diff --git a/.gitignore b/.gitignore
index 30b48e0df..075330f00 100644
--- a/.gitignore
+++ b/.gitignore
@@ -789,3 +789,6 @@ FodyWeavers.xsd
# debugging ipynb
debug.ipynb
test.xml
+
+# PyCharm Cache
+.idea/
\ No newline at end of file
diff --git a/README.md b/README.md
index fbfbca413..2dbda2a4a 100644
--- a/README.md
+++ b/README.md
@@ -29,6 +29,8 @@ DOI: 10.1021/acs.jctc.2c01297`
+ [OpenMM Plugin](docs/user_guide/4.7OpenMMplugin.md)
+ [5. Advanced examples](docs/user_guide/DMFF_example.ipynb)
+[And here is a tutorial notebook, which would tell you some basic usage of DMFF. Welcome to read it and get started witn DMFF！](docs/user_guide/test.ipynb)
+
## Developer Guide
+ [1. Introduction](docs/dev_guide/introduction.md)
+ [2. Software architecture](docs/dev_guide/arch.md)
diff --git a/docs/dev_guide/generator.ipynb b/docs/dev_guide/generator.ipynb
index 7b57713c9..559ee97cf 100644
--- a/docs/dev_guide/generator.ipynb
+++ b/docs/dev_guide/generator.ipynb
@@ -1 +1 @@
-{"metadata":{"language_info":{"name":"python","version":"3.10.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kernelspec":{"name":"python3","display_name":"Python 3 (ipykernel)","language":"python"}},"nbformat_minor":5,"nbformat":4,"cells":[{"cell_type":"markdown","source":"## 环境准备\n\n从github上获取DMFF，跳转到所需分支，而后安装","metadata":{},"id":"8ed5396a-d15b-4bdd-8b86-bafc9a2b4c1c"},{"cell_type":"code","source":"! rm -rf DMFF\n! rm -rf /opt/mamba/lib/python3.10/site-packages/dmff*\n! git clone https://github.com/deepmodeling/DMFF.git\n! git config --global --add safe.directory `pwd`/DMFF\n! cd DMFF && git checkout wangxy/v1.0.0-devel && pip install .","metadata":{"trusted":true},"execution_count":17,"outputs":[{"name":"stdout","text":"Cloning into 'DMFF'...\nremote: Enumerating objects: 3507, done.\u001b[K\nremote: Counting objects: 100% (956/956), done.\u001b[K\nremote: Compressing objects: 100% (340/340), done.\u001b[K\nremote: Total 3507 (delta 633), reused 912 (delta 608), pack-reused 2551\u001b[K\nReceiving objects: 100% (3507/3507), 18.81 MiB | 2.17 MiB/s, done.\nResolving deltas: 100% (2243/2243), done.\nUpdating files: 100% (273/273), done.\nBranch 'wangxy/v1.0.0-devel' set up to track remote branch 'wangxy/v1.0.0-devel' from 'origin'.\nSwitched to a new branch 'wangxy/v1.0.0-devel'\nLooking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\nProcessing /data/DMFF\n Preparing metadata (setup.py) ... \u001b[?25ldone\n\u001b[?25hRequirement already satisfied: numpy>=1.18 in /opt/mamba/lib/python3.10/site-packages (from dmff==0.2.1.dev222+g8efbe63) (1.23.4)\nRequirement already satisfied: openmm>=7.6.0 in /opt/mamba/lib/python3.10/site-packages (from dmff==0.2.1.dev222+g8efbe63) (7.7.0)\nRequirement already satisfied: freud-analysis in /opt/mamba/lib/python3.10/site-packages/freud_analysis-2.11.0-py3.10-linux-x86_64.egg (from dmff==0.2.1.dev222+g8efbe63) (2.11.0)\nRequirement already satisfied: rowan>=1.2.1 in /opt/mamba/lib/python3.10/site-packages/rowan-1.3.0.post1-py3.10.egg (from freud-analysis->dmff==0.2.1.dev222+g8efbe63) (1.3.0.post1)\nRequirement already satisfied: scipy>=1.1 in /opt/mamba/lib/python3.10/site-packages (from freud-analysis->dmff==0.2.1.dev222+g8efbe63) (1.9.3)\nBuilding wheels for collected packages: dmff\n Building wheel for dmff (setup.py) ... \u001b[?25ldone\n\u001b[?25h Created wheel for dmff: filename=dmff-0.2.1.dev222+g8efbe63-py3-none-any.whl size=93258 sha256=f20f32e539489412cad4c9a61a0e90b4c18f0c361fba252181ecc0ca6a337222\n Stored in directory: /tmp/pip-ephem-wheel-cache-cjaq0s7q/wheels/f3/08/c8/63a66e9272163ceeb3675eda2e65e58a3e3c8a96296799182d\nSuccessfully built dmff\nInstalling collected packages: dmff\nSuccessfully installed dmff-0.2.1.dev222+g8efbe63\n\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n\u001b[0m","output_type":"stream"}],"id":"cb612446-c7f3-4c27-9733-315c22700387"},{"cell_type":"markdown","source":"安装依赖库，比较耗时，需要稍等一会儿。","metadata":{},"id":"54a8934f-d0f8-47be-97b7-d896fdc4335e"},{"cell_type":"code","source":"! mamba install openmm=7.7.0 rdkit -c conda-forge -y\n! pip install parmed mdtraj pymbar networkx","metadata":{"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"\n __ __ __ __\n / \\ / \\ / \\ / \\\n / \\/ \\/ \\/ \\\n███████████████/ /██/ /██/ /██/ /████████████████████████\n / / \\ / \\ / \\ / \\ \\____\n / / \\_/ \\_/ \\_/ \\ o \\__,\n / _/ \\_____/ `\n |/\n ███╗ ███╗ █████╗ ███╗ ███╗██████╗ █████╗\n ████╗ ████║██╔══██╗████╗ ████║██╔══██╗██╔══██╗\n ██╔████╔██║███████║██╔████╔██║██████╔╝███████║\n ██║╚██╔╝██║██╔══██║██║╚██╔╝██║██╔══██╗██╔══██║\n ██║ ╚═╝ ██║██║ ██║██║ ╚═╝ ██║██████╔╝██║ ██║\n ╚═╝ ╚═╝╚═╝ ╚═╝╚═╝ ╚═╝╚═════╝ ╚═╝ ╚═╝\n\n mamba (0.27.0) supported by @QuantStack\n\n GitHub: https://github.com/mamba-org/mamba\n Twitter: https://twitter.com/QuantStack\n\n█████████████████████████████████████████████████████████████\n\n\nLooking for: ['openmm=7.7.0', 'rdkit']\n\n\u001b[?25l\u001b[2K\u001b[0G[+] 0.0s\n\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.1s\nconda-forge/linux-64 \u001b[90m━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.1s\nconda-forge/noarch \u001b[33m━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.1s\npkgs/main/linux-64 \u001b[33m━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.1s\npkgs/main/noarch \u001b[90m━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.1s\npkgs/r/linux-64 \u001b[90m━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 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11.1s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 11.2s\nconda-forge/linux-64 ━━━━━━━━━━━━━━━━━━━━━━ 34.5MB @ 3.5MB/s Finalizing 11.2s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 11.3s\nconda-forge/linux-64 ━━━━━━━━━━━━━━━━━━━━━━ 34.5MB @ 3.5MB/s Finalizing 11.3s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 11.4s\nconda-forge/linux-64 ━━━━━━━━━━━━━━━━━━━━━━ 34.5MB @ 3.5MB/s Finalizing 11.4s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 11.5s\n\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 11.6s\n\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 11.7s\n\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 11.8s\n\u001b[2K\u001b[1A\u001b[2K\u001b[0Gconda-forge/linux-64 @ 3.5MB/s 11.4s\n\u001b[?25h\nPinned packages:\n - python 3.10.*\n\n\nTransaction\n\n Prefix: /opt/mamba\n\n Updating specs:\n\n - openmm=7.7.0\n - rdkit\n - ca-certificates\n - certifi\n - openssl\n\n\n Package Version Build Channel Size\n──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n Install:\n──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n\u001b[32m + boost \u001b[00m 1.78.0 py310hc4a4660_4 conda-forge/linux-64 363kB\n\u001b[32m + boost-cpp \u001b[00m 1.78.0 h5adbc97_2 conda-forge/linux-64 16MB\n\u001b[32m + brotli \u001b[00m 1.1.0 hd590300_0 conda-forge/linux-64 19kB\n\u001b[32m + brotli-bin \u001b[00m 1.1.0 hd590300_0 conda-forge/linux-64 19kB\n\u001b[32m + cairo \u001b[00m 1.16.0 ha61ee94_1014 conda-forge/linux-64 2MB\n\u001b[32m + contourpy \u001b[00m 1.1.1 py310hd41b1e2_0 conda-forge/linux-64 224kB\n\u001b[32m + cycler \u001b[00m 0.11.0 pyhd8ed1ab_0 conda-forge/noarch 10kB\n\u001b[32m + expat \u001b[00m 2.5.0 hcb278e6_1 conda-forge/linux-64 137kB\n\u001b[32m + fmt \u001b[00m 10.1.1 h00ab1b0_0 conda-forge/linux-64 192kB\n\u001b[32m + font-ttf-dejavu-sans-mono\u001b[00m 2.37 hab24e00_0 conda-forge/noarch 397kB\n\u001b[32m + font-ttf-inconsolata \u001b[00m 3.000 h77eed37_0 conda-forge/noarch 97kB\n\u001b[32m + font-ttf-source-code-pro \u001b[00m 2.038 h77eed37_0 conda-forge/noarch 701kB\n\u001b[32m + font-ttf-ubuntu \u001b[00m 0.83 hab24e00_0 conda-forge/noarch 2MB\n\u001b[32m + fontconfig \u001b[00m 2.14.2 h14ed4e7_0 conda-forge/linux-64 272kB\n\u001b[32m + fonts-conda-ecosystem \u001b[00m 1 0 conda-forge/noarch 4kB\n\u001b[32m + fonts-conda-forge \u001b[00m 1 0 conda-forge/noarch 4kB\n\u001b[32m + fonttools \u001b[00m 4.42.1 py310h2372a71_0 conda-forge/linux-64 2MB\n\u001b[32m + freetype \u001b[00m 2.12.1 h267a509_2 conda-forge/linux-64 635kB\n\u001b[32m + freetype-py \u001b[00m 2.3.0 pyhd8ed1ab_0 conda-forge/noarch 59kB\n\u001b[32m + gettext \u001b[00m 0.21.1 h27087fc_0 conda-forge/linux-64 4MB\n\u001b[32m + greenlet \u001b[00m 2.0.2 py310hc6cd4ac_1 conda-forge/linux-64 191kB\n\u001b[32m + kiwisolver \u001b[00m 1.4.5 py310hd41b1e2_1 conda-forge/linux-64 73kB\n\u001b[32m + lcms2 \u001b[00m 2.15 haa2dc70_1 conda-forge/linux-64 242kB\n\u001b[32m + lerc \u001b[00m 4.0.0 h27087fc_0 conda-forge/linux-64 282kB\n\u001b[32m + libbrotlicommon \u001b[00m 1.1.0 hd590300_0 conda-forge/linux-64 69kB\n\u001b[32m + libbrotlidec \u001b[00m 1.1.0 hd590300_0 conda-forge/linux-64 33kB\n\u001b[32m + libbrotlienc \u001b[00m 1.1.0 hd590300_0 conda-forge/linux-64 282kB\n\u001b[32m + libdeflate \u001b[00m 1.18 h0b41bf4_0 conda-forge/linux-64 65kB\n\u001b[32m + libexpat \u001b[00m 2.5.0 hcb278e6_1 conda-forge/linux-64 78kB\n\u001b[32m + libglib \u001b[00m 2.78.0 hebfc3b9_0 conda-forge/linux-64 3MB\n\u001b[32m + libjpeg-turbo \u001b[00m 2.1.5.1 hd590300_1 conda-forge/linux-64 496kB\n\u001b[32m + libpng \u001b[00m 1.6.39 h753d276_0 conda-forge/linux-64 283kB\n\u001b[32m + libtiff \u001b[00m 4.5.1 h8b53f26_1 conda-forge/linux-64 417kB\n\u001b[32m + libwebp-base \u001b[00m 1.3.2 hd590300_0 conda-forge/linux-64 402kB\n\u001b[32m + libxcb \u001b[00m 1.13 h7f98852_1004 conda-forge/linux-64 400kB\n\u001b[32m + matplotlib-base \u001b[00m 3.8.0 py310h62c0568_1 conda-forge/linux-64 7MB\n\u001b[32m + munkres \u001b[00m 1.1.4 pyh9f0ad1d_0 conda-forge/noarch 12kB\n\u001b[32m + openjpeg \u001b[00m 2.5.0 hfec8fc6_2 conda-forge/linux-64 352kB\n\u001b[32m + pcre2 \u001b[00m 10.40 hc3806b6_0 conda-forge/linux-64 2MB\n\u001b[32m + pillow \u001b[00m 9.5.0 py310h065c6d2_0 conda-forge/linux-64 46MB\n\u001b[32m + pixman \u001b[00m 0.40.0 h36c2ea0_0 conda-forge/linux-64 643kB\n\u001b[32m + pthread-stubs \u001b[00m 0.4 h36c2ea0_1001 conda-forge/linux-64 6kB\n\u001b[32m + pycairo \u001b[00m 1.24.0 py310hda9f760_0 conda-forge/linux-64 113kB\n\u001b[32m + rdkit \u001b[00m 2023.03.3 py310h399bcf7_0 conda-forge/linux-64 36MB\n\u001b[32m + reportlab \u001b[00m 4.0.4 py310h2372a71_0 conda-forge/linux-64 2MB\n\u001b[32m + rlpycairo \u001b[00m 0.2.0 pyhd8ed1ab_0 conda-forge/noarch 15kB\n\u001b[32m + sqlalchemy \u001b[00m 2.0.21 py310h2372a71_0 conda-forge/linux-64 3MB\n\u001b[32m + typing-extensions \u001b[00m 4.8.0 hd8ed1ab_0 conda-forge/noarch 10kB\n\u001b[32m + typing_extensions \u001b[00m 4.8.0 pyha770c72_0 conda-forge/noarch 35kB\n\u001b[32m + unicodedata2 \u001b[00m 15.0.0 py310h2372a71_1 conda-forge/linux-64 374kB\n\u001b[32m + xorg-kbproto \u001b[00m 1.0.7 h7f98852_1002 conda-forge/linux-64 27kB\n\u001b[32m + xorg-libice \u001b[00m 1.0.10 h7f98852_0 conda-forge/linux-64 59kB\n\u001b[32m + xorg-libsm \u001b[00m 1.2.3 hd9c2040_1000 conda-forge/linux-64 26kB\n\u001b[32m + xorg-libx11 \u001b[00m 1.8.4 h0b41bf4_0 conda-forge/linux-64 830kB\n\u001b[32m + xorg-libxau \u001b[00m 1.0.11 hd590300_0 conda-forge/linux-64 14kB\n\u001b[32m + xorg-libxdmcp \u001b[00m 1.1.3 h7f98852_0 conda-forge/linux-64 19kB\n\u001b[32m + xorg-libxext \u001b[00m 1.3.4 h0b41bf4_2 conda-forge/linux-64 50kB\n\u001b[32m + xorg-libxrender \u001b[00m 0.9.10 h7f98852_1003 conda-forge/linux-64 33kB\n\u001b[32m + xorg-renderproto \u001b[00m 0.11.1 h7f98852_1002 conda-forge/linux-64 10kB\n\u001b[32m + xorg-xextproto \u001b[00m 7.3.0 h0b41bf4_1003 conda-forge/linux-64 30kB\n\u001b[32m + xorg-xproto \u001b[00m 7.0.31 h7f98852_1007 conda-forge/linux-64 75kB\n\n Change:\n──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n\u001b[31m - hdf5 \u001b[00m 1.12.1 h70be1eb_2 mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main \n\u001b[32m + hdf5 \u001b[00m 1.12.1 nompi_h4df4325_104 conda-forge/linux-64 4MB\n\u001b[31m - python \u001b[00m 3.10.6 h582c2e5_0_cpython conda-forge \n\u001b[32m + python \u001b[00m 3.10.6 ha86cf86_0_cpython conda-forge/linux-64 30MB\n\n Upgrade:\n──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n\u001b[31m - ca-certificates \u001b[00m 2022.10.11 h06a4308_0 mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main \n\u001b[32m + ca-certificates \u001b[00m 2023.7.22 hbcca054_0 conda-forge/linux-64 150kB\n\u001b[31m - certifi \u001b[00m 2022.9.24 py310h06a4308_0 mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main \n\u001b[32m + certifi \u001b[00m 2023.7.22 pyhd8ed1ab_0 conda-forge/noarch 154kB\n\u001b[31m - cryptography \u001b[00m 38.0.2 py310h597c629_1 conda-forge \n\u001b[32m + cryptography \u001b[00m 38.0.4 py310h600f1e7_0 conda-forge/linux-64 1MB\n\u001b[31m - krb5 \u001b[00m 1.19.3 h3790be6_0 conda-forge \n\u001b[32m + krb5 \u001b[00m 1.21.2 h659d440_0 conda-forge/linux-64 1MB\n\u001b[31m - libarchive \u001b[00m 3.5.2 hb890918_3 conda-forge \n\u001b[32m + libarchive \u001b[00m 3.6.2 h3d51595_0 conda-forge/linux-64 836kB\n\u001b[31m - libcurl \u001b[00m 7.86.0 h7bff187_0 conda-forge \n\u001b[32m + libcurl \u001b[00m 8.3.0 hca28451_0 conda-forge/linux-64 388kB\n\u001b[31m - libmamba \u001b[00m 0.27.0 h0dd8ff0_0 conda-forge \n\u001b[32m + libmamba \u001b[00m 1.5.1 h744094f_0 conda-forge/linux-64 2MB\n\u001b[31m - libmambapy \u001b[00m 0.27.0 py310hab0e683_0 conda-forge \n\u001b[32m + libmambapy \u001b[00m 1.5.1 py310h39ff949_0 conda-forge/linux-64 299kB\n\u001b[31m - libnghttp2 \u001b[00m 1.47.0 hdcd2b5c_1 conda-forge \n\u001b[32m + libnghttp2 \u001b[00m 1.52.0 h61bc06f_0 conda-forge/linux-64 622kB\n\u001b[31m - libsolv \u001b[00m 0.7.22 h6239696_0 conda-forge \n\u001b[32m + libsolv \u001b[00m 0.7.24 hfc55251_4 conda-forge/linux-64 468kB\n\u001b[31m - libssh2 \u001b[00m 1.10.0 haa6b8db_3 conda-forge \n\u001b[32m + libssh2 \u001b[00m 1.11.0 h0841786_0 conda-forge/linux-64 271kB\n\u001b[31m - mamba \u001b[00m 0.27.0 py310hf87f941_0 conda-forge \n\u001b[32m + mamba \u001b[00m 1.5.1 py310h51d5547_0 conda-forge/linux-64 51kB\n\u001b[31m - openssl \u001b[00m 1.1.1s h7f8727e_0 mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main \n\u001b[32m + openssl \u001b[00m 3.1.3 hd590300_0 conda-forge/linux-64 3MB\n\u001b[31m - zstd \u001b[00m 1.5.2 h6239696_4 conda-forge \n\u001b[32m + zstd \u001b[00m 1.5.5 hfc55251_0 conda-forge/linux-64 545kB\n\n Summary:\n\n Install: 61 packages\n Change: 2 packages\n Upgrade: 14 packages\n\n Total download: 180MB\n\n──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n\u001b[?25l\u001b[2K\u001b[0G[+] 0.0s\nDownloading \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0.0 B 0.0s\nExtracting \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0 0.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.1s\nDownloading (5) \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0.0 B ca-certificates 0.0s\nExtracting \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0 0.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.2s\nDownloading (5) \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0.0 B ca-certificates 0.1s\nExtracting \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0 0.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.3s\nDownloading (5) \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0.0 B ca-certificates 0.2s\nExtracting \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0 0.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.4s\nDownloading (5) \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0.0 B ca-certificates 0.3s\nExtracting \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0 0.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.5s\nDownloading (5) \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0.0 B fmt 0.4s\nExtracting \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0 0.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.6s\nDownloading (5) \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0.0 B fmt 0.5s\nExtracting \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0 0.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.7s\nDownloading (5) \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0.0 B fmt 0.6s\nExtracting \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0 0.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.8s\nDownloading (5) \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0.0 B fmt 0.7s\nExtracting \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0 0.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gxorg-xextproto 30.3kB @ 34.8kB/s 0.9s\n[+] 0.9s\nDownloading (5) \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 95.2kB libbrotlicommon 0.8s\nExtracting (1) \u001b[90m╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 0 xorg-xextproto 0.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.0s\nDownloading (5) \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 95.2kB libbrotlicommon 0.9s\nExtracting (1) \u001b[90m━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━\u001b[0m 0 xorg-xextproto 0.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.1s\nDownloading (5) \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 266.0kB libbrotlicommon 1.0s\nExtracting (1) \u001b[90m━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━\u001b[0m 0 xorg-xextproto 0.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibbrotlicommon 69.4kB @ 60.2kB/s 0.3s\n[+] 1.2s\nDownloading (5) \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 396.7kB ca-certificates 1.1s\nExtracting (2) \u001b[90m━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━\u001b[0m 0 xorg-xextproto 0.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.3s\nDownloading (5) \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 591.4kB ca-certificates 1.2s\nExtracting (1) \u001b[90m━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━\u001b[0m 1 libbrotlicommon 0.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.4s\nDownloading (5) \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 820.8kB ca-certificates 1.3s\nExtracting (1) \u001b[90m━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━\u001b[0m 1 libbrotlicommon 0.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gca-certificates 149.5kB @ 100.8kB/s 1.5s\n[+] 1.5s\nDownloading (5) \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 929.1kB fmt 1.4s\nExtracting (1) \u001b[90m━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━━\u001b[0m 2 ca-certificates 0.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.6s\nDownloading (5) \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 1.1MB fmt 1.5s\nExtracting (1) \u001b[90m━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━\u001b[0m 2 ca-certificates 0.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.7s\nDownloading (5) \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 1.2MB fmt 1.6s\nExtracting (1) \u001b[90m━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━\u001b[0m 2 ca-certificates 0.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gfmt 192.3kB @ 113.1kB/s 1.7s\nxorg-kbproto 27.3kB @ 15.7kB/s 0.3s\n[+] 1.8s\nDownloading (5) \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 1.5MB libsolv 1.7s\nExtracting (3) \u001b[90m━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━\u001b[0m 2 ca-certificates 0.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gzstd 545.2kB @ 297.8kB/s 1.8s\nlibsolv 467.8kB @ 254.4kB/s 1.8s\n[+] 1.9s\nDownloading (5) \u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 1.9MB freetype 1.8s\nExtracting (4) \u001b[90m━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━\u001b[0m 3 fmt 1.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gxorg-libice 59.4kB @ 29.9kB/s 0.3s\n[+] 2.0s\nDownloading (5) \u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 2.4MB freetype 1.9s\nExtracting (4) \u001b[90m━━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━\u001b[0m 3 fmt 1.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 2.1s\nDownloading (5) \u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 3.2MB freetype 2.0s\nExtracting (5) \u001b[33m━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 3 fmt 1.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 2.2s\nDownloading (5) \u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 3.8MB freetype 2.1s\nExtracting (4) ╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━\u001b[0m 4 libsolv 1.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gfreetype 635.0kB @ 285.6kB/s 0.4s\n[+] 2.3s\nDownloading (5) \u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 4.8MB libglib 2.2s\nExtracting (5) ╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━\u001b[0m 4 libsolv 1.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 2.4s\nDownloading (5) \u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 6.0MB libglib 2.3s\nExtracting (4) ╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━\u001b[0m 5 libsolv 1.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gpcre2 2.4MB @ 990.2kB/s 1.3s\nlibssh2 271.1kB @ 109.8kB/s 0.2s\n[+] 2.5s\nDownloading (5) \u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━\u001b[0m 7.1MB libglib 2.4s\nExtracting (6) ╸\u001b[33m━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━\u001b[0m 5 libsolv 1.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibxcb 399.9kB @ 155.6kB/s 0.6s\n[+] 2.6s\nDownloading (5) \u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━\u001b[0m 7.6MB libglib 2.5s\nExtracting (6) ╸\u001b[33m━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━\u001b[0m 6 libssh2 1.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 2.7s\nDownloading (5) \u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━\u001b[0m 8.7MB openjpeg 2.6s\nExtracting (6) ╸\u001b[33m━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━\u001b[0m 6 libssh2 1.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gopenjpeg 352.0kB @ 130.2kB/s 0.3s\nfontconfig 272.0kB @ 99.1kB/s 0.3s\nlibglib 2.7MB @ 970.5kB/s 0.9s\n[+] 2.8s\nDownloading (5) ╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 10.8MB boost 2.7s\nExtracting (9) ╸\u001b[33m━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━\u001b[0m 6 libssh2 1.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gopenssl 2.6MB @ 942.6kB/s 1.1s\n[+] 2.9s\nDownloading (5) ╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 11.4MB boost 2.8s\nExtracting (9) ━╸\u001b[33m━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━\u001b[0m 7 libssh2 2.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gunicodedata2 373.8kB @ 127.7kB/s 0.4s\nboost 362.6kB @ 121.5kB/s 0.3s\n[+] 3.0s\nDownloading (5) ╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 12.0MB font-ttf-inconsolata 2.9s\nExtracting (11) ━╸\u001b[33m━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 7 libxcb 2.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gxorg-libxext 50.1kB @ 16.5kB/s 0.3s\nlibmambapy 299.1kB @ 97.6kB/s 0.3s\n[+] 3.1s\nDownloading (5) ╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 12.8MB font-ttf-inconsolata 3.0s\nExtracting (12) ━╸\u001b[33m━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 8 libxcb 2.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 3.2s\nDownloading (5) ╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 13.6MB font-ttf-inconsolata 3.1s\nExtracting (12) ━╸\u001b[33m━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 8 libxcb 2.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gfont-ttf-inconsolata 96.5kB @ 30.1kB/s 0.3s\ntyping_extensions 35.1kB @ 10.8kB/s 0.3s\n[+] 3.3s\nDownloading (5) ╸\u001b[33m━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 14.1MB cycler 3.2s\nExtracting (13) ━╸\u001b[33m━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 9 libxcb 2.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gcycler 10.3kB @ 3.1kB/s 0.3s\ntyping-extensions 10.1kB @ 3.0kB/s 0.2s\n[+] 3.4s\nDownloading (5) ╸\u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 14.9MB libdeflate 3.3s\nExtracting (15) ━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 9 openjpeg 2.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 3.5s\nDownloading (5) ╸\u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 15.4MB libdeflate 3.4s\nExtracting (15) ━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 9 openjpeg 2.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gpycairo 113.5kB @ 32.3kB/s 0.3s\nlibdeflate 65.2kB @ 18.3kB/s 0.3s\n[+] 3.6s\nDownloading (5) ━╸\u001b[33m━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 17.4MB libexpat 3.5s\nExtracting (16) ━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 10 openjpeg 2.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 3.7s\nDownloading (5) ━╸\u001b[33m━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 20.0MB libexpat 3.6s\nExtracting (16) ━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 10 openjpeg 2.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gxorg-libxau 14.5kB @ 3.8kB/s 0.2s\n[+] 3.8s\nDownloading (5) ━╸\u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 23.0MB libexpat 3.7s\nExtracting (16) ━━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 11 openssl 2.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibexpat 78.0kB @ 20.4kB/s 0.3s\n[+] 3.9s\nDownloading (5) ━━╸\u001b[33m━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 25.6MB libtiff 3.8s\nExtracting (17) ━━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 11 openssl 3.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 4.0s\nDownloading (5) ━━╸\u001b[33m━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 28.2MB libtiff 3.9s\nExtracting (16) ━━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 12 openssl 3.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gpthread-stubs 5.6kB @ 1.4kB/s 0.2s\n[+] 4.1s\nDownloading (5) ━━╸\u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 30.5MB libtiff 4.0s\nExtracting (17) ━━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 12 openssl 3.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibtiff 416.5kB @ 101.3kB/s 0.3s\nsqlalchemy 2.6MB @ 631.3kB/s 0.9s\n[+] 4.2s\nDownloading (5) ━━━╸\u001b[33m━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 32.9MB expat 4.1s\nExtracting (19) ━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 12 pcre2 3.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gexpat 136.8kB @ 32.1kB/s 0.3s\n[+] 4.3s\nDownloading (5) ━━━╸\u001b[33m━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 33.3MB greenlet 4.2s\nExtracting (19) ━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 13 pcre2 3.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glcms2 242.1kB @ 55.1kB/s 0.3s\n[+] 4.4s\nDownloading (5) ━━━╸\u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 36.7MB greenlet 4.3s\nExtracting (20) ━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 13 pcre2 3.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibnghttp2 622.4kB @ 140.7kB/s 0.3s\n[+] 4.5s\nDownloading (5) ━━━╸\u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 37.8MB greenlet 4.4s\nExtracting (20) ━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 14 pcre2 3.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Ggreenlet 190.7kB @ 42.3kB/s 0.2s\n[+] 4.6s\nDownloading (5) ━━━╸\u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 38.0MB cryptography 4.5s\nExtracting (21) ━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 14 pthread-stubs 3.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gxorg-libxrender 32.9kB @ 7.0kB/s 0.3s\n[+] 4.7s\nDownloading (5) ━━━━╸\u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━\u001b[0m 41.4MB cryptography 4.6s\nExtracting (21) ━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 14 pthread-stubs 3.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 4.8s\nDownloading (5) ━━━━╸\u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━\u001b[0m 43.0MB cryptography 4.7s\nExtracting (22) ━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 14 pthread-stubs 3.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 4.9s\nDownloading (5) ━━━━╸\u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━\u001b[0m 43.0MB cryptography 4.8s\nExtracting (20) ━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 16 pthread-stubs 4.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.0s\nDownloading (5) ━━━━╸\u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━\u001b[0m 43.6MB font-ttf-ubuntu 4.9s\nExtracting (20) ━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 16 pycairo 4.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gcryptography 1.4MB @ 276.7kB/s 0.7s\n[+] 5.1s\nDownloading (5) ━━━━╸\u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━\u001b[0m 44.1MB font-ttf-ubuntu 5.0s\nExtracting (21) ━━━╸\u001b[33m━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 16 pycairo 4.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.2s\nDownloading (5) ━━━━╸\u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━\u001b[0m 44.1MB font-ttf-ubuntu 5.1s\nExtracting (21) ━━━╸\u001b[33m━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 16 pycairo 4.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.3s\nDownloading (5) ━━━━╸\u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━\u001b[0m 45.2MB font-ttf-ubuntu 5.2s\nExtracting (21) ━━━╸\u001b[33m━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 16 pycairo 4.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.4s\nDownloading (5) ━━━━╸\u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━\u001b[0m 45.9MB mamba 5.3s\nExtracting (19) ━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 18 unicodedata2 4.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gcertifi 153.8kB @ 28.4kB/s 0.3s\nfont-ttf-ubuntu 2.0MB @ 361.3kB/s 0.7s\n[+] 5.5s\nDownloading (5) ━━━━╸\u001b[33m━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━\u001b[0m 47.0MB mamba 5.4s\nExtracting (21) ━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 18 unicodedata2 4.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.6s\nDownloading (5) ━━━━━╸\u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━\u001b[0m 50.6MB mamba 5.5s\nExtracting (21) ━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 18 unicodedata2 4.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Grlpycairo 14.9kB @ 2.6kB/s 0.2s\n[+] 5.7s\nDownloading (5) ━━━━━╸\u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━\u001b[0m 50.6MB mamba 5.6s\nExtracting (21) ━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 19 unicodedata2 4.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.8s\nDownloading (5) ━━━━━╸\u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━\u001b[0m 50.9MB pillow 5.7s\nExtracting (20) ━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 20 xorg-libxau 4.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.9s\nDownloading (5) ━━━━━╸\u001b[33m━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━\u001b[0m 52.2MB pillow 5.8s\nExtracting (20) ━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 20 xorg-libxau 5.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.0s\nDownloading (5) ━━━━━╸\u001b[33m━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━\u001b[0m 52.2MB pillow 5.9s\nExtracting (20) ━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 20 xorg-libxau 5.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glerc 281.8kB @ 46.9kB/s 0.3s\nfonttools 2.2MB @ 366.6kB/s 0.7s\n[+] 6.1s\nDownloading (5) ━━━━━━╸\u001b[33m━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━\u001b[0m 57.4MB pillow 6.0s\nExtracting (21) ━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 21 xorg-libxau 5.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.2s\nDownloading (5) ━━━━━━╸\u001b[33m━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━\u001b[0m 60.1MB rdkit 6.1s\nExtracting (20) ━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 22 certifi 5.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gxorg-renderproto 9.6kB @ 1.5kB/s 0.2s\n[+] 6.3s\nDownloading (5) ━━━━━━╸\u001b[33m━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━\u001b[0m 62.1MB rdkit 6.2s\nExtracting (21) ━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 22 certifi 5.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.4s\nDownloading (5) ━━━━━━╸\u001b[33m━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━\u001b[0m 62.1MB rdkit 6.3s\nExtracting (21) ━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 22 certifi 5.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gmamba 51.2kB @ 7.9kB/s 2.0s\n[+] 6.5s\nDownloading (5) ━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━━\u001b[0m 63.2MB rdkit 6.4s\nExtracting (21) ━━━━━╸\u001b[33m━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 23 certifi 5.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.6s\nDownloading (5) ━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━━\u001b[0m 64.1MB gettext 6.5s\nExtracting (20) ━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 24 cryptography 5.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.7s\nDownloading (5) ━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━━\u001b[0m 65.0MB gettext 6.6s\nExtracting (20) ━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 24 cryptography 5.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibbrotlienc 282.2kB @ 41.5kB/s 0.5s\n[+] 6.8s\nDownloading (5) ━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━━\u001b[0m 66.8MB gettext 6.7s\nExtracting (20) ━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 24 cryptography 5.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.9s\nDownloading (5) ━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━━\u001b[0m 68.2MB gettext 6.8s\nExtracting (21) ━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 24 cryptography 6.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.0s\nDownloading (5) ━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━━\u001b[0m 68.2MB pillow 6.9s\nExtracting (21) ━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 24 cycler 6.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gbrotli-bin 19.0kB @ 2.7kB/s 0.3s\n[+] 7.1s\nDownloading (5) ━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━━\u001b[0m 68.3MB pillow 7.0s\nExtracting (21) ━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 25 cycler 6.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.2s\nDownloading (5) ━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━━\u001b[0m 68.3MB pillow 7.1s\nExtracting (21) ━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 25 cycler 6.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.3s\nDownloading (5) ━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━━\u001b[0m 68.3MB pillow 7.2s\nExtracting (20) ━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 26 cycler 6.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gcontourpy 223.7kB @ 30.4kB/s 0.3s\n[+] 7.4s\nDownloading (5) ━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━━\u001b[0m 68.6MB python 7.3s\nExtracting (21) ━━━━━━╸\u001b[33m━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 26 expat 6.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.5s\nDownloading (5) ━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━━\u001b[0m 69.2MB python 7.4s\nExtracting (20) ━━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 27 expat 6.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.6s\nDownloading (5) ━━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━\u001b[0m 74.0MB python 7.5s\nExtracting (20) ━━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 27 expat 6.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibmamba 1.6MB @ 211.5kB/s 0.3s\n[+] 7.7s\nDownloading (5) ━━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━\u001b[0m 75.7MB python 7.6s\nExtracting (20) ━━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 28 expat 6.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.8s\nDownloading (5) ━━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━\u001b[0m 77.1MB rdkit 7.7s\nExtracting (20) ━━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 28 font-ttf-ubuntu 6.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.9s\nDownloading (5) ━━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━\u001b[0m 79.1MB rdkit 7.8s\nExtracting (19) ━━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 29 font-ttf-ubuntu 7.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gfont-ttf-source-code-pro 700.8kB @ 87.8kB/s 0.3s\n[+] 8.0s\nDownloading (5) ━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━━\u001b[0m 81.8MB rdkit 7.9s\nExtracting (20) ━━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 29 font-ttf-ubuntu 7.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 8.1s\nDownloading (5) ━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━━\u001b[0m 83.2MB rdkit 8.0s\nExtracting (20) ━━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 29 font-ttf-ubuntu 7.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 8.2s\nDownloading (5) ━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━━\u001b[0m 83.2MB fonts-conda-forge 8.1s\nExtracting (20) ━━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 29 fonttools 7.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gfonts-conda-forge 4.1kB @ 498.0 B/s 0.3s\n[+] 8.3s\nDownloading (5) ━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━━\u001b[0m 83.8MB gettext 8.2s\nExtracting (19) ━━━━━━━━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 31 fonttools 7.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 8.4s\nDownloading (5) ━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━\u001b[0m 86.7MB gettext 8.3s\nExtracting (19) ━━━━━━━━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 31 fonttools 7.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Ggettext 4.3MB @ 511.6kB/s 2.4s\n[+] 8.5s\nDownloading (5) ━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━\u001b[0m 87.6MB libwebp-base 8.4s\nExtracting (20) ━━━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 31 fonttools 7.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 8.6s\nDownloading (5) ━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━\u001b[0m 91.1MB libwebp-base 8.5s\nExtracting (20) ━━━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 31 gettext 7.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 8.7s\nDownloading (5) ━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━\u001b[0m 91.1MB libwebp-base 8.6s\nExtracting (19) ━━━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 32 gettext 7.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 8.8s\nDownloading (5) ━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━\u001b[0m 91.2MB libwebp-base 8.7s\nExtracting (18) ━━━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 33 gettext 7.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 8.9s\nDownloading (5) ━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━\u001b[0m 91.2MB matplotlib-base 8.8s\nExtracting (18) ━━━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 33 gettext 8.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 9.0s\nDownloading (5) ━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━\u001b[0m 91.2MB matplotlib-base 8.9s\nExtracting (18) ━━━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 33 lerc 8.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibwebp-base 401.8kB @ 44.6kB/s 0.6s\n[+] 9.1s\nDownloading (5) ━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━\u001b[0m 91.7MB matplotlib-base 9.0s\nExtracting (18) ━━━━━━━━━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 34 lerc 8.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 9.2s\nDownloading (5) ━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━\u001b[0m 91.8MB matplotlib-base 9.1s\nExtracting (17) ━━━━━━━━━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 35 lerc 8.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gxorg-xproto 74.9kB @ 8.1kB/s 0.3s\n[+] 9.3s\nDownloading (5) ━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━\u001b[0m 92.1MB pillow 9.2s\nExtracting (18) ━━━━━━━━━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 35 lerc 8.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 9.4s\nDownloading (5) ━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━\u001b[0m 92.1MB pillow 9.3s\nExtracting (18) ━━━━━━━━━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 35 libbrotlienc 8.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 9.5s\nDownloading (5) ━━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━\u001b[0m 99.3MB pillow 9.4s\nExtracting (17) ━━━━━━━━━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 36 libbrotlienc 8.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibbrotlidec 32.6kB @ 3.4kB/s 0.2s\n[+] 9.6s\nDownloading (5) ━━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━\u001b[0m 101.0MB pillow 9.5s\nExtracting (18) ━━━━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 36 libbrotlienc 8.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 9.7s\nDownloading (5) ━━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━\u001b[0m 102.0MB python 9.6s\nExtracting (18) ━━━━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 36 libbrotlienc 8.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 9.8s\nDownloading (5) ━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━━\u001b[0m 103.9MB python 9.7s\nExtracting (18) ━━━━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 36 libdeflate 8.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 9.9s\nDownloading (5) ━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━━\u001b[0m 105.0MB python 9.8s\nExtracting (17) ━━━━━━━━━━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 37 libdeflate 9.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 10.0s\nDownloading (5) ━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━━\u001b[0m 105.8MB python 9.9s\nExtracting (16) ━━━━━━━━━━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 38 libdeflate 9.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 10.1s\nDownloading (5) ━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━━\u001b[0m 106.4MB rdkit 10.0s\nExtracting (16) ━━━━━━━━━━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 38 libdeflate 9.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gmatplotlib-base 6.8MB @ 667.1kB/s 1.9s\n[+] 10.2s\nDownloading (5) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━\u001b[0m 110.7MB rdkit 10.1s\nExtracting (17) ━━━━━━━━━━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 38 libexpat 9.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 10.3s\nDownloading (5) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━\u001b[0m 112.1MB rdkit 10.2s\nExtracting (17) ━━━━━━━━━━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 38 libexpat 9.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 10.4s\nDownloading (5) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━\u001b[0m 112.1MB rdkit 10.3s\nExtracting (16) ━━━━━━━━━━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 39 libexpat 9.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gkrb5 1.4MB @ 131.1kB/s 0.9s\n[+] 10.5s\nDownloading (5) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━\u001b[0m 114.4MB font-ttf-dejavu-sans-mono 10.4s\nExtracting (16) ━━━━━━━━━━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 40 libexpat 9.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 10.6s\nDownloading (5) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━\u001b[0m 114.5MB font-ttf-dejavu-sans-mono 10.5s\nExtracting (16) ━━━━━━━━━━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 40 libwebp-base 9.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 10.7s\nDownloading (5) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━\u001b[0m 115.5MB font-ttf-dejavu-sans-mono 10.6s\nExtracting (16) ━━━━━━━━━━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 40 libwebp-base 9.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibcurl 388.3kB @ 36.2kB/s 0.6s\nrdkit 36.4MB @ 3.4MB/s 7.5s\n[+] 10.8s\nDownloading (5) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━\u001b[0m 118.6MB font-ttf-dejavu-sans-mono 10.7s\nExtracting (17) ━━━━━━━━━━━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━\u001b[0m 41 contourpy 9.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gfont-ttf-dejavu-sans-mono 397.4kB @ 36.5kB/s 0.4s\n[+] 10.9s\nDownloading (5) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 119.3MB fonts-conda-ecosystem 10.8s\nExtracting (17) ━━━━━━━━━━━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━\u001b[0m 42 contourpy 10.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gfonts-conda-ecosystem 3.7kB @ 333.0 B/s 0.3s\n[+] 11.0s\nDownloading (5) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 119.8MB libarchive 10.9s\nExtracting (18) ━━━━━━━━━━━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━\u001b[0m 42 contourpy 10.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibjpeg-turbo 496.4kB @ 44.9kB/s 0.3s\n[+] 11.1s\nDownloading (5) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 120.9MB libarchive 11.0s\nExtracting (19) ━━━━━━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━\u001b[0m 42 contourpy 10.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gxorg-libxdmcp 19.1kB @ 1.7kB/s 0.3s\n[+] 11.2s\nDownloading (5) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 121.4MB libarchive 11.1s\nExtracting (20) ━━━━━━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━\u001b[0m 42 cycler 10.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gbrotli 19.4kB @ 1.7kB/s 0.2s\n[+] 11.3s\nDownloading (5) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 122.2MB libarchive 11.2s\nExtracting (20) ━━━━━━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━\u001b[0m 43 cycler 10.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibarchive 835.6kB @ 73.5kB/s 0.4s\nmunkres 12.5kB @ 1.1kB/s 0.3s\n[+] 11.4s\nDownloading (5) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 123.3MB hdf5 11.3s\nExtracting (21) ━━━━━━━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━\u001b[0m 44 brotli 10.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 11.5s\nDownloading (5) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 123.8MB hdf5 11.4s\nExtracting (21) ━━━━━━━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━\u001b[0m 44 brotli 10.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibpng 282.6kB @ 24.4kB/s 0.3s\n[+] 11.6s\nDownloading (5) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 124.6MB hdf5 11.5s\nExtracting (22) ━━━━━━━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━\u001b[0m 44 brotli 10.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gpillow 46.5MB @ 4.0MB/s 8.9s\nxorg-libsm 26.4kB @ 2.3kB/s 0.3s\n[+] 11.7s\nDownloading (5) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 124.9MB hdf5 11.6s\nExtracting (24) ━━━━━━━━━━━━╸\u001b[33m━━━━━━━╸\u001b[0m\u001b[90m━━\u001b[0m 44 brotli 10.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 11.8s\nDownloading (5) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 125.2MB python 11.7s\nExtracting (23) ━━━━━━━━━━━━╸\u001b[33m━━━━━━━╸\u001b[0m\u001b[90m━━\u001b[0m 45 contourpy 10.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 11.9s\nDownloading (5) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 125.8MB python 11.8s\nExtracting (23) ━━━━━━━━━━━━╸\u001b[33m━━━━━━━╸\u001b[0m\u001b[90m━━\u001b[0m 45 contourpy 11.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 12.0s\nDownloading (5) ━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━\u001b[0m 127.6MB python 11.9s\nExtracting (22) ━━━━━━━━━━━━╸\u001b[33m━━━━━━━╸\u001b[0m\u001b[90m━━\u001b[0m 46 contourpy 11.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gxorg-libx11 829.9kB @ 68.9kB/s 0.4s\n[+] 12.1s\nDownloading (5) ━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━\u001b[0m 129.6MB python 12.0s\nExtracting (23) ━━━━━━━━━━━━╸\u001b[33m━━━━━━━╸\u001b[0m\u001b[90m━━\u001b[0m 46 contourpy 11.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Greportlab 2.3MB @ 193.9kB/s 0.6s\ncairo 1.6MB @ 129.9kB/s 0.5s\n[+] 12.2s\nDownloading (5) ━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━\u001b[0m 130.7MB boost-cpp 12.1s\nExtracting (25) ━━━━━━━━━━━━╸\u001b[33m━━━━━━━━╸\u001b[0m\u001b[90m━\u001b[0m 46 expat 11.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 12.3s\nDownloading (5) ━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━\u001b[0m 131.0MB boost-cpp 12.2s\nExtracting (24) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━╸\u001b[0m\u001b[90m━\u001b[0m 47 expat 11.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gkiwisolver 73.1kB @ 5.9kB/s 0.3s\n[+] 12.4s\nDownloading (5) ━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━\u001b[0m 131.3MB boost-cpp 12.3s\nExtracting (24) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━╸\u001b[0m\u001b[90m━\u001b[0m 48 expat 11.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gpixman 642.5kB @ 51.4kB/s 0.4s\n[+] 12.5s\nDownloading (4) ━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━\u001b[0m 132.1MB boost-cpp 12.4s\nExtracting (24) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━╸\u001b[0m\u001b[90m━\u001b[0m 48 expat 11.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gfreetype-py 58.9kB @ 4.7kB/s 0.2s\n[+] 12.6s\nDownloading (3) ━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━\u001b[0m 132.5MB hdf5 12.5s\nExtracting (26) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━\u001b[0m 48 font-ttf-dejavu-sans-mono 11.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 12.7s\nDownloading (3) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 133.6MB hdf5 12.6s\nExtracting (25) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━\u001b[0m 49 font-ttf-dejavu-sans-mono 11.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 12.8s\nDownloading (3) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 134.6MB hdf5 12.7s\nExtracting (25) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━\u001b[0m 49 font-ttf-dejavu-sans-mono 11.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 12.9s\nDownloading (3) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 135.6MB hdf5 12.8s\nExtracting (25) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━\u001b[0m 49 font-ttf-dejavu-sans-mono 12.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 13.0s\nDownloading (3) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 136.5MB python 12.9s\nExtracting (25) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━\u001b[0m 49 freetype-py 12.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 13.1s\nDownloading (3) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 137.5MB python 13.0s\nExtracting (24) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━\u001b[0m 50 freetype-py 12.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 13.2s\nDownloading (3) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 138.5MB python 13.1s\nExtracting (24) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━\u001b[0m 50 freetype-py 12.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 13.3s\nDownloading (3) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 139.5MB python 13.2s\nExtracting (24) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━\u001b[0m 50 freetype-py 12.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 13.4s\nDownloading (3) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 140.4MB boost-cpp 13.3s\nExtracting (24) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━\u001b[0m 50 gettext 12.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Ghdf5 3.7MB @ 276.5kB/s 2.1s\n[+] 13.5s\nDownloading (2) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 141.5MB boost-cpp 13.4s\nExtracting (25) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━\u001b[0m 50 gettext 12.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 13.6s\nDownloading (2) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 142.4MB boost-cpp 13.5s\nExtracting (24) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 51 gettext 12.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 13.7s\nDownloading (2) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 143.3MB boost-cpp 13.6s\nExtracting (24) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 51 gettext 12.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 13.8s\nDownloading (2) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 143.9MB python 13.7s\nExtracting (23) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 52 cairo 12.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 13.9s\nDownloading (2) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 144.5MB python 13.8s\nExtracting (23) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 52 cairo 13.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 14.0s\nDownloading (2) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 145.3MB python 13.9s\nExtracting (22) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 53 cairo 13.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 14.1s\nDownloading (2) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 146.2MB python 14.0s\nExtracting (22) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 53 cairo 13.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 14.2s\nDownloading (2) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 146.5MB boost-cpp 14.1s\nExtracting (21) ━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━\u001b[0m 54 expat 13.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 14.3s\nDownloading (2) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 146.6MB boost-cpp 14.2s\nExtracting (21) ━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━\u001b[0m 54 expat 13.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 14.4s\nDownloading (2) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 146.6MB boost-cpp 14.3s\nExtracting (21) ━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━\u001b[0m 54 expat 13.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 14.5s\nDownloading (2) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 147.1MB boost-cpp 14.4s\nExtracting (21) ━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━\u001b[0m 54 expat 13.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 14.6s\nDownloading (2) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 149.8MB python 14.5s\nExtracting (20) ━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━\u001b[0m 55 font-ttf-dejavu-sans-mono 13.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 14.7s\nDownloading (2) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 149.8MB python 14.6s\nExtracting (20) ━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━\u001b[0m 55 font-ttf-dejavu-sans-mono 13.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gboost-cpp 15.9MB @ 1.1MB/s 2.7s\n[+] 14.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 150.5MB python 14.7s\nExtracting (20) ━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━\u001b[0m 55 font-ttf-dejavu-sans-mono 13.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 14.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 150.6MB python 14.8s\nExtracting (21) ━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━\u001b[0m 55 font-ttf-dejavu-sans-mono 14.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 15.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 150.6MB python 14.9s\nExtracting (21) ━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━\u001b[0m 55 freetype-py 14.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 15.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 150.6MB python 15.0s\nExtracting (19) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 57 freetype-py 14.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 15.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 150.7MB python 15.1s\nExtracting (19) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 57 freetype-py 14.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 15.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 150.7MB python 15.2s\nExtracting (19) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 57 freetype-py 14.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 15.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 150.7MB python 15.3s\nExtracting (18) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 58 hdf5 14.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 15.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 150.7MB python 15.4s\nExtracting (17) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 59 hdf5 14.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 15.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 150.7MB python 15.5s\nExtracting (17) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 59 hdf5 14.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 15.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 150.8MB python 15.6s\nExtracting (17) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 59 hdf5 14.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 15.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 150.8MB python 15.7s\nExtracting (16) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 60 kiwisolver 14.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 15.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 150.8MB python 15.8s\nExtracting (16) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 60 kiwisolver 15.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 16.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 150.8MB python 15.9s\nExtracting (16) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 60 kiwisolver 15.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 16.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 150.9MB python 16.0s\nExtracting (16) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 60 kiwisolver 15.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 16.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 150.9MB python 16.1s\nExtracting (15) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 61 libbrotlidec 15.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 16.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 150.9MB python 16.2s\nExtracting (15) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 61 libbrotlidec 15.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 16.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 150.9MB python 16.3s\nExtracting (14) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 62 libbrotlidec 15.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 16.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.0MB python 16.4s\nExtracting (14) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 62 libbrotlidec 15.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 16.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.0MB python 16.5s\nExtracting (14) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 62 libbrotlienc 15.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 16.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.0MB python 16.6s\nExtracting (13) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 63 libbrotlienc 15.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 16.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.0MB python 16.7s\nExtracting (13) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 63 libbrotlienc 15.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 16.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.0MB python 16.8s\nExtracting (13) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 63 libbrotlienc 16.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 17.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.1MB python 16.9s\nExtracting (13) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 63 pixman 16.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 17.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.1MB python 17.0s\nExtracting (12) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 64 pixman 16.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 17.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.1MB python 17.1s\nExtracting (12) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 64 pixman 16.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 17.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.1MB python 17.2s\nExtracting (12) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 64 pixman 16.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 17.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.2MB python 17.3s\nExtracting (12) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 64 pycairo 16.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 17.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.2MB python 17.4s\nExtracting (12) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 64 pycairo 16.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 17.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.2MB python 17.5s\nExtracting (11) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 65 pycairo 16.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 17.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.2MB python 17.6s\nExtracting (11) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 65 pycairo 16.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 17.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.3MB python 17.7s\nExtracting (11) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 65 rdkit 16.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 17.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.3MB python 17.8s\nExtracting (11) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 65 rdkit 17.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 18.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.3MB python 17.9s\nExtracting (9) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 67 rdkit 17.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 18.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.4MB python 18.0s\nExtracting (9) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 67 rdkit 17.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 18.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.4MB python 18.1s\nExtracting (9) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 67 reportlab 17.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 18.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.4MB python 18.2s\nExtracting (8) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 68 reportlab 17.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 18.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.4MB python 18.3s\nExtracting (7) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 69 expat 17.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 18.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.5MB python 18.4s\nExtracting (7) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 69 expat 17.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 18.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.5MB python 18.5s\nExtracting (7) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 69 expat 17.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 18.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.5MB python 18.6s\nExtracting (5) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 71 expat 17.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 18.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.5MB python 18.7s\nExtracting (5) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 71 libbrotlidec 17.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 18.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.6MB python 18.8s\nExtracting (5) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 71 libbrotlidec 18.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 19.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.6MB python 18.9s\nExtracting (4) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 72 expat 18.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 19.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.6MB python 19.0s\nExtracting (4) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 72 expat 18.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 19.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.6MB python 19.1s\nExtracting (4) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 72 expat 18.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 19.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.6MB python 19.2s\nExtracting (3) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 73 expat 18.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 19.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.7MB python 19.3s\nExtracting (2) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 74 pixman 18.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 19.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.8MB python 19.4s\nExtracting (2) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 74 pixman 18.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 19.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.8MB python 19.5s\nExtracting (2) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 74 pixman 18.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 19.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.8MB python 19.6s\nExtracting (2) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 74 pixman 18.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 19.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.9MB python 19.7s\nExtracting (2) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 74 rdkit 18.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 19.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.9MB python 19.8s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 75 rdkit 19.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 20.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.9MB python 19.9s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 75 rdkit 19.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 20.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.0MB python 20.0s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 75 rdkit 19.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 20.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.0MB python 20.1s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 20.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.0MB python 20.2s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 20.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.0MB python 20.3s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 20.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.1MB python 20.4s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 20.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.1MB python 20.5s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 20.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.2MB python 20.6s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 20.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.2MB python 20.7s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 20.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.2MB python 20.8s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 21.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.3MB python 20.9s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 21.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.3MB python 21.0s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 21.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.3MB python 21.1s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 21.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.4MB python 21.2s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 21.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.4MB python 21.3s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 21.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.5MB python 21.4s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 21.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.5MB python 21.5s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 21.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.5MB python 21.6s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 21.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.6MB python 21.7s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 21.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.6MB python 21.8s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 22.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.6MB python 21.9s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 22.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.7MB python 22.0s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 22.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.7MB python 22.1s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 22.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.8MB python 22.2s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 22.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.8MB python 22.3s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 22.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.8MB python 22.4s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 22.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.8MB python 22.5s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 22.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.8MB python 22.6s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 22.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.8MB python 22.7s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 22.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.9MB python 22.8s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 23.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.9MB python 22.9s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 23.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.9MB python 23.0s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 23.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 153.0MB python 23.1s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 23.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 153.0MB python 23.2s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 23.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 153.0MB python 23.3s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 23.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 153.0MB python 23.4s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 23.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 153.1MB python 23.5s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 23.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 153.2MB python 23.6s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 23.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 153.3MB python 23.7s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 23.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 153.3MB python 23.8s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 24.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 153.4MB python 23.9s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 24.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 153.4MB python 24.0s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 24.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 153.4MB python 24.1s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 24.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 153.5MB python 24.2s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 24.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 153.6MB python 24.3s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 24.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 153.6MB python 24.4s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 24.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 153.7MB python 24.5s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 24.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 153.7MB python 24.6s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 24.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 153.8MB python 24.7s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 24.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 153.8MB python 24.8s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 25.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 153.9MB python 24.9s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 25.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 153.9MB python 25.0s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 25.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 154.0MB python 25.1s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 25.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 154.0MB python 25.2s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 25.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 154.1MB python 25.3s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 25.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 154.1MB python 25.4s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 25.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 154.2MB python 25.5s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 25.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 154.3MB python 25.6s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 25.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 154.3MB python 25.7s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 25.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 154.4MB python 25.8s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 26.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 154.5MB python 25.9s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 26.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 154.5MB python 26.0s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 26.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 154.6MB python 26.1s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 26.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 154.6MB python 26.2s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 26.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 154.7MB python 26.3s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 26.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 154.8MB python 26.4s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 26.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 154.8MB python 26.5s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 26.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 154.9MB python 26.6s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 26.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 154.9MB python 26.7s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 26.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 155.0MB python 26.8s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 27.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 155.1MB python 26.9s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 27.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 155.2MB python 27.0s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 27.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 155.2MB python 27.1s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 27.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 155.3MB python 27.2s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 27.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 155.4MB python 27.3s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 27.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 155.5MB python 27.4s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 27.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 155.5MB python 27.5s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 27.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 155.6MB python 27.6s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 27.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 155.6MB python 27.7s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 27.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 155.7MB python 27.8s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 28.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 155.8MB python 27.9s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 28.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 155.9MB python 28.0s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 28.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 155.9MB python 28.1s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 28.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 156.0MB python 28.2s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 28.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 156.1MB python 28.3s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 28.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 156.2MB python 28.4s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 28.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 156.3MB python 28.5s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 28.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 156.4MB python 28.6s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 28.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 156.4MB python 28.7s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 28.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 156.5MB python 28.8s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 29.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 156.6MB python 28.9s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 29.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 156.7MB python 29.0s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 29.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 156.8MB python 29.1s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 29.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 156.8MB python 29.2s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 29.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 156.8MB python 29.3s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 29.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 157.0MB python 29.4s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 29.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 157.1MB python 29.5s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 29.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 157.2MB python 29.6s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 29.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 157.3MB python 29.7s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 29.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 157.4MB python 29.8s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 30.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 157.5MB python 29.9s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 30.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 157.6MB python 30.0s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 30.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 157.7MB python 30.1s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 30.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 157.8MB python 30.2s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 30.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 157.9MB python 30.3s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 30.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 158.0MB python 30.4s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 30.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 158.1MB python 30.5s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 30.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 158.2MB python 30.6s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 30.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 158.3MB python 30.7s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 30.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 158.3MB python 30.8s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 31.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 158.5MB python 30.9s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 31.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 158.6MB python 31.0s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 31.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 158.6MB python 31.1s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 31.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 158.7MB python 31.2s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 31.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 158.9MB python 31.3s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 31.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 159.0MB python 31.4s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 31.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 159.1MB python 31.5s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 31.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 159.2MB python 31.6s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 31.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 159.3MB python 31.7s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 31.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 159.5MB python 31.8s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 32.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 159.6MB python 31.9s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 32.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 159.7MB python 32.0s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 32.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 159.8MB python 32.1s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 32.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 160.0MB python 32.2s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 32.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 160.1MB python 32.3s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 32.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 160.2MB python 32.4s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 32.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 160.3MB python 32.5s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 32.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 160.4MB python 32.6s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 32.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 160.5MB python 32.7s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 32.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 160.6MB python 32.8s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 33.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 160.8MB python 32.9s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 33.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 160.9MB python 33.0s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 33.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 161.0MB python 33.1s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 33.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 161.2MB python 33.2s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 33.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 161.3MB python 33.3s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 33.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 161.3MB python 33.4s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 33.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 161.4MB python 33.5s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 33.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 161.4MB python 33.6s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 33.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 161.4MB python 33.7s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 33.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 161.5MB python 33.8s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 34.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 162.2MB python 33.9s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 34.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 162.4MB python 34.0s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 34.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 162.4MB python 34.1s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 34.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 162.4MB python 34.2s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 34.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 162.5MB python 34.3s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 34.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 162.5MB python 34.4s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 34.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 162.5MB python 34.5s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 34.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 162.5MB python 34.6s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 34.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 162.6MB python 34.7s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 34.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 162.7MB python 34.8s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 35.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 162.9MB python 34.9s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 35.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 163.0MB python 35.0s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 35.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 163.1MB python 35.1s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 35.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 163.3MB python 35.2s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 35.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 163.4MB python 35.3s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 35.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 163.6MB python 35.4s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 35.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 163.7MB python 35.5s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 35.