diff --git a/environment.yml b/environment.yml index 0b1d5fe..e221ce2 100644 --- a/environment.yml +++ b/environment.yml @@ -16,7 +16,7 @@ dependencies: - globus-compute-endpoint - netCDF4 - xarray - - xeofs + - xeofs<3 - cf_xarray - clisops - rooki diff --git a/notebooks/ex-regrid-plot.ipynb b/notebooks/ex-regrid-plot.ipynb index 199a306..f130e19 100644 --- a/notebooks/ex-regrid-plot.ipynb +++ b/notebooks/ex-regrid-plot.ipynb @@ -95,8 +95,8 @@ "# Run this on the DKRZ node in Germany\n", "os.environ[\"ROOK_URL\"] = \"http://rook.dkrz.de/wps\"\n", "intake_esgf.conf.set(indices={\"anl-dev\": False,\n", - " \"ornl-dev\": False,\n", - " \"esgf-data.dkrz.de\": True})\n", + " \"ornl-dev\": False,\n", + " \"esgf-data.dkrz.de\": True})\n", "\n", "import rooki.operators as ops\n", "import matplotlib.pyplot as plt\n", @@ -156,9 +156,11 @@ }, "outputs": [], "source": [ - "def separate_dataset_id(full_dataset):\n", - " dataset_id = full_dataset[0].split(\"|\")[0]\n", - " return dataset_id\n" + "def separate_dataset_id(id_list):\n", + " rooki_id = id_list[0]\n", + " rooki_id = rooki_id.split(\"|\")[0]\n", + " #rooki_id = f\"css03_data.{rooki_id}\" # <-- just something you have to know for now :(\n", + " return rooki_id\n" ] }, { diff --git a/notebooks/rooki_enso_nonlinear.ipynb b/notebooks/rooki_enso_nonlinear.ipynb index d00a2d3..cc60578 100644 --- a/notebooks/rooki_enso_nonlinear.ipynb +++ b/notebooks/rooki_enso_nonlinear.ipynb @@ -85,7 +85,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "a2339b90", "metadata": { "tags": [] @@ -95,10 +95,11 @@ "import os\n", "import intake_esgf\n", "\n", - "# Run this on the Oak Ridge National Laboratory Node\n", - "os.environ[\"ROOK_URL\"] = \"https://esgf-node.ornl.gov/wps\"\n", + "# Run this on the DKRZ node in Germany\n", + "os.environ[\"ROOK_URL\"] = \"http://rook.dkrz.de/wps\"\n", "intake_esgf.conf.set(indices={\"anl-dev\": False,\n", - " \"ornl-dev\": True})\n", + " \"ornl-dev\": False,\n", + " \"esgf-data.dkrz.de\": True})\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -122,44 +123,12 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "66b3b3c0-6aa0-465b-bc17-86ae2ce5f25b", "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "a3a223c41f6d42fa97f7b00fbe42276e", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " Searching indices: 0%| |0/1 [ ?index/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Summary information for 7 results:\n", - "mip_era [CMIP6]\n", - "activity_drs [CMIP]\n", - "institution_id [CNRM-CERFACS, CAS, NCAR, CMCC, CAMS, EC-Earth...\n", - "source_id [CNRM-ESM2-1, FGOALS-g3, CESM2, CMCC-CM2-SR5, ...\n", - "experiment_id [historical]\n", - "member_id [r1i1p1f2, r1i1p1f1]\n", - "table_id [Omon]\n", - "variable_id [tos]\n", - "grid_label [gn]\n", - "dtype: object\n" - ] - } - ], + "outputs": [], "source": [ "cat = ESGFCatalog()\n", "cat.search(\n", @@ -174,7 +143,6 @@ " \"CMCC-CM2-SR5\",\n", " \"CNRM-CM6-1\",\n", " \"CNRM-ESM2-1\",\n", - " \"EC-Earth3-Veg\",\n", " \"CESM2\",\n", " ],\n", ")\n", @@ -192,45 +160,27 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "9482b7d7", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "def build_rooki_id(id_list):\n", - " rooki_id = id_list[0]\n", - " rooki_id = rooki_id.split(\"|\")[0]\n", - " rooki_id = f\"css03_data.