From 2c7d5ba72060b9f4e177b897d4b98033625ea02c Mon Sep 17 00:00:00 2001 From: kierannp Date: Thu, 20 May 2021 16:33:43 -0400 Subject: [PATCH 1/8] Added sample pre-trained colab --- 3d_transfer.ipynb | 2452 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 2452 insertions(+) create mode 100644 3d_transfer.ipynb diff --git a/3d_transfer.ipynb b/3d_transfer.ipynb new file mode 100644 index 0000000..c8a9dd9 --- /dev/null +++ b/3d_transfer.ipynb @@ -0,0 +1,2452 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "3d-transfer.ipynb", + "provenance": [], + "collapsed_sections": [], + "authorship_tag": "ABX9TyO7vcZ1c5JmXcQwpkeJY3W3", + "include_colab_link": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uwzRkfxGDKaD" + }, + "source": [ + "# Unsupervised 3d Style Tranfer via 3dsnet" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DUajY7mxkAH8" + }, + "source": [ + "All credit for most of the code base and model to:\n", + "\n", + "\n", + "@article{segu20203dsnet,\n", + " title={3DSNet: Unsupervised Shape-to-Shape 3D Style Transfer},\n", + " author={Segu, Mattia and Grinvald, Margarita and Siegwart, Roland and Tombari, Federico},\n", + " journal={arXiv preprint arXiv:2011.13388},\n", + " year={2020}\n", + "}\n", + "\n", + "\n", + "Checkout the 3dsnet [repo](https://github.com/ethz-asl/3dsnet)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OxIfIaS0Drhi" + }, + "source": [ + "Author of this colab: [KieranNP](https://github.com/kierannp)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "P-Sh4i2EH89-" + }, + "source": [ + "List of available models compatible with current codebase\n", + "\n", + "CHAIRS (using our Adaptive-Meshflow backbone):\n", + "- 3dsnet (with reconstruction loss, adversarial loss and cycle-consistency loss)\n", + "- adanorm (only reconstruction loss, style transfer is attempted by relying only on the adaptive normalization layers)\n", + "\n", + "PLANES (using our Adaptive-Atlasnet backbone):\n", + "- 3dsnet (with reconstruction loss, adversarial loss and cycle-consistency loss)\n", + "- 3dsnet_no_cycle (with reconstruction loss and adversarial loss)\n", + "- adanorm (only reconstruction loss, style transfer is attempted by relying only on the adaptive normalization layers)\n", + "\n", + "Files included:\n", + "- log.txt, contains training statistics per each training epoch\n", + "- network.pth, model weights at last training epoch\n", + "- network_best.pth, model weights at best training epoch\n", + "- options.json, options used for training the model" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "rILowRnFIbWJ", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "c7334c06-664a-4ffa-a27f-b5afabf6b3ff" + }, + "source": [ + "#@title Installations required {display-mode: \"form\"}\n", + "\n", + "# This code will be hidden when the notebook is loaded.\n", + "\n", + "# %%capture\n", + "%cd /content/\n", + "%env PYTHONPATH=\n", + "! wget https://repo.anaconda.com/miniconda/Miniconda3-4.5.4-Linux-x86_64.sh\n", + "! chmod +x Miniconda3-4.5.4-Linux-x86_64.sh\n", + "! bash ./Miniconda3-4.5.4-Linux-x86_64.sh -b -f -p /usr/local\n", + "import sys\n", + "import os\n", + "\n", + "sys.path.append('/usr/local/lib/python3.6/site-packages/')\n", + "\n", + "!conda install --channel defaults conda python=3.6 --yes\n", + "!conda update --channel defaults --all --yes\n", + "if not os.path.exists(\"/content/3dsnet\"):\n", + " !git clone --recurse-submodules https://github.com/ethz-asl/3dsnet.git\n", + "\n", + "!conda create -n 3dsnet python=3.6 --yes\n", + "!source activate 3dsnet\n", + "\n", + "!pip install meshio[all]\n", + "\n", + "!conda install pytorch=1.7.1 torchvision=0.8.2 cudatoolkit=10.1 -c pytorch --yes\n", + "!conda install -y -c conda-forge pyembree\n", + "!conda install -y -c conda-forge trimesh seaborn\n", + "!conda install -y -c fvcore -c iopath -c conda-forge fvcore iopath\n", + "# !pip install \"git+https://github.com/facebookresearch/pytorch3d.git@stable\"\n", + "!conda install -y pytorch3d -c pytorch3d\n", + "# !pip install pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/py36_cu101_pyt170/download.html\n", + "!conda install -y -c conda-forge visdom\n", + "\n", + "if not os.path.exists(\"/content/3dsnet/PyMesh\"):\n", + " %cd /content/3dsnset\n", + " !git clone https://github.com/PyMesh/PyMesh.git\n", + " %cd PyMesh\n", + " !git submodule update --init\n", + " !export PYMESH_PATH=$(pwd)\n", + "\n", + " !apt-get install \\\n", + "\tlibeigen3-dev \\\n", + "\tlibgmp-dev \\\n", + "\tlibgmpxx4ldbl \\\n", + "\tlibmpfr-dev \\\n", + "\tlibboost-dev \\\n", + "\tlibboost-thread-dev \\\n", + "\tlibtbb-dev \\\n", + "\tpython3-dev \\\n", + "\tpython3-setuptools \\\n", + "\tpython3-numpy \\\n", + "\tpython3-scipy \\\n", + "\tpython3-nose \\\n", + "\tpython3-pip \\\n", + "\tcmake\n", + "\n", + " %cd $PYMESH_PATH/third_party\n", + " !mkdir build\n", + " !./build.py all\n", + " %cd $PYMESH_PATH\n", + " !mkdir build\n", + " !python3\n", + " setup.py build # This an alternative way of calling cmake/make\n", + " !python3 setup.py install\n", + " %cd ..\n", + "\n", + "!pip install trimesh\n", + "\n", + "!pip install git+https://github.com/rtqichen/torchdiffeq torchvision\n", + "!pip install git+https://github.com/cnr-isti-vclab/PyMeshLab\n", + "%cd /content/3dsnet\n", + "!pip install --user --requirement requirements.txt # pip dependencies" + ], + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "text": [ + "/content\n", + "env: PYTHONPATH=\n", + "--2021-05-20 19:42:12-- https://repo.anaconda.com/miniconda/Miniconda3-4.5.4-Linux-x86_64.sh\n", + "Resolving repo.anaconda.com (repo.anaconda.com)... 104.16.131.3, 104.16.130.3, 2606:4700::6810:8203, ...\n", + "Connecting to repo.anaconda.com (repo.anaconda.com)|104.16.131.3|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 58468498 (56M) [application/x-sh]\n", + "Saving to: ‘Miniconda3-4.5.4-Linux-x86_64.sh’\n", + "\n", + "Miniconda3-4.5.4-Li 100%[===================>] 55.76M 219MB/s in 0.3s \n", + "\n", + "2021-05-20 19:42:13 (219 MB/s) - ‘Miniconda3-4.5.4-Linux-x86_64.sh’ saved [58468498/58468498]\n", + "\n", + "PREFIX=/usr/local\n", + "installing: python-3.6.5-hc3d631a_2 ...\n", + "Python 3.6.5 :: Anaconda, Inc.\n", + "installing: ca-certificates-2018.03.07-0 ...\n", + "installing: conda-env-2.6.0-h36134e3_1 ...\n", + "installing: libgcc-ng-7.2.0-hdf63c60_3 ...\n", + "installing: libstdcxx-ng-7.2.0-hdf63c60_3 ...\n", + "installing: libffi-3.2.1-hd88cf55_4 ...\n", + "installing: ncurses-6.1-hf484d3e_0 ...\n", + "installing: openssl-1.0.2o-h20670df_0 ...\n", + "installing: tk-8.6.7-hc745277_3 ...\n", + "installing: xz-5.2.4-h14c3975_4 ...\n", + "installing: yaml-0.1.7-had09818_2 ...\n", + "installing: zlib-1.2.11-ha838bed_2 ...\n", + "installing: libedit-3.1.20170329-h6b74fdf_2 ...\n", + "installing: readline-7.0-ha6073c6_4 ...\n", + "installing: sqlite-3.23.1-he433501_0 ...\n", + "installing: asn1crypto-0.24.0-py36_0 ...\n", + "installing: certifi-2018.4.16-py36_0 ...\n", + "installing: chardet-3.0.4-py36h0f667ec_1 ...\n", + "installing: idna-2.6-py36h82fb2a8_1 ...\n", + "installing: pycosat-0.6.3-py36h0a5515d_0 ...\n", + "installing: pycparser-2.18-py36hf9f622e_1 ...\n", + "installing: pysocks-1.6.8-py36_0 ...\n", + "installing: ruamel_yaml-0.15.37-py36h14c3975_2 ...\n", + "installing: six-1.11.0-py36h372c433_1 ...\n", + "installing: cffi-1.11.5-py36h9745a5d_0 ...\n", + "installing: setuptools-39.2.0-py36_0 ...\n", + "installing: cryptography-2.2.2-py36h14c3975_0 ...\n", + "installing: wheel-0.31.1-py36_0 ...\n", + "installing: pip-10.0.1-py36_0 ...\n", + "installing: pyopenssl-18.0.0-py36_0 ...\n", + "installing: urllib3-1.22-py36hbe7ace6_0 ...\n", + "installing: requests-2.18.4-py36he2e5f8d_1 ...\n", + "installing: conda-4.5.4-py36_0 ...\n", + "installation finished.\n", + "Solving environment: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n", + "\n", + "## Package Plan ##\n", + "\n", + " environment location: /usr/local\n", + "\n", + " added / updated specs: \n", + " - conda\n", + " - python=3.6\n", + "\n", + "\n", + "The following packages will be downloaded:\n", + "\n", + " package | build\n", + " ---------------------------|-----------------\n", + " pysocks-1.7.1 | py36h06a4308_0 30 KB\n", + " pycparser-2.20 | py_2 94 KB\n", + " zlib-1.2.11 | h7b6447c_3 120 KB\n", + " brotlipy-0.7.0 |py36h27cfd23_1003 349 KB\n", + " _libgcc_mutex-0.1 | main 3 KB\n", + " openssl-1.1.1k | h27cfd23_0 3.8 MB\n", + " tqdm-4.59.0 | pyhd3eb1b0_1 90 KB\n", + " certifi-2020.12.5 | py36h06a4308_0 144 KB\n", + " libffi-3.3 | he6710b0_2 54 KB\n", + " idna-2.10 | pyhd3eb1b0_0 52 KB\n", + " pycosat-0.6.3 | py36h27cfd23_0 107 KB\n", + " tk-8.6.10 | hbc83047_0 3.2 MB\n", + " wheel-0.36.2 | pyhd3eb1b0_0 31 KB\n", + " pyopenssl-20.0.1 | pyhd3eb1b0_1 48 KB\n", + " ncurses-6.2 | he6710b0_1 1.1 MB\n", + " six-1.15.0 | pyhd3eb1b0_0 13 KB\n", + " libgcc-ng-9.1.0 | hdf63c60_0 8.1 MB\n", + " readline-8.1 | h27cfd23_0 464 KB\n", + " cffi-1.14.5 | py36h261ae71_0 224 KB\n", + " sqlite-3.35.4 | hdfb4753_0 1.4 MB\n", + " conda-4.10.1 | py36h06a4308_1 3.1 MB\n", + " requests-2.25.1 | pyhd3eb1b0_0 51 KB\n", + " python-3.6.13 | hdb3f193_0 33.9 MB\n", + " ca-certificates-2021.4.13 | h06a4308_1 120 KB\n", + " cryptography-3.4.7 | py36hd23ed53_0 1.0 MB\n", + " chardet-4.0.0 |py36h06a4308_1003 213 KB\n", + " pip-21.0.1 | py36h06a4308_0 2.0 MB\n", + " urllib3-1.26.4 | pyhd3eb1b0_0 99 KB\n", + " xz-5.2.5 | h7b6447c_0 438 KB\n", + " conda-package-handling-1.7.3| py36h27cfd23_1 946 KB\n", + " setuptools-52.0.0 | py36h06a4308_0 933 KB\n", + " ruamel_yaml-0.15.100 | py36h27cfd23_0 268 KB\n", + " yaml-0.2.5 | h7b6447c_0 87 KB\n", + " libstdcxx-ng-9.1.0 | hdf63c60_0 4.0 MB\n", + " ld_impl_linux-64-2.33.1 | h53a641e_7 645 KB\n", + " ------------------------------------------------------------\n", + " Total: 67.2 MB\n", + "\n", + "The following NEW packages will be INSTALLED:\n", + "\n", + " _libgcc_mutex: 0.1-main \n", + " brotlipy: 0.7.0-py36h27cfd23_1003\n", + " conda-package-handling: 1.7.3-py36h27cfd23_1 \n", + " ld_impl_linux-64: 2.33.1-h53a641e_7 \n", + " tqdm: 4.59.0-pyhd3eb1b0_1 \n", + "\n", + "The following packages will be UPDATED:\n", + "\n", + " ca-certificates: 2018.03.07-0 --> 2021.4.13-h06a4308_1 \n", + " certifi: 2018.4.16-py36_0 --> 2020.12.5-py36h06a4308_0\n", + " cffi: 1.11.5-py36h9745a5d_0 --> 1.14.5-py36h261ae71_0 \n", + " chardet: 3.0.4-py36h0f667ec_1 --> 4.0.0-py36h06a4308_1003 \n", + " conda: 4.5.4-py36_0 --> 4.10.1-py36h06a4308_1 \n", + " cryptography: 2.2.2-py36h14c3975_0 --> 3.4.7-py36hd23ed53_0 \n", + " idna: 2.6-py36h82fb2a8_1 --> 2.10-pyhd3eb1b0_0 \n", + " libffi: 3.2.1-hd88cf55_4 --> 3.3-he6710b0_2 \n", + " libgcc-ng: 7.2.0-hdf63c60_3 --> 9.1.0-hdf63c60_0 \n", + " libstdcxx-ng: 7.2.0-hdf63c60_3 --> 9.1.0-hdf63c60_0 \n", + " ncurses: 6.1-hf484d3e_0 --> 6.2-he6710b0_1 \n", + " openssl: 1.0.2o-h20670df_0 --> 1.1.1k-h27cfd23_0 \n", + " pip: 10.0.1-py36_0 --> 21.0.1-py36h06a4308_0 \n", + " pycosat: 0.6.3-py36h0a5515d_0 --> 0.6.3-py36h27cfd23_0 \n", + " pycparser: 2.18-py36hf9f622e_1 --> 2.20-py_2 \n", + " pyopenssl: 18.0.0-py36_0 --> 20.0.1-pyhd3eb1b0_1 \n", + " pysocks: 1.6.8-py36_0 --> 1.7.1-py36h06a4308_0 \n", + " python: 3.6.5-hc3d631a_2 --> 3.6.13-hdb3f193_0 \n", + " readline: 7.0-ha6073c6_4 --> 8.1-h27cfd23_0 \n", + " requests: 2.18.4-py36he2e5f8d_1 --> 2.25.1-pyhd3eb1b0_0 \n", + " ruamel_yaml: 0.15.37-py36h14c3975_2 --> 0.15.100-py36h27cfd23_0 \n", + " setuptools: 39.2.0-py36_0 --> 52.0.0-py36h06a4308_0 \n", + " six: 1.11.0-py36h372c433_1 --> 1.15.0-pyhd3eb1b0_0 \n", + " sqlite: 3.23.1-he433501_0 --> 3.35.4-hdfb4753_0 \n", + " tk: 8.6.7-hc745277_3 --> 8.6.10-hbc83047_0 \n", + " urllib3: 1.22-py36hbe7ace6_0 --> 1.26.4-pyhd3eb1b0_0 \n", + " wheel: 0.31.1-py36_0 --> 0.36.2-pyhd3eb1b0_0 \n", + " xz: 5.2.4-h14c3975_4 --> 5.2.5-h7b6447c_0 \n", + " yaml: 0.1.7-had09818_2 --> 0.2.5-h7b6447c_0 \n", + " zlib: 1.2.11-ha838bed_2 --> 1.2.11-h7b6447c_3 \n", + "\n", + "\n", + "Downloading and Extracting Packages\n", + "pysocks-1.7.1 | 30 KB | : 100% 1.0/1 [00:00<00:00, 25.95it/s]\n", + "pycparser-2.20 | 94 KB | : 100% 1.0/1 [00:00<00:00, 12.51it/s]\n", + "zlib-1.2.11 | 120 KB | : 100% 1.0/1 [00:00<00:00, 22.55it/s]\n", + "brotlipy-0.7.0 | 349 KB | : 100% 1.0/1 [00:00<00:00, 11.95it/s]\n", + "_libgcc_mutex-0.1 | 3 KB | : 100% 1.0/1 [00:00<00:00, 37.50it/s]\n", + "openssl-1.1.1k | 3.8 MB | : 100% 1.0/1 [00:00<00:00, 1.48it/s] \n", + "tqdm-4.59.0 | 90 KB | : 100% 1.0/1 [00:00<00:00, 18.55it/s]\n", + "certifi-2020.12.5 | 144 KB | : 100% 1.0/1 [00:00<00:00, 25.33it/s]\n", + "libffi-3.3 | 54 KB | : 100% 1.0/1 [00:00<00:00, 33.12it/s]\n", + "idna-2.10 | 52 KB | : 100% 1.0/1 [00:00<00:00, 29.32it/s]\n", + "pycosat-0.6.3 | 107 KB | : 100% 1.0/1 [00:00<00:00, 24.47it/s]\n", + "tk-8.6.10 | 3.2 MB | : 100% 1.0/1 [00:00<00:00, 1.45it/s] \n", + "wheel-0.36.2 | 31 KB | : 100% 1.0/1 [00:00<00:00, 28.73it/s]\n", + "pyopenssl-20.0.1 | 48 KB | : 100% 1.0/1 [00:00<00:00, 29.61it/s]\n", + "ncurses-6.2 | 1.1 MB | : 100% 1.0/1 [00:00<00:00, 1.19it/s] \n", + "six-1.15.0 | 13 KB | : 100% 1.0/1 [00:00<00:00, 38.13it/s]\n", + "libgcc-ng-9.1.0 | 8.1 MB | : 100% 1.0/1 [00:01<00:00, 1.25s/it] \n", + "readline-8.1 | 464 KB | : 100% 1.0/1 [00:00<00:00, 8.27it/s]\n", + "cffi-1.14.5 | 224 KB | : 100% 1.0/1 [00:00<00:00, 12.94it/s]\n", + "sqlite-3.35.4 | 1.4 MB | : 100% 1.0/1 [00:00<00:00, 4.08it/s] \n", + "conda-4.10.1 | 3.1 MB | : 100% 1.0/1 [00:00<00:00, 1.22it/s] \n", + "requests-2.25.1 | 51 KB | : 100% 1.0/1 [00:00<00:00, 22.32it/s]\n", + "python-3.6.13 | 33.9 MB | : 100% 1.0/1 [00:05<00:00, 5.19s/it] \n", + "ca-certificates-2021 | 120 KB | : 100% 1.0/1 [00:00<00:00, 24.39it/s]\n", + "cryptography-3.4.7 | 1.0 MB | : 100% 1.0/1 [00:00<00:00, 2.83it/s] \n", + "chardet-4.0.0 | 213 KB | : 100% 1.0/1 [00:00<00:00, 9.77it/s]\n", + "pip-21.0.1 | 2.0 MB | : 100% 1.0/1 [00:00<00:00, 1.53it/s] \n", + "urllib3-1.26.4 | 99 KB | : 100% 1.0/1 [00:00<00:00, 17.91it/s]\n", + "xz-5.2.5 | 438 KB | : 100% 1.0/1 [00:00<00:00, 7.81it/s] \n", + "conda-package-handli | 946 KB | : 100% 1.0/1 [00:00<00:00, 6.00it/s] \n", + "setuptools-52.0.0 | 933 KB | : 100% 1.0/1 [00:00<00:00, 3.20it/s] \n", + "ruamel_yaml-0.15.100 | 268 KB | : 100% 1.0/1 [00:00<00:00, 10.51it/s]\n", + "yaml-0.2.5 | 87 KB | : 100% 1.0/1 [00:00<00:00, 23.96it/s]\n", + "libstdcxx-ng-9.1.0 | 4.0 MB | : 100% 1.0/1 [00:00<00:00, 1.55it/s] \n", + "ld_impl_linux-64-2.3 | 645 KB | : 100% 1.0/1 [00:00<00:00, 5.71it/s] \n", + "Preparing transaction: / \b\b- \b\b\\ \b\b| \b\bdone\n", + "Verifying transaction: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n", + "Executing transaction: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n", + "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n", + "Solving environment: \\ \b\b| \b\b/ \b\bdone\n", + "\n", + "## Package Plan ##\n", + "\n", + " environment location: /usr/local\n", + "\n", + "\n", + "The following packages will be downloaded:\n", + "\n", + " package | build\n", + " ---------------------------|-----------------\n", + " six-1.15.0 | py36h06a4308_0 27 KB\n", + " ------------------------------------------------------------\n", + " Total: 27 KB\n", + "\n", + "The following packages will be REMOVED:\n", + "\n", + " asn1crypto-0.24.0-py36_0\n", + " conda-env-2.6.0-h36134e3_1\n", + " libedit-3.1.20170329-h6b74fdf_2\n", + "\n", + "The following packages will be SUPERSEDED by a higher-priority channel:\n", + "\n", + " six pkgs/main/noarch::six-1.15.0-pyhd3eb1~ --> pkgs/main/linux-64::six-1.15.0-py36h06a4308_0\n", + "\n", + "\n", + "\n", + "Downloading and Extracting Packages\n", + "six-1.15.0 | 27 KB | : 100% 1.0/1 [00:00<00:00, 10.04it/s]\n", + "Preparing transaction: \\ \b\bdone\n", + "Verifying transaction: / \b\bdone\n", + "Executing transaction: \\ \b\bdone\n", + "Cloning into '3dsnet'...\n", + "remote: Enumerating objects: 1203, done.\u001b[K\n", + "remote: Counting objects: 100% (1203/1203), done.\u001b[K\n", + "remote: Compressing objects: 100% (1045/1045), done.\u001b[K\n", + "remote: Total 1203 (delta 144), reused 1196 (delta 142), pack-reused 0\u001b[K\n", + "Receiving objects: 100% (1203/1203), 5.92 MiB | 15.30 MiB/s, done.\n", + "Resolving deltas: 100% (144/144), done.\n", + "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\bdone\n", + "Solving environment: - \b\bdone\n", + "\n", + "## Package Plan ##\n", + "\n", + " environment location: /usr/local/envs/3dsnet\n", + "\n", + " added / updated specs:\n", + " - python=3.6\n", + "\n", + "\n", + "The following packages will be downloaded:\n", + "\n", + " package | build\n", + " ---------------------------|-----------------\n", + " _libgcc_mutex-0.1 | main 3 KB\n", + " ca-certificates-2021.4.13 | h06a4308_1 114 KB\n", + " certifi-2020.12.5 | py36h06a4308_0 140 KB\n", + " ld_impl_linux-64-2.33.1 | h53a641e_7 568 KB\n", + " libffi-3.3 | he6710b0_2 50 KB\n", + " libgcc-ng-9.1.0 | hdf63c60_0 5.1 MB\n", + " libstdcxx-ng-9.1.0 | hdf63c60_0 3.1 MB\n", + " ncurses-6.2 | he6710b0_1 817 KB\n", + " openssl-1.1.1k | h27cfd23_0 2.5 MB\n", + " pip-21.0.1 | py36h06a4308_0 1.8 MB\n", + " python-3.6.13 | hdb3f193_0 29.7 MB\n", + " readline-8.1 | h27cfd23_0 362 KB\n", + " setuptools-52.0.0 | py36h06a4308_0 724 KB\n", + " sqlite-3.35.4 | hdfb4753_0 981 KB\n", + " tk-8.6.10 | hbc83047_0 3.0 MB\n", + " wheel-0.36.2 | pyhd3eb1b0_0 33 KB\n", + " xz-5.2.5 | h7b6447c_0 341 KB\n", + " zlib-1.2.11 | h7b6447c_3 103 KB\n", + " ------------------------------------------------------------\n", + " Total: 49.4 MB\n", + "\n", + "The following NEW packages will be INSTALLED:\n", + "\n", + " _libgcc_mutex pkgs/main/linux-64::_libgcc_mutex-0.1-main\n", + " ca-certificates pkgs/main/linux-64::ca-certificates-2021.4.13-h06a4308_1\n", + " certifi pkgs/main/linux-64::certifi-2020.12.5-py36h06a4308_0\n", + " ld_impl_linux-64 pkgs/main/linux-64::ld_impl_linux-64-2.33.1-h53a641e_7\n", + " libffi pkgs/main/linux-64::libffi-3.3-he6710b0_2\n", + " libgcc-ng pkgs/main/linux-64::libgcc-ng-9.1.0-hdf63c60_0\n", + " libstdcxx-ng pkgs/main/linux-64::libstdcxx-ng-9.1.0-hdf63c60_0\n", + " ncurses pkgs/main/linux-64::ncurses-6.2-he6710b0_1\n", + " openssl pkgs/main/linux-64::openssl-1.1.1k-h27cfd23_0\n", + " pip pkgs/main/linux-64::pip-21.0.1-py36h06a4308_0\n", + " python pkgs/main/linux-64::python-3.6.13-hdb3f193_0\n", + " readline pkgs/main/linux-64::readline-8.1-h27cfd23_0\n", + " setuptools pkgs/main/linux-64::setuptools-52.0.0-py36h06a4308_0\n", + " sqlite pkgs/main/linux-64::sqlite-3.35.4-hdfb4753_0\n", + " tk pkgs/main/linux-64::tk-8.6.10-hbc83047_0\n", + " wheel pkgs/main/noarch::wheel-0.36.2-pyhd3eb1b0_0\n", + " xz pkgs/main/linux-64::xz-5.2.5-h7b6447c_0\n", + " zlib pkgs/main/linux-64::zlib-1.2.11-h7b6447c_3\n", + "\n", + "\n", + "\n", + "Downloading and Extracting Packages\n", + "openssl-1.1.1k | 2.5 MB | : 100% 1.0/1 [00:00<00:00, 5.46it/s]\n", + "libstdcxx-ng-9.1.0 | 3.1 MB | : 100% 1.0/1 [00:00<00:00, 7.18it/s]\n", + "readline-8.1 | 362 KB | : 100% 1.0/1 [00:00<00:00, 15.98it/s]\n", + "sqlite-3.35.4 | 981 KB | : 100% 1.0/1 [00:00<00:00, 15.13it/s]\n", + "tk-8.6.10 | 3.0 MB | : 100% 1.0/1 [00:00<00:00, 6.50it/s]\n", + "libgcc-ng-9.1.0 | 5.1 MB | : 100% 1.0/1 [00:00<00:00, 4.00it/s]\n", + "certifi-2020.12.5 | 140 KB | : 100% 1.0/1 [00:00<00:00, 10.00it/s]\n", + "ca-certificates-2021 | 114 KB | : 100% 1.0/1 [00:00<00:00, 18.87it/s]\n", + "wheel-0.36.2 | 33 KB | : 100% 1.0/1 [00:00<00:00, 12.78it/s]\n", + "pip-21.0.1 | 1.8 MB | : 100% 1.0/1 [00:00<00:00, 5.13it/s]\n", + "zlib-1.2.11 | 103 KB | : 100% 1.0/1 [00:00<00:00, 18.33it/s]\n", + "ld_impl_linux-64-2.3 | 568 KB | : 100% 1.0/1 [00:00<00:00, 13.84it/s]\n", + "setuptools-52.0.0 | 724 KB | : 100% 1.0/1 [00:00<00:00, 9.88it/s]\n", + "xz-5.2.5 | 341 KB | : 100% 1.0/1 [00:00<00:00, 11.80it/s]\n", + "ncurses-6.2 | 817 KB | : 100% 1.0/1 [00:00<00:00, 3.17it/s]\n", + "_libgcc_mutex-0.1 | 3 KB | : 100% 1.0/1 [00:00<00:00, 21.93it/s]\n", + "libffi-3.3 | 50 KB | : 100% 1.0/1 [00:00<00:00, 16.29it/s]\n", + "python-3.6.13 | 29.7 MB | : 100% 1.0/1 [00:00<00:00, 1.15it/s]\n", + "Preparing transaction: | \b\b/ \b\b- \b\bdone\n", + "Verifying transaction: | \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n", + "Executing transaction: | \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n", + "#\n", + "# To activate this environment, use\n", + "#\n", + "# $ conda activate 3dsnet\n", + "#\n", + "# To deactivate an active environment, use\n", + "#\n", + "# $ conda deactivate\n", + "\n", + "Collecting meshio[all]\n", + " Downloading meshio-4.4.3-py3-none-any.whl (153 kB)\n", + "\u001b[K |████████████████████████████████| 153 kB 14.2 MB/s \n", + "\u001b[?25hCollecting importlib-metadata\n", + " Downloading importlib_metadata-4.0.1-py3-none-any.whl (16 kB)\n", + "Collecting numpy\n", + " Downloading numpy-1.19.5-cp36-cp36m-manylinux2010_x86_64.whl (14.8 MB)\n", + "\u001b[K |████████████████████████████████| 14.8 MB 275 kB/s \n", + "\u001b[?25hCollecting h5py\n", + " Downloading h5py-3.1.0-cp36-cp36m-manylinux1_x86_64.whl (4.0 MB)\n", + "\u001b[K |████████████████████████████████| 4.0 MB 51.6 MB/s \n", + "\u001b[?25hCollecting netCDF4\n", + " Downloading netCDF4-1.5.6-cp36-cp36m-manylinux2014_x86_64.whl (4.7 MB)\n", + "\u001b[K |████████████████████████████████| 4.7 MB 56.7 MB/s \n", + "\u001b[?25hCollecting cached-property\n", + " Downloading cached_property-1.5.2-py2.py3-none-any.whl (7.6 kB)\n", + "Collecting zipp>=0.5\n", + " Downloading zipp-3.4.1-py3-none-any.whl (5.2 kB)\n", + "Collecting typing-extensions>=3.6.4\n", + " Downloading typing_extensions-3.10.0.0-py3-none-any.whl (26 kB)\n", + "Collecting cftime\n", + " Downloading cftime-1.4.1-cp36-cp36m-manylinux2014_x86_64.whl (316 kB)\n", + "\u001b[K |████████████████████████████████| 316 kB 70.4 MB/s \n", + "\u001b[?25hInstalling collected packages: zipp, typing-extensions, numpy, importlib-metadata, cftime, cached-property, netCDF4, meshio, h5py\n", + "Successfully installed cached-property-1.5.2 cftime-1.4.1 h5py-3.1.0 importlib-metadata-4.0.1 meshio-4.4.3 netCDF4-1.5.6 numpy-1.19.5 typing-extensions-3.10.0.0 zipp-3.4.1\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.colab-display-data+json": { + "pip_warning": { + "packages": [ + "numpy" + ] + } + } + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n", + "Solving environment: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bfailed with initial frozen solve. Retrying with flexible solve.\n", + "Solving environment: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bfailed with repodata from current_repodata.json, will retry with next repodata source.\n", + "Collecting package metadata (repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n", + "Solving environment: | \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n", + "\n", + "## Package Plan ##\n", + "\n", + " environment location: /usr/local\n", + "\n", + " added / updated specs:\n", + " - cudatoolkit=10.1\n", + " - pytorch=1.7.1\n", + " - torchvision=0.8.2\n", + "\n", + "\n", + "The following packages will be downloaded:\n", + "\n", + " package | build\n", + " ---------------------------|-----------------\n", + " blas-1.0 | mkl 6 KB\n", + " cudatoolkit-10.1.243 | h6bb024c_0 347.4 MB\n", + " dataclasses-0.8 | pyh4f3eec9_6 22 KB\n", + " freetype-2.10.4 | h5ab3b9f_0 596 KB\n", + " intel-openmp-2021.2.0 | h06a4308_610 1.3 MB\n", + " jpeg-9b | h024ee3a_2 214 KB\n", + " lcms2-2.12 | h3be6417_0 312 KB\n", + " libpng-1.6.37 | hbc83047_0 278 KB\n", + " libtiff-4.1.0 | h2733197_1 449 KB\n", + " libuv-1.40.0 | h7b6447c_0 736 KB\n", + " lz4-c-1.9.3 | h2531618_0 186 KB\n", + " mkl-2020.2 | 256 138.3 MB\n", + " mkl-service-2.3.0 | py36he8ac12f_0 52 KB\n", + " mkl_fft-1.3.0 | py36h54f3939_0 170 KB\n", + " mkl_random-1.1.1 | py36h0573a6f_0 327 KB\n", + " ninja-1.10.2 | hff7bd54_1 1.4 MB\n", + " numpy-1.19.2 | py36h54aff64_0 22 KB\n", + " numpy-base-1.19.2 | py36hfa32c7d_0 4.1 MB\n", + " olefile-0.46 | py36_0 48 KB\n", + " pillow-8.2.0 | py36he98fc37_0 627 KB\n", + " pytorch-1.7.1 |py3.6_cuda10.1.243_cudnn7.6.3_0 553.7 MB pytorch\n", + " torchvision-0.8.2 | py36_cu101 17.8 MB pytorch\n", + " typing_extensions-3.7.4.3 | pyha847dfd_0 25 KB\n", + " zstd-1.4.9 | haebb681_0 480 KB\n", + " ------------------------------------------------------------\n", + " Total: 1.04 GB\n", + "\n", + "The following NEW packages will be INSTALLED:\n", + "\n", + " blas pkgs/main/linux-64::blas-1.0-mkl\n", + " cudatoolkit pkgs/main/linux-64::cudatoolkit-10.1.243-h6bb024c_0\n", + " dataclasses pkgs/main/noarch::dataclasses-0.8-pyh4f3eec9_6\n", + " freetype pkgs/main/linux-64::freetype-2.10.4-h5ab3b9f_0\n", + " intel-openmp pkgs/main/linux-64::intel-openmp-2021.2.0-h06a4308_610\n", + " jpeg pkgs/main/linux-64::jpeg-9b-h024ee3a_2\n", + " lcms2 pkgs/main/linux-64::lcms2-2.12-h3be6417_0\n", + " libpng pkgs/main/linux-64::libpng-1.6.37-hbc83047_0\n", + " libtiff pkgs/main/linux-64::libtiff-4.1.0-h2733197_1\n", + " libuv pkgs/main/linux-64::libuv-1.40.0-h7b6447c_0\n", + " lz4-c pkgs/main/linux-64::lz4-c-1.9.3-h2531618_0\n", + " mkl pkgs/main/linux-64::mkl-2020.2-256\n", + " mkl-service pkgs/main/linux-64::mkl-service-2.3.0-py36he8ac12f_0\n", + " mkl_fft pkgs/main/linux-64::mkl_fft-1.3.0-py36h54f3939_0\n", + " mkl_random pkgs/main/linux-64::mkl_random-1.1.1-py36h0573a6f_0\n", + " ninja pkgs/main/linux-64::ninja-1.10.2-hff7bd54_1\n", + " numpy pkgs/main/linux-64::numpy-1.19.2-py36h54aff64_0\n", + " numpy-base pkgs/main/linux-64::numpy-base-1.19.2-py36hfa32c7d_0\n", + " olefile pkgs/main/linux-64::olefile-0.46-py36_0\n", + " pillow pkgs/main/linux-64::pillow-8.2.0-py36he98fc37_0\n", + " pytorch pytorch/linux-64::pytorch-1.7.1-py3.6_cuda10.1.243_cudnn7.6.3_0\n", + " torchvision pytorch/linux-64::torchvision-0.8.2-py36_cu101\n", + " typing_extensions pkgs/main/noarch::typing_extensions-3.7.4.3-pyha847dfd_0\n", + " zstd pkgs/main/linux-64::zstd-1.4.9-haebb681_0\n", + "\n", + "\n", + "\n", + "Downloading and Extracting Packages\n", + "typing_extensions-3. | 25 KB | : 100% 1.0/1 [00:00<00:00, 15.24it/s]\n", + "libtiff-4.1.0 | 449 KB | : 100% 1.0/1 [00:00<00:00, 12.51it/s]\n", + "zstd-1.4.9 | 480 KB | : 100% 1.0/1 [00:00<00:00, 15.92it/s]\n", + "mkl_random-1.1.1 | 327 KB | : 100% 1.0/1 [00:00<00:00, 19.19it/s]\n", + "olefile-0.46 | 48 KB | : 100% 1.0/1 [00:00<00:00, 21.02it/s]\n", + "cudatoolkit-10.1.243 | 347.4 MB | : 100% 1.0/1 [00:08<00:00, 8.05s/it] \n", + "freetype-2.10.4 | 596 KB | : 100% 1.0/1 [00:00<00:00, 13.14it/s]\n", + "mkl-2020.2 | 138.3 MB | : 100% 1.0/1 [00:13<00:00, 13.71s/it] \n", + "dataclasses-0.8 | 22 KB | : 100% 1.0/1 [00:00<00:00, 17.59it/s]\n", + "numpy-base-1.19.2 | 4.1 MB | : 100% 1.0/1 [00:00<00:00, 4.16it/s]\n", + "numpy-1.19.2 | 22 KB | : 100% 1.0/1 [00:00<00:00, 18.33it/s]\n", + "blas-1.0 | 6 KB | : 100% 1.0/1 [00:00<00:00, 20.43it/s]\n", + "libuv-1.40.0 | 736 KB | : 100% 1.0/1 [00:00<00:00, 13.73it/s]\n", + "pillow-8.2.0 | 627 KB | : 100% 1.0/1 [00:00<00:00, 9.26it/s]\n", + "lcms2-2.12 | 312 KB | : 100% 1.0/1 [00:00<00:00, 15.80it/s]\n", + "mkl-service-2.3.0 | 52 KB | : 100% 1.0/1 [00:00<00:00, 18.19it/s]\n", + "mkl_fft-1.3.0 | 170 KB | : 100% 1.0/1 [00:00<00:00, 16.19it/s]\n", + "torchvision-0.8.2 | 17.8 MB | : 100% 1.0/1 [00:04<00:00, 4.67s/it] \n", + "ninja-1.10.2 | 1.4 MB | : 100% 1.0/1 [00:00<00:00, 10.92it/s]\n", + "libpng-1.6.37 | 278 KB | : 100% 1.0/1 [00:00<00:00, 15.03it/s]\n", + "pytorch-1.7.1 | 553.7 MB | : 100% 1.0/1 [01:30<00:00, 90.55s/it] \n", + "jpeg-9b | 214 KB | : 100% 1.0/1 [00:00<00:00, 13.75it/s]\n", + "intel-openmp-2021.2. | 1.3 MB | : 100% 1.0/1 [00:00<00:00, 9.17it/s]\n", + "lz4-c-1.9.3 | 186 KB | : 100% 1.0/1 [00:00<00:00, 13.41it/s]\n", + "Preparing transaction: / \b\b- \b\b\\ \b\bdone\n", + "Verifying transaction: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n", + "Executing transaction: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n", + "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n", + "Solving environment: | \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n", + "\n", + "## Package Plan ##\n", + "\n", + " environment location: /usr/local\n", + "\n", + " added / updated specs:\n", + " - pyembree\n", + "\n", + "\n", + "The following packages will be downloaded:\n", + "\n", + " package | build\n", + " ---------------------------|-----------------\n", + " ca-certificates-2020.12.5 | ha878542_0 137 KB conda-forge\n", + " certifi-2020.12.5 | py36h5fab9bb_1 143 KB conda-forge\n", + " conda-4.10.1 | py36h5fab9bb_0 3.1 MB conda-forge\n", + " embree-2.17.7 | 1 41.0 MB conda-forge\n", + " pyembree-0.1.6 | py36h830a2c2_1 86 KB conda-forge\n", + " python_abi-3.6 | 1_cp36m 4 KB conda-forge\n", + " ------------------------------------------------------------\n", + " Total: 44.4 MB\n", + "\n", + "The following NEW packages will be INSTALLED:\n", + "\n", + " embree conda-forge/linux-64::embree-2.17.7-1\n", + " pyembree conda-forge/linux-64::pyembree-0.1.6-py36h830a2c2_1\n", + " python_abi conda-forge/linux-64::python_abi-3.6-1_cp36m\n", + "\n", + "The following packages will be UPDATED:\n", + "\n", + " certifi pkgs/main::certifi-2020.12.5-py36h06a~ --> conda-forge::certifi-2020.12.5-py36h5fab9bb_1\n", + "\n", + "The following packages will be SUPERSEDED by a higher-priority channel:\n", + "\n", + " ca-certificates pkgs/main::ca-certificates-2021.4.13-~ --> conda-forge::ca-certificates-2020.12.5-ha878542_0\n", + " conda pkgs/main::conda-4.10.1-py36h06a4308_1 --> conda-forge::conda-4.10.1-py36h5fab9bb_0\n", + "\n", + "\n", + "\n", + "Downloading and Extracting Packages\n", + "pyembree-0.1.6 | 86 KB | : 100% 1.0/1 [00:00<00:00, 4.80it/s] \n", + "ca-certificates-2020 | 137 KB | : 100% 1.0/1 [00:00<00:00, 17.32it/s]\n", + "python_abi-3.6 | 4 KB | : 100% 1.0/1 [00:00<00:00, 28.10it/s]\n", + "embree-2.17.7 | 41.0 MB | : 100% 1.0/1 [00:07<00:00, 7.74s/it] \n", + "conda-4.10.1 | 3.1 MB | : 100% 1.0/1 [00:00<00:00, 1.58it/s]\n", + "certifi-2020.12.5 | 143 KB | : 100% 1.0/1 [00:00<00:00, 16.68it/s]\n", + "Preparing transaction: | \b\bdone\n", + "Verifying transaction: - \b\bdone\n", + "Executing transaction: | \b\bdone\n", + "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n", + "Solving environment: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n", + "\n", + "## Package Plan ##\n", + "\n", + " environment location: /usr/local\n", + "\n", + " added / updated specs:\n", + " - seaborn\n", + " - trimesh\n", + "\n", + "\n", + "The following packages will be downloaded:\n", + "\n", + " package | build\n", + " ---------------------------|-----------------\n", + " cycler-0.10.0 | py_2 9 KB conda-forge\n", + " kiwisolver-1.3.1 | py36h51d7077_0 86 KB conda-forge\n", + " libblas-3.9.0 |1_h6e990d7_netlib 176 KB conda-forge\n", + " libcblas-3.9.0 |3_h893e4fe_netlib 54 KB conda-forge\n", + " libgfortran-ng-7.5.0 | h14aa051_19 22 KB conda-forge\n", + " libgfortran4-7.5.0 | h14aa051_19 1.3 MB conda-forge\n", + " liblapack-3.9.0 |3_h893e4fe_netlib 2.9 MB conda-forge\n", + " matplotlib-base-3.3.3 | py36he12231b_0 6.8 MB conda-forge\n", + " pandas-1.1.4 | py36hd87012b_0 10.5 MB conda-forge\n", + " patsy-0.5.1 | py_0 187 KB conda-forge\n", + " pyparsing-2.4.7 | pyh9f0ad1d_0 60 KB conda-forge\n", + " python-dateutil-2.8.1 | py_0 220 KB conda-forge\n", + " pytz-2021.1 | pyhd8ed1ab_0 239 KB conda-forge\n", + " scipy-1.5.3 | py36h976291a_0 18.6 MB conda-forge\n", + " seaborn-0.11.1 | hd8ed1ab_1 4 KB conda-forge\n", + " seaborn-base-0.11.1 | pyhd8ed1ab_1 217 KB conda-forge\n", + " statsmodels-0.11.1 | py36h8c4c3a4_2 9.8 MB conda-forge\n", + " tornado-6.1 | py36h1d69622_0 644 KB conda-forge\n", + " trimesh-3.9.18 | pyh6c4a22f_0 508 KB conda-forge\n", + " ------------------------------------------------------------\n", + " Total: 52.3 MB\n", + "\n", + "The following NEW packages will be INSTALLED:\n", + "\n", + " cycler conda-forge/noarch::cycler-0.10.0-py_2\n", + " kiwisolver conda-forge/linux-64::kiwisolver-1.3.1-py36h51d7077_0\n", + " libblas conda-forge/linux-64::libblas-3.9.0-1_h6e990d7_netlib\n", + " libcblas conda-forge/linux-64::libcblas-3.9.0-3_h893e4fe_netlib\n", + " libgfortran-ng conda-forge/linux-64::libgfortran-ng-7.5.0-h14aa051_19\n", + " libgfortran4 conda-forge/linux-64::libgfortran4-7.5.0-h14aa051_19\n", + " liblapack conda-forge/linux-64::liblapack-3.9.0-3_h893e4fe_netlib\n", + " matplotlib-base conda-forge/linux-64::matplotlib-base-3.3.3-py36he12231b_0\n", + " pandas conda-forge/linux-64::pandas-1.1.4-py36hd87012b_0\n", + " patsy conda-forge/noarch::patsy-0.5.1-py_0\n", + " pyparsing conda-forge/noarch::pyparsing-2.4.7-pyh9f0ad1d_0\n", + " python-dateutil conda-forge/noarch::python-dateutil-2.8.1-py_0\n", + " pytz conda-forge/noarch::pytz-2021.1-pyhd8ed1ab_0\n", + " scipy conda-forge/linux-64::scipy-1.5.3-py36h976291a_0\n", + " seaborn conda-forge/noarch::seaborn-0.11.1-hd8ed1ab_1\n", + " seaborn-base conda-forge/noarch::seaborn-base-0.11.1-pyhd8ed1ab_1\n", + " statsmodels conda-forge/linux-64::statsmodels-0.11.1-py36h8c4c3a4_2\n", + " tornado conda-forge/linux-64::tornado-6.1-py36h1d69622_0\n", + " trimesh conda-forge/noarch::trimesh-3.9.18-pyh6c4a22f_0\n", + "\n", + "\n", + "\n", + "Downloading and Extracting Packages\n", + "libblas-3.9.0 | 176 KB | : 100% 1.0/1 [00:00<00:00, 7.46it/s] \n", + "trimesh-3.9.18 | 508 KB | : 100% 1.0/1 [00:00<00:00, 6.77it/s]\n", + "tornado-6.1 | 644 KB | : 100% 1.0/1 [00:00<00:00, 5.54it/s]\n", + "libgfortran-ng-7.5.0 | 22 KB | : 100% 1.0/1 [00:00<00:00, 23.27it/s]\n", + "liblapack-3.9.0 | 2.9 MB | : 100% 1.0/1 [00:00<00:00, 2.00it/s]\n", + "patsy-0.5.1 | 187 KB | : 100% 1.0/1 [00:00<00:00, 14.03it/s]\n", + "seaborn-0.11.1 | 4 KB | : 100% 1.0/1 [00:00<00:00, 31.35it/s]\n", + "kiwisolver-1.3.1 | 86 KB | : 100% 1.0/1 [00:00<00:00, 22.59it/s]\n", + "python-dateutil-2.8. | 220 KB | : 100% 1.0/1 [00:00<00:00, 15.91it/s]\n", + "pandas-1.1.4 | 10.5 MB | : 100% 1.0/1 [00:02<00:00, 2.33s/it]\n", + "matplotlib-base-3.3. | 6.8 MB | : 100% 1.0/1 [00:01<00:00, 1.24s/it]\n", + "libcblas-3.9.0 | 54 KB | : 100% 1.0/1 [00:00<00:00, 18.46it/s]\n", + "statsmodels-0.11.1 | 9.8 MB | : 100% 1.0/1 [00:01<00:00, 1.96s/it]\n", + "libgfortran4-7.5.0 | 1.3 MB | : 100% 1.0/1 [00:00<00:00, 3.97it/s]\n", + "scipy-1.5.3 | 18.6 MB | : 100% 1.0/1 [00:03<00:00, 3.16s/it]\n", + "seaborn-base-0.11.1 | 217 KB | : 100% 1.0/1 [00:00<00:00, 14.38it/s]\n", + "cycler-0.10.0 | 9 KB | : 100% 1.0/1 [00:00<00:00, 32.46it/s]\n", + "pytz-2021.1 | 239 KB | : 100% 1.0/1 [00:00<00:00, 8.12it/s]\n", + "pyparsing-2.4.7 | 60 KB | : 100% 1.0/1 [00:00<00:00, 23.38it/s]\n", + "Preparing transaction: - \b\b\\ \b\b| \b\bdone\n", + "Verifying transaction: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n", + "Executing transaction: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n", + "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n", + "Solving environment: | \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n", + "\n", + "## Package Plan ##\n", + "\n", + " environment location: /usr/local\n", + "\n", + " added / updated specs:\n", + " - fvcore\n", + " - iopath\n", + "\n", + "\n", + "The following packages will be downloaded:\n", + "\n", + " package | build\n", + " ---------------------------|-----------------\n", + " fvcore-0.1.5.post20210518 | py36 88 KB fvcore\n", + " iopath-0.1.8 | py36 31 KB iopath\n", + " portalocker-1.7.0 | py36h5fab9bb_1 19 KB conda-forge\n", + " pyyaml-5.3.1 | py36he6145b8_1 185 KB conda-forge\n", + " tabulate-0.8.9 | pyhd8ed1ab_0 26 KB conda-forge\n", + " termcolor-1.1.0 | py_2 6 KB conda-forge\n", + " yacs-0.1.6 | py_0 11 KB conda-forge\n", + " ------------------------------------------------------------\n", + " Total: 366 KB\n", + "\n", + "The following NEW packages will be INSTALLED:\n", + "\n", + " fvcore fvcore/linux-64::fvcore-0.1.5.post20210518-py36\n", + " iopath iopath/linux-64::iopath-0.1.8-py36\n", + " portalocker conda-forge/linux-64::portalocker-1.7.0-py36h5fab9bb_1\n", + " pyyaml conda-forge/linux-64::pyyaml-5.3.1-py36he6145b8_1\n", + " tabulate conda-forge/noarch::tabulate-0.8.9-pyhd8ed1ab_0\n", + " termcolor conda-forge/noarch::termcolor-1.1.0-py_2\n", + " yacs conda-forge/noarch::yacs-0.1.6-py_0\n", + "\n", + "\n", + "\n", + "Downloading and Extracting Packages\n", + "termcolor-1.1.0 | 6 KB | : 100% 1.0/1 [00:00<00:00, 11.39it/s]\n", + "tabulate-0.8.9 | 26 KB | : 100% 1.0/1 [00:00<00:00, 25.84it/s]\n", + "yacs-0.1.6 | 11 KB | : 100% 1.0/1 [00:00<00:00, 7.66it/s]\n", + "portalocker-1.7.0 | 19 KB | : 100% 1.0/1 [00:00<00:00, 7.86it/s] \n", + "pyyaml-5.3.1 | 185 KB | : 100% 1.0/1 [00:00<00:00, 14.21it/s]\n", + "fvcore-0.1.5.post202 | 88 KB | : 100% 1.0/1 [00:01<00:00, 1.08s/it]\n", + "iopath-0.1.8 | 31 KB | : 100% 1.0/1 [00:00<00:00, 1.05it/s]\n", + "Preparing transaction: - \b\bdone\n", + "Verifying transaction: | \b\bdone\n", + "Executing transaction: - \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n", + "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n", + "Solving environment: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n", + "\n", + "## Package Plan ##\n", + "\n", + " environment location: /usr/local\n", + "\n", + " added / updated specs:\n", + " - pytorch3d\n", + "\n", + "\n", + "The following packages will be downloaded:\n", + "\n", + " package | build\n", + " ---------------------------|-----------------\n", + " conda-4.10.1 | py36h06a4308_1 2.9 MB\n", + " pytorch3d-0.4.0 |py36_cu101_pyt171 36.8 MB pytorch3d\n", + " ------------------------------------------------------------\n", + " Total: 39.6 MB\n", + "\n", + "The following NEW packages will be INSTALLED:\n", + "\n", + " pytorch3d pytorch3d/linux-64::pytorch3d-0.4.0-py36_cu101_pyt171\n", + "\n", + "The following packages will be UPDATED:\n", + "\n", + " ca-certificates conda-forge::ca-certificates-2020.12.~ --> pkgs/main::ca-certificates-2021.4.13-h06a4308_1\n", + " conda conda-forge::conda-4.10.1-py36h5fab9b~ --> pkgs/main::conda-4.10.1-py36h06a4308_1\n", + "\n", + "\n", + "\n", + "Downloading and Extracting Packages\n", + "conda-4.10.1 | 2.9 MB | : 100% 1.0/1 [00:00<00:00, 4.41it/s]\n", + "pytorch3d-0.4.0 | 36.8 MB | : 100% 1.0/1 [00:08<00:00, 8.54s/it]\n", + "Preparing transaction: \\ \b\bdone\n", + "Verifying transaction: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n", + "Executing transaction: / \b\bdone\n", + "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n", + "Solving environment: | \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n", + "\n", + "## Package Plan ##\n", + "\n", + " environment location: /usr/local\n", + "\n", + " added / updated specs:\n", + " - visdom\n", + "\n", + "\n", + "The following packages will be downloaded:\n", + "\n", + " package | build\n", + " ---------------------------|-----------------\n", + " libsodium-1.0.18 | h36c2ea0_1 366 KB conda-forge\n", + " pyzmq-19.0.2 | py36h9947dbf_2 467 KB conda-forge\n", + " torchfile-0.1.0 | py_0 8 KB conda-forge\n", + " visdom-0.1.8.9 | 0 565 KB conda-forge\n", + " websocket-client-0.57.0 | py36h5fab9bb_4 59 KB conda-forge\n", + " zeromq-4.3.4 | h2531618_0 331 KB\n", + " ------------------------------------------------------------\n", + " Total: 1.8 MB\n", + "\n", + "The following NEW packages will be INSTALLED:\n", + "\n", + " libsodium conda-forge/linux-64::libsodium-1.0.18-h36c2ea0_1\n", + " pyzmq conda-forge/linux-64::pyzmq-19.0.2-py36h9947dbf_2\n", + " torchfile conda-forge/noarch::torchfile-0.1.0-py_0\n", + " visdom conda-forge/noarch::visdom-0.1.8.9-0\n", + " websocket-client conda-forge/linux-64::websocket-client-0.57.0-py36h5fab9bb_4\n", + " zeromq pkgs/main/linux-64::zeromq-4.3.4-h2531618_0\n", + "\n", + "The following packages will be SUPERSEDED by a higher-priority channel:\n", + "\n", + " ca-certificates pkgs/main::ca-certificates-2021.4.13-~ --> conda-forge::ca-certificates-2020.12.5-ha878542_0\n", + " conda pkgs/main::conda-4.10.1-py36h06a4308_1 --> conda-forge::conda-4.10.1-py36h5fab9bb_0\n", + "\n", + "\n", + "\n", + "Downloading and Extracting Packages\n", + "torchfile-0.1.0 | 8 KB | : 100% 1.0/1 [00:00<00:00, 13.33it/s]\n", + "libsodium-1.0.18 | 366 KB | : 100% 1.0/1 [00:00<00:00, 9.26it/s]\n", + "visdom-0.1.8.9 | 565 KB | : 100% 1.0/1 [00:00<00:00, 7.12it/s]\n", + "zeromq-4.3.4 | 331 KB | : 100% 1.0/1 [00:00<00:00, 10.28it/s]\n", + "pyzmq-19.0.2 | 467 KB | : 100% 1.0/1 [00:00<00:00, 4.04it/s]\n", + "websocket-client-0.5 | 59 KB | : 100% 1.0/1 [00:00<00:00, 19.68it/s]\n", + "Preparing transaction: \\ \b\bdone\n", + "Verifying transaction: / \b\bdone\n", + "Executing transaction: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n", + "[Errno 2] No such file or directory: '/content/3dsnset'\n", + "/content\n", + "Cloning into 'PyMesh'...\n", + "remote: Enumerating objects: 18131, done.\u001b[K\n", + "remote: Total 18131 (delta 0), reused 0 (delta 0), pack-reused 18131\u001b[K\n", + "Receiving objects: 100% (18131/18131), 21.40 MiB | 21.28 MiB/s, done.\n", + "Resolving deltas: 100% (12920/12920), done.\n", + "/content/PyMesh\n", + "Submodule 'third_party/Clipper' (https://github.com/PyMesh/Clipper.git) registered for path 'third_party/Clipper'\n", + "Submodule 'third_party/TetWild' (https://github.com/PyMesh/TetWild.git) registered for path 'third_party/TetWild'\n", + "Submodule 'third_party/WindingNumber' (https://github.com/PyMesh/WindingNumber.git) registered for path 'third_party/WindingNumber'\n", + "Submodule 'third_party/carve' (https://github.com/PyMesh/carve.git) registered for path 'third_party/carve'\n", + "Submodule 'third_party/cgal' (https://github.com/PyMesh/cgal.git) registered for path 'third_party/cgal'\n", + "Submodule 'third_party/cork' (https://github.com/PyMesh/cork.git) registered for path 'third_party/cork'\n", + "Submodule 'third_party/draco' (https://github.com/PyMesh/draco.git) registered for path 'third_party/draco'\n", + "Submodule 'third_party/eigen' (https://github.com/PyMesh/eigen.git) registered for path 'third_party/eigen'\n", + "Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/fmt'\n", + "Submodule 'third_party/geogram' (https://github.com/PyMesh/geogram.git) registered for path 'third_party/geogram'\n", + "Submodule 'third_party/jigsaw' (https://github.com/PyMesh/jigsaw.git) registered for path 'third_party/jigsaw'\n", + "Submodule 'third_party/json' (https://github.com/nlohmann/json.git) registered for path 'third_party/json'\n", + "Submodule 'libigl' (https://github.com/PyMesh/libigl.git) registered for path 'third_party/libigl'\n", + "Submodule 'third_party/mmg' (https://github.com/PyMesh/mmg.git) registered for path 'third_party/mmg'\n", + "Submodule 'third_party/pybind11' (https://github.com/PyMesh/pybind11.git) registered for path 'third_party/pybind11'\n", + "Submodule 'third_party/qhull' (https://github.com/PyMesh/qhull.git) registered for path 'third_party/qhull'\n", + "Submodule 'third_party/quartet' (https://github.com/PyMesh/quartet.git) registered for path 'third_party/quartet'\n", + "Submodule 'third_party/spdlog' (https://github.com/gabime/spdlog.git) registered for path 'third_party/spdlog'\n", + "Submodule 'third_party/tbb' (https://github.com/PyMesh/tbb.git) registered for path 'third_party/tbb'\n", + "Submodule 'third_party/tetgen' (https://github.com/PyMesh/tetgen.git) registered for path 'third_party/tetgen'\n", + "Submodule 'third_party/triangle' (https://github.com/PyMesh/triangle.git) registered for path 'third_party/triangle'\n", + "Cloning into '/content/PyMesh/third_party/Clipper'...\n", + "Cloning into '/content/PyMesh/third_party/TetWild'...\n", + "Cloning into '/content/PyMesh/third_party/WindingNumber'...\n", + "Cloning into '/content/PyMesh/third_party/carve'...\n", + "Cloning into '/content/PyMesh/third_party/cgal'...\n", + "Cloning into '/content/PyMesh/third_party/cork'...\n", + "Cloning into '/content/PyMesh/third_party/draco'...\n", + "Cloning into '/content/PyMesh/third_party/eigen'...\n", + "Cloning into '/content/PyMesh/third_party/fmt'...\n", + "Cloning into '/content/PyMesh/third_party/geogram'...\n", + "Cloning into '/content/PyMesh/third_party/jigsaw'...\n", + "Cloning into '/content/PyMesh/third_party/json'...\n", + "Cloning into '/content/PyMesh/third_party/libigl'...\n", + "Cloning into '/content/PyMesh/third_party/mmg'...\n", + "Cloning into '/content/PyMesh/third_party/pybind11'...\n", + "Cloning into '/content/PyMesh/third_party/qhull'...\n", + "Cloning into '/content/PyMesh/third_party/quartet'...\n", + "Cloning into '/content/PyMesh/third_party/spdlog'...\n", + "Cloning into '/content/PyMesh/third_party/tbb'...\n", + "Cloning into '/content/PyMesh/third_party/tetgen'...\n", + "Cloning into '/content/PyMesh/third_party/triangle'...\n", + "Submodule path 'third_party/Clipper': checked out '3fd3457741d275b887ad16abacccbd01eda2175c'\n", + "Submodule path 'third_party/TetWild': checked out '5b0f81552fbd66c1ed66168fd7bbf222e3391816'\n", + "Submodule path 'third_party/WindingNumber': checked out 'e011b7bc9fa1e2570651097936ccf2314fdcbe86'\n", + "Submodule path 'third_party/carve': checked out 'd328ad2136a4fa6413db8ad264ed219095bb6744'\n", + "Submodule path 'third_party/cgal': checked out '1ce145a3c611df5f3a71fb20275b755fdbfca21e'\n", + "Submodule path 'third_party/cork': checked out '360820dd981fa72117f255ddaa68367419f7526c'\n", + "Submodule path 'third_party/draco': checked out '063994c362871d6f149c24c669122e4ef3fa8196'\n", + "Submodule path 'third_party/eigen': checked out 'ed3db99ec3caff039f72645b7c5feb68717c8655'\n", + "Submodule path 'third_party/fmt': checked out '355eb6d29ad7dbcb017420442af237e3cf6d8054'\n", + "Submodule path 'third_party/geogram': checked out '25228ad2a88b793fc8de651ecf6ca7ee76819d5a'\n", + "Submodule path 'third_party/jigsaw': checked out '9c677b58234c64e2586e361d53bf60ce7e4ed3bb'\n", + "Submodule path 'third_party/json': checked out 'e7452d87783fbf6e9d320d515675e26dfd1271c5'\n", + "Submodule path 'third_party/libigl': checked out 'f6b406427400ed7ddb56cfc2577b6af571827c8c'\n", + "Submodule path 'third_party/mmg': checked out '8a93fc29e8ea61299c6c415aaf2c57cb5cd2f779'\n", + "Submodule path 'third_party/pybind11': checked out '80d452484c5409444b0ec19383faa84bb7a4d351'\n", + "Submodule path 'third_party/qhull': checked out 'd901974562b76b89947eea320423130851b2f164'\n", + "Submodule path 'third_party/quartet': checked out '7d789031d2f154e015f70a06eb2a1c01461e3cfc'\n", + "Submodule path 'third_party/spdlog': checked out 'b6b9d835c588c35227410a9830e7a4586f90777a'\n", + "Submodule path 'third_party/tbb': checked out 'ed6f6f15cece26ae4ab0816eab220c5e0691093f'\n", + "Submodule path 'third_party/tetgen': checked out '54e1149a1af5b586706b3d87a0152e77e76ade22'\n", + "remote: Enumerating objects: 23, done.\u001b[K\n", + "remote: Counting objects: 100% (23/23), done.\u001b[K\n", + "remote: Compressing objects: 100% (11/11), done.\u001b[K\n", + "remote: Total 19 (delta 12), reused 15 (delta 8), pack-reused 0\u001b[K\n", + "Unpacking objects: 100% (19/19), done.\n", + "From https://github.com/PyMesh/triangle\n", + " * branch a092f98815a38ee1d2f29341838947b3849fa2d0 -> FETCH_HEAD\n", + "Submodule path 'third_party/triangle': checked out 'a092f98815a38ee1d2f29341838947b3849fa2d0'\n", + "Reading package lists... Done\n", + "Building dependency tree \n", + "Reading state information... Done\n", + "libboost-dev is already the newest version (1.65.1.0ubuntu1).\n", + "libboost-dev set to manually installed.\n", + "libboost-thread-dev is already the newest version (1.65.1.0ubuntu1).\n", + "libboost-thread-dev set to manually installed.\n", + "libtbb-dev is already the newest version (2017~U7-8).\n", + "libtbb-dev set to manually installed.\n", + "cmake is already the newest version (3.10.2-1ubuntu2.18.04.1).\n", + "python3-dev is already the newest version (3.6.7-1~18.04).\n", + "python3-dev set to manually installed.\n", + "The following package was automatically installed and is no longer required:\n", + " libnvidia-common-460\n", + "Use 'apt autoremove' to remove it.\n", + "The following additional packages will be installed:\n", + " python-pip-whl python3-asn1crypto python3-cffi-backend python3-crypto\n", + " python3-cryptography python3-decorator python3-idna python3-keyring\n", + " python3-keyrings.alt python3-olefile python3-pil python3-pkg-resources\n", + " python3-secretstorage python3-six python3-wheel python3-xdg\n", + "Suggested packages:\n", + " libeigen3-doc libmrpt-dev gmp-doc libgmp10-doc libmpfr-doc python-crypto-doc\n", + " python-cryptography-doc python3-cryptography-vectors gnome-keyring\n", + " libkf5wallet-bin gir1.2-gnomekeyring-1.0 python-nose-doc python-numpy-doc\n", + " python3-numpy-dbg python-pil-doc python3-pil-dbg python-scipy-doc\n", + " python-secretstorage-doc python-setuptools-doc\n", + "The following NEW packages will be installed:\n", + " libeigen3-dev libgmp-dev libgmpxx4ldbl libmpfr-dev python-pip-whl\n", + " python3-asn1crypto python3-cffi-backend python3-crypto python3-cryptography\n", + " python3-decorator python3-idna python3-keyring python3-keyrings.alt\n", + " python3-nose python3-numpy python3-olefile python3-pil python3-pip\n", + " python3-pkg-resources python3-scipy python3-secretstorage python3-setuptools\n", + " python3-six python3-wheel python3-xdg\n", + "0 upgraded, 25 newly installed, 0 to remove and 34 not upgraded.\n", + "Need to get 16.3 MB of archives.\n", + "After this operation, 73.2 MB of additional disk space will be used.\n", + "Get:1 http://archive.ubuntu.com/ubuntu bionic/main amd64 libgmpxx4ldbl amd64 2:6.1.2+dfsg-2 [8,964 B]\n", + "Get:2 http://archive.ubuntu.com/ubuntu bionic/main amd64 libgmp-dev amd64 2:6.1.2+dfsg-2 [316 kB]\n", + "Get:3 http://archive.ubuntu.com/ubuntu bionic/main amd64 libmpfr-dev amd64 4.0.1-1 [249 kB]\n", + "Get:4 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 python-pip-whl all 9.0.1-2.3~ubuntu1.18.04.4 [1,653 kB]\n", + "Get:5 http://archive.ubuntu.com/ubuntu bionic/main amd64 python3-asn1crypto all 0.24.0-1 [72.8 kB]\n", + "Get:6 http://archive.ubuntu.com/ubuntu bionic/main amd64 python3-cffi-backend amd64 1.11.5-1 [64.6 kB]\n", + "Get:7 http://archive.ubuntu.com/ubuntu bionic/main amd64 python3-crypto amd64 2.6.1-8ubuntu2 [244 kB]\n", + "Get:8 http://archive.ubuntu.com/ubuntu bionic/main amd64 python3-idna all 2.6-1 [32.5 kB]\n", + "Get:9 http://archive.ubuntu.com/ubuntu bionic/main amd64 python3-six all 1.11.0-2 [11.4 kB]\n", + "Get:10 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 python3-cryptography amd64 2.1.4-1ubuntu1.4 [220 kB]\n", + "Get:11 http://archive.ubuntu.com/ubuntu bionic/universe amd64 python3-decorator all 4.1.2-1 [9,364 B]\n", + "Get:12 http://archive.ubuntu.com/ubuntu bionic/main amd64 python3-secretstorage all 2.3.1-2 [12.1 kB]\n", + "Get:13 http://archive.ubuntu.com/ubuntu bionic/main amd64 python3-keyring all 10.6.0-1 [26.7 kB]\n", + "Get:14 http://archive.ubuntu.com/ubuntu bionic/main amd64 python3-keyrings.alt all 3.0-1 [16.6 kB]\n", + "Get:15 http://archive.ubuntu.com/ubuntu bionic/main amd64 python3-pkg-resources all 39.0.1-2 [98.8 kB]\n", + "Get:16 http://archive.ubuntu.com/ubuntu bionic/universe amd64 python3-nose all 1.3.7-3 [115 kB]\n", + "Get:17 http://archive.ubuntu.com/ubuntu bionic/main amd64 python3-numpy amd64 1:1.13.3-2ubuntu1 [1,943 kB]\n", + "Get:18 http://archive.ubuntu.com/ubuntu bionic/main amd64 python3-olefile all 0.45.1-1 [33.3 kB]\n", + "Ign:19 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 python3-pil amd64 5.1.0-1ubuntu0.5\n", + "Get:20 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 python3-pip all 9.0.1-2.3~ubuntu1.18.04.4 [114 kB]\n", + "Get:21 http://archive.ubuntu.com/ubuntu bionic/main amd64 python3-setuptools all 39.0.1-2 [248 kB]\n", + "Get:22 http://archive.ubuntu.com/ubuntu bionic/universe amd64 python3-wheel all 0.30.0-0.2 [36.5 kB]\n", + "Get:23 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 python3-xdg all 0.25-4ubuntu1.1 [31.3 kB]\n", + "Get:24 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libeigen3-dev all 3.3.4-4 [810 kB]\n", + "Get:25 http://archive.ubuntu.com/ubuntu bionic/universe amd64 python3-scipy amd64 0.19.1-2ubuntu1 [9,619 kB]\n", + "Err:19 http://security.ubuntu.com/ubuntu bionic-updates/main amd64 python3-pil amd64 5.1.0-1ubuntu0.5\n", + " 404 Not Found [IP: 91.189.88.142 80]\n", + "Fetched 16.0 MB in 0s (41.1 MB/s)\n", + "E: Failed to fetch http://security.ubuntu.com/ubuntu/pool/main/p/pillow/python3-pil_5.1.0-1ubuntu0.5_amd64.deb 404 Not Found [IP: 91.189.88.142 80]\n", + "E: Unable to fetch some archives, maybe run apt-get update or try with --fix-missing?\n", + "[Errno 2] No such file or directory: '$PYMESH_PATH/third_party'\n", + "/content/PyMesh\n", + "/bin/bash: ./build.py: No such file or directory\n", + "[Errno 2] No such file or directory: '$PYMESH_PATH'\n", + "/content/PyMesh\n", + "mkdir: cannot create directory ‘build’: File exists\n", + "/bin/bash: pythonsetup.py: command not found\n", + "running install\n", + "running bdist_egg\n", + "running egg_info\n", + "creating python/pymesh2.egg-info\n", + "writing python/pymesh2.egg-info/PKG-INFO\n", + "writing dependency_links to python/pymesh2.egg-info/dependency_links.txt\n", + "writing top-level names to python/pymesh2.egg-info/top_level.txt\n", + "writing manifest file 'python/pymesh2.egg-info/SOURCES.txt'\n", + "package init file 'python/pymesh/tests/__init__.py' not found (or not a regular file)\n", + "package init file 'python/pymesh/meshutils/tests/__init__.py' not found (or not a regular file)\n", + "package init file 'python/pymesh/wires/tests/__init__.py' not found (or not a regular file)\n", + "reading manifest file 'python/pymesh2.egg-info/SOURCES.txt'\n", + "reading manifest template 'MANIFEST.in'\n", + "no previously-included directories found matching 'build'\n", + "no previously-included directories found matching 'third_party/build'\n", + "no previously-included directories found matching 'third_party/libigl/external'\n", + "writing manifest file 'python/pymesh2.egg-info/SOURCES.txt'\n", + "installing library code to build/bdist.linux-x86_64/egg\n", + "running install_lib\n", + "running build_py\n", + "creating build/lib.linux-x86_64-3.6\n", + "creating build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/map_attributes.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/Assembler.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/cell_partition.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/meshio.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/exact_arithmetic.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/igl_utils.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/material.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/selfintersection.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/minkowski_sum.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/timethis.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/Arrangement2.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/CSGTree.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/compression.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/cut_to_disk.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/HashGrid.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/boolean.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/SparseSolver.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/__init__.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/triangulate.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/submesh.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/straight_skeleton.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/Mesh.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/tetrahedralize.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/matrixio.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/slice_mesh.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/tetgen.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/boolean_unsupported.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/Boundary.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/TestCase.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/version.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/HarmonicSolver.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/aabb_tree.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/winding_number.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/PyMeshSetting.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/convex_hull.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/outerhull.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/predicates.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/snap_rounding.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/VoxelGrid.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/triangle.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/save_svg.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "creating build/lib.linux-x86_64-3.6/pymesh/misc\n", + "copying python/pymesh/misc/__init__.py -> build/lib.linux-x86_64-3.6/pymesh/misc\n", + "copying python/pymesh/misc/quaternion.py -> build/lib.linux-x86_64-3.6/pymesh/misc\n", + "creating build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/remove_duplicated_faces.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/cut_mesh.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/merge_meshes.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/face_utils.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/manifold_check.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/split_long_edges.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/hex_to_tet.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/generate_cylinder.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/generate_dodecahedron.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/generate_minimal_surface.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/remove_isolated_vertices.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/collapse_short_edges.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/__init__.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/attribute_utils.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/remove_degenerated_triangles.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/separate_mesh.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/subdivide.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/remove_obtuse_triangles.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/generate_box_mesh.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/mesh_to_graph.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/generate_tube.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/generate_equilateral_triangle.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/quad_to_tri.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/edge_utils.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/generate_regular_tetrahedron.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/remove_duplicated_vertices.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/voxel_utils.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/generate_icosphere.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "creating build/lib.linux-x86_64-3.6/pymesh/wires\n", + "copying python/pymesh/wires/merge_wires.py -> build/lib.linux-x86_64-3.6/pymesh/wires\n", + "copying python/pymesh/wires/WireNetwork.py -> build/lib.linux-x86_64-3.6/pymesh/wires\n", + "copying python/pymesh/wires/Parameters.py -> build/lib.linux-x86_64-3.6/pymesh/wires\n", + "copying python/pymesh/wires/Tiler.py -> build/lib.linux-x86_64-3.6/pymesh/wires\n", + "copying python/pymesh/wires/Inflator.py -> build/lib.linux-x86_64-3.6/pymesh/wires\n", + "copying python/pymesh/wires/wires_io.py -> build/lib.linux-x86_64-3.6/pymesh/wires\n", + "copying python/pymesh/wires/__init__.py -> build/lib.linux-x86_64-3.6/pymesh/wires\n", + "creating build/lib.linux-x86_64-3.6/pymesh/tests\n", + "copying python/pymesh/tests/test_HarmonicSolver.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", + "copying python/pymesh/tests/test_solver.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", + "copying python/pymesh/tests/test_boolean.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", + "copying python/pymesh/tests/test_map_attributes.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", + "copying python/pymesh/tests/test_slice_Mesh.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", + "copying python/pymesh/tests/test_selfintersection.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", + "copying python/pymesh/tests/test_minkowski_sum.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", + "copying python/pymesh/tests/test_assembler.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", + "copying python/pymesh/tests/test_aabb_tree.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", + "copying python/pymesh/tests/test_VoxelGrid.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", + "copying python/pymesh/tests/test_mesh.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", + "copying python/pymesh/tests/test_CSGTree.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", + "copying python/pymesh/tests/test_compression.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", + "copying python/pymesh/tests/test_snap_rounding.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", + "copying python/pymesh/tests/test_winding_number.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", + "copying python/pymesh/tests/test_triangulate.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", + "copying python/pymesh/tests/test_triangle.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", + "copying python/pymesh/tests/test_curvature.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", + "copying python/pymesh/tests/test_cut_to_disk.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", + "copying python/pymesh/tests/test_sparse_solver.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", + "copying python/pymesh/tests/test_material.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", + "copying python/pymesh/tests/test_meshio.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", + "copying python/pymesh/tests/test_predicates.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", + "copying python/pymesh/tests/test_outerhull.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", + "copying python/pymesh/tests/test_tetgen.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", + "creating build/lib.linux-x86_64-3.6/pymesh/meshutils/tests\n", + "copying python/pymesh/meshutils/tests/test_remove_isolated_vertices.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils/tests\n", + "copying python/pymesh/meshutils/tests/test_remove_duplicated_faces.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils/tests\n", + "copying python/pymesh/meshutils/tests/test_quad_to_tri.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils/tests\n", + "copying python/pymesh/meshutils/tests/test_remove_obtuse_triangles.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils/tests\n", + "copying python/pymesh/meshutils/tests/test_hex_to_tet.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils/tests\n", + "copying python/pymesh/meshutils/tests/test_merge_meshes.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils/tests\n", + "copying python/pymesh/meshutils/tests/test_collapse_short_edges.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils/tests\n", + "copying python/pymesh/meshutils/tests/test_separate_mesh.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils/tests\n", + "copying python/pymesh/meshutils/tests/test_attribute_utils.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils/tests\n", + "copying python/pymesh/meshutils/tests/test_remove_degenerated_triangles.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils/tests\n", + "copying python/pymesh/meshutils/tests/test_generate_icosphere.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils/tests\n", + "copying python/pymesh/meshutils/tests/test_split_long_edges.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils/tests\n", + "copying python/pymesh/meshutils/tests/test_remove_duplicated_vertices.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils/tests\n", + "copying python/pymesh/meshutils/tests/test_edge_utils.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils/tests\n", + "copying python/pymesh/meshutils/tests/test_cut_mesh.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils/tests\n", + "copying python/pymesh/meshutils/tests/test_generate_box_mesh.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils/tests\n", + "creating build/lib.linux-x86_64-3.6/pymesh/wires/tests\n", + "copying python/pymesh/wires/tests/test_wire_network.py -> build/lib.linux-x86_64-3.6/pymesh/wires/tests\n", + "copying python/pymesh/wires/tests/test_inflator.py -> build/lib.linux-x86_64-3.6/pymesh/wires/tests\n", + "copying python/pymesh/wires/tests/WireTestCase.py -> build/lib.linux-x86_64-3.6/pymesh/wires/tests\n", + "copying python/pymesh/wires/tests/test_tiler.py -> build/lib.linux-x86_64-3.6/pymesh/wires/tests\n", + "running build_ext\n", + "creating build/bdist.linux-x86_64\n", + "creating build/bdist.linux-x86_64/egg\n", + "creating build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/map_attributes.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "creating build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/remove_duplicated_faces.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/cut_mesh.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/merge_meshes.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/face_utils.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/manifold_check.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/split_long_edges.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/hex_to_tet.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/generate_cylinder.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/generate_dodecahedron.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/generate_minimal_surface.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/remove_isolated_vertices.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/collapse_short_edges.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/__init__.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/attribute_utils.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "creating build/bdist.linux-x86_64/egg/pymesh/meshutils/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/tests/test_remove_isolated_vertices.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/tests/test_remove_duplicated_faces.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/tests/test_quad_to_tri.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/tests/test_remove_obtuse_triangles.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/tests/test_hex_to_tet.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/tests/test_merge_meshes.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/tests/test_collapse_short_edges.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/tests/test_separate_mesh.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/tests/test_attribute_utils.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/tests/test_remove_degenerated_triangles.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/tests/test_generate_icosphere.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/tests/test_split_long_edges.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/tests/test_remove_duplicated_vertices.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/tests/test_edge_utils.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/tests/test_cut_mesh.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/tests/test_generate_box_mesh.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/remove_degenerated_triangles.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/separate_mesh.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/subdivide.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/remove_obtuse_triangles.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/generate_box_mesh.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/mesh_to_graph.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/generate_tube.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/generate_equilateral_triangle.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/quad_to_tri.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/edge_utils.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/generate_regular_tetrahedron.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/remove_duplicated_vertices.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/voxel_utils.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/generate_icosphere.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/Assembler.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/cell_partition.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshio.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/exact_arithmetic.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/igl_utils.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/material.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/selfintersection.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/minkowski_sum.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/timethis.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/Arrangement2.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/CSGTree.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/compression.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/cut_to_disk.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/HashGrid.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/boolean.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/SparseSolver.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "creating build/bdist.linux-x86_64/egg/pymesh/wires\n", + "copying build/lib.linux-x86_64-3.6/pymesh/wires/merge_wires.py -> build/bdist.linux-x86_64/egg/pymesh/wires\n", + "copying build/lib.linux-x86_64-3.6/pymesh/wires/WireNetwork.py -> build/bdist.linux-x86_64/egg/pymesh/wires\n", + "copying build/lib.linux-x86_64-3.6/pymesh/wires/Parameters.py -> build/bdist.linux-x86_64/egg/pymesh/wires\n", + "copying build/lib.linux-x86_64-3.6/pymesh/wires/Tiler.py -> build/bdist.linux-x86_64/egg/pymesh/wires\n", + "copying build/lib.linux-x86_64-3.6/pymesh/wires/Inflator.py -> build/bdist.linux-x86_64/egg/pymesh/wires\n", + "copying build/lib.linux-x86_64-3.6/pymesh/wires/wires_io.py -> build/bdist.linux-x86_64/egg/pymesh/wires\n", + "copying build/lib.linux-x86_64-3.6/pymesh/wires/__init__.py -> build/bdist.linux-x86_64/egg/pymesh/wires\n", + "creating build/bdist.linux-x86_64/egg/pymesh/wires/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/wires/tests/test_wire_network.py -> build/bdist.linux-x86_64/egg/pymesh/wires/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/wires/tests/test_inflator.py -> build/bdist.linux-x86_64/egg/pymesh/wires/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/wires/tests/WireTestCase.py -> build/bdist.linux-x86_64/egg/pymesh/wires/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/wires/tests/test_tiler.py -> build/bdist.linux-x86_64/egg/pymesh/wires/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/__init__.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/triangulate.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "creating build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tests/test_HarmonicSolver.py -> build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tests/test_solver.py -> build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tests/test_boolean.py -> build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tests/test_map_attributes.py -> build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tests/test_slice_Mesh.py -> build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tests/test_selfintersection.py -> build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tests/test_minkowski_sum.py -> build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tests/test_assembler.py -> build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tests/test_aabb_tree.py -> build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tests/test_VoxelGrid.py -> build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tests/test_mesh.py -> build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tests/test_CSGTree.py -> build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tests/test_compression.py -> build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tests/test_snap_rounding.py -> build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tests/test_winding_number.py -> build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tests/test_triangulate.py -> build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tests/test_triangle.py -> build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tests/test_curvature.py -> build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tests/test_cut_to_disk.py -> build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tests/test_sparse_solver.py -> build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tests/test_material.py -> build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tests/test_meshio.py -> build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tests/test_predicates.py -> build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tests/test_outerhull.py -> build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tests/test_tetgen.py -> build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/submesh.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "creating build/bdist.linux-x86_64/egg/pymesh/misc\n", + "copying build/lib.linux-x86_64-3.6/pymesh/misc/__init__.py -> build/bdist.linux-x86_64/egg/pymesh/misc\n", + "copying build/lib.linux-x86_64-3.6/pymesh/misc/quaternion.py -> build/bdist.linux-x86_64/egg/pymesh/misc\n", + "copying build/lib.linux-x86_64-3.6/pymesh/straight_skeleton.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/Mesh.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tetrahedralize.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/matrixio.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/slice_mesh.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tetgen.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/boolean_unsupported.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/Boundary.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/TestCase.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/version.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/HarmonicSolver.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/aabb_tree.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/winding_number.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/PyMeshSetting.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/convex_hull.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/outerhull.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/predicates.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/snap_rounding.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/VoxelGrid.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/triangle.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/save_svg.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/map_attributes.py to map_attributes.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/remove_duplicated_faces.py to remove_duplicated_faces.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/cut_mesh.py to cut_mesh.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/merge_meshes.py to merge_meshes.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/face_utils.py to face_utils.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/manifold_check.py to manifold_check.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/split_long_edges.py to split_long_edges.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/hex_to_tet.py to hex_to_tet.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/generate_cylinder.py to generate_cylinder.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/generate_dodecahedron.py to generate_dodecahedron.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/generate_minimal_surface.py to generate_minimal_surface.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/remove_isolated_vertices.py to remove_isolated_vertices.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/collapse_short_edges.py to collapse_short_edges.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/__init__.py to __init__.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/attribute_utils.py to attribute_utils.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/tests/test_remove_isolated_vertices.py to test_remove_isolated_vertices.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/tests/test_remove_duplicated_faces.py to test_remove_duplicated_faces.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/tests/test_quad_to_tri.py to test_quad_to_tri.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/tests/test_remove_obtuse_triangles.py to test_remove_obtuse_triangles.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/tests/test_hex_to_tet.py to test_hex_to_tet.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/tests/test_merge_meshes.py to test_merge_meshes.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/tests/test_collapse_short_edges.py to test_collapse_short_edges.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/tests/test_separate_mesh.py to test_separate_mesh.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/tests/test_attribute_utils.py to test_attribute_utils.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/tests/test_remove_degenerated_triangles.py to test_remove_degenerated_triangles.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/tests/test_generate_icosphere.py to test_generate_icosphere.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/tests/test_split_long_edges.py to test_split_long_edges.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/tests/test_remove_duplicated_vertices.py to test_remove_duplicated_vertices.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/tests/test_edge_utils.py to test_edge_utils.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/tests/test_cut_mesh.py to test_cut_mesh.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/tests/test_generate_box_mesh.py to test_generate_box_mesh.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/remove_degenerated_triangles.py to remove_degenerated_triangles.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/separate_mesh.py to separate_mesh.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/subdivide.py to subdivide.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/remove_obtuse_triangles.py to remove_obtuse_triangles.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/generate_box_mesh.py to generate_box_mesh.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/mesh_to_graph.py to mesh_to_graph.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/generate_tube.py to generate_tube.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/generate_equilateral_triangle.py to generate_equilateral_triangle.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/quad_to_tri.py to quad_to_tri.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/edge_utils.py to edge_utils.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/generate_regular_tetrahedron.py to generate_regular_tetrahedron.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/remove_duplicated_vertices.py to remove_duplicated_vertices.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/voxel_utils.py to voxel_utils.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/generate_icosphere.py to generate_icosphere.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/Assembler.py to Assembler.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/cell_partition.py to cell_partition.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshio.py to meshio.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/exact_arithmetic.py to exact_arithmetic.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/igl_utils.py to igl_utils.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/material.py to material.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/selfintersection.py to selfintersection.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/minkowski_sum.py to minkowski_sum.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/timethis.py to timethis.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/Arrangement2.py to Arrangement2.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/CSGTree.py to CSGTree.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/compression.py to compression.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/cut_to_disk.py to cut_to_disk.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/HashGrid.py to HashGrid.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/boolean.py to boolean.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/SparseSolver.py to SparseSolver.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/wires/merge_wires.py to merge_wires.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/wires/WireNetwork.py to WireNetwork.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/wires/Parameters.py to Parameters.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/wires/Tiler.py to Tiler.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/wires/Inflator.py to Inflator.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/wires/wires_io.py to wires_io.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/wires/__init__.py to __init__.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/wires/tests/test_wire_network.py to test_wire_network.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/wires/tests/test_inflator.py to test_inflator.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/wires/tests/WireTestCase.py to WireTestCase.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/wires/tests/test_tiler.py to test_tiler.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/__init__.py to __init__.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/triangulate.py to triangulate.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_HarmonicSolver.py to test_HarmonicSolver.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_solver.py to test_solver.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_boolean.py to test_boolean.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_map_attributes.py to test_map_attributes.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_slice_Mesh.py to test_slice_Mesh.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_selfintersection.py to test_selfintersection.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_minkowski_sum.py to test_minkowski_sum.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_assembler.py to test_assembler.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_aabb_tree.py to test_aabb_tree.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_VoxelGrid.py to test_VoxelGrid.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_mesh.py to test_mesh.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_CSGTree.py to test_CSGTree.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_compression.py to test_compression.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_snap_rounding.py to test_snap_rounding.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_winding_number.py to test_winding_number.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_triangulate.py to test_triangulate.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_triangle.py to test_triangle.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_curvature.py to test_curvature.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_cut_to_disk.py to test_cut_to_disk.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_sparse_solver.py to test_sparse_solver.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_material.py to test_material.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_meshio.py to test_meshio.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_predicates.py to test_predicates.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_outerhull.py to test_outerhull.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_tetgen.py to test_tetgen.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/submesh.py to submesh.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/misc/__init__.py to __init__.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/misc/quaternion.py to quaternion.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/straight_skeleton.py to straight_skeleton.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/Mesh.py to Mesh.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tetrahedralize.py to tetrahedralize.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/matrixio.py to matrixio.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/slice_mesh.py to slice_mesh.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tetgen.py to tetgen.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/boolean_unsupported.py to boolean_unsupported.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/Boundary.py to Boundary.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/TestCase.py to TestCase.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/version.py to version.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/HarmonicSolver.py to HarmonicSolver.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/aabb_tree.py to aabb_tree.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/winding_number.py to winding_number.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/PyMeshSetting.py to PyMeshSetting.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/convex_hull.py to convex_hull.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/outerhull.py to outerhull.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/predicates.py to predicates.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/snap_rounding.py to snap_rounding.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/VoxelGrid.py to VoxelGrid.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/triangle.py to triangle.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/save_svg.py to save_svg.cpython-36.pyc\n", + "creating build/bdist.linux-x86_64/egg/EGG-INFO\n", + "installing scripts to build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "running install_scripts\n", + "running build_scripts\n", + "creating build/scripts-3.6\n", + "copying and adjusting scripts/add_element_attribute.py -> build/scripts-3.6\n", + "copying and adjusting scripts/add_index.py -> build/scripts-3.6\n", + "copying and adjusting scripts/arrangement_2d.py -> build/scripts-3.6\n", + "copying and adjusting scripts/bbox.py -> build/scripts-3.6\n", + "copying and adjusting scripts/box_gen.py -> build/scripts-3.6\n", + "copying and adjusting scripts/boolean.py -> build/scripts-3.6\n", + "copying and adjusting scripts/carve.py -> build/scripts-3.6\n", + "copying and adjusting scripts/convex_hull.py -> build/scripts-3.6\n", + "copying and adjusting scripts/curvature.py -> build/scripts-3.6\n", + "copying and adjusting scripts/distortion.py -> build/scripts-3.6\n", + "copying and adjusting scripts/dodecahedron_gen.py -> build/scripts-3.6\n", + "copying and adjusting scripts/extract_self_intersecting_faces.py -> build/scripts-3.6\n", + "copying and adjusting scripts/fem_check.py -> build/scripts-3.6\n", + "copying scripts/find_file.py -> build/scripts-3.6\n", + "copying and adjusting scripts/fix_mesh.py -> build/scripts-3.6\n", + "copying and adjusting scripts/geodesic.py -> build/scripts-3.6\n", + "copying and adjusting scripts/highlight_boundary_edges.py -> build/scripts-3.6\n", + "copying and adjusting scripts/highlight_degenerated_faces.py -> build/scripts-3.6\n", + "copying and adjusting scripts/highlight_non_oriented_edges.py -> build/scripts-3.6\n", + "copying and adjusting scripts/highlight_self_intersection.py -> build/scripts-3.6\n", + "copying and adjusting scripts/highlight_zero_area_faces.py -> build/scripts-3.6\n", + "copying and adjusting scripts/highlight_inverted_tets.py -> build/scripts-3.6\n", + "copying and adjusting scripts/highlight_delaunay.py -> build/scripts-3.6\n", + "copying and adjusting scripts/hilbert_curve_gen.py -> build/scripts-3.6\n", + "copying and adjusting scripts/icosphere_gen.py -> build/scripts-3.6\n", + "copying and adjusting scripts/inflate.py -> build/scripts-3.6\n", + "copying and adjusting scripts/map_to_sphere.py -> build/scripts-3.6\n", + "copying and adjusting scripts/matrix_gen.py -> build/scripts-3.6\n", + "copying and adjusting scripts/mean_curvature_flow.py -> build/scripts-3.6\n", + "copying and adjusting scripts/merge.py -> build/scripts-3.6\n", + "copying and adjusting scripts/mesh_diff.py -> build/scripts-3.6\n", + "copying and adjusting scripts/meshconvert.py -> build/scripts-3.6\n", + "copying and adjusting scripts/meshstat.py -> build/scripts-3.6\n", + "copying and adjusting scripts/mesh_to_wire.py -> build/scripts-3.6\n", + "copying and adjusting scripts/microstructure_gen.py -> build/scripts-3.6\n", + "copying and adjusting scripts/minkowski_sum.py -> build/scripts-3.6\n", + "copying and adjusting scripts/outer_hull.py -> build/scripts-3.6\n", + "copying and adjusting scripts/point_cloud.py -> build/scripts-3.6\n", + "copying scripts/print_utils.py -> build/scripts-3.6\n", + "copying and adjusting scripts/quad_to_tri.py -> build/scripts-3.6\n", + "copying and adjusting scripts/refine_mesh.py -> build/scripts-3.6\n", + "copying and adjusting scripts/remove_degenerated_triangles.py -> build/scripts-3.6\n", + "copying and adjusting scripts/remove_duplicated_faces.py -> build/scripts-3.6\n", + "copying and adjusting scripts/remove_isolated_vertices.py -> build/scripts-3.6\n", + "copying and adjusting scripts/remove_nan.py -> build/scripts-3.6\n", + "copying and adjusting scripts/resolve_self_intersection.py -> build/scripts-3.6\n", + "copying and adjusting scripts/retriangulate.py -> build/scripts-3.6\n", + "copying and adjusting scripts/rigid_transform.py -> build/scripts-3.6\n", + "copying and adjusting scripts/scale_mesh.py -> build/scripts-3.6\n", + "copying and adjusting scripts/self_union.py -> build/scripts-3.6\n", + "copying and adjusting scripts/separate.py -> build/scripts-3.6\n", + "copying and adjusting scripts/slice_mesh.py -> build/scripts-3.6\n", + "copying and adjusting scripts/subdivide.py -> build/scripts-3.6\n", + "copying and adjusting scripts/submesh.py -> build/scripts-3.6\n", + "copying and adjusting scripts/svg_to_mesh.py -> build/scripts-3.6\n", + "copying and adjusting scripts/tet.py -> build/scripts-3.6\n", + "copying and adjusting scripts/tet_boundary.py -> build/scripts-3.6\n", + "copying and adjusting scripts/tet_to_hex.py -> build/scripts-3.6\n", + "copying and adjusting scripts/triangulate.py -> build/scripts-3.6\n", + "copying and adjusting scripts/uv.py -> build/scripts-3.6\n", + "copying and adjusting scripts/voxelize.py -> build/scripts-3.6\n", + "changing mode of build/scripts-3.6/add_element_attribute.py from 644 to 755\n", + "changing mode of build/scripts-3.6/add_index.py from 644 to 755\n", + "changing mode of build/scripts-3.6/arrangement_2d.py from 644 to 755\n", + "changing mode of build/scripts-3.6/bbox.py from 644 to 755\n", + "changing mode of build/scripts-3.6/box_gen.py from 644 to 755\n", + "changing mode of build/scripts-3.6/boolean.py from 644 to 755\n", + "changing mode of build/scripts-3.6/carve.py from 644 to 755\n", + "changing mode of build/scripts-3.6/convex_hull.py from 644 to 755\n", + "changing mode of build/scripts-3.6/curvature.py from 644 to 755\n", + "changing mode of build/scripts-3.6/distortion.py from 644 to 755\n", + "changing mode of build/scripts-3.6/dodecahedron_gen.py from 644 to 755\n", + "changing mode of build/scripts-3.6/extract_self_intersecting_faces.py from 644 to 755\n", + "changing mode of build/scripts-3.6/fem_check.py from 644 to 755\n", + "changing mode of build/scripts-3.6/find_file.py from 644 to 755\n", + "changing mode of build/scripts-3.6/fix_mesh.py from 644 to 755\n", + "changing mode of build/scripts-3.6/geodesic.py from 644 to 755\n", + "changing mode of build/scripts-3.6/highlight_boundary_edges.py from 644 to 755\n", + "changing mode of build/scripts-3.6/highlight_degenerated_faces.py from 644 to 755\n", + "changing mode of build/scripts-3.6/highlight_non_oriented_edges.py from 644 to 755\n", + "changing mode of build/scripts-3.6/highlight_self_intersection.py from 644 to 755\n", + "changing mode of build/scripts-3.6/highlight_zero_area_faces.py from 644 to 755\n", + "changing mode of build/scripts-3.6/highlight_inverted_tets.py from 644 to 755\n", + "changing mode of build/scripts-3.6/highlight_delaunay.py from 644 to 755\n", + "changing mode of build/scripts-3.6/hilbert_curve_gen.py from 644 to 755\n", + "changing mode of build/scripts-3.6/icosphere_gen.py from 644 to 755\n", + "changing mode of build/scripts-3.6/inflate.py from 644 to 755\n", + "changing mode of build/scripts-3.6/map_to_sphere.py from 644 to 755\n", + "changing mode of build/scripts-3.6/matrix_gen.py from 644 to 755\n", + "changing mode of build/scripts-3.6/mean_curvature_flow.py from 644 to 755\n", + "changing mode of build/scripts-3.6/merge.py from 644 to 755\n", + "changing mode of build/scripts-3.6/mesh_diff.py from 644 to 755\n", + "changing mode of build/scripts-3.6/meshconvert.py from 644 to 755\n", + "changing mode of build/scripts-3.6/meshstat.py from 644 to 755\n", + "changing mode of build/scripts-3.6/mesh_to_wire.py from 644 to 755\n", + "changing mode of build/scripts-3.6/microstructure_gen.py from 644 to 755\n", + "changing mode of build/scripts-3.6/minkowski_sum.py from 644 to 755\n", + "changing mode of build/scripts-3.6/outer_hull.py from 644 to 755\n", + "changing mode of build/scripts-3.6/point_cloud.py from 644 to 755\n", + "changing mode of build/scripts-3.6/print_utils.py from 644 to 755\n", + "changing mode of build/scripts-3.6/quad_to_tri.py from 644 to 755\n", + "changing mode of build/scripts-3.6/refine_mesh.py from 644 to 755\n", + "changing mode of build/scripts-3.6/remove_degenerated_triangles.py from 644 to 755\n", + "changing mode of build/scripts-3.6/remove_duplicated_faces.py from 644 to 755\n", + "changing mode of build/scripts-3.6/remove_isolated_vertices.py from 644 to 755\n", + "changing mode of build/scripts-3.6/remove_nan.py from 644 to 755\n", + "changing mode of build/scripts-3.6/resolve_self_intersection.py from 644 to 755\n", + "changing mode of build/scripts-3.6/retriangulate.py from 644 to 755\n", + "changing mode of build/scripts-3.6/rigid_transform.py from 644 to 755\n", + "changing mode of build/scripts-3.6/scale_mesh.py from 644 to 755\n", + "changing mode of build/scripts-3.6/self_union.py from 644 to 755\n", + "changing mode of build/scripts-3.6/separate.py from 644 to 755\n", + "changing mode of build/scripts-3.6/slice_mesh.py from 644 to 755\n", + "changing mode of build/scripts-3.6/subdivide.py from 644 to 755\n", + "changing mode of build/scripts-3.6/submesh.py from 644 to 755\n", + "changing mode of build/scripts-3.6/svg_to_mesh.py from 644 to 755\n", + "changing mode of build/scripts-3.6/tet.py from 644 to 755\n", + "changing mode of build/scripts-3.6/tet_boundary.py from 644 to 755\n", + "changing mode of build/scripts-3.6/tet_to_hex.py from 644 to 755\n", + "changing mode of build/scripts-3.6/triangulate.py from 644 to 755\n", + "changing mode of build/scripts-3.6/uv.py from 644 to 755\n", + "changing mode of build/scripts-3.6/voxelize.py from 644 to 755\n", + "creating build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/remove_duplicated_faces.