diff --git a/02_discrete_signal_translation.ipynb b/02_discrete_signal_translation.ipynb index 42fd214..95b7993 100644 --- a/02_discrete_signal_translation.ipynb +++ b/02_discrete_signal_translation.ipynb @@ -50,7 +50,6 @@ "import matplotlib.pyplot as plt\n", "import matplotlib.gridspec as gridspec\n", "import matplotlib.ticker as tck\n", - "import scipy.interpolate as interp\n", "\n", "rng = np.random.default_rng()" ] @@ -63,13 +62,34 @@ "source": [ "class TravelSignal:\n", " \"\"\"\n", - " Model an arbitrary signal that can be translated to another position and time.\n", + " Model an arbitrary digitised signal that can be translated to another position and time.\n", " \"\"\"\n", "\n", - " def __init__(self, signal, sample_rate, t_0 = 0, x_0 = 0, periodic=True):\n", - " assert periodic, \"Non-periodic signal is not yet supported\"\n", + " def __init__(self, signal, sample_rate, t_0 = 0, x_0 = 0, periodic=True, interp1d_kw = None):\n", + " \"\"\"\n", + " Initialise by saving the raw signal\n", + " \n", + " Parameters\n", + " ----------\n", + " signal : arraylike\n", + " The raw signal to wrap.\n", + " sample_rate : float\n", + " Sample rate of the raw signal.\n", + " t_0 : float, optional\n", + " Time that this signal is sent out.\n", + " x_0 : float, optional\n", + " Location that this signal is sent out from.\n", + " periodic : bool, optional\n", + " Translated signal is 0 if it is not periodic\n", + " and the time/distance is outside the samples.\n", + " interp1d_kw : bool or dict, optional\n", + " Use scipy.interpolate's interp1d_kw for interpolation.\n", + " Set to True, or a dictionary to enable.\n", + " Dictionary will be entered in as **kwargs.\n", + " \"\"\"\n", "\n", " self.raw = signal\n", + " self.periodic = periodic\n", "\n", " self.sample_rate = sample_rate # Hz\n", " self.sample_length = len(self.raw)\n", @@ -102,12 +122,10 @@ " interp1d_kw = { **interp1d_kw_defaults, **interp1d_kw }\n", "\n", " self.interp_f = interp.interp1d(\n", - " np.arange(0, self.sample_length),\n", - " self.raw,\n", - " **interp1d_kw\n", - " )\n", - " else:\n", - " self.interp_f = None\n", + " np.arange(0, self.sample_length),\n", + " self.raw,\n", + " **interp1d_kw\n", + " )\n", " \n", " def __len__(self):\n", " return self.sample_length\n", @@ -155,15 +173,23 @@ "\n", " # total offset\n", " total_time_offset = spatial_time_offset + temporal_time_offset\n", + " n_offset = (total_time_offset * sample_rate )\n", "\n", " # periodic signal\n", - " n_offset = (total_time_offset * sample_rate ) % self.sample_length\n", + " if self.periodic:\n", + " n_offset = n_offset % self.sample_length\n", + "\n", + " # non-periodic and outside the bounds\n", + " else:\n", + " mask_idx = np.nonzero( (0 > n_offset) | (n_offset >= self.sample_length) )\n", + " n_offset[mask_idx] = 0\n", "\n", " # offload to scipy interpolation\n", " if self.interp_f:\n", " amplitude = self.interp_f(n_offset)\n", + " \n", + " # self written linear interpolation\n", " else:\n", - " # self written interpolation\n", " n_offset_eps, n_offset_int = np.modf(n_offset)\n", " n_offset_int = n_offset.astype(int)\n", "\n", @@ -171,9 +197,12 @@ " amplitude = (1-n_offset_eps) * self.raw[n_offset_int] \\\n", " + n_offset_eps * self.raw[(n_offset_int + 1) % self.sample_length]\n", "\n", + " # use nearest value instead of interpolation\n", " else:\n", " amplitude = self.raw[n_offset_int]\n", "\n", + " if not self.periodic:\n", + " amplitude[mask_idx] = 0\n", " \n", " return amplitude, total_time_offset" ] @@ -289,39 +318,14 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "P Raw S1 S2\n", - "a 1.0 1.0 0.9130434782608696\n", - "T 0.4666666666666667 0.4666666666666667 0.4782608695652174\n", - "[0. 0.03333333 0.06666667 0.1 0.13333333 0.16666667\n", - " 0.2 0.23333333 0.26666667 0.3 0.33333333 0.36666667\n", - " 0.4 0.43333333 0.46666667 0.5 0.53333333 0.56666667\n", - " 0.6 0.63333333 0.66666667 0.7 0.73333333 0.76666667\n", - " 0.8 0.83333333 0.86666667 0.9 0.93333333 0.96666667] [0. 0.01449275 0.02898551 0.04347826 0.05797101 0.07246377\n", - " 0.08695652 0.10144928 0.11594203 0.13043478 0.14492754 0.15942029\n", - " 0.17391304 0.1884058 0.20289855 0.2173913 0.23188406 0.24637681\n", - " 0.26086957 0.27536232 0.28985507 0.30434783 0.31884058 0.33333333\n", - " 0.34782609 0.36231884 0.37681159 0.39130435 0.4057971 0.42028986\n", - " 0.43478261 0.44927536 0.46376812 0.47826087 0.49275362 0.50724638\n", - " 0.52173913 0.53623188 0.55072464 0.56521739 0.57971014 0.5942029\n", - " 0.60869565 0.62318841 0.63768116 0.65217391 0.66666667 0.68115942\n", - " 0.69565217 0.71014493 0.72463768 0.73913043 0.75362319 0.76811594\n", - " 0.7826087 0.79710145 0.8115942 0.82608696 0.84057971 0.85507246\n", - " 0.86956522 0.88405797 0.89855072 0.91304348 0.92753623 0.94202899\n", - " 0.95652174 0.97101449 0.98550725]\n" - ] - }, { "data": { - "image/png": 