{ "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": null, "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_gridded_data` 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": 2, "id": "f472b1ca-7928-4e1c-8847-b024f15d307b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "bounding box: (1224, 1739, 1345, 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": null, "id": "d1acd0e9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Reading downward_longwave pfb sequenceReading precipitation pfb sequence\n", "\n", "Reading air_temp pfb sequence\n", "Reading east_windspeed pfb sequence\n", "Reading downward_shortwave pfb sequence\n", "Reading north_windspeed pfb sequence\n", "Reading atmospheric_pressure pfb sequence\n", "Reading specific_humidity pfb sequence\n", "Finished writing air_temp to folder\n", "Finished writing east_windspeed to folder\n", "Finished writing downward_longwave to folder\n", "Finished writing north_windspeed to folder\n", "Finished writing downward_shortwave to folder\n", "Finished writing atmospheric_pressure to folder\n", "Finished writing precipitation to folder\n", "Finished writing specific_humidity 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/write/directory\",\n", ")" ] }, { "cell_type": "code", "execution_count": null, "id": "d8e677e2-d563-4c44-a67e-ea5a1c398e0b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Reading air_temp pfb sequence\n", "Reading precipitation 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/write/directory\",\n", " forcing_vars=('precipitation', 'air_temp',)\n", ")" ] }, { "cell_type": "markdown", "id": "beff70fe", "metadata": {}, "source": [ "### 2.2 Subset data using `hf.get_gridded_data` function\n", "The [`hf.get_gridded_data`](https://hf-hydrodata.readthedocs.io/en/latest/hf_hydrodata.gridded.html#hf_hydrodata.gridded.get_gridded_data) function is a general function to extract any gridded data from HydroData. Here we illustrate how to use it to 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": 7, "id": "1b013646", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "bounding box: (4057, 1914, 4058, 1915)\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": 15, "id": "b575e0c8", "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", " \"dataset_version\": \"1.0\",\n", " \"variable\": var,\n", " \"temporal_resolution\": \"hourly\",\n", " \"grid\": \"conus2\",\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_gridded_data(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": 16, "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_gridded_data` 