{ "cells": [ { "cell_type": "markdown", "id": "c8db6f12-c072-49ef-96e8-6392e46511ff", "metadata": {}, "source": [ "# Global VLBI conversion guide" ] }, { "cell_type": "code", "execution_count": 1, "id": "14a699d5-bf6e-4ebe-bfa5-dcccbec0ec2e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "XRADIO version 1.1.3 already installed.\n" ] } ], "source": [ "from importlib.metadata import version\n", "import os\n", "\n", "try:\n", " import xradio\n", "\n", " print(\"XRADIO version\", version(\"xradio\"), \"already installed.\")\n", "except ImportError as e:\n", " print(e)\n", " print(\"Installing XRADIO\")\n", "\n", " os.system(\"pip install xradio\")\n", "\n", " import xradio\n", "\n", " print(\"xradio version\", version(\"xradio\"), \" installed.\")" ] }, { "cell_type": "markdown", "id": "a939f97e-6c76-47b0-aef5-256df2e908f9", "metadata": {}, "source": [ "## Download dataset" ] }, { "cell_type": "markdown", "id": "4e478428", "metadata": {}, "source": [ "global (EVN+VLBA) VLBI observation with:\n", "- 5 scans \n", "- 2 fields \n", "- 2 spw" ] }, { "cell_type": "code", "execution_count": 2, "id": "134a99e3-c5a2-443c-96d4-f44fae59555a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[\u001b[38;2;128;05;128m2026-04-20 15:21:21,727\u001b[0m] \u001b[38;2;50;50;205m INFO\u001b[0m\u001b[38;2;112;128;144m toolviper: \u001b[0m Initializing download... \n", "[\u001b[38;2;128;05;128m2026-04-20 15:21:21,727\u001b[0m] \u001b[38;2;50;50;205m INFO\u001b[0m\u001b[38;2;112;128;144m toolviper: \u001b[0m File already exists: /Users/vdesouza/work/xradio/docs/source/measurement_set/guides/global_vlbi_gg084b_reduced.ms \n" ] } ], "source": [ "import toolviper\n", "toolviper.utils.data.download(\"global_vlbi_gg084b_reduced.ms\")" ] }, { "cell_type": "markdown", "id": "f8435c96-7010-4b79-8be6-51f7f6993b5f", "metadata": {}, "source": [ "## Convert to Processing Set" ] }, { "cell_type": "code", "execution_count": 3, "id": "ee52d124-2c17-450b-879f-1f86f0ae265c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[\u001b[38;2;128;05;128m2026-04-20 15:21:22,206\u001b[0m] \u001b[38;2;50;50;205m INFO\u001b[0m\u001b[38;2;112;128;144m toolviper: \u001b[0m Updated partition scheme used: ['DATA_DESC_ID', 'OBSERVATION_ID'] \n", "[\u001b[38;2;128;05;128m2026-04-20 15:21:22,207\u001b[0m] \u001b[38;2;50;50;205m INFO\u001b[0m\u001b[38;2;112;128;144m toolviper: \u001b[0m Number of partitions: 2 \n", "[\u001b[38;2;128;05;128m2026-04-20 15:21:22,207\u001b[0m] \u001b[38;2;50;50;205m INFO\u001b[0m\u001b[38;2;112;128;144m toolviper: \u001b[0m OBSERVATION_ID [0], DDI [0], STATE [None], FIELD [0, 1], SCAN [3, 319, 320, 321, 552], EPHEMERIS [None] \n", "[\u001b[38;2;128;05;128m2026-04-20 15:21:22,399\u001b[0m] \u001b[38;2;255;160;0m WARNING\u001b[0m\u001b[38;2;112;128;144m toolviper: \u001b[0m Source_id is -1. No source information will be included in the field_and_source_xds. \n", "[\u001b[38;2;128;05;128m2026-04-20 15:21:22,801\u001b[0m] \u001b[38;2;50;50;205m INFO\u001b[0m\u001b[38;2;112;128;144m toolviper: \u001b[0m OBSERVATION_ID [0], DDI [1], STATE [None], FIELD [0, 1], SCAN [3, 319, 320, 321, 552], EPHEMERIS [None] \n", "[\u001b[38;2;128;05;128m2026-04-20 15:21:22,981\u001b[0m] \u001b[38;2;255;160;0m WARNING\u001b[0m\u001b[38;2;112;128;144m toolviper: \u001b[0m Source_id is -1. No source information will be included in the field_and_source_xds. \n" ] } ], "source": [ "from xradio.measurement_set import convert_msv2_to_processing_set\n", "\n", "ms_file = \"global_vlbi_gg084b_reduced.ms\"\n", "\n", "main_chunksize = {\"frequency\": 1, \"time\": 20} # baseline, polarization\n", "outfile = \"global_vlbi_gg084b_reduced.ps.zarr\"\n", "convert_msv2_to_processing_set(\n", " in_file=ms_file,\n", " out_file=outfile,\n", " parallel_mode=\"none\",\n", " persistence_mode='w',\n", " main_chunksize=main_chunksize,\n", ")" ] }, { "cell_type": "markdown", "id": "fbd02679-0df8-4fa5-8036-6f22f534e386", "metadata": {}, "source": [ "## Processing Set" ] }, { "cell_type": "code", "execution_count": 4, "id": "dab986ca-55f8-4a4a-ba59-9c97fcc2ca84", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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namescan_intentsshapeexecution_block_UIDpolarizationscan_namespw_namespw_intentsfield_namesource_nameline_namefield_coordssession_reference_UIDscheduling_block_UIDproject_UIDstart_frequencyend_frequency
0global_vlbi_gg084b_reduced_0[None](480, 57, 32, 4)---[RR, RL, LR, LL][3, 319, 320, 321, 552]spw_0[UNSPECIFIED][EM170817_1, J1311-2329_0][Unknown][]Multi-Phase-Center------GG084B4.900500e+094.916000e+09
1global_vlbi_gg084b_reduced_1[None](480, 57, 32, 4)---[RR, RL, LR, LL][3, 319, 320, 321, 552]spw_1[UNSPECIFIED][EM170817_1, J1311-2329_0][Unknown][]Multi-Phase-Center------GG084B4.916000e+094.931500e+09
\n", "
" ], "text/plain": [ " name scan_intents shape \\\n", "0 global_vlbi_gg084b_reduced_0 [None] (480, 57, 32, 4) \n", "1 global_vlbi_gg084b_reduced_1 [None] (480, 57, 32, 4) \n", "\n", " execution_block_UID polarization scan_name spw_name \\\n", "0 --- [RR, RL, LR, LL] [3, 319, 320, 321, 552] spw_0 \n", "1 --- [RR, RL, LR, LL] [3, 319, 320, 321, 552] spw_1 \n", "\n", " spw_intents field_name source_name line_name \\\n", "0 [UNSPECIFIED] [EM170817_1, J1311-2329_0] [Unknown] [] \n", "1 [UNSPECIFIED] [EM170817_1, J1311-2329_0] [Unknown] [] \n", "\n", " field_coords session_reference_UID scheduling_block_UID project_UID \\\n", "0 Multi-Phase-Center --- --- GG084B \n", "1 Multi-Phase-Center --- --- GG084B \n", "\n", " start_frequency end_frequency \n", "0 4.900500e+09 4.916000e+09 \n", "1 4.916000e+09 4.931500e+09 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from xradio.measurement_set import open_processing_set\n", "\n", "ps_xdt = open_processing_set(ps_store=outfile)\n", "ps_xdt.xr_ps.summary()" ] }, { "cell_type": "code", "execution_count": 5, "id": "259aa56b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataTree 'global_vlbi_gg084b_reduced_0'>\n",
       "Group: /global_vlbi_gg084b_reduced_0\n",
       "│   Dimensions:                     (time: 480, baseline_id: 57, frequency: 32,\n",
       "│                                    polarization: 4, uvw_label: 3)\n",
       "│   Coordinates:\n",
       "│     * time                        (time) float64 4kB 1.527e+09 ... 1.527e+09\n",
       "│       field_name                  (time) <U32 61kB dask.array<chunksize=(480,), meta=np.ndarray>\n",
       "│       scan_name                   (time) <U21 40kB dask.array<chunksize=(480,), meta=np.ndarray>\n",
       "│     * baseline_id                 (baseline_id) int64 456B 0 1 2 3 ... 53 54 55 56\n",
       "│       baseline_antenna1_name      (baseline_id) <U2 456B dask.array<chunksize=(57,), meta=np.ndarray>\n",
       "│       baseline_antenna2_name      (baseline_id) <U2 456B dask.array<chunksize=(57,), meta=np.ndarray>\n",
       "│     * frequency                   (frequency) float64 256B 4.9e+09 ... 4.916e+09\n",
       "│     * polarization                (polarization) <U2 32B 'RR' 'RL' 'LR' 'LL'\n",
       "│     * uvw_label                   (uvw_label) <U1 12B 'u' 'v' 'w'\n",
       "│   Data variables:\n",
       "│       EFFECTIVE_INTEGRATION_TIME  (time, baseline_id) float64 219kB dask.array<chunksize=(20, 57), meta=np.ndarray>\n",
       "│       FLAG                        (time, baseline_id, frequency, polarization) bool 4MB dask.array<chunksize=(20, 57, 1, 4), meta=np.ndarray>\n",
       "│       TIME_CENTROID               (time, baseline_id) float64 219kB dask.array<chunksize=(20, 57), meta=np.ndarray>\n",
       "│       UVW                         (time, baseline_id, uvw_label) float64 657kB dask.array<chunksize=(20, 57, 3), meta=np.ndarray>\n",
       "│       VISIBILITY                  (time, baseline_id, frequency, polarization) complex64 28MB dask.array<chunksize=(20, 57, 1, 4), meta=np.ndarray>\n",
       "│       WEIGHT                      (time, baseline_id, frequency, polarization) float32 14MB dask.array<chunksize=(20, 57, 1, 4), meta=np.ndarray>\n",
       "│   Attributes:\n",
       "│       creation_date:     2026-04-20T21:21:22.222037+00:00\n",
       "│       creator:           {'software_name': 'xradio', 'version': '1.1.3'}\n",
       "│       data_groups:       {'base': {'correlated_data': 'VISIBILITY', 'date': '20...\n",
       "│       observation_info:  {'observer': ['GG084B'], 'observing_log': '[]', 'proje...\n",
       "│       processor_info:    {'sub_type': '', 'type': ''}\n",
       "│       schema_version:    4.0.0\n",
       "│       type:              visibility\n",
       "├── Group: /global_vlbi_gg084b_reduced_0/antenna_xds\n",
       "│       Dimensions:                 (antenna_name: 14, cartesian_pos_label: 3,\n",
       "│                                    receptor_label: 2)\n",
       "│       Coordinates:\n",
       "│         * antenna_name            (antenna_name) <U2 112B 'BD' 'HH' 'YS' ... 'BR' 'MK'\n",
       "│           mount                   (antenna_name) <U16 896B dask.array<chunksize=(14,), meta=np.ndarray>\n",
       "│           station_name            (antenna_name) <U2 112B dask.array<chunksize=(14,), meta=np.ndarray>\n",
       "│           telescope_name          (antenna_name) <U3 168B dask.array<chunksize=(14,), meta=np.ndarray>\n",
       "│         * cartesian_pos_label     (cartesian_pos_label) <U1 12B 'x' 'y' 'z'\n",
       "│         * receptor_label          (receptor_label) <U5 40B 'pol_0' 'pol_1'\n",
       "│           polarization_type       (antenna_name, receptor_label) <U1 112B dask.array<chunksize=(14, 2), meta=np.ndarray>\n",
       "│       Data variables:\n",
       "│           ANTENNA_DISH_DIAMETER   (antenna_name) float64 112B dask.array<chunksize=(14,), meta=np.ndarray>\n",
       "│           ANTENNA_POSITION        (antenna_name, cartesian_pos_label) float64 336B dask.array<chunksize=(14, 3), meta=np.ndarray>\n",
       "│           ANTENNA_RECEPTOR_ANGLE  (antenna_name, receptor_label) float64 224B dask.array<chunksize=(14, 2), meta=np.ndarray>\n",
       "│       Attributes:\n",
       "│           overall_telescope_name:  EVN\n",
       "│           relocatable_antennas:    False\n",
       "│           type:                    antenna\n",
       "├── Group: /global_vlbi_gg084b_reduced_0/field_and_source_base_xds\n",
       "│       Dimensions:                       (field_name: 2, sky_dir_label: 2)\n",
       "│       Coordinates:\n",
       "│         * field_name                    (field_name) <U32 256B 'J1311-2329_0' 'EM17...\n",
       "│           source_name                   (field_name) <U7 56B dask.array<chunksize=(2,), meta=np.ndarray>\n",
       "│         * sky_dir_label                 (sky_dir_label) <U3 24B 'ra' 'dec'\n",
       "│       Data variables:\n",
       "│           FIELD_PHASE_CENTER_DIRECTION  (field_name, sky_dir_label) float64 32B dask.array<chunksize=(2, 2), meta=np.ndarray>\n",
       "│       Attributes:\n",
       "│           type:     field_and_source\n",
       "├── Group: /global_vlbi_gg084b_reduced_0/gain_curve_xds\n",
       "│       Dimensions:                 (antenna_name: 14, poly_term: 3, receptor_label: 2)\n",
       "│       Coordinates:\n",
       "│         * antenna_name            (antenna_name) <U2 112B 'BD' 'HH' 'YS' ... 'BR' 'MK'\n",
       "│           antenna_id              (antenna_name) int32 56B dask.array<chunksize=(14,), meta=np.ndarray>\n",
       "│           gain_curve_type         (antenna_name) <U9 504B dask.array<chunksize=(14,), meta=np.ndarray>\n",
       "│           mount                   (antenna_name) <U16 896B dask.array<chunksize=(14,), meta=np.ndarray>\n",
       "│           station_name            (antenna_name) <U2 112B dask.array<chunksize=(14,), meta=np.ndarray>\n",
       "│           telescope_name          (antenna_name) <U3 168B dask.array<chunksize=(14,), meta=np.ndarray>\n",
       "│         * receptor_label          (receptor_label) <U5 40B 'pol_0' 'pol_1'\n",
       "│           polarization_type       (antenna_name, receptor_label) <U1 112B dask.array<chunksize=(14, 2), meta=np.ndarray>\n",
       "│       Dimensions without coordinates: poly_term\n",
       "│       Data variables:\n",
       "│           GAIN_CURVE              (antenna_name, poly_term, receptor_label) float64 672B dask.array<chunksize=(14, 3, 2), meta=np.ndarray>\n",
       "│           GAIN_CURVE_INTERVAL     (antenna_name) float64 112B dask.array<chunksize=(14,), meta=np.ndarray>\n",
       "│           GAIN_CURVE_SENSITIVITY  (antenna_name, receptor_label) float64 224B dask.array<chunksize=(14, 2), meta=np.ndarray>\n",
       "│       Attributes:\n",
       "│           measured_date:  2018-05-26T15:33:49.500000000\n",
       "│           type:           gain_curve\n",
       "└── Group: /global_vlbi_gg084b_reduced_0/system_calibration_xds\n",
       "        Dimensions:            (antenna_name: 14, time_system_cal: 10357,\n",
       "                                receptor_label: 2)\n",
       "        Coordinates:\n",
       "          * antenna_name       (antenna_name) <U2 112B 'BD' 'HH' 'YS' ... 'KP' 'BR' 'MK'\n",
       "            antenna_id         (antenna_name) int32 56B dask.array<chunksize=(14,), meta=np.ndarray>\n",
       "            mount              (antenna_name) <U16 896B dask.array<chunksize=(14,), meta=np.ndarray>\n",
       "            station_name       (antenna_name) <U2 112B dask.array<chunksize=(14,), meta=np.ndarray>\n",
       "            telescope_name     (antenna_name) <U3 168B dask.array<chunksize=(14,), meta=np.ndarray>\n",
       "          * time_system_cal    (time_system_cal) float64 83kB 1.527e+09 ... 1.527e+09\n",
       "          * receptor_label     (receptor_label) <U5 40B 'pol_0' 'pol_1'\n",
       "            polarization_type  (antenna_name, receptor_label) <U1 112B dask.array<chunksize=(14, 2), meta=np.ndarray>\n",
       "        Data variables:\n",
       "            TSYS               (antenna_name, time_system_cal, receptor_label) float64 2MB dask.array<chunksize=(7, 5179, 1), meta=np.ndarray>\n",
       "        Attributes:\n",
       "            type:     system_calibration
