diff --git a/.gitignore b/.gitignore index ffe84ea..f293e6d 100644 --- a/.gitignore +++ b/.gitignore @@ -2,9 +2,13 @@ __pycache__/ *.py[cod] -# Test things +# Internal test notebooks *test*.ipynb +# xagg /wm/ directories created during docs processing +wm/ +docs/notebooks/wm/ + # C extensions *.so diff --git a/docs/notebooks/base_run.ipynb b/docs/notebooks/base_run.ipynb index 4d7c404..cfc77c5 100644 --- a/docs/notebooks/base_run.ipynb +++ b/docs/notebooks/base_run.ipynb @@ -6,7 +6,7 @@ "metadata": {}, "source": [ "# Base run\n", - "A simple run of `xagg`, aggregating gridded temperature data over US counties. For a deeper dive into `xagg`'s functionality, see the [Detailed Code Run](./full_run.ipynb)." + "A simple run of `xagg`, aggregating gridded temperature data over US counties. For a deeper dive into `xagg`'s functionality, see the [Detailed Code Run](./full_run.ipynb). " ] }, { @@ -38,10 +38,455 @@ "execution_count": 2, "id": "simple-spelling", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.Dataset>\n",
+       "Dimensions:   (lon: 288, lat: 192, month: 12, bnds: 2)\n",
+       "Coordinates:\n",
+       "    height    float64 ...\n",
+       "  * lon       (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n",
+       "  * lat       (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
+       "  * month     (month) int64 1 2 3 4 5 6 7 8 9 10 11 12\n",
+       "Dimensions without coordinates: bnds\n",
+       "Data variables:\n",
+       "    lat_bnds  (month, lat, bnds) float64 ...\n",
+       "    lon_bnds  (month, lon, bnds) float64 ...\n",
+       "    tas       (month, lat, lon) float32 ...
" + ], + "text/plain": [ + "\n", + "Dimensions: (lon: 288, lat: 192, month: 12, bnds: 2)\n", + "Coordinates:\n", + " height float64 ...\n", + " * lon (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n", + " * lat (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n", + " * month (month) int64 1 2 3 4 5 6 7 8 9 10 11 12\n", + "Dimensions without coordinates: bnds\n", + "Data variables:\n", + " lat_bnds (month, lat, bnds) float64 ...\n", + " lon_bnds (month, lon, bnds) float64 ...\n", + " tas (month, lat, lon) float32 ..." + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Load some climate data as an xarray dataset\n", - "ds = xr.open_dataset('../../data/climate_data/tas_Amon_CCSM4_rcp85_monthavg_20700101-20991231.nc')" + "ds = xr.open_dataset('../../data/climate_data/tas_Amon_CCSM4_rcp85_monthavg_20700101-20991231.nc')\n", + "ds" ] }, { @@ -49,10 +494,111 @@ "execution_count": 3, "id": "indoor-darwin", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
NAMESTATE_NAMESTATE_FIPSCNTY_FIPSFIPSgeometry
0Lake of the WoodsMinnesota2707727077POLYGON ((-95.34283 48.54668, -95.34105 48.715...
1FerryWashington5301953019POLYGON ((-118.85163 47.94956, -118.84846 48.4...
2StevensWashington5306553065POLYGON ((-117.43883 48.04412, -117.54219 48.0...
3OkanoganWashington5304753047POLYGON ((-118.97209 47.93915, -118.97406 47.9...
4Pend OreilleWashington5305153051POLYGON ((-117.43858 48.99992, -117.03205 48.9...
\n", + "
" + ], + "text/plain": [ + " NAME STATE_NAME STATE_FIPS CNTY_FIPS FIPS \\\n", + "0 Lake of the Woods Minnesota 27 077 27077 \n", + "1 Ferry Washington 53 019 53019 \n", + "2 Stevens Washington 53 065 53065 \n", + "3 Okanogan Washington 53 047 53047 \n", + "4 Pend Oreille Washington 53 051 53051 \n", + "\n", + " geometry \n", + "0 POLYGON ((-95.34283 48.54668, -95.34105 48.715... \n", + "1 POLYGON ((-118.85163 47.94956, -118.84846 48.4... \n", + "2 POLYGON ((-117.43883 48.04412, -117.54219 48.0... \n", + "3 POLYGON ((-118.97209 47.93915, -118.97406 47.9... \n", + "4 POLYGON ((-117.43858 48.99992, -117.03205 48.9... " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Load US counties shapefile as a geopandas GeoDataFrame\n", - "gdf = gpd.read_file('../../data/geo_data/UScounties.shp')" + "gdf = gpd.read_file('../../data/geo_data/UScounties.shp')\n", + "gdf.head()" ] }, { @@ -156,6 +702,7 @@ "}\n", "\n", "html[theme=dark],\n", + "body[data-theme=dark],\n", "body.vscode-dark {\n", " --xr-font-color0: rgba(255, 255, 255, 1);\n", " --xr-font-color2: rgba(255, 255, 255, 0.54);\n", @@ -168,7 +715,7 @@ "}\n", "\n", ".xr-wrap {\n", - " display: block;\n", + " display: block !important;\n", " min-width: 300px;\n", " max-width: 700px;\n", "}\n", @@ -385,6 +932,11 @@ " grid-column: 4;\n", "}\n", "\n", + ".xr-index-preview {\n", + " grid-column: 2 / 5;\n", + " color: var(--xr-font-color2);\n", + "}\n", + "\n", ".xr-var-name,\n", ".xr-var-dims,\n", ".xr-var-dtype,\n", @@ -406,14 +958,16 @@ "}\n", "\n", ".xr-var-attrs,\n", - ".xr-var-data {\n", + ".xr-var-data,\n", + ".xr-index-data {\n", " display: none;\n", " background-color: var(--xr-background-color) !important;\n", " padding-bottom: 5px !important;\n", "}\n", "\n", ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n", - ".xr-var-data-in:checked ~ .xr-var-data {\n", + ".xr-var-data-in:checked ~ .xr-var-data,\n", + ".xr-index-data-in:checked ~ .xr-index-data {\n", " display: block;\n", "}\n", "\n", @@ -423,13 +977,16 @@ "\n", ".xr-var-name span,\n", ".xr-var-data,\n", + ".xr-index-name div,\n", + ".xr-index-data,\n", ".xr-attrs {\n", " padding-left: 25px !important;\n", "}\n", "\n", ".xr-attrs,\n", ".xr-var-attrs,\n", - ".xr-var-data {\n", + ".xr-var-data,\n", + ".xr-index-data {\n", " grid-column: 1 / -1;\n", "}\n", "\n", @@ -467,7 +1024,8 @@ "}\n", "\n", ".xr-icon-database,\n", - ".xr-icon-file-text2 {\n", + ".xr-icon-file-text2,\n", + ".xr-no-icon {\n", " display: inline-block;\n", " vertical-align: middle;\n", " width: 1em;\n", @@ -477,20 +1035,20 @@ " fill: currentColor;\n", "}\n", "
<xarray.Dataset>\n",
-       "Dimensions:     (month: 12, pix_idx: 3141)\n",
+       "Dimensions:     (poly_idx: 3141, month: 12)\n",
        "Coordinates:\n",
-       "  * pix_idx     (pix_idx) int64 0 1 2 3 4 5 6 ... 3135 3136 3137 3138 3139 3140\n",
+       "  * poly_idx    (poly_idx) int64 0 1 2 3 4 5 6 ... 3135 3136 3137 3138 3139 3140\n",
        "  * month       (month) int64 1 2 3 4 5 6 7 8 9 10 11 12\n",
        "Data variables:\n",
-       "    NAME        (pix_idx) object 'Lake of the Woods' 'Ferry' ... 'Broomfield'\n",
-       "    STATE_NAME  (pix_idx) object 'Minnesota' 'Washington' ... 'Colorado'\n",
-       "    STATE_FIPS  (pix_idx) object '27' '53' '53' '53' ... '02' '02' '02' '08'\n",
-       "    CNTY_FIPS   (pix_idx) object '077' '019' '065' '047' ... '240' '068' '014'\n",
-       "    FIPS        (pix_idx) object '27077' '53019' '53065' ... '02068' '08014'\n",
-       "    tas         (pix_idx, month) float64 263.9 268.8 274.0 ... 283.5 276.4 270.4
    • poly_idx
      PandasIndex
      PandasIndex(Index([   0,    1,    2,    3,    4,    5,    6,    7,    8,    9,\n",
+       "       ...\n",
+       "       3131, 3132, 3133, 3134, 3135, 3136, 3137, 3138, 3139, 3140],\n",
+       "      dtype='int64', name='poly_idx', length=3141))
    • month
      PandasIndex
      PandasIndex(Index([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype='int64', name='month'))
    • " ], "text/plain": [ "\n", - "Dimensions: (month: 12, pix_idx: 3141)\n", + "Dimensions: (poly_idx: 3141, month: 12)\n", "Coordinates:\n", - " * pix_idx (pix_idx) int64 0 1 2 3 4 5 6 ... 3135 3136 3137 3138 3139 3140\n", + " * poly_idx (poly_idx) int64 0 1 2 3 4 5 6 ... 3135 3136 3137 3138 3139 3140\n", " * month (month) int64 1 2 3 4 5 6 7 8 9 10 11 12\n", "Data variables:\n", - " NAME (pix_idx) object 'Lake of the Woods' 'Ferry' ... 'Broomfield'\n", - " STATE_NAME (pix_idx) object 'Minnesota' 'Washington' ... 'Colorado'\n", - " STATE_FIPS (pix_idx) object '27' '53' '53' '53' ... '02' '02' '02' '08'\n", - " CNTY_FIPS (pix_idx) object '077' '019' '065' '047' ... '240' '068' '014'\n", - " FIPS (pix_idx) object '27077' '53019' '53065' ... '02068' '08014'\n", - " tas (pix_idx, month) float64 263.9 268.8 274.0 ... 283.5 276.4 270.4" + " NAME (poly_idx) object 'Lake of the Woods' 'Ferry' ... 'Broomfield'\n", + " STATE_NAME (poly_idx) object 'Minnesota' 'Washington' ... 'Colorado'\n", + " STATE_FIPS (poly_idx) object '27' '53' '53' '53' ... '02' '02' '02' '08'\n", + " CNTY_FIPS (poly_idx) object '077' '019' '065' '047' ... '240' '068' '014'\n", + " FIPS (poly_idx) object '27077' '53019' '53065' ... '02068' '08014'\n", + " tas (poly_idx, month) float64 263.9 268.8 274.0 ... 276.4 270.4" ] }, "execution_count": 6, @@ -557,139 +1118,75 @@ " \n", " \n", " \n", + " \n", " NAME\n", " STATE_NAME\n", " STATE_FIPS\n", " CNTY_FIPS\n", " FIPS\n", - " tas0\n", - " tas1\n", - " tas2\n", - " tas3\n", - " tas4\n", - " tas5\n", - " tas6\n", - " tas7\n", - " tas8\n", - " tas9\n", - " tas10\n", - " tas11\n", + " tas\n", + " \n", + " \n", + " poly_idx\n", + " month\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " 0\n", + " 0\n", + " 1\n", " Lake of the Woods\n", " Minnesota\n", " 27\n", " 077\n", " 27077\n", " 263.918943\n", - " 268.834073\n", - " 273.977533\n", - " 283.141960\n", - " 290.623952\n", - " 297.858885\n", - " 302.068017\n", - " 300.362248\n", - " 293.471128\n", - " 283.798660\n", - " 275.109100\n", - " 266.016176\n", - " \n", - " \n", - " 1\n", - " Ferry\n", - " Washington\n", - " 53\n", - " 019\n", - " 53019\n", - " 271.794169\n", - " 275.631364\n", - " 276.947080\n", - " 279.837102\n", - " 286.630023\n", - " 293.769471\n", - " 299.073178\n", - " 297.151514\n", - " 289.866690\n", - " 281.648927\n", - " 276.727886\n", - " 272.256934\n", " \n", " \n", " 2\n", - " Stevens\n", - " Washington\n", - " 53\n", - " 065\n", - " 53065\n", - " 272.113155\n", - " 275.910279\n", - " 277.355354\n", - " 280.428965\n", - " 287.247099\n", - " 294.356788\n", - " 299.847098\n", - " 297.967740\n", - " 290.637124\n", - " 282.076344\n", - " 277.019222\n", - " 272.516056\n", + " Lake of the Woods\n", + " Minnesota\n", + " 27\n", + " 077\n", + " 27077\n", + " 268.834073\n", " \n", " \n", " 3\n", - " Okanogan\n", - " Washington\n", - " 53\n", - " 047\n", - " 53047\n", - " 271.772021\n", - " 275.539162\n", - " 276.654805\n", - " 279.317270\n", - " 285.794503\n", - " 292.650947\n", - " 297.741617\n", - " 295.915714\n", - " 289.090624\n", - " 281.372544\n", - " 276.598377\n", - " 272.208944\n", + " Lake of the Woods\n", + " Minnesota\n", + " 27\n", + " 077\n", + " 27077\n", + " 273.977533\n", " \n", " \n", " 4\n", - " Pend Oreille\n", - " Washington\n", - " 53\n", - " 051\n", - " 53051\n", - " 271.721285\n", - " 275.542011\n", - " 276.993355\n", - " 280.157156\n", - " 287.086018\n", - " 294.169635\n", - " 299.503768\n", - " 297.523382\n", - " 290.086946\n", - " 281.657134\n", - " 276.644670\n", - " 272.095152\n", + " Lake of the Woods\n", + " Minnesota\n", + " 27\n", + " 077\n", + " 27077\n", + " 283.141960\n", + " \n", + " \n", + " 5\n", + " Lake of the Woods\n", + " Minnesota\n", + " 27\n", + " 077\n", + " 27077\n", + " 290.623952\n", " \n", " \n", " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", + " ...\n", " ...\n", " ...\n", " ...\n", @@ -698,151 +1195,86 @@ " ...\n", " \n", " \n", - " 3136\n", - " Skagway-Hoonah-Angoon\n", - " Alaska\n", - " 02\n", - " 232\n", - " 02232\n", - " 270.709185\n", - " 272.455135\n", - " 273.717142\n", - " 276.188285\n", - " 281.253285\n", - " 286.791100\n", - " 288.361128\n", - " 287.822862\n", - " 284.093411\n", - " 278.681980\n", - " 274.221760\n", - " 271.175471\n", + " 3140\n", + " 8\n", + " Broomfield\n", + " Colorado\n", + " 08\n", + " 014\n", + " 08014\n", + " 297.646820\n", " \n", " \n", - " 3137\n", - " Yukon-Koyukuk\n", - " Alaska\n", - " 02\n", - " 290\n", - " 02290\n", - " 263.970656\n", - " 263.404975\n", - " 266.670047\n", - " 272.394716\n", - " 280.492861\n", - " 288.813169\n", - " 288.513645\n", - " 285.724033\n", - " 280.243361\n", - " 273.044271\n", - " 266.155923\n", - " 265.022613\n", + " 9\n", + " Broomfield\n", + " Colorado\n", + " 08\n", + " 014\n", + " 08014\n", + " 292.368988\n", " \n", " \n", - " 3138\n", - " Southeast Fairbanks\n", - " Alaska\n", - " 02\n", - " 240\n", - " 02240\n", - " 262.846312\n", - " 263.000185\n", - " 265.438037\n", - " 270.754788\n", - " 278.476096\n", - " 286.669566\n", - " 287.315147\n", - " 284.920161\n", - " 279.230840\n", - " 271.713061\n", - " 264.946526\n", - " 263.297936\n", + " 10\n", + " Broomfield\n", + " Colorado\n", + " 08\n", + " 014\n", + " 08014\n", + " 283.544708\n", " \n", " \n", - " 3139\n", - " Denali\n", - " Alaska\n", - " 02\n", - " 068\n", - " 02068\n", - " 265.084342\n", - " 264.547936\n", - " 267.203954\n", - " 271.782649\n", - " 278.898267\n", - " 287.059920\n", - " 287.375217\n", - " 285.069283\n", - " 279.833609\n", - " 272.514117\n", - " 266.145088\n", - " 265.682660\n", + " 11\n", + " Broomfield\n", + " Colorado\n", + " 08\n", + " 014\n", + " 08014\n", + " 276.383606\n", " \n", " \n", - " 3140\n", + " 12\n", " Broomfield\n", " Colorado\n", " 08\n", " 014\n", " 08014\n", - " 270.803864\n", - " 273.430206\n", - " 275.955505\n", - " 280.790070\n", - " 287.303619\n", - " 292.830048\n", - " 297.615662\n", - " 297.646820\n", - " 292.368988\n", - " 283.544708\n", - " 276.383606\n", " 270.444855\n", " \n", " \n", "\n", - "

    3141 rows × 17 columns

    \n", + "

    37692 rows × 6 columns