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\n", 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VEhLCww8/TL9+/ejXrx+PPPIIISHW9T5mzJhBamoqAA888AAxMTHExsayevVqXnjhhUZfuyVSdQ2bO6PExERd+R9RCGG8De8/wsBDL1J4fxp7iw+RXpgOWpO39EF8Xdpz7f2rGtX/py9fS7e89fT6y2HKVQXLf18OQPnns/H3H0Xyne80xcsQQgjhxH4+9jOnSqquPJ/QJoEO/h1qOcMxp0tP80P6D7bH7i7ujOw4Em837ybpvyXbs2cP0dHRRseoV2pqKnPmzGHtWsduYTKZTLi6uuLm5sb69euZNWsWW7dubaaUoiY1/YwppTZrrWvc60jugRVCNMrh0t/J9fZlrF8gn277lK8PW5e4J8wDH8tJxhbl4u8b1KC+LRUVvOq9hzjXTjzv4cmpwmM8/NPD1oOtA7nUdIjkJnodQgghnNe7u9/l5+M/V3kusU0i7yQ3/EPMrJIsNmRsICkiiWNn/3454/HBjzMxamKD+xfnz1NPPcWrr77aoOm8R44c4brrrsNiseDh4cGbb77ZDAlFU5ICVgjRKD/rNLa3CibZxYUH+j3APX3uAeCz71/mjdyvWPHz+0wafU+D+v5xyzJOubkQ7GOdntPGpw3fXWPdF23Gh8kU6JKmeRFCCCGc0ge7PyDcL5x/Dv0npoo/Vo/dlbWL6JDGjQr+cPQH5q+fz2cTPqN7cHfb75eyijImfDGB7FJZZ6GlePDBB3nwQfu24zlXVFQUW7ZsaeJEojlJASuEaJQiXYqfxbrUe4hXiO35m5PmMOnFtzhavAtGN6zvNbs/BmBS/1kAuLm40d7PuvhCqMUNF21uRHIhhBDO7p1d7zCk3RBGdRxV5fnK3wWNse7YOtr4tKFbUDeUUlX6XH3dagI9Axt9DSFE05MCVgjRKCWqDF9d/Z+S4MDWpLv3JjxzXYP73lO6j0gFvbr2q3bs/vwIQkp+a3DfQgghnF++Kb/WQnJ52nJyTblc1+M6h/stryhnfcZ6xnUeV+N+m628WzncpxDi/JBViIUQjVKkzHhr9xqP7evQhwWtStiy1/Ei9kTWUfZ7lNPTpeYFOsyegfhZChzuVwghRMtQai6ltKK09gL29+W8tu21OvfxrM3WzK0UlRcxtP3QGo9/vPdjPt3/qcP9CiGanxSwQohGKXKx4Ku8ajzWLmY0a3x9WP5risP9Hk/9no+PneCKqJtrPP6LdznzW7tjqahwuG8hhBDOL8+UB1BrATu0/VAySzLZd3qfw31vy9yGm4sbA8MH1nj8u7Tv+PLQlw73K4RoflLACiEa5ZWMHMZbutZ4bGDvMbQt1+wq3OZwv+Z9Kwgvc2PwwGtqPJ7jqfjB15vs/JMO9y2EEML55ZWdKWA9ai9gAdamO7ZtCsCMmBksv2Y5vu6+NR4P8gwivyzf4X5F81BKccstt9gem81mwsLCuOKKK+o8b9++fSQlJREfH090dDQzZ84ErFvuzJ49u8lzzp8/nwULFtge33ffffz444+AdT/X3bt313n+F198UW8bR2RmZpKc3PD9Gn777TcGDBhAVFQU119/PWVlZdXarFixgoSEBGJiYkhISGDVqj+2T0xKSqJHjx7Ex8cTHx/PqVOnqp3fEFLACiEarLSkiGhzMa282tV43MXVlWjdmj3uJRQU5drdr6Wigq9UKt8Ex+Du4VljGz+PYACOnTrseHAhhBBOLyooio03bSSpQ1KNx1t5tyI6JJp1xxq21kKYT1itxwI9A8k12f97SzQvX19fdu7cSUmJdfeBFStW0L59/Qt5zZ49mzlz5rB161b27NnDPfdYd0VITEzkpZdeatbMOTk5bNiwgeHDhwPw1ltvcckll9R5TkMKWLO59gUtw8LCCA8P56effqqzj5SUFObPn1/t+Xnz5jFnzhwOHDhAcHAwb7/9drU2rVq14ssvv2THjh28++67VT5oAFi0aBFbt25l69attG7d2r4XVQ8pYIUQDZZxKo0PAvw56eVaa5u+7UZQ6qL4cp39+6qt2fw5nwd6cCw8qtY2Ad7WNx6nco7aH1gIIUSLoZTC280bD1ePWtsMixhGQXkB5ZZyu/v96vBXzP1hLsXlxbW2CfQMJM+U16D7a0XzGDduHF9/bd1r/sMPP2TKlCkAWCwWoqKiyMzMtD3u1q0bWVlZZGRkEBERYesjJiYGgDVr1thGbzMzMxk9ejR9+/bljjvuoFOnTmRlZZGWlkZ0dDS33347vXr1YsyYMbYC+s0336Rfv37ExcVxzTXXUFxc/Wfp008/rTL6mZSURGpqKgB+fn787W9/Iy4ujoEDB3Ly5El+/vlnli1bxl/+8hfi4+M5dOgQhw4dIjk5mYSEBIYNG8bevXsBmDp1Kvfffz8jRoxg3rx5zJ8/n1tuuYWRI0cSFRVVZS/bq6++ukH742qtWbVqFddeey0At956K1988UW1dn369KFdO+tARq9evSgtLcVkMlVr15SkgBVCNNjvmft5OjSY4561f/p31dA7iS0tozhtk939/rDnEwDG9but1jbBfm0AyM4/bne/QgghWo5NJzbxzKZnKCovqrXNXXF38dmEz3B3qXkxwZqsSFvB9szteLt519om0DOQCl1BiVn2Gz/XtO+mVfvz0d6PACgxl9R4/IuD1sLndOnpasfsdcMNN/DRRx9RWlrK9u3bGTBgAAAuLi7cfPPNtiJt5cqVxMXF0apVK+bMmcPIkSMZN24cL7zwArm51UfVH3vsMUaOHMmvv/7KxIkTOXLkiO3YgQMHuPvuu9m1axdBQUEsWbIEgEmTJrFp0ya2bdtGdHR0jSOTP/30EwkJCTW+lqKiIgYOHMi2bdsYPnw4b775JoMHD2bChAk8++yzbN26la5duzJz5kz+85//sHnzZhYsWMBdd91l62P//v2sXLmS5557DoDt27fz9ddfs379eh5//HGOH7e+P0pMTGTtWsen2WdnZxMUFISbm3WniYiICI4dO1bnOUuWLKFPnz54ev4xe27atGnEx8fzxBNPNNkHQlLACiEaLDPX+g9ZsG/t07CCA8OYdzqCMSd32t3vHtM+OpdBdJea/+EHaBPUgfblZspK8uwPLIQQosXYnrmd93e/j4uq/e2qq4t1BpC9b4zLK8rZkLGBYe2H1bh9TqVbLrmFLbdswcfdx7HQotnExsaSlpbGhx9+yOWXX17l2PTp03nvvfcAWLhwIdOmWQvjadOmsWfPHiZPnsyaNWsYOHBgtdHBdevWccMNNwCQnJxMcHCw7Vjnzp2Jj48HICEhgbS0NAB27tzJsGHDiImJYdGiRezatata3oyMDMLCan5/5OHhYRsBPrvfsxUWFvLzzz8zefJk4uPjueOOO8jIyLAdnzx5Mq6uf8yAu+qqq/D29qZVq1aMGDGCjRs3AtC6dWtbMXu27Oxs272pjzzyCK+99prt8Y4dO2r8f6qu/2d27drFvHnzeP31123PLVq0iB07drB27VrWrl3L+++/X+v5jpB9YIUQDXa6yLqAUqh/zffAViruOIKoAws4dGALXaP61Nn2eObv7PcwM6aiU53teobH8t2Xx/klwN+x0EIIIVqEvLI8PFw88HKteaX7Su/vfp/F+xaz9OqldRa7AL+e+pVic3Gt2+dUcmRE92LzTvI7tR7zdvOu83iwV3Cdx+szYcIE5s6dy5o1a8jOzrY936FDB9q0acOqVav45ZdfqkyZbdeuHdOnT2f69On07t2bnTurfqBe14cfZ48kurq62qYQT506lS+++IK4uDhSUlJYs2ZNtXO9vb0pLS2tsV93d3dbMejq6lrjfawWi4WgoCC2bt1aYx++vlUXIDu3uKx8XFpaird39dkGoaGhtr5TUlJIS0urch+s1prc3FzMZjNubm6kp6fbpgqfKz09nYkTJ/Lee+/RtesfC3tW3qfs7+/PjTfeyMaNG/nTn/5UYx+OkBFYIUSDFZRkAdAmtGOd7bzihjOsY3s+2fifevv8Zf1HtDVXMKjLlXW28w+2fqqpi0/bmVYIIURLkm/KJ9AzsM5RH7BO903LT2Nvzt56+1ybvhZ3F3cGhA+os116QTrzf57PvhzHt+gRzWf69Ok88sgjtntZzzZjxgxuvvlmrrvuOtvI5HfffUd5ufX+6BMnTpCdnV1t8aehQ4eyePFiAJYvX87p0/W/rygoKCA8PJzy8vJa7y+Njo7m4MGDDr0+f39/Cgqse9wHBATQuXNnPvnEeluV1ppt22rf1WHp0qWUlpaSnZ3NmjVr6NevH2Cdaty7d2+HcoC1AB4xYgSffmrdD/ndd9/lqquuqtYuNzeX8ePH8+STTzJkyBDb82azmaws6/vE8vJyvvrqqwblqIkUsEKIBis0Wf+RDw+LrLNd3CXDCKlQ7CrcXm+f7Q9v5eP0PK4YUvd9MV7evtzdujUry+2fmiyEEKLlyDXl1roH7NmGtLO+abZnO502vm24utvV9U4NLjGXsOTAEtLy0+zKKs6PiIgI7r333hqPTZgwgcLCQtv0YbAWpL179yYuLo6xY8fy7LPP0rZt2yrnPfrooyxfvpy+ffvy7bffEh4ejr9/3bO7nnjiCQYMGMDo0aPp2bNnjW3Gjx9f48hsXW644QaeffZZ+vTpw6FDh1i0aBFvv/02cXFx9OrVi6VLl9Z6bv/+/Rk/fjwDBw7k4Ycfto2Wrl69mvHjxzuUo9LTTz/N888/T7du3cjOzua226xrkyxbtoxHHnkEgJdffpmDBw/yxBNPVNkux2QyMXbsWGJjY4mPj6d9+/bcfvvtDcpxLtXSVldLTEzUlSt4CSGMteb1u+h46hMiHzqBi2vtKxED3PvmKH5yO8n3k38k0C+kxjaWigoyn+jKMb9Y+s5dVu/1R77di+4VIbw20/HFCYQQQji3WStnUWIuISU5pd62N3x1A+4u7rx/edPcY3eq+BSjPhnFwwMf5roe1zVJny3Vnj17iI6ONjpGvVJTU5kzZ47DCxaZTCZcXV1xc3Nj/fr1zJo1q9Zpu44aOnQoX331FUFBQU3SX23mz5+Pn58fc+fOrXZs+PDhLF26tMq9vc6mpp8xpdRmrXViTe1lBFYI0WA+pnwCKnzqLV4BEtqPwuSi+HJt7dvprNr0Kdd19GFXJ/ummPhaXCim5vtLhBBCtGyvXvYqC8cutKvtsIhhbM/aTp6p9oX9skqy7N5uJ8AjAID8sny72gtjPfXUU1xzzTU8+eSTDp975MgR25Y4s2fPrrIFTWM999xzVVY1Pt8yMzO5//77nbp4bQhZxEkI0WC/kM52f39m2NF2wrCZvPTp/yP12EpuZl6NbX7c9yk5rq7072/fp92+FjeKVZkDiYUQQrQk9S3KVOmyjpdRVlFWZ4H62PrHOF54nCUTltTbn5ebF16uXnUWxMJ5PPjggzz44IMNOjcqKootW7Y0cSKryq1+mtvZiy+dLSwsjKuvvvq8ZDifpIAVQjTYOq88LF5udhWwQf6tuC0vkF7F6bW22Ws6QBcFPTrF23V9H+XBCVX7/oBCCCFarr+u/SuXdbyMUZ1G1du2R0gPeoT0qPV4WUUZv2T8woSuE+y+fmuf1lToCrvbCyHOD5lCLIRosGJlxkfbv9VAnzbjGV52nGOHq++Xln4qjf0eZnq6RtrdX1vtT4fy6kvPCyGEaNlKzCV8dfgrfsv/ze5zKotUi7ZUO7b55GZKzCUMaz/M7v6+nvQ1D/R7wO72Qojzo1kLWKVUslJqn1LqoFKq2ri+UqqjUmq1UmqLUmq7UurymvoRQjinQhcL3qru/fnO1j7xSlb7eLNyQ/X7S7786XUqlGJw5yvs7u9KFc3LJ0+hLdXfrAghhGi5KqfuBnnav/jN/9L+x4zlM9iTvafasXXH1uHu4k6/tv2aLKMQwhjNNoVYKeUKvAKMBtKBTUqpZVrr3Wc1+zuwWGv9qlLqEuAbILK5MgkhmlaBi8JX+9bf8IwOUTHMCGlFSelq1q+80/b8Q/0fonX6Hm7QJYwdfIvd/SmfENxVBYWFefgFXFgLFAghxMWssoC1ZxudSkPaD0Gh+PtPf6eNbxsA/jX0X4R4hXAo7xD92vard/ucsy3as4jDuYd5eNDDjoUXQjSr5hyB7Q8c1Fof1lqXAR8B5+5+q4GAM02NHNcAACAASURBVF8HAsebMY8QognlFmRhclH4ute9V9q5Rld0oV15Kbklp8k35ZNvysdsLufS7C0kl/fGy9P+Nxe7PIqY2L4tB4/vrr+xEEKIFsNWwHrYX8CGeIVw8yU34+3mbfv9Urld5NhOY7kp+iaHMhw4fYBVR1c5dI5oHunp6Vx11VVERUXRtWtX7r33XsrKal7E8fjx41x77bUO9f/VV1/Rp08f4uLiuOSSS3j99debInat5s+fz4IFC5qkr61bt/LNN980SV8tRXMu4tQeOHrW43Tg3KW45gPLlVL3AL7AZTV1pJSaCcwE6NixY5MHFUI4riy/gE1pR9no4BuCMd3/xNwfbmNb92uJGzEZgIPb1tGKXH7rWuM/AbXSXr4cLPHg5OnfgSEOnSuEEMJ5lVvKCfEKIcjLsf0za7tndWLURIczBHoGkmvKRWuNUsrh80XT0FozadIkZs2axdKlS6moqGDmzJn87W9/49lnn63S1mw2065dOz799FO7+y8vL2fmzJls3LiRiIgITCYTaWlpTfwqGsdsNuPmVnPZtnXrVlJTU7n88ovnTszmHIGt6f90fc7jKUCK1joCuBx4X6nq66Vrrd/QWidqrRPDwsKaIaoQwlFFuafw0ho//zYOndd9QDIl2oPS3d/Znsvc8hUAnQeeO0mjbsF+1mtn52c4dJ4QQgjnNqT9EH64/ge6B3c3LEOQZxBmi5kSc4lhGQSsWrUKLy8vpk2bBoCrqysvvPACCxcupLi4mJSUFCZPnsyVV17JmDFjSEtLo3dv637yxcXFXHfddcTGxnL99dczYMAAUlNTq/RfUFCA2WwmNDQUAE9PT3r0sK5o/eWXXzJgwAD69OnDZZddxsmTJwHrCOqtt97KmDFjiIyM5LPPPuOBBx4gJiaG5ORkysut2zlFRkYyb948+vfvT//+/Tl48GC113fo0CGSk5NJSEhg2LBh7N27F4CpU6dy//33M2LECObNm8fGjRsZPHgwffr0YfDgwezbt4+ysjIeeeQRPv74Y+Lj4/n4448pKipi+vTp9OvXjz59+rB06dJm+K9irOYcgU0HOpz1OILqU4RvA5IBtNbrlVJeQCvgVDPmEkI0gQOndrM4JIj+Xo59Dubl48c2n3jaZ62zPRd87AcOuHYjqm2HOs6srlVgezgKuUUnHTpPCCGEqE/l/be5plyH7p29kJ34178w7dnbpH16Rvek7UMP1Xp8165dJCQkVHkuICCAjh072grC9evXs337dkJCQqqMnv73v/8lODiY7du3s3PnTuLjq2/TFxISwoQJE+jUqROjRo3iiiuuYMqUKbi4uDB06FA2bNiAUoq33nqLZ555hueeew6wFp6rV69m9+7dDBo0iCVLlvDMM88wceJEvv76a9v+qwEBAWzcuJH33nuP++67j6+++qrK9WfOnMlrr71GVFQUv/zyC3fddRerVlmnru/fv5+VK1fi6upKfn4+P/74I25ubqxcuZKHHnqIJUuW8Pjjj5OamsrLL78MwEMPPcTIkSNZuHAhubm59O/fn8suuwxfX/vXLHF2zVnAbgKilFKdgWPADcCN57Q5AowCUpRS0YAXkNmMmYQQTSQt/yAfBAaQ4G3/KsSVSiNHEbHnSY4e3EFAcGuiyvawqcN0ohzsJzykEwD5pdkOZxBCCOG8Fu9bzKYTm3j20mfrb9xMWnm3ooN/B0wVJsMyCGqdwn3286NHjyYkJKRam3Xr1nHvvfcC0Lt3b2JjY2u8xltvvcWOHTtYuXIlCxYsYMWKFaSkpJCens71119PRkYGZWVldO7c2XbOuHHjcHd3JyYmhoqKCpKTkwGIiYmpUkRPmTLF9vecOXOqXLewsJCff/6ZyZMn254zmf74eZs8eTKurq4A5OXlceutt3LgwAGUUrZR3nMtX76cZcuW2e6xLS0t5ciRI0RHR9fYviVqtgJWa21WSv0f8D/AFViotd6llHocSNVaLwP+DLyplJqDdXrxVF15t70Qwqnll1iLxtahnRw+N6LfVbDnSY5tWsapgDYkKE1QnOP3brRr3Zn+JaX4adkLVgghLiQ7s3by68lfDc0wPGI4wyOGG5rB2dQ1UtpcevXqxZIlS6o8l5+fz9GjR+natSubN2+udXTRkbIiJiaGmJgYbrnlFjp37kxKSgr33HMP999/PxMmTGDNmjXMnz/f1t7T0xMAFxcX3N3dbcW0i4sLZvMf70vOLr7PLcQtFgtBQUFs3bq1xkxnv66HH36YESNG8Pnnn5OWlkZSUlKtr3nJkiW2adAXombdB1Zr/Y3WurvWuqvW+p9nnnvkTPGK1nq31nqI1jpOax2vtV7enHmEEE2nwJQLQPuwSIfPbd8lmqOqHT6/r8Kyfzm5+BHVJ8nhfvx8A/lPRh6JJY6PAgshhHBeeaY8Ar3sX4FYXLhGjRpFcXEx7733HgAVFRX8+c9/ZurUqfj41D21e+jQoSxevBiA3bt3s2PHjmptCgsLWbNmje3x1q1b6dTJ+uF8Xl4e7du3B+Ddd99tUP6PP/7Y9vegQYOqHAsICKBz58588skngLX43LZtW439nJ0lJSXF9ry/vz8FBQW2x2PHjuU///mPrXjfsmVLg3I7s2YtYIUQF64icx4eFk2wf8MWVjsWNoweJdvolreeg/4DcK1ldb365Ct/XEtzG3SuEEII55RXlufQFjrNIb8snxn/m8HyNBlfMZJSis8//5xPPvmEqKgounfvjpeXF//617/qPfeuu+4iMzOT2NhYnn76aWJjYwkMrPpzpbXmmWeeoUePHsTHx/Poo4/aCsT58+czefJkhg0bRqtWrRqU32QyMWDAAF588UVeeOGFascXLVrE22+/TVxcHL169ap10aUHHniAv/71rwwZMoSKigrb8yNGjGD37t22RZwefvhhysvLiY2NpXfv3jz88IW3j7FqaTN2ExMT9bmrhwkhzr/73khim2smq2/b1aDzd/zwGTGrrSsKftttPuNunlP3CbW47dVYXJUXb9y5sUHnCyGEcD4Tl06kU0An/j3i34ZlMFWYSPwgkdl9ZnN77O2G5TDanj17Wuz9kxUVFZSXl+Pl5cWhQ4cYNWoU+/fvx8PD47xcPzIyktTU1AYXvxeLmn7GlFKbtdaJNbVvzkWchBAXsFvzfAksafjiSUXhAyjWnnhRxuN7w2n9+2kSOgU73E+xqyvlyAIbQghxIWnt05qOAR0NzeDp6om3mze5Jpnl01IVFxczYsQIysvL0Vrz6quvnrfiVTQfKWCFEA3iUZ5HqWtAg8/ffKyEE5YEwsjjVIU/Gw5nN6iA9dYe5LoWNTiHEEII5/P66NeNjgBYt9LJM+UZHUM0kL+/f7V9X8+ns1cjFk1HClghRIN85JdHkArmkgaeP7BLKH9adTdmcwXubi4M7BLaoH58XLwpcilsYAohhBCidoEegeSVSQErhDORRZyEEA2y1tfCQc+G30Of0CmY92YMYfaYXiyaMbBBo68APi6+FLgoLGctaCCEEKLlyizO5Mavb2Rt+lqjo3BJ6CW09WlrdAwhxFlkBFYI0SCFLgpf7deoPhI6BTe4cK0U6dGO5Pw9nM7LJDRE3mQIIURLl1Oaw46sHZgqjF/f4PEhjxsdQQhxDhmBFUI4LLcgC5OLwsfd3+goJPrF8GRmNqZ8meIlhBAXgspFkwI9ZR9YIUR1UsAKIRyWfvIQAH4eQQYnAXffEAAKT580OIkQQoimULlokjMUsIv3LWbSskm0tG0nLzR+fvXP+Pr3v/9NcXFxs1y/uLiYm266iZiYGHr37s3QoUMpLGze9Tfsec32as7vjRGkgBVCOCwn9wRhZjPB3mFGRyHdo4T+nSJIPfWL0VGEEEI0gcpFkwI9jC9gi8uLOXD6AMXmC+fN/4WqIUVahZ3rZ7z44ou0adOGHTt2sHPnTt5++23c3d0bErNZaK2xWCy1HpcCVghx0QsxubDq6HEGhQ00OgpBAW0ocXHhdNEpo6MIIYRoAv4e/sSGxTrFCGxlBtlKxzmsWbOGpKQkrr32Wnr27MlNN92E1pqXXnqJ48ePM2LECEaMGAHA8uXLGTRoEH379mXy5Mm2EdPIyEgef/xxhg4dyieffEJSUhL33XcfgwcPpnfv3mzcuLHadTMyMmjfvr3tcY8ePfD09ATg6quvJiEhgV69evHGG2/Y2vj5+TFv3jwSEhK47LLL2LhxI0lJSXTp0oVly5YBkJKSwlVXXUVycjI9evTgscceq/F1P/vss/Tr14/Y2FgeffRRwLpFT3R0NHfddRd9+/bl6NGjzJo1i8TERHr16mVr58j3pqWQRZyEEA4rK8gGwDvA+BHYNqGdACgoyTY4iRBCiKaQHJlMcmSy0TEACPC07neea8qlnV87g9MYb+3i/WQdbdpip1UHP4Zd193u9lu2bGHXrl20a9eOIUOG8NNPPzF79myef/55Vq9eTatWrcjKyuIf//gHK1euxNfXl6effprnn3+eRx55BAAvLy/WrVsHwGuvvUZRURE///wzP/74I9OnT2fnzp1Vrjl9+nTGjBnDp59+yqhRo7j11luJiooCYOHChYSEhFBSUkK/fv245pprCA0NpaioiKSkJJ5++mkmTpzI3//+d1asWMHu3bu59dZbmTBhAgAbN25k586d+Pj40K9fP8aPH09iYqLt2suXL+fAgQNs3LgRrTUTJkzgxx9/pGPHjuzbt4933nmH//73vwD885//JCQkhIqKCkaNGsX27dsd/t60BFLACiEclpq7lXdat+JeP2+jo9A+rAsAhbJPnxBCiCYW5Gld60FGYJ1H//79iYiIACA+Pp60tDSGDh1apc2GDRvYvXs3Q4YMAaCsrIxBgwbZjl9//fVV2k+ZMgWA4cOHk5+fT25uLkFBf6zzER8fz+HDh1m+fDkrV66kX79+rF+/nujoaF566SU+//xzAI4ePcqBAwcIDQ3Fw8OD5GTrBzExMTF4enri7u5OTEwMaWlptr5Hjx5NaGgoAJMmTWLdunXVCtjly5fTp08fAAoLCzlw4AAdO3akU6dODBz4x2y4xYsX88Ybb2A2m8nIyGD37t3ExsY69L1pCaSAFUI4LN10nDW+PjweYvyn0b4+/vhYLBRVFBgdRQghRBOY//N88svyeT7peaOj0Nq7NQPDB+LtZvwHts7AkZHS5lI5dRfA1dUVs9lcrY3WmtGjR/Phhx/W2Ievr2+Vx0qpOh+DdUrwpEmTmDRpEi4uLnzzzTecPHmSlStXsn79enx8fEhKSqK0tBQAd3d3Wz8uLi623C4uLlUy13dtrTV//etfueOOO6o8n5aWVuV1/PbbbyxYsIBNmzYRHBzM1KlTbVkc+d60BHIPrBDCYcXlBXhaNEH+oUZHAeCK/Aq6meSfMyGEuBD8lvebbSsdo3UI6MCbY94kvnW80VFEPfz9/SkosH6YPXDgQH766ScOHjwIWFcR3r9/f63nfvzxxwCsW7eOwMBAAgOr3n/9008/cfr0acA6Yrl79246depEXl4ewcHB+Pj4sHfvXjZs2OBw7hUrVpCTk0NJSQlffPGFbWS00tixY1m4cKHtPtVjx45x6lT1dT/y8/Px9fUlMDCQkydP8u2339qONeZ744xkBFYI4bBiSxF+SqNcnKNonJLvQ6GHbHEghBAXgvyyfCIDIo2OIVqYmTNnMm7cOMLDw1m9ejUpKSlMmTIFk8kEwD/+8Q+6d695BDk4OJjBgweTn5/PwoULqx0/dOgQs2bNsq32O378eK655hrKysp47bXXiI2NpUePHlWm89pr6NCh3HLLLRw8eJAbb7yxyvRhgDFjxrBnzx7bNF8/Pz8++OADXF1dq7SLi4ujT58+9OrViy5dulQphBvzvXFGqqXta5WYmKhTU1ONjiHERW3q6/3Jcinhq9t3GB0FgB1PDqfCYiL+b7KVjhBCtHQjFo/g0ohLmT94vtFRAJi0bBJJEUnM7jvb6CiG2LNnD9HR0UbHaDZJSUksWLCgWuF4PqSkpJCamsrLL7983q/tTGr6GVNKbdZa1/gfRUZghRAOC6ow42lxnv3PXgwp45hrEd/W31QIIYQT01qTZ8pzii10KhWWFXKy+KTRMYQQZ0gBK4Rw2L1ZZnK9Oxgdw8bLxZtCF1nESQghWjqzNnNpxKX0DOlpdBSbQM9A8k35RscQzWTNmjWGXXvq1KlMnTrVsOu3VFLACiEc5mfJJ9PDeT4d93H1pcBFYamowOWce0KEEEK0HO4u7rww4gWjY1QR6BFI3kW+VZvWusaVeYVorIbczuocK7AIIVoMS0UF94V7ssa3+tLsRvFzD6RCKU7kpBsdRQghxAUmwDPAaVZFNoKXlxfZ2dkNKjSEqIvWmuzsbLy8vBw6T0ZghRAOySvMYbuXB91wnntg/TyCoRROZKbRLqyT0XGEEEI00KYTm5j7w1xeHvkyMWExRscBoF/bfrT2aW10DMNERESQnp5OZmam0VHEBcjLy4uIiAiHzpECVgjhkKOnDgHg5xFkcJI/dPPvxh0Zy6goKjQ6ihBCiEbIKc0hpzQHTzdPo6PYTOk5xegIhnJ3d6dz585GxxDCRqYQCyEccirnKACBPq0MTvKH7qGX8H+5eXgVm4yOIoQQohHyTNZ7TYM8nedDUrBOdZQptEI4BylghRAOyc4/BkCQTxuDk/zBMyCELBcXcvOPGx1FCCFEI1QWsM60jc43h78h8YNEjhfJ7xghnEG9BaxSykcp9bBS6s0zj6OUUlc0fzQhhDPSxYXEl5poG+w895qW+3gwolME6/I3Gx1FCCFEI+SZ8vB288bT1XmmEHu5eVFmKbMV10IIY9kzAvsOYAIGnXmcDvyj2RIJIZxa5zJv3s84Sc/wWKOj2ES06QpA4UW+zYEQQrR0PUJ6MKHrBKNjVFE5nfliXolYCGdizyJOXbXW1yulpgBorUuUbAQlxEXLUpwDQEBwmMFJ/uDj5YuPxUJRRYHRUYQQQjTClV2v5MquVxodo4rK6cz5pnyDkwghwL4R2DKllDegAZRSXbGOyAohLkLflG/j+vC2ePn4GR2lCv8KKLYUGx1DCCFEI1RYKoyOUE1lASsjsEI4B3sK2EeB74AOSqlFwPfAA/Z0rpRKVkrtU0odVEo9WEub65RSu5VSu5RS/8/u5EIIQ2RZ8jjl5nw7cPlqF0q0fLYmhBAt2TXLrmHej/OMjlFFoEcg13a/ls6BspWMEM6g3nehWusVSqlfgYGAAu7VWmfVd55SyhV4BRiN9b7ZTUqpZVrr3We1iQL+CgzRWp9WSl28u0QL0UKU6FJ8Lc63gPm4Ij98KsqMjiGEEKIRck25eLt5Gx2jCndXdx4d9KjRMYQQZ9RawCql+p7zVMaZvzsqpTpqrX+tp+/+wEGt9eEz/X0EXAXsPqvN7cArWuvTAFrrU46EF0Kcf0WqHB/tanSMavqZW9O6aL/RMYQQQjSQ1pq8sjyn2wMWrFObyyxlTldcC3ExqmsE9rkzf3sBicA2rCOwscAvwNB6+m4PHD3rcTow4Jw23QGUUj8BrsB8rfV353aklJoJzATo2LFjPZcVQjSnYlVBa4uP0TGqOe3lhzYV08HoIEIIIRqkxFyC2WJ2qj1gK035egqtfVrz8qiXjY4ixEWv1nmAWusRWusRwO9AX611otY6AegDHLSj75pWKtbnPHYDooAkYArwllKq2sduWus3zlw/MSzMeVY+FeJiFFtaRhftfG8uVvrkc1t7f8zmcqOjCCGEaIDKfVadsYAN8AyQfWCFcBL23MjWU2u9o/KB1nonEG/HeelQZTAkAjheQ5ulWutyrfVvwD6sBa0Qwglpi4XHsrK4TDnf/6a+7gFYlOJEzjGjowghhGgAd1d3br3kVnqE9DA6SjWBHoGyCrEQTsKeAnaPUuotpVSSUupSpdSbwB47ztsERCmlOiulPIAbgGXntPkCGAGglGqFdUrxYfvjCyHOp5LiAjyUGeUdYnSUavw9rZlOZKYZG0QIIUSDtPJuxdx+c+kV2svoKNUEeQaRXyb7wArhDOwpYKcBu4B7gfuwLsI0rb6TtNZm4P+A/2EteBdrrXcppR5XSk040+x/QLZSajewGviL1jrb8ZchhDgfDh3fzdCO7Un1cL5PoQO8rbcXnDp9xOAkQgghGqLUXEpxeTFan3vHmfECPQPJM+U5ZTYhLjb2bKNTCrxw5o9DtNbfAN+c89wjZ32tgfvP/BFCOLkTOUfIc3XFw9v5VogM9W8LOZBTkFF/YyGEEE5n2aFlPLHhCb6f/D2tfZxrZ8UB4QNwd3HHrM24K3ej