{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#PyTDA Demo\n", "\n", "Author
\n", "Timothy Lang, NASA MSFC
\n", "timothy.j.lang@nasa.gov\n", "\n", "Overview
\n", "PyTDA is a Python module that allows the use to estimate eddy dissipation rate (a measure of turbulence) from Doppler weather radar data. It interfaces seamlessly with Py-ART to make this calculation as simple as one line of code.\n", "\n", "To get started, you need the following to be installed:\n", "\n", "\n", "PyTDA is currently a pseudo-package, so you need to add the code location to your PYTHONPATH. Then you need to compile the Cython code using the provided compile_pytda_cython_code.sh script. Then you should be good to go for using this demo. PyTDA is tested and works under Python 2.7 and Python 3.4. If you switch between the two, you need to recompile the Cython code and recreate the shared object file after doing so." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from __future__ import division, print_function\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import os\n", "import glob\n", "import pyart\n", "import pytda\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So PyTDA is very simple to use. First you need a Py-ART radar object:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['cfrad.20101026_151323.000_to_20101026_151734.000_KGWX_v284_SUR.nc', 'file.nc']\n" ] } ], "source": [ "files = glob.glob('*.nc')\n", "print(files)\n", "radar = pyart.io.read(files[0])" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dict_keys(['DZ', 'VR', 'SW'])\n" ] } ], "source": [ "print(radar.fields.keys())" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.890625\n" ] } ], "source": [ "print(radar.instrument_parameters['radar_beam_width_h']['data'][0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once you have that, send the radar object as an argument to the desired PyTDA function. There are two: calc_turb_sweep and calc_turb_vol. The former returns the turbulence field as an ndarray. The latter just successively calls the former to process a volume and modify the radar object and add the turbulence field. Currently, PyTDA only works on PPI sweeps/volumes." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "pytda.calc_turb_vol(radar, name_sw='SW', name_dz='DZ', verbose=False,\n", " gate_spacing=250.0/1000.0, use_ntda=False,\n", " beamwidth=radar.instrument_parameters['radar_beam_width_h']['data'][0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The default is to do fuzzy-logic-based QC on the turbulence retrievals, to ensure better quality. This is like what the NCAR Turbulence Detection Algorithm (NTDA) does. However, there is an option to turn that off and just have straight inversion of spectrum width to EDR. You can use the verbose keyword to turn off the text, I just turned it on to show more of what was going on. See the documentation below to find out all the different keyword options.\n", "\n", "That's about it to do the processing. Usually takes about a minute or two per volume. Now let's use Py-ART to plot the results! First, let's define a function to do easy multi-panel plots in Py-ART." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def plot_list_of_fields(radar, sweep=0, fields=['reflectivity'], vmins=[0],\n", " vmaxs=[65], units=['dBZ'], cmaps=['RdYlBu_r'],\n", " return_flag=False, xlim=[-150, 150], ylim=[-150, 150],\n", " mask_tuple=None):\n", " num_fields = len(fields)\n", " if mask_tuple is None:\n", " mask_tuple = []\n", " for i in np.arange(num_fields):\n", " mask_tuple.append(None)\n", " nrows = (num_fields + 1) // 2\n", " ncols = (num_fields + 1) % 2 + 1\n", " fig = plt.figure(figsize=(14.0, float(nrows)*5.5))\n", " display = pyart.graph.RadarDisplay(radar)\n", " for index, field in enumerate(fields):\n", " ax = fig.add_subplot(nrows, 2, index+1)\n", " display.plot_ppi(field, sweep=sweep, vmin=vmins[index],\n", " vmax=vmaxs[index],\n", " colorbar_label=units[index], cmap=cmaps[index],\n", " mask_tuple=mask_tuple[index])\n", " display.set_limits(xlim=xlim, ylim=ylim)\n", " plt.tight_layout()\n", " if return_flag:\n", " return display" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And now let's plot." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/tjlang/anaconda/envs/python3/lib/python3.4/site-packages/matplotlib/colors.py:925: RuntimeWarning: invalid value encountered in subtract\n", " resdat -= vmin\n" ] }, { "data": { "image/png": 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2Kfy188uVtG6tstbMtoptRekRSkSRM2aD1sYiwWvrrQDfHFNcyrKy24Zj767D\nOk9g+wJ8sp54if0s41nie7YJ/d7y6WaO+H79WekctE58uz+DOZHtPVj4Zcac+G4KfjLzqPBWFEVZ\nK3y35EzfYwuEmvvKH4m0uXXX95Y4z2bv9zLebqU8saR1KsCVsqjYVpQeod8UG/ZdbCpsO6yZHKcu\n2YailpEHqx541487UU16GfzbNlY3qdJpvgBvms6s86H4dkJ4qpEWyNAW4PuMZP+t+OL701eMt9d/\nky7h5R/7l1NNqthQdCfkI8Ib4Af3NHj5OesB+N7XO1acLzm6ZltRlG5lC7BVhINzhFm21W7jkp3G\nqkE4poFjI+f5mYb/zJtrRVIJ5mJr728U4UHv/fNUfJdmrZWPVbGtKF3AjqniL+lGX9KnL0d0xzza\nIe5Lrhocp0SUeqYIV7K5+FNjDMhcFDjQea93B95hPzTbebwhEeMDvkc7EMizzfRn+uMb02L3mBPS\nkyONYM3/gP+mkv6bnDu0faDwhXeTeOTFnpiMEX2QVBRlBdheos8W+3NrzvdU8SKy9sP6A962WLbx\nYyPbHqkicEH4AmmczvsdPm19PvIZqwCPs9bmyFVsK0oXMEHxF7LTRbM5HulGfYhmwf/qrJBf/TJY\nHuYSoXnlvvpIi2+C92Foti++Z4KkZD6h+A4JxTdkJ1ujmRb2c+fwz+0/SNgHjT89e332AJYBTZCm\nKMpKsKW4S7T8ZsixpEV0jJ+WOM4RlPNiq/heGOEz0o5gW2zy4/MiTHjvX6z3HlCxvWSIyEeBZwMP\nGGOOCdreAPwDsI8xZrvddi7wchInyeuMMdct19gUpduYLvHN0ywhKvrqySuk4a39DctOxah564qz\n6ibvGm/xret2R9ue9My8IiRrC7+mdioDudfmtvt9Y+Lb9WvgZSCPiG8/3D8rGmHr9tacmJ7x+ofe\n7vtaLZAOR3dybu+8HeXF9hBLHUYuIncCYyS2qG6MeYLX