    \n", "" ], "text/plain": [ - " NAME STATE_NAME STATE_FIPS CNTY_FIPS FIPS \\\n", - "0 Lake of the Woods Minnesota 27 077 27077 \n", - "1 Ferry Washington 53 019 53019 \n", - "2 Stevens Washington 53 065 53065 \n", - "3 Okanogan Washington 53 047 53047 \n", - "4 Pend Oreille Washington 53 051 53051 \n", - "... ... ... ... ... ... \n", - "3136 Skagway-Hoonah-Angoon Alaska 02 232 02232 \n", - "3137 Yukon-Koyukuk Alaska 02 290 02290 \n", - "3138 Southeast Fairbanks Alaska 02 240 02240 \n", - "3139 Denali Alaska 02 068 02068 \n", - "3140 Broomfield Colorado 08 014 08014 \n", - "\n", - " tas0 tas1 tas2 tas3 tas4 tas5 \\\n", - "0 263.918943 268.834073 273.977533 283.141960 290.623952 297.858885 \n", - "1 271.794169 275.631364 276.947080 279.837102 286.630023 293.769471 \n", - "2 272.113155 275.910279 277.355354 280.428965 287.247099 294.356788 \n", - "3 271.772021 275.539162 276.654805 279.317270 285.794503 292.650947 \n", - "4 271.721285 275.542011 276.993355 280.157156 287.086018 294.169635 \n", - "... ... ... ... ... ... ... \n", - "3136 270.709185 272.455135 273.717142 276.188285 281.253285 286.791100 \n", - "3137 263.970656 263.404975 266.670047 272.394716 280.492861 288.813169 \n", - "3138 262.846312 263.000185 265.438037 270.754788 278.476096 286.669566 \n", - "3139 265.084342 264.547936 267.203954 271.782649 278.898267 287.059920 \n", - "3140 270.803864 273.430206 275.955505 280.790070 287.303619 292.830048 \n", - "\n", - " tas6 tas7 tas8 tas9 tas10 tas11 \n", - "0 302.068017 300.362248 293.471128 283.798660 275.109100 266.016176 \n", - "1 299.073178 297.151514 289.866690 281.648927 276.727886 272.256934 \n", - "2 299.847098 297.967740 290.637124 282.076344 277.019222 272.516056 \n", - "3 297.741617 295.915714 289.090624 281.372544 276.598377 272.208944 \n", - "4 299.503768 297.523382 290.086946 281.657134 276.644670 272.095152 \n", - "... ... ... ... ... ... ... \n", - "3136 288.361128 287.822862 284.093411 278.681980 274.221760 271.175471 \n", - "3137 288.513645 285.724033 280.243361 273.044271 266.155923 265.022613 \n", - "3138 287.315147 284.920161 279.230840 271.713061 264.946526 263.297936 \n", - "3139 287.375217 285.069283 279.833609 272.514117 266.145088 265.682660 \n", - "3140 297.615662 297.646820 292.368988 283.544708 276.383606 270.444855 \n", - "\n", - "[3141 rows x 17 columns]" + " NAME STATE_NAME STATE_FIPS CNTY_FIPS FIPS \\\n", + "poly_idx month \n", + "0 1 Lake of the Woods Minnesota 27 077 27077 \n", + " 2 Lake of the Woods Minnesota 27 077 27077 \n", + " 3 Lake of the Woods Minnesota 27 077 27077 \n", + " 4 Lake of the Woods Minnesota 27 077 27077 \n", + " 5 Lake of the Woods Minnesota 27 077 27077 \n", + "... ... ... ... ... ... \n", + "3140 8 Broomfield Colorado 08 014 08014 \n", + " 9 Broomfield Colorado 08 014 08014 \n", + " 10 Broomfield Colorado 08 014 08014 \n", + " 11 Broomfield Colorado 08 014 08014 \n", + " 12 Broomfield Colorado 08 014 08014 \n", + "\n", + " tas \n", + "poly_idx month \n", + "0 1 263.918943 \n", + " 2 268.834073 \n", + " 3 273.977533 \n", + " 4 283.141960 \n", + " 5 290.623952 \n", + "... ... \n", + "3140 8 297.646820 \n", + " 9 292.368988 \n", + " 10 283.544708 \n", + " 11 276.383606 \n", + " 12 270.444855 \n", + "\n", + "[37692 rows x 6 columns]" ] }, "execution_count": 7, diff --git a/docs/notebooks/full_run.ipynb b/docs/notebooks/full_run.ipynb index 331e3ba..349c46e 100644 --- a/docs/notebooks/full_run.ipynb +++ b/docs/notebooks/full_run.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "broken-labor", "metadata": {}, "outputs": [], @@ -40,6 +40,29 @@ "Let's get started." ] }, + { + "cell_type": "markdown", + "id": "c278bcbd-0e54-47a5-ae50-0d0fae0d6565", + "metadata": {}, + "source": [ + "### Extra downloads\n", + "Since we will be using an extra `weights` file that must be regridded, we need the optional dependency :py:mod:`xesmf`, which we can install through: \n", + "\n", + "`mamba install -c conda-forge xesmf`\n", + "\n", + "Since we will be using the optional feature :py:meth:`.diag_fig()`, we need the optional dependencies :py:mod:`matplotlib`, :py:mod:`cartopy`, and :py:mod:`cmocean`, which we can install through: \n", + "\n", + "`mamba install -c conda-forge matplotlib cartopy cmocean`" + ] + }, + { + "cell_type": "markdown", + "id": "a3de1c17-f535-46eb-8cc2-f7a19ab1aa58", + "metadata": {}, + "source": [ + "### Load data" + ] + }, { "cell_type": "code", "execution_count": 2, @@ -112,23 +135,7 @@ "output_type": "stream", "text": [ "creating polygons for each pixel...\n", - "regridding weights to data grid...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kevinschwarzwald/opt/anaconda3/envs/test/lib/python3.9/site-packages/xarray/core/dataarray.py:746: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", - " return key in self.data\n", - "/Users/kevinschwarzwald/opt/anaconda3/envs/test/lib/python3.9/site-packages/xesmf/frontend.py:466: FutureWarning: ``output_sizes`` should be given in the ``dask_gufunc_kwargs`` parameter. It will be removed as direct parameter in a future version.\n", - " dr_out = xr.apply_ufunc(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "regridding weights to data grid...\n", "calculating overlaps between pixels and output polygons...\n", "success!\n" ] @@ -139,6 +146,14 @@ "weightmap = xa.pixel_overlaps(ds,gdf,weights=ds_pop.pop)" ] }, + { + "cell_type": "markdown", + "id": "2e6c1fd7-a2d6-4ea8-8c38-441b62247a3e", + "metadata": {}, + "source": [ + "### Exporting / Importing the weightmap " + ] + }, { "cell_type": "markdown", "id": "ee2451e7-2e92-4cee-9d28-7b51625928ec", @@ -149,13 +164,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "9b5ba5c3-03ce-4fdb-866d-a937295b83f8", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kevinschwarzwald/opt/anaconda3/envs/xagg0320/lib/python3.12/site-packages/xagg/export.py:17: UserWarning: export_weightmap() is still an experimental feature. use with care.\n", + " warnings.warn('export_weightmap() is still an experimental feature. use with care.')\n" + ] + } + ], "source": [ - "# Export weightmap\n", - "weightmap.to_file('wm')" + "# Export weightmap to a directory called \"wm\" in the current directory\n", + "weightmap.to_file('./wm')" ] }, { @@ -168,7 +192,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "a6f23714-51b8-43f6-8fda-49299c6b56fc", "metadata": {}, "outputs": [], @@ -177,6 +201,129 @@ "weightmap = xa.read_wm('wm')" ] }, + { + "cell_type": "markdown", + "id": "e297b4c4-22b9-427c-893e-f3ab708f853d", + "metadata": {}, + "source": [ + "### Verifying the weightmap" + ] + }, + { + "cell_type": "markdown", + "id": "a0ceaf52-bc04-4630-a017-8157b24ce9d0", + "metadata": {}, + "source": [ + "Let's verify if the aggregation was successful. The `weightmap` class can produce diagnostic figures that show a given polygon + the grid cells of the original raster dataset that overlap it. (This feature is still a bit experimental and finicky, and as of v0.3.2.0 needs a little bit of manual processing) " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "d136f646-836b-4c0a-8d5c-e84e5e25a38b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "adjusting grid... (this may happen because only a subset of pixels were used for aggregation for efficiency - i.e. [subset_bbox=True] in xa.pixel_overlaps())\n", + "grid adjustment successful\n" + ] + } + ], + "source": [ + "# Load `subset_find()`, which allows you to find one grid within another\n", + "from xagg.auxfuncs import subset_find\n", + "\n", + "# weightmap.diag_fig() takes two required arguments: some information about\n", + "# a grid, and either the polygons of the raster grid, or the raster grid\n", + "# itself to calculate the polygons. \n", + "\n", + "# Let's get the raster grid.\n", + "# To match the internal indexing of `weightmap`, we need to subset the `ds`\n", + "# TODO: move this step internally to `weightmap.diag_fig()`\n", + "grid_polygon_info = subset_find(ds,weightmap.source_grid)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "9320f9f4-ff72-48cc-b2d2-32d4656a7e42", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create diagnostic figure of the polygon with index 50 in `gdf` (in this \n", + "# case, a county in Montana). You can verify this is the 50th row in `gdf`\n", + "# by printing `gdf.loc[50]`. \n", + "weightmap.diag_fig(50,grid_polygon_info)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "ee65af89-36b3-4dbe-b747-d30ede0e1120", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['NAME', 'STATE_NAME', 'STATE_FIPS', 'CNTY_FIPS', 'FIPS', 'geometry'], dtype='object')\n" + ] + } + ], + "source": [ + "# Alternatively, you can select a polygon based on other columns\n", + "# in the `gdf`, aka based on county metadata from the original \n", + "# shapefile. \n", + "print(gdf.columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "46c275c1-5224-4492-a21d-4161662453ca", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjEAAAG0CAYAAADD+nYCAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAADms0lEQVR4nOydd3wU1drHf1O2ZLPZ9N6oAQIJIDUgIh1ERcSugFjxqoiivhevXbnC1St2FBt6RbCCqAiiAirSCRB6gJCQ3sv2nZnz/rHZTTZ9d5Ns8Xz9zEcy88yZ8zs7M+eZU57DEEIIKBQKhUKhUHwM1tMZoFAoFAqFQnEF6sRQKBQKhULxSagTQ6FQKBQKxSehTgyFQqFQKBSfhDoxFAqFQqFQfBLqxFAoFAqFQvFJqBNDoVAoFArFJ6FODIVCoVAoFJ+EOjEUCoVCoVB8EqecmDVr1oBhGPvG8zwSEhKwYMECFBQUOH3xyy+/HJdffrnT53WU2bNn4+abbwYAEEIQGhqKNWvWONgUFRXhySefREZGBiIiIqDRaDBs2DCsXr0aoig62B4+fBgzZ85EUlISAgICEBYWhoyMDHz22WfNrk0Iwfvvv49hw4ZBo9EgPDwc48ePx48//thmnk+cOAGFQgGGYXDgwAGXtT/77LNgGAbl5eUup9GZvPHGG2AYBoMGDfJ0Vlqkq+9FZ6mtrcWyZcswfPhwaDQaKBQK9OjRA3fccQcOHTrkdHoXLlwAwzAO97/teb5w4YKD7ZNPPomkpCTwPI+QkBD3hLiA7d7tDkpLS3H77bcjIiICKpUKGRkZ+PXXX1u01el0ePrpp5GSkgKFQoHw8HBMmDAB2dnZLl/fYrHgueeeQ48ePaBQKNC/f3+8+eabzezWrVuHyy67DNHR0VAoFIiLi8NVV12Fv/76y8Fux44dDu/optvChQtdzqtWq8XixYsRFxcHpVKJIUOGYP369a3qevXVV5GWloaAgACEhIRgzJgxzfILAG+++Sb69+8PhUKBnj174rnnnoPFYnGw+eWXXzBlyhTExcVBoVAgKioKEydOxObNmx3sbPd5a9v06dNd1k/xTnhXTvr444/Rv39/GAwG/P7773jppZewc+dOZGVlITAwsLPz6DL79+/Ho48+CgA4deoUqqurMXLkSAebgwcP4tNPP8W8efPw1FNPQSaT4aeffsJ9992HPXv24KOPPrLbVldXIzExETfffDPi4+Oh0+mwdu1azJ07FxcuXMCTTz5pt33mmWfwwgsvYOHChVi+fDmMRiPefPNNXHnllfjmm29w7bXXNsuvKIq44447EBERgcLCwi4qFc9gK8fjx49j7969GDVqlIdz5L2cO3cOU6dORWlpKRYuXIjnnnsOarUaFy5cwJdffolhw4ahuroawcHBbl1n5syZ2L17N2JjY+37vvvuOyxbtgz/+te/MGPGDCgUCnflOM1dd93VLZWNyWTCpEmTUF1djddffx1RUVF4++23MX36dPzyyy8YP3683Var1WLChAkoLCzEP//5T6Snp6OmpgZ//fUX9Hq9y3n4xz/+gf/973944YUXMGLECGzduhUPPfQQ6urq8MQTT9jtKioqMHbsWDz00EOIiIhAUVERXn31VVx22WX49ddf7Xm95JJLsHv37mbXWbVqFT799FPMnj3b5bxee+212L9/P5YvX46UlBR8/vnnuPnmmyFJEm655Ra7nSiKmD17Nv788088/vjjGDNmDHQ6HQ4ePAidTueQ5rJly/DUU0/hn//8J6ZOnYr9+/fjySefREFBAVavXu2gf+DAgbjrrrsQExODyspKvPvuu5g5cyb+97//4bbbbgMAxMbGtqh/48aNWLFihVv6KV4KcYKPP/6YACD79+932P/UU08RAOSzzz5zJjkyfvx4Mn78eKfO6ShFRUUEAPnrr78IIYR89NFHJCgoiIii6GBXWVlJzGZzs/Pvv/9+AoDk5eW1e61Ro0aRxMREh33x8fHk0ksvddhnMBhIcHAwufrqq1tM5+WXXybx8fHk9ddfb7GcneGZZ54hAEhZWZnLaXQW+/fvJwDIzJkzCQBy9913ezpLzejKe9EZBEEgaWlpRKPRkKysrBZtNm/eTHQ6nVPp5uTkEADk448/btPuxRdfJABISUmJU+m3hbN57S7efvtth3cEIYRYLBaSmppKRo4c6WD70EMPkcDAQHLu3LlOu/6xY8cIwzDk3//+t8P+u+++mwQEBJCKioo2z6+uriYymYzMnTu3TTtJkkivXr1IcnJys/dfR/nxxx8JAPL555877J8yZQqJi4sjgiDY961cuZKwLEt2797dZprl5eVEqVSSe+65x2H/smXLCMMw5Pjx422ebzabSXx8PBk3bly7+b/88suJSqUiNTU17dpSfItOGRMzevRoAEBubi4AwGg0YunSpejZsyfkcjni4+Nx//33o7q6ui1nCn379sW0adOaHdNqtQgODsb999/f4Tzt27cPMpkMQ4cOBQDs3bsXw4cPB8s6Sg4NDYVMJmt2vq3FJj8/v91rRUREgOcdG7VkMlmzL2WlUmnfmpKdnY2nn34a77zzDjQaTbvX7Cw2bdqEjIwMqFQqBAUFYcqUKc2+ZMrKynDPPfcgMTERCoUCkZGRGDt2LH755ZcOXePDDz8EACxfvhxjxozB+vXrm3292pqBX3nlFbz66qvo2bMn1Go1MjIysGfPnmZpvv/++/Zm/dTUVHz++ee4/fbb0aNHDwc7s9mMF1980d5cHRkZiQULFqCsrKzdfHf03N9++w2XX345wsPDERAQgKSkJMyZM8elL/SNGzciKysLS5cubbXrbcaMGVCpVACAs2fPYsGCBejbty9UKhXi4+Nx1VVXISsrq91rNe1O6tGjh701MTo6GgzD4NlnnwUASJKE//znP/ayiIqKwrx585o9H5dffjkGDRqE33//HWPGjIFKpcIdd9zh1O/bUnfSF198galTpyI2NhYBAQEYMGAA/vnPfzb7sneGDRs2oF+/fsjIyLDv43ket912G/bt22fvItfr9fjggw9w/fXXo1evXi5frykbN24EIQQLFixw2L9gwQIYDAZs2bKlzfODgoKgVCqbvXuasn37dpw/fx4LFixo9v7rKBs2bIBarcb111/fLK+FhYXYu3evfd/rr7+Oyy67zF4vtMaWLVtgNBpb1E8IwcaNG9s8XyaTISQkpF39586dw86dO3HDDTd067uV0k044/G01hJjazlYvXo1kSSJTJs2jfA8T5566iny888/k1deeYUEBgaSoUOHEqPRaD+v6dfv66+/ThiGIWfOnHFI3/bF1J5nbstfR7acnJw205o/fz7heZ6Ul5c3OyaKIrFYLKS0tJS8/fbbhOd58u677zrYrFy5knAcRz744ANSWVlJCgsLycMPP0yUSiX5888/HWwlSSKXXXYZuf766x10NC1n29f0/Pnz28w7IR1riVm7di0BQKZOnUo2btxIvvjiCzJs2DAil8vJH3/8YbebNm0aiYyMJKtXryY7duwgGzduJE8//TRZv359u/nQ6/UkODiYjBgxghBCyAcffEAAkDVr1rSorUePHmT69Olk48aNZOPGjSQtLY2EhoaS6upqu+17771HAJA5c+aQH374gaxdu5akpKSQ5ORkkpycbLcTRZFMnz6dBAYGkueee45s27aNfPDBByQ+Pp6kpqYSvV5vt216L3b03JycHKJUKsmUKVPIxo0byY4dO8jatWvJ3LlzSVVVlT29+fPnd+i+u+eeewgAcvLkyXbLlhBCdu7cSZYsWUK+/vprsnPnTrJhwwZyzTXXkICAAHLq1Klm5du4JcZ2n9nydOjQIXLnnXcSAGTLli1k9+7d5OLFiw75euCBB8iWLVvIu+++SyIjI0liYqLDPTZ+/HgSFhZGEhMTyZtvvkm2b99Odu7c6dTva7t3G/PCCy+QlStXkh9//JHs2LGDvPvuu6Rnz55kwoQJDnbOPCMxMTH2Z64xP/zwAwFAtm7dSggh5PfffycAyLJly8jChQtJSEgIkclkZNiwYeSHH35odn7T+7A1brrpJhIZGdlsv1arJQDI0qVLmx0TBIGYzWaSk5ND7rnnHqJWq8mBAwfavM4tt9xCWJYlubm5zY4B6FAL5OjRo+3PcGOOHTtGAJD33nuPEEJIXl4eAUAefPBBsnTpUhIVFUU4jiOpqanNnvl//vOfBADRarXN0o2IiCA333xzs/22d29BQQF5+umniUwma/E3aMwTTzxBADR771L8A5ecmD179hCLxULq6urIDz/8QCIjI0lQUBApLi4mW7ZsIQDIf/7zH4dzv/jiC7ujY6NpxVFbW0uCgoLIQw895HBuampqs5dVS1RUVJDMzEySmZlJevXqRe677z6SmZlpz9P//vc/+3GTydRqOlu3biUsy5KHH364xeP33nuv3RmSy+XknXfeadHu3XffJQqFwm4bFhZGtm3b1szuzTffJKGhoaS4uJgQ0roTc+HCBcJxHLnjjjvaLYv2nBhRFElcXBxJS0tzaGKuq6sjUVFRZMyYMfZ9arWaLF68uN1rtsSnn35KANidvLq6OqJWq5s1Adsqn7S0NIem6X379hEAZN26dfZ8x8TEkFGjRjmcn5ubS2QymUPlsW7dOgKAfPPNNw62tu6txr9b03uxo+d+/fXXBAA5fPhwm+Vwxx13EI7jyIULF9q0mz59OgHg4Ow7g62S69u3r8P92xEnhpCW75uTJ08SAOQf//iHw7X27t1LAJAnnnjCvm/8+PEEAPn1118dbDv6+zbOQ2tIkkQsFgvZuXMnAUCOHDliP+bMMyKTyci9997bbP9ff/3l0HViuxc0Gg0ZO3Ys2bRpE/nhhx/IhAkTCMMwZMuWLQ7n9+7dm/Tu3bvd60+ZMoX069evxWNyubxZNwshhPTr18/+PomNjW23Yq6qqiJKpZJMmzatxeMcx5GJEye2m9e+ffu2mEZhYSEBYO8S2717t72sUlNTyZdffkm2bt1Krrvuumbv/7vvvpsoFIoWr5eSkkKmTp3abP+0adPs+jUaDfn222/bzLcgCCQ+Pp7079+/XY0U38QlJ6bplpaWZn+YHn/8cQKAlJaWOpwrSRIJDAwkN954o31fS+MQFi1aRIKDg+3e+a+//tpiZdIWlZWVhGVZu8Pw5ZdfEqVS2aGK4eDBgyQ4OJiMGTOmVfvc3Fyyf/9+8uOPP5KFCxcSlmXJyy+/7GDz0UcfEYVCQZYsWUJ++eUXsnnzZnLTTTcRlUrl8NK7cOECUavV5IMPPrDva82JcYb2nJgTJ0606GwSQsh9991HWJa1j2WYOHEiCQkJIS+88ALZvXt3i2OIWmP8+PEkICDA4Ut7wYIFBIBDi5utkvvnP//pcL7RaCQAyPLlyx3y/d///rfZtS6//HIHJ+bWW28lISEhxGw2E4vF4rDFxMSQG264wSGfje/Fjp579uxZIpfLyciRI8maNWvcHjPhrBNjsVjIsmXLyIABA4hMJnN4LqdPn263c8eJeeeddwgAsm/fvmbXHzBggINDOX78eBIaGtrMrqO/b+M8NObcuXPk5ptvJtHR0YRhGAedHWkRbAmZTEYWLlzYbL/NibE5VrYWy4iICFJbW2u30+l0JC4ujowdO9al60+ZMqXVylUul7foYB07dozs3buXfPXVV2TSpEkkKCiIbN++vdVrvPXWWwQA+eqrr1zKo42+ffs63E82bE7MSy+9RAghZNeuXfaPu8YOuyRJ5JJLLiEJCQn2fXfffTdRKpUtXi8lJaVFp+nMmTNk37595LvvviPXX389kclkzcbpNMbWqtb0/UzxH1zqIP3000+xf/9+ZGZmorCwEEePHsXYsWMBWEeR8zyPyMhIh3MYhkFMTAwqKiraTPvBBx9EXV0d1q5dCwB46623kJCQgFmzZrV5HiEEgiBAEATs3LkTLMtixIgREAQBv//+O0aMGAGO4yAIAgghLaaRmZmJKVOmoG/fvti8eXOrMzOSkpIwfPhwXHHFFVi1ahXuueceLF261D5WoqqqCvfffz/uuusuvPLKK5g0aRJmzJiBdevWYcSIEQ7THO+//34MGjQIc+bMQXV1Naqrq+1jKbRaLWpqatrU7Sq236HxzBQbcXFxkCQJVVVVAKzjEebPn48PPvgAGRkZCAsLw7x581BcXNzmNc6ePYvff/8dM2fOBCHEru+6664DAIeZXzbCw8Md/rb9BgaDwSHf0dHRzc5tuq+kpATV1dWQy+WQyWQOW3FxcZvTzzt6bu/evfHLL78gKioK999/P3r37o3evXvj9ddfb7NsWiMpKQkAkJOT0yH7Rx55BE899RSuueYafP/999i7dy/279+PwYMH28vMXdq7V5o+0y3Z2Wjv920JrVaLcePGYe/evXjxxRexY8cO7N+/H99++22757ZFeHh4i++jyspKAEBYWJhDnseMGYOgoCC7nUqlwvjx412a8t7W9XU6Hcxms/36jRk4cCBGjhyJ6667Dlu2bEFycjIeeuihVq/x4YcfIjIyst33p6t5ba2s+vfvj+TkZLsdwzCYNm0a8vPzUVpaarc1Go0tjh2rrKxsUX/fvn0xYsQIXH311fjyyy8xadIk3H///ZAkqcV8f/jhh5DJZJg3b56Tiim+gktTrAcMGIDhw4e3eCw8PByCIKCsrMzBkSGEoLi4GCNGjGgz7T59+mDGjBl4++23MWPGDGzatAnPPfccOI5r87ydO3diwoQJDvuaxriwDeDdvn17s5ggmZmZmDx5MpKTk/Hzzz87NX115MiRePfdd3H+/HlERkbi9OnTMBgMLWodPnw4du7cCa1WC7VajWPHjiE3NxehoaHNbCdMmIDg4OA2B0S7iu1lU1RU1OxYYWEhWJa15ykiIgKvvfYaXnvtNeTl5WHTpk345z//idLS0jYHH3700UcghODrr7/G119/3ez4J598ghdffLHd37alfJeUlDQ71tSpioiIQHh4eKt5bFwhNcWZc8eNG4dx48ZBFEUcOHAAb775JhYvXozo6GjcdNNN7WpqzLRp07B69Wps3LgR//znP9u1/+yzzzBv3jz8+9//dthfXl7eaTFeGt8rCQkJDscKCwsRERHhsK+zY7z89ttvKCwsxI4dOxymPbv7XKSlpbU4ANq2zzawOj09vdU0CCEuD5ZNS0vD+vXrUVxcjJiYmFav3xo8z+OSSy7Bl19+2eLxzMxMZGZmYsmSJS1OXnA2r+vWrYMgCA4DaZvmtXfv3vZB502xfTzayistLc2eRuOQC7aPhI7ElBo5ciS2bNmCsrKyZh8xpaWl+OGHH3D11VcjKiqqo1IpPkanR+ydNGkSADQLAPfNN99Ap9PZj7fFQw89hKNHj2L+/PngOA533313u+cMGzYM+/fvx/79+9GjRw/cf//92L9/P77//nt7fmzHhw0b5nDu4cOHMXnyZCQkJGDbtm0tOhRtsX37drAsa5+5EBcXBwDNZl0QQrBnzx6Ehoba4+msX78e27dvd9j+7//+DwDw7rvv4ocffnAqLx2lX79+iI+Px+eff+7QMqXT6fDNN9/YZyw1JSkpCQ888ACmTJnS5heoKIr45JNP0Lt372b6tm/fjiVLlqCoqAg//fST0/mOiYlp9uLOy8trFkjryiuvREVFBURRxPDhw5tt/fr1a/U6rpzLcRxGjRqFt99+GwBc+kKfNWsW0tLS8NJLL+HYsWMt2mzdutX+9cowTLMWwx9//NGl4JOtMXHiRADNn+n9+/fj5MmTHXqm3cHmFDXV+d5777mV7uzZs3Hq1CmHmTWCIOCzzz7DqFGj7M9xbGwsMjIysGvXLtTW1tpt9Xo9du7c2e4snNaYNWsWGIbBJ5984rB/zZo1CAgIaDdWjtFoxJ49e9CnT58Wj9tmBd55550u5a8xs2fPhlarxTfffOOw/5NPPkFcXJzdCeF5HrNmzcLJkycdgigSQrBlyxb07t3b7vROnz4dSqWyWQBS26y5a665ps08EUKwc+dOhISENGvhA6w9BhaLpVP0U7wYZ/qeOjJWwzY7SSaTkWeffZZs27aN/Pe//yVqtbrd2UmNSU1NJQDIbbfd5kwWSW5uLgFgH7H//vvvk6ioqFbjI5w6dYqEh4eTsLAw8v3335Pdu3c7bI3H9tx9991kyZIl5IsvviA7duwgX3/9NbnxxhsJAPLYY485pHvttdcSlmXJQw89RLZu3Uo2bdpE5syZQwCQF154oU0NnTmw96OPPiJfffVVs42Qhr7+K664gnz33Xfkyy+/JCNGjHCYnVRdXU2GDh1KXn75ZfL999+THTt2kJdffpkolUpyyy23tHr977//ngAgK1asaPF4WVkZUSgU5JprriGENIyZaKnvGgB55pln7H83np30448/2mcnJSUlkZ49e9rtBEEgM2bMIGFhYeS5554jP/30E/nll1/ImjVryPz58x0GBTa9Fzt67qpVq8j1119P1qxZQ3777TeyefNm+yBG2+wWQjo+O4kQ6zibXr16EbVaTR577DGyefNmsnPnTvLpp5+Sq6++mjAMYx9jNG/ePKJQKMjKlSvJr7/+Sv7zn/+QyMhIkpCQ4KDHnTExhFhnJzEMQxYvXky2bt1K3nvvPRIVFUUSExMdZvCNHz+eDBw4sJkmZ37fpmNiysvLSWhoKBk8eDD59ttvyffff09uuukm0rdv32aanJmdZDQaycCBA0liYiJZu3Yt2bZtG5k9ezbheZ7s2LHDwXbXrl1ELpeT0aNHkw0bNpCNGzeScePGEZlM5hBnhpCOz04ihJC77rqLKBQK8vLLL5MdO3aQJ554gjAMQ5YtW+Zgl5GRQV566SWyceNGsn37dvLxxx+TkSNHEo7jyKZNm5qlazAYSGhoqMMA/ZZAB2cnEWIdwxMaGkpWr15NfvvtN3L33XcToHl8sLNnz5KQkBDSr18/sm7dOvLjjz+S2bNnE4Zhmo3NefHFFwnDMOSJJ56wv1sUCkWzWFJXX301eeqpp8g333xDduzYQT7//HMydepUAoC8/fbbLea3f//+JDEx0eXYOBTfoNOdGEKsD9D//d//keTkZCKTyUhsbCy57777HKacEtK2E/Pss8/aZ0I5wxtvvEHi4uKIJEmEEOvNv2DBgnY1tbY1fkF+9NFHZNy4cSQiIoLwPE9CQkLI+PHjyf/+978Wy+Dll18m6enpJCgoiISFhZHRo0eTzz77zJ639vLUGVOsW9tsbNy4kYwaNYoolUoSGBhIJk2aRHbt2mU/bjQaycKFC0l6ejrRaDQkICCA9OvXjzzzzDNtBjG75ppriFwubzbAuzE33XQT4XmeFBcXO1XJEULI6tWrSZ8+fYhcLicpKSnko48+IrNmzSJDhw51sLNYLOSVV14hgwcPJkqlkqjVatK/f39y7733kuzsbLtdS/diR87dvXs3mT17NklOTiYKhYKEh4eT8ePHN6tY5syZQwICApo9A61RXV1NXnjhBXLJJZcQtVpNZDIZSUpKIrfddpvD71NVVUXuvPNOEhUVRVQqFbn00kvJH3/80UyPu06MKIpkxYoVJCUlhchkMhIREUFuu+02+xTsxuXY2U4MIdbBthkZGUSlUpHIyEhy1113kUOHDjXTlJWV1eIA4tYoLi4m8+bNI2FhYUSpVJLRo0e3OIOQEGIvV5VKRVQqFZk4caLDb2EjIiKCjB49ukPXN5vN5JlnniFJSUn2e/mNN95oZrdkyRIyePBgEhwcTHieJzExMWT27NktXp+Qhg+Ujz76qNVr19XVEQDkpptu6lBe6+rqyKJFi0hMTAyRy+UkPT3dYVZZY7KyssjMmTNJUFCQvVy///77Fm1ff/11kpKSQuRyOUlKSiLPPPNMs8kDK1asICNGjCChoaGE4zgSHh5Opk2b1ur0atsA46effrpD2ii+C0NIK6NcPczw4cPBMAz279/v6axQfIDq6mqkpKTgmmuucQhX7i3ExMRg7ty5ePnllz2dFb/mnXfeweOPP45z5861OPi7qzlx4gQGDhyIH374ATNnzuz26zvD5s2bceWVV+LIkSP28SkUiq/h0sDerqK2thbHjh3DDz/8gIMHD2LDhg2ezhLFCykuLsayZcswYcIEhIeHIzc3FytXrkRdXV2bMzU8xfHjx6HX6+1jnShdx/bt27Fo0SKPODC262dkZHi9AwNY83rTTTdRB4bi03hVS8yOHTvsFdMDDzxgD3lOoTSmqqoK8+bNw/79+1FZWQmVSoXRo0fjueeeowtLUigUyt8Ir3JiKBQKhUKhUDpKp0+xplAoFAqFQukOqBNDoVAoFArFJ6FODIVCoVAoFJ+EOjEUCoVCoVB8EurEUCgUCoVC8Un8yolZtWoV0tPTodFooNFokJGR4bA2z+233w6GYRy2puuenD59GmPHjkVCQgKef/55h2M9evQAwzDN1kRavHhxswUlKRQKhUKhdC1+5cQkJCRg+fLlOHDgAA4cOICJEydi1qxZOH78uN1m+vTpKCoqsm+bN292SOP+++/H3Llz8d133+H777/Hrl27HI4rlUoatIxCoVAoFC/Ar5yYq666CldccQVSUlKQkpKCZcuWQa1WO7ScKBQKxMTE2LewsDCHNKqrqzF06FCkp6cjLi4ONTU1Dsfvvfde7Nmzp5nzQ6FQKBQKpXvxqmUHOhNRFPHVV19Bp9MhIyPDvn/Hjh2IiopCSEgIxo8fj2XLliEqKsp+/Pnnn8eUKVNgMBhw5ZVXYtq0aQ7p9ujRAwsXLsTSpUsxffp0sKxzfqDRaITZbHZPHIVC+Vsil8uhVCo9nQ0KxWvwOycmKysLGRkZMBqNUKvV2LBhA1JTUwEAM2bMwPXXX4/k5GTk5OTgqaeewsSJE3Hw4EEoFAoAwBVXXIGysjLU1tYiMjKyxWs8+eST+Pjjj7F27VrMnTu3w3kzGo3o2bMniouL3RdKoVD+dsTExCAnJ4c6MhRKPX7nxPTr1w+HDx9GdXU1vvnmG8yfPx87d+5EamoqbrzxRrvdoEGDMHz4cCQnJ+PHH3/Etddeaz+mUChadWAAIDIyEo8++iiefvpphzTbw2w2o7i4GKdOnUJsbCwAQKfTgeM4KJVKSJIEnU6HgIAA8DwPs9kMs9kMtVoNANDr9WBZtlVbk8mEoKAguy3DMAgICAAhBFqtFkqlEjKZDBaLxe7kMQwDg8EAAB2yraiowLFjxzBs2DCo1WrU1dVBoVBALpdDEAQYDAYEBgaCZVkYjUZIkgSVSgUA0Gq1kMvlrdqKoojAwEC7rUwmg0KhgCiK0Ov1UKlU4DgOJpMJgiDYbXU6HXieb9XWYrHYy9BW3oIg4PDhw+jbty/Cw8M9Vt4AUFdX16YtIaTFMqyurkZmZiaGDRsGjUbTZnnbysVW3s6WYWu2tnKx2TpbhrZWyaNHjyIlJQWBgYFtlqGtvJ25Z5uWYVfds6WlpTh16hQuueQSBAQEdPo9azabkZiYCLPZTJ0YCqUevxoTA1ibW/v06YPhw4fjpZdewuDBg/H666+3aBsbG4vk5GRkZ2c7fZ1HHnkEBoMB77zzjtPnXrx40T6DKj8/HzU1NdBoNFAoFMjOzgbLstBoNNDr9Th//rzdtrCwEFVVVdBoNAgICEB2djYIIdBoNDCZTDh79qzdtqSkBBUVFdBoNAgMDER2djZEUYRGo4HFYkF2drbdtrS0FKWlpfa/s7OzYbFYoNFoIIoisrOzERgYCI1Gg7q6OgQGBiIiIgIajQZnz56FyWSCRqMBIQTZ2dkICAiARqNBVVUVCgsL7emeP38eer0eGo0GLMsiOzsbCoUCGo0GNTU1yM/Pt9vm5uZCp9NBo9GA4zhkZ2dDJpPZ85CXl2e3zcvLQ21tLTQaDeRyebMyvHDhgt22oKAA1dXViIiIQEpKCvLy8sAwDDQaDQwGA86dO2e3LSoqQmVlJTQaDVQqFbKzsyFJEjQaDcxmc7PyLi8vh0ajgVqtRnZ2NgRBgEajgSAIyM7ORlBQEDQaDcrKylBSUuJQ3mazGRqNBpIkITs7GyqVChqNBpWVlSguLrbbnj17Fkaj0a4VAIKDg+3lXVBQYLfNycmxl6GtvOVyub28G9+Hubm50Gq10Gg04Hke2dnZ4HkeGo0GWq0Wubm5dtuLFy/a79mm5a3T6ZCTk+NQ3k3vWQB2p+vs2bOIiIjAwIEDUVlZ2WZ5N75nS0pKUFZWBo1Gg6CgoBbLW61WQ6PRoLy83KG8z54922Z5FxUV2W3PnTsHg8EAjUYDhmGQnZ0NpVIJjUaD6upqh/IuKipCdHQ0IiIimpV3bW1ts3u2rq4OGo0GMpkM2dnZ4DjOXoaNy7vxO4JCoTSB+DkTJ04k8+fPb/FYeXk5USgU5JNPPulQWsnJyWTlypX2v9966y0SERFBFixYQMaPH9/u+TU1NQQAKSgosO/TarXEYDAQQggRRZHU1tYSi8VCCCHEZDKRuro6u61Op2vTtra21sFWr9cTQgiRJInU1tYSs9lMCCHEbDaT2tpaIkkSIYQQvV7vlK1Op7Nfp7a2lphMJkIIIRaLhdTW1hJRFAkhhBgMBgfburq6Nm21Wq2DrdFoJIQQIggCqa2tJYIgEEIIMRqNDrZarbZN28Zl6G3lbSvDjpZ3e2XYmq2tXGy2zpZha7a2crHZOluGTW3bK0Mbf8d71vb+qKmpIRQKxYpfrWL9xBNPYMaMGUhMTERdXR3Wr1+P5cuXY8uWLcjIyMCzzz6LOXPmIDY2FhcuXMATTzyBvLw8nDx50t5M3RY9evTA4sWLsXjxYgCAxWLBgAEDUFBQgFGjRmHHjh1tnl9bW4vg4GCf/qoSBAF1dXUICgoCz/tub6Q/6PAHDQDV0VH84f1BoXQ2ftWdVFJSgrlz56Jfv36YNGkS9u7diy1btmDKlCngOA5ZWVmYNWsWUlJSMH/+fKSkpGD37t0dcmBaQiaT4YUXXoDRaOxkJd6LwWDAkSNH7GMSfBV/0OEPGgCqg0KhuI5ftcR4O/7wJSVJEsxmM+RyudPTy70Jf9DhDxoAqqOj+MP7g0LpbHy37ZbiEWwzTXwdf9DhDxoAqoNCobiO7372UDyC0WjEmTNnfL4LzR90+IMGgOqgUCiuQ50YilOIooja2lqIoujprLiFP+jwBw0A1UGhUFyHjonpRmifNoVCcRX6/qBQmkNbYigUCoVC+Zvx+++/46qrrkJcXBwYhsHGjRvbPWfnzp0YNmwYlEolevXqhXfffbfrM9oO1ImhOIVWq8Vff/0FrVbr6ay4hT/o8AcNANVBoXgCnU6HwYMH46233uqQfU5ODq644gqMGzcOmZmZeOKJJ7Bo0SJ88803XZzTtqGzkyhOIZPJEB8fD5lM5umsuIU/6PAHDQDVQaF4ghkzZmDGjBkdtn/33XeRlJSE1157DQAwYMAAHDhwAK+88grmzJnTRblsH+rEUJxCoVAgOTnZ09lwG3/Q4Q8aAKqDQrFhNBrti6K6AiEEDMM47FMoFFAoFO5mDbt378bUqVMd9k2bNg0ffvghLBaLx5x36sRQnEIURWi1WqjVanAc5+nsuIw/6PAHDQDVQaEAVgfGtrK9q6jV6mbdmc888wyeffZZt9IFgOLiYkRHRzvsi46OhiAIKC8vR2xsrNvXcAU6JobiFHq9HpmZmdDr9Z7Oilv4gw5/0ABQHRQKgEYtMLzLm1arta8yb9uWLl3aaXls2spjm9zcdH93QltivJzhw0d5OgsOMAwDmUwGi8UCX56d7606poXe0XFjhgC8hG3CKYB47iXiNn9jHcu23dvFmaL4HoxLToHtPabRaLpkCn5MTAyKi4sd9pWWloLneYSHh3f69ToKbYmhOAUhBGaz2asqflfwCx2EASycb1f8ANVBofgAGRkZ2LZtm8O+n3/+GcOHD/foYHbqxFCcgud5REZGgOd9uxHPL3RwEpgQE8BJns6Je1AdFEojWDe2jqPVanH48GEcPnwYgHUK9eHDh5GXlwcAWLp0KebNm2e3X7hwIXJzc/HII4/g5MmT+Oijj/Dhhx/i0UcfdUOr+1AnhuIULMsiMDDQp1cbBvxEB0vABAgA68OtSQDVQaE0gnHjP2c4cOAAhg4diqFDhwIAHnnkEQwdOhRPP/00AKCoqMju0ABAz549sXnzZuzYsQNDhgzBCy+8gDfeeMOj06sBOiaG4iRmsxkXLuR6Ohtu4xc6LBykosBuuRSBtWImDAFhCMAQ6z6GAIQBL7rRnNyNOroUf9FB8SwMC7g0UJYATvjPl19+eZvd6WvWrGm2b/z48Th06JALees6qBND6VKI7auUaW8jjn8DIGzDv9u1b2Ej9rTqbdnW0mo9bYL681zJR/25xOHctvO9X/5Tg5NgcxhAHJ0Hpt6lYAgII9XrtDoVLTkZjvvQZvqtn9vWjwxcunUOwso9M8WSQqH8faFODMUp5HI54uNjUVBQ1G5QJjHdAmG8pZty5hwKUYFEXSIuBl6EiTM1HCDObPU1uwT7FxDTnn3jv5ucCwIIvAUMYRo2ibX+v94rY6zeCxiJgQw8IhCCCqkWIiQwktUTsp1rzY/1fKtP0jidepv6ffZ/t3ZuizYMRE7AkYzfYAxwY1qxTAQbYYRUrrQOjPVV/EUHxcM0/mpyhr9nNyZ1YihOIUkS6uq0kKQODF7UWh9E7iAPppxtXoETWCtx2CpZtOEAwNERAJo5AC2fz7SYDsfx0GuM4KrlkAtsfT7gdL9yZ5MRenXHjTkJjNqCWK0MED0ztscsN+JIxm9w6wUqMSB6HpB8fFaPv+igeBSGYV2Mu0LgrZMtT58+jXXr1uGPP/7AhQsXoNfrERkZiaFDh2LatGmYM2eOy1GFqRNDcQprdMaKDtmyORxQx4AoCfgz3nWriRYRFcZKAJ53XFxGZEFq3A8n3im4U4TepMMd/EUHxcNw8JeWmMzMTDz++OP4448/MGbMGIwcORLXXHMNAgICUFlZiWPHjuFf//oXHnzwQTz++ONYvHix086Md9UsFK/HmSBxDGHAHeMhjrCA7CJgTN7jLHhrsDunqA+uBqHxwJvuzkInXNcLdHQK/qKDQukkrrnmGjz22GP44osvEBYW1qrd7t27sXLlSvz3v//FE0884dQ1qBNDcQq5XI7k5CTk5ubBZDK1a88d5yGOtEBMFcBnes/qvs7q8Ep4CVysHmKRyrfHYFAdFIodhmHAMK50D3tffKLs7GzI5fJ27TIyMpCRkeHS4pc+HCSD4gnMZjPy8vI6fLMxBgZsNgcxTbBP0/UGnNXhlQgsxGKV9cvfY1hbHNz6bb1CRyfgLzooHoUB6/LmbXTEgXHHHqBODMVJCCEwGk1OdcFwR3kgmEBK9p4vBVd0eB2EAcyeDXPP2IrPnSx4gY5OwV90UDyKdWCva5u38+uvv+LKK69E79690adPH1x55ZX45Zdf3ErT+1VTvAqO4xAeHgaO63hzOVPCgillIKV7z3RrV3R4HZwEJthbwty74Qx6lQ438BcdFEoX8NZbb2H69OkICgrCQw89hEWLFkGj0eCKK67AW2+95XK6dEwMxSk4jkNwcDDq6rQQRbFD5zBgwB2VQZhkBtFIYGo97zu7osPrYAmYQME6rddTEupbHdxqfPAGHZ2Bv+igeBjn10HyBV566SWsXLkSDzzwgH3fokWLMHbsWCxbtsxhvzP4X0lRuhSz2Yzz53OcHkvCnuEAIyCmCV2UM+dwVYdXYeEgFQZ6ySBSN1pivEqHG/iLDopH8dfupNraWkyfPr3Z/qlTp6K2ttbldL1bNcVvYEQG3EkeYqoAwvvwOBQKhULpQvzVibn66quxYcOGZvu/++47XHXVVS6nS7uTKE4hl8sRGxuDoqJip1sxuCwe4lABUooI7oRnbz13dHgNvNQQ5t5DM2JsgQLd6k7yAh2dgr/ooFC6gAEDBmDZsmXYsWMHMjIyAAB79uzBrl27sGTJErzxxht220WLFnU4XerEUJxCkiQYDIaOLTvQBKaWBXuBhZhuAXuC82ikXHd0eA0EICbOs4E6SbN/uJSGx3V0Bv6ig+JRvHW6tLt8+OGHCA0NxYkTJ3DixAn7/pCQEHz44Yf2vxmGoU4MpesQBAGlpWUun88dlcEyywQSI4Ep9tzYAXd1eAUiC1Ll6TD3neCIeoWOTsBfdFA8ii90DblCTk5Ol6TrfyVF6VJs4fpdW6AMYPJYMNUMxHTPDvB1V4d3UB/m3oOf/p1Tep7X0Tn4iw6KJ/HXMTFdBW2JoTiFu+H6GTBgs3iIYywgfxAwBs84EX6x7IDMC8Lc19fXhHGj4vYGHZ2Bv+igeBTG+pZ04Uzvd57z8/OxadOmFqOlv/rqqy6lSZ0YilNYLBZcvJgPi8X1wHXcSR7iaAvEgQL4A55ZT6kzdHgcgYVYEuAVyw64hVfo6AT8RQeF0gX8+uuvuPrqq9GzZ0+cPn0agwYNwoULF0AIwSWXXOJyuvRpozhFZwyIZUwM2DP16ym58wXvBv4xsJcBTLxnlx1oyIzriXiBjk7BX3RQPArDcC5v3szSpUuxZMkSHDt2DEqlEt988w0uXryI8ePH4/rrr3c5XerEUJyC4ziEhoa6Ha6fOyoD1ARSL8+ENu0sHR6FlcAEmQHWg46YrcJ2K2KvF+joDPxFB8Wj+OuYmJMnT2L+/PkAAJ7nYTAYoFar8fzzz2PFihUup+vdqileB89zCAsLBc+7V/mz5SyYQtZjEXw7S4dH4QgYjRngPN8X7lYOvEiHW/iLDopHYd34z5sJDAy0jz+Mi4vDuXPn7MfKy8tdTpeOiaE4hclkxrlz5zslLe4oD2G6GVKoBLaqex/AztThMSwcpAK1R7Ngj/XjTregF+joFPxFB4XSBYwePRq7du1CamoqZs6ciSVLliArKwvffvstRo8e7XK61ImheAz2HAfoADFdALtT7unsUCgUisdxvWvIu1sAX331VWi1WgDAs88+C61Wiy+++AJ9+vTBypUrXU6XOjEUp5DLZYiJiUZxcQnMZvdm9jASA+44D3GIAPKXDIyl+wZEdqYOj8FLYMONkCo8HOaeAMSdF6i36HAXf9FB8Sj+6sT06tXL/m+VSoV33nmnU9KlTxrFKSSJwGQyQ5I654HhjvEAD0gDundsTGfr8AgEIBbWO95d7vif3qTDHfxFB8XDcGBc2AAfHt/nBrQlhuIUgiCgpKS009JjdCzYc9bp1uxRvtvWU+psHR5BZEEqlZ7OhftTir1Fh7v4iw4KpZMIDQ3tcFT0yspKl65BnRiK03AcB1HsvKnRXBYPy7UmkAQJTH73fU10to7uh1hnwogMOmsBAFdw/8reocN9/EUHxZO42p3EeGET4Guvvdbl16BODMUpFApFp4frZwpYMBXW9ZTYbnJiukJHt+M1Ye4Z98bEeI0ON/EXHRSP4uqyAwy8Lz6RLS6MIAhYu3Ytpk2bhpiYmE69Bh0TQ3EKi8WCgoLCTg3Xz4ABd5SH1FMEUXfPg9gVOrodgYVY6gVh7gnca3jwFh3u4i86KB7FHyP28jyP++67r0s+GOnTRnEKSZKg0+k6PVw/e5oHBEAc1D0DfLtKR7dCGMDo+TD31qu7ueyAF+hwG3/RQaF0AaNGjUJmZmanp0u7kyhOwXEcgoLUqKvTdup4EsbCWBeGHCiA2ycDI3VtRdBVOroVVgKjEkD0PCB57nuEMIDIuVGGXqLDbfxFB8WjuB5917vvuX/84x9YsmQJ8vPzMWzYMAQGBjocT09Pdyld6sRQnILneURGRsJgMHZ65c9m8RAHC5D6iODOdO2t2ZU6ug2OgAk1g5g4eLQ7nAAnhu1CVGESNDXhzp/vLTrcxV90UDyK6wN7vduJufHGGwEAixYtsu9jGAaEEDAM4/J7mDoxFKcwmUzIzj7bJWmzVSyYPBZiutDlTkxX6ug2LByki54Pcz9y50zsnfg99Opa15wYL9HhNv6ig+JRGuK+OHue981OakxOTk6XpEudGIpXwWXxEGaaIUVKYMu8+8uCYiW8JA4AIMh8eJA0hULpUpKTk7skXa+sJVatWoX09HRoNBpoNBpkZGTgp59+atH23nvvBcMw7c5HX7NmDRiGabYZjUa7jVarxU033YTY2FjcdNNN0Ol09mO33347GIbB8uXLHdLduHFjh4P5+AMymQwJCfGQyWRdkj6bwwF1DMS0rq0Qu1pHt8BLYKMMAO/ZvgtO5AECCLzZtQS8RIfb+IsOikfx11WsAeB///sfxo4di7i4OOTm5gKwxpL57rvvXE7TK1UnJCRg+fLlOHDgAA4cOICJEydi1qxZOH78uIPdxo0bsXfvXsTFxXUoXY1Gg6KiIodNqWyIsPnaa69BrVbj559/hkqlauYYKZVKrFixAlVVVW5r9F1Ifd9l1zRdMoQBl8VD6ieCKLuyebRrdXQLBCAi43EJDBjwFhlEV1tivESH2/iLDopH8ccp1oC1ceKRRx7BFVdcgerqavsYmJCQELeC4nmlE3PVVVfhiiuuQEpKClJSUrBs2TKo1Wrs2bPHblNQUIAHHngAa9eu7fDXNMMwiImJcdgaU11djZSUFKSlpaF///6oqalxOD558mTExMTgpZdecl+kj2KxCCgqKobF0nVTobnjPMAAYheup9QdOrockQWpUAKi5x9jTpBB4F10YrxIh1v4iw6KR7EFu3N+8+4egTfffBPvv/8+/vWvf4HjGhyu4cOHIysry+V0vf5pE0UR69evh06nQ0ZGBgBrjI+5c+fisccew8CBAzucllarRXJyMhISEnDllVc2m7P+wAMP4L333oNMJsPHH3+Mhx56yOE4x3H497//jTfffBP5+fnui/NRWLZrbxvGyIDNtq6nRJiu+6ztah1dDwEYAm/49OcFuRtjYrxHh3v4iw6KJ/HX7qScnBwMHTq02X6FQuEwdMNZvFZ1VlYW1Go1FAoFFi5ciA0bNiA1NRUAsGLFCvA87zBVqz369++PNWvWYNOmTVi3bh2USiXGjh2L7Oxsu02PHj2QnZ2Nixcv4sSJE4iPj2+WzuzZszFkyBA888wzLmvTarX2f+t0Ovu4HEmSUFdXB0Gwtg6YzWYoFHK7rVwuA883jMVWKBT2ipjjOCgUCvsxmUwGmaw1W7YFW5mDLcdZbVnW0TYwMBApKX3t+6y2XIu2PM9DLm+crryZrW08kdW2QavqhAp8EAcpWQLDMG3ayuVye7k0teU4rkVbhUKBPn16IygoqEkZdn55Ny0XmYxvtbzBEkDWqJuLkxzHWMhEgK3/Wy6CS66tt2/HlulAulwTW6aRrazR9EdearCF1VZmllvHxLDt29rTtdnKJHCJWiBAaG7L2mxJ++k2s+1AGXakXDpqGyCAS6oDZFL7ZdiovEVRRF1dnb153WQytfqOoFB8lZ49e+Lw4cPN9v/000/2ut0VvNaJ6devHw4fPow9e/bgvvvuw/z583HixAkcPHgQr7/+un2gbkcZPXo0brvtNgwePBjjxo3Dl19+iZSUFLz55psOdizLIiYmps20V6xYgU8++QQnTpxwSVvj806cOIGLFy8CsL68Dh48aH+BFRcXIyEhwW4bExON8PAwANZKNDk5CQEBAQCAoCA1kpIS7bbR0VGIiIiwa0pOToJKpQJgdUSSk5PstpGREYiMjLD/nZycZA9EpFKpkJycZK+Qg4M1sFgs9nD9SUmJCAqyTisNCAhAcnKS3VEJDw9DTEy0Pd2EhARoNBoADWsX2ZyEsLBQxMY2dO8lKBIRWhwGKc0ChUKO5OQke8UfEhKM+PhYu21cXCxCQ0MAWJ2J5OQku9MQHKxBQkKDMxobG42wsFBYLBaUlpYiLi4WSqXVVqMJQmJi4zKMRkREeIvlrVY3Le9Ie3kzDONQhrbytt1TkZGRiIqKdChvtbp+aq5CBBertz+ZTIgZbFhDBcZGG8Co6rvAeALrTExrukywGWx4I9soAxh1va3cuq4PeGulygRZwEYaGmwjjWDU9S0pvM3WWskyagvYyEbpRhrABNlsCbhYPVQWFQSZBYxasA5utdmGG8EE1w/45ay2UFgra0YlgI02ACILqVwJVmMBE1Jvy8LRNkCw/m1LN9QENrQhhDkXqwejtGpllPW2TMu2bIwBjKreoVDaypu0XIbRBjCBtjKst+XqbTVmsBGNbMNNIAbe2p0ka1KGQRZH24iG8jYYDDh48KD9a7SoqMiheb3xO4Li/9imWLuyeTOPPfYY7r//fnzxxRcghGDfvn1YtmwZnnjiCTz22GMup8sQQnyi7XPy5Mno3bs3BgwYgEceecShK0AURbAsi8TERFy4cKHDad59993Iz89vdeZTY26//XZUV1dj48aNAICZM2dCJpPh9ttvx+zZs9GRYqytrUVwcDAKCgrsg5F1Oh04joNSqbSHwg8ICADP8zCbzZgwYRJMJuuLXS6XQZKIvaVGoVDAYrFAkiRwHAee5+1rU1grfGIf8+Foy4LnZU1sYXdMFAoFBMECUZTAsixkssa2PACmia1g/w0a2/I8D5ZlYDbbbOUQBNHB1mw2gxBSb8vCbLZplcPS2wTjZAMU/wuAwqBs01aSJAiCAIZhIJfL7bYcx4HjuDZtHcuQ6/Ty7lgZWst7WvgC6xe6hQXAWP/NoGE9HploXSVZYq1f+Xwn2kqMtQK22QqsNRwvJ1kreduihrxkbYwQWVhbQSTsG7MFADDyjxnWSr4NW3u6rNS+rcha88WS+nJxxtaLy7DedtmW+yCKIvR6PVQqFTiOg8lkgsVisTu2tneE2WxGcHAwampq7B8DFP/BVj/0CJkJlnF+1qRELLhQ/aNX3x/vv/8+XnzxRbtTHh8fj2effRZ33nmny2n6TJwYQghMJhPmzp2LyZMnOxybNm0a5s6diwULFjiV3uHDh5GWluZSfpYvX44hQ4YgJSXF6XPtX92AQ+hllmURFBRk/1sul9srVAB2Z8BG48W0RFF0iHjYdGFDR1sJomjqkK0kSc3+DgwMhCSJEMXmxxr/bav8G9I1d9jWbDaDnAEwFhAGWYA/mTZtbdjukwatjuVis+U4FoGBgQ7rJzW37Zzybqq16WBih0XRJAaQGn1RNR0k2nh1ZAZg5BKIyFojxLZlSxjHv521bRxM02GBQ6stJ/IwKfXWyl5q29aOzZYlYAIEayuGfbmJprZNyqXNdJ0oQ3fLpTEiW6+DseahrTJsdMy6BEbDc69QKJp14QKO9znFf2EZDqxLM428