{ "cells": [ { "cell_type": "markdown", "id": "4768bbb9-77eb-4e56-83c9-0dbcbd35ff39", "metadata": {}, "source": [ "# Subset Gridded Forcing Data" ] }, { "cell_type": "markdown", "id": "40c3b0c1-9447-4e04-bf12-b4534a7f2c3e", "metadata": {}, "source": [ "To launch this notebook interactively in a Jupyter notebook-like browser interface, please click the \"Launch Binder\" button below. Note that Binder may take several minutes to launch.\n", "\n", "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/hydroframe/subsettools-binder/HEAD?labpath=subsettools%2Fsubset_forcing_data.ipynb)" ] }, { "cell_type": "markdown", "id": "83fb9a68-dca3-4193-9885-40f07596f6ba", "metadata": {}, "source": [ "There are several gridded forcing data products available on HydroData. Forcing data products are temporally complete gridded datasets of meterological variables that can be used to drive hydrologic models. Here we illustrate multiple ways to subset these datasets and create inputs for a ParFlow model or manipulate the files for other uses.\n", "\n", "You see a complete list of the forcing data products available on HydroData [here](https://hf-hydrodata.readthedocs.io/en/latest/available_datasets.html#meteorological-forcings). Each forcing product contains at a minimum the following 8 variables: \n", "- Precipitation \n", "- Downward longwave radiation\n", "- Downward shortwave radiation\n", "- Specific humidity \n", "- Air temperature\n", "- East/west wind speed\n", "- North/south wind speed\n", "- Atmospheric pressure\n", "\n", "All forcing products are gridded and you can see the projections and spatial resolution on the HydroData documentation page. \n", "\n", "Note that the subset tools and HydroData API tools do not provide any re-gridding options. Data will be returned at the spatial and temporal resolution of the initial dataset which is being subset\n", "\n", "\n", "#### Things to determine before you start\n", "1. Before you start you should browse the [data catalog](https://hf-hydrodata.readthedocs.io/en/latest/available_data.html) and determine which forcing dataset you would like to subset. \n", "2. Take note of the dataset name and the grid that it is available on as you will need this information for your subsetting. \n", "3. Also take note of the start and end dates of the dataset as well as the temporal resolution (e.g. hourly, daily), as these will set the limits of what you can subset. " ] }, { "cell_type": "markdown", "id": "a421017a", "metadata": {}, "source": [ "## 1. Setup \n", "\n", "In all examples you will need to import the following packages and register your pin in order to have access to the HydroData datasets\n", "\n", "Refer to the [getting started](https://hydroframesubsettools.readthedocs.io/en/latest/getting_started.html) instructions for creating your pin if you have not done this already." ] }, { "cell_type": "code", "execution_count": 1, "id": "61d84526-545e-41c4-a7c2-c2c4aee3f878", "metadata": {}, "outputs": [], "source": [ "import subsettools as st\n", "import hf_hydrodata as hf\n", "from parflow.tools.io import read_pfb\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "\n", "hf.register_api_pin(\"your_email\", \"your_pin\")" ] }, { "cell_type": "markdown", "id": "3c51a4e8", "metadata": {}, "source": [ "## 2.0 Subset the forcing variables\n", "There are two approaches to subsetting forcing data:\n", "- The `subset_forcing` function will automatically get the forcing data for all 8 forcing variables listed above and will write the data as pfb files into your output directory to be ready for a ParFlow run. You can modify this function to get a smaller list of variables if you would like. \n", "- The `hf.get_numpy` function provides more direct access to the HydroData API and can return any subset variables of interest as a numpy array. \n", "\n", "### 2.1 Subset forcings with the `subset_forcing` function\n", "This approach is recommended if you are planning on using forcings for a ParFlow run. By default, `subset_forcing` will get the data for all 8 forcing variables (API reference [here](https://hydroframesubsettools.readthedocs.io/en/latest/autoapi/subsettools/index.html#subsettools.subset_forcing)) and write them out as PFBs (ParFlow Binary Files) for a ParFlow-CLM simulation. \n", "\n", "This function will only work with hourly forcing datasets and will default to putting 24 hours of forcing data into a file. \n", "\n", "In addition to writing the files out the function returns a dictionary where the keys are forcing variable names (e.g. 'precipitation', 'air_temp', ...) and the values are list of filepaths where the subset data for that variable were written. We will show how to use these paths to load the data into an array and plot them later in this tutorial.\n", "\n", "**NOTE:** *As described above the grid that you use here must match the grid that was used to create the ij indices in step 2 and must also be consistent with the grid that the selected dataset is available on*" ] }, { "cell_type": "code", "execution_count": 9, "id": "f472b1ca-7928-4e1c-8847-b024f15d307b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "bounding box: (1225, 1738, 1347, 1811)\n" ] } ], "source": [ "# calculate the HUC bounds\n", "ij_huc_bounds, mask = st.define_huc_domain(hucs=[\"14050002\"], grid=\"conus2\")\n", "print(f\"bounding box: {ij_huc_bounds}\")" ] }, { "cell_type": "code", "execution_count": 11, "id": "d1acd0e9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Reading precipitation pfb sequence\n", "Reading downward_shortwave pfb sequence\n", "Reading downward_longwave pfb sequence\n", "Reading specific_humidity pfb sequence\n", "Reading air_temp pfb sequence\n", "Reading atmospheric_pressure pfb sequence\n", "Reading east_windspeed pfb sequence\n", "Reading north_windspeed pfb sequence\n", "Finished writing air_temp to folder\n", "Finished writing north_windspeed to folder\n", "Finished writing east_windspeed to folder\n", "Finished writing atmospheric_pressure to folder\n", "Finished writing downward_longwave to folder\n", "Finished writing specific_humidity to folder\n", "Finished writing downward_shortwave to folder\n", "Finished writing precipitation to folder\n" ] } ], "source": [ "# Example grabbing all 8 forcing variables\n", "filepaths = st.subset_forcing(\n", " ij_huc_bounds,\n", " grid=\"conus2\",\n", " start=\"2012-10-01\",\n", " end=\"2013-10-01\",\n", " dataset=\"CW3E\",\n", " write_dir=\"/path/to/your/chosen/directory\",\n", ")" ] }, { "cell_type": "code", "execution_count": 7, "id": "d8e677e2-d563-4c44-a67e-ea5a1c398e0b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Reading precipitation pfb sequence\n", "Reading air_temp pfb sequence\n", "Finished writing air_temp to folder\n", "Finished writing precipitation to folder\n" ] } ], "source": [ "# Example grabbing just two forcing variables \n", "filepaths = st.subset_forcing(\n", " ij_huc_bounds,\n", " grid=\"conus2\",\n", " start=\"2012-10-01\",\n", " end=\"2013-10-01\",\n", " dataset=\"CW3E\",\n", " write_dir=\"/path/to/your/chosen/directory\",\n", " forcing_vars=('precipitation', 'air_temp',)\n", ")" ] }, { "cell_type": "markdown", "id": "84f315cb", "metadata": {}, "source": [ "### 2.2 Subset data using `gridded.get_numpy` function\n", "The [`hf.get_numpy`](https://hf-hydrodata.readthedocs.io/en/latest/hf_hydrodata.gridded.html#hf_hydrodata.gridded.get_numpy) function is a general function to extract any gridded data from HydroData. Here we illustrate how to use it grab out a set of forcing variables. Here we show how to use this approach to grab out a single column of data but the function works the same if you provide it a bounding box. \n", "\n", "Note that