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 163.9MB python 35.6s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 35.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 164.0MB python 35.7s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 35.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 164.2MB python 35.8s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 36.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 164.3MB python 35.9s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 36.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 164.5MB python 36.0s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 36.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 164.7MB python 36.1s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 36.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 164.9MB python 36.2s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 36.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 165.0MB python 36.3s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 36.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 165.1MB python 36.4s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 36.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 165.3MB python 36.5s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 36.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 165.4MB python 36.6s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 36.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 165.6MB python 36.7s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 36.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 165.8MB python 36.8s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 37.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 166.0MB python 36.9s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 37.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 166.1MB python 37.0s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 37.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 166.3MB python 37.1s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 37.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 166.5MB python 37.2s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 37.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 166.6MB python 37.3s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 37.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 166.9MB python 37.4s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 37.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 167.0MB python 37.5s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 37.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 167.2MB python 37.6s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 37.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 167.3MB python 37.7s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 37.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 167.5MB python 37.8s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 38.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 167.7MB python 37.9s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 38.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 167.9MB python 38.0s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 38.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 168.1MB python 38.1s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 38.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 168.3MB python 38.2s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 38.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 168.5MB python 38.3s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 38.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 168.7MB python 38.4s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 38.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 168.9MB python 38.5s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 38.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 169.1MB python 38.6s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 38.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 169.3MB python 38.7s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 38.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 169.5MB python 38.8s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 39.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 169.7MB python 38.9s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 39.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 169.9MB python 39.0s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 39.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 170.1MB python 39.1s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 39.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 170.3MB python 39.2s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 39.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 170.5MB python 39.3s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 39.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 170.7MB python 39.4s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 39.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 170.8MB python 39.5s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 39.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 170.9MB python 39.6s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 39.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 170.9MB python 39.7s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 39.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 170.9MB python 39.8s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 40.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 171.1MB python 39.9s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 40.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 172.0MB python 40.0s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 40.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 172.2MB python 40.1s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 40.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 172.4MB python 40.2s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 40.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 172.6MB python 40.3s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 40.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 172.8MB python 40.4s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 40.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 173.0MB python 40.5s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 40.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 173.2MB python 40.6s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 40.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 173.4MB python 40.7s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 40.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 173.6MB python 40.8s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 41.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 173.8MB python 40.9s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 41.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 174.0MB python 41.0s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 41.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 174.3MB python 41.1s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 41.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 174.5MB python 41.2s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 41.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 174.7MB python 41.3s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 41.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 174.9MB python 41.4s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 41.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 175.1MB python 41.5s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 41.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 175.3MB python 41.6s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 41.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 175.6MB python 41.7s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 41.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 175.8MB python 41.8s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 42.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 176.0MB python 41.9s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 42.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 176.3MB python 42.0s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 42.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 176.5MB python 42.1s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 42.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 176.7MB python 42.2s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 42.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 176.8MB python 42.3s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 42.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 176.8MB python 42.4s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 42.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 176.8MB python 42.5s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 42.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 176.9MB python 42.6s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 42.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 177.9MB python 42.7s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 42.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 178.2MB python 42.8s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 43.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 178.5MB python 42.9s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 43.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 178.7MB python 43.0s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 43.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 179.0MB python 43.1s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 43.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 179.2MB python 43.2s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 43.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 179.5MB python 43.3s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 43.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 179.7MB python 43.4s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gpython 30.4MB @ 698.4kB/s 37.0s\n[+] 43.6s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 43.7s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 19.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 43.8s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 19.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 43.9s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 19.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 44.0s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 19.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 44.1s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 19.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 44.2s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 19.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 44.3s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 20.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 44.4s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 20.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 44.5s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 20.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 44.6s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 20.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 44.7s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 20.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 44.8s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 20.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 44.9s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 20.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 45.0s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 20.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 45.1s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 20.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 45.2s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 20.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 45.3s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 21.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 45.4s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 21.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 45.5s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 21.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 45.6s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 21.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 45.7s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 21.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 45.8s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 21.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 45.9s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 21.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 46.0s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 21.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 46.1s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 21.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 46.2s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 21.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 46.3s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 22.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 46.4s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 22.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 46.5s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 22.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 46.6s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 22.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 46.7s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 22.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 46.8s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 22.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 46.9s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 22.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 47.0s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 22.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 47.1s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 22.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 47.2s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 22.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 47.3s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 23.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 47.4s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 23.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 47.5s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 23.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 47.6s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting ━━━━━━━━━━━━━━━━━━━━━━━ 77 23.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G\u001b[?25h\nDownloading and Extracting Packages\n\nPreparing transaction: done\nVerifying transaction: done\nExecuting transaction: done\nLooking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\nCollecting parmed\n Downloading https://pypi.tuna.tsinghua.edu.cn/packages/dc/85/01007d38fe0945398c5e0ec7c7ce2d9cc433289bf05e32393c8e48e71cd4/ParmEd-4.1.0.tar.gz (2.2 MB)\n\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.2/2.2 MB\u001b[0m \u001b[31m27.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25ldone\n\u001b[?25hRequirement already satisfied: mdtraj in /opt/mamba/lib/python3.10/site-packages (1.9.7)\nRequirement already satisfied: pymbar in /opt/mamba/lib/python3.10/site-packages (4.0.1)\nCollecting networkx\n Downloading https://pypi.tuna.tsinghua.edu.cn/packages/a8/05/9d4f9b78ead6b2661d6e8ea772e111fc4a9fbd866ad0c81906c11206b55e/networkx-3.1-py3-none-any.whl (2.1 MB)\n\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.1/2.1 MB\u001b[0m \u001b[31m51.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m\n\u001b[?25hRequirement already satisfied: numpy>=1.6 in /opt/mamba/lib/python3.10/site-packages (from mdtraj) (1.23.4)\nRequirement already satisfied: astunparse in /opt/mamba/lib/python3.10/site-packages (from mdtraj) (1.6.3)\nRequirement already satisfied: pyparsing in /opt/mamba/lib/python3.10/site-packages (from mdtraj) (3.0.9)\nRequirement already satisfied: scipy in /opt/mamba/lib/python3.10/site-packages (from mdtraj) (1.9.3)\nRequirement already satisfied: jax in /opt/mamba/lib/python3.10/site-packages (from pymbar) (0.3.17)\nRequirement already satisfied: numexpr in /opt/mamba/lib/python3.10/site-packages (from pymbar) (2.8.4)\nRequirement already satisfied: jaxlib in /opt/mamba/lib/python3.10/site-packages (from pymbar) (0.3.15+cuda11.cudnn82)\nRequirement already satisfied: wheel<1.0,>=0.23.0 in /opt/mamba/lib/python3.10/site-packages (from astunparse->mdtraj) (0.37.1)\nRequirement already satisfied: six<2.0,>=1.6.1 in /opt/mamba/lib/python3.10/site-packages (from astunparse->mdtraj) (1.16.0)\nRequirement already satisfied: typing-extensions in /opt/mamba/lib/python3.10/site-packages (from jax->pymbar) (4.8.0)\nRequirement already satisfied: opt-einsum in /opt/mamba/lib/python3.10/site-packages (from jax->pymbar) (3.3.0)\nRequirement already satisfied: etils[epath] in /opt/mamba/lib/python3.10/site-packages (from jax->pymbar) (0.9.0)\nRequirement already satisfied: absl-py in /opt/mamba/lib/python3.10/site-packages (from jax->pymbar) (1.3.0)\nRequirement already satisfied: importlib_resources in /opt/mamba/lib/python3.10/site-packages (from etils[epath]->jax->pymbar) (5.10.0)\nRequirement already satisfied: zipp in /opt/mamba/lib/python3.10/site-packages (from etils[epath]->jax->pymbar) (3.10.0)\nBuilding wheels for collected packages: parmed\n Building wheel for parmed (setup.py) ... \u001b[?25ldone\n\u001b[?25h Created wheel for parmed: filename=ParmEd-4.1.0-cp310-cp310-linux_x86_64.whl size=1250051 sha256=8937550b1672378a608d73fce57d2b08e1708c0bafae003244ab6d6a0cfd5556\n Stored in directory: /root/.cache/pip/wheels/3c/92/a9/7282efbb63e0a2699132aa10ec070aad1688e7e0885b8832ea\nSuccessfully built parmed\nInstalling collected packages: parmed, networkx\nSuccessfully installed networkx-3.1 parmed-4.1.0\n\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n\u001b[0m","output_type":"stream"}],"id":"829d7d23-ad93-4d15-bf0b-eaf6bd1fe41d"},{"cell_type":"markdown","source":"拷贝示例文件到根目录","metadata":{},"id":"8ca10738-9594-4da1-ab94-f70aa9e238f7"},{"cell_type":"code","source":"! cp DMFF/tests/data/bond1.xml .\n! cp DMFF/tests/data/bond1.pdb .","metadata":{"trusted":true},"execution_count":6,"outputs":[],"id":"9c861cde-19e7-4dbc-9a29-5a3462bba31c"},{"cell_type":"markdown","source":"## 引入必要的库","metadata":{},"id":"bc3c4e1f-f874-430a-bc51-35245f4b861f"},{"cell_type":"code","source":"from typing import Tuple\nimport numpy as np\nimport jax.numpy as jnp\nimport jax\nfrom dmff.api.topology import DMFFTopology\nfrom dmff.api.paramset import ParamSet\nfrom dmff.api.xmlio import XMLIO\nfrom dmff.api.hamiltonian import _DMFFGenerators\nfrom dmff.classical.intra import HarmonicBondJaxForce\nfrom dmff.utils import DMFFException, isinstance_jnp","metadata":{"trusted":true},"execution_count":1,"outputs":[{"name":"stderr","text":"2023-09-23 18:08:32.369984: W external/org_tensorflow/tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: /usr/lib/x86_64-linux-gnu/libcuda.so.1: file too short; LD_LIBRARY_PATH: /usr/local/nvidia/lib:/usr/local/nvidia/lib64\nWARNING:absl:No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)\n2023-09-23 18:08:32.370046: W external/org_tensorflow/tensorflow/stream_executor/cuda/cuda_driver.cc:263] failed call to cuInit: UNKNOWN ERROR (303)\n","output_type":"stream"}],"id":"30abb3c9-c4e8-4975-86c2-2d1c227c7b01"},{"cell_type":"markdown","source":"## 创建Generator类","metadata":{},"id":"d1c59294-2440-4c4b-8417-676d03ca569d"},{"cell_type":"code","source":"class HarmonicBondGenerator:\n \"\"\"\n A class for generating harmonic bond force field parameters.\n\n Attributes:\n -----------\n name : str\n The name of the force field.\n ffinfo : dict\n The force field information.\n key_type : str\n The type of the key.\n bond_keys : list of tuple\n The keys of the bonds.\n bond_params : list of tuple\n The parameters of the bonds.\n bond_mask : list of float\n The mask of the bonds.\n _use_smarts : bool\n Whether to use SMARTS.\n \"\"\"\n\n def __init__(self, ffinfo: dict, paramset: ParamSet):\n \"\"\"\n Initializes the HarmonicBondGenerator.\n\n Parameters:\n -----------\n ffinfo : dict\n The force field information.\n paramset : ParamSet\n The parameter set.\n \"\"\"\n self.name = \"HarmonicBondForce\" # 初始化Generator所关联的势函数名称\n self.ffinfo = ffinfo # 绑定这一Generator所对应的力场文件信息\n paramset.addField(self.name) # 在参数集中注册一个Field，用于存储这一势函数相关的参数。ParamSet介绍见下文。\n self.key_type = None\n\n bond_keys, bond_params, bond_mask = [], [], [] # 创建bond_keys, bond_params, bond_mask三个List，每个key对应着相应位置的力场参数与mask。\n for node in self.ffinfo[\"Forces\"][self.name][\"node\"]:\n attribs = node[\"attrib\"]\n \n # 判断bond term使用\"type\"还是\"class\"进行匹配。目前仅支持基于这两个属性的参数匹配，并且不允许混搭。\n if self.key_type is None and \"type1\" in attribs:\n self.key_type = \"type\"\n elif self.key_type is None and \"class1\" in attribs:\n self.key_type = \"class\"\n elif self.key_type is not None and f\"{self.key_type}1\" not in attribs:\n raise ValueError(\"Keyword 'class' or 'type' cannot be used together.\")\n else:\n raise ValueError(\"Cannot find key type for HarmonicBondForce.\")\n key = (attribs[self.key_type + \"1\"], attribs[self.key_type + \"2\"])\n bond_keys.append(key)\n\n k = float(attribs[\"k\"])\n r0 = float(attribs[\"length\"])\n bond_params.append([k, r0])\n\n # when the node has mask attribute, it means that the parameter is not trainable. \n # the gradient of this parameter will be zero.\n mask = 1.0\n if \"mask\" in attribs and attribs[\"mask\"].upper() == \"TRUE\":\n mask = 0.0\n bond_mask.append(mask)\n\n self.bond_keys = bond_keys\n bond_length = jnp.array([i[1] for i in bond_params])\n bond_k = jnp.array([i[0] for i in bond_params])\n bond_mask = jnp.array(bond_mask)\n\n # 在ParamSet中注册参数。\n # 在Generator初始化结束后，我们可以通过ParamSet调用这些参数，不经过Generator，进而保证这些参数与Generator无关。\n # 可优化的参数与函数独立存在，不构成闭包，是可微分编程正确求导的前提。\n paramset.addParameter(bond_length, \"length\", field=self.name, mask=bond_mask) # register parameters to ParamSet\n paramset.addParameter(bond_k, \"k\", field=self.name, mask=bond_mask) # register parameters to ParamSet\n \n def getName(self) -> str:\n \"\"\"\n Returns the name of the force field.\n\n Returns:\n --------\n str\n The name of the force field.\n \"\"\"\n return self.name\n \n # 根据输入的ParamSet直接修改self.ffinfo的值。\n # self.ffinfo是解析xml力场文件后得到的dict，在保持格式约定的前提下，可以直接与xml文件互转。\n # 修改self.ffinfo中参数的值，而后我们可以直接将self.ffinfo渲染成新的力场参数文件。\n # 这一函数的入参是固定的。\n def overwrite(self, paramset: ParamSet) -> None:\n \"\"\"\n Overwrites the parameter set.\n\n Parameters:\n -----------\n paramset : ParamSet\n The parameter set.\n \"\"\"\n bond_node_indices = [i for i in range(len(self.ffinfo[\"Forces\"][self.name][\"node\"])) if self.ffinfo[\"Forces\"][self.name][\"node\"][i][\"name\"] == \"Bond\"]\n\n bond_length = paramset[self.name][\"length\"]\n bond_k = paramset[self.name][\"k\"]\n bond_msks = paramset.mask[self.name][\"length\"]\n for nnode, key in enumerate(self.bond_keys):\n self.ffinfo[\"Forces\"][self.name][\"node\"][bond_node_indices[nnode]][\"attrib\"] = {}\n self.ffinfo[\"Forces\"][self.name][\"node\"][bond_node_indices[nnode]][\"attrib\"][f\"{self.key_type}1\"] = key[0]\n self.ffinfo[\"Forces\"][self.name][\"node\"][bond_node_indices[nnode]][\"attrib\"][f\"{self.key_type}2\"] = key[1]\n r0 = bond_length[nnode]\n k = bond_k[nnode]\n mask = bond_msks[nnode]\n self.ffinfo[\"Forces\"][self.name][\"node\"][bond_node_indices[nnode]][\"attrib\"][\"k\"] = str(k)\n self.ffinfo[\"Forces\"][self.name][\"node\"][bond_node_indices[nnode]][\"attrib\"][\"length\"] = str(r0)\n if mask < 0.999:\n self.ffinfo[\"Forces\"][self.name][\"node\"][bond_node_indices[nnode]][\"attrib\"][\"mask\"] = \"true\"\n\n # 工具函数，用于查找与选定bond匹配的key的角标\n def _find_key_index(self, key: Tuple[str, str]) -> int:\n \"\"\"\n Finds the index of the key.\n\n Parameters:\n -----------\n key : tuple of str\n The key.\n\n Returns:\n --------\n int\n The index of the key.\n \"\"\"\n for i, k in enumerate(self.bond_keys):\n if k[0] == key[0] and k[1] == key[1]:\n return i\n if k[0] == key[1] and k[1] == key[0]:\n return i\n return None\n\n # 撰写方法来创建势函数。\n # 对于不同的topdata，我们所构造的势函数是不同的。\n # Generator负责基于输入的topdata构建从能量到力场参数的求导链，这使得Generator仅与力场参数相关，与各个体系的拓扑无关。\n # 这一函数的入参是固定的。\n def createPotential(self, topdata: DMFFTopology, nonbondedMethod,\n nonbondedCutoff, args):\n \"\"\"\n Creates the potential.\n\n Parameters:\n -----------\n topdata : DMFFTopology\n The topology data.\n nonbondedMethod : str\n The nonbonded method.\n nonbondedCutoff : float\n The nonbonded cutoff.\n args : list\n The arguments.\n\n Returns:\n --------\n function\n The potential function.\n \"\"\"\n # 按照HarmonicBondForce的要求遍历体系中所有的bond，进行匹配\n bond_a1, bond_a2, bond_indices = [], [], []\n for bond in topdata.bonds():\n a1, a2 = bond.atom1, bond.atom2\n i1, i2 = a1.index, a2.index\n if self.key_type == \"type\":\n key = (a1.meta[\"type\"], a2.meta[\"type\"])\n elif self.key_type == \"class\":\n key = (a1.meta[\"class\"], a2.meta[\"class\"])\n idx = self._find_key_index(key)\n if idx is None:\n continue\n bond_a1.append(i1)\n bond_a2.append(i2)\n bond_indices.append(idx)\n bond_a1 = jnp.array(bond_a1)\n bond_a2 = jnp.array(bond_a2)\n bond_indices = jnp.array(bond_indices)\n \n # 创建势函数\n harmonic_bond_force = HarmonicBondJaxForce(bond_a1, bond_a2, bond_indices)\n harmonic_bond_energy = harmonic_bond_force.generate_get_energy()\n \n # 包装成统一的potential_function函数形式，传入四个参数：positions, box, pairs, parameters。\n def potential_fn(positions: jnp.ndarray, box: jnp.ndarray, pairs: jnp.ndarray, params: ParamSet) -> jnp.ndarray:\n isinstance_jnp(positions, box, params)\n energy = harmonic_bond_energy(positions, box, pairs, params[self.name][\"k\"], params[self.name][\"length\"])\n return energy\n\n self._jaxPotential = potential_fn\n return potential_fn\n","metadata":{"trusted":true},"execution_count":2,"outputs":[],"id":"62d85f8b-d1fe-4842-b8c8-e2fcb6f09c25"},{"cell_type":"markdown","source":"## 注册Generator到DMFF，与XML文件中特定Force绑定","metadata":{},"id":"51168e46-d582-42b8-a65f-1320f7a09161"},{"cell_type":"code","source":"# register the generator\n_DMFFGenerators[\"HarmonicBondForce\"] = HarmonicBondGenerator","metadata":{"trusted":true},"execution_count":3,"outputs":[],"id":"b48500d8-4e24-4f3d-bb30-5dabf3475a24"},{"cell_type":"markdown","source":"## 测试\n\n### OpenMM计算测试体系能量","metadata":{},"id":"2a0f799e-c5ff-4d60-bbb8-4b075a1138d3"},{"cell_type":"code","source":"import openmm as mm\nimport openmm.app as app\nimport openmm.unit as unit\n\n\npdb = app.PDBFile(\"bond1.pdb\")\nff = app.ForceField(\"bond1.xml\")\nsystem = ff.createSystem(pdb.topology)\ninteg = mm.VerletIntegrator(1e-10)\ncontext = mm.Context(system, integ)\ncontext.setPositions(pdb.getPositions())\nenergy = context.getState(getEnergy=True).getPotentialEnergy()\nprint(\"OpenMM:\", energy)","metadata":{"trusted":true},"execution_count":4,"outputs":[{"name":"stdout","text":"OpenMM: 1389.1622953572387 kJ/mol\n","output_type":"stream"}],"id":"2d6c4b52-64a7-44e4-aaef-de243fdb0093"},{"cell_type":"markdown","source":"### DMFF计算测试体系能量","metadata":{},"id":"1d32e830-500c-4ae1-bbf9-f69214196423"},{"cell_type":"code","source":"from dmff.operators import TemplateATypeOperator\n\n# 体系坐标。\npos = pdb.getPositions(asNumpy=True).value_in_unit(unit.nanometer)\npos = jnp.array(pos)\n\n# DMFF格式下的体系拓扑，支持直接基于openmm的topology对象进行初始化。\ndmfftop = DMFFTopology(from_top=pdb.topology)\n\n# 盒子，在这一示例中并无用处。\nbox = np.eye(3) * 10.0\nbox = jnp.array(box)\n\n# XML力场读写工具\nxmlio = XMLIO()\nxmlio.loadXML(\"bond1.xml\")\n# 将xml文件解析为Dict，命名为ffinfo\nffinfo = xmlio.parseXML()\n\n# 根据力场文件中的residue template，基于图同构，为topology中各个atom赋予atom type，存储于Atom.meta中。\ntempOP = TemplateATypeOperator(ffinfo)\ntop_atype = tempOP(dmfftop)\nfor atom in top_atype.atoms():\n print(\"Meta data:\", atom.meta)\nprint()\n \n# 初始化ParamSet。\n# ParamSet是一个PyTree类。它类似一个字典，但被限制了深度，只有两层。\n# 第一层叫做Field，按照势函数名称分类，对于这个示例，就是HarmonicBondForce。\n# 第二层是势函数的各个参数，在这个示例中即为length和k。\n# mask也被初始化于ParamSet中，本示例中暂不展示。\nparamset = ParamSet()\n# 初始化Generator。\ngenerator = HarmonicBondGenerator(ffinfo, paramset)\n\n# 查看ParamSet。\nprint(paramset.parameters)\nprint()\n\n# init potential\npotential = generator.createPotential(top_atype, app.NoCutoff, 1.0, {})\nenergy = potential(pos, box, [], paramset)\nprint(f\"DMFF: {energy} kJ/mol\")","metadata":{"trusted":true},"execution_count":20,"outputs":[{"name":"stdout","text":"Meta data: {'element': 'N', 'external_bond': False, 'type': 'n1', 'class': 'n1'}\nMeta data: {'element': 'N', 'external_bond': False, 'type': 'n2', 'class': 'n2'}\n\n{'HarmonicBondForce': {'length': DeviceArray([0.09572], dtype=float32), 'k': DeviceArray([462750.4], dtype=float32)}}\n\nDMFF: 1389.1622314453125 kJ/mol\n","output_type":"stream"}],"id":"954a429e-b123-42e1-85cd-22497e31ac88"},{"cell_type":"markdown","source":"### XML力场文件更新示例","metadata":{},"id":"aa4d422d-ab5f-4d62-b611-de2460c56527"},{"cell_type":"code","source":"print(\">>>> Before updating <<<<\")\n! cat bond1.xml\n\nparamset[\"HarmonicBondForce\"][\"length\"] = paramset[\"HarmonicBondForce\"][\"length\"].at[0].set(0.1)\n\ngenerator.overwrite(paramset)\nxmlio.writeXML(\"bond_update.xml\", ffinfo)\nprint(\"\\n\\n>>>> After updating <<<<\")\n! cat bond_update.xml\n","metadata":{"trusted":true},"execution_count":27,"outputs":[{"name":"stdout","text":">>>> Before updating <<<<\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n>>>> After updating <<<<\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n","output_type":"stream"}],"id":"0f319818-f425-4141-9b92-71237e2fc62d"},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[],"id":"f1369677-c20f-42bd-9307-e04ae5246a28"}]}
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+{"metadata":{"language_info":{"name":"python","version":"3.10.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kernelspec":{"name":"python3","display_name":"Python 3 (ipykernel)","language":"python"}},"nbformat_minor":5,"nbformat":4,"cells":[{"cell_type":"markdown","source":"## Environment Setup\n\nRetrieve DMFF from GitHub and switch to the desired branch, then proceed with the installation.","metadata":{},"id":"8ed5396a-d15b-4bdd-8b86-bafc9a2b4c1c"},{"cell_type":"code","source":"! rm -rf DMFF\n! rm -rf /opt/mamba/lib/python3.10/site-packages/dmff*\n! git clone https://github.com/deepmodeling/DMFF.git\n! git config --global --add safe.directory `pwd`/DMFF\n! cd DMFF && git checkout wangxy/v1.0.0-devel && pip install .","metadata":{"trusted":true},"execution_count":17,"outputs":[{"name":"stdout","text":"Cloning into 'DMFF'...\nremote: Enumerating objects: 3507, done.\u001b[K\nremote: Counting objects: 100% (956/956), done.\u001b[K\nremote: Compressing objects: 100% (340/340), done.\u001b[K\nremote: Total 3507 (delta 633), reused 912 (delta 608), pack-reused 2551\u001b[K\nReceiving objects: 100% (3507/3507), 18.81 MiB | 2.17 MiB/s, done.\nResolving deltas: 100% (2243/2243), done.\nUpdating files: 100% (273/273), done.\nBranch 'wangxy/v1.0.0-devel' set up to track remote branch 'wangxy/v1.0.0-devel' from 'origin'.\nSwitched to a new branch 'wangxy/v1.0.0-devel'\nLooking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\nProcessing /data/DMFF\n Preparing metadata (setup.py) ... \u001b[?25ldone\n\u001b[?25hRequirement already satisfied: numpy>=1.18 in /opt/mamba/lib/python3.10/site-packages (from dmff==0.2.1.dev222+g8efbe63) (1.23.4)\nRequirement already satisfied: openmm>=7.6.0 in /opt/mamba/lib/python3.10/site-packages (from dmff==0.2.1.dev222+g8efbe63) (7.7.0)\nRequirement already satisfied: freud-analysis in /opt/mamba/lib/python3.10/site-packages/freud_analysis-2.11.0-py3.10-linux-x86_64.egg (from dmff==0.2.1.dev222+g8efbe63) (2.11.0)\nRequirement already satisfied: rowan>=1.2.1 in /opt/mamba/lib/python3.10/site-packages/rowan-1.3.0.post1-py3.10.egg (from freud-analysis->dmff==0.2.1.dev222+g8efbe63) (1.3.0.post1)\nRequirement already satisfied: scipy>=1.1 in /opt/mamba/lib/python3.10/site-packages (from freud-analysis->dmff==0.2.1.dev222+g8efbe63) (1.9.3)\nBuilding wheels for collected packages: dmff\n Building wheel for dmff (setup.py) ... \u001b[?25ldone\n\u001b[?25h Created wheel for dmff: filename=dmff-0.2.1.dev222+g8efbe63-py3-none-any.whl size=93258 sha256=f20f32e539489412cad4c9a61a0e90b4c18f0c361fba252181ecc0ca6a337222\n Stored in directory: /tmp/pip-ephem-wheel-cache-cjaq0s7q/wheels/f3/08/c8/63a66e9272163ceeb3675eda2e65e58a3e3c8a96296799182d\nSuccessfully built dmff\nInstalling collected packages: dmff\nSuccessfully installed dmff-0.2.1.dev222+g8efbe63\n\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n\u001b[0m","output_type":"stream"}],"id":"cb612446-c7f3-4c27-9733-315c22700387"},{"cell_type":"markdown","source":"Install the required libraries; this step is time-consuming, so please be patient.","metadata":{},"id":"54a8934f-d0f8-47be-97b7-d896fdc4335e"},{"cell_type":"code","source":"! mamba install openmm=7.7.0 rdkit -c conda-forge -y\n! pip install parmed mdtraj pymbar networkx","metadata":{"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"\n __ __ __ __\n / \\ / \\ / \\ / \\\n / \\/ \\/ \\/ \\\n███████████████/ /██/ /██/ /██/ /████████████████████████\n / / \\ / \\ / \\ / \\ \\____\n / / \\_/ \\_/ \\_/ \\ o \\__,\n / _/ \\_____/ `\n |/\n ███╗ ███╗ █████╗ ███╗ ███╗██████╗ █████╗\n ████╗ ████║██╔══██╗████╗ ████║██╔══██╗██╔══██╗\n ██╔████╔██║███████║██╔████╔██║██████╔╝███████║\n 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11.1s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 11.2s\nconda-forge/linux-64 ━━━━━━━━━━━━━━━━━━━━━━ 34.5MB @ 3.5MB/s Finalizing 11.2s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 11.3s\nconda-forge/linux-64 ━━━━━━━━━━━━━━━━━━━━━━ 34.5MB @ 3.5MB/s Finalizing 11.3s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 11.4s\nconda-forge/linux-64 ━━━━━━━━━━━━━━━━━━━━━━ 34.5MB @ 3.5MB/s Finalizing 11.4s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 11.5s\n\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 11.6s\n\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 11.7s\n\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 11.8s\n\u001b[2K\u001b[1A\u001b[2K\u001b[0Gconda-forge/linux-64 @ 3.5MB/s 11.4s\n\u001b[?25h\nPinned packages:\n - python 3.10.*\n\n\nTransaction\n\n Prefix: /opt/mamba\n\n Updating specs:\n\n - openmm=7.7.0\n - rdkit\n - ca-certificates\n - certifi\n - openssl\n\n\n Package Version Build Channel Size\n──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n Install:\n──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n\u001b[32m + boost \u001b[00m 1.78.0 py310hc4a4660_4 conda-forge/linux-64 363kB\n\u001b[32m + boost-cpp \u001b[00m 1.78.0 h5adbc97_2 conda-forge/linux-64 16MB\n\u001b[32m + brotli \u001b[00m 1.1.0 hd590300_0 conda-forge/linux-64 19kB\n\u001b[32m + brotli-bin \u001b[00m 1.1.0 hd590300_0 conda-forge/linux-64 19kB\n\u001b[32m + cairo \u001b[00m 1.16.0 ha61ee94_1014 conda-forge/linux-64 2MB\n\u001b[32m + contourpy \u001b[00m 1.1.1 py310hd41b1e2_0 conda-forge/linux-64 224kB\n\u001b[32m + cycler \u001b[00m 0.11.0 pyhd8ed1ab_0 conda-forge/noarch 10kB\n\u001b[32m + expat \u001b[00m 2.5.0 hcb278e6_1 conda-forge/linux-64 137kB\n\u001b[32m + fmt \u001b[00m 10.1.1 h00ab1b0_0 conda-forge/linux-64 192kB\n\u001b[32m + font-ttf-dejavu-sans-mono\u001b[00m 2.37 hab24e00_0 conda-forge/noarch 397kB\n\u001b[32m + font-ttf-inconsolata \u001b[00m 3.000 h77eed37_0 conda-forge/noarch 97kB\n\u001b[32m + font-ttf-source-code-pro \u001b[00m 2.038 h77eed37_0 conda-forge/noarch 701kB\n\u001b[32m + font-ttf-ubuntu \u001b[00m 0.83 hab24e00_0 conda-forge/noarch 2MB\n\u001b[32m + fontconfig \u001b[00m 2.14.2 h14ed4e7_0 conda-forge/linux-64 272kB\n\u001b[32m + fonts-conda-ecosystem \u001b[00m 1 0 conda-forge/noarch 4kB\n\u001b[32m + fonts-conda-forge \u001b[00m 1 0 conda-forge/noarch 4kB\n\u001b[32m + fonttools \u001b[00m 4.42.1 py310h2372a71_0 conda-forge/linux-64 2MB\n\u001b[32m + freetype \u001b[00m 2.12.1 h267a509_2 conda-forge/linux-64 635kB\n\u001b[32m + freetype-py \u001b[00m 2.3.0 pyhd8ed1ab_0 conda-forge/noarch 59kB\n\u001b[32m + gettext \u001b[00m 0.21.1 h27087fc_0 conda-forge/linux-64 4MB\n\u001b[32m + greenlet \u001b[00m 2.0.2 py310hc6cd4ac_1 conda-forge/linux-64 191kB\n\u001b[32m + kiwisolver \u001b[00m 1.4.5 py310hd41b1e2_1 conda-forge/linux-64 73kB\n\u001b[32m + lcms2 \u001b[00m 2.15 haa2dc70_1 conda-forge/linux-64 242kB\n\u001b[32m + lerc \u001b[00m 4.0.0 h27087fc_0 conda-forge/linux-64 282kB\n\u001b[32m + libbrotlicommon \u001b[00m 1.1.0 hd590300_0 conda-forge/linux-64 69kB\n\u001b[32m + libbrotlidec \u001b[00m 1.1.0 hd590300_0 conda-forge/linux-64 33kB\n\u001b[32m + libbrotlienc \u001b[00m 1.1.0 hd590300_0 conda-forge/linux-64 282kB\n\u001b[32m + libdeflate \u001b[00m 1.18 h0b41bf4_0 conda-forge/linux-64 65kB\n\u001b[32m + libexpat \u001b[00m 2.5.0 hcb278e6_1 conda-forge/linux-64 78kB\n\u001b[32m + libglib \u001b[00m 2.78.0 hebfc3b9_0 conda-forge/linux-64 3MB\n\u001b[32m + libjpeg-turbo \u001b[00m 2.1.5.1 hd590300_1 conda-forge/linux-64 496kB\n\u001b[32m + libpng \u001b[00m 1.6.39 h753d276_0 conda-forge/linux-64 283kB\n\u001b[32m + libtiff \u001b[00m 4.5.1 h8b53f26_1 conda-forge/linux-64 417kB\n\u001b[32m + libwebp-base \u001b[00m 1.3.2 hd590300_0 conda-forge/linux-64 402kB\n\u001b[32m + libxcb \u001b[00m 1.13 h7f98852_1004 conda-forge/linux-64 400kB\n\u001b[32m + matplotlib-base \u001b[00m 3.8.0 py310h62c0568_1 conda-forge/linux-64 7MB\n\u001b[32m + munkres \u001b[00m 1.1.4 pyh9f0ad1d_0 conda-forge/noarch 12kB\n\u001b[32m + openjpeg \u001b[00m 2.5.0 hfec8fc6_2 conda-forge/linux-64 352kB\n\u001b[32m + pcre2 \u001b[00m 10.40 hc3806b6_0 conda-forge/linux-64 2MB\n\u001b[32m + pillow \u001b[00m 9.5.0 py310h065c6d2_0 conda-forge/linux-64 46MB\n\u001b[32m + pixman \u001b[00m 0.40.0 h36c2ea0_0 conda-forge/linux-64 643kB\n\u001b[32m + pthread-stubs \u001b[00m 0.4 h36c2ea0_1001 conda-forge/linux-64 6kB\n\u001b[32m + pycairo \u001b[00m 1.24.0 py310hda9f760_0 conda-forge/linux-64 113kB\n\u001b[32m + rdkit \u001b[00m 2023.03.3 py310h399bcf7_0 conda-forge/linux-64 36MB\n\u001b[32m + reportlab \u001b[00m 4.0.4 py310h2372a71_0 conda-forge/linux-64 2MB\n\u001b[32m + rlpycairo \u001b[00m 0.2.0 pyhd8ed1ab_0 conda-forge/noarch 15kB\n\u001b[32m + sqlalchemy \u001b[00m 2.0.21 py310h2372a71_0 conda-forge/linux-64 3MB\n\u001b[32m + typing-extensions \u001b[00m 4.8.0 hd8ed1ab_0 conda-forge/noarch 10kB\n\u001b[32m + typing_extensions \u001b[00m 4.8.0 pyha770c72_0 conda-forge/noarch 35kB\n\u001b[32m + unicodedata2 \u001b[00m 15.0.0 py310h2372a71_1 conda-forge/linux-64 374kB\n\u001b[32m + xorg-kbproto \u001b[00m 1.0.7 h7f98852_1002 conda-forge/linux-64 27kB\n\u001b[32m + xorg-libice \u001b[00m 1.0.10 h7f98852_0 conda-forge/linux-64 59kB\n\u001b[32m + xorg-libsm \u001b[00m 1.2.3 hd9c2040_1000 conda-forge/linux-64 26kB\n\u001b[32m + xorg-libx11 \u001b[00m 1.8.4 h0b41bf4_0 conda-forge/linux-64 830kB\n\u001b[32m + xorg-libxau \u001b[00m 1.0.11 hd590300_0 conda-forge/linux-64 14kB\n\u001b[32m + xorg-libxdmcp \u001b[00m 1.1.3 h7f98852_0 conda-forge/linux-64 19kB\n\u001b[32m + xorg-libxext \u001b[00m 1.3.4 h0b41bf4_2 conda-forge/linux-64 50kB\n\u001b[32m + xorg-libxrender \u001b[00m 0.9.10 h7f98852_1003 conda-forge/linux-64 33kB\n\u001b[32m + xorg-renderproto \u001b[00m 0.11.1 h7f98852_1002 conda-forge/linux-64 10kB\n\u001b[32m + xorg-xextproto \u001b[00m 7.3.0 h0b41bf4_1003 conda-forge/linux-64 30kB\n\u001b[32m + xorg-xproto \u001b[00m 7.0.31 h7f98852_1007 conda-forge/linux-64 75kB\n\n Change:\n──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n\u001b[31m - hdf5 \u001b[00m 1.12.1 h70be1eb_2 mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main \n\u001b[32m + hdf5 \u001b[00m 1.12.1 nompi_h4df4325_104 conda-forge/linux-64 4MB\n\u001b[31m - python \u001b[00m 3.10.6 h582c2e5_0_cpython conda-forge \n\u001b[32m + python \u001b[00m 3.10.6 ha86cf86_0_cpython conda-forge/linux-64 30MB\n\n Upgrade:\n──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n\u001b[31m - ca-certificates \u001b[00m 2022.10.11 h06a4308_0 mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main \n\u001b[32m + ca-certificates \u001b[00m 2023.7.22 hbcca054_0 conda-forge/linux-64 150kB\n\u001b[31m - certifi \u001b[00m 2022.9.24 py310h06a4308_0 mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main \n\u001b[32m + certifi \u001b[00m 2023.7.22 pyhd8ed1ab_0 conda-forge/noarch 154kB\n\u001b[31m - cryptography \u001b[00m 38.0.2 py310h597c629_1 conda-forge \n\u001b[32m + cryptography \u001b[00m 38.0.4 py310h600f1e7_0 conda-forge/linux-64 1MB\n\u001b[31m - krb5 \u001b[00m 1.19.3 h3790be6_0 conda-forge \n\u001b[32m + krb5 \u001b[00m 1.21.2 h659d440_0 conda-forge/linux-64 1MB\n\u001b[31m - libarchive \u001b[00m 3.5.2 hb890918_3 conda-forge \n\u001b[32m + libarchive \u001b[00m 3.6.2 h3d51595_0 conda-forge/linux-64 836kB\n\u001b[31m - libcurl \u001b[00m 7.86.0 h7bff187_0 conda-forge \n\u001b[32m + libcurl \u001b[00m 8.3.0 hca28451_0 conda-forge/linux-64 388kB\n\u001b[31m - libmamba \u001b[00m 0.27.0 h0dd8ff0_0 conda-forge \n\u001b[32m + libmamba \u001b[00m 1.5.1 h744094f_0 conda-forge/linux-64 2MB\n\u001b[31m - libmambapy \u001b[00m 0.27.0 py310hab0e683_0 conda-forge \n\u001b[32m + libmambapy \u001b[00m 1.5.1 py310h39ff949_0 conda-forge/linux-64 299kB\n\u001b[31m - libnghttp2 \u001b[00m 1.47.0 hdcd2b5c_1 conda-forge \n\u001b[32m + libnghttp2 \u001b[00m 1.52.0 h61bc06f_0 conda-forge/linux-64 622kB\n\u001b[31m - libsolv \u001b[00m 0.7.22 h6239696_0 conda-forge \n\u001b[32m + libsolv \u001b[00m 0.7.24 hfc55251_4 conda-forge/linux-64 468kB\n\u001b[31m - libssh2 \u001b[00m 1.10.0 haa6b8db_3 conda-forge \n\u001b[32m + libssh2 \u001b[00m 1.11.0 h0841786_0 conda-forge/linux-64 271kB\n\u001b[31m - mamba \u001b[00m 0.27.0 py310hf87f941_0 conda-forge \n\u001b[32m + mamba \u001b[00m 1.5.1 py310h51d5547_0 conda-forge/linux-64 51kB\n\u001b[31m - openssl \u001b[00m 1.1.1s h7f8727e_0 mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main \n\u001b[32m + openssl \u001b[00m 3.1.3 hd590300_0 conda-forge/linux-64 3MB\n\u001b[31m - zstd \u001b[00m 1.5.2 h6239696_4 conda-forge \n\u001b[32m + zstd \u001b[00m 1.5.5 hfc55251_0 conda-forge/linux-64 545kB\n\n Summary:\n\n Install: 61 packages\n Change: 2 packages\n Upgrade: 14 packages\n\n Total download: 180MB\n\n──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n\u001b[?25l\u001b[2K\u001b[0G[+] 0.0s\nDownloading \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0.0 B 0.0s\nExtracting \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0 0.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.1s\nDownloading (5) \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0.0 B ca-certificates 0.0s\nExtracting \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0 0.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.2s\nDownloading (5) \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0.0 B ca-certificates 0.1s\nExtracting \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0 0.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.3s\nDownloading (5) \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0.0 B ca-certificates 0.2s\nExtracting \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0 0.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.4s\nDownloading (5) \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0.0 B ca-certificates 0.3s\nExtracting \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0 0.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.5s\nDownloading (5) \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0.0 B fmt 0.4s\nExtracting \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0 0.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.6s\nDownloading (5) \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0.0 B fmt 0.5s\nExtracting \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0 0.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.7s\nDownloading (5) \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0.0 B fmt 0.6s\nExtracting \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0 0.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.8s\nDownloading (5) \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0.0 B fmt 0.7s\nExtracting \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0 0.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gxorg-xextproto 30.3kB @ 34.8kB/s 0.9s\n[+] 0.9s\nDownloading (5) \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 95.2kB libbrotlicommon 0.8s\nExtracting (1) \u001b[90m╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 0 xorg-xextproto 0.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.0s\nDownloading (5) \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 95.2kB libbrotlicommon 0.9s\nExtracting (1) \u001b[90m━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━\u001b[0m 0 xorg-xextproto 0.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.1s\nDownloading (5) \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 266.0kB libbrotlicommon 1.0s\nExtracting (1) \u001b[90m━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━\u001b[0m 0 xorg-xextproto 0.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibbrotlicommon 69.4kB @ 60.2kB/s 0.3s\n[+] 1.2s\nDownloading (5) \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 396.7kB ca-certificates 1.1s\nExtracting (2) \u001b[90m━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━\u001b[0m 0 xorg-xextproto 0.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.3s\nDownloading (5) \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 591.4kB ca-certificates 1.2s\nExtracting (1) \u001b[90m━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━\u001b[0m 1 libbrotlicommon 0.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.4s\nDownloading (5) \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 820.8kB ca-certificates 1.3s\nExtracting (1) \u001b[90m━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━\u001b[0m 1 libbrotlicommon 0.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gca-certificates 149.5kB @ 100.8kB/s 1.5s\n[+] 1.5s\nDownloading (5) \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 929.1kB fmt 1.4s\nExtracting (1) \u001b[90m━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━━\u001b[0m 2 ca-certificates 0.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.6s\nDownloading (5) \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 1.1MB fmt 1.5s\nExtracting (1) \u001b[90m━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━\u001b[0m 2 ca-certificates 0.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.7s\nDownloading (5) \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 1.2MB fmt 1.6s\nExtracting (1) \u001b[90m━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━\u001b[0m 2 ca-certificates 0.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gfmt 192.3kB @ 113.1kB/s 1.7s\nxorg-kbproto 27.3kB @ 15.7kB/s 0.3s\n[+] 1.8s\nDownloading (5) \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 1.5MB libsolv 1.7s\nExtracting (3) \u001b[90m━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━\u001b[0m 2 ca-certificates 0.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gzstd 545.2kB @ 297.8kB/s 1.8s\nlibsolv 467.8kB @ 254.4kB/s 1.8s\n[+] 1.9s\nDownloading (5) \u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 1.9MB freetype 1.8s\nExtracting (4) \u001b[90m━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━\u001b[0m 3 fmt 1.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gxorg-libice 59.4kB @ 29.9kB/s 0.3s\n[+] 2.0s\nDownloading (5) \u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 2.4MB freetype 1.9s\nExtracting (4) \u001b[90m━━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━\u001b[0m 3 fmt 1.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 2.1s\nDownloading (5) \u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 3.2MB freetype 2.0s\nExtracting (5) \u001b[33m━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 3 fmt 1.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 2.2s\nDownloading (5) \u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 3.8MB freetype 2.1s\nExtracting (4) ╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━\u001b[0m 4 libsolv 1.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gfreetype 635.0kB @ 285.6kB/s 0.4s\n[+] 2.3s\nDownloading (5) \u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 4.8MB libglib 2.2s\nExtracting (5) ╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━\u001b[0m 4 libsolv 1.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 2.4s\nDownloading (5) \u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 6.0MB libglib 2.3s\nExtracting (4) ╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━\u001b[0m 5 libsolv 1.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gpcre2 2.4MB @ 990.2kB/s 1.3s\nlibssh2 271.1kB @ 109.8kB/s 0.2s\n[+] 2.5s\nDownloading (5) \u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━\u001b[0m 7.1MB libglib 2.4s\nExtracting (6) ╸\u001b[33m━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━\u001b[0m 5 libsolv 1.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibxcb 399.9kB @ 155.6kB/s 0.6s\n[+] 2.6s\nDownloading (5) \u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━\u001b[0m 7.6MB libglib 2.5s\nExtracting (6) ╸\u001b[33m━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━\u001b[0m 6 libssh2 1.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 2.7s\nDownloading (5) \u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━\u001b[0m 8.7MB openjpeg 2.6s\nExtracting (6) ╸\u001b[33m━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━\u001b[0m 6 libssh2 1.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gopenjpeg 352.0kB @ 130.2kB/s 0.3s\nfontconfig 272.0kB @ 99.1kB/s 0.3s\nlibglib 2.7MB @ 970.5kB/s 0.9s\n[+] 2.8s\nDownloading (5) ╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 10.8MB boost 2.7s\nExtracting (9) ╸\u001b[33m━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━\u001b[0m 6 libssh2 1.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gopenssl 2.6MB @ 942.6kB/s 1.1s\n[+] 2.9s\nDownloading (5) ╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 11.4MB boost 2.8s\nExtracting (9) ━╸\u001b[33m━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━\u001b[0m 7 libssh2 2.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gunicodedata2 373.8kB @ 127.7kB/s 0.4s\nboost 362.6kB @ 121.5kB/s 0.3s\n[+] 3.0s\nDownloading (5) ╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 12.0MB font-ttf-inconsolata 2.9s\nExtracting (11) ━╸\u001b[33m━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 7 libxcb 2.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gxorg-libxext 50.1kB @ 16.5kB/s 0.3s\nlibmambapy 299.1kB @ 97.6kB/s 0.3s\n[+] 3.1s\nDownloading (5) ╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 12.8MB font-ttf-inconsolata 3.0s\nExtracting (12) ━╸\u001b[33m━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 8 libxcb 2.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 3.2s\nDownloading (5) ╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 13.6MB font-ttf-inconsolata 3.1s\nExtracting (12) ━╸\u001b[33m━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 8 libxcb 2.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gfont-ttf-inconsolata 96.5kB @ 30.1kB/s 0.3s\ntyping_extensions 35.1kB @ 10.8kB/s 0.3s\n[+] 3.3s\nDownloading (5) ╸\u001b[33m━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 14.1MB cycler 3.2s\nExtracting (13) ━╸\u001b[33m━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 9 libxcb 2.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gcycler 10.3kB @ 3.1kB/s 0.3s\ntyping-extensions 10.1kB @ 3.0kB/s 0.2s\n[+] 3.4s\nDownloading (5) ╸\u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 14.9MB libdeflate 3.3s\nExtracting (15) ━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 9 openjpeg 2.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 3.5s\nDownloading (5) ╸\u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 15.4MB libdeflate 3.4s\nExtracting (15) ━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 9 openjpeg 2.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gpycairo 113.5kB @ 32.3kB/s 0.3s\nlibdeflate 65.2kB @ 18.3kB/s 0.3s\n[+] 3.6s\nDownloading (5) ━╸\u001b[33m━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 17.4MB libexpat 3.5s\nExtracting (16) ━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 10 openjpeg 2.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 3.7s\nDownloading (5) ━╸\u001b[33m━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 20.0MB libexpat 3.6s\nExtracting (16) ━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 10 openjpeg 2.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gxorg-libxau 14.5kB @ 3.8kB/s 0.2s\n[+] 3.8s\nDownloading (5) ━╸\u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 23.0MB libexpat 3.7s\nExtracting (16) ━━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 11 openssl 2.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibexpat 78.0kB @ 20.4kB/s 0.3s\n[+] 3.9s\nDownloading (5) ━━╸\u001b[33m━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 25.6MB libtiff 3.8s\nExtracting (17) ━━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 11 openssl 3.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 4.0s\nDownloading (5) ━━╸\u001b[33m━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 28.2MB libtiff 3.9s\nExtracting (16) ━━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 12 openssl 3.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gpthread-stubs 5.6kB @ 1.4kB/s 0.2s\n[+] 4.1s\nDownloading (5) ━━╸\u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 30.5MB libtiff 4.0s\nExtracting (17) ━━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 12 openssl 3.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibtiff 416.5kB @ 101.3kB/s 0.3s\nsqlalchemy 2.6MB @ 631.3kB/s 0.9s\n[+] 4.2s\nDownloading (5) ━━━╸\u001b[33m━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 32.9MB expat 4.1s\nExtracting (19) ━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 12 pcre2 3.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gexpat 136.8kB @ 32.1kB/s 0.3s\n[+] 4.3s\nDownloading (5) ━━━╸\u001b[33m━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 33.3MB greenlet 4.2s\nExtracting (19) ━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 13 pcre2 3.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glcms2 242.1kB @ 55.1kB/s 0.3s\n[+] 4.4s\nDownloading (5) ━━━╸\u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 36.7MB greenlet 4.3s\nExtracting (20) ━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 13 pcre2 3.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibnghttp2 622.4kB @ 140.7kB/s 0.3s\n[+] 4.5s\nDownloading (5) ━━━╸\u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 37.8MB greenlet 4.4s\nExtracting (20) ━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 14 pcre2 3.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Ggreenlet 190.7kB @ 42.3kB/s 0.2s\n[+] 4.6s\nDownloading (5) ━━━╸\u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 38.0MB cryptography 4.5s\nExtracting (21) ━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 14 pthread-stubs 3.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gxorg-libxrender 32.9kB @ 7.0kB/s 0.3s\n[+] 4.7s\nDownloading (5) ━━━━╸\u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━\u001b[0m 41.4MB cryptography 4.6s\nExtracting (21) ━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 14 pthread-stubs 3.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 4.8s\nDownloading (5) ━━━━╸\u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━\u001b[0m 43.0MB cryptography 4.7s\nExtracting (22) ━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 14 pthread-stubs 3.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 4.9s\nDownloading (5) ━━━━╸\u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━\u001b[0m 43.0MB cryptography 4.8s\nExtracting (20) ━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 16 pthread-stubs 4.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.0s\nDownloading (5) ━━━━╸\u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━\u001b[0m 43.6MB font-ttf-ubuntu 4.9s\nExtracting (20) ━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 16 pycairo 4.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gcryptography 1.4MB @ 276.7kB/s 0.7s\n[+] 5.1s\nDownloading (5) ━━━━╸\u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━\u001b[0m 44.1MB font-ttf-ubuntu 5.0s\nExtracting (21) ━━━╸\u001b[33m━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 16 pycairo 4.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.2s\nDownloading (5) ━━━━╸\u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━\u001b[0m 44.1MB font-ttf-ubuntu 5.1s\nExtracting (21) ━━━╸\u001b[33m━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 16 pycairo 4.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.3s\nDownloading (5) ━━━━╸\u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━\u001b[0m 45.2MB font-ttf-ubuntu 5.2s\nExtracting (21) ━━━╸\u001b[33m━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 16 pycairo 4.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.4s\nDownloading (5) ━━━━╸\u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━\u001b[0m 45.9MB mamba 5.3s\nExtracting (19) ━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 18 unicodedata2 4.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gcertifi 153.8kB @ 28.4kB/s 0.3s\nfont-ttf-ubuntu 2.0MB @ 361.3kB/s 0.7s\n[+] 5.5s\nDownloading (5) ━━━━╸\u001b[33m━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━\u001b[0m 47.0MB mamba 5.4s\nExtracting (21) ━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 18 unicodedata2 4.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.6s\nDownloading (5) ━━━━━╸\u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━\u001b[0m 50.6MB mamba 5.5s\nExtracting (21) ━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 18 unicodedata2 4.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Grlpycairo 14.9kB @ 2.6kB/s 0.2s\n[+] 5.7s\nDownloading (5) ━━━━━╸\u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━\u001b[0m 50.6MB mamba 5.6s\nExtracting (21) ━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 19 unicodedata2 4.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.8s\nDownloading (5) ━━━━━╸\u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━\u001b[0m 50.9MB pillow 5.7s\nExtracting (20) ━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 20 xorg-libxau 4.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.9s\nDownloading (5) ━━━━━╸\u001b[33m━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━\u001b[0m 52.2MB pillow 5.8s\nExtracting (20) ━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 20 xorg-libxau 5.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.0s\nDownloading (5) ━━━━━╸\u001b[33m━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━\u001b[0m 52.2MB pillow 5.9s\nExtracting (20) ━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 20 xorg-libxau 5.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glerc 281.8kB @ 46.9kB/s 0.3s\nfonttools 2.2MB @ 366.6kB/s 0.7s\n[+] 6.1s\nDownloading (5) ━━━━━━╸\u001b[33m━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━\u001b[0m 57.4MB pillow 6.0s\nExtracting (21) ━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 21 xorg-libxau 5.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.2s\nDownloading (5) ━━━━━━╸\u001b[33m━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━\u001b[0m 60.1MB rdkit 6.1s\nExtracting (20) ━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 22 certifi 5.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gxorg-renderproto 9.6kB @ 1.5kB/s 0.2s\n[+] 6.3s\nDownloading (5) ━━━━━━╸\u001b[33m━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━\u001b[0m 62.1MB rdkit 6.2s\nExtracting (21) ━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 22 certifi 5.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.4s\nDownloading (5) ━━━━━━╸\u001b[33m━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━\u001b[0m 62.1MB rdkit 6.3s\nExtracting (21) ━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 22 certifi 5.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gmamba 51.2kB @ 7.9kB/s 2.0s\n[+] 6.5s\nDownloading (5) ━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━━\u001b[0m 63.2MB rdkit 6.4s\nExtracting (21) ━━━━━╸\u001b[33m━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 23 certifi 5.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.6s\nDownloading (5) ━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━━\u001b[0m 64.1MB gettext 6.5s\nExtracting (20) ━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 24 cryptography 5.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.7s\nDownloading (5) ━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━━\u001b[0m 65.0MB gettext 6.6s\nExtracting (20) ━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 24 cryptography 5.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibbrotlienc 282.2kB @ 41.5kB/s 0.5s\n[+] 6.8s\nDownloading (5) ━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━━\u001b[0m 66.8MB gettext 6.7s\nExtracting (20) ━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 24 cryptography 5.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.9s\nDownloading (5) ━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━━\u001b[0m 68.2MB gettext 6.8s\nExtracting (21) ━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 24 cryptography 6.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.0s\nDownloading (5) ━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━━\u001b[0m 68.2MB pillow 6.9s\nExtracting (21) ━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 24 cycler 6.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gbrotli-bin 19.0kB @ 2.7kB/s 0.3s\n[+] 7.1s\nDownloading (5) ━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━━\u001b[0m 68.3MB pillow 7.0s\nExtracting (21) ━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 25 cycler 6.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.2s\nDownloading (5) ━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━━\u001b[0m 68.3MB pillow 7.1s\nExtracting (21) ━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 25 cycler 6.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.3s\nDownloading (5) ━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━━\u001b[0m 68.3MB pillow 7.2s\nExtracting (20) ━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 26 cycler 6.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gcontourpy 223.7kB @ 30.4kB/s 0.3s\n[+] 7.4s\nDownloading (5) ━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━━\u001b[0m 68.6MB python 7.3s\nExtracting (21) ━━━━━━╸\u001b[33m━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 26 expat 6.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.5s\nDownloading (5) ━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━━\u001b[0m 69.2MB python 7.4s\nExtracting (20) ━━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 27 expat 6.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.6s\nDownloading (5) ━━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━\u001b[0m 74.0MB python 7.5s\nExtracting (20) ━━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 27 expat 6.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibmamba 1.6MB @ 211.5kB/s 0.3s\n[+] 7.7s\nDownloading (5) ━━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━\u001b[0m 75.7MB python 7.6s\nExtracting (20) ━━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 28 expat 6.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.8s\nDownloading (5) ━━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━\u001b[0m 77.1MB rdkit 7.7s\nExtracting (20) ━━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 28 font-ttf-ubuntu 6.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.9s\nDownloading (5) ━━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━\u001b[0m 79.1MB rdkit 7.8s\nExtracting (19) ━━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 29 font-ttf-ubuntu 7.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gfont-ttf-source-code-pro 700.8kB @ 87.8kB/s 0.3s\n[+] 8.0s\nDownloading (5) ━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━━\u001b[0m 81.8MB rdkit 7.9s\nExtracting (20) ━━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 29 font-ttf-ubuntu 7.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 8.1s\nDownloading (5) ━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━━\u001b[0m 83.2MB rdkit 8.0s\nExtracting (20) ━━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 29 font-ttf-ubuntu 7.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 8.2s\nDownloading (5) ━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━━\u001b[0m 83.2MB fonts-conda-forge 8.1s\nExtracting (20) ━━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 29 fonttools 7.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gfonts-conda-forge 4.1kB @ 498.0 B/s 0.3s\n[+] 8.3s\nDownloading (5) ━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━━\u001b[0m 83.8MB gettext 8.2s\nExtracting (19) ━━━━━━━━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 31 fonttools 7.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 8.4s\nDownloading (5) ━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━\u001b[0m 86.7MB gettext 8.3s\nExtracting (19) ━━━━━━━━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 31 fonttools 7.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Ggettext 4.3MB @ 511.6kB/s 2.4s\n[+] 8.5s\nDownloading (5) ━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━\u001b[0m 87.6MB libwebp-base 8.4s\nExtracting (20) ━━━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 31 fonttools 7.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 8.6s\nDownloading (5) ━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━\u001b[0m 91.1MB libwebp-base 8.5s\nExtracting (20) ━━━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 31 gettext 7.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 8.7s\nDownloading (5) ━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━\u001b[0m 91.1MB libwebp-base 8.6s\nExtracting (19) ━━━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 32 gettext 7.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 8.8s\nDownloading (5) ━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━\u001b[0m 91.2MB libwebp-base 8.7s\nExtracting (18) ━━━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 33 gettext 7.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 8.9s\nDownloading (5) ━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━\u001b[0m 91.2MB matplotlib-base 8.8s\nExtracting (18) ━━━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 33 gettext 8.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 9.0s\nDownloading (5) ━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━\u001b[0m 91.2MB matplotlib-base 8.9s\nExtracting (18) ━━━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 33 lerc 8.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibwebp-base 401.8kB @ 44.6kB/s 0.6s\n[+] 9.1s\nDownloading (5) ━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━\u001b[0m 91.7MB matplotlib-base 9.0s\nExtracting (18) ━━━━━━━━━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 34 lerc 8.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 9.2s\nDownloading (5) ━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━\u001b[0m 91.8MB matplotlib-base 9.1s\nExtracting (17) ━━━━━━━━━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 35 lerc 8.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gxorg-xproto 74.9kB @ 8.1kB/s 0.3s\n[+] 9.3s\nDownloading (5) ━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━\u001b[0m 92.1MB pillow 9.2s\nExtracting (18) ━━━━━━━━━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 35 lerc 8.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 9.4s\nDownloading (5) ━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━\u001b[0m 92.1MB pillow 9.3s\nExtracting (18) ━━━━━━━━━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 35 libbrotlienc 8.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 9.5s\nDownloading (5) ━━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━\u001b[0m 99.3MB pillow 9.4s\nExtracting (17) ━━━━━━━━━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 36 libbrotlienc 8.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibbrotlidec 32.6kB @ 3.4kB/s 0.2s\n[+] 9.6s\nDownloading (5) ━━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━\u001b[0m 101.0MB pillow 9.5s\nExtracting (18) ━━━━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 36 libbrotlienc 8.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 9.7s\nDownloading (5) ━━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━\u001b[0m 102.0MB python 9.6s\nExtracting (18) ━━━━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 36 libbrotlienc 8.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 9.8s\nDownloading (5) ━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━━\u001b[0m 103.9MB python 9.7s\nExtracting (18) ━━━━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 36 libdeflate 8.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 9.9s\nDownloading (5) ━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━━\u001b[0m 105.0MB python 9.8s\nExtracting (17) ━━━━━━━━━━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 37 libdeflate 9.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 10.0s\nDownloading (5) ━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━━\u001b[0m 105.8MB python 9.9s\nExtracting (16) ━━━━━━━━━━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 38 libdeflate 9.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 10.1s\nDownloading (5) ━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━━\u001b[0m 106.4MB rdkit 10.0s\nExtracting (16) ━━━━━━━━━━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 38 libdeflate 9.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gmatplotlib-base 6.8MB @ 667.1kB/s 1.9s\n[+] 10.2s\nDownloading (5) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━\u001b[0m 110.7MB rdkit 10.1s\nExtracting (17) ━━━━━━━━━━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 38 libexpat 9.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 10.3s\nDownloading (5) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━\u001b[0m 112.1MB rdkit 10.2s\nExtracting (17) ━━━━━━━━━━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 38 libexpat 9.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 10.4s\nDownloading (5) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━\u001b[0m 112.1MB rdkit 10.3s\nExtracting (16) ━━━━━━━━━━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 39 libexpat 9.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gkrb5 1.4MB @ 131.1kB/s 0.9s\n[+] 10.5s\nDownloading (5) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━\u001b[0m 114.4MB font-ttf-dejavu-sans-mono 10.4s\nExtracting (16) ━━━━━━━━━━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 40 libexpat 9.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 10.6s\nDownloading (5) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━\u001b[0m 114.5MB font-ttf-dejavu-sans-mono 10.5s\nExtracting (16) ━━━━━━━━━━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 40 libwebp-base 9.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 10.7s\nDownloading (5) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━\u001b[0m 115.5MB font-ttf-dejavu-sans-mono 10.6s\nExtracting (16) ━━━━━━━━━━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 40 libwebp-base 9.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibcurl 388.3kB @ 36.2kB/s 0.6s\nrdkit 36.4MB @ 3.4MB/s 7.5s\n[+] 10.8s\nDownloading (5) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━\u001b[0m 118.6MB font-ttf-dejavu-sans-mono 10.7s\nExtracting (17) ━━━━━━━━━━━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━\u001b[0m 41 contourpy 9.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gfont-ttf-dejavu-sans-mono 397.4kB @ 36.5kB/s 0.4s\n[+] 10.9s\nDownloading (5) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 119.3MB fonts-conda-ecosystem 10.8s\nExtracting (17) ━━━━━━━━━━━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━\u001b[0m 42 contourpy 10.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gfonts-conda-ecosystem 3.7kB @ 333.0 B/s 0.3s\n[+] 11.0s\nDownloading (5) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 119.8MB libarchive 10.9s\nExtracting (18) ━━━━━━━━━━━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━\u001b[0m 42 contourpy 10.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibjpeg-turbo 496.4kB @ 44.9kB/s 0.3s\n[+] 11.1s\nDownloading (5) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 120.9MB libarchive 11.0s\nExtracting (19) ━━━━━━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━\u001b[0m 42 contourpy 10.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gxorg-libxdmcp 19.1kB @ 1.7kB/s 0.3s\n[+] 11.2s\nDownloading (5) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 121.4MB libarchive 11.1s\nExtracting (20) ━━━━━━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━\u001b[0m 42 cycler 10.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gbrotli 19.4kB @ 1.7kB/s 0.2s\n[+] 11.3s\nDownloading (5) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 122.2MB libarchive 11.2s\nExtracting (20) ━━━━━━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━━\u001b[0m 43 cycler 10.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibarchive 835.6kB @ 73.5kB/s 0.4s\nmunkres 12.5kB @ 1.1kB/s 0.3s\n[+] 11.4s\nDownloading (5) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 123.3MB hdf5 11.3s\nExtracting (21) ━━━━━━━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━\u001b[0m 44 brotli 10.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 11.5s\nDownloading (5) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 123.8MB hdf5 11.4s\nExtracting (21) ━━━━━━━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━\u001b[0m 44 brotli 10.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibpng 282.6kB @ 24.4kB/s 0.3s\n[+] 11.6s\nDownloading (5) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 124.6MB hdf5 11.5s\nExtracting (22) ━━━━━━━━━━━━╸\u001b[33m━━━━━━╸\u001b[0m\u001b[90m━━━\u001b[0m 44 brotli 10.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gpillow 46.5MB @ 4.0MB/s 8.9s\nxorg-libsm 26.4kB @ 2.3kB/s 0.3s\n[+] 11.7s\nDownloading (5) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 124.9MB hdf5 11.6s\nExtracting (24) ━━━━━━━━━━━━╸\u001b[33m━━━━━━━╸\u001b[0m\u001b[90m━━\u001b[0m 44 brotli 10.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 11.8s\nDownloading (5) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 125.2MB python 11.7s\nExtracting (23) ━━━━━━━━━━━━╸\u001b[33m━━━━━━━╸\u001b[0m\u001b[90m━━\u001b[0m 45 contourpy 10.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 11.9s\nDownloading (5) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 125.8MB python 11.8s\nExtracting (23) ━━━━━━━━━━━━╸\u001b[33m━━━━━━━╸\u001b[0m\u001b[90m━━\u001b[0m 45 contourpy 11.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 12.0s\nDownloading (5) ━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━\u001b[0m 127.6MB python 11.9s\nExtracting (22) ━━━━━━━━━━━━╸\u001b[33m━━━━━━━╸\u001b[0m\u001b[90m━━\u001b[0m 46 contourpy 11.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gxorg-libx11 829.9kB @ 68.9kB/s 0.4s\n[+] 12.1s\nDownloading (5) ━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━\u001b[0m 129.6MB python 12.0s\nExtracting (23) ━━━━━━━━━━━━╸\u001b[33m━━━━━━━╸\u001b[0m\u001b[90m━━\u001b[0m 46 contourpy 11.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Greportlab 2.3MB @ 193.9kB/s 0.6s\ncairo 1.6MB @ 129.9kB/s 0.5s\n[+] 12.2s\nDownloading (5) ━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━\u001b[0m 130.7MB boost-cpp 12.1s\nExtracting (25) ━━━━━━━━━━━━╸\u001b[33m━━━━━━━━╸\u001b[0m\u001b[90m━\u001b[0m 46 expat 11.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 12.3s\nDownloading (5) ━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━\u001b[0m 131.0MB boost-cpp 12.2s\nExtracting (24) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━╸\u001b[0m\u001b[90m━\u001b[0m 47 expat 11.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gkiwisolver 73.1kB @ 5.9kB/s 0.3s\n[+] 12.4s\nDownloading (5) ━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━\u001b[0m 131.3MB boost-cpp 12.3s\nExtracting (24) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━╸\u001b[0m\u001b[90m━\u001b[0m 48 expat 11.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gpixman 642.5kB @ 51.4kB/s 0.4s\n[+] 12.5s\nDownloading (4) ━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━\u001b[0m 132.1MB boost-cpp 12.4s\nExtracting (24) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━╸\u001b[0m\u001b[90m━\u001b[0m 48 expat 11.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gfreetype-py 58.9kB @ 4.7kB/s 0.2s\n[+] 12.6s\nDownloading (3) ━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━\u001b[0m 132.5MB hdf5 12.5s\nExtracting (26) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━\u001b[0m 48 font-ttf-dejavu-sans-mono 11.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 12.7s\nDownloading (3) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 133.6MB hdf5 12.6s\nExtracting (25) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━\u001b[0m 49 font-ttf-dejavu-sans-mono 11.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 12.8s\nDownloading (3) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 134.6MB hdf5 12.7s\nExtracting (25) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━\u001b[0m 49 font-ttf-dejavu-sans-mono 11.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 12.9s\nDownloading (3) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 135.6MB hdf5 12.8s\nExtracting (25) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━\u001b[0m 49 font-ttf-dejavu-sans-mono 12.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 13.0s\nDownloading (3) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 136.5MB python 12.9s\nExtracting (25) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━\u001b[0m 49 freetype-py 12.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 13.1s\nDownloading (3) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 137.5MB python 13.0s\nExtracting (24) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━\u001b[0m 50 freetype-py 12.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 13.2s\nDownloading (3) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 138.5MB python 13.1s\nExtracting (24) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━\u001b[0m 50 freetype-py 12.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 13.3s\nDownloading (3) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 139.5MB python 13.2s\nExtracting (24) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━\u001b[0m 50 freetype-py 12.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 13.4s\nDownloading (3) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 140.4MB boost-cpp 13.3s\nExtracting (24) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━\u001b[0m 50 gettext 12.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Ghdf5 3.7MB @ 276.5kB/s 2.1s\n[+] 13.5s\nDownloading (2) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 141.5MB boost-cpp 13.4s\nExtracting (25) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━\u001b[0m 50 gettext 12.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 13.6s\nDownloading (2) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 142.4MB boost-cpp 13.5s\nExtracting (24) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 51 gettext 12.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 13.7s\nDownloading (2) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 143.3MB boost-cpp 13.6s\nExtracting (24) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 51 gettext 12.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 13.8s\nDownloading (2) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 143.9MB python 13.7s\nExtracting (23) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 52 cairo 12.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 13.9s\nDownloading (2) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 144.5MB python 13.8s\nExtracting (23) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 52 cairo 13.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 14.0s\nDownloading (2) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 145.3MB python 13.9s\nExtracting (22) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 53 cairo 13.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 14.1s\nDownloading (2) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 146.2MB python 14.0s\nExtracting (22) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 53 cairo 13.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 14.2s\nDownloading (2) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 146.5MB boost-cpp 14.1s\nExtracting (21) ━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━\u001b[0m 54 expat 13.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 14.3s\nDownloading (2) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 146.6MB boost-cpp 14.2s\nExtracting (21) ━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━\u001b[0m 54 expat 13.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 14.4s\nDownloading (2) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 146.6MB boost-cpp 14.3s\nExtracting (21) ━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━\u001b[0m 54 expat 13.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 14.5s\nDownloading (2) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 147.1MB boost-cpp 14.4s\nExtracting (21) ━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━\u001b[0m 54 expat 13.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 14.6s\nDownloading (2) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 149.8MB python 14.5s\nExtracting (20) ━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━\u001b[0m 55 font-ttf-dejavu-sans-mono 13.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 14.7s\nDownloading (2) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 149.8MB python 14.6s\nExtracting (20) ━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━\u001b[0m 55 font-ttf-dejavu-sans-mono 13.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gboost-cpp 15.9MB @ 1.1MB/s 2.7s\n[+] 14.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 150.5MB python 14.7s\nExtracting (20) ━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━\u001b[0m 55 font-ttf-dejavu-sans-mono 13.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 14.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 150.6MB python 14.8s\nExtracting (21) ━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━\u001b[0m 55 font-ttf-dejavu-sans-mono 14.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 15.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 150.6MB python 14.9s\nExtracting (21) ━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━\u001b[0m 55 freetype-py 14.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 15.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 150.6MB python 15.0s\nExtracting (19) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 57 freetype-py 14.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 15.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 150.7MB python 15.1s\nExtracting (19) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 57 freetype-py 14.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 15.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 150.7MB python 15.2s\nExtracting (19) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 57 freetype-py 14.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 15.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 150.7MB python 15.3s\nExtracting (18) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 58 hdf5 14.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 15.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 150.7MB python 15.4s\nExtracting (17) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 59 hdf5 14.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 15.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 150.7MB python 15.5s\nExtracting (17) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 59 hdf5 14.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 15.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 150.8MB python 15.6s\nExtracting (17) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 59 hdf5 14.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 15.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 150.8MB python 15.7s\nExtracting (16) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 60 kiwisolver 14.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 15.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 150.8MB python 15.8s\nExtracting (16) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 60 kiwisolver 15.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 16.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 150.8MB python 15.9s\nExtracting (16) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 60 kiwisolver 15.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 16.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 150.9MB python 16.0s\nExtracting (16) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 60 kiwisolver 15.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 16.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 150.9MB python 16.1s\nExtracting (15) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 61 libbrotlidec 15.