4+3hDpxfw9tLAz8cLOr2AEAJ5Oj3ytQKB/t4QAvDxKojbx0sDL40GAgUxa+5lOnlaPbw0BRfpXG3BfBqNBt5eGuTkF3wXzb3vrxcCOfk6+Hp7IdCvYP1e9+bzLly/EPDxNr43yc7Twce7oOy1egENgHydgFZf8D19vTXIytOhrL+P0XK5Wh20OmGYrtcL5Ov10EADX28NhIChXAQEcvL0KOPndW8/QEGZoKB/XE5+wboC/b2hFwI6vTCUsUajgVanh4+3F/K0euiFQBnfgn1MqxP3ykaHcv4+hnLT6QvW4efjZdjnABh+Qz9vL8O8WXlaeHtp4O9TsK9l5+vg7+MNDYA8nR7ZeQX7XFl/H3h7aaDTC0OZBPp5I1dbsL9l5+sQ4Ott2N9y8nXw9yn4vtp7+5gQgJ93wbwCBb9/Yfn7ensZfoOC+IDse7+nr7cXtDo9vL000AtALwp+J2+v++Ws0xeUS55WDz8fL8P+4XPvt/C7F0u+Tg8NYNgPsvK0CPTzQb6uoGy9NAWxFB6vBb8fDN9PX2xbBf/thcy8gt+gME6dXhi+twYF+6yPV8F+6u2lQWaeDmX9vJGWrUWFQF9k5RUcN3n3fiugYN8ueqwazkNCGGItOG4K9pecfF2J8vXz9oLu3qUl795x7OdTcJ7w8dIgLUeLAF9veHsVxFi4r+Xk61DG1xv6e/tp4fyae8dRnu7+PuXr7YXMXC18vb2gv3f+8inymwFARq4W5fx9DMdWoF/B+c/P+/4+otUJw3fRCwFvjcawD+iFgL+PF3LyC37forFm5WlRxscbXve2lZOvMxzbFQJ8Db+Dj5cGOdqCc1ilQF/DsZV3L04fLy/Dca+9d0728tIg/95/5+vun0t1emE4f4t7f/t4aZCvLyiXosdYYUwFvxcM8/v7eCFfJwzJd3a+DoF+PoZjJye/4NjKvXfc+3gX/J6F57bCfbTo+RL3fncAhmtJxr3fpvB7aHUCZfy8DDHmafVIz8k3nMu8vTSGa0BhGRemJ1q9MPzucktLS0OFChVsun4zYUJBk1xgYCB+++03vPLKK4bpw4cPx8mTJ7Fr1y6Ly9tT4ERERKQO9ly/2SQHwM/PDy1btsS2bduMpm/btg3t2rVTKCoiIiJSCx/rs5QOo0aNwltvvYVWrVqhbdu2WLRoEeLj4zF48GClQyMiIiKFMWG6p3fv3rhz5w6++OIL3LhxA02aNMHGjRtRq1YtpUMjIiIihbEPkwzYh4mIiMj9sA8TERERkYyYMBERERFZwYSJiIiIyAomTERERERWMGEiIiIisoIJExEREZEVTJiIiIiIrGDCRERERGQFEyYiIiIiK/hqFBkUDpaelpamcCRERERkq8Lrti0vPWHCJIP09HQAQGhoqMKREBERkb3S09NRoUIFi/PwXXIy0Ov1uH79OoKCgqDRaJQOR3FpaWkIDQ1FQkIC363nRCxn12A5uw7L2jVYzvcJIZCeno6QkBB4eVnupcQaJhl4eXnh//7v/5QOQ3XKly9f6g9GV2A5uwbL2XVY1q7Bci5grWapEDt9ExEREVnBhImIiIjICiZMJDt/f39MnDgR/v7+Sofi0VjOrsFydh2WtWuwnKVhp28iIiIiK1jDRERERGQFEyYiIiIiK5gwEREREVnBhImIiIjICiZMZDc+J0BEpF48RzsHEyayS2pqKnQ6neFvHpjOER0djW3btikdhse7ePEiBg8ejD179igdikdLSEjAsWPHcP36daVD8XhJSUmG95sCPEfLiQkT2SQ/Px9DhgzB888/j+effx5ffvkldDod353nBKdPn0b9+vXx5ptv4sqVK0qH45H0ej1GjhyJ5s2bIzMz0+gCQ/LJz8/HoEGD8Oijj2LgwIF45JFHsG/fPqXD8kharRbvvPMOHn/8cXTu3Bl9+/bF7du3eY6WERMmsmrbtm1o1KgRIiMj8cknnyA0NBQ///wzwsPDAfAORm55eXno1q0bfH19MWPGDKXD8UibNm3CkSNHsGnTJqxcuRLPP/+84TPuz/LIyMjAa6+9hkuXLmHr1q349ddf8eijj2L8+PEAWM5y0mq1GDBgAM6dO4cVK1bgzTffxOnTp9GzZ09ERUUpHZ7HYMJEFqWlpeHXX39Ft27dsG3bNrz88stYsGAB3njjDRw5cgRZWVm8g5HZ8ePHUalSJfz8889YtGgRDh8+rHRIHmfJkiVo3rw52rdvj127dmH8+PFYvnw54uPjuT/L5Ny5c4iKisL48ePRokULNGjQAL169UJQUBD0ej3LWUY3btzA4cOHMWTIELRv3x4jR47Etm3bEBMTgwULFuDmzZtKh+gRmDBRCXl5eYb/1uv1eOKJJ/Duu+/C19cXQgj4+fkhJycH2dnZCAwM5J2iREXLGbh/x+3v749atWrhmWeewWOPPYZJkyYBKEheyX7FyzktLQ23b99Gp06dMHnyZLzxxhs4c+YMJkyYgGeeeQb//POPQpG6t+LlnJubi+joaMPrN27fvo3vv/8eISEh+PHHH5Gdna1EmB7pzp07uHr1Ktq0aQOgoOyrV6+OsWPHYuvWrdi9e7fCEXoGJkxkZNy4cejbty8GDRqE8+fPo2LFihgwYACaN28OoCCBAgo6f9epUwcAeKcoQWE5Dx48GOfPnwdwvxyPHz+OjIwMAMDPP/+MzZs347nnnkO3bt0M85JtipezXq9H+fLlkZeXhyVLluDixYtYu3Ytfv/9d1y5cgV169bFjz/+yHK2U9FyjoqKgl6vx1NPPYX27dvj7bffxnPPPYfg4GBUr14dfn5+GDt2LPr3748zZ84oHbrbmTZtGqZOnYo//vjDMO3hhx9GtWrVsGrVKgCAl1fBpX3IkCEICgrCpk2bkJubq0i8noQJEwEAdu/ejbp16yIiIgItWrTAli1bMHjwYFy9ehXA/dqPwgPxxIkTePLJJ40+I+uKl/PmzZsxePBgXLt2zTBPUlISXn75ZQDA9u3b4e/vj+3bt+Pjjz9Gw4YNFYrcvVgr50GDBmHTpk04dOgQHnroIfj4+ECj0eDzzz/HoUOHkJycrPA3cA+myvmDDz4wnDfWr1+PDRs2IC0tDTNmzMCmTZswd+5cbNu2DceOHWNiaod///0XYWFhWLduHU6cOIEPP/wQr7/+OuLj4+Hv749evXrhf//7H5KSkuDr64ucnBwAwNChQ7Fu3Tqep+UgiIQQAwcOFP379zf8feHCBaHRaERsbGyJeWNjY0XVqlXF+fPnDdMuX74shBBCp9M5O1S3Zks59+vXT7z11lviscceE1WrVhVffvmlqFSpkvjmm29cH7CbMlfOMTExQgghIiMjRYcOHUSjRo3EjRs3DPNlZ2eLcuXKid9++83VIbslW/bnY8eOiQYNGoikpCSh1+uFEEJotVru03bq3bu3GD58uOHvy5cvC41GI9577z2Rnp4uDhw4IB599FHx4YcfCiGEoawjIiJEtWrVxKlTp5QI26OwhomQkJCAnTt3GprdAODatWt4/fXXDf0Pitq8eTNCQ0PRoEEDnDhxAq1bt0abNm2g1WoNNVBUki3lnJubi/T0dGzYsAGPP/44Tpw4gc8//xxjxozBJ598gri4OGWCdyOWytnPzw8A0LBhQ4wYMQLR0dFYuHChoebp77//RtOmTfH0008rEbpbsfW8UbZsWVy8eBEJCQmGZud//vkHtWvXxjPPPOPqsN3SuXPnsGHDBrz66qsAgMzMTNSpUwePPfYY/vrrL6xevRpt2rTBW2+9heXLl2PdunXIz88HAOzbtw+NGjVC06ZNlfwKnkHpjI1c79ixYyIlJcVo2pNPPikee+wxsWjRIjFu3Djh4+MjGjduLCpVqiQ+/vhjcfbsWcO8Q4cOFa+99poYOXKk8PLyEu+8847Iyclx9ddQPXvLeeTIkeL69evi4sWL4vTp00bL5eTkiBkzZrAGzwR7y3nUqFEiKipKCCHE7NmzRUhIiGjQoIF45ZVXRNmyZcVXX32lxNdQPXvLefTo0SIqKkrodDrx+uuvi8DAQDF48GDRr18/ERQUJCZMmGCoBSFjxcs6NTVVVK1aVSxatMgwLSkpSXTu3Fk8/vjjomfPnuL27dsiOztbfPLJJyIoKEi0b99e9OrVSwQEBIjvv/9eCCFY3g5iwlSK/P777+L//u//RN26dcWDDz4oJkyYIBISEoQQQpw/f15MmjRJvPzyy6JmzZrin3/+EYmJiWLlypWiXbt2YvTo0Yb11KpVS2g0GtGhQwcRGRmp1NdRLanl3KZNG/HJJ58oHL37kFrObdu2NdqfDx48KObPny/Gjh0rLly4oNTXUS1Hyrlwf87OzhaffvqpGDBggOjXrx/L2YziZT1+/HiRlJQkhBBi3LhxQqPRiPDwcPHNN9+IoKAgMXr0aLF48WJRvnx5cfXqVcN6fvvtNzFx4kQxePBgw80BOY4JUylx5MgR0bBhQzFnzhxx6tQpMX/+fFG1alXxwQcfiFu3bhnmGzhwoPjss8+Mlu3Vq5fo2bOnyM3NFSkpKWLatGliy5Ytrv4KbkGucibL5Cjn7OxsV4ftdhwt51deeUVkZWUZpuXn57ssdndjqawLa5s+/fRT8eyzz4qGDRsaao2EEKJixYpi165dSoVeavgo3SRIziWEgEajwdGjR5GRkYG3334b5cuXR7NmzaDX67Fq1SosWLAA48ePR3Z2Nvbu3WsYXbpw2cJHsf38/ODn54cxY8Yo/K3UR65yrlChgqGfDZUkZzmXKVNG4W+jXnKVc8WKFREQEGBYr48PLznFWSvrlStXYu7cuZgwYQKmTZuGjIwMBAUFGZb/6aefUKZMGYSGhir4LUoH9tD1cIWdLGNjY1G/fn2jE9aAAQPQsmVLbN68GWfOnEFAQAAeeeQRjB07FuvXr0d0dDRGjBiBw4cPo2/fvgA4hIA5cpVznz59lPoKboHl7BosZ9exVtatWrXCli1bEBkZCY1GY0iW9Ho9kpKSsHHjRrz00kuoXbu2IvGXKkpVbZFzbN26VQwdOlTMmTNHHDp0yDD9r7/+EmXKlDE8/q/Vag3zt2vXTsyaNUsIIcSNGzdE8+bNRZ06dUSdOnVEmzZtxIkTJ1z+PdSO5ewaLGfXYDm7jpSyfuKJJwxlLYQQ27dvF+PGjRNVq1YVbdu2NSxDzsWEyUNcv35dvPDCC6JatWqib9++omnTpqJChQqGAzI7O1s0bNhQvP/++0II4/GSnnrqKfHBBx8Y/r579664dOmSOHr0qGu/hBtgObsGy9k1WM6u42hZF46vJIQQly5dEqNGjeJ4YS7GhMkDZGZmiv79+4vevXsbBuYTQojHHntMDBgwQAhRcLfy008/CS8vL7Fv3z6j5fv27Ss6duzo0pjdEcvZNVjOrsFydh2WtWdgHyYPEBgYCH9/fwwYMAC1a9eGVqsFALzwwguIiooCAHh7e+P111/HSy+9hHfffRe7du2CEAKJiYm4dOmSoY8Smcdydg2Ws2uwnF2HZe0ZNEKwF68nyM/Ph6+vL4D7T1289dZbCAgIwKJFiwzTcnJy8Nxzz+HcuXNo3rw5zp49iwcffBC//vorn7KwAcvZNVjOrsFydh2WtftjwuTBnn76aQwcOBADBgyAEAJ6vR7e3t64efMmTp8+jSNHjiAsLIxPsjiI5ewaLGfXYDm7DsvavTBh8lAxMTFo164dNmzYgJYtWwIA8vLyOMaPzFjOrsFydg2Ws+uwrN0P+zB5mML8d+/evShXrpzhQJw0aRKGDx+OpKQkJcPzGCxn12A5uwbL2XVY1u6Lw656mMJB0A4fPoxXX30V27Ztw/vvv4+srCysXLkS1apVUzhCz8Bydg2Ws2uwnF2HZe3GXPAkHrlYdna2eOihh4RGoxH+/v5i2rRpSofkkVjOrsFydg2Ws+uwrN0T+zB5qC5duqBevXqYNWsW35nlRCxn12A5uwbL2XVY1u6HCZOH0ul08Pb2VjoMj8dydg2Ws2uwnF2HZe1+mDARERERWcGn5IiIiIisYMJEREREZAUTJiIiIiIrmDARERERWcGEiYiIiMgKJkxEREREVjBhIqJSa+fOndBoNEhJSVE6FCJSOY7DRESlRocOHdC8eXPMmTMHQMHb4e/evYvg4GDDO76IiEzhy3eJqNTy8/ND9erVlQ6DiNwAm+SIqFQYMGAAdu3ahblz50Kj0UCj0WD58uVGTXLLly9HxYoVsX79ejRo0ACBgYF47bXXkJmZiRUrViAsLAyVKlXC0KFDodPpDOvOy8vDp59+ipo1a6Js2bJo3bo1du7cqcwXJSKnYA0TEZUKc+fOxcWLF9GkSRN88cUXAIDIyMgS82VlZWHevHn45ZdfkJ6ejp49e6Jnz56oWLEiNm7ciJiYGLz66qt48skn0bt3bwDA22+/jbi4OPzyyy8ICQnBunXr8Oyzz+LMmTOoV6+eS78nETkHEyYiKhUqVKgAPz8/BAYGGprhzp8/X2K+/Px8LFiwAHXr1gUAvPbaa1i5ciVu3ryJcuXKoVGjRujYsSMiIiLQu3dvXL58Gf/73/9w9epVhISEAAA+/vhjbN68GcuWLcOUKVNc9yWJyGmYMBERFREYGGhIlgAgODgYYWFhKFeunNG0pKQkAMDx48chhED9+vWN1pObm4sqVaq4JmgicjomTERERfj6+hr9rdFoTE7T6/UAAL1eD29vbxw7dgze3t5G8xVNsojIvTFhIqJSw8/Pz6izthxatGgBnU6HpKQkPPXUU7Kum4jUg0/JEVGpERYWhkOHDiEuLg63b9821BI5on79+ujbty/69euHtWvXIjY2FkeOHMH06dOxceNGGaImIjVgwkREpcbHH38Mb29vNGrUCFWrVkV8fLws6122bBn69euH0aNHo0GDBujRowcOHTqE0NBQWdZPRMrjSN9EREREVrCGiYiIiMgKJkxEREREVjBhIiIiIrKCCRMRERGRFUyYiIiIiKxgwkRERERkBRMmIiIiIiuYMBERERFZwYSJiIiIyAomTERERERWMGEiIiIisoIJExEREZEV/w96iywJL2AmYgAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# Show the corrected future precipitation.\n", - "corrected_fut_precip.plot()" + "# Compare it to the future precipitation without bias-correction.\n", + "future_pr.plot()" ] }, { @@ -598,15 +2391,12 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "# Compare it to the future precipitation without bias-correction.\n", - "future_pr.plot()" - ] + "source": [] } ], "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", +"kernelspec": { + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -620,7 +2410,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.9" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/docs/notebooks/09_Hydrological_impacts_of_climate_change.ipynb b/docs/notebooks/09_Hydrological_impacts_of_climate_change.ipynb index 4ab6a1ec..15fbd397 100644 --- a/docs/notebooks/09_Hydrological_impacts_of_climate_change.ipynb +++ b/docs/notebooks/09_Hydrological_impacts_of_climate_change.ipynb @@ -30,7 +30,6 @@ "import datetime as dt\n", "import warnings\n", "\n", - "import xarray as xr\n", "from matplotlib import pyplot as plt\n", "\n", "from ravenpy import Emulator\n", @@ -60,7 +59,6 @@ "source": [ "# Define the hydrological response unit. We can use the information from the tutorial notebook #02! Here we are using\n", "# arbitrary data for a test catchment.\n", - "hru = {}\n", "hru = dict(\n", " area=4250.6,\n", " elevation=843.0,\n", diff --git a/docs/notebooks/10_Data_assimilation.ipynb b/docs/notebooks/10_Data_assimilation.ipynb index 83adfbf3..ee839e3f 100644 --- a/docs/notebooks/10_Data_assimilation.ipynb +++ b/docs/notebooks/10_Data_assimilation.ipynb @@ -20,14 +20,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "\n", + "from numba.core.errors import NumbaDeprecationWarning\n", + "\n", + "warnings.simplefilter(\"ignore\", category=NumbaDeprecationWarning)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Import packages\n", "import datetime as dt\n", "import tempfile\n", - "from glob import glob\n", "from pathlib import Path\n", "\n", "import matplotlib.pyplot as plt\n", @@ -45,7 +57,6 @@ ")\n", "\n", "# Define HRU\n", - "hru = {}\n", "hru = dict(\n", " area=4250.6,\n", " elevation=843.0,\n", @@ -99,7 +110,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -201,11 +212,22 @@ }, { "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": [ - "# Get the paths to all of the ens_1...ens_N folders, one per member\n", + "# Get the paths to all the ens_1...ens_N folders, one per member\n", "paths_spinup = list(tmp_path.glob(\"ens_*\"))\n", "\n", "# Read those into memory in an EnsembleReader object\n", @@ -233,9 +255,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Set the start date equal to the assimilated date of the prior run, as we want to start from the assimilated\n", "# states. The end date is set 3 days later, after which assimilation will be automatically performed.\n", @@ -262,7 +295,7 @@ "# new 3-day period.\n", "loop = Emulator(config=conf_loop, workdir=tmp_path, overwrite=True).run(overwrite=True)\n", "\n", - "# Get the paths to all of the ens_1...ens_N folders, one per member\n", + "# Get the paths to all the ens_1...ens_N folders, one per member\n", "paths_loop = list(tmp_path.glob(\"ens_*\"))\n", "\n", "# Repeat the same process as the spinup to look at model results:\n", @@ -289,11 +322,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": false - }, - "outputs": [], + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Let's store the hydrograph from the previous 3-day run in a variable that we will append to at each time step.\n", "total_hydrograph = ens_loop.hydrograph\n", @@ -327,7 +369,7 @@ "\n", " # Extract the results for this 3-day hydrograph and store it into our \"total_hydrograph\" which keeps track\n", " # of the flows for each of the 3-day periods.\n", - " ens_loop = EnsembleReader(crun_name=onf_loop.run_name, paths=paths_loop)\n", + " ens_loop = EnsembleReader(run_name=conf_loop.run_name, paths=paths_loop)\n", " total_hydrograph = xr.concat([total_hydrograph, ens_loop.hydrograph], dim=\"time\")\n", "\n", "\n", @@ -350,9 +392,20 @@ }, { "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": [ "# Reset the start and end-dates to cover the entire period (spinup + 30 3-day steps)\n", "start_date = dt.datetime(1996, 9, 1)\n", @@ -399,9 +452,22 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 8, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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4eCR+/uzZM27SpAnny5ePc+fOze7u7uzt7c2lSpXiDh06JC43YsQI/vHHHzlfvnzs6OjIn376KQ8aNIhDQkISl4mLi+OuXbtyoUKF2MHBgYmI/f39zR6Hu3fvco0aNTh37tycL18+btasGT99+tRslphhFt7jx4/Zzc2Nc+fOzUSkl/EVGRnJY8aM4c8//5yzZcvGefLk4YoVK/KgQYP4xYsXicsREffp08fsGA05d+4cV6pUiXPkyMGFChXirl278rVr15iIeNWqVczMHBwczB07duTy5ctzzpw5OVeuXPz111/z7NmzWaPRMDOyKRs1asSlSpViR0dHLlCgAFepUoX37Nmjtz3DY2HuGjN3vXTo0IFz5syZ5DpNZeFZel0wM8+ZM4c/+eQTzpw5s95xMMzCY0Ym3fDhw7lUqVKcNWtWLlasGPfq1YvDwsL0litVqhTXqVOHDalSpYrJzEuJJCU4MJux00skEolEIpFITCJjoCQSiUQikUisRAooiUQikUgkEiuRAkoikUgkEonESqSAkkgkEolEIrESKaAkEolEIpFIrEQKKIlEIpFIJBIrkYU0UwlFUSgwMJBy585tccsHiUQikUgkaQsz09u3b6l48eJ6DdwNkQIqlQgMDKQSJUqk9TAkEolEIpHYQEBAQJKN1aWASiVy585NRDgBzs7OaTwaiUQikUgklhAREUElSpRInMfNIQVUKiHcds7OzlJASSQSiUSSwUgu/EYGkUskEolEIpFYiRRQEolEIpFIJFYiBZREIpFIJBKJlcgYqDSEmUmj0ZBWq03roUjSAZkzZ6YsWbLIshcSiUSSAZACKo2Ij4+noKAgio6OTuuhSNIROXLkoGLFilG2bNnSeigSiUQiSQIpoNIARVHI39+fMmfOTMWLF6ds2bJJq8MHDjNTfHw8vXr1ivz9/emzzz5LsoCbRCKRSNIWKaDSgPj4eFIUhUqUKEE5cuRI6+FI0glOTk6UNWtWevLkCcXHx1P27NnTekgSiUQiMYN8xE1DpIVBYoi8JiQSiSRjIO/WEolEIpFIJFYiBZREIpFIJBKJlUgBJZFIJBKJRGIlUkBJrKJjx47k4OBg9Pfw4cO0HppNeHp6Ut68edN6GBKJRCLJYMgsPInVuLu706pVq/TeK1SokNXriY+Pl/WOJBKJRJIhkRYoidU4OjpS0aJF9f4yZ85Mp06dov/973/k6OhIxYoVoxEjRpBGo0n8XtWqValv3740ePBgKliwINWoUYOIiO7evUu1a9emXLlyUZEiRahdu3YUEhKS+D1FUcjDw4PKli1Ljo6OVLJkSZo8eXLi58OHD6dy5cpRjhw56NNPP6WxY8dSQkJC4uc3b94kFxcXyp07Nzk7O9MPP/xAV65cIS8vL+rUqROFh4cnWtImTJiQ+gdQIpG8Nzx7RtSjB1GNGkSLFhFFRqb1iCTvCimgJHbh+fPnVLt2bfrpp5/o5s2btGjRIlqxYgVNmjRJb7nVq1dTlixZ6OzZs7RkyRIKCgqiKlWq0LfffktXrlyhQ4cOUXBwMDVv3jzxOyNHjiQPDw8aO3Ys3b17lzZs2EBFihRJ/Dx37tzk6elJd+/epblz59KyZcto9uzZiZ+3adOGPv74Y7p8+TJdvXqVRowYQVmzZqVff/2V5syZQ87OzhQUFERBQUE0dOjQ1D9YEokkw/P6NdHQoUT9+xP17Em0fz9R3rxEzZsTDRhA5OOT1iOUpDYOzMxpPYj3kYiICMqTJw+Fh4eTs7Oz3mexsbHk7+9Pn3zyiV6xxF69iJ4/f3dj/OgjPDFZQ8eOHWndunV6465VqxaVK1eOtm/fTvfu3Uusqr5w4UIaPnw4hYeHU6ZMmahq1aoUHh5O169fT/zuuHHj6OLFi3T48OHE9549e0YlSpQgHx8fKlasGBUqVIjmz59PXbt2tWiM06dPp82bN9OVK1eIiMjZ2Zn+/fdf6tChg9Gynp6eNHDgQHrz5o11ByKVMHdtSCSS9MHbt0SzZxNduEA0YgTRH38YL3P9OtHChUQvXxJ16UJUpw5R5szvfqwS20hq/tZFxkClI6wVM2mFi4sLLdIZbM6cOalPnz5UqVIlvZY0v/32G0VGRtKzZ8+oZMmSRET0448/6q3r6tWrdPLkScqVK5fRdh49ekRv3ryhuLg4cnV1NTuebdu20Zw5c+jhw4cUGRlJGo1G76IfPHgwde3aldauXUvVq1enZs2aUZkyZWzef4lE8uERF0e0eDHR3r1EAwcSjR1LZK4D13ffES1bRhQaSrRyJdHcuURubhBTBQu+02FLUhHpwpNYTc6cOals2bKJf8WKFSNmNurnJ4ybuu/nzJlTbxlFUahevXp048YNvT9fX1/6448/yMnJKcmxXLhwgVq2bEm1atWiffv20fXr12n06NEUHx+fuMyECRPozp07VKdOHTpx4gR98cUXtHPnzpQeBolE8gGg1RJ5ehLVrg3xc/gwUd265sWTLvnzw8135AjRV18Rde9O1Lkz0f8bxyUZHCmgJHbhiy++oHPnzpGuR/jcuXOUO3du+uijj8x+7/vvv6c7d+5Q6dKl9URZ2bJlKWfOnPTZZ5+Rk5MTHT9+3OT3z549S6VKlaLRo0fTjz/+SJ999hk9efLEaLly5crRoEGD6MiRI9S4cePELMJs2bKRVqtN4d5LJJL3DWaiHTtgOYqOJjp4kKhNG9tccZkzw423YwfRyJFEGzYQubsTrVsHy5YkYyIFlMQu9O7dmwICAqhfv350//592r17N40fP54GDx6cZH+3Pn36UGhoKLVq1YouXbpEfn5+dOTIEercuTNptVrKnj07DR8+nIYNG0Zr1qyhR48e0YULF2jFihVERFS2bFl6+vQpbdq0iR49ekTz5s3Tsy7FxMRQ3759ycvLi548eUJnz56ly5cvU4UKFYiIqHTp0hQZGUnHjx+nkJAQio6OTt0DJZFI0j3HjxPVqkX04AHRnj1EvXsT2aviymefEc2aRbR9O1FMDKxZo0cTBQTYZ/2Sd4cUUBK78NFHH9GBAwfo0qVL9M0331DPnj2pS5cuNGbMmCS/V7x4cTp79ixptVqqWbMmffXVVzRgwADKkydPovAaO3YsDRkyhMaNG0cVKlSgFi1a0MuXL4mIqEGDBjRo0CDq27cvffvtt3Tu3DkaO3Zs4vozZ85Mr1+/pvbt21O5cuWoefPmVKtWLZo4cSIREf3666/Us2dPatGiBRUqVIimTZuWSkdIIpGkdy5fJmrQgOjYMaKNGxEkbhB1YDdy5iTq1g3uPXd3JBHt2pU625KkDjILL5WwJQtPIpHXhkTy7rl/n2jiRMQsjRlDVKzYux9DfDxRs2Zw8f3yy7vfvkRFZuFJJBKJRJIMK1fCTTdzJlFaJudmy0a0di1R48bI9itbNu3GIrEM6cKTSCQSyQdHTAxcaAEBiEdKD5VNnJ2JVq9Gtt6rV2k9GklySAElkUgkkg8KPz+i+vWJmjQhGj8+fRW5/Ogj1I1q1w7Zf5L0ixRQEolEIvlg2LsXAdvLlyN4Oz1SsSLRn38SdeqEOlSS9ImMgZJIJBLJe49GQzRuHFFEBGKeHB3TekRJ4+pKFBhINHgw0Zw5lhXulLxbpAVKIpFIJO81wcFEjRoRffEF0fz56V88Cdq1IypcGL33JOkPKaAkEolE8t5y9ixRq1ZEU6cStW2b1qOxnlGjUNBz69a0HonEEOnCk0gkEsl7BzOCsS9eRIHKJMr5pGscHGA1a9EC9al+/z2tRyQRSAuURCKRSN4r3r6FtYkZfecyqngSZMmC8gZ//YWin5L0gRRQklShdOnSNGfOnLQeht3w8vIiBwcHevPmTVoPRSKRJMGdO2jH0qsX0aBB70/wda5cRGvWoC/fixdpPRoJkRRQEhsICAigLl26UPHixSlbtmxUqlQpGjBgAL1+/Tqth2YXqlatSgMHDtR779dff6WgoCDKkydP2gxKIpEky/r1RMOHw+r0Prq6ihYlWrCAqEMHoqiotB6NRAooiVX4+fnRjz/+SA8ePKCNGzfSw4cPafHixXT8+HGqVKkShYaGpsm4tFotKYqSauvPli0bFS1alBzel8dZieQ9Ii6OqG9fotu3Ee9UtGhajyj1qFAB/fo6dEBpBknaIQVUOoCZKSoqKk3+rO0l3adPH8qWLRsdOXKEqlSpQiVLlqRatWrRsWPH6Pnz5zR69OjEZd++fUutW7emXLlyUfHixenff//VW9eECROoZMmS5OjoSMWLF6f+/fsnfhYfH0/Dhg2jjz76iHLmzEk///wzeXl5JX7u6elJefPmpX379tEXX3xBjo6OtGzZMsqePbuRm61///5UpUoVIiJ6/fo1tWrVij7++GPKkSMHVaxYkTZu3Ji4bMeOHenUqVM0d+5ccnBwIAcHB3r8+LFJF9727dvpyy+/JEdHRypdujTNnDlTb7ulS5emKVOmUOfOnSl37txUsmRJWrp0qVXHWyKRJE1gIKqKu7kR/fMP4oXedypXRuPh/v0R5yVJI1iSKoSHhzMRcXh4uNFnMTExfPfuXY6JiWFm5sjISCaiNPmLjIy0eJ9ev37NDg4OPGXKFJOfd+vWjfPly8eKonCpUqU4d+7cPHXqVPbx8eF58+Zx5syZ+ciRI8zMvHXrVnZ2duYDBw7wkydP+OLFi7x06dLEdbVu3Zp//fVXPn36ND98+JCnT5/Ojo6O/ODBA2ZmXrVqFWfNmpV//fVXPnv2LN+/f58jIyO5SJEivHz58sT1aDQaLlKkCC9ZsoSZmZ89e8bTp0/n69ev86NHjxLHdeHCBWZmfvPmDVeqVIm7devGQUFBHBQUxBqNhk+ePMlExGFhYczMfOXKFc6UKRP/9ddf7OPjw6tWrWInJydetWpV4rZLlSrF+fPn5wULFrCvry9PnTqVM2XKxPfu3TN7jA2vDYlEYh5/f+aqVZnv30/rkaQN06czm7kdS1JAUvO3LlJApRLvo4C6cOECExHv3LnT5OezZs1iIuLg4GAuVaoUu7u7633eokULrlWrFjMzz5w5k8uVK8fx8fFG63n48CE7ODjw8+fP9d53dXXlkSNHMjMEFBHxjRs39Jbp378/V6tWLfH14cOHOVu2bBwaGmp2v2rXrs1DhgxJfF2lShUeMGCA3jKGAqp169Zco0YNvWX+/PNP/uKLLxJflypVitu2bZv4WlEULly4MC9atMjsWKSAkkgsw8eH2cWF+fHjtB5J2qEozH37Mq9bl9Yjeb+wVEB9AMbO9E+OHDkoMjIyzbZtL/j/bckiTqhSpUp6n1eqVCkxM69Zs2Y0Z84c+vTTT8nd3Z1q165N9erVoyxZstC1a9eImalcuXJ634+Li6MCBQokvs6WLRt9/fXXesu0adOGKlWqRIGBgVS8eHFav3491a5dm/Lly0dEiJX6559/aPPmzfT8+XOKi4ujuLg4ypkzp1X7eu/ePWrQoIHee7/99hvNmTOHtFotZf7/7qS643NwcKCiRYvSy5cvrdqWRCLR5/ZtooEDETRerFhajybtcHBAm5dWrYiKFydycUnrEX1YSAGVDnBwcLB6Ak8LypYtSw4ODnT37l1q2LCh0ef379+nfPnyUcGCBc2uQ4irEiVKkI+PDx09epSOHTtGvXv3punTp9OpU6dIURTKnDkzXb16NVGICHLlypX4fycnJ6Og7v/9739UpkwZ2rRpE/Xq1Yt27txJq1atSvx85syZNHv2bJozZw5VrFiRcubMSQMHDqT4+HirjgUzG22bTQQjZM2a1Wj/UzPYXSJ537l8GdW5N28mSuJW807x8SG6dQuxWO+6TUzmzESenkSNGxMVKkT01VfvdvsfMlJASSymQIECVKNGDVq4cCENGjSInJycEj978eIFrV+/ntq3b58oLC5cuKD3/QsXLlD58uUTXzs5OVH9+vWpfv361KdPHypfvjzdvn2bvvvuO9JqtfTy5UuqXLmy1eNs3bo1rV+/nj7++GPKlCkT1alTJ/GzM2fOUIMGDajt//d0UBSFfH19qUKFConLZMuWjbTJtED/4osv6L///tN779y5c1SuXDkj0SeRSOzDmTNEU6agrUnevGk9GiJ/f6JJk4gSEoh+/pmobl2i//2PqEcPopIl3904cuQgWrsW1cp37iSS1VbeDTILT2IV8+fPp7i4OKpZsyadPn2aAgIC6NChQ1SjRg366KOPaPLkyYnLnj17lqZNm0YPHjygBQsW0NatW2nAgAFEhCy6FStWkLe3N/n5+dHatWvJycmJSpUqReXKlaM2bdpQ+/btaceOHeTv70+XL18mDw8POnDgQLJjbNOmDV27do0mT55MTZs2pezZsyd+VrZsWTp69CidO3eO7t27Rz169KAXBlXpSpcuTRcvXqTHjx9TSEiISYvRkCFD6Pjx4/T333/TgwcPaPXq1TR//nwaOnSorYdWIpEkwdGjRNOnpw/xFBhI1KcP0ciRcCWuWYPXR44Q1apFNHo0xMyxY+8uS65QIYi53r1lZt474x3EY32QWBNEntF4/Pgxd+zYkYsWLcpZs2blEiVKcL9+/TgkJCRxmVKlSvHEiRO5efPmnCNHDi5SpAjPmTMn8fOdO3fyzz//zM7OzpwzZ07+5Zdf+NixY4mfx8fH87hx47h06dKcNWtWLlq0KDdq1Ihv3brFzAgiz5Mnj9kx/vTTT0xEfOLECb33X79+zQ0aNOBcuXJx4cKFecyYMdy+fXtu0KBB4jI+Pj78yy+/sJOTExMR+/v7GwWRMzNv27aNv/jiC86aNSuXLFmSp0+frretUqVK8ezZs/Xe++abb3j8+PFmx53Rrw2JJDXYtYu5SRPmtP5ZvHzJPHgwc8OGzJcvJ71sYCDzxInMrq7Mc+cyv3nzbsbo4cG8cOG72db7iqVB5A7MUqumBhEREZQnTx4KDw8nZ4NGTLGxseTv70+ffPKJnnVEIpHXhkSiz8aNRPv2Ea1aRZQtW9qM4c0bohkziG7cQKVzayILEhKIdu9GL7uPP4alKjXjlBQFNaLGjiX69tvU2877TFLzty7ShSeRSCSSdMmKFXCDrVmTNuIpMpJo8mS44/74g2jvXuvEExFR1qxETZviu337Ei1ahFipLVsgruxNpkxES5YQDR6MpsqS1EMKKIlEIpGkO+bNg8Vn2TJkmr1LYmKIZs8matgQ1qJDh1DpPKWdnL78Er3sNmwgevkS8VITJxIFBdll2IkULIj19ukj46FSEymgJBKJRJKumDqV6PlziKhM73CWio8nWrwYFqJixRAU3qBByoWTIc7OsEYdPQqL1qBBRG3bEtmzH3vlykTlyxMtX26/dUr0kQJKIpFIJOkCZmSwabXoa/euendrtYhRcndHHafDh4latkx98ebgQFStGtGmTURDhhC1awfrl70YMYJo/37UqJLYHymg0hAZvy8xRF4Tkg8VZlhiChYkGjPm3YknHx8Ip8hIooMHiTp1SpuGxN99h7ilTp0g6OxBpkxES5fiuKZRs4v3Gimg0gBRnTo6OjqNRyJJb4hrwrCCuUTyPqPVovjkF19gsn9XbNqE7Xl6Il7oXVcRN6R6daI6dTAmez1LFS6MjLx+/WQ8lL2RlcjTgMyZM1PevHkTe6LlyJHDqC2I5MOCmSk6OppevnxJefPmldXMJR8MCQlEXbogSPv/GwSkOrGxcJnlzk20Z0/aWJzM0a4d0bNnKBo6bJh91lm1KtHp0xCKnTrZZ50Sove+DlTp0qXpyZMnRu/37t2bFixYYPS+l5cXuZjoyHjv3j29NiTJkVwdCWamFy9e0Js3byxep+T9J2/evFS0aFEpqCUfDJ07I2i7ceN3sz0/P6KePYn698d20yPMsBj9+itR69b2WadWS9SoEQL0v/zSPut8X7G0DlQ60t2pw+XLl/X6mnl7e1ONGjWoWbNmSX7Px8dH78AVKlTIruNycHCgYsWKUeHChSkhNYqBSDIcWbNmlZYnyQfF2bPo2/auxNPOnciyW7aMqFSpd7NNW3BwIJo7l6hVK6IiRYhcXVO+zsyZEQ/Vpg2sbhmgf326570XUIbC559//qEyZcpQlSpVkvxe4cKFKe87aLiUOXNmOWlKJJIPDmZYQ1atSv1txccjI40ZBS3TqqK5NWTODJdb48boc/f11ylfZ9GiRKNGoX/fsmUpX9+HzgcVRB4fH0/r1q2jzp07J+si+e6776hYsWLk6upKJ0+eTHbdcXFxFBERofcnkUgkEtMcPkz0ww8QB6nJ06dE9esT/f47imNmBPEkyJGDaN06CJ6nT+2zTldX1Lhau9Y+6/uQ+aAE1K5du+jNmzfUsWNHs8sUK1aMli5dStu3b6cdO3bQ559/Tq6urnT69Okk1z116lTKkydP4l+JEiXsPHqJRCJ5P1AUolmzEMidmuzfT9S1K6p/vys3ob0pWBDFMDt1IgoLs886x48n2ryZ6P59+6zvQ+W9DyLXpWbNmpQtWzbau3evVd+rV68eOTg40J49e8wuExcXR3FxcYmvIyIiqESJEskGoUkkEsmHxpYtRAEBqSegNBqk7kdEEM2cSfQ+9OW+epVo3Dii7dvtsz+BgUTt28Ol6eSU8vW9T8hmwgY8efKEjh07Rl27drX6u7/88gv5+vomuYyjoyM5Ozvr/UkkEolEn4QENLvt3Tt11h8YiPYrX38Ny9P7IJ6I4O7s2xclHxQl5esrXpzozz/fbd2t940PRkCtWrWKChcuTHXq1LH6u9evX6dixYqlwqgkEonkw8LTE9llqWH1OHoUdZRmzsQ23jdq1UKxzT//tM/6atYkyp+faONG+6zvQ+O9z8IjIlIUhVatWkUdOnSgLAYV00aOHEnPnz+nNWvWEBHRnDlzqHTp0vTll18mBp1v376dtm/fnhZDl0gkkveGmBhU/z582L7r1WqJ/v4bBSj37kXw9ftKp05Ef/2FgHh7WI/++gsWux9+ICpXLuXr+5D4ICxQx44do6dPn1Lnzp2NPgsKCqKnOukN8fHxNHToUPr666+pcuXK9N9//9H+/fupcUaNQJRIJJJ0wsKFaNliz8rfISEIEC9dGsHW77N4EowdiwDwLVtSvq4sWeBS7dMHFdollvNBBZG/SywNQpNIJJIPgYgIoqZNiQ4dQpNbe/D8OQKhZ80i+uYb+6wzo6DRELVogYrqyZQ1tIgDB5C1aKJBxweHDCKXSCQSSbph5kyiwYPtJ578/BDvtGRJ2osnZqJ33Rs+SxaiNWuIpkwhunMn5eurXRtV4WWBTcuRAkoikUgkqcqrV0jDr1nTPuvz9ibq1g0ComxZ+6zTFphhtalZk6hJE6Lu3Ylu3Hh328+ZEwUx+/WDNS6lTJqE9jo7d6Z8XR8CUkBJJBKJJFWZMgUtROzRI/vSJQRPb9pE9PHHKV+fLTATHTsGq83Vq0TbthEdPAgL28qVeH/DBrSQSW0KF0aPuw4diMLDU7auTJlggVq7lujUKfuM733mg8jCk0gkEkna8PQpsuN+/TXl6/LyIpo2DYIlT56Ur88Wzpwh8vCA23D9epQBEJQvTzRvHlFkJFqw1KlD9PPPCJxPzeYUZcsSTZ6MDL1t21LmJs2aFQKqaVMc42+/tdsw3ztkEHkqIYPIJRKJBK1U+vdPeTPcffuIVqyAaEmLTLuLF9H8+LPPiIYNs6yHHzME15IlCPru3p2oWjX7WOJMsWwZ0cuXRKNHp3xdr18TNW+OdX76acrXl5GQQeQSiUQiSVPu3UPl8ZSKp40b4RLbvPndi6fr14maNcP2Fy0imj5dXzxptYjFGjTIOJjbwYHojz8g+ubMIfrvP6IaNYj+/RdZifama1ciX19Y6lJKgQIoetq1K1FwcMrX9z4iLVCphLRASSSSD53WrVHgskwZ29exZAnRlSsQL/asH5Ucd+4gqDpPHlh0DF1wikK0dSvijxo3hoty2TL0+GvfnqhhQ7jDDElIINq1C6KrRAm0tPnqK/uNOzKSqH59iM4iRVK+vnv3iAYMgGvwQ5nKLJ2/pYBKJaSAkkgkHzJXrhCtWpWyukLTphG9eEE0Y4b9yh8kh68vhFOWLBBOhu4rZqI9e4jmz0drlV699NvShIdDHO3eDetTt25E5jqBeXujuOjTpwgCb9rUPu6927cx9p07iTJnTvn6Ll7EMdm69f3pLZgUUkClMVJASSSSD5mGDWE1sqWNKDMEgKMj0bhxqRczpMvjxxAJcXFEY8YQff658ZgOH0YLlapVUTogVy7z62MmOnECVqnMmRFIXrmy6X2JiECdLK0WY7AHK1cieH/cOPus79AhotWrERxvD1GWnpECKo2RAkoikXyonDwJsfHPP9Z/V1EQdF62LNHAgXYfmhExMSix8OIFRJspd5qXF2KffvoJsU7WZgAGBMAVefEirExt2pgWXyNHEpUqRdSzp027ogczUZcuRG3bInDdHqxfjzpRCxa8G1GbVkgBlcZIASWRSD5EmNU6SPnyWfddjQZBy3/8QWSidandefAAPeCGDCFydzf+/Nw5iMAvvyQaOhSB1SkhLo5o+3YIkbJlEf+ka+lixn43bIgGvyklKgrxUOvXExUtmvL1ESEYPiyMaOJE+6wvPSIFVBojBZREIvkQ2b0bAdijRln3vdhYBF83bw4rTWqzcSPcUUuWGBfkvHoVxT9LliQaMcI+wdiGXL+O+KeXL1G/qW5dxF3Fx+MYDB9OVKlSyrdz9y7WtWuX/Vxvo0YRFS9O1LevfdaX3pACKo2RAkoikXxoaLVoa7J7N9qMWEpkJDL2evc2bQmyJzExqBieLx/RX3/pZ/bdvo2ClAUKwJ32Liqdh4Uh2H7vXlh3vvmG6O1bZPbNn28ci2ULa9YQPXyI/bUHzIjpql4dYu99QwqoNEYKKIlE8qGxZg0m/z59LP9OaCjE05gxRL//nnpjIzLvsvP1RbmFbNkQB/XJJ6k7DlO8fo16U56esHwFBeG4bNhgWyC+Id26Yf1ubilfFxHcrW3aYL3Vq9tnnekFKaDSGCmgJBLJh0R8PNL6Dx6EELEEZriu/v6b6PvvU3d8plx2zKhuvm8fSiaUK5e6Y0gOf3/EgG3dihYx9+8j22/HDqLcuVO27uhoxEOtWQP3mz2IiYG7deJEoh9/tM860wOyErlEIpFI3hlLlxJ17Gi5eCJCccYqVVJXPMXEoFbT7dtwLQrxFBmJ2KOXLyFQ0lo8EcHyNX06Ubt2iAkrXx7ipF27lDcmzpEDLsFu3WA9sgdOTghQHzEC1r0PDSmgJBKJRJIioqIgTlq3tvw7CQmoE9WvX+qN68EDWF0aNEBQuIh3un0b73XqhIDod1Wk0xK+/x5lHDp3RkmHX3+FMO3RAxazlFC+PM6RvWpDERHlzYvmwz17EgUG2m+9GYF0dNlIJBKJJCMydy4ysqzJ8lq5EpO5bhVve7JxI2o2rVqlxjsJl93Ysfi8SpXU2XZKqVkTf4MHY8wNGxL973/2aRLcpg3izg4eTPm6BMWKwTXavj2C4j8UpICSSCQSic2EhhKdOQNLj6VERxNt2QLLir1JymXXuTMKZu7YQVS4sP23bU86dEDT4pkz8bpXLxSvXLgw5euePRvrDQhI+boEn32GOLKWLYlevbLfetMzUkBJJBKJxGamTSMaNsy6ytRz5yIbzt7Ngc257Ly98V6HDrDi2NNlp9UiDmjoUDTetSejRhH5+cFaRoQ2L1euoMddSnByghDr3h2uVHvx/ffoW9iiBdGjR/Zbb3pFCiiJRCKR2ERQEDLFXFws/05oKNGpU0SNGtl3LOZcdqtWQTRt2IAedvZCUWBFc3PDPrVsiTpO9evDwmWPQG0HB6J58yCYTpzA6yVL0JPu7NmUrbtcOVgAx4xJ+Th1qVgRmX49ehBdumTfdac3pICSSCQSiU3Mno0q19YwdSqytuzVSy021rTLLioKveCePYOgsVc1cWZU9a5ZE+veuxfxX99+C3GzZg3R06f4fNIkouDglG0vSxbUhpo2jejWLaKsWRG0PWFCyi1eLVrAtblvX8rWY8jHH6NlzeTJRHv22Hfd6QkpoCQSiURiNRERaNliTbuRgAC4duxlCXr7FsUh69TRd9nduQNLULt2CBi3RwsTZqIDB2Ddun8fVqHBg2FlqVMHNbAGDUL8z8CBREePIvC7Xz+M4+xZ27PocuRADauBAyHOcueGiOrTB1bAlDBzJqxcT5+mbD2G5MmDelY7d9onbis9IgtpphKykKZEInmfmTWL6NNPkSFmKd26YdL/9tuUb//1azUl/7ff1Pc9PWFxWrbMPlYnZqJjx7C/lSpBxDg7q42GK1Qg+vNPooIFia5dQ2mGkBCUSKhTB+LtyROixYvRY69ZM4zbmlY3Aj8/HMNt29CKxscH1q/t2zEmW3n0COdlzx7r6nhZAjNErEYDkZueSkaYQxbSlEgkEkmqkJAAa0y9epZ/5+5dorg4+4inwED0YPPwUMVTVBSy7J4+hdXDHuLp1Cns4+nTiKEaNw6B6o0bw7qybBnGULAglv/+e7y3YgXaw9Ssic9z5oTrcs8eouzZUb170CAsYw2ffgpXXtu2cF1+/jn626W00GaZMqiAPnKk7eswh4MDXJmffoqYq7g4+28jrZAWqFRCWqAkEsn7yoYNcOH17Gn5d1q0gIj49NOUbdvPD7FNS5ao1cPv3oWrbPRoomrVUrZ+ItPWpZs3EdNTuDCExkcfqcsrCkoz5Mqlvx5FITp0CILK2RmxWv/7Hz67elW1VnXurFqrLOHQIbjw1q6FRWf3bojGlStTZuEZPBgCt31729eRFAcOYJ/XrkUBzvSK7IWXxkgBJZFI3keYEQe0cydicyzh/Hlkyc2bl7Jte3ujSvfq1UQlSuC93buJli+H5ado0ZSt//JliLxSpRDoXqQIxNmkSYg7GjUKnwmYsf0FC+D6KlqUqHdvoh9+MF63nx/Ew+3byNhr0QLlBF6/Rqbg/v0o7/D115aN1dMTQeWzZuH14sUIap80yfb912pR6qFFC+usi9Zw5QqO7cqVaJqcHrF4/mZJqhAeHs5ExOHh4Wk9FIlEIrEbx44xjx9v+fKKwlynDvOLFynb7oULzDVqML98qb63Zg1zu3bM8fEpW/etW8zNmjH36cP87Bnee/CAuX175o4dmR8+1F9eUZj37WN2c2OeOpX57Vu87+PDPHAgs7s7xhYTY7yt6GjmlSvx3T//ZPbzw/uvXjG7uDA/eWL5uP/6i3nmTPV1797Me/da/n1TxMUxN2jAfPp0ytaTFH5+zNWqMV+/nnrbSAmWzt9SQKUSUkBJJJL3kUaNmIODLV9+/37mceNSts1jx5hr12Z+80Z9b9485p49mTUa29erKMyzZ0M8PX6M9/z9mbt0YW7ThvnePePlDx9mrlkT4sXc7T0yknnpUgi+ESOwTlPbvnABAq1JE+aDByHUqlVjDg21fPw9ezJv3IjXMTHM1aur+2Irb99iH2/cSNl6kiIkhLlWLeYjR1JvG7Zi6fwtXXiphHThSSSS9w1vb6L58+EusgRFQSB1SrLEdu1S431y5IDbbPJk1C+aOtX2elKhoYjh+v13xE89f44ssdBQxFJVrKi//IkTqLL9008IADeM4WE2Hgsz3JeLF6PFTLduRNWrG8cpvXqFmKtChVCYc+xYHLPs2ZPfD42GqFUrZNFVrWq/jLrXrxGov3QpgsxTg5gYxLO5uaVOWx9bkTFQaYwUUBKJ5H2jSxe0bfn8c8uWX7cOE/GAAbZtb80a1FNasQJigFkN6h4xwrZ1EhFduIB4Jg8PxByNGwfhMWoUMul0OXMGmW8VKxINGUJUoID+5+fOYT0REUQ//wxRVrq08TaDgxGrdfIkUd26iDXKl0/9nBnH6aefEHvl6QnRaElgeVQUUZMmRNOnY5w7dhD9958aH2Urz54hoHz9ejQMTg0UBW1w8uaFcLRXgdWUIMsYSCQSicRuBAaicKWl4ikuDoHC1mTq6fLvvxA6np4QT1ot1lWmjO3iSVFgRZo3DyIjf34ES//2G2or6Yqn8+fRP2/PHuzHlCn64uniRbSj2bYNVpoTJ1C8c9w4lCk4dAjbExQpAsvWoUMIRO/YEb3obtzA5w4OqOx+4AD+7+6OrDhLTBw5c0JsDRhA9OYNyiwwp7xn3scfI/C9XTuisLCUrcscmTJB6Dk7o/2LPXvzpTrvwJ34QSJjoCQSyfvEiBHMZ85YvvzcuQikthZFYZ44EdtTFLwXF8fcujXzunXWr08QEoJYo/nzsd6tWxHsbRgvdOkS4rwGDmQOCjJez5UrWE///syBgaa3FRzMPHky4plmzjQf03TvHnO/fogF2rCBWatFkHmtWszXrmEd06ZZvo8XLjA3b479i4tDoLphALwtXLmCMUVFpXxdSbFtG469CMpPK2QQeRojBZREInlfiIiA2BCCxpLlXV2tD/DWapkHDWL+5x/1vago5oYNmffssW5dupw9iwy3q1cRaN23L/PQofrZe9evI5i8b181E08X8Xnv3swBAZZtNyGBeedO5vr1mbt2xfZN8fYt899/Mw8bhtevX0N8+flhe9YIxzlzmGfNwv8fP0Ygu6lsQGs5cQLCMaUZj8lx5gyEn2625btGCqg0RgooiUTyvjBnDiw2ljJ+PNL8rSEhAdlvixer7715A8vHyZPWrUug1UKMtWmDjDlfX2Sp6ab6h4aiXEGPHqZLCNy6xdyypfnPLeX+fVit3N2Z165ljo01XmbMGFjumCF+XFxgzWrenPnoUcu2oyjMLVownz+P13v3ojyDPdi+nblDBxzX1MTbG/suSjy8a6SASmOkgJJIJO8DCQnWWZOCg1FywFJrFTPERIsWajq+WE/16nCp2cLLl3AHLVqEsWzeDPGiK4IuXMBEffGi8ffv3IHbsGvX5CdyRbHcyhMZybxkCfZt5Eh9F6KiQEQKsXr9Osb8+jX+tbRuUlgY9iskBK+HDdM/tilh2TK4N605v7bw5AmscKlZSsEcUkClMVJASSSS94GNGxE3ZCn9+zOfO2f58m/fwsW1f7/63tOnEADe3pavR5fTp/H969chbHr3hogQ7idFYZ4xA5alsDD9796/j