4whxUau3gFVK/Ub1xZfQWndplkRCCKeVnW+9jT3Yt43BSarr0iqax1Oz8Q2SNxZCCNESOfMiTgPCBzAg/NzNNIQQRqi3gMW6hU4lL2Ay4Hw3wAkhmt3pIutWza0Cww1OUl27sM7EFBbxS4nJ6ChCCCEaINeUi7ebN56unkZHqcZsMXO69DT+Hv54uXkZHUeIi1q998BqrbPP+nNMa/1vYOR5yCaEcDIFJVkAtA2NNDZIDQJDWrPN04OM4nSjowghhGiAPFOeU46+AmzP3M7IT0by68lfjY4ixEXPninEfc966IJ1RNa/2RIJIZxWaDlcUVhEu7BIo6NU4+buwR1tWzOwYj9XGx1GCCGEw/JMeQR6OGcBG+RpXfshr0z2ghXCaPZMIX7urK/NwG/Adc0TRwjhzHoVu3LjqQK8/UONjlIjvwoothQbHUMIIUQDjO8ynmKzc/4bHuAZACB7wQrhBOwpYG/TWlfZm1Up1bmZ8gghnJkpl3zlj7fROWrhZ3GlWMs9sEII0RIld042OkKtKqc2Vy40JYQwjj37wH5q53NCiAvcq96HmNXO1+gYtfLRbhSrcqNjCCGEaICj+UcpLCs0OkaN3F3c8XX3lQJWCCdQ6wisUqon0AsIVEpNOutQANbViIUQF5liVY67XRM3jOGjPMl2KTA6hhBCCAdprZnwxQSm9p7KvX3vNTpOje7rex9dg7oaHUOIi15d70R7AFcAQcCVZz1fANzenKGEEM6pSJlpY3HeEdjR5g6E5W80OoYQQggHFZuLMWuz0y7iBHBDzxuMjiCEoI4CVmu9FFiqlBqktV5/HjMJIZxUkYvGx4knYHT0aE//klwsFRW4uLoaHUcIIYSdKqfmOus2OgCZxZkUlBfQJbCL0VGEuKjVNYX4Aa31M8CNSqkp5x7XWs9u1mRCCKdiqaigwFXhg/OOwJ728mS1rxexORm0DoswOo4QQgg7Va7u68wF7DObnmFvzl6+nPil0VGEuKjVNYV4z5m/U89HECGEcysoPM3NeQUEBHcwOkqt9nsU83abMBae2icFrBBCtCAtYQQ20DNQFnESwgnUNYX4yzN/v3v+4gghnFVxbjb3n85lY0S00VFqFegTBiVw6vRRo6MIIYRwQGRAJH8b8DciAyKNjlKrAI8A8srysGgLLsqejTyEEM2hrinEXwK6tuNa6wnNkkgI4ZRyc07gpxSuviFGR6lViF9byIac/BNGRxFCCOGAcL9wp18kKdAzEIu2UFRehL+Hv9FxhLho1TWFeMF5SyGEcHrbMjdzXWQHHnXPJ8HoMLUIC46A3yG3+JTRUYQQQjggozCD/LJ8eoT0MDpKrYI8gwDr/bpSwAphnLqmEP9Q+bVSygPoiXVEdp/Wuuw8ZBNCOJHTRScBaBXY3uAktWsb0hGAgtIcg5MIIYRwxAd7PuCT/Z+w8Sbn3Qqtb5u+/HPoP22FrBDCGHWNwAKglBoPvAYcAhTQWSl1h9b62+YOJ4RwHvkl2QC0DelkcJLatW/dhXcyTpLdOsDoKEIIIRyQa8p1+sKwg38HOvg770KGQlws6i1ggeeAEVrrgwBKqa7A14AUsEJcRArL8kBBeOtIo6PUytPTm+6lruwuKTY6ihBCCAfkm/KdegVigLKKMnZl76Kdbzva+LYxOo4QFy17llA7VVm8nnEYkBvMhLjIFJnz8bJoAv2cdxEngJU+geyzpBsdQwghhANyTbkEejh3AVtQVsCfvv0Tq46uMjqKEBc1e0ZgdymlvgEWY70HdjKwSSk1CUBr/Vkz5hNCOImYUnciy8uNjlGv94I98LNkcqvRQYQQQtgtryyPqKAoo2PUKcDTentKrinX4CRCXNzsKWC9gJPApWceZwIhwJVYC1opYIW4CMQXVxBg8jY6Rr18tBvFyvkLbSGEEH/4S+Jf8PPwMzpGndxd3PFz9yPflG90FCEuavUWsFrraecjiBDCuRVYcihz86Or0UHq4YMn2S6yULoQQrQkwyKGGR3BLoGegTICK4TB7FmFuDNwDxB5dnut9YTmiyWEcDZPhRbRVvsywOgg9fBR3hS6yKfjQgjRUpRVlLHpxCa6B3cnzCfM6Dh1CvQMJM+UZ3QMIS5q9kwh/gJ4G/gSsDRvHCGEsyp01XibnX8Ksa+rHwUupygvL8Pd3cPoOEIIIepxqvgUd668k8cHP87EqIlGx6nT3MS5eLs5/+9CIS5k9hSwpVrrl5o9iRDCaVkqKihwUfi6+hodpV4jPGOY9dtPFOZlE9wq3Og4Qggh6pFXZh3RdPZ9YAH6te1ndAQhLnr2bKPzolLqUaXUIKVU38o/zZ5MCOE0MnNPYFYKX/cAo6PUK9ivPR3NZopys42OIoQQwg55pdYC1tn3gQU4nHeY7498b3QMIS5q9ozAxgC3ACP5YwqxPvNYCHEROJ55GAB/T+feAxagwNuNlAB/orL2E9Gtt9FxhBBC1KMljcB+ffhr3trxFltu2YKLsmccSAjR1OwpYCcCXbTWsqynEBcpVVTMw1k5+EV3NzpKvQo9XXguNJi/5OxliNFhhBBC1KtyUaTKfVadWaBHIBZtoaCsoEWMGAtxIbKngN0GBAGnmjmLEMJJuRaVcl1BIbtDehgdpV5hwRGQBnlFOt7DvQAAIABJREFUmUZHEUIIYYekDkm092vfIkZgg7ysGfNN+VLACmEQe+Y+tAH2KqX+p5RadubPUns6V0olK6X2KaUOKqUerKPdtUoprZRKtDe4EOL8yco/wl4Pd9z9nf/NRdvQSADyTaeNDSKEEMIubX3bMixiGG4u9oyrGCvQw1q0Vk57FkKcf/b8S/HoWV8rYCgwpb6TlFKuwCvAaCAd2KSUWqa13n1OO39gNvCLvaGFEOfXxvxtfNA+nE99PI2OUq/wVh0BKCyXjeaFEKIl2HxyM6YKE4PbDTY6Sr0qR11lL1ghjFPvCKzW+gcgDxgPpACjgNf+f3t3HudWXe9//PXJPpnMPtOZ0tKNRTYRsbK4III/QB8K4narKOoVcQO54r2I13v5AS7IJm54VRB/LogsilZuFZFFVKi0VSyWpS0tpaUt7ez7ZJJ8f38k0yaTzJBpZ+ZMJu/n4zGPyTnfc5J38s03J99zTr6niPs+DtjonNuU+f3sz4GzCiz3ReAaYLDIzCIyzXri6aOZB8xZ7HGSlxYJR6lKpuhP9HodRUREivDDf/6Qr6/5utcxinJI3SH86Iwf8fKml3sdRaRsjXkE1swOBZaRPtraBtwOmHPujUXe9zxga9b0NuD4UY/xSuBA59w9Zvbv42Q5HzgfYMGCBUU+vIhMlv5EDxX+FFWVM/8UYoDvbh+mN1rldQwRESlC11BXSQzgBFAZrOTYZl1NUsRL4x2BfZr00da3Oede55z7FpCcwH1bgXluT6GZD7gB+OxL3ZFz7vvOuaXOuaVNTU0TiCAik6E/1U8s9dLLzRQRaojFe7yOISIiRegc6iyJAZxG3LPpHtbuXut1DJGyNV4H9p3ATuBBM7vJzE6lcKd0LNuAA7Om5wPbs6argKOAh8zsOeAEYLkGchKZefoZIpYqnevdPRgL8YdIm9cxRESkCN3x7j2DI5WCL6/8Mis2r/A6hkjZGvMbqXPubufcvwCHAQ8BnwGazex/zOy0Iu57FXCImS02sxDp05GXZ91/l3Ou0Tm3yDm3CFgJnOmcW73vT0dEpsLZXcZ7emJexyjaqooU98aGvY4hIiIvwTlH11BXSV2SpiZco0GcRDz0kqMQO+f6gFuBW82sHng3cCnw+5dYL2FmFwD3An7gFufcOjO7EljtnFs+3voiMnMcO9BHW/Qgr2MULeqL0uPr9jqGiIi8BIfj9rfeXnId2M4hjXQv4pUJXXDLOdcOfC/zV8zyK4AVo+ZdNsayJ08ki4hMn6dDAwxHKiiVYSsqAzF6zYjHhwiFZv6lf0REypXPfLys/mVex5iQ2nAt3UPaSSrildL5UZuIeCKVTHJpS5SHK0rnsjSVgRqcGTtat3gdRURExrGrfxe3P307L/a96HWUotWEdARWxEsTOgIrIuVnV8d2EmbEgqVzeld1pB76YWfbFhYecKjXcUREZAwbOzbypb9+iUPrD6W5stnrOEX5zKs+Q4oSGppfZJbREVgRGdf23ZsBiIXrPE5SvNfWH8/fNj9P7ZDf6ygiIjKOkSOZpTQK8dzYXObF5nkdQ6RsqQMrIuPa1bEVgJqKRo+TFK+yZg5BYLC71esoIiIyjq54ejTfUhrEaUPHBm755y30D/d7HUWkLKkDKyLjau9JX765vmqux0mKl6qM8qWGOtZ1POF1FBERGcfIEdjqcLXHSYr3dPvT3LDmBloHtJNUxAvqwIrIuObFI3xv5y4ObTzC6yhFq6xp4PbqKjYPPO91FBERGUf3UDexYIygL+h1lKKNHC3WtWBFvKFBnERkXJH+fo4fGKS1aZHXUYo2t3Eh5hy9wxolUkRkJvvkMZ/kfYe9z+sYE1IdSh8tHjn9WUSmlzqwIjKu5/o30xOt4PW1TV5HKVooFCaWcvQnS+fSPyIi5agqVEVVqMrrGBMycgRWl9IR8YZOIRaRca1MbuILTQ2EIxVeR5mQWMrodwNexxARkXHc8cwd/GHLH7yOMSG14VpApxCLeEVHYEVkXP2pfkpr33hadcqHS8W9jiEiIuP4yZM/4bD6w3jTwjd5HaVoNeEa/vCuP1AXKZ3Ly4nMJurAisi4+t0gla70Tta4rH0OkYT2jouIzGSdQ50ldQkdAJ/5aK5s9jqGSNkqvW+lIjKt+m2YqCu9fV3xUA3RZLfXMUREZAwpl6I73l1yHViAnz31M37z7G+8jiFSltSBFZFx9fuSRF3Y6xgT9khlgq81Oq9jiIjIGHqHe0m5FDWh0uvALn92OSs2r/A6hkhZKr3DKiIyrb78Yg/bao/0OsaE7QgZ90XCxONDhEKl1wEXEZntRgZBKsUjsDXhGg3iJOIRdWBFZEwuleLoeDdDoRavo0xYLFgDSdi++zkWzXuZ13FERGSU+bH5PPreRwn4Su/raE24hm0927yOIVKWdAqxiIyptWMnd1ZH2VlRel8uqsP1AOxo2+xxEhERKcTMiIViRAIRr6NMWE2oRteBFfGIOrAiMqatL27gK431bA4OeR1lwmoqmwDY3fmCx0lERKSQtbvX8rXVXyvJU3FrI7X0DfeRTCW9jiJSdtSBFZExvdjxPAC10SaPk0xcY9U85iYSDPS1eR1FREQK+GfrP/nhuh+SdKXXCfzoyz/Kmvevwe/zex1FpOyoAysiY2rv2QFAfaz0fgN79NxX8fut21kSj3kdRURECuiKp4+8VoeqPU4ycSF/SJ1XEY+oAysiY+ro3wVAY+08j5NMXFVd+qhxsq/d4yQiIlJI11AXsWCsJAdx2ty1mSsfvZLnup7zOopI2VEHVkTG1DOQPv22uX6hx0kmrrK6nk82N/HwwFqvo4iISAFdQ10leQkdgO54N3euv5Pne573OopI2Sm9XV4iMm1OjNfzvhe209x8kNdRJiwQDPKPcJhAstXrKCIiUkD/cH/JdmBrw7UAJTkAlUipUwdWRMYUGeihYThIJBL1Oso+iaWMfjfgdQwRESngG6d8g0Qq4XWMfVITSne81YEVmX46hVhExvS31BbuqCrNveMAlSkf/ZTeJYBERMpFKf7+FaAqVIVhewaiEpHpow6siIzp0WAbv64JeR1jn0VdkH4rzb37IiKz3RWPXsG9z93rdYx94vf5qY/UM5wc9jqKSNkpzd1eIjIt+myYqAt6HWOfHeAq6R7Wb2BFRGaalEvxi/W/oCHSwOmLTvc6zj558D0PYmZexxApO+rAisiY+n1J6lKl+ftXgHckD+WI3Ru8jiEiIqP0xHtwuJIdxAlQ51XEIzqFWETG1OtzRC3idYx95irqqKafxHDc6ygiIpKle6gboKQ7sD9a9yOuXXWt1zFEyo46sCJSUCIxTI/PiPpjXkfZZ38P9/LOA1rYtmuz11FERCRL51AnsPdyNKXoybYneXDrg17HECk7U9qBNbMzzOwZM9toZpcWKL/YzJ40s7Vmdr+ZLZzKPCJSvN7uTh7dso03h17pdZR9lgxXsD4cYnvrJq+jiIhIlqHkELXh2pI+AlsTrtFldEQ8MGUdWDPzAzcCbwaOAN5rZkeMWuzvwFLn3NHAXcA1U5VHRCamr7OVqHNUxuZ4HWWf1UbT2Xd3vuBxEhERyba0ZSl/WvYnXtH0Cq+j7LOacA098R6SqaTXUUTKylQegT0O2Oic2+SciwM/B87KXsA596Bzrj8zuRKYP4V5RGQCNu16imvra2mLlO4gFfXVLQB09O7wOImIiMw2teFaHI6eeI/XUUTKylR2YOcBW7Omt2XmjeUjwG8LFZjZ+Wa22sxW7969exIjishYtnRs4Mc11QxFSvcyOnPqDgSgs0+fGyIiM8nyZ5dz8UMX45zzOso+a6xoZF5sHgOJAa+jiJSVqbyMTqHDNgU/pczs/cBS4A2Fyp1z3we+D7B06dLS/aQTKSGd/elOX0Nt6Z4YcUDjEpYODFLh14XmRURmknWt61i5fWVJX4rm9EWnl+w1bEVK2VQegd0GHJg1PR/YPnohM3sT8AXgTOfc0BTmEZEJ6BlsBaClfoHHSfbd3MYF3LxjN0cPlu6lgEREZqOueFdJD+AkIt6Zyg7sKuAQM1tsZiFgGbA8ewEzeyXwPdKd111TmEVEJqg3nh5ZcX7zEo+T7Duf30+3xfANdnodRUREsnQNlX4HtnWglY/d9zEe3vaw11FEysqUdWCdcwngAuBe4CngDufcOjO70szOzCx2LRAD7jSzx81s+Rh3JyLTbCjRRyyZIhKOeh1lv3ymuYafBtZ7HUNERLLMhg5s0Bfkke2P8Hz3815HESkrU/kbWJxzK4AVo+ZdlnX7TVP5+CKy75b1VHNRz2avY+y3Hr+fuNMAGyIiM0ldpI55sfHG9pz5YsEYhtE5pLN8RKbTlHZgRaR0BYe7GPRVex1jv1W4EL0W9zqGiIhkufHUG72OsN/8Pj/V4Wq6hrq8jiJSVqbyN7AiUsLurmjl19WlewmdEVEL0+tLeR1DRERmoZpQjTqwItNMHVgRKejR6DBPV5Tu5Q1GRC1Kr19X3xIRmSm6492cs+IcHnj+Aa+j7LcjGo6goaLB6xgiZUWnEItIQX0+R9RVeB1jvy0KNBPqf5b40CChsC6nIyLitY7BDtbuXkvfcJ/XUfbbtW+41usIImVHR2BFJE8iMUyPz4j6Y15H2W8nVhzJNbvb6O1q8zqKiIjAnlNuS30UYhHxhjqwIpJnR+tWUmbEgqX/5cIfS5/a1dOx2+MkIiICs6sDe+tTt3LOinO8jiFSVtSBFZE8re1bqU8mqQ7Xex1lvz0b6OGEhfP5+47HvI4iIiJAVzzTgQ2Vfge2e6ibtbvXkkglvI4iUjbUgRWRPJWD8MfnX+CkhhO9jrLfqmJN9Pl8tPfu9DqKiIgA0UCUIxuOpC5S53WU/VYdTl9urjve7XESkfKhQZxEJM9gV/p020h1o8dJ9t+cugUAdPfrFGIRkZnglAWncMqCU7yOMSlqw7VA+rTo+kjpn7UkUgp0BFZE8vy943EumtPIYEXp7+NqaVoEQM9Qh7dBRERk1hn5Ha+uBSsyfdSBFZE8Lwxs44HKKNU1TV5H2W8t9fPwOUfvsL5ciIjMBFc/djUXPnCh1zEmRXO0meNbjifoD3odRaRslP7hFRGZdL3xLvDBAXMWex1lvwUCQd7eHac5aF5HERERYHP3ZroGZ8dOxUPqDuHm02/2OoZIWVEHVkTy9Cd6iPlTRMJRr6NMivM6/OyK6YQTEZGZoGuwa1ZcQkdEvKFvdCKSp8/1E0vNniOWff4qiHd6HUNEREhfRme2dGCdc7zt7rfxgyd+4HUUkbKhI7AikieWSDDf5/c6xqS5qilFp6+NX3sdRERE6BqaPR1YM6NzqJMdfTu8jiJSNtSBFZE8H2+HoUCD1zEmTdjC9PkGvY4hIlL2nHMsbV7K4fWHex1l0tSEa+ge0nVgRaaLOrAikqcy2U1vxTyvY0yaSl+UHp9OIRYR8ZqZ8Y1TvuF1jElVE6qhc0jbGJHpot/Aikiey5rhvljc6xiTJuqvot/no3+wz+soIiIyy9SEa+iKz45RlUVKgTqwIpJjeDjO6kiAruDs+Q1sLJT+rdULuzZ5nEREpLw9sfsJTr79ZFbvXO11lElz/NzjWdq81OsYImVDpxCLSI6drVtwZlQGZ8cAGwAHVy7ho1vuZ6i7w+soIiJlrWOog7bBNkL+kNdRJs0Hj/yg1xFEyoqOwIpIjh2tWwCoitR7nGTyHFZ3JJ/u6CLQp4GcRES81DWUPtV2toxCLCLTTx1YEcmxu2sbADXRRo+TTJ5QVT0dPh+dXbrMgYiIl/Z0YEOzpwP7q42/4rhbj6NtoM3rKCJlQR1YEckR7+3i6MEhmqsXeB1l0vRFjJMWzueRjlVeRxERKWtd8S4MoypU5XWUSRPyhRhIDGggJ5Fpog6siOQ4MB7m1h0vclTLMV5HmTRzmxYDsGXoBY+TiIiUt4NqDuLMg87E75s9AwXWReoAeLrtaY+TiJQHdWBFZI9nO59la+9zAMRq53gbZhK1NMzn7d19PJjayKqdOgorIuKVMxafwZde9yWvY0yqpc1LObz+cK567Cp29e/yOo7IrKcOrIgAMJgY5LMPfZZvxR8gAVTXNXkdadKYz8fH2oeZkwpz6Z8u3fMbLBERmR5bu7fy5ZVfpn+43+soky7oD/LVk77KYGKQm9be5HUckVlPHVgRAeD61dfzbNezvH9wAf1U4g/MrqtspayKT3Q3kkgl2Ni50es4IiJlYzg1zKV/upT/3fy/s3YH4pKaJdx02k38+6v/3esoIrOeOrAiwkNbH+Lnz/ycc484l6P7HT0W8zrSpOv3VXPowCC/e+fveFXzq7yOIyJSNv7n8f9hbetaLj/xcubG5nodZ8ocM+cYwv4wPfEetnRv8TqOyKylDqxImdvdv5vL/nIZh9UfxkXHXkQo3kWff/Zc3mDEYLCGikQ3FYEKnHPc9vRtbO7a7HUsEZFZbdXOVdz8xM2cffDZnLboNK/jTDnnHBfcfwEX3H/BrDxdWmQmmNIOrJmdYWbPmNlGM7u0QHnYzG7PlP/VzBZNZR4RyVcVquKtB72Vq19/NSF/iEiii8FAtdexJt1wqIbKZDcAHUMd3Pj4jVz6p0sZTg57nExEZHZKuRRf+etXWFC9gEuPy/saOCuZGZ865lNs6d7Ctauv9TqOyKw0ZR1YM/MDNwJvBo4A3mtmR4xa7CNAh3PuYOAG4OqpyjNd1mzp4MYHN7JmS8eUrjPd683Wxyr3jCmXIhKIcMmrL2FJ7RIAKhLdtCWjE8440/VYFVWulzVbOqiP1HPFa67gybYn+dbj3xp3vZlWZ6X4WMo4eeuJlBKf+fjmKd/k+jdcTzQY9TrOtDlu7nH861H/yl3r7+L+Lfd7HUdk1jHn3NTcsdmJwOXOudMz058HcM5dlbXMvZllHjWzALATaHLjhFq6dKlbvXr1lGTeX39e+zRbV7yDHzek9wtYVtklrbBoGP4chZ+NOjvTAZ/fkaIlAQ9X+/hlgbM3v/wi1KVgRQzuqdq73ojrdkAl8Mtq+ENl/vrf3pHeW/GzavhTVrkBYQc37ExP/7AWVlXkrluVgi++kALgpkZjXYXllDcl4YrMqPFfb4D1odznNi8Ol7yYIuj3cU0TbAnm3v/Bcbi4LX37iibYmTV2kAFHDcEn29PT/9kMnaN2u7xqAP61A4aTKS6d52PAl/vav7YfzsmMGfGpubmvG8AbehxndzqGDD43P3+fzpt74W090OWDzzfvfV4jzu6G0/pglx8uL3Dlmfd2wev74fkAXJU1sO9Ixg91wnEDsCEENzTkr39+OxzRl2JdBL7f5MNGlV/UBi+Lw+oI3FK3d/5IxotfTHFw0sdfKnPfeymDQUvX3cLMQUjn4MDUC9yaPJWr7CPcet4JvGph1p2WqDVbOvjjTf/BxYE7WZ+aR9DvwwxurIffVRkHxh2fbYWDhuHRCvhJ7d51R17Hz+1MsTDl448xuKPAAeov7oKGJNwbg19X5b5HDLhmJ8RcuuzeAj8x/vp2IJnirlrjT1WWU89+4Fs70rd/XAsrR7XRaAq+kmmj/6/Jx+OR3PKGZDofwLfr4clwbr55Cfiv3enb1zXCplFtdPEw/Edr+v3xxSbYEdz7vAAOH4ILM230v+dA26jLOx4zCB/anc73n/N89I9qZicMwLmd6dufnguJzPyRjK/vcby3x0hauny003rh7T3Qa3BJS/5rf2YPnNGbzvXfBdroe7rhDb2wxZfi6pb8z+8PdMKJA+nX5brG3HUd8OHWFMcMwIaojxsLtOFPtcORQ/C3CNxct3e9Hhelk2qufM2XecdRx3P/8/fzrb/l71D5+hu/zqKaRdyz6R5uXntzXvn3/s/3aK5s5q71d/HTJ3+aV/7jt/yY6lA1P3nyJ/xi/S/yyu98250E/UG+v/b7rNi0IqfM7/PzizPT63zjb9/gwecfzCmPhWL89C3px7z6sat5dPujOeVN0SZuOi09Kuvlj1zO47sezylfUL2Ab57yTQA+9/DneKb9mZzyQ+sP5ZqTrgHg0w98mue7n88pP2bOMVz+mssB+OjvP8ru/t055ScecCKfO+5zALx/xfvpjffmlL9xwRu56NiLAHjX8neRSCVyyt+y5C2cf/T5DKeGeffydzPaOw55B+ceeS7d8W7OXXFuXvk5R5zDuw99Ny/2vcjH7vtYXvl5R5/HW5e8lee6nuPfHvy3vPILj72QUxecytPtT3Ppw/lHMC959SW8Zt5r+Puuv3PFI1fklV924mUc23wsj7zwCNesuiav/KrXX8XhDYdP+XuvHA0nh/nAbz/AurZ13PvOezkgdgB3b7ibH637Ud6yPzzjh9RF6vjZUz/jjmfuyCu/7a23URGo4AdP/IDfPPubvPJfvf1XANz4+I3c99x9OWUVgQpue+ttAFy36jr+/MKfc8rrK+q55fRbAPjio19kzYtrcsrnVc3jxlNvBOALf/4C61rX5ZQfXHcw173hOgAufuhiNnVuyik/qvGoPZdN+vgfPs7O3p055UtblvJfJ/wXAB/87QfzBvk6af5JXLz0YgD+5Z5/YSgxlFN++qLT+cQxn8A5x9m/PjvvtXn7wW/nQ0d9iP7hft73v+/LK1922DKWHbaM1oFWzrv3vLzyDx/1Yc46+Cy29mzlwvsvzCv/xDGf4PRFp7O+Yz2X/PGSvPKLl17MSfNPYu3utVz2l8vyyr9wwhd4dcureWzHY3zlr1/JK//S677EUY1H8cetf+SGNTfklV9/8vUcVHsQv9v8O777j+/mlX/nTd8p+N4bedyZzMzWOOeWFiqbymFG5wFbs6a3AcePtYxzLmFmXUAD0Jq9kJmdD5wPsGDBgqnKu9/+sa0Hkk3UxfsACPh8BPzpr0ED4Trag0FccIAW17NnnUTSkUil2Jaqpsf5GWKYFjeQd989FQ0458cX6KPF9e1Zb8R2fwMN4RBBfy8tLv83F+3RJnwYuE7q4nvvP+DzEfH5aI+me1YV/m5a3GDOusGEscFles3DvTQGk3ueF0ANftqj6W9tVdZJi4vnPLfwsJ8Nrpoqf4Aa66bF5X5BqLYg7dH0t7pYspW61N5TOgM+H5UWpj2a7lE0uFYiLpWzfoU/zFZ/JT2JBFXxTiI+l/Pah/0R2qPpDWhzalfON9NE0jGYCLLBRRnG0ZjoznluAIFAJe3RSvosSYtry3vth1yM9mgVvb4ELa4977VPBatoj1bQOjxIXXzv0ZaRjMlQNe0WYdAfp8V15q3fYzE2OB+7UsPUxXtznhvAYLiW9kCIRGCQFted89oDbE9VEfZHcMHhnPceDmIpHxaqoz2Y7nH0DiZYl5jH3cnXM0yKlZvaZkUHduWmNn6bOo6Dk9vwk6TKHyAWCfCOwRRJfwed/iSDkXQbTWW10ezXcVuqGp8/jAvGaXG9eY/RE2nAnB8L9NGY6M15jwR8PrqiTcSdj0CgcBt9wV9HfyJJPNFPXXwop559zva00Ygvt40mkg5fAja49N6JQGqAFpd7WnRVVhut9HXSmBjKyVfpgrRH0z2zajry1q8h3UZ7BxOE4+3UueSe5xXwGzFfaE8braeNYKZ8z/NPBdng0r3uWLyLaICc93DEt7eNzkntJmUu57WPJ8Js9VcTifhpcbkdFIBAIEp7NMaApWhM7M577X2BGO3RSnoybXg0F4yx1R9iW3KIunh3znMDSIWqaLcKBvzDtLi9bXgkY1uykg0uRJelCr434qEa2v3hPW10ZL1hV03SzeGp7YNwFFQFq/acCZEt5E/vFawJ1RQsD/jSm/K6cF3Bcr+l23d9pL5g+chnYmNFY155wPZ+TZgTnZNXHg3sParWHG3OK6+P1O+5PbdyLt213Tnlcyv37pGYF5vHcCr3vTc/Nn/P7QOrDtzzXEe0VLbsub2weiFVoaqc8uZo857bi6oX0Z/IbXtzonv3aCypWUJi1PapsWLvHotCr119Rfr5+c1fsLwunP78DPgCBctrQul2G/KHCpZXBdPPJ+wPFyyvDKX3SEcD0YLlI0c9K0OVBcsjgciex5nK9145CvqDXH/y9Xzn8e8Q9KX3+tWEC7+OPkvvOBurjY6UF2qj2ZoqmvLKI/69ezQLteGa8N4923Njc1kylFue3UYOiB3AQCL3O+q82Lw9t7Pba/Y6IxZULcj5zIDcz4DFNYvpjud+RmQ//uKaxcST8Zzyxuj4bbShIr3tM7PCbTSSaaM2RhvNvD4hX+E2OrKDJuKPFG7Dmc+kikDFuG00GizchkfqLxaKjdtGq0PVBcvHeu+V+hkRU3kE9t3A6c658zLTHwCOc85dmLXMuswy2zLTz2aWyf+GkTGTj8Cu2dLBOTevZDiRIhjwFXX0al/Wme71ZutjKePkrTfTqc68eyxlnLz1REREysV4R2B1CvEkW7Olg5Wb2jhhSUPRX0j2ZZ3pXm+2PpYyTt56M53qzLvHUsbJW09ERKQceNWBDQDrgVOBF4BVwPucc+uylvkU8HLn3MfNbBnwDufce8a735negRUREREREZF958lvYDO/ab0AuJf0GCS3OOfWmdmVwGrn3HLgB8BPzGwj0A4sm6o8IiIiIiIiUtqmchAnnHMrgBWj5l2WdXsQyB/WT0RERERERGSUKbsOrIiIiIiIiMhkUgdWRERERERESoI6sCIiIiIiIlIS1IEVERERERGRkjBll9GZKma2G9jidY6X0Ai0eh1C8qheZibVy8ykepmZVC8zl+pmZlK9zEyql5lpJtXLQudcU6GCkuvAlgIzWz3WdYvEO6qXmUn1MjOpXmYm1cvMpbqZmVQvM5PqZWYqlXrRKcQiIiIiIiJSEtSBFRERERERkZKgDuzU+L7XAaQg1cvMpHqZmVQvM5PqZeZS3cxMqpeZSfUyM5VEveg3sCIiIiIiIlISdARWRERERERESoI6sCIiIiIiIlIS1IGdADM7w8yeMbPqXiFZAAAHZ0lEQVSNZnZpgfKwmd2eKf+rmS3KKvt8Zv4zZnb6dOae7Yqol4vN7EkzW2tm95vZwqyypJk9nvlbPr3JZ78i6uZDZrY7qw7Oyyr7oJltyPx9cHqTz25F1MsNWXWy3sw6s8rUZqaAmd1iZrvM7J9jlJuZfTNTZ2vN7NisMrWVKVRE3ZyTqZO1ZvaImb0iq+w5M3si015WT1/q2a+IejnZzLqyPq8uyyob9zNQ9l0R9fIfWXXyz8w2pT5TpvYyRczsQDN70MyeMrN1ZnZRgWVKZzvjnNNfEX+AH3gWWAKEgH8AR4xa5pPAdzO3lwG3Z24fkVk+DCzO3I/f6+c0G/6KrJc3AtHM7U+M1Etmutfr5zBb/4qsmw8B3y6wbj2wKfO/LnO7zuvnNBv+iqmXUctfCNySNa02MzX1chJwLPDPMcrfAvwWMOAE4K+Z+Wor3tfNa0Zec+DNI3WTmX4OaPT6OczGvyLq5WTgngLzJ/QZqL/JrZdRy74NeCBrWu1l6uplLnBs5nYVsL7Ad7KS2c7oCGzxjgM2Ouc2OefiwM+Bs0Ytcxbwo8ztu4BTzcwy83/unBtyzm0GNmbuT/bfS9aLc+5B51x/ZnIlMH+aM5arYtrMWE4H7nPOtTvnOoD7gDOmKGe5mWi9vBe4bVqSlTHn3MNA+ziLnAX82KWtBGrNbC5qK1PuperGOfdI5rUHbWOmTRFtZiz7s22SlzDBetH2ZZo453Y45/6Wud0DPAXMG7VYyWxn1IEt3jxga9b0NvIrfs8yzrkE0AU0FLmu7JuJvrYfIb13aUTEzFab2Uoze/tUBCxjxdbNOzOnqtxlZgdOcF2ZuKJf28zp9ouBB7Jmq814Y6x6U1uZWUZvYxzwezNbY2bne5SpnJ1oZv8ws9+a2ZGZeWozM4CZRUl3gn6RNVvtZRpY+ieOrwT+OqqoZLYzAS8fvMRYgXmjr0E01jLFrCv7pujX1szeDywF3pA1e4FzbruZLQEeMLMnnHPPTkHOclRM3fwGuM05N2RmHyd9BsMpRa4r+2Yir+0y4C7nXDJrntqMN7R9meHM7I2kO7Cvy5r92kx7mQPcZ2ZPZ45QydT7G7DQOddrZm8BfgUcgtrMTPE24C/OueyjtWovU8zMYqR3Gvybc657dHGBVWbkdkZHYIu3DTgwa3o+sH2sZcwsANSQPo2imHVl3xT12prZm4AvAGc654ZG5jvntmf+bwIeIr1HSibHS9aNc64tqz5uAl5V7Lqyzyby2i5j1OldajOeGave1FZmADM7GrgZOMs51zYyP6u97ALuRj8fmjbOuW7nXG/m9gogaGaNqM3MFONtX9RepoCZBUl3Xm91zv2ywCIls51RB7Z4q4BDzGyxmYVIN7zRI3AuB0ZG5noX6R+mu8z8ZZYepXgx6T2Aj01T7tnuJevFzF4JfI9053VX1vw6MwtnbjcCrwWenLbks18xdTM3a/JM0r/JALgXOC1TR3XAaZl5sv+K+SzDzF5GerCGR7Pmqc14ZzlwbmaUyBOALufcDtRWPGdmC4BfAh9wzq3Pml9pZlUjt0nXTcGRWWXymVlLZhwSzOw40t952yjyM1CmjpnVkD4b7tdZ89ReplCmLfwAeMo597UxFiuZ7YxOIS6Scy5hZheQrjA/6VE515nZlcBq59xy0m+Mn5jZRtJHXpdl1l1nZneQ/qKXAD416pQ82UdF1su1QAy4M7Mte945dyZwOPA9M0uR3rB91TmnL+OTpMi6+bSZnUm6XbSTHpUY51y7mX2R9BcNgCtHnWYk+6jIeoH04Bo/z+yEG6E2M0XM7DbSo6Y2mtk24P8CQQDn3HeBFaRHiNwI9AMfzpSprUyxIurmMtLjXXwns41JOOeWAs3A3Zl5AeBnzrnfTfsTmKWKqJd3AZ8wswQwACzLfJ4V/Az04CnMSkXUC8DZwO+dc31Zq6q9TK3XAh8AnjCzxzPz/hNYAKW3nbHc7yYiIiIiIiIiM5NOIRYREREREZGSoA6siIiIiIiIlAR1YEVERERERKQkqAMrIiIiIiIiJUEdWBERERERESkJuoyOiIjINDKzBuD+zGQLkAR2Z6b7nXOv8SSYiIhICdBldERERDxiZpcDvc6567zOIiIiUgp0CrGIiMgMYWa9mf8nm9kfzewOM1tvZl81s3PM7DEze8LMDsos12RmvzCzVZm/13r7DERERKaWOrAiIiIz0yuAi4CXAx8ADnXOHQfcDFyYWeYbwA3OuVcD78yUiYiIzFr6DayIiMjMtMo5twPAzJ4Ffp+Z/wTwxsztNwFHmNnIOtVmVuWc65nWpCIiItNEHVgREZGZaSjrdiprOsXe7bcPONE5NzCdwURERLyiU4hFRERK1++BC0YmzOwYD7OIiIhMOXVgRUREStengaVmttbMngQ+7nUgERGRqaTL6IiIiIiIiEhJ0BFYERERERERKQnqwIqIiIiIiEhJUAdWRERERERESoI6sCIiIiIiIlIS1IEVERERERGRkqAOrIiIiIiIiJQEdWBFRERERESkJPx/QA2nKr+0cgsAAAAASUVORK5CYII=\n", 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" ] }, "metadata": { @@ -335,14 +339,17 @@ "sample_rate = 3e1 # Hz\n", "interp_sample_rate = 2.3*sample_rate\n", "time = np.arange(0, 1, 1/sample_rate) #s\n", - "time2 = np.arange(0, 1, 1/(interp_sample_rate)) #s\n", + "time2 = np.arange(0, 2, 1/(interp_sample_rate)) #s\n", "\n", "signal = np.zeros(len(time))\n", - "signal[len(signal)//2-1] = 1\n", - "signal[len(signal)//2] = 0.75\n", - "signal[len(signal)//2+1] = 1\n", + "if False:\n", + " signal[len(signal)//2] = 1\n", + "else:\n", + " signal[len(signal)//2-1] = 1\n", + " signal[len(signal)//2] = 0.75\n", + " signal[len(signal)//2+1] = 1\n", "\n", - "mysignal = TravelSignal(signal, sample_rate+20)\n", + "mysignal = TravelSignal(signal, sample_rate)\n", "time_signal, time_offsets = mysignal._translate(time)\n", "time2_signal, time2_offsets = mysignal._translate(time2)\n", "\n", @@ -351,39 +358,73 @@ "my2_max_idx = np.argmax(mysignal(time2))\n", "\n", "\n", - "fig, ax = plt.subplots(1,1,sharex=True, figsize=(16,8))\n", + "fig, ax = plt.subplots(1,1,sharex=True, figsize=(16,4))\n", "ax.plot(time, mysignal.raw, '.-', label=\"Raw signal\")\n", "ax.plot(time, time_signal, '-', label=\"MySignal +0.5\")\n", "ax.plot(time2, time2_signal, '--', label=\"MySignal(interp) +0.25\")\n", - "ax.plot(np.array([0, 1/sample_rate]) + time[raw_max_idx], [1.02, 1.02], label=\"Orig Samplerate\")\n", - "ax.plot(np.array([0, 1/interp_sample_rate]) + time[raw_max_idx], [1.01, 1.01], label=\"Interp Samplerate\")\n", + "ax.plot(np.array([0, 1/sample_rate]) + time[raw_max_idx], [1.04, 1.04], label=\"Orig Samplerate\")\n", + "ax.plot(np.array([0, 1/interp_sample_rate]) + time[raw_max_idx], [1.02, 1.02], label=\"Interp Samplerate\")\n", "ax.set_ylabel(\"Amplitude\")\n", "ax.set_xlabel(\"Time\")\n", - "ax.set_xlim(0, 1)\n", "ax.legend()\n", "\n", - "\n", - "print(\"P\", \"Raw\", \"S1\", \"S2\")\n", - "print(\"a\", signal[raw_max_idx], mysignal(time)[my_max_idx], mysignal(time2)[my2_max_idx])\n", - "print(\"T\", time[raw_max_idx], time[my_max_idx], time2[my2_max_idx])\n", - "\n", - "print(time_offsets, time2_offsets)\n", "plt.show();" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "## Non-Periodicity: Gaussian pulse" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sample_rate = 3e2 # Hz\n", + "interp_sample_rate = sample_rate * 1/10 # Hz\n", + "\n", + "t_offset = 1\n", + "periodic = False\n", + "\n", + "time = t_offset + np.arange(0, 1, 1/sample_rate) #s\n", + "time2 = np.arange(0, 3*t_offset, 1/(interp_sample_rate)) #s\n", + "\n", + "def gaussian(x, mu, sigma ):\n", + " return 1/(sigma*np.sqrt(2*np.pi)) * np.exp(- (x-mu)**2 / sigma**2 )\n", + "\n", + "signal = gaussian(time, time[len(time)//2], (time[-1] - time[0])/10)\n", + "\n", + "\n", + "mysignal = TravelSignal(signal, sample_rate, t_0 = t_offset, periodic=periodic)\n", + "\n", + "fig, ax = plt.subplots(1, 1, figsize=(16,4))\n", + "ax.set_ylabel(\"Amplitude\")\n", + "ax.set_xlabel(\"Time\")\n", + "\n", + "ax.plot(time, signal, label='Raw signal')\n", + "ax.plot(time, mysignal(time)+0.5, '-', label=\"MySignal +0.5\")\n", + "ax.plot( time2, mysignal(time2)+0.25 , '--', label=\"MySignal(interp) +0.25\")\n", + "ax.legend()\n", + "\n", + "plt.show();" + ] } ], "metadata": {