1mG7VhNqtxWlPGXEdpmFNDFBHoaLPxTp\nE+KLutj6b6U8ZeqVu4R2g5F9sva9JJgMWavieynNtogcDFxM8l/SAP9hjPlnr33F7fZyOrMuAi4k\nuQFz2JvyDODX3rajgBeSJFw8ELheRI4wxmQ85ivK6qJMzrKhEp0G4to3FR6cVRvZJ7V21ysrVRYn\nwreO5e/0wjNHyx+0R0k9bLVMR+KaakXmPpO+isyJXV+MO/zPLuWR9toOWFehXk8V1Z77zRfeQ33t\ntuFakqyDOu8UAAAgAElEQVTN4cS3f47wzy8mvl9z9obOjb2HAU4KjXLMdq1C1G4rSknKPEBnCTWf\nvHXYjnUUL/W6I7LtkBLHDrm2wDt+6ioXiF8O1mtD52ftt4dZ5Ae935/Pm7mYd2aeS8X3klAH/tIY\n8wMRGQG+KyJfNsbc2i12e9nEtjHmmyJyaKTpfcDfAF/0tj0HuMwYUwfuFJFfAE8A/me5xqco3cRw\njhWtV1yfYrG9YdvSxNQWCeu8UOX58OkrktVqq1V0X3DFTuiDQU+pTgLDqQLYho2ex9sX1L74Xj+Q\n/lwGqnGPtKvPPVcLe6YdWl6vt6MWqhVhqC8uyv2MZzUvUqLujc2/hIqBpx3Szy03JdVcj37iEHuK\nZVqyHTtqzHatKtRuK0p5BnPanJe5TBj5RHGXDgYi22LebH/GcML2+fIivzSdGF+tons9SWiTT5H4\n9gnF90t4c+q9iu+l9WwbY+4D7rO/T4jIrSRzWLfSJXZ7jy7TFJHnAHcbY34UJLU5gLSBvptkplxR\n1gS1nKRnrr720XvnZyd7YHeLkQzPts9NGxq8/xnZidQ+fcU4k9PFX/CTdUN/QS1lP7HXWsTNj0zH\n3MAeo4HbOC24k59DVjxPefHlfh1sJ77dvZ7x+vlrvp347qukxfxIf9uTPtQnTDWSzOcH91UYm2mP\nZ1errfid+H7aIem67E507wmWIRu5IfHSNoF/N8Z8KMd2rXrUbitKnLxQbde2HyAZgmnc/n/aWfJ8\neRnNvyuSW6cbkgf+IaDo23mQch751Uqd5D7Vgu2+AC/7mV3OO3mBMYj837ltofj2CYX4ahXfy7Vm\n204W/w5wczfZ7T0mtkVkGDiPxJ0/tzlnl+hf1Pnnnz/3+0knncRJJ520BKNTlJWlTJLvwYH8L4tD\nBqpseTDtIp9ewP/wF545yjVXJnPtI/3Z55xqmJTwG4pkXvPLVoUlrdYCfuj/lCe4d1fb65v3q1eY\nIZ0QrdlKr+/uq7QnLkZqkpq88D+D0aow6/0JuFJgk/V0He7hfsmITmh3GuozDMypfs+lPtPe0Rfe\njv/93jf49ve+ETv4sjAfG3r7WIvbxwofVp5sjLlXRDYDXxaR24BzgWf6p53vOHsRtduKkk2WiC7L\nqN0/jOsaj3ypjRac6y6KH+hH7Lnc+bKyoPtxSaGHdy3g3xf/s/EFeFmx7TDmHan3vvj2mY8XfCn5\n2te+xte+9rU9ci6Yn9j+sX0VYUPIrwBeT/LQMh/btayIWcZZEjvDcJUx5hgROQa4nvYSxoOA3wDH\nAy8DMMa82+73JeCtxpibg+OZ5RyvoqwUH72s2KQ95oBi5fylB2dLne+8M/fKbXfh3Y7RiOierMf/\nL/oCe6TWud9EZL/Tn7M6w8gv/PSu1PuZwMJMVQz7zbY3+pMu4dzFweuzzZMT31MN01F728eJ7w1D\n6T6+ZvZD1WveZ1n3Jku2TbU7jc3AYw7O/tt81JOGMaZEkfgFICLm35+48DnjP7mpkTs2EXkryVLJ\nP6fTdj3BGFOURLjnULutKL1HrOxU+M2Y54l3tb7Hgc2R9jDU/bGr9P/0DSIdeVXCjFqjwK/s73lL\nCQBeUOI+XSLCS/hHb8uOzL6haF8uRGRZ7fZVi9j/dOgYm4jUgP8ErjXGvD/Hdq2I3d5jnm1jzI+B\nfd17EfkV8FhjzHYRuRK4VETeRxKG9lvA/+6psSnKSvPyc5amXNJxHWZiYYwFgth/v94K6JmwdpVl\n0q7xHa4Jew/FvN35+4dcf3V8Rdspz+7ufKsXf2qMDQgz3mUOttIh5QMtSb3313Y3TVp8n3xa+npv\nuKZ9X3yvdTMw7r74nm3C5pHOz6RSYprZF957D/k7tObWaO/J8PHlwHpyq8aYcRFZR+LNfpsxJmq7\nVmqcewq124rSGxQ9zDdoC+oiHsxpiwnxGL1ac9plfd/H2zYIqSerO2jf7wadIfdFAtznEhFmgYt5\nQ2p7p/hOlmqJvANj4l7xtYokMeIfAX5qjHk/5NuulRjjcpb+ugw4EdhbRLYCbzHGXOR1mftfZ4z5\nqYhcTlJKsAG8RqfCFWXlmMyp4TTpMmRXErGYxeGjiSDLSqY2bL3lx5+ysDJhoQjvNvHtwsCHSWcV\nHzBtAX5ffysVaj5TMQwEkdmDGfc4T3z7dIhv7wNpRhbT+8L78U9Lfzbf/mqSFMAX3vuNtnfwE6Pt\nKeG9xGu29wU+b9d39QGfjJSzWrW2Se22ovQmZcKapykuRVYkyIvWhefRSwI8LLXmi+8X8UYu5b1z\n70MhNd975DKe+HGJneL77XO/i6S9270ovsssnZwHTwb+CPiRiHzfbjvPGHOt12dF/9CWNYx8qdFw\nNEVZft72ucRs13Iykof6JlaW7PF7lZvLK6rJneXZDikS27fdnIi/I4/fM5my/VD8sHa1E9+/zrjJ\n/v18ysbO+1hmguKqL8ZX5B24V9zMxYR3KLZ9nPAu6rfc4WgfOmHhc8avujE/jFxZPGq3FWX5ucEK\n2TxrWaYuuP9Nnldq7NiC/9MfyVjOFIr5bhPbftmzrHvpPNkv4o2p7b4ABzhnAdfmJ0Rz4vuVvAvw\ns9+mE5L6LJXwXm67/aVF7P8sOsPIu509mo1cUZSVJVxDHMVmAKnneAxDjbjbhkL7u4QCE8rV+A6Z\nmE2OnZX5vH+eU6ROdIcstQj3h1utpjOyuyzgfgCBX9ltqiB7+c3Xp9POx8R3uA7+qi+OZwrtZIzp\n+3vciflLEvIE9p5EKj1lcxVFUZacUBjGpp6zwpt9T+xMpD1WZqyIImHfjckubgwyuvfRec/8+xyK\na198X8x7uURk3tnD/f5OeF/MubzYGETe7fUsJ767leXKRt6tqNhWFCVFM+dbsGoFdH/BpOKsmFTo\n9Nyx7f7VeQgkJ9obLRMV67NNeHDa8KHLxnjVIta+L6Xn+xtf2s2WdZW5iQKHn1SuWk3qUzsqJpXz\ne058t+9Z9vluvn53obfbF9/f+3pRMKGiKIrSDXw3J+mlS1SWF9flBGKWdezzfuYdp2wG7s8E442J\n/APsz2tFuqped2xNur+GvUFaOPnrtX3x7bYvpnRX2NeYN839ni28lW5ExbairCLKiK7F4IT4yKxQ\nl2yjMUBSpznL6+xClg/ap8ptN09lCtyPXjbGgOdxnWkm9aR9dniC9kMZWd0fvr7CppG2Wh0eXF5v\n6IhNDDcyJExMtcc30i9zAvy26Sb9CH4EfsO7p058u0RyA0F68jzxXUTote5l8b0MdbYVRVF6gu/O\no1Z3mawmeZk2nId7K/kiORQWsSRi/liuzZhMyLqmhYRnl+Fe7/f9vd99Ab6VzmsJr/cs3o4LAl+u\nutm+8IZEfIfbupm1ZrZVbCvKKsGFFochxj45+czmmOk31Br5HVu060RXMkoXNo1hylqlvHJUkB3a\n3QRmgrXEM3Yh2UBGWHkW2yda3u+kxPdS44v54UFhctoT3FaAt2y8nu/d7jdp8X14X2XuHk41DEPe\nN3Yovov4+bfb9/i3Hp+e3CgKGe9mVGwrirKaKVPrukwGbD8EOuvhv2hV1lbv9yyRHCM8X5Z3uAyX\nBeddKvHtl+kKvfNOfLuxFo8/Ce1+iSe8Yf51s10ytKK12L0ktEHFtqIoq5i8hCeOmRrM1Lzs2fXO\nPpMpoZv0HQ4O3vTLWNk+8/XGNiUZcyw5+mTTlM5oeft4k72DGPRtU81oaTKXkG0x2c37a+GWyEOJ\nzV8WmwBxAjyZWGhf/GyzvXY9FN/zIU949xq6ZFtRlF4lL0R8Pvhiu0w27JhA3IvO1b9hlpdwLXZs\n7XWReO4jnnwstLiDLC77+WKI1cf+jAgvCdZlO9KmuAb4D07tu+qL7/nUzF4NGch9VGwritKTNHOy\nhztmrYrry1lzXe+Lv695FjSWwGvKfnu6TNozMWXvhYE/YnM1OmZfkLes0GxJ3IOeN3ngC/TpCvym\n1WIwON+2ceZEeCi8Y1nQywrww44b4o7vtQVtKL7/69d1GsE99D8TJ8BnGoaZRujF7hTfp52x8IkB\nX3hD74lv9WwrirKacXFHed5rv3hwrF+NfA+5W889G2x31sDJxV8H7aNeH4cvtmNiuZExxmnK16x2\nxx0BrvImLE5fxvXfZ/FmLvc802cF2cgv5r1WVPXbl3834+K7CCeqQ7Hd6+J7rZltFduKskrwk29l\n4bTmrL82uOTxfRE+mZOedNKKwS0T8fHMeNa0FSk31WpCJRIi3sook+gSiYXe75hAn45crBPhe5fw\nk19/9cS8BHeIL8CHWkI9Y702wF6NCna5No26oW9OgKfFdxhmHyMU1IqiKMrKUybTt/v29uVaGDxV\n5AGeJhHGeecYCc4RG8OOSJvbtjHSljdBkCekw3XeoVgZjGyDtPB2LJUAd0L7LN7csc3RAC61NbJf\nxD96LaH4nh+hmA7FttLdqNhWlFXCtinDcEf4cpq8cl6OWokFVC3rlW1k6NNH7KgwYcVxLcOLPm0T\nhYUlp4C5OllZ68FjwrsZOwwmJWizmK4kXuKQ+ZYVK8IJ8IHbk0eaAWCm0r5P/lhbGGb9Bwfvkp34\nfsLDkw88L8kctL3Vq1F0q2dbUZTVTIFZB4rXbE/QFsVZD/57lTinCyOPhY/vsOMoU2Is9j4klmgt\nr325cSI7FNi++Aa41LY70e3wxbcxf7WosfSaJztkra3+UrGtKKuERgvGgiKZYamsZon/8SNT7a/B\nel+nqK01ZC4reb91lbeCkGhf1Ned6A5CuOsN99NQyxhXv4mHilcRmpJO1OaIie4iDqtVmG2ajjXl\nU410crfFhGv79Ptj9hZt++K7KYA3Iz8deuqD6w6TzMXEtx8iHhPevRZCDrBESx4VRVH2ODH7ForV\nIiG9nXjiLp+Y1zn0Um+I9AnXbLvjH0B8bbYT4bExu22x/WLZymPXUrb82FJziQiX2t8bwIsCgd32\nel/Y0RYX34sT273OEvsxuh4xXVTfrggRMb00XkVZSq65MpZSpM1Dk8WLtm8fLrGwexE4Eb4hZ8ra\nrek+el3n1+1wra2cbttWnM6tSVxc+57vWOh4yCGeyo7V8q5W4MC9Ose7kCzeH/zULgYL6pTvrLaT\nv4XL4/3Jhac9vPzar6L64T//9tSyiG0RwZiCC174sc0nTit/D0L+6JrZZRubkqB2W1mr7CgxE3h7\nieMMkp90LGstdtks3+5bf6+Mdl+Eb4+0++fxx5L1xJI1edCI/B7r60T4fd62vGnwpQgjv0wk17P+\nIt7MlZ7H+wwu9FrvT+0XZiRfaPmv5WS57fb/LGL/34Wes9vq2VYUJUWzQJxWc/S6a8sL3a5XDTXD\nXL1pH7dtpF+IVbZqRGxSLFO583wnFBsyf9lzswlhZHsj45pj9amLBHi9knj7Q09/KMDdVENT4rPA\nj93Yx+S0YaC/vV9etnff813k9e4lKhpGrihKj5IlcH0eDN6Ha6/LTPl2Wqo2bp121vS2E9jTOcfZ\nZH/6gtQJ5aJwcEesnFbWvH0owvOE/eeDSY/nLVDc5l3HxbyTM3jX3Psr+fNU37b4TgtvWLra273E\nWjPbKrYVZZUQE6ILYbZW7kB9uY7nxHhUMkRqHbhv2jCaUbdpd91QjbRVgWbL81p7QjpcGx4T4VlU\nJRDcy2jrXCb3qUrbyw+Ad11V0g8+qd898T09kx6oL7whW3yXCTlXFEVRlgcnYMs8hG/OaXuQ7KRm\nPk6Q54luN6Ys4bqRZC13bD23E8XrI22Q7fGOeaQfAg4LtoVrxCeIi3b/fu5D/FqWS3xfyrlzIeRn\n8GZgndc6wZWcyxm8vbD29iUia0JwryVUbCvKKmE20KYxsZklfh07R70s5Rl9W1bAxUqCtUmfvNaQ\n4D1MVM1cErWRSCz4aEWiId19VoQ3WtD0DFJTOtdvzyeEPCdPG4dtqtBs5iR0K8n7Lt+VmtL1S6iF\n4jv0Zofie8bOrsw0zFx28ukZw+BA+xjDg+XGWpRgrZvRBGmKonQjG3MEk1tD/bOCUPOd5D+ol/3W\nDsO/N0X6OEGc5ZGe8n5mnTcvBed6e45QYIeCeB/gjsj+vgC/w44vFmZeJOZj58zjMyWWA7jzXBoI\nZ3/9duL5dlMj8drb0Cm+VyNrzWyr2FaUHqBovTZAPSgdFQvlzgsBD2mV/DYM63LHaFgV7LzhtYaw\n21PGu6uGdYHgrjaYU5jDEYF7r7Q6vrDdNZd0zhcSFeARt7cT4GXWcIc1zv2SX1MVw/pGOwTed3z7\n4vvAaoWp1CRH/IKnZwybNqxus6YJ0hRF6VWKvNJ7UZwULOZpDo8bmmknrP3EakUh2zGBHYrrvHJd\nY3Zb7Hp8UexPMPhh9m7MD5EWsaHAdSLcH284zvmmOi0Khc8S+P7YRoAzeHvGGdLi+8XzHF+vsbqf\nSjpRsa0oPUDTmFRW7BitEqLDXyccSyxW5PluVcqVBsta9+22NyJu9zErGNfvTgY27Llyd7VMR5h4\no2ZAOsUrwIx0JhbLIuY9D9dot4L34TrhZtMw2C/8+MZ0kN4xJ6TFd5Uk4sD3Uvcb6fjs3O2pmvTn\n5PebrBsG7Df4RL2dNX2qYRjyvtl9j3UYPh7r02uoZ1tRlG6kTIK0WAZwn0mK13XHxGuZcmE+D5Iv\nQKeJC4Y+71yxc4YedXec2Ll2en0c99Hpve4DrgsE9jODDOAA3/T6lJkomA954snVCXe4az2DCyNr\nuTvFd6+X9SrDWjPbKrYVpQdotpKa0T7hetwy9aRT+y/A+1tpwbopyfVmz9YM1VZ+orVmJdtzvm2D\n4dBtlY4QcLc+e9CVG3PnExP/4s4Q4qEI769KKhw9xuRsO1Qbks8jLxmZIxTf7twVAq+1J8BrRlIC\nu2La2dWd+HbO9RnPog94Zdqc+A5LlYWiOkt89xIqthVF6UbKhG7nraGG/Id09/VfZs12Ec6znTeX\n3iB7TTbEa29DuzY3wD32Zyw8vi/46Z83HFdMXF9XIK6z+LIIz8h4Bri2ZOhUXqby9n29367l3neu\nzRff2V7v1cdaM9sqthWlB4hlww63zdpw5KwvsZlKeW9vEXne7Vk7vV0Usu7aY6LchUzHcrA5Ee4L\nUtcvnEDYXe3M+o0k3uTU+QKDWq2214M3W0m97dBjDMwJ8MEBod6EWk7xyP++u5EKBQ+91giM2g2h\n99uPlT5ipMK2KTs273on62HYe/GH3csebUVRlG7mkBJ9YqW0fPIKG7q2GvmCO8xmHku4VrSOGhLB\nmJfYbQtxwe1vKxuO/ZKImHYh2Q3a65r9fr4A/6onvMt4+b8cPAM48V0263kf8dJljhfxRu9dOyN5\nKL6V1YmKbUXpAcpkx3bCLSZQq8aK2pICeO6YESGc5ZEuCkH38TOZ9zWhEYhUJ5BrJKWyYmR55t19\nmLEWtk7n5IArvXWEVFNebV90u9+bGGbtBzDbTDzhDifAnZe7XodaLW20nQCvmrAkGeCfO6ynHVyr\n/7kOeI0zXsNyZlHvVjIS2iuKoqwoZZJw5XmKx/DTZmXTR7xsliMMQ3fCfGdOn1B8x+bXw21jtMVp\n7Lom6bwnoQhx+1/OOzvWkDdw4tpvmeZi3pvqFwr12EREjfz15U58T5O+F4N0rm1370PBjff+w7yX\nV6YEty/j2+LbmHdERrv6WGtmW8W2ovQAWXWefVo5/5tbkohkJ5TLCuNQfPc1i9djL4SwjFjF+yoe\naLXDqH3WIfHQedP28jtiYe91+89Myjq210E7wnvfaJnUOu/ZJqkyZUl2cO88gQD3RXUovmclbO/8\nvb8qbBqSuQmAEdI1y534fuGZYTXW1YmGkSuK0o3EMmWH5Any9aTFW1bIeVY4Nnb/LM+u83DXyQ8f\nzyLvvJORY7przVqDPkGnkA3v4eW8k7MCMf2SlIgFmM4N64bkmv1jh+f1Q9pj4fW+APf3zXoMWw9c\nzntTJdCyxffqJycIcFWiYltReoAZK6z6cqYDi7KC+2LY/30+GcoBWgWx6Pn1t2FgVmhV8sV5p5fX\nepldlm7rmXYPEaH3u2aEZr1znDPBU8dMszNBWhiOHfUWB/ds+1SLkf4gJjyFSYnqlKC2v09XbOky\nz7veDPvij7fdL098r3ZEXduKovQoRRLLF3K+tzgvZDlkO/ke9BrJWurnR0K3HVcWlKOKXUeW13qa\n7MznncKZDs91p+BOn/2rvJMa6QRo4T2KeaxDdgb7+f0ngp9Z/dw9iNUh/3BwXa+MXPtqZa3NkavY\nVpQewOnXvDDhsXUmdy11lgguW+ILoBKkzS4S3tnHSV5Z566LSXm3faqmM/N6x7psYD2SqmMNUJvt\nTCQXXQ9PImqr0m73Rbn/OTRazpvsn8ukxPdPdjZT1xNmgneCuilJ2HpqsiEQ347OLOpp8a0oiqKs\nHEUP2A2yRaejqL1MHz8xW9Ya8T7agjr2GOGfIyZMXRKzsv7ZrH6X8t7o+Z0Iv9wK1Msj4t8X4HXS\nmdIdToA7QRxGFoSCOiawY33jCdHavNhbMnaZtenDwX7G/APK6kTFtqKsEioZGcDnI6aL6BTzidEo\n8qr7otz3pGclSYuKTncsiZctg7QXuGJgXbOzrNYQST3rh1NNTa92hosnidKy1kdDIngnrfKebKbr\nge+uG9Z5lj4MhQ/Ftz/Olle6LLwPT3rmutRxvnXd7rmxZPVZzWidbUVRupEyodlF9Z6LhPQu0iG5\nsTl138M7FPyERIDHxuqbdV9oTtMplo/wfr+HOEV1vF1ZsNja5ywR7uMEeFgKzQ9bdwLc3ZNBOoXQ\nhNc3q4yXG29Wve+8R6JzIsJ7raGebUVR9jjXX52fSqXM+pahmfwv7amBYi/0QEZKU5fALCvkvGqz\nuDQrceEdesQ79veOu1ejLZDdz9CB7rzT4X1xIrVmZC75WEfWbxIRXu1Le6hj9ba3rKukQrKHa0l9\na59pb3DTxjDoXasT4LEQ8lB818Pa4BniO8QX1k54ryV0zbaiKCvBgwVCaSlW4WZ5YB2h59Z/X8/o\nA/lrkEMmSItpn9vtT99jPki8zNlPI+d1x3fn99dKx9ZdnxUpj3U5b0m9bwAv8ATt5yOfU5gpPhy/\nP6ZwosGNq4+0wIZs8Z3FOQVlR1cra81sq9hWlB4gb622Y7AgbenUQPExskRxvxXD9b78sl/VVmLg\n89aBF0WeR5OeCdQi+8Vm8Ztia3F7BtavVe3oy8jO7gvwoX57co+RfkkJ8HowiHrVpMY6jglKcqWp\nmmTNec2kr71Fp/h+/Vkbsg/E2vJoO5ZDbItIFfgOcLcx5nQReQLw/2gnsX2NMebbS39mRVF6hSJv\ncNEDdpkQ8ZjI9oVxntkv+4DvEq/llbXKSs52EEk4dDhPP25/+pnP8xKSZdX47pwEWMflvKFjayjC\nfdP5vEDQ3iCS8vZD5+TAhDeeLO+32+aPOya+10qG8fmwlGZbRD4KPBt4wBhzjN3WVTZbxbai9ADV\nEkmgBuaZ6Cxk/W4pDDlvVqBpi3vGBHW9LzFq7azn8w+RmqqYqLBuCPQZSa4zOGwouqvQUWY6zPzd\nXxX6q8wlFnP00Q4pH+wXBvthuiPhWHKg7481ojXBfdEciuimpD3yLYFhb2CTXsx4KL6VTirLM0X+\nehJHjEvp/vfA/zXG/JeInGrfn7wsZ1YUpScoCgHPe8BeSPZvhy+KQ9EYw9XZzspKnicqw3NujLTV\nScLSs9aDD0WOO0LnPYiFp/vnTpjlLN5FrAK5E+EvKOEtDicH6qTvjwtpdzSC9yOk64dnJa9LxLeK\n7ZAlNtsXARcCF3vbuspmq9hWlEXy6x8Uz0/fcle+ae0viBOPhTiHhGKs33RmxM6j2speQ+2IhaL3\ne4owrJdN1UQSs+UL8JaAi4jvDB83KWHqqJBe89wn3qxzJPN3k/Y9768Ks8EY+6tw8Kb2TRjsT58z\nJsDDS3eH3GAEDOwKy5F5h1zvlQZbjzDmhapPZhUUV5YFETkIOA34O+Cv7OZ7ARdWsBfwmxUYmqIo\nS8RPCkLAH7EE58h7wM5qC58miupsFxT/ABLPc4jvqY6NJcx27vqMR9qhnZAsK+t5xgq1lJDex/58\nKOjTRyLCn8/bvSOF8pg5EV5kMbPqa4djzHtqmyAt98eC9rzs78rSYoz5pogcGmzuKputYltRuoDZ\nJpx2RvY8+RWfHc9sA/hBrdkR3l0PTM58EqVlhYHHRLgT4NWMBG1umztmTDtmJTyLOcYrSDQs3F+b\n7UcCVIFmK913v+H0QGOTHffvanHKs9OfyY9vbD+ihAI8xB1yo80MvhFhx1R7HLvs+B81mvScyrDs\nTny/6hw13zGWIYz8n4C/Jv289CbgRhF5L8nczhOX/KyKovQUfcBQnhd1OCZz2zSmOv3S4UP5loIx\nZHm2nQjPcgX4I8vySEMicsOEY44Rr48jFJ0h90W27UcisN1Y+8iuxZ0mJuGzZH2bZwSf2ZeDiZf7\naAt//9rzxHfoZ3f34RVrdE12EXtgzXZX2WwV24rSA5TxbM+nzjZ0CuoyVbyKanIPzQizsRhwy97j\n2eW8HPWI8I2dN1YazF1DX3CMvoqkso03jWGqAdXAyBZFGBxzQvrh6RjgA5fvyuz/lH1