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 16.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 150.9MB python 16.2s\nExtracting (15) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 61 libbrotlidec 15.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 16.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 150.9MB python 16.3s\nExtracting (14) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 62 libbrotlidec 15.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 16.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.0MB python 16.4s\nExtracting (14) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 62 libbrotlidec 15.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 16.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.0MB python 16.5s\nExtracting (14) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 62 libbrotlienc 15.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 16.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.0MB python 16.6s\nExtracting (13) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 63 libbrotlienc 15.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 16.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.0MB python 16.7s\nExtracting (13) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 63 libbrotlienc 15.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 16.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.0MB python 16.8s\nExtracting (13) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 63 libbrotlienc 16.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 17.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.1MB python 16.9s\nExtracting (13) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 63 pixman 16.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 17.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.1MB python 17.0s\nExtracting (12) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 64 pixman 16.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 17.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.1MB python 17.1s\nExtracting (12) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 64 pixman 16.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 17.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.1MB python 17.2s\nExtracting (12) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 64 pixman 16.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 17.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.2MB python 17.3s\nExtracting (12) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 64 pycairo 16.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 17.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.2MB python 17.4s\nExtracting (12) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 64 pycairo 16.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 17.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.2MB python 17.5s\nExtracting (11) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 65 pycairo 16.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 17.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.2MB python 17.6s\nExtracting (11) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 65 pycairo 16.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 17.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.3MB python 17.7s\nExtracting (11) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 65 rdkit 16.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 17.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.3MB python 17.8s\nExtracting (11) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 65 rdkit 17.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 18.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.3MB python 17.9s\nExtracting (9) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 67 rdkit 17.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 18.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.4MB python 18.0s\nExtracting (9) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 67 rdkit 17.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 18.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.4MB python 18.1s\nExtracting (9) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 67 reportlab 17.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 18.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.4MB python 18.2s\nExtracting (8) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 68 reportlab 17.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 18.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.4MB python 18.3s\nExtracting (7) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 69 expat 17.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 18.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.5MB python 18.4s\nExtracting (7) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 69 expat 17.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 18.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.5MB python 18.5s\nExtracting (7) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 69 expat 17.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 18.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.5MB python 18.6s\nExtracting (5) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 71 expat 17.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 18.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.5MB python 18.7s\nExtracting (5) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 71 libbrotlidec 17.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 18.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.6MB python 18.8s\nExtracting (5) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 71 libbrotlidec 18.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 19.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.6MB python 18.9s\nExtracting (4) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 72 expat 18.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 19.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.6MB python 19.0s\nExtracting (4) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 72 expat 18.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 19.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.6MB python 19.1s\nExtracting (4) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 72 expat 18.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 19.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.6MB python 19.2s\nExtracting (3) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 73 expat 18.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 19.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.7MB python 19.3s\nExtracting (2) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 74 pixman 18.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 19.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.8MB python 19.4s\nExtracting (2) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 74 pixman 18.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 19.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.8MB python 19.5s\nExtracting (2) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 74 pixman 18.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 19.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.8MB python 19.6s\nExtracting (2) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 74 pixman 18.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 19.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.9MB python 19.7s\nExtracting (2) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 74 rdkit 18.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 19.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.9MB python 19.8s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 75 rdkit 19.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 20.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.9MB python 19.9s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 75 rdkit 19.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 20.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.0MB python 20.0s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 75 rdkit 19.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 20.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.0MB python 20.1s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 20.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.0MB python 20.2s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 20.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.0MB python 20.3s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 20.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.1MB python 20.4s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 20.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.1MB python 20.5s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 20.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.2MB python 20.6s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 20.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.2MB python 20.7s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 20.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.2MB python 20.8s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 21.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.3MB python 20.9s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 21.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.3MB python 21.0s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 21.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.3MB python 21.1s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 21.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.4MB python 21.2s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 21.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.4MB python 21.3s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 21.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.5MB python 21.4s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 21.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.5MB python 21.5s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 21.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.5MB python 21.6s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 21.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.6MB python 21.7s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 21.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.6MB python 21.8s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 22.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.6MB python 21.9s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 22.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.7MB python 22.0s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 22.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.7MB python 22.1s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 22.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.8MB python 22.2s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 22.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.8MB python 22.3s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 22.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.8MB python 22.4s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 22.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.8MB python 22.5s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 22.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.8MB python 22.6s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 22.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.8MB python 22.7s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 22.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.9MB python 22.8s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 23.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.9MB python 22.9s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 23.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.9MB python 23.0s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 23.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 153.0MB python 23.1s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 23.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 153.0MB python 23.2s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 23.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 153.0MB python 23.3s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 23.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 153.0MB python 23.4s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 23.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 153.1MB python 23.5s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 23.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 153.2MB python 23.6s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 23.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 153.3MB python 23.7s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 23.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 153.3MB python 23.8s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 24.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 153.4MB python 23.9s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 24.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 153.4MB python 24.0s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 24.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 153.4MB python 24.1s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 24.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 153.5MB python 24.2s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 24.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 153.6MB python 24.3s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 24.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 153.6MB python 24.4s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 24.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 153.7MB python 24.5s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 24.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 153.7MB python 24.6s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 24.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 153.8MB python 24.7s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 24.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 153.8MB python 24.8s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 25.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 153.9MB python 24.9s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 25.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 153.9MB python 25.0s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 25.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 154.0MB python 25.1s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 25.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 154.0MB python 25.2s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 25.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 154.1MB python 25.3s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 25.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 154.1MB python 25.4s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 25.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 154.2MB python 25.5s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 25.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 154.3MB python 25.6s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 25.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 154.3MB python 25.7s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 25.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 154.4MB python 25.8s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 26.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 154.5MB python 25.9s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 26.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 154.5MB python 26.0s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 26.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 154.6MB python 26.1s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 26.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 154.6MB python 26.2s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 26.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 154.7MB python 26.3s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 26.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 154.8MB python 26.4s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 26.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 154.8MB python 26.5s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 26.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 154.9MB python 26.6s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 26.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 154.9MB python 26.7s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 26.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 155.0MB python 26.8s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 27.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 155.1MB python 26.9s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 27.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 155.2MB python 27.0s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 27.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 155.2MB python 27.1s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 27.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 155.3MB python 27.2s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 27.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 155.4MB python 27.3s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 27.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 155.5MB python 27.4s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 27.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 155.5MB python 27.5s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 27.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 155.6MB python 27.6s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 27.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 155.6MB python 27.7s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 27.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 155.7MB python 27.8s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 28.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 155.8MB python 27.9s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 28.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 155.9MB python 28.0s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 28.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 155.9MB python 28.1s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 28.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 156.0MB python 28.2s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 28.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 156.1MB python 28.3s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 28.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 156.2MB python 28.4s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 28.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 156.3MB python 28.5s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 28.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 156.4MB python 28.6s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 28.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 156.4MB python 28.7s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 28.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 156.5MB python 28.8s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 29.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 156.6MB python 28.9s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 29.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 156.7MB python 29.0s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 29.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 156.8MB python 29.1s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 29.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 156.8MB python 29.2s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 29.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 156.8MB python 29.3s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 29.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 157.0MB python 29.4s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 29.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 157.1MB python 29.5s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 29.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 157.2MB python 29.6s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 29.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 157.3MB python 29.7s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 29.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 157.4MB python 29.8s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 30.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 157.5MB python 29.9s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 30.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 157.6MB python 30.0s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 30.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 157.7MB python 30.1s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 30.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 157.8MB python 30.2s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 30.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 157.9MB python 30.3s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 30.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 158.0MB python 30.4s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 30.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 158.1MB python 30.5s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 30.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 158.2MB python 30.6s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 30.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 158.3MB python 30.7s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 30.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 158.3MB python 30.8s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 31.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 158.5MB python 30.9s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 31.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 158.6MB python 31.0s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 31.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 158.6MB python 31.1s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 31.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 158.7MB python 31.2s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 31.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 158.9MB python 31.3s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 31.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 159.0MB python 31.4s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 31.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 159.1MB python 31.5s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 31.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 159.2MB python 31.6s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 31.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 159.3MB python 31.7s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 31.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 159.5MB python 31.8s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 32.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 159.6MB python 31.9s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 32.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 159.7MB python 32.0s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 32.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 159.8MB python 32.1s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 32.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 160.0MB python 32.2s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 32.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 160.1MB python 32.3s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 32.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 160.2MB python 32.4s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 32.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 160.3MB python 32.5s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 32.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 160.4MB python 32.6s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 32.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 160.5MB python 32.7s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 32.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 160.6MB python 32.8s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 33.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 160.8MB python 32.9s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 33.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 160.9MB python 33.0s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 33.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 161.0MB python 33.1s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 33.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 161.2MB python 33.2s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 33.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 161.3MB python 33.3s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 33.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 161.3MB python 33.4s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 33.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 161.4MB python 33.5s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 33.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 161.4MB python 33.6s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 33.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 161.4MB python 33.7s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 33.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 161.5MB python 33.8s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 34.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 162.2MB python 33.9s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 34.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 162.4MB python 34.0s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 34.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 162.4MB python 34.1s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 34.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 162.4MB python 34.2s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 34.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 162.5MB python 34.3s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 34.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 162.5MB python 34.4s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 34.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 162.5MB python 34.5s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 34.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 162.5MB python 34.6s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 34.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 162.6MB python 34.7s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 34.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 162.7MB python 34.8s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 35.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 162.9MB python 34.9s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 35.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 163.0MB python 35.0s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 35.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 163.1MB python 35.1s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 35.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 163.3MB python 35.2s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 35.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 163.4MB python 35.3s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 35.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 163.6MB python 35.4s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 35.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 163.7MB python 35.5s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 35.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 163.9MB python 35.6s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 35.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 164.0MB python 35.7s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 35.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 164.2MB python 35.8s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 36.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 164.3MB python 35.9s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 36.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 164.5MB python 36.0s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 36.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 164.7MB python 36.1s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 36.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 164.9MB python 36.2s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 36.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 165.0MB python 36.3s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 36.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 165.1MB python 36.4s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 36.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 165.3MB python 36.5s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 36.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 165.4MB python 36.6s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 36.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 165.6MB python 36.7s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 36.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 165.8MB python 36.8s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 37.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 166.0MB python 36.9s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 37.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 166.1MB python 37.0s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 37.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 166.3MB python 37.1s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 37.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 166.5MB python 37.2s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 37.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 166.6MB python 37.3s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 37.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 166.9MB python 37.4s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 37.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 167.0MB python 37.5s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 37.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 167.2MB python 37.6s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 37.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 167.3MB python 37.7s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 37.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 167.5MB python 37.8s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 38.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 167.7MB python 37.9s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 38.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 167.9MB python 38.0s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 38.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 168.1MB python 38.1s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 38.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 168.3MB python 38.2s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 38.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 168.5MB python 38.3s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 38.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 168.7MB python 38.4s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 38.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 168.9MB python 38.5s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 38.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 169.1MB python 38.6s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 38.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 169.3MB python 38.7s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 38.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 169.5MB python 38.8s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 39.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 169.7MB python 38.9s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 39.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 169.9MB python 39.0s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 39.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 170.1MB python 39.1s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 39.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 170.3MB python 39.2s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 39.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 170.5MB python 39.3s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 39.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 170.7MB python 39.4s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 39.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 170.8MB python 39.5s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 39.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 170.9MB python 39.6s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 39.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 170.9MB python 39.7s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 39.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 170.9MB python 39.8s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 40.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 171.1MB python 39.9s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 40.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 172.0MB python 40.0s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 40.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 172.2MB python 40.1s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 40.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 172.4MB python 40.2s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 40.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 172.6MB python 40.3s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 40.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 172.8MB python 40.4s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 40.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 173.0MB python 40.5s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 40.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 173.2MB python 40.6s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 40.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 173.4MB python 40.7s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 40.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 173.6MB python 40.8s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 41.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 173.8MB python 40.9s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 41.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 174.0MB python 41.0s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 41.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 174.3MB python 41.1s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 41.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 174.5MB python 41.2s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 41.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 174.7MB python 41.3s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 41.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 174.9MB python 41.4s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 41.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 175.1MB python 41.5s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 41.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 175.3MB python 41.6s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 41.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 175.6MB python 41.7s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 41.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 175.8MB python 41.8s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 42.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 176.0MB python 41.9s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 42.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 176.3MB python 42.0s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 42.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 176.5MB python 42.1s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 42.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 176.7MB python 42.2s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 42.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 176.8MB python 42.3s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 42.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 176.8MB python 42.4s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 42.6s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 176.8MB python 42.5s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 42.7s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 176.9MB python 42.6s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 42.8s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 177.9MB python 42.7s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 42.9s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 178.2MB python 42.8s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 43.0s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 178.5MB python 42.9s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 43.1s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 178.7MB python 43.0s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 43.2s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 179.0MB python 43.1s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 43.3s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 179.2MB python 43.2s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 43.4s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 179.5MB python 43.3s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 43.5s\nDownloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 179.7MB python 43.4s\nExtracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gpython 30.4MB @ 698.4kB/s 37.0s\n[+] 43.6s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 43.7s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 19.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 43.8s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 19.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 43.9s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 19.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 44.0s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 19.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 44.1s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 19.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 44.2s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 19.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 44.3s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 20.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 44.4s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 20.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 44.5s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 20.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 44.6s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 20.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 44.7s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 20.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 44.8s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 20.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 44.9s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 20.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 45.0s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 20.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 45.1s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 20.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 45.2s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 20.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 45.3s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 21.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 45.4s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 21.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 45.5s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 21.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 45.6s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 21.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 45.7s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 21.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 45.8s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 21.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 45.9s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 21.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 46.0s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 21.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 46.1s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 21.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 46.2s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 21.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 46.3s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 22.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 46.4s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 22.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 46.5s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 22.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 46.6s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 22.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 46.7s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 22.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 46.8s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 22.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 46.9s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 22.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 47.0s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 22.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 47.1s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 22.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 47.2s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 22.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 47.3s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 23.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 47.4s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 23.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 47.5s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 76 python 23.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 47.6s\nDownloading ━━━━━━━━━━━━━━━━━━━━━━━ 179.9MB 43.5s\nExtracting ━━━━━━━━━━━━━━━━━━━━━━━ 77 23.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G\u001b[?25h\nDownloading and Extracting Packages\n\nPreparing transaction: done\nVerifying transaction: done\nExecuting transaction: done\nLooking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\nCollecting parmed\n Downloading https://pypi.tuna.tsinghua.edu.cn/packages/dc/85/01007d38fe0945398c5e0ec7c7ce2d9cc433289bf05e32393c8e48e71cd4/ParmEd-4.1.0.tar.gz (2.2 MB)\n\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.2/2.2 MB\u001b[0m \u001b[31m27.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25ldone\n\u001b[?25hRequirement already satisfied: mdtraj in /opt/mamba/lib/python3.10/site-packages (1.9.7)\nRequirement already satisfied: pymbar in /opt/mamba/lib/python3.10/site-packages (4.0.1)\nCollecting networkx\n Downloading https://pypi.tuna.tsinghua.edu.cn/packages/a8/05/9d4f9b78ead6b2661d6e8ea772e111fc4a9fbd866ad0c81906c11206b55e/networkx-3.1-py3-none-any.whl (2.1 MB)\n\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.1/2.1 MB\u001b[0m \u001b[31m51.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m\n\u001b[?25hRequirement already satisfied: numpy>=1.6 in /opt/mamba/lib/python3.10/site-packages (from mdtraj) (1.23.4)\nRequirement already satisfied: astunparse in /opt/mamba/lib/python3.10/site-packages (from mdtraj) (1.6.3)\nRequirement already satisfied: pyparsing in /opt/mamba/lib/python3.10/site-packages (from mdtraj) (3.0.9)\nRequirement already satisfied: scipy in /opt/mamba/lib/python3.10/site-packages (from mdtraj) (1.9.3)\nRequirement already satisfied: jax in /opt/mamba/lib/python3.10/site-packages (from pymbar) (0.3.17)\nRequirement already satisfied: numexpr in /opt/mamba/lib/python3.10/site-packages (from pymbar) (2.8.4)\nRequirement already satisfied: jaxlib in /opt/mamba/lib/python3.10/site-packages (from pymbar) (0.3.15+cuda11.cudnn82)\nRequirement already satisfied: wheel<1.0,>=0.23.0 in /opt/mamba/lib/python3.10/site-packages (from astunparse->mdtraj) (0.37.1)\nRequirement already satisfied: six<2.0,>=1.6.1 in /opt/mamba/lib/python3.10/site-packages (from astunparse->mdtraj) (1.16.0)\nRequirement already satisfied: typing-extensions in /opt/mamba/lib/python3.10/site-packages (from jax->pymbar) (4.8.0)\nRequirement already satisfied: opt-einsum in /opt/mamba/lib/python3.10/site-packages (from jax->pymbar) (3.3.0)\nRequirement already satisfied: etils[epath] in /opt/mamba/lib/python3.10/site-packages (from jax->pymbar) (0.9.0)\nRequirement already satisfied: absl-py in /opt/mamba/lib/python3.10/site-packages (from jax->pymbar) (1.3.0)\nRequirement already satisfied: importlib_resources in /opt/mamba/lib/python3.10/site-packages (from etils[epath]->jax->pymbar) (5.10.0)\nRequirement already satisfied: zipp in /opt/mamba/lib/python3.10/site-packages (from etils[epath]->jax->pymbar) (3.10.0)\nBuilding wheels for collected packages: parmed\n Building wheel for parmed (setup.py) ... \u001b[?25ldone\n\u001b[?25h Created wheel for parmed: filename=ParmEd-4.1.0-cp310-cp310-linux_x86_64.whl size=1250051 sha256=8937550b1672378a608d73fce57d2b08e1708c0bafae003244ab6d6a0cfd5556\n Stored in directory: /root/.cache/pip/wheels/3c/92/a9/7282efbb63e0a2699132aa10ec070aad1688e7e0885b8832ea\nSuccessfully built parmed\nInstalling collected packages: parmed, networkx\nSuccessfully installed networkx-3.1 parmed-4.1.0\n\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n\u001b[0m","output_type":"stream"}],"id":"829d7d23-ad93-4d15-bf0b-eaf6bd1fe41d"},{"cell_type":"markdown","source":"Copy the example files to the root directory.","metadata":{},"id":"8ca10738-9594-4da1-ab94-f70aa9e238f7"},{"cell_type":"code","source":"! cp DMFF/tests/data/bond1.xml .\n! cp DMFF/tests/data/bond1.pdb .","metadata":{"trusted":true},"execution_count":6,"outputs":[],"id":"9c861cde-19e7-4dbc-9a29-5a3462bba31c"},{"cell_type":"markdown","source":"## Import the necessary libraries","metadata":{},"id":"bc3c4e1f-f874-430a-bc51-35245f4b861f"},{"cell_type":"code","source":"from typing import Tuple\nimport numpy as np\nimport jax.numpy as jnp\nimport jax\nfrom dmff.api.topology import DMFFTopology\nfrom dmff.api.paramset import ParamSet\nfrom dmff.api.xmlio import XMLIO\nfrom dmff.api.hamiltonian import _DMFFGenerators\nfrom dmff.classical.intra import HarmonicBondJaxForce\nfrom dmff.utils import DMFFException, isinstance_jnp","metadata":{"trusted":true},"execution_count":1,"outputs":[{"name":"stderr","text":"2023-09-23 18:08:32.369984: W external/org_tensorflow/tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: /usr/lib/x86_64-linux-gnu/libcuda.so.1: file too short; LD_LIBRARY_PATH: /usr/local/nvidia/lib:/usr/local/nvidia/lib64\nWARNING:absl:No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)\n2023-09-23 18:08:32.370046: W external/org_tensorflow/tensorflow/stream_executor/cuda/cuda_driver.cc:263] failed call to cuInit: UNKNOWN ERROR (303)\n","output_type":"stream"}],"id":"30abb3c9-c4e8-4975-86c2-2d1c227c7b01"},{"cell_type":"markdown","source":"## Create the Generator class","metadata":{},"id":"d1c59294-2440-4c4b-8417-676d03ca569d"},{"cell_type":"code","source":"class HarmonicBondGenerator:\n \"\"\"\n A class for generating harmonic bond force field parameters.\n\n Attributes:\n -----------\n name : str\n The name of the force field.\n ffinfo : dict\n The force field information.\n key_type : str\n The type of the key.\n bond_keys : list of tuple\n The keys of the bonds.\n bond_params : list of tuple\n The parameters of the bonds.\n bond_mask : list of float\n The mask of the bonds.\n _use_smarts : bool\n Whether to use SMARTS.\n \"\"\"\n\n def __init__(self, ffinfo: dict, paramset: ParamSet):\n \"\"\"\n Initializes the HarmonicBondGenerator.\n\n Parameters:\n -----------\n ffinfo : dict\n The force field information.\n paramset : ParamSet\n The parameter set.\n \"\"\"\n self.name = \"HarmonicBondForce\" # 初始化Generator所关联的势函数名称\n self.ffinfo = ffinfo # 绑定这一Generator所对应的力场文件信息\n paramset.addField(self.name) # 在参数集中注册一个Field，用于存储这一势函数相关的参数。ParamSet介绍见下文。\n self.key_type = None\n\n bond_keys, bond_params, bond_mask = [], [], [] # 创建bond_keys, bond_params, bond_mask三个List，每个key对应着相应位置的力场参数与mask。\n for node in self.ffinfo[\"Forces\"][self.name][\"node\"]:\n attribs = node[\"attrib\"]\n \n # 判断bond term使用\"type\"还是\"class\"进行匹配。目前仅支持基于这两个属性的参数匹配，并且不允许混搭。\n if self.key_type is None and \"type1\" in attribs:\n self.key_type = \"type\"\n elif self.key_type is None and \"class1\" in attribs:\n self.key_type = \"class\"\n elif self.key_type is not None and f\"{self.key_type}1\" not in attribs:\n raise ValueError(\"Keyword 'class' or 'type' cannot be used together.\")\n else:\n raise ValueError(\"Cannot find key type for HarmonicBondForce.\")\n key = (attribs[self.key_type + \"1\"], attribs[self.key_type + \"2\"])\n bond_keys.append(key)\n\n k = float(attribs[\"k\"])\n r0 = float(attribs[\"length\"])\n bond_params.append([k, r0])\n\n # when the node has mask attribute, it means that the parameter is not trainable. \n # the gradient of this parameter will be zero.\n mask = 1.0\n if \"mask\" in attribs and attribs[\"mask\"].upper() == \"TRUE\":\n mask = 0.0\n bond_mask.append(mask)\n\n self.bond_keys = bond_keys\n bond_length = jnp.array([i[1] for i in bond_params])\n bond_k = jnp.array([i[0] for i in bond_params])\n bond_mask = jnp.array(bond_mask)\n\n # 在ParamSet中注册参数。\n # 在Generator初始化结束后，我们可以通过ParamSet调用这些参数，不经过Generator，进而保证这些参数与Generator无关。\n # 可优化的参数与函数独立存在，不构成闭包，是可微分编程正确求导的前提。\n paramset.addParameter(bond_length, \"length\", field=self.name, mask=bond_mask) # register parameters to ParamSet\n paramset.addParameter(bond_k, \"k\", field=self.name, mask=bond_mask) # register parameters to ParamSet\n \n def getName(self) -> str:\n \"\"\"\n Returns the name of the force field.\n\n Returns:\n --------\n str\n The name of the force field.\n \"\"\"\n return self.name\n \n # 根据输入的ParamSet直接修改self.ffinfo的值。\n # self.ffinfo是解析xml力场文件后得到的dict，在保持格式约定的前提下，可以直接与xml文件互转。\n # 修改self.ffinfo中参数的值，而后我们可以直接将self.ffinfo渲染成新的力场参数文件。\n # 这一函数的入参是固定的。\n def overwrite(self, paramset: ParamSet) -> None:\n \"\"\"\n Overwrites the parameter set.\n\n Parameters:\n -----------\n paramset : ParamSet\n The parameter set.\n \"\"\"\n bond_node_indices = [i for i in range(len(self.ffinfo[\"Forces\"][self.name][\"node\"])) if self.ffinfo[\"Forces\"][self.name][\"node\"][i][\"name\"] == \"Bond\"]\n\n bond_length = paramset[self.name][\"length\"]\n bond_k = paramset[self.name][\"k\"]\n bond_msks = paramset.mask[self.name][\"length\"]\n for nnode, key in enumerate(self.bond_keys):\n self.ffinfo[\"Forces\"][self.name][\"node\"][bond_node_indices[nnode]][\"attrib\"] = {}\n self.ffinfo[\"Forces\"][self.name][\"node\"][bond_node_indices[nnode]][\"attrib\"][f\"{self.key_type}1\"] = key[0]\n self.ffinfo[\"Forces\"][self.name][\"node\"][bond_node_indices[nnode]][\"attrib\"][f\"{self.key_type}2\"] = key[1]\n r0 = bond_length[nnode]\n k = bond_k[nnode]\n mask = bond_msks[nnode]\n self.ffinfo[\"Forces\"][self.name][\"node\"][bond_node_indices[nnode]][\"attrib\"][\"k\"] = str(k)\n self.ffinfo[\"Forces\"][self.name][\"node\"][bond_node_indices[nnode]][\"attrib\"][\"length\"] = str(r0)\n if mask < 0.999:\n self.ffinfo[\"Forces\"][self.name][\"node\"][bond_node_indices[nnode]][\"attrib\"][\"mask\"] = \"true\"\n\n # 工具函数，用于查找与选定bond匹配的key的角标\n def _find_key_index(self, key: Tuple[str, str]) -> int:\n \"\"\"\n Finds the index of the key.\n\n Parameters:\n -----------\n key : tuple of str\n The key.\n\n Returns:\n --------\n int\n The index of the key.\n \"\"\"\n for i, k in enumerate(self.bond_keys):\n if k[0] == key[0] and k[1] == key[1]:\n return i\n if k[0] == key[1] and k[1] == key[0]:\n return i\n return None\n\n # 撰写方法来创建势函数。\n # 对于不同的topdata，我们所构造的势函数是不同的。\n # Generator负责基于输入的topdata构建从能量到力场参数的求导链，这使得Generator仅与力场参数相关，与各个体系的拓扑无关。\n # 这一函数的入参是固定的。\n def createPotential(self, topdata: DMFFTopology, nonbondedMethod,\n nonbondedCutoff, args):\n \"\"\"\n Creates the potential.\n\n Parameters:\n -----------\n topdata : DMFFTopology\n The topology data.\n nonbondedMethod : str\n The nonbonded method.\n nonbondedCutoff : float\n The nonbonded cutoff.\n args : list\n The arguments.\n\n Returns:\n --------\n function\n The potential function.\n \"\"\"\n # 按照HarmonicBondForce的要求遍历体系中所有的bond，进行匹配\n bond_a1, bond_a2, bond_indices = [], [], []\n for bond in topdata.bonds():\n a1, a2 = bond.atom1, bond.atom2\n i1, i2 = a1.index, a2.index\n if self.key_type == \"type\":\n key = (a1.meta[\"type\"], a2.meta[\"type\"])\n elif self.key_type == \"class\":\n key = (a1.meta[\"class\"], a2.meta[\"class\"])\n idx = self._find_key_index(key)\n if idx is None:\n continue\n bond_a1.append(i1)\n bond_a2.append(i2)\n bond_indices.append(idx)\n bond_a1 = jnp.array(bond_a1)\n bond_a2 = jnp.array(bond_a2)\n bond_indices = jnp.array(bond_indices)\n \n # 创建势函数\n harmonic_bond_force = HarmonicBondJaxForce(bond_a1, bond_a2, bond_indices)\n harmonic_bond_energy = harmonic_bond_force.generate_get_energy()\n \n # 包装成统一的potential_function函数形式，传入四个参数：positions, box, pairs, parameters。\n def potential_fn(positions: jnp.ndarray, box: jnp.ndarray, pairs: jnp.ndarray, params: ParamSet) -> jnp.ndarray:\n isinstance_jnp(positions, box, params)\n energy = harmonic_bond_energy(positions, box, pairs, params[self.name][\"k\"], params[self.name][\"length\"])\n return energy\n\n self._jaxPotential = potential_fn\n return potential_fn\n","metadata":{"trusted":true},"execution_count":2,"outputs":[],"id":"62d85f8b-d1fe-4842-b8c8-e2fcb6f09c25"},{"cell_type":"markdown","source":"## Register the Generator with DMFF and bind it to a specific Force in the XML file","metadata":{},"id":"51168e46-d582-42b8-a65f-1320f7a09161"},{"cell_type":"code","source":"# register the generator\n_DMFFGenerators[\"HarmonicBondForce\"] = HarmonicBondGenerator","metadata":{"trusted":true},"execution_count":3,"outputs":[],"id":"b48500d8-4e24-4f3d-bb30-5dabf3475a24"},{"cell_type":"markdown","source":"## Test\n\n### Test system energy calculation using OpenMM","metadata":{},"id":"2a0f799e-c5ff-4d60-bbb8-4b075a1138d3"},{"cell_type":"code","source":"import openmm as mm\nimport openmm.app as app\nimport openmm.unit as unit\n\n\npdb = app.PDBFile(\"bond1.pdb\")\nff = app.ForceField(\"bond1.xml\")\nsystem = ff.createSystem(pdb.topology)\ninteg = mm.VerletIntegrator(1e-10)\ncontext = mm.Context(system, integ)\ncontext.setPositions(pdb.getPositions())\nenergy = context.getState(getEnergy=True).getPotentialEnergy()\nprint(\"OpenMM:\", energy)","metadata":{"trusted":true},"execution_count":4,"outputs":[{"name":"stdout","text":"OpenMM: 1389.1622953572387 kJ/mol\n","output_type":"stream"}],"id":"2d6c4b52-64a7-44e4-aaef-de243fdb0093"},{"cell_type":"markdown","source":"### Test system energy calculation using DMFF","metadata":{},"id":"1d32e830-500c-4ae1-bbf9-f69214196423"},{"cell_type":"code","source":"from dmff.operators import TemplateATypeOperator\n\n# 体系坐标。\npos = pdb.getPositions(asNumpy=True).value_in_unit(unit.nanometer)\npos = jnp.array(pos)\n\n# DMFF格式下的体系拓扑，支持直接基于openmm的topology对象进行初始化。\ndmfftop = DMFFTopology(from_top=pdb.topology)\n\n# 盒子，在这一示例中并无用处。\nbox = np.eye(3) * 10.0\nbox = jnp.array(box)\n\n# XML力场读写工具\nxmlio = XMLIO()\nxmlio.loadXML(\"bond1.xml\")\n# 将xml文件解析为Dict，命名为ffinfo\nffinfo = xmlio.parseXML()\n\n# 根据力场文件中的residue template，基于图同构，为topology中各个atom赋予atom type，存储于Atom.meta中。\ntempOP = TemplateATypeOperator(ffinfo)\ntop_atype = tempOP(dmfftop)\nfor atom in top_atype.atoms():\n print(\"Meta data:\", atom.meta)\nprint()\n \n# 初始化ParamSet。\n# ParamSet是一个PyTree类。它类似一个字典，但被限制了深度，只有两层。\n# 第一层叫做Field，按照势函数名称分类，对于这个示例，就是HarmonicBondForce。\n# 第二层是势函数的各个参数，在这个示例中即为length和k。\n# mask也被初始化于ParamSet中，本示例中暂不展示。\nparamset = ParamSet()\n# 初始化Generator。\ngenerator = HarmonicBondGenerator(ffinfo, paramset)\n\n# 查看ParamSet。\nprint(paramset.parameters)\nprint()\n\n# init potential\npotential = generator.createPotential(top_atype, app.NoCutoff, 1.0, {})\nenergy = potential(pos, box, [], paramset)\nprint(f\"DMFF: {energy} kJ/mol\")","metadata":{"trusted":true},"execution_count":20,"outputs":[{"name":"stdout","text":"Meta data: {'element': 'N', 'external_bond': False, 'type': 'n1', 'class': 'n1'}\nMeta data: {'element': 'N', 'external_bond': False, 'type': 'n2', 'class': 'n2'}\n\n{'HarmonicBondForce': {'length': DeviceArray([0.09572], dtype=float32), 'k': DeviceArray([462750.4], dtype=float32)}}\n\nDMFF: 1389.1622314453125 kJ/mol\n","output_type":"stream"}],"id":"954a429e-b123-42e1-85cd-22497e31ac88"},{"cell_type":"markdown","source":"### XML force field file update example","metadata":{},"id":"aa4d422d-ab5f-4d62-b611-de2460c56527"},{"cell_type":"code","source":"print(\">>>> Before updating <<<<\")\n! cat bond1.xml\n\nparamset[\"HarmonicBondForce\"][\"length\"] = paramset[\"HarmonicBondForce\"][\"length\"].at[0].set(0.1)\n\ngenerator.overwrite(paramset)\nxmlio.writeXML(\"bond_update.xml\", ffinfo)\nprint(\"\\n\\n>>>> After updating <<<<\")\n! cat bond_update.xml\n","metadata":{"trusted":true},"execution_count":27,"outputs":[{"name":"stdout","text":">>>> Before updating <<<<\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n>>>> After updating <<<<\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n","output_type":"stream"}],"id":"0f319818-f425-4141-9b92-71237e2fc62d"},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[],"id":"f1369677-c20f-42bd-9307-e04ae5246a28"}]}
\ No newline at end of file
diff --git a/docs/user_guide/1.introduction.md b/docs/user_guide/1.introduction.md
index d64680bcc..3e1251bf3 100644
--- a/docs/user_guide/1.introduction.md
+++ b/docs/user_guide/1.introduction.md
@@ -7,7 +7,6 @@ In this user guide, you will learn:
- [New modules release](./4.modules.md)
- [Advanced examples](./DMFF_example.ipynb) of DMFF, which can help you quickly get started with DMFF
-
The first thing you should know is that DMFF is not an actual force field model (such as OPLS or AMBER), but a differentiable implementation of various force field (or "potential") functional forms. It contains following modules:
- Classical module: implements classical force fields (OPLS or GAFF like potentials)
diff --git a/docs/user_guide/4.modules.md b/docs/user_guide/4.modules.md
index 40d8bcb13..b47d43c87 100644
--- a/docs/user_guide/4.modules.md
+++ b/docs/user_guide/4.modules.md
@@ -2,10 +2,10 @@
In this part, you will see 7 modules of DMFF, some of which are newly released in version 1.0.0. Let's get started with them:
-+ [Classical](docs/user_guide/4.1classical.md)
-+ [ADMP](docs/user_guide/4.2ADMPPmeForce.md)
-+ [Qeq](docs/user_guide/4.3ADMPQeqForce.md)
-+ [Machine Learning](docs/user_guide/4.4MLForce.md)
-+ [Optimization](docs/user_guide/4.5Optimization.md)
-+ [Mbar Estimator](docs/user_guide/4.6MBAR.md)
-+ [OpenMM Plugin](docs/user_guide/4.7OpenMMplugin.md)
++ [Classical](./4.1classical.md)
++ [ADMP](./4.2ADMPPmeForce.md)
++ [Qeq](./4.3ADMPQeqForce.md)
++ [Machine Learning](./4.4MLForce.md)
++ [Optimization](./4.5Optimization.md)
++ [Mbar Estimator](./4.6MBAR.md)
++ [OpenMM Plugin](./4.7OpenMMplugin.md)
diff --git a/docs/user_guide/DMFF_example.ipynb b/docs/user_guide/DMFF_example.ipynb
new file mode 100644
index 000000000..51f4b2b1e
--- /dev/null
+++ b/docs/user_guide/DMFF_example.ipynb
@@ -0,0 +1,3698 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "1b1f04ae-398e-4aff-ac2e-e3e2beaea116",
+ "metadata": {},
+ "source": [
+ "# Advanced examples of DMFF 1.0.0\n",
+ "In our new tutorial notebook https://nb.bohrium.dp.tech/detail/6366839940 You must already have a basic understanding of DMFF version 1.0.0. As an advanced tutorial, we have also prepared this example-notebook for you as a supplement to the Tutorial, which includes an introduction to new modules in DMFF such as Qeq, ML Force, and the OpenMM plugin."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7efc4682-df1a-4137-827e-a210bf325819",
+ "metadata": {},
+ "source": [
+ "## Environment Setup\n",
+ "\n",
+ "Retrieve DMFF from GitHub and switch to the desired branch, then proceed with the installation."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "bd84a262-a875-4909-9c3d-55e9cbfeaea9",
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Cloning into 'DMFF'...\n",
+ "remote: Enumerating objects: 4439, done.\u001b[K\n",
+ "remote: Counting objects: 100% (4439/4439), done.\u001b[K\n",
+ "remote: Compressing objects: 100% (1432/1432), done.\u001b[K\n",
+ "remote: Total 4439 (delta 2961), reused 4361 (delta 2922), pack-reused 0\u001b[K\n",
+ "Receiving objects: 100% (4439/4439), 22.10 MiB | 5.00 MiB/s, done.\n",
+ "Resolving deltas: 100% (2961/2961), done.\n",
+ "Updating files: 100% (273/273), done.\n",
+ "Updating files: 100% (318/318), done.\n",
+ "Branch 'wangxy/v1.0.0-devel' set up to track remote branch 'wangxy/v1.0.0-devel' from 'origin'.\n",
+ "Switched to a new branch 'wangxy/v1.0.0-devel'\n",
+ "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
+ "Processing /data/DMFF\n",
+ " Preparing metadata (setup.py) ... \u001b[?25ldone\n",
+ "\u001b[?25hRequirement already satisfied: numpy>=1.18 in /opt/mamba/lib/python3.10/site-packages (from dmff==0.2.1.dev338+gcdb14ee) (1.23.4)\n",
+ "Requirement already satisfied: openmm>=7.6.0 in /opt/mamba/lib/python3.10/site-packages (from dmff==0.2.1.dev338+gcdb14ee) (7.7.0)\n",
+ "Requirement already satisfied: freud-analysis in /opt/mamba/lib/python3.10/site-packages/freud_analysis-2.11.0-py3.10-linux-x86_64.egg (from dmff==0.2.1.dev338+gcdb14ee) (2.11.0)\n",
+ "Collecting networkx>=3.0\n",
+ " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/d5/f0/8fbc882ca80cf077f1b246c0e3c3465f7f415439bdea6b899f6b19f61f70/networkx-3.2.1-py3-none-any.whl (1.6 MB)\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m10.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m \u001b[36m0:00:01\u001b[0m\n",
+ "\u001b[?25hCollecting optax>=0.1.4\n",
+ " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/13/71/787cc24c4b606f3bb9f1d14957ebd7cb9e4234f6d59081721230b2032196/optax-0.1.7-py3-none-any.whl (154 kB)\n",
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+ "\u001b[?25hCollecting jaxopt>=0.8.0\n",
+ " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/45/ee/a74d0ec01e2c90945cdb10f5c886441d2a78f11f612f14830df39a94b8b5/jaxopt-0.8.2-py3-none-any.whl (170 kB)\n",
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+ "\u001b[?25hRequirement already satisfied: pymbar>=4.0.0 in /opt/mamba/lib/python3.10/site-packages (from dmff==0.2.1.dev338+gcdb14ee) (4.0.1)\n",
+ "Requirement already satisfied: tqdm in /opt/mamba/lib/python3.10/site-packages (from dmff==0.2.1.dev338+gcdb14ee) (4.64.1)\n",
+ "Requirement already satisfied: jaxlib>=0.1.69 in /opt/mamba/lib/python3.10/site-packages (from jaxopt>=0.8.0->dmff==0.2.1.dev338+gcdb14ee) (0.3.15+cuda11.cudnn82)\n",
+ "Requirement already satisfied: scipy>=1.0.0 in /opt/mamba/lib/python3.10/site-packages (from jaxopt>=0.8.0->dmff==0.2.1.dev338+gcdb14ee) (1.9.3)\n",
+ "Requirement already satisfied: jax>=0.2.18 in /opt/mamba/lib/python3.10/site-packages (from jaxopt>=0.8.0->dmff==0.2.1.dev338+gcdb14ee) (0.3.17)\n",
+ "Requirement already satisfied: chex>=0.1.5 in /opt/mamba/lib/python3.10/site-packages (from optax>=0.1.4->dmff==0.2.1.dev338+gcdb14ee) (0.1.5)\n",
+ "Requirement already satisfied: absl-py>=0.7.1 in /opt/mamba/lib/python3.10/site-packages (from optax>=0.1.4->dmff==0.2.1.dev338+gcdb14ee) (1.3.0)\n",
+ "Requirement already satisfied: numexpr in /opt/mamba/lib/python3.10/site-packages (from pymbar>=4.0.0->dmff==0.2.1.dev338+gcdb14ee) (2.8.4)\n",
+ "Requirement already satisfied: rowan>=1.2.1 in /opt/mamba/lib/python3.10/site-packages/rowan-1.3.0.post1-py3.10.egg (from freud-analysis->dmff==0.2.1.dev338+gcdb14ee) (1.3.0.post1)\n",
+ "Requirement already satisfied: dm-tree>=0.1.5 in /opt/mamba/lib/python3.10/site-packages (from chex>=0.1.5->optax>=0.1.4->dmff==0.2.1.dev338+gcdb14ee) (0.1.7)\n",
+ "Requirement already satisfied: toolz>=0.9.0 in /opt/mamba/lib/python3.10/site-packages (from chex>=0.1.5->optax>=0.1.4->dmff==0.2.1.dev338+gcdb14ee) (0.12.0)\n",
+ "Requirement already satisfied: typing-extensions in /opt/mamba/lib/python3.10/site-packages (from jax>=0.2.18->jaxopt>=0.8.0->dmff==0.2.1.dev338+gcdb14ee) (4.4.0)\n",
+ "Requirement already satisfied: etils[epath] in /opt/mamba/lib/python3.10/site-packages (from jax>=0.2.18->jaxopt>=0.8.0->dmff==0.2.1.dev338+gcdb14ee) (0.9.0)\n",
+ "Requirement already satisfied: opt-einsum in /opt/mamba/lib/python3.10/site-packages (from jax>=0.2.18->jaxopt>=0.8.0->dmff==0.2.1.dev338+gcdb14ee) (3.3.0)\n",
+ "Requirement already satisfied: zipp in /opt/mamba/lib/python3.10/site-packages (from etils[epath]->jax>=0.2.18->jaxopt>=0.8.0->dmff==0.2.1.dev338+gcdb14ee) (3.10.0)\n",
+ "Requirement already satisfied: importlib_resources in /opt/mamba/lib/python3.10/site-packages (from etils[epath]->jax>=0.2.18->jaxopt>=0.8.0->dmff==0.2.1.dev338+gcdb14ee) (5.10.0)\n",
+ "Building wheels for collected packages: dmff\n",
+ " Building wheel for dmff (setup.py) ... \u001b[?25ldone\n",
+ "\u001b[?25h Created wheel for dmff: filename=dmff-0.2.1.dev338+gcdb14ee-py3-none-any.whl size=123185 sha256=1afdee2df70e0e259d2037d783a88c28a2edbe14c3f3816c5218753a503b687f\n",
+ " Stored in directory: /tmp/pip-ephem-wheel-cache-1dostt5y/wheels/f3/08/c8/63a66e9272163ceeb3675eda2e65e58a3e3c8a96296799182d\n",
+ "Successfully built dmff\n",
+ "Installing collected packages: networkx, jaxopt, optax, dmff\n",
+ " Attempting uninstall: optax\n",
+ " Found existing installation: optax 0.1.3\n",
+ " Uninstalling optax-0.1.3:\n",
+ " Successfully uninstalled optax-0.1.3\n",
+ "Successfully installed dmff-0.2.1.dev338+gcdb14ee jaxopt-0.8.2 networkx-3.2.1 optax-0.1.7\n",
+ "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
+ "\u001b[0m"
+ ]
+ }
+ ],
+ "source": [
+ "! rm -rf DMFF\n",
+ "! rm -rf /opt/mamba/lib/python3.10/site-packages/dmff*\n",
+ "! git clone https://github.com/deepmodeling/DMFF.git\n",
+ "! git config --global --add safe.directory `pwd`/DMFF\n",
+ "! cd DMFF && git checkout wangxy/v1.0.0-devel && pip install ."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "005cdb5d-ca06-4d89-9add-f8a4c65668b9",
+ "metadata": {},
+ "source": [
+ "Install the required libraries; this step is time-consuming, so please be patient."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "d645c00b-d218-4630-8737-87306d09fc52",
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ " __ __ __ __\n",
+ " / \\ / \\ / \\ / \\\n",
+ " / \\/ \\/ \\/ \\\n",
+ "███████████████/ /██/ /██/ /██/ /████████████████████████\n",
+ " / / \\ / \\ / \\ / \\ \\____\n",
+ " / / \\_/ \\_/ \\_/ \\ o \\__,\n",
+ " / _/ \\_____/ `\n",
+ " |/\n",
+ " ███╗ ███╗ █████╗ ███╗ ███╗██████╗ █████╗\n",
+ " ████╗ ████║██╔══██╗████╗ ████║██╔══██╗██╔══██╗\n",
+ " ██╔████╔██║███████║██╔████╔██║██████╔╝███████║\n",
+ " ██║╚██╔╝██║██╔══██║██║╚██╔╝██║██╔══██╗██╔══██║\n",
+ " ██║ ╚═╝ ██║██║ ██║██║ ╚═╝ ██║██████╔╝██║ ██║\n",
+ " ╚═╝ ╚═╝╚═╝ ╚═╝╚═╝ ╚═╝╚═════╝ ╚═╝ ╚═╝\n",
+ "\n",
+ " mamba (0.27.0) supported by @QuantStack\n",
+ "\n",
+ " GitHub: https://github.com/mamba-org/mamba\n",
+ " Twitter: https://twitter.com/QuantStack\n",
+ "\n",
+ "█████████████████████████████████████████████████████████████\n",
+ "\n",
+ "\n",
+ "Looking for: ['openmm=7.7.0', 'rdkit']\n",
+ "\n",
+ "\u001b[?25l\u001b[2K\u001b[0G[+] 0.0s\n",
+ "\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.1s\n",
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+ "pkgs/r/linux-64 \u001b[90m━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.6s\n",
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+ "conda-forge/noarch \u001b[90m━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.7s\n",
+ "pkgs/main/linux-64 \u001b[90m━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.7s\n",
+ "pkgs/main/noarch \u001b[33m━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.7s\n",
+ "pkgs/r/linux-64 \u001b[90m━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.8s\n",
+ "conda-forge/linux-64 \u001b[33m━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.8s\n",
+ "conda-forge/noarch \u001b[90m━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.8s\n",
+ "pkgs/main/linux-64 \u001b[90m━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.8s\n",
+ "pkgs/main/noarch \u001b[33m━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 352.0 B / ??.?MB @ 473.0 B/s 0.8s\n",
+ "pkgs/r/linux-64 \u001b[90m━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.9s\n",
+ "conda-forge/linux-64 \u001b[90m╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.9s\n",
+ "conda-forge/noarch \u001b[90m━━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.9s\n",
+ "pkgs/main/linux-64 \u001b[90m━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━━\u001b[0m 31.4kB / ??.?MB @ 38.6kB/s 0.9s\n",
+ "pkgs/main/noarch \u001b[33m━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 31.4kB / ??.?MB @ 38.6kB/s 0.9s\n",
+ "pkgs/r/linux-64 \u001b[90m━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━━\u001b[0m 31.4kB / ??.?MB @ 38.6kB/s 0.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.0s\n",
+ "conda-forge/linux-64 \u001b[90m━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 1.0s\n",
+ "conda-forge/noarch \u001b[33m━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 1.0s\n",
+ "pkgs/main/linux-64 \u001b[90m━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━\u001b[0m 94.3kB / ??.?MB @ 98.2kB/s 1.0s\n",
+ "pkgs/main/noarch \u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 135.3kB / ??.?MB @ 140.9kB/s 1.0s\n",
+ "pkgs/r/linux-64 \u001b[90m━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━\u001b[0m 135.3kB / ??.?MB @ 140.9kB/s 1.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.1s\n",
+ "conda-forge/linux-64 \u001b[90m━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━\u001b[0m 31.7kB / ??.?MB @ 29.3kB/s 1.1s\n",
+ "conda-forge/noarch \u001b[33m━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 32.2kB / ??.?MB @ 29.7kB/s 1.1s\n",
+ "pkgs/main/linux-64 \u001b[90m━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━\u001b[0m 131.6kB / ??.?MB @ 121.7kB/s 1.1s\n",
+ "pkgs/main/noarch \u001b[33m━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 176.5kB / ??.?MB @ 163.2kB/s 1.1s\n",
+ "pkgs/r/linux-64 \u001b[90m━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━\u001b[0m 151.7kB / ??.?MB @ 140.3kB/s 1.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.2s\n",
+ "conda-forge/linux-64 \u001b[90m━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━━\u001b[0m 31.7kB / ??.?MB @ 26.8kB/s 1.2s\n",
+ "conda-forge/noarch \u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 32.2kB / ??.?MB @ 27.2kB/s 1.2s\n",
+ "pkgs/main/linux-64 \u001b[33m━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 278.5kB / ??.?MB @ 235.5kB/s 1.2s\n",
+ "pkgs/main/noarch \u001b[90m━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━\u001b[0m 356.3kB / ??.?MB @ 301.3kB/s 1.2s\n",
+ "pkgs/r/linux-64 \u001b[90m━━━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━\u001b[0m 327.6kB / ??.?MB @ 277.1kB/s 1.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.3s\n",
+ "conda-forge/linux-64 \u001b[90m━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━\u001b[0m 136.6kB / ??.?MB @ 106.5kB/s 1.3s\n",
+ "conda-forge/noarch \u001b[33m━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 136.7kB / ??.?MB @ 106.6kB/s 1.3s\n",
+ "pkgs/main/linux-64 \u001b[33m━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 360.4kB / ??.?MB @ 281.0kB/s 1.3s\n",
+ "pkgs/main/noarch \u001b[90m━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━\u001b[0m 585.5kB / ??.?MB @ 456.6kB/s 1.3s\n",
+ "pkgs/r/linux-64 \u001b[33m━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 425.9kB / ??.?MB @ 332.1kB/s 1.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.4s\n",
+ "conda-forge/linux-64 \u001b[90m━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━\u001b[0m 136.6kB / ??.?MB @ 98.8kB/s 1.4s\n",
+ "conda-forge/noarch \u001b[90m━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━\u001b[0m 136.7kB / ??.?MB @ 98.9kB/s 1.4s\n",
+ "pkgs/main/linux-64 \u001b[33m━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 561.1kB / ??.?MB @ 405.7kB/s 1.4s\n",
+ "pkgs/main/noarch \u001b[90m━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━\u001b[0m 814.8kB / ??.?MB @ 589.2kB/s 1.4s\n",
+ "pkgs/r/linux-64 \u001b[33m━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 671.5kB / ??.?MB @ 485.6kB/s 1.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gpkgs/main/noarch 853.2kB @ 604.2kB/s 1.4s\n",
+ "[+] 1.5s\n",
+ "conda-forge/linux-64 \u001b[90m━━━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━\u001b[0m 330.2kB / ??.?MB @ 222.6kB/s 1.5s\n",
+ "conda-forge/noarch \u001b[90m━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━\u001b[0m 347.0kB / ??.?MB @ 233.9kB/s 1.5s\n",
+ "pkgs/main/linux-64 \u001b[33m━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 851.9kB / ??.?MB @ 574.3kB/s 1.5s\n",
+ "pkgs/r/linux-64 \u001b[33m━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 999.0kB / ??.?MB @ 673.5kB/s 1.5s\n",
+ "pkgs/r/noarch \u001b[90m━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.6s\n",
+ "conda-forge/linux-64 \u001b[33m━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 395.6kB / ??.?MB @ 249.8kB/s 1.6s\n",
+ "conda-forge/noarch \u001b[90m━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━\u001b[0m 379.7kB / ??.?MB @ 239.8kB/s 1.6s\n",
+ "pkgs/main/linux-64 \u001b[90m╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 1.2MB / ??.?MB @ 786.2kB/s 1.6s\n",
+ "pkgs/r/linux-64 \u001b[90m╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 1.4MB / ??.?MB @ 910.1kB/s 1.6s\n",
+ "pkgs/r/noarch \u001b[90m━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gpkgs/r/linux-64 1.9MB @ 1.1MB/s 1.7s\n",
+ "[+] 1.7s\n",
+ "conda-forge/linux-64 \u001b[33m━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 739.2kB / ??.?MB @ 438.8kB/s 1.7s\n",
+ "conda-forge/noarch \u001b[90m━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━\u001b[0m 756.0kB / ??.?MB @ 448.8kB/s 1.7s\n",
+ "pkgs/main/linux-64 \u001b[90m━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━\u001b[0m 1.8MB / ??.?MB @ 1.1MB/s 1.7s\n",
+ "pkgs/r/noarch \u001b[90m━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━\u001b[0m 237.6kB / ??.?MB @ 142.3kB/s 0.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.8s\n",
+ "conda-forge/linux-64 \u001b[33m━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 902.8kB / ??.?MB @ 505.7kB/s 1.8s\n",
+ "conda-forge/noarch \u001b[90m━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━\u001b[0m 886.9kB / ??.?MB @ 496.9kB/s 1.8s\n",
+ "pkgs/main/linux-64 \u001b[90m━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━\u001b[0m 2.6MB / ??.?MB @ 1.4MB/s 1.8s\n",
+ "pkgs/r/noarch \u001b[33m━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 860.2kB / ??.?MB @ 486.0kB/s 0.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.9s\n",
+ "conda-forge/linux-64 \u001b[90m╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 1.3MB / ??.?MB @ 708.7kB/s 1.9s\n",
+ "conda-forge/noarch \u001b[90m━━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━\u001b[0m 1.3MB / ??.?MB @ 704.3kB/s 1.9s\n",
+ "pkgs/main/linux-64 \u001b[90m━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━━\u001b[0m 3.2MB / ??.?MB @ 1.7MB/s 1.9s\n",
+ "pkgs/r/noarch \u001b[33m━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 1.4MB / ??.?MB @ 724.9kB/s 0.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 2.0s\n",
+ "conda-forge/linux-64 \u001b[90m━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━\u001b[0m 1.9MB / ??.?MB @ 980.9kB/s 2.0s\n",
+ "conda-forge/noarch \u001b[33m━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 1.9MB / ??.?MB @ 973.1kB/s 2.0s\n",
+ "pkgs/main/linux-64 \u001b[90m━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━\u001b[0m 3.6MB / ??.?MB @ 1.8MB/s 2.0s\n",
+ "pkgs/r/noarch \u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 1.8MB / ??.?MB @ 926.9kB/s 0.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 2.1s\n",
+ "conda-forge/linux-64 \u001b[90m━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━\u001b[0m 2.3MB / ??.?MB @ 1.1MB/s 2.1s\n",
+ "conda-forge/noarch \u001b[33m━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 2.3MB / ??.?MB @ 1.1MB/s 2.1s\n",
+ "pkgs/main/linux-64 \u001b[90m━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━\u001b[0m 3.9MB / ??.?MB @ 1.9MB/s 2.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gpkgs/r/noarch 2.3MB @ 1.1MB/s 0.7s\n",
+ "[+] 2.2s\n",
+ "conda-forge/linux-64 \u001b[90m━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━\u001b[0m 3.4MB / ??.?MB @ 1.6MB/s 2.2s\n",
+ "conda-forge/noarch \u001b[33m━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 3.4MB / ??.?MB @ 1.6MB/s 2.2s\n",
+ "pkgs/main/linux-64 \u001b[90m━━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━\u001b[0m 4.6MB / ??.?MB @ 2.1MB/s 2.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 2.3s\n",
+ "conda-forge/linux-64 \u001b[90m━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━\u001b[0m 4.3MB / ??.?MB @ 1.9MB/s 2.3s\n",
+ "conda-forge/noarch \u001b[33m━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 4.6MB / ??.?MB @ 2.0MB/s 2.3s\n",
+ "pkgs/main/linux-64 \u001b[33m━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 5.2MB / ??.?MB @ 2.3MB/s 2.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 2.4s\n",
+ "conda-forge/linux-64 \u001b[90m━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━\u001b[0m 5.1MB / ??.?MB @ 2.2MB/s 2.4s\n",
+ "conda-forge/noarch \u001b[90m╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 5.5MB / ??.?MB @ 2.3MB/s 2.4s\n",
+ "pkgs/main/linux-64 \u001b[33m━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 5.8MB / ??.?MB @ 2.4MB/s 2.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 2.5s\n",
+ "conda-forge/linux-64 \u001b[90m━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━\u001b[0m 6.1MB @ 2.5MB/s 2.5s\n",
+ "conda-forge/noarch \u001b[90m━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━\u001b[0m 6.5MB @ 2.6MB/s 2.5s\n",
+ "pkgs/main/linux-64 ━━━━━━━━━━━━━━━━━━━━━━ 6.3MB @ 2.6MB/s Finalizing 2.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gpkgs/main/linux-64 @ 2.6MB/s 2.5s\n",
+ "[+] 2.6s\n",
+ "conda-forge/linux-64 \u001b[33m━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 7.1MB / ??.?MB @ 2.7MB/s 2.6s\n",
+ "conda-forge/noarch \u001b[90m━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━\u001b[0m 7.6MB / ??.?MB @ 2.9MB/s 2.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 2.7s\n",
+ "conda-forge/linux-64 \u001b[33m━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 8.1MB / ??.?MB @ 3.0MB/s 2.7s\n",
+ "conda-forge/noarch \u001b[90m━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━━\u001b[0m 8.8MB / ??.?MB @ 3.3MB/s 2.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 2.8s\n",
+ "conda-forge/linux-64 \u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 9.0MB / ??.?MB @ 3.2MB/s 2.8s\n",
+ "conda-forge/noarch \u001b[90m━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━\u001b[0m 9.8MB / ??.?MB @ 3.5MB/s 2.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 2.9s\n",
+ "conda-forge/linux-64 \u001b[33m━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 10.0MB / ??.?MB @ 3.5MB/s 2.9s\n",
+ "conda-forge/noarch \u001b[90m━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━\u001b[0m 10.8MB / ??.?MB @ 3.7MB/s 2.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 3.0s\n",
+ "conda-forge/linux-64 \u001b[90m╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 10.5MB / ??.?MB @ 3.6MB/s 3.0s\n",
+ "conda-forge/noarch \u001b[90m━━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━\u001b[0m 11.3MB / ??.?MB @ 3.8MB/s 3.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 3.1s\n",
+ "conda-forge/linux-64 \u001b[90m╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 10.5MB / ??.?MB @ 3.6MB/s 3.1s\n",
+ "conda-forge/noarch \u001b[90m━━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━\u001b[0m 11.3MB / ??.?MB @ 3.8MB/s 3.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 3.2s\n",
+ "conda-forge/linux-64 \u001b[90m━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━\u001b[0m 10.8MB / ??.?MB @ 3.4MB/s 3.2s\n",
+ "conda-forge/noarch \u001b[90m━━━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━\u001b[0m 11.5MB / ??.?MB @ 3.6MB/s 3.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 3.3s\n",
+ "conda-forge/linux-64 \u001b[90m━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━\u001b[0m 10.8MB / ??.?MB @ 3.4MB/s 3.3s\n",
+ "conda-forge/noarch \u001b[90m━━━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━\u001b[0m 11.5MB / ??.?MB @ 3.6MB/s 3.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 3.4s\n",
+ "conda-forge/linux-64 \u001b[90m━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━\u001b[0m 10.8MB / ??.?MB @ 3.4MB/s 3.4s\n",
+ "conda-forge/noarch \u001b[90m━━━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━\u001b[0m 11.5MB / ??.?MB @ 3.6MB/s 3.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 3.5s\n",
+ "conda-forge/linux-64 \u001b[90m━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━\u001b[0m 11.8MB / ??.?MB @ 3.4MB/s 3.5s\n",
+ "conda-forge/noarch \u001b[33m━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 12.3MB / ??.?MB @ 3.5MB/s 3.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 3.6s\n",
+ "conda-forge/linux-64 \u001b[90m━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━\u001b[0m 12.2MB / ??.?MB @ 3.4MB/s 3.6s\n",
+ "conda-forge/noarch \u001b[33m━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 14.5MB / ??.?MB @ 4.0MB/s 3.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 3.7s\n",
+ "conda-forge/linux-64 \u001b[90m━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━\u001b[0m 12.2MB @ 3.4MB/s 3.7s\n",
+ "conda-forge/noarch ━━━━━━━━━━━━━━━━━━━━━━ 14.5MB @ 4.0MB/s Downloaded 3.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 3.8s\n",
+ "conda-forge/linux-64 \u001b[90m━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━\u001b[0m 12.2MB / ??.?MB @ 3.4MB/s 3.8s\u001b[2K\u001b[1A\u001b[2K\u001b[0Gconda-forge/noarch @ 4.0MB/s 3.8s\n",
+ "[+] 3.9s\n",
+ "conda-forge/linux-64 \u001b[90m━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━\u001b[0m 14.1MB / ??.?MB @ 3.7MB/s 3.9s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 4.0s\n",
+ "conda-forge/linux-64 \u001b[90m━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━\u001b[0m 15.8MB / ??.?MB @ 4.0MB/s 4.0s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 4.1s\n",
+ "conda-forge/linux-64 \u001b[90m━━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━\u001b[0m 16.9MB / ??.?MB @ 4.2MB/s 4.1s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 4.2s\n",
+ "conda-forge/linux-64 \u001b[33m━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 17.8MB / ??.?MB @ 4.3MB/s 4.2s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 4.3s\n",
+ "conda-forge/linux-64 \u001b[33m━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 18.7MB / ??.?MB @ 4.4MB/s 4.3s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 4.4s\n",
+ "conda-forge/linux-64 \u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 19.4MB / ??.?MB @ 4.4MB/s 4.4s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 4.5s\n",
+ "conda-forge/linux-64 \u001b[33m━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 20.1MB / ??.?MB @ 4.5MB/s 4.5s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 4.6s\n",
+ "conda-forge/linux-64 \u001b[90m━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━\u001b[0m 21.1MB / ??.?MB @ 4.6MB/s 4.6s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 4.7s\n",
+ "conda-forge/linux-64 \u001b[90m━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━\u001b[0m 22.0MB / ??.?MB @ 4.7MB/s 4.7s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 4.8s\n",
+ "conda-forge/linux-64 \u001b[90m━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━\u001b[0m 22.2MB / ??.?MB @ 4.7MB/s 4.8s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 4.9s\n",
+ "conda-forge/linux-64 \u001b[90m━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━━\u001b[0m 23.1MB / ??.?MB @ 4.8MB/s 4.9s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.0s\n",
+ "conda-forge/linux-64 \u001b[90m━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━\u001b[0m 24.0MB / ??.?MB @ 4.9MB/s 5.0s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.1s\n",
+ "conda-forge/linux-64 \u001b[90m━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━\u001b[0m 24.9MB / ??.?MB @ 4.9MB/s 5.1s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.2s\n",
+ "conda-forge/linux-64 \u001b[90m━━━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━\u001b[0m 25.7MB / ??.?MB @ 5.0MB/s 5.2s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.3s\n",
+ "conda-forge/linux-64 \u001b[33m━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 26.7MB / ??.?MB @ 5.1MB/s 5.3s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.4s\n",
+ "conda-forge/linux-64 \u001b[33m━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 27.7MB / ??.?MB @ 5.2MB/s 5.4s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.5s\n",
+ "conda-forge/linux-64 \u001b[33m━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 28.6MB / ??.?MB @ 5.2MB/s 5.5s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.6s\n",
+ "conda-forge/linux-64 \u001b[90m╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 29.6MB / ??.?MB @ 5.3MB/s 5.6s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.7s\n",
+ "conda-forge/linux-64 \u001b[90m━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━\u001b[0m 30.5MB / ??.?MB @ 5.4MB/s 5.7s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.8s\n",
+ "conda-forge/linux-64 \u001b[90m━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━\u001b[0m 30.6MB / ??.?MB @ 5.3MB/s 5.8s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.9s\n",
+ "conda-forge/linux-64 \u001b[90m━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━\u001b[0m 32.4MB / ??.?MB @ 5.5MB/s 5.9s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.0s\n",
+ "conda-forge/linux-64 \u001b[90m━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━\u001b[0m 33.4MB / ??.?MB @ 5.6MB/s 6.0s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.1s\n",
+ "conda-forge/linux-64 \u001b[90m━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━\u001b[0m 34.4MB / ??.?MB @ 5.7MB/s 6.1s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.2s\n",
+ "conda-forge/linux-64 \u001b[90m━━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━\u001b[0m 35.3MB / ??.?MB @ 5.7MB/s 6.2s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.3s\n",
+ "conda-forge/linux-64 \u001b[90m━━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━\u001b[0m 35.3MB / ??.?MB @ 5.7MB/s 6.3s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.4s\n",
+ "conda-forge/linux-64 ━━━━━━━━━━━━━━━━━━━━━━ 35.7MB @ 5.7MB/s Downloaded 6.4s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.5s\n",
+ "conda-forge/linux-64 ━━━━━━━━━━━━━━━━━━━━━━ 35.7MB @ 5.7MB/s Downloaded 6.5s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.6s\n",
+ "conda-forge/linux-64 ━━━━━━━━━━━━━━━━━━━━━━ 35.7MB @ 5.7MB/s Downloaded 6.6s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.7s\n",
+ "conda-forge/linux-64 ━━━━━━━━━━━━━━━━━━━━━━ 35.7MB @ 5.7MB/s Downloaded 6.7s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.8s\n",
+ "conda-forge/linux-64 ━━━━━━━━━━━━━━━━━━━━━━ 35.7MB @ 5.7MB/s Downloaded 6.8s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.9s\n",
+ "conda-forge/linux-64 ━━━━━━━━━━━━━━━━━━━━━━ 35.7MB @ 5.7MB/s Downloaded 6.9s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.0s\n",
+ "conda-forge/linux-64 ━━━━━━━━━━━━━━━━━━━━━━ 35.7MB @ 5.7MB/s Downloaded 7.0s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.1s\n",
+ "conda-forge/linux-64 ━━━━━━━━━━━━━━━━━━━━━━ 35.7MB @ 5.7MB/s Finalizing 7.1s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.2s\n",
+ "conda-forge/linux-64 ━━━━━━━━━━━━━━━━━━━━━━ 35.7MB @ 5.7MB/s Finalizing 7.2s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.3s\n",
+ "conda-forge/linux-64 ━━━━━━━━━━━━━━━━━━━━━━ 35.7MB @ 5.7MB/s Finalizing 7.3s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.4s\n",
+ "\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.5s\n",
+ "\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.6s\n",
+ "\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.7s\n",
+ "\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.8s\n",
+ "\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.9s\n",
+ "\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 8.0s\n",
+ "\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 8.1s\n",
+ "\u001b[2K\u001b[1A\u001b[2K\u001b[0Gconda-forge/linux-64 @ 5.7MB/s 7.3s\n",
+ "\u001b[?25h\n",
+ "Pinned packages:\n",
+ " - python 3.10.*\n",
+ "\n",
+ "\n",
+ "Transaction\n",
+ "\n",
+ " Prefix: /opt/mamba\n",
+ "\n",
+ " Updating specs:\n",
+ "\n",
+ " - openmm=7.7.0\n",
+ " - rdkit\n",
+ " - ca-certificates\n",
+ " - certifi\n",
+ " - openssl\n",
+ "\n",
+ "\n",
+ " Package Version Build Channel Size\n",
+ "──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
+ " Install:\n",
+ "──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
+ "\n",
+ "\u001b[32m + archspec \u001b[00m 0.2.2 pyhd8ed1ab_0 conda-forge/noarch 42kB\n",
+ "\u001b[32m + boltons \u001b[00m 23.0.0 pyhd8ed1ab_0 conda-forge/noarch 303kB\n",
+ "\u001b[32m + brotli \u001b[00m 1.1.0 hd590300_1 conda-forge/linux-64 19kB\n",
+ "\u001b[32m + brotli-bin \u001b[00m 1.1.0 hd590300_1 conda-forge/linux-64 19kB\n",
+ "\u001b[32m + cairo \u001b[00m 1.18.0 h3faef2a_0 conda-forge/linux-64 982kB\n",
+ "\u001b[32m + conda-libmamba-solver \u001b[00m 23.11.0 pyhd8ed1ab_0 conda-forge/noarch 47kB\n",
+ "\u001b[32m + conda-package-streaming \u001b[00m 0.9.0 pyhd8ed1ab_0 conda-forge/noarch 19kB\n",
+ "\u001b[32m + contourpy \u001b[00m 1.2.0 py310hd41b1e2_0 conda-forge/linux-64 239kB\n",
+ "\u001b[32m + cycler \u001b[00m 0.12.1 pyhd8ed1ab_0 conda-forge/noarch 13kB\n",
+ "\u001b[32m + expat \u001b[00m 2.5.0 hcb278e6_1 conda-forge/linux-64 137kB\n",
+ "\u001b[32m + fmt \u001b[00m 10.1.1 h00ab1b0_0 conda-forge/linux-64 192kB\n",
+ "\u001b[32m + font-ttf-dejavu-sans-mono\u001b[00m 2.37 hab24e00_0 conda-forge/noarch 397kB\n",
+ "\u001b[32m + font-ttf-inconsolata \u001b[00m 3.000 h77eed37_0 conda-forge/noarch 97kB\n",
+ "\u001b[32m + font-ttf-source-code-pro \u001b[00m 2.038 h77eed37_0 conda-forge/noarch 701kB\n",
+ "\u001b[32m + font-ttf-ubuntu \u001b[00m 0.83 hab24e00_0 conda-forge/noarch 2MB\n",
+ "\u001b[32m + fontconfig \u001b[00m 2.14.2 h14ed4e7_0 conda-forge/linux-64 272kB\n",
+ "\u001b[32m + fonts-conda-ecosystem \u001b[00m 1 0 conda-forge/noarch 4kB\n",
+ "\u001b[32m + fonts-conda-forge \u001b[00m 1 0 conda-forge/noarch 4kB\n",
+ "\u001b[32m + fonttools \u001b[00m 4.44.0 py310h2372a71_0 conda-forge/linux-64 2MB\n",
+ "\u001b[32m + freetype \u001b[00m 2.12.1 h267a509_2 conda-forge/linux-64 635kB\n",
+ "\u001b[32m + freetype-py \u001b[00m 2.3.0 pyhd8ed1ab_0 conda-forge/noarch 59kB\n",
+ "\u001b[32m + gettext \u001b[00m 0.21.1 h27087fc_0 conda-forge/linux-64 4MB\n",
+ "\u001b[32m + greenlet \u001b[00m 3.0.1 py310hc6cd4ac_0 conda-forge/linux-64 206kB\n",
+ "\u001b[32m + jsonpatch \u001b[00m 1.33 pyhd8ed1ab_0 conda-forge/noarch 17kB\n",
+ "\u001b[32m + jsonpointer \u001b[00m 2.4 py310hff52083_3 conda-forge/linux-64 16kB\n",
+ "\u001b[32m + kiwisolver \u001b[00m 1.4.5 py310hd41b1e2_1 conda-forge/linux-64 73kB\n",
+ "\u001b[32m + lcms2 \u001b[00m 2.15 h7f713cb_2 conda-forge/linux-64 241kB\n",
+ "\u001b[32m + lerc \u001b[00m 4.0.0 h27087fc_0 conda-forge/linux-64 282kB\n",
+ "\u001b[32m + libboost \u001b[00m 1.82.0 h6fcfa73_6 conda-forge/linux-64 3MB\n",
+ "\u001b[32m + libboost-python \u001b[00m 1.82.0 py310hcb52e73_6 conda-forge/linux-64 119kB\n",
+ "\u001b[32m + libbrotlicommon \u001b[00m 1.1.0 hd590300_1 conda-forge/linux-64 69kB\n",
+ "\u001b[32m + libbrotlidec \u001b[00m 1.1.0 hd590300_1 conda-forge/linux-64 33kB\n",
+ "\u001b[32m + libbrotlienc \u001b[00m 1.1.0 hd590300_1 conda-forge/linux-64 283kB\n",
+ "\u001b[32m + libdeflate \u001b[00m 1.19 hd590300_0 conda-forge/linux-64 67kB\n",
+ "\u001b[32m + libexpat \u001b[00m 2.5.0 hcb278e6_1 conda-forge/linux-64 78kB\n",
+ "\u001b[32m + libglib \u001b[00m 2.78.1 hebfc3b9_0 conda-forge/linux-64 3MB\n",
+ "\u001b[32m + libjpeg-turbo \u001b[00m 2.1.5.1 hd590300_1 conda-forge/linux-64 496kB\n",
+ "\u001b[32m + libpng \u001b[00m 1.6.39 h753d276_0 conda-forge/linux-64 283kB\n",
+ "\u001b[32m + libtiff \u001b[00m 4.6.0 h29866fb_1 conda-forge/linux-64 277kB\n",
+ "\u001b[32m + libwebp-base \u001b[00m 1.3.2 hd590300_0 conda-forge/linux-64 402kB\n",
+ "\u001b[32m + libxcb \u001b[00m 1.15 h0b41bf4_0 conda-forge/linux-64 384kB\n",
+ "\u001b[32m + matplotlib-base \u001b[00m 3.8.0 py310h62c0568_1 conda-forge/linux-64 7MB\n",
+ "\u001b[32m + munkres \u001b[00m 1.1.4 pyh9f0ad1d_0 conda-forge/noarch 12kB\n",
+ "\u001b[32m + openjpeg \u001b[00m 2.5.0 h488ebb8_3 conda-forge/linux-64 357kB\n",
+ "\u001b[32m + pcre2 \u001b[00m 10.40 hc3806b6_0 conda-forge/linux-64 2MB\n",
+ "\u001b[32m + pillow \u001b[00m 10.0.1 py310h29da1c1_1 conda-forge/linux-64 46MB\n",
+ "\u001b[32m + pixman \u001b[00m 0.42.2 h59595ed_0 conda-forge/linux-64 385kB\n",
+ "\u001b[32m + pthread-stubs \u001b[00m 0.4 h36c2ea0_1001 conda-forge/linux-64 6kB\n",
+ "\u001b[32m + pycairo \u001b[00m 1.25.1 py310hda9f760_0 conda-forge/linux-64 116kB\n",
+ "\u001b[32m + rdkit \u001b[00m 2023.09.1 py310hb79e901_0 conda-forge/linux-64 37MB\n",
+ "\u001b[32m + reportlab \u001b[00m 4.0.7 py310h2372a71_0 conda-forge/linux-64 2MB\n",
+ "\u001b[32m + rlpycairo \u001b[00m 0.2.0 pyhd8ed1ab_0 conda-forge/noarch 15kB\n",
+ "\u001b[32m + sqlalchemy \u001b[00m 2.0.23 py310h2372a71_0 conda-forge/linux-64 3MB\n",
+ "\u001b[32m + truststore \u001b[00m 0.8.0 pyhd8ed1ab_0 conda-forge/noarch 21kB\n",
+ "\u001b[32m + typing-extensions \u001b[00m 4.8.0 hd8ed1ab_0 conda-forge/noarch 10kB\n",
+ "\u001b[32m + typing_extensions \u001b[00m 4.8.0 pyha770c72_0 conda-forge/noarch 35kB\n",
+ "\u001b[32m + unicodedata2 \u001b[00m 15.1.0 py310h2372a71_0 conda-forge/linux-64 374kB\n",
+ "\u001b[32m + xorg-kbproto \u001b[00m 1.0.7 h7f98852_1002 conda-forge/linux-64 27kB\n",
+ "\u001b[32m + xorg-libice \u001b[00m 1.1.1 hd590300_0 conda-forge/linux-64 58kB\n",
+ "\u001b[32m + xorg-libsm \u001b[00m 1.2.4 h7391055_0 conda-forge/linux-64 27kB\n",
+ "\u001b[32m + xorg-libx11 \u001b[00m 1.8.7 h8ee46fc_0 conda-forge/linux-64 829kB\n",
+ "\u001b[32m + xorg-libxau \u001b[00m 1.0.11 hd590300_0 conda-forge/linux-64 14kB\n",
+ "\u001b[32m + xorg-libxdmcp \u001b[00m 1.1.3 h7f98852_0 conda-forge/linux-64 19kB\n",
+ "\u001b[32m + xorg-libxext \u001b[00m 1.3.4 h0b41bf4_2 conda-forge/linux-64 50kB\n",
+ "\u001b[32m + xorg-libxrender \u001b[00m 0.9.11 hd590300_0 conda-forge/linux-64 38kB\n",
+ "\u001b[32m + xorg-renderproto \u001b[00m 0.11.1 h7f98852_1002 conda-forge/linux-64 10kB\n",
+ "\u001b[32m + xorg-xextproto \u001b[00m 7.3.0 h0b41bf4_1003 conda-forge/linux-64 30kB\n",
+ "\u001b[32m + xorg-xproto \u001b[00m 7.0.31 h7f98852_1007 conda-forge/linux-64 75kB\n",
+ "\u001b[32m + zstandard \u001b[00m 0.22.0 py310h1275a96_0 conda-forge/linux-64 404kB\n",
+ "\n",
+ " Change:\n",
+ "──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
+ "\n",
+ "\u001b[31m - hdf5 \u001b[00m 1.12.1 h70be1eb_2 mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main \n",
+ "\u001b[32m + hdf5 \u001b[00m 1.12.1 nompi_h4df4325_104 conda-forge/linux-64 4MB\n",
+ "\u001b[31m - python \u001b[00m 3.10.6 h582c2e5_0_cpython conda-forge \n",
+ "\u001b[32m + python \u001b[00m 3.10.6 ha86cf86_0_cpython conda-forge/linux-64 30MB\n",
+ "\n",
+ " Upgrade:\n",
+ "──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
+ "\n",
+ "\u001b[31m - c-ares \u001b[00m 1.18.1 h7f98852_0 conda-forge \n",
+ "\u001b[32m + c-ares \u001b[00m 1.21.0 hd590300_0 conda-forge/linux-64 122kB\n",
+ "\u001b[31m - ca-certificates \u001b[00m 2022.10.11 h06a4308_0 mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main \n",
+ "\u001b[32m + ca-certificates \u001b[00m 2023.7.22 hbcca054_0 conda-forge/linux-64 150kB\n",
+ "\u001b[31m - certifi \u001b[00m 2022.9.24 py310h06a4308_0 mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main \n",
+ "\u001b[32m + certifi \u001b[00m 2023.7.22 pyhd8ed1ab_0 conda-forge/noarch 154kB\n",
+ "\u001b[31m - conda \u001b[00m 22.11.0 py310h06a4308_1 mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main \n",
+ "\u001b[32m + conda \u001b[00m 23.10.0 py310hff52083_1 conda-forge/linux-64 969kB\n",
+ "\u001b[31m - conda-package-handling \u001b[00m 1.9.0 py310h5764c6d_0 conda-forge \n",
+ "\u001b[32m + conda-package-handling \u001b[00m 2.2.0 pyh38be061_0 conda-forge/noarch 255kB\n",
+ "\u001b[31m - cryptography \u001b[00m 38.0.2 py310h597c629_1 conda-forge \n",
+ "\u001b[32m + cryptography \u001b[00m 41.0.5 py310h75e40e8_0 conda-forge/linux-64 2MB\n",
+ "\u001b[31m - icu \u001b[00m 70.1 h27087fc_0 conda-forge \n",
+ "\u001b[32m + icu \u001b[00m 73.2 h59595ed_0 conda-forge/linux-64 12MB\n",
+ "\u001b[31m - krb5 \u001b[00m 1.19.3 h3790be6_0 conda-forge \n",
+ "\u001b[32m + krb5 \u001b[00m 1.21.2 h659d440_0 conda-forge/linux-64 1MB\n",
+ "\u001b[31m - libarchive \u001b[00m 3.5.2 hb890918_3 conda-forge \n",
+ "\u001b[32m + libarchive \u001b[00m 3.7.2 h039dbb9_0 conda-forge/linux-64 866kB\n",
+ "\u001b[31m - libcurl \u001b[00m 7.86.0 h7bff187_0 conda-forge \n",
+ "\u001b[32m + libcurl \u001b[00m 8.4.0 hca28451_0 conda-forge/linux-64 386kB\n",
+ "\u001b[31m - libmamba \u001b[00m 0.27.0 h0dd8ff0_0 conda-forge \n",
+ "\u001b[32m + libmamba \u001b[00m 1.5.3 had39da4_1 conda-forge/linux-64 2MB\n",
+ "\u001b[31m - libmambapy \u001b[00m 0.27.0 py310hab0e683_0 conda-forge \n",
+ "\u001b[32m + libmambapy \u001b[00m 1.5.3 py310h39ff949_1 conda-forge/linux-64 303kB\n",
+ "\u001b[31m - libnghttp2 \u001b[00m 1.47.0 hdcd2b5c_1 conda-forge \n",
+ "\u001b[32m + libnghttp2 \u001b[00m 1.58.0 h47da74e_0 conda-forge/linux-64 631kB\n",
+ "\u001b[31m - libssh2 \u001b[00m 1.10.0 haa6b8db_3 conda-forge \n",
+ "\u001b[32m + libssh2 \u001b[00m 1.11.0 h0841786_0 conda-forge/linux-64 271kB\n",
+ "\u001b[31m - libuuid \u001b[00m 2.32.1 h7f98852_1000 conda-forge \n",
+ "\u001b[32m + libuuid \u001b[00m 2.38.1 h0b41bf4_0 conda-forge/linux-64 34kB\n",
+ "\u001b[31m - libxml2 \u001b[00m 2.10.3 h7463322_0 conda-forge \n",
+ "\u001b[32m + libxml2 \u001b[00m 2.11.5 h232c23b_1 conda-forge/linux-64 706kB\n",
+ "\u001b[31m - mamba \u001b[00m 0.27.0 py310hf87f941_0 conda-forge \n",
+ "\u001b[32m + mamba \u001b[00m 1.5.3 py310h51d5547_1 conda-forge/linux-64 52kB\n",
+ "\u001b[31m - openssl \u001b[00m 1.1.1s h7f8727e_0 mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main \n",
+ "\u001b[32m + openssl \u001b[00m 3.1.4 hd590300_0 conda-forge/linux-64 3MB\n",
+ "\u001b[31m - packaging \u001b[00m 21.3 pyhd3eb1b0_0 mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main \n",
+ "\u001b[32m + packaging \u001b[00m 23.2 pyhd8ed1ab_0 conda-forge/noarch 49kB\n",
+ "\u001b[31m - pyopenssl \u001b[00m 22.1.0 pyhd8ed1ab_0 conda-forge \n",
+ "\u001b[32m + pyopenssl \u001b[00m 23.3.0 pyhd8ed1ab_0 conda-forge/noarch 127kB\n",
+ "\u001b[31m - yaml-cpp \u001b[00m 0.7.0 h27087fc_2 conda-forge \n",
+ "\u001b[32m + yaml-cpp \u001b[00m 0.8.0 h59595ed_0 conda-forge/linux-64 205kB\n",
+ "\u001b[31m - zstd \u001b[00m 1.5.2 h6239696_4 conda-forge \n",
+ "\u001b[32m + zstd \u001b[00m 1.5.5 hfc55251_0 conda-forge/linux-64 545kB\n",
+ "\n",
+ " Summary:\n",
+ "\n",
+ " Install: 69 packages\n",
+ " Change: 2 packages\n",
+ " Upgrade: 22 packages\n",
+ "\n",
+ " Total download: 182MB\n",
+ "\n",
+ "──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
+ "\n",
+ "\u001b[?25l\u001b[2K\u001b[0G[+] 0.0s\n",
+ "Downloading \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0.0 B 0.0s\n",
+ "Extracting \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0 0.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.1s\n",
+ "Downloading (5) \u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0.0 B ca-certificates 0.0s\n",
+ "Extracting \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0 0.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.2s\n",
+ "Downloading (5) \u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0.0 B ca-certificates 0.1s\n",
+ "Extracting \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0 0.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.3s\n",
+ "Downloading (5) \u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0.0 B ca-certificates 0.2s\n",
+ "Extracting \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0 0.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.4s\n",
+ "Downloading (5) \u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0.0 B ca-certificates 0.3s\n",
+ "Extracting \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0 0.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.5s\n",
+ "Downloading (5) \u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0.0 B icu 0.4s\n",
+ "Extracting \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0 0.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.6s\n",
+ "Downloading (5) \u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0.0 B icu 0.5s\n",
+ "Extracting \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0 0.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.7s\n",
+ "Downloading (5) \u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0.0 B icu 0.6s\n",
+ "Extracting \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0 0.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.8s\n",
+ "Downloading (5) \u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 499.0 B icu 0.7s\n",
+ "Extracting \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0 0.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.9s\n",
+ "Downloading (5) \u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 130.6kB libuuid 0.8s\n",
+ "Extracting \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0 0.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibuuid 33.6kB @ 35.2kB/s 1.0s\n",
+ "xorg-libice 58.5kB @ 60.1kB/s 1.0s\n",
+ "[+] 1.0s\n",
+ "Downloading (5) \u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 189.8kB ca-certificates 0.9s\n",
+ "Extracting (2) \u001b[90m━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━\u001b[0m 0 libuuid 0.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.1s\n",
+ "Downloading (5) \u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 455.9kB ca-certificates 1.0s\n",
+ "Extracting (2) \u001b[90m━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━\u001b[0m 0 libuuid 0.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gca-certificates 149.5kB @ 132.2kB/s 1.1s\n",
+ "[+] 1.2s\n",
+ "Downloading (5) \u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 877.7kB icu 1.1s\n",
+ "Extracting (3) \u001b[90m━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━\u001b[0m 0 libuuid 0.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.3s\n",
+ "Downloading (5) \u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 955.9kB icu 1.2s\n",
+ "Extracting (3) \u001b[90m━━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━\u001b[0m 0 libuuid 0.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gpixman 385.3kB @ 295.5kB/s 1.3s\n",
+ "libdeflate 67.1kB @ 49.8kB/s 0.2s\n",
+ "libpng 282.6kB @ 203.8kB/s 0.4s\n",
+ "[+] 1.4s\n",
+ "Downloading (5) \u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 1.6MB icu 1.3s\n",
+ "Extracting (6) \u001b[33m━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 0 pixman 0.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.5s\n",
+ "Downloading (5) \u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 1.6MB icu 1.4s\n",
+ "Extracting (5) \u001b[33m━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 1 pixman 0.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibbrotlicommon 69.4kB @ 45.7kB/s 0.2s\n",
+ "xorg-xproto 74.9kB @ 47.8kB/s 0.2s\n",
+ "[+] 1.6s\n",
+ "Downloading (5) \u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━\u001b[0m 2.0MB libboost 1.5s\n",
+ "Extracting (6) \u001b[33m━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 2 pixman 0.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.7s\n",
+ "Downloading (5) \u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━\u001b[0m 2.8MB libboost 1.6s\n",
+ "Extracting (5) \u001b[33m━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 3 pixman 0.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.8s\n",
+ "Downloading (5) \u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━\u001b[0m 3.5MB libboost 1.7s\n",
+ "Extracting (4) \u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 4 xorg-xproto 0.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibxml2 705.5kB @ 385.1kB/s 0.4s\n",
+ "[+] 1.9s\n",
+ "Downloading (5) \u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━\u001b[0m 5.4MB libboost 1.8s\n",
+ "Extracting (4) ╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━\u001b[0m 5 xorg-xproto 0.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 2.0s\n",
+ "Downloading (5) \u001b[33m━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━\u001b[0m 6.7MB libbrotlidec 1.9s\n",
+ "Extracting (2) ╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━\u001b[0m 7 libpng 1.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibbrotlidec 32.8kB @ 16.0kB/s 0.2s\n",
+ "pcre2 2.4MB @ 1.2MB/s 1.1s\n",
+ "[+] 2.1s\n",
+ "Downloading (5) ╸\u001b[33m━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━\u001b[0m 9.2MB icu 2.0s\n",
+ "Extracting (3) ╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━\u001b[0m 8 libbrotlidec 1.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 2.2s\n",
+ "Downloading (5) ╸\u001b[33m━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━\u001b[0m 11.2MB icu 2.1s\n",
+ "Extracting (2) ━╸\u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━\u001b[0m 9 libbrotlidec 1.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibboost 2.6MB @ 1.2MB/s 0.7s\n",
+ "[+] 2.3s\n",
+ "Downloading (5) ╸\u001b[33m━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━\u001b[0m 14.1MB icu 2.2s\n",
+ "Extracting (3) ━╸\u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━\u001b[0m 9 libbrotlidec 1.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibnghttp2 631.4kB @ 268.3kB/s 0.3s\n",
+ "krb5 1.4MB @ 582.6kB/s 0.3s\n",
+ "libglib 2.7MB @ 1.1MB/s 0.8s\n",
+ "[+] 2.4s\n",
+ "Downloading (5) ━╸\u001b[33m━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━\u001b[0m 18.4MB icu 2.3s\n",
+ "Extracting (6) ━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━\u001b[0m 9 libbrotlidec 1.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gopenjpeg 356.7kB @ 146.3kB/s 0.2s\n",
+ "[+] 2.5s\n",
+ "Downloading (5) ━╸\u001b[33m━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 20.4MB pillow 2.4s\n",
+ "Extracting (6) ━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━\u001b[0m 10 krb5 1.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Ggreenlet 206.4kB @ 80.1kB/s 0.2s\n",
+ "[+] 2.6s\n",
+ "Downloading (5) ━╸\u001b[33m━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 22.1MB pillow 2.5s\n",
+ "Extracting (7) ━╸\u001b[33m━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━\u001b[0m 10 krb5 1.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gunicodedata2 374.1kB @ 143.0kB/s 0.3s\n",
+ "zstandard 404.0kB @ 154.4kB/s 0.2s\n",
+ "[+] 2.7s\n",
+ "Downloading (5) ━━╸\u001b[33m━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 25.5MB pillow 2.6s\n",
+ "Extracting (8) ━╸\u001b[33m━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━\u001b[0m 11 krb5 1.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gicu 12.1MB @ 4.4MB/s 2.7s\n",
+ "xorg-libxrender 37.8kB @ 13.6kB/s 0.2s\n",
+ "[+] 2.8s\n",
+ "Downloading (5) ━━╸\u001b[33m━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 28.1MB pillow 2.7s\n",
+ "Extracting (9) ━╸\u001b[33m━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 12 greenlet 1.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 2.9s\n",
+ "Downloading (5) ━━╸\u001b[33m━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 28.6MB truststore 2.8s\n",
+ "Extracting (8) ━━╸\u001b[33m━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 13 greenlet 1.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gmunkres 12.5kB @ 4.2kB/s 0.2s\n",
+ "truststore 20.7kB @ 6.9kB/s 0.2s\n",
+ "[+] 3.0s\n",
+ "Downloading (5) ━━╸\u001b[33m━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 29.3MB conda-package-streaming 2.9s\n",
+ "Extracting (7) ━━╸\u001b[33m━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 15 icu 2.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 3.1s\n",
+ "Downloading (5) ━━╸\u001b[33m━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 29.3MB conda-package-streaming 3.0s\n",
+ "Extracting (5) ━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 18 icu 2.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gpackaging 49.5kB @ 15.6kB/s 0.2s\n",
+ "[+] 3.2s\n",
+ "Downloading (5) ━━╸\u001b[33m━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 31.4MB conda-package-streaming 3.1s\n",
+ "Extracting (6) ━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 18 icu 2.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gconda-package-streaming 19.2kB @ 6.0kB/s 0.2s\n",
+ "font-ttf-source-code-pro 700.8kB @ 214.4kB/s 0.7s\n",
+ "libmambapy 302.8kB @ 92.2kB/s 0.7s\n",
+ "[+] 3.3s\n",
+ "Downloading (5) ━━╸\u001b[33m━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 32.5MB cairo 3.2s\n",
+ "Extracting (8) ━━━╸\u001b[33m━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 19 icu 2.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 3.4s\n",
+ "Downloading (5) ━━━╸\u001b[33m━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 33.9MB cairo 3.3s\n",
+ "Extracting (6) ━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 21 conda-package-streaming 2.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gtyping-extensions 10.1kB @ 3.0kB/s 0.2s\n",
+ "conda-libmamba-solver 46.5kB @ 13.3kB/s 0.2s\n",
+ "[+] 3.5s\n",
+ "Downloading (5) ━━━╸\u001b[33m━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 36.8MB cairo 3.4s\n",
+ "Extracting (6) ━━━━╸\u001b[33m━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 23 conda-package-streaming 2.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 3.6s\n",
+ "Downloading (5) ━━━╸\u001b[33m━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 36.8MB cairo 3.5s\n",
+ "Extracting (5) ━━━━╸\u001b[33m━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 24 conda-libmamba-solver 2.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 3.7s\n",
+ "Downloading (5) ━━━╸\u001b[33m━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 37.2MB fonttools 3.6s\n",
+ "Extracting (5) ━━━━╸\u001b[33m━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 24 conda-libmamba-solver 2.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gc-ares 121.7kB @ 32.7kB/s 0.2s\n",
+ "[+] 3.8s\n",
+ "Downloading (5) ━━━╸\u001b[33m━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 39.0MB fonttools 3.7s\n",
+ "Extracting (6) ━━━━╸\u001b[33m━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 24 conda-libmamba-solver 2.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 3.9s\n",
+ "Downloading (5) ━━━╸\u001b[33m━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 39.8MB fonttools 3.8s\n",
+ "Extracting (4) ━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 26 c-ares 2.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gxorg-xextproto 30.3kB @ 7.7kB/s 0.2s\n",
+ "[+] 4.0s\n",
+ "Downloading (5) ━━━━╸\u001b[33m━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 41.1MB fonttools 3.9s\n",
+ "Extracting (3) ━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 28 c-ares 3.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gfonttools 2.2MB @ 556.5kB/s 0.8s\n",
+ "[+] 4.1s\n",
+ "Downloading (5) ━━━━╸\u001b[33m━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 42.7MB cairo 4.0s\n",
+ "Extracting (3) ━━━━━━╸\u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 29 c-ares 3.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 4.2s\n",
+ "Downloading (5) ━━━━╸\u001b[33m━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 43.3MB cairo 4.1s\n",
+ "Extracting (3) ━━━━━━╸\u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 29 c-ares 3.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibjpeg-turbo 496.4kB @ 117.6kB/s 0.3s\n",
+ "xorg-libxau 14.5kB @ 3.4kB/s 0.2s\n",
+ "cairo 982.4kB @ 230.4kB/s 1.0s\n",
+ "[+] 4.3s\n",
+ "Downloading (5) ━━━━╸\u001b[33m━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 45.3MB libtiff 4.2s\n",
+ "Extracting (4) ━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 31 cairo 3.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 4.4s\n",
+ "Downloading (5) ━━━━╸\u001b[33m━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 47.0MB libtiff 4.3s\n",
+ "Extracting (4) ━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 31 cairo 3.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 4.5s\n",
+ "Downloading (5) ━━━━╸\u001b[33m━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 47.0MB libtiff 4.4s\n",
+ "Extracting (4) ━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 31 cairo 3.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gpthread-stubs 5.6kB @ 1.2kB/s 0.3s\n",
+ "reportlab 2.4MB @ 521.5kB/s 1.1s\n",
+ "libtiff 277.5kB @ 61.4kB/s 0.3s\n",
+ "[+] 4.6s\n",
+ "Downloading (5) ━━━━╸\u001b[33m━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━\u001b[0m 47.6MB fontconfig 4.5s\n",
+ "Extracting (6) ━━━━━━╸\u001b[33m━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 32 cairo 3.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 4.7s\n",
+ "Downloading (5) ━━━━╸\u001b[33m━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━\u001b[0m 47.6MB fontconfig 4.6s\n",
+ "Extracting (4) ━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 34 libtiff 3.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 4.8s\n",
+ "Downloading (5) ━━━━━╸\u001b[33m━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━\u001b[0m 51.3MB fontconfig 4.7s\n",
+ "Extracting (4) ━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 34 libtiff 3.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 4.9s\n",
+ "Downloading (5) ━━━━━╸\u001b[33m━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━\u001b[0m 51.3MB fontconfig 4.8s\n",
+ "Extracting (3) ━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 35 libtiff 3.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.0s\n",
+ "Downloading (5) ━━━━━╸\u001b[33m━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━\u001b[0m 51.4MB openssl 4.9s\n",
+ "Extracting (1) ━━━━━━━━╸\u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 37 libtiff 4.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gfontconfig 272.0kB @ 54.1kB/s 0.5s\n",
+ "xorg-libx11 828.7kB @ 163.5kB/s 0.6s\n",
+ "[+] 5.1s\n",
+ "Downloading (5) ━━━━━╸\u001b[33m━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━\u001b[0m 53.8MB openssl 5.0s\n",
+ "Extracting (2) ━━━━━━━━╸\u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 38 fontconfig 4.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gopenssl 2.6MB @ 517.0kB/s 0.9s\n",
+ "[+] 5.2s\n",
+ "Downloading (5) ━━━━━━╸\u001b[33m━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━\u001b[0m 58.7MB libboost-python 5.1s\n",
+ "Extracting (3) ━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 38 fontconfig 4.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibboost-python 119.1kB @ 22.6kB/s 0.2s\n",
+ "[+] 5.3s\n",
+ "Downloading (5) ━━━━━━╸\u001b[33m━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━\u001b[0m 59.7MB font-ttf-inconsolata 5.2s\n",
+ "Extracting (4) ━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 38 fontconfig 4.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.4s\n",
+ "Downloading (5) ━━━━━━╸\u001b[33m━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━\u001b[0m 60.5MB font-ttf-inconsolata 5.3s\n",
+ "Extracting (4) ━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 38 fontconfig 4.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.5s\n",
+ "Downloading (5) ━━━━━━╸\u001b[33m━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━\u001b[0m 60.5MB font-ttf-inconsolata 5.4s\n",
+ "Extracting (4) ━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 38 libboost-python 4.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gfont-ttf-inconsolata 96.5kB @ 17.5kB/s 0.3s\n",
+ "[+] 5.6s\n",
+ "Downloading (5) ━━━━━━╸\u001b[33m━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━\u001b[0m 60.8MB cycler 5.5s\n",
+ "Extracting (5) ━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 38 libboost-python 4.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.7s\n",
+ "Downloading (5) ━━━━━━╸\u001b[33m━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━\u001b[0m 62.2MB cycler 5.6s\n",
+ "Extracting (4) ━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 39 libboost-python 4.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibcurl 386.2kB @ 67.7kB/s 0.6s\n",
+ "cycler 13.5kB @ 2.3kB/s 0.2s\n",
+ "libmamba 1.7MB @ 286.7kB/s 0.6s\n",
+ "[+] 5.8s\n",
+ "Downloading (5) ━━━━━━━╸\u001b[33m━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━\u001b[0m 64.5MB archspec 5.7s\n",
+ "Extracting (7) ━━━━━━━━╸\u001b[33m━━╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 39 libboost-python 4.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.9s\n",
+ "Downloading (5) ━━━━━━━╸\u001b[33m━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━\u001b[0m 65.8MB archspec 5.8s\n",
+ "Extracting (4) ━━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 42 cycler 4.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Garchspec 42.3kB @ 7.1kB/s 0.2s\n",
+ "fonts-conda-ecosystem 3.7kB @ 614.0 B/s 0.2s\n",
+ "[+] 6.0s\n",
+ "Downloading (5) ━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━━\u001b[0m 68.3MB mamba 5.9s\n",
+ "Extracting (6) ━━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 42 cycler 5.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.1s\n",
+ "Downloading (5) ━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━━\u001b[0m 68.3MB mamba 6.0s\n",
+ "Extracting (4) ━━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 44 cycler 5.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gmamba 51.6kB @ 8.3kB/s 0.2s\n",
+ "[+] 6.2s\n",
+ "Downloading (5) ━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━━\u001b[0m 68.7MB matplotlib-base 6.1s\n",
+ "Extracting (5) ━━━━━━━━━╸\u001b[33m━━╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 44 cycler 5.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.3s\n",
+ "Downloading (5) ━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━━\u001b[0m 68.7MB matplotlib-base 6.2s\n",
+ "Extracting (5) ━━━━━━━━━╸\u001b[33m━━╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 44 fonts-conda-ecosystem 5.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.4s\n",
+ "Downloading (5) ━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━━\u001b[0m 68.7MB matplotlib-base 6.3s\n",
+ "Extracting (5) ━━━━━━━━━╸\u001b[33m━━╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 44 fonts-conda-ecosystem 5.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.5s\n",
+ "Downloading (5) ━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━━\u001b[0m 69.1MB matplotlib-base 6.4s\n",
+ "Extracting (3) ━━━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 46 fonts-conda-ecosystem 5.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.6s\n",
+ "Downloading (5) ━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━━\u001b[0m 69.1MB pillow 6.5s\n",
+ "Extracting ━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━\u001b[0m 49 5.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gzstd 545.2kB @ 81.5kB/s 0.5s\n",
+ "[+] 6.7s\n",
+ "Downloading (5) ━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━━\u001b[0m 69.9MB pillow 6.6s\n",
+ "Extracting (1) ━━━━━━━━━━━╸\u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 49 zstd 5.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gpyopenssl 127.1kB @ 19.0kB/s 1.0s\n",
+ "[+] 6.8s\n",
+ "Downloading (5) ━━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━\u001b[0m 76.2MB pillow 6.7s\n",
+ "Extracting (2) ━━━━━━━━━━━╸\u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 49 zstd 5.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.9s\n",
+ "Downloading (5) ━━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━\u001b[0m 76.5MB pillow 6.8s\n",
+ "Extracting (2) ━━━━━━━━━━━╸\u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 49 zstd 5.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gxorg-renderproto 9.6kB @ 1.4kB/s 0.2s\n",
+ "[+] 7.0s\n",
+ "Downloading (5) ━━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━\u001b[0m 78.8MB python 6.9s\n",
+ "Extracting (3) ━━━━━━━━━━━╸\u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 49 zstd 5.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.1s\n",
+ "Downloading (5) ━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━━\u001b[0m 81.1MB python 7.0s\n",
+ "Extracting (3) ━━━━━━━━━━━╸\u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 49 pyopenssl 6.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gxorg-libsm 27.4kB @ 3.8kB/s 0.2s\n",
+ "[+] 7.2s\n",
+ "Downloading (5) ━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━━\u001b[0m 83.5MB python 7.1s\n",
+ "Extracting (2) ━━━━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 51 xorg-libsm 6.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.3s\n",
+ "Downloading (5) ━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━━\u001b[0m 83.5MB python 7.2s\n",
+ "Extracting (2) ━━━━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 51 xorg-libsm 6.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.4s\n",
+ "Downloading (5) ━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━━\u001b[0m 83.5MB gettext 7.3s\n",
+ "Extracting (1) ━━━━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 52 xorg-libsm 6.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.5s\n",
+ "Downloading (5) ━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━━\u001b[0m 84.7MB gettext 7.4s\n",
+ "Extracting (1) ━━━━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 52 xorg-libsm 6.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.6s\n",
+ "Downloading (5) ━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━━\u001b[0m 84.7MB gettext 7.5s\n",
+ "Extracting ━━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━\u001b[0m 53 6.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.7s\n",
+ "Downloading (5) ━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━━\u001b[0m 84.9MB gettext 7.6s\n",
+ "Extracting ━━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━\u001b[0m 53 6.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.8s\n",
+ "Downloading (5) ━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━━\u001b[0m 85.0MB libbrotlienc 7.7s\n",
+ "Extracting ━━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━\u001b[0m 53 6.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibbrotlienc 282.5kB @ 36.0kB/s 0.7s\n",
+ "[+] 7.9s\n",
+ "Downloading (5) ━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━━\u001b[0m 86.2MB gettext 7.8s\n",
+ "Extracting (1) ━━━━━━━━━━━━╸\u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 53 libbrotlienc 6.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 8.0s\n",
+ "Downloading (5) ━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━━\u001b[0m 86.6MB gettext 7.9s\n",
+ "Extracting (1) ━━━━━━━━━━━━╸\u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 53 libbrotlienc 6.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 8.1s\n",
+ "Downloading (5) ━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━\u001b[0m 88.1MB gettext 8.0s\n",
+ "Extracting (1) ━━━━━━━━━━━━╸\u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 53 libbrotlienc 6.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Ggettext 4.3MB @ 528.9kB/s 1.5s\n",
+ "[+] 8.2s\n",
+ "Downloading (5) ━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━\u001b[0m 95.3MB kiwisolver 8.1s\n",
+ "Extracting (1) ━━━━━━━━━━━━╸\u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 54 gettext 6.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 8.3s\n",
+ "Downloading (5) ━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━\u001b[0m 95.6MB kiwisolver 8.2s\n",
+ "Extracting (1) ━━━━━━━━━━━━╸\u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 54 gettext 6.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gkiwisolver 73.1kB @ 8.7kB/s 0.2s\n",
+ "[+] 8.4s\n",
+ "Downloading (5) ━━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━\u001b[0m 97.2MB cryptography 8.3s\n",
+ "Extracting (2) ━━━━━━━━━━━━╸\u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 54 gettext 7.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 8.5s\n",
+ "Downloading (5) ━━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━\u001b[0m 98.2MB cryptography 8.4s\n",
+ "Extracting (2) ━━━━━━━━━━━━╸\u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 54 gettext 7.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 8.6s\n",
+ "Downloading (5) ━━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━\u001b[0m 100.1MB cryptography 8.5s\n",
+ "Extracting (2) ━━━━━━━━━━━━╸\u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 54 kiwisolver 7.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 8.7s\n",
+ "Downloading (5) ━━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━\u001b[0m 102.2MB cryptography 8.6s\n",
+ "Extracting (2) ━━━━━━━━━━━━╸\u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 54 kiwisolver 7.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 8.8s\n",
+ "Downloading (5) ━━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━\u001b[0m 102.2MB libarchive 8.7s\n",
+ "Extracting (1) ━━━━━━━━━━━━╸\u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 55 gettext 7.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 8.9s\n",
+ "Downloading (5) ━━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━\u001b[0m 102.4MB libarchive 8.8s\n",
+ "Extracting (1) ━━━━━━━━━━━━╸\u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 55 gettext 7.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibarchive 865.9kB @ 96.7kB/s 1.1s\n",
+ "[+] 9.0s\n",
+ "Downloading (5) ━━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━\u001b[0m 102.5MB cryptography 8.9s\n",
+ "Extracting (2) ━━━━━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 55 gettext 7.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 9.1s\n",
+ "Downloading (5) ━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━━\u001b[0m 106.0MB cryptography 9.0s\n",
+ "Extracting (2) ━━━━━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 55 gettext 7.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 9.2s\n",
+ "Downloading (5) ━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━━\u001b[0m 107.6MB cryptography 9.1s\n",
+ "Extracting (2) ━━━━━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 55 libarchive 7.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 9.3s\n",
+ "Downloading (5) ━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━━\u001b[0m 107.6MB cryptography 9.2s\n",
+ "Extracting (1) ━━━━━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 56 libarchive 7.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 9.4s\n",
+ "Downloading (5) ━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━━\u001b[0m 109.2MB hdf5 9.3s\n",
+ "Extracting ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━\u001b[0m 57 8.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 9.5s\n",
+ "Downloading (5) ━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━━\u001b[0m 109.3MB hdf5 9.4s\n",
+ "Extracting ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━\u001b[0m 57 8.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 9.6s\n",
+ "Downloading (5) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━\u001b[0m 110.9MB hdf5 9.5s\n",
+ "Extracting ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━\u001b[0m 57 8.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 9.7s\n",
+ "Downloading (5) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━\u001b[0m 112.4MB hdf5 9.6s\n",
+ "Extracting ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━\u001b[0m 57 8.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gmatplotlib-base 6.8MB @ 695.9kB/s 3.8s\n",
+ "[+] 9.8s\n",
+ "Downloading (5) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━\u001b[0m 116.0MB pillow 9.7s\n",
+ "Extracting (1) ━━━━━━━━━━━━━╸\u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 57 matplotlib-base 8.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 9.9s\n",
+ "Downloading (5) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━\u001b[0m 117.2MB pillow 9.8s\n",
+ "Extracting (1) ━━━━━━━━━━━━━╸\u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 57 matplotlib-base 8.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gtyping_extensions 35.1kB @ 3.5kB/s 0.2s\n",
+ "[+] 10.0s\n",
+ "Downloading (5) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━\u001b[0m 118.4MB pillow 9.9s\n",
+ "Extracting (2) ━━━━━━━━━━━━━╸\u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 57 matplotlib-base 8.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gcryptography 2.0MB @ 196.3kB/s 1.7s\n",
+ "[+] 10.1s\n",
+ "Downloading (5) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━\u001b[0m 119.6MB pillow 10.0s\n",
+ "Extracting (3) ━━━━━━━━━━━━━╸\u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 57 matplotlib-base 8.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 10.2s\n",
+ "Downloading (5) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 120.7MB python 10.1s\n",
+ "Extracting (3) ━━━━━━━━━━━━━╸\u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 57 typing_extensions 8.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gcertifi 153.8kB @ 15.0kB/s 0.3s\n",
+ "[+] 10.3s\n",
+ "Downloading (5) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 122.0MB python 10.2s\n",
+ "Extracting (3) ━━━━━━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 58 typing_extensions 8.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gconda-package-handling 255.1kB @ 24.8kB/s 0.2s\n",
+ "[+] 10.4s\n",
+ "Downloading (5) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 123.4MB python 10.3s\n",
+ "Extracting (3) ━━━━━━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 59 certifi 8.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gpycairo 116.2kB @ 11.1kB/s 0.3s\n",
+ "[+] 10.5s\n",
+ "Downloading (5) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 124.7MB python 10.4s\n",
+ "Extracting (4) ━━━━━━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 59 certifi 8.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 10.6s\n",
+ "Downloading (5) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 125.9MB fmt 10.5s\n",
+ "Extracting (2) ━━━━━━━━━━━━━━╸\u001b[33m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 61 conda-package-handling 8.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 10.7s\n",
+ "Downloading (5) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 127.1MB fmt 10.6s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━╸\u001b[33m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 62 pycairo 8.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 10.8s\n",
+ "Downloading (5) ━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━\u001b[0m 128.4MB fmt 10.7s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━╸\u001b[33m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 62 pycairo 9.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glerc 281.8kB @ 26.0kB/s 0.3s\n",
+ "[+] 10.9s\n",
+ "Downloading (5) ━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━\u001b[0m 129.7MB fmt 10.8s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━╸\u001b[33m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 63 lerc 9.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 11.0s\n",
+ "Downloading (5) ━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━\u001b[0m 130.9MB hdf5 10.9s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━╸\u001b[33m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 63 lerc 9.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gxorg-libxdmcp 19.1kB @ 1.7kB/s 0.2s\n",
+ "fmt 192.3kB @ 17.3kB/s 0.8s\n",
+ "[+] 11.1s\n",
+ "Downloading (5) ━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━\u001b[0m 132.1MB hdf5 11.0s\n",
+ "Extracting (3) ━━━━━━━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 63 lerc 9.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gpillow 46.4MB @ 4.2MB/s 8.7s\n",
+ "[+] 11.2s\n",
+ "Downloading (5) ━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━\u001b[0m 133.3MB hdf5 11.1s\n",
+ "Extracting (4) ━━━━━━━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 63 lerc 9.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 11.3s\n",
+ "Downloading (5) ━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━\u001b[0m 134.1MB hdf5 11.2s\n",
+ "Extracting (3) ━━━━━━━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 64 fmt 9.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gbrotli-bin 19.0kB @ 1.7kB/s 0.2s\n",
+ "brotli 19.4kB @ 1.7kB/s 0.2s\n",
+ "[+] 11.4s\n",
+ "Downloading (5) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 135.1MB jsonpatch 11.3s\n",
+ "Extracting (5) ━━━━━━━━━━━━━━╸\u001b[33m━━╸\u001b[0m\u001b[90m━━━━━\u001b[0m 64 fmt 9.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibxcb 384.2kB @ 33.7kB/s 0.4s\n",
+ "hdf5 3.7MB @ 325.5kB/s 2.5s\n",
+ "python 30.4MB @ 2.7MB/s 7.0s\n",
+ "[+] 11.5s\n",
+ "Downloading (5) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 135.8MB jsonpatch 11.4s\n",
+ "Extracting (7) ━━━━━━━━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━\u001b[0m 65 fmt 9.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gjsonpatch 17.4kB @ 1.5kB/s 0.2s\n",
+ "[+] 11.6s\n",
+ "Downloading (5) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 137.4MB conda 11.5s\n",
+ "Extracting (8) ━━━━━━━━━━━━━━━╸\u001b[33m━━╸\u001b[0m\u001b[90m━━━━\u001b[0m 65 fmt 9.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gxorg-kbproto 27.3kB @ 2.3kB/s 0.2s\n",
+ "[+] 11.7s\n",
+ "Downloading (5) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 137.6MB conda 11.6s\n",
+ "Extracting (8) ━━━━━━━━━━━━━━━╸\u001b[33m━━╸\u001b[0m\u001b[90m━━━━\u001b[0m 66 brotli 9.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gfont-ttf-ubuntu 2.0MB @ 167.5kB/s 0.4s\n",
+ "yaml-cpp 204.9kB @ 17.4kB/s 0.3s\n",
+ "[+] 11.8s\n",
+ "Downloading (5) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 138.4MB conda 11.7s\n",
+ "Extracting (9) ━━━━━━━━━━━━━━━╸\u001b[33m━━╸\u001b[0m\u001b[90m━━━━\u001b[0m 67 brotli 10.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibssh2 271.1kB @ 22.9kB/s 0.3s\n",
+ "jsonpointer 16.2kB @ 1.4kB/s 0.2s\n",
+ "[+] 11.9s\n",
+ "Downloading (5) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 139.0MB conda 11.8s\n",
+ "Extracting (11) ━━━━━━━━━━━━━━━╸\u001b[33m━━━╸\u001b[0m\u001b[90m━━━\u001b[0m 67 brotli 10.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gfreetype-py 58.9kB @ 4.9kB/s 0.2s\n",
+ "conda 968.8kB @ 80.9kB/s 0.6s\n",
+ "[+] 12.0s\n",
+ "Downloading (5) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 139.4MB expat 11.9s\n",
+ "Extracting (13) ━━━━━━━━━━━━━━━╸\u001b[33m━━━╸\u001b[0m\u001b[90m━━━\u001b[0m 67 brotli 10.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 12.1s\n",
+ "Downloading (5) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 139.5MB expat 12.0s\n",
+ "Extracting (12) ━━━━━━━━━━━━━━━╸\u001b[33m━━━╸\u001b[0m\u001b[90m━━━\u001b[0m 68 conda 10.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibexpat 78.0kB @ 6.4kB/s 0.2s\n",
+ "xorg-libxext 50.1kB @ 4.1kB/s 0.2s\n",
+ "expat 136.8kB @ 11.3kB/s 0.3s\n",
+ "fonts-conda-forge 4.1kB @ 336.0 B/s 0.2s\n",
+ "[+] 12.2s\n",
+ "Downloading (5) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 140.0MB contourpy 12.1s\n",
+ "Extracting (15) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━╸\u001b[0m\u001b[90m━━\u001b[0m 69 conda 10.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 12.3s\n",
+ "Downloading (5) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 140.2MB contourpy 12.2s\n",
+ "Extracting (15) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━╸\u001b[0m\u001b[90m━━\u001b[0m 69 conda 10.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Grlpycairo 14.9kB @ 1.2kB/s 0.2s\n",
+ "contourpy 238.9kB @ 19.3kB/s 0.2s\n",
+ "[+] 12.4s\n",
+ "Downloading (5) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 140.9MB boltons 12.3s\n",
+ "Extracting (17) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━╸\u001b[0m\u001b[90m━\u001b[0m 69 conda 10.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibwebp-base 401.8kB @ 32.4kB/s 0.3s\n",
+ "lcms2 240.6kB @ 19.3kB/s 0.3s\n",
+ "[+] 12.5s\n",
+ "Downloading (5) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 141.5MB boltons 12.4s\n",
+ "Extracting (18) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━╸\u001b[0m\u001b[90m━\u001b[0m 70 contourpy 10.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 12.6s\n",
+ "Downloading (5) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 141.6MB boltons 12.5s\n",
+ "Extracting (18) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━╸\u001b[0m\u001b[90m━\u001b[0m 70 contourpy 10.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 12.7s\n",
+ "Downloading (5) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 143.4MB boltons 12.6s\n",
+ "Extracting (16) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━╸\u001b[0m\u001b[90m━\u001b[0m 72 contourpy 10.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gboltons 302.8kB @ 23.8kB/s 0.4s\n",
+ "freetype 635.0kB @ 49.8kB/s 0.4s\n",
+ "[+] 12.8s\n",
+ "Downloading (3) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 144.3MB font-ttf-dejavu-sans-mono 12.7s\n",
+ "Extracting (18) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 72 contourpy 11.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gfont-ttf-dejavu-sans-mono 397.4kB @ 31.0kB/s 0.3s\n",
+ "[+] 12.9s\n",
+ "Downloading (2) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 145.3MB rdkit 12.8s\n",
+ "Extracting (18) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 73 expat 11.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 13.0s\n",
+ "Downloading (2) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 146.1MB rdkit 12.9s\n",
+ "Extracting (17) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 74 expat 11.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 13.1s\n",
+ "Downloading (2) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 147.0MB rdkit 13.0s\n",
+ "Extracting (16) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 75 expat 11.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gsqlalchemy 2.8MB @ 213.7kB/s 1.4s\n",
+ "[+] 13.2s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 147.9MB rdkit 13.1s\n",
+ "Extracting (16) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 76 expat 11.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 13.3s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 148.5MB rdkit 13.2s\n",
+ "Extracting (11) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 81 conda 11.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 13.4s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 148.5MB rdkit 13.3s\n",
+ "Extracting (11) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 81 conda 11.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 13.5s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 148.7MB rdkit 13.4s\n",
+ "Extracting (9) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 83 conda 11.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 13.6s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 148.7MB rdkit 13.5s\n",
+ "Extracting (9) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 83 conda 11.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 13.7s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 148.7MB rdkit 13.6s\n",
+ "Extracting (6) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 86 contourpy 11.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 13.8s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 149.2MB rdkit 13.7s\n",
+ "Extracting (5) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 87 contourpy 12.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 13.9s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 149.8MB rdkit 13.8s\n",
+ "Extracting (2) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 90 fonts-conda-forge 12.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 14.0s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 150.3MB rdkit 13.9s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 91 python 12.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 14.1s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 150.9MB rdkit 14.0s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 91 python 12.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 14.2s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.4MB rdkit 14.1s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 91 python 12.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 14.3s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 151.9MB rdkit 14.2s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 91 python 12.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 14.4s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 152.5MB rdkit 14.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 91 python 12.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 14.5s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 153.0MB rdkit 14.4s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 91 python 12.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 14.6s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 153.6MB rdkit 14.5s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 91 python 12.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 14.7s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 154.1MB rdkit 14.6s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 91 python 12.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 14.8s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 154.4MB rdkit 14.7s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 91 python 13.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 14.9s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 155.0MB rdkit 14.8s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 91 python 13.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 15.0s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 155.5MB rdkit 14.9s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 91 python 13.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 15.1s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 156.0MB rdkit 15.0s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 91 python 13.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 15.2s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 156.6MB rdkit 15.1s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 91 python 13.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 15.3s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 157.1MB rdkit 15.2s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 91 python 13.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 15.4s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 157.7MB rdkit 15.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 91 python 13.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 15.5s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 158.2MB rdkit 15.4s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 91 python 13.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 15.6s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 158.8MB rdkit 15.5s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 91 python 13.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 15.7s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 159.3MB rdkit 15.6s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 91 python 13.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 15.8s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 159.8MB rdkit 15.7s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 91 python 14.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 15.9s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 160.4MB rdkit 15.8s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 91 python 14.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 16.0s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 160.9MB rdkit 15.9s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 91 python 14.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 16.1s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 161.5MB rdkit 16.0s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 91 python 14.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 16.2s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 162.0MB rdkit 16.1s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 91 python 14.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 16.3s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 162.6MB rdkit 16.2s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 92 14.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 16.4s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 163.1MB rdkit 16.3s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 92 14.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 16.5s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 163.7MB rdkit 16.4s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 92 14.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 16.6s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 164.2MB rdkit 16.5s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 92 14.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 16.7s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 164.8MB rdkit 16.6s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 92 14.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 16.8s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 165.3MB rdkit 16.7s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 92 14.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 16.9s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 165.9MB rdkit 16.8s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 92 14.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 17.0s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 166.4MB rdkit 16.9s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 92 14.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 17.1s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 167.0MB rdkit 17.0s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 92 14.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 17.2s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 167.7MB rdkit 17.1s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 92 14.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 17.3s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 168.4MB rdkit 17.2s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 92 14.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 17.4s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 169.1MB rdkit 17.3s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 92 14.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 17.5s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 170.0MB rdkit 17.4s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 92 14.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 17.6s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 170.9MB rdkit 17.5s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 92 14.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 17.7s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 171.8MB rdkit 17.6s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 92 14.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 17.8s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 172.9MB rdkit 17.7s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 92 14.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 17.9s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 173.7MB rdkit 17.8s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 92 14.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 18.0s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 174.7MB rdkit 17.9s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 92 14.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 18.1s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 175.7MB rdkit 18.0s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 92 14.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 18.2s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 176.8MB rdkit 18.1s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 92 14.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 18.3s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 177.8MB rdkit 18.2s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 92 14.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 18.4s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 178.8MB rdkit 18.3s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 92 14.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 18.5s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 179.8MB rdkit 18.4s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 92 14.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 18.6s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 180.9MB rdkit 18.5s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 92 14.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Grdkit 37.1MB @ 2.0MB/s 6.3s\n",
+ "[+] 18.7s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 181.8MB 18.6s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 92 rdkit 14.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 18.8s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 181.8MB 18.6s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 92 rdkit 14.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 18.9s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 181.8MB 18.6s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 92 rdkit 14.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 19.0s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 181.8MB 18.6s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 92 rdkit 14.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 19.1s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 181.8MB 18.6s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 92 rdkit 14.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 19.2s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 181.8MB 18.6s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 92 rdkit 15.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 19.3s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 181.8MB 18.6s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 92 rdkit 15.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 19.4s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 181.8MB 18.6s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 92 rdkit 15.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 19.5s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 181.8MB 18.6s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 92 rdkit 15.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 19.6s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 181.8MB 18.6s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 92 rdkit 15.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G\u001b[?25h\n",
+ "Downloading and Extracting Packages\n",
+ "\n",
+ "Preparing transaction: done\n",
+ "Verifying transaction: done\n",
+ "Executing transaction: done\n",
+ "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
+ "Collecting parmed\n",
+ " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/5b/48/d94bf10284cac3daaecdeaa856fcdb5def06c5187d8c0431266b8805ff9d/ParmEd-4.2.2.tar.gz (20.2 MB)\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m20.2/20.2 MB\u001b[0m \u001b[31m29.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
+ "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25ldone\n",
+ "\u001b[?25hRequirement already satisfied: mdtraj in /opt/mamba/lib/python3.10/site-packages (1.9.7)\n",
+ "Requirement already satisfied: pymbar in /opt/mamba/lib/python3.10/site-packages (4.0.1)\n",
+ "Requirement already satisfied: networkx in /opt/mamba/lib/python3.10/site-packages (3.2.1)\n",
+ "Requirement already satisfied: astunparse in /opt/mamba/lib/python3.10/site-packages (from mdtraj) (1.6.3)\n",
+ "Requirement already satisfied: pyparsing in /opt/mamba/lib/python3.10/site-packages (from mdtraj) (3.0.9)\n",
+ "Requirement already satisfied: scipy in /opt/mamba/lib/python3.10/site-packages (from mdtraj) (1.9.3)\n",
+ "Requirement already satisfied: numpy>=1.6 in /opt/mamba/lib/python3.10/site-packages (from mdtraj) (1.23.4)\n",
+ "Requirement already satisfied: jaxlib in /opt/mamba/lib/python3.10/site-packages (from pymbar) (0.3.15+cuda11.cudnn82)\n",
+ "Requirement already satisfied: numexpr in /opt/mamba/lib/python3.10/site-packages (from pymbar) (2.8.4)\n",
+ "Requirement already satisfied: jax in /opt/mamba/lib/python3.10/site-packages (from pymbar) (0.3.17)\n",
+ "Requirement already satisfied: wheel<1.0,>=0.23.0 in /opt/mamba/lib/python3.10/site-packages (from astunparse->mdtraj) (0.37.1)\n",
+ "Requirement already satisfied: six<2.0,>=1.6.1 in /opt/mamba/lib/python3.10/site-packages (from astunparse->mdtraj) (1.16.0)\n",
+ "Requirement already satisfied: etils[epath] in /opt/mamba/lib/python3.10/site-packages (from jax->pymbar) (0.9.0)\n",
+ "Requirement already satisfied: typing-extensions in /opt/mamba/lib/python3.10/site-packages (from jax->pymbar) (4.8.0)\n",
+ "Requirement already satisfied: absl-py in /opt/mamba/lib/python3.10/site-packages (from jax->pymbar) (1.3.0)\n",
+ "Requirement already satisfied: opt-einsum in /opt/mamba/lib/python3.10/site-packages (from jax->pymbar) (3.3.0)\n",
+ "Requirement already satisfied: zipp in /opt/mamba/lib/python3.10/site-packages (from etils[epath]->jax->pymbar) (3.10.0)\n",
+ "Requirement already satisfied: importlib_resources in /opt/mamba/lib/python3.10/site-packages (from etils[epath]->jax->pymbar) (5.10.0)\n",
+ "Building wheels for collected packages: parmed\n",
+ " Building wheel for parmed (setup.py) ... \u001b[?25ldone\n",
+ "\u001b[?25h Created wheel for parmed: filename=ParmEd-4.2.2-cp310-cp310-linux_x86_64.whl size=19199927 sha256=9a99ceb4ee4b01a9311bc2d233af89b88dc3ec19cdef4e3af55199c54d85db6c\n",
+ " Stored in directory: /root/.cache/pip/wheels/69/fc/4b/be5259741ac3c58269f1c8f6db8ce9e3a492319611ed0017ae\n",
+ "Successfully built parmed\n",
+ "Installing collected packages: parmed\n",
+ "Successfully installed parmed-4.2.2\n",
+ "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
+ "\u001b[0m"
+ ]
+ }
+ ],
+ "source": [
+ "! mamba install openmm=7.7.0 rdkit -c conda-forge -y\n",
+ "! pip install parmed mdtraj pymbar networkx"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "59a45a2a-a1db-4261-b94a-1180199fc85a",
+ "metadata": {},
+ "source": [
+ "## 1. ADMPQeqForce"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "01ac23a0-0af1-41e2-b878-b80ad897bc86",
+ "metadata": {},
+ "source": [
+ "ADMPQeqForce provides a support to coulombic energy calculation for constant potential model and constant charge model. Net charges on all atoms were equilibrated at setted constraint first, then charge related energys were carried out next.\n",
+ "\n",
+ "You can directly run the test:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "e81f8cbf-1024-45bc-92e2-559d4aaf02f2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "run DMFF/tests/test_admp/test_qeq.py"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "76e0a6d0-e1f7-4d4c-bd43-6e13047c7273",
+ "metadata": {},
+ "source": [
+ "And we will provide a more detailed explanation as follows"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "47a2b80b-3320-46a0-bb64-8ba0cf7d220b",
+ "metadata": {},
+ "source": [
+ "### Import the necessary libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "3b039276-dd47-4072-a6af-360237cd05bf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import openmm.app as app\n",
+ "import openmm.unit as unit\n",
+ "from dmff.api import Hamiltonian\n",
+ "from dmff.api import DMFFTopology\n",
+ "from dmff.api.xmlio import XMLIO\n",
+ "from dmff import NeighborList\n",
+ "import jax\n",
+ "from jax import value_and_grad\n",
+ "import jax.numpy as jnp\n",
+ "import numpy as np\n",
+ "import time\n",
+ "import pickle\n",
+ "import sys"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "96762867-f1d2-4e08-9e10-5afa66eb72f2",
+ "metadata": {},
+ "source": [
+ "### Load your force field"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "0c828d89-e4b1-4848-a68f-d7c5ebdd216c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "xml = XMLIO()\n",
+ "xml.loadXML(\"DMFF/tests/data/qeq2.xml\")\n",
+ "\n",
+ "# get residues\n",
+ "res = xml.parseResidues()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "13a18793-c124-400a-9071-fe2fe10eaff9",
+ "metadata": {},
+ "source": [
+ "For information about the force field file, please refer to the user guide, which contains detailed explanations.\n",
+ "### Initialize the charge and type of each atom and aux"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "0a3952b0-5756-44cc-b9a1-7d92d5fb4cb6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "charges, types = [], []\n",
+ "for i in range(len(res)):\n",
+ " charges += [a[\"charge\"] for a in res[i][\"particles\"]]\n",
+ " types += [a[\"type\"] for a in res[i][\"particles\"]]\n",
+ "charges = np.zeros((len(charges),))\n",
+ "\n",
+ "# initialize aux\n",
+ "aux = {\n",
+ " \"q\": jnp.array(charges),\n",
+ " \"lagmt\": jnp.array([1.0, 1.0])\n",
+ " #\"lagmt\": jnp.array([1.0])\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3f8b891b-832e-4b4d-9ca1-3a6b898d48a1",
+ "metadata": {},
+ "source": [
+ "### Load the topological information and supplement it"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "45217801-ff92-4d5d-b8b1-3162ba4279da",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Load topology\n",
+ "pdb = app.PDBFile(\"DMFF/tests/data/qeq2.pdb\")\n",
+ "dmfftop = DMFFTopology(from_top=pdb.topology)\n",
+ "pos = pdb.getPositions(asNumpy=True).value_in_unit(unit.nanometer)\n",
+ "pos = jnp.array(pos)\n",
+ "box = dmfftop.getPeriodicBoxVectors()\n",
+ "\n",
+ "# Assign atom charges and types in te topology\n",
+ "atoms = [a for a in dmfftop.atoms()]\n",
+ "for na, a in enumerate(atoms):\n",
+ " a.meta[\"charge\"] = charges[na]\n",
+ " a.meta[\"type\"] = types[na]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cdcde6a1-bb19-460c-bf15-543e0c7f35cd",
+ "metadata": {},
+ "source": [
+ "### Preparation for potential function"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "7c01f458-4c79-4e59-888f-13f776e634d4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create Hamiltonian\n",
+ "hamilt = Hamiltonian(\"DMFF/tests/data/qeq2.xml\")\n",
+ "\n",
+ "# create neighborlist & pairs\n",
+ "nblist = NeighborList(box, 0.6, dmfftop.buildCovMat())\n",
+ "pairs = nblist.allocate(pos) \n",
+ "\n",
+ "# initialize const_list\n",
+ "const_list, map_atomtype = [], []\n",
+ "for i in dmfftop.residues():\n",
+ " temp = []\n",
+ " for j in i.atoms():\n",
+ " temp.append(int(j.id)-1)\n",
+ " const_list.append(np.array(temp))\n",
+ "\n",
+ "# create map_atomtype\n",
+ "for i in dmfftop.atoms():\n",
+ " map_atomtype.append(int(i.meta[\"type\"])-1) #temp set\n",
+ "\n",
+ "# assign const_val\n",
+ "n_template = len(const_list)\n",
+ "const_val = jnp.zeros(n_template)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b3843624-aa9e-4049-b21e-8ffb200e0f6c",
+ "metadata": {},
+ "source": [
+ "### Create potential function and Calculate the energy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "64315715-db77-487f-b6c0-3e340085b774",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "energy: 4817.286675 kj/mol\n",
+ "{'q': DeviceArray([-2.99605719e-04, -3.40972179e-04, -4.91927203e-04,\n",
+ " -7.57415141e-04, -9.72305199e-04, -9.04476306e-04,\n",
+ " -6.19403852e-04, -3.18511669e-04, -4.14033308e-04,\n",
+ " -5.08883423e-04, -5.64320831e-04, -5.92336096e-04,\n",
+ " -4.59863367e-04, -5.31103425e-04, -3.93029108e-04,\n",
+ " -4.28646761e-04, -2.86954295e-04, -3.30793706e-04,\n",
+ " -3.17696799e-04, -3.50221376e-04, -2.77981466e-04,\n",
+ " -3.21483470e-04, -2.96629949e-04, -3.72879359e-04,\n",
+ " -3.51565779e-04, -4.66407859e-04, -5.10171801e-04,\n",
+ " 3.04087838e-04, -3.70321220e-03, 3.20495493e-04,\n",
+ " -9.18942861e-04, -3.34922291e-04, -4.97683690e-04,\n",
+ " -3.47713379e-04, -3.55104058e-04, -2.89328710e-04,\n",
+ " -3.24275491e-04, -2.80456324e-04, -3.02740626e-04,\n",
+ " -2.75075995e-04, -2.91228744e-04, -2.70938814e-04,\n",
+ " -4.46974131e-04, -2.97128120e-04, -8.78905378e-04,\n",
+ " 2.95142992e-03, -3.88588557e-02, 1.29549998e-02,\n",
+ " -3.83818161e-02, 3.31452051e-03, -3.74277838e-03,\n",
+ " 1.69146046e-04, -5.05571766e-04, -3.41280745e-04,\n",
+ " -2.96877554e-04, -3.50184751e-04, -2.80700753e-04,\n",
+ " -3.08958051e-04, -2.75486154e-04, -2.53472624e-04,\n",
+ " -4.13053343e-04, 2.50543320e-04, -3.94790845e-03,\n",
+ " 3.08837051e-03, -3.81956267e-02, 9.60634373e-02,\n",
+ " 9.68818582e-02, -3.62979638e-02, 3.29579664e-03,\n",
+ " -1.02522463e-03, -4.53547548e-04, -5.09515948e-04,\n",
+ " -3.07122995e-04, -3.81540204e-04, -2.97175528e-04,\n",
+ " -3.59608561e-04, -2.77417496e-04, -2.72418702e-04,\n",
+ " -2.30206122e-04, -7.13972582e-04, 5.07442477e-04,\n",
+ " -3.56837088e-03, 1.48526525e-02, -3.69820579e-02,\n",
+ " 9.79521879e-02, -4.11810695e-02, 1.16109691e-02,\n",
+ " -6.61601264e-03, -1.50696486e-04, -1.15772010e-03,\n",
+ " -5.52013847e-04, -3.42282836e-04, -5.04247185e-04,\n",
+ " -3.06268072e-04, -3.79429085e-04, -2.97021154e-04,\n",
+ " -2.68281704e-04, -3.63524282e-04, -2.26827694e-04,\n",
+ " -4.84749988e-04, 4.63586556e-04, -1.14723757e-03,\n",
+ " 2.12659981e-03, -7.30877030e-03, -6.49662707e-03,\n",
+ " -6.09779870e-03, -3.32921379e-03, -1.63993637e-03,\n",
+ " -1.04092695e-03, -6.96354733e-04, -3.47717779e-04,\n",
+ " -5.12611257e-04, -3.22951316e-04, -4.07471526e-04,\n",
+ " -2.99612167e-04, -3.28875625e-04, -3.16545100e-04,\n",
+ " -3.53711007e-04, -4.15631694e-04, -5.04555443e-04,\n",
+ " -9.22541839e-04, -1.29533806e-03, -1.85409182e-03,\n",
+ " -2.33100318e-03, -2.61057252e-03, -1.54778026e-03,\n",
+ " -1.42172801e-03, -7.52259682e-04, -6.57063416e-04,\n",
+ " -3.36476383e-04, -4.57009550e-04, -3.20344580e-04,\n",
+ " -3.97260729e-04, -3.11855166e-04, -3.26788820e-04,\n",
+ " -3.32597288e-04, -3.68762724e-04, -4.01437776e-04,\n",
+ " -5.80259620e-04, -8.87751823e-04, -9.34733571e-04,\n",
+ " -6.93458239e-04, -4.85308224e-04, -1.23620433e-02,\n",
+ " -9.20549199e-01, 4.60306706e-01, 4.60242494e-01], dtype=float64), 'lagmt': DeviceArray([ -574.30738132, -1398.09352793], dtype=float64)}\n"
+ ]
+ }
+ ],
+ "source": [
+ "pot = hamilt.createPotential(dmfftop, nonbondedCutoff=0.6*unit.nanometer, nonbondedMethod=app.PME,\n",
+ " ethresh=1e-3, neutral=True, slab=False, constQ=True,\n",
+ " const_list=const_list, const_vals=const_val,\n",
+ " has_aux=True)\n",
+ "\n",
+ "#return energy\n",
+ "efunc = pot.getPotentialFunc()\n",
+ "energy, aux = efunc(pos, box, pairs, hamilt.paramset.parameters, aux)\n",
+ "print(\"energy: %f kj/mol\" %energy)\n",
+ "print(aux)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "49c81a82-7ce6-40f9-a3b8-9a8aa4cc0a79",
+ "metadata": {},
+ "source": [
+ "## 2. Machine Learning Force"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "584e398e-3c0e-49d0-b28c-1c3152e569b9",
+ "metadata": {},
+ "source": [
+ "## 2.1 SGNN\n",
+ "Navigate to the working directory"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "f684afc0-844f-4375-ab1c-23b0a51f8f90",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "os.chdir(os.path.join(\"DMFF\",\"examples\", \"sgnn\"))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f4cf0379-4118-4a9c-93c1-986302101260",
+ "metadata": {},
+ "source": [
+ "SGNN assume the remaining bonding energy can be written as a sum over different local fragments of the molecule. These fragments are defined as “subgraphs” (labeled as g):\n",
+ "\n",
+ "$$\n",
+ "E_{sGNN}=\\sum {E_{g}}\n",
+ "$$\n",
+ "\n",
+ "Each subgraph defines the local environment of a central bond, and $E_g$ represents the intramolcular energy attributed to that bond. This leads to a rigorously localized representation of the molecule, warranting the extendibility of the resulting model."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3088a4bf-2716-4fad-9c81-4d45374dbdd8",
+ "metadata": {},
+ "source": [
+ "### Create a SGNN potential function"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c1a0e526-7290-41be-a833-449b989fefe8",
+ "metadata": {},
+ "source": [
+ "For information about the force field file, please refer to the user guide, which contains detailed explanations. Now you need to do the following to create a SGNN potential:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "39ff4e27-40fc-4d09-be6b-89b9f7a344bb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "H = Hamiltonian('peg.xml')\n",
+ "app.Topology.loadBondDefinitions(\"residues.xml\")\n",
+ "pdb = app.PDBFile(\"peg4.pdb\")\n",
+ "rc = 0.6\n",
+ "# generator stores all force field parameters\n",
+ "pots = H.createPotential(pdb.topology, nonbondedCutoff=rc*unit.nanometer, ethresh=5e-4)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "94008719-c055-4ae0-baa0-8f9d8b0440c2",
+ "metadata": {},
+ "source": [
+ "### Preparation for energy calculation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "aa0c27d3-2a77-4a8e-8c6d-821127986474",
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "DeviceArray([[ 0, 1, 1],\n",
+ " [ 0, 2, 1],\n",
+ " [ 0, 3, 1],\n",
+ " [ 0, 4, 2],\n",
+ " [ 0, 5, 3],\n",
+ " [ 0, 6, 3],\n",
+ " [ 0, 7, 3],\n",
+ " [ 0, 8, 3],\n",
+ " [ 0, 9, 4],\n",
+ " [ 0, 10, 4],\n",
+ " [ 0, 11, 2],\n",
+ " [ 0, 12, 1],\n",
+ " [ 0, 13, 2],\n",
+ " [ 0, 14, 2],\n",
+ " [ 0, 19, 4],\n",
+ " [ 0, 20, 5],\n",
+ " [ 0, 21, 5],\n",
+ " [ 1, 2, 2],\n",
+ " [ 1, 3, 2],\n",
+ " [ 1, 4, 3],\n",
+ " [ 1, 5, 4],\n",
+ " [ 1, 6, 4],\n",
+ " [ 1, 7, 4],\n",
+ " [ 1, 8, 4],\n",
+ " [ 1, 9, 5],\n",
+ " [ 1, 10, 5],\n",
+ " [ 1, 11, 3],\n",
+ " [ 1, 12, 2],\n",
+ " [ 1, 13, 3],\n",
+ " [ 1, 14, 3],\n",
+ " [ 1, 19, 5],\n",
+ " [ 1, 20, 6],\n",
+ " [ 1, 21, 6],\n",
+ " [ 2, 3, 2],\n",
+ " [ 2, 4, 3],\n",
+ " [ 2, 5, 4],\n",
+ " [ 2, 6, 4],\n",
+ " [ 2, 7, 4],\n",
+ " [ 2, 8, 4],\n",
+ " [ 2, 9, 5],\n",
+ " [ 2, 10, 5],\n",
+ " [ 2, 11, 3],\n",
+ " [ 2, 12, 2],\n",
+ " [ 2, 13, 3],\n",
+ " [ 2, 14, 3],\n",
+ " [ 2, 19, 5],\n",
+ " [ 2, 20, 6],\n",
+ " [ 2, 21, 6],\n",
+ " [ 3, 4, 1],\n",
+ " [ 3, 5, 2],\n",
+ " [ 3, 6, 2],\n",
+ " [ 3, 7, 2],\n",
+ " [ 3, 8, 4],\n",
+ " [ 3, 9, 5],\n",
+ " [ 3, 10, 5],\n",
+ " [ 3, 11, 3],\n",
+ " [ 3, 12, 2],\n",
+ " [ 3, 13, 3],\n",
+ " [ 3, 14, 3],\n",
+ " [ 4, 5, 1],\n",
+ " [ 4, 6, 1],\n",
+ " [ 4, 7, 1],\n",
+ " [ 4, 8, 5],\n",
+ " [ 4, 11, 4],\n",
+ " [ 4, 12, 3],\n",
+ " [ 4, 13, 4],\n",
+ " [ 4, 14, 4],\n",
+ " [ 5, 6, 2],\n",
+ " [ 5, 7, 2],\n",
+ " [ 5, 11, 5],\n",
+ " [ 5, 12, 4],\n",
+ " [ 5, 13, 5],\n",
+ " [ 5, 14, 5],\n",
+ " [ 6, 7, 2],\n",
+ " [ 6, 11, 5],\n",
+ " [ 6, 12, 4],\n",
+ " [ 6, 13, 5],\n",
+ " [ 6, 14, 5],\n",
+ " [ 7, 11, 5],\n",
+ " [ 7, 12, 4],\n",
+ " [ 7, 13, 5],\n",
+ " [ 7, 14, 5],\n",
+ " [ 8, 9, 1],\n",
+ " [ 8, 10, 1],\n",
+ " [ 8, 11, 1],\n",
+ " [ 8, 12, 2],\n",
+ " [ 8, 13, 3],\n",
+ " [ 8, 14, 3],\n",
+ " [ 8, 15, 3],\n",
+ " [ 8, 16, 4],\n",
+ " [ 8, 17, 4],\n",
+ " [ 8, 18, 2],\n",
+ " [ 8, 19, 1],\n",
+ " [ 8, 20, 2],\n",
+ " [ 8, 21, 2],\n",
+ " [ 8, 22, 4],\n",
+ " [ 8, 23, 5],\n",
+ " [ 8, 24, 5],\n",
+ " [ 9, 10, 2],\n",
+ " [ 9, 11, 2],\n",
+ " [ 9, 12, 3],\n",
+ " [ 9, 13, 4],\n",
+ " [ 9, 14, 4],\n",
+ " [ 9, 15, 4],\n",
+ " [ 9, 16, 5],\n",
+ " [ 9, 17, 5],\n",
+ " [ 9, 18, 3],\n",
+ " [ 9, 19, 2],\n",
+ " [ 9, 20, 3],\n",
+ " [ 9, 21, 3],\n",
+ " [ 9, 22, 5],\n",
+ " [ 9, 23, 6],\n",
+ " [ 9, 24, 6],\n",
+ " [10, 11, 2],\n",
+ " [10, 12, 3],\n",
+ " [10, 13, 4],\n",
+ " [10, 14, 4],\n",
+ " [10, 15, 4],\n",
+ " [10, 16, 5],\n",
+ " [10, 17, 5],\n",
+ " [10, 18, 3],\n",
+ " [10, 19, 2],\n",
+ " [10, 20, 3],\n",
+ " [10, 21, 3],\n",
+ " [10, 22, 5],\n",
+ " [10, 23, 6],\n",
+ " [10, 24, 6],\n",
+ " [11, 12, 1],\n",
+ " [11, 13, 2],\n",
+ " [11, 14, 2],\n",
+ " [11, 15, 4],\n",
+ " [11, 16, 5],\n",
+ " [11, 17, 5],\n",
+ " [11, 18, 3],\n",
+ " [11, 19, 2],\n",
+ " [11, 20, 3],\n",
+ " [11, 21, 3],\n",
+ " [12, 13, 1],\n",
+ " [12, 14, 1],\n",
+ " [12, 15, 5],\n",
+ " [12, 18, 4],\n",
+ " [12, 19, 3],\n",
+ " [12, 20, 4],\n",
+ " [12, 21, 4],\n",
+ " [13, 14, 2],\n",
+ " [13, 18, 5],\n",
+ " [13, 19, 4],\n",
+ " [13, 20, 5],\n",
+ " [13, 21, 5],\n",
+ " [14, 18, 5],\n",
+ " [14, 19, 4],\n",
+ " [14, 20, 5],\n",
+ " [14, 21, 5],\n",
+ " [15, 16, 1],\n",
+ " [15, 17, 1],\n",
+ " [15, 18, 1],\n",
+ " [15, 19, 2],\n",
+ " [15, 20, 3],\n",
+ " [15, 21, 3],\n",
+ " [15, 22, 1],\n",
+ " [15, 23, 2],\n",
+ " [15, 24, 2],\n",
+ " [15, 25, 2],\n",
+ " [15, 26, 3],\n",
+ " [15, 27, 4],\n",
+ " [15, 28, 4],\n",
+ " [15, 29, 4],\n",
+ " [16, 17, 2],\n",
+ " [16, 18, 2],\n",
+ " [16, 19, 3],\n",
+ " [16, 20, 4],\n",
+ " [16, 21, 4],\n",
+ " [16, 22, 2],\n",
+ " [16, 23, 3],\n",
+ " [16, 24, 3],\n",
+ " [16, 25, 3],\n",
+ " [16, 26, 4],\n",
+ " [16, 27, 5],\n",
+ " [16, 28, 5],\n",
+ " [16, 29, 5],\n",
+ " [17, 18, 2],\n",
+ " [17, 19, 3],\n",
+ " [17, 20, 4],\n",
+ " [17, 21, 4],\n",
+ " [17, 22, 2],\n",
+ " [17, 23, 3],\n",
+ " [17, 24, 3],\n",
+ " [17, 25, 3],\n",
+ " [17, 26, 4],\n",
+ " [17, 27, 5],\n",
+ " [17, 28, 5],\n",
+ " [17, 29, 5],\n",
+ " [18, 19, 1],\n",
+ " [18, 20, 2],\n",
+ " [18, 21, 2],\n",
+ " [18, 22, 2],\n",
+ " [18, 23, 3],\n",
+ " [18, 24, 3],\n",
+ " [18, 25, 3],\n",
+ " [18, 26, 4],\n",
+ " [18, 27, 5],\n",
+ " [18, 28, 5],\n",
+ " [18, 29, 5],\n",
+ " [19, 20, 1],\n",
+ " [19, 21, 1],\n",
+ " [19, 22, 3],\n",
+ " [19, 23, 4],\n",
+ " [19, 24, 4],\n",
+ " [19, 25, 4],\n",
+ " [19, 26, 5],\n",
+ " [20, 21, 2],\n",
+ " [20, 22, 4],\n",
+ " [20, 23, 5],\n",
+ " [20, 24, 5],\n",
+ " [20, 25, 5],\n",
+ " [21, 22, 4],\n",
+ " [21, 23, 5],\n",
+ " [21, 24, 5],\n",
+ " [21, 25, 5],\n",
+ " [22, 23, 1],\n",
+ " [22, 24, 1],\n",
+ " [22, 25, 1],\n",