this will just return the data as numpy array's and will not write them out for a ParFlow run so if you use this option and want to run ParFlow some additional steps will be required to write the data out. " ] }, { "cell_type": "code", "execution_count": 4, "id": "6f337b71-2b2c-4602-a687-b1e31bd5a212", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "bounding box: (4057, 1915, 4058, 1916)\n" ] } ], "source": [ "# calculate column bounds for a single point\n", "lat = 39.8379\n", "lon = -74.3791\n", "# Since we want to subset only a single location, both lat-lon bounds are defined by this point:\n", "latlon_bounds = [[lat, lon],[lat, lon]]\n", "ij_column_bounds, _ = st.define_latlon_domain(latlon_bounds=latlon_bounds, grid=\"conus2\")\n", "print(f\"bounding box: {ij_column_bounds}\")" ] }, { "cell_type": "code", "execution_count": 5, "id": "68fcdabe", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "precipitation loaded: (8760,)\n", "downward_shortwave loaded: (8760,)\n", "downward_longwave loaded: (8760,)\n", "specific_humidity loaded: (8760,)\n", "air_temp loaded: (8760,)\n", "atmospheric_pressure loaded: (8760,)\n", "east_windspeed loaded: (8760,)\n", "north_windspeed loaded: (8760,)\n" ] } ], "source": [ "# list the variables that you would like to extract\n", "forcing_vars = ('precipitation',\n", " 'downward_shortwave',\n", " 'downward_longwave',\n", " 'specific_humidity',\n", " 'air_temp',\n", " 'atmospheric_pressure',\n", " 'east_windspeed',\n", " 'north_windspeed'\n", " )\n", "\n", "forcing_data = {}\n", "for var in forcing_vars:\n", " options = {\"dataset\": \"CW3E\",\n", " \"grid\": \"conus2\",\n", " \"period\": \"hourly\",\n", " \"variable\": var,\n", " \"start_time\": \"2012-10-01\",\n", " \"end_time\": \"2013-10-01\",\n", " \"grid_bounds\": ij_column_bounds\n", " }\n", " forcing_data[var] = hf.get_numpy(options).squeeze()\n", " print(f\"{var} loaded:\", forcing_data[var].shape)" ] }, { "cell_type": "markdown", "id": "cb8373af", "metadata": {}, "source": [ "**Saving single column subset outputs** \n", "Here we illustrate how to combine the single column subset values into a single dataframe and save it out as a csv. Note that this CSV is formatted to be compatible as a single column forcing file input for ParFlow. Note that ParFlow expects a specific order and format, described more in the [manual here](https://parflow.readthedocs.io/en/latest/keys.html#clm-solver-parameters).\n", "\n", "If you subset a bounding box of data with the functions above you could use the `write_pfb` function instead to write out files for the data. " ] }, { "cell_type": "code", "execution_count": 6, "id": "5f088881", "metadata": {}, "outputs": [], "source": [ "# Combine in a Pandas DataFrame in the order ParFlow expects\n", "forcing_df = pd.DataFrame({\"DSWR [W/m2]\": forcing_data[\"downward_shortwave\"],\n", " \"DLWR [W/m2]\": forcing_data[\"downward_longwave\"],\n", " \"APCP [mm/s]\": forcing_data[\"precipitation\"],\n", " \"Temp [K]\": forcing_data[\"air_temp\"],\n", " \"UGRD [m/s]\": forcing_data[\"east_windspeed\"],\n", " \"VGRD [m/s]\": forcing_data[\"north_windspeed\"],\n", " \"Press [pa]\": forcing_data[\"atmospheric_pressure\"],\n", " \"SPFH [kg/kg]\": forcing_data[\"specific_humidity\"]\n", " })\n", "\n", "# write to a ParFlow 1D forcing file\n", "forcing_df.to_csv('forcing1D.txt', sep=' ',header=None, index=False, index_label=False) " ] }, { "cell_type": "markdown", "id": "d3f1fb32", "metadata": {}, "source": [ "## 3.0 Visualize the data \n", "If you use the `subset_forcing` data will need to be read in first using the `read_pfb` function. If you use `get_numpy` the data are \n", "already available as a numpy array. \n", "\n", "### 3.1 loading and visualizing data that was written out using the `subset_forcing` function\n", "Here we use the `read_pfb` function [from PFTools](https://parflow.readthedocs.io/en/latest/python/tutorials/pfb.html#loading-pfb-from-python) to read the subset data in and plot it. \n", "\n", "First, we pick a location in the chosen HUC (\"14050002\") and calculate the corresponding grid point for that location using the `hf.to_ij` function (API [here](https://hf-hydrodata.readthedocs.io/en/latest/hf_hydrodata.grid.html#hf_hydrodata.grid.to_ij)). We can then calculate the indices for that point in our subset data array, by subtracting the grid indices of the lower left corner:" ] }, { "cell_type": "code", "execution_count": 16, "id": "f6cb5597-1b23-4a78-94ec-46bef7696dab", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "grid indices grid_i, grid_j: 1230 1749\n", "array indices i, j: 5 11\n" ] } ], "source": [ "# our location in the HUC\n", "lat, lon = 40.07, -108.89\n", "grid_i, grid_j = hf.to_ij(\"conus2\", lat, lon)\n", "print(\"grid indices grid_i, grid_j:\", grid_i, grid_j)\n", "# remember that our grid bounds for the HUC (that we calculated above) were: (imin, jmin, imax, jmax) = (1225, 1738, 1347, 1811)\n", "# so i, j = grid_i - imin, grid_j - jmin\n", "i, j = grid_i - 1225, grid_j - 1738\n", "print(\"array indices i, j:\", i, j)" ] }, { "cell_type": "code", "execution_count": 12, "id": "cd9a89df-5fcd-4a27-b3ca-9ce888bf3bb9", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Now we can concatenate all data for that point into a single array:\n", "temp_data = np.array([])\n", "for filepath in filepaths['air_temp']:\n", " data = read_pfb(filepath).squeeze()\n", " # select the (i, j) point calculated above:\n", " point_data = data[:, i, j]\n", " temp_data = np.concatenate((temp_data, point_data))\n", "\n", "plt.plot(temp_data, label='air temperature')\n", "plt.xlabel('Time [hr]') \n", "plt.ylabel('Temp [K]') \n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "9f58391b-85a9-4d98-b27c-a94933e2220d", "metadata": {}, "source": [ "Alternatively, if we wanted to plot a forcing variable over the whole bounding box as a snapshot in time, we could do the following:" ] }, { "cell_type": "code", "execution_count": 13, "id": "538718f7-b3cc-4d20-ad2b-345340b4197b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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u8qqrIsY6rW3J3PN3u+K55wfWV+We11Sb67S1p3LPI2H/Dreiwm8a1N1tliZOp/1BZKhZTyLhP0+lzHUyNO40/BdZKpkhs24jFKXhO2OX3peBnGy28G1wmI6tvNy8hEK0cbfIW/BQ2F8uQs9jMX/bclvZDDU2skSgkklz3pJ07s1x+stEImaDJ95vKOqPxwnRSiFz/04Rx+2lzMZMXO4k1eVfe7ZzID+Lx/1jdcV4+FgztE4FnbvYMPMHxC2ja5vGlk343y1H/BUXrvX7Ujg0Vx5dv9lEylhHvs6tk/bnx8uIJlb0BeB5M5YT5WPkWHO4fP2LayxN1w9tm6+9jDg/xVStkZcrr+Navj/yOuDXvD0+hpC8LsOhgssZPwhiP3ydO/R9DFXQ77K83nkb/JvE+w+J80HnJ1TuX3tOGf0DGhb/XKV2XjttyRQmLH0s92/HnqS/3BDyD+Irr7wSP/3pTwPXzWazmDdvHqZPn47Jkyfn3r/77rvx9a9/HfX19QiHw6ioqMCKFSswceJEADu/I+eddx6+853v4Igjjsh1q+0tJXez0HMRV4dDqAmHEIqYQwzTj1CaPgtTFz83ZF7wEYr9pOkaNW4WRBfADF28XISjit6X62Rd/8clSl+GClouLJoNpjz64THGTMuI2FW6H28WQkE3C7DcLNAclMt/MEK7cbNAcxWh9ctosuS2Mo79R76HhGgZkczSD6ntZkGcIF4uZPux252bBc8Rr/25TtJ+spmAmwXH/4zvcQJvFmidCtpPmThul793fLNA45E/+GFax7hZQHH/6DEefRs8eX4dy80CL9cfNwvGBngO6Xmmn28WQoWvUXlTYpwHvlmgdcL9fbNAF5nxu9wfNwv02rgpj9FNq7xZEJsYKNm6P/ayadMmoyhTMVGFpqYmrF+/Hs8++6zx/uWXX46WlhY89thjGDFiBO677z6cccYZeOaZZ3DIIYfgxhtvRHt7OxYsWNCnMZfczUIPnS3dcEMhVMsvMF08oQp/gmNV/vPuuPmvRDRS+B+dNP31snVbt7FOG/1VMT7m30V/ar9huef8gwgANfvQl4H/Kkj6f42FKAICAO1t/Fdk4S96yDV/yBNJ+quLv5j8VwXs8Cou3RCExT8YUfqepugOhX/g5Y99KMpzYNl/xv7D54UKnx/5V5LtGPj8RqPmAIL+SreNzYDPI/+giV8t4x8MHjZtO5syo195fz3ntsX/KJhzYPwVSvPD0Rk5Hr6uMjS/mc6EuWPeNn3n+B8MV1z/DkWcjL/E6VhlJMGjOYXtH3v5D3KGr3+at2zhm4g8+EYz4Kef55u35gbcYBj/cNM/gFmaa76uAfv3KWvsxzpMg8D7VJ43vgng4YgNGNelJUKU9w8/Y8w1vR223yyIUIn/XDaM63md7f9GcnuampqaXlVwnDNnDh566CE8/fTThtTy1ltv4aabbsL69etx8MEHAwAOO+wwPPPMM1i0aBGWLFmCJ554AmvWrMm7ITniiCNw9tlnF93orWRvFhRFURSllOhzBcderut5HubOnYsVK1bgqaeewoQJE4zPu7q6ACAvsTMUCiG760b6t7/9LX7+85/nPtu