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/uv.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/dodecahedron_gen.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/point_cloud.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/svg_to_mesh.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/meshconvert.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/retriangulate.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/highlight_self_intersection.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/highlight_boundary_edges.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/minkowski_sum.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/geodesic.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/bbox.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/fix_mesh.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/extract_self_intersecting_faces.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/highlight_delaunay.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/mean_curvature_flow.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/scale_mesh.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/remove_nan.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/tet_boundary.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/matrix_gen.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/map_to_sphere.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/mesh_to_wire.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/box_gen.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/tet.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/add_index.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/remove_isolated_vertices.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/outer_hull.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/refine_mesh.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/inflate.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/boolean.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/fem_check.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/highlight_zero_area_faces.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/triangulate.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/remove_degenerated_triangles.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/merge.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/self_union.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/submesh.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/subdivide.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/meshstat.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/tet_to_hex.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/add_element_attribute.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/slice_mesh.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/icosphere_gen.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/print_utils.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/curvature.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/voxelize.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/rigid_transform.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/separate.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/arrangement_2d.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/mesh_diff.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/microstructure_gen.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/distortion.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/convex_hull.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/find_file.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/highlight_non_oriented_edges.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/carve.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/highlight_inverted_tets.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/hilbert_curve_gen.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/quad_to_tri.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/resolve_self_intersection.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/highlight_degenerated_faces.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/remove_duplicated_faces.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/uv.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/dodecahedron_gen.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/point_cloud.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/svg_to_mesh.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/meshconvert.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/retriangulate.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/highlight_self_intersection.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/highlight_boundary_edges.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/minkowski_sum.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/geodesic.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/bbox.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/fix_mesh.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/extract_self_intersecting_faces.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/highlight_delaunay.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/mean_curvature_flow.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/scale_mesh.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/remove_nan.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/tet_boundary.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/matrix_gen.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/map_to_sphere.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/mesh_to_wire.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/box_gen.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/tet.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/add_index.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/remove_isolated_vertices.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/outer_hull.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/refine_mesh.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/inflate.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/boolean.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/fem_check.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/highlight_zero_area_faces.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/triangulate.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/remove_degenerated_triangles.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/merge.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/self_union.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/submesh.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/subdivide.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/meshstat.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/tet_to_hex.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/add_element_attribute.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/slice_mesh.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/icosphere_gen.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/print_utils.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/curvature.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/voxelize.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/rigid_transform.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/separate.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/arrangement_2d.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/mesh_diff.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/microstructure_gen.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/distortion.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/convex_hull.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/find_file.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/highlight_non_oriented_edges.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/carve.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/highlight_inverted_tets.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/hilbert_curve_gen.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/quad_to_tri.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/resolve_self_intersection.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/highlight_degenerated_faces.py to 755\n", + "copying python/pymesh2.egg-info/PKG-INFO -> build/bdist.linux-x86_64/egg/EGG-INFO\n", + "copying python/pymesh2.egg-info/SOURCES.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n", + "copying python/pymesh2.egg-info/dependency_links.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n", + "copying python/pymesh2.egg-info/not-zip-safe -> build/bdist.linux-x86_64/egg/EGG-INFO\n", + "copying python/pymesh2.egg-info/top_level.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n", + "creating dist\n", + "creating 'dist/pymesh2-0.3-py3.6-linux-x86_64.egg' and adding 'build/bdist.linux-x86_64/egg' to it\n", + "removing 'build/bdist.linux-x86_64/egg' (and everything under it)\n", + "Processing pymesh2-0.3-py3.6-linux-x86_64.egg\n", + "creating /usr/local/lib/python3.6/site-packages/pymesh2-0.3-py3.6-linux-x86_64.egg\n", + "Extracting pymesh2-0.3-py3.6-linux-x86_64.egg to /usr/local/lib/python3.6/site-packages\n", + "Adding pymesh2 0.3 to easy-install.pth file\n", + "Installing remove_duplicated_faces.py script to /usr/local/bin\n", + "Installing uv.py script to /usr/local/bin\n", + "Installing dodecahedron_gen.py script to /usr/local/bin\n", + "Installing point_cloud.py script to /usr/local/bin\n", + "Installing svg_to_mesh.py script to /usr/local/bin\n", + "Installing meshconvert.py script to /usr/local/bin\n", + "Installing retriangulate.py script to /usr/local/bin\n", + "Installing highlight_self_intersection.py script to /usr/local/bin\n", + "Installing highlight_boundary_edges.py script to /usr/local/bin\n", + "Installing minkowski_sum.py script to /usr/local/bin\n", + "Installing geodesic.py script to /usr/local/bin\n", + "Installing bbox.py script to /usr/local/bin\n", + "Installing fix_mesh.py script to /usr/local/bin\n", + "Installing extract_self_intersecting_faces.py script to /usr/local/bin\n", + "Installing highlight_delaunay.py script to /usr/local/bin\n", + "Installing mean_curvature_flow.py script to /usr/local/bin\n", + "Installing scale_mesh.py script to /usr/local/bin\n", + "Installing remove_nan.py script to /usr/local/bin\n", + "Installing tet_boundary.py script to /usr/local/bin\n", + "Installing matrix_gen.py script to /usr/local/bin\n", + "Installing map_to_sphere.py script to /usr/local/bin\n", + "Installing mesh_to_wire.py script to /usr/local/bin\n", + "Installing box_gen.py script to /usr/local/bin\n", + "Installing tet.py script to /usr/local/bin\n", + "Installing add_index.py script to /usr/local/bin\n", + "Installing remove_isolated_vertices.py script to /usr/local/bin\n", + "Installing outer_hull.py script to /usr/local/bin\n", + "Installing refine_mesh.py script to /usr/local/bin\n", + "Installing inflate.py script to /usr/local/bin\n", + "Installing boolean.py script to /usr/local/bin\n", + "Installing fem_check.py script to /usr/local/bin\n", + "Installing highlight_zero_area_faces.py script to /usr/local/bin\n", + "Installing triangulate.py script to /usr/local/bin\n", + "Installing remove_degenerated_triangles.py script to /usr/local/bin\n", + "Installing merge.py script to /usr/local/bin\n", + "Installing self_union.py script to /usr/local/bin\n", + "Installing submesh.py script to /usr/local/bin\n", + "Installing subdivide.py script to /usr/local/bin\n", + "Installing meshstat.py script to /usr/local/bin\n", + "Installing tet_to_hex.py script to /usr/local/bin\n", + "Installing add_element_attribute.py script to /usr/local/bin\n", + "Installing slice_mesh.py script to /usr/local/bin\n", + "Installing icosphere_gen.py script to /usr/local/bin\n", + "Installing print_utils.py script to /usr/local/bin\n", + "Installing curvature.py script to /usr/local/bin\n", + "Installing voxelize.py script to /usr/local/bin\n", + "Installing rigid_transform.py script to /usr/local/bin\n", + "Installing separate.py script to /usr/local/bin\n", + "Installing arrangement_2d.py script to /usr/local/bin\n", + "Installing mesh_diff.py script to /usr/local/bin\n", + "Installing microstructure_gen.py script to /usr/local/bin\n", + "Installing distortion.py script to /usr/local/bin\n", + "Installing convex_hull.py script to /usr/local/bin\n", + "Installing find_file.py script to /usr/local/bin\n", + "Installing highlight_non_oriented_edges.py script to /usr/local/bin\n", + "Installing carve.py script to /usr/local/bin\n", + "Installing highlight_inverted_tets.py script to /usr/local/bin\n", + "Installing hilbert_curve_gen.py script to /usr/local/bin\n", + "Installing quad_to_tri.py script to /usr/local/bin\n", + "Installing resolve_self_intersection.py script to /usr/local/bin\n", + "Installing highlight_degenerated_faces.py script to /usr/local/bin\n", + "\n", + "Installed /usr/local/lib/python3.6/site-packages/pymesh2-0.3-py3.6-linux-x86_64.egg\n", + "Processing dependencies for pymesh2==0.3\n", + "Finished processing dependencies for pymesh2==0.3\n", + "/content\n", + "Requirement already satisfied: 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collected packages: pymeshlab\n", + "Successfully installed pymeshlab-0.2\n", + "/content/3dsnet\n", + "Collecting pymesh2==0.2.1\n", + " Downloading https://github.com/PyMesh/PyMesh/releases/download/v0.2.1/pymesh2-0.2.1-cp36-cp36m-linux_x86_64.whl (56.4 MB)\n", + "\u001b[K |████████████████████████████████| 56.4 MB 58 kB/s \n", + "\u001b[?25hCollecting progress\n", + " Downloading progress-1.5.tar.gz (5.8 kB)\n", + "Collecting chumpy\n", + " Downloading chumpy-0.70.tar.gz (50 kB)\n", + "\u001b[K |████████████████████████████████| 50 kB 5.4 MB/s \n", + "\u001b[?25hCollecting numpy~=1.17.2\n", + " Downloading numpy-1.17.5-cp36-cp36m-manylinux1_x86_64.whl (20.0 MB)\n", + "\u001b[K |████████████████████████████████| 20.0 MB 1.4 MB/s \n", + "\u001b[?25hCollecting Pillow~=5.1.0\n", + " Downloading Pillow-5.1.0-cp36-cp36m-manylinux1_x86_64.whl (2.0 MB)\n", + "\u001b[K |████████████████████████████████| 2.0 MB 52.1 MB/s \n", + "\u001b[?25hCollecting easydict~=1.9\n", + " Downloading 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/root/.cache/pip/wheels/e5/d6/71/e87d26b0205f2c12e55a1a554214668ee324a962bad857c56a\n", + "Successfully built easydict chumpy progress\n", + "Installing collected packages: numpy, jsonpointer, Pillow, jsonpatch, pymesh2, progress, matplotlib, joblib, easydict, chumpy\n", + "\u001b[33m WARNING: The scripts f2py, f2py3 and f2py3.6 are installed in '/root/.local/bin' which is not on PATH.\n", + " Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\n", + "Successfully installed Pillow-5.1.0 chumpy-0.70 easydict-1.9 joblib-1.0.1 jsonpatch-1.32 jsonpointer-2.1 matplotlib-3.2.2 numpy-1.17.5 progress-1.5 pymesh2-0.2.1\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.colab-display-data+json": { + "pip_warning": { + "packages": [ + "matplotlib", + "mpl_toolkits" + ] + } + } + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "7cEwnsEfFgGI" + }, + "source": [ + "## Since PyMesh and pytorch3d both are having installations issues that I cant figure out....\n", + "\n", + "\n", + "# DO THIS ON THE FILES OR YOU WILL HAVE IMPORT ERRORS\n", + "\n", + "\n", + "# trainer.py from PyMesh.python.pymesh.meshio import form_mesh\n", + "# mesh_processor.py from PyMesh.python.pymesh.meshio import save_mesh\n", + "# template.py remove pymesh import\n", + "\n", + "# Removed the pytorch3d imports from train_loss.py, model.py due to improper installation issue from pytorch3d repo" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "b1nj8W0oY24U" + }, + "source": [ + "Need to link pre-trained models to your google drive account from [HERE](https://drive.google.com/drive/folders/1cyVRUmtN_YF-TXkytKfn1M0HlGH9Qux_?usp=sharing) then link your drive to colab. Or download the pretrained models then place in /content/3dsnet/\n", + "\n", + "Since the encoders are trained on each family objects individually, right now the only pre-trained models are for chairs and planes, although the training for other classes can be performed." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "nMfmXLOYfNRF", + "outputId": "2f86ad83-0b4e-4efc-8ec0-57e4dfbb3e14" + }, + "source": [ + "from google.colab import drive\n", + "drive.mount('/gdrive')\n", + "%cd /gdrive\n", + "# %cp -r /gdrive/MyDrive/Colab\\ Notebooks/3dsnet_models /content/3dsnet\n", + "# %cp -r /gdrive/MyDrive/Colab\\ Notebooks/3dsnet_models/aux_models/ /content/3dsnet" + ], + "execution_count": 35, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Drive already mounted at /gdrive; to attempt to forcibly remount, call drive.mount(\"/gdrive\", force_remount=True).\n", + "/gdrive\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "c9jnYHect6n8" + }, + "source": [ + "#@title Helper Functions {display-mode: \"form\"}\n", + "\n", + "# This code will be hidden when the notebook is loaded.\n", + "\n", + "def load_mesh_obj(path):\n", + " mesh = trimesh.load_mesh(path)\n", + " if isinstance(mesh, trimesh.Scene):\n", + " mesh = mesh.dump()[0]\n", + " return mesh\n", + "\n", + "def plot_meshes(mesh_list,\n", + " fig_size=8,\n", + " el=45,\n", + " rot_start=90,\n", + " vert_size=10,\n", + " vert_alpha=0.25,\n", + " n_cols=4):\n", + " \"\"\"Plots mesh data using matplotlib.\"\"\"\n", + "\n", + " n_plot = len(mesh_list)\n", + " n_cols = np.minimum(n_plot, n_cols)\n", + " n_rows = np.ceil(n_plot / n_cols).astype('int')\n", + " fig = plt.figure(figsize=(fig_size * n_cols, fig_size * n_rows))\n", + " for p_inc, mesh in enumerate(mesh_list):\n", + "\n", + " ax = fig.add_subplot(n_rows, n_cols, p_inc + 1, projection='3d')\n", + "\n", + " if 'faces' in mesh:\n", + " face_verts = mesh['vertices']\n", + " collection = []\n", + " for f in mesh['faces']:\n", + " collection.append(face_verts[f])\n", + " plt_mesh = Poly3DCollection(collection)\n", + " plt_mesh.set_edgecolor((0., 0., 0., 0.3))\n", + " plt_mesh.set_facecolor((1, 0, 0, 0.2))\n", + " ax.add_collection3d(plt_mesh)\n", + "\n", + " if mesh['vertices'] is not None:\n", + " ax.scatter3D(\n", + " mesh['vertices'][:, 0],\n", + " mesh['vertices'][:, 1],\n", + " mesh['vertices'][:, 2],\n", + " lw=0.,\n", + " s=vert_size,\n", + " c='g',\n", + " alpha=vert_alpha)\n", + "\n", + " # if mesh['pointcloud'] is not None:\n", + " # ax.scatter3D(\n", + " # mesh['pointcloud'][:, 0],\n", + " # mesh['pointcloud'][:, 1],\n", + " # mesh['pointcloud'][:, 2],\n", + " # lw=0.,\n", + " # s=2.5 * vert_size,\n", + " # c='b',\n", + " # alpha=1.)\n", + " \n", + " ax.view_init(el, rot_start)\n", + "\n", + " display_string = ''\n", + " if mesh['faces'] is not None:\n", + " display_string += 'Num. faces: {}\\n'.format(len(collection))\n", + " if mesh['vertices'] is not None:\n", + " num_verts = mesh['vertices'].shape[0]\n", + " # if mesh['vertices_conditional'] is not None:\n", + " # num_verts += mesh['vertices_conditional'].shape[0]\n", + " display_string += 'Num. verts: {}\\n'.format(num_verts)\n", + " # if mesh['class_name'] is not None:\n", + " # display_string += 'Synset: {}'.format(mesh['class_name'])\n", + " # if mesh['pointcloud'] is not None:\n", + " # display_string += 'Num. pointcloud: {}\\n'.format(\n", + " # mesh['pointcloud'].shape[0])\n", + " ax.text2D(0.05, 0.8, display_string, transform=ax.transAxes)\n", + " plt.subplots_adjust(\n", + " left=0., right=1., bottom=0., top=1., wspace=0.025, hspace=0.025)\n", + " plt.show()\n", + "\n", + "def load_data_from_file(path):\n", + " ext = path.split('.')[-1]\n", + " if ext == 'npy':\n", + " points = np.load(path)\n", + " elif ext == 'ply' or ext == 'obj':\n", + " points = trimesh.load_mesh(path)\n", + " if isinstance(points, trimesh.Scene):\n", + " points = points.dump()[0].vertices\n", + " else:\n", + " points = points.vertices\n", + " else:\n", + " print('invalid file extension')\n", + " raise IOError\n", + " points = torch.from_numpy(points.copy()).float()\n", + " operation = pointcloud_processor.Normalization(points, keep_track=True)\n", + " if opt.normalization == 'UnitBall':\n", + " operation.normalize_unitL2ball()\n", + " elif opt.normalization == 'BoundingBox':\n", + " operation.normalize_bounding_box()\n", + " else:\n", + " pass\n", + " return_dict = {\n", + " 'points': points,\n", + " 'operation': operation,\n", + " 'path': path,\n", + " }\n", + " return return_dict\n", + "\n", + "def unnormalize(mesh, operation=None):\n", + " if operation is not None:\n", + " # Undo any normalization that was used to preprocess the input.\n", + " vertices = torch.from_numpy(mesh.vertices).clone().unsqueeze(0)\n", + " norm_mesh = deepcopy(mesh)\n", + " norm_mesh.vertices = operation.apply(vertices).squeeze().numpy()\n", + " norm_mesh._data.__dict__['data']['vertices'] = norm_mesh.vertices\n", + " if np.sum(norm_mesh.vertices - mesh.vertices) == 0:\n", + " print(\"fucked normalization\")\n", + " return norm_mesh" + ], + "execution_count": 33, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "RsZseUSK_bc6", + "cellView": "form" + }, + "source": [ + "#@title HyperParameters\n", + "#@markdown Options to change in the model\n", + "\n", + "family = \"chair\" #@param [\"chair\", \"bananas\", \"oranges\"] {allow-input: true}\n", + "decoder = \"meshflow\" #@param [\"meshflow\", \"atlasnet\"] {allow-input: true}\n", + "noise_level = 1 #@param {type:\"number\"}\n", + "log_dir = \"log/\" #@param {type:\"string\"}\n", + "data_dir = \"/content/3dsnet/docs/points/\" #@param {type:\"string\"}\n", + "reload_model_path = '/content/3dsnet/3dsnet_models/3dsnet_models/chairs/meshflow/3dsnet/network.pth' #@param {type:\"string\"}\n", + "#@markdown ---\n" + ], + "execution_count": 8, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "H_l1hFOoMG0T" + }, + "source": [ + "#@title Options for trainer {display-mode: \"form\"}\n", + "\n", + "# This code will be hidden when the notebook is loaded.\n", + "\n", + "from easydict import EasyDict as edict\n", + "\n", + "opt = edict({ \"decoder_type\" : decoder,\n", + " 'demo': False,\n", + " \"SVR_0\": False,\n", + " \"SVR_1\" : False,\n", + " \"family\": family,\n", + " \"data_dir\" : data_dir,\n", + " \"dir_name\" : log_dir,\n", + " \"dataset\" :\"ShapeNet\",\n", + " \"weight_perceptual\": 1,\n", + " \"reload_model_path\" : reload_model_path, \n", + " \"batch_size\" : 4, \n", + " \"batch_size_test\" : 4,\n", + " \"class_choice\" : [\"armchair\",\"straight chair,side chair\"], \n", + " \"generator_norm\" : \"bn\",\n", + " \"discriminator_norm\" : \"bn\",\n", + " \"discriminator_activation\" : \"relu\",\n", + " \"dis_bottleneck_size\" : 1024,\n", + " \"style_bottleneck_size\" : 512,\n", + " \"generator_lrate\" : 0.001,\n", + " \"discriminator_lrate\" : 0.004,\n", + " \"batch_size\" : 16, \n", + " \"generator_update_skips\" : 1, \n", + " \"discriminator_update_skips\" : 1, \n", + " \"num_layers\" : 2,\n", + " \"num_layers_style\" : 1,\n", + " \"nb_primitives\" : 25, \n", + " \"template_type\" : \"SQUARE\",\n", + " \"weight_chamfer\" : 10,\n", + " \"weight_cycle_chamfer\" : 0,\n", + " \"weight_adversarial\" : 1,\n", + " \"weight_content_reconstruction\" : 1,\n", + " \"weight_style_reconstruction\" : 1,\n", + " \"lr_decay_1\" : 120,\n", + " \"lr_decay_2\" : 140, \n", + " \"lr_decay_3\" : 145, \n", + " \"decode_style\":True, \n", + " \"share_decoder\":True,\n", + " \"share_content_encoder\":True, \n", + " \"share_discriminator_encoder\":True,\n", + " \"gan_type\" : \"lsgan\",\n", + " \"use_visdom\":False,\n", + " \"start_epoch\":0,\n", + " \"adaptive\":True,\n", + " \"noise_magnitude\" : 1.0,\n", + " \"num_interpolations\" : 0,\n", + " \"normalization\":\"UnitBall\",\n", + " \"number_points\":2500,\n", + " \"multi_gpu\":[0],\n", + " \"use_default_demo_samples\":True,\n", + " \"multiscale_loss\":False,\n", + " \"bottleneck_size\":1024,\n", + " \"number_points_eval\":2500,\n", + " \"w_multiscale_1\":.1,\n", + " \"w_multiscale_2\":.2,\n", + " \"w_multiscale_3\":.7,\n", + " \"remove_all_batchNorms\": False,\n", + " \"dim_template\":2,\n", + " \"hidden_neurons\":512,\n", + " \"activation\": \"relu\",\n", + " \"share_style_encoder\": False,\n", + " \"num_layers_mlp\": 3,\n", + " \"no_learning\":True,\n", + " \"reload_decoder_path\" : '',\n", + " \"reload_pointnet_path\":'',\n", + " \"demo_input_dir\": \"./docs/points/\",\n", + " \"num_demo_pairs\":5,\n", + " \"share_style_mlp\": True})" + ], + "execution_count": 24, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "b_58lYhRFeO8", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "9c052a7b-caad-4f7a-f06a-b342cc60a57f" + }, + "source": [ + "import sys\n", + "import torch\n", + "import training.trainer as trainer\n", + "import auxiliary.my_utils as my_utils\n", + "import numpy as np\n", + "import os\n", + "from easydict import EasyDict\n", + "from PyMesh.python.pymesh.meshio import form_mesh\n", + "from dataset.dataset_shapenet import ShapeNet\n", + "import dataset.pointcloud_processor as pointcloud_processor\n", + "import torch\n", + "import matplotlib.pyplot as plt\n", + "from mpl_toolkits.mplot3d.art3d import Poly3DCollection\n", + "import trimesh\n", + "from copy import deepcopy\n", + "\n", + "path_a = \"./docs/points/armchair.points.ply.npy\"\n", + "path_b = \"./docs/points/straight chair,side chair.points.ply.npy\"\n", + "\n", + "torch.cuda.set_device(opt.multi_gpu[0])\n", + "\n", + "trainer = trainer.Trainer(opt)\n", + "trainer.build_network()\n", + "trainer.reload_best_network()\n", + "trainer.demo_pair_path = \"\"\n", + "\n", + "with torch.no_grad():\n", + " data_a = EasyDict(load_data_from_file(path_a))\n", + " data_b = EasyDict(load_data_from_file(path_b))\n", + " \n", + " # prepare normalization\n", + " trainer.make_network_input(data_a, opt.SVR_0)\n", + " trainer.make_network_input(data_b, opt.SVR_1)\n", + " x = {opt.class_choice[0]: data_a.network_input, opt.class_choice[1]: data_b.network_input}\n", + " \n", + " # set the normalization operation\n", + " trainer.set_operation(data_a, data_b)\n", + "\n", + " # Get results of forward pass\n", + " path_ab, mesh_ab_normalized = trainer.generate_mesh_from_classes(x, opt.class_choice[0], opt.class_choice[1], data_a.operation, save=False)\n", + " path_ba, mesh_ba_normalized = trainer.generate_mesh_from_classes(x, opt.class_choice[1], opt.class_choice[0], data_b.operation, save=False)\n", + " \n", + " # unnormalize mesh vertices\n", + " mesh_ab = unnormalize(mesh_ab_normalized, data_a.operation)\n", + " mesh_ba = unnormalize(mesh_ba_normalized, data_b.operation)" + ], + "execution_count": 26, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\u001b[36mPARAMETER: \u001b[0m\n", + " \u001b[33mdecoder_type\u001b[0m : \u001b[36mmeshflow\u001b[0m\n", + " \u001b[33mdemo\u001b[0m : \u001b[36mFalse\u001b[0m\n", + " \u001b[33mSVR_0\u001b[0m : \u001b[36mFalse\u001b[0m\n", + " \u001b[33mSVR_1\u001b[0m : \u001b[36mFalse\u001b[0m\n", + " \u001b[33mfamily\u001b[0m : \u001b[36mchair\u001b[0m\n", + " \u001b[33mdata_dir\u001b[0m : \u001b[36m/content/3dsnet/docs/points/\u001b[0m\n", + " \u001b[33mdir_name\u001b[0m : \u001b[36mlog/\u001b[0m\n", + " \u001b[33mdataset\u001b[0m : \u001b[36mShapeNet\u001b[0m\n", + " \u001b[33mweight_perceptual\u001b[0m : \u001b[36m1\u001b[0m\n", + " \u001b[33mreload_model_path\u001b[0m : \u001b[36m/content/3dsnet/3dsnet_models/3dsnet_models/chairs/meshflow/3dsnet/network.pth\u001b[0m\n", + " \u001b[33mbatch_size\u001b[0m : \u001b[36m16\u001b[0m\n", + " \u001b[33mbatch_size_test\u001b[0m : \u001b[36m4\u001b[0m\n", + " \u001b[33mclass_choice\u001b[0m : 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\u001b[33mdiscriminator_optimizer_path\u001b[0m : \u001b[36mlog/discriminator_optimizer.pth\u001b[0m\n", + " \u001b[33mmodel_path\u001b[0m : \u001b[36mlog/network.pth\u001b[0m\n", + " \u001b[33mreload_generator_optimizer_path\u001b[0m : \u001b[36m\u001b[0m\n", + " \u001b[33mreload_discriminator_optimizer_path\u001b[0m : \u001b[36m\u001b[0m\n", + " \u001b[33mbest_model_path\u001b[0m : \u001b[36mlog/network_best.pth\u001b[0m\n", + " \u001b[33mtraining_media_path\u001b[0m : \u001b[36mlog/training_media\u001b[0m\n", + " \u001b[33mdemo_media_path\u001b[0m : \u001b[36mlog/demo_media\u001b[0m\n", + " \u001b[33mdevice\u001b[0m : \u001b[36mcuda:0\u001b[0m\n", + "Neural Mesh Flow with 1024 length embedding initialized\n", + "\u001b[33mNetwork weights loaded from /content/3dsnet/3dsnet_models/3dsnet_models/chairs/meshflow/3dsnet/network.pth!\u001b[0m\n", + "\u001b[33mFailed to reload network weights from log/network_best.pth!\u001b[0m\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "WUZH_LOo9Vdf", + "outputId": "e282cede-4d31-4df0-bc35-4a6b6e24f18d" + }, + "source": [ + "plot_meshes([ mesh_ab._data.__dict__[\"data\"]], rot_start=270)\n", + "plot_meshes([ mesh_ba._data.__dict__[\"data\"]])" + ], + "execution_count": 34, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", 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jw654G0lfXx/hcBiA7u5utm3bds7J3EePHuVTn/oUv/vd75gzZ07y53V1dTQ2NtLU1EQkEmH9+vVnrHKdj0yvHm42myU4jQEJTmND1aE6lcTjcUMfY03TsFqt2Gw2otGoVJwmkXEdnAC+/vWvD7v8/p577mHr1q0sXryY7du343a7R3W8s80vSvXQQw9x6623smzZsmFX9n3rW9+ir6+PBQsWsHjxYl577TVKSkp44okn+PSnP82iRYtYsWIFBw8eZGhoiBtvvJFFixbx0Y9+lEcfffSsbThw4AC1tbUsXryYq6++OjmxHODxxx+noqKCtrY2Fi1axN133w3Aww8/TE9PD1/60pdYsmQJtbW1wMnK3Nq1a1m5ciXz5s3jtttuY/78+aN6rM5GH6rLFIvFolwIkeAkzkSCU/YZPZldH6qTitPkYzrHSVTOsGJEX/nKV1i5ciVXXnllRo7n8/k4evRoRpdLyLauri4CgcCIVzuOV83NzbhcLkpLS41uStref/99ZsyYkbyCVgW7du2itrZWqfC0a9cu6urqjG5G2sLhMAcPHmTx4sWG3H9zczMFBQV0d3fj9/uprq6mtLRUqedcnNUZn8hxX3ESmXeuhTGPHj3K1VdfzdKlS1m0aBEvvfTSabfxer0MDQ1lrE0yx2lsqFhxkjciMRKjN/jVh+qsVuuwK+zExCfBaZLRF8bcuHEj9fX1PP3009TX1w+7zXe/+11uu+023nnnHdavX8+XvvSl046T6TlOEpzGhorBSVUqBT4VXxPjYajOYrEkVw+XOU6ThwSnSSadhTFNJhODg4PAyUny06ZNO+04md7oV8XgJCuHi4lCxTlZRlecUuc46W2RvjU5jMvgZNRedReiv7+f//iP/zjvf//oo48mVxi/9tpraWlpSf7uTPvXJRIJ/vf//t/MmTOHefPm8fjjjwNn378unYUxH3roIZ588kkqKipYtWrViIuHZjo4qVi9kQUwxURh9BVq58PoipM+PKcvkSNDdZPHuAxO+l51qVfTjWeapl1wcFq6dCm7d+/m3Xff5ZZbbuEb3/hG8nf6iuZ79+7lhRdeSP78iSeeoLW1lYMHD3LgwAHWrFkD/G3/uv379/Ptb3+bz3/+86Nqy9NPP82dd95JW1sbL730Ep/5zGdOCzVut5tAIHDef++pVDtpg5phT4KTGImRe76dLyMrTiaTKVlx0itNKp7DxPkZlz3FiL3q1qxZw4svvnja/cRiMe6//37q6upYtGgRv/jFL5L397GPfSy5ce4DDzzA4cOHWbJkCffffz8dHR1cccUVLFmyhAULFvDGG2+c9f6vvvpqXC4XMHzPubP52c9+xoMPPpg84elXSp1p/zpIb2HMdevWcdtttwGwYsUKQqHQaSE2Nzc3o5PDVSTBSUwURldvzofRbY7FYpjNZmw2W7JPSd+aHMZtTxnrvepuv/12nn32WeDklimvvvoqn/zkJ1m3bh15eXns2rWLXbt28atf/YqmpiYA9uzZw49//GMaGhp45JFHmD17Nnv37uUHP/gBTz31FCtXrmTv3r3s27ePJUuWAHD33Xeze/fus7Yldc85OPP+dYcPH+aZZ56htraWG264gcbGxnMeK52FMadPn86rr74KnFxbKhQKUVJSMuw2mZ4criIJTmKiMDqEnA+jK076/2VS+OQzbp/xsd6r7oYbbuCrX/0q4XCYTZs2ccUVV+B0Onn55Zd59913k1WugYEBGhsbsdvtXHrppck97EZqy1133UU0GuXmm29OBqdf//rXZ23Hk08+ye7du9m6dWvyZ2favy4cDpOTk8Pu3bt57rnnuOuuu4ZVtkbavy51YcxYLMZdd93F/PnzefDBB6mtrWX16tX86Ec/4p577uGxxx7DZDLxxBNPnFaG9nq9zJ8/n127dp317xkNv9+f0eNlWzweJxQKKdXmaDRKIpGgs7PT6KakLRgMMjg4qNQcEhVfy+FwWKk269ttGfFaTv3wIZPCJ59xG5zg5F51l1xyCZ/97GeTP8vWXnU5OTlcddVV/OUvf+GZZ55JzhdKJBL85Cc/YeXKlcNuv2XLlrOuWn7FFVfw+uuv8+KLL3LnnXfyta99jTvuuOOsbdi8eTPf+9732Lp167C/ZaT962bPnk1FRQWf+tSnAPi7v/u7YY+Tvn/dxo0bk/vX6VatWsWqVauG/ezhhx9Ofl1TU8O2bduG/f76668fNlwXDoc5evRocgPiwsJCnn/++bP+feei2qKB0WiU9957j6VLlxrdlLQdP36cUCjEjBkzjG5K2urr65k+fbpyC2CqtJikigvQHj16FLvdzpQpU8b8vg8cOJC88jiVTBKfHMZ1bXas96q7/fbb+c1vfsMbb7yR3JB35cqV/OxnP0veT0NDw4hDVKcuCNnS0kJZWRn33HMPd999N3v27Dnrfb/zzjvce++9vPDCC8NWdT7b/nU333xzspK2devW5F51Z9q/7kJs2rSJ3bt3J/977bXXWLp0KVu3bmXr1q0XHJpAvSUJZKhu7KjYZpXIUN3oWK1WeU1OYuO64gQn96pbu3Zt8vt77rmHm266icWLF3P99def1151u3fvHlZh0X3iE5/gM5/5DDfddBN2ux04OSepubmZSy65hEQiQUlJybB5RrqioiIuv/xyFixYwA033MCCBQv4wQ9+gM1mw+Px8J//+Z/J433hC19I7imnu//++/H5fNx6663AyXlGL7zwAgcOHODee+9Nvkmn7l/3wAMP8I//+I889thjeDye5DBg6v51cLKTn2te1Whleq86+FtwUmXOgASnsaFKBVJlKl4VZmTYM5vNyftW6cOeyAzZq06cl0QiwZIlS4bNn7pQ7733HlVVVcmrC1Wg2pBMd3c3g4ODVFVVGd2UtB04cICKigq8Xq/RTUmbaq+Lvr4+uru7ueiii4xuStoaGhooLS0lPz9/zO/7yJEj9PT0cPnllzMwMMDu3bu5+uqrsVgsyQ/dQnmyV53IjkxWL1Ss4KhGxYqTyD4Zqhud1IqTpmmYTCbpW5OIWj1FjBvZKOurNsdJRXJyFyORobrRSb3fWCym5PZL4vxJcBIXJJNvwhKcsk+CkxiJVJzOn771iqZp0rcmCbV6ihhXHA7HsOUgLpQEp+yT4CRGomJwGi9t1jQNq9Wa0Su8xfhm/KtOKMvtdmf0yjoJTtknwUmMZLyEkNEYDyuHp7ZDzl2Th1o9RYwrp65ddaFUnByeSCSUCiISnLJPxcdX5jiNTmq/1zQNm82WHKpT8fkXoyPBSZy3TO9Xp+KnNrPZrNSJUoJT9kkIGRtGPs6JRCL5IU8fqlPt3CXOn1o9RYwrmV4EU9XgpFKVTNXgpFKbVWqrLh6PKxf2jJRaWYrFYthsNpnjNIlIcBLnTYITyl2GrGJwUvENXbU2JxIJ5SpORorH4yMO1YGawVmMjvQUcd68Xm9Gh+rMZrNywclisUhwEsPIUN3Ep6/dpH9tt9vRNI33338/ubeomLikp4jzJhUnqTiJ00lwyj6j5zfp1SU4WXFyOBxomobf7x8Xa0uJ7FKnp4hxJxvBSaUQAjI5XJxOxcdXtbBnZNAzmUzJilMikRhWcYrFYkoFUHF+5BkW5y03N3fSV5xkcrgYiUohBNSrOBkZUEwm02lX0qXOcVLtuRejp05PEeOODNVJcBKnU616A+oFp3g8buiQWCwWw2q1JsNS6tfSvyY+dXqKGHcyPTlcglP2SXDKPlWDk0ptNnqfukQiMazKpAcn6V+TgwQncd4yvQCmiicdCU7Zp9IbOqgZnFRbjsDoOU7AsP3pVPzQJ86fOj1FjDuZHqpT7c0GJDiJ06kYnFQbqjO64gQng1MkEsFisST7ldFtEmNDnZ4ixp1MTw5XkQSnsaFSm1Vqq061ipORwUl/fvXgZLVakz+3Wq1KPv9idNTpKWLcyfQcJxVJcBIjUbHipFKbja6Qmc1mLBYL0Wg0GeAkOE0eEpzEeXO73QQCgYwfV6UTj2oLYIrsk6G67DOy4qRpGhaL5bSKk8lkkqG6SUKdniLGnWwsWKlaRUTFipPILglO2Wdke/WlCPTJ4XpYMpvNyp2/xPlRp6eIjNu0aRNz586lurqaRx55ZMTbPPvss9TU1DB//nz+4R/+4bTfZ/pEodrVKaqtHC6yT9XXg0phz8iKk37f+hIEesVJD05i4rMa3QBhjFgsxn333ccrr7xCRUUFdXV1rF69mpqamuRtGhsb+fd//3e2bdtGQUEBXV1dw46RjU9XenCy2WwZPW62qFZxEmND3kCzKxaL4XA4DLvv1IpT6lCdVJwmB6k4TVI7d+6kurqaqqoq7HY7a9asYcOGDcNu86tf/Yr77ruPgoICAEpLS087Tqbn+KhYcZLgJFKpOFSnGiOH6lLnOKVWvvTgBOpWHUV6JDhNUu3t7VRWVia/r6iooL29fdhtGhoaaGho4PLLL2f58uVs2rTptONkeoK4akFEtfaK7JPglH1GD9XpFafUoTp5zicPGaoTZ6RpGo2NjWzZsoW2tjauuOIK9u/fT35+fvI2brcbn8+H1+vNyH1KxUmcSrU3JAlO2Wf05PCRKk5i8pCK0yRVXl5Oa2tr8vu2tjbKy8uH3aaiooLVq1djs9mYNWsWc+bMobGxcdhtvF7vpN7oV4KTOJUM02Sf0csRWK3W5FXFesUJ/vbcy2tgYpPgNEnV1dXR2NhIU1MTkUiE9evXs3r16mG3ufnmm9myZQsA3d3dNDQ0UFVVNew2md52RYKTGIlqb0RSccouoytOVqs1ORE8deVw1V6n4vzIUN0kZbVaWbt2LStXriQWi3HXXXcxf/58HnzwQWpra1m9ejUrV67k5ZdfpqamBovFwg9+8AOKioqGHSc3N5fBwUH6+voy0q5IJKLUVXXhcJhQKJSxv38saJqmVHvD4TBDQ0PKvCkNDQ0RDoeVeoxVe02EQiF8Ph/hcHjM7zsSiZCbmwswbH+6eDyuzGtUXBjTOZ5oeRWIYa6//nq6u7uT37e3t+NyuXA4HOTn57N27doLOr7P5yORSGRszlS26W84JSUlRjclbZ2dnUyZMsXoZqStr68v+RpTQTAYJBKJkJeXZ3RT0pJIJOjq6qKsrMzopqStq6uL4uLiMa86hUIhwuEw8+bNo6CggM2bN/PRj36UnJwctm/fjtvtZsGCBdjtdpn7pL4zlo2l4iRG5dQr677zne9QUVHBLbfckpHjd3Z2EolEmD59ekaOl23hcJiDBw9y0UUXGd2UtPX39yvV3oaGBkpLS4ddlDCenThxAp/Px6xZs4xuSlpisRhDQ0NKvSYGBga46KKLxjw49ff388EHHwwLRfqwbDwel2H7SULmOIkLIpPDzUq1V2SfasM1qm23AicfYyParF9JN9K8pkQikTwXqPYaEKOjVm8R444EJzW3XFGxzSpRaXK4isHJKPqVdKmLXupVJpPJhKZpgPSviU56i7ggXq8Xv9+fseOpGJxUK8/LthDZpdo6ThKc0mez2U4LTnpYApQ6d4nzJ71FXJBML0eg2tCXSm+QOhWDk0rtVS04qdZeI1kslmFX0qUGJ3kMJw8JTuKCeDyejFecVKvgqEa14KTaG5JqQUQqTunTh+ZT+5CmacTj8eRzrlr/EqMnvUVckMk+x0lFcmLPLtUe29Q3fRUY2d7U+9VXL9c0bdiedbIQ5sQnwUlckMk+x0lFEpyyT6UgYtQVaudrvFTIUoOT/rU+1UD618Rm/KtPKE0qTuqR4JRdMlSXXUbuU5e69ICmaadVnKxWK8FgkD179hjSPjE2ZAFMcUEyHZxUesNRlQSn7JIKTnbFYjHD2qs/VrFYLLmeU2pwisfjhMNhpR5PMXry7IoL4nA4iEQiRjfDcCoFEQlO2aVaxUm19qYuBzDWNE3DbDYPC0upQ3VWq5VIJILVapU+NoFJxUlcEFUXgMwkPYio8uYjwSm7VHtsVaw4GRWc9GqXHpZsNtuwEJVIJIhGo7JP3QSnTm8R45pqbxaZpNoimBKcsk+VEA3qBScj26tXu2KxGJqmYbPZkl+fWnESE5c6vUWMS9l6g1DpjV2Ck0ilUvUR1GuvkRWnUyeE2+32ZPVJnxwuFaeJT4KTuGD6ySe/zeAAACAASURBVCJTVAsiqrVXxeCkUntVCyJScUrfqUsQnDpUp58LZY7TxKZObxHjVqa3XVFtSQIJTtmlUggBCU7ZZnTFKTUs2Wy2YUsT6JPFZahuYlOnt4hxS4KTBCfxN6o9tqoFPSOXIxhp0Uv953rFSQ9Rqr0ORPokOIkLlo396iQ4ZY8Ep+xTKYioVnEycjmCkYbngGFDdVJxmvjk2RUXTCpOEpzE36hWwVEtOI2XobrUduhVJpPJlKw+iYlLnd4ixq1MBycVg4hq7ZXglD0SnLLL6Mnhdrs9uQTBqRPC9dXDZahuYlOnt4hxa7LvV6faIqASnLJLtcdWtaBn9AKYI11Jl/q1XnFS7XUg0if1RHHBZKhOvQqZnNSzS6UgolrFyeihOofDcdo2K/pjqPctWcdpYlOnt4hxy+v1yuRwCU7iQ6pVcFQLTuNhqE7TtORmzqnVJQlOk4M6vUWMW5keqtN3H1eFBCeRSrXgpFp7jao46RO/9YqTbqSJ4KoN34vRkeAk2LRpE3PnzqW6uppHHnnkjLf74x//iMlkYvfu3cN+7na7peIkwSmrVGqvakFEKk7pG2mpgZEmgicSCaVes2J01OktIitisRj33XcfGzdupL6+nqeffpr6+vrTbjc0NMSPf/xjLrvsstN+l5ubm/E5TioFEQlO2aVSCAEJTtlm5BwnfXgu1alt0a+yVamPidFRp7eIrNi5cyfV1dVUVVVht9tZs2YNGzZsOO123/72t/nmN79JTk7Oab+TyeESnMTfqPbYStBLT+pjlBqg9EnhettMJtOwoTwx8UhwmuTa29uprKxMfl9RUUF7e/uw2+zZs4fW1lY++clPjngMWY5AgpMYToJIdo2Hx1evNJlMpmR7NE3DbDZLcJrgZDkCcVbxeJyvfe1rPPHEE2e8TaavqpPJ4dklwSm7pIIzMaX2GbPZPOwx059vfR89/ao71V4LIj3SWya58vJyWltbk9+3tbVRXl6e/H5oaIj33nuPq666ipkzZ7Jjxw5Wr149bIJ4NvaqUy2IqNZeCU7Zo9qbpWrtNZIeliwWS/Lr1P6kb72i0gc/MXoSnCa5uro6GhsbaWpqIhKJsH79elavXp38fV5eHt3d3TQ3N9Pc3Mzy5ct54YUXqK2tTd5GhurUuvRYglN2qfbYjjThWZxOD0Vwsg/pj1nq861fdSdDdRObDNVNclarlbVr17Jy5UpisRh33XUX8+fP58EHH6S2tnZYiDoTm81GcXExXV1dGWlTPB4nFApl7HjZFggE8Pv9yrR3aGgITdOUaa9ezVQlTIfDYXp7ezP6YSKbotGoMq+FRCJhaHv1pQhSJ4SnLj2ghys9OEk1b2KS4CRYtWoVq1atGvazhx9+eMTbbtmyheuvv57u7u5hPz906FAyZOXn5/Pzn//8gtqkaRqBQOCCjjFWIpEI0WhUqfaq9PhqmkY4HFZmNWZN0wiFQspUHeLxuDKvBT2kGNHeY8eOUVhYCJw+OV0PTvo+dao89+L8SHASo7Zp06bTfrZ48WLeeOONjH26OnHiBDNnzszIsbItEAgQiUSUaW9XVxeBQECZ9sZiMfLz8ykqKjK6KWkZGBigsrISh8NhdFPSolJfi0aj9Pf3G9Lezs7OYYtfpk4IT6046ZsAS6Vp4pKBbZERk3nejFxVl30qtVeGZ7LHyMUvU+c1pT6/+tpNMDw4yerhE5cEJ5EROTk5hEIho5thCAlO2aVaCFHpsVXNeAlOqVKrS7FYLBmcxMQlwUlkhNvtVmYybKZJcBKnUi3sqcLINadS7/fUCeF6n9I0DbvdLsFpgpPgJDIi00sSqLQ2kgQnkUqloTrVXgdGV5xO3XYltU3xeHxYcOrr66Ovr8+QtorskuAkMiLTFSeVwohqQUS19qpGteCkSlvB2IrTqfOa9PNT6tpNsVgMh8OBpmn09/crc7WiGB0JTiIjJvMimKoFEdXaqxqVwohKbQVjK07wt/CUSCSGBSd9XpNecYrFYsMWzBQTiwQnkREej2fSBifVSHDKLpUeW9X2qTM6OOnPbWrFKXVCuB6i9J+nLl8gJg55VkVGZHqjX9WCk0qf2iU4ZZ8qrwfVgpOR7U3tM6nnpkQikRyqS60yScVp4lKnx4hxTSpO6pDglH2qBCcZqhsdvd8kEolh56fU1cL1YBeNRiU4TVASnERGZDo4mc1mCU5ZomJwUq29qpCK0+juWx+eOzVsnrrNin7+kuA0ManTY8S4lpubm/GKkypX1alGteCkUkVENaoFJ6PCiN5nTj0n6f3o1OBktVqJRqMyx2mCUqfHiHHN4/FM6jlOoE5VRLXgJLJHxaE6I+c4nRqc9CA3UnBSLZSK9MmzKjJiMi9HALLulFCTam/u8Xjc0OEvTdOSe9OZTKbkPCar1UokEkm2Ta80yX51E5M6PUaMa5muOKk2x0mllc4lOAmdasHJyKE6k8mUXJ/JarVitVoJh8PJr1OH5qxWq/SxCUydHiPGtck+VGexWJQ5UUpwEjrVgpPRK4frSw6cKTilVpykj01c6vQYMa7l5uYyNDSUseOpNjlc/zSqAglOQqfiHCcjKk6p+9LpbbBYLMOG6vRABScr5qmrjIuJRab8i4yY7Atgms1mZU6QEpyETrWKk1HBKR6PY7Vahw3VAUQikWTF6dTgpNLjKkZHnlmRES6Xi2AwmLHjqRicVKmQSXASOtWCk1HtTQ1FqVfS6fOa9POVHupUquKJ0VOnx4hxLdMVFwlO2SPBSehUG6pLJBKGBKfUUJQ6x0kfqtP71EjrNklfm3hkqE5kjH7yyMSJWKUgAmq1V8XgpFp7VaFaxcko+lBd6hIEZrN52JV0ZwpOYuKRHiMyItNvxlJxyh7VgpNKFRHVSHBKj75hb+rwnD6vSa9EpQYnec1ObNJjRMZkcu0lCU7ZIyd1oVNtqM4osVgsGZZSJ4TrP9elXkmnfzhR5bwg0ifBSWRMJtdyUukqNVArOAmhk4pTevR5TRaLJVll0r9OrTKNFJzExCM9RgCwadMm5s6dS3V1NY888shpv3/00Uepqalh0aJFXHvttbS0tJx2G7fbndFtV1QiwUmoSKXgpG91YoSRrqTTK06pyyPo7YvFYhKcJjA1eozIqlgsxn333cfGjRupr6/n6aefpr6+fthtli5dyu7du3n33Xe55ZZb+MY3vnHacTK9X51KVNpyRWSPam+WKg3VGRnyUofqUq+q0yeN61IXygT15hOK9EhwEuzcuZPq6mqqqqqw2+2sWbOGDRs2DLvN1VdfjcvlAmD58uW0tbWddpxMb7uiEtWGFkV2qPYaUKniZNTil8Bpw3N69enUOU66WCwmK4dPYGr0GJFV7e3tVFZWJr+vqKigvb39jLdft24dN9xww2k/z8ZQnSonHRmqEzpVKjigVnAajxWnRCIx7Ko6/XylaZpUmyYwWXRCjMqTTz7J7t272bp162m/83q9Gd2vTg8jRn3KHA0JTgLUGvoCtYKTkRWn1DlO+jlJrzKnPt/6OUCvShk5L0tkjwQnQXl5Oa2trcnv29raKC8vP+12mzdv5nvf+x5bt27F4XCc9vtMD9VJcBKqUS04qdReI88Fp85rSt3EV2cymdA0LXl7m802bJ0nMXFIcBLU1dXR2NhIU1MT5eXlrF+/nqeeemrYbd555x3uvfdeNm3aRGlp6YjH8Xq9BAIBIpFIRtplMpkIhUJKlLvj8TjRaDRjf3u2xeNxZdoai8WUeWw1TVPqsdU0jVgspkR7w+EwiUTCkLamznE6U9g0mUzJSeGapuF0OtE0bcQPmUJtEpwEVquVtWvXsnLlSmKxGHfddRfz58/nwQcfpLa2ltWrV3P//ffj8/m49dZbAZg+fTqRSITu7u7kcbq7uzGZTPz0pz8FIC8vjx/+8Ifn3S6fz0dDQ4MSn9ii0SjRaJRAIGB0U9ISCoVOu3JyvAoGg5jNZk6cOGF0U84pHo8TCASUeWyHhoY4fPiwEsN1kUgETdMIhUJjer+hUAiz2TxsXhMw7P96kNIrTvoq4pqmJec+qVLZE+cmwUkAsGrVKlatWjXsZw8//HDy682bN5/zGP/1X//F3r17eeCBBzLSpoMHDzJt2jRyc3MzcrxsGhgYoLOzk7lz5xrdlLTs2rWLJUuWGN2MtLS0tJCTk0NZWZnRTTmnSCRCfX29Mo/tvn37uPjii5WoinR1dREIBJg5c+aY3m97eztHjx5NznFKXXJAX4bEZDJhNpuTwQlIXnUnJh4JTiJjMj05XKVtV2SOkwC15gyBTA5Phz7J22w2D3usNE1LhiWTyZSsMOlO/V5MHGr0GKGEbEwOl+AkVCLBKXuMamtqlSmVPu8pFosNmwyu31YPTm1tbRKgJhg1eoxQQqZXDtc/6alAxZXDVZh0rxrVgpNK7TWq4jTSApdwsj16xUlf50m/OEAf1tM0jfb2dmUeY5EeCU4iY7xeb0YrTqoN1akURGRxvuxQ7TGV4HRuI81rgr8tUaBp2rCKU+rK4nqQUqWqJ9Ijc5xExmSj4qRScFKp4iTBKXtUCSI6VdprVABJvc/U85Ee5PQ5TjabjUQiMWzNJ1lBfGKSGCwyJtMVJ5njlD2qncxVaatKFZzxKhAN0BvsJR6P0xfqI6yFGQgPsK97Hx/0f4AWN26+kB6E9K9TK076kN6pwUmV165In1ScRMY4HA6i0WjGjicVp+xRKTipFEQkOF2YQDTAloYtvPHmG0TjUfp7++kOdRD0+WhvOoa7LJcrr72WZQuWMT13OsumLsNusY9Z+/ShutTKkn51nT6MmBqc9POhKn1NpEeCk8ioTJ4gVJscrtLJUbX2qkIe0/O3c+dOnvnj79h1cCvB3l7MeWaGev0ECxJoWhyn20qko5dnn/01//mHdVw8fT7f/9oPqSmuwWP3jEkb9flL+oRwm81GLBYjHo9js9mG3UYPTme6Kk+oS4KTyLhMfepWqeKkWpVBglP2qPZaMMJAeIBoLIotauPpp55ix+ZNxPt7CRw7wbTpHipnz6f5SCdFVVXso5UCh53+Xj/eQg8tjh4cwRjNHfV88dHP86W//wqfWvopCnIKstLW1A9vqcEpdUK4yWTC6XRitVqJRCLDhur0QCUmDglOImMy/YahUnACtaoNEpyyQ4bqzm0gPMATm57gheefx30iQpkvjrUsQajERGluOXNdUwh09VJbdxmtg0MkBiJ0HGsnN8fOwGAIXzRCbomDhDVCS+sRfvKbx7h+wfVZC06pV9Lpyw7oaze5XC4CgQAmkylZZYpEItjt9mTAcjqdWWmXMI4EJ5FRdrudSCSSkS0cVAtOKpHglB0SnE7X5e9iKDJETiSH9b97mjdff4lOfzM+n48CSy4mq4fePj82mwV7mYPGjnaCpjDbD7XTHwgy21GMyeymypJHsMhKuDtI3JfAZLWg5Wh0x4/x/T9/n3+6/p+YXTA74+0/04RwfahOn+OkV5kikQgulyv5b2SobuKR4CQyyuPxMDQ0lJHgpOKEa1VIcLow3YFuuoPdlLnLhlU6ugJddAY7mR2fjdU8vk+v+h5r2RKJRDh09BDP7XiOzRs3kjg+gKslTJkzB2eRF0qKuMwzg4A/wN5YJ1Pm5OHvCdBp8ZMIm/HFQkwrKqQk6uXumo/xTn09rcfj3OCZx+5gK+1agDyniW5fiKdfepIrllxBVX5Vxv+m1D3pUhe61DQNu91+WnBKvcJO3+xX+trEMr57tlCOx+PB5/NRXFx8wceSilP2SHA6f6FoiB1HdvB/fv0rLl1Wx0LvAiw2GwFzmN2tu+ns7KS0vJR5JfOMbupZZbI6Njg4SHt7O52dnZxoaaG7uZljR45Qf7ieVkcvdk1jtqeYT1xeS82MGbS2t7Nozhzy8vJ4cts2Hl9+I083vclvD2/h4vJpfKxyISZXghXVF/HE0xt5vegob3gOoWnwScsSTP5jaANRPA47HiIEAjGONhzFNC/zQVBfr+nUrVU0TcPhcAwLThaLhVAolLzCLpFIGLJop8guCU4iozK5X50Ep+yR4HR+Gnsb2d+yn//8/c/o3HOQ5t4hrp0dIxSJ8HZ7I+/1fUDXwACPHBvg9tWfY9WqVUY3+YzOZ0HJeDzO8ePH6ezspKOtje6WFvrb29H8Q2huP5ZEhKFD/RxsaKUvGCTfbmfNwgUsWTSX5eXzyHO4AGg6dpQBZ4SX3t5GrsvFc2+/TXNHF5cWTGeefQoRX5TP1V5FV1cX1y1azGuOFt5v7cRjdxCZEWdWoIBEToJQbxRz1EW35mPTX17k3hvuxWV3ZfRxOvVKupycnOTXenCyWCzDVgvXK0763CfpaxOLBCeRUXrFKRNUGvrSqTLHRbXgZHRb44k4vcFe/lr/V370/z5MuSeH6Z5Sunu6ebu8Be14nPl5U7moMJfNjQdYGnSy9Ze/ZMuf/8zq/+sfqJhfgd1sZ5p3mmF/gz/qxx/xU+QswmI++WaeGpz6Qn0MhAcoc5XhtDnp7e2lqamJ1tZWjjU303H4MD2dnWiBAEQiWDQNu9WKzWql3xKkmRN09fTiipiZGnKzdNYsambPJsdup/+Exv/01mM2mYglEmzoeo/YjgZisTiri5ewtGIWUZ+PotxZHGhtZd7UAqLxOOt37CCYSHDc2k2hPQe73UH7wHGaA71Yyy309/TR74gT1+Cd3j1saNzAmpo1Ge2Dp04I15cg0Oc4jbRaeGpwUuF8IEZHgpPIqEwGJ9Xoc7JUKM2rFJzGwxvPBz0f8Pyrz7N553OQF2BhUTUnjvXSFA7w2+2v8q0r/56b5l3Ky9u3s8I7gxx3Lv/fx1fyxzff5P/8+CHaXRrzay/h48s/zmXVl1GUV4Q/6ueY7xgFjgKKXec/tK0PG+n/RaPR5Nf6m33fUB/bmrbRfLSZXFsuZZThHxyks60Nr8vFwEAPzYk2+vt68Sas5HZaIBrFDjjNZnIsFlx2OxU5OThtNswuFz2RCEOaRjQSISccotgL1RXlrCi9mDlFVfQGAnT7/XT29hKIRIhEo2ixGHaTifbBfjwJJzPKyjgRDPDfb7+Nwx3nmMXHUH6UHUeOcCwQIBIIUDd7Nn31/dyWV0PIZ6Kz1MTxnCG8Fg8BLUqcBORBLKixbc82PlH1CYqcRRl77lO3VtHnNQWDQWD4a9NkMo0YnMD44C8yS4KTyKhM71enEglOE1dAC/DMM78lZvJh7klQUT2NKvKI+KwEQ2H+sHU7M52lhCIRaqZN462uLvr9fpbX1HCovoemQ2+zZfAw9VteptQ7jUrzdLqLQnQM9TJ37sXUlizBokFM007+F40S1zTi0ShaNEpM04jHYid/pgeiaJR4LIY5kYB4/GRgiscJBYP4g0EiwSCRUIhgOIxmjuIvihAIBMjBhrPj5HZGMU3D7XDgcdnJKYtiCwaoKCyjzOulJD+fqcXFeMtcFJbkcpFzCk0d3bx3rI0PutvxevJJdPfS1ddHHKjKqyTXkcfAoIkPop0UulzMKCigoLKSfKeTAq+XfJeLl3bvpm3vCb54441UuovobO3lt6+8wgGtm/eae1g0YzrzSvPwFMSx4eKDjg6uWrSIQEcHHYkQWshPf1+YBYUzcIY0Wk2DBInhGIzR+0FvxlcST53XpC90OTQ0NOJtrVYr8Xh8xIqTKtVocW4SnERGScVJjasAJTilL56I4+/14zaZCTtsOPOc+E748QzFWeasRPOYmeK188TmzcTicT61dCmVxcX8x0svUVlUxN/NvQSH1cYLR3dREAhQYvFR1N+Kr28Qf38XexsPcaT/NS6aUsFlVVXMmlJKvz2I02Rlmq0AW04OAL5wmKFQiKFwmMFAgKFYjO7+froGBvD7fGgfVnSsJhMOmw2v3Y7N5cKem4vH5ULLjWPKMTHdXEjZslzisRiDAwPker0M+nwc7D/GW8F2FlBGwhZjqK+P1p5jNDT3csLnwxS1UBzwEq+0YDLHMPsiTB/0sOKii7hs8WKKvF4KvF6c9jMHl31Hj7KjoYEbL17MQlcl+1vbeHLLFtw2G/GwmWvnzGHu1OnsbWth46H3qZpThjeUR4UdXjlyhPxcDzhNVOBlRqKIwUA/rgILg/4ITg163jmO1+7N2HOfSCSSc5n0+Zb6UN2pt4O/BafUIVAJSxOPBCeRUZne6BfU+aQmwWliaupv4qWtLxKyhAi0B8lzFRCJRHm3v4t/nLkCXyxGUV4eIWeILR0f8KN3NzMrXoTDGefyj1zBQOsAQ4f7mB0vZeVHFmLuj3Hr6mvo7O/mp2+9SrU9l4HjA+xubeXZzk5MZRa6vSEKc1xUBAuxBMxEwmGikQj+UIhAKET0wyE4i8mE1WzGajaTY7XittnIdTpPDq3ZbCerSTk5OOx2zJoZs9+E1RJjKDSApmmEgkHcTidet5sZWin9QzFmFpTSOzSEPRjElbDgLLZjnWoi1+rFejzOm7TwTms7lUXFLFl2CVMLCgnEYrhiMTyaNmJwGoqHONp5glffe49ZhYU09PZyaNMmPDYb0+125tXUMPDXbfzz0k8ys3gqfyp5m7c6D7K3q4PyaIiPFFcxv6iI6y69lN/s3M6V7ioaWjvos4WI5FjQwmAxQ7v/KP2hfvJz8jPy3JtMJjRNw+PxoGknNxe2WCxEo9HkOUkfxoOR12ySfjbxSHASGeX1eunq6srY8fQr6/TS93gmwWli0uIar7/6KpFQmByHnVAowFu+w0SsESxFFmrsU3m9sZHy+ZU0H9jBlRdfTI29jB3dDTz+2nNcVjybq5ctY/sHH1BpncZxfwsdJ05QWVZGbXE1MwsKWHZtNcFIhL/s3Mna5lc5HhnEl7ASPNqHuSdOJB7HbrHgsdmY4nQyrbSUGVOnMnPKFKYWFVHg9Z5crdpsxma1Yjabz3nFXCAQYGBgAE9eHi2dnRzp7mYwEABgYVUV5WVllBYXM0gQXzzEFEseNpOV+Fuv4HEX8tGC6bQ2H8ORgGgoRH1rK/2BANFEgrycHPLcbvJcLnyuEM2RbvY3tTA7WkLbiT4umzOH62tr2bZ3L4ncXOqPHaOmtIwSTyFbDhzklbf3U+Jz4nZ7qJ09h8G+EPkeD72RCPZonMrKEnYea+NEKMjUQSeRgSBRS4yecA9NA00szVmakefeZDINW+gSSO5Bpw/Jpz7WpwanRCKR/F6VD4Di3Mb/u5FQSiaXIwC1gpO+SJ4KJDilb2beTAriLqydDpwWJ4UeJ8F4hBPBINtaD3P/ZTX49+3jSHOAooJ8ZhZOpciSh63PQjgQpD8SIqevD7vVSnN3N8vnzOHtgwepLCsj1+lkIBAgGo2y5+BBAoOD3LPsCn554A3CoSg3zlvC5TNrmFJUhCmRYNDvZ9DnY8DnY9Dno+XYMZrb23G73eS63Xjdbgo8HvK9Xgpyc7F++OYeTkQ5EfeRb3KSk7DR0d1NQ0sLR48dw263M7WoiLL8fJbn5nLTxz6GpmmENY2BQIBQOExU0zgU6aB7cJBD7x2l2J7DnIopuEst7Dx0CBwOZk+bRk5ODvFgkOMDA7R0deEPh2m2dXMi0EdZYSFTphUw2+7i6kWL8AcCdHSfoN9mZ3F5OS/v2cMv/+d/mFNaypwcL/7EFC61TOPvqq/m+b/+lS2hVpoDCfoSfnYfPYZmSZBrszE1mos/EKBzaoiETSMWzOwSJrFYDLvdntxaRZ8Abv+wspa6Gfmp8xtTg5OYOMb/u5FQSqYnh6tUxVFpvRYJTulzWB3kB91Eek3kFjix+Sx4vblo9gTRrjC/fv11/IRpaepkSiiX97cd5uJly/jilTfzl7e2s6pqGdvfP0BTayvNJ07w8XnziEUivH+sGbfTwfuNh+nq6GBKfj43XX015hwL21vasOWYCUStmF0uSgsLASgpOv1qMX8wyODQEL0DA/T097O/s5P+oSFCmobNbseZk0O7d4i2QD/2hImCLhtOsw0TYDKbKSsspC0Q4PCxY/QFg/T19ZEA7BYLdpsNu9V68murlfr2dkptNqIWC26bjfnTp7Nw1iz2NTXR3NvLxxYsYHpJCe+3t3OwowOP3c7NM+twTLUTCWgMNvl4tn4r4XwXDaEWOiw+KrUSXn//fYpdLu656ipyrFbueeMNLp09m9pZs2g82kr51EKOdw1xuOF9ZhW4uab4YuJNcQYZpCPoQysyEwokiBfDn7b8iZp/rMFly9x6TqlVplP3oEutOJnN5mH9Kh6PK3P+EumT4CSG2bRpE1/96leJxWLcfffdPPDAA8N+Hw6HueOOO3j77bcpKirimWeeYebMmcnfZ6vipAK9rK8CCU6jY04ksJnNOMxmtEiU0nAR8UGN2dNLuWTuHL7xxu+JhYOYzDYWe6Zz7fIVAHTmdRIJRbiqrg6fycRATw+/evllbMUW/nrgVUwJC77DQ/zfV11DUX4+TV1dJ+cvtUdImExUVXv59aZNTCkuZm5ZGVrK1XNRTUP7cD2hiKZhNpmwWSxYLRZsLhf5FguDwSAfnDhB40AnaBHy8/Jwmh1EP3zuHYDJYmFGURH5Visuh4MrL7kE2wgVXn8oRMfAAHVLltDY08OSmprk7+bNns3OAwdY/+ab+OJxrlu0iL+vraXywx0E+gMBOiMDhG0Rir1e3mpvZHvnQfK8XuZdVMmV06ejAXluN0+/8Qb2RIJls2fTp2n81xtv4HbasVvtLJxVRH9okEZ7D3aHlYICJ++2d2Ozx7AWQdAEr+17jbs/dXdGglPqfnPRaBTrh8Og+lYq+m304HRqSEo9J0h/mzgkOImkWCzGfffdxyuvvEJFRQV1dXWsXr2ampQT5Lp16ygoKODQoUOsX7+eb37zmzzzzDPJ33u93jNeqns+VApOp37aHM8kOKVP0zTi0QgUmRjyhCkM2Ama4kxxuWg+cYKaqllcVFpGQ0c78YSJQCRCIBzG5XAws7ycA4cOcemUKSTMZq5dupRuv59Gl1uMzwAAIABJREFUWxd/2b0Pr9tFmdnJ0e5uOvr6sH447JOTSOCw2XDa7dy+bBnbGhro7O3lmoULybHZsFmt2G02bBYLdrs9GZjg5Jv3B52d7GpuJhgKcUVNDZ+bfR3BnCiFZg9lllz8oRBH2tvp6O0lbrXS2tvLvkOHiAKH/X4K3W6KvV5KcnMp9Xop9njYc/gwVSUlmM1mojYNfzyMVTPxwdGjNLS0ENM0PrlwIc29vexuaKBjcJAcu50Bvx+bxUKRywXRKIVuN10nTrCyZgGXXDyX0NEQT+3cSeVFU2hu8/HHlh0UmM28+N57LJs1i4/NmsX+9naW50xn8YyL+OGrz9Pu7WNebjmtXf04zGYKQhYi4SgRT4JIOEyhszCjrwF9QnjqViojLTmg72unM5lMRKPRjLZFGE+Ck0jauXMn1dXVVFVVAbBmzRo2bNgwLDht2LCBhx56CIBbbrmFL3/5y8MmPWb6qjrVgpMqZXnVgpORbT10/BAdBT0M2oPE/EECU93EXDnURD0cPdbHjvoDXJRTRklFPm2HOxjs7WP9X//KHR/7GDOmTGH73r3ENY1INMrMqVN5dfNmonkW7l15A/3dPnZsraemqoqFlZUAHOvpYSgcpqSgAIfDwYqaGmoXLGDjrl3sPHKEm+vq8LpOr6aEIhF2NzfzTksLORYLl8ycycJLL8U+QvXInZNDZXExU/PzKf5w+K/K7cbqcFBaUkKPz0fPwAD7e3ro9vkYCodpPHqUT86fz9t9R2hhgJa32nG2abgTdhwfTkzvGxykMi+PEpeL/ceOkTtlCndfdRUFHg8Am3bu5NX6ehYXlfL/XHU7FrMZ01QYaOthz0Abz760A7sJqvMr+dLHP8604mJeOLCT3R3b+UhBFYHGXsqm5rPnRAdOrYtpFgdxKwSiQWx5VkxaFN/xfmxmW0ae+9RlBvS96fSf6yEqNShpmnbaBHB9UrlK/U2cnQQnkdTe3k7lhydvgIqKCt56660z3sZqtZKXl0dPT09yU99MByez2SzBKQtUCk5GX4lUf7yeQ53HKCixYNWshKJhDnZ24Jk9lc6eXqpNZeRqbm5f9FF+dOAPrJgzh2NdXfzu9ddZdcklTCkpoauri0gsRv/gIIfb27lv2WouqayCSggf7GXLe+/xQWcnNy5ezEAggMfhYEZxMW81NQFgs1q58bLL2F5fz9Ovv86NdXVM+zDwdA4MsOPQIQ51dFBVXMz/WryYmaWl5/7DTrnKKxKNkpeXR77HQwzAbMbicGB3OHjnyBHygLaODvb6DnOMIEMF+cyxTWN28VSWzZ5N+ZQpWFOu5FsZjfL6/v3815tvcs3ixXT5fLyyfz/Vubn8rxUrONJ1gvr2dppPnKCho4PiGXlcM28efcd76TjqZ29nG8edQ7w48B7RoJ9D/m6c7gK04wmmRF14Kp0cPzKIpdzB8f4goWKNaAh6gj0c6jtETXHNaX/yaOkL2urBKXXyd+rXp1ac9L4lQ3UTkwQnkVFut1sqTgpQKTgZzdfjI0czMzWQC31QNsXNUJmZowODHDX1Yne18g9ll1FdUkIkkSDX6cRbUMCi6dN57q9/pdDrorXnBK2+Lt7av59VS5cSCoeTxy8vKuKKZct4+8gRfv4//0NFQQH5Ticzy8r48759yS08zGYzly9YQJHXy3/v2EF5WRl9wSD9Ph+LKiq45+qryXO7z/q3xONxBoJBev1+mjs6GAyHiZtM+EIhdtfX43S7mVZURJ7TSV5ODrkuFxW5ubzh81E9ZQpleXlcle8ib24xSypmYuuGw8c6eOmdd3C7XFRPmcLcigqKvF4cNhtXLV3E9PYO1r74IhG7hrUYmvqGeOngQQqdTi6eMoXLZszgPzo7mZ07ndn5BQQZ5Ji9nwP+dn74p01EHRE0t5XK3CJuW1hH6586WVBUxoFIPydMQexRsFhNWANgM0OiN4qJzIRt/bHXJ4SPNK8J/haKNE1Lngf0Vcb1ipOYOCQ4iaTy8nJaW1uT37e1tVFeXj7ibSoqKtA0jYGBAYpSrvTRV87NlNRLfcc7CU4Tjz/q58ChA9isYOpIkIcTjplYmFPOUFGIzlgvB451MHTRySGaEq+XcDiMX9O4ePp0youLWbf/FbY0HSAYjXLLjKXkO7wc6OjgI/PmAeDIyTk5P6iujob2dn6yaRMLS0q4cuFCitxumnt6qC4rA04Ox/mDQayRCJvfeotL5s7ly9ddl3xDj8fj9AcC9Pr99AcC9AUCDAYCDIZCDAWDBMNhnDYb3pwcrPE4+U4n00pKyHO7cfj91C5YQHVFBXAyBOxqbuaFPXtwxGKsuuwyZlZU8KcdO7jIUUlNTiVUwNyKSrRYjNauLg51dPCHN9/EYbfjneohmh9jz5EmXB4nbaYODrW0s2rJUj699DLKvSfPG9vef5+uQIB/qatj6+7dFLhcdIZCdPdqFNrthEJxSiOlLI5PpeVYJy6Ljatn1vBB2xv4iDO7z4I14ebE4BA5PTGCARPVBdUZef71fepO3U7pTJv36lUpfXsWq9VKOCUki4lBgpNIqquro7GxkaamJsrLy1m/fj1PPfXUsNusXr2a3/72t6xYsYI//OEPXHPNNSOeQDK12FvqqrzjnQSniac70M2ut3fhC0QJOWKUaRYGIhHK3QUc6uyiPJ6PxWHnrfePUDFYRGVBAV29vUyfMYO2nh6cDgc+sxmn3U40keBIexsOzcLGgwfpDAQo9Hhobm9nIBZjYShEodvNpVVVxHw+nt+yhYgnzhs9DdhtJg4dOUp7ZycFXi+1F1/MkrlzeXnfPr717LNUTZlCKBolGIngtNnIczrJzckhz+lk5ocVpHy3m3yXKxmyunt6sFqt5OflAfD+gQN4XC4C4TDbDx9m3//P3psGyXWeh3rPWXtfp2d6VswMZrCRWAiS4C4KJLVCMmXLsmTLllWWkvi6klQquZUqVZw4LlfJkerGSari6/jeSLbpkh2JsuRLURIlihQXcQNA7PsMBrN3T+/76bOf/Bj0aECCvLQwutDg9lPFYm/o/qa/Pqeffr/3e9/5eYaiUXp8Pn7t0CG2XhUq3bLwv6U6uCxJjKbTqH4/kViMM3NzfP/Scc4UFhmIxfjdnQ+x3T/EN4+22T+5nb7w6muWm02+f/IkB7dvRwCOzs5iCQIRn48+O8yv7f8Qrx95kzm7zouVaZzmDBM9fby6OE+mVCWsywgxkAURZ9khLIBedfEcDzagZWRHftbvruuwvgfd+ojTenFSFAXDMLrH2y1GV5y6rCHLMn/5l3/Jhz/8YRzH4Qtf+AK33347f/Inf8Ldd9/N448/zhe/+EU+97nPMTk5STKZ5Jvf/OY1zyEIAoODg8zPz2+IODWbTSzL2hRC0mg0VhunboKlxXq9jud5m0JKa7XaTdud1LJatJsanutieRZeyKHa0lCCHsVLFfwaDA7G2BMbYGp+nrOZZRBbhInxjZdeRBQl7h8b5v33jPJXP3yO3fvGmRgcxBJF+uJxUtEo9ZUVyvk8Jy2LumFwZHqavYkEQlDgSG6R5UaTo5EjTJgJfH4/tmnSbDYJ+/3cOzzM+UyGXC7Hof37GYrH37lYrGlSNc2f/22atvolf/V9XSoWWTl6lFytxkQqxUd27EAzDEqFAjGfj2KxCECpUqFVr3Ne08hUq+QaDYqNBuVWi6iqkggGKVSrJKUQf/C+h3HaFhemF5lbybM1lsK/5FJTq9i2zT8cPUqfIFBqtfjTb30Lv+Owf2iIWU1jMBAkZSooTYcH907w/1z6GSWzRWI0yM6BAYS6R6g/wGK1juUDKwxNH7giPPXcU9xz+z03PP/rk8DfupOug+u6a1LUKda7PuK0/rm63Bp0xanLNRw6dIhDhw5dc9uf/dmfrV32+/18+9vfvub+j3zkI2snVYCpqSk++9nPApBIJPibv/mbX3g8lmXhOA7+q41Of5UxTRPDMDbFWHVd3zTva6di880Yq9/vJ25GWejxqIQNLogV2orFtFDEFxbJaSbj4SQ13eC/ePhhFvQCJ8vLFJfPESXGn33gt4he3QE3ETjF1OIiO8fG2NrfT77ZZOfICJZhoGsaB/buBUBwXT66bx+nFy+zUK5RM0wGEj2EmgFSySTD6TTDvb1EruYz3bNvH8enpvjZhQt8eP9+BpPvbSu+2G4jyzJFTeP1K1d4dW6OT9x7L4f27Vsb83cPH+aOrVtpWxbZapVctcqb8/NkdJ1kIEBvNEp/NMrugQHS8Tg1TeOZ06fp7+3lD/bsIaAoCILAq9YFFuYWsRC4tJJn6/YhXrp4kZaus1guM6ko7OvvZ8/ICG9MTdE0DD60bx+Hz51jWdNYXMoipBWUtoQRsjmVm8MsNemd6GeuXCbuyHgOWBq0E/DS5Zd46M6HEIV3bzvzH+PKlSsMDg4Cq+Jzvbym9UUuO3lQbxWnrjTdWnTFqcsN86Mf/eia6w888ADf+c53CF/dgnwjqKqK53mkr+Z4/CojiiKNRmNTjNXzPEzT3BRjtSwLURRvylgzjQz5LTm0oofp6niWgE+RObOSwQwY1GNtxAEZua5gCAJ7t2zhnL6M5XmYjktPPE7wqvDtn5hgYX4en9/P7slJ/unVV4lFo6RTKeaXlohFo7iui+15XM5mkUyBf33wMzx99gSPjO1mazLFci5HJpfj5RMnUFWV/lSK4XSa9+3fz3B/P8+dPs1Dt93GnnVFad+Jc4uLnM5kMG2bu0ZHcbZt45MPPEDLslgolTizuMjhy5dpmSbnlpfpi8Xoi8fZ3dvLFz/+cWLrjm/XdXn14kWOzM1xcM8e7lr3+rppMlcqsWdiggPj4/zVuRd54+UFDNPkUN8e0pLEbz76KD969VW2bNnCv33hBf7rj36UpWaT7544QVRRGAon2BMwmM5pzAXL+JBRYkFaSzrJtp9AW0AxHAojLoIMl7OXEcIC6fCNfWZmZmauyWtaH33q4HneNeKkKAqO46zlRG2mtlFd3hvdmeyy4YRCodWlhA0Qp822q26z/LLs5ly8N/JannwmjydCUPLRkw/SJ4QZkFNUKbNglrhQyvCJofu4kMkgtAT6jCD9w0NoM0X+4pln+J0HHmAynaYnEqEZiXBqaop7d+9GlSQypRLBYBBN1wGYzmY5v7DAgeFhPvj+9yPLMvviW1golNgzNML2sTG2j43hui6FcpnlXI6zly7xs2PH6Ekm2dnfz0unTlGo1zm4e/fbGv26rsuJhQWOXrlCvVpl//g4/akUy5UKR5eXaT7/PJ7r0h+Nkstm+dhtt3FwXd0o07KYnpm5RprKzSZPHzuGK4r8wcMPk3zLcX94agrbMNAFgYuVCtVWE0+SSMfimAI0NY1aswEhkR9cOosMHFlcpFGrsSeVoqe3l6VajX5LRR4a44yU51yuyh3hNP35EBdEh7zXxFA8bB3EIFS1Ki1zY3b3Xk941uc1rV+e74jT+ohTp+r4qVOnePDBBzdkTF1uLl1x6rLhbGS/us0mTpshFwu64vReSYfSDIhprIUm8YpKH358PomQT8L1BRkOxsnNN8hHG9iNBsv5PHsC/ezo285UXWSkt5cfHj/OxMAAQUVhy8AA0wsL3LF9O+N9fczkcuwdHUUzTd6cneWpY8fYNTDA++6+e20Mo729/PjMmWvGJYoi6VSKdCrFnUBb11leWSFTKBAVRZ5+9VWeO3uW33zwAZS0RNszKc41OHV5EcMwCPn9GLbN8fl5hhoNksEgE8kkn3/oIRLhMNVmk+/Xanzw/vuvSQQ3TPOadizHrlzhpYsXuXtigoe2bbtG1DTD4MiVK/zDSy8hOQ7bRkbY1d9P8nSQz3z4EfYNjLA4tcI3z87w5de+j+a30AsmPbLMw5OTZHM5cisrnM7nuW9khOdOZgjtSFOvzRFSRXr7khQWyvjiIs2KSSgu4rc9NANUWyUeiN/w/L91eW797evFqYNt24RCoWuSwyVJwjCMtX9zs+uSdblxuuLUZcMJh8NdcfoVZ7OJ080a60B4gJ5iguKUgqK6qD0CddUkFvaRW64x6sToSSS4kFsgE2jheAaPJ3dw5/g4r5w7x1ZB4IuPPMIzJ07ws7k57hoaYrinh1OXLzOaTvOzixe5a2KCV+fmqIgij+3YQf0tx85wMkm93V5r43I9FFmmt7cXfzhMqr+f3nSa506c4E/+w/9HO+XSMgyGg0keCE1w18QEg4kEgmUxPjhIKBSi1mhQL5fXKnyfvnyZ7UNDb9s9pxsGiqrS0DSeOXmSYrvN7zzwAAPxVUnRDIOzy8tczGTI1Wro9TqPTkxgmiYfe/hhvvvmm+zpH+LO4DDDvl6cWJuP7NvHNxYOM5Mr0h+L4Y9FaVgmx8qXuWxVSPqjfOvIESKKwgPxAV5bkEmlIiyXqjTDOrrWJiSpBFYsPD+YAYgH4/QE3t4Q+RdhfaHL9awvbLm+AKaqqpimieu6BAIBFEXBNM3uUt0tRHcmu2w44XB4w/rVdcXpl8NmEqeb/QtdEEQ81cMadlkebtMomyzHqyxrNSZ8MVxdYHQ8xbGpBXyiSF3W6YtGGert5cL8PIfuv59P3X8/qufxwpkzfHD/fhZmrzA5/n5WSiX+/Qsv4BMEPn3vvVzKZIiuS4J33VXpCUoSz54+zUgiQUPXabXbtEyTtq7TNk1MyyKgqgQVhaDfT9jv5/bJSVqzU+So8PBttzGopNDyFtPFIrYgEJMkOp8Ay7bXet1pus5CJsOvHzz4tvfCtCxKrRZff+kltg8O8sl77sF0HA7PzHApmyVXqzGeTLJ/eJjeXbv46eHDHNi5k1NXrvD8uXP0xGLsiscp1+sM9/Uxnc3y7IULWDWd3//QQYalIJWFBs9dPMHzxSmWKxUe330Xo8sqHzz4Pv722ed4f+8EGavJ8XKGQFgBEURJoJnw0KLgeqD4Nqblyjt99t6poXdnqU7TtGuW6gzD6IrTLUR3JrtsOOFweMOqh28mGdlMY91M4nQz0W0dPW5gbfVoOhZNo4msiJxsLFNv6YRlmfHUIIVclcFUimKpxPHZLJphcGBykn+cmaFcr5OKx9k9NoataTSNNke9DJeOf4+Z5goHlEl64nGeO3uWc8vLRESR+XKZtmFg2jY+RaFWqVCpVAhu307Y7yeeSBAOBAj7/YQCAUJ+/9qS0kw+z/PnziEC/+PHfwNckxfOHOe+yXFu2z9Oplzm8soKb1y6xKuzs2zp6yOmKNhXReDM5cuM9vURfks/PN00eebECU7ncvyrj3wEzTT5xuuvU6jVGO/p4c6RESbvuWctSvXjw4fZNTZGoVwmr2moksTn772X85cvU6rVeOHCBb5/8iQ+UWQsOcAf7XqUI6dPMzqWYu7McURBZHd/P6VinaVgmXOZH1NJNNmd3EF5Pk8koOAbCDK3VMHoE9BksGQQqhAQAhsy/+sLXa5PAgfeVsqjc7+qqm/LcTJN8x0jV102H11x6rLhbGS/us0kI5tprF1xem/MVmfJkkOP2CiqTFRT6ReiBNp+htIJ8sUqesjjdG6Bg7v2sFT04xkaX3/5ZR7fvx8LmFpaIhWP41NVVFlmbGKUH5++wNGzZxmKJSnXavhEEUyTtKJw2/AwO8bHiQSDa0JUKJd54dgxPrQu9+mt5Ot1njt3jlK9zsPbt7NndHRNphLhOD994w3KjQYP7d3LcCrFRCxGIBplvlTiyMWLnJiboypJLC4u8tlHHwVAcw0ankGj2OIHJ04xn8sRUBR+dOoU4z093L1lC5MDA29b0ssWi1TKZR676y7+ryefxAqF+G/vvRdZlnFEkX8+eZJ7tm/njoEBXp6Z4V9drX5erNW4uLjI+cvzPLprJ7cN97NwaZ5TiTmW83n2b93Cp/bew+Jyhj78nNZKWKHVWpeqC4IFWOAzrr+keSOsjzIJgoBt29c0Ae7c1ylH0KkBJUkS7aulH7o5TrcGXXHqsuFsZI7TZjrJdMXp1sMn+0CRcB1QiiKJskgyEMDxPMZGepEbLtZ2m5nlIg8nXCb0NFl9mYcnJ/nOkSMEoiGev3KafbsmuVIo8MPz59ljmXx0xx08fvcBLpxeYPZilo/u3ctkXx/Tnseuqzvd1tMTj2NZFg1NW9vh1kEzDJ4/f57pTIYD4+N86sAB1LcsCyWjUT5+8CAvHj7MM6+9xmP33IPrecRCIe6KxYiJImOJBA5Q9/l48eJFfnD2NM1eg3yjRqumM+H0MRgKMRCP81sHD77tNdbz5vnz7Juc5OzsLGcLBf63Q4cI+Xy8Nj3Ni+fOkVYUPvvgg/x3f/VXpMNh9m/fTr5e53tnzxL1+xnu7eVA7xCZfI0jSxmMqE5fRKVZa/P84hkyvjrBmEw53yDgiPjKMi3FwaqC3QAxfWP1m65HR5Zc111rPt65vL7DwXpxWr+rbjPUTOvy3uiKU5cNJxKJXFMQ8z8XuuJ06zEeG2fIHKScWcZni4geFKMtkqKPGm0S4QCDvSlUUeLNuTn+cPRB3jh9jttHRuiNRvnykf/AC4szFF9x2dbuYSKR4A8f+8BaJOjXDu7n32T+kWfOnOHesTH8nve2JTK4uosukWApl2PX+DiwulT02swMR2dm2NXXx3/5yCNvk6r1+FWVDz34IIdPnuR7L77IzuFhhgSBqqYxlcsxUyiwUCwy3N+P6TgsV0vMNHMYus7W/iE+ffv9NAoFZL//XaXpSiaDaRik+/v56ne+wyO7d+NXFJ545RVc2+YLjzzCT372M146c4ayafLbu3dzuVDgmZMn6fX7yRsG/9Wjj/KDY0c42lqkSZ3BeYVH7ttPLtjmL079GCUBobAPz/aQbRmx5CJUVqNOtgdZO4vt2sjijX/FrT9OHMe5pshl53JHjuDt4qQoCpZlIUlS95i7ReiKU5cNJxKJMDc3d7OH8Z+crjjdegiCwGhinKnlYwQGRfKjFna7RjXpI2Ba7BnpY/lyidsSQ9hLJsfcRSRZ5lI2y+0jI+zfOcnL0+dYLOYYCsVZrFY5Nj/PRG8vyXAYURR5ZO9eYjMzTGWzlDWNDz30ENergDbQ00OmUGDX+DinFhZ4+dIlUoEAv/fAA6Tj77713rRtCo0G+XqdpqqypOs89fTTjJ46RV88TrvRoNZoMBgOs7W/n0vZLHcOjvLZ2x8kEFVpZFu8cPYsy/k8j+3e/Y6v47ouxy9cYPe2bXz36k64VDzO37z0EveMjfHgzp2rERpZ5gcnTrArmSTbbjN75gwf3LGDv7p8mZ5QiGPLy1zUsthug3bQZYcaox6SeaO2QKBXwmjr2JpIGJmW4KKFHEyAHtDrUNAKZJtZRqIjNzT3nXYqnSW2jix1etB1mgB3xEkUxTVxAtbu6whWl1uD7kx22XA2Mjl8M9E50W4GuuL03hkcHASfhxl1KNsmqVQEXA/LsDhfzxFSGuzqT1Os1TB1nZquc2Jujt5YjJWpCr+9+wC5isF9I+MYK0XmMhleuXABSZYZSiQISRKWZXHH4CCX83n+/pVXeN/OnRzYuvWacQz09/PC2bPMahqe4/DR3buZHBi45jFVTSNfq1FsNild/a+uabQNg3gwSDIUoicU4rE9e9gWiVBqNtk/MUFTb/LsiZMYPh+ZcpmP33EHO4eGfv7EY3Dv8Db+3VNP8dKlSxRMk0d37aIvGr3m9S/Nz+MTBE4vL+MKAiuNBkvlMr9z770M9vy8PMDFfJ6QLDO1skLPwAC//eB9/PDwUXKtFoFgECXkIERdVsoGCdlP0fAIygbmQo2HD+7l1dlpbMdFEWX05RYRn4CkQlEDZwCkhERICd3w3HeO6Y4g2ba9VgVc1/VrIk6dnXPr0wsEQVjLf+omh986dMWpy4azkQUw4ecnr+sVovtV4ld9fOvpitN7p2+wDycN1X4bGQEpKOHLiuzs20KlWaOh60yXigyF/PSEQsTDCt8rneTYyQV+fesd9HgKF4Rl3pyfJxWJ8NH9+wkHg+SqVebzeeZKJY4sLOB4HuN9fRwYHua1S5c4n8nwgf23I/gFJE3ghQsXObG4yO9MTLBrZIRis8nz589TbDaptVpUNQ1FENbkqCcUYltfH6lIZC26tZ6I55FMpfi7V37ET8pTeLj8T3vu54HxHdf9LMuyzGgqxYcnJliuVvnGK6+wNZ3m0V27iAaD2I7Dqakp5GCQi8UijWqV8VSKLz7yyDXRlmOXL9NwXVbyeYZCIR59/x6+lz3Ot5eO4nNttu0c4Z9rJzhbWibuKQzjY+jOUV5fmiHsD+DUJJyMi+x5xAIqghrAqVp4loN0OzgCqFEVSdwYUVm/JLd+qa5z3/qdc9eLKnWEq5Mc3mXz0xWnLhvORkecOrWcNpOY/KrTFaf3jh7RMRIuZgOiQZlGpYXkKZiyQ6ihMDieolCpYdQdzhs5RkdTvNrKUlms85GJffT5ezg3N8dju3fzb7/3PQ7VaoSDQdLxOOl4nHuAhOvyytmzDMRi1JpNbMfhQmaRH1ROoqoyki6w1eohGAjwwxMnmC4U6AkGSYbDbE+l6Nm6ld5olMBbdre9G3Vd58SlS8wLOk6zyZ6tWxkb63/X40w3TeLhMJPDw9w9OckrFy7w7194gduHh9CkJjNukeKSx9Z4D+OpFPfv3bsmE6Zns9ys8NTJE8iSiCq79KV6+OqLP+To3DRDkTDhiJ+Xi1Ocry+STKnEVyRmvTZvHD9OKhRkB0mSOYWJYIyi0EYqQQyFlaiBaYDoABUYfmD4hhv8dkoRXC+vqRM9emuO0/XEqdO7ritOtw5dceqy4WxkOQL4uTgpysYUteuy+cTpZo21YTawJAsE8CHgbyh4A1CLWkxZWfal05h5m4dT2ziZmyWjVcjVPYYGEvhRefnwGS5HVygXCnz4vvsYSib556NH+W8OHbrmS3Z0aIjXzp7ljtFR7tyzh9OLi7w4fZ5Fo0ijXCERitMTibC3vx+fKHLogQd+4b9JMwxevHjA8c9/AAAgAElEQVSRV682BL5NSiANbiEs+gnoErzLCpftOGulB0J+/2phzVSKJ8+8zo8WT6AoCnf3bWUsnebo6dOMt1rU5uaQFJHzLPODU8dxbZdQ0s+p6gpFzSFpRPnko/dwZOo8S7Uaaq7FVjWGZwrYS01CQ3Hu7I+hx2TmCy2aXpZ+NcZSvU4lYeGYDo2Ag94AJQ+SAX/42B8SUSO/8HvUoZPX1BGf9ZeBNYmSJIlWq3Xd5ThZltdqOnW5NejOZJcNZ6OX6jpbfzcDm0VGNpM43cyxKqJCf18/SkUkIAnYIRtPl9FxKLcbnIsKCBbcu3WCWD5MU9I4WVjhd3dt5QuTj/Kt1w4z0dPD0sIC//szzxBVVRxB4IfHj/P4PfesvY4/FeKcL089d4qXl5fYEk/wkR27+VT/vWiiSVhXmVvI8ebsLGevXCGQSHDX6CjRd9lF91Zs2+aV6WmOzc6yo7eXX9+3j6VymWF/jIQYYEsqxekLUzzyllpRncTyYqPBiaUlhDNnaOg6dU3Dtm0sy+Jyfom+aIQ7xsbYkxyjtljCr6pkKxXmikUyrTJnvGUs0+TusUmW6mUE28GWHXotH4dfv0CmkGOgP01SDpHVK1gXayTkIGkSaMs6YjDAlVqWsKJSNF0M00JRwBMFfIaI4Dg4BfAVYf/Y/huee9d1r4k4qapKs9lcizJJV3PT1kecglfnY71AdTaNdCNOtw5dceqy4Wy0OEmStGmSrjcLm0mcbiZ+2c/jBx7nTyv/A07UxpJcArZC0g3R44ZQdJmmqfPcxfPc1TvMdDGDTxE5NjvP3eoCj995J0+9+SaP7dpF1TQ5u7BAW9NYtGpkojV2pkdoLei8VLnIYrNGn9vDbx08xHi479qBKLDl9hTvu+02/t0//zPLuRzHZ2ZIJ5PsGxlh18DAO0Y0XNfl2Nwcr05NMRiN8rkHHyQRDPLE977HzvFxQn4/FV0nPTzMt37yExqADdTabarNJqZlEQsGifp8iJ7HZCpFPBSi3GhwcmGBlmlyR2yAT//aoygBlaCu8I+Xnsfz+5krl3Edh7Dg532Du2igM2xHyCwvMdg3yPiWQdq2TTtrIQeD9PWkOHllhrZhEvDZRCSH0BY/zUWbh5Lb6RNE5vQagiEw7AYx6gJatY2kO/hsKCZA6GFNYG6E60WZ3lqCwDRNQqHQ23bOrRenzrHWjTjdOnRnssuG4/f70XV9w55vM/Wr2ywFO7vi9N5JhpNIARHND9EemUhJobcWJh3voViu0DuaJmr4ydlN5holhvvj3OYb4M2L01zK5BhJpTi8sMBoKMQju3cjuC4vmfP8vz97gWQ4wh5lGE/16InHWKk1ePnkefIDDfyKQkBRVnvQ+XyEVBW/qrJ7bIxgKMSnxse5sLTE8ZkZnj1zhu0DA9w5NsZgPE7JbRIQVGazRV66cIGwovCJ/fuJBINkq1X+7tlnKVcqSL29HDl8mFg0ypZiEUGWOTs9zQfvvpu9Q0MkIxHiwSCiKFJrNBA1jaCq8tNz53AEgQe3byefzeImEswtlrmcy1FrNlnJZvnY/fezWKkwX60SCoXoc0Lct3Urf/Pss9TyJmk3xcnZBQrNKiHXI9mK0Zhq4lMk4gI0cFlOwELmMnv6h0n7E5w3JeZLVQKSxNawn5lGk4ACjaaHkwY3BKIOLbt1w0t1nfSATsSpkyrgOA5+vx9JkjBNk2g0uiZOHWFa36qlgyiK3WPuFqErTl02nI1O4t5M4gRsirYKXXH6F+ITMA2QCwKxtooHuP0COadJKWjza/1bqJQ1ZEcgu9Lkcw8MUcobDITDFGo1dEHgx9Pn+GifitVw0G2Hz9x7H4bloM+a5GdXeHhkG7ZmMH1pBkyXgN+PYVm0bRvDstBtG8d1aTSbVBsN9mWz+GUZn99PryxzfmGBN6an0WM2XtijqRtESz76AlE8z+PJw4fxKwrZXI6Ez8eBbdvYPjqKkc/z+Y9/nIDfj23bfPf55xmIxxnu7b3mLTi3sMArV65QlmXet307sWCQ16emePboUW4bH2eXLPPIjh3IjsNThsHrV67QchyGo1F27RrGiNr8z099i+XZCiICPruF63cIRX1QspATKoouEs4q1Gt1gqgM7kix0qozPpIiGJFpaCYxSUJQJKa9JnlahLbKtJccSIMjghcR8Ik33nKlE2WyLOsdE8Kvdxmufw7sHm+3Dl1x6vJLofPFvBECsZlynDby7/5l0hWnfxliTUL1gWN4WLKDNqCjD1ZYLNbAbvBa/jI7/AP40j48w+O5w2d43669TBeLfOKOO6hoLf7X+ln+z+M/ZTgc5cvv/1229KfwCTI/dU9w0hPRXAHH1Pj0ww9zZnqag/v305dIXDMO27YpNxr8009/yod27sR0HAzLoqppmKaJbtvMNfKUClWCgQAxuZ+gqnL70BD7t2zh6Llz7IxGeez++1mYm0NUFIbSaQJX24HIssy+bdt48/x5ht//fgAuLi/zysWL5KpVEpEIQ4kEz58/jyqK1IpFfvvee3nswAFEUaShafzFP/0TV2o19o6Pc9f4OMu1Gv/30ec4WrmMXxAYi8bQNFimSbXQwtTbuBGBaslCinmocZuQ5bJtdAsuCumUymtXZsmn27x/ZBuXFkXmjRZFz6Pab9AKO7R7QVgtr4Woiyw2FplITNzQnHciTu12G8dx8Pl8a7d3Clvqur4mTuuTxt96/HdypbrLdbcG3VnssuFs9JfyZoo4bZaaU11x+hfi9+Gva/j8AvkhDTcsIBZMZEEkJChUszX0sR5qmTYD4TCDfUmW83kcUeTJI0f49MF72TU+xGsXpqkbBk+8+BL9iSQhRWFhaYnfPHiQgUSC/+Pb3+bHU1OEPY/v/PSnfOaDHyS5rsikLMv0JRIMxuM0dZ2aZTG9skKhVmMoFuPQ3r18of/9lP1tAp5MqK4wVygwWyjw7VdeQRUEPnTgALP5PJ7rUlpZYWBdYUqAbWNjnJ+Z4SfHjjFXqbDcaBD0+WjqOqZpkvT7ue/eezENgzdOneLOu3aT9WosXS7wnTeOcGR6muH+fuaKRY7NzFBt1llINTBCNkJIZrpWJuQFcV0BSXDoj0axbAchpZAJVii7NhE/eG6OYENlJJ5Gdlws22JOrzFj1Vj0mgRwibUVSpqJHAejCEIcxJpMy7rxXb3rSwjYtk0otLrdsJMQ3qnP1JGodxKnzg+prjjdOnRnscsvhc5JZSNKCGym5PDNksfQFaf3TlWvIo0FcDMVzJCH13JRoyqhlo/RZgK3YBJSAlTlOneNj3FxdpGq1GYwGWYwGOSKWeJ/+dk/4cguD2/Zyt7gFkKhHqYzGRYWFtg2MMDp+XmeO3uWsmEwKstorstcrcaf/sM/8Hsf+wDhgRAx/Ng1lwvLy7y+vMzPFhd5+LbbODA2xkQ6vVYmAGCt0UgKhlIpbF3nQxMT7Nqxg8VymZfPn2cmk6HZavHonXcyUKlAFCpui2ZW43ihwNnjx9k9Ps62dJodAwOYzSYNXWfH8DCGZfHUG2+Q6Inw1WPf5+j0NHrNIOH5icdVkNucyRawLI96pElBb2AEoF2zkcMCRqCN4or4AxKqpzBoJnBbBlJbRBOhGZUwBQehafJAcpLASIBss8b7eiZ55flzVFstoqEgfk0kZCoIdQtLBMIQ6o0wFB66zkz+y1i/9NaJMnV20r1VljrnqOuJkW3biKK4tjNvM0Sku7w7XXHq8kshHA7TbDZJvGWp4Rehk4S5Gdgs/eq64vTesVyL3t5eZjMZ/JqHnFLBFegzwvQYYZpCk6A/hL/sUq1ajOtxxIBNvdmkqmlsvy3Fsbl52rU2J3NV7BB86uBWJNumXCphKgr7env5zP338/3XXsOnKKjBIEm/n8MXL/Lf//AJ5LCPSDDEYCNK2h8h7PfT0DR04Gw2y9lsdnWwV/uqrWd6cZF6o8EdO3ZwYmkJgEAwSERVWcjnefPKFZ65cJqlUJV6s0FQVNnlDBJRFOqtFldKJS5ks2QKBWzH4Xtnz7JYKFCq1wkmg7RTNqLoER4NsrxQpdzXwjJczIgDNtgJ8BxQwkALfKKM6om4po1jwyJVWnELtSagZ13sAESTQRoYKL4wz/z4TQKTMWqSzmy9wLAXRAwLtCsalu2iOB5qFZQktHVIbE/icuPH4Po2KdcrdLlenDrH0/XqOK0Xpy63Bl1x6vKulMtlPvOZzzA3N8fY2BhPPvnk22To5MmT/NEf/RH1eh1JkvjjP/7jDRenzbJU1xWnW49UIMW2vduYrpyi1bRJSApe1kQZVRFdj4bfpR2qEYnHqNebZK0a224bIVGIoGk6P7x8Gj1g8ZsH7yPz3BKzuSI/OX6cK5kMk8PDIAg8e/EiL01N0RfwEYjZ3H37VnpXEthAM2+gORq7+wcZiCaoNTTqus5cLkciHicZDtMTDtMbDhMPXVu98vTcDH4sPvrQQ/iuRqTKrRaL5TLLpRKSJGHYNnHVT9sfICyJ9AaT9DQjyIrIlL2EnJQRVhy0ep2m57EsN1hOlQikVKxFnZLcot6jY5bAjQExwAR8QAGEPvDqIAF2E6SoA1WXYMRHSomSt6qslKuEkgGkss1I2ceDBw/w9IVjhCSVif4kLSzqpoUL7Aj1YS/kaQcczvgrCGkP3QHHAK8J41vGiarX9tD7RXinQpftdvsacerI0vqSA+uPrc6/7YrTrUNXnLq8K1/5yld47LHH+NKXvsRXvvIVvvKVr/DVr371mscEg0H+/u//nm3btpHJZLjrrru47777NqyW02ZKDu+K0y+HmzlW3daJDcbwgGYfyBWLZI8PRXW5ECzgBlzK7TZyuYaSlFgpa3xr+jif7r+PsOVjMJlibvYCPzlylkRNRhZF6rpOIBAgEQ4zEouRToeYrhX40eIFZmdzJKeOMeTF2O7v59D4fVzITuNmHCZ3DuEbudrCw7LwSRKmbTOdy3F2aQkEgXgkQjIYpNAsc9lZYnzXFl4pzdLM62QrFSzbRgAW8nmSiQSaY9KQdMyqg0qAeqHNcrNMIVBnXi5yeH6RkK3SlkwsCQzBwRkEVA1EwA+EWRUl9+p1CQR5NWFb0ECUABsCcQEVCVOwMGybSqtBXAmgJxQapo4dgAAwczKHJbpons05schvpe5BqS8QM4LMFnLgutieh6iIaIqNB3gRoAEP7HsAn3zju+o6S2udaFFHljzPQxTFtcvrl93eSZw6u+4693WX6jY3XXHq8q489dRTvPjiiwB8/vOf5+DBg28Tp+3bt69dHhwcpK+vD1EUN6ztymbLcdoMY91MJ+6bPVZFUrhzx538PV/HU0HDJmKpLNk1yrTxezJxX5CQ4EM0PdSYRK3c5OixC6hthfKYjuNzuWPrFvx+kWcvvkmzbrF/cJKKpjFTK7AcrdDQTBy/g9CCRDBAzAlTb7c5PnUZx3HJFXLML+bZPjyMKsvotk2uUmbv6BgkwRVcpIZIMV/m9bNnmdfyuGmJI0uL9EgRkq3VekyeBE1VpyC2kMQgM06RnFfHFixYEdCrFrrs4YSBIcAHDa29GklygCSroqQDk0CdVdsJANXVfnEiQA1Un4BPVxFlD7Nho0oSog6hgTByTaQa1NBbNikhhOBYFJIWtqXgkwR2+pPMGzq6ZXH8yiJKRMV0NCzZZC5Ux7JMehoSRtDGSAAaCDps37X9OrP4L2d9/tL65bmOFL11WW69LHWqjsPbxanL5qcrTl3elVwux8DAAAD9/f3kcrl3ffyRI0cwTZPBwcENizh1l+q63ExkUebQ7Yf41zrQBmTwfAL5bIveoTD9epQBN8HM3DK9w3FEq8agL0or2Ob2u8c411jmYinDd6ZeR23LtJwmYl+YgtEmJCn4+xWqSosmGnvtIVZaNtGKgudYtEUHSRAQJYm+vj4WV1Y4tTBPdCBCPlih2KhzejpDVdIpNOrE/CGkrEtL1OkhguuKWIpNxWuzOFjBtB1aLYOmz8LdAucr1dXedGlWpagjRr2AChhX/y+z+rg2q2XFDRAAoQGSHyRZwFz28EdFnLKLEAAFAcmScQwb0RYRZQ8xJGKpDr6Why+lEq4aNBQbPeJgLRt4ErR74FJ1BbEkEN4ZQmq5yLrFnFwja9TwtloUCk2iVYmwqxLOGeg5UAPgNuCh2x/akHlfv2POMIy1yx1BemsLlc6O2s4PvY44ra863uXWoCtOXfjABz7AysrK227/8pe/fM3161XDXU82m+Vzn/scTzzxBE8//TSNRmNDxtcVpy43m4X6Aq4PiIKngFyRuHvrKJdyC6AIWBHQt7ucaWRohSzUpoLT43Ll+BH6RxJEw2EqxSaRXgWvJJGvV8Bx6ZGiFIINppwVlLjC1EIZp2HhGGWCwRCu52HKNpbPQTQEBBUWAis05AUcCdroKJEaUlNAcKDZKNO6w8ZVYH6+iXsOuAdIAQpQAUZZDQkJrEaJfFfvk4A4rOVVu1cjRw3AAsVZvU3wCSg1Cb+lYmDhk1VYtHGDHgkhiq7o6JKNUpERUgK238JUHZy2QKth4OtTabRNWg0DRRSJhH3Uqjqq5xGVwTRsqp7BYDhMs2JiWhZ1n5+P7t/P10+9iNlqE5BFHFVAKYMlgV8FaQXsCsRCsQ2Z8/URp87yWucyvH0HbachsKIoeOuS9Du3rV+q67K56YpTF5577rl3vC+dTpPNZhkYGCCbzdLX13fdx9XrdT72sY/x5S9/mfvuu48XXnhhQ5fquuLU5Wbil/yoso82BmETAg2ZYMxHrxegaBRppx0WtDqi7CB6En4AScUL22jNNtFkCG8oiNCCtgqO5zFlZ/FLVbyqRzvtoFct5mQHN+2sRnKaMpbsYPgcbNvBC4IH2El+fuaeBN20VoVnitXltKv3ewIINnhJVuUosLqU5dVWHyfKgA6iAqyA6AdfRUSRZYSoTMCSiVg+bNelpRq0NZ0eMYLQkon6AsQiflp6C1FRCW6RqZZa2FmHluAwHEwg9wt4nkTeraFbTfxBBVf2MHUHX1Sl3TAxdRMxIiMIHm0B4qafWCBMy25jFm1iY3HKrTqlFYNgEbYKKRZKbXyGBVlwLQ+lBYoElgPydXa1/aKsL3S5XpY6Px6vV6upI0frz1dvFacum5+uOHV5Vx5//HGeeOIJvvSlL/HEE0/wiU984m2PMU2T3/iN3+D3f//3+dSnPgVsbKPfrjh1udlsT24nLQ0ztzKDZ0M0GWBWX8FN2lg2LLVKDKcStOdbxIt+YluSlAMtTNWmKLTITzUQh2TsgoEbFpAUE8t1qYsWngmuCYIEpk/HYbVRLZqFJ7AqQy6r8mOxGiWSAY3VPCM/YIIcBsEPtrX6XEoe1JCMaTp4QQ9lViZVDxEK+chSRvLLJH1RRuw4ckShFdRpCQa71EGUtsiCUCJfqREQFOhx8TQNN+ayM9FH1AxQbTbRTAU74JC5Ukbu8yG5MBntJ9duYEoOPV6AaDJAs93GqtnEekO0JRurZePzizQFB9NzsEQTxwdeUETXDCTLww6B27QxaxZqPMKPXztFWFGIlUJY1RqOINKwTAQAAew0eJKI4zpI4o0LVCfitL420ztF3TtLc9eLKnWSzDfqh2SXm09XnLq8K1/60pf49Kc/zde//nVGR0d58sknAXjzzTf567/+a772ta/x5JNP8vLLL1Mqlfi7v/s7AD75yU/S29vL3NzcDY/B8zza7faGPNcvm1qtRqvV2hQnScMwNsV72mg0cF33psvzgcEDLL88g5WABaWM7QgICx7RqIrPECk5VcJtiabPRPeqLJgVRFz8yCDaeIKIu0XBallIDZBCAp7kYacAHaQ6iCEQw+Cpq9EiCXAl8CwQHHDbIIkCQgsEScCoucgiBFsy0d4Qtmnja6gMFiMkfRHEPpEZI0+MIBOBFJIgkKFK29AZkXoZDSfxl0XmhAorV0r0pSLYkklbMqhVK0hhCJoi5oqNLxlF8DwuXphFjvsYjCVJ4KPS0qjFLLxmm1grQDDoJxL1UbE0ym0NVmxExUONqngC2HWLeDRAxWwhSwJ+V0YLGDg+sF0PyzPALxIv2biegyU5KH6Fy4ksdb/FRG+SzBWLqCPT0C08FxwVmjHwbIc3L71JOpC+4fk2TXOtBEFHhNaLk+M411x+p5IDtm0TCASo1WoAa3lQXTYvXXHq8q709PTw/PPPv+32u+++m6997WsA/N7v/R7f+MY3KBaLa/c/8cQTGIZBIpEgkUjwt3/7tzc0DkEQCAaDN/Qc/ynQNA1JkjbFWEVR3BTjNAwDx3Fu+lg/dPBDPP3dbyLGoFVsk9jag2R5RIoy4yN9GJLApfwcZtBA7JXwlUUSyQgjSzEqRpWyaGAZNr6QwlA7Bq5EsVejUtfwBWQiKxL+QJCaT6emtRjwJYi6PuyAS0lqMyIl6LPC2I7Liq9KjTaBuETjUp1ELIbnE6mrAndER9maS2LaNlPVHI26RiwUQLJkgoqMa+qIhocaEIi0fFgjHkIb9t4+itU0mV1coWi18EVVIqJKtd1CTvjZGUrRbls0BtpU3BoXpxbo7Yuj+CU81yESChEdjNBomzSutAhPBnF0i5JqIYkyiiBjZU16R2Nopo5TcwiH/KhhFVdv4dqABZ7u4ngeqqgiAIoiUpUMxJBDvd1GG4fmkoMQFrBa4DpgJsAKgmRJJMNJgr4b+6wUCoU1cVq/PLee9ZHld6vV1Ik4dZfqbh264tRlQ/jRj350zfVnn32Wp59+mj//8z/fkOefn59/x/yqXyVM00QUxU0x1s3ynjqOg23bN32sj3zgEdxJsOPg80SCTRmwObBvOytnS+zYMcbF/gyW2cJuaCgVmTB+hlJptu4e5qeFkwQNP+GGwu6ecULJIOfbiyhChVFfLwOjERayK3iyi+h53BEYYYub4py+SLWiEQzIhHIybVvD6TWxdJN0MkkoLLMtNkhOLFGrNMm3i8TaKunhBEOJJO0eh/tC45CFQrDFYq7B1vgAvWqYmmdw7soimmmgRiSUpovY52dnbAuyLrBUL2HERBzToVBqEI8FUQyBRCTBwC6BjFelVmsh+WUC4QD12Qa+IT8ju3qpGBqNug1R8BUhNhgguK2HRqONk7OJhPxE42EqZgsECDsSSkXBDbgoskRbtxkIpJgvVEgrHg3NIREKoGgy+iC0YzbeEqgW2DLgguKXSfelifpurACmaZrUarW1CNP1xMm27WtKDnRSCtYv03X63Pl8vq443UJ0xanLL4VO5fD/3OjmON3CqKtLaK4Ots9DDUnM5spktSqyBJLlMhpMcLzRolfzscs/jFkyOWlPY5ZMov1hxlBoGzaLcgEhAwP9PeyNb+F8bomcVmZoIE2YCuelPCv+MlLeIyQrRKIqAcklFlHolQdJ7uzjUi3DHZFhCvNFkrEY5R6TtCKyTeyjOdvi9NIclXmduBpiLlRE9AlczmSwDBspIZKrlqmZOmLAZSgWw3VE6gkTO+CyOJsjmAri6Aap3jChgB9JlmnXDNqWR9ASqWRqNGUdJSkSkBVqtTrR3hCiKpKfqkJcwpcUkOoSw9v68WSP4uU64YQPLxqgbdoYpkPL0ZFtCNRlrLBDM2Yj1V0gREszSSVDtBd1hvQIFbVF+vYwJ6NgOuAbAPHKaiK8qEHAF8ByrRue6vVLaf8xcerIUafkwPqluI70q6qK67o0Gg1arRbj4+M3PMYuN4+uOHX5pbCRyeGbiW5Pql8OvwpbuGP+GBRWe6J5JhhWg9RAhGJRI+YpTFWyLAllRqNx9qmj1C83ufPeCcJijqnlFdyKTd1vU0vrCD6VsB6g7bZZKZpYhkXJ30JWJCoVnaZscMnMIrkOyVoMT4BaAMZTEXxNkYsrK2RXSgSiMj7bo+XXqS1UcP0itgDiqI9QxYccUVEsgYVWjqZr0khaiDZcyWRp1k2S41HCTQXThVDYR4+ioLkWvrEQy6UituYQtGWMmk0lXyISCyBLNq1Cm3AqTEyNUBV1pIZMUFawdJ1moYaXkvASUKlopIJxMD3MXJvhsR5cy6UwUyI0GMFzINQEwwWj10VTW9giBIPgVlzqpka11iCs+BgcTDJfqJFxy/gc8ESQTJDbIJureWC9Y2l6Aj03PNfXq9EEvK3MQOeH0vqdc+ubA3eud5qd67qOpmk3PL4uNxfxZg+gy61JJBLZ8ATpX4Uvz/8Y3YjTxnOzK4d3qBt1rB5wQxDyVGqOgGA4tMMW8riCJrdJSEHqNQvbFpB8Mi9eOc+RNy/TH0xwd88kFdVmsVSjXmtgam3OnJvjUilHSPKh6CILmRLpSJzB3gRBWcUWRFpmm2BEIRiVEAwHNwqRsJ/xyTTD/iher0DJ34KUj62pNFrQJONUacsCcV8YDIHlUpWa2EZvtPj/2XuTGMnS81zvOfMYc0bOmTVX9Ug2R0mURIGmKMi6FxJwl9cLrmxvBGihjbzQUgAXWhg2wM3diIBlwBdaSAYEydfy1ZUoipSaTXY32dVdc+WckTHHmaf/9yIZpexik6LUWajOVjxAVUVEBk4e1B/nP298w/ulcYr0YPNGm7rpUPoaoqnQ64853pugVJLDBz2MVOPmy5s4jk5Uxji+jjQVyDVMw8PXHGa7U7yhyXq3yfpaG69e48bGZWrYzHZnSCnQa1CJiobdRJYQRwmVrbFEjct6FzGVVKZCXlXoioovTZypjlloTPSUwCowdA0ME8e0iI5i/Ic67jEYuyBap38UD2orNabZ9EOv9dmCcPinveescDpbEP60cJrbGMwjUKp6equdO5AvuNgsVnDBM+G8I05zQfJR70ZZCKePL985+A7VVVBUkIHCqtLkYPcQbdXj7v4xbWETOBmeY+B3HDqaS48JL17eYjVtsrnVYXu2jAwlLcVG9ARXX9rAsDTapY9imRQip557EA+xpcO1lTX6ScAwGpGPK7TsNDJzMgpYsdocRGNGZkRwb8D6SocgCdk/7qN6Oo4FOjoAACAASURBVKol2Xs0RjQN1m4tUcqKg8mIRruOOi44uDvEW7JwVIMiVmmt1FBaOv3JBKGqNGseew+OGM9SNlfadBp1ZtMAu1On49foDwdYLZ8r3VWmacS9e/vU1lzG6YhCqViy6jhtm+BeyEjPqNoVVamw3++zstLE9kz2Rj0CE2qxjqubKI8VGolPdBRirqkcFjM0W6VMVNqxScPzkCcZ2kzHyEtyE6SEsgQ8qDfqp7MFrQ9ngvnTbAfORpzmg37n1gVFUTx5/aw9wfxYC+H08WCxggueCecdcZoXXi6E04LnxSybgQuVAWFRYqsKUgWynK1mB+WkQpewO4o49Ea4ms3+oxn2YUjtks1//va3qXserjDoTyM2N5YIhxG70xG3Lm+SJTknswDZVoiCFEWBUEkp9BJT09lY6sBMcNTvI3xJpsWEjZRQzenW6lRlxk4cIX3w6joileiuT6PpkhzHJEXK+nIDr7KpPJu1T3QokIwPQpRScjgaMR2ESBUaLY8qyqkv1fnEtWsUCIZRgOPVaCo2g8MxDw573Lp5Cc+x6FUTWk2XIEtJxxW2Z2B7Jt2iwca1JUqlZOfBIdPlDGMJIiXjIB1xqE1xXGjPXOxjHSUG3VShFEyclFyvsFSNmmERNkriQY7ZgEoRRNunReH6EKwxFAq8du01um73Q6/1T4pynrUgONtJN69jSpLkA4UT8OS9tm1/6PNb8HxZCKcFzwTDMM7Vd+eimGBeNOG0mNT+s/PK8iuo+wqaJ3GmOv1oCraBP9XYV4esdptMpjFXLi3R9hpsejXGXkh4kmArFZvXuqi6inqcs/zpDcogx6/7LLklR5MTTMfE8wwwJIomaPk+pSoZiohG3cWtHIKVBKPusJQ5HFRjeswwcw3T0BHjClu3aa57REcRdsei3nVIsgyraZEkKVqlEY4Lag2LvJ+TFgXdpRpSVhzFGaaqoxk6ZVZxEsRcdWrEQcZgNMKo2yzXfVzX4nF8zAuXNtE0jb978x3QYGOzw5psYb5ocTIcEU0LjoIp21eXcIXF8mqHUOtRpILSlFSDBEuryEtImiUzJwdPRegp6dUUOyvRhIYSQUevk1GhqAq5JtBaGnHt1PBTmmAHYN6DX37pl1GV861AORtletq76Wx6znXdJ8XgHyScdF0nz/NFxOljwGIFFzwz5vUA53FjVlV1IZzOmXlH0EI4/Wy82HkR56GLVkRsLvk4qkVddziOA9auLPHWzi7aisIkrGjoHgfJkOPjES/d2uTb37tHo+1Ra9e4tzvgldU6+0djatsOhQmO4zPOAqxMJyEnNSSWbpGoJbNZxrpVpx/0eXg8wFA0TBUsTcFyddorNYwDSd6BpucgDInRsXFrFr2jKUquotcVvMTA8k3aWz5FVbAvxnilxnA8IQxzfMtFN3XWvAZxWeGtnn752YmOMQ2bNcNnksV8Z/cOOirNustoMGBlq8VKs0HvZMZxHNI0S5iUXL7cpWV63N07pDec0lx18VSTWaCw5Xco6hkP44iyoRLGOZVZITSVwhcUtiAxcpzEwDjScQubaTnG61qI45LCLNDPlDyqGpQO3Lhx45ms/dn03NmI07yO6axX01nhVBTFkyj5PJV3dvbdgovJojh8wTPhvDeF+cTxjzrzCekXgblwWvCzoSgKiq5QrcHQibm+vs6arLGm1Pm8f4XrVzfRQ4mmSHYGJ3QtB9uxMVWd9UtdZjLFMCWt6zX0StC50WDUn6K4MAoCLOPUqmA36RNoCcdywsmgR5hGzIKQUgG/4XDpyiqbW120tsNm0aQrWgjVoC2bjI8DHr59SCYy5KjAaVlsX1kiqTJyX6dWORyfDHnzrQcwq+g0G1zprHD5hVUuNWs0TI9pXNF1W7TMGlVZIRMVXzOZxAEP5CFaSyA6goNgTJhUeL6LHJd4TZuXr24wHE4ZFyU5gnSakGgZ168so+Uq+aDgF1+4iUwqjkZjspmkOdVpC4926LKdtnFSHV1AJSHPCiqt5GG8Ty+a0O/NqLqSAoH9EJw7YD0CPQIpoN1un8tan70uznozPd1JNxdL8+jT2XqnpyNMc+G0iDhdfBbCacEz5bxuzItU3fmzEE7/MsbpmGw1I7sM/c2YSTsk9cCuW0RxRqdyqLsemqpyqb3E/cMjCjXl7QePmEQzLE/nzb3HFE7FO48OSaoU4WhEJAirIjJzFKOkdAWKIkhkTlFTWarXaa43EUs6HbOBWerc3Tmk6pdYho3opzS7LpurHZa2W3z+kzfQVZ274xOSacK9cI9hPiOtUibRDM3VuPXJbT61fo1YF9w97mPNdI4nM0gK6q5JS3eodX1YMvns9et4hsXBaMxoEDHNc9RKwWmbfPqFK1RlxaPJhBLJ5GQCQvJzL11j0B/zrTv3qfkeK40O2Ard1TajSUCvCCCQrBguVmahphIj0fCGBv5fV3Tu2Pg7Kk6gY400MqUicQrKZcEgmREqMaUH+hG4e5AvQ/4JuDO9cy5r/bTtwAeNVjkrls5aDswjTh8knM5aFSy4uCxWcMEzw7ZtsizDcZwPfayLJJwuihhZCKd/GRoa5VKJdEHN4P5Bn5puc9SbkLYqJnsxVa0kncK9dg/TMEEouA2T3tEYo6ESeyWyCjHXLI4fDaCtU04qhAO+ZpD7CnXHJE0qGp5LcpJDDplhMzicsb29TE21aG/XUKYliquR6QprRo3d4z7BJMHZ6lJPdLKaQ6FVDPMQ4Sl0MgfDMjnqTdnu2OALtLLi+tUV9L6ApoaheWw7K0zDgNvf38H2LO5UMZZnUN+usxrXGXsF0SClVCQPZnsomsb1yyvEIufuYB+3YXFvZwfFVPny5z7BUTLlH96+i91W6dRrvPPOLqsbDYqGJMpyIr8kK3MKVzDIItRVgb9TYmc269tdHKFw7EzJhMANdUxM0nqJyAqiF0EzIGqeGpPeG9/jU2uf+tBr/XQt05ynbQdM0yTP8x/zbpqPajkbYTornBapuovNQjgteGb4vk8QBOcinBY1TufPQjj9y7gzvINUJJRQSwzaI4tffPE6b6k9ZJLTXlmhH5xw9cYWB0d9tlY7RKrEMFXMtkkySQjbBdPjGL+j47ZcZFsSTROkqzFNYuyawXgcndbv5CqW1DF9nUkc0uzaWKrO/VEfJ9eJRIw2Uamv1DDRqOyK5VWP/nhMv4zY1tuUSsUgnLLeXaJeefSzMe1VG3cGD60BUVhQs3yOoj4lEqMjCQZT9tUBbsvisrlEoJfsHJ2wXDWoRMWGXUe51MbCZPfkhGCaEg53EZqgWhcoCUwzSbvmomoKhqFy8/oaB9MBx/sDbt5YJasqhknAjIIGJmVdRbFVoiBHkSB8ydLAIB9nGKbKZCUhcnKQDp/Kr/FN/T7SLZDJaTeddAATsio7l7UWQryvlml+rVRVhWVZT9J3hmEQx/GPCSdd158IJ8uyAJ5YF3zUO4MX/PMsUnULnhnnOXblIkWcFsLp/PkonOcgHUAOegjNExMhJe+OjvmNK5/BPNaQWYGFSZaVvHTjEu/c36W0Sn74YJ8oSjgezdBzle2VLiIRzAiZDEJkUyWXGaqrUpiCzCqxXRPVU7HaDu2lBkqiEk8qDg97JGVEuloguxramoVewNsHj+jtTShjaODQNDy6rSaWpbPa7vI59wrC1ZhFFW7mMqkSRsdDRCOnlw85Xo6IjYJ4kLCfjBgeR6z5S3RabVzX4NZLG+iFIPcVdscz0qwgH8Y4rsULVzYoNMnBdEpKTsf1ePHGOpdaLb777kPG/ZBgGqKUKpkKbsNnEAXk45It28ea6oheTvEgx4wBCZFX0V+PGDZDxt2MpCYQgBFL7ofHiERABooCZghGH7THsFXfOpe1PtsNNxc78+66ecQJ+DGTy3lBuK7rT1J6ZyNOi0jTx4NFxGnBM8PzvHPzcrooxeEL4XT+fFRuNLfat7BGBtIsKJsK/ZWQ3XBKM20i1lXe2d+ltuVwuLvLy+1LJIrCUX9AY9VDcxXq2w7DRyO8yw4VCqqvEQQxViYoJSiWQpUXSE2eRlFKweg4wN828Zs2rYbPLEkZhwPC0ZAsq2hUHq36Csu1Os6Gywp1dpURa1WbKhc82j2hWWsQmim+oXHlxjJXqi7vuHuUYxMlqnicHlGokobd4ZWVyxy6U1ZEh9Wkwf34hL3DPl3fx1BP5/FdvrSELjQeJsdohSQZztA1lY12k0pRuPu4x4svmjSsFrdubjLNZjx69xBnxebK2ir3fniAqmW0XINhf0zVhLQBCLAisDHJqpJQlqhXU5Ikp8hKNvw2K7MafTEkrRdUFRgnUHsIiYTMN3ht5bVzWeuqqt5XBD6PFsE/+THNH89fVxTlfQXgczuCs111cy7CdbfgJ7OIOC14Zpyne/gi4nT+XBTh9FHhSvMKxtiECqZVwkgkRLLkzuM9LnW6KIZKW3V49eoVVooaL9/cRAhJViUcHUyYxQlVUyOvCrSmiowEta5LlKRURgWFBENBiAorVymriqXNJqlRMhgFnIxGHN7vYUkdyzbxV1yWXJdZldBPEhqlwygKOXgwoCoFZZHhb7i8en2TUObc7p2QHeW8c/SY40c9TBXWqiaNjSYra8u0M4/Xd+/y5uv3ycY5QquwDIWbt9ZQ04qyqSGFwWg2ZffuDmiCJbeO5vhIobCldrimL/ML165zvDvkv715G1NqlJOCqqWSNkseBztYRkyYJUgSJiYkG1DVQdiQ5KBMBWZgYAGBXTJezdFqGsqBgF7OrFmSTys0DQRQLoM1BLtv0bSb57LWc8Fztkvu6SjT04/PPp8Xh5+NOD09xmXBxWURcVrwzPi3mKr7qERHfhYWwulfhqIoiEZFZYJiqtQri6buc6Va5f7RPnZDJdckugqxnvHg8RHmisVer0/9Ro2kH6F7OvE0Q1sxyDOBWah4pkNc5KBL8rzCkTqqoyFL2N8d4NVNdEVBr5msXV+mpTs8OO5hTHXa7TbBcIqQOUeDPlbX4vKNVTxh8dbJQ6zSoien5EmMUDIU6eJ0PDyn5LPNK9x7vIe8V9JcrnF1ZYOeOUEPTMqThLeSHQZVjOcYNC2TSX+CY5oYmknp2qwsN2kXJqUaYhQ241GCNFS2b1xhpYox6lO+Pfoh2TTD0xTCg4pKQCuFYgIzt6LRgigEowJLVUAzKTYFZqFhRhoyEUgJjcDCyzXkdZOwWVAKUA0o2yAaoNugnZzfZ3kuls7WMhVFAfyTkeXZeXRzdF0niiI0TXtiTTIXTvN5dQsuPgvhtOCZEccx9+/f50tf+tKHPpaU8kkny0ed+bl+1Lko/6dVVT1JmTxP3jx+k+RyitSgCgsmWUlkZfzcC7e4/e4B41lMlOXsPBrywq11dNWg2bCZSZ/gOCAxSjqaRd4QVMMSt2szHUf4NQuZKyR5BpYkswTBOMJCp73iY9VMVEPFFAYneyPUJUHDsOgut/Aqm3Er5hPmNSZhwN3be5gdE7uhkTVz6sIiCCMmRsqGuULTduidDFE0wZQQpaWw3OjyirnO0XTMe/f2ubS6SnfDZxztIdyM6TQisC06os7SWhNmBcvbDnqucPvgCGGXXGsuEYgCv6j4r3/3OqoukeswkgnCh+VYwzddfNMgzRP0DRVrLCmHkoQcsw3qkUQxJVFUENkFtqnhHeqoDQ2/tJAa3JvsI9UKpQ6lC6ILWQnZOphBwc5oh436xode67lR5dmU3Pw1TdNI0/R9wmkuinRdf99QXynlE+F09kvVRZi7ueAnsxBOC54Z85TVeURh5se4KBGdi3CeF+X/9KNynn9y90+QS4ABsQ3WTBKHBX/+6B9wrQaGYXDVaKNvuiiRQNYFD354SL4kmfk5mqMwnIXUPJtIFhj9HMNWmAxDbM/EtDWSuEJKgVUzaZkus/0UT7NJZin2JZOta11G0xlxVLKcqiRpQhHllJ2S1W6L0M2QacGDrE+Zw7plslZvkiR9ap5Fu+bT0wNuVh1OjsccJwGtTCFu5jQ7Llcaa6yHPj8Y7HE4naGtqDiWiSxgbbXBUI/pFzM20jrNWMU1S2aTgDf2JnRrJqZmYasV/dWCwhQoQAeV0YHkhutgGyp3RlNuNXwSkTFVC4ocrAHEuoLvqlieTpaVVLJCS3UuDRpst5cJaiknTohhKih5jjQBDbABBcqi5CQ5YbOx+aHWef6F4uzYKF3XybLsSQpuXsc07/Y1TfPJ+85Gcc8KpwUfHxYruuCZUa/XuXnz5pNN5cOgqiqGYZzLsZ41iqJcmPO0LOsjf67zm9XzPs8gDE6LalQoFXAFeBX4j1ISveST9W1ODmasXm8yTVLKWLJ6aYljY4IiM7K0wvN10mFJvWYBKmqp0WxLZrMMJ1fQHIWa5RIdxIz1gLrroHoaLa2BmJYcHwfodclSy8NxTEIjY8Vpk4YJP7j7CE3V2Fhv064cWutt6kc696oeLbOOq1rce3RALDJCTUPzNdYuL/Gaf5nD3pDvfe8BhqKSNHxqts0r3TqzUYho6IhRxd1oj/gkwlFVTnZmvNfPqddUuppFt6Uz0UvunExQHQU7EAQVuCrcGnscqjkzO0GzLNaWbVYbDe6GI8J+wWpNQ5M2ouNw0pui9iX1FZdEpiT1kmkZI1sq41nEtrfE3u6IVOaUNZAAKRCDlVo03eaH/pwIIZ6k2ObpubMRp6cLwM+Kow8SSfPXzgqq5/0lYMGHYyGcFjwzgiDA9/1zOdZF8T+5SDVDi9bofyH3JWSADmoB+hi2+gpTWxLLkl5vj1mmcXI34bOfvok61sAtkWMF1VewdB01gvaqT3CYsbRiIQTMRinNZY9ZEONMTCqrorvWoqhKgsOYLCjwNZv2RgPH0OhNAopcYZbFHD48QbFVthod1m51+ERji9t7eyiRxXW5StbK6B/PiPSQMNOQyxqv1LZ5dGeP6TRFmer80HpAZZXUtj1ec7Y4LAN2D4d4eU690hmfRLSlQjzImFDhtk0uL9W4tKKwN43o9XIspURWggqFy0s1tJqOFQXIDIJY8JJf4410wuMqpuOZPA4DBnnClm8i9hVs18ZwfSI9ITFy7NygVEsKq2KwnPD69C7ukolRd3A6LkUSoZbAmNMWpxCc0GHVXz2XpZ5Hkc6KpbkL+Fw4ua4LvD+q9HQBuJTySdrubNPI4rq72CyE04JnRhRF/+aE00WqXVgIp5+dSlS88973oAIKsDJQBUSlAikUgcSxdbYqyQ/TnNnjEdqKxe7JlKopUDOwTRtFg/FeQHezQXiU4y9ZtFY8ejsTNA/MpolrOEz2AlorNWodkwyBTAp2H/YQHniOiRKmjKcFatPi1Y1teodjpv2EwZUIGedsrbdpCIcjJ2N1dYUNp8nDgwMe3jtitDamckvKlsLVooVeGOzNIuqlR14HIxO0WzrT3RGpq+FUGl7dYkVabGoOk5HgH5UBnnYa8VFVaLUtutLnhZVlvt8/Ye9RxIZXZzaLmOqCz611aQcFcRWAKTk0QpIZTGSO6sD0qk6ppyT1HHFYMptEKHWVMqkQbRVVy1EVGD3I8G/ZKCZIE5QxkIHQQfEVfON89pt5xOlsEXgYhk9qnObF4/DjdUw/6Zo660a+4GKzKPNf8Mz4txhxOlsY+lHnogin5x3Fi4uYP3/rzznODqA4NV2UFSgaVDlclTU2TYMl12J/XaHyK3aKA753fJ9itSDRcpAKFQLT1vBXHHr3JtTWbYqwIulnLG3XSaMCJVAok5Ll7RZqqSJKFTER1HUfuaaSawmjMEQKk9VGG0foKIXCxqUlPvvpGyhpxcEkxEgNQj1m1ptiqgot4VLb8Liy3SVJMgZRTDwoaPlN6i/UMF+x2Fqqcf/BQ96+/ZBZb8yK42B3DerrBg9GM/aHGZNpTF5m+DZEpgAUfmF9HafwOExzSiHxdRO7rnFjrcVas0bLlPx/9x5hz1QuaTW0yMStVDxDRe0YTJoKlSyZaTFxVsCqBg2JmoPbtqlOBEVDIbHhyuVlXhLbaAXIAPQSZBdkHSIjYme286HXe35NnE3JnTWzfNpmQFGUJ3vTBwmnszPvnv4dCy4mi4jTgmdGGIbnKpyed43Lz8JCOH38GCZD/tM3/hPT6RQaoFZQAmUNGi2Nd40IXUoe6CGztGTqgK8ITAcm0xl5DuaSRVUIqkxiKzqtqzWO7g5YvtZGFSqj3Rm1joOmG1iZymh/xvJKg1IpKPOcmSmIT1Lcro3TsGjnFiQKTatGoQse3j6g2ahRtwzWr3X4hbVrvNc/ZlYJNjWXN47usDvos9xts6o0cFdzWiODd3fu0zsKQUhKjvESheW6SjzNeOBnFDNJrEvyropRaewWBd5Mxx5o+HWHMKkIrJLukssl1+DuyZAiF2xYPn9975BVy2Q2KyksE8fS6FQORRAiMvBMg+FuxsaShz9r4ioxgzigoMQyHAqzRM0F2pJKWQnUuKTl21yaNtF3wF+GwgFRAxqQD3Ki/HwMd+HH65qeFk4fJJaeFk6KojwpMD87umXBxWYhnBY8M6IoolarncuxFhGn82chnH42dEXnvfhdynVAh6oAy4AqhcN6QZqDWqlYueQFzWLkWoyDmCIs0XQF4UjyIkMxNAx0ZAXaVFLfqjE9nmH5NmpbJRpm1JdcCkNg13Qe3zum0fZwDJO1bhcr1Wm6Pjt3hkSdgsQoaPk+jcJh2PK4urrC3//gHbJScls5oGGqCGL2335M5gp8y8SeRgzilGS3YMmy6ZqSQRPWUxNyjZ1pgldXaWoapqkiTdgPU5S8ol1XUKQkqZU0Qw2nUrnRcHj7YICrGFzuNNkPYhxfYVuadOsWal3jslLHrCrupgkblo4vStJIo5AVHVVjO13DW2qQ9/bQ621OhkOKeo7mmRRpjoKkljusry4xeRyzX+9TOuBGIA1QdZD5j9Jr6vnd0uYRJ8MwnkSc5qm6s/5MPy09N3cPh/ebai642CyE04Jz49d//dcZDAZPnj969IgvfOELADQaDb72ta/9q4+dpim9Xo8HDx586PN8lgghyLKM73znO8/7VP5Z4jjmu9/97kdePBVFgZSS4+Pj5/L795N9gsMpcgXwAQOUGKQAd6ZRM0G3TS69I9gxC4RV4XZU4hkoiUTZgmkJiloRzUI0pUBJVQxVJ20LpuoUMoHTtdg97uMrBpais7HdxpAKg/2EwWhEcBLQ9WpsrzWoeRavnzymio/Z0o4QEXx/7wTIaWs6R7fv8g9lgpCSq7UOblvnjp6T7WS0NRCajqnDXpDjzARSEygdhbUXXKKkpH9SoYWSa6aHisTSFWIVKkvBjVXGYUleZBiBwDV0Kr9gJxugt+EoKAimCdcdh7ujkLpuEJsJI1thGsesZRrjUuDnKu3URL+sc6fa59gbY1omSsNAmRVIkVOoAgqJIwy67xpM3YhvW/cpXoRwBuggDKAOqPDH3/xj/v3av//Qn2nf95+IJdu2n5hhzi0IfpJwklL+WETprHCaR6s+6tfcgp/OQjgtODf+8i//8n3PX3vtNb71rW+dyyZx+/ZtNjY2aDQaH/pYz5IgCNjd3eXll19+3qfyz/L666/z6U9/+iMfyTs6OiLLMi5fvvxcfv8kneDmDpmMIQPVhGoGzRKuKR6PeiGpzGheWWMYDLlrp7RUnXbHoueXFIMKewOSDFAhm+S4dZ1RkiA0KGxObQ5mKfWWhRJLRFHQezSk3bbo1DUmhxNyWdIbHiGHFUXLwHUFvpAcTyaEQLupYuUq1bjg0tThlRWPumFgRYK/TcZMioJOW+fFyKXUNVarOs5WiZGUlJbOP0YnVFmOj0pny+O6WucHj08wJDQsh2wiMM2Mtcxj46UWB0S4I5Wrhsa3Rif0vRwlESiWzuZyg6tqHT0P6RsxUyGoZZA2VK7Wl1HzkECWxI7Fo3QMTTAUnTwQaE0VFB0NCc0KkcOkF3EQalh1nbgoUBTAgsoHXE79nAyYqBM++dlP4hruv3q95+InjuP3pefOFoQDP1E4zZlHn58WTouI08VnIZwWPBPOO49/UbrVhBAX5tvkIlX3s9G0m7gPdcpAZfhJgVDAsSCfwGGRoVsaYVRgaAa/cmWDk9lDtlyb+2lKHFVYNcinYBpQVuA2FYoB1DdMwiSnVMEwQasgjQpWaj5+pZNqJUolmQwSqEkuGxbxTFAZKrapQgC+brMkYc+JCWPBRBWsS5Pmhk2qQq5oLEubbT1jyTBoKA53BxGGhGbLIIsF4zDFzi0avoG0IIxLomnOYTal0XRpL1kY+zmf3+zyX/YfEYoCPVToD2M6vo0Tq3iqgWiAmyi4hcowDvlmNGMLk5mWM1gFvQb1XONvdo5otDQyA4y2RV0zaU8NnKlG2pTsT0dUliAxCqR62rlXdhXslo1bmhiFipwItAJKFfIGYHI6Q3A8xdbtc1n3s91z88fzPUhK+b7H8/3u7ON5au6scDo7umXBxeViFGMsuLCc1415UeN0/lwk4fS8z1OWFfkVTlNCDpgSmEIUlFyqNFYtg1911nkjnRGXsJvGNE0DM4Z6ZWJUCqahoOmQlgKxUpEd59R1k3qkYR2c1utkDcFRNuNoOCYJTzvYzLpkOi7IcoX1tsdrrSZ6qFIMBFkFUaHi5i6/WNviZb3F580W06zie5MxB3nKiR3ihxbqzOQL5RY/f3mT/3j1FrNC8lZ/xG4WIqoc+iXNPZXrqkcmCuKi4gW/yaOdgF6R83h/jFEYmA0Nt1K40vIY6DGHRcSnzTrlwxwxktiBziwSODWfz125jmkaWCWsT2BD87nkuVghpKZkNkz5lbUX0I41VqZ13BMddSgRRYWRa4joRxG+cUmvDNh3p2ixgvEQnLugBaCPgAkwBCd2UJUPd/3NP2tn65qejjidFU5nmc+2g/en5ubP527kz/vzvODDsYg4LXgmzNt4z4uLJJwuwnnCxRJOz5M0TUGUCIfT8IcGxQksuxqOKblf5ogA/vPJfa63fHaitYVXzQAAIABJREFUCUVV0hiWaKs2vX5K2YVCgOGByCBSBdoqZEGOJRSsZY0yrxAVSBc0XyU9UVhr+BRRyXbbZa1d4+HjKZEFuqqwVXN4wVnhe8kAUoWVrkeUlMxcnW5o8XLH4LVY47tZwNtFyIrjcNCcstcPeJAPWNEdbm61uZVZ7MqSoYxYVnXEYYamq9SaOnd3ByyvO5S1gqODgC9sr/G3u3sMy5QVzSTIUjxH4SRP6dg+l9ZafNFc4fZ4xPeVAd9Wj6mjU1UWgV7xeadLVk15Xa3oWj5N4fIP33+PvFYS13PEoKJZc4lnCfkyWLlEhAJ1TWeWZyhFgrAr5DpUEeQqaBpUCkgf7j26Q1ZmWLr1odd9PnduXtf0tFj6oGvnbMT5Jwmn5z1zccGH52J8NV5w4QjDEM/zzu14F0k4LcTIx4dKVPzNe39DtVyiFsppLZLkdIDtasXxsiBeElQO9IdTlo4TalJhPVOZ6CVRIsEERZx6P6UCNBMwQaZQ+mDrBlqm0/VcHF3BHGgUkYK1DL1RwLTM8QyDMMypZEVpC46TFCE1btRarPkOv9HeYBAm7EQRrm/QWHd4NBrzRpHwmSuXWF3y+K3aOm8e9nhrOCSjZGYX7JUhb+gzijClY5uolssnlrf4uStrhFmFZWkcpSPeKiYcLKX8/XifJK1Ya5m85Hb4yvUrLF2qE2nwP1x6gTwr+cuTXRqOgwC+Pelx4KZokUQd6shYcKcIGZQlV+1V/qdXfw1hCB4oxwxWYkY3CpKNisQTiJOSmu6gxgpiWKCUCq5hUpqnA36rDcAGJQcFwIB3gneYZbNzWfun03PwwSNVhBDvizL9JOE0t1RZ1DhdfBbCacEzIQiCcxdOFyEFdpFSdQv+ecbpmL/4b3/BuIihlBCAmkNlQGhAoUNT0XjR9nmx8LndgkFUkSgG7VDjU0qTddtDyU83W0uqFBH4pYoEjDHkWkmpCeJhgRuoNGsmrmUwOSmpNqFol/zgUZ9JFbPeqvGi28FQNLo1lzeyPg9HEw5kzmfqy3x5a5Nfr23x/cEJGTBYrviL9AFjM+Nv4ym/fukmv3Rzg19ylhnLmMjNKZyMEMGG3kR4CoYm6B9FzPIS3RGI/DSNaHjQweDnO016Sca+kdMVDncfjBjJgvtxQBDmZIpAxhlKLml6JnVDRYwl/3H5CkmaUyoVn7t2HVva/F9//00GMsJccUjTDFWDaJRh1QykITFSDce1URyFkoJaYlKpkLch9sDMwTji1NFdhThKGaWjc1n7p+fQPT2w92xd03ycylmvpg+KMJ0VUgsuLosdfsEz4Txdw+dchEjOQjidP8/TMNA3fQ76B5TbglIX4INQQKjgZ9DtweSw4jDMeceIaVYahqayIVzWZjajaUTb9RAloIIIBY6pI0cqegG2oaIEUCkSUROkKmihioKCvqkxGRaUlcKVm3VqUuXO4ZB+GdJwNV6tLxFNM6SmkTvwg7DPg9GYv374iBuOyxe211krTHbzlFBWGK7kO4N9fvi4zz/OBmylLq7Q6EcFeZixlpeMRwFvjHo0EoWGq9FLC7qVz6fFEuUBZKlOlGmMg4JMxDzcPcJUJVc363TQedGoE6k5bxdjPpu0uZ54RFMBhuStYMDfcsh+s+CLK9dIBgHLLzXJItgYebyQbdDYtakXDlVUUVkKgZEhrQphqWSG4LAbUNVAeqfdiFoMZgmaPHVyx4JxND7Xz8DZIvD5tX3W2PJp4TT3eSrLEtM0F8LpY8hih1/wTDhP1/CLxEI4fbzIqoxJPqHwIO1wWhVqghpBY6bS2lOwY4VYVCBKiHKams5v6kssXepyZxaShYKOZ1ClUJmghAKrpSGj0y56ZUmD/LRmRzMFfRERpTnKUGC6CqapMt5PcJs1PrXdZZLG3OlPeWNwTF1R+dRGl1fVBntxwiBJeKhM+GEw453ekGoEr7hNXjA8xIOIfhSgtCWbLQNlJGiPDZxc5RPNNoeTEoFxGsmxbFrUyHSVru9xeVznpl7nxfUGRqxyo+WR+CqP6zmfUH3efdDj/9x5j8fTEXqicpBk7IkIGUpMLLqGSbvtEFclOBr/uP8I0TV44/4O11ebrFZdPlFcodNzUfOKMMwwmjqFUhLMchRLoEhIsgoJKPJH3XYdEJ3Tx5LTv77z8Pw81J6eQzcX8WeNLZ82uZyLo7lwOlswbts2juOc2/kteD4sdvgFP5XRaMRXvvIVbty4wVe+8hXG45/8bW42m7G5uclv//ZvP5OI00VgIZw+fggpTru7FCA//aNpEESCQodrhcUX9AZ+Q+f/bQw49jP+b4a83FzlumUTiwSZqZgaCAmJLShnFVZLpRACMako7IrSqMhnkqbvYpsalCpLgYEfKOBq7EwnBLMCU7XY6tZR1Yq3R31uH55wW5ugNwssmWOlOhNRIIqMR9mEw8cBo8OMTrvOy26bKpC8M4lxllwaLZOihPtKhh0qLFkKllAofZWfr69gOyqzsqCtmTRLne89HNBc9hjnFW9HQw6siMd2ief6XF9v83PtLiulSk2vMNWS5dzmN1fWOPQLXp8cs1Y6fHHzZX7pk6/QWPKpWy7e0OCquopSCFa6LaQD6hJkRY5jmJi6BlOJrWrYhYYanZqPokCin0aeZAkYQBP+9PU/5SQ6OZe1PxtlgvfPmztbq/RBBeBnI07zfaFer7O1tXUu57bg+bHY4Rf8VL72ta/x5S9/mXv37vHlL3/5p7p///7v/z5f/OIXgfMvDr8oLITTx4uG1eCychn3UMF4DNiADi4ma1qDQoM9q+CBEhD4KmUpyXUYJjnf29lD1TVmcUKZCxTt9AZfeDBtlMhUUNiQeeI0PZfB0rpLMa1wHRu7rhFMT3N8NUdFkxqPwhmBSDAUjaAQrDdrFH7J38x2+P50xK5MQE1ZqRu0XZtX9SVevbnGqy+v8anNTWpmg5uby6xdbvCeOuW9WUDWgPaywy9euYyHx0GcIl3BnaMh1VQSaxW2b6JkKt0VF6+hUit0araOcMB2Tf7nqy9hGgZviJBPNTewK523wim7dsCf9O7R0wt6suRW/Rodrcb/8Q9/x7Lp8d8t3cScWAhTMPMyQjdFDcCMVdRYxcg1mqYLgYJIlCcDlhmAkYJmQdGByv3RzUyFh6OH5zKz7mx0af5vVVU/Zmx5Nm03tyyYCyfDMJ7UO51nl/GC58tih1/wU/mzP/szvvrVrwLw1a9+lT/90z/9wPe98cYb9Ho9fu3Xfg04rXE6rzl1F6lTbSGcng3Pc/3btLEfKihzh28FXNXk88s3WUp1hmrFXpZTz3Q+G9qszzT+15u/QGO1xrEQFLHA8iCLAQ9KAaUGWUtFnYEjDbQGGJbKsJdQ27KZxClKoLB5o4miwslBTK6ltFc1HjHjLTnkkTOjF01J8wofFd/TuNVokDsGjwYpvThnq95ERBXvPR7yZjrkEg4iLnmwM2SiZWyUDnVF46gfcFuZ8iVvk9c2l7h33OdhOObfXbmMaWu8NenjpQrTUcxfP9hHVVS8qYYca0ymCf/lwQ6TWc6sqtgJJ1QVGKYBKnQUjZai4Zkm/+Ezv4Bl2jRqPvv9CUd7fdhU2Vud8I/iHm/au0TtEreycYVOmuVklNhtE1szKI4rkGBOQQ9BS8CYnaZOlQTUBNJRiqmdz0DweXpubt0xF0Rzj6enRRTwJOJ01vdpIZw+Xix2+AU/lV6vx9raGgCrq6v0er0fe48Qgt/93d/lD//wD5+8dp5ddRfFigAWwunjSJIkaJqC0DitcdIhk4J3hweML3m4Nai7ks+JDjuuYKAX/G+Tu2zoNX55a4tuxyUdljSaGlUBmgGqALUQKMsqaJCIirwm0Nc1Du5PqbQKqwt7d0agw+oNnySv2IljWFKRHXAMBcW0aZYeLzXXaIYmDi6vLq3T2rK4tuzSPx5Q5Tl6XaHhwut7R0Rxgd40eElv8CllmfbIZKiXpFrK4SgkCgVDRdBo6nzvziFHxyF7VYAwVNqKRWe1Rr1jsRm5fGVlk5cvL1HKkquWjbImedscEI1ivqD6OIpBIBWuRQ0+Y13if//WnzMJJvwvv/xbXKraDNyYO0Gf28ePidQM6anMlJRiSSItBd3RCUmhkni+hb1qIkdACc67YN8GY3I68Ffrg1JBPIo4DA/Pbf3nYklVVaqqet+w3g8ao/L0z+bHmD9e7A8Xn4UEXsCv/uqvfuAA1T/4gz943/OfNAX861//Or/xG7/B5ubmk9fCMKTT6ZzL+S2E04LnhZSSk/SEcqNCkUACSgSN2GSYpKxfaTA8nlFUcCeeUDqSpIRdGfCL6iq6Ck3T4ThNKaIK3QMZgmkrSBSKoUBd0sCEPD51KNe7CmqkIDON9RfbKLkkeJjSuO4h0pgoKqAET/doNjTqnouZasQFSFOwN50xHWeMGw7bqx3icsLJJORvyoSXZIOb2BwFASO9YF1ovGrWueflHAcJfZHiuSqKB8OjHDfRuLXpMdIEiYSbmcd3wyl7ucJ1y0eOC743HbHu+xyJGfePJmTA59o12vVl3pztMooKug2PzsYKb753QvH4Mf9P4nJbOaanjSnNim7m8WJ9m0fTIwq3JJApmqZgGqfdh6WQzJKYsikorFOTUFWAG0A2A7sE0T7tdsSHftQ/l7Wfp+Hme9DZ2XVn03Ef1Dl3ViydtyHwgufLYiUX8Fd/9Vc/8WcrKyscHR2xtrbG0dERy8vLP/aeb3/723zzm9/k61//OmEYkuc5r7zyCr/zO79zLue3EE4LnhdH4REPqgekW1D+aCaaWoJbWqw7NW7U1nn36IhJknM4CXjNtHhDF7wwNbnbmPB342PqNY1Wy2RcpqgFKB4UkcS3VeI2xEmBJsE0VTQB1URg1BRM32CyE2N0NOQyFIMSJzPQPWiVFt3C4Gic8fhwwM31BustlyCKsSKbW7faaIrK0e0pM0ewsd7mZrPBpccW/7W/h7WsoZkF2jjl0TRC1W2MuopRwdsHA/KWSq1p8Svrl3nr4Ji+DGms1Ln/aIbTslCXNFatGg92x6zVXU60ACXLqWsGmq2w3GiiKCqvBA4/8AWXX1yn1AS//av/PW/efcCOccLb412KvOR/fOlX2Lsd4E0sTu722dhuMmrGRM2MNCgwFRU718iqgsytoAnCgOhVKGMQg9OUnQaggGLA3f27fOnyl3CMf30H29n0nKqqT4TT051zc0E0Lyl4+me6rpNl2UI4fYxYrOSCn8pv/uZv8o1vfIPf+73f4xvf+Aa/9Vu/9WPv+eM//uMnj//oj/6I7373u+i6fm5ddRdpjMlCOH28GKdjekWPxAX5o9mxQgXdUTmMxrRHTT5Tv8a4/xhnWPFD+/9n791jJMvuOs/Pue974x2Rka+qrHe/28amu2mQYcY2DG7D2MBg3MzYYM8a7cjrkZC12qElJP7YFdpGaGBHYmbRrr1LI7wwFuyMGTS0xjbLGrChsZfutt3uR70r35mR8bhx3/fcs39ERZJV3e32dGVRFcX9SKWMx82ok/fGOfGN3+97fr8Q3db5RjTg+Bjeftc8uSv5k7N9WJl8qMsUDAfSVKJSEB5Ymk4eFmhSw24ZZEnO+qUBdsfE1HTsoUZjxaO3GVGNDPJEw6rbzC0oaphEo5zz2hjHhaqUJOdzUgWP1Bd41Ovy5XSTFy722Ipt7vHa9OoZq/0Ra0ZKsycZNgMujmMcofM2t4vTtnh+c4c/H6/ysLuI3TT4m/VtPKHxk9WT/D/xGp9fu4QbwErh4ecxwtB50GpiZQZ/Ndrib5o9rCyFep2/ePksZ5aXKIxlClvwrdE2llGw4M4RbknaVpXBaIQmBCeDLl4wZK3do1fNyE1FzfRYkDaraY8kSClqk3MZu2AvQrYOKoSiBkqDL/9/X+ZnHv2ZGxJOUyH07dJzB7/UHRRKURRd0+cuTdNryhqUzDblCl/ybXniiSf4/Oc/z1133cUXvvAFnnjiCQC++tWv8nM/93Ov+3uHWcepjDiV3Co808OKLFQKegyEoEuI3IJLckCxBG9fOklXq9ByC5RQDCNFZuush3v0Bj5b/QBrTscARAKaAVkKCKi0DExN4BeSvKEIPYkUikwTaF1BPsow0RBdk+E4xswVlZqDchTVqkMygNF2il5InDaMRUqS59x1usN3PbDAd3e7hFFBp1ah0jFp2QZ1p4KSgsBTqBr4CCytRlYIalWLlYFO+K0xdqwzsFNezHa5QkTQhLSe8eeXL03SjjWDua6BrnSWqnWOLNZp1g3malXec+YkWSNn1JG0727zvu99lFrNRXR0Lgy3GK2HLGsd3uYv0O+NCcyYbdHHrlrIQhGvh9TPWTSGNnog6BPi69EkRScAHZQAHJAOyBro1UktJ2HCC5sv3PC1P5ieO7hbTkp5TckBwzAQQuyLo+lx01IG1wunktmnvJIl35ZOp8MXv/jFVz3+8MMP86lPfepVj3/0ox/lox/9KI8//nhpDr+NmaUGv7eycvhCZYF2vc3ehS1UF2iBiiDUcxqGyTfWL/FX6y8zOhLhLeQsSp1l1eDdcoHfVi/znNqiahkYQqHVJ3Wc8MGqgpQQjHLQQatBDBgmKEdQqdhkgxxZzxjkIfbYoHCgbVewNZ1xllIkBW7XQiSCYDehv53iVgxsIbh4foAvJUZD8BazzaUs49L6EMtpUdkLKESKVDlDQ+I2TI76JsXxNuMoo7dg8j1+m1Z3j6/t7PKCFSHOS8x7DPq6Qrdi3rbmMjjpcW5zSFWDf6yf4v/1r/CVsc+SF+O8DNkJCHUTlUF9YEIGn/qTpzk63+SxxXuRqYVoaDy7/jKVIw3WdjfxVjy4MKTqONh5gbltMpiL0CydfhGSOQW4TFqsqIkZnBikCyIDTZs8tzXY4PzgPB3vzfkspym614oyTX1NQRBcE2W6XjhNMQyj9DjdYZRXsuSmEATBoZUjmJU+dVAKpzsNz/RY0Ve4vPMt8pOQy0n1bxmlHNEbtNIGe90UvTait7XHuaakujMg7MVUGhaGlaNUzlCBHAMV0FwogkmbELMKuSnIUZgGFBJyUZBkKVbTQvcFMpMMSJkrDCqORqEE/TjlnL9HEGTYlkXDtPiuuTkqwiB9YYj+Fo+eLckHCX+WX2FLxWQLYEvwRhYd3eCIq+i0KuTbQ0aRpJbYbGkp9bbJ7lbExa2I1NZwGwbzhskryRihwJ3XyYcGcgy6Z9BoWlweD4mHGUs1lz0v454Hl1kMHB59y31cGG0zqEScv7KFaVnowmZ9b4S95KHPmez2U7ajNcZLGcNxSu2Yg1CSXjLCbJhoA0Vzz8bUNGQBl/QBVCeNk0UfDA2IIauCkYNKQUUZa/4aj/DIm772B71M08jRVExNK4JPj9F1/dsKpyAIylTdHcTtv8KXzCSHWTm8jDgdPqVw+s454h3BisC4ug1ezyFPFIN+zLzwuLu1xC+87cdY7raJJfQNSW2hxltXjrBgVtAznblMp+lY1IeTXXlmBbT6xO8kA4WZgZmBVUBGQWwq/HGC4ZkIU2AjUBhc2BmwOx7hOoKGZdNZrOJ6OrZhsrMTE/vQ6bbo70Ssnh9yKQvYCgKCUYwoYBhIZF3jIXuOjmHxwsVtzlkxx2oNTigPsoy/OL/GWT3gWOjS1k3S9Rwxgn+w1qJuWVzZCXmlExJvBTip5IWdPl+M17BzAzP18GjwtY0NHNsi35bkvYx//6U/xwgLfuTetzMKA4JuwZViyIWX1/nhh9+KvgeVwKRadfG9hCsLfYbHU3Ycn71Owlp7SOJm1C0HfRW42thXxZNWNZoOuTUps6VskJZinIxv6LpP152DUaaDqbqDwulgxGma3pvOr6mQKiNOdw7llSy5KQRBUAqn25hSOH3nbFe2SU9C2gJM0DJwdZuFusf5rM/w3DoqU5zsd8EsGNgB+oWYbwxDdFNjeaHGy6sDKqqguuiR5xHJQFFYoCzQXdAtgSogFwpbCLKkQKtBLw8RDhi+IJYpc3MV4jAlSwsiK2F3J6VhOZysN9EqOjVlYm0XOJagsmShj3PamYlbbbGmAqJqhhkrnpM91uIxkaM44VpcWu0zSgraixXC+ZxaKBi9EjFX0bHmPU52OvDNkIf9RZ5v9NgiIDiqI0KNel0jbho4qsOi2WGu0eKFnbOkmmA73iWQMQ+fOU7sCF6MN9lWY9w9m3c/9ABrYoT/Fz2qsYNu6HiZy0YFeqGPbek4VY84ShllKVEtx9/LEAtAF5QJnJz4xXIJZjEpgKkBaVrwtZe+xo/f++NUrTe3Dh30NQkhsCyLJEn2RdTBY6bCaWoGn0amgFfdL5l9SuFUclM4zG9Y0294s0ApnG4Ot2qsFwYXOC/PEy1CMQ/oE2+SkSi6ucvY1dlTIRXbYTPIqHfqFLHGnm3iORlDEUDfotbSiaREbSaYDYO4miHMiScnl5BnCmFMFuRQV7gmZJkiL8BD0Ky6ZCNJkOYsdesEesSGP0Y4Fve126z3xrywvounG9xHizBWhJsZHa/OgqhivZyQnLBYH0f8qb2BPpLklRzb1lnvxbQ7dd5tHuEFY4c/2Vqln2o82lngpNPiK3KbP3n+Mq4B8+c8stOSRAhyU1G1BYZ0aRhVLu6OKJZsKplLtJujt1y+Plpnrl3Fqnls7/Y5u7XGsWNt7m2dYOvSkPHApxf2aRt12qnLcDvkxOIcSubMVerYPZ211oCikuIYJsqB3GTicaoyCTHZkyievjXxOelzYBiwfnGdVKZv+tof9DVpmobjONfslJseo+s6uq6TZRmVSmXf0zRt5nsw4vR6tfBKZotSOJUcOodt5pVS4jjOob7mzaIUTncWaZ5CBEUEIp2UJNAs2NkdcUGZfODeH+BL33yOe/MW2/o2fhBwz/wKvhgz0lP8vRDqOemepDgJUUWClIgEZAYYkxSTpk0iJYkOJBAWioqpYefgCJPAz2l5JkUh2Y5D8rjAMHSSIOOv/Cuc0lucPF5nya7hvSgIpYVz3ODubpfv2mjxjfVdkuEOQztmwXawbYO9rOBUs8nlBZ+ubfHXz17kghFw97EmQRO0ROdrZzdJ65JKVaB00Gyd5WGde840GNkp8XZGoMcs5XXecvdxnu9f4b+8+DfUu1WqOSzWa4wGKS+N1lg+PUdsBESR5EV7nSs721BRjKyYhWUDI8sYmyl5GFMb21gbgiSNqY8t9I5kIesgazq7+uVJ0aaAyU9jsptOOYAFeQxCg81gE6nk61/cN2AaWUqSBCEEtm1fU5pgesw04jT1MWmaRlEU++JqKr7KVN2dQ3klS24Kh/nNapZSdbMiSGZlnLeaM+0zPPbgY5z762+hXMgaYAjw2jZjM+P3z/4VhZ/ynwbP8q4H7yMfXeJt7RVe2V4ja/j8yJnv5lub6+wkl5AFZGNFYYKra2RGQe4A7lXhlIMqoDDAK8BIBRVl0chMnJbB1s6YpKGQxDgNk0ZfR1Z03mq3GO3EbKwm7BgpJ1KHbr3BlfUN/mz9Cq+kO9QHOnpbx0HHH6d0vTanhhXOmSF7Vsh/2j3PkulyV9FkdRizNgpYZUS35tDZsVAVjbhqsdexeMepe6kObc7FO4wrEZpnEWxHfLX/Ms6SR6VtExIR7gY0jzc5sjRHPx5z9pur5F6B5wp2todYFRNNh4ZXQdMLXrmyTeWIiX/Bx67ZNAOP1nKTl1rrROTIygjRMBAZE5U5AGUANiChEKBVIbUmJSPOXz7Pqr9K1+u+qWs/jXSHYbifqjsYcRJC7O+Wu37nnFLqGuE0S7XoSt6YUjiVHDrTppiHxSwJJ5iNXTOlcPrO0DWdD3z/B/jUv/03pKqY1A0KwKmZxOd90rbi2FKL3cGI/zh4juGlAFs63H//Mt98/q+4iM6ZpWX20iF5S2eoRsRBiioKzBQyA5SCpABDTRZkrQB0DdPUkSPJXlRgkaI0iXAE6RBMWdA0PXbSBL8CK/Um68YGepHRiRvEw5RaW2OvUjCX2HSVh14zObOgeFkM6e4UXNnss2lLcAvkCY2jwwbJBlSGGicXq6zVE5zMI6yaPFAs88BDJ/ny4BX+8hsv0Jpvsmy3cZRJkRSsJRFpNyUZ7JLbkjm7ies5+HlI79kB1kmHvKEQnoFMMhJbsmR32Br2Oeq2MTMT2dW4HG2jtxRN6aB5JhcW9uipCM0UFFlENXYR7kQkscjkhGWg8knjZM0EM52UjDALDVd/cwUwp4Uvp+k5IQSmaaKUumYn3UEhddCecL1wms63cs7dGZTCqeTQieMY27YP7fVmTTjNArMknG5lHSeAlWMriGUN2SwAKFzY2fJZXKqiRxpztsfazgbKkZxe6PCWzhFe6m0RFAorD/n6OZ8gzej6FbQc+uGQyMxI25PXEkwiToWc7LJDQBgXEKWYSseVAqNqwZ7F8fkaYSXBHkEQ5+zVEjpuzOp4xLAvKYTi5XGPduiS2DD2M7Y7OTYp+nrEed/nImNeFIK2p+FgMLYzigK+Euyw1Jy0kenaFWzXZ0cEpNWIe1o11i/u4Tk2p44cZVsf8vLGFbyqg1f3OHayw4XhJoMswqzphKMY5Si0qol9yiAKI8Z7AU7Fo98LaRyt8uLqOmZD49zOLo2qS+RHiLiAeYOxFZG3FHupj2uY1HIHt6fRSmps5z7SmZwnrr41RAQUoJK/TafmImMQD970dc/zfD/KNG2lMn18KpamrVReKx130Bw+fQ/Pypwr+faUwqnk0PF9/9BqOEEpnG4G03YSJW9MpCKUoZEXQAGmEHgpiIpF/7LPyMo488BRruzu4Q/GfO3Ki7TvbfHAqaOEGz47LcUx0UR4Lp5lYyc2u+6IndSnEFDoYKRgSJA6aBK4KqQ0IQlyMMYKwxRsbg8Y+TkLuUVWZORVxUtRn9OixtEzHTZGAbbS8HsR9TEMTymuWCOkFpD1JEiNY2caDD3JYsMiiTTUKMbt2Ki7beo7BkENWrnDW5wWX5cXeGVnyF9m+70DAAAgAElEQVQPz9FZbtLGRSYSQ9ORLYtcK3jl3BXSNENvacw1apgViyRM2WWMe1lHaxskVobsakTDhPZKnTSX2A0dp2kRq5yNdA9bmdSWKqSmZM+K0fQUp9Dohg065x12TZ/t7hChgRZPzg/Z5BqJDKhNfGheBmEEYREyTIZv+rpPU3UHo0xTpnWdgiDYN4cfXKeEEPteR13XDz0KX3JrKYVTyaHj+z6NRoMsyw7l9bIsQyl1aK93M5mVcc7SOZVS3tKxPr/5PHmdSYQjBTTQdQM7NLi/26U+Nriws4sfZuiGYGXR5dnLF4iyjMqCw5XhLo35OsuBxShMKLIcFwtbtwhIUWISecr3QFMTQaAAywXlg2mCLyV6BJUCLA30XKJ7BmlX4riCzdUxmy+NGQnFnGUxJwz6ac5QKqIopVh2+W7rBA2nxro9ou/3WG0Kvvu+kxyPIB3nREFK34zY1X2IJGZqEe5F3HVsGVsZbA6HrA12mevUUElBP/MpcoWxoOMJh8HGmHyosO/WKHSFHepouiCczxkOAyqGQ4sqkUhBV4QqZbybocfQadbY7o04mXYZaTGRTNFDQW1kc2LQpuf69LsxY2Jyd1KKQI9A5iBqoBpAOCl+KXPQLbAxcTTnTb9vDnqWDm76mAqgqSCaRqMORpwOCqeDm0XKLyt3BqVwKrlhHnvsMXZ3d/fvh2GI7/s8+uijADQaDX71V3/1Tb++7/t861vfmolFJ4oinnvuuVs9jDdESkkcxzMx1iRJAK55j/1dcqF3gdzPEVUQVcgiNSm2uBWTJzZpkjDuxLzt+BJ3+3P8xaVzGHMW7zhxgq9GF0mFJBiPeTEKcA2D1JQUKPRYTvqrCZAFYIIRQa5A2JDGYFUM9FjHDArspo6UEOeSRFgEKkWlEGYKPRIs2h5mTeHkJvZKhTOWTdUc0G8n0Bfs1QMqscsp1aGxZPFSsMlXn3uRznyTFdFiLnBA5ezEKeupj2ZKwjjAW0txbRutCW3bpdhI2Mx8Mk9SaTkMN2O0JKAlqmjHdIajkEIpumadIs/Y9HfIbTADAZGk7wTgQZFKqpGNURf0/CFmS2fgj6nENk3XIU5TKolBGAdsnBkycGNc0yRJMnIH5ByTnnU5E6VZTAphJunE6xT3Yr7yza9Q6b251k/TnXQH72uahpSTnXoHI1CvJZym61VRFG/q/y+5fSmFU8kN8/TTT19z/0tf+hKf+cxn+PVf//VDef1nnnmGhx56aCa2+f/lX/4lDz/88K0exhvi+z6XL1/mgQceuNVDeUNWV1dRSrGysnJL/v/7s/v5X/7XX2d7tIEowAKaXo3tgQ8VQf27TPz1mJd2t2h7Td7+Xaf4yuWzpJnGg52TjIqEebfGYDRCVQyMWJHkBXEBlVAnrUpkLqkUkw/pogIyThGORr5e0O7UkYXiqNMgyXLGScxd7SUqVY/nq1cwGwYPNJcQexprep+eFfDA2+/CPqcYbZ6j3V5kzdzDrys2dvq4WyGtpMndrSXUvRpr/T0ujnvUmx4tu8axwiMf5mzu9Ujnq+ieSZbC2vk9arZNZddg5XSH1FP0g4B626bmO8w1mmzmAzTT4qTVQF9XrFdCBDrzmsfpuEPfCMmsApEqqoWDUTWIzYwolHimwaCIkKlCSYU+LkibiktHhuw4IZoCI54UupRtUBoTwWQCyUQ0kUNhX92lmCpOnzrNw/e/ufl4sKTAlKkhHK71Lk1vH1yjps8drCI+C1/+St6YUjiVHDqHWTUcZsePMyvjhNka663GNVzuX3mA7Y0NsMAZGTAsuGehy1y3xpd7Fwg8iZtKvn7pPEMjx9ENzu6uEg0KnLHB3KkmfiNDOJK+jDAqBuZIoNk2zrDAtE00BXkusaRBLylYqNUxWzpZkFL4OV7Vo1E3kdmQuVobY0NRHZtsXBixNVfjfrVM4qRs9/v8xd63cKXBMatJO2wiq5K19R7GnIHbNdja3iV+LsJZrtBYqWLYOgMV8ty5c8w1m7T7FaxAMH+swsgPyKRi0fOYt1t4b6uwEw9JjIRFrUG8F7Gw2GbHDQj2MqqZSb4ZY1RM8iRjOalzj30Ea1exWe1jjQXdZgvdl6xrI9JMoVcE+XaKLnSSLKM2NHHrVUTLIHElRgyOZlIVLqMkQwtB6VBkgJj0rVMCRH618W8Odtuh7bVv6NpPU3HTqJGu66+ZgjuYtrueUjjdedz+X+FLZo7RaHSowglmY8G5/hvn7cwsjRVu7fUvVMF9D903SQnlEKkc6eYE45hM6Vgp3NtZ5IHRAq5uUl90mKu5GIkiaETIZYmnWxiZwjIctEwnjTOKRGLmgiwscIWBrWxqTRctEbSkjdxQ2KHJSncep+syGvjsrPfYvtjjpZ0rpJrk3oWjHFluEiURq9ou9cDl7QunqM7bDMMhyVxBjuK43uUHTt2Haxq8dGWTwFZ4DY/QSlhf3WZ7e4+5scdDZ06zsNwinU/I53VG/YTYz1iSNe5uraC3LC7nPXp+xIKso7YzqhWPtWCXtbPbmJnirrjD99/9ANa8gbvgcbezRPSNMX7g0+nUOO0tsrBXRQs18kIhHLBzDcc10ao6aUNiNk3cikWkJ+R5RlO6LF+pI3pg6RNxpFaZfPXXQXmACdKEoj4pKhqlMVmekRf5t7/Ab8DBjSmapl1jAD/Yj+71zN9T4VSaw+8cyohTyaEzHo/xPO9WD+PvnFmpGg6zVY7gVqNrOu+8/538n//x30FeUHVMgp6EJMUY9pGZYPflAW2xjHBh4+KQheOnCPSAzd4GVdvkHJsknqDuCLrtOj3pQ02Q9hO8ukO0FVFtV4l2UjRHYDgmDcOhpruIUBD2Upa7baojnbSWsR0OqJsWta0aLbdOX43pN2IaWgVtR6HlGpbuEpmSNdmjGEkqqctxt0vz/hq6FOjjjNgVmAI8zWC0OmS4obFzKmQzHtCSHkf6Debn56lrFbbUkNW1XeYWm9zTbVNsJyR1QZCNCSop7zh+mmVtnkER8WejF1nb2UOrCgbeiGWjTuQmbPZ8eqHPitagoVu4mY1uQSwSRkcVWVhgB4LtVoDeMgj3IlasObIXAuxlh76XYBUasShQZ7haMhxIQIVgRlAUIC3QawaGNvl3Q9f/6o45uFYsHTSAX8/BXXR5nl/jjSqZfUrhVHLoHHY5gllh2tNqFiiF038dx44cw7UcgmGAkxi07SoPnl7k2c3L1DyPo8KjFhnsGDnLJ5q4UnGqc5xRNae33SPaCwmSmLGvY1YNSCT2nEkYKCxXkGgClebU6g5bvQFNyyPRJYKEZafFqWNzFFlBq9XCCV0uhzu85G9yopFzVOviOiZnVzfYLYacbi/gSsHKygpWaHJJ9tCFhmtbqFgR7USEYYSjGRzJO3SrklCPifSYPX/IeC+iYmnULQvXtRkSsJ0O0KTOmfkFdE1nLeux6w4RiUDPDE5ZS2ibJpcqW6zVd9jSh3SOecw5FfpXxrxCxNETbZyWydFRi/ZFD61rcSUJKPQCaUKaFQgm0ZtEl+BLPCyMdajkLlmjIOolaCsGeTX92zpOEkjBuNrCxtIgzEEl4oZarkw5GGXSNO01027Xp76LorhGOOm6XqbH7yBK4VRy6IzHY44dO3YorzVL4e1ZSn/NknC6HcZ6NjjLII+wbUj1gv5ln0utKrmWUplvMbqSUQ1y6pbBziBlR/OJ+znKzRFC58Ejpzg/3qQX+ziWRZLmaCHUmhbxICUPJQMkmavQWgWWbWEGGvkoRS8UnnC53NtFHNW5t7FE3XK5qO1w5dI223sDFrMG951cpO5V+ObeKqvbm4SF5KSc58RCl6yaw5ZkOPAZeiGmUkRpyGqUIKROvxLjpwk1y2RFdPBaFZzYwFcxO0GApQm8vYLd9T6BFTPohhRAy6ixGHjEF0acNXbZzQNGMkZK0IYaWZxhGjpFDf56dIlxL2PZrjFfr5HEEUaiiAYZTqGjHIkYgJFItHkTu2ai5YooChFnPPbyAK9jk0TppG9gzKTdSgrCmnic0hykNmlhV4yLG07TTd9703n9elGmqWH8IFNf1LQG1CyU/ij5ziiFU8mh4/v+oXmcZin9VUac7lwaToO5xTn8/jYVy0HTC6xCI7Rg88o2b7GPUq9XeMnfRDYM3vHIg1z+kytkS1UGScD2cMgxb55xlBGNYxzbwlQW6ThlaalDWuQMNoYUpiTZS9lQPdqxQ2IoVLFJPbIx9IKLZ9foiV0WsgaipnGs3SFSOWlRsNXb4eXNVYZOQmXFw8wU/fUeV/whvTigZtusJB2O6A0M26Q/NyZIY6hoLAYtXDVmgQYNrcYlf4eN4S6aEnSFjWyAmheEgwgVCSpmlbxWUCjFaBDhaxHjVo6WQseuUDVM6qs2vpaRK0ndsMjbNe6tVNlZ99kI9wgbCpUq2m4NmRTsZj5ZUyLGOrXMobpuMiJg3CgYNsfYoU41NMmMDKMHmQBywAOVQZFM2tbIZOKD8iyXY40b/wJ3fauUb2cAP9haZSqmpsLptcRVyWxSCqeSQ2c8HlOpvLnaKdczS1XDy4jTncvDiw/zk9/3QZ76nX9L7CW05hxGF4d836kzPD+4Qts0KXqKdyyf4guc5fNnn+OepWU2R1v4Rkz/SEQrrPLW+WO8tLtJViQkRUSjVWXw4pjl0x2qDYdBPKJyvIId6iwEHuf9XTZXB4hKnSXRpNusIzUNL7eotW3CNEPfibiQ9MhkjqoqtM2M5KSDH0YMnQIjFSw3GriahTQLepUAOc7pjwOStsKUAj8o0AuDS8U2xsYOo1ZKoYFTNejJMaMgIfJTUkPhVEysTUiTgkyTaPVJaYBcB60DwShjsCvQWhqF0DBsnb2tGG2nANPkAa/F2WyPitAZKkGe5JiOTqxL8jakRQZ7CeZAQ3QN4rkIOc5ZqnSRewnp0YLcndTUUorJFicxKUGgCtA9yEIoUrXfkuVGuN7XNI2CH4yGT9NxeZ5jmub+Y9OfU+FUzrk7g1I4lbwue3t7PP7441y8eJETJ07w2c9+llardc0xzz77LB//+McZjUbous4v/uIvMh6PDy3iNEvCaZaiY6Vw+q/DMz3+6Q/+Uz739O/Q3xngNhz8aEjFaDBai3nF3eVoWEcXNn4QE2gZNdPA8XXqusbFixsM0hGnnDYLuseo0NleG1DRdfKGTWEpVre2Oel0ae7Vubi1Q71R4QeWHsS+W2Pz/B6FppBuxmgtYk302Nn2adfrLFdanFhZoB+OcVOdaqvKdj6iH43pNio0hg5BlEBfstoc0stDrIaBt6mRhTlpAMLVkHlEUi8wWwb1VZ1EZfSLlLAL1MHZmoiVwMgIayAVKAvIQcuh0CYtT1JVYMwBpqQIwDF15psuLadOiEEau5xpnORCvolVS9BNgzRIUR4UV1NufjfGsXS8mocIY6rCINvKwIPAy6BxVTQZwNX6kopJuxolJo8rG8bZ+FCu/3SuHJw31/uYNE3bF0nTJsHT50zTLCNOdxClcCp5XZ588kl+8Ad/kCeeeIInn3ySJ598kl/5lV+55hjP8/jt3/5t7rrrLtbX13nooYd4+OGH/14KpzJVd+cihKBb67LgHidzAxIrp1oxSYcx8yc7nKxW2Tk3IElj7ut2uRANOHdpmzPzR1jy5tjKBgztMRfXdznmtWg7NpX5DqNxRG9vwJY9IE8ka3KAGhV06w5yPeC54Dytukfkx6zJIUEc0bGquKnBYrNGSMzZaJXoxYSsBeYWWJhUYwunBdEgJjQjkixFaRI5VNindGISkqOT46UnUH5BvKDIOiDGOYO7QFyAvDvZ3o8GCVD0IJtn0i9uACIBZYBCYORg2yZogszPMZSgaCuyXcnGeMxmZYymwaq+TXPoMm5k7MURZiioOA7aedBPAcWkrMCgHRMaEhErFlQDU2pcqOxi6gZZliMlUGFSANOc6CeNSeV1S4A21qhZb36TysHU25Tre85NfUzTdUpKuV9B/GDEyXXdUjjdQZTCqeR1+dznPsef/umfAvCRj3yEd77zna8STnfffff+7eXlZebn5+n3+4e2q26WhFOZqruzEQje/76f5H//vy4T7A04sXwC9UrIwAv5+t4Qo2qRb4Qsii732sfY7vYxbMne5oC5wKGoZDTaFWorTdKdgDTOcYTFYmeOnhNAV3LMbyJticxz/EpGbxjy4tYabcNFdzUqroNIQCYFSZ4T1TJCN0UAujDwHAMPk7iaM3ZT5CgjMCQqLHAbBiKCsczJaiDtq74gBzQb8gSKAApnEkEyqqBtTzatoQtM5bFgLdM93eWt97+VR+5/hLuX7sa2bOI8xjVdFrwFsiJj1V/FFjaXL1zmj770Rzzzl19mlG4xjMcUZspYpnjS4i0njrP98h4EBV7bJr6UIlJFsTIpaulqOnZmsdSrMzieEA8y5r0m298ckB6BZBFoMtlhd3Xq6VcLYTY7bRari4f6Hjgomg6WFziYqrve0zSNOEkpyzl3h1AKp5LXZWtri6WlJQAWFxfZ2tr6tsc/88wzpGmKlPJQzeGzIpzKVN3N43YY61J1iZ9650/x9a3n+fIf/hHrgx2auYsrDPZaMfWOztJ8h+AbIWcMm2LB49mzqyyc6PDQ287gbW7zSrjFuY3L7OYJlYHJgnIpWgq5HZHoOZeTjDiFaJRSMSxs26BTc9HQCBsZmZQEOQinYKxJskBShGCaOiLLiWwFviLNFIUNxfykgbCoQbaZkzdAVQAJavdq3+Jw8rOi2VT8GvVGh5WlFR55/BH+yff9E0zL5NLoEiv1Fe5q3wVAkic4hvO612UqWN6y+Bb+wUP/AEd32Bnu8Lt//Bk+8wefZrS3TiRTRl/bpSYq+FZIqBJES6AEaCPwQoN2XsHb1UiqKZvRCPNqyxrDhrjJZPvcVMuoSfpOaJDH4DTdQ9mVe/A1lFLX7Jab/v0HxdJUKIVhuP+cZVnEcXzDYym5PSiF099zfuiHfojNzc1XPf7Lv/zL19y/fmfJ9WxsbPAzP/MzPPXUU3z84x8/tAKYsxRxKoXTnY0QgmONY/y37/0XpOsp4douameP7fplhpWIvThi94rPXUaXMC/IrQz7EZuRP+Lff+PPSZMCzdKwNI2Gq+MUgi0xYuxnZEugfEVYVRiewNI1siAlSRKKsCBZnmwgK6pQNIH+ZPHWLCCFTObkNrAM2g4IH4Q5qaCtJBCCroEwNdQQmktt7j/2ID/97p+me1cX13W5b+4+5ivz+KlPmIV0vS6amLyf7+rcdc25cE33Oz5v03TZcnuZ//5D/wP//Mc/xlP/4Sn+j//tX7PlD6hWMhJLgQupo1AF4EBrbKBfzjErHmttn34UMK9V0FKIVyCvMmkcmAIKtAIKEzCYGNZNxSgd0a10b+i6H0zPXe9rOiicpj4mKeW+QRwma5hlWYzHh+O3Krn1lMLp7zlf+MIXXve5hYUFNjY2WFpaYmNjg/n5+dc8bjQa8aM/+qP88i//Mt/7vd8LHF6EoBRON4dSOL153r74dv7nTz5J02mSDBP+5VOf4IvPPo3mCUQOO+sDtsw+26dT0rTAsgQ1DNxCR0YFqZmTWBl7HUXmKYoItB6QQqpJshjCZCKSJKDXmPh+qpNWI2IE2tXdZOrqZ7Fk0tyWZGLQtjIXq+/hORWWv3uZx97+GB/7gY/Rz/uM0hEdp0PaS6lVaiwsLFzz99Ws2g15g96IdqXNJz/8Sd7xjnfwL/7lP6e/exm76aHiFENCGoHehoEd4Tgm68YOYyelKR3c2KGSGbxiD/+2+OVVihSEmJQmMAKYb83f8N8xFU0HhdPBiNP0mIPpuGn06SBlOYI7i1I4lbwu73//+3nqqad44okneOqpp/ixH/uxVx2Tpik/8RM/wc/+7M/ygQ984FXmyRulFE43h1I4vXlc0+V44/jkzjz8j//N/8R7136EqqjiP+vz3DN/w4VLL+A3XiBNR5hY1NY9SDRiLydyY6KaRAmJyDREJjDGJlZmYetQVBSWNMiVonALTNPBCHQ8qizOL9KYazBXmSMmZj1cZ7m7zEd+4CMsLS7xrf630ITGPzz2D7F0i51wh5pdo2E3AGjzt01vL/Yv7ouAW8HDJx7mjz7zX/jHH/4hLgeXkU0wd8HugzQgsgquHB1CrhAptMw6c6sVgpVsIh6v7uxDZ1KOQJ9oKcOcFMc8tXKKOW/uhsd5UCxNzd/T29OK4AcjTq8lnOr1+t/Lbgp3KqVwKnldnnjiCT74wQ/y6U9/muPHj/PZz34WgK9+9av85m/+Jp/61Kf47Gc/y5e+9CV6vR6/9Vu/BUAURYcacZoVMTJrwmmWxno7c2/nXu7t3AvA5cZl3vPu97C8vMx/Pvefefbiszy49CDvveu9GIaBVJILwwuc75+nH/Vpu22O1I7Qdtss15YZJSMs3cLUTM72z1KogmP1Y2wFW1i6xdH60f3/VxaS3XAXS7douZMyIXd1r02pHTz+ejRNu6XnVhMaR5pH+JVf+Tf83C99iL4IKRxQ7qQSuF4FcyzQPAMzh7k9D8PS2TOG6HWQV8VSIUBcLU2g5GR3XSGh1qiRyQzbsG9onFLKfeF0UGheL5amO+emRvHp78IkAn8wfVcy25TCqeR16XQ6fPGLX3zV4w8//DCf+tSnAPjwhz/Mhz/84f3n8jzne77new5tDFJKbPvGFr6/K4qieNU3zduVMuJ0czi4hf29p9/Lu46/C9dw98+1hsbd7bu5u333a/5+3a7v376nc8/+7VPWqVcdq2s6C9WFVz3+naJp2i2NOE15173v4se//3H+78//DqGWkc9PNsgZO9DCYb7fJL4c4AhBsJAQ7SbYHZ2skEgBmKBSIL+6uU5NSiTUOjV08eaj1dNredAQfrDf3NS7NPU1TX1M04jTtM3KrHxBKfnOKa9oyaESBMGhGcOhTNXdLErhdHM4eE41oeGZ3m17nm8X4WTqJr/63/1r5re6iCugpZALKHRQmwWNdYfq2GJQjNkzI6oLHm7fRL/q7xKSSbTpao86lQMpnJ47ja7d+NqR5/nrnifDMPZ9TZZlXeNxMgyDJEnKSNMdyGys8iUzw2H2qYNSON0sSuF0czjYkuN2RwhxWwgnANu2+dkPfRxLgognqTcFBHM5616fwlD43ZTd0Ke1Y1AdWeQ6+4JpilR/Wznc1u1DeY+/1lyZXuODJQim0afrhdN0/Srn253DbKzyJTOD7/uH1qcOSuF0s5g14TQrY50l4XSrPU7X81M/+zgs2BT5pCCnqkE4n7O5POLS94zZ7SbQVgxEhLFoUEyXGQUkoBeTfnXoIAtIZXqo45ueq4ORuqlwUkq9pnBK03Q/fT8r7+GSN2Y2VvmSmeEw+9RBKZxuFge9GiWHy+0kRr4dt0uqbkq70WZl/iTUQDkgNUATxEIytGLyikQzFKpQuKmDFoPIJm1fpv3qSCf96oQBpnG4KbLpnJkKIiHEfpsVuDb6ZBgGuq5fI5xK7hxmY5UvmRnKVN1sTKlZizjNCrMWcbqdhJNrujz+E/8MKaAIQeyB2YOqaWEFYBRg9AS2biBHEncXdH/iiUIDmU+qhqsMVAhNu3mo4zsoiJIk2Y8qTeszTUXU9RGn0uN05zEbq3zJzDAej8tU3QxQCqebwywJp9vJ4zTlxFtOoFmTiJFmgrapaEQejUsWR6+0WZFdZKFIYoloQl4B2QKcSWkCrhrFNRdMceOCZTqnrxdL00jSQeE0TX1ev6uu9DjdeczGKl8yM4xGo0Mt9FYKp5tDKZxuDrN0Tm+3iBNA023iZRXEGJQH0THwgwgvsqlu6DQCBzkuMGsawtDAZL/Br6aDftXfJCKIZHTD47ne6H1QOOm6vp+OO7hGTdesqXAqU3V3HrOxypfMDEEQHGrEqWzye3MohdPNYZYiTrebORzgHUffwY8/+hPoYlLEsmhAZKRkrYJQJPSbMWMtp9oTzIe1iWgSTCJNMejxxCAuHFiuLt/weKYiSNf1/ejR9PbBqNL14mjqhTpYRbycb3cOs7HKl8wMh+1xmqUP+FkTTrM01lliVsZ7O0acHMPhX/2zf4V5GUQA+hhUBINazJX7R1xu9BFdQUyOcsSk87ECikmPvkyblDNQQzg3OHfD45lWAb8+PXfw9vXC6WCpgusb/pbcGczGylkyMxy2cJolZk04zYognSVmKeJ0O3qcAE6vnMYNPfRt0FdBzUFSLwj0nDBJWDYb2IGJ3M4mv3A1VYc92YlXeKCaUDNv3DIgpXyVQDoYcdJ1/Zqo0sHo+DTiNCsR85LvnDL5WnKopGlKpVLh+eefP5TXi6Lo0F7rZuP7Pi+88MJMCJLRaEQYhjPxbTgIAjRNY3d391YP5Q1JkoQ0TQnD8FYP5Q2RUuL7/m03v5RS6CseWCF5AxgqLAl13cXKFUZPIVswKqJJocyrUSehAQbo6cTj9NK5l5jzb6zJb5IktFotNE0jyzJs294XS9NI1PXC6fpddmWq7s6jFE4lN8Rjjz12zQfapUuX+IM/+AM8z6Pdbu83Bn6zjMdjzpw5c6PD/Dvhueee4/Tp0zMRdXrllVdYXl4+VD/azWJtbQ3TNJmfn7/VQ3lDBoMBg8GAEydO3OqhvCFpmnL27Nnbbn7JQnLigVPsXNmd1GNqgLOmY8cKrdDwmymhHpEXBWYKqTX5PZWDkYEyJ8LpvpP3cWbpxv62oihwXZe1tTXCMHyVWHot4TSNMBmGsd+/shRNdxalcCq5IZ5++ulr7n/oQx/i53/+53nwwQdv+LWVUui6fqi9724mQoiZSVPquo7rujNxbi3LwrKsmRhrHMcYhjETYzVNEyHEbTnW933/+/jaf3gG4YM5BDES6C2TvJ8isgKz5lInZ5QnZEWBAjIbvALSEIQUzNXmbvhvm6a0D6bkrr99fVRp+sXpoHAqubO4/b8al8wUvu8fWjkCKeVMRG9mkdLjdPOYFY/T7WgOnzJ3eg6xCTPCW9EAACAASURBVFox8S0ljYKQFKdqkVcg3ZGc6NcxCmPSq84FHEjNyU9pKgbx4FDGopR6VZRpKpamc2gaZdI0bX/N0nV93/dYzrU7i1IKlxwqh1kAc5ZqOM0apXC6OZTm8MOh6TSpuBVCJ0ACZl1jeCmGRQ1yidU0sBMPM9Um5nCAHOwUwhSwOdQvXdPI0jQVdzDKNBVWMDmn03l1/c+SO4fy63zJoRIEwaFGnErhdHMohdPNYZaE0+1Yx2nKe0+/lzPteyAEpUMRF1g1wTCOKDJFIzNwYwdrKCZ96jKwMtAiMG3QTLHfQ+4wOBhlmt6erk0HhRO8tlAq59qdRSmcSg6VJEmwLOtQXqsUTjePWRJOt+uH+2sxS8LpdsbUTX7yhz8Aq6DFoI0VyhDUDJuj3TkyKfHnM5I83W/wm1Ug9aAQUAwVjuUc2niuF04HvUtTLyZcK5Cm74NZmmsl3xmlcCq5bSmF082jXMxvDqVwOjw+9CMfwlEmIoN8pBCBhlpLia4EJLsJW0mP3BToBaBAKVAaqAIMTadiHI5lYCqMpmLp4G24Nj138NoXRXFbp0NL3jylcCo5NKaLxmF9IM+ScJq1D8tSON0cynN6eBRGgTpukrVBHoesoRB1g0WrTXOuTm5qNGUVK2FSx4mJaBIpiFzhp/6hjWUqlnRd309xvtHalGUZmqbt13UquXMohVPJPk8//TT33HMPZ86c4cknn3zV80mS8Pjjj3PmzBkeffRRLl68eFPHM0t96maphQmUwulmUUacDo84jzHbNsqYiCHWJGGScaW6h99MGO2EnFANnMicpPMyQJvswlNVMLUbL+56cNfc9XPmteaPUmr/+ud5vi+cyrl2ZzE7K33JTUVKySc+8Qn++I//mBdeeIHf/d3f5YUXXrjmmE9/+tO0Wi3Onj3LJz/5SX7hF37hmueTJDnUStSzFHGahuVnhVI43TxK4XQ4eKbHg0sPYgwnpm/RAb1jsD0esBX7NOdqqHFOfWijNJD6pN2KVgWlKUz98NaiqXB6LQ6KpaIo9m9P1y8pZTnX7jBK4VQCwDPPPMOZM2c4deoUlmXx0z/903zuc5+75pjPfe5zfOQjHwHgAx/4AF/84hevWUzG4/GhFoCcNeFURpxuHrMy1jLidHh4pscnfuoTVHccMCCLoVZzaRQuC0mde/VFBkGMloFIAB2KAlQKKlEkeXJoY3mtuX3QmjDdwXdQOE3rPpWpujuP2VnpS24qa2trrKys7N8/evQoa2trr3uMYRg0Gg16vd7+877vH2oLj1I43TxmTTjNCrMonG7X8Xqmx7vufRfNTndiYGrCeBSix4JaaJGnBeM8IhcFhg6koBugApAx9KLeG/0X3zHfzvR9UDgppfZvZ1m2X2W8nGt3FrOz0pfc9vi+X0acZoRZSy3OCrN2Tm93oeeZHv/oH/0IxSWwpEbez9nrhFyu9rjMNoYGnnQwYkCAkJNUnVYDqQ6vjtNB8XPQu3TweZisWVNBVUac7lxmZ6UvuakcOXKEK1eu7N9fXV3lyJEjr3tMnucMh0M6nc7+8zdDOM2KGJk14QSz9yE/C9zuQuR6bucimACa0Hjfe98HJ3Qyq0DrmGSaIq0ViI7JaFkRWin4IHKQAoQBaJAXhydYpsJpGlHSNG1fJB0UUVLKa1J1pmkeaiHOktuD2VrpS24ajzzyCK+88goXLlwgTVN+7/d+j/e///3XHPP+97+fp556CoDf//3f593vfvc1H76lx6mcTjeD2/mD/bWYpfHezv3qpihToWGQ56AySXNs4yU2vR2fJJDYCnRTw0gm4mlamiCV6aGNYWrwLorimohTnuf7BnC4NgUupcQ0zTJVdwdSrvQlwMSz9Bu/8Ru85z3v4b777uODH/wgDzzwAL/0S7/EH/7hHwLwsY99jF6vx5kzZ/i1X/u1V5UsKIVTOZ3+vjOLEafbXTidap3isR/+UTQhkLIgTwqOpi3aQ4cTRotq5lIdGpOSBMHEHC778P+3d/fBUZV338C/J/uSTQADASKQABKDSEKDPBKJdW4UqQVjm9pKKTrjy63t2Irj3dpbfdp5ZCxPGZh2ykxHqrXIVK0zoFbbZKYSW+jA3MOogOAUjZpUQZOUF0Mk+5Lsy9lzPX/wnO1uXuBscnbP/na/n07HJLvs/mZfrv3u77rOddwu+07FmrxeyQxLuq4ndhMfaTouFovB6/Vyqi4P8SS/lNDU1ISmpqaUv23cuDHxs8/nwyuvvDLqv+ficAanQictOEnY2fqyssvwu//8Hf7XzgM4W34aQUTw+eRBeA0DCCkYUPC6ilDiAs5VAPACrplAIGzfBpjJ03PJXabkEJVMKQVd11FSUoJo1L7OF+UGjvRkGy4O59up0EmbkpHQcQIAr8uLBQ31AABjmobeYD/OeoM45xnA5NKJmBD1weUugssLwAVAB8pLym27/9G6TMnrmMzAnByqiouLOVWXhzjSk22CwSA7TpQRUj54JHRwkuX64nCTpmm4be1tUPAARUCJzwdjggsxLY5z3jC801yIFSm4gud3ENfCwJVTr7Tt/s0j5MxAlPyzuY7J/LsZnOLxOKfq8hRHerJNIBDApEmTbLs9BieSRkrAM0npOBVpRZg/dT4qp10G/AvA6Qgm97rh0304rfz4V4kfEaWg+QAjBBga8Gn/p7bdf3LHaWhYGhqczG5ULBaDz+eDz+ezrQ7KDRzpyTZ2Lw6XFEYk1UqZI22Nk5TgBABXTrsS/7X2vzBzSiUGLonjXFkMvtnF8EQVYkrDJLcX7kHAPRkwpgD/0/M/tt23OT03UpfJ6/WmdKLM4GQYBnw+HxYuXGhbHZQbONKTbexe4wTI+QbP4EQmScFJ0tRiibcEV5Vfhe/f+RBcE70IIIbPQ364jCJM/aIY5V+UwtfvAgYBLQRM9Ng3Fo3UZTKn4zweT2KbAnOqzpyekzJ+UXo40pNt7F7jJAmDEwEyO05S6jXXDi1bsgzzZl2JCcVuxIsU9LiO6ETAG4/D8BQBA0AJvLht/m223K+maSnBKXl/JvPvAFI6TsmbXjI85R+O9GSbUChk6xonSRicCJD3ISlpqs7cDmDR9EV4/n+/gIWTlgC6Qn95HOd8g+gqCWBgagxxHxD3augOddt232ZYSj5aLnk7AvM6ZnCKxWIAeE7IfMWRnmyTiak6KRicMkdKR0QiacFJKQWf24f5U+fjJ4/8H3hdl0CLAkaxwkAZoFyA5gUqJ1eiyMaPt5HWNSUvCAfOb3iZHJykHNhC6eNIT7YJhUK2TdVJCyLS6iUCZK1xGqqhsgHb/u/TWHbN9VC9CkUGoPUBE0IT8ON1P8bVM6+27b7i8XhKl8ncn8kMTpqmpQSnaDSaCFSUf/jMkm0Mw7DtW5akrQgAWcGJ0wdkkrTGaaiy4jJ8ff7Xcdl/XoYXal7AgQ8PYNA/iNtuug0319yMYnexbfeV3GUy92caeiRdLBZLdNzNEAVAzLhA1jE4kS3sHnzNUxxIweBEEkmaqhvNlyq+hP9e9d+4f8X9GIgNYFrpNFw64VJb70MplVjjBJxfqG6eViW5y+RyuRLdJ3PROOUfBieyzTXXXIPDhw/bcluGYWBwcBCHDh2y5fYybWBgAOfOnRPTJQsGg2Ie23A4DJfLhc8++8zpUiwZGBgQ89hGo1EopXDy5EmnS7HEymPb8///Z6fRzklnfmEyO07J03YlJSW21kC5g8GJxmT16tXo7e1N+dsHH3yQGNSmTp2K1157bcy3HwgE0N3dLWbzuPb2dsyZM0fE4nhd13Hs2DEsWbLE6VIsOXHiBCZMmIDp06c7XYolhw4dEvPYnjp1CtFoFHPmzHG6FEveeecdLF682JHurrkdwlCapsHlcmFwcDARnJIXjbO7m38YnGhM2traUn4PBoO46aabsHfvXtvuw+PxiFpgKaVepVTiW7IERUVFcLlcYurVNE1MrR6PJ2U9Tq4zp8KyXa/ZWRq6hMBcomDuFm7WZW5NQPmJzyzZopDPUwdwjRPJJG2Nk8vlcqxe8z1jjktmkAIYnAoNn1myRSAQsHXXcDuP0MsGBqfMkXrUlwQSg9NI02XZopRKBKLkEGf+bI4BhmFwqi6PMTiRLeze/FJax8mc/pJAWnCizJG2j1Nyl8cJycHJnEIGzneckt9Xydej/CNjpKecV+jBSdL2CQxOZJK2j5OTU3XJNQDnHzvzPT80JCmlRI1flB4ZIz3lvFAoVNDBSdJUnWEYDE4EgFN16TC7yuZ7Jzk4mfs6JV/XXMjO91r+kTHSU87z+/22rnGSFpwAOWsZ2HEik7Tg5PRUnaZpibCUHIpGej9J+SJF6eMzSwDOby+wYMEC1NTUYMuWLcMu37p1K2pra1FfX4+VK1fi008/TbnczvPUATKDkxQMTmSStsbJ6am65OBk/j7SzyP9TvmDwYkQj8exfv167N69G+3t7di5cyfa29tTrrNkyRIcPnwY//jHP7BmzRo8+uijKZf7/f6C3o5AEonBSVq9Ukhc4+R0x8nqa5FT4vmLwYlw8OBB1NTUoLq6Gl6vF+vWrUNLS0vKdVasWIHS0lIAQGNjI7q7u1MuDwaDBb3GSRKJwUkaKWGEU3XWpfO+0TTN0YBHmcXgROjp6cHs2bMTv1dVVaGnZ/RzPe3YsQM333xzyt8ycVQd1whkhqStEyTSNI3BKUOc7jgNfV6Ttx9Ivsw8Xx2/oOQnbjRBaXnxxRdx+PBh7N+/P+XvwWCQa5yEYMcpsyQ9tgxOY5ccluLxeEqIGvo3yi8MToTKykp0dXUlfu/u7kZlZeWw6+3ZswebNm3C/v37UVxcnHJZIU/VSekumKQFJ2mPr6SOExeHp2folgPm77quJ/5ujl3Jf6P8wn49oaGhAZ2dnTh+/Dii0Sh27dqF5ubmlOscPXoU999/P1pbW1FRUTHsNgo9OEma+pIWnCSSFJyk1Ao4t8bJfL8YhpF4vJJ/1nU9cR1d13OqM0b2kzPaU8a43W5s27YNq1atwsKFC7F27VrU1dVhw4YNaG1tBQA88sgjCAaD+Pa3v42rrrpqWLCyOzhJCiOSNr8EGJwyTdKRatJeB05vgKmUSnS8DMNI/GwGJ6VU4mS/uq6LGhfIOk7VEQCgqakJTU1NKX/buHFj4uc9e/Zc8N8Hg0FbtyOQhMGJhpISnKRxeqouucuUHOCSu0zJwYnyE4MT2WJwcBA+n8/pMhzB4ETJpE1/SZILO4eb9z80OJlhyfyZU3X5S85oTzmtkM/JxOCUeZLqZXDKHKen6gCM2EkaGpw8Hk/KuifKL3JGe8pZQ/cwseP2JGFwomR8bDPH6am60Ta2NMOSOVXn9Xo5VZfH5Iz2lPPs+sAwDEPMEXUAgxOlYscpc5w8qs48hcpoHSezy5Qcoig/yRntKWfFYjFbg46krQgABicajsEpM5x83cbj8cTapaH7OcXj8ZTgZHac+D7LT3JGe8pZoVCooHcNZ3DKLGkhRFrHSdommE4xg5P5s8vlSpk6NEOVGZzYccpfckZ7yll+v79gN78EGJwolbTgJGnfKSeZC8CTf07eIXzoUXWTJ092slzKIDmjPeWsTJynTlIQYXCiZNIeW2nnq3NKcsfJDEfJ+zUNDU61tbXiXgtkjZzRnnJWIZ9uBWBwolTSOk4Sp+qceHxH6zjFYjFomjZsA0zKX3JGe8pZgUCAwUlQcDKPDpJEWr2SgpO0jpNTWxIkr2uKRqOJjlMkEknpPjE45T85oz3lrExM1TE4ZQ47TpklreMkbY2TU5tgJk/PJQenWCyWEpzM8YDvsfzFWEzj5vf7MWvWLJw5c8aW2zt37hzi8bhtt5dp/f39cLlcYuoNBoOIxWJiwt7AwIDjp9pIRzgcRl9fHyKRiNOlWBKNRtHb2yvmlEmxWAxnzpyB1+vN6v329/djypQpiY6Ty+WCpmmJEJUc6Ar5TAqFgMGJ0rZ69Wr09vYmfv/888+haRqeeeYZAMCUKVPw29/+dsy3Pzg4CE3TEAwGx11rNoTDYbjdbjH1mh/oUuqNRqOJ14QEsVgMAwMDYro4uq4jFAqJ2enaMAwEg8GsByefz4fy8nL09/cjGo1i4sSJMAwDAwMDwxaKs6ub3xicKG1tbW0pv//iF79AeXk57rjjDltu/8SJE/D5fJgxY4Ytt5dpn3zyCSZOnIiKigqnS7HEMAyUlZVh+vTpTpdiSTweR3l5OaZOnep0KZbEYjFMnz4d5eXlTpdiSSQSwcyZM8UcPh8OhzFr1ixccsklWb1fcwrOXBBu7tsUi8Xg8/lSghPlNxm9esppXBwua7G1UkrMNJ1Ekl4LgLzF4U5P25oBKbnL5Ha7E3VJGrtobNhxonHjdgSyzq0nZQrJJK1eLg7PLKdP9Gtueul2uxPnrjN/VkoljqiTFqDJOgYnGjcGJx5VR6kkBRFpHSenjqozJXeZgNT9nRicCgODE40bgxOn6ujfpHWcpG2A6dRUnfm8msEt+bx1IwUnyl98hmncAoEAJk2aZNvtSQxOkuplxymzpAUndpzSYy4Kd7lcUEqlBCdzB3HKbwxONG7cAFNex0lSvYCsaQ9JtQIy1zg5efTahTpOmqYlNr+U9jog69ivp3ELhUK2T9VJmkpix4mSseOUWbnQcTLfQ+ZCdTM4maFJ0vNP6ZPz6UQZ0dbWhgULFqCmpgZbtmwZ9XqvvvoqNE3D4cOHh10Wi8Xg8Xhsq0lix0lS0GNwyjxJH5xc45Se5DVMZrdu6N8AeZ1Hsk7OaE+2i8fjWL9+PXbv3o329nbs3LkT7e3tw64XCATw61//GsuWLRt2WaY+ICQNOpyqo2TSOg4SO05O1WsuDh/6/Jpf9Pi+KgwMTgXs4MGDqKmpQXV1NbxeL9atW4eWlpZh13v88cfx2GOPXfBcVoU8YHCqLrMkhRBAZnCSVK/TU3VDn9+hR6lKem/R2DA4FbCenh7Mnj078XtVVRV6enpSrnPkyBF0dXXhlltuGfE2JA24mcKOEyWT9thK6zg5uR3BaMxxUCnFMbEA8Kg6GpVhGHj44Yfx3HPPjXqdSCSC4uLi7BWVoyR9WDI4ZZbEjpOk4OT0zuFDvyhpmpY4ss583nlUXX5jx6mAVVZWoqurK/F7d3c3KisrE78HAgG89957uOGGG3DZZZfhrbfeQnNzc8oCcbvPU8cP9czjY5xZ0oKTtMXhTk/V6bqeeP+Y7yWzHnacCgODUwFraGhAZ2cnjh8/jmg0il27dqG5uTlxeVlZGXp7e3HixAmcOHECjY2NaG1txdKlSxPXCQQCBb2Hk0QMTpkn6cNT2honp4+qS95DStd1FBUVJf5mGIa4qXtKH4NTAXO73di2bRtWrVqFhQsXYu3atairq8OGDRvQ2tpq6Tbs7jgxOGUeg1NmSes4caouPfF4PPEcm8EpOchJeixpbLjGqcA1NTWhqakp5W8bN24c8br79u0b9jcGJ3kkBidJ9TI4ZZbT9ZphyTAM6Lo+bCdzM1hR/mLHical0E/wK5HE4CSJtOAkbY2T02KxWGKd1dDgpGlayhooyk8MTjQuhd5xkvQBaWJwyixpj620NU5OM8coXdeh63ripL/m+8rJ8+hRdjA40bjwBL+yTrcCyNt3ShppHSenp76kSQ5L5s+6rifGLicXrlN2yBrxKedkouMkKYhIDE6ArK6IpBBiklQzg1N6ksPS0BDl8Xg4VVcA5I34lFMKfY2T1OBEmSOt4yRxjZOTNQ8NTmZYMn/mVHj+44hP41Loa5wYnGgoacFJ4honJ6fEzIAUj8cRj8dTgpPbzQPVCwFHfBoXu9c4STthLoNTdkj6Bi+pVkBmx8nJvZzi8Ti8Xm8iLHm93pT1ToC81wClhyM+jUsgEMCkSZNsuz12nEg6aR0niR/yudJxMoPT0I6TpOef0scRn8YlFArxqDoGJxqCH5yZ5dRpV8zz0iVPzxUXF6d0nMwOFOUvjvg0LsFgkB0nBidKIq3jJJHTp11JPpJuaMfp6quvhsfjcaw2yjyO+DQuXBzO4ESpGJwyz+n9kpKPqhvacaL8x2eZxsXn8yEYDGJgYMCW2wuFQjh37hwGBwdtub1M8/v9GBwcxOnTp50uxbJYLCaq3oGBAfT19SESiThdiiWhUAihUEjUYyztNTE4OIizZ88iFotl9X7NQGwGN/NgFvOEv5K+9NHYMThRWlavXo3e3t7E752dnWhubgYATJkyBc8888y4bj8ajSIcDotZIxAOh6Hrum3BMRsMwxBVbywWQzgcFrOIORKJ8DWRYbquIxwOZ73Do5TC3LlzEx0n4N+L63lUXeFgcKK0tLW1JX5WSmHJkiU4cOCAbQNFX18f5s2bJ2aNwMmTJxGJRHDZZZc5XYplp0+fxrx585wuw7JwOIyZM2di8uTJTpdiid/vh1JK1GMs7TXhcrngdrsxa9asrN6vUgpKKRiGMezLnRmcGJryHxdn0LhommbrQME1TiSdxDVO0mp26qg6k3mS32SxWIxrnAoER3was0wc1SLtBLTSgpOkD0fJpD3O0s5X59TicHNsMoOb+SXPDFLsOBUGOSM+5ZxQKITS0lLbb1fSwCMtOAGyHl+TpJqldW8AmcHJyXrN59jsMJlBTtpYQGPDZ5nGzO6tCCSSFpx4AtLMkxicpJ12xentCACkBCe32514b/H9lf/kjPiUcxicGJxoOImPr7QT/Tq9xsmUHJwAvr8KhZwRn3KO3cFJ0sBtYnCioSR2nDhVlz6lVMoaJ2nPOY2dnBGfck4wGLT1PHVKKVEhBGBwygaJH0jSapYYnJzuOBUVFSXe+8k/U/7jM13A2trasGDBAtTU1GDLli0jXufll19GbW0t6urqcMcdd6RcZndwkrYVASAv7EkMTtJI7DhJW+Pk5FSd+fwmr2ca+jPlN246UaDi8TjWr1+Pv/3tb6iqqkJDQwOam5tRW1ubuE5nZyc2b96MAwcOYMqUKThz5kzKbfj9/oI+wS8AcUfSSNvuQSKJwUlix8mpes3ndmhYMscBvr/yn5wRn2x18OBB1NTUoLq6Gl6vF+vWrUNLS0vKdbZv347169djypQpAICKioqUy0OhEDtO7DjREFKDk6Sac2GqDkgNSXxfFQ45Iz7ZqqenB7Nnz078XlVVhZ6enpTrdHR0oKOjA9dddx0aGxtTTrcCnO842bk4XGJwktZxYnDKPImPr7SOk9NH1SV3nYaS+PxTejhVR6PSdR2dnZ3Yt28furu7sXz5chw7dixxzrBgMIiZM2fadn8SgxMXh9NQEjtO0tY48TVMTpIz4pOtKisr0dXVlfi9u7sblZWVKdepqqpCc3MzPB4P5s2bhyuuuAKdnZ2Jy4PBYMGvcZI4VSepXpOkD0qJwUlaxykXJD/H0p5vGh95IyjZoqGhAZ2dnTh+/Dii0Sh27dqF5ubmlOvceuut2LdvHwCgt7cXHR0dqK6uTlzOo+pkTtVR5kl7nKWtccoFSqnEY5b8s6SQT2MjZ8QnW7ndbmzbtg2rVq3CwoULsXbtWtTV1WHDhg1obW0FAKxatQpTp05FbW0tVqxYgV/+8peYOnVq4jaCwaDta5wkhRBAXgeHU3WZx45TYRgtOFH+4xqnAtbU1ISmpqaUv23cuDHxs6Zp2Lp1K7Zu3Triv7d753CJHSdph/czOGWexOAkbY2TyYnXs3l/hmEknufknyn/yfmqTDknEx0nacEJkNWal9YhA+RNe0l6PZgkdpyc3stJ0zToug7g/NhlfomS+PxTemSNoJRTuDhcHnacaCQSg5PTWxJompa4/3g8zvdVAWFwojGLRCLwer223Z5hGAxOGcbgRCORuDg8FzbBNDtO5n+pMDA40ZjZ3ZZmxynzpAYniTVLInGNk5NTdeZYZQY3Xdc5TVdAGJxoTDJxFAmDU+ZJDU6UWZyqS4+u63C5XIlOk7m+SVrXjsaGwYnGhR0nWRicaCQSg5NTU3XmonCPx5Ny/052wCi7GJxoTKLRKDwej623KS04SQwhEmumzJO6xsnJqbqh4x+DU+FgcKIxsXvXcEBecJJ2njqAwYlGJrHj5PRUndt9fhtEM3C63W4uEi8QskZ9yhl+v9/WPZwABqdsYHCikUhdHO5UcIrH44ngZI5byWueKL/JGvUpZ2Si4yTtQ53BifKFxI6Tk8EpueMUi8Xgdrs5VVdAZI36lDPs3jVcIgan7JC29kYirnFKj3lUncvlQjQahdvtRkVFBXw+nyP1UHbxXHU0Jnafp04iicFJ2rn1KDskdpycXOMUj8dRXFwMl8uFSCQCt9uN8vJyWzcEptylXeRbhqyvIJQ1r732GgKBAObOnWvbbQYCAVtP4ZJp8XgckUgEpaWlTpdiWTQaBQBRA/zAwEDiQ0oKaa9lpRQGBgZsn37PpFgshng87kiXJxwOY9GiRejp6cG0adMQCoUwf/58FBcXi/syRaMa9RsmO05kyerVq9Hb25v4/ezZs4jFYqioqAAATJ06FX/605/GfPtKKRw6dAhLly4dd63Z0t/fj5MnT+LKK690uhTLurq6UFRUhMrKSqdLsez999/H3LlzRXU43377bVGvZV3X8e6774qqua+vD2fPnsX8+fOzft+GYcDj8eDUqVOJqToqHHy2yZK2traU37dt2wbDMPDd737Xlts3j0yR9m1NWs2apomsuaioSGTNUpgf/JJq9ng8jk+Xu91uxGIxrm0qMHLeJZRT7F4cLm0rAoBrnCh/SNyOwMk1TsD5LrnL5UocVQfwnIqFQtaoTznD7sXhDE7ZIfGoOqkkHaUm8TWRC4f/mx0nu8+iQLlN1qhPOYMdJwYnGh0f48xzch+n5BrMrQmocMga9SlnMDjJnPZicMoOTdNEdZwkcnqqDvj3aVbcbjc0TeN7q0AwONGYBAIBWw9dlhqcpNUsMThJDCAMTpnn5FSd+fyaXS9p4wCND4MTjUkwGMQll1xiSkRCQwAAEXFJREFU2+1JHHykdpykTS9KxeCUWbmwaafb7UY8HucapwLDEbQAtbW1YcGCBaipqcGWLVuGXf7ZZ59hxYoVWLJkCerr6/H6668Pu47d56qTGpyk1Syx4yQRO06FwQxO5lQdFQYGpwITj8exfv167N69G+3t7di5cyfa29tTrvPzn/8ca9euxdGjR7Fr1y488MADw24nFAoxOAldHE6Zx+BUGFwuV6KLy+BUOGSN+jRuBw8eRE1NDaqrq+H1erFu3Tq0tLSkXEfTNPj9fgDnd8eeNWvWsNsxv2XZJR6PiwshnKrLHmmPs7R6AYa9dCml4Ha7oZTi41ZguHN4genp6cHs2bMTv1dVVeHtt99Ouc4TTzyBr371q3jyyScRCoWwZ8+elMszMUjE43FR508DOFVHo5MYQsw1Q9Je004yO06AzLBMYyPvqydl3M6dO3HPPfegu7sbr7/+Ou68884RF2HaOVBInaqTNlgyOGWPtOAkcfdwp2uW2L2l8eOzXmAqKyvR1dWV+L27u3vYCV937NiBtWvXAgCuvfZahMPhlBP8ZqrjJDE4SRs4GZyyQ3LHSRKntiQw30PJzzHfV4VD1qhP49bQ0IDOzk4cP34c0WgUu3btQnNzc8p15syZg7179wIAPvjgA4TDYUyfPj1x+eDgIEpKSmytS2pwklazxOAkLYAAcoOTtJqd3j1c1/XE+0na+4rGjsGpwLjdbmzbtg2rVq3CwoULsXbtWtTV1WHDhg1obW0FAPzqV7/C9u3bsXjxYtx+++147rnnUgYFu89TB8gMIZyqo9FIDU7SOk5O7x6eHJyocHBxeAFqampCU1NTyt82btyY+Lm2thYHDhwY9d/bvWs4ILfjxKk6GonEx1hicHL6RL+6rjt23+QcWaM+5YRMdJwYnLKDwSk7JHacnF5oPRa5MFVnTnHyfVU4ZI36lBMYnM5jcKILkRacJK5xyoWpuqKiIsRiMcdqoOyTNepTTggGgxkJTtJCCIMTjUZix0nqVJ2TwSkWiyVOu0KFQ9aoTzkhE2ucAHnrQhicskdazQxO2eH0GiezU85F4oVF1qhPOSEUCtnecZJI4ulLpAYnaSQGJ65xSh87ToVJ1qhPOcHv9zM4CSVxCwWJJD7GEjtOTq9xMs/ZyeBUWBicKG2ZWONE2cGOU3ZI7DhJXByeCx0nj8eDeDzO91UBYXCitNl9VB27INkjMThJ+zA3SatbYscpF9Y4eTwe7udUYBicKG3BYNDWxeEStyKQSmJwkkhix0niGienp+p0XU90nKhwMDhR2gKBACZNmmTb7TE4ZReDU+ZJDE5SO05OByev18vgVGAYnChtoVCo4DtO0j4UpZMW9qQGJ2k1OzlVZ3bo2HEqPDxXHaXN7sXhEk/wyykvuhCJrw2JHSenp+oAYNKkSSgpKXG0BsouBidKWzAYLPipOombX1J2SeveSFzj5PRUHQAUFxdnZENgyl0MTpS2WbNm4eOPP7btW3UkEkE0GsWxY8dsub1sMAwDAwMDomoGgMHBQXE19/f346OPPhIVroPBIPr6+nD69GmnS7HMfB8ODAw4XYplhmGgv7/fkdc0u86Fi8GJLmj16tXo7e1N+duHH36If/7znwCA8vJyvPTSS+O6j7Nnz2JgYACzZ88e1+1kk/khU11d7XQpaQkEAuJq/vDDDzF79mz4fD6nS7Gsu7sbPp8P06ZNc7oUy/r7+9HX14d58+Y5XYpl8Xgc7e3tjrymlVKJMM8AVVgYnOiC2traUn5XSmHJkiU4cOCAbYOF3+9HSUmJqHa3pmnwer2iagbOrwmRVrPb7UZpaamodSTFxcXiXh+xWAx+v19UzeZ0qBM1K6US98/gVFi4SIPSpmmarQMF1zhRvpH4QSpxjZPEx5nk48hPacnEWcDj8bi4EMLglD3SFlkDcrcjkBacnMTQVrg48lNa7N7DCWDHifIPg1NhYYgqLBz5KS1+v9/2E/wyOFE+khicpNVM5ASO/JQWu89TBzA40cVJ+0YvseMkcY2TycnHmtsSFB6O/JQWu89TBzA4ZYu0D3LJJAYnqVN1Tp52BZAX6mn8ZI38NC733nsvKioqsGjRohEvV0rhoYceQk1NDerr63HkyJFh12HH6TyJwQngIJ8tEh9nqcEpF067QoVF3shPY3bPPfcM25cp2e7du9HZ2YnOzk787ne/ww9+8INh12FwOk9icOKUQvZI7ThJqxlwvuMEyAzKNHayRn4al+XLl6O8vHzUy1taWnDXXXdB0zQ0Njbi3LlzOHnyZMp1/H6/7VN1Ek/yy+CUPRI/zAF5dUtd4+T0+eokvqdofGSN/JRRPT09Kac9qaqqQk9PT8p12HE6T2JwMgyDg3yWSOw4SX1tcKqOsk3WyE+OCwQCtm9HEI1GxYWQWCzmdAlpMwxD5AeMruviQohSCrquO11G2iTWDMitm2SS9WlFGVVZWYmurq7E793d3aisrEy5TjAYtD04rV+/3tbby4Znn30Wf/3rX50uIy2ff/45fvKTnzhdRtp++tOf4tSpU06XkZa9e/fimWeecbqMtD344INOl5C2p59+Gvv373e6DCogPMkvJTQ3N2Pbtm1Yt24d3n77bZSVlWHmzJkp1zGDk90dAGkdhUgkguLiYlF1x2IxuFwuUTUnk1S3ue5GUs0maTWXlJQgFAo5Ure0x4rsoV3kieerIo/cfvvt2LdvH3p7e3HppZfiZz/7WWLK6fvf/z6UUnjwwQfR1taG0tJS/P73v8fSpUtTbmPr1q145ZVXEIlEbKvr+PHjmDdvnm23lw1ffPEFJk6cCI/H43QplsXjcZw5c2ZYGM51//rXvzBjxgxR07nRaBSDg4MoKytzupS0SHwvhkIhuFwu+Hw+R+5/+vTpeOONNxy5b8qoURf9MTiRo5RSuPrqq0fcMyqX/fCHP8S3vvUtLF++3OlSLPvkk0/w2GOP4ZVXXnG6lLR87Wtfw/bt20UFvjfeeAP79u3D5s2bnS4lLVdffTXeeecdp8tIy1NPPYXi4mLcd999TpdC+WXU4CTnKxzlpXA4jJKSEqfLSFs4HHbsG+5Y6boOt1ve7LzE6RCnD5EvJKWlpRgYGHC6DCogDE7kqFAoZPv2BtlgrnGSRGpwAuQdKi81OEncy4nBibJN5ihKeeX66693uoS0felLX7rgZqK5aMKECcPWrEmwbNkycSG1oqICtbW1TpeRtpUrV0LXdXi9XqdLsWzu3Lniwh7JxjVORERERKm4xomIiIhovBiciIiIiCxicCIiIiKyiMGJiIiIyCIGJyIiIiKLGJyIiIiILGJwIiIiIrKIwYmIiIjIIgYnIiIiIosYnIiIiIgsYnAiIiIisojBiYiIiMgiBifKmnvvvRcVFRVYtGjRiJcrpfDQQw+hpqYG9fX1OHLkSJYrHFlbWxsWLFiAmpoabNmyZdjln332GVasWIElS5agvr4er7/+ugNVprpYzQDw8ssvo7a2FnV1dbjjjjuyXOFwVmoGgFdffRWapuHw4cNZrG50F6t769atqK2tRX19PVauXIlPP/3UgSpTXazmSCSC73znO6ipqcGyZctw4sSJ7Bd5AX19fbjpppswf/583HTTTfjiiy+GXefdd9/Ftddei7q6OtTX1+Oll15yoFLKS0qpC/2fyDb79+9X77zzjqqrqxvx8r/85S9q9erVyjAM9eabb6prrrkmyxUOp+u6qq6uVh9//LGKRCKqvr5evf/++ynX+d73vqeeeuoppZRS77//vpo7d64Dlf6blZo7OjrUVVddpfr6+pRSSp0+fdqJUhOs1KyUUn6/X/3Hf/yHWrZsmTp06JADlaayUvff//53FQqFlFJKPfXUU2rt2rVOlJpgpebf/OY36v7771dKKbVz507Hax7qkUceUZs3b1ZKKbV582b16KOPDrvORx99pDo6OpRSSvX09KgZM2aoL774Iqt1kmijZiN2nChrli9fjvLy8lEvb2lpwV133QVN09DY2Ihz587h5MmTWaxwuIMHD6KmpgbV1dXwer1Yt24dWlpaUq6jaRr8fj8AoL+/H7NmzXKi1AQrNW/fvh3r16/HlClTAAAVFRVOlJpgpWYAePzxx/HYY4/B5/M5UOVwVupesWIFSktLAQCNjY3o7u52otQEKzW3tLTg7rvvBgCsWbMGe/fuhVLKiXJHlFzf3XffjT//+c/DrnPFFVdg/vz5AIBZs2ahoqICn3/+eVbrpPzE4EQ5o6enB7Nnz078XlVVhZ6eHgcrslbTE088gRdffBFVVVVoamrCk08+me0yU1ipuaOjAx0dHbjuuuvQ2NiItra2bJeZwkrNR44cQVdXF2655ZZslzeqdF+zO3bswM0335yN0kZlpebk67jdbpSVleHs2bNZrfNCTp8+jZkzZwIAZsyYgdOnT1/w+gcPHkQ0GsXll1+ejfIoz7mdLoBIup07d+Kee+7Bj3/8Y7z55pu488478d5776GoKHe/l+i6js7OTuzbtw/d3d1Yvnw5jh07hsmTJztd2ogMw8DDDz+M5557zulSxuzFF1/E4cOHsX//fqdLEeErX/kKTp06NezvmzZtSvld0zRomjbq7Zw8eRJ33nknnn/++Zx+T5IcDE6UMyorK9HV1ZX4vbu7G5WVlQ5WZK2mHTt2JDo21157LcLhMHp7ex2b/rJSc1VVFZYtWwaPx4N58+bhiiuuQGdnJxoaGrJdLoCL1xwIBPDee+/hhhtuAACcOnUKzc3NaG1txdKlS7NdboLV1+yePXuwadMm7N+/H8XFxdkscRgrNZvXqaqqgq7r6O/vx9SpU7Na5549e0a97NJLL8XJkycxc+ZMnDx5ctT3mt/vxy233IJNmzahsbExU6VSgWH8ppzR3NyMF154AUopvPXWWygrK0u0453S0NCAzs5OHD9+HNFoFLt27UJzc3PKdebMmYO9e/cCAD744AOEw2FMnz7diXIBWKv51ltvxb59+wAAvb296OjoQHV1tQPVnnexmsvKytDb24sTJ07gxIkTaGxsdDw0AdYe66NHj+L+++9Ha2ur42vJAGs1Nzc34/nnnwcA/PGPf8SNN954wa5OtiXX9/zzz+Mb3/jGsOtEo1F885vfxF133YU1a9Zku0TKZxdaOe7AKnbKY+vWrVMzZsxQbrdbVVZWqmeffVY9/fTT6umnn1ZKKWUYhnrggQdUdXW1WrRoUU4cNaXU+aP95s+fr6qrq9XPf/5zpZRSjz/+uGppaVFKnT+S7stf/rKqr69XixcvVm+88YaT5SqlLl6zYRjqRz/6kVq4cKFatGiR2rlzp5PlKqUuXnOy66+/XszrY+XKlaqiokItXrxYLV68WH396193slyl1MVrHhwcVGvWrFGXX365amhoUB9//LGT5Q7T29urbrzxRlVTU6NWrlypzp49q5RS6tChQ+q+++5TSin1hz/8Qbnd7sTjvnjxYnX06FEnyyZZRs1GmrrwkRK5cxgFERERUXaM2mLlVB0RERGRRQxORERERBYxOBERERFZxOBEREREZBGDExEREZFFDE5EREREFjE4EREREVnE4ERERERkEYMTERERkUUMTkREREQWMTgRERERWcTgRERERGQRgxMRERGRRQxORERERBYxOBERERFZxOBEREREZBGDExEREZFFDE5EREREFjE4EREREVnE4ERERERkEYMTERERkUUMTkREREQWMTgRERERWcTgRERERGQRgxMRERGRRQxORERERBYxOBERERFZxOBEREREZBGDExEREZFFDE5EREREFjE4EREREVnE4ERERERkEYMTERERkUUMTkREREQWMTgRERERWcTgRERERGQRgxMRERGRRQxORERERBYxOBERERFZxOBEREREZJH7IpdrWamCiIiISAB2nIiIiIgsYnAiIiIisojBiYiIiMgiBiciIiIiixiciIiIiCxicCIiIiKy6P8B44afr850WGsAAAAASUVORK5CYII=\n", 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0.00829677 -0.00920843 -0.22110354\\nv -0.22939847 0.32048753 -0.18508641\\nv 0.24157108 -0.08785685 0.21924024\\nv -0.18635425 0.13268590 -0.22374351\\nv -0.21689491 0.15829953 -0.15821872\\nv -0.10197775 -0.02405942 0.11980677\\nv -0.20792675 0.13490889 -0.11150412\\nv -0.17103688 -0.01393483 0.23155299\\nv -0.22779135 -0.08767587 0.16478683\\nv 0.25142612 -0.18607124 0.22237431\\nv -0.05954628 -0.28481541 0.22172585\\nv -0.24150024 -0.17391509 -0.17889905\\nv 0.01515525 -0.00397954 0.10334098\\nv -0.02713109 -0.01390299 -0.11471119\\nv 0.16347391 0.00849686 -0.00218100\\nv 0.24023943 -0.00451028 -0.06247011\\nf 395 1383 342\\nf 2501 36 1383\\nf 2177 342 36\\nf 1383 36 342\\nf 1887 1152 1357\\nf 1780 1289 1152\\nf 2501 1357 1289\\nf 1152 1289 1357\\nf 2335 757 573\\nf 2177 710 757\\nf 1780 573 710\\nf 757 710 573\\nf 2501 1289 36\\nf 1780 710 1289\\nf 2177 36 710\\nf 1289 710 36\\nf 2553 190 1022\\nf 2230 2175 190\\nf 1132 1022 2175\\nf 190 2175 1022\\nf 2279 1020 2540\\nf 2440 1453 1020\\nf 2230 2540 1453\\nf 1020 1453 2540\\nf 1887 273 597\\nf 1132 861 273\\nf 2440 597 861\\nf 273 861 597\\nf 2230 1453 2175\\nf 2440 861 1453\\nf 1132 2175 861\\nf 1453 861 2175\\nf 1592 222 2523\\nf 170 681 222\\nf 1555 2523 681\\nf 222 681 2523\\nf 2335 1685 645\\nf 459 1699 1685\\nf 170 645 1699\\nf 1685 1699 645\\nf 2279 249 430\\nf 1555 1337 249\\nf 459 430 1337\\nf 249 1337 430\\nf 170 1699 681\\nf 459 1337 1699\\nf 1555 681 1337\\nf 1699 1337 681\\nf 1887 597 1152\\nf 2440 2344 597\\nf 1780 1152 2344\\nf 597 2344 1152\\nf 2279 430 1020\\nf 459 2031 430\\nf 2440 1020 2031\\nf 430 2031 1020\\nf 2335 573 1685\\nf 1780 363 573\\nf 459 1685 363\\nf 573 363 1685\\nf 2440 2031 2344\\nf 459 363 2031\\nf 1780 2344 363\\nf 2031 363 2344\\nf 887 2447 76\\nf 2383 264 2447\\nf 718 76 264\\nf 2447 264 76\\nf 2361 613 2149\\nf 2245 1513 613\\nf 2383 2149 1513\\nf 613 1513 2149\\nf 2218 1580 908\\nf 718 66 1580\\nf 2245 908 66\\nf 1580 66 908\\nf 2383 1513 264\\nf 2245 66 1513\\nf 718 264 66\\nf 1513 66 264\\nf 2525 1145 446\\nf 1164 448 1145\\nf 1564 446 448\\nf 1145 448 446\\nf 1579 1930 452\\nf 1500 447 1930\\nf 1164 452 447\\nf 1930 447 452\\nf 2361 1677 786\\nf 1564 2460 1677\\nf 1500 786 2460\\nf 1677 2460 786\\nf 1164 447 448\\nf 1500 2460 447\\nf 1564 448 2460\\nf 447 2460 448\\nf 2553 340 1048\\nf 386 243 340\\nf 324 1048 243\\nf 340 243 1048\\nf 2218 2551 378\\nf 951 1416 2551\\nf 386 378 1416\\nf 2551 1416 378\\nf 1579 2345 2061\\nf 324 2025 2345\\nf 951 2061 2025\\nf 2345 2025 2061\\nf 386 1416 243\\nf 951 2025 1416\\nf 324 243 2025\\nf 1416 2025 243\\nf 2361 786 613\\nf 1500 1779 786\\nf 2245 613 1779\\nf 786 1779 613\\nf 1579 2061 1930\\nf 951 1884 2061\\nf 1500 1930 1884\\nf 2061 1884 1930\\nf 2218 908 2551\\nf 2245 1238 908\\nf 951 2551 1238\\nf 908 1238 2551\\nf 1500 1884 1779\\nf 951 1238 1884\\nf 2245 1779 1238\\nf 1884 1238 1779\\nf 300 434 1576\\nf 1635 1268 434\\nf 2483 1576 1268\\nf 434 1268 1576\\nf 227 1838 1558\\nf 1147 1254 1838\\nf 1635 1558 1254\\nf 1838 1254 1558\\nf 1011 1711 1411\\nf 2483 1398 1711\\nf 1147 1411 1398\\nf 1711 1398 1411\\nf 1635 1254 1268\\nf 1147 1398 1254\\nf 2483 1268 1398\\nf 1254 1398 1268\\nf 1592 2378 383\\nf 1577 2289 2378\\nf 45 383 2289\\nf 2378 2289 383\\nf 2195 853 1803\\nf 1160 1521 853\\nf 1577 1803 1521\\nf 853 1521 1803\\nf 227 2131 1139\\nf 45 1180 2131\\nf 1160 1139 1180\\nf 2131 1180 1139\\nf 1577 1521 2289\\nf 1160 1180 1521\\nf 45 2289 1180\\nf 1521 1180 2289\\nf 2525 444 502\\nf 1138 283 444\\nf 1258 502 283\\nf 444 283 502\\nf 1011 846 1665\\nf 2419 1167 846\\nf 1138 1665 1167\\nf 846 1167 1665\\nf 2195 977 145\\nf 1258 2426 977\\nf 2419 145 2426\\nf 977 2426 145\\nf 1138 1167 283\\nf 2419 2426 1167\\nf 1258 283 2426\\nf 1167 2426 283\\nf 227 1139 1838\\nf 1160 2444 1139\\nf 1147 1838 2444\\nf 1139 2444 1838\\nf 2195 145 853\\nf 2419 1003 145\\nf 1160 853 1003\\nf 145 1003 853\\nf 1011 1411 846\\nf 1147 1772 1411\\nf 2419 846 1772\\nf 1411 1772 846\\nf 1160 1003 2444\\nf 2419 1772 1003\\nf 1147 2444 1772\\nf 1003 1772 2444\\nf 2553 1048 190\\nf 324 312 1048\\nf 2230 190 312\\nf 1048 312 190\\nf 1579 443 2345\\nf 2052 2558 443\\nf 324 2345 2558\\nf 443 2558 2345\\nf 2279 2540 481\\nf 2230 294 2540\\nf 2052 481 294\\nf 2540 294 481\\nf 324 2558 312\\nf 2052 294 2558\\nf 2230 312 294\\nf 2558 294 312\\nf 2525 502 1145\\nf 1258 1202 502\\nf 1164 1145 1202\\nf 502 1202 1145\\nf 2195 1267 977\\nf 680 1274 1267\\nf 1258 977 1274\\nf 1267 1274 977\\nf 1579 452 2439\\nf 1164 74 452\\nf 680 2439 74\\nf 452 74 2439\\nf 1258 1274 1202\\nf 680 74 1274\\nf 1164 1202 74\\nf 1274 74 1202\\nf 1592 2523 2378\\nf 1555 2494 2523\\nf 1577 2378 2494\\nf 2523 2494 2378\\nf 2279 1224 249\\nf 1143 198 1224\\nf 1555 249 198\\nf 1224 198 249\\nf 2195 1803 2174\\nf 1577 177 1803\\nf 1143 2174 177\\nf 1803 177 2174\\nf 1555 198 2494\\nf 1143 177 198\\nf 1577 2494 177\\nf 198 177 2494\\nf 1579 2439 443\\nf 680 794 2439\\nf 2052 443 794\\nf 2439 794 443\\nf 2195 2174 1267\\nf 1143 1126 2174\\nf 680 1267 1126\\nf 2174 1126 1267\\nf 2279 481 1224\\nf 2052 2365 481\\nf 1143 1224 2365\\nf 481 2365 1224\\nf 680 1126 794\\nf 1143 2365 1126\\nf 2052 794 2365\\nf 1126 2365 794\\nf 395 342 594\\nf 2177 1795 342\\nf 707 594 1795\\nf 342 1795 594\\nf 2335 60 757\\nf 1023 796 60\\nf 2177 757 796\\nf 60 796 757\\nf 1101 380 1039\\nf 707 1434 380\\nf 1023 1039 1434\\nf 380 1434 1039\\nf 2177 796 1795\\nf 1023 1434 796\\nf 707 1795 1434\\nf 796 1434 1795\\nf 1592 730 222\\nf 528 2200 730\\nf 170 222 2200\\nf 730 2200 222\\nf 1019 2505 159\\nf 890 1112 2505\\nf 528 159 1112\\nf 2505 1112 159\\nf 2335 645 1842\\nf 170 1030 645\\nf 890 1842 1030\\nf 645 1030 1842\\nf 528 1112 2200\\nf 890 1030 1112\\nf 170 2200 1030\\nf 1112 1030 2200\\nf 692 1141 2268\\nf 359 1636 1141\\nf 414 2268 1636\\nf 1141 1636 2268\\nf 1101 773 1296\\nf 1198 1792 773\\nf 359 1296 1792\\nf 773 1792 1296\\nf 1019 1320 2537\\nf 414 1733 1320\\nf 1198 2537 1733\\nf 1320 1733 2537\\nf 359 1792 1636\\nf 1198 1733 1792\\nf 414 1636 1733\\nf 1792 1733 1636\\nf 2335 1842 60\\nf 890 506 1842\\nf 1023 60 506\\nf 1842 506 60\\nf 1019 2537 2505\\nf 1198 158 2537\\nf 890 2505 158\\nf 2537 158 2505\\nf 1101 1039 773\\nf 1023 69 1039\\nf 1198 773 69\\nf 1039 69 773\\nf 890 158 506\\nf 1198 69 158\\nf 1023 506 69\\nf 158 69 506\\nf 300 497 434\\nf 1991 1162 497\\nf 1635 434 1162\\nf 497 1162 434\\nf 768 519 1618\\nf 1650 2160 519\\nf 1991 1618 2160\\nf 519 2160 1618\\nf 227 1558 121\\nf 1635 1648 1558\\nf 1650 121 1648\\nf 1558 1648 121\\nf 1991 2160 1162\\nf 1650 1648 2160\\nf 1635 1162 1648\\nf 2160 1648 1162\\nf 1828 745 2212\\nf 1879 2260 745\\nf 2305 2212 2260\\nf 745 2260 2212\\nf 596 1412 821\\nf 209 1661 1412\\nf 1879 821 1661\\nf 1412 1661 821\\nf 768 1120 2249\\nf 2305 288 1120\\nf 209 2249 288\\nf 1120 288 2249\\nf 1879 1661 2260\\nf 209 288 1661\\nf 2305 2260 288\\nf 1661 288 2260\\nf 1592 383 885\\nf 45 1642 383\\nf 301 885 1642\\nf 383 1642 885\\nf 227 717 2131\\nf 2136 1462 717\\nf 45 2131 1462\\nf 717 1462 2131\\nf 596 105 2508\\nf 301 1222 105\\nf 2136 2508 1222\\nf 105 1222 2508\\nf 45 1462 1642\\nf 2136 1222 1462\\nf 301 1642 1222\\nf 1462 1222 1642\\nf 768 2249 519\\nf 209 212 2249\\nf 1650 519 212\\nf 2249 212 519\\nf 596 2508 1412\\nf 2136 1961 2508\\nf 209 1412 1961\\nf 2508 1961 1412\\nf 227 121 717\\nf 1650 1694 121\\nf 2136 717 1694\\nf 121 1694 717\\nf 209 1961 212\\nf 2136 1694 1961\\nf 1650 212 1694\\nf 1961 1694 212\\nf 1777 2543 1402\\nf 2366 244 2543\\nf 2038 1402 244\\nf 2543 244 1402\\nf 1653 2434 624\\nf 813 1032 2434\\nf 2366 624 1032\\nf 2434 1032 624\\nf 762 1932 442\\nf 2038 2176 1932\\nf 813 442 2176\\nf 1932 2176 442\\nf 2366 1032 244\\nf 813 2176 1032\\nf 2038 244 2176\\nf 1032 2176 244\\nf 692 1430 226\\nf 1442 2206 1430\\nf 1706 226 2206\\nf 1430 2206 226\\nf 1024 1130 2316\\nf 556 1172 1130\\nf 1442 2316 1172\\nf 1130 1172 2316\\nf 1653 2102 1741\\nf 1706 739 2102\\nf 556 1741 739\\nf 2102 739 1741\\nf 1442 1172 2206\\nf 556 739 1172\\nf 1706 2206 739\\nf 1172 739 2206\\nf 1828 1656 2124\\nf 1049 2269 1656\\nf 992 2124 2269\\nf 1656 2269 2124\\nf 762 1702 872\\nf 2081 1319 1702\\nf 1049 872 1319\\nf 1702 1319 872\\nf 1024 2409 1570\\nf 992 2003 2409\\nf 2081 1570 2003\\nf 2409 2003 1570\\nf 1049 1319 2269\\nf 2081 2003 1319\\nf 992 2269 2003\\nf 1319 2003 2269\\nf 1653 1741 2434\\nf 556 55 1741\\nf 813 2434 55\\nf 1741 55 2434\\nf 1024 1570 1130\\nf 2081 583 1570\\nf 556 1130 583\\nf 1570 583 1130\\nf 762 442 1702\\nf 813 2016 442\\nf 2081 1702 2016\\nf 442 2016 1702\\nf 556 583 55\\nf 2081 2016 583\\nf 813 55 2016\\nf 583 2016 55\\nf 1592 885 730\\nf 301 216 885\\nf 528 730 216\\nf 885 216 730\\nf 596 716 105\\nf 1114 1330 716\\nf 301 105 1330\\nf 716 1330 105\\nf 1019 159 499\\nf 528 2255 159\\nf 1114 499 2255\\nf 159 2255 499\\nf 301 1330 216\\nf 1114 2255 1330\\nf 528 216 2255\\nf 1330 2255 216\\nf 1828 2124 745\\nf 992 1604 2124\\nf 1879 745 1604\\nf 2124 1604 745\\nf 1024 95 2409\\nf 1084 851 95\\nf 992 2409 851\\nf 95 851 2409\\nf 596 821 1107\\nf 1879 956 821\\nf 1084 1107 956\\nf 821 956 1107\\nf 992 851 1604\\nf 1084 956 851\\nf 1879 1604 956\\nf 851 956 1604\\nf 692 2268 1430\\nf 414 2397 2268\\nf 1442 1430 2397\\nf 2268 2397 1430\\nf 1019 2059 1320\\nf 958 2158 2059\\nf 414 1320 2158\\nf 2059 2158 1320\\nf 1024 2316 504\\nf 1442 2130 2316\\nf 958 504 2130\\nf 2316 2130 504\\nf 414 2158 2397\\nf 958 2130 2158\\nf 1442 2397 2130\\nf 2158 2130 2397\\nf 596 1107 716\\nf 1084 2386 1107\\nf 1114 716 2386\\nf 1107 2386 716\\nf 1024 504 95\\nf 958 571 504\\nf 1084 95 571\\nf 504 571 95\\nf 1019 499 2059\\nf 1114 1913 499\\nf 958 2059 1913\\nf 499 1913 2059\\nf 1084 571 2386\\nf 958 1913 571\\nf 1114 2386 1913\\nf 571 1913 2386\\nf 395 594 1959\\nf 707 1744 594\\nf 1870 1959 1744\\nf 594 1744 1959\\nf 1101 2173 380\\nf 553 106 2173\\nf 707 380 106\\nf 2173 106 380\\nf 526 1937 267\\nf 1870 2236 1937\\nf 553 267 2236\\nf 1937 2236 267\\nf 707 106 1744\\nf 553 2236 106\\nf 1870 1744 2236\\nf 106 2236 1744\\nf 692 691 1141\\nf 780 2329 691\\nf 359 1141 2329\\nf 691 2329 1141\\nf 1225 254 1841\\nf 210 1806 254\\nf 780 1841 1806\\nf 254 1806 1841\\nf 1101 1296 397\\nf 359 1507 1296\\nf 210 397 1507\\nf 1296 1507 397\\nf 780 1806 2329\\nf 210 1507 1806\\nf 359 2329 1507\\nf 1806 1507 2329\\nf 1757 770 287\\nf 2449 1065 770\\nf 2389 287 1065\\nf 770 1065 287\\nf 526 1470 997\\nf 2103 1658 1470\\nf 2449 997 1658\\nf 1470 1658 997\\nf 1225 1156 1735\\nf 2389 2456 1156\\nf 2103 1735 2456\\nf 1156 2456 1735\\nf 2449 1658 1065\\nf 2103 2456 1658\\nf 2389 1065 2456\\nf 1658 2456 1065\\nf 1101 397 2173\\nf 210 2557 397\\nf 553 2173 2557\\nf 397 2557 2173\\nf 1225 1735 254\\nf 2103 530 1735\\nf 210 254 530\\nf 1735 530 254\\nf 526 267 1470\\nf 553 411 267\\nf 2103 1470 411\\nf 267 411 1470\\nf 210 530 2557\\nf 2103 411 530\\nf 553 2557 411\\nf 530 411 2557\\nf 1777 753 2543\\nf 657 334 753\\nf 2366 2543 334\\nf 753 334 2543\\nf 197 151 1581\\nf 2311 1399 151\\nf 657 1581 1399\\nf 151 1399 1581\\nf 1653 624 993\\nf 2366 1853 624\\nf 2311 993 1853\\nf 624 1853 993\\nf 657 1399 334\\nf 2311 1853 1399\\nf 2366 334 1853\\nf 1399 1853 334\\nf 744 2080 938\\nf 731 516 2080\\nf 524 938 516\\nf 2080 516 938\\nf 31 1962 1194\\nf 255 1935 1962\\nf 731 1194 1935\\nf 1962 1935 1194\\nf 197 79 1387\\nf 524 847 79\\nf 255 1387 847\\nf 79 847 1387\\nf 731 1935 516\\nf 255 847 1935\\nf 524 516 847\\nf 1935 847 516\\nf 692 226 889\\nf 1706 2170 226\\nf 1063 889 2170\\nf 226 2170 889\\nf 1653 998 2102\\nf 1443 1528 998\\nf 1706 2102 1528\\nf 998 1528 2102\\nf 31 2527 1301\\nf 1063 1862 2527\\nf 1443 1301 1862\\nf 2527 1862 1301\\nf 1706 1528 2170\\nf 1443 1862 1528\\nf 1063 2170 1862\\nf 1528 1862 2170\\nf 197 1387 151\\nf 255 1077 1387\\nf 2311 151 1077\\nf 1387 1077 151\\nf 31 1301 1962\\nf 1443 455 1301\\nf 255 1962 455\\nf 1301 455 1962\\nf 1653 993 998\\nf 2311 18 993\\nf 1443 998 18\\nf 993 18 998\\nf 255 455 1077\\nf 1443 18 455\\nf 2311 1077 18\\nf 455 18 1077\\nf 2282 321 1\\nf 71 205 321\\nf 876 1 205\\nf 321 205 1\\nf 2358 77 399\\nf 1200 1874 77\\nf 71 399 1874\\nf 77 1874 399\\nf 1099 2126 949\\nf 876 1815 2126\\nf 1200 949 1815\\nf 2126 1815 949\\nf 71 1874 205\\nf 1200 1815 1874\\nf 876 205 1815\\nf 1874 1815 205\\nf 1757 1881 854\\nf 2235 215 1881\\nf 5 854 215\\nf 1881 215 854\\nf 2231 1726 622\\nf 420 2299 1726\\nf 2235 622 2299\\nf 1726 2299 622\\nf 2358 1931 1679\\nf 5 303 1931\\nf 420 1679 303\\nf 1931 303 1679\\nf 2235 2299 215\\nf 420 303 2299\\nf 5 215 303\\nf 2299 303 215\\nf 744 2556 1173\\nf 926 705 2556\\nf 2352 1173 705\\nf 2556 705 1173\\nf 1099 298 849\\nf 891 1532 298\\nf 926 849 1532\\nf 298 1532 849\\nf 2231 1005 1270\\nf 2352 2258 1005\\nf 891 1270 2258\\nf 1005 2258 1270\\nf 926 1532 705\\nf 891 2258 1532\\nf 2352 705 2258\\nf 1532 2258 705\\nf 2358 1679 77\\nf 420 1300 1679\\nf 1200 77 1300\\nf 1679 1300 77\\nf 2231 1270 1726\\nf 891 346 1270\\nf 420 1726 346\\nf 1270 346 1726\\nf 1099 949 298\\nf 1200 1457 949\\nf 891 298 1457\\nf 949 1457 298\\nf 420 346 1300\\nf 891 1457 346\\nf 1200 1300 1457\\nf 346 1457 1300\\nf 692 889 691\\nf 1063 1885 889\\nf 780 691 1885\\nf 889 1885 691\\nf 31 2107 2527\\nf 1486 912 2107\\nf 1063 2527 912\\nf 2107 912 2527\\nf 1225 1841 352\\nf 780 1764 1841\\nf 1486 352 1764\\nf 1841 1764 352\\nf 1063 912 1885\\nf 1486 1764 912\\nf 780 1885 1764\\nf 912 1764 1885\\nf 744 1173 2080\\nf 2352 896 1173\\nf 731 2080 896\\nf 1173 896 2080\\nf 2231 1239 1005\\nf 999 2133 1239\\nf 2352 1005 2133\\nf 1239 2133 1005\\nf 31 1194 477\\nf 731 357 1194\\nf 999 477 357\\nf 1194 357 477\\nf 2352 2133 896\\nf 999 357 2133\\nf 731 896 357\\nf 2133 357 896\\nf 1757 287 1881\\nf 2389 1585 287\\nf 2235 1881 1585\\nf 287 1585 1881\\nf 1225 1669 1156\\nf 2286 835 1669\\nf 2389 1156 835\\nf 1669 835 1156\\nf 2231 622 1080\\nf 2235 572 622\\nf 2286 1080 572\\nf 622 572 1080\\nf 2389 835 1585\\nf 2286 572 835\\nf 2235 1585 572\\nf 835 572 1585\\nf 31 477 2107\\nf 999 263 477\\nf 1486 2107 263\\nf 477 263 2107\\nf 2231 1080 1239\\nf 2286 1197 1080\\nf 999 1239 1197\\nf 1080 1197 1239\\nf 1225 352 1669\\nf 1486 761 352\\nf 2286 1669 761\\nf 352 761 1669\\nf 999 1197 263\\nf 2286 761 1197\\nf 1486 263 761\\nf 1197 761 263\\nf 395 1959 2320\\nf 1870 1369 1959\\nf 2087 2320 1369\\nf 1959 1369 2320\\nf 526 1692 1937\\nf 165 918 1692\\nf 1870 1937 918\\nf 1692 918 1937\\nf 1729 2480 1695\\nf 2087 23 2480\\nf 165 1695 23\\nf 2480 23 1695\\nf 1870 918 1369\\nf 165 23 918\\nf 2087 1369 23\\nf 918 23 1369\\nf 1757 2073 770\\nf 1317 1535 2073\\nf 2449 770 1535\\nf 2073 1535 770\\nf 1177 679 1951\\nf 2496 1384 679\\nf 1317 1951 1384\\nf 679 1384 1951\\nf 526 997 2155\\nf 2449 2342 997\\nf 2496 2155 2342\\nf 997 2342 2155\\nf 1317 1384 1535\\nf 2496 2342 1384\\nf 2449 1535 2342\\nf 1384 2342 1535\\nf 256 2455 1992\\nf 981 1511 2455\\nf 2062 1992 1511\\nf 2455 1511 1992\\nf 1729 1957 1452\\nf 160 2288 1957\\nf 981 1452 2288\\nf 1957 2288 1452\\nf 1177 1210 438\\nf 2062 1484 1210\\nf 160 438 1484\\nf 1210 1484 438\\nf 981 2288 1511\\nf 160 1484 2288\\nf 2062 1511 1484\\nf 2288 1484 1511\\nf 526 2155 1692\\nf 2496 1283 2155\\nf 165 1692 1283\\nf 2155 1283 1692\\nf 1177 438 679\\nf 160 2528 438\\nf 2496 679 2528\\nf 438 2528 679\\nf 1729 1695 1957\\nf 165 1928 1695\\nf 160 1957 1928\\nf 1695 1928 1957\\nf 2496 2528 1283\\nf 160 1928 2528\\nf 165 1283 1928\\nf 2528 1928 1283\\nf 2282 319 321\\nf 683 1896 319\\nf 71 321 1896\\nf 319 1896 321\\nf 213 586 1614\\nf 449 994 586\\nf 683 1614 994\\nf 586 994 1614\\nf 2358 399 1725\\nf 71 1681 399\\nf 449 1725 1681\\nf 399 1681 1725\\nf 683 994 1896\\nf 449 1681 994\\nf 71 1896 1681\\nf 994 1681 1896\\nf 880 732 1700\\nf 1363 2354 732\\nf 415 1700 2354\\nf 732 2354 1700\\nf 598 605 2303\\nf 2355 2351 605\\nf 1363 2303 2351\\nf 605 2351 2303\\nf 213 1718 305\\nf 415 1527 1718\\nf 2355 305 1527\\nf 1718 1527 305\\nf 1363 2351 2354\\nf 2355 1527 2351\\nf 415 2354 1527\\nf 2351 1527 2354\\nf 1757 854 464\\nf 5 935 854\\nf 1115 464 935\\nf 854 935 464\\nf 2358 2257 1931\\nf 1240 2084 2257\\nf 5 1931 2084\\nf 2257 2084 1931\\nf 598 1936 897\\nf 1115 1353 1936\\nf 1240 897 1353\\nf 1936 1353 897\\nf 5 2084 935\\nf 1240 1353 2084\\nf 1115 935 1353\\nf 2084 1353 935\\nf 213 305 586\\nf 2355 715 305\\nf 449 586 715\\nf 305 715 586\\nf 598 897 605\\nf 1240 2473 897\\nf 2355 605 2473\\nf 897 2473 605\\nf 2358 1725 2257\\nf 449 2060 1725\\nf 1240 2257 2060\\nf 1725 2060 2257\\nf 2355 2473 715\\nf 1240 2060 2473\\nf 449 715 2060\\nf 2473 2060 715\\nf 1831 2183 1571\\nf 253 1469 2183\\nf 1425 1571 1469\\nf 2183 1469 1571\\nf 924 416 2051\\nf 1666 224 416\\nf 253 2051 224\\nf 416 224 2051\\nf 610 2058 1917\\nf 1425 2138 2058\\nf 1666 1917 2138\\nf 2058 2138 1917\\nf 253 224 1469\\nf 1666 2138 224\\nf 1425 1469 2138\\nf 224 2138 1469\\nf 256 2020 1904\\nf 2555 654 2020\\nf 2302 1904 654\\nf 2020 654 1904\\nf 1475 2086 2411\\nf 1876 2187 2086\\nf 2555 2411 2187\\nf 2086 2187 2411\\nf 924 1866 157\\nf 2302 1349 1866\\nf 1876 157 1349\\nf 1866 1349 157\\nf 2555 2187 654\\nf 1876 1349 2187\\nf 2302 654 1349\\nf 2187 1349 654\\nf 880 1153 1069\\nf 1517 1845 1153\\nf 2022 1069 1845\\nf 1153 1845 1069\\nf 610 701 1447\\nf 1667 944 701\\nf 1517 1447 944\\nf 701 944 1447\\nf 1475 259 1830\\nf 2022 65 259\\nf 1667 1830 65\\nf 259 65 1830\\nf 1517 944 1845\\nf 1667 65 944\\nf 2022 1845 65\\nf 944 65 1845\\nf 924 157 416\\nf 1876 2168 157\\nf 1666 416 2168\\nf 157 2168 416\\nf 1475 1830 2086\\nf 1667 1235 1830\\nf 1876 2086 1235\\nf 1830 1235 2086\\nf 610 1917 701\\nf 1666 1949 1917\\nf 1667 701 1949\\nf 1917 1949 701\\nf 1876 1235 2168\\nf 1667 1949 1235\\nf 1666 2168 1949\\nf 1235 1949 2168\\nf 1757 464 2073\\nf 1115 2381 464\\nf 1317 2073 2381\\nf 464 2381 2073\\nf 598 1823 1936\\nf 1256 1017 1823\\nf 1115 1936 1017\\nf 1823 1017 1936\\nf 1177 1951 2238\\nf 1317 80 1951\\nf 1256 2238 80\\nf 1951 80 2238\\nf 1115 1017 2381\\nf 1256 80 1017\\nf 1317 2381 80\\nf 1017 80 2381\\nf 880 1069 732\\nf 2022 1707 1069\\nf 1363 732 1707\\nf 1069 1707 732\\nf 1475 429 259\\nf 601 929 429\\nf 2022 259 929\\nf 429 929 259\\nf 598 2303 1362\\nf 1363 589 2303\\nf 601 1362 589\\nf 2303 589 1362\\nf 2022 929 1707\\nf 601 589 929\\nf 1363 1707 589\\nf 929 589 1707\\nf 256 1992 2020\\nf 2062 188 1992\\nf 2555 2020 188\\nf 1992 188 2020\\nf 1177 595 1210\\nf 278 173 595\\nf 2062 1210 173\\nf 595 173 1210\\nf 1475 2411 634\\nf 2555 175 2411\\nf 278 634 175\\nf 2411 175 634\\nf 2062 173 188\\nf 278 175 173\\nf 2555 188 175\\nf 173 175 188\\nf 598 1362 1823\\nf 601 1249 1362\\nf 1256 1823 1249\\nf 1362 1249 1823\\nf 1475 634 429\\nf 278 542 634\\nf 601 429 542\\nf 634 542 429\\nf 1177 2238 595\\nf 1256 921 2238\\nf 278 595 921\\nf 2238 921 595\\nf 601 542 1249\\nf 278 921 542\\nf 1256 1249 921\\nf 542 921 1249\\nf 395 2320 1383\\nf 2087 1322 2320\\nf 2501 1383 1322\\nf 2320 1322 1383\\nf 1729 119 2480\\nf 1946 2129 119\\nf 2087 2480 2129\\nf 119 2129 2480\\nf 1887 1357 7\\nf 2501 2339 1357\\nf 1946 7 2339\\nf 1357 2339 7\\nf 2087 2129 1322\\nf 1946 2339 2129\\nf 2501 1322 2339\\nf 2129 2339 1322\\nf 256 1671 2455\\nf 711 2507 1671\\nf 981 2455 2507\\nf 1671 2507 2455\\nf 400 1921 2057\\nf 1911 2503 1921\\nf 711 2057 2503\\nf 1921 2503 2057\\nf 1729 1452 2400\\nf 981 1236 1452\\nf 1911 2400 1236\\nf 1452 1236 2400\\nf 711 2503 2507\\nf 1911 1236 2503\\nf 981 2507 1236\\nf 2503 1236 2507\\nf 2553 1022 1282\\nf 1132 886 1022\\nf 2301 1282 886\\nf 1022 886 1282\\nf 1887 668 273\\nf 1140 232 668\\nf 1132 273 232\\nf 668 232 273\\nf 400 1926 1721\\nf 2301 93 1926\\nf 1140 1721 93\\nf 1926 93 1721\\nf 1132 232 886\\nf 1140 93 232\\nf 2301 886 93\\nf 232 93 886\\nf 1729 2400 119\\nf 1911 2450 2400\\nf 1946 119 2450\\nf 2400 2450 119\\nf 400 1721 1921\\nf 1140 129 1721\\nf 1911 1921 129\\nf 1721 129 1921\\nf 1887 7 668\\nf 1946 782 7\\nf 1140 668 782\\nf 7 782 668\\nf 1911 129 2450\\nf 1140 782 129\\nf 1946 2450 782\\nf 129 782 2450\\nf 1831 2044 2183\\nf 1996 43 2044\\nf 253 2183 43\\nf 2044 43 2183\\nf 1111 767 16\\nf 1872 100 767\\nf 1996 16 100\\nf 767 100 16\\nf 924 2051 2491\\nf 253 1480 2051\\nf 1872 2491 1480\\nf 2051 1480 2491\\nf 1996 100 43\\nf 1872 1480 100\\nf 253 43 1480\\nf 100 1480 43\\nf 1203 1923 61\\nf 199 2153 1923\\nf 2150 61 2153\\nf 1923 2153 61\\nf 318 1280 10\\nf 1188 270 1280\\nf 199 10 270\\nf 1280 270 10\\nf 1111 2054 6\\nf 2150 1572 2054\\nf 1188 6 1572\\nf 2054 1572 6\\nf 199 270 2153\\nf 1188 1572 270\\nf 2150 2153 1572\\nf 270 1572 2153\\nf 256 1904 1165\\nf 2302 2065 1904\\nf 1547 1165 2065\\nf 1904 2065 1165\\nf 924 2529 1866\\nf 2027 2379 2529\\nf 2302 1866 2379\\nf 2529 2379 1866\\nf 318 1459 281\\nf 1547 82 1459\\nf 2027 281 82\\nf 1459 82 281\\nf 2302 2379 2065\\nf 2027 82 2379\\nf 1547 2065 82\\nf 2379 82 2065\\nf 1111 6 767\\nf 1188 955 6\\nf 1872 767 955\\nf 6 955 767\\nf 318 281 1280\\nf 2027 1025 281\\nf 1188 1280 1025\\nf 281 1025 1280\\nf 924 2491 2529\\nf 1872 1066 2491\\nf 2027 2529 1066\\nf 2491 1066 2529\\nf 1188 1025 955\\nf 2027 1066 1025\\nf 1872 955 1066\\nf 1025 1066 955\\nf 887 76 1424\\nf 718 2201 76\\nf 2477 1424 2201\\nf 76 2201 1424\\nf 2218 2418 1580\\nf 740 83 2418\\nf 718 1580 83\\nf 2418 83 1580\\nf 806 2362 181\\nf 2477 1899 2362\\nf 740 181 1899\\nf 2362 1899 181\\nf 718 83 2201\\nf 740 1899 83\\nf 2477 2201 1899\\nf 83 1899 2201\\nf 2553 46 340\\nf 579 1284 46\\nf 386 340 1284\\nf 46 1284 340\\nf 1589 15 936\\nf 407 427 15\\nf 579 936 427\\nf 15 427 936\\nf 2218 378 1117\\nf 386 2079 378\\nf 407 1117 2079\\nf 378 2079 1117\\nf 579 427 1284\\nf 407 2079 427\\nf 386 1284 2079\\nf 427 2079 1284\\nf 1203 1413 769\\nf 1925 1450 1413\\nf 1791 769 1450\\nf 1413 1450 769\\nf 806 1292 1909\\nf 229 1531 1292\\nf 1925 1909 1531\\nf 1292 1531 1909\\nf 1589 2464 534\\nf 1791 2182 2464\\nf 229 534 2182\\nf 2464 2182 534\\nf 1925 1531 1450\\nf 229 2182 1531\\nf 1791 1450 2182\\nf 1531 2182 1450\\nf 2218 1117 2418\\nf 407 1473 1117\\nf 740 2418 1473\\nf 1117 1473 2418\\nf 1589 534 15\\nf 229 2064 534\\nf 407 15 2064\\nf 534 2064 15\\nf 806 181 1292\\nf 740 1248 181\\nf 229 1292 1248\\nf 181 1248 1292\\nf 407 2064 1473\\nf 229 1248 2064\\nf 740 1473 1248\\nf 2064 1248 1473\\nf 256 1165 1671\\nf 1547 1551 1165\\nf 711 1671 1551\\nf 1165 1551 1671\\nf 318 2318 1459\\nf 1696 578 2318\\nf 1547 1459 578\\nf 2318 578 1459\\nf 400 2057 1496\\nf 711 995 2057\\nf 1696 1496 995\\nf 2057 995 1496\\nf 1547 578 1551\\nf 1696 995 578\\nf 711 1551 995\\nf 578 995 1551\\nf 1203 769 1923\\nf 1791 53 769\\nf 199 1923 53\\nf 769 53 1923\\nf 1589 478 2464\\nf 326 1611 478\\nf 1791 2464 1611\\nf 478 1611 2464\\nf 318 10 193\\nf 199 1211 10\\nf 326 193 1211\\nf 10 1211 193\\nf 1791 1611 53\\nf 326 1211 1611\\nf 199 53 1211\\nf 1611 1211 53\\nf 2553 1282 46\\nf 2301 484 1282\\nf 579 46 484\\nf 1282 484 46\\nf 400 1783 1926\\nf 869 2244 1783\\nf 2301 1926 2244\\nf 1783 2244 1926\\nf 1589 936 1455\\nf 579 2047 936\\nf 869 1455 2047\\nf 936 2047 1455\\nf 2301 2244 484\\nf 869 2047 2244\\nf 579 484 2047\\nf 2244 2047 484\\nf 318 193 2318\\nf 326 1965 193\\nf 1696 2318 1965\\nf 193 1965 2318\\nf 1589 1455 478\\nf 869 1676 1455\\nf 326 478 1676\\nf 1455 1676 478\\nf 400 1496 1783\\nf 1696 1888 1496\\nf 869 1783 1888\\nf 1496 1888 1783\\nf 326 1676 1965\\nf 869 1888 1676\\nf 1696 1965 1888\\nf 1676 1888 1965\\nf 1777 1402 566\\nf 2038 643 1402\\nf 309 566 643\\nf 1402 643 566\\nf 762 2445 1932\\nf 1993 659 2445\\nf 2038 1932 659\\nf 2445 659 1932\\nf 487 2304 299\\nf 309 2562 2304\\nf 1993 299 2562\\nf 2304 2562 299\\nf 2038 659 643\\nf 1993 2562 659\\nf 309 643 2562\\nf 659 2562 643\\nf 1828 791 1656\\nf 1329 208 791\\nf 1049 1656 208\\nf 791 208 1656\\nf 2185 1997 1826\\nf 1465 1436 1997\\nf 1329 1826 1436\\nf 1997 1436 1826\\nf 762 872 1245\\nf 1049 1067 872\\nf 1465 1245 1067\\nf 872 1067 1245\\nf 1329 1436 208\\nf 1465 1067 1436\\nf 1049 208 1067\\nf 1436 1067 208\\nf 1191 1768 1273\\nf 130 2146 1768\\nf 341 1273 2146\\nf 1768 2146 1273\\nf 487 2322 117\\nf 2167 311 2322\\nf 130 117 311\\nf 2322 311 117\\nf 2185 972 1219\\nf 341 980 972\\nf 2167 1219 980\\nf 972 980 1219\\nf 130 311 2146\\nf 2167 980 311\\nf 341 2146 980\\nf 311 980 2146\\nf 762 1245 2445\\nf 1465 422 1245\\nf 1993 2445 422\\nf 1245 422 2445\\nf 2185 1219 1997\\nf 2167 792 1219\\nf 1465 1997 792\\nf 1219 792 1997\\nf 487 299 2322\\nf 1993 1379 299\\nf 2167 2322 1379\\nf 299 1379 2322\\nf 1465 792 422\\nf 2167 1379 792\\nf 1993 422 1379\\nf 792 1379 422\\nf 300 1652 497\\nf 2427 3 1652\\nf 1991 497 3\\nf 1652 3 497\\nf 1594 1537 619\\nf 2382 1279 1537\\nf 2427 619 1279\\nf 1537 1279 619\\nf 768 1618 1891\\nf 1991 960 1618\\nf 2382 1891 960\\nf 1618 960 1891\\nf 2427 1279 3\\nf 2382 960 1279\\nf 1991 3 960\\nf 1279 960 3\\nf 2402 1187 1569\\nf 1709 1627 1187\\nf 1209 1569 1627\\nf 1187 1627 1569\\nf 1609 1461 2435\\nf 2334 966 1461\\nf 1709 2435 966\\nf 1461 966 2435\\nf 1594 959 456\\nf 1209 726 959\\nf 2334 456 726\\nf 959 726 456\\nf 1709 966 1627\\nf 2334 726 966\\nf 1209 1627 726\\nf 966 726 1627\\nf 1828 2212 26\\nf 2305 1785 2212\\nf 1298 26 1785\\nf 2212 1785 26\\nf 768 1717 1120\\nf 441 1944 1717\\nf 2305 1120 1944\\nf 1717 1944 1120\\nf 1609 819 392\\nf 1298 1348 819\\nf 441 392 1348\\nf 819 1348 392\\nf 2305 1944 1785\\nf 441 1348 1944\\nf 1298 1785 1348\\nf 1944 1348 1785\\nf 1594 456 1537\\nf 2334 2055 456\\nf 2382 1537 2055\\nf 456 2055 1537\\nf 1609 392 1461\\nf 441 284 392\\nf 2334 1461 284\\nf 392 284 1461\\nf 768 1891 1717\\nf 2382 450 1891\\nf 441 1717 450\\nf 1891 450 1717\\nf 2334 284 2055\\nf 441 450 284\\nf 2382 2055 450\\nf 284 450 2055\\nf 1769 1124 1471\\nf 1553 1600 1124\\nf 2292 1471 1600\\nf 1124 1600 1471\\nf 2014 1599 1105\\nf 317 1567 1599\\nf 1553 1105 1567\\nf 1599 1567 1105\\nf 384 1929 1181\\nf 2292 1955 1929\\nf 317 1181 1955\\nf 1929 1955 1181\\nf 1553 1567 1600\\nf 317 1955 1567\\nf 2292 1600 1955\\nf 1567 1955 1600\\nf 1191 832 1790\\nf 1090 410 832\\nf 2169 1790 410\\nf 832 410 1790\\nf 1421 1771 1251\\nf 469 1731 1771\\nf 1090 1251 1731\\nf 1771 1731 1251\\nf 2014 1157 656\\nf 2169 11 1157\\nf 469 656 11\\nf 1157 11 656\\nf 1090 1731 410\\nf 469 11 1731\\nf 2169 410 11\\nf 1731 11 410\\nf 2402 2367 2414\\nf 700 884 2367\\nf 1377 2414 884\\nf 2367 884 2414\\nf 384 1085 1816\\nf 666 13 1085\\nf 700 1816 13\\nf 1085 13 1816\\nf 1421 75 1364\\nf 1377 1902 75\\nf 666 1364 1902\\nf 75 1902 1364\\nf 700 13 884\\nf 666 1902 13\\nf 1377 884 1902\\nf 13 1902 884\\nf 2014 656 1599\\nf 469 1603 656\\nf 317 1599 1603\\nf 656 1603 1599\\nf 1421 1364 1771\\nf 666 1306 1364\\nf 469 1771 1306\\nf 1364 1306 1771\\nf 384 1181 1085\\nf 317 742 1181\\nf 666 1085 742\\nf 1181 742 1085\\nf 469 1306 1603\\nf 666 742 1306\\nf 317 1603 742\\nf 1306 742 1603\\nf 1828 26 791\\nf 1298 1047 26\\nf 1329 791 1047\\nf 26 1047 791\\nf 1609 201 819\\nf 2210 21 201\\nf 1298 819 21\\nf 201 21 819\\nf 2185 1826 1110\\nf 1329 64 1826\\nf 2210 1110 64\\nf 1826 64 1110\\nf 1298 21 1047\\nf 2210 64 21\\nf 1329 1047 64\\nf 21 64 1047\\nf 2402 2414 1187\\nf 1377 1391 2414\\nf 1709 1187 1391\\nf 2414 1391 1187\\nf 1421 810 75\\nf 1640 1044 810\\nf 1377 75 1044\\nf 810 1044 75\\nf 1609 2435 2012\\nf 1709 1907 2435\\nf 1640 2012 1907\\nf 2435 1907 2012\\nf 1377 1044 1391\\nf 1640 1907 1044\\nf 1709 1391 1907\\nf 1044 1907 1391\\nf 1191 1273 832\\nf 341 1952 1273\\nf 1090 832 1952\\nf 1273 1952 832\\nf 2185 1103 972\\nf 804 492 1103\\nf 341 972 492\\nf 1103 492 972\\nf 1421 1251 547\\nf 1090 2135 1251\\nf 804 547 2135\\nf 1251 2135 547\\nf 341 492 1952\\nf 804 2135 492\\nf 1090 1952 2135\\nf 492 2135 1952\\nf 1609 2012 201\\nf 1640 669 2012\\nf 2210 201 669\\nf 2012 669 201\\nf 1421 547 810\\nf 804 1299 547\\nf 1640 810 1299\\nf 547 1299 810\\nf 2185 1110 1103\\nf 2210 1366 1110\\nf 804 1103 1366\\nf 1110 1366 1103\\nf 1640 1299 669\\nf 804 1366 1299\\nf 2210 669 1366\\nf 1299 1366 669\\nf 300 1576 1312\\nf 2483 1356 1576\\nf 527 1312 1356\\nf 1576 1356 1312\\nf 1011 1519 1711\\nf 1624 772 1519\\nf 2483 1711 772\\nf 1519 772 1711\\nf 2547 756 70\\nf 527 275 756\\nf 1624 70 275\\nf 756 275 70\\nf 2483 772 1356\\nf 1624 275 772\\nf 527 1356 275\\nf 772 275 1356\\nf 2525 834 444\\nf 991 115 834\\nf 1138 444 115\\nf 834 115 444\\nf 2467 1818 722\\nf 1587 1228 1818\\nf 991 722 1228\\nf 1818 1228 722\\nf 1011 1665 2433\\nf 1138 1125 1665\\nf 1587 2433 1125\\nf 1665 1125 2433\\nf 991 1228 115\\nf 1587 1125 1228\\nf 1138 115 1125\\nf 1228 1125 115\\nf 818 1385 44\\nf 895 1827 1385\\nf 1146 44 1827\\nf 1385 1827 44\\nf 2547 2217 1708\\nf 514 607 2217\\nf 895 1708 607\\nf 2217 607 1708\\nf 2467 92 529\\nf 1146 1628 92\\nf 514 529 1628\\nf 92 1628 529\\nf 895 607 1827\\nf 514 1628 607\\nf 1146 1827 1628\\nf 607 1628 1827\\nf 1011 2433 1519\\nf 1587 2159 2433\\nf 1624 1519 2159\\nf 2433 2159 1519\\nf 2467 529 1818\\nf 514 1565 529\\nf 1587 1818 1565\\nf 529 1565 1818\\nf 2547 70 2217\\nf 1624 1625 70\\nf 514 2217 1625\\nf 70 1625 2217\\nf 1587 1565 2159\\nf 514 1625 1565\\nf 1624 2159 1625\\nf 1565 1625 2159\\nf 887 948 2447\\nf 1302 507 948\\nf 2383 2447 507\\nf 948 507 2447\\nf 2521 315 2376\\nf 1441 432 315\\nf 1302 2376 432\\nf 315 432 2376\\nf 2361 2149 2223\\nf 2383 947 2149\\nf 1441 2223 947\\nf 2149 947 2223\\nf 1302 432 507\\nf 1441 947 432\\nf 2383 507 947\\nf 432 947 507\\nf 2478 1903 1382\\nf 1108 2123 1903\\nf 485 1382 2123\\nf 1903 2123 1382\\nf 67 544 1307\\nf 953 623 544\\nf 1108 1307 623\\nf 544 623 1307\\nf 2521 2297 2273\\nf 485 650 2297\\nf 953 2273 650\\nf 2297 650 2273\\nf 1108 623 2123\\nf 953 650 623\\nf 485 2123 650\\nf 623 650 2123\\nf 2525 446 1645\\nf 1564 454 446\\nf 466 1645 454\\nf 446 454 1645\\nf 2361 1748 1677\\nf 1166 515 1748\\nf 1564 1677 515\\nf 1748 515 1677\\nf 67 247 2371\\nf 466 2114 247\\nf 1166 2371 2114\\nf 247 2114 2371\\nf 1564 515 454\\nf 1166 2114 515\\nf 466 454 2114\\nf 515 2114 454\\nf 2521 2273 315\\nf 953 2179 2273\\nf 1441 315 2179\\nf 2273 2179 315\\nf 67 2371 544\\nf 1166 1770 2371\\nf 953 544 1770\\nf 2371 1770 544\\nf 2361 2223 1748\\nf 1441 1623 2223\\nf 1166 1748 1623\\nf 2223 1623 1748\\nf 953 1770 2179\\nf 1166 1623 1770\\nf 1441 2179 1623\\nf 1770 1623 2179\\nf 906 1867 428\\nf 1428 155 1867\\nf 1339 428 155\\nf 1867 155 428\\nf 2408 2002 152\\nf 1739 2538 2002\\nf 1428 152 2538\\nf 2002 2538 152\\nf 1168 391 87\\nf 1339 1501 391\\nf 1739 87 1501\\nf 391 1501 87\\nf 1428 2538 155\\nf 1739 1501 2538\\nf 1339 155 1501\\nf 2538 1501 155\\nf 818 1396 2396\\nf 665 1311 1396\\nf 323 2396 1311\\nf 1396 1311 2396\\nf 521 1375 1037\\nf 779 350 1375\\nf 665 1037 350\\nf 1375 350 1037\\nf 2408 1522 1878\\nf 323 240 1522\\nf 779 1878 240\\nf 1522 240 1878\\nf 665 350 1311\\nf 779 240 350\\nf 323 1311 240\\nf 350 240 1311\\nf 2478 2385 104\\nf 12 1004 2385\\nf 1649 104 1004\\nf 2385 1004 104\\nf 1168 1344 1472\\nf 1408 1207 1344\\nf 12 1472 1207\\nf 1344 1207 1472\\nf 521 1970 2520\\nf 1649 1869 1970\\nf 1408 2520 1869\\nf 1970 1869 2520\\nf 12 1207 1004\\nf 1408 1869 1207\\nf 1649 1004 1869\\nf 1207 1869 1004\\nf 2408 1878 2002\\nf 779 1429 1878\\nf 1739 2002 1429\\nf 1878 1429 2002\\nf 521 2520 1375\\nf 1408 822 2520\\nf 779 1375 822\\nf 2520 822 1375\\nf 1168 87 1344\\nf 1739 1060 87\\nf 1408 1344 1060\\nf 87 1060 1344\\nf 779 822 1429\\nf 1408 1060 822\\nf 1739 1429 1060\\nf 822 1060 1429\\nf 2525 1645 834\\nf 466 2323 1645\\nf 991 834 2323\\nf 1645 2323 834\\nf 67 2021 247\\nf 1185 1371 2021\\nf 466 247 1371\\nf 2021 1371 247\\nf 2467 722 2306\\nf 991 375 722\\nf 1185 2306 375\\nf 722 375 2306\\nf 466 1371 2323\\nf 1185 375 1371\\nf 991 2323 375\\nf 1371 375 2323\\nf 2478 104 1903\\nf 1649 580 104\\nf 1108 1903 580\\nf 104 580 1903\\nf 521 1969 1970\\nf 2550 460 1969\\nf 1649 1970 460\\nf 1969 460 1970\\nf 67 1307 2125\\nf 1108 1927 1307\\nf 2550 2125 1927\\nf 1307 1927 2125\\nf 1649 460 580\\nf 2550 1927 460\\nf 1108 580 1927\\nf 460 1927 580\\nf 818 44 1396\\nf 1146 1746 44\\nf 665 1396 1746\\nf 44 1746 1396\\nf 2467 2280 92\\nf 241 1852 2280\\nf 1146 92 1852\\nf 2280 1852 92\\nf 521 1037 2390\\nf 665 258 1037\\nf 241 2390 258\\nf 1037 258 2390\\nf 1146 1852 1746\\nf 241 258 1852\\nf 665 1746 258\\nf 1852 258 1746\\nf 67 2125 2021\\nf 2550 911 2125\\nf 1185 2021 911\\nf 2125 911 2021\\nf 521 2390 1969\\nf 241 1033 2390\\nf 2550 1969 1033\\nf 2390 1033 1969\\nf 2467 2306 2280\\nf 1185 482 2306\\nf 241 2280 482\\nf 2306 482 2280\\nf 2550 1033 911\\nf 241 482 1033\\nf 1185 911 482\\nf 1033 482 911\\nf 887 1424 1247\\nf 2477 306 1424\\nf 1133 1247 306\\nf 1424 306 1247\\nf 806 445 2362\\nf 1943 2232 445\\nf 2477 2362 2232\\nf 445 2232 2362\\nf 1291 1753 290\\nf 1133 939 1753\\nf 1943 290 939\\nf 1753 939 290\\nf 2477 2232 306\\nf 1943 939 2232\\nf 1133 306 939\\nf 2232 939 306\\nf 1203 983 1413\\nf 1608 354 983\\nf 1925 1413 354\\nf 983 354 1413\\nf 1668 741 467\\nf 231 831 741\\nf 1608 467 831\\nf 741 831 467\\nf 806 1909 153\\nf 1925 1068 1909\\nf 231 153 1068\\nf 1909 1068 153\\nf 1608 831 354\\nf 231 1068 831\\nf 1925 354 1068\\nf 831 1068 354\\nf 1510 382 91\\nf 49 234 382\\nf 697 91 234\\nf 382 234 91\\nf 1291 2205 662\\nf 2484 2533 2205\\nf 49 662 2533\\nf 2205 2533 662\\nf 1668 1976 2252\\nf 697 2237 1976\\nf 2484 2252 2237\\nf 1976 2237 2252\\nf 49 2533 234\\nf 2484 2237 2533\\nf 697 234 2237\\nf 2533 2237 234\\nf 806 153 445\\nf 231 1489 153\\nf 1943 445 1489\\nf 153 1489 445\\nf 1668 2252 741\\nf 2484 2214 2252\\nf 231 741 2214\\nf 2252 2214 741\\nf 1291 290 2205\\nf 1943 1549 290\\nf 2484 2205 1549\\nf 290 1549 2205\\nf 231 2214 1489\\nf 2484 1549 2214\\nf 1943 1489 1549\\nf 2214 1549 1489\\nf 1831 2423 2044\\nf 930 2099 2423\\nf 1996 2044 2099\\nf 2423 2099 2044\\nf 952 2514 1053\\nf 721 1809 2514\\nf 930 1053 1809\\nf 2514 1809 1053\\nf 1111 16 1395\\nf 1996 2253 16\\nf 721 1395 2253\\nf 16 2253 1395\\nf 930 1809 2099\\nf 721 2253 1809\\nf 1996 2099 2253\\nf 1809 2253 2099\\nf 116 1229 371\\nf 494 1897 1229\\nf 475 371 1897\\nf 1229 1897 371\\nf 40 274 1602\\nf 1689 2410 274\\nf 494 1602 2410\\nf 274 2410 1602\\nf 952 1728 894\\nf 475 426 1728\\nf 1689 894 426\\nf 1728 426 894\\nf 494 2410 1897\\nf 1689 426 2410\\nf 475 1897 426\\nf 2410 426 1897\\nf 1203 61 628\\nf 2150 698 61\\nf 797 628 698\\nf 61 698 628\\nf 1111 347 2054\\nf 1563 1918 347\\nf 2150 2054 1918\\nf 347 1918 2054\\nf 40 453 1095\\nf 797 588 453\\nf 1563 1095 588\\nf 453 588 1095\\nf 2150 1918 698\\nf 1563 588 1918\\nf 797 698 588\\nf 1918 588 698\\nf 952 894 2514\\nf 1689 2154 894\\nf 721 2514 2154\\nf 894 2154 2514\\nf 40 1095 274\\nf 1563 1015 1095\\nf 1689 274 1015\\nf 1095 1015 274\\nf 1111 1395 347\\nf 721 1747 1395\\nf 1563 347 1747\\nf 1395 1747 347\\nf 1689 1015 2154\\nf 1563 1747 1015\\nf 721 2154 1747\\nf 1015 1747 2154\\nf 2312 1315 228\\nf 20 2209 1315\\nf 733 228 2209\\nf 1315 2209 228\\nf 19 1657 2101\\nf 2388 2281 1657\\nf 20 2101 2281\\nf 1657 2281 2101\\nf 307 2360 1227\\nf 733 522 2360\\nf 2388 1227 522\\nf 2360 522 1227\\nf 20 2281 2209\\nf 2388 522 2281\\nf 733 2209 522\\nf 2281 522 2209\\nf 1510 1151 1368\\nf 62 1960 1151\\nf 2171 1368 1960\\nf 1151 1960 1368\\nf 554 1758 1894\\nf 1448 2522 1758\\nf 62 1894 2522\\nf 1758 2522 1894\\nf 19 824 2148\\nf 2171 616 824\\nf 1448 2148 616\\nf 824 616 2148\\nf 62 2522 1960\\nf 1448 616 2522\\nf 2171 1960 616\\nf 2522 616 1960\\nf 116 2353 915\\nf 2465 682 2353\\nf 582 915 682\\nf 2353 682 915\\nf 307 1954 1304\\nf 2291 1543 1954\\nf 2465 1304 1543\\nf 1954 1543 1304\\nf 554 1350 1690\\nf 582 702 1350\\nf 2291 1690 702\\nf 1350 702 1690\\nf 2465 1543 682\\nf 2291 702 1543\\nf 582 682 702\\nf 1543 702 682\\nf 19 2148 1657\\nf 1448 2472 2148\\nf 2388 1657 2472\\nf 2148 2472 1657\\nf 554 1690 1758\\nf 2291 1740 1690\\nf 1448 1758 1740\\nf 1690 1740 1758\\nf 307 1227 1954\\nf 2388 103 1227\\nf 2291 1954 103\\nf 1227 103 1954\\nf 1448 1740 2472\\nf 2291 103 1740\\nf 2388 2472 103\\nf 1740 103 2472\\nf 1203 628 983\\nf 797 2193 628\\nf 1608 983 2193\\nf 628 2193 983\\nf 40 133 453\\nf 1598 1086 133\\nf 797 453 1086\\nf 133 1086 453\\nf 1668 467 2293\\nf 1608 751 467\\nf 1598 2293 751\\nf 467 751 2293\\nf 797 1086 2193\\nf 1598 751 1086\\nf 1608 2193 751\\nf 1086 751 2193\\nf 116 915 1229\\nf 582 576 915\\nf 494 1229 576\\nf 915 576 1229\\nf 554 2544 1350\\nf 550 2347 2544\\nf 582 1350 2347\\nf 2544 2347 1350\\nf 40 1602 1839\\nf 494 1190 1602\\nf 550 1839 1190\\nf 1602 1190 1839\\nf 582 2347 576\\nf 550 1190 2347\\nf 494 576 1190\\nf 2347 1190 576\\nf 1510 91 1151\\nf 697 2502 91\\nf 62 1151 2502\\nf 91 2502 1151\\nf 1668 670 1976\\nf 1983 2116 670\\nf 697 1976 2116\\nf 670 2116 1976\\nf 554 1894 1226\\nf 62 2261 1894\\nf 1983 1226 2261\\nf 1894 2261 1226\\nf 697 2116 2502\\nf 1983 2261 2116\\nf 62 2502 2261\\nf 2116 2261 2502\\nf 40 1839 133\\nf 550 2132 1839\\nf 1598 133 2132\\nf 1839 2132 133\\nf 554 1226 2544\\nf 1983 2461 1226\\nf 550 2544 2461\\nf 1226 2461 2544\\nf 1668 2293 670\\nf 1598 2454 2293\\nf 1983 670 2454\\nf 2293 2454 670\\nf 550 2461 2132\\nf 1983 2454 2461\\nf 1598 2132 2454\\nf 2461 2454 2132\\nf 1831 1571 1013\\nf 1425 1981 1571\\nf 1916 1013 1981\\nf 1571 1981 1013\\nf 610 496 2058\\nf 2393 1118 496\\nf 1425 2058 1118\\nf 496 1118 2058\\nf 57 2317 667\\nf 1916 1541 2317\\nf 2393 667 1541\\nf 2317 1541 667\\nf 1425 1118 1981\\nf 2393 1541 1118\\nf 1916 1981 1541\\nf 1118 1541 1981\\nf 880 2046 1153\\nf 1788 1367 2046\\nf 1517 1153 1367\\nf 2046 1367 1153\\nf 1682 1843 771\\nf 1750 186 1843\\nf 1788 771 186\\nf 1843 186 771\\nf 610 1447 919\\nf 1517 1478 1447\\nf 1750 919 1478\\nf 1447 1478 919\\nf 1788 186 1367\\nf 1750 1478 186\\nf 1517 1367 1478\\nf 186 1478 1367\\nf 560 1710 413\\nf 975 150 1710\\nf 630 413 150\\nf 1710 150 413\\nf 57 520 1754\\nf 724 1683 520\\nf 975 1754 1683\\nf 520 1683 1754\\nf 1682 945 979\\nf 630 1259 945\\nf 724 979 1259\\nf 945 1259 979\\nf 975 1683 150\\nf 724 1259 1683\\nf 630 150 1259\\nf 1683 1259 150\\nf 610 919 496\\nf 1750 1533 919\\nf 2393 496 1533\\nf 919 1533 496\\nf 1682 979 1843\\nf 724 850 979\\nf 1750 1843 850\\nf 979 850 1843\\nf 57 667 520\\nf 2393 503 667\\nf 724 520 503\\nf 667 503 520\\nf 1750 850 1533\\nf 724 503 850\\nf 2393 1533 503\\nf 850 503 1533\\nf 2282 1704 319\\nf 1877 376 1704\\nf 683 319 376\\nf 1704 376 319\\nf 931 1216 840\\nf 2422 706 1216\\nf 1877 840 706\\nf 1216 706 840\\nf 213 1614 304\\nf 683 1144 1614\\nf 2422 304 1144\\nf 1614 1144 304\\nf 1877 706 376\\nf 2422 1144 706\\nf 683 376 1144\\nf 706 1144 376\\nf 1525 2526 1390\\nf 1406 402 2526\\nf 473 1390 402\\nf 2526 402 1390\\nf 238 1031 1655\\nf 1775 1158 1031\\nf 1406 1655 1158\\nf 1031 1158 1655\\nf 931 1449 1749\\nf 473 775 1449\\nf 1775 1749 775\\nf 1449 775 1749\\nf 1406 1158 402\\nf 1775 775 1158\\nf 473 402 775\\nf 1158 775 402\\nf 880 1700 587\\nf 415 125 1700\\nf 1414 587 125\\nf 1700 125 587\\nf 213 1723 1718\\nf 631 406 1723\\nf 415 1718 406\\nf 1723 406 1718\\nf 238 1820 2091\\nf 1414 1945 1820\\nf 631 2091 1945\\nf 1820 1945 2091\\nf 415 406 125\\nf 631 1945 406\\nf 1414 125 1945\\nf 406 1945 125\\nf 931 1749 1216\\nf 1775 699 1749\\nf 2422 1216 699\\nf 1749 699 1216\\nf 238 2091 1031\\nf 631 688 2091\\nf 1775 1031 688\\nf 2091 688 1031\\nf 213 304 1723\\nf 2422 1654 304\\nf 631 1723 1654\\nf 304 1654 1723\\nf 1775 688 699\\nf 631 1654 688\\nf 2422 699 1654\\nf 688 1654 699\\nf 2391 1401 1333\\nf 2043 1812 1401\\nf 1027 1333 1812\\nf 1401 1812 1333\\nf 720 1743 2485\\nf 349 2037 1743\\nf 2043 2485 2037\\nf 1743 2037 2485\\nf 760 1458 1119\\nf 1027 1257 1458\\nf 349 1119 1257\\nf 1458 1257 1119\\nf 2043 2037 1812\\nf 349 1257 2037\\nf 1027 1812 1257\\nf 2037 1257 1812\\nf 560 2248 2085\\nf 1643 194 2248\\nf 857 2085 194\\nf 2248 194 2085\\nf 154 373 512\\nf 2276 1051 373\\nf 1643 512 1051\\nf 373 1051 512\\nf 720 694 532\\nf 857 2172 694\\nf 2276 532 2172\\nf 694 2172 532\\nf 1643 1051 194\\nf 2276 2172 1051\\nf 857 194 2172\\nf 1051 2172 194\\nf 1525 901 1994\\nf 1331 1540 901\\nf 1237 1994 1540\\nf 901 1540 1994\\nf 760 1418 2239\\nf 2137 1854 1418\\nf 1331 2239 1854\\nf 1418 1854 2239\\nf 154 2143 708\\nf 1237 807 2143\\nf 2137 708 807\\nf 2143 807 708\\nf 1331 1854 1540\\nf 2137 807 1854\\nf 1237 1540 807\\nf 1854 807 1540\\nf 720 532 1743\\nf 2276 2030 532\\nf 349 1743 2030\\nf 532 2030 1743\\nf 154 708 373\\nf 2137 1014 708\\nf 2276 373 1014\\nf 708 1014 373\\nf 760 1119 1418\\nf 349 2387 1119\\nf 2137 1418 2387\\nf 1119 2387 1418\\nf 2276 1014 2030\\nf 2137 2387 1014\\nf 349 2030 2387\\nf 1014 2387 2030\\nf 880 587 2046\\nf 1414 1018 587\\nf 1788 2046 1018\\nf 587 1018 2046\\nf 238 2554 1820\\nf 2308 471 2554\\nf 1414 1820 471\\nf 2554 471 1820\\nf 1682 771 1999\\nf 1788 1361 771\\nf 2308 1999 1361\\nf 771 1361 1999\\nf 1414 471 1018\\nf 2308 1361 471\\nf 1788 1018 1361\\nf 471 1361 1018\\nf 1525 1994 2526\\nf 1237 266 1994\\nf 1406 2526 266\\nf 1994 266 2526\\nf 154 2296 2143\\nf 431 248 2296\\nf 1237 2143 248\\nf 2296 248 2143\\nf 238 1655 179\\nf 1406 2041 1655\\nf 431 179 2041\\nf 1655 2041 179\\nf 1237 248 266\\nf 431 2041 248\\nf 1406 266 2041\\nf 248 2041 266\\nf 560 413 2248\\nf 630 2265 413\\nf 1643 2248 2265\\nf 413 2265 2248\\nf 1682 1097 945\\nf 262 214 1097\\nf 630 945 214\\nf 1097 214 945\\nf 154 512 1724\\nf 1643 1910 512\\nf 262 1724 1910\\nf 512 1910 1724\\nf 630 214 2265\\nf 262 1910 214\\nf 1643 2265 1910\\nf 214 1910 2265\\nf 238 179 2554\\nf 431 608 179\\nf 2308 2554 608\\nf 179 608 2554\\nf 154 1724 2296\\nf 262 719 1724\\nf 431 2296 719\\nf 1724 719 2296\\nf 1682 1999 1097\\nf 2308 2363 1999\\nf 262 1097 2363\\nf 1999 2363 1097\\nf 431 719 608\\nf 262 2363 719\\nf 2308 608 2363\\nf 719 2363 608\\nf 2282 1 1463\\nf 876 874 1\\nf 1445 1463 874\\nf 1 874 1463\\nf 1099 1423 2126\\nf 647 2510 1423\\nf 876 2126 2510\\nf 1423 2510 2126\\nf 1388 122 593\\nf 1445 870 122\\nf 647 593 870\\nf 122 870 593\\nf 876 2510 874\\nf 647 870 2510\\nf 1445 874 870\\nf 2510 870 874\\nf 744 366 2556\\nf 32 1974 366\\nf 926 2556 1974\\nf 366 1974 2556\\nf 423 2324 2026\\nf 2549 1313 2324\\nf 32 2026 1313\\nf 2324 1313 2026\\nf 1099 849 368\\nf 926 2535 849\\nf 2549 368 2535\\nf 849 2535 368\\nf 32 1313 1974\\nf 2549 2535 1313\\nf 926 1974 2535\\nf 1313 2535 1974\\nf 1083 1745 1794\\nf 2229 293 1745\\nf 1042 1794 293\\nf 1745 293 1794\\nf 1388 196 1016\\nf 1012 1468 196\\nf 2229 1016 1468\\nf 196 1468 1016\\nf 423 1596 72\\nf 1042 1701 1596\\nf 1012 72 1701\\nf 1596 1701 72\\nf 2229 1468 293\\nf 1012 1701 1468\\nf 1042 293 1701\\nf 1468 1701 293\\nf 1099 368 1423\\nf 2549 2430 368\\nf 647 1423 2430\\nf 368 2430 1423\\nf 423 72 2324\\nf 1012 525 72\\nf 2549 2324 525\\nf 72 525 2324\\nf 1388 593 196\\nf 647 338 593\\nf 1012 196 338\\nf 593 338 196\\nf 2549 525 2430\\nf 1012 338 525\\nf 647 2430 338\\nf 525 338 2430\\nf 1777 1374 753\\nf 282 905 1374\\nf 657 753 905\\nf 1374 905 753\\nf 1705 2215 1437\\nf 1370 260 2215\\nf 282 1437 260\\nf 2215 260 1437\\nf 197 1581 546\\nf 657 2295 1581\\nf 1370 546 2295\\nf 1581 2295 546\\nf 282 260 905\\nf 1370 2295 260\\nf 657 905 2295\\nf 260 2295 905\\nf 961 2369 97\\nf 344 2287 2369\\nf 934 97 2287\\nf 2369 2287 97\\nf 531 1460 78\\nf 403 2509 1460\\nf 344 78 2509\\nf 1460 2509 78\\nf 1705 1262 2259\\nf 934 1755 1262\\nf 403 2259 1755\\nf 1262 1755 2259\\nf 344 2509 2287\\nf 403 1755 2509\\nf 934 2287 1755\\nf 2509 1755 2287\\nf 744 938 1797\\nf 524 1481 938\\nf 182 1797 1481\\nf 938 1481 1797\\nf 197 114 79\\nf 862 1613 114\\nf 524 79 1613\\nf 114 1613 79\\nf 531 1860 1297\\nf 182 827 1860\\nf 862 1297 827\\nf 1860 827 1297\\nf 524 1613 1481\\nf 862 827 1613\\nf 182 1481 827\\nf 1613 827 1481\\nf 1705 2259 2215\\nf 403 2246 2259\\nf 1370 2215 2246\\nf 2259 2246 2215\\nf 531 1297 1460\\nf 862 1680 1297\\nf 403 1460 1680\\nf 1297 1680 1460\\nf 197 546 114\\nf 1370 1593 546\\nf 862 114 1593\\nf 546 1593 114\\nf 403 1680 2246\\nf 862 1593 1680\\nf 1370 2246 1593\\nf 1680 1593 2246\\nf 2008 421 777\\nf 2147 759 421\\nf 746 777 759\\nf 421 759 777\\nf 2425 2097 877\\nf 575 1915 2097\\nf 2147 877 1915\\nf 2097 1915 877\\nf 29 990 1150\\nf 746 142 990\\nf 575 1150 142\\nf 990 142 1150\\nf 2147 1915 759\\nf 575 142 1915\\nf 746 759 142\\nf 1915 142 759\\nf 1083 1149 316\\nf 1464 2452 1149\\nf 1562 316 2452\\nf 1149 2452 316\\nf 829 996 1670\\nf 672 1123 996\\nf 1464 1670 1123\\nf 996 1123 1670\\nf 2425 728 541\\nf 1562 235 728\\nf 672 541 235\\nf 728 235 541\\nf 1464 1123 2452\\nf 672 235 1123\\nf 1562 2452 235\\nf 1123 235 2452\\nf 961 1871 2184\\nf 878 1804 1871\\nf 1634 2184 1804\\nf 1871 1804 2184\\nf 29 2009 1868\\nf 1479 1407 2009\\nf 878 1868 1407\\nf 2009 1407 1868\\nf 829 714 808\\nf 1634 2512 714\\nf 1479 808 2512\\nf 714 2512 808\\nf 878 1407 1804\\nf 1479 2512 1407\\nf 1634 1804 2512\\nf 1407 2512 1804\\nf 2425 541 2097\\nf 672 401 541\\nf 575 2097 401\\nf 541 401 2097\\nf 829 808 996\\nf 1479 2330 808\\nf 672 996 2330\\nf 808 2330 996\\nf 29 1150 2009\\nf 575 1688 1150\\nf 1479 2009 1688\\nf 1150 1688 2009\\nf 672 2330 401\\nf 1479 1688 2330\\nf 575 401 1688\\nf 2330 1688 401\\nf 744 1797 366\\nf 182 465 1797\\nf 32 366 465\\nf 1797 465 366\\nf 531 358 1860\\nf 1880 2098 358\\nf 182 1860 2098\\nf 358 2098 1860\\nf 423 2026 388\\nf 32 1221 2026\\nf 1880 388 1221\\nf 2026 1221 388\\nf 182 2098 465\\nf 1880 1221 2098\\nf 32 465 1221\\nf 2098 1221 465\\nf 961 2184 2369\\nf 1634 99 2184\\nf 344 2369 99\\nf 2184 99 2369\\nf 829 2271 714\\nf 2010 1986 2271\\nf 1634 714 1986\\nf 2271 1986 714\\nf 531 78 356\\nf 344 1933 78\\nf 2010 356 1933\\nf 78 1933 356\\nf 1634 1986 99\\nf 2010 1933 1986\\nf 344 99 1933\\nf 1986 1933 99\\nf 1083 1794 1149\\nf 1042 131 1794\\nf 1464 1149 131\\nf 1794 131 1149\\nf 423 2300 1596\\nf 417 1626 2300\\nf 1042 1596 1626\\nf 2300 1626 1596\\nf 829 1670 17\\nf 1464 296 1670\\nf 417 17 296\\nf 1670 296 17\\nf 1042 1626 131\\nf 417 296 1626\\nf 1464 131 296\\nf 1626 296 131\\nf 531 356 358\\nf 2010 374 356\\nf 1880 358 374\\nf 356 374 358\\nf 829 17 2271\\nf 417 2141 17\\nf 2010 2271 2141\\nf 17 2141 2271\\nf 423 388 2300\\nf 1880 474 388\\nf 417 2300 474\\nf 388 474 2300\\nf 2010 2141 374\\nf 417 474 2141\\nf 1880 374 474\\nf 2141 474 374\\nf 1034 1813 239\\nf 144 2152 1813\\nf 2314 239 2152\\nf 1813 2152 239\\nf 1850 461 1942\\nf 2088 967 461\\nf 144 1942 967\\nf 461 967 1942\\nf 1212 1560 435\\nf 2314 1499 1560\\nf 2088 435 1499\\nf 1560 1499 435\\nf 144 967 2152\\nf 2088 1499 967\\nf 2314 2152 1499\\nf 967 1499 2152\\nf 1497 1456 1263\\nf 329 920 1456\\nf 54 1263 920\\nf 1456 920 1263\\nf 1616 163 2326\\nf 1895 1397 163\\nf 329 2326 1397\\nf 163 1397 2326\\nf 1850 1906 1575\\nf 54 1550 1906\\nf 1895 1575 1550\\nf 1906 1550 1575\\nf 329 1397 920\\nf 1895 1550 1397\\nf 54 920 1550\\nf 1397 1550 920\\nf 1554 2453 2262\\nf 132 140 2453\\nf 1046 2262 140\\nf 2453 140 2262\\nf 1212 1631 1703\\nf 331 2417 1631\\nf 132 1703 2417\\nf 1631 2417 1703\\nf 1616 1730 245\\nf 1046 1327 1730\\nf 331 245 1327\\nf 1730 1327 245\\nf 132 2417 140\\nf 331 1327 2417\\nf 1046 140 1327\\nf 2417 1327 140\\nf 1850 1575 461\\nf 1895 748 1575\\nf 2088 461 748\\nf 1575 748 461\\nf 1616 245 163\\nf 331 510 245\\nf 1895 163 510\\nf 245 510 163\\nf 1212 435 1631\\nf 2088 1940 435\\nf 331 1631 1940\\nf 435 1940 1631\\nf 1895 510 748\\nf 331 1940 510\\nf 2088 748 1940\\nf 510 1940 748\\nf 1769 1175 1967\\nf 2019 1698 1175\\nf 2082 1967 1698\\nf 1175 1698 1967\\nf 320 2007 964\\nf 561 2534 2007\\nf 2019 964 2534\\nf 2007 2534 964\\nf 2165 648 472\\nf 2082 1619 648\\nf 561 472 1619\\nf 648 1619 472\\nf 2019 2534 1698\\nf 561 1619 2534\\nf 2082 1698 1619\\nf 2534 1619 1698\\nf 390 837 954\\nf 713 828 837\\nf 652 954 828\\nf 837 828 954\\nf 2134 620 1548\\nf 2213 1905 620\\nf 713 1548 1905\\nf 620 1905 1548\\nf 320 537 2024\\nf 652 2000 537\\nf 2213 2024 2000\\nf 537 2000 2024\\nf 713 1905 828\\nf 2213 2000 1905\\nf 652 828 2000\\nf 1905 2000 828\\nf 1497 1218 2348\\nf 1520 1134 1218\\nf 33 2348 1134\\nf 1218 1134 2348\\nf 2165 425 738\\nf 1076 2349 425\\nf 1520 738 2349\\nf 425 2349 738\\nf 2134 1092 89\\nf 33 1451 1092\\nf 1076 89 1451\\nf 1092 1451 89\\nf 1520 2349 1134\\nf 1076 1451 2349\\nf 33 1134 1451\\nf 2349 1451 1134\\nf 320 2024 2007\\nf 2213 860 2024\\nf 561 2007 860\\nf 2024 860 2007\\nf 2134 89 620\\nf 1076 1713 89\\nf 2213 620 1713\\nf 89 1713 620\\nf 2165 472 425\\nf 561 286 472\\nf 1076 425 286\\nf 472 286 425\\nf 2213 1713 860\\nf 1076 286 1713\\nf 561 860 286\\nf 1713 286 860\\nf 906 1987 844\\nf 2108 863 1987\\nf 2498 844 863\\nf 1987 863 844\\nf 723 2266 1630\\nf 2350 1488 2266\\nf 2108 1630 1488\\nf 2266 1488 1630\\nf 1178 1422 1244\\nf 2498 1234 1422\\nf 2350 1244 1234\\nf 1422 1234 1244\\nf 2108 1488 863\\nf 2350 1234 1488\\nf 2498 863 1234\\nf 1488 1234 863\\nf 1554 164 2457\\nf 618 552 164\\nf 1113 2457 552\\nf 164 552 2457\\nf 187 483 942\\nf 1309 2560 483\\nf 618 942 2560\\nf 483 2560 942\\nf 723 1381 2274\\nf 1113 1347 1381\\nf 1309 2274 1347\\nf 1381 1347 2274\\nf 618 2560 552\\nf 1309 1347 2560\\nf 1113 552 1347\\nf 2560 1347 552\\nf 390 261 617\\nf 893 1264 261\\nf 171 617 1264\\nf 261 1264 617\\nf 1178 1892 2005\\nf 291 1980 1892\\nf 893 2005 1980\\nf 1892 1980 2005\\nf 187 1343 1566\\nf 171 2015 1343\\nf 291 1566 2015\\nf 1343 2015 1566\\nf 893 1980 1264\\nf 291 2015 1980\\nf 171 1264 2015\\nf 1980 2015 1264\\nf 723 2274 2266\\nf 1309 764 2274\\nf 2350 2266 764\\nf 2274 764 2266\\nf 187 1566 483\\nf 291 568 1566\\nf 1309 483 568\\nf 1566 568 483\\nf 1178 1244 1892\\nf 2350 2357 1244\\nf 291 1892 2357\\nf 1244 2357 1892\\nf 1309 568 764\\nf 291 2357 568\\nf 2350 764 2357\\nf 568 2357 764\\nf 1497 2348 1456\\nf 33 2412 2348\\nf 329 1456 2412\\nf 2348 2412 1456\\nf 2134 2395 1092\\nf 285 969 2395\\nf 33 1092 969\\nf 2395 969 1092\\nf 1616 2326 2497\\nf 329 1316 2326\\nf 285 2497 1316\\nf 2326 1316 2497\\nf 33 969 2412\\nf 285 1316 969\\nf 329 2412 1316\\nf 969 1316 2412\\nf 390 617 837\\nf 171 932 617\\nf 713 837 932\\nf 617 932 837\\nf 187 1332 1343\\nf 1201 1417 1332\\nf 171 1343 1417\\nf 1332 1417 1343\\nf 2134 1548 2272\\nf 713 2341 1548\\nf 1201 2272 2341\\nf 1548 2341 2272\\nf 171 1417 932\\nf 1201 2341 1417\\nf 713 932 2341\\nf 1417 2341 932\\nf 1554 2262 164\\nf 1046 2539 2262\\nf 618 164 2539\\nf 2262 2539 164\\nf 1616 2432 1730\\nf 440 135 2432\\nf 1046 1730 135\\nf 2432 135 1730\\nf 187 942 257\\nf 618 814 942\\nf 440 257 814\\nf 942 814 257\\nf 1046 135 2539\\nf 440 814 135\\nf 618 2539 814\\nf 135 814 2539\\nf 2134 2272 2395\\nf 1201 1509 2272\\nf 285 2395 1509\\nf 2272 1509 2395\\nf 187 257 1332\\nf 440 2459 257\\nf 1201 1332 2459\\nf 257 2459 1332\\nf 1616 2497 2432\\nf 285 574 2497\\nf 440 2432 574\\nf 2497 574 2432\\nf 1201 2459 1509\\nf 440 574 2459\\nf 285 1509 574\\nf 2459 574 1509\\nf 1034 239 203\\nf 2314 581 239\\nf 1687 203 581\\nf 239 581 203\\nf 1212 1591 1560\\nf 219 736 1591\\nf 2314 1560 736\\nf 1591 736 1560\\nf 314 1159 925\\nf 1687 1116 1159\\nf 219 925 1116\\nf 1159 1116 925\\nf 2314 736 581\\nf 219 1116 736\\nf 1687 581 1116\\nf 736 1116 581\\nf 1554 2375 2453\\nf 793 124 2375\\nf 132 2453 124\\nf 2375 124 2453\\nf 1345 660 2298\\nf 1595 1727 660\\nf 793 2298 1727\\nf 660 1727 2298\\nf 1212 1703 2343\\nf 132 1737 1703\\nf 1595 2343 1737\\nf 1703 1737 2343\\nf 793 1727 124\\nf 1595 1737 1727\\nf 132 124 1737\\nf 1727 1737 124\\nf 565 379 1106\\nf 1205 2488 379\\nf 663 1106 2488\\nf 379 2488 1106\\nf 314 903 2247\\nf 917 1836 903\\nf 1205 2247 1836\\nf 903 1836 2247\\nf 1345 1840 505\\nf 663 629 1840\\nf 917 505 629\\nf 1840 629 505\\nf 1205 1836 2488\\nf 917 629 1836\\nf 663 2488 629\\nf 1836 629 2488\\nf 1212 2343 1591\\nf 1595 1720 2343\\nf 219 1591 1720\\nf 2343 1720 1591\\nf 1345 505 660\\nf 917 1858 505\\nf 1595 660 1858\\nf 505 1858 660\\nf 314 925 903\\nf 219 627 925\\nf 917 903 627\\nf 925 627 903\\nf 1595 1858 1720\\nf 917 627 1858\\nf 219 1720 627\\nf 1858 627 1720\\nf 906 1586 1987\\nf 2256 1856 1586\\nf 2108 1987 1856\\nf 1586 1856 1987\\nf 35 2074 2233\\nf 1170 2035 2074\\nf 2256 2233 2035\\nf 2074 2035 2233\\nf 723 1630 1029\\nf 2108 1800 1630\\nf 1170 1029 1800\\nf 1630 1800 1029\\nf 2256 2035 1856\\nf 1170 1800 2035\\nf 2108 1856 1800\\nf 2035 1800 1856\\nf 437 2392 1660\\nf 1799 551 2392\\nf 2163 1660 551\\nf 2392 551 1660\\nf 1886 2356 2486\\nf 1846 2479 2356\\nf 1799 2486 2479\\nf 2356 2479 2486\\nf 35 737 1360\\nf 2163 1855 737\\nf 1846 1360 1855\\nf 737 1855 1360\\nf 1799 2479 551\\nf 1846 1855 2479\\nf 2163 551 1855\\nf 2479 1855 551\\nf 1554 2457 110\\nf 1113 671 2457\\nf 48 110 671\\nf 2457 671 110\\nf 723 424 1381\\nf 1559 2446 424\\nf 1113 1381 2446\\nf 424 2446 1381\\nf 1886 783 1518\\nf 48 604 783\\nf 1559 1518 604\\nf 783 604 1518\\nf 1113 2446 671\\nf 1559 604 2446\\nf 48 671 604\\nf 2446 604 671\\nf 35 1360 2074\\nf 1846 1672 1360\\nf 1170 2074 1672\\nf 1360 1672 2074\\nf 1886 1518 2356\\nf 1559 2083 1518\\nf 1846 2356 2083\\nf 1518 2083 2356\\nf 723 1029 424\\nf 1170 2471 1029\\nf 1559 424 2471\\nf 1029 2471 424\\nf 1846 2083 1672\\nf 1559 2471 2083\\nf 1170 1672 2471\\nf 2083 2471 1672\\nf 2312 2384 789\\nf 864 1091 2384\\nf 2331 789 1091\\nf 2384 1091 789\\nf 790 1232 73\\nf 2531 42 1232\\nf 864 73 42\\nf 1232 42 73\\nf 149 2112 451\\nf 2331 90 2112\\nf 2531 451 90\\nf 2112 90 451\\nf 864 42 1091\\nf 2531 90 42\\nf 2331 1091 90\\nf 42 90 1091\\nf 565 892 1817\\nf 1310 508 892\\nf 1148 1817 508\\nf 892 508 1817\\nf 22 1271 2113\\nf 47 2191 1271\\nf 1310 2113 2191\\nf 1271 2191 2113\\nf 790 1321 1252\\nf 1148 734 1321\\nf 47 1252 734\\nf 1321 734 1252\\nf 1310 2191 508\\nf 47 734 2191\\nf 1148 508 734\\nf 2191 734 508\\nf 437 396 1893\\nf 766 1419 396\\nf 1833 1893 1419\\nf 396 1419 1893\\nf 149 1637 830\\nf 2424 1516 1637\\nf 766 830 1516\\nf 1637 1516 830\\nf 22 888 2373\\nf 1833 974 888\\nf 2424 2373 974\\nf 888 974 2373\\nf 766 1516 1419\\nf 2424 974 1516\\nf 1833 1419 974\\nf 1516 974 1419\\nf 790 1252 1232\\nf 47 1255 1252\\nf 2531 1232 1255\\nf 1252 1255 1232\\nf 22 2373 1271\\nf 2424 1684 2373\\nf 47 1271 1684\\nf 2373 1684 1271\\nf 149 451 1637\\nf 2531 1493 451\\nf 2424 1637 1493\\nf 451 1493 1637\\nf 47 1684 1255\\nf 2424 1493 1684\\nf 2531 1255 1493\\nf 1684 1493 1255\\nf 1554 110 2375\\nf 48 1057 110\\nf 793 2375 1057\\nf 110 1057 2375\\nf 1886 1848 783\\nf 1985 1924 1848\\nf 48 783 1924\\nf 1848 1924 783\\nf 1345 2298 1798\\nf 793 625 2298\\nf 1985 1798 625\\nf 2298 625 1798\\nf 48 1924 1057\\nf 1985 625 1924\\nf 793 1057 625\\nf 1924 625 1057\\nf 437 1893 2392\\nf 1833 2204 1893\\nf 1799 2392 2204\\nf 1893 2204 2392\\nf 22 486 888\\nf 436 1662 486\\nf 1833 888 1662\\nf 486 1662 888\\nf 1886 2486 1948\\nf 1799 1186 2486\\nf 436 1948 1186\\nf 2486 1186 1948\\nf 1833 1662 2204\\nf 436 1186 1662\\nf 1799 2204 1186\\nf 1662 1186 2204\\nf 565 1106 892\\nf 663 200 1106\\nf 1310 892 200\\nf 1106 200 892\\nf 1345 289 1840\\nf 904 2004 289\\nf 663 1840 2004\\nf 289 2004 1840\\nf 22 2113 1346\\nf 1310 2436 2113\\nf 904 1346 2436\\nf 2113 2436 1346\\nf 663 2004 200\\nf 904 2436 2004\\nf 1310 200 2436\\nf 2004 2436 200\\nf 1886 1948 1848\\nf 436 265 1948\\nf 1985 1848 265\\nf 1948 265 1848\\nf 22 1346 486\\nf 904 1378 1346\\nf 436 486 1378\\nf 1346 1378 486\\nf 1345 1798 289\\nf 1985 2070 1798\\nf 904 289 2070\\nf 1798 2070 289\\nf 436 1378 265\\nf 904 2070 1378\\nf 1985 265 2070\\nf 1378 2070 265\\nf 1034 203 1524\\nf 1687 146 203\\nf 1161 1524 146\\nf 203 146 1524\\nf 314 1914 1159\\nf 2441 2294 1914\\nf 1687 1159 2294\\nf 1914 2294 1159\\nf 1984 225 367\\nf 1161 1847 225\\nf 2441 367 1847\\nf 225 1847 367\\nf 1687 2294 146\\nf 2441 1847 2294\\nf 1161 146 1847\\nf 2294 1847 146\\nf 565 141 379\\nf 202 1281 141\\nf 1205 379 1281\\nf 141 1281 379\\nf 2040 1675 1174\\nf 2489 2321 1675\\nf 202 1174 2321\\nf 1675 2321 1174\\nf 314 2247 1206\\nf 1205 343 2247\\nf 2489 1206 343\\nf 2247 343 1206\\nf 202 2321 1281\\nf 2489 343 2321\\nf 1205 1281 343\\nf 2321 343 1281\\nf 658 2377 881\\nf 687 1512 2377\\nf 2110 881 1512\\nf 2377 1512 881\\nf 1984 118 1394\\nf 674 1242 118\\nf 687 1394 1242\\nf 118 1242 1394\\nf 2040 148 335\\nf 2110 127 148\\nf 674 335 127\\nf 148 127 335\\nf 687 1242 1512\\nf 674 127 1242\\nf 2110 1512 127\\nf 1242 127 1512\\nf 314 1206 1914\\nf 2489 841 1206\\nf 2441 1914 841\\nf 1206 841 1914\\nf 2040 335 1675\\nf 674 599 335\\nf 2489 1675 599\\nf 335 599 1675\\nf 1984 367 118\\nf 2441 1007 367\\nf 674 118 1007\\nf 367 1007 118\\nf 2489 599 841\\nf 674 1007 599\\nf 2441 841 1007\\nf 599 1007 841\\nf 2312 2104 2384\\nf 2270 271 2104\\nf 864 2384 271\\nf 2104 271 2384\\nf 236 1781 2431\\nf 501 1215 1781\\nf 2270 2431 1215\\nf 1781 1215 2431\\nf 790 73 2275\\nf 864 1253 73\\nf 501 2275 1253\\nf 73 1253 2275\\nf 2270 1215 271\\nf 501 1253 1215\\nf 864 271 1253\\nf 1215 1253 271\\nf 678 1647 957\\nf 1972 1028 1647\\nf 276 957 1028\\nf 1647 1028 957\\nf 811 558 533\\nf 217 2063 558\\nf 1972 533 2063\\nf 558 2063 533\\nf 236 1045 1789\\nf 276 1326 1045\\nf 217 1789 1326\\nf 1045 1326 1789\\nf 1972 2063 1028\\nf 217 1326 2063\\nf 276 1028 1326\\nf 2063 1326 1028\\nf 565 1817 336\\nf 1148 1308 1817\\nf 2222 336 1308\\nf 1817 1308 336\\nf 790 325 1321\\nf 1523 1082 325\\nf 1148 1321 1082\\nf 325 1082 1321\\nf 811 1454 511\\nf 2222 1975 1454\\nf 1523 511 1975\\nf 1454 1975 511\\nf 1148 1082 1308\\nf 1523 1975 1082\\nf 2222 1308 1975\\nf 1082 1975 1308\\nf 236 1789 1781\\nf 217 1863 1789\\nf 501 1781 1863\\nf 1789 1863 1781\\nf 811 511 558\\nf 1523 204 511\\nf 217 558 204\\nf 511 204 558\\nf 790 2275 325\\nf 501 1865 2275\\nf 1523 325 1865\\nf 2275 1865 325\\nf 217 204 1863\\nf 1523 1865 204\\nf 501 1863 1865\\nf 204 1865 1863\\nf 2391 372 1998\\nf 34 462 372\\nf 1220 1998 462\\nf 372 462 1998\\nf 836 488 916\\nf 2028 539 488\\nf 34 916 539\\nf 488 539 916\\nf 1756 2516 1477\\nf 1220 1035 2516\\nf 2028 1477 1035\\nf 2516 1035 1477\\nf 34 539 462\\nf 2028 1035 539\\nf 1220 462 1035\\nf 539 1035 462\\nf 658 1073 973\\nf 370 498 1073\\nf 1129 973 498\\nf 1073 498 973\\nf 322 2332 1796\\nf 2420 673 2332\\nf 370 1796 673\\nf 2332 673 1796\\nf 836 102 937\\nf 1129 98 102\\nf 2420 937 98\\nf 102 98 937\\nf 370 673 498\\nf 2420 98 673\\nf 1129 498 98\\nf 673 98 498\\nf 678 1778 377\\nf 1420 2144 1778\\nf 1098 377 2144\\nf 1778 2144 377\\nf 1756 549 965\\nf 1250 2346 549\\nf 1420 965 2346\\nf 549 2346 965\\nf 322 178 781\\nf 1098 2451 178\\nf 1250 781 2451\\nf 178 2451 781\\nf 1420 2346 2144\\nf 1250 2451 2346\\nf 1098 2144 2451\\nf 2346 2451 2144\\nf 836 937 488\\nf 2420 1934 937\\nf 2028 488 1934\\nf 937 1934 488\\nf 322 781 2332\\nf 1250 2166 781\\nf 2420 2332 2166\\nf 781 2166 2332\\nf 1756 1477 549\\nf 2028 1805 1477\\nf 1250 549 1805\\nf 1477 1805 549\\nf 2420 2166 1934\\nf 1250 1805 2166\\nf 2028 1934 1805\\nf 2166 1805 1934\\nf 565 336 141\\nf 2222 490 336\\nf 202 141 490\\nf 336 490 141\\nf 811 642 1454\\nf 1400 174 642\\nf 2222 1454 174\\nf 642 174 1454\\nf 2040 1174 1040\\nf 202 636 1174\\nf 1400 1040 636\\nf 1174 636 1040\\nf 2222 174 490\\nf 1400 636 174\\nf 202 490 636\\nf 174 636 490\\nf 678 377 1647\\nf 1098 548 377\\nf 1972 1647 548\\nf 377 548 1647\\nf 322 1908 178\\nf 563 2211 1908\\nf 1098 178 2211\\nf 1908 2211 178\\nf 811 533 2095\\nf 1972 1405 533\\nf 563 2095 1405\\nf 533 1405 2095\\nf 1098 2211 548\\nf 563 1405 2211\\nf 1972 548 1405\\nf 2211 1405 548\\nf 658 881 1073\\nf 2110 743 881\\nf 370 1073 743\\nf 881 743 1073\\nf 2040 826 148\\nf 2499 2178 826\\nf 2110 148 2178\\nf 826 2178 148\\nf 322 1796 1006\\nf 370 2552 1796\\nf 2499 1006 2552\\nf 1796 2552 1006\\nf 2110 2178 743\\nf 2499 2552 2178\\nf 370 743 2552\\nf 2178 2552 743\\nf 811 2095 642\\nf 563 1686 2095\\nf 1400 642 1686\\nf 2095 1686 642\\nf 322 1006 1908\\nf 2499 25 1006\\nf 563 1908 25\\nf 1006 25 1908\\nf 2040 1040 826\\nf 1400 180 1040\\nf 2499 826 180\\nf 1040 180 826\\nf 563 25 1686\\nf 2499 180 25\\nf 1400 1686 180\\nf 25 180 1686\\nf 1034 1524 1446\\nf 1161 63 1524\\nf 242 1446 63\\nf 1524 63 1446\\nf 1984 2156 225\\nf 2190 1008 2156\\nf 1161 225 1008\\nf 2156 1008 225\\nf 418 1277 865\\nf 242 439 1277\\nf 2190 865 439\\nf 1277 439 865\\nf 1161 1008 63\\nf 2190 439 1008\\nf 242 63 439\\nf 1008 439 63\\nf 658 332 2377\\nf 2267 94 332\\nf 687 2377 94\\nf 332 94 2377\\nf 2559 1483 2145\\nf 1093 137 1483\\nf 2267 2145 137\\nf 1483 137 2145\\nf 1984 1394 2513\\nf 687 774 1394\\nf 1093 2513 774\\nf 1394 774 2513\\nf 2267 137 94\\nf 1093 774 137\\nf 687 94 774\\nf 137 774 94\\nf 1075 1169 696\\nf 882 1351 1169\\nf 638 696 1351\\nf 1169 1351 696\\nf 418 2359 1610\\nf 2240 1971 2359\\nf 882 1610 1971\\nf 2359 1971 1610\\nf 2559 2029 1010\\nf 638 2234 2029\\nf 2240 1010 2234\\nf 2029 2234 1010\\nf 882 1971 1351\\nf 2240 2234 1971\\nf 638 1351 2234\\nf 1971 2234 1351\\nf 1984 2513 2156\\nf 1093 1982 2513\\nf 2190 2156 1982\\nf 2513 1982 2156\\nf 2559 1010 1483\\nf 2240 845 1010\\nf 1093 1483 845\\nf 1010 845 1483\\nf 418 865 2359\\nf 2190 1269 865\\nf 2240 2359 1269\\nf 865 1269 2359\\nf 1093 845 1982\\nf 2240 1269 845\\nf 2190 1982 1269\\nf 845 1269 1982\\nf 2391 2309 372\\nf 2327 1376 2309\\nf 34 372 1376\\nf 2309 1376 372\\nf 1295 50 58\\nf 1875 176 50\\nf 2327 58 176\\nf 50 176 58\\nf 836 916 2227\\nf 34 404 916\\nf 1875 2227 404\\nf 916 404 2227\\nf 2327 176 1376\\nf 1875 404 176\\nf 34 1376 404\\nf 176 404 1376\\nf 2194 2219 2181\\nf 2208 211 2219\\nf 1968 2181 211\\nf 2219 211 2181\\nf 664 2251 167\\nf 1467 251 2251\\nf 2208 167 251\\nf 2251 251 167\\nf 1295 2120 817\\nf 1968 1732 2120\\nf 1467 817 1732\\nf 2120 1732 817\\nf 2208 251 211\\nf 1467 1732 251\\nf 1968 211 1732\\nf 251 1732 211\\nf 658 973 333\\nf 1129 2199 973\\nf 2122 333 2199\\nf 973 2199 333\\nf 836 963 102\\nf 2328 1233 963\\nf 1129 102 1233\\nf 963 1233 102\\nf 664 1328 107\\nf 2122 913 1328\\nf 2328 107 913\\nf 1328 913 107\\nf 1129 1233 2199\\nf 2328 913 1233\\nf 2122 2199 913\\nf 1233 913 2199\\nf 1295 817 50\\nf 1467 570 817\\nf 1875 50 570\\nf 817 570 50\\nf 664 107 2251\\nf 2328 385 107\\nf 1467 2251 385\\nf 107 385 2251\\nf 836 2227 963\\nf 1875 1184 2227\\nf 2328 963 1184\\nf 2227 1184 963\\nf 1467 385 570\\nf 2328 1184 385\\nf 1875 570 1184\\nf 385 1184 570\\nf 2008 1404 2078\\nf 2364 1530 1404\\nf 2475 2078 1530\\nf 1404 1530 2078\\nf 128 2398 2018\\nf 292 1061 2398\\nf 2364 2018 1061\\nf 2398 1061 2018\\nf 2481 2075 1196\\nf 2475 2140 2075\\nf 292 1196 2140\\nf 2075 2140 1196\\nf 2364 1061 1530\\nf 292 2140 1061\\nf 2475 1530 2140\\nf 1061 2140 1530\\nf 1075 59 655\\nf 785 1714 59\\nf 1491 655 1714\\nf 59 1714 655\\nf 1305 328 545\\nf 562 899 328\\nf 785 545 899\\nf 328 899 545\\nf 128 2049 2006\\nf 1491 2487 2049\\nf 562 2006 2487\\nf 2049 2487 2006\\nf 785 899 1714\\nf 562 2487 899\\nf 1491 1714 2487\\nf 899 2487 1714\\nf 2194 2045 134\\nf 2042 1807 2045\\nf 1552 134 1807\\nf 2045 1807 134\\nf 2481 898 946\\nf 1514 1663 898\\nf 2042 946 1663\\nf 898 1663 946\\nf 1305 1204 695\\nf 1552 927 1204\\nf 1514 695 927\\nf 1204 927 695\\nf 2042 1663 1807\\nf 1514 927 1663\\nf 1552 1807 927\\nf 1663 927 1807\\nf 128 2006 2398\\nf 562 2068 2006\\nf 292 2398 2068\\nf 2006 2068 2398\\nf 1305 695 328\\nf 1514 2226 695\\nf 562 328 2226\\nf 695 2226 328\\nf 2481 1196 898\\nf 292 269 1196\\nf 1514 898 269\\nf 1196 269 898\\nf 562 2226 2068\\nf 1514 269 2226\\nf 292 2068 269\\nf 2226 269 2068\\nf 658 333 332\\nf 2122 195 333\\nf 2267 332 195\\nf 333 195 332\\nf 664 207 1328\\nf 1851 1989 207\\nf 2122 1328 1989\\nf 207 1989 1328\\nf 2559 2145 592\\nf 2267 1352 2145\\nf 1851 592 1352\\nf 2145 1352 592\\nf 2122 1989 195\\nf 1851 1352 1989\\nf 2267 195 1352\\nf 1989 1352 195\\nf 2194 134 2219\\nf 1552 1246 134\\nf 2208 2219 1246\\nf 134 1246 2219\\nf 1305 2142 1204\\nf 189 351 2142\\nf 1552 1204 351\\nf 2142 351 1204\\nf 664 167 725\\nf 2208 252 167\\nf 189 725 252\\nf 167 252 725\\nf 1552 351 1246\\nf 189 252 351\\nf 2208 1246 252\\nf 351 252 1246\\nf 1075 696 59\\nf 638 2203 696\\nf 785 59 2203\\nf 696 2203 59\\nf 2559 703 2029\\nf 747 749 703\\nf 638 2029 749\\nf 703 749 2029\\nf 1305 545 1001\\nf 785 1629 545\\nf 747 1001 1629\\nf 545 1629 1001\\nf 638 749 2203\\nf 747 1629 749\\nf 785 2203 1629\\nf 749 1629 2203\\nf 664 725 207\\nf 189 1272 725\\nf 1851 207 1272\\nf 725 1272 207\\nf 1305 1001 2142\\nf 747 1192 1001\\nf 189 2142 1192\\nf 1001 1192 2142\\nf 2559 592 703\\nf 1851 1810 592\\nf 747 703 1810\\nf 592 1810 703\\nf 189 1192 1272\\nf 747 1810 1192\\nf 1851 1272 1810\\nf 1192 1810 1272\\nf 1034 1446 1813\\nf 242 2476 1446\\nf 144 1813 2476\\nf 1446 2476 1813\\nf 418 1505 1277\\nf 1504 9 1505\\nf 242 1277 9\\nf 1505 9 1277\\nf 1850 1942 143\\nf 144 1829 1942\\nf 1504 143 1829\\nf 1942 1829 143\\nf 242 9 2476\\nf 1504 1829 9\\nf 144 2476 1829\\nf 9 1829 2476\\nf 1075 408 1169\\nf 2053 633 408\\nf 882 1169 633\\nf 408 633 1169\\nf 1288 591 2530\\nf 1127 345 591\\nf 2053 2530 345\\nf 591 345 2530\\nf 418 1610 1990\\nf 882 2541 1610\\nf 1127 1990 2541\\nf 1610 2541 1990\\nf 2053 345 633\\nf 1127 2541 345\\nf 882 633 2541\\nf 345 2541 633\\nf 1497 1263 362\\nf 54 943 1263\\nf 327 362 943\\nf 1263 943 362\\nf 1850 1659 1906\\nf 755 653 1659\\nf 54 1906 653\\nf 1659 653 1906\\nf 1288 2545 1697\\nf 327 1104 2545\\nf 755 1697 1104\\nf 2545 1104 1697\\nf 54 653 943\\nf 755 1104 653\\nf 327 943 1104\\nf 653 1104 943\\nf 418 1990 1505\\nf 1127 1056 1990\\nf 1504 1505 1056\\nf 1990 1056 1505\\nf 1288 1697 591\\nf 755 1214 1697\\nf 1127 591 1214\\nf 1697 1214 591\\nf 1850 143 1659\\nf 1504 1814 143\\nf 755 1659 1814\\nf 143 1814 1659\\nf 1127 1214 1056\\nf 755 1814 1214\\nf 1504 1056 1814\\nf 1214 1814 1056\\nf 2008 1427 1404\\nf 644 1078 1427\\nf 2364 1404 1078\\nf 1427 1078 1404\\nf 988 161 2506\\nf 268 875 161\\nf 644 2506 875\\nf 161 875 2506\\nf 128 2018 509\\nf 2364 2048 2018\\nf 268 509 2048\\nf 2018 2048 509\\nf 644 875 1078\\nf 268 2048 875\\nf 2364 1078 2048\\nf 875 2048 1078\\nf 2466 535 833\\nf 2336 1760 535\\nf 1055 833 1760\\nf 535 1760 833\\nf 489 1466 989\\nf 883 758 1466\\nf 2336 989 758\\nf 1466 758 989\\nf 988 1573 272\\nf 1055 360 1573\\nf 883 272 360\\nf 1573 360 272\\nf 2336 758 1760\\nf 883 360 758\\nf 1055 1760 360\\nf 758 360 1760\\nf 1075 655 183\\nf 1491 933 655\\nf 1439 183 933\\nf 655 933 183\\nf 128 2307 2049\\nf 1059 2263 2307\\nf 1491 2049 2263\\nf 2307 2263 2049\\nf 489 2096 1919\\nf 1439 1386 2096\\nf 1059 1919 1386\\nf 2096 1386 1919\\nf 1491 2263 933\\nf 1059 1386 2263\\nf 1439 933 1386\\nf 2263 1386 933\\nf 988 272 161\\nf 883 712 272\\nf 268 161 712\\nf 272 712 161\\nf 489 1919 1466\\nf 1059 517 1919\\nf 883 1466 517\\nf 1919 517 1466\\nf 128 509 2307\\nf 268 1217 509\\nf 1059 2307 1217\\nf 509 1217 2307\\nf 883 517 712\\nf 1059 1217 517\\nf 268 712 1217\\nf 517 1217 712\\nf 1769 1967 1287\\nf 2082 1081 1967\\nf 802 1287 1081\\nf 1967 1081 1287\\nf 2165 1213 648\\nf 2243 2066 1213\\nf 2082 648 2066\\nf 1213 2066 648\\nf 907 1633 156\\nf 802 1403 1633\\nf 2243 156 1403\\nf 1633 1403 156\\nf 2082 2066 1081\\nf 2243 1403 2066\\nf 802 1081 1403\\nf 2066 1403 1081\\nf 1497 206 1218\\nf 1122 2151 206\\nf 1520 1218 2151\\nf 206 2151 1218\\nf 2115 1752 1502\\nf 1941 1359 1752\\nf 1122 1502 1359\\nf 1752 1359 1502\\nf 2165 738 433\\nf 1520 172 738\\nf 1941 433 172\\nf 738 172 433\\nf 1122 1359 2151\\nf 1941 172 1359\\nf 1520 2151 172\\nf 1359 172 2151\\nf 2466 909 1474\\nf 727 2186 909\\nf 635 1474 2186\\nf 909 2186 1474\\nf 907 855 2372\\nf 2117 1776 855\\nf 727 2372 1776\\nf 855 1776 2372\\nf 2115 1782 543\\nf 635 1340 1782\\nf 2117 543 1340\\nf 1782 1340 543\\nf 727 1776 2186\\nf 2117 1340 1776\\nf 635 2186 1340\\nf 1776 1340 2186\\nf 2165 433 1213\\nf 1941 1021 433\\nf 2243 1213 1021\\nf 433 1021 1213\\nf 2115 543 1752\\nf 2117 1912 543\\nf 1941 1752 1912\\nf 543 1912 1752\\nf 907 156 855\\nf 2243 1088 156\\nf 2117 855 1088\\nf 156 1088 855\\nf 1941 1912 1021\\nf 2117 1088 1912\\nf 2243 1021 1088\\nf 1912 1088 1021\\nf 1075 183 408\\nf 1439 2413 183\\nf 2053 408 2413\\nf 183 2413 408\\nf 489 632 2096\\nf 1861 221 632\\nf 