From 5de8ed662c3faae9782f21c11bb1b6eab55008f3 Mon Sep 17 00:00:00 2001 From: Boris Bonev Date: Sun, 21 Jan 2024 11:29:02 +0100 Subject: [PATCH] Some adjustments regarding filter size --- notebooks/disco_conv_2d.ipynb | 144 ++++++++++++++++----------------- torch_harmonics/convolution.py | 11 ++- 2 files changed, 77 insertions(+), 78 deletions(-) diff --git a/notebooks/disco_conv_2d.ipynb b/notebooks/disco_conv_2d.ipynb index f021f2c..21454eb 100644 --- a/notebooks/disco_conv_2d.ipynb +++ b/notebooks/disco_conv_2d.ipynb @@ -13,22 +13,7 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [ - { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'triton'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[1], line 8\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmath\u001b[39;00m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfunctools\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m partial\n\u001b[0;32m----> 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorch_harmonics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mquadrature\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m legendre_gauss_weights, lobatto_weights, clenshaw_curtiss_weights\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[1;32m 12\u001b[0m cmap\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minferno\u001b[39m\u001b[38;5;124m\"\u001b[39m\n", - "File \u001b[0;32m~/Projects/torch-harmonics/torch_harmonics/__init__.py:35\u001b[0m\n\u001b[1;32m 32\u001b[0m __version__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m0.6.4\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 34\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msht\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m RealSHT, InverseRealSHT, RealVectorSHT, InverseRealVectorSHT\n\u001b[0;32m---> 35\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconvolution\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m DiscreteContinuousConvS2, DiscreteContinuousConvTransposeS2, DiscreteContinuousConv2d, DiscreteContinuousConvTranspose2d\n\u001b[1;32m 36\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m quadrature\n\u001b[1;32m 37\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m random_fields\n", - "File \u001b[0;32m~/Projects/torch-harmonics/torch_harmonics/convolution.py:43\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfunctools\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m partial\n\u001b[1;32m 42\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorch_harmonics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mquadrature\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _precompute_grid, _precompute_latitudes\n\u001b[0;32m---> 43\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorch_harmonics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_disco_convolution\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 44\u001b[0m _disco_s2_contraction_torch,\n\u001b[1;32m 45\u001b[0m _disco_s2_transpose_contraction_torch,\n\u001b[1;32m 46\u001b[0m _disco_s2_contraction_triton,\n\u001b[1;32m 47\u001b[0m _disco_s2_transpose_contraction_triton,\n\u001b[1;32m 48\u001b[0m )\n\u001b[1;32m 51\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_compute_support_vals_isotropic\u001b[39m(r: torch\u001b[38;5;241m.\u001b[39mTensor, phi: torch\u001b[38;5;241m.\u001b[39mTensor, nr: \u001b[38;5;28mint\u001b[39m, r_cutoff: \u001b[38;5;28mfloat\u001b[39m, norm: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124ms2\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 52\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 53\u001b[0m \u001b[38;5;124;03m Computes the index set that falls into the isotropic kernel's support and returns both indices and values.\u001b[39;00m\n\u001b[1;32m 54\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n", - "File \u001b[0;32m~/Projects/torch-harmonics/torch_harmonics/_disco_convolution.py:36\u001b[0m\n\u001b[1;32m 32\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmath\u001b[39;00m\n\u001b[1;32m 34\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\n\u001b[0;32m---> 36\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtriton\u001b[39;00m\n\u001b[1;32m 37\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtriton\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlanguage\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mtl\u001b[39;00m\n\u001b[1;32m 39\u001b[0m BLOCK_SIZE_BATCH \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m4\u001b[39m\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'triton'" - ] - } - ], + "outputs": [], "source": [ "import os\n", "import torch\n", @@ -46,7 +31,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -55,14 +40,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/home/bbonev/.local/lib/python3.10/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3526.)\n", + "/opt/homebrew/lib/python3.11/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/TensorShape.cpp:3527.)\n", " return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]\n" ] } @@ -86,7 +71,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -104,19 +89,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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KAvsPHrF8Rg3PVvOHU4TbD34RbbAGb/2q9FnZxj7Zz1bz2/lqUl+ttmTJEgwePLjLl7fHjRsX//fgwYNx4YUX4uyzz8b69etx1VVXdfHMmjULM2fOjP/e1tYmdIHEs9Wcd6nmFOH2g19EG6zBW78qfVa2sY9nq4nF1XeOevfujZycHDQ3Nyfc3tzcjHA4nDb38OHDWLlyJW677baM7QwYMAC9e/fGrl27kt4fCoWQn5+f8GOXAHi2mlGHGy7VnCLcfvCLaIM1eOtXpc/KNvbxbDWxuLo46tatGy6++GKsW7cufls0GsW6deswfPjwtLnPP/88Ojo6MH78+Izt7Nu3D19++SWKi525/Bbg2WpOOtxwqeYU4faDX0QbrMFbvyp9Vraxj2ericX1q9VmzpyJJ598Ek8//TR27tyJqVOn4vDhw5g0aRIAYMKECZg1a1aXvCVLlmDs2LH4h3/4h4TbDx06hLvuugt/+tOfsHv3bqxbtw7XX389zjnnHIwcOdLy4+TZaqmR7XwhVZ0i3H7wi2iDNXjrV6XPyjb28Ww1cQg5PuSxxx6LbwJZXl6OhQsXorKyEgAwYsQI9O/fH8uXL4/Hf/DBBxg4cCBee+01/K//9b8SXF999RXGjh2LLVu24MCBA+jbty+uvvpqzJkzp8sXv1PBs9W4Q7aXThFuP/hFtMEavPWr0mdlG/uyfYdsnq3mEjxbjRBCCFEPUfO36x+rEUIIIYSohNSX8rsND57lx2peOEW4/eAX0QZr8NavSp+VbezL9o/VRJDViyMePMuDZ0U7Rbj94BfRBmvw1q9Kn5Vt7OPBsz45eFZGePAsD571winC7Qe/iDZYg7d+VfqsbGMfD5710cGzqpLuSTJziB4PnnXHpZpThNsPfhFtsAZv/ar0WdnGPh48KxYujixi5BA9s4fy8eBZ/x04KdLtB7+INliDt35V+qxsYx8PnhVLVn/nSBR2DjvkwbP+cIpw+8Evog3W4K1flT4r29gn+8GzfoOLIwHw4FnnXao5Rbj94BfRBmvw1q9Kn5Vt7OPBs2Lhx2oWCYAHzxp1uOFSzSnC7Qe/iDZYg7d+VfqsbGMfD54VCxdHKQik+Lf+dx48a8zhhks1pwi3H/wi2mAN3vpV6bOyjX08eFYsXBx9DQ+eTY1shy+q6hTh9oNfRBuswVu/Kn1WtrGPB8+KI6v3OeLBs+ZecbLtEquqU4TbD34RbbAGb/2q9FnZxr5s3yGbB8+6BA+eJYQQQtSDB88SQgghhHgAF0eEEEIIITqyep+jFRsa+Z0jE8j2ubuqThFuP/hFtMEavPWr0mdlG/uy/TtHIsjqxdH8tR8iGNoHIPHUXyD9CcFmThi2cmJxMuycxCzDSdBuuVRzinD7wS+iDdbgrV+VPivb2GfXYSXf6TkuXQzQdc7Vz8/RjvaMNTpBVn8hu2TGKgRDeUljAkh9EF5s/ZvphOFYjJkTi5O9OO2cxCzDSdBuuVRzinD7wS+iDdbgrV+VPivb2GfXYSXfTI6RWCD1nGlkMRLtaMfeBTfyC9leke5JMnPCsJkTi5OdqGznJGYZToJ2y6WaU4TbD34RbbAGb/2q9FnZxj67Div5ZnKMxBqZM2WBiyOLGDlh2OyJxclOVLZzErMMJ0G75VLNKcLtB7+INliDt35V+qxsY59dh5V8MzlGYjPNmTKR1d85EoWdk6DtnIosw0nQbrlUc4pw+8Evog3W4K1flT4r29hn12ElX0RfkRUujgRg5yRoO6ciy3AStFsu1Zwi3H7wi2iDNXjrV6XPyjb22XVYyRfRV2SFH6tZJIDMJwwHYO7E4mQnKts5iVmGk6DdcqnmFOH2g19EG6zBW78qfVa2sc+uw0q+mRwjsZnmTJng4igFgRT/1v9u5IRhMycWJztR2c5JzDKcBO2WSzWnCLcf/CLaYA3e+lXps7KNfXYdVvLN5BiJNTJnJrvPC7g4+pqTX0/pTgg2esKwlROLk2HnJGYZToJ2y6WaU4TbD34RbbAGb/2q9FnZxj67Div5Ts9x6WJSzbkO78tqiKze52jRmq3cIdsEsu0Sq6pThNsPfhFtsAZv/ar0WdnGvmzfIXv66HLX9znK6sWR239cQgghhDiHqPmbH6sRQgghhOjg4ogQQgghRAcXR4QQQgghOrJ6E8gVGxr5hWwTyPalRFWdItx+8ItogzV461elz8o29mX7F7JFkNWLo/lrP0QwtA/AiUsF9ef1FRfkxvdsqKnbkXBmTOw+/UnEmWJimIl1Is9urpMON1yqOUW4/eAX0QZr8NavSp+Vbeyz67CS7/Qcly4G6Drn6ufnaEd7xhqdIKuvViuZsQrBUF7SmABSnxIcW/8uHj8UADD1mXe7xOpj9C8Io7F6rObZzXXS4YZLNacItx/8ItpgDd76Vemzso19dh1W8s3kGIkFUs+ZRhYj0Y527F1wI69W84p0T1LsvurV21G9ekfS2NhtNXU70BnV0BnVUFNnLFaP1Ty7uU463HCp5hTh9