f2r/3Xffjbvvvhvl5eWQJAlRUVFup+mVTswTTzyBGTNmIDExEXV1dVi/fj127NiBLVu2IDw8HOHhjlFBZTIZYmJi0K9fP/u+efPmIT4+3j6T6LnnnsPo0aPRt29f1NbW4o033sDhw4fx9ttvu5THtLQ03Hrrrc26o/wdnreWdW5unoMj1BUwIgPuRP16SntkYITOG33fnTq6DE4CG26EWKRyrLQ9AG+RQad2cWCvF+lwC3/RQaF0Ac899xxuu+029O7d29713hl45ZiYkpISzJ07F/369cOkSZOwd+9ebNmyBVOmTOlwGnl5eSgqKrL/XV1djXvuuQcDBgzA1KlTUVBQgN9//x0jR450OZ8vvPBCh7qR/AmTyYQzZ7K7ZEn1luCO8YACkFI6d32j7tbRJVhYiHnq+u4Nz8IJMtdnJ3mRDrfwFx0UD+PK9GoWXlqd2/nmm2+QkpKC0aNH46233kJZWVmnpOszY2L8AVufpzN9lsOHj+riXHk/liuNIGoC2Xql18dCcJdpoXd4OgsucSp9L/J6n8TUDbd7Ois+xbJt93bY1pX3B8V3sP2+fULngHNhTIxILDhb9Y1X3x/Hjx/H2rVrsX79euTn52Py5Mm47bbbcM0119gnnjiLd7tuFK9DJpMhLi62W8P1c0dlIJEEJLbz+nw9oaPT4SWwEd4R5p4XZBBkbiw74CU63MJfdFA8CktYlzdvZ+DAgfj3v/+N8+fPY/v27ejZsycWL17cLPCsM3i/asrfHiaPBVPNQEzz4ei6fg5vkUPkLSA00BuFQukAgYGBCAgIsM8QdRXqxFCcwmKxoLCwyK2bzlkYMGCzeEh9RBBV51SSntDR6QgspPKAJjN1PAMnyEBYAolzYeySF+lwC3/RQfEo/rrsAGCN2rts2TKkpqZi+PDhOHToEJ599lkUFxe7nKZXzk6iUJrCneQhjrZAHCiA3+/DXUCdSmOHzrMvMN5i/U0E3mxd1dopvEeHe/iLDoonYcGBdSFwHfHyYHcZGRnYt28f0tLSsGDBAtxyyy0tRsV3FurEUJzCFmk3NzevW2f2MCYG7GkO4iAB3EEejOReJeEpHZ1KfVRYsUjlGJPEA/BCvRMjs0DhbHF6kQ638BcdFI/SMNvI+fO8mQkTJuCDDz5war3DjkCdGIpTCIIFxcXFEITu74bhsmSQBhkhJYngLrh363pSR6chspC8ZNVk3mJdc8qllay9SIdb+IsOCqUL+Pe//23/t21StDNLB7UGfdooTiGKEmpr6yCK3T8Dgy1nwZQxkFLdjxnjSR2dhsSA6GSNotx6jsYtMU7jRTrcwl90UDyKv65iDQCffvop0tLSEBAQgICAAKSnp+N///ufW2nSlhgPUFRU5LXz+NuDZVmoVCro9Xp7uP5uvf4JHuKlFhAlAWN0vbLwtI5OgSVglAKIkfd4xWkbEyPyLkyz9iIdbuEvOigexVWHhHi5E/Pqq6/iqaeewgMPPICxY8eCEIJdu3Zh4cKFKC8vx8MPP+xSutSJ8QBr1qxB7969ERoaissuuwyRkZHtn+Ql2OKreGosCXfG6sSI/QTwR1wf4OtpHZ0CJ4GN8I4w95w7LTFepMMt/EUHxaOwxEUnxsvjxLz55ptYtWoV5s2bZ983a9YsDBw4EM8++6zLTox3q/ZTevfujbq6Oly8eBFr1qxBXV2dp7PUYUwmE86ePeexip8xMmBzOEipglsxSTyto1OwsBAvekeY+8azk5zGi3S4hb/ooFC6gKKiIowZM6bZ/jFjxjgsEeQs9GnzADfccAMefvhh3HPPPSCEYO3atTh37pzPrMPk6e4X9iQPEkFAIt0rL0/rcB/GurKyF0znZQkHVuRcXD/Je3S4h7/ooHgS12LEuDajqTvp06cPvvzyy2b7v/jiC/Tt29fldL1btZ+jUqkwdepUlJSU4LPPPsPWrVs9naV2kcl4xMbGQCbzXE8km8sCWgbiANcj+HqDDrfhJDDhRoDzDmeMt8hcm53kZTpcxl90UDxKdw/sfeedd9CzZ08olUoMGzYMf/zxR5v2a9euxeDBg6FSqRAbG4sFCxagoqKi3es899xzePrppzF9+nS88MILePHFFzF9+nQ899xzeP75513KO0CdGI8zZMgQ/Otf/8L06dOxd+9e7Ny508tbZBhwHAdPfm0yhAF3moPUTwDhXC0rz+twGwZgOOI1EniL3LX1k7xMh8v4iw6KR3E9Xq/zN94XX3yBxYsX41//+hcyMzMxbtw4zJgxA3l5eS3a//nnn5g3bx7uvPNOHD9+HF999RX279+Pu+66q91rzZkzB3v37kVERAQ2btyIb7/9FhEREdi3bx9mz57tdN5t+PBnqP/A8zxGjRoFk8mE7du3o6KiAldffTV43vt+HovFgvz8Ak9nA+xJHuIwAVJPEdxZ58vJW3S4hcBCKg3wdC7scIIMoistMV6mw2X8RQfFozCEcS3YHXHeiXn11Vdx55132p2Q1157DVu3bsWqVavw0ksvNbPfs2cPevTogUWLFgEAevbsiXvvvRf/+c9/OnS9YcOG4bPPPnM6n21BW2K8iMsuuwxz5szBiRMnsG3bNlRXV7u8PLm/w1axYIpYiKl0UUhvgbfIXBwTQ6FQOova2lqHrbXJC2azGQcPHsTUqVMd9k+dOhV//fVXi+eMGTMG+fn52Lx5MwghKCkpwddff42ZM2d2uo6OQp0YL2PQoEGYNGkS9u3bh9dffx2TJl2O1NT+ns6WHYVCgb59+0ChUHg6K+BO8iCJEkig82MQvEmHy8hEsIlaQOZ+8L/OgBdcHBPjZTpcxl90UDyKu2NiEhMTERwcbN9aalEBgPLycoiiiOjoaIf90dHRrS7IOGbMGKxduxY33ngj5HI5YmJiEBISgjfffLNzC8EJvK+/goJRo0ZBrVZDLpfj6aefQWrqAPA8j6ys4x4fLyMIAsrKyiAInm8BYbM5YBwg9hfBH3TOH/cmHS4jMiBVckD0jkEYLo+J8TIdLuMvOigexTbGxZXzAODixYsOwVTb+1BrGvqfENLqcgAnTpzAokWL8PTTT2PatGkoKirCY489hoULF+LDDz90Os+dAXVivBCWZZGWlgYAOHcuB2azBenpgyAIAk6cOOXRvImiiOrqGo/mwQZjZsCe4yANEEAO8k4NbPMmHS4jsSBauadzYYcXZDAG6Jw/0ct0uIy/6KB4FHedGI1G06GI8BEREeA4rlmrS2lpabPWGRsvvfQSxo4di8ceewwAkJ6ejsDAQIwbNw4vvvgiYmNjnc63u9DuJB/g4sV8nDp1Gr169URCQpxH88KyLAIDA8Gy3nHrcCd5kFACEuNcl5K36XAJhgBKwfp/L8DlMTFepsNl/EUH5W+BXC7HsGHDsG3bNof927ZtazEoHQDo9fpm70zrLE94rJeAtsT4COfO5SA4OBhDhw5Br169cOhQJrRaF7563UQmkyE+Ps5rwvUz+SwgAiRCAoo7Hurd23S4BC+BizJYw9xbPB/mnjAAYV2IkeJlOlzGX3RQPIqrgetcOeeRRx7B3LlzMXz4cGRkZGD16tXIy8vDwoULAQBLly5FQUEBPv30UwDAVVddhbvvvhurVq2ydyctXrwYI0eORFxc8w/sa6+9tsN5+fbbb53OP0CdGJ/i0KHDyM3Nw6BBA3HppWNQXl4Bnudx5kw2KiuruiUPJpMJ586dhyh6x+BFBgxgAojSufO8TYdLWFiIBYFeMwajOqIEwZUurAPmZTpcxl90UDyKu91JznDjjTeioqICzz//PIqKijBo0CBs3rwZycnJAKxLBTSOGXP77bejrq4Ob731FpYsWYKQkBBMnDgRK1asaDH94OBgp/PkLAzx9EjRvxG1tbUIDg5GTU1Nh1exHj58VLN9PM9jwIB+0GiCwHE8goLU2LVrD6qrqzs5x76B+TYD2BwO/C7fH48wLfQOT2fBJQTejJ9ueB9p+8ajx9lBns6Oz7Bs270dtnXl/UHxHWy/7+Wah8Azzs+aFIgJO2pf/9vdHz48IODviyAIyMo6jl279uCPP3ahuroGl1wyxN432ZXwPI/o6CjvCsRnZECUzvniXqnDWTgJTJh3hLmvjCwGYQnCS+OdP9mLdLiFv+igULoIQRDwyy+/4L333rMvfFxYWAitVutymj78BqcA1sFUmZlHMGnS5YiOjkJhoeurgXYElmWgUMjBsp5rMidBEqQeIiAwgACAJ4DCufx4gw63YQBGJsGFQJ2dTkVUIeTGAKhrQ5w/2Yt0uIW/6KB4FIZhWp3i3OZ5Xr7eRW5uLqZPn468POs4xClTpiAoKAj/+c9/YDQa8e6777qULm2J8QP0ej0MBgNCQrq+/9FstiAvLx9ms+cis4qDBQiXWyBMNkOYbrauZu3k0BZv0OE2AgupRAUInn+MK6IKEV4S59qL1It0uIW/6KB4FNfXsPZuJ+ahhx7C8OHDUVVVhYCAhuU5Zs+ejV9//dXldGlLjJ9QVVWN3r17IS/vokdmLXUnhAOYMgayL5QAB+td7EKMNUrnILICqiOKkXporKezQqH4PAxca13wbhfGunjkrl27IJc7jl1MTk5GQYHr69jRTwY/oba2FgC6vGVBoZCjd+9eUCg8OIiWASAx1oXSBAaMkQEjOfcIe4UOd5GJYOM9H+a+OqIEEichvNTFGEZeosNt/EUHhdIFSJLU4mzQ/Px8BAUFuZwudWL8hJoaqxOjVHbtWkCCIKKysgqC4MEXNUsAN+fUeYUOdxEZkFrPh7mviCoEb5ZDUx3uWgJeosNt/EUHxaMwbvznzUyZMgWvvfaa/W+GYaDVavHMM8/giiuucDld2p3kJ5SVlcNgMKBXr544fPhol11HFEVUVXVPTJpWYQC4OQHEK3S4i8SC1Hm+JakiqhDhpbFgiIvfRF6iw238RQfFo7AMA9aFgb3ePiZm5cqVmDBhAlJTU2E0GnHLLbcgOzsbERERWLduncvpUifGTyCE4Ny580hNHYDs7HPQ6bpmXAzLslAoFDCZTJAkD00lZeF2S4xX6HAXhgByETBz8NSUGIkRURlZjJSsEa4n4gU6OgV/0UHxKN0Z7K47iYuLw+HDh7F+/XocPHgQkiThzjvvxK233uow0NdZaHeSH5GbexEmkwn9+vXtsmvIZDIkJiZAJpN12TXapRNaYrxCh7vwErhoA8B7zgmrCSuHKLMgvNSNhd+8QEen4C86KJQuoKSkBAEBAViwYAHeeustvPPOO7jrrrsQEBCAo0dd7z2gTowfIUkSsrPPIS4uFgEBTsbh7yBmsxk5ORdgNntwOlAntMR4hQ53sbAQCwMBi+ce44qoAnACj5DKKNcT8QIdnYK/6KB4FIZxffNm0tLSsGnTpmb7X3nlFYwa1TwyfUeh3Ul+Rn5+AQYM6IdevXri+PGTnZ4+IQQWS+fNgCJyAuFSM0gEaVj91/YwtvJ/oiZgityrKDpbh2dgrAH/PEhFdCFCy2LASu5Ei/a8js7BX3RQPIm/dif93//9H2688UbMnz8fK1euRGVlJebOnYvjx4/jiy++cDld6sT4GaIo4ty5HPTt2xvnzuXAaDR2avo8zyMsLLR+Zo/gVlpSlAjLdDOgJGDPcoDEOrawkHq/hTT8bYPNcW+Jhc7U4TE4CYzGAlIrA8Tu//onIKiMLEKvU4PdS8jDOjoNf9FB8SgMXGtVYbx8FcQlS5Zg8uTJuO2225Ceno7KykqMHj0aR48eRXR0tMvpUifGDzl/Pgc9eyZjwID+OHfuPOrq6tBZ63yyLIuAgACwbI3LaRAQiEMEiGMsYMpYyDYqwNR270u/M3R4HAZgFCII45lxPXUhFbAoTK7Hh7HhYR2dhr/ooHgUf22JAYBevXph4MCB+OabbwAAN9xwg1sODEDHxPgloijixIlTSEiIw/jxl2L48Es6LW2z2Yzc3DyXx5IQJYFwpQniOAu4Izxk33S/AwO4r8MrEFhIxZ4Lc18RVQhGZBFS7t5LyNM6Og1/0UGhdAG7du1Ceno6zp49i6NHj2LVqlV48MEHccMNN7gV7oI+bX5Kfn4BKisrAQAxMdEYP/5SqNWBHs2TFCvCfJMRUowE/nsF+F1ypyPtUryHiqhChFREgRdpywOF0lmwjOubNzNx4kTceOON2L17NwYMGIC77roLmZmZyM/PR1pamsvp0u4kP2bXrj1gGAYREeEYODAV48aNxZEjWW6tdC2Xy5GQEI/8/IIOt2IQEIjDBYijLGCKWMi2KsDoPOs/u6LD65CJYCONkMqUgMW9MULOQkBQEV2IxHP93U/Mgzo6FX/RQfEoLq9i7eXTk37++WeMHz/eYV/v3r3x559/YtmyZS6nS1ti/BxCCMrKyvHHH7tQXFyCYcOGYtCgVLCsaz+9KIqoqalpcQ2MFq+vIrDMMkEcbQF3gIdsg+cdGMB5HV6JxIDoeMADrVm6oBqYAvQIL413PzEP6uhU/EUHxaOwbmzeTFMHxgbLsnjqqadcTpe2xPxNEEURmZlHUFlZhYEDByA0NAQHDmTCYDA4nU5FRWWHbKVEEZYpJgCAbKMCbL73fJ06o8NrEVmQmq5dK6s1ymIugpEYhJXFuJ+YB3V0Kv6ig0LpJN544w3cc889UCqVeOONN1q1YxgGDz74oEvXoE7M34zc3DxUV1dj2LBLcNlll+LUqdNISIgHIQS5uXkoLy+HydR69wrDMFAo5DCZzK3OeCIMgTjKAnG4AOYiC9nPCjAG7/o67YgOr4chgEyyBlfr5jD3xYnnEV4SD5mlEyptD+roVPxFB8WjuBq4zht7k1auXIlbb70VSqUSK1eubNWOOjEUp6ipqcXvv/+JIUPSkZ4+yL4/PDwMkiTh/PkcnDlztsWuFrlcjqSkJOTm5sFkMjU7TgIlWKabQWIkcLtl4A7yXrm6ans6fAJeAhejh1ik6tYxGGa5EeUxBRh0YFznJOghHZ2Ov+igeBTGxSnW3viezcnJafHfnQl1Yv6mCIKAAwcOITo6CklJCYiJiUF+fiG02jr07dsHcXGxOH36DOrqtDAYDDCbrdFtzWYzLlzIbTHardhDhDDZBAgMZN8qwBZ574u8LR0+g8BaK8xuntJbEn8BhJUQc7Fn5yToIR2djr/ooHgUf2qJaQ1b63dnDEamTszfnJKSUpSUlDrsKygoQlraQAwdOgSA9YY7f/4CTp06DUmSWpzNQwIlCDNNYHNZ8L8owBi9+4kihPjurCQbhPHIF39R4nmElkUjwKDunAQ9pKPT8RcdFEoX8eGHH2LlypXIzs4GAPTt2xeLFy/GXXfd5XKa1ImhNEOv12Pv3v1QKBRQKhWIiIhAv359ERkZgaNHs8BxHKqraxzC9ZNAArAAt0fu9Q4MYF12ICQkuJkOn4KTwKgtINruC3MvcBaUxeWh39GRnZeoB3R0Cf6ig+JRXI354u1xYp566imsXLkSDz74IDIyMgAAu3fvxsMPP4wLFy7gxRdfdCld6sRQWsVkMsFkMqGmphalpaUYOnQILr10DERRRGlpGfLyLqK0tMxqbIt35iO9MyzLIihIjdraOk9nxXVYAkYlgOh5oJtmipfG5UHkBcTk9eq8RD2go0vwFx0Uj8LAtfEtXu7DYNWqVXj//fdx88032/ddffXVSE9Px4MPPkidGErXUlenxZ9//oXY2BioVCrExcVi1KgRqKysRFbWcVTx1QAAxkcaNcxmM3Jycj2dDfewcJCKujcKc3HieQRVhUOtDem8RD2go0vwFx0Uj+KvLTGiKGL48OHN9g8bNsyt1nDa5knpMJIkoaCgENnZZ7Fz5x/YvXsvZDIZLrvsUvTq1wMAkNIrBZGREZ7NKKVLkFgRxQk5iL3Yia0wFArlb8Ftt92GVatWNdu/evVq3HrrrS6n61ctMatWrcKqVatw4cIFAMDAgQPx9NNPY8aMGc1s7733XqxevRorV67E4sWL7ftPnz6NO+64A7m5ubjnnnvw9NNP24/16NEDubm52L17N0aPHm3fv3jxYhw+fBg7duzoKmleg1wuR1xcLAoLi1BeXoGdO/9EcnISZOnWW6l4eCFCxVCkGvpBXicHW8VBNIqoqqpGTU0N6uq0kCTJ5evLZDIMGpSKgIAAmEwmGI1GmEzm+ungBOER4WACGdSKNVCGK2HkjKgRaiHxIkz7BdTk1TTT4bMDfGUi2AgjpPLuCXNfHl0AQW5GbF7vzk24m3V0Gf6ig+JR/LUlBrAO7P3555/t9eeePXtw8eJFzJs3D4888ojd7tVXX+1wmn7lxCQkJGD58uXo06cPAOCTTz7BrFmzkJmZiYEDB9rtNm7ciL179yIuLq5ZGvfffz/mzp2LESNGYOHChZg0aRLGjh1rP65UKvF///d/2LlzZ9cL8kIkSYJOp7M7IoQQXLiQC1JMwA+VoTKsClVR1RBjBCAGAAECzAEIFUIRZglBXF0M8g8XojavrsXFH+VyOZKTk9C7d0/wPA9RFFGn1UIn6SAL4QEVYOSMOK89B1EpQVQKsMgtMPNmmHgTTrEnm3UO8wIPwhCoZ6gxcM8AnDp2ppkOn0RiQAzdF+a+KPEcVHUaaKrDOzfhbtbRZfiLDopHYeDa+BZvv+uOHTuGSy65BABw7tw5AEBkZCQiIyNx7Ngxu52z0679yom56qqrHP5etmwZVq1ahT179tidmIKCAjzwwAPYunUrZs6c2SyN6upqDB06FOnp6YiLi0NNTY3D8XvvvRerVq3C5s2bccUVV3SdGC9FEASUlZU3288YGWtwu/q/OV4GEiaBhBOYwy0oiShBcXgxpEQJSITVgakEmAoWfDWPpNAkKEMUsMgtqONqsN28HSbOBFEpQlAIzZ/QYIDRMYCBAVPOAHoGjJ4Br5fX/xtg9PX7JQZSuITaG2tRm1aDyyPGYd++gy3q8ClEFqS668LcX+x5CmcHHgInyMBb5KgOL0Hy2dTOD6rVxTq6DX/RQfEo/toSs3379i5J16+cmMaIooivvvoKOp3OPp1LkiTMnTsXjz32mEPLTGOef/55TJkyBQaDAVdeeSWmTZvmcLxHjx5YuHAhli5diunTp7u8kKKvwjAMZDIZLBZLm+H6GYEBU8oBjiFoQJQEcekxCOkTjDx9HqqCqiH0EnBWlg1GZMAZOEAPSHXE7ozwepndSbE7JoJzTyxbwYLdzePs2LMIrgzG2LGjcfjwUZSUlPr2sgO8ZA2u1slh7qvDSnBk9G8IL0lAgF4NQWZGeGkcks+2/Ny4RRfq6Fb8RQeF4kP4nROTlZWFjIwMGI1GqNVqbNiwAampqQCAFStWgOd5LFq0qNXzr7jiCpSVlaG2thaRkZEt2jz55JP4+OOPsXbtWsydO7dLdHgrtu4eV8P1M0YGRftKoK5SY2L/idBX6xFgDsD5vBwcP3LS/pXfFSMKuEweUrKIQ9GHoDwTgOHDL0FW1nHk5uZ1wdW6AV4CF9v5Ye4tMhMOjNsKTVUERu2YCVbq4vEdXaSj2/EXHRSPwjCMS5FsOyP6rS/id80I/fr1w+HDh7Fnzx7cd999mD9/Pk6cOIGDBw/i9ddfx5o1a9r9sRUKRasODGDtx3v00Ufx9NNPuzQoVKvV2v+t0+lgNBoBWFuK6urq7NPNzGYzFAq53VYul4HnG/xOhUJhbwniOA4KRUNTtkwmg0zWmi3bgq3MwZbjrLYs62hLiISioobBsFZbrkVbnuchlzdOV263PXcuBxcv5kOlUqEgvxA5Z3KhkDecK5c32FoXa1TYfzdrunIHW1u5NLXlOM5uy4BB4E41OJbDHvUeFBYWIT19EGJjYxqVYeeXd9Nykcn4VssbLAFk1kHK1gtJ1q97+8kiwNb/LTIQKxSArVWqLVumA+lyEggIjoz6DQxPMOyPaVYHhpPqz62Hl6z7AGt6MtGaPmC9nrO2AguxWNWQr8a2rM2WtJ9uM9sOlGFHyqWjtgyBWKq0tsQwTbQ2LcP68gasrcZ1dXX2tcpMJlOr7wiK/8O6sf0d8Tvdcrkcffr0wfDhw/HSSy9h8ODBeP311/HHH3+gtLQUSUlJ4HkePM8jNzcXS5YsQY8ePZy+ziOPPAKDwYB33nnH6XNPnDjh8O+LFy8CsL68Dh48aH+BFRcXIyEhwW4bExON8PAwANZKNDk5CQEBAQCAoCA1kpIS7bbR0VGIiLBOdWZZFsnJSVCprBVFYGAgkpOT7LaRkREO06KTk5MQGGiNd6FSqZCcnGSvkMPDwxEcHGzvgklKSkRQkDX8fEBAAJKTk+zOR3h4GGJiou3pJiQkQKPRALBW3Hq9Abt370Nm5hEEBantzoTVNh4hIcH1ttbWH1vFHxISjPj4WLttXFwsQkNDAFidieTkJLvTEBysQUJCfIOtJhbRB2Mgpgg4rD8MrVaLtLSB4HkeGk0QEhMbl2E0IiLCWyxvtbppeUfay5thGIcytJW3zbGKjIxEVFSDk5ycnAS1uj6Ev0IEF6u3P5lMiBlsWEMFxkYbwKjqYyrIJXDhpgbbYDPY8Ea2UQYw6ka2sXqAr1+zJMgCNtLQYBtpBKO24ELKMVQmFCDj9AQEmq15YtQWsJGN0o00gAmqj2rIE2u6cqneVgAb1SjdcCOY4HpHn6u3VVgra0YlgI02WLtezBzYUBOYkHpbFo62AYL1b1u6oSawoQ0tgVysHozSqpVR1tsyLduyMQYwqnqHQmkrb9JyGUYbwATayrDelqu31ZjBRjQuFxMYObHqkdnKW2oo78a2EdbyBgCDwYCDBw9Cp9MBAIqKipCVlWW3bfyOoPg/trWTXNn+jjDEZwcEdIxJkyYhMTER//3vf1FUVORwbNq0aZg7dy4WLFiAfv36tZtWjx49sHjxYvuU7LfffhvPPvssrrrqKpw/f77dKda1tbUIDg5GQUGBfWaUTqcDx3FQKpX2GTMBAQHgeR5msxkTJkyCyWR9scvlMkgSsbfUKBQKWCwWSJIEjuPA87y9i8da4RNYLC3ZsuB5WRNb2BdDVCgUEAQLRFECy7KQyRpsFQoFgoM1qKiohCiK9bYCRFFsZsvzPFiWsS8eqVDIIQiig63ZbAYhpN6WtbfwyOVyiKLVlmEYyOXyNm0lSYIgCM1sOY4Dx3HNbC0TTQiO0YD5lUNG2igUFhbh+PGT4Hmu08u7ablYW2yYFst7WvgC6xe6hQXAWP/NoGFRQZkIiAwgsQAvgtFYQGpkgMi1bWsbr9FWuhKDnVO/gtIQiFG7ZjSM7eAkayVv6yLhJWtjhMjC2grSaBwIK1kreWdsJQZMoABiYq3HGtuKrHW2D0vqy6WNdJvZdqAMO1IuHbVVCmCUIkidzJqPxuNjmpZhfXkv23IfRFGEXq+HSqUCx3EwmUywWCx2x9b2jjCbzQgODkZNTY39Y4DiP9jqh3vjl0LOKp0+3ywZ8V7BS157f/z+++8YM2aMQ+s2YJ0s8tdff+Gyyy5zKV2/GhPzxBNPYMaMGUhMTERdXR3Wr1+PHTt2YMuWLQgPD0d4uOPUUJlMhpiYmA45MC1xzz33YOXKlVi3bh1GjRrV4fPsX92A/WsdsIXCD7L/LZfL7RUqALszYKPxmBRbhW+j6erMjrYSRNHUIVtJkhz+JoRArVajuroGoii2ads0CmNjLe3ZNu6mI4S4bNu0XOzdYH8pETozFHkjLuJE5ikMTk9Dfn4BKiurGtl2Tnk31WpzdFqyhcQAjcegNF2Dp/FYCwZgAuorTbEd26aLE7ZiG1oeg9K4vOa2jcPoO6zS3CRdiQUkJ21lIpggC4hB2ShfTW2blEub6TpRhh0slw7ZivXOmI63HmurDBsd4zjO4blXKBQO3Y+2d4TPxjOiUABMmDABRUVFiIqKcthfU1ODCRMmOLxPncGvupNKSkowd+5c9OvXD5MmTcLevXuxZcsWTJkypUuuJ5PJ8MILL/yt+qvNZjPOn8/x+ReqWWtBzp+5MCYYcD7sPCorKzF4cJpvzTazcJAKAjt1EGlUYRL0QTXQqqs7Lc126QIdHsFfdFA8i6tdSV7enUQIaXE8akVFhcPHvLP4VUvMhx9+6JS9LbKvO/Y333yzw4JWFN+BLeDAHeIhZlhw8IdMTEqbgL59++D06TOezlqXIzEiRF6EyFkg8gJEToDIC/bZYWVxeVCfCfFsJimUvyH+Fifm2muvBWAdK3j77bc7tDKKooijR49izJgxLqfvV04MpeuRy+WIjY1GUVGJT7fG2HQUHiqGLqkWtZdW4/ShbPTr0xcXLuS6NH282+Ele5h7M2vG6fR9sMhNdodE5C0N/27yf8JKbSZtUGnbPN6pNNLh2E3kY/iLDopHcXWmkbfeccHB1gkahBAEBQXZJ0cA1vfw6NGjcffdd7ucPnViKE4hSRIMBqNvh+tHgw4iEPA/K2C50YjT0aeRQvogISEe586d93QW24fAOhiWABVRhcjpfxShZTHgLXJwAg+ZWQFe4MGKPDiBByfKrP8XeHBi4//LwIlc/f+t+5T6blyNuZEOn8ZfdFAoncjHH38MwDox5tFHH3Wr66glqBNDcQpBEFBaWubpbLhNYx1sJQvuLxmEy8w4eew0khMTfcOJEVmQKussBomzDhbO+PVq8IK8rbO8j0Y6fBp/0UHxKK5Ol/b2KdaPP/64Q3T03NxcezDaqVOnupwudWK8nP3v3uPpLDggSQRmkUDOMWC9tRO2AzTVIRGC+3I34HT6BfS90Bsbl9+O+JDudwakAR2fKSdJEsxmEXI5h015NTh4AHj+y3TwrG8NLG2sw6cGVjfBX3RQPIu/jYmxMWvWLFx77bVYuHAhqqurMXLkSMjlcpSXl+PVV1/Ffffd51K69EmjOIXOLGFPTh10Zt/uTmqqg2UYPB8/FQJEHI3MxOH8bhwT4iI6vQV7D16ETm/B+doiyFje5xwYwFGHL+MvOiiehXFj82YOHTqEcePGAQC+/vprxMTEIDc3F59++ineeOMNl9OlTgzFKQJkLAYnBCJA5tu3Tks6omVqPB03GbkB+dhUdRKC6N2DGwKUMqQPjMWm/D/x6ZlfMC9lsqez5BI2HQFKWfvGXoy/6KBQugK9Xm+Ph/Tzzz/j2muvBcuyGD16NHJzc11O17drIkq3w3MMQlU8eM7b/f62aU3H1OC+mBrYD/tDMvF7QYmHctcxeJ5FESnFy8e+xry+k/HAwKs9nSWX4HkWoSEB4Hnffh35iw6KZ7F1J7myeTN9+vTBxo0bcfHiRWzdutU+Dqa0tNStCMP0aaM4hVmQcLHKBLPg291Jbel4NnEiVFDg5apfIRLv1Vmj1+OPU6fBSRz+Megqn13F1mwWkF9YA7NZaN/Yi/EXHRTPwrjxnzfz9NNP49FHH0WPHj0wcuRIZGRkALC2ygwdOtTldKkTQ3EKs0CQW2GCWfDurpb2aEuHmlNgUdAEFPBl2FTmfYHvtBYDnty/BlM3PYHfjp9AojIKSs7HZiQ1wmwRkXuxCmaLa2HHvQV/0UHxLP7aEnPdddchLy8PBw4cwNatW+37J02ahJUrV7qcLp2dRHEKtZLDpX28b3ExZ2lPx+zEXngjKwiby89hdlT/bsxZ2xyrvIClez9ChakWDw67CoNCe6B3cJyns+UW6kAFxo7q4elsuI2/6KBQuoqYmBhotVps27YNl112GQICAjBixAi3WpGpE0OhtICMY5HOJ+KQkANRtK787Wn+Kj6BRbveRr+QRLwz7kEkqiM9nSUKhdLJuDrTyMsbYlBRUYEbbrgB27dvB8MwyM7ORq9evXDXXXchJCQE//3vf11K1/NvZopPoTeLyLyog97s203mHdFxRWRv6DkDfisu7Mactc7a7F/RLyQRayY8ikR1JPR6Mw5nFUKv993lHwBQHRRKI/y1O+nhhx+GTCZDXl4eVCqVff+NN96ILVu2uJwudWIoTsEyDAJkLFgfHURqoyM6JkckgScctlR4PnpvmaEGu0tOYnbPsZCx1gZUlmUQoJT5dNBBgOqgUBrDwDUHxtvvup9//hkrVqxAQkKCw/6+ffu6NcWadidRnEIpY9E/JqB9Qy+nIzqUnAz9uTgcMV1Ehc6C8EDPxf/YcnE/eJbD1IRLGvKnlKFfX9/vUqI6KBT/R6fTObTA2CgvL3dY2dpZaEsMxSkkQmAWJEjEt2cndVTH9PBeKFOUI6da1005a5nvc/difFw6NPKGxdMkicBsFiFJPv5bUB0Uih0GxOXNm7nsssvw6aef2v9mGAaSJOHll1/GhAkTXE6XOjEUp9CZJPx1vg46k/fGT+kIHdUxWpUMwhCcFIu6KWfNOV2djzM1+bgqabTDfp3ejN37c6Hz8TEYVAeF0oC/jol5+eWX8d5772HGjBkwm814/PHHMWjQIPz+++9YsWKFy+lSJ4biFAEyFoPiVH6x7EBHdAQKgVCICpwWPTO4lxCC9ed2IFShRkZMqsOxACWPQQOiEaD07V5hqoNCacBf105KTU3F0aNHMXLkSEyZMgU6nQ7XXnstMjMz0bt3b5fTpU8bxSl4jkGE2vfXhumIDq1RwBM5v8IsM2FyWM9uylkDNWYdnj3wP2wvPIIl6XMga7K4I89zCA8LbOVs34HqoFAa8NdVrAFrnJjnnnuuU9OkTgzFKcyChHKtgAg1D7kPrxHTER2Pn9qJTPkZ3Kcej8uDe3Vr/sqNNbjt1xUwiGa8NmYhLo8b3MzGbBZRXqlDRFgg5HLfW73aBtVBofg/R48ebXE/wzBQKpVISkpyaYCv79ZCFI9gEgiyy4ww+fiyA23pIITgmRO78acsC7O44bgv2fV1PVzldHU+ig1VeHfcohYdGAAwmQWcPV8Ok4+v1UN1UCgNsG5srvDOO++gZ8+eUCqVGDZsGP7444827U0mE/71r38hOTkZCoUCvXv3xkcffdTudYYMGYKhQ4di6NChGDJkiP3vIUOGoH///ggODsb8+fNhNBqdyj91YihOEaTkML6vBkFK3/7SbEvHJ/nHsYHsxSR2EJ5NGePyNcotOpwylEJwYRHJCGUwALR5bpBagcvG9EKQ2vXpid4A1UGhNMAwrm/O8sUXX2Dx4sX417/+hczMTIwbNw4zZsxAXl5eq+fccMMN+PXXX/Hhhx/i9OnTWLduHfr3b39plg0bNqBv375YvXo1jhw5gsOHD2P16tXo168fPv/8c3z44Yf47bff8OSTTzqlgXYnUShN+LX2LBSiHCsGXA6Odc3PN1lE3HV+A84L5ZARHpGWMEyOSkJ6QCxGauIQwrcdoyZSaV3XqcxQ7dL1KRSKb9KdY2JeffVV3HnnnbjrrrsAAK+99hq2bt2KVatW4aWXXmpmv2XLFuzcuRPnz59HWFgYAKBHjx4dutayZcvw+uuvY9q0afZ96enpSEhIwFNPPYV9+/YhMDAQS5YswSuvvNJhDbQlhuIUerOIo/n+sexAazrmhw2HiTPjg7yW+3DbwmiRUKGz4LmDR3BeKMfQiqHoXzUAEHh8UZGFR4u+x2Wn38OErI8xe993+L9De7Dh3EUYm6x8bBAtAIAqk7Z1DQYLjh4vgt5gcTqf3gTVQaF0HrW1tQ6byWRq0c5sNuPgwYOYOnWqw/6pU6fir7/+avGcTZs2Yfjw4fjPf/6D+Ph4pKSk4NFHH4XBYGg3X1lZWUhOTm62Pzk5GVlZWQCsXU5FRc6Fs6AtMRSnYBgGHMu4teqoN9CWjkkx8Ugt7YFPa/fjdnEQVJy8w+n+fKIamfk67I45hjBjGHrV9QIDBpzW+qVUxWhRoahAXWAlagMr8RPJwU9GYNkpOVL4KIwThyAsIAhvHvsO8YHhuDR2UBsaAJ5jXWpG9iaoDgqlAXcXgExMTHTY/8wzz+DZZ59tZl9eXg5RFBEdHe2wPzo6GsXFxS1e4/z58/jzzz+hVCqxYcMGlJeX4x//+AcqKyvbHRfTv39/LF++HKtXr4Zcbn2nWiwWLF++3N4dVVBQ0Cw/7UGdGIpTBMhYDIxrHjra12hLB8MwWBQ9BveVr8PruQewJGk0qg0iItV8u84bzzEoU5ahQlmJsSVjwYDBqB5qKHgGv5+tgxpqqAU1ZvUZgsEJgdCKJuytLsQPhbk4rivC+ye3QGCsA0O/mvoUogNCWteglCG1v3MPvDdCdVAoDbjbnXTx4kVoNBr7/vZm/DR9pxFCWn3PSZIEhmGwdu1aBAdbx+29+uqruO666/D2228jIKD1bvK3334bV199NRISEpCeng6GYXD06FGIoogffvgBgNVJ+sc//tGu1sZQJ4biFIQQSKR+wTEf/uRsT8ewiAikXuyD9diP3zPzkaBNRIqUgLggJa5MC0VwQMuPTkG1GaeCTyHEFIIYQwwAIDZYhv4xAfj9bJ3d7lSxAYMTAqHmFJgU3hOTwnuiUifgtxKCzRf3IlEVCW2JiOge7WiQCFgfbxmjOiiUBlydaWQ7R6PRODgxrREREQGO45q1upSWlrbaGhIbG4v4+Hi7AwMAAwYMACEE+fn56Nu3b6vXGzNmDC5cuIDPPvsMZ86cASEE1113HW655RYEBQUBAObOndtuvptCx8RQnEJrkvDH2VpofXzZgfZ0KHgW7w+ZiquY4eACzdgbtRdfxnyHr5k/8crRYzhTpoPYwho5OlUVSgNK0b+mP5j6Bt6cChPkPIsnr4jHsCRrMLSWVs8OC+Rx7cxBeP6a69FDFod1Xx/G3gOtzxLQ6sz4c88FaHW+Heae6qBQuh+5XI5hw4Zh27ZtDvu3bduGMWNanpU5duxYFBYWQqttGKt35swZsCzbbHXqxlgsFvTq1Qt5eXlYuHAhXn31VaxcuRL33nuv3YFxFdoSQ3EKpYzFgBgVlD6+7EBHdKhlMrw4cCyAscgzVeOnmtP4rvIkvhN34Keiv9DjfCJmBPfDzb16QyXnIEoE25ksBFmCEK+PB2DttkqNtTaxsgyDmWmhmNBP0+Z142I0mHfTMGz/4xy27cgGyzIYcUliMzulgseAlCgoFb79GFMdFEoDrk6XduWcRx55BHPnzsXw4cORkZGB1atX2x0NAFi6dCkKCgrsCzfecssteOGFF7BgwQI899xzKC8vx2OPPYY77rijza4kmUwGk8nUJS2U9GmjOIWMYxCt8f1lB5zVkaQIwb1Ro3BP5EicMpbh69IT+Jk7g9fFc3j/ZAAGST0xRB2DHHkBhpcNx+D4QBwt0KNHuAJ9oxwfblUHorkyDIMJ43pDkgi2/nYGDMNg+FDHLx2ZjENUpLrDGrwVqoNCaYABAevCitSurGJ94403oqKiAs8//zyKioowaNAgbN682T6LqKioyCFmjFqtxrZt2/Dggw9i+PDhCA8Pxw033IAXX3yx3Ws9+OCDWLFiBT744APwfOe5HgwhxLdDr/oQtbW1CA4ORk1NTYf6LAGAHPiwi3PlHBaRoFJnQVigDDLOd/v9O0OHRAh2VV/EV6UnsNt8HibWDJWgwuO4FtcOjoBZkMCzDFgnRulJA/o5/E0IwbYd2dh38CKumNIflwyOb9BgEVFZrUdYiAoyme8GH/w76+ACL+1w+q68Pyi+g+33fXnAPxHAKZ0+3yAa8djJ5V57f8yePRu//vor1Go10tLSEBjouM7Yt99+61K6tCWG4hRGi4STxQYMS+Ig43y3wukMHSzDYFxoEsaFJkEvWLDuwllckhCGocERANApa0sxDIMpl/eFJBFs3nYKLMtgSFqcVYNJwKkzZbhkcLxPV/5UB4Xi/4SEhGDOnDmdni51YihOoVawuKyPxudjYXS2DhUvw519BnROYk1gGAbTJqZAkgh+2HoSDMNg8KBYqAPlGJfRw+dnwlAdFEoD7s5O8lY+/vjjLkmXOjEUp2AYxucdGMD3dDAMgxmT+0GSCL7fcgIsyyAtNcYvKkzrb0F1UChA9y474A9QJ4biFAaLhPNlRvSKVCLAh2co+aIOhmEwc2p/SBLBpp+OQxAkyOUcevUIQ4DSdwdbG4wWnL9QSXVQKHA/Yq838/XXX+PLL79EXl4ezGbHUASHDh1yKU3feHtTvAcCSMT6f5/GR3UwDIMrpw3AoAEx+PHnk8gvqPY5Dc0ggCQRqoNC8WPeeOMNLFiwAFFRUcjMzMTIkSMRHh6O8+fPY8aMGS6nS50YilMEyFmkxasQIPftW8eXdbAsg6ump2LggGgcPFKA3PwqT2fJLQICZEhLjUFAgG+3XviLDopnsXYnERc2T+e8bd555x2sXr0ab731FuRyOR5//HFs27YNixYtQk1Njcvp+t4bnEKhgGUZzJqRiv59I/Ht98dw5myZp7NEoVA6AcaNzZvJy8uzRwIOCAhAXZ11GZa5c+di3bp1LqdLnRiKU9QZRew4U4M6o+jprLiFP+jQ6S0IC1WhV3Iovt6UhYOH8+GLYZ/qtCbs3HUedVqTp7PiFv6ig+JZbAN7Xdm8mZiYGFRUVAAAkpOTsWfPHgBATk6OW+8t6sRQnEIpY9AvOgBKmZc/Me3gDzqUCh79+kbimpmDMCQtDj/9chrrvz0Crc63KlGlgkdKnwifD9fvLzoonoV1Y/NmJk6ciO+//x4AcOedd+Lhhx/GlClTcOONN2L27Nkup0ufNopTyDgWscFyT2fDbfxBh0zGITbaGpnziin90bdXBH7YehLvrdmLmVP6o39KlIdz2DEa6/Bl/EUHhdIVrF69GpJkXXB34cKFCAsLw59//omrrrrKvlaTK1AnhuIUFpGgWi8gRMX7/LIDvq7DYhFRU2tEsEYJmYxD394RuOf2Udj88yl8vSkLg1JjMGpYImKigrw6fklTHb6Kv+igeBaGIWAYF9ZOcuGc7oRlWbBsQ3vRDTfcgBtuuMHtdKkTQ3EKo0XC8SI9hiWpfX7ZAV/XYTQJOH6qxCHMfaBKjutmpeHIsSJs/+Msjp0oRkR4INJSYzCwfzRCgltfadZTtKTDF/EXHRTP4k8Re48ePdph2/T0dJeuQZ0YilOoFSzG9tagE5YF8ij+oEMdKMeYkcngm4hgGOv6SukDY5CTW4WjJ4rwx+4cbP/jHHokhWL6pH6ICA9sJdXupzUdvoa/6KB4FsbFQbre2Ng6ZMgQMAzT7sBdhmEgiq5NsqBODMUpGIaBP3xk+oMOhmHa/OJnWRa9e4ajd89wmMwCTmeXYdfeC3j/032YcGkvjByW5NQK211Fezp8BX/RQaF0Fjk5OV1+DerEUJzCYJFwodyEHhEKnwnX3xL+oMNgtCA3rwrJSaHthrlXyHmkD4zFgJQo7PjzPH7ZeRansstw1fRUhIepuinHLeOMDm/GX3RQPAsDAsaFsM+unNPVJCcnd/k1fPPtTfEYhBCYBMkn45E0xh90EAKYzAKckSCTcZgyoS/m3TQMOr0Z73+6F/sO5nm0HFzR4Y34iw6KZ/HXODFdBW2JoTiFSs5hSKL3jKdwFX/QoQqQYfCgOJfOTUoIwd3zRmH7n2fx8/ZsnM2pwOwrB3mkBcEdHd6Ev+igeBa6irVz0JYYCuVvilzOYdrEfrjluiEoKq7FR5/tR1m51tPZolAolA5DnRiKU2iNIv7IroXWh8P1A/6hQ6sz4c89OW5H6O3VIxx33DYSMhmHj9YewKkzpZ2Uw47RWTo8jb/ooHgW25gYV7a/I17pxKxatQrp6enQaDTQaDTIyMjATz/9ZD/+7LPPon///ggMDERoaCgmT56MvXv3tpnmmjVrwDBMs81oNNpttFotbrrpJsTGxuKmm26CTqezH7v99tvBMAyWL1/ukO7GjRu9OpBYZyPnGfSMUEDO+7Zmf9Ahl/HomRwGucz9XuHQkADcfvNw9O0Vjq83ZWHHn+e6bZxMZ+rwJP6ig+JZ/H1MzMGDB/HZZ59h7dq1OHTokNvpeaUTk5CQgOXLl+PAgQM4cOAAJk6ciFmzZuH48eMAgJSUFLz11lvIysrCn3/+iR49emDq1KkoK2t7JV+NRoOioiKHTalU2o+/9tprUKvV+Pnnn6FSqfDaa685nK9UKrFixQpUVVV1umZfQc6zSAhVQO7jsTD8QYdcziE+NhhyeedM65XLOcy+chAmjuuNP/dcwM5d5zsl3Y5ctzN1eAp/0UHxLP7aElNaWoqJEydixIgRWLRoER544AEMHz4ckyZNarfubguvfINfddVVuOKKK5CSkoKUlBQsW7YMarXavurlLbfcgsmTJ6NXr14YOHAgXn31VdTW1rYbHZBhGMTExDhsjamurkZKSgrS0tLQv39/1NTUOByfPHkyYmJi8NJLL3WuYB9CEAkqdQIE0bsfmPbwBx2CIKGySg9BkDotTYZhMGZUD0yod2Syz5V3Wtqt0RU6PIG/6KBQuoIHH3wQtbW1OH78OCorK1FVVYVjx46htrYWixYtcjldr3RiGiOKItavXw+dToeMjIxmx81mM1avXo3g4GAMHjy4zbS0Wi2Sk5ORkJCAK6+8EpmZmQ7HH3jgAbz33nuQyWT4+OOP8dBDDzkc5zgO//73v/Hmm28iPz/ffXE+iMEi4WiBDgaLb7+o/UGHwWhB1oliGIyWTk97zMhk9O0dge9+Oo6qakOnp9+YrtTRnfiLDopn8dfupC1btmDVqlUYMGCAfV9qairefvtth+EizuK1TkxWVhbUajUUCgUWLlyIDRs2IDU11X78hx9+gFqthlKpxMqVK7Ft2zZERES0ml7//v2xZs0abNq0CevWrYNSqcTYsWORnZ1tt+nRoweys7Nx8eJFnDhxAvHx8c3SmT17NoYMGYJnnnnGZW1abcMMEJ1OZx+XI0kS6urqIAgCAKuD1njgqd4swlhf6UqEoM4o2lsSzIKEuia2tgqa1Nta6m0totXWNubBYJZgMDe1leptiYMtywJD4gMRqLDeOnVGEeb6L0+h3laqtzVaJOjNDXnStmQrNdjqTI1sTSJM9baiZLUV621NTWx1JhEmSyu2ggRtE1ujRUKggsWoZDUkQhzKsGvK27EMDZaG8raVob28LSLqtKYGW6MFekNDpajVmWCuL1OFnMegAdH2adFGkwC93tzE1noviaKEOq3JvoqsySRA18hWpzPD1MhWqzNj5tT+UCp4fPXdUdTUNjgyOr0ZJpPVVpKs6Yr1+TebBYeBrXq9GUa7LUGd1mRvqTCbRWh1JgSq5Bg9PAkMw9gdAEKsthaL6FAuNgwGS7u2rZVhnbahDAVBrC+X+vvQaGm1DAXBsQyNTcqQEOCS9DgEquT28raVi8kkQKdrUt4mW3mLqKurs4ddN5lMrb4jKP4P68bmzUiSBJmseQgHmUxmf6ZcwWt19+vXD4cPH8aePXtw3333Yf78+Thx4oT9+IQJE3D48GH89ddfmD59Om644QaUlrY+q2L06NG47bbbMHjwYIwbNw5ffvklUlJS8OabbzrYsSyLmJiYNgfrrlixAp988olDfpyh8XknTpzAxYsXAVhfXgcPHrS/wIqLi3GkoGFw8aliI3IrrS9yi0hwME+LGoP1RVimFZB5scE2u9SInHLri0+UgIN5WlTprbYVWgEH8xpekufKjDhX1vCSPJinRYXWalult9rWv4uRW2FCbpUJbH35ZF7UoazetsZgtbVV3rmVJpwqbkj3SIEOxbXWykRrEnEwTwuTYLW9WGXCyeKGijKrQI+iGutLX2eWcDBPa3cSCmrMOFaot9seLzQgv9pqa7RYbW2OS3GNBUfzG2xPFRuQV2nNP8MyyLyoQ129bUmdBYfzG8rwdIkRFyqs5S3Ul3d1fXmXawUcalTeZ0uNOF9fhhKxlmGlzmpbqbPgYJ7WHgTtfJkRZ5uUd7mtDGuNOHSkwF7R5+ZVIftcQ3/x4axClFVYfzutzoRjJ0vsFWVefhVOZTfYHj1ejOLSunpbMw4dKYDRaL1OfmENTp4usdseO1WMwuJaAIDeYMGhIwUgBLju6nSUV+jw7ffH7LYnT5cgv9Da1Wo0Cjh0pADa+gq6uLQOR48XN5R3dhny8q1jyCwWEYeOFKC2zqq9rEKLw1mFYFkGCgWPcznlyM2z2gqChENHClBTa7Utr9Th0JGChvLOqcD5C5UArE7MoSMFqKy2/s5V1QYcOlJgd0xyLlTi7PmGbrHMowUor9S1XN4Xq3GmURfakWNFKC232tZpTTh0pMDuAF3Mr8ap7IZ3zvFTxaisNoBlmeblXVSDE43K+/jpEhTUl7fBYMDBgwftEwmKioqQlZVlt238jqD4P7ZVrF3ZvJmJEyfioYceQmFhoX1fQUEBHn74YUyaNMnldBniIyFLJ0+ejN69e+O9995r8Xjfvn1xxx13YOnSpR1O8+6770Z+fn6HmrJuv/12VFdXY+PGjQCAmTNnQiaT4fbbb8fs2bM7NJOjtrYWwcHBKCgoQFycNSiWTqcDx3FQKpWQJAk6nQ4BAQHgeR5msxmmfWugVloHCurNIliGgVLGQiIEOpOEABkLnmNgFiSYBIKgRrYMwyBAxoIQAq1JglLGQsYxsIgSjBYCtYK1fv3WtwoEyBvbMpBxLCwigdEi2W2r9QLyqkxIiQqAUsaizihCwTOQ8ywEkcBQ38rBMgyMFgkSIVDVD3TUGkXIm9rKWbCs1VaUCAIV9bYmETKOgYJnIUoEerMElZwFxzIwWSQIjWx1JhE8y0AhY1Fi0mLO6c/xSd/r0DsgDCZBgkUkUDey5erbXXMqjIhUyxASwNvL0CyQLihvxzI0WCSAWMsbsLbE2Mrb1KcPjCYB6kC5vWWCEGsgNcDquMhlPORyDlq9GedyytG3VyRUATIYTQIkUYJKJW9ky0Eu5yGKEvQGCwJVMrAsC5NJgCBKCKy31enM4GUsFI1sVQEycByL/ZkXsfXXMxg+JAGXX9oLokTAcywUCt56z+obbM1mAWaLCHWgwlouejNYjoVSwUOSCHR6MwKUMvA8C7NZhNkigOc55OVXITJcDaWSR4BSZi1DnRlKBQ+ZjIPFIsJoEhCktqZrMFgABm3atlaGdVoTFHJrGQqCCINRQKBKbr0PjRZIEmmxDAVBgsHYUIZGkwCxURlWVOlRXFKL3j0jIONZhzI0mQQIgoTAwEblzbNQhV0OURSh1+uhUqnAcRxMJhMsFgvUarXDO8JsNiM4OBg1NTXQaDTtvm8ovoWtfvhi2BKoOIXT5+tFE248+F+vvT8uXryIWbNm4dixY0hMTATDMMjLy0NaWhq+++47JCQkuJSuz8wFJITAZGo9/kJ7x1uyP3z4MNLS0lzKz/LlyzFkyBCkpKQ4fa7t5QQAgYENUWNZlkVQUJD9b7lcDpmyYaaDqtGsB5Zh7BUoYJ1tI2/0aza2ZZrYyjjWYfFDW2Xasi0DGdf4OtZK2dZl1NiW5xgENbJVNlmTSO2MraLhGMc65kkhY9H4Ebc5M4JI8PShQ6jV6DH7/KeIsURgBNsbKZZkGHUctGYRd4+NQqCCg94s2h0jnmPqtbVehu6Vt2MZNl2rycFWxjksItg0gq7NOQCszaiiQGBr4lEq+FZtOY61OwAAoFDwjmVYX7m2ZDtiaCIkiWDHn+dx8kwJJo3vi7RU66B4lnW0lct5yBsVjM0ZsNoyTWw5yOUc9HoztFozEmI5u16GcbRtVi4BDeXSrm2TMmxsy/McgtSN7sM2ypvnHbU2Le8ABQ+TSYQkSuAUfPPyblTgjuXNOTz3CoUCikbGtneE2dzQHUXxX1ztGvLabpV6EhMTcejQIWzbtg2nTp0CIQSpqamYPHmyW+l6pRPzxBNPYMaMGUhMTERdXR3Wr1+PHTt2YMuWLdDpdFi2bBmuvvpqxMbGoqKiAu+88w7y8/Nx/fXX29OYN28e4uPj7TOJnnvuOYwePRp9+/ZFbW0t3njjDRw+fBhvv/22S3lMS0vDrbfe2qw7yt9RyTkMS1K3b+gBWBZIrOqFCqLFmeAzqOXr8AP2gVHsR4o8HiHl8fhoD8Edo6OhCeC9VkdHUankuGRw83FbXcGoYUkYkBKFX3eexaafTiAzqxAzJvVDVKT7ZdidOroSf9FB8SwMCFgXuoa8fYr1p59+ihtvvBFTpkzBlClT7PvNZjPWr1+PefPmuZSuVzpvJSUlmDt3Lvr164dJkyZh79692LJlC6ZMmQKO43Dq1CnMmTMHKSkpuPLKK1FWVoY//vgDAwcOtKeRl5eHoqIi+9/V1dW45557MGDAAEydOhUFBQX4/fffMXLkSJfz+cILL/j0AoL+BsswWHhZNIZr0xArhSNKocLmlNvxWOw48IFm7Inag88iNuKuE5vxQ1EORDcGk/0d0QQpMfvKQbj1hqHQ6814/9N9+GVHtn3gK4VCcR/Gjc2bWbBgQbOwJQBQV1eHBQsWuJyuz4yJ8QdsfZ7O9FmSAx92ca6cQ2sScSRfj8EJKocuH2/iWKEea45dwPb4X3Fd2CAsjZ0AAMgxVeKr0hPYVHUSJgjor+2LhBA5pob2wYRY71i4TxrQr8O2Wp0JR48XI31gjEO3R3cgihL2HMjDH7tzEKiSY85VaYiLda0f3pM6OhNXdHCBl3Y4fVfeHxTfwfb7bhj+MAJ5558DnWDC7AMrvfb+YFkWJSUliIyMdNh/5MgRTJgwAZWVlS6l65XdSRTvRc4xSAiVQ855r98/MDYAkypjUVIxCOtwBJcH9UKGOhk9FWF4PPFSPBo/Bs/+dRznpFL8aj6BHysPIbosFDdEDcQ14QMQKfON1a3lMg4JcRrIZd3vTHIci7GjeiC1XzQ2/HAMa9YdwKwrUjGwf0z7JzfBkzo6E3/RQaF0JkOHDrUv8zNp0iTwfIPbIYoicnJyMH36dJfTp04MxSnkPIvkMO/+WmYYBpP6B+Pkzn4oMhThwQs/IFmIwQh1PMaFJmFkaDTuGZiC0yVJ6BszBj+UnsVPhtN4u/wvvFWxC4Pl8ZgW1B+zIvtC7cIsge5CLueRlBDq0TyEhgRg/s3D8P3Wk9j44wnIZBxSeke2f2IjvEFHZ+AvOiiexdXAdd4a7O6aa64BABw+fBjTpk1zmNgil8vRo0cPzJkzx+X0qRNDcQpRItCaRKgVnH2qsjci51k8eHkMBudNwUbtUZxjS7DeuA+fF+8BX8ijF6IxVJEINZuA+/oOwk36Adh4vBR/GM4hV52LFeZf8N/y7chQ9sRNUQOREZQMnvGuIWS2oHTqQDk4znN54zgWV08fAMEi4ptNx3DznMHokRTW4fO9RYe7+IsOimdxdR0kbx3YawsM26NHD9x4440O6xV2BtSJoTiF3iwh86IOw5LUDlODvREZx2JSz0hMgjWQUqXRjB2l+dhbl4/T+nIcr9biO8MPWJo0DrNDB2LBiFjMMUahWj8ShZZa/K/gGA5bzuMP01moocRVof0xKzQVqQFRHlZmRW+w4HBWIS4ZHO8wndcTsCyLa2YOwpcbj+DLDUdx6w1DER8b3KFzvUmHO/iLDopn8beWGBvz58/vknTp5wLFKQLlLEb2UCNQ7nu3TphSjmuTemHFwMvw7bDZmB04GEGCGru0FwBYu6GCA3gkhyuQEROJty+5HB/H3YybaqchtiYRG8pO4abzn+PH6lOeFVJPoEqGEUMTEKhqHsrbE/A8i+uuTkdUlBrrvjmM0jJt+yfB+3S4ir/ooFB8Cd+riSgehWUZqOQcWG93+9uBZRmo5TxCTWHI1Be2OFWeYRikRKuwdHR/vJIyCbdUXIWBQg+8UPQrck1VHsi1IyzLQqWSg2W95zGWyzncNHswgjVKfP51Jiqr9O2e4406XMFfdFA8i607yZXt7wh92ihOYbJIOFtmtC+46KuYLBLCA3lEGiJRJuhQZKlr1ZZhGPSMUELBcbik8hJE8IF4LH8zzJJn46OYTALO5VTYFxL0FpRKGW6ZMxQKBY+1X2WirLztFhlv1eEs/qKD4llYhri8/R2hTgzFKQSJoEonQJB8+4ERJAKGYRBmsg5APawvbOcMoE+UEgY9izu4CThnqsSrJX92dTbbRBAlVFXrIYje51AGBspxy3VDIZdx+Oiz/cg6UdSqrTfrcAZ/0UHxLLYxMa5svoDZbMbp06chCJ3j7FMnhuIUgQoOI3qo7esV+SqBCg7J4QooiAJhUhAyO+DETBkQgqGJgTh9hsfdwWPweeVh/FZ7thty2zKBKjmGD020L0DobQRrlFhw6wj0T4nCd5tP4MefT8JiEZvZebuOjuIvOiiUrkCv1+POO++ESqXCwIEDkZeXBwBYtGgRli9f7nK61Imh/G3JrbAuGJoRnIAjhtZbChozLTUYkUE86k7EIEPRE08VbEOhubYrs+nTyOUcrp6RiiunDUDWiWKs+fxAh8bJUCh/V/x1TMzSpUtx5MgR7Nixw2Ga9eTJk/HFF1+4nC51YihOoTOJ2JNTB52p+Re1L6EziTiYp0OkmscwdRzOGMuhE9tfJVjOs7hlRAQi1TJEnU1DACPH/+X/BAvp/vLQ6czYezAPOp13r27MMAyGpMVhwa3DYREkfPC/fThxusR+3Fd0tIe/6KB4Fn8dE7Nx40a89dZbuPTSS8EwDX1fqampOHfunMvpUieG4hQ8xyA6SAbei5cd6Ag8xyBMxUFvFjFYFQsJBFmG4g6dq5JzmDcqEtFKFaZpx+CYoRjvlO7u4hw3h5exiIpUg5f5xmMcHRmEO+eOQJ9eEfj2+2PY8utpCILkczpaw190UDwLw7i+eTNlZWWIimoeY0un0zk4Nc5CnzaKUyh41jpTh/ftW0fBs7isrwY6M0HhRRmCWEWHBvfaYFkGA2MDYCgJwghdOj4sP4C/tLldmOPmKOQ8eiaFQSH3nZiVCjmP2TMHYsbkfsg8WoBP1h2AXm/xOR0t4Yu/B4XSXYwYMQI//vij/W+b4/L+++8jIyPD5XTp00ZxClEi0JslqOSsVy870B6iRBCo4DCmtxq/Z9ehX0q0U04MAIzro0FUkAw/HktBWWAJXi7eiQ195nVRjpsjihL0BgtUATKfCnPPMAyGDUlAXIwG33yfhU/WHcDN1w1FRJjKp3Q0xVd/D4p3wYCA9aNlB2y89NJLmD59Ok6cOAFBEPD666/j+PHj2L17N3bu3OlyuvRJoziF3izhYJ4WerNvTyO16RiRpEZSqAJCmQZ7dfm4NftL/Oviz3ivZC8+ysvChgsXsDe/CqeLDSisNjsExeM5BgPjVEgOU8BsIYiWBXWvBoMFh44UQG+wdOt1O4vYGA3m3zwcoijhm++OQqf37bEkvv57ULwDfx0TM2bMGOzatQt6vR69e/fGzz//jOjoaOz+//beOzyqMv3/f02fyUwSSkJoIQk9dAhIExUFFBR0VxfbIrvq2n8u6u5XsaDiBxc/a2EtWFbUj66FZRXbYmEXQaQpCYFA6KSQkB6SyfR2fn8MGQg1Mykz5/C8rutcV3LmPs/c7+c5c+aep9zPpk1kZWVFXK7oiRGERZxeTVYvC3Ey3HbgRE7U8ZusTqh2DcRn9VOntvGTrgy79gAezfEvVZ1fh8VnIVkVT39LR0Z3TqaXoQM99YnsqW6gpGclN1subl8NJh2jhvcgziTfNPfxFgNXXT6IFV/sYPfeSsaO7hVtlyJGCe0hiD6Rzm+J9TkxAEOHDuX//u//WrVMEcQIwkKjVsX8xo/N4UQdcXoNt4zsyW8DPShv8FLn8GF3B0juBA0aG4XuOorddeyzH+WA4yj/dezja8/2UFmqnioklcRF8Rntq0GjVsRGgwP6JTNmVE/WrD9IelpHUpLbt0ertVBKewgEbcHkyZP57W9/y3XXXUdiYvM2h20OIogRhIXbF+BInYfuHfSyntx7Oh1qtYruiXq6J56YrMzCUHPXJtf+XGDj+7211KttOPV2rBobY1IT6KlvvQ9mc3B7fBwpt9K9a4KsJ5O6PT76ZHSmsOgoK7/ayW1zLkCnk1+grJT2EEQXdYRzYiK5pj0ZOnQojz/+OPfddx8zZsxgzpw5zJgxA72+Zckh5fstJIgKPr9ERYMXnz+2PzDnoiU6Lsiw8OcpPblxYC9m9xjAiyMv5P9Lj3xMN1J83gCVVTZ8Mt/HyucNUFPrYMbUgdRZXXz/w75ouxQRSmkPQXRRqaSIj1jm5ZdfprS0lC+++IL4+Hjmzp1L165dueOOO1o0sVclnW77XkGbYLVaSUxMpL6+noSEhGZdI21d1sZeCWKJQOaAaLsQVXJ2lLLq+z1cO2somf1PzSmhNDTmC5ttG8nzQyAfGtt340V3YdGGPyxp87mZ8OMbsrk/XC4XX331FYsWLSIvLw+/P7KEoaLPUyAQxAwjh3bnUEEN//5uN927JpCYYDz3RQKBgoi0VyXWe2JOpLy8nE8++YR//OMf7NixgzFjxkRclhhOigIrVqzg0KFDyLETzO7280uhTRHbDshdh93hYeu2w7JfmnyiDpVKxZWXZ6LXa/h81S4CMtotXSntIRC0BVarlXfffZepU6eSmprK66+/zsyZM9m3bx9btmyJuFzRExMF6urq+OCDD0hOTua66647bSrmWEWrVtHRrEUr40R3oAwdWo2ajh3i0Mo8sdrJOkxGHddcOZgPlufw0+YCLprQO8oeNg+ltIcguqhVRJTzJdYfZSkpKXTs2JHZs2fz7LPPtqj35UREEBMFbr/9do4ePcq3337Lu+++y0033URqamq03WoWBp2avsny7+JXgg6DQUufjM7RdqPFnE5Hr54duXBcBus3FZDeqxO9enaIjnNhoJT2EEQXpQ4nffHFF0yZMgW1unWDfPGTIQqoVCrS09P53e9+R0pKCh988AE1NTXRdqtZBAISDo9fVt38p0MJOgKBAA6Hh0BA3qthzqRj0vh0enZP5PN/78Lpiv0suEppD0F0UbfgiGWmTZvW6gEMxL5uRWM0GrnpppuIj4/nq6++irY7zcLuCfBzoQ27zLcdUIIOu8PLL9tKsDti/wv+