+KcnTrBYpUUisK8axdzqVLMefNiP27dsmyMigJ3VZs2zE2boh6SomCMjRoxnzqF5Y4ehQUqOBj7ZKqmlCmuXoXLTavFOmvVwr7Zg3/+YZ40yT7rSoqQENSj8vJK/W3pIgVUGiMFlEQiyegoCuJRIiMtW/7RI8QJWbP+hg1VscCMat5Vq2Jd1qLVYmJv1w5xWA8ewHqm6040DCYX+PqiqGX79hhDcuPet4/5k0+YnZxwjP79l7lECeYcOZg/+wxuurg4y8YdFATh6eGB12/fInZJCMi1a+GG8/ODiHr92rL1LlyorvPZMxyL6GjLvpsUioIYskWLUr6u5IiMhKDcvj31tyWQAiqNkQJKIpFkdE6eRFyOpbRvD9eXpezZg4w7wY0bcNs8f275OgTBwWhBsnQpJviNG+H2evpUXea//7D+a9fU9yIjYaFq04b57t2kt6EozAcOMJcpA+Hk6mqcqeftjW04OTEXLGh50LmiQCSJzMXgYAhJ8d1//kHbmOvXYU2yRAgpCnPbtmr25OHDzN26Jf89SxDuxs2b7bO+pIiPh0XwXQg2Zimg0hwpoCQSSUancWPLe9jduIEJ1VI0GggNkbJ+9iwsOSJuxxpOnsS6bt6EsOjZk3n4cNVlp9UyT5kCMaF7S751C987fjzp9SsK86FDsCw5OTH/8Ye+KNJqYfHSJS6OedQo5vz5mXPnZv7lF2wnqdghjQbuOtHexNcX4wsLw/f69YPAOnoUMWOWxKWFh8NqJbLaxo5lXr06+e9ZQkICLI7voh2LoqB34IQJqR9/JQVUGiMFlEQiycjcuWOdtaJRI31rT3KsWqXGVh05wly3rrEIsYQFC2D5evsWrjdXV/14quBguAmFZYoZ/y5diveT6uunKBjb55/DNffbb/pB6FotXEvVq6NhcqdOpoPeDx1i/uorrOOjj1DbSbevny6iDpQoeXDpEtYdGwvB1KIFxrR2LUouWCImbtyAdU6rxTrq1rU9vszUeOvUQUD+u2DmTFj1UtIDMTmkgEpjpICSSCQZma5dk3dpCby8mIcMsXzdMTGwisTFMe/YgSBqa2NzFAXxTn/+if9v2ADhoWsZOnECFhzdwO7wcFiipk5NOh3/xAnmChUgen75RT8TT1GYd+9GnNK0aWqMmK8v8+DBCHz29DTOzAsORmZfrlwIOm/QwHTQuW4dKGYIwtatjQttenhgPyxh2TLUmmKG29HFxX4FK9+8gYi0xn2bEtauZW7Vyj71rUwhBVQaIwWURCLJqAQFIdDaEhQFgsEa19uMGcybNsGtVLOm9cUZRRDz1KmYRHv0QEmAhAR8rtHA1dOpk75IuHIFwuG//8yv28uL+csvIZx+/BGB6Lrb3b8frsYpU1SLWUyMfnZcVBTz8uUQWMOGGWfOabUI8P7oI+acOWHhWr9e35r05AnG+uoVXq9YgSKjzBBYok5Sv36WueQUBUHyJ07g9YkTqOlkL3fYixcYk2Fl99Ti4EFY0sxZ8lKCFFBpjBRQEokkozJ6tOWp4zt2oOWIpYSFQVhotagnZE17GGaIo27dIEAiIjCJ6mbZBQbiPU9P9T1FQYHKZs3MZ7CdPs1csSIEzfff61vfRAyUuzvzX3+pcVQxMdiHvHmZ8+RBcPmyZap7SVFQ0qF9ewjSgweNrV63bzNXqcKcPTsscbrcvAmBKVqo/PUXxCezfqHNFi0QIJ4ckZGwbInA97//xnjthb8/At/tVS4hOS5ehOXLXEsdW5ECKo2RAkoikWREIiNhYbHEMpGQgAnZ0jIHzOhxd/QoJltDwZAcsbFw3WzYAIuXmxuCzwWHD2M8uq6k168RmD1njul9CgqCpSlnTuavv4agESgK87FjcJmNG6fWjIqJgcsyXz7m4sWx/oULmb/7jrlIEQSOd+qkH4CfXH+827eZHR3hktTlxAkcp4QEjKdnT+w/s36hTd24qaTw9obA1Ggg5ho0sLxApyUEBkIgnz5tv3Umxf37EJK6lsKUIgVUGiMFlEQiyYj8+y/ca5awZg2CuC3l2TNM2MxwJ+mKleSIjMR39+1DmQNXV7VKdUICMt66d9dveCt64F2+bHqde/dCBH30kbGI8PJCcPTo0arVKjYWLjkhnJo00a8ZJSxOTZsylywJq9SPP0LYCfFm2B9Pt6TC4cPM2bIZp+tv2gQ3paLg+02bqpmDotDmy5f6Lr+k8PRENh4zvle1qn52Ykp5+xYZnJZeRylF1Lgyd56tRQqoNEYKKIlEktEQFiURS5QUigL3iTWBvN27w0py8yZz586Wfy80FBaWU6dQYLNqVdXiEBSE1jHr16vLix54rVubjpERdYWcnFD/SdetduYMc716KIMgxEhsLGKs8udnLlYMGYf37iU95uBguMjKl2cuUADfGzdOX+CJ/ni1amH8Wi1cao6O+pmEzMyzZyOuixnrqFlTFZAiI+/KFYzNkl51Xbuqbr8zZ3Cs7FkeICEB2XL//JP6ZQeYYR2sXds+JRWkgEpjpICSSCQZjS1b1Ga2yXHggHXtPO7dg1hhhnXC0sa8QUEQalevwmLl4qKWSxCVuXXjlUTZgsWLTU/cjx8zlysHl92uXer7587BwjVkiFraIDYWhUSFAKpf33SmWUgI4nFMbS8hAdupUgVWq3z5IJh0ywi8fYtjOWwYXo8cCXFnaBUbMgTlF5gh7nSDtv/5BxmBixcjwD05oqJgtXn2DK+nTbOuZY8lKAoyBXv1skyUp5ToaFjjdMW0LUgBlcZIASWRSDISIpvO0tT2unUtbynCjEDnhw9hRRo82LLv+PtDJNy7B4FSo4ZaENLbG5/pCjHdgpqm2LgRAd+ffqpal16+RE+8AQPUYOS4OFiLChZkLloUlg1z5QZGjYKr788/IfQWLzZ/DO/fh5j49FOM47PPkK0ngs7HjlUFbMuWKHegW1tLq0VA+t696vGpVg3jUBRk1Z04gWUsSQK4fx/7lpCAdTdtmjqxS5s3QzTbq2xCUiQk4Fz06qVv7bMGKaDSGCmgJBJJRuL0aVg+LOHSJWSfWcr582rRR3d3y0oe3L2rWliOH8dEL9xxFy5ArAhLkUbDPH483IKmJumYGGTgOTkhCFvg5QUBIlxhcXFoLSOEk7u7+pkuoaGwTNWqhSBzYXmKjWVetw7v9+9v3s0XGQmh9e23CDovWBAutZgY/Lt1K5arVAmf6QrVuDi4GM+fx+tr19TWLm/fYn8ePcK/llSR37ABgf3M+H7NmvYNKhecOQMBbEubHls4dMi4BpilSAGVxkgBJZFIMhJNm1qeDt6qleUuOEWBhebFC8tLHly+DIH04gXcX40aqdaEY8ewPiGmXrzAa92yBbrcv4+mv7lyqfExGg2EUseOEA1xcXChFSoE4VSjhn5wtyAsDEKtVi2sK6nYnmvXIIbq1cN+m3JhKQqERaNGzIULM3/zDURY48aw1Gm1sFaVKqVfaDQ8HMdHBLAfOaK2drl9G9u8cQP/WlKxu1cvtRRESAiEhz2z2gQ+PhDF1iQPpIQXL+DONWwcnRxSQKUxUkBJJJKMwv37lgd1+/rCRWQp+/dDdFha8uDkSVibwsKQ5demDQQOM4RIkyaqmBAuPnMT8pIlzM7OzF98oWaZBQZCWKxahUn17FmIpiJFMD5TmVxv3kBwubvDsmHNZBwairIF1apBpJmzCt28Cbeeu7taSsLbG1apggUhrnTF0PPn2HdR02ntWtUq6OkJF+SqVTj2yRETA0EmRPGzZwjUF/FR9uTlS+zjsWP2X7cptFoc/2bNLC/2KgXU/1OqVCkmIqO/3r17m/2Ol5cXf//99+zo6MiffPIJL7KhBbQUUBKJJKPQo4flvdF69TLt1jKFRoNA5YgIZJctXpz08nv2wGIQFYVyCj17qqJh1SoIN1G1/M4d85Wv377FJO3kpN9i5vBhjOfuXYigESMgsL75xnQvt/BwZNK5uUEIpiSbTKuF+GralLldOwg3w/WdOIHGwz16wD3p4oLWNK9e4X3D+lz37kH4iIroQ4ciEYAZjZ2PHFH/TQ5fXxwzIVYfPFDjq+xNdDRivMxZDVODq1dxPE+eTH5ZKaD+n5cvX3JQUFDi39GjR5mI+KSZo+jn58c5cuTgAQMG8N27d3nZsmWcNWtW3rZtm1XblQJKIpFkBIKD4UKydNmGDS1f9+rVzPPmYcKsVi3pli3r1sHaFBsL0TJsmCoWZs9GDJVIzxcVqE1Zc65fZy5RAsJIVDlPSEB8V48eGMurVyh6mT8/Uu0NxxURAVejmxsCtu2dhu/vjzIJ1asjq07XKrd2LTIEp05F0L2rK6xxDx6gWrlhg+ezZ5E9GBeH/XB3h6tMZNk9fIhjb4k1ae9e9AkUx/naNesSC6xBo0EywYQJ76bMATP2o0sX1PZK6lqUAsoMAwYM4DJlyrBi5owNGzaMy5cvr/dejx49+JdffrFqO1JASSSSjMC4cWpRRnsuq9sw2MNDDYw2xYIFEDcJCbAYiQa5ioLMtDFj1En2+HHEIBnWd1IUpPI7O6MVi4iZevIEy2/ciNcHD6Iswccf67eAYcYEO3UqYqB277ZsYvf1RSyVqyusZtbc8mNiIDJr1kSfOyGkJk1CL74NG+BSrFMHwvLMGdSIMmwgvGsXxqAosFi5ukIo3rsHd6i3N46BJaUEPD3VgH9mxGI1aIDtpwZz58J9LCxf74INGyA0DXsUCqSAMkFcXBwXKFCAJycRxVi5cmXu37+/3ns7duzgLFmycHwSkjU2NpbDw8MT/wICAqSAkkgk6ZqoKIgFS4SCNS1emJlnzcJEFRqa9PemT4e1KSFB7XHHDCtIv35Yj2DnTgRYG6anh4YyV64Ml50oNskMEVSjBkSORgOXYJ48zP/7n342WGQk6iBVr45tWCqcOnWCxebOHVg0Nm9GeYdevawPlD54EPsmRE6XLrBEeXmh5lbr1jgmGzZARIl2LoLFi2FZYYarsHt3/H/9ergqN2yA1csSZs6EWBbs2YPtWxKQbguiMntqNAY2h58fRJSpaulSQJlg8+bNnDlzZn6eRB7lZ599ZiSwzp49y0TEgUmkqIwfP95krJUUUBKJJL2yYIHlRQfnzbO8NcebNxAjWi3EkTmrla8vssdEjzsxlvh41DRauVJdduVKxA4ZPsdeu4Yil/nyqQHgcXEIqB4wAOsOCFArgv/5p74QOH0aAdPbtllWwfvhQ1hMWrc2Hzd2+zZcg3XqQFQl5S7SZfNmiEhFUety5c4NS9KqVdgnRYGFKnt240bMffqoNaLGjoV1ixmCbv9+jEl8nhyjRukXVV2zButJLXfbxYuwnOnWvUpt4uOxn1276rtRpYAygZubG9etWzfJZT777DOeYlDG9b///mMi4iCR7mACaYGSSCQZCeFis2RyT0jA5GZpNenRoxGwHRCQdMxUmzaIWWrQAFYOZriemjZFxp1g1iz9GCjByZNw2f38s+oCevgQFq+dO/F640bEOpUsqR9ArNEw//UXRJklt+lHj2AVat3acutSeDjceq6uyIazpAbSnDmq9ScuDu7IfPkQ7zVpEvOMGfhMWKh0a03FxsLi5u+P/atbF2MVWXY+PvjXVOC9IaJx8dq1+mMbNcqyfbcFUb8qNepQJcXx49iuKF0hBZQBjx8/5kyZMvEu3dr9JrDVhWeIjIGSSCTpmcmT1Yyt5Ni4EULAEgID4Y5RFFhTTNVTYoa1qEcPuK2EsAkPh9VGpLgrCsTY2LHGlo9t21DbqVkz9b3Nm+GWefwY4qNlS5QGqFxZrWAuxli3LvOKFclbVPz8YKFo1cp8UcawMEz65talKMiwa9kSfydPJr3dP/9UMxYjIpjLlIGVLSICViDhvqtWDeJK99nezw+Wq7g4vO/igtguX1+8f/eu+nlyaDQQjLqxYmPHwsWXWrx+jXgtS2Pt7MWrVxDus2czv3kjBZQe48eP56JFi3JCMo9Qw4YN4woVKui917NnT5uDyK9dkwJKIpGkLwICICAscccoCqwaydVvEvTsCXF09y6sO+bWWb8+3EKiTtHLl9iOKCeg1cIlNXu28fcXLUKQdd++eB0dje0OHw6Lmo8PClAWLAgrk67l6uBBtZRBUvj7QwC2bGm+NYworOnuDnehmxtcjbpFLw0JCIAIqV4dLlRRgkAX0bJFPO8HBUFAff45rEzNm0NkarXMFSqgx57uXL97NyqhM0OstW+PY751KzLftm+3vJJ8bCwshKLFi6LgvKRmCYLoaAhjEfj/rlAUPCjUq/ceCajYFIb/a7VaLlmyJA83EUE3YsQIbqfzKxdlDAYNGsR3797lFStWpKiMwR9/hJv8gUgkEkla0a6deVFgyNGjlhVjZIZwad0a/2/eHC4ZUxw+jLICokbU06ewpgjXWHw8xmhqkp4wAcHiEyfi9aNHWM/Bg3i9aBFcdqVLq+1OxDqHDYMFJymB4++PAOwWLczXuwoLwzgMC2tGR0NAubmhJtPDh+a3ExeHmLI6dRCbZBhPJVq2nDuH1/fvY79+/x1B9KLNTEICCoF+/rm+VUm3JtSUKapFq39/uDcHDoSQsgTR4kUcD60WwfPJOHRSREICzlVqWrvMcfZsBhZQhw4d4g4dOvCnn37KWbJk4UyZMnGuXLn4jz/+4EmTJiUZBG6Kw4cPMxGxj6h7r0OHDh24SpUqeu95eXnxd999x9myZePSpUunqJDm4cPh3Ly5ZcGJEolEktqcOQMLgqU0bKjv/kqKVq1Qr+jcOWTQmUKrhaVp0SIEpov2Hn5++DwqCm49w8lZUTChOjnBcsMMl5qLCwRYZCRS9vPlw/rDwtTv+vtDACTlsnz8GC7F5s3Nx+DoViQ/eDBpl93Fiygt0KgRXGBJZbDdvIntGgpG0bJFxDmdPYuYr5Yt1ZYrjx9jX52d4aoUY4qPhyvMxwfHvFEjFJOMi4PAu3cPxyQpkaeL2J6vL17HxeHasKQwpa0oCiyIQ4a82zk0Q8ZA7dy5k8uVK8dFihThTp068aJFi3jPnj189OhR3rx5M48dO5arVq3Kjo6O3KNHD35p6a86DdA9AUuWqE9LEolEklZoNBAXllaXvn7dcrF18SIsKaL3nWj0a8j69SgZ4OIC65OLi9qDLywMk/6JE8bjbtoU4mnzZrx37hz25eVLCIOSJdFPbtYsfWGzbRsEgzlrWHQ0rDHNm5uP1xLCqWbNpIWTKV69Qn2qatWw3+baiWg0EKDCkiYQcUzCbiBiv0aMgDAU1cIfP0Zm3l9/qd8NCIAAi47GdqtWxb74++PYPXiAz2NiLNuXZ8/0xxIZCTF55Yrlx8MWli2DG/Jd1YrKkALqp59+4j179rA2Gan57Nkz/vPPP3mGSEdIhxiegF69UtfcKZFIJMmxZIlaZ8kS2rWzzEKhKIipCgqCtcWcyy8uDhPwtGkQUnPmoAI5M7LMqlc37kUXFweRkCOHGlx++DBEWng4LFn58jGXLatvOYqJgaAbOtT8xOvtDfffoUOmP3/zBoKkZk3UYkpJCr9Go7aq6dzZdM+9mBjs16VL+u+L7Dkxn8+ejQy8RYuwz7VqQSRt3owaURcvqt89fFitXn7uHKxXioI4qX79cL569bJ8P3x8cMyECA8Nxev79y1fhy3s2YO4uXeRl5UhBdT7hOEJiIvDRX7nThoPTCKRfJCEhmIStrQY4uPHajxTchw8iMBo3d53ppg3D7E4NWqo49Fq1abAhoHdkZHMP/0Ei4sQHFu3whoVHQ2rkLMzRIduuxHRI27/ftPjUBRYNerX189gE4SHQzjZoweeKXx9EcxdsybcdroWoLAwfVeZ4Px5xEQJMdivH0TU3r0Qls2b4/iLzEPdopTjxqnuwdmzIVyZIS43b0bwvWFhzqS4dk1teswMC2LVqqlfw+n8eZzXJCoK2YX3TkBpNBq+fv06h4aGpvVQLMLUCRCmWF3fvEQikbwLBgxAWw5rljdlJTFExDSFhyOrbt4808uJeJ6RI2HxGTMG/5prCvz6NfMXX0AgPXiA95YvR1xRXBxci87OiFvSFTienrCGmQuVffMGAdBTphjH1YjmwTVrWi6cFAXehQkT0DbGGqKisE81akDEiOD2gAAcE0M36J49sApqtdhuw4aquFy3Dq5IrRa9AL/5Rh2/RgOxePs23mvRAmJE9M67excP+Lo1pZLDywvb162/5eICl2Vqcv8+tmMipNluZHgBNWDAAF6+fDkzQzz99ttv7ODgwDlz5jTbCDg9Ye4EXLqEYL7UKokvkUgkhnh7o2ilpYSEwNphCevWwaIRG6v2vjPFuHFYtl49iJv69SEYTAmF589RhqBAAdXaMH06MsgSEmBtyZMHIkwQEQFxNXGi+fvrxYvY3tmz+u+Hh6NIpZsbXFrWCKcaNRDjdOIEjnGzZsxHjlgX9KwoEEfNmqnFSr29sW7DRr7LliGomhnL/vILjoW/P+pHbd4MEePkBCuXIDgYlq2ICDzEV62K8/z0KayGvr743NJyFcxwA7Ztqx7vmzexLivzvKzm+XNsR5S8sDcZXkB99NFHfPn/H3927tzJxYsXZx8fHx49ejT/+uuvaTy65EnqBKxejQBAiUQiSW0UBaLFGvfK338bBzObIjYWk25sLFxD5trCBAXBKtSzJx4ie/WC1WTGDOP2ML6+zB99pNY2UhRYrSZMwHaqV4eLSrihmBEH5OICq4gptFoIsJYt4TrU5dgxfHfvXsuF086dqnAyFDhBQXD/VauGY2KNx2HDBv12KWfOwMpjWMN5wgS1R2BUFEoYFC6MOLLatWGd2bcP8VC6BSlPn4YFS1EQeN+4MY7NgQOw5InGwdYEa69eDWugGLPIqjRXdNRevHmD69qwIbQ9yPACytHRkQMCApiZuVu3bjxgwABmRp2m3Llzp+HILCO5EzBwoJpNIpFIJKnF9u36mVnJER0NkWKJmFi0CC4z3d53pujTByUEWrXCBNumDYSFm5v+d65fZy5SBJW3Y2Jg2ejVCz3ZIiPRBDhfPjXwnBmirVEj866j4GB8vnCh/j4lJJjug2aO5ISTIQkJOPb16qGulLmaUobMmKF/vnbsQNNi3bErCgSPEJ+vXjF//DEC6Z88gXUmOhrbdXbWPzYeHmoiweLFzFOn4v+jRqFty549OD/WeElmzUIMnODlS7gGjxyxfB22EBsLC9iKFfZdb4YXUCVLluTDhw+zRqPhEiVK8N7/74Do7e3NefPmTePRJU9yJyAhAT8sS39UEolEYi3CRZZU4UhDFi9GLFNyaDSwssTHw5VmzmLl6wvLT+vWEE+tWuG9ESP0rSNeXnDZffstRFVcHCbyNWtgNfrySxSS1LU4/PsvhIS5yV70ODMsGvr4MSZ4SwKndYWTh0fywskUd++ianqtWhB8yVl4Bg9GbJRg4UJjr0VCAoLpRcmHR4/gymvZUj/zrmxZWKjEMdJq8b0rV7Bv7dvj2CckIBj/+nXbGgePGqVvFYyOxjnX3Y/UQKtlHjQIVlN7BftneAE1fvx4zpMnD5cvX55LliyZWI18xYoVVrdVSQssOQGvXr2boDuJRPJh8tdfllebZtYXRcmxZQsmTOGeMzd5tWmDzLlevRC43KePcZPh3bvhlnN1xYQYFQWr0e7diHcpU4a5UCHm//7D8oqCWKcRI0xvNyEBPfRMWZe2b4flK7nyDFotrD/Vq9sunAx5+xZWu+rVMT5zblWtFq62/7cbMDOWN+xHKCqSC4G4axfKPaxdq2behYcjW69zZ/V7r19j7gkNxZhcXOD+E8UyHzywvnGwKHSqWwxUq0X199Gj7Z/JaMiMGShbYY/44gwvoJiZt27dyrNmzUp05TEze3p6JtsQOD0gTsCjR0mfgBs3YImytMu5RCKRWMLTp2pTX0vZtk2NrUkKRYEIefsWk5Zu3SFdRMNgEThepw4m6m7d1JpNnp7MuXMjM4xZLaZ58iQm8hIl0KpExNRotcgQnDbN9DafPFEtPbpERydfF0qs397CyRBFQbxR69YIHD961Pg8xcbiuIl2NIrC3KULxKgur15B9IgMwG7dkJn34IGaeXfqFOKhdu9Wv3fxIoLxFQXL1KsH8fHsGQLMnz2zvnGwRoM4Nw8P/f1ZvBiCMIVd2ZJl3TrskzUWV1NkWAHVqlUr3rx5c7IDT++IE+DqGp7sydy82fLGjhKJRGIJbdoY91dLCkVB+r4lvTtPnIDbztcXLjlz66tfH26y0aNRFmD8eJQt6NABywQGwjoi2r7oFtO8dg0NdEuWVNu8xMcj086cW2jnTgg7wxpKd+/CunXggPl9EsKpRg2Is9QQTqYIDIQ1rVo14zICovSDKFIZHw/LnGE5Ct2K5BoNyj98/LHaY/DtWwjHXLkgjAT//gvLDTOE7Lhx+P+DB/heSAhcjytXWr4/igIB1auXvmHgwAEEuJurxG4vjh7FNWBYFsMaMqyAmjBhAn///ffs6OjINWrU4Pnz5/PT1K7OlQqIE9ChQzi3aJG8WXHECGQzSCQSSUo5dQop/9Z+Z+RIy5Zt2BDB2d27qw2ADTl8GC6g6tUxsYsCm82aYcJnZv7tN8TnMOsX0zxxAllln32mljGIjkbsjimXZEwMRNjgwfrWJUWB2KpXT20XY4p9+zDOdymcDDFs2SIIDNRvdxMZCYFgeNyvX4crNSEBQjR/fgiWU6cQ56QozF99hSbLwkWrKLCCCddojx6wQjJDwApB3a4dxKk1bNoEsacryEW2pKX992zl4UOMfeNG276fYQWUICAggBcsWMBubm7s6OjI3333HY8fP56vmWtWlM4QJ6BQoXD+5x/9ehym0GhwsRmW8JdIJBJrSEiAWLG25nDjxpZVeL5xA9aFoCB8xxRaLQTJ8uVwCa5eDWvHmTMI+GWGJcrREdaVO3fUxrjbtzMXLIjJXuzDmzdw/x09arwtPz9YjQzT2UXBzMmTzT/ARkfD5TR0aNoJJ10ePMC5060izoxjpNvK5eVLtYmyLuvWqXFLR47Aujd/PrIGFy9GzFTu3BCigvBwHPuXLyGsmjRRj7MoaxAZifnJsEdhcvz3H8ata/UKCMA+njtn3bqsJT4els/OnS2zquqS4QWULhEREbx582Zu3bo158uXj0uWLMl9+vRhb2vs0+8YcQImTQrnMmXgS547N+nvhIXhR5HaZeolEsn7y8KF6HlnDbdvw5pkCe3b4wl/5Ejzlc03bFAbBovWJLGxiE0SSTOff85cuTLcddWrw6K1ZAky8X75RQ3+Dg7G56birG7d0o//EVy6hG0Ly4q5fU6qD15acfEiLEmG8UKGrVwePcK+GwrlXr3UFjaDBsFtd/MmhNHVqzg2jo4INBfcvAmrokYDUVm7tlqkUpQ1iIjA+bOkOr0uDx7gXOhmQoaHY3uG8VypgZcXtm8uTs8U75WA0kWj0fCxY8e4f//+vGzZsrQejll0T0CVKvD5d+6cvBlUlNR/V12nJRLJ+8Pr17DGWJuJ1KmTZc1g/f3h8omIgIvEVIC6aBg8ezYsT6JY5o4daJ/CjOKVjo6wOAmRNXky3E7Vq6v3v8ePIRJM9RA9exauLN0sZlEws0UL8xY4RYE1pkGD9PuweuAARIthXS3dVi7MKEVQu7Z+L72YGFwDT55gue++Q22tp0/VY/3XX7BOPXqkfm/FCrX+lKjrJWwUoqyBaBxsTcsXZpwjd3d9sRofD5fh9Ompn6EXGopYvaSskbpkWAEVExPDvr6+HBcXx7t37+a36cGuagO6JyAmBv783bsRVJmcEt61y7ru2BKJRMKMgN8zZ6z7TkAAMpcsoX9/TNozZ5ovjzBvHixJrq4IGK5RA4KoWjW4kBQFweHNm6N2z65dWG/+/HAtiQnu7l18R8RL6XLwIKwxuq4ZcwUzdQkNRZ0kDw/rWq2kBatWIbnIcF+WLYPLUXD4MBvF2T56BIEbF4dzULAgc5UqsCq1aIF1/u9/qPiuK766dsX5YMbxdHFRj/+cOXCJiVgta/v+xcRAFOraPRQF7sXevVM/E11RIBLr1Em+Kn+GFVAtWrTgbNmy8YQJE/j333/nZs2apfWQbMLwBFy6hBuEqBKrq/xNMXEinpIkEonEEm7dgnXCWoYOtSweJSQED4BCDJl6khdZY+PGISZpxAhYm5YsUSfOlSuZs2fHJFarFizz+fPrF268dAnrefHCeBsbN0IE6E78x46ZLpipy3//YeJPrf5pqcHkyabLNUycqF9uYs0a/XYqzPB2/H8DD/7vP7jypkxBKMns2TiPefNCaAni4vRjnYSFUJyHsWOxXT8/vP/ypXX7o9WiafLIkfoCdtMmxNO9C3uJjw9EfVLuwwwroGrUqMHly5fnuLg4VhSFv/nmm7Qekk2YOgGjRqHK7pMnasqpObRaZKtcufIOBiuRSDI0ioK4GZ2SeRYRFgYXkCVMnAhrx+rV5mOsxo1D/FPt2hiLCEB2dYWFISEBrViGDsXkvmQJXEm6bUCOH8f3DQOpmWFd6tZN31oxaxZckObasWg0aBTctq3pdaZnFAVWRcPK8IqC4HfdLDMPD1hzdBkyRBUK48bhWF+4AHfWuXOIA8uenXnBAvU7UVH6sU6iBERYGLbbpw9KHty6BSFiS8WhxYtxPnRF8JkzxgHnqUVcHIRct26mr5sMK6Dq1KnDY3V+TVWqVEm7waQAcQK2bFFPgKIwf/MNnsquXzf2XRsiqsVmtB+9RCJ5t2zdCmuFtfzzD+JqkiMqCpOlVovJ1NR968ULuEf69sXk3K0bUuEnTVLdQuPHwxLy8CFceGXLwpUk2LEDwc5RUfrrVhTE5wwbplpZFAUPpePGmXfZPX8OV9+KFakfZ5NaaDRqexZdEhLwkH3sGF4rCmpk6bbHiY+HGHrwAJ//+isquj95onbBmD0b1ct1c7LCwiBm7t7F60uXsJ6oKFwDbdsiJOXcOQj3pOYxcxw4gHXq1oUSAeep3YhYcPw4tmdoqMiwAmq9TvnY2NhY7m5pakg6Q5yAn38O17M0BQfjAj59Gn78tm2T9sWfPQu/cUb98UskktQlKgqTgLWTWHINgHWZPx+Wpf37zQu1Pn0QF9W8OSbedu0wQdeqhftXZCTE07//IpNv3jxM3CLGZsUKJNsYtpHRahEL5OGhvqfbZNgcoraTtQHP6ZGYGIhTw4letHIRVd0jI+Hd0K0lFRCA4xAdDWtR0aLMP/4Icdu4MY6viwsCzXVdaC9e4H1RkPL4cSwfH6+6+ry8EBjerJltMUym6kK9fAm34smT1q/PFkJCcM1Om6b+FjKsgHpfECfghx/C+bff9AXQhg1oT/DyJfPSpTAlJoWHB/onSSQSiSHjx6sWHmsYONCyuj4JCZiUExJgbTCV3SYaBrdrB/HUvDniZAYMUEsJdO6MEgXXriFYuVgxxFQxI1Ovf39jMWeq8nhsLDIBddPwdYmNRfp+v362WUZsITzcdKagPREZcIZFKF+/1g+2v3sX3g1dQaPbXPjKFdSCGj4c88+UKVi2YEGUldCdq0Rx0+BgvN65U80CjIyEeLt6FTFMXbrYFphvqi5UZCQskZs2Wb8+W1AUuJPr14f4fC8EVExMDF+8eJH37t3Lu3fv1vtL74gTEBKCMgaVKukHRDZqhCq8Gg3M0EkFjGu1WP7GjVQftkQiyUA8fow4I2st1N7eECGWsGkTrEUXLpgvCNymDdxv3bpBMA0YAAElsvuCg5mdnOD2adAA8VQ5c2Ly/+sv5gkTjPfBVOXxt2/xfcOimQIRIPyu2qWGhWFf3NxgEXN3h7BLrZ5vAQH6gkYgyj0Id9iGDcYP5mPHqo1+p0/H8T96FFa/kychzLJnVwudCu7cwTEVoSQrV8JNqyiqqPPxwRw2eLBt3hJTdaESElDmwJLejPbi3j1Y6zZsyOAC6uDBg1yoUCF2cHAw+suUKVNaDy9ZdBVsXBzzzz8z//EHgv4UBRdM+fIIplQUmLRF8TNTvHyJH4i1FVUlEsn7S6tWapyKpSgK4oIs6ZClKJg8IyOR+WbqO5cvI6C5YUM1s+7lS9zTxNjc3JhLlcKEPXQosu5691ZdfYa8eQMriojvYYY4qFkT4Q+mWL1aDV5PbUJC0AvQ3R2hGEI0REbCqlOjBmJdTZVgSCne3mojZ11u3oSbT1ie+vRh3rtX/VyjwXm/fRvjdXXFefDzw9wSFIT5KXt242D0ixdxbEVf15kz1b55otVMQADcu6bEsCXEx6OYq25dKEWBQB0y5N2VnYiNZe7bN4MLqDJlynDv3r35hak81gyAoQnwxg08TU2YAFN3cDB8yl9+iaey2FjTPm5ddHsaSSSSD5vDh42tBZawZYvlAedHj8JF6OMDV5ohQoxt3owA7927Udvp2jW1svmDB5iUL13CxN+nD7OzM9xrwtWni2gorNvW6tkzTPimrPBv32JsEyZYX0DUWoKDYdmpWxfiTlEwsW/fjuMk4oUUBfGr7drhvn/woH0FwJkzEKyG8WKbNuE8MGNOcXPTF3EvXqgP4pGRqAP11VcI2q5XD8dv/nxYCw0bNh87Brea2OaoUagNxQzrlWgH4+EBQW1LTJSiwKXYt6/+uVy+HPHCqWXZMyTDu/By587ND1O742AqYuoETJoE8+7167hBbNmCm8n33yOGIDRU7QdljkmTEGwpkUg+XB49wj3EXOq+OSIjYS2wdCJq0ACB4D16mM6MOnYMlhY3N9VKHhmJ74l09G++QfmWjRshrpydIeCEq08X3YbCAh8f8w1ohVXK2h5t1hIYCPdU/fpq+xqNBsLR1RUWm717VcF06JAqmIKDIQpcXRHrlVT5GmvYsQPlGwwfqPv2hZBlhjh1c9M/36dPQ4woCqxZzs44v2vXquUkxo1DgP+OHcbbbN8e+6YocFuuXo3Pbt7EtiIiMLc1bGhbiQNmlG1o2VK1eDHDQ1O37rvJSs/wAqpTp0683FACZyBMnYCEBFxgAQHIYhg3Dk9gP/4I915UlGpODQszvV6NBjendNwGUCKRpCIREZiMra0EzQyrgbn4IUOuXsVknFTT4Pr18UA3bRr+XbQITWxHjsTnZ8+iZYtIT2/aFMHKCQn6ffGY4Voy7Gt37Rr2VTerTBAQgOVTMzY0IADHoFEjNchZo2Fev14VRKtWYRz166uhGH//jfdmzlSD7jUaCJuGDRFQb21POVMsWgQBq4uwPAnL3p492AddPDxQU4sZsUu5ciFAvHdvNQ6pZ0+8b5gNt3IlAvQVBfvUpo0q2P77D66+0FAcr+rVbXepHjmCa0RXcF66hOOe2rWiMryAioqK4tq1a3OHDh14xowZPHfuXL2/9I44AcHB+ifg7l38gMRTw9WrEFBff60+TVy+rN800pAXL3ARWfv0KZFIMjZaLR66rG3Xwgwrd5Mmli/fpg0m4VGjkK5uyJUrsECIosCiYbAIOFYUxD3VrInSBTNmYEJeuxaTta4b8fx51Yol8PJCjJGprL/795mrVjVtlbIH/v4QEM2aqa7EhARYW6pVQ/kEIZzmzlUtJX5+zH/+iX1ZuhQutQYNkHV49aq6fl9fWLTc3RHYrWtpsZYxYxDkbzh+XcvTsGH6GW1aLcSsEHH16qHAqQj8P3IE7zdtypwnj/7YmRHYLWKg4uIwpwmhdeWKmhUoXHvXrtm2b9euGYtqX1+I8dQ0ImR4AbVs2TLOnDkz58qVi0uVKsWlS5dO/Pvkk0/SenjJIk5AgwbhRkJo5kz8uASxsShwVro0AuiY8dTQubP5eKfjx5E2KpFIPhzGjdO/d1hDkyaWC45Hj+DmSappcOvWsGQsXQoX1tatyP4SWVNbtiD2SdQh+uMPlG+Jj1ddfcyYrOvW1Xf37N4Nq4+ph8QrV/AAGRho3f5bgq8vxE7Llmptpfh4WNeqVUPFbiGc5s0zXyYhOhrLubkhaP7oUZSNcHeHe0p8LyoKFp2aNSG8DAuIWoKiIPtxyxb99/fuRYgIM8Rf7dr6DaNFoebQUIynVCnmzz7DeahbF6KWGT30ChQwrqc1ejSKcDKrJQ1EDO/jxzhHFy9iO7VrJ50klRSibYyupTE4GMfWXEJBSsnwAqpIkSI8efJk1qb3jo9mECdg48ZwbtVKP6BOo8EFpRs8GRvL/NNPuFBF76O//8aP0Bzjxqn+Z4lE8n6zZQtcJ7awdy8mPEvp0wdP/7NmMW/bZvz5w4ewUAkhU7MmJmERX6XRoGBwp064T82bh7T548chuIQIDA2FxUpXiHh6IpbIlAX+xAm4dUxZpVKCjw/S+du2hSuRGfuxeDH2afFiVTjNn295fSlFQfmHDh3gBt2xA+uqUQPB6CLAW1EgMJo0sS34OiEB3zW0FI4YAXcjM9yghp6LS5dgZVMUnNO8eSF2Q0MhUG7fhrWqYkUU2tS1BIm2LmIOEiUNhEh78wZuzZ07cSzbt9dvGWMNr15BoOlWWX/7Fsc0qZ52tpLhBVS+fPnemyDyzZvxA9LNKnj4EFl3uvrwxg2YUkuWRMrwy5dYxsfH9DY0GjwpvA+VdiUSiXmuX8e9wTDryhKEsLHU5f/yJdxOwlJkKrOtd29YH6ZNg4XFywuuLFHccvp0BCEHBOAe9cUXyDjW7YvHDKuLbozNzJmI1zH13Lx9u+k2LylBUSDuGjVS76MxMRBJ1arB8qRrfUpJYU4RTO7iAnfmwYMQik2aqFl6W7bAAmZLpnVkpHHMUUIC5hARlH/smHHg+fz56kP79u1w2TVtivFWqwZrZEIC8yefMH/8sX4NKq0W+yDqbomSBsISFR+P/Zk9G8uOHYvrxRa7SFQUxN6GDep7Yv2GLsyUkuEF1MCBA3myLc2d0gmGJ2D1aqT16l44CxcatyKYPBmF5SpXVp98qlc3nzUjnipS4kOXSCTpl+BgxPvo9gyzhkmTjN07STFuHFxOq1ebLvAbHAyBVasW4lAaN4bbR7SFiYrCJDxhAiwUM2eqvdYmTYJFghnWDBGcLvrajR9vWjwsX266zUtKCA6GeJk3D9uMikJafrVq2HchnBYutG/6fEICjkH9+pj8jx1ThZW3NwTNqFG2rfvBA1hqdI9TYKC+5envv5mXLVM/VxRYE0Vc3ZIlOH89eqCul4sL1hETgzYwZcroZ8LFx+M8ikzI0FCINhFYrig47/36QYyvWgUXqS1COCEBcXczZujXiho3DnFe9nJYZXgB1a9fP86TJw//8ccf3LdvXx40aJDeX3rH1AlYsgTtCnRPfP36+n5pkak3axZMvBMmID4qqZaAhw7hYpdIJO8XcXFw99vaXPXJE1iuLLVoREYm3zR4zBg8+A0bhknx8mW8d+gQPu/XDxPw/fso9FmyJOJoXr5U++IxwxJy6xYm1Z49zVsRPDwQP2TPaI5DhyCObt7EeufNw/6uX68Kp0WLUr/u0L17OF7u7tieiwtEi6nAcEvZts24YvyJE6iVJepWNWqkxngxI9atWjXVujRpEs7h6NE4j66uEEZhYSi++dVX+gIoKgrnVgSlx8Xh/OoaCDZsgGXr7VuIxpo19btzWIoQZIbXxJIlENnmkq+sIcMLqKpVq5r9c3FxSevhJYu5EzBnjn5H8adPcSHp+r3v34ew6twZNUfu3MFNqHdv8zeRkSNRZ0UikbwfKAqEhbDY2ELr1tb1aJs3D9la5poGv32LB7xmzRAfU78+rBNCpIWEINbJ0xPi6a+/8Pr5c9R8OnsW67lxA/c3ZgRAr1tnvC1Fgbtn0iT7FQ8WffL69IHVPjAQ+zB/Pqxc1arB6vauCjYK3r7FserRQ23J0r277b3gBg3Sb4HDjPMpYs9evYJY07Uk3bqFYyFctv37Q0TNmoUsPHd3COznz1E76n//0xcrYWGwQgp3oagi3r+/us7TpyHQAwNxXRrW/LKGVatgOdMV+Xv2YB9srT8lyPACKqOT1AmYOhWWJcGqVXhPl7lzYaasWlWtGvv55+bTN0VdlQcP7LsfEokkbViwAJOqrRw/bl2lchHzJOJmTAVqz56NCbVrV7jbjhyByBOWh4YN4ea5eBFioHBhxHM+eqT2xWOGBeTpU9PFNJkxhi5d7NtE/e5dTPDCtbRnDywr27Zhv5cssY/1IiWMGYO4sFq1cN9v1ky/nY2lxMVB8Pj6qu9ptXC1iZIE587BlWbY6L5nT7wnXHt588Kl6eWF8xsXh4f8nDlxPHVj5ESJHd0q8mvXwvIkXIj372Meu3ULNcbc3GwvhHrwIK5V3bqJ58/jfKakzIEUUGlMcidg3DiYpplxoTZtqm+m12phut+0SXXf3b4Na1WzZvi+4VPS06e4oN9VB3KJRJI6nDwJC46tlhchhqyp2rx+PUTbhQumhZdYZ6dOmIRr1MBk2KYNPvf3R6zT0aOwSA0cyJw7N6wB7dqplobjx1UrvLu7fjFNZrXFy+bNNu26EYoCq1L9+rCeREfDbTZgAB5SGzfWrz+VligKxOm4cbjPR0RgHjCsw2QJYj7QjY8NCYF4EYJj9my1FIFg3jy1EKpWCzGXLx8E5549ON8aDURSjhxqFp8gLAzHWreKuZeXanlixvF2d4cAj4rC+bY1o/zyZYg23eD5oCCIvX//tbW5cQYUUD169OCnlnS4ZOZNmzbxOlN233RCcidAUfCkIfzcQUG42HWfgO7exYU1aJBaPXjBAvzoxVOTYUG9ffuMq85KJJKMg5+fbW1adJk9G240SxFNg6OiYJUwVeV8zRpYyps1w31r40ZYiYQw+uUX5nLlYBX4809MukOGICNLxGhqtbA4hIZigjV0E755A4uCKOSYUkJCYAGbNg3bvn0bk62Ix5kzJ/31FhVB2ePH4+H59WuM2Zak9EOHjONnL1zAMRFWppYt1SrrgokT1cy8hATm335DiR0vL1iUevXCd48cQd+8Xr2M96FbN/1gb1H8VBgKoqPxkLB8OQTZoEHmkwiS4+FDCENRgoIZ53vuXBxLa2OtMqSAGjNmDDs7O7O7uzsvXLiQL126xM+ePeOQkBD29fXl3bt3859//sklSpTgX375hW/ZGln5DrDkBCgKxI7wS2/apFZ3FYwahQyaatXwpKYouIFdvQql37Mn/nSfNIcMSZ3aGBKJJHV5+xaTZVL9MJMjKAhP99YEXR86BHfhgwcIxDVEUTCuQYMQlykqTTdrhs8vX0bRzFu3IP5at4aAiotTLT/MEF0zZmBSdnXVD0QODoaIu3DB1j3X5/hxNaVeUfDw2bAhHj5dXGyvjv0uiIyE0Bw+HKn/AQEYsy1B1+PGGYvpuXORHcmMucPFRd8SqCiwIIpsvZgYdMsoVAjHbd48NVNw0yaIKNFHT3cdU6Yg3kzE+ArL0+HDeK3Vwho5ahT+/++/CHa3xZX68iWOmWEtrFu3cL1a2sKIOYMKKGbm4OBgnjJlCn/99decKVMmvb88efJwkyZN+LA4+ukYS0+AVgulLuqntG6t1tBghkp3cUHwXevWaqCmiwtutsy4obm4qEGD8fFw9T16lAo7JpFIUgWtFpaBlFZX7tzZepdPvXq4r5hrGrx/PzKyatdGwPeCBXhQE5aLcuWYK1WClWrCBAQfz56NiVIU8IyLw0QWEwOXmm6rU39/fGZNwLs54uNRQLJbN9wjX72C0Js2DWPp0kW9d6ZnXr6ExaZ3b5RSuHsXAjMiwrr1aDQQsbrnVVEwn4ig/uvXIS51RbdWC3eteBiPiMB5LlYMtQnHj4cYZkYQfo4cxmV5mCGwGjVSxx0djW3rVtRfvBhFTGNi4CasV8+2psGRkShNYRh8Hx2NYPa+fS0r+ZNhBZQuYWFhfOPGDT5//jz7+vqykt5srUlg6QlgxgXevj0u1Fev1JuM4OBBZKNMnKhWlT1xQr+VS0wMAhCbN8fTnuiFlNZBkRKJxDLGj0cgc0o4dw4WaWu4fBmTy4sXmOhMUacOxrd7NyxMz5/jPWZUOc+eHfccFxdM8kWKwOpQvbo6Ec6dC4ElrGzCKuHtjXteSqxuAl9f3PdE3atjxzCmfftg+RD3z4zCo0cYf9u2sJydOwcxZG2WoCiKqTsdhYfjPRH/tXw5akTpkpAA8Slcqq9eISO8RAnEWPXvj7IPzLg+cuY0fYzPntUv8qnVQuQOH66KtgMHEG8VEgIrpK1NgxMSMF/27GnsBj9wwLIG1O+FgMrIWCOgmHHSW7aE+t69GzEEugjLlJubelGNHm1cukAEmi9ahHU1aJB+AiQlEolptm1T+5bZikYD8WJtwc1WrSBeRo3SrwouOH8eQdeurhAif/+N2KX9+zH5FS8O4TVrFlw2uXPjYXDtWsQYMUNEiUKbEyfi3sSsWthTeo9SFLipatdG/FZcHCbn7t0h2tzcUq/xcGpz5QrEX4MGcE/t2wdBZW1drDNnEACua4e4eRPr1WjUAHaRpSiIiUE1eWFtfPoUVqgyZSC6O3RQvR+9e+P8i56uuog4JV3X6dKlGJOwCt24gWV8feFOTknT4CNHIJYMrbGigOrMmeaPoRRQaYy1AooZP/omTXDiO3VCiq/g+XP8iLy9ccErCkzV7u76PfWY8WP491+o+YUL0cRTd10SiST9cOMGJqiUVtlesgS/d2vw9YX1O6mmwS1aIGN45UpYnZ49gxhSFFgtnJxwf3JzY/7uO+ZPP4WFRPTFY4Y4O3wYk1edOup2PDysq5JuivBwCIq//8a978EDjGXdOrgkhw/P+Jb4w4cR9O7mBtGzahWyCK11ysyYgblBl5Ur1bI6cXH6VcUFERHYtnAD3rvHXLAgWvSEhGBsotzCkCHMuXIhlsmwDdDr1zj/e/fq75u7uyqiAwLUUgiiafCpU9btp+DVK3hlPDz0xZKi4LfSoIEan6eLFFBpjC0Cihlqv359PGVUq6b/w//3X9wk58xRb5R+fsal+wUBAfD716nD/NFHuEGePWtbs0qJRGJ/Xr40DuC1hZAQ45o8ltCrFwScuabB9+/DwiAyfgcOxL1n7VpMQoULQ6SMHIkMvVy58LA2a5bqynn2DPc0ZsSgiCDxkBDzos1SXrzAfp85o2+FOnwYY7ZXNl96YM0ahG0Id+fUqcb1A5NDUSAoDAP1u3ZVj1VUFI6hqO0lEOElIrb20iVUJf/hBwij2rVR/4sZwjpnTsRwGcabxcbimtIVcrdu4XcgunKEheGhYt8+uOEaN7ZdaCsKLF116+qXOmBWa4MZFquVAiqNsVVAMeOCqV0bPuVZs9T3hYk+MBAXgyiauWFD8p3WY2Nx86tQAeto3BhC7Pbt9JfGK5F8CKS0TYsuvXtbb2W+dw/WpaSaBnfrhvvE9OlY1s8Py8bHo4Fs9uxwzdSvz1y2LCbTsDC1HQwz3GhXr+J+1aqVuu7Bg223LDCrLqG7d+EibNcOmYSiObBu09v3hX/+QXiHiF0aOBAWJGsIC8Nx03X1RkVBSIjwEFFV3DCoX2QDCqvN0aMotPnHHxBYNWqo3zlzBu68L74wjmVSFFi9Bg1Sr7vnz/F9cU3ExsKyuHw5Hvq7dzeuWWUN9+9j/YYZ6rGxiJnq0UONmZICKo1JiYDC93Gyf/1V/+n0yhWo9ydPYHkS1qQuXUzHLxhy5Qp+ACdOIIZh8GCYSFu3RjCgPQI5JRJJ0mi1uGHrFhu0lWvX4C6xBkWB+8LfH5YNU02DAwMhRNzdcd/o2BHZTSLTqmRJPIh1764GEPv4IDj46FEsc+cOXITMEE/ioc/fH24fW7l2TS2eeP06BMXBg7CuJBXbkhpERMBF1KYN7sGp+UCqKAjc/vtvWO8iIiAydF1ilnDtGs6d7nF68ABhH8Kb8eIF5gp/f/3v+vjgeL9+jddbtkBE1a2LEhquroibY8Z8UqAArhXd3nuCNWsQpC6ES2QkrgthvdRqIW7Gj1fj54YMsf38xsXh+uzSxTib8ehRtexFhhdQS5cu5QcZuC9JSgUUM54wvv/euC7LgAHwN3t6ImiTGWZSw6cKc4SF4UYzd676Yw8Oxs2xWzf8MLt3RyXglLoWJBKJPpGREBMpzbhjxu+3Vi21wrOl7NiByUjUdzKV2j18ONwsw4fjvnD7Nh7qIiMRUJw9OybK1q3VEIFnzyDMBM2awWp14YJ+kHyHDraXLDh+HPv8+jUeBN3dISBcXIzdTqnJmzfo0+fmBhdQQABqLlWvjjIP1pYbsBSNBsfcwwMi6O1bWAANi2Emx9KlxoVMd++GJU8IlMePcVyDgvSXu34d51u45xYvRumKdu1wXBo0UC09b9/COlm4sJo8oIthlfKEBFhUPTzU+WnOHFyDCQl40G/TJmX9Ck+exH4ZujJF4dUJEzK4gPr88885U6ZMXKxYMW7ZsiUvXryY7927l9bDshghoMLCUtbV8NEjpASLeh1YN05+dDSEkEjJvHoVry15AlIUuAdbtTJdb8PfH6bTFi3wFLply7tvsCmRvG+I9hrHj9tnfWvXqrV4LCUqCvePmBikdU+aZLxMeDiESZMmmCxFcsv48fj8yy+ZK1dGssuAAYh9evkSk5zIshIxU4qCOExRBPLaNSxnC1u2YCxRUYjZEtlU7dqlnmAxJDQUx8HdHTE6hvfbuDg8jNapA9Foa7PcpIiJwfo9PFD3S3gsrBGlioLzZxgwvmoVxi326949iGzdfnPMOL+6JRUmTYIlqndvHIMePRCDqygQZK6u+FwU8NRF9McTlcQVBfFd/fqpLr4tWyAYIyNx3Rr2wLOW0FAIUZF8oHtc/v03gwsoZuagoCDesGED9+jRI1FQFSlShFu0aJHWQ0sWIaD69ElhW2jGj7RIEf2nxK1b8QQZHIwLU1zEM2dal4lz7lzyVXlDQ7FOd3f8sERlX4lEYjmiyalug9eUIOr4WJu9N3YsLAHCeiVcMbpMmwYLdbduqBR9+jQmy1evIAiyZ0fMVaNGsCx07qzvrtPtc7d3r37zdHOZT8mxYAFcLwkJsHh06oTadyNHvpv70atXyCYUQeqWbPPmTQiJevUg+OyZwBMWhnv/xIlwSwmXmzWiIjIS6zA8H7Nn68fVXrmCa8WwrtL+/Si/I/arf3+47Bo3hsjz8EA9JnGN9u0Lsd2li3HMnWGVcma4+Fq0UOe+U6dg8QsOVnvgWdj9zSSKAsFYq5Z++EqGd+HpEhkZyYcOHeKOHTtylixZOHPmzGk9pGQRJ2D06HCrnxBN0aoVnvjEhaoouEgfPIA5fvhwvK/VoqKsbk+g5Hj1CjfCxYuTvyl4e8MnXaMGxNr7GKgpkdibNWvwe7WlurIpFAVWF8O2Fcnx8CHGoSh4ojdlfYqNhTDr2BGTlLs7/u3XD5//9hssUIMG4b7k7AyLUNOmarzM9u0ILxAtW4SrR7cyuTX7OnYsRIKIgxk6FA9z9ri3JseLF9he3bqw1hjeI/39IRJq1oR7yZQ7NCwMbihXV1g8bGnJYgoR1D14MATvxYtwm1oTI3TvHqw5huJu3Dj94+vlhbnFsCTEhg3I4hO99YYNg4iqVAlzy9at+J7QIgsXIl6uWjXjDD1RpVy3Sv3Ro7gGRXiKt7daK0oUGk1pIoavL87fhg14neEF1IEDB3j48OH8888/c/bs2fmHH37gQYMG8e7duzk0NDSth5cs4gS8eRPOvXunvAJuRARuWt26qT9gPz88FQpTrGgs/OKF+bgGc2i1MJm2b29Zm4P4eDxZtmiBG+fOnRm/1opEYm+0WjzcDBlifYmBpJg8WW1Ebg1Nm2KyiIxU3XiGrFgBC5Son7NrF4SSvz8mbCcnxGDWqoU0dmGhGjgQ3xdZfVFRmAgXLcL7Wi3cl9aEhWo0ECezZ+P/ffviPtWuHSwHqcnz59inBg1Mt9d58ACWt1at4OaMjsaYatbE+TZVuFOrhSu0WTMEf//3X8qtZ97eeKDt3BlxqwsWIFvPGjZuVB/CBSJgXVfM7NkDgWN4LS9cCJEp9mXVKoioChUQdH7+vL616NgxWKIqVjS2fukGjov13biBa0rUPHz2DOu7eBGWq5o1jV2R1hIfD6HeoQPz06cZXEA5ODhw4cKF2cPDg9/Y67HtHaKrYDUa3IxEZoqtLF+OG6Buw+GpUyHORENIIX4OHoQv2lq8vHChWmvBmjsXptWBA5Mvky+RfAhEROD3au+JfscOiAprJ959+1Trz+jRppurarX4/Q8cCHeJqyviU4RrrkEDZFRNmoSJJlcuiDDhrmPGZLp8OQSUrotxzRrrRF9MDB7QNmyAVaxNGwQ+N2oEUZdaPH0K61bjxmo2mS7e3hBw7dubjzm6dAkPtY0a4UHTlHh+/Bjuxxo1sF+G7jFrOH0a56ZOHVhj2re3LCtbl759jauQa7WwROqm/osCpYbX3+TJalKTGFORIsyffIKxPHqE60H0en3wAAK8dGnTc8bcubBsievnyRP97795A9fo3r241po1M+7MYQtnzjD//nsGF1CzZ8/mRo0accGCBblIkSLcvHlzXrhwId9NjYi8VMDQBBgdjSe2lHQA12jwBDdggPpUJxp0hoYiMLVXL3X5IUPwNGLtjfbFC1yYttz4r1/H+NzccLO0tq2ERPI+4OcH8WHvDgA3bmCStNbaGxODB6zISFigmjQxvdyuXYhXql2bedky1BgSDYZfv4brZds23IcKFYJIEO46ZjzAVasGd9CUKWqLj5gY48LASREWhv08fBhCtF49BGa7u1vvtrQUPz9kHzdrpt/QXXDtGgRd166Wx7G9egVrnqsrrHmmsppjY5EM4OKiehFsYft2tehpYCC2aU12ZmwsLDk+Pvrvx8fjQUC3KOn8+aYtVkOH4jOBry8E0scf41oKDYUrVGTjhYfj86JFERhuyLZtcP+JBIHQUFwXBw/idVwcxOLSpbjmevWC2zGlVr0Mb4HS5datW/zvv/9y48aNOWvWrFy0aNG0HlKyCAF16pR6Al6/1q/kagsnTiD2QNy4mKH0hXAaMEC9ELVa+Ns7drT+6UajwY20a1fbXHNxcXhSbtoU1rfNm/GUIJG87wjLjb1rqgUHW16qxJBJk1QrQuPGpt1LIvB73DgIKVHxunFjfN6jB9p3LFyIIOCcOdXq1OK3PWECLAKvXmFdYiKbPh33AEt4/ly/lUeNGhiPq6txXzN7EBEBa33LlqYtIRcuQHD26mVcE0kQGIj7sLmJW6OBxa9xY9y7RcVuXd6+xYOnrb3fmHHuRo+Gm+3uXQhha5IMAgNxjRkGZhv2w2PG3GLoKhQxUCNHqnFYoaFo8VOsGD6LjcUxEPXEtFrE9+bPb9pC+d9/uAZEKYWYGOyfKCCq1aK46Lhx+P/kybCmpSRrPMPHQAmuXbvGs2bN4nr16nHevHk5c+bM/OOPP6b1sJJFnIDKlcP1amg8farfAdsWmjeH6bhRI7Vqa9euMDcLs7luZs3x43jPFuPd7t14AkiJefnVK9S8qVcPZvjdu2VJBMn7ybJlsFJYEkdoDbGxsGDbMrk+eaLGSu7di8w1U5w5g2BkV1eIrenTEbh96hTuK7lzw6JdrRosCrVrYxIT9axevFD73A0YoFpTXr+2vGWLjw8mcB8fWIRcXPBAKN6zN5cuYd2m3F2nTuG4DRxoXElbEBCA4PqGDXFcXV0hApKadx8+hKWmZk1Y+XVjVYODcXwNW45YikaDMQ8diqD1zZvxf2sQ1eYNE4REP7ybN/FaUXC9mKpntmSJfpPguDhcG4ULQ0RGR6NqfP/+qnuzWzckJPTsaezyvHcP50lUMtJqsW1Ry4wZx124/HbtMl1J3VIyvICqV68e58uXjzNnzsw//PADDxkyhPfu3WtTYcpnz55xmzZtOH/+/Ozk5MTffPMNXzFlo/1/Tp48yURk9GdNHSpxAi5dCmd3d33BcPs2LkRbb7K+vjAzv30LZX7rltoLKyEBTzdt2+p/5/lz3PBEloE1nDqFH7s93HHPn+OHXasWgh6PHJG9+SQZn4QEiIYxY+xfBVtRYPExFbNkCa1a4eFJuPHMWYKbNIG7SQRCBwSoVqTx4zG5bdqkWp9ETIq4t/XpA2vNo0d4yBMMHWpZPM6lS2pK/c2bGOvBg3jPnICxFa0W1pOWLfUfNhUF96TatWEtMZct9/gxLFJNmuhbk+Ljkd1Yty4+T0rwRkdDgNasCQuKCJB+9Aj7bGuulLBUNmmC+oEDB6reCksRgemG4cfC4ihcmOLa3LTJeB2GTYIVBWMRGXovXyKeqmlTdS6cPRvXlpubcRJUUBDGpOvmnDULllExh2zbphYXDQyEsJ0/33qXXoYXUCkRTLqEhoZyqVKluGPHjnzx4kX29/fnY8eO8UNTNuz/RwgoHx8fDgoKSvzTWJFGo3sC9u/HRaZ7Ek+dggXJ1g7sgwfDnSeakT55ghufKFI2bpxxQF1CAvzWvXtbbwES7RJsfTIyhZ8fbmJubhjT6dPvtgWDRGIPwsJwo7ZHAKspZsyANcgWjhxRLRB//23cB0zg7Y0HmmrVkOwyciTE1M6duEfly4cMpRo1UFX655/hohfj0u1z166dau1+/Nh8vJXhOGvXxrEUtX727LHfg5suz57BGr5smXpPFta5mjWxn+a2+fAhLCUtWiQfz3rrFkRU3bo47ubu9YoCEdawoSpErl7F8bAmk1qXM2cgYqtVw/66u6ttdCzl4kXTYxClE4SoTUiAEBVxSbrcvq1vOWJGuZwCBZBVfv8+zneNGmo23v79SE744QfjmLGICBwn3cbXosCmEGFnzsCYcP8