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": "iVBORw0KGgoAAAANSUhEUgAAAgsAAAGFCAYAAABzDbD7AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAACVVUlEQVR4nO2deZhU5bXu371r6rkbGpqGAErEiIoag4pITiSKojEmHrkxek0c4mNM0pAgzzEJxiHHJGI8JpooguYxEE/kOiU4XcWLE2oEB9QTiQlxhgiNCvTcNe/7B3Tt91tV37aabppqe/186rGGPXz727uK3etd71qO53keFEVRFEVRLLh7ewCKoiiKopQ2erOgKIqiKEogerOgKIqiKEogerOgKIqiKEogerOgKIqiKEogerOgKIqiKEogerOgKIqiKEog4b09AEVRFEUpdeLxOJLJZJ+3E41GUVZW1g8jGlj0ZkFRFEVRAojH46gvr0AX+l7DsLGxEe+8886gu2HQmwVFURRFCSCZTKILHs5GJaJwdn878HBHczOSyaTeLCiKoijKJ5EyOH26WRjMSYJ6s6AoiqIoReDCgev04WZhEHdiGsw3OoqiKIqiDAAaWVAURVGUInDRt7+wB/Nf53qzoCiKoihF4DiAu/sqxM5sh0EqRQzmGx1FURRFUQYAjSwoiqIoShGoDKEoiqIoSiCu00c3BKAyhKIoiqIon0w0sqAoiqIoRaAyhKIoiqIogbh9dEPozYKiKIqifMIZypGFwTx2RVEURVEGAI0sKIqiKEoROI4Dpw9uiD4oGHsdvVlQFEVRlCJQGUJRFEVRFMWCRhYURVEUpQjUDaEoiqIoSiAO+vYP/mDOWRjMNzqKoiiKogwAGllQFEVRlCLol94QgxS9WVAURVGUIlA3hKIoiqIoigWNLCiKoihKEagbQlEURVGUQIayDKE3C4qiKIpSBC4cuH0wQA7mm4XBPHZFURRFUQYAjSwoiqIoShFozoKiKIqiKIEM5ZyFwTx2RVEURVEGAI0sKIqiKEoRqAyhKIqiKEogOxtJ7f7dggOv/wYzwAzmGx1FURRFUQYAjSwoiqIoShGoDKEoiqIoSiBD2Q2hNwuKoiiKUgRDObIwmMeuKIqiKMoAoJEFRVEURSmCvveG6ENYYi+jNwuKoiiKUgQqQyiKoiiKoljQyIKiKIqiFIGz69GX9QcrerOgKIqiKEWgMoSiKIqiKIoFjSwoiqIoShEMZTeERhaUTxxPPfUUHMfBU089tbeHoijKJ4geGaIvj8GK3iwoSgEefvhh/PSnP93bwyhJbr75ZixbtmxvD0NRlAHE8Txv8PbMVJQCZLNZJJNJRKNRuO7u3Q/PmTMHixYtgn498pk8eTJGjBihkRtlyNDW1oba2lr8rqYeFc7u/43d5WVxYds2tLa2oqamph9HuOfRyILyicN1XZSVlX3sjUJXV9cAjah08TwP3d3de3sYJTMORQnC6YfHYEVvFpRBw3vvvYfvfe97OOCAA1BeXo76+np87Wtfw7vvvmssVyhnYcaMGZg8eTLWrVuHL3zhC6ioqMCll15acD/nnXceFi1aBABwHCf36CGbzeKGG27AwQcfjLKyMowaNQoXXXQRduzYYWxn3333xZe//GU89dRTOOKII1BeXo5DDjkkN64///nPOOSQQ1BWVoYpU6bglVdeyRtHVVUV3n77bcyaNQuVlZUYM2YMrrrqqryIR2/H9Oijj+bGdMsttwAAli5diuOOOw4NDQ2IxWI46KCDsHjx4rz1//a3v2H16tW5eZkxYwYA4Kc//akxTz0sW7YMjuMY5yloHC0tLZg3bx7GjRuHWCyGiRMn4pe//CWy2WzB86UoA4XrOH1+DFbUDaEMGl588UU899xzOPPMMzF27Fi8++67WLx4MWbMmIHXX38dFRUVgetv27YNJ598Ms4880x84xvfwKhRowoud9FFF2Hz5s1YtWoV/vu//7vg58uWLcP555+P73//+3jnnXdw00034ZVXXsFf/vIXRCKR3LJvvvkm/vf//t+46KKL8I1vfAPXXXcdTj31VCxZsgSXXnopvve97wEAFi5ciDPOOAMbNmwwIiKZTAYnnXQSjj76aFx77bVYuXIlrrzySqTTaVx11VW7NaYNGzbgrLPOwkUXXYQLL7wQBxxwAABg8eLFOPjgg/GVr3wF4XAYDz74IL73ve8hm82iqakJAHDDDTdg7ty5qKqqwk9+8hMAsM7jx1FoHF1dXTj22GPx/vvv46KLLsL48ePx3HPPYcGCBdiyZQtuuOGG3dqXoih9xFOUQUJXV1fee2vWrPEAeLfffnvuvSeffNID4D355JO594499lgPgLdkyZKi9tXU1OQV+no888wzHgDvjjvuMN5fuXJl3vv77LOPB8B77rnncu89+uijHgCvvLzce++993Lv33LLLXljPvfccz0A3ty5c3PvZbNZ75RTTvGi0aj34Ycf7vaYVq5cmXdsheZ31qxZ3qc//WnjvYMPPtg79thj85a98sorC87Z0qVLPQDeO++887Hj+NnPfuZVVlZ6//znP433f/zjH3uhUMjbuHFj3vYVZU/T2trqAfCW1o7w7qpr2O3H0toRHgCvtbV1bx9Sr1EZQhk0lJeX556nUils27YNEydORF1dHV5++eWPXT8Wi+H888/v0xjuuece1NbW4oQTTsBHH32Ue0yZMgVVVVV48sknjeUPOuggTJs2Lfd66tSpAIDjjjsO48ePz3v/7bffztvnnDlzcs8dx8GcOXOQTCbx2GOP7daYJkyYgFmzZuXth