8eTNmzZqFu+66C1OnTi16LL1KHd13331z/bz50dTUBACIx+NoampCfX09qqqqMHv2bGzdurU3u1AURVGUkmSgrZNNTU344x//iOXLl6O6uhrNzc1obm5Gd/fOSPikSZMwceJEXHTRRXjhhRfw1ltv4Ve/+hVWrVqF0047DQAwfvx4TJ48Off4zGc+A2BnS/neJIT26mbhxRdfxJYtW3KPVatWAQC+9rWvAQAuvvhiPPjgg7jnnnuwevVqbN68GaeffnpvdqEoiqIoCoDFixejtbUVM2bMwOjRo3OPu+66CwAQiUTw8MMPY+TIkTj11FNx6KGH4vbbb8cf/vAHfOlLX+rXsfRKhhg5cqTx+pprrsF+++2HY489Fq2trbjtttuwfPlyHHfccQCApUuX4sADD8TatWtx9NFH92pgWz/oRqfroqxMJJzVlOWeR8iZwNpaaLuZf8DaXznprOwE+MeOLmOd4ZRMUz/c32emg7RdN8mrIFxD2eCVvj5UNn6Ev0rMdFCk3/6IX/ljJu0yk7brr65F5w1JfZues8LHcmVeHpmRREjjIQ05mTT111iU8gfoPpo15PzcAXaE0Dq07UjEfl+boW0HLccytk0el1q3xwn2vA5r5ULndQqnH5gEZNF7hvZuLGVdJyi8GZTH0UNKOEdA17lxHUTs2TA2B4MX9/VtI0cBgMf5CA5PAj2V54RfF5GHkr+cV+AZTN0cMJMfOUel2NwIr/BywasU/p5Foua8h4twHWVEHoyR0+gWngN57fJ17mT8+WGHi8y3Ma4XWz6D/M64li9k0vyNNehJHN/DtRUYF47xu7Y76/eGYppC77///taKjYXYd999i9quZLdnOZlM4o9//CO+9a1vwXEcrFu3DqlUCjNnzswtM2nSJIwfPx5r1qyxbieRSKCtrc14KIqiKEqpMdAyRCmx2zcL9913H1paWnDeeecB2FmfOhqNoq6uzlhu1KhRaG5utm5n4cKFqK2tzT20IJOiKIpSivQkOPblMVjZbTfEbbfdhpNPPnm360z3sGDBAsyfPz/3uq2tDePGjcOHyRQ6HRdjRQg+VE2h/mpfhnDb/cJJMirFoVcO2YUpZN0uQmkTqeBSlEKAHR0pet/cUSblN/0pJxkC9fX+OiL8U7HNXyeZ8qUQN1W4/gJQfEiTKSYyLilmPzKclaZ5DBd5L8rnxIgwszwhzo+tkA1Hm0Mi1Oka7ixaxxr2F8VvuOYCLZPn77aEhY3iRCJ8znY66d23wbvlImAyLB2j4kvsdffIHpyJm/JANiDMnFsmbtog2X5prGMporTzGCxzxTKGPCnGyS+8fl443XIB89p5x1mMzpN3wRQ+Vi6+lj8H/nPOamc7bF7NBA718zmlbbvF2kcDZAOzzkJx32fj+CzXTp4Mly18vbHkkze2yK7faJtHW+lXdutm4b333sNjjz2GP//5z7n3GhsbkUwm0dLSYkQXtm7disbGRuu2ii1zqSiKoih7k75KCYM4sLB7MsTSpUvR0NCAU045JffelClTEIlE8Pjjj+fe27BhAzZu3Ihp06b1faSKoiiKshdx+uG/wUqvIwvZbBZLly7FueeeizA5Bmpra3HBBRdg/vz5GD58OGpqajB37lxMmzat104IYOcdmOsAZdVm1CE8ym+ggfJyFKKiwnQcmDX0/XDr399u9dcRIa6RI3wHRBXVzW/b5ksFcVFPOEZyhcvVEEOFw4SAWU8/vMN3cQT2BLC8zxdisbmu4YCSb8UkmsuQOfcoKC/nqoIUzsyTByizm+UBw0Fh7jdFMg2Ha3neZNVHW+U9fl/KHfy3gFEtMyAj3hpa50z3gLLHRijZKDdtXqO2Kr2RmPm1DlF1UyOLPcl9HmSYm8ZNTolMmrLTxVwV069AVj21HoRxTRQZ/mbpwhHnhJ9zZcaAkLntnOxOWWFDahC/NVw1NEzVN0NBlRHNBi/0lKpGynUsvTNsMhMAOFyRlX/T+Lskx2n74WC5MUhqY/VHywaWDL0+FY899hg2btyIb33rW3mfXX/99XBdF7Nnz0YikcCsWbNw880398tAFUVRFGVv4jp9bFE9eAMLvb9ZOPHEE63JQmVlZVi0aBEWLVrU54EpiqIoSikxlHMWSjbIU+a6KHNdhKvLzA84npc2M7h72NLcabx+v9V3SoTodLVQY5JDR1QZ69R92i+kFB7mt8XOprb4z0VDmIoJftGqcCPJJRxalw17soVD06mUPTTI0kHaEvKTRZlskdOgUKdxF0whfYdDnUV2ETS2K26vpWvB3yXJA0UW30kbmfdyv7RtbrwUKZyBDpjNlnh7KXo/AvM6dBxuvETyS0D4msfA556lB5kRb+yTXSDlpgzHDZ+ylpBzUIdCbtXMLgl5jbKEw9cVh9Yj4npzSTLhhmssqckwt9mKmj8oUrrgcDg1RAq6dm2Fj+Q6NqkrGrUXs0qRYFC0xGHruhrQ5MpoHsW/SRm6foXeZxRiYpeCG9CqzvKbYpy3oO6WLBFy91L5he6Rd0O74/VSekvJ3iwoiqIoSimhkQVFURRFUQLpq6NhMLshtJqFoiiKoiiBlGxkocJ1Uem6cMuj5gdxP/+ALYmpbe25569vM3MWWLqrJ7vnpOG+JXLcQQ3GOtzwKf6u3+wpQ5ptWUONsU54ZK3/ggtNcTMUaVGjxjxVlf4+u7t9HVFq+qyXJxL+893RXAPdWaRjhyx2s6ywQPE6ZlMc1hWFBk3yJ2vntjHvXJC2ZrFbSvnXlGMLN6mS2n02YzmegOZekZB/jZjVKakxUMz+1XOcwuvLa4dzZjinw2s1G6lltvt2X1tRwrwGXBZrHecp5NtMaag0j0ZDJHlKbRUYi00bt1zzQdUG2bbnUCJLSDbZslXipBwOZE37tM0mypq8tCpyzout4VrQd4FzStiy7Yg8CeNapPPoZO1zlU35Y7Ndi/kVTHkDloZV0kJLeJaKn84ANoyy0deSzUOy3LOiKIqiDCVc9C0cv/dvd3YfvVlQFEVRlCLQBMcSZPiwMlSH3PxGRSQ3ZDqoeRTJBlERsiuj8NX4kb4Nsq7OlzhSLV3GOsmP/P1wODBCVs5QlWnrzHbQNug5h9yyCdNml+n2JYowNZ9qjBpxdmOdNK3T0elvL0WV9riSImD2Wim2emHIsDPRmGnT0kFls34ZxQ/TMgxL+wlzeJPHIuyWocJhUD4GGbrlZj4sSXBoXQ7fVomQQ+tSimF7oWOxuOVVvQuqiLcLI/wNwCVbZVebf00kk2I5tolaGk45rmjsQ+Fnvv7ZAiirq7oc9ua5oiZvnjj32W7fumhUlzQ2LPWkj68QKE+kMachCm1zZVFp0WQpJlm48mXgGLiiZZSrF9pXt1WXzLdoWsLzIft1ZLuuXDdS8H0AyJK1FLYKpuJ3w0FhycUlyTVvLMZ3w1aaNMCuqexxSvZmQVEURVFKCsfZrXLfvP5gRW8WFEVRFKUIVIYoQbq70wi5Lro3bjPeD5M7ItHhV0Os/rRfPbFKxMbbKW7e2UlhTwrzhUX2NIdro5QlzVnamS6zGqPxmu4guQplXlU1DlnTPrn5jwzZhZL+Z9E6avJD4d6EaHLFx23Lss7KKn6WkCgrQ7LBky1sH1R90KigyE1xjDC5ub6RmM0VLdm9kBeuprBukVnntrGxIyUocb/YBkRG6JU0Iw7bJ+Nm+NtwJgQ4YTw6R4ZrwljF3kTJcIvQdRkZblY95ZBzlqQywxWQp/PQU25a5NrPo5Qycuvw90SelGIaU8l1bLII7UcGxlkSMB0QhUP4cnvGcAxVJ0BWsbyfJ0NECje0cwKi+w5LDHyNGk2hhAxB+3XLqJopS6t5EolFQgr6cvX8rmcCdB2l3yjZmwVFURRFKSU0sqAoiqIoSiBOH3MW+pTvsJcp2ZuFf7Z1ocJxsUU0XvqQs7QplHZMhx9mD4vQ1Qccvm3zC9Z8JuI7I6QMwdnuHO51KdTvdCWNddiRwfIC95EP11XwKkaYjrOss3H/eIKytDkUHK71tx0VmfPh93fknieT3BzJ31ZaZvUb4VKWB+zrhCwyQsRoqGSsYs35McLksmEV74feZ7mDZR1ASEiWBlESY2x0HiKGRGI/P7v148BFaeiaSKelW8Up+Dwpdpmm6zeZYOeHvQAWOygqq/2fCW6qJq9LluHY9WNzSez8rLDrxwjhp8xr2cjQtzhMnIDMecN1QeeKCxrljYddJE7Az2bhr4xB3tgsDZYcwz0g5EsuLMXXfIDjwJAHuFEYuzvEl8GYU4vTQ8oQxrzR+WU3RFA1OGM/tG0PqQJLA16q8PtK/1KyNwuKoiiKUkq4TnAaRTHrD1b0ZkFRFEVRisBxneJLkRdafxBnLQzm6pOKoiiKogwAJRtZ6M5mAQfYLioR1pHWxp/8tdmvuCjvgLaSRtkY9XMEuHGTtBp2dfk6WISr1pXR85ipPcZIX2Mt3yN/YVZW4ePqjqQDZuN+PoQbsZ8m1olDlb5F0y03q7IZlScpH4KrKcaFNc9jjZ/Gxk2UIiIvIEQVGF1DE/eXybOPWmyMhoPKtWu2PGoejyM06AjnHIQL520E5hjYGifJYnS2CoxGNTt7BbsMnXu+LqVNtaKisBXN7TY13G6Woel881xnZO4JzR3n1bAlUs6GmWdD556bwUkLIGvsliZMRo4CxLVgsYxmRZVF27aNbUmNn557YcpzMBpJCVsnXyO2v0ADbK5wKIciVrgRFWBWfuVx8zWf9xewzZJo5IoUl09hbFbkYNgrkNK2xKZkflFusaD97/reesmBs05qIylFURRFUQLRmwVFURRFUQJR62QJcvToOlSH3DxrXhs1zPlrp2+D/FfCfz8tolXVFBaroudt7f46W9pMi2aaQtMjUlyBzl9GhsY5rJsku1oNL9faDQOu1MgVAim05grrmBFS5QqDbN2UMWtenzQBN+JvKyZsUxyaTlLDKg7bh0T4MGpYJLmJknU4RoMnPt+mimCXLrhCoSFjpOwNf/hQ2dJlWPkgbHY0p0alvqgZhuVtGBJLp3+95VWXpHCraW31FzFkB4jwPhETkx2JkP2S5tfsu2SuE+JjoGsxw9dlmTkew5pHYwtVUMOpgKZQNgtgXqifxsoShSHzBNggWVZxgyyWtFvPCK1TVUIxbzYbIl9HUoo0jpvkQ+P8ijlw0pbQO+9T/HYG2h179imvf7Yu2iyeYh2bLZKP28mzaPI8Fv6xkLJK7ns/iP8BHkz0OsHx/fffxze+8Q3U19ejvLwchxxyCF566aXc557n4YorrsDo0aNRXl6OmTNn4o033ujXQSuKoijKQNMjQ/TlMVjp1c3Cjh07MH36dEQiETzyyCN4/fXX8atf/QrDhg3LLXPttdfit7/9LZYsWYLnn38elZWVmDVrFuLxeMCWFUVRFKW06ZEh+vIYrPRKhvjlL3+JcePGYenSpbn3JkyYkHvueR5uuOEGXHbZZfjqV78KALj99tsxatQo3HfffTjzzDOL3teIfepQEwlh0z8+Mt7fmPDlgu0U2ouzk0Bs6+CYH87jyBxLD3/rMm9mxlIm8vAsN2Dxl8lIvYPgLPY4hYGlOlBOob1Ile9YyLKbQlxfsSoKT1LIL93alXsuM5K5oh5nc/PFG8oLH/pj42ZaSUrElg2iuFIjby9Dx8kVMSXsoMga8oK5TtoSRg1zKDzPcEDNb+hExoymOiIjnpsboXCmuXRdGNugcfL7fD4A85zYql3mhbw5Iz6gomSozL9ewjbZSsxnusW/lvjchQKq8FkbCNE1khcaZyzNiWT1QrjkZqC3baF1OTbjOuDvmQjt82ujimSQG8PSyMlwfcgQPDtPKlmWpOtFSGpmkyqSCmzjFHiWaz5vDrOFl3PK7U4NQ+5IWeQSJ+A6YPky4Bh69mtzXyj9S69m+YEHHsARRxyBr33ta2hoaMDhhx+O3/3ud7nP33nnHTQ3N2PmzJm592prazF16lSsWbOm4DYTiQTa2tqMh6IoiqKUGipDFMnbb7+NxYsXY//998ejjz6K7373u/j+97+PP/zhDwCA5uZmAMCoUaOM9UaNGpX7TLJw4ULU1tbmHuPGjdud41AURVGUPYrrOH1+DFZ6JUNks1kcccQRuPrqqwEAhx9+ONavX48lS5bg3HPP3a0BLFiwAPPnz8+9bmtrw7hx4/DiuvdR6br4SITftnJmNr0fppNQLkJX/zIKtPjPGynE3B4QwuT9cPguIYqBxPK62+9ajpr3ZGVWM/dMyfhOCd52VBTwSXT48kmEssGNcGbMfmpNh0lAgSUKnVZRZjaHcbmAEGAvIJWKc0OkgDkwij/Zmz3ZZIgECkssgFlQixtedXeTBJA2XTExKrzF82v0C4oEhVTpOWfky/NjO1+0aVloKJUo7Pbwsub7hjTk+mFuLuyT7jCPm88Dz1WoglwOFjdG3njoGPIaIrHjwCJPyXW4MZVHnxlSmwx/0zFYHQvimrIWBKJNS+nCkKRYeuBCUHLeinApSIzQO6/P8oJwFTg2iYTlMXGN8bbdCLtIWIawF0yzFTwLku5YHjPOj80BogwIvYosjB49GgcddJDx3oEHHoiNGzcCABobGwEAW7duNZbZunVr7jNJLBZDTU2N8VAURVGUUkNliCKZPn06NmzYYLz3z3/+E/vssw+AncmOjY2NePzxx3Oft7W14fnnn8e0adP6YbiKoiiKsndw0Ec3xCBuJNUrGeLiiy/GMcccg6uvvhpnnHEGXnjhBdx666249dZbAewMM82bNw8///nPsf/++2PChAm4/PLLMWbMGJx22ml7YvyKoiiKouxhenWzcOSRR2LFihVYsGABrrrqKkyYMAE33HADzj777NwyP/zhD9HZ2Ylvf/vbaGlpwec//3msXLkSZWVlAVvOpz2TMXTTHoaT/rqJNErOWYgG2G2GkS7v0l1eubCblTmk89L7XLUxKZpPxeNsf2Obnr+MtBryMaa6Sdcn/TYkjoc19ljMH2d5OS0kNMFQeWEdME4Ns2RVQda6w3UVueesKTpd9ip+rBuXkd7ITboAM4eCjzvJ1RNlMTpah06VUc3REXEznvsQ5SKwlVPmQrBtMBwurJlKexefL75eWLfO81sbzXwMv6S/SFacU5ofvo5kXgzbeKMe5bvUlhd8DgDhdGHrYqS+2h9Pham9Z7vM/JXccpSPkafJG82FOLnBUs0RYh7pfa40KX85jEqcbIm0VSgETNsr56sY1UPtlThteQWySqJxrHzu2Uoq8qOMkfL6/HaApdBqlwxsckXj4WqmovmUUVXWo89sxyk+A28vY89T6DmPA5nL4Lj5vyu9Wj9gekudXpd7/vKXv4wvf/nL1s8dx8FVV12Fq666qk8DUxRFUZSSoq+FlQZx0kLJ9oZQFEVRlFKir0mKg/heoXRvFqKug6jjYETIHGInhcy2cFMbXleckDoKIZaR3OBRsLJChJIz9FkXhdwy1MiqXDQQilOoM0WRdg5Fy2ZAYUvjJaalVdj5aL9U0BKuS2F/xwz1hyK+Zc6wv9EcJuL2cF6MpAeu7ueyvAEgXFeZe240+aFwc5kIdXZ1FbYAukZjLTNEbJMeIlRpsrLCHJsZ6eRKkWzX7H2cMK+yIksK9FHMsNXZY5mGhZCvS2kh5AZYGbtsxfPI0oGtwiAgbLgUNg8P889vXrMytr9F2cZLlSKFFZq3YKvamFdVM0zfIR5DwC9xMZbEvO+fRTZi2ckT61irNgZVSTTgEHxhWQYQFmU6V06AZTobL9x0i+UfeR2Y0kHha5a/23ljNSpnWocmmm6RHdwsYWruZtdxO0HSidJvlOzNgqIoiqKUEjsjC7sfHtDIgqIoiqJ8wlEZogQJw0HEcRATLoUkhauqKcSVpFBUXISl4hSqDHFGL0kNaZFuv40khS1U1Wwkhf/GijHXVvnh1o4OP5TWztm6XbBSXl447CnD9GbYvbArIA+qoBgKF14wKxvcWFwliXa/6Vak28yA56xzDmmGqnwZhOUJwGz4xDJNyghvmnPAc8UlVNnBESsXl7fFMdDW7o8nI0LE2Qy7GfznXA1SNsbKGhX16H0KwbuuDKnSJLDcwK4PIV2wy4DPVUj8IoVqTKeDPyB7Ay2WK0JVtH7MP4/o7DT3Q5KUU07uJ6r850TM8xiirmRpGc7ehQzbG0fnctifjiElrl36CtqqF+bB82hIS/QbIlxHRvVBo9kYVUmMm9d/tpO0REPKCWjWxMdgkYykhYjdKh79phnVJcXxWJtR8TUuG7YZY2O9kKUGcU5NxdBfjp0rFrmh2KqXSt8o2ZsFRVEURSkl+trfYcj0hlAURVGUoYrKECVIxHERcVzD/QCYIcgGCnN3ycxsgsOynVTkg+/y5B0fyx3DKTQXpeVaZcGQDv8ph5kjHu1fhFrbyOmQpecJOp6ECCeOoeeVdAo5Cz4OOy6FALlQkSx8xLADggsnZcTxxDy/GRZnbEdGVBV8HwDKy/1tRCjbnvcpm2lFo4WL5IQ4pCqK33CTnER34QJCEpZsyqv80Hy0we9h4nxgtlVPcKErGhsXn4oWm8Eti9cQRpiaJZ9KswAaO1aMIkRceEk2EeNmRyQjmJKCiB2z+4QrhPFy8ntKme/hsnjh5eSFyW4I/j7TfMgwt2eRGxx2RwUU9zHmjXVAKd+U8RgohE4F5LLi2svSfvn6lc2WjHHTZcHnMR3vLrB0/n75ejGaQkmXDkt33KjLcFMISc2QegtvK89J41hkHuNALb/xQT9cSr9RsjcLiqIoilJK9PR46Mv6gxW9WVAURVGUIhjKMkQfqlwriqIoijIUKNnIwpi6MlS5rtGcCTA14DrSLreTLa1F5BKwLTLJ8ha9XyYsQmwj5JyHt+JJ6zpcKXJstLAX6MNUSrz2x1pF+nRbxm7rHEb7iZMdqjFKWrXQbLnREIfCOM9BNlFispnCWqiUC21WwQzbw2SFQdJMXdLKnQ5/nbJKUcWPjiFBc8Aqr7RUZbnxUqawziktjZGw/zpc7c9vpN7Pwci0m3OTotwTswAd7VNaJwOanxXcGITljfMzArZlLFfGVQnF3w2cZ8D7TZP1MSx+PqLRws9tFRflZ7Zmc+I7Y7wOs73QP/vhiJm1Y8tH4PfTrSnrZ0b+AV3k0mqYZf3f0hAs0OrH587WXEzCDbQoL4dzdOR+w/w9C9i2mddiyaEIuN6MeeffILEto8IlWywtzbyMzwbwz/WhHFko2ZsFRVEURSklHNcp7sbetr43eO8W9GZBURRFUYpAIwslyOgJdagJh5DpNkODW7f4/kSWKCqzfhgrLkKdvAW2CmYCLDcJ2sS2FFdw9ENhO4RtcDuFKisorMsywjYRDh1FYeFyumMtc/1tbxbhxB20DbaChlv99UfXm1X7isnClctwODCRLBzGlU2LkrQcVzIMJf1qf9GoGfLm5bKbd/jjodBkVsw1h1F5/e4OsofJIn4kPbAsEyHboKzgGLI1BopRhcPaCnM/m9tRiDIK+8sGXEbVO1sjHtlQicfDzYTkqeZGQbyNssJVFotGyhA26cIyloLbKLQ+SxoA0NVVeDlGrOO0kb2VxsChetlEyZALWBLgS0Rc/2wJdNgizOF8OWY+J4a1lUP49vQyPgaWSyDsubbqksYpEd9zq2RiqWgZiEViAQJsqwHb7rmWA6twKv1Gyd4sKIqiKEopoRUcFUVRFEUJRGWIEiRUU45QJIxQrfl+5EM/BOnFUBBZ9dGymCFDyAqQCao9xg2rhlHYNJE1JRJuYNVO8kA3vR8WV0uMXvNn7KxIidBtynBx+GPbkvTHE9thhuYqK/1xR2XP+l1ERBU/IzxPx5BJ03jEFRQimYedBSwVpIUTgSOS3DTLSMIX63jUqCuRKJypHhbhyTA3mSIpJEnOkZSp+BhjMBwd7JapMEPeLM3wvIW4al5Awx5Dkiiyop9RHU985HmFQ7zG7KTFgbM8QC4DQ2oIifHwa6MJU0CYmK9to1ofvS/Hxvvh57xOgMRouBkCGi8Z66QslR7lrz+fh1ThEL4jKos6ljkwwvtBSXWW8L4rpCXr9RPgzjCcCSzZsOwljsc4BovEIiUSQ+4wvnTUmK6s8PEEOkWUfqNkbxYURVEUpZTQCo6KoiiKogTioI8yRL+NZOAp2ZsFL5neGRUU4aqRE4b5y8SpAAnFj8P/MrPRHSO057+fTPqhrx2ix3za81+zjFBFYbkKEeZOUriXpYIqWq5LhPw6SP6ocahpEYVuG0Q4keoEGTLERzQHb3WbRWmqkn7YsJGyomPUkKmm1gynh0mGSPFc03xERYiZ5zpMA01RSDaZMMPiIUszK5YxZBQ2Rdnl7HAJU6hfyiq8nzQ336GxeSIUzQ6KVLs/p5kOvxCTzMbm485QFTCj8JEsysTbYBmC15GhW0vztLwCS8Y6FFamcHrej5gxVg4lc7a92D9/xusESRI2uYO3HSRD2OSKpNmsyTpXNNey8ZgBXVdc3Ctve3R8Znicr2Xz+2yMLWuRZQJgSYuPMsglEHQtGiRShd/ndaRLJ8xzUFi+CZprzyap5blIdh13KKDIldJvlOzNgqIoiqKUEkNZhuiVQfWnP/1pbrJ6HpMmTcp9Ho/H0dTUhPr6elRVVWH27NnYunVrvw9aURRFUQYcx3dE7M5jMOsQva5mcfDBB2PLli25x7PPPpv77OKLL8aDDz6Ie+65B6tXr8bmzZtx+umn9+uAFUVRFEUZWHotQ4TDYTQ2Nua939raittuuw3Lly/HcccdBwBYunQpDjzwQKxduxZHH310r/aTTaSRLdDshy02RhU80tBGjjCrF1Y01uSehyp8IyVXPmt5Z5uxTnxTa+45WzHHV/nrp9Lm+HbQ9tpIv+WmVDVCR+dNsH3TtFGap6ksRrYl2lw06b/oELoqj6GDtEN+7ohbx1TM329rm68B19b4uQ1habckeyLnHLC9kXNFAKCiwt8PH2qU8imSeRUkeT/+uzyekLBUuZaqdS5pnhHY8xzY+pje5lcS5eZXAFA3aXTuefzdD2lHhRsLAcLWVmTtecO+Fg7Spwtvj7V3R+TSuGGLpdCi/Re9nKzYWOy2bXCeAjeYEnkOThHVJWWI2FZAx4kE5CywRs/VGHmuI0Ljp2uO7Za8nCd+C42h0ffECbB/WkPgnCcRtD43LrM1zALgcmVRWs7jXJw64Ynnc9fhV3t13MJVSne+0bPfgWwkpTJE0bzxxhsYM2YMPv3pT+Pss8/Gxo0bAQDr1q1DKpXCzJkzc8tOmjQJ48ePx5o1a6zbSyQSaGtrMx6KoiiKUmo4bt8fg5VeDX3q1KlYtmwZVq5cicWLF+Odd97Bv/3bv6G9vR3Nzc2IRqOoq6sz1hk1ahSam5ut21y4cCFqa2tzj3Hjxu3WgSiKoijKnkTm7O3OozcsXLgQRx55JKqrq9HQ0IDTTjsNGzZsMJZpbm7GN7/5TTQ2NqKyshKf+9zn8Kc//Sn3+bvvvosLLrgAEyZMQHl5Ofbbbz9ceeWVSArH0MfRKxni5JNPzj0/9NBDMXXqVOyzzz64++67UV5eHrCmnQULFmD+/Pm5121tbTtvGByncDiWoou2JiflI6uM17Gxw3PPnZEj/Q8o7DlM2H9GbvUrRdZSCHDfA0f4q4vQ+I4P/fDZjh1+tb84VwgUYb6OTOGQpqz0aIMbHVVX+WG6srQZsuPQf2fCn8R2msNNbabdMkRjSFGoksP55eXmfmIkkXBTJpYxOjvNEHE260s7bN8MmgK2J/J4WAYJlZnygGGLTPrnh7/AsZh5HbAU4lIFucjIan99aVFjOx43vCKZKttt/6Iadr4qewVHlhf4M2n/NODQeJYqX0pbJoWFjdNga/wEmDKCLewvpQbDGmqRIWSlSFulx6BKkY5FAuIKhWL/xu8LLxdQ8ZC3bVgAeZjyPLJ0ESp8TpxwkdIUzYE8HptU5ZGNOO/Y+Fq0SA9B17+xnwxLLKJxWcpi0eRtyfPb89uZtctCg53Vq1ejqakJRx55JNLpNC699FKceOKJeP3111FZWQkAOOecc9DS0oIHHngAI0aMwPLly3HGGWfgpZdewuGHH45//OMfyGazuOWWWzBx4kSsX78eF154ITo7O3HdddcVPZY+WSfr6urwmc98Bm+++SZOOOEEJJNJtLS0GNGFrVu3Fsxx6CEWiyEWsxVkVhRFUZQSwbX8Edub9YE8ud327+DKlSuN18uWLUNDQwPWrVuHL3zhCwCA5557DosXL8ZRRx0FALjssstw/fXXY926dTj88MNx0kkn4aSTTspt49Of/jQ2bNiAxYsX9+pmoU8KSkdHB9566y2MHj0aU6ZMQSQSweOPP577fMOGDdi4cSOmTZvWl90oiqIoyt6nL75J6kI1btw4Q35fuHBhUbtvbd2ZeD98uB8tP+aYY3DXXXdh+/btyGazuPPOOxGPxzFjxozA7fA2iqFXkYX/+I//wKmnnop99tkHmzdvxpVXXolQKISzzjoLtbW1uOCCCzB//nwMHz4cNTU1mDt3LqZNm9ZrJwQAROoqEImEkelKGO97loprnKkrG/sYIa9OXypAt1+FL1Rl3tVFKZxdVkYhb3JTROrN/TSOrss9H9HqyxhpqvyXSpgh+G3b/M84fFwWs58arl7IVQoNJ0JSVkksvNwwukne3m6GxpNcuZJCp10UTufxA2ZTJq6MyNuWUkx6h7+NujoK29P+syJMznKHDcMtg50Om0JwBcjycnPeWUZwuIofza8TNscWorniazFL1fCy3SLsSn+tsGOHka4GQzrgEK8MA5N04IH2G1CJ0LN8Fvg3FUsU/NwmAcjlbMjws01mCdKt2B3BsgbLgBm7DOHR+c7KbmM8VJaDeH0ac94wLdUMgyowmgOl+TCcEeb6LlVuNeUG/3ikhOWyGyFqqZwph5Pkuba4Q7aZ7jMDrrhrqwTKYwiS3UqUTZs2oabGd+kVE13PZrOYN28epk+fjsmTJ+fev/vuu/H1r38d9fX1CIfDqKiowIoVKzBx4sSC23nzzTdx44039iqqAPTyZuFf//oXzjrrLGzbtg0jR47E5z//eaxduxYjd+UBXH/99XBdF7Nnz0YikcCsWbNw880392pAiqIoilKK9Jd1sqamxrhZKIampiasX7/eqG0EAJdffjlaWlrw2GOPYcSIEbjvvvtwxhln4JlnnsEhhxxiLPv+++/jpJNOwte+9jVceOGFvdp/r24W7rzzzsDPy8rKsGjRIixatKhXg1AURVGUkqefchZ6y5w5c/DQQw/h6aefxtixY3Pvv/XWW7jpppuwfv16HHzwwQCAww47DM888wwWLVqEJUuW5JbdvHkzvvjFL+KYY47Brbfe2vuh79bIFUVRFEXZo3iehzlz5mDFihV44oknMGHCBOPzrq6dcrcrpLpQKIQsuWHef/99zJgxA1OmTMHSpUvzli+Gkm0klW7vRjqSPzzW3TjPwNCWKyrsG477+njyAz8jNbF5h7kf0rGH1fn7ybT6eQ5SK4t9yk8YCX+KLJrkZ83sMDtixja35J6Hqstyz6NkzWPLHQCkW/x8CNaWWQfPxEX+AVknE3nVEHci9Xpeh3MGWin/YEe3ObZOqtRoSKkUuqsVuQSsk374kX9+ONonO0hyKJCrLLKNUuYF8H74/EaoUkqozBybUZ3OkiMgFVNDnyY92tCJRQ6G8dpW6VFo2Ia9MRSg7XInRNJGja0FWOZ4nUB7IucGcV6AtD4yZf41b+QPBNo/aTlbp0qZC8HWbt42Pxfzy6+4AqrrUe6K6MqYtdkqWbsXh2b0ppRVCm0Ylk/jA/u2+LrKFH5fhtiNPAWG84lEDgdbNl3xfcotI37TzDwFy1/fpZCbQEmKu71+L2hqasLy5ctx//33o7q6OlezqLa2FuXl5Zg0aRImTpyIiy66CNdddx3q6+tx3333YdWqVXjooYcA+DcK++yzD6677jp8+KFfVTbIqSgp2ZsFRVEURSklHNex38wUuX5vWLx4MQDkORuWLl2K8847D5FIBA8//DB+/OMf49RTT0VHRwcmTpyIP/zhD/jSl74EAFi1ahXefPNNvPnmm4aEAXxMTRaB3iwoiqIoSglSzD/m+++/v1GxUXLeeefhvPPO6/NYSvZmIdOeQCaczguDGdXOyJqXBYXd45ZqYAAyZGNMbvWbRcXjZmh+dGNl7nmk1g9hGqFkEVLyuCkTf0DhWbbfAUB0lN9QJTzGrw6Jhobc01DCtI+GKIyU7fRlET62sLjIymisabJ12uxhAJCkeWQbZIptZGLe2BZZSWFQrowYEXPAX4ju7kzB96VEwnavcKRwiNdJmbZO/ixFx8OR9ZCorJj1pwoRw85KjaSE9S1Ldl/TPkdjC2j8lCdR9BBUOZCbZMnKfVwdL8SNeWjcrpQh6ByxDBEkFfA6LEnwBAfJGDxO3o+s7pe1fAdtFSQBIEo2Zy5zS5ZKR4yNj86oxEm/NfJoDMmQpQtu+iUaL9kqcQbOFa9Pc8W/QXm/TynLubNdb3I5W2MtcTzwSAqk3xBuMOV5Yh2jqiZLb2QJto9y4BhgGaKUKNmbBUVRFEUpJRynjzKE3iwoiqIoyiccjSyUHplECulMFqG0GeY2KqlxyJgbuMhwLSc/ZwqH08urzGqMYXImGNX1qvz3ZfjZaJRCrgtjbKIyXqiG9mNrxiWzyS0Nd9yAqo9S/siNh8Ysq2VGaD9cLbCK5qqiwgxh8nchTQ24WFKQOhw7LcrKCjeiysjqehTG5/XZ6eGKLyZntBsVIemUJBLmfpLsNskWlkVqqs1rh2WrcK3vzAlTKDtPi7T9tULHnQ3SL/l45PxSaJzdGUZlUxm252vO9gMnw+S2aoxB8gBXVuTvDK/TbjqIvLSluiSH8KurxYd0DCxrBDSi8hKW5WzbFcsZv0PsLhGuCw7Vc1VNo0KtdDbw+aF5d+h9LyEkNUsF0yA8+g47TuHGWHkyBB8fnStjWwF/nRvzw9e1E3D9K3uckr1ZUBRFUZSSwkUfizL120gGHL1ZUBRFUZQi6K9yz4ORkr1ZSGc8pOEBMMPCWWqiFJKFPXbhijs/W6ETbiAkTyIX9GGpgAsnuTK0z3IDh1dtBXcgCt7wOv/6V8HtAjBDuV3UiIrCfHnNbjotGfosiwQVCiLKy1kqEE2hDOmhsBMgLdbhJlcsQxiNpMQ6rqUQE0sPMkruyAzsAmPLmhVuEKVzzBIHu2eqq8Tx1PgyBEtDIZInskLycS3XKJ9TWf3JuCz4upIqle0Hyij4JPZvkxR4naAmUDbHggznsyTArh96P9NpulrM/VBom86VWyYcFLyfdOHfDSlfmqFydpvQPqWzgc8jSw9he9Es8xyz7ERFt+TYQuS8ihZ2nuT/7tEcFFMETCxnFQGkDMHj5O2x60M2ybI1G2OXj7x2ds2Jlw5wCSn9RsneLCiKoihKSbGXekOUAnqzoCiKoijFMITdEIM43UJRFEVRlIGgZCMLiWQG0RAQi5m2NLaSpS1aVUhoaCGLJucYOQbCosmaawdpqdzAiGyUAOBGC9vNPIt9CAC8Lr8Co8MNp0jTlhZNp8qvLsmNtbJxX4uVFk3DgsfjSdpzFjhnwNBv+X1xoxylaooZOj9GDyWxH24SFbKE6cJC4wyH6TzQ+kavnIz9eJi0YQ8z9885FJwnwTkUIVmRkueeGumEKvz8FJv9rzcY9jXOY8mY27ZWBQwFVAt0LRp7sdUYOS+Acxuk7ZftknT9G7Y/cd7Y/sl2QI+ufzdp2ga9pMUGKbVz3k+48PfZYR1f5hJwpUZ636heKK9Dy3fLyC0S17Jjydnh61c2oDPyKziHwrB4inwKzudJFK6M62TtTd5seRt5+RSW5l5cpTfPbtmznwH8a91xdz76sv5gpWRvFhRFURSlpFAZQlEURVEUpTAlG1lIJT2kQtk8u4whHViqqskon0PhcGmr9D+wW4a4UqRRpTFvHarQx9UPAxoA2cLRXNVN7icatlRvYxmEGiABZkjSqIJJ+88K6cIm8xhWRTE2DvXHYv540l3+/rmJEyCkC1qfm1fJU531/HVYqPKKzDZmqcqMSttlCD4ePgZ5fvl6yXCYu9hM6GLbxnLImqyBjqySaLWlWSQJ+dq2nFzH8MdaqgVKGYLlAvrMIfkxVBaDAR2f202N1LoLb2vngtysieaH50PIN7bjdvi7Lb6/1jPMll7RHM+QUnjcLPflba+wjJANqORpYJFi8n7T6DehmN9e+RlvzqhIKeQf4ztE6zsB35+e8eRVkNyDDHSL6lKiZG8WFEVRFKWkGMIyhN4sKIqiKEoxaJ2F0iOb9ZBxPCQSZpgvGi2cnZ6XYUwYoXJb9rOUO4wMcsq8r6CgtwylcXgyWdiZkJWyg6WSGmd2y5CfW0Yh2kqSPlg2kI2XLG6IDI0zmRTrcDjRcpFLh4HZl6fwOtKlwBUcM+nC55ElAABIG02mCjeIskpOAMqMeSvcLAoQ2e10cGF6LmUVt5slAVrfUqVR4llCyXmlYvn652ZRsrKirVJjkBuimOXkflh64OWC5Albk6lhw/znNTXGKiyzeNu3+8Ok5xBuCGNOKvzmXsY4UyLbn8fGlREz3OxJSFDJwpVbg869cVqNCpm0z4hwHNB3xnBN2BxMEr7G+DoKkEzZhQLLcQbCLgfpPuP9svMkSGLYC26IoUyfEhyvueYaOI6DefPm5d6Lx+NoampCfX09qqqqMHv2bGzdurWv41QURVGUvUpPb4i+PAYru32z8OKLL+KWW27BoYcearx/8cUX48EHH8Q999yD1atXY/PmzTj99NP7PFBFURRF2av0yBB9eQxSdkuG6OjowNlnn43f/e53+PnPf557v7W1FbfddhuWL1+O4447DgCwdOlSHHjggVi7di2OPvrooveRyWaRQX7ImrPluREUAppCceEidgwYoS+xH2N7FErmQkxO1D59RhEW3rZoimOMgY4tY5EKAMD5oDX3PExNixCUrcxjoH1yCF3OtQyv53bDidhCNuCpZ+mAw/uyaBZvj6UQdiakpaxCL2Vjqh5k5NfYL4duubiRvPO3ZH2n6bilayRExWucOMkQLme9B8hmVMiJJSM3JEPRAY15jAX76IawNYKSjgPbZ0FZ+ba/tLipVF5HMMs6JA/IgkRuJUkcZWYxNX8lcTw2R4d0TfDQ+DzQFHqpgHNP61gdA+IfGZY2WYIymzWJYmGG5MkfFC6CJD8zsBSSKjTW3GKWxn8ATLdIxHItS8kzu3MOtJHUwLBbkYWmpiaccsopmDlzpvH+unXrkEqljPcnTZqE8ePHY82aNQW3lUgk0NbWZjwURVEUpfRwfEfE7jzs5tqSp9eRhTvvvBMvv/wyXnzxxbzPmpubEY1GUVdXZ7w/atQoNDc3F9zewoUL8Z//+Z+9HYaiKIqiDCh9zTsYMjkLmzZtwg9+8APccccdKLOF8nrJggUL0Nramnts2rSpX7arKIqiKEr/0KvIwrp16/DBBx/gc5/7XO69TCaDp59+GjfddBMeffRRJJNJtLS0GNGFrVu3orGxseA2Y7EYYrFY3vtdXWm4rmvY6gDTDhelym6cVyCbKLHtyC33LXOORSvc+SE1ZIn7NizDShe225nYtuQY45SVywrnEnBVN6kbptr9vIc0NblywwH3fqT3JamCo6nXm6tkOLeBdEF5TpiwpcGNcd6i5vqc92C4XKlZlCdkSVtTKGO7AekHnZ1+XgGPTeZTRGlOs+nCuR4yt4NzKGpDdO3wNZanvxauDOrRXAcps1yV05F6PefscDVEm6USsDb2MQjK7yjmedC2eTxsiQRgrNHq5+9ku/zvRZ5VMUqWZ7ZRSrukjQw3g6Pvqfit4XwTziXgiqzScs0NxsC/hWlukkU5HGIMNntung3SYos0ToE4H0GVGm04buFcC/6ty/vtNJYrbKPMu1Z2fRZk9+x3tM5CcRx//PF47bXXjPfOP/98TJo0CT/60Y8wbtw4RCIRPP7445g9ezYAYMOGDdi4cSOmTZvWf6NWFEVRlAFmKMsQvbpZqK6uxuTJk433KisrUV9fn3v/ggsuwPz58zF8+HDU1NRg7ty5mDZtWq+cEIqiKIqilA79XsHx+uuvh+u6mD17NhKJBGbNmoWbb76519vZlkwj7rro3GGGmFIU9gtTSD8WLVxxEYC1UUqoyg/5haorjFW8JIUNjbKEJC/I/dByWarA6HBVNWl/41A72+TK/XAmjxMA3DY/3OpZKvylu80Kdmw55RB+cJSv8F1wUGgyFgvTcwqPsmtRWB35swidx6ylMiNgyiI2KSXfPcrWVArj0koRIROFQv45YVunTcrZ+bqw5BMJmDceQzbJoW2SMQLCrfwJ23slLl8vbA0Msg3aqjFK6cIYkMU6Ke2IbJFkeYClB1kpkuDwPFsD3VjUXJDHytUd46aV2YD3yzIRhfpdeU4t3/XAyoo2m2qAFdSQPC3SgyfWMSRPHoMt7A/xG2D5onlCIGOp1aw0Sde1bL6WLELuyGsktWu7QZbM/kZliN3nqaeeMl6XlZVh0aJFWLRoUV83rSiKoiilgzaSUhRFURQlCG1RXYJ0ZDPIeh7C4k4slfLDVYk4hXijlN1O2d+AqIrGboagqnecyU+SAIeIzXAxjBAkSwqOpcIgINwRXJ2ymiozSkcIOTrY+ZFp7/ZX8cw5cKiqYJZsAjZJAjBlBCO0nuIQvrlOmLKcQzTv5ZQlnombGei2m+0UjU02kuLXGYszQlbK42NN0j5TJC/IbRmuB5Iu2PEgZZkwnS+WLnj/Uu4wE8jZBUL7EfMGS1hZOns8uuY5nM3yWF44nUPGHI7nkLmULhh2GfC2hAyR6SI3T8T/LE2On1ClKcMZoW2W7ugaK2Dt8Z+zDGFrZCVfW0LwTjigMRad1BAdWzZhnsdsp/+9deh3jMPrQW4tQ/5M2EPybpm/DeN3w1IBEgDC1YUlrUyXP4fSjWDIHRYPjyd+E405MapQkqThiSqWlv0re4aSvVlQFEVRlJJCZQhFURRFUQJx0ccEx34byYBTsjcL2dxDNJLKcCiYstPJfRCTjWcII3PYkA1EtjGFIA2XQUsXLSQaVlmawGS5iUxA5i7LC9yIit/fuRF/3CkaT6jWd3S4olhMpsMP67oZ/znPpyxIZCa+UzjQ4iSQ22McIzQvimZZ7raDxhYmqcnNFs7mljITz73jFG4GFMrLuCanBBeSoehsZYX9axQWklhuLAHOCLeSzje7WMS1w6FbQ5qS54Cvc3rbCdm3zc4LJ0rHwDKE/J7xeeAiRobrwjxuLkjEkgKTl9VvC1MHuCas0kOQo8PmUgjaD2/bItO4ntn/hs+jUciJvifhavM64rnKSnmqZz8xc5xuuT/XmQ6SPngOhdzhVvpyaKbd/60JdCDwOeavExezkufadk6NwmHyevN6FrKPRek3SvZmQVEURVFKCS3KpCiKoihKMEO4zsIgVlAURVEURRkISjayEHZ2PlzYdXS2pUXCpLEKDc/b3um/YKsg6XOe0NHdctIEyY7U+s623POqkZXGOk4dvaY7yGynbw/r+qDdXIcbLFX4ujNXbXQ4TwJC4yRrX6jGF9LzKkVSDkSEtMxkwj/Ojg5z3thGyHp9NGK3LbKtMtxlVpHsIZE0tdxiInOOWIcrKxrnkfV6aamkOeF+PS7lQ0i7ZTRauEmPobEGNBSz2vlEXgHnHERGD/fH0035Je1mtUEjzyAgB4L1baMBUZiuF/Fng1EJkBs0cV6OtBpynkKKtXe7vu3YGiJRPoS02XHihRu1/L0T1LCq2FCwLbchaH3O47Dk0qCmxlyFP6PcimyzX8UyLc69MQSuSss5B3kaf+FmZUHN8djqyrkRxnUk8hxsdnAjn0hWijRyTwo3osqzBO8aQ0CKWv+jbghFURRFUQIZwjcLKkMoiqIoihJIyUYWXDh5EgQAJCiU1tlJYTEKM8bSIszdRbbKTj/MV86h/ohpoXIoNJ2ixk3NW31JoLbbDK/GtnbknrPVr4v2z2F6AAjTclHaXqSVrI6WBipyP0ZoUIQgM3H/uENlfsi7stp/nhLzlo4XbpYU4uZXKTPcm6bj68j454fDjtI2yDKAY4Qq/WXkOrYGTWbPInEdcLiWw5u0H1dWViTro3V+ZZg8YvnrwSssgQGASxIUW/McaogUEnMQgn/9cog4I+Qfw2JJx8Pz4ZbZ7bkcAncDQskcqnfKyVua9L8XeX9ZGc3XLFX85HTy3Nv+UpPvGyUyLX8jBcWzbc20pD3SVq2S1ymn6qwAUEFN7KjypbOthfZjnnuWZlyb9CArK/ILw2Js/2uXK2zaJIVQhXntGE35LA3Fgqou8jVvnMeouR9n11y5icLW0T1DHyMLBf5NGyyU7M2CoiiKopQUrtu3JIkBTbDoX/RmQVEURVGKYQjnLJTszYK369EtwnxpaiaSpLBYF1UsjMqqgvS8ipqpVJA8EBJZwBymbu/ww7rdtJ+Obd3mOvS8gkKDXIlQSgpRyubm5kRhS2h+52te399riBwDMmyfSpNMQw6IcnJQREUIPk4yBLseQhRKk8djjrPwZ0Fzzau4QaFogps9mY2xhJOG5jclm4AV2CcAeHFqzsWSUbSwMwIAQnSN2Rr+5FeXJMmn3Q/b25wMgKjsSWOQlRC5aZDHlUWp6ilX6gMAL04NnvhYOZQs9mNUUBw5Mvc0VEaSRJfp7Mm0+k4ldh0ZGfrloikauybYkREJaCRlq9QYVM2Rt8fLpQOqF9pcF7x+t/m7YYTXSaII1df5m6VrAgAyJKcaVR+5wZN0hNicYHyNkXNLHoNxzfK1HBbzNmwYCsISS0pIB3zt8HwEVdjsOQ/Rwq4rpX8p2ZsFRVEURSkpNLKgKIqiKEogerNQeqQ8Dyl4yMpwOk12kuY9QvJERFSY4WTfTLcf/uqkMGxInMQIheQzHL72Cj/fuV8qxGQJh+eFuSk6GadwMa8f5IaIxbgwlT15hkOS3d2Fw6ghERoPkkJyywjpIqA2UA4pd9jkhlAR+9+5TyoeFeHiLmYIM8Ph2qy/vVjMXy4l3CpBzawKDhpmdrosJGMbG8sIhpvBKH4jHDscIg5bikdByBDcYCxGkkLcDD+nW0l+qebiTfYCVg6HnyupQFkHySpJUTAtY8mQ56Jm3eY6XMgpZOpWsGJeWB//HDDC5obLgQkKk9vGI7fVRo2lLA4KeU658BGfq3Cd70QIlYmiWZamZOyGyMpzaimWxC6JjJAuXJZZuPoZnwOWpgBT8uF1+BwkhETS4yJxAs6B0m+U7M2CoiiKopQU6oZQFEVRFCWQISxDDN7bHEVRFEVRBoSSjSwkstmd3UDF+2G6M8vQ8zS7lBzRdIiWSzlkTyQLoMxZiFnEd4fWkfeIvEbKoqvK/AO2S0ao8l887q+fEDo6zwnbDoPyHMIWvZ2rS1bXmBXSZD5CoW3L/APO7zDsllw9UYwlbMm14FOS1xjLqMhH80tNsoKaCZVzsyeqahiPC2sfHUPU2A9prOJ4jOqMnI9BY3bLzK9elq2ctuY7Yg64MqPRvMrScAeAkQPB76dkszJpodtFpKHW3ydXHgSAqir/+Qcf+M/b/eZpsrpkuL6a9ikshT3jlNULqTKnYd/0/DHn5biwdZGvHc45kLkENhsk2/yCchZ4e7YKkIDRPMoYJz8X1Qv5N8XML6FzUlsLAzoPoRDtM8w5NuZ5d8oof8BiGU23mect3ew32wsPo2uCK1fKOeDj47nifXIuA7/2BvCvdY0sFMfixYtx6KGHoqamBjU1NZg2bRoeeeSR3OfxeBxNTU2or69HVVUVZs+eja1bt/b7oBVFURRlwOm5WejLY5DSq5uFsWPH4pprrsG6devw0ksv4bjjjsNXv/pV/O1vfwMAXHzxxXjwwQdxzz33YPXq1di8eTNOP/30PTJwRVEURVEGhl7JEKeeeqrx+he/+AUWL16MtWvXYuzYsbjtttuwfPlyHHfccQCApUuX4sADD8TatWtx9NFH92pgXRkPnuPJ4nhwKdhfxuFwCl3JezeXQl7cCIfcc8Z2ASCV8T8MW24GA/qvIEGh0zBJHxnReKmqgipKikp1PXAlRUDYMrl1vGxoRGQsY2WpQVovWWJguYQlhYzYp+FQ47B9gAwRVAUyt12RRWxU8SNLodGwSlQY5MG5Ff5cs/VLRkd5TljKcelc5TUhs/z14ATYOvnoDNkg4C8RDumHyd7oiuuIbXJsg8ySjTLTaoaSDYsl2eTcdl+uCFVXw4DC3BzaNs6DbCA0YoS/7XCLP7ZW2lZQJUJqOsS2zKyQFIr6qygivn9BtkobloqdhqQQ1OSKsVU1BAwbodnUjI5b7sdm/6RtG1U45X7jfkMxuHRNlJtjY3nM46qNtmqbgDkHPE62S8oGXD3bltUg9yRD2A2x2yPPZDK488470dnZiWnTpmHdunVIpVKYOXNmbplJkyZh/PjxWLNmjXU7iUQCbW1txkNRFEVRSg6VIYrntddeQ1VVFWKxGL7zne9gxYoVOOigg9Dc3IxoNIq6ujpj+VGjRqG5udm6vYULF6K2tjb3GDduXK8PQlEURVH2OA76eLOwtw9g9+m1G+KAAw7Aq6++itbWVtx7770499xzsXr16t0ewIIFCzB//vzc67a2NowbNw4pDwjBgysikCwjhOmzEIX8Atq8GAcsq0MyHLBjGSJKcf+wuEu0yRW8l6w8HgrBJyn0G+Q4YLjqoicni+DQOG+vpjpaaPG8bacpNGhLEt+5XGHJh5fLly68gsvlVTk0VvKfurRclpviCBcJzwE3LWJZp3pEpbGOUf2QQsmOxU0hPzMPyO5sKGb9vMZN/Jm8sIhQhX+OQ3V+drq3rb3Q4ju3bZGGjDkVTd6szoKgsp7sBGC3SLV/HrwuUyLJpLjCJTsO+CIzd2NIO1ank8B2ofP7QcdWjJtCkrWMM6C6ZKiKXAJB27Y1xuL9VJrXvxE2p3NluC4ccX7afbmCXTUhHpuUIVpbaQN08thxw8vwWIMaeyn9Rq9vFqLRKCZOnAgAmDJlCl588UX85je/wde//nUkk0m0tLQY0YWtW7eisbHRur1YLIaYtMQoiqIoSqmh1sndJ5vNIpFIYMqUKYhEInj88cdzn23YsAEbN27EtGnT+robRVEURdmrOK7b58dgpVeRhQULFuDkk0/G