oNfRBuswVu/Kn1WtrHPrsNKvpkcI7FG5kxZ4OLIIhqASFsHIm2pTyPWADS1HkFDYwsaGlsS3iZMF6vHap7dXCcdbrhUc4pw+8Evog3W4K1flT4r29hn12El30yOkdhMc6ZMCFkcPf744+jfvz9yc3NRWVmJhoaGlLHLly9HIBBI+MnNTdxKXNM0PPDAAyguLsZpp52GqqoqfPTRR26XYZn9B49g/0FjL4iT46zm2c110uGGSzWnCLcf/CLaYA3e+lXps7KNfXYdVvLN5NjpLzLi+uLoueeew8yZMzF79my8++67GDJkCEaOHIn9+/enzMnPz0dTU1P855NPPkm4f/78+Vi4cCFqa2uxadMmdO/eHSNHjsSRI3I+OYU9clHYIzdz4Nex6X43mmc310mHGy7VnCLcfvCLaIM1eOtXpc/KNvbZdVjJN5Njp7/IiOuLo0cffRSTJ0/GpEmTUFZWhtraWuTl5WHp0qUpcwKBAMLhcPynqKgofp+maViwYAHuu+8+XH/99bjwwguxYsUKfPbZZ3jppZfcLuebxwggnB9COD835QnCAZz4Bn5FaS9UlPZCcYGxWD1W8+zmOulww6WaU4TbD34RbbAGb/2q9FnZxj67Div5ZnKMxGaaM2XC1cXR0aNHsXnzZlRVVX3TYDCIqqoqbNy4MWXeoUOH0K9fP5SUlOD666/H9u3b4/c1NjYiEokkOAsKClBZWZnS2dHRgba2toSfTARS/Fv/e/WYC1A9pixtzOzrypATDCAnGIhfppgpVo/VPLu5TjrccKnmFOH2g19EG6zBW78qfVa2sc+uw0q+mRwjsUbmzGT3eYGri6MvvvgCnZ2dCe/8AEBRUREikUjSnPPOOw9Lly7Fyy+/jGeeeQbRaBSXXHIJ9u07sR9RLM+Mc+7cuSgoKIj/lJSUdIk5+fUULshF7fihqB0/FOGC3C73xS5fHDWoGIszxMQwE6vHap7dXCcdbrhUc4pw+8Evog3W4K1flT4r29hn12El3+k5Ll1MqjnX4X1ZDeHqPkefffYZzjzzTGzYsAHDhw+P33733XfjrbfewqZNmzI6jh07hvPPPx8333wz5syZgw0bNuDSSy/FZ599huLib56UG2+8EYFAAM8991wXR0dHBzo6OuK/t7W1oaSkBIvWbOUO2SaQbZdYVZ0i3H7wi2iDNXjrV6XPyjb2ZfsO2dNHl7u+z5Gri6OjR48iLy8Pv//97zF27Nj47RMnTsSBAwfw8ssvG/J8//vfxymnnILf/e53+J//+R+cffbZ2LJlC8rLy+MxV1xxBcrLy/HrX/86oy+2CaTbf1xCCCGEOIeo+dvVj9W6deuGiy++GOvWrYvfFo1GsW7duoR3ktLR2dmJ9957L/4uUWlpKcLhcIKzra0NmzZtMuwkhBBCCEmF62erzZw5ExMnTsSwYcNQUVGBBQsW4PDhw5g0aRIAYMKECTjzzDMxd+5cAMCDDz6I7373uzjnnHNw4MABPPzww/jkk0/wox/9CMCJK9lmzJiBX/ziFzj33HNRWlqK+++/H3379k14d4oQQgghxAquL45uuukmfP7553jggQcQiURQXl6O+vr6+Beq9+zZg2Dwmzew/v73v2Py5MmIRCI444wzcPHFF2PDhg0oKyuLx9x99904fPgwpkyZggMHDuCyyy5DfX19l80iCSGEEELMktUHz/I7R4QQQog6iJq/XX/nSGZWbGjk1WomkO2KDVWdItx+8ItogzV461elz8o29mX71WoiyOrF0fy1HyIYOrF/UjAA6A8zLi7IjW9oVVO3I+FAvdh9sf0d6rc1ZYyJYSbWiTy7uU463HCp5hTh9oNfRBuswVu/Kn1WtrHPrsNKvtNzXLoYoOucq5+fox3tGWt0gqz+WK1kxioEQ3lJYwI4cYpwqvsAYPH4oQCAqc+82yVWH6N/QRiN1WM1z26ukw43XKo5Rbj94BfRBmvw1q9Kn5Vt7LPrsJJvJsdILJB6zjSyGIl2tGPvghvVvpRfZdI9SbH7qldvR/XqHUljY7fV1O1AZ1RDZ1RDTZ2xWD1W8+zmOulww6WaU4TbD34RbbAGb/2q9FnZxj67Div5ZnKMxBqZM2WBiyOLaAAibR2ItB1JG9PUegQNjS1oaGxJeJswXaweq3l2c510uOFSzSnC7Qe/iDZYg7d+VfqsbGOfXYeVfDM5RmIzzZkykdXfORLF/oPGXwwnxxrNTRZnJ9dJhxsu1Zwi3H7wi2iDNXjrV6XPyjb22XVYyRfRV2SFiyMBFPYwvv/SybFGc5PF2cl10uGGSzWnCLcf/CLaYA3e+lXps7KNfXYdVvJF9BVZ4cdqFgkACOeHEM7PRSBNTHHBiUsUK0p7objAWKweq3l2c510uOFSzSnC7Qe/iDZYg7d+VfqsbGOfXYeVfDM5RmIzzZkywcVRCgIp/q3/vXrMBageU5Y2ZvZ1ZcgJBpATDMQvU8wUq8dqnt1cJx1uuFRzinD7wS+iDdbgrV+VPivb2GfXYSXfTI6RWCNzZrL7vICLo685+fUULshF7fihqB0/FOGC3C73xS5fHDWoGIszxMQwE6vHap7dXCcdbrhUc4pw+8Evog3W4K1flT4r29hn12El3+k5Ll1MqjnX4X1ZDZHV+xwtWrOVO2SbQLZdYlV1inD7wS+iDdbgrV+VPivb2JftO2RPH13u+j5HWb044tlqhBBCiDqImr/5sRohhBBCiA4ujgghhBBCdHBxRAghhBCig4sjQgghhBAdXBwRQgghhOjI6uNDVmxo5KX8JpDtclZVnSLcfvCLaIM1eOtXpc/KNvZl+6X8IsjqxdH8tR8iGNoH4MQmU1HdpgbFBbnx3T5r6nYknDYcuy+2+VX9tqaMMTHMxDqRZzfXSYcbLtWcItx+8ItogzV461elz8o29tl1WMl3eo5LFwN0nXP183O0oz1jjU6Q1fsclcxYhWAoL2lMAECqP0xs/bt4/FAAwNRn3u0Sq4/RvyCMxuqxmmc310mHGy7VnCLcfvCLaIM1eOtXpc/KNvbZdVjJN5NjJBZIPWcaWYxEO9qxd8GN3OfIK9I9SbH7qldvR/XqHUljY7fV1O1AZ1RDZ1RDTZ2xWD1W8+zmOulww6WaU4TbD34RbbAGb/2q9FnZxj67Div5ZnKMxBqZM2WBiyOLaAAibR2ItB1JG9PUegQNjS1oaGxJeJswXaweq3l2c510uOFSzSnC7Qe/iDZYg7d+VfqsbGOfXYeVfDM5RmIzzZkykdXfORLF/oPGXwwnxxrNTRZnJ9dJhxsu1Zwi3H7wi2iDNXjrV6XPyjb22XVYyRfRV2SFiyMBFPbIzRyUItZobrI4O7lOOtxwqeYU4faDX0QbrMFbvyp9Vraxz67DSr6IviIr/FjNIgEA4fwQwvm5CKSJKS44cYliRWkvFBcYi9VjNc9urpMON1yqOUW4/eAX0QZr8NavSp+Vbeyz67CSbybHSGymOVMmuDhKQSDFv/W/V4+5ANVjytLGzL6uDDnBAHKCgfhlipli9VjNs5vrpMMNl2pOEW4/+EW0wRq89avSZ2Ub++w6rOSbyTESa2TOTHafF3Bx9DUnv57CBbmoHT8UteOHIlyQ2+W+2OWLowYVY3GGmBhmYvVYzbOb66TDDZdqThFuP/hFtMEavPWr0mdlG/vsOqzkOz3HpYtJNec6vC+rIbJ6n6NFa7Zyh2wTyLZLrKpOEW4/+EW0wRq89avSZ2Ub+7J9h+zpo8td3+coqxdHbv9xCSGEEOIcouZvfqxGCCGEEKKDiyNCCCGEEB1cHBFCCCGE6ODiiBBCCCFEBxdHhBBCCCE6uDgihBBCCNGR1WerrdjQyH2OTCDbXh+qOkW4/eAX0QZr8NavSp+VbezL9n2ORCBkcfT444/j4YcfRiQSwZAhQ7Bo0SJUVFQkjX3yySexYsUKbNu2DQBw8cUX41e/+lVC/K233oqnn346IW/kyJGor6839bjmr/0QwdA+ACd24IzqdnwqLsiNb4VeU7cDTa1HutwX2xm0fltTxpgYZmKdyLOb66TDDZdqThFuP/hFtMEavPWr0mdlG/vsOqzkOz3HpYsBus65+vk52tGesUYncH0TyOeeew4TJkxAbW0tKisrsWDBAjz//PP44IMPUFhY2CX+lltuwaWXXopLLrkEubm5mDdvHl588UVs374dZ555JoATi6Pm5mYsW7YsnhcKhXDGGWcYekyxTaRKZqxCMJSXNCYAINUfJrb+XTx+KABg6jPvdonVx+hfEEZj9VjNs5vrpMMNl2pOEW4/+EW0wRq89avSZ2Ub++w6rOSbyTESC6SeM40sRqId7di74Eb1N4F89NFHMXnyZEyaNAllZWWora1FXl4eli5dmjT+t7/9LX7605+ivLwcAwcOxFNPPYVoNIp169YlxIVCIYTD4fhPuoVRR0cH2traEn4yke5Jit1XvXo7qlfvSBobu62mbgc6oxo6oxpq6ozF6rGaZzfXSYcbLtWcItx+8ItogzV461elz8o29tl1WMk3k2Mk1sicKQuuLo6OHj2KzZs3o6qq6psGg0FUVVVh48aNhhzt7e04duwYevXqlXD7+vXrUVhYiPPOOw9Tp07Fl1+m/hxy7ty5KCgoiP+UlJRYK0iHBiDS1oFI25G0MU2tR9DQ2IKGxpaEtwnTxeqxmmc310mHGy7VnCLcfvCLaIM1eOtXpc/KNvbZdVjJN5NjJDbTnCkTri6OvvjiC3R2dqKoqCjh9qKiIkQiEUOOe+65B3379k1YYI0aNQorVqzAunXrMG/ePLz11lsYPXo0Ojs7kzpmzZqF1tbW+M/evXutF2WB/QePYP9BYy+Ik+Os5tnNddLhhks1pwi3H/wi2mAN3vpV6bOyjX12HVbyzeTY6S8yIvXVag899BBWrlyJ9evXIzc3N377uHHj4v8ePHgwLrzwQpx99tlYv349rrrqqi6eUCiEUCgk5DEno7BHbuagFLFGc5PF2cl10uGGSzWnCLcf/CLaYA3e+lXps7KNfXYdVvJF9BVZcfWdo969eyMnJwfNzc0Jtzc3NyMcDqfNfeSRR/DQQw/htddew4UXXpg2dsCAAejduzd27dpl+zEbJQAgnB9COD83/mWzZDHFBScuUawo7YXiAmOxeqzm2c110uGGSzWnCLcf/CLaYA3e+lXps7KNfXYdVvLN5BiJzTRnyoSri6Nu3brh4osvTvgydezL1cOHD0+ZN3/+fMyZMwf19fUYNmxYxnb27duHL7/8EsXFzlx+CyDhyTv5iYz9Xj3mAlSPKUsbM/u6MuQEA8gJBuKXKWaK1WM1z26ukw43XKo5Rbj94BfRBmvw1q9Kn5Vt7LPrsJJvJsdIrJE5M9l9XuD61WozZ87Ek08+iaeffho7d+7E1KlTcfjwYUyaNAkAMGHCBMyaNSseP2/ePNx///1YunQp+vfvj0gkgkgkgkOHDgEADh06hLvuugt/+tOfsHv3bqxbtw7XX389zjnnHIwcOdLy4zz59RQuyEXt+KGoHT8U4YLcLvfFLl8cNagYizPExDATq8dqnt1cJx1uuFRzinD7wS+iDdbgrV+VPivb2GfXYSXf6TkuXUyqOdfhfVkN4fo+RwDw2GOPxTeBLC8vx8KFC1FZWQkAGDFiBPr374/ly5cDAPr3749PPvmki2P27Nmorq7GV199hbFjx2LLli04cOAA+vbti6uvvhpz5szp8sXvVMT2OVq0Zit3yDaBbLvEquoU4faDX0QbrMFbvyp9VraxL9t3yJ4+utz1fY6ELI5kI7Y4cvuPSwghhBDnEDV/8+BZQgghhBAdXBwRQgghhOjg4ogQQgghRAcXR4QQQgghOrg4IoQQQgjRwcURIYQQQogOqc9Wc5sVGxq5z5EJZNvrQ1WnCLcf/CLaYA3e+lXps7KNfdm+z5EIsnpxNH/thwiG9gE4sQNnVLfjU3FBbnwr9Jq6HWhqPdLlvtjOoPXbmjLGxDAT60Se3VwnHW64VHOKcPvBL6IN1uCtX5U+K9vYZ9dhJd/pOS5dDNB1ztXPz9GO9ow1OkFWbwJZMmMVgqG8pDEBAKn+MLH17+LxQwEAU595t0usPkb/gjAaq8dqnt1cJx1uuFRzinD7wS+iDdbgrV+VPivb2GfXYSXfTI6RWCD1nGlkMRLtaMfeBTdyE0ivSPckxe6rXr0d1at3JI2N3VZTtwOdUQ2dUQ01dcZi9VjNs5vrpMMNl2pOEW4/+EW0wRq89avSZ2Ub++w6rOSbyTESa2TOlAUujiyiAYi0dSDSdiRtTFPrETQ0tqChsSXhbcJ0sXqs5tnNddLhhks1pwi3H/wi2mAN3vpV6bOyjX12HVbyzeQYic00Z8pEVn/nSBT7Dxp/MZwcazQ3WZydXCcdbrhUc4pw+8Evog3W4K1flT4r29hn12ElX0RfkRUujgRQ2CPXcqzR3GRxdnKddLjhUs0pwu0Hv4g2WIO3flX6rGxjn12HlXwRfUVW+LGaRQIAwvkhhPNzEUgTU1xw4hLFitJeKC4wFqvHap7dXCcdbrhUc4pw+8Evog3W4K1flT4r29hn12El30yOkdhMc6ZMcHGUgkCKf+t/rx5zAarHlKWNmX1dGXKCAeQEA/HLFDPF6rGaZzfXSYcbLtWcItx+8ItogzV461elz8o29tl1WMk3k2Mk1sicmew+L+Di6GtOfj2FC3JRO34oascPRbggt8t9scsXRw0qxuIMMTHMxOqxmmc310mHGy7VnCLcfvCLaIM1eOtXpc/KNvbZdVjJd3qOSxeTas51eF9WQ2T1PkeL1mzlDtkmkG2XWFWdItx+8ItogzV461elz8o29mX7DtnTR5e7vs9RVi+O3P7jEkIIIcQ5RM3f/FiNEEIIIUQHF0eEEEIIITq4OCKEEEII0cHFESGEEEKIDi6OCCGEEEJ0cHFECCGEEKIjq89WW7GhkfscmUC2vT5UdYpw+8Evog3W4K1flT4r29iX7fsciSCrF0fz136IYGgfgBM7cEZ1Oz4VF+TGt0KvqduBptYjXe6L7Qxav60pY0wMM7FO5NnNddLhhks1pwi3H/wi2mAN3vpV6bOyjX12HVbynZ7j0sUAXedc/fwc7WjPWKMTZPUmkCUzViEYyksaEwCQ6g8TW/8uHj8UADD1mXe7xOpj9C8Io7F6rObZzXXS4YZLNacItx/8ItpgDd76Vemzso19dh1W8s3kGIkFUs+ZRhYj0Y527F1wIzeB9Ip0T1LsvurV21G9ekfS2NhtNXU70BnV0BnVUFNnLFaP1Ty7uU463HCp5hTh9oNfRBuswVu/Kn1WtrHPrsNKvpkcI7FG5kxZ4OLIIhqASFsHIm1H0sY0tR5BQ2MLGhpbEt4mTBerx2qe3VwnHW64VHOKcPvBL6IN1uCtX5U+K9vYZ9dhJd9MjpHYTHOmTGT1d45Esf+g8RfDybFGc5PF2cl10uGGSzWnCLcf/CLaYA3e+lXps7KNfXYdVvJF9BVZ4eJIAIU9ci3HGs1NFmcn10mHGy7VnCLcfvCLaIM1eOtXpc/KNvbZdVjJF9FXZIUfq1kkACCcH0I4PxeBNDHFBScuUawo7YXiAmOxeqzm2c110uGGSzWnCLcf/CLaYA3e+lXps7KNfXYdVvLN5BiJzTRnygQXRykIpPi3/vfqMRegekxZ2pjZ15UhJxhATjAQv0wxU6weq3l2c510uOFSzSnC7Qe/iDZYg7d+VfqsbGOfXYeVfDM5RmKNzJnJ7vMCLo6+5uTXU7ggF7Xjh6J2/FCEC3K73Be7fHHUoGIszhATw0ysHqt5dnOddLjhUs0pwu0Hv4g2WIO3flX6rGxjn12HlXyn57h0ManmXIf3ZTVEVu9ztGjNVu6QbQLZdolV1SnC7Qe/iDZYg7d+VfqsbGNftu+QPX10uev7HAlZHD3++ON4+OGHEYlEMGTIECxatAgVFRUp459//nncf//92L17N84991zMmzcP11xzTfx+TdMwe/ZsPPnkkzhw4AAuvfRSLF68GOeee66hxxNbHLn9xyWEEEKIc4iav13/WO25557DzJkzMXv2bLz77rsYMmQIRo4cif379yeN37BhA26++Wbcdttt2LJlC8aOHYuxY8di27Zt8Zj58+dj4cKFqK2txaZNm9C9e3eMHDkSR47473JCQgghhIjF9XeOKisr8Z3vfAePPfYYACAajaKkpATTp0/Hvffe2yX+pptuwuHDh/GHP/whftt3v/tdlJeXo7a2FpqmoW/fvrjzzjvxs5/9DADQ2tqKoqIiLF++HOPGjevi7OjoQEdHR/z3trY2lJSU8J0jQgghRCF88c7R0aNHsXnzZlRVVX3TYDCIqqoqbNy4MWnOxo0bE+IBYOTIkfH4xsZGRCKRhJiCggJUVlamdM6dOxcFBQXxn5KSErulEUIIIcSnuLo4+uKLL9DZ2YmioqKE24uKihCJRJLmRCKRtPGx/zfjnDVrFlpbW+M/e/futVQPIYQQQvxPVuyQHQqFEAqFvH4YhBBCCFEAV9856t27N3JyctDc3Jxwe3NzM8LhcNKccDicNj72/2achBBCCCFGcXVx1K1bN1x88cVYt25d/LZoNIp169Zh+PDhSXOGDx+eEA8Ar7/+ejy+tLQU4XA4IaatrQ2bNm1K6SSEEEIIMYrrH6vNnDkTEydOxLBhw1BRUYEFCxbg8OHDmDRpEgBgwoQJOPPMMzF37lwAwL/+67/iiiuuwP/5P/8H1157LVauXIm//OUveOKJJwAAgUAAM2bMwC9+8Quce+65KC0txf3334++ffti7Nixph7big2N3ATSBLJthKaqU4TbD34RbbAGb/2q9FnZxr5s3wRSBK4vjm666SZ8/vnneOCBBxCJRFBeXo76+vr4F6r37NmDYPCbN7AuueQSPPvss7jvvvvw85//HOeeey5eeuklDBo0KB5z99134/Dhw5gyZQoOHDiAyy67DPX19cjNNXcy8Py1HyIY2gfgxPbkUd2mBsUFufFzYmrqdqCp9UiX+2Lbptdva8oYE8NMrBN5dnOddLjhUs0pwu0Hv4g2WIO3flX6rGxjn12HlXyn57h0MUDXOVc/P0c72jPW6ARZfXxIyYxVCIbyksYEAKT6w8TWv4vHDwUATH3m3S6x+hj9C8JorB6reXZznXS44VLNKcLtB7+INliDt35V+qxsY59dh5V8MzlGYoHUc6aRxUi0ox17F9yo9j5HKpPuSYrdV716O6pX70gaG7utpm4HOqMaOqMaauqMxeqxmmc310mHGy7VnCLcfvCLaIM1eOtXpc/KNvbZdVjJN5NjJNbInCkLXBxZRAMQaetApC31kSUagKbWI2hobEFDY0vC24TpYvVYzbOb66TDDZdqThFuP/hFtMEavPWr0mdlG/vsOqzkm8kxEptpzpSJrNjnyGv2HzT+Yjg51mhusjg7uU463HCp5hTh9oNfRBuswVu/Kn1WtrHPrsNKvoi+IitcHAmgsIfxL4qfHGs0N1mcnVwnHW64VHOKcPvBL6IN1uCtX5U+K9vYZ9dhJV9EX5EVfqxmkQCAcH4I4fxcBNLEFBecuESxorQXiguMxeqxmmc310mHGy7VnCLcfvCLaIM1eOtXpc/KNvbZdVjJN5NjJDbTnCkTXBylIJDi3/rfq8dcgOoxZWljZl9XhpxgADnBQPwyxUyxeqzm2c110uGGSzWnCLcf/CLaYA3e+lXps7KNfXYdVvLN5BiJNTJnJrvPC7g4+pqTX0/hglzUjh+K2vFDES7I7XJf7PLFUYOKsThDTAwzsXqs5tnNddLhhks1pwi3H/wi2mAN3vpV6bOyjX12HVbynZ7j0sWkmnMd3pfVEFm9z9GiNVu5Q7YJZNslVlWnCLcf/CLaYA3e+lXps7KNfdm+Q/b00eWu73OU1Ysjt/+4hBBCCHEOUfM3P1YjhBBCCNHBxREhhBBCiA4ujgghhBBCdHBxRAghhBCig4sjQgghhBAdXBwRQgghhOjI6rPVVmxo5D5HJpBtrw9VnSLcfvCLaIM1eOtXpc/KNvZl+z5HIsjqxdH8tR8iGNoH4MQOnFHdjk/FBbnxrdBr6nagqfVIl/tiO4PWb2vKGBPDTKwTeXZznXS44VLNKcLtB7+INliDt35V+qxsY59dh5V8p+e4dDFA1zlXPz9HO9oz1ugEWb0JZMmMVQiG8pLGBACk+sPE1r+Lxw8FAEx95t0usfoY/QvCaKweq3l2c510uOFSzSnC7Qe/iDZYg7d+VfqsbGOfXYeVfDM5RmKB1HOmkcVItKMdexfcyE0gvSLdkxS7r3r1dlSv3pE0NnZbTd0OdEY1dEY11NQZi9VjNc9urpMON1yqOUW4/eAX0QZr8NavSp+Vbeyz67CSbybHSKyROVMWuDiyiAYg0taBSNuRtDFNrUfQ0NiChsaWhLcJ08XqsZpnN9dJhxsu1Zwi3H7wi2iDNXjrV6XPyjb22XVYyTeTYyQ205wpE1n9nSNR7D9o/MVwcqzR3GRxdnKddLjhUs0pwu0Hv4g2WIO3flX6rGxjn12HlXwRfUVWuDgSQGGPXMuxRnOTxdnJddLhhks1pwi3H/wi2mAN3vpV6bOyjX12HVbyRfQVWeHHahYJAAjnhxDOz0UgTUxxwYlLFCtKe6G4wFisHqt5dnOddLjhUs0pwu0Hv4g2WIO3flX6rGxjn12HlXwzOUZiM82ZMsHFUQoCKf6t/716zAWoHlOWNmb2dWXICQaQEwzEL1PMFKvHap7dXCcdbrhUc4pw+8Evog3W4K1flT4r29hn12El30yOkVgjc2ay+7yAi6OvOfn1FC7IRe34oagdPxThgtwu98UuXxw1qBiLM8TEMBOrx2qe3VwnHW64VHOKcPvBL6IN1uCtX5U+K9vYZ9dhJd/pOS5dTKo51+F9WQ2R1fscLVqzlTtkm0C2XWJVdYpw+8Evog3W4K1flT4r29iX7TtkTx9d7vo+R1m9OHL7j0sIIYQQ5xA1f/NjNUIIIYQQHVwcEUIIIYTo4OKIEEIIIUQHF0eEEEIIITq4OCKEEEII0cHFESGEEEKIjqw+W23Fhkbuc2QC2fb6UNUpwu0Hv4g2WIO3flX6rGxjX7bvcySCrF4czV/7IYKhfQBO7MAZ1e34VFyQG98KvaZuB5paj3S5L7YzaP22powxMczEOpFnN9dJhxsu1Zwi3H7wi2iDNXjrV6XPyjb22XVYyXd6jksXA3Sdc/Xzc7SjPWONTuDqJpAtLS2YPn066urqEAwGccMNN+DXv/41Tj/99JTxs2fPxmuvvYY9e/agT58+GDt2LObMmYOCgoJvHnSg62r1d7/7HcaNG2foccU2kSqZsQrBUF7SmACAVH+YWOuLxw8FAEx95t0usfoY/QvCaKweq3l2c510uOFSzSnC7Qe/iDZYg7d+VfqsbGOfXYeVfDM5RmKB1HOmkcVItKMdexfcqPYmkLfccgu2b9+O119/HX/4wx/w9ttvY8qUKSnjP/vsM3z22Wd45JFHsG3bNixfvhz19fW47bbbusQuW7YMTU1N8Z+xY8c6+tjTPUmx+6pXb0f16h1JY2O31dTtQGdUQ2dUQ02dsVg9VvPs5jrpcMOlmlOE2w9+EW2wBm/9qvRZ2cY+uw4r+WZyjMQamTNlwbXF0c6dO1FfX4+nnnoKlZWVuOyyy7Bo0SKsXLkSn332WdKcQYMG4T//8z9x3XXX4eyzz8aVV16JX/7yl6irq8Px48cTYnv27IlwOBz/yc3NTep0Cw1ApK0DkbYjaWOaWo+gobEFDY0tCW8TpovVYzXPbq6TDjdcqjlFuP3gF9EGa/DWr0qflW3ss+uwkm8mx0hspjlTJlxbHG3cuBE9e/bEsGHD4rdVVVUhGAxi06ZNhj2xt85OOSXx61G33347evfujYqKCixduhTpPh3s6OhAW1tbwo9I9h88gv0Hjb0gTo6zmmc310mHGy7VnCLcfvCLaIM1eOtXpc/KNvbZdVjJN5Njp7/IiGtfyI5EIigsLExs7JRT0KtXL0QiEUOOL774AnPmzOnyUdyDDz6IK6+8Enl5eXjttdfw05/+FIcOHcIdd9yR1DN37lzU1NRYK8QBCnsYf1fr5Fijucni7OQ66XDDpZpThNsPfhFtsAZv/ar0WdnGPrsOK/ki+oqsmH7n6N5770UgEEj78/7779t+YG1tbbj22mtRVlaG6urqhPvuv/9+XHrppbjoootwzz334O6778bDDz+c0jVr1iy0trbGf/bu3Wv78QUAhPNDCOfnouvXw7+JKS44cYliRWkvFBcYi9VjNc9urpMON1yqOUW4/eAX0QZr8NavSp+Vbeyz67CSbybHSGymOVMmTC+O7rzzTuzcuTPtz4ABAxAOh7F///6E3OPHj6OlpQXhcDhtGwcPHsSoUaPQo0cPvPjiizj11FPTxldWVmLfvn3o6OhIen8oFEJ+fn7CTyYCKf6t/716zAWoHlOWNmb2dWXICQaQEwzEL1PMFKvHap7dXCcdbrhUc4pw+8Evog3W4K1flT4r29hn12El30yOkVgjc2ay+7zA9OKoT58+GDhwYNqfbt26Yfjw4Thw4AA2b94cz33jjTcQjUZRWVmZ0t/W1oarr74a3bp1w+rVqw190Xrr1q0444wzEAqFzJYT5+TXU7ggF7Xjh6J2/FCEC3K73Be7fHHUoGIszhATw0ysHqt5dnOddLjhUs0pwu0Hv4g2WIO3flX6rGxjn12HlXyn57h0ManmXIf3ZTWEq/scjR49Gs3NzaitrcWxY8cwadIkDBs2DM8++ywA4NNPP8VVV12FFStWoKKiIr4wam9vx4svvoju3bvHXX369EFOTg7q6urQ3NyM7373u8jNzcXrr7+On/3sZ/jZz35m+HtFsX2OFq3Zyh2yTSDbLrGqOkW4/eAX0QZr8NavSp+VbezL9h2yp48ud32fI9c3gZw2bVrCJpALFy6MbwK5e/dulJaW4s0338SIESOwfv16fO9730vqamxsRP/+/VFfX49Zs2Zh165d0DQN55xzDqZOnYrJkycjGDT2RlhsceT2H5cQQgghziFq/nZ1cSQrXBwRQggh6iFq/nZ1h2xCCCGEENXg4ogQQgghRAcXR4QQQgghOrg4IoQQQgjRwcURIYQQQogO185WU4EVGxq5z5EJZNvrQ1WnCLcf/CLaYA3e+lXps7KNfdm+z5EIsnpxNH/thwiG9gE4sQNnVLepQXFBbnwr9Jq6HWhqPdLlvtjOoPXbmjLGxDAT60Se3VwnHW64VHOKcPvBL6IN1uCtX5U+K9vYZ9dhJd/pOS5dDNB1ztXPz9GO9ow1OkFW73NUMmMVgqG8pDEBAKn+MLH17+LxQwEAU595t0usPkb/gjAaq8dqnt1cJx1uuFRzinD7wS+iDdbgrV+VPivb2GfXYSXfTI6RWCD1nGlkMRLtaMfeBTdynyOvSPckxe6rXr0d1at3JI2N3VZTtwOdUQ2dUQ01dcZi9VjNs5vrpMMNl2pOEW4/+EW0wRq89avSZ2Ub++w6rOSbyTESa2TOlAUujiyiAYi0dSDSdiRtTFPrETQ0tqChsSXhbcJ0sXqs5tnNddLhhks1pwi3H/wi2mAN3vpV6bOyjX12HVbyzeQYic00Z8pEVn/nSBT7Dxp/MZwcazQ3WZydXCcdbrhUc4pw+8Evog3W4K1flT4r29hn12ElX0RfkRUujgRQ2CPXcqzR3GRxdnKddLjhUs0pwu0Hv4g2WIO3flX6rGxjn12HlXwRfUVW+LGaRQIAwvkhhPNzEUgTU1xw4hLFitJeKC4wFqvHap7dXCcdbrhUc4pw+8Evog3W4K1flT4r29hn12El30yOkdhMc6ZMcHGUgkCKf+t/rx5zAarHlKWNmX1dGXKCAeQEA/HLFDPF6rGaZzfXSYcbLtWcItx+8ItogzV461elz8o29tl1WMk3k2Mk1sicmew+L+Di6GtOfj2FC3JRO34oascPRbggt8t9scsXRw0qxuIMMTHMxOqxmmc310mHGy7VnCLcfvCLaIM1eOtXpc/KNvbZdVjJd3qOSxeTas51eF9WQ2T1PkeL1mzlDtkmkG2XWFWdItx+8ItogzV461elz8o29mX7DtnTR5e7vs9RVi+O3P7jEkIIIcQ5RM3f/FiNEEIIIUQHF0eEEEIIITq4OCKEEEII0cHFESGEEEKIDi6OCCGEEEJ0ZPXxISs2NPJSfhPIdjmrqk4Rbj/4RbTBGrz1q9JnZRv7sv1SfhFk9eJo/toPEQztA3Bik6moblOD4oLc+G6fNXU7Ek4bjt0X2/yqfltTxpgYZmKdyLOb66TDDZdqThFuP/hFtMEavPWr0mdlG/vsOqzkOz3HpYsBus65+vk52tGesUYnyOp9jkpmrEIwlJc0JgAg1R8mtv5dPH4oAGDqM+92idXH6F8QRmP1WM2zm+ukww2Xak4Rbj/4RbTBGrz1q9JnZRv77Dqs5JvJMRILpJ4zjSxGoh3t2LvgRu5z5BXpnqTYfdWrt6N69Y6ksbHbaup2oDOqoTOqoabOWKweq3l2c510uOFSzSnC7Qe/iDZYg7d+VfqsbGOfXYeVfDM5RmKNzJmywMWRRTQAkbYORNqOpI1paj2ChsYWNDS2JLxNmC5Wj9U8u7lOOtxwqeYU4faDX0QbrMFbvyp9Vraxz67DSr6ZHCOxmeZMmcjq7xyJYv9B4y+Gk2ON5iaLs5PrpMMNl2pOEW4/+EW0wRq89avSZ2Ub++w6rOSL6CuywsWRAAp75GYOShFrNDdZnJ1cJx1uuFRzinD7wS+iDdbgrV+VPivb2GfXYSVfRF+RFX6sZpEAgHB+COH8XATSxBQXnLhEsaK0F4oLjMXqsZpnN9dJhxsu1Zwi3H7wi2iDNXjrV6XPyjb22XVYyTeTYyQ205wpE1wcpSCQ4t/636vHXIDqMWVpY2ZfV4acYAA5wUD8MsVMsXqs5tnNddLhhks1pwi3H/wi2mAN3vpV6bOyjX12HVbyzeQYiTUyZya7zwu4OPqak19P4YJc1I4fitrxQxEuyO1yX+zyxVGDirE4Q0wMM7F6rObZzXXS4YZLNacItx/8ItpgDd76Vemzso19dh1W8p2e49LFpJpzHd6X1RBZvc/RojVbuUO2CWTbJVZVpwi3H/wi2mAN3vpV6bOyjX3ZvkP29NHlru9zlNWLI7f/uIQQQghxDlHzNz9WI4QQQgjR4eriqKWlBbfccgvy8/PRs2dP3HbbbTh06FDanBEjRiAQCCT8/OQnP0mI2bNnD6699lrk5eWhsLAQd911F44fP+5mKYQQQgjJElzd5+iWW25BU1MTXn/9dRw7dgyTJk3ClClT8Oyzz6bNmzx5Mh588MH473l535x/1tnZiWuvvRbhcBgbNmxAU1MTJkyYgFNPPRW/+tWvXKuFEEIIIdmBa9852rlzJ8rKyvDnP/8Zw4YNAwDU19fjmmuuwb59+9C3b9+keSNGjEB5eTkWLFiQ9P41a9bgn/7pn/DZZ5+hqKgIAFBbW4t77rkHn3/+Obp165bxsfE7R4QQQoh6iJq/XXvnaOPGjejZs2d8YQQAVVVVCAaD2LRpE/73//7fKXN/+9vf4plnnkE4HMZ1112H+++/P/7u0caNGzF48OD4wggARo4cialTp2L79u246KKLuvg6OjrQ0dER/72trQ0AsGJDI69WM4FsV2yo6hTh9oNfRBuswVu/Kn1WtrEv269WE4Fri6NIJILCwsLExk45Bb169UIkEkmZ94Mf/AD9+vVD37598be//Q333HMPPvjgA7zwwgtxr35hBCD+eyrv3LlzUVNT0+X2+Ws/RDC0D8CJfRT0hxkXF+TGN7SqqduRcKBe7L7Y/g7125oyxsQwE+tEnt1cJx1uuFRzinD7wS+iDdbgrV+VPivb2GfXYSXf6TkuXQzQdc7Vz8/RjvaMNTqB6Y/V7r33XsybNy9tzM6dO/HCCy/g6aefxgcffJBwX2FhIWpqajB16lRD7b3xxhu46qqrsGvXLpx99tmYMmUKPvnkE6xduzYe097eju7du+PVV1/F6NGjuziSvXNUUlKCkhmrEAzldYkHTuzQmeoPE1v/Lh4/FAAw9Zl3u8TqY/QvCKOxeqzm2c110uGGSzWnCLcf/CLaYA3e+lXps7KNfXYdVvLN5BiJBVLPmUYWI9GOduxdcKN8l/Lfeeed2LlzZ9qfAQMGIBwOY//+/Qm5x48fR0tLC8LhsOH2KisrAQC7du0CAITDYTQ3NyfExH5P5Q2FQsjPz0/4yUS6Jyl2X/Xq7ahevSNpbOy2mrod6Ixq6IxqqKkzFqvHap7dXCcdbrhUc4pw+8Evog3W4K1flT4r29hn12El30yOkVgjc6YsmF4c9enTBwMHDkz7061bNwwfPhwHDhzA5s2b47lvvPEGotFofMFjhK1btwIAiotPrEyHDx+O9957L2Hh9frrryM/Px9lZWVmy7GMBiDS1oFI25G0MU2tR9DQ2IKGxpaEtwnTxeqxmmc310mHGy7VnCLcfvCLaIM1eOtXpc/KNvbZdVjJN5NjJDbTnCkTru1zdP7552PUqFGYPHkyGhoa8M4772DatGkYN25c/Eq1Tz/9FAMHDkRDQwMA4OOPP8acOXOwefNm7N69G6tXr8aECRNw+eWX48ILLwQAXH311SgrK8MPf/hD/PWvf8XatWtx33334fbbb0coFHKrHFvsP3gE+w8ae0GcHGc1z26ukw43XKo5Rbj94BfRBmvw1q9Kn5Vt7LPrsJJvJsdOf5ERV/c5+u1vf4tp06bhqquuQjAYxA033ICFCxfG7z927Bg++OADtLef+IJVt27d8Mc//hELFizA4cOHUVJSghtuuAH33XdfPCcnJwd/+MMfMHXqVAwfPhzdu3fHxIkTE/ZFko3CHrmZg1LEGs1NFmcn10mHGy7VnCLcfvCLaIM1eOtXpc/KNvbZdVjJF9FXZMXVxVGvXr3SbvjYv39/6L8PXlJSgrfeeiujt1+/fnj11VcdeYxWCQAoyg8BCKC57UjSz0sDOHHScEVpLwAnvo0faTUWG6OitJelPLu5TjrccKnmFOH2g19EG6zBW78qfVa2sc+uw0q+2ZxMsZnmTJng2WopCKT4t/736jEXoHpMWdqY2deVIScYQE4wEL9MMVOsHqt5dnOddLjhUs0pwu0Hv4g2WIO3flX6rGxjn12HlXwzOUZijcyZye7zAi6Ovubk11O4IBe144eidvxQhAtyu9wXu3xx1KBiLM4QE8NMrB6reXZznXS44VLNKcLtB7+INliDt35V+qxsY59dh5V8p+e4dDGp5lyH92U1hGvHh8hMbPvxRWu2codsE8i2S6yqThFuP/hFtMEavPWr0mdlG/uyfYfs6aPLXd/nKKsXRzxbjRBCCFEHUfM3P1YjhBBCCNHBxREhhBBCiA4ujgghhBBCdLi6z5HsrNjQyC9km0C2LyWq6hTh9oNfRBuswVu/Kn1WtrEv27+QLYKsXhzNX/shgqF9AE5cKqg/r6+4IDe+Z0NN3Y6EM2Ni9+lPIs4UE8NMrBN5dnOddLjhUs0pwu0Hv4g2WIO3flX6rGxjn12HlXyn57h0MUDXOVc/P0c72jPW6ARZfbVayYxVCIbyksYEkPqU4Nj6d/H4oQCAqc+82yVWH6N/QRiN1WM1z26ukw43XKo5Rbj94BfRBmvw1q9Kn5Vt7LPrsJJvJsdILJB6zjSyGIl2tGPvght5tZpXpHuSYvdVr96O6tU7ksbGbqup24HOqIbOqIaaOmOxeqzm2c110uGGSzWnCLcf/CLaYA3e+lXps7KNfXYdVvLN5BiJNTJnygIXRxbRAETaOhBpS30SsQagqfUIGhpb0NDYkvA2YbpYPVbz7OY66XDDpZpThNsPfhFtsAZv/ar0WdnGPrsOK/lmcozEZpozZSKrv3Mkiv0Hjb8YTo41mpsszk6ukw43XKo5Rbj94BfRBmvw1q9Kn5Vt7LPrsJIvoq/IChdHAijskZs5KEWs0dxkcXZynXS44VLNKcLtB7+INliDt35V+qxsY59dh5V8EX1FVvixmkUCAML5IYTzc1OeIBzAiW/gV5T2QkVpLxQXGIvVYzXPbq6TDjdcqjlFuP3gF9EGa/DWr0qflW3ss+uwkm8mx0hspjlTJrg4SkEgxb/1v1ePuQDVY8rSxsy+rgw5wQBygoH4ZYqZYvVYzbOb66TDDZdqThFuP/hFtMEavPWr0mdlG/vsOqzkm8kxEmtkzkx2nxdwcfQ1J7+ewgW5qB0/FLXjhyJckNvlvtjli6MGFWNxhpgYZmL1WM2zm+ukww2Xak4Rbj/4RbTBGrz1q9JnZRv77Dqs5Ds9x6WLSTXnOrwvqyGyep+jRWu2codsE8i2S6yqThFuP/hFtMEavPWr0mdlG/uyfYfs6aPLXd/nKKsXR27/cQkhhBDiHKLmb36sRgghhBCig4sjQgghhBAdWb3P0YoNjfzOkQlk+9xdVacItx/8ItpgDd76Vemzso192f6dIxFk9eJo/toPEQztA5B46i+Q/oRgMycMWzmxOBl2TmKW4SRot1yqOUW4/eAX0QZr8NavSp+Vbeyz67CS7/Qcly4G6Drn6ufnaEd7xhqdIKu/kF0yYxWCobykMQGkPggvtv7NdMJwLMbMicXJXpx2TmKW4SRot1yqOUW4/eAX0QZr8NavSp+Vbeyz67CSbybHSCyQes40shiJdrRj74Ib+YVsr0j3JJk5YdjMicXJTlS2cxKzDCdBu+VSzSnC7Qe/iDZYg7d+VfqsbGOfXYeVfDM5RmKNzJmywMWRRYycMGz2xOJkJyrbOYlZhpOg3XKp5hTh9oNfRBuswVu/Kn1WtrHPrsNKvpkcI7GZ5kyZyOrvHInCzknQdk5FluEkaLdcqjlFuP3gF9EGa/DWr0qflW3ss+uwki+ir8gKF0cCsHMStJ1TkWU4Cdotl2pOEW4/+EW0wRq89avSZ2Ub++w6rOSL6Cuywo/VLBJA5hOGAzB3YnGyE5XtnMQsw0nQbrlUc4pw+8Evog3W4K1flT4r29hn12El30yOkdhMc6ZMcHGUgkCKf+t/N3LCsJkTi5OdqGznJGYZToJ2y6WaU4TbD34RbbAGb/2q9FnZxj67Div5ZnKMxBqZM5Pd5wVcHH3Nya+ndCcEGz1h2MqJxcmwcxKzDCdBu+VSzSnC7Qe/iDZYg7d+VfqsbGOfXYeVfKfnuHQxqeZch/dlNURW73O0aM1W7pBtAtl2iVXVKcLtB7+INliDt35V+qxsY1+275A9fXS56/scZfXiyO0/LiGEEEKcQ9T8zY/VCCGEEEJ0ZPWl/Dx4lh+reeEU4faDX0QbrMFbvyp9VraxL9s/VhOBq4ujlpYWTJ8+HXV1dQgGg7jhhhvw61//GqeffnrS+N27d6O0tDTpfatWrcL3v/99AEAg0PUJ+d3vfodx48aZenw8eJYHz4p2inD7wS+iDdbgrV+VPivb2MeDZ31w8Ozo0aPR1NSE3/zmNzh27BgmTZqE73znO3j22WeTxnd2duLzzz9PuO2JJ57Aww8/jKampviiKhAIYNmyZRg1alQ8rmfPnsjNNbYRFQ+e5cGzXjhFuP3gF9EGa/DWr0qflW3s48GzPjh4dufOnaivr8dTTz2FyspKXHbZZVi0aBFWrlyJzz77LGlOTk4OwuFwws+LL76IG2+8scu7TT179kyIM7owMkq6J8nMIXo8eNYdl2pOEW4/+EW0wRq89avSZ2Ub+3jwrFhcWxxt3LgRPXv2xLBhw+K3VVVVIRgMYtOmTYYcmzdvxtatW3Hbbbd1ue/2229H7969UVFRgaVLlyLdG2AdHR1oa2tL+LGLkUP0zB7Kx4Nn/XfgpEi3H/wi2mAN3vpV6bOyjX08eFYsrn3nKBKJoLCwMLGxU05Br169EIlEDDmWLFmC888/H5dccknC7Q8++CCuvPJK5OXl4bXXXsNPf/pTHDp0CHfccUdSz9y5c1FTU2OtEAfgwbPOu1RzinD7wS+iDdbgrV+VPivb2MeDZ8Vi+p2je++9F4FAIO3P+++/b/uBffXVV3j22WeTvmt0//3349JLL8VFF12Ee+65B3fffTcefvjhlK5Zs2ahtbU1/rN3717bj88MhT1yLR/gx4Nn/eEU4faDX0QbrMFbvyp9VraxT/aDZ/12+KzpxdGdd96JnTt3pv0ZMGAAwuEw9u/fn5B7/PhxtLS0IBwOZ2zn97//Pdrb2zFhwoSMsZWVldi3bx86OjqS3h8KhZCfn5/wY5cAePCsUYcbLtWcItx+8ItogzV461elz8o29vHgWbGYXhz16dMHAwcOTPvTrVs3DB8+HAcOHMDmzZvjuW+88Qai0SgqKysztrNkyRKMGTMGffr0yRi7detWnHHGGQiFQmbLSUkgxb/1v/PgWWMON1yqOUW4/eAX0QZr8NavSp+VbezjwbNice0L2eeffz5GjRqFyZMno6GhAe+88w6mTZuGcePGoW/fvgCATz/9FAMHDkRDQ0NC7q5du/D222/jRz/6URdvXV0dnnrqKWzbtg27du3C4sWL8atf/QrTp0+39Xh58GxqZDt8UVWnCLcf/CLaYA3e+lXps7KNfTx4Vhyu7nPU0tKCadOmJWwCuXDhwvhl+bFNH998802MGDEinvfzn/8czzzzDHbv3o1gMHH9Vl9fj1mzZmHXrl3QNA3nnHMOpk6dismTJ3eJTQUPnuUO2V46Rbj94BfRBmvw1q9Kn5Vt7Mv2HbJ58KxL8OBZQgghRD1Ezd88W43vHBlGtv96UtUpwu0Hv4g2WIO3flX6rGxjX7a/cySCrF4c8Ww1nq0m2inC7Qe/iDZYg7d+VfqsbGMfz1bzwdlqssKz1Xi2mhdOEW4/+EW0wRq89avSZ2Ub+3i2mg/OVlOddE+SmXNieLaaOy7VnCLcfvCLaIM1eOtXpc/KNvbxbDWxcHFkESPnxJg9d4Znq/nvTCWRbj/4RbTBGrz1q9JnZRv7eLaaWLL6O0ei4NlqzrtUc4pw+8Evog3W4K1flT4r29jHs9XEwsWRAOyc58Oz1fzhFOH2g19EG6zBW78qfVa2sU/2s9X8Bj9Ws0gAPFvNqMMNl2pOEW4/+EW0wRq89avSZ2Ub+3i2mli4OEpBIMW/9b/zbDVjDjdcqjlFuP3gF9EGa/DWr0qflW3s49lqYuHi6Gt4tlpqZDtfSFWnCLcf/CLaYA3e+lXps7KNfTxbTRxZvc/RhMVvoKSoN0p7d8dN3zkLz/15Dz5paUe/Xnn44fD+6HbKibXj0eNR/L+Nu5PeF8NIjJVYJ/Ls5jrpcMOlmlOE2w9+EW2wBm/9qvRZ2cY+uw4r+U7PceliTr4vNj9//MUhfNr8JVZMvZJnq7lBsk0guUN2emTbJVZVpwi3H/wi2mAN3vpV6bOyjX3cIVvMJpBcHHGHbO6QLcgpwu0Hv4g2WIO3flX6rGxjH3fI5g7ZnpPuSTKz2yd3yHbHpZpThNsPfhFtsAZv/ar0WdnGPu6QLRYujixiZLdPs7uHcods/+2MK9LtB7+INliDt35V+qxsYx93yBYLN4EUAHfIdt6lmlOE2w9+EW2wBm/9qvRZ2cY+7pAtFi6OBMAdsp13qeYU4faDX0QbrMFbvyp9Vraxjztki4Ufq1nEyG6fZncP5Q7Z/tsZV6TbD34RbbAGb/2q9FnZxj7ukC0WLo5SkG63TjO7fXKHbHdcqjlFuP3gF9EGa/DWr0qflW3s4w7ZYuHiKAWBAPDjy0vx48tLEQh0vW/K5aXx3T6nZIiJYSZWj9U8u7lOOtxwqeYU4faDX0QbrMFbvyp9Vraxz67DSr7Tc1y6mFRzrhdwcZSCqAb85u1G/ObtRpx8ZWRUA554uxH125pQv60JT2SIiWEmVo/VPLu5TjrccKnmFOH2g19EG6zBW78qfVa2sc+uw0q+03NcuphUc64XcHFkg3R7NsQwsgfEybF6rObZzXXS4YZLNacItx/8ItpgDd76Vemzso19dh1W8s3kGIk1MmfKAhdHFuE+R8YdbrhUc4pw+8Evog3W4K1flT4r29jHfY7Ewkv5BcB9jpx3qeYU4faDX0QbrMFbvyp9Vraxj/sciYWLIwFwnyPnXao5Rbj94BfRBmvw1q9Kn5Vt7OM+R2Lhx2oWCYD7HBl1uOFSzSnC7Qe/iDZYg7d+VfqsbGMf9zkSCxdHFtHwzZ4Nqb5cpqHrHhBGYvVYzbOb66TDDZdqThFuP/hFtMEavPWr0mdlG/vsOqzkm8kxEptpzpQJLo4IIYQQQnRwcWSRAL65LDFdzMmXORqJ1WM1z26ukw43XKo5Rbj94BfRBmvw1q9Kn5Vt7LPrsJJvJsdIbKY5Uya4OLIIL+U37nDDpZpThNsPfhFtsAZv/ar0WdnGPl7KLxZerSYAXsrvvEs1pwi3H/wi2mAN3vpV6bOyjX28lF8sXBwJgJfyO+9SzSnC7Qe/iDZYg7d+VfqsbGMfL+UXCz9Ws0HP005Bz7xT08ackXdq/DJHo7F6rObZzXXS4YZLNacItx/8ItpgDd76Vemzso19dh1W8s3kGIk1MmfKAhdHNtDi/5MhxozP6uPwINdJhxsu1Zwi3H7wi2iDNXjrV6XPyjb22XVYyTc9x6lwHT+4OLJF61fHceCrY2ljDrQfi39Z7UC7sVg9VvPs5jrpcMOlmlOE2w9+EW2wBm/9qvRZ2cY+uw4r+WZyjMQamTNlgd85EgC/kO28SzWnCLcf/CLaYA3e+lXps7KNffxCtlhce+fol7/8JS655BLk5eWhZ8+ehnI0TcMDDzyA4uJinHbaaaiqqsJHH32UENPS0oJbbrkF+fn56NmzJ2677TYcOnTIhQqco/fpIfQ+PWQ4Nt3vRvPs5jrpcMOlmlOE2w9+EW2wBm/9qvRZ2cY+uw4r+WZy7PQXGXFtcXT06FF8//vfx9SpUw3nzJ8/HwsXLkRtbS02bdqE7t27Y+TIkThy5JtV6S233ILt27fj9ddfxx/+8Ae8/fbbmDJlihslOEY0qiFqcPOxk+Os5tnNddLhhks1pwi3H/wi2mAN3vpV6bOyjX12HVbyzeTY6S8y4trHajU1NQCA5cuXG4rXNA0LFizAfffdh+uvvx4AsGLFChQVFeGll17CuHHjsHPnTtTX1+PPf/4zhg0bBgBYtGgRrrnmGjzyyCPo27dvUndHRwc6Ojriv7e2tgIAoh3tVsszxVvb9hhu761tezAkHEr43Uqe3VwnHW64VHOKcPvBL6IN1uCtX5U+K9vYZ9dhJd9MDiBmTo21oWkuL8Y0l1m2bJlWUFCQMe7jjz/WAGhbtmxJuP3yyy/X7rjjDk3TNG3JkiVaz549E+4/duyYlpOTo73wwgsp3bNnz9Zw4jvy/OEPf/jDH/7wR/Gfjz/+2PR6xAzSfCE7EokAAIqKihJuLyoqit8XiURQWFiYcP8pp5yCXr16xWOSMWvWLMycOTP++4EDB9CvXz/s2bMHBQUFTpUgPW1tbSgpKcHevXuRn5/v9cMRButm3dkA62bd2UBrayvOOuss9OplfR8yI5haHN17772YN29e2pidO3di4MCBth6U04RCIYRCXd9mLCgoyKoXVYz8/HzWnUWw7uyCdWcX2Vp3MOjuTkSmFkd33nknbr311rQxAwYMsPRAwuEwAKC5uRnFxcXx25ubm1FeXh6P2b9/f0Le8ePH0dLSEs8nhBBCCLGDqcVRnz590KdPH1ceSGlpKcLhMNatWxdfDLW1tWHTpk3xK96GDx+OAwcOYPPmzbj44osBAG+88Qai0SgqKytdeVyEEEIIyS5ce19qz5492Lp1K/bs2YPOzk5s3boVW7duTdiTaODAgXjxxRcBAIFAADNmzMAvfvELrF69Gu+99x4mTJiAvn37YuzYsQCA888/H6NGjcLkyZPR0NCAd955B9OmTcO4ceNSXqmWjFAohNmzZyf9qM3PsG7WnQ2wbtadDbBud+sOaJo718PdeuutePrpp7vc/uabb2LEiBEnGg8EsGzZsvhHdZqmYfbs2XjiiSdw4MABXHbZZfiP//gPfPvb347nt7S0YNq0aairq0MwGMQNN9yAhQsX4vTTT3ejDEIIIYRkGa4tjgghhBBCVIQHzxJCCCGE6ODiiBBCCCFEBxdHhBBCCCE6uDgihBBCCNHhy8XRL3/5S1xyySXIy8tDz549DeVomoYHHngAxcXFOO2001BVVYWPPvooIaalpQW33HIL8vPz0bNnT9x2220JWxN4jdnHt3v3bgQCgaQ/zz//fDwu2f0rV64UUZIhrDwvI0aM6FLTT37yk4SYPXv24Nprr0VeXh4KCwtx11134fjx426WYgqzdbe0tGD69Ok477zzcNppp+Gss87CHXfcET+IOYaMz/fjjz+O/v37Izc3F5WVlWhoaEgb//zzz2PgwIHIzc3F4MGD8eqrrybcb6S/y4CZup988kn84z/+I8444wycccYZqKqq6hJ/6623dnluR40a5XYZpjFT9/Lly7vUlJubmxDjx+c72RgWCARw7bXXxmNkf77ffvttXHfddejbty8CgQBeeumljDnr16/H0KFDEQqFcM455yQ93N7seJEUV09u84gHHnhAe/TRR7WZM2caOvRW0zTtoYce0goKCrSXXnpJ++tf/6qNGTNGKy0t1b766qt4zKhRo7QhQ4Zof/rTn7T/+q//0s455xzt5ptvdqkK85h9fMePH9eampoSfmpqarTTTz9dO3jwYDwOgLZs2bKEOP3fxWusPC9XXHGFNnny5ISaWltb4/cfP35cGzRokFZVVaVt2bJFe/XVV7XevXtrs2bNcrscw5it+7333tP++Z//WVu9erW2a9cubd26ddq5556r3XDDDQlxsj3fK1eu1Lp166YtXbpU2759uzZ58mStZ8+eWnNzc9L4d955R8vJydHmz5+v7dixQ7vvvvu0U089VXvvvffiMUb6u9eYrfsHP/iB9vjjj2tbtmzRdu7cqd16661aQUGBtm/fvnjMxIkTtVGjRiU8ty0tLaJKMoTZupctW6bl5+cn1BSJRBJi/Ph8f/nllwk1b9u2TcvJydGWLVsWj5H9+X711Ve1f//3f9deeOEFDYD24osvpo3/n//5Hy0vL0+bOXOmtmPHDm3RokVaTk6OVl9fH48x+3dMhS8XRzGWLVtmaHEUjUa1cDisPfzww/HbDhw4oIVCIe13v/udpmmatmPHDg2A9uc//zkes2bNGi0QCGiffvqp44/dLE49vvLycu1f/uVfEm4z8qL1Cqt1X3HFFdq//uu/prz/1Vdf1YLBYMIgu3jxYi0/P1/r6Ohw5LHbwanne9WqVVq3bt20Y8eOxW+T7fmuqKjQbr/99vjvnZ2dWt++fbW5c+cmjb/xxhu1a6+9NuG2yspK7cc//rGmacb6uwyYrftkjh8/rvXo0UN7+umn47dNnDhRu/76651+qI5itu5M43y2PN//9//+X61Hjx7aoUOH4rep8HzHMDLu3H333doFF1yQcNtNN92kjRw5Mv673b9jDF9+rGaWxsZGRCIRVFVVxW8rKChAZWUlNm7cCADYuHEjevbsiWHDhsVjqqqqEAwGsWnTJuGP+WSceHybN2/G1q1bcdttt3W57/bbb0fv3r1RUVGBpUuXQpNkeyw7df/2t79F7969MWjQIMyaNQvt7e0J3sGDB6OoqCh+28iRI9HW1obt27c7X4hJnHo9tra2Ij8/H6eckniSkCzP99GjR7F58+aEvhkMBlFVVRXvmyezcePGhHjgxHMXizfS373GSt0n097ejmPHjnU5vXz9+vUoLCzEeeedh6lTp+LLL7909LHbwWrdhw4dQr9+/VBSUoLrr78+oY9my/O9ZMkSjBs3Dt27d0+4Xebn2yyZ+rYTf8cYps5W8yuRSAQAEibC2O+x+yKRCAoLCxPuP+WUU9CrV694jJc48fiWLFmC888/H5dccknC7Q8++CCuvPJK5OXl4bXXXsNPf/pTHDp0CHfccYdjj98qVuv+wQ9+gH79+qFv377429/+hnvuuQcffPABXnjhhbg32eshdp/XOPF8f/HFF5gzZw6mTJmScLtMz/cXX3yBzs7OpM/F+++/nzQn1XOn78ux21LFeI2Vuk/mnnvuQd++fRMmilGjRuGf//mfUVpaio8//hg///nPMXr0aGzcuBE5OTmO1mAFK3Wfd955WLp0KS688EK0trbikUcewSWXXILt27fjW9/6VlY83w0NDdi2bRuWLFmScLvsz7dZUvXttrY2fPXVV/j73/9uu9/EUGZxdO+992LevHlpY3bu3ImBAwcKekRiMFq3Xb766is8++yzuP/++7vcp7/toosuwuHDh/Hwww+7Olm6Xbd+QTB48GAUFxfjqquuwscff4yzzz7bstcuop7vtrY2XHvttSgrK0N1dXXCfV4838RZHnroIaxcuRLr169P+HLyuHHj4v8ePHgwLrzwQpx99tlYv349rrrqKi8eqm2GDx+O4cOHx3+/5JJLcP755+M3v/kN5syZ4+EjE8eSJUswePBgVFRUJNzux+dbFMosju688874GWypGDBggCV3OBwGADQ3N6O4uDh+e3NzM8rLy+Mx+/fvT8g7fvw4Wlpa4vluYLRuu4/v97//Pdrb2zFhwoSMsZWVlZgzZw46OjpcO/xPVN0xKisrAQC7du3C2WefjXA43OUKh+bmZgBQ/vk+ePAgRo0ahR49euDFF1/EqaeemjZexPOdit69eyMnJyf+t4/R3Nycss5wOJw23kh/9xordcd45JFH8NBDD+GPf/wjLrzwwrSxAwYMQO/evbFr1y4pJks7dcc49dRTcdFFF2HXrl0A/P98Hz58GCtXrsSDDz6YsR3Znm+zpOrb+fn5OO2005CTk2P79RPH1DeUFMPsF7IfeeSR+G2tra1Jv5D9l7/8JR6zdu1a6b6QbfXxXXHFFV2uWkrFL37xC+2MM86w/FidxKnn5b//+781ANpf//pXTdO++UK2/gqH3/zmN1p+fr525MgR5wqwiNW6W1tbte9+97vaFVdcoR0+fNhQW14/3xUVFdq0adPiv3d2dmpnnnlm2i9k/9M//VPCbcOHD+/yhex0/V0GzNataZo2b948LT8/X9u4caOhNvbu3asFAgHt5Zdftv14ncJK3XqOHz+unXfeedq//du/aZrm7+db007Mc6FQSPviiy8ytiHj8x0DBr+QPWjQoITbbr755i5fyLbz+ok/HlPRivDJJ59oW7ZsiV+WvmXLFm3Lli0Jl6efd9552gsvvBD//aGHHtJ69uypvfzyy9rf/vY37frrr096Kf9FF12kbdq0Sfvv//5v7dxzz5XuUv50j2/fvn3aeeedp23atCkh76OPPtICgYC2Zs2aLs7Vq1drTz75pPbee+9pH330kfYf//EfWl5envbAAw+4Xo9RzNa9a9cu7cEHH9T+8pe/aI2NjdrLL7+sDRgwQLv88svjObFL+a+++mpt69atWn19vdanTx/pLuU3U3dra6tWWVmpDR48WNu1a1fC5b3Hjx/XNE3O53vlypVaKBTSli9fru3YsUObMmWK1rNnz/iVhD/84Q+1e++9Nx7/zjvvaKeccor2yCOPaDt37tRmz56d9FL+TP3da8zW/dBDD2ndunXTfv/73yc8t7Fx7+DBg9rPfvYzbePGjVpjY6P2xz/+URs6dKh27rnnSrHgj2G27pqaGm3t2rXaxx9/rG3evFkbN26clpubq23fvj0e48fnO8Zll12m3XTTTV1uV+H5PnjwYHx+BqA9+uij2pYtW7RPPvlE0zRNu/fee7Uf/vCH8fjYpfx33XWXtnPnTu3xxx9Peil/ur+jUXy5OJo4caIGoMvPm2++GY/B13u5xIhGo9r999+vFRUVaaFQSLvqqqu0Dz74IMH75ZdfajfffLN2+umna/n5+dqkSZMSFlxek+nxNTY2dvk7aJqmzZo1SyspKdE6Ozu7ONesWaOVl5drp59+uta9e3dtyJAhWm1tbdJYrzBb9549e7TLL79c69WrlxYKhbRzzjlHu+uuuxL2OdI0Tdu9e7c2evRo7bTTTtN69+6t3XnnnQmXvHuN2brffPPNpP0CgNbY2KhpmrzP96JFi7SzzjpL69atm1ZRUaH96U9/it93xRVXaBMnTkyIX7Vqlfbtb39b69atm3bBBRdor7zySsL9Rvq7DJipu1+/fkmf29mzZ2uapmnt7e3a1VdfrfXp00c79dRTtX79+mmTJ082PWmIwEzdM2bMiMcWFRVp11xzjfbuu+8m+Pz4fGuapr3//vsaAO21117r4lLh+U41JsXqnDhxonbFFVd0ySkvL9e6deumDRgwIGEej5Hu72iUgKZJck02IYQQQogEcJ8jQgghhBAdXBwRQgghhOjg4ogQQgghRAcXR4QQQgghOrg4IoQQQgjRwcURIYQQQogOLo4IIYQQQnRwcUQIIYQQooOLI0IIIYQQHVwcEUIIIYTo4OKIEEIIIUTH/wem1yVxMBiAdAAAAABJRU5ErkJggg==", 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", + "image/png": 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", "text/plain": [ - "
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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -168,7 +149,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -195,7 +176,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -215,7 +196,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -241,47 +222,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "/home/bbonev/.zshenv:export:2: not valid in this context: :/usr/local/cuda-11.7/lib64\n", - "--2024-01-17 18:43:57-- https://upload.wikimedia.org/wikipedia/commons/thumb/d/d3/Albert_Einstein_Head.jpg/360px-Albert_Einstein_Head.jpg\n", - "Resolving upload.wikimedia.org (upload.wikimedia.org)... 185.15.59.240, 2a02:ec80:600:ed1a::2:b\n", - "Connecting to upload.wikimedia.org (upload.wikimedia.org)|185.15.59.240|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 52845 (52K) [image/jpeg]\n", - "Saving to: ‘./data/einstein.jpg’\n", - "\n", - "./data/einstein.jpg 100%[===================>] 51.61K --.-KB/s in 0.03s \n", - "\n", - "2024-01-17 18:43:57 (1.81 MB/s) - ‘./data/einstein.jpg’ saved [52845/52845]\n", - "\n" + " % Total % Received % Xferd Average Speed Time Time Time Current\n", + " Dload Upload Total Spent Left Speed\n", + "100 52845 100 52845 0 0 53240 0 --:--:-- --:--:-- --:--:-- 53378\n" ] } ], "source": [ "# let us get some test data:\n", - "!wget https://upload.wikimedia.org/wikipedia/commons/thumb/d/d3/Albert_Einstein_Head.jpg/360px-Albert_Einstein_Head.jpg -O ./data/einstein.jpg" + "!curl https://upload.wikimedia.org/wikipedia/commons/thumb/d/d3/Albert_Einstein_Head.jpg/360px-Albert_Einstein_Head.jpg -o ./data/einstein.jpg" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_2653991/4109740787.py:5: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:206.)\n", - " data = nn.functional.interpolate(torch.from_numpy(img).unsqueeze(0).unsqueeze(0), size=(240,180)).squeeze().permute(1,0).flip(1).reshape(-1)\n" - ] - } - ], + "outputs": [], "source": [ "import imageio.v3 as iio\n", "\n", @@ -292,7 +255,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -314,7 +277,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -334,20 +297,44 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [ { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[14], line 7\u001b[0m\n\u001b[1;32m 5\u001b[0m plt\u001b[38;5;241m.