+e3tbUVH330EY499li8/fbbaG1tLXqOiqXQOO655x7827/9G4YNG2Ycy8yZM5HJZPD000/3+zgUpViGcs6CyhDKoKG7uxsLFy7E0qVL8f777xu6fTH/mH3qU59CNBrt0xjeeOMNtLa2oqGhoeDnH3zwgfGabwgAoLa2FgAwbty4gu/LHAPXdfHpT3/aeO8zn/kMAORyAHo7pgkTJhRc7i9/+QuuvPJKrFmzJi/5s7W1NTfG/qLQON544w389a9/xciRIwuuI49FUZSBQW8WlEHD3LlzsXTpUsybNw/Tpk1DbW0tHMfBmWeeWVTyG//lvLtks1k0NDTgjjvuKPi5/EcuFAoVXM72vrcbVs3ejqnQPLz11ls4/vjjMWnSJPz617/GuHHjEI1G8fDDD+P6668van4LJTcCO/MuClFoHNlsFieccAJ++MMfFlyn50ZJUfYG2khKUQYB9957L84991z86le/yr0Xj8fR0tLS7/uy/cO333774bHHHsP06dP75ebj48hms3j77beNfyT/+c9/AtjpKOivMT344INIJBJ44IEHjGiIlDAA+9wMGzYMwE43Q11dXe799957r+hx7Lfffujo6MDMmTOLXkdRBgrpjOr1+oP4dkFzFpRBQygUyvvL+8Ybb7T+5doXKisrASDvRuSMM85AJpPBz372s7x10un0Hrlxuemmm3LPPc/DTTfdhEgkguOPP77fxtQT6ZDSztKlS/OWraysLLjN/fbbDwCMvILOzk784Q9/+Nj993DGGWdgzZo1ePTRR/M+a2lpQTqdLnpbiqL0HxpZUAYNX/7yl/Hf//3fqK2txUEHHYQ1a9bgscceQ319fb/va8qUKQCA73//+5g1axZCoRDOPPNMHHvssbjooouwcOFCvPrqqzjxxBMRiUTwxhtv4J577sFvfvMb/K//9b/6bRxlZWVYuXIlzj33XEydOhWPPPII/u///b+49NJLc/JCf4zpxBNPRDQaxamnnoqLLroIHR0d+N3vfoeGhgZs2bIlb24WL16Mn//855g4cSIaGhpw3HHH4cQTT8T48eNxwQUX4JJLLkEoFMLvf/97jBw5Ehs3bizqeC+55BI88MAD+PKXv4zzzjsPU6ZMQWdnJ1577TXce++9ePfddzFixIjdm0xF6SMqQyjKIOA3v/kNQqEQ7rjjDsTjcUyfPh2PPfZYwcz+vnL66adj7ty5uPPOO/HHP/4RnufhzDPPBAAsWbIEU6ZMwS233IJLL70U4XAY++67L77xjW9g+vTp/TqOUCiElStX4rvf/S4uueQSVFdX48orr8QVV1xhLNfXMR1wwAG49957cdlll+E//uM/0NjYiO9+97sYOXIkvvWtbxnLXnHFFXjvvfdw7bXXor29HcceeyyOO+44RCIRrFixAt/73vdw+eWXo7GxEfPmzcOwYcOKdqFUVFRg9erVuPrqq3HPPffg9ttvR01NDT7zmc/gP//zP/s9yVJReoOLvoXjB3MoX3tDKEqJct555+Hee+9FR0fH3h6KogxpenpD3FE3ss+9Ic5u+XBQ9obQyIKiKIqiFIHj7Hzs9vr9N5QBR28WFEVRFKUInF3/9WX9wYreLCiKoihKEQzlBMfBnG+hKJ9oli1bpvkKijKEWbhwIY488khUV1ejoaEBp512GjZs2GAs09zcjG9+85tobGxEZWUlPve5z+FPf/pTwe0lEgl89rOfheM4ePXVV3s1Fr1ZUBRFUZQiGOjeEKtXr0ZTUxPWrl2LVatWIZVK4cQTT0RnZ2dumXPOOQcbNmzAAw88gNdeew2nn346zjjjjLyW9wDwwx/+EGPGjOnlKHZScjJENpvF5s2bUV1d3adKWYqiKMonH8/z0N7ejjFjxhjt3fcELgC3D/8sub30Hq5cudJ4vWzZMjQ0NGDdunX4whe+AAB47rnnsHjxYhx11FEAgMsuuwzXX3891q1bh8MPPzy37iOPPIL/9//+H/70pz/hkUce6fXYS+5mYfPmzXlNdhRFURQliE2bNmHs2LF7exhF0dbWZryOxWKIxWIfu15Pw7zhw4fn3jvmmGNw11134ZRTTkFdXR3uvvtuxONxzJgxI7fM1q1bceGFF+K+++5DRUXFbo255G4WqqurAQB/m3YQqsMhhCrNLoHh2src83SrH4rp/qA997yz0ywJm0j65YDTKb8hTpbu8qqrIsY6rW3J3PN3u+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", "text/plain": [ "
" ] }, "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 `hf.get_gridded_data`\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": 17, "id": "e5732626", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "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": "hydroframe", "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": "undefined.undefined.undefined" } }, "nbformat": 4, "nbformat_minor": 5 }