rTM/496M9\niFB8D3mf31CfpMT3aIGwX8sspdgWkd8nWff1fRE5yWv6CPA6Y8znReQFwEeBZyzdmRVF2ZM8rKC9\nTHKznaSzeofUI2Lap8wDeCiWwyn5fQv2v6PEOaC9XibGCPGEZ46s9dw+m72fD+Z1tMQE/ud4S3Ty\n4EWRpGnzIRTfl4ikvOv7pLtnTj74dAa5Kz574Immq2y2im1FWSSh13RqprNPmME6ZKAPrvpitve6\nGvMY++JxAYJjvvtMDLVDwjvCxS3VVjsretTLHcnK7VPPGNN8x7qjYaKhcwM205y7d01M6t7GBHgR\nsaRlvgAfHGgf78CBakp87z0E26ZanP6c5FHnhmvaQXVD+u1cCimRz8Dxo3ua/Oje3BmjJwFniMhp\nJPdZ6XsAACAASURBVM/b60XkEuAJxphTbJ8rgA8vcLiKonQBowUex6kSdmAvwOT0i3lC55uhvChL\neNEitrykZD55sXOjJP7irD6jZHvY+0jEaShQfyt4fzdxb3Y45tj980X4UmT3frExqQzm03Qmj9sH\nONWe65I1Wr5rMZSpsOP4ln3Nk66y2fo4pyiLZOd48Zd7sdie/5e1LxL9OtYLSUoGMGmzlRdlNQci\n67AT/FB2t5oqy+MeCx2vGKjYYy/wMpix92IGGAgOMmlFeJ6gdgLcCeCFEArw733dCz8fSJ/7QK+Q\n98mnpR+tfPGtxJmPZ/vYg6oce1D7fl/6vfQfsjHmPOA8ABE5EXijMebFIvI9ETnRGPN14Gm0K94o\nitKDjBcIpKxa0T5Z5bAcQ0N5fm/o8zzfMWHeyNg+tz9tr3JW8HRMIM/Xsm0OfoaeabdeG+JJ2Abp\nFM0/D977CdOyMrBnrbV251hKwgzmkC4hdqrX/uKIZzy2XWkzH//JCfbleF+53X7RTTZbxbaiLJJ0\nmHCcEl1ymY+3tZURD14UAu7Cv2c9a+knP8sT8e6csVMP1Omomx3i9vO93bH62JuzXN8eU94gpiqG\noeCkM0DVO/BMEwaCadbGEqf+Pu7ETj+7E+CxNkcovpU9jvtjejXwLyIyQOLEefXKDUlRlOUmf7V1\ngmyM5eb2GMyXgLE12z5+recsipKfxQgFeNa6cJdtO/RK10h7oUMhHa4BdyHZYTi2v68vRvaic5Ih\ndi8Wk9F9IcQEeAwV2XsWEbkMOBHYR0S2Am+hy2y2mB76oxAR00vjVdYG119d7H28dSI/Z+jB/fki\nsj+vUDPw9b7ipCAznoiOJUsrCtXemLHe2megYBhF3upY+TJ/014NT/xnrPrZ3td5EP/S9raCPXAw\np5Kq+UsDXnK2JiZbDCKCMR2p8Zfq2OZL/9/CfRrP+tfpZRubkqB2W+lG7i2YwI4Jw5Bagdie2rFj\nHiMqJvakcX9O/w2ky1PFaFCcUizPP9+g2KvsRwmEmcYhOyFaTEyHwt8X4P4xTtbvnEWx3Ha76O8y\njy3Qc3a7lGdbRNYBB5PM8t9tjNm9rKNSlC7i3VcUp8M4XOIrUMZseajtkZJZPlsK6ndUPW/tfNcU\nO7KEsBPhRWLbhYhn9au2isW0u4ysfi06w4uyhhVmI3f0G6EhnW1OgtdtWx2o+d/XTdMhwAEu/lRn\nflUV4N3DMtXZ7nnUbitrmQcL7OS2nLbQc5tFX4GYHtp//7nfzb33drRLQZj5VOD5jnm587J/z9IW\nrFkP+74XPOYlr5O4BbNCz/vIz2a+ibQYP4hOT7gLl49NJrjP4aHgvc9OkozmPjdEPn8V4N3DWjPb\nmWJbREaBVwFnk0zy3U+SQG5fEdkGfBL4kDFGFxUqa557s+KO7TfK5GD+l3xjHv+LmgswGCNTEs3y\nDTBg12jX+8jNJO6Ectaa6lgIeVHYeEhTkpIlCy0tPV1Jjt1vJHruhphUErY6hlrLfw8bKpWOpHch\nF39qTAV3l7AMpb96FrXbilLMXRSX1CoTL1MUxtyICOzUORaZrfwuEiF8YE6f8D96XpK0WPi5E+Bu\nv3DOfpR2pvKspFcxb7YfOeDcGYN0XrMT4S6VzANkZ/rOW9MNiQBXwd0drDWznfd/+QvAp4DTjTGp\nSBUR2Q84A/gi8PTlG56idBf1DPG4u0AdTg3kH7c5VmQAFhcxUzW0C29m9WnB4KyXgbOgdkUomGO3\nwG3LqqPt44vjlsQFed5xqiZt7Kum85L7jXR8hvUq+HMM62qwO+hUJL4VpUtQu60oJELUEQrrLRSL\n6cL12BR7tmsFnmtTILaLMo1DEuLte5bDI4YTAn3Bz6wQ8qztYQK0UGCHNvcBEm90eC1uEmCazs8i\nHPNekT4xb3oj+F2TUindQubfojEm0xgbY+4D/sO+FGVVM1GiYGKk2leKohrYi02gVpSBvKjkVmx7\nzbOO9Uqxt7k/bwmNSScuK0N4SZvqaW91eLiYEA8fBHZXDE3pvJZZaY+/vyr0V2G26XcSdhcvi1dW\ngIwVHGsStduKknBYQXuRV7peYr11UXjIxr0KgtFLJEiD7LHGtjsx7B45CubYM9dj508TtM8fe/bx\nfQtD3s/Y1ca82ZC+t87z7d/NA0gL+Kxwef8enape7a5BPdsRRORY4FCvvzHGfG65BqUo3cRUwXpr\nKBbTUwWpTScLlGy1IFa2KNO4T5Z3OCeCnIEW9LeE2RzB7PbPMu5F67nrfVDJuA53feHpy4Syh/2y\nvO1TYqgaGOl3DcLEbPuAvgB/7vMWXhZMWVrmU2d7LaF2W1nLzKf+dIwJih+QizKF1wvCyIuOnyXV\nnfjclNHuuKugHdprvrMWRbn22Lnc+GNrsF17TGD7Qv5B2y82ceHWYbu2WPYcl7ncnTPm0VYPd/eh\nYjtARC4CjgFuIZ0YWI22sib4x9/byJ/dkD/LvW1LvliuzRR4nuc9qjRZItVRJHRHG/E1zj5VmCuj\nFRvv3JrujHMWCeVmJXnFJg5aFRjMuIfTfvK4DCEdjs1tC0/1rM391L2a6AN9wkzKfZAW4MrKo2u2\nO1G7rax19l9kWS6AB4vWXBfsX5StvMh7XiSWi8R2KNZjYtWZNxeWnSUK8pKg5Y3lvoztfkIzl9E8\nPPeEfYX32Rf3OykuCaYh5d3HWjPbZf7+jgeO1todylrF1UP2qQXf5s2C/0m1ojjzAoqSoq2PLCaf\niojQLGoG/OTesbXpfiIxF6rmJxsLzxEK3w7PuYmfJyvb+ebBpHMYzj2MMNlMH7xm2lnHffoioe5+\n5vJaFcL18bUaHQJc6R5UbEdRu62saUwgZDsyf09PUy8I4y6i6AE6HENIzPvuH7NMre88Qm+xO7YT\np3dE9gmFakxE+xnQXf8HiN+PEeKTEg2SxGlhFvFwzDERPkLn+uyQvHByZeVZa2a7jNj+NnAUyQy5\noqw6YmI6ZHA2v71V8D+p1VcQJl44gnxi9akHPNexK3E1mRFnXQtEaC0lvA2DGW7vmufizgpPd6cc\niozRPf5MVQytyNj8tehV++28fgCawbHW1YTt0+n9q0Y6PPDDzXQ2ckiu3Yn+Wk06xHWCCmylp1C7\nraxp5IlPLOwzcdNNue1FYeJFa7ZHCzzbIyXWhS/GI5slMIuLmbbPnVVabD3ZHuPQC54V0h+7v+G2\nCXueWF93XNfmX1d47c/TeUdlBSnz//gi4CYRuY92LgRjjHn08g1LUbqL/qw05JbmQmtVlSQUlyFZ\nYtjh6kcPe/38KYa84VeNMFwVZprZncarhorJP85ASzJrY9dawo5Imy/AZ4LogIHg22t9vzATiW93\n426SCOtas9PrXcMmgbNCvDogqXvuC/BjTlisv0FZStSzHUXttrK2uS8rgLnNxoc/PLd96le/KjxG\n7kN0iVD1+R67qLyVzwj5EwJzk93zGZRljMTzHhP060nG9gD5IeCQCOR96CQ8btZ1TAdtoae87MSC\nsmdZa2a7jNj+CPBHwE/oXOKoKD3PcScO863rduf2CcPGO9o9ITi4u1P4Tq9bnBjPKuPdt4hvrJRk\nLHDc9lWgzyaiio1lHENL2uuwQyf1ulbiZa7aE4Ve8J9vzF+1fuT2KpOBQJ6sw3CtfaBkjJ3jG6ha\nr74fCo50ZIA/48h+Zhte6H0Vmt6wqgPq3e5GKmvNapdD7bayqhmX4u/j0QIxbayY7ggxtxR5rgty\njTOVseY7K7FYGfyH9sKM6rQzg4clu/z955aFBe2+CI+JBdc/69hZ92/E6wOJII5dyz6R84b9Bknf\nx+IpFqUbWGtmu4zYfsAYc+Wyj0RRVpB7xvOfR2PGxPfibnwg3/BPbljAoDyynMpODIae2kqgnosC\n5UcLMjpX/Wze1c4xhZ53J6ZTda/934Ma2OE67TBJWlOSawg95zOzZs5rv89wcpB+e6LZ4KZVg2sc\nprN+dn9f2qNdrWiN7W5HPdtR1G4rSsyz7G0rrKW9yDrYRWLdfwAvClmPUeTljk0h5Alo974R6ZtX\nZqyoKmZ4nmk6k6L1Rc6xk/iEhn9fR4h7rxcykaHsOdaa2S4jtr8vIpcCVwFu5aqWEFFWFQ91rNFN\nM1TNF6ODBRmqm4tclJ3l2Z47fpjZOwjJLsxGXuDajnnQ/S+PWkE69VhOsTnDHrl1HeLbF/bBsSaB\nw0c7B9hfFWa9cc00Tcd9DAV4sq2zT1EYv7JyaOmvKGq3lVVNmYfXqVtvXdQ5ijzXRYKuaIx+e1Gy\ntLLHLxqTL8CzzLY77gM5xxkk7oSoB33yxlWU2OwA4KfEPwc3OeFKtIWTFb4A1/Xa3YeK7U6GSdZ8\nPTPYrkZbWTVs7+tUU7uG21/QGyYX90BflI28qPRXkXe1VfDNNZVTHzs5QMEACthiFerujEmLSYH+\nnCHkjb/SyveKA3Oiuj+Y1HDvd9rkaVlh90WTGU6AH/Toxa3BU5Q9hNptZVUzZAwmEkpelKV6PhTt\nv9ztYURaUakvSIvOIuE9BeQtoBsku/42ZHu0nQgPPfexklwxIe0L5aPsz59G+vj7xjKvLyRaQFGW\ngzJi+w3GmG3+BhE5bJnGoygrwq5hw9i67PaiOtbT/Z3bprzj7XNfvlifLRDDE36yrkj5qiIx3Z/j\n2p6tmBKh0sn+odfXMWo9/6P9wnjEy7+72Wpn/I6M393fmOhuVTo9432kPeKuNNpUA6oZa/n84IQw\nLL+vAv99e50Xnjk6t+3uH2kgWi+gYeRR1G4rq54isTqUsRbbMR4JE/e/9YsekIvO74u9cJp2goXX\nrsbumyeEAdwXQNYqNl9oxx5/6rQFdXinikR4LAA/Fk7+UGQ7JEL6nuD9AZF+jw281ld59r8ojF9Z\nOaREzoVMejBSoYzYvkpETjXG7AIQkaOAzwBHL+vIFGUPcvdBC//PO7bRMDuQ32fLg/lfLEWOZb9c\nVT2StXvCiv2BjKnmvPxstaYwYY8ZE8LQ9vz6HmDfSzzkTTbsPZQcww/h/o33BOPWl/vlxdwa7Wor\nXme7YSKC2xfP3riamI5Jgb0GhakgXn0meFI68w9GU+/Vi90bqNiOonZbWfUUianMFdl23fZIRGz7\nArloTffUPOpoh2OdAJzZjHlg74rs43MA7bJckbl+BukU2buC974AcMF3zizWSYeJ1+j0ZPvtYVte\npnOXPC0vU/lD9qffxxffjscG70/vQSG2JulbRFG7elGWgO6jzNX+HYnhPg14JHAx8IfLOipF2cOM\nF8ZnJV/guzOmc4vqbPcVxImHtZ872gvsR93W8a5HxlFrCLWC0mUz1jPuC/maL2AjItwlZ6tKWliH\n9Ffja8ZnJMlavrPfpO5PXxMaXjj40duTN7G13Y6Yx90X4P1VsWu42wcZ6qNDgCu9h4rtKGq3lVVP\n0ZpqBgdhr5xeBWLZFLQXif286dqi9dp52x3h44hfE3swcn7//V3EBYDb5nu1Q2qRfcMEZ/5676wp\ni9j988fon+NUT0RftRivqNIdqNhOY4y5WkT6gS+TTMA93xjzs2UfmaLsQdYPx+ZMYYMN9PrF0KNy\n9z9cfpLbPlM7Jre9qLTYVMFs7UwsU8lcm2HnbFt5rm90GqqYFq9bwVsz+Wuak6GbzPXQs004vK/C\nLyIHaQkMNyCsPeYCBWb6Tcf67DDX3NGbky2TkfD1CTtL4WpyD/RJyqMdCnBFWQ2o3VbWAllms+Y8\n0kcembu/FNTijoWZ+xStCV7MmnFfaMdEe0yI+x7uonPHsn/7DNEZou1zQ47gXQ9sCbaFydby1lRP\nRNqu9c7nhMup6sVWeoRMsS0iFwab1gO/BF4rIsYY87plHZmiLCEfuDwMoEqz4ZT26qn1dM5mF4np\nk7kqt/26wXyxTSQ03KfeBwOz2catKEHaRL//e/tcI7ORzgFbmpWO7OY+64P60zFhHoZo+1z46V2s\na8LusK4XMByZGAjF9/CgzP2cnE4fY7hf2DBaoe6FBgxDqt9ALFW60jNone02areVtcScqD700HiH\nAjGduZ9l9M47c9unCsQ4ZHu/t2VsjxET1mWTf2V5x4syhRfhvOqxjOXTdE4QjJDYXsddQVvYN5wI\nWIQfVOlGFuPZ7kHyrva7pBWAey8UKQNF6TEO5o7c9t/iltz2k3Zfkdv+n4Pn5baHdaVjzNSy/9tN\n92cfo9KCsYxF22PrYPNOm/xsgf+rR/o7xepEQSk0n3U2xnxdS9gdSfRWJT9be63WPv+GmqSENcDQ\nQOf4wn7HnDDc0UfpDTSMPIXabWXt8MpX5rdfkW+Xmc6XmaZATBd5j/NKZ+UEowGwT0G7I+shPhT5\nofj1xb6LFisak4+7M6PE63k36Lx+N6YROgV2ON6YYNcsKqsIFdsJxpiP7cFxKMqKsiWaeqON7+0e\ni6xAkrDmVEB9oKgOd753tZLvmE+OkSE6KhlJxxxuPbW/rrqoUpjPaETMum3jM8UHGvCufcCGk88E\nod3zKVPui29I1nOPDHXWyh4aEKZKjE/pbrTOdhu128qq5Ljj4tsPPzx/v51eEanY2u289dwUi+m8\nNdsjdIZS+/w6Y7sTvEXrwXcG78NSA2M579fb8zwyIwz7JyXWRIcCOzYtsZFsAT8MHB+c3w8Vd8La\nv86YAFd6FBXbCSJyNfAx4GpjzGTQNgycDrzUGHPaso5QUZaQLA/pA9GiEuWRrAXL7rwFanG2QIyP\njGcbv3rBdHTNTt4XhZr7+MK7KIdYGKbt40K854sT4LtarY4M6eGt/NYddV5+TmfmuttuTpv/WBK1\nmNdb6S3Us91G7bayqsgS2Y7HPS677TvfSSc4iyQ7kyc+Mffw862D7VMkJbLKcTmc2M4qdBLmdPXF\n+QN0iu+QMeBnGaK6yMN9s0iHmPfHdVewbXOk3wkRoZ+1BjtMiBZONCg9iIrtOV4GvBZ4m4g0gXtJ\nQtH2s/t9Gnjpso9QUUpStC47LyF331eeR+Ppn597vyu7aEiUackPQ65nTMfWrSX1E7Rtr3cK/w3z\nWeAV4JfV8nHe7iIv9vRcqa7sGxh6k8vyycvH889dAYIa4PVgLK+KCG2AI4+P11j162dn1Q1XlB5F\n7bbS2xzmycSCMG++853cZnnGM/L3v/vu3OYsL6q4+t05YeZZ2b6Ljh0yk7E972lngHhpLZ8H7c+Y\nEC5ikuTaYk89DTqve4fXthC0nJfS6+SFkT8AvAV4i4jsBzzMNv3aGFOQdUJRFkfolXRkCagyNCT/\nC3trzlzwGBujidMcg2aSaRlm0MTnuusb89d+uaznABtq2zvad45kZ0OvzQh9OZUQ+gvKftVM/kRE\nU9zP5P6Fa7u/+qtZXnN2ep4+6/ObL7MVw2wF+oPaYdMYBkusc4+h9bNXFzKfNQarHLXbSs9xWI4P\ndr/98vctEuMPPZTf7hKoZXjIJ269Nb6fFdl54jEvVu4eij3fRe018hObFa2JfhhwsCdibwy8xzHP\nc0ieZz/G2vJlKrmoZ7sTa6TVUCvLylIJNCCaaKsZEZR+yaxdHYFZ+fj9TaPFABOZGYiOruXPwBex\nLUdQ1IBGTtzXYEHG8XXBjYllBfeJ3ceQxUyK+Pgiezb4TGfVK62gYeRZqN1Wegnzq19Ft895kX3c\nWutH5Zfk5MYbO7cNepOt7ji/+EV097wEZ5C/JjsMpfbpI74mez5ZwTfR9izHRG/W2LPGXEZcO06O\n9L0hUpoLFlf+TFnFqNheGkTko8CzgQeMMcfYbf8A/D4wS1KO5GXGmF227Vzg5STLal9njLluucam\nrC7OviXxBB+4IV8FDs0k7VMZ66MfNNlz0dUGjNWyQ8vNbF6+bHhsLWL0PX7B0bntzZz/qUXrwUcb\nwnROrPi6wGvs3rsJi8HW8q1r/sOz4iXBXHj5qK/sm8L4QlOmK6uW5RDbIlIFvgPcbYw5Pc92rSbU\nbitLxb2e+No/JuQ2JZPVJrKWOoUVxPV7721vs57lWkxMezxYcOzNMSHvcUj+yHJnsooCbmJPDH7M\nVZXya5PDcO7tZItqJ+ijn8ki8AV4rCa2oqRYQrE9X7u1Eizn/4OLgAuBi71t1wF/a4xpici7gXOB\nN4nIUcALgaOAA4HrReQIY8wCA0WVXuTebdmC9fsTTf736P+fvXuPc6uu88f/eieTmcx02plpp/RC\n2bZQodCCUIostWJBYFkWii5Y9auyXn66q66gqyK4q6LrBdH1u4s/v37FXS91lbWgcvGCFKWFCmJL\nWymFAqW0UsrQ20w7t0wzyfv7xzlnzuecnHOSySSZZPJ6Ph59JDknyflMb5+88v5cMsD28Or3oTyF\n6Ul90Z1L3PcVrH9o9lBDeMf89pbHcTv+MvR8vq3F8oXtKE15vg5PKJDwlaPNYeNTArbuAoAp9srg\nDTGgP2KYejk4Ifyun3vndE+H4ECKgZtckmcl/yJdB+ApWDvbACF9VzkuPM7Yb9OovJxn5epZHR0j\nwTqInHpq9AXsoeKJ+fODz/v30l64cOTu9I48a6/kGabeEzaM3Bb1yX1G9JXz7H9izdU2P3H4F0qL\nGsKdhLUAWtRHg1l5rl+sB0UCq9lm2AhbCI3qSGkr2wX3W6W86GhE/rQi0gDgB6r69tG+sao+LCLz\nfMfWGg8fA3CVff9KALerahrAbhHZCeA1AP4w2utSbfnVPd7BVDtSuYF7x6ys+5E3wuFZwf+BL2p5\nGABwYMf5ka+PmvcM5IZxv+cSp3seP4grRu5fg3+PfO1oF2QzteQZfR+0sJm5ZXdT2HKntrgInJ3N\njmVyf4/f+KYC/nCKsPF3/Ti+LYZB3zD4qc3A4cFsWa9N9UtE5gC4DMAXAfwTENl3VR322zTe8g4d\nNoZya0CgFTNY9+Sp7/q37zLDdzLP+hy+oN7rq4TniwNRgTooTJtj5/KtHFKK77fHY3USp8L9YMCX\nMPm2MyMqxij7rXER+X+Jqg6LyFwRaVLVsEURi/UeALfb92fD20HvhfVNOU0w/nDtt2OWWxSZtugh\n69Z5DOCFl18HAEhNyn2tE6odZ8M7xOzOttcFXvNop3WbL2wHnY8Znyq+kfhc6GvDFk4baYMUH7Zn\ndrud2kBj7vnoddKB7kHFNW+1VvS+925vJTnu6zAby1NFjNQc8DMd38iJumSJlf6vwv8G8AlY29EG\nMfuuqsN+m8olaP9l/xZWOT2dGXid4GwsbOYJ1k74NUNw2F7YzvE8e2Vj9+6cQ86QdP8HYP+iYvmG\ncYdtfwUEf+FgzuM2rxX0QTxfMF1sh9rHA/5Mwj7YVzJ8B83rJhpR2Tnb495nF/LTvgBgg4jcA/f/\nUVXVrxd7URH5ZwDHVPXHEU8L/Jd60003jdxfsWIFVqxYUWwzqApNW/QQPp7+mPUgINx+ZtZ3Q197\nFB34Z1wbev67nTeO3J+feNK9pn37Yjp6sZVYnq/rg7bsGpHnX1rUSucA0BPxkdmc1zw5oMrd1FB4\nQL7iyuqpFJ9zofcblY2/6x+nltBorFu3DuvWravY9UYzZ3vjM8PY+Ez4dBURuRzWvK8tIrIi4Hwh\nfVc1YL9NJRMUsh1HYC3SeSDk/CnmUO2gYdtmGA6qRBthOm1Wwe3AnJgVPSC615zr7ZMv0OYLpwHf\nA4/I8xVA5GtH42yGWiqBSvfbownb63p7sa6vuHER1dJni+b5hyoiN9l3nScKrE47vIznvnYegHud\nCev2sXcBeB+AN6hqyj52A6w3vdl+fB+Az6rqY77303ztpermVLZ7jwX/OV6w8OTQ16YPDKL31UtC\nz087/BykKfwf8D2T3hfZtp/oP+QcWyJutfzpo8GVcUc8Yo20L3ZcHfnau3BN5Pk9264MPbf8lej/\ntJoK2Brp6quqJ2TTxCIiUI3YpH1s761P/pe/HlW4xe/t87RNRL4E4J2wClNJWNXtn6rqNUF9V7Vi\nv02l5ITt8NgabiGAE8IWIps5c/TDxP2CViQ3twQLWWkcAA6ErIDuyN2E0ytqmZhX8ry2EIv574bG\nSbn7bT3nnOJfv3FjTtsK7bfGS96vFlT1plJdTEQuhTU87/W+H/weAD8Wka/DGob2KgB/LNV1qXo8\nFzAn23EgqVh+ILc029dlDC0/4bmc89k+a1JvFkBYkUviMaw88m083xa8nybgBusLcW/g+e0N0WF7\nUsiYsuY+wSMdF0W+dgG2R55PH3wjAKCvObfz7Ujm///wspXBgeTOn/YyaBPZVPVTAD4FACLyegAf\nt4N2WN9VldhvUyk5cdgfmZ3HQStfHzNvgyraThV75szcRc4AYN48AIBu2RLZNlmwAHjyyfAnRIT1\nfIE43yJnUf8ROLtxhs1Oi9itk4jGqNr67LxhW0SOA3A9rBVHnf9bVVUvzPO62wG8HkCniLwI4LOw\nVoNrBLBWrG9KH1XVD6rqUyKyBtbqr8MAPsivwiem61a14V/u8X6Tvb/D/aPu2+tdyDbtW0HcCdZh\nJB49pvQEPB94