{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Matched-Field Processing\n", "\n", "The match-field processing performed in this code is inspired from the one describe in [this git repository](https://github.com/schipp/fast_beamforming). The description below is taken from this repository.\n", "\n", "The beamformer formulation is written in the frequency domain as:\n", "\n", "$$B = \\frac{1}{N_f \\times N_s \\times (N_s-1)}\\sum^{N_f}_{\\omega} \\sum^{N_s}_{j} \\sum^{N_s}_{k \\neq j} \\frac{K_{jk}(\\omega) S_{kj}(\\omega)}{|K_{jk}(\\omega)|}$$\n", "\n", "where $K_{jk}(\\omega) = d_j(\\omega) d_k^H(\\omega)$ is the cross-spectral density matrix of the recorded signals $d$, and $S_{jk}(\\omega) = s_j(\\omega) s_k^H(\\omega)$ is the cross-spectral density matrix of the synthetic signals $s$. \n", "\n", "Here, $j$ and $k$ identify sensors, and $H$ denotes the complex conjugate. Auto-correlations ($j = k$) are excluded because they contain no phase information. Consequently, negative beam powers indicate anti-correlation.\n", "\n", "The synthetic signals $s$ (often called replica vectors or Green's functions) represent the expected wavefield for a given source origin and medium velocity, most often in an acoustic homogeneous half-space $s_j = \\exp(-i \\omega t_j)$ where $t_j$ is the travel time from the source to each receiver $j$.\n", "\n", "The travel time is computed as:\n", "$$t_j = \\frac{| \\mathbf{r}_j - \\mathbf{r}_s |}{c}$$\n", "\n", "with $| \\mathbf{r}_j - \\mathbf{r}_s |$ being the Euclidean distance between the sensor and the source, and $c$ the medium velocity.\n", "\n", "Using this formulation the Bartlett $B$ in contained in $[-1,1]$.\n", "\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# specific das_ice function\n", "import das_ice.io as di_io\n", "import das_ice.signal.filter as di_filter\n", "import das_ice.processes as di_p\n", "import das_ice.plot as di_plt\n", "import das_ice.mfp as di_mpf\n", "\n", "# classic librairy\n", "\n", "\n", "import time\n", "import xarray as xr\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from matplotlib.colors import LogNorm\n", "from tqdm import trange\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/chauvet/miniforge3/envs/das_ice/lib/python3.11/site-packages/distributed/node.py:182: UserWarning: Port 8787 is already in use.\n", "Perhaps you already have a cluster running?\n", "Hosting the HTTP server on port 44353 instead\n", " warnings.warn(\n" ] }, { "data": { "text/plain": [ "'http://127.0.0.1:44353/status'" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from dask.distributed import Client,LocalCluster\n", "\n", "cluster = LocalCluster(n_workers=8)\n", "client = Client(cluster)\n", "client.dashboard_link" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data pre-processing" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "ds=xr.Dataset()\n", "ds['velocity']=di_io.dask_Terra15('*.hdf5', chunks=\"auto\")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "d_optic_bottom=2505 #m\n", "depth_bh=97 #m" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "ds=ds.sel(distance=slice(d_optic_bottom-depth_bh,d_optic_bottom+depth_bh))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "ds['distance']=-1*(np.abs(ds.distance-d_optic_bottom)-depth_bh)\n", "ds = ds.sortby('distance')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "dsr=di_p.strain_rate(ds.velocity).T" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compute grid search\n", "\n", "To compute the grid search you need to provide the postion of each sensor along the optic fiber. Here, the optic distance is used in order to provid the position relative to the surface of the glacier." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "stations=np.array([np.zeros(len(dsr.distance)),dsr.distance.values]).T" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this example, the beamformer is compute for every sub-sample of $0.1~s$ without overlap over a spatial and temporal grid :\n", "\n", "- along $x$ : from $0~m$ to $120~m$ with a step of $2~m$\n", "- along $z$ : from $0~m$ to $100~m$ with a step of $2~m$\n", "- along $c$ : from $3400~m.s^{-1}$ to $3600~m.s^{-1}$ with a step of $100~m.s^{-1}$" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "freqrange=[100,200]\n", "ti='2024-08-29T05:05:00'\n", "tf='2024-08-29T05:07:00'" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/chauvet/miniforge3/envs/das_ice/lib/python3.11/site-packages/distributed/client.py:3362: UserWarning: Sending large graph of size 474.79 MiB.\n", "This may cause some slowdown.\n", "Consider loading the data with Dask directly\n", " or using futures or delayed objects to embed the data into the graph without repetition.\n", "See also https://docs.dask.org/en/stable/best-practices.html#load-data-with-dask for more information.\n", " warnings.warn(\n", "2024-12-04 15:49:48,323 - distributed.worker.memory - WARNING - Unmanaged memory use is high. This may indicate a memory leak or the memory may not be released to the OS; see https://distributed.dask.org/en/latest/worker-memory.html#memory-not-released-back-to-the-os for more information. -- Unmanaged memory: 3.19 GiB -- Worker memory limit: 3.87 GiB\n", "2024-12-04 15:49:52,470 - distributed.worker.memory - WARNING - Worker is at 86% memory usage. Pausing worker. Process memory: 3.33 GiB -- Worker memory limit: 3.87 GiB\n", "2024-12-04 15:49:57,509 - distributed.worker.memory - WARNING - Worker is at 81% memory usage. Pausing worker. Process memory: 3.14 GiB -- Worker memory limit: 3.87 GiB\n", "2024-12-04 15:49:57,596 - distributed.worker.memory - WARNING - Unmanaged memory use is high. This may indicate a memory leak or the memory may not be released to the OS; see https://distributed.dask.org/en/latest/worker-memory.html#memory-not-released-back-to-the-os for more information. -- Unmanaged memory: 3.16 GiB -- Worker memory limit: 3.87 GiB\n", "2024-12-04 15:49:59,866 - distributed.worker.memory - WARNING - Worker is at 7% memory usage. Resuming worker. Process memory: 292.79 MiB -- Worker memory limit: 3.87 GiB\n", "2024-12-04 15:50:00,322 - distributed.worker.memory - WARNING - Unmanaged memory use is high. This may indicate a memory leak or the memory may not be released to the OS; see https://distributed.dask.org/en/latest/worker-memory.html#memory-not-released-back-to-the-os for more information. -- Unmanaged memory: 3.00 GiB -- Worker memory limit: 3.87 GiB\n", "2024-12-04 15:50:00,389 - distributed.worker.memory - WARNING - Worker is at 81% memory usage. Pausing worker. Process memory: 3.14 GiB -- Worker memory limit: 3.87 GiB\n", "2024-12-04 15:50:00,636 - distributed.worker.memory - WARNING - Worker is at 6% memory usage. Resuming worker. Process memory: 268.22 MiB -- Worker memory limit: 3.87 GiB\n", "2024-12-04 15:50:01,801 - distributed.worker.memory - WARNING - Worker is at 86% memory usage. Pausing worker. Process memory: 3.36 GiB -- Worker memory limit: 3.87 GiB\n", "2024-12-04 15:50:03,388 - distributed.worker.memory - WARNING - Worker is at 7% memory usage. Resuming worker. Process memory: 