diff --git a/examples/render-reconstruct-confocal/fbp.ipynb b/examples/render-reconstruct-confocal/fbp.ipynb index ecb58da..6a7b12f 100644 --- a/examples/render-reconstruct-confocal/fbp.ipynb +++ b/examples/render-reconstruct-confocal/fbp.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -23,7 +23,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -37,7 +37,7 @@ "name": "stderr", "output_type": "stream", "text": [ - " " + "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:00<00:00, 41.43it/s]" ] }, { @@ -51,7 +51,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\r" + "\n" ] } ], @@ -61,12 +61,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 38, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -81,7 +81,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -96,7 +96,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "tal.resources progress: 100%|██████████| 2/2 [00:00<00:00, 20.79it/s] " + "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:00<00:00, 47.42it/s]\n", + "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:00<00:00, 44.59it/s]\n", + "tal.resources progress: 100%|██████████| 2/2 [00:00<00:00, 390.39it/s]\n" ] }, { @@ -111,8 +113,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\n", - "tal.resources progress: 100%|██████████| 2/2 [00:00<00:00, 142.17it/s]\n" + "tal.resources progress: 100%|██████████| 2/2 [00:00<00:00, 25653.24it/s]\n" ] } ], @@ -123,12 +124,12 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 40, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -143,7 +144,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -158,7 +159,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "tal.resources progress: 100%|██████████| 4/4 [00:00<00:00, 34.11it/s] " + "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:00<00:00, 137.28it/s]\n", + "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:00<00:00, 117.93it/s]\n", + "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:00<00:00, 103.25it/s]\n", + "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:00<00:00, 105.91it/s]\n", + "tal.resources progress: 100%|██████████| 4/4 [00:00<00:00, 1108.21it/s]\n" ] }, { @@ -173,8 +178,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\n", - "tal.resources progress: 100%|██████████| 4/4 [00:00<00:00, 313.40it/s]\n" + "tal.resources progress: 100%|██████████| 4/4 [00:00<00:00, 34952.53it/s]\n" ] } ], @@ -185,12 +189,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 42, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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WTZo00Z///Gdt375d27Zt0+9+9zsNGDBAn3/+uQFHVRFTwRE0srOzdcsttyg8PFzDhg3TwoULtXXrVl199dUV2pWWlmrcuHE6evSonnzySQ0ePFi/+93vtGHDBk2aNEl79uzR/Pnz9dBDD2nx4sUe99+9e7eGDBmie+65RyNHjtSSJUs0aNAg5eTk6Pe//32Vcm3YsKEWLlyoe++9VzfffLNuueUWSdJVV11VrWN2uVwaMGCAPvzwQ91zzz1q06aNVq1apZEjR1Zo+/nnn+vaa69V48aNNXnyZNWqVUuvv/66Bg4cqDfffFM333xztR4bAIBA8Z///EeSdNlll7n3jRs3TvXq1dOMGTO0b98+zZ07V+np6VqxYoW7zdKlS1W7dm1lZGSodu3aWrdunaZPny6Hw6GnnnpK0tmVk9PS0lRSUqJx48YpNjZWBw4c0Jo1a3Ts2DHZ7XZJ0uOPP65p06Zp8ODBuvvuu3X48GHNnz9fv/3tb/Xxxx+rbt26l+4JAfzAjTfe6PHz448/roULF+qjjz5Su3btDH88CmsEhe3bt+urr77S/PnzJUndu3dXkyZNlJ2dXaGwPnDggHbv3u3uyMrKypSZmakff/xR27ZtU1jY2bfN4cOHlZ2drYULF3pM4f7666/15ptvuovh0aNHq3Xr1po0aVKVC+tatWrptttu07333qurrrpKt99++0Ud9zvvvKONGzfqySef1MSJEyVJ9957r3r16lWh7fjx49W0aVNt3brVfTx//OMf1b17d02aNInCGiYpk/cjzmUXbgIA1VBUVKQjR47o1KlT+te//qWZM2cqKipK/fv319q1ayWdLbLff/992Ww2SZLT6dS8efNUVFTk/gyxbNkyRUX9vJbxPffco3vuuUcvvPCCZs+erYiICH3xxRfau3evVq5cqdtuu83ddvr06e7/79+/XzNmzNDs2bP1yCOPuPffcsst6tSpk1544QWP/YAvMWrhsfIYDofDY39ERMQFT6csKyvTypUrVVxcrJSUFAOyOXd+QEDLzs5WTEyMu6C02WwaMmSIli9fXmE61qBBg9wdoiT3CuK33367u6gu319aWqoDBw543D8+Pt6jCI2OjtaIESP08ccfq6CgwPBjO593331XYWFhuvfee937QkNDNW7cOI92R48e1bp16zR48GAdP35cR44c0ZEjR/T9998rLS1Nu3fvrnCcgDGYCg7A96Smpqphw4ZKSEjQ0KFDVbt2ba1atUqNGzd2txk7dqy7qJak6667TmVlZdq/f7973y+L6vL+9brrrtPJkyf11VdfSZL7M8ff//53nTx5stJ83nrrLTmdTg0ePNjdRx85ckSxsbFq1aqV1q9fb+jxA0Yyeip4QkKC7Ha7e8vMzDznY3/66aeqXbu2IiIidM8992jVqlVq27atGYfJiDUCX1lZmZYvX65evXp5nBuVnJysZ555Rrm5ubrhhhvc+5s2bepx//IOLyEhodL9P/zwg8f+yy+/3KOjlaQrrrhC0tlzp2NjY708oqrbv3+/4uLiVLt2bY/9V155pcfPe/bskcvl0rRp0zRt2rRKYx06dMjjAwUAAIFqwYIFuuKKKxQWFqaYmBhdeeWVCgnxHI/69eeFevXqSfL8XPD5559r6tSpWrduXYVRtvLzpxMTE5WRkaE5c+YoOztb1113nW666Sbdfvvt7s8au3fvlsvlUqtWrSrNt0aNGt4dMOBH8vPzFR0d7f75fKPVV155pXbu3KmioiK98cYbGjlypD744ANTimsKawS8devW6bvvvtPy5cu1fPnyCrdnZ2d7FNahoaGVxjnXfpfLZUyi1WCz2Sp93F+PvldV+WW+HnroIaWlpVXaxpcuTYZAYsSIMyPWAIzVtWtXdenS5bxtLvS54NixY+rRo4eio6M1c+ZMtWzZUpGRkdqxY4cmTZrkcYnNZ555Rnfeeafefvttvf/++7r//vuVmZmpjz76SE2aNJHT6ZTNZtN7771X6eP++gt0wJcYPRW8fJXvqggPD3d/hu3cubO2bt2q5557Ti+++KIBGXmisEbAy87OVqNGjbRgwYIKt7311ltatWqVsrKyDHu88tHfX45af/3115JUYcXu8/n1qPcv1atXT//9738r7P/l9DNJatasmXJzc3XixAmPTnfXrl0e7Vq0aCHp7DfeqampVc4R8B6FNYDAtGHDBn3//fd666239Nvf/ta9/5ez536pQ4cO6tChg6ZOnapNmzbp2muvVVZWlmbPnq2WLVvK5XIpMTHRPQsO8BdGF9becDqdKikpMSBSRZxjjYD2448/6q233lL//v112223VdjS09N1/PhxQy8pdfDgQa1atcr9s8Ph0CuvvKKkpKRqTQOvWbOmJFV6aayWLVvqq6++8risxyeffKJ//etfHu369u2rM2fOaOHChe59ZWVl7kXcyjVq1Eg9e/bUiy++qO+++67C4/368iEAAOD8ykeWfznDrLS0VC+88IJHO4fDoTNnPL8g7NChg0JCQtwFwC233KLQ0FA99thjFWasuVwuff/992YcAuDXpkyZoo0bN2rfvn369NNPNWXKFG3YsEHDhw835fEYsUZAe+edd3T8+HHddNNNld5+zTXXqGHDhsrOznYvUuatK664QqNHj9bWrVsVExOjxYsXq7CwUEuWLKlWnKioKLVt21YrVqzQFVdcofr166t9+/Zq37697rrrLs2ZM0dpaWkaPXq0Dh06pKysLLVr187jHK4bb7xR1157rSZPnqx9+/a5r6n96+tiSmfPJ+vevbs6dOigMWPGqEWLFiosLFReXp6+/fZbffLJJ14/N0BFZfJ+VW9WBQfge7p166Z69epp5MiRuv/++2Wz2fTqq69WKIzXrVun9PR0DRo0SFdccYXOnDmjV199VaGhobr11lslnf1Cffbs2ZoyZYr27dungQMHqk6dOtq7d69WrVqlsWPH6qGHHrLiMIELssmY0dxzz+Ws3KFDhzRixAh99913stvtuuqqq/T3v/+9ylfpqS4KawS07OxsRUZGnvMNFBISon79+ik7O9uwb3tbtWql+fPna+LEidq1a5cSExO1YsWKc567fD5/+ctfNG7cOD3wwAMqLS3VjBkz1L59e7Vp00avvPKKpk+froyMDLVt21avvvqqli1bpg0bNngc3zvvvKMJEybotddek81m00033aRnnnlGnTp18nistm3batu2bXrssce0dOlSff/992rUqJE6derkcckPwFhcbgtAYLrsssu0Zs0aPfjgg5o6darq1aun22+/Xddff73HZ4KOHTsqLS1Nf/vb33TgwAHVrFlTHTt21HvvvadrrrnG3W7y5Mm64oor9Oyzz+qxxx6TdHZh1RtuuOGcAwiAL7Cp+kXxueJUx//+7/8a8KhVZ3NZsfISEKCaN2+u9u3ba82aNVanAvg0h8Mhu92uoqJtio72btEdh+OE7PYuKioqqvJiJgAAwFzlff2TkqIu2PrCfpT0sOSz/T0j1gAAC7F4GQAAgSz0p82IOL6MwhoAYCEKawAAApkvrQpuJl/PDwAAAAAAn8aINWCgffv2WZ0C4GcYsQYAIJAFy4g1hTUAwEKsCg4AQCALlsLa1/MDAAAAAMCnBfyItdPp1MGDB1WnTh3ZbEZcQQ0AgovL5dLx48cVHx+vkBCjv49lKji8R18PAN4xs68PlhHrgC+sDx48qISEBKvTAAC/l5+fryZNmhgclcIa3qOvBwBjmNHXU1gHiDp16kiSYmXsL2OUgbHK3W1CTEn6zoSY9UyIecCEmCdNiNnAhJhm/aHYbULM3ibE/MaEmE4TYsaaEFOSckyIeb+BsVySTunnv6eAryl/ba6RVMvAuDEGxir3owkxJemECTFrmxDTjK/BzPgwW2pCTLP6ejP6OzOY8Xsy43UfbUJMSaphQsxvDYx1UtJtoq/3RsAX1uVTwoz6pqRcpIGxypn1Mj5uQkwzcjXyw1A5MyYEmvFBw6zOtqYJMc3ocMx4Ts34oGFWZ2vG78mM1745U2wZsYb3yl+btWTs3xMz+rpQE2KahcLaWBTW/sGM170khZsQ05TPzib09YxYAwBgOgprAAACmU3GfOHv6yto+HrhDwAAAACAT2PEGgBgIa5jDQBAIAuRMafB+PqIsK/nJ0lasGCBmjdvrsjISCUnJ2vLli1WpwQAMMQZgzYEAvp7AAg8IQZuvszX89OKFSuUkZGhGTNmaMeOHerYsaPS0tJ06NAhq1MDAAAGob8HAPgzny+s58yZozFjxmjUqFFq27atsrKyVLNmTS1evNjq1AAAXmPEGmfR3wNAYGLE2geUlpZq+/btSk1Nde8LCQlRamqq8vLyKr1PSUmJHA6HxwYA8FUU1qh+f09fDwD+g8LaBxw5ckRlZWWKiYnx2B8TE6OCgoJK75OZmSm73e7eEhISLkWqAADgIlW3v6evBwD4Gp8urC/GlClTVFRU5N7y8/OtTgkAcE6MWKP66OsBwH8Ey4i1T19uq0GDBgoNDVVhYaHH/sLCQsXGxlZ6n4iICEVERFyK9AAAXuNyW6h+f09fDwD+w6ii2NcLa5/OLzw8XJ07d1Zubq57n9PpVG5urlJSUizMDAAAGIX+HgDg73x6xFqSMjIyNHLkSHXp0kVdu3bV3LlzVVxcrFGjRlmdGgDAa2XyfsSZEetAQH8PAIEpWEasfb6wHjJkiA4fPqzp06eroKBASUlJysnJqbDACQDAHxlxjjTnWAcC+nsACEy2nzYj4vgyny+sJSk9PV3p6elWpwEAAExEfw8A8Fd+UVgDAAIVI9YAAASy0J82I+L4Ml+fqg4ACGjlq4J7s13cOdYLFixQ8+bNFRkZqeTkZG3ZsuW87VeuXKnWrVsrMjJSHTp00Lvvvutxu8vl0vTp0xUXF6eoqCilpqZq9+7dHm2OHj2q4cOHKzo6WnXr1tXo0aN14sSJSh9vz549qlOnjurWreuxf+nSpbLZbB5bZGRk9Z8AAAAuAZuMudSWr08Fp7AGAASdFStWKCMjQzNmzNCOHTvUsWNHpaWl6dChQ5W237Rpk4YNG6bRo0fr448/1sCBAzVw4EB99tln7jZPPvmk5s2bp6ysLG3evFm1atVSWlqaTp065W4zfPhwff7551q7dq3WrFmjjRs3auzYsRUe7/Tp0xo2bJiuu+66SvOJjo7Wd999597279/v5TMCAAC8YXO5XC6rkzCTw+GQ3W7Xc5KiDIw7pp+BwcpNNyGmJOWZENOMtWSOmhBzswkxf2NCzFMXbnJRDpsQ8yYTYprxe+phQswWJsSUpInGh1y81LhYP0pKl1RUVKTo6GhDYpb/bS4qelzR0d6Ntjocp2S3/79q5ZecnKyrr75azz//vKSzl3ZKSEjQuHHjNHny5ArthwwZouLiYq1Zs8a975prrlFSUpKysrLkcrkUHx+vBx98UA899JCks89XTEyMli5dqqFDh+rLL79U27ZttXXrVnXp0kWSlJOTo759++rbb79VfHy8O/akSZN08OBBXX/99ZowYYKOHTvmvm3p0qUV9gW78tfTZ5LqGBi3iYGxyp00IaZZcWubENOMPM04r9FpQkyzzr80I1czYpoxr8ZhQkwzXveSOcf/rYGxjku6Sub09ask1TIgXrGkm2VsjkZixBoAYCFvp4H/fI62w+Hw2EpKSip9xNLSUm3fvl2pqanufSEhIUpNTVVeXuXfRObl5Xm0l6S0tDR3+71796qgoMCjjd1uV3JysrtNXl6e6tat6y6qJSk1NVUhISHavPnnb5fWrVunlStXasGCBed81k6cOKFmzZopISFBAwYM0Oeff37OtgAAWMmIaeBGXbLLTL6eHwAAVZKQkCC73e7eMjMzK2135MgRlZWVVbiMU0xMjAoKCiq9T0FBwXnbl/97oTaNGjXyuD0sLEz169d3t/n+++915513aunSpef8Nv7KK6/U4sWL9fbbb+u1116T0+lUt27d9O23Ro5dAACA6mBVcACAhYxbFTw/P9+jGI2IiPAy7qU3ZswY/eEPf9Bvf/vbc7ZJSUlRSkqK++du3bqpTZs2evHFFzVr1qxLkSYAAFVm1Gizr48I+3p+AICAZtxU8OjoaI/tXIV1gwYNFBoaqsLCQo/9hYWFio2NrfQ+sbGx521f/u+F2vx6cbQzZ87o6NGj7jbr1q3T008/rbCwMIWFhWn06NEqKipSWFiYFi9eXGluNWrUUKdOnbRnz55KbwcAwEpMBQcAIACFh4erc+fOys3Nde9zOp3Kzc31GAn+pZSUFI/2krR27Vp3+8TERMXGxnq0cTgc2rx5s7tNSkqKjh07pu3bt7vbrFu3Tk6nU8nJyZLOnoe9c+dO9zZz5kzVqVNHO3fu1M0331xpbmVlZfr0008VFxd3Ec8GAAAwAlPBAQAWKr+OtbcxqicjI0MjR45Uly5d1LVrV82dO1fFxcUaNWqUJGnEiBFq3Lix+zzt8ePHq0ePHnrmmWfUr18/LV++XNu2bdOiRYskSTabTRMmTNDs2bPVqlUrJSYmatq0aYqPj9fAgQMlSW3atFHv3r01ZswYZWVl6fTp00pPT9fQoUPdK4K3adPGI89t27YpJCRE7du3d++bOXOmrrnmGl1++eU6duyYnnrqKe3fv1933313tZ8HAADMFixTwSmsAQAWOiMp1IAY1TNkyBAdPnxY06dPV0FBgZKSkpSTk+NefOybb75RSMjPXXi3bt20bNkyTZ06VY888ohatWql1atXexS8Dz/8sIqLizV27FgdO3ZM3bt3V05OjiIjf77ISnZ2ttLT03X99dcrJCREt956q+bNm1et3H/44QeNGTNGBQUFqlevnjp37qxNmzapbdu21X4eAAAwm+2nzYg4vozrWF8krmNtQkyuY208rmNtLK5jbcJ1rDMUHe3dImMOR4ns9jk+e11LmI/rWHMda6NxHWvjcR1r4/nLdaxzZNx1rHvLd69jzYg1AMBC1oxYAwCASyNU3vf05XF8GYU1AMBCFNYAAASyYDnH2tfzAwAAAADApzFiDQCwkDWrggMAgEvDJmNGc3198TIKawCAhc7I++6WqeAAAPgqpoIDAAAAAIALYsQaAGAhRqwBAAhkwTJiTWENALAQhTUAAIEsWAprX88PAAAAAACfxog1AMBCZfJ+VW9WBQcAwFcFy4g1hTUAwEJcbgsAgEBmkzGXyvL1y235euEPAAAAAIBPC5oR6yGSoo0M+ISRwX7S7jUTgkrq+ogJQeuaEPN640P+cYHxMdXehJjNTIgpSZEmxCwwPuTV642PGTXR+JgaYUJMSekdDA9511+Ni+VwSemlxsXzdEbefwfN4mU4q0RSDQPjnTIwlpkxzYxrNDPerU4TYvpLnpL/fJg34zk1o2vyl/eSZOzxnzYw1q+F/rQZEceXMWINALDQGYM2AADgi0IM3KoqMzNTV199terUqaNGjRpp4MCB2rVrl0FHVDkKawAAAABAwPjggw9033336aOPPtLatWt1+vRp3XDDDSouLjbtMf1l9ggAICAxFRwAgEBmxargOTk5Hj8vXbpUjRo10vbt2/Xb3/7WgGwqorAGAFiIwhoAgEBmkzGFdfmnBYfD4bE/IiJCERER571vUVGRJKl+/foGZFI5poIDAAAAAPxCQkKC7Ha7e8vMzDxve6fTqQkTJujaa69V+/ZmLEJ8FiPWAAALlcn7EWuuYw0AgK8yeip4fn6+oqN/vt7ThUar77vvPn322Wf68MMPDcji3CisAQAWMmIaN1PBAQDwVUYX1tHR0R6F9fmkp6drzZo12rhxo5o0aWJAFufm01PBrVgmHQAAXFr09wAAI7lcLqWnp2vVqlVat26dEhMTTX9Mny6srVgmHQBwKXEda9DfA0Ags+I61vfdd59ee+01LVu2THXq1FFBQYEKCgr0448/GnRUFfn0VPCLWSa9pKREJSUl7p9/vWocAMCXMBUc1e/v6esBwH/Y5P1qKuVxqmrhwoWSpJ49e3rsX7Jkie68804DsqnIp0esf60qy6RnZmZ6rBKXkJBwqdIDAAAGuFB/T18PADgfl8tV6WZWUS35UWFd1WXSp0yZoqKiIveWn59/CbMEAFRPmbyfBs6q4IGkKv09fT0A+I9QAzdf5tNTwX+pqsukV+UC4QAAX3FGksvLGBTWgaQq/T19PQD4D6NXBfdVflFYX8pl0gEAgDXo7wEA/sqnC2uXy6Vx48Zp1apV2rBhwyVZJh0AcCkxYg36ewAIZIxY+4D77rtPy5Yt09tvv+1eJl2S7Ha7oqKiLM4OAOA9CmvQ3wNAIAuWwtqn81u4cKGKiorUs2dPxcXFubcVK1ZYnRoAADAI/T0AwN/59Ii1y+XtKAYAwLcxYg36ewAIZDYZM5prxLWwzeTThTUAINCVyfvC2mlEIgAAwARMBQcAAAAAABfEiDUAwEKMWAMAEMiCZcQ6aAprh7z/6PZLUQcNDFau3R4Tgko68Y3xMQtNiFnj38bHPGZ8SLXdYXzMsBPGx5SkH742PmZd40NqiQkxBz9lfMwGJi2ktNX4kB+VGBer2LhQlTgj77tKCmuc5ZCxr4aTBsYyM6YkmdGLmPHOKjUhpq9/2C5nVp5nTIhpxu8p3ISYZryfzOpRzIhr5PvepE+ikoKnsPb1/AAAAAAA8GlBM2INAPBFjFgDABDIgmXEmsIaAGAhCmsAAAJZsBTWvp4fAAAAAAA+jRFrAICFyuT9iLORS1MCAAAjBcuINYU1AMBCZyTZvIxBYQ0AgK8KlsLa1/MDAAAAAMCnMWINALAQI9YAAASyYBmxprAGAFiIwhoAgEBmk2SzedvXSzaXb/f3vl74AwAAAADg0xixBgBYx+X0fsDZt7/ABgAguIWFSQaMWMvlks6c8T6OSSisAQDWccr7q215e38AAGCeICmsmQoOAAAAAIAXKKwBANYpM2i7CAsWLFDz5s0VGRmp5ORkbdmy5bztV65cqdatWysyMlIdOnTQu+++63G7y+XS9OnTFRcXp6ioKKWmpmr37t0ebY4eParhw4crOjpadevW1ejRo3XixIlKH2/Pnj2qU6eO6tatW+1cAADwGWFhxm0+jMIaAGAdiwrrFStWKCMjQzNmzNCOHTvUsWNHpaWl6dChQ5W237Rpk4YNG6bRo0fr448/1sCBAzVw4EB99tln7jZPPvmk5s2bp6ysLG3evFm1atVSWlqaTp065W4zfPhwff7551q7dq3WrFmjjRs3auzYsRUe7/Tp0xo2bJiuu+66i8oFAACfQWENAID/cDgcHltJSck5286ZM0djxozRqFGj1LZtW2VlZalmzZpavHhxpe2fe+459e7dWxMnTlSbNm00a9Ys/eY3v9Hzzz8v6exo9dy5czV16lQNGDBAV111lV555RUdPHhQq1evliR9+eWXysnJ0V/+8hclJyere/fumj9/vpYvX66DBw96PN7UqVPVunVrDR48uNq5AACAS4/CGgBgHadBm6SEhATZ7Xb3lpmZWelDlpaWavv27UpNTXXvCwkJUWpqqvLy8iq9T15enkd7SUpLS3O337t3rwoKCjza2O12JScnu9vk5eWpbt266tKli7tNamqqQkJCtHnzZve+devWaeXKlVqwYMFF5QIAgE8JDTVmtDo01OojOS/fHk8HAAQ2L86R9oghKT8/X9HR0e7dERERlTY/cuSIysrKFBMT47E/JiZGX331VaX3KSgoqLR9QUGB+/byfedr06hRI4/bw8LCVL9+fXeb77//Xnfeeadee+01j2OpTi4AAPiUsDApxIDxXKdvXwaEwhoAEBCio6PPWYz6izFjxugPf/iDfvvb31qdCgAAqAamggMArGPgVPCqatCggUJDQ1VYWOixv7CwULGxsZXeJzY29rzty/+9UJtfL4525swZHT161N1m3bp1evrppxUWFqawsDCNHj1aRUVFCgsLc5//faFcAADwKUGyeJlvZ2egbyXVNjBe7N8NDFbu90+aEFTSX02Iacbis8kmxGxrQsz9JsSM+dqEoJJeMSHmcBNimvEV3xsmxLwn0oSgkpobH7K+gbHCDYxVgVPeTwWvZmEdHh6uzp07Kzc3VwMHDjwbwulUbm6u0tPTK71PSkqKcnNzNWHCBPe+tWvXKiUlRZKUmJio2NhY5ebmKikpSdLZxdQ2b96se++91x3j2LFj2r59uzp37izpbCHtdDqVnHz2D2BeXp7Kyn5+Qt5++2098cQT2rRpkxo3blylXIJZPUl1DIxnxjverImMZsQ18nNTuVMXblJtZnyYNeP5NGs064wJMc34u29qX2IgM173kjl/T4yMacbryI2p4AAABKaMjAyNHDlSXbp0UdeuXTV37lwVFxdr1KhRkqQRI0aocePG7gXQxo8frx49euiZZ55Rv379tHz5cm3btk2LFi2SJNlsNk2YMEGzZ89Wq1atlJiYqGnTpik+Pt5dvLdp00a9e/fWmDFjlJWVpdOnTys9PV1Dhw5VfHy8u80vbdu2TSEhIWrfvr1734VyAQAAlx6FNQDAOgYuXlYdQ4YM0eHDhzV9+nQVFBQoKSlJOTk57kXBvvnmG4X84tv1bt26admyZZo6daoeeeQRtWrVSqtXr/YoeB9++GEVFxdr7NixOnbsmLp3766cnBxFRv48ppCdna309HRdf/31CgkJ0a233qp58+ZVK/eq5AIAgM8IDTVmRe8ybz8wmMvmcrlcVidhJofDIbvdrnUydmrH1Q8aGKzc0zVNCCrppZPGxwzmqeBGzjMsF3PhJhflf02IacZUcDOmbZvhnivMiZtj/KkAX/cxLtYJSZ0lFRUVGbY4WPnf5qKvpWgv31OO45L9CmPzg38pfz3tlLF/ohtduEm1mdAjmxaXqeDG8qep4GbkasZU8BMmxPSnqeDfGBjrhM5+FDelr7/iCkUbUFg7yspk//prn+3vWbwMAAAAAAAvMBUcAGAdi6aCAwCASyQszJip4Dab9zFMRGENALAOhTUAAIEtSAprv5oK/uc//9m98ioAAAg89PUAAH/kNyPWW7du1YsvvqirrrrK6lQAAEZxyvtVgnz7spaoBvp6AAhAjFj7jhMnTmj48OF66aWXVK9ePavTAQAYpcygDX6Pvh4AAlRo6Nni2tvNiOLcRH5RWN93333q16+fUlNTL9i2pKREDofDYwMAAL6Nvh4A4M98fir48uXLtWPHDm3durVK7TMzM/XYY4+ZnBUAwBAueT+V22VEIrASfT0ABLDyEecA59Mj1vn5+Ro/fryys7MVGVm1y6pPmTJFRUVF7i0/P9/kLAEAF42p4EGPvh4AApwR08D9oDj36ey2b9+uQ4cO6Te/+Y17X1lZmTZu3Kjnn39eJSUlCv3VXPuIiAhFRERc6lQBAMBFoK8HAAQCny6sr7/+en366ace+0aNGqXWrVtr0qRJFTpaAICf4TrWQY++HgACnB+MNhvBp4+wTp06at++vce+WrVq6bLLLquwHwDgh7jcVtCjrweAAFe+Kri3XL69qIpPn2MNAAAAAICv8+kR68ps2LDB6hQAAEZhKjgqQV8PAAHEqKngPj5i7XeFNQAggFBYAwAQ2IKksGYqOAAAAAAAXqCwBgBYx2nQBgAAfJNF17HeuHGjbrzxRsXHx8tms2n16tXmHN9PgmYqeB1JtY0M2N/IYOVmmhFUuv0h42PuMz6k2rQwPubO/xofs2VN42P+cNL4mJI02ISY4SbE7GtCzKa/NyHoNSbElHR8luEhIw2MddrAWBU45f1Ubgpr/ORHSUZenKvUwFjlzpgQ06y4/nL8Zj2n/sKMUTJ/eU795XVvFiO7P1O7UoumghcXF6tjx4666667dMstt3j/+BcQNIU1AAAAAMC/ORwOj58jIiIUERFRoV2fPn3Up0+fS5UWU8EBABZiKjgAAIGt/DrW3m6hZ+ckJSQkyG63u7fMzEyLD/AsRqwBANZhVXAAAAKbUVPBnWe/Sc/Pz1d0dLR7d2Wj1VagsAYAAAAA+IXo6GiPwtpXUFgDAKzDiDUAAIHN4BFrX0VhDQCwjhHnSPt2PwsAQHCjsAYAAAAAwP+cOHFCe/bscf+8d+9e7dy5U/Xr11fTpk0NfzwKawCAdZgKDgBAYLNoxHrbtm3q1auX++eMjAxJ0siRI7V06VLv8/kVCmsAgHUorAEACGzll9vyVln1OvyePXvK5XJ5/7hVxHWsAQAAAADwAiPWAADruOT94mOX7stoAABQXUZNBa/miPWlRmENALAOU8EBAAhsQVJYMxUcAAAAAAAvMGINALAO17EGACCwMWINAAAAAAAuhBFrAIB1OMcaAIDAZtTlts6c8T6GiSisAQDWobAGACCwGTUV3IgYJmIqOAAAAAAAXvDtsh8AENhYvAwAgMAWJCPWvp0dACCwMRUcAIDARmEdWP4jqaaB8Vq/YWCwct0eMiGopP+aEPO4CTHXmpBofeNDSqeMD3nM+JCSpKMmxIw1IeaHJsSsv9b4mL1zjY8pSYeND2nkW/SEgbEAM4XJ2A82ZnxI8qdz8Mw4/lITYoabEJOJMMYz47XvT++nSKsTgOmCprAGAPggp7wfceYTMAAAvosRawAATMY51gAABDajLrcVGup9DBP50wwKAAAAAAB8DiPWAADrsHgZAACBjangAACYjKngAAAEtiAprJkKDgAAAACAF3y+sD5w4IBuv/12XXbZZYqKilKHDh20bds2q9MCABihzKANfo/+HgACVPmItRGbD/Pp7H744Qdde+216tWrl9577z01bNhQu3fvVr169axODQBgBM6xhujvASCgsSq49Z544gklJCRoyZIl6tq1qxITE3XDDTeoZcuWVqcGAPBzCxYsUPPmzRUZGank5GRt2bLlvO1Xrlyp1q1bKzIyUh06dNC7777rcbvL5dL06dMVFxenqKgopaamavfu3R5tjh49quHDhys6Olp169bV6NGjdeLECfftu3btUq9evRQTE6PIyEi1aNFCU6dO1enTp91tli5dKpvN5rFFRkYa8IxYh/4eAODvfLqwfuedd9SlSxcNGjRIjRo1UqdOnfTSSy+d9z4lJSVyOBweGwDARzkN2qppxYoVysjI0IwZM7Rjxw517NhRaWlpOnToUKXtN23apGHDhmn06NH6+OOPNXDgQA0cOFCfffaZu82TTz6pefPmKSsrS5s3b1atWrWUlpamU6dOudsMHz5cn3/+udauXas1a9Zo48aNGjt2rPv2GjVqaMSIEXr//fe1a9cuzZ07Vy+99JJmzJjhkU90dLS+++4797Z///7qPwk+pLr9PX09APiRIJkK7tOF9X//+18tXLhQrVq10t///nfde++9uv/++/Xyyy+f8z6ZmZmy2+3uLSEh4RJmDACoFqe8P7/6IgrrOXPmaMyYMRo1apTatm2rrKws1axZU4sXL660/XPPPafevXtr4sSJatOmjWbNmqXf/OY3ev755yWdHa2eO3eupk6dqgEDBuiqq67SK6+8ooMHD2r16tWSpC+//FI5OTn6y1/+ouTkZHXv3l3z58/X8uXLdfDgQUlSixYtNGrUKHXs2FHNmjXTTTfdpOHDh+uf//ynRz42m02xsbHuLSYmpvpPgg+pbn9PXw8AfoTC2npOp1O/+c1v9Kc//UmdOnXS2LFjNWbMGGVlZZ3zPlOmTFFRUZF7y8/Pv4QZAwCs8usRzJKSkkrblZaWavv27UpNTXXvCwkJUWpqqvLy8iq9T15enkd7SUpLS3O337t3rwoKCjza2O12JScnu9vk5eWpbt266tKli7tNamqqQkJCtHnz5kofd8+ePcrJyVGPHj089p84cULNmjVTQkKCBgwYoM8///xcT4tfqG5/T18PAPA1Pl1Yx8XFqW3bth772rRpo2+++eac94mIiFB0dLTHBgDwUQZOBU9ISPAYxczMzKz0IY8cOaKysrIKo7wxMTEqKCio9D4FBQXnbV/+74XaNGrUyOP2sLAw1a9fv8LjduvWTZGRkWrVqpWuu+46zZw5033blVdeqcWLF+vtt9/Wa6+9JqfTqW7duunbb7+tNHd/UN3+nr4eAPxIkIxY+3R21157rXbt2uWx7+uvv1azZs0syggAYCgDVwXPz8/3KLAiIiK8DGydFStW6Pjx4/rkk080ceJEPf3003r44YclSSkpKUpJSXG37datm9q0aaMXX3xRs2bNsiplr9DfA0AAM6ooprC+eA888IC6deumP/3pTxo8eLC2bNmiRYsWadGiRVanBgDwMVUduWzQoIFCQ0NVWFjosb+wsFCxsbGV3ic2Nva87cv/LSwsVFxcnEebpKQkd5tfL4525swZHT16tMLjlp8z3LZtW5WVlWns2LF68MEHFVrJpUZq1KihTp06ac+ePRc6dJ9Ffw8A8Hc+PRX86quv1qpVq/TXv/5V7du316xZszR37lwNHz7c6tQAAEbwduGyixjxDg8PV+fOnZWbm+ve53Q6lZub6zES/EspKSke7SVp7dq17vaJiYmKjY31aONwOLR582Z3m5SUFB07dkzbt293t1m3bp2cTqeSk5PPma/T6dTp06fldFa+SltZWZk+/fRTj4Le39DfA0AAK7+Otbebj1/H2qdHrCWpf//+6t+/v9VpAADMcJGXy6oQo5oyMjI0cuRIdenSRV27dtXcuXNVXFysUaNGSZJGjBihxo0bu8/THj9+vHr06KFnnnlG/fr10/Lly7Vt2zb3iKrNZtOECRM0e/ZstWrVSomJiZo2bZri4+M1cOBASWfPGe7du7d7Ua7Tp08rPT1dQ4cOVXx8vCQpOztbNWrUUIcOHRQREaFt27ZpypQpGjJkiGrUqCFJmjlzpq655hpdfvnlOnbsmJ566int379fd999t5dPpLXo7wEgQDEVHACAwDRkyBAdPnxY06dPV0FBgZKSkpSTk+NefOybb75RSMjPk7q6deumZcuWaerUqXrkkUfUqlUrrV69Wu3bt3e3efjhh1VcXKyxY8fq2LFj6t69u3JychQZGeluk52drfT0dF1//fUKCQnRrbfeqnnz5rlvDwsL0xNPPKGvv/5aLpdLzZo1U3p6uh544AF3mx9++EFjxoxRQUGB6tWrp86dO2vTpk0VFv8CAACXjs3lcrmsTsJMDodDdrtdyyXVNDDujfcZGKzcHBNiStJuE2Ie95OY9U2I2dmEMyj2ejtkdw5HTYhZ+Smo3vnAhJhm/O57m3T2zAvG//4/N/Bv1AlJ10gqKioybPXl8r/NRf9Pio68cPvzxjol2R83Nj/4l/LX01ZJtQ2MG29grHInTYgpnX2fGs2Md5MZxx9uQkyTemW/Ycbxm/F7MuP15GWXdE5mvJ/2GRjrhKQUmdTXv/uuomvV8j5ecbHsffv6bH/PiDUAwDoGrgoOAAB8EFPBA0usjP0WW+0v3KTawvuYEFRSu38ZH/OAw/iYjY2cU/CTHBO+y/zGhO9xzfpq/JQJMc34PXU04fdkxoi1rjEjqFS8yfCQNXw0FmAmI07Z/3W8YOYvx3/G6gQs5tMrEf+Cv7yezBLsr9NgEDSFNQDAB7nk/aetgD6hCQAAP1e+KrgRcXwYhTUAwDpMBQcAILAFyVRwf5k9AgAAAACAT/Ltsh8AENgsuo41AAC4RIJkxNq3swMABDamggMAENiCpLBmKjgAAAAAAF7w7bIfABDYGLEGACCwBcmItW9nBwAIbJxjDQBAYAuSy20xFRwAAAAAAC8wYg0AsA5TwQEACGxMBQcAwGROeV8YMxUcAADfFSSFNVPBAQAAAADwgm+X/QCAwMbiZQAABLYgGbH27ewAAIGNc6wBAAhsrAoOAAAAAAAuhBFrAIB1mAoOAEBgYyo4AAAmYyo4AACBLUgKa6aCAwAAAAACzoIFC9S8eXNFRkYqOTlZW7ZsMe2xKKwBANYpM2gDAAC+qXzE2oitGlasWKGMjAzNmDFDO3bsUMeOHZWWlqZDhw6ZcpjVLqxHjhypjRs3mpELACDYOA3aYDj6ewCAISwqrOfMmaMxY8Zo1KhRatu2rbKyslSzZk0tXrzYnMOs7h2KioqUmpqqZs2aadSoURo5cqQaN25sRm6GskuqY2TAlkYGKzfAjKDSifeMj2nKCNHlxofs8m/jY9YyPqSi6psQVFKdoyYEPWN8yBjjQ+qkCTH1lRlBpVbGh9xjYCxTnkr4PH/s7/3hexZfz++XzMjVjKmSZsT0p9+TGfzld2/CJxLTpvOakauRvyd/es07HA6PnyMiIhQREeGxr7S0VNu3b9eUKVPc+0JCQpSamqq8vDxT8qr2a2f16tU6cOCA7r33Xq1YsULNmzdXnz599MYbb+j06dNm5AgACFROeT8N3J8+DfgR+nsAgBGcCjFsk6SEhATZ7Xb3lpmZWeExjxw5orKyMsXEeI7exMTEqKCgwJTjvKgvZRo2bKiMjAx98skn2rx5sy6//HLdcccdio+P1wMPPKDdu3cbnScAIBBxjrVPo78HAHjrzBnjNknKz89XUVGRe/vlqLSVvJrt8N1332nt2rVau3atQkND1bdvX3366adq27atnn32WaNyBAAAFqK/BwD4iujoaI/t19PAJalBgwYKDQ1VYWGhx/7CwkLFxsaakle1C+vTp0/rzTffVP/+/dWsWTOtXLlSEyZM0MGDB/Xyyy/rH//4h15//XXNnDnTjHwBAIGExct8Fv09AMAIRo9YV0V4eLg6d+6s3Nxc9z6n06nc3FylpKSYcJQXsXhZXFycnE6nhg0bpi1btigpKalCm169eqlu3boGpAcACGhl8n6lGKaCm4L+HgBghOoWxeeLUx0ZGRkaOXKkunTpoq5du2ru3LkqLi7WqFGjvE+mEtUurJ999lkNGjRIkZGR52xTt25d7d2716vEAACAdejvAQD+bMiQITp8+LCmT5+ugoICJSUlKScnp8KCZkapdmF9xx13mJEHACAYGTGVm6ngpqC/BwAYwaoRa0lKT09Xenq69w9eBWZdqs0QZWVlmjZtmhITExUVFaWWLVtq1qxZcrlcVqcGADACq4JD9PcAEMjKyow5v7rMx/v7ao9YX0pPPPGEFi5cqJdfflnt2rXTtm3bNGrUKNntdt1///1WpwcAAAxAfw8A8Hc+XVhv2rRJAwYMUL9+/SRJzZs311//+ldt2bLlnPcpKSlRSUmJ+2eHw2F6ngCAi8TiZVD1+3v6egDwH1ZOBb+UfHoqeLdu3ZSbm6uvv/5akvTJJ5/oww8/VJ8+fc55n8zMTNntdveWkJBwqdIFAFSXS95faovZwn6vuv09fT0A+A8rLrdlBZ8esZ48ebIcDodat26t0NBQlZWV6fHHH9fw4cPPeZ8pU6YoIyPD/bPD4aDDBQDAh1W3v6evBwD4Gp8urF9//XVlZ2dr2bJlateunXbu3KkJEyYoPj5eI0eOrPQ+ERERioiIuMSZAgAuSpkkmwEx4Neq29/T1wOA/wiWqeA+XVhPnDhRkydP1tChQyVJHTp00P79+5WZmXnOwhoA4EcorCH6ewAIZOWrghsRx5f59DnWJ0+eVEiIZ4qhoaFyOrloKQAAgYL+HgDg73x6xPrGG2/U448/rqZNm6pdu3b6+OOPNWfOHN11111WpwYAMEL5AmTexoBfo78HgMDFVHAfMH/+fE2bNk1//OMfdejQIcXHx+t//ud/NH36dKtTAwAYgangEP09AAQyCmsfUKdOHc2dO1dz5861OhUAAGAS+nsAgL/z6cIaABDgmAoOAEBAY8QaAACzMRUcAICAFiyFtU+vCg4AAAAAgK8LmhHrEkk1jAy408hgP0m5x4SgkuaaELOFCTH1b+ND/tP4kPqdCTHbHjUhqMw5/lqlxse80viQamVCTJ0yI6i02/iQRg7imjog7DTgAZgKjp+Eyfc/2Jg1omFGXDNimvF29ZdRIn/63fsLX3+//5IZuRoZ08znMliuY+1Pr0cAQKBxyvup4BTWAAD4LKaCAwAAAACAC6KwBgBYp8yg7SIsWLBAzZs3V2RkpJKTk7Vly5bztl+5cqVat26tyMhIdejQQe+++67H7S6XS9OnT1dcXJyioqKUmpqq3bs95/kfPXpUw4cPV3R0tOrWravRo0frxIkT7tt37dqlXr16KSYmRpGRkWrRooWmTp2q06dPVysXAAB8RfmItRGbL6OwBgBYx6LCesWKFcrIyNCMGTO0Y8cOdezYUWlpaTp06FCl7Tdt2qRhw4Zp9OjR+vjjjzVw4EANHDhQn332mbvNk08+qXnz5ikrK0ubN29WrVq1lJaWplOnfj43f/jw4fr888+1du1arVmzRhs3btTYsWPdt9eoUUMjRozQ+++/r127dmnu3Ll66aWXNGPGjGrlAgCArwiWwtrmcrlcVidhJofDIbvdro8k1TYwbrsnDQxW7l4TYkp+tHiZCfxm8TITYkomLV5mQkx/WbysXk0Tgkp66qThIf/2sHGxTkoaKqmoqEjR0dGGxCz/21zURooO9TJWmWT/snr5JScn6+qrr9bzzz8vSXI6nUpISNC4ceM0efLkCu2HDBmi4uJirVmzxr3vmmuuUVJSkrKysuRyuRQfH68HH3xQDz30kKSz+cTExGjp0qUaOnSovvzyS7Vt21Zbt25Vly5dJEk5OTnq27evvv32W8XHx1eaa0ZGhrZu3ap//vOfVcolGJW/nrbK2L4+1sBY5UxaAlHG/xWRzPiLZ8bxm7FgkD8tsmZGXBOWKVWkCTH95XUvmXP8Bw2MdUJSsszp6996q0i1ankfs7jYoVtusRuao5EYsQYAWMdp0KazHfgvt5KSkkofsrS0VNu3b1dqaqp7X0hIiFJTU5WXl1fpffLy8jzaS1JaWpq7/d69e1VQUODRxm63Kzk52d0mLy9PdevWdRfVkpSamqqQkBBt3ry50sfds2ePcnJy1KNHjyrnAgCALylfFdzbzddXBaewBgBYx8Cp4AkJCbLb7e4tMzOz0oc8cuSIysrKFBMT47E/JiZGBQUFld6noKDgvO3L/71Qm0aNGnncHhYWpvr161d43G7duikyMlKtWrXSddddp5kzZ1Y5FwAAfEmwTAXnclsAgICQn5/vMTUsIiLCwmy8s2LFCh0/flyffPKJJk6cqKeffloPP2zg/H4AAGAoCmsAgHUMvI51dHR0lc65atCggUJDQ1VYWOixv7CwULGxlZ9VGxsbe9725f8WFhYqLi7Oo01SUpK7za8XRztz5oyOHj1a4XETEhIkSW3btlVZWZnGjh2rBx98UKGhoRfMBQAAX8J1rAEAMJtT3k8Dr+YqQ+Hh4ercubNyc3N/TsPpVG5urlJSUiq9T0pKikd7SVq7dq27fWJiomJjYz3aOBwObd682d0mJSVFx44d0/bt291t1q1bJ6fTqeTk5HPm63Q6dfr0aTmdzirlAgCAL2EqOAAAASojI0MjR45Uly5d1LVrV82dO1fFxcUaNWqUJGnEiBFq3Lix+zzt8ePHq0ePHnrmmWfUr18/LV++XNu2bdOiRYskSTabTRMmTNDs2bPVqlUrJSYmatq0aYqPj9fAgQMlSW3atFHv3r01ZswYZWVl6fTp00pPT9fQoUPdK4JnZ2erRo0a6tChgyIiIrRt2zZNmTJFQ4YMUY0aNaqUCwAAuPQorAEA1imT5O1FHy/iujhDhgzR4cOHNX36dBUUFCgpKUk5OTnuRcG++eYbhYT8PKmrW7duWrZsmaZOnapHHnlErVq10urVq9W+fXt3m4cffljFxcUaO3asjh07pu7duysnJ0eRkT9fZCU7O1vp6em6/vrrFRISoltvvVXz5s1z3x4WFqYnnnhCX3/9tVwul5o1a6b09HQ98MAD1coFAABfESxTwbmO9UXiOtYmxDQD17E2HtexNl4wX8e6sRTt5UlJDqdkP2BsfvAvXMfaf67ny3Ws/SMu17E2XjBfxzorq0hRUd7H/PFHh+65h+tYAwAAAAAQkJgKDgCwjkVTwQEAwKURLFPBKawBANahsAYAIKAFS2HNVHAAAAAAALzAiDUAwDpGjDYzYg0AgM8KlhFrCmsAgHWc8n4qeEBf2wIAAP9WVmZMUVxW5n0MMzEVHAAAAAAALwTNiHUDSXWMDGjGdYdrDzIhqKSHVxofc6vxIfUbE2LeYELMBvEmBDXJQSOvcPiT35txhUcjrzz7kwOHjI9Z71rjY0pS67WGh7zawFjHDYxVgVOSzcsYjFjjJ6Uy9vq7ZpxlYNZMRjPiBvNZFmYcu1nPp7+MkvnL+8ms35Ovv6bMfL8zFRwAALOVicIaAIAAFiyFtb98yQUAAAAAgE9ixBoAYB1GrAEACGjBMmJNYQ0AsA7nWAMAENCCpbBmKjgAAAAAAF5gxBoAYB2mggMAENC4jvUlsHHjRt14442Kj4+XzWbT6tWrPW53uVyaPn264uLiFBUVpdTUVO3evduaZAEAxiszaIPPoq8HgOBWPhXciM2XWVpYFxcXq2PHjlqwYEGltz/55JOaN2+esrKytHnzZtWqVUtpaWk6derUJc4UAABcDPp6AEAwsHQqeJ8+fdSnT59Kb3O5XJo7d66mTp2qAQMGSJJeeeUVxcTEaPXq1Ro6dGil9yspKVFJSYn7Z4fDYXziAABjuMRU7gBHXw8Awe3MGSk01Jg4vsxnFy/bu3evCgoKlJqa6t5nt9uVnJysvLy8c94vMzNTdrvdvSUkJFyKdAEAF4GZ4MGNvh4AAh9TwS1WUFAgSYqJifHYHxMT476tMlOmTFFRUZF7y8/PNzVPAABwcejrAQCBIuBWBY+IiFBERITVaQAAqsCIEWdGrIMPfT0A+A9WBbdYbGysJKmwsNBjf2Fhofs2AIB/cxq0wT/R1wNA4GMquMUSExMVGxur3Nxc9z6Hw6HNmzcrJSXFwswAAIAR6OsBAIHC0qngJ06c0J49e9w/7927Vzt37lT9+vXVtGlTTZgwQbNnz1arVq2UmJioadOmKT4+XgMHDrQuaQCAYZgKHvjo6wEguJ05I4UYMJxr5oj1448/rv/7v//Tzp07FR4ermPHjlU7hqWF9bZt29SrVy/3zxkZGZKkkSNHaunSpXr44YdVXFyssWPH6tixY+revbtycnIUGRlpVcoAAAMZMZWbqeC+jb4eAIKbPxTWpaWlGjRokFJSUvS///u/FxXD0sK6Z8+ecrnOfQFTm82mmTNnaubMmZcwKwAAYBT6egCAr3vsscckSUuXLr3oGAG3KjgAwH8wFRwAgMBm9Ii1w+Hw2O8rV4rw2cXLAACBz6mfi+uL3ZgKDgCA7yq/3Ja3W/nlthISEmS3291bZmamtQf4EwprAAAAAIBfyM/PV1FRkXubMmVKpe0mT54sm8123u2rr74yLK+gmQq+T1ItA+M1MmWIxKRrdoab8P1JmQlPwLvGh9SXJsQcftD4mHWMDylJ+sKEmHVOGh/zCxNi1jc+pBofMCGopE+ND2nkr77YwFi/xuJlMFKYjP1gY8aHJLOWZDNjTZ9wE2Ka8X414/fkTx+QzRglKzUhphmvJzNimvW79/XjNyO/cmfOSDabMXEkKTo6WtHR0Rds/+CDD+rOO+88b5sWLVp4n9hP/OnvBgAgwHCONQAAgc3owrqqGjZsqIYNG3r/wFVEYQ0AAAAACFrffPONjh49qm+++UZlZWXauXOnJOnyyy9X7dq1qxSDwhoAYBlGrAEACGxWjVhXx/Tp0/Xyyy+7f+7UqZMkaf369erZs2eVYlBYAwAswznWAAAENn8orJcuXerVNawlVgUHAAAAAMArjFgDACzDVHAAAAJbWZkxI9ZlPt7hU1gDACzDVHAAAAKbUVO4zZwKbgSmggMAAAAA4AVGrAEAlnHK+6ncjFgDAOC7gmXEmsIaAGAZzrEGACCwBUthzVRwAAAAAAC8wIg1AMAyLF4GAEBgM2o1b1YFBwDgHJgKDgBAYDtzRnK5vI/j64U1U8EBAAAAAPACI9YAAMswYg0AQGALlhFrCmsAgGU4xxoAgMAWLIU1U8EBAAAAAPACI9YAAMswFRwAgMAWLCPWFNYAAMu45P1UbgP6agAAYJKyMmMKa6ePn/vFVHAAQFBasGCBmjdvrsjISCUnJ2vLli3nbb9y5Uq1bt1akZGR6tChg959912P210ul6ZPn664uDhFRUUpNTVVu3fv9mhz9OhRDR8+XNHR0apbt65Gjx6tEydOuG/fsGGDBgwYoLi4ONWqVUtJSUnKzs72iLF06VLZbDaPLTIy0stnAwAAeCNoRqzrSaptZMACI4OV22NGUGmLCV/vfGx8SA02IaYZ32yFmxCzQVMTgkrq+I3xMUuND6kYE2K2NSGmrjQjqNTwC8NDGvmUnrhwk4tm1VTwFStWKCMjQ1lZWUpOTtbcuXOVlpamXbt2qVGjRhXab9q0ScOGDVNmZqb69++vZcuWaeDAgdqxY4fat28vSXryySc1b948vfzyy0pMTNS0adOUlpamL774wl34Dh8+XN99953Wrl2r06dPa9SoURo7dqyWLVvmfpyrrrpKkyZNUkxMjNasWaMRI0bIbrerf//+7nyio6O1a9cu9882m+0inoXAc+anzSgnDYxVzow/oZK571MjmXH8ZnyY9fGBMdOZcfxGvjfLmfEeNWvU0Yzn1Mj3vZl/Q86ckUIMeGJ9fcQ6aAprAIDvsaqwnjNnjsaMGaNRo0ZJkrKysvR///d/Wrx4sSZPnlyh/XPPPafevXtr4sSJkqRZs2Zp7dq1ev7555WVlSWXy6W5c+dq6tSpGjBggCTplVdeUUxMjFavXq2hQ4fqyy+/VE5OjrZu3aouXbpIkubPn6++ffvq6aefVnx8vB555BGPxx0/frzef/99vfXWWx6Ftc1mU2xs7EUcOQAAl1awFNZMBQcABASHw+GxlZSUVNqutLRU27dvV2pqqntfSEiIUlNTlZeXV+l98vLyPNpLUlpamrv93r17VVBQ4NHGbrcrOTnZ3SYvL09169Z1F9WSlJqaqpCQEG3evPmcx1VUVKT69et77Dtx4oSaNWumhIQEDRgwQJ9//vk57w8AAMxHYQ0AsIzToE2SEhISZLfb3VtmZmalj3nkyBGVlZUpJsZzwnxMTIwKCio/z6egoOC87cv/vVCbX08zDwsLU/369c/5uK+//rq2bt3qHlmXpCuvvFKLFy/W22+/rddee01Op1PdunXTt99+W2kMAACsdOaMcZsvYyo4AMAyRk4Fz8/PV3R0tHt/RESEl5GttX79eo0aNUovvfSS2rVr596fkpKilJQU98/dunVTmzZt9OKLL2rWrFlWpAoAwDmVlRkzjduIlcXNxIg1ACAgREdHe2znKqwbNGig0NBQFRYWeuwvLCw853nLsbGx521f/u+F2hw6dMjj9jNnzujo0aMVHveDDz7QjTfeqGeffVYjRow432GrRo0a6tSpk/bsMWkBTAAAcEEU1gAAy5QZtFVHeHi4OnfurNzcXPc+p9Op3Nxcj5HgX0pJSfFoL0lr1651t09MTFRsbKxHG4fDoc2bN7vbpKSk6NixY9q+fbu7zbp16+R0OpWcnOzet2HDBvXr109PPPGExo4de8HjKSsr06effqq4uLgqHD0AAJdWsEwFt7Sw3rhxo2688UbFx8fLZrNp9erV7ttOnz6tSZMmqUOHDqpVq5bi4+M1YsQIHTx40LqEAQCGMvIc6+rIyMjQSy+9pJdffllffvml7r33XhUXF7vPZR4xYoSmTJnibj9+/Hjl5OTomWee0VdffaVHH31U27ZtU3p6uqSzq3RPmDBBs2fP1jvvvKNPP/1UI0aMUHx8vAYOHChJatOmjXr37q0xY8Zoy5Yt+te//qX09HQNHTpU8fHxks5O/+7Xr5/uv/9+3XrrrSooKFBBQYGOHj3qzmXmzJl6//339d///lc7duzQ7bffrv379+vuu+++iGfCfPT1ABDcKKwvgeLiYnXs2FELFiyocNvJkye1Y8cOTZs2TTt27NBbb72lXbt26aabbrIgUwBAIBkyZIiefvppTZ8+XUlJSdq5c6dycnLci4998803+u6779ztu3XrpmXLlmnRokXq2LGj3njjDa1evdp9DWtJevjhhzVu3DiNHTtWV199tU6cOKGcnBz3NawlKTs7W61bt9b111+vvn37qnv37lq0aJH79pdfflknT55UZmam4uLi3Nstt9zibvPDDz9ozJgxatOmjfr27SuHw6FNmzapbVtTLt7uNfp6AEAwsLlcvnEauM1m06pVq9zf7Fdm69at6tq1q/bv36+mTZtWKa7D4ZDdbtd2SbWNSVWSdMWiC7eptjF9TAgqact7xses/Io03hlsQswPTYjZzYSYjav2eq62Dd8YH9OMr+OOmxDTjBoj8WYTgkp6aZXhIT+/8AziKjsh6RqdvezTLxcH80b53+aVkmp6GeukpEEyNj+Yw+y+/l8ytq9vdOEm1VZqQkxJcpgQ08jnspwZx2/GSrw+frlc05lx/OEmxDxpQkwzXveSOcdv5NyeE5J6yJy+PiysSDab9zFdLofOnLH7bH/vV6uCFxUVyWazqW7duudsU1JS4nHtUofDjK4GAGCEi53K/esYCBz09QAQWM6ckWw27+P4xnDwufnN4mWnTp3SpEmTNGzYsPN+Q5GZmelxHdOEhIRLmCUAALhY9PUAAH/lF4X16dOnNXjwYLlcLi1cuPC8badMmaKioiL3lp+ff4myBABUlxWrgsM30dcDQKByyuXyfvP1OWo+PxW8vKPdv3+/1q1bd8H59BEREee8dikAwLcYURhTWPs/+noACGRGfQ3u2z2+TxfW5R3t7t27tX79el122WVWpwQAAAxEXw8ACASWFtYnTpzQnj173D/v3btXO3fuVP369RUXF6fbbrtNO3bs0Jo1a1RWVqaCggJJUv369RUebsbaegCAS4nFywIffT0ABDtGrE23bds29erVy/1zRkaGJGnkyJF69NFH9c4770iSkpKSPO63fv169ezZ81KlCQAwCVPBAx99PQAEOwpr0/Xs2VPnu4y2j1xiGwAAXCT6egBAMPDpc6wBAIGNEWsAAAKdUSt6+/bJXxTWAADLuOR9N8l4JwAAviw4poL7xXWsAQAAAADwVUEzYn15tBRtMzDgmJoGBiv3rgkxJXW9wfiYv1lrfMywucbHHLTI+JhqaULMJBNiSuq5wISgo02I+YEJMc34PY01Iaak36wyPGQ7Ay/x63BJKjUu3i8xFRxGCv9pM4oZPb1ZH7zMmCBZ24SYZvwpMWOUyLcnnHryl+M3Y41/M4490oSYkjnvJ4cJMc3hlDG9tW+/M4OmsAYA+B4utwUAQKBjKjgAAAAAALgARqwBAJZhKjgAAIEuOEasKawBAJahsAYAINAFx+W2mAoOAAAAAIAXGLEGAFiGxcsAAAh0TAUHAMBUTAUHACDQBUdhzVRwAAAAAAC8wIg1AMAyTnn//TNTwQEA8GXBMWJNYQ0AsAznWAMAEOiM+Bq9PI7vYio4AAAAAABeYMQaAGAZFi8DACDQBcd1rCmsAQCWYSo4AACBLjjOsWYqOAAAAAAgKO3bt0+jR49WYmKioqKi1LJlS82YMUOlpaXVisOINQDAMkwFBwAg0Pn2iPVXX30lp9OpF198UZdffrk+++wzjRkzRsXFxXr66aerHIfCGgBgGQprAAACnbGFtcPh8NgbERGhiIiIi47au3dv9e7d2/1zixYttGvXLi1cuLBahTVTwQEAAAAAfiEhIUF2u929ZWZmGv4YRUVFql+/frXuw4g1AMAyLF4GAECgM3bEOj8/X9HR0e693oxWV2bPnj2aP39+tUarJUasAQAWcurn7vZiNwprAAB8mUs/f5XuzeaSJEVHR3ts5yqsJ0+eLJvNdt7tq6++8rjPgQMH1Lt3bw0aNEhjxoyp1lEyYg0AAAAACCgPPvig7rzzzvO2adGihfv/Bw8eVK9evdStWzctWrSo2o9HYQ0AsAyLlwEAEOisWRW8YcOGatiwYZXaHjhwQL169VLnzp21ZMkShYRUf2J30BTWax1SLQPj9b77pIHRfvIXu/ExJWml48Jtqmud8SF19QTjY5rxlB7/wviYzf9mfExJWmFCzAFPGR9zh/Eh1XCL8TF/91fjY0qm/J5ySoyLVWxcqAo4xxpGqiWptoHxahoYq5xZH7zMeB+YcfxmiDQh5hkTYvoTM15P/lJ0mPF6knz/70mogbEq8u3LbR04cEA9e/ZUs2bN9PTTT+vw4cPu22JjY6scx19e4wAAAAAAGGrt2rXas2eP9uzZoyZNmnjc5nK5qhyHxcsAAJbxduEyo74DBwAAZjGqtzenx7/zzjvlcrkq3aqDEWsAgGWYCg4AQKDz7angRmHEGgAAAAAALzBiDQCwDKuCAwAQ6IyYn1Yex3dZOmK9ceNG3XjjjYqPj5fNZtPq1avP2faee+6RzWbT3LlzL1l+AABz+e4ZVzAKfT0ABDvfPsfaKJYW1sXFxerYsaMWLFhw3narVq3SRx99pPj4+EuUGQAAMAJ9PQAgGFg6FbxPnz7q06fPedscOHBA48aN09///nf169fvEmUGALgUXPJ+Ylf11uzEpUZfDwDBziljRpt9eyq4T59j7XQ6dccdd2jixIlq165dle5TUlKikpIS988Oh8Os9AAAXuIca9DXA0CgY1Vwyz3xxBMKCwvT/fffX+X7ZGZmym63u7eEhAQTMwQAAN6grwcABAKfLay3b9+u5557TkuXLpXNZqvy/aZMmaKioiL3lp+fb2KWAABvBP5SJjgf+noACAZOAzff5bOF9T//+U8dOnRITZs2VVhYmMLCwrR//349+OCDat68+TnvFxERoejoaI8NAOCbAr+bxfnQ1wNAMAiOVcF99hzrO+64Q6mpqR770tLSdMcdd2jUqFEWZQUAAIxCXw8ACBSWjlifOHFCO3fu1M6dOyVJe/fu1c6dO/XNN9/osssuU/v27T22GjVqKDY2VldeeaWVaQMADGLl99cLFixQ8+bNFRkZqeTkZG3ZsuW87VeuXKnWrVsrMjJSHTp00Lvvvutxu8vl0vTp0xUXF6eoqCilpqZq9+7dHm2OHj2q4cOHKzo6WnXr1tXo0aN14sQJ9+0bNmzQgAEDFBcXp1q1aikpKUnZ2dnVzsWX0NcDQLALjhFrSwvrbdu2qVOnTurUqZMkKSMjQ506ddL06dOtTAsAEOBWrFihjIwMzZgxQzt27FDHjh2VlpamQ4cOVdp+06ZNGjZsmEaPHq2PP/5YAwcO1MCBA/XZZ5+52zz55JOaN2+esrKytHnzZtWqVUtpaWk6deqUu83w4cP1+eefa+3atVqzZo02btyosWPHejzOVVddpTfffFP//ve/NWrUKI0YMUJr1qypVi6+hL4eABAMbC6XK6AvAepwOGS32/WGpFoGxu092sBg5f5i0jliK024DMk640PqahNi2k2IedyEmM1NiClJK0yIOcCEmDtMiNnQhJi/MyGmJL1ofMicp4yLVSzpNklFRUWGncta/rd5mKRwL2OVSvqrqpdfcnKyrr76aj3//POSzl7yKSEhQePGjdPkyZMrtB8yZIiKi4s9CtxrrrlGSUlJysrKksvlUnx8vB588EE99NBD0k/5xMTEaOnSpRo6dKi+/PJLtW3bVlu3blWXLl0kSTk5Oerbt6++/fZbxcfHV5prv379FBMTo8WLF1cpl2BU/nr6QlIdA+PGGhirXKkJMSXpxIWbVFttE2KeNCFmpAkxz5gQ05+YsXaFGeefmvF+MuP1JJnzfvqvgbGOS0qSOX299BdJNQ2IeFLS3YbmaCSfXbwMABD4jJwY5nA4PLZfXuf4l0pLS7V9+3aPc3tDQkKUmpqqvLy8Su+Tl5dX6bnA5e337t2rgoICjzZ2u13JycnuNnl5eapbt667qJak1NRUhYSEaPPmzed8joqKilS/fv0q5wIAgG9xypje3reXK6WwBgAEhISEBI9rG2dmZlba7siRIyorK1NMTIzH/piYGBUUFFR6n4KCgvO2L//3Qm0aNWrkcXtYWJjq169/zsd9/fXXtXXrVo+FvC6UCwAAuPR8dlVwAEDgK/8O29sYkpSfn+8xNSwiIsLLyNZav369Ro0apZdeeknt2rWzOh0AAC6SURfH9O0RawprAIBljOhqy+9f1esZN2jQQKGhoSosLPTYX1hYqNjYys+qjY2NPW/78n8LCwsVFxfn0SYpKcnd5teLo505c0ZHjx6t8LgffPCBbrzxRj377LMaMWJEtXIBAMC3GLWit2+vCh40hfUTMvZgnf9rYLCf9N1twiJjkgo2Gh/zC+NDqoUJa+4cNj6kik2ImWBCTMmcNcFam/B72md8SKWZEDPcjMXwJK0uMj7mgwbG8u3vh6svPDxcnTt3Vm5urgYOHCjp7OJlubm5Sk9Pr/Q+KSkpys3N1YQJE9z71q5dq5SUFElSYmKiYmNjlZub6y6kHQ6HNm/erHvvvdcd49ixY9q+fbs6d+4sSVq3bp2cTqeSk5PdcTds2KD+/fvriSee8FgxvKq5BLODMnahUjNe+6cu3OSimLEomBmLLZm1eJvRzMjTrA/dZsQ1Y/G2YF+8zIilu37tWwNjmfH5NtgETWENAPA9ZfJ+sY+L+f46IyNDI0eOVJcuXdS1a1fNnTtXxcXF7nOZR4wYocaNG7vP0x4/frx69OihZ555Rv369dPy5cu1bds2LVq0SJJks9k0YcIEzZ49W61atVJiYqKmTZum+Ph4d/Hepk0b9e7dW2PGjFFWVpZOnz6t9PR0DR061L0i+Pr169W/f3+NHz9et956q/u86fDwcPcCZhfKBQAA38KINQAAprKqsB4yZIgOHz6s6dOnq6CgQElJScrJyXEvCvbNN98oJOTnzLp166Zly5Zp6tSpeuSRR9SqVSutXr1a7du3d7d5+OGHVVxcrLFjx+rYsWPq3r27cnJyFBn58/hHdna20tPTdf311yskJES33nqr5s2b57795Zdf1smTJ5WZmemx+FqPHj20YcOGKucCAIDvCI7COmiuY321jP0WYaqBscr1/a0JQeVHU8FNiMlUcOO1NiHmPhNiMhXcOE6d/R2ZcW3L/pJqeBnrtKQ1MjY/+Jfy19M/ZOxU8MqvLO4dpoL7B6aCG4+p4MYz8jrWxZL6yqzrWD8rKcqAiD9KesBn+3tGrAEAljFy8TIAAOCLgmPEmsIaAGAZq6aCAwCASyU4Lrfl7ecZAAAAAACCGiPWAADLMBUcAIBA55Qx88t8u8ensAYAWMaIrta3u1kAAIJdcJxjzVRwAAAAAAC8wIg1AMAyZZJsBsQAAAC+KjhGrCmsAQCW4RxrAAACXXAU1kwFBwAAAADAC4xYAwAsw1RwAAACXXCMWFNYAwAsQ2ENAECgM+LEr/I4voup4AAAAAAAeIERawCAZVi8DACAQOeUMfPLfLvHp7AGAFiGqeAAAAS64DjHmqngAAAAAAB4gRFrAIBlXPJ+YpfLiEQAAIBJgmPEOuALa5fr7Ecuo38NJw2OJ0mOMyYElXTchJjFJsQ0I88TJsQ043dvxrFL5uTqL8+pw4SY4SZVcGYcv5FnIZXHKv97aqTA72ZxKZS/No3um8z421xiQkzJnL8jZvzJO21CTDOUmhDTrA/dZsQ14yOpGXma8Xsy6eO4KX2VkX/zyv+GmNHXB8uq4AFfWB8/frZb3GFw3EEGx5MkbTIjKADDmFGt+5Hjx4/LbrdbnQZQQXlfP8DiPADA39HXX7yAL6zj4+OVn5+vOnXqyGY79xI5DodDCQkJys/PV3R09CXMsPr8JVfyNJ6/5EqexrI6T5fLpePHjys+Pt7w2IxYwwhV7esl699PVUWexvOXXMnTWP6Sp2Rtrmb29UwFDxAhISFq0qRJldtHR0f7/JuunL/kSp7G85dcydNYVuZp1rfXTnm/KrhvTwzDpVDdvl7ifW80f8lT8p9cydNY/pKnZF2u5o1UB0dhzargAAAAAAB4IeBHrAEAvivwv78GACDYBceINYX1TyIiIjRjxgxFRERYncoF+Uuu5Gk8f8mVPI3lL3lejMDvZuFr/OX9RJ7G85dcydNY/pKn5F+5Vo9TxvTWvn3yl81lzprqAACck8PhkN1u1+WSQr2MVSZpj6SioiK/OX8OAIBAV97XS2MkhRsQsVTSSz7b3zNiDQCwDIuXAQAQ6LiONQAApgr8bhYAgGAXHOdYsyo4AAAAAABeYMQaAGAZRqwBAAh0jFgHlQULFqh58+aKjIxUcnKytmzZYnVKHjIzM3X11VerTp06atSokQYOHKhdu3ZZndYF/fnPf5bNZtOECROsTqVSBw4c0O23367LLrtMUVFR6tChg7Zt22Z1Wh7Kyso0bdo0JSYmKioqSi1bttSsWbPkC+sObty4UTfeeKPi4+Nls9m0evVqj9tdLpemT5+uuLg4RUVFKTU1Vbt37/apPE+fPq1JkyapQ4cOqlWrluLj4zVixAgdPHjQp/L8tXvuuUc2m01z5869ZPmZocygDagKX+/rJfp7M9DXe4e+/tLl+WuB0tcb19ub1+PfdNNNatq0qSIjIxUXF6c77rij2q8PCmtJK1asUEZGhmbMmKEdO3aoY8eOSktL06FDh6xOze2DDz7Qfffdp48++khr167V6dOndcMNN6i4uNjq1M5p69atevHFF3XVVVdZnUqlfvjhB1177bWqUaOG3nvvPX3xxRd65plnVK9ePatT8/DEE09o4cKFev755/Xll1/qiSee0JNPPqn58+dbnZqKi4vVsWNHLViwoNLbn3zySc2bN09ZWVnavHmzatWqpbS0NJ06dcpn8jx58qR27NihadOmaceOHXrrrbe0a9cu3XTTTZc0xwvl+UurVq3SRx99pPj4+EuUGeD//KGvl+jvjUZf7z36+kuX5y/R119avXr10uuvv65du3bpzTff1H/+8x/ddttt1Qvigqtr166u++67z/1zWVmZKz4+3pWZmWlhVud36NAhlyTXBx98YHUqlTp+/LirVatWrrVr17p69OjhGj9+vNUpVTBp0iRX9+7drU7jgvr16+e66667PPbdcsstruHDh1uUUeUkuVatWuX+2el0umJjY11PPfWUe9+xY8dcERERrr/+9a8WZHjWr/OszJYtW1ySXPv37780SVXiXHl+++23rsaNG7s+++wzV7NmzVzPPvvsJc/NCEVFRS5JrljJFe/lFiu5JLmKioqsPiz4MH/s610u+ntv0dcbi77eWMHS10tDXNIdBmxDXJJc+fn5rqKiIvd26tQpw3N/++23XTabzVVaWlrl+wT9iHVpaam2b9+u1NRU976QkBClpqYqLy/PwszOr6ioSJJUv359izOp3H333ad+/fp5PK++5p133lGXLl00aNAgNWrUSJ06ddJLL71kdVoVdOvWTbm5ufr6668lSZ988ok+/PBD9enTx+LMzm/v3r0qKCjweA3Y7XYlJyf79HtLOvv+stlsqlu3rtWpeHA6nbrjjjs0ceJEtWvXzup0DOE0aAPOx1/7eon+3lv09eairzdeIPb1Z3tqI6aBn+3xExISZLfb3VtmZqah2R49elTZ2dnq1q2batSoUeX7Bf3iZUeOHFFZWZliYmI89sfExOirr76yKKvzczqdmjBhgq699lq1b9/e6nQqWL58uXbs2KGtW7dancp5/fe//9XChQuVkZGhRx55RFu3btX999+v8PBwjRw50ur03CZPniyHw6HWrVsrNDRUZWVlevzxxzV8+HCrUzuvgoICSar0vVV+my86deqUJk2apGHDhik6OtrqdDw88cQTCgsL0/333291KoBf8ce+XqK/NwJ9vbno641HX39h+fn5Hr+3iIgIQ+JOmjRJzz//vE6ePKlrrrlGa9asqdb9g76w9kf33XefPvvsM3344YdWp1JBfn6+xo8fr7Vr1yoyMtLqdM7L6XSqS5cu+tOf/iRJ6tSpkz777DNlZWX5VGf7+uuvKzs7W8uWLVO7du20c+dOTZgwQfHx8T6VZyA4ffq0Bg8eLJfLpYULF1qdjoft27frueee044dO2Sz2axOxzBlOjuP2xuMWCNQ0d97j74ev0ZfbwWj5pedjREdHV2lL0QmT56sJ5544rxtvvzyS7Vu3VqSNHHiRI0ePVr79+/XY489phEjRmjNmjVV/l0EfWHdoEEDhYaGqrCw0GN/YWGhYmNjLcrq3NLT07VmzRpt3LhRTZo0sTqdCrZv365Dhw7pN7/5jXtfWVmZNm7cqOeff14lJSUKDQ21MMOfxcXFqW3bth772rRpozfffNOijCo3ceJETZ48WUOHDpUkdejQQfv371dmZqZPd7bl75/CwkLFxcW59xcWFiopKcmirM6tvKPdv3+/1q1b53PfYP/zn//UoUOH1LRpU/e+srIyPfjgg5o7d6727dtnXXJecMr7wtr6NXPh6/ytr5fo741CX28u+npjBWpff/ZrdCPOQK7equAPPvig7rzzzvO2adGihfv/DRo0UIMGDXTFFVeoTZs2SkhI0EcffaSUlJQqPV7QF9bh4eHq3LmzcnNzNXDgQElnv93Mzc1Venq6tcn9gsvl0rhx47Rq1Spt2LBBiYmJVqdUqeuvv16ffvqpx75Ro0apdevWmjRpkk90suWuvfbaCpcw+frrr9WsWTOLMqrcyZMnFRLi+ccoNDRUTqdvj9MlJiYqNjZWubm57s7V4XBo8+bNuvfee61N7lfKO9rdu3dr/fr1uuyyy6xOqYI77rijwjmMaWlpuuOOOzRq1CiLsgL8g7/09RL9vdHo681FX28s+npjNWzYUA0bNryo+5a/90pKSqp8n6AvrCUpIyNDI0eOVJcuXdS1a1fNnTtXxcXFPvUCvu+++7Rs2TK9/fbbqlOnjvu8FbvdrqioKIuz+1mdOnUqnAdWq1YtXXbZZT53ftgDDzygbt266U9/+pMGDx6sLVu2aNGiRVq0aJHVqXm48cYb9fjjj6tp06Zq166dPv74Y82ZM0d33XWX1anpxIkT2rNnj/vnvXv3aufOnapfv76aNm2qCRMmaPbs2WrVqpUSExM1bdo0xcfHuz/Y+kKecXFxuu2227Rjxw6tWbNGZWVl7vdX/fr1FR4e7hN5Nm3atMKHgBo1aig2NlZXXnnlJcvRaE5J3k52Y8QaVeEPfb1Ef280+nrv0ddfujwDta+3asS6qjZv3qytW7eqe/fuqlevnv7zn/9o2rRpatmyZZVHqyVxua1y8+fPdzVt2tQVHh7u6tq1q+ujjz6yOiUP+ulyMr/elixZYnVqF+SLl98o97e//c3Vvn17V0REhKt169auRYsWWZ1SBQ6HwzV+/HhX06ZNXZGRka4WLVq4/t//+3+ukpISq1NzrV+/vtLX5ciRI10u19nLcEybNs0VExPjioiIcF1//fWuXbt2+VSee/fuPef7a/369T6TZ2UC4RIctSVXHS+32lxuC1Xk6329y0V/bwb6eu/Q11+6PCsTCH291NclDTBg62tKf//vf//b1atXL1f9+vVdERERrubNm7vuuece17ffflutODaXy8WX/QCAS8rhcMhut6u2jBmxPqGzl07xtfPlAAAIVuV9vdRXUtUvW3VupyW967P9PVPBAQCWKRNTwQEACGylMmZV8DMGxDAPhTUA4JILDw9XbGysYdc5jY2NvaTnyAEAgPP7ua//h2Exfbm/Zyo4AMASp06dUmlpqSGxwsPDffpaugAABCMj+3rJt/t7CmsAAAAAALxgxLrnAAAAAAAELQprAAAAAAC8QGENAAAAAIAXKKwBAAAAAPAChTUAAAAAAF6gsAYAAAAAwAsU1gAAAAAAeIHCGrDA4cOHFRsbqz/96U/ufZs2bVJ4eLhyc3MtzAwAABiF/h4IHjaXy+WyOgkgGL377rsaOHCgNm3apCuvvFJJSUkaMGCA5syZY3VqAADAIPT3QHCgsAYsdN999+kf//iHunTpok8//VRbt25VRESE1WkBAAAD0d8DgY/CGrDQjz/+qPbt2ys/P1/bt29Xhw4drE4JAAAYjP4eCHycYw1Y6D//+Y8OHjwop9Opffv2WZ0OAAAwAf09EPgYsQYsUlpaqq5duyopKUlXXnml5s6dq08//VSNGjWyOjUAAGAQ+nsgOFBYAxaZOHGi3njjDX3yySeqXbu2evToIbvdrjVr1lidGgAAMAj9PRAcmAoOWGDDhg2aO3euXn31VUVHRyskJESvvvqq/vnPf2rhwoVWpwcAAAxAfw8ED0asAQAAAADwAiPWAAAAAAB4gcIaAAAAAAAvUFgDAAAAAOAFCmsAAAAAALxAYQ0AAAAAgBcorAEAAAAA8AKFNQAAAAAAXqCwBgAAAADACxTWAAAAAAB4gcIaAAAAAAAvUFgDAAAAAOCF/w9Wy6QQKMT+ZwAAAABJRU5ErkJggg==", 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" ] @@ -205,7 +209,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 43, "metadata": {}, "outputs": [ { @@ -220,7 +224,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "tal.resources progress: 100%|██████████| 2/2 [00:00<00:00, 21.85it/s] " + "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:00<00:00, 66.80it/s]\n", + "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:00<00:00, 63.91it/s]\n", + "tal.resources progress: 100%|██████████| 2/2 [00:00<00:00, 405.82it/s]\n" ] }, { @@ -228,7 +234,6 @@ "output_type": "stream", "text": [ "tal.reconstruct.utils: Assuming that volume_xyz is X_Y_Z_3\n", - "tal.reconstruct.bp: You have specified a camera_system with a projector and a projector_focus, but your data only contains one illumination point. Thus, you will not be able to implement the projector i.e. focus the illumination aperture anywhere on the scene.\n", "tal.resources: Using 2 CPU processes and downscale 2.\n" ] }, @@ -236,8 +241,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\n", - "tal.resources progress: 100%|██████████| 2/2 [00:00<00:00, 90.25it/s]\n" + "tal.resources progress: 100%|██████████| 2/2 [00:00<00:00, 25040.62it/s]\n" ] } ], @@ -250,12 +254,12 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 44, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -270,7 +274,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -285,7 +289,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "tal.resources progress: 100%|██████████| 2/2 [00:00<00:00, 17.17it/s] " + "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:00<00:00, 80.44it/s]\n", + "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:00<00:00, 63.19it/s]\n", + "tal.resources progress: 100%|██████████| 2/2 [00:00<00:00, 601.29it/s]\n" ] }, { @@ -293,7 +299,6 @@ "output_type": "stream", "text": [ "tal.reconstruct.utils: Assuming that volume_xyz is X_Y_Z_3\n", - "tal.reconstruct.bp: You have specified a camera_system with a projector and a projector_focus, but your data only contains one illumination point. Thus, you will not be able to implement the projector i.e. focus the illumination aperture anywhere on the scene.\n", "tal.resources: Using 2 CPU processes and downscale 2.\n" ] }, @@ -301,8 +306,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\n", - "tal.resources progress: 100%|██████████| 2/2 [00:00<00:00, 111.04it/s]\n" + "tal.resources progress: 100%|██████████| 2/2 [00:00<00:00, 33689.19it/s]\n" ] } ], @@ -315,12 +319,12 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 46, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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"text/plain": [ "
" ] @@ -335,7 +339,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -350,7 +354,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "tal.resources progress: 100%|██████████| 2/2 [00:00<00:00, 21.32it/s] " + "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:00<00:00, 59.51it/s]\n", + "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:00<00:00, 56.64it/s]\n", + "tal.resources progress: 100%|██████████| 2/2 [00:00<00:00, 527.85it/s]\n" ] }, { @@ -358,7 +364,6 @@ "output_type": "stream", "text": [ "tal.reconstruct.utils: Assuming that volume_xyz is X_Y_Z_3\n", - "tal.reconstruct.bp: You have specified a camera_system with a projector and a projector_focus, but your data only contains one illumination point. Thus, you will not be able to implement the projector i.e. focus the illumination aperture anywhere on the scene.\n", "tal.resources: Using 2 CPU processes and downscale 2.\n" ] }, @@ -366,8 +371,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\n", - "tal.resources progress: 100%|██████████| 2/2 [00:00<00:00, 139.74it/s]\n" + "tal.resources progress: 100%|██████████| 2/2 [00:00<00:00, 19373.23it/s]\n" ] } ], @@ -380,12 +384,12 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 48, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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9e7d27dql3//+9xoyZIj2799vwKsqj6ngqDNSU1N11113ydvbWyNHjtTKlSu1c+dO3XjjjeXaFRUV6aGHHtKpU6e0YMEC3XPPPfr973+vrVu36vHHH9ehQ4e0bNky/fnPf9bq1avtnn/w4EENHz5cDz74oMaMGaNXXnlFw4YNU1pamv7whz9UKdemTZtq5cqVmjhxov74xz/qrrvukiR17dq1Wq/ZarVqyJAh+uSTT/Tggw+qQ4cO2rhxo8aMGVOu7f79+3XzzTerefPmmjZtmho0aKA33nhDcXFxeuutt/THP/6xWucGAMBd/Pvf/5YkXXPNNbZ9Dz30kBo3bqzZs2fryJEjSk5OVkJCgtavX29rs2bNGjVs2FCJiYlq2LChtmzZolmzZik/P1/PPfecpIsrJ8fGxqqwsFAPPfSQQkJC9P3332vz5s06ffq0AgICJElPP/20Zs6cqXvuuUcPPPCATpw4oWXLlul3v/udPvvsMwUGBtbcGwLUAnfccYfd46efflorV67Up59+qk6dOhl+Pgpr1Am7d+/WN998o2XLlkmS+vTpoxYtWig1NbVcYf3999/r4MGDto6spKRESUlJ+vnnn7Vr1y55eV38sTlx4oRSU1O1cuVKuync3377rd566y1bMRwfH6/27dvr8ccfr3Jh3aBBA919992aOHGiunbtqnvvvfeqXvc777yjbdu2acGCBZo6daokaeLEierfv3+5tpMnT1bLli21c+dO2+v53//9X/Xp00ePP/44hTVMUiLHR5xLrtwEAKohLy9PJ0+e1Pnz5/Wvf/1Lc+bMkZ+fn26//Xalp6dLulhkv//++7JYLJKk0tJSLV26VHl5eba/IdauXSs/v1/XMn7wwQf14IMP6oUXXtC8efPk4+Ojr776SocPH9aGDRt0991329rOmjXL9v+jR49q9uzZmjdvnp544gnb/rvuukvdu3fXCy+8YLcfcCVGLTxWFiM/P99uv4+PzxUvpywpKdGGDRtUUFCg6OhoA7KpPD/AraWmpio4ONhWUFosFg0fPlzr1q0rNx1r2LBhtg5Rkm0F8XvvvddWVJftLyoq0vfff2/3/LCwMLsi1N/fX6NHj9Znn32mnJwcw1/b5bz33nvy8vLSxIkTbfs8PT310EMP2bU7deqUtmzZonvuuUdnzpzRyZMndfLkSf3444+KjY3VwYMHy71OwBhMBQfgemJiYtS0aVOFh4drxIgRatiwoTZu3KjmzZvb2kyYMMFWVEvSLbfcopKSEh09etS277dFdVn/esstt+jcuXP65ptvJMn2N8c///lPnTt3rsJ83n77bZWWluqee+6x9dEnT55USEiI2rZtqw8//NDQ1w8Yyeip4OHh4QoICLBtSUlJlZ77yy+/VMOGDeXj46MHH3xQGzduVMeOHc14mYxYw/2VlJRo3bp16t+/v921UVFRUVq0aJEyMjJ022232fa3bNnS7vllHV54eHiF+3/66Se7/dddd51dRytJ119/vaSL106HhIQ4+Iqq7ujRowoNDVXDhg3t9rdr187u8aFDh2S1WjVz5kzNnDmzwljHjx+3+4MCAAB3tWLFCl1//fXy8vJScHCw2rVrJw8P+/GoS/9eaNy4sST7vwv279+vGTNmaMuWLeVG2cqun46IiFBiYqIWL16s1NRU3XLLLbrzzjt177332v7WOHjwoKxWq9q2bVthvvXq1XPsBQO1SHZ2tvz9/W2PLzda3a5dO+3du1d5eXl68803NWbMGH300UemFNeMWMPtbdmyRT/88IPWrVuntm3b2rZ77rlHksotYubp6VlhnMr2W61WYxOugksL9zKXjr5XVdltvv785z8rPT29ws2Vbk0Gd+K8EesVK1aodevW8vX1VVRUlHbs2HHZ9hs2bFD79u3l6+urLl266L333rM7brVaNWvWLIWGhsrPz08xMTE6ePCgXZtTp05p1KhR8vf3V2BgoOLj43X27NkKz3fo0CE1atSo3HWTl95nt+xeuwCM06tXL8XExKhfv37q0KFDuaJauvLfBadPn1bfvn31+eefa86cOfr73/+u9PR0zZ8/X5LsbrG5aNEiffHFF3riiSf0888/6+GHH1anTp303//+19bWYrEoLS2twj76xRdfNPotAAxj9Ih12SrfZdvlCmtvb29dd9116tGjh5KSktStWzctWbLEjJfJiDXcX2pqqpo1a6YVK1aUO/b2229r48aNSklJMex8ZaO/vy1+v/32W0kqt2L35VRWPEsXPxX/z3/+U27/b6efSVKrVq2UkZGhs2fP2o1aHzhwwK5dmzZtJF38xDsmJqbKOQKOM2Iqd/Wfv379eiUmJiolJUVRUVFKTk5WbGysDhw4oGbNmpVrv337do0cOVJJSUm6/fbbtXbtWsXFxWnPnj3q3LmzJGnBggVaunSpXn31VUVERGjmzJmKjY3VV199ZSt8R40apR9++EHp6ekqLi7WuHHjNGHCBK1du9bufMXFxRo5cqRuueUWbd++vVw+/v7+dj/Hl/t9AcA5tm7dqh9//FFvv/22fve739n2/3b23G916dJFXbp00YwZM7R9+3bdfPPNSklJ0bx583TttdfKarUqIiLCNgsOqC2MvsbaEaWlpSosLDQgUnmMWMOt/fzzz3r77bd1++236+677y63JSQk6MyZM4beUurYsWPauHGj7XF+fr5ee+01RUZGVmsaeP369SWpwltjXXvttfrmm2/sbuvx+eef61//+pddu0GDBunChQtauXKlbV9JSYltEbcyzZo1U79+/fTiiy/qhx9+KHe+S28fArii/Px8u+1yHefixYs1fvx4jRs3Th07dlRKSorq169fbpX/MkuWLNGAAQM0depUdejQQXPnztUNN9yg5cuXS7o4QpWcnKwZM2ZoyJAh6tq1q1577TUdO3ZMmzZtkiR9/fXXSktL08svv6yoqCj16dNHy5Yt07p163Ts2DG7882YMUPt27e3zay5lMViUUhIiG0LDg6+incMgJnKRrR/O7OtqKhIL7zwgl27/Px8Xbhg/wFhly5d5OHhYfs9dtddd8nT01NPPfVUuZlyVqtVP/74oxkvAajVpk+frm3btunIkSP68ssvNX36dG3dulWjRo0y5XyMWMOtvfPOOzpz5ozuvPPOCo/fdNNNatq0qVJTU22LlDnq+uuvV3x8vHbu3Kng4GCtXr1aubm5euWVV6oVx8/PTx07dtT69et1/fXXKygoSJ07d1bnzp11//33a/HixYqNjVV8fLyOHz+ulJQUderUye4arjvuuEM333yzpk2bpiNHjtjuqX3pfTGli9Ni+/Tpoy5dumj8+PFq06aNcnNzlZmZqf/+97/6/PPPHX5vgPJK5Piq3heff+k6CLNnz7bd+/23ioqKtHv3bk2fPt22z8PDQzExMcrMzKzwDJmZmUpMTLTbFxsbayuaDx8+rJycHLsZHwEBAYqKilJmZqZGjBihzMxMBQYGqmfPnrY2MTEx8vDwUFZWlm3Rwy1btmjDhg3au3ev3n777QrzOXv2rFq1aqXS0lLdcMMNeuaZZ0y5dQiAq9e7d281btxYY8aM0cMPPyyLxaLXX3+9XGG8ZcsWJSQkaNiwYbr++ut14cIFvf766/L09NTQoUMlXfxAfd68eZo+fbqOHDmiuLg4NWrUSIcPH9bGjRs1YcIE/fnPf3bGywSuyCJjRnOrOzfr+PHjGj16tH744QcFBASoa9eu+uc//1nlu/RUF4U13Fpqaqp8fX0r/QHy8PDQ4MGDlZqaatinvW3bttWyZcs0depUHThwQBEREVq/fr1iY2OrHevll1/WQw89pEceeURFRUWaPXu2OnfurA4dOui1117TrFmzlJiYqI4dO+r111/X2rVrtXXrVrvX984772jKlCn661//KovFojvvvFOLFi1S9+7d7c7VsWNH7dq1S0899ZTWrFmjH3/8Uc2aNVP37t3tbvkBGMu4221VdTGTkydPqqSkpNwob3BwsG2V3kvl5ORU2L5spf+yf6/U5tJp5l5eXgoKCrK1+fHHHzV27Fj99a9/tXstv9WuXTutXr1aXbt2VV5enhYuXKjevXtr//79atGiRYXPAVDzrrnmGm3evFmPPvqoZsyYocaNG+vee+/Vrbfeavc3Qbdu3RQbG6u///3v+v7771W/fn1169ZN//jHP3TTTTfZ2k2bNk3XX3+9nn/+eT311FOSLn6geNttt1U6gAC4AouqXxRXFqc6/vKXvxhw1qqzWJ2x8hLgplq3bq3OnTtr8+bNzk4FcGn5+fkKCAhQXt4u+fs3vPITLhvrrAICeiovL6/SYvS3jh07pubNm2v79u1297J87LHH9NFHHykrK6vcc7y9vfXqq69q5MiRtn0vvPCCnnrqKeXm5tquhzx27JhCQ0Ntbe655x5ZLBatX79ezzzzjF599dVyaxw0a9ZMTz31lCZOnKi77rpL119/vZ599llJFxcqmzJlSoWXhJQpLi5Whw4dNHLkSM2dO/eKrx8AgJpQ1tcvkOR3xdZX9rOkx6Qq9/c1jWusAQBOVPOrgjdp0kSenp7Kzc2125+bm1vpOgghISGXbV/275XaHD9+3O74hQsXdOrUKVubLVu2aOHChfLy8pKXl5fi4+OVl5cnLy+vSq//rlevnrp3765Dhw5V5eUDAFCjPA3cXBmFNQDAiWq+sPb29laPHj2UkZFh21daWqqMjAy7Eezfio6OtmsvSenp6bb2ERERCgkJsWuTn5+vrKwsW5vo6GidPn1au3fvtrXZsmWLSktLbWs8ZGZmau/evbZtzpw5atSokfbu3Wu7BvtSJSUl+vLLL+1GygEAcBVG327LVXGNNQCgzklMTNSYMWPUs2dP9erVS8nJySooKNC4ceMkSaNHj1bz5s2VlJQkSZo8ebL69u2rRYsWafDgwVq3bp127dqlVatWSbq4SveUKVM0b948tW3b1na7rbCwMMXFxUmSOnTooAEDBmj8+PFKSUlRcXGxEhISNGLECIWFhdna/NauXbvk4eFhu6WXJM2ZM0c33XSTrrvuOp0+fVrPPfecjh49qgceeMDstw0AAFSCwhow0JEjR5ydAlDLOOc+1sOHD9eJEyc0a9Ys5eTkKDIyUmlpabbFx7777jt5ePz62Xjv3r21du1azZgxQ0888YTatm2rTZs22RW8jz32mAoKCjRhwgSdPn1affr0UVpamu0e1tLFBRUTEhJ06623ysPDQ0OHDtXSpUurlftPP/2k8ePHKycnR40bN1aPHj20fft2dezYsdrvAwAAZnOl+1ibicXLAAA17tfFyzLk79/AwVgFCgi41WUXMwEAoC4q6+uXybjFyx4Si5cBAAAAAOCW3H4qeGlpqY4dO6ZGjRrJYjHiDmoAULdYrVadOXNGYWFhdtOjjeGcqeBwL/T1AOAYM/v6ujIV3O0L62PHjik8PNzZaQBArZedna0WLVoYHJXCGo6jrwcAY5jR11NYu4lGjRpJknwlGfkZdmMDY5UJMiGmJDU0IWaACTGbmBDTjK+TY1eDVsys8ZWfTYiZe+Um1XbShJh5JsQ8a0JMyZxcfzIwllXSef36+xRwNb9+bx6SZOT36TMGxrro/7TE8JiS1NuEmIE33GB80JtuMj7mjTcaH7NlS+NjmuXf/zY+5m9uC2iYjz82POSpr74yPOYOwyNeNFz/Y0LUaQbGOiOpK329A9y+sC6bEmaRscWLGZ+YmHXTczO+yPVMiOltQkyfWhLTrE/gSk2IacbXyYzvJzO+7836GTXj62/GhzXmTLFlxBqO+/V7s5EkIxe0Mf43fn3DI15kxjI+/p4m/NbzMaEXrW/Cu9rQjGEJk5jx+r1N6O1N+H4y47e/WT+j5vwFaXwRbEZfz4g1AACmo7AGAMCdGTXA6eoraLh64Q8AAAAAgEtjxBoA4EQlcnzEucSIRAAAgAk8ZMzldK4+Iuzq+UmSVqxYodatW8vX11dRUVHascOsZQUAADXrgkEb3AH9PQC4Hw8DN1fm6vlp/fr1SkxM1OzZs7Vnzx5169ZNsbGxOn78uLNTAwAABqG/BwDUZi5fWC9evFjjx4/XuHHj1LFjR6WkpKh+/fpavXq1s1MDADiMEWtcRH8PAO6JEWsXUFRUpN27dysmJsa2z8PDQzExMcrMzKzwOYWFhcrPz7fbAACuisIa1e/v6esBoPagsHYBJ0+eVElJiYKDg+32BwcHKycnp8LnJCUlKSAgwLaFh4fXRKoAAOAqVbe/p68HALgaly6sr8b06dOVl5dn27Kzs52dEgCgUoxYo/ro6wGg9qgrI9YufbutJk2ayNPTU7m5uXb7c3NzFRISUuFzfHx85OPjUxPpAQAcxu22UP3+nr4eAGoPo4piVy+sXTo/b29v9ejRQxkZGbZ9paWlysjIUHR0tBMzAwAARqG/BwDUdi49Yi1JiYmJGjNmjHr27KlevXopOTlZBQUFGjdunLNTAwA4rESOjzgzYu0O6O8BwD3VlRFrly+shw8frhMnTmjWrFnKyclRZGSk0tLSyi1wAgCojYy4RpprrN0B/T0AuCfLL5sRcVyZyxfWkpSQkKCEhARnpwEAAExEfw8AqK1qRWENAHBXjFgDAODOPH/ZjIjjyiisAQBOxKrgAAC4M4uMuT7a1aeCu/o14AAAAAAAuLQ6M2LdWsZOH2htYKwyrUyIKUlNa0nMcBNihpkQs4EJMc0abys2IWaOCTGPmhDTjDxzr9zkqhwxIaaR72mJpAMGxrPHVHAYZ66C5WtgvHsNjFUmZOJEE6JKmjHD8JBbvjG+F/3gA8ND6pPlxsc8csT4mGa57rpehse86aZRhseMSTY8pH7f/pjhMW+fN8/wmJL0w8qlhsdcJ+Ninpc03bBo9lgVHAAA01FYAwDgzupKYe3q+QEAAAAA4NIYsQYAOBEj1gAAuLO6MmJNYQ0AcCIKawAA3FldKaxdPT8AAAAAAFwaI9YAACfiPtYAALizujJiTWENAHCiC3L8ZohMBQcAwFVZftmMiOPKXL3wBwAAAADApTFiDQBwIkasAQBwZ55yvKcvi+PKGLEGADjRBYO26luxYoVat24tX19fRUVFaceOHZdtv2HDBrVv316+vr7q0qWL3nvvPbvjVqtVs2bNUmhoqPz8/BQTE6ODBw/atTl16pRGjRolf39/BQYGKj4+XmfPnq3wfIcOHVKjRo0UGBhY7VwAAHAVHgZurszV8wMAwHDr169XYmKiZs+erT179qhbt26KjY3V8ePHK2y/fft2jRw5UvHx8frss88UFxenuLg47du3z9ZmwYIFWrp0qVJSUpSVlaUGDRooNjZW58+ft7UZNWqU9u/fr/T0dG3evFnbtm3ThAkTyp2vuLhYI0eO1C233HJVuQAAgJpFYQ0AcKKyVcEd2aq/KvjixYs1fvx4jRs3Th07dlRKSorq16+v1atXV9h+yZIlGjBggKZOnaoOHTpo7ty5uuGGG7R8+XJJF0erk5OTNWPGDA0ZMkRdu3bVa6+9pmPHjmnTpk2SpK+//lppaWl6+eWXFRUVpT59+mjZsmVat26djh07Zne+GTNmqH379rrnnnuqnQsAAK7EImNGq1m8DACAShk3FTw/P99uKywsrPCMRUVF2r17t2JiYmz7PDw8FBMTo8zMzAqfk5mZaddekmJjY23tDx8+rJycHLs2AQEBioqKsrXJzMxUYGCgevbsaWsTExMjDw8PZWVl2fZt2bJFGzZs0IoVK64qFwAAXAlTwQEAqEXCw8MVEBBg25KSkipsd/LkSZWUlCg4ONhuf3BwsHJycip8Tk5OzmXbl/17pTbNmjWzO+7l5aWgoCBbmx9//FFjx47VmjVr5O/vf1W5AACAmseq4AAAJ7ogxz/jvThinZ2dbVeM+vj4OBi35o0fP15/+tOf9Lvf/c7ZqQAAYAijRptdfUTY1fMDALg146aC+/v7222VFdZNmjSRp6encnNz7fbn5uYqJCSkwueEhIRctn3Zv1dqc+niaBcuXNCpU6dsbbZs2aKFCxfKy8tLXl5eio+PV15enry8vGzXf18pFwAAXAlTwQEAcEPe3t7q0aOHMjIybPtKS0uVkZGh6OjoCp8THR1t116S0tPTbe0jIiIUEhJi1yY/P19ZWVm2NtHR0Tp9+rR2795ta7NlyxaVlpYqKipK0sXrp/fu3Wvb5syZo0aNGmnv3r364x//WKVcAABAzWMqOADAiUp0Nat6l49RPYmJiRozZox69uypXr16KTk5WQUFBRo3bpwkafTo0WrevLntOu3Jkyerb9++WrRokQYPHqx169Zp165dWrVqlSTJYrFoypQpmjdvntq2bauIiAjNnDlTYWFhiouLkyR16NBBAwYM0Pjx45WSkqLi4mIlJCRoxIgRCgsLs7X5rV27dsnDw0OdO3e27btSLgAAuJK6MhWcwhoA4ERlt9tyNEb1DB8+XCdOnNCsWbOUk5OjyMhIpaWl2RYF++677+Th8WsX3rt3b61du1YzZszQE088obZt22rTpk12Be9jjz2mgoICTZgwQadPn1afPn2UlpYmX19fW5vU1FQlJCTo1ltvlYeHh4YOHaqlS5dWK/eq5AIAgKuwyJhbZbn67bYorAEAdVJCQoISEhIqPLZ169Zy+4YNG6Zhw4ZVGs9isWjOnDmaM2dOpW2CgoK0du3aKuc4duxYjR07ttq5AACAmlVnCusukuoZGC/SwFhl2pkQU5LCTYgZZkLMZk1MCNrahJi+V25SbY7OhK3MeRNi5l65SXXlHzM+5n+MD6mDJsSUpAMmxGxkYKximZPjRRfk+GfQjo54w1083KyZ/D0MnCz47rvGxfrFql03GB5TkubdZHzM7OzPjQ+qHSbEPGRCzONXblJt5nT22dmhhsf88MPrDI+ZlNTL8Jjh4d0Mj/nkky8YHlOS7s8aa3jMKUOGGBYrv7RU04+b8X0vef6yGRHHlbn6VHUAgFszblVwAADgepyxKnhSUpJuvPFGNWrUSM2aNVNcXJwOHDBvmECisAYAAAAAuJGPPvpIkyZN0qeffqr09HQVFxfrtttuU0FBgWnnrDNTwQEAroip4AAAuDNnrAqelpZm93jNmjVq1qyZdu/erd/97ncGZFMehTUAwIkorAEAcGcWGVNYl/21kJ+fb7ffx8dHPj4+l31uXl6epIuLiJqFqeAAAAAAgFohPDxcAQEBti0pKemy7UtLSzVlyhTdfPPNpt6akhFrAIATlcjxEWuzltUHAACOMnoqeHZ2tvz9/W37rzRaPWnSJO3bt0+ffPKJAVlUjsIaAOBERkzjZio4AACuyujC2t/f366wvpyEhARt3rxZ27ZtU4sWLQzIonIuPRXcGcukAwCAmkV/DwAwktVqVUJCgjZu3KgtW7YoIiLC9HO6dGHtjGXSAQA1iftYg/4eANyZM+5jPWnSJP31r3/V2rVr1ahRI+Xk5CgnJ0c///yzQa+qPJeeCn41y6QXFhaqsLDQ9vjSVeMAAK6EqeCofn9PXw8AtYdFjq+mUhanqlauXClJ6tevn93+V155RWPHjjUgm/JcesT6UlVZJj0pKclulbjw8PCaSg8AABjgSv09fT0A4HKsVmuFm1lFtVSLCuuqLpM+ffp05eXl2bbs7OwazBIAUD0lcnwaOKuCu5Oq9Pf09QBQe3gauLkyl54K/ltVXSa9KjcIBwC4iguSrA7GoLB2J1Xp7+nrAaD2MHpVcFdVKwrrmlwmHQAAOAf9PQCgtnLpwtpqteqhhx7Sxo0btXXr1hpZJh0AUJMYsQb9PQC4M0asXcCkSZO0du1a/e1vf7Mtky5JAQEB8vPzc3J2AADHUViD/h4A3FldKaxdOr+VK1cqLy9P/fr1U2hoqG1bv369s1MDAAAGob8HANR2Lj1ibbU6OooBAHBtjFiD/h4A3JlFxozmGnEvbDO5dGENAHB3JXK8sC41IhEAAGACpoIDAAAAAIArYsQaAOBEjFgDAODO6sqIdZ0prHtKMnJd0WgDY5WJNCGmJHm2MiGoGTG7mBDTjDzN4FmL4h4xPqT/PuNjRv7b+JjtDhsfU5JCTIhZz8BY5yVtNDCevQtyvKuksMYvPv9c8vc3LNxd99Y3LFaZjRs/NTzmRcYv9LZCyYbHvM7wiFKYCTGDTIhpllMmxMwxIea3JsSclD3F8Jjx8X8yPKYkbf5jL8Njvv1vA//Yyc+XQkONi/cbdaWwdvX8AAAAAABwaXVmxBoA4IoYsQYAwJ3VlRFrCmsAgBNRWAMA4M7qSmHt6vkBAAAAAODSGLEGADhRiRwfcXZ0VXEAAGCWujJiTWENAHCiC5IsDsagsAYAwFXVlcLa1fMDAAAAAMClMWINAHAiRqwBAHBndWXEmsIaAOBEFNYAALgziySLxdG+XrJYXbu/d/XCHwAAAAAAl0ZhDQBwHmupZC1xcLu6VcVXrFih1q1by9fXV1FRUdqxY8dl22/YsEHt27eXr6+vunTpovfee8/+pVitmjVrlkJDQ+Xn56eYmBgdPHjQrs2pU6c0atQo+fv7KzAwUPHx8Tp79qzt+IEDB9S/f38FBwfL19dXbdq00YwZM1RcXGxrs2bNGlksFrvN19f3qt4DAABM5+Vl3ObCKKwBAM5TatBWTevXr1diYqJmz56tPXv2qFu3boqNjdXx48crbL99+3aNHDlS8fHx+uyzzxQXF6e4uDjt27fP1mbBggVaunSpUlJSlJWVpQYNGig2Nlbnz5+3tRk1apT279+v9PR0bd68Wdu2bdOECRNsx+vVq6fRo0fr/fff14EDB5ScnKyXXnpJs2fPtsvH399fP/zwg207evRo9d8EAABqQh0prF07OwAAqig/P9/usY+Pj3x8fCpsu3jxYo0fP17jxo2TJKWkpOjdd9/V6tWrNW3atHLtlyxZogEDBmjq1KmSpLlz5yo9PV3Lly9XSkqKrFarkpOTNWPGDA0ZMkSS9Nprryk4OFibNm3SiBEj9PXXXystLU07d+5Uz549JUnLli3ToEGDtHDhQoWFhalNmzZq06aN7bytWrXS1q1b9fHHH9vlY7FYFBIScpXvFAAAMBoj1gAA5ykxaJMUHh6ugIAA25aUlFThKYuKirR7927FxMTY9nl4eCgmJkaZmZkVPiczM9OuvSTFxsba2h8+fFg5OTl2bQICAhQVFWVrk5mZqcDAQFtRLUkxMTHy8PBQVlZWhec9dOiQ0tLS1LdvX7v9Z8+eVatWrRQeHq4hQ4Zo//79FT4fAACnY8QaAACT/aYwdiiGpOzsbPn7+9t2VzZaffLkSZWUlCg4ONhuf3BwsL755psKn5OTk1Nh+5ycHNvxsn2Xa9OsWTO7415eXgoKCrK1KdO7d2/t2bNHhYWFmjBhgubMmWM71q5dO61evVpdu3ZVXl6eFi5cqN69e2v//v1q0aJFhfkDAOA0Xl6SAauCi1XBAQAwn7+/v91WWWFdG6xfv1579uzR2rVr9e6772rhwoW2Y9HR0Ro9erQiIyPVt29fvf3222ratKlefPFFJ2YMAEDdxog1AMB5rnLxsXIxqqFJkyby9PRUbm6u3f7c3NxKr1sOCQm5bPuyf3NzcxUaGmrXJjIy0tbm0sXRLly4oFOnTpU7b3h4uCSpY8eOKikp0YQJE/Too4/K09OzXG716tVT9+7ddejQoSu9dAAAap6np+RhwHhuqaN/MJiLEWsAgPMYeI11VXl7e6tHjx7KyMiw7SstLVVGRoaio6MrfE50dLRde0lKT0+3tY+IiFBISIhdm/z8fGVlZdnaREdH6/Tp09q9e7etzZYtW1RaWqqoqKhK8y0tLVVxcbFKK/mDoqSkRF9++aVdQQ8AgMvgGmsAANxTYmKixowZo549e6pXr15KTk5WQUGBbZXw0aNHq3nz5rYF0CZPnqy+fftq0aJFGjx4sNatW6ddu3Zp1apVki6u0j1lyhTNmzdPbdu2VUREhGbOnKmwsDDFxcVJkjp06KABAwZo/PjxSklJUXFxsRISEjRixAiFhYVJklJTU1WvXj116dJFPj4+2rVrl6ZPn67hw4erXr16kqQ5c+bopptu0nXXXafTp0/rueee09GjR/XAAw/U8LsIAADKUFgDAJzHCVPBJWn48OE6ceKEZs2apZycHEVGRiotLc22+Nh3330nj99MW+vdu7fWrl2rGTNm6IknnlDbtm21adMmde7c2dbmscceU0FBgSZMmKDTp0+rT58+SktLk6+vr61NamqqEhISdOutt8rDw0NDhw7V0qVLbce9vLw0f/58ffvtt7JarWrVqpUSEhL0yCOP2Nr89NNPGj9+vHJyctS4cWP16NFD27dvV8eOHav/RgAAYDYvrzoxFdxitbr48moOys/PV0BAgD6W1NDAuJFBBgYrU/EMRMd1MSFmuAkx25kQ04zbvJ43IWb5yyaN0ciEmDlXblJtn5sQ8z8mxPzKhJiStN34kJ/mGRerQFKMpLy8PLtVtx1R9rs577Dk7+D3af4ZKSDC2PxQu5R9P0VH58nLy7jvgY8//j/DYpV5Xn8yPKYk9TMhZuQvsyQMNWCA8TH79DE+ZuvWxscsLjY+piR9/73xMT/91PiYGzcaHnKP4RGlrSbElKRHtdbwmLfcMtKwWBcu5CszM8Ccvr5VK/kbUFjnl5Yq4OhRl+3vucYaAAAAAAAHMBUcAOA8Bt7HGgAAuCBPz4ubo0pcu8OnsAYAOI+TrrEGAAA1xMvLmMLaYnE8homYCg4AAAAAgAMYsQYAOA9TwQEAcG91ZMSawhoA4DwU1gAAuLc6UljXqqngzz77rCwWi6ZMmeLsVAAAgAno6wEAtVGtGbHeuXOnXnzxRXXt2tXZqQAAjMLiZfgN+noAcEOMWLuOs2fPatSoUXrppZfUuHFjZ6cDADBKiUEbaj36egBwU56eF4trRzcjinMT1YrCetKkSRo8eLBiYmKu2LawsFD5+fl2GwAAcG309QCA2szlp4KvW7dOe/bs0c6dO6vUPikpSU899ZTJWQEADGGV41O5rUYkAmeirwcAN1Y24uzmXHrEOjs7W5MnT1Zqaqp8fX2r9Jzp06crLy/PtmVnZ5ucJQDgqjEVvM6jrwcAN2fENPBaUJy7dHa7d+/W8ePHdcMNN9j2lZSUaNu2bVq+fLkKCwvleclcex8fH/n4+NR0qgAA4CrQ1wMA3IFLF9a33nqrvvzyS7t948aNU/v27fX444+X62gBALUM97Gu8+jrAcDN1YLRZiO49Cts1KiROnfubLevQYMGuuaaa8rtBwDUQtxuq86jrwcAN1e2KrijrK69qIpLX2MNAAAAAICrc+kR64ps3brV2SkAAIzCVHBUgL4eANyIUVPBXXzEutYV1gAAN0JhDQCAe6sjhTVTwQEAAAAAcACFNQDAeUoN2gAAgGty0n2st23bpjvuuENhYWGyWCzatGmTOa/vF3VmKnjXxpK/xcCAtxsYq0ysCTElyYxFVZuaEDO0vglBo0yIedSEmIEmxJSkm40PeW2G8THDvjI+Zq7xIfW9CTElKcz4kDdtNC5WvlXST8bFs1Mqx6dyU1jjF5mZ70kyri9ZoT8ZFqvMXYZHvChk5Ejjgz74oOEh8yN/Z3jMTz81PKQOHTI+plmuizQ+5k3Gf+nln7DF8Jg3LF9ueMyQjQZ2oL9R34TfJxM/9jUw2jkDY13CSVPBCwoK1K1bN91///266y6zfvv+qs4U1gAAAACA2i0/P9/usY+Pj3x8fMq1GzhwoAYOHFhTaTEVHADgREwFBwDAvZXdx9rRzdNTkhQeHq6AgADblpSU5OQXeBEj1gAA52FVcAAA3JtRU8FLL36Snp2dLX9/f9vuikarnYHCGgAAAABQK/j7+9sV1q6CwhoA4DyMWAMA4N4MHrF2VRTWAADnMeIaadfuZwEAqNsorAEAAAAAqH3Onj2rQ7+5d97hw4e1d+9eBQUFqWXLloafj8IaAOA8TAUHAMC9OWnEeteuXerfv7/tcWJioiRpzJgxWrNmjeP5XILCGgDgPBTWAAC4t7LbbTmqpHodfr9+/WS1Wh0/bxVxH2sAAAAAABzAiDUAwHmscnzxsZr7MBoAAFSXUVPBqzliXdMYsQYAOE+JQdtVWLFihVq3bi1fX19FRUVpx44dl22/YcMGtW/fXr6+vurSpYvee+89u+NWq1WzZs1SaGio/Pz8FBMTo4MHD9q1OXXqlEaNGiV/f38FBgYqPj5eZ8+etR0/cOCA+vfvr+DgYPn6+qpNmzaaMWOGiouLq5ULAAAuo6ywNmJzYRTWAIA6Z/369UpMTNTs2bO1Z88edevWTbGxsTp+/HiF7bdv366RI0cqPj5en332meLi4hQXF6d9+/bZ2ixYsEBLly5VSkqKsrKy1KBBA8XGxur8+fO2NqNGjdL+/fuVnp6uzZs3a9u2bZowYYLteL169TR69Gi9//77OnDggJKTk/XSSy9p9uzZ1coFAADULAprAIDzlBq0VdPixYs1fvx4jRs3Th07dlRKSorq16+v1atXV9h+yZIlGjBggKZOnaoOHTpo7ty5uuGGG7R8+XJJF0erk5OTNWPGDA0ZMkRdu3bVa6+9pmPHjmnTpk2SpK+//lppaWl6+eWXFRUVpT59+mjZsmVat26djh07Jklq06aNxo0bp27duqlVq1a68847NWrUKH388cdVzgUAAJfCiDUAALVHfn6+3VZYWFhhu6KiIu3evVsxMTG2fR4eHoqJiVFmZmaFz8nMzLRrL0mxsbG29ocPH1ZOTo5dm4CAAEVFRdnaZGZmKjAwUD179rS1iYmJkYeHh7Kysio876FDh5SWlqa+fftWORcAAFDzKKwBAM5j4DXW4eHhCggIsG1JSUkVnvLkyZMqKSlRcHCw3f7g4GDl5ORU+JycnJzLti/790ptmjVrZnfcy8tLQUFB5c7bu3dv+fr6qm3btrrllls0Z86cKucCAIBLKbvdlqObp6ezX8llufZ4OgDAvRl4H+vs7Gz5+/vbdvv4+DgY2HnWr1+vM2fO6PPPP9fUqVO1cOFCPfbYY85OCwCA6jNqGreLTwV37ewAAKgif39/u8K6Mk2aNJGnp6dyc3Pt9ufm5iokJKTC54SEhFy2fdm/ubm5Cg0NtWsTGRlpa3Pp4mgXLlzQqVOnyp03PDxcktSxY0eVlJRowoQJevTRR+Xp6XnFXAAAQM1jKjgAwHmcsHiZt7e3evTooYyMjF/TKC1VRkaGoqOjK3xOdHS0XXtJSk9Pt7WPiIhQSEiIXZv8/HxlZWXZ2kRHR+v06dPavXu3rc2WLVtUWlqqqKioSvMtLS1VcXGxSktLq5QLAAAupY4sXuba2QEA3JuBU8GrIzExUWPGjFHPnj3Vq1cvJScnq6CgQOPGjZMkjR49Ws2bN7ddpz158mT17dtXixYt0uDBg7Vu3Trt2rVLq1atkiRZLBZNmTJF8+bNU9u2bRUREaGZM2cqLCxMcXFxkqQOHTpowIABGj9+vFJSUlRcXKyEhASNGDFCYWFhkqTU1FTVq1dPXbp0kY+Pj3bt2qXp06dr+PDhqlevXpVyAQDApTAV3M20k7Gv9gYDY5XpZkJMSQo3IWYjE2KqvQkxm5gQ09EqoCLXmBBTklqZELOH8SFbf2V8zAbGh1TwlZtclXMmxDRyHatiSe8bGM8FDB8+XCdOnNCsWbOUk5OjyMhIpaWl2RYF++677+Th8eukrt69e2vt2rWaMWOGnnjiCbVt21abNm1S586dbW0ee+wxFRQUaMKECTp9+rT69OmjtLQ0+fr62tqkpqYqISFBt956qzw8PDR06FAtXbrUdtzLy0vz58/Xt99+K6vVqlatWikhIUGPPPJItXKpq6ZrpHyv3KzKbjcwVpmQiRNNiCrp2WcND/napitfWlFdm024K9zWrcbHPHGiwPigJmna1PgOr08fw0MqLu73hsccvabnlRtVU1gTM/52lAa99JLhMefqLsNinZf0tGHR6qa6U1gDAFxPqRz/rOoq7mMtSQkJCUpISKjw2NYK/lIfNmyYhg0bVmk8i8WiOXPm2K3gfamgoCCtXbu20uPDhw/X8OHDK0+6irkAAOAyGLEGAMBkV3GNdIUxAACAayq73ZYRcVwYi5cBAAAAAOAARqwBAM7jpMXLAABADWEqOAAAJmMqOAAA7q2OFNZMBQcAAAAAwAEuX1h///33uvfee3XNNdfIz89PXbp00a5du5ydFgDACCUGbaj16O8BwE2VjVgbsbkwl87up59+0s0336z+/fvrH//4h5o2baqDBw+qcePGzk4NAGAErrGG6O8BwK3VkVXBXbqwnj9/vsLDw/XKK6/Y9kVERDgxIwAAYDT6ewBAbefSU8Hfeecd9ezZU8OGDVOzZs3UvXt3vfTSS5d9TmFhofLz8+02AICLKjVoQ61W3f6evh4AapE6MhXcpQvr//znP1q5cqXatm2rf/7zn5o4caIefvhhvfrqq5U+JykpSQEBAbYtPDy8BjMGAFRLqRy/vprCutarbn9PXw8AtQiFtfOVlpbqhhtu0DPPPKPu3btrwoQJGj9+vFJSUip9zvTp05WXl2fbsrOzazBjAABQXdXt7+nrAQCuxqXL/tDQUHXs2NFuX4cOHfTWW29V+hwfHx/5+PiYnRoAwAjcxxqqfn9PXw8AtUgduY+1S2d3880368CBA3b7vv32W7Vq1cpJGQEADMWq4BD9PQC4tTpSWLv0VPBHHnlEn376qZ555hkdOnRIa9eu1apVqzRp0iRnpwYAAAxCfw8AqO1cuuy/8cYbtXHjRk2fPl1z5sxRRESEkpOTNWrUKGenBgAwAiPWEP09ALg17mPtGm6//Xbdfvvtzk4DAGAGrrHGL+jvAcBNMRUcAAAAAABciWuX/QAA98ZUcAAA3FsdGbF27ewAAO6NwhoAAPdGYe1mWkiqZ2C8YANjlWlkQkzJnD86fzQhZpNDJgQNMCHmf02IecGEmJJ01oSYOcaHLDA+pCnM+hltY0LMLgbGKpT0voHxAJP0l9TAwHgt//hHA6P9YuFC42NKmrOwvuExly83PKROnPjS+KDaZ0JMM/p6c5w4EWp4zI0bO165UTV98MENhsc8csTf8JizkpMNjylJLU+eNDzmbRs3GhbrrKSnDYtWN9WdwhoA4HqscnzxMasRiQAAAFOwKjgAACZjKjgAAO6tjkwFZ1VwAAAAAAAc4NplPwDAvXEfawAA3FsdGbF27ewAAO6NqeAAALi3OlJYMxUcAAAAAAAHuHbZDwBwb4xYAwDg3urIiLVrZwcAcG9cYw0AgHurI7fbYio4AAAAAAAOYMQaAOA8TAUHAMC9MRUcAACTlcrxwpip4AAAuK46UlgzFRwAAAAAAAe4dtkPAHBvLF4GAIB7Y8QaAACTlRi0XYUVK1aodevW8vX1VVRUlHbs2HHZ9hs2bFD79u3l6+urLl266L333rM7brVaNWvWLIWGhsrPz08xMTE6ePCgXZtTp05p1KhR8vf3V2BgoOLj43X27Fnb8a1bt2rIkCEKDQ1VgwYNFBkZqdTUVLsYa9askcVisdt8fX2v7k0AAMBsZauCO7qxKjgAAK5l/fr1SkxM1OzZs7Vnzx5169ZNsbGxOn78eIXtt2/frpEjRyo+Pl6fffaZ4uLiFBcXp3379tnaLFiwQEuXLlVKSoqysrLUoEEDxcbG6vz587Y2o0aN0v79+5Wenq7Nmzdr27ZtmjBhgt15unbtqrfeektffPGFxo0bp9GjR2vz5s12+fj7++uHH36wbUePHjX4HQIAANXh2uPpAAD35qSp4IsXL9b48eM1btw4SVJKSoreffddrV69WtOmTSvXfsmSJRowYICmTp0qSZo7d67S09O1fPlypaSkyGq1Kjk5WTNmzNCQIUMkSa+99pqCg4O1adMmjRgxQl9//bXS0tK0c+dO9ezZU5K0bNkyDRo0SAsXLlRYWJieeOIJu/NOnjxZ77//vt5++23dfvvttv0Wi0UhISHVf+EAANQ0poIDAGAyA6eC5+fn222FhYUVnrKoqEi7d+9WTEyMbZ+Hh4diYmKUmZlZ4XMyMzPt2ktSbGysrf3hw4eVk5Nj1yYgIEBRUVG2NpmZmQoMDLQV1ZIUExMjDw8PZWVlVfoW5eXlKSgoyG7f2bNn1apVK4WHh2vIkCHav39/pc8HAMCpjJgGblRxbiIKawCAWwgPD1dAQIBtS0pKqrDdyZMnVVJSouDgYLv9wcHBysnJqfA5OTk5l21f9u+V2jRr1szuuJeXl4KCgio97xtvvKGdO3faRtYlqV27dlq9erX+9re/6a9//atKS0vVu3dv/fe//60wBgAAdVV111NxhGuX/QAA9+bA4mN2MSRlZ2fL39/fttvHx8fBwM714Ycfaty4cXrppZfUqVMn2/7o6GhFR0fbHvfu3VsdOnTQiy++qLlz5zojVQAAKuekqeBl66mkpKQoKipKycnJio2N1YEDB8p90G2Eao9YjxkzRtu2bTM8EQBAHVRq0KaLC3r9dqussG7SpIk8PT2Vm5trtz83N7fS65ZDQkIu277s3yu1uXRxtAsXLujUqVPlzvvRRx/pjjvu0PPPP6/Ro0dXmFOZevXqqXv37jp06NBl21UX/T0AwBBOmgr+2/VUOnbsqJSUFNWvX1+rV68252VW9wl5eXmKiYlRq1atNG7cOI0ZM0bNmzc3IzdjdZVk5N1IuhgYq0zLliYElaQmJsQ8aULMISbENON784AJMc34GklSpAkxK75u1SEN/2VCzNbGx1TwlZtcjSYfGR/zlIE3dz5nXChX4O3trR49eigjI0NxcXGSpNLSUmVkZCghIaHC50RHRysjI0NTpkyx7UtPT7eNHEdERCgkJEQZGRmKjIyUdPGa76ysLE2cONEW4/Tp09q9e7d69OghSdqyZYtKS0sVFRVli7t161bdfvvtmj9/vt2K4ZUpKSnRl19+qUGDBlX3rbis2tjf3yjJ/4qtqqGS7wdHrPprfcNjSlIlVz445Pz5d4wPqp0mxPzG8IjP6U3DY5plqu42Iarxf+ucOWP85SpJSXcaHrNJE3N+Rv/3z382PGavS+4Y4Yh8q1W6cMGweGbKz8+3e+zj41Puw/Sy9VSmT59u23el9VQcVe0R602bNun777/XxIkTtX79erVu3VoDBw7Um2++qeLiYjNyBAC4q1I5vnDZVXyGkJiYqJdeekmvvvqqvv76a02cOFEFBQW2a5lHjx5t1xlPnjxZaWlpWrRokb755hs9+eST2rVrl60Qt1gsmjJliubNm6d33nlHX375pUaPHq2wsDBb8d6hQwcNGDBA48eP144dO/Svf/1LCQkJGjFihMLCwiRdnP49ePBgPfzwwxo6dKhycnKUk5OjU6dO2XKZM2eO3n//ff3nP//Rnj17dO+99+ro0aN64IEHqv9GXAb9PQDACKXyMGyTqramytWsp+Koq1q8rGnTpkpMTNTnn3+urKwsXXfddbrvvvsUFhamRx55RAcPHjQ6TwCAOzJwVfDqGD58uBYuXKhZs2YpMjJSe/fuVVpamq0D/u677/TDDz/Y2vfu3Vtr167VqlWr1K1bN7355pvatGmTOnfubGvz2GOP6aGHHtKECRN044036uzZs0pLS5Ov76/TpVJTU9W+fXvdeuutGjRokPr06aNVq1bZjr/66qs6d+6ckpKSFBoaatvuuusuW5uffvpJ48ePV4cOHTRo0CDl5+dr+/bt6tixY/XfiCugvwcAOOrCBeM26eKaKnl5ebbttx+EO5NDV5H/8MMPSk9PV3p6ujw9PTVo0CB9+eWX6tixoxYsWKBHHnnEqDwBADBUQkJCpVO/t27dWm7fsGHDNGzYsErjWSwWzZkzR3PmzKm0TVBQkNauXVvp8TVr1mjNmjWVHpek559/Xs8///xl2xiN/h4A4CrK1lK5nKtZT8VR1R6xLi4u1ltvvaXbb79drVq10oYNGzRlyhQdO3ZMr776qj744AO98cYbl/3DAgAASYYuXgZj0d8DAIxg9Ih1Vfx2PZUyZeup/PbOGkaq9oh1aGioSktLNXLkSO3YscO2SMtv9e/fX4GBgQakBwBwayW6youSLokBw9HfAwCMUN2i+HJxqiMxMVFjxoxRz5491atXLyUnJ9utp2K0ahfWzz//vIYNG2Z3zdilAgMDdfjwYYcSAwAAzkN/DwCozYYPH64TJ05o1qxZysnJUWRkpN16KkardmF93333mZEHAKAuMmIqN1PBTUF/DwAwgrNGrKXLr6diNEcn4JmqpKREM2fOVEREhPz8/HTttddq7ty5slqtzk4NAGAEJ60KDtdCfw8A7qukxJjrq0tcvL93aFVws82fP18rV67Uq6++qk6dOmnXrl0aN26cAgIC9PDDDzs7PQAAYAD6ewBAbefShfX27ds1ZMgQDR48WJLUunVr/d///Z927NhR6XMKCwtVWFhoe5yfn296ngCAq8TiZVD1+3v6egCoPZw5FbwmufRU8N69eysjI0PffvutJOnzzz/XJ598ooEDB1b6nKSkJAUEBNi28PDwmkoXAFBdVjl+qy1mC9d61e3v6esBoPZwxu22nMGlR6ynTZum/Px8tW/fXp6eniopKdHTTz+tUaNGVfqc6dOnKzEx0fY4Pz+fDhcAABdW3f6evh4A4GpcurB+4403lJqaqrVr16pTp07au3evpkyZorCwMI0ZM6bC5/j4+MjHx6eGMwUAXJUSSRYDYqBWq25/T18PALVHXZkK7tKF9dSpUzVt2jSNGDFCktSlSxcdPXpUSUlJlRbWAIBahMIaor8HAHdWtiq4EXFcmUtfY33u3Dl5eNin6OnpqdJSbloKAIC7oL8HANR2Lj1ifccdd+jpp59Wy5Yt1alTJ3322WdavHix7r//fmenBgAwQtkCZI7GQK1Gfw8A7oup4C5g2bJlmjlzpv73f/9Xx48fV1hYmP7nf/5Hs2bNcnZqAAAjMBUcor8HAHdGYe0CGjVqpOTkZCUnJzs7FQAAYBL6ewBAbefShTUAwM0xFRwAALfGiDUAAGZjKjgAAG6trhTWLr0qOAAAAAAArq7ujFj3ltTAwHidvA0MVibWhJiSFGhCTCPfzDJ9TYgZYkLMDBNiuvhHcHbMyPVWE2J2NiHmNSbElKQ840O22mNcrLPGhSqnVI6PODMVHL/wGDBAHvXqGRYvv+fvDYtVZuNzhoeUJJ0/v9OEqJ8aHvFpJRkes4XhEaVmJsQ061fVX/Sm4TH/a3hEabZmGB7z/PlQw2Nu2nSj4TElacSI3obHDBowwLhgxcVSWppx8X6jrtzHuu4U1gAA11Mqx6eCU1gDAOCymAoOAAAAAACuiBFrAIDzGDGty8WnhgEAUJfVlRFrCmsAgPNQWAMA4NbqSmHNVHAAAAAAABzAiDUAwHlYvAwAALfGquAAAJiNqeAAALg1poIDAAAAAIArYsQaAOA8TAUHAMCt1ZURawprAIDzGFEUU1gDAOCy6kphzVRwAAAAAAAcwIg1AMB5SiRZHYzBiDUAAC6rroxYU1gDAJyHqeAAALi1unK7LaaCAwDqpBUrVqh169by9fVVVFSUduzYcdn2GzZsUPv27eXr66suXbrovffesztutVo1a9YshYaGys/PTzExMTp48KBdm1OnTmnUqFHy9/dXYGCg4uPjdfbsWdvxrVu3asiQIQoNDVWDBg0UGRmp1NTUaucCAABqFoU1AMB5Sgzaqmn9+vVKTEzU7NmztWfPHnXr1k2xsbE6fvx4he23b9+ukSNHKj4+Xp999pni4uIUFxenffv22dosWLBAS5cuVUpKirKystSgQQPFxsbq/PnztjajRo3S/v37lZ6ers2bN2vbtm2aMGGC3Xm6du2qt956S1988YXGjRun0aNHa/PmzdXKBQAAV1E2FdyIzZVRWAMAnMdJhfXixYs1fvx4jRs3Th07dlRKSorq16+v1atXV9h+yZIlGjBggKZOnaoOHTpo7ty5uuGGG7R8+XJJF0erk5OTNWPGDA0ZMkRdu3bVa6+9pmPHjmnTpk2SpK+//lppaWl6+eWXFRUVpT59+mjZsmVat26djh07Jkl64oknNHfuXPXu3VvXXnutJk+erAEDBujtt9+uci4AALgSCmsAAGqR/Px8u62wsLDCdkVFRdq9e7diYmJs+zw8PBQTE6PMzMwKn5OZmWnXXpJiY2Nt7Q8fPqycnBy7NgEBAYqKirK1yczMVGBgoHr27GlrExMTIw8PD2VlZVX6uvLy8hQUFFTlXAAAQM2jsAYAOE+pQZuk8PBwBQQE2LakpKQKT3ny5EmVlJQoODjYbn9wcLBycnIqfE5OTs5l25f9e6U2zZo1szvu5eWloKCgSs/7xhtvaOfOnRo3blyVcwEAwJXUlRFrVgUHADhPqRy/3dYvz8/Ozpa/v79tt4+Pj4OBnevDDz/UuHHj9NJLL6lTp07OTgcAgKvCquAAANQi/v7+dltlhXWTJk3k6emp3Nxcu/25ubkKCQmp8DkhISGXbV/275XaXLo42oULF3Tq1Kly5/3oo490xx136Pnnn9fo0aOrlQsAAKh5dWfEuqmkRkYGNOMPGLOm8R0xIeZRE2LmXrlJtXUzIeZ+E2L6mhBTkk6bEDPDhJgnTYjZwoSYZuQpSYeMD3nawFgFBsa6VKkki4Mxqjni7e3trR49eigjI0NxcXEX0ygtVUZGhhISEip8TnR0tDIyMjRlyhTbvvT0dEVHR0uSIiIiFBISooyMDEVGRkq6eM13VlaWJk6caItx+vRp7d69Wz169JAkbdmyRaWlpYqKirLF3bp1q26//XbNnz/fbsXwquZSp/XsKfka9/t01y7DQtn861/Gx7zoq1oR04zfzK1NiNnsyk1cRkMTYppTIHxpQszrDI/48cc3Gh5TkvbuNT7m73/pbwxRWCilpRkX7zeMmsbNVHAAACpTohovrCUpMTFRY8aMUc+ePdWrVy8lJyeroKDAdi3z6NGj1bx5c9t12pMnT1bfvn21aNEiDR48WOvWrdOuXbu0atUqSZLFYtGUKVM0b948tW3bVhEREZo5c6bCwsJsxXuHDh00YMAAjR8/XikpKSouLlZCQoJGjBihsLAwSRenf99+++2aPHmyhg4dartu2tvb27aA2ZVyAQDAlVBYAwDgpoYPH64TJ05o1qxZysnJUWRkpNLS0myLgn333Xfy8Pj1aqnevXtr7dq1mjFjhp544gm1bdtWmzZtUufOnW1tHnvsMRUUFGjChAk6ffq0+vTpo7S0NPn+ZgQ1NTVVCQkJuvXWW+Xh4aGhQ4dq6dKltuOvvvqqzp07p6SkJLvF1/r27autW7dWORcAAFCzKKwBAM7jpBFrSUpISKh06ndZEftbw4YN07BhwyqNZ7FYNGfOHM2ZM6fSNkFBQVq7dm2lx9esWaM1a9ZUeryquQAA4CoYsQYAwGxOuMYaAADUnLpSWLMqOAAAAAAADmDEGgDgPE6cCg4AAMzHfaxrwLZt23THHXcoLCxMFotFmzZtsjtutVo1a9YshYaGys/PTzExMTp48KBzkgUAGK/EoA0ui74eAOq2sqngRmyuzKmFdUFBgbp166YVK1ZUeHzBggVaunSpUlJSlJWVpQYNGig2Nlbnz5+v4UwBAMDVoK8HANQFTp0KPnDgQA0cOLDCY1arVcnJyZoxY4aGDBkiSXrttdcUHBysTZs2acSIERU+r7CwUIWFhbbH+fn5xicOADCGVUzldnP09QBQt124IHl6GhPHlbns4mWHDx9WTk6OYmJibPsCAgIUFRWlzMzMSp+XlJSkgIAA2xYeHl4T6QIArgIzwes2+noAcH9MBXeynJwcSVJwcLDd/uDgYNuxikyfPl15eXm2LTs729Q8AQDA1aGvBwC4C7dbFdzHx0c+Pj7OTgMAUAVGjDgzYl330NcDQO3BquBOFhISIknKzc2125+bm2s7BgCo3UoN2lA70dcDgPtjKriTRUREKCQkRBkZGbZ9+fn5ysrKUnR0tBMzAwAARqCvBwC4C6dOBT979qwOHTpke3z48GHt3btXQUFBatmypaZMmaJ58+apbdu2ioiI0MyZMxUWFqa4uDjnJQ0AMAxTwd0ffT0A1G0XLkgeBgznmjli/fTTT+vdd9/V3r175e3trdOnT1c7hlML6127dql///62x4mJiZKkMWPGaM2aNXrsscdUUFCgCRMm6PTp0+rTp4/S0tLk6+vrrJQBAAYyYio3U8FdG309ANRttaGwLioq0rBhwxQdHa2//OUvVxXDqYV1v379ZLVWfgNTi8WiOXPmaM6cOTWYFQAAMAp9PQDA1T311FOSpDVr1lx1DLdbFRwAUHswFRwAAPdm9Ih1fn6+3X5XuVOEyy5eBgBwf6X6tbi+2o2p4AAAuK6y2205upXdbis8PFwBAQG2LSkpybkv8BcU1gAAAACAWiE7O1t5eXm2bfr06RW2mzZtmiwWy2W3b775xrC86s5U8J8kFRsYr/l3Bgb7hXdD42NKko4YH/KHc8bHDP2v8TE11ISYR02IeY0JMSWphwkxzfg+Ne6X2q/+aUJMk35l/pB/5TbVZeSPqAk/7mVYvAyGatJE8vMzLNxvFjM3zJkzJvy8S5KOGx7xOf3N8JhBhkc0J2YTE2KaxYz1nM6aENOM76epusXwmOfPFxgeU5KOHGlgfNAWLYyL9fPPxsW6xIULksViTBxJ8vf3l7+//xXbP/rooxo7duxl27Rp08bxxH5RdwprAIDL4RprAADcm9GFdVU1bdpUTZs2dfzEVURhDQAAAACos7777judOnVK3333nUpKSrR3715J0nXXXaeGDas2W5PCGgDgNIxYAwDg3pw1Yl0ds2bN0quvvmp73L17d0nShx9+qH79+lUpBoU1AMBpuMYaAAD3VhsK6zVr1jh0D2uJVcEBAAAAAHAII9YAAKdhKjgAAO6tpMSYEesSF+/wKawBAE7DVHAAANybUVO4zZwKbgSmggMAAAAA4ABGrAEATlMqx6dyM2INAIDrqisj1hTWAACn4RprAADcW10prJkKDgAAAACAAxixBgA4DYuXAQDg3oxazZtVwQEAqARTwQEAcG8XLkhWq+NxXL2wZio4AAAAAAAOYMQaAOA0jFgDAODeGLEGAMBkpQZtV2PFihVq3bq1fH19FRUVpR07dly2/YYNG9S+fXv5+vqqS5cueu+99+yOW61WzZo1S6GhofLz81NMTIwOHjxo1+bUqVMaNWqU/P39FRgYqPj4eJ09e9Z2/Pz58xo7dqy6dOkiLy8vxcXFlctj69atslgs5bacnJyrfCcAADDPhQvGba6MwhoAUOesX79eiYmJmj17tvbs2aNu3bopNjZWx48fr7D99u3bNXLkSMXHx+uzzz5TXFyc4uLitG/fPlubBQsWaOnSpUpJSVFWVpYaNGig2NhYnT9/3tZm1KhR2r9/v9LT07V582Zt27ZNEyZMsB0vKSmRn5+fHn74YcXExFz2NRw4cEA//PCDbWvWrJmD7woAALhaFNYAAKcpMWirrsWLF2v8+PEaN26cOnbsqJSUFNWvX1+rV6+usP2SJUs0YMAATZ06VR06dNDcuXN1ww03aPny5ZIujlYnJydrxowZGjJkiLp27arXXntNx44d06ZNmyRJX3/9tdLS0vTyyy8rKipKffr00bJly7Ru3TodO3ZMktSgQQOtXLlS48ePV0hIyGVfQ7NmzRQSEmLbPDzo0gEArocRawAATGaV49PAyy7bys/Pt9sKCwsrPGdRUZF2795tNyLs4eGhmJgYZWZmVviczMzMciPIsbGxtvaHDx9WTk6OXZuAgABFRUXZ2mRmZiowMFA9e/a0tYmJiZGHh4eysrKu9FaVExkZqdDQUP3hD3/Qv/71r2o/HwCAmlBSYkxRzTXWAADUgPDwcAUEBNi2pKSkCtudPHlSJSUlCg4OttsfHBxc6XXKOTk5l21f9u+V2lw6XdvLy0tBQUHVuj46NDRUKSkpeuutt/TWW28pPDxc/fr10549e6ocAwAAGKvurAqeJ8nI6QOmfCTR3Iyg5rjmKxOCtjAhpq8JMc9fuYnLuM6EmLkmxPzGhJhmzBdqaEJMSYEuHrOegbEuYeSq4NnZ2fL397ft9/HxcTCya2rXrp3atWtne9y7d2/9+9//1vPPP6/XX3/diZm5gOJiycu4P218GxkW6je8zQgqM/q7IsMjmvOb2Yxe+ZwJMc0azTLj61RbYprz82TOz6iBv5p+9XOxcbGKDYx1iQsXJCOuViq92tVKa0jdKawBAC7HyMLa39/frrCuTJMmTeTp6ancXPsPiXJzcyu9rjkkJOSy7cv+zc3NVWhoqF2byMhIW5tLF0e7cOGCTp06dcXrqa+kV69e+uSTTxyKAQCAGepKYc1UcABAneLt7a0ePXooIyPDtq+0tFQZGRmKjo6u8DnR0dF27SUpPT3d1j4iIkIhISF2bfLz85WVlWVrEx0drdOnT2v37t22Nlu2bFFpaamioqIcek179+61K+gBAEDNYsQaAOA0jtyH+rcxqisxMVFjxoxRz5491atXLyUnJ6ugoEDjxo2TJI0ePVrNmze3Xac9efJk9e3bV4sWLdLgwYO1bt067dq1S6tWrZIkWSwWTZkyRfPmzVPbtm0VERGhmTNnKiwszHYv6g4dOmjAgAEaP368UlJSVFxcrISEBI0YMUJhYWG23L766isVFRXp1KlTOnPmjPbu3StJtpHv5ORkRUREqFOnTjp//rxefvllbdmyRe+///5VvX8AAJiproxYU1gDAJzGyKng1TF8+HCdOHFCs2bNUk5OjiIjI5WWlmZbfOy7776zu31V7969tXbtWs2YMUNPPPGE2rZtq02bNqlz5862No899pgKCgo0YcIEnT59Wn369FFaWpp8fX+99jU1NVUJCQm69dZb5eHhoaFDh2rp0qV2uQ0aNEhHjx61Pe7evbuki7f0ki6uav7oo4/q+++/V/369dW1a1d98MEH6t+//1W8EwAAmKukxJii2Gq9chtnorAGANRJCQkJSkhIqPDY1q1by+0bNmyYhg0bVmk8i8WiOXPmaM6cOZW2CQoK0tq1ay+b15EjRy57/LHHHtNjjz122TYAAKBmUVgDAJzGWSPWAACgZly4IFksjsdx9RFrpy5etm3bNt1xxx0KCwuTxWLRpk2bbMeKi4v1+OOPq0uXLmrQoIHCwsI0evRoHTt2zHkJAwAMVWrQBtdFXw8AdduFC8ZtrsyphXVBQYG6deumFStWlDt27tw57dmzRzNnztSePXv09ttv68CBA7rzzjudkCkAALga9PUAgLrAqVPBBw4cqIEDB1Z4LCAgQOnp6Xb7li9frl69eum7775Ty5YtayJFAICJSuX4VG5GrF0bfT0A1G11ZSp4rbrGOi8vTxaLRYGBgZW2KSwsVGFhoe1xfn5+DWQGALgazrrdFlwXfT0AuJe6Ulg7dSp4dZw/f16PP/64Ro4cKX9//0rbJSUlKSAgwLaFh4fXYJYAAOBq0dcDAGqrWlFYFxcX65577pHVatXKlSsv23b69OnKy8uzbdnZ2TWUJQCgukoM2lD70dcDgLsqldXq+Obqc9Rcfip4WUd79OhRbdmy5bKfYEuSj4+PfHx8aig7AIAjuN0WJPp6AHBvRn0M7to9vksX1mUd7cGDB/Xhhx/qmmuucXZKAADAQPT1AAB34NTC+uzZszp06JDt8eHDh7V3714FBQUpNDRUd999t/bs2aPNmzerpKREOTk5kqSgoCB5e3s7K20AgEFYvMz90dcDQF3HiLXpdu3apf79+9seJyYmSpLGjBmjJ598Uu+8844kKTIy0u55H374ofr161dTaQIATMJUcPdHXw8AdR2Ften69esn62XWTb/cMQAA4Pro6wEAdYFLX2MNAHBvjFgDAODujFrR27Uv/qKwBgA4jVWOd5OMdwIA4MrqxlTwWnEfawAAAAAAXFXdGbEOktTQwHheXQ0MVuZWE2JKpnyZvQ8YH1NRJsS81oSYRn4jlWluQkzJvO8po/3XhJitTYjpa0JMSX4mfE+FbTcu1hnjQl2KqeAw1A8/SAbe37r1AMNC2TRqZM7vkTNnmhge8//pT4bH/IvWGh6zvuERa9cfyCdrSUwzvp8k47/vGzeuZ3hMSWrRwoSgW3ONi1VYaFysckplTG/NVHAAACrE7bYAAHB3TAUHAAAAAABXwIg1AMBpmAoOAIC7qxsj1hTWAACnobAGAMDd1Y3bbTEVHAAAAAAABzBiDQBwGhYvAwDA3TEVHAAAUzEVHAAAd1c3CmumggMAAAAA4ABGrAEATlMqxz9/Zio4AACurG6MWFNYAwCchmusAQBwd0Z8jF4Wx3UxFRwAAAAAAAcwYg0AcBoWLwMAwN3VjftYU1gDAJyGqeAAALi7unGNNVPBAQAAAAB10pEjRxQfH6+IiAj5+fnp2muv1ezZs1VUVFStOIxYAwCchqngAAC4O9cesf7mm29UWlqqF198Udddd5327dun8ePHq6CgQAsXLqxyHAprAIDTUFgDAODujC2s8/Pz7fb6+PjIx8fnqqMOGDBAAwYMsD1u06aNDhw4oJUrV1arsGYqOAAAAACgVggPD1dAQIBtS0pKMvwceXl5CgoKqtZzKKwBAE5TatB2NVasWKHWrVvL19dXUVFR2rFjx2Xbb9iwQe3bt5evr6+6dOmi9957z+641WrVrFmzFBoaKj8/P8XExOjgwYN2bU6dOqVRo0bJ399fgYGBio+P19mzZ23Hz58/r7Fjx6pLly7y8vJSXFxchbls3bpVN9xwg3x8fHTddddpzZo1V/UeAABgvhIDNyk7O1t5eXm2bfr06YZme+jQIS1btkz/8z//U63nUVgDAJymVI53sVdTWK9fv16JiYmaPXu29uzZo27duik2NlbHjx+vsP327ds1cuRIxcfH67PPPlNcXJzi4uK0b98+W5sFCxZo6dKlSklJUVZWlho0aKDY2FidP3/e1mbUqFHav3+/0tPTtXnzZm3btk0TJkywHS8pKZGfn58efvhhxcTEVJjL4cOHNXjwYPXv31979+7VlClT9MADD+if//znVbwTAACYzSpjPka3SpL8/f3ttsqmgU+bNk0Wi+Wy2zfffGP3nO+//14DBgzQsGHDNH78+Gq9Sq6xBgC4hepcc7V48WKNHz9e48aNkySlpKTo3Xff1erVqzVt2rRy7ZcsWaIBAwZo6tSpkqS5c+cqPT1dy5cvV0pKiqxWq5KTkzVjxgwNGTJEkvTaa68pODhYmzZt0ogRI/T1118rLS1NO3fuVM+ePSVJy5Yt06BBg7Rw4UKFhYWpQYMGWrlypSTpX//6l06fPl0ul5SUFEVERGjRokWSpA4dOuiTTz7R888/r9jY2Kt45wAAcD+PPvqoxo4de9k2bdq0sf3/2LFj6t+/v3r37q1Vq1ZV+3yMWAMAnMa4iWFVv+aqqKhIu3fvthsR9vDwUExMjDIzMyt8TmZmZrkR5NjYWFv7w4cPKycnx65NQECAoqKibG0yMzMVGBhoK6olKSYmRh4eHsrKyrrSW1XlXAAAcC3GTgWvqqZNm6p9+/aX3by9vSVdHKnu16+fevTooVdeeUUeHtUvk+vOiHW9XzbDmPHWhZgQU5JamBAz0oSY3UyIacbXqcCEmMEmxJSkJibE7GRCzL4mxDTjtZtll/EhPV001iUcuUb6tzGki9dc+fv72/ZXNlp98uRJlZSUKDjY/ucuODi43JSwMjk5ORW2z8nJsR0v23e5Ns2aNbM77uXlpaCgIFubqqgsl/z8fP3888/y8/Orciy388UXUj3jOvvOfzYslM1NNxkfU5LS0yNMiNrK8IhV/06vOjN6ekd/L9Wkii9gccxJE2Ka8f0kXWd4xMhIw0NKkjqZ8edT8l7jYhUXGxerHNe+3VZZUd2qVSstXLhQJ06csB0LCal6fVZ3CmsAgFsru9YKAACgqtLT03Xo0CEdOnRILVrYD0hardYqx2EqOADAaWp+YpjUpEkTeXp6Kjc3125/bm5upZ9Mh4SEXLZ92b9XanPp4mgXLlzQqVOnqvWJeGW5+Pv71+3RagCAi3LOVPCqGjt2rKxWa4VbdVBYAwCcxhm32/L29laPHj2UkZHxax6lpcrIyFB0dHSFz4mOjrZrL138hLusfUREhEJCQuza5OfnKysry9YmOjpap0+f1u7du21ttmzZotLSUkVFRVU5/yvlAgCAa3HtwtooTAUHANQ5iYmJGjNmjHr27KlevXopOTlZBQUFtlXCR48erebNm9sWQJs8ebL69u2rRYsWafDgwVq3bp127dplWzXUYrFoypQpmjdvntq2bauIiAjNnDlTYWFhtntRd+jQQQMGDND48eOVkpKi4uJiJSQkaMSIEQoLC7Pl9tVXX6moqEinTp3SmTNntHfvXklS5C8X/j344INavny5HnvsMd1///3asmWL3njjDb377rs18+YBAIByKKwBAE5jxOfPV/P84cOH68SJE5o1a5ZycnIUGRmptLQ026Jg3333nd2KoL1799batWs1Y8YMPfHEE2rbtq02bdqkzp0729o89thjKigo0IQJE3T69Gn16dNHaWlp8vX1tbVJTU1VQkKCbr31Vnl4eGjo0KFaunSpXW6DBg3S0aNHbY+7d+8u6dfrvCIiIvTuu+/qkUce0ZIlS9SiRQu9/PLL3GoLAOCijFiqtCyO63LqVPBt27bpjjvuUFhYmCwWizZt2lRp2wcffFAWi0XJyck1lh8AwFzOnBiWkJCgo0ePqrCwUFlZWXbTsbdu3ao1a9bYtR82bJgOHDigwsJC7du3T4MGDbI7brFYNGfOHOXk5Oj8+fP64IMPdP3119u1CQoK0tq1a3XmzBnl5eVp9erVatiwoV2bI0eOXPE6r379+umzzz5TYWGh/v3vf1/xPp3ORF8PAHVd3ZgK7tTCuqCgQN26ddOKFSsu227jxo369NNP7abKAQAA10dfDwCoC5w6FXzgwIEaOHDgZdt8//33euihh/TPf/5TgwcPrqHMAAA1wSrHJ3ZVb81O1DT6egCo60plzGiza08Fd+lrrEtLS3Xfffdp6tSp6lTFu6oXFhaqsLDQ9jg/P9+s9AAADnLWNdZwHfT1AODujJrG7do9vkvfbmv+/Pny8vLSww8/XOXnJCUlKSAgwLaFh4ebmCEAAHAEfT0AwB24bGG9e/duLVmyRGvWrJHFYqny86ZPn668vDzblp2dbWKWAABHuP9SJrgc+noAqAtKDdxcl8sW1h9//LGOHz+uli1bysvLS15eXjp69KgeffRRtW7dutLn+fj4yN/f324DALgm9+9mcTn09QBQF9SNVcFd9hrr++67TzExMXb7YmNjdd9992ncuHFOygoAABiFvh4A4C6cWlifPXtWhw4dsj0+fPiw9u7dq6CgILVs2VLXXHONXft69eopJCRE7dq1q+lUAQAmYPEy90dfDwB1Xd1YvMyphfWuXbvUv39/2+PExERJ0pgxY7RmzRonZQUAAIxCXw8AqAucWlj369dPVmvV70B65MgR85IBANQ4I66R5hpr10ZfDwB1HSPWAACYiqngAAC4u1IZ01u79kfpLrsqOAAAAAAAtQEj1gAApzHiM2zX/vwaAIC6zqibY7p2j09hDQBwGq6xBgDA3XGNtXv5RlJ9A+O12WNgsF/4ZRkfU5J0tpbEzDEhpq8JMU+aEPOCCTElc17//loSM9CEmGZ9nUz42d9rYKxzBsYCzPTBB4aGC/pmu6HxJOnWW3sbHlOS0tN7mBD1qOER/58eMzym9JXhEZdps+ExzWLGXyVP6VETot5QK2LGxhoeUpIU/L0JtUN6unGxqrHIJCpWdwprAIDLKZHji3249ufXAADUdYxYAwBgKgprAADcXd0orFkVHAAAAAAABzBiDQBwGhYvAwDA3dWNEWsKawCA0zAVHAAAd1c3brfFVHAAAAAAABzAiDUAwGmYCg4AgLsrlTHzy1y7x6ewBgA4jRFdrWt3swAA1HV14xprpoIDAAAAAOAARqwBAE5TIsliQAwAAOCq6saINYU1AMBpuMYaAAB3VzcKa6aCAwAAAADgAEasAQBOw1RwAADcXd0YsaawBgA4DYU1AADuzogLv8riuC6mggMAAAAA4ABGrAEATsPiZQAAuLtSGTO/zLV7fAprAIDTMBUcAAB3VzeusWYqOACgTlqxYoVat24tX19fRUVFaceOHZdtv2HDBrVv316+vr7q0qWL3nvvPbvjVqtVs2bNUmhoqPz8/BQTE6ODBw/atTl16pRGjRolf39/BQYGKj4+XmfPnrVr88UXX+iWW26Rr6+vwsPDtWDBArvja9askcVisdt8fX0deCcAAICjKKwBAE5j1a/Twa92s17FedevX6/ExETNnj1be/bsUbdu3RQbG6vjx49X2H779u0aOXKk4uPj9dlnnykuLk5xcXHat2+frc2CBQu0dOlSpaSkKCsrSw0aNFBsbKzOnz9vazNq1Cjt379f6enp2rx5s7Zt26YJEybYjufn5+u2225Tq1attHv3bj333HN68skntWrVKrt8/P399cMPP9i2o0ePXsW7AABATSgxcHNdbj8V3Gq9+CdX/s8GB843OJ4kFReZEFSSjH7xknT+yk2q7ZwJMc24FsOM99OM1y5JZ6/cpNrMyNWM7/1CE2JeMCGmdHWl4RUY+GXK/yVW2e9TIzlrYtjixYs1fvx4jRs3TpKUkpKid999V6tXr9a0adPKtV+yZIkGDBigqVOnSpLmzp2r9PR0LV++XCkpKbJarUpOTtaMGTM0ZMgQSdJrr72m4OBgbdq0SSNGjNDXX3+ttLQ07dy5Uz179pQkLVu2TIMGDdLChQsVFham1NRUFRUVafXq1fL29lanTp20d+9eLV682K4At1gsCgkJuYpX7p5sfb3RgQsKjI6o8+fN+ANCkopNiGnG73szfjcb/9rN6OnNYsY7ak5UM76fjP95On/e0/CYkpR/1oS/yQzsl/N/iWVGX19XVgV3+8L6zJkzkqTwiU5OpEr+4uwEAKBSZ86cUUBAgLPTqFR+vv0fWD4+PvLx8SnXrqioSLt379b06dNt+zw8PBQTE6PMzMwKY2dmZioxMdFuX2xsrDZt2iRJOnz4sHJychQTE2M7HhAQoKioKGVmZmrEiBHKzMxUYGCgraiWpJiYGHl4eCgrK0t//OMflZmZqd/97nfy9va2O8/8+fP1008/qXHjxpKks2fPqlWrViotLdUNN9ygZ555Rp06dariO+V+bH290YFvu83oiKglHnN2Ak633NkJOM2TT9auuEZz9b7elbl9YR0WFqbs7Gw1atRIFkvlS+Tk5+crPDxc2dnZ8vf3r8EMq6+25EqexqstuZKnsZydp9Vq1ZkzZxQWFmZ4bCNHrMPD7cuq2bNn68kK/pI5efKkSkpKFBwcbLc/ODhY33zzTYXnyMnJqbB9Tk6O7XjZvsu1adasmd1xLy8vBQUF2bWJiIgoF6PsWOPGjdWuXTutXr1aXbt2VV5enhYuXKjevXtr//79atGiRYX5u7uq9vWS83+eqoo8jVdbciVPY9WWPCXn5mpmX19XFi9z+8Law8OjWn9o+Pv7u/wPXZnakit5Gq+25EqexnJmnmZ9el0qx1cFL5sYdukfIhWNVruD6OhoRUdH2x737t1bHTp00Isvvqi5c+c6MTPnqW5fL/Fzb7TakqdUe3IlT2PVljwl5+Vq3kh13SisWbwMAOAWyv4QKdsqK6ybNGkiT09P5ebm2u3Pzc2t9LrlkJCQy7Yv+/dKbS5dHO3ChQs6deqUXZuKYvz2HJeqV6+eunfvrkOHDlV4HAAAmI/CGgDgNM5YI9Tb21s9evRQRkaGbV9paakyMjLsRoJ/Kzo62q69JKWnp9vaR0REKCQkxK5Nfn6+srKybG2io6N1+vRp7d6929Zmy5YtKi0tVVRUlK3Ntm3bVFxcbHeedu3a2a6vvlRJSYm+/PJLhYaGVudtAACghtSNVcEprH/h4+Oj2bNn14qpg7UlV/I0Xm3JlTyNVVvyvBrO6mYTExP10ksv6dVXX9XXX3+tiRMnqqCgwLZK+OjRo+0WN5s8ebLS0tK0aNEiffPNN3ryySe1a9cuJSQkSLq4SveUKVM0b948vfPOO/ryyy81evRohYWFKS4uTpLUoUMHDRgwQOPHj9eOHTv0r3/9SwkJCRoxYoTtmrY//elP8vb2Vnx8vPbv36/169dryZIldgunzZkzR++//77+85//aM+ePbr33nt19OhRPfDAA1fxTtQ9teXniTyNV1tyJU9j1ZY8pdqVa/WUypje3rVXBbdYzVlTHQCASuXn5ysgIEDXSXL0xiYlkg5JysvLq9Y1acuXL9dzzz2nnJwcRUZGaunSpbaR4379+ql169Zas2aNrf2GDRs0Y8YMHTlyRG3bttWCBQs0aNAg23Gr1arZs2dr1apVOn36tPr06aMXXnhB119/va3NqVOnlJCQoL///e/y8PDQ0KFDtXTpUjVs2NDW5osvvtCkSZO0c+dONWnSRA899JAef/xx2/FHHnlEb7/9tm0xsx49emjevHnq3r179d88AABMUtbXS+MleV+peRUUSXqp2v19TaGwBgDUuLLOto2MKaz/o+oX1gAAwDy/FtbxMq6w/ovL9vduvyo4AMB1GTGpy7UnhgEAUNexKjgAAAAAALgCRqwBAE7DiDUAAO6OEes6ZcWKFWrdurV8fX0VFRWlHTt2ODslO0lJSbrxxhvVqFEjNWvWTHFxcTpw4ICz07qiZ5991rZariv6/vvvde+99+qaa66Rn5+funTpol27djk7LTslJSWaOXOmIiIi5Ofnp2uvvVZz586VKyyPsG3bNt1xxx0KCwuTxWLRpk2b7I5brVbNmjVLoaGh8vPzU0xMjA4ePOhSeRYXF+vxxx9Xly5d1KBBA4WFhWn06NE6duyYS+V5qQcffFAWi0XJyck1lp8Z3P/mG3Alrt7XS/T3ZqCvdwx9fc3leSl36etrw+227rzzTrVs2VK+vr4KDQ3VfffdV+3vDwprSevXr1diYqJmz56tPXv2qFu3boqNjdXx48ednZrNRx99pEmTJunTTz9Venq6iouLddttt6mgoMDZqVVq586devHFF9W1a1dnp1Khn376STfffLPq1aunf/zjH/rqq6+0aNGiSu8V6yzz58/XypUrtXz5cn399deaP3++FixYoGXLljk7NRUUFKhbt25asWJFhccXLFigpUuXKiUlRVlZWWrQoIFiY2N1/vx5l8nz3Llz2rNnj2bOnKk9e/bo7bff1oEDB3TnnXfWaI5XyvO3Nm7cqE8//dR2iyYAV1Yb+nqJ/t5o9PWOo6+vuTx/i76+ZvXv319vvPGGDhw4oLfeekv//ve/dffdd1cviBXWXr16WSdNmmR7XFJSYg0LC7MmJSU5MavLO378uFWS9aOPPnJ2KhU6c+aMtW3bttb09HRr3759rZMnT3Z2SuU8/vjj1j59+jg7jSsaPHiw9f7777fbd9ddd1lHjRrlpIwqJsm6ceNG2+PS0lJrSEiI9bnnnrPtO336tNXHx8f6f//3f07I8KJL86zIjh07rJKsR48erZmkKlBZnv/973+tzZs3t+7bt8/aqlUr6/PPP1/juRkhLy/PKskaIlnDHNxCJKska15enrNfFlxYbezrrVb6e0fR1xuLvt5YdaWvl4ZbpfsM2IZbJVmzs7OteXl5tu38+fOG5/63v/3NarFYrEVFRVV+Tp0fsS4qKtLu3bsVExNj2+fh4aGYmBhlZmY6MbPLy8vLkyQFBQU5OZOKTZo0SYMHD7Z7X13NO++8o549e2rYsGFq1qyZunfvrpdeesnZaZXTu3dvZWRk6Ntvv5Ukff755/rkk080cOBAJ2d2eYcPH1ZOTo7d90BAQICioqJc+mdLuvjzZbFYFBgY6OxU7JSWluq+++7T1KlT1alTJ2enY4hSgzbgcmprXy/R3zuKvt5c9PXGc8e+/mJPbcQ08Is9fnh4uAICAmxbUlKSodmeOnVKqamp6t27t+rVq1fl59X5xctOnjypkpISBQcH2+0PDg7WN99846SsLq+0tFRTpkzRzTffrM6dOzs7nXLWrVunPXv2aOfOnc5O5bL+85//aOXKlUpMTNQTTzyhnTt36uGHH5a3t7fGjBnj7PRspk2bpvz8fLVv316enp4qKSnR008/rVGjRjk7tcvKycmRpAp/tsqOuaLz58/r8ccf18iRI13uHonz58+Xl5eXHn74YWenAtQqtbGvl+jvjUBfby76euPR119Zdna23dfNx8fHkLiPP/64li9frnPnzummm27S5s2bq/X8Ol9Y10aTJk3Svn379Mknnzg7lXKys7M1efJkpaeny9fX19npXFZpaal69uypZ555RpLUvXt37du3TykpKS7V2b7xxhtKTU3V2rVr1alTJ+3du1dTpkxRWFiYS+XpDoqLi3XPPffIarVq5cqVzk7Hzu7du7VkyRLt2bNHFovF2ekYpkQX53E7ghFruCv6e8fR1+NS9PXOYNT8sosx/P39q/SByLRp0zR//vzLtvn666/Vvn17SdLUqVMVHx+vo0eP6qmnntLo0aO1efPmKn8t6nxh3aRJE3l6eio3N9duf25urkJCQpyUVeUSEhK0efNmbdu2TS1atHB2OuXs3r1bx48f1w033GDbV1JSom3btmn58uUqLCyUp6enEzP8VWhoqDp27Gi3r0OHDnrrrbeclFHFpk6dqmnTpmnEiBGSpC5duujo0aNKSkpy6c627OcnNzdXoaGhtv25ubmKjIx0UlaVK+tojx49qi1btrjcJ9gff/yxjh8/rpYtW9r2lZSU6NFHH1VycrKOHDnivOQcUCrHC2vnr5kLV1fb+nqJ/t4o9PXmoq83lrv29Rc/RjfiCuTqrQr+6KOPauzYsZdt06ZNG9v/mzRpoiZNmuj6669Xhw4dFB4erk8//VTR0dFVOl+dL6y9vb3Vo0cPZWRkKC4uTtLFTzczMjKUkJDg3OR+w2q16qGHHtLGjRu1detWRUREODulCt1666368ssv7faNGzdO7du31+OPP+4SnWyZm2++udwtTL799lu1atXKSRlV7Ny5c/LwsP9l5OnpqdJS1x6ni4iIUEhIiDIyMmyda35+vrKysjRx4kTnJneJso724MGD+vDDD3XNNdc4O6Vy7rvvvnLXMMbGxuq+++7TuHHjnJQVUDvUlr5eor83Gn29uejrjUVfb6ymTZuqadOmV/Xcsp+9wsLCKj+nzhfWkpSYmKgxY8aoZ8+e6tWrl5KTk1VQUOBS38CTJk3S2rVr9be//U2NGjWyXbcSEBAgPz8/J2f3q0aNGpW7DqxBgwa65pprXO76sEceeUS9e/fWM888o3vuuUc7duzQqlWrtGrVKmenZueOO+7Q008/rZYtW6pTp0767LPPtHjxYt1///3OTk1nz57VoUOHbI8PHz6svXv3KigoSC1bttSUKVM0b948tW3bVhEREZo5c6bCwsJsf9i6Qp6hoaG6++67tWfPHm3evFklJSW2n6+goCB5e3u7RJ4tW7Ys90dAvXr1FBISonbt2tVYjkYrleToZDdGrFEVtaGvl+jvjUZf7zj6+prL0137emeNWFdVVlaWdu7cqT59+qhx48b697//rZkzZ+raa6+t8mi1JG63VWbZsmXWli1bWr29va29evWyfvrpp85OyY5+uZ3Mpdsrr7zi7NSuyBVvv1Hm73//u7Vz585WHx8fa/v27a2rVq1ydkrl5OfnWydPnmxt2bKl1dfX19qmTRvr//t//89aWFjo7NSsH374YYXfl2PGjLFarRdvwzFz5kxrcHCw1cfHx3rrrbdaDxw44FJ5Hj58uNKfrw8//NBl8qyIO9yCo6FkbeTg1pDbbaGKXL2vt1rp781AX+8Y+vqay7Mi7tDXS4Os0hADtkGm9PdffPGFtX///tagoCCrj4+PtXXr1tYHH3zQ+t///rdacSxWq5UP+wEANSo/P18BAQFqKGNGrM/q4q1TXO16OQAA6qqyvl4aJKnqt62qXLGk91y2v2cqOADAaUrEVHAAANxbkYxZFfyCATHMQ2ENAKhx3t7eCgkJMew+pyEhITV6jRwAALi8X/v6DwyL6cr9PVPBAQBOcf78eRUVFRkSy9vb26XvpQsAQF1kZF8vuXZ/T2ENAAAAAIADjFj3HAAAAACAOovCGgAAAAAAB1BYAwAAAADgAAprAAAAAAAcQGENAAAAAIADKKwBAAAAAHAAhTUAAAAAAA6gsAac4MSJEwoJCdEzzzxj27d9+3Z5e3srIyPDiZkBAACj0N8DdYfFarVanZ0EUBe99957iouL0/bt29WuXTtFRkZqyJAhWrx4sbNTAwAABqG/B+oGCmvAiSZNmqQPPvhAPXv21JdffqmdO3fKx8fH2WkBAAAD0d8D7o/CGnCin3/+WZ07d1Z2drZ2796tLl26ODslAABgMPp7wP1xjTXgRP/+97917NgxlZaW6siRI85OBwAAmID+HnB/jFgDTlJUVKRevXopMjJS7dq1U3Jysr788ks1a9bM2akBAACD0N8DdQOFNeAkU6dO1ZtvvqnPP/9cDRs2VN++fRUQEKDNmzc7OzUAAGAQ+nugbmAqOOAEW7duVXJysl5//XX5+/vLw8NDr7/+uj7++GOtXLnS2ekBAAAD0N8DdQcj1gAAAAAAOIARawAAAAAAHEBhDQAAAACAAyisAQAAAABwAIU1AAAAAAAOoLAGAAAAAMABFNYAAAAAADiAwhoAAAAAAAdQWAMAAAAA4AAKawAAAAAAHEBhDQAAAACAAyisAQAAAABwwP8HO94yLteOrIcAAAAASUVORK5CYII=", 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" ] @@ -400,7 +404,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 49, "metadata": {}, "outputs": [ { @@ -415,7 +419,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "tal.resources progress: 100%|██████████| 2/2 [00:00<00:00, 23.80it/s] " + "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:00<00:00, 64.10it/s]\n", + "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:00<00:00, 61.18it/s]\n", + "tal.resources progress: 100%|██████████| 2/2 [00:00<00:00, 579.08it/s]\n" ] }, { @@ -423,7 +429,6 @@ "output_type": "stream", "text": [ "tal.reconstruct.utils: Assuming that volume_xyz is X_Y_Z_3\n", - "tal.reconstruct.bp: You have specified a camera_system with a projector and a projector_focus, but your data only contains one illumination point. Thus, you will not be able to implement the projector i.e. focus the illumination aperture anywhere on the scene.\n", "tal.resources: Using 2 CPU processes and downscale 2.\n" ] }, @@ -431,8 +436,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\n", - "tal.resources progress: 100%|██████████| 2/2 [00:00<00:00, 141.21it/s]\n" + "tal.resources progress: 100%|██████████| 2/2 [00:00<00:00, 22919.69it/s]\n" ] } ], @@ -445,12 +449,12 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 50, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -465,18 +469,18 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ "import tal\n", "\n", - "data = tal.io.read_capture('/media/pleiades/vault/projects/202310-cascaded/data/tal-tests/goal0-test.hdf5')" + "data = tal.io.read_capture('confocal-scene.hdf5')" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 52, "metadata": {}, "outputs": [ { @@ -491,7 +495,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "tal.resources progress: 100%|██████████| 4/4 [00:19<00:00, 4.93s/it] " + "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:00<00:00, 107.59it/s]\n", + "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:00<00:00, 103.87it/s]\n", + "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:00<00:00, 127.02it/s]\n", + "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:00<00:00, 130.58it/s]\n", + "tal.resources progress: 100%|██████████| 4/4 [00:00<00:00, 958.92it/s]\n" ] }, { @@ -506,8 +514,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\n", - "tal.resources progress: 100%|██████████| 4/4 [00:04<00:00, 1.05s/it]\n" + "tal.resources progress: 100%|██████████| 4/4 [00:00<00:00, 37957.50it/s]\n" ] } ], @@ -518,12 +525,12 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 53, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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" ] diff --git a/examples/render-reconstruct-confocal/pf_dev.ipynb b/examples/render-reconstruct-confocal/pf_dev.ipynb deleted file mode 100644 index ab8c1b7..0000000 --- a/examples/render-reconstruct-confocal/pf_dev.ipynb +++ /dev/null @@ -1,736 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "ename": "AssertionError", - "evalue": "data.H_format must be T_Lx_Ly_Sx_Sy. It is not yet implemented for T_Li_Si.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m/media/pleiades/vault/projects/202206-tal/code/examples/render-reconstruct-confocal/pf_dev.ipynb Cell 1\u001b[0m line \u001b[0;36m4\n\u001b[1;32m 1\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mtal\u001b[39;00m\n\u001b[1;32m 3\u001b[0m data \u001b[39m=\u001b[39m tal\u001b[39m.\u001b[39mio\u001b[39m.\u001b[39mread_capture(\u001b[39m'\u001b[39m\u001b[39mconfocal-scene.hdf5\u001b[39m\u001b[39m'\u001b[39m) \u001b[39m# you'll need to generate this file using \"tal render confocal-scene\"\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m tal\u001b[39m.\u001b[39;49mreconstruct\u001b[39m.\u001b[39;49mcompensate_laser_cos_dsqr(data)\n", - "File \u001b[0;32m~/.pyenv/versions/3.11.3/envs/mitsuba3-py3.11/lib/python3.11/site-packages/tal/reconstruct/__init__.py:64\u001b[0m, in \u001b[0;36mcompensate_laser_cos_dsqr\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m 60\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 61\u001b[0m \u001b[39mCompensate for the cos decay and 1/d^2 decay of the laser signal (operates in place)\u001b[39;00m\n\u001b[1;32m 62\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 63\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mnumpy\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mnp\u001b[39;00m\n\u001b[0;32m---> 64\u001b[0m \u001b[39massert\u001b[39;00m data\u001b[39m.\u001b[39mH_format \u001b[39m==\u001b[39m HFormat\u001b[39m.\u001b[39mT_Lx_Ly_Sx_Sy, \u001b[39m'\u001b[39m\u001b[39mdata.H_format must be T_Lx_Ly_Sx_Sy. It is not yet implemented for T_Li_Si.\u001b[39m\u001b[39m'\u001b[39m\n\u001b[1;32m 65\u001b[0m nlx, nly, \u001b[39m=\u001b[39m data\u001b[39m.\u001b[39mlaser_grid_xyz\u001b[39m.\u001b[39mshape[:\u001b[39m2\u001b[39m]\n\u001b[1;32m 66\u001b[0m \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(nlx):\n", - "\u001b[0;31mAssertionError\u001b[0m: data.H_format must be T_Lx_Ly_Sx_Sy. It is not yet implemented for T_Li_Si." - ] - } - ], - "source": [ - "import tal\n", - "\n", - "data = tal.io.read_capture('confocal-scene.hdf5') # you'll need to generate this file using \"tal render confocal-scene\"\n", - "tal.reconstruct.compensate_laser_cos_dsqr(data)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "volume_xyz = tal.reconstruct.get_volume_project_rw(data, depths=[1.0,])" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tal.reconstruct.utils: Assuming that volume_xyz is X_Y_Z_3\n", - "tal.reconstruct.utils: Optimizing for projector convolutions.\n", - "tal.reconstruct.utils: Optimizing for camera convolutions.\n", - "tal.reconstruct.pf_dev: Using 50 wavelengths from 0.1664m to 0.5060m\n", - "tal.reconstruct.pf_dev: When projector_focus is not set, the projector convolution optimization is not implemented. Falling back to default method.\n", - "tal.reconstruct.pf_dev: projector_focus_mode=confocal\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - } - ], - "source": [ - "H_1_pf = tal.reconstruct.pf_dev.solve(data, wl_mean=0.25, wl_sigma=0.25, volume_xyz=volume_xyz, camera_system=tal.enums.CameraSystem.DIRECT_LIGHT)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "tal.plot.amplitude_phase(H_1_pf, title='Reconstruction')" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tal.reconstruct.utils: Assuming that volume_xyz is X_Y_Z_3\n", - "tal.reconstruct.utils: Optimizing for projector convolutions.\n", - "tal.reconstruct.utils: Optimizing for camera convolutions.\n", - "tal.reconstruct.pf_dev: Using 50 wavelengths from 0.1664m to 0.5060m\n", - "tal.reconstruct.pf_dev: When projector_focus is not set, the projector convolution optimization is not implemented. Falling back to default method.\n", - "tal.reconstruct.pf_dev: projector_focus_mode=confocal\n", - "tal.resources: Using 2 CPU processes and downscale 2.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tal.resources progress: 100%|██████████| 2/2 [00:42<00:00, 21.42s/it] \n", - " \r" - ] - } - ], - "source": [ - "with tal.resources(cpu_processes=2, downscale=2):\n", - " H_1_pf = tal.reconstruct.pf_dev.solve(data, wl_mean=0.25, wl_sigma=0.25, volume_xyz=volume_xyz, camera_system=tal.enums.CameraSystem.DIRECT_LIGHT)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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RhYWFJr1qILS1b99ejz/+uLZs2aLNmzfrkksu0TXXXKNPP/3UtHMyTRqWlZeXp+uvv15RUVG66aabtHDhQm3atEnnnXdeleMqKio0adIkfffdd3riiSc0cuRIXXLJJSooKNDUqVO1c+dOzZ8/X/fdd58WLVrk8/wdO3Zo1KhRmjBhgjIyMrR48WKNGDFCq1ev1mWXXVarXFu3bq2FCxfqrrvu0nXXXafrr79eknTuuef69Zo9Ho+uueYavf/++5owYYLOOeccrVy5UhkZGVWO/fTTTzVo0CC1a9dO06ZNU+PGjfXXv/5V1157rV555RVdd911fp0bqD2mSQOwtv/+97+SpJYtW3r3TZo0Sc2bN9esWbO0e/du5eTkKDMzUytWrPAes2TJEjVp0kRZWVlq0qSJ1qxZo5kzZ6q0tFRPPvmkpFMLDKWnp6u8vFyTJk1SQkKC9u3bp1WrVunw4cOKi4uTJD366KOaMWOGRo4cqTvuuEMHDx7U/Pnz9etf/1offfSRmjVrVn//IYCfgjFNetiwYT6PH330US1cuFAbNmxQjx49AsymejTDsKQtW7boiy++0Pz58yVJF154odq3b6+8vLwqzfC+ffu0Y8cOb/FxuVzKzs7WDz/8oM2bN6tBg1M/5gcPHlReXp4WLlzoM735yy+/1CuvvOJtYG+//XZ169ZNU6dOrXUz3LhxYw0fPlx33XWXzj33XN1yyy11et1vvPGG1q1bpyeeeEL333+/JOmuu+7SkCFDqhw7efJkdejQQZs2bfK+nrvvvlsXXnihpk6dSjMME7kUeDPrOvMhAFBLJSUlOnTokI4fP65///vfeuihhxQTE6OrrrpK7777rqRTjfE//vEPORwOSZLb7dYf/vAHlZSUeN9DLFu2TDExMd64EyZM0IQJE/Tss8/qkUcekdPp1GeffaZdu3bpb3/7m4YPH+49dubMmd5/79mzR7NmzdIjjzyi//f//p93//XXX6++ffvq2Wef9dkPWI2RzXBpaanPfqfTecZLDV0ul/72t7+prKxMAwcODDCTmjFNGpaUl5en+Ph4bxPocDg0atQoLV++vMpUpREjRniLmCTvytO33HKLtxGu3F9RUaF9+/b5PD8xMdGncYyNjdWYMWP00UcfGXY/1dp666231KBBA911113efZGRkZo0aZLPcd99953WrFmjkSNH6siRIzp06JAOHTqkb7/9Vunp6dqxY0eV1wkAQLhKS0tT69atlZSUpBtvvFFNmjTRypUr1a5dO+8xd955p7cRlqSLLrpILpdLe/bs8e77eSNcWV8vuugiHTt2TF988YUked9zvPPOOzp27Fi1+bz66qtyu90aOXKkt0YfOnRICQkJ6tKli9auXWvo6wesLCkpSXFxcd4tOzu7xmM//vhjNWnSRE6nUxMmTNDKlSvVvXt303JjZBiW43K5tHz5cg0ZMsTnWp/U1FQ99dRTys/P1+WXX+7d36FDB5/nVxappKSkavd///33PvvPOussn+IoSV27dpV06lrghISEAF9R7e3Zs0dt27ZVkyZNfPafffbZPo937twpj8ejGTNmaMaMGdXGOnDggM+bAMA4TJMGYC0LFixQ165d1aBBA8XHx+vss89WRITvmM8v3y80b95cku/7gk8//VQPPPCA1qxZU2U0q/J64JSUFGVlZWnevHnKy8vTRRddpKuvvlq33HKL973Gjh075PF41KVLl2rzbdiwYWAvGDCZkSPDhYWFPrdUPN2o8Nlnn61t27appKREL7/8sjIyMvTee++Z1hDTDMNy1qxZo2+++UbLly/X8uXLq3w9Ly/PpxmOjIysNk5N+z0ejzGJ+sHhcFR73l+OctdW5S2d7rvvPqWnp1d7jJVuQ4VwQzMMwFoGDBig/v37n/aYM70vOHz4sC6++GLFxsbqoYceUufOnRUdHa2tW7dq6tSpPrdTfOqppzR27Fi9/vrr+sc//qF77rlH2dnZ2rBhg9q3by+32y2Hw6G333672vP+8kNvwGqMbIYrV4eujaioKO972H79+mnTpk36/e9/r+eeey7AbKpHMwzLycvLU5s2bbRgwYIqX3v11Ve1cuVK5ebmGna+ylHWn48Of/nll5JUZaXn0/nl6PLPNW/eXF999VWV/T+fmiVJHTt2VH5+vo4ePepTKLdv3+5zXKdOnSSd+mQ5LS2t1jkCAIDqFRQU6Ntvv9Wrr76qX//61979P5+l9nO9evVSr1699MADD+iDDz7QoEGDlJubq0ceeUSdO3eWx+NRSkqKd7YZAP+53W6Vl5ebFp9rhmEpP/zwg1599VVdddVVGj58eJUtMzNTR44cMfT2Qfv379fKlSu9j0tLS/Xiiy+qT58+fk2RbtSokSRVexukzp0764svvvC5hcN//vMf/fvf//Y57je/+Y1OnjyphQsXeve5XC7vQmKV2rRpo8GDB+u5557TN998U+V8v7xVBGAs7jMMIPxUjuD+fCZXRUWFnn32WZ/jSktLdfKk79+wXr16KSIiwvum/frrr1dkZKRmz55dZWaYx+PRt99+a8ZLAAwTjPsMT58+XevWrdPu3bv18ccfa/r06SooKNDo0aONeEnVYmQYlvLGG2/oyJEjuvrqq6v9+vnnn6/WrVsrLy/Pu1BWoLp27arbb79dmzZtUnx8vBYtWqTi4mItXrzYrzgxMTHq3r27VqxYoa5du6pFixbq2bOnevbsqdtuu03z5s1Tenq6br/9dh04cEC5ubnq0aOHzzVJw4YN06BBgzRt2jTt3r3be8/jX963UDp1fdSFF16oXr16afz48erUqZOKi4u1fv16ff311/rPf/4T8P8NUD2XAl8NmtWkAVjLBRdcoObNmysjI0P33HOPHA6HXnrppSrN7Jo1a5SZmakRI0aoa9euOnnypF566SVFRkbqhhtukHTqQ/BHHnlE06dP1+7du3XttdeqadOm2rVrl1auXKk777xT9913XzBeJlArDgU+alrznMnqHThwQGPGjNE333yjuLg4nXvuuXrnnXdqfXeXuqAZhqXk5eUpOjq6xh/6iIgIXXnllcrLyzPsU9UuXbpo/vz5uv/++7V9+3alpKRoxYoVNV6Lezp//OMfNWnSJP3ud79TRUWFZs2apZ49e+qcc87Riy++qJkzZyorK0vdu3fXSy+9pGXLlqmgoMDn9b3xxhuaMmWK/vznP8vhcOjqq6/WU089pb59+/qcq3v37tq8ebNmz56tJUuW6Ntvv1WbNm3Ut29fn9s7AACAM2vZsqVWrVqle++9Vw888ICaN2+uW265RZdeeqnPe4LevXsrPT1df//737Vv3z41atRIvXv31ttvv63zzz/fe9y0adPUtWtXPf3005o9e7akU4t7Xn755TV+6A/Y2Z/+9Kd6P6fDE4zVhACLSE5OVs+ePbVq1apgpwJYXmlpqeLi4lRSslmxsYEt/lJaelRxcf1VUlJS60U1AACAuSpr/ROSYs549On9IOl/JEvXekaGAQB+YjVpAADCWeSPW6AxrI4FtAAAAAAAtsPIMADAT4wMAwAQzoy8z7CV0QzD1nbv3h3sFIAQRDMMAEA4s0szHAo5AgAg6dQtxZKTkxUdHa3U1FRt3LjxtMcfPnxYEydOVNu2beV0OtW1a1e99dZb9ZQtAACwMkaGAQB+cinwkV3/7zO8YsUKZWVlKTc3V6mpqcrJyVF6erq2b9+uNm3aVDm+oqJCl112mdq0aaOXX35Z7dq10549e9SsWbMAcwcAILzZZWTYcs2w2+3W/v371bRpUzkc/t6qGQAgSR6PR0eOHFFiYqIiIowuR8GZJj1v3jyNHz9e48aNkyTl5ubqzTff1KJFizRt2rQqxy9atEjfffedPvjgAzVs2FDSqdupwRqo9wAQGDNrPc1wkOzfv19JSUnBTgMAwkJhYaHat28f7DRqVFpa6vPY6XTK6XRWOa6iokJbtmzR9OnTvfsiIiKUlpam9evXVxv7jTfe0MCBAzVx4kS9/vrrat26tW6++WZNnTpVkZGhcMOH8Ea9BwBjWL3WW5nlmuGmTZtKkqIlGfk5cbSBsSq1MyGmWXFbmRCzuQkxm5gQs6EJMc1ywoSYP5gQ8zsTYh4wIeZeE2JKUpEJMY8bHM/zY8zKv6nGMm5k+JfN0KxZs/Tggw9WOfrQoUNyuVyKj4/32R8fH68vvvii2jN89dVXWrNmjUaPHq233npLO3fu1N13360TJ05o1qxZAeaPQP30s/kbGfmX+gW9blisSma8h5Akt0lxjWbG6E4ojBiFGjN+nuz8MypJFSbEvF3XGBjthKS3TKn1jAwHSeVUKYeMbYbNmIBl1riCGc1blAkxq47dBM6MNxyh1Ayb8TNlRiEz4+fJjO+TWb+jZvw9MWuSqDnTT41rhgsLCxUbG+vdW92ocF253W61adNGzz//vCIjI9WvXz/t27dPTz75JM2wBfz0s9lQRv4FaGRYpJ/QDIdGTLujGTaeOY2S8e94zKj1NMMAAJgsNjbWpxmuSatWrRQZGani4mKf/cXFxUpISKj2OW3btlXDhg19pkSfc845KioqUkVFhaKizPhYBwAAhIpQaNgBAJZy0qCt9qKiotSvXz/l5+d797ndbuXn52vgwIHVPmfQoEHauXOn3O6fxja+/PJLtW3blkYYAIDTcBi0WR3NMADAT5W3Vgpk8//WSllZWXrhhRe0dOlSff7557rrrrtUVlbmXV16zJgxPgts3XXXXfruu+80efJkffnll3rzzTf12GOPaeLEiXV94QAA2EKETl1uFsgWCo2maTkuWLBAycnJio6OVmpqqjZu3GjWqQAANjBq1CjNnTtXM2fOVJ8+fbRt2zatXr3au6jW3r179c0333iPT0pK0jvvvKNNmzbp3HPP1T333KPJkydXexsm1A21HgAQyky5ZnjFihXKyspSbm6uUlNTlZOTo/T0dG3fvl1t2rQx45QAgHoTnPsMS1JmZqYyMzOr/VpBQUGVfQMHDtSGDRvqdC6cHrUeAMKXXRbQMiXHefPmafz48Ro3bpy6d++u3NxcNWrUSIsWLTLjdACAelX/1wzDeqj1ABC+IgzarM7wHCsqKrRlyxalpaX9dJKICKWlpWn9+vVGnw4AANQzaj0AIBwYPk360KFDcrlc3mu4KsXHx+uLL76ocnx5ebnKy8u9j0tLS41OCQBgqOBNk4Y1+FvrJeo9AIQSpknXk+zsbMXFxXm3pKSkYKcEADgtpknDf9R7AAgdTJOuo1atWikyMlLFxcU++4uLi5WQkFDl+OnTp6ukpMS7FRYWGp0SAAAwkL+1XqLeAwCsx/BmOCoqSv369VN+fr53n9vtVn5+vgYOHFjleKfTqdjYWJ8NAGBlwbnPMKzD31ovUe8BIJTYZWTYlFsrZWVlKSMjQ/3799eAAQOUk5OjsrIyjRs3zozTAQDqlUuBN7M0w6GOWg8A4csu1wyb0gyPGjVKBw8e1MyZM1VUVKQ+ffpo9erVVRbaAAAAoYlaDwAIdaY0w5KUmZmpzMxMs8IDAIKG1aRxCrUeAMKT48ct0BhWZ1ozDAAIVzTDAACEs8gft0BjWF0oTOUGAAAAAMBQjAwDAPxUuZp0oDEAAIAVORT4qCnTpAMQLWP/A5sZGKtSCxNiSlKiCTGTTIhpRp5NTYgZbUJMsxw3IeZhE2Ka8X2KMiFmmQkxJemISXGN5JH0g2nRmSYN47yg19XIwHh9DIxVyYy/T5I5vwVmTPmzc0yzuG0c0wxmfe/NeF+Wp5cNi3VM0njDovmyy2rSoZAjAAAAAACGsuzIMADAqhgZBgAgnNllZJhmGADgJ5phAADCmV2a4VDIEQAAAAAAQzEyDADwEyPDAACEM7uMDNMMAwD8xK2VAAAIZ3ZphkMhRwAAAAAADMXIMADATyclRRoQAwAAWJHjxy3QGFZHMwwA8BPNMAAA4SxSgVf6QJ9fH5gmDQAAAACwHUaGAQB+YmQYAIBwZpcFtGiGAQB+YjVpAADCmUOBN7OhcM1wKDTsAAAAAAAYipFhAICfTirwz1KZJg0AgFUxTRoAgGrRDAMAEM7s0gyHQo4AAAAAABiKkWEAgJ8YGQYAIJzZZWSYZhgA4CeXAl8NmtWkAQCwKrs0w6GQIwAAAAAAhmJkGADgJ+4zDABAOHMo8PsEh8J9hi3bDCdIijQwXmsDY1VKNSGmJF1sQsxeJsRs18iEoGZ8o6JNiGnW+/jjJsQ8aHzIveXGx/zE+JCKMyGmZOzfpkpFBsdzSfre4Jg/OanASxzXDOOUaBn7ZzrKwFhmxpQs/CbsF8zIk6mJxnObEDNU/lKH0s+TkX/vzPieV4pU4O93zHi/ZLRQ+tkBAAAAAISh7OxsnXfeeWratKnatGmja6+9Vtu3bzf1nDTDAAA/nTRoAwAAVhRh0OaP9957TxMnTtSGDRv07rvv6sSJE7r88stVVlZmxEuqVqjM0AEAWAbTpAEACGfBWE169erVPo+XLFmiNm3aaMuWLfr1r38dYDbVY2QYAAAAAGApJSUlkqQWLVqYdg5GhgEAfmJkGACAcOZQ4KOmle8USktLffY7nU45nc7TPtftdmvKlCkaNGiQevbsGWAmNWNkGADgp8pbKwWycWslAACsyshrhpOSkhQXF+fdsrOzz3j+iRMn6pNPPtHy5csNfV2/xMgwAAAAAMAUhYWFio2N9T4+06hwZmamVq1apXXr1ql9+/am5mb4yHAwlsQGANQnVpO2O2o9AIQ3I0eGY2NjfbaammGPx6PMzEytXLlSa9asUUpKimmvr5LhzXAwlsQGANQnmmG7o9YDQHgLxq2VJk6cqD//+c9atmyZmjZtqqKiIhUVFemHH34w4iVVy/Bp0sFYEhsAANQfaj0AwGgLFy6UJA0ePNhn/+LFizV27FhTzmn6NcNnWhK7vLxc5eXl3se/XG0MAGA1RozqMjIcTmpz+wvqPQCEDocCv2+Ev8/3eDwBntF/pq4mXZslsbOzs31WF0tKSjIzJQBAwFhNGj+p7e0vqPcAEDoiDdqsztRmuDZLYk+fPl0lJSXerbCw0MyUAACAgWp7+wvqPQDAakybJl3bJbFrc9NlAICVnJQU6FQmRobDgT+3v6DeA0DoqMsCWNXFsDrDm2GPx6NJkyZp5cqVKigoqJclsQEA9Ylm2O6o9QAQ3miG62jixIlatmyZXn/9de+S2JIUFxenmJgYo08HAADqGbUeABAODG/YFy5cqJKSEg0ePFht27b1bitWrDD6VACAoOA+w3ZHrQeA8BaM+wwHgynTpAEA4Yxp0nZHrQeA8OZQ4M1soLdmqg+h0LADACBJWrBggZKTkxUdHa3U1FRt3LixxmOXLFkih8Phs0VHR9djtgAAwMpMW00aABCuXAp8ZNjt9zNWrFihrKws5ebmKjU1VTk5OUpPT9f27dvVpk2bap8TGxur7du3ex87HKHwOTUAAMFllwW0QiFHAICluAza/DNv3jyNHz9e48aNU/fu3ZWbm6tGjRpp0aJFNT7H4XAoISHBu8XHx/t9XgAA7IZrhoOsg6SGBsZLMjBWpYtNiClJV7QwIeiFJsT8lQkxO5oQs6kJMc1yxISY+40P2eFDE2K+Z3zMiBLjY0qSGWGNnrx7QtLnBscMpoqKCm3ZskXTp0/37ouIiFBaWprWr19f4/OOHj2qjh07yu1261e/+pUee+wx9ejRoz5SRi25VZd5AjUzY2k2s94sGfm6zRQqedqdGd8nu3/vzfh7YuT/qd2/P0awbDMMALCqkwr8895TJby0tNRnr9PplNPprHL0oUOH5HK5qozsxsfH64svvqj2DGeffbYWLVqkc889VyUlJZo7d64uuOACffrpp2rfvn2A+QMAEL6YJg0AQLWMu7VSUlKS4uLivFt2drZhWQ4cOFBjxoxRnz59dPHFF+vVV19V69at9dxzzxl2DgAAwhHTpAEAMFlhYaFiY2O9j6sbFZakVq1aKTIyUsXFxT77i4uLlZCQUKtzNWzYUH379tXOnTvrnjAAAAgbodCwAwAsxbiR4djYWJ+tpmY4KipK/fr1U35+vnef2+1Wfn6+Bg4cWKusXS6XPv74Y7Vt29bvVwwAgJ0wMgwAQLVcCnzZDv9vzZSVlaWMjAz1799fAwYMUE5OjsrKyjRu3DhJ0pgxY9SuXTvvVOuHHnpI559/vs466ywdPnxYTz75pPbs2aM77rgjwNwBAAhvdrlmmGYYABASRo0apYMHD2rmzJkqKipSnz59tHr1au+iWnv37lVExE+l9/vvv9f48eNVVFSk5s2bq1+/fvrggw/UvXv3YL0EAABgITTDAAA/nZTkCDCG/yPDkpSZmanMzMxqv1ZQUODz+Omnn9bTTz9dp/MAAGBnjAwDAFCt4DXDAADAfHZphkMhRwAAAAAADMXIMADAT4wMAwAQzhySHI7Aar3DY/1aTzMMAPCPxx14L2v9+ggAgH01aCAF2AzL45FOnjQmH5MwTRoAAAAAYDuMDAMA/ONW4LcZDvT5AADAPDYZGaYZBgD4x/XjFmgMAABgTUY1wxbHNGkAAAAAgO0wMgwA8A8jwwAAhDebjAzTDAMA/MM1wwAAhLfISCkiwEnEbusXe6ZJAwAAAABsh5FhAIB/mCYNAEB4a9DAFiPDNMMAAP8wTRoAgPBGMxxcrSRFGRivvYGxKvUyIaYk6WITYl5lQkwz8uxs5He9khnffbPumfa18SF3mfCHKNH4kGbo9YY5cbeaELPC4vEAGI9r1YzF/6fxQuX/1Kw8Q+X1o+4s2wwDACzKrcCnOVv/w2IAAOyLkWEAAKrBNcMAAIS3yMhTWyBc1i/2jP4DAAAAAGyHkWEAgH9YQAsAgPDWoEHgI8MOhzG5mIhmGADgH6ZJAwAQ3mzSDDNNGgAAAABgO6Y3w48//rgcDoemTJli9qkAAPXBZdCGsEGtB4Aw06CBMZvFmZrhpk2b9Nxzz+ncc8818zQAgPrENcP4GWo9AIQhpkkH5ujRoxo9erReeOEFNW/e3KzTAACAIKHWAwBCmWnN8MSJE3XllVcqLS3ttMeVl5ertLTUZwMAWBjTpPGj2tZ6iXoPACElMjLwKdKBjizXA1OmSS9fvlxbt27Vpk2bznhsdna2Zs+ebUYaAAAzeBT4NGePEYkgmPyp9RL1HgBCSohc8xsow0eGCwsLNXnyZOXl5Sk6OvqMx0+fPl0lJSXerbCw0OiUAACAgfyt9RL1HgBgPYa3+1u2bNGBAwf0q1/9yrvP5XJp3bp1euaZZ1ReXq7Inw2ZO51OOZ1Oo9MAAJiF+wzbnr+1XqLeA0BIscnIsOGv8NJLL9XHH3/ss2/cuHHq1q2bpk6dWqU4AgCA0EKtBwCEA8Ob4aZNm6pnz54++xo3bqyWLVtW2Q8ACEGMDNsetR4AwhwjwwAAVIP7DAMAEN4qV5MOhMf6q2XWSzNcUFBQH6cBAABBQq0HAIQaRoYBAP5hmjQAAOHNiGnSjAwDAMIOzTAAAOHNJs2w4fcZBgAAAADAH+vWrdOwYcOUmJgoh8Oh1157zfRzWnZkuJUkI+9GmGBgrErtzLpd4q/OfIjfLjYhZucBJgQ1I9GOJsQ8aUJMSdpjfMiUfxsf073R+JgmvPR2/zQ+piQlHDM+5ncGxys3OJ4PFtCCgRrI2DcjZryxMevNkhm/BmaMctg5pt3/VIXKqJlZeVr959TU708QRobLysrUu3dv3Xbbbbr++usDO3ctWbYZBgBYlFuBT3O2+ztMAACsLAjN8NChQzV06NDAzuknmmEAAAAAgClKS0t9HjudTjmdZk2x9U+ozH4AAFiF26ANAABYU+V9hgPZIiMlSUlJSYqLi/Nu2dnZQX5xP2FkGADgH1aTBgAgvBkxTdp96pPvwsJCxcbGendbZVRYohkGAAAAAJgkNjbWpxm2EpphAIB/GBkGACC8GTgybGU0wwAA/3BrJQAAwlsQmuGjR49q586d3se7du3Stm3b1KJFC3Xo0CGwXGpAMwwAAAAACKrNmzdryJAh3sdZWVmSpIyMDC1ZssSUc9IMAwD8wzRpAADCWxBGhgcPHiyPn/cmDhTNMADAPzTDAACEt8pbKwXCZf1iz32GAQAAAAC2w8gwAMA/HgW+AFb9zoICAAD+MGKadAiMDNMMAwD8wzRpAADCm02aYaZJAwAAAABsh5FhAIB/uM8wAADhzSYjwzTDAAD/ME0aAIDwZsRq0idPGpOLiZgmDQAAAACwHUaGAQD+YWQYAIDwZsQ06UCfXw8YGQYA+Mdt0FYHCxYsUHJysqKjo5WamqqNGzfW6nnLly+Xw+HQtddeW7cTAwBgJ5XNcKCbxdEMAwBCwooVK5SVlaVZs2Zp69at6t27t9LT03XgwIHTPm/37t267777dNFFF9VTpgAAIBRYtl2PkRRtYLymBsbySjAjqKT2JsTsHGVC0EtDJGZHE2Ka5b8mxDTyN+lHnbcZH7NjhfExWxgfUpKaHjMhpsHxGhocz0eQpknPmzdP48eP17hx4yRJubm5evPNN7Vo0SJNmzat+tO4XBo9erRmz56tf/3rXzp8+HAASSMUmPEpv91HDkLl/zRUYkqhs6B+qPzsm5Wn1X+mTP3+ME0aAIBquPVTQ1zX7cd3gqWlpT5beXl5taesqKjQli1blJaW5t0XERGhtLQ0rV+/vsZUH3roIbVp00a33357IK8YAAB7YZo0AADmSkpKUlxcnHfLzs6u9rhDhw7J5XIpPj7eZ398fLyKioqqfc7777+vP/3pT3rhhRcMzxsAAIQ+67frAABrCWABLJ8YkgoLCxUbG+vd7XQ6Awx8ypEjR3TrrbfqhRdeUKtWrQyJCQCAbRhxn+HISGNyMRHNMADAPwZeMxwbG+vTDNekVatWioyMVHFxsc/+4uJiJSRUXcDhv//9r3bv3q1hw4Z597ndpzrwBg0aaPv27ercuXMALwAAgDDGNcMAAFhDVFSU+vXrp/z8fO8+t9ut/Px8DRw4sMrx3bp108cff6xt27Z5t6uvvlpDhgzRtm3blJSUVJ/pAwAAC7J+uw4AsBYDp0n7IysrSxkZGerfv78GDBignJwclZWVeVeXHjNmjNq1a6fs7GxFR0erZ8+ePs9v1qyZJFXZDwAAfoGR4brbt2+fbrnlFrVs2VIxMTHq1auXNm/ebMapAAD1LdCVpOs4zXrUqFGaO3euZs6cqT59+mjbtm1avXq1d1GtvXv36ptvvgnstaHWqPUAEMZsspq04Rl+//33GjRokIYMGaK3335brVu31o4dO9S8eXOjTwUAsJnMzExlZmZW+7WCgoLTPnfJkiXGJ2RT1HoAQDgwvBmeM2eOkpKStHjxYu++lJQUo08DAAgWAxfQQmii1gNAmLPJatKGT5N+44031L9/f40YMUJt2rRR3759T3uPx/LycpWWlvpsAAALcxu0IWT5W+sl6j0AhBSbTJM2vBn+6quvtHDhQnXp0kXvvPOO7rrrLt1zzz1aunRptcdnZ2crLi7Ou7HCJwAA1uZvrZeo9wAA6zG8XXe73erfv78ee+wxSVLfvn31ySefKDc3VxkZGVWOnz59urKysryPS0tLKZAAYGVuBT7NmZHhkOZvrZeo9wAQUmyymrThGbZt21bdu3f32XfOOefolVdeqfZ4p9Mpp9NpdBoAALME6dZKsA5/a71EvQeAkGKTZtjwadKDBg3S9u3bffZ9+eWX6tixo9GnAgAAQUCtBwCEA8Pb9d/97ne64IIL9Nhjj2nkyJHauHGjnn/+eT3//PNGnwoAEAysJm171HoACHM2GRk2PMPzzjtPK1eu1PTp0/XQQw8pJSVFOTk5Gj16tNGnAgAEA82w7VHrASDM2eTWSqa061dddZWuuuoqM0IDAAALoNYDAEKd9ceuAQDWwgJaAACEN6ZJAwBQDaZJAwAQ3mzSDBu+mjQAAAAAAFZn2Xa94Y+bkfEMF21GUElNzQiaYELMdibENOO2HGeZEDOU7DQhZnvjQzb7yviYjY0PKZnzq2/0EhOmLlnByDAMFCFjP5m3+6f8Zrz+UIkZSkLl9ZuRZyhdJWP1n31Tf45sMjJs/QwBANbiUeDvZjxGJAIAAExhk9WkQ+WDKQAAAAAADMPIMADAP0yTBgAgvDFNGgCAanBrJQAAwptNmmGmSQMAAAAAbMf67ToAwFqYJg0AQHizyciw9TMEAFgLzTAAAOHNJs0w06QBAAAAALZj/XYdAGAtLKAFAEB4s8l9hmmGAQD+YZo0AADhjWnSAAAAAACEJ+u36wAAa3Er8JFdpkkDAGBdNhkZtn6GAABr4ZphAADCm02aYaZJAwAAAABsx/rtOgDAWlhACwCA8MZq0gAAVINp0gAAhDemSQMAAAAAEJ5ohgEA/nEZtAEAAGuqHBkOdKuDBQsWKDk5WdHR0UpNTdXGjRsNfnE/oRkGAPiHZhgAgPAWpGZ4xYoVysrK0qxZs7R161b17t1b6enpOnDggAkvkmYYAAAAAGAB8+bN0/jx4zVu3Dh1795dubm5atSokRYtWmTK+ax/VTMMcjLYCdSS3X8kQ+X1m/DzFEIjhWakavQnk6Z+0skCWoCtmPHrymgMYHEGLqBVWlrqs9vpdMrpdFY5vKKiQlu2bNH06dO9+yIiIpSWlqb169cHlksN+FsEAPCPW4FPkaYZBgDAstyKMGSTpKSkJMXFxXm37Ozsas956NAhuVwuxcfH++yPj49XUVGRKa8zVIahAAAAAAAhprCwULGxsd7H1Y0KBwvNMADAPy4FPq8ohKbFAwBgNydPntoCjSFJsbGxPs1wTVq1aqXIyEgVFxf77C8uLlZCQkJgydSAadIAAP+4DdoAAIAlVTbDgW7+iIqKUr9+/ZSfn+/d53a7lZ+fr4EDBxr8Ck9hZBgAAAAAEHRZWVnKyMhQ//79NWDAAOXk5KisrEzjxo0z5Xw0wwAA/zBNGgCAsGbkNGl/jBo1SgcPHtTMmTNVVFSkPn36aPXq1VUW1TIKzTAAwD/cWgkAgLAWrGZYkjIzM5WZmRnYyWvJ8GuGXS6XZsyYoZSUFMXExKhz5856+OGH5fF4jD4VAAAIAmo9ACAcGD4yPGfOHC1cuFBLly5Vjx49tHnzZo0bN05xcXG65557jD4dAKC+MU3a9qj1ABDeXK7AR4ZdIVDrDW+GP/jgA11zzTW68sorJUnJycn6y1/+oo0bNxp9KgBAMNAM2x61HgDCWzCnSdcnw6dJX3DBBcrPz9eXX34pSfrPf/6j999/X0OHDq32+PLycpWWlvpsAADAuvyt9RL1HgBgPYaPDE+bNk2lpaXq1q2bIiMj5XK59Oijj2r06NHVHp+dna3Zs2cbnQYAwCweBb4AFpeWhjR/a71EvQeAUMLIcB399a9/VV5enpYtW6atW7dq6dKlmjt3rpYuXVrt8dOnT1dJSYl3KywsNDolAICRXAZtCFn+1nqJeg8AoaSyGQ50szrDR4bvv/9+TZs2TTfeeKMkqVevXtqzZ4+ys7OVkZFR5Xin0ymn02l0GgAAwCT+1nqJeg8AsB7Dm+Fjx44pIsJ3wDkyMlJuNzeVBICw4JLkMCAGQha1HgDCG6tJ19GwYcP06KOPqkOHDurRo4c++ugjzZs3T7fddpvRpwIABINbgV8zTM8U0qj1ABDe7HLNsOHN8Pz58zVjxgzdfffdOnDggBITE/Xb3/5WM2fONPpUAAAgCKj1AIBwYHgz3LRpU+Xk5CgnJ8fo0AAAK2CatO1R6wEgvDEyDABAdZgmDQBAWLNLM2z4rZUAADDLggULlJycrOjoaKWmpmrjxo01Hvvqq6+qf//+atasmRo3bqw+ffropZdeqsdsAQCAlTEyDADwT5CmSa9YsUJZWVnKzc1VamqqcnJylJ6eru3bt6tNmzZVjm/RooX+93//V926dVNUVJRWrVqlcePGqU2bNkpPTw/wBQAAEL7sMjJs2Wb4hKRIg+MZ7rgZQSUdMSNokQkx95gQ878mxDTjN9GsX53dJsTcZ0LMr40PacbPvUm/o2b8PTE6pil/8yq5Ffg1v3WYJj1v3jyNHz9e48aNkyTl5ubqzTff1KJFizRt2rQqxw8ePNjn8eTJk7V06VK9//77NMMWYsSs+1/GszMzXr8Z0whD5ftk1hRKM15/qPyfhhKrf5/M/J7b5dZKTJMGAARNaWmpz1ZeXl7tcRUVFdqyZYvS0tK8+yIiIpSWlqb169ef8Twej0f5+fnavn27fv3rXxuWPwAACF2WHRkGAFiUW4FPk/7x4+ykpCSf3bNmzdKDDz5Y5fBDhw7J5XIpPj7eZ398fLy++OKLGk9TUlKidu3aqby8XJGRkXr22Wd12WWXBZg8AADhjWnSAABUx4hpTz/GKCwsVGxsrHe30+k0IPhPmjZtqm3btuno0aPKz89XVlaWOnXqVGUKNQAA+AnNMAAAJouNjfVphmvSqlUrRUZGqri42Gd/cXGxEhISanxeRESEzjrrLElSnz599Pnnnys7O5tmGAAAcM0wAMBPLoM2P0RFRalfv37Kz8/37nO73crPz9fAgQNrHcftdtd4XTIAADilcmQ40M3qGBkGAPjHwGuG/ZGVlaWMjAz1799fAwYMUE5OjsrKyryrS48ZM0bt2rVTdna2JCk7O1v9+/dX586dVV5errfeeksvvfSSFi5cGGDyAACEN7usJk0zDAAICaNGjdLBgwc1c+ZMFRUVqU+fPlq9erV3Ua29e/cqIuKnCU9lZWW6++679fXXXysmJkbdunXTn//8Z40aNSpYLwEAAFgIzTAAwD8GLqDlr8zMTGVmZlb7tYKCAp/HjzzyiB555JG6nQgAABtjAS0AAKoTpGnSAACgftilGWYBLQAAAACA7TAyDADwjxGjuowMAwBgWXYZGaYZBgD4xyXJE2AMmmEAACzLLs0w06QBAAAAALbDyDAAwD9MkwYAIKxxn2EAAKrDNGkAAMIa06QBAAAAAAhTjAwDAPzDyDAAAGHNLiPDNMMAAP9wzTAAAGHNLs0w06QBAAAAALbDyDAAwD9uBT5NOtDnAwAA07CadJD9IGNn0R0xMJbXQTOCSvrahJi7TJiTmPKe8TFN0THYCfhhjwkxTfg+mfHzZMbPfZEJMSUdNiGm0X+jyg2O58MtyRFgDJph/MgtY+u9GTPwzZrVHypXC4RKnqEkVH5OQyWmWUIpV6MxTRoAAAAAgDBl2ZFhAIBFucTIMAAAYcwuI8M0wwAA/9AMAwAQ1uzSDDNNGgAAAABgO4wMAwD8wwJaAACENbuMDNMMAwD8wzRpAADCml1ureT3NOl169Zp2LBhSkxMlMPh0GuvvebzdY/Ho5kzZ6pt27aKiYlRWlqaduzYYVS+AADAZNR6AIAd+N0Ml5WVqXfv3lqwYEG1X3/iiSf0hz/8Qbm5ufrwww/VuHFjpaen6/jx4wEnCwCwAJdBGyyLWg8A9lY5TTrQzer8niY9dOhQDR06tNqveTwe5eTk6IEHHtA111wjSXrxxRcVHx+v1157TTfeeGNg2QIAgs8jpjmHOWo9ANjbyZNSZGTgMazO0NWkd+3apaKiIqWlpXn3xcXFKTU1VevXr6/2OeXl5SotLfXZAACANdWl1kvUewCA9RjaDBcVFUmS4uPjffbHx8d7v/ZL2dnZiouL825JSUlGpgQAMBizpO2tLrVeot4DQCixyzTpoN9nePr06SopKfFuhYWFwU4JAHAaNMOoC+o9AISOytWkA9nCcjXp00lISJAkFRcX++wvLi72fu2XnE6nYmNjfTYAAGBNdan1EvUeAGA9hjbDKSkpSkhIUH5+vndfaWmpPvzwQw0cONDIUwEAgsRt0IbQRK0HgPBnl2nSfq8mffToUe3cudP7eNeuXdq2bZtatGihDh06aMqUKXrkkUfUpUsXpaSkaMaMGUpMTNS1115rZN4AgCAxYppzCMycsjVqPQDY28mTUkSAw6ZmNsOPPvqo3nzzTW3btk1RUVE6fPhwneL43Qxv3rxZQ4YM8T7OysqSJGVkZGjJkiX6n//5H5WVlenOO+/U4cOHdeGFF2r16tWKjo6uU4IAAKB+UesBAFZWUVGhESNGaODAgfrTn/5U5zh+N8ODBw+Wx1PzDSYdDoceeughPfTQQ3VOCgBgXUZMc2aatLVR6wHA3qw+Mjx79mxJ0pIlSwKK43czDACwN6ZJAwAQ3oxshn95X3mn0ymn0xlYcIME/dZKAAAAAIDwlJSU5HOf+ezs7GCn5MXIMADAL24FPrLLNGkAAKyr8j7DgcaQpMLCQp/b6dU0Kjxt2jTNmTPntDE///xzdevWLbDEfsayzfAhSVEGxoszMFalfcdMCCqp3VYTgrY3IaZ7o/ExO282PqYpL94sXxsfcpcJbcd7xofUJuND7i03PqYkHTQh5ncGx6swON7Pcc0wjGT0rbbM+Nky67I3M3I1Y8qfGa/fjDzNiGnW36pQ+Rtoxvc+VH7uJev/PTHzzkUnT0oOR+AxJNX63vL33nuvxo4de9pjOnXqFFhSv2DZZhgAAAAAYA+tW7dW69at6/WcNMMAAL+wgBYAAOHNyJFhM+zdu1ffffed9u7dK5fLpW3btkmSzjrrLDVp0qTWcWiGAQB+oRkGACC8Wb0ZnjlzppYuXep93LdvX0nS2rVrNXjw4FrHYTVpAAAAAEDIWLJkiTweT5XNn0ZYYmQYAOAnFtACACC8WX1k2Cg0wwAAvzBNGgCA8OZyBd4Mu0Kg2DNNGgAAAABgO4wMAwD8wjRpAADCmxFTnJkmDQAIO24FPs2ZZhgAAOuySzPMNGkAAAAAgO0wMgwA8AsLaAEAEN7sMjJMMwwA8AvXDAMAEN6MWAma1aQBAAAAALAgRoYBAH5hmjQAAOHt5EnJ4wksRiiMDNMMAwD8QjMMAEB4s0szzDRpAAAAAIDtMDIMAPALC2gBABDe7DIyTDMMAPAL06QBAAhvdmmGmSYNAAgZCxYsUHJysqKjo5WamqqNGzfWeOwLL7ygiy66SM2bN1fz5s2VlpZ22uMBAIC90AwDAPzi0U9Tpeu61eXD5hUrVigrK0uzZs3S1q1b1bt3b6Wnp+vAgQPVHl9QUKCbbrpJa9eu1fr165WUlKTLL79c+/btq8PZAQCwD5fr1OhwIFsojAxbdpr0IUkNDYwXbWCsSh+bEFOS2q0xIagZP4xfmRCzkwlXEjbda3xMsxwxIebXJsTcZEJME37uPzE+pCRz/ksPGhzvhMHxfi5Y06TnzZun8ePHa9y4cZKk3Nxcvfnmm1q0aJGmTZtW5fi8vDyfx3/84x/1yiuvKD8/X2PGjKlL2ggBdr8e3YzXHyojJ3zvYTQ7/5+ePClFBPjL7w6B/8BQ+fsGAAhDpaWlPlt5eXm1x1VUVGjLli1KS0vz7ouIiFBaWprWr19fq3MdO3ZMJ06cUIsWLQzJHQAAhDaaYQCAX1wGbZKUlJSkuLg475adnV3tOQ8dOiSXy6X4+Hif/fHx8SoqKqpV3lOnTlViYqJPQw0AAKoKdIp05WZ1lp0mDQCwJiNvrVRYWKjY2FjvfqfTGWDk6j3++ONavny5CgoKFB1txoUzAACED7tMk6YZBgAETWxsrE8zXJNWrVopMjJSxcXFPvuLi4uVkJBw2ufOnTtXjz/+uP75z3/q3HPPDShfAAAQPpgmDQDwi5HTpGsrKipK/fr1U35+vnef2+1Wfn6+Bg4cWOPznnjiCT388MNavXq1+vfv7+dZAQCwJ1aTBgCgGsFaTTorK0sZGRnq37+/BgwYoJycHJWVlXlXlx4zZozatWvnve54zpw5mjlzppYtW6bk5GTvtcVNmjRRkyZNAnwFAACEr5MnJYcjsBieutxHsZ75PTK8bt06DRs2TImJiXI4HHrttde8Xztx4oSmTp2qXr16qXHjxkpMTNSYMWO0f/9+I3MGANjQqFGjNHfuXM2cOVN9+vTRtm3btHr1au+iWnv37tU333zjPX7hwoWqqKjQ8OHD1bZtW+82d+7cYL2EkEGtBwDYgd8jw2VlZerdu7duu+02XX/99T5fO3bsmLZu3aoZM2aod+/e+v777zV58mRdffXV2rx5s2FJAwCCx8gFtPyVmZmpzMzMar9WUFDg83j37t11PAuo9QBgb3YZGfa7GR46dKiGDh1a7dfi4uL07rvv+ux75plnNGDAAO3du1cdOnSoW5YAAMtwK/Bp0iGwwKStUesBwN5ohg1SUlIih8OhZs2aVfv18vJylZeXex+XlpaanRIAADDQmWq9RL0HAFiPqatJHz9+XFOnTtVNN91U460zsrOzFRcX592SkpLMTAkAECC3QRvCQ21qvUS9B4BQEuhK0pWb1ZnWDJ84cUIjR46Ux+PRwoULazxu+vTpKikp8W6FhYVmpQQAMEAwbq0Ea6ptrZeo9wAQWtzyeALbQuGjb1OmSVcWxz179mjNmjWn/aTY6XTK6XSakQYAADCJP7Veot4DAKzH8Ga4sjju2LFDa9euVcuWLY0+BQAgiIJ1n2FYB7UeAMKdPaq9383w0aNHtXPnTu/jXbt2adu2bWrRooXatm2r4cOHa+vWrVq1apVcLpeKiookSS1atFBUVJRxmQMAgiKYt1ZC/aDWA4Dd0QxXa/PmzRoyZIj3cVZWliQpIyNDDz74oN544w1JUp8+fXyet3btWg0ePLjumQIAgHpBrQcA2IHfzfDgwYPlOc1No073NQBA6LPHZ8X2Rq0HALuzR7U3/T7DAIDwYo/yCACAndnjoihT7zMMAAAAAIAVMTIMAPCLR4F/1sskWwAArMwe88As2wzvlRRpYLwyA2NVijMhpiSpxPiQvd4wPma7d4yPqQQTYkabENMsZvygfmd8yH3HjI/5H+ND6l8mxJSkL0yIedDgeGaWH3uUR9SXCBk7Tc2MNzZ2n0Znxuu3+/9pqDBjkmso/TxZ/e+Jub9HbgVerZkmDQAAAACA5Vh2ZBgAYE32WFIDAAA7s8c8MJphAIBf7FEeAQCwM3tUe6ZJAwAAAABsh5FhAIBf7PFZMQAAdmaPi6JohgEAfrFHeQQAwM7s8dE306QBAAAAALbDyDAAwC/2+KwYAAA7s0e1pxkGAPjFrcDLG9OkAQCwMns0w0yTBgAAAADYDiPDAAC/sIAWAADhzh7zwGiGAQB+scfEKQAA7MweH30zTRoAAAAAYDs0wwAAv7gN2gAAgFW5DNqMt3v3bt1+++1KSUlRTEyMOnfurFmzZqmiosLvWEyTBgD4hWnSAACEO+tW+y+++EJut1vPPfeczjrrLH3yyScaP368ysrKNHfuXL9i0QwDAAAAAELCFVdcoSuuuML7uFOnTtq+fbsWLlxIMwwAMJd1PysGAADGMK7al5aW+ux1Op1yOp0BxvZVUlKiFi1a+P08rhkGAAAAAJgiKSlJcXFx3i07O9vQ+Dt37tT8+fP129/+1u/nMjIMAPCLPW62AACAnRk3MlxYWKjY2Fjv3ppGhadNm6Y5c+acNuLnn3+ubt26eR/v27dPV1xxhUaMGKHx48f7nSHNMADAL24FXh5phgEAsDKPAq/WHklSbGysTzNck3vvvVdjx4497TGdOnXy/nv//v0aMmSILrjgAj3//PN1ypBmGAAAAAAQVK1bt1br1q1rdey+ffs0ZMgQ9evXT4sXL1ZERN2u/rVsM1wkyWFgvCMGxjJbiQkxt5oQM6Hc+JjN9hgf04wL4yNNiClJx02IacbPfpEJMb82IeYXJsSUpK9MiGn098ljcLyfYwEtGOm4jP077f9dJoPnpAkxzah5oZJnKC2EY8bsGDvHNKuhMePviZHv9cx43/gT61b7ffv2afDgwerYsaPmzp2rgwcPer+WkJDgVyzLNsMAAGvimmEAAMKddZvhd999Vzt37tTOnTvVvn17n695PP4NB4TSh2gAAAAAABsbO3asPB5PtZu/GBkGAPjFup8VAwAAY9ij2tMMAwD8wjRpAADCnT2aYaZJAwAAAABsx+9meN26dRo2bJgSExPlcDj02muv1XjshAkT5HA4lJOTE0CKAAArcRm0wbqo9QBgd26DNmvzuxkuKytT7969tWDBgtMet3LlSm3YsEGJiYl1Tg4AYD00w+GPWg8AdmePau/3NcNDhw7V0KFDT3vMvn37NGnSJL3zzju68sor65wcAACof9R6AIAdGL6Altvt1q233qr7779fPXr0MDo8ACDIPAp84pP/Nz+AlVDrASDcuRX4yK71p0kb3gzPmTNHDRo00D333FOr48vLy1VeXu59XFpaanRKAAAD2WN9SZyOv7Veot4DQGixR7U3dDXpLVu26Pe//72WLFkih8NRq+dkZ2crLi7OuyUlJRmZEgAAMFBdar1EvQcAWI+hzfC//vUvHThwQB06dFCDBg3UoEED7dmzR/fee6+Sk5Orfc706dNVUlLi3QoLC41MCQBgMHssqYGa1KXWS9R7AAgt9lhN2tBp0rfeeqvS0tJ89qWnp+vWW2/VuHHjqn2O0+mU0+k0Mg0AgImMKG/WL4+oSV1qvUS9B4DQYo9p0n43w0ePHtXOnTu9j3ft2qVt27apRYsW6tChg1q2bOlzfMOGDZWQkKCzzz478GwBAIDpqPUAADvwuxnevHmzhgwZ4n2clZUlScrIyNCSJUsMSwwAYE32+KzY3qj1AGB39qj2fjfDgwcPlsdT+5ti7N69299TAAAsjGnS4Y9aDwB2Z49m2NAFtAAAAAAACAWG32cYABDe7PFZMQAAduZW4NXa+vPAGBkGAPilsjwGstW1PC5YsEDJycmKjo5WamqqNm7cWOOxn376qW644QYlJyfL4XAoJyenjmcFAMBu7HFrJZphAEBIWLFihbKysjRr1ixt3bpVvXv3Vnp6ug4cOFDt8ceOHVOnTp30+OOPKyEhoZ6zBQAAVmfZadLHJTmCncQZHDQpbmMTYh43IeZ3JsRsakLMhibENMsJE2IeMSHmYRNiFpsQ06zfUTP+T43+Ha390kf+C9YCWvPmzdP48eO997LNzc3Vm2++qUWLFmnatGlVjj/vvPN03nnnSVK1X4c1jNc1MvIvdZ5eNixWpWjDI55i/TGTU8wYOQmVmGYx43sfKjHNYNb33oz3z6N1vYHRTkj6u4Hxfs4eF0VZthkGAFiTS4G/8agsj6WlpT77nU6nnE5nleMrKiq0ZcsWTZ8+3bsvIiJCaWlpWr9+fYDZAAAAX/ZohkPpQzQAQJhJSkpSXFycd8vOzq72uEOHDsnlcik+Pt5nf3x8vIqKiuojVQAAEGYYGQYA+MXIkeHCwkLFxsZ691c3KgwAAOqbPUaGaYYBAH4x8prh2NhYn2a4Jq1atVJkZKSKi32vLi8uLmZxLAAADGePZphp0gAAy4uKilK/fv2Un5/v3ed2u5Wfn6+BAwcGMTMAABCqGBkGAPjFyGnS/sjKylJGRob69++vAQMGKCcnR2VlZd7VpceMGaN27dp5rzuuqKjQZ5995v33vn37tG3bNjVp0kRnnXVWgK8AAIBwFqx7R9QvmmEAgF+CVR5HjRqlgwcPaubMmSoqKlKfPn20evVq76Jae/fuVUTET236/v371bdvX+/juXPnau7cubr44otVUFAQ4CsAACCcuRX4NGeaYQAADJOZmanMzMxqv/bLBjc5OVkej5l3XAYAAKGMZhgA4Bd7fFYMAICd2WMBLZphAIBfXJIcBsQAAABWZY9mmNWkAQAAAAC2w8gwAMAv9lhfEgAAO7PHyDDNMADAL0yTBgAg3NmjGWaaNAAAAADAdhgZBgD4hZFhAADCnT0uiqIZBgD4xR7lEQAAO7PHjRSZJg0AAAAAsB1GhgEAfmGaNAAA4c4eC2jRDAMA/OJR4BOfPEYkAgAATEIzHBQez6m3SEa/UTLjjZdZ394TJsSsMCFmuQkxG5oQ0/q/hj8x43tvxvfJjJ8nM167Wd97M/6emPU3r/JvKmA1P/1sGvvbf8zQaKeYddWb9a+mO8WMa+pCJaZZzPjeh0pMM5j1vT9uSlQj/+adikWtrzvLNcNHjhyRZPwP3w8Gx5Ok702IKUmfmxQXgP0cOXJEcXFxhsY04kOGUPqQCuaorPfSW4bGHW9oNAAw2t8Nj2hGrbfLcpmWa4YTExNVWFiopk2byuE4/VVppaWlSkpKUmFhoWJjY+spQ/+Rp/FCJVfyNF6o5BrsPD0ej44cOaLExETDY9MMwwi1rffB/l2qrVDJUwqdXMnTeKGSK3nWjpm1nmnSQRIREaH27dv79ZzY2FhL/6JUIk/jhUqu5Gm8UMk1mHka/ykxYBx/6z2/88YLlVzJ03ihkit5nhm1PjCWa4YBANbmVuCrSVt/4hQAAHbGyDAAAFUwTRoAgHBnj2Y4lBbeq8LpdGrWrFlyOp3BTuW0yNN4oZIreRovVHINlTwBqwuV36VQyVMKnVzJ03ihkit5or44PKzFDQCohdLSUsXFxeksSZEBxnJJ2imppKQkJK4HAwDADipr/am1+aMCjFYh6QVL13qmSQMA/MI1wwAAhDt73FoppKdJAwAAAABQF4wMAwD8YsTnvNb/rBgAADuzxwJaNMMAAL/QDAMAEO7s0QyH7DTpBQsWKDk5WdHR0UpNTdXGjRuDnVIV2dnZOu+889S0aVO1adNG1157rbZv3x7stM7o8ccfl8Ph0JQpU4KdShX79u3TLbfcopYtWyomJka9evXS5s2bg51WFS6XSzNmzFBKSopiYmLUuXNnPfzwwwr2enXr1q3TsGHDlJiYKIfDoddee83n6x6PRzNnzlTbtm0VExOjtLQ07dixw1J5njhxQlOnTlWvXr3UuHFjJSYmasyYMdq/f3+953mmXH9pwoQJcjgcysnJqbf8gFBn9XpPrTdHKNR7ar25uVqp3lPrrefqq69Whw4dFB0drbZt2+rWW2+t089GSDbDK1asUFZWlmbNmqWtW7eqd+/eSk9P14EDB4Kdmo/33ntPEydO1IYNG/Tuu+/qxIkTuvzyy1VWVhbs1Gq0adMmPffcczr33HODnUoV33//vQYNGqSGDRvq7bff1meffaannnpKzZs3D3ZqVcyZM0cLFy7UM888o88//1xz5szRE088ofnz5wc1r7KyMvXu3VsLFiyo9utPPPGE/vCHPyg3N1cffvihGjdurPT0dB0/ftwyeR47dkxbt27VjBkztHXrVr366qvavn27rr766nrNsdKZ/k8rrVy5Uhs2bFBiYmI9ZWYel0EbcCahUO+p9cYLlXpPrQ9cqNR7O9Z6q1f7IUOG6K9//au2b9+uV155Rf/97381fPhw/wN5QtCAAQM8EydO9D52uVyexMRET3Z2dhCzOrMDBw54JHnee++9YKdSrSNHjni6dOnieffddz0XX3yxZ/LkycFOycfUqVM9F154YbDTqJUrr7zSc9ttt/nsu/766z2jR48OUkZVSfKsXLnS+9jtdnsSEhI8Tz75pHff4cOHPU6n0/OXv/wlCBme8ss8q7Nx40aPJM+ePXvqJ6ka1JTr119/7WnXrp3nk08+8XTs2NHz9NNP13tuRigpKfFI8iRInsQAtwTJI8lTUlIS7JcFCwvFek+tD1yo1HtqvbFCpd7bpdZLozzSrQFuo+qt1r/++useh8Phqaio8Ot5ITcyXFFRoS1btigtLc27LyIiQmlpaVq/fn0QMzuzkpISSVKLFi2CnEn1Jk6cqCuvvNLn/9ZK3njjDfXv318jRoxQmzZt1LdvX73wwgvBTqtaF1xwgfLz8/Xll19Kkv7zn//o/fff19ChQ4OcWc127dqloqIin+9/XFycUlNTQ+J3y+FwqFmzZsFOpQq3261bb71V999/v3r06BHsdICQEar1nlofuFCp99T6+mfVek+tP73S0lKfrby83ND43333nfLy8nTBBReoYcOGfj035JrhQ4cOyeVyKT4+3md/fHy8ioqKgpTVmbndbk2ZMkWDBg1Sz549g51OFcuXL9fWrVuVnZ0d7FRq9NVXX2nhwoXq0qWL3nnnHd1111265557tHTp0mCnVsW0adN04403qlu3bmrYsKH69u2rKVOmaPTo0cFOrUaVvz+h9rt1/PhxTZ06VTfddJMlb+g+Z84cNWjQQPfcc0+wUzGM26ANOJ1QrPfUemOESr2n1tcvK9f7cKz1pyp1oFOkT1X7pKQkxcXFeTej/gZNnTpVjRs3VsuWLbV37169/vrrfsdgNel6MnHiRH3yySd6//33g51KFYWFhZo8ebLeffddRUdHBzudGrndbvXv31+PPfaYJKlv37765JNPlJubq4yMjCBn5+uvf/2r8vLytGzZMvXo0UPbtm3TlClTlJiYaLlcQ9mJEyc0cuRIeTweLVy4MNjpVLFlyxb9/ve/19atW+VwOIKdjmFcOjXHORA0wwhH1HpjhEq9p9bXHyvX+3Ct9cZ8dH3q+YWFhT4fYDidzmqPnjZtmubMmXPaiJ9//rm6desmSbr//vt1++23a8+ePZo9e7bGjBmjVatW+fV9CLlmuFWrVoqMjFRxcbHP/uLiYiUkJAQpq9PLzMzUqlWrtG7dOrVv3z7Y6VSxZcsWHThwQL/61a+8+1wul9atW6dnnnlG5eXlioyMDGKGp7Rt21bdu3f32XfOOefolVdeCVJGNbv//vu9nxhLUq9evbRnzx5lZ2dbtkBW/v4UFxerbdu23v3FxcXq06dPkLKqWWVh3LNnj9asWWO5T4kl6V//+pcOHDigDh06ePe5XC7de++9ysnJ0e7du4OXHGBxoVbvqfXGCZV6T62vH1av99T6M4uNja3V9+3ee+/V2LFjT3tMp06dvP9u1aqVWrVqpa5du+qcc85RUlKSNmzYoIEDB9Y6t5BrhqOiotSvXz/l5+fr2muvlXTqE8T8/HxlZmYGN7lf8Hg8mjRpklauXKmCggKlpKQEO6VqXXrppfr444999o0bN07dunXT1KlTLVMcBw0aVOV2FV9++aU6duwYpIxqduzYMUVE+F6FEBkZKbfbuuNhKSkpSkhIUH5+vrcglpaW6sMPP9Rdd90V3OR+obIw7tixQ2vXrlXLli2DnVK1br311irX5aWnp+vWW2/VuHHjgpRV4NwKfGQ4uDceQSgIlXpPrTdeqNR7ar35QqHeh2utPzUPLNArav1bTbp169Zq3bp1nc5U+Xvn7/XIIdcMS1JWVpYyMjLUv39/DRgwQDk5OSorK7PcD9zEiRO1bNkyvf7662ratKn3Woy4uDjFxMQEObufNG3atMq1TZXz7610zdPvfvc7XXDBBXrsscc0cuRIbdy4Uc8//7yef/75YKdWxbBhw/Too4+qQ4cO6tGjhz766CPNmzdPt912W1DzOnr0qHbu3Ol9vGvXLm3btk0tWrRQhw4dNGXKFD3yyCPq0qWLUlJSNGPGDCUmJnrfiFohz7Zt22r48OHaunWrVq1aJZfL5f3datGihaKioiyTa4cOHaoU7oYNGyohIUFnn312veZpJLekQCeC0QyjNkKh3lPrjRcq9Z5ab26uVqr3dqz1wWiGa+vDDz/Upk2bdOGFF6p58+b673//qxkzZqhz585+jQpLCs1bK3k8Hs/8+fM9HTp08ERFRXkGDBjg2bBhQ7BTqkI/3jrkl9vixYuDndoZWfV2C3//+989PXv29DidTk+3bt08zz//fLBTqlZpaaln8uTJng4dOniio6M9nTp18vzv//6vp7y8PKh5rV27ttqfyYyMDI/Hc+qWCzNmzPDEx8d7nE6n59JLL/Vs377dUnnu2rWrxt+ttWvXWirX6oTD7RbiJE+zALc4bq2EWrJ6vafWmyMU6j213txcrVTv7VjrpWEe6foAt2Gm1Pr/+7//8wwZMsTTokULj9Pp9CQnJ3smTJjg+frrr/2O5fB4PHxADwA4o9LSUsXFxamJjBkZPqpTt8mw2vVfAADYVWWtl34jyb/bFFV1QtJblq71ITlNGgAQPEyTBgAg3Bm3mrSV0QwDAPxiRCNLMwwAgJWdtEgMc9EMAwBqJSoqSgkJCd4FTAKVkJBQ7wueAQCAmv1U6/9pSDyr13quGQYA1Nrx48dVUVFhSKyoqChFR0cbEgsAABjDTrWeZhgAAAAAYDuB3jwKAAAAAICQQzMMAAAAALAdmmEAAAAAgO3QDAMAAAAAbIdmGAAAAABgOzTDAAAAAADboRkGAAAAANjO/we8AeHJgjCiBwAAAABJRU5ErkJggg==", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "tal.plot.amplitude_phase(H_1_pf, title='Multiple threads')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tal.reconstruct.utils: Assuming that volume_xyz is X_Y_Z_3\n", - "tal.reconstruct.utils: Optimizing for projector convolutions.\n", - "tal.reconstruct.utils: Optimizing for camera convolutions.\n", - "tal.reconstruct.pf_dev: Using 50 wavelengths from 0.1664m to 0.5060m\n", - "tal.reconstruct.pf_dev: When projector_focus is not set, the projector convolution optimization is not implemented. Falling back to default method.\n", - "tal.reconstruct.pf_dev: projector_focus_mode=confocal\n", - "tal.resources: Using 2 CPU processes and downscale 4.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tal.resources progress: 100%|██████████| 4/4 [00:50<00:00, 12.67s/it] \n", - " \r" - ] - } - ], - "source": [ - "with tal.resources(cpu_processes=2, downscale=4):\n", - " H_1_pf = tal.reconstruct.pf_dev.solve(data, wl_mean=0.25, wl_sigma=0.25, volume_xyz=volume_xyz, camera_system=tal.enums.CameraSystem.DIRECT_LIGHT)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "tal.plot.amplitude_phase(H_1_pf, title='Multiple threads')" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tal.reconstruct.utils: Assuming that volume_xyz is X_Y_Z_3\n", - "tal.reconstruct.utils: Optimizing for projector convolutions.\n", - "tal.reconstruct.utils: Optimizing for camera convolutions.\n", - "tal.reconstruct.pf_dev: Using 50 wavelengths from 0.1664m to 0.5060m\n", - "tal.reconstruct.pf_dev: When projector_focus is a 3D point, the projector convolution optimization is not implemented. Falling back to default method.\n", - "tal.reconstruct.pf_dev: projector_focus_mode=single\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - } - ], - "source": [ - "with tal.resources(cpu_processes=1, downscale=1):\n", - " H_1_pf = tal.reconstruct.pf_dev.solve(data, wl_mean=0.25, wl_sigma=0.25, volume_xyz=volume_xyz,\n", - " projector_focus=[0, -0.5, 1],\n", - " camera_system=tal.enums.CameraSystem.PROJECTOR_CAMERA_T0)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "tal.plot.amplitude_phase(H_1_pf, title='Focus illumination')" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tal.reconstruct.utils: Assuming that volume_xyz is X_Y_Z_3\n", - "tal.reconstruct.utils: Optimizing for projector convolutions.\n", - "tal.reconstruct.utils: Optimizing for camera convolutions.\n", - "tal.reconstruct.pf_dev: Using 50 wavelengths from 0.1664m to 0.5060m\n", - "tal.reconstruct.pf_dev: When projector_focus is a 3D point, the projector convolution optimization is not implemented. Falling back to default method.\n", - "tal.reconstruct.pf_dev: projector_focus_mode=single\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - } - ], - "source": [ - "with tal.resources(cpu_processes=1, downscale=1):\n", - " H_1_pf = tal.reconstruct.pf_dev.solve(data, wl_mean=0.25, wl_sigma=0.25, volume_xyz=volume_xyz,\n", - " projector_focus=[-0.5, 0, 1],\n", - " camera_system=tal.enums.CameraSystem.PROJECTOR_CAMERA_T0)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "tal.plot.amplitude_phase(H_1_pf, title='Focus illumination')" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tal.reconstruct.utils: Assuming that volume_xyz is X_Y_Z_3\n", - "tal.reconstruct.utils: Optimizing for projector convolutions.\n", - "tal.reconstruct.utils: Optimizing for camera convolutions.\n", - "tal.reconstruct.pf_dev: Using 50 wavelengths from 0.1664m to 0.5060m\n", - "tal.reconstruct.pf_dev: When projector_focus is a 3D point, the projector convolution optimization is not implemented. Falling back to default method.\n", - "tal.reconstruct.pf_dev: projector_focus_mode=single\n", - "tal.resources: Using 2 CPU processes and downscale 2.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tal.resources progress: 100%|██████████| 2/2 [00:33<00:00, 16.94s/it] \n", - " \r" - ] - } - ], - "source": [ - "with tal.resources(cpu_processes=2, downscale=2):\n", - " H_1_pf = tal.reconstruct.pf_dev.solve(data, wl_mean=0.25, wl_sigma=0.25, volume_xyz=volume_xyz,\n", - " projector_focus=[data.laser_grid_xyz[9, 8, 0], data.laser_grid_xyz[9, 8, 1], 1],\n", - " camera_system=tal.enums.CameraSystem.PROJECTOR_CAMERA_T0)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "tal.plot.amplitude_phase(H_1_pf, title='Focus illumination')" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tal.reconstruct.utils: Assuming that volume_xyz is X_Y_Z_3\n", - "tal.reconstruct.utils: Optimizing for projector convolutions.\n", - "tal.reconstruct.utils: Optimizing for camera convolutions.\n", - "tal.reconstruct.pf_dev: Using 50 wavelengths from 0.1664m to 0.5060m\n", - "tal.reconstruct.pf_dev: projector_focus_mode=exhaustive\n", - "tal.resources: Using 2 CPU processes and downscale 2.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tal.resources progress: 100%|██████████| 2/2 [00:34<00:00, 17.46s/it] \n", - " \r" - ] - } - ], - "source": [ - "with tal.resources(cpu_processes=2, downscale=2):\n", - " H_1_pf = tal.reconstruct.pf_dev.solve(data, wl_mean=0.25, wl_sigma=0.25, volume_xyz=volume_xyz,\n", - " projector_focus=volume_xyz,\n", - " camera_system=tal.enums.CameraSystem.PROJECTOR_CAMERA)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(2048, 16, 16, 1, 16, 16, 1)\n" - ] - } - ], - "source": [ - "print(H_1_pf.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "i, j = 9, 8\n", - "tal.plot.amplitude_phase(H_1_pf[0, i, j, 0, :, :, 0], title=f'Focus at {i}, {j}')" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(16, 16, 3, 3)\n" - ] - } - ], - "source": [ - "volume_xyz = tal.reconstruct.get_volume_project_rw(data, depths=[0.95, 1.00, 1.05])\n", - "print(volume_xyz.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tal.reconstruct.utils: Assuming that volume_xyz is X_Y_Z_3\n", - "tal.reconstruct.utils: Optimizing for projector convolutions.\n", - "tal.reconstruct.utils: Optimizing for camera convolutions.\n", - "tal.reconstruct.pf_dev: Using 50 wavelengths from 0.1664m to 0.5060m\n", - "tal.reconstruct.pf_dev: When projector_focus is a 3D point, the projector convolution optimization is not implemented. Falling back to default method.\n", - "tal.reconstruct.pf_dev: projector_focus_mode=single\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tal.reconstruct.pf_dev Z slices: 0%| | 0/3 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "tal.plot.amplitude_phase(H_1_pf[..., 0], title='Z = 0.95')" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "tal.plot.amplitude_phase(H_1_pf[..., 2], title='Z = 1.05')" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(256, 3)\n" - ] - } - ], - "source": [ - "volume_xyz = tal.reconstruct.get_volume_project_rw(data, depths=1.0)\n", - "volume_xyz = volume_xyz.reshape((-1, 3))\n", - "print(volume_xyz.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tal.reconstruct.utils: Assuming that volume_xyz is N_3\n", - "tal.reconstruct.pf_dev: You have specified a time-gated camera system with an arbitrary reconstruction volume (that is not parallel to the relay wall). This will work, but the tal.reconstruct.bp or tal.reconstruct.fbp implementations are better suited for these cases.