bJxJh1qt5porh+Dx+Fj1/Z6Yn0OmlPYQCFqLUaNGcfToUQBGjhzJqFGjznhEihhOigJWq5X4+GBW0srKShITE6moqIiyV80jTq9mZKpZEdsOyF1HnEnHiKHdZZ/m/mw6EhOMXDltIJ9+tZPcvCOMHNYjCh42D6W0hyC6KGk46eqrr8ZgMIT+VrXB3ggiiIkCr732GnFxcaFuZ41Gw+TJk6PsVfPQqFUkmuR/2yhBh0ajVsQS5HPpyByQwsiiWr5fs4/UHh1I6mxuR++aj1LaQxBdVBFu5hhpELN06VL++te/UlZWxuDBg1myZAmTJk0653UbNmzg4osvZsiQIeTm5p7W5sknnwz9/dRTT0Xk37mQ91Ncplx77bV4vV70ej0dOnSgd+/ebRKhtgUeX4Ayq5duCTr0Mt52QAk6PB4f5ZUNdO0Sj17Gae6bo2PqJf0pLqlj5dc7+f3No9FqY29bAqW0hyC6tGdPzPLly5k3bx5Lly5l4sSJvPnmm0yfPp38/Hx69TrzZqz19fXccsstXHbZZc0eRejduze//PILnTs3nfxeV1fHqFGjOHToUNj+g5gTExX69+/P+PHjycrKok+fPrIJYAA8fomSox48Mt92QAk6PF4/JUeseLzyzXUDzdOh12v49VVDqK61s+bHg+3oXfNRSnsI5I3Vam1yuN3uM9q++OKL3Hbbbdx+++1kZmayZMmSUA6Xs3HnnXdy0003MX78+Gb7VVhYeNqsvG63m5KSkmaXczLi54IgLCwGDRP7yHOX4RNRgg6L2cCEC9Ki7UaLaa6OlC7xXHZxP75fs4+MtE7065PUDt41H6W0hyC6qIlsOKlxA8iT03U8+eSTpx3K8Xg8ZGdn88gjjzQ5P23aNDZu3HjG93n33Xc5ePAg//jHP/if//mfc/r15Zdfhv7+7rvvmuxg7ff7+e9//0tGRsY5yzkTIogRCASyYczInhwqrOGrb/P5w9yxxFvC32NGIIhlVKrgEcl1AIcPH26yd1LjxNqTqa6uxu/3k5KS0uR8SkoK5eXlp71m//79PPLII6xfvx6ttnnhwzXXXHPMPxVz585t8ppOpyM9PZ0XXnihWWWdDjGcJAgLh8dPdrENh0feXeZK0OFweMjZXopD5mnuw9GhUqmYecUg1GoVX36TH1PLrpXSHoLoolJLER8ACQkJTY4zBTGh9zspYpIk6bRTHPx+PzfddBNPP/00/fv3b7aeQCBAIBCgV69eVFZWhv4PBAK43W727t3LVVdd1ezyTkYEMYKwUKtUWAwa1DKax3M6lKBDrVFjsehRyzzNfbg6zHF6Zs0YTEFRLVu3RT6W3toopT0E5wdJSUloNJpTel0qKytP6Z0BaGhoYOvWrdx3331otVq0Wi0LFy5k+/btaLVa1qxZc9b3KygoICmp9YeAxXCSICyMOjUDUkzRdqPFKEGH0aClf5/kaLvRYiLR0TutEyOHdWfdhkMMHphCXJy+jbxrPkppD0F0UUe4xDrca/R6PVlZWaxevZpf/epXofOrV6/m6quvPsU+ISGBvLy8JueWLl3KmjVr+Ne//tWseS12u51169ZRXFyMx9O0x/L+++8Py/9GRBAjCIuAJOH1Sei0Kln3YihBRyAg4fX60ek0qGN997ezEKmOSyb2IX9PBes2HGL61IFt6GHzUEp7CKJLey6xfvDBB5kzZw6jR49m/PjxvPXWWxQXF3PXXXcBMH/+fEpLS3n//fdRq9UMGTKkyfVdunTBaDSecv50bNu2jRkzZuBwOLDb7XTq1Inq6mri4uLo0qVLxEGM6PcUhIXdHWBTQQN2t3zT9YMydNgdHjZvLcYu8zkYkeowm/VMmtCbnB2lVFbZ2si75qOU9hBEl8YgJpIjXK6//nqWLFnCwoULGTFiBD/++COrVq0iLS24yq6srIzi4uJW0fXAAw8wc+ZMamtrMZlMbN68maKiIrKysnj++ecjLlclxdLMOIVjtVpJTEykvr6+yezxsyFtXdbGXoWHzy9hdflJMGrQauT7azNWdQQyBzTb1ucLYG1wkRBvRCvThH3QMh1+f4A339tCvMXAb2ePjGrOpUh0aMwXNrv8SJ4fAvnQ2L67Z9xCvC784dEGr4fMVe/H7P3RoUMHtmzZwoABA+jQoQObNm0iMzOTLVu2MHfuXPbs2RNRufJ98gmiglajopNZG1Nf/JGgBB1arZpOHeNkHcBAy3RoNGqmTe5H0eGj7D1Q1QbeNR+ltIcgujQusY7kiGV0Ol3oR0ZKSkqohycxMbFFvT3i0yYIC48vQMlRNx6ffIdhQBk6PB4/pWX1eGS8TBxarqNv7yT6pHfmP2v34/NFry6U0h6C6KJWSxEfsczIkSPZunUrAJMnT2bBggV8+OGHzJs3j6FDh0ZcrghiBGHh8UkUVLvx+GL7A3MulKDD4/VRUFSLx+uLtistojV0TJ3cj3qri527o7cbvFLaQxBd2nNOTHvy7LPP0q1bNwCeeeYZOnfuzN13301lZSVvvfVWxOWK1UmCsLAYNUzqF3vjreGiBB0Ws4ELx0WerjtWaA0dSZ3NdO+awMGCGkYM7d5KnoWHUtpDEF1amrE3Vhk9enTo7+TkZFatWtUq5YqeGIFAoAh6p3emoLiWQCC2f5EKBILWQ/TECMLC4fGzr8JF/xQjcXpNtN2JGCXocDi97D9YRb8+ycSZdNF2J2JaS0fv9E6s31TAkXIrPbsnnvuCVkYp7SGILiduIRDudbHGyJHNXzGYk5MT0XuIIEYQFiqVCoNWHdWlrK2BEnSoVGDQa2O+G/lctJaOHt0SMBi0HCqsiUoQo5T2EESX9kx219Y0bv7YloggRhAWJp2azG7yTtcPytBhMuoY2L9LtN1oMa2lQ61Wk9GrI4cKa7loQu9W8Cw8lNIeguiipDkxTz75ZJu/h5gTIwgLSZLw+qWY2j04EpSgQ5KCae7lrAFaV0fvjM6UltXjcnlbwbPwUEp7CARtRV1dHW+//Tbz58+ntrYWCA4jlZaWRlymCGIEYWFzB9hw0IpNxun6QRk6bHYPG38uwmaXd5r71tTRJ70zkgQFxUdbwbPwUEp7CKLMsTkx4R7E4JyYE9mxYwf9+/fnueee4/nnn6eurg6AlStXMn/+/IjLVVQQ8/rrrzNs2DASEhJISEhg/PjxfPPNN6HXn3rqKQYOHIjZbKZjx45MmTKFLVu2NClj7969TJw4kZ49e7Jw4cImr6Wnp6NSqdi8eXOT8/PmzeOSSy5pM12xhFGnZnC3OIy62Lx1Khu8ZBfZqPd4WFa5lYOumtPaxbqO5mA0aBk8MAWjQd6jwq2pIzHBSOdOcRw4VN0KnoWHUtpDEF2UmifmwQcf5He/+x379+/HaDSGzk+fPp0ff/wx4nLl+wQ/DT179mTx4sVs3bqVrVu3cumll3L11Veza9cuAPr378+rr75KXl4eP/30E+np6UybNo2qquPpyu+9917mzJnDF198wVdffcWGDRuavIfRaOThhx9uV12xhE6jIjlehy4G0/Xvr3Tyxo8VvHtgH9Pz3+dvVT/xcOH3p+3ej2UdzUWn05DU2YxOJ8/VVY20to5hg7uxY1dZuwcySmkPQXRRqUCljuCI8UfZL7/8wp133nnK+R49elBeXh5xuYoKYmbOnMmMGTPo378//fv3Z9GiRVgsllDPyU033cSUKVPo3bs3gwcP5sUXX8RqtbJjx45QGXV1dYwcOZJhw4bRvXt36uvrm7zHnXfeyebNm1stUY/c8PoDlNV78PpjbxjmSL2Xwo4H+LHrj3QzWLjMNZp9/gpW1x46xTaWdTQXr9dPWYUVr1feae5bW8f4MWn07Z3EF6t24WvHbSWU0h4CQVtgNBqxWq2nnN+7dy/JyckRl6uoIOZE/H4/n3zyCXa7nfHjx5/yusfj4a233iIxMZHhw4eHzi9cuJCpU6cSFxeHWq3m8ssvb3Jdeno6d911F/PnzycQkO8XYKS4vBJ7K5y4vLHXdekPSHiPbSMwr9NFPJgxms6uzrxRvfmU3phY1tFcXG4f+w5U43LLO819a+tQq1VccmFvnC4fhcW1rVJmc1BKewiiS0S9MMeOWObqq69m4cKFeL3BSfcqlYri4mIeeeQRrr322ojLjXHZ4ZOXl4fFYsFgMHDXXXexcuVKBg0aFHr966+/xmKxYDQaeemll1i9ejVJSUmh12fMmEFVVRVHjhxh5cqVaDSndg0//vjjFBQU8OGHH0bko81mC/1tt9txuVwABAIBGhoa8PmCD0GPx4PNdfxXncPjx+UNBk4BSaLB5cfnD34Je3wBGk6ydR6zlY7Zeo/Zev1B28YvdqcngNNzsm3gmK3UxFarUXFBuoV4Y7BeGlz+0CaKvmO2gWO2Lm8Axwmb4dlOZxs4bmt3n2Dr9uM+ZusPBG39x2zdJ9na3X7c3gAXpFu4r9cokj2dee7Ienp00DPWPozDbiufVewL2bq8AeKNGi7qmxDypbEO26a+m9ah03u8vhvrMFTfXj8NNvdxW5cXh/P4Shub3R3aYNBk1DFqeA/McfpgHbp9OByek2yD95LfH6DB5g4F3m63D/sJtna7B/dJtv5jPrk9PuwnTFa1Ozy4j31RBwJNbT0eHza7+3i9ODyhL/VAQKLB5g71jng8fmx2N/EWAxdP7I1Go8Z5bFWRJAVtG3s1GuulEafTe1Zbk1FHh0Qje/ZXnVKHDbbjdejz+Y/Vy7H70OU9Yx36fE3r0HVSHapUMG5ML+IthlPr0H1SHdqP16Hf76ehoQG/33/M1n3GZ4RA+Sh1Tszzzz9PVVUVXbp0wel0cvHFF9O3b1/i4+NZtGhRxOUqLogZMGAAubm5bN68mbvvvpu5c+eSn58fen3y5Mnk5uayceNGrrjiCmbPnk1lZWWTMgwGw1m7t5KTk/nTn/7EggUL8HjCX4lwoj/5+fkcPnwYCD68srOzQw+w8vJytpfaQ7Z7yl0U1QYf5F6/RHaxjXpn8EFYZfOx7fBx2/2VLgqqgw8+fwCyi20cdQRta2w+souPPyQPVrk4WHX8IZldbKPGFrQ96gjaNo66FFS72F953HbbYTtVx2zrnUHbxi/volo3e8qP224vtVNuDX6Z2Nx+sottuI/1nBw+6mZ3uTNkm1fqoKw+WLd2T4DsYlsoSCit97DziCNku+uIk5I6DxaDhiHdLFzjm0CVuo4vKg9wfUYfhtYP5rXKTQQkiT3lToqP1aHnWB02HAuIKhq85JYcr8O9FS4Ka4K2vmO2dcfqu9rmI+eE+j5Q6eLQsToMSME6rLUHbWvtXrKLbTR2Bh2qcnHgpPqubqxDq4uc7aWhL/qi4qPsP3h8zlZu3hGqaoJtZ20I2jZ+eReXHGXP/uO2O3aVU17ZEKxvu4ec7aW4XMH3KTlSz+69xzdL3LmnnCPlwa5eh9NLzvbS0Bf/kXIrO/ccH7PevbeCkiPBYVaXy0fO9tLQipzyygZ27Dpuu2d/FcUlwZVCXq+fnO2lWBuC2qtqbOTmHQnZ7j9YRdGxVUU+X4Cc7aXUW4O21bV2crYfX4Z5oKCGQ4XBXhZJksjZXkptXfCeOFrnZNuOI/TrncTBghoOFdY0mR+zbUcp1bX209f34Tr2HTxuu31nGZXVQdsGm5uc7aWhAOhwSR179h9/duTll1NecYb6Lqsn/4T63rW3gtJj9e10OsnOzsZuD75PWVkZeXl5IdsTnxEC5aPUnpiEhAR++uknPv30UxYvXsx9993HqlWrWLduHWazOeJyVZLCkxpMmTKFPn368Oabb5729X79+nHrrbc2a4lXeno68+bNY968edhsNvr27csjjzxCYWEhubm5rF279qzXW61WEhMTKS0tpXv34CZ1drsdjUaD0WgkEAhgt9sxmUxotVo8Hg/un9/DcqzXw+Hxo1apMOrUBCQJuzuASadGq1Hh8QVw+6RQD4nD40elUmHSqZEkCZs7gFGnRqdR4fUHcHklLIZgxtrGXgGT/kRbFTqNGq9fwuUNhGyP2n0crHYxuFscJr2aBpcfg1aFXqvG55dwegOYDWrUKhUub4CAJIXS+ttcfvQn2+rVqNVBW39Awmw4Zuv2o9MEs+r6AxIOT4A4vRqNWoXbG8B3gq3d7UerVmHQBW0bnD5u2PMvvEYnX/Sfw78OFPFi4GseS7mUq+IHo1GrkCTYV+mke6KejnHaUB16fFIb1HfTOnR6AyAF6xuCPTGN9e3u2xeX24fFrA/aurxIEqE09ja7G71Oi16vocHmZs++SgYNSMFs1uNy+wj4A8Qd65kJ2mrQ67X4/QEcTi/mOB1qtRq324fPHwj14tjtHrQ6NYYTbONMOjQaNW6PD583gNl8zNbhQatRYzBog/es47itx+PD4/VjMRuC9eLwoNaoMRq0BAISdocHk1GHVqvG4/Hj8frQqNUcKKihR7cETCYdJqMuWId2D0aDFp1Og9frx+X2EW8Jlut0ekHFWW3Lyq388/Md3H7LBSTEG0N12GBzY9AH69Dn8+N0+TDH6YP3octLICCdtg59vgBO1/E6dLl9+E+ow+oaO8WldWT264Jer2lah24fPt8JdWj3oNWqiet0CX6/H4fDQVxcHBqNBrfbjdfrxWKxNHlGeDweEhMTqa+vJyFB3puXCk6l8fuh5LfXk6DXh3+9x0PPfyyPyfvD5/NhNBrJzc1lyJAhrVq24tcCSpKE2+2O+PUzYbFYeOKJJ3jqqaeYOXNm2Nc2cmIEqlariY+PD/2v1+vRZA2mceChcVFa4//mE/7XHjuaY6s59r907DCEYat3eTEU1BDI6EzAqGtiqz7pWv0JfwPERWirOslWd+xotDWdZJsAXFNRy+uOT/g8roqrLruULz7P59mKH+jcpz+X9hyB0+VFbawhLqMzaqMu7Dpsq/o2miZyfPEhWE76gZJ4wv8GtRNLx4Po4vugMZk4+cfMibYaQH/Ccy3uJNuEFth2OH7LYjIfbw+A+GbYOp1OtKaDWDr3wWQ6fnUHS9Nrz1Yvp7U11AE7cPj60iOp33Hbk3wynLBDwbnq8Gy2CWonJutBNOY+6E2m5tehRtPkc28wGDAYDKH/G58RkfT6CmRIpDlfYjhPjFarJS0tLTRk2prEeAdUeDz66KOsX7+ewsJC8vLyeOyxx1i7di0333wzdrudRx99lM2bN1NUVEROTg633347JSUl/OY3v4no/e644w4SExP5+OOPW1lJ7GIy6hic2RWTMbY3uBvRLYMMewZv7PoaTZzEogvn0sPegz9v/DubyvNlo+NsmEwmhgwZ0uSLX460lY7ExET0en2TFAptiVLaQxBdGrcdiOSIZR5//PEmmXpbC0UFMRUVFcyZM4cBAwZw2WWXsWXLFr799lumTp2KRqNhz549XHvttfTv35+rrrqKqqoq1q9fz+DBgyN6P51OxzPPPHNeTbqTJIlAIBDzqdX79k5iWMMQXF4vb+z6moH9UnhixBySHcn8cf3rbKs6IAsdZ0MubXEu2kqHSqUiKSmp3YIYpbSHILoodU7Myy+/zPr16+nevTsDBgxg1KhRTY5IUdRw0rJly874mtFo5LPPPmtR+YWFhaecu/HGG7nxxhtbVK6csNk95OSWMGpEz9D8hFikU6c4br7qAg7+5xDLD6xjdt+LGTsyjXn11/N88Yc8tObv3Nv5eqaNGxzTOs6GzWYjOzubrKysJsMRcqMtdSQnJ7dbEKOU9hAI2oK22tFaUUGMoO0xGrQM7N9FFqnV+/VJ4p66K/nTngKe2fgR71zxIFMnDWDvu1P4TvcD79Z8yVBPCvH0iLarEWE0GsnMzGySwluOtKWO5ORkdu/ejSRJqNq4v10p7SGIMmoiGyOJ8Z6YttrROsZlC2INnU5DSpd42aRWHzcqnUtUY8mx7mNjeT4ajZqrLhtKVukF+HU+7tvwCqW29t9npzXQ6XSkpKSg08l3Xg+0rY7k5GQ8Hs9pM4W2NkppD0F0UepwUnOIZChWAbIF7YnX66eyyiab1OoqlYqLugyjm68LL+T+C1/AT5+MzkwZN5CsQ+PQeLTcuW4Jlc66aLsaNl6vl4qKilAGTLnSljoa8z21x5CSUtpDEF2UNLE3MzOTjz766Jwr6/bv38/dd9/Nc889F/Z7iCBGEBYut4/deytklVrdaNSR1TCKQ9YyVh4Kbug5cngP0pOSGVl4AQ0OF3eseYmjbts5SootXC4Xu3fvlv3E8rbU0aFDB7RabbsEMUppD0GUUXF8SCmcIwaDmNdee42XXnqJlJQUrr/+ev7617/y4Ycf8umnn/L222/z4IMPcsEFFzBy5EgSExO55557wn6P2J/YIIgpLGY9F47PQK2OwU/MGejRPRHjRgsX9RnO0l1fcnmv0cRbTMy9OYstWw+j36bi247/4c4flrDs0oeI18tjiazFYmHSpEmo1fL+LdKWOlQqVbtN7lVKewgErcWll17KL7/8wsaNG1m+fDkfffQRhYWFOJ1OkpKSGDlyJLfccgu//e1v6dChQ0TvIYIYQVioVCo0GvkEMAB9MjrTJ6Mznjw3jtRdvL37Gx4cfi06nZYLx2eQNaInfAjfqFdz3/pXeOPiP2LSxv6KpWBbyGNu0tloax3JyclUV7f9vCeltIcgukQ6vyWW58RMmDCBCRMmtEnZMSxbEIs4XV7y91SENt+TAyqVil/NHELfrikMsQ/io31r2Fd9JKTDZNJx18yLmVQ1ifyaYh746Q08/tjX53Q62bVrF06n89zGMUxb6+jcuXO7BDFKaQ9BlIlkKCnSFU0K4DyVLYgUSZLw+eWX0Mto1HHFlIGkVmbQURPPY5vfocHlDOnompLA3VdeyoVVE/mlYh9/3vB3fIHYnrwsSRI+n092bXEyba0jKSkJp9MZ2mCxrVBKewiiy/m8OikSzlPZgkiJM+kZNrgbcabwNyiLNl2SLUzM6s2owrGUuWt4o/ZfaPTHh8b6ZHTmT7+awaS6iawvy+Oxje8RkAJnKTG6xMXFMXz4cOLi4s5tHMMIHQKBIFJEECM4r5gyuR+j0jKY7ryM3UeLeGzLO/gDxwOVHt0TeezaWUyyTeC70l94etOH4pe1zKmqqsJkMongQiAPxHBSWJynsgWR0mBz8+OGQzTYwt/5OxZQqVT06tkBTaWJO403sLFoD8/nrmgSqCQnWVj4m99wkWs8X5Rs4JmfYnODz4aGBtatW0dDQ0O0XWkRba2jqqqKLl26tHnGXqW0hyC6qNSqiI/zERHECMLCoNfSt3cSBr18F7YNGdQNv18iRduJeaOu4eMDP/B/e1c3sUlMMLJ49o1MZhyflf3Igu8/irkeGYPBQL9+/TAYYn8l1dloKx1Op5PvvvuOXbt2hZLetSVKaQ9BlFG14JAJTqcTq9Xa5IgUEcQIwkKv19C9WwJ6vXyXksbHG7ggqxc520vR7evEbzOmsmTHZ6wq+rmJXZxJzwvXzeVyywS+rPuRp/69PKYCGb1eT/fu3dHr5Tc/6UTaSsfq1avZvHkzAGPGjGnVsk+HUtpDIGgLHA4H9913H126dMFisdCxY8cmR6SIIEYQFj6fn5oaOz5fbK/cOReXTOrNJZP6UFR8FPe6RC7qOIIFv/wfWyr2NLFTq1Usnj6HaR3G8oVjLYu//TxmAhmfz0d1dTU+n3yyJ5+OttBhtVrZtm0bl156Kffffz9dunRptbLPhFLaQxBlFDon5s9//jNr1qxh6dKlGAwG3n77bZ5++mm6d+/O+++/H3G5MS5bEGs4XT527i7H6ZL3g9rl9uP3B5h702gGDUghObc3maZ0Htr4BnvrSkJ2kiRRVm7lt90vZ7x5GMut3/HGj99H0fPjOJ1Odu7cKfu8JG2hw2QyodFo0Ov1LfqVFw5KaQ9BdFHqnJivvvqKpUuXct1116HVapk0aRKPP/44zz77LB9++GHE5cp3YoMgKpjj9Iy/IB2tVt7x74k6rr5yCHqdFn9egKr0eu7+4WU+nPYISfpE3vngZyqrbTg0DrCYoCO8WbGS2s21PDbuxuhqMJuZMGECWq28P8ZtoUOn05GWlkZ+fj5jx45ttXLPhlLaQxBlIu1VifFHcm1tLRkZGQAkJCRQW1sLwIUXXsjdd98dcbni0yYIC7VaJev5MI2crGPG5QPp1CmOhJ16PjP+mzt/WEJfZx/2qopx9LNy1BtccZIQsGBV2xjcKT1Knh9HrVYrYv5FW+kYPXo0//znPykpKaFnz56tXv7JKKU9BIK2oHfv3hQWFpKWlsagQYP45z//yQUXXMBXX30V8b5JIIIYQZi4XF6KDh8lLbUjRqMu2u5EzMk6VCoV4y9IY0hmV2rea+A/qjWUqn+mb3IPpvWawIikPgztlEEnY3y0XQ/hcrkoLCwkPT0do9EYbXcipq10DBgwgE6dOrFx40Zmz57dauWeCaW0hyDKqFQQydBQG6cQaCm///3v2b59OxdffDHz58/nyiuv5JVXXsHn8/Hiiy9GXK4IYgRhEQhIOJ1eAoHYmNwaKWfSER9v4LaZFzKtbAhZI3pgMcXul1EgEMDpdBIIxG5W4ebQVjrUajUTJkzg66+/pqamhs6dO7dq+SejlPYQRBclbgAJ8MADD4T+njx5Mnv27GHr1q306dOH4cOHR1yuSoqVpRbnAVarlcTEROrr60lISGjWNX7nhjb2ShBLaEwTo+2CovD5fCxZsoTMzEyuvPLKaLvTIiJ5fgjkQ2P71jxxEwnG8IclrS4PnZ/5SBb3h8vlarXeyhiP3QQCgSBytFotgwYNorCwMNquCATnNX6/n2eeeYYePXpgsVg4dOgQAE888QTLli2LuFwRxAjCwmZzs2FzATaZbjvQiBJ02Gw21q9fj81mi7YrLaKtdSQnJ1NbW9vmwzxKaQ9BlFGrIj9imEWLFvHee+/xv//7v00mwA8dOpS333474nJFECMIC71eQ1pqR9mvUFKCDr1eT3p6uuxXxLS1jg4dOhAIBNp8TyOltIcgujTOiYnkiGXef/993nrrLW6++WY0muPP3WHDhrFnz56zXHl2xMReQVjo9Vp69ugQbTdajBJ06PV6UlNTo+1Gi2lrHY09MCc+ONsCpbSHIMqoVJGtNIrx1UmlpaX07dv3lPOBQACv1xtxuTEeuwliDZ8vwNE6Bz6fvFdgKEGHz+fj6NGjsk9z39Y6Gh+QOl3bpgRQSnsIooxCh5MGDx7M+vXrTzm/YsUKRo4cGXG5IogRhIXT5WXHzjKcrsgj51hACTqcTifbt2+XfZr7ttbhdgfnPbX1MI9S2kMgaAuefPJJ7rvvPp577jkCgQCfffYZf/jDH3j22WdZsGBBxOWK4SRBWJjjdIwd3UvWc0lAGTrMZjPjxo2T/RyMttbh8XjQ6/WojnW322w2Nm7cSE1NDR6PB4/HQ8eOHbnuuuta9D5KaQ9BlIm0VyXGe2JmzpzJ8uXLefbZZ1GpVCxYsIBRo0bx1VdfMXXq1IjLFUGMICzUajVGo/w78JSgI6ghdpPxNZe21uF2uzEYDADs3buXzz77DJ/Ph1arRafTodPpsNvtLX4fpbSHIMooMIjx+XwsWrSIW2+9lXXr1rVq2fJ+igvaHZfbx/6D1bjc8h73V4IOl8vFvn37cLlc0XalRbS1DrfbjV6v5+DBg/zzn//E4/GgUqnweDzY7XYaGhpoaGhgx44dLXofpbSHILqoVBHuYh3DE3u1Wi1//etf8fv9rV62CGIEYeH3B7A2uPD75TshFpShw+/3Y7Va2+TB0J60tY7G4aTc3NzQg75fv37ceeedDB48GL/fTyAQYOXKlfz73/+OeGKuUtpDIGgLpkyZwtq1a1u9XDGcJAgLc5yerBFtvyNwW6MEHWazmdGjR0fbjRbT1jrMZjNWq5Xk5GT8fj9arZZrr70WrVbLr3/9awYPHsx//vMfamtr2bZtGwcOHGDs2LGMHTs2rF+3SmkPQZRR4HASwPTp05k/fz47d+4kKysLs9nc5PVZs2ZFVK4IYgQCgaLp3bs369evp6CgAAiOz7///vuYTCZMJhP9+/dn+PDhbNy4kblz57J582a+++47TCZTizamEwgiQqFBzN133w1w2h2rVSpVxD2YYjhJEBY2u5tNPxdhs8s3XT8oQ0fjKhu5p7lvax1paWmkpqbS0NCAwWBgwoQJdOrUCYDKykpWrFjB3r17cbvdHDx4kF/96lcMGTKE77//Pqzl0kppD0GUUWiemEAgcMajJUOwIogRhIVOq6F7twR0WvkuTQZl6NDpdPTo0aPNk7i1NW2tQ6VShZZwqtVqpk6dyjXXXMONN97IH/7wB6655hqqq6sBWLt2LQ0NDVx++eV4PB62bdsWMzoEAsGpiOEkQVgYDFrSUjtG240WowQdBoOBtLS0aLvRYtpDR2pqKg899BAej6fJeZVKxfDhw+ncuTPLli3D7/ezZs0arr76arRabVibRiqlPQRRRqHDSQB2u51169ZRXFx8ymfx/vvvj6hM0RMjCAu/P0C9Vd6rekAZOvx+P/X19bJfDdNeOiwWS2gY6WR69uwZSn2em5tLVVUVfr8fSZKaXb5S2kMQZRr3TorkiIClS5eSkZGB0WgkKyvrtFsDNPLZZ58xdepUkpOTSUhIYPz48Xz33XfNep9t27bRt29fbrzxRu677z7+53/+h3nz5vHoo4+yZMmSiHwHEcQIwsTh9JK7oxSHU77p+kEZOhwOB9u2bcPhcETblRYRKzqmTJmCVhvsnN6yZQv9+vUjNze32b0xsaJDIG8iyhFz7AiX5cuXM2/ePB577DG2bdvGpEmTmD59OsXFxae1//HHH5k6dSqrVq0iOzubyZMnM3PmzGYNuz7wwAPMnDmT2tpaTCYTmzdvpqioiKysLJ5//vmwfW9EJYXzU0PQIqxWK4mJidTX15OQkNCsa/zODW3sVXj4/QFcLh9GoxaNRr4xcKzq0JgmNtvW7/fjcrkwGo1tvkNzWxJLOr788ku2bduGVqvl+uuv58MPP2T27NlkZmae89q21hHJ80MgHxrbt+7VO0gwhb91hdXpocN9b4V1f4wdO5ZRo0bx+uuvh85lZmZyzTXX8Je//KVZZQwePJjrr7/+nPsfdejQgS1btjBgwAA6dOjApk2byMzMZMuWLcydO5c9e/Y06/1OJnae3gJZoNGoMZv1MfXFHwlK0KHRaDCbzVH/4m8psaRj6NChQHAZts1mIy0tjY0bNzbr2ljSIZAxLVydZLVamxyNG6CejMfjITs7m2nTpjU5P23atGbf84FAgIaGhjMO056ITqcL5V1KSUkJ9fYkJiaeseenOcj3CS6ICm63j4MFNbhlnK4flKGjcUnwmR5SciGWdKSlpWGxWAAoKipizJgxlJSUUFdXd85rY0mHQMaoiTCICV6emppKYmJi6DhTj0p1dTV+v5+UlJQm51NSUigvL2+Wqy+88AJ2u53Zs2ef03bkyJFs3boVgMmTJ7NgwQI+/PBD5s2bF/rxEAkiiBGEhc8XoLbWjs8n3wmxoAwdPp+P6urqiNPkxwqxpEOtVtO7d28ACgoK8HqDc6Ya58qcjVjSIZAxLeyJOXz4MPX19aFj/vz5Z327k7NSS5LUrEzVH3/8MU899RTLly+nS5cu57R/9tln6datGwDPPPMMnTt35u6776ayspK33nrrnNefCbHEWhAWZrOeMVm9ou1Gi1GCDrPZzNixY6PtRouJNR2JiYlAMHndF198Qbdu3YiLizvndbGmQ3B+kpCQ0Kw5MUlJSWg0mlN6XSorK0/pnTmZ5cuXc9ttt7FixQqmTJnSLL9O3JIjOTmZVatWNeu6cyF6YgQCgeAEEhISUKlUzJkzh6lTp3LrrbeiVotHpaCdUKlBHcGhCu8e1ev1ZGVlsXr16ibnV69ezYQJE8543ccff8zvfvc7PvroI6688sqIJLYm4pMpCAu73cPP2cXY7Z5zG8cwStBht9vZsmULdrs92q60iFjTERcXhyRJdOnShQkTJjRrKAliT4dAprTjtgMPPvggb7/9Nu+88w67d+/mgQceoLi4mLvuuguA+fPnc8stt4TsP/74Y2655RZeeOEFxo0bR3l5OeXl5dTX17ea/HARw0mCsNBq1SR1NqPVyjv+VYIOrVZLcnJys79kY5VY02EwGIDgRF2TydTs62JNh0CmtGPG3uuvv56amhoWLlxIWVkZQ4YMYdWqVaHM02VlZU1WDr355pv4fD7uvfde7r333tD5uXPn8t5774XvcysgPm2CsDAYtPRO7xxtN1qMEnQYDIbQJFQ5E2s6jEYjAE6nkw4dOjT7uljTIZApkWbfjTBj7z333MM999xz2tdODkzWrl0b0Xu0JfL9GSqICn5/AJvdLet0/aAMHX6/H5vNJvs097GmozHnReOmkM0l1nQIBOcDIogRhIXD6SV7W4ms0