+ " [22, 26, 2],\n",
+ " [22, 27, 3],\n",
+ " [22, 28, 3],\n",
+ " [22, 29, 3],\n",
+ " [23, 24, 2],\n",
+ " [23, 25, 2],\n",
+ " [23, 26, 3],\n",
+ " [23, 27, 4],\n",
+ " [23, 28, 4],\n",
+ " [23, 29, 4],\n",
+ " [24, 25, 2],\n",
+ " [24, 26, 3],\n",
+ " [24, 27, 4],\n",
+ " [24, 28, 4],\n",
+ " [24, 29, 4],\n",
+ " [25, 26, 1],\n",
+ " [25, 27, 2],\n",
+ " [25, 28, 2],\n",
+ " [25, 29, 2],\n",
+ " [26, 27, 1],\n",
+ " [26, 28, 1],\n",
+ " [26, 29, 1],\n",
+ " [27, 28, 2],\n",
+ " [27, 29, 2],\n",
+ " [28, 29, 2],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0],\n",
+ " [30, 30, 0]], dtype=int64)"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# construct inputs\n",
+ "positions = jnp.array(pdb.positions._value)\n",
+ "a, b, c = pdb.topology.getPeriodicBoxVectors()\n",
+ "box = jnp.array([a._value, b._value, c._value])\n",
+ "# neighbor list\n",
+ "nbl = NeighborList(box, rc, pots.meta['cov_map']) \n",
+ "nbl.allocate(positions)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e8ef2d4a-6510-44a5-aca7-2c745359b112",
+ "metadata": {},
+ "source": [
+ "And you can get parameters by:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "01e48a54-e506-4322-8c00-c6566a787264",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "paramset = H.getParameters()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8f0caa3b-6320-4490-8e1f-3a9af1f9c708",
+ "metadata": {},
+ "source": [
+ "### Load data and fix it"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "fe602bb3-488e-44ec-b957-0c433b1a18ab",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "with open('test_backend/set_test_lowT.pickle', 'rb') as ifile:\n",
+ " data = pickle.load(ifile)\n",
+ "\n",
+ "# input in nm\n",
+ "pos = jnp.array(data['positions'][0:20]) / 10\n",
+ "box = jnp.eye(3) * 5"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1cd68aac-83b1-4a3e-b301-6263bc397761",
+ "metadata": {},
+ "source": [
+ "### Calculate the energy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "4b124b2c-46d7-4e70-96fd-c95407be39d2",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "-21.588284621154514\n",
+ "[-21.58828462 -39.79334159 10.03889335 -48.22451239 -32.90970162\n",
+ " -49.68568287 -47.58035178 -51.73860617 -37.39235277 -35.01933271\n",
+ " -46.06621902 -31.69327601 -6.86739655 -5.13698524 -27.4031207\n",
+ " -44.65301991 -52.00357797 3.1734038 -72.79081259 -28.27007722]\n"
+ ]
+ }
+ ],
+ "source": [
+ "efunc = jax.jit(pots.getPotentialFunc())\n",
+ "efunc_vmap = jax.vmap(jax.jit(pots.getPotentialFunc()), in_axes=(0, None, None, None), out_axes=0)\n",
+ "print(efunc(pos[0], box, nbl.pairs, paramset))\n",
+ "print(efunc_vmap(pos, box, nbl.pairs, paramset))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5b4bc26c-2974-4bcd-b0dd-996d60c62d85",
+ "metadata": {},
+ "source": [
+ "## 2.1 EANN\n",
+ "Navigate to the working directory"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "9781c244-156e-4a27-a5c2-5e957525eccd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "current_directory = os.getcwd()\n",
+ "parent_directory = os.path.dirname(current_directory)\n",
+ "os.chdir(parent_directory)\n",
+ "os.chdir(os.path.join(\"eann\"))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "13ae9c47-247e-432e-abe2-7a6688b34e99",
+ "metadata": {},
+ "source": [
+ "EANN framework born out from the EAM idea. This physically inspired embedded atom neuralnetworks (EANN) representation is not only conceptually andnumerically simple but also very efficient and accurate, as discussed below. EANN assume that the impurity experiences a locally uniform electron density, the embedding energy can be approximated as a function of the scalar local electron density at the impurity site plus an electrostatic interaction. Considering all atoms in the system as impurities embedded in the electron gas created by other atoms, in the EAM framework, the total energy of an $N$ atom system is just the sum over all individual impurity energies.\n",
+ "\n",
+ "$$\n",
+ "E=\\sum_{i=1}^{N} E_{i}=\\sum_{i=1}^{N}\\left[F_{i}\\left(\\rho_{i}\\right)+\\frac{1}{2} \\sum_{j \\neq i} \\phi_{i j}\\left(r_{i j}\\right)\\right]\n",
+ "$$\n",
+ "\n",
+ "where $F_i$ is the embedding function, $ρ_i$ is the embedded electron density at the position of atom $i$ given by the superposition of the densities of surrounding atoms, and $\\phi_{ij}$ is the short-range repulsive potential between atoms $i$ and $j$ depending on their distance $r_{ij}$. As the exact forms of these functions are generally unknown, they are often taken from electron gas computations or fit to experimental properties with semiempirical expres-sions. Given these intrinsic approximations, EAM or even its modified version has a limited accuracy and is mainly suitablefor metallic systems.\n",
+ "\n",
+ "To go beyond the EAM, we need to improve both expressions of the embedded density and the function $F$. To this end, EANN start from the commonly used Gaussian-type orbitals (GTOs) centered at each atom,\n",
+ "\n",
+ "$$\n",
+ "\\phi_{l_{x} y_{l} y_{z}}^{\\alpha, r_{s}}=x^{l_{x}} y^{l_{y}} z^{l_{z}} \\exp \\left(-\\alpha\\left|r-r_{s}\\right|^{2}\\right)\n",
+ "$$\n",
+ "\n",
+ "where each atom is taken as the origin, $r=(x,y,z)$ constitutes the coordinate vector of an electron, $r$ is the norm of the vector,$α$ and $r_s$ are parameters that determine radial distributions of atomic orbitals, ${l_x+l_y+l_z=L}$ specifies the orbital angular momentum ($L$), e.g., $L$ = 0, 1, and 2, correspond to the s, p, and d orbitals, respectively. In this representation, the embedded density of atom $i$ can be taken as the square of the linear combination of atomic orbitals from neighboring atoms, in a similar spirit as that in Hartree−Fock (HF) and densityfunctional theory (DFT). This would generate a scalar $ρ^i$ value for the embedding atom $i$, as used in the EAM, which has been proven to offer insufficient representability for the total energyand can be improved by including the gradients of density."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3567df74-e392-4679-9ec1-2fe087fef68d",
+ "metadata": {},
+ "source": [
+ "As for code, just follow the step in SGNN:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "581c3238-c476-4118-99a1-1f5321775680",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "-0.09797672247941636\n"
+ ]
+ }
+ ],
+ "source": [
+ "H = Hamiltonian('peg.xml')\n",
+ "app.Topology.loadBondDefinitions(\"residues.xml\")\n",
+ "pdb = app.PDBFile(\"peg4.pdb\")\n",
+ "rc = 0.4\n",
+ "# generator stores all force field parameters\n",
+ "pots = H.createPotential(pdb.topology, nonbondedCutoff=rc*unit.nanometer, ethresh=5e-4)\n",
+ "\n",
+ "# construct inputs\n",
+ "positions = jnp.array(pdb.positions._value)\n",
+ "a, b, c = pdb.topology.getPeriodicBoxVectors()\n",
+ "box = jnp.array([a._value, b._value, c._value])\n",
+ "# neighbor list\n",
+ "nbl = NeighborList(box, rc, pots.meta['cov_map']) \n",
+ "nbl.allocate(positions)\n",
+ "\n",
+ "\n",
+ "paramset = H.getParameters()\n",
+ "# params = paramset.parameters\n",
+ "paramset.parameters\n",
+ "\n",
+ "efunc = jax.jit(pots.getPotentialFunc())\n",
+ "print(efunc(positions, box, nbl.pairs, paramset))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "95704edc-1cd8-40fc-bfd9-c24b4e18ac8d",
+ "metadata": {},
+ "source": [
+ "## 3. OpenMM Plugin for DMFF"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6fb30805-b23d-4e70-be0b-5e3df0c31c6f",
+ "metadata": {},
+ "source": [
+ "OpenMM DMFF plugin was developed for [OpenMM](http://openmm.org) to incorporate the trained JAX model from [DMFF](https://github.com/deepmodeling/DMFF) as an independent Force class for molecular dynamics simulations.\n",
+ "To utilize this plugin, you need to save your DMFF model using the `DMFF/backend/save_dmff2tf.py` script.\n",
+ "The `save_dmff2tf.py` script converts the DMFF model to a TensorFlow module using the experimental feature of JAX called [`jax2tf`](https://github.com/google/jax/blob/main/jax/experimental/jax2tf/README.md).\n",
+ "The integration of the saved TensorFlow module with the DMFF plugin is accomplished using [cppflow](https://github.com/serizba/cppflow) and the OpenMM C++ interface. \n",
+ "\n",
+ "**Here you might need to shut down the original node and start up a new one**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e2bf5e5b-6429-4a86-b806-b4610928cd9e",
+ "metadata": {},
+ "source": [
+ "### Save the DMFF model with `save_dmff2tf.py` script"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b78d80d6-c0e6-4214-b80e-9ab40c0861d4",
+ "metadata": {},
+ "source": [
+ "#### Install TensorFlow and JAX"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "72997e8a-b190-445d-955a-88cedbdbcf14",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "os.chdir('/data')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "fd9049db-6633-4d93-94b1-7c9f517bacc6",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
+ "Collecting jax[cpu]==0.4.14\n",
+ " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/65/ce/e3a6e8669de6ff37b44b1f801c33c10dcdc05548ee5ded30c0327eb09c93/jax-0.4.14.tar.gz (1.3 MB)\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m5.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
+ "\u001b[?25h Installing build dependencies ... \u001b[?25ldone\n",
+ "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n",
+ "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n",
+ "\u001b[?25hRequirement already satisfied: numpy>=1.22 in /opt/mamba/lib/python3.10/site-packages (from jax[cpu]==0.4.14) (1.23.4)\n",
+ "Requirement already satisfied: opt-einsum in /opt/mamba/lib/python3.10/site-packages (from jax[cpu]==0.4.14) (3.3.0)\n",
+ "Collecting ml-dtypes>=0.2.0\n",
+ " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/90/31/ec94e33a799323a8c37d1883f44b517c38d9defa7667db97cba212384d71/ml_dtypes-0.3.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (206 kB)\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m206.7/206.7 kB\u001b[0m \u001b[31m4.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
+ "\u001b[?25hRequirement already satisfied: scipy>=1.7 in /opt/mamba/lib/python3.10/site-packages (from jax[cpu]==0.4.14) (1.9.3)\n",
+ "Collecting jaxlib==0.4.14\n",
+ " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/62/a1/beb609f27603cf2ce6f3fb14ad2e93bde9324158f872e46509d52b178cdc/jaxlib-0.4.14-cp310-cp310-manylinux2014_x86_64.whl (73.7 MB)\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m73.7/73.7 MB\u001b[0m \u001b[31m15.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
+ "\u001b[?25hBuilding wheels for collected packages: jax\n",
+ " Building wheel for jax (pyproject.toml) ... \u001b[?25ldone\n",
+ "\u001b[?25h Created wheel for jax: filename=jax-0.4.14-py3-none-any.whl size=1535363 sha256=87520dcb5f85c5073802ec72c4583f7acda7b09c0432e41f25a1d6ab1b34a94e\n",
+ " Stored in directory: /root/.cache/pip/wheels/7d/7a/8d/c75c9181b2ea75b6953c3a91fdacfec166038cb6d815807149\n",
+ "Successfully built jax\n",
+ "Installing collected packages: ml-dtypes, jaxlib, jax\n",
+ " Attempting uninstall: jaxlib\n",
+ " Found existing installation: jaxlib 0.3.15+cuda11.cudnn82\n",
+ " Uninstalling jaxlib-0.3.15+cuda11.cudnn82:\n",
+ " Successfully uninstalled jaxlib-0.3.15+cuda11.cudnn82\n",
+ " Attempting uninstall: jax\n",
+ " Found existing installation: jax 0.3.17\n",
+ " Uninstalling jax-0.3.17:\n",
+ " Successfully uninstalled jax-0.3.17\n",
+ "Successfully installed jax-0.4.14 jaxlib-0.4.14 ml-dtypes-0.3.1\n",
+ "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
+ "\u001b[0mLooking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
+ "Collecting tensorflow\n",
+ " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/e2/7a/c7762c698fb1ac41a7e3afee51dc72aa3ec74ae8d2f57ce19a9cded3a4af/tensorflow-2.14.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (489.8 MB)\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m489.8/489.8 MB\u001b[0m \u001b[31m1.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:02\u001b[0m\n",
+ "\u001b[?25hRequirement already satisfied: six>=1.12.0 in /opt/mamba/lib/python3.10/site-packages (from tensorflow) (1.16.0)\n",
+ "Requirement already satisfied: typing-extensions>=3.6.6 in /opt/mamba/lib/python3.10/site-packages (from tensorflow) (4.4.0)\n",
+ "Collecting tensorflow-estimator<2.15,>=2.14.0\n",
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+ " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/6f/bb/5deac77a9af870143c684ab46a7934038a53eb4aa975bc0687ed6ca2c610/requests_oauthlib-1.3.1-py2.py3-none-any.whl (23 kB)\n",
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+ "Collecting pyasn1<0.6.0,>=0.4.6\n",
+ " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/14/e5/b56a725cbde139aa960c26a1a3ca4d4af437282e20b5314ee6a3501e7dfc/pyasn1-0.5.0-py2.py3-none-any.whl (83 kB)\n",
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+ "\u001b[?25hCollecting oauthlib>=3.0.0\n",
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+ "\u001b[?25hInstalling collected packages: libclang, flatbuffers, wrapt, werkzeug, termcolor, tensorflow-io-gcs-filesystem, tensorflow-estimator, tensorboard-data-server, pyasn1, protobuf, oauthlib, numpy, markdown, keras, grpcio, google-pasta, gast, cachetools, rsa, requests-oauthlib, pyasn1-modules, ml-dtypes, google-auth, google-auth-oauthlib, tensorboard, tensorflow\n",
+ " Attempting uninstall: numpy\n",
+ " Found existing installation: numpy 1.23.4\n",
+ " Uninstalling numpy-1.23.4:\n",
+ " Successfully uninstalled numpy-1.23.4\n",
+ " Attempting uninstall: ml-dtypes\n",
+ " Found existing installation: ml-dtypes 0.3.1\n",
+ " Uninstalling ml-dtypes-0.3.1:\n",
+ " Successfully uninstalled ml-dtypes-0.3.1\n",
+ "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+ "scipy 1.9.3 requires numpy<1.26.0,>=1.18.5, but you have numpy 1.26.1 which is incompatible.\u001b[0m\u001b[31m\n",
+ "\u001b[0mSuccessfully installed cachetools-5.3.2 flatbuffers-23.5.26 gast-0.5.4 google-auth-2.23.4 google-auth-oauthlib-1.0.0 google-pasta-0.2.0 grpcio-1.59.2 keras-2.14.0 libclang-16.0.6 markdown-3.5.1 ml-dtypes-0.2.0 numpy-1.26.1 oauthlib-3.2.2 protobuf-4.25.0 pyasn1-0.5.0 pyasn1-modules-0.3.0 requests-oauthlib-1.3.1 rsa-4.9 tensorboard-2.14.1 tensorboard-data-server-0.7.2 tensorflow-2.14.0 tensorflow-estimator-2.14.0 tensorflow-io-gcs-filesystem-0.34.0 termcolor-2.3.0 werkzeug-3.0.1 wrapt-1.14.1\n",
+ "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
+ "\u001b[0m"
+ ]
+ }
+ ],
+ "source": [
+ "!pip install \"jax[cpu]==0.4.14\"\n",
+ "!pip install tensorflow"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f64a7c5d-c36a-4405-9fd1-f7269c567fb7",
+ "metadata": {},
+ "source": [
+ "#### Save DMFF with dmff2tf script"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6c282143-6795-49d8-830c-334e2c384336",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!python DMFF/backend/save_dmff2tf.py --input_pdb DMFF/examples/water_fullpol/water_dimer.pdb --xml_files DMFF/examples/water_fullpol/forcefield.xml --output /tmp/dmff_admp_water_dimer --has_aux True\n",
+ "!ls /tmp/dmff_admp_water_dimer"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d9bd39fd-4c0d-45f2-bcc0-882d8b2d49d7",
+ "metadata": {},
+ "source": [
+ "!python DMFF/backend/save_dmff2tf.py --input_pdb DMFF/examples/classical/lig.pdb --xml_files DMFF/examples/classical/lig-prm.xml --output /tmp/dmff_classical_lig"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0556b692-adeb-45db-970a-830d266dd769",
+ "metadata": {},
+ "source": [
+ "### Install OpenMM DMFF Plugin"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "43bc5a9e-54da-4dda-8f1b-ad53cd1b5215",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ " __ __ __ __\n",
+ " / \\ / \\ / \\ / \\\n",
+ " / \\/ \\/ \\/ \\\n",
+ "███████████████/ /██/ /██/ /██/ /████████████████████████\n",
+ " / / \\ / \\ / \\ / \\ \\____\n",
+ " / / \\_/ \\_/ \\_/ \\ o \\__,\n",
+ " / _/ \\_____/ `\n",
+ " |/\n",
+ " ███╗ ███╗ █████╗ ███╗ ███╗██████╗ █████╗\n",
+ " ████╗ ████║██╔══██╗████╗ ████║██╔══██╗██╔══██╗\n",
+ " ██╔████╔██║███████║██╔████╔██║██████╔╝███████║\n",
+ " ██║╚██╔╝██║██╔══██║██║╚██╔╝██║██╔══██╗██╔══██║\n",
+ " ██║ ╚═╝ ██║██║ ██║██║ ╚═╝ ██║██████╔╝██║ ██║\n",
+ " ╚═╝ ╚═╝╚═╝ ╚═╝╚═╝ ╚═╝╚═════╝ ╚═╝ ╚═╝\n",
+ "\n",
+ " mamba (0.27.0) supported by @QuantStack\n",
+ "\n",
+ " GitHub: https://github.com/mamba-org/mamba\n",
+ " Twitter: https://twitter.com/QuantStack\n",
+ "\n",
+ "█████████████████████████████████████████████████████████████\n",
+ "\n",
+ "\n",
+ "Looking for: ['python=3.9', 'openmm', 'libtensorflow_cc=2.9.1', 'swig=4.0.1', 'setuptools=59.5.0']\n",
+ "\n",
+ "\u001b[?25l\u001b[2K\u001b[0G[+] 0.0s\n",
+ "\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.1s\n",
+ "conda-forge/linux-64 \u001b[33m━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.1s\n",
+ "conda-forge/noarch \u001b[90m━━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.1s\n",
+ "pkgs/main/linux-64 \u001b[33m━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.1s\n",
+ "pkgs/main/noarch \u001b[90m━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.1s\n",
+ "pkgs/r/linux-64 \u001b[90m━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.2s\n",
+ "conda-forge/linux-64 \u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.2s\n",
+ "conda-forge/noarch \u001b[33m━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.2s\n",
+ "pkgs/main/linux-64 \u001b[90m╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.2s\n",
+ "pkgs/main/noarch \u001b[90m━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.2s\n",
+ "pkgs/r/linux-64 \u001b[90m━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.3s\n",
+ "conda-forge/linux-64 \u001b[90m╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.3s\n",
+ "conda-forge/noarch \u001b[33m━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.3s\n",
+ "pkgs/main/linux-64 \u001b[90m━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.3s\n",
+ "pkgs/main/noarch \u001b[90m━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.3s\n",
+ "pkgs/r/linux-64 \u001b[90m━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.4s\n",
+ "conda-forge/linux-64 \u001b[90m━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.4s\n",
+ "conda-forge/noarch \u001b[33m━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.4s\n",
+ "pkgs/main/linux-64 \u001b[90m━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.4s\n",
+ "pkgs/main/noarch \u001b[90m━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.4s\n",
+ "pkgs/r/linux-64 \u001b[90m━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.5s\n",
+ "conda-forge/linux-64 \u001b[90m━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.5s\n",
+ "conda-forge/noarch \u001b[33m━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.5s\n",
+ "pkgs/main/linux-64 \u001b[90m━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.5s\n",
+ "pkgs/main/noarch \u001b[90m━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.5s\n",
+ "pkgs/r/linux-64 \u001b[90m━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.6s\n",
+ "conda-forge/linux-64 \u001b[90m━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.6s\n",
+ "conda-forge/noarch \u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.6s\n",
+ "pkgs/main/linux-64 \u001b[90m━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.6s\n",
+ "pkgs/main/noarch \u001b[90m━━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.6s\n",
+ "pkgs/r/linux-64 \u001b[90m━━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.7s\n",
+ "conda-forge/linux-64 \u001b[90m━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.7s\n",
+ "conda-forge/noarch \u001b[33m━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.7s\n",
+ "pkgs/main/linux-64 \u001b[90m━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.7s\n",
+ "pkgs/main/noarch \u001b[33m━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 352.0 B / ??.?MB @ 565.0 B/s 0.7s\n",
+ "pkgs/r/linux-64 \u001b[33m━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.8s\n",
+ "conda-forge/linux-64 \u001b[90m━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.8s\n",
+ "conda-forge/noarch \u001b[33m━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.8s\n",
+ "pkgs/main/linux-64 \u001b[90m━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━━\u001b[0m 31.4kB / ??.?MB @ 43.2kB/s 0.8s\n",
+ "pkgs/main/noarch \u001b[33m━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 31.4kB / ??.?MB @ 43.2kB/s 0.8s\n",
+ "pkgs/r/linux-64 \u001b[33m━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 31.4kB / ??.?MB @ 43.2kB/s 0.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.9s\n",
+ "conda-forge/linux-64 \u001b[90m━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.9s\n",
+ "conda-forge/noarch \u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.9s\n",
+ "pkgs/main/linux-64 \u001b[90m━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━\u001b[0m 32.8kB / ??.?MB @ 38.0kB/s 0.9s\n",
+ "pkgs/main/noarch \u001b[33m━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 32.8kB / ??.?MB @ 38.0kB/s 0.9s\n",
+ "pkgs/r/linux-64 \u001b[33m━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 32.8kB / ??.?MB @ 38.0kB/s 0.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.0s\n",
+ "conda-forge/linux-64 \u001b[90m━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 1.0s\n",
+ "conda-forge/noarch \u001b[90m━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 1.0s\n",
+ "pkgs/main/linux-64 \u001b[90m━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━\u001b[0m 107.0kB / ??.?MB @ 110.4kB/s 1.0s\n",
+ "pkgs/main/noarch \u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 102.4kB / ??.?MB @ 105.6kB/s 1.0s\n",
+ "pkgs/r/linux-64 \u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 110.9kB / ??.?MB @ 114.3kB/s 1.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.1s\n",
+ "conda-forge/linux-64 \u001b[90m━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 1.1s\n",
+ "conda-forge/noarch \u001b[90m━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 1.1s\n",
+ "pkgs/main/linux-64 \u001b[90m━━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━\u001b[0m 156.1kB / ??.?MB @ 145.8kB/s 1.1s\n",
+ "pkgs/main/noarch \u001b[33m━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 176.5kB / ??.?MB @ 164.9kB/s 1.1s\n",
+ "pkgs/r/linux-64 \u001b[33m━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 180.8kB / ??.?MB @ 168.7kB/s 1.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.2s\n",
+ "conda-forge/linux-64 \u001b[33m━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 1.2s\n",
+ "conda-forge/noarch \u001b[90m━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 1.2s\n",
+ "pkgs/main/linux-64 \u001b[33m━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 229.4kB / ??.?MB @ 195.9kB/s 1.2s\n",
+ "pkgs/main/noarch \u001b[90m╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 262.1kB / ??.?MB @ 223.8kB/s 1.2s\n",
+ "pkgs/r/linux-64 \u001b[90m╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 278.5kB / ??.?MB @ 236.8kB/s 1.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.3s\n",
+ "conda-forge/linux-64 \u001b[33m━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 18.2kB / ??.?MB @ 14.3kB/s 1.3s\n",
+ "conda-forge/noarch \u001b[90m━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━\u001b[0m 32.2kB / ??.?MB @ 25.3kB/s 1.3s\n",
+ "pkgs/main/linux-64 \u001b[33m━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 344.0kB / ??.?MB @ 270.6kB/s 1.3s\n",
+ "pkgs/main/noarch \u001b[90m━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━\u001b[0m 393.1kB / ??.?MB @ 309.2kB/s 1.3s\n",
+ "pkgs/r/linux-64 \u001b[90m━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━\u001b[0m 409.5kB / ??.?MB @ 320.9kB/s 1.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.4s\n",
+ "conda-forge/linux-64 \u001b[33m━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 31.8kB / ??.?MB @ 23.1kB/s 1.4s\n",
+ "conda-forge/noarch \u001b[90m━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━\u001b[0m 32.2kB / ??.?MB @ 23.4kB/s 1.4s\n",
+ "pkgs/main/linux-64 \u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 475.0kB / ??.?MB @ 346.2kB/s 1.4s\n",
+ "pkgs/main/noarch \u001b[90m━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━\u001b[0m 577.5kB / ??.?MB @ 420.9kB/s 1.4s\n",
+ "pkgs/r/linux-64 \u001b[90m━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━\u001b[0m 606.0kB / ??.?MB @ 440.2kB/s 1.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gpkgs/main/noarch 853.2kB @ 583.5kB/s 1.5s\n",
+ "[+] 1.5s\n",
+ "conda-forge/linux-64 \u001b[33m━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 64.9kB / ??.?MB @ 44.0kB/s 1.5s\n",
+ "conda-forge/noarch \u001b[90m━━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━\u001b[0m 134.6kB / ??.?MB @ 91.3kB/s 1.5s\n",
+ "pkgs/main/linux-64 \u001b[33m━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 704.2kB / ??.?MB @ 477.7kB/s 1.5s\n",
+ "pkgs/r/linux-64 \u001b[90m━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━\u001b[0m 917.1kB / ??.?MB @ 620.9kB/s 1.5s\n",
+ "pkgs/r/noarch \u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.6s\n",
+ "conda-forge/linux-64 \u001b[90m╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 107.4kB / ??.?MB @ 68.2kB/s 1.6s\n",
+ "conda-forge/noarch \u001b[33m━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 175.4kB / ??.?MB @ 111.5kB/s 1.6s\n",
+ "pkgs/main/linux-64 \u001b[90m━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━\u001b[0m 1.0MB / ??.?MB @ 666.0kB/s 1.6s\n",
+ "pkgs/r/linux-64 \u001b[90m━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━\u001b[0m 1.4MB / ??.?MB @ 861.6kB/s 1.6s\n",
+ "pkgs/r/noarch \u001b[33m━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gpkgs/r/linux-64 1.9MB @ 1.1MB/s 1.7s\n",
+ "[+] 1.7s\n",
+ "conda-forge/linux-64 \u001b[90m━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━\u001b[0m 165.5kB / ??.?MB @ 98.4kB/s 1.7s\n",
+ "conda-forge/noarch \u001b[33m━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 330.6kB / ??.?MB @ 196.6kB/s 1.7s\n",
+ "pkgs/main/linux-64 \u001b[90m━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━\u001b[0m 1.4MB / ??.?MB @ 854.6kB/s 1.7s\n",
+ "pkgs/r/noarch \u001b[90m━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━\u001b[0m 28.7kB / ??.?MB @ 17.0kB/s 0.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.8s\n",
+ "conda-forge/linux-64 \u001b[90m━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━\u001b[0m 287.9kB / ??.?MB @ 161.5kB/s 1.8s\n",
+ "conda-forge/noarch \u001b[33m━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 494.3kB / ??.?MB @ 277.2kB/s 1.8s\n",
+ "pkgs/main/linux-64 \u001b[90m━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━\u001b[0m 2.2MB / ??.?MB @ 1.2MB/s 1.8s\n",
+ "pkgs/r/noarch \u001b[90m━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━\u001b[0m 593.9kB / ??.?MB @ 333.2kB/s 0.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.9s\n",
+ "conda-forge/linux-64 \u001b[90m━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━━\u001b[0m 418.8kB / ??.?MB @ 222.4kB/s 1.9s\n",
+ "conda-forge/noarch \u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 772.5kB / ??.?MB @ 410.2kB/s 1.9s\n",
+ "pkgs/main/linux-64 \u001b[90m━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━\u001b[0m 2.7MB / ??.?MB @ 1.5MB/s 1.9s\n",
+ "pkgs/r/noarch \u001b[90m━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━\u001b[0m 1.1MB / ??.?MB @ 591.8kB/s 0.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 2.0s\n",
+ "conda-forge/linux-64 \u001b[90m━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━\u001b[0m 647.8kB / ??.?MB @ 326.6kB/s 2.0s\n",
+ "conda-forge/noarch \u001b[90m━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━\u001b[0m 1.1MB / ??.?MB @ 554.5kB/s 2.0s\n",
+ "pkgs/main/linux-64 \u001b[90m━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━\u001b[0m 3.2MB / ??.?MB @ 1.6MB/s 2.0s\n",
+ "pkgs/r/noarch \u001b[90m━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━\u001b[0m 1.6MB / ??.?MB @ 813.5kB/s 0.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 2.1s\n",
+ "conda-forge/linux-64 \u001b[90m━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━\u001b[0m 1.0MB / ??.?MB @ 480.9kB/s 2.1s\n",
+ "conda-forge/noarch \u001b[90m━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━\u001b[0m 1.6MB / ??.?MB @ 786.8kB/s 2.1s\n",
+ "pkgs/main/linux-64 \u001b[90m━━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━\u001b[0m 3.7MB / ??.?MB @ 1.8MB/s 2.1s\n",
+ "pkgs/r/noarch \u001b[90m━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━\u001b[0m 2.1MB / ??.?MB @ 1.0MB/s 0.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gpkgs/r/noarch 2.3MB @ 1.1MB/s 0.7s\n",
+ "[+] 2.2s\n",
+ "conda-forge/linux-64 \u001b[90m━━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━\u001b[0m 1.2MB / ??.?MB @ 563.4kB/s 2.2s\n",
+ "conda-forge/noarch \u001b[90m━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━\u001b[0m 2.0MB / ??.?MB @ 935.1kB/s 2.2s\n",
+ "pkgs/main/linux-64 \u001b[90m━━━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━\u001b[0m 4.0MB / ??.?MB @ 1.8MB/s 2.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 2.3s\n",
+ "conda-forge/linux-64 \u001b[33m━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 2.1MB / ??.?MB @ 915.4kB/s 2.3s\n",
+ "conda-forge/noarch \u001b[90m━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━\u001b[0m 3.3MB / ??.?MB @ 1.5MB/s 2.3s\n",
+ "pkgs/main/linux-64 \u001b[33m━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 4.3MB / ??.?MB @ 1.9MB/s 2.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 2.4s\n",
+ "conda-forge/linux-64 \u001b[33m━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 3.2MB / ??.?MB @ 1.3MB/s 2.4s\n",
+ "conda-forge/noarch \u001b[90m━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━\u001b[0m 4.1MB / ??.?MB @ 1.7MB/s 2.4s\n",
+ "pkgs/main/linux-64 \u001b[33m━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 4.8MB / ??.?MB @ 2.1MB/s 2.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 2.5s\n",
+ "conda-forge/linux-64 \u001b[33m━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 4.2MB / ??.?MB @ 1.7MB/s 2.5s\n",
+ "conda-forge/noarch \u001b[90m━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━\u001b[0m 5.1MB / ??.?MB @ 2.1MB/s 2.5s\n",
+ "pkgs/main/linux-64 \u001b[33m━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 5.4MB / ??.?MB @ 2.2MB/s 2.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 2.6s\n",
+ "conda-forge/linux-64 \u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 5.2MB / ??.?MB @ 2.0MB/s 2.6s\n",
+ "conda-forge/noarch \u001b[33m━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 6.0MB / ??.?MB @ 2.4MB/s 2.6s\n",
+ "pkgs/main/linux-64 \u001b[90m╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 6.0MB / ??.?MB @ 2.3MB/s 2.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 2.7s\n",
+ "conda-forge/linux-64 \u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 5.7MB @ 2.2MB/s 2.7s\n",
+ "conda-forge/noarch \u001b[33m━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 6.6MB @ 2.5MB/s 2.7s\n",
+ "pkgs/main/linux-64 ━━━━━━━━━━━━━━━━━━━━━━ 6.3MB @ 2.4MB/s Finalizing 2.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gpkgs/main/linux-64 @ 2.4MB/s 2.7s\n",
+ "[+] 2.8s\n",
+ "conda-forge/linux-64 \u001b[90m━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━\u001b[0m 6.0MB / ??.?MB @ 2.1MB/s 2.8s\n",
+ "conda-forge/noarch \u001b[33m━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 6.8MB / ??.?MB @ 2.4MB/s 2.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 2.9s\n",
+ "conda-forge/linux-64 \u001b[90m━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━\u001b[0m 7.8MB / ??.?MB @ 2.7MB/s 2.9s\n",
+ "conda-forge/noarch \u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 8.9MB / ??.?MB @ 3.1MB/s 2.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 3.0s\n",
+ "conda-forge/linux-64 \u001b[90m━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━\u001b[0m 9.4MB / ??.?MB @ 3.1MB/s 3.0s\n",
+ "conda-forge/noarch \u001b[33m━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 10.4MB / ??.?MB @ 3.5MB/s 3.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 3.1s\n",
+ "conda-forge/linux-64 \u001b[90m━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━\u001b[0m 10.4MB / ??.?MB @ 3.4MB/s 3.1s\n",
+ "conda-forge/noarch \u001b[90m╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 11.5MB / ??.?MB @ 3.7MB/s 3.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 3.2s\n",
+ "conda-forge/linux-64 \u001b[90m━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━\u001b[0m 11.4MB / ??.?MB @ 3.6MB/s 3.2s\n",
+ "conda-forge/noarch \u001b[90m━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━\u001b[0m 12.5MB / ??.?MB @ 3.9MB/s 3.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 3.3s\n",
+ "conda-forge/linux-64 \u001b[90m━━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━\u001b[0m 12.3MB / ??.?MB @ 3.7MB/s 3.3s\n",
+ "conda-forge/noarch \u001b[90m━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━\u001b[0m 13.4MB / ??.?MB @ 4.1MB/s 3.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 3.4s\n",
+ "conda-forge/linux-64 \u001b[90m━━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━\u001b[0m 12.3MB / ??.?MB @ 3.7MB/s 3.4s\n",
+ "conda-forge/noarch \u001b[90m━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━\u001b[0m 13.4MB / ??.?MB @ 4.1MB/s 3.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 3.5s\n",
+ "conda-forge/linux-64 \u001b[90m━━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━\u001b[0m 12.3MB / ??.?MB @ 3.7MB/s 3.5s\n",
+ "conda-forge/noarch \u001b[90m━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━\u001b[0m 13.4MB / ??.?MB @ 4.1MB/s 3.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 3.6s\n",
+ "conda-forge/linux-64 \u001b[33m━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 12.8MB / ??.?MB @ 3.6MB/s 3.6s\n",
+ "conda-forge/noarch \u001b[90m━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━\u001b[0m 14.1MB / ??.?MB @ 3.9MB/s 3.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 3.7s\n",
+ "conda-forge/linux-64 \u001b[33m━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 15.4MB / ??.?MB @ 4.2MB/s 3.7s\n",
+ "conda-forge/noarch \u001b[90m━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━\u001b[0m 14.4MB / ??.?MB @ 3.9MB/s 3.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 3.8s\n",
+ "conda-forge/linux-64 \u001b[33m━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 15.4MB @ 4.2MB/s 3.8s\n",
+ "conda-forge/noarch ━━━━━━━━━━━━━━━━━━━━━━ 14.5MB @ 3.9MB/s Finalizing 3.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 3.9s\n",
+ "conda-forge/linux-64 \u001b[33m━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 15.4MB / ??.?MB @ 4.2MB/s 3.9s\u001b[2K\u001b[1A\u001b[2K\u001b[0Gconda-forge/noarch @ 3.9MB/s 3.9s\n",
+ "[+] 4.0s\n",
+ "conda-forge/linux-64 \u001b[33m━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 17.5MB / ??.?MB @ 4.4MB/s 4.0s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 4.1s\n",
+ "conda-forge/linux-64 \u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 19.1MB / ??.?MB @ 4.7MB/s 4.1s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 4.2s\n",
+ "conda-forge/linux-64 \u001b[90m━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━\u001b[0m 20.1MB / ??.?MB @ 4.8MB/s 4.2s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 4.3s\n",
+ "conda-forge/linux-64 \u001b[90m━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━\u001b[0m 21.0MB / ??.?MB @ 4.9MB/s 4.3s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 4.4s\n",
+ "conda-forge/linux-64 \u001b[90m━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━\u001b[0m 22.0MB / ??.?MB @ 5.0MB/s 4.4s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 4.5s\n",
+ "conda-forge/linux-64 \u001b[90m━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━\u001b[0m 22.9MB / ??.?MB @ 5.1MB/s 4.5s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 4.6s\n",
+ "conda-forge/linux-64 \u001b[90m━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━\u001b[0m 23.8MB / ??.?MB @ 5.2MB/s 4.6s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 4.7s\n",
+ "conda-forge/linux-64 \u001b[90m━━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━\u001b[0m 24.8MB / ??.?MB @ 5.3MB/s 4.7s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 4.8s\n",
+ "conda-forge/linux-64 \u001b[33m━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 25.7MB / ??.?MB @ 5.4MB/s 4.8s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 4.9s\n",
+ "conda-forge/linux-64 \u001b[33m━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 26.7MB / ??.?MB @ 5.5MB/s 4.9s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.0s\n",
+ "conda-forge/linux-64 \u001b[33m━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 27.7MB / ??.?MB @ 5.5MB/s 5.0s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.1s\n",
+ "conda-forge/linux-64 \u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 28.6MB / ??.?MB @ 5.6MB/s 5.1s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.2s\n",
+ "conda-forge/linux-64 \u001b[90m━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━\u001b[0m 29.4MB / ??.?MB @ 5.7MB/s 5.2s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.3s\n",
+ "conda-forge/linux-64 \u001b[90m━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━\u001b[0m 30.3MB / ??.?MB @ 5.7MB/s 5.3s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.4s\n",
+ "conda-forge/linux-64 \u001b[90m━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━\u001b[0m 31.2MB / ??.?MB @ 5.8MB/s 5.4s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.5s\n",
+ "conda-forge/linux-64 \u001b[90m━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━\u001b[0m 32.3MB / ??.?MB @ 5.9MB/s 5.5s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.6s\n",
+ "conda-forge/linux-64 \u001b[90m━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━\u001b[0m 33.3MB / ??.?MB @ 6.0MB/s 5.6s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.7s\n",
+ "conda-forge/linux-64 \u001b[90m━━━━━━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━\u001b[0m 34.1MB / ??.?MB @ 6.0MB/s 5.7s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.8s\n",
+ "conda-forge/linux-64 \u001b[33m━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 34.6MB / ??.?MB @ 6.0MB/s 5.8s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.9s\n",
+ "conda-forge/linux-64 \u001b[33m━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 35.0MB / ??.?MB @ 5.9MB/s 5.9s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.0s\n",
+ "conda-forge/linux-64 \u001b[33m━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 35.0MB / ??.?MB @ 5.9MB/s 6.0s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.1s\n",
+ "conda-forge/linux-64 \u001b[33m━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 35.0MB / ??.?MB @ 5.9MB/s 6.1s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.2s\n",
+ "conda-forge/linux-64 \u001b[33m━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 35.0MB / ??.?MB @ 5.9MB/s 6.2s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.3s\n",
+ "conda-forge/linux-64 \u001b[33m━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 35.0MB / ??.?MB @ 5.9MB/s 6.3s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.4s\n",
+ "conda-forge/linux-64 ━━━━━━━━━━━━━━━━━━━━━━ 35.7MB @ 6.0MB/s Downloaded 6.4s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.5s\n",
+ "conda-forge/linux-64 ━━━━━━━━━━━━━━━━━━━━━━ 35.7MB @ 6.0MB/s Downloaded 6.5s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.6s\n",
+ "conda-forge/linux-64 ━━━━━━━━━━━━━━━━━━━━━━ 35.7MB @ 6.0MB/s Downloaded 6.6s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.7s\n",
+ "conda-forge/linux-64 ━━━━━━━━━━━━━━━━━━━━━━ 35.7MB @ 6.0MB/s Downloaded 6.7s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.8s\n",
+ "conda-forge/linux-64 ━━━━━━━━━━━━━━━━━━━━━━ 35.7MB @ 6.0MB/s Finalizing 6.8s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.9s\n",
+ "conda-forge/linux-64 ━━━━━━━━━━━━━━━━━━━━━━ 35.7MB @ 6.0MB/s Finalizing 6.9s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.0s\n",
+ "conda-forge/linux-64 ━━━━━━━━━━━━━━━━━━━━━━ 35.7MB @ 6.0MB/s Finalizing 7.0s\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.1s\n",
+ "\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.2s\n",
+ "\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.3s\n",
+ "\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.4s\n",
+ "\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.5s\n",
+ "\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.6s\n",
+ "\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.7s\n",
+ "\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.8s\n",
+ "\u001b[2K\u001b[1A\u001b[2K\u001b[0Gconda-forge/linux-64 @ 6.0MB/s 7.0s\n",
+ "\u001b[?25h\u001b[33m\u001b[1mwarning libmamba\u001b[m Extracted package cache '/opt/mamba/pkgs/ocl-icd-2.3.1-h7f98852_0' has invalid url\n",
+ "\u001b[33m\u001b[1mwarning libmamba\u001b[m Extracted package cache '/opt/mamba/pkgs/ocl-icd-system-1.0.0-1' has invalid url\n",
+ "Transaction\n",
+ "\n",
+ " Prefix: /opt/mamba/envs/dmff_omm\n",
+ "\n",
+ " Updating specs:\n",
+ "\n",
+ " - python=3.9\n",
+ " - openmm\n",
+ " - libtensorflow_cc=2.9.1\n",
+ " - swig=4.0.1\n",
+ " - setuptools=59.5.0\n",
+ "\n",
+ "\n",
+ " Package Version Build Channel Size\n",
+ "──────────────────────────────────────────────────────────────────────────────────────────\n",
+ " Install:\n",
+ "──────────────────────────────────────────────────────────────────────────────────────────\n",
+ "\n",
+ "\u001b[32m + _libgcc_mutex \u001b[00m 0.1 conda_forge conda-forge/linux-64\u001b[32m Cached\u001b[00m\n",
+ "\u001b[32m + _openmp_mutex \u001b[00m 4.5 2_gnu conda-forge/linux-64\u001b[32m Cached\u001b[00m\n",
+ "\u001b[32m + abseil-cpp \u001b[00m 20211102.0 h93e1e8c_3 conda-forge/linux-64 13kB\n",
+ "\u001b[32m + bzip2 \u001b[00m 1.0.8 hd590300_5 conda-forge/linux-64 254kB\n",
+ "\u001b[32m + c-ares \u001b[00m 1.21.0 hd590300_0 conda-forge/linux-64 122kB\n",
+ "\u001b[32m + ca-certificates \u001b[00m 2023.7.22 hbcca054_0 conda-forge/linux-64 150kB\n",
+ "\u001b[32m + cuda-nvrtc \u001b[00m 12.0.76 h59595ed_1 conda-forge/linux-64 18MB\n",
+ "\u001b[32m + cuda-version \u001b[00m 12.0 hffde075_2 conda-forge/noarch 21kB\n",
+ "\u001b[32m + giflib \u001b[00m 5.2.1 h0b41bf4_3 conda-forge/linux-64 77kB\n",
+ "\u001b[32m + grpc-cpp \u001b[00m 1.46.4 h6fc47f4_3 conda-forge/linux-64 5MB\n",
+ "\u001b[32m + icu \u001b[00m 70.1 h27087fc_0 conda-forge/linux-64\u001b[32m Cached\u001b[00m\n",
+ "\u001b[32m + jpeg \u001b[00m 9e h0b41bf4_3 conda-forge/linux-64 240kB\n",
+ "\u001b[32m + keyutils \u001b[00m 1.6.1 h166bdaf_0 conda-forge/linux-64\u001b[32m Cached\u001b[00m\n",
+ "\u001b[32m + krb5 \u001b[00m 1.20.1 hf9c8cef_0 conda-forge/linux-64 1MB\n",
+ "\u001b[32m + ld_impl_linux-64\u001b[00m 2.40 h41732ed_0 conda-forge/linux-64 705kB\n",
+ "\u001b[32m + libabseil \u001b[00m 20211102.0 cxx17_h48a1fff_3 conda-forge/linux-64 1MB\n",
+ "\u001b[32m + libblas \u001b[00m 3.9.0 19_linux64_openblas conda-forge/linux-64 15kB\n",
+ "\u001b[32m + libcblas \u001b[00m 3.9.0 19_linux64_openblas conda-forge/linux-64 14kB\n",
+ "\u001b[32m + libcufft \u001b[00m 11.0.0.21 hcb278e6_1 conda-forge/linux-64 45MB\n",
+ "\u001b[32m + libcurl \u001b[00m 7.87.0 h6312ad2_0 conda-forge/linux-64 347kB\n",
+ "\u001b[32m + libedit \u001b[00m 3.1.20191231 he28a2e2_2 conda-forge/linux-64\u001b[32m Cached\u001b[00m\n",
+ "\u001b[32m + libev \u001b[00m 4.33 h516909a_1 conda-forge/linux-64\u001b[32m Cached\u001b[00m\n",
+ "\u001b[32m + libffi \u001b[00m 3.4.2 h7f98852_5 conda-forge/linux-64\u001b[32m Cached\u001b[00m\n",
+ "\u001b[32m + libgcc-ng \u001b[00m 13.2.0 h807b86a_2 conda-forge/linux-64 771kB\n",
+ "\u001b[32m + libgfortran-ng \u001b[00m 13.2.0 h69a702a_2 conda-forge/linux-64 24kB\n",
+ "\u001b[32m + libgfortran5 \u001b[00m 13.2.0 ha4646dd_2 conda-forge/linux-64 1MB\n",
+ "\u001b[32m + libgomp \u001b[00m 13.2.0 h807b86a_2 conda-forge/linux-64 421kB\n",
+ "\u001b[32m + liblapack \u001b[00m 3.9.0 19_linux64_openblas conda-forge/linux-64 14kB\n",
+ "\u001b[32m + libnghttp2 \u001b[00m 1.51.0 hdcd2b5c_0 conda-forge/linux-64 623kB\n",
+ "\u001b[32m + libnsl \u001b[00m 2.0.1 hd590300_0 conda-forge/linux-64 33kB\n",
+ "\u001b[32m + libopenblas \u001b[00m 0.3.24 pthreads_h413a1c8_0 conda-forge/linux-64 5MB\n",
+ "\u001b[32m + libpng \u001b[00m 1.6.39 h753d276_0 conda-forge/linux-64 283kB\n",
+ "\u001b[32m + libprotobuf \u001b[00m 3.20.3 h3eb15da_0 conda-forge/linux-64 2MB\n",
+ "\u001b[32m + libsqlite \u001b[00m 3.44.0 h2797004_0 conda-forge/linux-64 846kB\n",
+ "\u001b[32m + libssh2 \u001b[00m 1.10.0 haa6b8db_3 conda-forge/linux-64\u001b[32m Cached\u001b[00m\n",
+ "\u001b[32m + libstdcxx-ng \u001b[00m 13.2.0 h7e041cc_2 conda-forge/linux-64 4MB\n",
+ "\u001b[32m + libtensorflow_cc\u001b[00m 2.9.1 cpu_h0c3d7b9_0 conda-forge/linux-64 105MB\n",
+ "\u001b[32m + libuuid \u001b[00m 2.38.1 h0b41bf4_0 conda-forge/linux-64 34kB\n",
+ "\u001b[32m + libzlib \u001b[00m 1.2.13 hd590300_5 conda-forge/linux-64 62kB\n",
+ "\u001b[32m + ncurses \u001b[00m 6.4 h59595ed_2 conda-forge/linux-64 884kB\n",
+ "\u001b[32m + numpy \u001b[00m 1.26.0 py39h474f0d3_0 conda-forge/linux-64 7MB\n",
+ "\u001b[32m + ocl-icd \u001b[00m 2.3.1 h7f98852_0 conda-forge/linux-64\u001b[32m Cached\u001b[00m\n",
+ "\u001b[32m + ocl-icd-system \u001b[00m 1.0.0 1 conda-forge/linux-64\u001b[32m Cached\u001b[00m\n",
+ "\u001b[32m + openmm \u001b[00m 8.0.0 py39h5d72b6b_3 conda-forge/linux-64 11MB\n",
+ "\u001b[32m + openssl \u001b[00m 1.1.1w hd590300_0 conda-forge/linux-64 2MB\n",
+ "\u001b[32m + pcre \u001b[00m 8.45 h9c3ff4c_0 conda-forge/linux-64 259kB\n",
+ "\u001b[32m + pip \u001b[00m 23.3.1 pyhd8ed1ab_0 conda-forge/noarch 1MB\n",
+ "\u001b[32m + python \u001b[00m 3.9.15 h47a2c10_0_cpython conda-forge/linux-64 22MB\n",
+ "\u001b[32m + python_abi \u001b[00m 3.9 4_cp39 conda-forge/linux-64 6kB\n",
+ "\u001b[32m + re2 \u001b[00m 2022.06.01 h27087fc_1 conda-forge/linux-64 196kB\n",
+ "\u001b[32m + readline \u001b[00m 8.2 h8228510_1 conda-forge/linux-64 281kB\n",
+ "\u001b[32m + setuptools \u001b[00m 59.5.0 py39hf3d152e_0 conda-forge/linux-64 1MB\n",
+ "\u001b[32m + snappy \u001b[00m 1.1.10 h9fff704_0 conda-forge/linux-64 39kB\n",
+ "\u001b[32m + sqlite \u001b[00m 3.44.0 h2c6b66d_0 conda-forge/linux-64 837kB\n",
+ "\u001b[32m + swig \u001b[00m 4.0.1 he1b5a44_0 conda-forge/linux-64 1MB\n",
+ "\u001b[32m + tk \u001b[00m 8.6.13 noxft_h4845f30_101 conda-forge/linux-64 3MB\n",
+ "\u001b[32m + tzdata \u001b[00m 2023c h71feb2d_0 conda-forge/noarch 118kB\n",
+ "\u001b[32m + wheel \u001b[00m 0.41.3 pyhd8ed1ab_0 conda-forge/noarch 58kB\n",
+ "\u001b[32m + xz \u001b[00m 5.2.6 h166bdaf_0 conda-forge/linux-64\u001b[32m Cached\u001b[00m\n",
+ "\u001b[32m + zlib \u001b[00m 1.2.13 hd590300_5 conda-forge/linux-64 93kB\n",
+ "\n",
+ " Summary:\n",
+ "\n",
+ " Install: 60 packages\n",
+ "\n",
+ " Total download: 245MB\n",
+ "\n",
+ "──────────────────────────────────────────────────────────────────────────────────────────\n",
+ "\n",
+ "\u001b[?25l\u001b[2K\u001b[0G[+] 0.0s\n",
+ "Downloading \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0.0 B 0.0s\n",
+ "Extracting (2) \u001b[90m━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━\u001b[0m 0 ocl-icd 0.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.1s\n",
+ "Downloading (5) \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0.0 B ca-certificates 0.0s\n",
+ "Extracting (2) \u001b[90m━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━\u001b[0m 0 ocl-icd 0.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.2s\n",
+ "Downloading (5) \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0.0 B ca-certificates 0.1s\n",
+ "Extracting (2) \u001b[90m━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━\u001b[0m 0 ocl-icd 0.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.3s\n",
+ "Downloading (5) \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0.0 B ca-certificates 0.2s\n",
+ "Extracting (2) \u001b[90m━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━━\u001b[0m 0 ocl-icd 0.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.4s\n",
+ "Downloading (5) \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0.0 B ca-certificates 0.3s\n",
+ "Extracting (2) \u001b[90m━━━━━━━╸\u001b[0m\u001b[33m━━━━━━━━━━━━━━━\u001b[0m 0 ocl-icd-system 0.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.5s\n",
+ "Downloading (5) \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0.0 B ld_impl_linux-64 0.4s\n",
+ "Extracting \u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 2 0.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.6s\n",
+ "Downloading (5) \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0.0 B ld_impl_linux-64 0.5s\n",
+ "Extracting \u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 2 0.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.7s\n",
+ "Downloading (5) \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 0.0 B ld_impl_linux-64 0.6s\n",
+ "Extracting \u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 2 0.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gpython_abi 6.4kB @ 9.0kB/s 0.7s\n",
+ "[+] 0.8s\n",
+ "Downloading (5) \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 102.0kB ld_impl_linux-64 0.7s\n",
+ "Extracting (1) \u001b[33m━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 2 python_abi 0.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.9s\n",
+ "Downloading (5) \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 135.9kB libgomp 0.8s\n",
+ "Extracting (1) \u001b[33m━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 2 python_abi 0.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.0s\n",
+ "Downloading (5) \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 392.2kB libgomp 0.9s\n",
+ "Extracting (1) \u001b[33m━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 2 python_abi 0.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.1s\n",
+ "Downloading (5) \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 542.9kB libgomp 1.0s\n",
+ "Extracting ╸\u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━\u001b[0m 3 0.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gca-certificates 149.5kB @ 130.2kB/s 1.1s\n",
+ "[+] 1.2s\n",
+ "Downloading (5) \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 895.2kB libgomp 1.1s\n",
+ "Extracting (1) ╸\u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━\u001b[0m 3 ca-certificates 0.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.3s\n",
+ "Downloading (5) \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 1.3MB libstdcxx-ng 1.2s\n",
+ "Extracting (1) ╸\u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━\u001b[0m 3 ca-certificates 0.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gjpeg 240.4kB @ 179.4kB/s 0.6s\n",
+ "libgomp 421.1kB @ 313.0kB/s 1.3s\n",
+ "[+] 1.4s\n",
+ "Downloading (5) \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 1.7MB libstdcxx-ng 1.3s\n",
+ "Extracting (3) ╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━\u001b[0m 3 ca-certificates 1.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gld_impl_linux-64 704.7kB @ 485.8kB/s 1.5s\n",
+ "[+] 1.5s\n",
+ "Downloading (5) \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 2.2MB libstdcxx-ng 1.4s\n",
+ "Extracting (3) ╸\u001b[33m━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━\u001b[0m 4 jpeg 1.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibzlib 61.6kB @ 39.3kB/s 0.2s\n",
+ "pcre 259.4kB @ 164.3kB/s 0.4s\n",
+ "[+] 1.6s\n",
+ "Downloading (5) \u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 2.9MB libstdcxx-ng 1.5s\n",
+ "Extracting (5) ╸\u001b[33m━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━\u001b[0m 4 jpeg 1.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gbzip2 254.2kB @ 158.3kB/s 0.3s\n",
+ "[+] 1.7s\n",
+ "Downloading (5) \u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 3.4MB swig 1.6s\n",
+ "Extracting (6) ╸\u001b[33m━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━\u001b[0m 4 jpeg 1.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.8s\n",
+ "Downloading (5) \u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 4.5MB swig 1.7s\n",
+ "Extracting (5) ━╸\u001b[33m━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━\u001b[0m 5 bzip2 1.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.9s\n",
+ "Downloading (5) \u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 6.6MB swig 1.8s\n",
+ "Extracting (3) ━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━\u001b[0m 7 bzip2 1.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gswig 1.3MB @ 661.4kB/s 0.5s\n",
+ "libstdcxx-ng 3.8MB @ 2.0MB/s 2.0s\n",
+ "[+] 2.0s\n",
+ "Downloading (5) \u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 8.6MB krb5 1.9s\n",
+ "Extracting (5) ━━╸\u001b[33m━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 7 bzip2 1.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 2.1s\n",
+ "Downloading (5) \u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 9.5MB krb5 2.0s\n",
+ "Extracting (4) ━━╸\u001b[33m━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m 8 bzip2 1.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibblas 14.6kB @ 6.9kB/s 0.2s\n",
+ "krb5 1.3MB @ 621.7kB/s 0.5s\n",
+ "liblapack 14.5kB @ 6.7kB/s 0.2s\n",
+ "[+] 2.2s\n",
+ "Downloading (5) \u001b[33m━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━\u001b[0m 10.9MB cuda-nvrtc 2.1s\n",
+ "Extracting (5) ━━━╸\u001b[33m━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 10 krb5 1.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gcuda-version 20.9kB @ 9.1kB/s 0.2s\n",
+ "[+] 2.3s\n",
+ "Downloading (5) \u001b[33m━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━\u001b[0m 12.0MB cuda-nvrtc 2.2s\n",
+ "Extracting (6) ━━━╸\u001b[33m━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 10 krb5 1.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibprotobuf 2.3MB @ 961.8kB/s 0.8s\n",
+ "[+] 2.4s\n",
+ "Downloading (5) ╸\u001b[33m━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━\u001b[0m 14.6MB cuda-nvrtc 2.3s\n",
+ "Extracting (7) ━━━╸\u001b[33m━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 10 krb5 2.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gtk 3.3MB @ 1.4MB/s 0.8s\n",
+ "pip 1.4MB @ 581.9kB/s 0.2s\n",
+ "[+] 2.5s\n",
+ "Downloading (5) ╸\u001b[33m━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━\u001b[0m 17.2MB cuda-nvrtc 2.4s\n",
+ "Extracting (9) ━━━╸\u001b[33m━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m 10 krb5 2.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibgcc-ng 771.1kB @ 302.6kB/s 0.3s\n",
+ "c-ares 121.7kB @ 47.7kB/s 0.2s\n",
+ "[+] 2.6s\n",
+ "Downloading (5) ╸\u001b[33m━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━\u001b[0m 19.2MB libabseil 2.5s\n",
+ "Extracting (9) ━━━━╸\u001b[33m━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m 12 libblas 2.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibuuid 33.6kB @ 12.8kB/s 0.2s\n",
+ "libabseil 1.1MB @ 417.8kB/s 0.2s\n",
+ "[+] 2.7s\n",
+ "Downloading (5) ━╸\u001b[33m━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━\u001b[0m 22.2MB abseil-cpp 2.6s\n",
+ "Extracting (10) ━━━━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m 13 libblas 2.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gabseil-cpp 12.8kB @ 4.7kB/s 0.2s\n",
+ "libpng 282.6kB @ 102.6kB/s 0.2s\n",
+ "[+] 2.8s\n",
+ "Downloading (5) ━╸\u001b[33m━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━\u001b[0m 24.4MB cuda-nvrtc 2.7s\n",
+ "Extracting (11) ━━━━━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 14 libblas 2.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibcurl 347.4kB @ 121.5kB/s 0.2s\n",
+ "[+] 2.9s\n",
+ "Downloading (5) ━╸\u001b[33m━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 28.3MB cuda-nvrtc 2.8s\n",
+ "Extracting (9) ━━━━━━╸\u001b[33m━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 17 abseil-cpp 2.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gtzdata 117.6kB @ 40.2kB/s 0.2s\n",
+ "[+] 3.0s\n",
+ "Downloading (5) ━╸\u001b[33m━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 30.6MB cuda-nvrtc 2.9s\n",
+ "Extracting (10) ━━━━━━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 17 abseil-cpp 2.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 3.1s\n",
+ "Downloading (5) ━╸\u001b[33m━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 31.6MB cuda-nvrtc 3.0s\n",
+ "Extracting (10) ━━━━━━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 17 abseil-cpp 2.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 3.2s\n",
+ "Downloading (5) ━╸\u001b[33m━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 32.2MB libopenblas 3.1s\n",
+ "Extracting (10) ━━━━━━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 17 abseil-cpp 2.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 3.3s\n",
+ "Downloading (5) ━╸\u001b[33m━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m 32.3MB libopenblas 3.2s\n",
+ "Extracting (9) ━━━━━━━╸\u001b[33m━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 18 c-ares 2.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gsnappy 38.9kB @ 11.6kB/s 0.4s\n",
+ "[+] 3.4s\n",
+ "Downloading (5) ━╸\u001b[33m━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 33.7MB libopenblas 3.3s\n",
+ "Extracting (7) ━━━━━━━━╸\u001b[33m━━━╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 21 abseil-cpp 3.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gsetuptools 1.0MB @ 304.8kB/s 0.7s\n",
+ "[+] 3.5s\n",
+ "Downloading (5) ━━╸\u001b[33m━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 35.1MB libopenblas 3.4s\n",
+ "Extracting (7) ━━━━━━━━╸\u001b[33m━━━━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 22 abseil-cpp 3.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 3.6s\n",
+ "Downloading (5) ━━╸\u001b[33m━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 35.3MB ncurses 3.5s\n",
+ "Extracting (6) ━━━━━━━━━╸\u001b[33m━━━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 23 abseil-cpp 3.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 3.7s\n",
+ "Downloading (5) ━━╸\u001b[33m━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 35.7MB ncurses 3.6s\n",
+ "Extracting (5) ━━━━━━━━━╸\u001b[33m━━━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 24 abseil-cpp 3.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 3.8s\n",
+ "Downloading (5) ━━╸\u001b[33m━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 37.7MB ncurses 3.7s\n",
+ "Extracting (2) ━━━━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 27 setuptools 3.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gncurses 884.4kB @ 232.3kB/s 0.5s\n",
+ "[+] 3.9s\n",
+ "Downloading (5) ━━╸\u001b[33m━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 40.0MB cuda-nvrtc 3.8s\n",
+ "Extracting (2) ━━━━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 28 setuptools 3.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 4.0s\n",
+ "Downloading (5) ━━╸\u001b[33m━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m 40.2MB cuda-nvrtc 3.9s\n",
+ "Extracting (1) ━━━━━━━━━━━━╸\u001b[33m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 29 ncurses 3.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Greadline 281.5kB @ 70.1kB/s 0.6s\n",
+ "libopenblas 5.5MB @ 1.4MB/s 1.4s\n",
+ "[+] 4.1s\n",
+ "Downloading (5) ━━╸\u001b[33m━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 43.6MB cuda-nvrtc 4.0s\n",
+ "Extracting (3) ━━━━━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 29 ncurses 3.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 4.2s\n",
+ "Downloading (5) ━━╸\u001b[33m━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 43.6MB cuda-nvrtc 4.1s\n",
+ "Extracting (3) ━━━━━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 29 ncurses 3.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibcblas 14.5kB @ 3.4kB/s 0.2s\n",
+ "libsqlite 846.0kB @ 198.9kB/s 0.5s\n",
+ "[+] 4.3s\n",
+ "Downloading (5) ━━━╸\u001b[33m━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 44.9MB libcufft 4.2s\n",
+ "Extracting (4) ━━━━━━━━━━━━╸\u001b[33m━━╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 30 libcblas 3.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 4.4s\n",
+ "Downloading (5) ━━━╸\u001b[33m━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 44.9MB libcufft 4.3s\n",
+ "Extracting (3) ━━━━━━━━━━━━╸\u001b[33m━━╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 31 libcblas 4.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 4.5s\n",
+ "Downloading (5) ━━━╸\u001b[33m━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 45.9MB libcufft 4.4s\n",
+ "Extracting (2) ━━━━━━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 32 libcblas 4.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 4.6s\n",
+ "Downloading (5) ━━━╸\u001b[33m━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 45.9MB libcufft 4.5s\n",
+ "Extracting (2) ━━━━━━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m 32 libcblas 4.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 4.7s\n",
+ "Downloading (5) ━━━╸\u001b[33m━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m 45.9MB libgfortran5 4.6s\n",
+ "Extracting ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 34 4.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 4.8s\n",
+ "Downloading (5) ━━━╸\u001b[33m━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 47.9MB libgfortran5 4.7s\n",
+ "Extracting ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 34 4.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibgfortran5 1.4MB @ 298.5kB/s 0.6s\n",
+ "cuda-nvrtc 17.8MB @ 3.6MB/s 2.7s\n",
+ "[+] 4.9s\n",
+ "Downloading (5) ━━━╸\u001b[33m━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 51.9MB libcufft 4.8s\n",
+ "Extracting (2) ━━━━━━━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 34 cuda-nvrtc 4.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.0s\n",
+ "Downloading (5) ━━━━╸\u001b[33m━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m 55.5MB libcufft 4.9s\n",
+ "Extracting (2) ━━━━━━━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 34 cuda-nvrtc 4.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.1s\n",
+ "Downloading (5) ━━━━╸\u001b[33m━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 57.2MB libcufft 5.0s\n",
+ "Extracting (2) ━━━━━━━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 34 cuda-nvrtc 4.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.2s\n",
+ "Downloading (5) ━━━━╸\u001b[33m━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 58.6MB libcufft 5.1s\n",
+ "Extracting (2) ━━━━━━━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 34 cuda-nvrtc 4.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.3s\n",
+ "Downloading (5) ━━━━╸\u001b[33m━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 59.1MB libnghttp2 5.2s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 35 cuda-nvrtc 4.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibnghttp2 622.7kB @ 116.1kB/s 0.5s\n",
+ "[+] 5.4s\n",
+ "Downloading (5) ━━━━╸\u001b[33m━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 59.2MB libcufft 5.3s\n",
+ "Extracting (2) ━━━━━━━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 35 cuda-nvrtc 4.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.5s\n",
+ "Downloading (5) ━━━━╸\u001b[33m━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 60.2MB libcufft 5.4s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━╸\u001b[33m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 36 libnghttp2 4.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.6s\n",
+ "Downloading (5) ━━━━╸\u001b[33m━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 60.7MB libcufft 5.5s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━╸\u001b[33m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 36 libnghttp2 5.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.7s\n",
+ "Downloading (5) ━━━━╸\u001b[33m━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 61.3MB libcufft 5.6s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━╸\u001b[33m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m 36 libnghttp2 5.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gopenssl 2.0MB @ 339.7kB/s 1.5s\n",
+ "wheel 57.9kB @ 10.0kB/s 0.4s\n",
+ "[+] 5.8s\n",
+ "Downloading (5) ━━━━╸\u001b[33m━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m 64.5MB openmm 5.7s\n",
+ "Extracting (2) ━━━━━━━━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━━\u001b[0m 37 openssl 5.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gsqlite 836.6kB @ 144.2kB/s 0.9s\n",
+ "[+] 5.9s\n",
+ "Downloading (5) ━━━━━╸\u001b[33m━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━\u001b[0m 67.6MB openmm 5.8s\n",
+ "Extracting (3) ━━━━━━━━━━━━━━━╸\u001b[33m━━╸\u001b[0m\u001b[90m━━━━\u001b[0m 37 openssl 5.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gzlib 92.8kB @ 15.5kB/s 0.2s\n",
+ "giflib 77.4kB @ 12.9kB/s 0.2s\n",
+ "[+] 6.0s\n",
+ "Downloading (5) ━━━━━╸\u001b[33m━━━━━━━━━━━━━━━━━\u001b[0m 68.9MB openmm 5.9s\n",
+ "Extracting (5) ━━━━━━━━━━━━━━━╸\u001b[33m━━╸\u001b[0m\u001b[90m━━━━\u001b[0m 37 openssl 5.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.1s\n",
+ "Downloading (5) ━━━━━╸\u001b[33m━━━━━━━━━━━━━━━━━\u001b[0m 70.5MB openmm 6.0s\n",
+ "Extracting (5) ━━━━━━━━━━━━━━━╸\u001b[33m━━╸\u001b[0m\u001b[90m━━━━\u001b[0m 37 openssl 5.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.2s\n",
+ "Downloading (5) ━━━━━╸\u001b[33m━━━━━━━━━━━━━━━━━\u001b[0m 70.6MB python 6.1s\n",
+ "Extracting (4) ━━━━━━━━━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━━\u001b[0m 38 sqlite 5.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibnsl 33.4kB @ 5.4kB/s 0.2s\n",
+ "[+] 6.3s\n",
+ "Downloading (5) ━━━━━╸\u001b[33m━━━━━━━━━━━━━━━━━\u001b[0m 70.8MB python 6.2s\n",
+ "Extracting (3) ━━━━━━━━━━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━\u001b[0m 40 giflib 5.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.4s\n",
+ "Downloading (5) ━━━━━╸\u001b[33m━━━━━━━━━━━━━━━━━\u001b[0m 71.7MB python 6.3s\n",
+ "Extracting (3) ━━━━━━━━━━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━\u001b[0m 40 giflib 5.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibgfortran-ng 23.7kB @ 3.7kB/s 0.2s\n",
+ "[+] 6.5s\n",
+ "Downloading (5) ━━━━━╸\u001b[33m━━━━━━━━━━━━━━━━━\u001b[0m 72.7MB python 6.4s\n",
+ "Extracting (2) ━━━━━━━━━━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━━\u001b[0m 42 libgfortran-ng 5.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.6s\n",
+ "Downloading (5) ━━━━━╸\u001b[33m━━━━━━━━━━━━━━━━━\u001b[0m 75.2MB re2 6.5s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m╸\u001b[0m\u001b[90m━━━\u001b[0m 43 libgfortran-ng 6.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gopenmm 11.1MB @ 1.7MB/s 3.8s\n",
+ "[+] 6.7s\n",
+ "Downloading (5) ━━━━━╸\u001b[33m━━━━━━━━━━━━━━━━━\u001b[0m 75.5MB re2 6.6s\n",
+ "Extracting (2) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━\u001b[0m 43 libgfortran-ng 6.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.8s\n",
+ "Downloading (5) ━━━━━╸\u001b[33m━━━━━━━━━━━━━━━━━\u001b[0m 75.5MB re2 6.7s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━\u001b[0m 44 openmm 6.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.9s\n",
+ "Downloading (5) ━━━━━━╸\u001b[33m━━━━━━━━━━━━━━━━\u001b[0m 76.2MB re2 6.8s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━\u001b[0m 44 openmm 6.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gre2 196.0kB @ 28.3kB/s 0.5s\n",
+ "[+] 7.0s\n",
+ "Downloading (5) ━━━━━━╸\u001b[33m━━━━━━━━━━━━━━━━\u001b[0m 79.0MB grpc-cpp 6.9s\n",
+ "Extracting (2) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━\u001b[0m 44 openmm 6.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.1s\n",
+ "Downloading (5) ━━━━━━╸\u001b[33m━━━━━━━━━━━━━━━━\u001b[0m 79.0MB grpc-cpp 7.0s\n",
+ "Extracting (2) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━\u001b[0m 44 openmm 6.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.2s\n",
+ "Downloading (5) ━━━━━━╸\u001b[33m━━━━━━━━━━━━━━━━\u001b[0m 80.5MB grpc-cpp 7.1s\n",
+ "Extracting (2) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━━\u001b[0m 44 re2 6.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.3s\n",
+ "Downloading (5) ━━━━━━╸\u001b[33m━━━━━━━━━━━━━━━━\u001b[0m 80.5MB grpc-cpp 7.2s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 46 6.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.4s\n",
+ "Downloading (5) ━━━━━━╸\u001b[33m━━━━━━━━━━━━━━━━\u001b[0m 83.2MB libcufft 7.3s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 46 6.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.5s\n",
+ "Downloading (5) ━━━━━━╸\u001b[33m━━━━━━━━━━━━━━━━\u001b[0m 83.2MB libcufft 7.4s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 46 6.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.6s\n",
+ "Downloading (5) ━━━━━━╸\u001b[33m━━━━━━━━━━━━━━━━\u001b[0m 83.3MB libcufft 7.5s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 46 6.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.7s\n",
+ "Downloading (5) ━━━━━━╸\u001b[33m━━━━━━━━━━━━━━━━\u001b[0m 84.0MB libcufft 7.6s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 46 6.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.8s\n",
+ "Downloading (5) ━━━━━━╸\u001b[33m━━━━━━━━━━━━━━━━\u001b[0m 84.0MB libtensorflow_cc 7.7s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 46 6.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.9s\n",
+ "Downloading (5) ━━━━━━╸\u001b[33m━━━━━━━━━━━━━━━━\u001b[0m 84.0MB libtensorflow_cc 7.8s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 46 6.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 8.0s\n",
+ "Downloading (5) ━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━━\u001b[0m 86.1MB libtensorflow_cc 7.9s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 46 6.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 8.1s\n",
+ "Downloading (5) ━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━━\u001b[0m 91.5MB libtensorflow_cc 8.0s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 46 6.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 8.2s\n",
+ "Downloading (5) ━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━━\u001b[0m 96.2MB numpy 8.1s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 46 6.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 8.3s\n",
+ "Downloading (5) ━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━━\u001b[0m 97.8MB numpy 8.2s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 46 6.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 8.4s\n",
+ "Downloading (5) ━━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━\u001b[0m 98.9MB numpy 8.3s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 46 6.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 8.5s\n",
+ "Downloading (5) ━━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━\u001b[0m 98.9MB numpy 8.4s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 46 6.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 8.6s\n",
+ "Downloading (5) ━━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━\u001b[0m 99.5MB python 8.5s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 46 6.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 8.7s\n",
+ "Downloading (5) ━━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━\u001b[0m 99.8MB python 8.6s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 46 6.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 8.8s\n",
+ "Downloading (5) ━━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━\u001b[0m 100.3MB python 8.7s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 46 6.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 8.9s\n",
+ "Downloading (5) ━━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━\u001b[0m 100.4MB python 8.8s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 46 6.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 9.0s\n",
+ "Downloading (5) ━━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━\u001b[0m 102.5MB grpc-cpp 8.9s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 46 6.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 9.1s\n",
+ "Downloading (5) ━━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━\u001b[0m 102.6MB grpc-cpp 9.0s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 46 6.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gnumpy 6.9MB @ 747.8kB/s 2.3s\n",
+ "[+] 9.2s\n",
+ "Downloading (4) ━━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━\u001b[0m 104.2MB grpc-cpp 9.1s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━\u001b[0m 46 numpy 6.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Ggrpc-cpp 5.0MB @ 540.7kB/s 2.6s\n",
+ "[+] 9.3s\n",
+ "Downloading (3) ━━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━\u001b[0m 105.4MB libcufft 9.2s\n",
+ "Extracting (2) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━\u001b[0m 46 numpy 6.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 9.4s\n",
+ "Downloading (3) ━━━━━━━━╸\u001b[33m━━━━━━━━━━━━━━\u001b[0m 107.5MB libcufft 9.3s\n",
+ "Extracting (2) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━\u001b[0m 46 numpy 6.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 9.5s\n",
+ "Downloading (3) ━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━━\u001b[0m 111.8MB libcufft 9.4s\n",
+ "Extracting (2) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━\u001b[0m 46 numpy 7.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 9.6s\n",
+ "Downloading (3) ━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━━\u001b[0m 113.2MB libcufft 9.5s\n",
+ "Extracting (2) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━\u001b[0m 46 grpc-cpp 7.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 9.7s\n",
+ "Downloading (3) ━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━━\u001b[0m 114.7MB libtensorflow_cc 9.6s\n",
+ "Extracting (2) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━\u001b[0m 46 grpc-cpp 7.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 9.8s\n",
+ "Downloading (3) ━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━━\u001b[0m 116.3MB libtensorflow_cc 9.7s\n",
+ "Extracting (2) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━╸\u001b[0m\u001b[90m━\u001b[0m 46 grpc-cpp 7.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 9.9s\n",
+ "Downloading (3) ━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━\u001b[0m 117.9MB libtensorflow_cc 9.8s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m╸\u001b[0m\u001b[90m━\u001b[0m 47 grpc-cpp 7.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 10.0s\n",
+ "Downloading (3) ━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━\u001b[0m 118.8MB libtensorflow_cc 9.9s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m╸\u001b[0m\u001b[90m━\u001b[0m 47 grpc-cpp 7.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 10.1s\n",
+ "Downloading (3) ━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━\u001b[0m 118.8MB python 10.0s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m╸\u001b[0m\u001b[90m━\u001b[0m 47 grpc-cpp 7.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 10.2s\n",
+ "Downloading (3) ━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━\u001b[0m 119.2MB python 10.1s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m╸\u001b[0m\u001b[90m━\u001b[0m 47 grpc-cpp 7.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 10.3s\n",
+ "Downloading (3) ━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━\u001b[0m 120.0MB python 10.2s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m╸\u001b[0m\u001b[90m━\u001b[0m 47 grpc-cpp 7.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 10.4s\n",
+ "Downloading (3) ━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━\u001b[0m 120.3MB python 10.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m╸\u001b[0m\u001b[90m━\u001b[0m 47 grpc-cpp 7.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 10.5s\n",
+ "Downloading (3) ━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━\u001b[0m 122.0MB libcufft 10.4s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m╸\u001b[0m\u001b[90m━\u001b[0m 47 grpc-cpp 8.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 10.6s\n",
+ "Downloading (3) ━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━\u001b[0m 123.0MB libcufft 10.5s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 48 8.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 10.7s\n",
+ "Downloading (3) ━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━\u001b[0m 123.0MB libcufft 10.6s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 48 8.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 10.8s\n",
+ "Downloading (3) ━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━\u001b[0m 123.6MB libcufft 10.7s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 48 8.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 10.9s\n",
+ "Downloading (3) ━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━━\u001b[0m 126.9MB libtensorflow_cc 10.8s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 48 8.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 11.0s\n",
+ "Downloading (3) ━━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━\u001b[0m 130.6MB libtensorflow_cc 10.9s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 48 8.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 11.1s\n",
+ "Downloading (3) ━━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━\u001b[0m 132.3MB libtensorflow_cc 11.0s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 48 8.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gpython 21.9MB @ 2.0MB/s 5.3s\n",
+ "[+] 11.2s\n",
+ "Downloading (2) ━━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━\u001b[0m 133.5MB libtensorflow_cc 11.1s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 48 python 8.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 11.3s\n",
+ "Downloading (2) ━━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━\u001b[0m 134.4MB libcufft 11.2s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 48 python 8.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 11.4s\n",
+ "Downloading (2) ━━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━\u001b[0m 135.4MB libcufft 11.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 48 python 8.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 11.5s\n",
+ "Downloading (2) ━━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━\u001b[0m 136.4MB libcufft 11.4s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 48 python 8.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 11.6s\n",
+ "Downloading (2) ━━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━\u001b[0m 137.5MB libcufft 11.5s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 48 python 8.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 11.7s\n",
+ "Downloading (2) ━━━━━━━━━━━╸\u001b[33m━━━━━━━━━━━\u001b[0m 138.6MB libtensorflow_cc 11.6s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 48 python 8.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 11.8s\n",
+ "Downloading (2) ━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━━\u001b[0m 139.8MB libtensorflow_cc 11.7s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 48 python 8.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 11.9s\n",
+ "Downloading (2) ━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━━\u001b[0m 141.2MB libtensorflow_cc 11.8s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 48 python 8.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 12.0s\n",
+ "Downloading (2) ━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━━\u001b[0m 142.6MB libtensorflow_cc 11.9s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 49 8.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 12.1s\n",
+ "Downloading (2) ━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━━\u001b[0m 144.2MB libcufft 12.0s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 49 8.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 12.2s\n",
+ "Downloading (2) ━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━━\u001b[0m 145.9MB libcufft 12.1s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 49 8.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 12.3s\n",
+ "Downloading (2) ━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━━\u001b[0m 147.8MB libcufft 12.2s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 49 8.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 12.4s\n",
+ "Downloading (2) ━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━━\u001b[0m 148.2MB libcufft 12.3s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 49 8.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 12.5s\n",
+ "Downloading (2) ━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━━\u001b[0m 148.2MB libtensorflow_cc 12.4s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 49 8.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 12.6s\n",
+ "Downloading (2) ━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━━\u001b[0m 149.2MB libtensorflow_cc 12.5s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 49 8.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 12.7s\n",
+ "Downloading (2) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━\u001b[0m 150.3MB libtensorflow_cc 12.6s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 49 8.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 12.8s\n",
+ "Downloading (2) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━\u001b[0m 150.5MB libtensorflow_cc 12.7s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 49 8.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 12.9s\n",
+ "Downloading (2) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━\u001b[0m 150.5MB libcufft 12.8s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 49 8.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 13.0s\n",
+ "Downloading (2) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━\u001b[0m 153.4MB libcufft 12.9s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 49 8.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 13.1s\n",
+ "Downloading (2) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━\u001b[0m 154.9MB libcufft 13.0s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 49 8.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 13.2s\n",
+ "Downloading (2) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━\u001b[0m 158.1MB libcufft 13.1s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 49 8.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 13.3s\n",
+ "Downloading (2) ━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━━\u001b[0m 160.0MB libtensorflow_cc 13.2s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 49 8.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 13.4s\n",
+ "Downloading (2) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 161.8MB libtensorflow_cc 13.3s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 49 8.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 13.5s\n",
+ "Downloading (2) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 161.8MB libtensorflow_cc 13.4s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 49 8.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 13.6s\n",
+ "Downloading (2) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 161.8MB libtensorflow_cc 13.5s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 49 8.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 13.7s\n",
+ "Downloading (2) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 162.0MB libcufft 13.6s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 49 8.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 13.8s\n",
+ "Downloading (2) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 162.0MB libcufft 13.7s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 49 8.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 13.9s\n",
+ "Downloading (2) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 162.0MB libcufft 13.8s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 49 8.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 14.0s\n",
+ "Downloading (2) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 162.0MB libcufft 13.9s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 49 8.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 14.1s\n",
+ "Downloading (2) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 164.0MB libtensorflow_cc 14.0s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 49 8.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 14.2s\n",
+ "Downloading (2) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 165.5MB libtensorflow_cc 14.1s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 49 8.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 14.3s\n",
+ "Downloading (2) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 167.3MB libtensorflow_cc 14.2s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 49 8.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 14.4s\n",
+ "Downloading (2) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 169.3MB libtensorflow_cc 14.3s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 49 8.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 14.5s\n",
+ "Downloading (2) ━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━━\u001b[0m 171.4MB libcufft 14.4s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 49 8.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 14.6s\n",
+ "Downloading (2) ━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━\u001b[0m 173.5MB libcufft 14.5s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 49 8.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 14.7s\n",
+ "Downloading (2) ━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━\u001b[0m 175.7MB libcufft 14.6s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 49 8.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibcufft 44.8MB @ 3.0MB/s 10.8s\n",