\n", - "tal.reconstruct.pf_dev: Using 50 wavelengths from 0.1664m to 0.5060m\n", - "tal.reconstruct.pf_dev: When projector_focus is not set, the projector convolution optimization is not implemented. Falling back to default method.\n", - "tal.reconstruct.pf_dev: projector_focus_mode=confocal\n", - "tal.resources: Using 2 CPU processes and downscale 2.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tal.resources progress: 100%|██████████| 2/2 [00:37<00:00, 18.81s/it] \n", - " \r" - ] - } - ], - "source": [ - "with tal.resources(cpu_processes=2, downscale=2):\n", - " H_1_pf = tal.reconstruct.pf_dev.solve(data, wl_mean=0.25, wl_sigma=0.25, volume_xyz=volume_xyz, camera_system=tal.enums.CameraSystem.DIRECT_LIGHT)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "tal.plot.amplitude_phase(H_1_pf.reshape((16, 16)), title='Point to point propagation')" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tal.reconstruct.utils: Assuming that volume_xyz is N_3\n", - "tal.reconstruct.pf_dev: You have specified a time-gated camera system with an arbitrary reconstruction volume (that is not parallel to the relay wall). This will work, but the tal.reconstruct.bp or tal.reconstruct.fbp implementations are better suited for these cases.\n", - "tal.reconstruct.pf_dev: Using 50 wavelengths from 0.1664m to 0.5060m\n", - "tal.reconstruct.pf_dev: When projector_focus is a 3D point, the projector convolution optimization is not implemented. Falling back to default method.\n", - "tal.reconstruct.pf_dev: projector_focus_mode=single\n", - "tal.resources: Using 2 CPU processes and downscale 2.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tal.resources progress: 100%|██████████| 2/2 [00:38<00:00, 19.09s/it] \n", - " \r" - ] - } - ], - "source": [ - "with tal.resources(cpu_processes=2, downscale=2):\n", - " H_1_pf = tal.reconstruct.pf_dev.solve(data, wl_mean=0.25, wl_sigma=0.25, volume_xyz=volume_xyz,\n", - " projector_focus=[data.laser_grid_xyz[9, 8, 0], data.laser_grid_xyz[9, 8, 1], 1],\n", - " camera_system=tal.enums.CameraSystem.PROJECTOR_CAMERA_T0)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "tal.plot.amplitude_phase(H_1_pf.reshape((16, 16)), title='Point to point propagation')" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "mitsuba3-py3.11", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/render-reconstruct-exhaustive/fbp.ipynb b/examples/render-reconstruct-exhaustive/fbp.ipynb index a2e48bc..47c2552 100644 --- a/examples/render-reconstruct-exhaustive/fbp.ipynb +++ b/examples/render-reconstruct-exhaustive/fbp.ipynb @@ -8,7 +8,7 @@ "source": [ "import tal\n", "\n", - "data = tal.io.read_capture('/media/pleiades/vault/projects/202206-tal/code/examples/render-reconstruct-exhaustive/exhaustive-scene/20240621-193320/exhaustive-scene.hdf5') # you'll need to generate this file using \"tal render exhaustive-scene\"\n", + "data = tal.io.read_capture('exhaustive-scene.hdf5') # you'll need to generate this file using \"tal render exhaustive-scene\"\n", "tal.reconstruct.compensate_laser_cos_dsqr(data)" ] }, @@ -37,7 +37,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:29<00:00, 9.92s/it]\n" + "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:23<00:00, 7.91s/it]\n" ] }, { @@ -89,9 +89,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:15<00:00, 5.00s/it]\n", - "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:15<00:00, 5.00s/it]\n", - "tal.resources progress: 100%|██████████| 2/2 [00:04<00:00, 2.15s/it]\n" + "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:12<00:00, 4.05s/it]\n", + "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:12<00:00, 4.05s/it]\n", + "tal.resources progress: 100%|██████████| 2/2 [00:01<00:00, 1.01it/s]\n" ] }, { @@ -106,7 +106,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "tal.resources progress: 100%|██████████| 2/2 [00:00<00:00, 15033.35it/s]\n" + "tal.resources progress: 100%|██████████| 2/2 [00:00<00:00, 16513.01it/s]\n" ] } ], @@ -152,11 +152,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:05<00:00, 1.98s/it]\n", - "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:06<00:00, 2.10s/it]\n", - "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:05<00:00, 1.98s/it]\n", - "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:06<00:00, 2.01s/it]\n", - "tal.resources progress: 100%|██████████| 4/4 [00:01<00:00, 2.20it/s]\n" + "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:06<00:00, 2.31s/it]\n", + "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:07<00:00, 2.47s/it]\n", + "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:05<00:00, 1.91s/it]\n", + "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:05<00:00, 1.83s/it]\n", + "tal.resources progress: 100%|██████████| 4/4 [00:02<00:00, 1.80it/s]\n" ] }, { @@ -171,7 +171,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "tal.resources progress: 100%|██████████| 4/4 [00:00<00:00, 41020.09it/s]\n" + "tal.resources progress: 100%|██████████| 4/4 [00:00<00:00, 25970.92it/s]\n" ] } ], @@ -217,9 +217,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:13<00:00, 4.60s/it]\n", - "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:13<00:00, 4.60s/it]\n", - "tal.resources progress: 100%|██████████| 2/2 [00:02<00:00, 1.31s/it]\n" + "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:12<00:00, 4.10s/it]\n", + "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:12<00:00, 4.12s/it]\n", + "tal.resources progress: 100%|██████████| 2/2 [00:01<00:00, 1.00it/s]\n" ] }, { @@ -234,7 +234,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "tal.resources progress: 100%|██████████| 2/2 [00:00<00:00, 26546.23it/s]\n" + "tal.resources progress: 100%|██████████| 2/2 [00:00<00:00, 14004.35it/s]\n" ] } ], @@ -282,9 +282,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:17<00:00, 5.73s/it]\n", - "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:18<00:00, 6.12s/it]\n", - "tal.resources progress: 100%|██████████| 2/2 [00:04<00:00, 2.26s/it]\n" + "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:11<00:00, 3.93s/it]\n", + "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:12<00:00, 4.26s/it]\n", + "tal.resources progress: 100%|██████████| 2/2 [00:03<00:00, 1.60s/it]\n" ] }, { @@ -299,7 +299,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "tal.resources progress: 100%|██████████| 2/2 [00:00<00:00, 19195.90it/s]\n" + "tal.resources progress: 100%|██████████| 2/2 [00:00<00:00, 12671.61it/s]\n" ] } ], @@ -347,9 +347,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:16<00:00, 5.42s/it]\n", - "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:16<00:00, 5.42s/it]\n", - "tal.resources progress: 100%|██████████| 2/2 [00:04<00:00, 2.25s/it]\n" + "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:14<00:00, 4.72s/it]\n", + "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:14<00:00, 4.83s/it]\n", + "tal.resources progress: 100%|██████████| 2/2 [00:02<00:00, 1.20s/it]\n" ] }, { @@ -364,7 +364,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "tal.resources progress: 100%|██████████| 2/2 [00:00<00:00, 27503.63it/s]\n" + "tal.resources progress: 100%|██████████| 2/2 [00:00<00:00, 23497.50it/s]\n" ] } ], @@ -412,9 +412,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:14<00:00, 4.79s/it]\n", - "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:14<00:00, 4.82s/it]\n", - "tal.resources progress: 100%|██████████| 2/2 [00:05<00:00, 2.78s/it]\n" + "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:13<00:00, 4.61s/it]\n", + "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:13<00:00, 4.63s/it]\n", + "tal.resources progress: 100%|██████████| 2/2 [00:03<00:00, 1.68s/it]\n" ] }, { @@ -429,7 +429,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "tal.resources progress: 100%|██████████| 2/2 [00:00<00:00, 11831.61it/s]\n" + "tal.resources progress: 100%|██████████| 2/2 [00:00<00:00, 27776.85it/s]\n" ] } ], @@ -462,18 +462,18 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "import tal\n", "\n", - "data = tal.io.read_capture('/media/pleiades/vault/projects/202206-tal/code/examples/render-reconstruct-exhaustive/exhaustive-scene/20240621-193320/exhaustive-scene.hdf5')" + "data = tal.io.read_capture('exhaustive-scene.hdf5')" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -488,11 +488,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:06<00:00, 2.07s/it]\n", - "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:06<00:00, 2.08s/it]\n", - "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:06<00:00, 2.31s/it]\n", - "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:06<00:00, 2.31s/it]\n", - "tal.resources progress: 100%|██████████| 4/4 [00:02<00:00, 1.73it/s]\n" + "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:06<00:00, 2.02s/it]\n", + "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:06<00:00, 2.24s/it]\n", + "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:05<00:00, 1.96s/it]\n", + "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:05<00:00, 1.96s/it]\n", + "tal.resources progress: 100%|██████████| 4/4 [00:01<00:00, 2.19it/s]\n" ] }, { @@ -507,7 +507,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "tal.resources progress: 100%|██████████| 4/4 [00:00<00:00, 48349.33it/s]\n" + "tal.resources progress: 100%|██████████| 4/4 [00:00<00:00, 37449.14it/s]\n" ] } ], @@ -518,7 +518,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -538,7 +538,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "metadata": {}, "outputs": [ { diff --git a/examples/render-reconstruct-exhaustive/pf_dev.ipynb b/examples/render-reconstruct-exhaustive/pf_dev.ipynb index eee7518..6fe2941 100644 --- a/examples/render-reconstruct-exhaustive/pf_dev.ipynb +++ b/examples/render-reconstruct-exhaustive/pf_dev.ipynb @@ -8,7 +8,7 @@ "source": [ "import tal\n", "\n", - "data = tal.io.read_capture('/media/pleiades/vault/projects/202206-tal/code/examples/render-reconstruct-exhaustive/exhaustive-scene/20240621-193320/exhaustive-scene.hdf5') # you'll need to generate this file using \"tal render exhaustive-scene\"\n", + "data = tal.io.read_capture('exhaustive-scene.hdf5') # you'll need to generate this file using \"tal render exhaustive-scene\"\n", "tal.reconstruct.compensate_laser_cos_dsqr(data)" ] }, @@ -31,10 +31,8 @@ "output_type": "stream", "text": [ "tal.reconstruct.utils: Assuming that volume_xyz is X_Y_Z_3\n", - "tal.reconstruct.utils: Optimizing for projector convolutions.\n", "tal.reconstruct.utils: Optimizing for camera convolutions.\n", "tal.reconstruct.pf_dev: Using 50 wavelengths from 0.1664m to 0.5060m\n", - "tal.reconstruct.pf_dev: When projector_focus is not set, the projector convolution optimization is not implemented. Falling back to default method.\n", "tal.reconstruct.pf_dev: projector_focus_mode=confocal\n" ] }, @@ -42,8 +40,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "tal.reconstruct.pf_dev propagation (1/2): 64it [01:16, 1.20s/it] \n", - "tal.reconstruct.pf_dev ifft (2/2): 100%|██████████| 50/50 [00:00<00:00, 414.84it/s]\n" + "tal.reconstruct.pf_dev propagation (1/2): 64it [00:54, 1.17it/s] \n", + "tal.reconstruct.pf_dev ifft (2/2): 100%|██████████| 50/50 [00:00<00:00, 365.83it/s]\n" ] } ], @@ -81,10 +79,8 @@ "output_type": "stream", "text": [ "tal.reconstruct.utils: Assuming that volume_xyz is X_Y_Z_3\n", - "tal.reconstruct.utils: Optimizing for projector convolutions.\n", "tal.reconstruct.utils: Optimizing for camera convolutions.\n", "tal.reconstruct.pf_dev: Using 50 wavelengths from 0.1664m to 0.5060m\n", - "tal.reconstruct.pf_dev: When projector_focus is not set, the projector convolution optimization is not implemented. Falling back to default method.\n", "tal.reconstruct.pf_dev: projector_focus_mode=confocal\n", "tal.resources: Using 2 CPU processes and downscale 2.\n" ] @@ -93,10 +89,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "tal.reconstruct.pf_dev propagation (1/2): 100%|██████████| 32/32 [00:36<00:00, 1.14s/it]\n", - "tal.reconstruct.pf_dev propagation (1/2): 100%|██████████| 32/32 [01:00<00:00, 1.88s/it]\n", - "tal.resources progress: 100%|██████████| 2/2 [00:00<00:00, 9576.04it/s]\n", - "tal.reconstruct.pf_dev ifft (2/2): 100%|██████████| 50/50 [00:00<00:00, 247.01it/s]\n" + "tal.reconstruct.pf_dev propagation (1/2): 100%|██████████| 32/32 [00:23<00:00, 1.35it/s]\n", + "tal.reconstruct.pf_dev propagation (1/2): 100%|██████████| 32/32 [00:37<00:00, 1.17s/it]\n", + "tal.resources progress: 100%|██████████| 2/2 [00:00<00:00, 15169.27it/s]\n", + "tal.reconstruct.pf_dev ifft (2/2): 100%|██████████| 50/50 [00:00<00:00, 364.93it/s]\n" ] } ], @@ -135,10 +131,8 @@ "output_type": "stream", "text": [ "tal.reconstruct.utils: Assuming that volume_xyz is X_Y_Z_3\n", - "tal.reconstruct.utils: Optimizing for projector convolutions.\n", "tal.reconstruct.utils: Optimizing for camera convolutions.\n", "tal.reconstruct.pf_dev: Using 50 wavelengths from 0.1664m to 0.5060m\n", - "tal.reconstruct.pf_dev: When projector_focus is not set, the projector convolution optimization is not implemented. Falling back to default method.\n", "tal.reconstruct.pf_dev: projector_focus_mode=confocal\n", "tal.resources: Using 2 CPU processes and downscale 4.\n" ] @@ -147,12 +141,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "tal.reconstruct.pf_dev propagation (1/2): 100%|██████████| 16/16 [00:22<00:00, 1.43s/it]\n", - "tal.reconstruct.pf_dev propagation (1/2): 100%|██████████| 16/16 [00:22<00:00, 1.44s/it]\n", - "tal.reconstruct.pf_dev propagation (1/2): 100%|██████████| 16/16 [00:01<00:00, 8.22it/s]\n", - "tal.reconstruct.pf_dev propagation (1/2): 100%|██████████| 16/16 [00:14<00:00, 1.14it/s]\n", - "tal.resources progress: 100%|██████████| 4/4 [00:00<00:00, 14639.80it/s]\n", - "tal.reconstruct.pf_dev ifft (2/2): 100%|██████████| 50/50 [00:00<00:00, 605.92it/s]\n" + "tal.reconstruct.pf_dev propagation (1/2): 100%|██████████| 16/16 [00:19<00:00, 1.21s/it]\n", + "tal.reconstruct.pf_dev propagation (1/2): 100%|██████████| 16/16 [00:19<00:00, 1.24s/it]\n", + "tal.reconstruct.pf_dev propagation (1/2): 100%|██████████| 16/16 [00:02<00:00, 7.37it/s]\n", + "tal.reconstruct.pf_dev propagation (1/2): 100%|██████████| 16/16 [00:13<00:00, 1.14it/s]\n", + "tal.resources progress: 100%|██████████| 4/4 [00:00<00:00, 17331.83it/s]\n", + "tal.reconstruct.pf_dev ifft (2/2): 100%|██████████| 50/50 [00:00<00:00, 626.02it/s]\n" ] } ], @@ -191,10 +185,8 @@ "output_type": "stream", "text": [ "tal.reconstruct.utils: Assuming that volume_xyz is X_Y_Z_3\n", - "tal.reconstruct.utils: Optimizing for projector convolutions.\n", "tal.reconstruct.utils: Optimizing for camera convolutions.\n", "tal.reconstruct.pf_dev: Using 50 wavelengths from 0.1664m to 0.5060m\n", - "tal.reconstruct.pf_dev: When projector_focus is a 3D point, the projector convolution optimization is not implemented. Falling back to default method.\n", "tal.reconstruct.pf_dev: projector_focus_mode=single\n" ] }, @@ -202,8 +194,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "tal.reconstruct.pf_dev propagation (1/2): 64it [00:46, 1.38it/s] \n", - "tal.reconstruct.pf_dev ifft (2/2): 100%|██████████| 50/50 [00:00<00:00, 466.61it/s]\n" + "tal.reconstruct.pf_dev propagation (1/2): 64it [00:39, 1.62it/s] \n", + "tal.reconstruct.pf_dev ifft (2/2): 100%|██████████| 50/50 [00:00<00:00, 617.81it/s]\n" ] } ], @@ -244,10 +236,8 @@ "output_type": "stream", "text": [ "tal.reconstruct.utils: Assuming that volume_xyz is X_Y_Z_3\n", - "tal.reconstruct.utils: Optimizing for projector convolutions.\n", "tal.reconstruct.utils: Optimizing for camera convolutions.\n", "tal.reconstruct.pf_dev: Using 50 wavelengths from 0.1664m to 0.5060m\n", - "tal.reconstruct.pf_dev: When projector_focus is a 3D point, the projector convolution optimization is not implemented. Falling back to default method.\n", "tal.reconstruct.pf_dev: projector_focus_mode=single\n" ] }, @@ -255,8 +245,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "tal.reconstruct.pf_dev propagation (1/2): 64it [00:41, 1.53it/s] \n", - "tal.reconstruct.pf_dev ifft (2/2): 100%|██████████| 50/50 [00:00<00:00, 559.90it/s]\n" + "tal.reconstruct.pf_dev propagation (1/2): 64it [00:37, 1.69it/s] \n", + "tal.reconstruct.pf_dev ifft (2/2): 100%|██████████| 50/50 [00:00<00:00, 637.10it/s]\n" ] } ], @@ -297,10 +287,8 @@ "output_type": "stream", "text": [ "tal.reconstruct.utils: Assuming that volume_xyz is X_Y_Z_3\n", - "tal.reconstruct.utils: Optimizing for projector convolutions.\n", "tal.reconstruct.utils: Optimizing for camera convolutions.\n", "tal.reconstruct.pf_dev: Using 50 wavelengths from 0.1664m to 0.5060m\n", - "tal.reconstruct.pf_dev: When projector_focus is a 3D point, the projector convolution optimization is not implemented. Falling back to default method.\n", "tal.reconstruct.pf_dev: projector_focus_mode=single\n", "tal.resources: Using 2 CPU processes and downscale 2.\n" ] @@ -309,10 +297,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "tal.reconstruct.pf_dev propagation (1/2): 100%|██████████| 32/32 [00:16<00:00, 1.89it/s]\n", - "tal.reconstruct.pf_dev propagation (1/2): 100%|██████████| 32/32 [00:28<00:00, 1.11it/s]\n", - "tal.resources progress: 100%|██████████| 2/2 [00:00<00:00, 15448.63it/s]\n", - "tal.reconstruct.pf_dev ifft (2/2): 100%|██████████| 50/50 [00:00<00:00, 449.93it/s]\n" + "tal.reconstruct.pf_dev propagation (1/2): 100%|██████████| 32/32 [00:15<00:00, 2.05it/s]\n", + "tal.reconstruct.pf_dev propagation (1/2): 100%|██████████| 32/32 [00:27<00:00, 1.17it/s]\n", + "tal.resources progress: 100%|██████████| 2/2 [00:00<00:00, 17962.76it/s]\n", + "tal.reconstruct.pf_dev ifft (2/2): 100%|██████████| 50/50 [00:00<00:00, 617.10it/s]\n" ] } ], @@ -364,10 +352,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "tal.reconstruct.pf_dev propagation (1/2): 100%|██████████| 32/32 [00:18<00:00, 1.77it/s]\n", - "tal.reconstruct.pf_dev propagation (1/2): 100%|██████████| 32/32 [00:30<00:00, 1.06it/s]\n", - "tal.resources progress: 100%|██████████| 2/2 [00:00<00:00, 131.64it/s]\n", - "tal.reconstruct.pf_dev ifft (2/2): 100%|██████████| 50/50 [00:30<00:00, 1.66it/s]\n" + "tal.reconstruct.pf_dev propagation (1/2): 100%|██████████| 32/32 [00:15<00:00, 2.03it/s]\n", + "tal.reconstruct.pf_dev propagation (1/2): 100%|██████████| 32/32 [00:26<00:00, 1.20it/s]\n", + "tal.resources progress: 100%|██████████| 2/2 [00:00<00:00, 129.20it/s]\n", + "tal.reconstruct.pf_dev ifft (2/2): 100%|██████████| 50/50 [00:28<00:00, 1.78it/s]\n" ] } ], @@ -444,10 +432,8 @@ "output_type": "stream", "text": [ "tal.reconstruct.utils: Assuming that volume_xyz is X_Y_Z_3\n", - "tal.reconstruct.utils: Optimizing for projector convolutions.\n", "tal.reconstruct.utils: Optimizing for camera convolutions.\n", "tal.reconstruct.pf_dev: Using 50 wavelengths from 0.1664m to 0.5060m\n", - "tal.reconstruct.pf_dev: When projector_focus is a 3D point, the projector convolution optimization is not implemented. Falling back to default method.\n", "tal.reconstruct.pf_dev: projector_focus_mode=single\n" ] }, @@ -469,11 +455,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "tal.reconstruct.pf_dev propagation (1/2): 100%|██████████| 32/32 [00:16<00:00, 1.90it/s]\n", - "tal.reconstruct.pf_dev propagation (1/2): 100%|██████████| 32/32 [00:28<00:00, 1.11it/s]\n", - "tal.resources progress: 100%|██████████| 2/2 [00:00<00:00, 4194.30it/s]\n", - "tal.reconstruct.pf_dev ifft (2/2): 100%|██████████| 50/50 [00:00<00:00, 412.48it/s]\n", - "tal.reconstruct.pf_dev Z slices: 33%|███▎ | 1/3 [00:28<00:57, 28.97s/it]" + "tal.reconstruct.pf_dev propagation (1/2): 100%|██████████| 32/32 [00:16<00:00, 1.91it/s]\n", + "tal.reconstruct.pf_dev propagation (1/2): 100%|██████████| 32/32 [00:28<00:00, 1.13it/s]\n", + "tal.resources progress: 100%|██████████| 2/2 [00:00<00:00, 4905.62it/s]\n", + "tal.reconstruct.pf_dev ifft (2/2): 100%|██████████| 50/50 [00:00<00:00, 620.84it/s]\n", + "tal.reconstruct.pf_dev Z slices: 33%|███▎ | 1/3 [00:28<00:56, 28.42s/it]" ] }, { @@ -487,11 +473,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "tal.reconstruct.pf_dev propagation (1/2): 100%|██████████| 32/32 [00:19<00:00, 1.65it/s]\n", - "tal.reconstruct.pf_dev propagation (1/2): 100%|██████████| 32/32 [00:31<00:00, 1.02it/s]\n", - "tal.resources progress: 100%|██████████| 2/2 [00:00<00:00, 4364.52it/s]\n", - "tal.reconstruct.pf_dev ifft (2/2): 100%|██████████| 50/50 [00:00<00:00, 557.02it/s]\n", - "tal.reconstruct.pf_dev Z slices: 67%|██████▋ | 2/3 [01:00<00:30, 30.37s/it]" + "tal.reconstruct.pf_dev propagation (1/2): 100%|██████████| 32/32 [00:15<00:00, 2.04it/s]\n", + "tal.reconstruct.pf_dev propagation (1/2): 100%|██████████| 32/32 [00:27<00:00, 1.18it/s]\n", + "tal.resources progress: 100%|██████████| 2/2 [00:00<00:00, 5259.32it/s]\n", + "tal.reconstruct.pf_dev ifft (2/2): 100%|██████████| 50/50 [00:00<00:00, 649.90it/s]\n", + "tal.reconstruct.pf_dev Z slices: 67%|██████▋ | 2/3 [00:55<00:27, 27.67s/it]" ] }, { @@ -505,11 +491,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "tal.reconstruct.pf_dev propagation (1/2): 100%|██████████| 32/32 [00:17<00:00, 1.86it/s]\n", - "tal.reconstruct.pf_dev propagation (1/2): 100%|██████████| 32/32 [00:29<00:00, 1.10it/s]\n", - "tal.resources progress: 100%|██████████| 2/2 [00:00<00:00, 14027.77it/s]\n", - "tal.reconstruct.pf_dev ifft (2/2): 100%|██████████| 50/50 [00:00<00:00, 542.40it/s]\n", - "tal.reconstruct.pf_dev Z slices: 100%|██████████| 3/3 [01:29<00:00, 29.82s/it]\n" + "tal.reconstruct.pf_dev propagation (1/2): 100%|██████████| 32/32 [00:15<00:00, 2.10it/s]\n", + "tal.reconstruct.pf_dev propagation (1/2): 100%|██████████| 32/32 [00:27<00:00, 1.15it/s]\n", + "tal.resources progress: 100%|██████████| 2/2 [00:00<00:00, 4211.15it/s]\n", + "tal.reconstruct.pf_dev ifft (2/2): 100%|██████████| 50/50 [00:00<00:00, 620.79it/s]\n", + "tal.reconstruct.pf_dev Z slices: 100%|██████████| 3/3 [01:23<00:00, 27.79s/it]\n" ] } ], @@ -636,10 +622,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "tal.reconstruct.pf_dev propagation (1/2): 100%|██████████| 32/32 [00:19<00:00, 1.65it/s]\n", - "tal.reconstruct.pf_dev propagation (1/2): 100%|██████████| 32/32 [00:32<00:00, 1.02s/it]\n", - "tal.resources progress: 100%|██████████| 2/2 [00:00<00:00, 13315.25it/s]\n", - "tal.reconstruct.pf_dev ifft (2/2): 100%|██████████| 50/50 [00:00<00:00, 495.16it/s]\n" + "tal.reconstruct.pf_dev propagation (1/2): 100%|██████████| 32/32 [00:16<00:00, 1.99it/s]\n", + "tal.reconstruct.pf_dev propagation (1/2): 100%|██████████| 32/32 [00:26<00:00, 1.20it/s]\n", + "tal.resources progress: 100%|██████████| 2/2 [00:00<00:00, 5332.87it/s]\n", + "tal.reconstruct.pf_dev ifft (2/2): 100%|██████████| 50/50 [00:00<00:00, 535.23it/s]\n" ] } ], @@ -688,10 +674,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "tal.reconstruct.pf_dev propagation (1/2): 100%|██████████| 32/32 [00:19<00:00, 1.67it/s]\n", - "tal.reconstruct.pf_dev propagation (1/2): 100%|██████████| 32/32 [00:31<00:00, 1.00it/s]\n", - "tal.resources progress: 100%|██████████| 2/2 [00:00<00:00, 9576.04it/s]\n", - "tal.reconstruct.pf_dev ifft (2/2): 100%|██████████| 50/50 [00:00<00:00, 627.27it/s]\n" + "tal.reconstruct.pf_dev propagation (1/2): 100%|██████████| 32/32 [00:16<00:00, 1.96it/s]\n", + "tal.reconstruct.pf_dev propagation (1/2): 100%|██████████| 32/32 [00:27<00:00, 1.17it/s]\n", + "tal.resources progress: 100%|██████████| 2/2 [00:00<00:00, 8533.68it/s]\n", + "tal.reconstruct.pf_dev ifft (2/2): 100%|██████████| 50/50 [00:00<00:00, 622.92it/s]\n" ] } ], diff --git a/examples/render-reconstruct/reconstruct.ipynb b/examples/render-reconstruct/reconstruct.ipynb index 4d1a61d..68813db 100644 --- a/examples/render-reconstruct/reconstruct.ipynb +++ b/examples/render-reconstruct/reconstruct.