/WDMnQ4HA62bt0q+zT3sabDZDIRHx9PZWVlWNfFmg6BTIlkUm/jEeP897//5aqrrqJPnz707duXq666iv/85z8tKjMmVb/++usMGzYstFRs/PjxfPPNNwB4vV4efvhhhg4ditlspnv37txyyy0cOXLkrGW+9957qFSqUw6XyxWysdls3HDDDXTr1o0bbrihyQS93/3ud6hUKhYvXtyk3M8//7xZa+qVQpxJx8jhPYgz6aLtSotQgo64uDhGjRrVrOW/sUws6khJSQk7iIlFHQIZ0o4Te9uTV199lSuuuIL4+Hj++Mc/cv/995OQkMCMGTN49dVXIy43JoOYnj17snjxYrZu3crWrVu59NJLufrqq9m1axcOh4OcnByeeOIJcnJy+Oyzz9i3bx+zZs06Z7kJCQmUlZU1ORrHvwGWLFmCxWLh+++/Jy4u7pSdNY1GI8899xxHjx5tbcmyQaNRkxBvlHW6flCGDo1GQ0JCguzT3MeijqSkJGpqasK6JhZ1CGSIQoOYv/zlL7z00kt8/PHH3H///dx///189NFHvPTSSzz77LMRlxuTT/CZM2cyY8YM+vfvT//+/Vm0aBEWi4XNmzeTmJjI6tWrmT17NgMGDGDcuHG88sorZGdnn3P/BZVKRdeuXZscJ1JXV0f//v0ZOnQoAwcOPGXZ2JQpU+jatWuzN8ZSIm63j8LiWlmn6wdl6HC73RQWFso+zX0s6oiPj8dqtYZ1TSzqEAhiBavVyhVXXHHK+WnTpoX9WTuRmAxiTsTv9/PJJ59gt9sZP378aW3q6+tRqVTnXEnQuKFbz549ueqqq07ZPvy+++7jzTffRKfT8e677/LHP/6xyesajYZnn32WV155hZKSkhbpkiten5/yiga8PnlPXlSCDq/XS1lZWSg1vlyJRR06nS7sCbqxqEMgQxQ6J2bWrFmsXLnylPNffPEFM2fOjLjcmF1inZeXx/jx43G5XFgsFlauXMmgQYNOsXO5XDzyyCPcdNNNZ021PHDgQN577z2GDh2K1Wrlb3/7GxMnTmT79u3069cPgPT0dPbv3x9Ku3y6uS6/+tWvGDFiBE8++STLli1rPcEywWI2MG5MWrTdaDFK0GGxWM4Y2MuJWNQRCATCnusWizoEMqQd88S0J5mZmSxatIi1a9eGPiebN29mw4YNPPTQQ7z88ssh2/vvv7/Z5cZsEDNgwAByc3Opq6vj008/Ze7cuaxbt65JIOP1ernhhhsIBAIsXbr0rOWNGzeOcePGhf6fOHEio0aN4pVXXmlSeWq1+pRhppN57rnnuPTSS3nooYci0maz2UIBl91uR6PRYDQaCQQC2O12TCYTWq0Wj8eD0+bGYgkm33I4PKjVKoxGHYGAhN3hwWTUotVq8Hj8uD0+4httnR5UKhUmow5JkrDZPRgNWnQ6DV6vH5fbh8WsR6VS4Ty2Qsdkaoaty4skScSZ9AA02NwY9Fr0eg0+nx+ny4c5To9arcLl8hIISMTF6Y/pdqPXa9Drtfh8AZwuL+Y4HWq1Gpfbh98fwNxoa3ej02owGLT4/QEcTi9xJh0ajRq324fPF8Bs1h+rQw9arfqMtl6fH4s5WC92hweNRo3RoA3Wt8OLyahDq1Xj8fjwePxtX98uL0jB+m6sw+O23lDgHmwbZ7C+j00Wtdls6PV69Ho9Pp8Pp9OJ2WwO1qHLRSAQOK2t3+/H4XCEbN1uNz6fD7PZHLoPtVotBoMhZBsXF4dGozmrbeM922jr8XjweDyhnaAdDgdqtfqM9/fJtiqVCpPJFKxDmw2j0YhOpwvVS3x8PBDM4RK8Z89se6Y6bGhowGAwnLUOG+vMarViNBrPaOv3+0P1YrPZ0Ol0Ydfh6Wy9Xm+oXhqfEYLzBIUGMcuWLaNjx47k5+eTn58fOt+hQ4cmHQIqlSqsICZm+5/0ej19+/Zl9OjR/OUvf2H48OH87W9/C73u9XqZPXs2BQUFrF69ulkbXp2IWq1mzJgx7N+/P2zfLrroIi6//HIeffTRsK8FmjRgfn4+hw8fBoJj6tnZ2dhsNgDKy8vZsassZLt3fxXFh+sA8Hr95OSWUG8Nrq6qqrGRu6M0ZHvgYDWFRbUA+P0SObkl1NUHH/w1tQ5yco8Phx0sqOFgwfFJjDm5JdTUBpeJ1tU7ycktwe+XANizr5L/rNmP3RFM15+7o5SqmqC/9VYXObkleL3Bbvjiw3Xs3V8VKnfHrjIqKoO2NrubnNwS3O6gbUlpHXv2Hl8NsjO/nLKK4Dip3eEhJ7ck+OUPlJbVs2vP8U3L8veUU3IkOH/J5fKRk1uC7dh2AuWVDeTtOm67Z18lh0vqsDs8/Lz1MFt+KaLBFqzDyiob23ceX+W270AVRYeDk7h9vkCwvuuDttU1drZtP6G+D9VQUBis70AgWN9H64L1XXs0WN+SFKzDQwU1HDh0PAdJTm4J1TXBlXB1dXVkZ2eHdkIuLCxk3759x21zckKrZsrLy/nyyy+pq6sDoKioiN27d4dst2/fTllZ8P6x2WxkZ2eHVuMdPnyYXbt2hWzz8vJCK/wcDgfZ2dmhpcJHjhwhLy8vZLtr167QPetyuZrcs2VlZWzfvj1ku3v3boqKioDgZzY7Ozs0/l1ZWUlOTg52u51ffvmFvLw8CgsLj9W3j+zs7JC26upqsrOzQ+Xu37+fgwcPAsFdd7Ozs6mtDdZ/bW0t2dnZBALBHECHDh1q8jnPyckJ5YA5XX3v3bsXnU4Xsq2oqACCwU92djYeT/DeKi4ublLfW7duZe3atdjt9lPqu6SkhJ07d4Zsd+7cSWlp8P5xOp1kZ2eHVkOWlZU1qe8TnxECgVwpKCho1nHo0KGwylVJjU/WGOeyyy4jNTWV9957LxTA7N+/nx9++IHk5OSwy5MkiQsuuIChQ4fyzjvvnNP+d7/7HXV1dXz++edA8KE/YsQIHnroIf7617/SnGq0Wq0kJiZSWlpK9+7dgWb0xNT/FFM9MXX1TkqP1NOndxJGg1a2PTEAxYeP0qlTHB0STDHTExPQXtDsnhibzcahQ4fo27cvcXFxsu2J0Wq1HD58mKSkJIxGY0z0xJSVlfGPf/yD2267jS5dujSrJ6ampoaKigp69+6NTqdr9Z4Yj8dDYmIi9fX1Yf9oE8Q+jd8Pdf96mIRjz6uwrre76XDdc+fd/RGTQcyjjz7K9OnTSU1NpaGhgU8++YTFixfz7bffMnnyZK699lpycnL4+uuvm2wZ3qlTJ/T64BfbLbfcQo8ePUIriZ5++mnGjRtHv379sFqtvPzyy3zwwQds2LCBCy644Jw+nRzENL7HihUrcLlcYQUx4dxkfueGZtkJlIHGNDHaLgiAiooK3njjDW699VZSU1Oj7Q4Q2fNDIB9CQcxnj0QexPx6cUzfHyUlJXz55ZcUFxeHejQbefHFFyMqMybnxFRUVDBnzhzKyspITExk2LBhfPvtt0ydOpXCwkK+/PJLAEaMGNHkuh9++IFLLrkECHb1qk+YrV1XV8cdd9xBeXk5iYmJjBw5kh9//LFZAcyZeOaZZ/jnP/8Z8fVyJBAI4Hb7MRg0TepXbihBR1CDG4PBIFsNEJs6Gr8Ewln6GYs6BDJErYpspVGMz4n573//y6xZs8jIyGDv3r0MGTKEwsJCJEli1KhREZcbk0HM2Vb9pKenN6vX4+SNql566SVeeumliH063Q6daWlpTTL+ng/YHV5ycksYNaJnaChFjihBh91uJzs7m6ysrNAwixyJRR0mkwmj0RiaZ9McYlGHQBArzJ8/n4ceeoiFCxcSHx/Pp59+SpcuXbj55ptPmz+muYifC4KwMBl1DB/aHZNRvun6QRk6TCYTI0aMwGQyRduVFhGrOnr06MGuXbtCE4TPRazqEMgMhWbs3b17N3PnzgVAq9XidDqxWCwsXLiQ5557LuJyRRAjCAutVk2HxOBEWDmjBB1arZYOHTqg1cZkh2qziVUdF198MRUVFU1WCp2NWNUhkBkKTXZnNptD2ay7d+8eWl0I4e8YfyKxrVoQc3g8Pg6X1OHxyDddPyhDh8fjOe0EObkRqzpSU1PJzMxk3bp1zeqNiVUdApmh0J6YcePGsWFDcKHKlVdeyUMPPcSiRYu49dZbm+RwCxcRxAjCwuPxc7i0Do9Hvun6QRk6lPKl2R46amtrQ7lswmHSpEkcPXq0SU6dM6GU9hAI2oIXX3yRsWPHAvDUU08xdepUli9fTlpaWouy38fkEmulIpZYC86FWGLd+lit1tCk/gULFoS9ncCHH36I1WrlrrvuCvva1kQssVY2oSXW3zxJgtkY/vV2Fx2mP33e3R+iJ0YgECiaxgRzAFVVVWexPD2TJk2isrKySeZkgaDNUOhwUlshghhBWDgcHrZtL8XhkHeXuRJ0OBwOcnJyQtsDyJW21qHRaBg+fDhAk8mEzaVXr1706NGDnJycs9oppT0EUUZBE3s7duxIp06dmnVEiphGLwgLtVoV2lZAzihBh1qtDqXAlzPtoaN3795s3749tO9UuAwbNozvvvsutD3A6VBKewiiTKQBSQzed0uWLGnz9xBBjCAsjEYd/fuFv1dVrKEEHUajkQEDBkTbjRbTHjr69evHoEGDGDp0aETXDx48mG+//ZZdu3YxZsyY09oopT0EgtaiMS+Mz+fjww8/5PLLL6dr166t+h6xF7oJYppAQMLt9hEIyHs+uBJ0NKa5b24ytlilPXSYTCZ+85vf0Lt374iuN5vN9O3b96w5Y5TSHoIoo4pwPkwUJ52fC61Wy9133x3KE9OaiCBGEBZ2h4fNvxRhl/FcElCGDrvdzqZNm7Db7dF2pUXIRcewYcM4fPjwGYek5KJDEOMoaE7MiYwdO5Zt27a1erliOEkQFiajliGDumIyyvvWUYIOk8nE0KFDZZ/mXi46MjMzsVgsbNmyhZkzZ57yulx0CGIcVYS9KjHcEwNwzz338NBDD1FSUkJWVlaTVYMQ/JEQCfJ9gguiglaroXMn87kNYxwl6NBqtXTu3DnabrQYuejQaDSMGTOG9evXc9lll50ywVcuOgSCaHD99dcDcP/994fOqVQqJElCpVLh90eWeFQEMYKw8Hj8VNXYSO5sQa/XRNudiFGCDo/HQ1VVFcnJyej1+mi7EzFy0pGVlcWPP/5ITk4OF154YZPX5KRDEMOoVRGuTortnpiCgoI2KVcEMYKwcHt8HCqoISHeKNsvf1CGDrfbzYEDB0hISJD1l6acdJjNZoYNG8bPP//M+PHj0WiO3zty0iGIYRS0xPpE0tLS2qTc2FYtiDniLQYmTehNvMUQbVdahBJ0xMfHc/HFFxMfHx9tV1qE3HSMHTuWhoYGdu/e3eS83HQIYhQFZ+z94IMPmDhxIt27d6eoqAgI5pL54osvIi5TBDECgUAQBikpKWRkZLB58+ZouyIQyIbXX3+dBx98kBkzZlBXVxeaA9OhQ4cWJcUTQYwgLBxODzt2HsHhlO/SZFCGDofDwfbt22Wf5l6OOsaNG0dpaSklJSWhc3LUIYhBFLrE+pVXXuHvf/87jz32WJNh2NGjR581/9K5iG3VgphDpVKh02miuptva6AEHUENOllrAHnq6NevH506dWrSGyNHHYIYRKFBTEFBASNHjjzlvMFgaFFupdhWLYg5TEYdmQNSMBl10XalRShBh8lkYtCgQbLPSyJHHSqVigsuuID8/Hzq6+sBeeoQxCCNeWIiOWKYjIwMcnNzTzn/zTffMGjQoIjLFUGMICwkScLnCyBJ8k3XD8rQEdTgk7UGkK+OESNGoNfr+fnnnwH56hAI2oM///nP3HvvvSxfvhxJkvj5559ZtGgRjz76KH/+858jLlcssRaEhc3uISe3hFEjesp6ZY8SdNhsNrKzs8nKypL1ihi56jAYDIwZM4YNGzaEJvvKUYcgtpDUaqQIhoYiuaY9+f3vf4/P5+P//b//h8Ph4KabbqJHjx787W9/44Ybboi4XBHECMLCaNAyaGAKRoO8bx0l6DAajQwaNAij0RhtV1qEnHVMnjwZm83GypUr6d69O8nJyRQVFdG7d+9TMvoKBM1CFeH8FlVsBzEAf/jDH/jDH/5AdXU1gUCALl26tLhM+T7BBVFBp9OQnGSJthstRgk6dDpdqzwEoo2cdajVambNmkVaWhoHDx6koKCA7du3A8HJv9OnT6djx45R9lIgKxSa7O7pp5/mt7/9LX369CEpKanVyo1t1YKYw+v1U17RgNcb2T4XsYISdHi9XsrLy/F6vdF2pUXIXYdKpWLEiBHMmjWLG2+8kXvuuYdZs2ZRUVHB0qVL2bVrV7RdFAiizqeffkr//v0ZN24cr776KlVVVa1SrghiBGHhcvvYu78Sl9sXbVdahBJ0uFwu9uzZg8vlirYrLUJpOoxGIyNHjuTee+9l4MCBfPbZZ+zbty/a7gnkgkIz9u7YsYMdO3Zw6aWX8uKLL9KjRw9mzJjBRx991KLcSipJTKVvN6xWK4mJidTX15OQkNCsa/zODW3sVXiceLvIOR9GrOrQmCY22zZWNYSLknUEAgFWrFjBoUOHuOeee0hMTIy4/EieHwL50Ni+R3e9RUJ8+Mv0rQ1OOg6+Qzb3x4YNG/joo49YsWIFLpcLq9UaUTmiJ0YQFiqVKnTIGSXoUIIGULYOtVrN1VdfjV6v59tvv42idwLZoFIFJ+mGfcjr82M2mzGZTOj1+hYNJYsgRhAWTqeXnfnlOJ3ynL/QiBJ0OJ1O8vLycDqd0XalRShdh9Fo5LLLLmPPnj3U1tZGyTuBbFBoxl4IZu1dtGgRgwYNYvTo0eTk5PDUU09RXl4ecZmxr1ogEAhkzuDBg9Hr9S3aI0YgkDPjx4+nb9++rFixgt///vcUFRWxZs0abr/99hYNs4ol1oKwMJl0DBnUNdputBgl6DCZTAwdOjTabrSY80GHTqcjMzOTvLw8LrroItkPnQnaEIUusZ48eTJvv/02gwcPbtVyRRAjCAslT8KUG0rQAOePjqFDh7J9+3aOHDlCjx492tM1gZyIdKVRjK9OevbZZwGorq5GpVLRuXPnVik3tkM3Qcxhs3v4ccMhbHZPtF1pEUrQYbPZWLduHTabLdqutIjzRUdGRgYWi4UdO3a0s2cCWaHAOTF1dXXce++9JCUlkZKSQpcuXUhKSuK+++6jrq6uRWWLnhhBWBgNWgb06yLrdP2gDB1Go5GBAwfKMl3/iZwvOtRqNYMHD2bXrl1cccUVsu51EgiaS21tLePHj6e0tJSbb76ZzMxMJEli9+7dvPfee/z3v/9l48aNEWe2lu8TXBAVdDoNXVPkv7mdEnTodDq6dpX3vB44v3QMGjSILVu2cPjwYXr16tVOngnkhKRSI0WwD1Ik17QHCxcuRK/Xc/DgQVJSUk55bdq0aSxcuJCXXnopovJjU7UgZvF6/VRV22Sdrh+UocPr9VJZWSnbdP2NnE86UlNTsVgs5Ofnt6NnAlmhinAoKUaDmM8//5znn3/+lAAGoGvXrvzv//4vK1eujLj82FQtiFlcbh/5eypkna4flKHD5XKRn5+viHT954sOlUpFZmYmu3fvRiRLF5wWhc2JKSsrO+uKpCFDhog8MYL2w2LWM3FcBhazPtqutAgl6LBYLFx44YVYLPLejft80zFo0CCsViulpaXt5JlAED2SkpIoLCw84+sFBQUtWqkkghhBWKhUKrRatewnJSpBR1CDVtYa4PzT0atXL0wmE/v3728nzwSyQmE9MVdccQWPPfYYHs+pK0HdbjdPPPEEV1xxRcTli4m9grBwurwUFtWSntYJk1EXbXciRgk6nE4nBQUFZGRkYDKFv2FcrHC+6VCr1SQlJVFfX9+O3glkg8KS3T399NOMHj2afv36hXZ2B8jPz2fp0qW43W4++OCDiMsXQYwgLCRJwuv1y348Xwk6ghq8stYA56eOhISEiHftFSgctSrCICY2ezJ79uzJpk2buOeee5g/f37o86FSqZg6dSqvvvoqqampEZcvghhBWMSZ9Awb0j3abrQYJeiIi4tj+PDh0XajxZyPOuLj41s0mVEgkBMZGRl88803HD16NDSM2rdvXzp16tTiskUQIxAIBO1MY0+MJEmynwskaGVUquARyXUxTseOHbngggtatczYHEQTxCwNNjfrNx6iweaOtistQgk6GhoaWLduHQ0NDdF2pUWcjzoSEhLwer2yX1YuaAPaeWLv0qVLycjIwGg0kpWVxfr1689qv27dOrKysjAajfTu3Zs33ngjovdtLUQQIwgLg15L74zOGPTy7sRTgg6DwUDfvn0xGAzRdqVFnI86EhISAMS8GMGptGMQs3z5cubNm8djjz3Gtm3bmDRpEtOnT6e4uPi09gUFBcyYMYNJkyaxbds2Hn30Ue6//34+/fTTlqqOGBHECMJCr9fQo1sier0m2q60CCXo0Ov19OjRA71evrlu4PzUIYIYQSzw4osvctttt3H77beTmZnJkiVLSE1N5fXXXz+t/RtvvEGvXr1YsmQJmZmZ3H777dx66608//zz7ez5ceT7M1QQFXw+P/VWF4kJRrRa+QYAStDh8/mor68nMTERrVa+H+XzUUdjQjy73d4erglkhNXmiqhXxWoLDk2eHBgbDIbT9g56PB6ys7N55JFHmpyfNm0aGzduPO17bNq0iWnTpjU5d/nll7Ns2TK8Xi86Xfunq5DvE0MQFZwuHzvzyxk1oifxFnl++YMydDidTvLy8sjKyiI+Xr6bWZ6POjQaDQaDQQQxghB6vZ6uXbuSnjY74jIsFsspy5WffPJJnnrqqVNsq6ur8fv9p+xplJKScsaVc+Xl5ae19/l8VFdX061bt4h9jxQRxAjCwhynZ9yYNHQ6eX7xN6IEHWazmfHjx0fl109rcr7qiIuLw+FwtLFXArlgNBopKCg4bWbb5nK61W7nmqN1sv25Vsydzv5059sLRQUxr7/+Oq+//npon4bBgwezYMECpk+fjtfr5fHHH2fVqlUcOnSIxMREpkyZwuLFi+ne/Xi+kL1793LrrbdSVFTEHXfcwYIFC0KvpaenU1RUxKZNmxg3blzo/Lx588jNzWXt2rWtrkljmtjqZbYEDSDvr5ogStChVqtlPxkWzl8dZrNZBDGCJhiNRoxGY7u8V1JSEhqN5pRel8rKytPuOA3BXadPZ6/Valu0/1FLUNTE3p49e7J48WK2bt3K1q1bufTSS7n66qvZtWsXDoeDnJwcnnjiCXJycvjss8/Yt28fs2bNalLGvffey5w5c/jiiy/46quv2LBhQ5PXjUYjDz/8cHvKiilcLhd79+6V/dJQJehQggY4f3UkJSVRXFws+0zFAnmi1+vJyspi9erVTc6vXr2aCRMmnPaa8ePHn2L//fffM3r06Kj1pCoqiJk5cyYzZsygf//+9O/fn0WLFmGxWNi8eTOJiYmsXr2a2bNnM2DAAMaNG8crr7xCdnZ2k+VkdXV1jBw5kmHDhtG9e/dT9je588472bx5M6tWrWpveTFBIBDAbrcTCASi7UqLUIIOJWiA81fH8OHDqa2tpaioqI09EwhOz4MPPsjbb7/NO++8w+7du3nggQcoLi7mrrvuAmD+/PnccsstIfu77rqLoqIiHnzwQXbv3s0777zDsmXL+NOf/hQtCcoaTjoRv9/PihUrsNvtjB8//rQ29fX1qFQqOnToEDq3cOFCpk6ditPp5KqrruLyyy9vck16ejp33XUX8+fP54orrkAdo5tutRVxcXGMGjUq2m60GCXoUIIGOH91pKWl0bFjRzZu3EhaWprI3Ctod66//npqampYuHAhZWVlDBkyhFWrVpGWlgZAWVlZkx/5GRkZrFq1igceeIDXXnuN7t278/LLL3PttddGSwJICmPHjh2S2WyWNBqNlJiYKP373/8+rZ3T6ZSysrKkm2+++ZTXXC6XVFlZecr5tLQ06aWXXpIqKyul+Ph46f3335ckSZL++Mc/ShdffPE5fauvr5cAqbS0NHTOZrNJTqdTkiRJ8vv9ktVqlbxeryRJkuR2u6WGhoaQrd1uP6ut1WptYutwOCRJkqRAICBZrVbJ4/FIkiRJHo9HslqtUiAQkCRJkhwOR1i2drs99D5Wq1Vyu92SJEmS1+uVrFar5Pf7JUkK1vGJtg0NDWe1tdlsTWxdLpckSZLk8/kkq9Uq+Xw+SZKC7XOirc1mO6vtiXUYa/XdWIfNre9z1eGZbBvrpdE23Do8k21jvTTahluHJ9ueqw4bUco9m5+fLz311FPS+vXrz3nPNj4/6uvrJYFAEERx3QgDBgwgNzeXzZs3c/fddzN37lzy8/Ob2Hi9Xm644QYCgQBLly49pQyDwUBycvIZ3yM5OZk//elPLFiwIKKZ5Cf6k5+fz+HDhwFwu91kZ2djs9mA4HK23NzckO2ePXtCXc9er5fs7OzQcFdVVRU5OTkh2/3791NQUAAEe6Wys7M5evQoADU1NWRnZ4dsDxw4wIEDB0L/Z2dnU1NTA8DRo0fJzs7G7/cDsHv3bv7973+HfMzJyaGqqgoI9mxlZ2fj9XoBKCoqYs+ePaFyc3NzQ5PCbDYb2dnZuN3BtP+HDx9uUi95eXmUlZUBwVwa2dnZOJ1OAEpLS9m5c2fIdufOnZSUlADBeQkn1+GOHTtCtrt376a4uBibzca6devYtGlTKE18RUUF27ZtC9nu3bs3NEnc5/ORnZ1NXV0dEFyeeHJ9Hzp0CAgOK2RnZ1NbWwtAbW0t2dnZobkPBw8eDG2C1ljf1dXVQHA4Mzs7G5/PB0BhYSH79u0L2ebk5FBZWQkEfyWtXLky1K5FRUXs3r07ZLt9+/ZQHTbWd+N8jcOHD7Nr164m9X3kyBEAHA4H2dnZoUmnR44cIS8vL2S7a9eu0D17cn2XlZWxffv2JvV98j3bmMeisrKSnJwcbDYbP/30Ezt27DhrfZ94z+7fv5+DBw8CwdURp6vvxmGdQ4cONanvnJycs9b33r17Q7bbtm2joqICCG4rkJ2dHfrMFxcXN6nvX375he+//x6bzXZKfZeUlJxyz5aWlpKZmcnw4cNZs2ZNqJ7Kysqa1PeJzwiBQNAUlSQpe1bZlClT6NOnD2+++SYQfJDOnj2bQ4cOsWbNmrBmVKenpzNv3jzmzZuHzWajb9++PPLIIxQWFjZrdZLVaiUxMZHS0tLQiii73Y5Go8FoNIbG1E0mE1qtFo/Hg8fjCSXGcjgcqNXqM9q63e5QfgqHw4FKpcJkMiFJEjabDaPRiE6nC+3ZYrFYUKlUoeCgObb19fVUVFSQnp6OXq+noaEBg8GAXq/H5/PhdDoxm82o1WpcLheBQIC4uDgg+EWq1+vPaOv3+zGbzSFbnU6HwWDA7/fjcDiIi4tDo9Hgdrvx+XwhW7vdjlarPaOt1+ttklxMo9GgVqs5cuQICQkJJCQkRK2+IfjleDZbSZJOW4cOh4Pi4mLS09MxGo1nre/Gemms73Dr8Ey2jfXSaBtuHXo8HvR6PeXl5SQkJGAwGM5ah431Hc49e3IdttU9e/ToUWpra0lNTUWj0TS7Dl0uF2+//TZut5vMzEy6d+9Ot27dQitEGu9Zj8dDYmIi9fX1oYy/AsH5juKDmMsuu4zU1FTee++9UACzf/9+fvjhh7P2tpyOE4MYgNdee42nnnqKmTNncujQoWYHMeIhJBAITuTo0aOsX7+eoqKiUI+SyWRCp9Oh1WqJj49nypQppKamiueHQHACiprY++ijjzJ9+nRSU1NpaGjgk08+Ye3atXz77bf4fD6uu+46cnJy+Prrr/H7/aGhjU6dOkW0b8sdd9zBSy+9xMcff8zYsWNbW05M4vP5sNlsWCwW2aeIl7sOJWgAoQOgY8eOoXQPDQ0NFBUVcfToUXw+Hz6fj7y8PD7//PM28FogkDeKmhNTUVHBnDlzGDBgAJdddhlbtmzh22+/ZerUqZSUlPDll19SUlLCiBEj6NatW+g40z4R50Kn0/HMM8/IPr9FODidTnJzc0Pd+XJFCTqUoAGEjpOJj49nyJAhTJo0icmTJzN16lSuu+46SktLW8lTgUA5KH44KZZQwnBSIBDA7XZjMBhkvbxcCTqUoAGEjubg8/l44oknWLx4sayfHwJBayPfvltBVFCr1aEJqXJGCTqUoAGEjuag1WplHeAJBG2F+FQIwsLlcrF//37ZD6EpQYcSNIDQ0VyUsL+UQNDaiCBGEBZ+v5+6urpQ3hi5ogQdStAAQkdziWTxgUCgdMScmHZECXNiBAJBdHjhhRf405/+JJ4fAsEJiJ4YgUAgkAHR2iVYIIhlxMTeKNCYdl2O2Gw28vPzGTRoUCgrqxxRgg4laACho7n06dOn1csUCOSOCGLaEb1eT9euXUlNTY22KwKBQIZ07dpVzI0RCE5AzIlpZ1wuV0SbRgoEAoFer8doNEbbDYEgZhBBjEAgEAgEAlkiJvYKBAKBQCCQJSKIEQgEAoFAIEtEECMQCAQCgUCWiCBGIBAIBAKBLBFBjEAgEAgEAlkighiBQCAQCASyRAQxCubHH39k5syZdO/eHZVKxeeff97k9c8++4zLL7+cpKQkVCoVubm5p5Rx8OBBfvWrX5GcnExCQgKzZ8+moqKiic2mTZsYMWIEaWlp/P3vfw+dHzduHHfffXcT29dffx2VSsWyZcuanL/tttuYMGFCywTHMK3RFuXl5cyZM4euXbtiNpsZNWoU//rXv5rYiLY4Ny1ti8LCQlQq1WmPFStWhOxEWwgEbY8IYhSM3W5n+PDhvPrqq2d8feLEiSxevPiMr0+bNg2VSsWaNWvYsGEDHo+HmTNnEggEQna33norTzzxBB9//DHPPfccxcXFAEyePJkffvihSZlr164lNTX1tOcnT57cErkxTUvbAmDOnDns3buXL7/8kry8PH79619z/fXXs23btpCNaItz09K2SE1NpaysrMnx9NNPYzabmT59eshOtIVA0A5IgvMCQFq5cuVpXysoKJAAadu2bU3Of/fdd5JarZbq6+tD52prayVAWr16dehcr169pEOHDkk2m00aPXq0tGvXrtD1gHTkyJGQbUpKirR06VKpR48eoXPFxcWnlKlkImkLSZIks9ksvf/++03OderUSXr77bdD/4u2CI9I2+JkRowYId16661Nzom2EAjaHtETIzgjbrcblUqFwWAInTMajajVan766afQuQULFpCZmUliYiLjxo1j0KBBAEycOBGdTsfatWsByM/Px+l0cuutt2K1Wtm/fz8AP/zwA3q9XnSbn4MLL7yQ5cuXU1tbSyAQ4JNPPsHtdnPJJZeEbERbtD/Z2dnk5uZy2223NTkv2kIgaHtEECM4I+PGjcNsNvPwww/jcDiw2+38+c9/JhAIUFZWFrK77bbbqKmpoaqqildeeSV03mw2M2bMmNDDeu3atVx44YUYDAYmTpzY5PzYsWOJi4trT3myY/ny5fh8Pjp37ozBYODOO+9k5cqVTXY3Fm3R/ixbtozMzMxTgg3RFgJB2yOCGMEZSU5OZsWKFXz11VdYLBYSExOpr69n1KhRaDSaJrZms5mOHTueUsbkyZObPJQbew0uvvjiJucvvfTStpSiCB5//HGOHj3Kf/7zH7Zu3cqDDz7Ib37zG/Ly8prYibZoP5xOJx999NEpvTCNiLYQCNoWEcQIzsq0adM4ePAglZWVVFdX88EHH1BaWkpGRkazrp88eTL79u2jtLSUdevWcfHFFwPHH9bFxcUUFBSIyYvn4ODBg7z66qu88847XHbZZQwfPpwnn3yS0aNH89prrzWrDNEWrc+//vUvHA4Ht9xyS1jXibYQCFoHbbQdEMiDpKQkANasWUNlZSWzZs1q1nUTJkzAYDCwdOlSnE4nWVlZAIwePZr6+nrefPNNjEYj48aNazPflYDD4QBArW76u0Oj0TRZKXY2RFu0PsuWLWPWrFkkJyeHdZ1oC4GgdRBBjIKx2WwcOHAg9H9BQQG5ubl06tSJXr16UVtbS3FxMUeOHAFg7969AHTt2pWuXbsC8O6775KZmUlycjKbNm3ij3/8Iw888AADBgxolg8mk4mxY8fyyiuvMHHixNAwlE6nY/z48bzyyiuhB7qSaWlbDBw4kL59+3LnnXfy/PPP07lzZz7//HNWr17N119/3SwfRFsEaY3PBcCBAwf48ccfWbVqVdg+iLYQCFqJaC+PErQdP/zwgwSccsydO1eSJEl69913T/v6k08+GSrj4YcfllJSUiSdTif169dPeuGFF6RAIBCWH08++aQESIsXL25y/plnnpEA6Zlnnmmp1JinNdpi37590q9//WupS5cuUlxcnDRs2LBTllyfC9EWrdMWkiRJ8+fPl3r27Cn5/f6I/BBtIRC0HJUkSVLbhkkCgUAgEAgErY+Y2CsQCAQCgUCWiCBGIBAIBAKBLBFBjEAgEAgEAlkighiBQCAQCASyRAQxAoFAIBAIZIkIYgQCgUAgEMgSEcQIBAKBQCCQJSKIEQgEAoFAIEtEECMQCAQCgUCWiCBGIBAIBAKBLBFBjEAgEAgEAlny/wNwxrrtW9RJNAAAAABJRU5ErkJggg==", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Let's