+5pO5c/G5NaUjMryAshfDhw/n33//3arvCAEVloIyp4YnYNo0/cbAzHgqMBRWliKaPWo0qqn71SuYSZ88wY+1Rg3TgmfvXnwmnngsRTTvFB2z7cndu3jKrV4dwe+XL8tinZL0j3A5XbqUOuvfvx/CxpbfgkgwCQ+HkKlXz/x6OnWCC2TGDEw2T57ACqLVYsLLkYP50CHEU+bMiVghd3fVVSX63F25AouAoGPH5DN6N2/GZB8dDddLw4aYFBs2tL8rdOdO3GN0J/QHD7AvkyaZr9N1/z72pU0b0zWH3rwxb2168waTuKsr3FaGbVEECQk4DseO4fWRIxBqtpa/mDYNQrhBA5x/V1fr41CPHcP1YCj+fH1xbQmhGRsL16GpIPigIAhiXSvk8eNw55Upg/fF70i4B2/eRGuYcuWMY4bj4+GqEzFUzHj4dnNTRdKzZxjPokW45m/dwv5b2jPwvRBQXl5eXLduXS5TpgyXLVuW69Wrx6dNFeRIggoVKvDAgQO5adOmXKhQIf7222956dKlSX5HCKjSpUtz0aJFuVq1anwimSITsbGxHB4envgXEBCgdwIUBT/AQ4f0v7dgAarb2sKCBXiKYsbTkLu72s6FGRe57kWpi78/LjjDtNXkENkdqdVzSlFg7Ro+HNsZPRoXvxRTkvTGkSP4DdnbvSS4c4eN3P/WMH06XCTMEDjm4kECAhASULMmgoR79EAZghUr8LsrUgQTVv36zN98w/z558iEGzkS3xd97hQFIk1YEq5fx/eSQoQWaDTYZrt2cGu1aWPfunJRUYitGTJEPZ6Kgn2sU8d8NWtvb4RDdOhgOob02TO432rVwjGoWRPjNyVULKkBFR2N4yGsWxs2wIply/1PUbCt8eNhVTt2zDYxvnMnzouhkLtxA9e/yJB7+xb7b6ppcFQUxKCnp/qejw9KYpQsCe/Jq1c4jmKODAqCyCpZ0vgBRVGQsKDbWPjOHf1YOa0WDwRNm8JTExMDz03v3skLyQwvoNauXctZsmTh5s2b89y5c3nOnDncvHlzzpo1K6+3oiqlo6MjOzo68siRI/natWu8ePFizp49O69evdrsd+7fv89Lly7lq1ev8rlz57hXr17s4ODAp0TEtgnGjx9vMm4qNFQ9ATExuOAMLTijR+unflpKQgKeAsQFfPw4LpaRI1VhJKxSpoRSbCwCIP/80zpXYlgYLnR79fMyh6Lghj5okNqXa9o0+PVlELokrYiMxM27bVvbXSzJERKCycDWitXPn6sB3UePYqIxx+DByKwbORIp4ffuqbFNO3eqbVtatUIg+bFjCD948UK/z93Bg/oB6g0bJu/y794dD2NTpiA+ZtYsCAZ7Fh29fh3HUrdFyOvX2J+pU01v68YNfN6li2nX1927+KxZM/1SFVFRagzZgAH6li5dHj9GgH6NGngI1p3QQ0Px8CgsL7NmwXJlC6Gh2PeOHdW+h7Y0sV69GsLDUHydPQsXpSjI+vq1sbVJoNHAnTx2rLqeV6+Yv/4azaiHDcPvqW1blCVgxnEpWxZlDkxZj0RjYbH9589hYdSt33XjBo6ncDEeOYLrO6mMzgwvoMqXL8+zDH1ezDxz5kwuX768xevJmjUrV6pUSe+9fv368S+//GLVeOrWrcv16tUz+7k5C1TbtuF6P9Dnz3FB6wYIikA8Xb+upRw6pD4JMsOH3qkTc9Wqqvk7Lg5+e93u1rps2gRBZM2TdFQUBI0tY7aVt29x8544EU/DItBz927T9VUkEnsirCSurrjmUssqGh+PSSklBWnbtYPlWdeNZ4rQUPyOGjfGU36rVggtELFNpUtjLK1bo4XLxx9jXKJsyoEDsHCIGnViO0eO6N+XTHH7NgTbqFFIPR8zRn9yTSlaLYKSmzTRzyg7ccK82/XKFVjwe/QwHeJw9ixEU5cuyWc1X70KgVi3Lu6xpixqMTEQJ6J6uRBSAQE4b0JADx2qehus5coViNnq1bFPjRubrnOVHHPnmj6nhw7hmIhA7shIHENzNopFi3DeheiJjYXoKlJEbRQ9bJh6LWi1zH/8gTIHpgwNorGwmFPDw7H96dNV61R0NIwF/fvj/69ewSLm4WFaQGd4AZUtWzb2NVGtzNfXlx0dHS1eT8mSJblLly567y1cuJCLFy9u1XgmTZpklXATJ2Dx4nDu2lU/OPrSJfildbPP4uNxYSdh5DJLo0b69UnmzsWN8M8/1fcUBU8ynTubtt7cv4+JwZpq6fHxEGvJeERTDY0GN/MFC/AU4u6O/VuxAvsj3X4Se6AouElXr46bf2pmjSoKxMmOHbav49QptW/djBko0miOyZMxKXXvjsnl0iVM5uHhiGfJnh1P840aISZl7VpYCEQOTu3amLhWr1Zr+Gi1pjO4DGncGMe1Y0dYN+bMsX2fDXnxAmPWzcCKi0NoQLduxrFVly9jPH37GrvztFoI5lq1cE+11mX75g3uUdWrQ4CYqyV14AA8COL6unsXxzEiAmPo0MH6kAvBwoUQJTVrqrWeTDWRTo4JE+AFMGTLFswFYp7TaCAIzQnigwdxPMWDr7juCxZk/vVXuNz+/Rf3c+Ed6dABmaCDBhmv88oVzF9PnqjrW7oULmXd87l/P4TpjRtYZvlyCFzDc57hBVSZMmV48eLFRu8vXryYy5Yta/F6WrVqZRREPnDgQCOrVHI0adKEXVxcLF5e9wSsXIkblK6IWr8epnNdIiOhxK1tjHj3LgSELsOHM//0k3Gs0r59uHBN1aGKisIFO2GC5SZ0UY9jypT0IViePcOPecAA7GeTJnjKOHYMJnFbsx4lHybXr+NhZ8wY1VWemsyfb7rBr6UkJGCiDgvDRFmrlvnf5evXmKA7dEAcU716cL2ImMyvvmL+5Re1UW7BgvgNtW6Nz8+ehWXEsMr42rXJi6FjxzChN2iAe9eaNbbvsyH792My1b2P3r+PfTW0mCckwILWoYNxT7a4OMQzuboiWzilHcUUBe6+9u0h1ky1eNm8GXOFOGfnzuEYxcXhr0EDHHdbtt2uHe7TffqogtHarGdRisDUQ/OyZbjv6l5vCxdiu6Z6LopSFbr1vebNQ4ZexYqI4d2xA/ssfnt//40MvUaNjC16on6VbgyWjw/mVN0irsHBmBdmzcL+P3iA61u3FEOGF1ALFy7kbNmycc+ePXnNmjW8du1a7tGjBzs6OpoUVua4dOkSZ8mShSdPnsy+vr68fv16zpEjB68T0ZXMPGLECG7Xrl3i69mzZ/POnTv5wYMH7O3tzSNGjGAi4u1WFNMwPAHLl+NGpHvBjhhh3C5FlCUwF9Rojr599X3xioIb02efGbeT8PZOut/dihW4aC0t9qkoiCUYPDj9lSGIjITJftYs/LgbNcJEUbcubmTjx+MceHkhLkHWpJIw4/fXqROCoFMrSNyQI0cw2aTkQWTePLVCc4cOpgN6BX37QlC0aIGHpsOH1dgmb280DT5zBlam/PnxINKnjxrg3KwZRMe0aerkY0nLFtFUePt27O+AAbbvry4xMVhXv35qbJqwRNStaxyP5e8PgakzFTAzrG/Tp0M4LV+eOvGWr16p25g0ST8zb+5c/X6ne/fiXqXVQkjUqGFbUWTRZqdfPzzAL1lim1jXatXGxYZMmwYBrnsNG1qbdHn+HPuj63k5eBBtgsqVg7A/exbXi2hLs24dMkF/+81Y1IaEYFu68W7x8Ygz7tRJdTErCsRdgwYYQ3w8HpI6dsQyGV5AMTPv2LGDf/vtN86fPz/nz5+ff/vtN95lQxfJvXv38ldffcWOjo5cvnx5oyy8Dh06cJUqVRJfe3h4cJkyZTh79uycL18+/v3333n//v1WbdPUCViyRM1WYcbThyh2povoo2WNiTUkBEpbV8Botdhm/vzGFqKXL3FjNJfWeeMGhJw1vbyWLsUFmBGsPIqC43vlCiaRGTNwbho2xM22bl384P76C0/HZ85AYGWEfZPYzps3eLBp2NB09mpqIZ6UUxKU/uIFnqS1WqR19+ljftnr1/FbHTQIVqfq1fGeiG2qXBnZdiNHwlLh7IwJrEEDfO7tDXEZGqp/35kxI/l6WKtXIzapenX8paQrg8Db2zhNPSTEfJzLhg1w+eumyAcG4ty7uyN4/l08DIrMvBYtEHYhjsXw4SgfIVi5UnVdvXihX4fJGry9ES9Upw7+36mTWjbBGhISMGZTtZ9mz4bA0hXRwtpkqgROZCTEuK4V8sYN5uLFmT/9FOfi/n18XwjHM2eQ0PDll8bCODoaMWqjRumP4fRp4zlNuEmFbeTMGYjMw4czoICaO3cux/y/re/JkyespAefkI0IAfX8uf4JWLAATwBi18LDccIMLU7XrkHgWPP0M2uWaVN4cDBuhr/8oj8pxMbiQjPXfDE8HBe24RNaUmzfrgYCZmQUBTezixfxpPXPP5iQGjVSBVbTpjBnz5iBZc6dw49ZWrEyHvHxiLmoXl2/aeq7QGRKGbqQrKVLF7hmREC3uQcwRcFTuijeOG8eBEW7dohtevIEdZ8OHsR6ChbEdT5mjJp527EjJrXx49WnfUMxZYroaNzvli1DLFJK3JWCzZthVdZtnHvsGI6pYbB0RAREw9ix6u80PBz35CZNku5nl9rcuoXjFx6ulr3ZuVP9fPJkNf7I1xeC0ZZShWvW4HyKIHVbK73HxCCZx9RD9u7duEfqXoOBgRAr5jL0Bg7E9SSOf0AAMvBKloRV7sULfF9UMnr4EAVeP/3U9MPO1q3GWe9v3uC4jh2rPgzHxcGdLGLj3rxhbt06AwqozJkzc/D/p0tkypQp8f8ZESGgGjQIN3r6+fdfXCziQnn4EDcqQ9FhbRG9uDi147ohMTF46nBxQYaJOLSKAlOyuabBGg0CPGfMsGwMzLjJ1qpl2487IxETgxvZiRO4KU2eDDdtw4aqm7BZM7g2Z8+GP//GDfsXB5TYjqJA9Ferhho19kyft4SEBFh1UlqI8/x5XHvMeEhLKrFjzRp0LGjRApO2iwviQERsU6NGSCufPh1WkVy5cL9wc8PxevIEladF81txf/rzT/wWkmLqVDyQubgk3VbGUubPx71LiKG4OIyjRw/j++DFizjPuqUERUael1fKxmEvLlzAfTo6GhN8w4ZqcUpFgdAT2W2XL2NZU/FFydGjB85v27YQGO7utlnXk6r9dOUKjq1u04/kMvT+/ReuZ2E4CA9n/v57XI8DB+J148aIc2XGHPPxx/jclCXNsKCmYONGjFu3RIW4Fi5ezKAuvBIlSvDChQv58ePH7ODgwFevXuUnT56Y/EvviBOwZEm4UbA4M4IshwxRT+rx47iBGYqlmTNNZz2YY/duxDOYIj4eT5kTJuAH4+GhXqh798LiZcqcrigI3rMmxunyZTzZ6D4VfohERsI9c+wYYstGjcLkI8zo4nysXQsLVnBw+gjG/xA4dw5Cf8qU1KvnlBwDBpjuI2YNGg2ETEgIfr9ubuaF4Js3+F3u3w+hsX49REifPki7DwmBYNq0CaVQihaFiJo5Uw3AHjAAgm/2bHUie/IEE1tSvHypVvzu0cP2tHxm/EbGjYPLTfxe7t2DhcIwg1GjwTlu3VpNdY+Kwn707PlukgOs4dgxWMPi49V+p6IIqkaD/ThwAK8PHcL9xFrhHxODh/bRoyFatm+HQLGF169xTZlq9vz0KT7TtVIll6G3bx8ePsUDeFwcrpuiRXFc3r7F9SOqHCUkoMBr4cL6hToFWi2WNSxn8fQptqPb0ic0FMd39OgMKKCWLFnC2bJl40yZMpn9c3Bw4EyZMqX1UJNFV8GOGQMFbMjMmTAdipM3fz6Eii6KAtOipemrohqwuQJ2Gg2yPBYtwk3SxQU3RlHu3sXFfEuCZcsw4VvqVhTl+a3twfQh8fo1JqONGzGxdOqkiqsmTTDJLV6M8hJ+fjIGK6UEBeFYt2iBIOq0NHIvXYpJJKUsXqzeX7p3hxXDHIMGYdKtVg2TRbVqiO2rXx+f9+qFLChRoDBnTjwEVauGe8erV7i/iPpSYuLu2DH57OG+fTE51qiB79rq6tZoME4xgSoKYj3r1zd2RYlJcuVK9T57/jy2byp+J72wY4caOC6aA4t7emwszoE4z2vW6MfWWsrDh7DCNGuGYzJkiOnAcEsQ9Q1NJT9FREBcGzayTypD7/JlnCPdsgTduyO4vEoVCP2//4boE9dg/fpw6U2YYPpY3LxpHCen0cCQ0KKFfkmFRYsyoIBiZo6IiODbt2+zg4MDHz9+nG/cuGHyL72jK6C0WtyMdDMDBKJfkaLgr0cP4yeouDhMqJYGtd64gRuaORQFN9Lp0/HkPWkS1n/1Kn6stWvjCdUUe/fix2tpj+dnz3DDTC8m8oxEVBTE7O7dmCxEkHudOpgUmjTBU/T06RAF//0nA90NefkSVpJevTBZdOiArEt7NsW2FkXBw1OXLikPVA4Jwe9Lo4EQT6p1yu3buA9NmwbL07//Ists7FgI9MhI1HqaPRuTYalSmHBWrlRdguPHI0bM01N978YNhBokhY8PLCV9+2KMttYzio2FRUzUtkpIwMQ6darxsdy+HcdGWEZiY3Gv7dBBv5BxesXTE8dLUSB2XF31i0XWqKHG90ybZls82fbtCNGoVg1CuXZt25MnHj3CdWPKi5GQAPfj339bnqEn1qfrHvznHwj877/HvW7VKlwPQoQNHIiEh9atTbuHY2Iw9xm2c7l2DcdAtJHJkC48XTw9PTk2A/frMDwB0dG4gZvqRzV1KgI0FQViydRF/OoVLiZLn5i7d086rkJRcOMUQXuBgbihd+mC9N7OnTFpm1Ly58/rp5UmR2QkFP6KFZYtL7GMmBjcWL28MKFMnWoc6N6kCQokTpsGkeXlhYk0MPD9bIcTEoJJoW9f/N7atoVIePQofbhGY2Lw25o+3T7j6dULrkitFq47c/cHRcH1cOkS7i8iY09MxIqCCv+5c+P4tW4NV96DB5i4Y2PxOxZizdVVnbQaNky+7EqLFhBe4gHAln0PD8c+CPdVdDSsJ4Yu0MhIPIgOG6bGdd64oZZOyEjMmQM3GzNiinRjnoKC1OQDUZ9JlLCwhsGDEePasCGuH1dX270Gt2/j++auh7lzYWW3NENPlCXQTezYvBmJDV9+iYf+Q4ewjAhYnzcP1+4335hv1yLauegmGURF4b4xYADzixcZXEBldEwpWJF+auomN2kSxAwzFHzVqsZK3tsbF4olE9+LF0kX0RN4eOjHYl29ih/ppElqrIIp/7pIKzXl9zaFVounv6FD332g7odMTAzEg5cXgnenTUPcSLduEFdCaIm/Zs1wzkeNwk111SpYC86cQcrvixfpS3iFhjLv2oXJw90dE/+SJZgA0oNg0iUwEKJFCICUcvGiavlZsUKtBG6KTZsgsEWLF5GxN3s2JqT4eDzZ//knjuNnn+Epf9cu3COYMZlv3qz/3okTSLlPijNnIOJbtICg1e1TZikvXuj3OAsNxf3NsHOCsCSIgOKEBNzHmjdPW3dtShg3Tk3iOXxYP+ZJCNywMNxjbWnTEh+Pcz51KtxfwmVra6jxw4fG4kSX3bsxx1iaoRcdjX3WrZl49ixinsqVw+/p2jXMR6LK++XLcPeVKIHkHlNzTkgI1jtliv7n+/czV6limYByYGamdEK+fPnIwcHBomVDQ0NTeTQpIyIigvLkyUMeHuE0bJhz4vve3kR//km0cydR9uz63/nrLyIHB6KxY4lu3SIaNQrLZc2qLnPgANH27UTLl2PZpJg2jShvXqLu3ZNebuFCotu3iRYsIMqUiYiZaMcOvK5QgUirJVq0yHh7gYFE7dsTTZ5M9PPPyR8TIqI1a4j27CFatYood27LviN5d8TEEIWGEr1+bf7f8HCi+Hj1O+IOkjkzkbMzUZ48Sf+bMyeuKY0Gf7r/t+S1RoPr9c4drPOPP4hcXIjKl0/+N5FWXL5MNHw4fkeff57y9b14QdS6NdG2bfjNNm1KdOgQUZYsxstGRhI1aEA0ciR+e61bE61eTTR9Ot4/coRo2TKiIUOI9u0jWrIE95ljx4jGjyfavJnIyYnI3Z3o8GGievXwnrMzvr98OVGhQqbHyUxUqxZRv35EW7bgutm40bp99fcn6tKFaP58oi++IHr+HPedadOIfvgByygK0ezZRBcv4n5WsCDR/ftEAwYQtWtH1KZN+r02koOZaNAgoq+/JurcmWjtWqJLl4jmzcM+XblCNGECroXoaFwL27cT5ctn+TYCArDu0qWJatbEce3alWjDBqIiRawfc1gYUYcOOG8NGhh/fu0a0dChREuXEpUti/eioog6dSKqWxfnVxetFsvnz080Zgz2++FDIldXzI/DhmHcnTvjuv7+e9wnmjQhOn2a6KuvcNxKl9ZfLzORpyeO3YIF6uePHkVQ2bJ5KDw8nJydnckc6UpArV692uJlO3TokIojSTlCQA0aFE758jknnnQi3Og2bMBNzPBHPX48kaMjxNOmTURXr+KC0GXWLFwcw4YlPQZFwUXcvDluekmxejXRyZO4GYqbcGwsbkobNxJVqoQbqyHh4bhB9ehBVKdO0tsQnD2L/VyxgqhUKcu+I0n/aDREb98SRUTguoiIMP3/qChcY5kz49+k/m/us/Llib78MmNMiuvW4YFk5Uo80KSU2Fiihg3x26xQASKhSRMISVOMGIHPZs3CPaV1a/ymp00jqlaNqEYNoo8+wr/R0US+vjhHK1cSHTxINHUqHnxiYrC9Q4eIpkzBxL1mDSZyc2zZAgHk5UWUIwfWVa6c5ft68ybEg6cnUcmSEEW9emHi/ewzLPPqFVG3bhB4PXpgUpw3D9ucP5/o448t3156RVEgaGrXhkCaPp0oIQHzBBFE8KpVuNauXyfy8IDIzZTJ8m0cPgwhEREBIVKyJFH//jiH1ogxQXw8UZ8+uGYGDTL+rT57BsE0fjzR77/jPa0W81ru3Hjf8DuzZ+MamD8fwikkBNd2dDRE8uDBEF99+0K4E0FMdulCVKAA0cSJpsX0o0cYa9u2+PztW8zfyQko6cJLJXRdeKLNia5LYcEC1WWni6LAffLPP3jdv7+x396azDzRP0nUEkmKrVth6jZ00fj5MVeoABOrKfdNbCyCU63xv/v7w/R87pzl35FIMhIaDVxi9nRbKwqCoEUG2c2bcMuZ4/59/KbnzUMs2KJF+Lt3Dy41ZmTh6jYNzpuXec8euIOCguAaEjFPotULM9wfjx+b33ZsLNwq69cjaLd3b+v29dQpxHWJAOMLF+DG02174u+PbYiYUT8/uIcWL05/LtyUEh8PF/vhw6ZjntatU9viLFwIl5y1TJwIN3/9+pgzrl6Fe8/W2nWKAhdZr16msy7NZejNnQs3s6mEmC1bcB2KMUVHM//+O+pBtWmDWLlu3fDbE/NVWBjzd9/BrdekiekkgoQEuDDbtmV+/Pg9iYEKDg7m27dv882bN/X+0juGMVCLFiH7RPdGOmCA6SrfioK4gunT1RoYhrFG5oLNTSEK3lmy7P79CCY0zGDQahEj8/nnpv3UWi1+0JMmWX7jEkXRrKl0LpFkBMLCcJO3Z5NcZkxGolGvokAsmKsirSh4eLp8Gb//ly/xEJSQgAlSiJ9PPsHnHTsyV6rEXKwY7hU9euDz3bvxQHfjhlqs08cHQi4pZs1C3IqLC+KVhPCyhF27cB8SxTAPHMD9R7f32e3bahkG0fOudm399izvGzExOA4icaBNG/2M6REjIFgVBWUQTN2rk0JRkIiyeDHmnWvXIKTq17etYKdgyxacT1MNmRMSYCj46y/9uUM0EjalYc6cwbUsxLRGgweFokXx/ps3+H716vqJWx4eCDAvX958C5vz55krV87gAurKlSv85ZdfJtZ+0v3LaHWgBBs2IOVSqGKNBgLCVCl8RYHVav58pFy7uhpX1rUmM08s6+eX/LInT+LGbHjtiB9lzZoIXjUMchdVzfv0sfyJW6NBWuno0emvEbFEYgs+PpjYL16073p37YKoEZPMsmVJF9ndsQMWha5dkX3Xsycmh61b1Xpzp06hafCFC2rT4CVL8Dv39VXbvrx5o7Z6YcYTvrlm5Mx4wq9RA0HqAweaL+5riuXLsX2RqbV6tX6qOjPumUIUBgdDrM6c+WEkqEREqA/EsbEQVKImVEICXnt7q82DrS1mrNXiXK9di3nn3j1kujVvnrI2VaL+ljmrpcjQ0/VynDsHEWSqzdG9e5jT7t3Da0WBgCxYkPmnnyD4nz+HCJs3T7/zR4kSqF4+YIBpr8qzZxlcQFWsWJEbNWrEFy5cYH9/f378+LHeX3rHXB2JvXv1n6wiInCRmnpqEnWhPD2hljt2NLbuWJOZ9+SJ5YLrwgW1urEuGg1uZqIZ6PLlxsJn7Vq4B6x5Ylm6FOvN6D30JB82Bw/iASOlfe0MuXEDDzVCVPz3H9w55h46oqLUFiXdusEK1aUL7jsuLupvs2JF5h9+wERSvToy8fz88FtkRtuT4cNxf2rTBu89ewY3SFIMHQo3oKsrJk1LXECKArfTwIHqfpl6INu3D5NiRAQyQ11ckhZz7yOvXuG4+vqq1eWFl+LFC3wmjk+dOtYLn/h4nOOtW3F8Hz/G/zt2TNmDrp9f0g8Xe/caZ+iJgsymCjwHBen3x2PGA0DBgsxff40HjIQECKiGDVUxqdXiwcLZGdmmhkVgM3wdqFy5crGvr29aD8NmkjoBJ0/q94p78gQXlanecRoNnsa2bkU6pqmK5vv3Q7lb4jq7cwcXnCWFMEXtFMOmpLGxuIEdPw4hVbu2cX2rw4fxvjUF606cwNhsaWwpkaQlwvraqVPKXB2mEOVPRCzQ48eYMJNqQTJ2LBrR1qih1nwKDkYpEeHyOXAAlcZPn8ZyhQrBUtS3r5qC3rgxJp3eveHOYYY4OnvW/Lb9/PC9ESNgRV+4MPl91GphiZ4yBcdSq0V5FUO3zurVEHKxsbhfuLnpx0R9SDx7huvi2TO1Erg4FqdP4zgpCuq/DRtm/fqjonAP37UL81NQEGKuRHFPWwkLg0tQtAYyRJSi0PWWBAfjGjbVa/HtW1ggRVshZly/n3yCZsRubqhL5e2N341ug+aTJ2F1LV0apSKEOMzwAqpBgwa8zdwRzgCIE/D8uekTcOkSxImwBl26hArfpoLmEhJgPt27F08Fpgpkzpyp1mZJjnPnYOa15EZ//Tp84Ybuw8hIvH/1KlyMLVsi+F23p9iVK7hgran6/OBB0jVEJJL0RkwMnsxnzrR/4HJsLH5n4un77Vv8ppJyxT98CAGzZAlCAJYvR9VxHx9YrcSYS5dm/vlnuPnq1kURzcBAta2LiIN68QJP78x4IKpdO+kxt2sHi3m9evgtJ1cZPy4OD4miN158POKrlizRX27GDEzeGg2EVPPm0mLt64tj/OoVXFlubqo3YsYMuMWYcdx27bJ+/W/eQFzv2YP5KjQUMXgjR6Zs3PHxuLY8PEz/Zh4/xn5dvqy+FxWF63f9euPlRUV60d6HGXNRq1aoF/XddwihiY1FcHn37qpVNCYG28qfH/8GBLwHAurVq1dcu3ZtnjBhAm/bto13796t95feESfA1TWcR4wwbVXx9sYJE1Vbt21DnIKpC0pYffbswZOGoWvN2p55Bw5Y3oTy1CkofN3qscz4MQkfOTMEnouLfo+phw/1/dSWEBqKm/jWrZZ/RyJJC54/x6QlWkDYE0WBRUtYjLRaiAZdd4UpmjbFxCMe0KpXx4TVsKEqvEaPhvXJ3x+9xYoWxYQm2rowq3FQo0apwciTJiVdCPTSJaynUye07rCk8nfbtqpVIDIS9xrddlaKAgvKxIk4BuPHw1r1IcQ7WcLNm7gGIyJw/+/XD+8rCu7x585h/nBzsy3AXvTi27tXzcibOFHNFLcVRYGLrVs30yI7NBTuvH371Pc0Grh0haXScH2G/fGYcUyKF0flctFQ+sQJ7JNuUdeVK+HS++wzZk/PDC6gdu/ezc7OzkYB5BktiDw0NJzPnMGNr0MHuMV08fPTr+g9dSqeZE0RFQXX3+rVuBka3kCsycxjRvabOcFmyO7deLI09H+LdgIiLC0yEib+Nm1Uf3NQEG7i1gTVxsfDbWBNVp9E8i65cAEPQJZW47eWadP07wWjR8OalBT79mG5Pn2QqST+3bFDDeT290eMyJAhsEw1bozMpBcv1LYu/v54eg8Px6SpKLj/VK9u/vcosgKPHsXvX3wvKY4cgauPGVaUmjXxwCZISEDs1sKFuL917IgxS/TR9SoMGwa3HTMsSKI/3ePHOL+2uJhF/OyuXXi4jY7G9WOJezY5tm+HtdJUCEtsLOYdXWukEF69e5uO7fL0xEOEbuhJSAjzH39ASP34IwRUaCjE+4QJ6nqCg1Gyp3DhDC6gSpUqxX369OEX1uS+piOEgGrTJjzx5Dx4gBta/fp4ihM3F1E6//p1vNe1q3lzqzCpTphgOrPl1Su0gbH0sM2ebXlHeE9PPN0Y3hSFuVV3mzdv4gY6fz6E3ps3uLmaaqhsDkVB8F/79sYuRIkkrYiLw++mUSPTadn2YM8ePJmL39r69bC6JEVMDCa5c+fwsHbtGgSHCCgX7nUXFwiot28xqXz8MfZl1iw1jkTEQU2frlqD5s83rtejy+7dsA7Vrw/Rk1ztOa0WguzNG9xDdOs5Mau97rZswWRXty6Oi8Q0hw9D9MbH40H67l28f/MmzolGg3lHlKewlvv34XEQ9QLj4+EKE42dU8KlS+azxLVaiOxRo/Tnno0bEdJiam64eBHr0231oyhwaRYuzPztt4jPi4mBIcHdXc0wZWYeMiSDC6hcuXLxQ909ymAIAbVyZTi3bKnv/goJgWXF1RVPlDExeM/NDcGZcXG4WZhrhBgSgu82a6bvLhN4e+OCsLRn2ciRlj/VzZhhWrjdvYuboe5ThFaLeiI1akDxx8QgO088HVnK8eMQaCKIVSJJCxQFPeWqVUN9p9Qqu3HrFiZAcc+4eBEW5+QyqSZNgtioVQshA7Vq4eFszBhVeOzfj1inAwfwfrdusD6JRBaNBk/h9evj/lGtGvYzPh7/NzcG8fnOnbAMiFirpFi9GhParVv6fcyYcR+pXRuxVCJzS8ZFJs+CBXBviYdyIS5Wr1YflMeMwWtbEIU1166FOI+Ph7XRHlE14kHcXHHlhQvxYKA7l3p5qYkShkRGooDnoEH6VreHD+HO++QT5t9+w/Xn74/rbcUK/M4zfAxU+/bteZmIKsyA6J4AUdVXN8CaGTeoVasgPP76Cyexbl2YtV+/1s+qMCQoCE+Pv/1muq6GNZl5ioInCUuFzYgRpgXXlSu4aRs+EYSFIXCvaVNcrN27J9341BSvXuGmPHOmrBclefecPAn30vTp9s+y0+XlS9XlwoyA1mrVkrd0PX4M0bNmDR5yPD1hUXrwQC05EBODSePHHxHbVL8+86efMleujOWFm2TMGAiXZcvUStfr1iXtrlm4EN93dcVDkrB+mCM6GvsZFmbcOD0wEOu5ckV1k9ra2PZDQ1Fg/Tt4EA+tHTqoc0DPnpgXNBqce8PUfUs5dQqCfskSeCRiYzG/mStMaQ3h4Vi3bkadLnv3Grv77tzBtXT/vunviHIauuEzCQmIlSpUCGUMZs2CGJw6Fdevn18GF1CTJk3iggULcocOHXjGjBk8d+5cvb/0jqGCPXYM4shU6rGiwPzasCGeCN3d4Re+cweCxDB4W/DkCfMvv+AGZMraJAqTWVJKQGT6WeJiE4LLlDnfywv7YWrMfn7wOffqhbozY8daF9+kKLhRN2jw4aYuS94tt29DgAwdalzOw97ExuIpWNQ0iorC0/WDB8l/t2VLuEFERparK36DjRqprolRoxA4HhiICa9HD+Y8eXCfqVYNAksUaUxIwHtxcfjdVa9u/AAoCA/HssuXI/7GEhfRP/9gkpw8WT/Q/MEDNSZ0+3ZM9KnlJn1fiYnBdfPwIY7v4sX67/v7q4HhlpSzMcW+fXAXzpqFmLvoaIRo6AZl24pwDc6YYXp+uHQJ17dIvmLGNV29uumi1MywUDVpol+qgBken5IlEfdUqxYeWK5ceQ8qkZcuXdrs3yeffJLWw0sWUybA//7DU2xSgubWLZhGS5VCAbudOyE4zPHgAQqGdepk+vOzZ3Fz27o1ebESE4MfgSXB3hoNbtqmXIjix2UuS+bCBVysdepAMFqbTSPqeehmZ0gk9iQgALGIHTsm3e/NXojYx7171ddt2sB9nRxHjkDgDRmCmI+BA2Ex27VLddv4+eFpe8D/sXfV4VFcX/RGIFCCO8WdQikUfm0hRZLgENxdS3Ep7u5SXAMEd3dJsODuHgiBhLgna3N/f5xOZnez2WySDZvAnO+bb5Pd2Zm3M2/eO+/KucOwIh8wgLlsWSy+xFItzLDw7t+PTUwJP34cbqGEMHEi4qScnLBITExEVAwW9/PDOCCOS3fv4rn28UE7EsrOkpE4vL0lrbDWrSX357t3ktTB1avISktuks7OnSA6U6ciqDsszPSSYYlBEOCSHjrU8Pzw9i0I4P370nvh4Vi8J5S9LQiwmrm46JKvyEh8r0ABiMru2cPs65vOCVR6h0igDh/WvQF37oAp65dB0YePD+Toy5ZFRzE2gD1+DE0XQyKbzHhYpk2DCywxTabQULTPFNmBmBh0RkM+6507dYNg9SEIGKR/+gn1txJa3Ro799Ch2FLTpSLj+0JoKGICW7bUHZxTG4sX65ZlmT7dtAwnhQLE5dYtLFoePYKVNzoa5EjUSXJygsq4qERerx5StgMCpFItYvFflQoToaiT06SJ4QwpZoxTzZsjfXzyZJCpxDBsGALMBw6ULBYXLqAd/v5ItJk9W86+TSk8PEDCAwNxX8WF+/HjUmHnxYul2orJwerVIO/Dh4OcBAbCyiXKXqQUW7cmrPcVGIg+o+01USox7yxcmHD/efkS/Vs/ZGX3btSBrFKFuWNHmUBZFCKBatIkTCcTgFnSf0pMcVvUP+nVCwTJwQEdylDWwY0bWGGKNZEMQXQJrlplPI5IlCYwRQBTNPkbktlfswarYmMDoUKBVUzevLpFMU3F8eOYDAydX4YMU6FQYCKpXx8Wna+JEydQW1J8TvbtA4kwBQsWID7JxQWWMrG48JQpkrbS8eMgS8eOYSE1ahRqgU2cCE0pUaXa1RVxT+fPS5arK1eMq1gPHQordKNGsHgk5hJ68wYT4vPnsH4ww3rVpg3cSq1bG8/0k5E0LF0KMnHrFmJQxXF/wgQEgmtrRSUXs2Zhgd+3L0hJZCTiiMT4uZTiwgVJSV8fMTFYOGzeLL0nZm9rl27Rh1KJZ6R7d93FgZ8fxGV//PEbIFAfP37kVatW8dixY3nEiBE6W1qHSKD8/MK4efP4AXavX5te3Hf2bKzaateGWb1ZMwQKXrqkS06OHMEq05iEgUYDAtW4sfFAzzdvQPL0BTsNwd8f+2pn0YiYM0cqXGoMp0/DbeniknAwYELw9cXDsnq1vGqVkTRoNBj0nZwwoXztBAX9Wpb37iVckUAfPj4gTHv3YhLbsQPKzm/eIPZJEDDBlCyJQNl377D/L79AD0ejwX6fP+NvJydJsFe0kLdpk/Ak5O+Ptg4aBMJlSmJI5854vtu1Qzv9/EC+vLywEEpMJFRG0iAICCQ/dw7jvlitQq3GvXv8WFcrKrnn+OcfHL9TJ7iINRoQ77FjzfNMPX4Mi6ohzTWx7M+0abrj/5Mn6FPGxFyvXcNv17aYCQLz1KnpnECdP3+ef/jhB65YsSLb2tpylSpVOEeOHJw9e3Z2dHS0dPMShUig7t4N4+hoNkiiRHEyUwjD8uUYfBo0wKD49i18z87OMPeL5GXpUui6JBR4LsLbGyuS6dMT3vf+/aQXKjZE3saPR7sSw9OnCIpv3hw6NEl5oDUamKPbtZNqhsmQYQxiLbVFiyzjBg4I0C3u7esLEmNKsLpY4uXOHekYYtB3mzZS4LmoOP7pEyw/ffuiZMX587AcjR2L/Q4ehDXrzh1JyfrRI+MB4RMnInuvfXvp3MZw4wZiry5dgsuHGeRr2zZdMWEZ5oUogPruHUQpL17E+2KfCQtDhlqLFslXdxcrYWzbhnlq/Xq87+oKC5c5Su74+GC+S6gO47Jl6N/aiw+FAn28T5+EraPh4Wj7mDHSXJfuZQz+97//8eT/7Mj29vb89u1bjoiI4ObNm/Nqc8ifpjLEG5AxYxifOMFxJEo/KFTU6zAl8G7TJjwI2uZ+jQbsuVcvrCjc3LDi+PXXxB8GQcDq1dk5YRPu4cOwfpmCp08lYTz98wwdGr+2lSGIRMzNDRPE/PlJm9zEQpQpTalVKmHa9fEBwb17FxOApydWye7uWNWdOgUXyZEjWOns3QuLxrZtmFw2bsTvXr0a2TA7d2L/K1dwz728EJuQ1GrpMpIOQYDVY+NGWCxHj05asWtzIjQUixPxuY+JgZvClNhDQcAYcOwYSMzx43DLnT2L9yZMwH7v3kE0cNAg6D717ctcvjxkC8R4p4gIHK9hQ0wwnTpJi7EePWApN4SwMHynVy+ce/fuxNvcpAnGuwYNYNkWLVH6MgYyzI937+CeFr0FYhazdiD5pk1YlCcXYmLR0aPol//8g/c8PHDPE0suMAVhYSB6CZXJPXAAz7Y+77l0Cf09oSw9ZiwixHAQUwmUFTMzpUFkzZqVHjx4QKVKlaKcOXPS1atXqWLFivTw4UNq0aIFvX//3tJNNIrw8HDKnj075cwZRipVNpowgWjYMKJOnfDq5CTtGxSE92fNIvrtN+PH3bePaOZMot69iYYP1/0sMpLowAGi/fuJbt8mKlGCyN2dyM6OyNo64WOGhBCNHUuUKRPR7NlEWbPqfv7PP0Q1axK1aZP47755E+3bvx/HEyEIRP37E9WtS9Sli/FjBAZin4kTiT5/Jlq3jqhZM6J27YhsbYkUCiKlEq/6m1JJFBZG5OaG392wIZFGQxQbi+sTFYUtMpIoJibhNtjaEtnbE2XJgtcffiDKmJHIxgaf2dgk/W+NhigiAu0LD5dexU2jid+OjBmJsmXDlj07Ua5cRHnzEuXJg038O3PmxO/N9wZmIi8voosXsQUEEJUqhT5Yty6umyXw6hXRgAFEc+YQ/f472tmrF1HHjkSNGiX+/UWL8NqiBdH48UQzZuCZ27yZqEkTomPH0G/r1SN68IDo40eipk3RB2/fJnr9mmj9eqKyZfFMXbxIdO4cUc+eGIPc3Ijev8fzt2OH4TbMn0+UOzfRyZN4nk6dMj7GHDlC9OgRUZkyRD4+RKNG4ffmzInfYcrvlpEynDtHtHMnrv24cUSHDmFsWrqUyMoK88nffxPVr2/aWG8ICgX6UYMG6A8nTqBffvqEYy9ZQlSlSsp+h0pFNHAgUcWK8edAIqLr14kmTSL691+in3+W3g8Lw/6FChFNnYqxVR++vkSDBhH98Uc4jR2bncLCwihbtmwJtiXNEqgCBQqQu7s7/fTTT1SxYkWaO3cuNW/enB4+fEgODg4UGRlp6SYahUigbt0Ko4YNs1HmzCAhbm6GSVRYGN4fN46odm3jxz5xAmRkwwaixo0N7/PkCZGjIzpxuXIYqPTvtJWV7nuBgRjcS5Uiyp8f7zFju3ULnTFLlsR/e0AAkbc30a+/xj/H/ftEBQtiMwa1mujePaLixbHvly9EHz4QFShAVLUqfk/GjCBJ+pv4/t27GDRGjCAqXVqXEGXJAtJhZZX477EklEqJYIWFgWwHBmILCJD+jo7G/uL1zp5dl2BpbzlzYvvWSBczJn6RMPn7E5UsCbJUpw5RvnyWbR8R0enTGNg3bcJATgQyYmdneDLQx/HjRIcPYyJq3Zpo40aioUOJVq4k2rKFqFIlvH/8OFHXrpi8nj9HXzl8mKh9e0w+Q4fifysropYtsUiZMoVo8GA850OGgNT9+mv8NsTEELm4EFWogIVJu3aYMBOCWo2FzL59RG3bgnTdvYvxKyICiz4ZXwcLFmBhmzs30bNnWDAzY8E6aBAW8F26EPXpg3uWHAgC5jFbW/SL2bPRNzNlIurRg+ivv4iaN0/Z72AG2Q8OxoLCxkb3cz8//B4HBzxX2uR+3z48N8uWEZUvb/jYixeH0+jRiROoNOvCa9GiBa//z5E6evRoLl26NM+aNYt//fVXdnZ2tnDrEoe2CdDNDVkvNWow//wzYh9cXOAG0kZkJNx8plR2P3kSAeOi6J4hvH6NwPMmTRAXYUoplOho+Iy7dtWNZxILUZpaHmbPHrgM9IO6lUrEXhnSj9KHQoF2iBkWggAXaKtWUNU1RWDQ2xuBsytWJB6j8a1AEOAievMGaeLHjuEaLliAe/vXX3CduLjobs2b43oPGYIMlaVL4YY8cgRuy8eP4dIU3T5pAV5e+G09esDlO2AA+l5aK6EpCMiG6tVL1yV95AjuhynX8/FjuP3EGnFXrkhilO/ewXUhCPi8VCkEi3/4gO9UqYIUbY0GbhYxgeTePVyzz58lxfIvX+AmSQgrV8Il3aQJXDOJtX3NGmT3LVoEF7Yg4F61bCnHPX1tCAJcdhcv4r6LWnqiGOqXL+ifzZoZd3eZgvXrcS6xVM/duxiD+/RJWCQzqdiyBRl/hmRwBAF9T1/3iVmS31i50nA70r0L7927dxQZGUmVK1em6OhoGjVqFF29epVKly5NS5cupWLFilm6iUYhWqBEBtujB0zY1atjdXj+PMzvI0bAUiQiNpaoWzeizp2JWrUyfo5Dh4j69oVVp2hRw/soFETTphG9ewerS3Q0zJuVKhk/9oMHRGPGYBXbvTveO36c6MwZohUrTLsGa9fivAsW6L4fGwvz/YgRsAwYgyDA5Jw1K36HaDF6/Bim5+hoWPNq1Ej4GBoN0fbtsP716IHfpL9ikYFrHR4Ol672Fhqq+39YGK67aJ3Uhp2d5HLMmlX6W3v74QfcE7Va2lQq3f+NbSoVLKx+frBQihamAgUscNFMQEwMrD4//4w+r92HRVeKIXeCNgICYD3aswcWrDJlYPlp3hzus+7d4cYrWxaut2XLiF68IBo9Giv/48eJtm3DuS9ckJ7Jrl3hAly3DlalP/8kmjwZ1nHtcUmESgXLxB9/oB/UrYt2JISICFi4du3CuHbqFH7v6dPoC4sXJ+OCykgRIiNxT9asgZVm/Xo8R0+eEE2YQHTwIPps27awjqbE5XbuHO7xsmUYx/v2RZ+dPx/W4hUriDJkSNnvOX8e59i2zbBb/tUryaLasaP0viDg/JcuEa1apesV0Z+/E0KaJVDpHfo3IDoaMQnW1kQdOsBcvnUrzNgjR2IgEqFS4WY3bpx4vND69fDn3rlD9OOPCe/n6YmBsU8fxEWpVCBSZcsm/B21GoNx7twgU0R4rV4dg7kpmDULr5Mm6b4fFYVjTJ2aeNwXER72a9dwvbRjq3x88HA+ewbTsItLwrEYCgVMtwcPIgaldWvjcRsykg6FApOmdmyXuInvR0XBvG9oy5Ah4c/EzcYGbunE3MBpAZ8+IV5x5Ehdl8iTJ3Cj7dkDN6sxKJVYTM2dC9J1/z7RwoVE/fphLFGriS5fxufv3sFt0aIF4lh27IC7LGtWIg8PjClHjuD/a9cw6cydiwXbyZO4P+3bg+gYcm9v3Qo38qlTOO+5c8YXI+LzfeEC2lSzpuTuO3SIKEeOJF9SGWbAmzcgT8uWYQF69CgWP/v2YZG8fj0WTu3bE61ebXyeSAxPn4LALFkColK+PJ6HgwexqHVzQ0hBSvDoEVx169cjXEMfKhXmovfv8Zu1+93Tp/juwIGS0cJUApVmXXjMzCEhIbxhwwYeN24cB/2X23v37l32SUyBMg3AkAnwwQNE+derB1dUrlzQSXJxia/cqlajjIQponJjx8Jkn5imVEQE0pJHjEBbunZFNo+x7wkCZAhElWSlEi4BU9xnImbMgEtI31QaGgoXgHaRR2M4fRqmf0PumdBQZOw5O8NdYCxzLyoK+9avD1doWnFHyfi2cP063CL6z8qRI3B/GRIG1IeYHn7kCLJAW7fG2LBwIZ5JMZtOFNd1dsa4EhODczs7o96dnx/GGjFbTq3GOBQYiPfFEjILFyIbyRA0Ghxv1iy4ecVU9YTw6RNcQW/ewOXIDJdJt26Jf1dG6uPECalvaQu3rlsHmQlBgGvX0THlxZz9/JC1efYsxt7+/TGX3L6N44v1GlOCjx/RP43V47t+HefTD5+JjZXkDsLDvwEZg4cPH3LevHm5dOnSbGtry2/fvmVm5kmTJnG3bt0s3LrEkdANWLYMab+ieFyxYohJMESiVCpI8RsTAmNGR2/TBjEPpqRBHz+OwfXuXcRVdOiAmKKElMcFAZ1r4UL87+2NwTcp8gJz5+IY+mQlMBCd3pR2MyPF1NEx4dgvhQIxMfXqITbEWJp6aCiIXZMmSHOVIcNc2LIFz6S2pIdY36t/f9Pj8ZYuRZ26jx/xzIaGguz06SMdT6xcf/Qoc44cUDKfPx+TYsmSWDB9+IAxRnz+Vq4EiYmOxrOi0WAScXJKWPjw4EE8U87OkuimMfz1F3SlunTB8x0WxlynDr6fXL0hGebFrFmIExo3TnexvnAhxkZmLLDF+KiUIDoaMVEbNmBOc3HB+CzOJ+YQUQ0NRWyTsTkzIgJ9859/4vfhixcxv5w5k84JlLOzM48ePZqZJR0oZmZPT08uVqyYBVtmGhIiUIKAVeSmTbjRkZHQZfnlF1h2RJEzEUolhOoSK5wbFYWA8Zo1TQsWDwgAcZs5E0Tt7l0M+EOHShoh+u0eOxZilcyw3Ij1lEzFkiUYzPVJlFg6xhRVdmY8yI0aGQ9EFwSssFxcoGNlLFg1IACktmVLrIhkyEguVCr08XHjdElCVBSIREJBq4Zw8iSs0JGRsJa+fi0FksfGIrGjeXMpcLx0aRQW9/GBZbdqVeb8+UGIOnWCThsznp8GDfD+mjUoD8WMiS2h8huCgO8sWYLnSbtunyE8eQJL0/Xr0jgxcSKeMXmxknag0WB+uXQJyTba1SkmTZLGe1HjL6GaiEk53/jxEK28eVOyPkVEILlI7IspgUKBPjpwoOGyZyKOHAGZf/RI9/3QUObOndM5gcqWLRu/+c+up02g3r9/z3Z2dpZsmkkwZgL88gUd59AhWH9UKtzsAgXA9PVJVGwsMs8Sq9H14QNIlJNTwmqt2hAEdNiGDSU3w7VrGJRHj46v6C0IeF+s0q6/ajEFK1diZay/yvX2Nr3+HjOsX9264XiJ4dYtWNjq1wcJvHbN8ArYxwf3oUMHubaejKQjOBjZa/rPhLc3+p6+iK4xPHuGRUJUFFbt7u4QQRRVx6OiJFLFDPHMLFlAqrp2xbORLx+sVWfPYoEgok8fLBSio3E8pRLPgzE18XPnMKE6OoJIJVbzrk0bLIgaN0Z7fXzw3Y4dTb8GMr4OxAw8UYQ4PBzviwLIGzfi/9u3cT/NoSouKpS/fAkSc+UK+uA//4Bom6P8y9mz6HM3bya8j58f+uqiRbrnTPcuvHz58vG9/0wp2gTqzJkzXLhwYUs2zSQkdgPOnMGkvncvUps1GqwAc+SA3IE+iYqOhjVF/319XLyIeIMmTTheEeOEIJr3V62SVscXL+IYkybpDpaCgMF4yRIQvyZNkl67bv16mFD1HxKxPqCppmJBQP2jIUNMU/IWBLgUpkwBaezdG0rr+gPC27dY+ffogb9lyEgMz56h7965o/u+pycmiKTEeAQGSmWRpk2DlSg2FpPXo0dSKrqotv/2LRZfffqAaHXtCkvUb7+BEGlPip6eGHeY8RwcOYK/9+2TFkaG0KwZ2jFsGCwIxuDhgYnw4EG4iJjRtrp1DdfLlGF5vHgBwn7pEu61GJ6h0WCc3LMH/1+8CCuiOSRhzp8HGX/9GiRm2za8v3Yt+rA5yisFBcHyO316wnOEIOCczZpJsV7pnkD169ePW7ZsyUqlku3t7fndu3f84cMHrlq1Kg8ztbaIBWHKDRg1Cr5aV1fUfhMEWEdy5GCuWDG+qTsyEoQlscrZK1YgcLt1a5jaTTG7ajSoRt+yJVaLzGjPqVOYAFaulGoMicUj//1Xqk9kSIfDGLZsAXHUtwQ9fYrjmVIPTMTOnbDQJbYq1seHD/hdLVrgAd6wQdd9+fQpVkkDBkjXRIYMfRw7BnKj7/p2dcViRiQvpkCpxEB+/z4WV+K40KcPYpyYQaq0q1k5OzPnzIlnUIxPypEDfXbePMkiph04/uYNxgdBwFa/PtwohnD9OoKKnZ2x0EqsWHmDBtjH0RGLk0eP8N3/KnPJSKM4cgQW+DNn0G/F8V6lwjh44gT+P34cLmFzxLGJC48HD+D6njxZmneaNDGtmH1iEATm7dt1PS2G8PIl+u6OHd8AgQoLC2MHBwfOkSMH29jYcJEiRThDhgxcu3ZtjjTm2EwjEG/AtGlhCbJ1hQID18ePCBYVa1j5+jL/+COKAl+5ovud8HB0BGOxOuKAe+wY3ITNmuHBMMVS9PQp2qRd20qtRmyEkxOOJw66I0ciKP70aeNFRxPCzp1wNeivDO7exW9MysRz7RoG6ffvk94OZvi99+zByqdRIwTLPnkiWa3atIFV6vp1OWtPBiAI6Cf9+ukGo6pUIBxJdUUIAsj6gQN4BlxcMIktWoSgcGY8l2KxX2aQqpw58f6SJbDslikD4uXtjWdfP3BcTDoRrWLnzkkBw4bQti2e/yFD0D5j2LkT7V2xQhLAbdkSVvV0MGx/95g5E31t/36Md2L/jY3FQlP0gOzcaVgoOTnw98eYe/o0rJxdu2Ix8OCB+TL0mLFgbtYM1qaE2q1Uoh5g+/bpnECJuHDhAi9cuJDnz5/P50z1SaUBiARqw4Ywg3FNIl6+RPCeWo2VpThQqlTM1aohBVk/OyEkBCTHWAHimBhdiYAHD9DhW7UCuzc2sCuVWAl066ZrCYqKgkm+SROkVAsCJorlyzFZbN+e2FWJj/37sZrRJ5menrguSfG3e3mB5BlLYzUFSiXcECNG4DoPH474ladPEQNWvz4mInlC+H4RFYWBfvly3cE4KAgTza5dST/m8uV4vsQC40FBWO336oVz3LqFY4sLjuhokKWKFfGd+vUxZuTLh/Gkc2cpW1U7cPzIEV3C5OISP95RxOPHGDcaNUKwsTGXtiip8OULLF1qNZ6bunWTNzbI+PoQ41yXLYMFdcgQqX9HRsLSKi7e16zBItocJComBq62NWtgAWvUCH1SlCZIzOtiKjQaEPw2bYxbUi9cSMcESqVSsY2NDT82VqckjUPbBBgSghVhr16G43s2bcLAKVp11q6VPuvShTlTpvjlXcT0f+2sCX34+cEUO22aRFD8/XEuJyesSBMy2zODJDk6xid/fn6waHXpgtXBsGFw5zVrZrocgTaOHEE79VNKL1zA6tXU8jHMsCS1bJl4dXhTIQiwRM2di8mraVMEz48Zgwlp8GA54Px7gkaDWA1Hx/hB4QnFQZmCM2eYu3cHKWrYEM/RkydSxp2PD55Z0R0vCLA22dsjWLtnTxCm/PlhwTp3DmOJCO3AcScnyeV+6xYChRNC9+74vQMGgDAaw+LFcH+MG4cAXo1Gki2QrbbpB2Lw+Nq1sGpOmiR9FhICciyOefPmwWplDmg0ONc//yCg3dERLrewMIzp+/eb5zzMMD44O0sxgPpI9y68kiVL8gNTFRbTIAzdgDt3MOmuWaNrARIEkBHRPdSvn24mz5w5zBkySEF2Ivz8MBiKmTiGIAhwTTk7gxCJUCqxSm7UCANtQhIC4eEIpp4xI77P+/lzrEqHDUObRY2Y5GRpnDoF65h+LNXx4zhHUoIWVSoQmxkzzD9wq1SYiBYvxirGwQESFNWrI4bqe6m39z3i7FlMHsuWxb/Px4/jWTIkAZIYXrzAuBAVhWft9GmsvvUz7rTjN5YvZy5RAtahK1cQo/Lrr9jEwHFx6EkocJwZGacJub3fvsW41KIF2mVsOA4ORhu9vDDZMYNM/fEHSJqM9AVBQJ/ZvBneCFHOgBl90slJskaOG4f+aC6I9e1ev8Z84umJ+eqvv8xXQ48ZVq9RozB36YeLpHsCtWnTJm7cuHGcAnl6Q0I3QK1GAGjDhohzEBESggEzNFQyv2sPdAcOMNvaSlktIsT04MSyWwIDkU0xYkR819PNm3DXtW8P15V+BxUEWMmaNYOrQB+XLuH3ODjAMtWvn/G2JIRz5+BO0G/f4cMgK0kNVF++HAN/UixYSYVGAzfH4sUgUblzYxJbscLwtZKR/nD/PkjE+PG6wpjMeDbmzYOFJzn9LCgIz+/nzzjOv/+CADVpglWyqOGkbe06dYq5QgXmn37CvvXqwU2WPTviPBYskFxm2oHjYraTdqatMevT339jIde7NxY3xjBqFDIAe/ZE0HhMDJ6HHj2Sfk1kpA2IGXjbt+vKGTBLsjM+PuhPAwcyu7mZ79znzmFBIhbJ3rsX55k7FzI4pmRdmwoPD/wWbemfdE+gqlSpwvb29mxnZ8dly5blqlWr6mxpHeINuHLF8A3w88NgM3SoNChfvQqTOTMGxlatdDPx7t5lzpgRrkBtfPhguobSmTPY15Cm1KdPMKHWqwf/tz5hefoUK48zZ+J/VxBg0SpcGBYZMYA0qbh4EW4y/RXB2bMgcEkJLGdG5kjjxnBdfg1oNLD41agBlflff8V93rIF7k7ZlZF+8P49CEDfvoafrehoPK9LlybvviqV0Fy7exeLJVHao29fafE0ZYquS//pU+aff0acU1QUFgm9e4NQ/fUX2tmkieHA8datpYBcpRKr+4QydD9/xv4dO2JyNCaf4uWFfe/dQ9uZoWRdpYq8iEjvUKuxuN67F+PYvn3SZ69eYT4ICEC/7dYNSUbmwqNHmKuePUOS0oIF0jzTpo3x8JOkIiQEz/rEiXg20j2Bmjp1Kk+bNi3BLa1DvAG1aoXxp08J7+fhgU64c6ekaySqsUZGwrKjHSz+/j3zDz/A2qPtBnzzBp3NFBdCRAQCo3v3NiwXEBMDi1O9esgM1G5/VBQ687hxUpqr/ndr1kRsRnJJ1NWrWH3or/avXsX1SKpR8tEjXOMdO74ugfH3h1XBwQEm6R498Ltat4Zb1sPDvIOADPMgOBgWldat46sUi/jwAW43Q4sJUyCu2vftwzmaNMGiafFi9BlmTBTaFqKAAOb//Q+Wpjdv4EpxdkbgeJ48Uuknsc3ageOHD+sGji9ebFz1edQojElduqDPGntuuneHFdbFBRaJoCDoUM2enbxrIyNtQakEkT50CLGq2vG4jx7BdRsaiv3atDFdf9AUiEHknp6wPg0ciH5++TLOa2xuTQ727MFxb91K5wQqvUMkUHfvhsXFNyQEhQKDZvPmCM5r0kSKawoMxOSvHaP0+TMGzOLFdbWPXrzAvqZaW8Rip6J5VB+CIAlz9uwJV4YIMX7KUJFJ0e2QKxdcHwlNQsZw8yYGf/1advfu4YEylkFhCAoFBvSWLb++OKZGA7dLp06YZJYvx4Q2bx4GnEaN4C5xc0NWpmylsgxiYmA5adAgfrFREaGhWFS0bJm0gtraiIxEX1i7VlIX9/eHtVTMuLt5E+cQXRWiqy57duwnWq86dmQuWBADv7s7FkYixMDxqCicQ7Qof/qka6XSR1AQrMB9+iA+8vDhhH/LvXtw2Z84Iek8DRsG65M5hBBlpA0oFBirjh7FGHb1qvTZ9evoT1FR6GNt2pg36zIsDPPIwYOYd1q3xsLzxQv06+TML8bg48PcpEk6J1AlSpTgQAMqWiEhIVyiRAkLtChp0DYBurtjoEtME+b9e5CVIUNAEkQLj+ii087g+/wZLqJcuXT1nR4/xkBrrIiuNhQKWL3atzfO5l+9gu/ZxQX6UhoNSF79+oYzGTQaDPDVqsG10K6dcUl9Q7h7F79FP8VazHZKToXw16/RrvnzDVvQUhuRkcgQbN8eE6SrKyYsLy+s+IcOhcuxZUtkt5w7l3SBUBlJg3Zm3a5dhp9ThQLEt1695FudmPGM16sH66MY6/ToERZOjRqBdIiFg7Uz7nr3Robd3Ll4b/BgEPDq1eEyVyqlYsPMuoHjkydLIpzMyKYzJoEyfTqsU23bYhwyNm517AhrmKh27uWF2CxzunJkpA3ExGBcOn4cngDtBfWFCwg5USjg9hs+HHpK5loMKpUg9P/+q2t98vdHW8ytcBQSks4JlJWVFX8xkPPv5+fHGTJksECLkgZ9H+qGDYmXQBBx5AhzpUoYwMQOKApcat/Pjx+hAZMtm6QSy4xVYcOGSZt4xQF83TrjA2ZQEAZuJydkEwYH42EZNix+EK1GgwG+Z0+0dfhwELALF0x/sEQVY/0YlHfvMOEZy0BMCGINQGfnpJM6cyI8HCu1tm0x+Li5SRNgdDRWeYsWgWw1aoT4khUrUALh82fZUmUOiJl1//5rOAhcEOBmc3LC/UmJ+vLly+hz797h2ejTB8+6mHHn5weCXa+ebr9esACFgtu2xf+rV0Orp3ZtKI6/ewfLmeiSMxQ4LuLCBV0rlT4iImCBGzIElrYtWxLe9/lzPNvr10txWm3aIKBd7pvfJqKiEIt64gT6snaR9iNHkPwkPiOrViEuylyWSEEAuR8xQlpEP36MsbJjRyxGzYV0GwN15MgRPnLkCFtZWfHWrVvj/j9y5AgfPHiQBw0axGXLlrV0MxOFoRswapTxAUkbkZFYWdaqJenKXLsGAqI90H/4gLiInDklEU5mSBY0bpy0oGu1WtJzSoyYKBSY/Bs0QODdpk0gePqqsUolLGWiVoi/PwLVGzXCqtiUgVZc4WpnLTJL2jjJlQsLCMDKfuhQy1t5QkIwQbdujYlyx4749+7jR7gCFy/G5Nu0KSwYffrgvZMnYeEwRyHObx337iWcWSfiyhX003nzkp4Bqo/16xEHFxGBvta2LcYChQL3Ucy469gR1ikRR44gaLxiRXx+/jzcfw0awLrbr198l5x24HirVpLLWl/ewBCWLMFE5OJivLgws+QidHKCq/HuXRA9c7tUZKQtRERgbjl2DCTG21v6bMcOkGqx35w6hWfI1PqmpmDLFjwn79/DUHD+PJ6NUaMwt5iDvKdbAmVlZcVWVlZsbW0d97e4ZcyYkcuWLcvHjh2zdDMThaEboFbDlaVf4y4hBAYy//knLA9du2IgPHFCl+Uzw2z+55/MRYpgcBVx7RpIzaxZptXD0z5ey5YgZImliwoCfk+7dpgUataML2Lp64tip9orhNBQuCPq1YPrKrHzBAeD2OnHY/j7i0F/Jv+8eLhwAQNBWnE7BAXhWrVoAevTnj0Jq54LAqxRFy7AOjVwIK5T06ZY/c2di2v26pV5alelV/j4gPD36YNBd9CghLNWnz9Hfx4+PGGFblOhVMLdNnUqBnlxMeDpKWm+iX160iQQHxEPHmARJWbcvXoF4tSpE/rGzz/jGN26SS45/cDxqVOl4y1YgAkuIcTGwqowcSICzv/9N+F9vb3RN+fPR2yKICBZQpYt+D4QFoZ+dvQo+rM2QTp4EGOQGEby+DHG16dPzXd+UebA2xsLE9EwsWqVeaRr0i2BElG8eHEOSOnoZUEkdAMiIuKb6I1h717EKGmLVq5ahQlAm2m/eQPT+e+/M1etKq0ABEFK5R83zvTga0GARaR+fdM7/qtXiLsoXRqdW3vSv3QJlijtNFhmKRXbyQluTmMdX6EAmVyyRPe3h4bi95lKTA0hJgYTWIcOaatwsL8/3KouLpg4163DgGSKlSkwEFaUdetABpo3x8Dm4oLrOH06LIfnziGOLjkCqGkVnz+DmPfrB8LUuzdcXNqrZX34+kJxW1TYTykCAnC9RQXlCxfwPIltWLJEimnasUPXtebrq5txFxKCZ2TUKCymChTA8+XhgTFBhHbguLbi+MePunXxDGH9ergHGzXCGGWsVNGwYXjexBip48eZS5Y0T/FXGekDonjqwYNSJp6IO3ckJXFmzDsNGxqWz0kuHj7EOV68QLKDGHN17Bj6cHLrojJ/AwQqvcPYDRCDRE0J9BYEECfRTXX1KshC48ZYKWrj5Usct0cPFCPWX2FfvgyT/qBBpneuT59gjVq+3HTXUFAQsoly5sRKVhyIFy4EudMOaBWhUEgFi5cuNW5x0U5nFREZiVX5qVOmtTEhiOUzVqxIexYbf39YyUaPhoWpRQuQ6zNnEnZDGYJGA4Jx8yYm96VLMQC1a4dJtlkzHHvAAMgtbNuGbMy3b5lDQ2M4ODg4RVtUKjA1X19YPvv3x0Atam+Z0s8jIkAmmzY1X0ycqGHz4AH67MqVIGZRUbj+U6aAhAgC3O2tWkn9OSYG382RA25ZlQrP4Jw5uEc5c4JUiYHjonVZO3B80iTd56xzZ+Mlh1QqkKE5czARaVuu9BEQABK+ahWsemo1Ase1QwhkfB8ICEC/2b1bysQT4eMDIi5qiEVH4xnQ1jVLKUSZg+vXMWb36oW55NUrjAObNyfPpZduCdSNGzf45MmTOu+5ublx8eLFOW/evNyvXz+OTU1paTMhsRtw545UbT0xfPqEziBO6IKAibRoUXRIbSLx/DkG1YULMdC6ucUnPnfvYkDt1ct4LT0RgoDO2aJF0nQ33r8HYfr5Z2jFnDoFN1/dugkTHbUaLqt69Yy7HvfuxaSiTRxiY0E29a1cSYVGg4dcX4MrrUGhwOS7ZAksZ40aIeNxyxYMIMmNBdBoNPz27Qfevv0sjxixghs2HMxlytRne/siTEQp3qytrblWrVq8cOFCfqkdhZoEfPmCPjBgAO5T9+4g4Ikp8mtDpYJ1ztkZq1ZzBT4fOoTJxN8f9+jvv6ValxER6KPr1mFfb2/d7DlBgIVJO+Nu6FBIcDRujKxbMWFk0iTjiuMizp1DfTFj2LULcXTOzsaLCzMjq+/UKZA8lQrksGxZy2S1yrA8xJJiW7dKmXgixP6+aRP+F+vdjRxpvgVqaCis6wcPImbQxQXzhkqF56Z9+6QLKadbAtWoUSOeJyrJMfOjR4/Y1taW+/bty4sXL+YCBQrwVGPLIwPw8fHhLl26cK5cuThz5sz8yy+/8J1EKn5evHiRf/31V7azs+MSJUrwmjVrknRO8QY8fZrwDThwAIOrKQO3qysmSm0olYg7+PlndBzxOI8fYyA8dYr5t98w6P7xB1wa2mTqxQuY/Dt0kCpsG8PTpxikk1LUUa3GAOvkhIfGyQnWsRo1jJtztV2PY8cadqlcv47fqW1lUKlggTM1WN8YPn8GQR07Nn24twQBSQW7d0tyCK1bYyK+eDH+bwgPD+fbt2/z9u3befLkydy+fXv+5ZdfOHPmzGYhSqZuWbKU41KlRrODw1Vu1kzNLi5sdGvWDHE/GzfCKpZU4qNQgOQ4OcFtZa6yEIKA+ot//41z+PuDSIkxTu/eob9evoz/xYw77b49cyZzmTKwNDEj03XECJCVokVhIWKWJC9EJBY4biyZRBDgglm2DBZNbZegPsLDEfvi5oa2RUWhXcePJ+lSyfjGIJYUW78ehEU76UKjgdV87Fhp/nFzw2LaXELCSiXc9MuWYS5zdJTmtHv38AwY8nwkhHRLoAoUKMC3tWbzCRMmsIODQ9z/e/fu5QoVKph8vODgYC5WrBj37NmTb968yV5eXnz+/Hl+YyTI4d27d/zDDz/wsGHD+NmzZ7xhwwbOkCED708CcxBvQJs2Ydy9u65mhjbmzdMt1JgQBAEsW18EUqWCZahnTwzW16/j/QcPMDj7+6PTbtkCSYFcuaRAb7Ezf/iAwbhFC8O18LShUCCWqnfvpGWuffyIB2vKFMR7lC4NMdAePYwLEgoC3JZi1pmrq+5k4OUVX45Ao4GLb8UK09tnDCdO4IF0czNvDaavgagoZg8PDY8Y8YR//XU9FynSn/Pmrcv29gWNkpoMGTJw+fLluUWLFjxmzBh2dXXlq1evckBAACuVyhRtXl5evGLFCq5fvz7b2trqnDdPnjzcs2dPPnjwIEeYaXRVKvFczJkjxYEtWGBeFXhRHHP1avz/4AEsrWJGmrs7nkeR7MfGgiRpl0jZuxcK3pUq4f8LF/DM1KuHBZBoVbpxQ1dkUztw/NAhkCAR8+bBumQMx4/DQubkhPMZ01dbuBAWXmdnuBonTEDbZMgQ9Qq3b4dVWD/eduNGZM+JC7lLl9C3TSlBZgrESh4jR+KZ6NQJCxqVCn115EjDhYMNId0SKDs7O/bWivR0cHDgmWIOPDN7eXmxvb29yccbO3Ys//nnn0lqw5gxY7h8+fI67/Xv35//SMJIoX0D3rxB3FGLFohX0SYoggByYAo7fvsWK3B9ghMVBWvD+fPQb+nYEdalBw8wWYwYIXVSjQbm1GrVQKYcHDAgajQgWxMn4liJSQxcvoyH5coVky8JM8PM6uSEFcnEiRh8W7TAA/fvv8aD3CMjYSZ2cYHL5sIFtDs0FMfQdt0JAvOYMdKKPaWIicHkKAa7G0vvtjQiIyPZ3d2dZ82axY0bN+YcOXIkSJTs7fNz/vy1uUiRflyhwiKuU+cYDx36irdtU/Ht20mLrUoOQkNDeffu3dy5c+d47bSzs+MmTZrw2rVr+VMSfMdqNbIy588H0WjWDDE9Hh6po46tLY7JDAtt06Zwg4nxT507S3F9vr5wt2pbbW7fhnp3/vxSxl29erAgtmsHGQNBiO/yY05Ycdzb2/B4oQ1BwPO+fj1cc/p1NrURG4tz798PIhUbCxV0WbZAhoi3b9EHDx7Eq37cnbu7bgmW16+xXyIOoSRh82Y8M4GBiN9s2FDSq/LwwMImsXkr3RKookWL8qX/0qkUCgVnzpyZz58/H/f5o0ePOGfOnCYfr0KFCjx8+HBu27Yt582bl6tUqcLrtXOFDaBWrVo8VK9M+cGDB9nW1paVCTj6Y2NjOSwsLG77+PFjvBsQEABGXL8+iIA4CYtaMAlZqbSxdCkmcH0EB2NV+Po1ti5dEB9y7Rrce23bYqB9/lz6jkaDY4lk6s8/MTgGB0sSAwkpMzNjEO/VC6vQpBCK0FBYiMqVgzWqUSNYmQ4fllK03dyMrxTev8e1dHbGwP/sGX7vvHnShCEIWFmPG2e++BaFAispJycE0aaFchUfP37k3bt389ChQ7latWpsY2MTjyj98MMP7OjoyOPGjeOtW7fyzZs3OcRAgFlwMKx527eDdHTqJMki9OolBZWfOwdXcWCgOdWGlezu7s7Dhw/nEiVKxPsN1atX55kzZ/LDhw9Z0DqpRgMz/eLFIBxNmoCcnz+f+q5XfXHMadPQt5VK9JX+/aX4J2bJvaAde+jjA+tw9uw4jphxN2gQsiXz5wf5MuTy0w8c11Z46dQp8QzaS5fwfNSrByu2sf3XrcPk1KABns3Zs5H1K0OGNkTX9ebNGNu1a+cxg8w4OuKZZcaY4+JiXhmZmzdxjrNnpYXE6tV4DkND4UEZMybhrO90S6D++usvrlGjBl++fJlHjhzJuXPnZoXW7Lx9+3auXr26ycezs7NjOzs7Hj9+PN+7d4/Xrl3LmTJlYjc3twS/U6ZMGZ6tVwnT09OTiYg/J1BefOrUqQZX+IZuQHS0FLy6cCFuaGAgbnhi1cvVajBqQ6n2oh9aPMatW4ip+PtvkJK6daEVVbiwpOGyYwcG0RcvsFL+9VeQqdq1Ecy9eDFWqGJnN4S9e0EKtcmZKbhwAYHuixfj+6JQZlgYHr7mzbFyP3IkYTIlCFhN9OmDh7ZdO7gFtXnusmWY/M3pslGpQILFrMGvFSOlUqn47t27vHz5cu7YsSMXKWI4sLtw4cLcoUMHXrZsGd+5c4dVKfQ9CgLM4levol8sW4aJt2dP3CcxPqlFC0z6kyahP+3fj++8eWM8LT7++QR+8uQJz5kzh//44w+2srLS+X3FipXhZs3mcNOmPnFxcqdPf93izNrimPouvC9f8Nxo65Zt3w6LmHb2bWQknsscOdB+MeNuyhTEeeXMiQWRIZFN7cDxV68kpXJmWLpHjzbefjEsYOtW3Esx7soQVCr09ZMn0Ta1GtYnY+OCjO8XYvKE2I/F50JEYCAWZmIZMIUC7rWFC823IIuMxCJk6FD8/e+/iA8UrV8HD2IONpQoZCqBsmJmpjSEgIAAat26NXl6epK9vT25ublRq1at4j53dnamP/74g2bPnm3S8TJmzEjVq1ena9euxb03dOhQun37Nl2/ft3gd8qWLUu9evWi8ePHx73n6elJf/75J/n6+lKBAgXifUehUJBCoYj7Pzw8nIoUKUJhYWGULVs2g+cRBKJjx4jWryf66Seipk2J5s4lOnSI6IcfEv5NT58STZ9OtGcPkZWV7mcvXhANHkx04ABR9uyGv//6Nc7z8iVRnTpEOXIQffqETaFAu3x88H90NNGvvxIVLEiUPz/RzJlEOXPGP+anT0QDBxI1aIBX/XYlhMePidq0ISpblig0lGj1aqLKlaXPfX2J9u0junyZKCqKqFIltPnPP9FubURF4dotX47vLV9O1KIFkbU10cWLRDNmEM2bR/Tbb6a1zRRoNLjWGzYQ1a9PNGAAUdas5ju+iCdPntDYsWPp0qVLFBUVpfOZjY0N/fLLL+Tg4EA1a9YkBwcHKlKkiPkbYQI0GqLAQCI/P8NbVBQRs+n9QxydFAo/8vc/QX5+Rykg4BwJQgwREVlbW1PDhg2pd+/e5OLiQnZ2dqn0yyS8f080eTJRqVJEU6YQffxI1Lcv0cSJRHXrEt2/T/TPP+h/lSrhmowfj98ybx6RjQ2OIwhEnTsTXbpENGIE0ZgxRMOHo18/eEB05QrR1q0YFyZNIipeHOcRMXs2UYECRL17E7VuTbR4MVHJkniGmzQhOnzYeF88fJjo3j2iO3eISpcm6tSJqEYNw/vu3k0UEEB07hzR5s1EO3cSbdlCdPduii+njG8UzBjPr18nKlqUKDaWaOFCqf8rlUSDBhGVL080ciTeW7iQ6O1bopUriTJkME87Tp0iWrSIaMECosyZ8Yz17UvUvj3GpMGDMSf884/UtvDwcMqePbvR+fu/H5k2ERoaymoDeY5BQUE6FqnEULRoUe7Tp4/Oe6tXr+ZChQol+J3kuPD0YSqDFXH9OlaYDRrA7JmY5tL06bAEGMLNm3C5JOZeCgyEy6FBA8QPaV9ulQoWrTNn4F7ImBE190qVgtvPUPs0Gonl+/oaP7c29uyBSbVBAwSXJyQdoNHgsxUrpLT94cNh+tUX8BOF/WrUgDvnwQPs07kzXA/m1njSaCRBuZkzzRc7pFarecGCBZwxY8Y460v27Nm5UaNGPGPGDL5w4YLZgq3TCyIiInjTpk1cq1YtHatUrly5eOjQofzgwYNUOW9QEOQA2raVXF1XrsAyIyZ37NkDS5zYH0NC8DyIdepECAJc3+XKoS8zwyo9eDCOV7w4+ikzrMQjRuh+/+BBuAcFAX9rB47PmZPw2CAiMhLW6jNncOymTRPeV8zSc3dHIK4gIJP2xg3j55AhgxmudDHLUz/zThDgCenfX/IaHDgAS7Y54y8DAjC/zpoFb8GkSYijDQ5GGzZsgAfj3Tvsn25deOZGp06d4gWRDx8+nGvUqJHgd8aMGRMv0+/vv/9OdhB5UiAG1ZUtC/9tQuZMhUIy3xvCmTMYmE0hCqJ509k5YTVwQcA++fIx29gw29vDRWOofU+eoG0HDyZ+bhFDh2L/f/+F28KU7woCYknWrAExEkt07N2LYHRvb/ymNWvgzmjSBO6Rtm0RvG4s+y+5EASQt0aNEJsVFJT8Y719+5b//PPPOILQtGlTfvDgAWvkYndxePnyJY8fP54LFSqkQ6aqVq3KK1as4KCU3ID/EBODos4NGui60DZsgNsrPBwEeuJESACI3tIXL/As64tzirFRogQJM8hJ27bY38EBsVzMWFhpi2wyw23WrBmOox84/uED3HKJuUHGjkV2qYsL3Hd60ns6OHECE0/btlhUbdkiZQrKkGEKxBJGa9YYzrw7eBB9UXRv37iBsdtY5YCkQqyu0bgx2nPtGp6dc+fw+du3+GzjRubQUJlAMTPzrVu32NbWlmfPns2vX7/mHTt28A8//MDbRQU6Zh43bhx369Yt7n9RxmDEiBH87NkzdnV1TbaMQVIJlIiBAzHZG7IOSb8NMSgJYe9ekArtQd8YFArEHomxWQnFHfn5YUVhY8OcIQMG7KtXda1SsbEI0uvb17S0UYUCv/XtW1iZChRAXFPS4mZAQDduxOpCLOHxv/9hNS9OOB4emOjy5oXQ54QJ0Mwy54pHEPBgNm2KySopxTQFQeB169ZxlixZ/suUs+eNGzfqBE7L0IVareaTJ09yu3btOEOGDHFEKmPGjNy+fXs+ffq0QYu2MWg0iFtydIwv+9G9O1axGg1i9tq10631eOIESLR+TKOfHwbpBg0QjxgTgz5brx5IUefOUPUWNb30M+4+f0Z7xIXTxIm62XwdOyYei/j0KfY7dAjFlOvVM064mjTB892/P/4vWlTSspIhw1SEhUGKY8ECw5l3d+6gv4tlzt680Q02Nxe8vNCnN2yANWzgQMwH0dGYZ+fNY27RQiZQcTh27BhXqlSJ7ezsuHz58vGy8Hr06MF16tTRee/ixYtctWpVzpgxIxcvXjzZQprBwckjUGo1OpuHBwQ0nZ0xQOt7L0eNMr56DAyE9aVFC9NTRUU9mUaNMEAnpEqs0WD1ameHrUQJZMJdvCgRvosX8VDs2ZP4qlhMB4+JwcqjcmW44Iz9vsTg5QXZhlq1kM1Upw5ITe/ecHs0b47sw4kTEVjfqBGkIPbsSTyg31RcvIjzDB4MV6IxfP78mRs3bhxHAGrXrs3vRLuyDJMQEBDAy5Yt419++SVeYP3EiRP5tQmFKMW6dUuXShbZ0FCQ4VatpNR9cWV99Sr+FwQMwH36xHeh376N5IyKFZG9plDgmE5OSHIYNAh9NCJCqpmprfsWHQ3iJRKkFy90A8dPnUL7jEEQ0P9F69iSJRDlTAhXrmAh1K0bfuuBA7COy5CRHKjV6E+DB6Mf6mfe+fjguRMJelAQyE5K5oCE2rFgARY+X77g+E5Okvjm1asygbIoRALVtWuYyTXk9BESgkHO3x+D+MaNIFLateJEE35ihi4fH6wgO3fG4GkKBAGuhebNEfuREKFQq5HynicPc5YsiGOqWRNZGOfOoa2zZ+M4ia2OT5yQ0rK9vEB8Bg3Cd0WR0OTi/XtMOFOnYnV//TriS/r2RUaRgwMeVmdnbL/9hqzE5s0Rd3X/fsr0nx49wkqnYUOUitG3eO3Zs4dz5coVp4G0ePFi2V2XQty7d4+HDBnCOXPm1CFTdevW5ecGOuOjRyBIY8dKZYQUCriW69WTzP3M+Fu7OHBUFIjGv//GXyxs2wbyky8fVrzMcMu1aoUFzl9/wXX96hUWJu3b6xbHFku8iOr9IvESzy3Wz0ssHG7bNliXx40DGXJyMi4O27o1npOuXfF/yZIID5AhIyXYtg0elh494mfeRURgnN6xA//HxuK5MmcNPREPHmCsP3pUipGdMYM5KEgmUBaFSKDWrAnjfv1ML8Srj4cP4RsWBzm1WpINmD4dDN3dHSTDFLx6hcGwX7+k+Zc9PTGYDhqUsFKxvz/ISLVqcMEVKYKHpH59xDgdPYq4rDFjjA/0EybgAWOWzLi3buEYbdqYppeVEAQBqfVOTroq0CEhuC7Tp2Ol/+YNJkgxsLdGDRDD/PkxCZYpAyL0zz+4H2KQuiletpgY6Gs1bw4X7PHjQdyxY8e4yf3XX3/lJ8Yqv8pIMmJiYnjPnj3cqFGjOEmEXLly8Y3/IqF9fNB3e/WS+rcg4N6KKvTiMxwRgfTsrl0l+Qpvb/RzbYLFjOd11Cg8Ozly4DjisYcNA5Fp25Y5d25Jw2n8eCyWtDF9OmIOmSXipb2gmDkz8RqQISFS2nb79vHdf/p48AAW5f798Z2zZ0GgZMgwB0StptGjdYPImfHcDB6Mhbcg4NmbMEG3HIy5EBODcbx/f4SbbN/O7OwsEyiLQiRQr1+H8YYNWHUmN4Rlxw6QDm0IAnRjmjXDze/aNWlxCffuYVAfOdJ44VB93L0LEtS3r+Sr1sfNm1htDx8u1eJzcsJ36tVD/Mgff2ByMnRNVCr8LpFDeHnhQXv0CJPbX39hpZBU3SlthIZKQoVinLEggLg1bixlYxiCRoO2rFiBlVG1ashOLFYMWVX/+x8mqk6dMDgsWwbSdvMmzMXav3nTplNxZVWsrW145MgpJmd6ykgePnz4wL/99lucwGjHjqe5RQtdYn71Kty58+ZJAdoJFR++ehXv6VeHCg6G67xlS1hnxezS6GisvCdOBOkqUQKrXmZk6+kX/t2zR3eBNHy4rtvNywvnSWx8GTwYVq1mzRAaYEz3iRljiqen5CYsV05X10qGjJTCxwdzxZQpWFBqa/sKAlzM/fpJ5Gr9eohEp4aAsbs75plr15ifPpUJlEUhEqh69cI4NhZCYkOHJp9EDRuWcBHfa9cwgBYqBHXopODyZRCGadNMC/YW8fgxyEOPHobVi9VqTDb16yOeZMwYpD4XKwbS0r07YikqVzbsmvP11XVJ+PvjQRPdGq9f4xi9ehknO4lBLEi8bZt0b96+xeSplWdgEiIiMOGsWoWHvkEDiCS2awfSN2gQrlfTpswNG0ZwsWL946xOhQqV4+HDb3KLFrAOnDhhfqkFGRKCgiK4XLn6TERsa5uBd/1XMO7lSywQhg2TFhaCAME/sYyPtstLOxNPG0+e4N7/8QeIhzgOixPGxo34vHZtECxmPMdt2uje91u3MLGI51y3Dtmd2mjXLnG3/J07eFZ27ECVgZYtjT83r1/j+R45Es/IlSsIHpchw9yIjsZY/s8/upIgIg4e1JU1OH0ac1ZCGegpQUgIvAKjR8sEyqIQCdTQoWHcty8G4eXLJR2VpEKpRHyOdgkIfSxfjgDVLl2S5uYSU+/F+KqksPuXLxEw26mT4XMGBsIN0KsXSNHt2yBVOXMivmnkSCijlysHwqJ97osXsQoWr1dkJFwX2hIHjx8jo2jgQElhNqlQKsXMC8mKoFKBVHbrlrLsPJUKbdy2DQOEiwvzH39c5axZS8aRp+bNh/H69VE8bRoe3nr1mMuXh9WiWjW4dPbtw7Xz9ZWJVXIRGgo38ogRIOcbNyq4ffsOTERsZWXFtWuv4E6ddC2rN27guZs5UzcjVKlEssHkyfFdCocP41kqWhSESPz8+nVMEOvWwf07YICUcff+PT7THq/FygJiaveFC7C8ap/v+HH0D2NQq0HaXr+WEjqmTzf+nf79sbhq1gz/V6qU9AWFDBmmQky+6NwZfd7TU/fzmzfxTInu9QcPsJ8J+SDJwpYt6VSJ/FuBqGTarFkYFS6cjcqVgwLqkiVE/v5QAjdVjVmEnx9Rly5Q205IHLV7d6K2bYlOn8b+depA0TVDBiJbW2wJ/W1jA8Xu48ehftyyJVGmTNjs7HRfRcVWEe/fQ+nV359o9Gii33/X/fzePSg1N2gA5VcrK6jNrlhBFBND9MsvUEjPlg3K5926EdWqBWXarFmhbk5EpFIR9e8P5di//5aOf/culJlLlCAaN44ob96kXVsionfvoEYrqtJmzEh07RpUoGfMgPp5SqBQKGjKlCm0cOFCYmbKm7cItWixhaKjnSgkBNe2YkVci19+ISpWDEr1rq5EX77gt9nbQ7FdEHDMjBmhEv/jj0SFCum+ZsuW9D72LSEyksjTk8jdHYr3WbMS1a5N5OhIVKECrk1EhIbq1x9GN2+uIiKiKVOm0LRp0+jtWyuaMoUoVy6ojefLJx337Fn0y/798ayJEASiWbOgRH75Mvrh6NH4zM0N9/Knn6CSb2tLdPQo0atXUGxu2RJq9iVKYP/oaLy3ahVRmTJ4NgYP1q1SEBsLxfFjx4iyZEn4OqxZg9/64gWev0WLiE6exHNsCJ8/Ew0bhr7o4ECUJw9Rs2aoNiBDRmri2DHMCba2UNjXfr68vKAgvmABUbVqqJbRsyeqYySkoJ9cmKpELhOoVIJ4A3x8wqhHj2yULRsG3IYNUc4hOhqTclJx7Ro62M6dhifHgACUhzh5kigoiOjJEyK1GsRDrTbt79hYotu3UTJGnMgVCmyxsXhVq3XPK7YlJgZS/NHRKCuRLx9KUxQujIn99WtMauPGEbVqhe99+ID/z50DocuTByUxnj8n+t//QL6mTMFgToQJZ9w4yPJPnap7Ha5cIZo/n6hqVZAg/XIviYEZZStcXXF/atYkCgsjGjUK55k+HYQlqXj06BF17dqVHj9+TEREPXr0oGXLllF2rXo7sbFEz54RPXyI7e1bTMqFC6PUjb8/rolajetSvz5RlSoonfL5MyY47dewMOn82bLh+hcsSJQ7N66x9mv27Ch5k54RE4OyEe7uIDGZM6PPODkR/fyz7u/TaFAmZft2okGDmB4/nknTpk0lIqJKlf6mChVW0syZNlSunPSd+/eJpk3DscaM0V3EREYS9euHPnL6NNH+/TivWk00diz2+fwZC4s9e/AMeXqCNHfsSDR0qETQBQELpb/+AtkLCcFEsnUriLGIIUPQB5o3T/iafPmCRdWcOSgtU7gw+nTTpgl/Z/RojFMLF+K3VK+OtvTvb9p9kCEjJXj6FIuFvHmlxaw4xoeEYHH9998g9eHhRD16EHXtipJg5oJMoCwM7RsQHp6NunSB1WbtWkyGM2div8mTk37slStRU0wcmPWxcycsQhMmJLv5RAQS9O+/IG1z5ujWqEsM/v5ES5eCCHTujBW/nx9WDW/eoD5RaCjqIGXOjMmocGF879w5vBYrBsJy5QrR3r1Ezs6YLJydQQSWLsUKfuVKXYsYM9H586gNVrs2Jid7+6T99uBg1C+zscFvz5EDVq6pU6WH2tiqX2oLk6urKw0ePJgUCgXlzZuX1q9fTy1btjSpHcy4Zg8fEj16hN/r7w9yFBYG0pU9O1ZgrVvD4qhvHSTCQPP5M6wfQUEgXUFB0t9hYVLdOSIcI1cuiWBpk60cOXDO7NlxXS1FvJRKops3iTw8QPhtbXEdHB1BoG1tdfdXKLC/uzv6VOvWIAYZMqCvd+q0ho4eHURETO3ataNt27aRnZ0dffiA+54xI161SQwRLJd//YXzPXtGdOMGyGpICFGvXmjPsWMgWP/8AyuQqysmhXHj8Az07Ckdb/JkoiJFcEyVCuRp8mQQGRGrV6MfTJtm/Br16gXr7ZQpsAAvX45nKSGIE1Tduqj1V7482u/r+31bM2V8Xfj7gxgVKIAxZulS6XlWKkHmq1dHLT2VCoSrbFnU1DNHP5UJlIWhfwPu38fKTqOBGT5HDgxqP/yAQTQpYIZ5s2tXkAlDn7drB3dC+fIp/y2fPoGMZc0Kq0yuXKZ/NzgYRUdPnsTqu08fWKaIsNIYNw6TXr9+mMh9fCSStX07CrVmyIBCxpGRRH/8gUFeqSQqVw6FjT9/BmnMnDn+dTh6FBa7unWxqhddJKbiyhVMUv3745oS4ZjLl2Oi6dbNMGEhIoqKiqKBAwfS1q1biYioadOmtGnTJsqn7Q9KASIjMXk/eQLCeOcOSGrGjLhe1arBgli6NCbDYsVML9CpVuPeGSJboaEgXOHhaIPoTtSGvT1IsUi0tLcffsBzoFJJVs+k/K1SoX8wo085OsIip//b1GqQXnd3WKasrNB/nJxwbWxt0Q43N6IdOzAIK5V7qVu3rqRSqahOHWf65ZdD9OlTVpo+HS4tfVy4ANexnx8Iprs7jvv8OQb3Dh1QDLtnTxD5OXMk17ObG56BBQuk4+3cCTK4dCn+HzwYfVfblXHuHKxRW7canywuXcK5q1bFfbp4ESEExp6BmTPRZ5Yvh6vyzz9xbrHYqwwZXwuxsSjOLggwGGzZIi2EmbG4jojA82NtDc/O58+6ZCu5kAmUhWHoBpw4gXiEDBkwsNnYwMqRL1/SB6joaKIWLbCSLVo0/ucfP2LSP37cfBaCq1fRadu2hS86IeJgCMywZLm6YiLu0gXtz5gR12LtWrjJmjTR/V5sLK7NwYN4iJRKuFCqVcMxHzyAqysyEqv2n37CyqRUKVyXokVhrbpyhWjXLjxgTZqgErepPEapxMTj4YEHunlzTLzr1hEdOQISqE9kX7x4QW3btqWnT5+StbU1zZ49m8aMGUPWX8Fc4+tLdOYMiN6HDyC+2bKhzeKEa2eH1V3BgtIm/p8vX9LurT6YcT/CwyVLmfYWHa0bgyfG4Rn7W/+9YsXix/AIAqx07u7oq0ol+oKTE4iWnZ1uG0+dwn1t0wb9WSRgJ0+ep1atWpJSGUVly1ajq1dPUV69oDpmkAx3d1icunTBsfB9kHYHBxCp8uWJli3D8//771KV+lu3iDZtkq71jRuYDPbuxW9ctQqLhUmTpPO+eIH4pMOH4y8YtKFSETVuTLR+PcaB/v1hHZsyJeHvREXBrd66NUiugwPcfb6+6d+9KyN9ghmLjjt3MKZs3aobQuHmhud40yb02V27iA4c0CVbiSEwMJBevnypsz179oxev34tEyhLISEGu2oVBt3ixeFiYgZxKFYMK9Sk4N07TOhHjhgOCHV1hXVi8WLzDYAaDdHGjYjxmDo1eYHVoaHo6IcPE1WqhMmraFE8KK9eYSVRqpTud6KiQFhWr4brzNYW5CBPHhCxP/+Edez33+Huio4GEbC3x2QikgeNBgQuIAATV9WqcPP9+KPkqsqVC6/6Fo2ICBDgc+fQ5rZt8d6cObgXM2fCVbl7927q168fRUZGUoECBWj37t1Up06d5F7yFIEZk+7587BIxMTgt5Yvj4EoRw64tnx9sfn5wXyu0eD71ta4xtoEK08eWP7ELUsWy7h3xN/m7o7fFhkJ64mTEyZ+fRcrM0iEhwdITvXqsApnzYrPBQEWoE2biOrVu01LlzahwMBAKlu2LJ09e5aKFStGRCBFkyeDwBw/LIvMRwAAdUNJREFUDvLfoQOOv3AhPreyguXv9m30x1u3cN2USjznRYti8SReN29vuNsOHoSV7uxZWGDd3KR9goJgBd2xI/E4vIUL4Ua8fBltmzXLeOA4EUhevnx4vk+fhquxbl085zJkWBL792PsVanwqm0NdndH3OvWrbC8X7mChf7y5VhQEyGB5+3bt/GI0suXLyk4ODjB88oEykIwZgIcORJEoU0bDJrMyNArXx6EKCk4dQpEZN06w5+7umLC2LjR+OCZVAQHY2ANC0NGoX5ciKm4exdt8/aGVejXX+Ey++knTDBixpGI6GiievVAqIYNAxHdsAETRWws9pk2DQTn5k24WO7dA+EqVw6ErVw5TBT+/iCfFy7ge+XLI3AxLAy/T6XSPXeWLCBW2bJh4n75Etas1q0xMS5frqBXr/6hJ0+Q0VW3bl3atWsXFShQIHkXJxXADKL0+DHI9ePHcMtZW4O0VqoEC99PP4F8CgI+F8mVGEMVHAzrSEgI7oU+7O11SVauXNLf9vYgaPrJC4klN2i/PnmCdlSoAMJUqxaIh/5vff0a/f/iRRD3ChXg8qtdW3f/8+cxCDdrBhebnR3Ry5cvqUGDBuTt7U2FChWi7dvP0u7dFSkiAvf7yhUct0IFENMBA+Ce9vSEq3rKFPTPEydA1r98wfP+99+6gd+Rkci4W78e33/+nGjECLj6RSuTUonxYsYMkH5j+PgRcU8TJhBt3ow+nVjguFIJi1XPnlgUuLjgWRQzBmXIsDTu3MFzYWWFMd7JSfrs2TO4u1etIipcOILWrdtDa9c+o4wZX5JS+ZK8vLxIMBRr8B+KFi1K5cqVi9sKFy5MrVq1kgmUpWCMQGk0RJ06YQKfMweDGzM6QNWqmPyTghkzsNpM6HtnzoCNb9uWtPglU/DoEYJTa9YEMdR2kyQFUVFwXezfj0mnbFmQm4EDMXFoWzgUCkxAf/6JCaxtW0xMsbFYea9YAUJQsCAmpiFDsPp//RoT75MnmKRiYzFB/fQTAti9vEDoihVD4Hvt2pLljhltFGOBgoIwuZw8CetCwYLv6cWL9hQcfJuIiHLnnkC//Tad7Oxs4yxa+fLBzShuBQumnclJo4EVTSRWT5/i99rZgSCIxKps2cTjqEQXnkiwtMlWSAgmaH3XXELyGgm9V7Ys7qn+eb28JMIUGAgJAEdHBNcb6vsPH2IhUKECkjL0szY/ffpE9eo1oBcvnlGGDDmpWbPjdOlSTXJ2hpvghx8QI9i7NwjKkSMg9n36gCiJySL37sHSvGKF7upZEJCFN2QISKBoZdq5ExY/8Xf9/TcIjim5Bx07wu03YgTOv2SJ8cBxIhAtlQru9KNH4cqrVAmSBzJkpBX4+CCrlAhkX/ybCAu8zp1Dydu7Lr19+zDed7NmzapDksStTJky9IPeSl2OgbIwErsBUVEgAdHRSGsuWhSD6d9/I/agRw/TzyUIMNOPGYNgWkN48AAZQBs3Jj2QOjEwg/isXYtBu2nTlLl0nj5FO58+BcFRKmGS/e03aR88LLh2Z85gMmvWDJlLajUC7GvWxO++fBnv5c6NibJGDUxGP/0EEvX8uUSsXr+WLFBRUSANvXvD0pRQXNChQ8epa9fuFB0dQlmy5KJt27aRRtOE1qzBfWzQAMThyxdYB8TN11dylWXJokuuxC1vXsvGn8TGwtomEqtXryQJi0yZQAILFZIkEsS/c+T4Om49b28QJg8P9ImSJSXCZCzGzdsbxMnaGqvZIkXi76NUwrJ74EAwvX7djD5/vk7W1plp8+b91L07gvVu3MACon59rJAbNAARc3OTrEx79oAQbd4cn8RpZ+EplbBmzp4NV6SIpUuxaDAl2eTkSVjGfvwR5PfkycQDxzUayBYMGIB4wq5dcX5vb+NxVjJkWAKRkVgwh4VhET15MsaaqKgoqlevAd24cY2yZctHffp0oXLlylGWLOXI1bUc9exZgLp3tzJpXJIJlIVhyg3w88NKL2NGDHRZsoAM9e2LoOQuXUw/X2goBt89exIWkfT2BhmYOzdhopUSREXBDfLkCc6hraGTHCgUcGNs2QJikzUr3CKtW+PzO3cQ27F/Px6ggwfhBnF2hgVgxAj83asXruu9eyBbt26BNIWG4nv58mG17eAAYlWqFD578gQu0lOnQHiyZYN16s8/QaxKl1bTzp2TadGieURE9L///UadOu2l48eLkYsLyNPmzYhnmTTJeLxYZCRWV9oE6+NHxGoxY8uRA0S7WDEpQL5IESmG52sjJgZ9WJRH+PxZ+ls7rCBzZl1ylTs33tdocF/EV1P+1miwPXmC61W4MAhT3bo4dkKIjQXZ8fBAv8mWDS5iQ9IczLDYrFsHsr1nD5GNTRTlydOOrl07RTY2NrRlyxbSaLrS0aPoP7lyYUA/cAAB7KVKob2TJuHcCxbEtzZqZ+ExI9C7SRNdK9OJEzimKHuQ2P1o2hSLj4EDMY4kFjhOhON/+IB+uncvVvUFCyLWRIaMtAhBgIv69m2Mh8uWKaht2+Z09uxZypEjB3XseIkiIyvTsmV4NtVqaW5asSK+9VofMoGyMEy9AU+eYIIvWRICjmKgs+gSaN/e9HM+eoQB++DBhF1DoaFIvf/rL8Q5pAa8vDA5FS2K9hj5+SYjMhJEafFiuD7r1gVB+fIFk5DoahAEBPauXo2UdT8/uATHjjU8AanVyA48fx7E6tUrnMvKCqv4ypVBfBo3Bik4cgQTTUiIL/n4dKLQ0EtERFSixBBycVlEFStmjFOO3rsXFon27RGg6+0tpaUn1TrDjHv38SOOo71FRGAfW1u0WSRX2lmIKcmqSyliYiSC5esL15qVFdpkbY1N/NvYe9qflStn2GokQqXC4OrujvtqSMLAEDw8kMRQtSr6hK8vdMZatSJSqVTUq1cv2rFjBxER1agxi3LkGEVdu9rRypUgSp6eIIzh4SDxTZvqajwR4V6uWoX2iVl4S5fCAqWt7fb4Mf4/dMg01/jUqYhbOnQIz/ekSYkHjjOjbw8ZAoI5aBCsT69exY8pkyEjrWHLFqL169Xk5dWR/PwOUJYsWej8+fP0xx9/0O3bUjWARo2w/717+H/ECHgsEoJMoCwMU28AEbK6JkzADRUzXtRqrATbtzct7kHEzp2I65g/P+F9lEoMsL//nvSg9aTg3DlMRh07YhIxVYMoMXz8CIL2+DFWFz4+sKhNmYKJ1coKE8PZs4j9io6GdWLtWtOtNbGxyOy6cAFxUa9fwyLGTJQxowcFBHQipfIL2dpmpbJlXel//2tHv/wC69/nz3B7ffmCzdcX7sJWrWARePkSbpKOHZMfM2YIKhXOrU+wfH3Rn5gxKebLh3aKr9p/Z8+ePgUTNRoMjh4eIMQaDfqEoyNcv4ld58ePJZHMN29AuiZMgNtbRHg40bx5Au3ZM5LevVtGRET58xcnpXImtWjRmTZvhq/19Wu44mfPBmnThkKBLLxixaQsvBMnsOjZuFG69v7+cMvv3WtaWaLXr3G8QYOQVGJvn3jgOBGe0WvXcO1cXfH9jBkRLylDRlqHIAjUrFlfOnVqM1lZZaTt209Q58714j6PjsZzoVIhPjZLFoztEydiobxokeE5QSZQFkZSCBQRBs/Vq3FjRUl6lQpxPr16xddHMoYRI2A1MSZtzwzfsUoFd1tqxdmoVEgv3bkTv6V7d/MRKR8fELRPnxAAXb48rDQlS2LF4eSEieTSJalGWbduiBUz5u5JCIIg0Ny582jKlMkkCALlzPkzlSixnwICylJ0NIgpMybrkiURV9KrF9ogijUqlbDIiQHplSvD2vjnn7AUpSZ5YYa1KiAAm7+/7mtAAK6fNrJli0+2cuaUtKWyZcPfmTJ9XeIlCCA9Hh5wmcXGwmrk6Ai3m6mxOx8/IgZKjOs6dgyuc20xPpUKruHDh/HZ+fNMPj6b6dGjSRQS4ktERJUrV6a5c+eSjU1jWrLEijZtip+Z6ucnKYOL1t8nT9Afta1MsbEg24sWGRbv1Acz9p8/H8desAB/JxY4zoxF2/DhsFSNHw/r08OHpmukyZBhKTAzjRw5kv7991+ysbGhUqX2UdasrWj9elhitSEK3s6ahYUFEcaOmTMN1zmVCZSFkVQCRYSB9MwZTLZVquA9hQKWisGDDauOG4JKBavVokUImjYGUQJg40bzWkP0oVSCSO3ahUmoWzfzWqRmzED2kJsbMrROn4b7RqFAdpOYhTVwIKw0RYqA4LRuDWtAYggKCqLu3bvTyZMniYioV69etHLlSp3sDUGAdenyZZz//n2QE0HAb82ZE+fSaLD6adAAxOPoUVyffPlARooVQ0yWuJk7c9JUiIRLn2iFhuL98HBsERFw02lDJFNimR6RaGXLhvfUavxmlUp61f7b0KsYcE+E71euDMLk4JD0Uj2hoSDfL18iXmnzZgyi27ZJLmdmEJtVq/A8vX4Ni+LUqXAdbNwYTR07LqOdO+dT2H+FB/Pnr0W7ds0jR8eaOue7cwfP98qVkjaNvz+ebe24RbHKQPv2cK2Zgr170bYMGbAwOHAg8cBxIpz33TskWixejMVbRASsYTJkpHXMmDGDpv7nsnFzc6PmzbvHZbdPnx7fRRcaikzxAgWwaMqYEXGLI0Zg7J0+XZoDTZ6/WUaqICwsjImIw8LCTP6ORsPcpg3zL78wf/kivR8VxdysGfPly6af39eX2cmJ2ZTTnzzJ3KQJc1CQ6cdPLhQK5vXrmR0dmV1dmZVK8x374kXmIkWYW7dmvncP78XGMp8/zzx9OnOnTsxNmzL/73/MP/7I3K4dfreTE/O8ecyvX+seTxAEvnv3Lk+dOpULFy7MRMSZMmViV1fXJLUrPJz50CHmrl2Zf/qJuVgxtDNbNmY7O+ZMmZgLF2YuVYq5YkW09cgRtKlrV7TRxYV5xAjmVatwv54/Z46JMctlSzUIAvqunx/zq1fMd+4we3gwnzjBfPYs/vb0ZL51i/nBA+anT3EP3r9n/vyZOSAA/Tc6mlmtTnlbnj3D9evQAf1g6lT0g//9j/ndO939PT2ZGzdmnjaNecoU/H31KvOGDcx58zI7O0vPy6dPQfzzz6PZ1jYTExETETdv3pyfPHnCzMw7djC3aMEcHCwdPyiIuWFD5sePdc87ezbz0qWm/66wMDxLr19jjNi/H/0nMYSHo9/fv8/crx+zvz9zoULMXl6mn1uGDEth2bJlcc/asmXL4t5XKpn792euXp15yBCMHfrYv5+5Xj3mR4+k9w4c0H3P1PlbtkClEpJjgSKC+b5ePbjUzp8HSyaCxaJ9e6x+f//dtGPduoUguqlTYYExBjG4ztUVQdepDaUSq/jdu2GN6trVPBapM2dgVcubVyoILFrzRGg0iGsaNQoWoHz5sIL38yPSaBRUqJAHZclylF68OEp+fp/ivlemTBnat28f/aKdY54MhIUh7uTyZQT+q9WQOfDxkVZFUVFoW65cULSuVAluwZw5YfV59w6ZU6J4aM6csDiIW8mScCFZMnjckmBGLJOHB1y4wcFw8VaujADpAwewz5YtsGCJeP0asXS5c+ManjiBAOusWREUnjUrrKiVKmH/z59hMRo+nKhSJR+aPn06bdq0iQRBIGtra/rpp+5Uo8Y0Wr26WJxL8OlTHHPRIl1Xw8GDiElavdp0d+jIkXDvu7oiXmvMmMQDx4mwb5MmsApPmoT4EG9vWE5lyEjLcHNzo57/ZWbMmDGDJk+erPM5M/r1smUYT1eu1C3ETYSxfsgQxEn+8w/GST8/eHp+/52ob99wypVLduFZDMklUERwkzg7Y7Dftk0aTMPCQKLmzo3v4024HZJ+zLx5xjPiPnzAJDF/PjKVvgZEIrVnj0SkUiouKZbIad8evzkoCBPcH3/oTkzMiM3asCGQGjU6SXfuHKXTp89QVFRk3D5WVlkoV66GVK6cCzk5taXy5e2pZEmQlPz5zRP3ExMDsnvpEibQDx9gSi5bFn0hTx5IGHh5YcIWSZONDchfsWKSZlS2bLimHz4gNkwU3y1YEISgSJH4JWuyZfs2ap1pi2gGBIB4Vq+O98+dAznKmFEcIHVdZAEBcAMHBkJAdf9+BHE7OKBPBgVB1kD7OzdvItB8zRrcKxEvXrygMWMm0rFj8IVlzJiRBg0aRBMmTCBPzzy0bh1chvnzS9+5dw9uhQMHTF9I3L8PV13nzug7NjamBY4/fow4qUGDkAU4Zw6e95MnTYu5kiHDUjh48CC1a9eOBEGgkSNH0qJFi8gqgUHYxwfSIL6+0GSbNEl3bmFG/z9wACSrZEm85+pKtHdvOJ07JxMoiyElBIoIsRlNm0LVeMgQ6f3gYBCDf/+VVsGm4NIl+HgNFezVRkgIAr3//jvxgdicUCoxqYg6NF26JJ9IiXEkHTogmPzDBwQB37qFFUenTkSZMr2mI0eO0NGjR8nT01NH5r9QoULk4uJCLi4tKHNmRzp+PBPduweCUqQI1Kf9/BAPw4x2Fi1KccRKfE1qXI4IlQrp8KtXw0rFDItCqVK4Nu3aIZskMhK/6f596Fq9fQvS9F84DllZwTL144/w++fJI8UgKRSICQgOxv7aVQ6srZGNJxIs7ddcufBZ1qz4fZaqg0eE2DeRMPn6wnJaowauwdmzyHi0scFio2NHPDeiRVdEdDSepStXEOd09CiO0bMnUb9+iF2aPh2xcyKUStybK1cwAOun+798iezWrl1v0rZt4+jixYtERGRnl5UqVx5Np0+PoFy5pM7x+TNI2v79pse7ffmCfuzmBjmPJUtAhEwNHF+9Gr9v1y5YqJ88wXWUISOt4ty5c9SsWTNSKpXUu3dv2rhxY4LkSYRIkpYvx9jn6qq72CHCAmvwYDz/fftiPHv4MJyqVJEJlMWQUgJFBBdenz4Y5GpqxaQGBGBCWLUKbglTER2NwdLfH1aahMTEFArIHNSsCQb/NaFQgEjt25cyIhUbiyLDy5dD2kCj0dD16zdo3bqjdOLEUQoJeaGz/y+//EI//ticwsObk5vbr1SypK5JRnQJiYVrg4NBYB0dYdkKC4NbzcsL27t3Uo04e3sQqlKlYBUpXRoWIVOtPk+ewMXi6YnrExUFK4Woul23LiwHhQrpkhm1Gi7CO3dwjDdvJIIVG4vfJCJTJhCB3LnRL0Srlxj8nTkz+k9QkBQ4HhGB98TrI8LKCpu9PbasWaXN3l4KIhcDx/UDyRN7X6nEdShcGBaioCC4bh89Qjt+/hkks2vX+LUURWg0MPNv24YMtqtXQTLHjgVh2rcPGXMLFkhuUG3F/Z490Tf17+HJkxDq27QJ95iZ6ejRs9Sr1zgKCXlARET58+enyZMnU79+/UitzkgtW2IFrD+wJ4SYGLR5yRK0s1IlFB42JXDczQ3XK0MGXJuWLbGo2LEDxFGGjLSI69evU7169Sg6Opratm1Lu3fvJpskxCd4e2PB8OkT5rTBg3XHSo0GC6lr1/AsZskiZ+FZFOYgUEQYFFeuxI3VrkkrljLZsAETc1Jw+zZig/r1w6rcEIkXBMgcMCP982tbGRQKTEL79mGFnRzNpM+fiTp08Keff55L+/Ztp8DAwLjPbG1t6ZdfHMnOrjllyOBCLVsWo/btMTkNGQIJiN69E/7dzIhl8fCAlSgqCrFWTk4gntoTd0QESNXbt9jevEHboCkFIiQSq9KlQQwMjQ1qtaR1dOUKTNQxMdhEcpIrF6xhFStKW2KuRrFg8OvXaJ+3NwYaPz+QdZE0aWfBEeGYmTPDCmVvD7IlWq5y5JAsVVmyYMucGedSKHRr2zFLSuMqlaQ2rl1QWLuYsFJJdP064tg0GiwiWrdGP9GvZacNhQJike7uuH7OzrBORkfDfbZ/P4iqoyPcytoWxCtX8Bw4OUHHST/GiBlk6/VrPK/i5+/f4zmbMEGgL1/20qRJk+jt27dERFSyZEnKn38GTZ/eierXN41NCwIWFj16SAKxXbrA2qYXChIPISEglps2YRI5cQIW6Rs3ML7IkJEW8ejRI6pTpw6FhoZSw4YN6ciRI2SXjJRxZrjhly+HVX7LlvhSI0+eICuva9dw6tlTJlAWg7kIFBGI0osXiLnQjo8QCytu3mxaKr42lErEBz15AuadkC7SihWY8P/91zKuGoUCloIDB2BJ6tPHcPkNfYSHh9PixYtp4cLFFBMDU1COHDmoSZMm1Lx5c2rUqBFl/8/3olTCgrF3L9xazZrBJfTkCSZDbeKaEDQa6Oe4u8NSpFJhZe/khJgbfdeR9u/z8sI1fvMGE/DHj5gobW1hUShdGkVxS5WCC1E8lkoF65K7O3ROvnwBwciVC26rXLlAhL58wb0rWhRkQywFI8ZDJfe+KpUgWb6+kktTJFzBwbiWYWEgkJGRICoqFQYyUezUykpSGReLBhsqHpwxI17Fv3/7DQTXmF6RWi1dn+vXJTXyIkWk+odTpoDkiOKWu3frPksvXmCfwoWR5i+WodFGdDRc3tWq4Tji9RT1xzZskBIzlEolbdy4kWbMmEFfvnwhIlg/586dS40aNUrUJTF5MtrSqRNI48qVcC+aEjg+eDDc2hs2IHA2WzbEdC1bBkkPGTLSGl6/fk21atWiL1++kIODA505c4ayZMmSomO+fw/rsp8fnqfOnXU/VyqJJk4Mp0WLZAJlMZiTQKlUMK+XLYugZ228f4+JZNu2+GzaFDx9isG0XbuELS5r1yLwdMUKywYbP3oEH/bLl3A9dOoUP/5EoVDQ2rVradasWXEWpxIlqlOFCtPp4MH6ZGdnPEI3KgpiigcOYMXu54dVfrduSWurNrm5eRPX9fffYZ367beEXUvaEIPBRXL15g1Is1KJz+3scM8LFwYpyJcP5OXuXRAqf3/s89tvINolSoCsiQrlHz/C8iQif34cR7sMTOHC6aegrCDoklilEqSmeHHcy+vXcX8rVwZxyJwZ95UZz492dqufH1x5UVGwTpUsaficHz+C1I8Zg+xZIhxvzRq4BTdsgPVNG0ol0eDBUeTl9S/durWAwsPDiYioTp06NG/ePPpDX778P2zdKlUZaN8e5zx2zLTA8bt34fLv2hXfWbIE34mKQuxTelSfl/Ft4+PHj/Tnn3+St7c3ValShTw8PCiHMRNzEiAIeB5WrIAu26ZNuvGHspCmhWFOAkWEFX3VqpAaGDxY9zOxdMTOnbqZPaZCo4FZ8+JFKDAbmiw2boTrb80ay2dsxcZCFXr3bgRJ9+pFVLOmhnbu3EFTpkyhDx8+EBFR2bJlafbs2dSmTRvavNmKrlzBhGZqTFVICKxSS5Zg0hs5EhNXcq5xbCwCvq9fx2t0NAhxzZqI4ylcOOnHVChgYdIuQuzjA/egSoVBIjISfefLF/yGnDlhhapdG5a2ChVwPQQB1iP9WnsfP0pZfxkzop1iQLr+livX15VNYIYlyd0dbtTISJCj0qXhcrx1C68VK8It9+efIE1792Lg/PAB1hftepORkYgPvHMHlidjRbc9PbHP+vWSG12phBVKtFjpExN/fyxU+vbFIiAoKIjmzp1LK1euJIVCQURELVu2pDlz5lAFLRXcK1fQ5l270A9r1oRq+JQpiQeOCwISRzZsQOzWoUMIll+2DM+zfoq3DBmWRkBAANWqVYtevnxJZcuWpStXrlC+VJDHf/cOi8uAAHhZxCxbmUBZGOYmUERwOTg5YQWprwX17BkG7j17DLsZTMHbt0j3d3ZGHJD+ZOjmhoF83bq0oy/k5cU0fvxxOn58AkVFPSEiZNFNmzaNevXqRbZabGnfPmxbtybu7tDH+fNY8Ws0yMarVg1uDweH5Cm4M4P4XruGidjHB/E7NWrgmJUrm0cXKzYWx/bxgQXr5k1YE7294V5Tq0GIM2YEASpZEhNziRIgRWL5ljx5EM8kKpIHBmILCJD+Dg7WzeazsdENShe37NmxnxjbpNHoxjol9r5ajWchKAgEsHx5SQoiJATvOTpCgT5rVpCFHTvw2xUKuEO7dEGmnEhw1GpYN/ftQwxEkyYJW2ViY0E+7t7Fd8RaWl++gMz3748EBn3cvw9r7/Ll8TNovb29adq0aeTm5hanIdWjRw+aPn06KRRFaMAAEB8xCHzYMLjw3NwSJ9/r14NQh4fj/jo7Y6KoXh0ESoaMtISwsDBydHSk+/fvU5EiRejq1atUtGjRVDufIGCRvHo1xozVq4k0GplAWRSpQaCIQJCGD8cEok+UHj6EhWrvXuPBtMbADGuTWA5CLDshYscOaOps3JhyvaaU4urVqzRu3Djy9PQkIiJ7+xxUqtQ4Kl58CPXu/QM1aRK/jadP4wHZuTPpMgPM+P6iRXCL2dsjADdDBkzYDRtick6uOyQkBMfz9MS9ZAaZcXBA7E5qlXQJCsI5z54FCfnwAUTDxgb9KE8eWK7s7EBqtH+ftbUUPC4Gjouv9vbYVwwIj43FJB4WJsU82dhgs7aWsvf0r5/4vzhSMcNSdPcu2l6mDK5/nTo494ULEtmPjkZMU7NmsProkw1mEKzlyxEL0aNHwv1aENBvNm8GSWrXTmrbvXsIyF6xwrCW0p49eHY2bza+wHn27BlNnDiRDh8+TEREdnZ2lC/fYDpxYjx9+pSbdu3Cs9e+PfTdEhPVDQzE71qzBparw4dhhXr+HHF/OXMa/74MGV8TL1++pN69e9O1a9cob968dPXqVSpranpqCvHmDcJCIiKIVq0Kp3r1ZAJlMaQWgSLCKvb0acQE6VuC7tyBYNi+fYarTJsKHx8QtSpVkLGnfZ49e4iOH8dkYAkS9fjxY5owYQIdP36ciIgyZcpEw4YNo7Fjx1LOnDkpIABp3SdPwlLUu7duivjVqwju3bkzeaRETIHfvh1ihPXrI2D4zBmoXBcvDjLl7Bw/RispEGUIPD3h+gsJAUEoXx7EtkIFkIfUqmH45QtI1aVLIHQ+PiARomWpQAHETBUpAmJSuDBiu8LCpE0MJBf/Fmvm6ZMkQ4HjCb1myIBrUKcOrGPXriGGwcMDBK1QIeh/9esHd15CuHkT4pk1asDqZCw29fx5ZNk1awZ3uXZSwO7dcK1t3hy/P2k0CFSNiUGGn6nPy40bN2js2HF0+fIlIiKyt89GBQuOoevXh9PMmVno998x2CeGv/5CjNbixejzXl4Q4u3RA9YyGTLSAj59+hSn4q/RaCh79uzk4eFBVatW/art0GiQXLVmTTh9+iQTKIshNQkUM1bc+fIZjn+4dg0p13v3Gp8UTDnPli1ER47AVaG9cj5wAJubm/mKAieG9+/f09SpU2nbtm3EzGRjY0N9+vShKVOm0I8GIuiZMUlu2gSXVaVK0Ez680/4vseMAQkyJcvOEKKj4cq5fBmkVSwJ4uUFMuXujiDdP/4AoapWLeWuz7AwZIY9f47t9WtYizJlAkmsUEFya6WEQBtDZCSI3ZMnCOj38kIslr+/blHhzJnRR3/8ES7BsmWh0SSSWVGSQNtVJ0oViO8Z+l+lQrzeuXOwsOTNCxLbt2/iGZpfvuC7hw7BsjZlivFMvocPEUReoQKSCbQJsUaD+65QgFzpk6OwMJCXZs1g9UkKmIn692fKn/80HTo0np4+fUhERNmyFSAHhyl05EhfypDIg3f9Op7PJk0QvzhuHApY29sTnTpl+VhGGTJCQkJo/vz5tGzZMor9L9jSxcWF5s+frxMD+LVx9244Va8uEyiLQSRQfn5hlD+/eQkUESaesmVhJRo+PP7nly7BBbd7d8qzqO7fh/l/8WLdEjJHjsCKs21bwmn65kBAQADNnj2b1qxZQ8r/UtDatm1Ls2bNonLlypl0DEFAnNjFi7BAhYWBON29CxJlijRCwu3D6v7LF2RuaTdJpQKJO3MGbp5MmWDxqFMHSQHmsuDFxsL6JRKrFy9gihalECpUgNWqbFkQjtSePJkRgP7gAa77mzdwDfr64tqLLjtr6/h/G/pff6tUCZbFBBLW4hAcjGfBwwPXJ18+xBHWq2c8dujjR4jOWluDQOnvGxoKwubiAmuOPl69QozV7NmJt9EQFi7E65AhRC1bCuTktJv+/XcS+fp6ERFRqVKlaNasWdS+fXuyNnAz1WrEOW3ahPadOAFL2MOHcEGnsJyjDBkpQnR0NK1YsYLmzZtHoaGhRETk4OBA8+bNoz///NOyjSM5iNziEG9A7dphNHduNh0lcXPh0ydM/GIqsz7c3TGAN2wIU35KMkCDgjBhNG+ua/o/fhyr3O3bze9KYmZau3YtjR07liIiIoiIyMnJiebNm0f/M5YeZdKxQTQOHkTcSrlysBDVrYtAwuS49t6+hUUje3a8GrJsRUXBMnDpEoiprS3iWOrUQVCvuYmoWg0LkUisXr0C4ROf+mzZJPebuImSCOnRQhERgdgnd3dIdOTMiWvr5AR3XmLxaaGhMOG/egXipE+sVSoEZR86hGfLUAzS6dPI6Nm0KWF9NWM4dAjHWLNGUjwvWZJowAAlNWmynubPn0n+/v5ERFS5cmUaM2YMtW/fXscitWIFFk5eXiDsBQuibl+FCmibDBmWgFqtpk2bNtH06dPp8+fPRERUqVIlmjt3LjVt2jRRHbSvBZlAWRjiDfjwIYxmzMhG2bMTzZxpmv5PUuDujoDSly8NB6cKAkjO+vWYQIYPl0T9kgqNBpNKUBDkDkTCdPo0jr9zZ9Kz2xLCp0+fqE+fPnTmzBkiIvr1119p3rx5VL9+ffOcQAt+fpikBg4EubhyBZaLsmVBqGrXTlpm461biK0RK30bC1aPjYWF6tIlxK8RgUjVqYPJ2VzXMyGEh0tZeuL28aNU548IbmDtOKd8+XQLEn8tF64hREfDZe3hAUKaJQsIsJMTgrlNHY8VChCWkyfhqnN21v2cGWR71SqkPXfrFt8dywzrzosX2C859+7uXVgxDxxArFLu3OibrVtLWm+RkZG0dOlSWrhwYdzComjRojR8+HDq27cvRUVlpZ49YYGePh2Lm0aNQP6OHUtZXJ4MGckBM9OBAwdo4sSJ9OrVKyIiKlasGM2YMYO6dOmSpLIsXwMygbIw9G/AqVMYXKdPRwyOOTF/PjReXr0ybjW4cwcxOxoN0qATy+BJCEePIpNt40bJtXHuHCaNnTtTRhKZmXbt2kWDBg2i0NBQypQpE82bN4+GDBli0FVhLgQHI1tp0iTcH1Fm4OJFxDgFBWHy+vVXWKoqVzbuGmWG22TpUhDcPn1Mc9cplbhPly+DWKlUcPXVqQNLQgpFeJOFyEhdciWqjYuK4yqV7v7ZsiG+SCRZ+lu2bLoyBtqxTqZsKhVisG7fBnlzcEBMYJUqSY8xEwTECm7YgHvUsWP8Z8jTEy7aOnUgFWKof8fEILi8alU8W8lZSPv4wN22fz9cvtevw23eti00pfSNrsHBwbRmzRpavnx5nEUqR44cVKTIAFq4cAitWlWQVqxA0se9e3A3dumS9HbJkJESXLhwgcaNG0d3/lsh5smThyZNmkR///13skqyfA3IBMrCMHQDwsKQ6mxvD/O/Oa1RLVticvkvMc0oRAHBly+RqeTikvSJ5/VrxHhMmgQrDRGsAEuXIiMpORN9UFAQDRgwgPbt20dERNWrV6dt27ZR+aRUTE4BIiNBogYOxIpdH76+sBDcu4dYkthYWGNEUvXLL/F/t1qNQPydOzHBtm6dtLgntRpxRJcuYUKNiYEF4eefpa1o0bSjJM0Mq5ZIsPS3wEB8bqhki7FNex8bG7iiqldPmfXr4kVYeRo2xD3Xtxi9fIk4qAIF0M8TKr5tSI08qYiMRIHgNWtwjRYsALEbORKEXlvsUx+xsbG0bds2WrRoUdzq3tY2I1Wp0o1mzfqHliypQBkzYuGTVvqJjG8fd+/epfHjx9O5c+eIiMje3p7++ecfGjlypNkTq8wNk5PAWEaqICwsjImIw8LC4n126hSzoyPz5cvmO59Gw1y6NPOsWaZ/JzSUeeFCZicn5lWrmKOiknbOyEjmrl2ZFy1iFgS8d+kSc9OmzBERSTvW8ePHuUCBAkxEbGtry9OnT2elUpm0g5gBMTHM7dsz791r2v5+fswnTzLPnMncti1zkybM3boxL12K+xsejv0iI/GekxOuV2ho8tsYEsJ85Qrz6tXMAwbgejdtyty/P/PKlbgHwcHJP/63DC8v5k2bmFu2ZB492vB18vNjHjiQuXNn5jdvjB/v6lXc09evk98mtRp95/JltM/ZmTksDPd32jTTjxMbq+Gffz7E1arVZCKK2/Lnd2E3t8ssiA+pDBmpiFevXnH79u3j+l+GDBl46NCh/OXLF0s3zWQYm7+1IVugUgmJMdiwMKxYM2eGNcocrpmQEOgC7dqFtG5ToVJBN2rLFrj1Bg82vVwJM6xZd+6gZp69vSSjsGsX3DXGEBERQf/88w9t2LCBiIgqVKhA27Zto2rVqpn+A8wMtRqWuVq1kOmVVAQEIB7n7l28RkYimLlqVbj+fH1xvUuVgktILAOSEjDDBfT4Mdxbjx8jINrWFgHyorWqfPnUj6tKS/j8GZZRd3ckXRQvjvgoR0dkI2ojMhJxQzdvIgkgMRf3hg1wXWurkScH//wD92OLFrBQbtyIrMUtWxD3ZKrVaOFCBIvfvk1Upsw12rx5Id27d4QwjxH9/vvvNHr0aGrZsmWaizmRkf4RGxtLkydPpqVLl5JGoyErKyvq0qULzZgxg0qUKGHp5iUJsgXKwjCVwZ4+DWvUpUvmOe+tW8w5czJ7eyf9u4LA7O7O3KYNc9++zE+emP7dS5ewEn/xAv/fuMHcqJFxS8ulS5e4RIkSTERsZWXFI0eO5Ojo6KQ3PBWg0TAPHgyrkTkQFMR8/jzz4sWwUDVpwly3LnPlysw1ajDv2AFLhLmhVDI/fsy8cyfz+PHMrVszN2uGrVUr/Mb583H+y5eZ371jVijM346vBX9/WA8HDGBu0IC5Vy/mrVuZP35M+DsqFfO6dei/R49K1tSE4OUFy+uUKegnKcHatcyTJqENrVszX7vG/PIl2p4Ui/DHj7BC3r3L3LMn85cvsGT9/vsL7tXrL7azs4uzCJQuXZrXrFmTZp41Gekfd+7c4Z9++imujzVt2pQfPnxo6WYlG6bO3zKBSiWYegOYQTL++ot5yBC4elKK1auZixRJ2UT47Bna1Lq16a5GHx+QpkOH8P/t25gI9N0kMTExPGrUKLaysmIi4mLFivHFixeT39hUgiAwT5zIPHVq4pNqchATgwlv8WLm6tWZ8+Rh/uUXTP4bNzLfuYN9UgsKBcjA5csgWPPng1C1aiWRrNat0S8XLMA+V66AJPv7Y9K3NEJCmI8cYR42jLlhQxCbjRuZ375N/J4JAr7r5MS8fn3ivycoiHnkSLjbnj5NedvPnkV7NRrmoUOZd+3Cs+LoyPzpU9KO1akT86NHuAZ+fiDpAwcy79mDz/38/HjSpEmcM2fOuEkub968PH36dJPGKBkyDEGpVPL06dPZ1tb2P3dxfj569Kilm5ViyC48CyM5SuRnz0KDZsoUKTA7uejaFerbly+n7DhfvsCt8fQp3E316xt3KSiVcElkzQrZhkeP4KrcsgVZbPfu3aPu3bvT06dPiYioT58+tGTJkjQdVLhwIdw/ixenbhHlmBiUiNm6FYHhP/4IHR+FAi7AX36BO65UKYhjplQg1RQoFHCDffwobYGBcBeHhMDdKYIZ7sFcudDeXLkM/501q1QfT9y0Vca1/zb0v5iF9/gxAupr14Zbrlw509xdzKg5OGsWRC5HjjTuQo+Nha7ShQsoa1SnTsqv69GjcAHu3Qu9qMBA6DS1aYPA9aR4sM+ehSuxTBlcqxIlUPPOzw+v2tckMjKSNm3aREuXLqX3798TESQQtmzZQo6Ojin/YTK+G7x48YK6d+9Ot2/fJiKIG69Zs4byJJRtkY4gZ+FZGMkt5RIeDh0aW1tkCCW14K0IZsRV5M+PWKSk6BgZQnAwCq7euIHsOxcX45IJW7dCN8fVFRNt375q+uWXebR69XRSq9WUP39+2rBhA7m4uKSsYV8J27ZhsluzxriCtTnAjAlx7Vrct2HDIMj48CGkKt6+BbGKjcW++fKBVJUqBa2vUqUsVyQ2Nhb3OzgYm6G/RYV07dp2+rXuEvvsp5+gRm6qsoW3N+KgPDywKKhUCYW3jcX6CQIKAG/ZggzKtm1TnsUmCNAI8/eHoKWHB87h5obYQ0dHnMdUKBRQHHd1RRv37kUcVY4cRHPm6Kria0OtVtP+/ftpwoQJ5OUFdfPhw4fTnDlzKPPXYOYy0i0EQaCVK1fS2LFjKTY2lnLkyEGrVq2iTp06pRkhzJRCjoGyMJLiwjOEc+dgyj93LvltiIlh7tOHOW9e5smTzeNyCQtjnjePuV49uByMxe3cu4ffsG/fC65a9bc410GbNm04ICAg5Y35ynjxAr/78OGvd85nz5j//pu5RQvmEyfi30NBQLyLpydifaZOhVtIdMF16MA8YQKzqyvzxYvM798zx8Z+vfZbCr6+cDn26we3Vr9++N/X17Tvnz3LXL8+8/Ll5osJCwtjbtcO7kJmxBg2aoTndOVK5hkzkn7MmTMR89WnD+Ifhw/HexMmmPb98PBw/uuvv+KezfLly/OtW7eS3hAZ3wU+fPjAjo6Ocf2lfv36/NFYgGE6hezCszDMUUw4MhJmfbGSe3JLsXh5QSDQ15do5UqUY0kpoqORLXTkCNyFXbvG1+QRBIEWLFhFkyaNJY0mhrJnz06lSq2iKVM6U4sW6XOlolDgnigUuCdfa7EeFAQrw8WLcIHVrw/9oiJFjH8vIgKFk9++lSxXX77A1UoEC1bWrLDEFCggbeL/efOar15faiI4GNdGrHlXoACsOY6ORMWKmX6cBw+gtl+xIlzP5lLtfvkSWlMzZ6Ls0uPHsCzu3o1zbt8OK1RSFvDv3sH9+M8/OE6PHni+fXygOJ6UzN6TJ09S3759ydfXl2xsbGjSpEk0ceLERAsWy/g+wMy0detWGjp0KIWHh9MPP/xAixYtor///vubsTppQ3bhWRjmIFAiPD0RF4XCosk/zrFjkkTBrl3mSZ9XKOCu270b8Ru9eyMO5uPHj9SrVy+6cOECERGVK1efSpfeRBs2FKZx4yAR0Ldvys9vKZw6hZioZcsw2X5NhIQQnT+PEjo+PnBHNWqEa5ociYLISJAqPz9s2n8HBCBeSRwlcuSAWzFnTvyd0GuWLKkr2qhd8+7JE5y3bl3Ta97p48MHECdbW8QgmdNNe/w4VPpdXeGKPXCAaPNmxD4FB6O80qFDSSPjkZEYC1atIho0CEKtXbrAbe/gkLxxIigoiAYNGkR79uwhIqJq1arR1q1b6aeffkr6wWR8M/D396e//vqLjhw5QkRENWrUIDc3NypTpoyFW5Z6kAmUhWFOAkWE2JKZM4nev0dQt6k6TfpQq1FOZv16xE6sXJn8OCv94+7ZQ7RpE1PevNvo1KkhFB4eTpkzZ6aFCxfSwIED6d49KxozBhPUsWPQiJo0Kf2qI/v5Ie6kcWMUa7bE72BGgP/p0yAU1tawujRqhKBic7aJGdpSwcF4DQlJ+DUqKv53f/gB5MreXrd8i36AuHZguvb3tZEpU/Jq3ukjJASxhm/fgkD9/HPyjmMIggA9NB8fxA9myIC+HxYG8h0eTtSuHWKgChY0/bgaDVGnTlgM3bkDhfRPn1Cb0tMTpWBSct93795NAwcOpJCQELKzs6O5c+fSsGHDUrWUkoy0iUOHDlH//v0pICCAMmTIQDNmzKDRo0d/8zpicgyUhZHSGKiEcP8+4nC2bk1Zar2/P3PjxswFCzKvWJFyPRsc059btmwV5x8vXPgPvnPnlc4+YWFIsZ41CyroAwakjv7R14JGgxT/jh2R5m5pREYyHz8O6YEGDRA/dfiwpIhuKQgCdI0+fYLO0du30Crz9WUODISUR1QUdKu+hmB2bCzkI+rXZ75wwfzHDw+Hov2aNfg9oaGQP3B1xedhYdACu3cv6ccePRpq6qL208uXUFZv1Spx5XRT8enTJ27UqFHcs1ynTh328vIyz8FlpHmEhoZy9+7d4+7/zz//zA8ePLB0s74aZB0oCyO1CBQzJpm5czFgfviQsmPduMFcvjxzuXIIRE4uDh06xHnz5o2T7p89ew4fO6biJk2gnSMKbDJjQtm4kbl5c5SQad+eOb1r+t26ZV5BVHPh7VvogrVpg6DyadOY9+1DAHN6FsxMDoKCmA8cgNaVkxOCys2xcNDHq1c4/tWr+P/FC/x//bru58l53jZuZB47Fs9Lw4bMz5/jvq5diwQCc0IQBF67di1nyZKFiYizZs3Krq6uckmYbxznz5/nIkWKMBGxtbU1jx07lmO/h8wTLcgEysJITQIl4sULWJFWrUrZRCAIOEahQrBafP5s+ndDQ0O5R48ecSuVSpUq8f3793X2uXMHopzNmkHxWhSHfPIEE8mcOViNp/f6beHhUIGeOjVtiEzqQ6EAYd68mXnMGEkws3VrTMpbtjDfvGl5a5W5EBYGa9zIkch269wZiuOvX6eelevECTxDPj74/9gxnFt8psTsvuQkLnl4YLGhUjF36YJjbdyIrFhHx9RbhLx584YdHBzinnEXFxf2NTWdUUa6gUKh4KFDh8bd51KlSvFVcRXwnUHOwrMwzB0DlRAEAdpEJ08SLV1KVLZs8o8VEYFA9bNnkVU3Y4bxoGR3d3fq2bMnffz4kaytrWn06NE0ffp0srOzS/D4u3YhiLZiRdSbK1YMmUQKBQJ53dwSzyxL69i+HUG9a9YkLQPMUoiNJXr9muj5c2wvXuBeWVujdlyFCtKWL1/ajVmLjkYMkLs7NLPs7SGy6egI3ajUbDczYqm8vCC6aWeH+KePHxH/lDEjEg7u3SNaty7p2ZuvXyOL7/BhovnzEYzesiUy76pWRTxY06ap8csAjUZDixcvpsmTJ5NSqaTcuXPTunXrqE2bNql3UhlfDYGBgdS6dWu6cuUKERENGDCAFixYQPbmCJBNh5CDyC2Mr0WgRHz4gGyeP/5AWnNKUs9fviTq3h3ZWPPnE7Vvrzv5REdH0/jx42n58uVERFSqVClyc3MjBwcHk89x9y6UmH18EBArCJBFUKnwmt4Tf968QXZUv35JE0ZMS9BokLQgEqtnzyAAKcYS58qlK3mg/XeuXKlPtBQKCLt6eKCArp0dMtCcnFC0+WvFuUZG4j7/+SdITlQU/q9dG0kGSiX6QpkykEZI6nUJCUGG69atkGq4dw8q6q1b47jbtyOB42vg8ePH1K1bN3r48CEREXXt2pVWrFhBOZKrsSLD4nj27Bk1a9aMvLy8KFu2bLRz505qmppsPB1AJlAWxtcmUERYBW/bhgF14UKU/kjJsQ4eJBo1CqvdXbtQXuTWrVvUvXt3evnyJRER/f3337Rw4cJkr1QiIyGBsH8/MpGePAGJWrUKk2F6hlJJNHkysq6WLEEW2rcCZkzs+tIH4mtICEgxEQhXnjwSwcqVC59pNLqbWm38f/E9tRoaSERYMDg6ovSJJSSL3r4l6t8fMiO1a0v/T50Kq5CfH1GvXiA6zZol/fgqFcjTlCmwFP77L6ybXbsi83PFCli4vqalU6lU0vTp02nevHkkCALlyJGDmjVrRs2bN6eGDRum6bJMMnRx+vRp6tChA4WHh1PJkiXp2LFjsmwFyQQqDtOmTaPp06frvJc/f37y8/MzuP/FixcN1oR6/vw5lS9f3uTzWoJAifjyhWjECKKSJSHWlydP8q0BMTFwsR0+rKIyZWbStWtzSKPRUKFChcjV1ZUaNWpktnbfv4/yJWfOwB2zdCm0bdI7xBqHixfD3fK9QaOBEKg2ubK2hoVI3Gxtdf839J72/0WKwOJkSZw+DWLs6or2nDuHhYv4/507sDitWgX3Z1LBDItWvXroN/37w/09fjwWFwoFxHEnTDD/bzMF169fp549e9KrV6/i3suQIQM5OjpS8+bNycXFhYoWLWqZxskwCmamlStX0vDhw0kQBKpVqxYdPHjwm6hjZw7IBOo/TJs2jfbv30/nz5+Pe8/Gxoby5s1rcH+RQL18+VLnwuXNmzdJ2heWJFAiDh+GUnhQkKSjkzEjLD0FC8KypP2aO7fh2mJPnz6l9u2707Nn94iIqH79TrR790rKlStXqrQ7MhITz6ZNmDg2bIBYZHqGvz/RuHG4vtOno1CwjPSJ0FDEOwUGQkctUyaQ46dPiVavRnzTzp1wq23Zkvy6hP/+i4XEwIGwQm3ZAgtzhgxQM1+yBLXvLCnJo9Fo6Pr163T06FE6cuSIDpkiIqpSpQo1b96cWrRoQVWrVv0mVavTG1QqFQ0bNozWrFlDREQ9e/aktWvXJhi7+j1CJlD/Ydq0aXT48GF68OCBSfuLBCokJCRFfv20QKAMQaGAFeDzZ6xetV/1iVaePLHk7b2Kzp+fSGq1grJnz0WNGq2hy5fbU+3aIDZZs6ZeWz98wErbzg7WtAoViJo0IapT5+uVUDE37t+He+eXX0ASU/P6yTAvYmNhTTpzBoWI69cHwRkwAArgw4fDNTlxIty3CxYkPxbxxAm4tdetg9jmpEmIQbt/H1blvn2hXp6GhhYiInr58iUdPXqUjh49SteuXSNB9OMS0Y8//kjNmzen5s2bk6OjozxhWwAhISHUvn17On/+PFlZWdH8+fNp1KhRMrHVgyyk+R+mTp3KP/zwAxcsWJCLFy/OHTp04Ldv3ya4v4eHBxMRFy9enAsUKMBOTk7s7u6e6HliY2M5LCwsbvv48WOqyxikBgICAnjLli3csmVr/uGHLHEprRUrNuFhwz5znz5Iy86XjzlTJuZff2Xu2xfFilevZj50CKnyHz6YR2dIpYLEQeHCSBFfsgT6US1bQgDUyK1MsxAE5pMnmZ2dIbSYFiUPZEhQq5nd3CAVsGuXJBny4QMkCcSC36Gh0NvavDll53v0CPIkMTEQmt23j/n0aeZOnSD14ezMnB40Lf39/XnLli3cunXrOC0pcbO3t+c2bdqwm5tbuiwsnh7x6tUrLleuHBMRZ8mShY8cOWLpJqVZyDpQ/+HkyZO8f/9+fvToEZ87d47r1KnD+fPn58DAQIP7v3jxgtevX893797la9eu8YABA9jKyoovJaKQOHXqVJ0BQtzSA4F6+fIlL1y4kGvVqsXW1tY67f/xxx95/fr1BsXzzp5lLlYMJOr4cWwbNqCq/IABks5Q06b4e9gw5qVLQbLu32cOCTG9jVu3MufOzTxlCia0iAjmI0eY+/fHJDZ8ONqTnvTeVCpcLycn6AXJ+oRpC4IA0u7sDLIuLggEAferXj3md+/w3qtXIFjXrqXsnH5+zHXrolLAkiXQSLtzB4uIyEhodqX0HJZATEwMnzx5kvv378+FChXSGWOsra15+vTpskBnKsLd3Z1z5szJRMRFihT5rlTFkwNZByoBREVFUalSpWjMmDE0cuRIk77j4uJCVlZWdPTo0QT3USgUpFAo4v4PDw+nIkWKpDkXHpFu3MLRo0fjMupEiHELzZs3p19//dWoeVehgCtqzx6kbk+bZjgmQ6GAJs7793DNvX+PLTQUn2fIgCy/YsWgPSRuOXJIAfDe3qg7lykT4kHEumXMcG+cPEl0+TLcj/XrY9/0oMMUGYkYmjt3cP2qVbN0i2TcvInak9WqIRNVdLXevg19tF9/Rb/PkgVJAosWIWYvJUWIY2Oh7bR4MbIMDx+GO7BfP7jzxPak98QKQRDo3r17ceOPKIkwYcIEmjVrluxOMjM2bNhAAwcOJLVaTb///jsdPnyYChQoYOlmpWnIMVBGUL9+fSpdunRcEF1imD17Nm3fvp2eP39u8jnSWgxUZGQknT17lo4ePUonTpygwMDAuM/MkTnz6hVRhw4gA9u2Ib08KVAqJYKlvYWE4HNra4g45stHdP48grLr1kVsSPHiurEm4eHY59QpkLXKlbFvpUogaWm1JqqvLwhUbCwm6fRA/r41vHyJe5ArFyQoxHnmzRvErmXPDkmBAgVA3P/9l+jBA2SPpiQujxmimF26QOph0iQU/O7aFYuF06fxfMycmfLfmNawbNkyGj58OBERjRs3jubMmSOTKDNAo9HQ6NGjaenSpURE1KlTJ3J1daXM6TWA9CvC1Pk7BXKL6RMKhYKeP39OtWrVMvk79+/fp4JJKZeeRuDr6xu3yrtw4YKOhSxHjhzUtGlTat68OTVq1CjFJK9sWUlluXVriAq6upoeJJ0xI1GpUtgMQaNB1tPnz8hA2rULAopnziDAXEwItLHBBFSoENFvvxG1aAFS9/AhVvSfP2O/fPmghi5uRYtaXmG7YEFcvydPIB1RujRS1mWNwtSHry/ISVgYyGuZMnjf3x/vBwaCWJUrh/cVCmg7lS0LgpPSvjNzJtHvv4Psi6Spd28QtDdvIKC5fXvKzpFWMWzYMLK2tqahQ4fSvHnzSKPR0Pz582USlQKEh4dT586d6cSJE0RENGPGDJo0aZJ8Tc2Mb94CNWrUqDirir+/P82aNYsuXbpEjx8/pmLFitH48ePp06dPtHXrViIi+vfff6l48eJUsWJFUiqVtH37dpo3bx4dOHCAWrdubfJ5LWWBUiqVdOzYMdq0aROdPn1aJwumZMmS1KJFC2revDk5ODhQhlRSHgwNhZL5nTtQMu/WLVVOQ56ecHEULQoX4IIFsBD4+4MoaW8BASBSkZEQYlQoUK4kMhKvMTGYBLNmBbn68UdYgH78EZlO9vb4LFs23desWUH+UgPnz+P6ubhA0Tq1zvM9Izwc2k0PH8Li9L//4f3ISMgE3LyJ90WLKjOI+MqVUPxv0iTlbdizh+jqVdzrVq2gfzZ1KrL7ChcmGjwY5/yWhFgNYdWqVTR48GAiIvrnn39o4cKF8oSfDLx//55cXFzoyZMnlClTJnJzc6P27dtbulnpCrIF6j/4+PhQp06dKDAwkPLmzUt//PEH3bhxg4r95x/x9fUlb2/vuP2VSiWNGjWKPn36RJkzZ6aKFSvSiRMnqIk5RspUxKNHj2jTpk20Y8cOHffcb7/9Ri1btqQWLVpQhQoVvsqAlCMH0dGjRJcuQYV5yRKUZzF3bI+DA/R2+vSB2GDHjkQ9exJ17gwLVFKhUCD25MEDosePUb7kyhWoQdvb43dlyYIYLFtbuB0jIvC5IWTJoku0cuaE66dgQenVmIB7vXooS7J9O1GjRrB4tGqVdl2Q6QkKBdxux48jxmnGDBBosZTQ/v2QJZg8WbIu3byJ/WrWJDp2zDyE5uZNWFP37oULb+xYqIu3bg1phLZtcf+/dfJERDRo0CCytramgQMH0uLFi0kQBFq8eLFMopKAa9euUcuWLSkgIIAKFixIR44cof+JqwIZZsc3b4GyFL6GBSo4OJh27dpFmzZtonv37sW9X6hQIerevTv16tWLyqakurAZoFYjKHb5crjaNm2S3CPmgkIBbZx8+RCHcusWzlmypHmOzwwV7TdvQLDevsUmBsBnySK5H0uWxGuhQmhXeDhIVng4UXAwjuPrCy0uX19YOsT5IVMmqaacNskqUABEa80aBCxXrYpYmcqVLe92TE8ICgKp9/BA0kGvXiDb1ta4x4cOwbLUpQvIjBhX9+4d4p5y5MBrvnzmaY+3N9pw8CCU6suUQZ/IlIlo6FCQqOnTEbD+PWHdunX0999/ExHce0uXLpVJVCIQBIHWr19Pw4YNI6VSSVWrVqWjR49S4ZRkNXzHkIPILYzUIlAajYYuXLhAmzZtosOHD8fFNWXIkIFatGhBvXr1ogYNGpBtSqoJpwJiYxFDsmULUfXqEOE0d1jZhg0gGBMnwgXy558oaZPalyIigsjLSyJW796hSLIg4NxFioBYlS6NrWRJw2VIYmKk2nLaJMvPDy5IjQb7iWQsOhqZiC4usFYUKoTJ3ZLK1GkJoaHIyvTwgDUxVy6IsDo6gqyIc/KVK0Rz5qCW3bBhkrUnKAhFez9/BpFJQiWnROHjAzf3xo0gdS9eQCj24UOQ/4EDiRo2hMXxe8SGDRvor7/+IiKiIUOG0LJly2QSlQAePHhAAwYMoBs3bhARUevWrWnr1q2UJUsWC7cs/UImUBaGuQnU27dvacuWLbRlyxby8fGJe79y5crUu3dv6tKlS7qoYxQRAZfJ4cNEDRpgxZ89u/mOf+MGCNSKFUR37yIjcM4ckDZLQK1G9tS7d7BgvX4NsqVQYAIvUgSTeZkyErlKLNZJo0Gc18ePCKI/dQoEq0ABuApFAmVjI1mxChXCVqAASvbkyoV9v6U5KSICZMjDA4H42bKBFDk6gpzo/9Znz0C0CxdGnxEfn9hY9Mvz51FnrnZt87bT0xOWrHXrQKTWrgWZ2rED/XXZMvSPcePMe970BldXV+rXrx8xMw0cOJBWrlwpkygthIeH05QpU2jFihUkCALZ29vTzJkzaejQoWQt+/lTBJlAWRjmIFBRUVF04MAB2rRpE126dCnu/Zw5c1KXLl2oV69e6ba+VGAgYno8PIg6dYILw1zZtX5+yGDq14+oVi2U3ciYEUG/FvZo6kAQMIG+fo3tzRuJXFlbxydXJUokTK5iYhCXs28f3FHt2kEHKzJSN5jezw/Wq6AgEA5t2NggTkskWLlzG/47rWRBR0WBjHh4wHKTJQusjo6OkKwwNIeIJOvAARDRqVNxXYlwP3bvhlXor7+I2rc3f7zZhg0oOuzqCuvi4MEgU/PmIe7qwgW0zdX12yK3ycXmzZupT58+xMw0YMAAWrly5XdPDpiZ9uzZQyNHjiRfX18iImrfvj0tWbKEfpSLbJoFMoGyMFJKoCZPnkzLli2jiP9mOSsrK2rQoAH16tWLWrRoQZkyZTJ3ky0CHx8EgT94AEI1YYJ5XG5KJaQAcuaE6/DZM6SER0QgvsTBIW1PUBqNYXIlBqxnzgyCVbiw7muBAnBd7d8PQpU3Lwiqk1Pi11Wthu6WSLCCgqS/td+LjTX8fRsbWLXEjMWEXjNlkjIhlcqkbwoF9L3s7HAfHR1RW9CQ6zI6mujaNZCs+/fhnqtVC+4xbZfcxYsoENywIfqhucu0qVRwJ+fODdL25g1R//6wjk6ahPvl44Mg8kOHzH/+9Aw3Nzfq1asXMTP179+fVq9e/d2SqJcvX9KgQYPowoULRERUunRpWrVqFTVo0MDCLfu2IBMoCyOlBGrSpEk0e/ZsKlWqFPXq1Yu6d+9ORYoUSYWWpg28fAmr0bt3mFAGDjQPwdm8GZlWGzeCTH3+DPfegwcgbq1apc+YoehoTLg+PnDlffyIv/38pFipH36Ae/TzZ3xeoQLETp2czOs2FaFWS1IRERG6MhHarzExsKRlyIDX5GyFChkmhAoFMtvc3SGjkSEDsuacnBAnpn+vnz9Hpl3x4nDj5cxp/usSEIBg8d69ERh+4gT64MKFiLlycwNh6tgRkgZ585q/Dekd27Ztox49ehAzU79+/Wjt2rXfFYmKjo6mOXPm0IIFC0ilUpGdnR1NnDiRRo8e/c0sptMSZAJlYaSUQPn4+NC7d++oVq1a6dJFl1zcvQtiExyMCaZDh5Qf8/ZtxJN064ZYE2trTOabNkFuoVUrTHDfWsxlVJREsHx8cB1u3QKhUqthEcqVC9ll+fIhBkjccueO/39atIqoVCBKHh6If7OygiCloyPi3hKSOvPzQ2B4ZCSkCUQ3nrnx4AEsocuWwa04dy4siZMmwcW8eDFctK1aoRxMxYqp045vATt27KDu3buTIAjUp08fWr9+/XdBoo4fP05Dhgyh9+/fExFR48aNacWKFVQqIdVhGSmGTKAsjLRWyiW9wd0d4pGCgCDbevVSdjyVimj1atTLmzEDkywRiMSBAyBT//sfYlK+hzJRgoDMr+vXEUf08SPcgmXKQJg0WzaocgcGSptSqXsMUXg0WzZYtLJlkzbt/7X/1idhzLCYqVQ4vimvCgXRo0dwzanV0BdzcsI9TYjkCQLIjLs7fq+1NUh1akrk7NmDwPDNm9Gu/v1hDatYEX1w5UpYBXv1gvWpUaPUa8u3gl27dlHXrl1JEATq2bMnbdy4kWzSownZBHh7e9OwYcPo8OHDRERUuHBhWrZsGbVq1eq7WlRbAjKBsjBkAmUe7N+PIHAioiFDQHBSosgdEACXjUqFFHVRSoEZKe8rVsCNM2IE0U8/pbz96QkhIbDiXL+OoGyNBpN9zZpENWrE1z8SBFhwwsNBtsLDE/9brCakPf7b2Oi69Ex5rVAB7UpIYJIZbmF3d8gERETAhefkhLip1AyE12hgYYqNhRXV2xtB6RMnwgL49CnIvL090ezZIJZDhqRee7417Nmzh7p06UIajYZ69OhBrq6u3xSJUiqVtHTpUpoxYwZFR0eTra0tjRgxgqZMmUL2xpR3ZZgNMoGyMGQCZV5cu4YA2xcvUBh44ULErSQX9+5hkqtbF3Eo2paL589RTiMoCBNbnTppO+A8taDRYLK/fh2bvz9ceRUq4NoXL45yNwULpg11dG9vECZ3d7S1fHkQpjp1UifmyxDCwhDr1Lw5xDjPnUNfXbwYLkNnZ1hWraywOLh4EaT9e+xfKcG+ffuoU6dOpNFoqFu3brR58+ZvgkRdvHiRBg4cGFe4vlatWrR69WqqVKmShVv2fUEmUBaGTKBSB6GhSPveuxfBtlOmoNxFciYgZqStb9gAiYOmTXU/9/MjWrUKQck9exK1aZM244C+JoKCkBX4/r20+frCGmVlhULOIrESSVbBgqkTqB8QgNinCxeQlVekCAiKoyPa8bXx4gUy+ObOhWtwyRKUBOrbF/10wQJJj2zrVgSTb9+ecJyWDOM4cOAAdezYkdRqdZyIcM2aNSnvV4zCj42NpTt37pCnpyd5eXml6FifP3+mY8eOERFR3rx5aeHChdS9e3fZXWcByATKwpAJVOqCGZpHM2YgPqdDB/ydHEtDVBQmvWfP8FquXPzPt26FLEDhwij/Ubt22rC6pCUww/KjTa7ev0fQuljTOl8+yW0qCIlvGo3h9wIDQaAdHWFlSok10hwQM+s2b0YfHDAA0gqZM4PgrV+PgH21mmjMGBDxWbPSZwZoWsLBgwepQ4cOpFar494rU6YMOTg4UM2aNcnBwYHKly9vtmDzL1++kKenJ127do08PT3p7t27pEqoGGYyYGVlRf3796fZs2dTrly5zHZcGUmDTKAsDJlAfT28f484qYsX4bZZuJDojz+Sfpx376BDVbgw4qQMkbE3b1DA+MoV1CgTa9LJSBzMsBr5+sJaZW0df7OxMfy+9udWVogbSgsLc2aIYL57h6BwPz9YnIYORXJC+fIgTNbWyCzt3Rtkv1MnS7f824Gnpye5ubmRp6cnPXv2LN7nOXPmpBo1asSRqt9++41+MKE6syAI9PTp0ziy5OnpSe/evYu3X/78+cnBwYEqVaqUIjeitbU1NW7cmKqZu+q6jCRDJlAWhkygvj5UKlgBVqyApWLIEMQ3JdVFcuECBA67dIHrztDilRnp8zt2wHLl7AzL1Dcs1SVDD1FRyKz74w+47i5dglVpzBii+fNBwuvWxb5Pn6I/Llr0/RUH/poICQmh69evx5GemzdvUkxMjM4+tra2VKVKFXJwcIgjVT/++CNFRkbSrVu34sjSjRs3KCwsTOe7VlZWVKlSpTjrVs2aNalkyZKym+0bg0ygLAyZQFkWt27BKvX0KYKIxWr3pkKtJlqzBiKc06cbt2ip1Qhc3rkTMULNmyMuKzVEGWWkDbx9Czfd+PEgSatWIdC+Xj1YnrSLZR85grp3mzdbJjbre4ZKpaKHDx/qWJE+ffoUb78CBQpQQEAAaUQV2v+QJUsW+uOPP+II0++//045cuT4Sq2XYSnIBMrCkAlU2kBEBAJ4DxxACn358nCfdOkCDaPEEBCA78fGYrJMrJZedDTEOffvh7upfXsEp8tiwd8GgoMhPfDxI4LCCxSAtEbx4nBNZstGNHMmVNKZse+nTxDSTIn8hgzzwdvbWyeO6eHDhyT8F6RXtGhRnfipn3/+mWzNUVtKRrqCTKAsDJlApT1ERMBKtGsXpArEWmp9+iAY2Vj4wpMnyKqKjDS9ll5AAALdjx9H6ZGOHRF8Lk+k6Q+xsXANX7iAOLnatUGMevcGId+1C0TKxQX7R0VJxaxF2QIZaRORkZH06NEjKlq0KBUuXNjSzZGRBiATKAtDJlBpH2/fIjvq+HFkj+XPD2vRX38RJVQlQayld/++VEvPlAXqu3ewSl2+DAHFJk2IGjeW656ldQgC4ty2bAEREiUzzp5FnFOHDriv69ZJ5WDevwd5mjQJ7mMZMmSkL8gEysKQCVT6gkYDTSFXV5T6UCggGNmpE4LD9d19kZEo/3LkCFGLFrBEmCoSHBREdPo0ysqEhMBK0awZaqXJloq0g/Pn4aZr1gzkKWNGlIOZNg2uXEFAYeTFiyUX7cWLcNtt2GB5aQUZMmQkDzKBsjBkApW+Ycjd98svqFfWurUUIKxWEx06BOJVtSoyrQoVMv08KhXR1auwgj1+jInZxQWWCzluyjJ4+BAkqUIFqN9nzw6hzqlT4eatWRMCrD17IpaOCPFOa9aAfK9f/+0VppYh43uCTKAsDJlAfVt49w7q5+fPo8aaUkmUJw9qxLVsCTHHe/eIli+HJWrECKKff076eV69Apny8IAIY6NGcPd9DwWOLY2PH0GSrKyQeVm4sBQ0/v49Uf36RIcPg9wOGSJZHJVKxMUVKYL4KNmKKENG+oZMoCwMmUB921CpYG04cAAWJF9f6EWVLAlLVUAA3ICDByO1PTmTamgo0ZkzULkOCIDl488/oSP0tWq7fQ8IDYXMxatXsDxVrgzX3IoVkKdo2BA17X77DcRY+9p/+ULUqxdcfM2bW+oXyJAhw5yQCZSFIROo7w9BQbAeHTuGWJnQUMRWZciAOJoZM2DVSA7UaqIbN7DdvUsUHk6UIwdRtWqor1a1qmmyDDIkKBRwu508CfHLevVwv7Ztw1a3LvTEfv6ZaNQolGLRxt270BpbsYKoYkWL/AQZMmSkAmQCZWHIBEoGM1TKd+8m2rOHyNsbVqo8eSDM2aAB3HNJiZnSRnAw3IZ37iArMDKSKHduEKrq1YmqVCEyoWLFd4eoKAT/b9yITMpOnWAhPHUKUhW//go3benSiIHKl0/3+yoV4pzOnUMigVyyTIaMbwsygbIwZAIlwxDu3ydauhS19IigLyQIEGCsXBklYZo1QzxNctx+gYGwjIikKjoa8gwiqapcGbFV3xOUSqKbN+GOu30b2XT16iFzMlMmvDdjBoisvz+u/bhx8YktM0RSly9HZmbPnnIxYBkyvkXIBMrCkAmUDGMQBNRO27ED2lJVquC927dRsDgmBqSqYkWQqqZNEV+VHFLl5yeRqsePQdqsrBDLU6IE0u1LlMBWpIhpulZpGRoNyKO7O9G1a7iuv/+O61itmlQb8e1bxDypVCBZ+fIhCLxo0fjHvHkTJKtGDcRByVl2MmR8u5AJlIUhEygZpiImBnFTe/fCxdehA0Q237xBTNXFiwhwjomBnEK+fJA7qFoViugVKyYvqDw0lMjLS3fz8QEBIcJ5RGIlkqwCBQwXV7YkRFepuzuESqOi4IZzckLgvbYchEKBOLJ9+/B7M2SA23PiRBBUfbx9i8y87NlR0keuZSdDxrcPmUBZGDKBkpEcBASASJ04AWtQ164gSdbWIAqfP8OadOMG0aNHkFcID0eQeaZMiK8qVw4EokYNaBklp6gxM9xZ2uTq/XtYs7SRLRvOmTs3XvW33LnNX7qGGe1xd4fcQ3Aw0U8/gTDVrq0bTK9Ww/Lm7g4rkkKBdkVEoO2TJuF66SMoCDXtfH1heTK0jwwZMr5NyATKwpAJlIyU4vVruPiuXoULqmtXECJDEATUZtMmV+/fS+Qqc2aQmTJlYEkqUwakoFAhWJWSI9rJDCISGGh8U6mk71hZwf2lVmMTrV36xxX3NfQ+M9xsTk7IlMuTR/c6PHoEwnTlClFYGIiSeL78+SEF4eRkuFxPbCxinNzdYZWqVSvp10WGDBnpGzKBsjBkAiXDXGAGKdqxAy6lPHkwsdeuDRKUWFyURoMMwPv3QbBEV11AAAiDQoH97OxApHLkgNxC8eIgWqVKQXm9YEF8lhKhSEFAYLutLdxn1tYpOx4z0YsXIDwXL8Ji9MMPIEsZMiCTrlYtkCZjEhKCAOX5TZug6dSunSyIKUPG9wqZQFkYMoGSkVrw94dV6soVkIesWRHrU7s2RDyTkxmmUIB8fPoEkvbyJYjWx49wkSkU2AQBRCtDBrjmMmYEqcqdGzFTBQvCopU7N97PmRNbjhxopzFSwgziIwZ1q1SG/1YqYWW6cAFttLWVMhmrVAFhqlnTdNflhQsoDNy0KdGAAeZ3OcqQISN9QSZQFoZMoGR8LYSHI9vsyhXUcbO1hWp2rVpE//ufeWvqRUbCLRcUBGIVEIC4rM+fER8VGIjgdJHoqFQgRSJxyphRyvJj1t2IsJ+1tWSZEv/X/psIhKlAAUmd/bffki7P8OQJAsRLlyYaPx4kT4YMGTJkAmVhyARKhqUQGwsF7StX8KpWI2OvVi0IeH7tMjCCAFIVHAziFR4OImRjI23a/5vyWb58yZNbiIzEddm/H+2aPt2wbIEMGTK+X8gEysKQCZSMtAK1GqVlREIVEQFrTrFiCEovXx5boULfXtyPUon4MXd3ZOPZ2YFINm4sZ9bJkCHDMGQCZWHIBEpGWoYYWP7iBbbnz+GGI0JpEm1iVbq0JD6Z1qHRgCy6u6PYs7aIZvXq6V8kVIYMGakPmUBZGDKBkpFeERQkEasXLyDoqVLBelOmDFxeBQrobpaquceMgHd3dyi7R0QgkNzZGfFR31vZGhkyZKQcMoGyMGQCJeNbQ2wsyJSPDzL2/PykLTpa2i9z5vgES9wMxS4xw1IkajVpb2IQuv57otbTly9wxTk7E9Wp8/Xju2TIkPHtQSZQFoZMoGR8r4iOBrHRJlji5u+vm5UnwsYGxCqhLUMG3f8rVCBydAQpkyFDhgxzwtT5W44IkCFDhlnxww9SDT0ZMmTI+FaRxsqCypAhQ4YMGTJkpH3IBEqGDBkyZMiQISOJkAmUDBkyZMiQIUNGEiETKBkyZMiQIUOGjCRCJlAyZMiQIUOGDBlJhEygZMiQIUOGDBkykgiZQMmQIUOGDBkyZCQRMoGSIUOGDBkyZMhIImQCJUOGDBkyZMiQkUTIBEqGDBkyZMiQISOJkAmUDBkyZMiQIUNGEiETKBkyZMiQIUOGjCRCJlAyZMiQIUOGDBlJhEygZMiQIUOGDBkykghbSzfgWwUzExFReHi4hVsiQ4YMGTJkyDAV4rwtzuMJQSZQqYSgoCAiIipSpIiFWyJDhgwZMmTISCqCgoIoe/bsCX4uE6hUQq5cuYiIyNvb2+gNSE2Eh4dTkSJF6OPHj5QtWzaLtCEtQL4OgHwdAPk6SJCvBSBfB0C+DkBYWBgVLVo0bh5PCDKBSiVYWyO8LHv27BbviNmyZbN4G9IC5OsAyNcBkK+DBPlaAPJ1AOTrAIjzeIKff6V2yJAhQ4YMGTJkfDOQCZQMGTJkyJAhQ0YSIROoVIKdnR1NnTqV7Ozsvus2pAXI1wGQrwMgXwcJ8rUA5OsAyNcBMPU6WHFieXoyZMiQIUOGDBkydCBboGTIkCFDhgwZMpIImUDJkCFDhgwZMmQkETKBkiFDhgwZMmTISCJkAiVDhgwZMmTIkJFEyARKhgwZMmTIkCEjiZAJlAwZMmTIkCFDRhIhEygZMmTI+Ebx6dMnUqlUlm6GDBnfJGQClUQEBwfT27dvKTIykoiILCGjpVQqv/o50yI+f/5Mbdq0od27d1u6KRZFWuiTaQXBwcH06NEj+vLli6WbYlH4+vpS27ZtqWXLlhQUFGTp5lgM8lgJyGMlYO6xUiZQScDYsWOpTJky1K1bN6pSpQqdPn2aYmJiiOjrTVoTJ04kBwcH8vHx+SrnS6sYMWIEFS5cmKysrKh+/fqWbo7FkBb6ZFrBmDFjqFKlStSrVy+qWLEinTx58ru7BkR4NooWLUrPnj2je/fukUajsXSTLAJ5rATksRJIlbGSZZiEESNG8O+//85Xrlzhhw8f8oABA/jHH3/klStXfpXzf/jwgTt06MDly5dna2trnjRp0lc5b1rDlStXuECBAlyhQgW+ceOGzmeCIFioVZaBpftkWsHdu3f5f//7H1evXp09PDz48ePH3LVrVy5btqylm/ZVsXLlSs6ZMydXrVqVr1+/zi9evOCffvqJd+/ebemmfVXIYyUgj5USUmuslAlUIhAEgX19fblq1aq8bt06nc9Kly7NJUqU4MuXL8ftm1q4du0aDxgwgN3d3XnNmjWcOXNmfvz4caqdL61iyZIlXLx4cd65cyczMz969IhXr17NZ8+e5Y8fP1q4dV8HaaVPphXs2rWLJ0yYwF++fIl77/Tp01ytWjWOjIxk5u/jOnTo0IFXrFgR9//nz585Z86cvG3bNmZm1mg0lmraV4U8VgLyWJn6Y6VcC88EeHl5UYUKFejatWv066+/xr1fv3598vb2pv/973+0bds2srKyMts5BUEga2vJwxoVFUWfP3+mMmXKEBFR9erVqXjx4rR3716d/b416F+HgIAAGjVqFPn4+FCOHDnozp07VLhwYXr58iXZ29uTq6srOTs7W7DFqQP962CJPpnWoNFoyMbGhqKioigoKIiKFi1KRESRkZHk4uJC+fPnJ2dnZ+rUqRPZ29tbuLWpB5VKRRkyZNB5T61Wk62tLdWuXZvKlClDrq6uFmrd18f3OlbqIzAwkP7555/vbqzUR2qOld9PbzIRq1atojFjxtDKlSspLCyMiIhKlChBv/32G40bN46eP39ORETjx48nlUpFdevWpbdv39KzZ8/M1oa5c+dS586dady4ceTl5UWCIFCWLFniBgQiooULF9LBgwfpzJkzZjtvWoP+dVCr1ZQ3b15q0qQJBQQEUGxsLB08eJAOHjxIXl5eVLJkSVq4cCE9fvzY0k03K8TrMH78+LjrUKJECapevfpX65NpBXv27KFHjx4REZGNjQ0REWXJkiWOPHl4eFDOnDlJpVJRrly5aObMmdS2bVu6cuWKxdqcGtC+DvrkiYjI1taWFP9v796Doqr/MI5/DrcIsUxDUuu3gFccSlFTAgXJLpYmhY52pRrTqZnMG9V0ZbKsaLqhpmPeYxTTCivyVqaWlZfBCkXRVNQQFUhNRFhYeX5/fGfPsoDGwrKcXZ7XP8mybh/ewNmv55w9azZLly5d5Pz581JWVubqEV3Cur2ePXu2vr1ujdvK+jpcf/31MnLkyFa1rXT587cT9pJ5hB9//BFdu3ZFZGQkHnzwQXTo0AFxcXEoKSkBAOzduxedOnVC9+7d0bZtW5hMJuzfvx9Hjx6Fr68vcnJymjzDqVOnMHjwYPTs2RPJycno1q0bIiIiMHfu3HrvP3bsWERGRqK0tLTJ/28juVwH6/HqqqoqLFq0CHv37gVg2/Wak5ODwMBArFu3rsVmd6bLdZg3bx4AYM+ePc3+M2kUO3bsQP/+/aFpGiZPnqwfmqtt//792Lp1q/5xYWEhwsLC7A5tubOGdrAerktOTkZERAQAzzqMWd/2eujQoSguLq73/p66rbxcB+vhbIvFgsWLF3v8trKlnr+5gAKwa9cuxMbG4vXXX0dlZSUA4OTJk/Dy8kJWVpZ+v+PHj2PTpk3YsGGDfttff/2FoKAgbNu2rclzfPHFF+jduzcKCwsBqI3gU089hZtvvhlbtmwBoBYPVkeOHMHVV1+NTz75BBaLBevWrcPmzZubPEdLu1KHH3/8EQDq3RCeP38e/v7++PTTT106b3O5UodNmzYBUD8DzfkzaQQnT57EM888g6effhpvvvkmAgMD7RZJ/+XGG2/Eiy++2IwTuoYjHaxPlGvWrEFQUBAOHjzoylGbVUO217XP9/LEbWVDn7cqKirq/F1P2la25PM3F1AA9u3bh3vvvVc/sc76TYiKikJKSsoV/+7MmTMRFxfnlBM033//ffTu3RtlZWX6bTk5OUhISMDQoUP122r+SzIlJQXt2rVD//794e3tja+//rrJc7S0hnaobeHChejfvz9OnTrlijGb3ZU6xMXFXfbvOfNn0gguXLiAzMxMZGdnAwD69euHkSNHXnZvQ01ZWVmIiorCn3/+2dxjNrvGdPj2228REhKCX375xVVjNrvGbq89bVvZlOctT9pWtuTzd6tfQFkXI+Xl5Xa3WywWhIaG1ruL89ixY/j9998xefJkdOjQQV/FN3UX+VtvvYXIyEjk5+fb3Z6eno7w8HD95cjWb/bRo0fx6KOPQtM0PP744/ruSnfX0A6AWlDs378f06ZNQ3BwMFJTU3Hp0iWPOFzhSIfm+pk0ippfx/bt26FpGjIyMurd8O3duxd5eXmYPn06goKC8Morr9jtuXVnjnQAgIsXL0LTNKxZs8ZVIzarxmyvAc/bVjamgyduK1v6+bvVL6BqqrkRys3NRbdu3XDs2DFYLBa7+33//fcYMWIEBgwYUOf6Go1h/cYVFBTAy8tLf9mx1ZEjR3DPPfdg8uTJ+oxFRUVISEiAyWTCrl27mjyDETSmQ0pKCrp06YKBAwc65XthBI3p4OyfSaOy/i6OHTsWt9xyS53FJaD+VdmtWzdERUV5bIuGdADUOWBTpkzBX3/95cLpXKOh2+vTp0973LaypoZ28MRtZU0t8fzdqhZQ9a0wa//L1PrxggULEB4ebnf82Hoopby83OEN0pX+BVzzc+PHj0evXr30816sEhMTMXbsWP3jyspKtzyvwdkdTp065dD5MEbh7A4VFRVu+yTZ0BY1P/7nn3/g6+uLd955R7/t+PHjANTPhDsesnJWh4KCguYb0gUa06G+7fXFixcBAGaz2aO3lTU/vlKHwsJCj95W1vzY2c/fl+PRlzEAIB999JH+/j81r/NQXV0tIurlviKiv4zRep2QdevWSXx8vFx11VVSWFgoDz/8sCxevFiqqqrE399funXr1uAZXnnlFRk/frxMnjxZDhw4oF823mKx6DNUV1fLgQMHZObMmXLq1Cl59913paSkxG7eDh066B/7+vravVTX6JqrQ3BwsMTGxrr2i2mC5upw1VVXNfhn0iga2kLE9vvp4+MjFotF2rdvL6+++qrMmjVLVq1aJSNGjJBp06bJ+fPnJTg4WKKjo1vmi2oEZ3eYMmWKlJaWtswX0wSN6XCl7fWiRYukqqpK/Pz8PHJbKdLwDpWVldKpUyeP3FaKNN/zd0OG9EibNm1CZGQkNE3DmDFj9F3ctfdCzZ8/H23btsWMGTNgNpsBqJVqdHQ01q9fj5kzZyIgIAC33Xab3ZWOG+Kbb76ByWRCTEwMXnvtNfzvf/9DXFxcnVd/WGewnvC2YsUKhIaGIj4+HqtWrcJLL72EoKAg/PDDD41q0dLYQWEHG0db1Pz9tP4Ol5SUQNM0aJqGYcOG1dlL5w7YQWlKB2dtr42AHRR36eCRC6jy8nJMmjQJEydOxAcffIABAwbgo48+srtPVVUVnn/+eVx33XVYuHCh3XHSn376CZqmwdfXFyaTCd9//73DM+Tm5uK+++5DSkqK3Yaue/fu+klrZrNZn2HBggV2uyPXrVuHhIQExMTEoG/fvm77klt2UNjBxtEWtX8/ASAzMxP+/v645ZZb8Ntvv7n8a3AGdlCa2sEZ22sjYAfFnTp41AKq5t6lHTt26O9/9PDDD2P48OHYuXOn3f0PHTqEs2fP1nmcvXv3wt/fH8uWLWv0DDk5OZg6dSqOHTsGAPrqOC4uDs8995x+39oz1H41TVFRkcMzGAE7KOxg09QWNR/nu+++c9s3TWYHxVkdmrK9NgJ2UNyxg0csoKxXEb3cyxC3bduGfv364aWXXtKvEXE51icsR68LYZ2h5t+rPc/FixfRp08frFq1yqHHdifsoLCDDVso7KA4s0Njt9dGwA6KO3dw65PI165dK6GhofLYY49Jfn6+aJqmnxxeU0xMjMTHx8vPP/8sGzduvOJjWk9Ca+ibTtaewcvLS5+h9jylpaVSXl4uvXr1auiX6DbYQWEHG7ZQ2EFpjg6Obq+NgB0UT+jgPrVrSU9Pl5dfflnCw8MlMDBQlixZIiJ1w1m/CZMmTZLq6mr55ptv5OzZsyIikpeXZ3ef5pjBy8tLf+XAr7/+KmVlZWIymfTPnzlzRkREv487YgeFHWzYQmEHhR0UdlA8pYPbLqBCQ0PljjvukPnz58uAAQNk8+bN8uuvv4qI/YLI+k0wmUwybtw4ycnJkdTUVImJiZHRo0eL2Wxu9Gq1oTNYZWZmyl133SXXXHON7N+/X0aMGCEvv/yyWCwWu0ssuBt2UNjBhi0UdlDYQWEHxWM6uORAYTOxXiRr+/btGDZsGMaPH69/ruYxVOufc3Nz0aZNG2iahqSkJKe8M3dDZ6ioqMA999yDJUuWYNq0afDx8UFiYqLd+5y5M3ZQ2MGGLRR2UNhBYQfFEzq49QIKsIWeOXMmBg0ahM8//9zudqv09HRomobY2Fjs27fP5TPk5eXp12qJjIzE7t27nTqDEbCDwg42bKGwg8IOCjso7t7BsIfwDh06JFlZWSJS9xjnpUuX9D9bP/fQQw9Jp06dJCMjQ86dOyeapulXKxURiYqKks8++0y2bt0q4eHhLpuhqqpKREQuXLggsbGxkpWVJbt375bIyMgGzWAE7KCwgw1bKOygsIPCDkqr6eDqFdt/MZvNmDhxIjRNg8lksvtczQsL1veSx0WLFiEqKgppaWnYs2cPEhISGvUu7M6cYdSoUf956QSjYgeFHWzYQmEHhR0UdlBaWwdDLaA++OAD+Pv7Y8iQIZgyZQr69u2LAwcO1LlfWloaBg4ciLy8PAC2b0BZWRnGjRuHNm3awNfXFzExMSgrK7vs9aFcNcOFCxfc7voc7KCwgw1bKOygsIPCDkpr7GCIBVRJSQnCw8PRsWNHfPHFFwCAH374AW3bttXfWby6uhq5ubno0aMHunbtioyMDLvHuHDhAmbPng0/Pz9ER0dj165dbjeDEbCDwg42bKGwg8IOCjsorbmDIRZQ586dw/r16+1WmgUFBWjXrp1+UhkA5Ofn47333sO///5b5zH27duHLl26YP78+W47gxGwg8IONmyhsIPCDgo7KK25Q4stoA4dOlTvrjnrbYcPH0bfvn3x4YcfArj827T81+eMPoMRsIPCDjZsobCDwg4KOyjsoLj8VXiLFy/WL2oZHR0ty5cv1y+cBUC/qGVYWJgAkPz8fBG58tXCHb2QlhFmMAJ2UNjBhi0UdlDYQWEHhR3s+bjyf5aWliZz5syR1NRUuemmm2Tjxo2SlJQk//77r0yYMEF8fX0Faq+YeHl5SWxsrOzcuVNERLy9vT1mBiNgB4UdbNhCYQeFHRR2UNihHs27g8umrKwMd955J1JSUgDYdtsNGTIEJpMJa9assbsdAJKTkxEdHY0zZ854zAxGwA4KO9iwhcIOCjso7KCwQ/1cdgjPx8dHsrOzpWfPniIiYjabRUSkY8eOUlVVJV999ZUUFxfbXQAzPj5esrOzPWoGI2AHhR1s2EJhB4UdFHZQ2KF+zbKAWr16tUyYMEHS0tJkz549IiLi5+cnd999t8yYMUNOnDgh/v7+snz5cjlz5ozce++9sn37djlx4oSIqG+W9b+BgYHyxx9/uOUMRsAOCjvYsIXCDgo7KOygsIMDnLk7q6SkBGPGjMENN9yAp59+GoMHD0anTp3w2WefAQAOHjyIsLAwhIWFoXPnzggICMCXX34JAPDx8cF3330HALBYLADUSyF37tzpdjMYATso7GDDFgo7KOygsIPCDo5z6gJq9erVGDhwoH7xLABISEhASEgIMjMzAQB///03NmzYgGXLlumXaS8qKkJYWBhWr17tETMYATso7GDDFgo7KOygsIPCDo5z6gLqgQceQGJiIgCgtLQUALB06VJomoZhw4ahqKgIAOpcP+Lzzz9Hr169cPLkSY+YwQjYQWEHG7ZQ2EFhB4UdFHZwXKPPgfrpp59kw4YN+gljIiLdu3eX3NxcEREJDAwUEZG8vDy5/fbbpaKiQtasWSMiIl5eXlJcXCx5eXkyZ84cmTp1qiQmJsr1119f552bjT6DEbCDwg42bKGwg8IOCjso7OAkjq64iouLkZSUBE3T0KdPH+Tn5+ufO3z4MIKCghAXF4fU1FTcdtttCA0NxaZNm9CnTx+89tpr+n2zs7Nx//33IzQ0FOnp6W43gxGwg8IONmyhsIPCDgo7KOzgXA4toKqqqjB37lzcfffdWLlyJQICAvDOO++goqJCv8+2bdswYcIE9OvXD88++yyKi4sBAI899hhGjx5t93i7d+92eGAjzGAE7KCwgw1bKOygsIPCDgo7OJ/De6C2b9+Ob7/9FgDwxhtvICgoCL///nud+5nNZv3Pp0+fRkREBN566y0A6hvZFEaYwQjYQWEHG7ZQ2EFhB4UdFHZwLocXULXf+K9z586YOHEizp8/X+fz5eXlqKysxNy5cxEZGYmcnJwmjmucGYyAHRR2sGELhR0UdlDYQWEH52r0q/CsK9RVq1bBx8cHGzdutPt8QUEB5s6diwEDBqB9+/ZYsWJF0yY16AxGwA4KO9iwhcIOCjso7KCwg3NoQNNPm4+OjpY2bdrI8uXLpWPHjlJcXCxBQUGSkZEhhYWFMn36dGec7274GYyAHRR2sGELhR0UdlDYQWGHxmvSAspisYiPj4/k5uZKnz595MMPP5TDhw/Ltm3bZNmyZRIREeHMWQ07gxGwg8IONmyhsIPCDgo7KOzgBM7alXXrrbdC0zSYTCasX7/eWQ/rdjMYATso7GDDFgo7KOygsIPCDo3T5AXUoUOHEBERgYCAACxcuNAZM7nlDEbADgo72LCFwg4KOyjsoLBD0zT6SuRW3t7eMnr0aCkpKZHx48c7Y6eYW85gBOygsIMNWyjsoLCDwg4KOzSNU04iJyIiImpNmrwHioiIiKi14QKKiIiIyEFcQBERERE5iAsoIiIiIgdxAUVERETkIC6giIiIiBzEBRQRERGRg7iAIiKqYcuWLaJpmpw7d66lRyEiA+OFNImoVRs6dKj07dtXPv74YxERqayslDNnzkhwcLBomtaywxGRYfm09ABEREbi5+cnN9xwQ0uPQUQGx0N4RNRqPfHEE7J161ZJS0sTTdNE0zRZunSp3SG8pUuXSrt27SQrK0t69uwpAQEBMmbMGCkrK5Nly5ZJSEiIXHfddTJp0iS5dOmS/tiVlZXywgsvSJcuXaRNmzYyaNAg2bJlS8t8oUTkdNwDRUStVlpamhw8eFAiIiJkxowZIiKSm5tb534XL16UWbNmycqVK6W0tFQSExMlMTFR2rVrJ2vXrpUjR47I6NGjZfDgwTJu3DgREXnyySfl6NGjsnLlSuncubNkZmbK8OHDZc+ePdK9e3eXfp1E5HxcQBFRq3XttdeKn5+fBAQE6Ift8vLy6tyvqqpK5s2bJ127dhURkTFjxkh6erqcPn1aAgMDpXfv3hIfHy+bN2+WcePGyeHDhyUjI0MKCgqkc+fOIiKSnJws69evlyVLlsjbb7/tui+SiJoFF1BERP8hICBAXzyJiAQHB0tISIgEBgba3VZUVCQiIrt37xYA0qNHD7vHMZvN0qFDB9cMTUTNigsoIqL/4Ovra/expmn13lZdXS0iItXV1eLt7S3Z2dni7e1td7+aiy4icl9cQBFRq+bn52d38rczREZGyqVLl6SoqEiGDBni1McmImPgq/CIqFULCQmRHTt2yNGjR6WkpETfi9QUPXr0kEceeUSSkpLkq6++kvz8fNm1a5ekpqbK2rVrnTA1EbU0LqCIqFVLTk4Wb29v6d27twQFBcnx48ed8rhLliyRpKQkmT59uvTs2VNGjRolO3bskJtuuskpj09ELYtXIiciIiJyEPdAERERETmICygiIiIiB3EBRUREROQgLqCIiIiIHMQFFBEREZGDuIAiIiIichAXUEREREQO4gKKiIiIyEFcQBERERE5iAsoIiIiIgdxAUVERETkoP8DvMchJJ2/xZEAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Set up the forecast configuration, basing it on the previous (final) assimilation step.