+ "[+] 14.8s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━\u001b[0m 178.1MB libtensorflow_cc 14.7s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 49 libcufft 8.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 14.9s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━\u001b[0m 179.3MB libtensorflow_cc 14.8s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 49 libcufft 9.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 15.0s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━━\u001b[0m 180.6MB libtensorflow_cc 14.9s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 49 libcufft 9.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 15.1s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 182.0MB libtensorflow_cc 15.0s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 49 libcufft 9.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 15.2s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 183.5MB libtensorflow_cc 15.1s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 49 libcufft 9.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 15.3s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 185.2MB libtensorflow_cc 15.2s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 49 libcufft 9.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 15.4s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 186.1MB libtensorflow_cc 15.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 49 libcufft 9.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 15.5s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 186.1MB libtensorflow_cc 15.4s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 49 libcufft 9.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 15.6s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 186.2MB libtensorflow_cc 15.5s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 49 libcufft 9.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 15.7s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 188.1MB libtensorflow_cc 15.6s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 49 libcufft 9.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 15.8s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 189.4MB libtensorflow_cc 15.7s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 9.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 15.9s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 191.0MB libtensorflow_cc 15.8s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 9.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 16.0s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━━\u001b[0m 192.7MB libtensorflow_cc 15.9s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 9.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 16.1s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 194.4MB libtensorflow_cc 16.0s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 9.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 16.2s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 196.0MB libtensorflow_cc 16.1s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 9.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 16.3s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 197.7MB libtensorflow_cc 16.2s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 9.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 16.4s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 197.7MB libtensorflow_cc 16.3s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 9.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 16.5s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 197.7MB libtensorflow_cc 16.4s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 9.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 16.6s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 199.3MB libtensorflow_cc 16.5s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 9.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 16.7s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 200.3MB libtensorflow_cc 16.6s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 9.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 16.8s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 201.7MB libtensorflow_cc 16.7s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 9.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 16.9s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━━\u001b[0m 203.1MB libtensorflow_cc 16.8s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 9.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 17.0s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 204.8MB libtensorflow_cc 16.9s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 9.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 17.1s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 206.5MB libtensorflow_cc 17.0s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 9.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 17.2s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 208.1MB libtensorflow_cc 17.1s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 9.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 17.3s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 209.8MB libtensorflow_cc 17.2s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 9.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 17.4s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 211.4MB libtensorflow_cc 17.3s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 9.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 17.5s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 212.3MB libtensorflow_cc 17.4s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 9.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 17.6s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 212.3MB libtensorflow_cc 17.5s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 9.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 17.7s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━━\u001b[0m 212.4MB libtensorflow_cc 17.6s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 9.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 17.8s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 214.1MB libtensorflow_cc 17.7s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 9.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 17.9s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 215.5MB libtensorflow_cc 17.8s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 9.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 18.0s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 217.5MB libtensorflow_cc 17.9s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 9.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 18.1s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 219.3MB libtensorflow_cc 18.0s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 9.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 18.2s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 221.4MB libtensorflow_cc 18.1s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 9.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 18.3s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 222.1MB libtensorflow_cc 18.2s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 9.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 18.4s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 222.1MB libtensorflow_cc 18.3s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 9.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 18.5s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 222.8MB libtensorflow_cc 18.4s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 9.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 18.6s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 223.9MB libtensorflow_cc 18.5s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 9.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 18.7s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━━\u001b[0m 225.4MB libtensorflow_cc 18.6s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 9.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 18.8s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 226.6MB libtensorflow_cc 18.7s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 9.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 18.9s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 228.5MB libtensorflow_cc 18.8s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 9.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 19.0s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 230.3MB libtensorflow_cc 18.9s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 9.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 19.1s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 232.1MB libtensorflow_cc 19.0s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 9.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 19.2s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 233.1MB libtensorflow_cc 19.1s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 9.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 19.3s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 233.1MB libtensorflow_cc 19.2s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 9.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 19.4s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━━\u001b[0m 234.5MB libtensorflow_cc 19.3s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 9.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 19.5s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 235.5MB libtensorflow_cc 19.4s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 9.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 19.6s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 236.7MB libtensorflow_cc 19.5s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 9.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 19.7s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 238.8MB libtensorflow_cc 19.6s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 9.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 19.8s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 240.9MB libtensorflow_cc 19.7s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 9.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 19.9s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 241.9MB libtensorflow_cc 19.8s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 9.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 20.0s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 243.0MB libtensorflow_cc 19.9s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 9.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 20.1s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 243.0MB libtensorflow_cc 20.0s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 9.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 20.2s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 243.5MB libtensorflow_cc 20.1s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 9.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 20.3s\n",
+ "Downloading (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 243.6MB libtensorflow_cc 20.2s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 9.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibtensorflow_cc 105.3MB @ 5.2MB/s 14.3s\n",
+ "[+] 20.4s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 9.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 20.5s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 10.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 20.6s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 10.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 20.7s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 10.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 20.8s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 10.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 20.9s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 10.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 21.0s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 10.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 21.1s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 10.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 21.2s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 10.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 21.3s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 10.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 21.4s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 10.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 21.5s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 11.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 21.6s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 11.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 21.7s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 11.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 21.8s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 11.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 21.9s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 11.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 22.0s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 11.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 22.1s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 11.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 22.2s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 11.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 22.3s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 11.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 22.4s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 11.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 22.5s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 12.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 22.6s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 12.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 22.7s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 12.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 22.8s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 12.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 22.9s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 12.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 23.0s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 12.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 23.1s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 12.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 23.2s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 12.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 23.3s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 12.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 23.4s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 12.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 23.5s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 13.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 23.6s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 13.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 23.7s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 13.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 23.8s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 13.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 23.9s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 13.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 24.0s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 13.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 24.1s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 13.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 24.2s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 13.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 24.3s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 13.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 24.4s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 13.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 24.5s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 14.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 24.6s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 14.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 24.7s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 14.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 24.8s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 14.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 24.9s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 14.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 25.0s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 14.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 25.1s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 14.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 25.2s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 14.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 25.3s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 14.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 25.4s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 14.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 25.5s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 15.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 25.6s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 15.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 25.7s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 15.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 25.8s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 15.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 25.9s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 15.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 26.0s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 15.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 26.1s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 15.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 26.2s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 15.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 26.3s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 15.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 26.4s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 15.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 26.5s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 16.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 26.6s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 16.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 26.7s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 16.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 26.8s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 16.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 26.9s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 16.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 27.0s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 16.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 27.1s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 16.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 27.2s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 16.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 27.3s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 16.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 27.4s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 16.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 27.5s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 17.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 27.6s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 17.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 27.7s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 17.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 27.8s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 17.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 27.9s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 17.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 28.0s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 17.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 28.1s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 17.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 28.2s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 17.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 28.3s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 17.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 28.4s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 17.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 28.5s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 18.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 28.6s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 18.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 28.7s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 18.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 28.8s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 18.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 28.9s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 18.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 29.0s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 18.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 29.1s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 18.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 29.2s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 18.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 29.3s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 18.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 29.4s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 18.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 29.5s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 19.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 29.6s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 19.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 29.7s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 19.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 29.8s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 19.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 29.9s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 19.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 30.0s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 19.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 30.1s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 19.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 30.2s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 19.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 30.3s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 19.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 30.4s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 19.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 30.5s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 20.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 30.6s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 20.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 30.7s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 20.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 30.8s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 20.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 30.9s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 20.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 31.0s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 20.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 31.1s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 20.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 31.2s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 20.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 31.3s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 20.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 31.4s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 20.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 31.5s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 21.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 31.6s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 21.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 31.7s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 21.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 31.8s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 21.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 31.9s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 21.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 32.0s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 21.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 32.1s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 21.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 32.2s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 21.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 32.3s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 21.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 32.4s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 21.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 32.5s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 22.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 32.6s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 22.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 32.7s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 22.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 32.8s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 22.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 32.9s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 22.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 33.0s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 22.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 33.1s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 22.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 33.2s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 22.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 33.3s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 22.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 33.4s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 22.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 33.5s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 23.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 33.6s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 23.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 33.7s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 23.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 33.8s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 23.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 33.9s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 23.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 34.0s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 23.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 34.1s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 23.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 34.2s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 23.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 34.3s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 23.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 34.4s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 23.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 34.5s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 24.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 34.6s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 24.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 34.7s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 24.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 34.8s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 24.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 34.9s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 24.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 35.0s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 24.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 35.1s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 24.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 35.2s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 24.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 35.3s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 24.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 35.4s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 24.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 35.5s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 25.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 35.6s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 25.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 35.7s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 25.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 35.8s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 25.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 35.9s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 25.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 36.0s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 25.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 36.1s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 25.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 36.2s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 25.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 36.3s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 25.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 36.4s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 25.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 36.5s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 26.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 36.6s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 26.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 36.7s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 26.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 36.8s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 26.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 36.9s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 26.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 37.0s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 26.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 37.1s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 26.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 37.2s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 26.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 37.3s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 26.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 37.4s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 26.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 37.5s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 27.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 37.6s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 27.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 37.7s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 27.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 37.8s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 27.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 37.9s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 27.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 38.0s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 27.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 38.1s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 27.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 38.2s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 27.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 38.3s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 27.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 38.4s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 27.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 38.5s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 28.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 38.6s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 28.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 38.7s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 28.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 38.8s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 28.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 38.9s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 28.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 39.0s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 28.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 39.1s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 28.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 39.2s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 28.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 39.3s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 28.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 39.4s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 28.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 39.5s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 29.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 39.6s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 29.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 39.7s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 29.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 39.8s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 29.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 39.9s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 29.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 40.0s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 29.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 40.1s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 29.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 40.2s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 29.7s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 40.3s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 29.8s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 40.4s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 29.9s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 40.5s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 30.0s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 40.6s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 30.1s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 40.7s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 30.2s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 40.8s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 30.3s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 40.9s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 30.4s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 41.0s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting (1) ━━━━━━━━━━━━━━━━━━━━━╸\u001b[33m━\u001b[0m 50 libtensorflow_cc 30.5s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 41.1s\n",
+ "Downloading ━━━━━━━━━━━━━━━━━━━━━━━ 245.1MB 20.3s\n",
+ "Extracting ━━━━━━━━━━━━━━━━━━━━━━━ 51 30.6s\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G\u001b[?25h\n",
+ "Downloading and Extracting Packages\n",
+ "\n",
+ "Preparing transaction: done\n",
+ "Verifying transaction: done\n",
+ "Executing transaction: done\n",
+ "\n",
+ "To activate this environment, use\n",
+ "\n",
+ " $ mamba activate dmff_omm\n",
+ "\n",
+ "To deactivate an active environment, use\n",
+ "\n",
+ " $ mamba deactivate\n",
+ "\n",
+ "Run 'mamba init' to be able to run mamba activate/deactivate\n",
+ "and start a new shell session. Or use conda to activate/deactivate.\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "!mamba create -n dmff_omm -c conda-forge -y python=3.9 openmm libtensorflow_cc=2.9.1 swig=4.0.1 setuptools=59.5.0\n",
+ "!mamba activate dmff_omm"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "23491ef9-77c7-45b8-b71e-05452c7ba798",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "--2023-11-09 06:26:46-- https://github.com/tensorflow/tensorflow/archive/refs/tags/v2.9.1.tar.gz\n",
+ "Resolving ga.dp.tech (ga.dp.tech)... 10.255.254.7, 10.255.254.37, 10.255.254.18\n",
+ "Connecting to ga.dp.tech (ga.dp.tech)|10.255.254.7|:8118... connected.\n",
+ "Proxy request sent, awaiting response... 302 Found\n",
+ "Location: https://codeload.github.com/tensorflow/tensorflow/tar.gz/refs/tags/v2.9.1 [following]\n",
+ "--2023-11-09 06:26:47-- https://codeload.github.com/tensorflow/tensorflow/tar.gz/refs/tags/v2.9.1\n",
+ "Connecting to ga.dp.tech (ga.dp.tech)|10.255.254.7|:8118... connected.\n",
+ "Proxy request sent, awaiting response... 200 OK\n",
+ "Length: unspecified [application/x-gzip]\n",
+ "Saving to: ‘v2.9.1.tar.gz’\n",
+ "\n",
+ "v2.9.1.tar.gz [ <=>] 63.57M 5.04MB/s in 14s \n",
+ "\n",
+ "2023-11-09 06:27:02 (4.66 MB/s) - ‘v2.9.1.tar.gz’ saved [66654318]\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "!wget https://github.com/tensorflow/tensorflow/archive/refs/tags/v2.9.1.tar.gz\n",
+ "!tar -xf v2.9.1.tar.gz --no-same-owner\n",
+ "!cp -r tensorflow-2.9.1/tensorflow/c /opt/mamba/envs/dmff_omm/include/tensorflow"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "33b4746d-9d70-4304-ba2f-27b481ed2407",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Cloning into 'cppflow'...\n",
+ "remote: Enumerating objects: 1011, done.\u001b[K\n",
+ "remote: Counting objects: 100% (299/299), done.\u001b[K\n",
+ "remote: Compressing objects: 100% (138/138), done.\u001b[K\n",
+ "remote: Total 1011 (delta 182), reused 237 (delta 157), pack-reused 712\u001b[K\n",
+ "Receiving objects: 100% (1011/1011), 8.70 MiB | 5.32 MiB/s, done.\n",
+ "Resolving deltas: 100% (483/483), done.\n"
+ ]
+ }
+ ],
+ "source": [
+ "!git clone https://github.com/serizba/cppflow.git\n",
+ "!cd /data/cppflow && git apply /data/DMFF/backend/openmm_dmff_plugin/tests/cppflow_empty_constructor.patch\n",
+ "!mkdir /opt/mamba/envs/dmff_omm/include/cppflow\n",
+ "!cd /data/cppflow && cp -r include/cppflow /opt/mamba/envs/dmff_omm/include/"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "d07d498d-536d-4ed8-8fa8-f012fd8d6281",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "-- The C compiler identification is GNU 9.4.0\n",
+ "-- The CXX compiler identification is GNU 9.4.0\n",
+ "-- Check for working C compiler: /usr/bin/cc\n",
+ "-- Check for working C compiler: /usr/bin/cc -- works\n",
+ "-- Detecting C compiler ABI info\n",
+ "-- Detecting C compiler ABI info - done\n",
+ "-- Detecting C compile features\n",
+ "-- Detecting C compile features - done\n",
+ "-- Check for working CXX compiler: /usr/bin/c++\n",
+ "-- Check for working CXX compiler: /usr/bin/c++ -- works\n",
+ "-- Detecting CXX compiler ABI info\n",
+ "-- Detecting CXX compiler ABI info - done\n",
+ "-- Detecting CXX compile features\n",
+ "-- Detecting CXX compile features - done\n",
+ "-- Looking for pthread.h\n",
+ "-- Looking for pthread.h - found\n",
+ "-- Performing Test CMAKE_HAVE_LIBC_PTHREAD\n",
+ "-- Performing Test CMAKE_HAVE_LIBC_PTHREAD - Failed\n",
+ "-- Looking for pthread_create in pthreads\n",
+ "-- Looking for pthread_create in pthreads - not found\n",
+ "-- Looking for pthread_create in pthread\n",
+ "-- Looking for pthread_create in pthread - found\n",
+ "-- Found Threads: TRUE \n",
+ "-- CUDA found, building CUDA implementation\n",
+ "-- Python and SWIG found, building Python wrappers\n",
+ "-- Configuring done\n",
+ "-- Generating done\n",
+ "-- Build files have been written to: /data/DMFF/backend/openmm_dmff_plugin/build\n",
+ "\u001b[35m\u001b[1mScanning dependencies of target OpenMMDMFF\u001b[0m\n",
+ "[ 5%] \u001b[32mBuilding CXX object CMakeFiles/OpenMMDMFF.dir/openmmapi/src/DMFFForce.cpp.o\u001b[0m\n",
+ "[ 10%] \u001b[32mBuilding CXX object CMakeFiles/OpenMMDMFF.dir/openmmapi/src/DMFFForceImpl.cpp.o\u001b[0m\n",
+ "[ 15%] \u001b[32mBuilding CXX object CMakeFiles/OpenMMDMFF.dir/serialization/src/DMFFForceProxy.cpp.o\u001b[0m\n",
+ "[ 21%] \u001b[32mBuilding CXX object CMakeFiles/OpenMMDMFF.dir/serialization/src/DMFFSerializationProxyRegistration.cpp.o\u001b[0m\n",
+ "[ 26%] \u001b[32m\u001b[1mLinking CXX shared library libOpenMMDMFF.so\u001b[0m\n",
+ "[ 26%] Built target OpenMMDMFF\n",
+ "\u001b[35m\u001b[1mScanning dependencies of target TestSerializeDMFFForce\u001b[0m\n",
+ "[ 31%] \u001b[32mBuilding CXX object serialization/tests/CMakeFiles/TestSerializeDMFFForce.dir/TestSerializeDMFFForce.cpp.o\u001b[0m\n",
+ "[ 36%] \u001b[32m\u001b[1mLinking CXX executable ../../TestSerializeDMFFForce\u001b[0m\n",
+ "[ 36%] Built target TestSerializeDMFFForce\n",
+ "\u001b[35m\u001b[1mScanning dependencies of target OpenMMDMFFReference\u001b[0m\n",
+ "[ 42%] \u001b[32mBuilding CXX object platforms/reference/CMakeFiles/OpenMMDMFFReference.dir/src/ReferenceDMFFKernelFactory.cpp.o\u001b[0m\n",
+ "[ 47%] \u001b[32mBuilding CXX object platforms/reference/CMakeFiles/OpenMMDMFFReference.dir/src/ReferenceDMFFKernels.cpp.o\u001b[0m\n",
+ "[ 52%] \u001b[32m\u001b[1mLinking CXX shared library ../../libOpenMMDMFFReference.so\u001b[0m\n",
+ "[ 52%] Built target OpenMMDMFFReference\n",
+ "\u001b[35m\u001b[1mScanning dependencies of target TestDMFFPlugin4Reference\u001b[0m\n",
+ "[ 57%] \u001b[32mBuilding CXX object platforms/reference/tests/CMakeFiles/TestDMFFPlugin4Reference.dir/TestDMFFPlugin4Reference.cpp.o\u001b[0m\n",
+ "[ 63%] \u001b[32m\u001b[1mLinking CXX executable ../../../TestDMFFPlugin4Reference\u001b[0m\n",
+ "[ 63%] Built target TestDMFFPlugin4Reference\n",
+ "[ 68%] \u001b[34m\u001b[1mGenerating src/CudaDMFFKernelSources.cpp, src/CudaDMFFKernelSources.h\u001b[0m\n",
+ "\u001b[35m\u001b[1mScanning dependencies of target OpenMMDMFFCUDA\u001b[0m\n",
+ "[ 73%] \u001b[32mBuilding CXX object platforms/cuda/CMakeFiles/OpenMMDMFFCUDA.dir/src/CudaDMFFKernelFactory.cpp.o\u001b[0m\n",
+ "[ 78%] \u001b[32mBuilding CXX object platforms/cuda/CMakeFiles/OpenMMDMFFCUDA.dir/src/CudaDMFFKernels.cpp.o\u001b[0m\n",
+ "[ 84%] \u001b[32mBuilding CXX object platforms/cuda/CMakeFiles/OpenMMDMFFCUDA.dir/src/CudaDMFFKernelSources.cpp.o\u001b[0m\n",
+ "[ 89%] \u001b[32m\u001b[1mLinking CXX shared library ../../libOpenMMDMFFCUDA.so\u001b[0m\n",
+ "[ 89%] Built target OpenMMDMFFCUDA\n",
+ "\u001b[35m\u001b[1mScanning dependencies of target TestDMFFPlugin4CUDA\u001b[0m\n",
+ "[ 94%] \u001b[32mBuilding CXX object platforms/cuda/tests/CMakeFiles/TestDMFFPlugin4CUDA.dir/TestDMFFPlugin4CUDA.cpp.o\u001b[0m\n",
+ "[100%] \u001b[32m\u001b[1mLinking CXX executable ../../../TestDMFFPlugin4CUDA\u001b[0m\n",
+ "[100%] Built target TestDMFFPlugin4CUDA\n",
+ "[ 26%] Built target OpenMMDMFF\n",
+ "[ 36%] Built target TestSerializeDMFFForce\n",
+ "[ 52%] Built target OpenMMDMFFReference\n",
+ "[ 63%] Built target TestDMFFPlugin4Reference\n",
+ "[ 89%] Built target OpenMMDMFFCUDA\n",
+ "[100%] Built target TestDMFFPlugin4CUDA\n",
+ "\u001b[36mInstall the project...\u001b[0m\n",
+ "-- Install configuration: \"\"\n",
+ "-- Installing: /opt/mamba/envs/dmff_omm/include/DMFFForce.h\n",
+ "-- Installing: /opt/mamba/envs/dmff_omm/include/DMFFKernels.h\n",
+ "-- Installing: /opt/mamba/envs/dmff_omm/include/internal/DMFFForceImpl.h\n",
+ "-- Installing: /opt/mamba/envs/dmff_omm/include/internal/windowsExportDMFF.h\n",
+ "-- Installing: /opt/mamba/envs/dmff_omm/lib/libOpenMMDMFF.so\n",
+ "-- Set runtime path of \"/opt/mamba/envs/dmff_omm/lib/libOpenMMDMFF.so\" to \"\"\n",
+ "-- Installing: /opt/mamba/envs/dmff_omm/lib/plugins/libOpenMMDMFFReference.so\n",
+ "-- Set runtime path of \"/opt/mamba/envs/dmff_omm/lib/plugins/libOpenMMDMFFReference.so\" to \"\"\n",
+ "-- Installing: /opt/mamba/envs/dmff_omm/lib/plugins/libOpenMMDMFFCUDA.so\n",
+ "-- Set runtime path of \"/opt/mamba/envs/dmff_omm/lib/plugins/libOpenMMDMFFCUDA.so\" to \"\"\n",
+ "\u001b[35m\u001b[1mScanning dependencies of target PythonInstall\u001b[0m\n",
+ "[100%] \u001b[34m\u001b[1mGenerating OpenMMDMFFPluginWrapper.cpp\u001b[0m\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:724: Warning 314: 'None' is a python keyword, renaming to '_None'\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:470: Warning 453: Can't apply (OpenMM::Context &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:493: Warning 453: Can't apply (OpenMM::Context &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:570: Warning 453: Can't apply (std::vector< double,std::allocator< double > > &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:587: Warning 453: Can't apply (std::string &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:588: Warning 453: Can't apply (std::vector< double,std::allocator< double > > &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:598: Warning 453: Can't apply (std::string &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:599: Warning 453: Can't apply (std::string &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:605: Warning 453: Can't apply (std::set< int > &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:606: Warning 453: Can't apply (std::set< int > &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:611: Warning 453: Can't apply (OpenMM::Context &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:633: Warning 453: Can't apply (std::vector< double,std::allocator< double > > &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:666: Warning 453: Can't apply (std::vector< double,std::allocator< double > > &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:671: Warning 453: Can't apply (OpenMM::Context &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:707: Warning 453: Can't apply (OpenMM::Context &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:777: Warning 453: Can't apply (std::vector< int,std::allocator< int > > &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:794: Warning 453: Can't apply (OpenMM::Context &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:808: Warning 453: Can't apply (std::vector< double,std::allocator< double > > &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:834: Warning 453: Can't apply (OpenMM::Context &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:866: Warning 453: Can't apply (std::vector< double,std::allocator< double > > &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:874: Warning 453: Can't apply (OpenMM::Context &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:905: Warning 453: Can't apply (std::vector< double,std::allocator< double > > &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:912: Warning 453: Can't apply (OpenMM::Context &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:987: Warning 453: Can't apply (OpenMM::Context &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:1104: Warning 453: Can't apply (std::string &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:1117: Warning 453: Can't apply (std::string &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:1135: Warning 453: Can't apply (OpenMM::Context &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:1183: Warning 453: Can't apply (ContextImpl &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:1184: Warning 453: Can't apply (ContextImpl &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:1223: Warning 453: Can't apply (OpenMM::Context &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:1224: Warning 453: Can't apply (std::vector< double,std::allocator< double > > &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:1228: Warning 453: Can't apply (OpenMM::Context &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:1231: Warning 453: Can't apply (OpenMM::Context &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:1255: Warning 453: Can't apply (Vec3 &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:1256: Warning 453: Can't apply (Vec3 &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:1257: Warning 453: Can't apply (Vec3 &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:1304: Warning 453: Can't apply (OpenMM::Context &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:1317: Warning 453: Can't apply (std::vector< double,std::allocator< double > > &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:1354: Warning 453: Can't apply (std::vector< int,std::allocator< int > > &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:1355: Warning 453: Can't apply (std::vector< double,std::allocator< double > > &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:1361: Warning 453: Can't apply (std::vector< int,std::allocator< int > > &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:1362: Warning 453: Can't apply (std::vector< double,std::allocator< double > > &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:1371: Warning 453: Can't apply (OpenMM::Context &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:1438: Warning 453: Can't apply (OpenMM::Context &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:1501: Warning 453: Can't apply (ContextImpl &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:1583: Warning 453: Can't apply (OpenMM::Context &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:1611: Warning 453: Can't apply (std::vector< double,std::allocator< double > > &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:1721: Warning 453: Can't apply (std::vector< double,std::allocator< double > > &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:1722: Warning 453: Can't apply (std::vector< double,std::allocator< double > > &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:1775: Warning 453: Can't apply (std::vector< Vec3,std::allocator< Vec3 > > &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:1778: Warning 453: Can't apply (std::vector< Vec3,std::allocator< Vec3 > > &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:1781: Warning 453: Can't apply (OpenMM::Context &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:1827: Warning 453: Can't apply (std::vector< double,std::allocator< double > > &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:1832: Warning 453: Can't apply (std::string &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:1833: Warning 453: Can't apply (std::string &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:1841: Warning 453: Can't apply (std::string &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:1859: Warning 453: Can't apply (std::string &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:1860: Warning 453: Can't apply (std::vector< double,std::allocator< double > > &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:1869: Warning 453: Can't apply (OpenMM::Context &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:1896: Warning 453: Can't apply (std::vector< double,std::allocator< double > > &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:1983: Warning 453: Can't apply (Vec3 &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:1984: Warning 453: Can't apply (Vec3 &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:1985: Warning 453: Can't apply (Vec3 &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:2017: Warning 453: Can't apply (std::vector< double,std::allocator< double > > &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:2023: Warning 453: Can't apply (OpenMM::Context &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:2054: Warning 453: Can't apply (OpenMM::Context &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:2089: Warning 453: Can't apply (std::vector< Vec3,std::allocator< Vec3 > > &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:2092: Warning 453: Can't apply (std::vector< Vec3,std::allocator< Vec3 > > &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:2107: Warning 453: Can't apply (std::string &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:2108: Warning 453: Can't apply (std::string &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:2178: Warning 453: Can't apply (OpenMM::Context &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:2238: Warning 453: Can't apply (std::vector< int,std::allocator< int > > &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:2253: Warning 453: Can't apply (std::vector< double,std::allocator< double > > &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:2254: Warning 453: Can't apply (std::vector< double,std::allocator< double > > &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:2275: Warning 453: Can't apply (std::vector< int,std::allocator< int > > &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:2278: Warning 453: Can't apply (std::vector< std::vector< int,std::allocator< int > >,std::allocator< std::vector< int,std::allocator< int > > > > &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:2289: Warning 453: Can't apply (std::vector< Vec3,std::allocator< Vec3 > > &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:2292: Warning 453: Can't apply (std::vector< Vec3,std::allocator< Vec3 > > &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:2295: Warning 453: Can't apply (std::vector< Vec3,std::allocator< Vec3 > > &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:2298: Warning 453: Can't apply (std::vector< double,std::allocator< double > > &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:2301: Warning 453: Can't apply (OpenMM::Context &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:2302: Warning 453: Can't apply (std::vector< double,std::allocator< double > > &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:2306: Warning 453: Can't apply (OpenMM::Context &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:2337: Warning 453: Can't apply (std::vector< int,std::allocator< int > > &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:2338: Warning 453: Can't apply (std::vector< double,std::allocator< double > > &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:2348: Warning 453: Can't apply (std::string &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:2349: Warning 453: Can't apply (std::vector< double,std::allocator< double > > &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:2358: Warning 453: Can't apply (OpenMM::Context &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:2374: Warning 453: Can't apply (OpenMM::SerializationNode &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:2384: Warning 453: Can't apply (std::vector< double,std::allocator< double > > &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:2436: Warning 453: Can't apply (std::vector< double,std::allocator< double > > &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:2447: Warning 453: Can't apply (std::vector< double,std::allocator< double > > &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:2466: Warning 453: Can't apply (std::string &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:2467: Warning 453: Can't apply (std::vector< double,std::allocator< double > > &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:2476: Warning 453: Can't apply (OpenMM::Context &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:2520: Warning 453: Can't apply (std::vector< double,std::allocator< double > > &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:2534: Warning 453: Can't apply (std::set< int > &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:2542: Warning 453: Can't apply (OpenMM::Context &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:2600: Warning 453: Can't apply (std::vector< double,std::allocator< double > > &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:2603: Warning 453: Can't apply (std::vector< double,std::allocator< double > > &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:2606: Warning 453: Can't apply (std::vector< double,std::allocator< double > > &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:2679: Warning 453: Can't apply (OpenMM::Context &OUTPUT). No typemaps are defined.\n",
+ "/opt/mamba/envs/dmff_omm/include/swig/OpenMMSwigHeaders.i:2723: Warning 453: Can't apply (std::vector< double,std::allocator< double > > &OUTPUT). No typemaps are defined.\n",
+ "running build\n",
+ "running build_py\n",
+ "creating build\n",
+ "creating build/lib.linux-x86_64-3.9\n",
+ "creating build/lib.linux-x86_64-3.9/OpenMMDMFFPlugin\n",
+ "copying OpenMMDMFFPlugin/__init__.py -> build/lib.linux-x86_64-3.9/OpenMMDMFFPlugin\n",
+ "copying OpenMMDMFFPlugin/tools.py -> build/lib.linux-x86_64-3.9/OpenMMDMFFPlugin\n",
+ "package init file 'OpenMMDMFFPlugin/tests/__init__.py' not found (or not a regular file)\n",
+ "creating build/lib.linux-x86_64-3.9/OpenMMDMFFPlugin/tests\n",
+ "copying OpenMMDMFFPlugin/tests/test_dmff_plugin_nve.py -> build/lib.linux-x86_64-3.9/OpenMMDMFFPlugin/tests\n",
+ "package init file 'OpenMMDMFFPlugin/tests/__init__.py' not found (or not a regular file)\n",
+ "running build_ext\n",
+ "building 'OpenMMDMFFPlugin._OpenMMDMFFPlugin' extension\n",
+ "creating build/temp.linux-x86_64-3.9\n",
+ "/usr/bin/cc -Wno-unused-result -Wsign-compare -DNDEBUG -fwrapv -O2 -Wall -fPIC -O2 -isystem /opt/mamba/envs/dmff_omm/include -fPIC -O2 -isystem /opt/mamba/envs/dmff_omm/include -fPIC -I/opt/mamba/envs/dmff_omm/include -I/opt/mamba/envs/dmff_omm/include -I/opt/mamba/envs/dmff_omm/include -I/data/DMFF/backend/openmm_dmff_plugin/openmmapi/include -I/opt/mamba/envs/dmff_omm/include/python3.9 -c OpenMMDMFFPluginWrapper.cpp -o build/temp.linux-x86_64-3.9/OpenMMDMFFPluginWrapper.o -std=c++17 -fPIC\n",
+ "/usr/bin/c++ -shared -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/mamba/envs/dmff_omm/lib -Wl,-rpath-link,/opt/mamba/envs/dmff_omm/lib -L/opt/mamba/envs/dmff_omm/lib -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/mamba/envs/dmff_omm/lib -Wl,-rpath-link,/opt/mamba/envs/dmff_omm/lib -L/opt/mamba/envs/dmff_omm/lib build/temp.linux-x86_64-3.9/OpenMMDMFFPluginWrapper.o -L/opt/mamba/envs/dmff_omm/lib -L/opt/mamba/envs/dmff_omm/lib -L/opt/mamba/envs/dmff_omm/lib -L/data/DMFF/backend/openmm_dmff_plugin/build -lOpenMM -lOpenMMDMFF -o build/lib.linux-x86_64-3.9/OpenMMDMFFPlugin/_OpenMMDMFFPlugin.cpython-39-x86_64-linux-gnu.so\n",
+ "running install\n",
+ "running build\n",
+ "running build_py\n",
+ "copying OpenMMDMFFPlugin/OpenMMDMFFPlugin.py -> build/lib.linux-x86_64-3.9/OpenMMDMFFPlugin\n",
+ "package init file 'OpenMMDMFFPlugin/tests/__init__.py' not found (or not a regular file)\n",
+ "package init file 'OpenMMDMFFPlugin/tests/__init__.py' not found (or not a regular file)\n",
+ "running build_ext\n",
+ "running install_lib\n",
+ "creating /opt/mamba/envs/dmff_omm/lib/python3.9/site-packages/OpenMMDMFFPlugin\n",
+ "copying build/lib.linux-x86_64-3.9/OpenMMDMFFPlugin/OpenMMDMFFPlugin.py -> /opt/mamba/envs/dmff_omm/lib/python3.9/site-packages/OpenMMDMFFPlugin\n",
+ "copying build/lib.linux-x86_64-3.9/OpenMMDMFFPlugin/_OpenMMDMFFPlugin.cpython-39-x86_64-linux-gnu.so -> /opt/mamba/envs/dmff_omm/lib/python3.9/site-packages/OpenMMDMFFPlugin\n",
+ "copying build/lib.linux-x86_64-3.9/OpenMMDMFFPlugin/__init__.py -> /opt/mamba/envs/dmff_omm/lib/python3.9/site-packages/OpenMMDMFFPlugin\n",
+ "creating /opt/mamba/envs/dmff_omm/lib/python3.9/site-packages/OpenMMDMFFPlugin/tests\n",
+ "copying build/lib.linux-x86_64-3.9/OpenMMDMFFPlugin/tests/test_dmff_plugin_nve.py -> /opt/mamba/envs/dmff_omm/lib/python3.9/site-packages/OpenMMDMFFPlugin/tests\n",
+ "copying build/lib.linux-x86_64-3.9/OpenMMDMFFPlugin/tools.py -> /opt/mamba/envs/dmff_omm/lib/python3.9/site-packages/OpenMMDMFFPlugin\n",
+ "byte-compiling /opt/mamba/envs/dmff_omm/lib/python3.9/site-packages/OpenMMDMFFPlugin/OpenMMDMFFPlugin.py to OpenMMDMFFPlugin.cpython-39.pyc\n",
+ "byte-compiling /opt/mamba/envs/dmff_omm/lib/python3.9/site-packages/OpenMMDMFFPlugin/__init__.py to __init__.cpython-39.pyc\n",
+ "byte-compiling /opt/mamba/envs/dmff_omm/lib/python3.9/site-packages/OpenMMDMFFPlugin/tests/test_dmff_plugin_nve.py to test_dmff_plugin_nve.cpython-39.pyc\n",
+ "byte-compiling /opt/mamba/envs/dmff_omm/lib/python3.9/site-packages/OpenMMDMFFPlugin/tools.py to tools.cpython-39.pyc\n",
+ "running install_egg_info\n",
+ "Writing /opt/mamba/envs/dmff_omm/lib/python3.9/site-packages/OpenMMDMFFPlugin-cdb14ee-py3.9.egg-info\n",
+ "[100%] Built target PythonInstall\n"
+ ]
+ }
+ ],
+ "source": [
+ "!cd /data/DMFF/backend/openmm_dmff_plugin/ && mkdir build && cd build && cmake .. -DOPENMM_DIR=/opt/mamba/envs/dmff_omm -DCPPFLOW_DIR=/opt/mamba/envs/dmff_omm -DTENSORFLOW_DIR=/opt/mamba/envs/dmff_omm -DSWIG_EXECUTABLE=/opt/mamba/envs/dmff_omm/bin/swig -DPYTHON_EXECUTABLE=/opt/mamba/envs/dmff_omm/bin/python && make && make install && make PythonInstall"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "9b9bc4df-09cd-4bcc-ae19-59b684cd84ed",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2023-11-09 06:35:31.878151: I tensorflow/cc/saved_model/reader.cc:43] Reading SavedModel from: ./openmm_dmff_plugin/python/OpenMMDMFFPlugin/data/admp_water_dimer_aux\n",
+ "2023-11-09 06:35:32.138075: I tensorflow/cc/saved_model/reader.cc:81] Reading meta graph with tags { serve }\n",
+ "2023-11-09 06:35:32.138139: I tensorflow/cc/saved_model/reader.cc:122] Reading SavedModel debug info (if present) from: ./openmm_dmff_plugin/python/OpenMMDMFFPlugin/data/admp_water_dimer_aux\n",
+ "2023-11-09 06:35:32.139209: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE4.1 SSE4.2 AVX AVX2 AVX512F FMA\n",
+ "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
+ "2023-11-09 06:35:32.854369: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:354] MLIR V1 optimization pass is not enabled\n",
+ "2023-11-09 06:35:32.946199: I tensorflow/cc/saved_model/loader.cc:228] Restoring SavedModel bundle.\n",
+ "2023-11-09 06:35:36.536096: I tensorflow/cc/saved_model/loader.cc:212] Running initialization op on SavedModel bundle at path: ./openmm_dmff_plugin/python/OpenMMDMFFPlugin/data/admp_water_dimer_aux\n",
+ "2023-11-09 06:35:40.039418: I tensorflow/cc/saved_model/loader.cc:301] SavedModel load for tags { serve }; Status: success: OK. Took 8161272 microseconds.\n",
+ "Running dynamics\n",
+ "2023-11-09 06:35:59.797664: I tensorflow/compiler/xla/service/service.cc:170] XLA service 0x55b197670fa0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n",
+ "2023-11-09 06:35:59.797707: I tensorflow/compiler/xla/service/service.cc:178] StreamExecutor device (0): Host, Default Version\n",
+ "2023-11-09 06:35:59.809487: I tensorflow/compiler/jit/xla_compilation_cache.cc:478] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n",
+ "2023-11-09 06:35:59.984554: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:263] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
+ "Running on Reference platform, time cost: 40.6564 s\n",
+ "Total energy std: 0.0051 kJ/mol\n",
+ "Mean total energy: -0.0556 kJ/mol\n"
+ ]
+ }
+ ],
+ "source": [
+ "!cd /data/DMFF/backend && /opt/mamba/envs/dmff_omm/bin/python -m OpenMMDMFFPlugin.tests.test_dmff_plugin_nve -n 100 --pdb ../examples/water_fullpol/water_dimer.pdb --model ./openmm_dmff_plugin/python/OpenMMDMFFPlugin/data/admp_water_dimer_aux --has_aux True"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/user_guide/test.ipynb b/docs/user_guide/test.ipynb
new file mode 100644
index 000000000..d304f8fa8
--- /dev/null
+++ b/docs/user_guide/test.ipynb
@@ -0,0 +1 @@
+{"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.6"}},"nbformat_minor":5,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Getting started with DMFF: A comprehensive beginner's tutorial.\n**In the simulation of molecular systems, the underlying force field (FF) model plays an extremely important role, determining the reliability of the simulation. However, the quality of the state-of-the-art molecular force fields is still unsatisfactory in many cases, and the FF parameterization process largely relies on human experience, which is not scalable. To address this issue, we introduce DMFF (Differentiable Molecular Force Field), an open-source molecular FF development platform based on automatic differentiation technique. Using DMFF, both energies/forces and thermodynamic quantities such as ensemble averages and free energies can be evaluated in a differentiable way, realizing an automatic, yet highly\nflexible force field optimization workflow**\n\nFor more details, please refer to this link: https://mp.weixin.qq.com/s/eVXTr1eU1-dGbC5UFybr0g\n\n**Note**: JAX, DMFF, and other related tools are still undergoing rapid development and iteration. This notebook has been successfully executed using DMFF 0.2.0.\n> **Choose dmff:0.2.0-notebook and you can directly run this notebook on Bohrium** \n\nThis notebook is primarily organized by Wei Feng.","metadata":{},"id":"5a7fe9e6-825a-4963-a55e-18a7366fb8b0"},{"cell_type":"markdown","source":"# Before You Run","metadata":{"jp-MarkdownHeadingCollapsed":true},"id":"410936e3-f066-44e7-8ee4-930417d62809"},{"cell_type":"markdown","source":"Molecular systems are a highly important category of systems, encompassing various organic molecules such as biomolecules, drug molecules, various coatings used in industries, porous materials like COFs, polymers and small molecule electrolytes in batteries, and more. Problems of interest in industries, such as structure prediction of biomolecules, drug screening, screening and design of materials, heavily rely on molecular dynamics (MD) simulations of molecular systems. Although the applications of MD have extended beyond molecular systems, simulating molecular systems remains one of the core applications of MD.\n\nMD simulations usually rely on a potential energy surface (PES), which describes the energies and the forces of the simulated atoms. For the sake of computational efficiency, the PES is typically approximated using a classical model, namely molecular force field, instead of being computed ab initioly on-the-fly. Therefore, the quality of the underlying force field limits the accuracy and the predictive power of the simulation. \n\nSo it's obvious that the core of simulation is fitting microscale interactions, or potential functions, to accurately predict macroscopic properties. In classical molecular dynamics, the potential function is expressed mathematically in terms of a molecular force field.\n\n## What Is Molecular Force Field\n\nAs we mentioned above, one of the most important aspects in simulating molecular systems is characterizing the system's potential energy function and describing the interactions within the system. In Classical Molecular Dynamics (CMD) simulations, the potential energy function follows a fixed mathematical form, and the force field provides the mathematical functions for intermolecular and intramolecular interaction potentials. The traditional force fields commonly used in the industry, such as OPLS and GAFF, generally share a similar form:\n\n\n\nThe total energy can be divided into bonded and nonbonded components. The bonded component naturally depends on the intramolecular coordinates (bond lengths, bond angles, dihedral angles), while the nonbonded component (further decomposable into van der Waals interactions and electrostatic interactions) naturally depends on the interatomic distances. This form is almost universally used as the standard form in the entire industry. However, the molecular force fields currently employed in industrial applications face several challenges:\n- Lack of portability and predictive capability: When studying new systems, it is often uncertain which force field will yield better results until actual simulations are performed. Apart from relying on \"experience,\" it can be challenging to determine the appropriate force field selection criteria.\n- Parameter's non-uniqueness and inconsistency: It is common to encounter multiple sets of completely different parameters that yield similar macroscopic predictions. As a result, it is not possible to solely rely on macroscopic data to determine which set of parameters is more reasonable at the microscale. Moreover, similar molecular systems often have entirely different force field parameters, making it challenging to combine force fields developed by different research groups.\n\nOver the past few decades, the development of empirical force fields and the improvement of computational accuracy have been both an age-old and emerging topic. The quest to accurately and efficiently describe atomic interactions and establish a technical roadmap for developing a molecular force field applicable to the majority of systems has been the focus of researchers in the field of molecular force fields and molecular dynamics.\n\nThe optimization of parameters in force fields has long relied on manual intervention and \"empirical\" parameter tuning methods. This reliance raises concerns about the reliability and efficiency of empirical force field fitting. However, with the advent of the artificial intelligence era, one underlying technology, automatic differentiation, offers a new solution. This technology has paved the way for the development of Differentiable Molecular Force Field (DMFF), which holds the promise of becoming a powerful tool for molecular force field developers.","metadata":{},"id":"6dd2486f-c47b-4d46-a42c-ffe2e94827c3"},{"cell_type":"markdown","source":"## Automatic DIfferentiation & DMFF\nAutomatic differentiation is a high-precision and versatile method for computing derivatives in computer programs. It follows the mathematical chain rule to compute derivatives of composite functions. By tracing the derivative chains of every data point based on the computation graph, it can calculate the differentiation of the output with respect to the input variables.\n\nAutomatic differentiation plays a crucial role in optimizing neural network models. During model training, it is necessary to compute the gradients of the output with respect to the input variables through backpropagation and utilize gradient descent to optimize the model parameters. Automatic differentiation frameworks excel at achieving this task. With their efficient optimization capabilities for high-dimensional parameters, automatic differentiation techniques can be applied not only to neural network models but also to any framework that follows the \"input model parameters → apply model computation → obtain computed results\" structure. Molecular dynamics simulations precisely follow this workflow.\n\nTherefore, leveraging automatic differentiation techniques and utilizing experimental or first-principles computed data as references, it is possible to optimize force field parameters by calculating the differentiation of the output results with respect to the input parameters through backpropagation. This enables the optimization of force field parameters in molecular dynamics simulations, just as in the optimization of neural network models.\n\n\n\nSeveral attempts have been made to harness automatic differentiation techniques for molecular dynamics simulations, such as TorchMD, JAX-MD, and SPONGE. However, the deep computational graph involved in molecular dynamics simulations often introduces additional challenges. The backpropagation process, which calculates the differentiation from the output results to the input variables, can be computationally expensive. Additionally, there is still a lack of simulation engines specifically designed for rapid implementation and parameter optimization of force fields. Molecular force field developers urgently require comprehensive support for a wider range of force field functional forms and various types of objective functions. Now DMFF comes.\n\n\n\nDMFF provides a comprehensive and rapid implementation of force field models and offers differentiable estimators of system energy, forces, and thermodynamic quantities. These differentiable estimators allow for the definition of corresponding object functions, enabling an automatic optimization process.\n\nAs mentioned above, the deep computational graph spanning the entire trajectory in molecular dynamics simulations would be computationally expensive and time-consuming. However, this limitation can be mitigated by employing a reweighting scheme for the trajectory. In DMFF, the reweighting algorithm is incorporated into the MBAR method, and extends the differentiable estimators for average properties and free energy calculations. We will demonstrate this method with practical examples to showcase its effectiveness.\n\nDMFF gives a solution to two major problems in molecular force field development:\n- How to ensure the rapid implementation and iteration of complex force fields in molecular dynamics simulations?\n- How to improve the efficiency of optimizing parameters in high-dimensional functions and automate this process, while increasing the transferability of the parameters?\n\nIn our latest release, DMFF 0.2.0, we have explored a pathway for automatic optimization of force field parameters. The corresponding workflow has been validated in both simple small molecular systems and more complex electrolyte systems. We will guide you through a step-by-step notebook experience, demonstrating the complete workflow of DMFF for automatic optimization of force field parameters. Furthermore, using the notebook as a teaching template, we will provide a real hands-on experience showcasing the impact of DMFF on force field development.","metadata":{},"id":"79f62281-a2e6-4e78-afe2-1aeeac9d934e"},{"cell_type":"markdown","source":"# Table of Contents:\n* [1. Quick Start Guide to DMFF](#1)\n * [1.0 Import dependencies and prepare files](#1-1)\n * [1.1 Load existing force field parameters and topology](#1-2)\n * [1.2 Calculation](#1-3)\n * [1.3 [Review] Basic interface and key usage points of DMFF](#1-4)\n* [2. Mutipolar polarizable force field with fluctuating charges](#2)\n * [2.1 Genearate auto-differentiable multipolar polarizable (ADMP) forces](#2-1)\n * [2.2 Implement fluctuating charges](#2-2)\n * [3. Bottom-Up Fitting](#3)\n * [3.1 Problem introduction](#3-1)\n * [3.2 Definition of potential function](#3-2)\n * [3.3 Preparing inputs for the potential function](#3-3)\n * [3.4 Definition of loss function](#3-5)\n * [3.5 Optimize](#3-6)\n* [4.Using DMFF for force field optimization of liquid dimethyl carbonate (DMC)](#4)\n * [4.1 Introduction](#4-1)\n * [4.2 Definition of potential function](#4-2)\n * [4.3 Definition of OpenMM sampler](#4-3)\n * [4.4 Initial MD sampling](#4-4)\n * [4.5 Definition of property calculation functions](#4-5)\n * [4.6 Read the data and perform the comparison](#4-6)\n * [4.7 Estimator initialization](#4-7)\n * [4.8 Definition of target ensemble](#4-8)\n * [4.9 Definition of loss function](#4-9)\n * [4.10 Optimizer setup and optimization loop](#4-10)\n* [5. Summary & Outlook](#summary)","metadata":{},"id":"51c192bb-7ed8-4462-afcc-4b88dbc6e9ce"},{"cell_type":"markdown","source":"## 1. Quick Start Guide to DMFF \nThis case is primarily contributed by Yanbo Han.\n### 1.0 Import dependencies and prepare files \nFirst, we set up the runtime environment and import the potential functions needed.","metadata":{"jp-MarkdownHeadingCollapsed":true},"id":"b9c18f06-2ab4-46dd-ae07-cc82edeb81b2"},{"cell_type":"code","source":"! rm -rf DMFF\n! git clone https://github.com/deepmodeling/DMFF.git\n! git config --global --add safe.directory `pwd`/DMFF\n! cd DMFF && git checkout wangxy/v1.0.0-devel && pip install .","metadata":{"collapsed":true,"jupyter":{"outputs_hidden":true}},"execution_count":1,"outputs":[{"name":"stdout","output_type":"stream","text":"Cloning into 'DMFF'...\nremote: Enumerating objects: 4249, done.\u001b[K\nremote: Counting objects: 100% (532/532), done.\u001b[K\nremote: Compressing objects: 100% (204/204), done.\u001b[K\nremote: Total 4249 (delta 364), reused 466 (delta 328), pack-reused 3717\u001b[K\nReceiving objects: 100% (4249/4249), 19.60 MiB | 4.03 MiB/s, done.\nResolving deltas: 100% (2754/2754), done.\nUpdating files: 100% (273/273), done.\nUpdating files: 100% (281/281), done.\nBranch 'wangxy/v1.0.0-devel' set up to track remote branch 'wangxy/v1.0.0-devel' from 'origin'.\nSwitched to a new branch 'wangxy/v1.0.0-devel'\nLooking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\nProcessing /data/DMFF\n Preparing metadata (setup.py) ... \u001b[?25ldone\n\u001b[?25hRequirement already satisfied: numpy>=1.18 in /opt/mamba/lib/python3.10/site-packages (from dmff==0.2.1.dev306+geaba9a4) (1.23.4)\nRequirement already satisfied: openmm>=7.6.0 in /opt/mamba/lib/python3.10/site-packages (from dmff==0.2.1.dev306+geaba9a4) (7.7.0)\nRequirement already satisfied: freud-analysis in /opt/mamba/lib/python3.10/site-packages/freud_analysis-2.11.0-py3.10-linux-x86_64.egg (from dmff==0.2.1.dev306+geaba9a4) (2.11.0)\nCollecting networkx>=3.0\n Downloading https://pypi.tuna.tsinghua.edu.cn/packages/d5/f0/8fbc882ca80cf077f1b246c0e3c3465f7f415439bdea6b899f6b19f61f70/networkx-3.2.1-py3-none-any.whl (1.6 MB)\n\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m6.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n\u001b[?25hCollecting optax>=0.1.4\n Downloading https://pypi.tuna.tsinghua.edu.cn/packages/13/71/787cc24c4b606f3bb9f1d14957ebd7cb9e4234f6d59081721230b2032196/optax-0.1.7-py3-none-any.whl (154 kB)\n\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m154.1/154.1 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n\u001b[?25hCollecting jaxopt>=0.8.0\n Downloading https://pypi.tuna.tsinghua.edu.cn/packages/4d/a2/46c5f5cf6808bd9b316a022497c7e76e9106360aec3522fde562224a8b9d/jaxopt-0.8.1-py3-none-any.whl (169 kB)\n\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m169.7/169.7 kB\u001b[0m \u001b[31m3.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n\u001b[?25hRequirement already satisfied: pymbar>=4.0.0 in /opt/mamba/lib/python3.10/site-packages (from dmff==0.2.1.dev306+geaba9a4) (4.0.1)\nRequirement already satisfied: tqdm in /opt/mamba/lib/python3.10/site-packages (from dmff==0.2.1.dev306+geaba9a4) (4.64.1)\nRequirement already satisfied: scipy>=1.0.0 in /opt/mamba/lib/python3.10/site-packages (from jaxopt>=0.8.0->dmff==0.2.1.dev306+geaba9a4) (1.9.3)\nRequirement already satisfied: jax>=0.2.18 in /opt/mamba/lib/python3.10/site-packages (from jaxopt>=0.8.0->dmff==0.2.1.dev306+geaba9a4) (0.3.17)\nRequirement already satisfied: jaxlib>=0.1.69 in /opt/mamba/lib/python3.10/site-packages (from jaxopt>=0.8.0->dmff==0.2.1.dev306+geaba9a4) (0.3.15+cuda11.cudnn82)\nRequirement already satisfied: chex>=0.1.5 in /opt/mamba/lib/python3.10/site-packages (from optax>=0.1.4->dmff==0.2.1.dev306+geaba9a4) (0.1.5)\nRequirement already satisfied: absl-py>=0.7.1 in /opt/mamba/lib/python3.10/site-packages (from optax>=0.1.4->dmff==0.2.1.dev306+geaba9a4) (1.3.0)\nRequirement already satisfied: numexpr in /opt/mamba/lib/python3.10/site-packages (from pymbar>=4.0.0->dmff==0.2.1.dev306+geaba9a4) (2.8.4)\nRequirement already satisfied: rowan>=1.2.1 in /opt/mamba/lib/python3.10/site-packages/rowan-1.3.0.post1-py3.10.egg (from freud-analysis->dmff==0.2.1.dev306+geaba9a4) (1.3.0.post1)\nRequirement already satisfied: toolz>=0.9.0 in /opt/mamba/lib/python3.10/site-packages (from chex>=0.1.5->optax>=0.1.4->dmff==0.2.1.dev306+geaba9a4) (0.12.0)\nRequirement already satisfied: dm-tree>=0.1.5 in /opt/mamba/lib/python3.10/site-packages (from chex>=0.1.5->optax>=0.1.4->dmff==0.2.1.dev306+geaba9a4) (0.1.7)\nRequirement already satisfied: etils[epath] in /opt/mamba/lib/python3.10/site-packages (from jax>=0.2.18->jaxopt>=0.8.0->dmff==0.2.1.dev306+geaba9a4) (0.9.0)\nRequirement already satisfied: typing-extensions in /opt/mamba/lib/python3.10/site-packages (from jax>=0.2.18->jaxopt>=0.8.0->dmff==0.2.1.dev306+geaba9a4) (4.4.0)\nRequirement already satisfied: opt-einsum in /opt/mamba/lib/python3.10/site-packages (from jax>=0.2.18->jaxopt>=0.8.0->dmff==0.2.1.dev306+geaba9a4) (3.3.0)\nRequirement already satisfied: zipp in /opt/mamba/lib/python3.10/site-packages (from etils[epath]->jax>=0.2.18->jaxopt>=0.8.0->dmff==0.2.1.dev306+geaba9a4) (3.10.0)\nRequirement already satisfied: importlib_resources in /opt/mamba/lib/python3.10/site-packages (from etils[epath]->jax>=0.2.18->jaxopt>=0.8.0->dmff==0.2.1.dev306+geaba9a4) (5.10.0)\nBuilding wheels for collected packages: dmff\n Building wheel for dmff (setup.py) ... \u001b[?25ldone\n\u001b[?25h Created wheel for dmff: filename=dmff-0.2.1.dev306+geaba9a4-py3-none-any.whl size=122980 sha256=92f4a1caf2d4709325963c07231c0589b031244315b53c5a034f0ce9336d9a16\n Stored in directory: /tmp/pip-ephem-wheel-cache-wzaeoh64/wheels/f3/08/c8/63a66e9272163ceeb3675eda2e65e58a3e3c8a96296799182d\nSuccessfully built dmff\nInstalling collected packages: networkx, jaxopt, optax, dmff\n Attempting uninstall: optax\n Found existing installation: optax 0.1.3\n Uninstalling optax-0.1.3:\n Successfully uninstalled optax-0.1.3\n Attempting uninstall: dmff\n Found existing installation: dmff 0.2.1.dev128+g7e83028\n Uninstalling dmff-0.2.1.dev128+g7e83028:\n Successfully uninstalled dmff-0.2.1.dev128+g7e83028\nSuccessfully installed dmff-0.2.1.dev306+geaba9a4 jaxopt-0.8.1 networkx-3.2.1 optax-0.1.7\n\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n\u001b[0m"}],"id":"3575eb67-60da-446b-8bcf-5f0ee1d51fbd"},{"cell_type":"code","source":"# CPU version\n! pip install jax==0.4.14\n# GPU version\n! pip install optax==0.1.3 pymbar==4.0.1 jaxopt==0.8.1\n! mamba install openmm=7.7.0 rdkit -c conda-forge -y","metadata":{},"execution_count":1,"outputs":[{"name":"stdout","output_type":"stream","text":"Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\nRequirement already satisfied: jax==0.4.14 in /opt/mamba/lib/python3.10/site-packages (0.4.14)\nRequirement already satisfied: opt-einsum in /opt/mamba/lib/python3.10/site-packages (from jax==0.4.14) (3.3.0)\nRequirement already satisfied: numpy>=1.22 in /opt/mamba/lib/python3.10/site-packages (from jax==0.4.14) (1.23.4)\nRequirement already satisfied: ml-dtypes>=0.2.0 in /opt/mamba/lib/python3.10/site-packages (from jax==0.4.14) (0.3.1)\nRequirement already satisfied: scipy>=1.7 in /opt/mamba/lib/python3.10/site-packages (from jax==0.4.14) (1.9.3)\n\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n\u001b[0mLooking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\nRequirement already satisfied: optax==0.1.3 in /opt/mamba/lib/python3.10/site-packages (0.1.3)\nRequirement already satisfied: pymbar==4.0.1 in /opt/mamba/lib/python3.10/site-packages (4.0.1)\nRequirement already satisfied: jaxopt==0.8.1 in /opt/mamba/lib/python3.10/site-packages (0.8.1)\nRequirement already satisfied: jax>=0.1.55 in /opt/mamba/lib/python3.10/site-packages (from optax==0.1.3) (0.4.14)\nRequirement already satisfied: typing-extensions>=3.10.0 in /opt/mamba/lib/python3.10/site-packages (from optax==0.1.3) (4.8.0)\nRequirement already satisfied: jaxlib>=0.1.37 in /opt/mamba/lib/python3.10/site-packages (from optax==0.1.3) (0.4.11)\nRequirement already satisfied: chex>=0.0.4 in /opt/mamba/lib/python3.10/site-packages (from optax==0.1.3) (0.1.5)\nRequirement already satisfied: absl-py>=0.7.1 in /opt/mamba/lib/python3.10/site-packages (from optax==0.1.3) (1.3.0)\nRequirement already satisfied: numpy>=1.18.0 in /opt/mamba/lib/python3.10/site-packages (from optax==0.1.3) (1.23.4)\nRequirement already satisfied: numexpr in /opt/mamba/lib/python3.10/site-packages (from pymbar==4.0.1) (2.8.4)\nRequirement already satisfied: scipy in /opt/mamba/lib/python3.10/site-packages (from pymbar==4.0.1) (1.9.3)\nRequirement already satisfied: dm-tree>=0.1.5 in /opt/mamba/lib/python3.10/site-packages (from chex>=0.0.4->optax==0.1.3) (0.1.7)\nRequirement already satisfied: toolz>=0.9.0 in /opt/mamba/lib/python3.10/site-packages (from chex>=0.0.4->optax==0.1.3) (0.12.0)\nRequirement already satisfied: opt-einsum in /opt/mamba/lib/python3.10/site-packages (from jax>=0.1.55->optax==0.1.3) (3.3.0)\nRequirement already satisfied: ml-dtypes>=0.2.0 in /opt/mamba/lib/python3.10/site-packages (from jax>=0.1.55->optax==0.1.3) (0.3.1)\n\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n\u001b[0mTraceback (most recent call last):\n File \"/opt/mamba/bin/mamba\", line 7, in \n from mamba.mamba import main\n File \"/opt/mamba/lib/python3.10/site-packages/mamba/mamba.py\", line 44, in \n import libmambapy as api\n File \"/opt/mamba/lib/python3.10/site-packages/libmambapy/__init__.py\", line 7, in \n raise e\n File \"/opt/mamba/lib/python3.10/site-packages/libmambapy/__init__.py\", line 4, in \n from libmambapy.bindings import * # noqa: F401,F403\nImportError: /opt/mamba/lib/python3.10/site-packages/libmambapy/../../../libmamba.so.2: undefined symbol: solver_ruleinfo2str, version SOLV_1.0\n"}],"id":"3f7f70e2-5fd5-4d4e-b178-58a4a3c053fe"},{"cell_type":"markdown","source":"In addition to DMFF, we also need to use JAX and OpenMM.\n- OpenMM: Manages the force field files and parameter data\n- JAX：framework of differentiation\n\nAt the same time, we will also use other libraries and trajectory analysis software such as mdtraj in the subsequent examples. Let's import them as well.","metadata":{},"id":"6758cdea-f98d-4749-8286-3b23d24adfc5"},{"cell_type":"code","source":"import os\nimport sys\nimport numpy as np\nimport jax\nimport jax.numpy as jnp\nfrom jax import value_and_grad, jit, vmap\nimport openmm as mm\nimport openmm.app as app\nimport openmm.unit as unit\nimport dmff\nfrom dmff import Hamiltonian, NeighborList\nfrom dmff.common import nblist\nfrom dmff.mbar import ReweightEstimator, MBAREstimator, SampleState, TargetState, Sample, OpenMMSampleState, buildTrajEnergyFunction\nfrom dmff.api.xmlio import XMLIO\nfrom dmff.api.paramset import ParamSet\nfrom dmff.generators.classical import LennardJonesGenerator\nfrom dmff.api.topology import DMFFTopology\nfrom dmff.operators.templatetype import TemplateATypeOperator\nfrom dmff.operators.templatevsite import TemplateVSiteOperator\nfrom dmff.api.vstools import insertVirtualSites, pickTheSame\nfrom dmff import NeighborListFreud\nimport pickle\nfrom pprint import pprint\nimport optax\nimport mdtraj as md\nimport xml.etree.ElementTree as ET\nfrom itertools import combinations\nimport matplotlib.pyplot as plt\nfrom tqdm import tqdm, trange","metadata":{},"execution_count":2,"outputs":[{"ename":"AttributeError","evalue":"module 'ml_dtypes' has no attribute 'float8_e4m3b11'","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)","Cell \u001b[0;32mIn [2], line 4\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msys\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mjnp\u001b[39;00m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m value_and_grad, jit, vmap\n","File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/jax/__init__.py:35\u001b[0m\n\u001b[1;32m 30\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m _cloud_tpu_init\n\u001b[1;32m 32\u001b[0m \u001b[38;5;66;03m# Confusingly there are two things named \"config\": the module and the class.