use the \"NAME\" (aka, county name) column to plot a \n", + "# diagnostic plot of Los Angeles county\n", + "weightmap.diag_fig({'NAME':'Los Angeles'},grid_polygon_info)" + ] + }, + { + "cell_type": "markdown", + "id": "91a18949-7473-4d26-9ec4-a34b58157c96", + "metadata": {}, + "source": [ + "Note that, as before, the colormap is set so that \"1\" / the darkest shade is the area overlap between LA county (in green) and the pixel that most overlaps the county polygon, which in this case is the top left pixel. The top right has a similarly large (but smaller) overlap, and also has a dark shade. \n", + "\n", + "Note that grid cells covering (nearly uninhabited) San Clemente and (sparsely populated) Catalina Islands will contribute to any aggregated county-level metrics, since the islands are part of LA County (depending on the application, this may perhaps be an argument for additionally using a population weight layer, as we do in this example). " + ] + }, { "cell_type": "markdown", "id": "automated-version", @@ -190,7 +337,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 12, "id": "proved-administration", "metadata": {}, "outputs": [ @@ -231,7 +378,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 13, "id": "dressed-curve", "metadata": {}, "outputs": [ @@ -269,6 +416,7 @@ "}\n", "\n", "html[theme=dark],\n", + "body[data-theme=dark],\n", "body.vscode-dark {\n", " --xr-font-color0: rgba(255, 255, 255, 1);\n", " --xr-font-color2: rgba(255, 255, 255, 0.54);\n", @@ -281,7 +429,7 @@ "}\n", "\n", ".xr-wrap {\n", - " display: block;\n", + " display: block !important;\n", " min-width: 300px;\n", " max-width: 700px;\n", "}\n", @@ -498,6 +646,11 @@ " grid-column: 4;\n", "}\n", "\n", + ".xr-index-preview {\n", + " grid-column: 2 / 5;\n", + " color: var(--xr-font-color2);\n", + "}\n", + "\n", ".xr-var-name,\n", ".xr-var-dims,\n", ".xr-var-dtype,\n", @@ -519,14 +672,16 @@ "}\n", "\n", ".xr-var-attrs,\n", - ".xr-var-data {\n", + ".xr-var-data,\n", + ".xr-index-data {\n", " display: none;\n", " background-color: var(--xr-background-color) !important;\n", " padding-bottom: 5px !important;\n", "}\n", "\n", ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n", - ".xr-var-data-in:checked ~ .xr-var-data {\n", + ".xr-var-data-in:checked ~ .xr-var-data,\n", + ".xr-index-data-in:checked ~ .xr-index-data {\n", " display: block;\n", "}\n", "\n", @@ -536,13 +691,16 @@ "\n", ".xr-var-name span,\n", ".xr-var-data,\n", + ".xr-index-name div,\n", + ".xr-index-data,\n", ".xr-attrs {\n", " padding-left: 25px !important;\n", "}\n", "\n", ".xr-attrs,\n", ".xr-var-attrs,\n", - ".xr-var-data {\n", + ".xr-var-data,\n", + ".xr-index-data {\n", " grid-column: 1 / -1;\n", "}\n", "\n", @@ -580,7 +738,8 @@ "}\n", "\n", ".xr-icon-database,\n", - ".xr-icon-file-text2 {\n", + ".xr-icon-file-text2,\n", + ".xr-no-icon {\n", " display: inline-block;\n", " vertical-align: middle;\n", " width: 1em;\n", @@ -590,49 +749,52 @@ " fill: currentColor;\n", "}\n", "
    <xarray.Dataset>\n",
-       "Dimensions:     (month: 12, pix_idx: 3141)\n",
+       "Dimensions:     (poly_idx: 3141, month: 12)\n",
        "Coordinates:\n",
-       "  * pix_idx     (pix_idx) int64 0 1 2 3 4 5 6 ... 3135 3136 3137 3138 3139 3140\n",
+       "  * poly_idx    (poly_idx) int64 0 1 2 3 4 5 6 ... 3135 3136 3137 3138 3139 3140\n",
        "  * month       (month) int64 1 2 3 4 5 6 7 8 9 10 11 12\n",
        "Data variables:\n",
-       "    NAME        (pix_idx) object 'Lake of the Woods' 'Ferry' ... 'Broomfield'\n",
-       "    STATE_NAME  (pix_idx) object 'Minnesota' 'Washington' ... 'Colorado'\n",
-       "    STATE_FIPS  (pix_idx) object '27' '53' '53' '53' ... '02' '02' '02' '08'\n",
-       "    CNTY_FIPS   (pix_idx) object '077' '019' '065' '047' ... '240' '068' '014'\n",
-       "    FIPS        (pix_idx) object '27077' '53019' '53065' ... '02068' '08014'\n",
-       "    tas         (pix_idx, month) float64 264.0 268.9 274.0 ... 283.5 276.4 270.4
    • poly_idx
      PandasIndex
      PandasIndex(Index([   0,    1,    2,    3,    4,    5,    6,    7,    8,    9,\n",
+       "       ...\n",
+       "       3131, 3132, 3133, 3134, 3135, 3136, 3137, 3138, 3139, 3140],\n",
+       "      dtype='int64', name='poly_idx', length=3141))
    • month
      PandasIndex
      PandasIndex(Index([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype='int64', name='month'))
    • " ], "text/plain": [ "\n", - "Dimensions: (month: 12, pix_idx: 3141)\n", + "Dimensions: (poly_idx: 3141, month: 12)\n", "Coordinates:\n", - " * pix_idx (pix_idx) int64 0 1 2 3 4 5 6 ... 3135 3136 3137 3138 3139 3140\n", + " * poly_idx (poly_idx) int64 0 1 2 3 4 5 6 ... 3135 3136 3137 3138 3139 3140\n", " * month (month) int64 1 2 3 4 5 6 7 8 9 10 11 12\n", "Data variables:\n", - " NAME (pix_idx) object 'Lake of the Woods' 'Ferry' ... 'Broomfield'\n", - " STATE_NAME (pix_idx) object 'Minnesota' 'Washington' ... 'Colorado'\n", - " STATE_FIPS (pix_idx) object '27' '53' '53' '53' ... '02' '02' '02' '08'\n", - " CNTY_FIPS (pix_idx) object '077' '019' '065' '047' ... '240' '068' '014'\n", - " FIPS (pix_idx) object '27077' '53019' '53065' ... '02068' '08014'\n", - " tas (pix_idx, month) float64 264.0 268.9 274.0 ... 283.5 276.4 270.4" + " NAME (poly_idx) object 'Lake of the Woods' 'Ferry' ... 'Broomfield'\n", + " STATE_NAME (poly_idx) object 'Minnesota' 'Washington' ... 'Colorado'\n", + " STATE_FIPS (poly_idx) object '27' '53' '53' '53' ... '02' '02' '02' '08'\n", + " CNTY_FIPS (poly_idx) object '077' '019' '065' '047' ... '240' '068' '014'\n", + " FIPS (poly_idx) object '27077' '53019' '53065' ... '02068' '08014'\n", + " tas (poly_idx, month) float64 264.0 268.9 274.0 ... 276.4 270.4" ] }, - "execution_count": 7, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -645,7 +807,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 14, "id": "electrical-ticket", "metadata": {}, "outputs": [ @@ -670,139 +832,75 @@ " \n", " \n", " \n", + " \n", " NAME\n", " STATE_NAME\n", " STATE_FIPS\n", " CNTY_FIPS\n", " FIPS\n", - " tas0\n", - " tas1\n", - " tas2\n", - " tas3\n", - " tas4\n", - " tas5\n", - " tas6\n", - " tas7\n", - " tas8\n", - " tas9\n", - " tas10\n", - " tas11\n", + " tas\n", + " \n", + " \n", + " poly_idx\n", + " month\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " 0\n", + " 0\n", + " 1\n", " Lake of the Woods\n", " Minnesota\n", " 27\n", " 077\n", " 27077\n", " 263.978006\n", - " 268.887868\n", - " 274.012152\n", - " 283.158717\n", - " 290.630598\n", - " 297.850779\n", - " 302.038199\n", - " 300.327744\n", - " 293.465816\n", - " 283.815233\n", - " 275.141634\n", - " 266.054430\n", - " \n", - " \n", - " 1\n", - " Ferry\n", - " Washington\n", - " 53\n", - " 019\n", - " 53019\n", - " 271.780440\n", - " 275.618485\n", - " 276.934183\n", - " 279.826777\n", - " 286.621100\n", - " 293.757010\n", - " 299.056368\n", - " 297.131708\n", - " 289.844308\n", - " 281.633456\n", - " 276.714475\n", - " 272.242004\n", " \n", " \n", " 2\n", - " Stevens\n", - " Washington\n", - " 53\n", - " 065\n", - " 53065\n", - " 273.217250\n", - " 276.940380\n", - " 278.414225\n", - " 281.319652\n", - " 287.817911\n", - " 294.926457\n", - " 300.903109\n", - " 299.304529\n", - " 292.245363\n", - " 283.273956\n", - " 278.063277\n", - " 273.666181\n", + " Lake of the Woods\n", + " Minnesota\n", + " 27\n", + " 077\n", + " 27077\n", + " 268.887868\n", " \n", " \n", " 3\n", - " Okanogan\n", - " Washington\n", - " 53\n", - " 047\n", - " 53047\n", - " 271.831071\n", - " 275.586124\n", - " 276.689357\n", - " 279.324166\n", - " 285.771338\n", - " 292.635899\n", - " 297.756402\n", - " 295.956748\n", - " 289.177685\n", - " 281.440422\n", - " 276.654779\n", - " 272.275232\n", + " Lake of the Woods\n", + " Minnesota\n", + " 27\n", + " 077\n", + " 27077\n", + " 274.012152\n", " \n", " \n", " 4\n", - " Pend Oreille\n", - " Washington\n", - " 53\n", - " 051\n", - " 53051\n", - " 272.092353\n", - " 275.888818\n", - " 277.346070\n", - " 280.446389\n", - " 287.268406\n", - " 294.357705\n", - " 299.851527\n", - " 297.965815\n", - " 290.622763\n", - " 282.058301\n", - " 276.996473\n", - " 272.484589\n", + " Lake of the Woods\n", + " Minnesota\n", + " 27\n", + " 077\n", + " 27077\n", + " 283.158717\n", + " \n", + " \n", + " 5\n", + " Lake of the Woods\n", + " Minnesota\n", + " 27\n", + " 077\n", + " 27077\n", + " 290.630598\n", " \n", " \n", " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", + " ...\n", " ...\n", " ...\n", " ...\n", @@ -811,154 +909,89 @@ " ...\n", " \n", " \n", - " 3136\n", - " Skagway-Hoonah-Angoon\n", - " Alaska\n", - " 02\n", - " 232\n", - " 02232\n", - " 273.605147\n", - " 275.477240\n", - " 276.792992\n", - " 279.194054\n", - " 283.764519\n", - " 288.635583\n", - " 290.038044\n", - " 289.689574\n", - " 286.058608\n", - " 280.958010\n", - " 276.904109\n", - " 274.048888\n", + " 3140\n", + " 8\n", + " Broomfield\n", + " Colorado\n", + " 08\n", + " 014\n", + " 08014\n", + " 297.646820\n", " \n", " \n", - " 3137\n", - " Yukon-Koyukuk\n", - " Alaska\n", - " 02\n", - " 290\n", - " 02290\n", - " 264.534558\n", - " 264.088869\n", - " 267.621423\n", - " 273.426228\n", - " 281.649435\n", - " 289.319370\n", - " 288.936030\n", - " 286.209616\n", - " 280.807791\n", - " 273.683875\n", - " 266.722855\n", - " 265.538685\n", + " 9\n", + " Broomfield\n", + " Colorado\n", + " 08\n", + " 014\n", + " 08014\n", + " 292.368988\n", " \n", " \n", - " 3138\n", - " Southeast Fairbanks\n", - " Alaska\n", - " 02\n", - " 240\n", - " 02240\n", - " 263.919168\n", - " 263.899079\n", - " 266.771514\n", - " 272.144709\n", - " 279.739283\n", - " 287.625174\n", - " 287.933732\n", - " 285.436821\n", - " 279.768225\n", - " 272.377963\n", - " 265.702026\n", - " 264.414302\n", + " 10\n", + " Broomfield\n", + " Colorado\n", + " 08\n", + " 014\n", + " 08014\n", + " 283.544708\n", " \n", " \n", - " 3139\n", - " Denali\n", - " Alaska\n", - " 02\n", - " 068\n", - " 02068\n", - " 265.049599\n", - " 264.794849\n", - " 268.193156\n", - " 273.612534\n", - " 281.097223\n", - " 288.917064\n", - " 288.898311\n", - " 286.233612\n", - " 280.504391\n", - " 273.142605\n", - " 266.534916\n", - " 265.575153\n", + " 11\n", + " Broomfield\n", + " Colorado\n", + " 08\n", + " 014\n", + " 08014\n", + " 276.383606\n", " \n", " \n", - " 3140\n", + " 12\n", " Broomfield\n", " Colorado\n", " 08\n", " 014\n", " 08014\n", - " 270.803864\n", - " 273.430206\n", - " 275.955505\n", - " 280.790070\n", - " 287.303619\n", - " 292.830048\n", - " 297.615662\n", - " 297.646820\n", - " 292.368988\n", - " 283.544708\n", - " 276.383606\n", " 270.444855\n", " \n", " \n", "\n", - "

    3141 rows × 17 columns

    \n", + "

    37692 rows × 6 columns

    \n", "" ], "text/plain": [ - " NAME STATE_NAME STATE_FIPS CNTY_FIPS FIPS \\\n", - "0 Lake of the Woods Minnesota 27 077 27077 \n", - "1 Ferry Washington 53 019 53019 \n", - "2 Stevens Washington 53 065 53065 \n", - "3 Okanogan Washington 53 047 53047 \n", - "4 Pend Oreille Washington 53 051 53051 \n", - "... ... ... ... ... ... \n", - "3136 Skagway-Hoonah-Angoon Alaska 02 232 02232 \n", - "3137 Yukon-Koyukuk Alaska 02 290 02290 \n", - "3138 Southeast Fairbanks Alaska 02 240 02240 \n", - "3139 Denali Alaska 02 068 02068 \n", - "3140 Broomfield Colorado 08 014 08014 \n", - "\n", - " tas0 tas1 tas2 tas3 tas4 tas5 \\\n", - "0 263.978006 268.887868 274.012152 283.158717 290.630598 297.850779 \n", - "1 271.780440 275.618485 276.934183 279.826777 286.621100 293.757010 \n", - "2 273.217250 276.940380 278.414225 281.319652 287.817911 294.926457 \n", - "3 271.831071 275.586124 276.689357 279.324166 285.771338 292.635899 \n", - "4 272.092353 275.888818 277.346070 280.446389 287.268406 294.357705 \n", - "... ... ... ... ... ... ... \n", - "3136 273.605147 275.477240 276.792992 279.194054 283.764519 288.635583 \n", - "3137 264.534558 264.088869 267.621423 273.426228 281.649435 289.319370 \n", - "3138 263.919168 263.899079 266.771514 272.144709 279.739283 287.625174 \n", - "3139 265.049599 264.794849 268.193156 273.612534 281.097223 288.917064 \n", - "3140 270.803864 273.430206 275.955505 280.790070 287.303619 292.830048 \n", + " NAME STATE_NAME STATE_FIPS CNTY_FIPS FIPS \\\n", + "poly_idx month \n", + "0 1 Lake of the Woods Minnesota 27 077 27077 \n", + " 2 Lake of the Woods Minnesota 27 077 27077 \n", + " 3 Lake of the Woods Minnesota 27 077 27077 \n", + " 4 Lake of the Woods Minnesota 27 077 27077 \n", + " 5 Lake of the Woods Minnesota 27 077 27077 \n", + "... ... ... ... ... ... \n", + "3140 8 Broomfield Colorado 08 014 08014 \n", + " 9 Broomfield Colorado 08 014 08014 \n", + " 10 Broomfield Colorado 08 014 08014 \n", + " 11 Broomfield Colorado 08 014 08014 \n", + " 12 Broomfield Colorado 08 014 08014 \n", "\n", - " tas6 tas7 tas8 tas9 tas10 tas11 \n", - "0 302.038199 300.327744 293.465816 283.815233 275.141634 266.054430 \n", - "1 299.056368 297.131708 289.844308 281.633456 276.714475 272.242004 \n", - "2 300.903109 299.304529 292.245363 283.273956 278.063277 273.666181 \n", - "3 297.756402 295.956748 289.177685 281.440422 276.654779 272.275232 \n", - "4 299.851527 297.965815 290.622763 282.058301 276.996473 272.484589 \n", - "... ... ... ... ... ... ... \n", - "3136 290.038044 289.689574 286.058608 280.958010 276.904109 274.048888 \n", - "3137 288.936030 286.209616 280.807791 273.683875 266.722855 265.538685 \n", - "3138 287.933732 285.436821 279.768225 272.377963 265.702026 264.414302 \n", - "3139 288.898311 286.233612 280.504391 273.142605 266.534916 265.575153 \n", - "3140 297.615662 297.646820 292.368988 283.544708 276.383606 270.444855 \n", + " tas \n", + "poly_idx month \n", + "0 1 263.978006 \n", + " 2 268.887868 \n", + " 3 274.012152 \n", + " 4 283.158717 \n", + " 5 290.630598 \n", + "... ... \n", + "3140 8 297.646820 \n", + " 9 292.368988 \n", + " 10 283.544708 \n", + " 11 276.383606 \n", + " 12 270.444855 \n", "\n", - "[3141 rows x 17 columns]" + "[37692 rows x 6 columns]" ] }, - "execution_count": 8, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -1057,8 +1090,8 @@ "metadata": {}, "outputs": [], "source": [ - "# Export to csv\n", - "aggregated.to_csv('file_out.shp')" + "# Export to shapefile\n", + "aggregated.to_shp('file_out.shp')" ] } ],