\n", "conf_forecast = conf_loop.duplicate(\n", @@ -435,8 +501,8 @@ } ], "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", +"kernelspec": { + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -450,7 +516,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.9" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/docs/notebooks/11_Climatological_ESP_forecasting.ipynb b/docs/notebooks/11_Climatological_ESP_forecasting.ipynb index cb4a7cc5..c6611b3c 100644 --- a/docs/notebooks/11_Climatological_ESP_forecasting.ipynb +++ b/docs/notebooks/11_Climatological_ESP_forecasting.ipynb @@ -28,7 +28,6 @@ "\n", "from matplotlib import pyplot as plt\n", "\n", - "from ravenpy import Emulator, RavenWarning\n", "from ravenpy.config import commands as rc\n", "from ravenpy.config.emulators import GR4JCN\n", "from ravenpy.utilities import forecasting\n", diff --git a/docs/notebooks/12_Performing_hindcasting_experiments.ipynb b/docs/notebooks/12_Performing_hindcasting_experiments.ipynb index 94ccb991..970d8598 100644 --- a/docs/notebooks/12_Performing_hindcasting_experiments.ipynb +++ b/docs/notebooks/12_Performing_hindcasting_experiments.ipynb @@ -22,14 +22,11 @@ "source": [ "# This entire section is cookie-cutter template to import required packages and prepare the temporary writing space.\n", "import datetime as dt\n", - "import json\n", "import tempfile\n", "from pathlib import Path\n", "\n", "import xarray as xr\n", - "import xskillscore\n", "from clisops.core import average, subset\n", - "from matplotlib import pyplot as plt\n", "\n", "from ravenpy import Emulator, RavenWarning\n", "from ravenpy.config import commands as rc\n", @@ -113,14 +110,13 @@ "ts = get_file(\"notebook_inputs/ERA5_weather_data_Salmon.nc\")\n", "\n", "# This is the model start date, on which the simulation will be launched for a certain duration\n", - "# to setup the initial states. We will then save the final states as a launching point for the\n", + "# to set up the initial states. We will then save the final states as a launching point for the\n", "# forecasts.\n", "\n", "start_date = dt.datetime(2000, 1, 1)\n", "end_date = dt.datetime(2018, 6, 2)\n", "\n", "# Define HRU to build the hydrological model\n", - "hru = {}\n", "hru = dict(\n", " area=4250.6,\n", " elevation=843.0,\n", diff --git a/docs/notebooks/Assess_probabilistic_flood_risk.ipynb b/docs/notebooks/Assess_probabilistic_flood_risk.ipynb index b2ac3743..0db93331 100644 --- a/docs/notebooks/Assess_probabilistic_flood_risk.ipynb +++ b/docs/notebooks/Assess_probabilistic_flood_risk.ipynb @@ -12,7 +12,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, + "id": "79d923bd-4ce5-41f1-a441-f439b23fc388", + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "\n", + "from numba.core.errors import NumbaDeprecationWarning\n", + "\n", + "warnings.simplefilter(\"ignore\", category=NumbaDeprecationWarning)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, "id": "descending-bedroom", "metadata": { "scrolled": true @@ -23,11 +37,10 @@ "\n", "import datetime as dt\n", "\n", - "import xarray as xr\n", "import xclim\n", "from matplotlib import pyplot as plt\n", "\n", - "from ravenpy.utilities.testdata import get_file" + "from ravenpy.utilities.testdata import get_file, open_dataset" ] }, { @@ -40,14 +53,422 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "quiet-queens", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.DataArray 'fa_1maxannual' (return_period: 6)>\n",
+       "array([186.42526386, 283.04234564, 347.01126071, 427.83615447,\n",
+       "       487.79667785, 547.31446213])\n",
+       "Coordinates:\n",
+       "  * return_period  (return_period) int64 2 5 10 25 50 100\n",
+       "Attributes:\n",
+       "    units:               m**3 s**-1\n",
+       "    original_long_name:  discharge observation\n",
+       "    long_name:           N-year return level\n",
+       "    description:         Frequency analysis for the maximal annual 1-day valu...\n",
+       "    method:              ML\n",
+       "    estimator:           Maximum likelihood\n",
+       "    scipy_dist:          gumbel_r\n",
+       "    history:             [2023-05-31 13:22:25] fa_1maxannual: xclim.core.indi...\n",
+       "    cell_methods:        \n",
+       "    mode:                max
" + ], + "text/plain": [ + "\n", + "array([186.42526386, 283.04234564, 347.01126071, 427.83615447,\n", + " 487.79667785, 547.31446213])\n", + "Coordinates:\n", + " * return_period (return_period) int64 2 5 10 25 50 100\n", + "Attributes:\n", + " units: m**3 s**-1\n", + " original_long_name: discharge observation\n", + " long_name: N-year return level\n", + " description: Frequency analysis for the maximal annual 1-day valu...\n", + " method: ML\n", + " estimator: Maximum likelihood\n", + " scipy_dist: gumbel_r\n", + " history: [2023-05-31 13:22:25] fa_1maxannual: xclim.core.indi...\n", + " cell_methods: \n", + " mode: max" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Get the data that we will be using for the demonstration.\n", - "fn = get_file(\"raven-gr4j-cemaneige/Salmon-River-Near-Prince-George_meteo_daily.nc\")\n", - "ts = xr.open_dataset(fn).qobs\n", + "file = \"raven-gr4j-cemaneige/Salmon-River-Near-Prince-George_meteo_daily.nc\"\n", + "ts = open_dataset(file).qobs\n", "\n", "# Perform the frequency analysis for various return periods. We compute 2, 5, 10, 25, 50 and 100 year return\n", "# periods, but later on we will only compare the forecasts to the 2 year return period.\n", @@ -59,10 +480,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "appointed-toner", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Threshold: 186.4\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(25, 10, 'Flow threshold, set at 2-year return period')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Plot the results of the flows as a function of return period.\n", "fig, ax = plt.subplots(1)\n", @@ -95,19 +544,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "excessive-apparatus", "metadata": { - "execution": { - "iopub.execute_input": "2021-09-08T20:34:06.026173Z", - "iopub.status.busy": "2021-09-08T20:34:06.025792Z", - "iopub.status.idle": "2021-09-08T20:34:21.490991Z", - "shell.execute_reply": "2021-09-08T20:34:21.490647Z" + "pycharm": { + "is_executing": true } }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "hwloc/linux: Ignoring PCI device with non-16bit domain.\n", + "Pass --enable-32bits-pci-domain to configure to support such devices\n", + "(warning: it would break the library ABI, don't enable unless really needed).\n" + ] + } + ], "source": [ - "from ravenpy import Emulator, RavenWarning\n", "from ravenpy.config import commands as rc\n", "from ravenpy.config.emulators import GR4JCN\n", "from ravenpy.utilities.forecasting import climatology_esp, compute_forecast_flood_risk\n", @@ -120,7 +575,6 @@ "duration = 30 # Length in days of the climatological ESP forecast\n", "\n", "# Define HRU to build the hydrological model\n", - "hru = {}\n", "hru = dict(\n", " area=4250.6,\n", " elevation=843.0,\n", @@ -151,7 +605,10 @@ " params=[0.529, -3.396, 407.29, 1.072, 16.9, 0.947],\n", " Gauge=[\n", " rc.Gauge.from_nc(\n", - " fn, data_type=data_type, alt_names=alt_names, data_kwds=data_kwds\n", + " get_file(file),\n", + " data_type=data_type,\n", + " alt_names=alt_names,\n", + " data_kwds=data_kwds,\n", " )\n", " ],\n", " HRUs=[hru],\n", @@ -171,10 +628,586 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "0c0b126a", - "metadata": {}, - "outputs": [], + "metadata": { + "pycharm": { + "is_executing": true + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.Dataset>\n",
+       "Dimensions:     (member: 57, time: 31, nbasins: 1)\n",
+       "Coordinates:\n",
+       "  * member      (member) int64 1954 1955 1956 1957 1958 ... 2007 2008 2009 2010\n",
+       "  * time        (time) datetime64[ns] 2003-04-13 2003-04-14 ... 2003-05-13\n",
+       "    basin_name  (nbasins) object 'sub_001'\n",
+       "Dimensions without coordinates: nbasins\n",
+       "Data variables:\n",
+       "    precip      (member, time) float64 nan 0.2054 0.0 4.304 ... 0.0 0.0 0.0 0.0\n",
+       "    q_sim       (member, time, nbasins) float64 0.0 0.1644 ... 0.5947 0.5763\n",
+       "    q_obs       (member, time, nbasins) float64 nan nan nan nan ... nan nan nan\n",
+       "    q_in        (member, time, nbasins) float64 nan nan nan nan ... nan nan nan\n",
+       "Attributes:\n",
+       "    Conventions:  CF-1.6\n",
+       "    featureType:  timeSeries\n",
+       "    history:      Created on 2023-05-31T13:22:36 by Raven 3.7\n",
+       "    description:  Standard Output\n",
+       "    references:   Craig J.R. and the Raven Development Team Raven user's and ...\n",
+       "    model_id:     GR4JCN
" + ], + "text/plain": [ + "\n", + "Dimensions: (member: 57, time: 31, nbasins: 1)\n", + "Coordinates:\n", + " * member (member) int64 1954 1955 1956 1957 1958 ... 2007 2008 2009 2010\n", + " * time (time) datetime64[ns] 2003-04-13 2003-04-14 ... 2003-05-13\n", + " basin_name (nbasins) object 'sub_001'\n", + "Dimensions without coordinates: nbasins\n", + "Data variables:\n", + " precip (member, time) float64 nan 0.2054 0.0 4.304 ... 0.0 0.0 0.0 0.0\n", + " q_sim (member, time, nbasins) float64 0.0 0.1644 ... 0.5947 0.5763\n", + " q_obs (member, time, nbasins) float64 nan nan nan nan ... nan nan nan\n", + " q_in (member, time, nbasins) float64 nan nan nan nan ... nan nan nan\n", + "Attributes:\n", + " Conventions: CF-1.6\n", + " featureType: timeSeries\n", + " history: Created on 2023-05-31T13:22:36 by Raven 3.7\n", + " description: Standard Output\n", + " references: Craig J.R. and the Raven Development Team Raven user's and ...\n", + " model_id: GR4JCN" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Launch the ESP forecasting method\n", "ESP_sims = climatology_esp(\n", @@ -187,10 +1220,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "embedded-patrol", - "metadata": {}, - "outputs": [], + "metadata": { + "pycharm": { + "is_executing": true + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Plot the forecasts and the 2-year threshold previously estimated\n", "fig, ax = plt.subplots(1)\n", @@ -202,10 +1250,35 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "british-bunch", - "metadata": {}, - "outputs": [], + "metadata": { + "pycharm": { + "is_executing": true + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Flood risk')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Now compute the flood risk given the probabilistic forecast and the threshold associated to the 2-year return\n", "# period.\n", @@ -250,7 +1323,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.9" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/docs/notebooks/Comparing_hindcasts_and_ESP_forecasts.ipynb b/docs/notebooks/Comparing_hindcasts_and_ESP_forecasts.ipynb index 959ba475..2821714a 100644 --- a/docs/notebooks/Comparing_hindcasts_and_ESP_forecasts.ipynb +++ b/docs/notebooks/Comparing_hindcasts_and_ESP_forecasts.ipynb @@ -40,7 +40,7 @@ "from ravenpy.config.emulators import GR4JCN\n", "from ravenpy.extractors.forecasts import get_CASPAR_dataset\n", "from ravenpy.utilities import forecasting\n", - "from ravenpy.utilities.testdata import get_file" + "from ravenpy.utilities.testdata import get_file, open_dataset" ] }, { @@ -132,7 +132,7 @@ "metadata": {}, "outputs": [], "source": [ - "dss = xr.open_dataset(ts)\n", + "dss = open_dataset(ts)\n", "dss" ] }, diff --git a/docs/notebooks/Distributed_hydrological_modelling.ipynb b/docs/notebooks/Distributed_hydrological_modelling.ipynb index 4670a023..ab884bb6 100644 --- a/docs/notebooks/Distributed_hydrological_modelling.ipynb +++ b/docs/notebooks/Distributed_hydrological_modelling.ipynb @@ -52,7 +52,7 @@ " open_shapefile,\n", " upstream_from_coords,\n", ")\n", - "from ravenpy.utilities.testdata import get_file, get_local_testdata\n", + "from ravenpy.utilities.testdata import get_file, open_dataset\n", "\n", "tmp_path = Path(tempfile.mkdtemp())" ] @@ -208,7 +208,7 @@ ")\n", "\n", "# Plot the observed streamflow\n", - "qobs_data = xr.open_dataset(qobs_fn)\n", + "qobs_data = open_dataset(qobs_fn)\n", "qobs_data.discharge.plot.line(x=\"time\", label=\"Observations\", color=\"red\", lw=1.5)\n", "\n", "plt.legend()" diff --git a/docs/notebooks/HydroShare_integration.ipynb b/docs/notebooks/HydroShare_integration.ipynb index 54d35875..2fa10331 100644 --- a/docs/notebooks/HydroShare_integration.ipynb +++ b/docs/notebooks/HydroShare_integration.ipynb @@ -8,7 +8,7 @@ "source": [ "# Accessing HydroShare content\n", "\n", - "The following code snippets show examples for how to use the HS RDF HydroShare Python Client for performing basic resource operations." + "The following code snippets show examples for how to use the HydroShare Python Client for search and acquire data. See the [documentation](https://hydroshare.github.io/hsclient/) to explore further." ] }, { @@ -19,472 +19,171 @@ "source": [ "## Authenticating with HydroShare\n", "\n", - "Before you start interacting with resources in HydroShare you will need to authenticate. To authenticate with HydroShare, you can either specify your username and password or you can call the `sign_in()` function, which will prompt you to input your username and password." + "Before you start interacting with resources in HydroShare you will need to authenticate. Just call `hsclient.Hydroshare()` to be prompted for your username and password. You may also pass your credentials programatically. For this public notebook, we use a token and client_id to authenticate. " ] }, { "cell_type": "markdown", "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [] + }, + "outputs": [], "source": [ - "In most cases you will not want anyone to see your username and password, so you can also call the `sign_in()` function to be prompted for your username and password. This is better to use if you are sharing a Jupyter Notebook." + "import os\n", + "\n", + "from hsclient import HydroShare, Token\n", + "\n", + "# Authentication method using username and password\n", + "\"\"\"\n", + "username = 'XXXXX'\n", + "password = 'XXXXX'\n", + "hs = HydroShare(username=username, password=password)\n", + "\"\"\"\n", + "\n", + "client_id = os.environ.get(\"HYDROSHARE_AUTH_CLIENT_ID\", \"\")\n", + "access_token = os.environ.get(\"HYDROSHARE_AUTH_TOKEN\", \"\")\n", + "\n", + "token = Token(access_token=access_token, token_type=\"bearer\")\n", + "hs = HydroShare(client_id=client_id, token=token)" ] }, { - "cell_type": "code", - "execution_count": 11, + "cell_type": "markdown", "metadata": {}, + "source": [ + "Now that we're authenticated, let's search for data from the 2017 Harvey flood. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Username: richardarsenault\n", - "Password for richardarsenault: Ā·Ā·Ā·Ā·Ā·Ā·Ā·Ā·\n" + "test harvey netcdf file : 75d17f265dba4c8396725cf98f652302\n", + "RAPID: Archiving and Enabling Community Access to Data from Recent US Hurricanes : 564b6d73040142579ad3236e1aeb4712\n", + "Hurricane Harvey 2017 Collection : 2836494ee75e43a9bfb647b37260e461\n", + "USGS - Harvey Gaged Streamflow Timeseries : 51d1539bf6e94b15ac33f7631228118c\n", + "Harvey Flood Data Collections : 12e69ee668124fdf833b29b5167e03c3\n", + "NOAA NHC - Harvey 2017 Storm Track : 6168b9969c984b658952a896710b65ef\n", + "USGS - Harvey High Water Marks : 615d426f70cc4346875c725b4b8fdc59\n", + "Harvey Basemap Data Collections : 7661752c688a4f3ebcf58f8657773530\n", + "Texas-Harvey Basemap - Addresses and Boundaries : d2bab32e7c1d4d55b8cba7221e51b02d\n", + "Preserving a Flood of Data: Hurricane Harvey 2017 Data Archive : 64f6af3dcea4475688cac0d6b4917d8d\n", + "Hurricane Harvey Streamflow Preview Notebook : 4c089adbbad74aeda3932d8ff283b2b5\n", + "Harvey Basemap - Hydrology Map Data : adb14c9c073e4eee8be82fadb21a0a93\n", + "Civil Air Patrol - Harvey Oblique Aerial Photos : 85c5f592e347452a84f552f17a9a05c1\n", + "CDC Social Vulnerability Index 2014 : c2df2a80b9d6490788704a24854f4879\n", + "HUC 120200 : a29b1b3c4889429bb23072c214e432e8\n", + "Hurricane Harvey NWM Subsetting Exercise : 3db192783bcb4599bab36d43fc3413db\n", + "NOAA NHC - Irma Storm Track - Best Track + Advisories : aa5c9982a4694a19be2fa9299b78e5ca\n", + "Hurricane Harvey 2017 Story Map : 8161a96a08474d12bba219852409be61\n", + "Perspectives on research gaps from the differences amongst Hurricane Harvey, Irma and Maria : 7be94dcca60c428e81a6846e97122579\n", + "Hurricane Harvey NWIS Data : 2f469f714ea541dc86b6578066e7815f\n", + "Data for \"A Computationally Efficient and Physically Based Approach for Urban Flood Modeling Using a Flexible Spatiotemporal Structure\" : e314ed52a83a46dd9e575304c299bd83\n", + "Test for versioning published resource : c4037062b89d4e989b66a60b6ef176e4\n", + "Test Collection Inner : c34cacb091074999aba351698eba3861\n", + "FEMA - Harvey Damage Assessments and Claims : a52d209d46eb42578be0a7472c48e2d5\n", + "FEMA - Harvey Flood Depths Grid : e8768f4cb4d5478a96d2b1cbd00d9e85\n", + "ECMWF GloFAS - Harvey+Irma Flood Area Grids : 9ff2b9ad3eb74b06a5af8491c399ee57\n", + "NOAA NWC - Harvey National Water Model Streamflow Forecasts : 35d4502200764c2985c24ae5c8836ab9\n", + "Hurricane Harvey flood inundation simulation : fae24734d6fc47be8bf0b54d6a175d86\n", + "Coupling coastal and hydrologic models through BMI and Nextgen National Water Model Framework in low gradient coastal regions of Galveston Bay, Texas, USA Results : 379b4c8c663c460d87c246641dc5cea2\n", + "IGUIDE Shapefile Testing Resource : 9d413b9d57824a79b8239a5f7c4fdf51\n" ] } ], "source": [ - "from hs_restclient import HydroShare, HydroShareAuthBasic\n", - "\n", - "# Auth method\n", - "\"\"\"\n", - "username = 'XXXXX'\n", - "password = 'XXXXX'\n", - "auth = HydroShareAuthBasic(username=username, password=password)\n", - "hs = HydroShare(auth=auth)\n", - "\"\"\"\n", - "# Sign-in method, will prompt for HydroShare account username/password:\n", - "hs = HydroShare()" + "results = hs.search(subject=[\"Harvey\"])\n", + "for r in results:\n", + " print(r.resource_title, \": \", r.resource_id)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "HydroShare resources are identified uniquely by their `resource_id`. Here we use the ID for the `USGS - Harvey Gaged Streamflow Timeseries` to see which files are stored. " ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 3, "metadata": { - "scrolled": false + "tags": [] }, "outputs": [ { "data": { "text/plain": [ - "{'resource_type': 'CollectionResource',\n", - " 'resource_id': 'bdb2e6ac7e9f46d58e985a9739145fb3',\n", - " 'resource_title': 'NCC Tall Grass Prairie Preserve',\n", - " 'abstract': \"The NCC Tall Grass Prairie Preserve resource collection contains a LiDAR-based digital elevation model, derivative grids, and ancillary spatial data in the vicinity of Nature Conservancy Canada's Weston Family Tall Grass Prairie Interpretive Centre, located in Stuartburn Rural Municipality, southeast Manitoba, Canada.\",\n", - " 'authors': ['Phil Gerla'],\n", - " 'creator': 'Phil Gerla',\n", - " 'doi': None,\n", - " 'public': True,\n", - " 'discoverable': True,\n", - " 'shareable': True,\n", - " 'immutable': False,\n", - " 'published': False,\n", - " 'date_created': '2016-07-13T19:46:45.156387Z',\n", - " 'date_last_updated': '2016-10-05T14:53:09.128611Z',\n", - " 'bag_url': 'http://www.hydroshare.org/django_irods/rest_download/bags/bdb2e6ac7e9f46d58e985a9739145fb3.zip?url_download=False&zipped=False&aggregation=False',\n", - " 'coverages': [{'type': 'point',\n", - " 'value': {'units': 'Decimal degrees',\n", - " 'east': -96.72920644283295,\n", - " 'north': 49.152752125384296,\n", - " 'name': 'NCC Weston Family Tall Grass Prairie Interpretive Centre',\n", - " 'projection': 'WGS 84 EPSG:4326'}}],\n", - " 'science_metadata_url': 'http://www.hydroshare.org/hsapi/resource/bdb2e6ac7e9f46d58e985a9739145fb3/scimeta/',\n", - " 'resource_map_url': 'http://www.hydroshare.org/hsapi/resource/bdb2e6ac7e9f46d58e985a9739145fb3/map/',\n", - " 'resource_url': 'http://www.hydroshare.org/resource/bdb2e6ac7e9f46d58e985a9739145fb3/',\n", - " 'content_types': []}" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "{'resource_type': 'CompositeResource',\n", - " 'resource_id': '6be7b304e0ca4ae7a046647b07e851af',\n", - " 'resource_title': 'NCC Tall Grass Prairie Interpretive Centre AOI',\n", - " 'abstract': \"The NCC Tall Grass Prairie Preserve resource collection contains a LiDAR-based digital elevation model and derivative grids in the vicinity of Nature Conservancy Canada's Weston Family Tall Grass Prairie Interpretive Centre, located in Stuartburn Rural Municipality, southeast Manitoba, Canada. This shape file, visitor_centre_clip, contains one polygon delineating the area of interest (AOI), which outlines the associated grids.\",\n", - " 'authors': ['Phil Gerla'],\n", - " 'creator': 'Phil Gerla',\n", - " 'doi': None,\n", - " 'public': True,\n", - " 'discoverable': True,\n", - " 'shareable': True,\n", - " 'immutable': False,\n", - " 'published': False,\n", - " 'date_created': '2016-07-13T21:48:50.885138Z',\n", - " 'date_last_updated': '2016-07-13T22:07:54.774309Z',\n", - " 'bag_url': 'http://www.hydroshare.org/django_irods/rest_download/bags/6be7b304e0ca4ae7a046647b07e851af.zip?url_download=False&zipped=False&aggregation=False',\n", - " 'coverages': [{'type': 'box',\n", - " 'value': {'northlimit': 49.18476672335955,\n", - " 'projection': 'WGS 84 EPSG:4326',\n", - " 'units': 'Decimal degrees',\n", - " 'southlimit': 49.131218840945195,\n", - " 'eastlimit': -96.66762281348629,\n", - " 'westlimit': -96.74719437796493}}],\n", - " 'science_metadata_url': 'http://www.hydroshare.org/hsapi/resource/6be7b304e0ca4ae7a046647b07e851af/scimeta/',\n", - " 'resource_map_url': 'http://www.hydroshare.org/hsapi/resource/6be7b304e0ca4ae7a046647b07e851af/map/',\n", - " 'resource_url': 'http://www.hydroshare.org/resource/6be7b304e0ca4ae7a046647b07e851af/',\n", - " 'content_types': []}" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "{'resource_type': 'CompositeResource',\n", - " 'resource_id': '6a5acc2746ef42ec848948efb9b8e111',\n", - " 'resource_title': 'NCC Tall Grass Prairie Preserve, Interpretive Centre digital elevation model',\n", - " 'abstract': \"The NCC Tall Grass Prairie Preserve resource collection contains a LiDAR-based digital elevation model and derivative grids in the vicinity of Nature Conservancy Canada's Weston Family Tall Grass Prairie Interpretive Centre, located in Stuartburn Rural Municipality, southeast Manitoba, Canada. This ESRI grid file, vc_dem, contains a 1 m square raster of processed LiDAR ground return data, and is hydrologically corrected to include culverts. The area of the grid is give in the associated polygon shape file that delineates the area of interest (AOI).\",\n", - " 'authors': ['Phil Gerla'],\n", - " 'creator': 'Phil Gerla',\n", - " 'doi': None,\n", - " 'public': True,\n", - " 'discoverable': True,\n", - " 'shareable': True,\n", - " 'immutable': False,\n", - " 'published': False,\n", - " 'date_created': '2016-07-13T22:22:32.602685Z',\n", - " 'date_last_updated': '2016-07-13T22:26:53.605505Z',\n", - " 'bag_url': 'http://www.hydroshare.org/django_irods/rest_download/bags/6a5acc2746ef42ec848948efb9b8e111.zip?url_download=False&zipped=False&aggregation=False',\n", - " 'coverages': [],\n", - " 'science_metadata_url': 'http://www.hydroshare.org/hsapi/resource/6a5acc2746ef42ec848948efb9b8e111/scimeta/',\n", - " 'resource_map_url': 'http://www.hydroshare.org/hsapi/resource/6a5acc2746ef42ec848948efb9b8e111/map/',\n", - " 'resource_url': 'http://www.hydroshare.org/resource/6a5acc2746ef42ec848948efb9b8e111/',\n", - " 'content_types': []}" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "{'resource_type': 'CompositeResource',\n", - " 'resource_id': '5b714198621c4aa6adbc929d5eeb0bf1',\n", - " 'resource_title': 'NCC Tall Grass Prairie Preserve, Interpretive Centre MrSID imagery',\n", - " 'abstract': \"The NCC Tall Grass Prairie Preserve resource collection contains a LiDAR-based digital elevation model and derivative grids in the vicinity of Nature Conservancy Canada's Weston Family Tall Grass Prairie Interpretive Centre, located in Stuartburn Rural Municipality, southeast Manitoba, Canada. This ESRI grid file, vc_mrsid, contains aerial imagery obtained from the Manitoba Land Initiative. The area of the grid is given in the associated polygon shape file that delineates the area of interest (AOI). \",\n", - " 'authors': ['Phil Gerla'],\n", - " 'creator': 'Phil Gerla',\n", - " 'doi': None,\n", - " 'public': True,\n", - " 'discoverable': True,\n", - " 'shareable': True,\n", - " 'immutable': False,\n", - " 'published': False,\n", - " 'date_created': '2016-07-13T23:41:35.872910Z',\n", - " 'date_last_updated': '2016-07-13T23:51:27.612657Z',\n", - " 'bag_url': 'http://www.hydroshare.org/django_irods/rest_download/bags/5b714198621c4aa6adbc929d5eeb0bf1.zip?url_download=False&zipped=False&aggregation=False',\n", - " 'coverages': [],\n", - " 'science_metadata_url': 'http://www.hydroshare.org/hsapi/resource/5b714198621c4aa6adbc929d5eeb0bf1/scimeta/',\n", - " 'resource_map_url': 'http://www.hydroshare.org/hsapi/resource/5b714198621c4aa6adbc929d5eeb0bf1/map/',\n", - " 'resource_url': 'http://www.hydroshare.org/resource/5b714198621c4aa6adbc929d5eeb0bf1/',\n", - " 'content_types': []}" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "{'resource_type': 'CompositeResource',\n", - " 'resource_id': '672f7f05ec064ee5940ff9c80f1f28ac',\n", - " 'resource_title': 'NCC Tall Grass Prairie Preserve, Interpretive Centre ditch pour point',\n", - " 'abstract': \"The NCC Tall Grass Prairie Preserve resource collection contains a LiDAR-based digital elevation model and derivative grids in the vicinity of Nature Conservancy Canada's Weston Family Tall Grass Prairie Interpretive Centre, located in Stuartburn Rural Municipality, southeast Manitoba, Canada. This ESRI point shape file, vc_ditch_pour_point, contains one point that lies within the channel of the ditch that drains the area around the Interpretive Centre, immediately upstream from the confluence with Vita Drain. \",\n", - " 'authors': ['Phil Gerla'],\n", - " 'creator': 'Phil Gerla',\n", - " 'doi': None,\n", - " 'public': True,\n", - " 'discoverable': True,\n", - " 'shareable': True,\n", - " 'immutable': False,\n", - " 'published': False,\n", - " 'date_created': '2016-07-14T00:58:56.760656Z',\n", - " 'date_last_updated': '2016-07-14T01:07:04.746329Z',\n", - " 'bag_url': 'http://www.hydroshare.org/django_irods/rest_download/bags/672f7f05ec064ee5940ff9c80f1f28ac.zip?url_download=False&zipped=False&aggregation=False',\n", - " 'coverages': [],\n", - " 'science_metadata_url': 'http://www.hydroshare.org/hsapi/resource/672f7f05ec064ee5940ff9c80f1f28ac/scimeta/',\n", - " 'resource_map_url': 'http://www.hydroshare.org/hsapi/resource/672f7f05ec064ee5940ff9c80f1f28ac/map/',\n", - " 'resource_url': 'http://www.hydroshare.org/resource/672f7f05ec064ee5940ff9c80f1f28ac/',\n", - " 'content_types': []}" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "{'resource_type': 'CompositeResource',\n", - " 'resource_id': 'd79be06bbb584994b8e0ca238850948d',\n", - " 'resource_title': 'NCC Tall Grass Prairie Preserve, Interpretive Centre, proposed ditch diversion',\n", - " 'abstract': \"The NCC Tall Grass Prairie Preserve resource collection contains a LiDAR-based digital elevation model and derivative grids in the vicinity of Nature Conservancy Canada's Weston Family Tall Grass Prairie Interpretive Centre, located in Stuartburn Rural Municipality, southeast Manitoba, Canada. This ESRI point shape file,diversion_channel.shp, contains a single line with multiple vertices. It follows roughly the natural course that storm water would flow if the drainage ditch due east of the interpretive centre were to be plugged.