1439 2096 221\\nf 632 221 2096\\nf 1288 2530 2490\\nf 2053 470 2530\\nf 1861 2490 470\\nf 2530 470 2490\\nf 1439 221 2413\\nf 1861 470 221\\nf 2053 2413 470\\nf 221 470 2413\\nf 2466 1474 535\\nf 635 856 1474\\nf 2336 535 856\\nf 1474 856 535\\nf 2115 2017 1782\\nf 2313 1071 2017\\nf 635 1782 1071\\nf 2017 1071 1782\\nf 489 989 1674\\nf 2336 1849 989\\nf 2313 1674 1849\\nf 989 1849 1674\\nf 635 1071 856\\nf 2313 1849 1071\\nf 2336 856 1849\\nf 1071 1849 856\\nf 1497 362 206\\nf 327 1590 362\\nf 1122 206 1590\\nf 362 1590 206\\nf 1288 1898 2545\\nf 803 1583 1898\\nf 327 2545 1583\\nf 1898 1583 2545\\nf 2115 1502 2310\\nf 1122 2241 1502\\nf 803 2310 2241\\nf 1502 2241 2310\\nf 327 1583 1590\\nf 803 2241 1583\\nf 1122 1590 2241\\nf 1583 2241 1590\\nf 489 1674 632\\nf 2313 2442 1674\\nf 1861 632 2442\\nf 1674 2442 632\\nf 2115 2310 2017\\nf 803 523 2310\\nf 2313 2017 523\\nf 2310 523 2017\\nf 1288 2490 1898\\nf 1861 2394 2490\\nf 803 1898 2394\\nf 2490 2394 1898\\nf 2313 523 2442\\nf 803 2394 523\\nf 1861 2442 2394\\nf 523 2394 2442\\nf 300 1312 1652\\nf 527 1834 1312\\nf 2427 1652 1834\\nf 1312 1834 1652\\nf 2547 1495 756\\nf 1043 365 1495\\nf 527 756 365\\nf 1495 365 756\\nf 1594 619 577\\nf 2427 910 619\\nf 1043 577 910\\nf 619 910 577\\nf 527 365 1834\\nf 1043 910 365\\nf 2427 1834 910\\nf 365 910 1834\\nf 818 2515 1385\\nf 982 2111 2515\\nf 895 1385 2111\\nf 2515 2111 1385\\nf 1487 1062 108\\nf 987 1438 1062\\nf 982 108 1438\\nf 1062 1438 108\\nf 2547 1708 2093\\nf 895 2428 1708\\nf 987 2093 2428\\nf 1708 2428 2093\\nf 982 1438 2111\\nf 987 2428 1438\\nf 895 2111 2428\\nf 1438 2428 2111\\nf 2402 1569 686\\nf 1209 693 1569\\nf 639 686 693\\nf 1569 693 686\\nf 1594 280 959\\nf 1964 1716 280\\nf 1209 959 1716\\nf 280 1716 959\\nf 1487 353 1290\\nf 639 2077 353\\nf 1964 1290 2077\\nf 353 2077 1290\\nf 1209 1716 693\\nf 1964 2077 1716\\nf 639 693 2077\\nf 1716 2077 693\\nf 2547 2093 1495\\nf 987 603 2093\\nf 1043 1495 603\\nf 2093 603 1495\\nf 1487 1290 1062\\nf 1964 162 1290\\nf 987 1062 162\\nf 1290 162 1062\\nf 1594 577 280\\nf 1043 1568 577\\nf 1964 280 1568\\nf 577 1568 280\\nf 987 162 603\\nf 1964 1568 162\\nf 1043 603 1568\\nf 162 1568 603\\nf 906 844 1867\\nf 2498 2500 844\\nf 1428 1867 2500\\nf 844 2500 1867\\nf 1178 1002 1422\\nf 1808 2033 1002\\nf 2498 1422 2033\\nf 1002 2033 1422\\nf 2408 152 641\\nf 1428 1584 152\\nf 1808 641 1584\\nf 152 1584 641\\nf 2498 2033 2500\\nf 1808 1584 2033\\nf 1428 2500 1584\\nf 2033 1584 2500\\nf 390 184 261\\nf 1100 689 184\\nf 893 261 689\\nf 184 689 261\\nf 2224 799 1054\\nf 250 729 799\\nf 1100 1054 729\\nf 799 729 1054\\nf 1178 2005 1303\\nf 893 1837 2005\\nf 250 1303 1837\\nf 2005 1837 1303\\nf 1100 729 689\\nf 250 1837 729\\nf 893 689 1837\\nf 729 1837 689\\nf 818 2396 928\\nf 323 398 2396\\nf 1278 928 398\\nf 2396 398 928\\nf 2408 1824 1522\\nf 1265 389 1824\\nf 323 1522 389\\nf 1824 389 1522\\nf 2224 1009 1766\\nf 1278 2162 1009\\nf 1265 1766 2162\\nf 1009 2162 1766\\nf 323 389 398\\nf 1265 2162 389\\nf 1278 398 2162\\nf 389 2162 398\\nf 1178 1303 1002\\nf 250 2198 1303\\nf 1808 1002 2198\\nf 1303 2198 1002\\nf 2224 1766 799\\nf 1265 1978 1766\\nf 250 799 1978\\nf 1766 1978 799\\nf 2408 641 1824\\nf 1808 1266 641\\nf 1265 1824 1266\\nf 641 1266 1824\\nf 250 1978 2198\\nf 1265 1266 1978\\nf 1808 2198 1266\\nf 1978 1266 2198\\nf 1769 1471 1175\\nf 2292 611 1471\\nf 2019 1175 611\\nf 1471 611 1175\\nf 384 1873 1929\\nf 609 1358 1873\\nf 2292 1929 1358\\nf 1873 1358 1929\\nf 320 964 1380\\nf 2019 1334 964\\nf 609 1380 1334\\nf 964 1334 1380\\nf 2292 1358 611\\nf 609 1334 1358\\nf 2019 611 1334\\nf 1358 1334 611\\nf 2402 637 2367\\nf 28 1440 637\\nf 700 2367 1440\\nf 637 1440 2367\\nf 871 859 1538\\nf 2 1041 859\\nf 28 1538 1041\\nf 859 1041 1538\\nf 384 1816 1335\\nf 700 704 1816\\nf 2 1335 704\\nf 1816 704 1335\\nf 28 1041 1440\\nf 2 704 1041\\nf 700 1440 704\\nf 1041 704 1440\\nf 390 954 1832\\nf 652 2370 954\\nf 1432 1832 2370\\nf 954 2370 1832\\nf 320 233 537\\nf 950 2495 233\\nf 652 537 2495\\nf 233 2495 537\\nf 871 2128 1508\\nf 1432 1365 2128\\nf 950 1508 1365\\nf 2128 1365 1508\\nf 652 2495 2370\\nf 950 1365 2495\\nf 1432 2370 1365\\nf 2495 1365 2370\\nf 384 1335 1873\\nf 2 166 1335\\nf 609 1873 166\\nf 1335 166 1873\\nf 871 1508 859\\nf 950 2277 1508\\nf 2 859 2277\\nf 1508 2277 859\\nf 320 1380 233\\nf 609 2492 1380\\nf 950 233 2492\\nf 1380 2492 233\\nf 2 2277 166\\nf 950 2492 2277\\nf 609 166 2492\\nf 2277 2492 166\\nf 818 928 2515\\nf 1278 1761 928\\nf 982 2515 1761\\nf 928 1761 2515\\nf 2224 237 1009\\nf 412 2448 237\\nf 1278 1009 2448\\nf 237 2448 1009\\nf 1487 108 2207\\nf 982 1137 108\\nf 412 2207 1137\\nf 108 1137 2207\\nf 1278 2448 1761\\nf 412 1137 2448\\nf 982 1761 1137\\nf 2448 1137 1761\\nf 390 1832 184\\nf 1432 788 1832\\nf 1100 184 788\\nf 1832 788 184\\nf 871 1977 2128\\nf 457 409 1977\\nf 1432 2128 409\\nf 1977 409 2128\\nf 2224 1054 14\\nf 1100 1715 1054\\nf 457 14 1715\\nf 1054 1715 14\\nf 1432 409 788\\nf 457 1715 409\\nf 1100 788 1715\\nf 409 1715 788\\nf 2402 686 637\\nf 639 1646 686\\nf 28 637 1646\\nf 686 1646 637\\nf 1487 1950 353\\nf 2319 2337 1950\\nf 639 353 2337\\nf 1950 2337 353\\nf 871 1538 168\\nf 28 1693 1538\\nf 2319 168 1693\\nf 1538 1693 168\\nf 639 2337 1646\\nf 2319 1693 2337\\nf 28 1646 1693\\nf 2337 1693 1646\\nf 2224 14 237\\nf 457 1793 14\\nf 412 237 1793\\nf 14 1793 237\\nf 871 168 1977\\nf 2319 1938 168\\nf 457 1977 1938\\nf 168 1938 1977\\nf 1487 2207 1950\\nf 412 84 2207\\nf 2319 1950 84\\nf 2207 84 1950\\nf 457 1938 1793\\nf 2319 84 1938\\nf 412 1793 84\\nf 1938 84 1793\\nf 2312 789 1315\\nf 2331 676 789\\nf 20 1315 676\\nf 789 676 1315\\nf 149 120 2112\\nf 2421 1534 120\\nf 2331 2112 1534\\nf 120 1534 2112\\nf 19 2101 1136\\nf 20 1821 2101\\nf 2421 1136 1821\\nf 2101 1821 1136\\nf 2331 1534 676\\nf 2421 1821 1534\\nf 20 676 1821\\nf 1534 1821 676\\nf 437 1738 396\\nf 2325 812 1738\\nf 766 396 812\\nf 1738 812 396\\nf 1953 1154 569\\nf 1087 2072 1154\\nf 2325 569 2072\\nf 1154 2072 569\\nf 149 830 1615\\nf 766 538 830\\nf 1087 1615 538\\nf 830 538 1615\\nf 2325 2072 812\\nf 1087 538 2072\\nf 766 812 538\\nf 2072 538 812\\nf 1510 1368 2157\\nf 2171 1734 1368\\nf 2333 2157 1734\\nf 1368 1734 2157\\nf 19 1498 824\\nf 1958 2034 1498\\nf 2171 824 2034\\nf 1498 2034 824\\nf 1953 1485 1072\\nf 2333 1230 1485\\nf 1958 1072 1230\\nf 1485 1230 1072\\nf 2171 2034 1734\\nf 1958 1230 2034\\nf 2333 1734 1230\\nf 2034 1230 1734\\nf 149 1615 120\\nf 1087 1260 1615\\nf 2421 120 1260\\nf 1615 1260 120\\nf 1953 1072 1154\\nf 1958 585 1072\\nf 1087 1154 585\\nf 1072 585 1154\\nf 19 1136 1498\\nf 2421 513 1136\\nf 1958 1498 513\\nf 1136 513 1498\\nf 1087 585 1260\\nf 1958 513 585\\nf 2421 1260 513\\nf 585 513 1260\\nf 906 428 1586\\nf 1339 1920 428\\nf 2256 1586 1920\\nf 428 1920 1586\\nf 1168 590 391\\nf 86 1433 590\\nf 1339 391 1433\\nf 590 1433 391\\nf 35 2233 1070\\nf 2256 1639 2233\\nf 86 1070 1639\\nf 2233 1639 1070\\nf 1339 1433 1920\\nf 86 1639 1433\\nf 2256 1920 1639\\nf 1433 1639 1920\\nf 2478 684 2385\\nf 2429 2546 684\\nf 12 2385 2546\\nf 684 2546 2385\\nf 2092 848 1883\\nf 2094 2001 848\\nf 2429 1883 2001\\nf 848 2001 1883\\nf 1168 1472 56\\nf 12 1995 1472\\nf 2094 56 1995\\nf 1472 1995 56\\nf 2429 2001 2546\\nf 2094 1995 2001\\nf 12 2546 1995\\nf 2001 1995 2546\\nf 437 1660 136\\nf 2163 2468 1660\\nf 2121 136 2468\\nf 1660 2468 136\\nf 35 1444 737\\nf 1802 1900 1444\\nf 2163 737 1900\\nf 1444 1900 737\\nf 2092 1763 838\\nf 2121 2109 1763\\nf 1802 838 2109\\nf 1763 2109 838\\nf 2163 1900 2468\\nf 1802 2109 1900\\nf 2121 2468 2109\\nf 1900 2109 2468\\nf 1168 56 590\\nf 2094 677 56\\nf 86 590 677\\nf 56 677 590\\nf 2092 838 848\\nf 1802 2220 838\\nf 2094 848 2220\\nf 838 2220 848\\nf 35 1070 1444\\nf 86 2039 1070\\nf 1802 1444 2039\\nf 1070 2039 1444\\nf 2094 2220 677\\nf 1802 2039 2220\\nf 86 677 2039\\nf 2220 2039 677\\nf 887 1247 948\\nf 1133 615 1247\\nf 1302 948 615\\nf 1247 615 948\\nf 1291 2532 1753\\nf 1128 866 2532\\nf 1133 1753 866\\nf 2532 866 1753\\nf 2521 2376 1341\\nf 1302 348 2376\\nf 1128 1341 348\\nf 2376 348 1341\\nf 1133 866 615\\nf 1128 348 866\\nf 1302 615 348\\nf 866 348 615\\nf 1510 1605 382\\nf 330 476 1605\\nf 49 382 476\\nf 1605 476 382\\nf 2197 1393 313\\nf 1822 1864 1393\\nf 330 313 1864\\nf 1393 1864 313\\nf 1291 662 1503\\nf 49 191 662\\nf 1822 1503 191\\nf 662 191 1503\\nf 330 1864 476\\nf 1822 191 1864\\nf 49 476 191\\nf 1864 191 476\\nf 2478 1382 971\\nf 485 2196 1382\\nf 1318 971 2196\\nf 1382 2196 971\\nf 2521 1285 2297\\nf 81 2404 1285\\nf 485 2297 2404\\nf 1285 2404 2297\\nf 2197 825 2548\\nf 1318 978 825\\nf 81 2548 978\\nf 825 978 2548\\nf 485 2404 2196\\nf 81 978 2404\\nf 1318 2196 978\\nf 2404 978 2196\\nf 1291 1503 2532\\nf 1822 1163 1503\\nf 1128 2532 1163\\nf 1503 1163 2532\\nf 2197 2548 1393\\nf 81 2127 2548\\nf 1822 1393 2127\\nf 2548 2127 1393\\nf 2521 1341 1285\\nf 1128 1882 1341\\nf 81 1285 1882\\nf 1341 1882 1285\\nf 1822 2127 1163\\nf 81 1882 2127\\nf 1128 1163 1882\\nf 2127 1882 1163\\nf 437 136 1738\\nf 2121 1844 136\\nf 2325 1738 1844\\nf 136 1844 1738\\nf 2092 868 1763\\nf 2067 750 868\\nf 2121 1763 750\\nf 868 750 1763\\nf 1953 569 126\\nf 2325 1355 569\\nf 2067 126 1355\\nf 569 1355 126\\nf 2121 750 1844\\nf 2067 1355 750\\nf 2325 1844 1355\\nf 750 1355 1844\\nf 2478 971 684\\nf 1318 1622 971\\nf 2429 684 1622\\nf 971 1622 684\\nf 2197 500 825\\nf 985 1410 500\\nf 1318 825 1410\\nf 500 1410 825\\nf 2092 1883 1231\\nf 2429 986 1883\\nf 985 1231 986\\nf 1883 986 1231\\nf 1318 1410 1622\\nf 985 986 1410\\nf 2429 1622 986\\nf 1410 986 1622\\nf 1510 2157 1605\\nf 2333 2338 2157\\nf 330 1605 2338\\nf 2157 2338 1605\\nf 1953 394 1485\\nf 798 976 394\\nf 2333 1485 976\\nf 394 976 1485\\nf 2197 313 1759\\nf 330 1109 313\\nf 798 1759 1109\\nf 313 1109 1759\\nf 2333 976 2338\\nf 798 1109 976\\nf 330 2338 1109\\nf 976 1109 2338\\nf 2092 1231 868\\nf 985 1131 1231\\nf 2067 868 1131\\nf 1231 1131 868\\nf 2197 1759 500\\nf 798 2401 1759\\nf 985 500 2401\\nf 1759 2401 500\\nf 1953 126 394\\nf 2067 754 126\\nf 798 394 754\\nf 126 754 394\\nf 985 2401 1131\\nf 798 754 2401\\nf 2067 1131 754\\nf 2401 754 1131\\nf 2391 1998 1401\\nf 1220 922 1998\\nf 2043 1401 922\\nf 1998 922 1401\\nf 1756 111 2516\\nf 1275 2161 111\\nf 1220 2516 2161\\nf 111 2161 2516\\nf 720 2485 1751\\nf 2043 1293 2485\\nf 1275 1751 1293\\nf 2485 1293 1751\\nf 1220 2161 922\\nf 1275 1293 2161\\nf 2043 922 1293\\nf 2161 1293 922\\nf 678 1691 1778\\nf 2254 1607 1691\\nf 1420 1778 1607\\nf 1691 1607 1778\\nf 1784 1811 2023\\nf 2050 337 1811\\nf 2254 2023 337\\nf 1811 337 2023\\nf 1756 965 763\\nf 1420 101 965\\nf 2050 763 101\\nf 965 101 763\\nf 2254 337 1607\\nf 2050 101 337\\nf 1420 1607 101\\nf 337 101 1607\\nf 560 2085 38\\nf 857 1606 2085\\nf 468 38 1606\\nf 2085 1606 38\\nf 720 1102 694\\nf 675 277 1102\\nf 857 694 277\\nf 1102 277 694\\nf 1784 1544 27\\nf 468 1241 1544\\nf 675 27 1241\\nf 1544 1241 27\\nf 857 277 1606\\nf 675 1241 277\\nf 468 1606 1241\\nf 277 1241 1606\\nf 1756 763 111\\nf 2050 1767 763\\nf 1275 111 1767\\nf 763 1767 111\\nf 1784 27 1811\\nf 675 626 27\\nf 2050 1811 626\\nf 27 626 1811\\nf 720 1751 1102\\nf 1275 1000 1751\\nf 675 1102 1000\\nf 1751 1000 1102\\nf 2050 626 1767\\nf 675 1000 626\\nf 1275 1767 1000\\nf 626 1000 1767\\nf 2312 228 2104\\nf 733 1490 228\\nf 2270 2104 1490\\nf 228 1490 2104\\nf 307 1373 2360\\nf 1325 2517 1373\\nf 733 2360 2517\\nf 1373 2517 2360\\nf 236 2431 1176\\nf 2270 1545 2431\\nf 1325 1176 1545\\nf 2431 1545 1176\\nf 733 2517 1490\\nf 1325 1545 2517\\nf 2270 1490 1545\\nf 2517 1545 1490\\nf 116 169 2353\\nf 1620 139 169\\nf 2465 2353 139\\nf 169 139 2353\\nf 297 1276 1859\\nf 1762 1557 1276\\nf 1620 1859 1557\\nf 1276 1557 1859\\nf 307 1304 2036\\nf 2465 2443 1304\\nf 1762 2036 2443\\nf 1304 2443 2036\\nf 1620 1557 139\\nf 1762 2443 1557\\nf 2465 139 2443\\nf 1557 2443 139\\nf 678 957 1536\\nf 276 1179 957\\nf 1183 1536 1179\\nf 957 1179 1536\\nf 236 1956 1045\\nf 192 614 1956\\nf 276 1045 614\\nf 1956 614 1045\\nf 297 1064 1435\\nf 1183 1651 1064\\nf 192 1435 1651\\nf 1064 1651 1435\\nf 276 614 1179\\nf 192 1651 614\\nf 1183 1179 1651\\nf 614 1651 1179\\nf 307 2036 1373\\nf 1762 1973 2036\\nf 1325 1373 1973\\nf 2036 1973 1373\\nf 297 1435 1276\\nf 192 1835 1435\\nf 1762 1276 1835\\nf 1435 1835 1276\\nf 236 1176 1956\\nf 1325 2139 1176\\nf 192 1956 2139\\nf 1176 2139 1956\\nf 1762 1835 1973\\nf 192 2139 1835\\nf 1325 1973 2139\\nf 1835 2139 1973\\nf 1831 1013 2423\\nf 1916 839 1013\\nf 930 2423 839\\nf 1013 839 2423\\nf 57 2374 2317\\nf 123 2069 2374\\nf 1916 2317 2069\\nf 2374 2069 2317\\nf 952 1053 112\\nf 930 2100 1053\\nf 123 112 2100\\nf 1053 2100 112\\nf 1916 2069 839\\nf 123 2100 2069\\nf 930 839 2100\\nf 2069 2100 839\\nf 560 1765 1710\\nf 1342 900 1765\\nf 975 1710 900\\nf 1765 900 1710\\nf 1678 602 1431\\nf 2416 940 602\\nf 1342 1431 940\\nf 602 940 1431\\nf 57 1754 621\\nf 975 1542 1754\\nf 2416 621 1542\\nf 1754 1542 621\\nf 1342 940 900\\nf 2416 1542 940\\nf 975 900 1542\\nf 940 1542 900\\nf 116 371 2469\\nf 475 1578 371\\nf 567 2469 1578\\nf 371 1578 2469\\nf 952 1719 1728\\nf 185 2407 1719\\nf 475 1728 2407\\nf 1719 2407 1728\\nf 1678 649 279\\nf 567 2290 649\\nf 185 279 2290\\nf 649 2290 279\\nf 475 2407 1578\\nf 185 2290 2407\\nf 567 1578 2290\\nf 2407 2290 1578\\nf 57 621 2374\\nf 2416 873 621\\nf 123 2374 873\\nf 621 873 2374\\nf 1678 279 602\\nf 185 1476 279\\nf 2416 602 1476\\nf 279 1476 602\\nf 952 112 1719\\nf 123 52 112\\nf 185 1719 52\\nf 112 52 1719\\nf 2416 1476 873\\nf 185 52 1476\\nf 123 873 52\\nf 1476 52 873\\nf 678 1536 1691\\nf 1183 2340 1536\\nf 2254 1691 2340\\nf 1536 2340 1691\\nf 297 1556 1064\\nf 2011 1142 1556\\nf 1183 1064 1142\\nf 1556 1142 1064\\nf 1784 2023 1889\\nf 2254 1286 2023\\nf 2011 1889 1286\\nf 2023 1286 1889\\nf 1183 1142 2340\\nf 2011 1286 1142\\nf 2254 2340 1286\\nf 1142 1286 2340\\nf 116 2469 169\\nf 567 2493 2469\\nf 1620 169 2493\\nf 2469 2493 169\\nf 1678 1963 649\\nf 493 2090 1963\\nf 567 649 2090\\nf 1963 2090 649\\nf 297 1859 2188\\nf 1620 661 1859\\nf 493 2188 661\\nf 1859 661 2188\\nf 567 2090 2493\\nf 493 661 2090\\nf 1620 2493 661\\nf 2090 661 2493\\nf 560 38 1765\\nf 468 2462 38\\nf 1342 1765 2462\\nf 38 2462 1765\\nf 1784 1223 1544\\nf 902 805 1223\\nf 468 1544 805\\nf 1223 805 1544\\nf 1678 1431 690\\nf 1342 8 1431\\nf 902 690 8\\nf 1431 8 690\\nf 468 805 2462\\nf 902 8 805\\nf 1342 2462 8\\nf 805 8 2462\\nf 297 2188 1556\\nf 493 2406 2188\\nf 2011 1556 2406\\nf 2188 2406 1556\\nf 1678 690 1963\\nf 902 1323 690\\nf 493 1963 1323\\nf 690 1323 1963\\nf 1784 1889 1223\\nf 2011 1121 1889\\nf 902 1223 1121\\nf 1889 1121 1223\\nf 493 1323 2406\\nf 902 1121 1323\\nf 2011 2406 1121\\nf 1323 1121 2406\\nf 2008 2078 421\\nf 2475 2076 2078\\nf 2147 421 2076\\nf 2078 2076 421\\nf 2481 2504 2075\\nf 393 2536 2504\\nf 2475 2075 2536\\nf 2504 2536 2075\\nf 2425 877 2056\\nf 2147 776 877\\nf 393 2056 776\\nf 877 776 2056\\nf 2475 2536 2076\\nf 393 776 2536\\nf 2147 2076 776\\nf 2536 776 2076\\nf 2194 923 2045\\nf 1038 223 923\\nf 2042 2045 223\\nf 923 223 2045\\nf 2458 852 787\\nf 752 2474 852\\nf 1038 787 2474\\nf 852 2474 787\\nf 2481 946 458\\nf 2042 96 946\\nf 752 458 96\\nf 946 96 458\\nf 1038 2474 223\\nf 752 96 2474\\nf 2042 223 96\\nf 2474 96 223\\nf 1083 316 1094\\nf 1562 51 316\\nf 555 1094 51\\nf 316 51 1094\\nf 2425 1742 728\\nf 795 2180 1742\\nf 1562 728 2180\\nf 1742 2180 728\\nf 2458 2250 2315\\nf 555 1426 2250\\nf 795 2315 1426\\nf 2250 1426 2315\\nf 1562 2180 51\\nf 795 1426 2180\\nf 555 51 1426\\nf 2180 1426 51\\nf 2481 458 2504\\nf 752 2438 458\\nf 393 2504 2438\\nf 458 2438 2504\\nf 2458 2315 852\\nf 795 220 2315\\nf 752 852 220\\nf 2315 220 852\\nf 2425 2056 1742\\nf 393 41 2056\\nf 795 1742 41\\nf 2056 41 1742\\nf 752 220 2438\\nf 795 41 220\\nf 393 2438 41\\nf 220 41 2438\\nf 2391 1333 2309\\nf 1027 2511 1333\\nf 2327 2309 2511\\nf 1333 2511 2309\\nf 760 559 1458\\nf 1773 2105 559\\nf 1027 1458 2105\\nf 559 2105 1458\\nf 1295 58 2524\\nf 2327 138 58\\nf 1773 2524 138\\nf 58 138 2524\\nf 1027 2105 2511\\nf 1773 138 2105\\nf 2327 2511 138\\nf 2105 138 2511\\nf 1525 2482 901\\nf 2542 2202 2482\\nf 1331 901 2202\\nf 2482 2202 901\\nf 88 1058 564\\nf 815 685 1058\\nf 2542 564 685\\nf 1058 685 564\\nf 760 2239 37\\nf 1331 1199 2239\\nf 815 37 1199\\nf 2239 1199 37\\nf 2542 685 2202\\nf 815 1199 685\\nf 1331 2202 1199\\nf 685 1199 2202\\nf 2194 2181 480\\nf 1968 2415 2181\\nf 1354 480 2415\\nf 2181 2415 480\\nf 1295 1539 2120\\nf 1988 1787 1539\\nf 1968 2120 1787\\nf 1539 1787 2120\\nf 88 540 2368\\nf 1354 218 540\\nf 1988 2368 218\\nf 540 218 2368\\nf 1968 1787 2415\\nf 1988 218 1787\\nf 1354 2415 218\\nf 1787 218 2415\\nf 760 37 559\\nf 815 1193 37\\nf 1773 559 1193\\nf 37 1193 559\\nf 88 2368 1058\\nf 1988 361 2368\\nf 815 1058 361\\nf 2368 361 1058\\nf 1295 2524 1539\\nf 1773 1135 2524\\nf 1988 1539 1135\\nf 2524 1135 1539\\nf 815 361 1193\\nf 1988 1135 361\\nf 1773 1193 1135\\nf 361 1135 1193\\nf 2282 1463 1704\\nf 1445 302 1463\\nf 1877 1704 302\\nf 1463 302 1704\\nf 1388 2284 122\\nf 765 495 2284\\nf 1445 122 495\\nf 2284 495 122\\nf 931 840 1825\\nf 1877 2380 840\\nf 765 1825 2380\\nf 840 2380 1825\\nf 1445 495 302\\nf 765 2380 495\\nf 1877 302 2380\\nf 495 2380 302\\nf 1083 2519 1745\\nf 2463 843 2519\\nf 2229 1745 843\\nf 2519 843 1745\\nf 584 1389 1096\\nf 1632 2071 1389\\nf 2463 1096 2071\\nf 1389 2071 1096\\nf 1388 1016 1612\\nf 2229 1774 1016\\nf 1632 1612 1774\\nf 1016 1774 1612\\nf 2463 2071 843\\nf 1632 1774 2071\\nf 2229 843 1774\\nf 2071 1774 843\\nf 1525 1390 968\\nf 473 1089 1390\\nf 1736 968 1089\\nf 1390 1089 968\\nf 931 1372 1449\\nf 24 2032 1372\\nf 473 1449 2032\\nf 1372 2032 1449\\nf 584 518 1939\\nf 1736 30 518\\nf 24 1939 30\\nf 518 30 1939\\nf 473 2032 1089\\nf 24 30 2032\\nf 1736 1089 30\\nf 2032 30 1089\\nf 1388 1612 2284\\nf 1632 2221 1612\\nf 765 2284 2221\\nf 1612 2221 2284\\nf 584 1939 1389\\nf 24 1801 1939\\nf 1632 1389 1801\\nf 1939 1801 1389\\nf 931 1825 1372\\nf 765 2283 1825\\nf 24 1372 2283\\nf 1825 2283 1372\\nf 1632 1801 2221\\nf 24 2283 1801\\nf 765 2221 2283\\nf 1801 2283 2221\\nf 2194 480 923\\nf 1354 1574 480\\nf 1038 923 1574\\nf 480 1574 923\\nf 88 2189 540\\nf 2089 230 2189\\nf 1354 540 230\\nf 2189 230 540\\nf 2458 787 1712\\nf 1038 1050 787\\nf 2089 1712 1050\\nf 787 1050 1712\\nf 1354 230 1574\\nf 2089 1050 230\\nf 1038 1574 1050\\nf 230 1050 1574\\nf 1525 968 2482\\nf 1736 1819 968\\nf 2542 2482 1819\\nf 968 1819 2482\\nf 584 109 518\\nf 1673 1243 109\\nf 1736 518 1243\\nf 109 1243 518\\nf 88 564 606\\nf 2542 1722 564\\nf 1673 606 1722\\nf 564 1722 606\\nf 1736 1243 1819\\nf 1673 1722 1243\\nf 2542 1819 1722\\nf 1243 1722 1819\\nf 1083 1094 2519\\nf 555 1195 1094\\nf 2463 2519 1195\\nf 1094 1195 2519\\nf 2458 491 2250\\nf 2278 858 491\\nf 555 2250 858\\nf 491 858 2250\\nf 584 1096 2285\\nf 2463 85 1096\\nf 2278 2285 85\\nf 1096 85 2285\\nf 555 858 1195\\nf 2278 85 858\\nf 2463 1195 85\\nf 858 85 1195\\nf 88 606 2189\\nf 1673 463 606\\nf 2089 2189 463\\nf 606 463 2189\\nf 584 2285 109\\nf 2278 1052 2285\\nf 1673 109 1052\\nf 2285 1052 109\\nf 2458 1712 491\\nf 2089 310 1712\\nf 2278 491 310\\nf 1712 310 491\\nf 1673 1052 463\\nf 2278 310 1052\\nf 2089 463 310\\nf 1052 310 463\\nf 1769 1287 1124\\nf 802 842 1287\\nf 1553 1124 842\\nf 1287 842 1124\\nf 907 984 1633\\nf 2437 1182 984\\nf 802 1633 1182\\nf 984 1182 1633\\nf 2014 1105 1294\\nf 1553 809 1105\\nf 2437 1294 809\\nf 1105 809 1294\\nf 802 1182 842\\nf 2437 809 1182\\nf 1553 842 809\\nf 1182 809 842\\nf 2466 784 909\\nf 479 1171 784\\nf 727 909 1171\\nf 784 1171 909\\nf 2216 651 2242\\nf 1966 4 651\\nf 479 2242 4\\nf 651 4 2242\\nf 907 2372 1261\\nf 727 1561 2372\\nf 1966 1261 1561\\nf 2372 1561 1261\\nf 479 4 1171\\nf 1966 1561 4\\nf 727 1171 1561\\nf 4 1561 1171\\nf 1191 1790 1155\\nf 2169 2399 1790\\nf 1492 1155 2399\\nf 1790 2399 1155\\nf 2014 941 1157\\nf 1208 1409 941\\nf 2169 1157 1409\\nf 941 1409 1157\\nf 2216 646 246\\nf 1492 2470 646\\nf 1208 246 2470\\nf 646 2470 246\\nf 2169 1409 2399\\nf 1208 2470 1409\\nf 1492 2399 2470\\nf 1409 2470 2399\\nf 907 1261 984\\nf 1966 68 1261\\nf 2437 984 68\\nf 1261 68 984\\nf 2216 246 651\\nf 1208 801 246\\nf 1966 651 801\\nf 246 801 651\\nf 2014 1294 941\\nf 2437 600 1294\\nf 1208 941 600\\nf 1294 600 941\\nf 1966 801 68\\nf 1208 600 801\\nf 2437 68 600\\nf 801 600 68\\nf 2008 777 1427\\nf 746 2192 777\\nf 644 1427 2192\\nf 777 2192 1427\\nf 29 1979 990\\nf 1324 709 1979\\nf 746 990 709\\nf 1979 709 990\\nf 988 2506 962\\nf 644 778 2506\\nf 1324 962 778\\nf 2506 778 962\\nf 746 709 2192\\nf 1324 778 709\\nf 644 2192 778\\nf 709 778 2192\\nf 961 1529 1871\\nf 1641 823 1529\\nf 878 1871 823\\nf 1529 823 1871\\nf 1546 816 369\\nf 339 295 816\\nf 1641 369 295\\nf 816 295 369\\nf 29 1868 914\\nf 878 612 1868\\nf 339 914 612\\nf 1868 612 914\\nf 1641 295 823\\nf 339 612 295\\nf 878 823 612\\nf 295 612 823\\nf 2466 833 867\\nf 1055 2225 833\\nf 879 867 2225\\nf 833 2225 867\\nf 988 2106 1573\\nf 557 1588 2106\\nf 1055 1573 1588\\nf 2106 1588 1573\\nf 1546 2405 1644\\nf 879 2164 2405\\nf 557 1644 2164\\nf 2405 2164 1644\\nf 1055 1588 2225\\nf 557 2164 1588\\nf 879 2225 2164\\nf 1588 2164 2225\\nf 29 914 1979\\nf 339 1494 914\\nf 1324 1979 1494\\nf 914 1494 1979\\nf 1546 1644 816\\nf 557 381 1644\\nf 339 816 381\\nf 1644 381 816\\nf 988 962 2106\\nf 1324 1597 962\\nf 557 2106 1597\\nf 962 1597 2106\\nf 339 381 1494\\nf 557 1597 381\\nf 1324 1494 1597\\nf 381 1597 1494\\nf 1777 566 1374\\nf 309 2228 566\\nf 282 1374 2228\\nf 566 2228 1374\\nf 487 1857 2304\\nf 2264 1415 1857\\nf 309 2304 1415\\nf 1857 1415 2304\\nf 1705 1437 1074\\nf 282 2013 1437\\nf 2264 1074 2013\\nf 1437 2013 1074\\nf 309 1415 2228\\nf 2264 2013 1415\\nf 282 2228 2013\\nf 1415 2013 2228\\nf 1191 1036 1768\\nf 1026 1638 1036\\nf 130 1768 1638\\nf 1036 1638 1768\\nf 364 536 1621\\nf 1336 800 536\\nf 1026 1621 800\\nf 536 800 1621\\nf 487 117 419\\nf 130 1515 117\\nf 1336 419 1515\\nf 117 1515 419\\nf 1026 800 1638\\nf 1336 1515 800\\nf 130 1638 1515\\nf 800 1515 1638\\nf 961 97 405\\nf 934 820 97\\nf 1338 405 820\\nf 97 820 405\\nf 1705 1890 1262\\nf 2119 1901 1890\\nf 934 1262 1901\\nf 1890 1901 1262\\nf 364 1664 2403\\nf 1338 308 1664\\nf 2119 2403 308\\nf 1664 308 2403\\nf 934 1901 820\\nf 2119 308 1901\\nf 1338 820 308\\nf 1901 308 820\\nf 487 419 1857\\nf 1336 113 419\\nf 2264 1857 113\\nf 419 113 1857\\nf 364 2403 536\\nf 2119 1506 2403\\nf 1336 536 1506\\nf 2403 1506 536\\nf 1705 1074 1890\\nf 2264 387 1074\\nf 2119 1890 387\\nf 1074 387 1890\\nf 1336 1506 113\\nf 2119 387 1506\\nf 2264 113 387\\nf 1506 387 113\\nf 2466 867 784\\nf 879 355 867\\nf 479 784 355\\nf 867 355 784\\nf 1546 2518 2405\\nf 2561 1392 2518\\nf 879 2405 1392\\nf 2518 1392 2405\\nf 2216 2242 640\\nf 479 1617 2242\\nf 2561 640 1617\\nf 2242 1617 640\\nf 879 1392 355\\nf 2561 1617 1392\\nf 479 355 1617\\nf 1392 1617 355\\nf 961 405 1529\\nf 1338 1582 405\\nf 1641 1529 1582\\nf 405 1582 1529\\nf 364 2118 1664\\nf 1314 1922 2118\\nf 1338 1664 1922\\nf 2118 1922 1664\\nf 1546 369 1786\\nf 1641 1526 369\\nf 1314 1786 1526\\nf 369 1526 1786\\nf 1338 1922 1582\\nf 1314 1526 1922\\nf 1641 1582 1526\\nf 1922 1526 1582\\nf 1191 1155 1036\\nf 1492 1079 1155\\nf 1026 1036 1079\\nf 1155 1079 1036\\nf 2216 1189 646\\nf 735 147 1189\\nf 1492 646 147\\nf 1189 147 646\\nf 364 1621 39\\nf 1026 1482 1621\\nf 735 39 1482\\nf 1621 1482 39\\nf 1492 147 1079\\nf 735 1482 147\\nf 1026 1079 1482\\nf 147 1482 1079\\nf 1546 1786 2518\\nf 1314 1601 1786\\nf 2561 2518 1601\\nf 1786 1601 2518\\nf 364 39 2118\\nf 735 970 39\\nf 1314 2118 970\\nf 39 970 2118\\nf 2216 640 1189\\nf 2561 1947 640\\nf 735 1189 1947\\nf 640 1947 1189\\nf 1314 970 1601\\nf 735 1947 970\\nf 2561 1601 1947\\nf 970 1947 1601'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 29 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "pyBAYFy0cQuj" + }, + "source": [ + "" + ], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file From df0fba058ffdecb15e54667584fcd966b94fcea1 Mon Sep 17 00:00:00 2001 From: kierannp Date: Sat, 22 May 2021 19:08:50 -0400 Subject: [PATCH 2/8] Pretrained 3dsnet sample --- Sample_pretrained_chair_3dsnet.ipynb | 2452 ++++++++++++++++++++++++++ 1 file changed, 2452 insertions(+) create mode 100644 Sample_pretrained_chair_3dsnet.ipynb diff --git a/Sample_pretrained_chair_3dsnet.ipynb b/Sample_pretrained_chair_3dsnet.ipynb new file mode 100644 index 0000000..497e871 --- /dev/null +++ b/Sample_pretrained_chair_3dsnet.ipynb @@ -0,0 +1,2452 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Sample_pretrained_chair_3dsnet.ipynb", + "provenance": [], + "collapsed_sections": [], + "authorship_tag": "ABX9TyO7vcZ1c5JmXcQwpkeJY3W3", + "include_colab_link": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uwzRkfxGDKaD" + }, + "source": [ + "# Unsupervised 3d Style Tranfer via 3dsnet" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DUajY7mxkAH8" + }, + "source": [ + "All credit for most of the code base and model to:\n", + "\n", + "\n", + "@article{segu20203dsnet,\n", + " title={3DSNet: Unsupervised Shape-to-Shape 3D Style Transfer},\n", + " author={Segu, Mattia and Grinvald, Margarita and Siegwart, Roland and Tombari, Federico},\n", + " journal={arXiv preprint arXiv:2011.13388},\n", + " year={2020}\n", + "}\n", + "\n", + "\n", + "Checkout the 3dsnet [repo](https://github.com/ethz-asl/3dsnet)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OxIfIaS0Drhi" + }, + "source": [ + "Author of this colab: [KieranNP](https://github.com/kierannp)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "P-Sh4i2EH89-" + }, + "source": [ + "List of available models compatible with current codebase\n", + "\n", + "CHAIRS (using our Adaptive-Meshflow backbone):\n", + "- 3dsnet (with reconstruction loss, adversarial loss and cycle-consistency loss)\n", + "- adanorm (only reconstruction loss, style transfer is attempted by relying only on the adaptive normalization layers)\n", + "\n", + "PLANES (using our Adaptive-Atlasnet backbone):\n", + "- 3dsnet (with reconstruction loss, adversarial loss and cycle-consistency loss)\n", + "- 3dsnet_no_cycle (with reconstruction loss and adversarial loss)\n", + "- adanorm (only reconstruction loss, style transfer is attempted by relying only on the adaptive normalization layers)\n", + "\n", + "Files included:\n", + "- log.txt, contains training statistics per each training epoch\n", + "- network.pth, model weights at last training epoch\n", + "- network_best.pth, model weights at best training epoch\n", + "- options.json, options used for training the model" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "rILowRnFIbWJ", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "c7334c06-664a-4ffa-a27f-b5afabf6b3ff" + }, + "source": [ + "#@title Installations required {display-mode: \"form\"}\n", + "\n", + "# This code will be hidden when the notebook is loaded.\n", + "\n", + "# %%capture\n", + "%cd /content/\n", + "%env PYTHONPATH=\n", + "! wget https://repo.anaconda.com/miniconda/Miniconda3-4.5.4-Linux-x86_64.sh\n", + "! chmod +x Miniconda3-4.5.4-Linux-x86_64.sh\n", + "! bash ./Miniconda3-4.5.4-Linux-x86_64.sh -b -f -p /usr/local\n", + "import sys\n", + "import os\n", + "\n", + "sys.path.append('/usr/local/lib/python3.6/site-packages/')\n", + "\n", + "!conda install --channel defaults conda python=3.6 --yes\n", + "!conda update --channel defaults --all --yes\n", + "if not os.path.exists(\"/content/3dsnet\"):\n", + " !git clone --recurse-submodules https://github.com/ethz-asl/3dsnet.git\n", + "\n", + "!conda create -n 3dsnet python=3.6 --yes\n", + "!source activate 3dsnet\n", + "\n", + "!pip install meshio[all]\n", + "\n", + "!conda install pytorch=1.7.1 torchvision=0.8.2 cudatoolkit=10.1 -c pytorch --yes\n", + "!conda install -y -c conda-forge pyembree\n", + "!conda install -y -c conda-forge trimesh seaborn\n", + "!conda install -y -c fvcore -c iopath -c conda-forge fvcore iopath\n", + "# !pip install \"git+https://github.com/facebookresearch/pytorch3d.git@stable\"\n", + "!conda install -y pytorch3d -c pytorch3d\n", + "# !pip install pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/py36_cu101_pyt170/download.html\n", + "!conda install -y -c conda-forge visdom\n", + "\n", + "if not os.path.exists(\"/content/3dsnet/PyMesh\"):\n", + " %cd /content/3dsnset\n", + " !git clone https://github.com/PyMesh/PyMesh.git\n", + " %cd PyMesh\n", + " !git submodule update --init\n", + " !export PYMESH_PATH=$(pwd)\n", + "\n", + " !apt-get install \\\n", + "\tlibeigen3-dev \\\n", + "\tlibgmp-dev \\\n", + "\tlibgmpxx4ldbl \\\n", + "\tlibmpfr-dev \\\n", + "\tlibboost-dev \\\n", + "\tlibboost-thread-dev \\\n", + "\tlibtbb-dev \\\n", + "\tpython3-dev \\\n", + "\tpython3-setuptools \\\n", + "\tpython3-numpy \\\n", + "\tpython3-scipy \\\n", + "\tpython3-nose \\\n", + "\tpython3-pip \\\n", + "\tcmake\n", + "\n", + " %cd $PYMESH_PATH/third_party\n", + " !mkdir build\n", + " !./build.py all\n", + " %cd $PYMESH_PATH\n", + " !mkdir build\n", + " !python3\n", + " setup.py build # This an alternative way of calling cmake/make\n", + " !python3 setup.py install\n", + " %cd ..\n", + "\n", + "!pip install trimesh\n", + "\n", + "!pip install git+https://github.com/rtqichen/torchdiffeq torchvision\n", + "!pip install git+https://github.com/cnr-isti-vclab/PyMeshLab\n", + "%cd /content/3dsnet\n", + "!pip install --user --requirement requirements.txt # pip dependencies" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "/content\n", + "env: PYTHONPATH=\n", + "--2021-05-20 19:42:12-- https://repo.anaconda.com/miniconda/Miniconda3-4.5.4-Linux-x86_64.sh\n", + "Resolving repo.anaconda.com (repo.anaconda.com)... 104.16.131.3, 104.16.130.3, 2606:4700::6810:8203, ...\n", + "Connecting to repo.anaconda.com (repo.anaconda.com)|104.16.131.3|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 58468498 (56M) [application/x-sh]\n", + "Saving to: ‘Miniconda3-4.5.4-Linux-x86_64.sh’\n", + "\n", + "Miniconda3-4.5.4-Li 100%[===================>] 55.76M 219MB/s in 0.3s \n", + "\n", + "2021-05-20 19:42:13 (219 MB/s) - ‘Miniconda3-4.5.4-Linux-x86_64.sh’ saved [58468498/58468498]\n", + "\n", + "PREFIX=/usr/local\n", + "installing: python-3.6.5-hc3d631a_2 ...\n", + "Python 3.6.5 :: Anaconda, Inc.\n", + "installing: ca-certificates-2018.03.07-0 ...\n", + "installing: conda-env-2.6.0-h36134e3_1 ...\n", + "installing: libgcc-ng-7.2.0-hdf63c60_3 ...\n", + "installing: libstdcxx-ng-7.2.0-hdf63c60_3 ...\n", + "installing: libffi-3.2.1-hd88cf55_4 ...\n", + "installing: ncurses-6.1-hf484d3e_0 ...\n", + "installing: openssl-1.0.2o-h20670df_0 ...\n", + "installing: tk-8.6.7-hc745277_3 ...\n", + "installing: xz-5.2.4-h14c3975_4 ...\n", + "installing: yaml-0.1.7-had09818_2 ...\n", + "installing: zlib-1.2.11-ha838bed_2 ...\n", + "installing: libedit-3.1.20170329-h6b74fdf_2 ...\n", + "installing: readline-7.0-ha6073c6_4 ...\n", + "installing: sqlite-3.23.1-he433501_0 ...\n", + "installing: asn1crypto-0.24.0-py36_0 ...\n", + "installing: certifi-2018.4.16-py36_0 ...\n", + "installing: chardet-3.0.4-py36h0f667ec_1 ...\n", + "installing: idna-2.6-py36h82fb2a8_1 ...\n", + "installing: pycosat-0.6.3-py36h0a5515d_0 ...\n", + "installing: pycparser-2.18-py36hf9f622e_1 ...\n", + "installing: pysocks-1.6.8-py36_0 ...\n", + "installing: ruamel_yaml-0.15.37-py36h14c3975_2 ...\n", + "installing: six-1.11.0-py36h372c433_1 ...\n", + "installing: cffi-1.11.5-py36h9745a5d_0 ...\n", + "installing: setuptools-39.2.0-py36_0 ...\n", + "installing: cryptography-2.2.2-py36h14c3975_0 ...\n", + "installing: wheel-0.31.1-py36_0 ...\n", + "installing: pip-10.0.1-py36_0 ...\n", + "installing: pyopenssl-18.0.0-py36_0 ...\n", + "installing: urllib3-1.22-py36hbe7ace6_0 ...\n", + "installing: requests-2.18.4-py36he2e5f8d_1 ...\n", + "installing: conda-4.5.4-py36_0 ...\n", + "installation finished.\n", + "Solving environment: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n", + "\n", + "## Package Plan ##\n", + "\n", + " environment location: /usr/local\n", + "\n", + " added / updated specs: \n", + " - conda\n", + " - python=3.6\n", + "\n", + "\n", + "The following packages will be downloaded:\n", + "\n", + " package | build\n", + " ---------------------------|-----------------\n", + " pysocks-1.7.1 | py36h06a4308_0 30 KB\n", + " pycparser-2.20 | py_2 94 KB\n", + " zlib-1.2.11 | h7b6447c_3 120 KB\n", + " brotlipy-0.7.0 |py36h27cfd23_1003 349 KB\n", + " _libgcc_mutex-0.1 | main 3 KB\n", + " openssl-1.1.1k | h27cfd23_0 3.8 MB\n", + " tqdm-4.59.0 | pyhd3eb1b0_1 90 KB\n", + " certifi-2020.12.5 | py36h06a4308_0 144 KB\n", + " libffi-3.3 | he6710b0_2 54 KB\n", + " idna-2.10 | pyhd3eb1b0_0 52 KB\n", + " pycosat-0.6.3 | py36h27cfd23_0 107 KB\n", + " tk-8.6.10 | hbc83047_0 3.2 MB\n", + " wheel-0.36.2 | pyhd3eb1b0_0 31 KB\n", + " pyopenssl-20.0.1 | pyhd3eb1b0_1 48 KB\n", + " ncurses-6.2 | he6710b0_1 1.1 MB\n", + " six-1.15.0 | pyhd3eb1b0_0 13 KB\n", + " libgcc-ng-9.1.0 | hdf63c60_0 8.1 MB\n", + " readline-8.1 | h27cfd23_0 464 KB\n", + " cffi-1.14.5 | py36h261ae71_0 224 KB\n", + " sqlite-3.35.4 | hdfb4753_0 1.4 MB\n", + " conda-4.10.1 | py36h06a4308_1 3.1 MB\n", + " requests-2.25.1 | pyhd3eb1b0_0 51 KB\n", + " python-3.6.13 | hdb3f193_0 33.9 MB\n", + " ca-certificates-2021.4.13 | h06a4308_1 120 KB\n", + " cryptography-3.4.7 | py36hd23ed53_0 1.0 MB\n", + " chardet-4.0.0 |py36h06a4308_1003 213 KB\n", + " pip-21.0.1 | py36h06a4308_0 2.0 MB\n", + " urllib3-1.26.4 | pyhd3eb1b0_0 99 KB\n", + " xz-5.2.5 | h7b6447c_0 438 KB\n", + " conda-package-handling-1.7.3| py36h27cfd23_1 946 KB\n", + " setuptools-52.0.0 | py36h06a4308_0 933 KB\n", + " ruamel_yaml-0.15.100 | py36h27cfd23_0 268 KB\n", + " yaml-0.2.5 | h7b6447c_0 87 KB\n", + " libstdcxx-ng-9.1.0 | hdf63c60_0 4.0 MB\n", + " ld_impl_linux-64-2.33.1 | h53a641e_7 645 KB\n", + " ------------------------------------------------------------\n", + " Total: 67.2 MB\n", + "\n", + "The following NEW packages will be INSTALLED:\n", + "\n", + " _libgcc_mutex: 0.1-main \n", + " brotlipy: 0.7.0-py36h27cfd23_1003\n", + " conda-package-handling: 1.7.3-py36h27cfd23_1 \n", + " ld_impl_linux-64: 2.33.1-h53a641e_7 \n", + " tqdm: 4.59.0-pyhd3eb1b0_1 \n", + "\n", + "The following packages will be UPDATED:\n", + "\n", + " ca-certificates: 2018.03.07-0 --> 2021.4.13-h06a4308_1 \n", + " certifi: 2018.4.16-py36_0 --> 2020.12.5-py36h06a4308_0\n", + " cffi: 1.11.5-py36h9745a5d_0 --> 1.14.5-py36h261ae71_0 \n", + " chardet: 3.0.4-py36h0f667ec_1 --> 4.0.0-py36h06a4308_1003 \n", + " conda: 4.5.4-py36_0 --> 4.10.1-py36h06a4308_1 \n", + " cryptography: 2.2.2-py36h14c3975_0 --> 3.4.7-py36hd23ed53_0 \n", + " idna: 2.6-py36h82fb2a8_1 --> 2.10-pyhd3eb1b0_0 \n", + " libffi: 3.2.1-hd88cf55_4 --> 3.3-he6710b0_2 \n", + " libgcc-ng: 7.2.0-hdf63c60_3 --> 9.1.0-hdf63c60_0 \n", + " libstdcxx-ng: 7.2.0-hdf63c60_3 --> 9.1.0-hdf63c60_0 \n", + " ncurses: 6.1-hf484d3e_0 --> 6.2-he6710b0_1 \n", + " openssl: 1.0.2o-h20670df_0 --> 1.1.1k-h27cfd23_0 \n", + " pip: 10.0.1-py36_0 --> 21.0.1-py36h06a4308_0 \n", + " pycosat: 0.6.3-py36h0a5515d_0 --> 0.6.3-py36h27cfd23_0 \n", + " pycparser: 2.18-py36hf9f622e_1 --> 2.20-py_2 \n", + " pyopenssl: 18.0.0-py36_0 --> 20.0.1-pyhd3eb1b0_1 \n", + " pysocks: 1.6.8-py36_0 --> 1.7.1-py36h06a4308_0 \n", + " python: 3.6.5-hc3d631a_2 --> 3.6.13-hdb3f193_0 \n", + " readline: 7.0-ha6073c6_4 --> 8.1-h27cfd23_0 \n", + " requests: 2.18.4-py36he2e5f8d_1 --> 2.25.1-pyhd3eb1b0_0 \n", + " ruamel_yaml: 0.15.37-py36h14c3975_2 --> 0.15.100-py36h27cfd23_0 \n", + " setuptools: 39.2.0-py36_0 --> 52.0.0-py36h06a4308_0 \n", + " six: 1.11.0-py36h372c433_1 --> 1.15.0-pyhd3eb1b0_0 \n", + " sqlite: 3.23.1-he433501_0 --> 3.35.4-hdfb4753_0 \n", + " tk: 8.6.7-hc745277_3 --> 8.6.10-hbc83047_0 \n", + " urllib3: 1.22-py36hbe7ace6_0 --> 1.26.4-pyhd3eb1b0_0 \n", + " wheel: 0.31.1-py36_0 --> 0.36.2-pyhd3eb1b0_0 \n", + " xz: 5.2.4-h14c3975_4 --> 5.2.5-h7b6447c_0 \n", + " yaml: 0.1.7-had09818_2 --> 0.2.5-h7b6447c_0 \n", + " zlib: 1.2.11-ha838bed_2 --> 1.2.11-h7b6447c_3 \n", + "\n", + "\n", + "Downloading and Extracting Packages\n", + "pysocks-1.7.1 | 30 KB | : 100% 1.0/1 [00:00<00:00, 25.95it/s]\n", + "pycparser-2.20 | 94 KB | : 100% 1.0/1 [00:00<00:00, 12.51it/s]\n", + "zlib-1.2.11 | 120 KB | : 100% 1.0/1 [00:00<00:00, 22.55it/s]\n", + "brotlipy-0.7.0 | 349 KB | : 100% 1.0/1 [00:00<00:00, 11.95it/s]\n", + "_libgcc_mutex-0.1 | 3 KB | : 100% 1.0/1 [00:00<00:00, 37.50it/s]\n", + "openssl-1.1.1k | 3.8 MB | : 100% 1.0/1 [00:00<00:00, 1.48it/s] \n", + "tqdm-4.59.0 | 90 KB | : 100% 1.0/1 [00:00<00:00, 18.55it/s]\n", + "certifi-2020.12.5 | 144 KB | : 100% 1.0/1 [00:00<00:00, 25.33it/s]\n", + "libffi-3.3 | 54 KB | : 100% 1.0/1 [00:00<00:00, 33.12it/s]\n", + "idna-2.10 | 52 KB | : 100% 1.0/1 [00:00<00:00, 29.32it/s]\n", + "pycosat-0.6.3 | 107 KB | : 100% 1.0/1 [00:00<00:00, 24.47it/s]\n", + "tk-8.6.10 | 3.2 MB | : 100% 1.0/1 [00:00<00:00, 1.45it/s] \n", + "wheel-0.36.2 | 31 KB | : 100% 1.0/1 [00:00<00:00, 28.73it/s]\n", + "pyopenssl-20.0.1 | 48 KB | : 100% 1.0/1 [00:00<00:00, 29.61it/s]\n", + "ncurses-6.2 | 1.1 MB | : 100% 1.0/1 [00:00<00:00, 1.19it/s] \n", + "six-1.15.0 | 13 KB | : 100% 1.0/1 [00:00<00:00, 38.13it/s]\n", + "libgcc-ng-9.1.0 | 8.1 MB | : 100% 1.0/1 [00:01<00:00, 1.25s/it] \n", + "readline-8.1 | 464 KB | : 100% 1.0/1 [00:00<00:00, 8.27it/s]\n", + "cffi-1.14.5 | 224 KB | : 100% 1.0/1 [00:00<00:00, 12.94it/s]\n", + "sqlite-3.35.4 | 1.4 MB | : 100% 1.0/1 [00:00<00:00, 4.08it/s] \n", + "conda-4.10.1 | 3.1 MB | : 100% 1.0/1 [00:00<00:00, 1.22it/s] \n", + "requests-2.25.1 | 51 KB | : 100% 1.0/1 [00:00<00:00, 22.32it/s]\n", + "python-3.6.13 | 33.9 MB | : 100% 1.0/1 [00:05<00:00, 5.19s/it] \n", + "ca-certificates-2021 | 120 KB | : 100% 1.0/1 [00:00<00:00, 24.39it/s]\n", + "cryptography-3.4.7 | 1.0 MB | : 100% 1.0/1 [00:00<00:00, 2.83it/s] \n", + "chardet-4.0.0 | 213 KB | : 100% 1.0/1 [00:00<00:00, 9.77it/s]\n", + "pip-21.0.1 | 2.0 MB | : 100% 1.0/1 [00:00<00:00, 1.53it/s] \n", + "urllib3-1.26.4 | 99 KB | : 100% 1.0/1 [00:00<00:00, 17.91it/s]\n", + "xz-5.2.5 | 438 KB | : 100% 1.0/1 [00:00<00:00, 7.81it/s] \n", + "conda-package-handli | 946 KB | : 100% 1.0/1 [00:00<00:00, 6.00it/s] \n", + "setuptools-52.0.0 | 933 KB | : 100% 1.0/1 [00:00<00:00, 3.20it/s] \n", + "ruamel_yaml-0.15.100 | 268 KB | : 100% 1.0/1 [00:00<00:00, 10.51it/s]\n", + "yaml-0.2.5 | 87 KB | : 100% 1.0/1 [00:00<00:00, 23.96it/s]\n", + "libstdcxx-ng-9.1.0 | 4.0 MB | : 100% 1.0/1 [00:00<00:00, 1.55it/s] \n", + "ld_impl_linux-64-2.3 | 645 KB | : 100% 1.0/1 [00:00<00:00, 5.71it/s] \n", + "Preparing transaction: / \b\b- \b\b\\ \b\b| \b\bdone\n", + "Verifying transaction: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n", + "Executing transaction: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n", + "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n", + "Solving environment: \\ \b\b| \b\b/ \b\bdone\n", + "\n", + "## Package Plan ##\n", + "\n", + " environment location: /usr/local\n", + "\n", + "\n", + "The following packages will be downloaded:\n", + "\n", + " package | build\n", + " ---------------------------|-----------------\n", + " six-1.15.0 | py36h06a4308_0 27 KB\n", + " ------------------------------------------------------------\n", + " Total: 27 KB\n", + "\n", + "The following packages will be REMOVED:\n", + "\n", + " asn1crypto-0.24.0-py36_0\n", + " conda-env-2.6.0-h36134e3_1\n", + " libedit-3.1.20170329-h6b74fdf_2\n", + "\n", + "The following packages will be SUPERSEDED by a higher-priority channel:\n", + "\n", + " six pkgs/main/noarch::six-1.15.0-pyhd3eb1~ --> pkgs/main/linux-64::six-1.15.0-py36h06a4308_0\n", + "\n", + "\n", + "\n", + "Downloading and Extracting Packages\n", + "six-1.15.0 | 27 KB | : 100% 1.0/1 [00:00<00:00, 10.04it/s]\n", + "Preparing transaction: \\ \b\bdone\n", + "Verifying transaction: / \b\bdone\n", + "Executing transaction: \\ \b\bdone\n", + "Cloning into '3dsnet'...\n", + "remote: Enumerating objects: 1203, done.\u001b[K\n", + "remote: Counting objects: 100% (1203/1203), done.\u001b[K\n", + "remote: Compressing objects: 100% (1045/1045), done.\u001b[K\n", + "remote: Total 1203 (delta 144), reused 1196 (delta 142), pack-reused 0\u001b[K\n", + "Receiving objects: 100% (1203/1203), 5.92 MiB | 15.30 MiB/s, done.\n", + "Resolving deltas: 100% (144/144), done.\n", + "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\bdone\n", + "Solving environment: - \b\bdone\n", + "\n", + "## Package Plan ##\n", + "\n", + " environment location: /usr/local/envs/3dsnet\n", + "\n", + " added / updated specs:\n", + " - python=3.6\n", + "\n", + "\n", + "The following packages will be downloaded:\n", + "\n", + " package | build\n", + " ---------------------------|-----------------\n", + " _libgcc_mutex-0.1 | main 3 KB\n", + " ca-certificates-2021.4.13 | h06a4308_1 114 KB\n", + " certifi-2020.12.5 | py36h06a4308_0 140 KB\n", + " ld_impl_linux-64-2.33.1 | h53a641e_7 568 KB\n", + " libffi-3.3 | he6710b0_2 50 KB\n", + " libgcc-ng-9.1.0 | hdf63c60_0 5.1 MB\n", + " libstdcxx-ng-9.1.0 | hdf63c60_0 3.1 MB\n", + " ncurses-6.2 | he6710b0_1 817 KB\n", + " openssl-1.1.1k | h27cfd23_0 2.5 MB\n", + " pip-21.0.1 | py36h06a4308_0 1.8 MB\n", + " python-3.6.13 | hdb3f193_0 29.7 MB\n", + " readline-8.1 | h27cfd23_0 362 KB\n", + " setuptools-52.0.0 | py36h06a4308_0 724 KB\n", + " sqlite-3.35.4 | hdfb4753_0 981 KB\n", + " tk-8.6.10 | hbc83047_0 3.0 MB\n", + " wheel-0.36.2 | pyhd3eb1b0_0 33 KB\n", + " xz-5.2.5 | h7b6447c_0 341 KB\n", + " zlib-1.2.11 | h7b6447c_3 103 KB\n", + " ------------------------------------------------------------\n", + " Total: 49.4 MB\n", + "\n", + "The following NEW packages will be INSTALLED:\n", + "\n", + " _libgcc_mutex pkgs/main/linux-64::_libgcc_mutex-0.1-main\n", + " ca-certificates pkgs/main/linux-64::ca-certificates-2021.4.13-h06a4308_1\n", + " certifi pkgs/main/linux-64::certifi-2020.12.5-py36h06a4308_0\n", + " ld_impl_linux-64 pkgs/main/linux-64::ld_impl_linux-64-2.33.1-h53a641e_7\n", + " libffi pkgs/main/linux-64::libffi-3.3-he6710b0_2\n", + " libgcc-ng pkgs/main/linux-64::libgcc-ng-9.1.0-hdf63c60_0\n", + " libstdcxx-ng pkgs/main/linux-64::libstdcxx-ng-9.1.0-hdf63c60_0\n", + " ncurses pkgs/main/linux-64::ncurses-6.2-he6710b0_1\n", + " openssl pkgs/main/linux-64::openssl-1.1.1k-h27cfd23_0\n", + " pip pkgs/main/linux-64::pip-21.0.1-py36h06a4308_0\n", + " python pkgs/main/linux-64::python-3.6.13-hdb3f193_0\n", + " readline pkgs/main/linux-64::readline-8.1-h27cfd23_0\n", + " setuptools pkgs/main/linux-64::setuptools-52.0.0-py36h06a4308_0\n", + " sqlite pkgs/main/linux-64::sqlite-3.35.4-hdfb4753_0\n", + " tk pkgs/main/linux-64::tk-8.6.10-hbc83047_0\n", + " wheel pkgs/main/noarch::wheel-0.36.2-pyhd3eb1b0_0\n", + " xz pkgs/main/linux-64::xz-5.2.5-h7b6447c_0\n", + " zlib pkgs/main/linux-64::zlib-1.2.11-h7b6447c_3\n", + "\n", + "\n", + "\n", + "Downloading and Extracting Packages\n", + "openssl-1.1.1k | 2.5 MB | : 100% 1.0/1 [00:00<00:00, 5.46it/s]\n", + "libstdcxx-ng-9.1.0 | 3.1 MB | : 100% 1.0/1 [00:00<00:00, 7.18it/s]\n", + "readline-8.1 | 362 KB | : 100% 1.0/1 [00:00<00:00, 15.98it/s]\n", + "sqlite-3.35.4 | 981 KB | : 100% 1.0/1 [00:00<00:00, 15.13it/s]\n", + "tk-8.6.10 | 3.0 MB | : 100% 1.0/1 [00:00<00:00, 6.50it/s]\n", + "libgcc-ng-9.1.0 | 5.1 MB | : 100% 1.0/1 [00:00<00:00, 4.00it/s]\n", + "certifi-2020.12.5 | 140 KB | : 100% 1.0/1 [00:00<00:00, 10.00it/s]\n", + "ca-certificates-2021 | 114 KB | : 100% 1.0/1 [00:00<00:00, 18.87it/s]\n", + "wheel-0.36.2 | 33 KB | : 100% 1.0/1 [00:00<00:00, 12.78it/s]\n", + "pip-21.0.1 | 1.8 MB | : 100% 1.0/1 [00:00<00:00, 5.13it/s]\n", + "zlib-1.2.11 | 103 KB | : 100% 1.0/1 [00:00<00:00, 18.33it/s]\n", + "ld_impl_linux-64-2.3 | 568 KB | : 100% 1.0/1 [00:00<00:00, 13.84it/s]\n", + "setuptools-52.0.0 | 724 KB | : 100% 1.0/1 [00:00<00:00, 9.88it/s]\n", + "xz-5.2.5 | 341 KB | : 100% 1.0/1 [00:00<00:00, 11.80it/s]\n", + "ncurses-6.2 | 817 KB | : 100% 1.0/1 [00:00<00:00, 3.17it/s]\n", + "_libgcc_mutex-0.1 | 3 KB | : 100% 1.0/1 [00:00<00:00, 21.93it/s]\n", + "libffi-3.3 | 50 KB | : 100% 1.0/1 [00:00<00:00, 16.29it/s]\n", + "python-3.6.13 | 29.7 MB | : 100% 1.0/1 [00:00<00:00, 1.15it/s]\n", + "Preparing transaction: | \b\b/ \b\b- \b\bdone\n", + "Verifying transaction: | \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n", + "Executing transaction: | \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n", + "#\n", + "# To activate this environment, use\n", + "#\n", + "# $ conda activate 3dsnet\n", + "#\n", + "# To deactivate an active environment, use\n", + "#\n", + "# $ conda deactivate\n", + "\n", + "Collecting meshio[all]\n", + " Downloading meshio-4.4.3-py3-none-any.whl (153 kB)\n", + "\u001b[K |████████████████████████████████| 153 kB 14.2 MB/s \n", + "\u001b[?25hCollecting importlib-metadata\n", + " Downloading importlib_metadata-4.0.1-py3-none-any.whl (16 kB)\n", + "Collecting numpy\n", + " Downloading numpy-1.19.5-cp36-cp36m-manylinux2010_x86_64.whl (14.8 MB)\n", + "\u001b[K |████████████████████████████████| 14.8 MB 275 kB/s \n", + "\u001b[?25hCollecting h5py\n", + " Downloading h5py-3.1.0-cp36-cp36m-manylinux1_x86_64.whl (4.0 MB)\n", + "\u001b[K |████████████████████████████████| 4.0 MB 51.6 MB/s \n", + "\u001b[?25hCollecting netCDF4\n", + " Downloading netCDF4-1.5.6-cp36-cp36m-manylinux2014_x86_64.whl (4.7 MB)\n", + "\u001b[K |████████████████████████████████| 4.7 MB 56.7 MB/s \n", + "\u001b[?25hCollecting cached-property\n", + " Downloading cached_property-1.5.2-py2.py3-none-any.whl (7.6 kB)\n", + "Collecting zipp>=0.5\n", + " Downloading zipp-3.4.1-py3-none-any.whl (5.2 kB)\n", + "Collecting typing-extensions>=3.6.4\n", + " Downloading typing_extensions-3.10.0.0-py3-none-any.whl (26 kB)\n", + "Collecting cftime\n", + " Downloading cftime-1.4.1-cp36-cp36m-manylinux2014_x86_64.whl (316 kB)\n", + "\u001b[K |████████████████████████████████| 316 kB 70.4 MB/s \n", + "\u001b[?25hInstalling collected packages: zipp, typing-extensions, numpy, importlib-metadata, cftime, cached-property, netCDF4, meshio, h5py\n", + "Successfully installed cached-property-1.5.2 cftime-1.4.1 h5py-3.1.0 importlib-metadata-4.0.1 meshio-4.4.3 netCDF4-1.5.6 numpy-1.19.5 typing-extensions-3.10.0.0 zipp-3.4.1\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.colab-display-data+json": { + "pip_warning": { + "packages": [ + "numpy" + ] + } + } + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n", + "Solving environment: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bfailed with initial frozen solve. Retrying with flexible solve.\n", + "Solving environment: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bfailed with repodata from current_repodata.json, will retry with next repodata source.\n", + "Collecting package metadata (repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n", + "Solving environment: | \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n", + "\n", + "## Package Plan ##\n", + "\n", + " environment location: /usr/local\n", + "\n", + " added / updated specs:\n", + " - cudatoolkit=10.1\n", + " - pytorch=1.7.1\n", + " - torchvision=0.8.2\n", + "\n", + "\n", + "The following packages will be downloaded:\n", + "\n", + " package | build\n", + " ---------------------------|-----------------\n", + " blas-1.0 | mkl 6 KB\n", + " cudatoolkit-10.1.243 | h6bb024c_0 347.4 MB\n", + " dataclasses-0.8 | pyh4f3eec9_6 22 KB\n", + " freetype-2.10.4 | h5ab3b9f_0 596 KB\n", + " intel-openmp-2021.2.0 | h06a4308_610 1.3 MB\n", + " jpeg-9b | h024ee3a_2 214 KB\n", + " lcms2-2.12 | h3be6417_0 312 KB\n", + " libpng-1.6.37 | hbc83047_0 278 KB\n", + " libtiff-4.1.0 | h2733197_1 449 KB\n", + " libuv-1.40.0 | h7b6447c_0 736 KB\n", + " lz4-c-1.9.3 | h2531618_0 186 KB\n", + " mkl-2020.2 | 256 138.3 MB\n", + " mkl-service-2.3.0 | py36he8ac12f_0 52 KB\n", + " mkl_fft-1.3.0 | py36h54f3939_0 170 KB\n", + " mkl_random-1.1.1 | py36h0573a6f_0 327 KB\n", + " ninja-1.10.2 | hff7bd54_1 1.4 MB\n", + " numpy-1.19.2 | py36h54aff64_0 22 KB\n", + " numpy-base-1.19.2 | py36hfa32c7d_0 4.1 MB\n", + " olefile-0.46 | py36_0 48 KB\n", + " pillow-8.2.0 | py36he98fc37_0 627 KB\n", + " pytorch-1.7.1 |py3.6_cuda10.1.243_cudnn7.6.3_0 553.7 MB pytorch\n", + " torchvision-0.8.2 | py36_cu101 17.8 MB pytorch\n", + " typing_extensions-3.7.4.3 | pyha847dfd_0 25 KB\n", + " zstd-1.4.9 | haebb681_0 480 KB\n", + " ------------------------------------------------------------\n", + " Total: 1.04 GB\n", + "\n", + "The following NEW packages will be INSTALLED:\n", + "\n", + " blas pkgs/main/linux-64::blas-1.0-mkl\n", + " cudatoolkit pkgs/main/linux-64::cudatoolkit-10.1.243-h6bb024c_0\n", + " dataclasses pkgs/main/noarch::dataclasses-0.8-pyh4f3eec9_6\n", + " freetype pkgs/main/linux-64::freetype-2.10.4-h5ab3b9f_0\n", + " intel-openmp pkgs/main/linux-64::intel-openmp-2021.2.0-h06a4308_610\n", + " jpeg pkgs/main/linux-64::jpeg-9b-h024ee3a_2\n", + " lcms2 pkgs/main/linux-64::lcms2-2.12-h3be6417_0\n", + " libpng pkgs/main/linux-64::libpng-1.6.37-hbc83047_0\n", + " libtiff pkgs/main/linux-64::libtiff-4.1.0-h2733197_1\n", + " libuv pkgs/main/linux-64::libuv-1.40.0-h7b6447c_0\n", + " lz4-c pkgs/main/linux-64::lz4-c-1.9.3-h2531618_0\n", + " mkl pkgs/main/linux-64::mkl-2020.2-256\n", + " mkl-service pkgs/main/linux-64::mkl-service-2.3.0-py36he8ac12f_0\n", + " mkl_fft pkgs/main/linux-64::mkl_fft-1.3.0-py36h54f3939_0\n", + " mkl_random pkgs/main/linux-64::mkl_random-1.1.1-py36h0573a6f_0\n", + " ninja pkgs/main/linux-64::ninja-1.10.2-hff7bd54_1\n", + " numpy pkgs/main/linux-64::numpy-1.19.2-py36h54aff64_0\n", + " numpy-base pkgs/main/linux-64::numpy-base-1.19.2-py36hfa32c7d_0\n", + " olefile pkgs/main/linux-64::olefile-0.46-py36_0\n", + " pillow pkgs/main/linux-64::pillow-8.2.0-py36he98fc37_0\n", + " pytorch pytorch/linux-64::pytorch-1.7.1-py3.6_cuda10.1.243_cudnn7.6.3_0\n", + " torchvision pytorch/linux-64::torchvision-0.8.2-py36_cu101\n", + " typing_extensions pkgs/main/noarch::typing_extensions-3.7.4.3-pyha847dfd_0\n", + " zstd pkgs/main/linux-64::zstd-1.4.9-haebb681_0\n", + "\n", + "\n", + "\n", + "Downloading and Extracting Packages\n", + "typing_extensions-3. | 25 KB | : 100% 1.0/1 [00:00<00:00, 15.24it/s]\n", + "libtiff-4.1.0 | 449 KB | : 100% 1.0/1 [00:00<00:00, 12.51it/s]\n", + "zstd-1.4.9 | 480 KB | : 100% 1.0/1 [00:00<00:00, 15.92it/s]\n", + "mkl_random-1.1.1 | 327 KB | : 100% 1.0/1 [00:00<00:00, 19.19it/s]\n", + "olefile-0.46 | 48 KB | : 100% 1.0/1 [00:00<00:00, 21.02it/s]\n", + "cudatoolkit-10.1.243 | 347.4 MB | : 100% 1.0/1 [00:08<00:00, 8.05s/it] \n", + "freetype-2.10.4 | 596 KB | : 100% 1.0/1 [00:00<00:00, 13.14it/s]\n", + "mkl-2020.2 | 138.3 MB | : 100% 1.0/1 [00:13<00:00, 13.71s/it] \n", + "dataclasses-0.8 | 22 KB | : 100% 1.0/1 [00:00<00:00, 17.59it/s]\n", + "numpy-base-1.19.2 | 4.1 MB | : 100% 1.0/1 [00:00<00:00, 4.16it/s]\n", + "numpy-1.19.2 | 22 KB | : 100% 1.0/1 [00:00<00:00, 18.33it/s]\n", + "blas-1.0 | 6 KB | : 100% 1.0/1 [00:00<00:00, 20.43it/s]\n", + "libuv-1.40.0 | 736 KB | : 100% 1.0/1 [00:00<00:00, 13.73it/s]\n", + "pillow-8.2.0 | 627 KB | : 100% 1.0/1 [00:00<00:00, 9.26it/s]\n", + "lcms2-2.12 | 312 KB | : 100% 1.0/1 [00:00<00:00, 15.80it/s]\n", + "mkl-service-2.3.0 | 52 KB | : 100% 1.0/1 [00:00<00:00, 18.19it/s]\n", + "mkl_fft-1.3.0 | 170 KB | : 100% 1.0/1 [00:00<00:00, 16.19it/s]\n", + "torchvision-0.8.2 | 17.8 MB | : 100% 1.0/1 [00:04<00:00, 4.67s/it] \n", + "ninja-1.10.2 | 1.4 MB | : 100% 1.0/1 [00:00<00:00, 10.92it/s]\n", + "libpng-1.6.37 | 278 KB | : 100% 1.0/1 [00:00<00:00, 15.03it/s]\n", + "pytorch-1.7.1 | 553.7 MB | : 100% 1.0/1 [01:30<00:00, 90.55s/it] \n", + "jpeg-9b | 214 KB | : 100% 1.0/1 [00:00<00:00, 13.75it/s]\n", + "intel-openmp-2021.2. | 1.3 MB | : 100% 1.0/1 [00:00<00:00, 9.17it/s]\n", + "lz4-c-1.9.3 | 186 KB | : 100% 1.0/1 [00:00<00:00, 13.41it/s]\n", + "Preparing transaction: / \b\b- \b\b\\ \b\bdone\n", + "Verifying transaction: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n", + "Executing transaction: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n", + "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n", + "Solving environment: | \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n", + "\n", + "## Package Plan ##\n", + "\n", + " environment location: /usr/local\n", + "\n", + " added / updated specs:\n", + " - pyembree\n", + "\n", + "\n", + "The following packages will be downloaded:\n", + "\n", + " package | build\n", + " ---------------------------|-----------------\n", + " ca-certificates-2020.12.5 | ha878542_0 137 KB conda-forge\n", + " certifi-2020.12.5 | py36h5fab9bb_1 143 KB conda-forge\n", + " conda-4.10.1 | py36h5fab9bb_0 3.1 MB conda-forge\n", + " embree-2.17.7 | 1 41.0 MB conda-forge\n", + " pyembree-0.1.6 | py36h830a2c2_1 86 KB conda-forge\n", + " python_abi-3.6 | 1_cp36m 4 KB conda-forge\n", + " ------------------------------------------------------------\n", + " Total: 44.4 MB\n", + "\n", + "The following NEW packages will be INSTALLED:\n", + "\n", + " embree conda-forge/linux-64::embree-2.17.7-1\n", + " pyembree conda-forge/linux-64::pyembree-0.1.6-py36h830a2c2_1\n", + " python_abi conda-forge/linux-64::python_abi-3.6-1_cp36m\n", + "\n", + "The following packages will be UPDATED:\n", + "\n", + " certifi pkgs/main::certifi-2020.12.5-py36h06a~ --> conda-forge::certifi-2020.12.5-py36h5fab9bb_1\n", + "\n", + "The following packages will be SUPERSEDED by a higher-priority channel:\n", + "\n", + " ca-certificates pkgs/main::ca-certificates-2021.4.13-~ --> conda-forge::ca-certificates-2020.12.5-ha878542_0\n", + " conda pkgs/main::conda-4.10.1-py36h06a4308_1 --> conda-forge::conda-4.10.1-py36h5fab9bb_0\n", + "\n", + "\n", + "\n", + "Downloading and Extracting Packages\n", + "pyembree-0.1.6 | 86 KB | : 100% 1.0/1 [00:00<00:00, 4.80it/s] \n", + "ca-certificates-2020 | 137 KB | : 100% 1.0/1 [00:00<00:00, 17.32it/s]\n", + "python_abi-3.6 | 4 KB | : 100% 1.0/1 [00:00<00:00, 28.10it/s]\n", + "embree-2.17.7 | 41.0 MB | : 100% 1.0/1 [00:07<00:00, 7.74s/it] \n", + "conda-4.10.1 | 3.1 MB | : 100% 1.0/1 [00:00<00:00, 1.58it/s]\n", + "certifi-2020.12.5 | 143 KB | : 100% 1.0/1 [00:00<00:00, 16.68it/s]\n", + "Preparing transaction: | \b\bdone\n", + "Verifying transaction: - \b\bdone\n", + "Executing transaction: | \b\bdone\n", + "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n", + "Solving environment: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n", + "\n", + "## Package Plan ##\n", + "\n", + " environment location: /usr/local\n", + "\n", + " added / updated specs:\n", + " - seaborn\n", + " - trimesh\n", + "\n", + "\n", + "The following packages will be downloaded:\n", + "\n", + " package | build\n", + " ---------------------------|-----------------\n", + " cycler-0.10.0 | py_2 9 KB conda-forge\n", + " kiwisolver-1.3.1 | py36h51d7077_0 86 KB conda-forge\n", + " libblas-3.9.0 |1_h6e990d7_netlib 176 KB conda-forge\n", + " libcblas-3.9.0 |3_h893e4fe_netlib 54 KB conda-forge\n", + " libgfortran-ng-7.5.0 | h14aa051_19 22 KB conda-forge\n", + " libgfortran4-7.5.0 | h14aa051_19 1.3 MB conda-forge\n", + " liblapack-3.9.0 |3_h893e4fe_netlib 2.9 MB conda-forge\n", + " matplotlib-base-3.3.3 | py36he12231b_0 6.8 MB conda-forge\n", + " pandas-1.1.4 | py36hd87012b_0 10.5 MB conda-forge\n", + " patsy-0.5.1 | py_0 187 KB conda-forge\n", + " pyparsing-2.4.7 | pyh9f0ad1d_0 60 KB conda-forge\n", + " python-dateutil-2.8.1 | py_0 220 KB conda-forge\n", + " pytz-2021.1 | pyhd8ed1ab_0 239 KB conda-forge\n", + " scipy-1.5.3 | py36h976291a_0 18.6 MB conda-forge\n", + " seaborn-0.11.1 | hd8ed1ab_1 4 KB conda-forge\n", + " seaborn-base-0.11.1 | pyhd8ed1ab_1 217 KB conda-forge\n", + " statsmodels-0.11.1 | py36h8c4c3a4_2 9.8 MB conda-forge\n", + " tornado-6.1 | py36h1d69622_0 644 KB conda-forge\n", + " trimesh-3.9.18 | pyh6c4a22f_0 508 KB conda-forge\n", + " ------------------------------------------------------------\n", + " Total: 52.3 MB\n", + "\n", + "The following NEW packages will be INSTALLED:\n", + "\n", + " cycler conda-forge/noarch::cycler-0.10.0-py_2\n", + " kiwisolver conda-forge/linux-64::kiwisolver-1.3.1-py36h51d7077_0\n", + " libblas conda-forge/linux-64::libblas-3.9.0-1_h6e990d7_netlib\n", + " libcblas conda-forge/linux-64::libcblas-3.9.0-3_h893e4fe_netlib\n", + " libgfortran-ng conda-forge/linux-64::libgfortran-ng-7.5.0-h14aa051_19\n", + " libgfortran4 conda-forge/linux-64::libgfortran4-7.5.0-h14aa051_19\n", + " liblapack conda-forge/linux-64::liblapack-3.9.0-3_h893e4fe_netlib\n", + " matplotlib-base conda-forge/linux-64::matplotlib-base-3.3.3-py36he12231b_0\n", + " pandas conda-forge/linux-64::pandas-1.1.4-py36hd87012b_0\n", + " patsy conda-forge/noarch::patsy-0.5.1-py_0\n", + " pyparsing conda-forge/noarch::pyparsing-2.4.7-pyh9f0ad1d_0\n", + " python-dateutil conda-forge/noarch::python-dateutil-2.8.1-py_0\n", + " pytz conda-forge/noarch::pytz-2021.1-pyhd8ed1ab_0\n", + " scipy conda-forge/linux-64::scipy-1.5.3-py36h976291a_0\n", + " seaborn conda-forge/noarch::seaborn-0.11.1-hd8ed1ab_1\n", + " seaborn-base conda-forge/noarch::seaborn-base-0.11.1-pyhd8ed1ab_1\n", + " statsmodels conda-forge/linux-64::statsmodels-0.11.1-py36h8c4c3a4_2\n", + " tornado conda-forge/linux-64::tornado-6.1-py36h1d69622_0\n", + " trimesh conda-forge/noarch::trimesh-3.9.18-pyh6c4a22f_0\n", + "\n", + "\n", + "\n", + "Downloading and Extracting Packages\n", + "libblas-3.9.0 | 176 KB | : 100% 1.0/1 [00:00<00:00, 7.46it/s] \n", + "trimesh-3.9.18 | 508 KB | : 100% 1.0/1 [00:00<00:00, 6.77it/s]\n", + "tornado-6.1 | 644 KB | : 100% 1.0/1 [00:00<00:00, 5.54it/s]\n", + "libgfortran-ng-7.5.0 | 22 KB | : 100% 1.0/1 [00:00<00:00, 23.27it/s]\n", + "liblapack-3.9.0 | 2.9 MB | : 100% 1.0/1 [00:00<00:00, 2.00it/s]\n", + "patsy-0.5.1 | 187 KB | : 100% 1.0/1 [00:00<00:00, 14.03it/s]\n", + "seaborn-0.11.1 | 4 KB | : 100% 1.0/1 [00:00<00:00, 31.35it/s]\n", + "kiwisolver-1.3.1 | 86 KB | : 100% 1.0/1 [00:00<00:00, 22.59it/s]\n", + "python-dateutil-2.8. | 220 KB | : 100% 1.0/1 [00:00<00:00, 15.91it/s]\n", + "pandas-1.1.4 | 10.5 MB | : 100% 1.0/1 [00:02<00:00, 2.33s/it]\n", + "matplotlib-base-3.3. | 6.8 MB | : 100% 1.0/1 [00:01<00:00, 1.24s/it]\n", + "libcblas-3.9.0 | 54 KB | : 100% 1.0/1 [00:00<00:00, 18.46it/s]\n", + "statsmodels-0.11.1 | 9.8 MB | : 100% 1.0/1 [00:01<00:00, 1.96s/it]\n", + "libgfortran4-7.5.0 | 1.3 MB | : 100% 1.0/1 [00:00<00:00, 3.97it/s]\n", + "scipy-1.5.3 | 18.6 MB | : 100% 1.0/1 [00:03<00:00, 3.16s/it]\n", + "seaborn-base-0.11.1 | 217 KB | : 100% 1.0/1 [00:00<00:00, 14.38it/s]\n", + "cycler-0.10.0 | 9 KB | : 100% 1.0/1 [00:00<00:00, 32.46it/s]\n", + "pytz-2021.1 | 239 KB | : 100% 1.0/1 [00:00<00:00, 8.12it/s]\n", + "pyparsing-2.4.7 | 60 KB | : 100% 1.0/1 [00:00<00:00, 23.38it/s]\n", + "Preparing transaction: - \b\b\\ \b\b| \b\bdone\n", + "Verifying transaction: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n", + "Executing transaction: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n", + "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n", + "Solving environment: | \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n", + "\n", + "## Package Plan ##\n", + "\n", + " environment location: /usr/local\n", + "\n", + " added / updated specs:\n", + " - fvcore\n", + " - iopath\n", + "\n", + "\n", + "The following packages will be downloaded:\n", + "\n", + " package | build\n", + " ---------------------------|-----------------\n", + " fvcore-0.1.5.post20210518 | py36 88 KB fvcore\n", + " iopath-0.1.8 | py36 31 KB iopath\n", + " portalocker-1.7.0 | py36h5fab9bb_1 19 KB conda-forge\n", + " pyyaml-5.3.1 | py36he6145b8_1 185 KB conda-forge\n", + " tabulate-0.8.9 | pyhd8ed1ab_0 26 KB conda-forge\n", + " termcolor-1.1.0 | py_2 6 KB conda-forge\n", + " yacs-0.1.6 | py_0 11 KB conda-forge\n", + " ------------------------------------------------------------\n", + " Total: 366 KB\n", + "\n", + "The following NEW packages will be INSTALLED:\n", + "\n", + " fvcore fvcore/linux-64::fvcore-0.1.5.post20210518-py36\n", + " iopath iopath/linux-64::iopath-0.1.8-py36\n", + " portalocker conda-forge/linux-64::portalocker-1.7.0-py36h5fab9bb_1\n", + " pyyaml conda-forge/linux-64::pyyaml-5.3.1-py36he6145b8_1\n", + " tabulate conda-forge/noarch::tabulate-0.8.9-pyhd8ed1ab_0\n", + " termcolor conda-forge/noarch::termcolor-1.1.0-py_2\n", + " yacs conda-forge/noarch::yacs-0.1.6-py_0\n", + "\n", + "\n", + "\n", + "Downloading and Extracting Packages\n", + "termcolor-1.1.0 | 6 KB | : 100% 1.0/1 [00:00<00:00, 11.39it/s]\n", + "tabulate-0.8.9 | 26 KB | : 100% 1.0/1 [00:00<00:00, 25.84it/s]\n", + "yacs-0.1.6 | 11 KB | : 100% 1.0/1 [00:00<00:00, 7.66it/s]\n", + "portalocker-1.7.0 | 19 KB | : 100% 1.0/1 [00:00<00:00, 7.86it/s] \n", + "pyyaml-5.3.1 | 185 KB | : 100% 1.0/1 [00:00<00:00, 14.21it/s]\n", + "fvcore-0.1.5.post202 | 88 KB | : 100% 1.0/1 [00:01<00:00, 1.08s/it]\n", + "iopath-0.1.8 | 31 KB | : 100% 1.0/1 [00:00<00:00, 1.05it/s]\n", + "Preparing transaction: - \b\bdone\n", + "Verifying transaction: | \b\bdone\n", + "Executing transaction: - \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n", + "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n", + "Solving environment: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n", + "\n", + "## Package Plan ##\n", + "\n", + " environment location: /usr/local\n", + "\n", + " added / updated specs:\n", + " - pytorch3d\n", + "\n", + "\n", + "The following packages will be downloaded:\n", + "\n", + " package | build\n", + " ---------------------------|-----------------\n", + " conda-4.10.1 | py36h06a4308_1 2.9 MB\n", + " pytorch3d-0.4.0 |py36_cu101_pyt171 36.8 MB pytorch3d\n", + " ------------------------------------------------------------\n", + " Total: 39.6 MB\n", + "\n", + "The following NEW packages will be INSTALLED:\n", + "\n", + " pytorch3d pytorch3d/linux-64::pytorch3d-0.4.0-py36_cu101_pyt171\n", + "\n", + "The following packages will be UPDATED:\n", + "\n", + " ca-certificates conda-forge::ca-certificates-2020.12.~ --> pkgs/main::ca-certificates-2021.4.13-h06a4308_1\n", + " conda conda-forge::conda-4.10.1-py36h5fab9b~ --> pkgs/main::conda-4.10.1-py36h06a4308_1\n", + "\n", + "\n", + "\n", + "Downloading and Extracting Packages\n", + "conda-4.10.1 | 2.9 MB | : 100% 1.0/1 [00:00<00:00, 4.41it/s]\n", + "pytorch3d-0.4.0 | 36.8 MB | : 100% 1.0/1 [00:08<00:00, 8.54s/it]\n", + "Preparing transaction: \\ \b\bdone\n", + "Verifying transaction: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n", + "Executing transaction: / \b\bdone\n", + "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n", + "Solving environment: | \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n", + "\n", + "## Package Plan ##\n", + "\n", + " environment location: /usr/local\n", + "\n", + " added / updated specs:\n", + " - visdom\n", + "\n", + "\n", + "The following packages will be downloaded:\n", + "\n", + " package | build\n", + " ---------------------------|-----------------\n", + " libsodium-1.0.18 | h36c2ea0_1 366 KB conda-forge\n", + " pyzmq-19.0.2 | py36h9947dbf_2 467 KB conda-forge\n", + " torchfile-0.1.0 | py_0 8 KB conda-forge\n", + " visdom-0.1.8.9 | 0 565 KB conda-forge\n", + " websocket-client-0.57.0 | py36h5fab9bb_4 59 KB conda-forge\n", + " zeromq-4.3.4 | h2531618_0 331 KB\n", + " ------------------------------------------------------------\n", + " Total: 1.8 MB\n", + "\n", + "The following NEW packages will be INSTALLED:\n", + "\n", + " libsodium conda-forge/linux-64::libsodium-1.0.18-h36c2ea0_1\n", + " pyzmq conda-forge/linux-64::pyzmq-19.0.2-py36h9947dbf_2\n", + " torchfile conda-forge/noarch::torchfile-0.1.0-py_0\n", + " visdom conda-forge/noarch::visdom-0.1.8.9-0\n", + " websocket-client conda-forge/linux-64::websocket-client-0.57.0-py36h5fab9bb_4\n", + " zeromq pkgs/main/linux-64::zeromq-4.3.4-h2531618_0\n", + "\n", + "The following packages will be SUPERSEDED by a higher-priority channel:\n", + "\n", + " ca-certificates pkgs/main::ca-certificates-2021.4.13-~ --> conda-forge::ca-certificates-2020.12.5-ha878542_0\n", + " conda pkgs/main::conda-4.10.1-py36h06a4308_1 --> conda-forge::conda-4.10.1-py36h5fab9bb_0\n", + "\n", + "\n", + "\n", + "Downloading and Extracting Packages\n", + "torchfile-0.1.0 | 8 KB | : 100% 1.0/1 [00:00<00:00, 13.33it/s]\n", + "libsodium-1.0.18 | 366 KB | : 100% 1.0/1 [00:00<00:00, 9.26it/s]\n", + "visdom-0.1.8.9 | 565 KB | : 100% 1.0/1 [00:00<00:00, 7.12it/s]\n", + "zeromq-4.3.4 | 331 KB | : 100% 1.0/1 [00:00<00:00, 10.28it/s]\n", + "pyzmq-19.0.2 | 467 KB | : 100% 1.0/1 [00:00<00:00, 4.04it/s]\n", + "websocket-client-0.5 | 59 KB | : 100% 1.0/1 [00:00<00:00, 19.68it/s]\n", + "Preparing transaction: \\ \b\bdone\n", + "Verifying transaction: / \b\bdone\n", + "Executing transaction: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n", + "[Errno 2] No such file or directory: '/content/3dsnset'\n", + "/content\n", + "Cloning into 'PyMesh'...\n", + "remote: Enumerating objects: 18131, done.\u001b[K\n", + "remote: Total 18131 (delta 0), reused 0 (delta 0), pack-reused 18131\u001b[K\n", + "Receiving objects: 100% (18131/18131), 21.40 MiB | 21.28 MiB/s, done.\n", + "Resolving deltas: 100% (12920/12920), done.\n", + "/content/PyMesh\n", + "Submodule 'third_party/Clipper' (https://github.com/PyMesh/Clipper.git) registered for path 'third_party/Clipper'\n", + "Submodule 'third_party/TetWild' (https://github.com/PyMesh/TetWild.git) registered for path 'third_party/TetWild'\n", + "Submodule 'third_party/WindingNumber' (https://github.com/PyMesh/WindingNumber.git) registered for path 'third_party/WindingNumber'\n", + "Submodule 'third_party/carve' (https://github.com/PyMesh/carve.git) registered for path 'third_party/carve'\n", + "Submodule 'third_party/cgal' (https://github.com/PyMesh/cgal.git) registered for path 'third_party/cgal'\n", + "Submodule 'third_party/cork' (https://github.com/PyMesh/cork.git) registered for path 'third_party/cork'\n", + "Submodule 'third_party/draco' (https://github.com/PyMesh/draco.git) registered for path 'third_party/draco'\n", + "Submodule 'third_party/eigen' (https://github.com/PyMesh/eigen.git) registered for path 'third_party/eigen'\n", + "Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/fmt'\n", + "Submodule 'third_party/geogram' (https://github.com/PyMesh/geogram.git) registered for path 'third_party/geogram'\n", + "Submodule 'third_party/jigsaw' (https://github.com/PyMesh/jigsaw.git) registered for path 'third_party/jigsaw'\n", + "Submodule 'third_party/json' (https://github.com/nlohmann/json.git) registered for path 'third_party/json'\n", + "Submodule 'libigl' (https://github.com/PyMesh/libigl.git) registered for path 'third_party/libigl'\n", + "Submodule 'third_party/mmg' (https://github.com/PyMesh/mmg.git) registered for path 'third_party/mmg'\n", + "Submodule 'third_party/pybind11' (https://github.com/PyMesh/pybind11.git) registered for path 'third_party/pybind11'\n", + "Submodule 'third_party/qhull' (https://github.com/PyMesh/qhull.git) registered for path 'third_party/qhull'\n", + "Submodule 'third_party/quartet' (https://github.com/PyMesh/quartet.git) registered for path 'third_party/quartet'\n", + "Submodule 'third_party/spdlog' (https://github.com/gabime/spdlog.git) registered for path 'third_party/spdlog'\n", + "Submodule 'third_party/tbb' (https://github.com/PyMesh/tbb.git) registered for path 'third_party/tbb'\n", + "Submodule 'third_party/tetgen' (https://github.com/PyMesh/tetgen.git) registered for path 'third_party/tetgen'\n", + "Submodule 'third_party/triangle' (https://github.com/PyMesh/triangle.git) registered for path 'third_party/triangle'\n", + "Cloning into '/content/PyMesh/third_party/Clipper'...\n", + "Cloning into '/content/PyMesh/third_party/TetWild'...\n", + "Cloning into '/content/PyMesh/third_party/WindingNumber'...\n", + "Cloning into '/content/PyMesh/third_party/carve'...\n", + "Cloning into '/content/PyMesh/third_party/cgal'...\n", + "Cloning into '/content/PyMesh/third_party/cork'...\n", + "Cloning into '/content/PyMesh/third_party/draco'...\n", + "Cloning into '/content/PyMesh/third_party/eigen'...\n", + "Cloning into '/content/PyMesh/third_party/fmt'...\n", + "Cloning into '/content/PyMesh/third_party/geogram'...\n", + "Cloning into '/content/PyMesh/third_party/jigsaw'...\n", + "Cloning into '/content/PyMesh/third_party/json'...\n", + "Cloning into '/content/PyMesh/third_party/libigl'...\n", + "Cloning into '/content/PyMesh/third_party/mmg'...\n", + "Cloning into '/content/PyMesh/third_party/pybind11'...\n", + "Cloning into '/content/PyMesh/third_party/qhull'...\n", + "Cloning into '/content/PyMesh/third_party/quartet'...\n", + "Cloning into '/content/PyMesh/third_party/spdlog'...\n", + "Cloning into '/content/PyMesh/third_party/tbb'...\n", + "Cloning into '/content/PyMesh/third_party/tetgen'...\n", + "Cloning into '/content/PyMesh/third_party/triangle'...\n", + "Submodule path 'third_party/Clipper': checked out '3fd3457741d275b887ad16abacccbd01eda2175c'\n", + "Submodule path 'third_party/TetWild': checked out '5b0f81552fbd66c1ed66168fd7bbf222e3391816'\n", + "Submodule path 'third_party/WindingNumber': checked out 'e011b7bc9fa1e2570651097936ccf2314fdcbe86'\n", + "Submodule path 'third_party/carve': checked out 'd328ad2136a4fa6413db8ad264ed219095bb6744'\n", + "Submodule path 'third_party/cgal': checked out '1ce145a3c611df5f3a71fb20275b755fdbfca21e'\n", + "Submodule path 'third_party/cork': checked out '360820dd981fa72117f255ddaa68367419f7526c'\n", + "Submodule path 'third_party/draco': checked out '063994c362871d6f149c24c669122e4ef3fa8196'\n", + "Submodule path 'third_party/eigen': checked out 'ed3db99ec3caff039f72645b7c5feb68717c8655'\n", + "Submodule path 'third_party/fmt': checked out '355eb6d29ad7dbcb017420442af237e3cf6d8054'\n", + "Submodule path 'third_party/geogram': checked out '25228ad2a88b793fc8de651ecf6ca7ee76819d5a'\n", + "Submodule path 'third_party/jigsaw': checked out '9c677b58234c64e2586e361d53bf60ce7e4ed3bb'\n", + "Submodule path 'third_party/json': checked out 'e7452d87783fbf6e9d320d515675e26dfd1271c5'\n", + "Submodule path 'third_party/libigl': checked out 'f6b406427400ed7ddb56cfc2577b6af571827c8c'\n", + "Submodule path 'third_party/mmg': checked out '8a93fc29e8ea61299c6c415aaf2c57cb5cd2f779'\n", + "Submodule path 'third_party/pybind11': checked out '80d452484c5409444b0ec19383faa84bb7a4d351'\n", + "Submodule path 'third_party/qhull': checked out 'd901974562b76b89947eea320423130851b2f164'\n", + "Submodule path 'third_party/quartet': checked out '7d789031d2f154e015f70a06eb2a1c01461e3cfc'\n", + "Submodule path 'third_party/spdlog': checked out 'b6b9d835c588c35227410a9830e7a4586f90777a'\n", + "Submodule path 'third_party/tbb': checked out 'ed6f6f15cece26ae4ab0816eab220c5e0691093f'\n", + "Submodule path 'third_party/tetgen': checked out '54e1149a1af5b586706b3d87a0152e77e76ade22'\n", + "remote: Enumerating objects: 23, done.\u001b[K\n", + "remote: Counting objects: 100% (23/23), done.\u001b[K\n", + "remote: Compressing objects: 100% (11/11), done.\u001b[K\n", + "remote: Total 19 (delta 12), reused 15 (delta 8), pack-reused 0\u001b[K\n", + "Unpacking objects: 100% (19/19), done.\n", + "From https://github.com/PyMesh/triangle\n", + " * branch a092f98815a38ee1d2f29341838947b3849fa2d0 -> FETCH_HEAD\n", + "Submodule path 'third_party/triangle': checked out 'a092f98815a38ee1d2f29341838947b3849fa2d0'\n", + "Reading package lists... Done\n", + "Building dependency tree \n", + "Reading state information... Done\n", + "libboost-dev is already the newest version (1.65.1.0ubuntu1).\n", + "libboost-dev set to manually installed.\n", + "libboost-thread-dev is already the newest version (1.65.1.0ubuntu1).\n", + "libboost-thread-dev set to manually installed.\n", + "libtbb-dev is already the newest version (2017~U7-8).\n", + "libtbb-dev set to manually installed.\n", + "cmake is already the newest version (3.10.2-1ubuntu2.18.04.1).\n", + "python3-dev is already the newest version (3.6.7-1~18.04).\n", + "python3-dev set to manually installed.\n", + "The following package was automatically installed and is no longer required:\n", + " libnvidia-common-460\n", + "Use 'apt autoremove' to remove it.\n", + "The following additional packages will be installed:\n", + " python-pip-whl python3-asn1crypto python3-cffi-backend python3-crypto\n", + " python3-cryptography python3-decorator python3-idna python3-keyring\n", + " python3-keyrings.alt python3-olefile python3-pil python3-pkg-resources\n", + " python3-secretstorage python3-six python3-wheel python3-xdg\n", + "Suggested packages:\n", + " libeigen3-doc libmrpt-dev gmp-doc libgmp10-doc libmpfr-doc python-crypto-doc\n", + " python-cryptography-doc python3-cryptography-vectors gnome-keyring\n", + " libkf5wallet-bin gir1.2-gnomekeyring-1.0 python-nose-doc python-numpy-doc\n", + " python3-numpy-dbg python-pil-doc python3-pil-dbg python-scipy-doc\n", + " python-secretstorage-doc python-setuptools-doc\n", + "The following NEW packages will be installed:\n", + " libeigen3-dev libgmp-dev libgmpxx4ldbl libmpfr-dev python-pip-whl\n", + " python3-asn1crypto python3-cffi-backend python3-crypto python3-cryptography\n", + " python3-decorator python3-idna python3-keyring python3-keyrings.alt\n", + " python3-nose python3-numpy python3-olefile python3-pil python3-pip\n", + " python3-pkg-resources python3-scipy python3-secretstorage python3-setuptools\n", + " python3-six python3-wheel python3-xdg\n", + "0 upgraded, 25 newly installed, 0 to remove and 34 not upgraded.\n", + "Need to get 16.3 MB of archives.\n", + "After this operation, 73.2 MB of additional disk space will be used.\n", + "Get:1 http://archive.ubuntu.com/ubuntu bionic/main amd64 libgmpxx4ldbl amd64 2:6.1.2+dfsg-2 [8,964 B]\n", + "Get:2 http://archive.ubuntu.com/ubuntu bionic/main amd64 libgmp-dev amd64 2:6.1.2+dfsg-2 [316 kB]\n", + "Get:3 http://archive.ubuntu.com/ubuntu bionic/main amd64 libmpfr-dev amd64 4.0.1-1 [249 kB]\n", + "Get:4 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 python-pip-whl all 9.0.1-2.3~ubuntu1.18.04.4 [1,653 kB]\n", + "Get:5 http://archive.ubuntu.com/ubuntu bionic/main amd64 python3-asn1crypto all 0.24.0-1 [72.8 kB]\n", + "Get:6 http://archive.ubuntu.com/ubuntu bionic/main amd64 python3-cffi-backend amd64 1.11.5-1 [64.6 kB]\n", + "Get:7 http://archive.ubuntu.com/ubuntu bionic/main amd64 python3-crypto amd64 2.6.1-8ubuntu2 [244 kB]\n", + "Get:8 http://archive.ubuntu.com/ubuntu bionic/main amd64 python3-idna all 2.6-1 [32.5 kB]\n", + "Get:9 http://archive.ubuntu.com/ubuntu bionic/main amd64 python3-six all 1.11.0-2 [11.4 kB]\n", + "Get:10 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 python3-cryptography amd64 2.1.4-1ubuntu1.4 [220 kB]\n", + "Get:11 http://archive.ubuntu.com/ubuntu bionic/universe amd64 python3-decorator all 4.1.2-1 [9,364 B]\n", + "Get:12 http://archive.ubuntu.com/ubuntu bionic/main amd64 python3-secretstorage all 2.3.1-2 [12.1 kB]\n", + "Get:13 http://archive.ubuntu.com/ubuntu bionic/main amd64 python3-keyring all 10.6.0-1 [26.7 kB]\n", + "Get:14 http://archive.ubuntu.com/ubuntu bionic/main amd64 python3-keyrings.alt all 3.0-1 [16.6 kB]\n", + "Get:15 http://archive.ubuntu.com/ubuntu bionic/main amd64 python3-pkg-resources all 39.0.1-2 [98.8 kB]\n", + "Get:16 http://archive.ubuntu.com/ubuntu bionic/universe amd64 python3-nose all 1.3.7-3 [115 kB]\n", + "Get:17 http://archive.ubuntu.com/ubuntu bionic/main amd64 python3-numpy amd64 1:1.13.3-2ubuntu1 [1,943 kB]\n", + "Get:18 http://archive.ubuntu.com/ubuntu bionic/main amd64 python3-olefile all 0.45.1-1 [33.3 kB]\n", + "Ign:19 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 python3-pil amd64 5.1.0-1ubuntu0.5\n", + "Get:20 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 python3-pip all 9.0.1-2.3~ubuntu1.18.04.4 [114 kB]\n", + "Get:21 http://archive.ubuntu.com/ubuntu bionic/main amd64 python3-setuptools all 39.0.1-2 [248 kB]\n", + "Get:22 http://archive.ubuntu.com/ubuntu bionic/universe amd64 python3-wheel all 0.30.0-0.2 [36.5 kB]\n", + "Get:23 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 python3-xdg all 0.25-4ubuntu1.1 [31.3 kB]\n", + "Get:24 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libeigen3-dev all 3.3.4-4 [810 kB]\n", + "Get:25 http://archive.ubuntu.com/ubuntu bionic/universe amd64 python3-scipy amd64 0.19.1-2ubuntu1 [9,619 kB]\n", + "Err:19 http://security.ubuntu.com/ubuntu bionic-updates/main amd64 python3-pil amd64 5.1.0-1ubuntu0.5\n", + " 404 Not Found [IP: 91.189.88.142 80]\n", + "Fetched 16.0 MB in 0s (41.1 MB/s)\n", + "E: Failed to fetch http://security.ubuntu.com/ubuntu/pool/main/p/pillow/python3-pil_5.1.0-1ubuntu0.5_amd64.deb 404 Not Found [IP: 91.189.88.142 80]\n", + "E: Unable to fetch some archives, maybe run apt-get update or try with --fix-missing?\n", + "[Errno 2] No such file or directory: '$PYMESH_PATH/third_party'\n", + "/content/PyMesh\n", + "/bin/bash: ./build.py: No such file or directory\n", + "[Errno 2] No such file or directory: '$PYMESH_PATH'\n", + "/content/PyMesh\n", + "mkdir: cannot create directory ‘build’: File exists\n", + "/bin/bash: pythonsetup.py: command not found\n", + "running install\n", + "running bdist_egg\n", + "running egg_info\n", + "creating python/pymesh2.egg-info\n", + "writing python/pymesh2.egg-info/PKG-INFO\n", + "writing dependency_links to python/pymesh2.egg-info/dependency_links.txt\n", + "writing top-level names to python/pymesh2.egg-info/top_level.txt\n", + "writing manifest file 'python/pymesh2.egg-info/SOURCES.txt'\n", + "package init file 'python/pymesh/tests/__init__.py' not found (or not a regular file)\n", + "package init file 'python/pymesh/meshutils/tests/__init__.py' not found (or not a regular file)\n", + "package init file 'python/pymesh/wires/tests/__init__.py' not found (or not a regular file)\n", + "reading manifest file 'python/pymesh2.egg-info/SOURCES.txt'\n", + "reading manifest template 'MANIFEST.in'\n", + "no previously-included directories found matching 'build'\n", + "no previously-included directories found matching 'third_party/build'\n", + "no previously-included directories found matching 'third_party/libigl/external'\n", + "writing manifest file 'python/pymesh2.egg-info/SOURCES.txt'\n", + "installing library code to build/bdist.linux-x86_64/egg\n", + "running install_lib\n", + "running build_py\n", + "creating build/lib.linux-x86_64-3.6\n", + "creating build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/map_attributes.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/Assembler.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/cell_partition.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/meshio.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/exact_arithmetic.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/igl_utils.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/material.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/selfintersection.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/minkowski_sum.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/timethis.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/Arrangement2.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/CSGTree.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/compression.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/cut_to_disk.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/HashGrid.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/boolean.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/SparseSolver.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/__init__.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/triangulate.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/submesh.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/straight_skeleton.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/Mesh.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/tetrahedralize.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/matrixio.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/slice_mesh.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/tetgen.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/boolean_unsupported.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/Boundary.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/TestCase.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/version.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/HarmonicSolver.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/aabb_tree.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/winding_number.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/PyMeshSetting.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/convex_hull.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/outerhull.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/predicates.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/snap_rounding.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/VoxelGrid.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/triangle.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "copying python/pymesh/save_svg.py -> build/lib.linux-x86_64-3.6/pymesh\n", + "creating build/lib.linux-x86_64-3.6/pymesh/misc\n", + "copying python/pymesh/misc/__init__.py -> build/lib.linux-x86_64-3.6/pymesh/misc\n", + "copying python/pymesh/misc/quaternion.py -> build/lib.linux-x86_64-3.6/pymesh/misc\n", + "creating build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/remove_duplicated_faces.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/cut_mesh.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/merge_meshes.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/face_utils.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/manifold_check.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/split_long_edges.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/hex_to_tet.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/generate_cylinder.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/generate_dodecahedron.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/generate_minimal_surface.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/remove_isolated_vertices.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/collapse_short_edges.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/__init__.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/attribute_utils.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/remove_degenerated_triangles.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/separate_mesh.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/subdivide.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/remove_obtuse_triangles.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/generate_box_mesh.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/mesh_to_graph.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/generate_tube.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/generate_equilateral_triangle.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/quad_to_tri.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/edge_utils.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/generate_regular_tetrahedron.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/remove_duplicated_vertices.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/voxel_utils.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "copying python/pymesh/meshutils/generate_icosphere.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", + "creating build/lib.linux-x86_64-3.6/pymesh/wires\n", + "copying python/pymesh/wires/merge_wires.py -> build/lib.linux-x86_64-3.6/pymesh/wires\n", + "copying python/pymesh/wires/WireNetwork.py -> build/lib.linux-x86_64-3.6/pymesh/wires\n", + "copying python/pymesh/wires/Parameters.py -> build/lib.linux-x86_64-3.6/pymesh/wires\n", + "copying python/pymesh/wires/Tiler.py -> build/lib.linux-x86_64-3.6/pymesh/wires\n", + "copying python/pymesh/wires/Inflator.py -> build/lib.linux-x86_64-3.6/pymesh/wires\n", + "copying python/pymesh/wires/wires_io.py -> build/lib.linux-x86_64-3.6/pymesh/wires\n", + "copying python/pymesh/wires/__init__.py -> build/lib.linux-x86_64-3.6/pymesh/wires\n", + "creating build/lib.linux-x86_64-3.6/pymesh/tests\n", + "copying python/pymesh/tests/test_HarmonicSolver.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", + "copying python/pymesh/tests/test_solver.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", + "copying python/pymesh/tests/test_boolean.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", + "copying python/pymesh/tests/test_map_attributes.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", + "copying python/pymesh/tests/test_slice_Mesh.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", + "copying python/pymesh/tests/test_selfintersection.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", + "copying python/pymesh/tests/test_minkowski_sum.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", + "copying python/pymesh/tests/test_assembler.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", + "copying python/pymesh/tests/test_aabb_tree.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", + "copying python/pymesh/tests/test_VoxelGrid.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", + "copying python/pymesh/tests/test_mesh.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", + "copying python/pymesh/tests/test_CSGTree.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", + "copying python/pymesh/tests/test_compression.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", + "copying python/pymesh/tests/test_snap_rounding.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", + "copying python/pymesh/tests/test_winding_number.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", + "copying python/pymesh/tests/test_triangulate.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", + "copying python/pymesh/tests/test_triangle.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", + "copying python/pymesh/tests/test_curvature.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", + "copying python/pymesh/tests/test_cut_to_disk.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", + "copying python/pymesh/tests/test_sparse_solver.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", + "copying python/pymesh/tests/test_material.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", + "copying python/pymesh/tests/test_meshio.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", + "copying python/pymesh/tests/test_predicates.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", + "copying python/pymesh/tests/test_outerhull.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", + "copying python/pymesh/tests/test_tetgen.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", + "creating build/lib.linux-x86_64-3.6/pymesh/meshutils/tests\n", + "copying python/pymesh/meshutils/tests/test_remove_isolated_vertices.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils/tests\n", + "copying python/pymesh/meshutils/tests/test_remove_duplicated_faces.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils/tests\n", + "copying python/pymesh/meshutils/tests/test_quad_to_tri.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils/tests\n", + "copying python/pymesh/meshutils/tests/test_remove_obtuse_triangles.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils/tests\n", + "copying python/pymesh/meshutils/tests/test_hex_to_tet.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils/tests\n", + "copying python/pymesh/meshutils/tests/test_merge_meshes.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils/tests\n", + "copying python/pymesh/meshutils/tests/test_collapse_short_edges.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils/tests\n", + "copying python/pymesh/meshutils/tests/test_separate_mesh.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils/tests\n", + "copying python/pymesh/meshutils/tests/test_attribute_utils.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils/tests\n", + "copying python/pymesh/meshutils/tests/test_remove_degenerated_triangles.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils/tests\n", + "copying python/pymesh/meshutils/tests/test_generate_icosphere.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils/tests\n", + "copying python/pymesh/meshutils/tests/test_split_long_edges.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils/tests\n", + "copying python/pymesh/meshutils/tests/test_remove_duplicated_vertices.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils/tests\n", + "copying python/pymesh/meshutils/tests/test_edge_utils.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils/tests\n", + "copying python/pymesh/meshutils/tests/test_cut_mesh.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils/tests\n", + "copying python/pymesh/meshutils/tests/test_generate_box_mesh.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils/tests\n", + "creating build/lib.linux-x86_64-3.6/pymesh/wires/tests\n", + "copying python/pymesh/wires/tests/test_wire_network.py -> build/lib.linux-x86_64-3.6/pymesh/wires/tests\n", + "copying python/pymesh/wires/tests/test_inflator.py -> build/lib.linux-x86_64-3.6/pymesh/wires/tests\n", + "copying python/pymesh/wires/tests/WireTestCase.py -> build/lib.linux-x86_64-3.6/pymesh/wires/tests\n", + "copying python/pymesh/wires/tests/test_tiler.py -> build/lib.linux-x86_64-3.6/pymesh/wires/tests\n", + "running build_ext\n", + "creating build/bdist.linux-x86_64\n", + "creating build/bdist.linux-x86_64/egg\n", + "creating build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/map_attributes.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "creating build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/remove_duplicated_faces.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/cut_mesh.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/merge_meshes.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/face_utils.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/manifold_check.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/split_long_edges.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/hex_to_tet.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/generate_cylinder.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/generate_dodecahedron.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/generate_minimal_surface.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/remove_isolated_vertices.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/collapse_short_edges.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/__init__.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/attribute_utils.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "creating build/bdist.linux-x86_64/egg/pymesh/meshutils/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/tests/test_remove_isolated_vertices.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/tests/test_remove_duplicated_faces.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/tests/test_quad_to_tri.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/tests/test_remove_obtuse_triangles.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/tests/test_hex_to_tet.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/tests/test_merge_meshes.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/tests/test_collapse_short_edges.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/tests/test_separate_mesh.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/tests/test_attribute_utils.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/tests/test_remove_degenerated_triangles.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/tests/test_generate_icosphere.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/tests/test_split_long_edges.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/tests/test_remove_duplicated_vertices.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/tests/test_edge_utils.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/tests/test_cut_mesh.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/tests/test_generate_box_mesh.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/remove_degenerated_triangles.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/separate_mesh.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/subdivide.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/remove_obtuse_triangles.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/generate_box_mesh.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/mesh_to_graph.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/generate_tube.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/generate_equilateral_triangle.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/quad_to_tri.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/edge_utils.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/generate_regular_tetrahedron.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/remove_duplicated_vertices.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/voxel_utils.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/generate_icosphere.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", + "copying build/lib.linux-x86_64-3.6/pymesh/Assembler.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/cell_partition.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/meshio.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/exact_arithmetic.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/igl_utils.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/material.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/selfintersection.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/minkowski_sum.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/timethis.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/Arrangement2.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/CSGTree.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/compression.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/cut_to_disk.