315.20 MiB -- Worker memory limit: 3.87 GiB\n", "2024-12-04 15:50:03,871 - distributed.worker.memory - WARNING - Unmanaged memory use is high. This may indicate a memory leak or the memory may not be released to the OS; see https://distributed.dask.org/en/latest/worker-memory.html#memory-not-released-back-to-the-os for more information. -- Unmanaged memory: 2.76 GiB -- Worker memory limit: 3.87 GiB\n", "2024-12-04 15:50:04,256 - distributed.worker.memory - WARNING - Worker is at 81% memory usage. Pausing worker. Process memory: 3.16 GiB -- Worker memory limit: 3.87 GiB\n", "2024-12-04 15:50:05,560 - distributed.worker.memory - WARNING - Worker is at 87% memory usage. Pausing worker. Process memory: 3.38 GiB -- Worker memory limit: 3.87 GiB\n", "2024-12-04 15:50:06,111 - distributed.worker.memory - WARNING - Worker is at 86% memory usage. Pausing worker. Process memory: 3.33 GiB -- Worker memory limit: 3.87 GiB\n", "2024-12-04 15:50:07,501 - distributed.worker.memory - WARNING - Worker is at 4% memory usage. Resuming worker. Process memory: 195.65 MiB -- Worker memory limit: 3.87 GiB\n", "2024-12-04 15:50:07,960 - distributed.worker.memory - WARNING - Unmanaged memory use is high. This may indicate a memory leak or the memory may not be released to the OS; see https://distributed.dask.org/en/latest/worker-memory.html#memory-not-released-back-to-the-os for more information. -- Unmanaged memory: 2.87 GiB -- Worker memory limit: 3.87 GiB\n", "2024-12-04 15:50:08,110 - distributed.worker.memory - WARNING - Worker is at 84% memory usage. Pausing worker. Process memory: 3.26 GiB -- Worker memory limit: 3.87 GiB\n", "2024-12-04 15:50:08,319 - distributed.worker.memory - WARNING - Worker is at 6% memory usage. Resuming worker. Process memory: 267.83 MiB -- Worker memory limit: 3.87 GiB\n", "2024-12-04 15:50:09,486 - distributed.worker.memory - WARNING - Worker is at 84% memory usage. Pausing worker. Process memory: 3.26 GiB -- Worker memory limit: 3.87 GiB\n", "2024-12-04 15:50:10,394 - distributed.worker.memory - WARNING - Worker is at 7% memory usage. Resuming worker. Process memory: 311.75 MiB -- Worker memory limit: 3.87 GiB\n", "2024-12-04 15:50:12,171 - distributed.worker.memory - WARNING - Worker is at 87% memory usage. Pausing worker. Process memory: 3.38 GiB -- Worker memory limit: 3.87 GiB\n", "2024-12-04 15:51:26,220 - distributed.worker.memory - WARNING - Worker is at 5% memory usage. Resuming worker. Process memory: 206.14 MiB -- Worker memory limit: 3.87 GiB\n", "2024-12-04 15:54:57,674 - distributed.worker.memory - WARNING - Unmanaged memory use is high. This may indicate a memory leak or the memory may not be released to the OS; see https://distributed.dask.org/en/latest/worker-memory.html#memory-not-released-back-to-the-os for more information. -- Unmanaged memory: 3.41 GiB -- Worker memory limit: 3.87 GiB\n", "2024-12-04 15:55:00,350 - distributed.worker.memory - WARNING - Unmanaged memory use is high. This may indicate a memory leak or the memory may not be released to the OS; see https://distributed.dask.org/en/latest/worker-memory.html#memory-not-released-back-to-the-os for more information. -- Unmanaged memory: 3.43 GiB -- Worker memory limit: 3.87 GiB\n", "2024-12-04 15:55:07,964 - distributed.worker.memory - WARNING - Unmanaged memory use is high. This may indicate a memory leak or the memory may not be released to the OS; see https://distributed.dask.org/en/latest/worker-memory.html#memory-not-released-back-to-the-os for more information. -- Unmanaged memory: 3.43 GiB -- Worker memory limit: 3.87 GiB\n", "2024-12-04 15:56:31,691 - distributed.worker.memory - WARNING - Worker is at 8% memory usage. Resuming worker. Process memory: 346.84 MiB -- Worker memory limit: 3.87 GiB\n", "2024-12-04 15:56:36,734 - distributed.worker.memory - WARNING - Worker is at 9% memory usage. Resuming worker. Process memory: 369.12 MiB -- Worker memory limit: 3.87 GiB\n", "2024-12-04 15:56:41,345 - distributed.worker.memory - WARNING - Worker is at 9% memory usage. Resuming worker. Process memory: 365.27 MiB -- Worker memory limit: 3.87 GiB\n" ] } ], "source": [ "MFP=di_mpf.MFP_2D_series(dsr.sel(time=slice(ti,tf)),0.1,stations,xrange=[0,120],zrange=[0,100],vrange=[3400,3600],dx=2,dz=2,dv=100,freqrange=freqrange)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Extract Bartlett value\n", "\n", "In this section, it is shown how to extract the maximum Bartlett value for each sample of $0.1~s$." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "bc_max=MFP.max(dim=['z','x','velocity'])\n", "pos_bc_max=[]\n", "for i in range(len(MFP.true_time)):\n", " sources_find=MFP[...,i].where(MFP[...,i] == bc_max[i], drop=True).coords\n", " pos_bc_max.append([sources_find['x'].values[0],sources_find['z'].values[0],sources_find['velocity'].values[0],bc_max[i]])\n", "\n", "bc=xr.DataArray(np.stack(pos_bc_max),dims=['true_time','bc'])\n", "bc['true_time']=MFP.true_time" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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MlClTzH777Wd23nlns88++xhJ5p577knJ/ubfo3PPPTf5+9ScDzvttFOr7D/ssMPIfpvsf+ONN8yYMWPMgw8+aJ599lkzefJk07VrV1NWVmbefffd5HxkP9nvB9nvHtlP9mcz+/fbbz/TsWNH07FjR3PJJZeYv/zlL+aSSy4xklJ+B8l+st8Pst+9uGd/S3avaXP2P/vss+bee+81AwcOTGZ/S82/Ry1/f73kU6a1kwenn366/vSnP2nWrFkaMWKEpB0XRGxqatLpp58uSVq1apVeeeUV3XTTTVq/fn3K0cNRo0Zp0qRJ+vrrr7Xzzju3Wv/cuXO1fPly3XDDDSorK0tOP/bYY7X33nvr+eef14033qgVK1bo9ddf12WXXabevXunrKPlxf28OO2003TZZZfpkUce0S9+8QtJ0osvvqiVK1cmL1aXjtt2FTcXkJOkffbZRy+99JI2btyoN998Uy+//HKrI/z/+te/dP3112vixInq3r17m+vavHmzJKm0tLTVY2VlZcnHm+dta76W6wpT3759NWrUKF/LGmP0l7/8RaeddpqMMVq5cmXysVGjRumxxx7Tu+++q0MPPTSs4abo3LmzzjjjjOT9vfbaS126dNHOO++s4cOHJ6c3/7u5eSOMcf/mN7/R6tWrHcfYs2dPz8/r17/+tV5++WXdc8896tKlS3L65s2bVVJSYruM9b1k9eijj+rBBx9MNre09NOf/lT9+vXTD3/4Q89jdXp/t5xHkrp3765BgwZp/PjxGjBggObPn69bb71VY8eOTWl2caut1+qHP/yh7rvvPp122mm688471aNHDz3xxBPJb8T9/C55+f188MEHU+b5/ve/rwsvvFAPPPCArrjiCh100EHJx4ylM8X6mrbM/uZtPffcc8nsnzhxolatWqUrrrhCN910k37wgx9oxIgRuvbaa3Xccccls3+PPfaQMUazZs3S9OnTU7a1evVq/epXv0rJ/r59++rNN98k+y3Zf8ghh+iQQw5J3j/hhBN0yimnaL/99tOECRM0Y8YMSWQ/2U/2k/1kf1vyMfs3bNigTZs26aKLLkq2RH73u99VQ0OD7r//ft10003aY489yH6y3/PzIvvdK4Tsd9rWG2+8kTLPD37wAw0ePFg/+9nPdN555yXPjDviiCM8bysTmdMWTwfCRo8ercrKSj3++OPJA2GPP/64DjjgAO25556SpMWLF8sYo4kTJ2rixIm261m+fLntgbAvvvhC0o5Qsdp77731j3/8Q9J/QqX5TzHD0KVLFx1//PF69NFHkzvERx55RDvvvLOrpgW/p1a2paKiIrnOE088UY8++qhOPPFEvfvuu9p///0lSdddd52qqqp0ySWXpF1X85vR7vTsLVu2pJzG2aFDhzbna7muMPXt29f3sitWrNCaNWs0depUTZ061Xae5cuX+16/k1122aXVh7DKykr16tWr1TRJyR1YGOMePHiw32Gn9fjjj+u6667T+eefr4svvjjlsQ4dOqihocF2Oet7qaW///3vOv/88zVq1Cj96le/Snnsrbfe0sMPP6yZM2embebZvHmz1q5dmzKtpqbG8f3dPG5pR3YceeSR+uMf/6gxY8ZI2vH7teuuu+q8887TCy+8oGOOOUYbNmxI+QBaXFxse7A53Wu133776dFHH9VFF12U/GBTU1OjcePG6Y477tBjjz2mBx98UE8//bROOumkNp93S35+P2+44QbdeOONKdOOP/54rVixIu12pP+8pi2zv/lPGF5++WVX2d/yPy122d9yzNbs37Jli9q3b5/cN5D9+7e53O67764TTzxRTz31lBobG1VcXEz2k/2ekP1kP9kf7exvfs3OPPPMlGW/973v6f7779fs2bO1xx57kP1kvydkP9nv5TW1U1JSovHjx+uiiy7SvHnz0rbNesmnTPN0IKy0tDT59/733HOP6uvr9cYbb+jXv/51cp6mpiZJ0lVXXdXm0f7dd989wJAz55xzztGTTz6pN998U/vuu6/+53/+Rz/+8Y9d1eY2/62wk8rKSl8/4O9+97v6/