" ], "text/plain": [ "\n", "Group: /global_vlbi_gg084b_reduced_0\n", "│ Dimensions: (time: 480, baseline_id: 57, frequency: 32,\n", "│ polarization: 4, uvw_label: 3)\n", "│ Coordinates:\n", "│ * time (time) float64 4kB 1.527e+09 ... 1.527e+09\n", "│ field_name (time) \n", "│ scan_name (time) \n", "│ * baseline_id (baseline_id) int64 456B 0 1 2 3 ... 53 54 55 56\n", "│ baseline_antenna1_name (baseline_id) \n", "│ baseline_antenna2_name (baseline_id) \n", "│ * frequency (frequency) float64 256B 4.9e+09 ... 4.916e+09\n", "│ * polarization (polarization) \n", "│ FLAG (time, baseline_id, frequency, polarization) bool 4MB dask.array\n", "│ TIME_CENTROID (time, baseline_id) float64 219kB dask.array\n", "│ UVW (time, baseline_id, uvw_label) float64 657kB dask.array\n", "│ VISIBILITY (time, baseline_id, frequency, polarization) complex64 28MB dask.array\n", "│ WEIGHT (time, baseline_id, frequency, polarization) float32 14MB dask.array\n", "│ Attributes:\n", "│ creation_date: 2026-04-20T21:21:22.222037+00:00\n", "│ creator: {'software_name': 'xradio', 'version': '1.1.3'}\n", "│ data_groups: {'base': {'correlated_data': 'VISIBILITY', 'date': '20...\n", "│ observation_info: {'observer': ['GG084B'], 'observing_log': '[]', 'proje...\n", "│ processor_info: {'sub_type': '', 'type': ''}\n", "│ schema_version: 4.0.0\n", "│ type: visibility\n", "├── Group: /global_vlbi_gg084b_reduced_0/antenna_xds\n", "│ Dimensions: (antenna_name: 14, cartesian_pos_label: 3,\n", "│ receptor_label: 2)\n", "│ Coordinates:\n", "│ * antenna_name (antenna_name) \n", "│ station_name (antenna_name) \n", "│ telescope_name (antenna_name) \n", "│ * cartesian_pos_label (cartesian_pos_label) \n", "│ Data variables:\n", "│ ANTENNA_DISH_DIAMETER (antenna_name) float64 112B dask.array\n", "│ ANTENNA_POSITION (antenna_name, cartesian_pos_label) float64 336B dask.array\n", "│ ANTENNA_RECEPTOR_ANGLE (antenna_name, receptor_label) float64 224B dask.array\n", "│ Attributes:\n", "│ overall_telescope_name: EVN\n", "│ relocatable_antennas: False\n", "│ type: antenna\n", "├── Group: /global_vlbi_gg084b_reduced_0/field_and_source_base_xds\n", "│ Dimensions: (field_name: 2, sky_dir_label: 2)\n", "│ Coordinates:\n", "│ * field_name (field_name) \n", "│ * sky_dir_label (sky_dir_label) \n", "│ Attributes:\n", "│ type: field_and_source\n", "├── Group: /global_vlbi_gg084b_reduced_0/gain_curve_xds\n", "│ Dimensions: (antenna_name: 14, poly_term: 3, receptor_label: 2)\n", "│ Coordinates:\n", "│ * antenna_name (antenna_name) \n", "│ gain_curve_type (antenna_name) \n", "│ mount (antenna_name) \n", "│ station_name (antenna_name) \n", "│ telescope_name (antenna_name) \n", "│ * receptor_label (receptor_label) \n", "│ Dimensions without coordinates: poly_term\n", "│ Data variables:\n", "│ GAIN_CURVE (antenna_name, poly_term, receptor_label) float64 672B dask.array\n", "│ GAIN_CURVE_INTERVAL (antenna_name) float64 112B dask.array\n", "│ GAIN_CURVE_SENSITIVITY (antenna_name, receptor_label) float64 224B dask.array\n", "│ Attributes:\n", "│ measured_date: 2018-05-26T15:33:49.500000000\n", "│ type: gain_curve\n", "└── Group: /global_vlbi_gg084b_reduced_0/system_calibration_xds\n", " Dimensions: (antenna_name: 14, time_system_cal: 10357,\n", " receptor_label: 2)\n", " Coordinates:\n", " * antenna_name (antenna_name) \n", " mount (antenna_name) \n", " station_name (antenna_name) \n", " telescope_name (antenna_name) \n", " * time_system_cal (time_system_cal) float64 83kB 1.527e+09 ... 1.527e+09\n", " * receptor_label (receptor_label) \n", " Data variables:\n", " TSYS (antenna_name, time_system_cal, receptor_label) float64 2MB dask.array\n", " Attributes:\n", " type: system_calibration" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ms_xdt = ps_xdt['global_vlbi_gg084b_reduced_0']\n", "ms_xdt" ] }, { "cell_type": "code", "execution_count": 6, "id": "ba761deb-b7a9-4b2b-8163-3050413fe59c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataTree 'antenna_xds'>\n",
       "Group: /global_vlbi_gg084b_reduced_0/antenna_xds\n",
       "    Dimensions:                 (time: 480, baseline_id: 57, frequency: 32,\n",
       "                                 polarization: 4, uvw_label: 3, antenna_name: 14,\n",
       "                                 cartesian_pos_label: 3, receptor_label: 2)\n",
       "    Coordinates:\n",
       "      * antenna_name            (antenna_name) <U2 112B 'BD' 'HH' 'YS' ... 'BR' 'MK'\n",
       "        mount                   (antenna_name) <U16 896B dask.array<chunksize=(14,), meta=np.ndarray>\n",
       "        station_name            (antenna_name) <U2 112B dask.array<chunksize=(14,), meta=np.ndarray>\n",
       "        telescope_name          (antenna_name) <U3 168B dask.array<chunksize=(14,), meta=np.ndarray>\n",
       "      * cartesian_pos_label     (cartesian_pos_label) <U1 12B 'x' 'y' 'z'\n",
       "      * receptor_label          (receptor_label) <U5 40B 'pol_0' 'pol_1'\n",
       "        polarization_type       (antenna_name, receptor_label) <U1 112B dask.array<chunksize=(14, 2), meta=np.ndarray>\n",
       "    Inherited coordinates:\n",
       "      * baseline_id             (baseline_id) int64 456B 0 1 2 3 4 ... 53 54 55 56\n",
       "      * frequency               (frequency) float64 256B 4.9e+09 ... 4.916e+09\n",
       "      * polarization            (polarization) <U2 32B 'RR' 'RL' 'LR' 'LL'\n",
       "      * time                    (time) float64 4kB 1.527e+09 1.527e+09 ... 1.527e+09\n",
       "      * uvw_label               (uvw_label) <U1 12B 'u' 'v' 'w'\n",
       "    Data variables:\n",
       "        ANTENNA_DISH_DIAMETER   (antenna_name) float64 112B dask.array<chunksize=(14,), meta=np.ndarray>\n",
       "        ANTENNA_POSITION        (antenna_name, cartesian_pos_label) float64 336B dask.array<chunksize=(14, 3), meta=np.ndarray>\n",
       "        ANTENNA_RECEPTOR_ANGLE  (antenna_name, receptor_label) float64 224B dask.array<chunksize=(14, 2), meta=np.ndarray>\n",
       "    Attributes:\n",
       "        overall_telescope_name:  EVN\n",
       "        relocatable_antennas:    False\n",
       "        type:                    antenna
" ], "text/plain": [ "\n", "Group: /global_vlbi_gg084b_reduced_0/antenna_xds\n", " Dimensions: (time: 480, baseline_id: 57, frequency: 32,\n", " polarization: 4, uvw_label: 3, antenna_name: 14,\n", " cartesian_pos_label: 3, receptor_label: 2)\n", " Coordinates:\n", " * antenna_name (antenna_name) \n", " station_name (antenna_name) \n", " telescope_name (antenna_name) \n", " * cartesian_pos_label (cartesian_pos_label) \n", " Inherited coordinates:\n", " * baseline_id (baseline_id) int64 456B 0 1 2 3 4 ... 53 54 55 56\n", " * frequency (frequency) float64 256B 4.9e+09 ... 4.916e+09\n", " * polarization (polarization) \n", " ANTENNA_POSITION (antenna_name, cartesian_pos_label) float64 336B dask.array\n", " ANTENNA_RECEPTOR_ANGLE (antenna_name, receptor_label) float64 224B dask.array\n", " Attributes:\n", " overall_telescope_name: EVN\n", " relocatable_antennas: False\n", " type: antenna" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ant_xds = ms_xdt.antenna_xds # or ms_xdt[\"antenna_xds\"]\n", "ant_xds" ] }, { "cell_type": "code", "execution_count": 7, "id": "cf6106c1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['BD', 'HH', 'YS', 'SC', 'HN', 'GB', 'NL', 'FD', 'YY', 'LA', 'PT',\n", " 'KP', 'BR', 'MK'], dtype='\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DatasetView> Size: 5kB\n",
       "Dimensions:                       (field_name: 2, sky_dir_label: 2,\n",
       "                                   baseline_id: 57, frequency: 32,\n",
       "                                   polarization: 4, time: 480, uvw_label: 3)\n",
       "Coordinates:\n",
       "  * field_name                    (field_name) <U32 256B 'J1311-2329_0' 'EM17...\n",
       "    source_name                   (field_name) <U7 56B dask.array<chunksize=(2,), meta=np.ndarray>\n",
       "  * sky_dir_label                 (sky_dir_label) <U3 24B 'ra' 'dec'\n",
       "  * baseline_id                   (baseline_id) int64 456B 0 1 2 3 ... 54 55 56\n",
       "  * frequency                     (frequency) float64 256B 4.9e+09 ... 4.916e+09\n",
       "  * polarization                  (polarization) <U2 32B 'RR' 'RL' 'LR' 'LL'\n",
       "  * time                          (time) float64 4kB 1.527e+09 ... 1.527e+09\n",
       "  * uvw_label                     (uvw_label) <U1 12B 'u' 'v' 'w'\n",
       "Data variables:\n",
       "    FIELD_PHASE_CENTER_DIRECTION  (field_name, sky_dir_label) float64 32B dask.array<chunksize=(2, 2), meta=np.ndarray>\n",
       "Attributes:\n",
       "    type:     field_and_source
" ], "text/plain": [ " Size: 5kB\n", "Dimensions: (field_name: 2, sky_dir_label: 2,\n", " baseline_id: 57, frequency: 32,\n", " polarization: 4, time: 480, uvw_label: 3)\n", "Coordinates:\n", " * field_name (field_name) \n", " * sky_dir_label (sky_dir_label) \n", "Attributes:\n", " type: field_and_source" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ms_xdt.xr_ms.get_field_and_source_xds()" ] }, { "cell_type": "code", "execution_count": 10, "id": "b013c3d3-45e1-456f-9fa8-8baff88c5b17", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataTree 'gain_curve_xds'>\n",
       "Group: /global_vlbi_gg084b_reduced_0/gain_curve_xds\n",
       "    Dimensions:                 (time: 480, baseline_id: 57, frequency: 32,\n",
       "                                 polarization: 4, uvw_label: 3, antenna_name: 14,\n",
       "                                 poly_term: 3, receptor_label: 2)\n",
       "    Coordinates:\n",
       "      * antenna_name            (antenna_name) <U2 112B 'BD' 'HH' 'YS' ... 'BR' 'MK'\n",
       "        antenna_id              (antenna_name) int32 56B dask.array<chunksize=(14,), meta=np.ndarray>\n",
       "        gain_curve_type         (antenna_name) <U9 504B dask.array<chunksize=(14,), meta=np.ndarray>\n",
       "        mount                   (antenna_name) <U16 896B dask.array<chunksize=(14,), meta=np.ndarray>\n",
       "        station_name            (antenna_name) <U2 112B dask.array<chunksize=(14,), meta=np.ndarray>\n",
       "        telescope_name          (antenna_name) <U3 168B dask.array<chunksize=(14,), meta=np.ndarray>\n",
       "      * receptor_label          (receptor_label) <U5 40B 'pol_0' 'pol_1'\n",
       "        polarization_type       (antenna_name, receptor_label) <U1 112B dask.array<chunksize=(14, 2), meta=np.ndarray>\n",
       "    Inherited coordinates:\n",
       "      * baseline_id             (baseline_id) int64 456B 0 1 2 3 4 ... 53 54 55 56\n",
       "      * frequency               (frequency) float64 256B 4.9e+09 ... 4.916e+09\n",
       "      * polarization            (polarization) <U2 32B 'RR' 'RL' 'LR' 'LL'\n",
       "      * time                    (time) float64 4kB 1.527e+09 1.527e+09 ... 1.527e+09\n",
       "      * uvw_label               (uvw_label) <U1 12B 'u' 'v' 'w'\n",
       "    Dimensions without coordinates: poly_term\n",
       "    Data variables:\n",
       "        GAIN_CURVE              (antenna_name, poly_term, receptor_label) float64 672B dask.array<chunksize=(14, 3, 2), meta=np.ndarray>\n",
       "        GAIN_CURVE_INTERVAL     (antenna_name) float64 112B dask.array<chunksize=(14,), meta=np.ndarray>\n",
       "        GAIN_CURVE_SENSITIVITY  (antenna_name, receptor_label) float64 224B dask.array<chunksize=(14, 2), meta=np.ndarray>\n",
       "    Attributes:\n",
       "        measured_date:  2018-05-26T15:33:49.500000000\n",
       "        type:           gain_curve
" ], "text/plain": [ "\n", "Group: /global_vlbi_gg084b_reduced_0/gain_curve_xds\n", " Dimensions: (time: 480, baseline_id: 57, frequency: 32,\n", " polarization: 4, uvw_label: 3, antenna_name: 14,\n", " poly_term: 3, receptor_label: 2)\n", " Coordinates:\n", " * antenna_name (antenna_name) \n", " gain_curve_type (antenna_name) \n", " mount (antenna_name) \n", " station_name (antenna_name) \n", " telescope_name (antenna_name) \n", " * receptor_label (receptor_label) \n", " Inherited coordinates:\n", " * baseline_id (baseline_id) int64 456B 0 1 2 3 4 ... 53 54 55 56\n", " * frequency (frequency) float64 256B 4.9e+09 ... 4.916e+09\n", " * polarization (polarization) \n", " GAIN_CURVE_INTERVAL (antenna_name) float64 112B dask.array\n", " GAIN_CURVE_SENSITIVITY (antenna_name, receptor_label) float64 224B dask.array\n", " Attributes:\n", " measured_date: 2018-05-26T15:33:49.500000000\n", " type: gain_curve" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ms_xdt.gain_curve_xds" ] }, { "cell_type": "code", "execution_count": 11, "id": "b90b349e-37f2-43f0-92a8-589068323473", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataTree 'system_calibration_xds'>\n",
       "Group: /global_vlbi_gg084b_reduced_0/system_calibration_xds\n",
       "    Dimensions:            (time: 480, baseline_id: 57, frequency: 32,\n",
       "                            polarization: 4, uvw_label: 3, antenna_name: 14,\n",
       "                            time_system_cal: 10357, receptor_label: 2)\n",
       "    Coordinates:\n",
       "      * antenna_name       (antenna_name) <U2 112B 'BD' 'HH' 'YS' ... 'KP' 'BR' 'MK'\n",
       "        antenna_id         (antenna_name) int32 56B dask.array<chunksize=(14,), meta=np.ndarray>\n",
       "        mount              (antenna_name) <U16 896B dask.array<chunksize=(14,), meta=np.ndarray>\n",
       "        station_name       (antenna_name) <U2 112B dask.array<chunksize=(14,), meta=np.ndarray>\n",
       "        telescope_name     (antenna_name) <U3 168B dask.array<chunksize=(14,), meta=np.ndarray>\n",
       "      * time_system_cal    (time_system_cal) float64 83kB 1.527e+09 ... 1.527e+09\n",
       "      * receptor_label     (receptor_label) <U5 40B 'pol_0' 'pol_1'\n",
       "        polarization_type  (antenna_name, receptor_label) <U1 112B dask.array<chunksize=(14, 2), meta=np.ndarray>\n",
       "    Inherited coordinates:\n",
       "      * baseline_id        (baseline_id) int64 456B 0 1 2 3 4 5 ... 52 53 54 55 56\n",
       "      * frequency          (frequency) float64 256B 4.9e+09 4.901e+09 ... 4.916e+09\n",
       "      * polarization       (polarization) <U2 32B 'RR' 'RL' 'LR' 'LL'\n",
       "      * time               (time) float64 4kB 1.527e+09 1.527e+09 ... 1.527e+09\n",
       "      * uvw_label          (uvw_label) <U1 12B 'u' 'v' 'w'\n",
       "    Data variables:\n",
       "        TSYS               (antenna_name, time_system_cal, receptor_label) float64 2MB dask.array<chunksize=(7, 5179, 1), meta=np.ndarray>\n",
       "    Attributes:\n",
       "        type:     system_calibration