\u001b[39mxlim(\u001b[38;5;241m0\u001b[39m,\u001b[38;5;241m2\u001b[39m)\n\u001b[1;32m 6\u001b[0m plt\u001b[38;5;241m.\u001b[39mylim(\u001b[38;5;241m0\u001b[39m,\u001b[38;5;241m3\u001b[39m)\n\u001b[0;32m----> 7\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/homebrew/lib/python3.11/site-packages/matplotlib/pyplot.py:527\u001b[0m, in \u001b[0;36mshow\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 483\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 484\u001b[0m \u001b[38;5;124;03mDisplay all open figures.\u001b[39;00m\n\u001b[1;32m 485\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 524\u001b[0m \u001b[38;5;124;03mexplicitly there.\u001b[39;00m\n\u001b[1;32m 525\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 526\u001b[0m _warn_if_gui_out_of_main_thread()\n\u001b[0;32m--> 527\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_get_backend_mod\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Library/Python/3.11/lib/python/site-packages/matplotlib_inline/backend_inline.py:90\u001b[0m, in \u001b[0;36mshow\u001b[0;34m(close, block)\u001b[0m\n\u001b[1;32m 88\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 89\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m figure_manager \u001b[38;5;129;01min\u001b[39;00m Gcf\u001b[38;5;241m.\u001b[39mget_all_fig_managers():\n\u001b[0;32m---> 90\u001b[0m \u001b[43mdisplay\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 91\u001b[0m \u001b[43m \u001b[49m\u001b[43mfigure_manager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 92\u001b[0m \u001b[43m \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_fetch_figure_metadata\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfigure_manager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 93\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 94\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 95\u001b[0m show\u001b[38;5;241m.\u001b[39m_to_draw \u001b[38;5;241m=\u001b[39m []\n", + "File \u001b[0;32m~/Library/Python/3.11/lib/python/site-packages/IPython/core/display_functions.py:298\u001b[0m, in \u001b[0;36mdisplay\u001b[0;34m(include, exclude, metadata, transient, display_id, raw, clear, *objs, **kwargs)\u001b[0m\n\u001b[1;32m 296\u001b[0m publish_display_data(data\u001b[38;5;241m=\u001b[39mobj, metadata\u001b[38;5;241m=\u001b[39mmetadata, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 297\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 298\u001b[0m format_dict, md_dict \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minclude\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minclude\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexclude\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexclude\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 299\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m format_dict:\n\u001b[1;32m 300\u001b[0m \u001b[38;5;66;03m# nothing to display (e.g. _ipython_display_ took over)\u001b[39;00m\n\u001b[1;32m 301\u001b[0m \u001b[38;5;28;01mcontinue\u001b[39;00m\n", + "File \u001b[0;32m~/Library/Python/3.11/lib/python/site-packages/IPython/core/formatters.py:179\u001b[0m, in \u001b[0;36mDisplayFormatter.format\u001b[0;34m(self, obj, include, exclude)\u001b[0m\n\u001b[1;32m 177\u001b[0m md \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 178\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 179\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[43mformatter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 180\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n\u001b[1;32m 181\u001b[0m \u001b[38;5;66;03m# FIXME: log the exception\u001b[39;00m\n\u001b[1;32m 182\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m\n", + "File \u001b[0;32m~/Library/Python/3.11/lib/python/site-packages/decorator.py:232\u001b[0m, in \u001b[0;36mdecorate..fun\u001b[0;34m(*args, **kw)\u001b[0m\n\u001b[1;32m 230\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m kwsyntax:\n\u001b[1;32m 231\u001b[0m args, kw \u001b[38;5;241m=\u001b[39m fix(args, kw, sig)\n\u001b[0;32m--> 232\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcaller\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mextras\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Library/Python/3.11/lib/python/site-packages/IPython/core/formatters.py:223\u001b[0m, in \u001b[0;36mcatch_format_error\u001b[0;34m(method, self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 221\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"show traceback on failed format call\"\"\"\u001b[39;00m\n\u001b[1;32m 222\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 223\u001b[0m r \u001b[38;5;241m=\u001b[39m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 224\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m:\n\u001b[1;32m 225\u001b[0m \u001b[38;5;66;03m# don't warn on NotImplementedErrors\u001b[39;00m\n\u001b[1;32m 226\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_return(\u001b[38;5;28;01mNone\u001b[39;00m, args[\u001b[38;5;241m0\u001b[39m])\n", + "File \u001b[0;32m~/Library/Python/3.11/lib/python/site-packages/IPython/core/formatters.py:340\u001b[0m, in \u001b[0;36mBaseFormatter.__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m 338\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[1;32m 339\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 340\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mprinter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 341\u001b[0m \u001b[38;5;66;03m# Finally look for special method names\u001b[39;00m\n\u001b[1;32m 342\u001b[0m method \u001b[38;5;241m=\u001b[39m get_real_method(obj, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprint_method)\n", + "File \u001b[0;32m~/Library/Python/3.11/lib/python/site-packages/IPython/core/pylabtools.py:152\u001b[0m, in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, base64, **kwargs)\u001b[0m\n\u001b[1;32m 149\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_bases\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m FigureCanvasBase\n\u001b[1;32m 150\u001b[0m FigureCanvasBase(fig)\n\u001b[0;32m--> 152\u001b[0m \u001b[43mfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprint_figure\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbytes_io\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 153\u001b[0m data \u001b[38;5;241m=\u001b[39m bytes_io\u001b[38;5;241m.\u001b[39mgetvalue()\n\u001b[1;32m 154\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fmt \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msvg\u001b[39m\u001b[38;5;124m'\u001b[39m:\n", + "File \u001b[0;32m/opt/homebrew/lib/python3.11/site-packages/matplotlib/backend_bases.py:2193\u001b[0m, in \u001b[0;36mFigureCanvasBase.print_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m 2189\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 2190\u001b[0m \u001b[38;5;66;03m# _get_renderer may change the figure dpi (as vector formats\u001b[39;00m\n\u001b[1;32m 2191\u001b[0m \u001b[38;5;66;03m# force the figure dpi to 72), so we need to set it again here.\u001b[39;00m\n\u001b[1;32m 2192\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m cbook\u001b[38;5;241m.