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -41,7 +41,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -55,7 +55,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:00<00:00, 24.99it/s]" + "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:00<00:00, 29.68it/s]" ] }, { @@ -97,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -112,15 +112,15 @@ "name": "stderr", "output_type": "stream", "text": [ - "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:00<00:00, 70.34it/s]\n", - "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:00<00:00, 67.15it/s]\n", - "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:00<00:00, 59.09it/s]\n", - "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:00<00:00, 68.67it/s]\n", - "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:00<00:00, 64.65it/s]\n", - "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:00<00:00, 59.18it/s]\n", - "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:00<00:00, 71.56it/s]\n", - "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:00<00:00, 97.91it/s]\n", - "tal.resources progress: 100%|██████████| 8/8 [00:00<00:00, 498.20it/s]" + "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:00<00:00, 69.50it/s]\n", + "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:00<00:00, 67.96it/s]\n", + "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:00<00:00, 62.24it/s]\n", + "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:00<00:00, 62.29it/s]\n", + "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:00<00:00, 94.50it/s]\n", + "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:00<00:00, 61.56it/s]\n", + "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:00<00:00, 97.18it/s]\n", + "tal.reconstruct.filter_H (pf, 3/3): 100%|██████████| 3/3 [00:00<00:00, 86.35it/s]\n", + "tal.resources progress: 100%|██████████| 8/8 [00:00<00:00, 508.82it/s]" ] }, { @@ -136,7 +136,7 @@ "output_type": "stream", "text": [ "\n", - "tal.resources progress: 100%|██████████| 8/8 [00:00<00:00, 35209.27it/s]\n" + "tal.resources progress: 100%|██████████| 8/8 [00:00<00:00, 30559.59it/s]\n" ] }, { @@ -162,7 +162,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -179,8 +179,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "tal.reconstruct.pf_dev propagation (1/2): 64it [00:00, 114.57it/s] \n", - "tal.reconstruct.pf_dev ifft (2/2): 100%|██████████| 41/41 [00:00<00:00, 188.19it/s]\n" + "tal.reconstruct.pf_dev propagation (1/2): 64it [00:00, 126.58it/s] \n", + "tal.reconstruct.pf_dev ifft (2/2): 100%|██████████| 41/41 [00:00<00:00, 204.15it/s]\n" ] }, { @@ -206,16 +206,16 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 6, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, @@ -241,16 +241,16 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 7, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, diff --git a/pytest.ini b/pytest.ini new file mode 100644 index 0000000..e3c1c79 --- /dev/null +++ b/pytest.ini @@ -0,0 +1,4 @@ +[pytest] +testpaths="tests" +filterwarnings= + ignore::DeprecationWarning \ No newline at end of file diff --git a/tal/__init__.py b/tal/__init__.py index 7ba5ae8..970d6dd 100644 --- a/tal/__init__.py +++ b/tal/__init__.py @@ -5,4 +5,4 @@ from tal.config import set_resources, ResourcesConfig as resources from tal.log import set_log_level, LogLevel -__version__ = '0.17.0' +__version__ = '0.17.1' diff --git a/tal/__main__.py b/tal/__main__.py index 4021b1d..f4529f6 100644 --- a/tal/__main__.py +++ b/tal/__main__.py @@ -95,9 +95,6 @@ def main(): render_parser.add_argument('-n', '--nice', type=int, default=0, required=False, help='Change +/- in nice factor. Positive values = lower priority. Negative values = higher priority (needs sudo)') - render_parser.add_argument('-q', '--quiet', - dest='quiet', action='store_true', - help='Disable progress bars and other verbose outputs') render_parser.add_argument('-g', '--gpu', dest='gpus', nargs='*', type=int, default=[], required=False, help='Select which GPUs should be used by Mitsuba via the CUDA_VISIBLE_DEVICES env. variable') diff --git a/tal/io/capture_data.py b/tal/io/capture_data.py index c4e745b..a9396aa 100644 --- a/tal/io/capture_data.py +++ b/tal/io/capture_data.py @@ -69,7 +69,7 @@ def write_hdf5(filename: str, capture_data: dict): if value is None: file[key] = h5py.Empty(float) elif isinstance(value, dict): - file[key] = yaml.dump(value) + file[key] = yaml.dump(value, Dumper=yaml.CDumper) elif isinstance(value, Enum): dt = h5py.enum_dtype(dict((item.name, item.value) for item in value.__class__), basetype='i') @@ -242,7 +242,7 @@ def __init__(self, filename: str = None, file_format: FileFormat = FileFormat.AU if key not in own_dict_keys: raise AssertionError(f'raw_data contains unknown key: {key}') if key == 'scene_info' and not isinstance(value, h5py.Empty): - value = yaml.load(value, Loader=yaml.Loader) + value = yaml.load(value, Loader=yaml.CLoader) setattr(self, key, value) diff --git a/tal/reconstruct/bp/__init__.py b/tal/reconstruct/bp/__init__.py index 648dafa..fbb0c7f 100644 --- a/tal/reconstruct/bp/__init__.py +++ b/tal/reconstruct/bp/__init__.py @@ -71,6 +71,8 @@ def solve(data: NLOSCaptureData, If True, shows a progress bar with estimated time remaining. """ from tal.reconstruct.util import convert_to_N_3, convert_reconstruction_from_N_3 + if projector_focus is not None: + projector_focus = np.array(projector_focus) H, laser_grid_xyz, sensor_grid_xyz, volume_xyz_n3, _, __ = \ convert_to_N_3(data, volume_xyz, volume_format) diff --git a/tal/render/__init__.py b/tal/render/__init__.py index a5d54dd..dfd24de 100644 --- a/tal/render/__init__.py +++ b/tal/render/__init__.py @@ -27,4 +27,4 @@ def render_nlos_scene(config_path, args): See tal render -h for more information. You probably want to do tal render , or tal render new """ from tal.render import render - render.render_nlos_scene(config_path, args) + return render.render_nlos_scene(config_path, args) diff --git a/tal/render/mitsuba2_transient_nlos.py b/tal/render/mitsuba2_transient_nlos.py index 44d5c83..66fd05b 100644 --- a/tal/render/mitsuba2_transient_nlos.py +++ b/tal/render/mitsuba2_transient_nlos.py @@ -111,7 +111,7 @@ def get_materials(): } -def get_scene_xml(config, random_seed=0, quiet=False): +def get_scene_xml(config, random_seed=0): import os import tal from tal.util import fdent @@ -295,7 +295,7 @@ def g(key): name = g('name') is_relay_wall = name == relay_wall_name - if is_relay_wall and g('mesh')['type'] != 'rectangle' and not quiet: + if is_relay_wall and g('mesh')['type'] != 'rectangle': log(LogLevel.WARNING, 'Relay wall does not work well with meshes that are ' 'not of type "rectangle" because of wrong UV mapping. ' 'Please make sure that you know what you are doing') @@ -492,62 +492,57 @@ def run_mitsuba(scene_xml_path, hdr_path, defines, command = ['/bin/bash', '-c', f'source "{setpath_location}" && {" ".join(command)}'] - if args.quiet: - # simplified version, block until done rendering - mitsuba_process = subprocess.Popen( - command, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) - else: - # need to pass the command through stdbuf to be able to read the progress bar - command = ['stdbuf', '-o0'] + command - mitsuba_process = subprocess.Popen( - command, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) - - # read the progress bar and pass the info to the user through a tqdm bar - # this is totally not overengineering-trust me-this saves so much time - progress_re = re.compile( - r'Rendering \[(=* *)\] \([\d\.]+\w+, ETA: ([\d\.]+\w+)\)') - read_opl = defines.get('auto_detect_bins', False) - if read_opl: - opl_output = '' - opl_re = re.compile( - r'limits: \[(\d+\.\d+), \d+\.\d+\] with bin width (\d+\.\d+)') - with tqdm(desc=experiment_name, total=100, ascii=True, leave=False, - file=TQDMLogRedirect(), - bar_format='{desc} |{bar}| [{n:.2f}%{postfix}] ') as pbar: - output = None - while output is None or len(output) > 0: - output = mitsuba_process.stdout.read(160) - try: - output = output.decode('utf-8') - except UnicodeDecodeError: - continue - if logfile is not None: - logfile.write(output) - logfile.flush() - if read_opl: - opl_output += output - matches = opl_re.findall(opl_output) - if len(matches) > 0: - start_opl, bin_width_opl = matches[-1] - start_opl = float(start_opl) - bin_width_opl = float(bin_width_opl) - log(LogLevel.INFO, 'Auto-detected histogram: ' - f'start_opl={start_opl:.4f}, bin_width_opl={bin_width_opl:.6f}') - defines.update(start_opl=start_opl) - defines.update(bin_width_opl=bin_width_opl) - read_opl = False - del opl_output - matches = progress_re.findall(output) + # need to pass the command through stdbuf to be able to read the progress bar + command = ['stdbuf', '-o0'] + command + mitsuba_process = subprocess.Popen( + command, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) + + # read the progress bar and pass the info to the user through a tqdm bar + # this is totally not overengineering-trust me-this saves so much time + progress_re = re.compile( + r'Rendering \[(=* *)\] \([\d\.]+\w+, ETA: ([\d\.]+\w+)\)') + read_opl = defines.get('auto_detect_bins', False) + if read_opl: + opl_output = '' + opl_re = re.compile( + r'limits: \[(\d+\.\d+), \d+\.\d+\] with bin width (\d+\.\d+)') + with tqdm(desc=experiment_name, total=100, ascii=True, leave=False, + file=TQDMLogRedirect(), + bar_format='{desc} |{bar}| [{n:.2f}%{postfix}] ') as pbar: + output = None + while output is None or len(output) > 0: + output = mitsuba_process.stdout.read(160) + try: + output = output.decode('utf-8') + except UnicodeDecodeError: + continue + if logfile is not None: + logfile.write(output) + logfile.flush() + if read_opl: + opl_output += output + matches = opl_re.findall(opl_output) if len(matches) > 0: - progress, eta = matches[-1] - completed = progress.count('=') - not_completed = progress.count(' ') - progress = 100 * completed / (completed + not_completed) - pbar.update(progress - pbar.n) - pbar.set_postfix_str(f'ETA: {eta}') - if not_completed == 0: - break - time.sleep(1) + start_opl, bin_width_opl = matches[-1] + start_opl = float(start_opl) + bin_width_opl = float(bin_width_opl) + log(LogLevel.INFO, 'Auto-detected histogram: ' + f'start_opl={start_opl:.4f}, bin_width_opl={bin_width_opl:.6f}') + defines.update(start_opl=start_opl) + defines.update(bin_width_opl=bin_width_opl) + read_opl = False + del opl_output + matches = progress_re.findall(output) + if len(matches) > 0: + progress, eta = matches[-1] + completed = progress.count('=') + not_completed = progress.count(' ') + progress = 100 * completed / (completed + not_completed) + pbar.update(progress - pbar.n) + pbar.set_postfix_str(f'ETA: {eta}') + if not_completed == 0: + break + time.sleep(1) # wait for the process to end mitsuba_process.communicate() diff --git a/tal/render/mitsuba3_transient_nlos.py b/tal/render/mitsuba3_transient_nlos.py index 6370323..b792f8a 100644 --- a/tal/render/mitsuba3_transient_nlos.py +++ b/tal/render/mitsuba3_transient_nlos.py @@ -124,7 +124,7 @@ def get_materials(): } -def get_scene_xml(config, random_seed=0, quiet=False): +def get_scene_xml(config, random_seed=0): import os import tal from tal.util import fdent @@ -342,7 +342,7 @@ def g(key): name = g('name') is_relay_wall = name == relay_wall_name - if is_relay_wall and g('mesh')['type'] != 'rectangle' and not quiet: + if is_relay_wall and g('mesh')['type'] != 'rectangle': log(LogLevel.WARNING, 'Relay wall does not work well with meshes that are ' 'not of type "rectangle" because of wrong UV mapping. ' 'Please make sure that you know what you are doing') @@ -516,7 +516,7 @@ def shapify(content): def run_mitsuba(scene_xml_path, hdr_path, defines, - experiment_name, logfile, args, sensor_index=0, queue=None): + experiment_name, args, pipe_output, sensor_index=0): try: import mitsuba as mi import mitransient as mitr @@ -526,8 +526,8 @@ def run_mitsuba(scene_xml_path, hdr_path, defines, import sys import os - sys.stdout = queue - sys.stderr = queue + sys.stdout = pipe_output + sys.stderr = pipe_output if os.name == 'posix': # Nice only available in posix systems os.nice(args.nice) @@ -569,16 +569,13 @@ def find_id(array, eid): if isinstance(integrator, TransientADIntegrator): integrator.prepare_transient(scene, sensor_index) - progress_bar = None - if not args.quiet: - progress_bar = tqdm(total=100, desc=experiment_name, - file=TQDMLogRedirect(), - ascii=True, leave=False) + progress_bar = tqdm(total=100, desc=experiment_name, + file=TQDMLogRedirect(), + ascii=True, leave=False) def update_progress(p): - if not args.quiet: - progress_bar.n = int(p * 100) - progress_bar.refresh() + progress_bar.n = int(p * 100) + progress_bar.refresh() steady_image, transient_image = integrator.render( scene, progress_callback=update_progress) @@ -591,8 +588,7 @@ def update_progress(p): if result.ndim == 4: # sum all channels result = np.sum(result, axis=-1) - if not args.quiet: - progress_bar.close() + progress_bar.close() del steady_image, transient_image, progress_bar else: image = integrator.render(scene, sensor_index) @@ -602,10 +598,9 @@ def update_progress(p): del result, scene, integrator except Exception as e: - queue.write(e) - - sys.stdout = sys.__stdout__ - sys.stderr = sys.__stderr__ + print('/!\ Mitsuba process threw an exception:', e, file=sys.stderr) + finally: + pipe_output.close() def read_mitsuba_bitmap(path: str): diff --git a/tal/render/render.py b/tal/render/render.py index e46d9c7..4f79378 100644 --- a/tal/render/render.py +++ b/tal/render/render.py @@ -1,23 +1,32 @@ import os -import sys import shutil import yaml import tal +import platform from tal.io.capture_data import NLOSCaptureData from tal.enums import FileFormat, GridFormat, HFormat, GroundTruthFormat from tal.config import local_file_path from tal.log import log, LogLevel, TQDMLogRedirect +from tal.render.util import get_grid_xyz, expand_xy_dims import datetime import numpy as np from tqdm import tqdm import multiprocessing -from multiprocessing.queues import Queue from functools import partial from tal.render.util import import_mitsuba_backend -def render_nlos_scene(config_path, args, num_retries=0): +def _read_config_and_init_mitsuba_variant(config_path, args): + # FIXME: on macOS "spawn" method, which is the default since 3.8, + # is considered more safe than "fork", but requires serialization methods available + # to send the objects to the spawned process. So a proper fix would be to add them + # (see e.g. https://stackoverflow.com/a/65513291 and + # https://docs.python.org/3/library/multiprocessing.html#the-spawn-and-forkserver-start-methods + # for more details) + if platform.system() == 'Darwin': + multiprocessing.set_start_method('fork') + config_path = os.path.abspath(config_path) assert os.path.exists(config_path), \ @@ -31,8 +40,6 @@ def render_nlos_scene(config_path, args, num_retries=0): assert os.path.isfile(config_path), \ f'{config_path} is not a TAL config file' - config_dir, config_filename = os.path.split(config_path) - try: scene_config = yaml.safe_load( open(config_path, 'r')) or dict() @@ -58,21 +65,23 @@ def render_nlos_scene(config_path, args, num_retries=0): if not args.dry_run: mitsuba_backend.set_variant(scene_config['mitsuba_variant']) - steady_xml, ground_truth_xml, nlos_xml = mitsuba_backend.get_scene_xml( - scene_config, random_seed=args.seed, quiet=args.quiet) + return mitsuba_backend, scene_config, config_path + +def _check_progress_and_create_folders(config_path, args): + config_dir, config_filename = os.path.split(config_path) try: in_progress = False progress_file = os.path.join(config_dir, 'IN_PROGRESS') if os.path.exists(progress_file): with open(progress_file, 'r') as f: progress_folder = f.read() - if os.path.exists(os.path.join(config_dir, progress_folder)) and not args.quiet: + if os.path.exists(os.path.join(config_dir, progress_folder)): in_progress = True else: log(LogLevel.INFO, 'The IN_PROGRESS file is stale, removing it...') os.remove(progress_file) - if in_progress and not args.quiet: + if in_progress: log(LogLevel.INFO, f'Found a render in progress ({progress_folder}), continuing...') if not in_progress: @@ -96,413 +105,411 @@ def render_nlos_scene(config_path, args, num_retries=0): except OSError as exc: raise AssertionError(f'Invalid permissions: {exc}') from exc - try: - steady_scene_xml = os.path.join(root_dir, 'steady_scene.xml') - with open(steady_scene_xml, 'w') as f: - f.write(steady_xml) - - ground_truth_scene_xml = os.path.join( - root_dir, 'ground_truth_scene.xml') - with open(ground_truth_scene_xml, 'w') as f: - f.write(ground_truth_xml) - - nlos_scene_xml = os.path.join(root_dir, 'nlos_scene.xml') - with open(nlos_scene_xml, 'w') as f: - f.write(nlos_xml) - - laser_lookats = [] - name = scene_config['name'] - scan_type = scene_config['scan_type'] - num_bins = scene_config['num_bins'] - sensor_width = scene_config['sensor_width'] - sensor_height = scene_config['sensor_height'] - laser_width = scene_config['laser_width'] - laser_height = scene_config['laser_height'] - if scan_type == 'single': - laser_width = 1 - laser_height = 1 - - relay_wall = next(filter( - lambda g: g['name'] == scene_config['relay_wall'], - scene_config['geometry'])) - assert 'rot_degrees_x' not in relay_wall and \ - 'rot_degrees_y' not in relay_wall and \ - 'rot_degrees_z' not in relay_wall, \ - 'Relay wall displacement/rotation is NYI' - - def get_grid_xyz(nx, ny, rw_scale_x, rw_scale_y, ax0=0, ax1=1, ay0=0, ay1=1): - px0 = -rw_scale_x + 2 * rw_scale_x * ax0 - px1 = rw_scale_x - 2 * rw_scale_x * (1 - ax1) - py0 = -rw_scale_y + 2 * rw_scale_y * ay0 - py1 = rw_scale_y - 2 * rw_scale_y * (1 - ay1) - xg = np.stack( - (np.linspace(px0, px1, num=2*nx + 1)[1::2],)*ny, axis=1) - yg = np.stack( - (np.linspace(py0, py1, num=2*ny + 1)[1::2],)*nx, axis=0) - assert xg.shape[0] == yg.shape[0] == nx and xg.shape[1] == yg.shape[1] == ny, \ - 'Incorrect shapes' - return np.stack([xg, yg, np.zeros((nx, ny))], axis=-1).astype(np.float32) - - def expand(vec, x, y): - assert len(vec) == 3 - return vec.reshape(1, 1, 3).repeat(x, axis=0).repeat(y, axis=1) - - laser_aperture_start_x = scene_config['laser_aperture_start_x'] or 0 - laser_aperture_start_y = scene_config['laser_aperture_start_y'] or 0 - laser_aperture_end_x = scene_config['laser_aperture_end_x'] or 1 - laser_aperture_end_y = scene_config['laser_aperture_end_y'] or 1 - - if scan_type == 'single': - laser_lookat_x = \ - scene_config['laser_lookat_x'] or sensor_width / 2 - laser_lookat_y = \ - scene_config['laser_lookat_y'] or sensor_height / 2 - laser_lookats.append((laser_lookat_x, laser_lookat_y)) - elif scan_type == 'exhaustive' or scan_type == 'confocal': - assert not (scan_type == 'confocal' and - (laser_width != sensor_width or - laser_height != sensor_height)), \ - 'If using scan_type=confocal, sensor_{width|height} must match laser_{width|height}' - - for y in range(laser_height): - for x in range(laser_width): - # start in (0, 1) space - laser_lookat_x = (x + 0.5) / laser_width - laser_lookat_y = (y + 0.5) / laser_height - # take aperture into account - laser_lookat_x = laser_aperture_start_x + \ - laser_lookat_x * \ - (laser_aperture_end_x - laser_aperture_start_x) - laser_lookat_y = laser_aperture_start_y + \ - laser_lookat_y * \ - (laser_aperture_end_y - laser_aperture_start_y) - # finally store in sensor space (0, sensor_width) - laser_lookat_x *= sensor_width - laser_lookat_y *= sensor_height - laser_lookats.append((laser_lookat_x, laser_lookat_y)) - else: - raise AssertionError( - 'Invalid scan_type, must be one of {single|exhaustive|confocal}') - - # TODO(diego): rotate - displacement = np.array([ - relay_wall['displacement_x'], - relay_wall['displacement_y'], - relay_wall['displacement_z']]) - sensor_grid_xyz = get_grid_xyz( - sensor_width, sensor_height, relay_wall['scale_x'], relay_wall['scale_y']) - sensor_grid_xyz += displacement - if scan_type == 'single': - px = relay_wall['scale_x'] * \ - ((laser_lookat_x / sensor_width) * 2 - 1) - py = relay_wall['scale_y'] * \ - ((laser_lookat_y / sensor_height) * 2 - 1) - laser_grid_xyz = np.array([[ - [px, py, 0], - ]], dtype=np.float32) - else: - laser_grid_xyz = get_grid_xyz( - laser_width, laser_height, relay_wall['scale_x'], relay_wall['scale_y'], - ax0=laser_aperture_start_x, ax1=laser_aperture_end_x, - ay0=laser_aperture_start_y, ay1=laser_aperture_end_y) - laser_grid_xyz += displacement - # TODO(diego): rotate [0, 0, 1] by rot_degrees_x (assmes RW is a plane) - # or use a more generalist approach - sensor_grid_normals = expand( - np.array([0, 0, 1]), sensor_width, sensor_height) - laser_grid_normals = expand( - np.array([0, 0, 1]), laser_width, laser_height) - - experiment_name = scene_config['name'] - - class StdoutQueue(Queue): - def __init__(self, *args, **kwargs): - ctx = multiprocessing.get_context() - super(StdoutQueue, self).__init__(*args, **kwargs, ctx=ctx) - - def write(self, msg): - self.put(msg) - - # TODO(diego): move this to a separate function - - if args.do_steady_renders: - def render_steady(render_name, sensor_index): - if not args.quiet: - log(LogLevel.INFO, - f'{render_name} for {experiment_name} steady render...') - hdr_ext = mitsuba_backend.get_hdr_extension() - hdr_path = os.path.join(partial_results_dir, - f'{experiment_name}_{render_name}.{hdr_ext}') - ldr_path = os.path.join(steady_dir, - f'{experiment_name}_{render_name}.png') - if os.path.exists(ldr_path) and not args.quiet: - pass # skip + return root_dir, partial_results_dir, steady_dir, log_dir, progress_file + + +def __write_scene_xmls(args, mitsuba_backend, scene_config, root_dir): + steady_xml, ground_truth_xml, nlos_xml = mitsuba_backend.get_scene_xml( + scene_config, random_seed=args.seed) + + steady_scene_xml = os.path.join(root_dir, 'steady_scene.xml') + with open(steady_scene_xml, 'w') as f: + f.write(steady_xml) + + ground_truth_scene_xml = os.path.join( + root_dir, 'ground_truth_scene.xml') + with open(ground_truth_scene_xml, 'w') as f: + f.write(ground_truth_xml) + + nlos_scene_xml = os.path.join(root_dir, 'nlos_scene.xml') + with open(nlos_scene_xml, 'w') as f: + f.write(nlos_xml) + + return steady_scene_xml, ground_truth_scene_xml, nlos_scene_xml + + +def __write_metadata_and_get_laser_lookats(args, scene_config): + """ Compute laser_lookats """ + + laser_lookats = [] + scan_type = scene_config['scan_type'] + num_bins = scene_config['num_bins'] + sensor_width = scene_config['sensor_width'] + sensor_height = scene_config['sensor_height'] + laser_width = scene_config['laser_width'] + laser_height = scene_config['laser_height'] + if scan_type == 'single': + laser_width = 1 + laser_height = 1 + + relay_wall = next(filter( + lambda g: g['name'] == scene_config['relay_wall'], + scene_config['geometry'])) + assert 'rot_degrees_x' not in relay_wall and \ + 'rot_degrees_y' not in relay_wall and \ + 'rot_degrees_z' not in relay_wall, \ + 'Relay wall displacement/rotation is NYI' + + laser_aperture_start_x = scene_config['laser_aperture_start_x'] or 0 + laser_aperture_start_y = scene_config['laser_aperture_start_y'] or 0 + laser_aperture_end_x = scene_config['laser_aperture_end_x'] or 1 + laser_aperture_end_y = scene_config['laser_aperture_end_y'] or 1 + + if scan_type == 'single': + laser_lookat_x = \ + scene_config['laser_lookat_x'] or sensor_width / 2 + laser_lookat_y = \ + scene_config['laser_lookat_y'] or sensor_height / 2 + laser_lookats.append((laser_lookat_x, laser_lookat_y)) + elif scan_type == 'exhaustive' or scan_type == 'confocal': + assert not (scan_type == 'confocal' and + (laser_width != sensor_width or + laser_height != sensor_height)), \ + 'If using scan_type=confocal, sensor_{width|height} must match laser_{width|height}' + + for y in range(laser_height): + for x in range(laser_width): + # start in (0, 1) space + laser_lookat_x = (x + 0.5) / laser_width + laser_lookat_y = (y + 0.5) / laser_height + # take aperture into account + laser_lookat_x = laser_aperture_start_x + \ + laser_lookat_x * \ + (laser_aperture_end_x - laser_aperture_start_x) + laser_lookat_y = laser_aperture_start_y + \ + laser_lookat_y * \ + (laser_aperture_end_y - laser_aperture_start_y) + # finally store in sensor space (0, sensor_width) + laser_lookat_x *= sensor_width + laser_lookat_y *= sensor_height + laser_lookats.append((laser_lookat_x, laser_lookat_y)) + else: + raise AssertionError( + 'Invalid scan_type, must be one of {single|exhaustive|confocal}') + + """ Create NLOSCaptureData and write all metadata """ + + # TODO(diego): rotate sensor_grid_xyz and laser_grid_xyz based on relay wall rotation + displacement = np.array([ + relay_wall['displacement_x'], + relay_wall['displacement_y'], + relay_wall['displacement_z']]) + sensor_grid_xyz = get_grid_xyz( + sensor_width, sensor_height, relay_wall['scale_x'], relay_wall['scale_y']) + sensor_grid_xyz += displacement + if scan_type == 'single': + px = relay_wall['scale_x'] * \ + ((laser_lookat_x / sensor_width) * 2 - 1) + py = relay_wall['scale_y'] * \ + ((laser_lookat_y / sensor_height) * 2 - 1) + laser_grid_xyz = np.array([[ + [px, py, 0], + ]], dtype=np.float32) + else: + laser_grid_xyz = get_grid_xyz( + laser_width, laser_height, relay_wall['scale_x'], relay_wall['scale_y'], + ax0=laser_aperture_start_x, ax1=laser_aperture_end_x, + ay0=laser_aperture_start_y, ay1=laser_aperture_end_y) + laser_grid_xyz += displacement + + # TODO(diego): rotate [0, 0, 1] by rot_degrees_x (assmes RW is a plane) + # or use a more generalist approach + sensor_grid_normals = expand_xy_dims( + np.array([0, 0, 1]), sensor_width, sensor_height) + laser_grid_normals = expand_xy_dims( + np.array([0, 0, 1]), laser_width, laser_height) + + capture_data = NLOSCaptureData() + if scan_type == 'single' or scan_type == 'confocal': + capture_data.H = np.zeros( + (num_bins, sensor_width, sensor_height), + dtype=np.float32) + capture_data.H_format = HFormat.T_Sx_Sy + elif scan_type == 'exhaustive': + capture_data.H = np.zeros( + (num_bins, laser_width, laser_height, sensor_width, sensor_height), + dtype=np.float32) + capture_data.H_format = HFormat.T_Lx_Ly_Sx_Sy + else: + raise AssertionError( + 'Invalid scan_type, must be one of {single|exhaustive|confocal}') + capture_data.sensor_xyz = np.array([ + scene_config['sensor_x'], + scene_config['sensor_y'], + scene_config['sensor_z'], + ], dtype=np.float32) + capture_data.sensor_grid_xyz = sensor_grid_xyz + capture_data.sensor_grid_normals = sensor_grid_normals + capture_data.sensor_grid_format = GridFormat.X_Y_3 + capture_data.laser_xyz = np.array([ + scene_config['laser_x'], + scene_config['laser_y'], + scene_config['laser_z'], + ], dtype=np.float32) + capture_data.laser_grid_xyz = laser_grid_xyz + capture_data.laser_grid_normals = laser_grid_normals + capture_data.laser_grid_format = GridFormat.X_Y_3 + capture_data.delta_t = scene_config['bin_width_opl'] + capture_data.t_start = scene_config['start_opl'] + capture_data.t_accounts_first_and_last_bounces = \ + scene_config['account_first_and_last_bounces'] + capture_data.scene_info = { + 'tal_version': tal.