+PHj0d7ejuXLl+Opp57Co48+itraWlxwwQWYP38+hg8fjpqaGsydOxfTpk3rtRMCANJeYTkhTEH9eOG+M3nhRH5tRCop+pYW4UQuehKiLXgUMpcByKzHYXdajqORYp3WDj+cGKXwcyxGLgXRQMhWYIn3L6PIUWqWFLY0jkmnRViZYGcDF1GSjZeMwlCWRlC2EHfeckaTILGOEQmmuabiRpyVDQApOr4ohebLGmr8fYrmO5kOP6TqUeMx3qdRCArCqcGNeCK0nIimObawPc9BN2WjizHwnDqySQ9vj/bD7oyQzGiPUxZ70HlgOLRMzYRYAuBGRwDMeeDn1HzKEXKH8W3gTH4K1TtJs/gTh91ZznGiXFxLNveyFFKyFVuS2Io/yXX4M87+5yz/LrNoFl8Xofo6/312DHCRrKCx8fmVrguWDvg8UOg/JObNI4kv3c4OCrpGu83iTx45drjoVriBzpu4/p2euUoMoBtiCNOrm4UPPvgA55xzDrZs2YLa2loceuihePTRR3HCCScAAK6//nq4rovZs2cjkUhg1qxZuPnmm/fIwBVFURRlYOmro2HwyhC9ulm47bbbAj8vKyvDokWLsGjRoj4NSlEURVFKDs1ZUBRFURRFKUzJNpKywXc33NiHbYx5OQtsMbPIwZGAOz6jKqEll0HC+RBG4ycpvRtF3vwXKaPSo2gk5Ra2FHoBnkYjN4He50ZSEq60GDLyD6yrwOOh0jCLlb13B9bEuVphRlQLNCpKWiowenGhf9I2jEqRnIsgGjcZ1kc+2CDrmK1CIFcmlZXqbHbJIK2atO9QBV98ARZArnwZ1KiLj4l0fbcsWngZuQ3W0Xk5yl8AYDY3ogqQRi6BnCuuJMjau81OKLdnq34YZJ202Gbz5oCxfZZ37mmslB9iLFdsZUOed2m9tF2zbFcUc5A18g+oSihVHDUqhMLMuWEbcHaz35RKVv90djU/S6UG0Do5hCMLg+5mQVEURVH2ClruWVEURVEUpTAlG1lIeR5CyJcHWHqIcoQ3ILxjygCFpQuJR948HkOYpAcZTudILq9v2gHF2CyRSpYUsiICmg1ZKtDR+jKqnEwWtkV6AWHUqko/7MgVJeOJwtUcAZiNrSzWPtk4ytgGV6Tk9QMkH6PKIoU902kRHqXQbZjXifrvG1YvmBIHV0I0QvNChjCqBWYsFQ9l1Tl+XWTTImuFS2mzq/GtoUZYmasA1pqV+0Ijh/kv2Jra0ek/bzEr6jlcbY/tm0Gherbq8V9dVK0v025WCDTGxs2s2C7Zba5jhOR5P3xOAuyWBvxFk6F+m6xiW1/uh88Pry+3lTCthznoOvJknx2b1GR8zwSyWViBdTKtHYWXgdlkKjKMrjHxG+BYbKaZFn/bXHUVANK7rot0yiIR7QkGWIZYuHAh/vznP+Mf//gHysvLccwxx+CXv/wlDjjggNwyzc3NuOSSS7Bq1Sq0t7fjgAMOwE9+8hPMnj07t8z27dsxd+5cPPjggznH4m9+8xtUBUmWAo0sKIqiKEoxDHAjqdWrV6OpqQlr167FqlWrkEqlcOKJJ6Kz079pP+ecc7BhwwY88MADeO2113D66afjjDPOwCuvvJJb5uyzz8bf/vY3rFq1Cg899BCefvppfPvb3+7VWEo2sqAoiqIoJcUARxZWrlxpvF62bBkaGhqwbt06fOELXwAAPPfcc1i8eDGOOuooAMBll12G66+/HuvWrcPhhx+Ov//971i5ciVefPFFHHHEEQCAG2+8EV/60pdw3XXXYcyYMUWNpWRvFtxdD9mcyRYKyQSE0rihEbsRWF7I5IVKC8sVZqVIEUpzDB2i4Hhk4JhlEQ7PWysZBmAegmf9jJ0RRqhf7Ke7m6u0WeQBsY5jOUFGoyNhCTFkCUO6ICdAoAWj4Op5EksqoEJlbj+iwZOXKbyOkb0tGjwZx8rHEyRD8GeG/SZU+DmAUI2o1NiDDFnbtschSJmFzzIChdqz21keMCWbCF+/tbX+B7I5kY3Nm/1tU2g7K8LM3JDIifhhbo8lASlD8GuLa8hLmDKEIfPYXAqyWibvxyZxSOnCJjcEXS9B0o4NljZZhqOqp078Q3NoLDUZ5W+psqKo4spyHTuNjAqO8prg+aXzG6L3vQ92mGPbdX7c3ZmLvUybkIiK7ZHUukueGz58eO69Y445BnfddRdOOeUU1NXV4e6770Y8HseMGTMAAGvWrEFdXV3uRgEAZs6cCdd18fzzz+Pf//3fixqzyhCKoiiKUgw9boi+PACMGzcOtbW1ucfChQs/dtfZbBbz5s3D9OnTMXny5Nz7d999N1KpFOrr6xGLxXDRRRdhxYoVuYaPzc3NaGhoMLYVDocxfPhwNDc3F33oJRtZUBRFUZSSop9kiE2bNqGGEo+LiSo0NTVh/fr1ePbZZ433L7/8crS0tOCxxx7DiBEjcN999+GMM87AM888g0MOOWT3xyoo2ZsFBx9fRTtJ0aewxb0gcbkpFK2TEWH7GMXTg1wTTMiynCmD2EPwtgZRbkDY3iYJRMJm0IgLFHFA07OE8AEgTZWlslRtKWTIJQWHvHMdS5OqsBibsQ2WSKQNhKGmWSwVZAyJxVyluorcHdQwiovIOHKuYxzythTDksViWLLhj4LCpbamQxyeFes7/ANjy/YH7MWFeNsyu55dChRCz3aTJJE0Q+OZDn8b4SiF41nukMWNPvTD3iw9GO4Z0ajLaDjFc8WhfuEIyXZRQzA6X9LJYmBrJBXkUuB543MSUMTIKjXxXEkZwtZ4LACbPAbj90SsQxJblhqp8TpumShKRnPqcGMrllWk44dfW+Zayn1u5c7rwE0V5x4qJWpqaoybhY9jzpw5ucTEsWPH5t5/6623cNNNN2H9+vU4+OCDAQCHHXYYnnnmGSxatAhLlixBY2MjPvjgA2N76XQa27dvR2NjY9FjUBlCURRFUYphgN0Qnudhzpw5WLFiBZ544glMmDDB+Lxr102xK25aQ6FQzio+bdo0tLS0YN26dbnPn3jiCWSzWUydOrXosZRsZEFRFEVRSooBruDY1NSE5cuX4/7770d1dXUux6C2thbl5eWYNGkSJk6ciIsuugjXXXcd6uvrcd999+UskgBw4IEH4qSTTsKFF16IJUuWIJVKYc6cOTjzzDOLdkIAGllQFEVRlJJk8eLFaG1txYwZMzB69Ojc46677gIARCIRPPzwwxg5ciROPfVUHHroobj99tvxhz/8AV/60pdy27njjjswadIkHH/88fjSl76Ez3/+87j11lt7NZaSjSxEXQdRZ+eD4fwBloOLvevhrXGBP9kUivMeWEJLU26DHBsrZ54l40Kqi6zrZ0mHD2q2xJ9lLRUlZZ4D2xBtBQJl46VsYVkzENboOZ8iRbbSsLAwmk2qLPkYovqcYbek5xHStyMiZyhEzaNYt/ZIi5V2S5ebR4ULP5d4tkp5XJ2Pte2dOy68nC1/Qa5jlv80l2OtmK1+rP23tJjrbN/ur97uF4DJdFkqBwLmBcN6Pe8/4LhD1VQBkscm8ylofrw45Ua0t/u77DStk0ZeiUWvd+T82kLGQfkhvA6fR54DWRnS9lmQddIGXS+O2I+HwjlAxmHKL7qlA5xDeUdGozDAvE75+uVrLyivhuynnq1SJYBw9c7thZMD2UgKfUxw7N3iQRV2e9h///3xpz/9KXCZ4cOHY/ny5b3buaBkbxYURVEUpaQYwl0nVYZQFEVRFCWQko0sVIcclDtuXgVHth5y1UV+XwZuQhbpgrcdEaUHebk0N02h5xHR0ClkbMMr8Cwf3p5LVkWPQ34Zcwtpt/AWMxYbZRAhmoTKMjMMyyEwtlFm0vYjYlklY4Tj/RlNpcyxZbOFq1ga0WIR9TeqXZIkEK71rVqeqPxna/BkSBJ5trbCdkveZ57swCFVrgjJYemw+OrZQs4ctpf6Ea/Dod+gCoG8DW46JK2G3b4NkS2RQfKLSzKPMR6eD242BZjVHXncfNyySiLPHY+7w7deSpsd/0VnykkB58SGJWQOwDxum8VSzgHD2wtqKGarKMlIq22KzjfPR8QuKXjcfM1mM2V7JGCeL9t1ILHIaB5JDK74fXIiO8fjDGQFxyEcWSjZmwVFURRFKSmcProhbPXwBwF6s6AoiqIoxaCRhdIjnvXgOB5qZCVCDo3bexMZJCmUFqHQHjsr5PpxWodVAF5MNpKC5TOzQKFnXc5WjVHejGYt2fZZGmjatTdNMtwdXNAvoLIifxayyCCSZLJweD8tZBXb98c8TjEJMWoyxaF+WkdWXMwLTRcgrzGWcR4oXEsuCVnB0SO3h2xMlUNmebOkwH+5BFX+49dB1f7i1PCJtx2Qac6VDd36wlUkZQVHN0bLcSg6KLzPMgRXYOTwtbxAWK5g6SHpv58nl7DcF7PIJUEld3lOg8LpvByfU962dIQwvE51tf982zZzuYjlGPiaENKSU+aPIRSxVIAUVQUd2zUSdF3arkWbHCbXsUhGkE3deo774/vDKf1Ar2IiCxcuxJFHHonq6mo0NDTgtNNOw4YNG4xl4vE4mpqaUF9fj6qqKsyePRtbt27t10EriqIoyoAzwBUcS4le3SysXr0aTU1NWLt2LVatWoVUKoUTTzwRnZ2+D/viiy/Ggw8+iHvuuQerV6/G5s2bcfrpp/f7wBVFURRlQHHcvj8GKb2SIVauXGm8XrZsGRoaGrBu3Tp84QtfQGtrK2677TYsX74cxx13HABg6dKlOPDAA7F27VocffTR/TdyRVEURVEGhD7lLLS2tgLYWR0KANatW4dUKoWZM2fmlpk0aRLGjx+PNWvWFLxZSCQSSJAu1tbWBgDozHjIONk8OaqY+7JokeUGWWlLCd0tY5HlbdZNAEhwUzXLEOR2Q1TZkAsbuqy9S1snWyzZ1knrh8V+jEJslk6XMpeAu0vauk6WlZnaY6TC10Xj7b5WnkoVJywmaTnOWYiI4zGqttGBs+XPEVYvzjNg3drj+QyyW7JdM8pVBIVV0QbrvzKjml+zbs06fnCLT/+5sE56CdqGWzgHw+HcAQnnH9B+Qru+/7n90GcOa9U87qBug7wfXkfaE2W1yZ5VKn1LoqxeaMwj5w/wc2nRtI2Bty31es4PCTpuZtdvXh5B6/MYOCeE15Hb5bHaxhPUEZOvK86TqKsz17F1GeX15X4o9yRwfpkey6Y7gKl3rlN8OVvb+oOU3Y6JZLNZzJs3D9OnT8fkyZMBAM3NzYhGo6gTF8+oUaNyDTAkCxcuRG1tbe4xbty43R2SoiiKouw5hrAMsdsjb2pqwvr163HnnXf2aQALFixAa2tr7rFp06Y+bU9RFEVRlP5lt+I3c+bMwUMPPYSnn34aY8eOzb3f2NiIZDKJlpYWI7qwdetWNDY2FtxWLBZDrIBlKeHtbMUkbWksA0SNiE5hq6KErYscGc+v+ug/dy1blBGlBIXqK+gOksec9mQ4nkP9NE5uMCXkAa6MmKHPzOqH0p5Y+DPTTSgaSdE6yVRhu1g8br4f6Src1MWIgIqJ48+SCX97tn5MAJBo9ZdzLY2oysvMsZWP9MOjbnlh+1o2YYbwDeujpQJkXqjUsFvSvHOYPigcH9QUirF9Ju2fRrVJGjdX3pMheJZF2MLH2xa2Oq/FD3s7brzwcvK4yUbIjZw8DlnLipTDhlnHkINlB8AucdiqDQJmCJ0/4znk8LncNv+uBVXY5O3Z5AEpQdkqPdrGWeh1ofXlvPF1YKt2ybIDAIetoTSHXpCsYrnm+agz29rBeFs+2Pn+gDaSGrp1FnoVWfA8D3PmzMGKFSvwxBNPYMKECcbnU6ZMQSQSweOPP557b8OGDdi4cSOmTZvWPyNWFEVRlL2B6/b9MUjpVWShqakJy5cvx/3334/q6upcHkJtbS3Ky8tRW1uLCy64APPnz8fw4cNRU1ODuXPnYtq0aeqEUBRFUZRBSq9uFhYvXgwAmDFjhvH+0qVLcd555wEArr/+eriui9mzZyORSGDWrFm4+eabez0wFw5cOPnFubgSG92lcVMoGeixNVVKGxUTzc9CLGsYDaf8F9JBwVUfo7QcyxgycFxOIWJDeqDnSeEk8AwpxX8epUCRrPLIc5CRldBy79urF/L2eP8sGwCA022vPNmDG5ARnKZj5f3n7aeIbctzWpb0t2FUhrM0pQJE8xqWcngO5Vyz66KCwtz8V0VQONIW+g2QO4wQs6wwyI15SE5yOOQsQ9Q8BgptOyQBeCL8jFY/TGxILuwQkMfAzayowZJDIXxPVoDkMD6H+vl9+RecJUPfoX16ct5slQgDKl8a+7GF3eUccNiejyHIPWNzzPB+gtZhbJVAJbbmVR9+aG6Ozwm7bGhsTszeHMw437TPsPjOeN07r6sB/Vt9CMsQvbpZyOvIV4CysjIsWrQIixYt2u1BKYqiKErJ0VdHwyB2Q5RsbwhFURRFKSkc9DGy0G8jGXBK9mYh5jqIfcxJ4Xs0lzPQxXK2cigclJO9kTgYxxKHa7gX7JEWbnKVyPpbi4hjilIv+TiF2lni6BZOBHZqsFEi4/nLdYumSTxuVgHYqRESlaS4YFOYxsmHbZN4AMAzDAMknaTMeWOJg8dgKAUB0oURRaWJTwv5hgs2pVv8EuXcYMpoMgTAY/mFwriGDCGaRXETJiPbvtiwvzHobOHncp2gxj4EN9dix4HXFS+0+M7dsEuBw+yinoq7Y4e/HBeCkhn2jK3ZUoRcEjHxDU4XLoLldfnnVDaFcqqpQRLPFYe8t31kbpAbMbGUwuuIxkuGpMDPgxwu3CSKZQjep2w+ZZMLWCqQhbZ4e7YGTwEykXEMPB7pbGD5xOI88eTvBv9+swQVMLaeQmJOosiiaEqfKNmbBUVRFEUpKfrqaBgqbghFURRFGbIM4QTHwXuboyiKoijKgFDykYW6sHk/E2eroEXHDvJssOr78d6OnbDF0qMtZMUWQpbslYRRtVEcD+UpGJZEbhCVN9DC9kBbXgJguvtsd4jSbmnrhsUVJfNtnf7zCOU58HNXaI+U0mGt2lhRYeYScBXIJFkieTxSfk3wXO/w9W03YmnEA1PjdxzS0SlPQTas4gZNVpudDEfyflmP5vWFTu3ZrHABcJVEsFUxLarg2ZoGccVCaWm0VSm0bQsw8xn4OeUleEnKRQDgVFLeRJhsnXwhRYU1j3MYaE69Dmq2JBpjGeNhjb6+3n8uj4e19zKqkJnw8wU8yu0AYFZjtDWFCgpfc84An0dpbeXKl+1UDTGoWqa8LgqNTc4Bj5WPjbctczAsDb0418STuRE934d4QE5Mf6NuCEVRFEVRAnH62HVSZQhFURRFUT6plHxkoUzcxYUtN2b8toxk82uWDoIsgA4tx6FxtjQGFdTjao4sI3hi/BnaSIKrNtLzmJgDll+yliqUcg7MYnKF7xFlZUc+bj7WNB1QWugGYRpbZYV/efE+5X6qqvwwJssILIvIG3JeB/Cfd3f7YVMpkRjbI1+mW0nygrwOWG5gSYyrOcomW1z0MahyH8PhXnrOja080TDHS1OVRKoaGaoRljluGMXjIRkib2wckme5o43C9iNHmutQmNvhcDqHvKUswyFrowIjjbPTlCF4pMb8kiSBcjo2AGjd7q/P2+MwOc8TYEoCLNnUi+NmuHFSF0k2XOWQ5xCwh/R5/7JhFUNyg9MwqvD6gGFH9Xg/fH6kzVXKEoXGKWUI3i+vz9sWc+2wzZMlJJJynHJpBd157pxIQEXN/kZlCEVRFEVRAlE3hKIoiqIoSmFKNrKQ9Xb6DjpEyLrGEkIv1gFhcwXI9Q3pwFjOLkOYrwv3ZJcGA5YhWCJhuSMmXBaG9FBk/dAi2npA1oTjsL2tgqJ0L7A8MHy4H05MU8g8I+wd6XThOWVJwJV35DQebvZUVeVnVafazKqE7JoAX1f0PE/C4vNDckOIXBJ5DgpbRjsT0LTIqA7JExJQKdJoWCWrCrLcwPv9iCoWyvAzZ6uzJGGrGgnkVwzsgSoHenEpQ5B7gF0KSVpOZsFz5Ul67lRaXAGAGdq2VEZ0qquNVTyWCzjUzhUkxX681hb/xXZf+jCqQYrqkkaVTxtSvrFIJKip859LKWYHVYo0bUv+cznXNocLX0fyx4U/szVCk84evv74nLKTLCzkju5dclK3yhADQcneLCiKoihKSeH20Q3Rl3X3MnqzoCiKoijFMIRzFkr2ZiHheXAAtKVNGYKDOBUUluX35ekwQsn0flBJG96GrQdMsdIHj00WWOrOsvTgr8VFmdJinQiNJxYu7DKQ67B8wsvZnBGAKR2kLS4Fs+AUEKOmP+FyCmV3c5hbFnKyN5nqgRtuAUCCXA9O3H8eIxkiOtwMiztUiIn36VZQ4xrhOMh2+yHRTMgPX7skQ7AMsnNBCrHuxo+DE6PQeA2FrEW418jqZ9kgLppCsZuBPsts98PsoWFmCJ7XyXT4jgHDaSFD/Twei+vCkSFrW/a/Ib+I0Lit6RCHrxPdxiqoIMdAiq7FKjrumHBDWIoVeR9s9d9nqUGuw9cBH4MslmSTAXh96dSgz4xCW20t/vNukj4AeCxDsAskqLiXrSgTryMdE25h15BxDGKfxjGQa4PdEHnHs8sh4g1kUaYhTMneLCiKoihKSaE5C4qiKIqiBDKEcxYG722OoiiKoigDQslGFmKOg5jjICE0zjauHkh6eQV5EqNCJ+YtsFWR7XhpsR/D7likPdG0aBbOk+iQljmCxxAO0Lo5zyGUKaw3yiZXDOdDxGhbUk6Os5WTtPsY5TlEpG2QXmdTvt7J+Q/JpDlmbvDERKKUkyLuyPOaXu2is8XXYsMdpqUqRNcI2zfLKOcgK/MCaBvpVn/bbG+M1Jm5EaFqaoTjWGxkQY2BWNtlm52wNzp1dYW3zRX5AEOfzrRT/kE17UdU4eN8CK4Uaeyny9SQjQuIx8bWPmkB5OPj3IZo4VyEQmPNrWOzNwJAO9k/jXwKbkolGoJRwyrDEsnVFGX+AR8fj5uPU1hbHbZOkpZvHLf8PdiyxV+OLbBBtl3OE7A9lzkKNs81z7VsCsWvOY+EralyuyEaN1s+6TroqdiYo2dOZVncPYnj9FGGGLyRhZK9WVAURVGUkmIIuyFUhlAURVEUJZCSjSy8k0gjCidPUqgK+eGrbgorx7MuLWO/e+MAeJQWi4owN5u1+BOWLuReOPTvWkJVGRF+4wA8h/DLXXuon18nLM2WZDg9SuMxm2HR8wDpwqhiSS/KhPWyvIwshTEK73f54ciQOD8V1HDKsDRywywhO6QoNB6i5TIs0eQV/qMQL20uG6dwr9gPN5Ly0v7Zynb6kkAmZobFnRhVVuT3bdZAwAwFs1TFoX4ZIubwNX8mJQ567bKddRQ1HRKhfofGE2ZZhKs0yuZGfAFy+HmE33gp7wqjcLbDcgWHolPCGkfHatgla2r991k2AEwJh47B4f3Ixktst6QQuMcSi7zI+Dzy+aHlnNphMGD7ZotvxTRkkG3bYMDfW2pS5bAEII+nGHkrqNQrn1+ew4ZGczlqAubtIImEJZ8KId/w7y9bWPncR81Kl05FZtewBvCfsSHshuj1yJ9++mmceuqpGDNmDBzHwX333Wd87nkerrjiCowePRrl5eWYOXMm3njjjf4ar6IoiqLsHXrcEH15DFJ6fbPQ2dmJww47DIsWLSr4+bXXXovf/va3WLJkCZ5//nlUVlZi1qxZiMtCMYqiKIoymOiJLPTlMUjpdfzm5JNPxsknn1zwM8/zcMMNN+Cyyy7DV7/6VQDA7bffjlGjRuG+++7DmWeeWfR+vFxQXGTBW5bn0HpSZMey3MAuA77Jk/d7IVrO1khKNnGy9fxhN4Un3QOWfVbTBsojZqiT95Om0HiSQuhSVuFxswpgyCDieEK0DjsgYlF/PCwhAEBZHfWfpzGUU2VFDucDpqSQiPufpclxIBtJZbhEJQ0hHCBBJVOFpYvUNj+cHqoym/xERvohYh53hipSyuPxEn6Y3FbJ0wmL8DWHhTmUbKsCCNhLi8rleBXOSOdtS+mCx8Dha962dCXIBkm59cnlMHy48ZHRQEg2CupZR4TTWaoywvEdwgXC2OaEs+3l/lmaIUmArzBPSjE8j+x6YJeEbPBkq1hoa/YEmOeny1KNUUoKNgcOLyeXYbcHfWZcR8NHWLfndJALhSWFSiFDVFoqafL6CfMPTq9lpzRjNB1T9hj9epvzzjvvoLm5GTNnzsy9V1tbi6lTp2LNmjUF10kkEmhrazMeiqIoilJy9Lgh+vIYpPTrzUJzczMAYBQnTu163fOZZOHChaitrc09xo0b159DUhRFUZT+wXX7/hik7HU3xIIFCzB//vzc67a2NowbNw5VrouY4+QVJ6qhcHgtNVHi7H95OjiEXUbh56DTZmskxciIt2ze1EOEQ94i2z5GFw+PrbrMPzXRmBlCTVIRoxAlwbPTIhxwdNyIih0HeXVSaK5Zbqir9UO3FQ1mONEoykTheI/Co/G4mdUftxRlMrYl8uhdmvwIXQdl5MZg94NcjkPZ3R1+eLRSyAPhfThz3g+PZtv88HPqIzP8nenww6Uh+BncHs01N4vaNXDaqaV4kwyly2z33IBShd+X2+BQdnd3/rI9cAicw9wVFfnLFhobNwAqF829KilUn6XrgC9GWciMX9saN+UVWKL9VtfScgGFnPhYedwsndikF5hOCy/oHwmWHnjcLTv855XmvKHVD8875XTtVAS4OxjbvJWZjgOjUBZvj50JEVmUiT4b7jth0EmSDTe8Ettw6hpyz70QnZ8dFkdIkIND6Tf69WahsXGnhWbr1q0YPXp07v2tW7fis5/9bMF1YrEYYgFfOEVRFEUpDfoqJagMAQCYMGECGhsb8fjjj+fea2trw/PPP49p06b1564URVEUZWBRN0TxdHR04M0338y9fuedd/Dqq69i+PDhGD9+PObNm4ef//zn2H///TFhwgRcfvnlGDNmDE477bT+HLeiKIqiKANEr28WXnrpJXzxi1/Mve7JNzj33HOxbNky/PCHP0RnZye+/e1vo6WlBZ///OexcuVKlEkt7OMGtitfoUxYAMvpNVsi02B928SwrFmey3VYJY3SkpynICsrJgIqINrgPIM4DcIlC2FMJENwBUS2O/JwwiLaFYmwlu9vm3MWaqpN7ZGtizHKm6gaQ5XyZEXKNl+v76YmTNy4SUbxuLEU5xVE6ASnM+Z+ohGyllYJzbRnffHao8ZjWYsHNxM3qwXy8YRJ43erfC031GWuk9rm5zDw/ERillwEwN6AKMiqaGsaJL9rlsZLxn7kMrb9ckVJOYl8TJTfwZZGR9oGbVUoyQ7nBeUssKZOY/Zkwyob2/3qh3nWSd7PSErapqZHjsgj8bIt/gvKgTCWk1a/br9pF+rIhsi6vqzeyeeebYyc2yC1fN4G56jwcrIpFFtTaT4c+i4ZOSkAwHkodfX+c24WJaty0hx4GZo3qvTocf4DAKdz53XlRAewhs8Q7g3R65uFGTNm5P0DwTiOg6uuugpXXXVVnwamKIqiKCVFXx0Ng9gNMXhHriiKoijKgLDXrZM24lmvYJvyWormcXQ+GWCdZMGBw/5cTTEtoiVGlTau+kifxIUNkrcR46qNXCBNHBNXnkxQaI/HmRJjK88UthSyLOKKHcVi/qxEo/7z7m4/NBkS2kV5uR+WDZM84JT576c/NG2DCQrJ8xjYBiktjVx1kRtGsfRQJuyjsXLLpWs0yTI/yhpNpooLB2Y6/RCnS3ZWt4IskWJHHo3boyZVXtIP43rdZujUqOjIIWsON0srnK26o5QUOPycsdgTZXjUZou0SSSAWe2PZQDevww/26yLHJqXkgJtz2aJdMR3s6faHwDgw63+807aj5BvjCqFbGNM+ufOkyF43iePmxvQdYmqj1Ka6YGrS8rzw42chlFjKq6MKKQLr50K3rGcxFZQIUNwYyqj2iafR2mD5ONhW2ia3pcOOG4sxfPL5zEh7L31uyyWXQG23/5GZQhFURRFUQJxnD52ndSbBUVRFEX5ZKORhdJFhvq76XUdhcZltjzD8oBpLKCKh+IkGiF0ep/lgYTIBs8ay9ELw0Fh3pWGHH9BXodXlzJEmKPH9H7UKSx9AKbjoLrKD/1yOJ6XAYDKSn85bhDlUshcNlGKknSRSfph0CA3BFdd5HlnB4YjVvIy7Gzwt82XS1Y6KEh+CZdTeJW2hYw5BxlydDhR/+tifHHENeqS64G3ndzqV90Lx8vNdSr8sCyv78YoTC8njmUJlgRsVg/5mVGRr7CjBICZOc8yhAwlGxn/liqH8sK0/Hh6HEKX6/BY+fvElQSjYmwsQ2yj53xs3PgJgGdUoaTlOIQflLDGx8ZODVlhk2UVPlaWYmQDunpyGdRRcy52Hwh3h7PtA38MfK6C/gGjaozGUiwvSBmFz0mYG5KRZCT12FrfBeK4NFdxmoMK0XyqYtf56rRLQUr/UfI3C4qiKIpSEvS1sNJQKsqkKIqiKEMS18mPivR2/UFKyd4suCjs62RZQjoY/PeDXxdCFjHifdtWl6FxDiGyS4HXlw2RzH1y8SdqlBQQJuTPWBDoFIVshlMIPkTyTZiKG6WFS4FfGxn+3LCqxgynZ7sp+58kihAVhfI8M0s7TJPPc+pQI6tsWsgDfB3QOHmqIhHzCorWmmPNjSdLywnpgmUEdjZkaGw8TgAI0ZywyyHbVTg7HgA8kmw8koaM5lOeyPrmcDyH+qU8YJMe2PEgHRQcaufnLA/I659C8h5vz5A7xNgs42bHgCddChxC5/V5P7KIUUdH4c/YTSGLWfH8smuC50O6RliW4Of8WyXdHSRLsPPKOG/yt27Up/zno8fnnjoUzodnfmc8CuMbTaHoXHlx06XjdZLbicbg1JL0IWWIKmrUxdJBud0W5gzzi1453IiKnBG2hlFOrKPg+0r/UrI3C4qiKIpSUqgMoSiKoihKIEPYDTF4b3MURVEURRkQSjay0JNHEg24E9tCFfHChsZvLpelXAC+O2LZLCo0NN4eF/tjSTsb0CODP2LrY0T0M+dXnKcwnO1zYmxcAbGiwl+uvdPXPrN5DjXKbUgXbhAVrQi4HCynQer1TtTfXrjM1zI5lyEaNbVUI0+BNH4zZ8Gs/Je1WDGjtP9Q1DwetiHa7JKebCmWLqyz8vpuman3h4dR5T+243EeidCG01v9CoEujduJFPkV5RwBaedj7ZvzAqponKKyorUREx2PI6yGhp2Omw7xeGSzJr5QWWOn5Rz5PWONvJKOIUb737rZXIePh2yHTi3p6zVUCVHuh22HbHUcPhzFYNglZSVOHhs/5/Mm7ZacyzLMb7DkVPvj8dLmOkZV2gw3EfNzM5z2VhhkqeooNQQzGnBlRaMvDrXTOXXqR/vvV5jXjlNNcx+ma5S/9wnTIum17bLApopsGtYfqAyhKIqiKEoQjuPkJbb3dv3ByuC9zVEURVEUZUAo2cjCsHAIZY6TZ2lso/BvmEI6LD2UibA93xHx9lhqkBUc2eIYpi14YHukCW/DpecsV4hgoiGz8LiHDfNDcZGweU/X2SVsYbvgZlpSVkmQZMO2Q65+WFtn2trKG/wQb7TBDxtycxdPVDw0JBNezvHHbFQlFNvgqo98SjKySiJpQxGSLtwKCqkKicTzCts/Dc1IWCddkjW4gqMbYalANFRi2KbHVrykGfZ3yObp1FKIlm11smEOhakNO6GUIcopPC9tlT3bDrDzGfZAthpyRT7ADDnHyP7GlRVlCJ5fczib15F/jbHcwGPIkP1UNDdyRvnWPDSO9Z+zlVOG06kxFcjK6Yxq9N9voNC6pJPW4fel5LP9I9oPhdqN61KMjWQsr52aXPG2q4WswpZEtlhSxcQ8GyTbJWPb/fe5gVZeBUc62iRdV1TN0amkuQWAcr7mSaIjKcWTMkR3x67/d2LAUBlCURRFUZRAhvDNwuAduaIoiqIMJI7jZ9/vzqOXOQsLFy7EkUceierqajQ0NOC0007Dhg0bcp+/++67uTwK+bjnnntyy7344os4/vjjUVdXh2HDhmHWrFn4n//5n16NpWQjC1UhB+WOkxfqdzMU3jcaQfn3PVKG4Ndc8TCosCNHANO0ZJJCv7KCZDmFf2OW/YTEtcIOiBitH6WwdEWV2eSHQ/1dJEnwnV+lcAJESKdJpagZFskTnZ2mSFLBx8CSAldTlLYLIiuaTOXWF9IFuxFs3yVZJZWdDtx4KUgSMKokWsbtiXPKTbNYenCpYZaUVQzpgRsQsfQg9hOqplAuuwza2vxVMma41UtRhUyH9iNlCFllsGf9BIV7u4XEwdUMSSpwqmlsUtKI0zY4hM9OjTJRRZND4BxCjwQ0tmLpgh0UHPaX1RiH+44B4yLjcHpSSjGciU+fcbOmWuGG4G3zc65k2ErhfABgGYIdECwtCRnC+2irvxuqcujx2EY0wqCSnCzc4IkkCU9KF3wt8zkhWQajSNYBgBA5WbgRVQU5V6KiKqdhafKvEY+kpTyZqGuXO6P7k9tIavXq1WhqasKRRx6JdDqNSy+9FCeeeCJef/11VFZWYty4cdiyZYuxzq233or/+q//wsknnwwA6OjowEknnYSvfOUruPnmm5FOp3HllVdi1qxZ2LRpEyKyequFkr1ZUBRFUZSSYoBliJUrVxqvly1bhoaGBqxbtw5f+MIXEAqF0Nho3hSuWLECZ5xxBqqqdt6g/uMf/8D27dtx1VVXYdy4cQCAK6+8Eoceeijee+89TJw4saixqAyhKIqiKMXQU8GxLw8AbW1txiNhq2siaG3dWQdjuKW+x7p16/Dqq6/iggsuyL13wAEHoL6+HrfddhuSySS6u7tx22234cADD8S+++5b9KGXbGQh4wGFcv6lO6IQ0gnA0gMHwDkQLMPPvG+eJA6EBd1pFdtcjN0VKQqNp7gYUIDO1dXlSwdGUafhZoi4ptoPIX7woR8urqr0jy4lOm7ZHAPcECkbN6WLLIX62VlgFDGK2i87Dq3z/qW4YEgPPNlcYEk0xmJZxAloBMWYy1EhHJY7AopzGZ9xISYpDXBIn+WKTOExA4BDzcGMa0QUfDKaIDEBYW5DOrCFKeU6xuDYdhQp/BwwZQgZZrZhOD/oOReZatlmrsPFhjgEzlKDaLxkhNoZllKEswFt5ExgiYKvg3ZfWgJgShx0XTjkYvHa23kN0/3CcoMhsYjxc4Elkhucyjr/uXSr8LjJOeLRtpy6BtgwmkKVUyOrqClHGb81XDCK30+K67rn+KSENgjo+Qu/hyuvvBI//elPA9fJZrOYN28epk+fjsmTJxdcpucm4Jhjjsm9V11djaeeegqnnXYafvaznwEA9t9/fzz66KMIy/MdwB6LLCxatAj77rsvysrKMHXqVLzwwgt7aleKoiiKsudxHF+K2K3Hzpu5TZs2obW1NfdYsGDBx+66qakJ69evx5133lnw8+7ubixfvtyIKvS8f8EFF2D69OlYu3Yt/vKXv2Dy5Mk45ZRT0N2LG609Elm46667MH/+fCxZsgRTp07FDTfcgFmzZmHDhg1oaLDfhSqKoihKydJPjaRqampQI8ulBzBnzhw89NBDePrppzF27NiCy9x7773o6urCOeecY7y/fPlyvPvuu1izZg3cXcnPy5cvx7Bhw3D//ffjzDPPLGoMeySy8Otf/xoXXnghzj//fBx00EFYsmQJKioq8Pvf/35P7E5RFEVRPnF4noc5c+ZgxYoVeOKJJzBhwgTrsrfddhu+8pWvYOTIkcb7XV1dcF3XcJz0vM5mpd/QTr9HFpLJJNatW2eEVVzXxcyZM7FmzZq85ROJhJHc0bbLKtaaySLhOKgWOm056dNcJZHzFOQdUDHTIVVntkXamlnJqo+cT8H5A7xUJkDe5s2FuFGM2E93t58X0JXwtcNyqjZYXWVqw+X7+RGdxhjpqqTDd+8wQ1KcW5Bp9/XC9A7fqiRtkDL3oxCu8I963BSKjsGoBinmgPV7Y5+U98FVEfP2y8PkdeS55uuP8xfYViqtoHzubFY4qRVyngLZJY0KeqNEVI5tbax7y5wF1ru5iiTlIjjUXClvrJzBLTV62zq25/K4yWaXp0n34IqMFa7gyBZNng9paeQqf7Y8BZlPwctxU6bmfxXeP2D/q5PyH7xtH5qf8W9FPf3QV/jVKR3xo84VNx0eG1tEQ+J4+DywdTjL15GoMcvbYBskvy9zTUJ0jnk5Oo+ebP7E2+DrIMWVKlvMdXryULos182eYIDdEE1NTVi+fDnuv/9+VFdXo7m5GQBQW1uLcsppefPNN/H000/j4YcfztvGCSecgEsuuQRNTU2YO3custksrrnmGoTDYXzxi18seiz9Hln46KOPkMlkMIrLqwIYNWpU7kCZhQsXora2NveQiR+KoiiKUhL0pSBTz6MXLF68GK2trZgxYwZGjx6de9x1113Gcr///e8xduxYnHjiiXnbmDRpEh588EH89a9/xbRp0/Bv//Zv2Lx5M1auXInRowPKlQv2uhtiwYIFmD9/fu51a2srxo8fj8Suv6gi4i9V7rPAdzr812XEM0+Ia+mvHPQ3cMZwAvhPk5b9A4DDCb30h0BQZCFk/PHsv2inTHNPFDdqp413cpGorL9+m1gnTS6FBH9GcxWXhV9ShXtQpGn93YkshNKiZwNPCs9HUGSBi0TZ+jyIsRiFmCyRhbyx0hyEaQ5DdOI8eVLZpGBsmx0C4i+4ZKrwcz6GhPirns9jwrI+YJxjxGkbLjlK0iI6Yoss8F/iaXkxcwSAez7Q21nxXQzRNca9L7rpr0XpuuDlXPrLleejW2bO01+ytuMJi/1wfwz+6z3M0SKxH75Oy2icNGYvLs8j9UMxxk3z2W3+Je5RJMrhv6xjVKAoKLJAx+B4PB/ynNI22KXA70s3EV9XfClSAbi8sXFkgXtDUAQi2ymKL+067rZdc1bMb89go9hjuvrqq3H11VdbPz/hhBNwwgkn9Gks/X6zMGLECIRCIWzdutV4f+vWrXnFIwAgFoshRraxHhni+vSuUKzsvDRU+PDjFwnkffH6hX/0fhuv9HEMiqIoA0R7eztqa2s/fsG+MIR7Q/T7zUI0GsWUKVPw+OOP47TTTgOw0x/6+OOPY86cOR+7/pgxY7Bp0yZ4nofx48dj06ZNvcoa/STR1taGcePG6RzoHOgc6BzoHKDwHHieh/b2dowZM2bPD6Cf3BCDkT0iQ8yfPx/nnnsujjjiCBx11FG44YYb0NnZifPPP/9j13VdF2PHjs1FGHprMfkkonOgcwDoHAA6B4DOAZA/B3s8otCDRhb6l69//ev48MMPccUVV6C5uRmf/exnsXLlyrykR0VRFEVRSp89luA4Z86comQHRVEURRkUqAxResRiMVx55ZVG8uNQQ+dA5wDQOQB0DgCdA6AE5mAIyxCO90n0myiKoihKP9HW1oba2lrseP5R1FRVfvwKtu10dGLY1FlobW0ddHknJRtZUBRFUZSSwnV3Pvqy/iBFbxYURVEUpQgcx8kvCd/L9Qcrg/c2R1EURVGUAUEjC4qiKIpSDI7TxwRHjSz0K4sWLcK+++6LsrIyTJ06FS+88MLeHtIeY+HChTjyyCNRXV2NhoYGnHbaadiwYYOxTDweR1NTE+rr61FVVYXZs2fnldP+JHHNNdfAcRzMmzcv995QmIP3338f3/jGN1BfX4/y8nIccsgheOmll3Kfe56HK664AqNHj0Z5eTlmzpyJN954Yy+OuH/JZDK4/PLLMWHCBJSXl2O//fbDz372M6M+/idxDp5++mmceuqpGDNmDBzHwX333Wd8Xswxb9++HWeffTZqampQV1eHCy64AB0dHRgsBM1BKpXCj370IxxyyCGorKzEmDFjcM4552Dz5s3GNgZkDnqsk315DFJK7mbhrrvuwvz583HllVfi5ZdfxmGHHYZZs2bhgw8+2NtD2yOsXr0aTU1NWLt2LVatWoVUKoUTTzwRnZ1+S92LL74YDz74IO655x6sXr0amzdvxumnn74XR73nePHFF3HLLbfg0EMPNd7/pM/Bjh07MH36dEQiETzyyCN4/fXX8atf/QrDhg3LLXPttdfit7/9LZYsWYLnn38elZWVmDVrFuKyJfUg5Ze//CUWL16Mm266CX//+9/xy1/+Etdeey1uvPHG3DKfxDno7OzEYYcdhkWLFhX8vJhjPvvss/G3v/0Nq1atwkMPPYSnn34a3/72twfqEPpM0Bx0dXXh5ZdfxuWXX46XX34Zf/7zn7FhwwZ85StfMZYb7HNQ8nglxlFHHeU1NTXlXmcyGW/MmDHewoUL9+KoBo4PPvjAA+CtXr3a8zzPa2lp8SKRiHfPPffklvn73//uAfDWrFmzt4a5R2hvb/f2339/b9WqVd6xxx7r/eAHP/A8b2jMwY9+9CPv85//vPXzbDbrNTY2ev/1X/+Ve6+lpcWLxWLe//k//2cghrjHOeWUU7xvfetbxnunn366d/bZZ3ueNzTmAIC3YsWK3Otijvn111/3AHgvvvhibplHHnnEcxzHe//99wds7P2FnINCvPDCCx4A77333vM8b8/PQWtrqwfAa3n5KS/7xku7/Wh5+SkPgNfa2trnMQ00JRVZSCaTWLduHWbOnJl7z3VdzJw5E2vWrNmLIxs4WltbAQDDhw8HAKxbtw6pVMqYk0mTJmH8+PGfuDlpamrCKaecYhwrMDTm4IEHHsARRxyBr33ta2hoaMDhhx+O3/3ud7nP33nnHTQ3NxtzUFtbi6lTp35i5uCYY47B448/jn/+858AgP/5n//Bs88+i5NPPhnA0JgDSTHHvGbNGtTV1eGII47ILTNz5ky4rovnn39+wMc8ELS2tsJxHNTV1QEYwDkYwjJESSU4fvTRR8hkMnk9JEaNGoV//GM3WiwPMrLZLObNm4fp06dj8uTJAIDm5mZEo9Hcl6KHUaNGobm5eS+Mcs9w55134uWXX8aLL76Y99lQmIO3334bixcvxvz583HppZfixRdfxPe//31Eo1Gce+65ueMs9N34pMzBj3/8Y7S1tWHSpEkIhULIZDL4xS9+gbPPPhsAhsQcSIo55ubmZjQ0NBifh8NhDB8+/BM5L/F4HD/60Y9w1lln5QobDdgcaJ0FpRRoamrC+vXr8eyzz+7toQwomzZtwg9+8AOsWrUKZWVle3s4e4VsNosjjjgCV199NQDg8MMPx/r167FkyRKce+65e3l0A8Pdd9+NO+64A8uXL8fBBx+MV199FfPmzcOYMWOGzBwowaRSKZxxxhnwPA+LFy/e28MZUpTUbc6IESMQCoXysty3bt2KxsbGvTSqgWHOnDl46KGH8OSTT2Ls2LG59xsbG5FMJtHS0mIs/0mak3Xr1uGDDz7A5z73OYTDYYTDYaxevRq//e1vEQ6HMWrUqE/8HIwePRoHHXSQ8d6BBx6IjRs3AkDuOD/J341LLrkEP/7xj3HmmWfikEMOwTe/+U1cfPHFWLhwIYChMQeSYo65sbExLwE8nU5j+/btn6h56blReO+997Bq1SqjXPKAzcEQliFK6mYhGo1iypQpePzxx3PvZbNZPP7445g2bdpeHNmew/M8zJkzBytWrMATTzyBCRMmGJ9PmTIFkUjEmJMNGzZg48aNn5g5Of744/Haa6/h1VdfzT2OOOIInH322bnnn/Q5mD59ep5l9p///Cf22WcfAMCECRPQ2NhozEFbWxuef/75T8wcdHV1wRVh2lAohGw2C2BozIGkmGOeNm0aWlpasG7dutwyTzzxBLLZLKZOnTrgY94T9NwovPHGG3jsscdQX19vfD5gc9DTSKovj8HK3s6wlNx5551eLBbzli1b5r3++uvet7/9ba+urs5rbm7e20PbI3z3u9/1amtrvaeeesrbsmVL7tHV1ZVb5jvf+Y43fvx474knnvBeeuklb9q0ad60adP24qj3POyG8LxP/hy88MILXjgc9n7xi194b7zxhnfHHXd4FRUV3h//+MfcMtdcc41XV1fn3X///d5f//pX76tf/ao3YcIEr7u7ey+OvP8499xzvU996lPeQw895L3zzjven//8Z2/EiBHeD3/4w9wyn8Q5aG9v91555RXvlVde8QB4v/71r71XXnkll+lfzDGfdNJJ3uGHH+49//zz3rPPPuvtv//+3llnnbW3DqnXBM1BMpn0vvKVr3hjx471Xn31VeN3MpFI5LaxJ+cg54Z47Tkv++5fd/vR8tpzg9YNUXI3C57neTfeeKM3fvx4LxqNekcddZS3du3avT2kPQaAgo+lS5fmlunu7va+973vecOGDfMqKiq8f//3f/e2bNmy9wY9AMibhaEwBw8++KA3efJkLxaLeZMmTfJuvfVW4/NsNutdfvnl3qhRo7xYLOYdf/zx3oYNG/bSaPuftrY27wc/+IE3fvx4r6yszPv0pz/t/eQnPzH+QfgkzsGTTz5Z8Dfg3HPP9TyvuGPetm2bd9ZZZ3lVVVVeTU2Nd/7553vt7e174Wh2j6A5eOedd6y/k08++WRuG3tyDnI3C+vXeNn3XtvtR8v6NYP2ZkFbVCuKoihKAD0tqlvWr0VNddXub6e9A3WTjx6ULaoHsYCiKIqiKMpAoNZJRVEURSmGvjoaBrEbQm8WFEVRFKUYhvDNgsoQiqIoiqIEopEFRVEURSkKZ9ejL+sPTvRmQVEURVGKYQjLEHqzoCiKoijFMHQDC5qzoCiKoihKMBpZUBRFUZSiGLqhBb1ZUBRFUZRiGMI5CypDKIqiKIoSiEYWFEVRFKUYHPQxstBvIxlw9GZBURRFUYpi6OYsqAyhKIqiKEogGllQFEVRlGIYwgmOerOgKIqiKEWhMoSiKIqiKEpBNLKgKIqiKMWgMoSiKIqiKIHozYKiKIqiKMFozoKiKIqiKEpBNLKgKIqiKEXgOA6cPkgJfVl3b6M3C4qiKIpSDEM4Z0FlCEVRFEVRAtHIgqIoiqIUxdBNcNSbBUVRFEUpij7KEIP4ZkFlCEVRFEVRAtHIgqIoiqIUwxBOcNSbBUVRFEUpiqGbs6AyhKIoiqIogWhkQVEURVGKQWUIRVEURVECGboqhN4sKIqiKEpxDN27Bc1ZUBRFURQlEI0sKIqiKEoxaM6CoiiKoiiBDOGbBZUhFEVRFEUJRCMLiqIoilIUQzfBUW8WFEVRFKUYHPRRhui3kQw4KkMoiqIoihKIRhYURVEUpRiGcIKj3iwoiqIoSlEM3ZwFlSEURVEURQlEIwuKoiiKUgRtHR19khLaOjr6cTQDi94sKIqiKEoA0WgUjY2NGPeZg/u8rcbGRkSj0X4Y1cDieJ7n7e1BKIqiKEopE4/HkUwm+7ydaDSKsrKyfhjRwKI3C4qiKIqiBKIJjoqiKIqiBKI3C4qiKIqiBKI3C4qiKIqiBKI3C4qiKIqiBKI3C4qiKIqiBKI3C4qiKIqiBKI3C4qiKIqiBPL/Abh+aea+KzbPAAAAAElFTkSuQmCC", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# here we are choosing the first day at noon for the temperature data\n", "filename = filepaths[\"air_temp\"][0]\n", "data = read_pfb(filename)\n", "plt.imshow(data[12, :, :], cmap=\"Reds\", origin='lower')\n", "plt.title(\"air temperature\")\n", "plt.colorbar()" ] }, { "cell_type": "markdown", "id": "af064bdc", "metadata": {}, "source": [ "### 3.2 Visualize data that was subset using `gridded.get_numpy`\n", "Here we just visualize directly from the dataframe that was created in section 2.2. Note that you can also visualize your data from the numpy arrays directly and don't need to convert to a dataframe if you don't want to. " ] }, { "cell_type": "code", "execution_count": 14, "id": "e5732626", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(forcing_df['Temp [K]'], label='Temp [K]')\n", "plt.xlabel('Time [hr]') \n", "plt.ylabel('Temp [K]') \n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "1b93c3d1-8cc8-43dc-9b86-30a6d794864d", "metadata": {}, "source": [ "## 4. Cite the data sources\n", "\n", "Please make sure to cite all data sources that you use. We will use the `get_citations` function, which takes as an argument a dataset name and returns citation information about that dataset:" ] }, { "cell_type": "code", "execution_count": 15, "id": "a2b30185-9952-4a43-b5df-5641a33bab67", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Meteorological Forcing Data for Conus2 Grid\\n Source: https://cw3e.ucsd.edu\\n'" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hf.get_citations(\"CW3E\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.17" } }, "nbformat": 4, "nbformat_minor": 5 }