\u001b[39m_setattr_cm(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure, dpi\u001b[38;5;241m=\u001b[39mdpi):\n\u001b[0;32m-> 2193\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mprint_method\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2194\u001b[0m \u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2195\u001b[0m \u001b[43m \u001b[49m\u001b[43mfacecolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfacecolor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2196\u001b[0m \u001b[43m \u001b[49m\u001b[43medgecolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43medgecolor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2197\u001b[0m \u001b[43m \u001b[49m\u001b[43morientation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morientation\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2198\u001b[0m \u001b[43m \u001b[49m\u001b[43mbbox_inches_restore\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_bbox_inches_restore\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2199\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2200\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 2201\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bbox_inches \u001b[38;5;129;01mand\u001b[39;00m restore_bbox:\n", + "File \u001b[0;32m/opt/homebrew/lib/python3.11/site-packages/matplotlib/backend_bases.py:2043\u001b[0m, in \u001b[0;36mFigureCanvasBase._switch_canvas_and_return_print_method..\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 2039\u001b[0m optional_kws \u001b[38;5;241m=\u001b[39m { \u001b[38;5;66;03m# Passed by print_figure for other renderers.\u001b[39;00m\n\u001b[1;32m 2040\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdpi\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfacecolor\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124medgecolor\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124morientation\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 2041\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbbox_inches_restore\u001b[39m\u001b[38;5;124m\"\u001b[39m}\n\u001b[1;32m 2042\u001b[0m skip \u001b[38;5;241m=\u001b[39m optional_kws \u001b[38;5;241m-\u001b[39m {\u001b[38;5;241m*\u001b[39minspect\u001b[38;5;241m.\u001b[39msignature(meth)\u001b[38;5;241m.\u001b[39mparameters}\n\u001b[0;32m-> 2043\u001b[0m print_method \u001b[38;5;241m=\u001b[39m functools\u001b[38;5;241m.\u001b[39mwraps(meth)(\u001b[38;5;28;01mlambda\u001b[39;00m \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: \u001b[43mmeth\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2044\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m{\u001b[49m\u001b[43mk\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m 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\u001b[38;5;21mprint_png\u001b[39m(\u001b[38;5;28mself\u001b[39m, filename_or_obj, \u001b[38;5;241m*\u001b[39m, metadata\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, pil_kwargs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 451\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 452\u001b[0m \u001b[38;5;124;03m Write the figure to a PNG file.\u001b[39;00m\n\u001b[1;32m 453\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 495\u001b[0m \u001b[38;5;124;03m *metadata*, including the default 'Software' key.\u001b[39;00m\n\u001b[1;32m 496\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 497\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_print_pil\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename_or_obj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m 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\u001b[38;5;124;03m *pil_kwargs* and *metadata* are forwarded).\u001b[39;00m\n\u001b[1;32m 444\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 445\u001b[0m \u001b[43mFigureCanvasAgg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 446\u001b[0m mpl\u001b[38;5;241m.\u001b[39mimage\u001b[38;5;241m.\u001b[39mimsave(\n\u001b[1;32m 447\u001b[0m filename_or_obj, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuffer_rgba(), \u001b[38;5;28mformat\u001b[39m\u001b[38;5;241m=\u001b[39mfmt, origin\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mupper\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 448\u001b[0m dpi\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure\u001b[38;5;241m.\u001b[39mdpi, metadata\u001b[38;5;241m=\u001b[39mmetadata, pil_kwargs\u001b[38;5;241m=\u001b[39mpil_kwargs)\n", + "File \u001b[0;32m/opt/homebrew/lib/python3.11/site-packages/matplotlib/backends/backend_agg.py:388\u001b[0m, in \u001b[0;36mFigureCanvasAgg.draw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 385\u001b[0m \u001b[38;5;66;03m# Acquire a lock on the shared font cache.\u001b[39;00m\n\u001b[1;32m 386\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtoolbar\u001b[38;5;241m.\u001b[39m_wait_cursor_for_draw_cm() \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtoolbar\n\u001b[1;32m 387\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m nullcontext()):\n\u001b[0;32m--> 388\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 389\u001b[0m \u001b[38;5;66;03m# A GUI class may be need to update a window using this draw, so\u001b[39;00m\n\u001b[1;32m 390\u001b[0m \u001b[38;5;66;03m# don't forget to call the superclass.\u001b[39;00m\n\u001b[1;32m 391\u001b[0m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mdraw()\n", + "File \u001b[0;32m/opt/homebrew/lib/python3.11/site-packages/matplotlib/artist.py:95\u001b[0m, in \u001b[0;36m_finalize_rasterization..draw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m 93\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(draw)\n\u001b[1;32m 94\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdraw_wrapper\u001b[39m(artist, renderer, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m---> 95\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 96\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m renderer\u001b[38;5;241m.\u001b[39m_rasterizing:\n\u001b[1;32m 97\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstop_rasterizing()\n", + "File \u001b[0;32m/opt/homebrew/lib/python3.11/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization..draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 70\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 74\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[0;32m/opt/homebrew/lib/python3.11/site-packages/matplotlib/figure.py:3154\u001b[0m, in \u001b[0;36mFigure.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m 3151\u001b[0m \u001b[38;5;66;03m# ValueError can occur when resizing a window.\u001b[39;00m\n\u001b[1;32m 3153\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpatch\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[0;32m-> 3154\u001b[0m \u001b[43mmimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3155\u001b[0m \u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3157\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sfig \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msubfigs:\n\u001b[1;32m 3158\u001b[0m sfig\u001b[38;5;241m.\u001b[39mdraw(renderer)\n", + "File \u001b[0;32m/opt/homebrew/lib/python3.11/site-packages/matplotlib/image.py:132\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m 130\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[1;32m 131\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[0;32m--> 132\u001b[0m \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 133\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 134\u001b[0m \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[1;32m 135\u001b[0m image_group \u001b[38;5;241m=\u001b[39m []\n", + "File \u001b[0;32m/opt/homebrew/lib/python3.11/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization..draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 70\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 74\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[0;32m/opt/homebrew/lib/python3.11/site-packages/matplotlib/axes/_base.py:3070\u001b[0m, in \u001b[0;36m_AxesBase.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m 3067\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artists_rasterized:\n\u001b[1;32m 3068\u001b[0m _draw_rasterized(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure, artists_rasterized, renderer)\n\u001b[0;32m-> 3070\u001b[0m \u001b[43mmimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3071\u001b[0m \u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3073\u001b[0m renderer\u001b[38;5;241m.