" ], "text/plain": [ "\n", "Group: /global_vlbi_gg084b_reduced_0/system_calibration_xds\n", " Dimensions: (time: 480, baseline_id: 57, frequency: 32,\n", " polarization: 4, uvw_label: 3, antenna_name: 14,\n", " time_system_cal: 10357, receptor_label: 2)\n", " Coordinates:\n", " * antenna_name (antenna_name) \n", " mount (antenna_name) \n", " station_name (antenna_name) \n", " telescope_name (antenna_name) \n", " * time_system_cal (time_system_cal) float64 83kB 1.527e+09 ... 1.527e+09\n", " * receptor_label (receptor_label) \n", " Inherited coordinates:\n", " * baseline_id (baseline_id) int64 456B 0 1 2 3 4 5 ... 52 53 54 55 56\n", " * frequency (frequency) float64 256B 4.9e+09 4.901e+09 ... 4.916e+09\n", " * polarization (polarization) \n", " Attributes:\n", " type: system_calibration" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ms_xdt.system_calibration_xds # or ms_xdt[\"system_calibration_xds\"]" ] }, { "cell_type": "code", "execution_count": 12, "id": "ab743d16", "metadata": {}, "outputs": [ { "data": { "image/png": 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3724SJ04s577OnTubHTt2yHksWbJkJmnSpCZPnjwyXkf3+Z2F/cScjfPHpk2bzE8//WSOHTtmNmzYYPLnzx/n11cCG52HKe6kZcuWZvr06WH/f/TRR02RIkVMnz59TL58+eS2VKlShd3POY+FfokSJUzz5s1N4cKFPfKFXL161TzxxBMyf4ppDRTP4qypxLhD+SKvXLniVEDh/v37MoEfOXKkWbp0qUmdOrUMnLly5ZKT0LRp08yZM2dkMvvWW2+ZN99802TJkkW/BT+D73v06NEyuSEAxMSCyYyv4TdEsKNSpUoaUAhS7t27JwsH+8I56OzZs+bUqVOmYcOGZvDgwaZjx45RBhEmTpxoFi1aZNauXWtu375tsmfPbl5//XXTrl27kDrX2MdK1qxZzdatW83+/fvNgQMH5Prvv/+WhUetWrVMjx49TN68eX29uYoSBsG/AQMGSPDwySefNPXq1ZP5B8HGzz77zFy6dEnOASys7YU4k8uXXnrJ5MiRQy5MRB0noY5jW9GiReW+9evXy+TUF2MLcyQCByRs0qdPL7dxfvv5558lgOIYcCWoMmfOHPk/QZQffvjB5cAB51WCECRyVq5caU6cOCHBRfYT+4PgBq/JtJgEz5IlS+R5nBtKlixpypYtK+cLTfIoOg9T3Enjxo1lXUgCGjgnduvWzezdu1fOU8C5iftfffVVUc7/9ttvZuzYsWbu3Lky53v77bd9u/4loKBEz5UrVwi6yLUzNGvWTB5vXx5++OGwv1OlSmW1bt3a2rt3r+72AGHOnDny3R08eNDyB+7cuWPNnTtXrpXQ4rfffpPf4vLly6N8zIQJE+QxyZMnt4YNG2YdOnTIClWiO1YuX75sjR071sqaNauVOHFi6+jRoz7ZRkVx5P79+9aMGTOsJ554wkqUKJHVs2dP6+bNm+Eec+3aNeuTTz6R323OnDnlOYxTZcuWtTJnzhw233jkkUes9evXR7qDd+3aJc/n8tJLL8l7ents2bZtm2zn7t27w27r1auXFT9+fOuFF16wOnbsaM2fP9+6d++e3Ldp0ybrvffes3r06GENHjzYOnbsWIzvcebMGWvSpEnWm2++KfuD90uZMqVVtWpVq3379tbrr79uPfnkk3J7/fr1ZX+3atVK/j9mzBjrwoULHt0HSmCi8zDFnTRq1EjOSY5w7uY8dPbsWfk/f3Oej8jbb79tpUiRwrp48aJP17/Br3n1AUT3x48fL1FvIttcyHBzTaYwFKTGgZQFQk7Jd5U5c2bJ7pIJRlXyxhtvyGOefvppuUatQMYoefLkPt5qJVT59ddf5ZqsWlQQpabUauDAgZKFQ0qnPAhR93fffdfUrVtXFAxIq8mYKoqv2Llzp2TsN27caKpVq2aGDh0qc4aIPPzwwyL7p1SgUKFCkrni8Vzsce333383bdu2NRUqVJBsPgolR5DIkv0iC0/mnwwZCgdvwnHHfGjhwoXyOYCShccee0z2ARJg9gGKLOS2u3fvlrIwssN8ZtQZL774oqg3UDZQUgqoG+bNmyevu23bNrkNBYe9P/g74jyM8yZlDai6UIGwf1u0aOGW8glFURRXoLwNNTvnf9Yj0YHydOrUqaK8QmXlM9wezghCXFUoKIHDvHnz5LuNFy9eOFUJmQwbsj99+vSRTA5Zo1mzZsltvkIj46ELagN+n2QRY2Lp0qWS6Rs/frwVqjh7rJCVTJo0qWRtVfmj+IIPP/xQxqF8+fJZK1eujPaxjD9k8FEnXL16NcrHoWyoVq2a9dBDD1knTpyI8nG8Rtq0aa13333XowoFlAYog1ADnTx50rp06ZKVN29eGW8jG1NRF9hjMuNv0aJFrS5dulinT58Wlca0adOsSpUqyedDafTZZ59ZL774ojyejB2qhMmTJ8vrKIo70XmY4m6FwkMPPSTKUi6cwx5//HFr586dYY+JSqHAeZ77UG35cv2rbn9KSGNbiGBQRfb3r7/+kjrJ559/PuwxZCgwg6LumiwKnhfUMFFTpCjeBCNB1Alk32LilVdeEe8WspNK9LRu3VrqyDFBQoFEvbRdt6go3oDMOKaCZOFffvnlaB+Lkg6jwAYNGoRl5QHjaLLyGAfu27fPnDt3Toye8Q7AtDAqeA18RBxNweIK78k2kmWbNWuW1P6mSZPGPPLII9I1C0Ugyk3GXBSdkSkBMJ20PwsZO/wPUF7ht4BKo379+uIVQy0xPhMoEPB+mD17tjl//ryc+1Aa8DqKoij+TNmyZcU/hgvKKuZwnL8xh3VmHeNrNZVq75WQhvKUxx9/3LRv395MmDBBpEWYL1IGERHcsOnWgVs+0lImK82aNfPJdiuhS9WqVeW3iuw3JuM0gl9MsukcgYxYiRwWOkjEGcgJLA4fPlyCCiyIWPgoiqeh+wpyVcYffnvRwWKaUjwWzY6weKZLUUQKFiwov/HowCl8zJgx8ndsvboptUB2yzhJYIPyQUfKlCkjJVg4mCPRPXnypJiJUfoQFZQ0xARj8+rVq+U8F9PnVBRF8UeSJ08ersSNQCulmePGjTP9+vWL8nkYTQOBWl+iCgUlpGFBRraXmklad3700Ufiqk+kMCrwVsBzAdd4RfE2ZCWZOLPojQnc0MkUOqNoCHXInLLgadWqlWRF6ajBwkhRvAHHKgt/1HAxQecWOg4RBLNBMUcbyEGDBomabvPmzeKPMGPGDKeUB4kSJZLsPxBQRw2AKsBZtm/fLm3MCLYTiMOTgeMINQXnK4IHKAEJmuBj8M0338j/owsmuAKdFzSYoChKsBAvXjw5r7EmiQ66/tCBoXz58saXaEBBCXlo34kZVp06dcIyNB9//LFMhKIC8yoWG/T+ZuGhKN6CYBaKGtqZIvWNDib4tJHTsgfXyJgxo2RRL1++HKfvSlGchYkjwWpKH1h8RwXqAZRxGK86Bh9Q1hA8RHFH6QRjFKV5tKamtMAZkNcCwUrGRUoh6EGOyik6eF+UBygFDh06JBkzjBRRF1BKxLHEdmm7RUVRQp1Tp4zp1eu/64iBYtpFcuEcikEtQV3bIB6Yk3A/ZRCowVChEjBm7UJSxJdoQEFR/l+pgLwIlQIsX75con1kViKjQ4cOUm+Nu2qmTJmkVp0DmomVongagglI8VlY3L9/P9rH4miOY7riGgzOly5d0t2meAWC2nQ0eO+99ySgFRVIX/ElmDx5criuDAQAatSoIeeEuCqS8DWghpeyBMoAKSeIDsoW2P4vv/xSfF4URVGUyCGQ0Lv3gwGFpUuXSuCVy3PPPSeqL5JBKCdtmjRpIvfjpUUQl/I3ztV0uvE1GlBQlP+HTArZIepQgVrWqNpo5ciRQ+SkZ86ckXp2Agnvv/9+jBljRXHXb5W6uvXr15uOHTtKZDsyrl69KmZoZCkV5zl27Ji5deuWKhQUr3DhwgUpeShQoIDIVyOC+qBnz56iNMA8sXfv3uGOaUoc+vfvb/744w/xVkEdEBdQJhCgoHyBckBaTEYFQXcUfbRddMbvQFEURQkPAWLUZ/aFuRuBAsYFG8f7KYOg5I3nkdz0B9SUUVEimKJwgNKzG5UCyoPowOiOiCFu2nv27JFAg6J4A3w+hgwZImZuixcvlgBDRDO3Tz/9VCRzLEIU5zKz1JGzL5Fps6BSFE+DISIS1sqVK0tGCtUbYxHH9cyZM0X+So0shqzDhg0LK02wwZOAMYjJJ+o5zIbjCsH1unXrijdDdH4t3bp1c0sQQ1EUJVg5dep/ioRdu8Jfw+OP/3cJZFShoCiRQF3S2LFjxQDPGah5pS1XTOYpngQTLExc7AtSVWpo9+7dG/YYx/uZoJKFUsO+wAV1wq5du0Sej1cCEjgWAGQVaZ3GQoCWiCxQlMhhX7EPkZrjnD9lyhQpJ2GBRmBxzpw5uusUj4I7N94oGCg2bNjQlCtXTiSvI0aMkDI8OgvRMYESBIIFEduDderUSRb1hw8flgAEi3xUDCgHInZacBaCGNTqjhw5UtqXRYRyoM6dO5uvvvpK3gu/FkVRFOVBvv7amKJF/7u8++5/t3Ft38b9gY4qFBTFDWBaRVYTk6zIJKveggACEndgMsjEkkXRiRMnwh7D/TwOSdXo0aMleMKCKn/+/D7bbiX2sBDBTR0PDxYQBIgeeughybajsOE25X+wX3DAp0sLF2q/CQQSYGO/YcaaJUsWkaFzjBCYUxRPguqAi9168e+//5ZSgkKFCkk3h5jAvCtBggTS9hQvIEf4TUfXciwq8GIgoEGwon79+hKwoAyQMquffvpJjh28hxjzCFoqiqIokdOihTFVqvxPmUAwYdw4Y+xqhUBXJ4AGFBQljlC/jhM2WRoyNizWkaAykfM2TD7tTBHXXbp0kcw12da0adPK7WSzuY8L7cE+//xz6V2uAYXAhd8ak3ok0ZRA8DvA0yO62udQg+AAi6Ov/z8VQNCALDDHAMcH5SOUOFDyQPkSXV+4L6ayJ0Vxtz+Kq6VzdE+g3AElA1A6gcoBDyB+49GBazhBNFQPtsEr5Q60UKZGl/HshRdeEKUb99OakWMCA0mOkfTp08fh0yqKogQ/j0dS0kAwwU/sD9yCBhQUJQ5gxkiGGAUA5QPp0qWT+lbqX59//nmZiGFUxaQuVapUXt3X1M7jBp49e/ZIs6xIvTGUtHuQK4EPgS1k0Up48EOhpIHFEwG02rVrhwXevvnmG6k7537awJLlxZiV+nGCM4ri7yxatEi8fAgsYBZMEMAGHwbUD3iuMCbZbR0xcrTLGmwo26PsgsAk6ghUCJRgYNJIIA6Plly5cj1QcqEoiqKENhpQUJQ4QB0pWRyCBdSpI1UFMj6bNm0SaShSUSZgZHVoq5U3b16P7fOFCxdKGxl7G2gvw22O/b9ZKJHRRuZNxunJJ5+UBZai+BIW8rjUc0xxQfrt6PnBJUmSJBII4HdNaz2uWexEXOBcu3bNnDp1Si5Qq1YtOf5YFGXNmjWc9wlycbxSKP/Bf4LHEiSkFZ4unBR/hnM4qhsCYQQRSpcuLZ4fO3bskPM6JQqMObiADxo0SI4pyhRQQOTJk8e8++67co1qDedwAuSMCYwZBCBoW0YAgoAcqjtFURQlbjz+uDE9ewZHmYMj8SxGESVGqSwLRiab1NkqSkRjPIyrmLhhoEXnByZyZHdoP4dRFu0kOdSYrOG38Mknn4iLfGwgQEHWCXMuJoeOpowENKilBxZlLJJ4LO1nWEixQOL+8uXLy+KtXbt2su0vv/yyfqmKT+C4oFyIiw3nWVQ+wCKH3zzXqGpYFEWUiBNYQIWD7wEZV4JpjhlXlAm0tXMMEPC+NWrUkMAfmVqORxZeBDaA+5CR057PMSCnKP4CwTCCCZiJojLjd0wwjGPg/PnzEoBDnUD5E/MYbn/qqafCjRuRjS2UShGYwBeIY4cOFL4o4VMUfyWqeZiihOr6VxUKihJHMLzC1JBsDzDxQhqKDNVeFNFHnIwPCxoCD0zUWMwTXHBnKQTvR4mDDQZdvD7vZxtzkeHlMVwwaGRARP5KuYaieNt/5J133jHffvut/D4pO+D3isGcDeZ0jiU71HDnzp1bAmSofQgaoDQgmMB9toKBa37TBPTq1av3gNrgxx9/NHPnzpU2fQQBKX2wgwkcm5SO0CmDTC+lS4rijwEFAmz8lglm8xsuimX4/yvUCMLZk0COKy6c8xmPeG5EtQ6vg7cIxxfPrVixoiggNJigKIqiRIcGFBQljmCAt2rVKonk7du3Twzd2rdvLxLUDz/80FSpUkUWPhhdoQxgofPMM8+IBJWOENRvf/DBByLddjcsosiuRtXOsnjx4jIB7d+/v9SWK4o3oJUdC3kCXQcPHjTfffedGLxFBgsbJNoEBhyfj+qHQATeJbTL5PmoFRyxW+lFhCBFq1atTPXq1UWFYB8LGDNyO9F4HO7J+iL3RvnD/8nw0jUF80bMTjUzpfiSnDlzig+O7YXjiB3MjgjKOUp/GHso7+F3jxqBTjGMQQTSaAXJuEUphKIoiqLEhOo4FcVNkAkik8nihkwPCyEmbPS0RylAEGHdunVinId6gckbC6U+ffpIFpQAgys9wwcONOb/S8TDZXxZ9HBB/koWCnPGN954I8rXQSXBBNP2f1AUT8DxQAaVnvb4H/C7w3dk2bJlch9dKmhPh2IG8ziypBiaEqAjeMA1AQCCAfyf57Dgp867adOmssh3BsomCBpwrKAWspULmM1RvmSXUHAMU/ZAIAGPEcqVCBKy+KJEiGOW11CUQILjDzj+CC7QmYixCOXCkSNHwnx2NJigKIqiOIsqFBTFA9DpgQvSUtrQNW/eXBQMLEwwavzll1/Ey4AFFu0aybJ269ZNvBVYINmy1egYNMiY118Pb+yydOlSWQwBk0QWZWSCy5QpE+XrsEjKli2bqBTwXFAUd3Pnzh3xMJg5c2aYOSkBNkoKWLQTJOD/tDbFywCjOK43b94sZnAcS8OHD5fHY7jI7QTnuHC83Lp1S0ofYoJacIIWPJ9jzz5WIoPt4sK24zdiu9uz7WwPt6lCQfEnCHrxG2fcsS+UMuCbwO+XC2UOqNYoeUO9Q1DMVvag6FEURVEUV9GAgqJ4EEodkJCy8CD7SSkECyeyRLTmQjlARpZaVRYoPP6LL74wkydPdvm9eE5Mz4vMg5VFEmoGRfEEKGRYmLP4p40pCoGxY8eajRs3iucBHgrNmjUT+XZkv1cCbQMGDJCacIJxBMo4Xri4Au307Mwr24L6wRlQQRCYs6FzBCVOdFNBVo76iCCFovgazBdRmwHHCeMJaiC6PlAe5GhoyrGEZ0nEMiFFURRFcRUNKCiKhyEbhJyUDOvKlStFMo1fAVJqGxZXLJpYPPXs2VMe62hEpyiBCE7zlStXFtNPlAmUGuAaj5cIagWCaizYI4OShqFDh4qvAYod/BbiAgEFvBGoDY+LySIKCbK7LNZ2795tFixYoAEFxS9AYUBgjnGGUjtHI1LUC5S10QaSizMqOEVRFEVxBg0oKIqXwEmekgccszGhY+Jnt/IiY8oiBYUBEuv58+ebJk2ahHs+fglc7t//3227dv3vb9TbwdbXVglc/vzzT1ERnDhxQkoZWORgUopXAm3uYgJFD8Zx1HjTUjKqwIOzKgnKIgAvBoIVjp0kXAHZuK3ooUQJAzuCDJ4wVVUUV+C4mjVrlpQ6oMRx/E0S2Ma/hwsBPUVRFEVxF2rKqCheBpd4JNS0tEM+jREjqgXk3fgYsGChDWVEULKSVCpV6n+30amS27j8v9JVUXwOmXtUAHRwIDNKS0daNKLKcSaYAJQU0Oaub9++cQom2PJuFA54lVB2xLZwWbhwofiOcMFrBLd8tpEyJQIhjhw7dkwUFXhBcNwCBqwEK/BVUBRfQ6CA4waPDzxISpQoYT7++GMJ5kVW7qYoiqIo7kAVCorigzaTdHTAlZ5sacGCBeVvJnzUmlNXvnPnzgee16KFMVWq/KdQsBsyoAIvUuS/v1WdoPgD06dPF08Eu1UpNdwobigRcDXwRjABA0VaNMYFjB2LFCkiBo6NGjWK9rH4IuDXAJjZNWjQwBQuXFgUQwQ2CABSvsFnJNvLduIN0aVLlzhto6LEFVpA/vXXX2K2uGbNGmlVTLkd4w1BBvw+OB4VJSYIBBNEpfSSwK6iKEp0qEJBUXwARozIwMmE2hBUKF26tNSdc19ECBgQPChY8H+38X/7ogEFxZdgttihQwfxE7CDCeXKlTPbtm1zOZgAxYoVExUPLVYJtrGQd7ZNI49HlcA2gd1eku2iPOHSpUvm3LlzojSgNAM1ArfxeBQHv//+u3Q+wSiVtq60qyxbtqx4n0DJkiXlGj8IDBo5nhXFH8A3gXK6Fi1amBkzZshvHJ8PVEP58uUz8+bN8/UmKn4GnUFGjRolahYCrrTtpTQTNVlMAVhfQsnolClT5HeOCk5RFN+hCgVF8QF0fahXr544bw8bNkwmgVxoJYkhI2Z0ihJI1KpVS8oIbPBK+PTTT2PdWpGab7L/33//vUx48R1Bwr1hwwaZ7EZHr169JBCwfPnycK+XJEkSp967bdu25ujRo/I3288xyefBz4HgBPcR9KM8giDHRx99FKvPqCiehnGF42jfvn3SUaVmzZpyTHGtBB506kA5QBCU8yFeGa4uwlGxYFDLBXNcWuiCHYCN6P3kb8oJPKcmTpxofvzxRwkSE0Dm/I5CB4UnQWMCwwSl8bhRFMXzaEBBUXyEnfEk85o+fXq5LU+ePCJLdQYU1qpKUPyFQoUKif8HpQU4yDvrlRAdZFmZODKBfvzxx82WLVtE9YBCgCws5oos6CkjIpNGoIGWlAQTAFd7asldhc4Sr776qrw+qiHKkoDPlSFDhnCKhDZt2shtiuLPIF3HsLFhw4bmrbfeEs8QVHFKYIBy8eWXXzZ79+4Nuw1TWHyXnOGXX34xI0eOlPIs26AWOHcNHjxYggucNwm60gmH1taY6tItxNfQJQiVDeVvmzZtEnUZJtZ8fjxtCCbgg0MAmc+WKVMmUSwwFlA2F5niU1EUN2MpMXLlyhXcjORaUdzF3r175Xe1ceNGl553584da+7cuXKtKP5Go0aNrKpVqz5w+5o1a+T3funSpbC/n3nmGevff/8N97hUqVJZkyZNCvt/iRIlrDRp0sjjo7okSZLEOnPmjDVv3jwrfvz4VuvWra3UqVNbb7zxhtuPlfv371v79u2zZs6caa1evfqB7VcUf+bu3btWrVq1rIQJE1oLFiwId5+OLf5L7dq1rccee0zOjevWrbMqVqxoJUiQwMqTJ49Vo0YNq1u3bta3335rHT58+IHndujQQc6TGTNmtPr162dt2LDB+uWXX6zjx4+HnRs5f77yyivyuJEjR1r+NE9KmjSplTx5cqtcuXLyOfn8nIej49SpU/JZpk2b5pHt0mNFCQWuuLD+VYWCovgIarSBOm0c8RUl1OC3j49IxBapjlBW8O6770qGDsNEJNzIWZMlSybZKAzDUDLQaYFSIfwOaDdJHTCtJ5F5k93C8NQd8P54QsTGF0JRfA3tUim1q1OnjpQ9IH8P1AwuUndb7h7MUBZJaQJeAahLACUj/jK0sCWDj5Lr5MmTch/Kxw8++CDs+TwGVdfq1aujLEHjN4D57LJly6RTiK+5e/euOXTokPxGc+TIIeoEzvnOwmfgXE0bbkVRPE9wn4UVxY9hwMN07siRI77eFEXxCZQL9OzZM1qzReqEkfliqmjLW+1aYuppMVOkROHrr7+WUgQkvUxGT58+LRNKsE0iFUX5zxeEQBuLrUBccCHPt1vQEmRE4k6pFcGRYIJgSe/evSUw2rVrVwkC2dBh5r333pPgAcGEpk2bhgWG8MhwhLIGzGdZoPOadLEh+EBg1n6fbt26STcQfG+iC/B6EraDoHD+/Pnle+WaQDKlOq4EE+wSD16PUjxFUTyPKhQUxYcwAaAeUFFCEcwPMQRjsly+fHlZEBBcIPPG3yx2CA5kyZLFtGzZUky2yNbRwYFaXwJyXKgPJ9CAWeK4ceMkwIC7ffPmzeV9ateu7euPqih+hR1w4/gJJDZu3Cj+JpwnOK4JJLRv314WyJw3OEewiIzJuDUQIJjAZcCAAdKBIargCudHKFOmjJxLuXaEFrcrVqyQBTr7BW8YG5QM+BLs2LFDzq3t2rUzvgI/iO7du0unIIIlqMD4LqMzniQoQmtUWm3TyeTixYvmmWeekWAzahz+VhTFC3ilCCPAUQ8FxRNQA5gyZUpr8ODBLj1Pa/f81zuAU+rAgQPD3T5nzhy5PaKPQDDvh4ceekhqXh0v+BxE9FD48ssvw3khxIsXT66TJUsW7nZ8FCKrmb148aI1a9Ysq0WLFtbTTz8tj8VDgXrjQ4cO6bGiKFHQt29fK23atAE3tkyfPl2Oc45vmyeeeMIqVaqUlThxYrmPcXXEiBFWIHLjxg3r119/lc/36KOPWk2aNIn28efPn7cKFSoUdq7Mli3bAx4InDtXrVplFS5cOFIPmieffFI8YXzJsGHDZFv4XbrC5MmT5XnFihWz3n33Xevjjz+2qlWrZmXPnl08dDxFIBwriuLN9a+WPCiKjyCSTgunp556Sr+DIIGsOY7ZyPBDvYPJzz//HO4SWfcSZKmAwqBVq1ZSmoC01c6a0lVh9uzZkpW0yxd+/fVXkec+99xz0iLszTfflNpgXMnnzJkj7t5IfukCoShK1AqFQOxOYnsA0NnFplOnTtJKkFr7tWvXyjUyeSTvgQZZejLzdMwhwx6TuopzJ5n5y5cvy7mSzjbr16+X+yhtoBSsQIEC0iHi8OHDouCg0welBbTjxVuGLjqcM5csWWJ8wejRo02HDh2kawPn9pigDIKxg2vmUYwZlMTR1QE1B+MAn3X+/Ple2X5FUbTkQVF8akgHGlAIHpDt//7772bgwIEiuw9VqH+lrZcjdn0zE15qfoHJLnXBTHY//PBDkeXijcBigRZg7E8CCcib6TlOT3Emy/RGr1Chgpg1cp01a1affE5FCVQoCaIVq6/hvEDAEBNVW7ofHbSOtf2HbFq3bi3/L168uHnyySfFm6Vx48ZyQQ4fSKaNBEReeuklCbBWrVpVgtTOgDktXhKUiREoYB/gJ0PS4o033hCjWlru2oFZGwIv06dPl/IwzrGcezGJZoHv2B7Xk2ASCrR4dAaMKVetWhX2f3/4HStKqBM4Z1lFCTJsM0aMpZTggPpUMiRffPFF0BmExQXqm8meQb58+cwPP/wgf5NZ++677yQwwELgt99+k2xTly5dJFDA5JfAQu7cuaWulowdmTQWQzyP2mANJihK4CkUrl27ZmrVqiXmgiwQ6XrEwnfevHniCxAVtplk586dzb///ht2O5l8ziHQqFEjUTLQQYZzTKBA1xq8DNgvBFqdDSbYoD5ACUZQgHMs51XmGXPnzpWgbcRgAnAb51ZMGznv8ptYs2aNqVy5spx3T5w4YTwN/jhsB0FlZ1QlKNMgT548pl+/fpGq3xRF8S4aUFAUHyoUyLSSWVCCB9oWYiRF94Jg59QpY3r1+u/aESaFSJCRsGKaRuYMWStwbQcUHIMBGKuB3fGB0gUWBpQyoOKh1IHSBhYOiRIl8t6HVBQfgSEpv3nk3e7qVMKxSecUMv0Rg9kogOrVqyfBUMxNCxYsKGoATPFoKYjyKq5gQrx48WLZBoKFLAZZzKJWIvBYrVo18+ijj0ZpDsgiku0gaIuUn8VyZItQW76Pud+ZM2dMIEAwgWDJiy++GKvno8Qg4MK5l4ARKjk7yBITzEXYr5Q/ULJXt25d6QrBuZdFuydp0KCB/PZGjRolQY2Yggp0tuD3QbBj69atXlNSKIoSNdrlQVF8xK5du7TOO0jBRwF5KbLRYIZAQu/exlSpguz0v9twEEfCTP0qXUwICHz00UcSOKtSpYpMVPFUiAgZNPv5TBjJQBJcYMFBS7TIsmuKEoyQeUeu3qdPH+lcAvz+yeZTSkTmmkUXTvZcU2JENpnFNtcoeaipR+7O88jW2xeegySe2x3d83kfOgmQ1V65cqV0YBk5cqQpXbq0HIe0bt23b1+U28x7cewSDIgq+05bQhatLJrZVhaDbC+gNuKCSgl5PoFHPn+KFCkeeC3OJ2wX20sAl78JICRNmjTc+YQxlsUmwQs+S8WKFcN5L/gTBD1YTLP/CJT4En4XlEFQNoFqhM4LqD1QLhAc9gSUXFDuRqCAsZNxIyrSp08v7S0JVuMHQSAGdYaiKL5DAwqK4gPIHiDtpE2TEnyUKlVKJkRMeKnjDRVYrLBwwAyMLCT7wLF+2c480dYsYhYKtQJGYWTFKGkgI4pBmdbHKqECC/IZM2ZIq0C8WDDMo7QHpQImcyy2kbCzICcgYF9YzNN+1dkyq8yZM8vYQ4tBFoxkscls41mCoeqECRNECcRtNhgFOh7nHKebN28Ou+zfv1+OaRbEKB8cL5Qx9ejRQ7LfGCgSIOQ9I/M2wEyVx4wZM0aCBFGZEmLYisJh6dKlEqgkiNsLuZQDnENQYmDcymNSpkxpXn/9dVkoE2TwF1BmYDLL97xgwQIJCPkD+FqcPXtWvi8CXCz0+X2yXz0R4CU4RECB35czEJzGd4HfFgFsf9lvihKK6NGnKD6ArCvZIiTdSnBCNo7SB9y6g02VYJc47NoV/nr06B7ilYC512uvvebS6zJBDXZFh6JEB1n1n376SRb5M2fOlIWbjTMyeHwJWOjbWWYW0EAAgsWqfcH8kMw/BoAsFglcoD5AOYRbPnX0mJ+iZIjMowQTRBb8HLMEGjDxo2SJwAEBD/uCUskOcmA0SBAdZUJE2KYvv/zSbNu2TQIjBBs4dxJMwU8gusXrq6++ajp27CjnW6TzEc1g2X5el4AH5yVek4UyGXEWo47mjr6C7SA7jwrEUwqA2ELQZ/LkyRJ8IihDIIffFoEe9nlUipTYQBANnN0HeBax7/CbQLXAbwHvB64p4VAUxYvEuUllCOBKH05FcYY8efJY9evXj9XO0v7HvuXkScvq2fO/a0caNWpkVa1aNdxtDRs2tJIkSSLnD1izZo38vW7dOmv37t1hl59//tkKFPjsfJwHL/csYxJYL77YxfIX9FhRAolWrVpZ8ePHl/723uDMmTNWmjRp5D3nzJkTdrwcP37cKlGihJyrcubMKee277//3rp37548L1euXFa9evWsy5cvx/geN2/etI4ePRr23IicO3fOyps3r7wXlwMHDsjtY8eOlf83b95ctis6bty4YWXNmtUqXLiwdeTIkWgf+++//1qjRo2yUqRIYSVMmNB68803rWXLlkW5fZ6GfcN2DBo0yPJ32M/sq+7du1spU6a00qdPL2Oau/j777+t1KlTW88//7x1/fp1p5+3bds2q1u3bvL985t59NFHrb/++svyJDq2KKHAFRfWv2rKqCg+IH/+/FL3F4h9skMd2zcgohFhZFADHJl8k5IIso/2haxPoNCihTE7d/53GTfuv9u43rz5LtXfplat/2qiFUVxDbxDyPYPHz7cK7uO7HyLFi1ENYAZog1lRpQxYAaJ8z6eDqjpyPxiDknpBWVLzhgK4/cQVXkDJR50c6EUwm4DuJMTizHi5UB7Wdo+onZCeh8VKCNQRuEbgbKB+v/ostq0ZOQ9KbE6cOCAlGahJBs7dqzTcnt3gSIEhQb7wd9hP+NDwbjGfkP94U5VWcaMGaWEhd8dqhnboDcmnn32WVEq4JnB98rvFYNHRVG8h5Y8KIoPYEKDkRQO3rYZnRLYIAuNCBNpx0lRZN4BgQbmixHbfhcpYkyhQgllQrh27Vyzb98Gaf1G3a12ZFAU52Cxmzp1ailR8BbUnUdVe06LVy7vv/++dEugbGHTpk2ynY4tG2MLvhCUWVDeQX0+Uvc9e/ZIG0No0qSJdBmg7IEgPOfYqEqpMOjjtWhByPPxV+D5BAgo7yBo61g6gSSeYMkHH3wgwROMIAmu0H2J1r+RBUA8AeUttMykKxD+CXHh77//lg4PdM3g+yFgY1/z2QmcUDrjDv8DzvWYSLLwpwOPo8dGXMBckdIYvmcMF/Fs4PfmLHR+oPSHABHf65w5c+Q3hakkZRGKongGDSgoig9gYkYdK9F4DSgEtm9AdAttfzQDxWV90aJFUttMhpGaY2qPyUCSgSIIQu00MKlmEsbkDhO36OpleSyTckcTNTJH1DcriuIcZNlZrPkbzzzzjFxjcsixzqItruTIkUPUEHgtsCBlQUyrSkcIvJOxJjhAxwb8GziH0c4yInhGEMRk4UzQfsqUKWH3YfSIcWNEWFyjCuHCOY7z1bJlyyQgQZcaT4OCg+w652A8BCJ6QEQHwWm2E18IAgkEQwCPAzyaWIhzIWCEsoT2j+xnAics1h27fMQGAuRAkMldAQUguPT999+bmjVrSiCLlqLOBnjw/mCfMhYRSOA7xJ+CFpreUv4oSkhi+SEDBw6Umo0PP/wwXB3e+++/bz322GNW8uTJrRo1alinT58O97x58+ZZOXLkkJq/BQsWhKtR4/XSpk1rXb16NdxzChYsaPWkKDga1ENB8QRFihSx3nvvPZefp7V7/uQb8L9LDKcRn0NtcYYMGazcuXNLPfT+/fvlNmqmK1WqJOdPoBa5T58+1qlTp6QOdfXq1Vb27NmtBg0aPPCaR4/eslq02G317TtS7q9bt25YLTSXpUuXWr5EjxUl0KhZs6ZVsmRJj3u+2DD/YR7keLzgW8A5YMOGDdaxY8eszZs3W6+99pqVNGlSOa7LlStnnT171i3bxzwPP4Nnn31Wzk+3b9+O9HH379+3Ro4cKZ40GTNmtL755ptofQ8uXrxo/fbbb9bChQvDzkeXLl2KcXtGjx4tj7XPh94AvwDmtVmyZBEvALbbGd+AsmXLyrbiQfHBBx9Ys2bNEl+MyGBfrVixQn5fCRIkkO+ySZMm8t2yb12FeTXP5/3/+OMPyxNMmTLFihcvntWiRQunPS62b98e9n0PGzZMPDN69Oghn5ffs7vQsUUJBa644KHgdwEFTpJPPvmkVaBAgXABBRZeTzzxhLVq1Sprx44dYhj0wgsvhN1/69YtK3PmzHLCXL58ufxtD0x2QIGBiBOLIxpQUHwFk0ZM+1xFBzLvw2R8587/LuPG/RdA4Nq+jUtUE3Z/4ZVXXpHzYlRmV/akkoDCiBEjwt3Xt29fCURMmDAhzPjK8ZIoUSI5J5cpU8YqX768nGd/+ukny9fosaIEGtOmTZNjauPGjW55Pc5NnK+4djag8N1331kvv/yyJHAeeughK1myZNbDDz8sC9FPP/3UrQaGFy5cEINi3oeFY0yweMVIkX3EXLF06dISyMTcNjIIIjBX5PH2Ipp5ZlRwrkuVKlWUgQ1PwHutXLlSztH2OZV9j8kv29u+fXurf//+EuyYPn26VatWrbBAAskzVwMCJ0+etPr16yfnel4Hc0UCwizgMUYEFuInTpyw1q5da/3www9iJPz777/Lopz9j5En5odDhgyxPMnEiRMlqPDxxx87/RzGID4XawUgkViqVCkxv+T13IGOLUoocCVQAwrXrl0ThQFBAQYJO6CAkzAnAk5qNjgB8yGJrgIflpMjjsFcGGhsNYIdUOjYsaMMio4RXA0oKL7s9ODMBCoiOpD5lpgm6P7I+fPnZVKG+ismIgYUOH+i7ooYRLAvHTp0sP755x/LH9FjRQk0WKw/8sgj1oABA7x+vrKPl6lTp4Yd34kTJ7YKFSokC849e/ZY7gL1UtGiRUVRyvvYC1RnsvPAQrdt27bSbYLXoOvAokWLogx22ItoVAC8H+9NZ4uIi/F33nlH1BKxBVXXjz/+aHXq1EkUBARm2E7OvV9++aXMT23+/PNPUXtEdW6tXLmydDygqwbnYIIu3E5geNKkSbLojws8nyQd2+oYKM6UKZMEiaPaLubpfBZXOjHEhcGDB8v4RdDFGbZs2SLbOXToUOvgwYPyHRO4Ys7F7ey7uKJjixIKXHEhoOBXHgrUvNFDtnz58lLrZYPrLy643G6TO3duqUHHdKVEiRJSO0eNHfV41MTxfIx4HKlbt65ZsWKFONSqA6ziS06ePCkuyRgxKYqnoTaXADJGVY6kSZPG3Lp1K+z8a9cYd+7c2XTr1k3MvOz7o3KJp841adKkHv4EihIacJwyh2HO4ynPl5j8Xuh2gIkhtfkYBrpiiucsV69elbkd87dx48aJ7wFzvNdff118GqLza7G9FbgAfg6YAzJ/5JyGiePIkSPDmU0yN+zatauc59auXStzwOrVq4svwxdffCHmj/bYnCFDBqc/x6lTp8TgMXHixPK+s2bNktsxmHzuuefkbzxrLl68aC5cuCCfm/MrJoGYQmIayXyVOSy+B/b9zFO7d+/+wG+Dz4rXTVRGmq7A94pfARfO/efOnROjaLwH2H72Cd8/PgR02WDf4GXAfveWaSXQSQKvCHwfMNHE74JOSVGBDxDgh8GFz8L+ZywrW7asdAJRFMXNWH7CjBkzrHz58olXAjgqFL799luJlkaEKDKRVUdQM0T0SbAVCkjiiIqjdkC6BapQUHwpa7Xlha6gkXHfElNNsj9iZ2xmz54d7nY8FA4fPmwVL1487HyLQqFr165ye+PGjcOyUvSrb9WqldQuk0WMTd2tt9FjRQk0yBg7qi894fkSld8LWXwUCmTDPV2yhMw/Xbp0onCy4ZzDZx87dmyssu3r16+3unTpImoHSjkinqPIVlO+QenDc889J5l+Mt+oMObPnx9WXosi4O7duzG+J4oNnl+sWDHJ4FEqwmsxl2VfRgQ1QrZs2ayKFStGmf3nQnmvt7L/gQLKD+bu9j6i9CYqKAvhMfyeFi9eLOpkFCLHjx932/bo2KKEAlcCTaFAixsitagHcGeNCzH1RSYKXrJkSYn8RterODLIGMQla6AoNk8//bQoaMhEkJ1xJQNk/wb1t+gb0qQxpmtX+7swAQH9wsl67t+/XzKANk888YRcc94lU2b/psgO8pxPP/1U3LZxIOd3ynPtriTuaBvnafRYUQINWgfmzJlT2iDG9hzfrJkx9mG+Z48xbdoY88UXxtgNFEjAR3xpst8c52SAFy5caAoXLuzRMYbzEZ+RzjP2+5ClR+3E+Sc2740igAutanH1p+UgHQJolcj5jfaXZNzfeecd6Rxx5swZUTRcv35dzm+vvvqqKBZQD/AcVAzRZbPprMC5k7aJdMLhuWTAf/rpJ9O0aVMzf/78cI8fM2aMvC6dCPicKAA++eQTkzx5ctlm+8L3z7WO8f9j9+7d4Vqc7tixI8r9w/5lLUBXB/Z3//79w+5z1z7VsUUJBe66cLzEI6pgfMzcuXNFeua4qLL75iKrooUPUrhLly6Fa3PDhLdt27amXbt20b7+sWPHZBDhhMTgsG3bNvP888/LCYkyiWrVqplevXpF+XwkaJycCEAgNVMURQk0OMcRvP3yyy8fCNwiBeYc2axZM/Puu+9Kb/QqVaqE6xePdJTJb/HixX2w9YqiKIqiKIq3oOVqvXr1zJUrVyTgGx1+oVAg40W02BEW+vgkkMEli0Zt2apVq6ReFw4dOmROnDghgQFXYUJMvV2XLl1cel7FihVj3KGK4goEw+jBTZDLlYghap4KFSrIcaEojpw5Y8zEicY0bWpM+vT/ux3/BPqG49uBQit//vwSsCWwev78efk9kc27ceOG1JwSvGUgwSeB8x51tG3atDGpU6cOmB2ux4oSSL9V5iWMBdThv/TSS255XRQKlJuvW/c/hUJkkGEHjnFvjS1vv/22qANmz54tCgLq95njkfHPnDlznF+fQChzRFQXLVq0kPEWPwPOhfhDoBC4ffu2WbNmjczvHH0ByHKjyCKQip9MVKDqYLtRQ9ieDlGxePFi8fIiYDthwgTxXVCcA/VI+vTpzZQpU5x6PF4TOXLkkO+vffv2bt/NOrYoocDVq1edf7Dlpzh6KNh1bbjz0hOdVjA433JxBkcPBZtDhw5JCyRaSVJr564aEkVxhTFjxohzM21PQ7V2r1GjRtIeKzpww6Z+kjZZSuxd3anrbd26tdTxsj/peoNnAg7uN27ciLSeF++ESpUqRdmWzZ8JtmNFCV62bt0qx5tdy+9NzxeOD3wH8DPw5vHCeYeOAXi74KfAnAxPAne+f58+fWSMtVtF0tmB/bxkyRKnxiY6bvzyyy9RPgavA7pM4PNFzX507Ny5U86ndFGg/aLiHLZXBW2LnYU2l3zPdK/As8rdbUB1bFFCgSsurH+9Z9MaR0aMGCHRYqLXuLviwktUO7ZQo0aNW3QO5oriaQoUKCDlPT/88IPu7GiYPHmyqV27tkRLt27dqvsqluB0jqP5H3/8Ye7cuSPdH44fP26mTZsm5VwRfWVw9cb5m6yeOmMriuegNBPweHIndHOgojO6rg6oEVBs0t3Bm1BihToKZQZj4YABA0Ql5U51ROvWrUUJQB39oEGDzMcffyy32x0YouPzzz+XbmJkxykXiwxKaX/77TcpweX8Gh14Rvz888/in2CrbZXowYcCHwTUBqhBnIXjCC8QFHU8j+4eiqJ4Dr8oeYgM2vo4Qs0vtb9cXIUWMpFZRTBwcVEUX0HLrPr160twCzkfUlMlPBy7kyZNMqNHjxYZLFJRZyaDoURs28RhKuZInTp1zMqVK83EiRMlkIBhmaIonofAMhw9ejTGlomeOMdiEuxtE0BKBCh35ZzDOOiOVogRYY5HOQWlrgQTaO3ItTP7GO+sJUuWSJtCElobNmx4oB05YxEmj4xLeNEQuMBkMSoIihPEdbXkNlRh7GcOz77HqNJZ8GCjjSgXglVaHqooniVgFAqKEoxQs8mASf0mxqSafX8Q6lsxhsGYlUwDta/U+Sv/g7ho0aL/Xd5997/buLZviyxuOmzYMJnEOwZx7RpiMmj0VlcUxTu8+eab4mvC4pXsKh5R3gJvARbZmFx7G/xaGjVq5FIwAYNugqF4LdCpgY4zKK5YqONJNHPmTHH4xzsBRRbdFwiS0oGBrjV4NzhLxowZJdNNoAd/iYiwUKVDBGqGr776Sry/ovNEatmypagZMLhVYgb/HhKKMXVwiy5YhiKPzlqKongODSgoio9hQsIEiDZd5cqVk5ZVyv8g8/PWW29JF5h8+fKJkZaWiISnRQtjdu787zJu3H+3cW3fxv0RmTdvXriMJOa4RYsWlYn3yJEjwxmUKYri+XGAjlbI8lkUY/TnDQjOUuqAWWvv3r1NIEDgA3UD4wHlr+w7yhrIZBOMQWmF8SzBBMq2KI+lZIG2jWSuXYX3wdxvzpw5kbbLpWSM7PnSpUvl9RmzolrcXrhwQYIgtAdVYgbVJsbBBw4ciNXuYl/zG8+ePbvubkXxIDpjVBQ/gPp1JiPUapKt2eWoVw8SGjduLJMtLky+GOD79Okjbtosbu37Il6o63esneTvqCZsoQrlDEWK/O8Cjv+PrNyB/c4kDSUCnRyQBfO7o20ktc2KongXavBZLNPZik5Wni6x2L59u0jvb968KeNOIMB5irGSTgmoFBg7GA+4cDslFBcvXgzrBkBJAovSb7/9Vro7xBZeg3KFyNQH33zzjSgPUED8/fff0jkiMhjPCBbRUQNPIEoklOihRIXxiYAXx4Srne7tUqLIAkGKooSAh4KihBrUXSJDJ5tCRB4Dp2ADgySyPUz2aKGFURL1jXny5DEHDx4M55lAcIVrpKHFihWTjDkTMu5nUokRFuaqSuxBnks7tYEDB4YzwFUUxXew+PGkQohzLSVkLH4ffvhhKQ3Ili2b1z0UbDins9BGecY4yBhBi1vKDSJ7LKoEMvwEQCnbatiwoTzHEQIMeBRt3rzZLdvIGER5BmoOylJsxo8fLwEZoJQCpQTBjugk/JTtUd6CioJyDEwHlchhf7Kf+I3y+8D7gt8GJS38ZmOC74zkhd2SVFEUz6AKBUXxIxgg69WrJ32vqRMNNpDU43JNJomMDpNapKgEBwgyVKtWLWzSxaSRfuzsE7JMPI/Lnj17pEc70nwl7jgaiJF9i85QTFEUz3L69Glz6tQpyXLjcE+2HUNazpUE/6jVj4vXDudbPHseeeQRMbrj9XlNd8Mim4AwXbkIGKMOIACMOeLcuXPN4cOH5f0ps2DBxzmdYDrZfoIEdevWFRUVwYIff/wx7HUpfWMcIFBQtWpVWdCzOCcw/f7778s+I+CcN29es2/fPvnbHeDx0L59e1E6OPpbEPAmc46qq0ePHhJwiC6gAATG8awgSEEXCjuLrkQO+4j50PLly8Xvg84adB2aMWNGtLuM7x7vCzwrdlL7pyiKx4hH70jPvXxwgMwNQ5grV67IQkdRPAk1n0y+kKCOGjXqgfvJIpHdJ7MRSM7FlDycPn3ZJElS3axc2dr88stekffyWZjcrlixQia31PLjqN2pUyeZjJJB279/v8iAbaiFpVyCybEnnMEDGbo9YMKIb0IMXcwEsmVM3jG+un79ukzYg4VAPVaU0IVFNovryCT3Z8+eFXUBwQbMasnUOsJ0jvMorRj5vdu/eRas9oXzJot9FAFkb2M6XjjHYnaImsHxwrZwvrh27Zpc+JsLQYNnn31WjAw5n7/yyiuyuEdxwQVVAV0dbDi/Y0jJeMf5nsfggUA22p6ecvtPP/0U6f7CjJFtpvRhy5Ytsn8IMFBGyHYRVIiNb0Jk8PloI8nnwWcG3yO8FQhq/PXXXy4HYwngEixnrGOfoUzhO8IHgkWwI/gAsE85P3Ou5kJAgrEzKth/n332mewXyjH4LpjLEszBiyht2rQmEGEuTkKCgAJzcvaVfaFbFiaMGHYyb7ANnIcOHWo6dOjgtm3QsUUJBa66sv4loKBEz5UrVxjV5FpRvMHw4cOt+PHjW7t3737gvjt37lhz586V60CiUaNGVunSVS3OOqVKVbEKFChgJUiQwHrooYfk+Ip4qVixouyDyZMnP/Bap06dkvvmzZvnk88SbCxdulT2+ebNm61gIlCPFSV0uXXrlrVgwQLrwIED1h9//GFNnDjR2rhxY9j9d+/etQoXLmyVKVPGun//ftjtW7ZssZ5//vlIz6WOl0yZMlm//fZbtMfLpk2brM6dO1u5cuUK99xkyZJZOXLkkPeuU6eO1axZM6tdu3ZWjx49rCFDhlijRo2ymjZtauXNm9f66KOPrMOHDz/wHmwz55nVq1dbv/76q/XPP/888JjPP//cqlChgrznww8/HO7zx8T69evDnvvll19a7mbFihVh+6V27dpWy5Yt5e+vvvoqVq/Xpk2bB76jRIkSWe3bt7cuXrwY9jj2d8THpUuXzvrxxx8jfd2bN29a9evXl8cVKVLkgXG2VatWViDD72jZsmXWoEGD5DuoVKmS/O7SpEljFStWTOYbQ4cOtRYvXmwdP37c7e+vY4sSClxxYf2rAQU371BFcddgxeBYokQJ6/bt20GxSGKA/29Sk9xKmDBR2MRmxowZ4R7HbUxYCRhUrVrVZ9sbaouYePHiWePHj7eCiUA9VhQlOubPny/nyQ8//NC6du2aNWXKFDl+CxYsaK1atcq6ceOGdfnyZevcuXNyYWHK/OX69evWv//+G+XrHjp0SI6XpEmTWqlTp7aaNGlizZ4929q3b5916dKlcAEMT8PCnc/I54kNR48ete7du2d5AoI648aNk+BK2rRprYwZM8r/YwP71P6Orl69Kte9e/e2kidPbj366KMSSChUqJDsi7Fjx8r3evr0aWv//v1WtWrV5PaaNWtKUIcgwVtvvSUBlWzZsllJkiSxvv/+e3kfvn8C8Pa4e+bMGTfvldBCxxYlFLjiwvpXtcKK4ocgN/3666+ljSSeCsjSA1XajwSfy4ULxuTMWdYcODDG9O2byBw69JVZt26+KV36rQeegwP22rVrxUtC8Y7xFSUlv//+u+5uRfFzqNHHVwHPANv/gLIBxglXSpYoZ6AkAGk4F7wN8Kb5/vvvpROBL8cc25UfyW1soIWkp2C/4JkQsRsONfuummlSjpEmTZpwt+HF0Lx5cylP2bt3r5RDtGnTRsoGeX0kyEj78Zzgu+rcubOUNdCxB+NjLvhk0LEH3yJArlylShXp6jFkyBB5XXw5FEVR3EFgrlAUJQTA+RlTwho1akh96dSpUwOyvp16/v+1N6fGNLvp0oW/E8kpiPt79Qr/HPtzBmoQJRB5+umnNaCgKAEC3gOOEIDFS8A2BGRxi+ArsjHjyJEjUvs/Z84c+T9+A3bHF6DDjq/PvaVLlxbfiOrVq4tpb44cOYw/g5cDnSZo/YnHD/vz8uXL4ndBwMBVDxdMiQkaxRSMeOutt+TiCO+JgSEeC0DQCN8H+PDDD8VPAK8NDSgoiuIutMuDovgxTA6nT58umScyFu5yrHYHZEvsrgw2BEAwi6pYcbipU6exTHh6945nEiRIZJIlm20yZKC3+r9m3DhjmjdHsfCfeaDiW+hHT4aLybCiKP4NhnC9evWS8QGDPbLNmPuRgaZjAmZ7LGAJFNA6kUXktGnTZJFJIIGuC7QmpgvDsWPHxLwQF3w6LvgLSZMmlcw7BIJ3OMEZvot169bJ4p1ADkoBWl+iAGMsLFiwoLSzdLeSg84gKBodDRv5LTgGi23oQsG+5XegKIriLjT9pyh+Tq1atcytW7ekxzXZGpya/RGcrlu1amU+/vgr07t3E1O+fGOTPfurZtasSeaRR26Lc/d/7c6GmiJFPjZ//cWk0blOBIrnoAUak08mu4MHD9ZdrSh+zpdffint8ChRIIAACxYsEBUbwQVb9s64QSeBRYsWSVcCIMjQvXt3aYHI3/4M7RhRStA62N9Jly6ddJf45Zdfwt1Om0wCDbQ95LuhTILyFMoW3AFdkQhiAIoE1IyUwVC+RpcoxmRHEiVKJPs0YpmFoihKXNCAgqIEAPTipm81rbP8MaBATWbPnj1FSZE1a3Upcbh925jff09s7t3LYOiARQa8RImKZuvW+caYj6N8LbJRZM0Uz0FGa+nSpSKRJmv56KOPSts1MleKovgvBBK6du1qWrduLQtYGzLgLCajAvm93QLMXYtZT8IYQNYdNUUgtHylLSMBA9o+8h2xqGdBT8tNFCO0NCTIU7VqVbfuf/wVCCjgtUS7SRQQBBNoJ0m7zYjQ9tL+LWgbdEVR3IWWPChKgEBv8BEjRsi1bdy0evVqX2+WyFL79OlrRoxYKMGEXbv+u/3Klf+uDxwwchvGjEmSsGC9I7cj2f3555+jNNQisFCoUCGvfY5QgH06aNAgkeE2bdpUJrvt2rWTCSkZNkVR/Pv4pfSN7PKAAQNcei4LTBaTgRBMIMhZpEgRKcWYMmWKCSQoO0A18sUXX8j3RfAWpcXu3bvNypUrxVzRnaA2IUhByQVBJsrW5s+fb86dOxcu4GTDnIHAcSCUkSiKEjioQkFRAkjmSm0mta6AwzOLQ7wMuB3lQmQTCE+yZMkSyWy//fYq07JluXD37d3733WDBvxrmUqVVpnNm5eZ559v41KZA58vskklMtJ+/fqF3YeME5frAgUKmLp164Y5Yiv/QeaMWmp+Rzh9f/TRR5I5UxTF/8H0b+DAgbIo5bwb0ZQxmCBwniJFChnrUE8pUUOAiNIXyltu3LghKoiooCwGBQOmkYEQWFIUJXDQ2baiBAjIPlkQ2qZO1GoOHz7cbNy4UcoNypQpIxl/bxo3snhHTXDwYE+zfv11MVoMz0L8yI0xSczixa+ZPHnqmBUrernsm4Dr+KlTp8Jd7Lpa+z4ksky0kZ6yn15//fWw1mOhDPsEfwQWIEwkkRGzMNFggqL4P5zPCR4TNKaNIKVvnPOCGcwm//77b1Fh4AuhxAxGjNEFEwg24N/AuGh381AURXEXGlBQlACFmlmyzLTU2rNnj0gYCxcuLFLRv3A89AKZMmWSdmXnzv1tunR51XTocM1Mm/bffQUK8G9Z07//z2bhwsNm69abZsmSKSZ5clpHugYu2bTRcrzY7dDs+9gWPvsnn3wiqgkW0pMnTzahTLdu3aREhnIGggj4WCCZVhTFv6H138SJE6UVYc2aNUVGz3ne1VKHQAQDX7pTEEjBF4IxRokb33zzjTl//ryYczJ3UBRFcScaUFCUIIDJ5smTJ6X29OLFi+b55583v/76q1feG7MpzCKpFW3c+FWTJcs1uf0/RWVy8+qr2U3lyllM8eIJvNbRoVy5cpKVZ0Iaquzatcv0799fvDaWLVtm2rZtKwZeiqL4N9TAo/wio5wzZ06zfv16CZASXAgFyLRv2LBBzAXpXNCkSRMzevRoUaIpsVO5fPrpp9IlKhA6ZiiKEnhoQEFRgkjyWKFCBclC4yXAoposlztgHter13/XkfHEE09IFuns2bOmdWu6UFw17mThwoUi2bcvtNKMidy5c4dkt4h//vlHgggvvPCCLEZQbKiXhKIEBgQOaCtI8O/AgQNSH497fyhCJp3MOsFhzAwxky1atKic3+hQ89lnn4mKA+XG3bt3PbotjKV4OtDJiAAPY12gGBvS0hnPIRSNiqIonkBNGRUlyGDS9e2334pBIwZelStXjvNrEkigFWSVKiZKlUHmzJklqFCqVFmTOfMrJmHCx02uXFE/3hXwRaD+38aZsgkme6Em7USRQdcG1CIdOnSQYAIlIYqi+D8sVGvUqGFee+01M2vWrIBol+hpCAwTVEGuj9Jq0aJF0o6xb9++EkRnoc+53g6oUy6BB4O7WiJeuHBBzqWMqRGDFnTOyJUrl7Rs/OCDD4w/m1wShCfIrCiK4gk0oKAoQQgGXs8884yZNGmSWwIKkRGZPwE+BkeO/Ob29yKAQFmHK5DdCxV5J+aT7du3l/pYTLdWrVrl8v5SFMW30AIQo9vvv/9egwkRoFVm/fr15cL5jpa3lNvR3QAz4s2bN0vrxAYNGkgQlaAMqgaUerGF13vvvfckaEFHoVKlSkkAgfJCWkFy2b59u5gAo5x7+eWXZZv8iT///NMcPHhQtl9RFMVTaEBBUYIQMvNkTN5//31z5MgR8/TTT7usSHAsb9i1K/w1oDzwlieCq9Brmy4YZOtDoZ0cWc2lS5dKS0i+c0VRAq9UiUXx0KFDJduuRA0tgu0xjYU8JSFcOnbsaE6cOCHqDrpDEExn/EO15yrbtm2T0hMCtKjjHF+DVpYE7QF1RO3atc0777wj/6d9M4EHAgtZsmSR0hVffp+U0DAfQOWnKIriKdRDQVGClLffflu8FKgzjQyMrlh84jOAlNOxHvTrr40pWvR/l3ff/e92ru3beIw/QPYIiT9txjAixAW9atWqMhFkHwQ7mC1ixonPhAYTFCUwmT59upyHOW8psYdFPF4BGAUnS5ZMSiNiA91w6Jr0448/RhuQYLE+Y8YMs3XrVvF7uHLlirRzRilBYKF8+fLm3Llz5syZM6JsQDGAcXJsccW3AfUG4yElIMwFFEVRPIUGFBQlSEmaNKlp3bq1GDKRtXGEhTclC2RxyPQgEcXsCs8FoE31zp3/u4wb99/zuLZvc3cr65iMH6OCzPzjjz8uruj0Z1+zZo1I/2kdabeWDFao6yV7Rk1xsPemV5RghUUigd8qVaqYp556ytebExSkSpXKdOnSRca/BQsWyELfle+DADUlDKghYoLHFC9eXIIIqEzwXbh27Zr4PWCSTNteu7UxAQ+6WNDimO1jvEJlFhXr1q0zFStWNHny5BHPBt4LXwk8ERjHo4NgAqUhLVu2dPqzK4qixIZ4VqDY1PqQq1evyuDEgOQuox9FiS1ksXC4rlSpUox1tkxqcPovXbq0uFPbUKP71ltvmf3795tNmzbJhIbsCpMhDKamTZsWztCQeQuqBAIJRYp45rvzxnsEG8hYCQYRVFHidqwoiq9gUUmtP6a2nKt9RbAdLzdv3pS2k4xzjGcsygkSoAJhP0dnWNu4cWOzY8cOs2/fvjhtAx0o/vjjDwkEEODmglqB4D3KMv5mXkmpBNtFmQYBB3whCBTTxYKyCUo6CErgJ4R3A8EKxm3mp5Rf8DzGg/Tp08vrMcZjyst9/K4SJUoUp8+hBPexoihxXf+qh4KiBDEpUqQwgwYNkskRcngkmMDkhQnGihUrwtypMZ/6+OOPzZAhQySgoPg39+7dE0MwWqgpihK4XL58Wa7VSNX9Kj28dFiAU5JAwBzl2hdffCELczL/lAOQ7ceLwRHUAL/++qu5ceOGU12FooKWl1wiQjne/fv3JeBAcAAVRaNGjSTgwO+AbUbRQPkEY3REtR1jOqpCAgYEEVAjdO3aNex+WgVjYIlaT4MJiqJ4Gg0oKEqQ07BhQzN69GgxKGQBykSDyRJu4nv37g17HBmcQoUKidyTntUoG2wwX+zZ0/0mjI7mj5EZP/q7+WNM4EbO/oyYweA2Ir5MYp2R1EbGqVOnZLKbI0cON22toii+gCw67Ny5UxaRivtgvEOZwIVFOOdeVAd4znDBTJG2kJQH0hWC4EHv3r3N559/brp16xanYIIz24ZPAxfeCy8gOksQ/Bg4cKAEO6IaH3gOgQiCFXg84CVEZyPKLbhwOx0pFEVRvIF6KChKkMOk5dNPP5V6SzwTbGipiBTTkWrVqkm2g6yIIyzo8Tdw98Le0fwxMuNHfzJ/dAbMtvr06SOTvbRp00pmCIdv6mZtgy7qXwkk4BSeOXNmqaON6HEBMVWjsfDgPZDlKooSuHAe4FgmoKB4FgLn+fPnFzXexo0bzdGjR8X7YNiwYXKexsOC8W/w4MGxNnSMLZQ04HeAv1H16tVjDDbTGtM2jKR8g4QAJR10nQiWYAIdO1CSLFu2TMZExtiI8xZFUXyPBhQUJQR48cUXTZ06dWQSRU0UMKnCMGr37t1hj2PxS9cAJjR0f/A0juaPkRk/esL80RVctZih5rVnz54SlGE/jh071nz11Vcib33iiSckiMAkkIkqru5M/L7++msx6LK/B9QhrVq1kqADtbNk0aKaGJcpUybMSFNRlMCEYxn5ugYUvA/tHVEjYF5IcJegOpn+Tp06mUBRwUUWkA5kUF5Q/kGZJl5PlJ5gOowaj8Bbvnz5pFuGoih+BKaMSvRcuXKFVYVcK4qvuXPnjjV37ly5doXjx49bSZMmtT766CP5/40bN6xixYpZmTJlsg4dOhT2OH7nWbJksV566SXr33//tbzFzp2s3v+79gX37t2ztm/fbvXr188qV66c7IMECRJYzzzzjNWtWzdr/vz51hdffGF9+OGH1ieffGJdu3btgdc4ffq0nCsmTZrk9PteuHDBKl68uJUqVSqrfPnyVrx48ay0adNaXbp0ke146KGH5PuOjOnTp8v7OX5/StyPFUXxJuvXr7cefvhhq2HDhj7d8Xq8BB5NmzaNcQxgbAskunbtKp8pc+bM1rvvvmvdvXvXWrRokdWkSRNrzJgxVrp06ay3337bp9uox4oSClxxYf2rCgVFCRGQc3bv3l3KH3C4xqBq/vz54tyKg7Tdgor/Y8qIHBRDx1CAjNQzzzwjigCkrqgDkMGyr6hvRnlASzf6myO9pMUbZQ3btm0Le41Lly6JWzs4elPEBP3BMcekXpauHKgayDhRQ4sLONJX1CX2azvCfageJkyY4KY9oSiKN9mwYYOoljjP0AJWUVwB5RvgAxEZlAhgTok5Y6CAmSSMGDFCxkNKP+imQMcLzKPxuKArVUxtMxVF8SJeCXEEOKpQUPyJuETG79+/b82ZM0cy4hz+n332mXXu3Dnr2WeftVKmTGlt3rw57LEdOnSwEiVK5LXs7smTltWz53/X3mTBggVWihQprLx581pr1qyJ9PNy24kTJ8IUG7/99pvsM9QDefLkEaUHCgP2KZc0adK4Td1x69Ytq2LFipLB3Ldv3wP3f/DBB6JouHz5slveL5jQLJLir3B+mDJlihzXZcqUsa5fv+7rTdLjJQA5efJk2LgTmUqhXr16ch/Z/UBh586dss2LFy+O9H4UC7lz57bKli0rcxpfoGOLEgpcUYWCoihR1epSI4p3AjWi1PmTuVi1apXUJZLxtk3+qlatKn2u8QDwBp4yfowKvCTwO0B5gFkiig08CSLrKc1tZILs1l3UcqLgoPaWFpyoFfA9oNaTLAqvFbHNV2zBbGv27Nnm8ccflxrfiJCZ+ueff8K1DFMUxX/BJwXPBOrEUSfgk+LJbgJK8MK4gL8A9OvXL9x9169fF68eaNasmQkUGE9RDI6zjZUigGKB9tao9lBZKoriB/g6+hEIqEJB8SfcGRkfNGiQZAJGjx5t/fnnn9ZTTz0l2fqDBw9K5L9du3Zy/9ixY61g4fDhw5LVJzOIR0LPnj0DosZ02rRp8l1EplIYMWKEeC9s27bNJ9vmr2gWSfE3NmzYYKVOndrKlStXOEWYP6DHS2CydOnSSFUK8+bNC7v90qVLViDBnCR+/PjW0aNHI72f+clrr71mZciQwTp79qzXt0+PFSUUuKIKBUVRnKFz587mySefFIdrWpd98cUXUsffokULUTPQPov+3M2bNxdvAVe7HvgTbDueEDlz5pSszYcffiidLHr16iWtNf2dZMmShSkWIsJ3hPeFdnxQFP+C8yndG+bMmSMZZNr65c2b12zatMmUKFHC15unBAG0VSxbtqz8jdru1KlT8vd3330n16jr8FIIJOiMlCpVqii9H5if4B109+5dmZ8E8txEUYIB/59FK4ridi5fvmzWr18vAQQW1cgLAeMjShy478KFCzJoM6D36NFD5PblypUTY8K//voroL6V+/fvS39v2mZSGoDpIZP7TJkymUBh3rx58j1lz549UgloihQp5DtTFMU/QI6NGS6GizVq1DD9+/c39erVE7NVzFgVxR0wTtvlAbRTpHRv8eLFYQEFbnv66afN1KlTA2aHUwJE20g+F/OVqMo9uH/u3LliOK2tJBXFd2hAQVFCECa1pUqVMh988IFktxs2bBh2H94J1P+TIaAGk8kKrspMRli0Tpo0ybRv394EEkysvv76azN+/HjTt29fcb0ONG7duiWBg6gyMa+//rooL8jYKIriW4YNGybnUjLHdIM5c+aMeJ3gsRKZykhR4gIBA8Zp+O2330zlypVFjccim98fnRNYoB88eDBgdjRzE3ycatWqJb5PkYHvU8eOHUV9SPCuYMGCErjjeYqieA8NKChKCMJAbUf4mfgSNLDJkCGDmIT99NNP4UwACTqQcfvkk0/kGlPDQIEWjEyw3nnnHROoYOBGO8qlS5dGej8KDKSualKlKL5l3759ooYi8Prjjz9KO9p06dKFO88qirthYc34XaFCBcnYYxxMKSO/P5SHlD7Url3b3Lx5MyB2Pp+FZABBkeeff16Uk5GBQeP58+fN999/bwoUKCCBFRImKBGdhQAESkZFUWKHBhQUJQShtGHRokVSV5knTx6J6J87dy5cTWafPn3M6NGjJfLvmBWvW7euZMHffffdgJmY4BFBuUYgg5M3n6FNmzaiVogIE6kXXnjhga4c9+7dk8/PpOzs2bNSwuL4XSuK4v7yKjLGnFc1iKB4C5R3dP2h+wHB89SpU4fd9/DDD5uZM2ea33//XZSJgQIKhP3794vygDnH1q1bI33cI488IsGSb775xmzYsEGC63SLoPQjKnbv3i37Cy8T9s9TTz0lpZCMmYqiuIYGFBQlhIMKtDcsXbq0THzJoGHodODAAbmflpL4DZBps4MIQMaDrMGCBQskC0DW3N/5999/pVwgkGFh8uWXX0rWhSBPZDRu3FiMGa9cuSL/P336tHxujDeRg6ZPn17MKPmuMdzU8ghFcS8YxbGgGTNmjJY2KF7nvffeEzNDsvYRyZ8/vxk1apSU/rHwDhQwTZ42bZoYE6NUaNeuXbQmjMWLF5dgAY9FmViyZElJkDDfYS6AcoP5T5EiRcyMGTMk+Md4yPwHZQfJFkVRXEMDCooSwhDBxxOBIAJmf6tWrTKFChUyAwYMkCg9fgPICLngUm7z5ptvyqCMwoHMAbXC/iwXTJQoUcCoKaIjd+7cImulhINe9hEhywJ28OTTTz994DEZM2aUazIzdo9yRVHixtGjR6VOHcf5Jk2ahLnuK4q3zQwxTiawdfLkyQfu57eJPxIqGnw9AoV8+fKJF0S3bt3MZ599JsdbdGB6Svnf5MmTJYDOWIiCj2ALAQZUe99++61cf/7551LywGN4H4IMiqK4hgYUFEUxWbNmFSMnTMOYjNDVgSj/zz//LDJC/mbwjRiMwODJzor7c0CBbg6RLcADEVQjBAUw2IqYpaFFHUoG23SS7+61116TrBUZGFQljh4MBCgURYkbR44ckezv6tWrzdixY+WiKL6iVatWMgbQ6jkijA8syAk6o0wMJNjmmjVryt90miLpgXJhxIgRkSoWUDbgPTR79mzxWKBVKwEJkiO//PKLmFOj0qtSpYoE2FEVcRuBiBs3bvjgEypK4KIBBUVRBHshSvabOkUGa+r2uW7QoIHUIkZsS5gwYUKpxyfy788lBbhEI/EMBu8AZJ+UPhDEwXzKsWyBEgeyMUykgHZ1fG9MlAgU0QmC+22QfCqKEjc4tsiIHjp0SOq8/flcqAQ/ZOExVKa8IbLuCI8++qjp1KmT+O0EQsmiI7t27ZJrlAT4HWAWzfHH54kOjklKICjhrFatmoyRBA0YEymFANoyv/XWW5JYoaU2SYhgUDYqijfQgIKiKA9QtGhRWbQiiWQAr1OnjgzAkdXup02b1uzYsUNKJ/y1VRMlDwRMaIcZDFD/SbcNSlIIltgsW7ZMSlCiAx8FHofihICQoiixZ/v27ZLRpFtOypQpdVcqfgEdRhjHURDevn37gfsxZkShxiJ7586dJlBAGQm0gUZ1x+ekDDCiGXFMoOZjHCV5wmsSaH/55ZdNtmzZpFyJwANtNwng2/dxG+oG1BHReTi4Cq9FsgbVRLAoKZXQQ8PoiqJEynPPPSfXZDBoO8XilaxHjRo1ZBJiQ90hJoBNmzY1PXv2lEEXib2/uJsT5CBTQ90oWcRgAbkqpSrs61mzZkkGBkknf0fGsWPHpB0oNbR08VAUJe5wXMErr7yiu1PxGxgPCPITYGb8Y+Ed0W+HzDwdDpD7U67jL2N2dODx1KxZMwmoA54lqPUi63wUFX///bcoE/744w+zfPlyMVFlfmOXChJwx/yYC92ROMYxeZw6dWpYUoUgA6VNlB7myJFDumURoEHRSYcJ/Cu4JilDQIfbuVAayjVtt3mMfbGTMXwHEydOlECQogQSGlBQFCVS7AGOCD0wIfnhhx+kPpOsnJ3tRz5JjSK91xlsGWAZQOkS4Q/QB56B3V+2x53QfQMlia1SIPBDPWhEaBXGpAfwyqB1pKIocQdzOBZvmOEpij+RN29e06JFC+lwQCKA7Lsj/GbxS8JrB2NmJP+BAOoEOlXA9evXpdQoTZo0Tj0XXyiCCSguCSTgfXL58mV5HQIH7COUe3R+4BJZMAJFB+8J7De6XvH8iKBYQhGYJEkSmS/xnlxzIaDDmEyXLTyR7AtBIJIzBB1o/akogYIGFBRFiRQ74o+RX5kyZczjjz8u5kcvvfSSWbt2rUgAHaGmEYOkDBkySMaD+nzaSvoaPATwEgiUyZIrpEiRQiZITITOnj0r5pmRZZno0sHkkfpu3L9RN/BcRVFiDy3oRo8eLSVh6pug+CP4DGDEi28ACraIgS/bU4eFcqCMkSzMCYpTtkGXBroVYb4YE3x+fKFYyDOvsTseMW6ywCchQrAiJoNnLngXMbdYs2aNHPvsP4IMBCN4XeZLsQkysi28HioMAhF0jlGUQEA9FBRFiZTUqVPLwnPJkiUSqcekiAw40FYJiWRkdYQMygQd6C5A5N7XrFu3TrIAwQoBBLIqlKhEJVll0sUEiO8UwykCP4qixB68Zai9RhJNAFVR/BEUhPPmzZNOJJS7RRyzGatZxGJqSCeEQKF169ZSmoDfAcaJJDlQItqBvohdp1AjUJbEMfvTTz+FBRMAdcPQoUOlhIHW2a7CuJs5c2ZJspBEoQV3bBVLBEsoUaGbBR0pUCooSiCgAQVFUaIcJKlTJPuNBI9sAAMydfvU+DF4oliICNF1avXJiCDJ//DDD31m1njx4kWpg2TCFMqQzcG9ulevXpKRomRFUZTYlzkQMCUbuX79eqnrVhR/BfUg9f+ULNLFKeIClmQBLaBZZNPR6dKlSyYQ5id4AT3yyCNSzkgZAttPCUPixImlrIBr7mexX758eUmIoCpAjRARvBgITlAySFtNXy7k+WydO3cO8z1SlEBASx4URYmxBZUdMccoiOAAzsh4E+CrQIDB7g1tQ3SeLDjmjbR0ogvEokWLZHD3JphFApn5UAbpJN/h3LlzJYsTMXujKIpzUG9NMIH6aDKkdLlRFH8HDwUCYASVGbdZbNsQcEdpw5iNOSFGjYzXZNoDATwi6JKwbds2CRrg40RnJ8o2UeTRHpLPS3LD9oSKCIEVurWQRGHOwmsWKFBAgoUc75R9Ou4zT8PnePHFF0316tVFYUmgh7IOLa1S/BUNKCiK4hRE7xmwieAzuGLESASd7gnUJDL4RoyyI0ukdVXlypVFscAE3JuDMk7KoO3c/vNboEsHss6I/heKojjHkCFDpG0c3iUaTFACCRbT1OZHNgYj+yfTj2EhPgMkD1DfBELnB4IBY8aMifProFxAzcechrkKHa4Iwn/++eeyf/BLQO3hLVBTkLihxBT/o8mTJ0u75yxZsnhtGxTFWbTkIURgcGBgoMVcRHDt5z67TQ3XLP4coRUdWU76/iqhC47FDKpkMFq2bCmLU2rzaR0ZFagUMEDasmWLRNi9mR0PloAC8ktKN6j9RJ7asGFDyaIgX2WyQ6cHJht2DWlU0NITuTbPVxTFNZCCo/RhzAyU7K2iOCoHY6rt53fNb3zjxo2yqA5FMHHu2rWrmBkzXhJYwGQRlQcKJW/BvAXfC1pbMvbjhUQb0KhaQyuKL1GFQgjxxBNPiEkede92v10kYTjkRhfxpD0PE6ivvvpKTm5KaEOWA+NFZJJPPfWUGCChQIgOpHv8zt58802pZxw2bJhXSx4CsaMB5llMIsaNGydqEMc+28gwMaI6d+6c3M53QCkKmZoKFSqIPBLneSSuiqK4Byby165dk2CeogQaefLkkQA0qsHI2gvb4JXEmI0pIH8HgkrBU/DZ8WWYM2eOBBoIxmNy6W0wz9yzZ48oFWgTTTtQEgl2+27lf/zxxx9iwknwDNUJ6lkCQorrRGa8HhWqUAghaONHUIHFiQ1/E0zA+TYqeWebNm0kEKHBBMWxvo8JCfV8DLQMcDFBdJ/JDCoXZHze4LfffpPgmd0aK1Bgu5nQUWaC2RSGmLh1O7aKpGb08OHDUh96+/ZtMaAk6EfNKJMevh9aWeFGz3eEC7aiKLHHloqH8gJLCeyAGGNK1apVxSuAQHRk8PumwxNjD74CipFOVyRF8JYgaE85iLdhDoCxJq0tSTTgd6E8yIABA0QNSwKL8h0CQsyFlJihxSy/bRJVeI44dkOJCQ0o+DFE2KiXcie49U+aNCns/7j1RxUowGWWExYusxjDKIojROlPnz4tkxNnwVOBchpMk1gAexoW3JhGBpKREUoDyo7OnDkjwReCekgvKRWhp7jtKE+LLz4fMmyiyEw2yF7QJnPlypVy/kANQjaKYA4TSTo9BIKDt6L443E5ePBgUWZRT60ogQamyCSRGFO40CUBlVtkYARIIH737t1e305/BXNGFvS0zGYs9gUEe0jg5M6dW5SilJwq4WHeg5KWOdKBAwckEIwvhppRRw1zSM4J/MbLlSsnpTUErlCnO4sGFPwU6s2ROCFdZtHvLng9FhrUYnGhTo7bIrJkyRL5cbFoVAM3xZ2DITK9s2fPSqTf0+DfQDuskydPmkCAmlW8SjZv3ixqIty4CdgQJUaVQF0nagO7qwZeFo899pjUWmIWxYIHvwWOWZQJTH4w0iQ4QXCS1ycgwXGvKIpzkN1C+o1yCN8YSosUJVDH4I4dO5pVq1aZX3/91VSqVClSpQILDEoGQ71DUkSee+45kdGj+PUVb7zxhpyLSDSEelkjqleCXxhO81vu1KmTmTBhgiyM+e0SeKFlKkEgR3W28h8oEdgv/KZIIjPHJPhCQIa5I2VPzhI4absQA9d8+uaSaaRtnw2SKyYzRJZZSLgKrtTUu2PgxoDB35FlW6g5IrrXs2dPychE1rdXUWIDi2N+X0zM69at61H1ACdJFuiYRnrLtyE6OOaowY7KJJLSBrtmDf8EFB0YqTIo2rWSDJJIVwkS0GUDs0aCg/v27RPTRT4rgykXZJqYSDGJrFevnilZsqRcMwDTPozHaw2mokQPk9X9+/eLORqZG0UJdBgDGEdKlSol5XDMNyNC60W8epT/gX8KtfmcE3wVmGfexHuTlKErRyiM4ZRyojYgCMaFTjvMHSnJwR+Bck8CYCRD+d2SXLEhwULyhTIRyiCCjd27d0tZOupTSmVJRmfLli3K0jyOaVq/k2Qi6cR+Q8nK+pLfVmTG5k5h+QkDBgywihUrZj388MNW2rRprapVq1oHDx4M95ibN29a77//vvXYY49ZyZMnt2rUqGGdPn063GPmzZtn5ciRw8qZM6e1YMGCsNuPHj3KLF1e++rVq+GeU7BgQatnz55RbtuVK1fkuVx7m8uXL4f9/dtvv8l2cMmdO7d169Yt6/79+069TqNGjWSfwsKFC60nn3xSLosWLZLbuI/HOD722LFjVrZs2awXX3zxgX2m+I47d+5Yc+fOletA5aeffrLix49v9evXz+PvNXDgQHmvn3/+2ePvdf36dWv58uVWnz59rEqVKlnp06e30qVLJ8darly55PzG8duwYUPr3r17DzyfcwzPnzFjhnXkyBGnj2+bnTt3Wp07d7by588fdq7g8tprr4U95u7du1b37t2tePHiWWXKlLH+/PNPl98nUAiGY0XxPYyBb7/9thXs6PESWnDeZ2xq0aJFpPc/99xzIfG7d5bvvvtOxtOpU6f6/FiZPHmy9dBDD1m1atWStUBU/PHHH9bEiROtf//91wpURowYIfMVez6TNWtWq1SpUlbJkiWt0qVLy/qOfcCa5dKlSw+sHWHQoEFWypQprWDi9OnTVp06dWSfpEmTxsqbN2/YPsqYMaPVoEED6/z58/LYYcOGWS+//LL1xBNPhO1Ljv1u3bpZBw4ccMv6128CCq+88oo1adIka9++fTLxZzKeJUsWmaDbvPfee7IzVq1aZe3YscMqUaKE9cILL4Tdzw8qc+bM1ooVK2RSzt+3b98OF1BIkiSJ1aNHj4AJKEQ8+b/xxhthPxgmOfYPadOmTdGe2BwDCpxY+LFlypQp7CQTWUABTpw4YT399NOynzWo4B/4eiBzFxzjXDwN+4mFPQttT7Fnzx7r3XfftVKkSCHH5COPPCLnNE7WBE0++eQT66OPPrKGDh0qf/OYX375xfIkBAoIMDAY834EJB1Zs2aNnAO4L2HChPJdTJ8+PSCCC2fPng0bDxIlSiTfb8WKFa0NGzaEPWbXrl0SdE6VKpWVOHFiK3v27FazZs2sQ4cO+XTblcCDwLonzx/+QrCMLYrzdOjQQRJtkS04W7ZsKedWe1ESyvTq1UvGytq1a0sywB+OFd6fsa1ChQrWtWvXwm7nuyShyphuLx6XLVtmBSrMnTJkyGBt2bIl1usQkjTsh5MnT1qBzv3792W9/Oijj8r6jwAXiSK4cOGCNX/+fKto0aKSSCMxZSej+Z106dLFmjBhgrVt2zan5noBGVCIbMLIhyCTaWfqmfT+8MMPYY8hqsJjNm/eLP/nAxO5OnfunFyIvtg/Pjug0LFjR8kSnjlzJuACCsCJ7Pjx4/J5mjZtGi4LmSxZMgmmcLzwcRyPG8cgAfBZHD9PVAEFe2HCZPz555/3i30Q6vjDQBZXWOxxPHtrkl6vXj3r2Wef9chrczxy7LG4JVj566+/Rqo+sJk9e7Ycr2QOvME///wjAUQCgwyqGzduDFM+MVEcN26cRK85D7Jdn3/+ubV27Vp5nr/y0ksvSfZs9erVkpXYunWrqNxQqAGTKQINlStXtnr37i1BBCYj7du3lwmhorjCU089JaofjvVgJhjGFsU13nzzTStBggQPqH3h77//lkULjwmEQLMnEwYszEkQ2PvBX44VxkDWNAQ969evLwEi1kGM5Swqx48fL0F1b6hBPQVjOwvnuMBch/3QqlUrK5D5448/RGnA94sCgbVuZBQuXFgS0NC/f3+Zo964ccPl9wuKgMLhw4fDZfFQJfB/5CyOoGL49NNPw0UROTmyWBk8eHDY7XZAgYVMoUKFwv2oAimgEJWk2w4qtG3b1tq5kyJs5M//e0zEIEFEogsowF9//SWlJKhC/HU/hAr+MpDFFhbblC7xe+U49wYMqkRricq6G4IITLqciZzzeSmBYDHsTQkiUeoCBQqEnSdQUBDhdoTFuGOAEoWXP0olGQPYPoIekcGgyeSjWrVqkR4rEccQRYkJVJNM0jl2mUsEK4E+tiiu8/jjj1vt2rWL8v6ZM2fK+fabb74Jud2Lwg+pPGpg5r+Ox4U/HSu7d+8WhSRKYr7PJk2ahJvrsACNbv7vz6C8oByeoG5cYU1ImQiJ10Dk6NGjohhiLFqyZEm0j+3UqZOVOnVqUemzxqU0IjYEfECBBQeZJQ5im2+//VYyThEh68iOc4TsW8TJvR1Q4MBbunSpBBx+//33oAgoEDGlngo5NbXSjRp1soxB7uzrLVM8gT8NZLHBVhbly5dPlEjegGw7wTCkne4OYjB4EyCICcq3GBTxd4kqquzp88TFixdFoUAtIYFXR5mk7VODeouJFLWZdmmVP4G0j4wMwdPIakdtBYhdBhbIx4riP3CuYrKO2ilY0eMl9EDtVbdu3WgfQ+YbaT2//f3791uhAPMEShj53IwntvotEI8VxkrmHYEG8xVKGfkeSAbHFb4rSlGZ/3i65NTdUMqADxdKU0eFfVTs3btXfrdJkyaV688++yxW7+vK+tcvuzzQ9xLHctobxgbHrgiRQfsn3M67d+8urpbOQr9Xf+35+vLL9czq1TvN2rU9jDFrTdKkY8yAAZ1M586dxOkzQwZj0qf39VYq7sD+DfrrbzEmnnrqKVOmTBnZfrqHeONz4AZMu0W6o9CGkRZCsemSEhl0Q6HFY0yfY+bMmeb06dNmx44dco7yxfd3/fp107ZtW3nvwoULm3bt2kkXCFyiM2XKJB1fcPylN/mff/5pkiZNKu2D7HMfLZpw/WXfZc+e3eTNm1deE7dwzjO4X+MWzmt7kvHjx5uWLVuKszPvhatx7dq1pXsIbUKBDheBfqwo/sMjjzxiOnToIG206NRC95hgQ4+X0IMOQnQwie4cSatnzq2TJk0yFSpUMAsWLJA2xcHMlClTpBsTaxG6BtCe0XEfBdKxwrkq4vb7M6dOnZLfHL832mAzd+P35o7tnzFjhnn11VdNjRo1ZP5AdwPmOP7M7du3pTvFjRs3pPPfo48+GuW+YD5K5wu6hDGf4znM4+joxVyNtpqu4Mo+j0dUwfgRtEmbN2+eWbdunbS9sGEBQOsP2mIwsNtkzZpVJshMjKOD9mq8Hu016MNOf0161DO5b9KkialWrZq0UYsMJtAsAAg++PsPT1EUJRS4c+eOTIQPHTpkdu3aZQ4fPizBaAIi9J2eNm2atrtVFEVRFEWJZbtOWo0zr4qq3bmN3ygUiGu0adNGIlH0xHUMJgBRJCJsq1atMjVr1pTbmEjSg53AgKsUL15cIlT0knUWspsx7VBfceaMMf379zfffz/K9O590HTuvN8Y86rJli2PmTJliyoUgggihitWrJBMAcdEoDJq1CjTtWtXM2TIENOsWTOv9VIm+Eiv4r1790rmIS7Qx/f999+XAGWuXLmifWzDhg3N0qVL5ZxFxDjQGD16tES5gXPwwIEDJXvDZ0ElwDXZBD4j2R36HLNP6P9Mb2SUYe6GQLBNixYtJBg9bNgwCShkyZJFxo1gOFYU/6Bz585m+fLlkpgIRoJlbFGch3M45+e5c+easmXLxvh4FG2o/C5evGj27Nlj4sePH5S7u0qVKpJI/OabbwL+WEGNvWjRIgm8+yvMyRo0aCBZdRIDJHpTpEjhsfdjroJagffinP7cc88Zf8SyLJM/f36ZPw0fPjzKx505c0bWwmnTppV1dMaMGcPu27lzpxyzKEpRF7ky7yWh7srG+gW0p8GBE6OtU6dOhV0cncZpE4YJI66mtI2k6wAXZ3D0ULDB+Zs6YlpJBrKHgg37ju18+OFUcp0rV+FwbdSU4CCQavdiqunnmOa3Sv/cOXPmeMVJmhpQ6iIxMYoteA98+OGH4vzsrNkRdar4RgQyfD/422CSZLcpigo6StD5gu+X67Fjx4pHg6cYPny4mBDhVaGmjIonwMuJOYi3vF+8TbCMLYpr53RM3lxxv2deyXl93bp1QberMfWl7V6RIkWkXj0YjpWPP/5YWkT7K5gk4pFXs2bNSH2RPAXtFL3ZbSy2hsDOtP1cuXKlPI4uYxGPbwy58RuMTcvNgDRldHQXd7w4OpEzGcUdHkd1WmBUr15dgg6xDShA8+bN5fZgCCjQ4id37txWkiTJLGNmWtu3R926TglcAmkgcwba+ZUrV06OMYwTveGiTvcC3q9UqVLSGYZgXHSLXRbPBDHpKENvZ84/mN2wiI1pYQ28NkaCffv2tQKJNWvWSOteWorZwR5+ezglY9roTAcInJXpGMFz6LDDIO4KEdvg0v6pbNmy4jpOOy/aKOFEjvsxrXTtbWSiYLeN5D23b98ubYNj63asKPv27QvahVQwji2Kc7Rp00aCvs4G9DFO5/EkAoMFPvuPP/4oAUN7/RGdWWUgHSs//PCDfB7WCP4GwSnmUxjK05HAF13y6ACGWbU/cvz4cZlrtmjRwqkgX0TTVDvRHFNXiKAKKPgzgRJQABY3K1eee6BtpBI8BNJA5mqUml7KtGdyxsU2rkyZMkXUBbRQ5Pgmu03QgIkF1wQZa9euLZFdggE8BjVT+fLlZZF87NixGN+DTOaTTz4ZNkHxlUM2v5UPPvhAehOjBuFz0yrsu+++i/J3dOLEiXDBXRRk+fPnt1599VUrY8aMEiDgMY7nHiZkU6dOlSzDkCFDrGHDhkkPaf5esGCBDNwEj2x4bPbs2aX7BdmgMmXKSFCAhZtNxDa4vHaXLl0kg8Q2MRnB/Zge4Y6KNgIItI7kMShSeB8CyN5qVaoEH2R4OBZQKgQjwTq2KNFD0JjfNeOds+qbrl27yrnXmXEwEKBTGvvg9ddflywvnZiiC7AE0rFy8uRJ+WwE3v0J5ll0XGDcdxy7vQnzFhJZzA9QN/ojX3/9tXx/s2bNivIxdObiMczzaBNOVwjmeCRWmLfFtv23BhRCOKAQWUZPCS4CaSBzFU6EGTJkkAX8m2++KVloToyehBMtLYmKFy8uwQU7Q8HfDHTvvPOOBBCIYLsqx6M1rb0gZ1HrK6ZPny7bQItcVF4EBQjc2OUIixYtilSdwP0ERGwFieOFcjEyVAxy/BZpKWbfR8CBgYwJJ4Eau3UR145lWAQCCCa0b99eIvAEAHgc7fns33fEgIIrBPOxovgGAlScD4IRPV5Cl2nTpsm5mtbK0S1cHINrKM/ILHujVNHTNGjQQEr5gvVYIWDPYtOfEkjMsWi5HRspvjuh/J25ib+WPty/f1/KQdhfKBYig7kpJcS02LTnYcyj+/XrJ2U8sUUDCiEeUFCCm0AbyFyFrHf//v1FGWCfGJnosLj3pGTv0qVL1ttvvy21pNSjuWv/Fi1aVAbzgwcPWr4M1FBHh0KgWbNm1uLFi6UvMb2p7X0cMVjCIDZ79mxRjfA8ygcaNmxolS5d2nrjjTdkX9lBicaNG8s1kfROnTrJZevWreH6SaNWwP/GEUoX8KGIGKyoWvUta8eO+xJEGDfuv4AC1/yfi7PB0mA/VhTvQ8AzuhLJQEaPl9AGZSClxJyDkcnHBF4DPBZ/nECD8Y4SRAIilDESIP/kk0+C9lhp1KiRqPr8gdGjR4vC8ZVXXrEuX75s+QOoF5955hnLX7l48aIku1566aVo1QYED0jEoRalVDWuaEDBzWhAQfEnAm0giwt//fWXnBwJMJANIZsdWTbdX0FdwYJ56NChvt4U+b1QemArMBjQUR4QIEBpEFX9IrdHpxIhq8PrYZBLba2rkkcCG3jloKIgS0YQo3v3uxJEiOri7HoulI4VxfMgy+W3jjw6GNHjRSGQjIKNxZUzMmkUfBwTlKr5SrYeG1588UVR0VHiUKVKFQkmOOOHFKjHyldffSVjflyy1XGFeQRJCVua78r+9lbJC4klf2XdunWS3PGmF5cGFHy4QxXF0wTaQOYuKB8go82xyIQHQz5/h8Gb7cXM0Z8mjHg5YGborqwWngYHDhyw3AUKBFuNoAoFxV/guFFTRiXYQV3G75xOPc6MJ+PHj5fF6pgxY6xAAJNkthfVXKjMwyjr5Dtdv369T96fRAHGycj2/TEgi3Ez+2fp0qWWP9OtWzf57W7evNnv1r/B2UBWUZSg4+mnnzarVq2SftmHDx82BQsWNC+99JKZPn26uX37tvFHjh8/LteZMmUy/kK8ePFMnjx5TLZs2dzyeunSpTMDBw40uXPnNu7i8ceNKVLkfxdw/D/3K4q3Wbp0qYkfP77JmTOn7nwlaClevLipVKmS6dOnj7l3716M48k777wj4/F3331n7ty5Y/yNEydOmBYtWpgCBQqYWrVqyfjHdj/33HMmVMifP79JmjSp2bZtm9ff+5NPPjE1atSQ/f3rr7+aRo0aGX/j7t27cs3vIipu3bplJk+ebK5fv258RY8ePcyzzz5r6tevb65evWr8CQ0oKIoSMHCyr1q1qjlw4ICZOXOmSZgwoZxYn3jiCdO4cWPz2WefmU2bNtG9xvgDdqAjffr0vt4URVHiwJUrV0zfvn1l8aTHsxLssHBhnCWI5gwElbds2WI6dOhgfM39+/fN6tWrzUcffWSqVKlismfPbmbPni2BkvPnz5tixYqZffv2mZIlS5pQIUGCBKZo0aJm69atXn9vfkNvvfWWJIMyZsxo/BG2MUmSJJKkiow//vjDvPjii6ZJkyamX79+xlckTJjQfPvtt+bcuXMSmPFlcCMiGlBQFCXg4KRKpoFJA5Oehg0bmv3795uPP/5YTvpEcBm8mFg4i7uDEAQTRo8ebRIlSiTboyhK4HL69Glz6dIlU7t2bV9viqJ4HBbfLMTnz5/v1OMrVqxo2rZta2bMmBGjqsGTfPPNN6K+e/nll82PP/4oigkWgEePHjXjx483a9asMT/88IPJlSuXCTVQCHhTocCcrGnTpmbv3r2yv6PL/vuaZcuWmVKlSomKI+K8cOrUqaZIkSLm8uXLpl69embkyJEyHviKp556SrZpxYoVorpZt26d8Qc0oKAoSkCD1H748OEyUF67dk1Osg8//LCpXr26KVSokMg2N2zYEKkU85dffhFlAxJmotNIIbt27Wp27doVpwADz3399dfNuHHjTP/+/U2yZMmizHouWrTI/PbbbzIJ27hxo8gzlf9BeUPPnlrmoPgWFilMiH///Xf9KpSgh9/6G2+8YRYuXOj0WMgiHgUAi3dfwIKvZcuWJl++fDKWHjt2TDLPnTp1kjlBqEOQiH1y9uxZj7w+c5i///5bFruoP/LmzWuWLFlihg4darp06WL8lZs3b8qi/Pnnnw93O6W15cuXFyVA5cqVzc6dO82XX35pEidOLIocX1KtWjWzZ88ekzlzZlOmTBmZt/pamZvAp++uKIriZlkfAwAXBghKID799FPTs2dP88gjj4jsjlq+kydPSpaCxTzlEjVr1jRPPvmk+fnnn81XX31lBgwYIFLJUaNGyf2uQgBj5cqV8jfReQbXI0eOmBQpUkiQg/ci28lkzY50U5ttKyoIkhDoaNeunSgcQj2g0KuXr7dCCXXWrl0rE7ZQzGwqoQlj4IgRI2QcZSxyRjkIFy5cEHWDt0GV+M8//4gS4XE12nkA1JvMM9g/+Bq4k7/++ktKSc6cOSP/Zw5GWSolqv4+h+E3Q8Cpd+/ekpBiPwFKBPyvUC+gwLGpW7du2PzO175ia9asMUOGDJHvE+UCJXm+QgMKiqIEJcjXuBA1J1CA/BE5JAEDYGFPJJ0ggz0Rgn///VfqLZFvkulg0R9VXV1UFC5cWKLaU6ZMEQkodYN16tSR4MGpU6dEAZEyZUoJZDRo0MBcvHhRDBxZrFAbt3jxYok4sx2YAKGcUBTFN3BOoKacGmSyQYoSCpQuXVo8EfAiIPM8aNAgCdpHBaoAlH6Mf96EMX7BggWyAMTfRIMJkcPimO+ThTMZ7meeecZt38GECROknp/vAQPIrFmzmkAhderU5s8//5SAFMais2bNkrkaQbTu3bs/oDAl4TNx4kRJABGg8SUPPfSQlPqiPGndurUEdTBI9Qle6TsR4GjbSMWfCLR2Rf4EfbX37t3rVAsc+hHTpjJp0qTWsmXLYvV+tNTiPbmOTeuurFmzSsseejf7U8/mQEGPFcUdDBgwQPp/b9iwIah3qB4vSmR8/vnn8vsvUaKEtNeLiooVK8p49eeff3p8RzKm7t692+rTp4+VJUsWeV+2b/ny5ZY3CNRjhZaZuXPntooXL+62OQVzHL6DZs2aWcFCdHO2RYsWye/t+PHjlj99r4UKFbLy5MkTq/lmVGjbSEVRlCiiuUTPUQfEBCUSlESUK1dOakmJXsemDpX3jI0ZEfWOhw4dMmPGjBFlBbI2RVG8W1uLDwolU9Rh21JYRQklPvjgA7N+/XpRz6HsY0yKrF6bTC94qpabDPi8efNM8+bNpRQRJcTgwYNN2bJlzfbt283mzZtNhQoVPPLewQIKErLr7C/KWdwBZQ54P7366qsmWIhuzpYjR44wjwV/+l4HDBggJuW+2i41ZVQURYkCHH8pO6AO8M033zTTpk3z6r7C/AfZKWjZg6J4F8qhWLxQ5oBMWFFClRdeeEFKB+mo9P7775tKlSpJ+Z4jLFAfffRR6W7kTghQfPHFFyZt2rQi1cfPhC5P1Lvj10BZIFJvxTkwH6SMBTm/O0xmM2TIIGUByO5DATywWMBzPPgTJUuWlAQWx4cv0ICCoihKNGAoNH36dPP222/LZIqB+O7du17bZ2nSpJFrOlgoiuI9PvzwQ6nJZuJIDbmihDIY1+FBhHKPY4KFqWNXIo6V1157TRb67jTMY9xFJdGsWTPpiMSF4AXGfwTdFdfp27evLIq//fbbOO8+fATwFSA7HgrguUWix52/c3eA6Tc+Pz/99JPxBRpQUBRFiQGMqDAdwu2aTAm9f2kfxMQKl11PLvbpGAHOlGkoiuI+KHeiSwsTyG7duumuVRRjRJ1Am2YWkpQbOAbbWORjOmy7/cfGYJGgBe2WMYEkaDFnzhwJ6jP22nJzJe7qS74rOhi4AxSUoRJQAEprWLjfunXL+BOlS5cO60jkbTSgoCiK4mRNHRlL6jQZPNu0aWNef/11WXSkS5dOWglFlIC6A9pe0t+bsgtFUbwLxzYeCnRroe+3oihGPAxWr14tHVBYmOKvYLeaJFM6bNgwl3fTH3/8IeMpJRVDhw4VfwRea8uWLTK+Ku6FfY2XgjsWxcyJ8HwKFWgjyX6zEz7+FFCgLTptyr2Nto1UFEVxAWo18VVAlYBJ1KVLl0SpgAST3tv0Bsb4kZZDdl0ntYX0LUYOSusmZ7lz545Mpnjt2Bg7KooSd5o0aSKtuTBH9VlLLkXxw1pyxjXaKr/yyiui1sOYsUSJEubo0aPRmp2S3aVV3+XLl8358+clOLFjxw4JVPA6tneQ4jmYnxAQ+uWXX8yzzz4bp9ei3AU/CxQm1PEHO7QUz5Ytm3jr8FtNGKH1OAobftu0lqRUCL8KSky84aOAcgiVAvNRb6IBBUVRlFhA5oQLPa/p59yoUSMxhzp+/LhZvny5DNDIMwkwUPNJ9gWYPNlu2DHBYxmcmLgpiuIbmChfvHhRXOUVRfkfjHGMdyyqUCcgob9x44Z57LHHIt1NKBkomSB4AKlSpZKOSnQ1wicBJZ6W93kHSjcp59y5c2ecAwp8h3D16lUx5gx2SPBg0s3vnnK4wYMHh91HgKZXr14y97t9+7b566+/TNOmTeX/nobjifnl2bNnjbfRkgdFURQ3SaNpLYe3wsGDB6W9FXV2uB/nzJkz7HHff/+9S3WO9iCtKIpvQCkEBBAVRXlwYbpw4UKRz2MKB6j27HELJd/WrVtF4ZMxY0aptacNJcFysrgo+GbOnCnmixpM8B5kzPPmzSsBhbiC6sTxXBkqnU8aNGhgli5d+kAAGjBtpEw2efLkXksKoYgg+O1s0sqdaEBBURTFzSD5I1tDcOHHH380ixcvNjVr1pT7KJFwFiL9RJyRhvojGP9QV64O+EowgwwbSS+LIkVRHuTFF18UfyECAhs3bpSSQLv8jzI/yiDolERGl/puu8Wd4lswvWR+QjeNuDBmzBj5bjlPhhL4KERU41y8eFGuuZ3kEvuWuRwJolKlSnm0reOVK1ek7MTuDuZNNKCgKIriBXkcGZi2bduKPI7yCGcGcAx/GCC8UXsXm2BChw4dTKFChUzmzJmlnaajszCLL2+211QUTx6/LIj8zYBLUfwJ/EUIJmBYB61bt5YuEPgJUeLAmMDiNdQWnf4MYzhlKPg0xRYCSVzat29vQg3UCBHVAGfPnpVgGcE1joEBAwZICQTXzPs4JjDa3rdvn9u3hzJZUIWCoihKkIJRDoP21KlTzaxZs8TJOiYwf2Ty1aJFC+NPIGVt2bKldKAYMmSIyPr4bEhe7UADCzCMiRQlGEBhtG7dOvPee+/5elMUxW+h0xF1+agT6I4ycuRIWUxRCoFPQqJEieL8HowxqP/wb8iVK5fUrCuxg++pVatW0qIztq0+x44dKwaAlStXDrmvgZKdiAmfRYsWmeeee04C0SRcunTpIgkXAmvMkWiDiuq0Tp06UqLgTuxyC1UoKIqiBDnUiTLJmjJliunXr1+0/YIZ4LNmzeo3CgW2FaUFnhAERr7++uuwQZJazCJFiphff/3VDB8+XByQCTLY8j9FCfTjlsAZv3kyre6eCCpKoEMtOeMVHRswWHQn+C3Q/YEgNe0OyfyyWMPw2B0eAKEMqkmCQLFVGJAVx5iapEmogU8WZa0nTpwIC6hhUooKNTIIMlSrVk3mf/v375ekkTv5/fff5VoDCoqiKCEAjr+0G6KV0DvvvCOTpYjgDoyMkJZDvoLB0faBoM0XDtxE1al/pec0tbDI+jZt2mRq1KghgyVBEK5x8qaWj3IIBjdqNe1OF4oSiBAAxIAO9Q2/aVcMVhUl2GFRiWcCxnwYL1KuFxsI1pHFffPNN0UeTqAa02MCCcOGDZPHzJ8/XwLXwH1K7EEeT5Lj22+/lUSBq0SXFAl2UB8wRxs6dKj8n31IC8natWtH+zzmQ5Q99O3b123B6YkTJ5pmzZpJG1dfHBPaNlJRFMXLsODu0aOHeeqppyTzyQSMFkSOYN5IL++PP/7Yq9tGIAOJ6t69eyWIQAswG1pkElGvXr26/J+BE3dnBs+TJ0+Kgzf1g5hwlS9fXky4qDvnNSdMmCALMSaCXHsKBudQzJQonoeJI79