vLH/CD7Rsi3ytVGGE1aoLlbUauTt9/8tptjbcE0JuMYJs62fORUyWiAKgzbVujLu\nsy1wK8PfQEDfVbYrjxH7bSqloLq0ua6m2SsHrtdpDA8fDFgQrfmss0KvHbjPtvm+ebb/SkcMI88X\nePOF8agFEZyvt8Ou0YLoyvmr+U+JJrLSbv1VcL9VsouOUiE/7Y8A/ATWfmV/D+BdCJ+eM0JV3xZw\nOHTCrap+CcCXCmgP1TgzXPe3eTuUz7ftxo2PzUXM7qFivp5KGgr4ZB3xnMZ+Y7OOYW+wzw5Er5A2\nbE+yCtvzuq8tvHPMNyf7znTuEHbHSgBNEXtdp+3BAnHf4mWJAoaPE9U6iZVn0T5VXQdgnX3/VWW5\nSPmw36aS8W9Y4Q+QTsAOXR3PmJfdHBaeAxYyA5Azj7vbXDF8cDBnUbMwQVuHnQLgsYih5PnmXUeF\n7bDVFR0DyF2QDSjdXG6iqlbCsD3afms8FPLTTlPV/xSRa1V1PYD1IrKp3A2jicsJ2JmQMJhoJiRi\nuQAAIABJREFULe7Dsw5noZkskClum9dYS/B30E7AnyrRO2P2TQpfIK0tT9juiPgKfXKeMJEJ2I7L\nOh75MqIJQfilUhD221Qyi1XxjEjoHOWohcSSQPR+1l1d1hzrPHteOzp8z0uH7IWdsKvlaXsYejok\nVEd9CM63F3ZUZTx6/5H8K52fkOc8UU2r7Grk466Qn9YZIdRlr9S6DxjDpsBU98JCtlMxjgf0YA3N\nVuAc2DOA5uNzu/Zsb/Twcud8w6zw78GzvUOId1jfuqckd8OstjzLpRydEv7PIl9le/oruYE6bf9+\nzJgk6IsYIp4t7rsFogmhjMPIaxn7bSqpFgTPUU7ax4OqwH32r46gOdlOaJ45E5gzJ/iizgrEO3aE\ntithDkE3r2PfD7iyR1T12tyAJOjDctAyLc7Hl3xrJ+fdi5xoImPYzvFFEWkH8DFY89amAPhoWVtF\nE1rYMGyHE6wdTVO9LzCDdXrQG0IT8IbuoaPeJBqfHp5MpSEWGLId+QLzMqwNPXc0z+fcbEBgiNtN\nbYwLpjaHV7fzFfKXXRKwKTkRTWTst6mkgqrXw3BDoxku2323OVVr/6Jl/m19fOF8sDu872023ztg\nMbRjEXO2gfyh2BEUjvNVp6NwgTSi+lHIauTO0sw9AFaUtTVEcMN1w/Tg4Du4qxfHjgYnzGNHc8O6\nyT/nO9+CalH8AfpVESuKv4gTI98rE9GMQqap+wP1I/dzT2qqD6xs52K/TaVmhs2garATxiVo32tn\nmHhYBdtZTdwJy77Q3Ox/TyNg51ut3JzcFVSZPxjx2nz7bEf16r15XpsGsJyLoFG9YmXbIiLfMB4q\nrJVZnftQ1WvL2C6awO44ZSre/Ez4kOymk6eFnntczsdJT96BWENwoG6eFv3JeyRchyTYoxE7Z+ar\nTkdVvqNWGweCK9uORBH/J7GaTfUixjnbI9hvU7k48bcHwRXdWRErir+8ZQtmtbeH73mdb752xHk5\n6ywcCAncryB/BTlqGHm+qvf+iHPc54MoAsP2iMft22Wwtg/5CayO+81Ank2BiYqU7Bc8Pv38wHO/\nwxUAgMXTfppzLt7hfgedHQqeDRWb1AgJWQStEPmGkUfJV9lORzQrG71IOlFdK9dq5DWK/TaVRbMq\nXpTgf2tzYAXqILOamzGruTm4qm2uNB4WxAtYPC1qi658H+mjVv/OF7ajFjGLHrxOVOcYti2q+n0A\nEJEPAFiuqmn78bcARG9sSJRHsj+30+6ws+y93V/FpLMeGjm+WV/neZ4ZrAEg1uYb7NXvnbcda3L/\nmufbOmxKxCJoH8ZnsRofCT1vVq+P+KrgLw4sjrxuYkb4cLKXnwgeMt/ayJBBxGHkLvbbVE5Bg8Cd\nSvcs35Ze6cFBz20iX7BO+QZ5O0PJk/kGcwMzIs4FLWJm2mPc93/nne977qhFzoK2/jI/Xfw1h5BT\nPWPYztEO6/+NQ/bjyci//SBRpA6jSDwlIHhvtAN2a0/uXpROuJbG4PGjsWHrr3Vsin93UEvUImhJ\njd6w4zjf9l9mtfsu/F3o6xJRG3ICGIzYLPToUHCnHHaciOoe+20qOXP4eIPvtndwcGSOc9AK4Cc4\nAdsfqh1OuO7sLKwxe/eO3I0K1EFD3gcLuF+IqHFyQUGdQ8uJ6lMhYftmAJtFZJ39+PUAbipXg6g+\nOAG7fSi4OtsascznNY2PY/Wxs6HHgjeSPth2UvS17e+Xg+ZnNw1HDxw7IbEr9NwBDd9ne0qefT4a\nEuHd/LCyiyYKw322A7HfppJzPjBO7ghevyRtrxoeOOh79+7CLtIT0Pk71e2QoB7VvQadM0NyVMAO\n+VpgRFD1OugaROTDyraXqn5PRO4DcC6sRVY+qar5ti4kivSFle34zu3B30d3NAp+1xocpKcm9qEZ\nAAZyh1arvQfWk5PODr1uG7oj51n5h5lHVcH9miJ67aFJQFORC4Q3xTlcnCgMh5HnYr9N5eDE4J6A\nrbiOAAj6mtt5TetgcAfpBNrpUfOy81S7Z7/wQuT5KOETx8b22vyD34nqGMN2oBSs9R6SAE4WkZNV\n9aE8ryGK1BEw53iavW3X1IR3uPZibPI8VmNz6Wyfd45229TcDwKLjNe/GPiRwJYn10YtkhYWphvt\nIeSpIhcIn2R8RT4U/B0EUd1iZTsU+20qqSMBx+bat+3IHbI94LtvVoL9QXV62PBywBoyHrZtGKI/\nyP4FgHUR54M4QTnfAmmFjjnjGqdEPgzbXiLyPgDXwlobYyuAvwTwKIALy9s0muimGftht03ylqfM\ncH0B7oWfE7D7d+d20CftvgPJ8+cDABr7cz8eHJ2UOwTOXdAsfJh4lBdxYuCib6bYcMQc64Hm0FPm\nVuAtrOIReXA18lzst6kcFqui11iRfLJvka/99rmwQV7DwMjkrfBNNoHugJXNO4Kq2/Y876i9sgHg\nuJDjrQD+HHB82DgfJd8c74u5CBpRMIbtHNcBOAfAo6p6gYgsBPDl8jaL6oETsFuSuR+W3/jcWzB9\n3umhr/3zQ9Fjslv3HwAABK3jfcQI2/69s/Nt0QUAj2N54PFsQ3THGhXGG8ew2NkVV3JONxF5sN+m\nsvAHbNNJp54a+doDTz8deq7XCNiBH0ztYP3kHXfknAr/qtrSivDAHfX1ethrHGMZgk5E9aOQsJ1S\n1UERgYgkVXWHiJxS9pbRhBcUsh0Lz23G/idzA3Wm26pkd+8J3g5rsNs6Puu1xwLPA7kBO9+554wt\nvfzbkJk68gxnbY4Yk9YUslAcANw3ZRjfvCC8zUT1jHO2A7HfpsprD1nwvtWqETeEhO2Ojg70BswD\n99hkjXZbPH9+zqkHA+ZsTzfuDyF8SHjU4mr5FkgjoiKxsp1jr4h0ALgLwFoR6Qawu6ytorqw8Nzo\n76OdYO3IGsOwnVDtOOYbz3XsUO4sqb591oTn/fPDVw0HrCHlYYG8uTf8dZk8/5qmHw4P1Mnw7waI\nKEKMc7aDsN+mynvkEeCSS0JPd/hXMV+4cOTuZAC9jz6a8xqnW30xYhE0J1hPK7CZpn0R56I3AuWK\n40RFY9j2UtU32ndvsrcRmQLgvnI2ighww3UsaX2ajhl/W5e8bxL+cGs/hgaCh7Q5wRoABg+Nboj2\n9oHw6vXkiA09p3RHzx2dPhB+vjfPEHQiCsbKdi722zTuLroo91hf9JJj5gdS/2reYeuR9wA4Pk9T\noq4atWp41NZeQP5h5kQUgmHbJSINAJ5U1YUAoKrrKtEoIgBo6GiKPN/XHTyUHACOvhR+zu8433fb\nM18ID8VZo4rW4qumJ/LspT0D4algxjDw9lWce01EY8N+m8bV1VeHn1u6NPKlDUZl29+dmiudH/Kd\nm4toTg+/P+Bc1CJoebp07ANwLhdBI6I8IsO2qg6LyDMiMldV91SqUUQAIMngv54N0509tILneCWa\nBJmIYdmv+48P4Znr/jn0/HEhFeoZg4K9k8NDfEsqurJ9whSW4IhKjauRe7HfpnH1wAPBx5cutbbv\n+v73g88vWBAZbs1PAzN851IIrnz/3r6Nmg2er3pNRGXAynaOqQC2i8gfATgrVqmqrixfs4iA4xa+\nhMOHTvYcu00+NXJ/XtPfe841tbgfurMh22xl7N48ar/sGYPW+yQ090N8Yjj8g326AWiNCPmtzfUR\nCnY8FrwhSr45+kRFYdgOwn6bxoe/er1zp/d2wQLveWd/7VQqZ59ux6z58/FExJxtAHjGvjVXATzF\ndy5I1PZd+Xqs6LF3RBSKYTvHpwOOcdwMldTq/wmeDL3vEitc/+Fo7mfEU1r+AQDQGNAjZvKN/wrR\nhm68ZH92H5Lcv+axPKPTG7PWi4PWbHr4lTSuW9VWXMNqRFjQNs8xdFNJMWwHYb9N5Tc7YLHRyy93\n77e3565Qvnu3dTtzZs5LZ5krjXd1ee6HzdkGrDnZztzroAXPogK1U9kOm7v9ag4TJyo9hm0vzvei\ncgsL2gCwc8eVAIKHiLV0hH/IHupVNE0WxEP+hrdFVLYHY4V3rnFf+E4WPlV8QspkgXjEaHkGbSo1\nDiPPxX6byiooZDt+8Qu3um0GZse8eaEvHYyoXs8G8KzxOO67H9Wz+PfDbjHuOx8Rivx+fkLYIO7/\nocv55QJVAsM2UXUYUiCWCT53dKqGBmkAaGgOD9qOsKHkQxFh0R+u/WKo7w/+Q8fcjrqpsb5/L4iI\nJiJ9+eXQc+Lf3suUTAZWtAEACxZgw3e+E3ndWfAOEzftinhd9GafZDKDt4MBnGhsGLZp3A0F/D/e\nG7cOHp3qPXnUKHE3BMyBNgN2rCE47MUbgVfQgSMhe2lnjLCd9QXvfMPI610m4/55DQwq4nEGbioz\nrjtIVFGiCkydmnM83d0NdHcjkYzYUMs/Zxuw5nPv3ImLP/1pqzIe4uUtW3Aw5NzJIccB4I8R5/gh\nOHeYPcefUdmxsk1UWU6wHnls7DlthutXJx72PO83n7wdl/3b2wAAiZb8oc4M4g/iitDnTYn4V5GJ\nRVe343X+BXB62P/Y+g1J8H8aKpcSDiMXkSSA9bDWPmoEcLeq3mif+zCADwLIAPilqn6yZBcmqjHp\nbndkmH8IdsJ84B827iyUBgCLF3tvAW/YNo8DaNiyJbQ90hweEdOD4bO22wOOmftnd4ugY4JXdnO/\nNrH4h98TlUyJw7aItAP4TwCLYK1P8h5V/UNJLzIGeX9aEbkCwOcBzDOer6rKHROoJJxwfXRSbof2\n0cT1ka9NTon+oB1W3X4hvTjn2CR7KdTWvHO2AyrqdgBvqvNC7nDIFxHDEau0E42FBAx7LJaqpkTk\nAlUdsPer3iAiy2Hlh5UAzlDVtIhML9lFy4D9NpVb5Bznd7wj/Jw5j7snYP3xxYuhP/yhdd8Xrv1r\nt5htiArUYUPPw9Tb/G2Gaqq