ve/r8cee0z777+/Pv30U02dOlVTpkzRN998k5xvy5Yt2rZtmz7//HNVVFSoqqpKtbW1kqRly5a1Wu+yZctSvjmora3V119/bTuf5O9bBid2r0db3/A1Njam3G9+v5199tk699xzbZfZb7/9Ao6wbcXFxZ6mNx8VD2Pcq1atanPn1FKHDh2SO2QnL730ks455xwde+yxuu+++1o9Xltbq8bGRi1fvlzV1dXJ6Q0NDfrXv/5l+/745z//qRNOOEEDBw7Un//8Z7Vrlxo711xzjf7rv/5Lffv21eeffy5JyW/Kli1bpi+//FK9e/fW448/rrFjx6Ysa4xRVVWVSktL23x/S/95306bNk1btmzRcccdlzLfCSecIGnHNxzHHHOMbr/99pSdSJ8+fZJjc/taSdIpp5yiE044Qf/85z/V2NioAw88ULfddpukHR9cH3jgAdvl2uL393PAgAF6+eWXtWnTJu2222465ZRT0m7H+pq2zP7mDwbvvvtu2uyfM2eOrrvuOt14443Js5fssr85n+wsW7Ys5SyBTMmn7E+nV69eamho0MaNG1VRUUH2k/1kP9lP9qeRb9nfs2dPffTRR+rRo0fKvM2/k80HXch+sp/sJ/uDZL+XbTVrPhi8atWqtPN5yadM83QgTNpxmvT06dM1c+ZMLVy4UMaY5J9FSlK/fv0kSe3bt/f8bUmfPn0k7biIovXbmEWLFiUfb97Ghx9+6HX4aU+hHj16tLp3765HHnlEw4cP16ZNm/T973/f1XrT7dBbeuihh3Teeee5mrelrVu3qqmpKXlk/Ouvv1ZTU5MuvfTS5EUpW+rbt68uu+wyTZkyRQMHDlS7du00d+5cnXbaacl5GhoaNH/+/JRpBxxwgF599VWtW7cu5cJ8c+bMST7ulZ/T1nfaaSdJSrlQqvSfswabde/eXeXl5WpsbAzl2zm/p9h7Fca4v/vd7+q1115znO/cc8/VtGnTHOebM2eOTj75ZA0ZMkRPPPFEqx2X9J+f/9y5c/Wd73wnOX3u3Llqampq9f747LPPNHr0aFVXV+uvf/2rOnfu3GqdX375pb744gvbbwhPOOEEVVZWas2aNRo1apTtnyIUFRVp3333bXWh8Obn1K9fP5WXl0vacaFSY0yrD1bNF5Lcvn27pB0fjlt+m2H90ObmtWpWUlKioUOHJu83/w5fddVVtjvEpqYmffbZZ9p55521Zs0aDRw4ULfccouOOOII37+f7dq1U01NTTIzrX9aYmX3mjZn/wsvvKCuXbvqX//6V9rs//jjjyVJ3/72t3XwwQe3ua2BAwequLhYjY2NKdnfnE/t2rUj+1tkfzr/93//p7KysuTvGdnvHtlP9ktkP9kf7ewfPHiwXnrppeTF8ps1fyHefLCS7HeP7Cf7JbLfy2valuYzd9Ndrknylk8Z5/WiYg0NDaaqqsqMHTvWHHTQQWbYsGGt5jniiCNMVVWV+eabb1o91nxRd2Pavlj+fvvtl3Kh/b/+9a+hXTRz+PDhZv/992/z+V166aWmW7du5sgjjzT77rtvm/NZvfTSS65udq9JS6tXrzYNDQ2tpt9+++1GUrJid8WKFebpp59udRswYIDp3bu3efrpp83777+fXH706NGmtrbWrFu3LjntD3/4g5FSq2PfeustI8ncdtttyWlbtmwxu+++uxk+fLjr16Ol5orap59+utVjzTXKdrp162ZOPvnklGk/+clPWl1077zzzjMlJSXmgw8+aLWOlu83N9p6f6SrUbZq6zlJO6pmwxr33LlzXb3nPvroI8d1LViwwHTt2tUMGDDArFq1qs35Nm3aZKqqqsxxxx2XMv3ss882HTt2NP/617+S05YtW2b69etnevbsmfI7aPXiiy+2eh83X/z19ttvN88995zj+G+++eZWF9n8+OOPTXFxsfnpT3+anNb8e2S9GOuUKVOMJPPYY485bsvta2Xnk08+MeXl5cnXz+73okuXLqaiosK8/vrrZvHixea2224zpaWl5pNPPnH9+7l27dpkhk6aNMl07NjR1NbWmk6dOhlJrV5Tuxpl62va0NBgKisrTSKRMD179kxmf8v3anP2f/LJJ2a33XYz3bp1M1u3bk3O11yj/Mwzz6T8Ph199NGmqKjIDBw4MDnu5nxqmf0bN240Q4YMMZ07d04Z78KFC1MuTtqc/S+//HJyWlyy3xj7fJg/f75p3769OeGEE1Kmk/3ukP1kvxOyn+xvKRfZ/+677xpJ5nvf+17KvGeeeaZp166d+frrr5PTyH53yH6y30mhZr8x9q+p3Xt13bp1rbLfGJPM/hUrVqTM7zaf2lrebvxffPFFSvmJG54PhBljzA9/+EPTuXNnk0gkzG9+85tWj3/00Udmp512Ml27djXXXnutmTp1qvnFL35hvvOd75j99tsvOZ9dyDTvxIYPH26mTJliJkyYYDp27Gh23XXXlGaG+fPnm86dO5uuXbuaCRMmmKlTp5qf/exnKWFmdyDsxz/+sUkkEuYXv/iF+dOf/mRmzpyZMva5c+cmd8C33HKLn5cnkKefftr06tXLXHHFFeaee+4xU6ZMMWPGjDGJRMIMGTIk5c1lp62QnjdvniktLTWDBg0y9957r/n5z39uysrKzNFHH91q3lNPPdW0a9fOXH311eb+++83hxxyiGnXrp157bXXUuabNGlSq5+fnWXLlpni4mJz0EEHmWnTppk//elPpr6+3hiTfod47bXXGknm/PPPN/fee68588wzzeDBg1vtEOvq6kyfPn1Mx44dzWWXXWbuv/9+M3nyZHPqqaeanXbaKe3YrNp6f2RihxjmuINYt26d6dWrlykqKjI333yzefjhh1Nub775Zsr8ze0hp5xyinnggQfMOeecYySZX/3qVynz7b///kaSueaaa1qt829/+1vaMXlpj2l+Drvttpuprq42t956q7nzzjtNr169TM+ePVMCe+XKlaampsaUlJSYSy+91Nx///3mRz/6kSkuLjYDBgxw/P3y+lr179/fXH/99eYPf/iD+fnPf26qqqpMnz59zFdffWWMab1D/OKLL4wkc9BBB6WsZ8SIEWbChAnGGHe/n6+++qqpqakxV1xxhfnxj39svv/975sDDjjASDI9evQwvXv3Ttn5yNIe09Zr2qlTJ5NIJFKyf9KkSWb//fc31113nbnhhhtMhw4dTFFRUfJ3t2X22zWHGbMjn9q1a2ckmV133dV8+9vfNsXFxaaoqCgl+5uXLykpScl+SaZTp07JsTe/f1p+1xOn7D/yyCPNd77zHfPLX/7STJ061Vx++eWmY8eOprKy0ixYsCBlvWS/O2Q/2R/ma0X2k/1uef3c/4Mf/MBIO5rg7r77bnPqqacaScn3STOy3x2yn+wP87WKW/bbvaYts3/q1KnmxhtvNH369DGJRML8v//3/1LWadcaaYz7fGprebvxN7eqeuHrQNhLL71kJJlEImGWLl1qO89nn31mzjnnHFNTU2Pat29vdt55Z3PccceZP//5z8l57ELGGGMef/xxM2jQIFNaWmqqqqrMWWedlXwDtfThhx+ak08+2XTp0sWUlZWZvfbay0ycODH5uN2BsLq6OnPsscea8vJy2xfRmB1VpUVFRbbbzLTFixebc845x/Tr18906NDBlJWVmQEDBphJkyaZDRs2OC7fVkgbY8zf//53c8ghh5iysjLTvXt3M27cuJRfimabN282V111lampqTGlpaVm6NChrWqOjdnxLU0ikXB19PWBBx4w/fr1M8XFxSk/83Q7xE2bNpnzzz/fVFZWmvLycnPaaaeZ5cuX2/5C1NfXm3HjxplevXqZ9u3bm5qaGjNixAgzdepUx7G11Nb7IxM7xDDHHURzZXVbN7sa5qlTp5q99trLlJSUmN12283ceeedKWdjGpO+WrytmuBmXneIxhizdOlSc8opp5iKigrTuXNnc9xxx5lPP/201XxfffWV+cEPfmD69u1rSkpKTG1trbngggtafdtgx+trdcYZZ5hevXqZkpIS07NnT3PRRRclPwwa03qH+NxzzxlpRw13p06dkrd27dol64/nz5+fdgySzI9+9CNz6qmnml133dWUlZWZjh07msGDB5v77rvPrFq1ylRUVJg//OEPKeOw+5lYX9Phw4e3yv6//e1v5tvf/nYy68vLy01tba2pqqpqlf1t/WfImB35tOeee5pEImEkmdLSUnPaaael5HDz8hdffHFK9ksyvXv3Ts5n95+hOGX/b3/7WzNs2DBTVVVl2rVrZ2pra83ZZ59t+343hux3g+wn+9Mh+8n+TPH6ub+hocHccMMNpk+fPqZ9+/Zm9913N3feeaftusl+Z2Q/2Z9OoWe/3Wtqzf4uXbqYo48+utWXDMa0fSDLGHf5lOkDYYl/rwwtDBo0SFVVVZo5c2auhxJpw4YNU58+fXxVzwLYcW2Klu0xjz/+uM466yx99NFHrS682rlzZ9XU1KihoSH5d/ht6dq1a9q/0R86dKhGjhypyZMnB34OcUL2u0P2A8GQ/dFC9rtD9gPBkP3R4vli+XE3d+5czZ8/39VFBgvZunXr9M9//lPTp0/P9VCA2Bg0aFCynee//uu/bOcpKSnR3nvv7XsbGzZs0Geffeb6gsCFgux3h+wHwkf25w7Z7w7ZD4SP7M8tDoT924cffqh58+bpN7/5jWpra1MacdBaRUWFtm7dmuthuLZixYpWjSEtlZSUqKqqKosjQqHasGGDFi9enLy/ZMkSzZ8/X1VVVdpzzz111lln6ZxzztFvfvMbDRo0SCtWrNDMmTO133776dhjj/W8vauuukrHH3+8+vTpo2+++UaTJk1ScXGxzjzzzDCfVt4i+70h+wF/yP5oIfu9IfsBf8j+CPP0h5QxNmnSJJNIJMzee+9tZs2alevhIGR9+vQJ9LfrQFia/97demu+zkBDQ4O5/vrrza677mrat29vamtrzcknn5zSAuvF6aefbmpra01JSYnZeeedzemnn24WL14c4jPKb2R/vJH9iAqyP1rI/ngj+xEVZH90cY0wFIQ33nhDmzdvbvPxnXbaSYMHD87iiAAAmUb2A0DhIfsBOOFAGAAAAAAAAApCUa4HAAAAAAAAAGQDF8sP0d13363bbrtNdXV12n///fW73/1Ow4YNc7VsU1OTvvnmG5WXlyuRSGR4pACiwhij9evXq2fPnioq8vfdxJYtW9TQ0OBr2ZKSEpWVlflaFsGR/UBhIvsLG9kPFCayPzo4EBaSxx9/XFdeeaXuu+8+DR8+XFOmTNGoUaO0aNEiVVdXOy7/zTffqFevXlkYKYAoWrp0qXbZZRfPy23ZskXdO3TQBp/bramp0ZIlS9gp5gjZDxQ2sr8wkf1AYSP7c49rhIVk+PDhGjp0qH7/+99L2vFNT69evXTJJZfo2muvdVx+7dq16tKli95/f6nKyyskSe0shymd7pe0a2q94u3bg923qx72ug5r3bL1cUnasiX9dq2PW9dpfdx6380y1sc3bEj/uN06Nm70tg2n5f2sw+lxN/NY7lvfBTY/QVnffdZ5rPetwWN93Obd7DiP0zbs1um0DuvjXu87jWOjpJMkrVmzRpWVlTZLp7du3TpVVlbqJ5JKPS67VdJvtCN/KioqPG8bwcUq+52y3pozdvuCoPlml9NOWR40xzOxDTfrCGOfZfkZOGW9032pdQY6rdP6uM07zzFnrcs4PW73wddr9rvZv6Tb7kZJp4jsL1TN2b9gwX+y38qa9db/txbZvevCzv4wPrM77SvslsmXnPY6jhz838LY/Aydstzps7GbnA66Dbucti6zzWEZN5/Rve5f3OxP7LbTbJOkU0X2RwFnhIWgoaFB8+bN04QJE5LTioqKNHLkSM2ePdt2ma1bt2pri+Bav369JKm8vCL5xozEf4bsdoBel2nf3nmd1idjnae4OP1966mldqeaWqdZT0W33rcbp5X1OPI2SyQ3NaW/b/3wYH0d7KY5vTZuXgun526532h5nmEcCPN6ACqMdeTqQJibeYL+aUQHSV6/2+EikdkX6+x3ynqnLLObJ4zst+as0++adVzWnLf7/rCkJPW+16y3y/6gWW/3PANmvZ8DYU7LhHEgzCm3M3EgzE2uu9ku2V8Y3GS/VU4OhLnZFwTNeru885r11ox1+lxrNw4rr5/pJcn652nWrHfKfrsxBc16y303eef183Wu/h+QiwNhTvssrwfCmpH9ucfrEYKVK1eqsbFRPXr0SJneo0cP1dXV2S4zefJkVVZWJm+cHg0giCKfN2QX2Q8gTGR/fiD7AYSJ7A+O1yNHJkyYoLVr1yZvS5cuzfWQAAAZRvYDQOEh+wEgWvjTyBB069ZNxcXFqq+vT5leX1+vmpoa22VKS0tVWtr6L3vf+2qVNmu9unUu09Bdq1RcRJMMAGd+vunhm5DsI/sBhInszw9tZf+7S1dpk9are+cyHdiL7AfgDtkfHAfCQlBSUqLBgwdr5syZOumkkyTtuFj+zJkzNX78eE/ruuwv76iotKMkqUdFmX42ah99u3+tJBfXBrC7iKPT3/o7XdDSzXVinNbh5kL2TvN4vW+9GKWfZfys02kdXud3sw6nC93bXM/G63Vf/Fy/KxPX8wp6LQA31zTwem2ZoNcfsKlH8IUdYn7zlP3bLdck2RJCTrvJ/qBZn4l9gZsM9ZrtudiGj9fCbN6ccj8T13DxmuN+lnHKXLtpQa/f5ecalE7XfPGa/Zsc1ucW2Z/fxj8RIPvD+Izup9TEKb+sy4TxWddrxobxmT1X2W9ZxliuVRb0M7zknMvWz9tOGWy3jkxcz8trTvu5nlcmrg3cchxkf3TweoTkyiuv1AMPPKDp06dr4cKFuvjii7Vx40aNHTvW9zqXr9uiy598Vy8tXBbiSAHEEdcKiA+yH4BbZH98kP0A3CL7g+OMsJCcfvrpWrFiha6//nrV1dXpgAMO0IwZM1pdQN8LIykhafKLC3TUXjX/vgcArfHNUHyQ/QDcIvvjg+wH4BbZHxyvR4jGjx+vL774Qlu3btWcOXM0fPjwwOs0kurWbdG8L1cFHyCA2OKboXgh+wG4QfbHC9kPwA2yPzhejzyxYoPN35ADAGKN7AeAwkP2A0Bm8aeReaJ75zLnmQAUrIS8f7PBH11EH9kPIB2yP57IfgDpkP3BcSAsDxQlpEN2r1TRFkvPhJ9ml2y0xYTRFBZ2y5ebeSLQGmlthpGCt8O4afWytqpY7/tpjXRqavHT0OV1G0EbHd2uI92YnMa1WeFIyPsOjh1itBUlpEN27ZSZ7Pf6uJ9lQmjLykirVza2EbDx0W5aJrLfa4tkGE28frI/aNaHsS/wOgY7ZD/ccJ39mWiLD6PdNxuf0f1sIwfZ76fdN+zs99Ma6fVxN/MEbZF0s04/2e+mWdLrOtPtD7Y6rN8tsj84/jQyDzQZad6Xq3M9DAARVuzzhuhqMtK8pWtyPQwAEUb2xw/ZD8AJ2R8cZ4TlieXrt0rqlOthAIgo2mPiaUf2A4A9sj+eyH4A6ZD9wXEgLE9Ul5fmeggAIowdYjyR/QDSIfvjiewHkA7ZHxyvR8QlJNVWlmnYrlW5HgoAIEsSkmorSjWsz065HgoAIEvIfgDIDs4IywOTju2v4iIubwegbXwzFD+TvrPXjux3cxVuAAWJ7I8fsh+AE7I/OA6ERVhRQrpgcA+NrmknrVzpr+XLaxuMn+Yw2mKSrK2P1gaVBst9P+0x1paVTDQ8Wu/noiksjLYYN00wXlshg7ZKWufZ1OZc3rBDjI9k9vcsldasyU7DYy6awtzkdNB9g5t5PD4PawuYFDzr7TI1aNaH0RrptfVLCt7o6Gb/YpWNBuGg80up4wyrNZLsj49IZn+2Wti9ZnsY+wKP2b/NtP4UGTT77TLUKeuz0fDotaXdzTqDfoa3227Q3A5jHXbjtGo5brI/Ong9IswYaerces34dFWuhwIg4op83hA9ZD8At8j++CD7AbhF9gfH6xFhzUePb5y1VI1NdsfeAWCHbOwQJ0+erKFDh6q8vFzV1dU66aSTtGjRorCeAv6N7AfgFtkfH2Q/ALfI/uA4EBZxRtKy9Q16++v1uR4KgAhL+Lx58dprr2ncuHF666239NJLL2nbtm06+uijtXHjxrCeBv6N7AfgBtkfL2Q/ADfI/uC4RlieWL5xm9S9JNfDAFDAZsyYkXJ/2rRpqq6u1rx583T44YfnaFTxRvYDyDWyP/vIfgC5Fvfs50BYnqju1D7XQwAQYUWSin0sI0nr1q1LmV5aWqrS0lLH5deuXStJqqqq8rhluEX2A0iH7I8nsh9AOmR/cBwIywNdSos0rDKxo0GmpTBavgqpLcZhHI0eGx/t5gn7vpt5wmiL8doamY1GxzC24bXB0W5cVm7G6bSNlra6WN6NIO0xvXr1Spk+adIk3XDDDWmXbWpq0uWXX65DDz1UAwcO9LhluJHR7HfKaRctuVlp9cpA9ltbH51avpwet5uWiex3yvowmsKCtv9KwRsc/WSqn/YxK6/ZTvaT/ZnSpbRIwzo1+st+r9kehc/sdtOszz2E/2tYWx+9Zn02/h8gBc96NzkdtDE4jEbHbHyGd3OlvTCaJ73MY/Nb6wvZHxwHwvJAwvNf9AIoNEF2iEuXLlVFRUVyuptvhcaNG6cPP/xQ//jHPzxuFW6R/QCckP3xQ/IDcEL2B8eBsDywemuj3q7bpIMrnOcFUJiC7BArKipSdohOxo8fr+eee06vv/66dtllF49bhVtkPwAnZH/8rN7apLfrN+vgneg0A2CP7A+OA2F5Yvmm7VKF178EBlAoguwQ3TLG6JJLLtHTTz+tWbNmqW/fvh7XAK/IfgDpkP3xtHxTo8SBMABtIPuD40BYnqju2E7u/tIZADJj3LhxevTRR/Xss8+qvLxcdXV1kqTKykp16NAhx6OLJ7IfQK6R/dlX3ZEvQADkVtyzn68aIi4hqbZTOw2r6ZjroQCIsCKfNy/uvfderV27VkcccYRqa2uTt8cffzysp4F/I/sBuEH2x0sy+3vk/38yAWQO2R8cZ4RFnJE0aUiVihu2RqeBK4LrtDY+uml2iUJTmFNriyRts9y3nhvitenFbpmgLV9u5vHawuKmkcbrGMJYJow2mZai0B7jljGcmZQtabPfqU3LbprXtl/rNtws43WdPvYFTo2PfrLfmrHW7AljX+CU9dYxSMGz3m6cYTc6+tkXeG3mbWs7XrbpdX1hrSPdc92c5jEvyP54SWZ/U2NGMjQjOe214dHH/sXa+BhGw6PTOtzktPV33Os67H6zgma9m+bETDQ6Bm1wjMJneDfz+GkhbonP/dHBgTAAiIGEvDdN0UwFAPmN7AeAwkP2B8efRkZcQtKN7/xLjU3xPiILIJhinzdEE9kPwA2yP17IfgBukP3BcSAs4oykZZsa9fbysE6kBBBHCXm/TgDfDEUX2Q/ADbI/Xsh+AG6Q/cFxICxPLN/s5i+SAQBxQvYDQOEh+wEgs7hGWJ6o7lAs+0vfAkB2LpqJ7CP7AaRD9scT2Q8gHbI/OA6E5YEupUUa1rOztGpr6gPWFjC7FpaVK9PP43Tfri3GaR6vj8u59dFrO4ybtpig7TFS62YRp/Yxp6YXu6YSp4aZTLR6+Wl+yUarV9A2mKBNL3626TTP1jSPecEOMX66lBRpWHWZtGpj6gPZyH43rV4B9wXWFjDJOcu97hvs5nHK6TCaw7xmvZvWyFw0OvppEPa6Taf5/czjp5nS6za8NgRbl7H7nOIH2R8/yexfvjb1AWvGWnNeap27EdgXNDq0/dpNs963flby0+4btCXSTcOj16zPRqOjXVaF3SBsJ+z/F0jBsz1oboexjN3+3g+yPzgOhOUB/p4XgBN2iPGTIPwBOCD744fsB+CE7A+O1yMPrN7apLfrNzvPCKBgeb1gpp8dKLJr9dYmLpgMIC2yP37IfgBOyP7gOCMsTyzf1ChV5noUAKKKb4biafnmRqk816MAEFVkfzwt39wodcj1KABEFdkfHK9HnqjuWJzrIQAAsmzHBZMBAIWE7AeAzOJAWMQlJNV2aqdhPfhaCEDbEj5viKaEpNqOxTsumAwAbSD744XsB+AG2R8cfxqZByYd1lPFHTtI6yw/ru2WbhJrk5jk3B5TV5f+cbu2GIdGGWs7jHVUftpivDaFhdEc5tT6ZTfNug6ndeai5cvtdoPMbzdPNlq8/MyfiXV6WSas5rDif9+8LoPomnRojbvsd9Pu65T1Tvft1mm5v9XSAml9b1v3BXaNqdZcdspt6303Oe31vp+GR6d1+Mn+MBods5H92Wj1isK+IOjydu9VP8j++Jl0ULWKS9pL7Xxkv9PnfK+f++2a3tevT7nvlPVumt3D/tzvpj3e6XO+n9bIKDY6ZqLd1y7nw25szIfP8H7Wwef+6OBAWIQVJaQL9u+m0btxcTAA6XGtgPgoSkgX7Ful0X0rcj0UABFH9sdHUUK6YEAXjd61c66HAiDiyP7geD0izBhp6vyVmvHZ2lwPBUDEJeS9OYZTpKPJGGnq+6s0Y8m6XA8FQMSR/fFhjDT1wzWa8bnNmV4A0ALZHxwHwiKs+RTTG99YpsYmN384BqBQUaMcH8nsf7Oe7AeQFtkfH8nsn7OC7AeQFtkfHK9HxBlJyzZs09vLNuZ6KACALDGSlm3crrfrNuV6KACALElmf/1mx3kBAP5xjbA8sXzTdqk816MAEFVcKyCelm/aLnG5GABtIPvjafmmRqlTrkcBIKrI/uA4EJYnqruWSyWWH1dnH/87sjbMOLVK2jSHmdWrU+5bz1ewtsVYv9Oya9zKRmtk0KYwu3U6taw4Nc74afnyOgY/LV9xafXKRTtMrprD2CHGU3XXcilh+Y0sK3Ne0KldzGurpKRtlkZgp+zPRmukNbvs2picctlrY7CbnA6jNdJpG1HI+nzZF4S9fBjrIPuRTnWXDlKx5TfW2iJpx9og79QC6dAyudm0Tglrljvdd9oXSM6tj14/9/tp9/Wa21L4DY9+l/EyJjvZyPp8+Awf1jrSrZPsjw5ejzxQlJAG9+R0MABt41oB8VOUkAbXcjoYgLaR/fFTlJAG9+iY62EAiDCyPzhejzzQZKR536zP9TAARFjC5w3R1WSkectoDwPQNrI/fpqMNK+e60MCaBvZHxx/Gpknlm/YJlXlehQAoqr43zevyyDalm/cxjXCALSJ7I8nrg0MIB2yPzjOCMsT1Z3b53oIAIAsq+5E9gNAoanuyLkKAJBJHAiLuISk2vISDdulItdDARBhXCsgXpLZvzOnBABoG9kfLwlJtZ3ba1gtlZEA2kb2B8fXDXlg0rF7q7iiXGqwdGxZm1+6dGm9sLUNxto25tRAY22fkfdmF2szmF2rl1PDjFNbjFOzmBS8GcxNA43Xphc/7THW5+F1THbCbnrJ1TozvXxY68jE+miPiZ9Jo/fckf2buqQ+EEb2O7HJfq9tvl6bxaTW+wunVsgwGoOdcthPa2QY2R/FrM9GbsepKSzdNmgOQ1smjez77+zfmPqAtS3erj0+4Of8RktLpJvP7EFbJO2mOWW9U/uvm32B0+drp/832E3LRrtvFBseM7XOsNeR6UbIbI6B7A+OA2ERVpSQLjikj0bv0yPXQwEQcQl538Fx0cxoKkpIFxzUS6P7d8/1UABEHNkfH0UJ6YKhtRq9Z9dcDwVAxJH9wXFgMMKMkaa+8YVmLKjP9VAARBynSMeHMdLU2Us1Y+GKXA8FQMSR/fFhjDT17WWa8cm/cj0UABFH9gfH6xFhzafC3vjXRWpssp4YCwD/wQ4xPpLZ/+KnZD+AtMj++Ehm/8zPyX4AaZH9wfF6RJyRtGzdVr39xepcDwUAkCXJ7P9yTa6HAgDIEiNp2foGvf3VulwPBQBijQNhLkyePFlDhw5VeXm5qqurddJJJ2nRokUp82zZskXjxo1T165d1blzZ40ZM0b19eH9SePy9dZLCAPAf/DNUDwt32B3WWEA2IHsj6flG+wqmgBgB7I/OC6W78Jrr72mcePGaejQodq+fbt+9rOf6eijj9aCBQvUqdOOeuMrrrhCzz//vJ588klVVlZq/Pjx+u53v6s33ngjlDF079JZ6tw+dWK3bqn3bVq+tGFD+nmc7luXl9TeMk+JQ8OM9U1m9986r7+YmWgecbpvd5J62E1hbprDnE6W99NQQ1NYeHLVHpOQ94tgctHM6Kuu6ix1KU6daM1+m5xuNS3ofUklq1PPTLZmufX95JTrbnLf6ffDT6uX0+NeWyXdzON0364hMopZH9V9QSbWkY11hoHsj6fq7hVSueVd5yb7ra3CTtluaR0utjxesq31ATlr9luz3Om+H06fycNoYXfan4SR/V4/07sdh5fHw1pH0GWikvVRzXYnZH9wHAhzYcaMGSn3p02bpurqas2bN0+HH3641q5dqwcffFCPPvqojjrqKEnSQw89pP79++utt97SQQcdFGj7XTq219Bdq6RN6wOtB0B8Ff/75nUZRFeXju01bNcqad3aXA8FQESR/fHTpUM7Deuzk7SKi+YDsEf2B8eBMB/Wrt3xn5KqqipJ0rx587Rt2zaNHDkyOc/ee++t3r17a/bs2bYHwrZu3aqtW//z547r1rV9LQCO3gJw4ueUZ06Rzj6yH0CYyP784Cn7E6Q/gPTI/uB4PTxqamrS5ZdfrkMPPVQDBw6UJNXV1amkpERdLKcX9+jRQ3V1dbbrmTx5siorK5O3Xr16tbnN1Zu26Z3PV4X2HADED9cKyA9es/9tsh9AGmR/fvCc/ZRkAUiD7A+O18OjcePG6cMPP9Rjjz0WaD0TJkzQ2rVrk7elS5emnX/5epvrfwEA8or37KcoBQDyHdkPANHCn0Z6MH78eD333HN6/fXXtcsuuySn19TUqKGhQWvWrEk5K6y+vl41NTW26yotLVVpaanrbVeXl/keN4D44xTp/OA9+93PC6DwkP35wV/25+tlvAFkGtkfHAfCXDDG6JJLLtHTTz+tWbNmqW/fvimPDx48WO3bt9fMmTM1ZswYSdKiRYv05Zdf6uCDDw607YSkmsqyHRfLb7I0t1j+FNO2NdJumhftbN4iZakH5Tp99VXqIpaGGWunjd0voXWU1nmso3BqJ7PbhrUJzMq6jNP8kn3TV6Y5bTNXIWf9uGYdh9PHOa/z+10mE+tIt74w1ulGoo1tOy2DaGrO/iG7dpMaNqc+aM1+uy9ctm9Pf9/KmvU22d/eMq3LihWpi1jm32S5b31/2u2drPNY28mc2srctBJ73RdYO9P87F/8iGrWOwl7X+BmG07rCCODM7HOMJD98dKc/cN26y5t2pj6oJu2eKest3LI/g42l3gpWp9a3mXNfj8tktZpTp/z/bQWB90X2P3OZyP7g2a93bidnpvTNv2sM+j82VqHVVSy3orsD44DYS6MGzdOjz76qJ599lmVl5cnr/tVWVmpDh06qLKyUueff76uvPJKVVVVqaKiQpdccokOPvjgwI2RRtLEY/dRcVEiur+JAHKOb4biJSX7AaANZH+8GEmTju1P9gNIi+wPjgNhLtx7772SpCOOOCJl+kMPPaTzzjtPknTnnXeqqKhIY8aM0datWzVq1Cjdc889WR4pgELFDhEACg/ZDwCFh+wPjtfDBWOM7a35IJgklZWV6e6779aqVau0ceNGPfXUU21eH8yLhKRfPL9AjU0m8LoAAPmB7AeAwpOQdOPzC8l+AMgwDoRFnJG0bO0WvfP5qlwPBUCEUaMcL2Q/ADfI/nhpzv63yX4AaZD9wfGnkXli+fotkspzPQwAEcUp0vG0I/s75HoYACKK7I+n5eu3StW0BgOwR/YHx4GwPFFdXtZ6orXpxdokJrVuj3FqBuvc2Xmd1mmWFptSS4tk6cqVKfc7brN2cLVuD7P05Gir5b71o4F1jXbNYdZp1qYX6+PWddo1eFn7C6zrtD5uXaf1xHe7PgTrtKD3pdbPpdjHuKyc5vH6uJ/msDCaX7LdDBbWTokdYjzZZr+luddX9lvXYc1+632b7SQsbWLllvsdV69OuW9tkbTel1rvC5zuW3Pbuq+QWued077A630peNZnIvvt9llBsz4X+wI7uWgnCxvZj3Sqy0udP6NbWyTdsGa/077AZhutPudbsr/M8jnfmvWWHmRJrbPbOo/1cadGYbucdvrc7/QZ3k32O60jE9nvp2XSaw67+bwd9DN5VBuDw0b2RwcHwvJAl47tNXTXKqmp9QEkAJDYIcZRMvsb7P7bAABkfxx16dhew3atkhrsDu0DANkfBg6E5QEKlAE4YYcYP2Q/ACdkf/yQ/QCckP3B8XrkgdWbtnHBZAAoMGQ/ABSe1Zu2cbF8AMgwzgjLE1wsH0A6fDMUT1wsH0A6ZH88LV+/VVKnXA8DQESR/cFxICxP2F4wGQD+LSEpkfD2BxUJY71UNqKG7AeQDtkfT9XlNEYCaBvZHxwHwiIuIammolTDd+ksbbH2ZVlY22Uk++avdMu4aQ6zNshY2mJUU5P28Q6WFkm7aRXr16fctz5zawON9XKidpcXdWqY8doqaTeP9b5Tc5hTu4ybeaxtMblqorRyGpfTmPzM46cdJhPr9LKO0EK4XTvJ4w5RxrRuF0QkpGS/9YLJTg2QUusmyTAag52y33K/2JLr1lbJcpt9wWbLh7SgLZJ207xmv5t9gVNzWBhtZF6zPhPZb2XXTBk066O6LwhjGy2R/bDTnP3Ddqlwvli+3Wd0p5+r15ZIa87bzWPJ8g4On/sbLZ/xpdaf671mv/WVsqsXC5r9bpoovWa9m2Z3P/93cHrcT+twujH5HYeXx3O1Tj9nUaXbbmhnZZH9gXEgLA9M+s5eKi7i0pkA0mCHGDtkPwBHZH/skP0AHJH9gXEgLMKKEtIFh/TR6H165HooAKLO7w4RkUP2A3CN7I8Nsh+Aa2R/YFwzLcKMkaa+8YVmLKjP9VAAAFlC9gNA4SH7ASB7OBAWYc3HbG/86yI1NnEEF0Aa7dr5uyFyyH4ArpH9sUH2A3CN7A+MVyPijKRl67bq7S9W6+DajrkeDoCoKi6Wijx+t9EURhUAMoHsB+AK2R8rZD8AV8j+wDgQlieWr98qWXeIbi5259QMZuXUJiO1boux3re2RlqbwWyawqzTrG1jndasSb1vbaDZvDnlvl2/plO7mNN9u+4ea6tK0AYaN4001m1a77tp/cpGE6XT436aKMNotXFap9fHsz1/m9q1Y4cYQ66y3+4bPrsmyXTL+Ml+p6y3to252BdY28U6WLLfrF6dct9Na2TQ7HfKdTfz+Mn+oFmfjSZKP7kdRguxVT62EofaGkn2x45t9lu5aYv3mvV+GoSdst3hM77UukXYen/bttQeSK8tknbTvLbFh5H91t88u3bLoFnv5rNy0KwPI/utvLbNu1lnFJsoyf7o4E8j80R1eWmuhwAgyrJ0ivTrr7+u448/Xj179lQikdAzzzwT/nNBEtkPIC2yP5bIfgBpkf2BcSAsDxQlpMG9uuR6GACiLEs7xI0bN2r//ffX3XffnYEngZbIfgCOyP7YIfsBOCL7A+NPI/NAk5HmLV3DtQIA5NwxxxyjY445JtfDKAhkP4CoIPuzh+wHEBVxzn4OhOUJV9cKAFC4iot33Lxo3HFFhnXr1qVMLi0tVWkpf5YRBWQ/gLTI/lgi+wGkRfYHxp9G5gmuFQAgrQCnSPfq1UuVlZXJ2+TJk3P8ZNCM7AeQFtkfS2Q/gLTI/sA4IyziEpJqKss0bLfuUoNdB4oDp78FtrbDWNtk7JrHnBplrG0yltavVvel1g0z1nkcHi+2tkrabMM6zaxfn3Lf2vzipo3M+hPx2iLpppHGOs3aMOPULuOmOcy6Tqc2GTfrdLrvpwUsaItNGG2XVk7PQ0rffONmeVfatfP+zVAiIUlaunSpKioqkpML8VuhqEnJ/k0bva/Amv3WLLc+br1v1xppzXqn7HdqlfSxL0g4tEpa70tSpWXaNpP6Gxm0VdJumtest8vUoFnvponSmk1O7WNuMjeMFmKndQbN/mw0VTptJ9TGYLI/NtJmvzWn7drjnbLf6/xuWiM9fmZ30x5vXaa99b61ZdLyeOO21n2MTlnv9Lif1kinx/20RjplvZv2xaBZb5dfUWghdprfTrZbicn+6OBAWB6YdGx/FRclcj0MAFEWYIdYUVGRskNENJD9AByR/bFD9gNwRPYHxoGwCCtKSBcc1lejB9Y4zwygsAXYISJayH4ArpH9sUH2A3CN7A+MA2ERZow09e9LNKhXF3aKACJhw4YNWrx4cfL+kiVLNH/+fFVVVal37945HFl8kP0AoobszzyyH0DUxDn7ORAWYUY7rhVw4/ML9e19esjjMV8AhaS42PmagCGYO3eujjzyyOT9K6+8UpJ07rnnatq0aRnffiEg+wG4RvbHBtkPwDWyPzAOhEWckbRs7Ra9/fkqHdyzU66HAyCqWrTBZNIRRxwhY9xcJhpBpGR/dWFexBSAC2R/rJD9AFwh+wPjQFieWLamQepdmTrR2uyyxdp34oJTA43dL5h1u9Z2MadWyQ0bWq/T2i7m1DTp9b7NtITlfqllXKXWddiM29pK47VtzKl1UgrePuanNdKpscau8cSpgcZrm4ybcTs97qap0muDWRgtN+m271uWdojIrvr1Ntlv5Sb7vWa9XdOYU/Y7Zb1T05jdtAxkf3vLuKxtZNb5nXLebloYjcFBs95Na6TXdrIw9gVhNFEGbSEOo0GY7Ecm2Wa/Nevtcto6j1NDsFNbvF2DcCba471mu1ObvM1ndmt7fKs2+c2bU+47tUpKztnudV9gN4/X+045b7eMU745tcu7WUcumimtyH60xKuXJ7qXO9QfAyhs7BBjqZrsB5AO2R9LZD+AtMj+wHj18kCXDu01pE+VWh+PB4B/83OtgJie6hwXXTq219Bdq6Qmsh9AG8j+2CH7ATgi+wMryvUA4AJNpwBQcIh+ACg8ZD8AZB4HwvLAmk3bNPeLVbkeBoAoaz5F2usNkbV60za98znZDyANsj92yH4Ajsj+wHg18sSK9Vskled6GACiys8OjlOkI2852Q8gHbI/lsh+AGmR/YFxICxPdC8vU1O7kpRpRdbraNr9MljbY6xNYVZOj9vN49Q446Y10jpO6zxODY5O87tZh9fH1bqVpoPDfes6t1kCyU17jNcmMTfrdGoKC6OJ0hq9ftrIvDaH+WkKC6PVxirduHLaHsMOMfK6dsxQ9ju1SNrtC5yaiq1NYtb8c7MvCJrLdtnvdRnL/MWWx1vlut00j1nvpzXS675A8t4Q7LWdzM06vTZT2q3TKdvDaKZ0etxpTE7rIPuRjqvs9/OZPYwGYWvWO32GDyP7Pea2n2bKhLU93qlN3ma7XrM+E5/7nXLebhmv2e8mQ71+zneTqZn4zJ7tFmKyPzo4EBZxCUk1FWX/vlg+ALSBHWKskP0AXCH7Y4XsB+AK2R8YB8Iizkj62Xf2UXERl84EkIaf9pgmN+ezIRfIfgCukP2xQvYDcIXsD4yL5QMAAAAAAKAgcCAs4hKSfv3XBWps4lRGAGnQHhMrZD8AV8j+WCH7AbhC9gfGgbCIM5KWrduiuV9QowwgDXaIsUL2A3CF7I8Vsh+AK2R/YLwaeaJu7RabYhdLm4zdm9trM5ifBhora8OMU8uk5Nw+5tQ443TfzTxemyn9LGO5397hvqRWr43ZvDnlvtd2GT/LuGmL8dpa46c5LGgjTRitkZlopgyFnx0c1wqIvEhnv9M2vLZM2s0TRvYHbR0OYRuOWW993mqd9UFbJO2mBb3vpjXSazuZ3TqDtg67yeCwmynbmhY6sj+WIpP9do97bY93apmUvDdNes1tN/P4yX5rtlvW0d6hUdOa81L4jcHZ2BdI3j+jZ6I93ml/4qeJ0s/86eYJLX3J/sA4EJYnune2OXgEAM3YIcYS2Q8gLbI/lsh+AGmR/YFxICwPdOnQXoN7U6MMIA0/7TGNdudeICrIfgCOyP7YIfsBOCL7A+MaYQAAAAAAACgIHAjLA2s2b9O8L7loJoA0uGhm7JD9AByR/bFD9gNwRPYHxquRJ1ZssLmwJAA087OD4xTpyCP7AaRF9scS2Q8gLbI/MA6E5YnunctclHq1PsHPsWEmE62RTut00xppXcapbcyhlcXVPJlooszANhKW5+GrjWxbaqdjJhpogrZG2m3Dug6ndTptw24er81hftouI9MayQ4x8mKd/XbrC5r1dhkatHkyF/sCSQnLtFLLcy+1PN7J8njjNmsKO+dsGPsCr9nuJ6eDNlH6aQ4LozWy5TjsxuAL2R9LVR1aZ3/rFsksZL+b1kgrp8Zgu+WtWe/0WdZry6TdNK/3ra2SIazTmvOSi6y33G80qWnilOt284TRGumUy17/X+Cmod7pM7zTNtxs12ubvOSuUT4wsj8wDoRFXEJSj4oyLpoJID0/F810c/ADOUH2A3CF7I+V5uw/sBfZDyANsj8wDoTlgQmj9lFxUSLXwwAQZX6+GeJaAZFG9gNwRPbHzjUjyX4ADsj+wHg1IqwoIZ13UF99u39trocCAMgSsh8ACk9RQjpnWF+N3JvsB4BMozUywoyRHpq9RC8tXJbroQCIOtpjYoPsB+Aa2R8bxkjT5yzRyx+T/QAckP2BcSAswpovtDf5xQVqbArtsqoA4ogdYmyQ/QBcI/tjozntb32Z7AfggOwPjANhHt18881KJBK6/PLLk9O2bNmicePGqWvXrurcubPGjBmj+vr6ULZnJNWt26J5X64KZX0AYoodYqyQ/QBcIftjpTn7311K9gNIg+wPjFfDg3feeUf333+/9ttvv5TpV1xxhZ5//nk9+eSTqqys1Pjx4/Xd735Xb7zxRmjbXrFhS6tp/oofUo99OtYs2/FTtez0uHW71nm8VjFba5jtlvFazWx93G4ep/tO23BT9+x1HTbjttY1t6pqdtiGtapZCl69bK0W9lPN7HUbkveqZaf7duO2rrPlqxfa5XD9tMcUF4e1dWRI3do8zv4w9gVO2W99PBPZ7yang64jA9lfbJP91mmllmU6WR63Zr1TxrqZx3p/m+W+Xbm7U85aH7eu07rHshu316x32oabdYSC7I+lFRu2tIpIp4/K9gJmv91GvGa7m4Fbs9wp+61Z7+czexjZHzTrQ8h+a64XO3zGlySzeXPK/aCf4f0s4/UzvJtl/HxGd1om3Wd4u/mdxhXauZ5kf2AcCHNpw4YNOuuss/TAAw/ol7/8ZXL62rVr9eCDD+rRRx/VUUcdJUl66KGH1L9/f7311ls66KCDQtl+985lzjMBKFx+vunhm6HII/sBpEX2xxLZDyAtsj8w/jTSpXHjxunYY4/VyJEjU6bPmzdP27ZtS5m+9957q3fv3po9e3ab69u6davWrVuXcmtLUUI6YJedgj8JAEBOec3+/Xcm+wEg35H9ABAtHAhz4bHHHtO7776ryZMnt3qsrq5OJSUl6tKlS8r0Hj16qK6urs11Tp48WZWVlclbr1692py3yUjzv1rte/wACgDXCsgLXrP/n1+T/QDSIPvzAtkPIFRkf2AcCHOwdOlSXXbZZXrkkUdUZv179QAmTJigtWvXJm9Lly5NO7/dNcIAIIkdYl4g+wGEiuzPC2Q/gFCR/YHxajiYN2+eli9frgMPPDA5rbGxUa+//rp+//vf68UXX1RDQ4PWrFmTclZYfX29ampq2lxvaWmpSktLXY+DawUASIuLZuYFsh9AqMj+vED2AwgV2R8YB8IcjBgxQh988EHKtLFjx2rvvffWT3/6U/Xq1Uvt27fXzJkzNWbMGEnSokWL9OWXX+rggw8OvP2EpB4VZdq/Z5Wv5b0Xu1jbZWwWsjbOWPsynNph7FbqdaBem8Ts5rE2zvhpjQy7iTKM5jA36/S6DodGGsmmjcxhHX7ayLw2zFjv27WRObVEem2k8bONUHDRzFhpzv4De1X5bIlMLyfZ7zQIN+vw2iBsN0/Q7A8jUzPRRJmBcbZqI3NonZQksy21TzFok5ifZfxsw2kdTs1hftrIQkH2x0rL7Ldysy/wWtjomP2W3Jd8NE26aRR2GrjXz/12n9kzkf1RaCH28f+XhGVae8sy7R0+w1tzXgrePOnm/wFem97dZL9TA7CfdabL/tDa4sn+wHg1HJSXl2vgwIEp0zp16qSuXbsmp59//vm68sorVVVVpYqKCl1yySU6+OCDQ2uMvGbkPiouCu3XBkAcsUOMHbIfgCOyP3bIfgCOyP7AeDVCcOedd6qoqEhjxozR1q1bNWrUKN1zzz2B11uUkM4Z1lcj964NYZQAgHxA9gNA4SH7ASB7OBDmw6xZs1Lul5WV6e6779bdd98d6naMkabPWaL9dt6JnSKA9PhmKDbIfgCukf2xQfYDcI3sD4zWyAhr/hvlW19eoMYm618sA0ALtMfEBtkPwDWyPzbIfgCukf2BcSAs4oykunVb9O7SVbkeCoAoa26P8XKjPSayyH4ArpD9sUL2A3CF7A+Mw4J5YsWGLRlpDnPiptTLejzVqXGmVdOY3ULea2/SPy55b5xx00DjtY0sCs2UbuYJY5xOzZNObWQutuHUPOnU9OJnGT/tMW7mCYxTpGNpxQab3wMXgu4vspL9bhqEne6Hkf1e28esOe9mnbloosxG9rtpI3O4n4k2sjByOmg7md08ZD/c8pv9Vl73BW6K3j1nv9dGYbt5grZK2q3DKdut+ecm+zOR00HXmYF9gTXnJe9Zn432eOv8rfcuzg3AfvYnTi3DoSD7A+PVyBPdO5c5zwSgcLFDjCWyH0BaZH8skf0A0iL7A+NPI/NAlw7tdWCvqlwPAwCQRWQ/ABQesh8AMo/DggAQB3wzBACFh+wHgMJD9gfGGWF5YM3mbVw0E0B6XDQzdsh+AI7I/tgh+wE4IvsD47BgngjropkAYopvhmKJ7AeQFtkfS2Q/gLTI/sB4NfJEVYfsXDQzjGZK58aZ1iciBm4bc2qXcTMw6+NhNNA43XdqmbSb5rXpJYy2GD8NZ2Hft5nm1DzZah0274ugzZN+msNazhNakww7xFjKp+x35tA0Jpt2Ma9Z7yf7nbLeKeftpnnNej+Zmo2msCw0CFvvh9FGZnLQRmbXHGZtKDNt/DsQsj+WqjqU+SrJdRLGOrxv0/K53/IZX2qjUT51Jenvu6m79LovyER7fC7a4mPUHm9tFQ6a236WSZfrbsZlbZT0jewPjFcj4hKSqsvLNGgXLpoJIA12iLFC9gNwheyPFbIfgCtkf2BcIyzijKSrRuyj4qJErocCAMgSsh8ACg/ZDwDZwWFBAIgDvhkCgMJD9gNA4SH7A+OMsIhLSLp95gI1NoV2NQkAcUR7TKyQ/QBcIftjhewH4ArZHxgHwiLOSKpfv0XvfUWNMoA0vO4M/XyThKwh+wG4QvbHCtkPwBWyPzBejTyxcsMWV4Uo2ZCddjGr9G1jjk1jkve2sUw00PhoNfTcSJON5rAMNIXlZBuyaa1xaIjz2ljjNI9DT5J7nCIdS2Flv9fcdhN3meHQLGltFHZTgeY0cGsu+Gkj85r11vvW5d0s43Vf4GaeMFqJI5D91ubJVq2TLva11kZhrw3CTvOQ/Uhn5YbWvweZaHa3ykWr5I7tpm+WDNwqKXlvj3f6TC8Fz/psNFFmq5E+C/8PsGa7Y/Ok5Wdq/QwveW+Dd5P9tMXnB84IyxPdOtuELwAg1sh+ACg8ZD8AZBaHBfNAZVl7apQBpMc3Q7FD9gNwRPbHDtkPwBHZHxivRh5I0KAMwEnzRTO9LoPIIvsBOCL7Y4fsB+CI7A+MP43MA2s2b+OimQDS46KZsUP2A3BE9scO2Q/AEdkfGAfC8oTdRTMBICmLO8S7775bu+66q8rKyjR8+HC9/fbbIT8ZNCP7AaRF9scS2Q8gLbI/MA4L5gm7i2ZmokXSTzuM03Yj0TQmeW8bc7pvN81pnU5tMW5aI702T9o1uwRtnszX1sgQXt+EZR1OjTV2223ZWpNvzWGPP/64rrzySt13330aPny4pkyZolGjRmnRokWqrq72vD6k5yb7MyE37cDeOTWNSS6y3q4JrCWnVkk368xCNoWSoWG0EudD9rvYhlOjsLWJ0u5nmK55kuxHOrm6WL6b7M9Es6TTx+nArZL2K/U2CLsnGjTrw8i7MBqE86Q1Mmj2W1snJecsL/XYKCzFqzE4ztmft2eEHXXUUbrxxhtbTV+9erWOOuqoHIwoMxKSepSXcdFMAJFwxx136IILLtDYsWO1zz776L777lPHjh313//937keWsZlc79D9gOIErKf7AdQeOKc/Xl7RtisWbP0wQcf6L333tMjjzyiTp06SZIaGhr02muv5Xh04bpqxD4qLuLKmQDSCPDN0Lp161Iml5aWqrS0tNXsDQ0NmjdvniZMmJCcVlRUpJEjR2r27Nnex5xnsr3fIfsBOCL7M47sBxA5ZH9geXtGmCS9/PLLqqur00EHHaTPP/8818MJXVFC+v6wvhqxZ22uhwIg4ppU5OsmSb169VJlZWXyNnnyZNttrFy5Uo2NjerRo0fK9B49eqiuri7jzzEKsrHfIfsBuEX2ZwfZDyBKyP7g8vaMMEmqra3Va6+9prFjx2ro0KF68skn1b9//1wPKzRNRnr47SXat+dO7BQBpLV9u/frdDTPv3TpUlVUVCSn230rhB2ysd8h+wG4RfZnB9kPIErI/uDy9oywRGLHKcOlpaV69NFHddlll2n06NG65557cjyy8N0+c4Eam4zzjAAKVvMO0etNkioqKlJube0Qu3XrpuLiYtXX16dMr6+vV01NTaafYs5le79D9gNwQvZnHtkPIGrI/uDy9owwY2lsuO6669S/f3+de+65ORpRZhhJ9eu36L2vVmlI765p581Vy5fX7bop3HJaJozn6thA42ajTgNxaotxsw3rPF6bJ920rjgtE9FWr1y0Rnp+rWyWadlak2hqkv71r9bLeBTkmyG3SkpKNHjwYM2cOVMnnXSSJKmpqUkzZ87U+PHjva0sD2Vzv+Ml+6MiFznuRuCst2aum32BU9Y75bibeXKRoX72BXHZv/jIfmvTZMsmyvbG2I/DI7I/88j+VJnI+qD7B1cNwl77+vxkf9BmSrvtZiPvotgYnIHWyDCy36lRWGrdRNmyLT6sjz5kf3B5eyBsyZIl6t69e8q0MWPGaO+999bcuXNzNKrMWbkh+IclAAjqyiuv1LnnnqshQ4Zo2LBhmjJlijZu3KixY8fmemgZl4v9DtkPIArIfrIfQOGJc/bn7YGwPn362E4fMGCABgwYkOXRZF63zmXOMwEoWNn4ZkiSTj/9dK1YsULXX3+96urqdMABB2jGjBmtLqQZR7nY75D9ANIh+zOP7AcQNWR/cHl7IKyQFCWk/XrulOthAIiwxkbvO7jGRn/bGj9+fCxOiY46sh+AE7I/fsh+AE7I/uDy9mL5haTJSO9/szrXwwAQYUEumoloIvsBOCH744fsB+CE7A+OM8LyBNcKAJBOtk6RRnaR/QDSIfvjiewHkA7ZHxwHwvJEnK4VkIn2mFy0SkohtI053d8xEG/bsLadWB+3m8drG1au2hezsc6gr4XXbTQ25k1rJLKP7M/MWFK3mYEGYa/33dQpe22edJP9mWh4DLqObGR/GOMOmv1NTdIXX7RexiOyP57yOfu9fmZ3s0wYAmd9GI3BfrI/Cg3CUWklzsT+JQvZn2ixTD61xccdB8IiLiGpurxMg3apyvVQAABZQvYDQOEh+wEgOzgQlgeuGrGPiosSuR4GgAjL5kUzkR1kPwAnZH/8kP0AnJD9wXEgLMKKEtLZQ/tqxJ61uR4KgIjjFOn4IPsBuEX2xwfZD8Atsj84WiMjrMlID7+9RDM/WZbroQCIONpj4oPsB+AW2R8fZD8At8j+4DgQlgdun7lAjU0m18MAEGHsEOOH7AfghOyPH7IfgBOyPzj+NDLijKT69Vv03lerNKR311wPJ2u8Ns64+cXORbOk5wYaP400Xhtq3Mzj1FBjt86wW72ysY0wxhV0GyHtlThFOl7IfntRbJWUQsh6N9nvtX3Mbp1esz0KDcJhjDOK+5fGRloj0UohZH+uPrM7b9PjZ3opeNa7Wac1W6LYIBzGOrLRGhmV7Kc1MhI4IyxPrNxg80sGAIg1sh8ACg/ZDwCZxRlheaJbZ5uj+QDwb7THxBPZDyAdsj+eyH4A6ZD9wXEgLA9UlrXXoF2qcj0MABHGKdLxQ/YDcEL2xw/ZD8AJ2R8cB8LyQCKR6xEAiDp2iPFD9gNwQvbHD9kPwAnZHxzXCMsDazZv03tfrcr1MABEGO0x8UP2A3BC9scP2Q/ACdkfHGeE5QkumpnKa7OYn2Ui0SppNy2MNjKvDWfZaCPLRHNYNtrI3Gwj3TzbtrWe3weuFRBPZH8qN4VbXpfJRNa3ynavmetmHj9PzKl9zGmdTjnvZp6wG7iytY2w10n2I418yn4/uex1nVaRaJVUCFnvJvud1uk11+3m8do8mavP03FopA/pzUr2B8cZYXmCi2YCQOEh+wGg8JD9AJBZnBEWcQlJ1eVlXDQTQFpcKyBeyH4AbpD98UL2A3CD7A+OA2ERZyRdNWIfFRdx5UwAbWOHGC9kPwA3yP54IfsBuEH2B8eBMACIAXaIAFB4yH4AKDxkf3BcI8yFr7/+Wmeffba6du2qDh06aN9999XcuXOTjxtjdP3116u2tlYdOnTQyJEj9emnn4ay7YSk22cuUGOTCWV9AOKJ9ph4IfsBuEH2xwvZD8ANsj84zghzsHr1ah166KE68sgj9cILL6h79+769NNPtdNOOyXnufXWW3XXXXdp+vTp6tu3ryZOnKhRo0ZpwYIFKrO2bHhkJNWv36L3vlqlIb27Bnw2hcPNL7rXxhk/TZTOY3BoGrPbcBitkZloI/O6jNeGGrt5wmiNDLspzE0jTct5Ghpaz+8D7THxEsfsz0WzmN12stFGZtcu1pJj05jdhsPI/rAbg93kdBhtZLnOaT+POy1D9sNGPmS/0/vNz+fpoGPIRIOwu/JFj1nfegWtpwX9jO4m74I+ebtxB836MD6zR6WRPt08NAZHBgfCHNxyyy3q1auXHnrooeS0vn37Jv9tjNGUKVN03XXX6cQTT5Qk/fGPf1SPHj30zDPP6IwzzghlHPlUowwACAfZDwCFh+wHgMziTyMd/M///I+GDBmiU089VdXV1Ro0aJAeeOCB5ONLlixRXV2dRo4cmZxWWVmp4cOHa/bs2W2ud+vWrVq3bl3KLR1qlAGkwynS+YHsBxAmsj8/kP0AwkT2B8eBMAf/93//p3vvvVd77LGHXnzxRV188cW69NJLNX36dElSXV2dJKlHjx4py/Xo0SP5mJ3JkyersrIyeevVq1eb81aWtadGGUBa7BDzA9kPIExkf34g+wGEiewPjgNhDpqamnTggQfq17/+tQYNGqQLL7xQF1xwge67775A650wYYLWrl2bvC1durTNeRM0KANwwA4xP5D9AMJE9ucHsh9AmMj+4LhGmIPa2lrts88+KdP69++vv/zlL5KkmpoaSVJ9fb1qa2uT89TX1+uAAw5oc72lpaUqLS11NYY1m7dF+qKZAHKPi2bmB7IfQJjI/vxA9gMIE9kfHAfCHBx66KFatGhRyrRPPvlEffr0kbTjwvk1NTWaOXNm8sDXunXrNGfOHF188cWhjaPQLppp/cUOo2HGaRtWfprDgpewOJ+k6dg2Zl2p3cCd5vF6325a2O1kUvBGGruGM+t2st0es3Vr6/l98PNND98MRV8+Z7/T+8tPpmZiHLloEPaV9U4DyUT2O23DLu+C7giz0URp99pkO/tDao0k++Mpn7PfSSayP4zP6JnQKuvblaTcLVJTGBtJf99NNnn/D4vzOjPxmT0XTZReH7ebh+yPJA6EObjiiit0yCGH6Ne//rVOO+00vf3225o6daqmTp0qSUokErr88sv1y1/+UnvssYf69u2riRMnqmfPnjrppJNCGwcXzQSAwkP2A0DhIfsBILM4EOZg6NChevrppzVhwgTddNNN6tu3r6ZMmaKzzjorOc8111yjjRs36sILL9SaNWt02GGHacaMGSqzO5LtUUJSdXkZF80EkBbfDMUL2Q/ADbI/Xsh+AG6Q/cFxIMyF4447Tscdd1ybjycSCd1000266aabMrL9q0bso+IirpwJoG3sEOOH7AfghOyPH7IfgBOyPzgOhEVYUUI6e2hfjdiz1nlmAAWNHWJ8kP0A3CL744PsB+AW2R+c89VakTNNRnr47SWa+cmyXA8FQMQ1t8d4udEeE01kPwC3yP74IPsBuEX2B8cZYXng9pkLdMTuNbE+TTqMdrGWMtE05madOWmgsXBsGtuxktT7mWiNDLudzM8yTm0yfpaxPh60kcZufh+2b5eKi70vg+gqhOx34qfBMeg2nNZpN6ZMtJFlpG0s7Ox30xxmzTg/bZdBs94pt+2WyXRrZIiNwWR/vJD9mcn+oFkfRmOwld1n+lDa4Vty05gehex385k9H9rjneYh+yODM8IizkiqX79F7321KtdDAQBkCdkPAIWH7AeA7OCMsDyxckM4Z40AiCe+GYonsh9AOmR/PJH9ANIh+4PjQFie6NbZ5lROAPg3dojxRPYDSIfsjyeyH0A6ZH9wHAjLA0UJab+eO+V6GAAirPmimV6XQXSR/QCckP3xQ/YDcEL2B8c1wvJAk5He/2Z1rocBIMK8Nsf4qV1GdpH9AJyQ/fFD9gNwQvYHxxlheSLu1woIu3HLzfJeG2fCWKefokSvnJrGJBdtY34aaoI20PjZhtdxumm58dpi4zS/3TIt59m8ufX8PmzfLhV5/GqDHWL0xT37g8pF1vtpDnOKKjfrtM7TZPku07FpzG6lTsLYFzitw00DV9Csz0Uzpd06MtQYTPbHD9mfntcGSDfrsPLzGd1r1ttHUcB2eD8t7FH4jO7UMulnG9loj3fTbtlyG2R/ZHBGWJ7gWgEAUHjIfgAoPGQ/AGQWZ4RFXEJSdXmZBu1SleuhAIgwvhmKF7IfgBtkf7yQ/QDcIPuD40BYHrhqxD4qLkrkehgAIowdYvyQ/QCckP3xQ/YDcEL2B8eBsAgrSkhnD+2rEXvW5nooACKO9pj4IPsBuEX2xwfZD8Atsj84DoRFWJORHn57ifbtuRM7RQBpbd8uJTx+gcw3Q9FE9gNwi+yPD7IfgFtkf3AcCMsDt89coCN2r+E06ZCF0TjjtE6rXDXQtF6nQ9uYH17bYcJopPF6309rZBhtZOkaZrye19wGdojxk+/Zn4lWXD/Czno3hYNulgmbU9OYFFLbmPNA0q/Dz07Ma9NkJlojw24jC7ExmOyPl6hnf1SyPd0YMvGZ3s06c9EOH4nP8H6W8fNiOTVLZuL/AV6bKe3G2XKZMH5eIvvDQGtkxBlJ9eu36L2vVuV6KACALCH7AaDwkP0AkB2cEZYnVm6w+aYRAP6Nb4biiewHkA7ZH09kP4B0yP7gOBCWJ7p1tjntEgD+jR1iPJH9ANIh++OJ7AeQDtkfHAfC8kBlWXsN2qUq18MAEGGNjd53iLTHRBvZD8AJ2R8/ZD8AJ2R/cBwIywNe3+QACo+fb3n4ZijayH4ATsj++CH7ATgh+4PjQFgeWLN5m977apWG9O6a66HEmtfGxzC2kY0GGjcFKU5tY60aadys1GkguWikCWOdftrI0q0jxNbIbCyD7CH7MyMTWR90nX5KDP0I3Dbmp+ExE+v0msNOLZN+1hm0jYzsRxvI/uD8tLL7WWfQxmB3n9GdHvf4Gd7NSp2E0cLu5zO707i9Ngq72W7YbfHFxa3n94HsD47WyDzBRTMBoPCQ/QBQeMh+AMgszgjLE1w0E0A6fDMUT2Q/gHTI/ngi+wGkQ/YHx4GwiEtIqi4v46KZANLycwFMLpoZXWQ/ADfI/ngh+wG4QfYHx59GRpyRdNWIfVRcxJUzAbRt+3Z/t0z61a9+pUMOOUQdO3ZUly5dMruxmCH7AbhB9scL2Q/Ajahlfz7mPgfCACAGorZDlKSGhgadeuqpuvjiizO7IQAoUGQ/ABSeqGV/PuY+fxoZcQlJt89coCN2rymob4cy0ZYVlJ+2GD/rdNqG1waaMF47p0YayUf7mPNGW0/LRANN0G24aSNL10ATUk/69u2SMd6WaT5Fet26dSnTS0tLVVpaGnhMN954oyRp2rRpgddVaOKY/VHMdTtuSqaysU6vy/gpx/LVNuaVn8atsNfpp5nSKduDtpGF2BpJ9sdHHLM/qsLIeq+fyd0sH/a+MpTP8Jn5z0X6dbrJaa9Zb7dOr02TfsaZobb4KGV/PuY+Z4RFnJFUv36L3vtqVa6HAiCmevXqpcrKyuRt8uTJuR5SwSP7AWQa2R89ZD+ATCP7d+CMsDxBjTKAdIJ8M7R06VJVVFQkp4dxRgDCQfYDSIfsjyeyH0A6ZH9wnBGWJ6hRBpBOY6P36wQ07xArKipSbul2iNdee60SiUTa28cff5ylZx1/ZD+AdMj+eCL7AaSTjeyPe+5zRlgeqCxrT40ygLS2b/d+2YGmJu/b+clPfqLzzjsv7Tz9+vXzvmK0QvYDcEL2xw/ZD8BJNrI/7rnPgbA8ENK1tAHEWLb+M9S9e3d1797d+4LwjOwH4ITsjx+yH4CTbGR/3HOfA2F5YM3mbXrvq1Ua0rtrrocCi6BtMX62EUZzmNdl3KyzyfKX1p7bxvw00nhtcnHT7OJ1G37ayKJalxeyL7/8UqtWrdKXX36pxsZGzZ8/X5K0++67q3PnzrkdXB4g+6MhGy2SdvKhMTj0tuAdG209LWiG+snpMPYv6drImv9GJYbI/mDyLfvzpRHYSVwbg900Uwb+DG8nCo3BbrbpdZ1B2+JDao2MmnzMfQ6E5QkumgkgnWydFeDF9ddfr+nTpyfvDxo0SJL06quv6ogjjsjsxmOC7AeQDtkfT2Q/gHSilv35mPscCMsTXDQTQDqNjd53cF7bZryaNm2apk2bltmNxBzZDyAdsj+eyH4A6UQt+/Mx9zkQFnEJSdXlZVw0E0Ba27d7v65Ipv8zBP/IfgBukP3xQvYDcIPsD44DYXngqhH7qLiIK2cCaBs7xPgh+wE4Ifvjh+wH4ITsD44DYRFWlJDOHtpXI/aszfVQAEQcO8T4IPsBuEX2xwfZD8Atsj84DoRFWJORHn57ifbtuVPB7xTztZEmCg00mWgncyMjbWOZqFHz2jDjpwUn3Tz58mZG1pD90eYnU8PYThQbg61NY1IeNQZnYxv5+uEFOUH2R0sUPsPbLWPlp+ndeZ0RaAz20/QexjqD/j/AaR1ej14hY+LZ3xkzt89coMYmDuECaNv27f5uiC6yH4ATsj9+yH4ATsj+4DgQFnFGUv36LXrvq1W5HgqASGuSMd5uUgZ7lBEI2Q/AHbI/Tsh+AO6Q/UHxp5F5YuWGLbkeAoBIa/z3zesyiDKyH0B6ZH8ckf0A0iP7g+JAWJ7o1rks10MAEGnsEOOI7AeQHtkfR2Q/gPTI/qA4EJYHihLSfj13yvUwAEQaO8S4IfsBOCP744bsB+CM7A+KA2F5oMlI73+zWkN6d831UBBQNhporMJoDnOzTe+FjjlopHHDTytkuuWd1sGVK9GGuGe/nyarfBGFtrFYNQYHFUZzWNjrjMubHaHL9+yPa1lqJhqDw1inn1JDp21EtjHY6zpy1RhM9ucFDoTlCa4VACA9PxfB5KKZUUf2A0iP7I8jsh9AemR/UBwIyxNcKwBAepwiHUdkP4D0yP44IvsBpEf2B8WBsIhLSKouL9OgXapyPRQAkdYk7zs4vhmKKrIfgDtkf5yQ/QDcIfuD4kBYHrhqxD4qLkrkehgAIo1vhuKG7AfgjOyPG7IfgDOyP6j0VzSFGhsbNXHiRPXt21cdOnTQbrvtpl/84hcyxiTnMcbo+uuvV21trTp06KCRI0fq008/DbztooT0/WF9NWLP2sDrAhB3jT5viBqyH4B7ZH9ckP0A3CP7g+KMMAe33HKL7r33Xk2fPl0DBgzQ3LlzNXbsWFVWVurSSy+VJN1666266667NH36dPXt21cTJ07UqFGjtGDBApWV+f8b/yYjPfz2Eu3bcyd2ihZxaaTJRAONm20EbRvz0/YWSiON9ZTeMNrHvNddpp/f6w912zbn+VFQyP74iWtjsJt9gZtsbymUVslM1KYFzXrrMvn6wQUZQ/bnvyg2BoexL3AXdx6z3c9Aw/7MbjfNazbTFp+3OCPMwZtvvqkTTzxRxx57rHbddVedcsopOvroo/X2229L2nE22JQpU3TdddfpxBNP1H777ac//vGP+uabb/TMM8+EMobbZy5QY5NxnhFAAWvyeUNUkf0AnJH9cUP2A3BG9gfFgTAHhxxyiGbOnKlPPvlEkvTPf/5T//jHP3TMMcdIkpYsWaK6ujqNHDkyuUxlZaWGDx+u2bNnt7nerVu3at26dSk3O0ZS/foteu+rVeE9KQAxxCnS+YDsBxAusj8fkP0AwkX2B8WBMAfXXnutzjjjDO29995q3769Bg0apMsvv1xnnXWWJKmurk6S1KNHj5TlevTokXzMzuTJk1VZWZm89erVK+04Vm7YEvCZAIg3doj5gOwHEC6yPx+Q/QDCRfYHxYEwB0888YQeeeQRPfroo3r33Xc1ffp03X777Zo+fXqg9U6YMEFr165N3pYuXZp2/m6d/V9rDEAhYIeYD8h+AOEi+/MB2Q8gXGR/UFws38HVV1+dPCtMkvbdd1998cUXmjx5ss4991zV1NRIkurr61Vb+58LW9bX1+uAAw5oc72lpaUqLS11NYbKsvYatEuV/ycBoAA0yfsOjmsFZBvZDyBcZH8+IPsBhIvsD4oDYQ42bdqkoqLUE+eKi4vV1LTjjdS3b1/V1NRo5syZyQNf69at05w5c3TxxReHMoZEIpTVIE9FsW0sjCbKrDTShCFoQ43dMi3vZ2LMiIVCzP64NAI7yUSuO23DzXbCKOAKGpm2jcFRzEk/bWQtFReHOx7ERiFmf1xFsUXSTiY+6jpleyRzXcp8g3BcP9jkIf400sHxxx+vX/3qV3r++ef1+eef6+mnn9Ydd9yhk08+WZKUSCR0+eWX65e//KX+53/+Rx988IHOOecc9ezZUyeddFIoY1izeRsXzQTggPaYuCH7ATgj++OG7AfgjOwPKqKHYqPjd7/7nSZOnKgf//jHWr58uXr27Kkf/ehHuv7665PzXHPNNdq4caMuvPBCrVmzRocddphmzJihsrLw/r6fi2YCSM/P3/5zrYCoI/sBpEf2xxHZDyA9sj8oDoQ5KC8v15QpUzRlypQ250kkErrpppt00003ZWwcXDQTQHrsEOOI7AeQHtkfR2Q/gPTI/qA4EBZxCUnV5WVcNBOAA3aIcUL2A3CH7I8Tsh+AO2R/UBwIizgj6aoR+6i4iCtnAkiHHWKckP0A3CH744TsB+AO2R8UB8IQC4XSNCblpm3MzTbCaBtz2qbXRhqrSDbURHFMALLKT6tXGNvxuk43ywfdF9itMxLZ7q/quO1ltm0LPiYgDxTSZ3QnUWiRDGMd4TQGRyDXdwwk9b7XN6jXtngagyOD1siIS0i6feYCNTaZXA8FQKQZeW+OIVeiiuwH4A7ZHydkPwB3yP6gOBAWcUZS/fot1CgDcNDo84YoIvsBuEP2xwnZD8Adsj8o/iYnT1CjDCA9rhUQR2Q/gPTI/jgi+wGkR/YHxYGwPEGNMoD02CHGEdkPID2yP47IfgDpkf1BcSAsD1SWtadGGYADdohxQ/YDcEb2xw3ZD8AZ2R8UB8LyQIIGZaThp30x6Dbyp5Em9b5d81iRmrxtJIwXtOU6GhqCrw+xRPbTNtZSFNvG3OS01236KWO0ZrtjrkdBFMeESCD7C4efDPWzTq/bCKM9Pug2fH1mz4agOy0agyODi+XngTWbt3HRTAAOvDbHNN8QVWQ/AGdkf9yQ/QCckf1B8XVUnuCimQDS4xTpOCL7AaRH9scR2Q8gPbI/KA6E5QkumgkgvSZ538HxzVDUkf0A0iP744jsB5Ae2R8UB8IiLiGpuryMi2YCcMA3Q3FC9gNwh+yPE7IfgDtkf1AcCMsDV43YR8VFXDkTQDp+/vafb4aijOwH4IzsjxuyH4Azsj8oDoRFWFFCOntoX43YszbXQ8k7diUihdo2li+NNG6W97qMu3Gm7wwpynQjDc1hsCD74UY2WiSt/DSHOa3Dz/7ET9tYSxnJda87reLi8MeAvFYo2c9n9PQyke1BtxFGTnud325MOcl2P9I9uaiMEbRGRlmTkR5+e4lmfrIs10MBEHmNPm+IGrIfgHtkf1yQ/QDcI/uD4kBYHrh95gI1NplcDwNApLFDjBuyH4Azsj9uyH4Azsj+oDgQFnFGUv36LXrvq1W5HgqASGOHGCdkPwB3yP44IfsBuEP2B8UfqeaJlRu25HoIACKNGuU4IvsBpEf2xxHZDyA9sj8oDoTliW6dy3I9BACRRntMHJH9ANIj++OI7AeQHtkfFAfC8kBRQtqv5065HgZiJIqNNE7Lu1mHn23mvH2M9hi0gexvzU/jYKHIh1z3uw6nZby2k9nlepH1PwheK8287rTIfrSB7EdLcc32oLlut4w12x1z3c1AwkZjcGRwjbA80GSk979ZnethAIg0rhUQN2Q/AGdkf9yQ/QCckf1B8XVUnuBaAQDS87ODY4cYdWQ/gPTI/jgi+wGkR/YHxYGwPMG1AgCkxw4xjsh+AOmR/XFE9gNIj+wPigNhEZeQVF1epkG7VOV6KAAijR1inJD9ANwh++OE7AfgDtkfFAfC8sBVI/ZRcVEi18MAEGm0x8QN2Q/AGdkfN2Q/AGdkf1AcCIuwooR09tC+GrFnba6HAgDIErIfAAoP2Q8A2cOBsAhrMtLDby/Rvj13YqcYAj+1vIUiX6uZrcKoXva6DceqZr8D86xJ3k955puhKCL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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "i=0\n", "fig, axs = plt.subplots(1, 3, sharey=True, sharex=True, figsize=(14, 4))\n", "MFP[...,0,i].plot(yincrease=False,ax=axs[0],vmin=-bc[i,3],vmax=bc[i,3],cmap='seismic')\n", "MFP[...,1,i].plot(yincrease=False,ax=axs[1],vmin=-bc[i,3],vmax=bc[i,3],cmap='seismic')\n", "MFP[...,2,i].plot(yincrease=False,ax=axs[2],vmin=-bc[i,3],vmax=bc[i,3],cmap='seismic')\n", "axs[0].scatter(stations[:,0],stations[:,1],label='stations')\n", "axs[1].scatter(stations[:,0],stations[:,1],label='stations')\n", "axs[2].scatter(stations[:,0],stations[:,1],label='stations')\n", "#if MFP['v'][i]==sources_find['v']:\n", "# plt.scatter(sources_find['x'][0],sources_find['z'],marker='^',label='best beam')\n", "\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0, 'Bartlett value')" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(bc.values[:,3])\n", "plt.ylabel('Count')\n", "plt.xlabel('Bartlett value')" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Velocity')" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axs = plt.subplots(1, 2, sharey=True, sharex=True, figsize=(10, 4))\n", "\n", "scatter1=axs[0].scatter(bc.values[:,0],bc.values[:,1],c=bc.values[:,3])\n", "fig.colorbar(scatter1, ax=axs[0])\n", "axs[0].invert_yaxis()\n", "axs[0].grid()\n", "\n", "scatter2=axs[1].scatter(bc.values[:,0],bc.values[:,1],c=bc.values[:,2])\n", "fig.colorbar(scatter2, ax=axs[1])\n", "#axs[1].invert_yaxis()\n", "axs[1].grid()\n", "\n", "axs[0].set_xlabel('x')\n", "axs[1].set_xlabel('x')\n", "axs[0].set_ylabel('z')\n", "axs[1].set_xlabel('z')\n", "axs[0].set_title('Bartlett')\n", "axs[1].set_title('Velocity')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Code efficiency\n", "\n", "In this exemple we performed multiple MFP in order to look at the numerical efficiency of the algorimth." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/chauvet/Documents/Projets/Gricad/das_ice/das_ice/mfp.py:28: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:206.)\n", " multi_waveform_spectra=torch.fft.fft(torch.from_numpy(ds_cube.values),axis=2).to(dtype=torch.complex128)\n", "/home/chauvet/miniforge3/envs/das_ice/lib/python3.11/site-packages/distributed/client.py:3362: UserWarning: Sending large graph of size 56.30 MiB.\n", "This may cause some slowdown.\n", "Consider loading the data with Dask directly\n", " or using futures or delayed objects to embed the data into the graph without repetition.\n", "See also https://docs.dask.org/en/stable/best-practices.html#load-data-with-dask for more information.