__version__, + 'config': scene_config, + 'args': vars(args), + } + + return scan_type, laser_lookats, capture_data + + +def __run_mitsuba(args, log_path, mitsuba_backend, scene_xml, hdr_path, defines, experiment_name, render_name, sensor_index, + check_done=lambda: False): + if check_done(): + log(LogLevel.INFO, f'Skipping {render_name} for {experiment_name}') + return + + class StdoutPipe: + def __init__(self, pipe, logfile=None): + self.pipe = pipe + self.logfile = logfile + + def write(self, data): + self.pipe.send(data) + if logfile is not None: + self.logfile.write(data) + + def close(self): + self.pipe.send(None) + self.pipe.close() + if logfile is not None: + self.logfile.close() + + pipe_r, pipe_w = multiprocessing.Pipe() + logfile = None + if args.do_logging and not args.dry_run: + logfile = open(log_path, 'w') + # NOTE: something here has a memory leak (probably Mitsuba-related) + # We run Mitsuba in a separate process to ensure that the leaks do not add up + # as they can fill your RAM in exhaustive scans + run_mitsuba_f = partial(mitsuba_backend.run_mitsuba, scene_xml, hdr_path, defines, + render_name, args, StdoutPipe(pipe_w, logfile), sensor_index) + if os.name == 'nt': + # NOTE: Windows does not support multiprocessing + run_mitsuba_f() + else: + process = multiprocessing.Process(target=run_mitsuba_f) + try: + process.start() + while True: + line = pipe_r.recv() + if line: + if len(line.strip()) > 0: + log(LogLevel.INFO, line) else: - logfile = None - if args.do_logging and not args.dry_run: - logfile = open(os.path.join( - log_dir, f'{experiment_name}_{render_name}.log'), 'w') - # NOTE: something here has a memory leak (probably Mitsuba-related) - # We run Mitsuba in a separate process to ensure that the leaks do not add up - # as they can fill your RAM in exhaustive scans - queue = StdoutQueue() - run_mitsuba_f = partial(mitsuba_backend.run_mitsuba, steady_scene_xml, hdr_path, dict(), - render_name, logfile, args, sensor_index, queue) - if os.name == 'nt': - # NOTE: Windows does not support multiprocessing - run_mitsuba_f() - else: - process = multiprocessing.Process(target=run_mitsuba_f) - try: - process.start() - process.join() - except KeyboardInterrupt: - process.terminate() - raise KeyboardInterrupt - if args.do_logging and not args.dry_run: - while not queue.empty(): - e = queue.get() - if isinstance(e, Exception): - raise e - else: - logfile.write(e) - queue.close() - logfile.close() - if not args.dry_run: - mitsuba_backend.convert_hdr_to_ldr(hdr_path, ldr_path) - - render_steady('back_view', 0) - render_steady('side_view', 1) - - if args.do_ground_truth_renders: - if not args.quiet: - log(LogLevel.INFO, - f'ground_truth for {experiment_name}...') - gt_ext = mitsuba_backend.get_hdr_extension() - gt_path = os.path.join(partial_results_dir, - f'{experiment_name}_ground_truth.{gt_ext}') - if os.path.exists(gt_path) and not args.quiet: - pass # skip - else: - logfile = None - if args.do_logging and not args.dry_run: - logfile = open(os.path.join( - log_dir, f'{experiment_name}_ground_truth.log'), 'w') - # NOTE: something here has a memory leak (probably Mitsuba-related) - # We run Mitsuba in a separate process to ensure that the leaks do not add up - # as they can fill your RAM in exhaustive scans - queue = StdoutQueue() - run_mitsuba_f = partial(mitsuba_backend.run_mitsuba, ground_truth_scene_xml, gt_path, dict(), - 'ground_truth', logfile, args, 0, queue) - if os.name == 'nt': - # NOTE: Windows does not support multiprocessing - run_mitsuba_f() + raise EOFError + except EOFError: + pipe_r.close() + process.join() + except KeyboardInterrupt: + process.terminate() + raise KeyboardInterrupt + + if args.do_logging and not args.dry_run: + logfile.flush() + logfile.close() + + +class RenderException(Exception): + pass + + +def __merge_gt_results(args, mitsuba_backend, capture_data, gt_path): + if not args.do_ground_truth_renders: + return + + gt_image = mitsuba_backend.read_mitsuba_bitmap(gt_path) + depth = gt_image[:, :, 0:3] + normals = gt_image[:, :, 3:6] + capture_data.scene_info['ground_truth'] = { + 'format': GroundTruthFormat.X_Y, + 'depth': depth, + 'normals': normals, + } + + return capture_data + + +def __merge_nlos_results(args, mitsuba_backend, capture_data, partial_results_dir, experiment_name, scan_type, laser_lookats): + + if scan_type == 'single': + hdr_path, _ = mitsuba_backend.partial_laser_path( + partial_results_dir, + experiment_name, + *laser_lookats[0]) + capture_data.H = mitsuba_backend.read_transient_image(hdr_path) + elif scan_type == 'exhaustive' or scan_type == 'confocal': + laser_width = capture_data.H.shape[1] + + e_laser_lookats = enumerate(laser_lookats) + if len(laser_lookats) > 1: + e_laser_lookats = tqdm( + e_laser_lookats, desc='Merging partial results...', + file=TQDMLogRedirect(), ascii=True, total=len(laser_lookats)) + try: + for i, (laser_lookat_x, laser_lookat_y) in e_laser_lookats: + x = i % laser_width + y = i // laser_width + hdr_path, _ = mitsuba_backend.partial_laser_path( + partial_results_dir, + experiment_name, + laser_lookat_x, laser_lookat_y) + if scan_type == 'confocal': + capture_data.H[:, x:x+1, y:y+1, ...] = \ + mitsuba_backend.read_transient_image(hdr_path) + elif scan_type == 'exhaustive': + capture_data.H[:, x, y, ...] = \ + mitsuba_backend.read_transient_image(hdr_path) else: - process = multiprocessing.Process(target=run_mitsuba_f) - try: - process.start() - process.join() - except KeyboardInterrupt: - process.terminate() - raise KeyboardInterrupt - if args.do_logging and not args.dry_run: - while not queue.empty(): - e = queue.get() - if isinstance(e, Exception): - raise e - else: - logfile.write(e) - queue.close() - logfile.close() - - pbar = tqdm( - enumerate(laser_lookats), desc=f'Rendering {experiment_name} ({scan_type})...', - file=TQDMLogRedirect(), ascii=True, total=len(laser_lookats)) - for i, (laser_lookat_x, laser_lookat_y) in pbar: - try: - hdr_path, is_dir = mitsuba_backend.partial_laser_path( - partial_results_dir, experiment_name, laser_lookat_x, laser_lookat_y) - if os.path.exists(hdr_path) and not args.quiet: - continue # skip - if is_dir: - os.mkdir(hdr_path) - except OSError as exc: - raise AssertionError(f'Invalid permissions: {exc}') from exc - defines = { - 'laser_lookat_x': laser_lookat_x, - 'laser_lookat_y': laser_lookat_y, - } - logfile = None - if args.do_logging and not args.dry_run: - logfile = open(os.path.join( - log_dir, - f'{experiment_name}_L[{laser_lookat_x}][{laser_lookat_y}].log'), 'w') - queue = StdoutQueue() - render_name = f'Laser {i + 1} of {len(laser_lookats)}' - # NOTE: something here has a memory leak (probably Mitsuba-related) - # We run Mitsuba in a separate process to ensure that the leaks do not add up - # as they can fill your RAM in exhaustive scans - run_mitsuba_f = partial(mitsuba_backend.run_mitsuba, nlos_scene_xml, hdr_path, defines, - render_name, sys.stdout, args, queue=queue) - if os.name == 'nt': - # NOTE: Windows does not support multiprocessing - run_mitsuba_f() - else: - process = multiprocessing.Process(target=run_mitsuba_f) - try: - process.start() - process.join() - except KeyboardInterrupt: - process.terminate() - raise KeyboardInterrupt - if scan_type == 'exhaustive' and i == 0: - size_bytes = os.path.getsize(hdr_path) - final_size_gb = size_bytes * len(laser_lookats) / 2**30 - pbar.set_description( - f'Rendering {experiment_name} ({scan_type}, estimated size: {final_size_gb:.2f} GB)...') - if args.do_logging and not args.dry_run: - past_elems = [] - while not queue.empty(): - e = queue.get() - past_elems.append(e) - if isinstance(e, Exception): - log(LogLevel.ERROR, '') - log(LogLevel.ERROR, '/!\ Mitsuba thread got an exception!') - log(LogLevel.ERROR, '') - for pe in past_elems[:-1]: - log(LogLevel.ERROR, pe) - log(LogLevel.ERROR, '') - raise e - else: - logfile.write(e) - logfile.flush() - queue.close() - logfile.close() - - if args.dry_run: - return - - if not args.quiet: - log(LogLevel.INFO, 'Merging partial results...') - - capture_data = NLOSCaptureData() - capture_data.sensor_xyz = np.array([ - scene_config['sensor_x'], - scene_config['sensor_y'], - scene_config['sensor_z'], - ], dtype=np.float32) - capture_data.sensor_grid_xyz = sensor_grid_xyz - capture_data.sensor_grid_normals = sensor_grid_normals - capture_data.sensor_grid_format = GridFormat.X_Y_3 - capture_data.laser_xyz = np.array([ - scene_config['laser_x'], - scene_config['laser_y'], - scene_config['laser_z'], - ], dtype=np.float32) - capture_data.laser_grid_xyz = laser_grid_xyz - capture_data.laser_grid_normals = laser_grid_normals - capture_data.laser_grid_format = GridFormat.X_Y_3 - # NOTE(diego): we do not store volume information for now - # capture_data.volume_format = VolumeFormat.X_Y_Z_3 - capture_data.delta_t = scene_config['bin_width_opl'] - capture_data.t_start = scene_config['start_opl'] - capture_data.t_accounts_first_and_last_bounces = \ - scene_config['account_first_and_last_bounces'] - capture_data.scene_info = { - 'tal_version': tal.__version__, - 'config': scene_config, - 'args': vars(args), + raise AssertionError + except Exception as exc: + raise RenderException from exc + else: + raise AssertionError( + 'Invalid scan_type, must be one of {single|exhaustive|confocal}') + + return capture_data + + +def _main_render(config_path, args, + mitsuba_backend, scene_config, + root_dir, partial_results_dir, steady_dir, log_dir, + progress_file, num_retries=0): + """ General initialization """ + + steady_scene_xml, ground_truth_scene_xml, nlos_scene_xml = \ + __write_scene_xmls(args, mitsuba_backend, scene_config, root_dir) + experiment_name = scene_config['name'] + + """ Steady state + ground truth """ + + if args.do_steady_renders: + def render_steady(render_name, sensor_index): + log(LogLevel.INFO, + f'{render_name} for {experiment_name} steady render...') + hdr_ext = mitsuba_backend.get_hdr_extension() + hdr_path = os.path.join(partial_results_dir, + f'{experiment_name}_{render_name}.{hdr_ext}') + ldr_path = os.path.join(steady_dir, + f'{experiment_name}_{render_name}.png') + log_path = os.path.join( + log_dir, f'{experiment_name}_{render_name}.log') + + __run_mitsuba(args, log_path, mitsuba_backend, steady_scene_xml, hdr_path, dict(), + experiment_name, render_name, sensor_index, check_done=lambda: os.path.exists(ldr_path)) + + if not args.dry_run: + mitsuba_backend.convert_hdr_to_ldr(hdr_path, ldr_path) + + render_steady('back_view', 0) + render_steady('side_view', 1) + + if args.do_ground_truth_renders: + gt_render_name = 'ground_truth' + log(LogLevel.INFO, f'{gt_render_name} for {experiment_name}...') + gt_ext = mitsuba_backend.get_hdr_extension() + gt_path = os.path.join(partial_results_dir, + f'{experiment_name}_{gt_render_name}.{gt_ext}') + log_path = os.path.join( + log_dir, f'{experiment_name}_{gt_render_name}.log') + + __run_mitsuba(args, log_path, mitsuba_backend, ground_truth_scene_xml, gt_path, dict(), + experiment_name, gt_render_name, 0, check_done=lambda: os.path.exists(gt_path)) + + """ NLOS renders """ + + scan_type, laser_lookats, capture_data = \ + __write_metadata_and_get_laser_lookats(args, scene_config) + + pbar = tqdm( + enumerate(laser_lookats), desc=f'Rendering {experiment_name} ({scan_type})...', + file=TQDMLogRedirect(), ascii=True, total=len(laser_lookats)) + for i, (laser_lookat_x, laser_lookat_y) in pbar: + try: + hdr_path, is_dir = mitsuba_backend.partial_laser_path( + partial_results_dir, experiment_name, laser_lookat_x, laser_lookat_y) + if is_dir and not os.path.exists(hdr_path): + os.mkdir(hdr_path) + except OSError as exc: + raise AssertionError(f'Invalid permissions: {exc}') from exc + defines = { + 'laser_lookat_x': laser_lookat_x, + 'laser_lookat_y': laser_lookat_y, } + log_path = os.path.join( + log_dir, + f'{experiment_name}_L[{laser_lookat_x}][{laser_lookat_y}].log') + render_name = f'Laser {i + 1} of {len(laser_lookats)}' - if args.do_ground_truth_renders: - gt_image = mitsuba_backend.read_mitsuba_bitmap(gt_path) - depth = gt_image[:, :, 0:3] - normals = gt_image[:, :, 3:6] - capture_data.scene_info['ground_truth'] = { - 'format': GroundTruthFormat.X_Y, - 'depth': depth, - 'normals': normals, - } - - if scan_type == 'single': - hdr_path, _ = mitsuba_backend.partial_laser_path( - partial_results_dir, - experiment_name, - *laser_lookats[0]) - capture_data.H = mitsuba_backend.read_transient_image(hdr_path) - capture_data.H_format = HFormat.T_Sx_Sy - elif scan_type == 'exhaustive' or scan_type == 'confocal': - if scan_type == 'exhaustive': - capture_data.H = np.empty( - (num_bins, laser_width, laser_height, - sensor_width, sensor_height), - dtype=np.float32) - capture_data.H_format = HFormat.T_Lx_Ly_Sx_Sy - elif scan_type == 'confocal': - capture_data.H = np.empty( - (num_bins, laser_width, laser_height), - dtype=np.float32) - capture_data.H_format = HFormat.T_Sx_Sy - else: - raise AssertionError + __run_mitsuba(args, log_path, mitsuba_backend, nlos_scene_xml, hdr_path, defines, + experiment_name, render_name, 0, check_done=lambda: os.path.exists(hdr_path)) - e_laser_lookats = enumerate(laser_lookats) - if not args.quiet and len(laser_lookats) > 1: - e_laser_lookats = tqdm( - e_laser_lookats, desc='Merging partial results...', - file=TQDMLogRedirect(), ascii=True, total=len(laser_lookats)) - try: - for i, (laser_lookat_x, laser_lookat_y) in e_laser_lookats: - x = i % laser_width - y = i // laser_width - hdr_path, _ = mitsuba_backend.partial_laser_path( - partial_results_dir, - experiment_name, - laser_lookat_x, laser_lookat_y) - if scan_type == 'confocal': - capture_data.H[:, x:x+1, y:y+1, ...] = \ - mitsuba_backend.read_transient_image(hdr_path) - elif scan_type == 'exhaustive': - capture_data.H[:, x, y, ...] = \ - mitsuba_backend.read_transient_image(hdr_path) - else: - raise AssertionError - except Exception as exc: - if num_retries >= 10: - raise AssertionError( - f'Failed to read partial results after {num_retries} retries') - mitsuba_backend.remove_transient_image(hdr_path) - # TODO Mitsuba sometimes fails to write some images, - # it seems like some sort of race condition - # If there is a partial result missing, just re-launch for now - log(LogLevel.INFO, - f'We missed some partial results (iteration {i} failed because: {exc}), re-launching...') - return render_nlos_scene(config_path, args, num_retries=num_retries + 1) - else: + if scan_type == 'exhaustive' and i == 0: + size_bytes = os.path.getsize(hdr_path) + final_size_gb = size_bytes * len(laser_lookats) / 2**30 + pbar.set_description( + f'Rendering {experiment_name} ({scan_type}, estimated size: {final_size_gb:.2f} GB)...') + + if args.dry_run: + return + + """ Generate final HDF5 file""" + + log(LogLevel.INFO, 'Reading partial results and generating HDF5 file...') + try: + capture_data = __merge_gt_results( + args, mitsuba_backend, capture_data, gt_path) + capture_data = __merge_nlos_results( + args, mitsuba_backend, capture_data, partial_results_dir, experiment_name, scan_type, laser_lookats) + except RenderException: + if num_retries >= 10: raise AssertionError( - 'Invalid scan_type, must be one of {single|exhaustive|confocal}') + f'Failed to read partial results after {num_retries} retries') + # TODO(diego): Mitsuba sometimes fails to write some images, + # it seems like some sort of race condition + # If there is a partial result missing, just re-launch for now + mitsuba_backend.remove_transient_image(hdr_path) + log(LogLevel.INFO, + f'We missed some partial results (iteration {i} failed because: {exc}), re-launching...') + return _main_render(config_path, args, num_retries=num_retries + 1) - hdf5_path = os.path.join(root_dir, f'{experiment_name}.hdf5') - tal.io.write_capture(hdf5_path, capture_data, - file_format=FileFormat.HDF5_TAL) + hdf5_path = os.path.join(root_dir, f'{experiment_name}.hdf5') + tal.io.write_capture(hdf5_path, capture_data, + file_format=FileFormat.HDF5_TAL) + log(LogLevel.INFO, f'Stored result in {hdf5_path}') - if not args.quiet: - log(LogLevel.INFO, f'Stored result in {hdf5_path}') + if not args.keep_partial_results: + log(LogLevel.INFO, + f'Cleaning partial results in {partial_results_dir}...') + shutil.rmtree(partial_results_dir, ignore_errors=True) + log(LogLevel.INFO, f'All clean.') - # remove IN_PROGRESS file - os.remove(progress_file) + os.remove(progress_file) - if args.keep_partial_results: - return + return hdf5_path - if not args.quiet: - log(LogLevel.INFO, - f'Cleaning partial results in {partial_results_dir}...') - shutil.rmtree(partial_results_dir) +def render_nlos_scene(config_path, args): + mitsuba_backend, scene_config, config_path = \ + _read_config_and_init_mitsuba_variant(config_path, args) - if not args.quiet: - log(LogLevel.INFO, f'All clean.') + root_dir, partial_results_dir, steady_dir, log_dir, progress_file = \ + _check_progress_and_create_folders(config_path, args) + + try: + return _main_render(config_path, args, + mitsuba_backend, scene_config, + root_dir, partial_results_dir, steady_dir, log_dir, + progress_file) except KeyboardInterrupt: delete = None while delete is None: @@ -518,5 +525,5 @@ def render_steady(render_name, sensor_index): except KeyboardInterrupt: pass if delete: - shutil.rmtree(root_dir) + shutil.rmtree(root_dir, ignore_errors=True) os.remove(progress_file) diff --git a/tal/render/util.py b/tal/render/util.py index 69c5465..5458dbb 100644 --- a/tal/render/util.py +++ b/tal/render/util.py @@ -9,3 +9,23 @@ def import_mitsuba_backend(): raise AssertionError( f'Invalid MITSUBA_VERSION={mitsuba_version}, must be one of (2, 3)') return mitsuba_backend + + +def get_grid_xyz(nx, ny, rw_scale_x, rw_scale_y, ax0=0, ax1=1, ay0=0, ay1=1): + import numpy as np + px0 = -rw_scale_x + 2 * rw_scale_x * ax0 + px1 = rw_scale_x - 2 * rw_scale_x * (1 - ax1) + py0 = -rw_scale_y + 2 * rw_scale_y * ay0 + py1 = rw_scale_y - 2 * rw_scale_y * (1 - ay1) + xg = np.stack( + (np.linspace(px0, px1, num=2*nx + 1)[1::2],)*ny, axis=1) + yg = np.stack( + (np.linspace(py0, py1, num=2*ny + 1)[1::2],)*nx, axis=0) + assert xg.shape[0] == yg.shape[0] == nx and xg.shape[1] == yg.shape[1] == ny, \ + 'Incorrect shapes' + return np.stack([xg, yg, np.zeros((nx, ny))], axis=-1).astype(np.float32) + + +def expand_xy_dims(vec, x, y): + assert len(vec) == 3 + return vec.reshape(1, 1, 3).repeat(x, axis=0).repeat(y, axis=1) diff --git a/tests/render/conftest.py b/tests/render/conftest.py new file mode 100644 index 0000000..6ad0d9b --- /dev/null +++ b/tests/render/conftest.py @@ -0,0 +1,6 @@ +import pytest + + +@pytest.fixture(scope="session", autouse=True) +def setup_path(): + pass diff --git a/tests/render/test_confocal.py b/tests/render/test_confocal.py new file mode 100644 index 0000000..3503cbb --- /dev/null +++ b/tests/render/test_confocal.py @@ -0,0 +1,37 @@ +import pytest + +_EXPERIMENT_FOLDER = 'examples/render-reconstruct-confocal' + + +@pytest.fixture(autouse=True) +def change_test_dir(request, monkeypatch): + monkeypatch.chdir(_EXPERIMENT_FOLDER) + + +def test00_Z_single(): + import tal + from tal.render import render_nlos_scene + import numpy as np + import argparse + import os + + args = { + 'threads': os.cpu_count() - 1, + 'seed': 0, + 'nice': 0, + 'gpu': None, + 'dry_run': False, + 'do_steady_renders': True, + 'do_ground_truth_renders': True, + 'do_logging': True, + 'keep_partial_results': True, + } + args = argparse.Namespace(**args) + config_path = 'confocal-scene' + + hdf5_path = render_nlos_scene(config_path, args) + + data = tal.io.read_capture(hdf5_path) + + assert data.H.shape == (2048, 16, 16) + assert np.isclose(data.H.sum(), 20.65128) diff --git a/tests/render/test_exhaustive.py b/tests/render/test_exhaustive.py new file mode 100644 index 0000000..d348d50 --- /dev/null +++ b/tests/render/test_exhaustive.py @@ -0,0 +1,37 @@ +import pytest + +_EXPERIMENT_FOLDER = 'examples/render-reconstruct-exhaustive' + + +@pytest.fixture(autouse=True) +def change_test_dir(request, monkeypatch): + monkeypatch.chdir(_EXPERIMENT_FOLDER) + + +def test00_Z_single(): + import tal + from tal.render import render_nlos_scene + import numpy as np + import argparse + import os + + args = { + 'threads': os.cpu_count() - 1, + 'seed': 0, + 'nice': 0, + 'gpu': None, + 'dry_run': False, + 'do_steady_renders': True, + 'do_ground_truth_renders': True, + 'do_logging': True, + 'keep_partial_results': True, + } + args = argparse.Namespace(**args) + config_path = 'exhaustive-scene' + + hdf5_path = render_nlos_scene(config_path, args) + + data = tal.io.read_capture(hdf5_path) + + assert data.H.shape == (2048, 16, 16, 16, 16) + assert np.isclose(data.H.sum(), 3640.9846) diff --git a/tests/render/test_single.py b/tests/render/test_single.py new file mode 100644 index 0000000..bee598f --- /dev/null +++ b/tests/render/test_single.py @@ -0,0 +1,37 @@ +import pytest + +_EXPERIMENT_FOLDER = 'examples/render-reconstruct' + + +@pytest.fixture(autouse=True) +def change_test_dir(request, monkeypatch): + monkeypatch.chdir(_EXPERIMENT_FOLDER) + + +def test00_Z_single(): + import tal + from tal.render import render_nlos_scene + import numpy as np + import argparse + import os + + args = { + 'threads': os.cpu_count() - 1, + 'seed': 0, + 'nice': 0, + 'gpu': None, + 'dry_run': False, + 'do_steady_renders': True, + 'do_ground_truth_renders': True, + 'do_logging': True, + 'keep_partial_results': True, + } + args = argparse.Namespace(**args) + config_path = 'nlos-z' + + hdf5_path = render_nlos_scene(config_path, args) + + data = tal.io.read_capture(hdf5_path) + + assert data.H.shape == (320, 64, 64) + assert np.isclose(data.H.sum(), 135.80711)