40le2/wPAr1T1arvvnpTvBZVUyE/77wDeBOBJVeUgWiq5Q23h4faX\nj38V/3D2VYHnpqf/jN0hYRoAYonQUyPBOtk/uu29AKApHX6uJVHfafv8S63/3x66rz/PM4lKpMQL\npKnqgH23EdbaS90APgPgy6qatp9zoKQXLT322zR+duwIP3fRRd7qtmnBAuCBByBvfnPg6fQddwAI\nDsPNn/hE6CWTX/1qeHsATI6oiteDi+3K/dqALy4vnuBVfRonJQzbItIG4HWq+ncAoKrDAI6U7AIl\nUMhPuxfAdnbYVC63vqEDH1kbvjr49PSfPY91wEq7iuBA3ZSn2g0AbYfCn9PVqViwz3t+ihHAjzaE\ndz4tXBQMgBu6iWqNiMQAbAZwEoBvqep2ETkZwPki8iUAKQAfV9VN49nOPNhvU1kFzcrus297P/lJ\nTH78ce/JTZvcW/9e3AcPurfOvtsBEvbia0Hfow/6ArVZM5+VJ0wPRlTFgfqZw8xgTTVqPoADIvI9\nAK8G8DiA64wvzsddIWH7kwB+LSIPAnAGg6qqfr18zaJ65p8T7YTrIGawbpnuXSkp5qsyNx3XNHL/\ni1iIz3eHf/t+Qta3U7bx1uls+OfX1oiw3cR5y0SlV/rKdhbAmfa35b8RkRWw+soOVf1LETkHwBoA\nJ5b0wqXFfpvKqs/3ePKpp2KyecAJ184+2+Z+2wfDljkLYMz51nvvDX2av9rdWvgVQvfYFoZPovIo\n7TDyBgBLAPyjqm4UkX8HcAOsEWlVoZCf9l8B9ML6/6ixvM2heuUE7EmDwR+cpSV4TPjU0yNWPQUg\n8Rji01oCzyUCBoKOzL2O555zxCImdDFQE1XWaPbZ/v3GITyysbAFBFT1iIj8EsBSWJXin9nHN4pI\nVkSmqeqhIppcCey3qawmqwKnnWY9WJy7Bgra23Mr2I6wYeSAG64DVjQP3FbMDvGpiD26U4ODOfO9\nR96zudlaXG2gaopgRBPfKML2umeewbpnn416yl4Ae1V1o/34Tlhhu2oU8tPOUtWLy94SqmvzesL3\n74lNaQo9F28LD9vpqdPwIk4Kf+0YBlh++C1txb+YiEpnFGH7tecm8dpz3f8zvvYt79oCItIJYFhV\ne0SkGcDFAD4HK7heCGC9PaS8sYqDNsB+myrhoovCzy1YELwAWjIZOVQ88DUOszq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bAAAg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X9UpTzivX11ByKzaHL+Yb8LqWc9fWfA1rQ19++HZg/hnBk+0GV/DGsaD6ZrP7uKkGdeQObnIr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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_list_of_fields(radar, sweep=4, xlim=[-50, 50], ylim=[50, 150],\n", " fields=['DZ', 'VR', 'SW', 'turbulence'],\n", " vmins=[0, -30, 0, 0], vmaxs=[60, 30, 10, 1.0], \n", " units=['dBZ', 'm/s', 'm/s', 'EDR^0.33'],\n", " cmaps=['pyart_LangRainbow12', 'seismic', 'YlOrRd', 'cubehelix'])" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pyart.io.write_cfradial('file_with_turbulence.nc', radar)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on module pytda:\n", "\n", "NAME\n", " pytda\n", "\n", "DESCRIPTION\n", " Python Turbulence Detection Algorithm (PyTDA)\n", " Version 1.0\n", " Last Updated 08/28/2015\n", " \n", " \n", " Major References\n", " ----------------\n", " Bohne, A. R. (1982). Radar detection of turbulence in precipitation\n", " environments. Journal of the Atmospheric Sciences, 39(8), 1819-1837.\n", " Doviak, R. J., and D. S. Zrnic, 1993: Doppler Radar and Weather Observations,\n", " Academic Press, 562 pp.\n", " Labitt, M. (1981). Coordinated radar and aircraft observations of turbulence\n", " (No. ATC-108). Federal Aviation Administration, Systems Research and\n", " Development Service.\n", " Williams, J. K., L. B. Cornman, J. Yee, S. G. Carson, G. Blackburn, and\n", " J. Craig, 2006: NEXRAD detection of hazardous turbulence. 44th AIAA\n", " Aerospace Sciences Meeting and Exhibit, Reno, NV.\n", " \n", " \n", " Author\n", " ------\n", " Timothy James Lang\n", " timothy.j.lang@nasa.gov\n", " (256) 961-7861\n", " \n", " \n", " Overview\n", " --------\n", " This software will estimate the cubic root of eddy dissipation rate, given\n", " input radar data containing reflectivity and spectrum width. Can be done\n", " on an individual sweep basis or by processing a full volume at once. If\n", " the latter, a new turbulence field is created within the Py-ART radar object.\n", " Based on the NCAR Turbulence Detection Algorithm (NTDA). For 2010 and older\n", " NEXRAD data (V06 and earlier), recommend running on UFs produced from the\n", " native Level 2 files via Radx due to conflicts between PyART and older NEXRAD\n", " data models.\n", " \n", " \n", " Change Log\n", " ----------\n", " Version 1.0 Major Changes (08/28/2015):\n", " 1. Fixed issues for when radar object fields lack masks or fill values.\n", " 2. Fixed failure when radar.sweep_number attribute is not sequential\n", " \n", " Version 0.9 Major Changes (08/03/2015):\n", " 1. Made compliant with Python 3.\n", " \n", " Version 0.8 Major Changes (07/02/2015):\n", " 1. Made all code pep8 compliant.\n", " \n", " Version 0.7 Major Changes (03/16/2015):\n", " 1. Minor edits to improve documentation and reduce number of local variables.\n", " \n", " Version 0.6 Major Changes (11/26/2014):\n", " 1. Changed from NTDA's lookup table to basic equations for relating spectrum\n", " width to EDR. Now can account for radars with other beamwidths and\n", " gate spacings than NEXRAD.\n", " 2. Added use_ntda flag to turn on/off NTDA-based filtering (i.e., can turn off\n", " to straight up convert SW to EDR).\n", " 3. Performance improvements leading to significant code speedup.\n", " 4. Removed variables and imports related to NTDA lookup tables.\n", " 5. Changed name of different_sweeps flag to split_cut for improved clarity.\n", " 6. Changed atan2_cython function's name to atan2c_longitude to better indicate\n", " its specialized nature. Added more generic atan2c function to\n", " pytda_cython_tools, although this is not currently used.\n", " \n", " Version 0.5 Major Changes:\n", " 1. Fixed a bug that prevented actual filtering of DZ/SW fields before\n", " turbulence calculations in certain radar volumes. Performance improved\n", " considerably!\n", " 2. Fixed bug that set turbulence to 0 where it was never calculated to\n", " begin with. Should instead be the bad data fill value for spectrum width.\n", " 3. Fixed bug that caused a crash when running calc_turb_vol() with\n", " different_sweeps keyword set to True.\n", " \n", " Version 0.4 Major Changes:\n", " 1. Refactoring to reduce number of local variables in calc_turb_sweep() proper.