{rooki_id}\" # <-- just something you have to know for now :(\n", - " return rooki_id" + "def keep_ds_id(ds):\n", + " return ds[0].split(\"|\")[0]" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "46726e56-030d-4e54-a1a4-5e2f2ca11b43", "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "text/plain": [ - "['css03_data.CMIP6.CMIP.CNRM-CERFACS.CNRM-ESM2-1.historical.r1i1p1f2.Omon.tos.gn.v20181206',\n", - " 'css03_data.CMIP6.CMIP.CAS.FGOALS-g3.historical.r1i1p1f1.Omon.tos.gn.v20191107',\n", - " 'css03_data.CMIP6.CMIP.NCAR.CESM2.historical.r1i1p1f1.Omon.tos.gn.v20190308',\n", - " 'css03_data.CMIP6.CMIP.CMCC.CMCC-CM2-SR5.historical.r1i1p1f1.Omon.tos.gn.v20200616',\n", - " 'css03_data.CMIP6.CMIP.CNRM-CERFACS.CNRM-CM6-1.historical.r1i1p1f2.Omon.tos.gn.v20180917',\n", - " 'css03_data.CMIP6.CMIP.CAMS.CAMS-CSM1-0.historical.r1i1p1f1.Omon.tos.gn.v20190708',\n", - " 'css03_data.CMIP6.CMIP.EC-Earth-Consortium.EC-Earth3-Veg.historical.r1i1p1f1.Omon.tos.gn.v20211207']" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "collections = cat.df.id.apply(build_rooki_id).to_list()\n", + "collections = cat.df.id.apply(keep_ds_id).to_list()\n", "collections" ] }, @@ -254,9 +204,11 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "30e8c66b", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "def get_pacific_ocean(dataset_id):\n", @@ -336,7 +288,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "f43be532-c565-45e7-84d8-21be6e4e351e", "metadata": { "tags": [] @@ -398,7 +350,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "140ee71c-01ad-4df5-a4af-d3f7f28c622a", "metadata": { "tags": [] @@ -424,23 +376,12 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "6b327ec5-f261-4ae8-9ee8-19fff28b62dc", "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "image/png": 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UAQBIkqIKAECSFFUAAJKkqAIAkCRFFQCAJCmqAAAkSVEFACBJiioAAElSVAEASJKiCgBAkhRVAACSpKgCAJAkRRUAgCQpqgAAJElRBQAgSYoqAABJUlQBAEiSogoAQJIUVQAAkqSoAgCQJEUVAIAkKaoAACRJUQUAIEmKKgAASVJUAQBIkqIKAECSFFUAAJKkqAIAkCRFFQCAJCmqAAAkSVEFACBJiioAAElSVAEASJKiCgBAksZVVDdt2hQ1NTVRVlYWtbW10dHRcdq5zz77bFx33XXxyU9+MsrLy2PJkiXx85//fNwbBigEchTgzPIuqq2trbF69epYv359dHV1xbJly2L58uXR3d095vyXXnoprrvuumhra4vOzs748pe/HDfddFN0dXWd9eYBpiI5CpCboizLsnwWLF68OBYuXBibN28eHps/f36sWLEiWlpacvoZn//856OxsTHuv//+nOYPDg5GRUVFDAwMRHl5eT7bBcjJZOaMHAUK0UTkTF5XVI8dOxadnZ3R0NAwYryhoSF27dqV0884efJkHD16NC6++OJ8XhqgIMhRgNwV5zO5r68vTpw4EZWVlSPGKysro7e3N6ef8f3vfz/ee++9uPnmm087Z2hoKIaGhoafDw4O5rNNgGTJUYDcjeuPqYqKikY8z7Js1NhYnnzyyfjOd74Tra2tcckll5x2XktLS1RUVAw/5s6dO55tAiRLjgKcWV5FddasWTF9+vRR7/qPHDky6urA72ttbY0777wz/t//+39x7bXXfuTcdevWxcDAwPDj0KFD+WwTIFlyFCB3eRXVkpKSqK2tjfb29hHj7e3tUV9ff9p1Tz75ZNx+++3xxBNPxI033njG1yktLY3y8vIRD4BCIEcBcpfXd1QjIpqbm+PWW2+Nurq6WLJkSfzkJz+J7u7uaGpqiogP38X/5je/iZ/97GcR8WG4rly5Mn7wgx/El770peGrCBdccEFUVFScw6MATA1yFCA3eRfVxsbG6O/vj40bN0ZPT08sWLAg2traorq6OiIienp6RtwL8Mc//nEcP348vvGNb8Q3vvGN4fHbbrsttm3bdvYnAJhi5ChAbvK+j+r54P5/wEQr9Jwp9PMB5995v48qAABMFkUVAIAkKaoAACRJUQUAIEmKKgAASVJUAQBIkqIKAECSFFUAAJKkqAIAkCRFFQCAJCmqAAAkSVEFACBJiioAAElSVAEASJKiCgBAkhRVAACSpKgCAJAkRRUAgCQpqgAAJElRBQAgSYoqAABJUlQBAEiSogoAQJIUVQAAkqSoAgCQJEUVAIAkKaoAACRJUQUAIEmKKgAASVJUAQBIkqIKAECSFFUAAJKkqAIAkCRFFQCAJCmqAAAkSVEFACBJiioAAElSVAEASJKiCgBAkhRVAACSpKgCAJCkcRXVTZs2RU1NTZSVlUVtbW10dHR85PydO3dGbW1tlJWVxbx58+LRRx8d12YBCoUcBTizvItqa2trrF69OtavXx9dXV2xbNmyWL58eXR3d485/+DBg3HDDTfEsmXLoqurK7797W/HqlWr4plnnjnrzQNMRXIUIDdFWZZl+SxYvHhxLFy4MDZv3jw8Nn/+/FixYkW0tLSMmv+tb30rXnjhhdi/f//wWFNTU7z66quxe/funF5zcHAwKioqYmBgIMrLy/PZLkBOJjNn5ChQiCYiZ4rzmXzs2LHo7OyMtWvXjhhvaGiIXbt2jblm9+7d0dDQMGLs+uuvjy1btsQHH3wQM2bMGLVmaGgohoaGhp8PDAxExIf/BQBMhFP5kud797zJUaBQTUSO5lVU+/r64sSJE1FZWTlivLKyMnp7e8dc09vbO+b848ePR19fX1RVVY1a09LSEhs2bBg1Pnfu3Hy2C5C3/v7+qKiomLCfL0eBQncuczSvonpKUVHRiOdZlo0aO9P8scZPWbduXTQ3Nw8/f+edd6K6ujq6u7sn9P9AzpfBwcGYO3duHDp0qGA/kiv0Mzrf1DcwMBCXX355XHzxxZPyenL03Po4/DNa6Gd0vqlvInI0r6I6a9asmD59+qh3/UeOHBn1bv+USy+9dMz5xcXFMXPmzDHXlJaWRmlp6ajxioqKgv3lRkSUl5cX9PkiCv+Mzjf1TZs2sXftk6MT6+Pwz2ihn9H5pr5zmaN5/aSSkpKora2N9vb2EePt7e1RX18/5polS5aMmr9jx46oq6sb83tVAIVMjgLkLu/K29zcHI899lhs3bo19u/fH2vWrInu7u5oamqKiA8/blq5cuXw/KampnjjjTeiubk59u/fH1u3bo0tW7bEvffee+5OATCFyFGA3OT9HdXGxsbo7++PjRs3Rk9PTyxYsCDa2tqiuro6IiJ6enpG3AuwpqYm2traYs2aNfHII4/E7Nmz4+GHH46vfvWrOb9maWlpPPDAA2N+jFUICv18EYV/Rueb+ibzjHL03Cv080UU/hmdb+qbiDPmfR9VAACYDBP7VwMAADBOiioAAElSVAEASJKiCgBAkpIpqps2bYqampooKyuL2tra6Ojo+Mj5O3fujNra2igrK4t58+bFo48+Okk7HZ98zvfss8/GddddF5/85CejvLw8lixZEj//+c8ncbf5y/f3d8orr7wSxcXF8cUvfnFiN3gO5HvGoaGhWL9+fVRXV0dpaWl8+tOfjq1bt07SbvOX7/m2b98eV111VVx44YVRVVUVd9xxR/T390/SbvPz0ksvxU033RSzZ8+OoqKieP7558+4ZqplTIQc/b+mYo5GFH6WytGR5GgOsgT84z/+YzZjxozspz/9abZv377snnvuyS666KLsjTfeGHP+gQMHsgsvvDC75557sn379mU//elPsxkzZmRPP/30JO88N/me75577sm++93vZv/5n/+Zvfbaa9m6deuyGTNmZL/4xS8meee5yfd8p7zzzjvZvHnzsoaGhuyqq66anM2O03jO+JWvfCVbvHhx1t7enh08eDD7j//4j+yVV16ZxF3nLt/zdXR0ZNOmTct+8IMfZAcOHMg6Ojqyz3/+89mKFSsmeee5aWtry9avX58988wzWURkzz333EfOn2oZk2Vy9PdNtRzNssLPUjk6khzNTRJFddGiRVlTU9OIsc9+9rPZ2rVrx5z/t3/7t9lnP/vZEWNf//rXsy996UsTtsezke/5xvK5z30u27Bhw7ne2jkx3vM1NjZmf/d3f5c98MADSYdrluV/xn/+53/OKioqsv7+/snY3lnL93x///d/n82bN2/E2MMPP5zNmTNnwvZ4ruQSsFMtY7JMjuYi5RzNssLPUjk6khzNzXn/6P/YsWPR2dkZDQ0NI8YbGhpi165dY67ZvXv3qPnXX3997NmzJz744IMJ2+t4jOd8v+/kyZNx9OjRuPjiiydii2dlvOd7/PHH4/XXX48HHnhgord41sZzxhdeeCHq6urie9/7Xlx22WVx5ZVXxr333hu//e1vJ2PLeRnP+err6+Pw4cPR1tYWWZbFW2+9FU8//XTceOONk7HlCTeVMiZCjuYi5RyNKPwslaOjydHc5P1vpjrX+vr64sSJE1FZWTlivLKyMnp7e8dc09vbO+b848ePR19fX1RVVU3YfvM1nvP9vu9///vx3nvvxc033zwRWzwr4znfr3/961i7dm10dHREcfF5/0fwjMZzxgMHDsTLL78cZWVl8dxzz0VfX1/cfffd8fbbbyf3/arxnK++vj62b98ejY2N8bvf/S6OHz8eX/nKV+KHP/zhZGx5wk2ljImQo7lIOUcjCj9L5ehocjS3jDnvV1RPKSoqGvE8y7JRY2eaP9Z4KvI93ylPPvlkfOc734nW1ta45JJLJmp7Zy3X8504cSJuueWW2LBhQ1x55ZWTtb1zIp/f4cmTJ6OoqCi2b98eixYtihtuuCEeeuih2LZtW5JXAyLyO9++ffti1apVcf/990dnZ2e8+OKLcfDgweF/V30hmGoZEyFHT2eq5GhE4WepHP1fcjQ35/0t2KxZs2L69Omj3nEcOXJkVBM/5dJLLx1zfnFxccycOXPC9joe4znfKa2trXHnnXfGU089Fddee+1EbnPc8j3f0aNHY8+ePdHV1RXf/OY3I+LDMMqyLIqLi2PHjh1xzTXXTMreczWe32FVVVVcdtllUVFRMTw2f/78yLIsDh8+HFdcccWE7jkf4zlfS0tLLF26NO67776IiPjCF74QF110USxbtiwefPDBpK7GjcdUypgIOfpRpkKORhR+lsrR0eRobs77FdWSkpKora2N9vb2EePt7e1RX18/5polS5aMmr9jx46oq6uLGTNmTNhex2M854v48ArA7bffHk888UTS31fJ93zl5eXxy1/+Mvbu3Tv8aGpqis985jOxd+/eWLx48WRtPWfj+R0uXbo03nzzzXj33XeHx1577bWYNm1azJkzZ0L3m6/xnO/999+PadNGxsf06dMj4n/fMU9lUyljIuTo6UyVHI0o/CyVo6PJ0Rzl9adXE+TULR22bNmS7du3L1u9enV20UUXZf/93/+dZVmWrV27Nrv11luH55+65cGaNWuyffv2ZVu2bJkSt1XJ9XxPPPFEVlxcnD3yyCNZT0/P8OOdd945X0f4SPme7/el/peqWZb/GY8ePZrNmTMn+4u/+IvsV7/6VbZz587siiuuyO66667zdYSPlO/5Hn/88ay4uDjbtGlT9vrrr2cvv/xyVldXly1atOh8HeEjHT16NOvq6sq6urqyiMgeeuihrKura/i2MVM9Y7JMjk71HM2yws9SOSpHp+ztqbIsyx555JGsuro6KykpyRYuXJjt3Llz+D+77bbbsquvvnrE/H/7t3/L/viP/zgrKSnJPvWpT2WbN2+e5B3nJ5/zXX311VlEjHrcdtttk7/xHOX7+/u/Ug/XU/I94/79+7Nrr702u+CCC7I5c+Zkzc3N2fvvvz/Ju85dvud7+OGHs8997nPZBRdckFVVVWV/+Zd/mR0+fHiSd52bf/3Xf/3I/00VQsZkmRyd6jmaZYWfpXL06hHz5eiZFWVZAVxfBgCg4Jz376gCAMBYFFUAAJKkqAIAkCRFFQCAJCmqAAAkSVEFACBJiioAAElSVAEASJKiCgBAkhRVAACSpKgCAJAkRRUAgCQpqgAAJElRBQAgSYoqAABJUlQBAEiSogoAQJIUVQAAkqSoAgCQJEUVAIAkKaoAACRJUQUAIEmKKgAASVJUAQBIkqIKAECSFFUAAJKkqAIAkCRFFQCAJCmqAAAkSVEFACBJiioAAElSVAEASJKiCgBAkhRVAACSpKgCAJAkRRUAgCQpqgAAJElRBQAgSYoqAABJUlQBAEiSogoAQJIUVQAAkqSoAgCQJEUVAIAkKaoAACRJUQUAIEmKKgAASVJUAQBIkqIKAECSFFUAAJKkqAIAkKS8i+pLL70UN910U8yePTuKiori+eefP+OanTt3Rm1tbZSVlcW8efPi0UcfHc9eAQqCHAXITd5F9b333ourrroqfvSjH+U0/+DBg3HDDTfEsmXLoqurK7797W/HqlWr4plnnsl7swCFQI4C5KYoy7Js3IuLiuK5556LFStWnHbOt771rXjhhRdi//79w2NNTU3x6quvxu7du8f70gAFQY4CnF7xRL/A7t27o6GhYcTY9ddfH1u2bIkPPvggZsyYMWrN0NBQDA0NDT8/efJkvP322zFz5swoKiqa6C0DH0NZlsXRo0dj9uzZMW1aWl/fl6PAVDAROTrhRbW3tzcqKytHjFVWVsbx48ejr68vqqqqRq1paWmJDRs2TPTWAEY5dOhQzJkz53xvYwQ5Ckwl5zJHJ7yoRsSod++nvm1wunf169ati+bm5uHnAwMDcfnll8ehQ4eivLx84jYKfGwNDg7G3Llz4w//8A/P91bGJEeB1E1Ejk54Ub300kujt7d3xNiRI0eiuLg4Zs6cOeaa0tLSKC0tHTVeXl4uYIEJleLH4nIUmErOZY5O+BexlixZEu3t7SPGduzYEXV1dWN+rwqAkeQo8HGVd1F99913Y+/evbF3796I+PC2KXv37o3u7u6I+PDjppUrVw7Pb2pqijfeeCOam5tj//79sXXr1tiyZUvce++95+YEAFOMHAXITd4f/e/Zsye+/OUvDz8/9R2o2267LbZt2xY9PT3DYRsRUVNTE21tbbFmzZp45JFHYvbs2fHwww/HV7/61XOwfYCpR44C5Oas7qM6WQYHB6OioiIGBgZ8twqYEIWeM4V+PuD8m4icSetmgQAA8