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/HashGrid.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/boolean.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/SparseSolver.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "creating build/bdist.linux-x86_64/egg/pymesh/wires\n", + "copying build/lib.linux-x86_64-3.6/pymesh/wires/merge_wires.py -> build/bdist.linux-x86_64/egg/pymesh/wires\n", + "copying build/lib.linux-x86_64-3.6/pymesh/wires/WireNetwork.py -> build/bdist.linux-x86_64/egg/pymesh/wires\n", + "copying build/lib.linux-x86_64-3.6/pymesh/wires/Parameters.py -> build/bdist.linux-x86_64/egg/pymesh/wires\n", + "copying build/lib.linux-x86_64-3.6/pymesh/wires/Tiler.py -> build/bdist.linux-x86_64/egg/pymesh/wires\n", + "copying build/lib.linux-x86_64-3.6/pymesh/wires/Inflator.py -> build/bdist.linux-x86_64/egg/pymesh/wires\n", + "copying build/lib.linux-x86_64-3.6/pymesh/wires/wires_io.py -> build/bdist.linux-x86_64/egg/pymesh/wires\n", + "copying build/lib.linux-x86_64-3.6/pymesh/wires/__init__.py -> build/bdist.linux-x86_64/egg/pymesh/wires\n", + "creating build/bdist.linux-x86_64/egg/pymesh/wires/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/wires/tests/test_wire_network.py -> build/bdist.linux-x86_64/egg/pymesh/wires/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/wires/tests/test_inflator.py -> build/bdist.linux-x86_64/egg/pymesh/wires/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/wires/tests/WireTestCase.py -> build/bdist.linux-x86_64/egg/pymesh/wires/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/wires/tests/test_tiler.py -> build/bdist.linux-x86_64/egg/pymesh/wires/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/__init__.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/triangulate.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "creating build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tests/test_HarmonicSolver.py -> build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tests/test_solver.py -> build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tests/test_boolean.py -> build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tests/test_map_attributes.py -> build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tests/test_slice_Mesh.py -> build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tests/test_selfintersection.py -> build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tests/test_minkowski_sum.py -> build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tests/test_assembler.py -> build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tests/test_aabb_tree.py -> build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tests/test_VoxelGrid.py -> build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tests/test_mesh.py -> build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tests/test_CSGTree.py -> build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tests/test_compression.py -> build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tests/test_snap_rounding.py -> build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tests/test_winding_number.py -> build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tests/test_triangulate.py -> build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tests/test_triangle.py -> build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tests/test_curvature.py -> build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tests/test_cut_to_disk.py -> build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tests/test_sparse_solver.py -> build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tests/test_material.py -> build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tests/test_meshio.py -> build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tests/test_predicates.py -> build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tests/test_outerhull.py -> build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tests/test_tetgen.py -> build/bdist.linux-x86_64/egg/pymesh/tests\n", + "copying build/lib.linux-x86_64-3.6/pymesh/submesh.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "creating build/bdist.linux-x86_64/egg/pymesh/misc\n", + "copying build/lib.linux-x86_64-3.6/pymesh/misc/__init__.py -> build/bdist.linux-x86_64/egg/pymesh/misc\n", + "copying build/lib.linux-x86_64-3.6/pymesh/misc/quaternion.py -> build/bdist.linux-x86_64/egg/pymesh/misc\n", + "copying build/lib.linux-x86_64-3.6/pymesh/straight_skeleton.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/Mesh.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tetrahedralize.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/matrixio.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/slice_mesh.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/tetgen.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/boolean_unsupported.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/Boundary.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/TestCase.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/version.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/HarmonicSolver.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/aabb_tree.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/winding_number.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/PyMeshSetting.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/convex_hull.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/outerhull.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/predicates.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/snap_rounding.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/VoxelGrid.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/triangle.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "copying build/lib.linux-x86_64-3.6/pymesh/save_svg.py -> build/bdist.linux-x86_64/egg/pymesh\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/map_attributes.py to map_attributes.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/remove_duplicated_faces.py to remove_duplicated_faces.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/cut_mesh.py to cut_mesh.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/merge_meshes.py to merge_meshes.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/face_utils.py to face_utils.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/manifold_check.py to manifold_check.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/split_long_edges.py to split_long_edges.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/hex_to_tet.py to hex_to_tet.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/generate_cylinder.py to generate_cylinder.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/generate_dodecahedron.py to generate_dodecahedron.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/generate_minimal_surface.py to generate_minimal_surface.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/remove_isolated_vertices.py to remove_isolated_vertices.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/collapse_short_edges.py to collapse_short_edges.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/__init__.py to __init__.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/attribute_utils.py to attribute_utils.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/tests/test_remove_isolated_vertices.py to test_remove_isolated_vertices.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/tests/test_remove_duplicated_faces.py to test_remove_duplicated_faces.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/tests/test_quad_to_tri.py to test_quad_to_tri.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/tests/test_remove_obtuse_triangles.py to test_remove_obtuse_triangles.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/tests/test_hex_to_tet.py to test_hex_to_tet.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/tests/test_merge_meshes.py to test_merge_meshes.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/tests/test_collapse_short_edges.py to test_collapse_short_edges.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/tests/test_separate_mesh.py to test_separate_mesh.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/tests/test_attribute_utils.py to test_attribute_utils.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/tests/test_remove_degenerated_triangles.py to test_remove_degenerated_triangles.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/tests/test_generate_icosphere.py to test_generate_icosphere.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/tests/test_split_long_edges.py to test_split_long_edges.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/tests/test_remove_duplicated_vertices.py to test_remove_duplicated_vertices.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/tests/test_edge_utils.py to test_edge_utils.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/tests/test_cut_mesh.py to test_cut_mesh.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/tests/test_generate_box_mesh.py to test_generate_box_mesh.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/remove_degenerated_triangles.py to remove_degenerated_triangles.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/separate_mesh.py to separate_mesh.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/subdivide.py to subdivide.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/remove_obtuse_triangles.py to remove_obtuse_triangles.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/generate_box_mesh.py to generate_box_mesh.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/mesh_to_graph.py to mesh_to_graph.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/generate_tube.py to generate_tube.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/generate_equilateral_triangle.py to generate_equilateral_triangle.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/quad_to_tri.py to quad_to_tri.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/edge_utils.py to edge_utils.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/generate_regular_tetrahedron.py to generate_regular_tetrahedron.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/remove_duplicated_vertices.py to remove_duplicated_vertices.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/voxel_utils.py to voxel_utils.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/generate_icosphere.py to generate_icosphere.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/Assembler.py to Assembler.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/cell_partition.py to cell_partition.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshio.py to meshio.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/exact_arithmetic.py to exact_arithmetic.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/igl_utils.py to igl_utils.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/material.py to material.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/selfintersection.py to selfintersection.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/minkowski_sum.py to minkowski_sum.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/timethis.py to timethis.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/Arrangement2.py to Arrangement2.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/CSGTree.py to CSGTree.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/compression.py to compression.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/cut_to_disk.py to cut_to_disk.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/HashGrid.py to HashGrid.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/boolean.py to boolean.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/SparseSolver.py to SparseSolver.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/wires/merge_wires.py to merge_wires.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/wires/WireNetwork.py to WireNetwork.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/wires/Parameters.py to Parameters.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/wires/Tiler.py to Tiler.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/wires/Inflator.py to Inflator.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/wires/wires_io.py to wires_io.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/wires/__init__.py to __init__.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/wires/tests/test_wire_network.py to test_wire_network.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/wires/tests/test_inflator.py to test_inflator.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/wires/tests/WireTestCase.py to WireTestCase.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/wires/tests/test_tiler.py to test_tiler.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/__init__.py to __init__.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/triangulate.py to triangulate.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_HarmonicSolver.py to test_HarmonicSolver.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_solver.py to test_solver.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_boolean.py to test_boolean.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_map_attributes.py to test_map_attributes.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_slice_Mesh.py to test_slice_Mesh.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_selfintersection.py to test_selfintersection.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_minkowski_sum.py to test_minkowski_sum.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_assembler.py to test_assembler.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_aabb_tree.py to test_aabb_tree.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_VoxelGrid.py to test_VoxelGrid.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_mesh.py to test_mesh.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_CSGTree.py to test_CSGTree.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_compression.py to test_compression.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_snap_rounding.py to test_snap_rounding.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_winding_number.py to test_winding_number.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_triangulate.py to test_triangulate.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_triangle.py to test_triangle.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_curvature.py to test_curvature.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_cut_to_disk.py to test_cut_to_disk.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_sparse_solver.py to test_sparse_solver.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_material.py to test_material.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_meshio.py to test_meshio.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_predicates.py to test_predicates.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_outerhull.py to test_outerhull.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_tetgen.py to test_tetgen.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/submesh.py to submesh.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/misc/__init__.py to __init__.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/misc/quaternion.py to quaternion.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/straight_skeleton.py to straight_skeleton.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/Mesh.py to Mesh.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tetrahedralize.py to tetrahedralize.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/matrixio.py to matrixio.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/slice_mesh.py to slice_mesh.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tetgen.py to tetgen.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/boolean_unsupported.py to boolean_unsupported.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/Boundary.py to Boundary.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/TestCase.py to TestCase.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/version.py to version.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/HarmonicSolver.py to HarmonicSolver.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/aabb_tree.py to aabb_tree.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/winding_number.py to winding_number.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/PyMeshSetting.py to PyMeshSetting.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/convex_hull.py to convex_hull.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/outerhull.py to outerhull.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/predicates.py to predicates.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/snap_rounding.py to snap_rounding.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/VoxelGrid.py to VoxelGrid.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/triangle.py to triangle.cpython-36.pyc\n", + "byte-compiling build/bdist.linux-x86_64/egg/pymesh/save_svg.py to save_svg.cpython-36.pyc\n", + "creating build/bdist.linux-x86_64/egg/EGG-INFO\n", + "installing scripts to build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "running install_scripts\n", + "running build_scripts\n", + "creating build/scripts-3.6\n", + "copying and adjusting scripts/add_element_attribute.py -> build/scripts-3.6\n", + "copying and adjusting scripts/add_index.py -> build/scripts-3.6\n", + "copying and adjusting scripts/arrangement_2d.py -> build/scripts-3.6\n", + "copying and adjusting scripts/bbox.py -> build/scripts-3.6\n", + "copying and adjusting scripts/box_gen.py -> build/scripts-3.6\n", + "copying and adjusting scripts/boolean.py -> build/scripts-3.6\n", + "copying and adjusting scripts/carve.py -> build/scripts-3.6\n", + "copying and adjusting scripts/convex_hull.py -> build/scripts-3.6\n", + "copying and adjusting scripts/curvature.py -> build/scripts-3.6\n", + "copying and adjusting scripts/distortion.py -> build/scripts-3.6\n", + "copying and adjusting scripts/dodecahedron_gen.py -> build/scripts-3.6\n", + "copying and adjusting scripts/extract_self_intersecting_faces.py -> build/scripts-3.6\n", + "copying and adjusting scripts/fem_check.py -> build/scripts-3.6\n", + "copying scripts/find_file.py -> build/scripts-3.6\n", + "copying and adjusting scripts/fix_mesh.py -> build/scripts-3.6\n", + "copying and adjusting scripts/geodesic.py -> build/scripts-3.6\n", + "copying and adjusting scripts/highlight_boundary_edges.py -> build/scripts-3.6\n", + "copying and adjusting scripts/highlight_degenerated_faces.py -> build/scripts-3.6\n", + "copying and adjusting scripts/highlight_non_oriented_edges.py -> build/scripts-3.6\n", + "copying and adjusting scripts/highlight_self_intersection.py -> build/scripts-3.6\n", + "copying and adjusting scripts/highlight_zero_area_faces.py -> build/scripts-3.6\n", + "copying and adjusting scripts/highlight_inverted_tets.py -> build/scripts-3.6\n", + "copying and adjusting scripts/highlight_delaunay.py -> build/scripts-3.6\n", + "copying and adjusting scripts/hilbert_curve_gen.py -> build/scripts-3.6\n", + "copying and adjusting scripts/icosphere_gen.py -> build/scripts-3.6\n", + "copying and adjusting scripts/inflate.py -> build/scripts-3.6\n", + "copying and adjusting scripts/map_to_sphere.py -> build/scripts-3.6\n", + "copying and adjusting scripts/matrix_gen.py -> build/scripts-3.6\n", + "copying and adjusting scripts/mean_curvature_flow.py -> build/scripts-3.6\n", + "copying and adjusting scripts/merge.py -> build/scripts-3.6\n", + "copying and adjusting scripts/mesh_diff.py -> build/scripts-3.6\n", + "copying and adjusting scripts/meshconvert.py -> build/scripts-3.6\n", + "copying and adjusting scripts/meshstat.py -> build/scripts-3.6\n", + "copying and adjusting scripts/mesh_to_wire.py -> build/scripts-3.6\n", + "copying and adjusting scripts/microstructure_gen.py -> build/scripts-3.6\n", + "copying and adjusting scripts/minkowski_sum.py -> build/scripts-3.6\n", + "copying and adjusting scripts/outer_hull.py -> build/scripts-3.6\n", + "copying and adjusting scripts/point_cloud.py -> build/scripts-3.6\n", + "copying scripts/print_utils.py -> build/scripts-3.6\n", + "copying and adjusting scripts/quad_to_tri.py -> build/scripts-3.6\n", + "copying and adjusting scripts/refine_mesh.py -> build/scripts-3.6\n", + "copying and adjusting scripts/remove_degenerated_triangles.py -> build/scripts-3.6\n", + "copying and adjusting scripts/remove_duplicated_faces.py -> build/scripts-3.6\n", + "copying and adjusting scripts/remove_isolated_vertices.py -> build/scripts-3.6\n", + "copying and adjusting scripts/remove_nan.py -> build/scripts-3.6\n", + "copying and adjusting scripts/resolve_self_intersection.py -> build/scripts-3.6\n", + "copying and adjusting scripts/retriangulate.py -> build/scripts-3.6\n", + "copying and adjusting scripts/rigid_transform.py -> build/scripts-3.6\n", + "copying and adjusting scripts/scale_mesh.py -> build/scripts-3.6\n", + "copying and adjusting scripts/self_union.py -> build/scripts-3.6\n", + "copying and adjusting scripts/separate.py -> build/scripts-3.6\n", + "copying and adjusting scripts/slice_mesh.py -> build/scripts-3.6\n", + "copying and adjusting scripts/subdivide.py -> build/scripts-3.6\n", + "copying and adjusting scripts/submesh.py -> build/scripts-3.6\n", + "copying and adjusting scripts/svg_to_mesh.py -> build/scripts-3.6\n", + "copying and adjusting scripts/tet.py -> build/scripts-3.6\n", + "copying and adjusting scripts/tet_boundary.py -> build/scripts-3.6\n", + "copying and adjusting scripts/tet_to_hex.py -> build/scripts-3.6\n", + "copying and adjusting scripts/triangulate.py -> build/scripts-3.6\n", + "copying and adjusting scripts/uv.py -> build/scripts-3.6\n", + "copying and adjusting scripts/voxelize.py -> build/scripts-3.6\n", + "changing mode of build/scripts-3.6/add_element_attribute.py from 644 to 755\n", + "changing mode of build/scripts-3.6/add_index.py from 644 to 755\n", + "changing mode of build/scripts-3.6/arrangement_2d.py from 644 to 755\n", + "changing mode of build/scripts-3.6/bbox.py from 644 to 755\n", + "changing mode of build/scripts-3.6/box_gen.py from 644 to 755\n", + "changing mode of build/scripts-3.6/boolean.py from 644 to 755\n", + "changing mode of build/scripts-3.6/carve.py from 644 to 755\n", + "changing mode of build/scripts-3.6/convex_hull.py from 644 to 755\n", + "changing mode of build/scripts-3.6/curvature.py from 644 to 755\n", + "changing mode of build/scripts-3.6/distortion.py from 644 to 755\n", + "changing mode of build/scripts-3.6/dodecahedron_gen.py from 644 to 755\n", + "changing mode of build/scripts-3.6/extract_self_intersecting_faces.py from 644 to 755\n", + "changing mode of build/scripts-3.6/fem_check.py from 644 to 755\n", + "changing mode of build/scripts-3.6/find_file.py from 644 to 755\n", + "changing mode of build/scripts-3.6/fix_mesh.py from 644 to 755\n", + "changing mode of build/scripts-3.6/geodesic.py from 644 to 755\n", + "changing mode of build/scripts-3.6/highlight_boundary_edges.py from 644 to 755\n", + "changing mode of build/scripts-3.6/highlight_degenerated_faces.py from 644 to 755\n", + "changing mode of build/scripts-3.6/highlight_non_oriented_edges.py from 644 to 755\n", + "changing mode of build/scripts-3.6/highlight_self_intersection.py from 644 to 755\n", + "changing mode of build/scripts-3.6/highlight_zero_area_faces.py from 644 to 755\n", + "changing mode of build/scripts-3.6/highlight_inverted_tets.py from 644 to 755\n", + "changing mode of build/scripts-3.6/highlight_delaunay.py from 644 to 755\n", + "changing mode of build/scripts-3.6/hilbert_curve_gen.py from 644 to 755\n", + "changing mode of build/scripts-3.6/icosphere_gen.py from 644 to 755\n", + "changing mode of build/scripts-3.6/inflate.py from 644 to 755\n", + "changing mode of build/scripts-3.6/map_to_sphere.py from 644 to 755\n", + "changing mode of build/scripts-3.6/matrix_gen.py from 644 to 755\n", + "changing mode of build/scripts-3.6/mean_curvature_flow.py from 644 to 755\n", + "changing mode of build/scripts-3.6/merge.py from 644 to 755\n", + "changing mode of build/scripts-3.6/mesh_diff.py from 644 to 755\n", + "changing mode of build/scripts-3.6/meshconvert.py from 644 to 755\n", + "changing mode of build/scripts-3.6/meshstat.py from 644 to 755\n", + "changing mode of build/scripts-3.6/mesh_to_wire.py from 644 to 755\n", + "changing mode of build/scripts-3.6/microstructure_gen.py from 644 to 755\n", + "changing mode of build/scripts-3.6/minkowski_sum.py from 644 to 755\n", + "changing mode of build/scripts-3.6/outer_hull.py from 644 to 755\n", + "changing mode of build/scripts-3.6/point_cloud.py from 644 to 755\n", + "changing mode of build/scripts-3.6/print_utils.py from 644 to 755\n", + "changing mode of build/scripts-3.6/quad_to_tri.py from 644 to 755\n", + "changing mode of build/scripts-3.6/refine_mesh.py from 644 to 755\n", + "changing mode of build/scripts-3.6/remove_degenerated_triangles.py from 644 to 755\n", + "changing mode of build/scripts-3.6/remove_duplicated_faces.py from 644 to 755\n", + "changing mode of build/scripts-3.6/remove_isolated_vertices.py from 644 to 755\n", + "changing mode of build/scripts-3.6/remove_nan.py from 644 to 755\n", + "changing mode of build/scripts-3.6/resolve_self_intersection.py from 644 to 755\n", + "changing mode of build/scripts-3.6/retriangulate.py from 644 to 755\n", + "changing mode of build/scripts-3.6/rigid_transform.py from 644 to 755\n", + "changing mode of build/scripts-3.6/scale_mesh.py from 644 to 755\n", + "changing mode of build/scripts-3.6/self_union.py from 644 to 755\n", + "changing mode of build/scripts-3.6/separate.py from 644 to 755\n", + "changing mode of build/scripts-3.6/slice_mesh.py from 644 to 755\n", + "changing mode of build/scripts-3.6/subdivide.py from 644 to 755\n", + "changing mode of build/scripts-3.6/submesh.py from 644 to 755\n", + "changing mode of build/scripts-3.6/svg_to_mesh.py from 644 to 755\n", + "changing mode of build/scripts-3.6/tet.py from 644 to 755\n", + "changing mode of build/scripts-3.6/tet_boundary.py from 644 to 755\n", + "changing mode of build/scripts-3.6/tet_to_hex.py from 644 to 755\n", + "changing mode of build/scripts-3.6/triangulate.py from 644 to 755\n", + "changing mode of build/scripts-3.6/uv.py from 644 to 755\n", + "changing mode of build/scripts-3.6/voxelize.py from 644 to 755\n", + "creating build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/remove_duplicated_faces.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/uv.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/dodecahedron_gen.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/point_cloud.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/svg_to_mesh.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/meshconvert.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/retriangulate.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/highlight_self_intersection.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/highlight_boundary_edges.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/minkowski_sum.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/geodesic.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/bbox.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/fix_mesh.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/extract_self_intersecting_faces.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/highlight_delaunay.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/mean_curvature_flow.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/scale_mesh.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/remove_nan.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/tet_boundary.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/matrix_gen.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/map_to_sphere.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/mesh_to_wire.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/box_gen.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/tet.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/add_index.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/remove_isolated_vertices.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/outer_hull.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/refine_mesh.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/inflate.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/boolean.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/fem_check.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/highlight_zero_area_faces.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/triangulate.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/remove_degenerated_triangles.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/merge.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/self_union.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/submesh.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/subdivide.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/meshstat.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/tet_to_hex.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/add_element_attribute.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/slice_mesh.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/icosphere_gen.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/print_utils.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/curvature.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/voxelize.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/rigid_transform.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/separate.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/arrangement_2d.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/mesh_diff.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/microstructure_gen.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/distortion.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/convex_hull.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/find_file.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/highlight_non_oriented_edges.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/carve.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/highlight_inverted_tets.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/hilbert_curve_gen.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/quad_to_tri.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/resolve_self_intersection.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "copying build/scripts-3.6/highlight_degenerated_faces.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/remove_duplicated_faces.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/uv.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/dodecahedron_gen.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/point_cloud.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/svg_to_mesh.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/meshconvert.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/retriangulate.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/highlight_self_intersection.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/highlight_boundary_edges.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/minkowski_sum.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/geodesic.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/bbox.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/fix_mesh.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/extract_self_intersecting_faces.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/highlight_delaunay.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/mean_curvature_flow.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/scale_mesh.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/remove_nan.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/tet_boundary.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/matrix_gen.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/map_to_sphere.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/mesh_to_wire.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/box_gen.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/tet.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/add_index.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/remove_isolated_vertices.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/outer_hull.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/refine_mesh.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/inflate.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/boolean.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/fem_check.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/highlight_zero_area_faces.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/triangulate.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/remove_degenerated_triangles.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/merge.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/self_union.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/submesh.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/subdivide.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/meshstat.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/tet_to_hex.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/add_element_attribute.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/slice_mesh.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/icosphere_gen.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/print_utils.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/curvature.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/voxelize.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/rigid_transform.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/separate.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/arrangement_2d.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/mesh_diff.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/microstructure_gen.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/distortion.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/convex_hull.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/find_file.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/highlight_non_oriented_edges.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/carve.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/highlight_inverted_tets.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/hilbert_curve_gen.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/quad_to_tri.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/resolve_self_intersection.py to 755\n", + "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/highlight_degenerated_faces.py to 755\n", + "copying python/pymesh2.egg-info/PKG-INFO -> build/bdist.linux-x86_64/egg/EGG-INFO\n", + "copying python/pymesh2.egg-info/SOURCES.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n", + "copying python/pymesh2.egg-info/dependency_links.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n", + "copying python/pymesh2.egg-info/not-zip-safe -> build/bdist.linux-x86_64/egg/EGG-INFO\n", + "copying python/pymesh2.egg-info/top_level.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n", + "creating dist\n", + "creating 'dist/pymesh2-0.3-py3.6-linux-x86_64.egg' and adding 'build/bdist.linux-x86_64/egg' to it\n", + "removing 'build/bdist.linux-x86_64/egg' (and everything under it)\n", + "Processing pymesh2-0.3-py3.6-linux-x86_64.egg\n", + "creating /usr/local/lib/python3.6/site-packages/pymesh2-0.3-py3.6-linux-x86_64.egg\n", + "Extracting pymesh2-0.3-py3.6-linux-x86_64.egg to /usr/local/lib/python3.6/site-packages\n", + "Adding pymesh2 0.3 to easy-install.pth file\n", + "Installing remove_duplicated_faces.py script to /usr/local/bin\n", + "Installing uv.py script to /usr/local/bin\n", + "Installing dodecahedron_gen.py script to /usr/local/bin\n", + "Installing point_cloud.py script to /usr/local/bin\n", + "Installing svg_to_mesh.py script to /usr/local/bin\n", + "Installing meshconvert.py script to /usr/local/bin\n", + "Installing retriangulate.py script to /usr/local/bin\n", + "Installing highlight_self_intersection.py script to /usr/local/bin\n", + "Installing highlight_boundary_edges.py script to /usr/local/bin\n", + "Installing minkowski_sum.py script to /usr/local/bin\n", + "Installing geodesic.py script to /usr/local/bin\n", + "Installing bbox.py script to /usr/local/bin\n", + "Installing fix_mesh.py script to /usr/local/bin\n", + "Installing extract_self_intersecting_faces.py script to /usr/local/bin\n", + "Installing highlight_delaunay.py script to /usr/local/bin\n", + "Installing mean_curvature_flow.py script to /usr/local/bin\n", + "Installing scale_mesh.py script to /usr/local/bin\n", + "Installing remove_nan.py script to /usr/local/bin\n", + "Installing tet_boundary.py script to /usr/local/bin\n", + "Installing matrix_gen.py script to /usr/local/bin\n", + "Installing map_to_sphere.py script to /usr/local/bin\n", + "Installing mesh_to_wire.py script to /usr/local/bin\n", + "Installing box_gen.py script to /usr/local/bin\n", + "Installing tet.py script to /usr/local/bin\n", + "Installing add_index.py script to /usr/local/bin\n", + "Installing remove_isolated_vertices.py script to /usr/local/bin\n", + "Installing outer_hull.py script to /usr/local/bin\n", + "Installing refine_mesh.py script to /usr/local/bin\n", + "Installing inflate.py script to /usr/local/bin\n", + "Installing boolean.py script to /usr/local/bin\n", + "Installing fem_check.py script to /usr/local/bin\n", + "Installing highlight_zero_area_faces.py script to /usr/local/bin\n", + "Installing triangulate.py script to /usr/local/bin\n", + "Installing remove_degenerated_triangles.py script to /usr/local/bin\n", + "Installing merge.py script to /usr/local/bin\n", + "Installing self_union.py script to /usr/local/bin\n", + "Installing submesh.py script to /usr/local/bin\n", + "Installing subdivide.py script to /usr/local/bin\n", + "Installing meshstat.py script to /usr/local/bin\n", + "Installing tet_to_hex.py script to /usr/local/bin\n", + "Installing add_element_attribute.py script to /usr/local/bin\n", + "Installing slice_mesh.py script to /usr/local/bin\n", + "Installing icosphere_gen.py script to /usr/local/bin\n", + "Installing print_utils.py script to /usr/local/bin\n", + "Installing curvature.py script to /usr/local/bin\n", + "Installing voxelize.py script to /usr/local/bin\n", + "Installing rigid_transform.py script to /usr/local/bin\n", + "Installing separate.py script to /usr/local/bin\n", + "Installing arrangement_2d.py script to /usr/local/bin\n", + "Installing mesh_diff.py script to /usr/local/bin\n", + "Installing microstructure_gen.py script to /usr/local/bin\n", + "Installing distortion.py script to /usr/local/bin\n", + "Installing convex_hull.py script to /usr/local/bin\n", + "Installing find_file.py script to /usr/local/bin\n", + "Installing highlight_non_oriented_edges.py script to /usr/local/bin\n", + "Installing carve.py script to /usr/local/bin\n", + "Installing highlight_inverted_tets.py script to /usr/local/bin\n", + "Installing hilbert_curve_gen.py script to /usr/local/bin\n", + "Installing quad_to_tri.py script to /usr/local/bin\n", + "Installing resolve_self_intersection.py script to /usr/local/bin\n", + "Installing highlight_degenerated_faces.py script to /usr/local/bin\n", + "\n", + "Installed /usr/local/lib/python3.6/site-packages/pymesh2-0.3-py3.6-linux-x86_64.egg\n", + "Processing dependencies for pymesh2==0.3\n", + "Finished processing dependencies for pymesh2==0.3\n", + "/content\n", + "Requirement already satisfied: 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collected packages: pymeshlab\n", + "Successfully installed pymeshlab-0.2\n", + "/content/3dsnet\n", + "Collecting pymesh2==0.2.1\n", + " Downloading https://github.com/PyMesh/PyMesh/releases/download/v0.2.1/pymesh2-0.2.1-cp36-cp36m-linux_x86_64.whl (56.4 MB)\n", + "\u001b[K |████████████████████████████████| 56.4 MB 58 kB/s \n", + "\u001b[?25hCollecting progress\n", + " Downloading progress-1.5.tar.gz (5.8 kB)\n", + "Collecting chumpy\n", + " Downloading chumpy-0.70.tar.gz (50 kB)\n", + "\u001b[K |████████████████████████████████| 50 kB 5.4 MB/s \n", + "\u001b[?25hCollecting numpy~=1.17.2\n", + " Downloading numpy-1.17.5-cp36-cp36m-manylinux1_x86_64.whl (20.0 MB)\n", + "\u001b[K |████████████████████████████████| 20.0 MB 1.4 MB/s \n", + "\u001b[?25hCollecting Pillow~=5.1.0\n", + " Downloading Pillow-5.1.0-cp36-cp36m-manylinux1_x86_64.whl (2.0 MB)\n", + "\u001b[K |████████████████████████████████| 2.0 MB 52.1 MB/s \n", + "\u001b[?25hCollecting easydict~=1.9\n", + " Downloading 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/root/.cache/pip/wheels/e5/d6/71/e87d26b0205f2c12e55a1a554214668ee324a962bad857c56a\n", + "Successfully built easydict chumpy progress\n", + "Installing collected packages: numpy, jsonpointer, Pillow, jsonpatch, pymesh2, progress, matplotlib, joblib, easydict, chumpy\n", + "\u001b[33m WARNING: The scripts f2py, f2py3 and f2py3.6 are installed in '/root/.local/bin' which is not on PATH.\n", + " Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\n", + "Successfully installed Pillow-5.1.0 chumpy-0.70 easydict-1.9 joblib-1.0.1 jsonpatch-1.32 jsonpointer-2.1 matplotlib-3.2.2 numpy-1.17.5 progress-1.5 pymesh2-0.2.1\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.colab-display-data+json": { + "pip_warning": { + "packages": [ + "matplotlib", + "mpl_toolkits" + ] + } + } + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "7cEwnsEfFgGI" + }, + "source": [ + "## Since PyMesh and pytorch3d both are having installations issues that I cant figure out....\n", + "\n", + "\n", + "# DO THIS ON THE FILES OR YOU WILL HAVE IMPORT ERRORS\n", + "\n", + "\n", + "# trainer.py from PyMesh.python.pymesh.meshio import form_mesh\n", + "# mesh_processor.py from PyMesh.python.pymesh.meshio import save_mesh\n", + "# template.py remove pymesh import\n", + "\n", + "# Removed the pytorch3d imports from train_loss.py, model.py due to improper installation issue from pytorch3d repo" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "b1nj8W0oY24U" + }, + "source": [ + "Need to link pre-trained models to your google drive account from [HERE](https://drive.google.com/drive/folders/1cyVRUmtN_YF-TXkytKfn1M0HlGH9Qux_?usp=sharing) then link your drive to colab. Or download the pretrained models then place in /content/3dsnet/\n", + "\n", + "Since the encoders are trained on each family objects individually, right now the only pre-trained models are for chairs and planes, although the training for other classes can be performed." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "nMfmXLOYfNRF", + "outputId": "2f86ad83-0b4e-4efc-8ec0-57e4dfbb3e14" + }, + "source": [ + "from google.colab import drive\n", + "drive.mount('/gdrive')\n", + "%cd /gdrive\n", + "# %cp -r /gdrive/MyDrive/Colab\\ Notebooks/3dsnet_models /content/3dsnet\n", + "# %cp -r /gdrive/MyDrive/Colab\\ Notebooks/3dsnet_models/aux_models/ /content/3dsnet" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Drive already mounted at /gdrive; to attempt to forcibly remount, call drive.mount(\"/gdrive\", force_remount=True).\n", + "/gdrive\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "c9jnYHect6n8" + }, + "source": [ + "#@title Helper Functions {display-mode: \"form\"}\n", + "\n", + "# This code will be hidden when the notebook is loaded.\n", + "\n", + "def load_mesh_obj(path):\n", + " mesh = trimesh.load_mesh(path)\n", + " if isinstance(mesh, trimesh.Scene):\n", + " mesh = mesh.dump()[0]\n", + " return mesh\n", + "\n", + "def plot_meshes(mesh_list,\n", + " fig_size=8,\n", + " el=45,\n", + " rot_start=90,\n", + " vert_size=10,\n", + " vert_alpha=0.25,\n", + " n_cols=4):\n", + " \"\"\"Plots mesh data using matplotlib.\"\"\"\n", + "\n", + " n_plot = len(mesh_list)\n", + " n_cols = np.minimum(n_plot, n_cols)\n", + " n_rows = np.ceil(n_plot / n_cols).astype('int')\n", + " fig = plt.figure(figsize=(fig_size * n_cols, fig_size * n_rows))\n", + " for p_inc, mesh in enumerate(mesh_list):\n", + "\n", + " ax = fig.add_subplot(n_rows, n_cols, p_inc + 1, projection='3d')\n", + "\n", + " if 'faces' in mesh:\n", + " face_verts = mesh['vertices']\n", + " collection = []\n", + " for f in mesh['faces']:\n", + " collection.append(face_verts[f])\n", + " plt_mesh = Poly3DCollection(collection)\n", + " plt_mesh.set_edgecolor((0., 0., 0., 0.3))\n", + " plt_mesh.set_facecolor((1, 0, 0, 0.2))\n", + " ax.add_collection3d(plt_mesh)\n", + "\n", + " if mesh['vertices'] is not None:\n", + " ax.scatter3D(\n", + " mesh['vertices'][:, 0],\n", + " mesh['vertices'][:, 1],\n", + " mesh['vertices'][:, 2],\n", + " lw=0.,\n", + " s=vert_size,\n", + " c='g',\n", + " alpha=vert_alpha)\n", + "\n", + " # if mesh['pointcloud'] is not None:\n", + " # ax.scatter3D(\n", + " # mesh['pointcloud'][:, 0],\n", + " # mesh['pointcloud'][:, 1],\n", + " # mesh['pointcloud'][:, 2],\n", + " # lw=0.,\n", + " # s=2.5 * vert_size,\n", + " # c='b',\n", + " # alpha=1.)\n", + " \n", + " ax.view_init(el, rot_start)\n", + "\n", + " display_string = ''\n", + " if mesh['faces'] is not None:\n", + " display_string += 'Num. faces: {}\\n'.format(len(collection))\n", + " if mesh['vertices'] is not None:\n", + " num_verts = mesh['vertices'].shape[0]\n", + " # if mesh['vertices_conditional'] is not None:\n", + " # num_verts += mesh['vertices_conditional'].shape[0]\n", + " display_string += 'Num. verts: {}\\n'.format(num_verts)\n", + " # if mesh['class_name'] is not None:\n", + " # display_string += 'Synset: {}'.format(mesh['class_name'])\n", + " # if mesh['pointcloud'] is not None:\n", + " # display_string += 'Num. pointcloud: {}\\n'.format(\n", + " # mesh['pointcloud'].shape[0])\n", + " ax.text2D(0.05, 0.8, display_string, transform=ax.transAxes)\n", + " plt.subplots_adjust(\n", + " left=0., right=1., bottom=0., top=1., wspace=0.025, hspace=0.025)\n", + " plt.show()\n", + "\n", + "def load_data_from_file(path):\n", + " ext = path.split('.')[-1]\n", + " if ext == 'npy':\n", + " points = np.load(path)\n", + " elif ext == 'ply' or ext == 'obj':\n", + " points = trimesh.load_mesh(path)\n", + " if isinstance(points, trimesh.Scene):\n", + " points = points.dump()[0].vertices\n", + " else:\n", + " points = points.vertices\n", + " else:\n", + " print('invalid file extension')\n", + " raise IOError\n", + " points = torch.from_numpy(points.copy()).float()\n", + " operation = pointcloud_processor.Normalization(points, keep_track=True)\n", + " if opt.normalization == 'UnitBall':\n", + " operation.normalize_unitL2ball()\n", + " elif opt.normalization == 'BoundingBox':\n", + " operation.normalize_bounding_box()\n", + " else:\n", + " pass\n", + " return_dict = {\n", + " 'points': points,\n", + " 'operation': operation,\n", + " 'path': path,\n", + " }\n", + " return return_dict\n", + "\n", + "def unnormalize(mesh, operation=None):\n", + " if operation is not None:\n", + " # Undo any normalization that was used to preprocess the input.\n", + " vertices = torch.from_numpy(mesh.vertices).clone().unsqueeze(0)\n", + " norm_mesh = deepcopy(mesh)\n", + " norm_mesh.vertices = operation.apply(vertices).squeeze().numpy()\n", + " norm_mesh._data.__dict__['data']['vertices'] = norm_mesh.vertices\n", + " if np.sum(norm_mesh.vertices - mesh.vertices) == 0:\n", + " print(\"fucked normalization\")\n", + " return norm_mesh" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "RsZseUSK_bc6", + "cellView": "form" + }, + "source": [ + "#@title HyperParameters\n", + "#@markdown Options to change in the model\n", + "\n", + "family = \"chair\" #@param [\"chair\", \"bananas\", \"oranges\"] {allow-input: true}\n", + "decoder = \"meshflow\" #@param [\"meshflow\", \"atlasnet\"] {allow-input: true}\n", + "noise_level = 1 #@param {type:\"number\"}\n", + "log_dir = \"log/\" #@param {type:\"string\"}\n", + "data_dir = \"/content/3dsnet/docs/points/\" #@param {type:\"string\"}\n", + "reload_model_path = '/content/3dsnet/3dsnet_models/3dsnet_models/chairs/meshflow/3dsnet/network.pth' #@param {type:\"string\"}\n", + "#@markdown ---\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "H_l1hFOoMG0T" + }, + "source": [ + "#@title Options for trainer {display-mode: \"form\"}\n", + "\n", + "# This code will be hidden when the notebook is loaded.\n", + "\n", + "from easydict import EasyDict as edict\n", + "\n", + "opt = edict({ \"decoder_type\" : decoder,\n", + " 'demo': False,\n", + " \"SVR_0\": False,\n", + " \"SVR_1\" : False,\n", + " \"family\": family,\n", + " \"data_dir\" : data_dir,\n", + " \"dir_name\" : log_dir,\n", + " \"dataset\" :\"ShapeNet\",\n", + " \"weight_perceptual\": 1,\n", + " \"reload_model_path\" : reload_model_path, \n", + " \"batch_size\" : 4, \n", + " \"batch_size_test\" : 4,\n", + " \"class_choice\" : [\"armchair\",\"straight chair,side chair\"], \n", + " \"generator_norm\" : \"bn\",\n", + " \"discriminator_norm\" : \"bn\",\n", + " \"discriminator_activation\" : \"relu\",\n", + " \"dis_bottleneck_size\" : 1024,\n", + " \"style_bottleneck_size\" : 512,\n", + " \"generator_lrate\" : 0.001,\n", + " \"discriminator_lrate\" : 0.004,\n", + " \"batch_size\" : 16, \n", + " \"generator_update_skips\" : 1, \n", + " \"discriminator_update_skips\" : 1, \n", + " \"num_layers\" : 2,\n", + " \"num_layers_style\" : 1,\n", + " \"nb_primitives\" : 25, \n", + " \"template_type\" : \"SQUARE\",\n", + " \"weight_chamfer\" : 10,\n", + " \"weight_cycle_chamfer\" : 0,\n", + " \"weight_adversarial\" : 1,\n", + " \"weight_content_reconstruction\" : 1,\n", + " \"weight_style_reconstruction\" : 1,\n", + " \"lr_decay_1\" : 120,\n", + " \"lr_decay_2\" : 140, \n", + " \"lr_decay_3\" : 145, \n", + " \"decode_style\":True, \n", + " \"share_decoder\":True,\n", + " \"share_content_encoder\":True, \n", + " \"share_discriminator_encoder\":True,\n", + " \"gan_type\" : \"lsgan\",\n", + " \"use_visdom\":False,\n", + " \"start_epoch\":0,\n", + " \"adaptive\":True,\n", + " \"noise_magnitude\" : 1.0,\n", + " \"num_interpolations\" : 0,\n", + " \"normalization\":\"UnitBall\",\n", + " \"number_points\":2500,\n", + " \"multi_gpu\":[0],\n", + " \"use_default_demo_samples\":True,\n", + " \"multiscale_loss\":False,\n", + " \"bottleneck_size\":1024,\n", + " \"number_points_eval\":2500,\n", + " \"w_multiscale_1\":.1,\n", + " \"w_multiscale_2\":.2,\n", + " \"w_multiscale_3\":.7,\n", + " \"remove_all_batchNorms\": False,\n", + " \"dim_template\":2,\n", + " \"hidden_neurons\":512,\n", + " \"activation\": \"relu\",\n", + " \"share_style_encoder\": False,\n", + " \"num_layers_mlp\": 3,\n", + " \"no_learning\":True,\n", + " \"reload_decoder_path\" : '',\n", + " \"reload_pointnet_path\":'',\n", + " \"demo_input_dir\": \"./docs/points/\",\n", + " \"num_demo_pairs\":5,\n", + " \"share_style_mlp\": True})" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "b_58lYhRFeO8", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "9c052a7b-caad-4f7a-f06a-b342cc60a57f" + }, + "source": [ + "import sys\n", + "import torch\n", + "import training.trainer as trainer\n", + "import auxiliary.my_utils as my_utils\n", + "import numpy as np\n", + "import os\n", + "from easydict import EasyDict\n", + "from PyMesh.python.pymesh.meshio import form_mesh\n", + "from dataset.dataset_shapenet import ShapeNet\n", + "import dataset.pointcloud_processor as pointcloud_processor\n", + "import torch\n", + "import matplotlib.pyplot as plt\n", + "from mpl_toolkits.mplot3d.art3d import Poly3DCollection\n", + "import trimesh\n", + "from copy import deepcopy\n", + "\n", + "path_a = \"./docs/points/armchair.points.ply.npy\"\n", + "path_b = \"./docs/points/straight chair,side chair.points.ply.npy\"\n", + "\n", + "torch.cuda.set_device(opt.multi_gpu[0])\n", + "\n", + "trainer = trainer.Trainer(opt)\n", + "trainer.build_network()\n", + "trainer.reload_best_network()\n", + "trainer.demo_pair_path = \"\"\n", + "\n", + "with torch.no_grad():\n", + " data_a = EasyDict(load_data_from_file(path_a))\n", + " data_b = EasyDict(load_data_from_file(path_b))\n", + " \n", + " # prepare normalization\n", + " trainer.make_network_input(data_a, opt.SVR_0)\n", + " trainer.make_network_input(data_b, opt.SVR_1)\n", + " x = {opt.class_choice[0]: data_a.network_input, opt.class_choice[1]: data_b.network_input}\n", + " \n", + " # set the normalization operation\n", + " trainer.set_operation(data_a, data_b)\n", + "\n", + " # Get results of forward pass\n", + " path_ab, mesh_ab_normalized = trainer.generate_mesh_from_classes(x, opt.class_choice[0], opt.class_choice[1], data_a.operation, save=False)\n", + " path_ba, mesh_ba_normalized = trainer.generate_mesh_from_classes(x, opt.class_choice[1], opt.class_choice[0], data_b.operation, save=False)\n", + " \n", + " # unnormalize mesh vertices\n", + " mesh_ab = unnormalize(mesh_ab_normalized, data_a.operation)\n", + " mesh_ba = unnormalize(mesh_ba_normalized, data_b.operation)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\u001b[36mPARAMETER: \u001b[0m\n", + " \u001b[33mdecoder_type\u001b[0m : \u001b[36mmeshflow\u001b[0m\n", + " \u001b[33mdemo\u001b[0m : \u001b[36mFalse\u001b[0m\n", + " \u001b[33mSVR_0\u001b[0m : \u001b[36mFalse\u001b[0m\n", + " \u001b[33mSVR_1\u001b[0m : \u001b[36mFalse\u001b[0m\n", + " \u001b[33mfamily\u001b[0m : \u001b[36mchair\u001b[0m\n", + " \u001b[33mdata_dir\u001b[0m : \u001b[36m/content/3dsnet/docs/points/\u001b[0m\n", + " \u001b[33mdir_name\u001b[0m : \u001b[36mlog/\u001b[0m\n", + " \u001b[33mdataset\u001b[0m : \u001b[36mShapeNet\u001b[0m\n", + " \u001b[33mweight_perceptual\u001b[0m : \u001b[36m1\u001b[0m\n", + " \u001b[33mreload_model_path\u001b[0m : \u001b[36m/content/3dsnet/3dsnet_models/3dsnet_models/chairs/meshflow/3dsnet/network.pth\u001b[0m\n", + " \u001b[33mbatch_size\u001b[0m : \u001b[36m16\u001b[0m\n", + " \u001b[33mbatch_size_test\u001b[0m : \u001b[36m4\u001b[0m\n", + " \u001b[33mclass_choice\u001b[0m : 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\u001b[33mdiscriminator_optimizer_path\u001b[0m : \u001b[36mlog/discriminator_optimizer.pth\u001b[0m\n", + " \u001b[33mmodel_path\u001b[0m : \u001b[36mlog/network.pth\u001b[0m\n", + " \u001b[33mreload_generator_optimizer_path\u001b[0m : \u001b[36m\u001b[0m\n", + " \u001b[33mreload_discriminator_optimizer_path\u001b[0m : \u001b[36m\u001b[0m\n", + " \u001b[33mbest_model_path\u001b[0m : \u001b[36mlog/network_best.pth\u001b[0m\n", + " \u001b[33mtraining_media_path\u001b[0m : \u001b[36mlog/training_media\u001b[0m\n", + " \u001b[33mdemo_media_path\u001b[0m : \u001b[36mlog/demo_media\u001b[0m\n", + " \u001b[33mdevice\u001b[0m : \u001b[36mcuda:0\u001b[0m\n", + "Neural Mesh Flow with 1024 length embedding initialized\n", + "\u001b[33mNetwork weights loaded from /content/3dsnet/3dsnet_models/3dsnet_models/chairs/meshflow/3dsnet/network.pth!\u001b[0m\n", + "\u001b[33mFailed to reload network weights from log/network_best.pth!\u001b[0m\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "WUZH_LOo9Vdf", + "outputId": "e282cede-4d31-4df0-bc35-4a6b6e24f18d" + }, + "source": [ + "plot_meshes([ mesh_ab._data.__dict__[\"data\"]], rot_start=270)\n", + "plot_meshes([ mesh_ba._data.__dict__[\"data\"]])" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", 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\n", 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0.00829677 -0.00920843 -0.22110354\\nv -0.22939847 0.32048753 -0.18508641\\nv 0.24157108 -0.08785685 0.21924024\\nv -0.18635425 0.13268590 -0.22374351\\nv -0.21689491 0.15829953 -0.15821872\\nv -0.10197775 -0.02405942 0.11980677\\nv -0.20792675 0.13490889 -0.11150412\\nv -0.17103688 -0.01393483 0.23155299\\nv -0.22779135 -0.08767587 0.16478683\\nv 0.25142612 -0.18607124 0.22237431\\nv -0.05954628 -0.28481541 0.22172585\\nv -0.24150024 -0.17391509 -0.17889905\\nv 0.01515525 -0.00397954 0.10334098\\nv -0.02713109 -0.01390299 -0.11471119\\nv 0.16347391 0.00849686 -0.00218100\\nv 0.24023943 -0.00451028 -0.06247011\\nf 395 1383 342\\nf 2501 36 1383\\nf 2177 342 36\\nf 1383 36 342\\nf 1887 1152 1357\\nf 1780 1289 1152\\nf 2501 1357 1289\\nf 1152 1289 1357\\nf 2335 757 573\\nf 2177 710 757\\nf 1780 573 710\\nf 757 710 573\\nf 2501 1289 36\\nf 1780 710 1289\\nf 2177 36 710\\nf 1289 710 36\\nf 2553 190 1022\\nf 2230 2175 190\\nf 1132 1022 2175\\nf 190 2175 1022\\nf 2279 1020 2540\\nf 2440 1453 1020\\nf 2230 2540 1453\\nf 1020 1453 2540\\nf 1887 273 597\\nf 1132 861 273\\nf 2440 597 861\\nf 273 861 597\\nf 2230 1453 2175\\nf 2440 861 1453\\nf 1132 2175 861\\nf 1453 861 2175\\nf 1592 222 2523\\nf 170 681 222\\nf 1555 2523 681\\nf 222 681 2523\\nf 2335 1685 645\\nf 459 1699 1685\\nf 170 645 1699\\nf 1685 1699 645\\nf 2279 249 430\\nf 1555 1337 249\\nf 459 430 1337\\nf 249 1337 430\\nf 170 1699 681\\nf 459 1337 1699\\nf 1555 681 1337\\nf 1699 1337 681\\nf 1887 597 1152\\nf 2440 2344 597\\nf 1780 1152 2344\\nf 597 2344 1152\\nf 2279 430 1020\\nf 459 2031 430\\nf 2440 1020 2031\\nf 430 2031 1020\\nf 2335 573 1685\\nf 1780 363 573\\nf 459 1685 363\\nf 573 363 1685\\nf 2440 2031 2344\\nf 459 363 2031\\nf 1780 2344 363\\nf 2031 363 2344\\nf 887 2447 76\\nf 2383 264 2447\\nf 718 76 264\\nf 2447 264 76\\nf 2361 613 2149\\nf 2245 1513 613\\nf 2383 2149 1513\\nf 613 1513 2149\\nf 2218 1580 908\\nf 718 66 1580\\nf 2245 908 66\\nf 1580 66 908\\nf 2383 1513 264\\nf 2245 66 1513\\nf 718 264 66\\nf 1513 66 264\\nf 2525 1145 446\\nf 1164 448 1145\\nf 1564 446 448\\nf 1145 448 446\\nf 1579 1930 452\\nf 1500 447 1930\\nf 1164 452 447\\nf 1930 447 452\\nf 2361 1677 786\\nf 1564 2460 1677\\nf 1500 786 2460\\nf 1677 2460 786\\nf 1164 447 448\\nf 1500 2460 447\\nf 1564 448 2460\\nf 447 2460 448\\nf 2553 340 1048\\nf 386 243 340\\nf 324 1048 243\\nf 340 243 1048\\nf 2218 2551 378\\nf 951 1416 2551\\nf 386 378 1416\\nf 2551 1416 378\\nf 1579 2345 2061\\nf 324 2025 2345\\nf 951 2061 2025\\nf 2345 2025 2061\\nf 386 1416 243\\nf 951 2025 1416\\nf 324 243 2025\\nf 1416 2025 243\\nf 2361 786 613\\nf 1500 1779 786\\nf 2245 613 1779\\nf 786 1779 613\\nf 1579 2061 1930\\nf 951 1884 2061\\nf 1500 1930 1884\\nf 2061 1884 1930\\nf 2218 908 2551\\nf 2245 1238 908\\nf 951 2551 1238\\nf 908 1238 2551\\nf 1500 1884 1779\\nf 951 1238 1884\\nf 2245 1779 1238\\nf 1884 1238 1779\\nf 300 434 1576\\nf 1635 1268 434\\nf 2483 1576 1268\\nf 434 1268 1576\\nf 227 1838 1558\\nf 1147 1254 1838\\nf 1635 1558 1254\\nf 1838 1254 1558\\nf 1011 1711 1411\\nf 2483 1398 1711\\nf 1147 1411 1398\\nf 1711 1398 1411\\nf 1635 1254 1268\\nf 1147 1398 1254\\nf 2483 1268 1398\\nf 1254 1398 1268\\nf 1592 2378 383\\nf 1577 2289 2378\\nf 45 383 2289\\nf 2378 2289 383\\nf 2195 853 1803\\nf 1160 1521 853\\nf 1577 1803 1521\\nf 853 1521 1803\\nf 227 2131 1139\\nf 45 1180 2131\\nf 1160 1139 1180\\nf 2131 1180 1139\\nf 1577 1521 2289\\nf 1160 1180 1521\\nf 45 2289 1180\\nf 1521 1180 2289\\nf 2525 444 502\\nf 1138 283 444\\nf 1258 502 283\\nf 444 283 502\\nf 1011 846 1665\\nf 2419 1167 846\\nf 1138 1665 1167\\nf 846 1167 1665\\nf 2195 977 145\\nf 1258 2426 977\\nf 2419 145 2426\\nf 977 2426 145\\nf 1138 1167 283\\nf 2419 2426 1167\\nf 1258 283 2426\\nf 1167 2426 283\\nf 227 1139 1838\\nf 1160 2444 1139\\nf 1147 1838 2444\\nf 1139 2444 1838\\nf 2195 145 853\\nf 2419 1003 145\\nf 1160 853 1003\\nf 145 1003 853\\nf 1011 1411 846\\nf 1147 1772 1411\\nf 2419 846 1772\\nf 1411 1772 846\\nf 1160 1003 2444\\nf 2419 1772 1003\\nf 1147 2444 1772\\nf 1003 1772 2444\\nf 2553 1048 190\\nf 324 312 1048\\nf 2230 190 312\\nf 1048 312 190\\nf 1579 443 2345\\nf 2052 2558 443\\nf 324 2345 2558\\nf 443 2558 2345\\nf 2279 2540 481\\nf 2230 294 2540\\nf 2052 481 294\\nf 2540 294 481\\nf 324 2558 312\\nf 2052 294 2558\\nf 2230 312 294\\nf 2558 294 312\\nf 2525 502 1145\\nf 1258 1202 502\\nf 1164 1145 1202\\nf 502 1202 1145\\nf 2195 1267 977\\nf 680 1274 1267\\nf 1258 977 1274\\nf 1267 1274 977\\nf 1579 452 2439\\nf 1164 74 452\\nf 680 2439 74\\nf 452 74 2439\\nf 1258 1274 1202\\nf 680 74 1274\\nf 1164 1202 74\\nf 1274 74 1202\\nf 1592 2523 2378\\nf 1555 2494 2523\\nf 1577 2378 2494\\nf 2523 2494 2378\\nf 2279 1224 249\\nf 1143 198 1224\\nf 1555 249 198\\nf 1224 198 249\\nf 2195 1803 2174\\nf 1577 177 1803\\nf 1143 2174 177\\nf 1803 177 2174\\nf 1555 198 2494\\nf 1143 177 198\\nf 1577 2494 177\\nf 198 177 2494\\nf 1579 2439 443\\nf 680 794 2439\\nf 2052 443 794\\nf 2439 794 443\\nf 2195 2174 1267\\nf 1143 1126 2174\\nf 680 1267 1126\\nf 2174 1126 1267\\nf 2279 481 1224\\nf 2052 2365 481\\nf 1143 1224 2365\\nf 481 2365 1224\\nf 680 1126 794\\nf 1143 2365 1126\\nf 2052 794 2365\\nf 1126 2365 794\\nf 395 342 594\\nf 2177 1795 342\\nf 707 594 1795\\nf 342 1795 594\\nf 2335 60 757\\nf 1023 796 60\\nf 2177 757 796\\nf 60 796 757\\nf 1101 380 1039\\nf 707 1434 380\\nf 1023 1039 1434\\nf 380 1434 1039\\nf 2177 796 1795\\nf 1023 1434 796\\nf 707 1795 1434\\nf 796 1434 1795\\nf 1592 730 222\\nf 528 2200 730\\nf 170 222 2200\\nf 730 2200 222\\nf 1019 2505 159\\nf 890 1112 2505\\nf 528 159 1112\\nf 2505 1112 159\\nf 2335 645 1842\\nf 170 1030 645\\nf 890 1842 1030\\nf 645 1030 1842\\nf 528 1112 2200\\nf 890 1030 1112\\nf 170 2200 1030\\nf 1112 1030 2200\\nf 692 1141 2268\\nf 359 1636 1141\\nf 414 2268 1636\\nf 1141 1636 2268\\nf 1101 773 1296\\nf 1198 1792 773\\nf 359 1296 1792\\nf 773 1792 1296\\nf 1019 1320 2537\\nf 414 1733 1320\\nf 1198 2537 1733\\nf 1320 1733 2537\\nf 359 1792 1636\\nf 1198 1733 1792\\nf 414 1636 1733\\nf 1792 1733 1636\\nf 2335 1842 60\\nf 890 506 1842\\nf 1023 60 506\\nf 1842 506 60\\nf 1019 2537 2505\\nf 1198 158 2537\\nf 890 2505 158\\nf 2537 158 2505\\nf 1101 1039 773\\nf 1023 69 1039\\nf 1198 773 69\\nf 1039 69 773\\nf 890 158 506\\nf 1198 69 158\\nf 1023 506 69\\nf 158 69 506\\nf 300 497 434\\nf 1991 1162 497\\nf 1635 434 1162\\nf 497 1162 434\\nf 768 519 1618\\nf 1650 2160 519\\nf 1991 1618 2160\\nf 519 2160 1618\\nf 227 1558 121\\nf 1635 1648 1558\\nf 1650 121 1648\\nf 1558 1648 121\\nf 1991 2160 1162\\nf 1650 1648 2160\\nf 1635 1162 1648\\nf 2160 1648 1162\\nf 1828 745 2212\\nf 1879 2260 745\\nf 2305 2212 2260\\nf 745 2260 2212\\nf 596 1412 821\\nf 209 1661 1412\\nf 1879 821 1661\\nf 1412 1661 821\\nf 768 1120 2249\\nf 2305 288 1120\\nf 209 2249 288\\nf 1120 288 2249\\nf 1879 1661 2260\\nf 209 288 1661\\nf 2305 2260 288\\nf 1661 288 2260\\nf 1592 383 885\\nf 45 1642 383\\nf 301 885 1642\\nf 383 1642 885\\nf 227 717 2131\\nf 2136 1462 717\\nf 45 2131 1462\\nf 717 1462 2131\\nf 596 105 2508\\nf 301 1222 105\\nf 2136 2508 1222\\nf 105 1222 2508\\nf 45 1462 1642\\nf 2136 1222 1462\\nf 301 1642 1222\\nf 1462 1222 1642\\nf 768 2249 519\\nf 209 212 2249\\nf 1650 519 212\\nf 2249 212 519\\nf 596 2508 1412\\nf 2136 1961 2508\\nf 209 1412 1961\\nf 2508 1961 1412\\nf 227 121 717\\nf 1650 1694 121\\nf 2136 717 1694\\nf 121 1694 717\\nf 209 1961 212\\nf 2136 1694 1961\\nf 1650 212 1694\\nf 1961 1694 212\\nf 1777 2543 1402\\nf 2366 244 2543\\nf 2038 1402 244\\nf 2543 244 1402\\nf 1653 2434 624\\nf 813 1032 2434\\nf 2366 624 1032\\nf 2434 1032 624\\nf 762 1932 442\\nf 2038 2176 1932\\nf 813 442 2176\\nf 1932 2176 442\\nf 2366 1032 244\\nf 813 2176 1032\\nf 2038 244 2176\\nf 1032 2176 244\\nf 692 1430 226\\nf 1442 2206 1430\\nf 1706 226 2206\\nf 1430 2206 226\\nf 1024 1130 2316\\nf 556 1172 1130\\nf 1442 2316 1172\\nf 1130 1172 2316\\nf 1653 2102 1741\\nf 1706 739 2102\\nf 556 1741 739\\nf 2102 739 1741\\nf 1442 1172 2206\\nf 556 739 1172\\nf 1706 2206 739\\nf 1172 739 2206\\nf 1828 1656 2124\\nf 1049 2269 1656\\nf 992 2124 2269\\nf 1656 2269 2124\\nf 762 1702 872\\nf 2081 1319 1702\\nf 1049 872 1319\\nf 1702 1319 872\\nf 1024 2409 1570\\nf 992 2003 2409\\nf 2081 1570 2003\\nf 2409 2003 1570\\nf 1049 1319 2269\\nf 2081 2003 1319\\nf 992 2269 2003\\nf 1319 2003 2269\\nf 1653 1741 2434\\nf 556 55 1741\\nf 813 2434 55\\nf 1741 55 2434\\nf 1024 1570 1130\\nf 2081 583 1570\\nf 556 1130 583\\nf 1570 583 1130\\nf 762 442 1702\\nf 813 2016 442\\nf 2081 1702 2016\\nf 442 2016 1702\\nf 556 583 55\\nf 2081 2016 583\\nf 813 55 2016\\nf 583 2016 55\\nf 1592 885 730\\nf 301 216 885\\nf 528 730 216\\nf 885 216 730\\nf 596 716 105\\nf 1114 1330 716\\nf 301 105 1330\\nf 716 1330 105\\nf 1019 159 499\\nf 528 2255 159\\nf 1114 499 2255\\nf 159 2255 499\\nf 301 1330 216\\nf 1114 2255 1330\\nf 528 216 2255\\nf 1330 2255 216\\nf 1828 2124 745\\nf 992 1604 2124\\nf 1879 745 1604\\nf 2124 1604 745\\nf 1024 95 2409\\nf 1084 851 95\\nf 992 2409 851\\nf 95 851 2409\\nf 596 821 1107\\nf 1879 956 821\\nf 1084 1107 956\\nf 821 956 1107\\nf 992 851 1604\\nf 1084 956 851\\nf 1879 1604 956\\nf 851 956 1604\\nf 692 2268 1430\\nf 414 2397 2268\\nf 1442 1430 2397\\nf 2268 2397 1430\\nf 1019 2059 1320\\nf 958 2158 2059\\nf 414 1320 2158\\nf 2059 2158 1320\\nf 1024 2316 504\\nf 1442 2130 2316\\nf 958 504 2130\\nf 2316 2130 504\\nf 414 2158 2397\\nf 958 2130 2158\\nf 1442 2397 2130\\nf 2158 2130 2397\\nf 596 1107 716\\nf 1084 2386 1107\\nf 1114 716 2386\\nf 1107 2386 716\\nf 1024 504 95\\nf 958 571 504\\nf 1084 95 571\\nf 504 571 95\\nf 1019 499 2059\\nf 1114 1913 499\\nf 958 2059 1913\\nf 499 1913 2059\\nf 1084 571 2386\\nf 958 1913 571\\nf 1114 2386 1913\\nf 571 1913 2386\\nf 395 594 1959\\nf 707 1744 594\\nf 1870 1959 1744\\nf 594 1744 1959\\nf 1101 2173 380\\nf 553 106 2173\\nf 707 380 106\\nf 2173 106 380\\nf 526 1937 267\\nf 1870 2236 1937\\nf 553 267 2236\\nf 1937 2236 267\\nf 707 106 1744\\nf 553 2236 106\\nf 1870 1744 2236\\nf 106 2236 1744\\nf 692 691 1141\\nf 780 2329 691\\nf 359 1141 2329\\nf 691 2329 1141\\nf 1225 254 1841\\nf 210 1806 254\\nf 780 1841 1806\\nf 254 1806 1841\\nf 1101 1296 397\\nf 359 1507 1296\\nf 210 397 1507\\nf 1296 1507 397\\nf 780 1806 2329\\nf 210 1507 1806\\nf 359 2329 1507\\nf 1806 1507 2329\\nf 1757 770 287\\nf 2449 1065 770\\nf 2389 287 1065\\nf 770 1065 287\\nf 526 1470 997\\nf 2103 1658 1470\\nf 2449 997 1658\\nf 1470 1658 997\\nf 1225 1156 1735\\nf 2389 2456 1156\\nf 2103 1735 2456\\nf 1156 2456 1735\\nf 2449 1658 1065\\nf 2103 2456 1658\\nf 2389 1065 2456\\nf 1658 2456 1065\\nf 1101 397 2173\\nf 210 2557 397\\nf 553 2173 2557\\nf 397 2557 2173\\nf 1225 1735 254\\nf 2103 530 1735\\nf 210 254 530\\nf 1735 530 254\\nf 526 267 1470\\nf 553 411 267\\nf 2103 1470 411\\nf 267 411 1470\\nf 210 530 2557\\nf 2103 411 530\\nf 553 2557 411\\nf 530 411 2557\\nf 1777 753 2543\\nf 657 334 753\\nf 2366 2543 334\\nf 753 334 2543\\nf 197 151 1581\\nf 2311 1399 151\\nf 657 1581 1399\\nf 151 1399 1581\\nf 1653 624 993\\nf 2366 1853 624\\nf 2311 993 1853\\nf 624 1853 993\\nf 657 1399 334\\nf 2311 1853 1399\\nf 2366 334 1853\\nf 1399 1853 334\\nf 744 2080 938\\nf 731 516 2080\\nf 524 938 516\\nf 2080 516 938\\nf 31 1962 1194\\nf 255 1935 1962\\nf 731 1194 1935\\nf 1962 1935 1194\\nf 197 79 1387\\nf 524 847 79\\nf 255 1387 847\\nf 79 847 1387\\nf 731 1935 516\\nf 255 847 1935\\nf 524 516 847\\nf 1935 847 516\\nf 692 226 889\\nf 1706 2170 226\\nf 1063 889 2170\\nf 226 2170 889\\nf 1653 998 2102\\nf 1443 1528 998\\nf 1706 2102 1528\\nf 998 1528 2102\\nf 31 2527 1301\\nf 1063 1862 2527\\nf 1443 1301 1862\\nf 2527 1862 1301\\nf 1706 1528 2170\\nf 1443 1862 1528\\nf 1063 2170 1862\\nf 1528 1862 2170\\nf 197 1387 151\\nf 255 1077 1387\\nf 2311 151 1077\\nf 1387 1077 151\\nf 31 1301 1962\\nf 1443 455 1301\\nf 255 1962 455\\nf 1301 455 1962\\nf 1653 993 998\\nf 2311 18 993\\nf 1443 998 18\\nf 993 18 998\\nf 255 455 1077\\nf 1443 18 455\\nf 2311 1077 18\\nf 455 18 1077\\nf 2282 321 1\\nf 71 205 321\\nf 876 1 205\\nf 321 205 1\\nf 2358 77 399\\nf 1200 1874 77\\nf 71 399 1874\\nf 77 1874 399\\nf 1099 2126 949\\nf 876 1815 2126\\nf 1200 949 1815\\nf 2126 1815 949\\nf 71 1874 205\\nf 1200 1815 1874\\nf 876 205 1815\\nf 1874 1815 205\\nf 1757 1881 854\\nf 2235 215 1881\\nf 5 854 215\\nf 1881 215 854\\nf 2231 1726 622\\nf 420 2299 1726\\nf 2235 622 2299\\nf 1726 2299 622\\nf 2358 1931 1679\\nf 5 303 1931\\nf 420 1679 303\\nf 1931 303 1679\\nf 2235 2299 215\\nf 420 303 2299\\nf 5 215 303\\nf 2299 303 215\\nf 744 2556 1173\\nf 926 705 2556\\nf 2352 1173 705\\nf 2556 705 1173\\nf 1099 298 849\\nf 891 1532 298\\nf 926 849 1532\\nf 298 1532 849\\nf 2231 1005 1270\\nf 2352 2258 1005\\nf 891 1270 2258\\nf 1005 2258 1270\\nf 926 1532 705\\nf 891 2258 1532\\nf 2352 705 2258\\nf 1532 2258 705\\nf 2358 1679 77\\nf 420 1300 1679\\nf 1200 77 1300\\nf 1679 1300 77\\nf 2231 1270 1726\\nf 891 346 1270\\nf 420 1726 346\\nf 1270 346 1726\\nf 1099 949 298\\nf 1200 1457 949\\nf 891 298 1457\\nf 949 1457 298\\nf 420 346 1300\\nf 891 1457 346\\nf 1200 1300 1457\\nf 346 1457 1300\\nf 692 889 691\\nf 1063 1885 889\\nf 780 691 1885\\nf 889 1885 691\\nf 31 2107 2527\\nf 1486 912 2107\\nf 1063 2527 912\\nf 2107 912 2527\\nf 1225 1841 352\\nf 780 1764 1841\\nf 1486 352 1764\\nf 1841 1764 352\\nf 1063 912 1885\\nf 1486 1764 912\\nf 780 1885 1764\\nf 912 1764 1885\\nf 744 1173 2080\\nf 2352 896 1173\\nf 731 2080 896\\nf 1173 896 2080\\nf 2231 1239 1005\\nf 999 2133 1239\\nf 2352 1005 2133\\nf 1239 2133 1005\\nf 31 1194 477\\nf 731 357 1194\\nf 999 477 357\\nf 1194 357 477\\nf 2352 2133 896\\nf 999 357 2133\\nf 731 896 357\\nf 2133 357 896\\nf 1757 287 1881\\nf 2389 1585 287\\nf 2235 1881 1585\\nf 287 1585 1881\\nf 1225 1669 1156\\nf 2286 835 1669\\nf 2389 1156 835\\nf 1669 835 1156\\nf 2231 622 1080\\nf 2235 572 622\\nf 2286 1080 572\\nf 622 572 1080\\nf 2389 835 1585\\nf 2286 572 835\\nf 2235 1585 572\\nf 835 572 1585\\nf 31 477 2107\\nf 999 263 477\\nf 1486 2107 263\\nf 477 263 2107\\nf 2231 1080 1239\\nf 2286 1197 1080\\nf 999 1239 1197\\nf 1080 1197 1239\\nf 1225 352 1669\\nf 1486 761 352\\nf 2286 1669 761\\nf 352 761 1669\\nf 999 1197 263\\nf 2286 761 1197\\nf 1486 263 761\\nf 1197 761 263\\nf 395 1959 2320\\nf 1870 1369 1959\\nf 2087 2320 1369\\nf 1959 1369 2320\\nf 526 1692 1937\\nf 165 918 1692\\nf 1870 1937 918\\nf 1692 918 1937\\nf 1729 2480 1695\\nf 2087 23 2480\\nf 165 1695 23\\nf 2480 23 1695\\nf 1870 918 1369\\nf 165 23 918\\nf 2087 1369 23\\nf 918 23 1369\\nf 1757 2073 770\\nf 1317 1535 2073\\nf 2449 770 1535\\nf 2073 1535 770\\nf 1177 679 1951\\nf 2496 1384 679\\nf 1317 1951 1384\\nf 679 1384 1951\\nf 526 997 2155\\nf 2449 2342 997\\nf 2496 2155 2342\\nf 997 2342 2155\\nf 1317 1384 1535\\nf 2496 2342 1384\\nf 2449 1535 2342\\nf 1384 2342 1535\\nf 256 2455 1992\\nf 981 1511 2455\\nf 2062 1992 1511\\nf 2455 1511 1992\\nf 1729 1957 1452\\nf 160 2288 1957\\nf 981 1452 2288\\nf 1957 2288 1452\\nf 1177 1210 438\\nf 2062 1484 1210\\nf 160 438 1484\\nf 1210 1484 438\\nf 981 2288 1511\\nf 160 1484 2288\\nf 2062 1511 1484\\nf 2288 1484 1511\\nf 526 2155 1692\\nf 2496 1283 2155\\nf 165 1692 1283\\nf 2155 1283 1692\\nf 1177 438 679\\nf 160 2528 438\\nf 2496 679 2528\\nf 438 2528 679\\nf 1729 1695 1957\\nf 165 1928 1695\\nf 160 1957 1928\\nf 1695 1928 1957\\nf 2496 2528 1283\\nf 160 1928 2528\\nf 165 1283 1928\\nf 2528 1928 1283\\nf 2282 319 321\\nf 683 1896 319\\nf 71 321 1896\\nf 319 1896 321\\nf 213 586 1614\\nf 449 994 586\\nf 683 1614 994\\nf 586 994 1614\\nf 2358 399 1725\\nf 71 1681 399\\nf 449 1725 1681\\nf 399 1681 1725\\nf 683 994 1896\\nf 449 1681 994\\nf 71 1896 1681\\nf 994 1681 1896\\nf 880 732 1700\\nf 1363 2354 732\\nf 415 1700 2354\\nf 732 2354 1700\\nf 598 605 2303\\nf 2355 2351 605\\nf 1363 2303 2351\\nf 605 2351 2303\\nf 213 1718 305\\nf 415 1527 1718\\nf 2355 305 1527\\nf 1718 1527 305\\nf 1363 2351 2354\\nf 2355 1527 2351\\nf 415 2354 1527\\nf 2351 1527 2354\\nf 1757 854 464\\nf 5 935 854\\nf 1115 464 935\\nf 854 935 464\\nf 2358 2257 1931\\nf 1240 2084 2257\\nf 5 1931 2084\\nf 2257 2084 1931\\nf 598 1936 897\\nf 1115 1353 1936\\nf 1240 897 1353\\nf 1936 1353 897\\nf 5 2084 935\\nf 1240 1353 2084\\nf 1115 935 1353\\nf 2084 1353 935\\nf 213 305 586\\nf 2355 715 305\\nf 449 586 715\\nf 305 715 586\\nf 598 897 605\\nf 1240 2473 897\\nf 2355 605 2473\\nf 897 2473 605\\nf 2358 1725 2257\\nf 449 2060 1725\\nf 1240 2257 2060\\nf 1725 2060 2257\\nf 2355 2473 715\\nf 1240 2060 2473\\nf 449 715 2060\\nf 2473 2060 715\\nf 1831 2183 1571\\nf 253 1469 2183\\nf 1425 1571 1469\\nf 2183 1469 1571\\nf 924 416 2051\\nf 1666 224 416\\nf 253 2051 224\\nf 416 224 2051\\nf 610 2058 1917\\nf 1425 2138 2058\\nf 1666 1917 2138\\nf 2058 2138 1917\\nf 253 224 1469\\nf 1666 2138 224\\nf 1425 1469 2138\\nf 224 2138 1469\\nf 256 2020 1904\\nf 2555 654 2020\\nf 2302 1904 654\\nf 2020 654 1904\\nf 1475 2086 2411\\nf 1876 2187 2086\\nf 2555 2411 2187\\nf 2086 2187 2411\\nf 924 1866 157\\nf 2302 1349 1866\\nf 1876 157 1349\\nf 1866 1349 157\\nf 2555 2187 654\\nf 1876 1349 2187\\nf 2302 654 1349\\nf 2187 1349 654\\nf 880 1153 1069\\nf 1517 1845 1153\\nf 2022 1069 1845\\nf 1153 1845 1069\\nf 610 701 1447\\nf 1667 944 701\\nf 1517 1447 944\\nf 701 944 1447\\nf 1475 259 1830\\nf 2022 65 259\\nf 1667 1830 65\\nf 259 65 1830\\nf 1517 944 1845\\nf 1667 65 944\\nf 2022 1845 65\\nf 944 65 1845\\nf 924 157 416\\nf 1876 2168 157\\nf 1666 416 2168\\nf 157 2168 416\\nf 1475 1830 2086\\nf 1667 1235 1830\\nf 1876 2086 1235\\nf 1830 1235 2086\\nf 610 1917 701\\nf 1666 1949 1917\\nf 1667 701 1949\\nf 1917 1949 701\\nf 1876 1235 2168\\nf 1667 1949 1235\\nf 1666 2168 1949\\nf 1235 1949 2168\\nf 1757 464 2073\\nf 1115 2381 464\\nf 1317 2073 2381\\nf 464 2381 2073\\nf 598 1823 1936\\nf 1256 1017 1823\\nf 1115 1936 1017\\nf 1823 1017 1936\\nf 1177 1951 2238\\nf 1317 80 1951\\nf 1256 2238 80\\nf 1951 80 2238\\nf 1115 1017 2381\\nf 1256 80 1017\\nf 1317 2381 80\\nf 1017 80 2381\\nf 880 1069 732\\nf 2022 1707 1069\\nf 1363 732 1707\\nf 1069 1707 732\\nf 1475 429 259\\nf 601 929 429\\nf 2022 259 929\\nf 429 929 259\\nf 598 2303 1362\\nf 1363 589 2303\\nf 601 1362 589\\nf 2303 589 1362\\nf 2022 929 1707\\nf 601 589 929\\nf 1363 1707 589\\nf 929 589 1707\\nf 256 1992 2020\\nf 2062 188 1992\\nf 2555 2020 188\\nf 1992 188 2020\\nf 1177 595 1210\\nf 278 173 595\\nf 2062 1210 173\\nf 595 173 1210\\nf 1475 2411 634\\nf 2555 175 2411\\nf 278 634 175\\nf 2411 175 634\\nf 2062 173 188\\nf 278 175 173\\nf 2555 188 175\\nf 173 175 188\\nf 598 1362 1823\\nf 601 1249 1362\\nf 1256 1823 1249\\nf 1362 1249 1823\\nf 1475 634 429\\nf 278 542 634\\nf 601 429 542\\nf 634 542 429\\nf 1177 2238 595\\nf 1256 921 2238\\nf 278 595 921\\nf 2238 921 595\\nf 601 542 1249\\nf 278 921 542\\nf 1256 1249 921\\nf 542 921 1249\\nf 395 2320 1383\\nf 2087 1322 2320\\nf 2501 1383 1322\\nf 2320 1322 1383\\nf 1729 119 2480\\nf 1946 2129 119\\nf 2087 2480 2129\\nf 119 2129 2480\\nf 1887 1357 7\\nf 2501 2339 1357\\nf 1946 7 2339\\nf 1357 2339 7\\nf 2087 2129 1322\\nf 1946 2339 2129\\nf 2501 1322 2339\\nf 2129 2339 1322\\nf 256 1671 2455\\nf 711 2507 1671\\nf 981 2455 2507\\nf 1671 2507 2455\\nf 400 1921 2057\\nf 1911 2503 1921\\nf 711 2057 2503\\nf 1921 2503 2057\\nf 1729 1452 2400\\nf 981 1236 1452\\nf 1911 2400 1236\\nf 1452 1236 2400\\nf 711 2503 2507\\nf 1911 1236 2503\\nf 981 2507 1236\\nf 2503 1236 2507\\nf 2553 1022 1282\\nf 1132 886 1022\\nf 2301 1282 886\\nf 1022 886 1282\\nf 1887 668 273\\nf 1140 232 668\\nf 1132 273 232\\nf 668 232 273\\nf 400 1926 1721\\nf 2301 93 1926\\nf 1140 1721 93\\nf 1926 93 1721\\nf 1132 232 886\\nf 1140 93 232\\nf 2301 886 93\\nf 232 93 886\\nf 1729 2400 119\\nf 1911 2450 2400\\nf 1946 119 2450\\nf 2400 2450 119\\nf 400 1721 1921\\nf 1140 129 1721\\nf 1911 1921 129\\nf 1721 129 1921\\nf 1887 7 668\\nf 1946 782 7\\nf 1140 668 782\\nf 7 782 668\\nf 1911 129 2450\\nf 1140 782 129\\nf 1946 2450 782\\nf 129 782 2450\\nf 1831 2044 2183\\nf 1996 43 2044\\nf 253 2183 43\\nf 2044 43 2183\\nf 1111 767 16\\nf 1872 100 767\\nf 1996 16 100\\nf 767 100 16\\nf 924 2051 2491\\nf 253 1480 2051\\nf 1872 2491 1480\\nf 2051 1480 2491\\nf 1996 100 43\\nf 1872 1480 100\\nf 253 43 1480\\nf 100 1480 43\\nf 1203 1923 61\\nf 199 2153 1923\\nf 2150 61 2153\\nf 1923 2153 61\\nf 318 1280 10\\nf 1188 270 1280\\nf 199 10 270\\nf 1280 270 10\\nf 1111 2054 6\\nf 2150 1572 2054\\nf 1188 6 1572\\nf 2054 1572 6\\nf 199 270 2153\\nf 1188 1572 270\\nf 2150 2153 1572\\nf 270 1572 2153\\nf 256 1904 1165\\nf 2302 2065 1904\\nf 1547 1165 2065\\nf 1904 2065 1165\\nf 924 2529 1866\\nf 2027 2379 2529\\nf 2302 1866 2379\\nf 2529 2379 1866\\nf 318 1459 281\\nf 1547 82 1459\\nf 2027 281 82\\nf 1459 82 281\\nf 2302 2379 2065\\nf 2027 82 2379\\nf 1547 2065 82\\nf 2379 82 2065\\nf 1111 6 767\\nf 1188 955 6\\nf 1872 767 955\\nf 6 955 767\\nf 318 281 1280\\nf 2027 1025 281\\nf 1188 1280 1025\\nf 281 1025 1280\\nf 924 2491 2529\\nf 1872 1066 2491\\nf 2027 2529 1066\\nf 2491 1066 2529\\nf 1188 1025 955\\nf 2027 1066 1025\\nf 1872 955 1066\\nf 1025 1066 955\\nf 887 76 1424\\nf 718 2201 76\\nf 2477 1424 2201\\nf 76 2201 1424\\nf 2218 2418 1580\\nf 740 83 2418\\nf 718 1580 83\\nf 2418 83 1580\\nf 806 2362 181\\nf 2477 1899 2362\\nf 740 181 1899\\nf 2362 1899 181\\nf 718 83 2201\\nf 740 1899 83\\nf 2477 2201 1899\\nf 83 1899 2201\\nf 2553 46 340\\nf 579 1284 46\\nf 386 340 1284\\nf 46 1284 340\\nf 1589 15 936\\nf 407 427 15\\nf 579 936 427\\nf 15 427 936\\nf 2218 378 1117\\nf 386 2079 378\\nf 407 1117 2079\\nf 378 2079 1117\\nf 579 427 1284\\nf 407 2079 427\\nf 386 1284 2079\\nf 427 2079 1284\\nf 1203 1413 769\\nf 1925 1450 1413\\nf 1791 769 1450\\nf 1413 1450 769\\nf 806 1292 1909\\nf 229 1531 1292\\nf 1925 1909 1531\\nf 1292 1531 1909\\nf 1589 2464 534\\nf 1791 2182 2464\\nf 229 534 2182\\nf 2464 2182 534\\nf 1925 1531 1450\\nf 229 2182 1531\\nf 1791 1450 2182\\nf 1531 2182 1450\\nf 2218 1117 2418\\nf 407 1473 1117\\nf 740 2418 1473\\nf 1117 1473 2418\\nf 1589 534 15\\nf 229 2064 534\\nf 407 15 2064\\nf 534 2064 15\\nf 806 181 1292\\nf 740 1248 181\\nf 229 1292 1248\\nf 181 1248 1292\\nf 407 2064 1473\\nf 229 1248 2064\\nf 740 1473 1248\\nf 2064 1248 1473\\nf 256 1165 1671\\nf 1547 1551 1165\\nf 711 1671 1551\\nf 1165 1551 1671\\nf 318 2318 1459\\nf 1696 578 2318\\nf 1547 1459 578\\nf 2318 578 1459\\nf 400 2057 1496\\nf 711 995 2057\\nf 1696 1496 995\\nf 2057 995 1496\\nf 1547 578 1551\\nf 1696 995 578\\nf 711 1551 995\\nf 578 995 1551\\nf 1203 769 1923\\nf 1791 53 769\\nf 199 1923 53\\nf 769 53 1923\\nf 1589 478 2464\\nf 326 1611 478\\nf 1791 2464 1611\\nf 478 1611 2464\\nf 318 10 193\\nf 199 1211 10\\nf 326 193 1211\\nf 10 1211 193\\nf 1791 1611 53\\nf 326 1211 1611\\nf 199 53 1211\\nf 1611 1211 53\\nf 2553 1282 46\\nf 2301 484 1282\\nf 579 46 484\\nf 1282 484 46\\nf 400 1783 1926\\nf 869 2244 1783\\nf 2301 1926 2244\\nf 1783 2244 1926\\nf 1589 936 1455\\nf 579 2047 936\\nf 869 1455 2047\\nf 936 2047 1455\\nf 2301 2244 484\\nf 869 2047 2244\\nf 579 484 2047\\nf 2244 2047 484\\nf 318 193 2318\\nf 326 1965 193\\nf 1696 2318 1965\\nf 193 1965 2318\\nf 1589 1455 478\\nf 869 1676 1455\\nf 326 478 1676\\nf 1455 1676 478\\nf 400 1496 1783\\nf 1696 1888 1496\\nf 869 1783 1888\\nf 1496 1888 1783\\nf 326 1676 1965\\nf 869 1888 1676\\nf 1696 1965 1888\\nf 1676 1888 1965\\nf 1777 1402 566\\nf 2038 643 1402\\nf 309 566 643\\nf 1402 643 566\\nf 762 2445 1932\\nf 1993 659 2445\\nf 2038 1932 659\\nf 2445 659 1932\\nf 487 2304 299\\nf 309 2562 2304\\nf 1993 299 2562\\nf 2304 2562 299\\nf 2038 659 643\\nf 1993 2562 659\\nf 309 643 2562\\nf 659 2562 643\\nf 1828 791 1656\\nf 1329 208 791\\nf 1049 1656 208\\nf 791 208 1656\\nf 2185 1997 1826\\nf 1465 1436 1997\\nf 1329 1826 1436\\nf 1997 1436 1826\\nf 762 872 1245\\nf 1049 1067 872\\nf 1465 1245 1067\\nf 872 1067 1245\\nf 1329 1436 208\\nf 1465 1067 1436\\nf 1049 208 1067\\nf 1436 1067 208\\nf 1191 1768 1273\\nf 130 2146 1768\\nf 341 1273 2146\\nf 1768 2146 1273\\nf 487 2322 117\\nf 2167 311 2322\\nf 130 117 311\\nf 2322 311 117\\nf 2185 972 1219\\nf 341 980 972\\nf 2167 1219 980\\nf 972 980 1219\\nf 130 311 2146\\nf 2167 980 311\\nf 341 2146 980\\nf 311 980 2146\\nf 762 1245 2445\\nf 1465 422 1245\\nf 1993 2445 422\\nf 1245 422 2445\\nf 2185 1219 1997\\nf 2167 792 1219\\nf 1465 1997 792\\nf 1219 792 1997\\nf 487 299 2322\\nf 1993 1379 299\\nf 2167 2322 1379\\nf 299 1379 2322\\nf 1465 792 422\\nf 2167 1379 792\\nf 1993 422 1379\\nf 792 1379 422\\nf 300 1652 497\\nf 2427 3 1652\\nf 1991 497 3\\nf 1652 3 497\\nf 1594 1537 619\\nf 2382 1279 1537\\nf 2427 619 1279\\nf 1537 1279 619\\nf 768 1618 1891\\nf 1991 960 1618\\nf 2382 1891 960\\nf 1618 960 1891\\nf 2427 1279 3\\nf 2382 960 1279\\nf 1991 3 960\\nf 1279 960 3\\nf 2402 1187 1569\\nf 1709 1627 1187\\nf 1209 1569 1627\\nf 1187 1627 1569\\nf 1609 1461 2435\\nf 2334 966 1461\\nf 1709 2435 966\\nf 1461 966 2435\\nf 1594 959 456\\nf 1209 726 959\\nf 2334 456 726\\nf 959 726 456\\nf 1709 966 1627\\nf 2334 726 966\\nf 1209 1627 726\\nf 966 726 1627\\nf 1828 2212 26\\nf 2305 1785 2212\\nf 1298 26 1785\\nf 2212 1785 26\\nf 768 1717 1120\\nf 441 1944 1717\\nf 2305 1120 1944\\nf 1717 1944 1120\\nf 1609 819 392\\nf 1298 1348 819\\nf 441 392 1348\\nf 819 1348 392\\nf 2305 1944 1785\\nf 441 1348 1944\\nf 1298 1785 1348\\nf 1944 1348 1785\\nf 1594 456 1537\\nf 2334 2055 456\\nf 2382 1537 2055\\nf 456 2055 1537\\nf 1609 392 1461\\nf 441 284 392\\nf 2334 1461 284\\nf 392 284 1461\\nf 768 1891 1717\\nf 2382 450 1891\\nf 441 1717 450\\nf 1891 450 1717\\nf 2334 284 2055\\nf 441 450 284\\nf 2382 2055 450\\nf 284 450 2055\\nf 1769 1124 1471\\nf 1553 1600 1124\\nf 2292 1471 1600\\nf 1124 1600 1471\\nf 2014 1599 1105\\nf 317 1567 1599\\nf 1553 1105 1567\\nf 1599 1567 1105\\nf 384 1929 1181\\nf 2292 1955 1929\\nf 317 1181 1955\\nf 1929 1955 1181\\nf 1553 1567 1600\\nf 317 1955 1567\\nf 2292 1600 1955\\nf 1567 1955 1600\\nf 1191 832 1790\\nf 1090 410 832\\nf 2169 1790 410\\nf 832 410 1790\\nf 1421 1771 1251\\nf 469 1731 1771\\nf 1090 1251 1731\\nf 1771 1731 1251\\nf 2014 1157 656\\nf 2169 11 1157\\nf 469 656 11\\nf 1157 11 656\\nf 1090 1731 410\\nf 469 11 1731\\nf 2169 410 11\\nf 1731 11 410\\nf 2402 2367 2414\\nf 700 884 2367\\nf 1377 2414 884\\nf 2367 884 2414\\nf 384 1085 1816\\nf 666 13 1085\\nf 700 1816 13\\nf 1085 13 1816\\nf 1421 75 1364\\nf 1377 1902 75\\nf 666 1364 1902\\nf 75 1902 1364\\nf 700 13 884\\nf 666 1902 13\\nf 1377 884 1902\\nf 13 1902 884\\nf 2014 656 1599\\nf 469 1603 656\\nf 317 1599 1603\\nf 656 1603 1599\\nf 1421 1364 1771\\nf 666 1306 1364\\nf 469 1771 1306\\nf 1364 1306 1771\\nf 384 1181 1085\\nf 317 742 1181\\nf 666 1085 742\\nf 1181 742 1085\\nf 469 1306 1603\\nf 666 742 1306\\nf 317 1603 742\\nf 1306 742 1603\\nf 1828 26 791\\nf 1298 1047 26\\nf 1329 791 1047\\nf 26 1047 791\\nf 1609 201 819\\nf 2210 21 201\\nf 1298 819 21\\nf 201 21 819\\nf 2185 1826 1110\\nf 1329 64 1826\\nf 2210 1110 64\\nf 1826 64 1110\\nf 1298 21 1047\\nf 2210 64 21\\nf 1329 1047 64\\nf 21 64 1047\\nf 2402 2414 1187\\nf 1377 1391 2414\\nf 1709 1187 1391\\nf 2414 1391 1187\\nf 1421 810 75\\nf 1640 1044 810\\nf 1377 75 1044\\nf 810 1044 75\\nf 1609 2435 2012\\nf 1709 1907 2435\\nf 1640 2012 1907\\nf 2435 1907 2012\\nf 1377 1044 1391\\nf 1640 1907 1044\\nf 1709 1391 1907\\nf 1044 1907 1391\\nf 1191 1273 832\\nf 341 1952 1273\\nf 1090 832 1952\\nf 1273 1952 832\\nf 2185 1103 972\\nf 804 492 1103\\nf 341 972 492\\nf 1103 492 972\\nf 1421 1251 547\\nf 1090 2135 1251\\nf 804 547 2135\\nf 1251 2135 547\\nf 341 492 1952\\nf 804 2135 492\\nf 1090 1952 2135\\nf 492 2135 1952\\nf 1609 2012 201\\nf 1640 669 2012\\nf 2210 201 669\\nf 2012 669 201\\nf 1421 547 810\\nf 804 1299 547\\nf 1640 810 1299\\nf 547 1299 810\\nf 2185 1110 1103\\nf 2210 1366 1110\\nf 804 1103 1366\\nf 1110 1366 1103\\nf 1640 1299 669\\nf 804 1366 1299\\nf 2210 669 1366\\nf 1299 1366 669\\nf 300 1576 1312\\nf 2483 1356 1576\\nf 527 1312 1356\\nf 1576 1356 1312\\nf 1011 1519 1711\\nf 1624 772 1519\\nf 2483 1711 772\\nf 1519 772 1711\\nf 2547 756 70\\nf 527 275 756\\nf 1624 70 275\\nf 756 275 70\\nf 2483 772 1356\\nf 1624 275 772\\nf 527 1356 275\\nf 772 275 1356\\nf 2525 834 444\\nf 991 115 834\\nf 1138 444 115\\nf 834 115 444\\nf 2467 1818 722\\nf 1587 1228 1818\\nf 991 722 1228\\nf 1818 1228 722\\nf 1011 1665 2433\\nf 1138 1125 1665\\nf 1587 2433 1125\\nf 1665 1125 2433\\nf 991 1228 115\\nf 1587 1125 1228\\nf 1138 115 1125\\nf 1228 1125 115\\nf 818 1385 44\\nf 895 1827 1385\\nf 1146 44 1827\\nf 1385 1827 44\\nf 2547 2217 1708\\nf 514 607 2217\\nf 895 1708 607\\nf 2217 607 1708\\nf 2467 92 529\\nf 1146 1628 92\\nf 514 529 1628\\nf 92 1628 529\\nf 895 607 1827\\nf 514 1628 607\\nf 1146 1827 1628\\nf 607 1628 1827\\nf 1011 2433 1519\\nf 1587 2159 2433\\nf 1624 1519 2159\\nf 2433 2159 1519\\nf 2467 529 1818\\nf 514 1565 529\\nf 1587 1818 1565\\nf 529 1565 1818\\nf 2547 70 2217\\nf 1624 1625 70\\nf 514 2217 1625\\nf 70 1625 2217\\nf 1587 1565 2159\\nf 514 1625 1565\\nf 1624 2159 1625\\nf 1565 1625 2159\\nf 887 948 2447\\nf 1302 507 948\\nf 2383 2447 507\\nf 948 507 2447\\nf 2521 315 2376\\nf 1441 432 315\\nf 1302 2376 432\\nf 315 432 2376\\nf 2361 2149 2223\\nf 2383 947 2149\\nf 1441 2223 947\\nf 2149 947 2223\\nf 1302 432 507\\nf 1441 947 432\\nf 2383 507 947\\nf 432 947 507\\nf 2478 1903 1382\\nf 1108 2123 1903\\nf 485 1382 2123\\nf 1903 2123 1382\\nf 67 544 1307\\nf 953 623 544\\nf 1108 1307 623\\nf 544 623 1307\\nf 2521 2297 2273\\nf 485 650 2297\\nf 953 2273 650\\nf 2297 650 2273\\nf 1108 623 2123\\nf 953 650 623\\nf 485 2123 650\\nf 623 650 2123\\nf 2525 446 1645\\nf 1564 454 446\\nf 466 1645 454\\nf 446 454 1645\\nf 2361 1748 1677\\nf 1166 515 1748\\nf 1564 1677 515\\nf 1748 515 1677\\nf 67 247 2371\\nf 466 2114 247\\nf 1166 2371 2114\\nf 247 2114 2371\\nf 1564 515 454\\nf 1166 2114 515\\nf 466 454 2114\\nf 515 2114 454\\nf 2521 2273 315\\nf 953 2179 2273\\nf 1441 315 2179\\nf 2273 2179 315\\nf 67 2371 544\\nf 1166 1770 2371\\nf 953 544 1770\\nf 2371 1770 544\\nf 2361 2223 1748\\nf 1441 1623 2223\\nf 1166 1748 1623\\nf 2223 1623 1748\\nf 953 1770 2179\\nf 1166 1623 1770\\nf 1441 2179 1623\\nf 1770 1623 2179\\nf 906 1867 428\\nf 1428 155 1867\\nf 1339 428 155\\nf 1867 155 428\\nf 2408 2002 152\\nf 1739 2538 2002\\nf 1428 152 2538\\nf 2002 2538 152\\nf 1168 391 87\\nf 1339 1501 391\\nf 1739 87 1501\\nf 391 1501 87\\nf 1428 2538 155\\nf 1739 1501 2538\\nf 1339 155 1501\\nf 2538 1501 155\\nf 818 1396 2396\\nf 665 1311 1396\\nf 323 2396 1311\\nf 1396 1311 2396\\nf 521 1375 1037\\nf 779 350 1375\\nf 665 1037 350\\nf 1375 350 1037\\nf 2408 1522 1878\\nf 323 240 1522\\nf 779 1878 240\\nf 1522 240 1878\\nf 665 350 1311\\nf 779 240 350\\nf 323 1311 240\\nf 350 240 1311\\nf 2478 2385 104\\nf 12 1004 2385\\nf 1649 104 1004\\nf 2385 1004 104\\nf 1168 1344 1472\\nf 1408 1207 1344\\nf 12 1472 1207\\nf 1344 1207 1472\\nf 521 1970 2520\\nf 1649 1869 1970\\nf 1408 2520 1869\\nf 1970 1869 2520\\nf 12 1207 1004\\nf 1408 1869 1207\\nf 1649 1004 1869\\nf 1207 1869 1004\\nf 2408 1878 2002\\nf 779 1429 1878\\nf 1739 2002 1429\\nf 1878 1429 2002\\nf 521 2520 1375\\nf 1408 822 2520\\nf 779 1375 822\\nf 2520 822 1375\\nf 1168 87 1344\\nf 1739 1060 87\\nf 1408 1344 1060\\nf 87 1060 1344\\nf 779 822 1429\\nf 1408 1060 822\\nf 1739 1429 1060\\nf 822 1060 1429\\nf 2525 1645 834\\nf 466 2323 1645\\nf 991 834 2323\\nf 1645 2323 834\\nf 67 2021 247\\nf 1185 1371 2021\\nf 466 247 1371\\nf 2021 1371 247\\nf 2467 722 2306\\nf 991 375 722\\nf 1185 2306 375\\nf 722 375 2306\\nf 466 1371 2323\\nf 1185 375 1371\\nf 991 2323 375\\nf 1371 375 2323\\nf 2478 104 1903\\nf 1649 580 104\\nf 1108 1903 580\\nf 104 580 1903\\nf 521 1969 1970\\nf 2550 460 1969\\nf 1649 1970 460\\nf 1969 460 1970\\nf 67 1307 2125\\nf 1108 1927 1307\\nf 2550 2125 1927\\nf 1307 1927 2125\\nf 1649 460 580\\nf 2550 1927 460\\nf 1108 580 1927\\nf 460 1927 580\\nf 818 44 1396\\nf 1146 1746 44\\nf 665 1396 1746\\nf 44 1746 1396\\nf 2467 2280 92\\nf 241 1852 2280\\nf 1146 92 1852\\nf 2280 1852 92\\nf 521 1037 2390\\nf 665 258 1037\\nf 241 2390 258\\nf 1037 258 2390\\nf 1146 1852 1746\\nf 241 258 1852\\nf 665 1746 258\\nf 1852 258 1746\\nf 67 2125 2021\\nf 2550 911 2125\\nf 1185 2021 911\\nf 2125 911 2021\\nf 521 2390 1969\\nf 241 1033 2390\\nf 2550 1969 1033\\nf 2390 1033 1969\\nf 2467 2306 2280\\nf 1185 482 2306\\nf 241 2280 482\\nf 2306 482 2280\\nf 2550 1033 911\\nf 241 482 1033\\nf 1185 911 482\\nf 1033 482 911\\nf 887 1424 1247\\nf 2477 306 1424\\nf 1133 1247 306\\nf 1424 306 1247\\nf 806 445 2362\\nf 1943 2232 445\\nf 2477 2362 2232\\nf 445 2232 2362\\nf 1291 1753 290\\nf 1133 939 1753\\nf 1943 290 939\\nf 1753 939 290\\nf 2477 2232 306\\nf 1943 939 2232\\nf 1133 306 939\\nf 2232 939 306\\nf 1203 983 1413\\nf 1608 354 983\\nf 1925 1413 354\\nf 983 354 1413\\nf 1668 741 467\\nf 231 831 741\\nf 1608 467 831\\nf 741 831 467\\nf 806 1909 153\\nf 1925 1068 1909\\nf 231 153 1068\\nf 1909 1068 153\\nf 1608 831 354\\nf 231 1068 831\\nf 1925 354 1068\\nf 831 1068 354\\nf 1510 382 91\\nf 49 234 382\\nf 697 91 234\\nf 382 234 91\\nf 1291 2205 662\\nf 2484 2533 2205\\nf 49 662 2533\\nf 2205 2533 662\\nf 1668 1976 2252\\nf 697 2237 1976\\nf 2484 2252 2237\\nf 1976 2237 2252\\nf 49 2533 234\\nf 2484 2237 2533\\nf 697 234 2237\\nf 2533 2237 234\\nf 806 153 445\\nf 231 1489 153\\nf 1943 445 1489\\nf 153 1489 445\\nf 1668 2252 741\\nf 2484 2214 2252\\nf 231 741 2214\\nf 2252 2214 741\\nf 1291 290 2205\\nf 1943 1549 290\\nf 2484 2205 1549\\nf 290 1549 2205\\nf 231 2214 1489\\nf 2484 1549 2214\\nf 1943 1489 1549\\nf 2214 1549 1489\\nf 1831 2423 2044\\nf 930 2099 2423\\nf 1996 2044 2099\\nf 2423 2099 2044\\nf 952 2514 1053\\nf 721 1809 2514\\nf 930 1053 1809\\nf 2514 1809 1053\\nf 1111 16 1395\\nf 1996 2253 16\\nf 721 1395 2253\\nf 16 2253 1395\\nf 930 1809 2099\\nf 721 2253 1809\\nf 1996 2099 2253\\nf 1809 2253 2099\\nf 116 1229 371\\nf 494 1897 1229\\nf 475 371 1897\\nf 1229 1897 371\\nf 40 274 1602\\nf 1689 2410 274\\nf 494 1602 2410\\nf 274 2410 1602\\nf 952 1728 894\\nf 475 426 1728\\nf 1689 894 426\\nf 1728 426 894\\nf 494 2410 1897\\nf 1689 426 2410\\nf 475 1897 426\\nf 2410 426 1897\\nf 1203 61 628\\nf 2150 698 61\\nf 797 628 698\\nf 61 698 628\\nf 1111 347 2054\\nf 1563 1918 347\\nf 2150 2054 1918\\nf 347 1918 2054\\nf 40 453 1095\\nf 797 588 453\\nf 1563 1095 588\\nf 453 588 1095\\nf 2150 1918 698\\nf 1563 588 1918\\nf 797 698 588\\nf 1918 588 698\\nf 952 894 2514\\nf 1689 2154 894\\nf 721 2514 2154\\nf 894 2154 2514\\nf 40 1095 274\\nf 1563 1015 1095\\nf 1689 274 1015\\nf 1095 1015 274\\nf 1111 1395 347\\nf 721 1747 1395\\nf 1563 347 1747\\nf 1395 1747 347\\nf 1689 1015 2154\\nf 1563 1747 1015\\nf 721 2154 1747\\nf 1015 1747 2154\\nf 2312 1315 228\\nf 20 2209 1315\\nf 733 228 2209\\nf 1315 2209 228\\nf 19 1657 2101\\nf 2388 2281 1657\\nf 20 2101 2281\\nf 1657 2281 2101\\nf 307 2360 1227\\nf 733 522 2360\\nf 2388 1227 522\\nf 2360 522 1227\\nf 20 2281 2209\\nf 2388 522 2281\\nf 733 2209 522\\nf 2281 522 2209\\nf 1510 1151 1368\\nf 62 1960 1151\\nf 2171 1368 1960\\nf 1151 1960 1368\\nf 554 1758 1894\\nf 1448 2522 1758\\nf 62 1894 2522\\nf 1758 2522 1894\\nf 19 824 2148\\nf 2171 616 824\\nf 1448 2148 616\\nf 824 616 2148\\nf 62 2522 1960\\nf 1448 616 2522\\nf 2171 1960 616\\nf 2522 616 1960\\nf 116 2353 915\\nf 2465 682 2353\\nf 582 915 682\\nf 2353 682 915\\nf 307 1954 1304\\nf 2291 1543 1954\\nf 2465 1304 1543\\nf 1954 1543 1304\\nf 554 1350 1690\\nf 582 702 1350\\nf 2291 1690 702\\nf 1350 702 1690\\nf 2465 1543 682\\nf 2291 702 1543\\nf 582 682 702\\nf 1543 702 682\\nf 19 2148 1657\\nf 1448 2472 2148\\nf 2388 1657 2472\\nf 2148 2472 1657\\nf 554 1690 1758\\nf 2291 1740 1690\\nf 1448 1758 1740\\nf 1690 1740 1758\\nf 307 1227 1954\\nf 2388 103 1227\\nf 2291 1954 103\\nf 1227 103 1954\\nf 1448 1740 2472\\nf 2291 103 1740\\nf 2388 2472 103\\nf 1740 103 2472\\nf 1203 628 983\\nf 797 2193 628\\nf 1608 983 2193\\nf 628 2193 983\\nf 40 133 453\\nf 1598 1086 133\\nf 797 453 1086\\nf 133 1086 453\\nf 1668 467 2293\\nf 1608 751 467\\nf 1598 2293 751\\nf 467 751 2293\\nf 797 1086 2193\\nf 1598 751 1086\\nf 1608 2193 751\\nf 1086 751 2193\\nf 116 915 1229\\nf 582 576 915\\nf 494 1229 576\\nf 915 576 1229\\nf 554 2544 1350\\nf 550 2347 2544\\nf 582 1350 2347\\nf 2544 2347 1350\\nf 40 1602 1839\\nf 494 1190 1602\\nf 550 1839 1190\\nf 1602 1190 1839\\nf 582 2347 576\\nf 550 1190 2347\\nf 494 576 1190\\nf 2347 1190 576\\nf 1510 91 1151\\nf 697 2502 91\\nf 62 1151 2502\\nf 91 2502 1151\\nf 1668 670 1976\\nf 1983 2116 670\\nf 697 1976 2116\\nf 670 2116 1976\\nf 554 1894 1226\\nf 62 2261 1894\\nf 1983 1226 2261\\nf 1894 2261 1226\\nf 697 2116 2502\\nf 1983 2261 2116\\nf 62 2502 2261\\nf 2116 2261 2502\\nf 40 1839 133\\nf 550 2132 1839\\nf 1598 133 2132\\nf 1839 2132 133\\nf 554 1226 2544\\nf 1983 2461 1226\\nf 550 2544 2461\\nf 1226 2461 2544\\nf 1668 2293 670\\nf 1598 2454 2293\\nf 1983 670 2454\\nf 2293 2454 670\\nf 550 2461 2132\\nf 1983 2454 2461\\nf 1598 2132 2454\\nf 2461 2454 2132\\nf 1831 1571 1013\\nf 1425 1981 1571\\nf 1916 1013 1981\\nf 1571 1981 1013\\nf 610 496 2058\\nf 2393 1118 496\\nf 1425 2058 1118\\nf 496 1118 2058\\nf 57 2317 667\\nf 1916 1541 2317\\nf 2393 667 1541\\nf 2317 1541 667\\nf 1425 1118 1981\\nf 2393 1541 1118\\nf 1916 1981 1541\\nf 1118 1541 1981\\nf 880 2046 1153\\nf 1788 1367 2046\\nf 1517 1153 1367\\nf 2046 1367 1153\\nf 1682 1843 771\\nf 1750 186 1843\\nf 1788 771 186\\nf 1843 186 771\\nf 610 1447 919\\nf 1517 1478 1447\\nf 1750 919 1478\\nf 1447 1478 919\\nf 1788 186 1367\\nf 1750 1478 186\\nf 1517 1367 1478\\nf 186 1478 1367\\nf 560 1710 413\\nf 975 150 1710\\nf 630 413 150\\nf 1710 150 413\\nf 57 520 1754\\nf 724 1683 520\\nf 975 1754 1683\\nf 520 1683 1754\\nf 1682 945 979\\nf 630 1259 945\\nf 724 979 1259\\nf 945 1259 979\\nf 975 1683 150\\nf 724 1259 1683\\nf 630 150 1259\\nf 1683 1259 150\\nf 610 919 496\\nf 1750 1533 919\\nf 2393 496 1533\\nf 919 1533 496\\nf 1682 979 1843\\nf 724 850 979\\nf 1750 1843 850\\nf 979 850 1843\\nf 57 667 520\\nf 2393 503 667\\nf 724 520 503\\nf 667 503 520\\nf 1750 850 1533\\nf 724 503 850\\nf 2393 1533 503\\nf 850 503 1533\\nf 2282 1704 319\\nf 1877 376 1704\\nf 683 319 376\\nf 1704 376 319\\nf 931 1216 840\\nf 2422 706 1216\\nf 1877 840 706\\nf 1216 706 840\\nf 213 1614 304\\nf 683 1144 1614\\nf 2422 304 1144\\nf 1614 1144 304\\nf 1877 706 376\\nf 2422 1144 706\\nf 683 376 1144\\nf 706 1144 376\\nf 1525 2526 1390\\nf 1406 402 2526\\nf 473 1390 402\\nf 2526 402 1390\\nf 238 1031 1655\\nf 1775 1158 1031\\nf 1406 1655 1158\\nf 1031 1158 1655\\nf 931 1449 1749\\nf 473 775 1449\\nf 1775 1749 775\\nf 1449 775 1749\\nf 1406 1158 402\\nf 1775 775 1158\\nf 473 402 775\\nf 1158 775 402\\nf 880 1700 587\\nf 415 125 1700\\nf 1414 587 125\\nf 1700 125 587\\nf 213 1723 1718\\nf 631 406 1723\\nf 415 1718 406\\nf 1723 406 1718\\nf 238 1820 2091\\nf 1414 1945 1820\\nf 631 2091 1945\\nf 1820 1945 2091\\nf 415 406 125\\nf 631 1945 406\\nf 1414 125 1945\\nf 406 1945 125\\nf 931 1749 1216\\nf 1775 699 1749\\nf 2422 1216 699\\nf 1749 699 1216\\nf 238 2091 1031\\nf 631 688 2091\\nf 1775 1031 688\\nf 2091 688 1031\\nf 213 304 1723\\nf 2422 1654 304\\nf 631 1723 1654\\nf 304 1654 1723\\nf 1775 688 699\\nf 631 1654 688\\nf 2422 699 1654\\nf 688 1654 699\\nf 2391 1401 1333\\nf 2043 1812 1401\\nf 1027 1333 1812\\nf 1401 1812 1333\\nf 720 1743 2485\\nf 349 2037 1743\\nf 2043 2485 2037\\nf 1743 2037 2485\\nf 760 1458 1119\\nf 1027 1257 1458\\nf 349 1119 1257\\nf 1458 1257 1119\\nf 2043 2037 1812\\nf 349 1257 2037\\nf 1027 1812 1257\\nf 2037 1257 1812\\nf 560 2248 2085\\nf 1643 194 2248\\nf 857 2085 194\\nf 2248 194 2085\\nf 154 373 512\\nf 2276 1051 373\\nf 1643 512 1051\\nf 373 1051 512\\nf 720 694 532\\nf 857 2172 694\\nf 2276 532 2172\\nf 694 2172 532\\nf 1643 1051 194\\nf 2276 2172 1051\\nf 857 194 2172\\nf 1051 2172 194\\nf 1525 901 1994\\nf 1331 1540 901\\nf 1237 1994 1540\\nf 901 1540 1994\\nf 760 1418 2239\\nf 2137 1854 1418\\nf 1331 2239 1854\\nf 1418 1854 2239\\nf 154 2143 708\\nf 1237 807 2143\\nf 2137 708 807\\nf 2143 807 708\\nf 1331 1854 1540\\nf 2137 807 1854\\nf 1237 1540 807\\nf 1854 807 1540\\nf 720 532 1743\\nf 2276 2030 532\\nf 349 1743 2030\\nf 532 2030 1743\\nf 154 708 373\\nf 2137 1014 708\\nf 2276 373 1014\\nf 708 1014 373\\nf 760 1119 1418\\nf 349 2387 1119\\nf 2137 1418 2387\\nf 1119 2387 1418\\nf 2276 1014 2030\\nf 2137 2387 1014\\nf 349 2030 2387\\nf 1014 2387 2030\\nf 880 587 2046\\nf 1414 1018 587\\nf 1788 2046 1018\\nf 587 1018 2046\\nf 238 2554 1820\\nf 2308 471 2554\\nf 1414 1820 471\\nf 2554 471 1820\\nf 1682 771 1999\\nf 1788 1361 771\\nf 2308 1999 1361\\nf 771 1361 1999\\nf 1414 471 1018\\nf 2308 1361 471\\nf 1788 1018 1361\\nf 471 1361 1018\\nf 1525 1994 2526\\nf 1237 266 1994\\nf 1406 2526 266\\nf 1994 266 2526\\nf 154 2296 2143\\nf 431 248 2296\\nf 1237 2143 248\\nf 2296 248 2143\\nf 238 1655 179\\nf 1406 2041 1655\\nf 431 179 2041\\nf 1655 2041 179\\nf 1237 248 266\\nf 431 2041 248\\nf 1406 266 2041\\nf 248 2041 266\\nf 560 413 2248\\nf 630 2265 413\\nf 1643 2248 2265\\nf 413 2265 2248\\nf 1682 1097 945\\nf 262 214 1097\\nf 630 945 214\\nf 1097 214 945\\nf 154 512 1724\\nf 1643 1910 512\\nf 262 1724 1910\\nf 512 1910 1724\\nf 630 214 2265\\nf 262 1910 214\\nf 1643 2265 1910\\nf 214 1910 2265\\nf 238 179 2554\\nf 431 608 179\\nf 2308 2554 608\\nf 179 608 2554\\nf 154 1724 2296\\nf 262 719 1724\\nf 431 2296 719\\nf 1724 719 2296\\nf 1682 1999 1097\\nf 2308 2363 1999\\nf 262 1097 2363\\nf 1999 2363 1097\\nf 431 719 608\\nf 262 2363 719\\nf 2308 608 2363\\nf 719 2363 608\\nf 2282 1 1463\\nf 876 874 1\\nf 1445 1463 874\\nf 1 874 1463\\nf 1099 1423 2126\\nf 647 2510 1423\\nf 876 2126 2510\\nf 1423 2510 2126\\nf 1388 122 593\\nf 1445 870 122\\nf 647 593 870\\nf 122 870 593\\nf 876 2510 874\\nf 647 870 2510\\nf 1445 874 870\\nf 2510 870 874\\nf 744 366 2556\\nf 32 1974 366\\nf 926 2556 1974\\nf 366 1974 2556\\nf 423 2324 2026\\nf 2549 1313 2324\\nf 32 2026 1313\\nf 2324 1313 2026\\nf 1099 849 368\\nf 926 2535 849\\nf 2549 368 2535\\nf 849 2535 368\\nf 32 1313 1974\\nf 2549 2535 1313\\nf 926 1974 2535\\nf 1313 2535 1974\\nf 1083 1745 1794\\nf 2229 293 1745\\nf 1042 1794 293\\nf 1745 293 1794\\nf 1388 196 1016\\nf 1012 1468 196\\nf 2229 1016 1468\\nf 196 1468 1016\\nf 423 1596 72\\nf 1042 1701 1596\\nf 1012 72 1701\\nf 1596 1701 72\\nf 2229 1468 293\\nf 1012 1701 1468\\nf 1042 293 1701\\nf 1468 1701 293\\nf 1099 368 1423\\nf 2549 2430 368\\nf 647 1423 2430\\nf 368 2430 1423\\nf 423 72 2324\\nf 1012 525 72\\nf 2549 2324 525\\nf 72 525 2324\\nf 1388 593 196\\nf 647 338 593\\nf 1012 196 338\\nf 593 338 196\\nf 2549 525 2430\\nf 1012 338 525\\nf 647 2430 338\\nf 525 338 2430\\nf 1777 1374 753\\nf 282 905 1374\\nf 657 753 905\\nf 1374 905 753\\nf 1705 2215 1437\\nf 1370 260 2215\\nf 282 1437 260\\nf 2215 260 1437\\nf 197 1581 546\\nf 657 2295 1581\\nf 1370 546 2295\\nf 1581 2295 546\\nf 282 260 905\\nf 1370 2295 260\\nf 657 905 2295\\nf 260 2295 905\\nf 961 2369 97\\nf 344 2287 2369\\nf 934 97 2287\\nf 2369 2287 97\\nf 531 1460 78\\nf 403 2509 1460\\nf 344 78 2509\\nf 1460 2509 78\\nf 1705 1262 2259\\nf 934 1755 1262\\nf 403 2259 1755\\nf 1262 1755 2259\\nf 344 2509 2287\\nf 403 1755 2509\\nf 934 2287 1755\\nf 2509 1755 2287\\nf 744 938 1797\\nf 524 1481 938\\nf 182 1797 1481\\nf 938 1481 1797\\nf 197 114 79\\nf 862 1613 114\\nf 524 79 1613\\nf 114 1613 79\\nf 531 1860 1297\\nf 182 827 1860\\nf 862 1297 827\\nf 1860 827 1297\\nf 524 1613 1481\\nf 862 827 1613\\nf 182 1481 827\\nf 1613 827 1481\\nf 1705 2259 2215\\nf 403 2246 2259\\nf 1370 2215 2246\\nf 2259 2246 2215\\nf 531 1297 1460\\nf 862 1680 1297\\nf 403 1460 1680\\nf 1297 1680 1460\\nf 197 546 114\\nf 1370 1593 546\\nf 862 114 1593\\nf 546 1593 114\\nf 403 1680 2246\\nf 862 1593 1680\\nf 1370 2246 1593\\nf 1680 1593 2246\\nf 2008 421 777\\nf 2147 759 421\\nf 746 777 759\\nf 421 759 777\\nf 2425 2097 877\\nf 575 1915 2097\\nf 2147 877 1915\\nf 2097 1915 877\\nf 29 990 1150\\nf 746 142 990\\nf 575 1150 142\\nf 990 142 1150\\nf 2147 1915 759\\nf 575 142 1915\\nf 746 759 142\\nf 1915 142 759\\nf 1083 1149 316\\nf 1464 2452 1149\\nf 1562 316 2452\\nf 1149 2452 316\\nf 829 996 1670\\nf 672 1123 996\\nf 1464 1670 1123\\nf 996 1123 1670\\nf 2425 728 541\\nf 1562 235 728\\nf 672 541 235\\nf 728 235 541\\nf 1464 1123 2452\\nf 672 235 1123\\nf 1562 2452 235\\nf 1123 235 2452\\nf 961 1871 2184\\nf 878 1804 1871\\nf 1634 2184 1804\\nf 1871 1804 2184\\nf 29 2009 1868\\nf 1479 1407 2009\\nf 878 1868 1407\\nf 2009 1407 1868\\nf 829 714 808\\nf 1634 2512 714\\nf 1479 808 2512\\nf 714 2512 808\\nf 878 1407 1804\\nf 1479 2512 1407\\nf 1634 1804 2512\\nf 1407 2512 1804\\nf 2425 541 2097\\nf 672 401 541\\nf 575 2097 401\\nf 541 401 2097\\nf 829 808 996\\nf 1479 2330 808\\nf 672 996 2330\\nf 808 2330 996\\nf 29 1150 2009\\nf 575 1688 1150\\nf 1479 2009 1688\\nf 1150 1688 2009\\nf 672 2330 401\\nf 1479 1688 2330\\nf 575 401 1688\\nf 2330 1688 401\\nf 744 1797 366\\nf 182 465 1797\\nf 32 366 465\\nf 1797 465 366\\nf 531 358 1860\\nf 1880 2098 358\\nf 182 1860 2098\\nf 358 2098 1860\\nf 423 2026 388\\nf 32 1221 2026\\nf 1880 388 1221\\nf 2026 1221 388\\nf 182 2098 465\\nf 1880 1221 2098\\nf 32 465 1221\\nf 2098 1221 465\\nf 961 2184 2369\\nf 1634 99 2184\\nf 344 2369 99\\nf 2184 99 2369\\nf 829 2271 714\\nf 2010 1986 2271\\nf 1634 714 1986\\nf 2271 1986 714\\nf 531 78 356\\nf 344 1933 78\\nf 2010 356 1933\\nf 78 1933 356\\nf 1634 1986 99\\nf 2010 1933 1986\\nf 344 99 1933\\nf 1986 1933 99\\nf 1083 1794 1149\\nf 1042 131 1794\\nf 1464 1149 131\\nf 1794 131 1149\\nf 423 2300 1596\\nf 417 1626 2300\\nf 1042 1596 1626\\nf 2300 1626 1596\\nf 829 1670 17\\nf 1464 296 1670\\nf 417 17 296\\nf 1670 296 17\\nf 1042 1626 131\\nf 417 296 1626\\nf 1464 131 296\\nf 1626 296 131\\nf 531 356 358\\nf 2010 374 356\\nf 1880 358 374\\nf 356 374 358\\nf 829 17 2271\\nf 417 2141 17\\nf 2010 2271 2141\\nf 17 2141 2271\\nf 423 388 2300\\nf 1880 474 388\\nf 417 2300 474\\nf 388 474 2300\\nf 2010 2141 374\\nf 417 474 2141\\nf 1880 374 474\\nf 2141 474 374\\nf 1034 1813 239\\nf 144 2152 1813\\nf 2314 239 2152\\nf 1813 2152 239\\nf 1850 461 1942\\nf 2088 967 461\\nf 144 1942 967\\nf 461 967 1942\\nf 1212 1560 435\\nf 2314 1499 1560\\nf 2088 435 1499\\nf 1560 1499 435\\nf 144 967 2152\\nf 2088 1499 967\\nf 2314 2152 1499\\nf 967 1499 2152\\nf 1497 1456 1263\\nf 329 920 1456\\nf 54 1263 920\\nf 1456 920 1263\\nf 1616 163 2326\\nf 1895 1397 163\\nf 329 2326 1397\\nf 163 1397 2326\\nf 1850 1906 1575\\nf 54 1550 1906\\nf 1895 1575 1550\\nf 1906 1550 1575\\nf 329 1397 920\\nf 1895 1550 1397\\nf 54 920 1550\\nf 1397 1550 920\\nf 1554 2453 2262\\nf 132 140 2453\\nf 1046 2262 140\\nf 2453 140 2262\\nf 1212 1631 1703\\nf 331 2417 1631\\nf 132 1703 2417\\nf 1631 2417 1703\\nf 1616 1730 245\\nf 1046 1327 1730\\nf 331 245 1327\\nf 1730 1327 245\\nf 132 2417 140\\nf 331 1327 2417\\nf 1046 140 1327\\nf 2417 1327 140\\nf 1850 1575 461\\nf 1895 748 1575\\nf 2088 461 748\\nf 1575 748 461\\nf 1616 245 163\\nf 331 510 245\\nf 1895 163 510\\nf 245 510 163\\nf 1212 435 1631\\nf 2088 1940 435\\nf 331 1631 1940\\nf 435 1940 1631\\nf 1895 510 748\\nf 331 1940 510\\nf 2088 748 1940\\nf 510 1940 748\\nf 1769 1175 1967\\nf 2019 1698 1175\\nf 2082 1967 1698\\nf 1175 1698 1967\\nf 320 2007 964\\nf 561 2534 2007\\nf 2019 964 2534\\nf 2007 2534 964\\nf 2165 648 472\\nf 2082 1619 648\\nf 561 472 1619\\nf 648 1619 472\\nf 2019 2534 1698\\nf 561 1619 2534\\nf 2082 1698 1619\\nf 2534 1619 1698\\nf 390 837 954\\nf 713 828 837\\nf 652 954 828\\nf 837 828 954\\nf 2134 620 1548\\nf 2213 1905 620\\nf 713 1548 1905\\nf 620 1905 1548\\nf 320 537 2024\\nf 652 2000 537\\nf 2213 2024 2000\\nf 537 2000 2024\\nf 713 1905 828\\nf 2213 2000 1905\\nf 652 828 2000\\nf 1905 2000 828\\nf 1497 1218 2348\\nf 1520 1134 1218\\nf 33 2348 1134\\nf 1218 1134 2348\\nf 2165 425 738\\nf 1076 2349 425\\nf 1520 738 2349\\nf 425 2349 738\\nf 2134 1092 89\\nf 33 1451 1092\\nf 1076 89 1451\\nf 1092 1451 89\\nf 1520 2349 1134\\nf 1076 1451 2349\\nf 33 1134 1451\\nf 2349 1451 1134\\nf 320 2024 2007\\nf 2213 860 2024\\nf 561 2007 860\\nf 2024 860 2007\\nf 2134 89 620\\nf 1076 1713 89\\nf 2213 620 1713\\nf 89 1713 620\\nf 2165 472 425\\nf 561 286 472\\nf 1076 425 286\\nf 472 286 425\\nf 2213 1713 860\\nf 1076 286 1713\\nf 561 860 286\\nf 1713 286 860\\nf 906 1987 844\\nf 2108 863 1987\\nf 2498 844 863\\nf 1987 863 844\\nf 723 2266 1630\\nf 2350 1488 2266\\nf 2108 1630 1488\\nf 2266 1488 1630\\nf 1178 1422 1244\\nf 2498 1234 1422\\nf 2350 1244 1234\\nf 1422 1234 1244\\nf 2108 1488 863\\nf 2350 1234 1488\\nf 2498 863 1234\\nf 1488 1234 863\\nf 1554 164 2457\\nf 618 552 164\\nf 1113 2457 552\\nf 164 552 2457\\nf 187 483 942\\nf 1309 2560 483\\nf 618 942 2560\\nf 483 2560 942\\nf 723 1381 2274\\nf 1113 1347 1381\\nf 1309 2274 1347\\nf 1381 1347 2274\\nf 618 2560 552\\nf 1309 1347 2560\\nf 1113 552 1347\\nf 2560 1347 552\\nf 390 261 617\\nf 893 1264 261\\nf 171 617 1264\\nf 261 1264 617\\nf 1178 1892 2005\\nf 291 1980 1892\\nf 893 2005 1980\\nf 1892 1980 2005\\nf 187 1343 1566\\nf 171 2015 1343\\nf 291 1566 2015\\nf 1343 2015 1566\\nf 893 1980 1264\\nf 291 2015 1980\\nf 171 1264 2015\\nf 1980 2015 1264\\nf 723 2274 2266\\nf 1309 764 2274\\nf 2350 2266 764\\nf 2274 764 2266\\nf 187 1566 483\\nf 291 568 1566\\nf 1309 483 568\\nf 1566 568 483\\nf 1178 1244 1892\\nf 2350 2357 1244\\nf 291 1892 2357\\nf 1244 2357 1892\\nf 1309 568 764\\nf 291 2357 568\\nf 2350 764 2357\\nf 568 2357 764\\nf 1497 2348 1456\\nf 33 2412 2348\\nf 329 1456 2412\\nf 2348 2412 1456\\nf 2134 2395 1092\\nf 285 969 2395\\nf 33 1092 969\\nf 2395 969 1092\\nf 1616 2326 2497\\nf 329 1316 2326\\nf 285 2497 1316\\nf 2326 1316 2497\\nf 33 969 2412\\nf 285 1316 969\\nf 329 2412 1316\\nf 969 1316 2412\\nf 390 617 837\\nf 171 932 617\\nf 713 837 932\\nf 617 932 837\\nf 187 1332 1343\\nf 1201 1417 1332\\nf 171 1343 1417\\nf 1332 1417 1343\\nf 2134 1548 2272\\nf 713 2341 1548\\nf 1201 2272 2341\\nf 1548 2341 2272\\nf 171 1417 932\\nf 1201 2341 1417\\nf 713 932 2341\\nf 1417 2341 932\\nf 1554 2262 164\\nf 1046 2539 2262\\nf 618 164 2539\\nf 2262 2539 164\\nf 1616 2432 1730\\nf 440 135 2432\\nf 1046 1730 135\\nf 2432 135 1730\\nf 187 942 257\\nf 618 814 942\\nf 440 257 814\\nf 942 814 257\\nf 1046 135 2539\\nf 440 814 135\\nf 618 2539 814\\nf 135 814 2539\\nf 2134 2272 2395\\nf 1201 1509 2272\\nf 285 2395 1509\\nf 2272 1509 2395\\nf 187 257 1332\\nf 440 2459 257\\nf 1201 1332 2459\\nf 257 2459 1332\\nf 1616 2497 2432\\nf 285 574 2497\\nf 440 2432 574\\nf 2497 574 2432\\nf 1201 2459 1509\\nf 440 574 2459\\nf 285 1509 574\\nf 2459 574 1509\\nf 1034 239 203\\nf 2314 581 239\\nf 1687 203 581\\nf 239 581 203\\nf 1212 1591 1560\\nf 219 736 1591\\nf 2314 1560 736\\nf 1591 736 1560\\nf 314 1159 925\\nf 1687 1116 1159\\nf 219 925 1116\\nf 1159 1116 925\\nf 2314 736 581\\nf 219 1116 736\\nf 1687 581 1116\\nf 736 1116 581\\nf 1554 2375 2453\\nf 793 124 2375\\nf 132 2453 124\\nf 2375 124 2453\\nf 1345 660 2298\\nf 1595 1727 660\\nf 793 2298 1727\\nf 660 1727 2298\\nf 1212 1703 2343\\nf 132 1737 1703\\nf 1595 2343 1737\\nf 1703 1737 2343\\nf 793 1727 124\\nf 1595 1737 1727\\nf 132 124 1737\\nf 1727 1737 124\\nf 565 379 1106\\nf 1205 2488 379\\nf 663 1106 2488\\nf 379 2488 1106\\nf 314 903 2247\\nf 917 1836 903\\nf 1205 2247 1836\\nf 903 1836 2247\\nf 1345 1840 505\\nf 663 629 1840\\nf 917 505 629\\nf 1840 629 505\\nf 1205 1836 2488\\nf 917 629 1836\\nf 663 2488 629\\nf 1836 629 2488\\nf 1212 2343 1591\\nf 1595 1720 2343\\nf 219 1591 1720\\nf 2343 1720 1591\\nf 1345 505 660\\nf 917 1858 505\\nf 1595 660 1858\\nf 505 1858 660\\nf 314 925 903\\nf 219 627 925\\nf 917 903 627\\nf 925 627 903\\nf 1595 1858 1720\\nf 917 627 1858\\nf 219 1720 627\\nf 1858 627 1720\\nf 906 1586 1987\\nf 2256 1856 1586\\nf 2108 1987 1856\\nf 1586 1856 1987\\nf 35 2074 2233\\nf 1170 2035 2074\\nf 2256 2233 2035\\nf 2074 2035 2233\\nf 723 1630 1029\\nf 2108 1800 1630\\nf 1170 1029 1800\\nf 1630 1800 1029\\nf 2256 2035 1856\\nf 1170 1800 2035\\nf 2108 1856 1800\\nf 2035 1800 1856\\nf 437 2392 1660\\nf 1799 551 2392\\nf 2163 1660 551\\nf 2392 551 1660\\nf 1886 2356 2486\\nf 1846 2479 2356\\nf 1799 2486 2479\\nf 2356 2479 2486\\nf 35 737 1360\\nf 2163 1855 737\\nf 1846 1360 1855\\nf 737 1855 1360\\nf 1799 2479 551\\nf 1846 1855 2479\\nf 2163 551 1855\\nf 2479 1855 551\\nf 1554 2457 110\\nf 1113 671 2457\\nf 48 110 671\\nf 2457 671 110\\nf 723 424 1381\\nf 1559 2446 424\\nf 1113 1381 2446\\nf 424 2446 1381\\nf 1886 783 1518\\nf 48 604 783\\nf 1559 1518 604\\nf 783 604 1518\\nf 1113 2446 671\\nf 1559 604 2446\\nf 48 671 604\\nf 2446 604 671\\nf 35 1360 2074\\nf 1846 1672 1360\\nf 1170 2074 1672\\nf 1360 1672 2074\\nf 1886 1518 2356\\nf 1559 2083 1518\\nf 1846 2356 2083\\nf 1518 2083 2356\\nf 723 1029 424\\nf 1170 2471 1029\\nf 1559 424 2471\\nf 1029 2471 424\\nf 1846 2083 1672\\nf 1559 2471 2083\\nf 1170 1672 2471\\nf 2083 2471 1672\\nf 2312 2384 789\\nf 864 1091 2384\\nf 2331 789 1091\\nf 2384 1091 789\\nf 790 1232 73\\nf 2531 42 1232\\nf 864 73 42\\nf 1232 42 73\\nf 149 2112 451\\nf 2331 90 2112\\nf 2531 451 90\\nf 2112 90 451\\nf 864 42 1091\\nf 2531 90 42\\nf 2331 1091 90\\nf 42 90 1091\\nf 565 892 1817\\nf 1310 508 892\\nf 1148 1817 508\\nf 892 508 1817\\nf 22 1271 2113\\nf 47 2191 1271\\nf 1310 2113 2191\\nf 1271 2191 2113\\nf 790 1321 1252\\nf 1148 734 1321\\nf 47 1252 734\\nf 1321 734 1252\\nf 1310 2191 508\\nf 47 734 2191\\nf 1148 508 734\\nf 2191 734 508\\nf 437 396 1893\\nf 766 1419 396\\nf 1833 1893 1419\\nf 396 1419 1893\\nf 149 1637 830\\nf 2424 1516 1637\\nf 766 830 1516\\nf 1637 1516 830\\nf 22 888 2373\\nf 1833 974 888\\nf 2424 2373 974\\nf 888 974 2373\\nf 766 1516 1419\\nf 2424 974 1516\\nf 1833 1419 974\\nf 1516 974 1419\\nf 790 1252 1232\\nf 47 1255 1252\\nf 2531 1232 1255\\nf 1252 1255 1232\\nf 22 2373 1271\\nf 2424 1684 2373\\nf 47 1271 1684\\nf 2373 1684 1271\\nf 149 451 1637\\nf 2531 1493 451\\nf 2424 1637 1493\\nf 451 1493 1637\\nf 47 1684 1255\\nf 2424 1493 1684\\nf 2531 1255 1493\\nf 1684 1493 1255\\nf 1554 110 2375\\nf 48 1057 110\\nf 793 2375 1057\\nf 110 1057 2375\\nf 1886 1848 783\\nf 1985 1924 1848\\nf 48 783 1924\\nf 1848 1924 783\\nf 1345 2298 1798\\nf 793 625 2298\\nf 1985 1798 625\\nf 2298 625 1798\\nf 48 1924 1057\\nf 1985 625 1924\\nf 793 1057 625\\nf 1924 625 1057\\nf 437 1893 2392\\nf 1833 2204 1893\\nf 1799 2392 2204\\nf 1893 2204 2392\\nf 22 486 888\\nf 436 1662 486\\nf 1833 888 1662\\nf 486 1662 888\\nf 1886 2486 1948\\nf 1799 1186 2486\\nf 436 1948 1186\\nf 2486 1186 1948\\nf 1833 1662 2204\\nf 436 1186 1662\\nf 1799 2204 1186\\nf 1662 1186 2204\\nf 565 1106 892\\nf 663 200 1106\\nf 1310 892 200\\nf 1106 200 892\\nf 1345 289 1840\\nf 904 2004 289\\nf 663 1840 2004\\nf 289 2004 1840\\nf 22 2113 1346\\nf 1310 2436 2113\\nf 904 1346 2436\\nf 2113 2436 1346\\nf 663 2004 200\\nf 904 2436 2004\\nf 1310 200 2436\\nf 2004 2436 200\\nf 1886 1948 1848\\nf 436 265 1948\\nf 1985 1848 265\\nf 1948 265 1848\\nf 22 1346 486\\nf 904 1378 1346\\nf 436 486 1378\\nf 1346 1378 486\\nf 1345 1798 289\\nf 1985 2070 1798\\nf 904 289 2070\\nf 1798 2070 289\\nf 436 1378 265\\nf 904 2070 1378\\nf 1985 265 2070\\nf 1378 2070 265\\nf 1034 203 1524\\nf 1687 146 203\\nf 1161 1524 146\\nf 203 146 1524\\nf 314 1914 1159\\nf 2441 2294 1914\\nf 1687 1159 2294\\nf 1914 2294 1159\\nf 1984 225 367\\nf 1161 1847 225\\nf 2441 367 1847\\nf 225 1847 367\\nf 1687 2294 146\\nf 2441 1847 2294\\nf 1161 146 1847\\nf 2294 1847 146\\nf 565 141 379\\nf 202 1281 141\\nf 1205 379 1281\\nf 141 1281 379\\nf 2040 1675 1174\\nf 2489 2321 1675\\nf 202 1174 2321\\nf 1675 2321 1174\\nf 314 2247 1206\\nf 1205 343 2247\\nf 2489 1206 343\\nf 2247 343 1206\\nf 202 2321 1281\\nf 2489 343 2321\\nf 1205 1281 343\\nf 2321 343 1281\\nf 658 2377 881\\nf 687 1512 2377\\nf 2110 881 1512\\nf 2377 1512 881\\nf 1984 118 1394\\nf 674 1242 118\\nf 687 1394 1242\\nf 118 1242 1394\\nf 2040 148 335\\nf 2110 127 148\\nf 674 335 127\\nf 148 127 335\\nf 687 1242 1512\\nf 674 127 1242\\nf 2110 1512 127\\nf 1242 127 1512\\nf 314 1206 1914\\nf 2489 841 1206\\nf 2441 1914 841\\nf 1206 841 1914\\nf 2040 335 1675\\nf 674 599 335\\nf 2489 1675 599\\nf 335 599 1675\\nf 1984 367 118\\nf 2441 1007 367\\nf 674 118 1007\\nf 367 1007 118\\nf 2489 599 841\\nf 674 1007 599\\nf 2441 841 1007\\nf 599 1007 841\\nf 2312 2104 2384\\nf 2270 271 2104\\nf 864 2384 271\\nf 2104 271 2384\\nf 236 1781 2431\\nf 501 1215 1781\\nf 2270 2431 1215\\nf 1781 1215 2431\\nf 790 73 2275\\nf 864 1253 73\\nf 501 2275 1253\\nf 73 1253 2275\\nf 2270 1215 271\\nf 501 1253 1215\\nf 864 271 1253\\nf 1215 1253 271\\nf 678 1647 957\\nf 1972 1028 1647\\nf 276 957 1028\\nf 1647 1028 957\\nf 811 558 533\\nf 217 2063 558\\nf 1972 533 2063\\nf 558 2063 533\\nf 236 1045 1789\\nf 276 1326 1045\\nf 217 1789 1326\\nf 1045 1326 1789\\nf 1972 2063 1028\\nf 217 1326 2063\\nf 276 1028 1326\\nf 2063 1326 1028\\nf 565 1817 336\\nf 1148 1308 1817\\nf 2222 336 1308\\nf 1817 1308 336\\nf 790 325 1321\\nf 1523 1082 325\\nf 1148 1321 1082\\nf 325 1082 1321\\nf 811 1454 511\\nf 2222 1975 1454\\nf 1523 511 1975\\nf 1454 1975 511\\nf 1148 1082 1308\\nf 1523 1975 1082\\nf 2222 1308 1975\\nf 1082 1975 1308\\nf 236 1789 1781\\nf 217 1863 1789\\nf 501 1781 1863\\nf 1789 1863 1781\\nf 811 511 558\\nf 1523 204 511\\nf 217 558 204\\nf 511 204 558\\nf 790 2275 325\\nf 501 1865 2275\\nf 1523 325 1865\\nf 2275 1865 325\\nf 217 204 1863\\nf 1523 1865 204\\nf 501 1863 1865\\nf 204 1865 1863\\nf 2391 372 1998\\nf 34 462 372\\nf 1220 1998 462\\nf 372 462 1998\\nf 836 488 916\\nf 2028 539 488\\nf 34 916 539\\nf 488 539 916\\nf 1756 2516 1477\\nf 1220 1035 2516\\nf 2028 1477 1035\\nf 2516 1035 1477\\nf 34 539 462\\nf 2028 1035 539\\nf 1220 462 1035\\nf 539 1035 462\\nf 658 1073 973\\nf 370 498 1073\\nf 1129 973 498\\nf 1073 498 973\\nf 322 2332 1796\\nf 2420 673 2332\\nf 370 1796 673\\nf 2332 673 1796\\nf 836 102 937\\nf 1129 98 102\\nf 2420 937 98\\nf 102 98 937\\nf 370 673 498\\nf 2420 98 673\\nf 1129 498 98\\nf 673 98 498\\nf 678 1778 377\\nf 1420 2144 1778\\nf 1098 377 2144\\nf 1778 2144 377\\nf 1756 549 965\\nf 1250 2346 549\\nf 1420 965 2346\\nf 549 2346 965\\nf 322 178 781\\nf 1098 2451 178\\nf 1250 781 2451\\nf 178 2451 781\\nf 1420 2346 2144\\nf 1250 2451 2346\\nf 1098 2144 2451\\nf 2346 2451 2144\\nf 836 937 488\\nf 2420 1934 937\\nf 2028 488 1934\\nf 937 1934 488\\nf 322 781 2332\\nf 1250 2166 781\\nf 2420 2332 2166\\nf 781 2166 2332\\nf 1756 1477 549\\nf 2028 1805 1477\\nf 1250 549 1805\\nf 1477 1805 549\\nf 2420 2166 1934\\nf 1250 1805 2166\\nf 2028 1934 1805\\nf 2166 1805 1934\\nf 565 336 141\\nf 2222 490 336\\nf 202 141 490\\nf 336 490 141\\nf 811 642 1454\\nf 1400 174 642\\nf 2222 1454 174\\nf 642 174 1454\\nf 2040 1174 1040\\nf 202 636 1174\\nf 1400 1040 636\\nf 1174 636 1040\\nf 2222 174 490\\nf 1400 636 174\\nf 202 490 636\\nf 174 636 490\\nf 678 377 1647\\nf 1098 548 377\\nf 1972 1647 548\\nf 377 548 1647\\nf 322 1908 178\\nf 563 2211 1908\\nf 1098 178 2211\\nf 1908 2211 178\\nf 811 533 2095\\nf 1972 1405 533\\nf 563 2095 1405\\nf 533 1405 2095\\nf 1098 2211 548\\nf 563 1405 2211\\nf 1972 548 1405\\nf 2211 1405 548\\nf 658 881 1073\\nf 2110 743 881\\nf 370 1073 743\\nf 881 743 1073\\nf 2040 826 148\\nf 2499 2178 826\\nf 2110 148 2178\\nf 826 2178 148\\nf 322 1796 1006\\nf 370 2552 1796\\nf 2499 1006 2552\\nf 1796 2552 1006\\nf 2110 2178 743\\nf 2499 2552 2178\\nf 370 743 2552\\nf 2178 2552 743\\nf 811 2095 642\\nf 563 1686 2095\\nf 1400 642 1686\\nf 2095 1686 642\\nf 322 1006 1908\\nf 2499 25 1006\\nf 563 1908 25\\nf 1006 25 1908\\nf 2040 1040 826\\nf 1400 180 1040\\nf 2499 826 180\\nf 1040 180 826\\nf 563 25 1686\\nf 2499 180 25\\nf 1400 1686 180\\nf 25 180 1686\\nf 1034 1524 1446\\nf 1161 63 1524\\nf 242 1446 63\\nf 1524 63 1446\\nf 1984 2156 225\\nf 2190 1008 2156\\nf 1161 225 1008\\nf 2156 1008 225\\nf 418 1277 865\\nf 242 439 1277\\nf 2190 865 439\\nf 1277 439 865\\nf 1161 1008 63\\nf 2190 439 1008\\nf 242 63 439\\nf 1008 439 63\\nf 658 332 2377\\nf 2267 94 332\\nf 687 2377 94\\nf 332 94 2377\\nf 2559 1483 2145\\nf 1093 137 1483\\nf 2267 2145 137\\nf 1483 137 2145\\nf 1984 1394 2513\\nf 687 774 1394\\nf 1093 2513 774\\nf 1394 774 2513\\nf 2267 137 94\\nf 1093 774 137\\nf 687 94 774\\nf 137 774 94\\nf 1075 1169 696\\nf 882 1351 1169\\nf 638 696 1351\\nf 1169 1351 696\\nf 418 2359 1610\\nf 2240 1971 2359\\nf 882 1610 1971\\nf 2359 1971 1610\\nf 2559 2029 1010\\nf 638 2234 2029\\nf 2240 1010 2234\\nf 2029 2234 1010\\nf 882 1971 1351\\nf 2240 2234 1971\\nf 638 1351 2234\\nf 1971 2234 1351\\nf 1984 2513 2156\\nf 1093 1982 2513\\nf 2190 2156 1982\\nf 2513 1982 2156\\nf 2559 1010 1483\\nf 2240 845 1010\\nf 1093 1483 845\\nf 1010 845 1483\\nf 418 865 2359\\nf 2190 1269 865\\nf 2240 2359 1269\\nf 865 1269 2359\\nf 1093 845 1982\\nf 2240 1269 845\\nf 2190 1982 1269\\nf 845 1269 1982\\nf 2391 2309 372\\nf 2327 1376 2309\\nf 34 372 1376\\nf 2309 1376 372\\nf 1295 50 58\\nf 1875 176 50\\nf 2327 58 176\\nf 50 176 58\\nf 836 916 2227\\nf 34 404 916\\nf 1875 2227 404\\nf 916 404 2227\\nf 2327 176 1376\\nf 1875 404 176\\nf 34 1376 404\\nf 176 404 1376\\nf 2194 2219 2181\\nf 2208 211 2219\\nf 1968 2181 211\\nf 2219 211 2181\\nf 664 2251 167\\nf 1467 251 2251\\nf 2208 167 251\\nf 2251 251 167\\nf 1295 2120 817\\nf 1968 1732 2120\\nf 1467 817 1732\\nf 2120 1732 817\\nf 2208 251 211\\nf 1467 1732 251\\nf 1968 211 1732\\nf 251 1732 211\\nf 658 973 333\\nf 1129 2199 973\\nf 2122 333 2199\\nf 973 2199 333\\nf 836 963 102\\nf 2328 1233 963\\nf 1129 102 1233\\nf 963 1233 102\\nf 664 1328 107\\nf 2122 913 1328\\nf 2328 107 913\\nf 1328 913 107\\nf 1129 1233 2199\\nf 2328 913 1233\\nf 2122 2199 913\\nf 1233 913 2199\\nf 1295 817 50\\nf 1467 570 817\\nf 1875 50 570\\nf 817 570 50\\nf 664 107 2251\\nf 2328 385 107\\nf 1467 2251 385\\nf 107 385 2251\\nf 836 2227 963\\nf 1875 1184 2227\\nf 2328 963 1184\\nf 2227 1184 963\\nf 1467 385 570\\nf 2328 1184 385\\nf 1875 570 1184\\nf 385 1184 570\\nf 2008 1404 2078\\nf 2364 1530 1404\\nf 2475 2078 1530\\nf 1404 1530 2078\\nf 128 2398 2018\\nf 292 1061 2398\\nf 2364 2018 1061\\nf 2398 1061 2018\\nf 2481 2075 1196\\nf 2475 2140 2075\\nf 292 1196 2140\\nf 2075 2140 1196\\nf 2364 1061 1530\\nf 292 2140 1061\\nf 2475 1530 2140\\nf 1061 2140 1530\\nf 1075 59 655\\nf 785 1714 59\\nf 1491 655 1714\\nf 59 1714 655\\nf 1305 328 545\\nf 562 899 328\\nf 785 545 899\\nf 328 899 545\\nf 128 2049 2006\\nf 1491 2487 2049\\nf 562 2006 2487\\nf 2049 2487 2006\\nf 785 899 1714\\nf 562 2487 899\\nf 1491 1714 2487\\nf 899 2487 1714\\nf 2194 2045 134\\nf 2042 1807 2045\\nf 1552 134 1807\\nf 2045 1807 134\\nf 2481 898 946\\nf 1514 1663 898\\nf 2042 946 1663\\nf 898 1663 946\\nf 1305 1204 695\\nf 1552 927 1204\\nf 1514 695 927\\nf 1204 927 695\\nf 2042 1663 1807\\nf 1514 927 1663\\nf 1552 1807 927\\nf 1663 927 1807\\nf 128 2006 2398\\nf 562 2068 2006\\nf 292 2398 2068\\nf 2006 2068 2398\\nf 1305 695 328\\nf 1514 2226 695\\nf 562 328 2226\\nf 695 2226 328\\nf 2481 1196 898\\nf 292 269 1196\\nf 1514 898 269\\nf 1196 269 898\\nf 562 2226 2068\\nf 1514 269 2226\\nf 292 2068 269\\nf 2226 269 2068\\nf 658 333 332\\nf 2122 195 333\\nf 2267 332 195\\nf 333 195 332\\nf 664 207 1328\\nf 1851 1989 207\\nf 2122 1328 1989\\nf 207 1989 1328\\nf 2559 2145 592\\nf 2267 1352 2145\\nf 1851 592 1352\\nf 2145 1352 592\\nf 2122 1989 195\\nf 1851 1352 1989\\nf 2267 195 1352\\nf 1989 1352 195\\nf 2194 134 2219\\nf 1552 1246 134\\nf 2208 2219 1246\\nf 134 1246 2219\\nf 1305 2142 1204\\nf 189 351 2142\\nf 1552 1204 351\\nf 2142 351 1204\\nf 664 167 725\\nf 2208 252 167\\nf 189 725 252\\nf 167 252 725\\nf 1552 351 1246\\nf 189 252 351\\nf 2208 1246 252\\nf 351 252 1246\\nf 1075 696 59\\nf 638 2203 696\\nf 785 59 2203\\nf 696 2203 59\\nf 2559 703 2029\\nf 747 749 703\\nf 638 2029 749\\nf 703 749 2029\\nf 1305 545 1001\\nf 785 1629 545\\nf 747 1001 1629\\nf 545 1629 1001\\nf 638 749 2203\\nf 747 1629 749\\nf 785 2203 1629\\nf 749 1629 2203\\nf 664 725 207\\nf 189 1272 725\\nf 1851 207 1272\\nf 725 1272 207\\nf 1305 1001 2142\\nf 747 1192 1001\\nf 189 2142 1192\\nf 1001 1192 2142\\nf 2559 592 703\\nf 1851 1810 592\\nf 747 703 1810\\nf 592 1810 703\\nf 189 1192 1272\\nf 747 1810 1192\\nf 1851 1272 1810\\nf 1192 1810 1272\\nf 1034 1446 1813\\nf 242 2476 1446\\nf 144 1813 2476\\nf 1446 2476 1813\\nf 418 1505 1277\\nf 1504 9 1505\\nf 242 1277 9\\nf 1505 9 1277\\nf 1850 1942 143\\nf 144 1829 1942\\nf 1504 143 1829\\nf 1942 1829 143\\nf 242 9 2476\\nf 1504 1829 9\\nf 144 2476 1829\\nf 9 1829 2476\\nf 1075 408 1169\\nf 2053 633 408\\nf 882 1169 633\\nf 408 633 1169\\nf 1288 591 2530\\nf 1127 345 591\\nf 2053 2530 345\\nf 591 345 2530\\nf 418 1610 1990\\nf 882 2541 1610\\nf 1127 1990 2541\\nf 1610 2541 1990\\nf 2053 345 633\\nf 1127 2541 345\\nf 882 633 2541\\nf 345 2541 633\\nf 1497 1263 362\\nf 54 943 1263\\nf 327 362 943\\nf 1263 943 362\\nf 1850 1659 1906\\nf 755 653 1659\\nf 54 1906 653\\nf 1659 653 1906\\nf 1288 2545 1697\\nf 327 1104 2545\\nf 755 1697 1104\\nf 2545 1104 1697\\nf 54 653 943\\nf 755 1104 653\\nf 327 943 1104\\nf 653 1104 943\\nf 418 1990 1505\\nf 1127 1056 1990\\nf 1504 1505 1056\\nf 1990 1056 1505\\nf 1288 1697 591\\nf 755 1214 1697\\nf 1127 591 1214\\nf 1697 1214 591\\nf 1850 143 1659\\nf 1504 1814 143\\nf 755 1659 1814\\nf 143 1814 1659\\nf 1127 1214 1056\\nf 755 1814 1214\\nf 1504 1056 1814\\nf 1214 1814 1056\\nf 2008 1427 1404\\nf 644 1078 1427\\nf 2364 1404 1078\\nf 1427 1078 1404\\nf 988 161 2506\\nf 268 875 161\\nf 644 2506 875\\nf 161 875 2506\\nf 128 2018 509\\nf 2364 2048 2018\\nf 268 509 2048\\nf 2018 2048 509\\nf 644 875 1078\\nf 268 2048 875\\nf 2364 1078 2048\\nf 875 2048 1078\\nf 2466 535 833\\nf 2336 1760 535\\nf 1055 833 1760\\nf 535 1760 833\\nf 489 1466 989\\nf 883 758 1466\\nf 2336 989 758\\nf 1466 758 989\\nf 988 1573 272\\nf 1055 360 1573\\nf 883 272 360\\nf 1573 360 272\\nf 2336 758 1760\\nf 883 360 758\\nf 1055 1760 360\\nf 758 360 1760\\nf 1075 655 183\\nf 1491 933 655\\nf 1439 183 933\\nf 655 933 183\\nf 128 2307 2049\\nf 1059 2263 2307\\nf 1491 2049 2263\\nf 2307 2263 2049\\nf 489 2096 1919\\nf 1439 1386 2096\\nf 1059 1919 1386\\nf 2096 1386 1919\\nf 1491 2263 933\\nf 1059 1386 2263\\nf 1439 933 1386\\nf 2263 1386 933\\nf 988 272 161\\nf 883 712 272\\nf 268 161 712\\nf 272 712 161\\nf 489 1919 1466\\nf 1059 517 1919\\nf 883 1466 517\\nf 1919 517 1466\\nf 128 509 2307\\nf 268 1217 509\\nf 1059 2307 1217\\nf 509 1217 2307\\nf 883 517 712\\nf 1059 1217 517\\nf 268 712 1217\\nf 517 1217 712\\nf 1769 1967 1287\\nf 2082 1081 1967\\nf 802 1287 1081\\nf 1967 1081 1287\\nf 2165 1213 648\\nf 2243 2066 1213\\nf 2082 648 2066\\nf 1213 2066 648\\nf 907 1633 156\\nf 802 1403 1633\\nf 2243 156 1403\\nf 1633 1403 156\\nf 2082 2066 1081\\nf 2243 1403 2066\\nf 802 1081 1403\\nf 2066 1403 1081\\nf 1497 206 1218\\nf 1122 2151 206\\nf 1520 1218 2151\\nf 206 2151 1218\\nf 2115 1752 1502\\nf 1941 1359 1752\\nf 1122 1502 1359\\nf 1752 1359 1502\\nf 2165 738 433\\nf 1520 172 738\\nf 1941 433 172\\nf 738 172 433\\nf 1122 1359 2151\\nf 1941 172 1359\\nf 1520 2151 172\\nf 1359 172 2151\\nf 2466 909 1474\\nf 727 2186 909\\nf 635 1474 2186\\nf 909 2186 1474\\nf 907 855 2372\\nf 2117 1776 855\\nf 727 2372 1776\\nf 855 1776 2372\\nf 2115 1782 543\\nf 635 1340 1782\\nf 2117 543 1340\\nf 1782 1340 543\\nf 727 1776 2186\\nf 2117 1340 1776\\nf 635 2186 1340\\nf 1776 1340 2186\\nf 2165 433 1213\\nf 1941 1021 433\\nf 2243 1213 1021\\nf 433 1021 1213\\nf 2115 543 1752\\nf 2117 1912 543\\nf 1941 1752 1912\\nf 543 1912 1752\\nf 907 156 855\\nf 2243 1088 156\\nf 2117 855 1088\\nf 156 1088 855\\nf 1941 1912 1021\\nf 2117 1088 1912\\nf 2243 1021 1088\\nf 1912 1088 1021\\nf 1075 183 408\\nf 1439 2413 183\\nf 2053 408 2413\\nf 183 2413 408\\nf 489 632 2096\\nf 1861 221 632\\nf 1439 2096 221\\nf 632 221 2096\\nf 1288 2530 2490\\nf 2053 470 2530\\nf 1861 2490 470\\nf 2530 470 2490\\nf 1439 221 2413\\nf 1861 470 221\\nf 2053 2413 470\\nf 221 470 2413\\nf 2466 1474 535\\nf 635 856 1474\\nf 2336 535 856\\nf 1474 856 535\\nf 2115 2017 1782\\nf 2313 1071 2017\\nf 635 1782 1071\\nf 2017 1071 1782\\nf 489 989 1674\\nf 2336 1849 989\\nf 2313 1674 1849\\nf 989 1849 1674\\nf 635 1071 856\\nf 2313 1849 1071\\nf 2336 856 1849\\nf 1071 1849 856\\nf 1497 362 206\\nf 327 1590 362\\nf 1122 206 1590\\nf 362 1590 206\\nf 1288 1898 2545\\nf 803 1583 1898\\nf 327 2545 1583\\nf 1898 1583 2545\\nf 2115 1502 2310\\nf 1122 2241 1502\\nf 803 2310 2241\\nf 1502 2241 2310\\nf 327 1583 1590\\nf 803 2241 1583\\nf 1122 1590 2241\\nf 1583 2241 1590\\nf 489 1674 632\\nf 2313 2442 1674\\nf 1861 632 2442\\nf 1674 2442 632\\nf 2115 2310 2017\\nf 803 523 2310\\nf 2313 2017 523\\nf 2310 523 2017\\nf 1288 2490 1898\\nf 1861 2394 2490\\nf 803 1898 2394\\nf 2490 2394 1898\\nf 2313 523 2442\\nf 803 2394 523\\nf 1861 2442 2394\\nf 523 2394 2442\\nf 300 1312 1652\\nf 527 1834 1312\\nf 2427 1652 1834\\nf 1312 1834 1652\\nf 2547 1495 756\\nf 1043 365 1495\\nf 527 756 365\\nf 1495 365 756\\nf 1594 619 577\\nf 2427 910 619\\nf 1043 577 910\\nf 619 910 577\\nf 527 365 1834\\nf 1043 910 365\\nf 2427 1834 910\\nf 365 910 1834\\nf 818 2515 1385\\nf 982 2111 2515\\nf 895 1385 2111\\nf 2515 2111 1385\\nf 1487 1062 108\\nf 987 1438 1062\\nf 982 108 1438\\nf 1062 1438 108\\nf 2547 1708 2093\\nf 895 2428 1708\\nf 987 2093 2428\\nf 1708 2428 2093\\nf 982 1438 2111\\nf 987 2428 1438\\nf 895 2111 2428\\nf 1438 2428 2111\\nf 2402 1569 686\\nf 1209 693 1569\\nf 639 686 693\\nf 1569 693 686\\nf 1594 280 959\\nf 1964 1716 280\\nf 1209 959 1716\\nf 280 1716 959\\nf 1487 353 1290\\nf 639 2077 353\\nf 1964 1290 2077\\nf 353 2077 1290\\nf 1209 1716 693\\nf 1964 2077 1716\\nf 639 693 2077\\nf 1716 2077 693\\nf 2547 2093 1495\\nf 987 603 2093\\nf 1043 1495 603\\nf 2093 603 1495\\nf 1487 1290 1062\\nf 1964 162 1290\\nf 987 1062 162\\nf 1290 162 1062\\nf 1594 577 280\\nf 1043 1568 577\\nf 1964 280 1568\\nf 577 1568 280\\nf 987 162 603\\nf 1964 1568 162\\nf 1043 603 1568\\nf 162 1568 603\\nf 906 844 1867\\nf 2498 2500 844\\nf 1428 1867 2500\\nf 844 2500 1867\\nf 1178 1002 1422\\nf 1808 2033 1002\\nf 2498 1422 2033\\nf 1002 2033 1422\\nf 2408 152 641\\nf 1428 1584 152\\nf 1808 641 1584\\nf 152 1584 641\\nf 2498 2033 2500\\nf 1808 1584 2033\\nf 1428 2500 1584\\nf 2033 1584 2500\\nf 390 184 261\\nf 1100 689 184\\nf 893 261 689\\nf 184 689 261\\nf 2224 799 1054\\nf 250 729 799\\nf 1100 1054 729\\nf 799 729 1054\\nf 1178 2005 1303\\nf 893 1837 2005\\nf 250 1303 1837\\nf 2005 1837 1303\\nf 1100 729 689\\nf 250 1837 729\\nf 893 689 1837\\nf 729 1837 689\\nf 818 2396 928\\nf 323 398 2396\\nf 1278 928 398\\nf 2396 398 928\\nf 2408 1824 1522\\nf 1265 389 1824\\nf 323 1522 389\\nf 1824 389 1522\\nf 2224 1009 1766\\nf 1278 2162 1009\\nf 1265 1766 2162\\nf 1009 2162 1766\\nf 323 389 398\\nf 1265 2162 389\\nf 1278 398 2162\\nf 389 2162 398\\nf 1178 1303 1002\\nf 250 2198 1303\\nf 1808 1002 2198\\nf 1303 2198 1002\\nf 2224 1766 799\\nf 1265 1978 1766\\nf 250 799 1978\\nf 1766 1978 799\\nf 2408 641 1824\\nf 1808 1266 641\\nf 1265 1824 1266\\nf 641 1266 1824\\nf 250 1978 2198\\nf 1265 1266 1978\\nf 1808 2198 1266\\nf 1978 1266 2198\\nf 1769 1471 1175\\nf 2292 611 1471\\nf 2019 1175 611\\nf 1471 611 1175\\nf 384 1873 1929\\nf 609 1358 1873\\nf 2292 1929 1358\\nf 1873 1358 1929\\nf 320 964 1380\\nf 2019 1334 964\\nf 609 1380 1334\\nf 964 1334 1380\\nf 2292 1358 611\\nf 609 1334 1358\\nf 2019 611 1334\\nf 1358 1334 611\\nf 2402 637 2367\\nf 28 1440 637\\nf 700 2367 1440\\nf 637 1440 2367\\nf 871 859 1538\\nf 2 1041 859\\nf 28 1538 1041\\nf 859 1041 1538\\nf 384 1816 1335\\nf 700 704 1816\\nf 2 1335 704\\nf 1816 704 1335\\nf 28 1041 1440\\nf 2 704 1041\\nf 700 1440 704\\nf 1041 704 1440\\nf 390 954 1832\\nf 652 2370 954\\nf 1432 1832 2370\\nf 954 2370 1832\\nf 320 233 537\\nf 950 2495 233\\nf 652 537 2495\\nf 233 2495 537\\nf 871 2128 1508\\nf 1432 1365 2128\\nf 950 1508 1365\\nf 2128 1365 1508\\nf 652 2495 2370\\nf 950 1365 2495\\nf 1432 2370 1365\\nf 2495 1365 2370\\nf 384 1335 1873\\nf 2 166 1335\\nf 609 1873 166\\nf 1335 166 1873\\nf 871 1508 859\\nf 950 2277 1508\\nf 2 859 2277\\nf 1508 2277 859\\nf 320 1380 233\\nf 609 2492 1380\\nf 950 233 2492\\nf 1380 2492 233\\nf 2 2277 166\\nf 950 2492 2277\\nf 609 166 2492\\nf 2277 2492 166\\nf 818 928 2515\\nf 1278 1761 928\\nf 982 2515 1761\\nf 928 1761 2515\\nf 2224 237 1009\\nf 412 2448 237\\nf 1278 1009 2448\\nf 237 2448 1009\\nf 1487 108 2207\\nf 982 1137 108\\nf 412 2207 1137\\nf 108 1137 2207\\nf 1278 2448 1761\\nf 412 1137 2448\\nf 982 1761 1137\\nf 2448 1137 1761\\nf 390 1832 184\\nf 1432 788 1832\\nf 1100 184 788\\nf 1832 788 184\\nf 871 1977 2128\\nf 457 409 1977\\nf 1432 2128 409\\nf 1977 409 2128\\nf 2224 1054 14\\nf 1100 1715 1054\\nf 457 14 1715\\nf 1054 1715 14\\nf 1432 409 788\\nf 457 1715 409\\nf 1100 788 1715\\nf 409 1715 788\\nf 2402 686 637\\nf 639 1646 686\\nf 28 637 1646\\nf 686 1646 637\\nf 1487 1950 353\\nf 2319 2337 1950\\nf 639 353 2337\\nf 1950 2337 353\\nf 871 1538 168\\nf 28 1693 1538\\nf 2319 168 1693\\nf 1538 1693 168\\nf 639 2337 1646\\nf 2319 1693 2337\\nf 28 1646 1693\\nf 2337 1693 1646\\nf 2224 14 237\\nf 457 1793 14\\nf 412 237 1793\\nf 14 1793 237\\nf 871 168 1977\\nf 2319 1938 168\\nf 457 1977 1938\\nf 168 1938 1977\\nf 1487 2207 1950\\nf 412 84 2207\\nf 2319 1950 84\\nf 2207 84 1950\\nf 457 1938 1793\\nf 2319 84 1938\\nf 412 1793 84\\nf 1938 84 1793\\nf 2312 789 1315\\nf 2331 676 789\\nf 20 1315 676\\nf 789 676 1315\\nf 149 120 2112\\nf 2421 1534 120\\nf 2331 2112 1534\\nf 120 1534 2112\\nf 19 2101 1136\\nf 20 1821 2101\\nf 2421 1136 1821\\nf 2101 1821 1136\\nf 2331 1534 676\\nf 2421 1821 1534\\nf 20 676 1821\\nf 1534 1821 676\\nf 437 1738 396\\nf 2325 812 1738\\nf 766 396 812\\nf 1738 812 396\\nf 1953 1154 569\\nf 1087 2072 1154\\nf 2325 569 2072\\nf 1154 2072 569\\nf 149 830 1615\\nf 766 538 830\\nf 1087 1615 538\\nf 830 538 1615\\nf 2325 2072 812\\nf 1087 538 2072\\nf 766 812 538\\nf 2072 538 812\\nf 1510 1368 2157\\nf 2171 1734 1368\\nf 2333 2157 1734\\nf 1368 1734 2157\\nf 19 1498 824\\nf 1958 2034 1498\\nf 2171 824 2034\\nf 1498 2034 824\\nf 1953 1485 1072\\nf 2333 1230 1485\\nf 1958 1072 1230\\nf 1485 1230 1072\\nf 2171 2034 1734\\nf 1958 1230 2034\\nf 2333 1734 1230\\nf 2034 1230 1734\\nf 149 1615 120\\nf 1087 1260 1615\\nf 2421 120 1260\\nf 1615 1260 120\\nf 1953 1072 1154\\nf 1958 585 1072\\nf 1087 1154 585\\nf 1072 585 1154\\nf 19 1136 1498\\nf 2421 513 1136\\nf 1958 1498 513\\nf 1136 513 1498\\nf 1087 585 1260\\nf 1958 513 585\\nf 2421 1260 513\\nf 585 513 1260\\nf 906 428 1586\\nf 1339 1920 428\\nf 2256 1586 1920\\nf 428 1920 1586\\nf 1168 590 391\\nf 86 1433 590\\nf 1339 391 1433\\nf 590 1433 391\\nf 35 2233 1070\\nf 2256 1639 2233\\nf 86 1070 1639\\nf 2233 1639 1070\\nf 1339 1433 1920\\nf 86 1639 1433\\nf 2256 1920 1639\\nf 1433 1639 1920\\nf 2478 684 2385\\nf 2429 2546 684\\nf 12 2385 2546\\nf 684 2546 2385\\nf 2092 848 1883\\nf 2094 2001 848\\nf 2429 1883 2001\\nf 848 2001 1883\\nf 1168 1472 56\\nf 12 1995 1472\\nf 2094 56 1995\\nf 1472 1995 56\\nf 2429 2001 2546\\nf 2094 1995 2001\\nf 12 2546 1995\\nf 2001 1995 2546\\nf 437 1660 136\\nf 2163 2468 1660\\nf 2121 136 2468\\nf 1660 2468 136\\nf 35 1444 737\\nf 1802 1900 1444\\nf 2163 737 1900\\nf 1444 1900 737\\nf 2092 1763 838\\nf 2121 2109 1763\\nf 1802 838 2109\\nf 1763 2109 838\\nf 2163 1900 2468\\nf 1802 2109 1900\\nf 2121 2468 2109\\nf 1900 2109 2468\\nf 1168 56 590\\nf 2094 677 56\\nf 86 590 677\\nf 56 677 590\\nf 2092 838 848\\nf 1802 2220 838\\nf 2094 848 2220\\nf 838 2220 848\\nf 35 1070 1444\\nf 86 2039 1070\\nf 1802 1444 2039\\nf 1070 2039 1444\\nf 2094 2220 677\\nf 1802 2039 2220\\nf 86 677 2039\\nf 2220 2039 677\\nf 887 1247 948\\nf 1133 615 1247\\nf 1302 948 615\\nf 1247 615 948\\nf 1291 2532 1753\\nf 1128 866 2532\\nf 1133 1753 866\\nf 2532 866 1753\\nf 2521 2376 1341\\nf 1302 348 2376\\nf 1128 1341 348\\nf 2376 348 1341\\nf 1133 866 615\\nf 1128 348 866\\nf 1302 615 348\\nf 866 348 615\\nf 1510 1605 382\\nf 330 476 1605\\nf 49 382 476\\nf 1605 476 382\\nf 2197 1393 313\\nf 1822 1864 1393\\nf 330 313 1864\\nf 1393 1864 313\\nf 1291 662 1503\\nf 49 191 662\\nf 1822 1503 191\\nf 662 191 1503\\nf 330 1864 476\\nf 1822 191 1864\\nf 49 476 191\\nf 1864 191 476\\nf 2478 1382 971\\nf 485 2196 1382\\nf 1318 971 2196\\nf 1382 2196 971\\nf 2521 1285 2297\\nf 81 2404 1285\\nf 485 2297 2404\\nf 1285 2404 2297\\nf 2197 825 2548\\nf 1318 978 825\\nf 81 2548 978\\nf 825 978 2548\\nf 485 2404 2196\\nf 81 978 2404\\nf 1318 2196 978\\nf 2404 978 2196\\nf 1291 1503 2532\\nf 1822 1163 1503\\nf 1128 2532 1163\\nf 1503 1163 2532\\nf 2197 2548 1393\\nf 81 2127 2548\\nf 1822 1393 2127\\nf 2548 2127 1393\\nf 2521 1341 1285\\nf 1128 1882 1341\\nf 81 1285 1882\\nf 1341 1882 1285\\nf 1822 2127 1163\\nf 81 1882 2127\\nf 1128 1163 1882\\nf 2127 1882 1163\\nf 437 136 1738\\nf 2121 1844 136\\nf 2325 1738 1844\\nf 136 1844 1738\\nf 2092 868 1763\\nf 2067 750 868\\nf 2121 1763 750\\nf 868 750 1763\\nf 1953 569 126\\nf 2325 1355 569\\nf 2067 126 1355\\nf 569 1355 126\\nf 2121 750 1844\\nf 2067 1355 750\\nf 2325 1844 1355\\nf 750 1355 1844\\nf 2478 971 684\\nf 1318 1622 971\\nf 2429 684 1622\\nf 971 1622 684\\nf 2197 500 825\\nf 985 1410 500\\nf 1318 825 1410\\nf 500 1410 825\\nf 2092 1883 1231\\nf 2429 986 1883\\nf 985 1231 986\\nf 1883 986 1231\\nf 1318 1410 1622\\nf 985 986 1410\\nf 2429 1622 986\\nf 1410 986 1622\\nf 1510 2157 1605\\nf 2333 2338 2157\\nf 330 1605 2338\\nf 2157 2338 1605\\nf 1953 394 1485\\nf 798 976 394\\nf 2333 1485 976\\nf 394 976 1485\\nf 2197 313 1759\\nf 330 1109 313\\nf 798 1759 1109\\nf 313 1109 1759\\nf 2333 976 2338\\nf 798 1109 976\\nf 330 2338 1109\\nf 976 1109 2338\\nf 2092 1231 868\\nf 985 1131 1231\\nf 2067 868 1131\\nf 1231 1131 868\\nf 2197 1759 500\\nf 798 2401 1759\\nf 985 500 2401\\nf 1759 2401 500\\nf 1953 126 394\\nf 2067 754 126\\nf 798 394 754\\nf 126 754 394\\nf 985 2401 1131\\nf 798 754 2401\\nf 2067 1131 754\\nf 2401 754 1131\\nf 2391 1998 1401\\nf 1220 922 1998\\nf 2043 1401 922\\nf 1998 922 1401\\nf 1756 111 2516\\nf 1275 2161 111\\nf 1220 2516 2161\\nf 111 2161 2516\\nf 720 2485 1751\\nf 2043 1293 2485\\nf 1275 1751 1293\\nf 2485 1293 1751\\nf 1220 2161 922\\nf 1275 1293 2161\\nf 2043 922 1293\\nf 2161 1293 922\\nf 678 1691 1778\\nf 2254 1607 1691\\nf 1420 1778 1607\\nf 1691 1607 1778\\nf 1784 1811 2023\\nf 2050 337 1811\\nf 2254 2023 337\\nf 1811 337 2023\\nf 1756 965 763\\nf 1420 101 965\\nf 2050 763 101\\nf 965 101 763\\nf 2254 337 1607\\nf 2050 101 337\\nf 1420 1607 101\\nf 337 101 1607\\nf 560 2085 38\\nf 857 1606 2085\\nf 468 38 1606\\nf 2085 1606 38\\nf 720 1102 694\\nf 675 277 1102\\nf 857 694 277\\nf 1102 277 694\\nf 1784 1544 27\\nf 468 1241 1544\\nf 675 27 1241\\nf 1544 1241 27\\nf 857 277 1606\\nf 675 1241 277\\nf 468 1606 1241\\nf 277 1241 1606\\nf 1756 763 111\\nf 2050 1767 763\\nf 1275 111 1767\\nf 763 1767 111\\nf 1784 27 1811\\nf 675 626 27\\nf 2050 1811 626\\nf 27 626 1811\\nf 720 1751 1102\\nf 1275 1000 1751\\nf 675 1102 1000\\nf 1751 1000 1102\\nf 2050 626 1767\\nf 675 1000 626\\nf 1275 1767 1000\\nf 626 1000 1767\\nf 2312 228 2104\\nf 733 1490 228\\nf 2270 2104 1490\\nf 228 1490 2104\\nf 307 1373 2360\\nf 1325 2517 1373\\nf 733 2360 2517\\nf 1373 2517 2360\\nf 236 2431 1176\\nf 2270 1545 2431\\nf 1325 1176 1545\\nf 2431 1545 1176\\nf 733 2517 1490\\nf 1325 1545 2517\\nf 2270 1490 1545\\nf 2517 1545 1490\\nf 116 169 2353\\nf 1620 139 169\\nf 2465 2353 139\\nf 169 139 2353\\nf 297 1276 1859\\nf 1762 1557 1276\\nf 1620 1859 1557\\nf 1276 1557 1859\\nf 307 1304 2036\\nf 2465 2443 1304\\nf 1762 2036 2443\\nf 1304 2443 2036\\nf 1620 1557 139\\nf 1762 2443 1557\\nf 2465 139 2443\\nf 1557 2443 139\\nf 678 957 1536\\nf 276 1179 957\\nf 1183 1536 1179\\nf 957 1179 1536\\nf 236 1956 1045\\nf 192 614 1956\\nf 276 1045 614\\nf 1956 614 1045\\nf 297 1064 1435\\nf 1183 1651 1064\\nf 192 1435 1651\\nf 1064 1651 1435\\nf 276 614 1179\\nf 192 1651 614\\nf 1183 1179 1651\\nf 614 1651 1179\\nf 307 2036 1373\\nf 1762 1973 2036\\nf 1325 1373 1973\\nf 2036 1973 1373\\nf 297 1435 1276\\nf 192 1835 1435\\nf 1762 1276 1835\\nf 1435 1835 1276\\nf 236 1176 1956\\nf 1325 2139 1176\\nf 192 1956 2139\\nf 1176 2139 1956\\nf 1762 1835 1973\\nf 192 2139 1835\\nf 1325 1973 2139\\nf 1835 2139 1973\\nf 1831 1013 2423\\nf 1916 839 1013\\nf 930 2423 839\\nf 1013 839 2423\\nf 57 2374 2317\\nf 123 2069 2374\\nf 1916 2317 2069\\nf 2374 2069 2317\\nf 952 1053 112\\nf 930 2100 1053\\nf 123 112 2100\\nf 1053 2100 112\\nf 1916 2069 839\\nf 123 2100 2069\\nf 930 839 2100\\nf 2069 2100 839\\nf 560 1765 1710\\nf 1342 900 1765\\nf 975 1710 900\\nf 1765 900 1710\\nf 1678 602 1431\\nf 2416 940 602\\nf 1342 1431 940\\nf 602 940 1431\\nf 57 1754 621\\nf 975 1542 1754\\nf 2416 621 1542\\nf 1754 1542 621\\nf 1342 940 900\\nf 2416 1542 940\\nf 975 900 1542\\nf 940 1542 900\\nf 116 371 2469\\nf 475 1578 371\\nf 567 2469 1578\\nf 371 1578 2469\\nf 952 1719 1728\\nf 185 2407 1719\\nf 475 1728 2407\\nf 1719 2407 1728\\nf 1678 649 279\\nf 567 2290 649\\nf 185 279 2290\\nf 649 2290 279\\nf 475 2407 1578\\nf 185 2290 2407\\nf 567 1578 2290\\nf 2407 2290 1578\\nf 57 621 2374\\nf 2416 873 621\\nf 123 2374 873\\nf 621 873 2374\\nf 1678 279 602\\nf 185 1476 279\\nf 2416 602 1476\\nf 279 1476 602\\nf 952 112 1719\\nf 123 52 112\\nf 185 1719 52\\nf 112 52 1719\\nf 2416 1476 873\\nf 185 52 1476\\nf 123 873 52\\nf 1476 52 873\\nf 678 1536 1691\\nf 1183 2340 1536\\nf 2254 1691 2340\\nf 1536 2340 1691\\nf 297 1556 1064\\nf 2011 1142 1556\\nf 1183 1064 1142\\nf 1556 1142 1064\\nf 1784 2023 1889\\nf 2254 1286 2023\\nf 2011 1889 1286\\nf 2023 1286 1889\\nf 1183 1142 2340\\nf 2011 1286 1142\\nf 2254 2340 1286\\nf 1142 1286 2340\\nf 116 2469 169\\nf 567 2493 2469\\nf 1620 169 2493\\nf 2469 2493 169\\nf 1678 1963 649\\nf 493 2090 1963\\nf 567 649 2090\\nf 1963 2090 649\\nf 297 1859 2188\\nf 1620 661 1859\\nf 493 2188 661\\nf 1859 661 2188\\nf 567 2090 2493\\nf 493 661 2090\\nf 1620 2493 661\\nf 2090 661 2493\\nf 560 38 1765\\nf 468 2462 38\\nf 1342 1765 2462\\nf 38 2462 1765\\nf 1784 1223 1544\\nf 902 805 1223\\nf 468 1544 805\\nf 1223 805 1544\\nf 1678 1431 690\\nf 1342 8 1431\\nf 902 690 8\\nf 1431 8 690\\nf 468 805 2462\\nf 902 8 805\\nf 1342 2462 8\\nf 805 8 2462\\nf 297 2188 1556\\nf 493 2406 2188\\nf 2011 1556 2406\\nf 2188 2406 1556\\nf 1678 690 1963\\nf 902 1323 690\\nf 493 1963 1323\\nf 690 1323 1963\\nf 1784 1889 1223\\nf 2011 1121 1889\\nf 902 1223 1121\\nf 1889 1121 1223\\nf 493 1323 2406\\nf 902 1121 1323\\nf 2011 2406 1121\\nf 1323 1121 2406\\nf 2008 2078 421\\nf 2475 2076 2078\\nf 2147 421 2076\\nf 2078 2076 421\\nf 2481 2504 2075\\nf 393 2536 2504\\nf 2475 2075 2536\\nf 2504 2536 2075\\nf 2425 877 2056\\nf 2147 776 877\\nf 393 2056 776\\nf 877 776 2056\\nf 2475 2536 2076\\nf 393 776 2536\\nf 2147 2076 776\\nf 2536 776 2076\\nf 2194 923 2045\\nf 1038 223 923\\nf 2042 2045 223\\nf 923 223 2045\\nf 2458 852 787\\nf 752 2474 852\\nf 1038 787 2474\\nf 852 2474 787\\nf 2481 946 458\\nf 2042 96 946\\nf 752 458 96\\nf 946 96 458\\nf 1038 2474 223\\nf 752 96 2474\\nf 2042 223 96\\nf 2474 96 223\\nf 1083 316 1094\\nf 1562 51 316\\nf 555 1094 51\\nf 316 51 1094\\nf 2425 1742 728\\nf 795 2180 1742\\nf 1562 728 2180\\nf 1742 2180 728\\nf 2458 2250 2315\\nf 555 1426 2250\\nf 795 2315 1426\\nf 2250 1426 2315\\nf 1562 2180 51\\nf 795 1426 2180\\nf 555 51 1426\\nf 2180 1426 51\\nf 2481 458 2504\\nf 752 2438 458\\nf 393 2504 2438\\nf 458 2438 2504\\nf 2458 2315 852\\nf 795 220 2315\\nf 752 852 220\\nf 2315 220 852\\nf 2425 2056 1742\\nf 393 41 2056\\nf 795 1742 41\\nf 2056 41 1742\\nf 752 220 2438\\nf 795 41 220\\nf 393 2438 41\\nf 220 41 2438\\nf 2391 1333 2309\\nf 1027 2511 1333\\nf 2327 2309 2511\\nf 1333 2511 2309\\nf 760 559 1458\\nf 1773 2105 559\\nf 1027 1458 2105\\nf 559 2105 1458\\nf 1295 58 2524\\nf 2327 138 58\\nf 1773 2524 138\\nf 58 138 2524\\nf 1027 2105 2511\\nf 1773 138 2105\\nf 2327 2511 138\\nf 2105 138 2511\\nf 1525 2482 901\\nf 2542 2202 2482\\nf 1331 901 2202\\nf 2482 2202 901\\nf 88 1058 564\\nf 815 685 1058\\nf 2542 564 685\\nf 1058 685 564\\nf 760 2239 37\\nf 1331 1199 2239\\nf 815 37 1199\\nf 2239 1199 37\\nf 2542 685 2202\\nf 815 1199 685\\nf 1331 2202 1199\\nf 685 1199 2202\\nf 2194 2181 480\\nf 1968 2415 2181\\nf 1354 480 2415\\nf 2181 2415 480\\nf 1295 1539 2120\\nf 1988 1787 1539\\nf 1968 2120 1787\\nf 1539 1787 2120\\nf 88 540 2368\\nf 1354 218 540\\nf 1988 2368 218\\nf 540 218 2368\\nf 1968 1787 2415\\nf 1988 218 1787\\nf 1354 2415 218\\nf 1787 218 2415\\nf 760 37 559\\nf 815 1193 37\\nf 1773 559 1193\\nf 37 1193 559\\nf 88 2368 1058\\nf 1988 361 2368\\nf 815 1058 361\\nf 2368 361 1058\\nf 1295 2524 1539\\nf 1773 1135 2524\\nf 1988 1539 1135\\nf 2524 1135 1539\\nf 815 361 1193\\nf 1988 1135 361\\nf 1773 1193 1135\\nf 361 1135 1193\\nf 2282 1463 1704\\nf 1445 302 1463\\nf 1877 1704 302\\nf 1463 302 1704\\nf 1388 2284 122\\nf 765 495 2284\\nf 1445 122 495\\nf 2284 495 122\\nf 931 840 1825\\nf 1877 2380 840\\nf 765 1825 2380\\nf 840 2380 1825\\nf 1445 495 302\\nf 765 2380 495\\nf 1877 302 2380\\nf 495 2380 302\\nf 1083 2519 1745\\nf 2463 843 2519\\nf 2229 1745 843\\nf 2519 843 1745\\nf 584 1389 1096\\nf 1632 2071 1389\\nf 2463 1096 2071\\nf 1389 2071 1096\\nf 1388 1016 1612\\nf 2229 1774 1016\\nf 1632 1612 1774\\nf 1016 1774 1612\\nf 2463 2071 843\\nf 1632 1774 2071\\nf 2229 843 1774\\nf 2071 1774 843\\nf 1525 1390 968\\nf 473 1089 1390\\nf 1736 968 1089\\nf 1390 1089 968\\nf 931 1372 1449\\nf 24 2032 1372\\nf 473 1449 2032\\nf 1372 2032 1449\\nf 584 518 1939\\nf 1736 30 518\\nf 24 1939 30\\nf 518 30 1939\\nf 473 2032 1089\\nf 24 30 2032\\nf 1736 1089 30\\nf 2032 30 1089\\nf 1388 1612 2284\\nf 1632 2221 1612\\nf 765 2284 2221\\nf 1612 2221 2284\\nf 584 1939 1389\\nf 24 1801 1939\\nf 1632 1389 1801\\nf 1939 1801 1389\\nf 931 1825 1372\\nf 765 2283 1825\\nf 24 1372 2283\\nf 1825 2283 1372\\nf 1632 1801 2221\\nf 24 2283 1801\\nf 765 2221 2283\\nf 1801 2283 2221\\nf 2194 480 923\\nf 1354 1574 480\\nf 1038 923 1574\\nf 480 1574 923\\nf 88 2189 540\\nf 2089 230 2189\\nf 1354 540 230\\nf 2189 230 540\\nf 2458 787 1712\\nf 1038 1050 787\\nf 2089 1712 1050\\nf 787 1050 1712\\nf 1354 230 1574\\nf 2089 1050 230\\nf 1038 1574 1050\\nf 230 1050 1574\\nf 1525 968 2482\\nf 1736 1819 968\\nf 2542 2482 1819\\nf 968 1819 2482\\nf 584 109 518\\nf 1673 1243 109\\nf 1736 518 1243\\nf 109 1243 518\\nf 88 564 606\\nf 2542 1722 564\\nf 1673 606 1722\\nf 564 1722 606\\nf 1736 1243 1819\\nf 1673 1722 1243\\nf 2542 1819 1722\\nf 1243 1722 1819\\nf 1083 1094 2519\\nf 555 1195 1094\\nf 2463 2519 1195\\nf 1094 1195 2519\\nf 2458 491 2250\\nf 2278 858 491\\nf 555 2250 858\\nf 491 858 2250\\nf 584 1096 2285\\nf 2463 85 1096\\nf 2278 2285 85\\nf 1096 85 2285\\nf 555 858 1195\\nf 2278 85 858\\nf 2463 1195 85\\nf 858 85 1195\\nf 88 606 2189\\nf 1673 463 606\\nf 2089 2189 463\\nf 606 463 2189\\nf 584 2285 109\\nf 2278 1052 2285\\nf 1673 109 1052\\nf 2285 1052 109\\nf 2458 1712 491\\nf 2089 310 1712\\nf 2278 491 310\\nf 1712 310 491\\nf 1673 1052 463\\nf 2278 310 1052\\nf 2089 463 310\\nf 1052 310 463\\nf 1769 1287 1124\\nf 802 842 1287\\nf 1553 1124 842\\nf 1287 842 1124\\nf 907 984 1633\\nf 2437 1182 984\\nf 802 1633 1182\\nf 984 1182 1633\\nf 2014 1105 1294\\nf 1553 809 1105\\nf 2437 1294 809\\nf 1105 809 1294\\nf 802 1182 842\\nf 2437 809 1182\\nf 1553 842 809\\nf 1182 809 842\\nf 2466 784 909\\nf 479 1171 784\\nf 727 909 1171\\nf 784 1171 909\\nf 2216 651 2242\\nf 1966 4 651\\nf 479 2242 4\\nf 651 4 2242\\nf 907 2372 1261\\nf 727 1561 2372\\nf 1966 1261 1561\\nf 2372 1561 1261\\nf 479 4 1171\\nf 1966 1561 4\\nf 727 1171 1561\\nf 4 1561 1171\\nf 1191 1790 1155\\nf 2169 2399 1790\\nf 1492 1155 2399\\nf 1790 2399 1155\\nf 2014 941 1157\\nf 1208 1409 941\\nf 2169 1157 1409\\nf 941 1409 1157\\nf 2216 646 246\\nf 1492 2470 646\\nf 1208 246 2470\\nf 646 2470 246\\nf 2169 1409 2399\\nf 1208 2470 1409\\nf 1492 2399 2470\\nf 1409 2470 2399\\nf 907 1261 984\\nf 1966 68 1261\\nf 2437 984 68\\nf 1261 68 984\\nf 2216 246 651\\nf 1208 801 246\\nf 1966 651 801\\nf 246 801 651\\nf 2014 1294 941\\nf 2437 600 1294\\nf 1208 941 600\\nf 1294 600 941\\nf 1966 801 68\\nf 1208 600 801\\nf 2437 68 600\\nf 801 600 68\\nf 2008 777 1427\\nf 746 2192 777\\nf 644 1427 2192\\nf 777 2192 1427\\nf 29 1979 990\\nf 1324 709 1979\\nf 746 990 709\\nf 1979 709 990\\nf 988 2506 962\\nf 644 778 2506\\nf 1324 962 778\\nf 2506 778 962\\nf 746 709 2192\\nf 1324 778 709\\nf 644 2192 778\\nf 709 778 2192\\nf 961 1529 1871\\nf 1641 823 1529\\nf 878 1871 823\\nf 1529 823 1871\\nf 1546 816 369\\nf 339 295 816\\nf 1641 369 295\\nf 816 295 369\\nf 29 1868 914\\nf 878 612 1868\\nf 339 914 612\\nf 1868 612 914\\nf 1641 295 823\\nf 339 612 295\\nf 878 823 612\\nf 295 612 823\\nf 2466 833 867\\nf 1055 2225 833\\nf 879 867 2225\\nf 833 2225 867\\nf 988 2106 1573\\nf 557 1588 2106\\nf 1055 1573 1588\\nf 2106 1588 1573\\nf 1546 2405 1644\\nf 879 2164 2405\\nf 557 1644 2164\\nf 2405 2164 1644\\nf 1055 1588 2225\\nf 557 2164 1588\\nf 879 2225 2164\\nf 1588 2164 2225\\nf 29 914 1979\\nf 339 1494 914\\nf 1324 1979 1494\\nf 914 1494 1979\\nf 1546 1644 816\\nf 557 381 1644\\nf 339 816 381\\nf 1644 381 816\\nf 988 962 2106\\nf 1324 1597 962\\nf 557 2106 1597\\nf 962 1597 2106\\nf 339 381 1494\\nf 557 1597 381\\nf 1324 1494 1597\\nf 381 1597 1494\\nf 1777 566 1374\\nf 309 2228 566\\nf 282 1374 2228\\nf 566 2228 1374\\nf 487 1857 2304\\nf 2264 1415 1857\\nf 309 2304 1415\\nf 1857 1415 2304\\nf 1705 1437 1074\\nf 282 2013 1437\\nf 2264 1074 2013\\nf 1437 2013 1074\\nf 309 1415 2228\\nf 2264 2013 1415\\nf 282 2228 2013\\nf 1415 2013 2228\\nf 1191 1036 1768\\nf 1026 1638 1036\\nf 130 1768 1638\\nf 1036 1638 1768\\nf 364 536 1621\\nf 1336 800 536\\nf 1026 1621 800\\nf 536 800 1621\\nf 487 117 419\\nf 130 1515 117\\nf 1336 419 1515\\nf 117 1515 419\\nf 1026 800 1638\\nf 1336 1515 800\\nf 130 1638 1515\\nf 800 1515 1638\\nf 961 97 405\\nf 934 820 97\\nf 1338 405 820\\nf 97 820 405\\nf 1705 1890 1262\\nf 2119 1901 1890\\nf 934 1262 1901\\nf 1890 1901 1262\\nf 364 1664 2403\\nf 1338 308 1664\\nf 2119 2403 308\\nf 1664 308 2403\\nf 934 1901 820\\nf 2119 308 1901\\nf 1338 820 308\\nf 1901 308 820\\nf 487 419 1857\\nf 1336 113 419\\nf 2264 1857 113\\nf 419 113 1857\\nf 364 2403 536\\nf 2119 1506 2403\\nf 1336 536 1506\\nf 2403 1506 536\\nf 1705 1074 1890\\nf 2264 387 1074\\nf 2119 1890 387\\nf 1074 387 1890\\nf 1336 1506 113\\nf 2119 387 1506\\nf 2264 113 387\\nf 1506 387 113\\nf 2466 867 784\\nf 879 355 867\\nf 479 784 355\\nf 867 355 784\\nf 1546 2518 2405\\nf 2561 1392 2518\\nf 879 2405 1392\\nf 2518 1392 2405\\nf 2216 2242 640\\nf 479 1617 2242\\nf 2561 640 1617\\nf 2242 1617 640\\nf 879 1392 355\\nf 2561 1617 1392\\nf 479 355 1617\\nf 1392 1617 355\\nf 961 405 1529\\nf 1338 1582 405\\nf 1641 1529 1582\\nf 405 1582 1529\\nf 364 2118 1664\\nf 1314 1922 2118\\nf 1338 1664 1922\\nf 2118 1922 1664\\nf 1546 369 1786\\nf 1641 1526 369\\nf 1314 1786 1526\\nf 369 1526 1786\\nf 1338 1922 1582\\nf 1314 1526 1922\\nf 1641 1582 1526\\nf 1922 1526 1582\\nf 1191 1155 1036\\nf 1492 1079 1155\\nf 1026 1036 1079\\nf 1155 1079 1036\\nf 2216 1189 646\\nf 735 147 1189\\nf 1492 646 147\\nf 1189 147 646\\nf 364 1621 39\\nf 1026 1482 1621\\nf 735 39 1482\\nf 1621 1482 39\\nf 1492 147 1079\\nf 735 1482 147\\nf 1026 1079 1482\\nf 147 1482 1079\\nf 1546 1786 2518\\nf 1314 1601 1786\\nf 2561 2518 1601\\nf 1786 1601 2518\\nf 364 39 2118\\nf 735 970 39\\nf 1314 2118 970\\nf 39 970 2118\\nf 2216 640 1189\\nf 2561 1947 640\\nf 735 1189 1947\\nf 640 1947 1189\\nf 1314 970 1601\\nf 735 1947 970\\nf 2561 1601 1947\\nf 970 1947 1601'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 29 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "pyBAYFy0cQuj" + }, + "source": [ + "" + ], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file From b06242d5e935f3b1e6b38be09165f9999e27d331 Mon Sep 17 00:00:00 2001 From: Kieran Nehil-Puleo Date: Sat, 22 May 2021 19:12:22 -0400 Subject: [PATCH 3/8] Revert "Added sample pre-trained colab" This reverts commit 2c7d5ba72060b9f4e177b897d4b98033625ea02c. --- 3d_transfer.ipynb | 2452 --------------------------------------------- 1 file changed, 2452 deletions(-) delete mode 100644 3d_transfer.ipynb diff --git a/3d_transfer.ipynb b/3d_transfer.ipynb deleted file mode 100644 index c8a9dd9..0000000 --- a/3d_transfer.ipynb +++ /dev/null @@ -1,2452 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "name": "3d-transfer.ipynb", - "provenance": [], - "collapsed_sections": [], - "authorship_tag": "ABX9TyO7vcZ1c5JmXcQwpkeJY3W3", - "include_colab_link": true - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "name": "python" - }, - "accelerator": "GPU" - }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "uwzRkfxGDKaD" - }, - "source": [ - "# Unsupervised 3d Style Tranfer via 3dsnet" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "DUajY7mxkAH8" - }, - "source": [ - "All credit for most of the code base and model to:\n", - "\n", - "\n", - "@article{segu20203dsnet,\n", - " title={3DSNet: Unsupervised Shape-to-Shape 3D Style Transfer},\n", - " author={Segu, Mattia and Grinvald, Margarita and Siegwart, Roland and Tombari, Federico},\n", - " journal={arXiv preprint arXiv:2011.13388},\n", - " year={2020}\n", - "}\n", - "\n", - "\n", - "Checkout the 3dsnet [repo](https://github.com/ethz-asl/3dsnet)\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "OxIfIaS0Drhi" - }, - "source": [ - "Author of this colab: [KieranNP](https://github.com/kierannp)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "P-Sh4i2EH89-" - }, - "source": [ - "List of available models compatible with current codebase\n", - "\n", - "CHAIRS (using our Adaptive-Meshflow backbone):\n", - "- 3dsnet (with reconstruction loss, adversarial loss and cycle-consistency loss)\n", - "- adanorm (only reconstruction loss, style transfer is attempted by relying only on the adaptive normalization layers)\n", - "\n", - "PLANES (using our Adaptive-Atlasnet backbone):\n", - "- 3dsnet (with reconstruction loss, adversarial loss and cycle-consistency loss)\n", - "- 3dsnet_no_cycle (with reconstruction loss and adversarial loss)\n", - "- adanorm (only reconstruction loss, style transfer is attempted by relying only on the adaptive normalization layers)\n", - "\n", - "Files included:\n", - "- log.txt, contains training statistics per each training epoch\n", - "- network.pth, model weights at last training epoch\n", - "- network_best.pth, model weights at best training epoch\n", - "- options.json, options used for training the model" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "rILowRnFIbWJ", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "outputId": "c7334c06-664a-4ffa-a27f-b5afabf6b3ff" - }, - "source": [ - "#@title Installations required {display-mode: \"form\"}\n", - "\n", - "# This code will be hidden when the notebook is loaded.\n", - "\n", - "# %%capture\n", - "%cd /content/\n", - "%env PYTHONPATH=\n", - "! wget https://repo.anaconda.com/miniconda/Miniconda3-4.5.4-Linux-x86_64.sh\n", - "! chmod +x Miniconda3-4.5.4-Linux-x86_64.sh\n", - "! bash ./Miniconda3-4.5.4-Linux-x86_64.sh -b -f -p /usr/local\n", - "import sys\n", - "import os\n", - "\n", - "sys.path.append('/usr/local/lib/python3.6/site-packages/')\n", - "\n", - "!conda install --channel defaults conda python=3.6 --yes\n", - "!conda update --channel defaults --all --yes\n", - "if not os.path.exists(\"/content/3dsnet\"):\n", - " !git clone --recurse-submodules https://github.com/ethz-asl/3dsnet.git\n", - "\n", - "!conda create -n 3dsnet python=3.6 --yes\n", - "!source activate 3dsnet\n", - "\n", - "!pip install meshio[all]\n", - "\n", - "!conda install pytorch=1.7.1 torchvision=0.8.2 cudatoolkit=10.1 -c pytorch --yes\n", - "!conda install -y -c conda-forge pyembree\n", - "!conda install -y -c conda-forge trimesh seaborn\n", - "!conda install -y -c fvcore -c iopath -c conda-forge fvcore iopath\n", - "# !pip install \"git+https://github.com/facebookresearch/pytorch3d.git@stable\"\n", - "!conda install -y pytorch3d -c pytorch3d\n", - "# !pip install pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/py36_cu101_pyt170/download.html\n", - "!conda install -y -c conda-forge visdom\n", - "\n", - "if not os.path.exists(\"/content/3dsnet/PyMesh\"):\n", - " %cd /content/3dsnset\n", - " !git clone https://github.com/PyMesh/PyMesh.git\n", - " %cd PyMesh\n", - " !git submodule update --init\n", - " !export PYMESH_PATH=$(pwd)\n", - "\n", - " !apt-get install \\\n", - "\tlibeigen3-dev \\\n", - "\tlibgmp-dev \\\n", - "\tlibgmpxx4ldbl \\\n", - "\tlibmpfr-dev \\\n", - "\tlibboost-dev \\\n", - "\tlibboost-thread-dev \\\n", - "\tlibtbb-dev \\\n", - "\tpython3-dev \\\n", - "\tpython3-setuptools \\\n", - "\tpython3-numpy \\\n", - "\tpython3-scipy \\\n", - "\tpython3-nose \\\n", - "\tpython3-pip \\\n", - "\tcmake\n", - "\n", - " %cd $PYMESH_PATH/third_party\n", - " !mkdir build\n", - " !./build.py all\n", - " %cd $PYMESH_PATH\n", - " !mkdir build\n", - " !python3\n", - " setup.py build # This an alternative way of calling cmake/make\n", - " !python3 setup.py install\n", - " %cd ..\n", - "\n", - "!pip install trimesh\n", - "\n", - "!pip install git+https://github.com/rtqichen/torchdiffeq torchvision\n", - "!pip install git+https://github.com/cnr-isti-vclab/PyMeshLab\n", - "%cd /content/3dsnet\n", - "!pip install --user --requirement requirements.txt # pip dependencies" - ], - "execution_count": 5, - "outputs": [ - { - "output_type": "stream", - "text": [ - "/content\n", - "env: PYTHONPATH=\n", - "--2021-05-20 19:42:12-- https://repo.anaconda.com/miniconda/Miniconda3-4.5.4-Linux-x86_64.sh\n", - "Resolving repo.anaconda.com (repo.anaconda.com)... 104.16.131.3, 104.16.130.3, 2606:4700::6810:8203, ...\n", - "Connecting to repo.anaconda.com (repo.anaconda.com)|104.16.131.3|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 58468498 (56M) [application/x-sh]\n", - "Saving to: ‘Miniconda3-4.5.4-Linux-x86_64.sh’\n", - "\n", - "Miniconda3-4.5.4-Li 100%[===================>] 55.76M 219MB/s in 0.3s \n", - "\n", - "2021-05-20 19:42:13 (219 MB/s) - ‘Miniconda3-4.5.4-Linux-x86_64.sh’ saved [58468498/58468498]\n", - "\n", - "PREFIX=/usr/local\n", - "installing: python-3.6.5-hc3d631a_2 ...\n", - "Python 3.6.5 :: Anaconda, Inc.\n", - "installing: ca-certificates-2018.03.07-0 ...\n", - "installing: conda-env-2.6.0-h36134e3_1 ...\n", - "installing: libgcc-ng-7.2.0-hdf63c60_3 ...\n", - "installing: libstdcxx-ng-7.2.0-hdf63c60_3 ...\n", - "installing: libffi-3.2.1-hd88cf55_4 ...\n", - "installing: ncurses-6.1-hf484d3e_0 ...\n", - "installing: openssl-1.0.2o-h20670df_0 ...\n", - "installing: tk-8.6.7-hc745277_3 ...\n", - "installing: xz-5.2.4-h14c3975_4 ...\n", - "installing: yaml-0.1.7-had09818_2 ...\n", - "installing: zlib-1.2.11-ha838bed_2 ...\n", - "installing: libedit-3.1.20170329-h6b74fdf_2 ...\n", - "installing: readline-7.0-ha6073c6_4 ...\n", - "installing: sqlite-3.23.1-he433501_0 ...\n", - "installing: asn1crypto-0.24.0-py36_0 ...\n", - "installing: certifi-2018.4.16-py36_0 ...\n", - "installing: chardet-3.0.4-py36h0f667ec_1 ...\n", - "installing: idna-2.6-py36h82fb2a8_1 ...\n", - "installing: pycosat-0.6.3-py36h0a5515d_0 ...\n", - "installing: pycparser-2.18-py36hf9f622e_1 ...\n", - "installing: pysocks-1.6.8-py36_0 ...\n", - "installing: ruamel_yaml-0.15.37-py36h14c3975_2 ...\n", - "installing: six-1.11.0-py36h372c433_1 ...\n", - "installing: cffi-1.11.5-py36h9745a5d_0 ...\n", - "installing: setuptools-39.2.0-py36_0 ...\n", - "installing: cryptography-2.2.2-py36h14c3975_0 ...\n", - "installing: wheel-0.31.1-py36_0 ...\n", - "installing: pip-10.0.1-py36_0 ...\n", - "installing: pyopenssl-18.0.0-py36_0 ...\n", - "installing: urllib3-1.22-py36hbe7ace6_0 ...\n", - "installing: requests-2.18.4-py36he2e5f8d_1 ...\n", - "installing: conda-4.5.4-py36_0 ...\n", - "installation finished.\n", - "Solving environment: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n", - "\n", - "## Package Plan ##\n", - "\n", - " environment location: /usr/local\n", - "\n", - " added / updated specs: \n", - " - conda\n", - " - python=3.6\n", - "\n", - "\n", - "The following packages will be downloaded:\n", - "\n", - " package | build\n", - " ---------------------------|-----------------\n", - " pysocks-1.7.1 | py36h06a4308_0 30 KB\n", - " pycparser-2.20 | py_2 94 KB\n", - " zlib-1.2.11 | h7b6447c_3 120 KB\n", - " brotlipy-0.7.0 |py36h27cfd23_1003 349 KB\n", - " _libgcc_mutex-0.1 | main 3 KB\n", - " openssl-1.1.1k | h27cfd23_0 3.8 MB\n", - " tqdm-4.59.0 | pyhd3eb1b0_1 90 KB\n", - " certifi-2020.12.5 | py36h06a4308_0 144 KB\n", - " libffi-3.3 | he6710b0_2 54 KB\n", - " idna-2.10 | pyhd3eb1b0_0 52 KB\n", - " pycosat-0.6.3 | py36h27cfd23_0 107 KB\n", - " tk-8.6.10 | hbc83047_0 3.2 MB\n", - " wheel-0.36.2 | pyhd3eb1b0_0 31 KB\n", - " pyopenssl-20.0.1 | pyhd3eb1b0_1 48 KB\n", - " ncurses-6.2 | he6710b0_1 1.1 MB\n", - " six-1.15.0 | pyhd3eb1b0_0 13 KB\n", - " libgcc-ng-9.1.0 | hdf63c60_0 8.1 MB\n", - " readline-8.1 | h27cfd23_0 464 KB\n", - " cffi-1.14.5 | py36h261ae71_0 224 KB\n", - " sqlite-3.35.4 | hdfb4753_0 1.4 MB\n", - " conda-4.10.1 | py36h06a4308_1 3.1 MB\n", - " requests-2.25.1 | pyhd3eb1b0_0 51 KB\n", - " python-3.6.13 | hdb3f193_0 33.9 MB\n", - " ca-certificates-2021.4.13 | h06a4308_1 120 KB\n", - " cryptography-3.4.7 | py36hd23ed53_0 1.0 MB\n", - " chardet-4.0.0 |py36h06a4308_1003 213 KB\n", - " pip-21.0.1 | py36h06a4308_0 2.0 MB\n", - " urllib3-1.26.4 | pyhd3eb1b0_0 99 KB\n", - " xz-5.2.5 | h7b6447c_0 438 KB\n", - " conda-package-handling-1.7.3| py36h27cfd23_1 946 KB\n", - " setuptools-52.0.0 | py36h06a4308_0 933 KB\n", - " ruamel_yaml-0.15.100 | py36h27cfd23_0 268 KB\n", - " yaml-0.2.5 | h7b6447c_0 87 KB\n", - " libstdcxx-ng-9.1.0 | hdf63c60_0 4.0 MB\n", - " ld_impl_linux-64-2.33.1 | h53a641e_7 645 KB\n", - " ------------------------------------------------------------\n", - " Total: 67.2 MB\n", - "\n", - "The following NEW packages will be INSTALLED:\n", - "\n", - " _libgcc_mutex: 0.1-main \n", - " brotlipy: 0.7.0-py36h27cfd23_1003\n", - " conda-package-handling: 1.7.3-py36h27cfd23_1 \n", - " ld_impl_linux-64: 2.33.1-h53a641e_7 \n", - " tqdm: 4.59.0-pyhd3eb1b0_1 \n", - "\n", - "The following packages will be UPDATED:\n", - "\n", - " ca-certificates: 2018.03.07-0 --> 2021.4.13-h06a4308_1 \n", - " certifi: 2018.4.16-py36_0 --> 2020.12.5-py36h06a4308_0\n", - " cffi: 1.11.5-py36h9745a5d_0 --> 1.14.5-py36h261ae71_0 \n", - " chardet: 3.0.4-py36h0f667ec_1 --> 4.0.0-py36h06a4308_1003 \n", - " conda: 4.5.4-py36_0 --> 4.10.1-py36h06a4308_1 \n", - " cryptography: 2.2.2-py36h14c3975_0 --> 3.4.7-py36hd23ed53_0 \n", - " idna: 2.6-py36h82fb2a8_1 --> 2.10-pyhd3eb1b0_0 \n", - " libffi: 3.2.1-hd88cf55_4 --> 3.3-he6710b0_2 \n", - " libgcc-ng: 7.2.0-hdf63c60_3 --> 9.1.0-hdf63c60_0 \n", - " libstdcxx-ng: 7.2.0-hdf63c60_3 --> 9.1.0-hdf63c60_0 \n", - " ncurses: 6.1-hf484d3e_0 --> 6.2-he6710b0_1 \n", - " openssl: 1.0.2o-h20670df_0 --> 1.1.1k-h27cfd23_0 \n", - " pip: 10.0.1-py36_0 --> 21.0.1-py36h06a4308_0 \n", - " pycosat: 0.6.3-py36h0a5515d_0 --> 0.6.3-py36h27cfd23_0 \n", - " pycparser: 2.18-py36hf9f622e_1 --> 2.20-py_2 \n", - " pyopenssl: 18.0.0-py36_0 --> 20.0.1-pyhd3eb1b0_1 \n", - " pysocks: 1.6.8-py36_0 --> 1.7.1-py36h06a4308_0 \n", - " python: 3.6.5-hc3d631a_2 --> 3.6.13-hdb3f193_0 \n", - " readline: 7.0-ha6073c6_4 --> 8.1-h27cfd23_0 \n", - " requests: 2.18.4-py36he2e5f8d_1 --> 2.25.1-pyhd3eb1b0_0 \n", - " ruamel_yaml: 0.15.37-py36h14c3975_2 --> 0.15.100-py36h27cfd23_0 \n", - " setuptools: 39.2.0-py36_0 --> 52.0.0-py36h06a4308_0 \n", - " six: 1.11.0-py36h372c433_1 --> 1.15.0-pyhd3eb1b0_0 \n", - " sqlite: 3.23.1-he433501_0 --> 3.35.4-hdfb4753_0 \n", - " tk: 8.6.7-hc745277_3 --> 8.6.10-hbc83047_0 \n", - " urllib3: 1.22-py36hbe7ace6_0 --> 1.26.4-pyhd3eb1b0_0 \n", - " wheel: 0.31.1-py36_0 --> 0.36.2-pyhd3eb1b0_0 \n", - " xz: 5.2.4-h14c3975_4 --> 5.2.5-h7b6447c_0 \n", - " yaml: 0.1.7-had09818_2 --> 0.2.5-h7b6447c_0 \n", - " zlib: 1.2.11-ha838bed_2 --> 1.2.11-h7b6447c_3 \n", - "\n", - "\n", - "Downloading and Extracting Packages\n", - "pysocks-1.7.1 | 30 KB | : 100% 1.0/1 [00:00<00:00, 25.95it/s]\n", - "pycparser-2.20 | 94 KB | : 100% 1.0/1 [00:00<00:00, 12.51it/s]\n", - "zlib-1.2.11 | 120 KB | : 100% 1.0/1 [00:00<00:00, 22.55it/s]\n", - "brotlipy-0.7.0 | 349 KB | : 100% 1.0/1 [00:00<00:00, 11.95it/s]\n", - "_libgcc_mutex-0.1 | 3 KB | : 100% 1.0/1 [00:00<00:00, 37.50it/s]\n", - "openssl-1.1.1k | 3.8 MB | : 100% 1.0/1 [00:00<00:00, 1.48it/s] \n", - "tqdm-4.59.0 | 90 KB | : 100% 1.0/1 [00:00<00:00, 18.55it/s]\n", - "certifi-2020.12.5 | 144 KB | : 100% 1.0/1 [00:00<00:00, 25.33it/s]\n", - "libffi-3.3 | 54 KB | : 100% 1.0/1 [00:00<00:00, 33.12it/s]\n", - "idna-2.10 | 52 KB | : 100% 1.0/1 [00:00<00:00, 29.32it/s]\n", - "pycosat-0.6.3 | 107 KB | : 100% 1.0/1 [00:00<00:00, 24.47it/s]\n", - "tk-8.6.10 | 3.2 MB | : 100% 1.0/1 [00:00<00:00, 1.45it/s] \n", - "wheel-0.36.2 | 31 KB | : 100% 1.0/1 [00:00<00:00, 28.73it/s]\n", - "pyopenssl-20.0.1 | 48 KB | : 100% 1.0/1 [00:00<00:00, 29.61it/s]\n", - "ncurses-6.2 | 1.1 MB | : 100% 1.0/1 [00:00<00:00, 1.19it/s] \n", - "six-1.15.0 | 13 KB | : 100% 1.0/1 [00:00<00:00, 38.13it/s]\n", - "libgcc-ng-9.1.0 | 8.1 MB | : 100% 1.0/1 [00:01<00:00, 1.25s/it] \n", - "readline-8.1 | 464 KB | : 100% 1.0/1 [00:00<00:00, 8.27it/s]\n", - "cffi-1.14.5 | 224 KB | : 100% 1.0/1 [00:00<00:00, 12.94it/s]\n", - "sqlite-3.35.4 | 1.4 MB | : 100% 1.0/1 [00:00<00:00, 4.08it/s] \n", - "conda-4.10.1 | 3.1 MB | : 100% 1.0/1 [00:00<00:00, 1.22it/s] \n", - "requests-2.25.1 | 51 KB | : 100% 1.0/1 [00:00<00:00, 22.32it/s]\n", - "python-3.6.13 | 33.9 MB | : 100% 1.0/1 [00:05<00:00, 5.19s/it] \n", - "ca-certificates-2021 | 120 KB | : 100% 1.0/1 [00:00<00:00, 24.39it/s]\n", - "cryptography-3.4.7 | 1.0 MB | : 100% 1.0/1 [00:00<00:00, 2.83it/s] \n", - "chardet-4.0.0 | 213 KB | : 100% 1.0/1 [00:00<00:00, 9.77it/s]\n", - "pip-21.0.1 | 2.0 MB | : 100% 1.0/1 [00:00<00:00, 1.53it/s] \n", - "urllib3-1.26.4 | 99 KB | : 100% 1.0/1 [00:00<00:00, 17.91it/s]\n", - "xz-5.2.5 | 438 KB | : 100% 1.0/1 [00:00<00:00, 7.81it/s] \n", - "conda-package-handli | 946 KB | : 100% 1.0/1 [00:00<00:00, 6.00it/s] \n", - "setuptools-52.0.0 | 933 KB | : 100% 1.0/1 [00:00<00:00, 3.20it/s] \n", - "ruamel_yaml-0.15.100 | 268 KB | : 100% 1.0/1 [00:00<00:00, 10.51it/s]\n", - "yaml-0.2.5 | 87 KB | : 100% 1.0/1 [00:00<00:00, 23.96it/s]\n", - "libstdcxx-ng-9.1.0 | 4.0 MB | : 100% 1.0/1 [00:00<00:00, 1.55it/s] \n", - "ld_impl_linux-64-2.3 | 645 KB | : 100% 1.0/1 [00:00<00:00, 5.71it/s] \n", - "Preparing transaction: / \b\b- \b\b\\ \b\b| \b\bdone\n", - "Verifying transaction: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n", - "Executing transaction: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n", - "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n", - "Solving environment: \\ \b\b| \b\b/ \b\bdone\n", - "\n", - "## Package Plan ##\n", - "\n", - " environment location: /usr/local\n", - "\n", - "\n", - "The following packages will be downloaded:\n", - "\n", - " package | build\n", - " ---------------------------|-----------------\n", - " six-1.15.0 | py36h06a4308_0 27 KB\n", - " ------------------------------------------------------------\n", - " Total: 27 KB\n", - "\n", - "The following packages will be REMOVED:\n", - "\n", - " asn1crypto-0.24.0-py36_0\n", - " conda-env-2.6.0-h36134e3_1\n", - " libedit-3.1.20170329-h6b74fdf_2\n", - "\n", - "The following packages will be SUPERSEDED by a higher-priority channel:\n", - "\n", - " six pkgs/main/noarch::six-1.15.0-pyhd3eb1~ --> pkgs/main/linux-64::six-1.15.0-py36h06a4308_0\n", - "\n", - "\n", - "\n", - "Downloading and Extracting Packages\n", - "six-1.15.0 | 27 KB | : 100% 1.0/1 [00:00<00:00, 10.04it/s]\n", - "Preparing transaction: \\ \b\bdone\n", - "Verifying transaction: / \b\bdone\n", - "Executing transaction: \\ \b\bdone\n", - "Cloning into '3dsnet'...\n", - "remote: Enumerating objects: 1203, done.\u001b[K\n", - "remote: Counting objects: 100% (1203/1203), done.\u001b[K\n", - "remote: Compressing objects: 100% (1045/1045), done.\u001b[K\n", - "remote: Total 1203 (delta 144), reused 1196 (delta 142), pack-reused 0\u001b[K\n", - "Receiving objects: 100% (1203/1203), 5.92 MiB | 15.30 MiB/s, done.\n", - "Resolving deltas: 100% (144/144), done.\n", - "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\bdone\n", - "Solving environment: - \b\bdone\n", - "\n", - "## Package Plan ##\n", - "\n", - " environment location: /usr/local/envs/3dsnet\n", - "\n", - " added / updated specs:\n", - " - python=3.6\n", - "\n", - "\n", - "The following packages will be downloaded:\n", - "\n", - " package | build\n", - " ---------------------------|-----------------\n", - " _libgcc_mutex-0.1 | main 3 KB\n", - " ca-certificates-2021.4.13 | h06a4308_1 114 KB\n", - " certifi-2020.12.5 | py36h06a4308_0 140 KB\n", - " ld_impl_linux-64-2.33.1 | h53a641e_7 568 KB\n", - " libffi-3.3 | he6710b0_2 50 KB\n", - " libgcc-ng-9.1.0 | hdf63c60_0 5.1 MB\n", - " libstdcxx-ng-9.1.0 | hdf63c60_0 3.1 MB\n", - " ncurses-6.2 | he6710b0_1 817 KB\n", - " openssl-1.1.1k | h27cfd23_0 2.5 MB\n", - " pip-21.0.1 | py36h06a4308_0 1.8 MB\n", - " python-3.6.13 | hdb3f193_0 29.7 MB\n", - " readline-8.1 | h27cfd23_0 362 KB\n", - " setuptools-52.0.0 | py36h06a4308_0 724 KB\n", - " sqlite-3.35.4 | hdfb4753_0 981 KB\n", - " tk-8.6.10 | hbc83047_0 3.0 MB\n", - " wheel-0.36.2 | pyhd3eb1b0_0 33 KB\n", - " xz-5.2.5 | h7b6447c_0 341 KB\n", - " zlib-1.2.11 | h7b6447c_3 103 KB\n", - " ------------------------------------------------------------\n", - " Total: 49.4 MB\n", - "\n", - "The following NEW packages will be INSTALLED:\n", - "\n", - " _libgcc_mutex pkgs/main/linux-64::_libgcc_mutex-0.1-main\n", - " ca-certificates pkgs/main/linux-64::ca-certificates-2021.4.13-h06a4308_1\n", - " certifi pkgs/main/linux-64::certifi-2020.12.5-py36h06a4308_0\n", - " ld_impl_linux-64 pkgs/main/linux-64::ld_impl_linux-64-2.33.1-h53a641e_7\n", - " libffi pkgs/main/linux-64::libffi-3.3-he6710b0_2\n", - " libgcc-ng pkgs/main/linux-64::libgcc-ng-9.1.0-hdf63c60_0\n", - " libstdcxx-ng pkgs/main/linux-64::libstdcxx-ng-9.1.0-hdf63c60_0\n", - " ncurses pkgs/main/linux-64::ncurses-6.2-he6710b0_1\n", - " openssl pkgs/main/linux-64::openssl-1.1.1k-h27cfd23_0\n", - " pip pkgs/main/linux-64::pip-21.0.1-py36h06a4308_0\n", - " python pkgs/main/linux-64::python-3.6.13-hdb3f193_0\n", - " readline pkgs/main/linux-64::readline-8.1-h27cfd23_0\n", - " setuptools pkgs/main/linux-64::setuptools-52.0.0-py36h06a4308_0\n", - " sqlite pkgs/main/linux-64::sqlite-3.35.4-hdfb4753_0\n", - " tk pkgs/main/linux-64::tk-8.6.10-hbc83047_0\n", - " wheel pkgs/main/noarch::wheel-0.36.2-pyhd3eb1b0_0\n", - " xz pkgs/main/linux-64::xz-5.2.5-h7b6447c_0\n", - " zlib pkgs/main/linux-64::zlib-1.2.11-h7b6447c_3\n", - "\n", - "\n", - "\n", - "Downloading and Extracting Packages\n", - "openssl-1.1.1k | 2.5 MB | : 100% 1.0/1 [00:00<00:00, 5.46it/s]\n", - "libstdcxx-ng-9.1.0 | 3.1 MB | : 100% 1.0/1 [00:00<00:00, 7.18it/s]\n", - "readline-8.1 | 362 KB | : 100% 1.0/1 [00:00<00:00, 15.98it/s]\n", - "sqlite-3.35.4 | 981 KB | : 100% 1.0/1 [00:00<00:00, 15.13it/s]\n", - "tk-8.6.10 | 3.0 MB | : 100% 1.0/1 [00:00<00:00, 6.50it/s]\n", - "libgcc-ng-9.1.0 | 5.1 MB | : 100% 1.0/1 [00:00<00:00, 4.00it/s]\n", - "certifi-2020.12.5 | 140 KB | : 100% 1.0/1 [00:00<00:00, 10.00it/s]\n", - "ca-certificates-2021 | 114 KB | : 100% 1.0/1 [00:00<00:00, 18.87it/s]\n", - "wheel-0.36.2 | 33 KB | : 100% 1.0/1 [00:00<00:00, 12.78it/s]\n", - "pip-21.0.1 | 1.8 MB | : 100% 1.0/1 [00:00<00:00, 5.13it/s]\n", - "zlib-1.2.11 | 103 KB | : 100% 1.0/1 [00:00<00:00, 18.33it/s]\n", - "ld_impl_linux-64-2.3 | 568 KB | : 100% 1.0/1 [00:00<00:00, 13.84it/s]\n", - "setuptools-52.0.0 | 724 KB | : 100% 1.0/1 [00:00<00:00, 9.88it/s]\n", - "xz-5.2.5 | 341 KB | : 100% 1.0/1 [00:00<00:00, 11.80it/s]\n", - "ncurses-6.2 | 817 KB | : 100% 1.0/1 [00:00<00:00, 3.17it/s]\n", - "_libgcc_mutex-0.1 | 3 KB | : 100% 1.0/1 [00:00<00:00, 21.93it/s]\n", - "libffi-3.3 | 50 KB | : 100% 1.0/1 [00:00<00:00, 16.29it/s]\n", - "python-3.6.13 | 29.7 MB | : 100% 1.0/1 [00:00<00:00, 1.15it/s]\n", - "Preparing transaction: | \b\b/ \b\b- \b\bdone\n", - "Verifying transaction: | \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n", - "Executing transaction: | \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n", - "#\n", - "# To activate this environment, use\n", - "#\n", - "# $ conda activate 3dsnet\n", - "#\n", - "# To deactivate an active environment, use\n", - "#\n", - "# $ conda deactivate\n", - "\n", - "Collecting meshio[all]\n", - " Downloading meshio-4.4.3-py3-none-any.whl (153 kB)\n", - "\u001b[K |████████████████████████████████| 153 kB 14.2 MB/s \n", - "\u001b[?25hCollecting importlib-metadata\n", - " Downloading importlib_metadata-4.0.1-py3-none-any.whl (16 kB)\n", - "Collecting numpy\n", - " Downloading numpy-1.19.5-cp36-cp36m-manylinux2010_x86_64.whl (14.8 MB)\n", - "\u001b[K |████████████████████████████████| 14.8 MB 275 kB/s \n", - "\u001b[?25hCollecting h5py\n", - " Downloading h5py-3.1.0-cp36-cp36m-manylinux1_x86_64.whl (4.0 MB)\n", - "\u001b[K |████████████████████████████████| 4.0 MB 51.6 MB/s \n", - "\u001b[?25hCollecting netCDF4\n", - " Downloading netCDF4-1.5.6-cp36-cp36m-manylinux2014_x86_64.whl (4.7 MB)\n", - "\u001b[K |████████████████████████████████| 4.7 MB 56.7 MB/s \n", - "\u001b[?25hCollecting cached-property\n", - " Downloading cached_property-1.5.2-py2.py3-none-any.whl (7.6 kB)\n", - "Collecting zipp>=0.5\n", - " Downloading zipp-3.4.1-py3-none-any.whl (5.2 kB)\n", - "Collecting typing-extensions>=3.6.4\n", - " Downloading typing_extensions-3.10.0.0-py3-none-any.whl (26 kB)\n", - "Collecting cftime\n", - " Downloading cftime-1.4.1-cp36-cp36m-manylinux2014_x86_64.whl (316 kB)\n", - "\u001b[K |████████████████████████████████| 316 kB 70.4 MB/s \n", - "\u001b[?25hInstalling collected packages: zipp, typing-extensions, numpy, importlib-metadata, cftime, cached-property, netCDF4, meshio, h5py\n", - "Successfully installed cached-property-1.5.2 cftime-1.4.1 h5py-3.1.0 importlib-metadata-4.0.1 meshio-4.4.3 netCDF4-1.5.6 numpy-1.19.5 typing-extensions-3.10.0.0 zipp-3.4.1\n" - ], - "name": "stdout" - }, - { - "output_type": "display_data", - "data": { - "application/vnd.colab-display-data+json": { - "pip_warning": { - "packages": [ - "numpy" - ] - } - } - }, - "metadata": { - "tags": [] - } - }, - { - "output_type": "stream", - "text": [ - "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n", - "Solving environment: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bfailed with initial frozen solve. Retrying with flexible solve.\n", - "Solving environment: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bfailed with repodata from current_repodata.json, will retry with next repodata source.\n", - "Collecting package metadata (repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n", - "Solving environment: | \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n", - "\n", - "## Package Plan ##\n", - "\n", - " environment location: /usr/local\n", - "\n", - " added / updated specs:\n", - " - cudatoolkit=10.1\n", - " - pytorch=1.7.1\n", - " - torchvision=0.8.2\n", - "\n", - "\n", - "The following packages will be downloaded:\n", - "\n", - " package | build\n", - " ---------------------------|-----------------\n", - " blas-1.0 | mkl 6 KB\n", - " cudatoolkit-10.1.243 | h6bb024c_0 347.4 MB\n", - " dataclasses-0.8 | pyh4f3eec9_6 22 KB\n", - " freetype-2.10.4 | h5ab3b9f_0 596 KB\n", - " intel-openmp-2021.2.0 | h06a4308_610 1.3 MB\n", - " jpeg-9b | h024ee3a_2 214 KB\n", - " lcms2-2.12 | h3be6417_0 312 KB\n", - " libpng-1.6.37 | hbc83047_0 278 KB\n", - " libtiff-4.1.0 | h2733197_1 449 KB\n", - " libuv-1.40.0 | h7b6447c_0 736 KB\n", - " lz4-c-1.9.3 | h2531618_0 186 KB\n", - " mkl-2020.2 | 256 138.3 MB\n", - " mkl-service-2.3.0 | py36he8ac12f_0 52 KB\n", - " mkl_fft-1.3.0 | py36h54f3939_0 170 KB\n", - " mkl_random-1.1.1 | py36h0573a6f_0 327 KB\n", - " ninja-1.10.2 | hff7bd54_1 1.4 MB\n", - " numpy-1.19.2 | py36h54aff64_0 22 KB\n", - " numpy-base-1.19.2 | py36hfa32c7d_0 4.1 MB\n", - " olefile-0.46 | py36_0 48 KB\n", - " pillow-8.2.0 | py36he98fc37_0 627 KB\n", - " pytorch-1.7.1 |py3.6_cuda10.1.243_cudnn7.6.3_0 553.7 MB pytorch\n", - " torchvision-0.8.2 | py36_cu101 17.8 MB pytorch\n", - " typing_extensions-3.7.4.3 | pyha847dfd_0 25 KB\n", - " zstd-1.4.9 | haebb681_0 480 KB\n", - " ------------------------------------------------------------\n", - " Total: 1.04 GB\n", - "\n", - "The following NEW packages will be INSTALLED:\n", - "\n", - " blas pkgs/main/linux-64::blas-1.0-mkl\n", - " cudatoolkit pkgs/main/linux-64::cudatoolkit-10.1.243-h6bb024c_0\n", - " dataclasses pkgs/main/noarch::dataclasses-0.8-pyh4f3eec9_6\n", - " freetype pkgs/main/linux-64::freetype-2.10.4-h5ab3b9f_0\n", - " intel-openmp pkgs/main/linux-64::intel-openmp-2021.2.0-h06a4308_610\n", - " jpeg pkgs/main/linux-64::jpeg-9b-h024ee3a_2\n", - " lcms2 pkgs/main/linux-64::lcms2-2.12-h3be6417_0\n", - " libpng pkgs/main/linux-64::libpng-1.6.37-hbc83047_0\n", - " libtiff pkgs/main/linux-64::libtiff-4.1.0-h2733197_1\n", - " libuv pkgs/main/linux-64::libuv-1.40.0-h7b6447c_0\n", - " lz4-c pkgs/main/linux-64::lz4-c-1.9.3-h2531618_0\n", - " mkl pkgs/main/linux-64::mkl-2020.2-256\n", - " mkl-service pkgs/main/linux-64::mkl-service-2.3.0-py36he8ac12f_0\n", - " mkl_fft pkgs/main/linux-64::mkl_fft-1.3.0-py36h54f3939_0\n", - " mkl_random pkgs/main/linux-64::mkl_random-1.1.1-py36h0573a6f_0\n", - " ninja pkgs/main/linux-64::ninja-1.10.2-hff7bd54_1\n", - " numpy pkgs/main/linux-64::numpy-1.19.2-py36h54aff64_0\n", - " numpy-base pkgs/main/linux-64::numpy-base-1.19.2-py36hfa32c7d_0\n", - " olefile pkgs/main/linux-64::olefile-0.46-py36_0\n", - " pillow pkgs/main/linux-64::pillow-8.2.0-py36he98fc37_0\n", - " pytorch pytorch/linux-64::pytorch-1.7.1-py3.6_cuda10.1.243_cudnn7.6.3_0\n", - " torchvision pytorch/linux-64::torchvision-0.8.2-py36_cu101\n", - " typing_extensions pkgs/main/noarch::typing_extensions-3.7.4.3-pyha847dfd_0\n", - " zstd pkgs/main/linux-64::zstd-1.4.9-haebb681_0\n", - "\n", - "\n", - "\n", - "Downloading and Extracting Packages\n", - "typing_extensions-3. | 25 KB | : 100% 1.0/1 [00:00<00:00, 15.24it/s]\n", - "libtiff-4.1.0 | 449 KB | : 100% 1.0/1 [00:00<00:00, 12.51it/s]\n", - "zstd-1.4.9 | 480 KB | : 100% 1.0/1 [00:00<00:00, 15.92it/s]\n", - "mkl_random-1.1.1 | 327 KB | : 100% 1.0/1 [00:00<00:00, 19.19it/s]\n", - "olefile-0.46 | 48 KB | : 100% 1.0/1 [00:00<00:00, 21.02it/s]\n", - "cudatoolkit-10.1.243 | 347.4 MB | : 100% 1.0/1 [00:08<00:00, 8.05s/it] \n", - "freetype-2.10.4 | 596 KB | : 100% 1.0/1 [00:00<00:00, 13.14it/s]\n", - "mkl-2020.2 | 138.3 MB | : 100% 1.0/1 [00:13<00:00, 13.71s/it] \n", - "dataclasses-0.8 | 22 KB | : 100% 1.0/1 [00:00<00:00, 17.59it/s]\n", - "numpy-base-1.19.2 | 4.1 MB | : 100% 1.0/1 [00:00<00:00, 4.16it/s]\n", - "numpy-1.19.2 | 22 KB | : 100% 1.0/1 [00:00<00:00, 18.33it/s]\n", - "blas-1.0 | 6 KB | : 100% 1.0/1 [00:00<00:00, 20.43it/s]\n", - "libuv-1.40.0 | 736 KB | : 100% 1.0/1 [00:00<00:00, 13.73it/s]\n", - "pillow-8.2.0 | 627 KB | : 100% 1.0/1 [00:00<00:00, 9.26it/s]\n", - "lcms2-2.12 | 312 KB | : 100% 1.0/1 [00:00<00:00, 15.80it/s]\n", - "mkl-service-2.3.0 | 52 KB | : 100% 1.0/1 [00:00<00:00, 18.19it/s]\n", - "mkl_fft-1.3.0 | 170 KB | : 100% 1.0/1 [00:00<00:00, 16.19it/s]\n", - "torchvision-0.8.2 | 17.8 MB | : 100% 1.0/1 [00:04<00:00, 4.67s/it] \n", - "ninja-1.10.2 | 1.4 MB | : 100% 1.0/1 [00:00<00:00, 10.92it/s]\n", - "libpng-1.6.37 | 278 KB | : 100% 1.0/1 [00:00<00:00, 15.03it/s]\n", - "pytorch-1.7.1 | 553.7 MB | : 100% 1.0/1 [01:30<00:00, 90.55s/it] \n", - "jpeg-9b | 214 KB | : 100% 1.0/1 [00:00<00:00, 13.75it/s]\n", - "intel-openmp-2021.2. | 1.3 MB | : 100% 1.0/1 [00:00<00:00, 9.17it/s]\n", - "lz4-c-1.9.3 | 186 KB | : 100% 1.0/1 [00:00<00:00, 13.41it/s]\n", - "Preparing transaction: / \b\b- \b\b\\ \b\bdone\n", - "Verifying transaction: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n", - "Executing transaction: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n", - "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n", - "Solving environment: | \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n", - "\n", - "## Package Plan ##\n", - "\n", - " environment location: /usr/local\n", - "\n", - " added / updated specs:\n", - " - pyembree\n", - "\n", - "\n", - "The following packages will be downloaded:\n", - "\n", - " package | build\n", - " ---------------------------|-----------------\n", - " ca-certificates-2020.12.5 | ha878542_0 137 KB conda-forge\n", - " certifi-2020.12.5 | py36h5fab9bb_1 143 KB conda-forge\n", - " conda-4.10.1 | py36h5fab9bb_0 3.1 MB conda-forge\n", - " embree-2.17.7 | 1 41.0 MB conda-forge\n", - " pyembree-0.1.6 | py36h830a2c2_1 86 KB conda-forge\n", - " python_abi-3.6 | 1_cp36m 4 KB conda-forge\n", - " ------------------------------------------------------------\n", - " Total: 44.4 MB\n", - "\n", - "The following NEW packages will be INSTALLED:\n", - "\n", - " embree conda-forge/linux-64::embree-2.17.7-1\n", - " pyembree conda-forge/linux-64::pyembree-0.1.6-py36h830a2c2_1\n", - " python_abi conda-forge/linux-64::python_abi-3.6-1_cp36m\n", - "\n", - "The following packages will be UPDATED:\n", - "\n", - " certifi pkgs/main::certifi-2020.12.5-py36h06a~ --> conda-forge::certifi-2020.12.5-py36h5fab9bb_1\n", - "\n", - "The following packages will be SUPERSEDED by a higher-priority channel:\n", - "\n", - " ca-certificates pkgs/main::ca-certificates-2021.4.13-~ --> conda-forge::ca-certificates-2020.12.5-ha878542_0\n", - " conda pkgs/main::conda-4.10.1-py36h06a4308_1 --> conda-forge::conda-4.10.1-py36h5fab9bb_0\n", - "\n", - "\n", - "\n", - "Downloading and Extracting Packages\n", - "pyembree-0.1.6 | 86 KB | : 100% 1.0/1 [00:00<00:00, 4.80it/s] \n", - "ca-certificates-2020 | 137 KB | : 100% 1.0/1 [00:00<00:00, 17.32it/s]\n", - "python_abi-3.6 | 4 KB | : 100% 1.0/1 [00:00<00:00, 28.10it/s]\n", - "embree-2.17.7 | 41.0 MB | : 100% 1.0/1 [00:07<00:00, 7.74s/it] \n", - "conda-4.10.1 | 3.1 MB | : 100% 1.0/1 [00:00<00:00, 1.58it/s]\n", - "certifi-2020.12.5 | 143 KB | : 100% 1.0/1 [00:00<00:00, 16.68it/s]\n", - "Preparing transaction: | \b\bdone\n", - "Verifying transaction: - \b\bdone\n", - "Executing transaction: | \b\bdone\n", - "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n", - "Solving environment: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n", - "\n", - "## Package Plan ##\n", - "\n", - " environment location: /usr/local\n", - "\n", - " added / updated specs:\n", - " - seaborn\n", - " - trimesh\n", - "\n", - "\n", - "The following packages will be downloaded:\n", - "\n", - " package | build\n", - " ---------------------------|-----------------\n", - " cycler-0.10.0 | py_2 9 KB conda-forge\n", - " kiwisolver-1.3.1 | py36h51d7077_0 86 KB conda-forge\n", - " libblas-3.9.0 |1_h6e990d7_netlib 176 KB conda-forge\n", - " libcblas-3.9.0 |3_h893e4fe_netlib 54 KB conda-forge\n", - " libgfortran-ng-7.5.0 | h14aa051_19 22 KB conda-forge\n", - " libgfortran4-7.5.0 | h14aa051_19 1.3 MB conda-forge\n", - " liblapack-3.9.0 |3_h893e4fe_netlib 2.9 MB conda-forge\n", - " matplotlib-base-3.3.3 | py36he12231b_0 6.8 MB conda-forge\n", - " pandas-1.1.4 | py36hd87012b_0 10.5 MB conda-forge\n", - " patsy-0.5.1 | py_0 187 KB conda-forge\n", - " pyparsing-2.4.7 | pyh9f0ad1d_0 60 KB conda-forge\n", - " python-dateutil-2.8.1 | py_0 220 KB conda-forge\n", - " pytz-2021.1 | pyhd8ed1ab_0 239 KB conda-forge\n", - " scipy-1.5.3 | py36h976291a_0 18.6 MB conda-forge\n", - " seaborn-0.11.1 | hd8ed1ab_1 4 KB conda-forge\n", - " seaborn-base-0.11.1 | pyhd8ed1ab_1 217 KB conda-forge\n", - " statsmodels-0.11.1 | py36h8c4c3a4_2 9.8 MB conda-forge\n", - " tornado-6.1 | py36h1d69622_0 644 KB conda-forge\n", - " trimesh-3.9.18 | pyh6c4a22f_0 508 KB conda-forge\n", - " ------------------------------------------------------------\n", - " Total: 52.3 MB\n", - "\n", - "The following NEW packages will be INSTALLED:\n", - "\n", - " cycler conda-forge/noarch::cycler-0.10.0-py_2\n", - " kiwisolver conda-forge/linux-64::kiwisolver-1.3.1-py36h51d7077_0\n", - " libblas conda-forge/linux-64::libblas-3.9.0-1_h6e990d7_netlib\n", - " libcblas conda-forge/linux-64::libcblas-3.9.0-3_h893e4fe_netlib\n", - " libgfortran-ng conda-forge/linux-64::libgfortran-ng-7.5.0-h14aa051_19\n", - " libgfortran4 conda-forge/linux-64::libgfortran4-7.5.0-h14aa051_19\n", - " liblapack conda-forge/linux-64::liblapack-3.9.0-3_h893e4fe_netlib\n", - " matplotlib-base conda-forge/linux-64::matplotlib-base-3.3.3-py36he12231b_0\n", - " pandas conda-forge/linux-64::pandas-1.1.4-py36hd87012b_0\n", - " patsy conda-forge/noarch::patsy-0.5.1-py_0\n", - " pyparsing conda-forge/noarch::pyparsing-2.4.7-pyh9f0ad1d_0\n", - " python-dateutil conda-forge/noarch::python-dateutil-2.8.1-py_0\n", - " pytz conda-forge/noarch::pytz-2021.1-pyhd8ed1ab_0\n", - " scipy conda-forge/linux-64::scipy-1.5.3-py36h976291a_0\n", - " seaborn conda-forge/noarch::seaborn-0.11.1-hd8ed1ab_1\n", - " seaborn-base conda-forge/noarch::seaborn-base-0.11.1-pyhd8ed1ab_1\n", - " statsmodels conda-forge/linux-64::statsmodels-0.11.1-py36h8c4c3a4_2\n", - " tornado conda-forge/linux-64::tornado-6.1-py36h1d69622_0\n", - " trimesh conda-forge/noarch::trimesh-3.9.18-pyh6c4a22f_0\n", - "\n", - "\n", - "\n", - "Downloading and Extracting Packages\n", - "libblas-3.9.0 | 176 KB | : 100% 1.0/1 [00:00<00:00, 7.46it/s] \n", - "trimesh-3.9.18 | 508 KB | : 100% 1.0/1 [00:00<00:00, 6.77it/s]\n", - "tornado-6.1 | 644 KB | : 100% 1.0/1 [00:00<00:00, 5.54it/s]\n", - "libgfortran-ng-7.5.0 | 22 KB | : 100% 1.0/1 [00:00<00:00, 23.27it/s]\n", - "liblapack-3.9.0 | 2.9 MB | : 100% 1.0/1 [00:00<00:00, 2.00it/s]\n", - "patsy-0.5.1 | 187 KB | : 100% 1.0/1 [00:00<00:00, 14.03it/s]\n", - "seaborn-0.11.1 | 4 KB | : 100% 1.0/1 [00:00<00:00, 31.35it/s]\n", - "kiwisolver-1.3.1 | 86 KB | : 100% 1.0/1 [00:00<00:00, 22.59it/s]\n", - "python-dateutil-2.8. | 220 KB | : 100% 1.0/1 [00:00<00:00, 15.91it/s]\n", - "pandas-1.1.4 | 10.5 MB | : 100% 1.0/1 [00:02<00:00, 2.33s/it]\n", - "matplotlib-base-3.3. | 6.8 MB | : 100% 1.0/1 [00:01<00:00, 1.24s/it]\n", - "libcblas-3.9.0 | 54 KB | : 100% 1.0/1 [00:00<00:00, 18.46it/s]\n", - "statsmodels-0.11.1 | 9.8 MB | : 100% 1.0/1 [00:01<00:00, 1.96s/it]\n", - "libgfortran4-7.5.0 | 1.3 MB | : 100% 1.0/1 [00:00<00:00, 3.97it/s]\n", - "scipy-1.5.3 | 18.6 MB | : 100% 1.0/1 [00:03<00:00, 3.16s/it]\n", - "seaborn-base-0.11.1 | 217 KB | : 100% 1.0/1 [00:00<00:00, 14.38it/s]\n", - "cycler-0.10.0 | 9 KB | : 100% 1.0/1 [00:00<00:00, 32.46it/s]\n", - "pytz-2021.1 | 239 KB | : 100% 1.0/1 [00:00<00:00, 8.12it/s]\n", - "pyparsing-2.4.7 | 60 KB | : 100% 1.0/1 [00:00<00:00, 23.38it/s]\n", - "Preparing transaction: - \b\b\\ \b\b| \b\bdone\n", - "Verifying transaction: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n", - "Executing transaction: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n", - "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n", - "Solving environment: | \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n", - "\n", - "## Package Plan ##\n", - "\n", - " environment location: /usr/local\n", - "\n", - " added / updated specs:\n", - " - fvcore\n", - " - iopath\n", - "\n", - "\n", - "The following packages will be downloaded:\n", - "\n", - " package | build\n", - " ---------------------------|-----------------\n", - " fvcore-0.1.5.post20210518 | py36 88 KB fvcore\n", - " iopath-0.1.8 | py36 31 KB iopath\n", - " portalocker-1.7.0 | py36h5fab9bb_1 19 KB conda-forge\n", - " pyyaml-5.3.1 | py36he6145b8_1 185 KB conda-forge\n", - " tabulate-0.8.9 | pyhd8ed1ab_0 26 KB conda-forge\n", - " termcolor-1.1.0 | py_2 6 KB conda-forge\n", - " yacs-0.1.6 | py_0 11 KB conda-forge\n", - " ------------------------------------------------------------\n", - " Total: 366 KB\n", - "\n", - "The following NEW packages will be INSTALLED:\n", - "\n", - " fvcore fvcore/linux-64::fvcore-0.1.5.post20210518-py36\n", - " iopath iopath/linux-64::iopath-0.1.8-py36\n", - " portalocker conda-forge/linux-64::portalocker-1.7.0-py36h5fab9bb_1\n", - " pyyaml conda-forge/linux-64::pyyaml-5.3.1-py36he6145b8_1\n", - " tabulate conda-forge/noarch::tabulate-0.8.9-pyhd8ed1ab_0\n", - " termcolor conda-forge/noarch::termcolor-1.1.0-py_2\n", - " yacs conda-forge/noarch::yacs-0.1.6-py_0\n", - "\n", - "\n", - "\n", - "Downloading and Extracting Packages\n", - "termcolor-1.1.0 | 6 KB | : 100% 1.0/1 [00:00<00:00, 11.39it/s]\n", - "tabulate-0.8.9 | 26 KB | : 100% 1.0/1 [00:00<00:00, 25.84it/s]\n", - "yacs-0.1.6 | 11 KB | : 100% 1.0/1 [00:00<00:00, 7.66it/s]\n", - "portalocker-1.7.0 | 19 KB | : 100% 1.0/1 [00:00<00:00, 7.86it/s] \n", - "pyyaml-5.3.1 | 185 KB | : 100% 1.0/1 [00:00<00:00, 14.21it/s]\n", - "fvcore-0.1.5.post202 | 88 KB | : 100% 1.0/1 [00:01<00:00, 1.08s/it]\n", - "iopath-0.1.8 | 31 KB | : 100% 1.0/1 [00:00<00:00, 1.05it/s]\n", - "Preparing transaction: - \b\bdone\n", - "Verifying transaction: | \b\bdone\n", - "Executing transaction: - \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n", - "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n", - "Solving environment: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n", - "\n", - "## Package Plan ##\n", - "\n", - " environment location: /usr/local\n", - "\n", - " added / updated specs:\n", - " - pytorch3d\n", - "\n", - "\n", - "The following packages will be downloaded:\n", - "\n", - " package | build\n", - " ---------------------------|-----------------\n", - " conda-4.10.1 | py36h06a4308_1 2.9 MB\n", - " pytorch3d-0.4.0 |py36_cu101_pyt171 36.8 MB pytorch3d\n", - " ------------------------------------------------------------\n", - " Total: 39.6 MB\n", - "\n", - "The following NEW packages will be INSTALLED:\n", - "\n", - " pytorch3d pytorch3d/linux-64::pytorch3d-0.4.0-py36_cu101_pyt171\n", - "\n", - "The following packages will be UPDATED:\n", - "\n", - " ca-certificates conda-forge::ca-certificates-2020.12.~ --> pkgs/main::ca-certificates-2021.4.13-h06a4308_1\n", - " conda conda-forge::conda-4.10.1-py36h5fab9b~ --> pkgs/main::conda-4.10.1-py36h06a4308_1\n", - "\n", - "\n", - "\n", - "Downloading and Extracting Packages\n", - "conda-4.10.1 | 2.9 MB | : 100% 1.0/1 [00:00<00:00, 4.41it/s]\n", - "pytorch3d-0.4.0 | 36.8 MB | : 100% 1.0/1 [00:08<00:00, 8.54s/it]\n", - "Preparing transaction: \\ \b\bdone\n", - "Verifying transaction: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n", - "Executing transaction: / \b\bdone\n", - "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n", - "Solving environment: | \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n", - "\n", - "## Package Plan ##\n", - "\n", - " environment location: /usr/local\n", - "\n", - " added / updated specs:\n", - " - visdom\n", - "\n", - "\n", - "The following packages will be downloaded:\n", - "\n", - " package | build\n", - " ---------------------------|-----------------\n", - " libsodium-1.0.18 | h36c2ea0_1 366 KB conda-forge\n", - " pyzmq-19.0.2 | py36h9947dbf_2 467 KB conda-forge\n", - " torchfile-0.1.0 | py_0 8 KB conda-forge\n", - " visdom-0.1.8.9 | 0 565 KB conda-forge\n", - " websocket-client-0.57.0 | py36h5fab9bb_4 59 KB conda-forge\n", - " zeromq-4.3.4 | h2531618_0 331 KB\n", - " ------------------------------------------------------------\n", - " Total: 1.8 MB\n", - "\n", - "The following NEW packages will be INSTALLED:\n", - "\n", - " libsodium conda-forge/linux-64::libsodium-1.0.18-h36c2ea0_1\n", - " pyzmq conda-forge/linux-64::pyzmq-19.0.2-py36h9947dbf_2\n", - " torchfile conda-forge/noarch::torchfile-0.1.0-py_0\n", - " visdom conda-forge/noarch::visdom-0.1.8.9-0\n", - " websocket-client conda-forge/linux-64::websocket-client-0.57.0-py36h5fab9bb_4\n", - " zeromq pkgs/main/linux-64::zeromq-4.3.4-h2531618_0\n", - "\n", - "The following packages will be SUPERSEDED by a higher-priority channel:\n", - "\n", - " ca-certificates pkgs/main::ca-certificates-2021.4.13-~ --> conda-forge::ca-certificates-2020.12.5-ha878542_0\n", - " conda pkgs/main::conda-4.10.1-py36h06a4308_1 --> conda-forge::conda-4.10.1-py36h5fab9bb_0\n", - "\n", - "\n", - "\n", - "Downloading and Extracting Packages\n", - "torchfile-0.1.0 | 8 KB | : 100% 1.0/1 [00:00<00:00, 13.33it/s]\n", - "libsodium-1.0.18 | 366 KB | : 100% 1.0/1 [00:00<00:00, 9.26it/s]\n", - "visdom-0.1.8.9 | 565 KB | : 100% 1.0/1 [00:00<00:00, 7.12it/s]\n", - "zeromq-4.3.4 | 331 KB | : 100% 1.0/1 [00:00<00:00, 10.28it/s]\n", - "pyzmq-19.0.2 | 467 KB | : 100% 1.0/1 [00:00<00:00, 4.04it/s]\n", - "websocket-client-0.5 | 59 KB | : 100% 1.0/1 [00:00<00:00, 19.68it/s]\n", - "Preparing transaction: \\ \b\bdone\n", - "Verifying transaction: / \b\bdone\n", - "Executing transaction: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n", - "[Errno 2] No such file or directory: '/content/3dsnset'\n", - "/content\n", - "Cloning into 'PyMesh'...