nHOpxrH/rrbekZpysqXaAUEKdLFmymG3btplRo0bJ+Gab9TkDZQssxhYsWCCBasYMZOOMkblz55asLkFqSicYP20XfaDsQYkbzEPwt/jkk09M/fr1XTbMtL+TUIP5BnOk9OnTS2AF5QGJF7uNZnRwjGBkye+e/R9bMEClzJTSE8pjP//8c5/MgeJZoRxacqFeGKd1oq3a0kbxNRjdceJncGVBpwQuLMJZfAMqBcdBnFMzNYlM0JDT0X7SG9tDrfju3bvNK6+8Iq7ELJqIeHMfZQyOigkCIfny5TOHDx+Wz1G3bl15PrWyEQc0ZJFMCnft2iWfx1GSx4B4+vRpaa9EUCIi9HE+fvy4tB/jsVwz4UQBwWDO9pYpU8Z89dVX8jfRf1QTeqwonoLjk4UTrWKZTPK7C/T+63q8KO4CA7o+ffqYVatWSQcIx5pzXO4pVaA7EOd2pN+44eOKT7Aac8dnn302xsAzwQe6KTF2eNvoMRiPFeYaBHGWLFliXn31VaefxzwFbwxUJaEGCk5K4U6cOCHqTNQ0eCjwu3AGgje0GMefp3Dhwi4FZpj/TJo0SUpWWJ8yHqF49dX6VxUKiqIoPoKMf4YMGcRIhwmKY0CBgcU2nho/frzba1Ijg3IGBkYysEzoHInMNZhJBBM5AgqYNHbt2jXK1yaAQAQdrwWCELYvBIEGWwXB6xFdJ8puD6xkAMhQOSolImPGjBmSJWZ/sbhTFE/C77NNmzYycWQBxLHKxE5RFCOLLGrJqTHH9A9Q97Bo5ZzOIoVyOALRBKwx9yWI7YppIwo+cIfRo2JkzKdshUWqswEFFIkE+CO2TgwVVq5cKQGzjBkzStkD8yG7y4kzMCdCDYryhjkSJUMEBQg0RAeBOvyrUPbwWAJ4KIR8iQYUFEVRfLgoYQBBfcACG0NDet7bkWAUAZj7IEMsVqyYeeGFFzy2LbQzwrOBhVHEYEJUoALYsmWL/E2mKSby5MkjAQMCKETXyfISqCCwwoXuFwQmiNijMmBfIJklmMA1mRAUElwGDx4sHhSNGzeWIALBDEylkMvGtae2orjikk55Dz4jTPBQ2ShKqMNxQKab4DDndRRrLJpYLBFkwLwxLhCAx9OEsrpAVwb503ykSZMmMpaiJIlJts8YjIcARGVCGOwwb2HOcffuXTN9+nTToEEDMbh05Tgh0EaAgOAAcypeg3JTfC0iM+Qm4IMPF4EMDIJRhPoFlDwo0XPlyhXKQuRaUXzNnTt3rLlz58q1Evg0btzYSpMmjTVgwAA5z3A5duxY2P03btywSpcubaVMmdJau3atx7Zj3bp18t4///yzU4+/evWqlTlzZuvFF1+02rdvb8WPH99asWJFnLdj+vTp1ksvvWSlTp1atqdYsWKRfu7r169buXLlstKnT28lT548bN9xqVixojxGjxXFG6xZs0Z+g1mzZrUWL14csDtdjxclUFi/fr2c67dt2+aT9w/WY+XPP/+U/TplypRoH3fkyBErceLEVvPmza2tW7daO3futPbt22eFGoMHD7Yefvhha/ny5bLfdu/eHefXnDBhgpUkSRKZ+9y9ezfcfVOnTrXixYtntWjRwrp//77lT+tfda5SFEXxIfR/JsOJCsGmY8eOYXJO1AvUiqIaQBJK1ic6yPqPHj1a/Awok6DO0xm3bWR31IJSkuAMmEaiSsBMaODAgeK3QFuvuMJ2U09IKQTvgUQW46LIIvvsFwwh8U/AvAtwTh43blyct0NRnIXfH94glOZgyLVw4ULdeYriQcjokkGnZl1xH5ShoIT84Ycfon0caqy0adOaTz/9VDLkKBv4Lug0EErgBULZzu3bt+X/7vDyQH3APIj5laOnAuaNKDK5nzmevxlhakBBURTFhzAAUf9PCzpq75AbUnuKjP/dd98VuT/eABj9NG/eXBYskZkfYeqI6zUmitSsHjt2zHzxxRdSJsDEixKCyCYJdGlggEK6SB2ks3I96vXYlhEjRpjt27fLdhLwcCdsd3SDJvsIWSALOGSHlErQrowyEmdKMBTFXeTMmVMCXG+88YYEuShfUhTFM6xevVoCea52I1BiplatWjIHiS4RgZEmQX2MmTFsJiFBS11KWvDPCAW/f8o2bWPQDBkyhHkizJw509y6dStORojM1Zjv8ftmH7dt21ZKISjx4T38spOVx/USQYCWPCj+RLBK7RQrXJnDiBEjREpHSYEtbUP+VqJECZGg1a1b15o1a5b1zz//WN99952UH3D7U089ZQ0bNswaOHCg1bRpU2vUqFEiX6xQoYLc37NnT+vevXth5zbKKZImTWr16dPH+vXXX136GrZs2SKvSblD2rRpH5Dn+YLTp0/LNn3zzTd6rCg+OXbz5s1rPfPMM/J3IKFjixIInDx50kqQIIH15Zdf+mwbgvlYOXHiRNgYGllJYqVKlaxEiRLJYyj3mjhxosjwz58/L/MNZ0omgoFNmzbJZ92wYYN16dIlmYOxH7jtvffei/XrUk7Ca3z99dfWH3/8YT377LNWwoQJrZEjR3qlzCG2618NKLh5hyqKpwnmgUwJT9++feXcc+3atbDbZs+ebeXJkyecZwCXqlWrWjt27JDfBQsaarozZswo91HnSBChf//+YR4DQ4YMsYoWLWqlSpVK6lHjUu/31VdfuRyM8CRPP/20BFyoa9RjRfE2HAtMLCdNmhRQO1/HFiUQICjO+MYizlcE+7FSqlQp64knnpDFrQ1eRswfSpYsKQkPFrtQp04dWfTa1K9f33rkkUck8BPMDBo0yEqRIoX177//ht3GPKt3796SpDl37lysX5t9ircW8zOSRNu3b7d8gXooKIqiBAG0YwLqFOnTvXHjRik12Ldvn8jtvvnmG3H83blzp5k9e7Y5deqUtOBC9o9L8PHjx81nn30mErlevXpJF4SlS5dK9wjaDF28eFEeV7JkyVhvI+USOHnTbspfwP2YMhJKMmDq1KmyvxTFG3As4OnBcaooivugVp2OP3QViKkLgRJ76FhAhwH8i37//Xe5rXfv3qZQoULicYQEn3kE4yqllo5tJunkRCvPjz/+OKi/AroyUCJKqaUNpQjvv/++lHx8/fXXJrbQQYPfOh1R8Oehy5ff45OQR4ChCgXFnwj2yLjyP27fvm21bt3aSpYsWTg1QrZs2azatWtbBw8eDHssUXHuQ50QUXFApJv7+N2EAkjNyRLv3btXPjPZggIFClirVq3y9aYpIUKVKlWs8uXLW4GEji2Kv4PLPWPZgQMHfLodoXCs/P3339JJCaVjy5YtZb+jkHSE7g7cTpcoR4YPHy5lKcePH7eCFVSh9pysf//+4e5r1qyZKCXjws2bNy1fowoFRVGUIIAoP8aKf//9t3nzzTdN69atxd36tddeE7UCpj1k35ctWyZqA/jqq68eUBw899xzkjHFMC4UoDMGWWJc94F99vDDD0sHiClTpvh685QQoFKlSvK7U3NGRXEP9+/fN0OGDJHxzz63K54jY8aMZu3atSZVqlRm/PjxZvjw4aZatWrhHmObOEc0c8ZQmjEX0+ZgBcNr21i7a9euovq0KVeunBhF060qtqAQCST80CZSURRFcQRpJ66/BBcYqL788ksJHCA9RPqJ3BCJHFDycO7cuXDPpwXRH3/8IRK9UAS54IYNG2SSQ4lGTC2xFCWucFzS+YHAAqVHiqLEDRz1KSNybLGseBa6F2zZskXaI3700UcPdF3KmjWrXNNVyhE6UyH9J4Afl44H/gzdfChJILnTu3dvCSowRwPafMOOHTtMqKABBUVRlADk9ddfl0GeFk1Ay0nIly/fAy2FatasKRl72jrixRCKMBGi9pa2mvXr1zdnzpzx9SYpQQzZpZUrV0rbr7Jly5o1a9b4epMUJWChJr1fv35S0x8Xzx/FdVKmTGmefPLJSO9DvUDCI7KgKYH8S5cuibdTMMJnp73m008/bbp37y4Blw8++MBs3rxZbsucObPp2bOntIEMBTSgoCiKEqDkyJHD9O/fX6R3DOqYN9IPOnXq1A8sbtavXy9mhQQeLly4YEIRFncdOnQwd+/eFXPKYM2cKP4BE0oCCUhiURbRr5zjEOm2oijOw8KNbG+3bt10t/kZBBsiKhQAuT/JDUonQiFhMXToUFFsoMrg/5g1kvShRIcgw40bN0wwowEFRVGUACZ58uRm4cKFMni1a9fuAUmizWOPPSYdH/755x/TsmVLE6owuaHes3HjxlIecvbsWV9vkhLEMMHctGmT+e6776TkqFSpUlKHrMEsRXEOAnBkgPECwgdH8S/o9rB9+3ZRkThCdwi6UiVOnNiEAvHjx5dStwkTJpgff/zRFClSRPyt6Lr1wgsviKJh2LBhJlgJ76KhKIqiBC1PPPGEtI7EPIhJWsTSiFCAbDHlDig5KH9APsugz6RIUTwBQb46deqIser8+fNNvXr1RK0wd+5cMRANBJDtYjJ29OhRc+fOHbmNBQRt4/7991/xi2DSrCjuhowvC1bUPVEFzBXfgVcCXgK0mqSckPMCnkW0bs6fP39IfTXdu3cXVQLn+23btpnixYtLyQfmvLT57tixo5wzO3XqFHS/ZQ0oKIqihBBFixY1169fF5PG7Nmzm1AEtUaZMmUkc4yvRIkSJcRMiX1jBxYoiUDGSfCBbhtklpkchEq2RfFMyU316tXNkiVLxAOF39TEiRNNgQIF/G53UxbFMYAPxOrVq82JEyeifTyTY8xiQ1n9pLifK1eumC5dukgQTr0T/JPy5ctLsJRyQkq8OGfgqfD888+HmRSGCgkSJJDAwe7duyW4sGjRIpkzoFYoXLiwmFXye8YYevHixSZdunQmWAi99JSiKEoIQ4YeQtWc0RFaaRJUILBCRoFrWl1xwZcCw0vaZVEbyeIPnwokt8gWydQqSmwgmPXTTz+ZmzdvShCLunB/KIHAWwTefvtt8/jjj5sGDRqYXbt2ianrt99+a7Zu3SolQteuXQu78BlQLGBGRqYS9VNE6bOixJY+ffpI7TntIhX/Bf8mzh+cIyjpoiMHihLG2FAjQYIE0qI7Ystggq78ngnQ0gqcQEww+VmpQkFRFCWEoL0kPgIqT/6PtGnTijyTAX7//v0yCeBSsGBB06pVKykLQaK4Z88e6cnNZIAFINJFMg4EItq2bavKBcUlCCSwWB80aJAYq5KtohwCI0dvQ40vgbNZs2aZkSNHinqJBRwlQbSNcwb6zaP8oXVa3rx5TdWqVT2+3Upww++S3yOLsEyZMvl6c5QYyilpVx0MMn5ak/bq1UvK0Tin4QOB8aR9efTRR2P8nEeOHJEysMig68+qVasksEzLb5IaCRMmNIGOBhQURVFCbAGNhJ+BUvkPJgcs5LjY7TcjStWRLHKhNRSZWWTrs2fPNj169DBz5swREyad9CqugBSWtmIsvvFUoHc5QQW7h3lMoGo4fPiw1Oxy4W9+m2QKMYNksorsmKAYUtuIk2B8Efj9IkumMwzKBCDA5uoEl9fmtZgoE7TUgIISF1C9NG3aVH7HmA0r/k8wBBPg559/lvGcoC/qGMq9MLO2QalIoKthw4ZRvsbp06dF6RgVtPEmiIzR6PDhw6UMwt8gkfLVV185/XgteVAURQkh6OONN0BMNdFK1LA4q127tjj32+oGJh9IPBXFVQoVKiQGXmS/KK0ZO3ZsmPFhZGAo+t5775k0adKI/wL1yygKyKxRc85zMXysVKmSZNNwFyeD2KZNG1nwf//99/L7Rak0btw4CQD89ddfZuDAgXH+8qijpkUaSp6YoHyCbVaUiGBat3PnTjH6o+2xongL20iS8yUqGTynOFdxjp45c6YkHZo0aSIBgaj48MMPzYoVKyQwERXFihUTdWPv3r0jbbvpS1CbMFfs3Lmz80+ylBi5cuUKBYFyrSi+5s6dO9bcuXPlWlFc5dSpU3I++/7774N+53nrWDlz5oxVqlQpK0GCBNY333zj0fdSgpebN29ajRo1kuMzc+bM1qeffmpdvXpV7jt37pw1cuRIq2jRonJ/xowZrZ49e1obN26U++7fvx/utfj//v37renTp1szZsywPvjgA3lNnsuF1+nfv7914sQJtx4v9+7ds0qWLGmlS5fOOnr0aLjbt27dKts0adIkq2LFimHbwn2KYvPDDz/I74Lfu7+i87DghXNn3bp1rYQJE1qLFy9+4P67d+9aVapUsZIlS2Zt2bIlyteoVKmSVaRIkWjfi/M7v/UJEyZY/sL58+etPHnyWOnTp7eWLFni9PpXAwpOoAEFxZ/QgUyJC0uXLpUBYvfu3UG/I715rPAetWrVkoXUkSNHPP5+SvDyyy+/SGCBAFXSpEmttGnTyt9cmMj++OOPsfpNM8ndtWtXuIW+J46Xs2fPWk8//bSVI0cOq23btlanTp2s4sWLhwUQ4sWLZ5UuXdqqXr26/L9q1apxej8lePjtt9+sFClSWLVr134gSOZP6DwsuOH7feONN6wkSZJYa9eufeD+f/75RwKnqVOntvbu3Rvpa3z22Wfy/H///Tfa93r00UetQYMGWf7A7du3rRIlSsjn4lh0Zf2rJQ+KoighxLJly8SFODpJteI61Jxj5HT58mXz9NNPi9u9osSGfPnymcmTJ4s5Yt++fU3r1q3F9JDSmnnz5pkaNWrEysSLGmeMRCmt8LRPC+3S6BSxfPlykf2yvfiOUHJBuRUGp3iQTJkyRT4T5yUltMHxnnaqlOJgEhosNflK4MH5ivKGF198UTo2UO7gSNKkScXvBsNG2pmujqTEC58EfG44j0cFiX3mY44eDb6kffv2UmrE+TtHjhwuPVcDCoqiKCFE8+bNxYBNTdPcDxOI33//3TRr1kzq0i9duuSBd1FCBXwPmOBhdkhQIZB6lufKlUtaY9IxhWMCrxFMIsuVKxeukwWtKTE75b6XXnpJTCWV0OP27dumevXq5uLFi7KYwadGUXwJ3h140eCpQGcGgp+O4E/DOQ7j21dffdVMnTo13P34KqVMmVJa6v7777+Rvsfx48fFrwA/BV+DOe+oUaPMZ599JmaRrqIBBUVRlBCCAezMmTNinqZ4ZhFImzOMnGjDpyhK1NCBAhNHjhUyY/369dPdFWKQpaWjA1lg1CoovBTFH6BTA+aKtNBt3LixadSokbl582bY/QS+FixYIB1yuA8jR4JjQCct1FkrV66U2/mdRyR58uSixCGQ5ksw6sVIkgD2+++/H6vX0ICCoihKiEDGHHdiXIq1FZfnQOqNQzKSbkVRooc2tjVr1jQff/yxlHbQ/lIJHWidSjcHMrwvvPCCrzdHUcKRLFkyM2HCBPl9UgbRvXv3B8ojxo0bZ0aPHi0delBh0W4Sypcvb7788ktpv7hly5ZIy8NQc+3atcure52SV7oFTZs2TYIItMCsX7++dAuKLRpQUBRFCXKQ2yE7btmypdTqTZw4UTKDiuegzp16cTwVFEWJGbLUd+/eNXv27NHdFQKQsf3000/FJ4SWpbQyVRR/hUU3wS9KAliMOxIvXjyZX61bt07OXyga7DIHzmsoEfCTiYxs2bKZ/fv3R6pgcDeowfCEQHlRsGBB+UwkPtjGuM4LdUapKIoSxDBBpzYZgx1kbdTIZcqUydebFfRUq1ZN9j31wIqixMzBgwfDTCmV4AazOtRyZEc7duzoWr97RfERH330kcylCB7YpQ2OPPfcc1K+hQEtvje26SLBMkybKX2w1QuOcwXKIiip8CSUYaKeOHr0qBk6dKhZv369JDz4PwqK2Bj9OpLAbVuqKIqi+B1ff/211KZ+9913Iielxl/xPBjPMbkg+o+UUFGU6Nm9e7e4p7vqLq4EFidPnhQDRjK5yMjJkipKoJRnMaeqUKGCefbZZ6VkoECBAuEeg0EjpQ/vvPOO+CjwfxQA/E1ZxOLFi6WLD4ke2ygbZcC7775r3nvvPTF59JQ6gUAeZRsYSLsbVSgoiqIEMbSaI4hQp04dDSb4oOyBTAWmTY5GToqiPAitI5lkP/TQQ7p7ghSyt4UKFZKgAp0/NJigBBqlSpUy27dvF/UBQYXhw4c/UK5AAAFFAqU8+ClxXuNxtOwlsMBtqHIcyyJy5swpJUBxhaAB7VdRIyxdulS27fz58xJIoMNXnjx5jCfQgIKiKEqQwgDGAOUvPY5DDVriZc+e3VSpUsUjGQFFCRaQ3q5du1bb2QYpZ8+eFdl3rVq1TMmSJaWjhz+0ylOU2IAqgaBCq1atpGPW4sWLH3gMfgt//vmnOXTokLTPxVOJxMKxY8dElcD8DKUDxwYKhbZt24qikU5cseXevXsSpOO9rl69al577TUxicb8cfz48aKWxO/BEwR1QAFnTaJB9BJFeors14Yv+MUXXxRZqrYpUhQl2Lh//75IiHHzpRWX4n0yZswo5k0EdZhE8J0oivIgZNLI1mEYpgQPZEfJjObNm9esXr1aSu9opZcuXTpfb5qixAnWlgQFSpQoIUqEyGCNifKAhAKKAYIQeMTQNWLAgAFixlikSBFZ7J86dUrmCEeOHIn1NvXo0UOCEhxnvBfHHImNb775RuYgn3/+ufEUQRtQwHwM8wwiRLTjwM3ylVdekUgQYJbBTmaizWXTpk2+3mRFUZQ4Q4QamT1B1G+//dbUq1fPFC1aVPesD3nkkUfkWpUiihI5uKODp7Jnive5du2auN1TbofEm8UTf+t3rAQL/JZpd7tx40Yp4YmJNGnSSLcH2kmSaMCMMWvWrOKfgFEi5Qi5c+eOU0kR6oeqVavKtpUtW9YMGzZM1ru8jycJ2oACXxRfEC6yRIZwsKSXKG0x7H7sTLKRrZBF0tZeiqIEOigSaEFUqVIl+T8uvgQVMBJSfAMOyp988onIe2kdpSjKgzABhn379unuCQLIsmIuR3CbbCkLHVUlKMEIfgVPP/20mTRpklOPp5sCa1EUWb1795Z5GuUJJBwIurEmjQ2oG1Ah+Kq8MigDCkh8qc8iAmRDfQr/x+US+vTpI/8nyMB9qBcURVECmd9//11q9pAPU+JFrariO86cOWMqVqwoPZ8XLlyomTlFiebcxURbz1mBD/XbxYsXl7Z6W7duFVWCogQrrCHr1KkjpQasP50BXwMS3ZT/vP3229LSMa7KndOnT8v7U+rvC4IyoICbJbLf9OnTh7ud/7PDgQzeuXPnxGl2zpw56iqsKErAc+HCBbnOlSuXLl79APpKM8YgcYw4HimK8j8IgpLRJvimBC6Y09EmD0UWQW1POcorij/x1ltvidJ9xowZTj/nzTfflPaRdLdhzmYr6GPL3bt35TpBggTGF/jmXf2ExIkTi/OlK1+W/YUpiq+wf4P6W1QcYTDr37+/lHllypRJfx9+cKxUrlzZ/PTTT6ZatWphTstckP5yYeLtyhikKMF4vCD3RfaL/FfHtcAFEziyrZSvYALHwiZYv09fjy2Kf5ErVy7poNCmTRsxa6RltDPgcUUnBpoETJ06NU5tVDNkyCAtKWk64KjQjwuu/L7jWRGbZwYBSD4oZaBmi4mcTaNGjWTS7arjOYNdqlSpzPTp0+V1FUVRFEVRFEVRFCUYwdeBoMeVK1dMypQpQ0+hgAEZhovUcdkBBcwq+D/dHWILtbAx7VBF8TREDFesWCH9a6k5VRSgJdETTzxhpkyZojskAI4VXM8LFSokygVaTtEJ4uWXX5ZtJbugygUlFI4XOgHQkQYjMZJASuBBuzt+M5SrULpid7UJZvx5bFF8x/37902HDh2kLSSG2Bg2xgTKBvz9Dh48GGcDbTpOTJs2zfz8888mderUJq6QUHeWoAwoAC0jUSRQx4U5zGeffWZu3LghcuDYwklDTxyKv6C/RwWIHLdo0UJaFiGZ03NUYBwrDz30kNRP3rx503Tp0kXkwTiiMxmAUqVKiXyYOksUcooSjMcLLbsxZMS01N+OUcU5WBBxHiNpF2qBUH8cWxTfMnLkSOly0rFjRyl7jClIULhwYTFnZK1KqUSPHj1i/Zvq1KmTGTdunJS/fvHFFyauuLIdQWnKCDhu0nuTL4YsENEaIqdqjKUoSjBx+PBh8/3330tkmomdEhiQzcM8mCwGCoW+ffuaHTt2iHEw7aeow6T1MXWRdevWlWADbaYUJZggsGZnuZXAY8uWLRLM/vLLL03mzJl9vTmK4nPixYtnhgwZIi2jx44dG+Pjmbvt2rVLFKb9+vWTJEJswZupa9euZsyYMdJwwJuuBkHpoeBubA8FZ2pIFMUbUjuclOlUopFxhRq3FClSyMD1zjvv6A4JkGOFIDfDL9mJqPj7778l4EAZC/2pmbAjs82dO7dXt1UJDXxxvCARfuGFFyTDTbtvXzmUK67D+Ynfyq1bt8yBAwekfV6o4M9ji+IfNG3aVJRXKLCcXTvSShJjx0uXLsW64w3HY/Xq1SWJjkEqgYXYtqR0Zf0bOke/oihKEIJRbM6cOc2ePXt8vSmKC6Cciy6YAHTrQMK4b98+WWwxoL/xxhvm4sWLuq+VoIBFKNLcX375xalsnuIfQew+ffrIuHP27FlZsIRSMEFRnKFPnz7iEYNa3hVvJZSIc+fOjfVORt1IsGv48OHShIDghDfQM4CiKEoAc+LECckUUad3+/Ztkc6rfDi4ILtQpEgRs2DBApkc4BHkmI1QlEDm2WeflWxet27dzJ9//unrzVGiAVUVRnPUaGNyjpEchpqKooQHReGHH34oAQVUCs6QJ08e8VHA2BHVD3O62M4ZaEVpzxG9gQYUFEVRApgzZ87IoIMhVoMGDcQ35pNPPvH1Zike4KmnnjJp0qQRxQKTeUofkiZNavLnz2/at28vEkeyh4oSaAwaNEhKt5hMU/6g+B/r1q0TY/M1a9aY+fPnm8GDB6thrKJEQ7du3cQHiXJUyrucNXVkTkegDrVBiRIlpOTAVfBkAG8FaTWgoCiKEuDZPYyxWEjSdi1r1qzafi1IuXz5sjl06JCUPxA8KFOmjJgv4Q49c+ZM89prr5lHH31U2k8y2UfRgHv09OnTxeiR+sxly5Z51ahJUZyBQBnSeYJlLVu21N+oH0LnNDxcevXqZV555RVfb46i+D0PP/ywtJAkGEe3QWeg1PG3336TY+3rr78WpQIKLlfHbZoQ4EmD/xIG0DaeGv/V/UZRFCXAoRafRSYtBz/44APTrl07X2+S4gHo70528Mknn5SLzXvvvSeTBIINy5cvl4kIpS+0So4og+Rx1GlSX1m0aFH9nhS/gbIefBRol5olSxbTu3fvWJuJKe7l/Pnz5tixY2bGjBnmrbfe0t2rKE5StmxZURBSxkCwgC6EMUH71fLly4f9jXKLNtMohFzpoJM9e3ZpS929e3c5p5J4woOJ7aFsCWhxiXkkagiUFLE1xtWAgqIoShDAQEEEHPk7i0klOEGVEBksvCiB4EJQ6c6dOzJxSJw4sfhrcM1EAWUDE5tixYqZhg0byqTClkYqiq/hN3ny5EmZBJNVo42aBhV8D0onWxGnKIprDBkyRFpCc34jMeCKwqdatWpiyMx50VVQRtCSEg8HAoIoJjBSHTBggLzetm3bRMHAHAEzSAK69erVkyAGxzoBEGfRgIKiKEqAs337dolgs4jE1ZfBQQlt+A1QuxmRV199VTIfyDDx2/jhhx+kzpNe2OrUrvgDnTt3luAXgS/a81G+o0EF3/Hrr7+atm3bSnb06aef9uGWKEpgEj9+fCk7xAuBlo6UqRYoUMCp56IgoH0jSQBXITBA8MIxgIFKkfLJRYsWmcqVK0vQtkKFChJYwEQSZRjdKVAsbNq0yen30oCCoihKAPPtt9+KTI02hGSRXIkoK6EJi7UWLVqYunXrSqaCgAKTiYkTJ4qSQVF8DZJcfqcsZAkmEFRQfAPnBbKqo0eP1q9AUWJJwoQJxevoueeeM/Xr15dEEIv2mNixY4dcu6tEkfMp88aI4MW0efNm+ZvAB6W07777rtOvq6aMiqIoAQqS9ubNm4s64aefftJgguISyChx12eSQzAK9QKZC0XxB8iWffrppyIXpnZf8T4401NfTSbTmcWPoihRQ1cmTJIPHz4spQXnzp0zMYE/Fp5JGNd6i1SpUplx48ZJuYSzaEBBURQlQKHejTpjvBM0s6zEllq1apmVK1eaPXv2SO9qb/WtVpSYQKFAO9xmzZqJ9F7xHpi6vvnmm7L4Qc2kKErcyZcvnwQVSALlyZPHfPPNN9F2XsD7IEeOHF7f9XSL+uOPP5x+vAYUFEVRAhTaRFL7li5dOl9vihLglCxZUuolMfdE+ohZo7f6VytKdPJc2p0+9dRTosSilljxDqhDlixZYubOnSuLC0VR3EONGjWkHWTFihWlqw0eB1Et3m/fvu0zddBjjz3m9GM1oKAoihKgVKpUyaxatUrcgxUlrtAhArMoSh/wVsiaNavJmzevef755yVw9cUXX3ish7WiREXy5MmlJOfUqVNiJKp4Ho5z2tShTKhSpYruckVxM+nSpROlwuLFi6Xlc8GCBc2GDRseeBxm24FgtK0BBUVRlAClXbt2MtCoYZniLtKnTy8LCYJUdIIgM/nMM89IhoR2lJjlKYq3yZkzp/jFYCZG5wfFs7CwIWPauHFj3dWK4kFee+01s2/fPmnTSDB//fr1DygUAqGkVQMKiqIoAcqjjz5q3nvvPTNmzBhz69YtX2+OEkSkSJFC2sSNHDlSAgsLFiwwvXr1EpUCLaUUxdvgjH7+/HmzYsUK3fkehqAiZSaUQimK4vnxduHChVJuSIBh2rRpUm7EuEtwH3NUvEz4+/r1636pFNSAgqIoSoDCwIIUOH/+/AEhiVMCf0H377//mnXr1vl6U5QQhNa4nOsIoCqeZf78+aZOnTomfnxdJiiKN0iWLJkEFUqUKGEaNmwoJa2UGx08eND88MMPotJ6/PHHJfhAS118FyKqGXyJnikURVECFAaa33//XdQJOAYriid5+umnxVeBjhCK4guDRrLm/jSJDlYefvhhv8yCKkqwBxWWL18unR0wRabsKFeuXOJltHr1alEs4LswYsQIc/LkSVOqVClREfXu3VuC/dHNFS9duuTRbdeAgqIoSoBSrFgxs3nzZjEtK1eunHn//fe1vljx6IKufPnyKjlXfAbnuStXrkiHG8Vz0M6O7hqUPKlnhaJ4j/jx40vgPnPmzGbo0KHSxnnGjBmmbNmy5vXXXxejVPyMaPNMBxbmf5Qj0uKVBNOZM2fMzZs3wwKCu3fvFh+k1KlTy5yxc+fOZtmyZdIW1q3b7dZXUxRFUbwK8jiCCsiAx40bJ/V3no5EK6ELAYVff/1VsiOK4m3atGkjUnzMApk8K57zUKhdu7Zp27atadCgge5mRfEy+/btk3kdAYZ33nlHznmO/kXcXrVqVQkOUBIxb948kyNHDpMhQwZROiRMmFB8tkqXLi23jx8/XtQOU6dOFfNH7kPh4C5PmgRueRVFURTFp5ljzBlp+1ezZk3zwgsvmB07dkjkWlHcHVDg98YkpFGjRrpzFa/Cbw+T0CeeeEImxn369NFvwEMt7b7++msxg1u7dq3uY0XxQYnh559/bv766y9z7tw5MWqk9IFOSxG9TZj3/fzzz+bixYsSdOBy9erVsL8JPKBOaNq0qSgXKIGg5fiUKVMkYIjhY8qUKeO0vRpQUBRFCRLKlCkjhnkFChSQwadFixa+3iQlyEiTJo0s5vbu3evrTVFCFAKllStXloycBhQ8BwsRSktYhCiK4l2SJk0qpQ02d+7cMZ06dTIDBw40zz//vCSOuNBukraSeGmhTkC1ynOjC8pS0sSFQANmjyi/MF2m1CJLlizRPj8qtORBURQliCCCjTMw7f3UVEtxN7Tto6azcOHCunMVn8E5jqDW0aNH9VvwYKeHy5cvh1vUKIriGyZOnCjKQMqQ7t27J/4KeMqkSpXKPPbYYxJIwCuBcgeCBd26dRPVQnTzQJID/fv3lwQUXSNQufJ8AhMVK1Z0yYBZFQqKoihBBgMOagUGCgYVRXEXGzZskGtqLxXFV1ADTKtcVAqc7xT3w0LFzmgqiuJbEidOLCWHXIBypAMHDphNmzZJoB/TRlQMR44ckXF69OjRMgekdIIWlEWKFJFEAMEGxzbjH330kSgUKK04fvx42GXRokVSSuEsGlBQFEUJMjDhoY1Q9+7dxYwHEzNFcQeU1NiySEXxFfRiZ2KNy7kGFDwD3TQA8zZFUfyL+PHjiyKViyME+5s0aSJdWtasWSOGjRg3olq1wS8BVYN9bFMucfv2bVO0aFHz9ttvm3r16sn8kYBt9erVndoeDSgoiqIEIQwGtAv6+OOPJcqcIIGe7pW4s379elUnKH7Bk08+Kd0IkPRqFt392FJpspX58uXzwDsoiuIp6PJA2QIX2xOFVpMYMGLeeOHCBbkmMJEkSRK5Xr58uXnjjTdM2rRppQSCBIKz6AxTURQlCGGC3aNHD5G5zZw5UyLOihIXmIDs2rXLNG/eXHek4lOQ+dJSjf7rGkzwDLSNZP9iBLd48WIPvYuiKN4AVVfJkiXlEh0EHeigg1/Dyy+/bCZNmuTU66spo6IoSpBCvdxrr70mrsDU2ylKbOH3Qx9s6qpff/113ZGKT0HCS1ata9eu+k14COqsGTuWLFlitm7dqvtZUUKAggULmuHDh4vp7Weffeb08zSgoCiKEsRQ8rBv3z4x2FGU2IK5E78h3KAff/xx3ZGKT8GA7ObNm1KCo3iOGjVqmGzZspkvv/xSd7OiKFGiAQVFUZQg5qWXXhKJ24ABA7SNpBIrRowYIeUzyJ9xi1YUfzCepcWZLnQ9y0MPPWRatmxpvv/+e3P27FkPv5uiKIGKBhQURVFCQKWwZcsW89NPP/l6U5QAY9iwYdJWqnPnzmL0qSj+AL4JtDqbPXu2mAYqnqNp06Zi2Mb133//rbtaUZQH0ICCoihKkIOPAnVx/fr18/WmKAHE4MGDTceOHaVOnVpqNb9T/IlGjRpJ+zN+n3ZHAsX9pE6d2nz77bdmx44dpmrVqrqLFUV5AA0oKIqiBDksBJGrr1q1yqxcudLXm6MEiGdCly5dpNShb9++GkxQ/I7kyZObUaNGyWJ34sSJvt6coPdS+Prrr83OnTvNr7/+6uvNURTFz9CAgqIoSghAZun555+XRaJ2fFCi4/PPPzfdunWTIFTv3r01mKD4LfXr1zclSpTQci4vUKxYMbnWEhNFUSKiAQVFUZQQUSkMGjRIMkyzZs3y9eYofgo10p988olp3bq16dmzp683R1FihK4jahjoeeiqYStDFEVRHNGAgqIoSohQqlQpcemn5vju3bu+3hzFD0GZkCxZMilzUJRA4NKlS+aRRx7x9WYEPTdu3JBrzg+KoiiOaEBBURQlhKB95JEjR8yECRN8vSmKn7Fr1y4zZcoUKXPQBZoSKBw+fNhkz57d15sR9Ny7d88n74v6ZO/evT55b0VRnEMDCoqiKCEE3R7q1asni8Zr1675enMUP+H27dumVatWJnfu3KZ58+a+3hxFcYpFixZJmU6RIkV0j3mYJ598Uq6PHTvmtX39zz//mJdfftkUL17cbN++3WvvqyiKa2hAQVEUJQQd/K9evSq18opCy71mzZqZ3bt3i1t+ggQJdKcofg8L2yZNmpjKlSub6tWr+3pzgp7HHntMLr///rvX3rNPnz6iqCPQWadOHXP58mWvvbeiKM6jAQVFUZQQI2vWrBJU+PLLL82mTZt8vTmKj8EvYdq0aVLugGO+ogSCbwJ+MClSpDCTJk3STiReIkuWLObEiRPeejuzb98+U6FCBTN79mxz7tw5DYIrip+iAQVFUZQQpE2bNiIjJTON3F0JPU6ePCnBJbo59OvXTzKAihIItfwoEqitX7JkiUmbNq2vNylkSJo0qdfGC5RTfMe851NPPWV69Ohhxo0bpx09FMUP0YCCoihKCPLQQw+Z8ePHi3wVtYISekyePFmyfjNnztTMnxIwcN766aefJGudM2dOX29OSJEoUSJz584dr7zXyJEjxTehVq1a8v+mTZuKEmX69OleeX9FUZxHAwqKoighSr58+Uz58uXNjz/+6OtNUbwM2T+k4m+++aZM2JmoK0ogqBOoq2/YsKG0wVW8S+LEiT2qUOC8ZHtitG/f3nz00UemZs2acl/q1KmlzGXu3Lkee39FUWKHBhQURVFCGNpxPfvss77eDMXLnD9/XtQpr7/+uu57JWBYs2aNlOq0bt3a15sSknhDobB//34pZXnjjTfM4MGDw91XoEABr5pCKoriHBpQUBRFCWFwSUfyfubMGV9viuJFkiRJItf379/X/a4EDJiH5siRQ4OgPgI/AwwxPcW///4r56bXXnvNzJkz54GOM3gp0Cb05s2bHtsGRVFcRwMKiqIoIUy7du1MwoQJRUaM3FQJnYUB6MRcCSTmz58v5qFaouMbypUrJ52BPBGAJriJ8eKhQ4fkOjIIKNgtQxVF8R80oKAoihLC0Fccl//Ro0dLx4dbt275epMUL0DmD/nyP//8o/tbCQguXrwo2XG8XxTfkCdPHvGxcHfZwYULF0yVKlXMoEGDpONM4cKFI30c6hRYv369W99fUZS4oQEFRVGUEAfjKxz/v/32W1O6dGlz9OhRX2+S4iWVggYUlEBhwYIFcl2iRAlfb0rIMnHiRJMtWza3fge7d+82hQoVMlu2bDGLFi2KtuPM448/bmrXrm169+6twW9F8SM0oKAoiqKYRo0amQ0bNoiUNW/evGbAgAFe6zeu+IZkyZJpyYMSMBD0RHKfNWtWX29KSIIZ5vfff2/ee+89aTvsrmDCyy+/bNKnTy9/08UhKlavXm2KFStmXnjhBdmWy5cvu2UbFEWJOxpQUBRFUQQma/v27RMHdcogChYsaFatWqV7J4jLHjRopAQC1MyvXbtWTGQV39CtWzeTIkUK06JFizi9zqlTp0yXLl1MjRo1JEBEGcPKlSvNE088Ee3zeN+dO3eatm3bilIhQ4YMcdoORVHchwYUFEVRlDAefvhhM2TIEMkWpUuXzpQvX97Uq1dPJoFK8ECpw19//RVmcqYo/oxdM08rQcX7UI6AQqRXr14mVapULj33ypUrEjD45ptvJIiAwmTMmDGijqpVq5ZZtmyZeeSRR2J8nbRp04b9/c4778TqcyiK4hnC92NRFEVRFGPE+Oynn36SSWCHDh1Mrly5zMcff2zatGkjQQclsPn111+lqwd93RXF3zl48KDJlCmTy4tZJe5s375d2jgWL17cNG/e3OXnv//++2b69OnyN2aLBKwbN27sVBDBEdQMdICYOnWqqVChgsvboSiK51CFgqIoihIptGZ7++23ZTKPxwJlEBhyDR061Ny4cUP3WgCzd+9eEz9+fPHLUBR/h3NQ7ty5fb0ZIcW///5rZs2aJSo1ujugJKDFsKts3LjRtGzZUpQKu3btkpIFV4MJ8PTTT0unD4yD6VCjKIr/oAEFRVEUJcbWkl988YW0CkOyigs3UvkRI0aoqV+AsmfPHqldxphRUfyd+/fvSwBM8Txnz541/fv3l3M8JQmYIBJMiI06BE+e48ePm7Jly5qUKVPGepuuXbtmpkyZYnLmzKkKOUXxQ/TsrCiKojhFlixZzNdff21+++03qWXu2LGjTDpHjx4t8nklsBQKWu6gBAosJA8fPuzrzQhqMDxEiYY5IgGFihUrym1LliwRM8bYBIHoCEG5XJUqVeK0bZ9++qnZtm2bGTVqVJxeR1EUz6ABBUVRFMUlKHsYP3681LPS8qtVq1Yia1UCh/3794tPhqIEyjnnxIkTskhV3MuOHTvMiy++KF1+8M0hmIBhK+f4IkWKxMnIkXHhs88+M4kTJ47TNv75558mf/786p2gKH6KBhQURVGUWEFNKzLU5MmTm759+4q0VfF/Tp8+bc6dOyd10YoSCODwTzDh8uXLvt6UoIJWnJQj0HFhzpw55siRI2LCS5lbXEmSJMkD3Rliy4ULF0zq1Knj/DqKongGDSgoiqIoseahhx4S1+1ffvlFTNO6d+9url+/rnvUT2FR9u6770o9dMmSJX29OYriFLSwhZ9//ln3mJuglIHuDc8//7y05axWrZqcz939neHJEBcop8OUM3PmzG7aMkVR3I0GFBRFUZQ4gVEjvgofffSRdICg9lbxT5AgL1y4UJQljz/+uK83R1Gc4rnnnpOFb/369UWOr8SNH3/80VStWtW88sorZv78+aIyczcoE+gURKlKXFvcElBgexVF8U/8KqAwcOBA8+yzz4r5C5FNoqXU6Dpy69YtqddF+kQv9Jo1a5ozZ86EewwnRwx8MIJh4uQIkq4SJUpIdob3oWUWLWwURVGU2MP5mNrbzz//3GzdutXcvn1bd6cfgrEdk3wWEooSKNAmkPkb1ywstW1t7CAY07x5c1O7dm3p4PDDDz+ElSa4G3wTihcvbpYvXx6n1/n+++9lzl6hQgW3bZuiKEEcUMAMhmABRi4rVqwwd+/elUyX48DRrl07s2DBAjkJ8viTJ09KdsyGSSyvges4brD0vr1z547ct2rVKlOnTh0JQuAWi3stE2DeR1EURYk71OUjq//jjz90d/ohfC+ZMmXy2CJCUTxF+vTpJWFEoomyHcX1Yx+Vx7x588ygQYOkVC1hwoQe3Y10A6LlJG0fY1vuMHPmTFO9enUJJimK4p8kMH7E0qVLw/1/8uTJolRg4V+qVClz5coVM2HCBDN9+nRTrlw5ecykSZNkAksQAuUBAQVqwAoVKiT3J0iQQG7jREQgAidbWp3ZoGRACaEoiqLEHc6pQAmEmv7556KCVp+KEogULFhQugYQUEDVmjVrVl9vUkCAYS7mi0mTJhUFWcaMGb3yvm+//bbp3bu3GTNmjOnUqZPLz9+9e7eMJSjfFEXxX/xKoRARAghgu80SWEBNUL58+bDHYAJGb/TNmzfL/1OmTGmaNGkitaGcMFEo2P1zM2TIILVY+/bt88nnURRFCYUsIudcJoH/196dwNlUvw8cf2YY6xj7MrJHRHZlK4QiSfq1UcmaLWQpS9sMla2UlLXyI/0QflFIkvUXigghCmMpYy3GPszc/+v5+t/bzJgZ98x67zmf9+t1zdx7ztz5znHPvec85/k+D3yLXu3Tz0ACCvBn7dq1MyfGc+bMyeyh+Dz3FX4twKoX2FatWpVhwQRVsmRJad26tcksSYnPPvvMXFiMe9wPwPf4VIZCXJoyq7UNNKPA3StbW11ppkG+fPluOIDVZW5hYWHmZwMDAz3BBNW3b19TyVZ72WpUWzMadEqFFvnxpkeuBjOYHoHM5n4N8lqEr9LMBC3EldmvUfaV+PQqn9ZQ0Cu7mf1/A9/jL/uLHq/p/P9Zs2aZabB6rIekM387depkujmMGzfOHC9n9P+vTlsuU6aM5d8bExNj6mboMboGRnzpdekv+wqQGlZe3wEu3Ut9kGYWaEub77//3tMqRqc6aPZBwmJfWvRFU7nGjBnj1XNrn93Vq1ebaRJa6dad4ZArV65E14+KijIFYfT3J7UOAAAAAAD+7uLFi/LUU0+ZGQM6A8DvMhT69OljujOsW7cuXt9ZnbKgBRbPnDkTL0tBuzzoMm/deuut5tatWzd55ZVXzJxfrSKrwYrkaDbDzTYokBERQy1aqhWP07ugEpASevVQs8n+85//ZOoGZF+5TosX33PPPaarkV7xS8te87APf9tfdJxaXFTrYyFxmsGhNRM2bNiQaRcHly5dKj/99JOZumD1Z3XsOt1ZO9P4En/bV4CU0Avq3vKpgIImS+i0BD3gWbNmjZQtWzbe8tq1a5sdV7s1aKcGpdV+NbVWK9emhKZhadaBNy2I9HfzxgFfwesRvkqDCRr49ZX3SyfvKxqEb9++vfl81arudHeAXfaX3r17m85dWhfEXYgb8VWuXNl0Pdu6davUrVs3QzePHsdrIfWPP/7YdJax+r61YMEC6devn093d/CXfQVICSuvbZ8KKGi7R51WoC1ttPaBuy6CTjfQAjz6tWvXrjJw4EBTqFGzBTQAocEErYdwM+Hh4SZ9o1WrVqaGgh7wTpgwwUQa6W8LAGkjd+7cpt85Mp9eRdMpfVo/qHDhwpk9HCDNaMtwLTCq3QO0NaGvXcX2BT169JDp06dL9+7dTZZARp38ah00zfzVrISbZf8mRi8capq1ZrsB8H0+VclG28roG0iTJk1Mlwb3Td+U3N577z1TMVYzFLSVpE51+OKLL7x6/saNG5uWWdrGRrtDaJEaDVp8++23UrFixXT8ywDAOYKDg+X8+fOZPQyImK5GGqDXAseAnWjXgg8//NAEzej4kDid3jRt2jT55ZdfMmwKml64q1atmkyZMkXuvPPOFBXN1OyEChUqeIqyA/BtPpWh4E19SE3XnDhxorlZpYUb9QYASN95d5qlgMy3e/duk/bM1VvYkV4YeuKJJ0xnr6ZNm1qqp+UUtWrVMhfg9OKcdnxIb1qvQaehaKtIvQBo1bVr10ymsmZV8L4FZCztBKUX27W97DvvvOOfGQoAAP+nnXT06hIyl0510L7zGlAA7Gr8+PEmlV8LZ//111+ZPRyf9Mgjj5gWklpzLCNqJxQqVMgEE1ISEFi7dq2cPn3aUysNQMbQYJ6+V7Rr185kF82bN8/rnyWgAABIU/v27TOddJA59KRBDwoaNGhg6g0NGjSI/wrYlk6N1WkP+rofO3ZsZg/HJ7mn9Q4ZMiTdf9fBgwfN70tpdoFOd9CC6ZpZASDjfPTRRya7SDtD6VQlrbviLQIKAIA0c+7cOdPKt3z58mzVTOJut/bZZ5/Jzz//bNpFAnamWTg6/UHbjfs7nZbQtm3bRK/860m6FhSP+31CejKuWRtxafZG0aJFTd2J9FayZMkUF+XVqc86VUILbjLdAchYo0aN8hRv1uBswYIF/bOGAgDA/6c7KAIKGePPP/+UH374QS5dumTuX7582aQ2a0G0p59+OoNGAWS+mjVrytdff53Zw/BJerVRT/S1Y1p605bvR44cMUXWtTub1SKyenVUg0MAMr6gtjZG0HqDHTt2tFRcm4ACACBNpzsoAgrpl06sc4z1pldj3QGcuPQKw5NPPplOIwB8k54sR0dHZ/YwfJae4GdEQEFrJ/Tp00c++eQT0+bdCu26pmO8++670218ABKnU5X0IkVKWr0y5QEAkKYBBb0qpXP3kXb0Q16LlOnVP/2w37p1q7Rq1crMN46MjDSZCZqloDe9whcSEsLmh6NoOv/Vq1czexg+ac+ePaZ6u7ZPT29aHV4DmhMmTDBF3qxYvny5GaN2dAOQcXS60bZt21I8RZIMBQBAmhdkZP5r6sXExJjAgU5h0PZNeuVuxowZ0qZNG8mfP38a/AbAPrTTg+4zemDs7+8/S5YsMenHcenfllCJEiVueOzixYs3PHby5EmzTfR5tT5BeuvZs6ep4bJlyxapW7euVz+j49asq9GjR6f7+ADEN23aNJMBqccXKUFAAQCQZvbu3SuVKlVii6aCpv1OmjTJFF7TNOXcuXNLhw4dZOTIkQQSgCS4s3K05aC2LfRnOod58uTJ8R7TQqvPPPNMvMf+97//SZ48eeI9pnOgE6pfv77JbtJgS0YoV66cJ5DhrS+//FKuXLkiDz74YDqODEBitJBr+/btpXnz5pISBBQAAGmaWqsVxWFdVFSUafH48ccfS506dcz842bNmsldd91lrr4CSFrt2rXN182bN/t9UT8NIiasQ5NY5wQNEuTLly/eYwk7Ofz999+mQOuhQ4eke/fukhHc1eFPnTrl9c/o+16jRo2kQoUK6TgyAAlduHDBXAx68cUXJaWooQAASBN6ZVAPIN09z+G91atXS7Vq1WTOnDmmQ8OmTZvk9ddfl4YNGxJMALy8Kq6ZCdr1BGKmS3Xr1s1Mi1i/fr3pgKGZChkhW7Zspk3lxo0bvVpfi8uuWrXKjBdAxsqSJYupe6U1mVKaxURAAQCQJjTCrZjyYI32e27atKnpH//LL79Ijx49/H4OOJDRdJ+pV6+eXwUUIiNFwsOvf00LWmdBaxFoQUTN2NDpU8OGDcuUzDHNsJo+fbqpi5AU7cqxcuVK6d+/vynmq4VnAWQsLYKqFzK0XtOOHTtS9BwEFAAAaUIPWvWgnpRV72lGwpAhQ8xBv16h0xRmACmjAQWtNRAbG+sXm1ADCcOHpz6goCfmWndFg7nuugVffPGFHDhwQF599VUJDQ2VjKatI7VivHZt0DaQCxcu9BSWPHz4sFmuUyN0zvbPP/9sCs/mypUrw8cJQEwxRs0qeuONN1K0OQgoAADSLEOhdOnSGdLr3A42bNhgWkBqobW33npLAgP5SAZSQ1P6tZCpZvr4K+3ksmjRohse12KLmo6sNRPifq/TzPSkvG/fvlKrVi0TUNFAwiOPPHJDPYWMpMEBnXahf4sGmrW7xG233WYKv2knoLlz58qAAQNMMOHIkSNMdwAy0U8//SQvv/yy/Pe//5XFixdb/nmKMgIA0sRvv/1mDhhxc3qFrm3btnLnnXeaYmRMcQBST6+Ea0vV+fPnS/Xq1X1yk2o2gjsjYevW+F+VJhNYSSjo1KmTyQ7Tjg8NGjQQX6JB0ocfftjctFjme++9Z1pJamtIndqVsDUmgMyh9Zrcnn/+eWnVqpWlnyegAABIE0ePHpWqVauyNb0wc+ZM0yJN04CzZ8/ONgPSqBigXpn//PPP5c033/TJbTp16vVpDnE999w/34eFXa+rYOWkXac0FC5cWHyZBk9nz56d2cMAkMixW1yaMXTu3DlLWZPkVwIA0sSxY8fMHDzcnKYC6wG2VqUHkLZzgfft22cOin1Rjx4iW7Zcv3300fXH9Kv7MV1uhbaC1EJq4VaiEAAQ59gtIavdHggoAABSTT98jh8/LsWKFWNremHnzp1SuXJlthWQxtxp/1qjxBfpdIZatf65qbj3rdZPbN26tdxyyy0UdAWQIjVr1vR8r+2q9YKHTh2zgoACACDVtBCapvATUPCOVmPfvn07rzwgjWnqf/ny5WX9+vWO2LZaP+HPP/80GU8AYJXWcNL2rUqnYMYNMHiLgAIAIM1S5pjy4B2tyq4nPNeuXePVB6SxZs2ayYIFC+TixYs+vW01G0FrJqSmq6PWi8iTJ4+0aNEiLYcGwEGaNm0qQ4cOlbCwMFM41SoCCgCANAsokKHgHff0kMxs6wbY1ZAhQ0w7xffff198mQYStPRBagIK2pZRuyjkyJEjLYcGwGFGjBhhLgppS1erCCgAAFJtyZIlkjdvXilZsiRb0wvbtm3z2bZ2gL8rW7as9OzZ07Qn3L17t9hVbGysmfJQu3btzB4KAD8XFBRkjkt+/fVXyz9LQAEAkCqnT5+WKVOmSJ8+fSRnzpxsTS8KWBJQANLX8OHDpXTp0iaVV0+67dru7fLly6ZmBACk1u233y6bNm2SP/74w9LPEVAAAKSKphXrSXL//v3Zkl749ttvJTIyUu677z62F5BOtEq5FhrT1qwaVPj9999tt62XLl1qvlapUiWzhwLABnr37m0uDNWvX99S610CCgCAFIuKipIPPvhAevToYQ7ckTw9qenXr5/UqVNHmjRpwuYC0rnjgwYVgoODpWPHjpZ7q/t669nXXntNnnnmGVpGAkgT5cqVk40bN8rJkydl8eLFXv8cAQUAQIppNeAzZ85I586d2Yo38dVXX5lAgvrss89MqyYA6UuLjE2cONEcJH/xxRd+v7k1KPLRRx+ZNpH6t40dOzazhwTARooXL27eX2bNmuX1zxBQAACkmDsr4fz582zFZGiLyLZt25rU682bN0vFihXZXkAG0elFLVu2NG3RoqOj/Xa769iffvpp6d69u8m40LnOoalpEQEAiRg/fryl4oz0q7KY2gtktqtXr5re2vp61IqsQGbSVGK1f/9+n5vH6yv7ip4EdO3aVWrVqiXTp083j/F5Al/jK/tLetH+6g0aNDAHytoBwh8zE1544QWZP3++eR959NFHzf+Z3pCx7L6vABUqVJDWrVt7Pe0hwGWnCWXpRCvoagsid591AAAAAADsqlixYhIRESE5cuRIdj0CChaCCv6cJgcAAAAAgDeyZct202CCIqAAAAAAAAAsoygjAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACkI7WrVsnDz30kBQvXlwCAgJk0aJFnmVXr16VIUOGSNWqVSV37txmnWeffVaOHj0a7zn++usvefrppyUkJETy5csnXbt2lfPnz8db56OPPpLSpUtLzZo15ccffzSP6TpBQUEyd+7ceOu2a9fOjOXgwYPxHi9Tpoy89tpr6bAVgJT5888/5ZlnnpGCBQtKzpw5zb7y008/eZa7XC55/fXXJTQ01Cxv3ry5/P777/GeY+PGjVKjRg3z+v7kk088j9erV0969uwZb90pU6aYfWPGjBnxHu/UqZPcc889/DfCViZOnGj2C+0xXrduXdm0aZNn2d69e6Vhw4ZSokQJefPNNzN1nIA/fsYo/TxJ7JbwuAzwdwQUgHR04cIFqV69ujlwS+jixYuydetWcxKvX7/44gtzENemTZt462kwYdeuXbJixQpZsmSJCVJ0797ds/zw4cMyduxY8wH1yiuvSOfOnc3jwcHBUqdOHVmzZk2859P7JUuWjPd4RESEHDp0SJo2bZoOWwGw7u+//zYnNBoUW7ZsmezevVvGjRsn+fPn96yjr/sJEyaYQIAG0jQw16JFC7l8+bJnHQ3A6T42e/ZsGTVqlBw5csQ8fu+9996wb6xevfqGfUPpffYN2Mnnn38uAwcOlLCwMPP5o59Tuu+cOHHCLO/Tp4850fryyy/NbcOGDZk9ZMCvPmPc/v3vf0tkZGS8W9u2bfnfhL24AGQI3d0WLlyY7DqbNm0y6x06dMjc3717t7m/efNmzzrLli1zBQQEuP78809z/5dffnHVqVPHdf78edeBAwdcZcqU8aw7bNgwV8WKFT339fny5s3rGjlypKtjx46ex6dPn+7Knj2769KlS2n6NwMpNWTIENfdd9+d5PLY2FhXsWLFXG+//bbnsTNnzpjX8Zw5czyPlSpVyuwXun/ofrJr1y7z+PLly82+FRkZ6Vm3aNGirokTJ7pKly7teUx/VtdbvXo1/5mwjbvuusv1/PPPe+7HxMS4ihcv7ho1apS5X7t2bdePP/7oio6OdrVp08a1dOnSTBwt4H+fMd4e9wF2QIYC4EPOnj1r0uF0aoM7lU6/10wDN025CwwM9ExtuOOOO6RatWqSN29eqVKlSrz0VL0Kq1kPGhF3X4G9++67zdXWuFdh9fH69eub1FfAF3z11Vfmdf/4449LkSJFzHQendoTN6vm2LFjZn9w031AU7d1v3HTdNXbb7/dLNNpDpUrVzaPu69M6Wtf6dWpS5cumatNp0+fNs+vdLnuF7p/AHYQHR0tW7Zsibfv6GeK3nfvOyNGjDD3c+XKZZbpVVnATtL7MwZwEgIKgI/QFDqtqdC+fXtTL0Hph5l+0MWVNWtWKVCggFnmpvP2jh8/bk6EdIqEm540ZcuWzRM80K+NGzeW2rVry6lTpzwnTWvXrjXBB8BXHDhwQCZPniwVKlSQ5cuXS69evaRfv34yc+ZMs9z9+i9atGi8n9P7cfcNd4Dg5MmT8sEHH3ge19TVu+66K96+ocG27NmzS4MGDeI9rsEEfRywA33vj4mJSXbfadWqldlntKbPwoULJUuWLJk0WsA/P2Pc9JhOp6DGvelUVcBOsmb2AABcL9D4xBNPmAJA+gGXElpUKCG9unTnnXeakyL9UNPAwUsvvWSCEu6TJv2d+uFGQAG+JDY21lw9GjlypLmvV4927txp5rJ27NjR0nNp8EBvCTVp0kTmz59vvtd9Qe8rDbrpfa1Hol+fe+65NPmbAH+iQbTChQtn9jAAv/2MUe+99168LAelRbgBOyFDAfCRYIIWRdTCi+7sBFWsWDFPkSy3a9eumc4PuswbGijQtG0t7Kgp3bVq1fKcNOnjetPAg6bxAb5Cq2onTB3VtFL3lR33618zc+LS+1b2jd9++81U+nZn78QNKOzfv98U2KIgI+ykUKFCJuMgNfsO4O8y4jPG/Tzly5ePd9OLOoCdEFAAfCCYoG2IvvvuuxuyDDTV+syZM2a+q9uqVatMZN3bAICeNOnzawViTel2p642atTIZCzoiZN7agTgK/Q1qfU/4tKTf22PqsqWLWsO1FauXOlZHhUVZWqLeFvvQLN09HU/adIkM+VIpwIpzerR9NXp06d7pkYAdqGveX2tx9139DNF71MrBE6REZ8xgFMQIgPS0fnz52Xfvn2e+1qzYNu2baYGgkbHH3vsMdOyS9tB6pxW97w8Xa4HfRotb9mypUm51jQ8DUBoO6927dp5nTKnJ02auqpz+7StpJueJGn2g7YEGzZsWDr89UDKDRgwwLx2NR1Vg26bNm2SadOmmZvS4qX9+/c3RUh1Dqwe/GnrLt0vvG3JpX3FtYiW7ht6cOkOtum+F/dxLd4I2Im2jNS0bk351s+C8ePHmzbH7rbDgN1lxGeM0otCcWsuqDx58iQ5RQLwS5ndZgKwM201p7tZwpu2bIyIiEh0WcIWdadPn3a1b9/eFRwc7AoJCXF17tzZde7cOUvjaNy4sXneH374Id7jTZo0MY9v3Lgxzf5mIK0sXrzYdccdd5g2XZUqVXJNmzbthrZer732mmn3qOs0a9bMtXfvXku/IywszOwDo0ePjvd4eHi4edzdRg+wmw8++MC0vMuWLZtpI5nw8wGwu/T+jEnqGI/PFdhNgP6T2UENAAAAAADgX6ihAAAAAAAALCOgAAAAAAAALCOgAAAAAAAALCOgAAAAAAAALCOgAAAAAAAALCOgAAAAAAAALCOgAAAAAAAALCOgAAAAAAAALCOgAAAAAAAALCOgAAAAAAAALCOgAAAAAAAALCOgAAAAAAAACCgAAAAAAID0R4YCAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAACwjIACAAAAAAAgoAAAAAAAANIfGQoAAAAAAMAyAgoAAAAAAMAyAgoAAAAAAMAyAgoAAAAAAMAyAgoAAAAAAMAyAgoAAAAAAMAyAgoAAAAAAMAyAgoAAAAAAMAyAgoAAAAAAMAyAgoAAAAAAMAyAgoAAAAAAMAyAgoAAAAAAMAyAgoAAAAAAMAyAgoAAAAAAMAyAgoAAAAAAMAyAgoAAAAAAMAyAgoAAAAAAMAyAgoAAAAAAMAyAgoAAAAAAMAyAgoAAAAAAMCyrNZ/xHliY2Pl6NGjkidPHgkICMjs4QAAAAAAkC5cLpecO3dOihcvLoGByecgEFDwggYTSpYsmVb/PwAAAAAA+LQjR45IiRIlkl2HgIIXNDPBvUFDQkLS5n8HAAAAAAAfExUVZS6ou8+Dk0NAwQvuaQ4aTCCgAAAAAACwO2+m+1OUEQAAAAAAWEZAAQAAAAAAWEZAAQAAAAAAWEZAAQAAAAAAWEZAAQAAAAAAWEaXBwAAAAAA0lhk5PVbUkJDr9/8GQEFAAAAAADS2NSpIsOHJ708LEwkPNy/NzsBBQAAAAAA0liPHiJt2iS93N+zExQBBQAAAAAA0lioDaY03AxFGQEAAAAAgGUEFAAAAAAAgGUEFAAAAAAAgGUEFAAAAAAAgGUEFAAAAAAAgGUEFAAAAAAAgGUEFAAAAAAAgGUEFAAAAAAAgGUEFAAAAAAAgGUEFAAAAAAAgGUEFAAAAAAAgGVZvVkpKirK8hOHhIRYHw0AAAAAALBPQCFfvnwSEBDg9ZPqur/99puUK1cuNWMDAAAAAAD+HFBQCxYskAIFCtx0PZfLJa1atUrtuAAAAAAAgL8HFEqXLi2NGjWSggULevWkmpkQFBSU2rEBAAAAAAB/DihERERYetKdO3emdDwAAAAAAMAP0OUBAAAAAACkXw2FuDZv3iyrV6+WEydOSGxsbLxl7777bkqeEgAAAAAA2DmgMHLkSHn11VelYsWKUrRo0XjdH6x0ggAAAAAAAA4KKLz//vsyffp06dSpU/qMCAAAAAAA2K+GQmBgoDRs2DB9RgMAAAAAAOwZUBgwYIBMnDgxfUYDAAAAAADsOeXhxRdflAcffFBuvfVWqVy5sgQFBcVb/sUXX6Tl+AAAAAAAgB0CCv369TMdHu69914pWLAghRgBAAAAAHAgywGFmTNnyn//+1+TpQAAAAAAAJzJcg2FAgUKmOkOAAAAAADAuSwHFMLDwyUsLEwuXryYPiMCAAAAAAD2m/IwYcIE2b9/vxQtWlTKlClzQ1HGrVu3puX4AAAAAACAHQIKbdu2TZ+RAAAAAAAAvxHgcrlcmfXLJ0+ebG4HDx4096tUqSKvv/66PPDAA+b+5cuXZdCgQTJ37ly5cuWKtGjRQiZNmmSyI9wOHz4svXr1Mp0ngoODpWPHjjJq1CjJmvWfWMmaNWtk4MCBsmvXLilZsqS8+uqr0qlTJ6/HGRUVJXnz5pWzZ89KSEhImm4DAAAAAAB8hZXzX8s1FNJSiRIlZPTo0bJlyxb56aefpGnTpvLwww+bE381YMAAWbx4scyfP1/Wrl0rR48elX/961+en4+JiTHdJqKjo2XDhg2mA8WMGTNMUMItIiLCrKNtLrdt2yb9+/eXbt26yfLlyzPlbwYAAAAAwDEZCtrZ4bfffpNChQp59aSlSpWS//3vf1K6dGnLA9Lf9fbbb8tjjz0mhQsXltmzZ5vv1Z49e+T222+XjRs3Sr169WTZsmXSunVrE2hwZy1MmTJFhgwZIidPnpRs2bKZ75cuXSo7d+70/I527drJmTNn5JtvvvFqTGQoAAAAAACcIMpChoJXNRT05FtP3vVJvXH69GmTPWCFrq+ZCBcuXJD69eubrIWrV69K8+bNPetUqlTJBCvcAQX9WrVq1XhTIHRahE6B0CyHmjVrmnXiPod7Hc1UAAAAAAAA6VyUUWsTpIdffvnFBBC0XoLWQFi4cKFUrlzZTE/QDIN8+fLFW1+DB8eOHTPf69e4wQT3cvey5NbRqMulS5ckZ86cN4xJ6zXozU3XBQAAAAAAFgMKsbGxkl4qVqxoggeaTrFgwQITuNB6CZlJizoOHz48U8cAAAAAAIAvy9SijEqzEMqXLy+1a9c2J/LVq1eX999/X4oVK2aKLep0i7iOHz9ulin9qvcTLncvS24dnQuSWHaCGjZsmAlwuG9HjhxJ078ZAAAAAAB/l+kBhcSyIXS6gQYYgoKCZOXKlZ5le/fuNW0idYqE0q86ZeLEiROedVasWGGCBTptwr1O3Odwr+N+jsRkz57dPEfcGwAAAAAASEENhfSgmQAPPPCAKbR47tw509FhzZo1pqWjFoDs2rWrDBw40HR+0JP6vn37mkCAFmRU999/vwkcdOjQQcaOHWvqJbz66qvy/PPPm6CA6tmzp3z44YcyePBg6dKli6xatUrmzZtnOj8AAAAAAAA/DChoZsGzzz4rkZGRJoBQrVo1E0y47777zPL33ntPAgMD5dFHHzVZC9qdYdKkSZ6fz5IliyxZssR0ddBAQ+7cuU0NhhEjRnjWKVu2rAkeDBgwwEylKFGihHz88cfmuQAAAAAAQMoEuFwuVwp/1jGs9OEEAAAAAMAJ579ZU1rnYN++fSbDIGEHiEaNGqXkKQEAAAAAgB+xHFD44Ycf5KmnnpJDhw5JwuSGgIAAiYmJScvxAQAAAAAAOwQUtMhhnTp1TF2C0NBQE0QAAAAAAADOYjmg8Pvvv8uCBQukfPny6TMiAAAAAADg8wKt/kDdunVN/QQAAAAAAOBcljMU+vbtK4MGDZJjx45J1apVJSgoKN5ybf0IAAAAAADszXLbyMDAG5MatI6CPo1dizLSNhIAAAAA4ARR6dk2MiIiIjVjAwAAAAAANmA5oFC6dOn0GQkAAAAAALBvQEHt379fxo8fL7/++qu5X7lyZXnhhRfk1ltvTevxAQAAAAAAO3R5WL58uQkgbNq0yRRg1NuPP/4oVapUkRUrVqTPKAEAAAAAgH8XZaxZs6a0aNFCRo8eHe/xoUOHyrfffitbt24Vu6EoIwAAAADACaIsFGW0nKGg0xy6du16w+NdunSR3bt3W306AAAAAADghywHFAoXLizbtm274XF9rEiRImk1LgAAAAAAYKeijM8995x0795dDhw4IA0aNDCPrV+/XsaMGSMDBw5MjzECAAAAAAB/r6Ggq2uHh3HjxsnRo0fNY8WLF5eXXnpJ+vXrJwEBAWI31FAAAAAAADhBlIUaCpYDCnGdO3fOfM2TJ4/YGQEFAAAAAIATRFkIKFie8hCX3QMJAAAAAAAgFQGFWrVqycqVKyV//vymbWRy0xrs2DYSAAAAAACkIKDw8MMPS/bs2T3f27FOAgAAAAAA8F6qaig4BTUUAAAAAABOEGWhhkKg1ScvV66cnD59+obHz5w5Y5YBAAAAAAD7sxxQOHjwoMTExNzw+JUrV+SPP/5Iq3EBAAAAAAAf5nWXh6+++srz/fLly00KhJsGGLRoY9myZdN+hAAAAAB8SmTk9VtyQkOv3wDYl9cBhbZt25qvWpCxY8eO8ZYFBQVJmTJlZNy4cWk/QgAAAAA+ZepUkeHDk18nLEwkPDyjRgTApwMKsbGx5qtmIWzevFkKFSqUnuMCAAAA4KN69BBp0yb5dchOAOzP64CCW0RERPqMBAAAAIBfYDoDgBQFFNSFCxdk7dq1cvjwYYmOjo63rF+/fmxZAAAAAABsznJA4eeff5ZWrVrJxYsXTWChQIECcurUKcmVK5cUKVKEgAIAAAAAAA5guW3kgAED5KGHHpK///5bcubMKT/88IMcOnRIateuLe+88076jBIAAAAAAPh3QGHbtm0yaNAgCQwMlCxZssiVK1ekZMmSMnbsWHn55ZfTZ5QAAAAAAMC/AwraIlKDCUqnOGgdBZU3b145cuRI2o8QAAAAAAD4fw2FmjVrmraRFSpUkMaNG8vrr79uaijMmjVL7rjjjvQZJQAAAAAA8O8MhZEjR0ro/zeVfeuttyR//vzSq1cvOXnypEybNi09xggAAAAAAHxMgMvlcmX2IHxdVFSUmdJx9uxZCQkJyezhAAAAAACQ6ee/lqc8vPnmm/L0009L2bJlUzNGAAAA+IHIyOu3pGji6v8nrwIAHMbylIf58+dL+fLlpUGDBjJp0iRTPwEAAAD2NHWqSO3aSd90OQDAmSwHFLZv3y47duyQJk2ayDvvvCPFixeXBx98UGbPni0XL1609FyjRo2SO++8U/LkyWM6RrRt21b27t0bb53Lly/L888/LwULFpTg4GB59NFH5fjx4/HW0U4TOoZcuXKZ53nppZfk2rVr8dZZs2aN1KpVS7Jnz24CIjNmzLD6pwMAADhOjx4iW7YkfdPlAABnSnUNhfXr15tggmYu6Mm/zrfwVsuWLaVdu3YmqKABgJdffll27twpu3fvlty5c5t1tODj0qVLTQBA53H06dPHtK3U36tiYmKkRo0aUqxYMXn77bclMjJSnn32WXnuuedMAUkVERFhOlD07NlTunXrJitXrpT+/fub523RosVNx0kNBQAAAACAE0RZqKGQ6oDCtm3b5LPPPpO5c+fK6dOn5dKlSyl+Lu0UoRkGa9eulUaNGpk/oHDhwiZg8dhjj5l19uzZI7fffrts3LhR6tWrJ8uWLZPWrVvL0aNHpWjRomadKVOmyJAhQ8zzZcuWzXyvwQMNVrhpIOPMmTPyzTff3HRcBBQAAAAAAE4QZSGgYHnKg/uKv7aMrFKlitSpU0d+/vlnGT58uBw7dkxSQwesChQoYL5u2bJFrl69Ks2bN/esU6lSJSlVqpQJKCj9WrVqVU8wQWnWgW6EXbt2edaJ+xzuddzPAQAAAAAArLHc5UGzAjZv3izVqlWTzp07S/v27eWWW26R1IqNjTXTEBo2bGimJygNUGiGQb58+eKtq8EDd/BCv8YNJriXu5clt44GHTSjImfOnPGWXblyxdzcrEzjAAAAAADACSwHFJo1aybTp0+XypUrp+lAtPCiTkn4/vvvJbNpsUjNuAAAAADcaKEJAKmc8qBTHTSYEB0dbToyJOymkBJaaHHJkiWyevVqKVGihOdxLbSov0drHcSlXR50mXudhF0f3Pdvto7OB0mYnaCGDRtmpl+4b0eOHEn13wgAAAD/RgtNAEhlQEGnCHTt2tW0aNQaCtqyUfXt21dGjx5t6bm0HqQGExYuXCirVq2SsmXLxlteu3ZtCQoKMl0Z3DSIob+zfv365r5+/eWXX+TEiROedVasWGGCBe4sCl0n7nO413E/R0LaWlJ/Pu4NAAAAzkYLTQBI5ZSHoUOHyvbt22XNmjWm7aObFj0MDw83y61Mc9AODl9++aXkyZPHU/NAK0pq5oB+1eDFwIEDTaFGPbHXwIUGArSWg7r//vtN4KBDhw4yduxY8xyvvvqqeW4NDChtF/nhhx/K4MGDpUuXLiZ4MW/ePNP5AQAAAPBGaOj1G2BlKgyvHdiZ5baRpUuXls8//9yc0GsQQIML5cqVk3379kmtWrUsFTAMCAhI9PF///vf0qlTJ/P95cuXZdCgQTJnzhxTKFG7M0yaNMkznUEdOnRIevXqZYIcuXPnlo4dO5psiaxZ/4mX6LIBAwbI7t27zbSK1157zfM7boa2kQAAAAASEx4ucrPya2Fh19cD/IGV81/LAQWd6qDFEzWIEDegoF8bNWrkaf1oJwQUAAAAACSGDAU4+fzX8pSHOnXqmKkCOvUgbpbBxx9/nGRNAgAAAACwI6bCwMksBxRGjhwpDzzwgJk6oB0e3n//ffP9hg0bZO3atekzSgAAAAAA4N9dHu6++27Ztm2bCSZUrVpVvv32WylSpIhs3LjRdGUAAAAAAAD2Z7mGghNRQwEAAAAA4ARRaV1DwUrnhpv9QgAAAAAA4P+8Cijky5cvyRaPbprooOvExMSk1dgAAAAAAIA/BxRWr16d/iMBAAAAAAD2Cig0btw4/UcCAAAAAADs1+Vh7NixcunSJc/99evXy5UrVzz3z507J7179077EQIAAAAAAP/t8pAlSxaJjIw0LSLdxRe1fWS5cuXM/ePHj0vx4sVtWUOBLg8AAAAAACeIstDlwesMhYRxB7pNAgAAAADgXF4HFAAAAAAAACwVZQQAAHCSyMjrt6SEhl6/AQDgZJYCCh9//LEEBweb769duyYzZsyQQoUKeYoyAgAA2MHUqSLDhye9PCxMJDw8I0cEAIAfF2UsU6aMBAQE3HS9iIgIsRuKMgIA4CxkKAAAnCrKQlFGrzMUDh48mBZjAwAA8HlMaQAA4OYoyggAAAAAACwjoAAAAAAAACwjoAAAAAAAACwjoAAAAAAAACwjoAAAAAAAACzzustDYi5fvizR0dHxHrtZWwkgvdHqCwAAAAB8MKBw8eJFGTx4sMybN09Onz59w/KYmJi0GhuQIlOnigwfnvTysDCR8HA2LgAAAABkaEDhpZdektWrV8vkyZOlQ4cOMnHiRPnzzz9l6tSpMnr06FQNBkgLPXqItGmTfG9xAAAAAEDqBLhcLpeVHyhVqpR8+umn0qRJEzO9YevWrVK+fHmZNWuWzJkzR77++muxm6ioKMmbN6+cPXuWKR0AAAAAANuycv5ruSjjX3/9JeXKlTPf65PrfXX33XfLunXrUjpmAAAAAADgRywHFDSYEBERYb6vVKmSqaWgFi9eLPny5Uv7EQIAAAAAAJ9jOaDQuXNn2b59u/l+6NChpoZCjhw5ZMCAAaa+AgAAAAAAsD/LNRQSOnTokGzZssXUUahWrZrYETUUAAAAAABOEGWhhoLlLg8JlS5d2twAAAAAAIBzpCigsHLlSnM7ceKExMbGxls2ffr0tBobAAAAAACwS0Bh+PDhMmLECKlTp46EhoZKQEBA+owMAAAAAADYJ6AwZcoUmTFjhnTo0CF9RgQAAAAAAOzX5SE6OloaNGiQPqMBAAAAAAD2DCh069ZNZs+enT6jAQAAAAAA9pzycPnyZZk2bZp89913pk1kUFBQvOXvvvtuWo4PAAAAAADYIaCwY8cOqVGjhvl+586d8ZZRoBEAAAAAAGewHFBYvXp1+owEAAAAAADYt4ZCWlq3bp089NBDUrx4cZPdsGjRonjLXS6XvP7666Y9Zc6cOaV58+by+++/x1vnr7/+kqefflpCQkIkX7580rVrVzl//vwNWRX33HOP5MiRQ0qWLCljx47NkL8PAAAAAAC7shxQuHDhgrz22mum00P58uWlXLly8W5Wn6t69eoyceLERJfrif+ECRNMq8off/xRcufOLS1atDB1HNw0mLBr1y5ZsWKFLFmyxAQpunfv7lkeFRUl999/v5QuXVq2bNkib7/9toSHh5s6EAAAAAAAIGUCXJoGYEH79u1l7dq10qFDB5M5kLBuwgsvvJCygQQEyMKFC6Vt27bmvg5LMxcGDRokL774onns7NmzUrRoUZkxY4a0a9dOfv31V6lcubJs3rxZ6tSpY9b55ptvpFWrVvLHH3+Yn588ebK88sorcuzYMcmWLZtZZ+jQoSYbYs+ePV6NTYMSefPmNb9fMyEAAAAAALAjK+e/lmsoLFu2TJYuXSoNGzaU9BQREWGCADrNwU3/qLp168rGjRtNQEG/6jQHdzBB6fqBgYEmo+GRRx4x6zRq1MgTTFCa5TBmzBj5+++/JX/+/Df87itXrphb3A0K+KPIyOu3pISGXr8BAAAAQLpPedAT8AIFCkh602CC0oyEuPS+e5l+LVKkSLzlWbNmNeOLu05izxH3dyQ0atQoE7xw37TuAuCPpk4VqV076ZsuBwAAAICUsJyh8MYbb5hCiTNnzpRcuXKJHQ0bNkwGDhwYL0OBoAL8UY8eIm3aJL2c7AQAAAAAGRZQGDdunOzfv99c5S9TpowEBQXFW75161ZJC8WKFTNfjx8/bmo1uOn9GjVqeNY5ceJEvJ+7du2a6fzg/nn9qj8Tl/u+e52EsmfPbm6Av2NKAwAAAACfCSi4iyamt7Jly5oT/pUrV3oCCJopoLURevXqZe7Xr19fzpw5Y7o31Nb8bRFZtWqVxMbGmloL7nW0KOPVq1c9wQ/tCFGxYsVE6ycAAAAAAIB06PKQls6fPy/79u0z39esWVPeffdduffee00NhFKlSpnCiaNHjzbTKzTAoO0qd+zYIbt375YcOXKYn3vggQdMxoG2ltSgQefOnU2RxtmzZ5vlWplSgwfaOnLIkCGyc+dO6dKli7z33nvx2ksmhy4PAAAAAAAniLLQ5SFFAQXNCliwYIGZ+vDSSy+ZAIBOddBpELfccovXz7NmzRoTQEioY8eOpjWkDi0sLEymTZtmfufdd98tkyZNkttuu82zrk5v6NOnjyxevNh0d3j00UdlwoQJEhwc7FlHgxDPP/+8aS9ZqFAh6du3rwkueIuAAgAAAADACaLSM6CgJ+famlF/wcGDB2Xv3r1Srlw5efXVV+Xw4cPy6aefit0QUAAAAAAAOEGUhYCC5RoK2v2gU6dOMnbsWMmTJ4/n8VatWslTTz2VshEj1SIjr9+SQnE+AOD9EwAAIC1ZDijotIGpiTSv16kOx44dS6txwSL9Lxk+POnlYWEi4eFsVgDg/RMAACCTAgraTlFTIBL67bffpHDhwmk0LFjVo4dImzZJL4/TeRMAwPsnAABAxgcU2rRpIyNGjJB58+aZ+wEBAaZ2ghY51IKIyBxMaQAA3j8BAAAyUqDVHxg3bpxp91ikSBG5dOmSNG7cWMqXL2/qKbz11lvpM0oAAAAAAODfGQpa7XHFihWyfv162b59uwku1KpVy3R+AAAAAAAAzmA5oKBtIZ988klp2LChublFR0fL3Llz5dlnn03rMQIAAAAAAB8T4HK5XFZ+IEuWLBIZGWmmPMR1+vRp81hMTIw4uQ8nAAAAAABOOP+1XENB4w9aiDGhP/74w/xSAAAAAABgf15PeahZs6YJJOitWbNmkjXrPz+qWQkRERHSsmXL9BonAAAAAADwx4BC27Ztzddt27ZJixYtJDg42LMsW7ZsUqZMGdpGAgAAAADgEF4HFMLCwsxXDRxoUcYcOXKk57gAAAAAAICdujx07NjR09XhxIkTEhsbG295qVKl0m50AAAAAADAHgGF33//Xbp06SIbNmxItFijHbs8AAAAAACAVAYUOnXqZAoyLlmyREJDQxPt+AAAAAAAAOzNckBBizJu2bJFKlWqlD4jAgAAAAAAPi/Q6g9UrlxZTp06lT6jAQAAAAAA9gwojBkzRgYPHixr1qyR06dPS1RUVLwbAAAAAACwvwCXVlO0IDDwegwiYe0EOxdl1EBJ3rx55ezZswPRj5MAABiaSURBVBISEpLZwwEAAAAAINPPfy3XUFi9enVqxgYAAAAAAGzAckChcePGSS7buXNnascDAAAAAADsWEMhoXPnzsm0adPkrrvukurVq6fNqAAAAAAAgD0DCuvWrZOOHTtKaGiovPPOO9K0aVP54Ycf0nZ0AAAAAADA/6c8HDt2TGbMmCGffPKJKdTwxBNPyJUrV2TRokWmnSQAAAAAAHAGrzMUHnroIalYsaLs2LFDxo8fL0ePHpUPPvggfUcHAAAAAAD8O0Nh2bJl0q9fP+nVq5dUqFAhfUcFAAAAAADsEVD4/vvvzVSH2rVry+233y4dOnSQdu3ape/oAABpLjLy+i05oaHXbwAAAECqpzzUq1dPPvroI4mMjJQePXrI3LlzpXjx4hIbGysrVqww3R4AAL5v6lSR2rWTv+k6AAAAQHICXC6XS1Jo7969Jmth1qxZcubMGbnvvvvkq6++ErvRApR58+aVs2fPSkhISGYPBwBShQwFAACQHjjGcN75b6oCCm4xMTGyePFimT59OgEFAABseiDIVBgAQHLCw0WGD09+G4WFXV8PvivDAwp2R4YCAMAJbnYgyEEgACA5ZCg47/zX66KMAADA3nr0EGnTJunlFOoEACSHTDbnIaAAAAAMDgQBAEC6dHkAAAAAAABwI6AAAAAAAAAsI6AAAAAAAAAsI6AAAAAAAAAsc1RAYeLEiVKmTBnJkSOH1K1bVzZt2pTZQwIAAAAAwC85JqDw+eefy8CBAyUsLEy2bt0q1atXlxYtWsiJEycye2gAAAAAAPgdxwQU3n33XXnuueekc+fOUrlyZZkyZYrkypVLpk+fntlDAwAAAADA7zgioBAdHS1btmyR5s2bex4LDAw09zdu3HjD+leuXJGoqKh4NwAAAAAA8I+s4gCnTp2SmJgYKVq0aLzH9f6ePXtuWH/UqFEyfPjwDBwhgPQWGXn9lpzQ0Os3AAAAADfniICCVcOGDTP1Ftw0Q6FkyZKZOiYAqTN1qsjN4oRhYSLh4WxpAAAAwBuOCCgUKlRIsmTJIsePH4/3uN4vVqzYDetnz57d3ADYR48eIm3aJL8O2QkAAACA9xwRUMiWLZvUrl1bVq5cKW3btjWPxcbGmvt9+vTJ7OEByABMZwAAAADSliMCCkqnMHTs2FHq1Kkjd911l4wfP14uXLhguj4AAAAAAABrHBNQePLJJ+XkyZPy+uuvy7Fjx6RGjRryzTff3FCoEQAAAAAA3FyAy+VyebGeo2lRxrx588rZs2clJCQks4cDAAAAAECmn/8Gps8QAAAAAACAnRFQAAAAAAAAljmmhkJquGeFaOoHAAAAAAB25T7v9aY6AgEFL5w7d858LVmyZGr/bwAAAAAA8IvzYK2lkByKMnohNjZWjh49Knny5JGAgADxh4iSBj+OHDni+CKSbAteF+wjvGfw/slnCZ+rHGNwvMWxJ8fimYNjcf/cFpqZoMGE4sWLS2Bg8lUSyFDwgm7EEiVKiL/RF6qvv1gzCtuCbcHrgv2E9wzeP/ks4XOVYwyOtzj+zBwci/vftrhZZoIbRRkBAAAAAIBlBBQAAAAAAIBlBBRsKHv27BIWFma+Oh3bgm3B64L9hPcM3j/5LOFzlWMMjrc4/swcHIvbf1tQlBEAAAAAAFhGhgIAAAAAALCMgAIAAAAAALCMgAIAAAAAALCMgAIAAADimTRpElsEAHBTFGUEHGLt2rVy4cIFqV+/vuTPnz+zhwMA8GEFChSQO++8U/79739L8eLFM3s4AAAfRYYCYDNjxoyR1157zXPf5XJJy5Yt5d5775XWrVvL7bffLrt27RKn+P3336V9+/YSFRV1w7KzZ8/KU089JQcOHMiUscG36L6yatUqWbp0qfz999+ZPRwgU+3cuVOyZs0qd9xxh3z22Wf8b3ghNjZWlixZwrYC4JXz58+LHZChYBN6YrRixQo5ePCgBAQESNmyZaV58+YSEhIiTjJhwgSv1uvXr5/YVa1atWTIkCHy5JNPmvvz58+Xjh07mteHBhOeffZZyZUrl8ybN0+coHv37pIvXz4ZO3Zsost1W2mwYfLkyeIEn376qVfr6evEzs6cOSMvvPCCbN26VerVqyfjxo2TVq1ayYYNG8zyIkWKyLfffivVqlXL7KH6hB07dkidOnUkOjpanHJiOGPGDPniiy/ifa4+9thj0qFDB3PfKXQ7DBw40ASlX3nlFRNkiIt9RGTfvn0yffp0s61OnjwpV69eFbsrV66cbN68WQoWLChOp+8Xb7/9tnz11VfmPbJZs2YSFhYmOXPmzOyh+ZzIyEh566235MMPPxS7e++992TAgAFJLj937py54Ld+/XrxdwQUbECvHPTp0+eGK7B58+aVKVOmeE4snUAP+OI6cuSIhIaGxjsA0gNBO1+R1ukMelKkwQPVuXNniYmJ8ZxI/vDDD/L444+bbeMEFStWNPuIpu4mZsuWLSZLYe/eveIEyU130X1Dp8Vcu3bNvGbsrFu3brJu3ToTbFu8eLEEBgaaDIXx48eb7wcPHizBwcFmGUS2b99ugpV2f10ofR089NBD8vXXX0v16tWlUqVK5rFff/1VfvnlF2nTpo0sWrRInOS7774zB766HfSm7xXur054TSTm0qVLJmD/8ccfmxOCe+65R9q1ayePPPKIFC1aVOxO3yePHTtmgq9O98Ybb0h4eLi5kKdBhOXLl5vMSA0yOZFmwa5evVqyZcsmTzzxhLmoc+rUKRNI0PMSDUY5IVM2Z86cMnXq1EQv0Oix1v333y+nT5+WPXv2iN9zwa9t2bLFlTVrVlfHjh1d27Ztc12+fNl16dIl83iHDh1cQUFB5nGnCg4Odu3fv9/l5L+5YsWKrsmTJ3vuHzp0yJUjRw6XU+jfevDgwSSX67KcOXO6nO7o0aOuHj16mPeMFi1auOyuePHirjVr1pjv//jjD1dAQIBr9erVnuU//vijq2jRopk4Qt+inyOBgYEuJ5g+fborT548rlWrVt2wbOXKlWbZzJkzXU4xbtw4V65cucxxxr59+8x7Ztyb02zatMnVvXt3V0hIiKtmzZqud955x5UlSxbXrl27XE6i75nHjx/P7GH4hPLly7umTJniub9ixQpXtmzZXDExMS6n+fLLL81xhL4+9Hbrrbea99JChQqZY4tly5a5nGL+/PnmGFS3SVznz593NWzY0FWhQgVz7GUH8fPW4Hc++OADadu2rUmzi0uvJOkV6YsXL8r777/v2CipE916663myqtGgA8fPiy//fabNGrUyLP8jz/+cFSKombq7N+/X0qXLp1kqqrTpgYlTLnTuhv6PlGlShVzZUVTm+3u+PHjctttt5nvb7nlFsmRI4eULFnSs7xUqVImdRnOM2fOHHn55ZcT3Q+aNm0qQ4cOlf/85z+2nxakmXyawaN1aGbPni0PP/ywOJ1O79BsUM1q00xAfc9U+ppwIv280M/Y5GhGj93psZZOmXPTTAXN3jl69KiUKFFCnOTNN9+U559/3mRtaPaOTpfSacaa8ZVUpqhdPfbYY2Z6pWaraG2mJk2amMwEzfbSYxAtlq5Z1HZAQMHPaZpdcq2devbsKb17987QMSFz6Ru5ToH53//+Z6Y3aFeHypUre5Zr0bmaNWuKU2gwRQNveiKQVN0NTVV1Gp3jq9tl5MiRJsCkldz1w89Jc16zZMniua/fx50X76Q58iqxoqUJA09OqheRVM0V9cADD3hdr8ffp4tpEGHhwoVSqFChzB6OT9CpcTqNVINNcT9XnUoDTslxypQYnSaoQem4goKCHFFLI7F9RAOQOmWwb9++8uKLL5paAk4LJsSdXvnXX3+Z99Ivv/xSXn/9dRNo0mCCnbrnEFDwc/qidF9lS4wu+/PPPzN0TMhcDz74oKkZocWB9GRaCwMlfM106dJFnGLYsGEmqKInyzovXg+Slc5Z05MGvcLiLsTnBDrvWbOX9ENND4I0oNC1a9d4J9dOoVdP9KBH6bbQTC/3iZOTTqCVznFNLojini/vBHrwl9wceF3mhC4g+n/+r3/9i2BCgqwNfZ/o1auXqaGgVx6ffvppx+wbCVFD4Z99pVOnTpI9e3bPtrl8+bK5qJc7d27PY1rk1e70s9Od9anHFVpHQDNmnWzw4MHmc0WLdZYpU0bWrFlju8wVijLavCiOptRoBMwJEeLErrLpDvv999+bHTguO6e4a+9wrZ6rKZm4Ttt4aRBFi9/EPQDQk0c9qXRCSqZb1apVzUGxXjno37+/6fiRGDvvI0rfE7w5CYiIiBAn0AMcb7ZH48aNxe70IFg/VwsXLuzoz1XNftQuOJqeq4XUnDRVzhua7afTSfUkUU8e9UqsXo1M7iKPnVCU8R9a/NqbE+0FCxaIE14XM2fO9EyF0aCbFjtOGKR1wnHXv/71r3j33YV+dZplXHYINBFQsNmOm5DO3XFX+XfK9oh7UJzwqpoTqlLHPQjU6rIaYMD1itzffPONqZmgrwPNVNAKu05r66T7iFtiJ5BO2EeAm+0jOq0h7tXGuK5cuWLeS5ywj2hATTOYdu/eLR999JHpfoEb23ZrTQ0NLmgb2jvuuMNMm7E7AgrObA9o5RgjKU45xujsRaBJ6ZRTf0dAwc+x48bHVbbrOAj8x8aNG01mQuvWrT2PaRBOWzxpcRwtaqq1BJI6ebAbnbfnDbtfidYritoKz/260KkxeqLoptOGRowYccO8WKcEYxOjy3VqiN1p6rI32Rp2OAj0lma96QmTtiOO24ZZ6Uk0rtPaRTol4pNPPrH9JunQoYPJxtDgWnR0tEnn1imWTgvSe9MesEWLFqZtoi3aAwKJoIaCDQqLIX53C4iULVvWpGPqQaCmXDn5IFBPCrWyrvvEUfvIP/fcc6aYlG6Xt99+26Qva4DBCZxUkDM5etCvVZfdrwvdV7Riu/tgWA/8ihUrZipUO4EW3ksuKKdFCJ3yeaP1RXRKjDcBeyc4dOiQScnNnz+/KSyW8LME8aeKOSWgoMEE/dzUjgb6vqmdgk6cOOHIrmKzZs0yARatRRM3lf/8+fMmM0G3i17wchK9kOOeJnXkyBGT4aSBfM1yckoh7C5e1CvT4LUd3i/IULAJdlzvr7IpJ6Ra6UGgplvt3LlTevToccNBYMJijXalLXkWL14sderUMfdfeeUVc5Vea2uo+fPnm22hKb1OwD5ynR7QaKEkdwp3njx5ZPv27Z7iUZ999plMnDjRnEw7lVbr1nZ4uv9o4TkNziXVftVuNRQiIyM9tYm0qr8GVJIr1GhXehIwaNAgc9KoV2CTqiuB6/Q9RC9sOOEYQwMK+trQ4wulGV9aFFqnFzoxGKf1mF544YUb2gNqPRa7VfRPjl600c9VDSJUqFBB5s6da7aDbg99XehXrSWh2aF2FxgYaD4z9UKOTidNSUDfXxBmtvmO++677zpmx1WrV6/2fK87r/YF1jf5hAVQnHQQuGvXLkcfBGo19rgnAvrBrvOj3bSVke4/TsE+cp3W0tAClW46tSHuQfBdd91lWrA6kXaC0SCbTg3SVN1t27aZeeFOkfDATwtpjRo1SpxGjyU2bdpksncSS+WGs+lFCz3GctPjDb2go+8fdqtg7w2ntAe8GQ3U62er1hXRzA3NAtRAkx6XKi0IPXr0aEecl/Tq1UvmzJljpiHrBb5nnnnGvnXNXPBrLVu2dLVu3dr1/fffu3r06OG65ZZbXF26dHHFxMSYW+/evV1169Z1OVVwcLBr//79Lidp0aKFK3/+/K6ZM2dm9lB8QqlSpVxr164131+5csWVM2dO13fffedZvmPHDrO9nMqJ+4jKkSOHa8+ePUku//XXX13Zs2d3OcmZM2dcgwcPNvtI/fr1XevWrXM5UUBAgOv48eMup+8jzZs3dx05ciSzh+FXtm3b5goMDHQ5gf6dJ06ciPeY7isHDhxwOdmQIUPMtilXrpzr8OHDLqcpWLCga/v27eb7c+fOmffTn376Kd5na968eV1OcfnyZdfs2bPN+2muXLlcjz/+uOubb75xxcbGuuyEDAU/t3nzZjNXvlq1aqYVybRp06R3796eK20aCaxXr15mDxMZSFMttcK0E68QJEavoGja9pgxY2TRokWmTWLc+Xu6rW699dZMHSMynu4fOh1Iu30kxmn70NixY80+onUj9IqKXmVzKr3KmnDqnDdT6exmxYoVmT0En28Dl1hnLSdl8mgB07gFjXWOfM+ePSV37ty2aoln9XURFBRk2lLrFIi4nLAtNEtDP0dUcHCweS1o/RU3/V67XjhF9uzZTetMvWlWj9ZY0fM0LXCsGcS6jeyAgIKfY8dFQhwExvfGG2+YD3vtWqBv3JrGnS1bNs9yLSCl7SPhvECTpqRqKmbCTg46B3j48OFmmVNo0E0Lq5UvX97sI3pLjBMOiBOeKCV2kuSUbYH4kmrRHXe5U6aHaGHjhDSl24kSvi705NHJCMgmX8NKP2PsVmeFoow2eHEeP37cM0deC4vplTWt8q90mc7dstsL11sJtwec3StcAwpacC1hUE4fjxtkcBKn7iP63lijRg3z/96nTx9TYMxdiFDnjOvVg59//tkxhfholejM3uEAkNbnJVqnyh2Q1aK+TZs29QRktT2zthp1ynnJlStXTPBZL15pMXCtKaGfMVqjxk7FSwko+Dl23OTTzhK+kblxZQlOxT7yDy2UpEWTNKvHXYhPrx7cd999MmnSJE/HBwAAcHMEZP+hUxu0WH7JkiVNC0ntlqRTYeyIgIKfY8dlewC8Z6SOZqlo1welKf+2rcIMAAAy7KJvqVKlTNvI5Orw2OEiJwEFAAAAAADSSKdOnbwq6GuH6XMEFAAAAAAAgGX2qQYBAAAAAAAyDAEFAAAAAABgGQEFAAAAAABgGQEFAAAAAABgGQEFAABg2caNGyVLlizy4IMPWv7Z8PBwqVGjBlsdAAA/R0ABAABY9sknn0jfvn1l3bp1cvToUbYgAAAOREABAABYcv78efn888+lV69eJkNhxowZnmVr1qwxvbdXrlwpderUkVy5ckmDBg1k7969ZrmuO3z4cNm+fbtZT2/unz9z5ox069ZNChcuLCEhIdK0aVOzXsLMhlmzZkmZMmUkb9680q5dOzl37pxnnSZNmki/fv1k8ODBUqBAASlWrJj5ubjeffddqVq1quTOnVtKliwpvXv3Nn8TAACwhoACAACwZN68eVKpUiWpWLGiPPPMMzJ9+nRxuVzx1nnllVdk3Lhx8tNPP0nWrFmlS5cu5vEnn3xSBg0aJFWqVJHIyEhz08fU448/LidOnJBly5bJli1bpFatWtKsWTP566+/PM+7f/9+WbRokSxZssTc1q5dK6NHj473u2fOnGmCBT/++KOMHTtWRowYIStWrPjn4CcwUCZMmCC7du0y665atcoEIAAAgDUEFAAAgOXpDhpIUC1btpSzZ8+aE/u43nrrLWncuLFUrlxZhg4dKhs2bJDLly9Lzpw5JTg42AQZNHtAb/rY999/L5s2bZL58+ebzIYKFSrIO++8I/ny5ZMFCxZ4njc2NtZkNNxxxx1yzz33SIcOHUw2RFzVqlWTsLAw8xzPPvuseb646/Tv31/uvfdek+WgWRBvvvmmCZIAAABrCCgAAACv6dQFPfFv3769ua+BAc0w0CBDwpN6t9DQUPNVsw+SolMbdNpBwYIFTcDBfYuIiDBZCW4aBMiTJ0+85074vHF/d2LrfPfddybz4ZZbbjHPpUGJ06dPy8WLF3klAABgQVYrKwMAAGfTwMG1a9ekePHinsd0ukP27Nnlww8/9DwWFBTk+V7rJLizC5KiwQQ98dcaDAlplkJiz+t+7oTPm9w6Bw8elNatW5v6D5pFoXUWNDuia9euEh0dbWo+AAAA7xBQAAAAXtFAwqeffmpqI9x///3xlrVt21bmzJljaivcTLZs2SQmJibeY1ov4dixYybjQbMQ0ovWZtDggv4NWktBMd0BAICUYcoDAADwihZB/Pvvv83VfK1hEPf26KOP3jDtISkaMNCpDNu2bZNTp07JlStXpHnz5lK/fn0TmPj2229NJoHWXdDijlrYMa2UL19erl69Kh988IEcOHDAdIyYMmVKmj0/AABOQkABAAB4RQMGeuKv7RoT0oCCnvjv2LHjps+j62oxRy2MqC0iNbNBpyV8/fXX0qhRI+ncubPcdtttpiXkoUOHpGjRomn2P1S9enXTNnLMmDEmEPKf//xHRo0alWbPDwCAkwS4EvZ5AgAAAAAAuAkyFAAAAAAAgGUEFAAAAAAAgGUEFAAAAAAAgGUEFAAAAAAAgGUEFAAAAAAAgGUEFAAAAAAAgGUEFAAAAAAAgGUEFAAAAAAAgGUEFAAAAAAAgGUEFAAAAAAAgGUEFAAAAAAAgGUEFAAAAAAAgFj1f6r2SHm576oMAAAAAElFTkSuQmCC", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ps_xdt.xr_ps.plot_antenna_positions_2d()" ] }, { "cell_type": "code", "execution_count": 13, "id": "25735290", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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