\",\n", - " 'authors': ['Phil Gerla'],\n", - " 'creator': 'Phil Gerla',\n", - " 'doi': None,\n", - " 'public': True,\n", - " 'discoverable': True,\n", - " 'shareable': True,\n", - " 'immutable': False,\n", - " 'published': False,\n", - " 'date_created': '2016-07-14T01:26:23.376041Z',\n", - " 'date_last_updated': '2016-07-14T01:31:22.807069Z',\n", - " 'bag_url': 'http://www.hydroshare.org/django_irods/rest_download/bags/d79be06bbb584994b8e0ca238850948d.zip?url_download=False&zipped=False&aggregation=False',\n", - " 'coverages': [],\n", - " 'science_metadata_url': 'http://www.hydroshare.org/hsapi/resource/d79be06bbb584994b8e0ca238850948d/scimeta/',\n", - " 'resource_map_url': 'http://www.hydroshare.org/hsapi/resource/d79be06bbb584994b8e0ca238850948d/map/',\n", - " 'resource_url': 'http://www.hydroshare.org/resource/d79be06bbb584994b8e0ca238850948d/',\n", - " 'content_types': []}" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "{'resource_type': 'CompositeResource',\n", - " 'resource_id': '650ba9303d5942c2b36fa5beafbf612a',\n", - " 'resource_title': 'BEREV data 2017-2018',\n", - " 'abstract': \"This dataset is supplementary information to: Schilling et al. (2021): Quantifying groundwater recharge dynamics and unsaturated zone processes in snowā€dominated catchments via onā€site dissolved gas analysis. Water Resour. Res., e2020WR028479. doi: 10.1029/2020WR028479\\r\\n\\r\\nThe data was used to develop a novel tracer method for the quantification of groundwater recharge from snowmelt and includes hydraulic, snow, meteorologic and tracer measurements covering the period of Nov-2017 to Dec-2018. The data was recorded in the experimental boral headwater catchment 'Bassin ExpĆ©rimental du Ruisseau des Eaux-VolĆ©es' (BEREV) of UniversitĆ© Laval, located in the ForĆŖt Montmorency, QuĆ©bec, Canada.\",\n", - " 'authors': ['Schilling, Oliver S.',\n", - " 'Parajuli, Achut',\n", - " 'Tremblay Otis, Catherine',\n", - " 'MĆ¼ller, Tanja U.',\n", - " 'Antolinez Quijano, Walter',\n", - " 'Tremblay, Yohann',\n", - " 'Nadeau, Daniel F.',\n", - " 'Brennwald, Matthias S.',\n", - " 'Jutras, Sylvain',\n", - " 'Kipfer, Rolf',\n", - " 'Therrien, RenĆ©'],\n", - " 'creator': 'Schilling, Oliver S.',\n", - " 'doi': '10.4211/hs.650ba9303d5942c2b36fa5beafbf612a',\n", - " 'public': True,\n", - " 'discoverable': True,\n", - " 'shareable': False,\n", - " 'immutable': True,\n", - " 'published': True,\n", - " 'date_created': '2019-08-08T04:36:34.446735Z',\n", - " 'date_last_updated': '2021-01-06T09:34:45.249213Z',\n", - " 'bag_url': 'http://www.hydroshare.org/django_irods/rest_download/bags/650ba9303d5942c2b36fa5beafbf612a.zip?url_download=False&zipped=False&aggregation=False',\n", - " 'coverages': [{'type': 'period',\n", - " 'value': {'start': '2017-11-01', 'end': '2018-12-31'}},\n", - " {'type': 'point',\n", - " 'value': {'units': 'Decimal degrees',\n", - " 'east': -71.1613,\n", - " 'north': 47.2898,\n", - " 'name': 'Sub-basin 7A, BEREV, Foret Montmorency, Quebec, Canada',\n", - " 'projection': 'WGS 84 EPSG:4326'}}],\n", - " 'science_metadata_url': 'http://www.hydroshare.org/hsapi/resource/650ba9303d5942c2b36fa5beafbf612a/scimeta/',\n", - " 'resource_map_url': 'http://www.hydroshare.org/hsapi/resource/650ba9303d5942c2b36fa5beafbf612a/map/',\n", - " 'resource_url': 'http://www.hydroshare.org/resource/650ba9303d5942c2b36fa5beafbf612a/',\n", - " 'content_types': []}" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "{'resource_type': 'CompositeResource',\n", - " 'resource_id': '82326ee6288241f3a85ed1d01a3083d0',\n", - " 'resource_title': 'MODIS snow persistence for Canada and Alaska 2001-2019',\n", - " 'abstract': 'Snow persistence (SP) or the snow cover index (SCI), is the fraction of time that snow is present on the ground for a defined period. SP was calculated on a pixel by pixel basis using MODIS/Terra Snow Cover 8-Day L3 Global 500m Grid, Collection 6 obtained from the National Snow and Ice Data Center (NSIDC). We computed the 1 January ā€“ 3 July SP for each year as the fraction of 8-day MODIS images with snow present. \\r\\n\\r\\nFor more information on MODIS snow persistence please see:\\r\\n\\r\\n-Hammond, J. C., Saavedra, F. A., & Kampf, S. K. (2018). Global snow zone maps and trends in snow persistence 2001ā€“2016. International Journal of Climatology, 38(12), 4369-4383.\\r\\n-Hammond, J. C., Saavedra, F. A., & Kampf, S. K. (2018). How does snow persistence relate to annual streamflow in mountain watersheds of the Western US with wet maritime and dry continental climates?. Water Resources Research, 54(4), 2605-2623.\\r\\n-Hammond, J. C., F. A. Saavedra, S. K. Kampf (2017). MODIS MOD10A2 derived snow persistence and no data index for the western U.S., HydroShare, https://doi.org/10.4211/hs.1c62269aa802467688d25540caf2467e',\n", - " 'authors': ['Hammond, John'],\n", - " 'creator': 'Hammond, John',\n", - " 'doi': '10.4211/hs.82326ee6288241f3a85ed1d01a3083d0',\n", - " 'public': True,\n", - " 'discoverable': True,\n", - " 'shareable': False,\n", - " 'immutable': True,\n", - " 'published': True,\n", - " 'date_created': '2020-03-18T18:14:02.496618Z',\n", - " 'date_last_updated': '2020-03-18T19:55:46.946250Z',\n", - " 'bag_url': 'http://www.hydroshare.org/django_irods/rest_download/bags/82326ee6288241f3a85ed1d01a3083d0.zip?url_download=False&zipped=False&aggregation=False',\n", - " 'coverages': [{'type': 'box',\n", - " 'value': {'northlimit': 79.99999999281273,\n", - " 'southlimit': 39.999999996428876,\n", - " 'eastlimit': 28.94754257670456,\n", - " 'westlimit': -156.64887469755448,\n", - " 'projection': 'WGS 84 EPSG:4326',\n", - " 'units': 'Decimal degrees'}},\n", - " {'type': 'period', 'value': {'start': '2001-01-01', 'end': '2019-07-03'}}],\n", - " 'science_metadata_url': 'http://www.hydroshare.org/hsapi/resource/82326ee6288241f3a85ed1d01a3083d0/scimeta/',\n", - " 'resource_map_url': 'http://www.hydroshare.org/hsapi/resource/82326ee6288241f3a85ed1d01a3083d0/map/',\n", - " 'resource_url': 'http://www.hydroshare.org/resource/82326ee6288241f3a85ed1d01a3083d0/',\n", - " 'content_types': []}" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "{'resource_type': 'CompositeResource',\n", - " 'resource_id': '97d4169fd7f54299aa9d60e61a56314f',\n", - " 'resource_title': 'Hydroviewer Canada',\n", - " 'abstract': 'Streams and Gauging Stations Network in Canada',\n", - " 'authors': ['Sanchez-Lozano, Jorge Luis'],\n", - " 'creator': 'Sanchez-Lozano, Jorge Luis',\n", - " 'doi': None,\n", - " 'public': True,\n", - " 'discoverable': True,\n", - " 'shareable': True,\n", - " 'immutable': False,\n", - " 'published': False,\n", - " 'date_created': '2022-05-27T09:48:56.975338Z',\n", - " 'date_last_updated': '2022-05-27T09:57:42.894757Z',\n", - " 'bag_url': 'http://www.hydroshare.org/django_irods/rest_download/bags/97d4169fd7f54299aa9d60e61a56314f.zip?url_download=False&zipped=False&aggregation=False',\n", - " 'coverages': [{'type': 'box',\n", - " 'value': {'northlimit': 59.98958333325731,\n", - " 'southlimit': 41.43708333334351,\n", - " 'eastlimit': -52.68333333370196,\n", - " 'westlimit': -137.3566666667138,\n", - " 'projection': 'WGS 84 EPSG:4326',\n", - " 'units': 'Decimal degrees'}}],\n", - " 'science_metadata_url': 'http://www.hydroshare.org/hsapi/resource/97d4169fd7f54299aa9d60e61a56314f/scimeta/',\n", - " 'resource_map_url': 'http://www.hydroshare.org/hsapi/resource/97d4169fd7f54299aa9d60e61a56314f/map/',\n", - " 'resource_url': 'http://www.hydroshare.org/resource/97d4169fd7f54299aa9d60e61a56314f/',\n", - " 'content_types': []}" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "{'resource_type': 'CompositeResource',\n", - " 'resource_id': '790fa65aebc24e4c98f2ee5a82cd3d7e',\n", - " 'resource_title': 'Boreal peat extraction affects riverine export and processing of organic carbon and nutrients in contrasting wet and dry years',\n", - " 'abstract': 'Land use and hydrology influence the transport and attenuation of dissolved organic carbon (DOC) and nutrients through river networks, affecting aquatic food webs, habitat, and water quality. Here, we studied how horticultural peat extraction affects DOC and nutrient dynamics in western Canada. During two contrasting hydrological years, we sampled >50 subcatchments and analyzed DOC and nutrient concentrations, dissolved organic matter (DOM) chemical composition, solute export, and relative downstream losses in the stream network. We found that headwaters with substantial peat extraction had twice the DOC concentration and 8-fold the dissolved inorganic nitrogen (DIN) concentration (primarily NH4+) compared to headwaters with intact peatlands. Peat extraction sites were point sources of DOC and DIN across the landscape, even in catchments with substantial agricultural activity. Conversely, DOM chemical composition differed by hydrological status rather than land use, with lower aromaticity in the dry year driven by a combination of deeper flowpaths and greater in-stream processing. Neither land use nor hydrology appeared to influence PO43- concentration, which was relatively high across the study region due to geology. Downstream attenuation of DOC and inorganic nutrients varied by solute and was modulated by stream discharge. The most rapid attenuation was found for PO43- and DIN, while DOC acted largely conservative. Attenuation of all solutes except PO43- was much greater in the dry than wet year. These results suggest that DOC from extracted peatlands will persist further downstream than DIN, and that climate-induced changes in hydrology will affect DOM composition and inorganic nutrient processing. Our study shows that peat extraction can influence DOC and nutrient dynamics even in watersheds with predominantly agricultural land use, and that mitigation options should be explored to improve water quality.',\n", - " 'authors': ['Frei, Rebecca'],\n", - " 'creator': 'Frei, Rebecca',\n", - " 'doi': None,\n", - " 'public': True,\n", - " 'discoverable': True,\n", - " 'shareable': True,\n", - " 'immutable': False,\n", - " 'published': False,\n", - " 'date_created': '2022-08-29T19:55:10.986889Z',\n", - " 'date_last_updated': '2022-08-29T21:19:57.456038Z',\n", - " 'bag_url': 'http://www.hydroshare.org/django_irods/rest_download/bags/790fa65aebc24e4c98f2ee5a82cd3d7e.zip?url_download=False&zipped=False&aggregation=False',\n", - " 'coverages': [{'type': 'box',\n", - " 'value': {'projection': 'WGS 84 EPSG:4326',\n", - " 'units': 'Decimal degrees',\n", - " 'northlimit': 53.5578,\n", - " 'eastlimit': -114.6627,\n", - " 'southlimit': 53.3321,\n", - " 'westlimit': -115.1214}},\n", - " {'type': 'period', 'value': {'start': '2020-06-01', 'end': '2021-09-30'}}],\n", - " 'science_metadata_url': 'http://www.hydroshare.org/hsapi/resource/790fa65aebc24e4c98f2ee5a82cd3d7e/scimeta/',\n", - " 'resource_map_url': 'http://www.hydroshare.org/hsapi/resource/790fa65aebc24e4c98f2ee5a82cd3d7e/map/',\n", - " 'resource_url': 'http://www.hydroshare.org/resource/790fa65aebc24e4c98f2ee5a82cd3d7e/',\n", - " 'content_types': []}" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "{'resource_type': 'CompositeResource',\n", - " 'resource_id': 'c949a3dc4da44e42a09f768d370f33ef',\n", - " 'resource_title': 'Global Hydrological Station Network',\n", - " 'abstract': 'This resource contains the shapefiles with the global stations network with available streamflow data.',\n", - " 'authors': ['Sanchez-Lozano, Jorge Luis'],\n", - " 'creator': 'Sanchez-Lozano, Jorge Luis',\n", - " 'doi': None,\n", - " 'public': True,\n", - " 'discoverable': True,\n", - " 'shareable': True,\n", - " 'immutable': False,\n", - " 'published': False,\n", - " 'date_created': '2023-03-04T09:26:58.907168Z',\n", - " 'date_last_updated': '2023-03-06T10:05:56.974026Z',\n", - " 'bag_url': 'http://www.hydroshare.org/django_irods/rest_download/bags/c949a3dc4da44e42a09f768d370f33ef.zip?url_download=False&zipped=False&aggregation=False',\n", - " 'coverages': [{'type': 'box',\n", - " 'value': {'northlimit': 59.56666900000005,\n", - " 'southlimit': -50.26999999999998,\n", - " 'eastlimit': 177.87560470000005,\n", - " 'westlimit': -135.346275,\n", - " 'projection': 'WGS 84 EPSG:4326',\n", - " 'units': 'Decimal degrees'}}],\n", - " 'science_metadata_url': 'http://www.hydroshare.org/hsapi/resource/c949a3dc4da44e42a09f768d370f33ef/scimeta/',\n", - " 'resource_map_url': 'http://www.hydroshare.org/hsapi/resource/c949a3dc4da44e42a09f768d370f33ef/map/',\n", - " 'resource_url': 'http://www.hydroshare.org/resource/c949a3dc4da44e42a09f768d370f33ef/',\n", - " 'content_types': []}" + "['USGS_Gages_TxLaMsAr_shapefile/USGS_Gages_TxLaMsAr.dbf',\n", + " 'USGS_Gages_TxLaMsAr_shapefile.zip',\n", + " 'download_usgs_gage_height_inst.R',\n", + " 'USGS_gage_discharge_timeseries.zip',\n", + " 'USGS_Harvey_gages_TxLaMsAr.csv',\n", + " 'README.md',\n", + " 'USGS_Gages_TxLaMsAr_shapefile/USGS_Gages_TxLaMsAr.prj',\n", + " 'USGS gage timeseries example.png',\n", + " 'USGS-NWS gages in Harvey study area.png',\n", + " 'USGS_Gages_TxLaMsAr_shapefile/USGS_Gages_TxLaMsAr.shp',\n", + " 'USGS_Gages_TxLaMsAr_shapefile/USGS_Gages_TxLaMsAr.shx',\n", + " 'USGS_Gages_TxLaMsAr_shapefile/USGS_Gages_TxLaMsAr.shp.xml',\n", + " 'USGS_gage_height_timeseries.zip',\n", + " 'README-USGS Gaged Streamflow Timeseries.pdf',\n", + " 'USGS_Gages_TxLaMsAr_shapefile/USGS_Gages_TxLaMsAr.cpg',\n", + " 'USGS_Gages_TxLaMsAr_shapefile/USGS_Gages_TxLaMsAr.sbx',\n", + " 'USGS_Gages_TxLaMsAr_shapefile/USGS_Gages_TxLaMsAr.sbn',\n", + " 'download_usgs_gage_discharge_inst.R']" ] }, + "execution_count": 3, "metadata": {}, - "output_type": "display_data" - }, + "output_type": "execute_result" + } + ], + "source": [ + "res = hs.resource(\"51d1539bf6e94b15ac33f7631228118c\", validate=False)\n", + "res.files()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can simply use the `file_download` method to save a copy locally. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "tags": [] + }, + "outputs": [ { "data": { "text/plain": [ - "{'resource_type': 'CompositeResource',\n", - " 'resource_id': 'cff2657bc8244560b559320162bf8ce4',\n", - " 'resource_title': 'Global Hydrological Stream Network',\n", - " 'abstract': 'This resource contains the shapefiles with the streams network for different countries from the GEOGloWS ECMWF Streamflow Services (GESS) Global Hydrological Model.',\n", - " 'authors': ['Sanchez-Lozano, Jorge Luis'],\n", - " 'creator': 'Sanchez-Lozano, Jorge Luis',\n", - " 'doi': None,\n", - " 'public': True,\n", - " 'discoverable': True,\n", - " 'shareable': True,\n", - " 'immutable': False,\n", - " 'published': False,\n", - " 'date_created': '2023-03-06T03:10:31.608533Z',\n", - " 'date_last_updated': '2023-03-06T03:35:20.421805Z',\n", - " 'bag_url': 'http://www.hydroshare.org/django_irods/rest_download/bags/cff2657bc8244560b559320162bf8ce4.zip?url_download=False&zipped=False&aggregation=False',\n", - " 'coverages': [{'type': 'box',\n", - " 'value': {'northlimit': 59.98958333325731,\n", - " 'southlimit': -43.6004166235268,\n", - " 'eastlimit': 153.58583331691884,\n", - " 'westlimit': -138.17791666658826,\n", - " 'projection': 'WGS 84 EPSG:4326',\n", - " 'units': 'Decimal degrees'}}],\n", - " 'science_metadata_url': 'http://www.hydroshare.org/hsapi/resource/cff2657bc8244560b559320162bf8ce4/scimeta/',\n", - " 'resource_map_url': 'http://www.hydroshare.org/hsapi/resource/cff2657bc8244560b559320162bf8ce4/map/',\n", - " 'resource_url': 'http://www.hydroshare.org/resource/cff2657bc8244560b559320162bf8ce4/',\n", - " 'content_types': []}" + "'/tmp/USGS_Harvey_gages_TxLaMsAr.csv'" ] }, + "execution_count": 4, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "# for resource in hs.resources(): # Careful, this takes quite a while. Probably better to use filters...\n", - "# print(resource) <-- Scans ALL packages/resources\n", - "\n", - "# Here I reduce the selection by filtering for \"streamflow\" and \"Canada\". Can use comma separated values, or other filters. See here:\n", - "# https://hs-restclient.readthedocs.io/en/latest/\n", - "for resource in hs.resources(subject=[\"streamflow\", \"Canada\"]):\n", - " display(resource)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "# Get one of the datasets based on \"resource_id\"\n", - "\n", - "# Make folder where results will be written\n", - "workdir = \"/tmp/tmp_Hydroshare/\"\n", - "os.mkdir(workdir)\n", - "\n", - "# Donwload the data from the resource.\n", - "hs.getResource(\"650ba9303d5942c2b36fa5beafbf612a\", destination=workdir, unzip=True)" + "res.file_download(\"USGS_Harvey_gages_TxLaMsAr.csv\", save_path=\"/tmp\")" ] }, { @@ -517,7 +216,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.9" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/docs/notebooks/Hydrological_realtime_forecasting.ipynb b/docs/notebooks/Hydrological_realtime_forecasting.ipynb index 8e46446d..3fc7e182 100644 --- a/docs/notebooks/Hydrological_realtime_forecasting.ipynb +++ b/docs/notebooks/Hydrological_realtime_forecasting.ipynb @@ -38,7 +38,7 @@ "from ravenpy.config.emulators import GR4JCN\n", "from ravenpy.extractors.forecasts import get_recent_ECCC_forecast\n", "from ravenpy.utilities import forecasting\n", - "from ravenpy.utilities.testdata import get_file" + "from ravenpy.utilities.testdata import get_file, open_dataset" ] }, { @@ -80,9 +80,8 @@ "start_date_wu = dt.datetime(2010, 1, 1)\n", "end_date_wu = dt.datetime(2020, 3, 30)\n", "\n", - "# Define some of the catchment properties. Could also be replaced by a call to the properties WPS as in\n", + "# Define some catchment properties. Could also be replaced by a call to the properties WPS as in\n", "# the Tutorial Notebook 02.\n", - "hru = {}\n", "hru = dict(\n", " area=4250.6,\n", " elevation=843.0,\n", @@ -175,7 +174,7 @@ "}\n", "\n", "# ECCC forecast time format is a bit complex to work with, so we will use cftime to make it more manageable.\n", - "fcst_tmp = xr.open_dataset(fname, use_cftime=True)\n", + "fcst_tmp = open_dataset(fname, use_cftime=True)\n", "\n", "# Get the first timestep that will be used for the model simulation\n", "start_date = fcst_tmp.time.data[0] + dt.timedelta(days=1)\n", diff --git a/docs/notebooks/Managing_Jupyter_Environments.ipynb b/docs/notebooks/Managing_Jupyter_Environments.ipynb index ecf091d2..1822ce55 100644 --- a/docs/notebooks/Managing_Jupyter_Environments.ipynb +++ b/docs/notebooks/Managing_Jupyter_Environments.ipynb @@ -76,7 +76,7 @@ "--> File\n", " --> Hub Control Panel\n", " --> Stop my server.\n", - " \n", + "\n", "\n", "Doing so will kill the server, but it will nonetheless keep all of your files. Respawning the server will open a fresh default environment. You can test this now! When you try and re-run the notebook, the first cell will fail again because 'seaborn' will have not been installed yet on this server instance." ] @@ -91,40 +91,30 @@ "To export your environment to replicate it elsewhere (such as a local installation, or to make a backup in case of future updates), you need to export two elements:\n", " - The data\n", " - The installed packages\n", - " \n", + "\n", "The data can be exported using the explorer on the left. You can select the files you want to download directly, or you can select \"Download current folder as an archive\". This will allow you to keep a copy of your data on your personal computer. However, note that data stored on this server is not removed or purged. Users are encouraged to use storage on an as-needed basis and to remove data that is not required to free-up resources for other users. PAVICS developers will contact users that use unreasonable amounts of storage space in order to find an alternative solution. The same reasoning also applies to computing power. Users can run multiple kernels/notebooks in parallel, but users are encouraged to use resources on an as-needed basis, with power users potentially being contacted to find alternative solutions.\n", "\n", "The environment can be exported using the following commands:\n", "\n", - "**Export it to text in this Notebook:**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2ba89850-ebe1-483f-9a83-3378beb061d7", - "metadata": {}, - "outputs": [], - "source": [ - "conda env export" - ] - }, - { - "cell_type": "markdown", - "id": "858a1bc0-c829-4e79-9823-cde88bfdb7f0", - "metadata": {}, - "source": [ + "**Export it to text in this Notebook:**\n", + "\n", + "```shell\n", + "conda env export\n", + "```\n", "\n", "### Other methods to export environments\n", "You can also export the environment to files, using these commands:\n", "\n", "**Export it to file with explicit packages and channels:**\n", "\n", + "```shell\n", "conda list --explicit>ENV.txt\n", - "\n", + "```\n", "**Export it cross-platform:**\n", "\n", - "conda env export --from-history>ENV.yml\n" + "```shell\n", + "conda env export --from-history>ENV.yml\n", + "```\n" ] } ], diff --git a/docs/notebooks/Perform_Regionalization.ipynb b/docs/notebooks/Perform_Regionalization.ipynb index b0f5e67b..8baf90a9 100644 --- a/docs/notebooks/Perform_Regionalization.ipynb +++ b/docs/notebooks/Perform_Regionalization.ipynb @@ -24,11 +24,8 @@ "source": [ "import datetime as dt\n", "\n", - "import numpy as np\n", - "import xarray as xr\n", "from matplotlib import pyplot as plt\n", "\n", - "from ravenpy import Emulator\n", "from ravenpy.config import commands as rc\n", "from ravenpy.config.emulators import GR4JCN\n", "from ravenpy.utilities.regionalization import (\n", @@ -36,8 +33,6 @@ " read_gauged_properties,\n", " regionalize,\n", ")\n", - "\n", - "# from ravenpy.utilities.regionalization import\n", "from ravenpy.utilities.testdata import get_file" ] }, diff --git a/docs/notebooks/Running_HMETS_with_CANOPEX_dataset.ipynb b/docs/notebooks/Running_HMETS_with_CANOPEX_dataset.ipynb index 44c4d0dc..3ebf2d97 100644 --- a/docs/notebooks/Running_HMETS_with_CANOPEX_dataset.ipynb +++ b/docs/notebooks/Running_HMETS_with_CANOPEX_dataset.ipynb @@ -11,15 +11,21 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2021-09-08T20:36:56.268818Z", - "iopub.status.busy": "2021-09-08T20:36:56.268370Z", - "iopub.status.idle": "2021-09-08T20:37:00.359280Z", - "shell.execute_reply": "2021-09-08T20:37:00.357631Z" - } - }, + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "\n", + "from numba.core.errors import NumbaDeprecationWarning\n", + "\n", + "warnings.simplefilter(\"ignore\", category=NumbaDeprecationWarning)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, "outputs": [], "source": [ "# Cookie-cutter template necessary to provide the tools, packages and paths for the project. All notebooks\n", @@ -37,38 +43,487 @@ "from ravenpy.utilities.calibration import SpotSetup\n", "from ravenpy.utilities.testdata import get_file\n", "\n", - "# DATA MAIN SOURCE - DAP link to CANOPEX dataset. Can be DAP or direct URL:\n", - "CANOPEX_DAP = \"https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/dodsC/birdhouse/ets/Watersheds_5797_cfcompliant.nc\"\n", - "# CANOPEX_URL = \"https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/fileServer/birdhouse/ets/Watersheds_5797_cfcompliant.nc\"\n", - "\n", - "# Prefer the DAP link\n", - "ds = xr.open_dataset(CANOPEX_DAP)\n", - "\n", "# Make a temporary folder\n", "tmp = Path(tempfile.mkdtemp())" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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+       "Dimensions:        (time: 22280, watershed: 5797)\n",
+       "Coordinates:\n",
+       "  * time           (time) datetime64[ns] 1950-01-01 1950-01-02 ... 2010-12-31\n",
+       "  * watershed      (watershed) |S64 b'St. John River at Ninemile Bridge, Main...\n",
+       "Data variables:\n",
+       "    drainage_area  (watershed) float64 ...\n",
+       "    pr             (watershed, time) float64 ...\n",
+       "    tasmax         (watershed, time) float64 ...\n",
+       "    tasmin         (watershed, time) float64 ...\n",
+       "    discharge      (watershed, time) float64 ...\n",
+       "Attributes: (12/15)\n",
+       "    title:          Hydrometeorological data for lumped hydrological modellin...\n",
+       "    institute_id:   ETS\n",
+       "    contact:        Richard Arsenault: richard.arsenault@etsmtl.ca\n",
+       "    date_created:   2020-08-01\n",
+       "    source:         Hydrometric data from USGS National Water Information Ser...\n",