\n", " warnings.warn(\n", "/home/chauvet/miniforge3/envs/das_ice/lib/python3.11/site-packages/distributed/client.py:3362: UserWarning: Sending large graph of size 62.70 MiB.\n", "This may cause some slowdown.\n", "Consider loading the data with Dask directly\n", " or using futures or delayed objects to embed the data into the graph without repetition.\n", "See also https://docs.dask.org/en/stable/best-practices.html#load-data-with-dask for more information.\n", " warnings.warn(\n", "/home/chauvet/miniforge3/envs/das_ice/lib/python3.11/site-packages/distributed/client.py:3362: UserWarning: Sending large graph of size 93.04 MiB.\n", "This may cause some slowdown.\n", "Consider loading the data with Dask directly\n", " or using futures or delayed objects to embed the data into the graph without repetition.\n", "See also https://docs.dask.org/en/stable/best-practices.html#load-data-with-dask for more information.\n", " warnings.warn(\n", "2024-12-04 15:57:30,288 - distributed.worker.memory - WARNING - Unmanaged memory use is high. This may indicate a memory leak or the memory may not be released to the OS; see https://distributed.dask.org/en/latest/worker-memory.html#memory-not-released-back-to-the-os for more information. -- Unmanaged memory: 2.77 GiB -- Worker memory limit: 3.87 GiB\n", "/home/chauvet/miniforge3/envs/das_ice/lib/python3.11/site-packages/distributed/client.py:3362: UserWarning: Sending large graph of size 160.45 MiB.\n", "This may cause some slowdown.\n", "Consider loading the data with Dask directly\n", " or using futures or delayed objects to embed the data into the graph without repetition.\n", "See also https://docs.dask.org/en/stable/best-practices.html#load-data-with-dask for more information.\n", " warnings.warn(\n", "2024-12-04 15:59:13,337 - distributed.worker.memory - WARNING - Unmanaged memory use is high. This may indicate a memory leak or the memory may not be released to the OS; see https://distributed.dask.org/en/latest/worker-memory.html#memory-not-released-back-to-the-os for more information. -- Unmanaged memory: 2.83 GiB -- Worker memory limit: 3.87 GiB\n", "/home/chauvet/miniforge3/envs/das_ice/lib/python3.11/site-packages/distributed/client.py:3362: UserWarning: Sending large graph of size 261.57 MiB.\n", "This may cause some slowdown.\n", "Consider loading the data with Dask directly\n", " or using futures or delayed objects to embed the data into the graph without repetition.\n", "See also https://docs.dask.org/en/stable/best-practices.html#load-data-with-dask for more information.\n", " warnings.warn(\n", "2024-12-04 16:03:32,484 - distributed.worker.memory - WARNING - Unmanaged memory use is high. This may indicate a memory leak or the memory may not be released to the OS; see https://distributed.dask.org/en/latest/worker-memory.html#memory-not-released-back-to-the-os for more information. -- Unmanaged memory: 2.80 GiB -- Worker memory limit: 3.87 GiB\n", "2024-12-04 16:03:39,506 - distributed.worker.memory - WARNING - Unmanaged memory use is high. This may indicate a memory leak or the memory may not be released to the OS; see https://distributed.dask.org/en/latest/worker-memory.html#memory-not-released-back-to-the-os for more information. -- Unmanaged memory: 2.85 GiB -- Worker memory limit: 3.87 GiB\n", "2024-12-04 16:08:32,563 - distributed.worker.memory - WARNING - Unmanaged memory use is high. This may indicate a memory leak or the memory may not be released to the OS; see https://distributed.dask.org/en/latest/worker-memory.html#memory-not-released-back-to-the-os for more information. -- Unmanaged memory: 2.97 GiB -- Worker memory limit: 3.87 GiB\n", "/home/chauvet/miniforge3/envs/das_ice/lib/python3.11/site-packages/distributed/client.py:3362: UserWarning: Sending large graph of size 463.81 MiB.\n", "This may cause some slowdown.\n", "Consider loading the data with Dask directly\n", " or using futures or delayed objects to embed the data into the graph without repetition.\n", "See also https://docs.dask.org/en/stable/best-practices.html#load-data-with-dask for more information.\n", " warnings.warn(\n", "2024-12-04 16:09:19,938 - distributed.worker.memory - WARNING - Unmanaged memory use is high. This may indicate a memory leak or the memory may not be released to the OS; see https://distributed.dask.org/en/latest/worker-memory.html#memory-not-released-back-to-the-os for more information. -- Unmanaged memory: 2.93 GiB -- Worker memory limit: 3.87 GiB\n", "2024-12-04 16:09:28,883 - distributed.worker.memory - WARNING - Unmanaged memory use is high. This may indicate a memory leak or the memory may not be released to the OS; see https://distributed.dask.org/en/latest/worker-memory.html#memory-not-released-back-to-the-os for more information. -- Unmanaged memory: 2.82 GiB -- Worker memory limit: 3.87 GiB\n", "2024-12-04 16:09:31,961 - distributed.worker.memory - WARNING - Unmanaged memory use is high. This may indicate a memory leak or the memory may not be released to the OS; see https://distributed.dask.org/en/latest/worker-memory.html#memory-not-released-back-to-the-os for more information. -- Unmanaged memory: 2.90 GiB -- Worker memory limit: 3.87 GiB\n", "2024-12-04 16:14:19,970 - distributed.worker.memory - WARNING - Unmanaged memory use is high. This may indicate a memory leak or the memory may not be released to the OS; see https://distributed.dask.org/en/latest/worker-memory.html#memory-not-released-back-to-the-os for more information. -- Unmanaged memory: 3.03 GiB -- Worker memory limit: 3.87 GiB\n", "2024-12-04 16:14:28,972 - distributed.worker.memory - WARNING - Unmanaged memory use is high. This may indicate a memory leak or the memory may not be released to the OS; see https://distributed.dask.org/en/latest/worker-memory.html#memory-not-released-back-to-the-os for more information. -- Unmanaged memory: 2.89 GiB -- Worker memory limit: 3.87 GiB\n", "2024-12-04 16:14:32,049 - distributed.worker.memory - WARNING - Unmanaged memory use is high. This may indicate a memory leak or the memory may not be released to the OS; see https://distributed.dask.org/en/latest/worker-memory.html#memory-not-released-back-to-the-os for more information. -- Unmanaged memory: 2.90 GiB -- Worker memory limit: 3.87 GiB\n" ] } ], "source": [ "ti='2024-08-29T05:05:00'\n", "tf=['2024-08-29T05:05:00.1','2024-08-29T05:05:01','2024-08-29T05:05:10','2024-08-29T05:05:30','2024-08-29T05:06:00','2024-08-29T05:07:00']\n", "run_time=[]\n", "for ttf in tf:\n", " stime=time.time()\n", " MFP=di_mpf.MFP_2D_series(dsr.sel(time=slice(ti,ttf)),0.1,stations,xrange=[0,100],zrange=[0,100],vrange=[2500,3500],dx=2,dz=2,dv=500)\n", " etime=time.time()\n", " run_time.append(etime-stime)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The reference time $t_{0.1}$ is the time to process one signal of $0.1~s$. Therefore, the speed up is compute as the duration time to process $n$ signals of $0.1~s$ over $t_{0.1}$." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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