\n", " 2. Added calc_turb_vol(), which leverages calc_turb_sweep() to process and\n", " entire volume at once, and add the turbulence field to the Py-ART radar\n", " object.\n", " 3. Added add_turbulence_field() to create a Py-ART radar field for turbulence\n", " \n", " Version 0.3 Major Changes:\n", " 1. Refactoring of calc_turb_sweep() to drastically speed up processing.\n", " \n", " Version 0.2 Functionality:\n", " 1. calc_turb_sweep() - Input Py-ART radar object, receive back turbulence on\n", " the specified sweep plus longitude and latitude in that coordinate system.\n", "\n", "FUNCTIONS\n", " add_turbulence_field(radar, turbulence, turb_name='turbulence')\n", " \n", " atan2c_longitude(...)\n", " \n", " calc_cartesian_coords_radians(lon1r, lat1r, lon2r, lat2r)\n", " Assumes conversion to radians has already occurred\n", " \n", " calc_cswv_cython(...)\n", " \n", " calc_turb_sweep(radar, sweep_number, radius=2.0, split_cut=False, xran=[-300.0, 300.0], yran=[-300.0, 300.0], verbose=False, name_dz='reflectivity', name_sw='spectrum_width', use_ntda=True, beamwidth=0.96, gate_spacing=0.25)\n", " Provide a Py-ART radar object containing reflectivity and spectrum width\n", " variables as an argument, along with the sweep number and any necessary\n", " changes to the keywords, and receive back turbulence for the same sweep\n", " as spectrum width (along with longitude and latitude on the same coordinate\n", " system).\n", " radar = Py-ART radar object\n", " sweep_number = Can be as low as 0, as high as # of sweeps minus 1\n", " radius = radius of influence (km)\n", " split_cut = Set to True if using NEXRAD or similar radar that has two\n", " separate low-level sweeps at the same tilt angle for DZ & SW\n", " verbose = Set to True to get more information about calculation progress.\n", " xran = [Min X from radar, Max X from radar], subsectioning improves\n", " performance\n", " yran = [Min Y from radar, Max Y from radar], subsectioning improves\n", " performance\n", " name_dz = Name of reflectivity field, used by Py-ART to access field\n", " name_sw = Name of spectrum width field, used by Py-ART to access field\n", " use_ntda = Flag to use the spatial averaging and weighting employed by NTDA\n", " beamwidth = Beamwidth of radar in degrees\n", " gate_spacing = Gate spacing of radar in km\n", " \n", " calc_turb_vol(radar, radius=2.0, split_cut=False, xran=[-300.0, 300.0], yran=[-300.0, 300.0], verbose=False, name_dz='reflectivity', name_sw='spectrum_width', turb_name='turbulence', max_split_cut=2, use_ntda=True, beamwidth=0.96, gate_spacing=0.25)\n", " Leverages calc_turb_sweep() to process an entire radar volume for\n", " turbulence. Has ability to account for split-cut sweeps in a volume\n", " (i.e., DZ & SW on different, mismatched sweeps).\n", " radar = Py-ART radar object\n", " radius = Search radius for calculating EDR\n", " split_cut = Set to True for split-cut volumes\n", " xran = Spatial range in X to consider\n", " yran = Spatial range in Y to consider\n", " verbose = Set to True to get more information on calculation status\n", " name_dz = Name of reflectivity field\n", " name_sw = Name of spectrum width field\n", " turb_name = Name for created turbulence field\n", " max_split_cut = Total number of tilts that are affected by split cuts\n", " use_ntda = Flag to use the spatial averaging and weighting employed by NTDA\n", " beamwidth = Beamwidth of radar in degrees\n", " gate_spacing = Gate spacing of radar in km\n", " \n", " edr_long_range(sw, rng, theta, gs)\n", " For gate spacing < range * beamwidth\n", " sw (spectrum width) in m/s,\n", " rng (range) in km,\n", " theta (beamwidth) in deg,\n", " gs (gate spacing) in km\n", " \n", " edr_short_range(sw, rng, theta, gs)\n", " For gate spacing > range * beamwidth\n", " sw (spectrum width) in m/s,\n", " rng (range) in km,\n", " theta (beamwidth) in deg,\n", " gs (gate spacing) in km\n", " \n", " flatten_and_reduce_data_array(array, condition)\n", " \n", " get_radar_latlon_plus_radians(radar)\n", " Input Py-ART radar object, get lat/lon first in deg then in radians\n", " \n", " get_range_adjusted_nexrad_sweep(file, sweep)\n", " Function under construction, not used yet.\n", " Credit: JJ Helmus, DOE\n", " file: Path and name of original radar file\n", " sweep: Number of sweep needing range adjustment\n", " Function will return sweep as radar object with modified range.\n", " \n", " get_sweep_azimuths(radar, sweep_number)\n", " \n", " get_sweep_data(radar, field_name, sweep_number)\n", " \n", " get_sweep_elevations(radar, sweep_number)\n", " \n", " polar_coords_to_latlon(radar, sweep_number)\n", " Function under construction, not currently used\n", "\n", "DATA\n", " BAD_DATA_VAL = -32768\n", " CONSTANT = 2.1665887030822408\n", " DEFAULT_BEAMWIDTH = 0.96\n", " DEFAULT_DZ = 'reflectivity'\n", " DEFAULT_GATE_SPACING = 0.25\n", " DEFAULT_RADIUS = 2.0\n", " DEFAULT_SW = 'spectrum_width'\n", " DEFAULT_TURB = 'turbulence'\n", " KOLMOGOROV_CONSTANT = 1.6\n", " MAX_INT = [-300.0, 300.0]\n", " RNG_MULT = 1000.0\n", " RRV_SCALING_FACTOR = 3.3302184446307908\n", " SPLIT_CUT_MAX = 2\n", " VARIANCE_RADIUS_SW = 1.0\n", " VERSION = '1.0'\n", " __warningregistry__ = {('invalid value encountered in sqrt', \n", " hypergeometric_gaussian = \n", " print_function = _Feature((2, 6, 0, 'alpha', 2), (3, 0, 0, 'alpha', 0)...\n", " re = 6371.1\n", "\n", "FILE\n", " /Users/tjlang/Documents/Python/pytda/pytda.py\n", "\n", "\n" ] } ], "source": [ "help(pytda)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 0 }