+       "    featureType:    timeSeries\n",
+       "    ...             ...\n",
+       "    activity:       PAVICS_Hydro\n",
+       "    Conventions:    CF-1.6, ACDD-1.3\n",
+       "    summary:        Hydrometeorological database for the PAVICS-Hydro platfor...\n",
+       "    institution:    ETS (Ɖcole de technologie supĆ©rieure)\n",
+       "    DODS.strlen:    72\n",
+       "    DODS.dimName:   string72
" + ], + "text/plain": [ + "\n", + "Dimensions: (time: 22280, watershed: 5797)\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 1950-01-01 1950-01-02 ... 2010-12-31\n", + " * watershed (watershed) |S64 b'St. John River at Ninemile Bridge, Main...\n", + "Data variables:\n", + " drainage_area (watershed) float64 ...\n", + " pr (watershed, time) float64 ...\n", + " tasmax (watershed, time) float64 ...\n", + " tasmin (watershed, time) float64 ...\n", + " discharge (watershed, time) float64 ...\n", + "Attributes: (12/15)\n", + " title: Hydrometeorological data for lumped hydrological modellin...\n", + " institute_id: ETS\n", + " contact: Richard Arsenault: richard.arsenault@etsmtl.ca\n", + " date_created: 2020-08-01\n", + " source: Hydrometric data from USGS National Water Information Ser...\n", + " featureType: timeSeries\n", + " ... ...\n", + " activity: PAVICS_Hydro\n", + " Conventions: CF-1.6, ACDD-1.3\n", + " summary: Hydrometeorological database for the PAVICS-Hydro platfor...\n", + " institution: ETS (Ɖcole de technologie supĆ©rieure)\n", + " DODS.strlen: 72\n", + " DODS.dimName: string72" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ + "# DATA MAIN SOURCE - DAP link to CANOPEX dataset.\n", + "CANOPEX_DAP = \"https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/dodsC/birdhouse/ets/Watersheds_5797_cfcompliant.nc\"\n", + "ds = xr.open_dataset(CANOPEX_DAP)\n", + "\n", "# Explore the dataset:\n", "display(ds)" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2021-09-08T20:37:00.369961Z", - "iopub.status.busy": "2021-09-08T20:37:00.368559Z", - "iopub.status.idle": "2021-09-08T20:37:00.374515Z", - "shell.execute_reply": "2021-09-08T20:37:00.373296Z" - } - }, + "execution_count": 4, + "metadata": {}, "outputs": [], "source": [ "# We could explore the dataset and find a watershed of interest, but for now, let's pick one at random\n", @@ -76,41 +531,40 @@ "watershedID = 5600\n", "\n", "# And show what it includes:\n", - "ds = ds.isel({\"watershed\": watershedID})" + "ts = ds.isel({\"watershed\": watershedID})" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ds" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Let's write the file to disk to make it more efficient to retrieve:\n", "fname = tmp / \"CANOPEX_extracted.nc\"\n", - "ds.to_netcdf(fname)" + "ts.to_netcdf(fname)\n", + "ds.close()\n", + "ts.close()" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2021-09-08T20:37:00.388499Z", - "iopub.status.busy": "2021-09-08T20:37:00.387053Z", - "iopub.status.idle": "2021-09-08T20:37:00.419350Z", - "shell.execute_reply": "2021-09-08T20:37:00.419020Z" + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Basin name: [b'St. John River at Ninemile Bridge, Maine'\n", + " b'St. John River at Dickey, Maine' b'Fish River near Fort Kent, Maine'\n", + " ... b'MIDDLE THAMES RIVER AT THAMESFORD'\n", + " b'BIG OTTER CREEK AT TILLSONBURG' b'KETTLE CREEK AT ST. THOMAS']\n", + "Latitude: 49.51119663557124 Ā°N\n", + "Area: 3650.476384548832 km^2\n" + ] } - }, - "outputs": [], + ], "source": [ "# With this info, we can gather some properties from the CANOPEX database. This same database is used for\n", "# regionalization, so let's query it there where more information is available:\n", @@ -131,21 +585,32 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, we might have the model and data, but we don't have model parameters! We need to calibrate. This next snippet shows how to do so." + "Now, we might have the model and data, but we don't have model parameters! We need to calibrate. This next snippets show how to configure the model and the calibration." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2021-09-08T20:37:02.449304Z", - "iopub.status.busy": "2021-09-08T20:37:02.447661Z", - "iopub.status.idle": "2021-09-08T20:37:06.197246Z", - "shell.execute_reply": "2021-09-08T20:37:06.196910Z" + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: registry._helper_single_adder(): Redefining 'percent' ()\n", + "WARNING: registry._helper_single_adder(): Redefining '%' ()\n", + "WARNING: registry._helper_single_adder(): Redefining 'year' ()\n", + "WARNING: registry._helper_single_adder(): Redefining 'yr' ()\n", + "WARNING: registry._helper_single_adder(): Redefining 'C' ()\n", + "WARNING: registry._helper_single_adder(): Redefining 'd' ()\n", + "WARNING: registry._helper_single_adder(): Redefining 'h' ()\n", + "WARNING: registry._helper_single_adder(): Redefining 'degrees_north' ()\n", + "WARNING: registry._helper_single_adder(): Redefining 'degrees_east' ()\n", + "WARNING: registry._helper_single_adder(): Redefining '[speed]' ()\n", + "WARNING: registry._helper_single_adder(): Redefining '[radiation]' ()\n" + ] } - }, - "outputs": [], + ], "source": [ "# We will also calibrate on only a subset of the years for now to keep the computations faster in this notebook.\n", "start_calib = dt.datetime(1998, 1, 1)\n", @@ -203,16 +668,9 @@ ")" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that the model is setup, we can focus on calibrating the parameters using SpotPy:" - ] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -281,9 +739,87 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initializing the Dynamically Dimensioned Search (DDS) algorithm with 50 repetitions\n", + "The objective function will be maximized\n", + "Starting the DDS algotrithm with 50 repetitions...\n", + "Finding best starting point for trial 1 using 5 random samples.\n", + "Initialize database...\n", + "['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']\n", + "40 of 50, maximal objective function=-24.7202, time remaining: 00:00:00\n", + "Best solution found has obj function value of -24.6817 at 5\n", + "\n", + "\n", + "\n", + "*** Final SPOTPY summary ***\n", + "Total Duration: 2.61 seconds\n", + "Total Repetitions: 50\n", + "Maximal objective value: -24.6817\n", + "Corresponding parameter setting:\n", + "GAMMA_SHAPE: 7.63524\n", + "GAMMA_SCALE: 0.769725\n", + "GAMMA_SHAPE2: 11.4388\n", + "GAMMA_SCALE2: 1.0265\n", + "MIN_MELT_FACTOR: 17.5457\n", + "MAX_MELT_FACTOR: 0.0603\n", + "DD_MELT_TEMP: -0.420604\n", + "DD_AGGRADATION: 0.01893\n", + "SNOW_SWI_MIN: 0.0308324\n", + "SNOW_SWI_MAX: 0.0106\n", + "SWI_REDUCT_COEFF: 0.1\n", + "DD_REFREEZE_TEMP: -0.0332151\n", + "REFREEZE_FACTOR: 0.664621\n", + "REFREEZE_EXP: 0.958967\n", + "PET_CORRECTION: 1.10548\n", + "HMETS_RUNOFF_COEFF: 0.214056\n", + "PERC_COEFF: 0.0117801\n", + "BASEFLOW_COEFF_1: 0.0155179\n", + "BASEFLOW_COEFF_2: 0.00345655\n", + "TOPSOIL: 0.494935\n", + "PHREATIC: 0.145286\n", + "******************************\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "[{'sbest': spotpy.parameter.ParameterSet(),\n", + " 'trial_initial': [2.4446232183696073,\n", + " 3.648993786086633,\n", + " 6.808285937892062,\n", + " 0.9190984550605087,\n", + " 18.936595179011256,\n", + " 6.460481436876682,\n", + " -0.48087709565992487,\n", + " 0.07545129002831279,\n", + " 0.09286020935352743,\n", + " 0.03369469595949492,\n", + " 0.09584518083667387,\n", + " 1.375621144444363,\n", + " 0.7970479176500463,\n", + " 0.6446325223885151,\n", + " 1.9186703071461322,\n", + " 0.06648882787110477,\n", + " 0.0029416036552497686,\n", + " 0.06919911158176603,\n", + " 0.0024456422338723837,\n", + " 0.44961927013891523,\n", + " 0.30659009606737986],\n", + " 'objfunc_val': -24.6817}]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# We'll definitely want to adjust the random seed and number of model evaluations:\n", "model_evaluations = (\n", @@ -306,16 +842,29 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2021-09-08T20:37:06.200767Z", - "iopub.status.busy": "2021-09-08T20:37:06.200208Z", - "iopub.status.idle": "2021-09-08T20:37:06.202836Z", - "shell.execute_reply": "2021-09-08T20:37:06.202446Z" + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Nash-Sutcliffe value is: [-24.6817]\n", + "Best parameter set:\n", + "GAMMA_SHAPE=7.635242457794534, GAMMA_SCALE=0.7697246308702375, GAMMA_SHAPE2=11.438816553712229, GAMMA_SCALE2=1.026504937776411, MIN_MELT_FACTOR=17.545677044445164, MAX_MELT_FACTOR=0.0603, DD_MELT_TEMP=-0.42060403078707786, DD_AGGRADATION=0.018930000290900063, SNOW_SWI_MIN=0.030832364134558664, SNOW_SWI_MAX=0.0106, SWI_REDUCT_COEFF=0.1, DD_REFREEZE_TEMP=-0.03321510587203654, REFREEZE_FACTOR=0.6646210627773466, REFREEZE_EXP=0.9589666833784665, PET_CORRECTION=1.1054800200214736, HMETS_RUNOFF_COEFF=0.2140562916769115, PERC_COEFF=0.011780138728707873, BASEFLOW_COEFF_1=0.015517857213688549, BASEFLOW_COEFF_2=0.0034565506868557516, TOPSOIL=0.49493522146275265, PHREATIC=0.14528642601470373\n" + ] + }, + { + "data": { + "text/plain": [ + "(7.63524246, 0.76972463, 11.43881655, 1.02650494, 17.54567704, 0.0603, -0.42060403, 0.01893, 0.03083236, 0.0106, 0.1, -0.03321511, 0.66462106, 0.95896668, 1.10548002, 0.21405629, 0.01178014, 0.01551786, 0.00345655, 0.49493522, 0.14528643)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" } - }, - "outputs": [], + ], "source": [ "# Get the model diagnostics\n", "diag = spot_setup.diagnostics\n", @@ -340,7 +889,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -360,67 +909,83 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2021-09-08T20:37:08.194893Z", - "iopub.status.busy": "2021-09-08T20:37:08.194524Z", - "iopub.status.idle": "2021-09-08T20:37:08.235636Z", - "shell.execute_reply": "2021-09-08T20:37:08.235272Z" - } - }, - "outputs": [], - "source": [ - "out.hydrograph.q_sim" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2021-09-08T20:37:08.238599Z", - "iopub.status.busy": "2021-09-08T20:37:08.238201Z", - "iopub.status.idle": "2021-09-08T20:37:08.361328Z", - "shell.execute_reply": "2021-09-08T20:37:08.361914Z" - } - }, + "execution_count": 12, + "metadata": {}, "outputs": [], "source": [ - "# Plot the simulated hydrograph\n", - "from pandas.plotting import register_matplotlib_converters\n", - "\n", - "register_matplotlib_converters()\n", - "out.hydrograph.q_sim.plot()" + "q = out.hydrograph.q_sim" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2021-09-08T20:37:08.367532Z", - "iopub.status.busy": "2021-09-08T20:37:08.366839Z", - "iopub.status.idle": "2021-09-08T20:37:08.375481Z", - "shell.execute_reply": "2021-09-08T20:37:08.375119Z" + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Max: 1522.001921641669\n", + "Mean: 54.27677750325202\n", + "Monthly means: [[6.93032482e-02]\n", + " [7.86399173e+00]\n", + " [2.87806769e+00]\n", + " [2.16481233e+01]\n", + " [1.84055794e+02]\n", + " [1.31270602e+02]\n", + " [8.73476499e+01]\n", + " [9.45753187e+01]\n", + " [5.70102246e+01]\n", + " [5.79744837e+01]\n", + " [1.88086182e+00]\n", + " [8.44691306e-02]]\n" + ] } - }, - "outputs": [], + ], "source": [ - "# You can also get statistics from the data directly here.\n", - "print(\"Max: \", out.hydrograph.q_sim.max())\n", - "print(\"Mean: \", out.hydrograph.q_sim.mean())\n", + "# You can also get statistics from the data directly.\n", + "print(\"Max: \", q.max().values)\n", + "print(\"Mean: \", q.mean().values)\n", "print(\n", " \"Monthly means: \",\n", - " out.hydrograph.q_sim.groupby(out.hydrograph.time.dt.month).mean(dim=\"time\"),\n", + " q.groupby(\"time.month\").mean(dim=\"time\").values,\n", ")" ] }, { - "cell_type": "markdown", - "metadata": {}, + "cell_type": "code", + "execution_count": 14, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "For an example of how to download the data directly to analyze locally on your own computer/server, see here:\n" + "# Plot the simulated hydrograph\n", + "from pandas.plotting import register_matplotlib_converters\n", + "\n", + "register_matplotlib_converters()\n", + "q.plot()" ] } ], @@ -440,9 +1005,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.9" + "version": "3.9.16" } }, "nbformat": 4, - "nbformat_minor": 1 + "nbformat_minor": 4 } diff --git a/docs/notebooks/time_series_analysis.ipynb b/docs/notebooks/time_series_analysis.ipynb index d36a12a8..a6084a75 100644 --- a/docs/notebooks/time_series_analysis.ipynb +++ b/docs/notebooks/time_series_analysis.ipynb @@ -24,19 +24,17 @@ "source": [ "%matplotlib inline\n", "\n", - "import os\n", - "\n", "import xarray as xr\n", "import xclim\n", "from pandas.plotting import register_matplotlib_converters\n", "\n", - "from ravenpy.utilities.testdata import get_file\n", + "from ravenpy.utilities.testdata import get_file, open_dataset\n", "\n", "register_matplotlib_converters()\n", "\n", "# Get the file we will use to analyze flows\n", - "fn = get_file(\"hydro_simulations/raven-gr4j-cemaneige-sim_hmets-0_Hydrographs.nc\")\n", - "ds = xr.open_dataset(fn)" + "file = \"hydro_simulations/raven-gr4j-cemaneige-sim_hmets-0_Hydrographs.nc\"\n", + "ds = open_dataset(file)" ] }, { @@ -45,9 +43,18 @@ "source": [ "## Base flow index\n", "\n", - "The base flow index is the minimum 7-day average flow divided by the mean flow. " + "The base flow index is the minimum 7-day average flow divided by the mean flow." ] }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false + } + }, { "cell_type": "code", "execution_count": null, @@ -68,7 +75,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The base flow index needs as input arguments the link to a NetCDF file storing the stream flow time series, the name of the stream flow variable, and the frequency at which the index is computed (`YS`: yearly, `QS-DEC`: seasonally). " + "The base flow index needs as input arguments the link to a NetCDF file storing the stream flow time series, the name of the stream flow variable, and the frequency at which the index is computed (`YS`: yearly, `QS-DEC`: seasonally)." ] }, { @@ -135,7 +142,7 @@ "source": [ "## Frequency analysis\n", "\n", - "The process `freq_analysis` is similar to the previous stat sin the it fits a series of annual maxima or minima to a statistical distribution, and returns the values corresponding to different return periods. " + "The process `freq_analysis` is similar to the previous stat in that it fits a series of annual maxima or minima to a statistical distribution, and returns the values corresponding to different return periods." ] }, { @@ -158,7 +165,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "For example, computing the Q(2,7), the minimum 7-days streamflow of reccurrence two years, can be done using the following. " + "For example, computing the Q(2,7), the minimum 7-days streamflow with a two-year reoccurrence, can be done using the following." ] }, { @@ -182,7 +189,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "An array of return periods can be passed. " + "An array of return periods can be passed." ] }, { @@ -210,7 +217,7 @@ "source": [ "## Getting the parameters of the distribution and comparing the fit\n", "\n", - "It's sometimes more useful to store the fitted parameters of the distribution rather than storing only the quantiles. In the example below, we're first computing the annual maxima of the simulated time series, then fitting them to a gumbel distribution using the `fit` process. " + "It's sometimes more useful to store the fitted parameters of the distribution rather than storing only the quantiles. In the example below, we're first computing the annual maxima of the simulated time series, then fitting them to a gumbel distribution using the `fit` process." ] }, { diff --git a/docs/user_api.rst b/docs/user_api.rst index 2ff77fb9..ce5f82ab 100644 --- a/docs/user_api.rst +++ b/docs/user_api.rst @@ -7,36 +7,30 @@ Execution .. automodule:: ravenpy.ravenpy :members: - :noindex: Configuration ============= .. automodule:: ravenpy.config.commands :members: - :noindex: .. automodule:: ravenpy.config.rvs :members: - :noindex: Emulators ========= .. automodule:: ravenpy.config.emulators :members: - :noindex: Extractors ========== .. automodule:: ravenpy.extractors.routing_product :members: - :noindex: .. automodule:: ravenpy.extractors.forecasts :members: - :noindex: Utilities ========= @@ -45,25 +39,20 @@ Geospatial ---------- .. automodule:: ravenpy.utilities.io :members: - :noindex: .. automodule:: ravenpy.utilities.geo :members: - :noindex: .. automodule:: ravenpy.utilities.geoserver :members: - :noindex: Graphics -------- .. automodule:: ravenpy.utilities.graphs :members: - :noindex: Regionalization --------------- .. automodule:: ravenpy.utilities.regionalization :members: - :noindex: diff --git a/environment-rtd.yml b/environment-rtd.yml index f3e050cf..57f312ae 100644 --- a/environment-rtd.yml +++ b/environment-rtd.yml @@ -3,7 +3,8 @@ channels: - conda-forge - defaults dependencies: - - python >=3.8,<3.12 + - python >=3.9,<3.10 # fixed to reduce solver time + - raven-hydro ==0.2.1 - autodoc-pydantic - click # - clisops # mocked @@ -11,13 +12,15 @@ dependencies: - ipykernel - ipython - jupyter_client - - matplotlib + - matplotlib-base - myst-nb - jupytext - nbsphinx # - netCDF4 # mocked + - notebook - pandoc - pydantic + - seaborn - sphinx - sphinx-autoapi - sphinx-click diff --git a/pyproject.toml b/pyproject.toml index bb94e084..3568fa15 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -86,7 +86,7 @@ docs = [ "cartopy", "clisops", "gcsfs", - "hs_restclient", + "hsclient", "intake", "intake-esm", "intake-xarray", diff --git a/ravenpy/config/base.py b/ravenpy/config/base.py index f1847b74..5770c349 100644 --- a/ravenpy/config/base.py +++ b/ravenpy/config/base.py @@ -10,12 +10,12 @@ ----- In pydantic, BaseModel and dataclasses behave slightly differently. A dataclass object can be instantiated using positional arguments, while BaseModels cannot. -This is why Params is defined as a dataclass +This is why Params is defined as a dataclass:: -Two cases -RV - Command, alias="A1" - Sequence[Command], alias="A2" + Two cases + RV + Command, alias="A1" + Sequence[Command], alias="A2" Sometimes, we want A2 to be printed as a command (HydrologicProcesses) Sometime, we don't (Gauges) @@ -37,20 +37,18 @@ class Params: def encoder(v: dict) -> dict: - """ - Return string representation of objects in dictionary. + r"""Return string representation of objects in dictionary. This is meant to be applied to BaseModel attributes that either have an `alias` defined, or have a `__root__` attribute. The objective is to avoid creating `Command` objects for every - configuration option. - - - bool: ':{cmd}\n' if obj else '' - - dict: ':{cmd} {key} {value}' - - enum: ':{cmd} {obj.value}' - - Command: obj.to_rv() - - Sequence: complicated - - Any other: ':{cmd} {obj}' - + configuration option: + + - bool: ':{cmd}\n' if obj else '' + - dict: ':{cmd} {key} {value}' + - enum: ':{cmd} {obj.value}' + - Command: obj.to_rv() + - Sequence: complicated + - Any other: ':{cmd} {obj}' """ import warnings diff --git a/ravenpy/config/commands.py b/ravenpy/config/commands.py index 6b156bd2..fc7845d2 100644 --- a/ravenpy/config/commands.py +++ b/ravenpy/config/commands.py @@ -42,16 +42,18 @@ from .utils import filter_for, nc_specs """ -Notes ------ - -TODO: Strip command outputs of white space to facilitate testing. -TODO: Add docstrings -TODO: Create tests in tests/config/test_commands.py +Raven commands +============== +The syntax of those commands match as closely as possible the Raven documentation. """ +# TODO: Strip command outputs of white space to facilitate testing. +# TODO: Add docstrings +# TODO: Create tests in tests/config/test_commands.py + + INDENT = " " * 4 VALUE_PADDING = 10 T12 = Tuple[ @@ -139,18 +141,12 @@ def to_rv(self): class RainSnowTransition(Command): - """Specify the range of temperatures over which there will be a rain/snow mix when partitioning total precipitation into rain and snow components. - - Attributes - ---------- - temp : float - Midpoint of the temperature range [C]. - delta : float - Range [C]. - """ + """Specify the range of temperatures over which there will be a rain/snow mix when partitioning total precipitation into rain and snow components.""" temp: Sym + """Midpoint of the temperature range [C].""" delta: Sym + """Range [C].""" def to_rv(self): cmd = "RainSnowTransition" @@ -177,15 +173,16 @@ class CustomOutput(FlatCommand): Parameters ---------- time_per : {'DAILY', 'MONTHLY', 'YEARLY', 'WATER_YEARLY', 'CONTINUOUS'} - Time period. + Time period. stat : {'AVERAGE', 'MAXIMUM', 'MINIMUM', 'RANGE', 'MEDIAN', 'QUARTILES', 'HISTOGRAM [min] [max] [# bins]' - Statistic reported for each time inverval. - variable: str - Variable or parameter name. Consult the Raven documentation for the list of allowed names. + Statistic reported for each time interval. + variable : str + Variable or parameter name. Consult the Raven documentation for the list of allowed names. space_agg : {'BY_BASIN', 'BY_HRU', 'BY_HRU_GROUP', 'BY_SB_GROUP', 'ENTIRE_WATERSHED'} - Spatial evaluation domain. + Spatial evaluation domain. filename : str - Output file name. Defaults to something approximately like `____.nc + Output file name. Defaults to something approximately like + `____.nc` """ time_per: Literal["DAILY", "MONTHLY", "YEARLY", "WATER_YEARLY", "CONTINUOUS"] @@ -400,8 +397,9 @@ def to_rv(self): class GridWeights(Command): """GridWeights command. - Important note: this command can be embedded in both a `GriddedForcing` - or a `StationForcing`. + Notes + ----- + command can be embedded in both a `GriddedForcing` or a `StationForcing`. The default is to have a single cell that covers an entire single HRU, with a weight of 1. @@ -445,6 +443,8 @@ def parse(cls, s): class RedirectToFile(Command): """RedirectToFile command (RVT). + Notes + ----- For the moment, this command can only be used in the context of a `GriddedForcingCommand` or a `StationForcingCommand`, as a `grid_weights` field replacement when inlining is not desired. @@ -640,27 +640,27 @@ def from_nc( Parameters ---------- fn : Union[Path, Sequence[Path]], - NetCDF file path or paths. - data_type: Sequence[str], None - Raven data types to extract from netCDF files, e.g. 'PRECIP', 'AVE_TEMP'. The algorithm tries to find all - forcings in each file until one is found, then it stops searching for it in the following files. - station_idx: int - Index along station dimension. Starts at 1. Should be the same for all netCDF files. - alt_names: dict - Alternative variable names keyed by data type. - Use this if variables do not correspond to CF standard defaults. - mon_ave: bool - If True, compute the monthly average. - data_kwds: Dict[options.Forcings, Dict[str, str]]] - Additional `:Data` parameters keyed by forcing type and station id. Overrides inferred parameters. - Use keyword "ALL" to pass parameters to all variables. + NetCDF file path or paths. + data_type : Sequence[str], None + Raven data types to extract from netCDF files, e.g. 'PRECIP', 'AVE_TEMP'. The algorithm tries to find all + forcings in each file until one is found, then it stops searching for it in the following files. + station_idx : int + Index along station dimension. Starts at 1. Should be the same for all netCDF files. + alt_names : dict + Alternative variable names keyed by data type. + Use this if variables do not correspond to CF standard defaults. + mon_ave : bool + If True, compute the monthly average. + data_kwds : Dict[options.Forcings, Dict[str, str]]] + Additional `:Data` parameters keyed by forcing type and station id. Overrides inferred parameters. + Use keyword "ALL" to pass parameters to all variables. **kwds - Additional arguments for Gauge. + Additional arguments for Gauge. Returns ------- Gauge - Gauge instance. + Gauge instance. """ if alt_names is None: alt_names = {} @@ -1032,6 +1032,8 @@ class AssimilatedState(FlatCommand): class AssimilateStreamflow(FlatCommand): + """Subbasin ID to assimilate streamflow for.""" + sb_id: str _template = ":{_cmd} {sb_id}\n" diff --git a/ravenpy/config/parsers.py b/ravenpy/config/parsers.py index 548f08eb..fcca9eb2 100644 --- a/ravenpy/config/parsers.py +++ b/ravenpy/config/parsers.py @@ -105,10 +105,10 @@ def parse_outputs(run_name: str = None, outputdir: [str, Path] = None): Parameters ---------- - run_name: str - RunName value identifying model outputs. - outputdir: str, Path - Path to model output directory. Current directory if None. + run_name : str + RunName value identifying model outputs. + outputdir : str or Path + Path to model output directory. Current directory if None. Returns ------- @@ -118,7 +118,11 @@ def parse_outputs(run_name: str = None, outputdir: [str, Path] = None): - storage: xarray.Dataset - solution: Dict[str, Command] - diagnostics: Dict[str, list] - Values are set to None if no file is found. + + Notes + ----- + Values are set to None if no file is found. + """ if outputdir is None: diff --git a/tests/test_commands.py b/tests/test_commands.py index 41369bee..870d317b 100644 --- a/tests/test_commands.py +++ b/tests/test_commands.py @@ -1,4 +1,5 @@ import re +from shutil import copyfile from textwrap import dedent from typing import Sequence, Union @@ -350,12 +351,16 @@ def test_gridded_forcing(get_local_testdata): # assert gf.dim_names_nc == ("lon_dim", "lat_dim", "time") -def test_gauge(get_local_testdata): +def test_gauge(get_local_testdata, tmp_path): f = get_local_testdata( "raven-gr4j-cemaneige/Salmon-River-Near-Prince-George_meteo_daily.nc" ) + f_tmp = copyfile( + f, tmp_path.joinpath("Salmon-River-Near-Prince-George_meteo_daily.nc") + ) + g = rc.Gauge.from_nc( - f, + f_tmp, alt_names={"RAINFALL": "rain", "SNOWFALL": "snow"}, data_kwds={"ALL": {"Deaccumulate": True}}, ) @@ -365,6 +370,12 @@ def test_gauge(get_local_testdata): assert "RAINFALL" in g.ds assert g.data[0].read_from_netcdf.deaccumulate + +def test_gauge_raises(get_local_testdata): + f = get_local_testdata( + "raven-gr4j-cemaneige/Salmon-River-Near-Prince-George_meteo_daily.nc" + ) + with pytest.raises(ValueError): rc.Gauge.from_nc( f, diff --git a/tests/test_emulators.py b/tests/test_emulators.py index 1761d9dc..670fb456 100644 --- a/tests/test_emulators.py +++ b/tests/test_emulators.py @@ -160,6 +160,7 @@ def test_evaluation_periods(gr4jcn_config, tmp_path): @pytest.mark.slow @pytest.mark.online +@pytest.mark.xfail(error=OSError, reason="Network may be unreliable") def test_run_with_dap_link(minimal_emulator, tmp_path): """Test Raven with DAP link instead of local netCDF file.""" # Link to THREDDS Data Server netCDF testdata