\u001b[39mclose_group(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maxes\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 3074\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstale \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n", + "File \u001b[0;32m/opt/homebrew/lib/python3.11/site-packages/matplotlib/image.py:132\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m 130\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[1;32m 131\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[0;32m--> 132\u001b[0m \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 133\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 134\u001b[0m \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[1;32m 135\u001b[0m image_group \u001b[38;5;241m=\u001b[39m []\n", + "File \u001b[0;32m/opt/homebrew/lib/python3.11/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization..draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 70\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 74\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[0;32m/opt/homebrew/lib/python3.11/site-packages/matplotlib/collections.py:1005\u001b[0m, in \u001b[0;36m_CollectionWithSizes.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m 1002\u001b[0m \u001b[38;5;129m@artist\u001b[39m\u001b[38;5;241m.\u001b[39mallow_rasterization\n\u001b[1;32m 1003\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdraw\u001b[39m(\u001b[38;5;28mself\u001b[39m, renderer):\n\u001b[1;32m 1004\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_sizes(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sizes, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure\u001b[38;5;241m.\u001b[39mdpi)\n\u001b[0;32m-> 1005\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/homebrew/lib/python3.11/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization..draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 70\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 74\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[0;32m/opt/homebrew/lib/python3.11/site-packages/matplotlib/collections.py:423\u001b[0m, in \u001b[0;36mCollection.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m 414\u001b[0m ipaths, ilinestyles \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_inverse_paths_linestyles()\n\u001b[1;32m 415\u001b[0m renderer\u001b[38;5;241m.\u001b[39mdraw_path_collection(\n\u001b[1;32m 416\u001b[0m gc, transform\u001b[38;5;241m.\u001b[39mfrozen(), ipaths,\n\u001b[1;32m 417\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_transforms(), offsets, offset_trf,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 420\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_antialiaseds, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_urls,\n\u001b[1;32m 421\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mscreen\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 423\u001b[0m \u001b[43mrenderer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw_path_collection\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 424\u001b[0m \u001b[43m \u001b[49m\u001b[43mgc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtransform\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrozen\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpaths\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 425\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_transforms\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moffsets\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moffset_trf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 426\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_facecolor\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_edgecolor\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 427\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_linewidths\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_linestyles\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 428\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_antialiaseds\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_urls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 429\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mscreen\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# offset_position, kept for backcompat.\u001b[39;00m\n\u001b[1;32m 431\u001b[0m gc\u001b[38;5;241m.\u001b[39mrestore()\n\u001b[1;32m 432\u001b[0m renderer\u001b[38;5;241m.\u001b[39mclose_group(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m)\n", + "File \u001b[0;32m/opt/homebrew/lib/python3.11/site-packages/matplotlib/path.py:223\u001b[0m, in \u001b[0;36mPath.codes\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 220\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_vertices \u001b[38;5;241m=\u001b[39m vertices\n\u001b[1;32m 221\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_update_values()\n\u001b[0;32m--> 223\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[1;32m 224\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcodes\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 225\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 226\u001b[0m \u001b[38;5;124;03m The list of codes in the `Path` as a 1D array.\u001b[39;00m\n\u001b[1;32m 227\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 231\u001b[0m \u001b[38;5;124;03m of `vertices` and `codes` is always the same.\u001b[39;00m\n\u001b[1;32m 232\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m 233\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_codes\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] } ], "source": [ @@ -381,7 +368,16 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click here for more info. View Jupyter log for further details." + ] + } + ], "source": [ "# conv = DiscreteContinuousConv2d(1, 1, grid_in=grid_in, quad_weights=weights, grid_out=grid_out, kernel_shape=[2,4], radius_cutoff=2*3./180)\n", "# alternatively, btu normalization will be wrong due to the wrong domain [-1, 1]^2\n", diff --git a/torch_harmonics/convolution.py b/torch_harmonics/convolution.py index 46eaada..0f742ef 100644 --- a/torch_harmonics/convolution.py +++ b/torch_harmonics/convolution.py @@ -688,14 +688,18 @@ def __init__( groups: Optional[int] = 1, bias: Optional[bool] = True, radius_cutoff: Optional[float] = None, + padding_mode: str = "circular", + **kwargs ): super().__init__(in_channels, out_channels, kernel_shape, groups, bias) + self.padding_mode = padding_mode + # compute the cutoff radius based on the assumption that the grid is [-1, 1]^2 # this still assumes a quadratic domain if radius_cutoff is None: - radius_cutoff = 2 * (self.kernel_shape[0] + 1) / float(max(*in_shape)) - self.psi_local_size = math.ceil(2 * radius_cutoff * max(*in_shape) / 2) + 1 + radius_cutoff = 2 * (self.kernel_shape[0]) / float(max(*in_shape)) + self.psi_local_size = math.floor(2*radius_cutoff * max(*in_shape) / 2) + 1 # psi_local is essentially the support of the hat functions evaluated locally x = torch.linspace(-radius_cutoff, radius_cutoff, self.psi_local_size) @@ -724,8 +728,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: left_pad = self.psi_local_size // 2 right_pad = (self.psi_local_size+1) // 2 - 1 - x = F.pad(x, (left_pad, right_pad, left_pad, right_pad), mode="circular") - print(x.shape) + x = F.pad(x, (left_pad, right_pad, left_pad, right_pad), mode=self.padding_mode) out = F.conv2d(x, kernel, self.bias, stride=1, dilation=1, padding=0, groups=self.groups) return out \ No newline at end of file