\n", - "remote: Enumerating objects: 18131, done.\u001b[K\n", - "remote: Total 18131 (delta 0), reused 0 (delta 0), pack-reused 18131\u001b[K\n", - "Receiving objects: 100% (18131/18131), 21.40 MiB | 21.28 MiB/s, done.\n", - "Resolving deltas: 100% (12920/12920), done.\n", - "/content/PyMesh\n", - "Submodule 'third_party/Clipper' (https://github.com/PyMesh/Clipper.git) registered for path 'third_party/Clipper'\n", - "Submodule 'third_party/TetWild' (https://github.com/PyMesh/TetWild.git) registered for path 'third_party/TetWild'\n", - "Submodule 'third_party/WindingNumber' (https://github.com/PyMesh/WindingNumber.git) registered for path 'third_party/WindingNumber'\n", - "Submodule 'third_party/carve' (https://github.com/PyMesh/carve.git) registered for path 'third_party/carve'\n", - "Submodule 'third_party/cgal' (https://github.com/PyMesh/cgal.git) registered for path 'third_party/cgal'\n", - "Submodule 'third_party/cork' (https://github.com/PyMesh/cork.git) registered for path 'third_party/cork'\n", - "Submodule 'third_party/draco' (https://github.com/PyMesh/draco.git) registered for path 'third_party/draco'\n", - "Submodule 'third_party/eigen' (https://github.com/PyMesh/eigen.git) registered for path 'third_party/eigen'\n", - "Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/fmt'\n", - "Submodule 'third_party/geogram' (https://github.com/PyMesh/geogram.git) registered for path 'third_party/geogram'\n", - "Submodule 'third_party/jigsaw' (https://github.com/PyMesh/jigsaw.git) registered for path 'third_party/jigsaw'\n", - "Submodule 'third_party/json' (https://github.com/nlohmann/json.git) registered for path 'third_party/json'\n", - "Submodule 'libigl' (https://github.com/PyMesh/libigl.git) registered for path 'third_party/libigl'\n", - "Submodule 'third_party/mmg' (https://github.com/PyMesh/mmg.git) registered for path 'third_party/mmg'\n", - "Submodule 'third_party/pybind11' (https://github.com/PyMesh/pybind11.git) registered for path 'third_party/pybind11'\n", - "Submodule 'third_party/qhull' (https://github.com/PyMesh/qhull.git) registered for path 'third_party/qhull'\n", - "Submodule 'third_party/quartet' (https://github.com/PyMesh/quartet.git) registered for path 'third_party/quartet'\n", - "Submodule 'third_party/spdlog' (https://github.com/gabime/spdlog.git) registered for path 'third_party/spdlog'\n", - "Submodule 'third_party/tbb' (https://github.com/PyMesh/tbb.git) registered for path 'third_party/tbb'\n", - "Submodule 'third_party/tetgen' (https://github.com/PyMesh/tetgen.git) registered for path 'third_party/tetgen'\n", - "Submodule 'third_party/triangle' (https://github.com/PyMesh/triangle.git) registered for path 'third_party/triangle'\n", - "Cloning into '/content/PyMesh/third_party/Clipper'...\n", - "Cloning into '/content/PyMesh/third_party/TetWild'...\n", - "Cloning into '/content/PyMesh/third_party/WindingNumber'...\n", - "Cloning into '/content/PyMesh/third_party/carve'...\n", - "Cloning into '/content/PyMesh/third_party/cgal'...\n", - "Cloning into '/content/PyMesh/third_party/cork'...\n", - "Cloning into '/content/PyMesh/third_party/draco'...\n", - "Cloning into '/content/PyMesh/third_party/eigen'...\n", - "Cloning into '/content/PyMesh/third_party/fmt'...\n", - "Cloning into '/content/PyMesh/third_party/geogram'...\n", - "Cloning into '/content/PyMesh/third_party/jigsaw'...\n", - "Cloning into '/content/PyMesh/third_party/json'...\n", - "Cloning into '/content/PyMesh/third_party/libigl'...\n", - "Cloning into '/content/PyMesh/third_party/mmg'...\n", - "Cloning into '/content/PyMesh/third_party/pybind11'...\n", - "Cloning into '/content/PyMesh/third_party/qhull'...\n", - "Cloning into '/content/PyMesh/third_party/quartet'...\n", - "Cloning into '/content/PyMesh/third_party/spdlog'...\n", - "Cloning into '/content/PyMesh/third_party/tbb'...\n", - "Cloning into '/content/PyMesh/third_party/tetgen'...\n", - "Cloning into '/content/PyMesh/third_party/triangle'...\n", - "Submodule path 'third_party/Clipper': checked out '3fd3457741d275b887ad16abacccbd01eda2175c'\n", - "Submodule path 'third_party/TetWild': checked out '5b0f81552fbd66c1ed66168fd7bbf222e3391816'\n", - "Submodule path 'third_party/WindingNumber': checked out 'e011b7bc9fa1e2570651097936ccf2314fdcbe86'\n", - "Submodule path 'third_party/carve': checked out 'd328ad2136a4fa6413db8ad264ed219095bb6744'\n", - "Submodule path 'third_party/cgal': checked out '1ce145a3c611df5f3a71fb20275b755fdbfca21e'\n", - "Submodule path 'third_party/cork': checked out '360820dd981fa72117f255ddaa68367419f7526c'\n", - "Submodule path 'third_party/draco': checked out '063994c362871d6f149c24c669122e4ef3fa8196'\n", - "Submodule path 'third_party/eigen': checked out 'ed3db99ec3caff039f72645b7c5feb68717c8655'\n", - "Submodule path 'third_party/fmt': checked out '355eb6d29ad7dbcb017420442af237e3cf6d8054'\n", - "Submodule path 'third_party/geogram': checked out '25228ad2a88b793fc8de651ecf6ca7ee76819d5a'\n", - "Submodule path 'third_party/jigsaw': checked out '9c677b58234c64e2586e361d53bf60ce7e4ed3bb'\n", - "Submodule path 'third_party/json': checked out 'e7452d87783fbf6e9d320d515675e26dfd1271c5'\n", - "Submodule path 'third_party/libigl': checked out 'f6b406427400ed7ddb56cfc2577b6af571827c8c'\n", - "Submodule path 'third_party/mmg': checked out '8a93fc29e8ea61299c6c415aaf2c57cb5cd2f779'\n", - "Submodule path 'third_party/pybind11': checked out '80d452484c5409444b0ec19383faa84bb7a4d351'\n", - "Submodule path 'third_party/qhull': checked out 'd901974562b76b89947eea320423130851b2f164'\n", - "Submodule path 'third_party/quartet': checked out '7d789031d2f154e015f70a06eb2a1c01461e3cfc'\n", - "Submodule path 'third_party/spdlog': checked out 'b6b9d835c588c35227410a9830e7a4586f90777a'\n", - "Submodule path 'third_party/tbb': checked out 'ed6f6f15cece26ae4ab0816eab220c5e0691093f'\n", - "Submodule path 'third_party/tetgen': checked out '54e1149a1af5b586706b3d87a0152e77e76ade22'\n", - "remote: Enumerating objects: 23, done.\u001b[K\n", - "remote: Counting objects: 100% (23/23), done.\u001b[K\n", - "remote: Compressing objects: 100% (11/11), done.\u001b[K\n", - "remote: Total 19 (delta 12), reused 15 (delta 8), pack-reused 0\u001b[K\n", - "Unpacking objects: 100% (19/19), done.\n", - "From https://github.com/PyMesh/triangle\n", - " * branch a092f98815a38ee1d2f29341838947b3849fa2d0 -> FETCH_HEAD\n", - "Submodule path 'third_party/triangle': checked out 'a092f98815a38ee1d2f29341838947b3849fa2d0'\n", - "Reading package lists... Done\n", - "Building dependency tree \n", - "Reading state information... Done\n", - "libboost-dev is already the newest version (1.65.1.0ubuntu1).\n", - "libboost-dev set to manually installed.\n", - "libboost-thread-dev is already the newest version (1.65.1.0ubuntu1).\n", - "libboost-thread-dev set to manually installed.\n", - "libtbb-dev is already the newest version (2017~U7-8).\n", - "libtbb-dev set to manually installed.\n", - "cmake is already the newest version (3.10.2-1ubuntu2.18.04.1).\n", - "python3-dev is already the newest version (3.6.7-1~18.04).\n", - "python3-dev set to manually installed.\n", - "The following package was automatically installed and is no longer required:\n", - " libnvidia-common-460\n", - "Use 'apt autoremove' to remove it.\n", - "The following additional packages will be installed:\n", - " python-pip-whl python3-asn1crypto python3-cffi-backend python3-crypto\n", - " python3-cryptography python3-decorator python3-idna python3-keyring\n", - " python3-keyrings.alt python3-olefile python3-pil python3-pkg-resources\n", - " python3-secretstorage python3-six python3-wheel python3-xdg\n", - "Suggested packages:\n", - " libeigen3-doc libmrpt-dev gmp-doc libgmp10-doc libmpfr-doc python-crypto-doc\n", - " python-cryptography-doc python3-cryptography-vectors gnome-keyring\n", - " libkf5wallet-bin gir1.2-gnomekeyring-1.0 python-nose-doc python-numpy-doc\n", - " python3-numpy-dbg python-pil-doc python3-pil-dbg python-scipy-doc\n", - " python-secretstorage-doc python-setuptools-doc\n", - "The following NEW packages will be installed:\n", - " libeigen3-dev libgmp-dev libgmpxx4ldbl libmpfr-dev python-pip-whl\n", - " python3-asn1crypto python3-cffi-backend python3-crypto python3-cryptography\n", - " python3-decorator python3-idna python3-keyring python3-keyrings.alt\n", - " python3-nose python3-numpy python3-olefile python3-pil python3-pip\n", - " python3-pkg-resources python3-scipy python3-secretstorage python3-setuptools\n", - " python3-six python3-wheel python3-xdg\n", - "0 upgraded, 25 newly installed, 0 to remove and 34 not upgraded.\n", - "Need to get 16.3 MB of archives.\n", - "After this operation, 73.2 MB of additional disk space will be used.\n", - "Get:1 http://archive.ubuntu.com/ubuntu bionic/main amd64 libgmpxx4ldbl amd64 2:6.1.2+dfsg-2 [8,964 B]\n", - "Get:2 http://archive.ubuntu.com/ubuntu bionic/main amd64 libgmp-dev amd64 2:6.1.2+dfsg-2 [316 kB]\n", - "Get:3 http://archive.ubuntu.com/ubuntu bionic/main amd64 libmpfr-dev amd64 4.0.1-1 [249 kB]\n", - "Get:4 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 python-pip-whl all 9.0.1-2.3~ubuntu1.18.04.4 [1,653 kB]\n", - "Get:5 http://archive.ubuntu.com/ubuntu bionic/main amd64 python3-asn1crypto all 0.24.0-1 [72.8 kB]\n", - "Get:6 http://archive.ubuntu.com/ubuntu bionic/main amd64 python3-cffi-backend amd64 1.11.5-1 [64.6 kB]\n", - "Get:7 http://archive.ubuntu.com/ubuntu bionic/main amd64 python3-crypto amd64 2.6.1-8ubuntu2 [244 kB]\n", - "Get:8 http://archive.ubuntu.com/ubuntu bionic/main amd64 python3-idna all 2.6-1 [32.5 kB]\n", - "Get:9 http://archive.ubuntu.com/ubuntu bionic/main amd64 python3-six all 1.11.0-2 [11.4 kB]\n", - "Get:10 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 python3-cryptography amd64 2.1.4-1ubuntu1.4 [220 kB]\n", - "Get:11 http://archive.ubuntu.com/ubuntu bionic/universe amd64 python3-decorator all 4.1.2-1 [9,364 B]\n", - "Get:12 http://archive.ubuntu.com/ubuntu bionic/main amd64 python3-secretstorage all 2.3.1-2 [12.1 kB]\n", - "Get:13 http://archive.ubuntu.com/ubuntu bionic/main amd64 python3-keyring all 10.6.0-1 [26.7 kB]\n", - "Get:14 http://archive.ubuntu.com/ubuntu bionic/main amd64 python3-keyrings.alt all 3.0-1 [16.6 kB]\n", - "Get:15 http://archive.ubuntu.com/ubuntu bionic/main amd64 python3-pkg-resources all 39.0.1-2 [98.8 kB]\n", - "Get:16 http://archive.ubuntu.com/ubuntu bionic/universe amd64 python3-nose all 1.3.7-3 [115 kB]\n", - "Get:17 http://archive.ubuntu.com/ubuntu bionic/main amd64 python3-numpy amd64 1:1.13.3-2ubuntu1 [1,943 kB]\n", - "Get:18 http://archive.ubuntu.com/ubuntu bionic/main amd64 python3-olefile all 0.45.1-1 [33.3 kB]\n", - "Ign:19 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 python3-pil amd64 5.1.0-1ubuntu0.5\n", - "Get:20 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 python3-pip all 9.0.1-2.3~ubuntu1.18.04.4 [114 kB]\n", - "Get:21 http://archive.ubuntu.com/ubuntu bionic/main amd64 python3-setuptools all 39.0.1-2 [248 kB]\n", - "Get:22 http://archive.ubuntu.com/ubuntu bionic/universe amd64 python3-wheel all 0.30.0-0.2 [36.5 kB]\n", - "Get:23 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 python3-xdg all 0.25-4ubuntu1.1 [31.3 kB]\n", - "Get:24 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libeigen3-dev all 3.3.4-4 [810 kB]\n", - "Get:25 http://archive.ubuntu.com/ubuntu bionic/universe amd64 python3-scipy amd64 0.19.1-2ubuntu1 [9,619 kB]\n", - "Err:19 http://security.ubuntu.com/ubuntu bionic-updates/main amd64 python3-pil amd64 5.1.0-1ubuntu0.5\n", - " 404 Not Found [IP: 91.189.88.142 80]\n", - "Fetched 16.0 MB in 0s (41.1 MB/s)\n", - "E: Failed to fetch http://security.ubuntu.com/ubuntu/pool/main/p/pillow/python3-pil_5.1.0-1ubuntu0.5_amd64.deb 404 Not Found [IP: 91.189.88.142 80]\n", - "E: Unable to fetch some archives, maybe run apt-get update or try with --fix-missing?\n", - "[Errno 2] No such file or directory: '$PYMESH_PATH/third_party'\n", - "/content/PyMesh\n", - "/bin/bash: ./build.py: No such file or directory\n", - "[Errno 2] No such file or directory: '$PYMESH_PATH'\n", - "/content/PyMesh\n", - "mkdir: cannot create directory ‘build’: File exists\n", - "/bin/bash: pythonsetup.py: command not found\n", - "running install\n", - "running bdist_egg\n", - "running egg_info\n", - "creating python/pymesh2.egg-info\n", - "writing python/pymesh2.egg-info/PKG-INFO\n", - "writing dependency_links to python/pymesh2.egg-info/dependency_links.txt\n", - "writing top-level names to python/pymesh2.egg-info/top_level.txt\n", - "writing manifest file 'python/pymesh2.egg-info/SOURCES.txt'\n", - "package init file 'python/pymesh/tests/__init__.py' not found (or not a regular file)\n", - "package init file 'python/pymesh/meshutils/tests/__init__.py' not found (or not a regular file)\n", - "package init file 'python/pymesh/wires/tests/__init__.py' not found (or not a regular file)\n", - "reading manifest file 'python/pymesh2.egg-info/SOURCES.txt'\n", - "reading manifest template 'MANIFEST.in'\n", - "no previously-included directories found matching 'build'\n", - "no previously-included directories found matching 'third_party/build'\n", - "no previously-included directories found matching 'third_party/libigl/external'\n", - "writing manifest file 'python/pymesh2.egg-info/SOURCES.txt'\n", - "installing library code to build/bdist.linux-x86_64/egg\n", - "running install_lib\n", - "running build_py\n", - "creating build/lib.linux-x86_64-3.6\n", - "creating build/lib.linux-x86_64-3.6/pymesh\n", - "copying python/pymesh/map_attributes.py -> build/lib.linux-x86_64-3.6/pymesh\n", - "copying python/pymesh/Assembler.py -> build/lib.linux-x86_64-3.6/pymesh\n", - "copying python/pymesh/cell_partition.py -> build/lib.linux-x86_64-3.6/pymesh\n", - "copying python/pymesh/meshio.py -> build/lib.linux-x86_64-3.6/pymesh\n", - "copying python/pymesh/exact_arithmetic.py -> build/lib.linux-x86_64-3.6/pymesh\n", - "copying python/pymesh/igl_utils.py -> build/lib.linux-x86_64-3.6/pymesh\n", - "copying python/pymesh/material.py -> build/lib.linux-x86_64-3.6/pymesh\n", - "copying python/pymesh/selfintersection.py -> build/lib.linux-x86_64-3.6/pymesh\n", - "copying python/pymesh/minkowski_sum.py -> build/lib.linux-x86_64-3.6/pymesh\n", - "copying python/pymesh/timethis.py -> build/lib.linux-x86_64-3.6/pymesh\n", - "copying python/pymesh/Arrangement2.py -> build/lib.linux-x86_64-3.6/pymesh\n", - "copying python/pymesh/CSGTree.py -> build/lib.linux-x86_64-3.6/pymesh\n", - "copying python/pymesh/compression.py -> build/lib.linux-x86_64-3.6/pymesh\n", - "copying python/pymesh/cut_to_disk.py -> build/lib.linux-x86_64-3.6/pymesh\n", - "copying python/pymesh/HashGrid.py -> build/lib.linux-x86_64-3.6/pymesh\n", - "copying python/pymesh/boolean.py -> build/lib.linux-x86_64-3.6/pymesh\n", - "copying python/pymesh/SparseSolver.py -> build/lib.linux-x86_64-3.6/pymesh\n", - "copying python/pymesh/__init__.py -> build/lib.linux-x86_64-3.6/pymesh\n", - "copying python/pymesh/triangulate.py -> build/lib.linux-x86_64-3.6/pymesh\n", - "copying python/pymesh/submesh.py -> build/lib.linux-x86_64-3.6/pymesh\n", - "copying python/pymesh/straight_skeleton.py -> build/lib.linux-x86_64-3.6/pymesh\n", - "copying python/pymesh/Mesh.py -> build/lib.linux-x86_64-3.6/pymesh\n", - "copying python/pymesh/tetrahedralize.py -> build/lib.linux-x86_64-3.6/pymesh\n", - "copying python/pymesh/matrixio.py -> build/lib.linux-x86_64-3.6/pymesh\n", - "copying python/pymesh/slice_mesh.py -> build/lib.linux-x86_64-3.6/pymesh\n", - "copying python/pymesh/tetgen.py -> build/lib.linux-x86_64-3.6/pymesh\n", - "copying python/pymesh/boolean_unsupported.py -> build/lib.linux-x86_64-3.6/pymesh\n", - "copying python/pymesh/Boundary.py -> build/lib.linux-x86_64-3.6/pymesh\n", - "copying python/pymesh/TestCase.py -> build/lib.linux-x86_64-3.6/pymesh\n", - "copying python/pymesh/version.py -> build/lib.linux-x86_64-3.6/pymesh\n", - "copying python/pymesh/HarmonicSolver.py -> build/lib.linux-x86_64-3.6/pymesh\n", - "copying python/pymesh/aabb_tree.py -> build/lib.linux-x86_64-3.6/pymesh\n", - "copying python/pymesh/winding_number.py -> build/lib.linux-x86_64-3.6/pymesh\n", - "copying python/pymesh/PyMeshSetting.py -> build/lib.linux-x86_64-3.6/pymesh\n", - "copying python/pymesh/convex_hull.py -> build/lib.linux-x86_64-3.6/pymesh\n", - "copying python/pymesh/outerhull.py -> build/lib.linux-x86_64-3.6/pymesh\n", - "copying python/pymesh/predicates.py -> build/lib.linux-x86_64-3.6/pymesh\n", - "copying python/pymesh/snap_rounding.py -> build/lib.linux-x86_64-3.6/pymesh\n", - "copying python/pymesh/VoxelGrid.py -> build/lib.linux-x86_64-3.6/pymesh\n", - "copying python/pymesh/triangle.py -> build/lib.linux-x86_64-3.6/pymesh\n", - "copying python/pymesh/save_svg.py -> build/lib.linux-x86_64-3.6/pymesh\n", - "creating build/lib.linux-x86_64-3.6/pymesh/misc\n", - "copying python/pymesh/misc/__init__.py -> build/lib.linux-x86_64-3.6/pymesh/misc\n", - "copying python/pymesh/misc/quaternion.py -> build/lib.linux-x86_64-3.6/pymesh/misc\n", - "creating build/lib.linux-x86_64-3.6/pymesh/meshutils\n", - "copying python/pymesh/meshutils/remove_duplicated_faces.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", - "copying python/pymesh/meshutils/cut_mesh.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", - "copying python/pymesh/meshutils/merge_meshes.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", - "copying python/pymesh/meshutils/face_utils.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", - "copying python/pymesh/meshutils/manifold_check.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", - "copying python/pymesh/meshutils/split_long_edges.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", - "copying python/pymesh/meshutils/hex_to_tet.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", - "copying python/pymesh/meshutils/generate_cylinder.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", - "copying python/pymesh/meshutils/generate_dodecahedron.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", - "copying python/pymesh/meshutils/generate_minimal_surface.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", - "copying python/pymesh/meshutils/remove_isolated_vertices.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", - "copying python/pymesh/meshutils/collapse_short_edges.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", - "copying python/pymesh/meshutils/__init__.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", - "copying python/pymesh/meshutils/attribute_utils.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", - "copying python/pymesh/meshutils/remove_degenerated_triangles.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", - "copying python/pymesh/meshutils/separate_mesh.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", - "copying python/pymesh/meshutils/subdivide.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", - "copying python/pymesh/meshutils/remove_obtuse_triangles.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", - "copying python/pymesh/meshutils/generate_box_mesh.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", - "copying python/pymesh/meshutils/mesh_to_graph.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", - "copying python/pymesh/meshutils/generate_tube.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", - "copying python/pymesh/meshutils/generate_equilateral_triangle.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", - "copying python/pymesh/meshutils/quad_to_tri.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", - "copying python/pymesh/meshutils/edge_utils.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", - "copying python/pymesh/meshutils/generate_regular_tetrahedron.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", - "copying python/pymesh/meshutils/remove_duplicated_vertices.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", - "copying python/pymesh/meshutils/voxel_utils.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", - "copying python/pymesh/meshutils/generate_icosphere.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils\n", - "creating build/lib.linux-x86_64-3.6/pymesh/wires\n", - "copying python/pymesh/wires/merge_wires.py -> build/lib.linux-x86_64-3.6/pymesh/wires\n", - "copying python/pymesh/wires/WireNetwork.py -> build/lib.linux-x86_64-3.6/pymesh/wires\n", - "copying python/pymesh/wires/Parameters.py -> build/lib.linux-x86_64-3.6/pymesh/wires\n", - "copying python/pymesh/wires/Tiler.py -> build/lib.linux-x86_64-3.6/pymesh/wires\n", - "copying python/pymesh/wires/Inflator.py -> build/lib.linux-x86_64-3.6/pymesh/wires\n", - "copying python/pymesh/wires/wires_io.py -> build/lib.linux-x86_64-3.6/pymesh/wires\n", - "copying python/pymesh/wires/__init__.py -> build/lib.linux-x86_64-3.6/pymesh/wires\n", - "creating build/lib.linux-x86_64-3.6/pymesh/tests\n", - "copying python/pymesh/tests/test_HarmonicSolver.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", - "copying python/pymesh/tests/test_solver.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", - "copying python/pymesh/tests/test_boolean.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", - "copying python/pymesh/tests/test_map_attributes.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", - "copying python/pymesh/tests/test_slice_Mesh.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", - "copying python/pymesh/tests/test_selfintersection.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", - "copying python/pymesh/tests/test_minkowski_sum.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", - "copying python/pymesh/tests/test_assembler.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", - "copying python/pymesh/tests/test_aabb_tree.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", - "copying python/pymesh/tests/test_VoxelGrid.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", - "copying python/pymesh/tests/test_mesh.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", - "copying python/pymesh/tests/test_CSGTree.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", - "copying python/pymesh/tests/test_compression.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", - "copying python/pymesh/tests/test_snap_rounding.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", - "copying python/pymesh/tests/test_winding_number.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", - "copying python/pymesh/tests/test_triangulate.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", - "copying python/pymesh/tests/test_triangle.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", - "copying python/pymesh/tests/test_curvature.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", - "copying python/pymesh/tests/test_cut_to_disk.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", - "copying python/pymesh/tests/test_sparse_solver.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", - "copying python/pymesh/tests/test_material.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", - "copying python/pymesh/tests/test_meshio.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", - "copying python/pymesh/tests/test_predicates.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", - "copying python/pymesh/tests/test_outerhull.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", - "copying python/pymesh/tests/test_tetgen.py -> build/lib.linux-x86_64-3.6/pymesh/tests\n", - "creating build/lib.linux-x86_64-3.6/pymesh/meshutils/tests\n", - "copying python/pymesh/meshutils/tests/test_remove_isolated_vertices.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils/tests\n", - "copying python/pymesh/meshutils/tests/test_remove_duplicated_faces.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils/tests\n", - "copying python/pymesh/meshutils/tests/test_quad_to_tri.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils/tests\n", - "copying python/pymesh/meshutils/tests/test_remove_obtuse_triangles.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils/tests\n", - "copying python/pymesh/meshutils/tests/test_hex_to_tet.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils/tests\n", - "copying python/pymesh/meshutils/tests/test_merge_meshes.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils/tests\n", - "copying python/pymesh/meshutils/tests/test_collapse_short_edges.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils/tests\n", - "copying python/pymesh/meshutils/tests/test_separate_mesh.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils/tests\n", - "copying python/pymesh/meshutils/tests/test_attribute_utils.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils/tests\n", - "copying python/pymesh/meshutils/tests/test_remove_degenerated_triangles.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils/tests\n", - "copying python/pymesh/meshutils/tests/test_generate_icosphere.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils/tests\n", - "copying python/pymesh/meshutils/tests/test_split_long_edges.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils/tests\n", - "copying python/pymesh/meshutils/tests/test_remove_duplicated_vertices.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils/tests\n", - "copying python/pymesh/meshutils/tests/test_edge_utils.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils/tests\n", - "copying python/pymesh/meshutils/tests/test_cut_mesh.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils/tests\n", - "copying python/pymesh/meshutils/tests/test_generate_box_mesh.py -> build/lib.linux-x86_64-3.6/pymesh/meshutils/tests\n", - "creating build/lib.linux-x86_64-3.6/pymesh/wires/tests\n", - "copying python/pymesh/wires/tests/test_wire_network.py -> build/lib.linux-x86_64-3.6/pymesh/wires/tests\n", - "copying python/pymesh/wires/tests/test_inflator.py -> build/lib.linux-x86_64-3.6/pymesh/wires/tests\n", - "copying python/pymesh/wires/tests/WireTestCase.py -> build/lib.linux-x86_64-3.6/pymesh/wires/tests\n", - "copying python/pymesh/wires/tests/test_tiler.py -> build/lib.linux-x86_64-3.6/pymesh/wires/tests\n", - "running build_ext\n", - "creating build/bdist.linux-x86_64\n", - "creating build/bdist.linux-x86_64/egg\n", - "creating build/bdist.linux-x86_64/egg/pymesh\n", - "copying build/lib.linux-x86_64-3.6/pymesh/map_attributes.py -> build/bdist.linux-x86_64/egg/pymesh\n", - "creating build/bdist.linux-x86_64/egg/pymesh/meshutils\n", - "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/remove_duplicated_faces.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", - "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/cut_mesh.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", - "copying build/lib.linux-x86_64-3.6/pymesh/meshutils/merge_meshes.py -> build/bdist.linux-x86_64/egg/pymesh/meshutils\n", - 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"byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/remove_isolated_vertices.py to remove_isolated_vertices.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/collapse_short_edges.py to collapse_short_edges.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/__init__.py to __init__.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/attribute_utils.py to attribute_utils.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/tests/test_remove_isolated_vertices.py to test_remove_isolated_vertices.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/tests/test_remove_duplicated_faces.py to test_remove_duplicated_faces.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/tests/test_quad_to_tri.py to test_quad_to_tri.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/tests/test_remove_obtuse_triangles.py to test_remove_obtuse_triangles.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/tests/test_hex_to_tet.py to test_hex_to_tet.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/tests/test_merge_meshes.py to test_merge_meshes.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/tests/test_collapse_short_edges.py to test_collapse_short_edges.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/tests/test_separate_mesh.py to test_separate_mesh.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/tests/test_attribute_utils.py to test_attribute_utils.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/tests/test_remove_degenerated_triangles.py to test_remove_degenerated_triangles.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/tests/test_generate_icosphere.py to test_generate_icosphere.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/tests/test_split_long_edges.py to test_split_long_edges.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/tests/test_remove_duplicated_vertices.py to test_remove_duplicated_vertices.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/tests/test_edge_utils.py to test_edge_utils.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/tests/test_cut_mesh.py to test_cut_mesh.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/tests/test_generate_box_mesh.py to test_generate_box_mesh.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/remove_degenerated_triangles.py to remove_degenerated_triangles.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/separate_mesh.py to separate_mesh.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/subdivide.py to subdivide.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/remove_obtuse_triangles.py to remove_obtuse_triangles.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/generate_box_mesh.py to generate_box_mesh.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/mesh_to_graph.py to mesh_to_graph.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/generate_tube.py to generate_tube.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/generate_equilateral_triangle.py to generate_equilateral_triangle.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/quad_to_tri.py to quad_to_tri.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/edge_utils.py to edge_utils.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/generate_regular_tetrahedron.py to generate_regular_tetrahedron.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/remove_duplicated_vertices.py to remove_duplicated_vertices.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/voxel_utils.py to voxel_utils.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshutils/generate_icosphere.py to generate_icosphere.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/Assembler.py to Assembler.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/cell_partition.py to cell_partition.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/meshio.py to meshio.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/exact_arithmetic.py to exact_arithmetic.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/igl_utils.py to igl_utils.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/material.py to material.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/selfintersection.py to selfintersection.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/minkowski_sum.py to minkowski_sum.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/timethis.py to timethis.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/Arrangement2.py to Arrangement2.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/CSGTree.py to CSGTree.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/compression.py to compression.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/cut_to_disk.py to cut_to_disk.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/HashGrid.py to HashGrid.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/boolean.py to boolean.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/SparseSolver.py to SparseSolver.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/wires/merge_wires.py to merge_wires.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/wires/WireNetwork.py to WireNetwork.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/wires/Parameters.py to Parameters.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/wires/Tiler.py to Tiler.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/wires/Inflator.py to Inflator.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/wires/wires_io.py to wires_io.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/wires/__init__.py to __init__.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/wires/tests/test_wire_network.py to test_wire_network.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/wires/tests/test_inflator.py to test_inflator.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/wires/tests/WireTestCase.py to WireTestCase.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/wires/tests/test_tiler.py to test_tiler.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/__init__.py to __init__.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/triangulate.py to triangulate.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_HarmonicSolver.py to test_HarmonicSolver.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_solver.py to test_solver.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_boolean.py to test_boolean.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_map_attributes.py to test_map_attributes.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_slice_Mesh.py to test_slice_Mesh.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_selfintersection.py to test_selfintersection.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_minkowski_sum.py to test_minkowski_sum.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_assembler.py to test_assembler.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_aabb_tree.py to test_aabb_tree.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_VoxelGrid.py to test_VoxelGrid.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_mesh.py to test_mesh.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_CSGTree.py to test_CSGTree.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_compression.py to test_compression.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_snap_rounding.py to test_snap_rounding.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_winding_number.py to test_winding_number.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_triangulate.py to test_triangulate.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_triangle.py to test_triangle.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_curvature.py to test_curvature.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_cut_to_disk.py to test_cut_to_disk.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_sparse_solver.py to test_sparse_solver.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_material.py to test_material.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_meshio.py to test_meshio.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_predicates.py to test_predicates.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_outerhull.py to test_outerhull.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tests/test_tetgen.py to test_tetgen.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/submesh.py to submesh.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/misc/__init__.py to __init__.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/misc/quaternion.py to quaternion.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/straight_skeleton.py to straight_skeleton.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/Mesh.py to Mesh.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tetrahedralize.py to tetrahedralize.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/matrixio.py to matrixio.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/slice_mesh.py to slice_mesh.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/tetgen.py to tetgen.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/boolean_unsupported.py to boolean_unsupported.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/Boundary.py to Boundary.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/TestCase.py to TestCase.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/version.py to version.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/HarmonicSolver.py to HarmonicSolver.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/aabb_tree.py to aabb_tree.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/winding_number.py to winding_number.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/PyMeshSetting.py to PyMeshSetting.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/convex_hull.py to convex_hull.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/outerhull.py to outerhull.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/predicates.py to predicates.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/snap_rounding.py to snap_rounding.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/VoxelGrid.py to VoxelGrid.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/triangle.py to triangle.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pymesh/save_svg.py to save_svg.cpython-36.pyc\n", - "creating build/bdist.linux-x86_64/egg/EGG-INFO\n", - "installing scripts to build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "running install_scripts\n", - "running build_scripts\n", - "creating build/scripts-3.6\n", - "copying and adjusting scripts/add_element_attribute.py -> build/scripts-3.6\n", - "copying and adjusting scripts/add_index.py -> build/scripts-3.6\n", - "copying and adjusting scripts/arrangement_2d.py -> build/scripts-3.6\n", - "copying and adjusting scripts/bbox.py -> build/scripts-3.6\n", - "copying and adjusting scripts/box_gen.py -> build/scripts-3.6\n", - "copying and adjusting scripts/boolean.py -> build/scripts-3.6\n", - "copying and adjusting scripts/carve.py -> build/scripts-3.6\n", - "copying and adjusting scripts/convex_hull.py -> build/scripts-3.6\n", - "copying and adjusting scripts/curvature.py -> build/scripts-3.6\n", - "copying and adjusting scripts/distortion.py -> build/scripts-3.6\n", - "copying and adjusting scripts/dodecahedron_gen.py -> build/scripts-3.6\n", - "copying and adjusting scripts/extract_self_intersecting_faces.py -> build/scripts-3.6\n", - "copying and adjusting scripts/fem_check.py -> build/scripts-3.6\n", - "copying scripts/find_file.py -> build/scripts-3.6\n", - "copying and adjusting scripts/fix_mesh.py -> build/scripts-3.6\n", - "copying and adjusting scripts/geodesic.py -> build/scripts-3.6\n", - "copying and adjusting scripts/highlight_boundary_edges.py -> build/scripts-3.6\n", - "copying and adjusting scripts/highlight_degenerated_faces.py -> build/scripts-3.6\n", - "copying and adjusting scripts/highlight_non_oriented_edges.py -> build/scripts-3.6\n", - "copying and adjusting scripts/highlight_self_intersection.py -> build/scripts-3.6\n", - "copying and adjusting scripts/highlight_zero_area_faces.py -> build/scripts-3.6\n", - "copying and adjusting scripts/highlight_inverted_tets.py -> build/scripts-3.6\n", - "copying and adjusting scripts/highlight_delaunay.py -> build/scripts-3.6\n", - "copying and adjusting scripts/hilbert_curve_gen.py -> build/scripts-3.6\n", - "copying and adjusting scripts/icosphere_gen.py -> build/scripts-3.6\n", - "copying and adjusting scripts/inflate.py -> build/scripts-3.6\n", - "copying and adjusting scripts/map_to_sphere.py -> build/scripts-3.6\n", - "copying and adjusting scripts/matrix_gen.py -> build/scripts-3.6\n", - "copying and adjusting scripts/mean_curvature_flow.py -> build/scripts-3.6\n", - "copying and adjusting scripts/merge.py -> build/scripts-3.6\n", - "copying and adjusting scripts/mesh_diff.py -> build/scripts-3.6\n", - "copying and adjusting scripts/meshconvert.py -> build/scripts-3.6\n", - "copying and adjusting scripts/meshstat.py -> build/scripts-3.6\n", - "copying and adjusting scripts/mesh_to_wire.py -> build/scripts-3.6\n", - "copying and adjusting scripts/microstructure_gen.py -> build/scripts-3.6\n", - "copying and adjusting scripts/minkowski_sum.py -> build/scripts-3.6\n", - "copying and adjusting scripts/outer_hull.py -> build/scripts-3.6\n", - "copying and adjusting scripts/point_cloud.py -> build/scripts-3.6\n", - "copying scripts/print_utils.py -> build/scripts-3.6\n", - "copying and adjusting scripts/quad_to_tri.py -> build/scripts-3.6\n", - "copying and adjusting scripts/refine_mesh.py -> build/scripts-3.6\n", - "copying and adjusting scripts/remove_degenerated_triangles.py -> build/scripts-3.6\n", - "copying and adjusting scripts/remove_duplicated_faces.py -> build/scripts-3.6\n", - "copying and adjusting scripts/remove_isolated_vertices.py -> build/scripts-3.6\n", - "copying and adjusting scripts/remove_nan.py -> build/scripts-3.6\n", - "copying and adjusting scripts/resolve_self_intersection.py -> build/scripts-3.6\n", - "copying and adjusting scripts/retriangulate.py -> build/scripts-3.6\n", - "copying and adjusting scripts/rigid_transform.py -> build/scripts-3.6\n", - "copying and adjusting scripts/scale_mesh.py -> build/scripts-3.6\n", - "copying and adjusting scripts/self_union.py -> build/scripts-3.6\n", - "copying and adjusting scripts/separate.py -> build/scripts-3.6\n", - "copying and adjusting scripts/slice_mesh.py -> build/scripts-3.6\n", - "copying and adjusting scripts/subdivide.py -> build/scripts-3.6\n", - "copying and adjusting scripts/submesh.py -> build/scripts-3.6\n", - "copying and adjusting scripts/svg_to_mesh.py -> build/scripts-3.6\n", - "copying and adjusting scripts/tet.py -> build/scripts-3.6\n", - "copying and adjusting scripts/tet_boundary.py -> build/scripts-3.6\n", - "copying and adjusting scripts/tet_to_hex.py -> build/scripts-3.6\n", - "copying and adjusting scripts/triangulate.py -> build/scripts-3.6\n", - "copying and adjusting scripts/uv.py -> build/scripts-3.6\n", - "copying and adjusting scripts/voxelize.py -> build/scripts-3.6\n", - "changing mode of build/scripts-3.6/add_element_attribute.py from 644 to 755\n", - "changing mode of build/scripts-3.6/add_index.py from 644 to 755\n", - "changing mode of build/scripts-3.6/arrangement_2d.py from 644 to 755\n", - "changing mode of build/scripts-3.6/bbox.py from 644 to 755\n", - "changing mode of build/scripts-3.6/box_gen.py from 644 to 755\n", - "changing mode of build/scripts-3.6/boolean.py from 644 to 755\n", - "changing mode of build/scripts-3.6/carve.py from 644 to 755\n", - "changing mode of build/scripts-3.6/convex_hull.py from 644 to 755\n", - "changing mode of build/scripts-3.6/curvature.py from 644 to 755\n", - "changing mode of build/scripts-3.6/distortion.py from 644 to 755\n", - "changing mode of build/scripts-3.6/dodecahedron_gen.py from 644 to 755\n", - "changing mode of build/scripts-3.6/extract_self_intersecting_faces.py from 644 to 755\n", - "changing mode of build/scripts-3.6/fem_check.py from 644 to 755\n", - "changing mode of build/scripts-3.6/find_file.py from 644 to 755\n", - "changing mode of build/scripts-3.6/fix_mesh.py from 644 to 755\n", - "changing mode of build/scripts-3.6/geodesic.py from 644 to 755\n", - "changing mode of build/scripts-3.6/highlight_boundary_edges.py from 644 to 755\n", - "changing mode of build/scripts-3.6/highlight_degenerated_faces.py from 644 to 755\n", - "changing mode of build/scripts-3.6/highlight_non_oriented_edges.py from 644 to 755\n", - "changing mode of build/scripts-3.6/highlight_self_intersection.py from 644 to 755\n", - "changing mode of build/scripts-3.6/highlight_zero_area_faces.py from 644 to 755\n", - "changing mode of build/scripts-3.6/highlight_inverted_tets.py from 644 to 755\n", - "changing mode of build/scripts-3.6/highlight_delaunay.py from 644 to 755\n", - "changing mode of build/scripts-3.6/hilbert_curve_gen.py from 644 to 755\n", - "changing mode of build/scripts-3.6/icosphere_gen.py from 644 to 755\n", - "changing mode of build/scripts-3.6/inflate.py from 644 to 755\n", - "changing mode of build/scripts-3.6/map_to_sphere.py from 644 to 755\n", - "changing mode of build/scripts-3.6/matrix_gen.py from 644 to 755\n", - "changing mode of build/scripts-3.6/mean_curvature_flow.py from 644 to 755\n", - "changing mode of build/scripts-3.6/merge.py from 644 to 755\n", - "changing mode of build/scripts-3.6/mesh_diff.py from 644 to 755\n", - "changing mode of build/scripts-3.6/meshconvert.py from 644 to 755\n", - "changing mode of build/scripts-3.6/meshstat.py from 644 to 755\n", - "changing mode of build/scripts-3.6/mesh_to_wire.py from 644 to 755\n", - "changing mode of build/scripts-3.6/microstructure_gen.py from 644 to 755\n", - "changing mode of build/scripts-3.6/minkowski_sum.py from 644 to 755\n", - "changing mode of build/scripts-3.6/outer_hull.py from 644 to 755\n", - "changing mode of build/scripts-3.6/point_cloud.py from 644 to 755\n", - "changing mode of build/scripts-3.6/print_utils.py from 644 to 755\n", - "changing mode of build/scripts-3.6/quad_to_tri.py from 644 to 755\n", - "changing mode of build/scripts-3.6/refine_mesh.py from 644 to 755\n", - "changing mode of build/scripts-3.6/remove_degenerated_triangles.py from 644 to 755\n", - "changing mode of build/scripts-3.6/remove_duplicated_faces.py from 644 to 755\n", - "changing mode of build/scripts-3.6/remove_isolated_vertices.py from 644 to 755\n", - "changing mode of build/scripts-3.6/remove_nan.py from 644 to 755\n", - "changing mode of build/scripts-3.6/resolve_self_intersection.py from 644 to 755\n", - "changing mode of build/scripts-3.6/retriangulate.py from 644 to 755\n", - "changing mode of build/scripts-3.6/rigid_transform.py from 644 to 755\n", - "changing mode of build/scripts-3.6/scale_mesh.py from 644 to 755\n", - "changing mode of build/scripts-3.6/self_union.py from 644 to 755\n", - "changing mode of build/scripts-3.6/separate.py from 644 to 755\n", - "changing mode of build/scripts-3.6/slice_mesh.py from 644 to 755\n", - "changing mode of build/scripts-3.6/subdivide.py from 644 to 755\n", - "changing mode of build/scripts-3.6/submesh.py from 644 to 755\n", - "changing mode of build/scripts-3.6/svg_to_mesh.py from 644 to 755\n", - "changing mode of build/scripts-3.6/tet.py from 644 to 755\n", - "changing mode of build/scripts-3.6/tet_boundary.py from 644 to 755\n", - "changing mode of build/scripts-3.6/tet_to_hex.py from 644 to 755\n", - "changing mode of build/scripts-3.6/triangulate.py from 644 to 755\n", - "changing mode of build/scripts-3.6/uv.py from 644 to 755\n", - "changing mode of build/scripts-3.6/voxelize.py from 644 to 755\n", - "creating build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/remove_duplicated_faces.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/uv.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/dodecahedron_gen.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/point_cloud.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/svg_to_mesh.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/meshconvert.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/retriangulate.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/highlight_self_intersection.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/highlight_boundary_edges.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/minkowski_sum.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/geodesic.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/bbox.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/fix_mesh.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/extract_self_intersecting_faces.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/highlight_delaunay.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/mean_curvature_flow.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/scale_mesh.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/remove_nan.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/tet_boundary.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/matrix_gen.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/map_to_sphere.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/mesh_to_wire.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/box_gen.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/tet.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/add_index.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/remove_isolated_vertices.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/outer_hull.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/refine_mesh.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/inflate.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/boolean.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/fem_check.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/highlight_zero_area_faces.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/triangulate.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/remove_degenerated_triangles.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/merge.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/self_union.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/submesh.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/subdivide.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/meshstat.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/tet_to_hex.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/add_element_attribute.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/slice_mesh.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/icosphere_gen.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/print_utils.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/curvature.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/voxelize.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/rigid_transform.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/separate.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/arrangement_2d.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/mesh_diff.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/microstructure_gen.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/distortion.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/convex_hull.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/find_file.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/highlight_non_oriented_edges.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/carve.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/highlight_inverted_tets.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/hilbert_curve_gen.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/quad_to_tri.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/resolve_self_intersection.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "copying build/scripts-3.6/highlight_degenerated_faces.py -> build/bdist.linux-x86_64/egg/EGG-INFO/scripts\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/remove_duplicated_faces.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/uv.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/dodecahedron_gen.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/point_cloud.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/svg_to_mesh.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/meshconvert.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/retriangulate.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/highlight_self_intersection.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/highlight_boundary_edges.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/minkowski_sum.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/geodesic.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/bbox.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/fix_mesh.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/extract_self_intersecting_faces.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/highlight_delaunay.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/mean_curvature_flow.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/scale_mesh.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/remove_nan.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/tet_boundary.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/matrix_gen.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/map_to_sphere.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/mesh_to_wire.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/box_gen.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/tet.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/add_index.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/remove_isolated_vertices.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/outer_hull.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/refine_mesh.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/inflate.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/boolean.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/fem_check.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/highlight_zero_area_faces.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/triangulate.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/remove_degenerated_triangles.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/merge.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/self_union.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/submesh.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/subdivide.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/meshstat.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/tet_to_hex.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/add_element_attribute.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/slice_mesh.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/icosphere_gen.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/print_utils.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/curvature.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/voxelize.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/rigid_transform.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/separate.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/arrangement_2d.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/mesh_diff.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/microstructure_gen.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/distortion.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/convex_hull.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/find_file.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/highlight_non_oriented_edges.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/carve.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/highlight_inverted_tets.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/hilbert_curve_gen.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/quad_to_tri.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/resolve_self_intersection.py to 755\n", - "changing mode of build/bdist.linux-x86_64/egg/EGG-INFO/scripts/highlight_degenerated_faces.py to 755\n", - "copying python/pymesh2.egg-info/PKG-INFO -> build/bdist.linux-x86_64/egg/EGG-INFO\n", - "copying python/pymesh2.egg-info/SOURCES.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n", - "copying python/pymesh2.egg-info/dependency_links.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n", - "copying python/pymesh2.egg-info/not-zip-safe -> build/bdist.linux-x86_64/egg/EGG-INFO\n", - "copying python/pymesh2.egg-info/top_level.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n", - "creating dist\n", - "creating 'dist/pymesh2-0.3-py3.6-linux-x86_64.egg' and adding 'build/bdist.linux-x86_64/egg' to it\n", - "removing 'build/bdist.linux-x86_64/egg' (and everything under it)\n", - "Processing pymesh2-0.3-py3.6-linux-x86_64.egg\n", - "creating /usr/local/lib/python3.6/site-packages/pymesh2-0.3-py3.6-linux-x86_64.egg\n", - "Extracting pymesh2-0.3-py3.6-linux-x86_64.egg to /usr/local/lib/python3.6/site-packages\n", - "Adding pymesh2 0.3 to easy-install.pth file\n", - "Installing remove_duplicated_faces.py script to /usr/local/bin\n", - "Installing uv.py script to /usr/local/bin\n", - "Installing dodecahedron_gen.py script to /usr/local/bin\n", - "Installing point_cloud.py script to /usr/local/bin\n", - "Installing svg_to_mesh.py script to /usr/local/bin\n", - "Installing meshconvert.py script to /usr/local/bin\n", - "Installing retriangulate.py script to /usr/local/bin\n", - "Installing highlight_self_intersection.py script to /usr/local/bin\n", - "Installing highlight_boundary_edges.py script to /usr/local/bin\n", - "Installing minkowski_sum.py script to /usr/local/bin\n", - "Installing geodesic.py script to /usr/local/bin\n", - "Installing bbox.py script to /usr/local/bin\n", - "Installing fix_mesh.py script to /usr/local/bin\n", - "Installing extract_self_intersecting_faces.py script to /usr/local/bin\n", - "Installing highlight_delaunay.py script to /usr/local/bin\n", - "Installing mean_curvature_flow.py script to /usr/local/bin\n", - "Installing scale_mesh.py script to /usr/local/bin\n", - "Installing remove_nan.py script to /usr/local/bin\n", - "Installing tet_boundary.py script to /usr/local/bin\n", - "Installing matrix_gen.py script to /usr/local/bin\n", - "Installing map_to_sphere.py script to /usr/local/bin\n", - "Installing mesh_to_wire.py script to /usr/local/bin\n", - "Installing box_gen.py script to /usr/local/bin\n", - 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"Successfully built easydict chumpy progress\n", - "Installing collected packages: numpy, jsonpointer, Pillow, jsonpatch, pymesh2, progress, matplotlib, joblib, easydict, chumpy\n", - "\u001b[33m WARNING: The scripts f2py, f2py3 and f2py3.6 are installed in '/root/.local/bin' which is not on PATH.\n", - " Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\n", - "Successfully installed Pillow-5.1.0 chumpy-0.70 easydict-1.9 joblib-1.0.1 jsonpatch-1.32 jsonpointer-2.1 matplotlib-3.2.2 numpy-1.17.5 progress-1.5 pymesh2-0.2.1\n" - ], - "name": "stdout" - }, - { - "output_type": "display_data", - "data": { - "application/vnd.colab-display-data+json": { - "pip_warning": { - "packages": [ - "matplotlib", - "mpl_toolkits" - ] - } - } - }, - "metadata": { - "tags": [] - } - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "7cEwnsEfFgGI" - }, - "source": [ - "## Since PyMesh and pytorch3d both are having installations issues that I cant figure out....\n", - "\n", - "\n", - "# DO THIS ON THE FILES OR YOU WILL HAVE IMPORT ERRORS\n", - "\n", - "\n", - "# trainer.py from PyMesh.python.pymesh.meshio import form_mesh\n", - "# mesh_processor.py from PyMesh.python.pymesh.meshio import save_mesh\n", - "# template.py remove pymesh import\n", - "\n", - "# Removed the pytorch3d imports from train_loss.py, model.py due to improper installation issue from pytorch3d repo" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "b1nj8W0oY24U" - }, - "source": [ - "Need to link pre-trained models to your google drive account from [HERE](https://drive.google.com/drive/folders/1cyVRUmtN_YF-TXkytKfn1M0HlGH9Qux_?usp=sharing) then link your drive to colab. Or download the pretrained models then place in /content/3dsnet/\n", - "\n", - "Since the encoders are trained on each family objects individually, right now the only pre-trained models are for chairs and planes, although the training for other classes can be performed." - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "nMfmXLOYfNRF", - "outputId": "2f86ad83-0b4e-4efc-8ec0-57e4dfbb3e14" - }, - "source": [ - "from google.colab import drive\n", - "drive.mount('/gdrive')\n", - "%cd /gdrive\n", - "# %cp -r /gdrive/MyDrive/Colab\\ Notebooks/3dsnet_models /content/3dsnet\n", - "# %cp -r /gdrive/MyDrive/Colab\\ Notebooks/3dsnet_models/aux_models/ /content/3dsnet" - ], - "execution_count": 35, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Drive already mounted at /gdrive; to attempt to forcibly remount, call drive.mount(\"/gdrive\", force_remount=True).\n", - "/gdrive\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "c9jnYHect6n8" - }, - "source": [ - "#@title Helper Functions {display-mode: \"form\"}\n", - "\n", - "# This code will be hidden when the notebook is loaded.\n", - "\n", - "def load_mesh_obj(path):\n", - " mesh = trimesh.load_mesh(path)\n", - " if isinstance(mesh, trimesh.Scene):\n", - " mesh = mesh.dump()[0]\n", - " return mesh\n", - "\n", - "def plot_meshes(mesh_list,\n", - " fig_size=8,\n", - " el=45,\n", - " rot_start=90,\n", - " vert_size=10,\n", - " vert_alpha=0.25,\n", - " n_cols=4):\n", - " \"\"\"Plots mesh data using matplotlib.\"\"\"\n", - "\n", - " n_plot = len(mesh_list)\n", - " n_cols = np.minimum(n_plot, n_cols)\n", - " n_rows = np.ceil(n_plot / n_cols).astype('int')\n", - " fig = plt.figure(figsize=(fig_size * n_cols, fig_size * n_rows))\n", - " for p_inc, mesh in enumerate(mesh_list):\n", - "\n", - " ax = fig.add_subplot(n_rows, n_cols, p_inc + 1, projection='3d')\n", - "\n", - " if 'faces' in mesh:\n", - " face_verts = mesh['vertices']\n", - " collection = []\n", - " for f in mesh['faces']:\n", - " collection.append(face_verts[f])\n", - " plt_mesh = Poly3DCollection(collection)\n", - " plt_mesh.set_edgecolor((0., 0., 0., 0.3))\n", - " plt_mesh.set_facecolor((1, 0, 0, 0.2))\n", - " ax.add_collection3d(plt_mesh)\n", - "\n", - " if mesh['vertices'] is not None:\n", - " ax.scatter3D(\n", - " mesh['vertices'][:, 0],\n", - " mesh['vertices'][:, 1],\n", - " mesh['vertices'][:, 2],\n", - " lw=0.,\n", - " s=vert_size,\n", - " c='g',\n", - " alpha=vert_alpha)\n", - "\n", - " # if mesh['pointcloud'] is not None:\n", - " # ax.scatter3D(\n", - " # mesh['pointcloud'][:, 0],\n", - " # mesh['pointcloud'][:, 1],\n", - " # mesh['pointcloud'][:, 2],\n", - " # lw=0.,\n", - " # s=2.5 * vert_size,\n", - " # c='b',\n", - " # alpha=1.)\n", - " \n", - " ax.view_init(el, rot_start)\n", - "\n", - " display_string = ''\n", - " if mesh['faces'] is not None:\n", - " display_string += 'Num. faces: {}\\n'.format(len(collection))\n", - " if mesh['vertices'] is not None:\n", - " num_verts = mesh['vertices'].shape[0]\n", - " # if mesh['vertices_conditional'] is not None:\n", - " # num_verts += mesh['vertices_conditional'].shape[0]\n", - " display_string += 'Num. verts: {}\\n'.format(num_verts)\n", - " # if mesh['class_name'] is not None:\n", - " # display_string += 'Synset: {}'.format(mesh['class_name'])\n", - " # if mesh['pointcloud'] is not None:\n", - " # display_string += 'Num. pointcloud: {}\\n'.format(\n", - " # mesh['pointcloud'].shape[0])\n", - " ax.text2D(0.05, 0.8, display_string, transform=ax.transAxes)\n", - " plt.subplots_adjust(\n", - " left=0., right=1., bottom=0., top=1., wspace=0.025, hspace=0.025)\n", - " plt.show()\n", - "\n", - "def load_data_from_file(path):\n", - " ext = path.split('.')[-1]\n", - " if ext == 'npy':\n", - " points = np.load(path)\n", - " elif ext == 'ply' or ext == 'obj':\n", - " points = trimesh.load_mesh(path)\n", - " if isinstance(points, trimesh.Scene):\n", - " points = points.dump()[0].vertices\n", - " else:\n", - " points = points.vertices\n", - " else:\n", - " print('invalid file extension')\n", - " raise IOError\n", - " points = torch.from_numpy(points.copy()).float()\n", - " operation = pointcloud_processor.Normalization(points, keep_track=True)\n", - " if opt.normalization == 'UnitBall':\n", - " operation.normalize_unitL2ball()\n", - " elif opt.normalization == 'BoundingBox':\n", - " operation.normalize_bounding_box()\n", - " else:\n", - " pass\n", - " return_dict = {\n", - " 'points': points,\n", - " 'operation': operation,\n", - " 'path': path,\n", - " }\n", - " return return_dict\n", - "\n", - "def unnormalize(mesh, operation=None):\n", - " if operation is not None:\n", - " # Undo any normalization that was used to preprocess the input.\n", - " vertices = torch.from_numpy(mesh.vertices).clone().unsqueeze(0)\n", - " norm_mesh = deepcopy(mesh)\n", - " norm_mesh.vertices = operation.apply(vertices).squeeze().numpy()\n", - " norm_mesh._data.__dict__['data']['vertices'] = norm_mesh.vertices\n", - " if np.sum(norm_mesh.vertices - mesh.vertices) == 0:\n", - " print(\"fucked normalization\")\n", - " return norm_mesh" - ], - "execution_count": 33, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "RsZseUSK_bc6", - "cellView": "form" - }, - "source": [ - "#@title HyperParameters\n", - "#@markdown Options to change in the model\n", - "\n", - "family = \"chair\" #@param [\"chair\", \"bananas\", \"oranges\"] {allow-input: true}\n", - "decoder = \"meshflow\" #@param [\"meshflow\", \"atlasnet\"] {allow-input: true}\n", - "noise_level = 1 #@param {type:\"number\"}\n", - "log_dir = \"log/\" #@param {type:\"string\"}\n", - "data_dir = \"/content/3dsnet/docs/points/\" #@param {type:\"string\"}\n", - "reload_model_path = '/content/3dsnet/3dsnet_models/3dsnet_models/chairs/meshflow/3dsnet/network.pth' #@param {type:\"string\"}\n", - "#@markdown ---\n" - ], - "execution_count": 8, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "H_l1hFOoMG0T" - }, - "source": [ - "#@title Options for trainer {display-mode: \"form\"}\n", - "\n", - "# This code will be hidden when the notebook is loaded.\n", - "\n", - "from easydict import EasyDict as edict\n", - "\n", - "opt = edict({ \"decoder_type\" : decoder,\n", - " 'demo': False,\n", - " \"SVR_0\": False,\n", - " \"SVR_1\" : False,\n", - " \"family\": family,\n", - " \"data_dir\" : data_dir,\n", - " \"dir_name\" : log_dir,\n", - " \"dataset\" :\"ShapeNet\",\n", - " \"weight_perceptual\": 1,\n", - " \"reload_model_path\" : reload_model_path, \n", - " \"batch_size\" : 4, \n", - " \"batch_size_test\" : 4,\n", - " \"class_choice\" : [\"armchair\",\"straight chair,side chair\"], \n", - " \"generator_norm\" : \"bn\",\n", - " \"discriminator_norm\" : \"bn\",\n", - " \"discriminator_activation\" : \"relu\",\n", - " \"dis_bottleneck_size\" : 1024,\n", - " \"style_bottleneck_size\" : 512,\n", - " \"generator_lrate\" : 0.001,\n", - " \"discriminator_lrate\" : 0.004,\n", - " \"batch_size\" : 16, \n", - " \"generator_update_skips\" : 1, \n", - " \"discriminator_update_skips\" : 1, \n", - " \"num_layers\" : 2,\n", - " \"num_layers_style\" : 1,\n", - " \"nb_primitives\" : 25, \n", - " \"template_type\" : \"SQUARE\",\n", - " \"weight_chamfer\" : 10,\n", - " \"weight_cycle_chamfer\" : 0,\n", - " \"weight_adversarial\" : 1,\n", - " \"weight_content_reconstruction\" : 1,\n", - " \"weight_style_reconstruction\" : 1,\n", - " \"lr_decay_1\" : 120,\n", - " \"lr_decay_2\" : 140, \n", - " \"lr_decay_3\" : 145, \n", - " \"decode_style\":True, \n", - " \"share_decoder\":True,\n", - " \"share_content_encoder\":True, \n", - " \"share_discriminator_encoder\":True,\n", - " \"gan_type\" : \"lsgan\",\n", - " \"use_visdom\":False,\n", - " \"start_epoch\":0,\n", - " \"adaptive\":True,\n", - " \"noise_magnitude\" : 1.0,\n", - " \"num_interpolations\" : 0,\n", - " \"normalization\":\"UnitBall\",\n", - " \"number_points\":2500,\n", - " \"multi_gpu\":[0],\n", - " \"use_default_demo_samples\":True,\n", - " \"multiscale_loss\":False,\n", - " \"bottleneck_size\":1024,\n", - " \"number_points_eval\":2500,\n", - " \"w_multiscale_1\":.1,\n", - " \"w_multiscale_2\":.2,\n", - " \"w_multiscale_3\":.7,\n", - " \"remove_all_batchNorms\": False,\n", - " \"dim_template\":2,\n", - " \"hidden_neurons\":512,\n", - " \"activation\": \"relu\",\n", - " \"share_style_encoder\": False,\n", - " \"num_layers_mlp\": 3,\n", - " \"no_learning\":True,\n", - " \"reload_decoder_path\" : '',\n", - " \"reload_pointnet_path\":'',\n", - " \"demo_input_dir\": \"./docs/points/\",\n", - " \"num_demo_pairs\":5,\n", - " \"share_style_mlp\": True})" - ], - "execution_count": 24, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "b_58lYhRFeO8", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "9c052a7b-caad-4f7a-f06a-b342cc60a57f" - }, - "source": [ - "import sys\n", - "import torch\n", - "import training.trainer as trainer\n", - "import auxiliary.my_utils as my_utils\n", - "import numpy as np\n", - "import os\n", - "from easydict import EasyDict\n", - "from PyMesh.python.pymesh.meshio import form_mesh\n", - "from dataset.dataset_shapenet import ShapeNet\n", - "import dataset.pointcloud_processor as pointcloud_processor\n", - "import torch\n", - "import matplotlib.pyplot as plt\n", - "from mpl_toolkits.mplot3d.art3d import Poly3DCollection\n", - "import trimesh\n", - "from copy import deepcopy\n", - "\n", - "path_a = \"./docs/points/armchair.points.ply.npy\"\n", - "path_b = \"./docs/points/straight chair,side chair.points.ply.npy\"\n", - "\n", - "torch.cuda.set_device(opt.multi_gpu[0])\n", - "\n", - "trainer = trainer.Trainer(opt)\n", - "trainer.build_network()\n", - "trainer.reload_best_network()\n", - "trainer.demo_pair_path = \"\"\n", - "\n", - "with torch.no_grad():\n", - " data_a = EasyDict(load_data_from_file(path_a))\n", - " data_b = EasyDict(load_data_from_file(path_b))\n", - " \n", - " # prepare normalization\n", - " trainer.make_network_input(data_a, opt.SVR_0)\n", - " trainer.make_network_input(data_b, opt.SVR_1)\n", - " x = {opt.class_choice[0]: data_a.network_input, opt.class_choice[1]: data_b.network_input}\n", - " \n", - " # set the normalization operation\n", - " trainer.set_operation(data_a, data_b)\n", - "\n", - " # Get results of forward pass\n", - " path_ab, mesh_ab_normalized = trainer.generate_mesh_from_classes(x, opt.class_choice[0], opt.class_choice[1], data_a.operation, save=False)\n", - " path_ba, mesh_ba_normalized = trainer.generate_mesh_from_classes(x, opt.class_choice[1], opt.class_choice[0], data_b.operation, save=False)\n", - " \n", - " # unnormalize mesh vertices\n", - " mesh_ab = unnormalize(mesh_ab_normalized, data_a.operation)\n", - " mesh_ba = unnormalize(mesh_ba_normalized, data_b.operation)" - ], - "execution_count": 26, - 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\u001b[33mdiscriminator_optimizer_path\u001b[0m : \u001b[36mlog/discriminator_optimizer.pth\u001b[0m\n", - " \u001b[33mmodel_path\u001b[0m : \u001b[36mlog/network.pth\u001b[0m\n", - " \u001b[33mreload_generator_optimizer_path\u001b[0m : \u001b[36m\u001b[0m\n", - " \u001b[33mreload_discriminator_optimizer_path\u001b[0m : \u001b[36m\u001b[0m\n", - " \u001b[33mbest_model_path\u001b[0m : \u001b[36mlog/network_best.pth\u001b[0m\n", - " \u001b[33mtraining_media_path\u001b[0m : \u001b[36mlog/training_media\u001b[0m\n", - " \u001b[33mdemo_media_path\u001b[0m : \u001b[36mlog/demo_media\u001b[0m\n", - " \u001b[33mdevice\u001b[0m : \u001b[36mcuda:0\u001b[0m\n", - "Neural Mesh Flow with 1024 length embedding initialized\n", - "\u001b[33mNetwork weights loaded from /content/3dsnet/3dsnet_models/3dsnet_models/chairs/meshflow/3dsnet/network.pth!\u001b[0m\n", - "\u001b[33mFailed to reload network weights from log/network_best.pth!\u001b[0m\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "WUZH_LOo9Vdf", - "outputId": "e282cede-4d31-4df0-bc35-4a6b6e24f18d" - }, - "source": [ - "plot_meshes([ mesh_ab._data.__dict__[\"data\"]], rot_start=270)\n", - "plot_meshes([ mesh_ba._data.__dict__[\"data\"]])" - ], - "execution_count": 34, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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\n", 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0.00829677 -0.00920843 -0.22110354\\nv -0.22939847 0.32048753 -0.18508641\\nv 0.24157108 -0.08785685 0.21924024\\nv -0.18635425 0.13268590 -0.22374351\\nv -0.21689491 0.15829953 -0.15821872\\nv -0.10197775 -0.02405942 0.11980677\\nv -0.20792675 0.13490889 -0.11150412\\nv -0.17103688 -0.01393483 0.23155299\\nv -0.22779135 -0.08767587 0.16478683\\nv 0.25142612 -0.18607124 0.22237431\\nv -0.05954628 -0.28481541 0.22172585\\nv -0.24150024 -0.17391509 -0.17889905\\nv 0.01515525 -0.00397954 0.10334098\\nv -0.02713109 -0.01390299 -0.11471119\\nv 0.16347391 0.00849686 -0.00218100\\nv 0.24023943 -0.00451028 -0.06247011\\nf 395 1383 342\\nf 2501 36 1383\\nf 2177 342 36\\nf 1383 36 342\\nf 1887 1152 1357\\nf 1780 1289 1152\\nf 2501 1357 1289\\nf 1152 1289 1357\\nf 2335 757 573\\nf 2177 710 757\\nf 1780 573 710\\nf 757 710 573\\nf 2501 1289 36\\nf 1780 710 1289\\nf 2177 36 710\\nf 1289 710 36\\nf 2553 190 1022\\nf 2230 2175 190\\nf 1132 1022 2175\\nf 190 2175 1022\\nf 2279 1020 2540\\nf 2440 1453 1020\\nf 2230 2540 1453\\nf 1020 1453 2540\\nf 1887 273 597\\nf 1132 861 273\\nf 2440 597 861\\nf 273 861 597\\nf 2230 1453 2175\\nf 2440 861 1453\\nf 1132 2175 861\\nf 1453 861 2175\\nf 1592 222 2523\\nf 170 681 222\\nf 1555 2523 681\\nf 222 681 2523\\nf 2335 1685 645\\nf 459 1699 1685\\nf 170 645 1699\\nf 1685 1699 645\\nf 2279 249 430\\nf 1555 1337 249\\nf 459 430 1337\\nf 249 1337 430\\nf 170 1699 681\\nf 459 1337 1699\\nf 1555 681 1337\\nf 1699 1337 681\\nf 1887 597 1152\\nf 2440 2344 597\\nf 1780 1152 2344\\nf 597 2344 1152\\nf 2279 430 1020\\nf 459 2031 430\\nf 2440 1020 2031\\nf 430 2031 1020\\nf 2335 573 1685\\nf 1780 363 573\\nf 459 1685 363\\nf 573 363 1685\\nf 2440 2031 2344\\nf 459 363 2031\\nf 1780 2344 363\\nf 2031 363 2344\\nf 887 2447 76\\nf 2383 264 2447\\nf 718 76 264\\nf 2447 264 76\\nf 2361 613 2149\\nf 2245 1513 613\\nf 2383 2149 1513\\nf 613 1513 2149\\nf 2218 1580 908\\nf 718 66 1580\\nf 2245 908 66\\nf 1580 66 908\\nf 2383 1513 264\\nf 2245 66 1513\\nf 718 264 66\\nf 1513 66 264\\nf 2525 1145 446\\nf 1164 448 1145\\nf 1564 446 448\\nf 1145 448 446\\nf 1579 1930 452\\nf 1500 447 1930\\nf 1164 452 447\\nf 1930 447 452\\nf 2361 1677 786\\nf 1564 2460 1677\\nf 1500 786 2460\\nf 1677 2460 786\\nf 1164 447 448\\nf 1500 2460 447\\nf 1564 448 2460\\nf 447 2460 448\\nf 2553 340 1048\\nf 386 243 340\\nf 324 1048 243\\nf 340 243 1048\\nf 2218 2551 378\\nf 951 1416 2551\\nf 386 378 1416\\nf 2551 1416 378\\nf 1579 2345 2061\\nf 324 2025 2345\\nf 951 2061 2025\\nf 2345 2025 2061\\nf 386 1416 243\\nf 951 2025 1416\\nf 324 243 2025\\nf 1416 2025 243\\nf 2361 786 613\\nf 1500 1779 786\\nf 2245 613 1779\\nf 786 1779 613\\nf 1579 2061 1930\\nf 951 1884 2061\\nf 1500 1930 1884\\nf 2061 1884 1930\\nf 2218 908 2551\\nf 2245 1238 908\\nf 951 2551 1238\\nf 908 1238 2551\\nf 1500 1884 1779\\nf 951 1238 1884\\nf 2245 1779 1238\\nf 1884 1238 1779\\nf 300 434 1576\\nf 1635 1268 434\\nf 2483 1576 1268\\nf 434 1268 1576\\nf 227 1838 1558\\nf 1147 1254 1838\\nf 1635 1558 1254\\nf 1838 1254 1558\\nf 1011 1711 1411\\nf 2483 1398 1711\\nf 1147 1411 1398\\nf 1711 1398 1411\\nf 1635 1254 1268\\nf 1147 1398 1254\\nf 2483 1268 1398\\nf 1254 1398 1268\\nf 1592 2378 383\\nf 1577 2289 2378\\nf 45 383 2289\\nf 2378 2289 383\\nf 2195 853 1803\\nf 1160 1521 853\\nf 1577 1803 1521\\nf 853 1521 1803\\nf 227 2131 1139\\nf 45 1180 2131\\nf 1160 1139 1180\\nf 2131 1180 1139\\nf 1577 1521 2289\\nf 1160 1180 1521\\nf 45 2289 1180\\nf 1521 1180 2289\\nf 2525 444 502\\nf 1138 283 444\\nf 1258 502 283\\nf 444 283 502\\nf 1011 846 1665\\nf 2419 1167 846\\nf 1138 1665 1167\\nf 846 1167 1665\\nf 2195 977 145\\nf 1258 2426 977\\nf 2419 145 2426\\nf 977 2426 145\\nf 1138 1167 283\\nf 2419 2426 1167\\nf 1258 283 2426\\nf 1167 2426 283\\nf 227 1139 1838\\nf 1160 2444 1139\\nf 1147 1838 2444\\nf 1139 2444 1838\\nf 2195 145 853\\nf 2419 1003 145\\nf 1160 853 1003\\nf 145 1003 853\\nf 1011 1411 846\\nf 1147 1772 1411\\nf 2419 846 1772\\nf 1411 1772 846\\nf 1160 1003 2444\\nf 2419 1772 1003\\nf 1147 2444 1772\\nf 1003 1772 2444\\nf 2553 1048 190\\nf 324 312 1048\\nf 2230 190 312\\nf 1048 312 190\\nf 1579 443 2345\\nf 2052 2558 443\\nf 324 2345 2558\\nf 443 2558 2345\\nf 2279 2540 481\\nf 2230 294 2540\\nf 2052 481 294\\nf 2540 294 481\\nf 324 2558 312\\nf 2052 294 2558\\nf 2230 312 294\\nf 2558 294 312\\nf 2525 502 1145\\nf 1258 1202 502\\nf 1164 1145 1202\\nf 502 1202 1145\\nf 2195 1267 977\\nf 680 1274 1267\\nf 1258 977 1274\\nf 1267 1274 977\\nf 1579 452 2439\\nf 1164 74 452\\nf 680 2439 74\\nf 452 74 2439\\nf 1258 1274 1202\\nf 680 74 1274\\nf 1164 1202 74\\nf 1274 74 1202\\nf 1592 2523 2378\\nf 1555 2494 2523\\nf 1577 2378 2494\\nf 2523 2494 2378\\nf 2279 1224 249\\nf 1143 198 1224\\nf 1555 249 198\\nf 1224 198 249\\nf 2195 1803 2174\\nf 1577 177 1803\\nf 1143 2174 177\\nf 1803 177 2174\\nf 1555 198 2494\\nf 1143 177 198\\nf 1577 2494 177\\nf 198 177 2494\\nf 1579 2439 443\\nf 680 794 2439\\nf 2052 443 794\\nf 2439 794 443\\nf 2195 2174 1267\\nf 1143 1126 2174\\nf 680 1267 1126\\nf 2174 1126 1267\\nf 2279 481 1224\\nf 2052 2365 481\\nf 1143 1224 2365\\nf 481 2365 1224\\nf 680 1126 794\\nf 1143 2365 1126\\nf 2052 794 2365\\nf 1126 2365 794\\nf 395 342 594\\nf 2177 1795 342\\nf 707 594 1795\\nf 342 1795 594\\nf 2335 60 757\\nf 1023 796 60\\nf 2177 757 796\\nf 60 796 757\\nf 1101 380 1039\\nf 707 1434 380\\nf 1023 1039 1434\\nf 380 1434 1039\\nf 2177 796 1795\\nf 1023 1434 796\\nf 707 1795 1434\\nf 796 1434 1795\\nf 1592 730 222\\nf 528 2200 730\\nf 170 222 2200\\nf 730 2200 222\\nf 1019 2505 159\\nf 890 1112 2505\\nf 528 159 1112\\nf 2505 1112 159\\nf 2335 645 1842\\nf 170 1030 645\\nf 890 1842 1030\\nf 645 1030 1842\\nf 528 1112 2200\\nf 890 1030 1112\\nf 170 2200 1030\\nf 1112 1030 2200\\nf 692 1141 2268\\nf 359 1636 1141\\nf 414 2268 1636\\nf 1141 1636 2268\\nf 1101 773 1296\\nf 1198 1792 773\\nf 359 1296 1792\\nf 773 1792 1296\\nf 1019 1320 2537\\nf 414 1733 1320\\nf 1198 2537 1733\\nf 1320 1733 2537\\nf 359 1792 1636\\nf 1198 1733 1792\\nf 414 1636 1733\\nf 1792 1733 1636\\nf 2335 1842 60\\nf 890 506 1842\\nf 1023 60 506\\nf 1842 506 60\\nf 1019 2537 2505\\nf 1198 158 2537\\nf 890 2505 158\\nf 2537 158 2505\\nf 1101 1039 773\\nf 1023 69 1039\\nf 1198 773 69\\nf 1039 69 773\\nf 890 158 506\\nf 1198 69 158\\nf 1023 506 69\\nf 158 69 506\\nf 300 497 434\\nf 1991 1162 497\\nf 1635 434 1162\\nf 497 1162 434\\nf 768 519 1618\\nf 1650 2160 519\\nf 1991 1618 2160\\nf 519 2160 1618\\nf 227 1558 121\\nf 1635 1648 1558\\nf 1650 121 1648\\nf 1558 1648 121\\nf 1991 2160 1162\\nf 1650 1648 2160\\nf 1635 1162 1648\\nf 2160 1648 1162\\nf 1828 745 2212\\nf 1879 2260 745\\nf 2305 2212 2260\\nf 745 2260 2212\\nf 596 1412 821\\nf 209 1661 1412\\nf 1879 821 1661\\nf 1412 1661 821\\nf 768 1120 2249\\nf 2305 288 1120\\nf 209 2249 288\\nf 1120 288 2249\\nf 1879 1661 2260\\nf 209 288 1661\\nf 2305 2260 288\\nf 1661 288 2260\\nf 1592 383 885\\nf 45 1642 383\\nf 301 885 1642\\nf 383 1642 885\\nf 227 717 2131\\nf 2136 1462 717\\nf 45 2131 1462\\nf 717 1462 2131\\nf 596 105 2508\\nf 301 1222 105\\nf 2136 2508 1222\\nf 105 1222 2508\\nf 45 1462 1642\\nf 2136 1222 1462\\nf 301 1642 1222\\nf 1462 1222 1642\\nf 768 2249 519\\nf 209 212 2249\\nf 1650 519 212\\nf 2249 212 519\\nf 596 2508 1412\\nf 2136 1961 2508\\nf 209 1412 1961\\nf 2508 1961 1412\\nf 227 121 717\\nf 1650 1694 121\\nf 2136 717 1694\\nf 121 1694 717\\nf 209 1961 212\\nf 2136 1694 1961\\nf 1650 212 1694\\nf 1961 1694 212\\nf 1777 2543 1402\\nf 2366 244 2543\\nf 2038 1402 244\\nf 2543 244 1402\\nf 1653 2434 624\\nf 813 1032 2434\\nf 2366 624 1032\\nf 2434 1032 624\\nf 762 1932 442\\nf 2038 2176 1932\\nf 813 442 2176\\nf 1932 2176 442\\nf 2366 1032 244\\nf 813 2176 1032\\nf 2038 244 2176\\nf 1032 2176 244\\nf 692 1430 226\\nf 1442 2206 1430\\nf 1706 226 2206\\nf 1430 2206 226\\nf 1024 1130 2316\\nf 556 1172 1130\\nf 1442 2316 1172\\nf 1130 1172 2316\\nf 1653 2102 1741\\nf 1706 739 2102\\nf 556 1741 739\\nf 2102 739 1741\\nf 1442 1172 2206\\nf 556 739 1172\\nf 1706 2206 739\\nf 1172 739 2206\\nf 1828 1656 2124\\nf 1049 2269 1656\\nf 992 2124 2269\\nf 1656 2269 2124\\nf 762 1702 872\\nf 2081 1319 1702\\nf 1049 872 1319\\nf 1702 1319 872\\nf 1024 2409 1570\\nf 992 2003 2409\\nf 2081 1570 2003\\nf 2409 2003 1570\\nf 1049 1319 2269\\nf 2081 2003 1319\\nf 992 2269 2003\\nf 1319 2003 2269\\nf 1653 1741 2434\\nf 556 55 1741\\nf 813 2434 55\\nf 1741 55 2434\\nf 1024 1570 1130\\nf 2081 583 1570\\nf 556 1130 583\\nf 1570 583 1130\\nf 762 442 1702\\nf 813 2016 442\\nf 2081 1702 2016\\nf 442 2016 1702\\nf 556 583 55\\nf 2081 2016 583\\nf 813 55 2016\\nf 583 2016 55\\nf 1592 885 730\\nf 301 216 885\\nf 528 730 216\\nf 885 216 730\\nf 596 716 105\\nf 1114 1330 716\\nf 301 105 1330\\nf 716 1330 105\\nf 1019 159 499\\nf 528 2255 159\\nf 1114 499 2255\\nf 159 2255 499\\nf 301 1330 216\\nf 1114 2255 1330\\nf 528 216 2255\\nf 1330 2255 216\\nf 1828 2124 745\\nf 992 1604 2124\\nf 1879 745 1604\\nf 2124 1604 745\\nf 1024 95 2409\\nf 1084 851 95\\nf 992 2409 851\\nf 95 851 2409\\nf 596 821 1107\\nf 1879 956 821\\nf 1084 1107 956\\nf 821 956 1107\\nf 992 851 1604\\nf 1084 956 851\\nf 1879 1604 956\\nf 851 956 1604\\nf 692 2268 1430\\nf 414 2397 2268\\nf 1442 1430 2397\\nf 2268 2397 1430\\nf 1019 2059 1320\\nf 958 2158 2059\\nf 414 1320 2158\\nf 2059 2158 1320\\nf 1024 2316 504\\nf 1442 2130 2316\\nf 958 504 2130\\nf 2316 2130 504\\nf 414 2158 2397\\nf 958 2130 2158\\nf 1442 2397 2130\\nf 2158 2130 2397\\nf 596 1107 716\\nf 1084 2386 1107\\nf 1114 716 2386\\nf 1107 2386 716\\nf 1024 504 95\\nf 958 571 504\\nf 1084 95 571\\nf 504 571 95\\nf 1019 499 2059\\nf 1114 1913 499\\nf 958 2059 1913\\nf 499 1913 2059\\nf 1084 571 2386\\nf 958 1913 571\\nf 1114 2386 1913\\nf 571 1913 2386\\nf 395 594 1959\\nf 707 1744 594\\nf 1870 1959 1744\\nf 594 1744 1959\\nf 1101 2173 380\\nf 553 106 2173\\nf 707 380 106\\nf 2173 106 380\\nf 526 1937 267\\nf 1870 2236 1937\\nf 553 267 2236\\nf 1937 2236 267\\nf 707 106 1744\\nf 553 2236 106\\nf 1870 1744 2236\\nf 106 2236 1744\\nf 692 691 1141\\nf 780 2329 691\\nf 359 1141 2329\\nf 691 2329 1141\\nf 1225 254 1841\\nf 210 1806 254\\nf 780 1841 1806\\nf 254 1806 1841\\nf 1101 1296 397\\nf 359 1507 1296\\nf 210 397 1507\\nf 1296 1507 397\\nf 780 1806 2329\\nf 210 1507 1806\\nf 359 2329 1507\\nf 1806 1507 2329\\nf 1757 770 287\\nf 2449 1065 770\\nf 2389 287 1065\\nf 770 1065 287\\nf 526 1470 997\\nf 2103 1658 1470\\nf 2449 997 1658\\nf 1470 1658 997\\nf 1225 1156 1735\\nf 2389 2456 1156\\nf 2103 1735 2456\\nf 1156 2456 1735\\nf 2449 1658 1065\\nf 2103 2456 1658\\nf 2389 1065 2456\\nf 1658 2456 1065\\nf 1101 397 2173\\nf 210 2557 397\\nf 553 2173 2557\\nf 397 2557 2173\\nf 1225 1735 254\\nf 2103 530 1735\\nf 210 254 530\\nf 1735 530 254\\nf 526 267 1470\\nf 553 411 267\\nf 2103 1470 411\\nf 267 411 1470\\nf 210 530 2557\\nf 2103 411 530\\nf 553 2557 411\\nf 530 411 2557\\nf 1777 753 2543\\nf 657 334 753\\nf 2366 2543 334\\nf 753 334 2543\\nf 197 151 1581\\nf 2311 1399 151\\nf 657 1581 1399\\nf 151 1399 1581\\nf 1653 624 993\\nf 2366 1853 624\\nf 2311 993 1853\\nf 624 1853 993\\nf 657 1399 334\\nf 2311 1853 1399\\nf 2366 334 1853\\nf 1399 1853 334\\nf 744 2080 938\\nf 731 516 2080\\nf 524 938 516\\nf 2080 516 938\\nf 31 1962 1194\\nf 255 1935 1962\\nf 731 1194 1935\\nf 1962 1935 1194\\nf 197 79 1387\\nf 524 847 79\\nf 255 1387 847\\nf 79 847 1387\\nf 731 1935 516\\nf 255 847 1935\\nf 524 516 847\\nf 1935 847 516\\nf 692 226 889\\nf 1706 2170 226\\nf 1063 889 2170\\nf 226 2170 889\\nf 1653 998 2102\\nf 1443 1528 998\\nf 1706 2102 1528\\nf 998 1528 2102\\nf 31 2527 1301\\nf 1063 1862 2527\\nf 1443 1301 1862\\nf 2527 1862 1301\\nf 1706 1528 2170\\nf 1443 1862 1528\\nf 1063 2170 1862\\nf 1528 1862 2170\\nf 197 1387 151\\nf 255 1077 1387\\nf 2311 151 1077\\nf 1387 1077 151\\nf 31 1301 1962\\nf 1443 455 1301\\nf 255 1962 455\\nf 1301 455 1962\\nf 1653 993 998\\nf 2311 18 993\\nf 1443 998 18\\nf 993 18 998\\nf 255 455 1077\\nf 1443 18 455\\nf 2311 1077 18\\nf 455 18 1077\\nf 2282 321 1\\nf 71 205 321\\nf 876 1 205\\nf 321 205 1\\nf 2358 77 399\\nf 1200 1874 77\\nf 71 399 1874\\nf 77 1874 399\\nf 1099 2126 949\\nf 876 1815 2126\\nf 1200 949 1815\\nf 2126 1815 949\\nf 71 1874 205\\nf 1200 1815 1874\\nf 876 205 1815\\nf 1874 1815 205\\nf 1757 1881 854\\nf 2235 215 1881\\nf 5 854 215\\nf 1881 215 854\\nf 2231 1726 622\\nf 420 2299 1726\\nf 2235 622 2299\\nf 1726 2299 622\\nf 2358 1931 1679\\nf 5 303 1931\\nf 420 1679 303\\nf 1931 303 1679\\nf 2235 2299 215\\nf 420 303 2299\\nf 5 215 303\\nf 2299 303 215\\nf 744 2556 1173\\nf 926 705 2556\\nf 2352 1173 705\\nf 2556 705 1173\\nf 1099 298 849\\nf 891 1532 298\\nf 926 849 1532\\nf 298 1532 849\\nf 2231 1005 1270\\nf 2352 2258 1005\\nf 891 1270 2258\\nf 1005 2258 1270\\nf 926 1532 705\\nf 891 2258 1532\\nf 2352 705 2258\\nf 1532 2258 705\\nf 2358 1679 77\\nf 420 1300 1679\\nf 1200 77 1300\\nf 1679 1300 77\\nf 2231 1270 1726\\nf 891 346 1270\\nf 420 1726 346\\nf 1270 346 1726\\nf 1099 949 298\\nf 1200 1457 949\\nf 891 298 1457\\nf 949 1457 298\\nf 420 346 1300\\nf 891 1457 346\\nf 1200 1300 1457\\nf 346 1457 1300\\nf 692 889 691\\nf 1063 1885 889\\nf 780 691 1885\\nf 889 1885 691\\nf 31 2107 2527\\nf 1486 912 2107\\nf 1063 2527 912\\nf 2107 912 2527\\nf 1225 1841 352\\nf 780 1764 1841\\nf 1486 352 1764\\nf 1841 1764 352\\nf 1063 912 1885\\nf 1486 1764 912\\nf 780 1885 1764\\nf 912 1764 1885\\nf 744 1173 2080\\nf 2352 896 1173\\nf 731 2080 896\\nf 1173 896 2080\\nf 2231 1239 1005\\nf 999 2133 1239\\nf 2352 1005 2133\\nf 1239 2133 1005\\nf 31 1194 477\\nf 731 357 1194\\nf 999 477 357\\nf 1194 357 477\\nf 2352 2133 896\\nf 999 357 2133\\nf 731 896 357\\nf 2133 357 896\\nf 1757 287 1881\\nf 2389 1585 287\\nf 2235 1881 1585\\nf 287 1585 1881\\nf 1225 1669 1156\\nf 2286 835 1669\\nf 2389 1156 835\\nf 1669 835 1156\\nf 2231 622 1080\\nf 2235 572 622\\nf 2286 1080 572\\nf 622 572 1080\\nf 2389 835 1585\\nf 2286 572 835\\nf 2235 1585 572\\nf 835 572 1585\\nf 31 477 2107\\nf 999 263 477\\nf 1486 2107 263\\nf 477 263 2107\\nf 2231 1080 1239\\nf 2286 1197 1080\\nf 999 1239 1197\\nf 1080 1197 1239\\nf 1225 352 1669\\nf 1486 761 352\\nf 2286 1669 761\\nf 352 761 1669\\nf 999 1197 263\\nf 2286 761 1197\\nf 1486 263 761\\nf 1197 761 263\\nf 395 1959 2320\\nf 1870 1369 1959\\nf 2087 2320 1369\\nf 1959 1369 2320\\nf 526 1692 1937\\nf 165 918 1692\\nf 1870 1937 918\\nf 1692 918 1937\\nf 1729 2480 1695\\nf 2087 23 2480\\nf 165 1695 23\\nf 2480 23 1695\\nf 1870 918 1369\\nf 165 23 918\\nf 2087 1369 23\\nf 918 23 1369\\nf 1757 2073 770\\nf 1317 1535 2073\\nf 2449 770 1535\\nf 2073 1535 770\\nf 1177 679 1951\\nf 2496 1384 679\\nf 1317 1951 1384\\nf 679 1384 1951\\nf 526 997 2155\\nf 2449 2342 997\\nf 2496 2155 2342\\nf 997 2342 2155\\nf 1317 1384 1535\\nf 2496 2342 1384\\nf 2449 1535 2342\\nf 1384 2342 1535\\nf 256 2455 1992\\nf 981 1511 2455\\nf 2062 1992 1511\\nf 2455 1511 1992\\nf 1729 1957 1452\\nf 160 2288 1957\\nf 981 1452 2288\\nf 1957 2288 1452\\nf 1177 1210 438\\nf 2062 1484 1210\\nf 160 438 1484\\nf 1210 1484 438\\nf 981 2288 1511\\nf 160 1484 2288\\nf 2062 1511 1484\\nf 2288 1484 1511\\nf 526 2155 1692\\nf 2496 1283 2155\\nf 165 1692 1283\\nf 2155 1283 1692\\nf 1177 438 679\\nf 160 2528 438\\nf 2496 679 2528\\nf 438 2528 679\\nf 1729 1695 1957\\nf 165 1928 1695\\nf 160 1957 1928\\nf 1695 1928 1957\\nf 2496 2528 1283\\nf 160 1928 2528\\nf 165 1283 1928\\nf 2528 1928 1283\\nf 2282 319 321\\nf 683 1896 319\\nf 71 321 1896\\nf 319 1896 321\\nf 213 586 1614\\nf 449 994 586\\nf 683 1614 994\\nf 586 994 1614\\nf 2358 399 1725\\nf 71 1681 399\\nf 449 1725 1681\\nf 399 1681 1725\\nf 683 994 1896\\nf 449 1681 994\\nf 71 1896 1681\\nf 994 1681 1896\\nf 880 732 1700\\nf 1363 2354 732\\nf 415 1700 2354\\nf 732 2354 1700\\nf 598 605 2303\\nf 2355 2351 605\\nf 1363 2303 2351\\nf 605 2351 2303\\nf 213 1718 305\\nf 415 1527 1718\\nf 2355 305 1527\\nf 1718 1527 305\\nf 1363 2351 2354\\nf 2355 1527 2351\\nf 415 2354 1527\\nf 2351 1527 2354\\nf 1757 854 464\\nf 5 935 854\\nf 1115 464 935\\nf 854 935 464\\nf 2358 2257 1931\\nf 1240 2084 2257\\nf 5 1931 2084\\nf 2257 2084 1931\\nf 598 1936 897\\nf 1115 1353 1936\\nf 1240 897 1353\\nf 1936 1353 897\\nf 5 2084 935\\nf 1240 1353 2084\\nf 1115 935 1353\\nf 2084 1353 935\\nf 213 305 586\\nf 2355 715 305\\nf 449 586 715\\nf 305 715 586\\nf 598 897 605\\nf 1240 2473 897\\nf 2355 605 2473\\nf 897 2473 605\\nf 2358 1725 2257\\nf 449 2060 1725\\nf 1240 2257 2060\\nf 1725 2060 2257\\nf 2355 2473 715\\nf 1240 2060 2473\\nf 449 715 2060\\nf 2473 2060 715\\nf 1831 2183 1571\\nf 253 1469 2183\\nf 1425 1571 1469\\nf 2183 1469 1571\\nf 924 416 2051\\nf 1666 224 416\\nf 253 2051 224\\nf 416 224 2051\\nf 610 2058 1917\\nf 1425 2138 2058\\nf 1666 1917 2138\\nf 2058 2138 1917\\nf 253 224 1469\\nf 1666 2138 224\\nf 1425 1469 2138\\nf 224 2138 1469\\nf 256 2020 1904\\nf 2555 654 2020\\nf 2302 1904 654\\nf 2020 654 1904\\nf 1475 2086 2411\\nf 1876 2187 2086\\nf 2555 2411 2187\\nf 2086 2187 2411\\nf 924 1866 157\\nf 2302 1349 1866\\nf 1876 157 1349\\nf 1866 1349 157\\nf 2555 2187 654\\nf 1876 1349 2187\\nf 2302 654 1349\\nf 2187 1349 654\\nf 880 1153 1069\\nf 1517 1845 1153\\nf 2022 1069 1845\\nf 1153 1845 1069\\nf 610 701 1447\\nf 1667 944 701\\nf 1517 1447 944\\nf 701 944 1447\\nf 1475 259 1830\\nf 2022 65 259\\nf 1667 1830 65\\nf 259 65 1830\\nf 1517 944 1845\\nf 1667 65 944\\nf 2022 1845 65\\nf 944 65 1845\\nf 924 157 416\\nf 1876 2168 157\\nf 1666 416 2168\\nf 157 2168 416\\nf 1475 1830 2086\\nf 1667 1235 1830\\nf 1876 2086 1235\\nf 1830 1235 2086\\nf 610 1917 701\\nf 1666 1949 1917\\nf 1667 701 1949\\nf 1917 1949 701\\nf 1876 1235 2168\\nf 1667 1949 1235\\nf 1666 2168 1949\\nf 1235 1949 2168\\nf 1757 464 2073\\nf 1115 2381 464\\nf 1317 2073 2381\\nf 464 2381 2073\\nf 598 1823 1936\\nf 1256 1017 1823\\nf 1115 1936 1017\\nf 1823 1017 1936\\nf 1177 1951 2238\\nf 1317 80 1951\\nf 1256 2238 80\\nf 1951 80 2238\\nf 1115 1017 2381\\nf 1256 80 1017\\nf 1317 2381 80\\nf 1017 80 2381\\nf 880 1069 732\\nf 2022 1707 1069\\nf 1363 732 1707\\nf 1069 1707 732\\nf 1475 429 259\\nf 601 929 429\\nf 2022 259 929\\nf 429 929 259\\nf 598 2303 1362\\nf 1363 589 2303\\nf 601 1362 589\\nf 2303 589 1362\\nf 2022 929 1707\\nf 601 589 929\\nf 1363 1707 589\\nf 929 589 1707\\nf 256 1992 2020\\nf 2062 188 1992\\nf 2555 2020 188\\nf 1992 188 2020\\nf 1177 595 1210\\nf 278 173 595\\nf 2062 1210 173\\nf 595 173 1210\\nf 1475 2411 634\\nf 2555 175 2411\\nf 278 634 175\\nf 2411 175 634\\nf 2062 173 188\\nf 278 175 173\\nf 2555 188 175\\nf 173 175 188\\nf 598 1362 1823\\nf 601 1249 1362\\nf 1256 1823 1249\\nf 1362 1249 1823\\nf 1475 634 429\\nf 278 542 634\\nf 601 429 542\\nf 634 542 429\\nf 1177 2238 595\\nf 1256 921 2238\\nf 278 595 921\\nf 2238 921 595\\nf 601 542 1249\\nf 278 921 542\\nf 1256 1249 921\\nf 542 921 1249\\nf 395 2320 1383\\nf 2087 1322 2320\\nf 2501 1383 1322\\nf 2320 1322 1383\\nf 1729 119 2480\\nf 1946 2129 119\\nf 2087 2480 2129\\nf 119 2129 2480\\nf 1887 1357 7\\nf 2501 2339 1357\\nf 1946 7 2339\\nf 1357 2339 7\\nf 2087 2129 1322\\nf 1946 2339 2129\\nf 2501 1322 2339\\nf 2129 2339 1322\\nf 256 1671 2455\\nf 711 2507 1671\\nf 981 2455 2507\\nf 1671 2507 2455\\nf 400 1921 2057\\nf 1911 2503 1921\\nf 711 2057 2503\\nf 1921 2503 2057\\nf 1729 1452 2400\\nf 981 1236 1452\\nf 1911 2400 1236\\nf 1452 1236 2400\\nf 711 2503 2507\\nf 1911 1236 2503\\nf 981 2507 1236\\nf 2503 1236 2507\\nf 2553 1022 1282\\nf 1132 886 1022\\nf 2301 1282 886\\nf 1022 886 1282\\nf 1887 668 273\\nf 1140 232 668\\nf 1132 273 232\\nf 668 232 273\\nf 400 1926 1721\\nf 2301 93 1926\\nf 1140 1721 93\\nf 1926 93 1721\\nf 1132 232 886\\nf 1140 93 232\\nf 2301 886 93\\nf 232 93 886\\nf 1729 2400 119\\nf 1911 2450 2400\\nf 1946 119 2450\\nf 2400 2450 119\\nf 400 1721 1921\\nf 1140 129 1721\\nf 1911 1921 129\\nf 1721 129 1921\\nf 1887 7 668\\nf 1946 782 7\\nf 1140 668 782\\nf 7 782 668\\nf 1911 129 2450\\nf 1140 782 129\\nf 1946 2450 782\\nf 129 782 2450\\nf 1831 2044 2183\\nf 1996 43 2044\\nf 253 2183 43\\nf 2044 43 2183\\nf 1111 767 16\\nf 1872 100 767\\nf 1996 16 100\\nf 767 100 16\\nf 924 2051 2491\\nf 253 1480 2051\\nf 1872 2491 1480\\nf 2051 1480 2491\\nf 1996 100 43\\nf 1872 1480 100\\nf 253 43 1480\\nf 100 1480 43\\nf 1203 1923 61\\nf 199 2153 1923\\nf 2150 61 2153\\nf 1923 2153 61\\nf 318 1280 10\\nf 1188 270 1280\\nf 199 10 270\\nf 1280 270 10\\nf 1111 2054 6\\nf 2150 1572 2054\\nf 1188 6 1572\\nf 2054 1572 6\\nf 199 270 2153\\nf 1188 1572 270\\nf 2150 2153 1572\\nf 270 1572 2153\\nf 256 1904 1165\\nf 2302 2065 1904\\nf 1547 1165 2065\\nf 1904 2065 1165\\nf 924 2529 1866\\nf 2027 2379 2529\\nf 2302 1866 2379\\nf 2529 2379 1866\\nf 318 1459 281\\nf 1547 82 1459\\nf 2027 281 82\\nf 1459 82 281\\nf 2302 2379 2065\\nf 2027 82 2379\\nf 1547 2065 82\\nf 2379 82 2065\\nf 1111 6 767\\nf 1188 955 6\\nf 1872 767 955\\nf 6 955 767\\nf 318 281 1280\\nf 2027 1025 281\\nf 1188 1280 1025\\nf 281 1025 1280\\nf 924 2491 2529\\nf 1872 1066 2491\\nf 2027 2529 1066\\nf 2491 1066 2529\\nf 1188 1025 955\\nf 2027 1066 1025\\nf 1872 955 1066\\nf 1025 1066 955\\nf 887 76 1424\\nf 718 2201 76\\nf 2477 1424 2201\\nf 76 2201 1424\\nf 2218 2418 1580\\nf 740 83 2418\\nf 718 1580 83\\nf 2418 83 1580\\nf 806 2362 181\\nf 2477 1899 2362\\nf 740 181 1899\\nf 2362 1899 181\\nf 718 83 2201\\nf 740 1899 83\\nf 2477 2201 1899\\nf 83 1899 2201\\nf 2553 46 340\\nf 579 1284 46\\nf 386 340 1284\\nf 46 1284 340\\nf 1589 15 936\\nf 407 427 15\\nf 579 936 427\\nf 15 427 936\\nf 2218 378 1117\\nf 386 2079 378\\nf 407 1117 2079\\nf 378 2079 1117\\nf 579 427 1284\\nf 407 2079 427\\nf 386 1284 2079\\nf 427 2079 1284\\nf 1203 1413 769\\nf 1925 1450 1413\\nf 1791 769 1450\\nf 1413 1450 769\\nf 806 1292 1909\\nf 229 1531 1292\\nf 1925 1909 1531\\nf 1292 1531 1909\\nf 1589 2464 534\\nf 1791 2182 2464\\nf 229 534 2182\\nf 2464 2182 534\\nf 1925 1531 1450\\nf 229 2182 1531\\nf 1791 1450 2182\\nf 1531 2182 1450\\nf 2218 1117 2418\\nf 407 1473 1117\\nf 740 2418 1473\\nf 1117 1473 2418\\nf 1589 534 15\\nf 229 2064 534\\nf 407 15 2064\\nf 534 2064 15\\nf 806 181 1292\\nf 740 1248 181\\nf 229 1292 1248\\nf 181 1248 1292\\nf 407 2064 1473\\nf 229 1248 2064\\nf 740 1473 1248\\nf 2064 1248 1473\\nf 256 1165 1671\\nf 1547 1551 1165\\nf 711 1671 1551\\nf 1165 1551 1671\\nf 318 2318 1459\\nf 1696 578 2318\\nf 1547 1459 578\\nf 2318 578 1459\\nf 