D8UVQAAkqSoAgCQJEUVAIAkKaoAACRJUQUAIEmKKgAASVJUAQBIkqIKAECSFFUAAJKkqAIAkCRFFQCAJCmqAAAkSVEFACBJiioAAElSVAEASJKiCgBAkhRVAACSpKgCAJAkRRUAgCQpqgAAJElRBQAgSYoqAABJUlQBAEiSogoAQJIUVQAAkqSoAgCQJEUVAIAkKaoAACRJUQUAIEmKKgAASVJUAQBIkqIKAECSFFUAAJKkqAIAkCRFFQCAJCmqAAAkSVEFACBJiioAAElSVAEASNK4iuqmTZuipqYmysrKora2Njo6Oj5y/vbt2+Oqq66KCy+8MKqqquKOO+6I/v7+cW0YoBDIUYAzy7uotra2xurVq2P9+vXR1dUVy5Yti+XLl0d3d/eY819++eVYuXJl3HnnnfGrX/0qnnrqqfiv//qvuOuuu8568wBTkRwFyE3eRfWhhx6KO++8M+66666YP39+/MM//EPMnTs3Nm/ePOb8f//3f49PfepTsWrVqqipqYk/+ZM/ia9//euxZ8+es948wFQkRwFyk1dRPXbsWHR2dkZDQ8OI8YaGhti1a9eYa+rr6+Pw4cPR1tYWWZbFW2+9FU8//XTceOONp32doaGhGBwcHPEAKARyFCB3eRXVvr6+OHHiRFRWVo4Yr6ysjN7e3jHX1NfXx/bt26OxsTFKSkri0ksvjU984hPxwx/+8LSv09LSEhUVFcOPuXPn5rNNgGTJUYDcjeuPqYqKikY8z7Js1Ngp+/bti1WrVsX9998fnZ2d8eKLL8bBgwejqanptD9/3bp1MTAwMPw4dOjQeLYJkCw5CnBmxflMnjVrVkyfPn3Uu/4jR46MujpwSktLSyxdujTuu+++iIj4whe+EBdddFEsW7YsHnzwwaiqqhq1prS0NEpLS/PZGsCUIEcBcpfXFdWSkpKora2N9vb2EePt7e1RX18/5pr3338/pk0b+TLTp0+PiA+vIAB8nMhRgNzl/dF/c3NzPPbYY7F169bYv39/rFmzJrq7u4c/glq3bl2sXLlyeP5NN90Uzz77bGzevDkOHDgQr7zySqxatSoWLVoUs2fPPncnAZgi5ChAbvL66D8iorGxMfr7+2Pjxo3R09MTCxYsiLa2tqiuro6IiJ6enhH3Arz99tvj6NGj8aMf/Sj+5m/+Jj7xiU/ENddcE9/97nfP3SkAphA5CpCbomwKfG40ODgYFRUVMTAwEOXl5ed7O0ABKvScKfTzAeffROTMuP7qHwAAJpqiCgBAkhRVAACSpKgCAJAkRRUAgCQpqgAAJElRBQAgSYoqAABJUlQBAEiSogoAQJIUVQAAkqSoAgCQJEUVAIAkKaoAACRJUQUAIEmKKgAASVJUAQBIkqIKAECSFFUAAJKkqAIAkCRFFQCAJCmqAAAkSVEFACBJiioAAElSVAEASJKiCgBAkhRVAACSpKgCAJAkRRUAgCQpqgAAJElRBQAgSYoqAABJUlQBAEiSogoAQJIUVQAAkqSoAgCQJEUVAIAkKaoAACRJUQUAIEmKKgAASRpXUd20aVPU1NREWVlZ1NbWRkdHx0fOHxoaivXr10d1dXWUlpbGpz/96di6deu4NgxQCOQowJkV57ugtbU1Vq9eHZs2bYqlS5fGj3/841i+fHns27cvLr/88jHX3HzzzfHWW2/Fli1b4o/+6I/iyJEjcfz48bPePMBUJEcBclOUZVmWz4LFixfHwoULY/PmzcNj8+fPjxUrVkRLS8uo+S+++GJ87WtfiwMHDsTFF188rk0ODg5GRUVFDAwMRHl5+bh+BsBHmcyckaNAIZqInMnro/9jx45FZ2dnNDQ0jBhvaGiIXbt2jbnmhRdeiLq6uvje974Xl112WVx55ZVx7733xm9/+9vx7xpgipKjALnL66P/vr6+OHHiRFRWVo4Yr6ysjN7e3jHXHDhwIF5++eUoKyuL5557Lvr6+uLuu++Ot99++7TfrxoaGoqhoaHh54ODg/lsEyBZchQgd+P6Y6qioqIRz7MsGzV2ysmTJ6OoqCi2b98eixYtihtuuCEeeuih2LZt22mvBrS0tERFRcXwY+7cuePZJkCy5CjAmeVVVGfNmhXTp08f9a7/yJEjo64OnFJVVRWXXXZZVFRUDI/Nnz8/siyLw4cPj7lm3bp1MTAwMPw4dOhQPtsESJYcBchdXkW1pKQkamtro729fcR4e3t71NfXj7lm6dKl8eabb8a77747PPbaa6/FtGnTYs6cOWOuKS0tjfLy8hEPgEIgRwFyl/dH/83NzfHYY4/F1q1bY//+/bFmzZro7u6OpqamiPjwXfzKlSuH599yyy0xc+bMuOOOO2Lfvn3x0ksvxX333Rd/9Vd/FRdccMG5OwnAFCFHAXKT931UGxsbo7+/PzZu3Bg9PT2xYMGCaGtri+rq6oiI6Onpie7u7uH5f/AHfxDt7e3x13/911FXVxczZ86Mm2++OR588MFzdwqAKUSOAuQm7/uong/u/wdMtELPmUI/H3D+nff7qAIAwGRRVAEASJKiCgBAkhRVAACSpKgCAJAkRRUAgCQpqgAAJElRBQAgSYoqAABJUlQBAEiSogoAQJIUVQAAkqSoAgCQJEUVAIAkKaoAACRJUQUAIEmKKgAASVJUAQBIkqIKAECSFFUAAJKkqAIAkCRFFQCAJCmqAAAkSVEFACBJiioAAElSVAEASJKiCgBAkhRVAACSpKgCAJAkRRUAgCQpqgAAJElRBQAgSYoqAABJUlQBAEiSogoAQJIUVQAAkqSoAgCQJEUVAIAkKaoAACRJUQUAIEmKKgAASRpXUd20aVPU1NREWVlZ1NbWRkdHR07rXnnllSguLo4vfvGL43lZgIIhRwHOLO+i2traGqtXr47169dHV1dXLFu2LJYvXx7d3d0fuW5gYCBWrlwZf/qnfzruzQIUAjkKkJuiLMuyfBYsXrw4Fi5cGJs3bx4emz9/fqxYsSJaWlpOu+5rX/taXHHFFTF9+vR4/vnnY+/evTm/5uDgYFRUVMTAwECUl5fns12AnExmzshRoBBNRM7kdUX12LFj0dnZGQ0NDSPGGxoaYteuXadd9/jjj8frr78eDzzwQE6vMzQ0FIODgyMeAIVAjgLkLq+i2tfXFydOnIjKysoR45WVldHb2zvmml//+texdu3a2L59exQXF+f0Oi0tLVFRUTH8mDt3bj7bBEiWHAXI3bj+mKqoqGjE8yzLRo1FRJw4cSJuueWW2LBhQ1x55ZU5//x169bFwMDA8OPQoUPj2SZAsuQowJnl9tb8f8yaNSumT58+6l3/kSNHRl0diIg4evRo7NmzJ7q6uuKb3/xmREScPHkysiyL4uLi2LFjR1xzzTWj1pWWlkZpaWk+WwOYEuQoQO7yuqJaUlIStbW10d7ePmK8vb096uvrR80vLy+PX/7yl7F3797hR1NTU3zmM5+JvXv3xuLFi89u9wBTjBwFyF1eV1QjIpqbm+PWW2+Nurq6WLJkSfzkJz+J7u7uaGpqiogPP276zW9+Ez/72c9i2rRpsWDBghHrL7nkkigrKxs1DvBxIUcBcpN3UW1sbIz+/v7YuHFj9PT0xIIFC6KtrS2qq6sjIqKnp+eM9wIE+DiTowC5yfs+queD+/8BE63Qc6bQzwecf+f9PqoAADBZFFUAAJKkqAIAkCRFFQCAJCmqAAAkSVEFACBJiioAAElSVAEASJKiCgBAkhRVAACSpKgCAJAkRRUAgCQpqgAAJElRBQAgSYoqAABJUlQBAEiSogoAQJIUVQAAkqSoAgCQJEUVAIAkKaoAACRJUQUAIEmKKgAASVJUAQBIkqIKAECSFFUAAJKkqAIAkCRFFQCAJCmqAAAkSVEFACBJiioAAElSVAEASJKiCgBAkhRVAACSpKgCAJAkRRUAgCQpqgAAJElRBQAgSYoqAABJUlQBAEjSuIrqpk2boqamJsrKyqK2tjY6OjpOO/fZZ5+N6667Lj75yU9GeXl5LFmyJH7+85+Pe8MAhUCOApxZ3kW1tbU1Vq9eHevXr4+urq5YtmxZLF++PLq7u8ec/9JLL8V1110XbW1t0dnZGV/+8pfjpptuiq6urrPePMBUJEcBclOUZVmWz4LFixfHwoULY/PmzcNj8+fPjxUrVkRLS0tOP+Pzn/98NDY2xv3335/T/MHBwaioqIiBgYEoLy/PZ7sAOZnMnJGjQCGaiJzJ64rqsWPHorOzMxoaGkaMNzQ0xK5du3L6GSdPnoyjR4/GxRdffNo5Q0NDMTg4OOIBUAjkKEDu8iqqfX19ceLEiaisrBwxXllZGb29vTn9jO9///vx3nvvxc0333zaOS0tLVFRUTH8mDt3bj7bBEiWHAXI3bj+mKqoqGjE8yzLRo2N5cknn4zvfOc70draGpdccslp561bty4GBgaGH4cOHRrPNgGSJUcBzqw4n8mzZs2K6dOnj3rXf+TIkVFXB35fa2tr3HnnnfHUU0/Ftdde+5FzS0tLo7S0NJ+tAUwJchQgd3ldUS0pKYna2tpob28fMd7e3h719fWnXffkk0/G7bffHk888UTceOON49spQAGQowC5y+uKakREc3Nz3HrrrVFXVxdLliyJn/zkJ9Hd3R1NTU0R8eHHTb/5zW/iZz/7WUR8GK4rV66MH/zgB/GlL31p+CrCBRdcEBUVFefwKABTgxwFyE3eRbWxsTH6+/tj48aN0dPTEwsWLIi2traorq6OiIienp4R9wL88Y9/HMePH49vfOMb8Y1vfGN4/Lbbbott27ad/QkAphg5CpCbvO+jej64/x8w0Qo9Zwr9fMD5d97vowoAAJNFUQUAIEmKKgAASVJUAQBIkqIKAECSFFUAAJKkqAIAkCRFFQCAJCmqAAAkSVEFACBJiioAAElSVAEASJKiCgBAkhRVAACSpKgCAJAkRRUAgCQpqgAAJElRBQAgSYoqAABJUlQBAEiSogoAQJIUVQAAkqSoAgCQJEUVAIAkKaoAACRJUQUAIEmKKgAASVJUAQBIkqIKAECSFFUAAJKkqAIAkCRFFQCAJCmqAAAkSVEFACBJiioAAElSVAEASJKiCgBAkhRVAACSpKgCAJAkRRUAgCSNq6hu2rQpampqoqysLGpra6Ojo+Mj5+/cuTNqa2ujrKws5s2bF48++ui4NgtQKOQowJnlXVRbW1tj9erVsX79+ujq6oply5bF8uXLo7u7e8z5Bw8ejBtuuCGWLVsWXV1d8e1vfztWrVoVzzzzzFlvHmAqkqMAuSnKsizLZ8HixYtj4cKFsXnz5uGx+fPnx4oVK6KlpWXU/G9961vxwgsvxP79+4fHmpqa4tVXX43du3fn9JqDg4NRUVERAwMDUV5ens92AXIymTkjR4FCNBE5k9cV1WPHjkVnZ2c0NDSMGG9oaIhdu3aNuWb37t2j5l9//fWxZ8+e+OCDD/LcLsDUJkcBclecz+S+vr44ceJEVFZWjhivrKyM3t7eMdf09vaOOf/48ePR19cXVVVVo9YMDQ3F0NDQ8POBgYGI+LCpA0yEU/mS54dMeZOjQKGaiBzNq6ieUlRUNOJ5lmWjxs40f6zxU1paWmLDhg2jxufOnZvvVgHy0t/fHxUVFRP+OnIUKFTnMkfzKqqzZs2K6dOnj3rXf+TIkVHv9k+59NJLx5xfXFwcM2fOHHPNunXrorm5efj5O++8E9XV1dHd3T0p/wcy2QYHB2Pu3Llx6NChgv3uWKGf0fmmvoGBgbj88svj4osvntDXkaMT4+Pwz2ihn9H5pr6JyNG8impJSUnU1tZGe3t7/Pmf//nweHt7e/zZn/3ZmGuWLFkS//RP/zRibMeOHVFXVxczZswYc01paWmUlpaOGq+oqCjYX25ERHl5eUGfL6Lwz+h8U9+0aRN7e2k5OrE+Dv+MFvoZnW/qO5c5mvdPam5ujsceeyy2bt0a+/fvjzVr1kR3d3c0NTVFxIfv4leuXDk8v6mpKd54441obm6O/fv3x9atW2PLli1x7733nrNDAEwlchQgN3l/R7WxsTH6+/tj48aN0dPTEwsWLIi2traorq6OiIienp4R9wKsqamJtra2WLNmTTzyyCMxe/bsePjhh+OrX/3quTsFwBQiRwFyM64/prr77rvj7rvvHvM/27Zt26ixq6++On7xi1+M56Ui4sOPsB544IExP8YqBIV+vojCP6PzTX2TfUY5em4V+vkiCv+Mzjf1TcQZ877hPwAATIaJ/asBAAAYJ0UVAIAkKaoAACRJUQUAIEnJFNVNmzZFTU1NlJWVRW1tbXR0dHzk/J07d0ZtbW2UlZXFvHnz4tFHH52knY5PPud79tln47rrrotPfvKTUV5eHkuWLImf//znk7jb/OX7+zvllVdeieLi4vjiF784sRs8B/I949DQUKxfvz6qq6ujtLQ0Pv3pT8fWrVsnabf5y/d827dvj6uuuiouvPDCqKqqijvuuCP6+/snabf5eemll+Kmm26K2bNnR1FRUTz//PNnXDPVMiZCjv5fUzFHIwo/S+XoSHI0B1kC/vEf/zGbMWNG9tOf/jTbt29fds8992QXXXRR9sYbb4w5/8CBA9mFF16Y3XPPPdm+ffuyn/70p9mMGTOyp59+epJ3npt8z3fPPfdk3/3ud7P//M//zF577bVs3bp12YwZM7Jf/OIXk7zz3OR7vlPeeeedbN68eVlDQ0N21VVXTc5mx2k8Z/zKV76SLV68OGtvb88OHjyY/cd//Ef2yiuvTOKuc5fv+To6OrJp06ZlP/jBD7IDBw5kHR0d2ec///lsxYoVk7zz3LS1tWXr16/PnnnmmSwisueee+4j50+1jMkyOfr7plqOZlnhZ6kcHUmO5iaJorpo0aKsqalpxNhnP/vZbO3atWPO/9u//dvss5/97Iixr3/969mXvvSlCdvj2cj3fGP53Oc+l23YsOFcb+2cGO/5Ghsbs7/7u7/LHnjggaTDNcvyP+M///M/ZxUVFVl/f/9kbO+s5Xu+v//7v8/mzZs3Yuzhhx/O5syZM2F7PFdyCdipljFZJkdzkXKOZlnhZ6kcHUmO5ua8f/R/7Nix6OzsjIaGhhHjDQ0NsWvXrjHX7N69e9T866+/Pvbs2RMffPDBhO11PMZzvt938uTJOHr0aFx88cUTscWzMt7zPf744/H666/HAw88MNFbPGvjOeMLL7wQdXV18b3vfS8uu+yyuPLKK+Pee++N3/72t5Ox5byM53z19fVx+PDhaGtriyzL4q233oqnn346brzxxsnY8oSbShkTIUdzkXKORhR+lsrR0eRobsb1b6Y6l/r6+uLEiRNRWVk5YryysjJ6e3vHXNPb2zvm/OPHj0dfX19UVVVN2H7zNZ7z/b7vf//78d5778XNN988EVs8K+M5369//etYu3ZtdHR0RHHxef9H8IzGc8YDBw7Eyy+/HGVlZfHcc89FX19f3H333fH2228n9/2q8Zyvvr4+tm/fHo2NjfG73/0ujh8/Hl/5ylfihz/84WRsecJNpYyJkKO5SDlHIwo/S+XoaHI0t4w571dUTykqKhrxPMuyUWNnmj/WeCryPd8pTz75ZHznO9+J1tbWuOSSSyZqe2ct