400 2057 1496\\nf 711 995 2057\\nf 1696 1496 995\\nf 2057 995 1496\\nf 1547 578 1551\\nf 1696 995 578\\nf 711 1551 995\\nf 578 995 1551\\nf 1203 769 1923\\nf 1791 53 769\\nf 199 1923 53\\nf 769 53 1923\\nf 1589 478 2464\\nf 326 1611 478\\nf 1791 2464 1611\\nf 478 1611 2464\\nf 318 10 193\\nf 199 1211 10\\nf 326 193 1211\\nf 10 1211 193\\nf 1791 1611 53\\nf 326 1211 1611\\nf 199 53 1211\\nf 1611 1211 53\\nf 2553 1282 46\\nf 2301 484 1282\\nf 579 46 484\\nf 1282 484 46\\nf 400 1783 1926\\nf 869 2244 1783\\nf 2301 1926 2244\\nf 1783 2244 1926\\nf 1589 936 1455\\nf 579 2047 936\\nf 869 1455 2047\\nf 936 2047 1455\\nf 2301 2244 484\\nf 869 2047 2244\\nf 579 484 2047\\nf 2244 2047 484\\nf 318 193 2318\\nf 326 1965 193\\nf 1696 2318 1965\\nf 193 1965 2318\\nf 1589 1455 478\\nf 869 1676 1455\\nf 326 478 1676\\nf 1455 1676 478\\nf 400 1496 1783\\nf 1696 1888 1496\\nf 869 1783 1888\\nf 1496 1888 1783\\nf 326 1676 1965\\nf 869 1888 1676\\nf 1696 1965 1888\\nf 1676 1888 1965\\nf 1777 1402 566\\nf 2038 643 1402\\nf 309 566 643\\nf 1402 643 566\\nf 762 2445 1932\\nf 1993 659 2445\\nf 2038 1932 659\\nf 2445 659 1932\\nf 487 2304 299\\nf 309 2562 2304\\nf 1993 299 2562\\nf 2304 2562 299\\nf 2038 659 643\\nf 1993 2562 659\\nf 309 643 2562\\nf 659 2562 643\\nf 1828 791 1656\\nf 1329 208 791\\nf 1049 1656 208\\nf 791 208 1656\\nf 2185 1997 1826\\nf 1465 1436 1997\\nf 1329 1826 1436\\nf 1997 1436 1826\\nf 762 872 1245\\nf 1049 1067 872\\nf 1465 1245 1067\\nf 872 1067 1245\\nf 1329 1436 208\\nf 1465 1067 1436\\nf 1049 208 1067\\nf 1436 1067 208\\nf 1191 1768 1273\\nf 130 2146 1768\\nf 341 1273 2146\\nf 1768 2146 1273\\nf 487 2322 117\\nf 2167 311 2322\\nf 130 117 311\\nf 2322 311 117\\nf 2185 972 1219\\nf 341 980 972\\nf 2167 1219 980\\nf 972 980 1219\\nf 130 311 2146\\nf 2167 980 311\\nf 341 2146 980\\nf 311 980 2146\\nf 762 1245 2445\\nf 1465 422 1245\\nf 1993 2445 422\\nf 1245 422 2445\\nf 2185 1219 1997\\nf 2167 792 1219\\nf 1465 1997 792\\nf 1219 792 1997\\nf 487 299 2322\\nf 1993 1379 299\\nf 2167 2322 1379\\nf 299 1379 2322\\nf 1465 792 422\\nf 2167 1379 792\\nf 1993 422 1379\\nf 792 1379 422\\nf 300 1652 497\\nf 2427 3 1652\\nf 1991 497 3\\nf 1652 3 497\\nf 1594 1537 619\\nf 2382 1279 1537\\nf 2427 619 1279\\nf 1537 1279 619\\nf 768 1618 1891\\nf 1991 960 1618\\nf 2382 1891 960\\nf 1618 960 1891\\nf 2427 1279 3\\nf 2382 960 1279\\nf 1991 3 960\\nf 1279 960 3\\nf 2402 1187 1569\\nf 1709 1627 1187\\nf 1209 1569 1627\\nf 1187 1627 1569\\nf 1609 1461 2435\\nf 2334 966 1461\\nf 1709 2435 966\\nf 1461 966 2435\\nf 1594 959 456\\nf 1209 726 959\\nf 2334 456 726\\nf 959 726 456\\nf 1709 966 1627\\nf 2334 726 966\\nf 1209 1627 726\\nf 966 726 1627\\nf 1828 2212 26\\nf 2305 1785 2212\\nf 1298 26 1785\\nf 2212 1785 26\\nf 768 1717 1120\\nf 441 1944 1717\\nf 2305 1120 1944\\nf 1717 1944 1120\\nf 1609 819 392\\nf 1298 1348 819\\nf 441 392 1348\\nf 819 1348 392\\nf 2305 1944 1785\\nf 441 1348 1944\\nf 1298 1785 1348\\nf 1944 1348 1785\\nf 1594 456 1537\\nf 2334 2055 456\\nf 2382 1537 2055\\nf 456 2055 1537\\nf 1609 392 1461\\nf 441 284 392\\nf 2334 1461 284\\nf 392 284 1461\\nf 768 1891 1717\\nf 2382 450 1891\\nf 441 1717 450\\nf 1891 450 1717\\nf 2334 284 2055\\nf 441 450 284\\nf 2382 2055 450\\nf 284 450 2055\\nf 1769 1124 1471\\nf 1553 1600 1124\\nf 2292 1471 1600\\nf 1124 1600 1471\\nf 2014 1599 1105\\nf 317 1567 1599\\nf 1553 1105 1567\\nf 1599 1567 1105\\nf 384 1929 1181\\nf 2292 1955 1929\\nf 317 1181 1955\\nf 1929 1955 1181\\nf 1553 1567 1600\\nf 317 1955 1567\\nf 2292 1600 1955\\nf 1567 1955 1600\\nf 1191 832 1790\\nf 1090 410 832\\nf 2169 1790 410\\nf 832 410 1790\\nf 1421 1771 1251\\nf 469 1731 1771\\nf 1090 1251 1731\\nf 1771 1731 1251\\nf 2014 1157 656\\nf 2169 11 1157\\nf 469 656 11\\nf 1157 11 656\\nf 1090 1731 410\\nf 469 11 1731\\nf 2169 410 11\\nf 1731 11 410\\nf 2402 2367 2414\\nf 700 884 2367\\nf 1377 2414 884\\nf 2367 884 2414\\nf 384 1085 1816\\nf 666 13 1085\\nf 700 1816 13\\nf 1085 13 1816\\nf 1421 75 1364\\nf 1377 1902 75\\nf 666 1364 1902\\nf 75 1902 1364\\nf 700 13 884\\nf 666 1902 13\\nf 1377 884 1902\\nf 13 1902 884\\nf 2014 656 1599\\nf 469 1603 656\\nf 317 1599 1603\\nf 656 1603 1599\\nf 1421 1364 1771\\nf 666 1306 1364\\nf 469 1771 1306\\nf 1364 1306 1771\\nf 384 1181 1085\\nf 317 742 1181\\nf 666 1085 742\\nf 1181 742 1085\\nf 469 1306 1603\\nf 666 742 1306\\nf 317 1603 742\\nf 1306 742 1603\\nf 1828 26 791\\nf 1298 1047 26\\nf 1329 791 1047\\nf 26 1047 791\\nf 1609 201 819\\nf 2210 21 201\\nf 1298 819 21\\nf 201 21 819\\nf 2185 1826 1110\\nf 1329 64 1826\\nf 2210 1110 64\\nf 1826 64 1110\\nf 1298 21 1047\\nf 2210 64 21\\nf 1329 1047 64\\nf 21 64 1047\\nf 2402 2414 1187\\nf 1377 1391 2414\\nf 1709 1187 1391\\nf 2414 1391 1187\\nf 1421 810 75\\nf 1640 1044 810\\nf 1377 75 1044\\nf 810 1044 75\\nf 1609 2435 2012\\nf 1709 1907 2435\\nf 1640 2012 1907\\nf 2435 1907 2012\\nf 1377 1044 1391\\nf 1640 1907 1044\\nf 1709 1391 1907\\nf 1044 1907 1391\\nf 1191 1273 832\\nf 341 1952 1273\\nf 1090 832 1952\\nf 1273 1952 832\\nf 2185 1103 972\\nf 804 492 1103\\nf 341 972 492\\nf 1103 492 972\\nf 1421 1251 547\\nf 1090 2135 1251\\nf 804 547 2135\\nf 1251 2135 547\\nf 341 492 1952\\nf 804 2135 492\\nf 1090 1952 2135\\nf 492 2135 1952\\nf 1609 2012 201\\nf 1640 669 2012\\nf 2210 201 669\\nf 2012 669 201\\nf 1421 547 810\\nf 804 1299 547\\nf 1640 810 1299\\nf 547 1299 810\\nf 2185 1110 1103\\nf 2210 1366 1110\\nf 804 1103 1366\\nf 1110 1366 1103\\nf 1640 1299 669\\nf 804 1366 1299\\nf 2210 669 1366\\nf 1299 1366 669\\nf 300 1576 1312\\nf 2483 1356 1576\\nf 527 1312 1356\\nf 1576 1356 1312\\nf 1011 1519 1711\\nf 1624 772 1519\\nf 2483 1711 772\\nf 1519 772 1711\\nf 2547 756 70\\nf 527 275 756\\nf 1624 70 275\\nf 756 275 70\\nf 2483 772 1356\\nf 1624 275 772\\nf 527 1356 275\\nf 772 275 1356\\nf 2525 834 444\\nf 991 115 834\\nf 1138 444 115\\nf 834 115 444\\nf 2467 1818 722\\nf 1587 1228 1818\\nf 991 722 1228\\nf 1818 1228 722\\nf 1011 1665 2433\\nf 1138 1125 1665\\nf 1587 2433 1125\\nf 1665 1125 2433\\nf 991 1228 115\\nf 1587 1125 1228\\nf 1138 115 1125\\nf 1228 1125 115\\nf 818 1385 44\\nf 895 1827 1385\\nf 1146 44 1827\\nf 1385 1827 44\\nf 2547 2217 1708\\nf 514 607 2217\\nf 895 1708 607\\nf 2217 607 1708\\nf 2467 92 529\\nf 1146 1628 92\\nf 514 529 1628\\nf 92 1628 529\\nf 895 607 1827\\nf 514 1628 607\\nf 1146 1827 1628\\nf 607 1628 1827\\nf 1011 2433 1519\\nf 1587 2159 2433\\nf 1624 1519 2159\\nf 2433 2159 1519\\nf 2467 529 1818\\nf 514 1565 529\\nf 1587 1818 1565\\nf 529 1565 1818\\nf 2547 70 2217\\nf 1624 1625 70\\nf 514 2217 1625\\nf 70 1625 2217\\nf 1587 1565 2159\\nf 514 1625 1565\\nf 1624 2159 1625\\nf 1565 1625 2159\\nf 887 948 2447\\nf 1302 507 948\\nf 2383 2447 507\\nf 948 507 2447\\nf 2521 315 2376\\nf 1441 432 315\\nf 1302 2376 432\\nf 315 432 2376\\nf 2361 2149 2223\\nf 2383 947 2149\\nf 1441 2223 947\\nf 2149 947 2223\\nf 1302 432 507\\nf 1441 947 432\\nf 2383 507 947\\nf 432 947 507\\nf 2478 1903 1382\\nf 1108 2123 1903\\nf 485 1382 2123\\nf 1903 2123 1382\\nf 67 544 1307\\nf 953 623 544\\nf 1108 1307 623\\nf 544 623 1307\\nf 2521 2297 2273\\nf 485 650 2297\\nf 953 2273 650\\nf 2297 650 2273\\nf 1108 623 2123\\nf 953 650 623\\nf 485 2123 650\\nf 623 650 2123\\nf 2525 446 1645\\nf 1564 454 446\\nf 466 1645 454\\nf 446 454 1645\\nf 2361 1748 1677\\nf 1166 515 1748\\nf 1564 1677 515\\nf 1748 515 1677\\nf 67 247 2371\\nf 466 2114 247\\nf 1166 2371 2114\\nf 247 2114 2371\\nf 1564 515 454\\nf 1166 2114 515\\nf 466 454 2114\\nf 515 2114 454\\nf 2521 2273 315\\nf 953 2179 2273\\nf 1441 315 2179\\nf 2273 2179 315\\nf 67 2371 544\\nf 1166 1770 2371\\nf 953 544 1770\\nf 2371 1770 544\\nf 2361 2223 1748\\nf 1441 1623 2223\\nf 1166 1748 1623\\nf 2223 1623 1748\\nf 953 1770 2179\\nf 1166 1623 1770\\nf 1441 2179 1623\\nf 1770 1623 2179\\nf 906 1867 428\\nf 1428 155 1867\\nf 1339 428 155\\nf 1867 155 428\\nf 2408 2002 152\\nf 1739 2538 2002\\nf 1428 152 2538\\nf 2002 2538 152\\nf 1168 391 87\\nf 1339 1501 391\\nf 1739 87 1501\\nf 391 1501 87\\nf 1428 2538 155\\nf 1739 1501 2538\\nf 1339 155 1501\\nf 2538 1501 155\\nf 818 1396 2396\\nf 665 1311 1396\\nf 323 2396 1311\\nf 1396 1311 2396\\nf 521 1375 1037\\nf 779 350 1375\\nf 665 1037 350\\nf 1375 350 1037\\nf 2408 1522 1878\\nf 323 240 1522\\nf 779 1878 240\\nf 1522 240 1878\\nf 665 350 1311\\nf 779 240 350\\nf 323 1311 240\\nf 350 240 1311\\nf 2478 2385 104\\nf 12 1004 2385\\nf 1649 104 1004\\nf 2385 1004 104\\nf 1168 1344 1472\\nf 1408 1207 1344\\nf 12 1472 1207\\nf 1344 1207 1472\\nf 521 1970 2520\\nf 1649 1869 1970\\nf 1408 2520 1869\\nf 1970 1869 2520\\nf 12 1207 1004\\nf 1408 1869 1207\\nf 1649 1004 1869\\nf 1207 1869 1004\\nf 2408 1878 2002\\nf 779 1429 1878\\nf 1739 2002 1429\\nf 1878 1429 2002\\nf 521 2520 1375\\nf 1408 822 2520\\nf 779 1375 822\\nf 2520 822 1375\\nf 1168 87 1344\\nf 1739 1060 87\\nf 1408 1344 1060\\nf 87 1060 1344\\nf 779 822 1429\\nf 1408 1060 822\\nf 1739 1429 1060\\nf 822 1060 1429\\nf 2525 1645 834\\nf 466 2323 1645\\nf 991 834 2323\\nf 1645 2323 834\\nf 67 2021 247\\nf 1185 1371 2021\\nf 466 247 1371\\nf 2021 1371 247\\nf 2467 722 2306\\nf 991 375 722\\nf 1185 2306 375\\nf 722 375 2306\\nf 466 1371 2323\\nf 1185 375 1371\\nf 991 2323 375\\nf 1371 375 2323\\nf 2478 104 1903\\nf 1649 580 104\\nf 1108 1903 580\\nf 104 580 1903\\nf 521 1969 1970\\nf 2550 460 1969\\nf 1649 1970 460\\nf 1969 460 1970\\nf 67 1307 2125\\nf 1108 1927 1307\\nf 2550 2125 1927\\nf 1307 1927 2125\\nf 1649 460 580\\nf 2550 1927 460\\nf 1108 580 1927\\nf 460 1927 580\\nf 818 44 1396\\nf 1146 1746 44\\nf 665 1396 1746\\nf 44 1746 1396\\nf 2467 2280 92\\nf 241 1852 2280\\nf 1146 92 1852\\nf 2280 1852 92\\nf 521 1037 2390\\nf 665 258 1037\\nf 241 2390 258\\nf 1037 258 2390\\nf 1146 1852 1746\\nf 241 258 1852\\nf 665 1746 258\\nf 1852 258 1746\\nf 67 2125 2021\\nf 2550 911 2125\\nf 1185 2021 911\\nf 2125 911 2021\\nf 521 2390 1969\\nf 241 1033 2390\\nf 2550 1969 1033\\nf 2390 1033 1969\\nf 2467 2306 2280\\nf 1185 482 2306\\nf 241 2280 482\\nf 2306 482 2280\\nf 2550 1033 911\\nf 241 482 1033\\nf 1185 911 482\\nf 1033 482 911\\nf 887 1424 1247\\nf 2477 306 1424\\nf 1133 1247 306\\nf 1424 306 1247\\nf 806 445 2362\\nf 1943 2232 445\\nf 2477 2362 2232\\nf 445 2232 2362\\nf 1291 1753 290\\nf 1133 939 1753\\nf 1943 290 939\\nf 1753 939 290\\nf 2477 2232 306\\nf 1943 939 2232\\nf 1133 306 939\\nf 2232 939 306\\nf 1203 983 1413\\nf 1608 354 983\\nf 1925 1413 354\\nf 983 354 1413\\nf 1668 741 467\\nf 231 831 741\\nf 1608 467 831\\nf 741 831 467\\nf 806 1909 153\\nf 1925 1068 1909\\nf 231 153 1068\\nf 1909 1068 153\\nf 1608 831 354\\nf 231 1068 831\\nf 1925 354 1068\\nf 831 1068 354\\nf 1510 382 91\\nf 49 234 382\\nf 697 91 234\\nf 382 234 91\\nf 1291 2205 662\\nf 2484 2533 2205\\nf 49 662 2533\\nf 2205 2533 662\\nf 1668 1976 2252\\nf 697 2237 1976\\nf 2484 2252 2237\\nf 1976 2237 2252\\nf 49 2533 234\\nf 2484 2237 2533\\nf 697 234 2237\\nf 2533 2237 234\\nf 806 153 445\\nf 231 1489 153\\nf 1943 445 1489\\nf 153 1489 445\\nf 1668 2252 741\\nf 2484 2214 2252\\nf 231 741 2214\\nf 2252 2214 741\\nf 1291 290 2205\\nf 1943 1549 290\\nf 2484 2205 1549\\nf 290 1549 2205\\nf 231 2214 1489\\nf 2484 1549 2214\\nf 1943 1489 1549\\nf 2214 1549 1489\\nf 1831 2423 2044\\nf 930 2099 2423\\nf 1996 2044 2099\\nf 2423 2099 2044\\nf 952 2514 1053\\nf 721 1809 2514\\nf 930 1053 1809\\nf 2514 1809 1053\\nf 1111 16 1395\\nf 1996 2253 16\\nf 721 1395 2253\\nf 16 2253 1395\\nf 930 1809 2099\\nf 721 2253 1809\\nf 1996 2099 2253\\nf 1809 2253 2099\\nf 116 1229 371\\nf 494 1897 1229\\nf 475 371 1897\\nf 1229 1897 371\\nf 40 274 1602\\nf 1689 2410 274\\nf 494 1602 2410\\nf 274 2410 1602\\nf 952 1728 894\\nf 475 426 1728\\nf 1689 894 426\\nf 1728 426 894\\nf 494 2410 1897\\nf 1689 426 2410\\nf 475 1897 426\\nf 2410 426 1897\\nf 1203 61 628\\nf 2150 698 61\\nf 797 628 698\\nf 61 698 628\\nf 1111 347 2054\\nf 1563 1918 347\\nf 2150 2054 1918\\nf 347 1918 2054\\nf 40 453 1095\\nf 797 588 453\\nf 1563 1095 588\\nf 453 588 1095\\nf 2150 1918 698\\nf 1563 588 1918\\nf 797 698 588\\nf 1918 588 698\\nf 952 894 2514\\nf 1689 2154 894\\nf 721 2514 2154\\nf 894 2154 2514\\nf 40 1095 274\\nf 1563 1015 1095\\nf 1689 274 1015\\nf 1095 1015 274\\nf 1111 1395 347\\nf 721 1747 1395\\nf 1563 347 1747\\nf 1395 1747 347\\nf 1689 1015 2154\\nf 1563 1747 1015\\nf 721 2154 1747\\nf 1015 1747 2154\\nf 2312 1315 228\\nf 20 2209 1315\\nf 733 228 2209\\nf 1315 2209 228\\nf 19 1657 2101\\nf 2388 2281 1657\\nf 20 2101 2281\\nf 1657 2281 2101\\nf 307 2360 1227\\nf 733 522 2360\\nf 2388 1227 522\\nf 2360 522 1227\\nf 20 2281 2209\\nf 2388 522 2281\\nf 733 2209 522\\nf 2281 522 2209\\nf 1510 1151 1368\\nf 62 1960 1151\\nf 2171 1368 1960\\nf 1151 1960 1368\\nf 554 1758 1894\\nf 1448 2522 1758\\nf 62 1894 2522\\nf 1758 2522 1894\\nf 19 824 2148\\nf 2171 616 824\\nf 1448 2148 616\\nf 824 616 2148\\nf 62 2522 1960\\nf 1448 616 2522\\nf 2171 1960 616\\nf 2522 616 1960\\nf 116 2353 915\\nf 2465 682 2353\\nf 582 915 682\\nf 2353 682 915\\nf 307 1954 1304\\nf 2291 1543 1954\\nf 2465 1304 1543\\nf 1954 1543 1304\\nf 554 1350 1690\\nf 582 702 1350\\nf 2291 1690 702\\nf 1350 702 1690\\nf 2465 1543 682\\nf 2291 702 1543\\nf 582 682 702\\nf 1543 702 682\\nf 19 2148 1657\\nf 1448 2472 2148\\nf 2388 1657 2472\\nf 2148 2472 1657\\nf 554 1690 1758\\nf 2291 1740 1690\\nf 1448 1758 1740\\nf 1690 1740 1758\\nf 307 1227 1954\\nf 2388 103 1227\\nf 2291 1954 103\\nf 1227 103 1954\\nf 1448 1740 2472\\nf 2291 103 1740\\nf 2388 2472 103\\nf 1740 103 2472\\nf 1203 628 983\\nf 797 2193 628\\nf 1608 983 2193\\nf 628 2193 983\\nf 40 133 453\\nf 1598 1086 133\\nf 797 453 1086\\nf 133 1086 453\\nf 1668 467 2293\\nf 1608 751 467\\nf 1598 2293 751\\nf 467 751 2293\\nf 797 1086 2193\\nf 1598 751 1086\\nf 1608 2193 751\\nf 1086 751 2193\\nf 116 915 1229\\nf 582 576 915\\nf 494 1229 576\\nf 915 576 1229\\nf 554 2544 1350\\nf 550 2347 2544\\nf 582 1350 2347\\nf 2544 2347 1350\\nf 40 1602 1839\\nf 494 1190 1602\\nf 550 1839 1190\\nf 1602 1190 1839\\nf 582 2347 576\\nf 550 1190 2347\\nf 494 576 1190\\nf 2347 1190 576\\nf 1510 91 1151\\nf 697 2502 91\\nf 62 1151 2502\\nf 91 2502 1151\\nf 1668 670 1976\\nf 1983 2116 670\\nf 697 1976 2116\\nf 670 2116 1976\\nf 554 1894 1226\\nf 62 2261 1894\\nf 1983 1226 2261\\nf 1894 2261 1226\\nf 697 2116 2502\\nf 1983 2261 2116\\nf 62 2502 2261\\nf 2116 2261 2502\\nf 40 1839 133\\nf 550 2132 1839\\nf 1598 133 2132\\nf 1839 2132 133\\nf 554 1226 2544\\nf 1983 2461 1226\\nf 550 2544 2461\\nf 1226 2461 2544\\nf 1668 2293 670\\nf 1598 2454 2293\\nf 1983 670 2454\\nf 2293 2454 670\\nf 550 2461 2132\\nf 1983 2454 2461\\nf 1598 2132 2454\\nf 2461 2454 2132\\nf 1831 1571 1013\\nf 1425 1981 1571\\nf 1916 1013 1981\\nf 1571 1981 1013\\nf 610 496 2058\\nf 2393 1118 496\\nf 1425 2058 1118\\nf 496 1118 2058\\nf 57 2317 667\\nf 1916 1541 2317\\nf 2393 667 1541\\nf 2317 1541 667\\nf 1425 1118 1981\\nf 2393 1541 1118\\nf 1916 1981 1541\\nf 1118 1541 1981\\nf 880 2046 1153\\nf 1788 1367 2046\\nf 1517 1153 1367\\nf 2046 1367 1153\\nf 1682 1843 771\\nf 1750 186 1843\\nf 1788 771 186\\nf 1843 186 771\\nf 610 1447 919\\nf 1517 1478 1447\\nf 1750 919 1478\\nf 1447 1478 919\\nf 1788 186 1367\\nf 1750 1478 186\\nf 1517 1367 1478\\nf 186 1478 1367\\nf 560 1710 413\\nf 975 150 1710\\nf 630 413 150\\nf 1710 150 413\\nf 57 520 1754\\nf 724 1683 520\\nf 975 1754 1683\\nf 520 1683 1754\\nf 1682 945 979\\nf 630 1259 945\\nf 724 979 1259\\nf 945 1259 979\\nf 975 1683 150\\nf 724 1259 1683\\nf 630 150 1259\\nf 1683 1259 150\\nf 610 919 496\\nf 1750 1533 919\\nf 2393 496 1533\\nf 919 1533 496\\nf 1682 979 1843\\nf 724 850 979\\nf 1750 1843 850\\nf 979 850 1843\\nf 57 667 520\\nf 2393 503 667\\nf 724 520 503\\nf 667 503 520\\nf 1750 850 1533\\nf 724 503 850\\nf 2393 1533 503\\nf 850 503 1533\\nf 2282 1704 319\\nf 1877 376 1704\\nf 683 319 376\\nf 1704 376 319\\nf 931 1216 840\\nf 2422 706 1216\\nf 1877 840 706\\nf 1216 706 840\\nf 213 1614 304\\nf 683 1144 1614\\nf 2422 304 1144\\nf 1614 1144 304\\nf 1877 706 376\\nf 2422 1144 706\\nf 683 376 1144\\nf 706 1144 376\\nf 1525 2526 1390\\nf 1406 402 2526\\nf 473 1390 402\\nf 2526 402 1390\\nf 238 1031 1655\\nf 1775 1158 1031\\nf 1406 1655 1158\\nf 1031 1158 1655\\nf 931 1449 1749\\nf 473 775 1449\\nf 1775 1749 775\\nf 1449 775 1749\\nf 1406 1158 402\\nf 1775 775 1158\\nf 473 402 775\\nf 1158 775 402\\nf 880 1700 587\\nf 415 125 1700\\nf 1414 587 125\\nf 1700 125 587\\nf 213 1723 1718\\nf 631 406 1723\\nf 415 1718 406\\nf 1723 406 1718\\nf 238 1820 2091\\nf 1414 1945 1820\\nf 631 2091 1945\\nf 1820 1945 2091\\nf 415 406 125\\nf 631 1945 406\\nf 1414 125 1945\\nf 406 1945 125\\nf 931 1749 1216\\nf 1775 699 1749\\nf 2422 1216 699\\nf 1749 699 1216\\nf 238 2091 1031\\nf 631 688 2091\\nf 1775 1031 688\\nf 2091 688 1031\\nf 213 304 1723\\nf 2422 1654 304\\nf 631 1723 1654\\nf 304 1654 1723\\nf 1775 688 699\\nf 631 1654 688\\nf 2422 699 1654\\nf 688 1654 699\\nf 2391 1401 1333\\nf 2043 1812 1401\\nf 1027 1333 1812\\nf 1401 1812 1333\\nf 720 1743 2485\\nf 349 2037 1743\\nf 2043 2485 2037\\nf 1743 2037 2485\\nf 760 1458 1119\\nf 1027 1257 1458\\nf 349 1119 1257\\nf 1458 1257 1119\\nf 2043 2037 1812\\nf 349 1257 2037\\nf 1027 1812 1257\\nf 2037 1257 1812\\nf 560 2248 2085\\nf 1643 194 2248\\nf 857 2085 194\\nf 2248 194 2085\\nf 154 373 512\\nf 2276 1051 373\\nf 1643 512 1051\\nf 373 1051 512\\nf 720 694 532\\nf 857 2172 694\\nf 2276 532 2172\\nf 694 2172 532\\nf 1643 1051 194\\nf 2276 2172 1051\\nf 857 194 2172\\nf 1051 2172 194\\nf 1525 901 1994\\nf 1331 1540 901\\nf 1237 1994 1540\\nf 901 1540 1994\\nf 760 1418 2239\\nf 2137 1854 1418\\nf 1331 2239 1854\\nf 1418 1854 2239\\nf 154 2143 708\\nf 1237 807 2143\\nf 2137 708 807\\nf 2143 807 708\\nf 1331 1854 1540\\nf 2137 807 1854\\nf 1237 1540 807\\nf 1854 807 1540\\nf 720 532 1743\\nf 2276 2030 532\\nf 349 1743 2030\\nf 532 2030 1743\\nf 154 708 373\\nf 2137 1014 708\\nf 2276 373 1014\\nf 708 1014 373\\nf 760 1119 1418\\nf 349 2387 1119\\nf 2137 1418 2387\\nf 1119 2387 1418\\nf 2276 1014 2030\\nf 2137 2387 1014\\nf 349 2030 2387\\nf 1014 2387 2030\\nf 880 587 2046\\nf 1414 1018 587\\nf 1788 2046 1018\\nf 587 1018 2046\\nf 238 2554 1820\\nf 2308 471 2554\\nf 1414 1820 471\\nf 2554 471 1820\\nf 1682 771 1999\\nf 1788 1361 771\\nf 2308 1999 1361\\nf 771 1361 1999\\nf 1414 471 1018\\nf 2308 1361 471\\nf 1788 1018 1361\\nf 471 1361 1018\\nf 1525 1994 2526\\nf 1237 266 1994\\nf 1406 2526 266\\nf 1994 266 2526\\nf 154 2296 2143\\nf 431 248 2296\\nf 1237 2143 248\\nf 2296 248 2143\\nf 238 1655 179\\nf 1406 2041 1655\\nf 431 179 2041\\nf 1655 2041 179\\nf 1237 248 266\\nf 431 2041 248\\nf 1406 266 2041\\nf 248 2041 266\\nf 560 413 2248\\nf 630 2265 413\\nf 1643 2248 2265\\nf 413 2265 2248\\nf 1682 1097 945\\nf 262 214 1097\\nf 630 945 214\\nf 1097 214 945\\nf 154 512 1724\\nf 1643 1910 512\\nf 262 1724 1910\\nf 512 1910 1724\\nf 630 214 2265\\nf 262 1910 214\\nf 1643 2265 1910\\nf 214 1910 2265\\nf 238 179 2554\\nf 431 608 179\\nf 2308 2554 608\\nf 179 608 2554\\nf 154 1724 2296\\nf 262 719 1724\\nf 431 2296 719\\nf 1724 719 2296\\nf 1682 1999 1097\\nf 2308 2363 1999\\nf 262 1097 2363\\nf 1999 2363 1097\\nf 431 719 608\\nf 262 2363 719\\nf 2308 608 2363\\nf 719 2363 608\\nf 2282 1 1463\\nf 876 874 1\\nf 1445 1463 874\\nf 1 874 1463\\nf 1099 1423 2126\\nf 647 2510 1423\\nf 876 2126 2510\\nf 1423 2510 2126\\nf 1388 122 593\\nf 1445 870 122\\nf 647 593 870\\nf 122 870 593\\nf 876 2510 874\\nf 647 870 2510\\nf 1445 874 870\\nf 2510 870 874\\nf 744 366 2556\\nf 32 1974 366\\nf 926 2556 1974\\nf 366 1974 2556\\nf 423 2324 2026\\nf 2549 1313 2324\\nf 32 2026 1313\\nf 2324 1313 2026\\nf 1099 849 368\\nf 926 2535 849\\nf 2549 368 2535\\nf 849 2535 368\\nf 32 1313 1974\\nf 2549 2535 1313\\nf 926 1974 2535\\nf 1313 2535 1974\\nf 1083 1745 1794\\nf 2229 293 1745\\nf 1042 1794 293\\nf 1745 293 1794\\nf 1388 196 1016\\nf 1012 1468 196\\nf 2229 1016 1468\\nf 196 1468 1016\\nf 423 1596 72\\nf 1042 1701 1596\\nf 1012 72 1701\\nf 1596 1701 72\\nf 2229 1468 293\\nf 1012 1701 1468\\nf 1042 293 1701\\nf 1468 1701 293\\nf 1099 368 1423\\nf 2549 2430 368\\nf 647 1423 2430\\nf 368 2430 1423\\nf 423 72 2324\\nf 1012 525 72\\nf 2549 2324 525\\nf 72 525 2324\\nf 1388 593 196\\nf 647 338 593\\nf 1012 196 338\\nf 593 338 196\\nf 2549 525 2430\\nf 1012 338 525\\nf 647 2430 338\\nf 525 338 2430\\nf 1777 1374 753\\nf 282 905 1374\\nf 657 753 905\\nf 1374 905 753\\nf 1705 2215 1437\\nf 1370 260 2215\\nf 282 1437 260\\nf 2215 260 1437\\nf 197 1581 546\\nf 657 2295 1581\\nf 1370 546 2295\\nf 1581 2295 546\\nf 282 260 905\\nf 1370 2295 260\\nf 657 905 2295\\nf 260 2295 905\\nf 961 2369 97\\nf 344 2287 2369\\nf 934 97 2287\\nf 2369 2287 97\\nf 531 1460 78\\nf 403 2509 1460\\nf 344 78 2509\\nf 1460 2509 78\\nf 1705 1262 2259\\nf 934 1755 1262\\nf 403 2259 1755\\nf 1262 1755 2259\\nf 344 2509 2287\\nf 403 1755 2509\\nf 934 2287 1755\\nf 2509 1755 2287\\nf 744 938 1797\\nf 524 1481 938\\nf 182 1797 1481\\nf 938 1481 1797\\nf 197 114 79\\nf 862 1613 114\\nf 524 79 1613\\nf 114 1613 79\\nf 531 1860 1297\\nf 182 827 1860\\nf 862 1297 827\\nf 1860 827 1297\\nf 524 1613 1481\\nf 862 827 1613\\nf 182 1481 827\\nf 1613 827 1481\\nf 1705 2259 2215\\nf 403 2246 2259\\nf 1370 2215 2246\\nf 2259 2246 2215\\nf 531 1297 1460\\nf 862 1680 1297\\nf 403 1460 1680\\nf 1297 1680 1460\\nf 197 546 114\\nf 1370 1593 546\\nf 862 114 1593\\nf 546 1593 114\\nf 403 1680 2246\\nf 862 1593 1680\\nf 1370 2246 1593\\nf 1680 1593 2246\\nf 2008 421 777\\nf 2147 759 421\\nf 746 777 759\\nf 421 759 777\\nf 2425 2097 877\\nf 575 1915 2097\\nf 2147 877 1915\\nf 2097 1915 877\\nf 29 990 1150\\nf 746 142 990\\nf 575 1150 142\\nf 990 142 1150\\nf 2147 1915 759\\nf 575 142 1915\\nf 746 759 142\\nf 1915 142 759\\nf 1083 1149 316\\nf 1464 2452 1149\\nf 1562 316 2452\\nf 1149 2452 316\\nf 829 996 1670\\nf 672 1123 996\\nf 1464 1670 1123\\nf 996 1123 1670\\nf 2425 728 541\\nf 1562 235 728\\nf 672 541 235\\nf 728 235 541\\nf 1464 1123 2452\\nf 672 235 1123\\nf 1562 2452 235\\nf 1123 235 2452\\nf 961 1871 2184\\nf 878 1804 1871\\nf 1634 2184 1804\\nf 1871 1804 2184\\nf 29 2009 1868\\nf 1479 1407 2009\\nf 878 1868 1407\\nf 2009 1407 1868\\nf 829 714 808\\nf 1634 2512 714\\nf 1479 808 2512\\nf 714 2512 808\\nf 878 1407 1804\\nf 1479 2512 1407\\nf 1634 1804 2512\\nf 1407 2512 1804\\nf 2425 541 2097\\nf 672 401 541\\nf 575 2097 401\\nf 541 401 2097\\nf 829 808 996\\nf 1479 2330 808\\nf 672 996 2330\\nf 808 2330 996\\nf 29 1150 2009\\nf 575 1688 1150\\nf 1479 2009 1688\\nf 1150 1688 2009\\nf 672 2330 401\\nf 1479 1688 2330\\nf 575 401 1688\\nf 2330 1688 401\\nf 744 1797 366\\nf 182 465 1797\\nf 32 366 465\\nf 1797 465 366\\nf 531 358 1860\\nf 1880 2098 358\\nf 182 1860 2098\\nf 358 2098 1860\\nf 423 2026 388\\nf 32 1221 2026\\nf 1880 388 1221\\nf 2026 1221 388\\nf 182 2098 465\\nf 1880 1221 2098\\nf 32 465 1221\\nf 2098 1221 465\\nf 961 2184 2369\\nf 1634 99 2184\\nf 344 2369 99\\nf 2184 99 2369\\nf 829 2271 714\\nf 2010 1986 2271\\nf 1634 714 1986\\nf 2271 1986 714\\nf 531 78 356\\nf 344 1933 78\\nf 2010 356 1933\\nf 78 1933 356\\nf 1634 1986 99\\nf 2010 1933 1986\\nf 344 99 1933\\nf 1986 1933 99\\nf 1083 1794 1149\\nf 1042 131 1794\\nf 1464 1149 131\\nf 1794 131 1149\\nf 423 2300 1596\\nf 417 1626 2300\\nf 1042 1596 1626\\nf 2300 1626 1596\\nf 829 1670 17\\nf 1464 296 1670\\nf 417 17 296\\nf 1670 296 17\\nf 1042 1626 131\\nf 417 296 1626\\nf 1464 131 296\\nf 1626 296 131\\nf 531 356 358\\nf 2010 374 356\\nf 1880 358 374\\nf 356 374 358\\nf 829 17 2271\\nf 417 2141 17\\nf 2010 2271 2141\\nf 17 2141 2271\\nf 423 388 2300\\nf 1880 474 388\\nf 417 2300 474\\nf 388 474 2300\\nf 2010 2141 374\\nf 417 474 2141\\nf 1880 374 474\\nf 2141 474 374\\nf 1034 1813 239\\nf 144 2152 1813\\nf 2314 239 2152\\nf 1813 2152 239\\nf 1850 461 1942\\nf 2088 967 461\\nf 144 1942 967\\nf 461 967 1942\\nf 1212 1560 435\\nf 2314 1499 1560\\nf 2088 435 1499\\nf 1560 1499 435\\nf 144 967 2152\\nf 2088 1499 967\\nf 2314 2152 1499\\nf 967 1499 2152\\nf 1497 1456 1263\\nf 329 920 1456\\nf 54 1263 920\\nf 1456 920 1263\\nf 1616 163 2326\\nf 1895 1397 163\\nf 329 2326 1397\\nf 163 1397 2326\\nf 1850 1906 1575\\nf 54 1550 1906\\nf 1895 1575 1550\\nf 1906 1550 1575\\nf 329 1397 920\\nf 1895 1550 1397\\nf 54 920 1550\\nf 1397 1550 920\\nf 1554 2453 2262\\nf 132 140 2453\\nf 1046 2262 140\\nf 2453 140 2262\\nf 1212 1631 1703\\nf 331 2417 1631\\nf 132 1703 2417\\nf 1631 2417 1703\\nf 1616 1730 245\\nf 1046 1327 1730\\nf 331 245 1327\\nf 1730 1327 245\\nf 132 2417 140\\nf 331 1327 2417\\nf 1046 140 1327\\nf 2417 1327 140\\nf 1850 1575 461\\nf 1895 748 1575\\nf 2088 461 748\\nf 1575 748 461\\nf 1616 245 163\\nf 331 510 245\\nf 1895 163 510\\nf 245 510 163\\nf 1212 435 1631\\nf 2088 1940 435\\nf 331 1631 1940\\nf 435 1940 1631\\nf 1895 510 748\\nf 331 1940 510\\nf 2088 748 1940\\nf 510 1940 748\\nf 1769 1175 1967\\nf 2019 1698 1175\\nf 2082 1967 1698\\nf 1175 1698 1967\\nf 320 2007 964\\nf 561 2534 2007\\nf 2019 964 2534\\nf 2007 2534 964\\nf 2165 648 472\\nf 2082 1619 648\\nf 561 472 1619\\nf 648 1619 472\\nf 2019 2534 1698\\nf 561 1619 2534\\nf 2082 1698 1619\\nf 2534 1619 1698\\nf 390 837 954\\nf 713 828 837\\nf 652 954 828\\nf 837 828 954\\nf 2134 620 1548\\nf 2213 1905 620\\nf 713 1548 1905\\nf 620 1905 1548\\nf 320 537 2024\\nf 652 2000 537\\nf 2213 2024 2000\\nf 537 2000 2024\\nf 713 1905 828\\nf 2213 2000 1905\\nf 652 828 2000\\nf 1905 2000 828\\nf 1497 1218 2348\\nf 1520 1134 1218\\nf 33 2348 1134\\nf 1218 1134 2348\\nf 2165 425 738\\nf 1076 2349 425\\nf 1520 738 2349\\nf 425 2349 738\\nf 2134 1092 89\\nf 33 1451 1092\\nf 1076 89 1451\\nf 1092 1451 89\\nf 1520 2349 1134\\nf 1076 1451 2349\\nf 33 1134 1451\\nf 2349 1451 1134\\nf 320 2024 2007\\nf 2213 860 2024\\nf 561 2007 860\\nf 2024 860 2007\\nf 2134 89 620\\nf 1076 1713 89\\nf 2213 620 1713\\nf 89 1713 620\\nf 2165 472 425\\nf 561 286 472\\nf 1076 425 286\\nf 472 286 425\\nf 2213 1713 860\\nf 1076 286 1713\\nf 561 860 286\\nf 1713 286 860\\nf 906 1987 844\\nf 2108 863 1987\\nf 2498 844 863\\nf 1987 863 844\\nf 723 2266 1630\\nf 2350 1488 2266\\nf 2108 1630 1488\\nf 2266 1488 1630\\nf 1178 1422 1244\\nf 2498 1234 1422\\nf 2350 1244 1234\\nf 1422 1234 1244\\nf 2108 1488 863\\nf 2350 1234 1488\\nf 2498 863 1234\\nf 1488 1234 863\\nf 1554 164 2457\\nf 618 552 164\\nf 1113 2457 552\\nf 164 552 2457\\nf 187 483 942\\nf 1309 2560 483\\nf 618 942 2560\\nf 483 2560 942\\nf 723 1381 2274\\nf 1113 1347 1381\\nf 1309 2274 1347\\nf 1381 1347 2274\\nf 618 2560 552\\nf 1309 1347 2560\\nf 1113 552 1347\\nf 2560 1347 552\\nf 390 261 617\\nf 893 1264 261\\nf 171 617 1264\\nf 261 1264 617\\nf 1178 1892 2005\\nf 291 1980 1892\\nf 893 2005 1980\\nf 1892 1980 2005\\nf 187 1343 1566\\nf 171 2015 1343\\nf 291 1566 2015\\nf 1343 2015 1566\\nf 893 1980 1264\\nf 291 2015 1980\\nf 171 1264 2015\\nf 1980 2015 1264\\nf 723 2274 2266\\nf 1309 764 2274\\nf 2350 2266 764\\nf 2274 764 2266\\nf 187 1566 483\\nf 291 568 1566\\nf 1309 483 568\\nf 1566 568 483\\nf 1178 1244 1892\\nf 2350 2357 1244\\nf 291 1892 2357\\nf 1244 2357 1892\\nf 1309 568 764\\nf 291 2357 568\\nf 2350 764 2357\\nf 568 2357 764\\nf 1497 2348 1456\\nf 33 2412 2348\\nf 329 1456 2412\\nf 2348 2412 1456\\nf 2134 2395 1092\\nf 285 969 2395\\nf 33 1092 969\\nf 2395 969 1092\\nf 1616 2326 2497\\nf 329 1316 2326\\nf 285 2497 1316\\nf 2326 1316 2497\\nf 33 969 2412\\nf 285 1316 969\\nf 329 2412 1316\\nf 969 1316 2412\\nf 390 617 837\\nf 171 932 617\\nf 713 837 932\\nf 617 932 837\\nf 187 1332 1343\\nf 1201 1417 1332\\nf 171 1343 1417\\nf 1332 1417 1343\\nf 2134 1548 2272\\nf 713 2341 1548\\nf 1201 2272 2341\\nf 1548 2341 2272\\nf 171 1417 932\\nf 1201 2341 1417\\nf 713 932 2341\\nf 1417 2341 932\\nf 1554 2262 164\\nf 1046 2539 2262\\nf 618 164 2539\\nf 2262 2539 164\\nf 1616 2432 1730\\nf 440 135 2432\\nf 1046 1730 135\\nf 2432 135 1730\\nf 187 942 257\\nf 618 814 942\\nf 440 257 814\\nf 942 814 257\\nf 1046 135 2539\\nf 440 814 135\\nf 618 2539 814\\nf 135 814 2539\\nf 2134 2272 2395\\nf 1201 1509 2272\\nf 285 2395 1509\\nf 2272 1509 2395\\nf 187 257 1332\\nf 440 2459 257\\nf 1201 1332 2459\\nf 257 2459 1332\\nf 1616 2497 2432\\nf 285 574 2497\\nf 440 2432 574\\nf 2497 574 2432\\nf 1201 2459 1509\\nf 440 574 2459\\nf 285 1509 574\\nf 2459 574 1509\\nf 1034 239 203\\nf 2314 581 239\\nf 1687 203 581\\nf 239 581 203\\nf 1212 1591 1560\\nf 219 736 1591\\nf 2314 1560 736\\nf 1591 736 1560\\nf 314 1159 925\\nf 1687 1116 1159\\nf 219 925 1116\\nf 1159 1116 925\\nf 2314 736 581\\nf 219 1116 736\\nf 1687 581 1116\\nf 736 1116 581\\nf 1554 2375 2453\\nf 793 124 2375\\nf 132 2453 124\\nf 2375 124 2453\\nf 1345 660 2298\\nf 1595 1727 660\\nf 793 2298 1727\\nf 660 1727 2298\\nf 1212 1703 2343\\nf 132 1737 1703\\nf 1595 2343 1737\\nf 1703 1737 2343\\nf 793 1727 124\\nf 1595 1737 1727\\nf 132 124 1737\\nf 1727 1737 124\\nf 565 379 1106\\nf 1205 2488 379\\nf 663 1106 2488\\nf 379 2488 1106\\nf 314 903 2247\\nf 917 1836 903\\nf 1205 2247 1836\\nf 903 1836 2247\\nf 1345 1840 505\\nf 663 629 1840\\nf 917 505 629\\nf 1840 629 505\\nf 1205 1836 2488\\nf 917 629 1836\\nf 663 2488 629\\nf 1836 629 2488\\nf 1212 2343 1591\\nf 1595 1720 2343\\nf 219 1591 1720\\nf 2343 1720 1591\\nf 1345 505 660\\nf 917 1858 505\\nf 1595 660 1858\\nf 505 1858 660\\nf 314 925 903\\nf 219 627 925\\nf 917 903 627\\nf 925 627 903\\nf 1595 1858 1720\\nf 917 627 1858\\nf 219 1720 627\\nf 1858 627 1720\\nf 906 1586 1987\\nf 2256 1856 1586\\nf 2108 1987 1856\\nf 1586 1856 1987\\nf 35 2074 2233\\nf 1170 2035 2074\\nf 2256 2233 2035\\nf 2074 2035 2233\\nf 723 1630 1029\\nf 2108 1800 1630\\nf 1170 1029 1800\\nf 1630 1800 1029\\nf 2256 2035 1856\\nf 1170 1800 2035\\nf 2108 1856 1800\\nf 2035 1800 1856\\nf 437 2392 1660\\nf 1799 551 2392\\nf 2163 1660 551\\nf 2392 551 1660\\nf 1886 2356 2486\\nf 1846 2479 2356\\nf 1799 2486 2479\\nf 2356 2479 2486\\nf 35 737 1360\\nf 2163 1855 737\\nf 1846 1360 1855\\nf 737 1855 1360\\nf 1799 2479 551\\nf 1846 1855 2479\\nf 2163 551 1855\\nf 2479 1855 551\\nf 1554 2457 110\\nf 1113 671 2457\\nf 48 110 671\\nf 2457 671 110\\nf 723 424 1381\\nf 1559 2446 424\\nf 1113 1381 2446\\nf 424 2446 1381\\nf 1886 783 1518\\nf 48 604 783\\nf 1559 1518 604\\nf 783 604 1518\\nf 1113 2446 671\\nf 1559 604 2446\\nf 48 671 604\\nf 2446 604 671\\nf 35 1360 2074\\nf 1846 1672 1360\\nf 1170 2074 1672\\nf 1360 1672 2074\\nf 1886 1518 2356\\nf 1559 2083 1518\\nf 1846 2356 2083\\nf 1518 2083 2356\\nf 723 1029 424\\nf 1170 2471 1029\\nf 1559 424 2471\\nf 1029 2471 424\\nf 1846 2083 1672\\nf 1559 2471 2083\\nf 1170 1672 2471\\nf 2083 2471 1672\\nf 2312 2384 789\\nf 864 1091 2384\\nf 2331 789 1091\\nf 2384 1091 789\\nf 790 1232 73\\nf 2531 42 1232\\nf 864 73 42\\nf 1232 42 73\\nf 149 2112 451\\nf 2331 90 2112\\nf 2531 451 90\\nf 2112 90 451\\nf 864 42 1091\\nf 2531 90 42\\nf 2331 1091 90\\nf 42 90 1091\\nf 565 892 1817\\nf 1310 508 892\\nf 1148 1817 508\\nf 892 508 1817\\nf 22 1271 2113\\nf 47 2191 1271\\nf 1310 2113 2191\\nf 1271 2191 2113\\nf 790 1321 1252\\nf 1148 734 1321\\nf 47 1252 734\\nf 1321 734 1252\\nf 1310 2191 508\\nf 47 734 2191\\nf 1148 508 734\\nf 2191 734 508\\nf 437 396 1893\\nf 766 1419 396\\nf 1833 1893 1419\\nf 396 1419 1893\\nf 149 1637 830\\nf 2424 1516 1637\\nf 766 830 1516\\nf 1637 1516 830\\nf 22 888 2373\\nf 1833 974 888\\nf 2424 2373 974\\nf 888 974 2373\\nf 766 1516 1419\\nf 2424 974 1516\\nf 1833 1419 974\\nf 1516 974 1419\\nf 790 1252 1232\\nf 47 1255 1252\\nf 2531 1232 1255\\nf 1252 1255 1232\\nf 22 2373 1271\\nf 2424 1684 2373\\nf 47 1271 1684\\nf 2373 1684 1271\\nf 149 451 1637\\nf 2531 1493 451\\nf 2424 1637 1493\\nf 451 1493 1637\\nf 47 1684 1255\\nf 2424 1493 1684\\nf 2531 1255 1493\\nf 1684 1493 1255\\nf 1554 110 2375\\nf 48 1057 110\\nf 793 2375 1057\\nf 110 1057 2375\\nf 1886 1848 783\\nf 1985 1924 1848\\nf 48 783 1924\\nf 1848 1924 783\\nf 1345 2298 1798\\nf 793 625 2298\\nf 1985 1798 625\\nf 2298 625 1798\\nf 48 1924 1057\\nf 1985 625 1924\\nf 793 1057 625\\nf 1924 625 1057\\nf 437 1893 2392\\nf 1833 2204 1893\\nf 1799 2392 2204\\nf 1893 2204 2392\\nf 22 486 888\\nf 436 1662 486\\nf 1833 888 1662\\nf 486 1662 888\\nf 1886 2486 1948\\nf 1799 1186 2486\\nf 436 1948 1186\\nf 2486 1186 1948\\nf 1833 1662 2204\\nf 436 1186 1662\\nf 1799 2204 1186\\nf 1662 1186 2204\\nf 565 1106 892\\nf 663 200 1106\\nf 1310 892 200\\nf 1106 200 892\\nf 1345 289 1840\\nf 904 2004 289\\nf 663 1840 2004\\nf 289 2004 1840\\nf 22 2113 1346\\nf 1310 2436 2113\\nf 904 1346 2436\\nf 2113 2436 1346\\nf 663 2004 200\\nf 904 2436 2004\\nf 1310 200 2436\\nf 2004 2436 200\\nf 1886 1948 1848\\nf 436 265 1948\\nf 1985 1848 265\\nf 1948 265 1848\\nf 22 1346 486\\nf 904 1378 1346\\nf 436 486 1378\\nf 1346 1378 486\\nf 1345 1798 289\\nf 1985 2070 1798\\nf 904 289 2070\\nf 1798 2070 289\\nf 436 1378 265\\nf 904 2070 1378\\nf 1985 265 2070\\nf 1378 2070 265\\nf 1034 203 1524\\nf 1687 146 203\\nf 1161 1524 146\\nf 203 146 1524\\nf 314 1914 1159\\nf 2441 2294 1914\\nf 1687 1159 2294\\nf 1914 2294 1159\\nf 1984 225 367\\nf 1161 1847 225\\nf 2441 367 1847\\nf 225 1847 367\\nf 1687 2294 146\\nf 2441 1847 2294\\nf 1161 146 1847\\nf 2294 1847 146\\nf 565 141 379\\nf 202 1281 141\\nf 1205 379 1281\\nf 141 1281 379\\nf 2040 1675 1174\\nf 2489 2321 1675\\nf 202 1174 2321\\nf 1675 2321 1174\\nf 314 2247 1206\\nf 1205 343 2247\\nf 2489 1206 343\\nf 2247 343 1206\\nf 202 2321 1281\\nf 2489 343 2321\\nf 1205 1281 343\\nf 2321 343 1281\\nf 658 2377 881\\nf 687 1512 2377\\nf 2110 881 1512\\nf 2377 1512 881\\nf 1984 118 1394\\nf 674 1242 118\\nf 687 1394 1242\\nf 118 1242 1394\\nf 2040 148 335\\nf 2110 127 148\\nf 674 335 127\\nf 148 127 335\\nf 687 1242 1512\\nf 674 127 1242\\nf 2110 1512 127\\nf 1242 127 1512\\nf 314 1206 1914\\nf 2489 841 1206\\nf 2441 1914 841\\nf 1206 841 1914\\nf 2040 335 1675\\nf 674 599 335\\nf 2489 1675 599\\nf 335 599 1675\\nf 1984 367 118\\nf 2441 1007 367\\nf 674 118 1007\\nf 367 1007 118\\nf 2489 599 841\\nf 674 1007 599\\nf 2441 841 1007\\nf 599 1007 841\\nf 2312 2104 2384\\nf 2270 271 2104\\nf 864 2384 271\\nf 2104 271 2384\\nf 236 1781 2431\\nf 501 1215 1781\\nf 2270 2431 1215\\nf 1781 1215 2431\\nf 790 73 2275\\nf 864 1253 73\\nf 501 2275 1253\\nf 73 1253 2275\\nf 2270 1215 271\\nf 501 1253 1215\\nf 864 271 1253\\nf 1215 1253 271\\nf 678 1647 957\\nf 1972 1028 1647\\nf 276 957 1028\\nf 1647 1028 957\\nf 811 558 533\\nf 217 2063 558\\nf 1972 533 2063\\nf 558 2063 533\\nf 236 1045 1789\\nf 276 1326 1045\\nf 217 1789 1326\\nf 1045 1326 1789\\nf 1972 2063 1028\\nf 217 1326 2063\\nf 276 1028 1326\\nf 2063 1326 1028\\nf 565 1817 336\\nf 1148 1308 1817\\nf 2222 336 1308\\nf 1817 1308 336\\nf 790 325 1321\\nf 1523 1082 325\\nf 1148 1321 1082\\nf 325 1082 1321\\nf 811 1454 511\\nf 2222 1975 1454\\nf 1523 511 1975\\nf 1454 1975 511\\nf 1148 1082 1308\\nf 1523 1975 1082\\nf 2222 1308 1975\\nf 1082 1975 1308\\nf 236 1789 1781\\nf 217 1863 1789\\nf 501 1781 1863\\nf 1789 1863 1781\\nf 811 511 558\\nf 1523 204 511\\nf 217 558 204\\nf 511 204 558\\nf 790 2275 325\\nf 501 1865 2275\\nf 1523 325 1865\\nf 2275 1865 325\\nf 217 204 1863\\nf 1523 1865 204\\nf 501 1863 1865\\nf 204 1865 1863\\nf 2391 372 1998\\nf 34 462 372\\nf 1220 1998 462\\nf 372 462 1998\\nf 836 488 916\\nf 2028 539 488\\nf 34 916 539\\nf 488 539 916\\nf 1756 2516 1477\\nf 1220 1035 2516\\nf 2028 1477 1035\\nf 2516 1035 1477\\nf 34 539 462\\nf 2028 1035 539\\nf 1220 462 1035\\nf 539 1035 462\\nf 658 1073 973\\nf 370 498 1073\\nf 1129 973 498\\nf 1073 498 973\\nf 322 2332 1796\\nf 2420 673 2332\\nf 370 1796 673\\nf 2332 673 1796\\nf 836 102 937\\nf 1129 98 102\\nf 2420 937 98\\nf 102 98 937\\nf 370 673 498\\nf 2420 98 673\\nf 1129 498 98\\nf 673 98 498\\nf 678 1778 377\\nf 1420 2144 1778\\nf 1098 377 2144\\nf 1778 2144 377\\nf 1756 549 965\\nf 1250 2346 549\\nf 1420 965 2346\\nf 549 2346 965\\nf 322 178 781\\nf 1098 2451 178\\nf 1250 781 2451\\nf 178 2451 781\\nf 1420 2346 2144\\nf 1250 2451 2346\\nf 1098 2144 2451\\nf 2346 2451 2144\\nf 836 937 488\\nf 2420 1934 937\\nf 2028 488 1934\\nf 937 1934 488\\nf 322 781 2332\\nf 1250 2166 781\\nf 2420 2332 2166\\nf 781 2166 2332\\nf 1756 1477 549\\nf 2028 1805 1477\\nf 1250 549 1805\\nf 1477 1805 549\\nf 2420 2166 1934\\nf 1250 1805 2166\\nf 2028 1934 1805\\nf 2166 1805 1934\\nf 565 336 141\\nf 2222 490 336\\nf 202 141 490\\nf 336 490 141\\nf 811 642 1454\\nf 1400 174 642\\nf 2222 1454 174\\nf 642 174 1454\\nf 2040 1174 1040\\nf 202 636 1174\\nf 1400 1040 636\\nf 1174 636 1040\\nf 2222 174 490\\nf 1400 636 174\\nf 202 490 636\\nf 174 636 490\\nf 678 377 1647\\nf 1098 548 377\\nf 1972 1647 548\\nf 377 548 1647\\nf 322 1908 178\\nf 563 2211 1908\\nf 1098 178 2211\\nf 1908 2211 178\\nf 811 533 2095\\nf 1972 1405 533\\nf 563 2095 1405\\nf 533 1405 2095\\nf 1098 2211 548\\nf 563 1405 2211\\nf 1972 548 1405\\nf 2211 1405 548\\nf 658 881 1073\\nf 2110 743 881\\nf 370 1073 743\\nf 881 743 1073\\nf 2040 826 148\\nf 2499 2178 826\\nf 2110 148 2178\\nf 826 2178 148\\nf 322 1796 1006\\nf 370 2552 1796\\nf 2499 1006 2552\\nf 1796 2552 1006\\nf 2110 2178 743\\nf 2499 2552 2178\\nf 370 743 2552\\nf 2178 2552 743\\nf 811 2095 642\\nf 563 1686 2095\\nf 1400 642 1686\\nf 2095 1686 642\\nf 322 1006 1908\\nf 2499 25 1006\\nf 563 1908 25\\nf 1006 25 1908\\nf 2040 1040 826\\nf 1400 180 1040\\nf 2499 826 180\\nf 1040 180 826\\nf 563 25 1686\\nf 2499 180 25\\nf 1400 1686 180\\nf 25 180 1686\\nf 1034 1524 1446\\nf 1161 63 1524\\nf 242 1446 63\\nf 1524 63 1446\\nf 1984 2156 225\\nf 2190 1008 2156\\nf 1161 225 1008\\nf 2156 1008 225\\nf 418 1277 865\\nf 242 439 1277\\nf 2190 865 439\\nf 1277 439 865\\nf 1161 1008 63\\nf 2190 439 1008\\nf 242 63 439\\nf 1008 439 63\\nf 658 332 2377\\nf 2267 94 332\\nf 687 2377 94\\nf 332 94 2377\\nf 2559 1483 2145\\nf 1093 137 1483\\nf 2267 2145 137\\nf 1483 137 2145\\nf 1984 1394 2513\\nf 687 774 1394\\nf 1093 2513 774\\nf 1394 774 2513\\nf 2267 137 94\\nf 1093 774 137\\nf 687 94 774\\nf 137 774 94\\nf 1075 1169 696\\nf 882 1351 1169\\nf 638 696 1351\\nf 1169 1351 696\\nf 418 2359 1610\\nf 2240 1971 2359\\nf 882 1610 1971\\nf 2359 1971 1610\\nf 2559 2029 1010\\nf 638 2234 2029\\nf 2240 1010 2234\\nf 2029 2234 1010\\nf 882 1971 1351\\nf 2240 2234 1971\\nf 638 1351 2234\\nf 1971 2234 1351\\nf 1984 2513 2156\\nf 1093 1982 2513\\nf 2190 2156 1982\\nf 2513 1982 2156\\nf 2559 1010 1483\\nf 2240 845 1010\\nf 1093 1483 845\\nf 1010 845 1483\\nf 418 865 2359\\nf 2190 1269 865\\nf 2240 2359 1269\\nf 865 1269 2359\\nf 1093 845 1982\\nf 2240 1269 845\\nf 2190 1982 1269\\nf 845 1269 1982\\nf 2391 2309 372\\nf 2327 1376 2309\\nf 34 372 1376\\nf 2309 1376 372\\nf 1295 50 58\\nf 1875 176 50\\nf 2327 58 176\\nf 50 176 58\\nf 836 916 2227\\nf 34 404 916\\nf 1875 2227 404\\nf 916 404 2227\\nf 2327 176 1376\\nf 1875 404 176\\nf 34 1376 404\\nf 176 404 1376\\nf 2194 2219 2181\\nf 2208 211 2219\\nf 1968 2181 211\\nf 2219 211 2181\\nf 664 2251 167\\nf 1467 251 2251\\nf 2208 167 251\\nf 2251 251 167\\nf 1295 2120 817\\nf 1968 1732 2120\\nf 1467 817 1732\\nf 2120 1732 817\\nf 2208 251 211\\nf 1467 1732 251\\nf 1968 211 1732\\nf 251 1732 211\\nf 658 973 333\\nf 1129 2199 973\\nf 2122 333 2199\\nf 973 2199 333\\nf 836 963 102\\nf 2328 1233 963\\nf 1129 102 1233\\nf 963 1233 102\\nf 664 1328 107\\nf 2122 913 1328\\nf 2328 107 913\\nf 1328 913 107\\nf 1129 1233 2199\\nf 2328 913 1233\\nf 2122 2199 913\\nf 1233 913 2199\\nf 1295 817 50\\nf 1467 570 817\\nf 1875 50 570\\nf 817 570 50\\nf 664 107 2251\\nf 2328 385 107\\nf 1467 2251 385\\nf 107 385 2251\\nf 836 2227 963\\nf 1875 1184 2227\\nf 2328 963 1184\\nf 2227 1184 963\\nf 1467 385 570\\nf 2328 1184 385\\nf 1875 570 1184\\nf 385 1184 570\\nf 2008 1404 2078\\nf 2364 1530 1404\\nf 2475 2078 1530\\nf 1404 1530 2078\\nf 128 2398 2018\\nf 292 1061 2398\\nf 2364 2018 1061\\nf 2398 1061 2018\\nf 2481 2075 1196\\nf 2475 2140 2075\\nf 292 1196 2140\\nf 2075 2140 1196\\nf 2364 1061 1530\\nf 292 2140 1061\\nf 2475 1530 2140\\nf 1061 2140 1530\\nf 1075 59 655\\nf 785 1714 59\\nf 1491 655 1714\\nf 59 1714 655\\nf 1305 328 545\\nf 562 899 328\\nf 785 545 899\\nf 328 899 545\\nf 128 2049 2006\\nf 1491 2487 2049\\nf 562 2006 2487\\nf 2049 2487 2006\\nf 785 899 1714\\nf 562 2487 899\\nf 1491 1714 2487\\nf 899 2487 1714\\nf 2194 2045 134\\nf 2042 1807 2045\\nf 1552 134 1807\\nf 2045 1807 134\\nf 2481 898 946\\nf 1514 1663 898\\nf 2042 946 1663\\nf 898 1663 946\\nf 1305 1204 695\\nf 1552 927 1204\\nf 1514 695 927\\nf 1204 927 695\\nf 2042 1663 1807\\nf 1514 927 1663\\nf 1552 1807 927\\nf 1663 927 1807\\nf 128 2006 2398\\nf 562 2068 2006\\nf 292 2398 2068\\nf 2006 2068 2398\\nf 1305 695 328\\nf 1514 2226 695\\nf 562 328 2226\\nf 695 2226 328\\nf 2481 1196 898\\nf 292 269 1196\\nf 1514 898 269\\nf 1196 269 898\\nf 562 2226 2068\\nf 1514 269 2226\\nf 292 2068 269\\nf 2226 269 2068\\nf 658 333 332\\nf 2122 195 333\\nf 2267 332 195\\nf 333 195 332\\nf 664 207 1328\\nf 1851 1989 207\\nf 2122 1328 1989\\nf 207 1989 1328\\nf 2559 2145 592\\nf 2267 1352 2145\\nf 1851 592 1352\\nf 2145 1352 592\\nf 2122 1989 195\\nf 1851 1352 1989\\nf 2267 195 1352\\nf 1989 1352 195\\nf 2194 134 2219\\nf 1552 1246 134\\nf 2208 2219 1246\\nf 134 1246 2219\\nf 1305 2142 1204\\nf 189 351 2142\\nf 1552 1204 351\\nf 2142 351 1204\\nf 664 167 725\\nf 2208 252 167\\nf 189 725 252\\nf 167 252 725\\nf 1552 351 1246\\nf 189 252 351\\nf 2208 1246 252\\nf 351 252 1246\\nf 1075 696 59\\nf 638 2203 696\\nf 785 59 2203\\nf 696 2203 59\\nf 2559 703 2029\\nf 747 749 703\\nf 638 2029 749\\nf 703 749 2029\\nf 1305 545 1001\\nf 785 1629 545\\nf 747 1001 1629\\nf 545 1629 1001\\nf 638 749 2203\\nf 747 1629 749\\nf 785 2203 1629\\nf 749 1629 2203\\nf 664 725 207\\nf 189 1272 725\\nf 1851 207 1272\\nf 725 1272 207\\nf 1305 1001 2142\\nf 747 1192 1001\\nf 189 2142 1192\\nf 1001 1192 2142\\nf 2559 592 703\\nf 1851 1810 592\\nf 747 703 1810\\nf 592 1810 703\\nf 189 1192 1272\\nf 747 1810 1192\\nf 1851 1272 1810\\nf 1192 1810 1272\\nf 1034 1446 1813\\nf 242 2476 1446\\nf 144 1813 2476\\nf 1446 2476 1813\\nf 418 1505 1277\\nf 1504 9 1505\\nf 242 1277 9\\nf 1505 9 1277\\nf 1850 1942 143\\nf 144 1829 1942\\nf 1504 143 1829\\nf 1942 1829 143\\nf 242 9 2476\\nf 1504 1829 9\\nf 144 2476 1829\\nf 9 1829 2476\\nf 1075 408 1169\\nf 2053 633 408\\nf 882 1169 633\\nf 408 633 1169\\nf 1288 591 2530\\nf 1127 345 591\\nf 2053 2530 345\\nf 591 345 2530\\nf 418 1610 1990\\nf 882 2541 1610\\nf 1127 1990 2541\\nf 1610 2541 1990\\nf 2053 345 633\\nf 1127 2541 345\\nf 882 633 2541\\nf 345 2541 633\\nf 1497 1263 362\\nf 54 943 1263\\nf 327 362 943\\nf 1263 943 362\\nf 1850 1659 1906\\nf 755 653 1659\\nf 54 1906 653\\nf 1659 653 1906\\nf 1288 2545 1697\\nf 327 1104 2545\\nf 755 1697 1104\\nf 2545 1104 1697\\nf 54 653 943\\nf 755 1104 653\\nf 327 943 1104\\nf 653 1104 943\\nf 418 1990 1505\\nf 1127 1056 1990\\nf 1504 1505 1056\\nf 1990 1056 1505\\nf 1288 1697 591\\nf 755 1214 1697\\nf 1127 591 1214\\nf 1697 1214 591\\nf 1850 143 1659\\nf 1504 1814 143\\nf 755 1659 1814\\nf 143 1814 1659\\nf 1127 1214 1056\\nf 755 1814 1214\\nf 1504 1056 1814\\nf 1214 1814 1056\\nf 2008 1427 1404\\nf 644 1078 1427\\nf 2364 1404 1078\\nf 1427 1078 1404\\nf 988 161 2506\\nf 268 875 161\\nf 644 2506 875\\nf 161 875 2506\\nf 128 2018 509\\nf 2364 2048 2018\\nf 268 509 2048\\nf 2018 2048 509\\nf 644 875 1078\\nf 268 2048 875\\nf 2364 1078 2048\\nf 875 2048 1078\\nf 2466 535 833\\nf 2336 1760 535\\nf 1055 833 1760\\nf 535 1760 833\\nf 489 1466 989\\nf 883 758 1466\\nf 2336 989 758\\nf 1466 758 989\\nf 988 1573 272\\nf 1055 360 1573\\nf 883 272 360\\nf 1573 360 272\\nf 2336 758 1760\\nf 883 360 758\\nf 1055 1760 360\\nf 758 360 1760\\nf 1075 655 183\\nf 1491 933 655\\nf 1439 183 933\\nf 655 933 183\\nf 128 2307 2049\\nf 1059 2263 2307\\nf 1491 2049 2263\\nf 2307 2263 2049\\nf 489 2096 1919\\nf 1439 1386 2096\\nf 1059 1919 1386\\nf 2096 1386 1919\\nf 1491 2263 933\\nf 1059 1386 2263\\nf 1439 933 1386\\nf 2263 1386 933\\nf 988 272 161\\nf 883 712 272\\nf 268 161 712\\nf 272 712 161\\nf 489 1919 1466\\nf 1059 517 1919\\nf 883 1466 517\\nf 1919 517 1466\\nf 128 509 2307\\nf 268 1217 509\\nf 1059 2307 1217\\nf 509 1217 2307\\nf 883 517 712\\nf 1059 1217 517\\nf 268 712 1217\\nf 517 1217 712\\nf 1769 1967 1287\\nf 2082 1081 1967\\nf 802 1287 1081\\nf 1967 1081 1287\\nf 2165 1213 648\\nf 2243 2066 1213\\nf 2082 648 2066\\nf 1213 2066 648\\nf 907 1633 156\\nf 802 1403 1633\\nf 2243 156 1403\\nf 1633 1403 156\\nf 2082 2066 1081\\nf 2243 1403 2066\\nf 802 1081 1403\\nf 2066 1403 1081\\nf 1497 206 1218\\nf 1122 2151 206\\nf 1520 1218 2151\\nf 206 2151 1218\\nf 2115 1752 1502\\nf 1941 1359 1752\\nf 1122 1502 1359\\nf 1752 1359 1502\\nf 2165 738 433\\nf 1520 172 738\\nf 1941 433 172\\nf 738 172 433\\nf 1122 1359 2151\\nf 1941 172 1359\\nf 1520 2151 172\\nf 1359 172 2151\\nf 2466 909 1474\\nf 727 2186 909\\nf 635 1474 2186\\nf 909 2186 1474\\nf 907 855 2372\\nf 2117 1776 855\\nf 727 2372 1776\\nf 855 1776 2372\\nf 2115 1782 543\\nf 635 1340 1782\\nf 2117 543 1340\\nf 1782 1340 543\\nf 727 1776 2186\\nf 2117 1340 1776\\nf 635 2186 1340\\nf 1776 1340 2186\\nf 2165 433 1213\\nf 1941 1021 433\\nf 2243 1213 1021\\nf 433 1021 1213\\nf 2115 543 1752\\nf 2117 1912 543\\nf 1941 1752 1912\\nf 543 1912 1752\\nf 907 156 855\\nf 2243 1088 156\\nf 2117 855 1088\\nf 156 1088 855\\nf 1941 1912 1021\\nf 2117 1088 1912\\nf 2243 1021 1088\\nf 1912 1088 1021\\nf 1075 183 408\\nf 1439 2413 183\\nf 2053 408 2413\\nf 183 2413 408\\nf 489 632 2096\\nf 1861 221 632\\nf 1439 2096 221\\nf 632 221 2096\\nf 1288 2530 2490\\nf 2053 470 2530\\nf 1861 2490 470\\nf 2530 470 2490\\nf 1439 221 2413\\nf 1861 470 221\\nf 2053 2413 470\\nf 221 470 2413\\nf 2466 1474 535\\nf 635 856 1474\\nf 2336 535 856\\nf 1474 856 535\\nf 2115 2017 1782\\nf 2313 1071 2017\\nf 635 1782 1071\\nf 2017 1071 1782\\nf 489 989 1674\\nf 2336 1849 989\\nf 2313 1674 1849\\nf 989 1849 1674\\nf 635 1071 856\\nf 2313 1849 1071\\nf 2336 856 1849\\nf 1071 1849 856\\nf 1497 362 206\\nf 327 1590 362\\nf 1122 206 1590\\nf 362 1590 206\\nf 1288 1898 2545\\nf 803 1583 1898\\nf 327 2545 1583\\nf 1898 1583 2545\\nf 2115 1502 2310\\nf 1122 2241 1502\\nf 803 2310 2241\\nf 1502 2241 2310\\nf 327 1583 1590\\nf 803 2241 1583\\nf 1122 1590 2241\\nf 1583 2241 1590\\nf 489 1674 632\\nf 2313 2442 1674\\nf 1861 632 2442\\nf 1674 2442 632\\nf 2115 2310 2017\\nf 803 523 2310\\nf 2313 2017 523\\nf 2310 523 2017\\nf 1288 2490 1898\\nf 1861 2394 2490\\nf 803 1898 2394\\nf 2490 2394 1898\\nf 2313 523 2442\\nf 803 2394 523\\nf 1861 2442 2394\\nf 523 2394 2442\\nf 300 1312 1652\\nf 527 1834 1312\\nf 2427 1652 1834\\nf 1312 1834 1652\\nf 2547 1495 756\\nf 1043 365 1495\\nf 527 756 365\\nf 1495 365 756\\nf 1594 619 577\\nf 2427 910 619\\nf 1043 577 910\\nf 619 910 577\\nf 527 365 1834\\nf 1043 910 365\\nf 2427 1834 910\\nf 365 910 1834\\nf 818 2515 1385\\nf 982 2111 2515\\nf 895 1385 2111\\nf 2515 2111 1385\\nf 1487 1062 108\\nf 987 1438 1062\\nf 982 108 1438\\nf 1062 1438 108\\nf 2547 1708 2093\\nf 895 2428 1708\\nf 987 2093 2428\\nf 1708 2428 2093\\nf 982 1438 2111\\nf 987 2428 1438\\nf 895 2111 2428\\nf 1438 2428 2111\\nf 2402 1569 686\\nf 1209 693 1569\\nf 639 686 693\\nf 1569 693 686\\nf 1594 280 959\\nf 1964 1716 280\\nf 1209 959 1716\\nf 280 1716 959\\nf 1487 353 1290\\nf 639 2077 353\\nf 1964 1290 2077\\nf 353 2077 1290\\nf 1209 1716 693\\nf 1964 2077 1716\\nf 639 693 2077\\nf 1716 2077 693\\nf 2547 2093 1495\\nf 987 603 2093\\nf 1043 1495 603\\nf 2093 603 1495\\nf 1487 1290 1062\\nf 1964 162 1290\\nf 987 1062 162\\nf 1290 162 1062\\nf 1594 577 280\\nf 1043 1568 577\\nf 1964 280 1568\\nf 577 1568 280\\nf 987 162 603\\nf 1964 1568 162\\nf 1043 603 1568\\nf 162 1568 603\\nf 906 844 1867\\nf 2498 2500 844\\nf 1428 1867 2500\\nf 844 2500 1867\\nf 1178 1002 1422\\nf 1808 2033 1002\\nf 2498 1422 2033\\nf 1002 2033 1422\\nf 2408 152 641\\nf 1428 1584 152\\nf 1808 641 1584\\nf 152 1584 641\\nf 2498 2033 2500\\nf 1808 1584 2033\\nf 1428 2500 1584\\nf 2033 1584 2500\\nf 390 184 261\\nf 1100 689 184\\nf 893 261 689\\nf 184 689 261\\nf 2224 799 1054\\nf 250 729 799\\nf 1100 1054 729\\nf 799 729 1054\\nf 1178 2005 1303\\nf 893 1837 2005\\nf 250 1303 1837\\nf 2005 1837 1303\\nf 1100 729 689\\nf 250 1837 729\\nf 893 689 1837\\nf 729 1837 689\\nf 818 2396 928\\nf 323 398 2396\\nf 1278 928 398\\nf 2396 398 928\\nf 2408 1824 1522\\nf 1265 389 1824\\nf 323 1522 389\\nf 1824 389 1522\\nf 2224 1009 1766\\nf 1278 2162 1009\\nf 1265 1766 2162\\nf 1009 2162 1766\\nf 323 389 398\\nf 1265 2162 389\\nf 1278 398 2162\\nf 389 2162 398\\nf 1178 1303 1002\\nf 250 2198 1303\\nf 1808 1002 2198\\nf 1303 2198 1002\\nf 2224 1766 799\\nf 1265 1978 1766\\nf 250 799 1978\\nf 1766 1978 799\\nf 2408 641 1824\\nf 1808 1266 641\\nf 1265 1824 1266\\nf 641 1266 1824\\nf 250 1978 2198\\nf 1265 1266 1978\\nf 1808 2198 1266\\nf 1978 1266 2198\\nf 1769 1471 1175\\nf 2292 611 1471\\nf 2019 1175 611\\nf 1471 611 1175\\nf 384 1873 1929\\nf 609 1358 1873\\nf 2292 1929 1358\\nf 1873 1358 1929\\nf 320 964 1380\\nf 2019 1334 964\\nf 609 1380 1334\\nf 964 1334 1380\\nf 2292 1358 611\\nf 609 1334 1358\\nf 2019 611 1334\\nf 1358 1334 611\\nf 2402 637 2367\\nf 28 1440 637\\nf 700 2367 1440\\nf 637 1440 2367\\nf 871 859 1538\\nf 2 1041 859\\nf 28 1538 1041\\nf 859 1041 1538\\nf 384 1816 1335\\nf 700 704 1816\\nf 2 1335 704\\nf 1816 704 1335\\nf 28 1041 1440\\nf 2 704 1041\\nf 700 1440 704\\nf 1041 704 1440\\nf 390 954 1832\\nf 652 2370 954\\nf 1432 1832 2370\\nf 954 2370 1832\\nf 320 233 537\\nf 950 2495 233\\nf 652 537 2495\\nf 233 2495 537\\nf 871 2128 1508\\nf 1432 1365 2128\\nf 950 1508 1365\\nf 2128 1365 1508\\nf 652 2495 2370\\nf 950 1365 2495\\nf 1432 2370 1365\\nf 2495 1365 2370\\nf 384 1335 1873\\nf 2 166 1335\\nf 609 1873 166\\nf 1335 166 1873\\nf 871 1508 859\\nf 950 2277 1508\\nf 2 859 2277\\nf 1508 2277 859\\nf 320 1380 233\\nf 609 2492 1380\\nf 950 233 2492\\nf 1380 2492 233\\nf 2 2277 166\\nf 950 2492 2277\\nf 609 166 2492\\nf 2277 2492 166\\nf 818 928 2515\\nf 1278 1761 928\\nf 982 2515 1761\\nf 928 1761 2515\\nf 2224 237 1009\\nf 412 2448 237\\nf 1278 1009 2448\\nf 237 2448 1009\\nf 1487 108 2207\\nf 982 1137 108\\nf 412 2207 1137\\nf 108 1137 2207\\nf 1278 2448 1761\\nf 412 1137 2448\\nf 982 1761 1137\\nf 2448 1137 1761\\nf 390 1832 184\\nf 1432 788 1832\\nf 1100 184 788\\nf 1832 788 184\\nf 871 1977 2128\\nf 457 409 1977\\nf 1432 2128 409\\nf 1977 409 2128\\nf 2224 1054 14\\nf 1100 1715 1054\\nf 457 14 1715\\nf 1054 1715 14\\nf 1432 409 788\\nf 457 1715 409\\nf 1100 788 1715\\nf 409 1715 788\\nf 2402 686 637\\nf 639 1646 686\\nf 28 637 1646\\nf 686 1646 637\\nf 1487 1950 353\\nf 2319 2337 1950\\nf 639 353 2337\\nf 1950 2337 353\\nf 871 1538 168\\nf 28 1693 1538\\nf 2319 168 1693\\nf 1538 1693 168\\nf 639 2337 1646\\nf 2319 1693 2337\\nf 28 1646 1693\\nf 2337 1693 1646\\nf 2224 14 237\\nf 457 1793 14\\nf 412 237 1793\\nf 14 1793 237\\nf 871 168 1977\\nf 2319 1938 168\\nf 457 1977 1938\\nf 168 1938 1977\\nf 1487 2207 1950\\nf 412 84 2207\\nf 2319 1950 84\\nf 2207 84 1950\\nf 457 1938 1793\\nf 2319 84 1938\\nf 412 1793 84\\nf 1938 84 1793\\nf 2312 789 1315\\nf 2331 676 789\\nf 20 1315 676\\nf 789 676 1315\\nf 149 120 2112\\nf 2421 1534 120\\nf 2331 2112 1534\\nf 120 1534 2112\\nf 19 2101 1136\\nf 20 1821 2101\\nf 2421 1136 1821\\nf 2101 1821 1136\\nf 2331 1534 676\\nf 2421 1821 1534\\nf 20 676 1821\\nf 1534 1821 676\\nf 437 1738 396\\nf 2325 812 1738\\nf 766 396 812\\nf 1738 812 396\\nf 1953 1154 569\\nf 1087 2072 1154\\nf 2325 569 2072\\nf 1154 2072 569\\nf 149 830 1615\\nf 766 538 830\\nf 1087 1615 538\\nf 830 538 1615\\nf 2325 2072 812\\nf 1087 538 2072\\nf 766 812 538\\nf 2072 538 812\\nf 1510 1368 2157\\nf 2171 1734 1368\\nf 2333 2157 1734\\nf 1368 1734 2157\\nf 19 1498 824\\nf 1958 2034 1498\\nf 2171 824 2034\\nf 1498 2034 824\\nf 1953 1485 1072\\nf 2333 1230 1485\\nf 1958 1072 1230\\nf 1485 1230 1072\\nf 2171 2034 1734\\nf 1958 1230 2034\\nf 2333 1734 1230\\nf 2034 1230 1734\\nf 149 1615 120\\nf 1087 1260 1615\\nf 2421 120 1260\\nf 1615 1260 120\\nf 1953 1072 1154\\nf 1958 585 1072\\nf 1087 1154 585\\nf 1072 585 1154\\nf 19 1136 1498\\nf 2421 513 1136\\nf 1958 1498 513\\nf 1136 513 1498\\nf 1087 585 1260\\nf 1958 513 585\\nf 2421 1260 513\\nf 585 513 1260\\nf 906 428 1586\\nf 1339 1920 428\\nf 2256 1586 1920\\nf 428 1920 1586\\nf 1168 590 391\\nf 86 1433 590\\nf 1339 391 1433\\nf 590 1433 391\\nf 35 2233 1070\\nf 2256 1639 2233\\nf 86 1070 1639\\nf 2233 1639 1070\\nf 1339 1433 1920\\nf 86 1639 1433\\nf 2256 1920 1639\\nf 1433 1639 1920\\nf 2478 684 2385\\nf 2429 2546 684\\nf 12 2385 2546\\nf 684 2546 2385\\nf 2092 848 1883\\nf 2094 2001 848\\nf 2429 1883 2001\\nf 848 2001 1883\\nf 1168 1472 56\\nf 12 1995 1472\\nf 2094 56 1995\\nf 1472 1995 56\\nf 2429 2001 2546\\nf 2094 1995 2001\\nf 12 2546 1995\\nf 2001 1995 2546\\nf 437 1660 136\\nf 2163 2468 1660\\nf 2121 136 2468\\nf 1660 2468 136\\nf 35 1444 737\\nf 1802 1900 1444\\nf 2163 737 1900\\nf 1444 1900 737\\nf 2092 1763 838\\nf 2121 2109 1763\\nf 1802 838 2109\\nf 1763 2109 838\\nf 2163 1900 2468\\nf 1802 2109 1900\\nf 2121 2468 2109\\nf 1900 2109 2468\\nf 1168 56 590\\nf 2094 677 56\\nf 86 590 677\\nf 56 677 590\\nf 2092 838 848\\nf 1802 2220 838\\nf 2094 848 2220\\nf 838 2220 848\\nf 35 1070 1444\\nf 86 2039 1070\\nf 1802 1444 2039\\nf 1070 2039 1444\\nf 2094 2220 677\\nf 1802 2039 2220\\nf 86 677 2039\\nf 2220 2039 677\\nf 887 1247 948\\nf 1133 615 1247\\nf 1302 948 615\\nf 1247 615 948\\nf 1291 2532 1753\\nf 1128 866 2532\\nf 1133 1753 866\\nf 2532 866 1753\\nf 2521 2376 1341\\nf 1302 348 2376\\nf 1128 1341 348\\nf 2376 348 1341\\nf 1133 866 615\\nf 1128 348 866\\nf 1302 615 348\\nf 866 348 615\\nf 1510 1605 382\\nf 330 476 1605\\nf 49 382 476\\nf 1605 476 382\\nf 2197 1393 313\\nf 1822 1864 1393\\nf 330 313 1864\\nf 1393 1864 313\\nf 1291 662 1503\\nf 49 191 662\\nf 1822 1503 191\\nf 662 191 1503\\nf 330 1864 476\\nf 1822 191 1864\\nf 49 476 191\\nf 1864 191 476\\nf 2478 1382 971\\nf 485 2196 1382\\nf 1318 971 2196\\nf 1382 2196 971\\nf 2521 1285 2297\\nf 81 2404 1285\\nf 485 2297 2404\\nf 1285 2404 2297\\nf 2197 825 2548\\nf 1318 978 825\\nf 81 2548 978\\nf 825 978 2548\\nf 485 2404 2196\\nf 81 978 2404\\nf 1318 2196 978\\nf 2404 978 2196\\nf 1291 1503 2532\\nf 1822 1163 1503\\nf 1128 2532 1163\\nf 1503 1163 2532\\nf 2197 2548 1393\\nf 81 2127 2548\\nf 1822 1393 2127\\nf 2548 2127 1393\\nf 2521 1341 1285\\nf 1128 1882 1341\\nf 81 1285 1882\\nf 1341 1882 1285\\nf 1822 2127 1163\\nf 81 1882 2127\\nf 1128 1163 1882\\nf 2127 1882 1163\\nf 437 136 1738\\nf 2121 1844 136\\nf 2325 1738 1844\\nf 136 1844 1738\\nf 2092 868 1763\\nf 2067 750 868\\nf 2121 1763 750\\nf 868 750 1763\\nf 1953 569 126\\nf 2325 1355 569\\nf 2067 126 1355\\nf 569 1355 126\\nf 2121 750 1844\\nf 2067 1355 750\\nf 2325 1844 1355\\nf 750 1355 1844\\nf 2478 971 684\\nf 1318 1622 971\\nf 2429 684 1622\\nf 971 1622 684\\nf 2197 500 825\\nf 985 1410 500\\nf 1318 825 1410\\nf 500 1410 825\\nf 2092 1883 1231\\nf 2429 986 1883\\nf 985 1231 986\\nf 1883 986 1231\\nf 1318 1410 1622\\nf 985 986 1410\\nf 2429 1622 986\\nf 1410 986 1622\\nf 1510 2157 1605\\nf 2333 2338 2157\\nf 330 1605 2338\\nf 2157 2338 1605\\nf 1953 394 1485\\nf 798 976 394\\nf 2333 1485 976\\nf 394 976 1485\\nf 2197 313 1759\\nf 330 1109 313\\nf 798 1759 1109\\nf 313 1109 1759\\nf 2333 976 2338\\nf 798 1109 976\\nf 330 2338 1109\\nf 976 1109 2338\\nf 2092 1231 868\\nf 985 1131 1231\\nf 2067 868 1131\\nf 1231 1131 868\\nf 2197 1759 500\\nf 798 2401 1759\\nf 985 500 2401\\nf 1759 2401 500\\nf 1953 126 394\\nf 2067 754 126\\nf 798 394 754\\nf 126 754 394\\nf 985 2401 1131\\nf 798 754 2401\\nf 2067 1131 754\\nf 2401 754 1131\\nf 2391 1998 1401\\nf 1220 922 1998\\nf 2043 1401 922\\nf 1998 922 1401\\nf 1756 111 2516\\nf 1275 2161 111\\nf 1220 2516 2161\\nf 111 2161 2516\\nf 720 2485 1751\\nf 2043 1293 2485\\nf 1275 1751 1293\\nf 2485 1293 1751\\nf 1220 2161 922\\nf 1275 1293 2161\\nf 2043 922 1293\\nf 2161 1293 922\\nf 678 1691 1778\\nf 2254 1607 1691\\nf 1420 1778 1607\\nf 1691 1607 1778\\nf 1784 1811 2023\\nf 2050 337 1811\\nf 2254 2023 337\\nf 1811 337 2023\\nf 1756 965 763\\nf 1420 101 965\\nf 2050 763 101\\nf 965 101 763\\nf 2254 337 1607\\nf 2050 101 337\\nf 1420 1607 101\\nf 337 101 1607\\nf 560 2085 38\\nf 857 1606 2085\\nf 468 38 1606\\nf 2085 1606 38\\nf 720 1102 694\\nf 675 277 1102\\nf 857 694 277\\nf 1102 277 694\\nf 1784 1544 27\\nf 468 1241 1544\\nf 675 27 1241\\nf 1544 1241 27\\nf 857 277 1606\\nf 675 1241 277\\nf 468 1606 1241\\nf 277 1241 1606\\nf 1756 763 111\\nf 2050 1767 763\\nf 1275 111 1767\\nf 763 1767 111\\nf 1784 27 1811\\nf 675 626 27\\nf 2050 1811 626\\nf 27 626 1811\\nf 720 1751 1102\\nf 1275 1000 1751\\nf 675 1102 1000\\nf 1751 1000 1102\\nf 2050 626 1767\\nf 675 1000 626\\nf 1275 1767 1000\\nf 626 1000 1767\\nf 2312 228 2104\\nf 733 1490 228\\nf 2270 2104 1490\\nf 228 1490 2104\\nf 307 1373 2360\\nf 1325 2517 1373\\nf 733 2360 2517\\nf 1373 2517 2360\\nf 236 2431 1176\\nf 2270 1545 2431\\nf 1325 1176 1545\\nf 2431 1545 1176\\nf 733 2517 1490\\nf 1325 1545 2517\\nf 2270 1490 1545\\nf 2517 1545 1490\\nf 116 169 2353\\nf 1620 139 169\\nf 2465 2353 139\\nf 169 139 2353\\nf 297 1276 1859\\nf 1762 1557 1276\\nf 1620 1859 1557\\nf 1276 1557 1859\\nf 307 1304 2036\\nf 2465 2443 1304\\nf 1762 2036 2443\\nf 1304 2443 2036\\nf 1620 1557 139\\nf 1762 2443 1557\\nf 2465 139 2443\\nf 1557 2443 139\\nf 678 957 1536\\nf 276 1179 957\\nf 1183 1536 1179\\nf 957 1179 1536\\nf 236 1956 1045\\nf 192 614 1956\\nf 276 1045 614\\nf 1956 614 1045\\nf 297 1064 1435\\nf 1183 1651 1064\\nf 192 1435 1651\\nf 1064 1651 1435\\nf 276 614 1179\\nf 192 1651 614\\nf 1183 1179 1651\\nf 614 1651 1179\\nf 307 2036 1373\\nf 1762 1973 2036\\nf 1325 1373 1973\\nf 2036 1973 1373\\nf 297 1435 1276\\nf 192 1835 1435\\nf 1762 1276 1835\\nf 1435 1835 1276\\nf 236 1176 1956\\nf 1325 2139 1176\\nf 192 1956 2139\\nf 1176 2139 1956\\nf 1762 1835 1973\\nf 192 2139 1835\\nf 1325 1973 2139\\nf 1835 2139 1973\\nf 1831 1013 2423\\nf 1916 839 1013\\nf 930 2423 839\\nf 1013 839 2423\\nf 57 2374 2317\\nf 123 2069 2374\\nf 1916 2317 2069\\nf 2374 2069 2317\\nf 952 1053 112\\nf 930 2100 1053\\nf 123 112 2100\\nf 1053 2100 112\\nf 1916 2069 839\\nf 123 2100 2069\\nf 930 839 2100\\nf 2069 2100 839\\nf 560 1765 1710\\nf 1342 900 1765\\nf 975 1710 900\\nf 1765 900 1710\\nf 1678 602 1431\\nf 2416 940 602\\nf 1342 1431 940\\nf 602 940 1431\\nf 57 1754 621\\nf 975 1542 1754\\nf 2416 621 1542\\nf 1754 1542 621\\nf 1342 940 900\\nf 2416 1542 940\\nf 975 900 1542\\nf 940 1542 900\\nf 116 371 2469\\nf 475 1578 371\\nf 567 2469 1578\\nf 371 1578 2469\\nf 952 1719 1728\\nf 185 2407 1719\\nf 475 1728 2407\\nf 1719 2407 1728\\nf 1678 649 279\\nf 567 2290 649\\nf 185 279 2290\\nf 649 2290 279\\nf 475 2407 1578\\nf 185 2290 2407\\nf 567 1578 2290\\nf 2407 2290 1578\\nf 57 621 2374\\nf 2416 873 621\\nf 123 2374 873\\nf 621 873 2374\\nf 1678 279 602\\nf 185 1476 279\\nf 2416 602 1476\\nf 279 1476 602\\nf 952 112 1719\\nf 123 52 112\\nf 185 1719 52\\nf 112 52 1719\\nf 2416 1476 873\\nf 185 52 1476\\nf 123 873 52\\nf 1476 52 873\\nf 678 1536 1691\\nf 1183 2340 1536\\nf 2254 1691 2340\\nf 1536 2340 1691\\nf 297 1556 1064\\nf 2011 1142 1556\\nf 1183 1064 1142\\nf 1556 1142 1064\\nf 1784 2023 1889\\nf 2254 1286 2023\\nf 2011 1889 1286\\nf 2023 1286 1889\\nf 1183 1142 2340\\nf 2011 1286 1142\\nf 2254 2340 1286\\nf 1142 1286 2340\\nf 116 2469 169\\nf 567 2493 2469\\nf 1620 169 2493\\nf 2469 2493 169\\nf 1678 1963 649\\nf 493 2090 1963\\nf 567 649 2090\\nf 1963 2090 649\\nf 297 1859 2188\\nf 1620 661 1859\\nf 493 2188 661\\nf 1859 661 2188\\nf 567 2090 2493\\nf 493 661 2090\\nf 1620 2493 661\\nf 2090 661 2493\\nf 560 38 1765\\nf 468 2462 38\\nf 1342 1765 2462\\nf 38 2462 1765\\nf 1784 1223 1544\\nf 902 805 1223\\nf 468 1544 805\\nf 1223 805 1544\\nf 1678 1431 690\\nf 1342 8 1431\\nf 902 690 8\\nf 1431 8 690\\nf 468 805 2462\\nf 902 8 805\\nf 1342 2462 8\\nf 805 8 2462\\nf 297 2188 1556\\nf 493 2406 2188\\nf 2011 1556 2406\\nf 2188 2406 1556\\nf 1678 690 1963\\nf 902 1323 690\\nf 493 1963 1323\\nf 690 1323 1963\\nf 1784 1889 1223\\nf 2011 1121 1889\\nf 902 1223 1121\\nf 1889 1121 1223\\nf 493 1323 2406\\nf 902 1121 1323\\nf 2011 2406 1121\\nf 1323 1121 2406\\nf 2008 2078 421\\nf 2475 2076 2078\\nf 2147 421 2076\\nf 2078 2076 421\\nf 2481 2504 2075\\nf 393 2536 2504\\nf 2475 2075 2536\\nf 2504 2536 2075\\nf 2425 877 2056\\nf 2147 776 877\\nf 393 2056 776\\nf 877 776 2056\\nf 2475 2536 2076\\nf 393 776 2536\\nf 2147 2076 776\\nf 2536 776 2076\\nf 2194 923 2045\\nf 1038 223 923\\nf 2042 2045 223\\nf 923 223 2045\\nf 2458 852 787\\nf 752 2474 852\\nf 1038 787 2474\\nf 852 2474 787\\nf 2481 946 458\\nf 2042 96 946\\nf 752 458 96\\nf 946 96 458\\nf 1038 2474 223\\nf 752 96 2474\\nf 2042 223 96\\nf 2474 96 223\\nf 1083 316 1094\\nf 1562 51 316\\nf 555 1094 51\\nf 316 51 1094\\nf 2425 1742 728\\nf 795 2180 1742\\nf 1562 728 2180\\nf 1742 2180 728\\nf 2458 2250 2315\\nf 555 1426 2250\\nf 795 2315 1426\\nf 2250 1426 2315\\nf 1562 2180 51\\nf 795 1426 2180\\nf 555 51 1426\\nf 2180 1426 51\\nf 2481 458 2504\\nf 752 2438 458\\nf 393 2504 2438\\nf 458 2438 2504\\nf 2458 2315 852\\nf 795 220 2315\\nf 752 852 220\\nf 2315 220 852\\nf 2425 2056 1742\\nf 393 41 2056\\nf 795 1742 41\\nf 2056 41 1742\\nf 752 220 2438\\nf 795 41 220\\nf 393 2438 41\\nf 220 41 2438\\nf 2391 1333 2309\\nf 1027 2511 1333\\nf 2327 2309 2511\\nf 1333 2511 2309\\nf 760 559 1458\\nf 1773 2105 559\\nf 1027 1458 2105\\nf 559 2105 1458\\nf 1295 58 2524\\nf 2327 138 58\\nf 1773 2524 138\\nf 58 138 2524\\nf 1027 2105 2511\\nf 1773 138 2105\\nf 2327 2511 138\\nf 2105 138 2511\\nf 1525 2482 901\\nf 2542 2202 2482\\nf 1331 901 2202\\nf 2482 2202 901\\nf 88 1058 564\\nf 815 685 1058\\nf 2542 564 685\\nf 1058 685 564\\nf 760 2239 37\\nf 1331 1199 2239\\nf 815 37 1199\\nf 2239 1199 37\\nf 2542 685 2202\\nf 815 1199 685\\nf 1331 2202 1199\\nf 685 1199 2202\\nf 2194 2181 480\\nf 1968 2415 2181\\nf 1354 480 2415\\nf 2181 2415 480\\nf 1295 1539 2120\\nf 1988 1787 1539\\nf 1968 2120 1787\\nf 1539 1787 2120\\nf 88 540 2368\\nf 1354 218 540\\nf 1988 2368 218\\nf 540 218 2368\\nf 1968 1787 2415\\nf 1988 218 1787\\nf 1354 2415 218\\nf 1787 218 2415\\nf 760 37 559\\nf 815 1193 37\\nf 1773 559 1193\\nf 37 1193 559\\nf 88 2368 1058\\nf 1988 361 2368\\nf 815 1058 361\\nf 2368 361 1058\\nf 1295 2524 1539\\nf 1773 1135 2524\\nf 1988 1539 1135\\nf 2524 1135 1539\\nf 815 361 1193\\nf 1988 1135 361\\nf 1773 1193 1135\\nf 361 1135 1193\\nf 2282 1463 1704\\nf 1445 302 1463\\nf 1877 1704 302\\nf 1463 302 1704\\nf 1388 2284 122\\nf 765 495 2284\\nf 1445 122 495\\nf 2284 495 122\\nf 931 840 1825\\nf 1877 2380 840\\nf 765 1825 2380\\nf 840 2380 1825\\nf 1445 495 302\\nf 765 2380 495\\nf 1877 302 2380\\nf 495 2380 302\\nf 1083 2519 1745\\nf 2463 843 2519\\nf 2229 1745 843\\nf 2519 843 1745\\nf 584 1389 1096\\nf 1632 2071 1389\\nf 2463 1096 2071\\nf 1389 2071 1096\\nf 1388 1016 1612\\nf 2229 1774 1016\\nf 1632 1612 1774\\nf 1016 1774 1612\\nf 2463 2071 843\\nf 1632 1774 2071\\nf 2229 843 1774\\nf 2071 1774 843\\nf 1525 1390 968\\nf 473 1089 1390\\nf 1736 968 1089\\nf 1390 1089 968\\nf 931 1372 1449\\nf 24 2032 1372\\nf 473 1449 2032\\nf 1372 2032 1449\\nf 584 518 1939\\nf 1736 30 518\\nf 24 1939 30\\nf 518 30 1939\\nf 473 2032 1089\\nf 24 30 2032\\nf 1736 1089 30\\nf 2032 30 1089\\nf 1388 1612 2284\\nf 1632 2221 1612\\nf 765 2284 2221\\nf 1612 2221 2284\\nf 584 1939 1389\\nf 24 1801 1939\\nf 1632 1389 1801\\nf 1939 1801 1389\\nf 931 1825 1372\\nf 765 2283 1825\\nf 24 1372 2283\\nf 1825 2283 1372\\nf 1632 1801 2221\\nf 24 2283 1801\\nf 765 2221 2283\\nf 1801 2283 2221\\nf 2194 480 923\\nf 1354 1574 480\\nf 1038 923 1574\\nf 480 1574 923\\nf 88 2189 540\\nf 2089 230 2189\\nf 1354 540 230\\nf 2189 230 540\\nf 2458 787 1712\\nf 1038 1050 787\\nf 2089 1712 1050\\nf 787 1050 1712\\nf 1354 230 1574\\nf 2089 1050 230\\nf 1038 1574 1050\\nf 230 1050 1574\\nf 1525 968 2482\\nf 1736 1819 968\\nf 2542 2482 1819\\nf 968 1819 2482\\nf 584 109 518\\nf 1673 1243 109\\nf 1736 518 1243\\nf 109 1243 518\\nf 88 564 606\\nf 2542 1722 564\\nf 1673 606 1722\\nf 564 1722 606\\nf 1736 1243 1819\\nf 1673 1722 1243\\nf 2542 1819 1722\\nf 1243 1722 1819\\nf 1083 1094 2519\\nf 555 1195 1094\\nf 2463 2519 1195\\nf 1094 1195 2519\\nf 2458 491 2250\\nf 2278 858 491\\nf 555 2250 858\\nf 491 858 2250\\nf 584 1096 2285\\nf 2463 85 1096\\nf 2278 2285 85\\nf 1096 85 2285\\nf 555 858 1195\\nf 2278 85 858\\nf 2463 1195 85\\nf 858 85 1195\\nf 88 606 2189\\nf 1673 463 606\\nf 2089 2189 463\\nf 606 463 2189\\nf 584 2285 109\\nf 2278 1052 2285\\nf 1673 109 1052\\nf 2285 1052 109\\nf 2458 1712 491\\nf 2089 310 1712\\nf 2278 491 310\\nf 1712 310 491\\nf 1673 1052 463\\nf 2278 310 1052\\nf 2089 463 310\\nf 1052 310 463\\nf 1769 1287 1124\\nf 802 842 1287\\nf 1553 1124 842\\nf 1287 842 1124\\nf 907 984 1633\\nf 2437 1182 984\\nf 802 1633 1182\\nf 984 1182 1633\\nf 2014 1105 1294\\nf 1553 809 1105\\nf 2437 1294 809\\nf 1105 809 1294\\nf 802 1182 842\\nf 2437 809 1182\\nf 1553 842 809\\nf 1182 809 842\\nf 2466 784 909\\nf 479 1171 784\\nf 727 909 1171\\nf 784 1171 909\\nf 2216 651 2242\\nf 1966 4 651\\nf 479 2242 4\\nf 651 4 2242\\nf 907 2372 1261\\nf 727 1561 2372\\nf 1966 1261 1561\\nf 2372 1561 1261\\nf 479 4 1171\\nf 1966 1561 4\\nf 727 1171 1561\\nf 4 1561 1171\\nf 1191 1790 1155\\nf 2169 2399 1790\\nf 1492 1155 2399\\nf 1790 2399 1155\\nf 2014 941 1157\\nf 1208 1409 941\\nf 2169 1157 1409\\nf 941 1409 1157\\nf 2216 646 246\\nf 1492 2470 646\\nf 1208 246 2470\\nf 646 2470 246\\nf 2169 1409 2399\\nf 1208 2470 1409\\nf 1492 2399 2470\\nf 1409 2470 2399\\nf 907 1261 984\\nf 1966 68 1261\\nf 2437 984 68\\nf 1261 68 984\\nf 2216 246 651\\nf 1208 801 246\\nf 1966 651 801\\nf 246 801 651\\nf 2014 1294 941\\nf 2437 600 1294\\nf 1208 941 600\\nf 1294 600 941\\nf 1966 801 68\\nf 1208 600 801\\nf 2437 68 600\\nf 801 600 68\\nf 2008 777 1427\\nf 746 2192 777\\nf 644 1427 2192\\nf 777 2192 1427\\nf 29 1979 990\\nf 1324 709 1979\\nf 746 990 709\\nf 1979 709 990\\nf 988 2506 962\\nf 644 778 2506\\nf 1324 962 778\\nf 2506 778 962\\nf 746 709 2192\\nf 1324 778 709\\nf 644 2192 778\\nf 709 778 2192\\nf 961 1529 1871\\nf 1641 823 1529\\nf 878 1871 823\\nf 1529 823 1871\\nf 1546 816 369\\nf 339 295 816\\nf 1641 369 295\\nf 816 295 369\\nf 29 1868 914\\nf 878 612 1868\\nf 339 914 612\\nf 1868 612 914\\nf 1641 295 823\\nf 339 612 295\\nf 878 823 612\\nf 295 612 823\\nf 2466 833 867\\nf 1055 2225 833\\nf 879 867 2225\\nf 833 2225 867\\nf 988 2106 1573\\nf 557 1588 2106\\nf 1055 1573 1588\\nf 2106 1588 1573\\nf 1546 2405 1644\\nf 879 2164 2405\\nf 557 1644 2164\\nf 2405 2164 1644\\nf 1055 1588 2225\\nf 557 2164 1588\\nf 879 2225 2164\\nf 1588 2164 2225\\nf 29 914 1979\\nf 339 1494 914\\nf 1324 1979 1494\\nf 914 1494 1979\\nf 1546 1644 816\\nf 557 381 1644\\nf 339 816 381\\nf 1644 381 816\\nf 988 962 2106\\nf 1324 1597 962\\nf 557 2106 1597\\nf 962 1597 2106\\nf 339 381 1494\\nf 557 1597 381\\nf 1324 1494 1597\\nf 381 1597 1494\\nf 1777 566 1374\\nf 309 2228 566\\nf 282 1374 2228\\nf 566 2228 1374\\nf 487 1857 2304\\nf 2264 1415 1857\\nf 309 2304 1415\\nf 1857 1415 2304\\nf 1705 1437 1074\\nf 282 2013 1437\\nf 2264 1074 2013\\nf 1437 2013 1074\\nf 309 1415 2228\\nf 2264 2013 1415\\nf 282 2228 2013\\nf 1415 2013 2228\\nf 1191 1036 1768\\nf 1026 1638 1036\\nf 130 1768 1638\\nf 1036 1638 1768\\nf 364 536 1621\\nf 1336 800 536\\nf 1026 1621 800\\nf 536 800 1621\\nf 487 117 419\\nf 130 1515 117\\nf 1336 419 1515\\nf 117 1515 419\\nf 1026 800 1638\\nf 1336 1515 800\\nf 130 1638 1515\\nf 800 1515 1638\\nf 961 97 405\\nf 934 820 97\\nf 1338 405 820\\nf 97 820 405\\nf 1705 1890 1262\\nf 2119 1901 1890\\nf 934 1262 1901\\nf 1890 1901 1262\\nf 364 1664 2403\\nf 1338 308 1664\\nf 2119 2403 308\\nf 1664 308 2403\\nf 934 1901 820\\nf 2119 308 1901\\nf 1338 820 308\\nf 1901 308 820\\nf 487 419 1857\\nf 1336 113 419\\nf 2264 1857 113\\nf 419 113 1857\\nf 364 2403 536\\nf 2119 1506 2403\\nf 1336 536 1506\\nf 2403 1506 536\\nf 1705 1074 1890\\nf 2264 387 1074\\nf 2119 1890 387\\nf 1074 387 1890\\nf 1336 1506 113\\nf 2119 387 1506\\nf 2264 113 387\\nf 1506 387 113\\nf 2466 867 784\\nf 879 355 867\\nf 479 784 355\\nf 867 355 784\\nf 1546 2518 2405\\nf 2561 1392 2518\\nf 879 2405 1392\\nf 2518 1392 2405\\nf 2216 2242 640\\nf 479 1617 2242\\nf 2561 640 1617\\nf 2242 1617 640\\nf 879 1392 355\\nf 2561 1617 1392\\nf 479 355 1617\\nf 1392 1617 355\\nf 961 405 1529\\nf 1338 1582 405\\nf 1641 1529 1582\\nf 405 1582 1529\\nf 364 2118 1664\\nf 1314 1922 2118\\nf 1338 1664 1922\\nf 2118 1922 1664\\nf 1546 369 1786\\nf 1641 1526 369\\nf 1314 1786 1526\\nf 369 1526 1786\\nf 1338 1922 1582\\nf 1314 1526 1922\\nf 1641 1582 1526\\nf 1922 1526 1582\\nf 1191 1155 1036\\nf 1492 1079 1155\\nf 1026 1036 1079\\nf 1155 1079 1036\\nf 2216 1189 646\\nf 735 147 1189\\nf 1492 646 147\\nf 1189 147 646\\nf 364 1621 39\\nf 1026 1482 1621\\nf 735 39 1482\\nf 1621 1482 39\\nf 1492 147 1079\\nf 735 1482 147\\nf 1026 1079 1482\\nf 147 1482 1079\\nf 1546 1786 2518\\nf 1314 1601 1786\\nf 2561 2518 1601\\nf 1786 1601 2518\\nf 364 39 2118\\nf 735 970 39\\nf 1314 2118 970\\nf 39 970 2118\\nf 2216 640 1189\\nf 2561 1947 640\\nf 735 1189 1947\\nf 640 1947 1189\\nf 1314 970 1601\\nf 735 1947 970\\nf 2561 1601 1947\\nf 970 1947 1601'" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 29 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "pyBAYFy0cQuj" - }, - "source": [ - "" - ], - "execution_count": null, - "outputs": [] - } - ] -} \ No newline at end of file From 7895986c319bf97268f404d6c2c97f2e568b2cbe Mon Sep 17 00:00:00 2001 From: Kieran Nehil-Puleo Date: Tue, 25 May 2021 19:32:57 -0400 Subject: [PATCH 4/8] Replaced pymesh with trimesh andremoved pytorch3d dependencies --- .../sampling_and_meshing/O-CNN/randomizePointCloud.py | 5 +++-- auxiliary/sampling_and_meshing/O-CNN/sample30kpoints.py | 1 - .../sampling_and_meshing/Shuffle/parallel_shuffle.py | 9 +++++---- auxiliary/sampling_and_meshing/Shuffle/shuffle.py | 9 +++++---- dataset/dataset_shapenet.py | 5 +++-- dataset/download_shapenet_pointclouds.sh | 3 +-- dataset/mesh_processor.py | 3 +-- model/atlasnet.py | 7 ++++--- model/model.py | 2 -- model/styleatlasnet.py | 7 ++++--- model/template.py | 7 ++++--- training/metro.py | 7 ++++--- training/trainer.py | 5 +++-- training/trainer_loss.py | 9 +++++---- 14 files changed, 42 insertions(+), 37 deletions(-) diff --git a/auxiliary/sampling_and_meshing/O-CNN/randomizePointCloud.py b/auxiliary/sampling_and_meshing/O-CNN/randomizePointCloud.py index 571813d..337d977 100644 --- a/auxiliary/sampling_and_meshing/O-CNN/randomizePointCloud.py +++ b/auxiliary/sampling_and_meshing/O-CNN/randomizePointCloud.py @@ -1,7 +1,8 @@ import argparse from os import listdir from os.path import isfile, join -import pymesh +# import pymesh +import trimesh import numpy as np import copy import joblib @@ -15,7 +16,7 @@ def shuffle_pc(file, output_path, limit=None, count=None): try: if not os.path.exists(output_path + ".npy"): - mesh = pymesh.load_mesh(file) + mesh = trimesh.load_mesh(file) vertices = copy.deepcopy(mesh.vertices) permutation = np.random.permutation(len(vertices)) diff --git a/auxiliary/sampling_and_meshing/O-CNN/sample30kpoints.py b/auxiliary/sampling_and_meshing/O-CNN/sample30kpoints.py index 055634d..339db52 100644 --- a/auxiliary/sampling_and_meshing/O-CNN/sample30kpoints.py +++ b/auxiliary/sampling_and_meshing/O-CNN/sample30kpoints.py @@ -1,7 +1,6 @@ import argparse from os import listdir from os.path import isfile, join -import pymesh import numpy as np import copy import joblib diff --git a/auxiliary/sampling_and_meshing/Shuffle/parallel_shuffle.py b/auxiliary/sampling_and_meshing/Shuffle/parallel_shuffle.py index eb5e55e..d34e141 100644 --- a/auxiliary/sampling_and_meshing/Shuffle/parallel_shuffle.py +++ b/auxiliary/sampling_and_meshing/Shuffle/parallel_shuffle.py @@ -4,7 +4,8 @@ import argparse from os import listdir from os.path import isfile, join -import pymesh +# import pymesh +import trimesh import numpy as np import copy import joblib @@ -13,18 +14,18 @@ def shuffle_pc(file, output_path): - mesh = pymesh.load_mesh(file) + mesh = trimesh.load_mesh(file) vertices = copy.deepcopy(mesh.vertices) permutation = np.random.permutation(len(vertices)) vertices = vertices[permutation] - new_mesh = pymesh.meshio.form_mesh(vertices, mesh.faces) + new_mesh = trimesh.Trimesh(vertices, faces = mesh.faces) new_mesh.add_attribute("vertex_nx") new_mesh.set_attribute("vertex_nx", mesh.get_vertex_attribute("vertex_nx")[permutation]) new_mesh.add_attribute("vertex_ny") new_mesh.set_attribute("vertex_ny", mesh.get_vertex_attribute("vertex_ny")[permutation]) new_mesh.add_attribute("vertex_nz") new_mesh.set_attribute("vertex_nz", mesh.get_vertex_attribute("vertex_nz")[permutation]) - pymesh.save_mesh(output_path, new_mesh, ascii=True, anonymous=True, use_float=True, *new_mesh.get_attribute_names()) + new_mesh.export(output_path) def main(): diff --git a/auxiliary/sampling_and_meshing/Shuffle/shuffle.py b/auxiliary/sampling_and_meshing/Shuffle/shuffle.py index 4342d15..8453dad 100644 --- a/auxiliary/sampling_and_meshing/Shuffle/shuffle.py +++ b/auxiliary/sampling_and_meshing/Shuffle/shuffle.py @@ -4,7 +4,8 @@ import argparse from os import listdir from os.path import isfile, join -import pymesh +# import pymesh +import trimesh import numpy as np import copy @@ -13,18 +14,18 @@ def shuffle_pc(file, output_path): """ Function to shuffle a point cloud produced by virtual scanner. """ - mesh = pymesh.load_mesh(file) + mesh = trimesh.load_mesh(file) vertices = copy.deepcopy(mesh.vertices) permutation = np.random.permutation(len(vertices)) vertices = vertices[permutation] - new_mesh = pymesh.meshio.form_mesh(vertices, mesh.faces) + new_mesh = trimesh.Trimesh(vertices, faces = mesh.faces) new_mesh.add_attribute("vertex_nx") new_mesh.set_attribute("vertex_nx", mesh.get_vertex_attribute("vertex_nx")[permutation]) new_mesh.add_attribute("vertex_ny") new_mesh.set_attribute("vertex_ny", mesh.get_vertex_attribute("vertex_ny")[permutation]) new_mesh.add_attribute("vertex_nz") new_mesh.set_attribute("vertex_nz", mesh.get_vertex_attribute("vertex_nz")[permutation]) - pymesh.save_mesh(output_path, new_mesh, ascii=True, anonymous=True, use_float=True, *new_mesh.get_attribute_names()) + new_mesh.export(output_path) def main(): diff --git a/dataset/dataset_shapenet.py b/dataset/dataset_shapenet.py index 82fc884..c6fbd07 100644 --- a/dataset/dataset_shapenet.py +++ b/dataset/dataset_shapenet.py @@ -253,8 +253,9 @@ def load_point_input(self, path): if ext == 'npy': points = np.load(path) elif ext == 'ply' or ext == 'obj': - import pymesh - points = pymesh.load_mesh(path).vertices + # import pymesh + import trimesh + points = trimesh.load_mesh(path).vertices else: print('invalid file extension') diff --git a/dataset/download_shapenet_pointclouds.sh b/dataset/download_shapenet_pointclouds.sh index 38afdce..a0cd2cf 100755 --- a/dataset/download_shapenet_pointclouds.sh +++ b/dataset/download_shapenet_pointclouds.sh @@ -5,8 +5,7 @@ function gdrive_download () { wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$CONFIRM&id=$1" -O $2 rm -rf /tmp/cookies.txt } -cd dataset -mkdir data + gdrive_download 1MMCYOqSalz77dduKahqDEQKFP9aCvUCy data/ShapeNetV1PointCloud.zip cd data unzip ShapeNetV1PointCloud.zip diff --git a/dataset/mesh_processor.py b/dataset/mesh_processor.py index 7a8732d..da0e0fc38 100644 --- a/dataset/mesh_processor.py +++ b/dataset/mesh_processor.py @@ -1,4 +1,3 @@ -import pymesh import numpy as np from os.path import join, dirname @@ -34,4 +33,4 @@ def save(mesh, path, colormap): mesh.set_attribute("vertex_blue", colormap.colormap[vertex_sources][:, 2]) except: pass - pymesh.save_mesh(path[:-3] + "ply", mesh, *mesh.get_attribute_names(), ascii=True) + mesh.export(path[:-3] + "ply") diff --git a/model/atlasnet.py b/model/atlasnet.py index ea4c5dd..ec2ce8d 100644 --- a/model/atlasnet.py +++ b/model/atlasnet.py @@ -61,7 +61,7 @@ def forward(self, latent_vector, train=True): def generate_mesh(self, latent_vector): assert latent_vector.size(0)==1, "input should have batch size 1!" - import pymesh + # import pymesh input_points = [self.template[i].get_regular_points(self.nb_pts_in_primitive, latent_vector.device) for i in range(self.opt.nb_primitives)] input_points = [input_points[i] for i in range(self.opt.nb_primitives)] @@ -70,12 +70,13 @@ def generate_mesh(self, latent_vector): output_points = [self.decoder[i](input_points[i], latent_vector.unsqueeze(2)).squeeze() for i in range(0, self.opt.nb_primitives)] - output_meshes = [pymesh.form_mesh(vertices=output_points[i].transpose(1, 0).contiguous().cpu().numpy(), + output_meshes = [trimesh.Trimesh(vertices=output_points[i].transpose(1, 0).contiguous().cpu().numpy(), faces=self.template[i].mesh.faces) for i in range(self.opt.nb_primitives)] # Deform return the deformed pointcloud - mesh = pymesh.merge_meshes(output_meshes) + mesh = trimesh.util.concatenate(output_meshes) + # mesh = pymesh.merge_meshes(output_meshes) return mesh diff --git a/model/model.py b/model/model.py index a443d98..d9c3356 100644 --- a/model/model.py +++ b/model/model.py @@ -10,8 +10,6 @@ from model.meshflow import NeuralMeshFlow import torch import torch.nn as nn -from pytorch3d.ops import sample_points_from_meshes -from pytorch3d.structures import Meshes class StyleNetBase(nn.Module): diff --git a/model/styleatlasnet.py b/model/styleatlasnet.py index 95e9dbf..ed5e2fc 100644 --- a/model/styleatlasnet.py +++ b/model/styleatlasnet.py @@ -87,7 +87,7 @@ def forward(self, content_latent_vector, style_latent_vector, train=True): def generate_mesh(self, content_latent_vector, style_latent_vector, train=False): assert content_latent_vector.size(0) == 1, "input should have batch size 1!" - import pymesh + import trimesh if train: input_points = [self.template[i].get_random_points( @@ -118,12 +118,13 @@ def generate_mesh(self, content_latent_vector, style_latent_vector, train=False) for i in range(0, self.opt.nb_primitives)] output_points = torch.cat(output_patches, dim=1).squeeze(0) - output_meshes = [pymesh.form_mesh(vertices=output_points[i].transpose(1, 0).contiguous().cpu().numpy(), + output_meshes = [trimesh.Trimesh(vertices=output_points[i].transpose(1, 0).contiguous().cpu().numpy(), faces=self.template[i].mesh.faces) for i in range(self.opt.nb_primitives)] # Deform return the deformed pointcloud - mesh = pymesh.merge_meshes(output_meshes) + mesh = trimesh.util.concatenate(output_meshes) + # mesh = pymesh.merge_meshes(output_meshes) return mesh diff --git a/model/template.py b/model/template.py index 5d9580c..caa70be 100644 --- a/model/template.py +++ b/model/template.py @@ -1,4 +1,5 @@ -import pymesh +# import pymesh +import trimesh import numpy as np import torch from torch.autograd import Variable @@ -48,7 +49,7 @@ def get_regular_points(self, npoints=None, device="gpu0"): Return Tensor of Size [x, 3] """ if not self.npoints == npoints: - self.mesh = pymesh.generate_icosphere(1, [0, 0, 0], 4) # 2562 vertices + self.mesh = trimesh.creation.icosphere(subdivisions = 4 ,radius = 1) # 2562 vertices self.vertex = torch.from_numpy(self.mesh.vertices).to(device).float() self.num_vertex = self.vertex.size(0) self.vertex = self.vertex.transpose(0,1).contiguous().unsqueeze(0) @@ -80,7 +81,7 @@ def get_regular_points(self, npoints=2500, device="gpu0"): if not self.npoints == npoints: self.npoints = npoints vertices, faces = self.generate_square(np.sqrt(npoints)) - self.mesh = pymesh.form_mesh(vertices=vertices, faces=faces) # 10k vertices + self.mesh = trimesh.Trimesh(vertices=vertices, faces=faces) # 10k vertices self.vertex = torch.from_numpy(self.mesh.vertices).to(device).float() self.num_vertex = self.vertex.size(0) self.vertex = self.vertex.transpose(0,1).contiguous().unsqueeze(0) diff --git a/training/metro.py b/training/metro.py index 1c6066a..9f92fdc 100644 --- a/training/metro.py +++ b/training/metro.py @@ -1,6 +1,7 @@ import argparse import numpy as np -import pymesh +# import pymesh +import trimesh from os.path import exists import os import subprocess @@ -46,12 +47,12 @@ def isolate_files(): path_png = '/'.join(['.', 'dataset', 'data', 'ShapeNetV1Renderings', cat, name, "rendering", '00.png']) path_obj = '/'.join(['', 'home', 'thibault', 'hdd', 'data', 'ShapeNetCore.v1', cat, name, 'model.obj']) - mesh = pymesh.load_mesh(path_obj) + mesh = trimesh.load_mesh(path_obj) points = np.load((path_points)) if not exists('/'.join(['.', 'dataset', 'data', 'metro_files', cat])): os.mkdir('/'.join(['.', 'dataset', 'data', 'metro_files', cat])) - pymesh.save_mesh('/'.join(['.', 'dataset', 'data', 'metro_files', cat, name + '.ply']), mesh, ascii=True) + mesh.export('/'.join(['.', 'dataset', 'data', 'metro_files', cat, name + '.ply'])) np.save('/'.join(['.', 'dataset', 'data', 'metro_files', cat, name + '.npy']), points) copy(path_png, '/'.join(['.', 'dataset', 'data', 'metro_files', cat, name + '.png'])) diff --git a/training/trainer.py b/training/trainer.py index 3b78460..815e191 100644 --- a/training/trainer.py +++ b/training/trainer.py @@ -9,7 +9,8 @@ import auxiliary.html_report as html_report import numpy as np from easydict import EasyDict -import pymesh +# import pymesh +import trimesh from termcolor import colored import auxiliary.my_utils as my_utils # import pymeshlab as ml @@ -576,7 +577,7 @@ def unnormalize(mesh, operation=None): # Undo any normalization that was used to preprocess the input. vertices = torch.from_numpy(mesh.vertices).clone().unsqueeze(0) unnormalized_vertices = operation.apply(vertices) - mesh = pymesh.form_mesh(vertices=unnormalized_vertices.squeeze().numpy(), faces=mesh.faces) + mesh = trimesh.Trimesh(vertices=unnormalized_vertices.squeeze().numpy(), face = mesh.faces) return mesh def rename_path(path, unnormalized=False, demo=False, interpolated=False, ext='ply'): diff --git a/training/trainer_loss.py b/training/trainer_loss.py index 07d23b5..dc1168c 100644 --- a/training/trainer_loss.py +++ b/training/trainer_loss.py @@ -7,8 +7,8 @@ import torch.nn as nn import torch.nn.functional as F import auxiliary.ChamferDistancePytorch.chamfer3D.dist_chamfer_3D as dist_chamfer_3D -from pytorch3d.ops import sample_points_from_meshes -from pytorch3d.structures import Meshes +# from pytorch3d.ops import sample_points_from_meshes +# from pytorch3d.structures import Meshes from auxiliary.ChamferDistancePytorch.fscore import fscore import os @@ -65,8 +65,9 @@ def fuse_primitives(self, points, faces, sample=True): points = points.transpose(2, 3).contiguous() points = points.view(points.size(0), -1, 3) elif self.opt.decoder_type.lower() == "meshflow" and sample and self.flags.train: - meshes = Meshes(verts=points, faces=faces) - points = sample_points_from_meshes(meshes, num_samples=points.size(1)) + pass + # meshes = Meshes(verts=points, faces=faces) + # points = sample_points_from_meshes(meshes, num_samples=points.size(1)) return points def l1_distance(self, inputs, targets): From f2080b8089a35fc9a0d89a2e17622eb290cc9819 Mon Sep 17 00:00:00 2001 From: Kieran Nehil-Puleo Date: Wed, 26 May 2021 12:03:43 -0400 Subject: [PATCH 5/8] fixed save_mesh with new trimesh procedure --- training/trainer.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/training/trainer.py b/training/trainer.py index 815e191..951308a 100644 --- a/training/trainer.py +++ b/training/trainer.py @@ -223,12 +223,13 @@ def save_mesh(self, mesh, path, operation=None): if self.opt.decoder_type.lower() == 'atlasnet': if operation: unnormalized_mesh = unnormalize(mesh, operation) - for name in mesh.get_attribute_names(): - val = mesh.get_attribute(name) - unnormalized_mesh.add_attribute(name) - unnormalized_mesh.set_attribute(name, val) + # for name in mesh.get_attribute_names(): + # val = mesh.get_attribute(name) + # unnormalized_mesh.add_attribute(name) + # unnormalized_mesh.set_attribute(name, val) mesh = unnormalized_mesh - mesh_processor.save(mesh, path, self.colormap) + mesh.export(path) + # mesh_processor.save(mesh, path, self.colormap) elif self.opt.decoder_type.lower() == 'meshflow': # mesh_processor.save(mesh, path, self.colormap) mesh.export(path) From e07a1a97e42d86fcc59aeedf517a72ec12825241 Mon Sep 17 00:00:00 2001 From: Kieran Nehil-Puleo Date: Sun, 6 Jun 2021 16:45:31 -0400 Subject: [PATCH 6/8] added trimesh to dataset_smxl --- dataset/dataset_smxl.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/dataset/dataset_smxl.py b/dataset/dataset_smxl.py index 8a04cda..5e989e6 100644 --- a/dataset/dataset_smxl.py +++ b/dataset/dataset_smxl.py @@ -155,8 +155,9 @@ def load_point_input(self, path): if ext == 'npy': points = np.load(path) elif ext == 'ply' or ext == 'obj': - import pymesh - points = pymesh.load_mesh(path).vertices + # import pymesh + import trimesh + points = trimesh.load_mesh(path).vertices else: print('invalid file extension') From 90418d7629640b731f3c4163404a05699b5816af Mon Sep 17 00:00:00 2001 From: Kieran Nehil-Puleo Date: Mon, 7 Jun 2021 14:37:00 -0400 Subject: [PATCH 7/8] face to faces bug --- training/trainer.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/training/trainer.py b/training/trainer.py index 951308a..e983b9b 100644 --- a/training/trainer.py +++ b/training/trainer.py @@ -221,7 +221,7 @@ def save_mesh(self, mesh, path, operation=None): if not os.path.exists(base_dir): os.makedirs(base_dir) if self.opt.decoder_type.lower() == 'atlasnet': - if operation: + if operation is not None: unnormalized_mesh = unnormalize(mesh, operation) # for name in mesh.get_attribute_names(): # val = mesh.get_attribute(name) @@ -578,7 +578,7 @@ def unnormalize(mesh, operation=None): # Undo any normalization that was used to preprocess the input. vertices = torch.from_numpy(mesh.vertices).clone().unsqueeze(0) unnormalized_vertices = operation.apply(vertices) - mesh = trimesh.Trimesh(vertices=unnormalized_vertices.squeeze().numpy(), face = mesh.faces) + mesh = trimesh.Trimesh(vertices=unnormalized_vertices.squeeze().numpy(), faces = mesh.faces) return mesh def rename_path(path, unnormalized=False, demo=False, interpolated=False, ext='ply'): From 429c3d431b36358e43c61692e0d02df3ea255635 Mon Sep 17 00:00:00 2001 From: Kieran Nehil-Puleo Date: Wed, 9 Jun 2021 15:22:40 -0400 Subject: [PATCH 8/8] Added caesar dataset, not tested --- dataset/dataset_caesar.py | 211 +++++++++++++++++++++++++++++++++++++ dataset/trainer_dataset.py | 4 + 2 files changed, 215 insertions(+) create mode 100644 dataset/dataset_caesar.py diff --git a/dataset/dataset_caesar.py b/dataset/dataset_caesar.py new file mode 100644 index 0000000..8a5bb3c --- /dev/null +++ b/dataset/dataset_caesar.py @@ -0,0 +1,211 @@ +from copy import deepcopy +from easydict import EasyDict +import numpy as np +import os +import pickle +from termcolor import colored +import torch +import torch.utils.data as data + +import auxiliary.my_utils as my_utils +import dataset.pointcloud_processor as pointcloud_processor + + +class Caesar(data.Dataset): + """ + Caesar Dataloader + Uses caesar dataset + Make sure to respect dataset Licence. + """ + + def __init__(self, opt, unused_category, subcategory, unused_svr=False, train=True): + self.opt = opt + self.num_sample = opt.number_points if train else 2500 + + self.train = train + self.mode = 'training' if train else 'validation' + self.subcategory = subcategory + + self.id2names = {0: 'person'} + self.names2id = {'person': 0} + + # Initialize pointcloud normalization functions + self.init_normalization() + + if not opt.demo or not opt.use_default_demo_samples: + if len(opt.class_choice) > 0 and len(opt.class_choice) == 2: + print('Initializing {} dataset for class {}.'.format( + self.mode, subcategory)) + else: + raise ValueError('Argument class_choice must contain exactly two classes.') + + my_utils.red_print('Create Caesar Dataset...') + # Define core path array + self.dataset_path = os.path.join(opt.data_dir, 'Caesar') + + # Create Cache path + self.path_dataset = os.path.join(self.dataset_path, 'cache') + if not os.path.exists(self.path_dataset): + os.makedirs(self.path_dataset) + self.path_dataset = os.path.join(self.path_dataset, '_'.join((self.opt.normalization, self.mode))) + self.cache_file = self.path_dataset + self.subcategory + '_info.pkl' + + if not os.path.exists(self.cache_file): + # Compile list of pointcloud path by selected categories + dir_pointcloud = os.path.join(self.dataset_path, self.subcategory, self.mode) + list_pointcloud = sorted(os.listdir(dir_pointcloud)) + print( + ' subcategory ' + + colored(self.names2id[self.subcategory], 'yellow') + + ' ' + + colored(self.subcategory, 'cyan') + + ' Number Files: ' + + colored(str(len(list_pointcloud)), 'yellow') + ) + + if len(list_pointcloud) != 0: + self.datapath = [] + for pointcloud in list_pointcloud: + pointcloud_path = os.path.join(dir_pointcloud, pointcloud) + self.datapath.append((pointcloud_path, pointcloud, self.subcategory)) + + # Preprocess and cache files + self.preprocess() + + def preprocess(self): + if os.path.exists(self.cache_file): + # Reload dataset + my_utils.red_print('Reload dataset : {}'.format(self.cache_file)) + with open(self.cache_file, 'rb') as fp: + self.data_metadata = pickle.load(fp) + + self.data_points = torch.load(self.path_dataset + self.subcategory + '_points.pth') + else: + # Preprocess dataset and put in cache for future fast reload + my_utils.red_print('Preprocess dataset...') + self.datas = [self._getitem(i) for i in range(len(self.datapath))] + + # Concatenate all processed files + self.data_points = [data[0] for data in self.datas] + # TODO(msegu): consider adding option to randomly select num_samples if we want to train with less samples + self.data_points = torch.cat(self.data_points, 0) + + self.data_metadata = [{'pointcloud_path': data[1], 'name': data[2], 'subcategory': data[3]} + for data in self.datas] + + # Save in cache + with open(self.cache_file, 'wb') as fp: # Pickling + pickle.dump(self.data_metadata, fp) + torch.save(self.data_points, self.path_dataset + self.subcategory + '_points.pth') + + my_utils.red_print('Dataset Size: ' + str(len(self.data_metadata))) + + def init_normalization(self): + if not self.opt.demo: + my_utils.red_print('Dataset normalization : ' + self.opt.normalization) + + if self.opt.normalization == 'UnitBall': + self.normalization_function = pointcloud_processor.Normalization.normalize_unitL2ball_functional + elif self.opt.normalization == 'BoundingBox': + self.normalization_function = pointcloud_processor.Normalization.normalize_bounding_box_functional + else: + self.normalization_function = pointcloud_processor.Normalization.identity_functional + + def _getitem(self, index): + + pointcloud_path, pointcloud, subcategory = self.datapath[index] + points = self.load(pointcloud_path)['points'][0] + points[:, :3] = self.normalization_function(points[:, :3]) + return points.unsqueeze(0), pointcloud_path, pointcloud, subcategory + + def __getitem__(self, index): + return_dict = deepcopy(self.data_metadata[index]) + # Point processing + points = self.data_points[index] + points = points.clone() + if self.opt.sample: + choice = np.random.choice(points.size(0), self.num_sample, replace=True) + points = points[choice, :] + points = points[:, :3].contiguous() + + return_dict = {'points': points, + 'pointcloud_path': return_dict['pointcloud_path'], + 'subcategory': return_dict['subcategory']} + return return_dict + + def __len__(self): + return len(self.data_metadata) + + @staticmethod + def int2str(N): + if N < 10: + return '0' + str(N) + else: + return str(N) + + def load(self, path): + ext = path.split('.')[-1] + if ext == 'npy' or ext == 'ply' or ext == 'obj': + return self.load_point_input(path) + else: + raise IOError("File extension .{} not supported. Must be one of '.npy', '.ply' or '.obj'.".format(ext)) + + def load_point_input(self, path): + ext = path.split('.')[-1] + if ext == 'npy': + points = np.load(path) + elif ext == 'ply' or ext == 'obj': + # import pymesh + import trimesh + points = trimesh.load_mesh(path).vertices + else: + print('invalid file extension') + + points = torch.from_numpy(points.copy()).float() + operation = pointcloud_processor.Normalization(points, keep_track=True) + if self.opt.normalization == 'UnitBall': + operation.normalize_unitL2ball() + elif self.opt.normalization == 'BoundingBox': + operation.normalize_bounding_box() + else: + pass + return_dict = { + 'points': points, + 'operation': operation, + 'path': path, + } + return return_dict + + +if __name__ == '__main__': + print('Testing SMXL dataset') + opt = EasyDict({'normalization': 'UnitBall', 'class_choice': ['cats', 'male'], 'sample': True, 'npoints': 2500, + 'num_epochs': 5}) + dataset_a = SMXL(opt, subcategory=opt.class_choice[0], train=False) + dataset_b = SMXL(opt, subcategory=opt.class_choice[1], train=False) + + print(dataset_a[1]) + a = len(dataset_a) + b = len(dataset_b) + + # Check that random pairwise loading works as expected + dataloader_a = torch.utils.data.DataLoader(dataset_a, batch_size=1, shuffle=True) + + dataloader_b = torch.utils.data.DataLoader(dataset_b, batch_size=1, shuffle=True) + + for epoch in range(opt.num_epochs): + for i, (data_a, data_b) in enumerate(zip(dataloader_a, dataloader_b)): + if i == 2: break + data_a = EasyDict(data_a) + data_b = EasyDict(data_b) + print(data_a.pointcloud_path, data_a.pointcloud_path) + + + + + + + + + + diff --git a/dataset/trainer_dataset.py b/dataset/trainer_dataset.py index a47d560..0edc85d 100644 --- a/dataset/trainer_dataset.py +++ b/dataset/trainer_dataset.py @@ -1,6 +1,8 @@ import torch import dataset.dataset_shapenet as dataset_shapenet import dataset.dataset_smxl as dataset_smxl +import dataset.dataset_caesar as dataset_caesar +import dataset import dataset.augmenter as augmenter from easydict import EasyDict @@ -16,6 +18,8 @@ def __init__(self, opt): self.dataset_class = dataset_smxl.SMXL elif opt.dataset == 'ShapeNet': self.dataset_class = dataset_shapenet.ShapeNet + elif opt.dataset == 'Caesar': + self.dataset_class = dataset_shapenet.Caesar def build_dataset(self): """