1/OdOHEibrnlltiwYUNceeWVk7W9cyKf3+HJkyejqKgotm/fHosWLYobbrghHnroodi2bVuSVwMi8jvfvn37YtWqVXH//fdHZ2dnvPjii3Hw4MFoamqajK1OiqmWMRFy9HSmSo5GFH6WytH/JUdzc97fgs2aNSumT58+6h3HkSNHRjXxUy699NIx5xcXF8fMmTMnbK/jMZ7zndLa2hp33nlnPPXUU3HttddO5DbHLd/zHT16NPbs2RNdXV3xzW9+MyI+DKMsy6K4uDh27NgR11xzzaTsPVfj+R1WVVXFZZddFhUVFcNj8+fPjyzL4vDhw3HFFVdM6J7zMZ7ztbS0xNKlS+O+++6LiIgvfOELcdFFF8WyZcviwQcfTOpq3HhMpYyJkKMfZSrkaEThZ6kcHU2O5ua8X1EtKSmJ2traaG9vHzHe3t4e9fX1Y65ZsmTJqPk7duyIurq6mDFjxoTtdTzGc76ID68A3H777fHEE08k/X2VfM9XXl4ev/zlL2Pv3r3Dj6ampvjMZz4Te/fujcWLF0/W1nM2nt/h0qVL480334x33313eOy1116LadOmxZw5cyZ0v/kaz/nef//9mDZtZHxMnz49Iv73HfNUNpUyJkKOns5UydGIws9SOTqaHM1RXn96NUFO3dJhy5Yt2b59+7LVq1dnF110Ufbf//3fWZZl2dq1a7Nbb711eP6pWx6sWbMm27dvX7Zly5YpcVuVXM/3xBNPZMXFxdkjjzyS9fT0DD/eeeed83WEj5Tv+X5f6n+pmmX5n/Ho0aPZnDlzsr/4i7/IfvWrX2U7d+7Mrrjiiuyuu+46X0f4SPme7/HHH8+Ki4uzTZs2Za+//nr28ssvZ3V1ddmiRYvO1xE+0tGjR7Ourq6sq6sri4jsoYceyrq6uoZvGzPVMybL5OhUz9EsK/wslaNydMrenirLsuyRRx7Jqqurs5KSkmzhwoXZzp07h/+z2267Lbv66qtHzP+3f/u37I//+I+zkpKS7FOf+lS2efPmSd5xfvI539VXX51FxKjHbbfdNvkbz1G+v7//K/VwPSXfM+7fvz+79tprswsuuCCbM2dO1tzcnL3//vuTvOvc5Xu+hx9+OPvc5z6XXXDBBVlVVVX2l3/5l9nhw4cnede5+dd//deP/N9UIWRMlsnRqZ6jWVb4WSpHrx4xX46eWVGWFcD1ZQAACs55/44qAACMRVEFACBJiioAAElSVAEASJKiCgBAkhRVAACSpKgCAJAkRRUAgCQpqgAAJElRBQAgSYoqAABJUlQBAEiSogoAQJIUVQAAkqSoAgCQJEUVAIAkKaoAACRJUQUAIEmKKgAASVJUAQBIkqIKAECSFFUAAJKkqAIAkCRFFQCAJCmqAAAkSVEFACBJiioAAElSVAEASJKiCgBAkhRVAACSpKgCAJAkRRUAgCQpqgAAJElRBQAgSYoqAABJUlQBAEiSogoAQJIUVQAAkqSoAgCQJEUVAIAkKaoAACRJUQUAIEmKKgAASVJUAQBIkqIKAECSFFUAAJKkqAIAkCRFFQCAJCmqAAAkSVEFACBJeRfVl156KW666aaYPXt2FBUVxfPPP3/GNTt37oza2tooKyuLefPmxaOPPjqevQIUBDkKkJu8i+p7770XV111VfzoRz/Kaf7BgwfjhhtuiGXLlkVXV1d8+9vfjlWrVsUzzzyT92YBCoEcBchNUZZl2bgXFxXFc889FytWrDjtnG9961vxwgsvxP79+4fHmpqa4tVXX43du3eP96UBCoIcBTi9Cf+O6u7du6OhoWHE2PXXXx979uyJDz74YKJfHmDKk6PAx1XxRL9Ab29vVFZWjhirrKyM48ePR19fX1RVVY1aMzQ0FENDQ8PPT548GW+//XbMnDkzioqKJnrLwMdQlmVx9OjRmD17dkybltbfmcpRYCqYiByd8KIaEaNC8dS3DU4Xli0tLbFhw4YJ3xfA7zt06FDMmTPnfG9jFDkKTBXnMkcnvKheeuml0dvbO2LsyJEjUVxcHDNnzhxzzbp166K5uXn4+cDAQFx++eVx6NChKC8vn9D9Ah9Pg4ODMXfu3PjDP/zD872VUeQoMBVMRI5OeFFdsmRJ/NM//dOIsR07dkRdXV3MmDFjzDWlpaVRWlo6ary8vFzAAhMqxY/F5SgwlZzLHM37CwTvvvtu7N27N/bu3RsRH942Ze/evdHd3R0RH76LX7ly5fD8pqameOONN6K5uTn2798fW7dujS1btsS99957bk4AMMXIUYDc5H1Fdc+ePfHlL395+Pmpj5Zuu+222LZtW/T09AyHbURETU1NtLW1xZo1a+KRRx6J2bNnx8MPPxxf/epXz8H2AaYeOQqQm7O6j+pkGRwcjIqKihgYGPCRFTAhCj1nCv18wPk3ETmT1j1YAADgfyiqAAAkSVEFACBJiioAAElSVAEASJKiCgBAkhRVAACSpKgCAJAkRRUAgCQpqgAAJElRBQAgSYoqAABJUlQBAEiSogoAQJIUVQAAkqSoAgCQJEUVAIAkKaoAACRJUQUAIEmKKgAASVJUAQBIkqIKAECSFFUAAJKkqAIAkCRFFQCAJCmqAAAkSVEFACBJiioAAElSVAEASJKiCgBAkhRVAACSpKgCAJAkRRUAgCQpqgAAJElRBQAgSYoqAABJUlQBAEiSogoAQJIUVQAAkqSoAgCQJEUVAIAkjauobtq0KWpqaqKsrCxqa2ujo6PjI+dv3749rrrqqrjwwgujqqoq7rjjjujv7x/XhgEKgRwFOLO8i2pra2usXr061q9fH11dXbFs2bJYvnx5dHd3jzn/5ZdfjpUrV8add94Zv/rVr+Kpp56K//qv/4q77rrrrDcPMBXJUYDc5F1UH3roobjzzjvjrrvuivnz58c//MM/xNy5c2Pz5s1jzv/3f//3+NSnPhWrVq2Kmpqa+JM/+ZP4+te/Hnv27DnrzQNMRXIUIDd5FdVjx45FZ2dnNDQ0jBhvaGiIXbt2jbmmvr4+Dh8+HG1tbZFlWbz11lvx9NNPx4033nja1xkaGorBwcERD4BCIEcBcpdXUe3r64sTJ05EZWXliPHKysro7e0dc019fX1s3749Ghsbo6SkJC699NL4xCc+ET/84Q9P+zotLS1RUVEx/Jg7d24+2wRIlhwFyN24/piqqKhoxPMsy0aNnbJv375YtWpV3H///dHZ2RkvvvhiHDx4MJqamk7789etWxcDAwPDj0OHDo1nmwDJkqMAZ1acz+RZs2bF9OnTR73rP3LkyKirA6e0tLTE0qVL47777ouIiC984Qtx0UUXxbJly+LBBx+MqqqqUWtKS0ujtLQ0n60BTAlyFCB3eV1RLSkpidra2mhvbx8x3t7eHvX19WOuef/992PatJEvM3369Ij48AoCwMeJHAXIXd4f/Tc3N8djjz0WW7dujf3798eaNWuiu7t7+COodevWxcqVK4fn33TTTfHss8/G5s2b48CBA/HKK6/EqlWrYtGiRTF79uxzdxKAKUKOAuQmr4/+IyIaGxujv78/Nm7cGD09PbFgwYJoa2uL6urqiIjo6ekZcS/A22+/PY4ePRo/+tGP4m/+5m/iE5/4RFxzzTXx3e9+99ydAmAKkaMAuSnKpsDnRoODg1FRUREDAwNRXl5+vrcDFKBCz5lCPx9w/k1Ezozrr/4BAGCiKaoAACRJUQUAIEmKKgAASVJUAQBIkqIKAECSFFUAAJKkqAIAkCRFFQCAJCmqAAAkSVEFACBJiioAAElSVAEASJKiCgBAkhRVAACSpKgCAJAkRRUAgCQpqgAAJElRBQAgSYoqAABJUlQBAEiSogoAQJIUVQAAkqSoAgCQJEUVAIAkKaoAACRJUQUAIEmKKgAASVJUAQBIkqIKAECSFFUAAJKkqAIAkCRFFQCAJCmqAAAkSVEFACBJiioAAElSVAEASJKiCgBAkhRVAACSpKgCAJCkcRXVTZs2RU1NTZSVlUVtbW10dHR85PyhoaFYv359VFdXR2lpaXz605+OrVu3jmvDAIVAjgKcWXG+C1pbW2P16tWxadOmWLp0afz4xz+O5cuXx759++Lyyy8fc83NN98cb731VmzZsiX+6I/+KI4cORLHjx8/680DTEVyFCA3RVmWZfksWLx4cSxcuDA2b948PDZ//vxYsWJFtLS0jJr/4osvxte+9rU4cOBAXHzxxePa5ODgYFRUVMTAwECUl5eP62cAfJTJzBk5ChSiiciZvD76P3bsWHR2dkZDQ8OI8YaGhti1a9eYa1544YWoq6uL733ve3HZZZfFlVdeGffee2/89re/Pe3rDA0NxeDg4IgHQCGQowC5y+uj/76+vjhx4kRUVlaOGK+srIze3t4x1xw4cCBefvnlKCsri+eeey76+vri7rvvjrfffvu0369qaWmJDRs25LM1gClBjgLkblx/TFVUVDTieZZlo8ZOOXnyZBQVFcX27dtj0aJFccMNN8RDDz0U27ZtO+3VgHXr1sXAwMDw49ChQ+PZJkCy5CjAmeV1RXXWrFkxffr0Ue/6jxw5MurqwClVVVVx2WWXRUVFxfDY/PnzI8uyOHz4cFxxxRWj1pSWlkZpaWk+WwOYEuQoQO7yuqJaUlIStbW10d7ePmK8vb096uvrx1yzdOnSePPNN+Pdd98dHnvttddi2rRpMWfOnHFsGWDqkqMAucv7o//m5uZ47LHHYuvWrbF///5Ys2ZNdHd3R1NTU0R8+HHTypUrh+ffcsstMXPmzLjjjjti37598dJLL8V9990Xf/VXfxUXXHDBuTsJwBQhRwFyk/d9VBsbG6O/vz82btwYPT09sWDBgmhra4vq6uqIiOjp6Ynu7u7h+X/wB38Q7e3t8dd//ddRV1cXM2fOjJtvvjkefPDBc3cKgClEjgLkJu/7qJ4P7v8HTLRCz5lCPx9w/p33+6gCAMBkUVQBAEiSogoAQJIUVQAAkqSoAgCQJEUVAIAkKaoAACRJUQUAIEmKKgAASVJUAQBIkqIKAECSFFUAAJKkqAIAkCRFFQCAJCmqAAAkSVEFACBJiioAAElSVAEASJKiCgBAkhRVAACSpKgCAJAkRRUAgCQpqgAAJElRBQAgSYoqAABJUlQBAEiSogoAQJIUVQAAkqSoAgCQJEUVAIAkKaoAACRJUQUAIEmKKgAASVJUAQBIkqIKAECSFFUAAJKkqAIAkCRFFQCAJCmqAAAkSVEFACBJ4yqqmzZtipqamigrK4va2tro6OjIad0rr7wSxcXF8cUvfnE8LwtQMOQowJnlXVRbW1tj9erVsX79+ujq6oply5bF8uXLo7u7+yPXDQwMxMqVK+NP//RPx71ZgEIgRwFyU5RlWZbPgsWLF8fChQtj8+bNw2Pz58+PFStWREtLy2nXfe1rX4srrrgipk+fHs8//3zs3bs359ccHByMioqKGBgYiPLy8ny2C5CTycwZOQoUoonImbyuqB47diw6OzujoaFhxHhDQ0Ps2rXrtOsef/zxeP311+OBBx4Y3y4BCoQcBchdcT6T+/r64sSJE1FZWTlivLKyMnp7e8dc8+tf/zrWrl0bHR0dUVyc28sNDQ3F0NDQ8PPBwcF8tgmQLDkKkLtx/TFVUVHRiOdZlo0ai4g4ceJE3HLLLbFhw4a48sorc/75LS0tUVFRMfyYO3fueLYJkCw5CnBmeRXVWbNmxfTp00e96z9y5MioqwMREUePHo09e/bEN7/5zSguLo7i4uLYuHFjvPrqq1FcXBz/8i//MubrrFu3LgYGBoYfhw4dymebAMmSowC5y+uj/5KSkqitrY329vb48z//8+Hx9vb2+LM/+7NR88vLy+OXv/zliLFNmzbFv/zLv8TTTz8dNTU1Y75OaWlplJaW5rM1gClBjgLkLq+iGhHR3Nwct956a9TV1cWSJUviJz/5SXR3d0dTU1NEfPgu/je/+U387Gc/i2nTpsWCBQtGrL/kkkuirKxs1DjAx4UcBchN3kW1sbEx+vv7Y+PGjdHT0xMLFiyItra2qK6ujoiInp6eM94LEODjTI4C5Cbv+6ieD+7/B0y0Qs+ZQj8fcP6d9/uoAgDAZFFUAQBIkqIKAECSFFUAAJKkqAIAkCRFFQCAJCmqAAAkSVEFACBJiioAAElSVAEASJKiCgBAkhRVAACSpKgCAJAkRRUAgCQpqgAAJElRBQAgSYoqAABJUlQBAEiSogoAQJIUVQAAkqSoAgCQJEUVAIAkKaoAACRJUQUAIEmKKgAASVJUAQBIkqIKAECSFFUAAJKkqAIAkCRFFQCAJCmqAAAkSVEFACBJiioAAElSVAEASJKiCgBAkhRVAACSpKgCAJAkRRUAgCQpqgAAJElRBQAgSYoqAABJGldR3bRpU9TU1ERZWVnU1tZGR0fHaec+++yzcd1118UnP/nJKC8vjyVLlsTPf/7zcW8YoBDIUYAzy7uotra2xurVq2P9+vXR1dUVy5Yti+XLl0d3d/eY81966aW47rrroq2tLTo7O+PLX/5y3HTTTdHV1XXWmweYiuQoQG6KsizL8lmwePHiWLhwYWzevHl4bP78+bFixYpoaWnJ6Wd8/vOfj8bGxrj//vtzmj84OBgVFRUxMDAQ5eXl+WwXICeTmTNyFChEE5EzeV1RPXbsWHR2dkZDQ8OI8YaGhti1a1dOP+PkyZNx9OjRuPjii087Z2hoKAYHB0c8AAqBHAXIXV5Fta+vL06cOBGVlZUjxisrK6O3tzenn/H9738/3nvvvbj55ptPO6elpSUqKiqGH3Pnzs1nmwDJkqMAuRvXH1MVFRWNeJ5l2aixsTz55JPxne98J1pbW+OSSy457bx169bFwMDA8OPQoUPj2SZAsuQowJkV5zN51qxZMX369FHv+o8cOTLq6sDva21tjTvvvDOeeuqpuPbaaz9ybmlpaZSWluazNYApQY4C5C6vK6olJSVRW1sb7e3tI8bb29ujvr7+tOuefPLJuP322+OJJ56IG2+8cXw7BSgAchQgd3ldUY2IaG5ujltvvTXq6upiyZIl8ZOf/CS6u7ujqakpIj78uOk3v/lN/OxnP4uID8N15cqV8YMf/CC+9KUvDV9FuOCCC6KiouIcHgVgapCjALnJu6g2NjZGf39/bNy4MXp6emLBggXR1tYW1dXVERHR09Mz4l6AP/7xj+P48ePxjW98I77xjW8Mj992222xbdu2sz8BwBQjRwFyk/d9VM8H9/8DJlqh50yhnw84/877fVQBAGCyKKoAACRJUQUAIEmKKgAASVJUAQBIkqIKAECSFFUAAJKkqAIAkCRFFQCAJCmqAAAkSVEFACBJiioAAElSVAEASJKiCgBAkhRVAACSpKgCAJAkRRUAgCQpqgAAJElRBQAgSYoqAABJUlQBAEiSogoAQJIUVQAAkqSoAgCQJEUVAIAkKaoAACRJUQUAIEmKKgAASVJUAQBIkqIKAECSFFUAAJKkqAIAkCRFFQCAJCmqAAAkSVEFACBJiioAAElSVAEASJKiCgBAkhRVAACSpKgCAJCkcRXVTZs2RU1NTZSVlUVtbW10dHR85PydO3dGbW1tlJWVxbx58+LRRx8d12YBCoUcBTizvItqa2trrF69OtavXx9dXV2xbNmyWL58eXR3d485/+DBg3HDDTfEsmXLoqurK7797W/HqlWr4plnnjnrzQNMRXIUIDdFWZZl+SxYvHhxLFy4MDZv3jw8Nn/+/FixYkW0tLSMmv+tb30rXnjhhdi/f//wWFNTU7z66quxe/funF5zcHAwKioqYmBgIMrLy/PZLkBOJjNn5ChQiCYiZ4rzmXzs2LHo7OyMtWvXjhhvaGiIXbt2jblm9+7d0dDQMGLs+uuvjy1btsQHH3wQM2bMGLVmaGgohoaGhp8PDAxExIf/BQBMhFP5kud797zJUaBQTUSO5lVU+/r64sSJE1FZWTlivLKyMnp7e8dc09vbO+b848ePR19fX1RVVY1a09LSEhs2bBg1Pnfu3Hy2C5C3/v7+qKiomLCfL0eBQncuczSvonpKUVHRiOdZlo0aO9P8scZPWbduXTQ3Nw8/f+edd6K6ujq6u7sn9P9AzpfBwcGYO3duHDp0qGA/kiv0Mzrf1DcwMBCXX355XHzxxZPyenL03Po4/DNa6Gd0vqlvInI0r6I6a9asmD59+qh3/UeOHBn1bv+USy+9dMz5xcXFMXPmzDHXlJaWRmlp6ajxioqKgv3lRkSUl5cX9PkiCv+Mzjf1TZs2sXftk6MT6+Pwz2ihn9H5pr5zmaN5/aSSkpKora2N9vb2EePt7e1RX18/5polS5aMmr9jx46oq6sb83tVAIVMjgLkLu/K29zcHI899lhs3bo19u/fH2vWrInu7u5oamqKiA8/blq5cuXw/KampnjjjTeiubk59u/fH1u3bo0tW7bEvffee+5OATCFyFGA3OT9HdXGxsbo7++PjRs3Rk9PTyxYsCDa2tqiuro6IiJ6enpG3AuwpqYm2traYs2aNfHII4/E7Nmz4+GHH46vfvWrOb9maWlpPPDAA2N+jFUICv18EYV/Rueb+ibzjHL03Cv080UU/hmdb+qbiDPmfR9VAACYDBP7VwMAADBOiioAAElSVAEASJKiCgBAkpIpqps2bYqampooKyuL2tra6Ojo+Mj5O3fujNra2igrK4t58+bFo48+Okk7HZ98zvfss8/GddddF5/85CejvLw8lixZEj//+c8ncbf5y/f3d8orr7wSxcXF8cUvfnFiN3gO5HvGoaGhWL9+fVRXV0dpaWl8+tOfjq1bt07SbvOX7/m2b98eV111VVx44YVRVVUVd9xxR/T390/SbvPz0ksvxU033RSzZ8+OoqKieP7558+4ZqplTIQc/b+mYo5GFH6WytGR5GgOsgT84z/+YzZjxozspz/9abZv377snnvuyS666KLsjTfeGHP+gQMHsgsvvDC75557sn379mU//elPsxkzZmRPP/30JO88N/me75577sm++93vZv/5n/+Zvfbaa9m6deuyGTNmZL/4xS8meee5yfd8p7zzzjvZvHnzsoaGhuyqq66anM2O03jO+JWvfCVbvHhx1t7enh08eDD7j//4j+yVV16ZxF3nLt/zdXR0ZNOmTct+8IMfZAcOHMg6Ojqyz3/+89mKFSsmeee5aWtry9avX58988wzWURkzz333EfOn2oZk2Vy9PdNtRzNssLPUjk6khzNTRJFddGiRVlTU9OIsc9+9rPZ2rVrx5z/t3/7t9lnP/vZEWNf//rXsy996UsTtsezke/5xvK5z30u27Bhw7ne2jkx3vM1NjZmf/d3f5c98MADSYdrluV/xn/+53/OKioqsv7+/snY3lnL93x///d/n82bN2/E2MMPP5zNmTNnwvZ4ruQSsFMtY7JMjuYi5RzNssLPUjk6khzNzXn/6P/YsWPR2dkZDQ0NI8YbGhpi165dY67ZvXv3qPnXX3997NmzJz744IMJ2+t4jOd8v+/kyZNx9OjRuPjiiydii2dlvOd7/PHH4/XXX48HHnhgord41sZzxhdeeCHq6urie9/7Xlx22WVx5ZVXxr333hu//e1vJ2PLeRnP+err6+Pw4cPR1tYWWZbFW2+9FU8//XTceOONk7HlCTeVMiZCjuYi5RyNKPwslaOjydHc5P1vpjrX+vr64sSJE1FZWTlivLKyMnp7e8dc09vbO+b848ePR19fX1RVVU3YfvM1nvP9vu9///vx3nvvxc033zwRWzwr4znfr3/961i7dm10dHREcfF5/0fwjMZzxgMHDsTLL78cZWVl8dxzz0VfX1/cfffd8fbbbyf3/arxnK++vj62b98ejY2N8bvf/S6OHz8eX/nKV+KHP/zhZGx5wk2ljImQo7lIOUcjCj9L5ehocjS3jDnvV1RPKSoqGvE8y7JRY2eaP9Z4KvI93ylPPvlkfOc734nW1ta45JJLJmp7Zy3X8504cSJuueWW2LBhQ1x55ZWTtb1zIp/f4cmTJ6OoqCi2b98eixYtihtuuCEeeuih2LZtW5JXAyLyO9++ffti1apVcf/990dnZ2e8+OKLcfDgweF/V30hmGoZEyFHT2eq5GhE4WepHP1fcjQ35/0t2KxZs2L69Omj3nEcOXJkVBM/5dJLLx1zfnFxccycOXPC9joe4znfKa2trXHnnXfGU089Fddee+1EbnPc8j3f0aNHY8+ePdHV1RXf/OY3I+LDMMqyLIqLi2PHjh1xzTXXTMreczWe32FVVVVcdtllUVFRMTw2f/78yLIsDh8+HFdcccWE7jkf4zlfS0tLLF26NO67776IiPjCF74QF110USxbtiwefPDBpK7GjcdUypgIOfpRpkKORhR+lsrR0eRobs77FdWSkpKora2N9vb2EePt7e1RX18/5polS5aMmr9jx46oq6uLGTNmTNhex2M854v48ArA7bffHk888UTS31fJ93zl5eXxy1/+Mvbu3Tv8aGpqis985jOxd+/eWLx48WRtPWfj+R0uXbo03nzzzXj33XeHx1577bWYNm1azJkzZ0L3m6/xnO/999+PadNGxsf06dMj4n/fMU9lUyljIuTo6UyVHI0o/CyVo6PJ0Rzl9adXE+TULR22bNmS7du3L1u9enV20UUXZf/93/+dZVmWrV27Nrv11luH55+65cGaNWuyffv2ZVu2bJkSt1XJ9XxPPPFEVlxcnD3yyCNZT0/P8OOdd945X0f4SPme7/el/peqWZb/GY8ePZrNmTMn+4u/+IvsV7/6VbZz587siiuuyO66667zdYSPlO/5Hn/88ay4uDjbtGlT9vrrr2cvv/xyVldXly1atOh8HeEjHT16NOvq6sq6urqyiMgeeuihrKura/i2MVM9Y7JMjk71HM2yws9SOSpHp+ztqbIsyx555JGsuro6KykpyRYuXJjt3Llz+D+77bbbsquvvnrE/H/7t3/L/viP/zgrKSnJPvWpT2WbN2+e5B3nJ5/zXX311VlEjHrcdtttk7/xHOX7+/u/Ug/XU/I94/79+7Nrr702u+CCC7I5c+Zkzc3N2fvvvz/Ju85dvud7+OGHs8997nPZBRdckFVVVWV/+Zd/mR0+fHiSd52bf/3Xf/3I/00VQsZkmRyd6jmaZYWfpXL06hHz5eiZFWVZAVxfBgCg4Jz376gCAMBYFFUAAJKkqAIAkCRFFQCAJCmqAAAkSVEFACBJiioAAElSVAEASJKiCgBAkhRVAACSpKgCAJAkRRUAgCT9f05dmn+3heueAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig, axs = plt.subplots(nrows=3, ncols=2, figsize=(8, 12))\n", "axs = axs.ravel()\n",