\u001b[39;00m\n\u001b[1;32m 33\u001b[0m \u001b[38;5;66;03m# We want the exported object to be the class, so we first import the module\u001b[39;00m\n\u001b[1;32m 34\u001b[0m \u001b[38;5;66;03m# to make sure a later import doesn't overwrite the class.\u001b[39;00m\n\u001b[0;32m---> 35\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m config \u001b[38;5;28;01mas\u001b[39;00m _config_module\n\u001b[1;32m 36\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m _config_module\n\u001b[1;32m 38\u001b[0m \u001b[38;5;66;03m# Force early import, allowing use of `jax.core` after importing `jax`.\u001b[39;00m\n","File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/jax/config.py:17\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Copyright 2018 The JAX Authors.\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Licensed under the Apache License, Version 2.0 (the \"License\");\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 14\u001b[0m \n\u001b[1;32m 15\u001b[0m \u001b[38;5;66;03m# TODO(phawkins): fix users of this alias and delete this file.\u001b[39;00m\n\u001b[0;32m---> 17\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconfig\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m config \u001b[38;5;66;03m# noqa: F401\u001b[39;00m\n","File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/jax/_src/config.py:27\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mthreading\u001b[39;00m\n\u001b[1;32m 25\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtyping\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Any, Callable, Generic, NamedTuple, Optional, TypeVar\n\u001b[0;32m---> 27\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m lib\n\u001b[1;32m 28\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m jax_jit\n\u001b[1;32m 29\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m transfer_guard_lib\n","File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/jax/_src/lib/__init__.py:87\u001b[0m\n\u001b[1;32m 84\u001b[0m cpu_feature_guard\u001b[38;5;241m.\u001b[39mcheck_cpu_features()\n\u001b[1;32m 86\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mjaxlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mutils\u001b[39;00m\n\u001b[0;32m---> 87\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mjaxlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mxla_client\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mxla_client\u001b[39;00m\n\u001b[1;32m 88\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mjaxlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlapack\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mlapack\u001b[39;00m\n\u001b[1;32m 90\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mjaxlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mducc_fft\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mducc_fft\u001b[39;00m\n","File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/jaxlib/xla_client.py:225\u001b[0m\n\u001b[1;32m 223\u001b[0m bfloat16 \u001b[38;5;241m=\u001b[39m ml_dtypes\u001b[38;5;241m.\u001b[39mbfloat16\n\u001b[1;32m 224\u001b[0m float8_e4m3fn \u001b[38;5;241m=\u001b[39m ml_dtypes\u001b[38;5;241m.\u001b[39mfloat8_e4m3fn\n\u001b[0;32m--> 225\u001b[0m float8_e4m3b11fnuz \u001b[38;5;241m=\u001b[39m \u001b[43mml_dtypes\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfloat8_e4m3b11\u001b[49m\n\u001b[1;32m 226\u001b[0m float8_e5m2 \u001b[38;5;241m=\u001b[39m ml_dtypes\u001b[38;5;241m.\u001b[39mfloat8_e5m2\n\u001b[1;32m 228\u001b[0m XLA_ELEMENT_TYPE_TO_DTYPE \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 229\u001b[0m PrimitiveType\u001b[38;5;241m.\u001b[39mPRED: np\u001b[38;5;241m.\u001b[39mdtype(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbool\u001b[39m\u001b[38;5;124m'\u001b[39m),\n\u001b[1;32m 230\u001b[0m PrimitiveType\u001b[38;5;241m.\u001b[39mS8: np\u001b[38;5;241m.\u001b[39mdtype(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mint8\u001b[39m\u001b[38;5;124m'\u001b[39m),\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 248\u001b[0m PrimitiveType\u001b[38;5;241m.\u001b[39mTOKEN: np\u001b[38;5;241m.\u001b[39mdtype(np\u001b[38;5;241m.\u001b[39mobject_),\n\u001b[1;32m 249\u001b[0m }\n","\u001b[0;31mAttributeError\u001b[0m: module 'ml_dtypes' has no attribute 'float8_e4m3b11'"]}],"id":"aab99ecb-ffd3-4633-8b95-a12a7dc95bc2"},{"cell_type":"markdown","source":"Navigate to the working directory","metadata":{},"id":"a89fa3f5-2dc8-4c4e-ab8b-1ffca85e3c72"},{"cell_type":"code","source":"import os\nos.chdir(os.path.join(\"DMFF\",\"examples\", \"classical\"))","metadata":{},"execution_count":3,"outputs":[],"id":"7d8f746b-1176-4cf3-a302-ba637e69efbf"},{"cell_type":"markdown","source":"### 1.1 Load existing force field parameters and topology | OpenMM Frontend \n\nIf you are using a force field in OpenMM, you can load the force field parameters using the `ForceField` class, like:\n```python\napp.Topology.loadBondDefinitions(\"lig-top.xml\")\npdb = app.PDBFile('lig.pdb')\npdb_system = pdb.topology\n\nforcefield = ForceField('someforcefield.xml')\n```\n\nIn DMFF, there is a functionality class called `Hamiltonian` that is similar to OpenMM's `ForceField` class. It allows you to read force field parameters and define a more generalized system potential energy function. At the same time, it is compatible with reading existing force field parameters.\n\n- We can use OpenMM to read PDB and topology files：\n - topology（`log-top.xml`）\n - PDB（`lig.pdb`）\n- Load the force field parameters using DMFF's Hamiltonian class and create a differentiable potential energy function:\n - GAFF force field file （`gaff-2.11.xml`）\n - Additional parameters for the corresponding molecule (assigned charges) （`lig-prm.xml`）\n \nThe DMFF potential function, apart from the name \"Hamiltonian,\" is used in a similar way to OpenMM. The XML files of OpenMM force fields can also be directly reused.","metadata":{},"id":"2143158a-3914-4023-ac83-72901975d82a"},{"cell_type":"code","source":"app.Topology.loadBondDefinitions(\"lig-top.xml\")\npdb = app.PDBFile(\"lig.pdb\")\nff = Hamiltonian(\"gaff-2.11.xml\", \"lig-prm.xml\")\npotentials = ff.createPotential(pdb.topology)","metadata":{},"execution_count":3,"outputs":[],"id":"5e8c689e-d13f-47cd-99fd-c5f60c49614b"},{"cell_type":"markdown","source":"In DMFF, the parameters and calculations of the potential function are managed by JAX. For example, the DMFF potential function includes the re-implemented HarmonicBondForce, HarmonicAngleForce, PeriodicTorsionForce, and NonbondedForce. The parameters in DMFF are JAX-wrapped Array objects. For example, if we define:\n```python\njnp.array([1.])\n```\nYou will obtain a `DeviceArray` type. This type has a similar interface to numpy.ndarray(), as both are high-performance arrays/matrices. However, unlike numpy, JAX's jax.numpy.array() can be extended to work with GPUs/TPUs, making it suitable for efficient automatic differentiation.","metadata":{},"id":"087c7d7e-499d-480b-b7e2-683634330a11"},{"cell_type":"code","source":"for k in potentials.dmff_potentials.keys():\n pot = potentials.dmff_potentials[k]\n print(k, pot)\n\nparams = ff.getParameters()\n# print(params.keys()) # GAFF2有四种 Force，'NonbondedForce', 'HarmonicBondForce', 'HarmonicAngleForce', 'PeriodicTorsionForce'\nnbparam = params['NonbondedForce']\nfor k,v in nbparam.items():\n print(k, type(v), v if v.shape[0]<10 else f\"shape: {v.shape}\")","metadata":{},"execution_count":4,"outputs":[{"name":"stdout","output_type":"stream","text":"HarmonicBondForce .potential_fn at 0x7f4e401437f0>\nHarmonicAngleForce .potential_fn at 0x7f4e40143be0>\nPeriodicTorsionForce .potential_fn at 0x7f4dfc538280>\nNonbondedForce .potential_fn at 0x7f4dfc538e50>\nsigma shape: (97,)\nepsilon shape: (97,)\n"}],"id":"6546efbf-6c0b-4790-9b4a-aa136cfb3f2d"},{"cell_type":"code","source":"potentials.meta[\"cov_map\"]","metadata":{},"execution_count":5,"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"DeviceArray([[0, 1, 2, ..., 0, 0, 2],\n [1, 0, 1, ..., 0, 0, 3],\n [2, 1, 0, ..., 0, 0, 4],\n ...,\n [0, 0, 0, ..., 0, 2, 0],\n [0, 0, 0, ..., 2, 0, 0],\n [2, 3, 4, ..., 0, 0, 0]], dtype=int64)"},"metadata":{}}],"id":"7f6bf26c-0d91-4b00-8c1a-3cb8498041f9"},{"cell_type":"markdown","source":"### 1.2 Calculation | JAX Differentiable Backend \nIn the calculation of the defined potential functions, we require the following parameters:\n\n- **coordinates**: just use the coordinates in PDB file\n\n- **box**: We need to add the definition of the box since our PDB file does not contain this information. However, it is worth mentioning that if we use **None** for the box size, we can still obtain results because our system does not involve periodic boundaries.\n\n- **pairs**: The GAFF2 potential function requires the input of **NeighborList** to calculate nonbonded forces. Therefore, we can use the NeighborList class to obtain the pairs for the energy calculation.\n\nThen, we can pass these to the `get_energy` function, which is generated by the `generator` from the parsed XML force field parameters in **potentials.dmff_potentials**. For example, we can calculate the energy associated with the `NonbondedForce` interactions as follows:","metadata":{},"id":"3895de0e-010d-4125-bdc3-fbf53850cd83"},{"cell_type":"code","source":"positions = jnp.array(pdb.getPositions(asNumpy=True).value_in_unit(unit.nanometer))\n\nbox = jnp.array([\n [10.0, 0.0, 0.0],\n [ 0.0, 10.0, 0.0],\n [ 0.0, 0.0, 10.0]\n])\n# box=None # 使用这个也可以\n\nnbList = NeighborList(box, 4, potentials.meta[\"cov_map\"])\nnbList.allocate(positions)\npairs = nbList.pairs\n\n# pairs的格式是[原子索引1, 原子索引2, nbond]，nbond为0表示没有bond\n# print(pairs)\n\nnbfunc = potentials.dmff_potentials['NonbondedForce']\n\n# 可以用 inspect 看看，`nbfunc`是一个【函数】,而inspect.signature()方法会告诉我们这个函数的输入参数有哪些\n# \n# import inspect\n# print(inspect.signature(nbfunc))\n\nnbene = nbfunc(positions, box, pairs, params)\nprint(nbene)","metadata":{},"execution_count":6,"outputs":[{"name":"stdout","output_type":"stream","text":"-425.4047017461835\n"}],"id":"f81744a2-085e-4dae-9a0b-64e1dee88d70"},{"cell_type":"markdown","source":"For the system we defined above, the total energy can be calculated as follows:\n\n$$E_{\\rm{total}}^{\\rm{GAFF2}}=E_{\\rm{bond}}+E_{\\rm{angle}}+E_{\\rm{torsion}}+E_{\\rm{nonbond}}$$\n\nTo calculate the total energy using the previously defined potential, you can use the `getPotentialFunc()` method, which will return the function for calculating the total energy.","metadata":{},"id":"7cd6a691-42b0-4800-a89a-49f29b479221"},{"cell_type":"code","source":"efunc = potentials.getPotentialFunc()\nparams = ff.getParameters()\ntotene = efunc(positions, box, pairs, params)\nprint(totene)","metadata":{},"execution_count":7,"outputs":[{"name":"stdout","output_type":"stream","text":"-52.35775535703641\n"}],"id":"fc12600d-1e31-4405-83f3-d0d2270e0c03"},{"cell_type":"markdown","source":"So far, the methods we have used to calculate the system energy are similar to those in OpenMM.\n\nThe main advantage of using JAX as a computational backend is that we can use the `jax.grad` function to obtain the gradients of a function. Its syntax is `jax.grad(func, argnums)`, where it calculates the (partial) derivatives of the function with respect to the **argument** specified by argnums.\n\nThe interface of the total energy calculation function efunc that we obtained is [coordinates, box, bond pairs, force field parameters]. ","metadata":{},"id":"72329b6d-66c5-433f-bff5-d7bea991a91f"},{"cell_type":"code","source":"# you can use inspect.signature to view the function interface signature\n# import inspect\n# print(inspect.signature(efunc))","metadata":{},"execution_count":8,"outputs":[],"id":"ce0b6451-1956-49da-bff3-7ab18389a943"},{"cell_type":"markdown","source":"By applying the \"differentiation of the function\" operation, specifically taking the partial derivatives of the total energy with respect to the coordinates, we can compute the partial derivatives of the total energy with respect to the coordinates. These derivatives can then be used to calculate the forces acting on the atoms in the molecule.\n\n$$\\frac{\\partial{E_{\\rm{total}}}}{\\partial{\\mathbf{Z_i}}}=-\\mathbf{F_i}, \\ i=x,y,z$$","metadata":{},"id":"0ae128d7-3d1a-47ce-8d78-b35cd856b144"},{"cell_type":"code","source":"pos_grad_func = jax.grad(efunc, argnums=0)\nforce = -pos_grad_func(positions, box, pairs, params)\nprint(force)","metadata":{},"execution_count":9,"outputs":[{"name":"stdout","output_type":"stream","text":"[[ 803.52957747 3400.29774026 -661.96213523]\n [ 1150.36392033 -2973.80535655 4723.1401719 ]\n [-1806.30563334 -1691.22853598 -4400.66468133]\n [-1975.18818512 -3796.76170845 -2135.01995789]\n [ 300.94474783 -2505.35668706 1973.24600965]\n [ 1417.86850059 4929.0136063 690.09213683]\n [-1214.69075969 -752.04890746 -728.64363448]\n [ 506.19084362 -1762.15983054 -183.44743699]\n [ 666.47812556 -96.44574992 1381.96013269]\n [-1764.39328602 -1823.64962431 -1339.25100614]\n [ 716.92780964 2224.67413422 -611.09476826]\n [ -668.08596611 -2633.60075168 -45.50003995]\n [ 525.3188336 1892.76410092 -324.60902175]\n [ -222.93562587 -1252.5822138 1038.35399886]\n [ 1050.2831757 1406.00072105 859.35930808]\n [ -149.99815788 -816.80260414 -112.40342883]\n [-2460.68641394 1360.33433118 -797.03282339]\n [-1690.67958659 -4545.06401473 67.80091833]\n [ 2421.74312888 2531.32994259 411.40292148]\n [-1881.71637547 2700.43869309 -938.8271789 ]\n [ 2230.84228769 -2105.44762878 996.80519838]\n [ 2820.17595634 3725.34103792 531.68421385]\n [ 1008.60327391 -1133.9584703 -123.14349352]\n [ -276.50743719 160.26940872 -402.33483739]\n [ 119.18246059 482.13137215 -187.78600837]\n [ 350.39374172 -1206.41303817 97.6235938 ]\n [ -646.06449223 625.31531552 662.35075182]\n [ -848.36122598 793.56182029 -1179.70627373]\n [ -110.52131871 -297.51678589 1608.69051833]\n [ 907.24924949 -712.65640858 -804.73153243]\n [ -23.81344251 -403.63449895 221.90382764]\n [ -974.84347195 1265.96450714 34.97803045]\n [ 1254.77065898 408.1267468 160.6793413 ]\n [ -523.41026695 797.07980959 -1009.97526506]\n [ 384.26997895 -426.25633961 378.53995254]\n [ -935.39210743 511.55922005 -1090.50523632]\n [ -150.10284632 -666.4265588 1039.41296827]\n [ 397.37612952 -338.39787242 339.2815683 ]\n [ 414.71386197 479.49633356 767.07953947]\n [ 965.36948355 160.06498779 13.46682867]\n [ -527.06588314 650.7009065 441.7987455 ]\n [ 317.29196525 998.74765406 -60.58825503]\n [ -273.48532288 918.78223009 -523.6331768 ]\n [ 1251.03183781 930.39541048 445.93120648]\n [ -647.65431898 -1616.51320224 -86.54822059]\n [-1666.19786619 -537.25322843 -452.23497364]\n [ -638.9467378 -822.28086508 -414.43010717]\n [ 751.15184138 454.18755264 783.01090009]\n [ 428.44509773 -256.58883573 -690.36786701]\n [ -603.21827361 231.03790584 -406.60888759]\n [ -831.82950411 -148.26754971 -253.45934033]\n [ -156.60888744 425.77912229 815.50717525]\n [ -181.66985391 242.79408619 13.22389549]\n [ -527.76583611 -599.48002448 -484.46773728]\n [ -955.47257532 -160.50351122 388.18000331]\n [ -212.87534091 -710.95427149 291.92603185]\n [ 34.79793147 149.08675492 746.77258693]\n [ -35.16803002 -257.83600642 -725.78935376]\n [ -63.9175139 318.12674851 679.27357666]\n [ 737.52783774 43.30127308 -182.65304035]\n [ 723.9084714 492.98317644 -177.7631343 ]\n [ 326.54325749 -76.56914279 -903.24205277]\n [ 8.258139 342.50611657 700.35028221]\n [ 721.14445971 20.56613577 -288.54115124]\n [ -195.67178466 -755.86375714 93.28108171]\n [ 128.54774337 1809.56507834 -670.14135826]]\n"}],"id":"55a230f0-7d8c-4639-a430-6fabe07a5348"},{"cell_type":"markdown","source":"### 1.3 [Review] Basic interface and key usage points of DMFF \n\n### 1.3.1 Applications of DMFF：\n**Molecular Force Field Optimization Platform**\n- DMFF is compatible with OpenMM's XML format for molecular force field parameters\n- Automatic differentiation and GPU support enable fast implementation of complex molecular dynamics force fields in DMFF\n- **flexible force field parameter optimization capability of DMFF**：\n - Improving a specific component among them×\n - Complex parameter optimization√ Rapid implementation through automatic differentiation frameworks\n### 1.3.2 General Operations\n### Generating Potential Functions\n- Defining force parameters in an XML file (OpenMM interface)\n- Description of the system in PDB and topology in XML format\n----\n```py\npdb = app.PDBFile(\"lig.pdb\")\nff = Hamiltonian(\"gaff-2.11.xml\", \"lig-prm.xml\")\npotentials = ff.createPotential(pdb.topology)\n```\n----\n### Calculating System's Energy and Forces\n----\n```py\nefunc = potentials.getPotentialFunc()\nparams = ff.getParameters()\n\n# energy function\ntotene = efunc(positions, box, pairs, params)\nprint(totene)\n\n# 力\npos_grad_func = jax.grad(efunc, argnums=0)\nforce = -pos_grad_func(positions, box, pairs, params)\nprint(force)\n```\n----\n### 1.3.3 [More] Why do we need \"differentiable\" JAX: The first step towards infinite possibilities for new force fields.\n> Why JAX？\n\nHere we take the example of the implementation of the HarmonicBondForce in DMFF to build a simple model to illustrate what we can do with the JAX backend of DMFF.\n\nIn this part we will use some customized files (modifying these files might help you understand what the DMFF frontend does):\n\nwe an create a new file `dummy-prm.xml`\n\n-----\n```xml\n\n \n \n \n \n \n \n \n\n```\n-----\n\n`dummy-top.xml`：\n\n-----\n```xml\n\n \n \n \n\n```\n-----\n\n`dummy.pdb`：\n\n-----\n```pdb\nREMARK 1 CREATED WITH MANUAL\nHETATM 1 C1 DUM A 1 0.000 0.000 0.000 1.00 0.00 C \nHETATM 2 C2 DUM A 1 1.000 0.000 0.000 1.00 0.00 C \nTER 3 DUM A 1\nEND\n```\n-----\n\n\n> and `dummy.xml`:\n\n-----\n```xml\n\n \n \n \n \n \n \n\n```\n-----","metadata":{},"id":"805db12e-cb86-4b17-9ef9-e210f8b00134"},{"cell_type":"code","source":"# from dmff/classical/intra.py\nimport jax.numpy as jnp\nfrom jax import value_and_grad\nfrom dmff.classical.intra import distance\n'''\ndef distance(p1v, p2v):\n return jnp.sqrt(jnp.sum(jnp.power(p1v - p2v, 2), axis=1))\n\nclass HarmonicBondJaxForce:\n def __init__(self, p1idx, p2idx, prmidx):\n self.p1idx = p1idx\n self.p2idx = p2idx\n self.prmidx = prmidx\n self.refresh_calculators()\n\n def generate_get_energy(self):\n def get_energy(positions, box, pairs, k, length):\n p1 = positions[self.p1idx,:]\n p2 = positions[self.p2idx,:]\n kprm = k[self.prmidx]\n b0prm = length[self.prmidx]\n dist = distance(p1, p2)\n return jnp.sum(0.5 * kprm * jnp.power(dist - b0prm, 2))\n\n return get_energy\n\n def update_env(self, attr, val):\n \"\"\"\n Update the environment of the calculator\n \"\"\"\n setattr(self, attr, val)\n self.refresh_calculators()\n\n def refresh_calculators(self):\n \"\"\"\n refresh the energy and force calculators according to the current environment\n \"\"\"\n self.get_energy = self.generate_get_energy()\n self.get_forces = value_and_grad(self.get_energy)\n'''\nimport openmm.app as app\napp.Topology.loadBondDefinitions(\"dummy-top.xml\")\npdb = app.PDBFile('dummy.pdb')\nff = Hamiltonian(\"dummy.xml\",\"dummy-prm.xml\")\npotentials = ff.createPotential(pdb.topology)\nefunc = potentials.getPotentialFunc()\nparams = ff.getParameters()\ntotene = efunc(jnp.array([[0,0,0],[0.11,0,0]]), box=None, pairs=[[0,1,1]], prms=params)\nprint(\"dmff energy:\",totene)\npos_grad_func = jax.grad(efunc, argnums=0)\nforce = -pos_grad_func(jnp.array([[0,0,0],[0.11,0,0]]), box=None, pairs=[[0,1,1]], prms=params)\nprint(\"dmff force:\",force)\n\ndef get_energy(positions, box, pairs, k, length):\n p1 = positions[[0],:]\n p2 = positions[[1],:]\n kprm = k[0]\n b0prm = length[0]\n dist = distance(p1, p2)\n return jnp.sum(0.5 * kprm * jnp.power(dist - b0prm, 2))\n\npos = jnp.array([[0,0,0],[0.11,0,0]])\nbox = None\npairs = [[0,1,1]]\nk = [100]\nlength = [0.1]\n\nprint(\"func energy:\", get_energy(pos,box,pairs,k,length))\n\npos_grad_func = jax.grad(get_energy, argnums=0)\nprint(\"func force:\", -pos_grad_func(pos,box,pairs,k,length))\n\ndef get_rmse_force(pos,box,pairs,k,length):\n return jnp.sum(jnp.power(pos_grad_func(pos,box,pairs,k,length),2))\n\nparam_grad_func = jax.grad(get_rmse_force, argnums=4)\nprint(\"param grad:\", param_grad_func(pos,box,pairs,k,length))\nprint(\"param grad, optimized:\", param_grad_func(pos,box,pairs,k,[0.11]))","metadata":{},"execution_count":11,"outputs":[{"name":"stdout","output_type":"stream","text":"dmff energy: 0.004999999999999995\ndmff force: [[ 1. -0. -0.]\n [-1. -0. -0.]]\nfunc energy: 0.004999999999999995\nfunc force: [[ 1. -0. -0.]\n [-1. -0. -0.]]\nparam grad: [DeviceArray(-400., dtype=float64, weak_type=True)]\nparam grad, optimized: [DeviceArray(-0., dtype=float64, weak_type=True)]\n"}],"id":"0920c0dd-5fb1-4c2c-86cc-4b1186d6f17c"},{"cell_type":"markdown","source":"- By taking derivatives with respect to coordinates, we can perform molecular dynamics (MD) simulations.\n- By taking derivatives with respect to the box, we can perform NPT ensemble molecular dynamics (MD) simulations.\n- By taking derivatives with respect to the parameters, we will be able to optimize the parameters of our defined force field using techniques such as gradient descent.\n\n### 1.4 Code\n[https://gitee.com/deepmodeling/DMFF](https://gitee.com/deepmodeling/DMFF)\n[https://github.com/deepmodeling/DMFF](https://github.com/deepmodeling/DMFF) ","metadata":{},"id":"4fff2fe9-7bf8-4ad2-a02d-98a85a522ec6"},{"cell_type":"markdown","source":"## 2. Mutipolar polarizable force field with fluctuating charges \nThis case is primarily contributed by Professor Kuang Yu\n\nIn the following example, we show how to implement a **multipolar polarizable potential with fluctuating charges** with DMFF API.\n\nIn conventional models, atomic charges are pre-defined and remain unchanged during the simulation. Here, we want to implement a model that considers atomic charges as *conformer-dependent*, so that the charges can vary during a molecular dynamics simulation. This will give better description of the system's behavior.\n\n### System preparation\nLoad the coordinates, box of a water dimer system. The unit of the frontend API is **nanometer**.\n\nNavigate to the working directory","metadata":{"jp-MarkdownHeadingCollapsed":true},"id":"b3383627-ed6b-4cd2-9988-116151e9b4c5"},{"cell_type":"code","source":"current_directory = os.getcwd()\nparent_directory = os.path.dirname(current_directory)\nos.chdir(parent_directory)\nos.chdir(os.path.join(\"fluctuated_leading_term_waterff\"))\n\nfrom dmff.api import Hamiltonian\nfrom jax_md import space, partition\nfrom jax import value_and_grad, jit\nimport pickle\nfrom dmff.admp.pme import trim_val_0\nfrom dmff.admp.spatial import v_pbc_shift\nfrom dmff.common import nblist\nfrom dmff.utils import jit_condition\nfrom dmff.admp.pairwise import (\n TT_damping_qq_c6_kernel,\n generate_pairwise_interaction,\n slater_disp_damping_kernel,\n slater_sr_kernel,\n TT_damping_qq_kernel\n)\n\nrc = 0.4\npdb = app.PDBFile(\"water_dimer.pdb\")\n# construct inputs\npositions = jnp.array(pdb.positions._value)\na, b, c = pdb.topology.getPeriodicBoxVectors()\nbox = jnp.array([a._value, b._value, c._value])","metadata":{},"execution_count":4,"outputs":[],"id":"b48345d8-5abf-4379-8168-c07ec26f0d4b"},{"cell_type":"markdown","source":"### 2.1 Genearate auto-differentiable multipolar polarizable (ADMP) forces \nFirst, we will use the `dmff` to create a multipolar polarizable potential with **fixed** atomic charges.\n\nHere, we have two types of force: \n\n- Dispersion force\n- Multipolar polarizable PME force.\n\nWe will focus on the PME force.","metadata":{},"id":"b0835b43-9b05-44db-87b7-dbe42240f15f"},{"cell_type":"code","source":"H = Hamiltonian('forcefield.xml')\n# generator stores all force field parameters \npots = H.createPotential(pdb.topology, nonbondedCutoff=rc*unit.nanometer, step_pol=5)\npme_pot = pots.dmff_potentials['ADMPPmeForce']\ndisp_generator, pme_generator = H.getGenerators()","metadata":{},"execution_count":5,"outputs":[],"id":"39ea05df-2ae2-498b-a6ff-46ea745da82f"},{"cell_type":"markdown","source":"The function `pme_pot` takes the following actions:\n\n- Expand **force field parameters** (oxygen and hydrogen charges, polarizabilites, etc.) pre-defined in `forcefield.xml` to each atom, which we called **atomic parameters**\n- Calls the real PME kernel function to evaluate energy.\n\nThe force field parameters are stored in Hamiltonian `H`. And the expansion is implemented with the *broadcast* feature of `jax.numpy.ndarray`. The expansion can be done using the variable `map_atomtype`, which maps each atom to the corrsponding atomtype.","metadata":{},"id":"168f3bf8-37dd-4137-a45c-036f5983c8ac"},{"cell_type":"code","source":"params = H.getParameters()['ADMPPmeForce']\nmap_atomtype = pots.meta[\"ADMPPmeForce_map_atomtype\"]\nparams['Q_local'][map_atomtype]","metadata":{},"execution_count":6,"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"DeviceArray([[-0.803721 , -0.0784325 , 0. , 0. ,\n 0.00459693, 0. , 0. , 0.12960503,\n 0. ],\n [ 0.401876 , -0.0095895 , -0.0121713 , 0. ,\n 0.00812139, 0.00436148, 0. , 0.00701541,\n 0. ],\n [ 0.401876 , -0.0095895 , -0.0121713 , 0. ,\n 0.00812139, 0.00436148, 0. , 0.00701541,\n 0. ],\n [-0.803721 , -0.0784325 , 0. , 0. ,\n 0.00459693, 0. , 0. , 0.12960503,\n 0. ],\n [ 0.401876 , -0.0095895 , -0.0121713 , 0. ,\n 0.00812139, 0.00436148, 0. , 0.00701541,\n 0. ],\n [ 0.401876 , -0.0095895 , -0.0121713 , 0. ,\n 0.00812139, 0.00436148, 0. , 0.00701541,\n 0. ]], dtype=float64)"},"metadata":{}}],"id":"d0e6e127-c530-4509-b0a3-7fee93d2b36b"},{"cell_type":"markdown","source":"### 2.2 Implement fluctuating charges \nSince this expansion process is done internally within `pme_pot`, it is **not flexible** enough for us to specify atom-specific charges, i.e. **fluctuating charges**. \n\nAs a result, we must re-write `pme_pot` to enable modifying the atomic charges after force field parameter expansion. \n\nBenifiting from the flexible APIs in DMFF, we will reuse most of the functions and variables in the `pme_generator`, only modify charges in the input parameters, i.e. the `Q_local` argument in `pme_generator.pme_force.get_energy` function. One particular thing to be careful is that all ADMP backend functions assumes the inputs (`positions` and `box`) are in Angstrom, instead of nm!","metadata":{},"id":"964c81b1-7dde-473a-95e6-ea6dccdb8cf4"},{"cell_type":"code","source":"from dmff.utils import jit_condition\nfrom dmff.admp.pme import trim_val_0\nfrom dmff.admp.spatial import v_pbc_shift\n\n\n@jit_condition(static_argnums=())\ndef compute_leading_terms(positions, box):\n n_atoms = len(positions)\n c0 = jnp.zeros(n_atoms)\n c6_list = jnp.zeros(n_atoms)\n box_inv = jnp.linalg.inv(box)\n O = positions[::3]\n H1 = positions[1::3]\n H2 = positions[2::3]\n ROH1 = H1 - O\n ROH2 = H2 - O\n ROH1 = v_pbc_shift(ROH1, box, box_inv)\n ROH2 = v_pbc_shift(ROH2, box, box_inv)\n dROH1 = jnp.linalg.norm(ROH1, axis=1)\n dROH2 = jnp.linalg.norm(ROH2, axis=1)\n costh = jnp.sum(ROH1 * ROH2, axis=1) / (dROH1 * dROH2)\n angle = jnp.arccos(costh) * 180 / jnp.pi\n dipole = -0.016858755 + 0.002287251 * angle + 0.239667591 * dROH1 + (-0.070483437) * dROH2\n charge_H = dipole / dROH1\n charge_O = charge_H * (-2)\n C6_H = (-2.36066199 + (-0.007049238) * angle + 1.949429648 * dROH1+ 2.097120784 * dROH2) * 0.529**6 * 2625.5\n C6_O = (-8.641301261 + 0.093247893 * angle + 11.90395358 * (dROH1+ dROH2)) * 0.529**6 * 2625.5\n C6_H = trim_val_0(C6_H)\n c0 = c0.at[::3].set(charge_O)\n c0 = c0.at[1::3].set(charge_H)\n c0 = c0.at[2::3].set(charge_H)\n c6_list = c6_list.at[::3].set(jnp.sqrt(C6_O))\n c6_list = c6_list.at[1::3].set(jnp.sqrt(C6_H))\n c6_list = c6_list.at[2::3].set(jnp.sqrt(C6_H))\n return c0, c6_list\n\n\ndef generate_calculator(pots, pme_generator, params):\n map_atomtype = pots.meta[\"ADMPPmeForce_map_atomtype\"]\n map_poltype = pots.meta[\"ADMPPmeForce_map_poltype\"]\n def admp_calculator(positions, box, pairs):\n positions = positions * 10 # convert from nm to angstrom\n box = box * 10\n c0, c6_list = compute_leading_terms(positions, box) # compute fluctuated charges\n Q_local = params[\"Q_local\"][map_atomtype]\n Q_local = Q_local.at[:,0].set(c0) # change fixed charge into fluctuated one\n pol = params[\"pol\"][map_poltype]\n tholes = params[\"thole\"][map_poltype]\n mScales = pme_generator.mScales\n pScales = pme_generator.pScales\n dScales = pme_generator.dScales\n E_pme = pme_generator.pme_force.get_energy(\n positions, \n box, \n pairs, \n Q_local, \n pol, \n tholes, \n mScales, \n pScales, \n dScales\n )\n return E_pme \n return jax.jit(admp_calculator)","metadata":{},"execution_count":7,"outputs":[],"id":"c85107f6-0063-46de-8905-e26591f2c7c5"},{"cell_type":"markdown","source":"**Finally, compute the energy and force!**","metadata":{},"id":"ba9f7c49-f139-49d1-8dad-c83b409895e2"},{"cell_type":"code","source":"# neighbor list\nnbl = nblist.NeighborList(box, rc, pots.meta[\"cov_map\"])\nnbl.allocate(positions)\npairs = nbl.pairs\n\npotential_fn = generate_calculator(pots, pme_generator, params)\nene = potential_fn(positions, box, pairs)\nprint(ene)","metadata":{},"execution_count":8,"outputs":[{"name":"stdout","output_type":"stream","text":"-41.261709056188494\n"}],"id":"8ef82930-52ad-4a3a-853d-d05e34268616"},{"cell_type":"code","source":"force_fn = jax.grad(potential_fn, argnums=(0))\nforce = -force_fn(positions, box, pairs)\nprint(force)","metadata":{},"execution_count":9,"outputs":[{"name":"stdout","output_type":"stream","text":"[[ -76.31268719 117.49783627 -79.89266772]\n [ 751.2499921 -582.24588471 -251.82070224]\n [ -18.97483886 -49.68783375 146.28345763]\n [-675.35013452 382.30839617 204.50616711]\n [ -25.65479533 -52.55337869 41.92507785]\n [ 45.04246381 184.68086471 -61.00133263]]\n"}],"id":"da83ef43-70a4-4a5e-9437-2bff7fdd2b50"},{"cell_type":"markdown","source":"The running speed of the first pass is slow because JAX is trying to track the data flow and compile the code. Once the code is compiled, it runs much faster, until the shapes of the input parameters change, trigerring a recompilation. ","metadata":{},"id":"8dfc4b83-c290-4fc6-b37c-e265249290df"},{"cell_type":"code","source":"print(-force_fn(positions, box, pairs))","metadata":{},"execution_count":10,"outputs":[{"name":"stdout","output_type":"stream","text":"[[ -76.31268719 117.49783627 -79.89266772]\n [ 751.2499921 -582.24588471 -251.82070224]\n [ -18.97483886 -49.68783375 146.28345763]\n [-675.35013452 382.30839617 204.50616711]\n [ -25.65479533 -52.55337869 41.92507785]\n [ 45.04246381 184.68086471 -61.00133263]]\n"}],"id":"3340e66b-4d53-4a97-a1ae-afe6b2cdaeba"},{"cell_type":"markdown","source":"## 3. Bottom-Up Fitting \nThis case is primarily contributed by Junmin Chen\n\nIn this case, we will demonstrate how to use DMFF in a bottom-up fitting approach.\n\nNavigate to the working directory","metadata":{"jp-MarkdownHeadingCollapsed":true},"id":"37315f53-ed4c-4c46-97de-2a4c931ed9c0"},{"cell_type":"code","source":"current_directory = os.getcwd()\nparent_directory = os.path.dirname(current_directory)\nos.chdir(parent_directory)\nos.chdir(os.path.join(\"peg_slater_isa\"))","metadata":{},"execution_count":104,"outputs":[],"id":"0281ede7-0764-484b-9f28-fbc16989e64a"},{"cell_type":"markdown","source":"### 3.1 Problem introduction: \nThe main objective of bottom-up fitting is energies and forces calculated from first-principle calculations. The definition of this object function is relatively straightforward. In this example, we will focus on fitting the Pauli Exchange interaction between two PEG dimers.\n\n\n\nThe intermolecular exchange repulsion term for this system can be written in the following form:\n\n$$\n\\begin{align}\n\\begin{cases}\nE_{ex} & = \\sum_{i\nWe define the relevant `Hamiltonian` (we will define different Hamiltonians and potential functions for the dimer and the two monomers separately):","metadata":{},"id":"5af6e793-7557-4eb7-af31-a0c33021f965"},{"cell_type":"code","source":"restart = 'params.0.pickle' # None\n#restart = None\nff = 'forcefield.xml'\npdb_AB = app.PDBFile('peg2_dimer.pdb')\npdb_A = app.PDBFile('peg2.pdb')\npdb_B = app.PDBFile('peg2.pdb')\nparam_file = 'params.0.pickle'\nH_AB = Hamiltonian(ff)\nH_A = Hamiltonian(ff)\nH_B = Hamiltonian(ff)\npme_generator_AB, \\\n disp_generator_AB, \\\n ex_generator_AB, \\\n sr_es_generator_AB, \\\n sr_pol_generator_AB, \\\n sr_disp_generator_AB, \\\n dhf_generator_AB, \\\n dmp_es_generator_AB, \\\n dmp_disp_generator_AB = H_AB.getGenerators()\npme_generator_A, \\\n disp_generator_A, \\\n ex_generator_A, \\\n sr_es_generator_A, \\\n sr_pol_generator_A, \\\n sr_disp_generator_A, \\\n dhf_generator_A, \\\n dmp_es_generator_A, \\\n dmp_disp_generator_A = H_A.getGenerators()\npme_generator_B, \\\n disp_generator_B, \\\n ex_generator_B, \\\n sr_es_generator_B, \\\n sr_pol_generator_B, \\\n sr_disp_generator_B, \\\n dhf_generator_B, \\\n dmp_es_generator_B, \\\n dmp_disp_generator_B = H_B.getGenerators()","metadata":{},"execution_count":117,"outputs":[],"id":"403aad12-5cf0-4c08-b243-758a8bf15ce1"},{"cell_type":"markdown","source":"And obtain the corresponding potential functionss:","metadata":{},"id":"1682c9ce-7e95-48a0-866f-686865b27eb4"},{"cell_type":"code","source":"rc = 1.45\n\n# get potential functions\npots_AB = H_AB.createPotential(pdb_AB.topology, nonbondedCutoff=rc*unit.nanometer, nonbondedMethod=app.CutoffPeriodic, ethresh=1e-4)\npot_pme_AB = pots_AB.dmff_potentials['ADMPPmeForce']\npot_disp_AB = pots_AB.dmff_potentials['ADMPDispPmeForce']\npot_ex_AB = pots_AB.dmff_potentials['SlaterExForce']\npot_sr_es_AB = pots_AB.dmff_potentials['SlaterSrEsForce']\npot_sr_pol_AB = pots_AB.dmff_potentials['SlaterSrPolForce']\npot_sr_disp_AB = pots_AB.dmff_potentials['SlaterSrDispForce']\npot_dhf_AB = pots_AB.dmff_potentials['SlaterDhfForce']\npot_dmp_es_AB = pots_AB.dmff_potentials['QqTtDampingForce']\npot_dmp_disp_AB = pots_AB.dmff_potentials['SlaterDampingForce']\npots_A = H_A.createPotential(pdb_A.topology, nonbondedCutoff=rc*unit.nanometer, nonbondedMethod=app.CutoffPeriodic, ethresh=1e-4)\npot_pme_A = pots_A.dmff_potentials['ADMPPmeForce']\npot_disp_A = pots_A.dmff_potentials['ADMPDispPmeForce']\npot_ex_A = pots_A.dmff_potentials['SlaterExForce']\npot_sr_es_A = pots_A.dmff_potentials['SlaterSrEsForce']\npot_sr_pol_A = pots_A.dmff_potentials['SlaterSrPolForce']\npot_sr_disp_A = pots_A.dmff_potentials['SlaterSrDispForce']\npot_dhf_A = pots_A.dmff_potentials['SlaterDhfForce']\npot_dmp_es_A = pots_A.dmff_potentials['QqTtDampingForce']\npot_dmp_disp_A = pots_A.dmff_potentials['SlaterDampingForce']\npots_B = H_B.createPotential(pdb_B.topology, nonbondedCutoff=rc*unit.nanometer, nonbondedMethod=app.CutoffPeriodic, ethresh=1e-4)\npot_pme_B = pots_B.dmff_potentials['ADMPPmeForce']\npot_disp_B = pots_B.dmff_potentials['ADMPDispPmeForce']\npot_ex_B = pots_B.dmff_potentials['SlaterExForce']\npot_sr_es_B = pots_B.dmff_potentials['SlaterSrEsForce']\npot_sr_pol_B = pots_B.dmff_potentials['SlaterSrPolForce']\npot_sr_disp_B = pots_B.dmff_potentials['SlaterSrDispForce']\npot_dhf_B = pots_B.dmff_potentials['SlaterDhfForce']\npot_dmp_es_B = pots_B.dmff_potentials['QqTtDampingForce']\npot_dmp_disp_B = pots_B.dmff_potentials['SlaterDampingForce']","metadata":{},"execution_count":118,"outputs":[],"id":"b2d8efa0-c3bb-4150-9344-2322b7d304a0"},{"cell_type":"markdown","source":"Note that in `forcefield.xml`, there is a complete force field that contains many components. Here, we are only interested in the exchange part, so we extract the relevant potential function from it.\n\nIn the subsequent calculation of the loss function, we will process minibatches of structures as input.","metadata":{},"id":"ab0a9beb-536b-4b77-a0ec-051fcb7395d7"},{"cell_type":"markdown","source":"### 3.3 Preparing inputs for the potential function (such as neighbor lists, etc) \n\nNext, we prepare the inputs required for these potential functions, with the most important one, the neighbor list. For a simple system with only two molecules, we can set `rc` to a relatively large value to include all possible interactions. This way, we don't need to update the neighbor list due to structural changes, which will speed up the calculations.\n\nFirst, we obtain the initial structure and box size:","metadata":{},"id":"7a16932d-253a-424d-bb34-89872ed27975"},{"cell_type":"code","source":"# init positions used to set up neighbor list\npos_AB0 = jnp.array(pdb_AB.positions._value)\nn_atoms = len(pos_AB0)\nn_atoms_A = n_atoms // 2\nn_atoms_B = n_atoms // 2\npos_A0 = jnp.array(pdb_AB.positions._value[:n_atoms_A])\npos_B0 = jnp.array(pdb_AB.positions._value[n_atoms_A:n_atoms])\nbox = jnp.array(pdb_AB.topology.getPeriodicBoxVectors()._value)","metadata":{},"execution_count":119,"outputs":[],"id":"222e9182-ea9a-4d2b-9e2a-3fe970110a0a"},{"cell_type":"markdown","source":"Then, construct a list of all atom pairs using the initial structure:","metadata":{},"id":"dcc775f6-d09c-4633-b0c7-07cd5807f6bb"},{"cell_type":"code","source":"# nn list initial allocation\nnbl_AB = nblist.NeighborList(box, rc, pots_AB.meta[\"cov_map\"])\nnbl_AB.allocate(pos_AB0)\npairs_AB = nbl_AB.pairs\nnbl_A = nblist.NeighborList(box, rc, pots_A.meta[\"cov_map\"])\nnbl_A.allocate(pos_A0)\npairs_A = nbl_A.pairs\nnbl_B = nblist.NeighborList(box, rc, pots_B.meta[\"cov_map\"])\nnbl_B.allocate(pos_B0)\npairs_B = nbl_B.pairs\n\npairs_AB = pairs_AB[pairs_AB[:, 0] < pairs_AB[:, 1]]\npairs_A = pairs_A[pairs_A[:, 0] < pairs_A[:, 1]]\npairs_B = pairs_B[pairs_B[:, 0] < pairs_B[:, 1]]","metadata":{},"execution_count":120,"outputs":[],"id":"44f4e571-d3d9-4c59-9e2d-26d0dc41c331"},{"cell_type":"markdown","source":"Next, obtain the initial parameters as the starting point for optimization:","metadata":{},"id":"b51a57ee-9d11-4599-9d83-e99cc3f51436"},{"cell_type":"code","source":"params0 = H_AB.getParameters()","metadata":{},"execution_count":121,"outputs":[],"id":"28981b3f-7604-4028-b6b6-0aaea4a81cc5"},{"cell_type":"code","source":"# construct total force field params\ncomps = ['ex', 'es', 'pol', 'disp', 'dhf', 'tot']\nweights_comps = jnp.array([0.1, 0.1, 0.1, 0.1, 0.1, 1.0])\nif restart is None:\n params = {}\n sr_forces = {\n 'ex': 'SlaterExForce',\n 'es': 'SlaterSrEsForce',\n 'pol': 'SlaterSrPolForce',\n 'disp': 'SlaterSrDispForce',\n 'dhf': 'SlaterDhfForce',\n }\n for k in params0['ADMPPmeForce']:\n params[k] = params0['ADMPPmeForce'][k]\n for k in params0['ADMPDispPmeForce']:\n params[k] = params0['ADMPDispPmeForce'][k]\n for c in comps:\n if c == 'tot':\n continue\n force = sr_forces[c]\n for k in params0[sr_forces[c]]:\n if k == 'A':\n params['A_'+c] = params0[sr_forces[c]][k]\n else:\n params[k] = params0[sr_forces[c]][k]\n # a random initialization of A\n for c in comps:\n if c == 'tot':\n continue\n params['A_'+c] = jnp.array(np.random.random(params['A_'+c].shape))\n # specify charges for es damping\n params['Q'] = params0['QqTtDampingForce']['Q']\nelse:\n with open(restart, 'rb') as ifile:\n params = pickle.load(ifile)","metadata":{},"execution_count":122,"outputs":[{"ename":"ImportError","evalue":"cannot import name 'TracerBoolConversionError' from 'jax._src.errors' (/opt/mamba/lib/python3.10/site-packages/jax/_src/errors.py)","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)","Cell \u001b[0;32mIn [122], line 35\u001b[0m\n\u001b[1;32m 33\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 34\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(restart, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrb\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m ifile:\n\u001b[0;32m---> 35\u001b[0m params \u001b[38;5;241m=\u001b[39m \u001b[43mpickle\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[43mifile\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/jax/_src/array.py:30\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m api_util\n\u001b[1;32m 29\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m basearray\n\u001b[0;32m---> 30\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m core\n\u001b[1;32m 31\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m dispatch\n\u001b[1;32m 32\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m dtypes\n","File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/jax/_src/core.py:42\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m effects\n\u001b[1;32m 41\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconfig\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m config\n\u001b[0;32m---> 42\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01merrors\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 43\u001b[0m ConcretizationTypeError, TracerArrayConversionError, TracerBoolConversionError,\n\u001b[1;32m 44\u001b[0m TracerIntegerConversionError, UnexpectedTracerError)\n\u001b[1;32m 45\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m linear_util \u001b[38;5;28;01mas\u001b[39;00m lu\n\u001b[1;32m 47\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m source_info_util\n","\u001b[0;31mImportError\u001b[0m: cannot import name 'TracerBoolConversionError' from 'jax._src.errors' (/opt/mamba/lib/python3.10/site-packages/jax/_src/errors.py)"]}],"id":"b6139479-8ca5-41aa-a7d6-b14c554cc8e9"},{"cell_type":"markdown","source":"### 3.4 Definition of loss function ","metadata":{},"id":"75342d96-db47-46ed-9c0b-6db56fa1f2c8"},{"cell_type":"code","source":"@jit\ndef MSELoss(params, scan_res):\n '''\n The weighted mean squared error loss function\n Conducted for each scan\n '''\n E_tot_full = scan_res['tot_full']\n kT = 2.494 # 300 K = 2.494 kJ/mol\n weights_pts = jnp.piecewise(E_tot_full, [E_tot_full<25, E_tot_full>=25], [lambda x: jnp.array(1.0), lambda x: jnp.exp(-(x-25)/kT)])\n npts = len(weights_pts)\n\n energies = {\n 'ex': jnp.zeros(npts), \n 'es': jnp.zeros(npts), \n 'pol': jnp.zeros(npts),\n 'disp': jnp.zeros(npts),\n 'dhf': jnp.zeros(npts),\n 'tot': jnp.zeros(npts)\n }\n\n # setting up params for all calculators\n params_ex = {}\n params_sr_es = {}\n params_sr_pol = {}\n params_sr_disp = {}\n params_dhf = {}\n params_dmp_es = {} # electrostatic damping\n params_dmp_disp = {} # dispersion damping\n for k in ['B', 'mScales']:\n params_ex[k] = params[k]\n params_sr_es[k] = params[k]\n params_sr_pol[k] = params[k]\n params_sr_disp[k] = params[k]\n params_dhf[k] = params[k]\n params_dmp_es[k] = params[k]\n params_dmp_disp[k] = params[k]\n params_ex['A'] = params['A_ex']\n params_sr_es['A'] = params['A_es']\n params_sr_pol['A'] = params['A_pol']\n params_sr_disp['A'] = params['A_disp']\n params_dhf['A'] = params['A_dhf']\n # damping parameters\n params_dmp_es['Q'] = params['Q']\n params_dmp_disp['C6'] = params['C6']\n params_dmp_disp['C8'] = params['C8']\n params_dmp_disp['C10'] = params['C10']\n p = {}\n p['SlaterExForce'] = params_ex\n p['SlaterSrEsForce'] = params_sr_es\n p['SlaterSrPolForce'] = params_sr_pol\n p['SlaterSrDispForce'] = params_sr_disp\n p['SlaterDhfForce'] = params_dhf\n p['QqTtDampingForce'] = params_dmp_es\n p['SlaterDampingForce'] = params_dmp_disp\n\n # calculate each points, only the short range and damping components\n for ipt in range(npts):\n # get position array\n pos_A = jnp.array(scan_res['posA'][ipt]) / 10\n pos_B = jnp.array(scan_res['posB'][ipt]) / 10\n pos_AB = jnp.concatenate([pos_A, pos_B], axis=0)\n\n #####################\n # exchange repulsion\n #####################\n E_ex_AB = pot_ex_AB(pos_AB, box, pairs_AB, p)\n E_ex_A = pot_ex_A(pos_A, box, pairs_A, p)\n E_ex_B = pot_ex_B(pos_B, box, pairs_B, p)\n E_ex = E_ex_AB - E_ex_A - E_ex_B\n\n #######################\n # electrostatic + pol\n #######################\n E_dmp_es = pot_dmp_es_AB(pos_AB, box, pairs_AB, p) \\\n - pot_dmp_es_A(pos_A, box, pairs_A, p) \\\n - pot_dmp_es_B(pos_B, box, pairs_B, p)\n E_sr_es = pot_sr_es_AB(pos_AB, box, pairs_AB, p) \\\n - pot_sr_es_A(pos_A, box, pairs_A, p) \\\n - pot_sr_es_B(pos_B, box, pairs_B, p)\n\n ###################################\n # polarization (induction) energy\n ###################################\n E_sr_pol = pot_sr_pol_AB(pos_AB, box, pairs_AB, p) \\\n - pot_sr_pol_A(pos_A, box, pairs_A, p) \\\n - pot_sr_pol_B(pos_B, box, pairs_B, p)\n\n #############\n # dispersion\n #############\n E_dmp_disp = pot_dmp_disp_AB(pos_AB, box, pairs_AB, p) \\\n - pot_dmp_disp_A(pos_A, box, pairs_A, p) \\\n - pot_dmp_disp_B(pos_B, box, pairs_B, p)\n E_sr_disp = pot_sr_disp_AB(pos_AB, box, pairs_AB, p) \\\n - pot_sr_disp_A(pos_A, box, pairs_A, p) \\\n - pot_sr_disp_B(pos_B, box, pairs_B, p)\n\n ###########\n # dhf\n ###########\n E_AB_dhf = pot_dhf_AB(pos_AB, box, pairs_AB, p)\n E_A_dhf = pot_dhf_A(pos_A, box, pairs_A, p)\n E_B_dhf = pot_dhf_B(pos_B, box, pairs_B, p)\n E_dhf = E_AB_dhf - E_A_dhf - E_B_dhf\n\n energies['ex'] = energies['ex'].at[ipt].set(E_ex)\n energies['es'] = energies['es'].at[ipt].set(E_dmp_es + E_sr_es)\n energies['pol'] = energies['pol'].at[ipt].set(E_sr_pol)\n energies['disp'] = energies['disp'].at[ipt].set(E_dmp_disp + E_sr_disp)\n energies['dhf'] = energies['dhf'].at[ipt].set(E_dhf)\n energies['tot'] = energies['tot'].at[ipt].set(E_ex \n + E_dmp_es + E_sr_es\n + E_sr_pol \n + E_dmp_disp + E_sr_disp \n + E_dhf)\n errs = jnp.zeros(len(comps))\n for ic, c in enumerate(comps):\n dE = energies[c] - scan_res[c]\n mse = dE**2 * weights_pts / jnp.sum(weights_pts)\n errs = errs.at[ic].set(jnp.sum(mse))\n\n return jnp.sum(weights_comps * errs)","metadata":{},"execution_count":49,"outputs":[],"id":"e29e556a-12e8-4a71-8421-7fa6f5e1d9b3"},{"cell_type":"markdown","source":"In this function, we have added a statistical weight to each data point. By assigning weights to the data points, we can effectively ignore those with significantly higher total energies, which may not be important for the actual simulations.","metadata":{},"id":"f60b5c5e-f3e1-43cd-a426-c1b6f7ab92e6"},{"cell_type":"code","source":"# load data\nwith open('data_sr.pickle', 'rb') as ifile:\n data = pickle.load(ifile)\n\nMSELoss_grad = jit(value_and_grad(MSELoss, argnums=(0)))\nerr, gradients = MSELoss_grad(params, data['000'])\nsids = np.array(list(data.keys()))","metadata":{},"execution_count":null,"outputs":[],"id":"240786a9-8e8e-44cc-a8fe-05e506f2c779"},{"cell_type":"markdown","source":"### 3.5 Optimize \n\nonly optimize these parameters A/B","metadata":{},"id":"463259a5-b39b-433a-a902-089108885816"},{"cell_type":"code","source":"def mask_fn(grads):\n for k in grads:\n if k.startswith('A_') or k == 'B':\n continue\n else:\n grads[k] = 0.0\n return grads","metadata":{},"execution_count":24,"outputs":[],"id":"0e470b13-0c4e-4b14-a11a-0cb55ecb2250"},{"cell_type":"markdown","source":"To start our optimization loop, we can iterate over the data for a specified number of epochs and update the parameters using the optimizer. After each epoch, we can save the parameters as a pickle file.","metadata":{},"id":"788d6ad5-e851-46d5-ba2d-aa8e37467bb5"},{"cell_type":"code","source":"# start to do optmization\nlr = 0.01\noptimizer = optax.adam(lr)\nopt_state = optimizer.init(params)\n\nn_epochs = 2000\nfor i_epoch in range(n_epochs):\n np.random.shuffle(sids)\n for sid in sids:\n loss, grads = MSELoss_grad(params, data[sid])\n grads = mask_fn(grads)\n print(loss)\n sys.stdout.flush()\n updates, opt_state = optimizer.update(grads, opt_state)\n params = optax.apply_updates(params, updates)\n with open('params.pickle', 'wb') as ofile:\n pickle.dump(params, ofile)","metadata":{},"execution_count":null,"outputs":[],"id":"e6bf7507-20fb-43f1-93ff-fd261339e68f"},{"cell_type":"markdown","source":"## 4.Using DMFF for force field optimization of liquid dimethyl carbonate (DMC) ","metadata":{"jp-MarkdownHeadingCollapsed":true},"id":"55664308-ca38-48fa-898d-a5fa7e2d90d2"},{"cell_type":"markdown","source":"In the following case, we will provide a practical application of DMFF and build the corresponding workflow.\n\nThis case is primarily contributed by Wei Feng\n\n### 4.1 Introduction \nThe initial step in building a workflow using DMFF is to generate the potential function. The process and API are similar to OpenMM and generally follow the following steps:\n\n1. The first step in building a force field using DMFF is to define the force field object, known as the `Hamiltonian`. It encapsulates the `typification` rules and all the force field parameters. It can be seen as the equivalent of an XML file in OpenMM or an ITP file in GROMACS, but represented as a Python object. \n2. Obtain the molecular bond connectivity information for the system you want to simulate (you can use a PDB file).\n3. Based on the system's connectivity topology, you can use the `Hamiltonian` to classify and parameterize each atom, bond length, bond angle, dihedral angle, etc., and create a potential function. The function takes inputs such as atomic positions, box size, and force field parameters, making it suitable for subsequent derivative calculations.\n4. If necessary, the potential function can be modified and extended as needed. For example, using `jax.grad`, one can obtain a function to compute forces and the Virial matrix, which can be directly used in molecular dynamics (MD) simulations.\n5. `Estimator` and loss functions can be defined based on the potential function to compute desired properties. The optimization of parameters can be performed using a gradient descent optimizer.\n\n**In DMFF, we provide JAX implementations of various potential functions commonly used in traditional physics force fields, such as PME, pairwise interactions, multipole moments, and polarizable force fields. With the help of JAX's transformation tools, users can differentiate, vectorize, compile, and modify these potential functions. They can also create new potential functions by re-encapsulating and recombining existing ones. Additionally, users can develop `property estimators` based on these potential functions, and these new functions can also be further differentiated or vectorized. This function-centric programming paradigm in DMFF offers unbelievable flexibility in defining new force fields and objective functions.**\n\nTo provide users with a comprehensive understanding of the main modules and functionalities in DMFF and how these modules work together in force field development, we will demonstrate the complete process of model definition, optimization, and deployment using a coarse-grained model of dimethyl carbonate (DMC). The goal of this task is to optimize the interactions between three sites representing the DMC chain (3-site model) and reproduce the radial distribution function (RDF) of liquid DMC obtained from X-ray diffraction (XRD) experiments. We will also compare the DMC coarse-grained model force field with the all-atom OPLS-AA force field.\n\n\n\nNavigate to the working directory","metadata":{"jp-MarkdownHeadingCollapsed":true},"id":"475eefde-d99c-412d-b2f4-7ed93c179f86"},{"cell_type":"code","source":"current_directory = os.getcwd()\nparent_directory = os.path.dirname(current_directory)\nos.chdir(parent_directory)\nos.chdir(os.path.join(\"DMC\"))","metadata":{},"execution_count":61,"outputs":[],"id":"63321944-cf2b-4ad0-8188-7dc18c190247"},{"cell_type":"markdown","source":"### 4.2 Definition of potential function \nLoad the topology of the DMC system and construct the potential function for the simulation box:","metadata":{},"id":"2f05bf27-37f0-4eaf-9675-9d0538aa54a1"},{"cell_type":"code","source":"# Load PDB and topology data\npdb = app.PDBFile(\"box_DMC.pdb\")\ntop = pdb.topology\ntopdata = dmff.DMFFTopology(top)\ncov_mat = topdata.buildCovMat()","metadata":{},"execution_count":62,"outputs":[],"id":"e0a7c162-5ee8-4a36-a5c7-46848e837cb4"},{"cell_type":"markdown","source":"From the `prm1.xml` force field file, we can see that the function includes the following terms for intramolecular atomic interactions: bond, angle, and electrostatics, as well as intermolecular interactions represented by the Lennard-Jones potential:\n\n$$\\begin{align*}\n V(\\mathbf{R}) &= V_{\\mathrm{bond}} + V_\\mathrm{angle} + V_{\\mathrm{elec}} + V_{\\mathrm{vdW}} \\\\\n &= \\sum_{\\mathrm{bonds}}k_r(r - r_0)^2 + \\sum_{\\mathrm{angles}}k_\\theta(\\theta - \\theta_{eq})^2 + \\sum_{\\mathrm{i\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n```\nand we take the rest part as the res.xml. Then we can create potential & generator for this part in DMFF, and we need to define a xml-render function between xml files","metadata":{},"id":"a86a3823-6599-4f79-b6f5-e7678c054caf"},{"cell_type":"code","source":"# Load XML forcefield parameters\nio = XMLIO()\nio.loadXML(\"lj.xml\")\nffinfo = io.parseXML()\noperator = TemplateATypeOperator(ffinfo)\noperator.operate(topdata)\nfor na, a in enumerate(topdata._meta):\n print(a)\n \n# Create LJ potential\nparamset = ParamSet()\nlj_gen = LennardJonesGenerator(ffinfo, paramset)\nprint(paramset.parameters)\nprint()\nlj_force = lj_gen.createPotential(\n topdata, nonbondedMethod=app.CutoffPeriodic, nonbondedCutoff=1.0, args={})","metadata":{},"execution_count":63,"outputs":[{"name":"stdout","output_type":"stream","text":"{'LennardJonesForce': {'sigma': DeviceArray([0.5, 0.5], dtype=float64), 'epsilon': DeviceArray([0.75, 0.75], dtype=float64), 'sigma_nbfix': DeviceArray([], dtype=float64), 'epsilon_nbfix': DeviceArray([], dtype=float64)}}\n\n"}],"id":"712dab2c-cbcd-47ec-9674-95b89d2bded4"},{"cell_type":"code","source":"def xmlrender(file1, file2, file3):\n lj_tree = ET.parse(file1)\n lj_root = lj_tree.getroot()\n\n fullff_tree = ET.parse(file2)\n fullff_root = fullff_tree.getroot()\n\n nonbonded_force_element = fullff_root.find(\".//NonbondedForce\")\n\n lj_force_element = lj_root.find(\".//LennardJonesForce\")\n for atom_element in lj_force_element:\n nonbonded_force_element.append(atom_element)\n \n combined_tree = ET.ElementTree(fullff_root)\n combined_tree.write(file3, encoding='utf-8')","metadata":{},"execution_count":64,"outputs":[],"id":"93a0ee4f-11f6-48f6-a4f2-a41f3d5c2b8b"},{"cell_type":"markdown","source":"### 4.3 Definition of OpenMM sampler ","metadata":{},"id":"4cb1282a-58e8-4719-b907-ad6bc73a8412"},{"cell_type":"code","source":"# OpenMM sampler, for resampling during optimization\ndef runMD(ffile, trajfile):\n pdb = app.PDBFile('box_DMC.pdb')\n forcefield = app.ForceField(ffile)\n system = forcefield.createSystem(pdb.topology, nonbondedMethod=app.PME, nonbondedCutoff=1.0*unit.nanometer, constraints=app.HBonds)\n for force in system.getForces():\n if isinstance(force, mm.NonbondedForce):\n force.setUseDispersionCorrection(False)\n system.addForce(mm.MonteCarloBarostat(1.0*unit.bar, 293*unit.kelvin, 20))\n \n # Create integrator and simulation\n integ = mm.LangevinIntegrator(293*unit.kelvin, 5/unit.picosecond, 1*unit.femtosecond)\n simulation = app.Simulation(pdb.topology, system, integ)\n \n # Add reporters and perform simulation\n simulation.reporters.append(app.DCDReporter(trajfile, 4000))\n simulation.reporters.append(app.StateDataReporter(sys.stdout, 10000, density=True, step=True, remainingTime=True, speed=True, totalSteps=500*1000))\n simulation.context.setPositions(pdb.getPositions())\n simulation.minimizeEnergy()\n simulation.step(500*1000)\n\n# Usage\nrunMD(\"prm1.xml\", \"init.dcd\")","metadata":{},"execution_count":78,"outputs":[{"name":"stdout","output_type":"stream","text":"#\"Step\",\"Density (g/mL)\",\"Speed (ns/day)\",\"Time Remaining\"\n10000,0.6530003030971635,0,--\n20000,0.6446899494552302,636,1:05\n30000,0.654480923076971,635,1:03\n40000,0.671459932046347,635,1:02\n50000,0.6414382719916313,635,1:01\n60000,0.6637136428436129,635,0:59\n70000,0.6577889530955275,635,0:58\n80000,0.6682431392147973,635,0:57\n90000,0.6417739622327542,635,0:55\n100000,0.6588650508213222,635,0:54\n110000,0.6487744293895138,635,0:53\n120000,0.6360648337854733,635,0:51\n130000,0.6352417039403969,635,0:50\n140000,0.6560213808456982,635,0:49\n150000,0.6623521021910665,634,0:47\n160000,0.6521971277778645,634,0:46\n170000,0.6589500151573725,634,0:44\n180000,0.6704886539606272,634,0:43\n190000,0.6458769035922026,634,0:42\n200000,0.6534911856804638,634,0:40\n210000,0.6204816291822809,634,0:39\n220000,0.636381553562392,634,0:38\n230000,0.6567372679532382,634,0:36\n240000,0.660606478056156,634,0:35\n250000,0.650192833823221,634,0:34\n260000,0.6514872174830811,634,0:32\n270000,0.6400983888702015,634,0:31\n280000,0.6285535488055957,634,0:29\n290000,0.6573162752760268,634,0:28\n300000,0.6359023006772347,634,0:27\n310000,0.6386603469589097,635,0:25\n320000,0.6288081268924041,635,0:24\n330000,0.6656420040710469,635,0:23\n340000,0.6503358580491692,635,0:21\n350000,0.6528482463172317,635,0:20\n360000,0.6376504075487313,634,0:19\n370000,0.649412819278512,634,0:17\n380000,0.6561046947238908,634,0:16\n390000,0.650230647082696,634,0:14\n400000,0.6546968354167091,634,0:13\n410000,0.6525136526949809,634,0:12\n420000,0.647013386295085,634,0:10\n430000,0.6703421935342975,634,0:09\n440000,0.6680338729151056,634,0:08\n450000,0.6407888426631222,634,0:06\n460000,0.628742500395471,634,0:05\n470000,0.6567841435191818,634,0:04\n480000,0.6550768983884313,634,0:02\n490000,0.6556400271026187,631,0:01\n500000,0.6586471077786411,631,0:00\n"}],"id":"63951106-00b1-41c5-b452-3fcc3585b8e0"},{"cell_type":"markdown","source":"The function generates an NPT ensemble using OpenMM based on the input parameters and saves the generated samples in the specified trajectory file. This function will be repeatedly called for resampling purposes during subsequent optimization steps.\n\n### 4.4 Definition of Energy Rerun function in OpenMM & DMFF \n\nTo enhance computational efficiency, we opt to use DMFF only for calculating the energy of the Lennard Jones part, while the energy for the other parts is still obtained from OpenMM. Therefore, we need to design energy rerun functions pertaining to both OpenMM and DMFF.","metadata":{},"id":"060d68cf-7df9-489f-8899-666bda63be9c"},{"cell_type":"code","source":"def rerun_energy(ffile, traj, skip=20, removeLJ=True, skpi=0):\n samples = md.load(traj, top='box_DMC.pdb')[skip:]\n pdb = app.PDBFile('box_DMC.pdb')\n forcefield = app.ForceField(ffile)\n system = forcefield.createSystem(pdb.topology, nonbondedMethod=app.PME, nonbondedCutoff=1.0*unit.nanometer, constraints=app.HBonds)\n for force in system.getForces():\n if isinstance(force, mm.NonbondedForce):\n force.setUseDispersionCorrection(False)\n if removeLJ:\n for npart in range(force.getNumParticles()):\n chrg, sig, eps = force.getParticleParameters(npart)\n force.setParticleParameters(npart, chrg, 1.0, 0.0)\n for nex in range(force.getNumExceptions()):\n p1, p2, chrg, sig, eps = force.getExceptionParameters(nex)\n force.setExceptionParameters(nex, p1, p2, chrg, 1.0, 0.0)\n integ = mm.LangevinIntegrator(293*unit.kelvin, 5/unit.picosecond, 1*unit.femtosecond)\n ctx = mm.Context(system, integ)\n energies = []\n for frame in tqdm(samples):\n ctx.setPositions(frame.xyz[0] * unit.nanometer)\n ctx.setPeriodicBoxVectors(*frame.unitcell_vectors[0])\n ctx.applyConstraints(1e-10)\n state = ctx.getState(getEnergy=True)\n energy = state.getPotentialEnergy().value_in_unit(unit.kilojoule_per_mole)\n energies.append(energy)\n return np.array(energies)","metadata":{},"execution_count":79,"outputs":[],"id":"23a3b075-6749-415d-b0e5-2658db14be95"},{"cell_type":"code","source":"def rerun_dmff_lennard_jones(params, pdb, traj, efunc, skip=0):\n samples = md.load(traj, top=pdb)[skip:]\n energies = []\n nblist = NeighborListFreud(samples.unitcell_vectors[0], 1.0, cov_mat)\n xyzs_jnp = jnp.array(samples.xyz)\n cell_jnp = jnp.array(samples.unitcell_vectors)\n energies = []\n nblist = NeighborListFreud(samples.unitcell_vectors[0], 1.0, cov_mat)\n xyzs_jnp = jnp.array(samples.xyz)\n cell_jnp = jnp.array(samples.unitcell_vectors)\n energies = []\n for nframe in trange(len(samples)):\n frame = samples[nframe]\n # calc pair\n pairs = jnp.array(nblist.allocate(frame.xyz[0], frame.unitcell_vectors[0]))\n ener = efunc(xyzs_jnp[nframe,:,:], cell_jnp[nframe,:,:], pairs, params)\n energies.append(ener.reshape((1,)))\n energies = jnp.concatenate(energies)\n return energies","metadata":{},"execution_count":80,"outputs":[],"id":"82ec485f-95c1-4fb6-a393-ef82a9db06bb"},{"cell_type":"markdown","source":"### 4.5 Definition of property calculation functions \n\nIn the fitting process, we need to simultaneously fit the Radial Distribution Function (RDF) and density. Therefore, we will define separate calculation functions for RDF and density:","metadata":{},"id":"b50ba93c-27bb-4b9b-9f68-bdd92cbb50c6"},{"cell_type":"code","source":"# define property calculator, in our case, rdf for each frame:\ndef compute_rdf_frame(traj, xaxis):\n rdf_list = []\n delta = xaxis[1] - xaxis[0]\n\n coordinates = traj.xyz\n masses = np.array([15, 15, 60]) # mass of each site\n coordinates_3d = coordinates.reshape((traj.n_frames, 175, 3, 3))\n com = np.sum(masses[:, np.newaxis] * coordinates_3d, axis=2) / 90\n\n pairs = np.array(list(combinations(range(175), 2)))\n\n tidx = np.arange(0, 525, 3, dtype=int)\n tsub = traj.atom_slice(tidx)\n tsub.xyz = com\n \n for frame in tsub:\n _, g_r = md.compute_rdf(frame, pairs, r_range=(xaxis[0]-0.5*delta, xaxis[-1]+0.5*delta+1e-10), bin_width=delta)\n rdf_list.append(g_r.reshape((1, -1)))\n return np.concatenate(rdf_list, axis=0)","metadata":{},"execution_count":81,"outputs":[],"id":"9c977f6e-29a0-4624-b608-a5ea5eb31ca1"},{"cell_type":"markdown","source":"When calculating the ensemble average of an observable quantity $A$, the following statistical quantities need to be computed: $\\langle A \\rangle_p = \\sum_n W (x_n ; \\theta) A(x_n ; \\theta)$, code above has defined the $A(x_n ; \\theta)$ function.\n\nIn general, we need to encapsulate $\\langle A \\rangle_p $ in a function and then take its overall derivative during each optimization iteration.\nHowever, in this case, since the RDF and density are purely structural properties that are independent of $\\theta$ and do not participate in the derivative calculation, we can precompute the RDF for each sample ($A(x_n)$) and store it. It is not necessary to recalculate it in every iteration.\n\nIn the code above, we use the existing tools in `mdtraj` to compute the RDF for each frame, rather than using a differentiable implementation with jax.\n\n### 4.6 Read the data and perform the comparison \n\nRead the experimental data and pre-compute the RDF for the DMC full-atom model in the OPLS-AA force field (generated by LigPargen) as comparison:","metadata":{},"id":"066a1963-01cc-4f72-82b1-a937c3e64ee0"},{"cell_type":"code","source":"# Prepare reference data\ndef readRDF(fname):\n with open(fname, \"r\") as f:\n data = np.array([[float(j) for j in i.strip().split()] for i in f])\n xaxis = np.linspace(2.0, 14.0, 121)\n yinterp = np.interp(xaxis, data[:,0], data[:,1])\n return xaxis, yinterp\n\n# read experimental benzene RDF\nx_ref, y_ref = readRDF(\"DMC_Experi.txt\")\nm_ref, n_ref = readRDF(\"DMC_OPLS.txt\")","metadata":{},"execution_count":82,"outputs":[],"id":"ad92d3c4-895e-4d81-ad41-1709a4a3d053"},{"cell_type":"markdown","source":"We will compare the RDF results obtained from the RDF calculation function with the experimental data and the RDF results from the full-atom model in the OPLS-AA force field. Let's start by looking at the RDF generated from the initial parameter sampling, which serves as our starting point for optimization.","metadata":{},"id":"b7f635fc-07f5-47d5-9efa-1056af9acfe7"},{"cell_type":"code","source":"traj_init = md.load(\"init.dcd\", top=\"box_DMC.pdb\")[20:]\nrdf_frames_init = compute_rdf_frame(traj_init, x_ref*0.1)\nrdf_init = rdf_frames_init.mean(axis=0)\n\nplt.plot(x_ref, rdf_init, label = \"Initial\")\n#plt.plot(x_ref, y_ref, label = \"Experiment\")\nplt.plot(m_ref, n_ref, label = \"OPLS-AA\")\n\nplt.legend()\nplt.show()","metadata":{},"execution_count":83,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAiMAAAGdCAYAAADAAnMpAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAA9hAAAPYQGoP6dpAABtZUlEQVR4nO3dd3iUVfbA8e/MpPdeSSD03jtKERBZdcUGVuyrLlZ2LehaV+Xnuta1YIHFVREbIqKiiKCg9N5rSID0hPQ+M78/biYFCKTMvO/M5HyeJ89MJu/MPUkgObn33HMNVqvVihBCCCGETox6ByCEEEKItk2SESGEEELoSpIRIYQQQuhKkhEhhBBC6EqSESGEEELoSpIRIYQQQuhKkhEhhBBC6EqSESGEEELoykPvAJrCYrGQlpZGYGAgBoNB73CEEEII0QRWq5WioiLi4uIwGhuf/3CJZCQtLY2EhAS9wxBCCCFECxw7dox27do1+nGXSEYCAwMB9ckEBQXpHI0QQgghmqKwsJCEhITa3+ONcYlkxLY0ExQUJMmIEEII4WLOVWIhBaxCCCGE0JUkI0IIIYTQlSQjQgghhNCVS9SMCCGEaDusVivV1dWYzWa9QxHnYDKZ8PDwaHXbDUlGhBBCOI3KykrS09MpLS3VOxTRRH5+fsTGxuLl5dXi15BkRAghhFOwWCwkJydjMpmIi4vDy8tLGl06MavVSmVlJdnZ2SQnJ9OlS5ezNjY7G0lGhBBCOIXKykosFgsJCQn4+fnpHY5oAl9fXzw9PUlJSaGyshIfH58WvY4UsAohhHAqLf3rWujDHt8v+Y4LIYQQQleSjAghhBBCV5KMCCGEEBozGAwsXrz4rNfcfPPNTJkypcmvefToUQwGA9u2bWtVbHqQAlYhhBCilW6++Wby8/PPmWDYpKenExoaCqgkIikpia1bt9K/f//aa15//XWsVqsDonU+MjMihBMprqjmzV8OkpJboncoQggHiomJwdvb+6zXBAcHExISok1AOpNkRAgn8tKyffz7pwPcNG8DxRXVeocjhO6sViulldWav7VmRmLs2LHcd999PPzww4SFhRETE8PTTz/d4Jr6yzRJSUkADBgwAIPBwNixY4HTl2mWLVvGeeedR0hICOHh4VxyySUcPny4xXE6E1mmEcJJZBaW8+nGYwAczS3lqW928/LUfjpHJYS+yqrM9HzyR83H3fPsJPy8Wv4r8sMPP2TmzJmsX7+etWvXcvPNNzNq1CgmTpx42rUbNmxg6NCh/Pzzz/Tq1avRTqYlJSXMnDmTvn37UlxczJNPPsnll1/Otm3bXH47tCQjQjiJOb8eprLaQodwP1LzSvlqy3FGd43gsv7xeocmhGimvn378tRTTwHQpUsX3nzzTVasWHHGZCQyMhKA8PBwYmJiGn3NK6+8ssH78+bNIzIykj179tC7d287Rq89SUaEcAJZheUsWJ8KwD+n9Gbj0ZO8seIg//h6FwMTQ0kIk26Uom3y9TSx59lJuozbGn379m3wfmxsLFlZWa16zYMHD/Lkk0+yfv16cnJysFgsAKSmpkoyIoRonspqCwYDeJrqplXf/e0IFdUWBiaGcF7nCEZ0DOePQzlsSjnJ/Qu38vmdI/AwufY0rBAtYTAYWrVcohdPT88G7xsMhtrkoaUuvfRS2rdvz/vvv09cXBwWi4XevXtTWVnZqtd1BvLTTQgNWa1Wrn1/HX2e/pHXfz5IeZWZ7KIKPlmfAsB947tgMBjwMBl57Zr+BPp4sCU1n6U70nWOXAjhKLYaEbPZ3Og1ubm57N+/n3/84x+MHz+eHj16cPLkSa1CdDjXSzeFcGEpuaVsTlE/QF79+QCfbzpG1+gAyqss9EsIYUzXyNpr24X6cc2QBN5fncyW1JNMGSC1I0K4o6ioKHx9fVm2bBnt2rXDx8eH4ODgBteEhoYSHh7Oe++9R2xsLKmpqTz66KM6RWx/MjMihIbWHckFIDHMj7hgH07kl7FyfzYAD9TMitTXp10IADtPFGgapxBCOx4eHrzxxhu8++67xMXFcdlll512jdFoZOHChWzevJnevXvz4IMP8tJLL+kQrWMYrC7Q3q2wsJDg4GAKCgoICgrSOxwhWuzBz7bx9dYT3HtBZ/46tjPv/HqYd389zNCkMP5369DTkpEj2cVc8PKv+Hga2fX0JKkbEW6tvLyc5ORkkpKSWnwUvdDe2b5vTf39Lcs0QmjEarXWzowM7xiOr5eJmRO7ct8FnQFOS0QAOoT7E+DtQXFFNYeyi+keI8m4EML9yJ9ZQmjkWF4Z6QXleJoMDEwMrX3cw2RsdMbDaDTQK04lILtOFGoSpxBCaE2SESE0YpsV6dcuBF+vpvcw6B2vCtl2Sd2IEMJNSTIihEbWJatkZFjHsGY9r09NMiJFrEIIdyXJiBAaWX8kD1D1Is1hmxnZk1aI2eL09eZCCNFskowIoYFjeaWcyC/Dw2hgUPvQcz+hnqQIf/y8TJRVmTmSXeygCIUQQj+SjAihgfXJalakb7vgZre2NtUrYpWlGiGEO5JkRAgN2IpXhzVzicamt9SNCCHcmCQjQmhgva14Nal5xas2veNkR40Qwn1JMiKEg53IL+NYXhkmo4HBHVqWjPRpp5KR3WmFWKSIVQindOzYMW699Vbi4uLw8vKiffv23H///eTm5tZeM3bsWAwGAwaDAR8fH3r27Mnbb79d+/H58+cTEhLS6BjZ2dncfffdJCYm4u3tTUxMDJMmTeL3339vUozdu3fH29ubjIyMRq8pKysjLCyMiIgIKioqmvS6rSXJiBAOtr5miaZ3fDAB3i1retwpMgAfTyOllWaO5JTYMzwhhB0cOXKEwYMHc/DgQT799FMOHTrEnDlzWLFiBSNGjCAvL6/22jvuuIP09HT27NnD1KlTmTFjBp9++mmTxrnyyivZunUrH374IQcOHGDJkiWMHTu2QcLTmDVr1lBWVsZVV13Fhx9+2Oh1X331Fb169aJ79+4sXry4SXG1lrSDF8LB6rb0tmxWBFQRa8/YILak5rPrRAGdowLsFZ4Qwg5mzJiBl5cXP/30E76+vgAkJiYyYMAAOnXqxOOPP84777wDgJ+fHzExMQA8/fTTLFiwgCVLlnDttdeedYz8/HxWr17NqlWrGDNmDADt27dn6NChTYpx7ty5XHfddYwZM4b777+fRx55pNHrbrjhBqxWK3PnzmXatGlNev3WkJkRIRzMVnQ6KLF5W3pPJc3PRJtktUJlifZvzThDNi8vjx9//JG//vWvtYmITUxMDNdffz2fffYZjZ1L6+vrS2Vl5TnHCQgIICAggMWLFzd7+aSoqIgvvviCG264gYkTJ1JQUMDq1atPu+7w4cOsXbuWqVOnMnXqVFavXk1KSkqzxmoJmRkRwoHMFiuHanqDdIsJbNVrSVt40SZVlcILcdqP+1gaePk36dKDBw9itVrp0aPHGT/eo0cPTp48SXZ2doPHzWYzn376KTt27OAvf/nLOcfx8PBg/vz53HHHHcyZM4eBAwcyZswYrrnmGvr27XvW5y5cuJAuXbrQq1cvAK655hrmzp3L+eef3+C6efPmMXnyZEJD1R9PkyZN4r///S9PP/30OeNrDZkZEcKBUnJLqKy24ONpJCHUr1WvZUtGpIhVCOfU2MzHqd5++20CAgLw9fXljjvu4MEHH+Tuu+9u0nOvvPJK0tLSWLJkCRdddBGrVq1i4MCBzJ8/H4C77rqrdgYlIKBuOXfevHnccMMNte/fcMMNfPHFFxQVFdU+Zjab+fDDD0+7bv78+VgslibF11IyMyKEAx3IVP/Ru0YHYjQaWvVaXaIC8PYwUlxRTXJuCZ0ipW5EtAGefmqWQo9xm6hz584YDAb27t3L5ZdfftrH9+7dS2hoKJGRkQBcf/31PP744/j6+hIbG4vR2Lx5AR8fHyZOnMjEiRN54oknuP3223nqqae4+eabefbZZ/n73//e4Po9e/awbt06NmzY0KBOxGw2s3DhQu644w4AfvzxR06cOHFajYjZbGbFihVMnDixWXE2h8yMCOFABzLVEk2XqNYt0QB4mIz0tHViPS5LNaKNMBjUconWb4am//EQHh7OxIkTefvttykrK2vwsYyMDD755BOmTZuGoeY1g4OD6dy5M/Hx8c1ORM6kZ8+elJSoXXZRUVF07ty59g1UQero0aPZvn0727Ztq32bOXMmc+fOrX2duXPncs011zS4Ztu2bbVLOo4kMyNCOND+mpmRbjH2mcXo1y6Eran57DhewJQB8XZ5TSFE67355puMHDmSSZMm8dxzz5GUlMTu3bt56KGHiI+P5/nnn2/ya5nNZrZt29bgMW9vb6Kiorj66qu59dZb6du3L4GBgWzatIl//etfXHbZZWd8raqqKj766COeffZZevfu3eBjt99+O6+88gq7d+8mKiqKb7/9liVLlpx23fTp07n88svJy8sjLKzluwLPRpIRIRzoQEbdMo099K1pfrbjeL5dXk8IYR9dunRh06ZNPPXUU0ydOpW8vDxiYmKYMmUKTz31VLN+iRcXFzNgwIAGj3Xq1Indu3czbNgwXn31VQ4fPkxVVRUJCQnccccdPPbYY2d8rSVLlpCbm3vG5aMePXrQo0cP5s6dS3x8PP7+/owfP/6068aPH4+vry8ff/wx9913X5M/j+YwWJtacaOjwsJCgoODKSgoICgoSO9whGiSymoLPZ9cRrXFyh+PXkBciO+5n3QOh7KKmfDKr/h4Gtn19CQ8TLLSKtxHeXk5ycnJJCUl4ePjo3c4oonO9n1r6u9v+UkmhIMk55RQbbES6O1BbLB9frB2jPAnwNuD8ioLB7OK7fKaQgihN0lGhHAQW71I15jA2sK11jIaDfSOV39dyFKNEMJdSDIihIMcrN3Wa98tuP3ahQCwQ3bUCCHchCQjQjjIfjsXr9r0lWRECOFmJBkRwkFsDc+62T0ZUTtq9mUUUlFttutrCyGEHiQZEcIByqvMpOSVAqpmxJ7ahfoS6udJldnK3vSicz9BCBfjAps8RT32+H5JMiKEAxzKKsZqhTB/LyICvO362gaDoXapZqcUsQo34unpCUBpaanOkYjmsH2/bN+/lpCmZ0I4QF29iGPOj+nXLphfD2Sz/XgBNzpkBCG0ZzKZCAkJISsrCwA/Pz+77UQT9me1WiktLSUrK4uQkBBMJlOLX0uSESEcwFH1IjZ1Raz5Dnl9IfQSExMDUJuQCOcXEhJS+31rKUlGhHAAWzLSxWHJiCpiPZRVTElFNf7e8l9ZuAeDwUBsbCxRUVFUVVXpHY44B09Pz1bNiNjITzAhHMB2Wm83Oxev2kQF+RAT5ENGYTm70woZmuSYw6uE0IvJZLLLLznhGqSAVQg7Kyqv4kS+Oka8a5RjkhGQQ/OEEO6jWcnI7NmzGTJkCIGBgURFRTFlyhT2799/zud98cUXdO/eHR8fH/r06cP333/f4oCFcHa2M2Oig7wJ9mt5dfm59EsIAWC7ND8TQri4ZiUjv/76KzNmzGDdunUsX76cqqoqLrzwQkpKShp9zh9//MG1117LbbfdxtatW5kyZQpTpkxh165drQ5eCGd0wEGdV08lMyNCCHfRrJqRZcuWNXh//vz5REVFsXnzZkaPHn3G57z++utcdNFFPPTQQwD885//ZPny5bz55pvMmTOnhWEL4bxsMyNdHLhEA3U7alJyS8krqSTM38uh4wkhhKO0qmakoEBND4eFNV48t3btWiZMmNDgsUmTJrF27drWDC2E0zpUk4x0jnJMjxGbYF/P2jG2pp506FhCCOFILU5GLBYLDzzwAKNGjaJ3796NXpeRkUF0dHSDx6Kjo8nIyGj0ORUVFRQWFjZ4E8JVHM7WJhkBGFBTN7JFkhEhhAtrcTIyY8YMdu3axcKFC+0ZD6AKZYODg2vfEhIS7D6GEI5QVmmu3UmjRTIysH0oAFtT8x0+lhBCOEqLkpF77rmHpUuXsnLlStq1a3fWa2NiYsjMzGzwWGZm5lm7tc2aNYuCgoLat2PHjrUkTCE0dyRHnUkT6uepSQ3HgMQQALYfy8dskcPFhBCuqVnJiNVq5Z577uHrr7/ml19+ISkp6ZzPGTFiBCtWrGjw2PLlyxkxYkSjz/H29iYoKKjBmxCuQKt6EZsuUYEEeHtQUmmu7foqhBCuplnJyIwZM/j4449ZsGABgYGBZGRkkJGRQVlZWe0106dPZ9asWbXv33///SxbtoyXX36Zffv28fTTT7Np0ybuuece+30WQjiJwzXJSKdIbZIRk9FAvwS1xVfqRoQQrqpZycg777xDQUEBY8eOJTY2tvbts88+q70mNTWV9PT02vdHjhzJggULeO+99+jXrx9ffvklixcvPmvRqxCu6nC26rmj1cwIwMBEqRsRQri2ZvUZsVrPvSa9atWq0x67+uqrufrqq5szlBAuybZM00nDZMRWNyIzI0IIVyVn0whhJ9VmC8k5NTMjGi3TAAxIUDMjR7JLyC+t1GxcIYSwF0lGhLCT4yfLqDRb8PE0Eh/iq9m4of5eJEX4A7D1WL5m4wohhL1IMiKEndiWaDpGBGA0GjQd27ZUI3UjQghXJMmIEHZi67yqZb2IzYDaIlapGxFCuB5JRoSwk9oeIxrWi9gMrJkZ2Zaaj0WanwkhXIwkI0LYyaHamRF/zcfuFh2In5eJoorq2jiEEMJVSDIihB1Yrdbahmda9hix8TAZ6duupvlZiizVCCFciyQjQthBdnEFheXVGA3QIVz7mRGoqxuRfiNCCFcjyYgQdmCrF0kI88PH06RLDP0TQgDYcbxAl/GFEKKlJBkRwg5q28DrULxqY1umOZhVTFmlWbc4hBCiuSQZEcIO9KwXsYkJ8iEiwBuzxcqe9ELd4hBCiOaSZEQIO6jtMaLjzIjBYKidHdl5PF+3OIQQorkkGRHCDvQ4IO9M+sSrZGTHCakbEUK4DklGhGil4opq0gvKAX1rRoB6MyOSjAghXIckI0K00pGaJZqIAG+C/Tx1jcU2M3Iou5iSimpdYxFCiKaSZESIVkrOUTtpOkbo01+kvqggH2KCfLBaYXeaFLEKIVyDJCNCtJItGekQ4adzJEof21KN1I0IIVyEJCNCtNLR2mRE/5kRgL7xsqNGCOFaJBkRopWSc0sBSNKpDfypbDMjsqNGCOEqJBkRopWcbWbEVsR6JLuEovIqnaMRQohzk2REiFY4WVJJQZn6ha/XAXmnCg/wJj7EF4BdJ6SIVQjh/CQZEaIVknPVrEhMkA++XvockHcmtf1GTuTrG4gQQjSBJCNCtMJRJ9tJY1NbNyLNz4QQLsBD7wCEcEkluWCuqE1GkpykXsSmb3wIINt7hRCuQZIRIZrLYoH3xkBROl3DpmNinNPUi9jYilhTckspKK3SvTOsEEKcjSzTCNFcuQeh4BhYqrkkZx6LvJ6il1d649dn74cN70N1pWYhBvt50j5cLR3J7IgQwtlJMiJEc53YDIA1qB2FVn/6GY8w8ucrYM1rYD5lK+2+7+C9cfD932H315qG2bv2BN98TccVQojmkmREiOY6vgmAsq6XMrHiRVaa+2E0V8DPT8F7Y+HYRrBaVXKy8HqoUnUlpG3RNExbJ9ZdMjMihHByUjMiRHPVzIyk+/ckkzD+4fcUv0/OhB8fh8xdMHcixA+svY6YPpCxU71pyFY3IjtqhBDOTmZGhGiOqnKVcAD7TF0BSIoMgP7XwT2boP/1gFUlIgYjTH4JLn9XPTdjp5ox0UivmmTk+MkyTpZoV68ihBDNJcmIEM2RsRMs1eAXwZ5S9cu+tseIfzhMeRtuWgq9r4IbFsGwv0BEVzB5QUUh5KdoFmqwb10R6640mR0RQjgvSUaEaA7b0ku7wRzNLQPO0AY+6Xy4ai50GqfeN3lCZHd1X+OlGlsRq+yoEUI4M0lGhGgOWzISP4jk5jQ8i+mrbjN2OSiwM+sjRaxCCBcgyYgQzWHb1hs3kKO5zTitN6aPutV4ZqSvFLEKIVyAJCNCNFVpHuQdBiAnqBellWaMBkgIbcK5NDolI1LEKoRwBZKMCNFUtj4hYR05UuIFQLtQP7w8mvDfKLqXui1IhbKTDgrwdFLEKoRwBZKMCNFUJ2qSkfhBzVuiAfANgZBEdV/juhEpYhVCODtJRoRoqgbFq6UAJIU3YYnGpraIVZ/mZ1LEKoRwVpKMCNEUVmuDZORoTjNnRqCubiRT25mRvjIzIoRwcpKMCNEUBcegJBuMHhDTt/nLNFCviHWHAwJsnK2I9VieFLEKIZyTJCNCNIVtViS6N1YP77pk5NSGZ2djS0ay9kG1dkmBFLEKIZydJCNCNEW9JZr80irKqywAxIX4NP01ghPAJxgsVZCz3wFBNk6KWIUQzkySESHOpbwQdi9W99sNJr2gHIBwfy+8PUxNfx2DAaL16TciRaxCCGcmyYgQ57JslqoZCUmEHpeSWaiSkeigZsyK2NTWjejTFl5mRoQQzkiSESHOZu9S2PYxYIDL3wXvQDJqkpGY4NYkI9oWsfaOkyJWIYTzkmREiMYUZ8G396n7o+6D9iMBapdpWpeM7FTbhTUS7OdJx0hVbLs+OVezcYUQoikkGRHiTKxWWHIflOZCdG8Y93jthzJtyUhLlmkiu4PRE8rzIfeQnYJtmrFdowD4eW+WpuMKIcS5SDIixJns+BwO/AAmL7jiPfDwrv1Q7TJNS5IRDy/oOEbd3/WVPSJtsgk9VDKycl8WZot2szJCCHEukowIcSpzFax8Xt0f83DdIXc1MlqzTAPQZ6q63fGZpks1Q5LCCPT2ILekku3H8zUbVwghzkWSESFOtX0h5KeAfyQM/+tpH25VAStA94vB0w/yjtT1L9GAp8nI6G6RAKzYm6nZuEIIcS6SjAhRn7kKfntJ3R/1AHg17LBaXmWmoKwKaOHWXgDvAOh+ibq/4/MWBtoytqWaFVI3IoRwIpKMCFHf9k9rZkWiYPCtp33YtkTj62kiyMej5eP0rVmq2fWVSoA0MrZrFEYD7Mso4vjJUs3GFUKIs5FkRAib6sq6WZHzHgAvv9MusW3rjQ32wWAwtHysjuPALwJKc+DIqpa/TjOF+nsxqH0oAL/sk9kRIYRzkGRECJvtn0J+qpoVGXTLGS9pVffV+kwe0OcqdX/HZ617rWYa3yMakC2+QgjnIcmIEKBmRVb/W90/78EzzoqAHYpX67Ptqtn3HVQUt/71mmh8d1U3su5wLiUV1ZqNK4QQjZFkRAiAlDU1syKRMPjMsyJQVzPS6pkRgPiBENYJqkpVQqKRzlEBJIb5UWm2sPpgjmbjCiFEYyQZEQLqDq7rcB54+jZ+Wb2akVYzGKDvNHVfw6Uag8HA+JpdNb/sky2+Qgj9STIiBEDWXnUb1fOsl2XYq2bEpveV6jb5N42XalTdyMr92Vg1bLwmhBBnIsmIEABZu9XtOZKRTHvWjABEdIbQDmCpgqOr7fOaTTC4Qygmo4HsogoyCys0G1cIIc5EkhEhLGbI3q/uR/Vo9DKzxUpWkfrFbZdlGptO49XtoRX2e81z8PE00TkyAIBdJwo0G1cIIc5EkhEh8pKhuhw8fNUsRSNyiiswW6yYjAYiArwbva7ZOtckI4e1S0YAesUHAbArTZIRIYS+JBkRImuPuo3qDkZTo5fZilcjA7wxGVvR8OxUHc4Ho4c6qyYv2X6vew6944IB2J1WqNmYQghxJs1ORn777TcuvfRS4uLiMBgMLF68+KzXr1q1CoPBcNpbRkZGS2MWwr6aWLxq674abc8lGgCfIEgYpu5rODvSK07NjOyWZRohhM6anYyUlJTQr18/3nrrrWY9b//+/aSnp9e+RUVFNXdoIRyjmcWrsfbaSVNfpwvU7aFf7P/ajehZk4ykFZSTV1Kp2bhCCHGqZp/0NXnyZCZPntzsgaKioggJCWn284RwuNqZkcaLV8HO3VdP1Xk8/PJPtcXXXAUmT/uPcYpAH086hPtxNLeU3WkFnN8l0uFjCiHEmWhWM9K/f39iY2OZOHEiv//++1mvraiooLCwsMGbEA5RVQ65h9X96F5nvTTTnt1XTxXTTx2cV1kExzbY//Ub0Ste1Y3sOiH/x4QQ+nF4MhIbG8ucOXP46quv+Oqrr0hISGDs2LFs2bKl0efMnj2b4ODg2reEhARHhynaqpwDYDWDbygERJ/10nR7dl89ldFYt1SjYd1IXRGr1I0IIfTj8GSkW7du3HnnnQwaNIiRI0cyb948Ro4cyauvvtroc2bNmkVBQUHt27FjxxwdpmiranfS9FTt2c/Cbif2Nqaz9v1GaotYZUeNEEJHza4ZsYehQ4eyZs2aRj/u7e2Nt7cd+zgI0Zj6ychZWK1Wx9aMQN3MSPo2KM6GAMfXcNiSkeScEorKqwj0cXytihBCnEqXPiPbtm0jNjZWj6GFaKiJxauF5dWUVpoBiHHUzEhAFMT0UfePrHTMGKcID/Amria52ptepMmYQghxqmbPjBQXF3Po0KHa95OTk9m2bRthYWEkJiYya9YsTpw4wf/+9z8AXnvtNZKSkujVqxfl5eV88MEH/PLLL/z000/2+yyEaKnMmpmRcxWv1syKBPt64uvVeGO0Vus0HjJ2wpFV0Heq48app2dcMGkF5ew6UcDQpDBNxhRCiPqaPTOyadMmBgwYwIABAwCYOXMmAwYM4MknnwQgPT2d1NTU2usrKyv529/+Rp8+fRgzZgzbt2/n559/Zvz48Xb6FIRoofICKDyu7kd2P+ultu6rDpsVsWk/Ut0e3+TYcerpHS91I0IIfTV7ZmTs2LFnPXJ8/vz5Dd5/+OGHefjhh5sdmBAOZ1uiCYoH35CzXmqrF7F799VTxQ1UtzkHoKIIvAMdOx7QS3bUCCF0JmfTiLaricWrUH9mxMGF1QGREJwAWCFtm2PHqmGbGTmYVUx5lVmTMYUQoj5JRkTbZasXOUfxKtTvvurryIiUOLUEyonNjh8LtfQU7u+F2WJlf4YUsQohtCfJiGi7bMs05yhehbruqw6vGQGIH6Ru0xpvDGhPBoOh9pyaXbJUI4TQgSQjom2yWOodkHfumZGsogp1aaAG/W/ia+pGTmx1/Fg1ektbeCGEjiQZEW3TwR+h7CR4BUJEt3NenlOskpEILZKR2P6AAQpSVfMzDfRrFwLA+iO5mownhBD1STIi2h6rFVa/rO4PuQ08z770YrVayS2uBCAiwMvR0YFPEER0Ufc1WqoZ1TkcT5OBIzklHMku1mRMIYSwkWREtD1H18DxjWDyhuF/PeflhWXVVJotAEQEaHRMgW2L7wltkpFAH8/ahme/7MvSZEwhhLCRZES0PWteUbcDb4TAs5/UC5Bds0QT6O2Bj6cDu6/Wp3ERK8AF3dXXQpIRIYTWJBkRbUvaVjj8CxhMMPK+Jj0lV8t6EZv4ejMjZ2kyaE/ju0cBsCE5j6LyKk3GFEIIkGREtDWra2ZF+lwFoe2b9JQcLetFbKJ7g9EDSnMgP/Xc19tBhwh/Okb6U22xsvpgjiZjCiEESDIi2pLsA7D3W3X/vAeb/LTanTRa1YuAKqq19T/Rcqmmm5odWbFXlmqEENqRZES0HX+8AVih28VN6i1io0syAnV1IxoVsQJc0EMlI6v2Z2GxaLM8JIQQkoyItuPQz+p22F+a9TTdkhGNd9QADOkQRqC3B7kllWw/nq/ZuEKItk2SEdE2FJyAonRVuNpuSLOeml2kakbCtawZgboi1vRtYNHmADtPk5HR3SIB2VUjhNCOJCOibTixSd1G9QQv/2Y9VbeZkYhu4OkHlcWQc1CzYaVuRAihNUlGRNtwvCYZaTeo2U+1JSORgRrPjJg8alrDU5dMaWBst0gMBtiTXkh6QZlm4woh2i5JRkTbYKu7iB/crKdZrVb9ZkYAEoer26NrNBsyPMCbAQkhAKzar83ZOEKItk2SEeH+LGbV7AygXfOSkZJKM+VVGreCry9ptLpN/k2z5mcAwzqGA7DzRIFmYwoh2i5JRoT7y9oLVSU1J/R2bdZTbd1XfT1N+Ht7OCK6s0sYBiYvKDwBeUc0G7Z7TCAA+zOKNBtTCNF2STIi3J+t3iJ+ABibd7ZM7RKN1vUiNl5+0G6oup/8q2bD9ogNAlQyIv1GhBCOJsmIcH+24tVm1otA3bZeXZZobOov1Wg1ZIQ/XiYjxRXVnMiXIlYhhGNJMiLc34nN6raZ9SKg47be+mqTkdVgsWgypKfJSKeoAAD2phdqMqYQou2SZES4t4oiVTMCLZoZcYpkJH6Q6jdSmgPZezUbtofUjQghNCLJiHBvaVsBKwQnQGB0s59el4zoVDMC4OEFiSPU/SPa1Y10j1XJyD5JRoQQDibJiHBvtfUiA1v09BxnqBkB6DhG3WpYN9I9RhWx7s2QZRohhGNJMiLcm61epAVLNOAkyzRQVzeS8juYqzUZ0ra992hOCeVV2pyNI4RomyQZEe7Laq3XBr61yYiOyzQAMX3BJxgqCiF9uyZDRgZ6E+bvhcUKBzOLNRlTCNE2STIi3FfhCSjOUCf12s54aaac4pplmkCdZ0aMJuhwvrqvUb8Rg8FQOzsiSzVCCEeSZES4L9usSHRP1TysmcqrzBRXqCUR3ZdpQJd+I7a6kX3pUsQqhHAcSUaE+0r5Q922sl7Ey2QkyEeHVvCnsiUjqeugukKTIet21MjMiBDCcSQZEe7JYoY9i9X9rhe16CVql2gCvDAYDHYKrBUiu4N/JFSX1Z1C7GC2ZZp9GUVYNTyoTwjRtkgyItxTyu9QnAk+IdDpgha9RE6R7VwaJ1iiATAYIHG4un9snSZDdokKxGiAvJJKsou1mY0RAmDXiQKmz9vAjXPXs+ZgzhmTYdnl5T6cYO5ZCAfY+aW67fln1TSsBZxmW299CcNh77eQul6T4Xy9THSI8OdIdgn70ouICvTRZFzRdmUVlfPvH/fzxebj2PKP1Qdz6J8QwoxxnTEAvx3MZvXBHJJzSujbLpirBrXjz/3iCPHTedebaDFJRoT7qa6EvUvU/d5XtvhlbMlIuL8T/YCzdWI9tk6dU2N0/ORmj5gglYxkFDK6a6TDxxNt15ebj/PUN7soqVQzHpf1jyPUz4tPN6Sy7Vg+d/xv02nP2XG8gB3HC3hu6V5GdQ4nPtSXcH9vIgK8GJAYSu/4YK0/DdECkowI93NkJZSdBP+ouu2wLeA023rri+0LHr7q88s9CJHdHD5kt5hAvtuZLm3hhUOtO5LLI1/twGyx0q9dME9e2otB7UMBmDGuMx+sPsLCjccI9vVkdNcIRneJpHtMED/vzeTLzcfZk17Iyv3ZDV7TYIBHLurOnaM7Okfdl2iUJCPC/ez6St32ulz152ihbGdcpjF5qgZuR1dD6lpNkpHaIlbZ3iscJKOgnHsWbMFssXL5gHhevrofRmNd8hAZ6M2sP/Vg1p96nPbcW89L4tbzktiTVsimlDxyiivJLa7gaG4Jvx/K5f9+2Me+9EL+78q++Hi2/OeBcCxJRoR7qSqDfd+p+32uatVL1Raw6t199VQJw2qSkfUw6GaHD9cjVvUaOZRVTJXZgqdJ6t6F/VRWW5ixYAs5xZV0jwnkhcv7NEhEmqpnXBA944Jq37darXy8LoWnv93D4m1pJOeU8O6Ng4kJlronZyQ/VYR7OfAjVBZDcCK0G9Kql7LVjEQ608wI1NWNpK7VZLj4EF8CvD2oNFs4kCmzI8K+Xvh+L5tTThLo48GcGwbh62Wf2QuDwcCNIzrw0a1DCfHzZPvxAq59fx0FpVV2eX1hXzIzItyLbYmm9xVqwbgVckucsGYEIGEIYICTyVCUCYHRDh3OaDQwvGM4P+/NZNGWE/SKk4JA0XzH8kr5eH0Ki7eeoLi8GpPRgMlo4GRNcvDK1P50iPC3+7gjO0fwzYxRXPf+epJzSrjn0y389+YheMgMn1OR74ZwH+WFcPAndb8Vu2gAqswW8mt+SDpVzQioA/Oie6n7GvUbuX5YIqB2O0hvB9EcG4/mcdv8jYx+aSXv/nqEzMIKSirNFJZX1yYiD0zowsSejkuq24f78970Qfh6mlh9MIfZP+xz2FiiZWRmRLiPo2uguhzCO0NMn1a9VG7NThqT0UCIr6c9orOvhGGQuUvVjfS8zOHDje4aSXyILyfyy1i6I52rBrVz+JjC9W1JPck1763DbFENQ87vEsGNw9vTLSYQs8WK2WLFz9uD+BBfh8fSKy6YV6b24+5PtjB3TTLdYgIZ2y2SH3dl8N3OdI7llTG6awSX9I1jeMdwTC2oWxEtJ8mIcB/ZNX/txA1s9RJN/R4jLSmmc7jEEbBprmZ1IyajgeuGJfLSj/v5ZH2KJCPinIorqnnws22YLVbGdYvkiUt60jEyQNeYJveJ5f7xXXh9xUFmLdqJxWqlfmPXTzcc49MNx4gI8OLyAfHcPbYzYc7UZ8iNyTKNcB85B9RtRNdWv5RTbuutL3GYus3YAZUlmgw5dXACHkYDW1Pz2Z1WoMmYwnX989s9pOSWEh/iy2vXDNA9EbG5f3wXJveOwWxRiUi/hBAe+1N35t08mGuHJhDi50lOcSXvr05mzEsree+3w1RUy9Kko8nMiHAftmQksvXJiG1bb7izbeu1CU6AwDgoSoMTm+tO9HWgyEBvJvWO4bsd6XyyPpUXLm/dUphwD6WV1aw+mMOg9qG1yfuyXRl8tukYBgO8PLUfwU601Gk0Gnj9mgFcvj+LnnFBtAv1q/3YBd2jefay3vy6P5tXlh9gT3ohL3y/j4/WpfD8lD7SgdiBZGZEuAerFbJtMyOtbwRm677qdNt6beofmqfROTVQV8j6zdYTFFdUazaucE7lVWamz93AnR9tZtgLK7hp3gYWbkhl1qIdANw5uhPDO4brHOXpvDyMXNgrpkEiYuNpMjKhZzTf3nse/7qqL1GB3hzLK+P2Dzex6WieDtG2DZKMCPdQlAGVRWAwQVjHVr9cds3MSKSzbeutrzYZ0aZuBGBEx3A6RvpTUmnm660nNBtXOB+LxcrMz7exKeUkniYDZouVXw9k8+iinZwsraJnbBAzJ7Z+llIvJqOBqYMTWPXQWCb2jKbSbOEvH20mJVebZdG2RpIR4R5y9qvbsKQWn9JbX2ZROQDRQU7crdGWjBzfCBZt1rQNBgPXD2sPwCfrUs54rLtoG57/fi/f78zAy2Tkf7cO45e/jeH+8V1IivAnIsCb16/pj5eH6/+K8fPy4PVr+tMnPpi8kkpumb9RGqc5gOv/SxECIOegurVD8SpAZoELJCNRvcArACoK63YSaeDKgfF4mYzsyyhiv3RkbZPmrklm7ppkAF66ui8jOoXTMTKAByd2ZeXfx7LpHxPoEh2oc5T24+flwdybBhMb7MOR7BLu+ngzldUWvcNyK5KMCPeQXTMzYq9kpHZmxImXaUweED9I3U/VpvkZQIifF2O6qUK+b7enaTaucA4bkvN47rs9ADw6uTuX9Y/XOSJtRAX5MPemIfh7mVh7JJc7P9pEQdnpMyTHT5aSnCNLOc0lyYhwD3bc1mu1WsksVDUjTj0zAnVLNcc2aDrspf3iAPh2e7os1Tij6kr44RH4+Eo4efTM15TlQ2Pfu11fwaoXobritA/955eDWK1wxYB47hzd+vosV9IzLog3rx+It4eRlfuzmfLW7xzKUrODBaVVPL1kN2NeWsWk137joMwaNoskI8I91G7rbf1OmoKyqtop2ChnnhkBSBiqbjVqC28zoUcUvp4mUvNK2XFceo44lYoiWDAV1s+BQz/D++MbJqsVRfDd3+DFDvDBBMjaW/exqnL45h748lZY9QJ8+0CDhGXn8QJWH8zBZDTw4MSuGFrZXNAVjesWxZd3jSQu2IfknBIue/N3Zv+wl7H/Xsn8P45itliprLbw75/26x2qS5FkRLi+8kIoSlf3I7q0+uVssyKhfp54e9jnBFGHaWc7NO+oOjRPI35eHkyoOUtkiSzVOI/iLJh/MRxZCZ5+ENkDSnNg/iWw80s4tALeHgEbPwCscGITzDlfzYLkHoZ5k2DrR2Awqp1p2xfA6pdrX37Ob4cBuKRvLAlhp2+LbSv6tAtmyb3nMSwpjJJKM+/+eoSTpVV0jQ5g9hV9MBrgx92ZbEk9qXeoLkOSEeH6bMWrATHqELlWyix0geJVG59giOqp7h/Trt8IwKV9YwFYuiMNi0WWanRVdhL2fQdzJ0L6dvALh5uWwh0roNvFYK6Ar26Dj6+AgmMQ0h6ung9dLwJLlZoF+c9ASN8GvmFwwyL407/Ua//yT9i1iKM5JfywUyX9d43ppNun6iwiArz5+PZh3HF+Eh3C/Xj2sl58f9/5XDs0sfa4hBd/2CfLmE0kHViF67Nt67XDrAhARk0yEuUKyQiopZqs3SoZ6flnzYYd0y2SQB8PMgsr2Hg0j2FO2NzK7ZTmqQMSizKgMA3yU9X3PXM3UPNLL6Q93Pg1hNckDNM+gp+fgj/+Axhg2F0w/gnw8oeeU1R9yA8PQ2kuxPZX14ckQqdxarZk3dvw9V0s7/ASFms4Y7tF0SM2SL12ZQkkr4bcQ9DjEgjtoPmXRE+eJiOPX9yTxy/u2eDxByZ0ZfG2NNYn5/HrgWzGdovSKULXIcmIcH12rBcByLLNjDhzw7P6EofD5v9qXsTq7WHiol4xfLH5ON/uSJNkxNGObYSPpkBl8Zk/Ht4FOo6B0Q9DYHTd40YTXPgcdJmkZtJi+9Z9zGCAPldBx3Gq7qjTePCsl4Rf+BzkHYEDy7jj8H1c6R2AsWog/NQPMnZByu9gVt2K+flpGHIbjH4I/CPs/dm7lLgQX24a0Z73Vyfzr2X7Gd0l0jkP3HQikowI12fHNvCA6+yksbEVsaZvUwWIntrFfWm/OL7YfJzvd2bw9KW98DDJym+rnEyBQ8uhz1TwCap7PP8YLLxOJSJB8arLcFAcBMZCXH9oPwoCzvHXd9L5jX/MPxy6X3z640YTXDmXA3OupUPe74QZiiHtN/VmE5Ko4ji2XhXNbv0Eht4OCcMhqof6eBssdP3r2M4s3HCMPemFfLsjrc1sgW4pSUaE66vd1mufZZrampFgF0lGQpPAPwpKslRCYtvuq4GRncIJ8/cir6SS3w/nMkYOEmu5k0dh7iQozoA/3oSpH0JsP6gohk+vVd/f6N5w6zLw1qah2KGsYhasT2VBzl+xVN3G/y72Z7hPCmTuUQlRlwvV/zuDAQ6vVLMj6dtgzat1L+IVCAlDoPdVaimnfl1XUYZaaoruDV7uVRAb6u/FnWM68u+fDvDiD/sY2y3KqQ4MdDaSjAjXVl2pppHBbss0mTXn0rjMMo3BoGZH9i1Vzc80TEY8TEb+1CeGj9elsnR7miQjLVWcBf+bohIRgJPJatvtpBfUL/nMneAfCdd+qkkisu5ILq/9fIB1R+oOhhvRMYaho4ZBY8sNncZB0hjY+40qps3co/5QqCyCw7+ot6UPQpeJYK5SSUtxzQ4wTz/1eI8/Q9dJmiVbjnbreUl8vuk4qXmlPLZoJ29eN+C07dAWi1WWcJBkRLi6k8lgNau/vgJj7fKSWa60m8YmcbhKRjSuGwGY1EslI38cztV8bJeTlwyf3wjeQTDoZvXL11yhdrmcTFZLGtd+BiuehQM/wPd/V88zecM1C9THHWzT0Tymz9tAZbUFowEu6B7N9cMTm1b3YDRCr8vVG6ikI+cA7Psedn5ec39p3fUGI/iGquLZPd+oN69A+PMb0PsKx32SGvHz8uCNawdw1Tt/8N3OdM7bGMG1Q9X3sKi8ikcX7eS3A9m8e+MgRnZq23U2kowI15ZdbyeNHdalzRYrWUUuVjMCkDBM3R5br5pUabhGPyAxFKMBTuSXkVFQToyrLG9prShDFaDaOqKm/A6+j6haj+x9aubjxsVqF8y1n8Lat9QuGEs1XPZmXW2QAx3JLub2/22istrCBd2jeG5Kb+JCfFv+giZPiO6l3kb/HTJ2woEf1VJNXH/1uKefmiXZswT2LFYznV/eAmlbYPzT6tgDgMpS9e87vDOEJLT+k9VI/4QQHprUjdk/7OOZb3czuH0oBgP85aPNHMlWbeP/sXgXy+4f7RYHC7aUJCPCtdmxDTxAbkkFZosVgwEiAlp/+q9mYvupv55Lc9QP83Dt+kAEeHvQPSaIPemFbErJ45K+cZqN7TLKTsJHV6hEJLQD9L1GNRcrPAFleWqm5IZFdd83gwFG3qOWLspOarL0lltcwc3/3Uh+aRX92gXz1nUD8fWyY9M/g0Ht5Km/m8cmboB6u+Afalbo99fUVuT0HWqHzp4lsP8HqCoBoycMnK527QTZZzbU0e44vyNrDuWw+mAOt324idziCkoqzcQG+1BZbeFIdgn/W3uU289vW+3162u7aZhwD7Xbeu2TjGTV7KSJCPB2rZ0hHt7qhzlo3vwMYHCHUAA2p0jHydNUlsCCaaoXTEC0mv0YNwvu3wHXLlTLNTcuPvMv6chumiQi5VVmbv/fJlLzSkkI8+WDm4bYNxFpKqMJJj6jGrJ5+kPyr/D5dNj1pUpE/MJVk7ZNc+GN/vDj45p2Hm4po9HAy1P7ERHgRWpeKSWVZkZ0DOfbe8/joUmq1u31FQfJLT79LKC2otk/bX/77TcuvfRS4uLiMBgMLF68+JzPWbVqFQMHDsTb25vOnTszf/78FoQqxBnYeWakrvuqixSv1mebxk9dq/nQg9q38WQkc49qtb53qWq7vuF99Yvy0+tU+/Vj69XSxA2LICxJPcfkAd0mw6WvQ7tBuoVutVp56MsdbE3NJ9jXk//ePJRIvYu3e10Ot/8Mkd0hMA6G/xVuXwEPHYabv4PEEVBdDmvfhNd6w5J767b4O6moQB/euGYAHcL9uHtsJz66bSgRAd5cPTiBnrFBFJVX88py5/4cHKnZyzQlJSX069ePW2+9lSuuOHeBUXJyMhdffDF33XUXn3zyCStWrOD2228nNjaWSZMmtShoIQBVG2FrBW/nHiMxrlQvYtN+FPzxhuqIqbHBHcIA2J1WSGllNX5ebWgFeMWzDc5vOSNPf7juc4jprU1MzTB3TTLfbk/Dw2jg3RsH0TkqQO+QlOie8Nd1p9c/dTgPbvkBDq+AX/+lEr0t/1NvnSeqpa32IyGqlyqodSIjO0ew6qFxDR4zGQ08dWlPpr23jk83pHLD8PZ1HW7bkGb/xJg8eTKTJ09u8vVz5swhKSmJl19W/1l79OjBmjVrePXVVyUZEa2TtVc1gfLwrftrs5UyXa0VfH0dRqnDzU4m19UmaCQ+xJfYYB/SC8rZdiy/7ewM2P5ZXSIS1Uu1WPf0VVtTQ9qr70FYklpCc8KupH8czmH2D/sAeOKSngx3ti66jRViGwzQeYJ6S12vkvB936mGcYeWq2t8gqHbn2DCMw070jqhYR3DubhvLN/tSOfpJbv5321Dnf+QTjtz+J8va9euZcKECQ0emzRpEg888ECjz6moqKCiom7trLCw0FHhCVd2ZKW67TBKVe3bQe0yTaALJiPegeoU32Pr4MivMKiDpsMPbB/KdzvS2ZJysm0kI8c3qeUBgPNmwoSn9I2nmdLyy7hnwVbMFitXDIhn+oj2eofUMonDIPETyDmkduOk/KFmS8oLYPunqvD1ov+DfteoJKYwDbZ+DMm/qR09fadBTB+9PwtmTe7Oz3syWZ+cx8VvrGH2FX0YUjPj2BY4PBnJyMggOrphVhodHU1hYSFlZWX4+p6+bWz27Nk888wzjg5NuLrDv6jbjuPOfl0zuHTNCEDHsTXJyCoYdJOmQw+uSUY2tYW6kYITqj27uUL99X3BE3pH1CyZheXc/fFm8koq6RkbxAtX9DmtGZfLieistg8DmKvh+Ab44RHI2AGL71JFsCYvOLAMrBZ13dHVatdOVE9Vv2P0VGftWKrUbFavKzTbJt8u1I+3rx/II1/t5FBWMVfPWct1wxJ55KLu9u/cmrVP9XQJba8KpEPa696y3ykXdmfNmsXMmTNr3y8sLCQhwXX2lQsNVFfA0d/V/U4X2O1lXe5cmlN1HAu//p/ahWCxaLpmPri9+ituS8pJ9+4qWVWuEpHiTPVL7Ir3nK424UzMFiu/HcxmwfpUftmXhdliJcTPk3dvHISPp5stCZg8VN3IHb+oJZxV/weHfq77ePtR0ONSNYtyYBlk7VFvpyrOguF3tzwOiwXWvwNpW9WMTGGa2so97h8w7C+nXT6+RzQrZobxf8v28umGYyxYn8ofh3L47M4Rp/9MKkyH/d+rYt6oHk1PJvZ+C4vuVLuTbAJi1AzT+X9TbQJ04PBkJCYmhszMhluvMjMzCQoKOuOsCIC3tzfe3i76l6nQRuo6qC5T/4mietjtZbOKbDUjLvrvr91g8ApQHS0zd515u6iD9IgNxNfTRGF5NYeyi+ka7R4tvU+zfo5q0uUbpll79tYqrzIz7d21bD9eUPvYkA6hPPanHiSEudeZMA2YPNUv2O6XwMoXILid6lFiOzpi+N2qj8ueb+DEZjUzYvKCkmw1k/LjY+oMnq7nqG+0mNW25FPt/EK9xql+fEzNSJzh/2ewnyezr+jLlP7xzPx8O0dzS7n+g/V89pfhhAfU/FwqOA7/nazO9QFVm9TtYug8XiXIgTGnJydWK/z2Eqx8Xr0fP1hdk7ZNHUOw5xsYdf/ZP08HcngyMmLECL7//vsGjy1fvpwRI0Y4emjhzmxLNJ3G2W16scpsIadYHYfusjMjJk+12+DAMrVUo2Ey4mEy0j8hhLVHctl09KR7JiMlOXUFq5Ne0LRIuDV+2ZfF9uMF+HuZmDYkkWuHJtDFHb8/jYnspg4ePBPfUNXrZdDNdY9ZraoQeetH8OWtcOuPZ94JVXYSvrlH7WCb9j81M2lTVaZ2WgH0u04lCkHxajvyvqWw6C/wl1WNnrI9rGM4C/8ynKnvruVQVjE3zN3AwjuGE2zOhQ8vhfxUrH7hGCqKVcH6urfUG6gmehFd1MnO3sHqBOjcw3Dwx5oXvxsufE7NIFWVwYktank3RrufF6dq9txicXEx27ZtY9u2bYDaurtt2zZSU1WGNmvWLKZPn157/V133cWRI0d4+OGH2bdvH2+//Taff/45Dz74oH0+A9E22YpX7Vgvkl3TBt7TZCDMz4W6r57K9gPxyCrNh7Y1P9uUkneOK13Ury9CRaH6od13mt7RNNmyXeoAvhuGt+fJS3u2rUSkJQwGuPgV6HC+2rG3YJpq519f+nZ4d4xKLCoKVHJRUu98pnXvQOFxCE6AS16BPldB+xGqr4x/FGTvhV/+2XgMVWUkHP2SJSMOMsA/l73pBdwzdzl570yGvCOcIIrz859h4bhfYepHqqtvWCd13k9FoZrp2fstbPsY1r2tEhGjJ1z6Bkz+v7o2+56+ahPA+X+z20aAlmj2zMimTZsYN67uF4CttuOmm25i/vz5pKen1yYmAElJSXz33Xc8+OCDvP7667Rr144PPvhAtvWKlivJUT8IoOFfIq1Uu6030Me16x1sX5OUP1R9QyN/eTmCWzc/yzkIm+ap+xc+5xJ1IgAV1WZ+2ZcFwKTeMTpH40I8vGDaR+r05NxD8MYAdQZU0mjV8fjnZ1QBc0h7tbSTexCW3KMONCzNhdWvqNcZ/6T6hW/jH6EOAvz0GnX+ULfJajbTxmKGbZ/AytlQlEYk8DWQ6RNGZbaJMGM26dYwplU+xnFrGI8uPUL1lH7ccMWf1fOrK9SRENn71XJTeYFKTqorVULUbrBWX8FmaXYyMnbsWKxWa6MfP1N31bFjx7J169bmDiXEmdn+4o/uY9f+AXU9Rly0XsQmsrtqO16cqXYUJI3WbOgBieoQsJTcUrKLKvTv5GlPy2sOret6EXQco3c0TfbHoVyKK6qJDvKmf7sQvcNxLb6hqlndx1eq/j1HVtbNyoL6t3D5HFXD8f4FqqB0839VR97KIojtD72vOv11u02GATeqZaBFd0LfqarmxGBUtRvZqvcLwYnqpObjG4g254ERCk2hrB46jzd69OeHnem8vzqZfyzeBaiZLzy8VR2dHWvptOCUu2mEOKvDNT8MOo2168vW7qRxxR4j9RkManZkx2cqcdMwGQn29aRrVCD7M4vYnHKSi9zlL/Gja2D/d6qp3MSzTK07IdsSzaReMa4946eX8E5w7xa1rJK8Wm0Hzj2sEohRD6gZMt9QmPC0Kkxd9pjaHgxnn0G7aLbqdZKfAmteafgx31A4/+8w5HY1s1lVpnqnpG0jqMelTK05UHFAQghAbUJiBW4cfnq/mJziCj7beIyJPaOdtpZLkhHhWqzWesWr9tvSC27QY6S++snI+Cc1HXpoUhj7M4v4eW+m6ycj1ZXqL9WVz6n3B99it0MZtVBttrB8r9rNeFEvF/9e6MlohOhe6m34XWe+ZtjdcHB53cxJtz9B0vmNv6Z3IFz/hWrAZq4Cq1kt0QTFqSTEN6TuWk9f9X/6lGVpg8HAY39SMyDvr07micW7WHs4hycv6UVMsPqjatmuDB77eid5JZX89/dkltxzHnEhZ97JqidJRoRryTkARWlg8lb76+3INjPikq3gT5VUs4yQtlVV/PuGajb0lAFxfLQuhaU70njy0p4E+ehXFNdi5QXqsLuNH0BRunrMPwrGPKpvXM208ehJ8koqCfHzZGhS2+nmqQujEaa8A3POg4oi1Yb+XCK7wYWtm2mzJSR+Xh68ufIQ3+/M4LcDOTwwoQt70gtZtOUEoM7AySmu5M6PNvPFXSOcrreMa1RgCWFjmxVpP7JhUZgd2HqMuOy23vqC49VJxlaLag2voYGJoXSNDqC8ysI3W09oOrZdlOXDvMlqp0NRuqq/GfcP+OtaCIjUO7pm+XG3WqKZ0CMaD5P8uHe4oFi4+3eYsU7TGTSDwcCDE7uy5J5R9E8Iobiimue+28uiLScwGuDusZ1Y/uBoQv082XmigMe+3nnW2k89yL9O4ToK09WUJqj+InZmW6ZxyRN7z6TrRep24weaDmswGLh2aCIACzYcc7ofemdVVQ4Lr4es3Wom5PJ34YFdMOYhpzzo7mysVmttMiJLNBoKjFGN0nTQKy6YRXeP5PnLexPs60lShD9f3DWCRy7qTsfIAN66biAmo4FFW07w39+PAmCxWCmuqCarsJzKaosucYMs0whXsX8ZLL5btVL2CoCeU+w+REaBG9WMAAy7U/UXOLoajm+GdoM0G/ryAfHM/mEfe9ML2XG8gH41hXZOzWKGr/8CKWvAKxBu+ErTpnH2tuN4AekF5fh5mTivi2slUqLljEYD1w9rz9WDEvAwGhoULY/sHMHjf+rBs0v38M/v9vDSj/spqzLXfnzpvefROz5Yj7BlZkQ4ueoK+P5h+HSaSkRi+sAdK9UBT3ZUVmmmsLwacJOaEVCtr/tMVfd/f1XToUP8vLi4TywAn25IPcfVTsBqhWWPqmJVoydc84lLJyJWq5WlO9IAGNc9yunqA4TjeXkYz7h76pZRHbh6UDusVhokIkZDw/e1JjMjwrmteRU2vKvuD/+r2j7nYf+ZC1u9iI+nkSAfN/pvMep+2L4A9i5VTbsiumg29LVDE/l66wmWbE/j8Yt7EKh3IeuxDar5U8cLGm63LMlVicjOz9X7l89xqT4iNkdzSvhw7VF2pxWyN72QoprkWpZoRH0Gg4EXr+zLXWM74WUy4udlwt/bA28Po64nN7vRT13hlvZ+q24n/0stOzhI7U6aQB/XP0q9vqjuaovh/u/h99fhsjc1G3pIh1A6RfpzOLuEJdvTuH6YfWezmsxqhdX/hl9qtueGd4GR96j22Xu+UYlIWR5ggMkvqi6VLmZPWiE3zF1PXkll7WOeJgPDksKZ0MN+jQGFezAaDXSKDNA7jAYkGRHOqyhDnTyLAXpf6dCh0gvKAIgNdpMlmvpGPaCSke0LYdxjqo+BBmyFrM99t5eFG45pk4wUZ4FfeN0JqpWl8M0M2L1Ive/pp9p2f3s/LJsFVaXq8aheqkW3k7bKPptdJwq4Ye568kur6BUXxK2jkugZF0SnyAC8PGQlXrgGSUaE87Jt443r7/CdDLbiVbdMRhKHQeJISP1DFbRe+JxmQ18xsB3/WrafnScKWHs4lxGdwh032O9vwPInVIFz3ACVWBxeCenbwOgBf3oJ+lwNW/4Ha99Wh5iZvGHMw2o5S8dDwlpqa+pJps/bQFF5Nf0TQvjw1qEE+7re5yGEpM3CeR1aoW47jXf4UOk1yUhMsPN1JrSL8x5Qt5v+C6Xanagb5u/F1YPbAfD44p2UO6pALmtf3QmolcVqB9GaV1Ui4hcO05fA4FtV18sRM+D+berMkRnrYfTfXTIR2ZNWyI1zVSIyuH0oH90miYhwXZKMCOdksdS1Ve6sRTLixss0AF0uhOje6hf1by9pOvTDF3UnMtCbI9klvLXykP0HsJhhyb3qPJAuF8Lda+HP/4GB02HADWr3VYdRDZ9j8oSukyAsyf7xaKC8ysz9C7dSXFHN0KQwPrx1qP4FwkK0giQjwjmlb1PHcHsFQrshDh/OrZdpQB2eN/FZdX/D++qgL40E+3ryz8t6AfDOqsPsyyi07wAb3lOnE3sFwiWvQnRPlYj8+T9w2Vt23wbuDP61bD8Hs4qJCPBmzg2D8PeWFXfh2iQZEc7pcM0STccxmkyhp9cmI266TANqhqnzBLBUwc9PaTr0Rb1jmdQrmmqLlUe+2onZYqeurCePwoqaJGviM6q3ipv7/VAO835PBuClq/oS5u+lc0RCtJ4kI8I51daL2Pdk3jOprLaQXay29sa468yIzYXPgcGotkwf/V3ToZ+9rDeBPh5sP5bPh38cbf0LWsxqV0xVKbQ/Dwbd0vrXdHIFpVX8/YvtAFw/LJFx3aN0jkgI+5BkRDif8gLVoAo0qRfJKirHagUvk5Fwd/8rM6oHDLxJ3f/pcVWbo5HoIJ/a485f/mk/ZZWtKGY9vhk+GA9HVoGHj9qWa3T/H2dPLtlFekE5HcL9ePziHnqHI4TduP//XuF6kn8DqxnCOkFoB4cPV3smTbD3Gdsnu51xj6ntr2lbYecXmg49bXAC8SG+lFSaWXMo5/QLSnLVwX5FGWd+gZJcWHKfSkTStoJ3EEx5G8I7OTZwJ/D9znS+2ZaGyWjg1Wn98fOSOhHhPiQZEc7HtkSjwawI1KsXCXLjepH6AqLg/Jnq/opnoLJEs6GNRgMTe6qOoD/tPiXhqCqDj6+A7/4Gbw2DHV+o7qkA5mpY/x78ZyBs+RCwQr9r4Z5NDm+I5wxyiyt4YvEuAO4e04kBiaE6RySEfUkyIpyL1VpXvKpBfxGo29br9vUi9Q3/K4QkQuEJWP2KpkNfWJOM/LIvq66Q1WpV9R/p29T75fmw6Hb4fDrsWQJzRsEPD6nHo3vDLcvUGTKBbaPV+ZPf7Ca3pJJu0YHcO76z3uEIYXeSjAjnknsI8lPVyakdztNkyNqZkZA2lIx4+sKkF9T9P97QdKvvkKQwgnw8yC2pZEvqSfXg2rdgx2dgMMGNX8PYx1TX1L1L4PMbIXsf+Iaprbt3/gbtR2gWr96W7kjju53pmIwGXp7aD28POYFXuB9JRoRzsW3TTDofvLU5yKm2x0hQG0pGALpfonYrmSvVOS31lebBvu+hqtzuw3qajFxQswtk+Z5M1bJ9+RPqg5NeUDGNfQTu+EWdGWP0UDM5921RXVSNbeeXcU5xBU9+sxuAGWM70Ts+WOeIhHAMSUaE89i9WP0lbPSACc9oNqzbt4JvjMGgTkM2esLBH2H/MvX4niXw1lBYeC28Pw4yd9t96Ik91bH2B3ZuwPrlLWC1QP/rG57MHNsP7loDDyfDRbPBt+3VSTz5zS7ySirpHhPIPRd00TscIRxGkhHhHErz4Pu/q/vnPQixfTUb2u1bwZ9NRBcY8Vd1f9kj8OWtalmkJBswQNYeeG8crJtTV0xqB2O6RTLE4zCvlc7CUHYS4gfBxa+oBKk+oxF8guw2ritZtiud73dm4GE08O+r+8kJvMKtyb9u4RyWPap+AUZ2h9EPaTZsldlCVpFqeNamakbqG/0QBMaqbqa7vlJ1G+f/DR7cpc56MVeoRGXBVLst2wQc+5WPPZ8nxFBCRmAfuP5L8GyjX/8zKCit4oma5Zk7x3SU5Rnh9mSjutDfgZ9qiheN6iwRD2/Nhs4uqsBqBQ+jgQh/7cZ1Kt6BMPlF+OJmiOim+nbED1Qfu+5z1ffjp3/AwZ/UIXvjn2je6x/4CfZ/r8bxDVHbdH97CW9rFb+Z+/CW9zN85hdm78/KpT3//R6yiyroGOnPvbI8I9oASUaEvqorYOkD6v7wv0K7wZoOb6sXiQ7yaRsNzxrT8zKYuQ/8wsFU78eCwQBD74CAaLV88/tr0PsKiO7VtNdNWQsLr1Pn4ZyivOufuW3HVVQdLyersJyotlZA3IjfD+Xw+abjALx4ZV98PNtOwa5ou2SZRujr8C+q10VANIx7XPPhbfUicW11iaa+wOiGiUh9Pf+sdt9YqlUHVEsTWrnnH1MJjKUKkkbD8BmqSLX7JTD+KXyumU/PhAgAlu/NtOMn4rpKK6uZtWgnADcOb8+QDjJjJNoGmRkR+tq1SN32ugK8/DQfPqOt7qRpiT+9BEd+hRObYONcGPaXxq+tLFG7cUqyIaYPXLsQvPxPu+zCntFsP5bPk9/s5otNxxnVOZzzu0QyLCkMw6nFrG7OarXy7Ld7SM0rJS7Yh4cv6qZ3SEJoRmZGhH6qylQtAUCvy3UJobbhWVvcSdNcQXEw4Sl1f8UzUHD8zNdZrbD4r5CxE/wi4JpPz5iIAFw5sB3dYwIxW6xsO5bPWysPc81763j+u70O+iSc10s/7mfhxmMYDPDCFX0I9PHUOyQhNCPJiNDPoZ+hshiC2kG7IbqEUDszIvUKTTP4NkgYpr5vi+48PSEpzoKvboM9i1X/kmkfQ0hCoy8XE+zDsgdG88ejF/DSVX25rH8cAB+sSWbRlkaSHRdntVqxnrJN+p1Vh3l7leqC+/yUPoztFqVHaELoRpZphH5ql2im6Hb8e5rUjDSP0QiXvg7vjoGUNfCfQTD8bhh1v/p+rngGygsAA1z6WpPbtseF+HL14ASuHpxAYpgf//nlELMW7aRrdKBbbWvdl1HIDR9swGCA8ztHMLprJDnFFby4bB8AsyZ357phiTpHKYT2JBkR+qgsgQM1HT97XaFbGFIz0gJRPeC2n+DHx1VCsuZV+OM/qrgVVOfUS15Vjcxa4MEJXdmdVsgv+7K486PNLLlnFOEBrr/t+kR+GTfN20BOseprs2jrCRZtPVH78RnjOnHnmE56hSeErmSZRujj4E9QVQoh7et6Wmisun7DM6kZaZ64/nDzUlWYGtFVJSJegaq9/B0rW5yIABiNBl6d1p+OEf6cyC9jxoItlFZW2y92HeSXVnLTvA1kFlbQJSqA+bcM4a4xnegVp7rL3joqib9fKAWrou2SmRGhj9olmstPbwGukZziSswWq2p45gZ/eWvOYIBuk6HzRDiySu2aCYy2y0sH+3ry3vRBTHnrD9YdyWPy66t56ap+DE1yva2u5VVmbv9wE4eyiokJ8uHDW4cSF+LL2G5RPDq5O5XVFmn1Lto8+R8gtFdRpGZGQLddNFBXLxId5IOpLTc8ay2TB3SZYLdExKZzVCBzbxpMbLAPKbmlTHtvLc9+u4eyyib0OHESJ/LLuON/m9iUcpJAH4/aRKQ+SUSEkGRE6GH/Mqguh7COqr5AJ3X1IrJE46yGdQznxwdHM3VwO6xWmPd7Mpf8ZzVHc0r0Du2sKqrNvPnLQca/vIrVB3Pw8jDy/vTBdIsJ1Ds0IZySJCNCe7u+Ure9rtBtiQbqeoxIMuLcgnw8+ddV/fjvLUOIDvLmcHYJl7/9OxuP5ukd2hltTjnJpFd/498/HaC8ysLQDmEsuWcUwzuG6x2aEE5LkhGhrax9dbto+lytaygZtm29koy4hHHdovj2nvPo2y6Yk6VVXP/+ehbX243iDPZnFHHzvA0czS0lKtCb16/pz2d3Dqd7TJDeoQnh1KSAVWjrt5cAqzqfJKq7rqGkybZelxMV5MNnfxnBg59tY9nuDB74bBufbkglNtiH6CAfYoN9mNAzmnah2h8tkFlYzi3/3UBRRTVDO4Qx9+bB0kVViCaSZERoJ/tA3RLNmIf1jYW6mhHZ1utafL1MvH39QF78cR/v/nqE9ckNl2ueWbqHcd2iuGF4ImO6RmlSnFxSUc1tH24kraCcjpH+vDd9kCQiQjSDJCNCO6v/DVih2590LVy1kQJW12U0Gpg1uQd/7hfHwcxisorKySysYHdaAeuO5PHLvix+2ZdFQpgv947rwhUD4/EwOWZVutps4d5Pt7LrRCHh/l7Mv3koIX5eDhlLCHclyYjQRs4h2PmFuu8EsyJmi5XMQpkZcXW94oLpFdewXfyR7GI+WZ/Kl5uPcyyvjIe/2sGcXw/z4MSuXNwnFqMdZ0rMFiuPfLWTX/Zl4e1h5P2bBpMYrv0SkRCuTgpYhTZW/xusFuh6EcQN0DsacoorqLZYMRkNRAVKMuJOOkYG8MQlPVk3azz/uLgHoX6eHMkp4d5PtzLx1V95a+UhjuWVnvU1rFYrFdVn72disVh59KsdfLXlOCajgTeuHcDAxFB7fipCtBkyMyIcL/cw7Phc3R/ziL6x1EjLr2l4FugtDc/clK+XidvP78i0IQnMW3OUD1Yf4XB2CS/9uJ+XftzP4PahXNI3lgt7xdQ2IiuvMrNkWxpz1ySTnFvCM3/uxbVDTz+4zmKx8tjXO/li83GMBnj9mv5M6hWj9acohNuQZEQ43soXwGqGLhfqdg7NqaTHSNsR6OPJ/RO6cMt5HVi2M4PF206w9kgum1JOsinlJE9/u4d+7YLpnxDCdzvTySmurH3urEU7Sc4p4ZGLutcmrYXlVcz+fi8LNx7DaIBXp/Xnkr5xen16QrgFSUaEY6Wug11fAgYY97je0dSyzYzEhsi23rYiyMeTqUMSmDokgczCcr7dnsaPuzPYlHKS7ccL2H68AFA1RLeM6kBxhZk3Vhzkvd+OkJxTwl/HduKLzcdZvPUEpZVmDAZ4eWo/Lusfr/NnJoTrk2REOI7FAj/ULMsMvFGd9OokbDtppOFZ2xQd5MPt53fk9vM7kl1UwfI9mew4ns/IzhFM7h2DZ83Om85RAfz9i+0s35PJ8j2Ztc/vHBXA3y/sxkW9ZWlGCHuQZEQ4zvYFkL4NvIPggif0jqaB9NoeIzIz0tZFBnpz3bBErht2em3In/vFER/iy50fbeJkaRWTekVz4/AODO8YhkHHowyEcDeSjAjHKC+En59R90c/BAFR+sZzCtuJvbKtV5zLoPahrHpoHFXVFkL9pX+IEI4gyYhwjNX/hpIsCOsEw+7SO5rTpOfXzIxIzYhoggBvD/DWOwoh3Jf0GRH2l5cMa99W9ye9AB7O9ddktdlCVpHUjAghhLOQZETY3+b5YKmCpDHQdZLe0Zwmq6gCixU8jAYiAuTPXSGE0JskI8K+LOa6BmdDbgMnLPJLr6kXiQ7ysWtrcCGEEC0jyYiwr6OroSgNfIJV63cnZNtJExciSzRCCOEMJBkR9rV9obrtfSV4OOcSSG3xqmzrFUIIpyDJiLCfyhLYs0Td73uNvrGcRe22XpkZEUIIpyDJiLCfvUuhqgRCkyBhqN7RNKp2ZiRIkhEhhHAGkowI+9n+qbrtd61TFq7apBdKjxEhhHAmkowI+yhMgyOr1P2+U3UN5VzSaw7Ji5OaESGEcAqSjAj72PE5YIXEERCWpHc0jaqstpBdXAFIzYgQQjgLSUZE61mtdbto+jlv4SpAZmE5Vit4mYyE+TlXZ1ghhGirWpSMvPXWW3To0AEfHx+GDRvGhg0bGr12/vz5GAyGBm8+PvIXqVs5uhqy94LJG3pO0Tuas8qoqReJCZaGZ0II4SyanYx89tlnzJw5k6eeeootW7bQr18/Jk2aRFZWVqPPCQoKIj09vfYtJSWlVUELJ2K1wsoX1P1BN4FviK7hnEtavpzWK4QQzqbZycgrr7zCHXfcwS233ELPnj2ZM2cOfn5+zJs3r9HnGAwGYmJiat+io6NbFbRwIkdWQupaNSty3ky9ozmnuu6rUrwqhBDOolnJSGVlJZs3b2bChAl1L2A0MmHCBNauXdvo84qLi2nfvj0JCQlcdtll7N69+6zjVFRUUFhY2OBNOCGrFVbOVvcH3wpBsfrG0wS2nTQxMjMihBBOo1nJSE5ODmaz+bSZjejoaDIyMs74nG7dujFv3jy++eYbPv74YywWCyNHjuT48eONjjN79myCg4Nr3xISEpoTptDKoRVwfAN4+MJ5D+odTZPUzoxIMiKEEE7D4btpRowYwfTp0+nfvz9jxoxh0aJFREZG8u677zb6nFmzZlFQUFD7duzYMUeHKZrLaoWVz6v7Q26DQNdYerMlI3IujRBCOA+P5lwcERGByWQiMzOzweOZmZnExMQ06TU8PT0ZMGAAhw4davQab29vvL2d85C1Ns9qhcpiOPAjpG0BTz8Y9YDeUTVZupxLI4QQTqdZMyNeXl4MGjSIFStW1D5msVhYsWIFI0aMaNJrmM1mdu7cSWys89cXiHr2fgsvd4d/RsDsdvDVberxoXdAQKS+sTVRRbWZnOJKQGZGhBDCmTRrZgRg5syZ3HTTTQwePJihQ4fy2muvUVJSwi233ALA9OnTiY+PZ/ZsVdj47LPPMnz4cDp37kx+fj4vvfQSKSkp3H777fb9TIRj/f4GFKXXvW/ygqgeLjUrklmgOq96exgJ9fPUORohhBA2zU5Gpk2bRnZ2Nk8++SQZGRn079+fZcuW1Ra1pqamYjTWTbicPHmSO+64g4yMDEJDQxk0aBB//PEHPXv2tN9nIRyrJAeOb1T37/odwjqCp69TH4Z3Jmk1SzRxIb4YXCx2IYRwZwar1WrVO4hzKSwsJDg4mIKCAoKCgvQOp+3Z9iksvgti+sJdq/WOpsW+3nqcBz/bzshO4Sy4Y7je4QghhNtr6u9vOZtGnNuBZeq260X6xtFKafl1reCFEEI4D0lGxNlVV8LhX9R9F09GMmp7jEjxqhBCOBNJRsTZpa6FikLwj4S4AXpH0yqyrVcIIZyTJCPi7A7+pG67TAKja/9zOX6ypoBVZkaEEMKpuPZvF+F4tfUiF+obRytZrVZS80oBaB/up3M0Qggh6pNkRDQu5xDkHgKjJ3Qcp3c0rZJdXEFppRmjAdqFSjIihBDORJIR0biDP6rbDqPAx7W3VKfkqlmRuBBfvDzkn70QQjgT+aksGucmW3qhLhnpEO6vcyRCCCFOJcmIOLPyAkj5Q93vOknfWOwgJbcEgESpFxFCCKcjyYg4s4PLwVINEV1V+3cXd7R2ZkSSESGEcDaSjIgz2zRP3fa6XN847CS1ZmakvSzTCCGE05FkRJwuczek/A4GEwy6We9o7MI2MyLbeoUQwvlIMiJOt/EDddv9YgiK0zcWO8gvraSgrAqAxDBJRoQQwtlIMiIaKi+A7Z+p+0Pv0DcWO7HtpIkK9MbPy0PnaIQQQpxKkhHR0PbPoKoEIrtDh/P1jsYujtbUi8i2XiGEcE6SjIg6VmvdEs2Q28Fg0DceO0mVehEhhHBqkoyIOsm/Qc5+8AqAvtP0jsZupHhVCCGcmyQjos7G99Vtv2tcvv17fal5sq1XCCGcmSQjQinKgH3fq/tDbtc3FjuTmREhhHBukowIZc8SsJqh3RCI6qF3NHZTUlFNdlEFAO3DZGZECCGckSQjQtnzjbrtOUXXMOwtNU/NioT6eRLs56lzNEIIIc5EkhEBRZmq4ypAzz/rG4ud1R2QJ7MiQgjhrCQZEbDvW8AK8YMgJFHvaOxKDsgTQgjnJ8mIqLdEc5m+cThASm3xqsyMCCGEs5JkpK0rzoaja9R9t0xGarb1ypk0QgjhtCQZaev2LQWrBWL7Q2gHvaOxO9vMSIcISUaEEMJZSTLS1u1ZrG7dcFakotpMWkEZAImyrVcIIZyWJCNtWUkuJK9W990wGTmWV4bVCv5eJiICvPQORwghRCMkGWnL9i1Vjc5i+kB4J72jsbv6beANbnLonxBCuCNJRtqq4mzY/F91380andkczZE28EII4Qo89A5AaMxcDZvmwS/PQUUBmLyh95V6R+UQe9ILAegYKfUiQgjhzCQZaUtyD8MXN0HGTvV+TF+4+BUIS9I3LgewWq2sPZwLwLCkcJ2jEUIIcTaSjLQlPz6uEhGfYBj/JAy6BYwmvaNyiNS8Uk7kl+FpMjCkQ5je4QghhDgLSUbaiqpySP5V3Z++BOL66xqOo/1+SM2KDEgMxdfLPRMuIYRwF1LA2lakroWqUgiIgdh+ekfjcH8czgFgZCdZohFCCGcnyUhbcXiFuu08Htx8m2v9epGRnSJ0jkYIIcS5SDLSVhyql4y4uQOZxeSWVOLjaaR/Qoje4QghhDgHSUbagoITkLUHDEboOE7vaBzOtkQzpEMYXh7yT1wIIZyd/KRuCw7/om7jBoKf++8s+UOWaIQQwqVIMtIWHPpZ3XaeoG8cGqg2W1h3xJaMSPGqEEK4AklG3J25Go6sUvfbQDKyO62QovJqAn086B0frHc4QgghmkCSEXeXtgXK88EnBOIH6h2Nw9mWaIZ3DMdkdO9dQ0II4S4kGXF3tiWaTuPctttqfdJfRAghXI8kI+6udkuv+y/RVFZb2Hg0D5DiVSGEcCWSjLiz0jw4sVnd7+T+/UW2pJ6kvMpCuL8XXaMD9A5HCCFEE0ky4q7K8uGHhwErRPWCoFi9I3K4n3ZnAjC6ayQGN+8yK4QQ7kQOynNHB36Eb++HonTAACNm6B2Rw1mtVn7cnQHARb1jdI5GCCFEc0gy4k4sZlj6AGz5n3o/rBNc9ha0H6FrWFrYdaKQE/ll+HqaGN0lUu9whBBCNIMkI+5k77c1iUjNbMi4x8HLT++oNPHDrnQAxnWPxNfL/XcNCSGEO5FkxJ2sfVPdnv83GP+EvrFoyGq1smyXWqKZ1EuWaIQQwtVIAau7OLYBjm8EkxcM/Yve0WjqUFYxR3JK8DIZuaB7lN7hCCGEaCZJRtzFH/9Rt32nQmC0vrFozDYrMqpzOIE+njpHI4QQorkkGXEHecmwb6m6P9z9d86c6oeaZGRyb/ffviyEEO5IkhF3sH4OWC2qsVl0T72j0VRqbil70gsxGmBCz7Y1IySEEO5CkhFXV3YStnyk7o+8R99YdGDrLTIsKZwwfy+doxFCCNESkoy4us3zoapEdVntOE7vaDS3rCYZmdxHdtEIIYSrkmTElRVnwdq31P0RM6CNtUDfnJLH5pSTAFzYU5IRIYRwVZKMuCqLBRb9BUqyIbI79LlK74g0VWW28NiiXQBcPagdMcE+OkckhBCipSQZcVVrXoEjK8HDF66eDx7eekekqfdXH2F/ZhFh/l489qceeocjhBCiFSQZcUUpf8DK59X9i/8NUW3rl3Fqbimv/3wQgH9c3INQKVwVQgiXJu3gXYnFAieT4cvb1FbevtdA/+v1jkpTVquVf3yzi4pqCyM7hXP5gHi9QxJCCNFKLZoZeeutt+jQoQM+Pj4MGzaMDRs2nPX6L774gu7du+Pj40OfPn34/vvvWxRsm5R7GL66Hd4eCS/Ewn8GQlEahHeBi19uc0WrX289wW8HsvHyMPLclN4Y2tjnL4QQ7qjZychnn33GzJkzeeqpp9iyZQv9+vVj0qRJZGVlnfH6P/74g2uvvZbbbruNrVu3MmXKFKZMmcKuXbtaHbzbO7wS3h8HO7+ArN1QXQ5GD4gbANM+Au8AvSPUTHmVmRe+38vfvtgOwD3jOtMxsu18/kII4c4MVqvV2pwnDBs2jCFDhvDmm+qEWIvFQkJCAvfeey+PPvroaddPmzaNkpISli5dWvvY8OHD6d+/P3PmzGnSmIWFhQQHB1NQUEBQUFBzwnVNVits/AB+eASsZmg3FEY/BBGdITgRTG1rdW3n8QJmfr6Ng1nFgNo98/zlffDykJInIYRwZk39/d2s32qVlZVs3ryZWbNm1T5mNBqZMGECa9euPeNz1q5dy8yZMxs8NmnSJBYvXtzoOBUVFVRUVNS+X1hY2Jwwm2zdgn9CfqpDXrs1gisz6ZH/KwDbwiazNPIRzPu9YH8ZsF/f4DRgtUJeSSWZheVkFVWQmleK2WIlIsCb2Vf0YaK0fRdCCLfSrGQkJycHs9lMdHTDXwbR0dHs27fvjM/JyMg44/UZGRmNjjN79myeeeaZ5oTWIiFHvqN79V6Hj9MSFquB2dXX8n7axZCWpnc4uvtTnxiem9JHWr4LIYQbcsr5/lmzZjWYTSksLCQhIcHu4xR0u4q1+cfs/rqtZyA5ZBheQQNoe2fwKqF+XkQF+RAV6E18iC8JYX56hySEEMJBmpWMREREYDKZyMzMbPB4ZmYmMTFnbscdExPTrOsBvL298fZ2fBOvYVf/3eFjtNQIvQMQQgghNNKsCkAvLy8GDRrEihUrah+zWCysWLGCESPO/OtzxIgRDa4HWL58eaPXCyGEEKJtafYyzcyZM7npppsYPHgwQ4cO5bXXXqOkpIRbbrkFgOnTpxMfH8/s2bMBuP/++xkzZgwvv/wyF198MQsXLmTTpk2899579v1MhBBCCOGSmp2MTJs2jezsbJ588kkyMjLo378/y5Ytqy1STU1NxWism3AZOXIkCxYs4B//+AePPfYYXbp0YfHixfTu3dt+n4UQQgghXFaz+4zooc31GRFCCCHcQFN/f0vXKCGEEELoSpIRIYQQQuhKkhEhhBBC6EqSESGEEELoSpIRIYQQQuhKkhEhhBBC6EqSESGEEELoSpIRIYQQQuhKkhEhhBBC6KrZ7eD1YGsSW1hYqHMkQgghhGgq2+/tczV7d4lkpKioCICEhASdIxFCCCFEcxUVFREcHNzox13ibBqLxUJaWhqBgYEYDAa7vW5hYSEJCQkcO3ZMzrw5B/laNY98vZpOvlZNJ1+rppOvVdM58mtltVopKioiLi6uwSG6p3KJmRGj0Ui7du0c9vpBQUHyj7WJ5GvVPPL1ajr5WjWdfK2aTr5WTeeor9XZZkRspIBVCCGEELqSZEQIIYQQumrTyYi3tzdPPfUU3t7eeofi9ORr1Tzy9Wo6+Vo1nXytmk6+Vk3nDF8rlyhgFUIIIYT7atMzI0IIIYTQnyQjQgghhNCVJCNCCCGE0JUkI0IIIYTQVZtMRmbPns2QIUMIDAwkKiqKKVOmsH//fr3Dcgn/93//h8Fg4IEHHtA7FKd04sQJbrjhBsLDw/H19aVPnz5s2rRJ77Ccjtls5oknniApKQlfX186derEP//5z3OeX9FW/Pbbb1x66aXExcVhMBhYvHhxg49brVaefPJJYmNj8fX1ZcKECRw8eFCfYHV2tq9VVVUVjzzyCH369MHf35+4uDimT59OWlqafgHr6Fz/ruq76667MBgMvPbaa5rE1iaTkV9//ZUZM2awbt06li9fTlVVFRdeeCElJSV6h+bUNm7cyLvvvkvfvn31DsUpnTx5klGjRuHp6ckPP/zAnj17ePnllwkNDdU7NKfz4osv8s477/Dmm2+yd+9eXnzxRf71r3/xn//8R+/QnEJJSQn9+vXjrbfeOuPH//Wvf/HGG28wZ84c1q9fj7+/P5MmTaK8vFzjSPV3tq9VaWkpW7Zs4YknnmDLli0sWrSI/fv38+c//1mHSPV3rn9XNl9//TXr1q0jLi5Oo8gAq7BmZWVZAeuvv/6qdyhOq6ioyNqlSxfr8uXLrWPGjLHef//9eofkdB555BHreeedp3cYLuHiiy+23nrrrQ0eu+KKK6zXX3+9ThE5L8D69ddf175vsVisMTEx1pdeeqn2sfz8fKu3t7f1008/1SFC53Hq1+pMNmzYYAWsKSkp2gTlpBr7Wh0/ftwaHx9v3bVrl7V9+/bWV199VZN42uTMyKkKCgoACAsL0zkS5zVjxgwuvvhiJkyYoHcoTmvJkiUMHjyYq6++mqioKAYMGMD777+vd1hOaeTIkaxYsYIDBw4AsH37dtasWcPkyZN1jsz5JScnk5GR0eD/YnBwMMOGDWPt2rU6RuYaCgoKMBgMhISE6B2K07FYLNx444089NBD9OrVS9OxXeKgPEeyWCw88MADjBo1it69e+sdjlNauHAhW7ZsYePGjXqH4tSOHDnCO++8w8yZM3nsscfYuHEj9913H15eXtx00016h+dUHn30UQoLC+nevTsmkwmz2czzzz/P9ddfr3doTi8jIwOA6OjoBo9HR0fXfkycWXl5OY888gjXXnutHJ53Bi+++CIeHh7cd999mo/d5pORGTNmsGvXLtasWaN3KE7p2LFj3H///SxfvhwfHx+9w3FqFouFwYMH88ILLwAwYMAAdu3axZw5cyQZOcXnn3/OJ598woIFC+jVqxfbtm3jgQceIC4uTr5WwiGqqqqYOnUqVquVd955R+9wnM7mzZt5/fXX2bJlCwaDQfPx2/QyzT333MPSpUtZuXIl7dq10zscp7R582aysrIYOHAgHh4eeHh48Ouvv/LGG2/g4eGB2WzWO0SnERsbS8+ePRs81qNHD1JTU3WKyHk99NBDPProo1xzzTX06dOHG2+8kQcffJDZs2frHZrTi4mJASAzM7PB45mZmbUfEw3ZEpGUlBSWL18usyJnsHr1arKyskhMTKz9WZ+SksLf/vY3OnTo4PDx2+TMiNVq5d577+Xrr79m1apVJCUl6R2S0xo/fjw7d+5s8Ngtt9xC9+7deeSRRzCZTDpF5nxGjRp12hbxAwcO0L59e50icl6lpaUYjQ3/FjKZTFgsFp0ich1JSUnExMSwYsUK+vfvD0BhYSHr16/n7rvv1jc4J2RLRA4ePMjKlSsJDw/XOySndOONN55WEzhp0iRuvPFGbrnlFoeP3yaTkRkzZrBgwQK++eYbAgMDa9dZg4OD8fX11Tk65xIYGHhaLY2/vz/h4eFSY3OKBx98kJEjR/LCCy8wdepUNmzYwHvvvcd7772nd2hO59JLL+X5558nMTGRXr16sXXrVl555RVuvfVWvUNzCsXFxRw6dKj2/eTkZLZt20ZYWBiJiYk88MADPPfcc3Tp0oWkpCSeeOIJ4uLimDJlin5B6+RsX6vY2FiuuuoqtmzZwtKlSzGbzbU/78PCwvDy8tIrbF2c69/VqYmap6cnMTExdOvWzfHBabJnx8kAZ3z773//q3doLkG29jbu22+/tfbu3dvq7e1t7d69u/W9997TOySnVFhYaL3//vutiYmJVh8fH2vHjh2tjz/+uLWiokLv0JzCypUrz/gz6qabbrJarWp77xNPPGGNjo62ent7W8ePH2/dv3+/vkHr5Gxfq+Tk5EZ/3q9cuVLv0DV3rn9Xp9Jya6/BapWWh0IIIYTQT5suYBVCCCGE/iQZEUIIIYSuJBkRQgghhK4kGRFCCCGEriQZEUIIIYSuJBkRQgghhK4kGRFCCCGEriQZEUIIIYSuJBkRQgghhK4kGRFCCCGEriQZEUIIIYSuJBkRQgghhK7+HyFpxaBm0UTcAAAAAElFTkSuQmCC\n","text/plain":""},"metadata":{}}],"id":"ea4602ca-3493-43f8-b25e-73cc3a13e975"},{"cell_type":"markdown","source":"Up to now, we have completed the preparation for force field optimization. Before starting the optimization loop, we need to prepare some few tools. Here, we introduce the distinctive feature of DMFF - Property Estimator.\n\nThe computational cost of computing deep computational graphs that span the entire trajectory in molecular dynamics simulations is significantly high in terms of both time and computation. However, this drawback can be mitigated by a trajectory reweighting approach. In DMFF, the reweighting algorithm is introduced in the MBAR method, which extends the differentiable estimators for ensemble averages and free energies.\n\n\n\n### Optimization of Physical Properties Based on the Zwanzig Reweighting Scheme\n\nThe core of this optimization scheme is the following derivation process:\n\n$$\\left_{H_1}=\\frac{\\int{A\\left(r\\right)\\exp\\left[-\\beta U^{H_1}\\left(r\\right)\\right]dr}}{Z_{H_1}}$$\n$$=\\frac{\\int{A\\exp\\left[-\\beta \\left(U^{H_1}-U^{H_2}+U_{H_2}\\right)\\right]dr}}{Z_{H_1}}$$\n$$=\\frac{\\int{A\\exp\\left[-\\beta \\left(U^{H_1}-U^{H_2}\\right)\\right]\\exp\\left(-\\beta U^{H_2}\\right)dr}}{Z_{H_1}}$$\n$$=\\frac{\\int{A\\exp\\left[-\\beta \\left(U^{H_1}-U^{H_2}\\right)\\right]\\exp\\left(-\\beta U^{H_2}\\right)dr}}{Z_{H_2}}\\frac{Z_{H_2}}{Z_{H_1}}$$\n$$=\\left_{H_2}\\frac{Z_{H_2}}{Z_{H_1}}$$\n\nwhere\n$$Z_{H_1}=\\int{\\exp\\left[-\\beta U^{H_1}\\left(r\\right)\\right]dr}$, $Z_{H_2}=\\int{\\exp\\left[-\\beta U^{H_2}\\left(r\\right)\\right]dr}$$\n\n\nThis implies that we can estimate the ensemble average of$H_1$sing the average value under the$H_2$ensemble.\n\nand $\\frac{Z_{H_1}}{Z_{H_2}}$can be further transformed into:\n$$\\frac{Z_{H_1}}{Z_{H_2}}=\\frac{\\int{\\exp\\left(-\\beta U^{H_1}\\right)dr}}{Z_{H_2}}$$\n$$=\\frac{\\int{\\exp\\left[-\\beta \\left(U^{H_1}-U^{H_2}+U^{H_2}\\right)\\right]dr}}{Z_{H_2}}$$\n$$=\\frac{\\int{\\exp\\left[-\\beta \\left(U^{H_1}-U^{H_2}\\right)\\right]\\exp\\left(-\\beta U^{H_2}\\right)dr}}{Z_{H_2}}$$\n$$=\\left<\\exp\\left[-\\beta \\left(U^{H_1}-U^{H_2}\\right)\\right]\\right>_{H_2}$$\n\nThen:\n$$\\left_{H_1}=\\left<\\frac{\\exp\\left[-\\beta \\left(U^{H_1}-U^{H_2}\\right)\\right]}{\\left<\\exp\\left[-\\beta \\left(U^{H_1}-U^{H_2}\\right)\\right]\\right>_{H_2}} A\\right>_{H_2}=\\sum^{H_2}_i {\\frac{\\exp\\left[-\\beta \\left(U^{H_1}_i-U^{H_2}_i\\right)\\right]}{\\left<\\exp\\left[-\\beta \\left(U^{H_1}-U^{H_2}\\right)\\right]\\right>_{H_2}} A_i}$$\n\n$\\frac{\\exp\\left[-\\beta \\left(U^{H_1}_i-U^{H_2}_i\\right)\\right]}{\\left<\\exp\\left[-\\beta \\left(U^{H_1}-U^{H_2}\\right)\\right]\\right>_{H_2}}$can be viewed as the weight of conformation when estimating the sampling of$H_1$using samples from the$H_2$potential function.\n\n`ReweightEstimator`is a module provided by DMFF that employs the reweighting method to estimate physical quantities in a differentiable manner. The use of this module presupposes the implementation of various sampling and recalculating functions.","metadata":{},"id":"b6e7772f-3fe0-4bc8-b4da-54a7e16f3fba"},{"cell_type":"markdown","source":"### 4.7 Optimize ","metadata":{},"id":"0b91e520-8e9f-45e0-90ab-d857bc8b87d3"},{"cell_type":"code","source":"optimizer = optax.adam(0.001)\nopt_state = optimizer.init(paramset)","metadata":{},"execution_count":84,"outputs":[],"id":"07e008c0-a73d-4f84-9405-663337c3e31c"},{"cell_type":"code","source":"lbfgs = None\nos.system(\"cp lj.xml loop-0.xml\")\nNL = 60\nfor nloop in range(1, NL+1):\n xmlrender(f\"loop-{nloop-1}.xml\", 'res.xml', f\"loop-{nloop-1}.xml\")\n # sample\n print(\"SAMPLE\")\n print(nloop)\n runMD(f\"loop-{nloop-1}.xml\", f\"loop-{nloop}.dcd\")\n print(\"RERUN\")\n ener = rerun_energy(f\"loop-{nloop-1}.xml\", f\"loop-{nloop}.dcd\", removeLJ=False, skip=20)\n ener_no_lj = rerun_energy(f\"loop-{nloop-1}.xml\", f\"loop-{nloop}.dcd\", skip=20)\n print(\"ESTIMATOR\")\n traj = md.load(f\"loop-{nloop}.dcd\", top=\"box_DMC.pdb\")[20:]\n estimator = ReweightEstimator(ener, base_energies=ener_no_lj, volume=traj.unitcell_volumes)\n\n print(\"CALC DENSE & RDF\")\n density = md.density(traj) * 0.001\n\n # get loss & grad\n # Define loss function\n rdf_frames = compute_rdf_frame(traj, x_ref*0.1)\n\n def loss(paramset):\n lj_jax = rerun_dmff_lennard_jones(paramset, \"box_DMC.pdb\", f\"loop-{nloop}.dcd\", lj_force, skip=20)\n weight = estimator.estimate_weight(lj_jax)\n\n rdf_pert = (rdf_frames * weight.reshape((-1, 1))).sum(axis=0)\n loss_ref = jnp.log(jnp.power(rdf_pert - n_ref, 2).mean())\n\n # loss function of density \n dens = weight * density\n dens = dens.mean()\n loss_den = jnp.power(dens - 1.07, 2) * 10.0\n \n return loss_ref + loss_den\n \n v_and_g = jax.value_and_grad(loss, 0)\n v, g = v_and_g(paramset)\n print(\"Loss:\", v)\n \n plt.plot(m_ref, n_ref, label = \"OPLS-AA\")\n # plt.plot(x_ref, y_ref, label = \"Experiment\")\n plt.plot(x_ref, rdf_frames.mean(axis=0), label = \"Current\")\n plt.legend()\n plt.title(f\"Loop-{nloop}\")\n # plt.savefig(\"compare.png\")\n plt.show()\n \n # update parameters\n updates, opt_state = optimizer.update(g, opt_state)\n paramset = optax.apply_updates(paramset, updates)\n paramset = jax.tree_map(lambda x: jnp.clip(x, 0.0, 1e8), paramset)\n\n lj_gen.overwrite(paramset)\n io.writeXML(f\"loop-{nloop}.xml\", ffinfo)","metadata":{"collapsed":true,"jupyter":{"outputs_hidden":true}},"execution_count":88,"outputs":[{"name":"stdout","output_type":"stream","text":"SAMPLE\n1\n#\"Step\",\"Density (g/mL)\",\"Speed (ns/day)\",\"Time Remaining\"\n10000,0.6730764728182231,0,--\n20000,0.6696147699946959,630,1:05\n30000,0.6463700406350699,631,1:04\n40000,0.6513872754825832,632,1:02\n50000,0.6525823187238825,632,1:01\n60000,0.6697139951608344,632,1:00\n70000,0.6565018659501556,632,0:58\n80000,0.6609978504701608,632,0:57\n90000,0.6501425402456501,632,0:56\n100000,0.640095565199735,632,0:54\n110000,0.6443495006874734,631,0:53\n120000,0.6453797909596053,631,0:52\n130000,0.6530586356467012,631,0:50\n140000,0.6511252331292993,631,0:49\n150000,0.6493585685714136,631,0:47\n160000,0.6611654692166531,631,0:46\n170000,0.6299647190742841,631,0:45\n180000,0.6550507623880887,631,0:43\n190000,0.6495030541186503,631,0:42\n200000,0.6487052133702255,631,0:41\n210000,0.647436641281221,631,0:39\n220000,0.6407083815064721,631,0:38\n230000,0.6835766784819463,631,0:36\n240000,0.6523389185956995,631,0:35\n250000,0.6386700641861797,631,0:34\n260000,0.6366873388067018,631,0:32\n270000,0.6554711466088526,631,0:31\n280000,0.6733783358224606,631,0:30\n290000,0.6475780246181619,631,0:28\n300000,0.6518880842322466,631,0:27\n310000,0.6698398872549354,631,0:26\n320000,0.6499392023521838,631,0:24\n330000,0.6405015051374844,631,0:23\n340000,0.6569710829177986,631,0:21\n350000,0.653853314089067,631,0:20\n360000,0.6559514775052917,631,0:19\n370000,0.640813435250535,631,0:17\n380000,0.6577314006594823,631,0:16\n390000,0.6689406807401618,631,0:15\n400000,0.6389693056414331,631,0:13\n410000,0.6566227267576341,631,0:12\n420000,0.6518080774545063,631,0:10\n430000,0.6499563052222843,631,0:09\n440000,0.6534193803863104,631,0:08\n450000,0.6529777427799393,631,0:06\n460000,0.6570511948370427,631,0:05\n470000,0.6563692544539336,631,0:04\n480000,0.6474223491655458,631,0:02\n490000,0.6493028200087201,631,0:01\n500000,0.6427357003549519,631,0:00\nRERUN\nESTIMATOR\nCALC DENSE & RDF\nLoss: 15.870180676782528\n"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":""},"metadata":{}},{"name":"stdout","output_type":"stream","text":"SAMPLE\n2\n#\"Step\",\"Density (g/mL)\",\"Speed (ns/day)\",\"Time Remaining\"\n10000,0.651065525767448,0,--\n20000,0.6714756355507168,618,1:07\n30000,0.6594873077207757,621,1:05\n40000,0.6665784796791466,621,1:03\n50000,0.6831123394657903,622,1:02\n60000,0.6637870831199422,622,1:01\n70000,0.667129160757806,622,0:59\n80000,0.6633039299251599,622,0:58\n90000,0.6565053933125344,623,0:56\n100000,0.671684912119932,623,0:55\n110000,0.6500951019425772,623,0:54\n120000,0.6666584351669924,623,0:52\n130000,0.6659556977742978,623,0:51\n140000,0.6611373098958204,623,0:49\n150000,0.6513470763921477,623,0:48\n160000,0.6417160185435988,623,0:47\n170000,0.6658724588754958,623,0:45\n180000,0.6495778453765773,623,0:44\n190000,0.6599069482963903,623,0:42\n200000,0.663304724598376,623,0:41\n210000,0.6712636904995878,623,0:40\n220000,0.6717563053654046,623,0:38\n230000,0.665811826826304,623,0:37\n240000,0.6676410416092742,623,0:36\n250000,0.6662819912851742,623,0:34\n260000,0.6700363479990482,623,0:33\n270000,0.6637966195304331,623,0:31\n280000,0.6567964211698348,623,0:30\n290000,0.6634398033762469,623,0:29\n300000,0.6728267456743389,623,0:27\n310000,0.6656202701834719,623,0:26\n320000,0.6626901966612553,623,0:24\n330000,0.652366375353711,623,0:23\n340000,0.6713163038686302,623,0:22\n350000,0.6462063695698634,623,0:20\n360000,0.646684679633897,623,0:19\n370000,0.6628132052754169,623,0:18\n380000,0.6631817349169167,623,0:16\n390000,0.6715043497403834,623,0:15\n400000,0.6540802138048043,623,0:13\n410000,0.6591383653279173,623,0:12\n420000,0.6759337429338593,623,0:11\n430000,0.676770720977082,623,0:09\n440000,0.6590515943855906,623,0:08\n450000,0.6506206795975595,623,0:06\n460000,0.6724418739103425,623,0:05\n470000,0.6587260488654286,623,0:04\n480000,0.6675405514238612,623,0:02\n490000,0.6598407154930133,623,0:01\n500000,0.6699306037728279,623,0:00\nRERUN\nESTIMATOR\nCALC DENSE & RDF\nLoss: 15.812784653771704\n"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":""},"metadata":{}},{"name":"stdout","output_type":"stream","text":"SAMPLE\n3\n#\"Step\",\"Density (g/mL)\",\"Speed (ns/day)\",\"Time Remaining\"\n10000,0.6718645245702801,0,--\n20000,0.6755685973414902,629,1:05\n30000,0.6713169715821765,630,1:04\n40000,0.6514334918471593,628,1:03\n50000,0.6760486967657545,630,1:01\n60000,0.6848578358624342,630,1:00\n70000,0.6561299856470861,630,0:58\n80000,0.6880366455873881,630,0:57\n90000,0.667097398444723,630,0:56\n100000,0.6667884760772556,630,0:54\n110000,0.696671586450088,630,0:53\n120000,0.654081476330793,631,0:52\n130000,0.6626347019403445,631,0:50\n140000,0.6731411830908128,631,0:49\n150000,0.6655217808268538,631,0:47\n160000,0.6538876323508401,631,0:46\n170000,0.6611437435731256,631,0:45\n180000,0.6637788251159248,631,0:43\n190000,0.6650875672903395,631,0:42\n200000,0.6714764348067651,631,0:41\n210000,0.6807755044242513,631,0:39\n220000,0.6769567213505293,631,0:38\n230000,0.6671596753641728,631,0:36\n240000,0.6845538694955645,631,0:35\n250000,0.6822774711981981,631,0:34\n260000,0.6783156195936104,631,0:32\n270000,0.676567338642897,632,0:31\n280000,0.6790158243891763,632,0:30\n290000,0.6686322282476979,632,0:28\n300000,0.6634648795512493,632,0:27\n310000,0.6769263925170507,632,0:25\n320000,0.6526637233762382,632,0:24\n330000,0.669435433400108,632,0:23\n340000,0.6641843095689863,632,0:21\n350000,0.6645577787557228,632,0:20\n360000,0.6668230758084706,631,0:19\n370000,0.6703211650552884,631,0:17\n380000,0.6806966229131148,631,0:16\n390000,0.6616890707646312,631,0:15\n400000,0.6819525784896312,631,0:13\n410000,0.665157274642379,631,0:12\n420000,0.6699321065074668,631,0:10\n430000,0.6808958547292827,631,0:09\n440000,0.6666969170415316,631,0:08\n450000,0.6777106136813664,632,0:06\n460000,0.6610028091781511,632,0:05\n470000,0.668538804771872,632,0:04\n480000,0.6803365654986357,632,0:02\n490000,0.6695076178907728,632,0:01\n500000,0.6651491924259733,632,0:00\nRERUN\nESTIMATOR\nCALC DENSE & RDF\nLoss: 15.7829902574545\n"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":""},"metadata":{}},{"name":"stdout","output_type":"stream","text":"SAMPLE\n4\n#\"Step\",\"Density (g/mL)\",\"Speed (ns/day)\",\"Time Remaining\"\n10000,0.6885785506889522,0,--\n20000,0.6855313267936995,620,1:06\n30000,0.6738399334137635,622,1:05\n40000,0.6758683360787152,622,1:03\n50000,0.6580422879144204,622,1:02\n60000,0.685476791854041,623,1:01\n70000,0.6723658334244308,623,0:59\n80000,0.6841155136543882,622,0:58\n90000,0.6697960378777031,622,0:56\n100000,0.6693224815511806,622,0:55\n110000,0.6776514780480505,622,0:54\n120000,0.6782084962583117,622,0:52\n130000,0.6711639748591046,622,0:51\n140000,0.6729686574938444,622,0:50\n150000,0.6808904909553964,622,0:48\n160000,0.6604370949042647,622,0:47\n170000,0.6654119614943933,622,0:45\n180000,0.6754897723338992,622,0:44\n190000,0.6930922087205912,622,0:43\n200000,0.6927413572065216,622,0:41\n210000,0.6635566632819155,622,0:40\n220000,0.675689996696178,622,0:38\n230000,0.6852580120296231,622,0:37\n240000,0.685205721714706,622,0:36\n250000,0.6651119129848674,622,0:34\n260000,0.6580544201013442,622,0:33\n270000,0.6846049610241778,622,0:31\n280000,0.6775557366503023,622,0:30\n290000,0.6554876975217213,622,0:29\n300000,0.6581194305919392,622,0:27\n310000,0.6827285529423294,622,0:26\n320000,0.6383643164545799,622,0:24\n330000,0.6501500644317023,622,0:23\n340000,0.671710134862776,622,0:22\n350000,0.6704489382635833,622,0:20\n360000,0.6798675299158504,622,0:19\n370000,0.6789445843269181,622,0:18\n380000,0.6806685240362235,622,0:16\n390000,0.6869812586105256,622,0:15\n400000,0.6685174916291445,622,0:13\n410000,0.6572229884392974,622,0:12\n420000,0.6835066235630598,622,0:11\n430000,0.6748251163937088,622,0:09\n440000,0.6610883621925576,622,0:08\n450000,0.67780721138058,622,0:06\n460000,0.6805647441371488,622,0:05\n470000,0.6858561866730586,622,0:04\n480000,0.679362446480657,622,0:02\n490000,0.6756215720243536,622,0:01\n500000,0.6804034797403086,622,0:00\nRERUN\nESTIMATOR\nCALC DENSE & RDF\nLoss: 15.77247390426107\n"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":""},"metadata":{}},{"name":"stdout","output_type":"stream","text":"SAMPLE\n5\n#\"Step\",\"Density (g/mL)\",\"Speed (ns/day)\",\"Time Remaining\"\n10000,0.6976382888743065,0,--\n20000,0.6725511283112715,616,1:07\n30000,0.6882838337373404,616,1:05\n40000,0.6791127582776632,617,1:04\n50000,0.6633986213858268,617,1:02\n60000,0.669689813467961,617,1:01\n70000,0.6572277858788221,618,1:00\n80000,0.6692980231309368,618,0:58\n90000,0.6663853828712928,618,0:57\n100000,0.6609102909544062,618,0:55\n110000,0.6877572224355598,618,0:54\n120000,0.6897426343741119,618,0:53\n130000,0.6710449057208788,618,0:51\n140000,0.672662971700982,618,0:50\n150000,0.6732441403013656,618,0:48\n160000,0.653905534581875,617,0:47\n170000,0.6786849938297382,618,0:46\n180000,0.6688867374873644,617,0:44\n190000,0.660636763570619,617,0:43\n200000,0.658570550674693,617,0:41\n210000,0.6717448165358701,617,0:40\n220000,0.6838297466520072,617,0:39\n230000,0.6745011684412172,617,0:37\n240000,0.6571798790920299,617,0:36\n250000,0.6649566841621759,617,0:34\n260000,0.6737761415172699,617,0:33\n270000,0.6862126163247438,617,0:32\n280000,0.6793622119282605,617,0:30\n290000,0.6698848029677942,617,0:29\n300000,0.67949488553041,617,0:27\n310000,0.6791970638701499,617,0:26\n320000,0.6706660558087095,617,0:25\n330000,0.6870114426546609,618,0:23\n340000,0.6996630771627976,618,0:22\n350000,0.6751726604081996,618,0:20\n360000,0.6887400497278416,618,0:19\n370000,0.6666566269288412,618,0:18\n380000,0.6608656121363888,618,0:16\n390000,0.6841990926345275,618,0:15\n400000,0.6631456681390829,618,0:13\n410000,0.6841451570582064,618,0:12\n420000,0.6971576348474124,618,0:11\n430000,0.6720146802210868,618,0:09\n440000,0.689983221350112,618,0:08\n450000,0.6839190853894165,618,0:06\n460000,0.6738649744303744,618,0:05\n470000,0.6644421666324579,618,0:04\n480000,0.6740747827356015,618,0:02\n490000,0.6736973878013252,617,0:01\n500000,0.6773923272948968,617,0:00\nRERUN\nESTIMATOR\nCALC DENSE & RDF\nLoss: 15.746867555993234\n"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":""},"metadata":{}},{"name":"stdout","output_type":"stream","text":"SAMPLE\n6\n#\"Step\",\"Density (g/mL)\",\"Speed (ns/day)\",\"Time Remaining\"\n10000,0.688020305959254,0,--\n20000,0.6893909023822415,620,1:06\n30000,0.6917729602538208,623,1:05\n40000,0.6739143910369427,624,1:03\n50000,0.6558510853681656,624,1:02\n60000,0.6989558022509464,624,1:00\n70000,0.6895262296439162,624,0:59\n"},{"ename":"KeyboardInterrupt","evalue":"","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)","Cell \u001b[0;32mIn [88], line 9\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSAMPLE\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28mprint\u001b[39m(nloop)\n\u001b[0;32m----> 9\u001b[0m \u001b[43mrunMD\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mloop-\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mnloop\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m.xml\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mloop-\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mnloop\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m.dcd\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRERUN\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 11\u001b[0m ener \u001b[38;5;241m=\u001b[39m rerun_energy(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mloop-\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnloop\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.xml\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mloop-\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnloop\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.dcd\u001b[39m\u001b[38;5;124m\"\u001b[39m, removeLJ\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, skip\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m20\u001b[39m)\n","Cell \u001b[0;32mIn [78], line 20\u001b[0m, in \u001b[0;36mrunMD\u001b[0;34m(ffile, trajfile)\u001b[0m\n\u001b[1;32m 18\u001b[0m simulation\u001b[38;5;241m.\u001b[39mcontext\u001b[38;5;241m.\u001b[39msetPositions(pdb\u001b[38;5;241m.\u001b[39mgetPositions())\n\u001b[1;32m 19\u001b[0m simulation\u001b[38;5;241m.\u001b[39mminimizeEnergy()\n\u001b[0;32m---> 20\u001b[0m \u001b[43msimulation\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m500\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m1000\u001b[39;49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/openmm/app/simulation.py:141\u001b[0m, in \u001b[0;36mSimulation.step\u001b[0;34m(self, steps)\u001b[0m\n\u001b[1;32m 139\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mstep\u001b[39m(\u001b[38;5;28mself\u001b[39m, steps):\n\u001b[1;32m 140\u001b[0m \u001b[38;5;124;03m\"\"\"Advance the simulation by integrating a specified number of time steps.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 141\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_simulate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mendStep\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcurrentStep\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43msteps\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/openmm/app/simulation.py:206\u001b[0m, in \u001b[0;36mSimulation._simulate\u001b[0;34m(self, endStep, endTime)\u001b[0m\n\u001b[1;32m 204\u001b[0m stepsToGo \u001b[38;5;241m=\u001b[39m nextSteps\n\u001b[1;32m 205\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m stepsToGo \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m10\u001b[39m:\n\u001b[0;32m--> 206\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mintegrator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# Only take 10 steps at a time, to give Python more chances to respond to a control-c.\u001b[39;00m\n\u001b[1;32m 207\u001b[0m stepsToGo \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m10\u001b[39m\n\u001b[1;32m 208\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m endTime \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m datetime\u001b[38;5;241m.\u001b[39mnow() \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m endTime:\n","File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/openmm/openmm.py:14726\u001b[0m, in \u001b[0;36mLangevinIntegrator.step\u001b[0;34m(self, steps)\u001b[0m\n\u001b[1;32m 14716\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mstep\u001b[39m(\u001b[38;5;28mself\u001b[39m, steps):\n\u001b[1;32m 14717\u001b[0m \u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 14718\u001b[0m \u001b[38;5;124;03m step(self, steps)\u001b[39;00m\n\u001b[1;32m 14719\u001b[0m \u001b[38;5;124;03m Advance a simulation through time by taking a series of time steps.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 14724\u001b[0m \u001b[38;5;124;03m the number of time steps to take\u001b[39;00m\n\u001b[1;32m 14725\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m> 14726\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_openmm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mLangevinIntegrator_step\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msteps\u001b[49m\u001b[43m)\u001b[49m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}],"id":"9dd67aff-d540-4285-850f-d5700f7dbd1d"},{"cell_type":"code","source":"xmlrender(f\"loop-{NL}.xml\", 'res.xml', f\"loop-{NL}.xml\")\nsample_with_prm(f\"loop-{NL}.xml\", f\"loop-{NL}.dcd\")\ntraj = md.load(f\"loop-{NL}.dcd\", top=\"box_DMC.pdb\")[20:]\nrdf_final = compute_rdf_frame(traj, x_ref*0.1).mean(axis=0)\n\nplt.plot(x_ref, rdf_init, label = \"Initial\")\nplt.plot(m_ref, n_ref, label = \"OPLS-AA\")\n#plt.plot(x_ref, y_ref, label = \"Experiment\")\nplt.plot(x_ref, rdf_final, label = \"Current\")\nplt.legend()\nplt.title(f\"Final\")\n# plt.savefig(\"compare.png\")\nplt.show()","metadata":{},"execution_count":57,"outputs":[{"name":"stdout","output_type":"stream","text":"#\"Step\",\"Density (g/mL)\",\"Speed (ns/day)\",\"Time Remaining\"\n20000,0.9034204023148491,0,--\n40000,0.9134589209326665,1.21e+03,0:32\n60000,0.9311857845324879,1.21e+03,0:31\n80000,0.9209480174841933,1.21e+03,0:30\n100000,0.9036050529338874,1.21e+03,0:28\n120000,0.9120823292515017,1.21e+03,0:27\n140000,0.9411380111912142,1.21e+03,0:25\n160000,0.9263761966211715,1.21e+03,0:24\n180000,0.9349105015898002,1.21e+03,0:22\n200000,0.9028592257494273,1.21e+03,0:21\n220000,0.9397294615775963,1.21e+03,0:20\n240000,0.916016085063393,1.21e+03,0:18\n260000,0.9369981516116607,1.21e+03,0:17\n280000,0.911334635669278,1.21e+03,0:15\n300000,0.9169613328351074,1.21e+03,0:14\n320000,0.9107559836942581,1.21e+03,0:12\n340000,0.9008690012597967,1.21e+03,0:11\n360000,0.9255158510125162,1.21e+03,0:10\n380000,0.9173521261898001,1.21e+03,0:08\n400000,0.9199973033288819,1.21e+03,0:07\n420000,0.9071096082489485,1.21e+03,0:05\n440000,0.9137287359774533,1.21e+03,0:04\n460000,0.8926880220808483,1.21e+03,0:02\n480000,0.9341117246446181,1.21e+03,0:01\n500000,0.923685862805699,1.21e+03,0:00\n"},{"output_type":"display_data","data":{"remote/url":"https://bohrium.oss-cn-zhangjiakou.aliyuncs.com/article/110115/ab171957574140a085c76a5f33b95db9/d7d33c74b4b24d199cb47f46046c6cc4.png","text/plain":""},"metadata":{}}],"id":"2e077eba-6894-4643-be75-709039b0e315"},{"cell_type":"markdown","source":"## 5. Summary & Outlook \nAs an old saying goes in China, to do a good job, one must first sharpen his tools (工欲善其事，必先利其器). In the era of rapid development of differentiable programming techniques driven by the wave of deep learning, we have witnessed a new paradigm in force field development. We aim to transform force field development into an engineering-oriented, automated, and reproducible process, allowing us to reap the benefits of continuous integration/development and the open-source spirit. DMFF, along with other related projects in **DeepModeling** community, will promote and implement this transformative change.\n\nDMFF is currently in the early stages of rapid iteration and development. There are many areas that require further improvement and numerous possibilities worth exploring:\n\n- DMFF, along with projects like dflow, aims to implement common force field fitting workflows, such as fitting dihedral angles, free energies, probability distributions, and more. These workflows will be integrated into the software to provide users with a comprehensive suite of tools for force field development and parameterization.\n\n- DMFF is committed to continuous development and will strive to meet the evolving needs of the scientific community. This includes expanding the range of supported force field function forms to accommodate a wider variety of molecular systems.\n\n- DMFF recognizes the importance of integrating advanced molecular dynamics algorithms with force field development. By leveraging state-of-the-art sampling techniques and enhanced sampling methods, DMFF aims to enhance the accuracy and efficiency of force field optimization and simulations.\n\n- Absolutely! DMFF recognizes the importance of user experience and aims to continuously improve its documentation, API, and overall usability. Providing clear and comprehensive documentation is essential for users to understand the functionality and usage of DMFF effectively.\n\nWelcome to write Issues, initiate Discussions, and even submit Pull Requests in the DMFF GitHub project. Specifically:\n\n- If you are a hardcore developer in the field of molecular force fields and are exploring new forms of force field functions, we welcome you to engage in in-depth discussions with the developers and contribute to enriching the force field calculation capabilities of DMFF.\n\n- If you are dedicated to simulating a specific system and are struggling to find suitable force field parameters, you can become an angel user of DMFF. You can use DMFF to build a force field optimization workflow based on your own needs and provide valuable suggestions to us based on your practical requirements. Your feedback will contribute to the further development and improvement of DMFF.","metadata":{},"id":"1bfb9193-e469-45df-9196-0b0eea5f144d"}]}
\ No newline at end of file
diff --git a/examples/DMC/init.dcd b/examples/DMC/init.dcd
new file mode 100644
index 000000000..1436a2bbd
Binary files /dev/null and b/examples/DMC/init.dcd differ
diff --git a/examples/DMC/lj.xml b/examples/DMC/lj.xml
new file mode 100644
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diff --git a/examples/DMC/res.xml b/examples/DMC/res.xml
new file mode 100644
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+++ b/examples/DMC/res.xml
@@ -0,0 +1,23 @@
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diff --git a/examples/mbar/ben-prm.xml b/examples/mbar/ben-prm.xml
deleted file mode 100644
index 51ca52004..000000000
--- a/examples/mbar/ben-prm.xml
+++ /dev/null
@@ -1,24 +0,0 @@
-
-
- 2022-09-18
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
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-
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-
\ No newline at end of file
diff --git a/examples/mbar/ben-top.xml b/examples/mbar/ben-top.xml
deleted file mode 100644
index 9ef62d2c0..000000000
--- a/examples/mbar/ben-top.xml
+++ /dev/null
@@ -1,7 +0,0 @@
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/examples/mbar/ben.pdb b/examples/mbar/ben.pdb
deleted file mode 100644
index 5af843280..000000000
--- a/examples/mbar/ben.pdb
+++ /dev/null
@@ -1,4 +0,0 @@
-REMARK 1 CREATED WITH OPENMM 7.7, 2022-09-18
-HETATM 1 C1 BEN A 1 3.131 2.883 -0.435 1.00 0.00 C
-HETATM 3 C3 BEN A 1 3.131 1.046 1.132 1.00 0.00 C
-HETATM 5 C5 BEN A 1 4.939 2.647 1.147 1.00 0.00 C
\ No newline at end of file
diff --git a/examples/mbar/benz.txt b/examples/mbar/benz.txt
deleted file mode 100644
index 2eeb0748b..000000000
--- a/examples/mbar/benz.txt
+++ /dev/null
@@ -1,106 +0,0 @@
-3.50000e+0 1.36691e-2
-3.60000e+0 2.33244e-2
-3.70000e+0 3.88271e-2
-3.80000e+0 5.73159e-2
-3.90000e+0 8.12927e-2
-4.00000e+0 1.10869e-1
-4.10000e+0 1.47153e-1
-4.20000e+0 1.97505e-1
-4.30000e+0 2.51627e-1
-4.40000e+0 3.33850e-1
-4.50000e+0 4.46947e-1
-4.60000e+0 6.06233e-1
-4.70000e+0 8.30737e-1
-4.80000e+0 1.02379e+0
-4.90000e+0 1.28467e+0
-5.00000e+0 1.48432e+0
-5.10000e+0 1.60134e+0
-5.20000e+0 1.66708e+0
-5.30000e+0 1.70344e+0
-5.40000e+0 1.73484e+0
-5.50000e+0 1.77387e+0
-5.60000e+0 1.81304e+0
-5.70000e+0 1.85190e+0
-5.80000e+0 1.87969e+0
-5.90000e+0 1.88458e+0
-6.00000e+0 1.86235e+0
-6.10000e+0 1.80173e+0
-6.20000e+0 1.71302e+0
-6.30000e+0 1.57397e+0
-6.40000e+0 1.44336e+0
-6.50000e+0 1.31439e+0
-6.60000e+0 1.19057e+0
-6.70000e+0 1.06929e+0
-6.80000e+0 9.66103e-1
-6.90000e+0 8.82179e-1
-7.00000e+0 8.16834e-1
-7.10000e+0 7.64724e-1
-7.20000e+0 7.26786e-1
-7.30000e+0 6.98183e-1
-7.40000e+0 6.78272e-1
-7.50000e+0 6.68151e-1
-7.60000e+0 6.58833e-1
-7.70000e+0 6.54363e-1
-7.80000e+0 6.55446e-1
-7.90000e+0 6.60430e-1
-8.00000e+0 6.67523e-1
-8.10000e+0 6.81308e-1
-8.20000e+0 6.96787e-1
-8.30000e+0 7.15460e-1
-8.40000e+0 7.40184e-1
-8.50000e+0 7.81555e-1
-8.60000e+0 8.11797e-1
-8.70000e+0 8.41624e-1
-8.80000e+0 8.73386e-1
-8.90000e+0 9.11023e-1
-9.00000e+0 9.48117e-1
-9.10000e+0 9.79008e-1
-9.20000e+0 1.01636e+0
-9.30000e+0 1.05518e+0
-9.40000e+0 1.08475e+0
-9.50000e+0 1.11409e+0
-9.60000e+0 1.14053e+0
-9.70000e+0 1.16202e+0
-9.80000e+0 1.18017e+0
-9.90000e+0 1.19236e+0
-1.00000e+1 1.19962e+0
-1.01000e+1 1.20169e+0
-1.02000e+1 1.19914e+0
-1.03000e+1 1.19294e+0
-1.04000e+1 1.18467e+0
-1.05000e+1 1.17460e+0
-1.06000e+1 1.16124e+0
-1.07000e+1 1.15031e+0
-1.08000e+1 1.13482e+0
-1.09000e+1 1.11122e+0
-1.10000e+1 1.09903e+0
-1.11000e+1 1.08409e+0
-1.12000e+1 1.06913e+0
-1.13000e+1 1.05144e+0
-1.14000e+1 1.03694e+0
-1.15000e+1 1.02233e+0
-1.16000e+1 1.00696e+0
-1.17000e+1 9.92727e-1
-1.18000e+1 9.79214e-1
-1.19000e+1 9.68538e-1
-1.20000e+1 9.60193e-1
-1.21000e+1 9.49812e-1
-1.22000e+1 9.41204e-1
-1.23000e+1 9.33168e-1
-1.24000e+1 9.29037e-1
-1.25000e+1 9.24243e-1
-1.26000e+1 9.23099e-1
-1.27000e+1 9.23096e-1
-1.28000e+1 9.24229e-1
-1.29000e+1 9.26455e-1
-1.30000e+1 9.31653e-1
-1.31000e+1 9.35617e-1
-1.32000e+1 9.41629e-1
-1.33000e+1 9.53214e-1
-1.34000e+1 9.67656e-1
-1.35000e+1 9.75437e-1
-1.36000e+1 9.86745e-1
-1.37000e+1 9.98225e-1
-1.38000e+1 1.00970e+0
-1.39000e+1 1.01895e+0
-1.40000e+1 1.02877e+0
\ No newline at end of file
diff --git a/examples/mbar/box_relaxed.pdb b/examples/mbar/box_relaxed.pdb
deleted file mode 100644
index 5122b01fc..000000000
--- a/examples/mbar/box_relaxed.pdb
+++ /dev/null
@@ -1,607 +0,0 @@
-HEADER
-TITLE Built with Packmol
-REMARK Packmol generated pdb file
-REMARK Home-Page: http://m3g.iqm.unicamp.br/packmol
-REMARK
-CRYST1 30.05 30.05 30.05 90.00 90.00 90.00 P 1 1
-HETATM 1 C1 BEN A 1 24.148 20.445 23.381 1.00 0.00 C
-HETATM 2 C3 BEN A 1 25.088 21.452 25.364 1.00 0.00 C
-HETATM 3 C5 BEN A 1 22.693 21.264 25.125 1.00 0.00 C
-HETATM 4 C1 BEN A 2 4.869 26.635 27.351 1.00 0.00 C
-HETATM 5 C3 BEN A 2 2.513 26.374 26.890 1.00 0.00 C
-HETATM 6 C5 BEN A 2 3.255 28.017 28.498 1.00 0.00 C
-HETATM 7 C1 BEN A 3 18.723 9.938 9.174 1.00 0.00 C
-HETATM 8 C3 BEN A 3 19.013 8.044 7.705 1.00 0.00 C
-HETATM 9 C5 BEN A 3 16.797 8.883 8.172 1.00 0.00 C
-HETATM 10 C1 BEN A 4 17.984 7.600 2.626 1.00 0.00 C
-HETATM 11 C3 BEN A 4 16.822 5.636 3.414 1.00 0.00 C
-HETATM 12 C5 BEN A 4 16.533 6.377 1.134 1.00 0.00 C
-HETATM 13 C1 BEN A 5 17.570 5.323 30.003 1.00 0.00 C
-HETATM 14 C3 BEN A 5 19.932 4.820 30.001 1.00 0.00 C
-HETATM 15 C5 BEN A 5 18.339 3.146 29.299 1.00 0.00 C
-HETATM 16 C1 BEN A 6 11.251 3.589 11.059 1.00 0.00 C
-HETATM 17 C3 BEN A 6 9.721 5.428 11.388 1.00 0.00 C
-HETATM 18 C5 BEN A 6 12.073 5.715 11.855 1.00 0.00 C
-HETATM 19 C1 BEN A 7 9.659 22.692 21.411 1.00 0.00 C
-HETATM 20 C3 BEN A 7 8.485 20.584 21.496 1.00 0.00 C
-HETATM 21 C5 BEN A 7 10.889 20.620 21.265 1.00 0.00 C
-HETATM 22 C1 BEN A 8 27.278 13.927 19.581 1.00 0.00 C
-HETATM 23 C3 BEN A 8 29.530 13.477 20.328 1.00 0.00 C
-HETATM 24 C5 BEN A 8 28.672 15.734 20.367 1.00 0.00 C
-HETATM 25 C1 BEN A 9 22.837 10.589 27.153 1.00 0.00 C
-HETATM 26 C3 BEN A 9 24.122 11.194 29.105 1.00 0.00 C
-HETATM 27 C5 BEN A 9 24.464 9.049 28.051 1.00 0.00 C
-HETATM 28 C1 BEN A 10 8.528 29.627 17.543 1.00 0.00 C
-HETATM 29 C3 BEN A 10 8.172 28.020 15.777 1.00 0.00 C
-HETATM 30 C5 BEN A 10 7.039 27.759 17.894 1.00 0.00 C
-HETATM 31 C1 BEN A 11 19.875 6.973 13.839 1.00 0.00 C
-HETATM 32 C3 BEN A 11 19.751 8.784 12.247 1.00 0.00 C
-HETATM 33 C5 BEN A 11 21.658 7.303 12.246 1.00 0.00 C
-HETATM 34 C1 BEN A 12 7.958 29.226 20.865 1.00 0.00 C
-HETATM 35 C3 BEN A 12 9.940 29.179 22.243 1.00 0.00 C
-HETATM 36 C5 BEN A 12 8.911 27.112 21.534 1.00 0.00 C
-HETATM 37 C1 BEN A 13 16.240 27.827 18.591 1.00 0.00 C
-HETATM 38 C3 BEN A 13 18.157 28.743 19.739 1.00 0.00 C
-HETATM 39 C5 BEN A 13 17.612 26.402 19.975 1.00 0.00 C
-HETATM 40 C1 BEN A 14 2.948 7.869 20.826 1.00 0.00 C
-HETATM 41 C3 BEN A 14 3.522 9.947 21.913 1.00 0.00 C
-HETATM 42 C5 BEN A 14 1.204 9.354 21.586 1.00 0.00 C
-HETATM 43 C1 BEN A 15 25.704 24.134 22.005 1.00 0.00 C
-HETATM 44 C3 BEN A 15 25.067 26.029 23.360 1.00 0.00 C
-HETATM 45 C5 BEN A 15 23.874 23.939 23.567 1.00 0.00 C
-HETATM 46 C1 BEN A 16 0.812 18.252 10.232 1.00 0.00 C
-HETATM 47 C3 BEN A 16 0.582 16.870 8.265 1.00 0.00 C
-HETATM 48 C5 BEN A 16 1.414 15.916 10.322 1.00 0.00 C
-HETATM 49 C1 BEN A 17 26.389 13.045 4.179 1.00 0.00 C
-HETATM 50 C3 BEN A 17 24.814 11.859 5.573 1.00 0.00 C
-HETATM 51 C5 BEN A 17 26.779 10.730 4.740 1.00 0.00 C
-HETATM 52 C1 BEN A 18 7.136 6.278 4.342 1.00 0.00 C
-HETATM 53 C3 BEN A 18 9.399 5.581 4.812 1.00 0.00 C
-HETATM 54 C5 BEN A 18 7.862 6.001 6.627 1.00 0.00 C
-HETATM 55 C1 BEN A 19 28.274 28.266 22.125 1.00 0.00 C
-HETATM 56 C3 BEN A 19 29.999 28.703 23.757 1.00 0.00 C
-HETATM 57 C5 BEN A 19 29.919 29.983 21.711 1.00 0.00 C
-HETATM 58 C1 BEN A 20 17.598 6.239 16.695 1.00 0.00 C
-HETATM 59 C3 BEN A 20 15.672 6.646 15.298 1.00 0.00 C
-HETATM 60 C5 BEN A 20 17.353 8.359 15.566 1.00 0.00 C
-HETATM 61 C1 BEN A 21 21.879 26.236 25.198 1.00 0.00 C
-HETATM 62 C3 BEN A 21 22.109 24.469 26.827 1.00 0.00 C
-HETATM 63 C5 BEN A 21 23.563 26.394 26.920 1.00 0.00 C
-HETATM 64 C1 BEN A 22 11.509 21.789 18.241 1.00 0.00 C
-HETATM 65 C3 BEN A 22 9.148 21.287 18.169 1.00 0.00 C
-HETATM 66 C5 BEN A 22 9.894 23.583 18.218 1.00 0.00 C
-HETATM 67 C1 BEN A 23 20.524 0.600 10.775 1.00 0.00 C
-HETATM 68 C3 BEN A 23 22.015 2.364 10.071 1.00 0.00 C
-HETATM 69 C5 BEN A 23 21.420 0.600 8.533 1.00 0.00 C
-HETATM 70 C1 BEN A 24 23.381 28.881 11.610 1.00 0.00 C
-HETATM 71 C3 BEN A 24 22.052 28.191 9.717 1.00 0.00 C
-HETATM 72 C5 BEN A 24 21.144 28.041 11.949 1.00 0.00 C
-HETATM 73 C1 BEN A 25 12.290 5.318 21.654 1.00 0.00 C
-HETATM 74 C3 BEN A 25 10.097 5.045 20.681 1.00 0.00 C
-HETATM 75 C5 BEN A 25 11.422 6.992 20.147 1.00 0.00 C
-HETATM 76 C1 BEN A 26 30.000 14.399 4.497 1.00 0.00 C
-HETATM 77 C3 BEN A 26 28.758 16.469 4.454 1.00 0.00 C
-HETATM 78 C5 BEN A 26 29.488 15.455 2.387 1.00 0.00 C
-HETATM 79 C1 BEN A 27 29.600 16.851 24.027 1.00 0.00 C
-HETATM 80 C3 BEN A 27 27.473 16.656 25.153 1.00 0.00 C
-HETATM 81 C5 BEN A 27 28.732 14.672 24.596 1.00 0.00 C
-HETATM 82 C1 BEN A 28 8.330 10.530 5.762 1.00 0.00 C
-HETATM 83 C3 BEN A 28 6.772 12.297 5.234 1.00 0.00 C
-HETATM 84 C5 BEN A 28 8.622 11.834 3.752 1.00 0.00 C
-HETATM 85 C1 BEN A 29 4.849 12.492 18.746 1.00 0.00 C
-HETATM 86 C3 BEN A 29 2.852 13.170 19.921 1.00 0.00 C
-HETATM 87 C5 BEN A 29 2.678 11.866 17.896 1.00 0.00 C
-HETATM 88 C1 BEN A 30 8.511 28.110 25.809 1.00 0.00 C
-HETATM 89 C3 BEN A 30 8.678 28.565 28.175 1.00 0.00 C
-HETATM 90 C5 BEN A 30 7.348 30.000 26.759 1.00 0.00 C
-HETATM 91 C1 BEN A 31 26.832 21.092 7.099 1.00 0.00 C
-HETATM 92 C3 BEN A 31 28.739 20.252 5.880 1.00 0.00 C
-HETATM 93 C5 BEN A 31 26.991 21.446 4.717 1.00 0.00 C
-HETATM 94 C1 BEN A 32 25.050 5.984 24.165 1.00 0.00 C
-HETATM 95 C3 BEN A 32 26.064 7.614 22.700 1.00 0.00 C
-HETATM 96 C5 BEN A 32 24.801 8.340 24.626 1.00 0.00 C
-HETATM 97 C1 BEN A 33 8.547 28.952 8.815 1.00 0.00 C
-HETATM 98 C3 BEN A 33 7.997 30.011 6.716 1.00 0.00 C
-HETATM 99 C5 BEN A 33 10.277 30.018 7.511 1.00 0.00 C
-HETATM 100 C1 BEN A 34 1.919 27.170 6.277 1.00 0.00 C
-HETATM 101 C3 BEN A 34 3.502 28.447 7.578 1.00 0.00 C
-HETATM 102 C5 BEN A 34 2.195 26.710 8.631 1.00 0.00 C
-HETATM 103 C1 BEN A 35 6.862 11.711 29.247 1.00 0.00 C
-HETATM 104 C3 BEN A 35 5.642 12.942 27.566 1.00 0.00 C
-HETATM 105 C5 BEN A 35 7.598 11.666 26.948 1.00 0.00 C
-HETATM 106 C1 BEN A 36 0.600 4.929 4.123 1.00 0.00 C
-HETATM 107 C3 BEN A 36 0.780 6.531 2.325 1.00 0.00 C
-HETATM 108 C5 BEN A 36 2.592 4.995 2.760 1.00 0.00 C
-HETATM 109 C1 BEN A 37 14.754 28.711 26.368 1.00 0.00 C
-HETATM 110 C3 BEN A 37 15.860 30.000 24.652 1.00 0.00 C
-HETATM 111 C5 BEN A 37 15.416 27.652 24.302 1.00 0.00 C
-HETATM 112 C1 BEN A 38 29.592 11.484 26.286 1.00 0.00 C
-HETATM 113 C3 BEN A 38 29.520 13.120 28.061 1.00 0.00 C
-HETATM 114 C5 BEN A 38 27.512 12.580 26.833 1.00 0.00 C
-HETATM 115 C1 BEN A 39 1.247 5.969 13.377 1.00 0.00 C
-HETATM 116 C3 BEN A 39 1.688 8.113 12.357 1.00 0.00 C
-HETATM 117 C5 BEN A 39 3.512 6.605 12.836 1.00 0.00 C
-HETATM 118 C1 BEN A 40 15.508 26.266 9.683 1.00 0.00 C
-HETATM 119 C3 BEN A 40 13.668 26.171 11.244 1.00 0.00 C
-HETATM 120 C5 BEN A 40 15.357 24.445 11.261 1.00 0.00 C
-HETATM 121 C1 BEN A 41 17.331 21.393 19.947 1.00 0.00 C
-HETATM 122 C3 BEN A 41 19.187 21.359 18.404 1.00 0.00 C
-HETATM 123 C5 BEN A 41 17.982 19.323 18.890 1.00 0.00 C
-HETATM 124 C1 BEN A 42 3.361 5.282 17.050 1.00 0.00 C
-HETATM 125 C3 BEN A 42 4.768 4.410 15.292 1.00 0.00 C
-HETATM 126 C5 BEN A 42 2.625 3.392 15.742 1.00 0.00 C
-HETATM 127 C1 BEN A 43 8.010 5.057 15.838 1.00 0.00 C
-HETATM 128 C3 BEN A 43 9.103 3.580 17.404 1.00 0.00 C
-HETATM 129 C5 BEN A 43 9.585 5.947 17.436 1.00 0.00 C
-HETATM 130 C1 BEN A 44 21.415 22.505 20.595 1.00 0.00 C
-HETATM 131 C3 BEN A 44 21.873 24.611 19.506 1.00 0.00 C
-HETATM 132 C5 BEN A 44 20.890 24.579 21.712 1.00 0.00 C
-HETATM 133 C1 BEN A 45 21.978 25.813 15.341 1.00 0.00 C
-HETATM 134 C3 BEN A 45 20.510 27.723 15.172 1.00 0.00 C
-HETATM 135 C5 BEN A 45 20.153 26.074 16.900 1.00 0.00 C
-HETATM 136 C1 BEN A 46 17.990 21.529 28.654 1.00 0.00 C
-HETATM 137 C3 BEN A 46 19.626 23.273 28.992 1.00 0.00 C
-HETATM 138 C5 BEN A 46 19.037 22.586 26.753 1.00 0.00 C
-HETATM 139 C1 BEN A 47 17.014 2.643 7.846 1.00 0.00 C
-HETATM 140 C3 BEN A 47 17.887 0.600 8.790 1.00 0.00 C
-HETATM 141 C5 BEN A 47 15.896 1.604 9.716 1.00 0.00 C
-HETATM 142 C1 BEN A 48 29.279 9.080 12.126 1.00 0.00 C
-HETATM 143 C3 BEN A 48 29.170 10.110 14.307 1.00 0.00 C
-HETATM 144 C5 BEN A 48 30.000 11.366 12.419 1.00 0.00 C
-HETATM 145 C1 BEN A 49 2.724 19.194 7.318 1.00 0.00 C
-HETATM 146 C3 BEN A 49 3.574 18.224 5.277 1.00 0.00 C
-HETATM 147 C5 BEN A 49 3.750 17.009 7.357 1.00 0.00 C
-HETATM 148 C1 BEN A 50 5.034 21.105 5.945 1.00 0.00 C
-HETATM 149 C3 BEN A 50 3.267 21.367 4.320 1.00 0.00 C
-HETATM 150 C5 BEN A 50 4.978 23.058 4.527 1.00 0.00 C
-HETATM 151 C1 BEN A 51 11.172 16.114 11.197 1.00 0.00 C
-HETATM 152 C3 BEN A 51 12.506 16.105 9.184 1.00 0.00 C
-HETATM 153 C5 BEN A 51 12.853 17.813 10.855 1.00 0.00 C
-HETATM 154 C1 BEN A 52 8.471 0.948 13.454 1.00 0.00 C
-HETATM 155 C3 BEN A 52 8.964 0.672 15.801 1.00 0.00 C
-HETATM 156 C5 BEN A 52 6.822 1.548 15.111 1.00 0.00 C
-HETATM 157 C1 BEN A 53 10.411 21.049 6.167 1.00 0.00 C
-HETATM 158 C3 BEN A 53 10.578 19.969 4.015 1.00 0.00 C
-HETATM 159 C5 BEN A 53 10.478 18.640 6.029 1.00 0.00 C
-HETATM 160 C1 BEN A 54 12.667 28.038 19.670 1.00 0.00 C
-HETATM 161 C3 BEN A 54 13.988 29.246 21.290 1.00 0.00 C
-HETATM 162 C5 BEN A 54 14.043 26.832 21.245 1.00 0.00 C
-HETATM 163 C1 BEN A 55 12.353 2.849 14.706 1.00 0.00 C
-HETATM 164 C3 BEN A 55 11.768 1.049 13.207 1.00 0.00 C
-HETATM 165 C5 BEN A 55 13.091 0.600 15.177 1.00 0.00 C
-HETATM 166 C1 BEN A 56 21.291 17.031 25.755 1.00 0.00 C
-HETATM 167 C3 BEN A 56 22.400 15.823 27.527 1.00 0.00 C
-HETATM 168 C5 BEN A 56 23.297 17.928 26.754 1.00 0.00 C
-HETATM 169 C1 BEN A 57 19.011 30.000 10.366 1.00 0.00 C
-HETATM 170 C3 BEN A 57 17.928 29.991 12.524 1.00 0.00 C
-HETATM 171 C5 BEN A 57 16.601 30.000 10.506 1.00 0.00 C
-HETATM 172 C1 BEN A 58 12.697 10.687 18.061 1.00 0.00 C
-HETATM 173 C3 BEN A 58 14.169 11.362 16.270 1.00 0.00 C
-HETATM 174 C5 BEN A 58 13.931 9.023 16.821 1.00 0.00 C
-HETATM 175 C1 BEN A 59 16.131 23.467 23.003 1.00 0.00 C
-HETATM 176 C3 BEN A 59 13.897 22.599 22.709 1.00 0.00 C
-HETATM 177 C5 BEN A 59 15.006 22.386 24.844 1.00 0.00 C
-HETATM 178 C1 BEN A 60 21.137 18.937 16.418 1.00 0.00 C
-HETATM 179 C3 BEN A 60 21.249 18.607 18.807 1.00 0.00 C
-HETATM 180 C5 BEN A 60 21.659 16.756 17.311 1.00 0.00 C
-HETATM 181 C1 BEN A 61 28.576 19.874 12.985 1.00 0.00 C
-HETATM 182 C3 BEN A 61 26.869 20.448 11.377 1.00 0.00 C
-HETATM 183 C5 BEN A 61 28.995 21.591 11.340 1.00 0.00 C
-HETATM 184 C1 BEN A 62 30.000 19.399 9.087 1.00 0.00 C
-HETATM 185 C3 BEN A 62 30.000 17.515 10.598 1.00 0.00 C
-HETATM 186 C5 BEN A 62 28.188 17.804 9.027 1.00 0.00 C
-HETATM 187 C1 BEN A 63 9.588 26.733 6.606 1.00 0.00 C
-HETATM 188 C3 BEN A 63 11.455 27.733 5.447 1.00 0.00 C
-HETATM 189 C5 BEN A 63 9.307 27.563 4.357 1.00 0.00 C
-HETATM 190 C1 BEN A 64 2.584 4.572 21.893 1.00 0.00 C
-HETATM 191 C3 BEN A 64 0.600 3.713 22.968 1.00 0.00 C
-HETATM 192 C5 BEN A 64 2.678 3.842 24.192 1.00 0.00 C
-HETATM 193 C1 BEN A 65 4.824 30.000 16.412 1.00 0.00 C
-HETATM 194 C3 BEN A 65 3.046 29.788 18.032 1.00 0.00 C
-HETATM 195 C5 BEN A 65 3.709 27.880 16.708 1.00 0.00 C
-HETATM 196 C1 BEN A 66 21.712 20.958 4.222 1.00 0.00 C
-HETATM 197 C3 BEN A 66 23.551 20.962 5.788 1.00 0.00 C
-HETATM 198 C5 BEN A 66 21.947 22.767 5.803 1.00 0.00 C
-HETATM 199 C1 BEN A 67 5.751 19.330 13.093 1.00 0.00 C
-HETATM 200 C3 BEN A 67 7.638 18.221 14.114 1.00 0.00 C
-HETATM 201 C5 BEN A 67 7.457 20.628 14.203 1.00 0.00 C
-HETATM 202 C1 BEN A 68 22.385 5.738 15.730 1.00 0.00 C
-HETATM 203 C3 BEN A 68 23.137 7.381 17.332 1.00 0.00 C
-HETATM 204 C5 BEN A 68 24.209 5.219 17.224 1.00 0.00 C
-HETATM 205 C1 BEN A 69 13.689 4.612 3.242 1.00 0.00 C
-HETATM 206 C3 BEN A 69 14.409 4.558 5.546 1.00 0.00 C
-HETATM 207 C5 BEN A 69 12.875 6.267 4.799 1.00 0.00 C
-HETATM 208 C1 BEN A 70 17.229 18.173 29.377 1.00 0.00 C
-HETATM 209 C3 BEN A 70 19.261 16.887 29.595 1.00 0.00 C
-HETATM 210 C5 BEN A 70 18.927 18.301 27.666 1.00 0.00 C
-HETATM 211 C1 BEN A 71 7.180 16.087 22.607 1.00 0.00 C
-HETATM 212 C3 BEN A 71 7.562 13.746 22.154 1.00 0.00 C
-HETATM 213 C5 BEN A 71 7.520 15.338 20.338 1.00 0.00 C
-HETATM 214 C1 BEN A 72 20.961 5.856 24.996 1.00 0.00 C
-HETATM 215 C3 BEN A 72 22.493 6.853 26.574 1.00 0.00 C
-HETATM 216 C5 BEN A 72 20.123 7.278 26.758 1.00 0.00 C
-HETATM 217 C1 BEN A 73 5.231 22.622 30.002 1.00 0.00 C
-HETATM 218 C3 BEN A 73 2.879 22.074 30.002 1.00 0.00 C
-HETATM 219 C5 BEN A 73 3.728 23.758 28.492 1.00 0.00 C
-HETATM 220 C1 BEN A 74 15.407 14.189 26.926 1.00 0.00 C
-HETATM 221 C3 BEN A 74 14.613 15.723 25.239 1.00 0.00 C
-HETATM 222 C5 BEN A 74 16.882 15.889 26.051 1.00 0.00 C
-HETATM 223 C1 BEN A 75 21.574 11.782 16.293 1.00 0.00 C
-HETATM 224 C3 BEN A 75 21.545 10.882 14.053 1.00 0.00 C
-HETATM 225 C5 BEN A 75 20.329 9.769 15.818 1.00 0.00 C
-HETATM 226 C1 BEN A 76 18.888 4.249 5.581 1.00 0.00 C
-HETATM 227 C3 BEN A 76 20.526 5.176 7.092 1.00 0.00 C
-HETATM 228 C5 BEN A 76 20.191 6.169 4.916 1.00 0.00 C
-HETATM 229 C1 BEN A 77 17.223 8.529 21.107 1.00 0.00 C
-HETATM 230 C3 BEN A 77 16.280 10.107 19.541 1.00 0.00 C
-HETATM 231 C5 BEN A 77 16.006 7.735 19.179 1.00 0.00 C
-HETATM 232 C1 BEN A 78 4.302 14.912 4.839 1.00 0.00 C
-HETATM 233 C3 BEN A 78 2.925 12.962 5.205 1.00 0.00 C
-HETATM 234 C5 BEN A 78 1.896 15.114 4.829 1.00 0.00 C
-HETATM 235 C1 BEN A 79 10.079 8.039 13.374 1.00 0.00 C
-HETATM 236 C3 BEN A 79 8.948 9.358 15.051 1.00 0.00 C
-HETATM 237 C5 BEN A 79 11.339 9.552 14.770 1.00 0.00 C
-HETATM 238 C1 BEN A 80 6.652 9.138 12.120 1.00 0.00 C
-HETATM 239 C3 BEN A 80 6.390 11.004 13.630 1.00 0.00 C
-HETATM 240 C5 BEN A 80 4.443 9.911 12.710 1.00 0.00 C
-HETATM 241 C1 BEN A 81 12.483 21.846 28.850 1.00 0.00 C
-HETATM 242 C3 BEN A 81 13.935 19.974 29.316 1.00 0.00 C
-HETATM 243 C5 BEN A 81 14.826 22.218 29.292 1.00 0.00 C
-HETATM 244 C1 BEN A 82 29.695 5.717 16.509 1.00 0.00 C
-HETATM 245 C3 BEN A 82 27.533 4.920 17.229 1.00 0.00 C
-HETATM 246 C5 BEN A 82 29.531 4.202 18.381 1.00 0.00 C
-HETATM 247 C1 BEN A 83 10.805 12.360 26.313 1.00 0.00 C
-HETATM 248 C3 BEN A 83 9.445 13.999 25.176 1.00 0.00 C
-HETATM 249 C5 BEN A 83 11.751 13.685 24.530 1.00 0.00 C
-HETATM 250 C1 BEN A 84 14.131 10.923 26.703 1.00 0.00 C
-HETATM 251 C3 BEN A 84 16.246 9.780 26.480 1.00 0.00 C
-HETATM 252 C5 BEN A 84 15.659 11.548 24.942 1.00 0.00 C
-HETATM 253 C1 BEN A 85 24.305 10.196 12.381 1.00 0.00 C
-HETATM 254 C3 BEN A 85 24.875 11.135 14.531 1.00 0.00 C
-HETATM 255 C5 BEN A 85 25.630 12.210 12.505 1.00 0.00 C
-HETATM 256 C1 BEN A 86 3.668 16.512 1.416 1.00 0.00 C
-HETATM 257 C3 BEN A 86 3.178 14.183 1.823 1.00 0.00 C
-HETATM 258 C5 BEN A 86 1.609 15.552 0.600 1.00 0.00 C
-HETATM 259 C1 BEN A 87 18.605 23.358 15.843 1.00 0.00 C
-HETATM 260 C3 BEN A 87 17.084 24.864 16.960 1.00 0.00 C
-HETATM 261 C5 BEN A 87 16.226 22.958 15.750 1.00 0.00 C
-HETATM 262 C1 BEN A 88 16.063 15.153 30.000 1.00 0.00 C
-HETATM 263 C3 BEN A 88 15.924 12.743 30.001 1.00 0.00 C
-HETATM 264 C5 BEN A 88 18.081 13.829 29.972 1.00 0.00 C
-HETATM 265 C1 BEN A 89 10.294 12.611 13.843 1.00 0.00 C
-HETATM 266 C3 BEN A 89 10.825 12.847 11.499 1.00 0.00 C
-HETATM 267 C5 BEN A 89 9.381 11.057 12.237 1.00 0.00 C
-HETATM 268 C1 BEN A 90 4.733 25.507 6.722 1.00 0.00 C
-HETATM 269 C3 BEN A 90 4.535 26.323 4.458 1.00 0.00 C
-HETATM 270 C5 BEN A 90 6.704 25.747 5.350 1.00 0.00 C
-HETATM 271 C1 BEN A 91 17.229 3.703 19.954 1.00 0.00 C
-HETATM 272 C3 BEN A 91 16.142 1.668 20.666 1.00 0.00 C
-HETATM 273 C5 BEN A 91 18.525 1.929 20.955 1.00 0.00 C
-HETATM 274 C1 BEN A 92 6.334 13.251 1.580 1.00 0.00 C
-HETATM 275 C3 BEN A 92 7.360 14.905 3.008 1.00 0.00 C
-HETATM 276 C5 BEN A 92 7.360 15.211 0.613 1.00 0.00 C
-HETATM 277 C1 BEN A 93 12.524 20.222 24.625 1.00 0.00 C
-HETATM 278 C3 BEN A 93 14.491 19.049 23.860 1.00 0.00 C
-HETATM 279 C5 BEN A 93 13.727 18.773 26.135 1.00 0.00 C
-HETATM 280 C1 BEN A 94 0.636 14.357 17.094 1.00 0.00 C
-HETATM 281 C3 BEN A 94 0.847 16.003 15.340 1.00 0.00 C
-HETATM 282 C5 BEN A 94 2.493 14.249 15.554 1.00 0.00 C
-HETATM 283 C1 BEN A 95 29.278 10.454 29.989 1.00 0.00 C
-HETATM 284 C3 BEN A 95 27.137 9.339 29.964 1.00 0.00 C
-HETATM 285 C5 BEN A 95 27.243 11.752 30.013 1.00 0.00 C
-HETATM 286 C1 BEN A 96 12.647 11.937 6.470 1.00 0.00 C
-HETATM 287 C3 BEN A 96 13.557 10.706 8.336 1.00 0.00 C
-HETATM 288 C5 BEN A 96 13.999 13.070 8.117 1.00 0.00 C
-HETATM 289 C1 BEN A 97 18.548 13.492 18.376 1.00 0.00 C
-HETATM 290 C3 BEN A 97 17.447 12.944 16.298 1.00 0.00 C
-HETATM 291 C5 BEN A 97 19.444 14.302 16.286 1.00 0.00 C
-HETATM 292 C1 BEN A 98 9.420 1.713 2.666 1.00 0.00 C
-HETATM 293 C3 BEN A 98 7.278 0.600 2.606 1.00 0.00 C
-HETATM 294 C5 BEN A 98 7.610 2.504 4.054 1.00 0.00 C
-HETATM 295 C1 BEN A 99 19.626 24.343 12.378 1.00 0.00 C
-HETATM 296 C3 BEN A 99 18.861 25.962 10.757 1.00 0.00 C
-HETATM 297 C5 BEN A 99 18.260 26.204 13.084 1.00 0.00 C
-HETATM 298 C1 BEN A 100 15.141 3.322 26.190 1.00 0.00 C
-HETATM 299 C3 BEN A 100 16.367 5.265 26.932 1.00 0.00 C
-HETATM 300 C5 BEN A 100 14.924 4.087 28.469 1.00 0.00 C
-HETATM 301 C1 BEN A 101 8.127 9.398 9.067 1.00 0.00 C
-HETATM 302 C3 BEN A 101 10.128 8.515 10.089 1.00 0.00 C
-HETATM 303 C5 BEN A 101 10.288 10.268 8.436 1.00 0.00 C
-HETATM 304 C1 BEN A 102 7.684 20.274 7.855 1.00 0.00 C
-HETATM 305 C3 BEN A 102 5.660 19.371 8.814 1.00 0.00 C
-HETATM 306 C5 BEN A 102 6.835 18.145 7.097 1.00 0.00 C
-HETATM 307 C1 BEN A 103 6.184 21.307 26.038 1.00 0.00 C
-HETATM 308 C3 BEN A 103 4.479 20.533 27.562 1.00 0.00 C
-HETATM 309 C5 BEN A 103 6.809 20.373 28.175 1.00 0.00 C
-HETATM 310 C1 BEN A 104 4.985 19.935 23.020 1.00 0.00 C
-HETATM 311 C3 BEN A 104 4.947 22.245 22.319 1.00 0.00 C
-HETATM 312 C5 BEN A 104 4.888 20.481 20.671 1.00 0.00 C
-HETATM 313 C1 BEN A 105 13.046 25.672 7.465 1.00 0.00 C
-HETATM 314 C3 BEN A 105 12.182 27.583 8.661 1.00 0.00 C
-HETATM 315 C5 BEN A 105 11.283 25.383 9.088 1.00 0.00 C
-HETATM 316 C1 BEN A 106 14.853 22.372 0.599 1.00 0.00 C
-HETATM 317 C3 BEN A 106 14.818 23.878 2.486 1.00 0.00 C
-HETATM 318 C5 BEN A 106 13.360 24.269 0.601 1.00 0.00 C
-HETATM 319 C1 BEN A 107 20.270 6.229 18.869 1.00 0.00 C
-HETATM 320 C3 BEN A 107 20.097 5.235 21.062 1.00 0.00 C
-HETATM 321 C5 BEN A 107 21.811 6.875 20.610 1.00 0.00 C
-HETATM 322 C1 BEN A 108 26.741 24.542 17.937 1.00 0.00 C
-HETATM 323 C3 BEN A 108 26.625 25.797 15.877 1.00 0.00 C
-HETATM 324 C5 BEN A 108 25.404 23.725 16.100 1.00 0.00 C
-HETATM 325 C1 BEN A 109 6.482 26.595 9.845 1.00 0.00 C
-HETATM 326 C3 BEN A 109 7.735 27.536 11.682 1.00 0.00 C
-HETATM 327 C5 BEN A 109 5.878 28.756 10.736 1.00 0.00 C
-HETATM 328 C1 BEN A 110 28.593 12.991 7.129 1.00 0.00 C
-HETATM 329 C3 BEN A 110 29.999 12.893 9.089 1.00 0.00 C
-HETATM 330 C5 BEN A 110 29.601 15.007 7.992 1.00 0.00 C
-HETATM 331 C1 BEN A 111 24.276 28.997 24.563 1.00 0.00 C
-HETATM 332 C3 BEN A 111 26.454 29.997 24.266 1.00 0.00 C
-HETATM 333 C5 BEN A 111 24.865 29.998 22.447 1.00 0.00 C
-HETATM 334 C1 BEN A 112 16.307 13.918 5.640 1.00 0.00 C
-HETATM 335 C3 BEN A 112 17.707 12.708 7.191 1.00 0.00 C
-HETATM 336 C5 BEN A 112 17.671 15.122 7.226 1.00 0.00 C
-HETATM 337 C1 BEN A 113 15.953 22.892 7.466 1.00 0.00 C
-HETATM 338 C3 BEN A 113 14.357 23.225 5.686 1.00 0.00 C
-HETATM 339 C5 BEN A 113 15.988 24.936 6.182 1.00 0.00 C
-HETATM 340 C1 BEN A 114 8.042 14.821 14.824 1.00 0.00 C
-HETATM 341 C3 BEN A 114 5.698 14.255 14.711 1.00 0.00 C
-HETATM 342 C5 BEN A 114 6.745 14.640 16.852 1.00 0.00 C
-HETATM 343 C1 BEN A 115 20.102 27.794 1.773 1.00 0.00 C
-HETATM 344 C3 BEN A 115 20.246 27.663 4.179 1.00 0.00 C
-HETATM 345 C5 BEN A 115 21.466 26.093 2.809 1.00 0.00 C
-HETATM 346 C1 BEN A 116 26.648 24.394 30.000 1.00 0.00 C
-HETATM 347 C3 BEN A 116 26.936 22.488 28.545 1.00 0.00 C
-HETATM 348 C5 BEN A 116 24.906 23.792 28.442 1.00 0.00 C
-HETATM 349 C1 BEN A 117 20.804 1.888 3.320 1.00 0.00 C
-HETATM 350 C3 BEN A 117 22.180 0.600 4.830 1.00 0.00 C
-HETATM 351 C5 BEN A 117 21.998 2.992 5.104 1.00 0.00 C
-HETATM 352 C1 BEN A 118 22.726 1.439 27.980 1.00 0.00 C
-HETATM 353 C3 BEN A 118 23.182 3.767 28.427 1.00 0.00 C
-HETATM 354 C5 BEN A 118 21.179 3.136 27.235 1.00 0.00 C
-HETATM 355 C1 BEN A 119 17.569 1.376 4.624 1.00 0.00 C
-HETATM 356 C3 BEN A 119 15.741 0.953 3.104 1.00 0.00 C
-HETATM 357 C5 BEN A 119 15.444 0.611 5.476 1.00 0.00 C
-HETATM 358 C1 BEN A 120 25.680 17.300 6.895 1.00 0.00 C
-HETATM 359 C3 BEN A 120 24.665 15.371 5.857 1.00 0.00 C
-HETATM 360 C5 BEN A 120 24.662 17.499 4.716 1.00 0.00 C
-HETATM 361 C1 BEN A 121 27.272 7.959 9.260 1.00 0.00 C
-HETATM 362 C3 BEN A 121 27.348 10.221 8.420 1.00 0.00 C
-HETATM 363 C5 BEN A 121 28.317 8.421 7.134 1.00 0.00 C
-HETATM 364 C1 BEN A 122 20.146 11.194 21.235 1.00 0.00 C
-HETATM 365 C3 BEN A 122 21.812 11.039 19.494 1.00 0.00 C
-HETATM 366 C5 BEN A 122 20.006 9.438 19.583 1.00 0.00 C
-HETATM 367 C1 BEN A 123 3.551 8.728 0.809 1.00 0.00 C
-HETATM 368 C3 BEN A 123 5.686 8.375 1.880 1.00 0.00 C
-HETATM 369 C5 BEN A 123 4.093 9.916 2.839 1.00 0.00 C
-HETATM 370 C1 BEN A 124 6.935 27.734 2.025 1.00 0.00 C
-HETATM 371 C3 BEN A 124 6.800 29.806 3.257 1.00 0.00 C
-HETATM 372 C5 BEN A 124 4.802 28.824 2.321 1.00 0.00 C
-HETATM 373 C1 BEN A 125 28.523 23.399 14.060 1.00 0.00 C
-HETATM 374 C3 BEN A 125 28.734 25.629 13.159 1.00 0.00 C
-HETATM 375 C5 BEN A 125 26.690 24.384 12.836 1.00 0.00 C
-HETATM 376 C1 BEN A 126 10.955 16.873 20.721 1.00 0.00 C
-HETATM 377 C3 BEN A 126 12.695 15.504 21.686 1.00 0.00 C
-HETATM 378 C5 BEN A 126 11.527 17.116 23.054 1.00 0.00 C
-HETATM 379 C1 BEN A 127 18.567 26.962 26.235 1.00 0.00 C
-HETATM 380 C3 BEN A 127 18.472 25.916 24.061 1.00 0.00 C
-HETATM 381 C5 BEN A 127 18.754 28.308 24.239 1.00 0.00 C
-HETATM 382 C1 BEN A 128 23.760 30.000 17.496 1.00 0.00 C
-HETATM 383 C3 BEN A 128 22.795 27.814 17.839 1.00 0.00 C
-HETATM 384 C5 BEN A 128 21.364 29.748 17.634 1.00 0.00 C
-HETATM 385 C1 BEN A 129 16.957 3.806 14.157 1.00 0.00 C
-HETATM 386 C3 BEN A 129 19.010 3.228 13.025 1.00 0.00 C
-HETATM 387 C5 BEN A 129 17.291 1.560 13.337 1.00 0.00 C
-HETATM 388 C1 BEN A 130 10.309 19.879 9.844 1.00 0.00 C
-HETATM 389 C3 BEN A 130 8.327 19.184 11.035 1.00 0.00 C
-HETATM 390 C5 BEN A 130 8.775 21.533 10.702 1.00 0.00 C
-HETATM 391 C1 BEN A 131 27.783 24.369 24.760 1.00 0.00 C
-HETATM 392 C3 BEN A 131 29.303 25.422 26.312 1.00 0.00 C
-HETATM 393 C5 BEN A 131 26.962 26.013 26.325 1.00 0.00 C
-HETATM 394 C1 BEN A 132 3.766 28.350 24.564 1.00 0.00 C
-HETATM 395 C3 BEN A 132 4.741 29.857 22.948 1.00 0.00 C
-HETATM 396 C5 BEN A 132 5.746 27.697 23.346 1.00 0.00 C
-HETATM 397 C1 BEN A 133 1.488 26.285 21.661 1.00 0.00 C
-HETATM 398 C3 BEN A 133 1.839 28.598 21.063 1.00 0.00 C
-HETATM 399 C5 BEN A 133 2.782 26.842 19.701 1.00 0.00 C
-HETATM 400 C1 BEN A 134 21.531 1.073 22.467 1.00 0.00 C
-HETATM 401 C3 BEN A 134 20.044 1.265 24.360 1.00 0.00 C
-HETATM 402 C5 BEN A 134 22.436 1.253 24.698 1.00 0.00 C
-HETATM 403 C1 BEN A 135 2.437 3.379 11.663 1.00 0.00 C
-HETATM 404 C3 BEN A 135 2.460 5.153 10.026 1.00 0.00 C
-HETATM 405 C5 BEN A 135 4.263 3.548 10.094 1.00 0.00 C
-HETATM 406 C1 BEN A 136 10.419 17.837 1.485 1.00 0.00 C
-HETATM 407 C3 BEN A 136 12.190 16.227 1.803 1.00 0.00 C
-HETATM 408 C5 BEN A 136 10.433 16.432 3.447 1.00 0.00 C
-HETATM 409 C1 BEN A 137 13.942 9.115 21.653 1.00 0.00 C
-HETATM 410 C3 BEN A 137 14.635 8.479 23.877 1.00 0.00 C
-HETATM 411 C5 BEN A 137 12.306 8.927 23.419 1.00 0.00 C
-HETATM 412 C1 BEN A 138 22.272 20.852 28.372 1.00 0.00 C
-HETATM 413 C3 BEN A 138 20.881 19.737 30.000 1.00 0.00 C
-HETATM 414 C5 BEN A 138 23.245 19.256 29.899 1.00 0.00 C
-HETATM 415 C1 BEN A 139 8.030 8.109 20.978 1.00 0.00 C
-HETATM 416 C3 BEN A 139 6.813 10.073 21.679 1.00 0.00 C
-HETATM 417 C5 BEN A 139 9.205 9.961 21.986 1.00 0.00 C
-HETATM 418 C1 BEN A 140 13.350 25.360 25.611 1.00 0.00 C
-HETATM 419 C3 BEN A 140 11.339 25.327 24.275 1.00 0.00 C
-HETATM 420 C5 BEN A 140 11.228 24.843 26.639 1.00 0.00 C
-HETATM 421 C1 BEN A 141 9.769 11.694 19.202 1.00 0.00 C
-HETATM 422 C3 BEN A 141 8.049 11.392 17.535 1.00 0.00 C
-HETATM 423 C5 BEN A 141 9.888 12.900 17.114 1.00 0.00 C
-HETATM 424 C1 BEN A 142 24.379 17.110 23.422 1.00 0.00 C
-HETATM 425 C3 BEN A 142 23.330 16.873 21.261 1.00 0.00 C
-HETATM 426 C5 BEN A 142 25.209 15.475 21.851 1.00 0.00 C
-HETATM 427 C1 BEN A 143 15.474 4.649 23.007 1.00 0.00 C
-HETATM 428 C3 BEN A 143 17.702 3.944 23.617 1.00 0.00 C
-HETATM 429 C5 BEN A 143 17.121 6.288 23.662 1.00 0.00 C
-HETATM 430 C1 BEN A 144 13.754 14.931 16.942 1.00 0.00 C
-HETATM 431 C3 BEN A 144 12.605 13.904 18.801 1.00 0.00 C
-HETATM 432 C5 BEN A 144 14.821 14.816 19.104 1.00 0.00 C
-HETATM 433 C1 BEN A 145 2.873 16.109 23.966 1.00 0.00 C
-HETATM 434 C3 BEN A 145 4.825 17.052 25.029 1.00 0.00 C
-HETATM 435 C5 BEN A 145 2.684 18.146 25.247 1.00 0.00 C
-HETATM 436 C1 BEN A 146 5.535 24.064 11.968 1.00 0.00 C
-HETATM 437 C3 BEN A 146 4.562 21.955 11.306 1.00 0.00 C
-HETATM 438 C5 BEN A 146 5.423 23.467 9.631 1.00 0.00 C
-HETATM 439 C1 BEN A 147 26.559 13.591 9.656 1.00 0.00 C
-HETATM 440 C3 BEN A 147 25.712 15.832 9.956 1.00 0.00 C
-HETATM 441 C5 BEN A 147 27.331 14.934 11.507 1.00 0.00 C
-HETATM 442 C1 BEN A 148 12.767 30.043 3.494 1.00 0.00 C
-HETATM 443 C3 BEN A 148 12.265 28.114 2.132 1.00 0.00 C
-HETATM 444 C5 BEN A 148 10.791 30.026 2.107 1.00 0.00 C
-HETATM 445 C1 BEN A 149 6.228 5.185 19.150 1.00 0.00 C
-HETATM 446 C3 BEN A 149 6.290 3.137 20.426 1.00 0.00 C
-HETATM 447 C5 BEN A 149 6.225 5.267 21.562 1.00 0.00 C
-HETATM 448 C1 BEN A 150 27.888 1.583 10.933 1.00 0.00 C
-HETATM 449 C3 BEN A 150 27.470 3.855 10.230 1.00 0.00 C
-HETATM 450 C5 BEN A 150 29.525 2.753 9.600 1.00 0.00 C
-HETATM 451 C1 BEN A 151 14.809 18.693 7.931 1.00 0.00 C
-HETATM 452 C3 BEN A 151 16.787 18.675 6.546 1.00 0.00 C
-HETATM 453 C5 BEN A 151 14.945 17.197 6.041 1.00 0.00 C
-HETATM 454 C1 BEN A 152 13.800 6.594 9.166 1.00 0.00 C
-HETATM 455 C3 BEN A 152 12.071 5.341 8.039 1.00 0.00 C
-HETATM 456 C5 BEN A 152 12.255 7.724 7.694 1.00 0.00 C
-HETATM 457 C1 BEN A 153 14.036 20.344 3.067 1.00 0.00 C
-HETATM 458 C3 BEN A 153 16.150 21.007 4.028 1.00 0.00 C
-HETATM 459 C5 BEN A 153 16.076 19.270 2.351 1.00 0.00 C
-HETATM 460 C1 BEN A 154 5.488 21.951 0.594 1.00 0.00 C
-HETATM 461 C3 BEN A 154 5.750 20.364 2.395 1.00 0.00 C
-HETATM 462 C5 BEN A 154 7.222 22.273 2.241 1.00 0.00 C
-HETATM 463 C1 BEN A 155 0.929 12.558 26.795 1.00 0.00 C
-HETATM 464 C3 BEN A 155 0.916 12.041 24.437 1.00 0.00 C
-HETATM 465 C5 BEN A 155 2.996 12.559 25.548 1.00 0.00 C
-HETATM 466 C1 BEN A 156 16.614 18.751 14.571 1.00 0.00 C
-HETATM 467 C3 BEN A 156 16.271 16.361 14.576 1.00 0.00 C
-HETATM 468 C5 BEN A 156 18.345 17.286 15.398 1.00 0.00 C
-HETATM 469 C1 BEN A 157 28.754 7.231 19.919 1.00 0.00 C
-HETATM 470 C3 BEN A 157 26.796 7.817 18.634 1.00 0.00 C
-HETATM 471 C5 BEN A 157 28.728 9.266 18.621 1.00 0.00 C
-HETATM 472 C1 BEN A 158 11.810 1.813 27.939 1.00 0.00 C
-HETATM 473 C3 BEN A 158 12.542 1.356 25.684 1.00 0.00 C
-HETATM 474 C5 BEN A 158 10.500 2.588 26.065 1.00 0.00 C
-HETATM 475 C1 BEN A 159 11.451 29.998 11.317 1.00 0.00 C
-HETATM 476 C3 BEN A 159 11.151 27.910 12.492 1.00 0.00 C
-HETATM 477 C5 BEN A 159 12.913 29.426 13.150 1.00 0.00 C
-HETATM 478 C1 BEN A 160 22.890 8.234 5.892 1.00 0.00 C
-HETATM 479 C3 BEN A 160 23.963 6.663 7.378 1.00 0.00 C
-HETATM 480 C5 BEN A 160 24.377 9.030 7.619 1.00 0.00 C
-HETATM 481 C1 BEN A 161 25.845 6.129 2.136 1.00 0.00 C
-HETATM 482 C3 BEN A 161 23.972 7.315 1.180 1.00 0.00 C
-HETATM 483 C5 BEN A 161 26.122 8.399 1.363 1.00 0.00 C
-HETATM 484 C1 BEN A 162 19.145 5.051 10.277 1.00 0.00 C
-HETATM 485 C3 BEN A 162 17.569 6.353 11.561 1.00 0.00 C
-HETATM 486 C5 BEN A 162 17.036 5.699 9.298 1.00 0.00 C
-HETATM 487 C1 BEN A 163 23.316 24.346 12.194 1.00 0.00 C
-HETATM 488 C3 BEN A 163 23.608 22.051 11.503 1.00 0.00 C
-HETATM 489 C5 BEN A 163 22.218 22.572 13.408 1.00 0.00 C
-HETATM 490 C1 BEN A 164 4.825 7.738 26.139 1.00 0.00 C
-HETATM 491 C3 BEN A 164 5.253 5.849 24.697 1.00 0.00 C
-HETATM 492 C5 BEN A 164 3.191 7.089 24.486 1.00 0.00 C
-HETATM 493 C1 BEN A 165 3.620 29.249 13.074 1.00 0.00 C
-HETATM 494 C3 BEN A 165 2.705 27.159 12.285 1.00 0.00 C
-HETATM 495 C5 BEN A 165 4.825 27.190 13.441 1.00 0.00 C
-HETATM 496 C1 BEN A 166 20.369 13.120 4.944 1.00 0.00 C
-HETATM 497 C3 BEN A 166 21.080 13.915 2.778 1.00 0.00 C
-HETATM 498 C5 BEN A 166 18.781 13.266 3.132 1.00 0.00 C
-HETATM 499 C1 BEN A 167 18.805 20.714 9.072 1.00 0.00 C
-HETATM 500 C3 BEN A 167 19.005 21.770 6.910 1.00 0.00 C
-HETATM 501 C5 BEN A 167 18.912 23.120 8.910 1.00 0.00 C
-HETATM 502 C1 BEN A 168 24.138 21.035 19.448 1.00 0.00 C
-HETATM 503 C3 BEN A 168 23.082 21.779 17.408 1.00 0.00 C
-HETATM 504 C5 BEN A 168 24.989 20.300 17.312 1.00 0.00 C
-HETATM 505 C1 BEN A 169 14.446 19.723 19.911 1.00 0.00 C
-HETATM 506 C3 BEN A 169 13.082 18.752 18.172 1.00 0.00 C
-HETATM 507 C5 BEN A 169 14.758 20.400 17.615 1.00 0.00 C
-HETATM 508 C1 BEN A 170 5.523 8.724 18.255 1.00 0.00 C
-HETATM 509 C3 BEN A 170 5.740 7.420 16.235 1.00 0.00 C
-HETATM 510 C5 BEN A 170 4.079 9.167 16.372 1.00 0.00 C
-HETATM 511 C1 BEN A 171 7.001 2.400 26.082 1.00 0.00 C
-HETATM 512 C3 BEN A 171 6.248 0.596 24.665 1.00 0.00 C
-HETATM 513 C5 BEN A 171 6.317 2.854 23.812 1.00 0.00 C
-HETATM 514 C1 BEN A 172 10.161 23.650 1.703 1.00 0.00 C
-HETATM 515 C3 BEN A 172 11.446 25.094 3.150 1.00 0.00 C
-HETATM 516 C5 BEN A 172 9.342 24.183 3.910 1.00 0.00 C
-HETATM 517 C1 BEN A 173 21.295 17.710 2.848 1.00 0.00 C
-HETATM 518 C3 BEN A 173 19.167 17.155 1.851 1.00 0.00 C
-HETATM 519 C5 BEN A 173 19.384 17.264 4.254 1.00 0.00 C
-HETATM 520 C1 BEN A 174 26.923 28.911 2.652 1.00 0.00 C
-HETATM 521 C3 BEN A 174 26.874 27.634 0.603 1.00 0.00 C
-HETATM 522 C5 BEN A 174 24.874 27.852 1.940 1.00 0.00 C
-HETATM 523 C1 BEN A 175 27.977 7.855 25.378 1.00 0.00 C
-HETATM 524 C3 BEN A 175 28.904 9.156 23.568 1.00 0.00 C
-HETATM 525 C5 BEN A 175 30.000 7.126 24.280 1.00 0.00 C
-HETATM 526 C1 BEN A 176 23.712 14.276 16.589 1.00 0.00 C
-HETATM 527 C3 BEN A 176 23.347 13.908 18.947 1.00 0.00 C
-HETATM 528 C5 BEN A 176 24.682 15.781 18.209 1.00 0.00 C
-HETATM 529 C1 BEN A 177 17.782 25.079 1.635 1.00 0.00 C
-HETATM 530 C3 BEN A 177 16.919 27.068 2.697 1.00 0.00 C
-HETATM 531 C5 BEN A 177 15.988 26.318 0.599 1.00 0.00 C
-HETATM 532 C1 BEN A 178 7.235 14.111 9.571 1.00 0.00 C
-HETATM 533 C3 BEN A 178 6.834 15.734 11.313 1.00 0.00 C
-HETATM 534 C5 BEN A 178 8.248 16.298 9.437 1.00 0.00 C
-HETATM 535 C1 BEN A 179 14.064 10.333 11.575 1.00 0.00 C
-HETATM 536 C3 BEN A 179 15.185 8.419 12.530 1.00 0.00 C
-HETATM 537 C5 BEN A 179 16.475 10.225 11.578 1.00 0.00 C
-HETATM 538 C1 BEN A 180 20.060 19.989 23.597 1.00 0.00 C
-HETATM 539 C3 BEN A 180 17.716 20.444 23.955 1.00 0.00 C
-HETATM 540 C5 BEN A 180 19.239 22.255 23.470 1.00 0.00 C
-HETATM 541 C1 BEN A 181 26.336 3.055 24.976 1.00 0.00 C
-HETATM 542 C3 BEN A 181 27.687 4.160 26.645 1.00 0.00 C
-HETATM 543 C5 BEN A 181 28.597 2.313 25.384 1.00 0.00 C
-HETATM 544 C1 BEN A 182 25.568 27.192 19.516 1.00 0.00 C
-HETATM 545 C3 BEN A 182 27.887 27.482 18.910 1.00 0.00 C
-HETATM 546 C5 BEN A 182 26.539 29.402 19.483 1.00 0.00 C
-HETATM 547 C1 BEN A 183 13.249 20.932 11.858 1.00 0.00 C
-HETATM 548 C3 BEN A 183 11.890 22.619 10.791 1.00 0.00 C
-HETATM 549 C5 BEN A 183 13.610 21.480 9.535 1.00 0.00 C
-HETATM 550 C1 BEN A 184 24.801 11.976 21.999 1.00 0.00 C
-HETATM 551 C3 BEN A 184 25.748 10.258 20.592 1.00 0.00 C
-HETATM 552 C5 BEN A 184 23.747 9.809 21.866 1.00 0.00 C
-HETATM 553 C1 BEN A 185 8.956 24.250 12.564 1.00 0.00 C
-HETATM 554 C3 BEN A 185 7.660 24.343 14.599 1.00 0.00 C
-HETATM 555 C5 BEN A 185 9.798 25.459 14.476 1.00 0.00 C
-HETATM 556 C1 BEN A 186 7.440 6.713 30.000 1.00 0.00 C
-HETATM 557 C3 BEN A 186 7.098 8.521 28.436 1.00 0.00 C
-HETATM 558 C5 BEN A 186 9.313 8.054 29.278 1.00 0.00 C
-HETATM 559 C1 BEN A 187 21.671 29.929 27.724 1.00 0.00 C
-HETATM 560 C3 BEN A 187 23.818 30.000 28.828 1.00 0.00 C
-HETATM 561 C5 BEN A 187 22.096 28.507 29.628 1.00 0.00 C
-HETATM 562 C1 BEN A 188 16.638 10.466 4.117 1.00 0.00 C
-HETATM 563 C3 BEN A 188 15.062 9.572 2.521 1.00 0.00 C
-HETATM 564 C5 BEN A 188 15.552 11.936 2.540 1.00 0.00 C
-HETATM 565 C1 BEN A 189 28.135 1.475 14.937 1.00 0.00 C
-HETATM 566 C3 BEN A 189 26.143 2.774 14.518 1.00 0.00 C
-HETATM 567 C5 BEN A 189 28.309 3.651 13.907 1.00 0.00 C
-HETATM 568 C1 BEN A 190 27.203 1.451 7.649 1.00 0.00 C
-HETATM 569 C3 BEN A 190 25.024 2.489 7.699 1.00 0.00 C
-HETATM 570 C5 BEN A 190 26.417 2.699 5.738 1.00 0.00 C
-HETATM 571 C1 BEN A 191 16.110 16.688 22.155 1.00 0.00 C
-HETATM 572 C3 BEN A 191 18.125 15.663 23.003 1.00 0.00 C
-HETATM 573 C5 BEN A 191 18.099 16.627 20.789 1.00 0.00 C
-HETATM 574 C1 BEN A 192 4.908 9.466 7.294 1.00 0.00 C
-HETATM 575 C3 BEN A 192 2.995 9.015 8.697 1.00 0.00 C
-HETATM 576 C5 BEN A 192 2.732 9.139 6.300 1.00 0.00 C
-HETATM 577 C1 BEN A 193 21.358 27.917 22.060 1.00 0.00 C
-HETATM 578 C3 BEN A 193 21.415 30.001 23.278 1.00 0.00 C
-HETATM 579 C5 BEN A 193 20.977 30.001 20.903 1.00 0.00 C
-HETATM 580 C1 BEN A 194 18.924 11.664 25.960 1.00 0.00 C
-HETATM 581 C3 BEN A 194 19.188 10.518 28.069 1.00 0.00 C
-HETATM 582 C5 BEN A 194 20.212 12.679 27.731 1.00 0.00 C
-HETATM 583 C1 BEN A 195 25.322 17.101 12.977 1.00 0.00 C
-HETATM 584 C3 BEN A 195 26.301 18.177 14.904 1.00 0.00 C
-HETATM 585 C5 BEN A 195 23.917 18.295 14.535 1.00 0.00 C
-HETATM 586 C1 BEN A 196 22.371 14.087 22.807 1.00 0.00 C
-HETATM 587 C3 BEN A 196 20.616 14.069 24.466 1.00 0.00 C
-HETATM 588 C5 BEN A 196 22.749 13.046 24.952 1.00 0.00 C
-HETATM 589 C1 BEN A 197 11.933 17.944 14.750 1.00 0.00 C
-HETATM 590 C3 BEN A 197 10.824 16.360 16.196 1.00 0.00 C
-HETATM 591 C5 BEN A 197 10.175 18.686 16.229 1.00 0.00 C
-HETATM 592 C1 BEN A 198 11.970 6.082 27.888 1.00 0.00 C
-HETATM 593 C3 BEN A 198 13.836 7.376 27.069 1.00 0.00 C
-HETATM 594 C5 BEN A 198 12.838 5.678 25.672 1.00 0.00 C
-HETATM 595 C1 BEN A 199 25.628 6.194 12.294 1.00 0.00 C
-HETATM 596 C3 BEN A 199 25.088 7.442 14.289 1.00 0.00 C
-HETATM 597 C5 BEN A 199 27.376 6.816 13.838 1.00 0.00 C
-HETATM 598 C1 BEN A 200 8.681 7.740 25.312 1.00 0.00 C
-HETATM 599 C3 BEN A 200 9.870 6.135 23.957 1.00 0.00 C
-HETATM 600 C5 BEN A 200 8.460 5.379 25.766 1.00 0.00 C
-END
diff --git a/examples/mbar/demo.ipynb b/examples/mbar/demo.ipynb
deleted file mode 100644
index cbd7ea21d..000000000
--- a/examples/mbar/demo.ipynb
+++ /dev/null
@@ -1,523 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "16a0d3cf",
- "metadata": {},
- "source": [
- "# Optimize CG benzene using MBAR to fit radial distribution function\n",
- "\n",
- "\n",
- "In this demo, we would try to optimize a coarse-grained benzene model with three beads to fit experimental center-of-mass radial distribution function. The potential function only has harmonic bond term and Lennard-Jones term as:\n",
- "\n",
- "$$\\begin{align*}\n",
- " V(\\mathbf{R}) &= V_{\\mathrm{bond}} + V_\\mathrm{vdW} \\\\\n",
- " &= \\sum_{\\mathrm{bonds}}\\frac{1}{2}k_b(r - r_0)^2 \\\\\n",
- " &\\quad+ \\sum_{ij}4\\varepsilon_{ij}\\left[\\left(\\frac{\\sigma_{ij}}{r_{ij}}\\right)^{12} - \\left(\\frac{\\sigma_{ij}}{r_{ij}}\\right)^6\\right]\n",
- "\\end{align*}$$\n",
- "\n",
- "## Import necessary packages & functions "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "68a9fa08",
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/xinyan/miniconda3/envs/openmm/lib/python3.8/site-packages/dm_haiku-0.0.6-py3.8.egg/haiku/_src/data_structures.py:37: FutureWarning: jax.tree_structure is deprecated, and will be removed in a future release. Use jax.tree_util.tree_structure instead.\n",
- " PyTreeDef = type(jax.tree_structure(None))\n",
- "WARNING:pymbar.timeseries:Warning on use of the timeseries module: If the inherent timescales of the system are long compared to those being analyzed, this statistical inefficiency may be an underestimate. The estimate presumes the use of many statistically independent samples. Tests should be performed to assess whether this condition is satisfied. Be cautious in the interpretation of the data.\n"
- ]
- }
- ],
- "source": [
- "import openmm as mm\n",
- "import openmm.app as app\n",
- "import openmm.unit as unit\n",
- "import numpy as np\n",
- "import sys\n",
- "import mdtraj as md\n",
- "from tqdm import tqdm, trange\n",
- "import matplotlib.pyplot as plt\n",
- "from dmff.mbar import MBAREstimator, SampleState, TargetState, Sample, OpenMMSampleState, buildTrajEnergyFunction\n",
- "from dmff.optimize import MultiTransform, genOptimizer\n",
- "from dmff import Hamiltonian, NeighborListFreud\n",
- "import optax\n",
- "import jax\n",
- "import jax.numpy as jnp\n",
- "\n",
- "app.Topology.loadBondDefinitions(\"ben-top.xml\")\n",
- "kbT = 8.314 * 303 / 1000.0\n",
- "\n",
- "\n",
- "def readRDF(fname):\n",
- " with open(fname, \"r\") as f:\n",
- " data = np.array([[float(j) for j in i.strip().split()] for i in f])\n",
- " xaxis = np.linspace(2.0, 14.0, 121)\n",
- " yinterp = np.interp(xaxis, data[:,0], data[:,1])\n",
- " return xaxis, yinterp\n",
- "\n",
- "# read experimental benzene RDF\n",
- "x_ref, y_ref = readRDF(\"benz.txt\")\n",
- "\n",
- "\n",
- "def sample_with_prm(parameter, trajectory, init_struct=\"box_relaxed.pdb\"):\n",
- " pdb = app.PDBFile(init_struct)\n",
- " ff = app.ForceField(parameter)\n",
- " system = ff.createSystem(pdb.topology, nonbondedMethod=app.PME, nonbondedCutoff=1.1*unit.nanometer, constraints=None)\n",
- " system.addForce(mm.MonteCarloBarostat(1.0*unit.bar, 303.0*unit.kelvin, 20))\n",
- " for force in system.getForces():\n",
- " if isinstance(force, mm.NonbondedForce):\n",
- " force.setUseDispersionCorrection(False)\n",
- " force.setUseSwitchingFunction(False)\n",
- " integ = mm.LangevinIntegrator(303*unit.kelvin, 5/unit.picosecond, 1*unit.femtosecond)\n",
- "\n",
- " simulation = app.Simulation(pdb.topology, system, integ)\n",
- " simulation.context.setPositions(pdb.getPositions())\n",
- " simulation.reporters.append(app.DCDReporter(trajectory, 4000))\n",
- " simulation.reporters.append(app.StateDataReporter(sys.stdout, 20000, density=True, step=True, remainingTime=True, speed=True, totalSteps=500*1000))\n",
- " simulation.minimizeEnergy()\n",
- " simulation.step(500*1000)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "8ee7668b",
- "metadata": {},
- "source": [
- "## sample with initial parameter set"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "4b29effb",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "#\"Step\",\"Density (g/mL)\",\"Speed (ns/day)\",\"Time Remaining\"\n",
- "20000,0.5609269274230193,0,--\n",
- "40000,0.5459109958008428,154,4:18\n",
- "60000,0.5327412068778505,152,4:09\n",
- "80000,0.5458843337093509,153,3:57\n",
- "100000,0.5499541448891778,153,3:45\n",
- "120000,0.5559041043396893,152,3:35\n",
- "140000,0.552264674142506,152,3:24\n",
- "160000,0.5497341640178879,153,3:12\n",
- "180000,0.5429753752031576,153,3:01\n",
- "200000,0.5483391135340221,153,2:49\n",
- "220000,0.5505668336047832,153,2:38\n",
- "240000,0.5335132149170563,153,2:26\n",
- "260000,0.5612835029864318,153,2:15\n",
- "280000,0.5539964740924908,153,2:04\n",
- "300000,0.5562792831995799,153,1:52\n",
- "320000,0.5810152336723056,153,1:41\n",
- "340000,0.5578735559454526,153,1:30\n",
- "360000,0.5646608102872624,153,1:19\n",
- "380000,0.5715438669104802,153,1:07\n",
- "400000,0.5615453644018904,153,0:56\n",
- "420000,0.5729058327239069,153,0:45\n",
- "440000,0.5551077875519419,153,0:33\n",
- "460000,0.575714406552948,153,0:22\n",
- "480000,0.5542066355298249,153,0:11\n",
- "500000,0.5602510266387776,153,0:00\n"
- ]
- }
- ],
- "source": [
- "sample_with_prm(\"ben-prm.xml\", \"init.dcd\")\n",
- "traj = md.load(\"init.dcd\", top=\"box_relaxed.pdb\")[50:]"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "e39138c2",
- "metadata": {},
- "source": [
- "## compute radial distribution function per frame"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "ae1d2a94",
- "metadata": {},
- "outputs": [],
- "source": [
- "def compute_rdf_frame(traj, xaxis):\n",
- " rdf_list = []\n",
- " delta = xaxis[1] - xaxis[0]\n",
- "\n",
- " tidx = []\n",
- " for ii in range(200):\n",
- " tidx.append(3*ii)\n",
- " tidx = np.array(tidx)\n",
- " tsub = traj.atom_slice(tidx)\n",
- " xyzs = traj.xyz\n",
- " com = np.zeros((traj.n_frames, 200, 3))\n",
- "\n",
- " for na in range(3):\n",
- " com += xyzs[:,tidx+na,:]\n",
- " com = com / 3\n",
- "\n",
- " pairs = []\n",
- " for na in range(200):\n",
- " for nb in range(na+1, 200):\n",
- " pairs.append([na, nb])\n",
- " tsub.xyz = com\n",
- "\n",
- " for frame in tsub:\n",
- " _, g_r = md.compute_rdf(frame, pairs, r_range=(xaxis[0]-0.5*delta, xaxis[-1]+0.5*delta+1e-10), bin_width=delta)\n",
- " rdf_list.append(g_r.reshape((1, -1)))\n",
- " return np.concatenate(rdf_list, axis=0)\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a8b76cf8",
- "metadata": {},
- "source": [
- "## initialize MBAR estimator"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "724a10d0",
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- " 0%| | 0/75 [00:00
-
- 200000
-
- 12345
-
-
- unix_dmff_444824
-
-
- unix_eann_444824
-
-
-
- density_0.03338_init.pdb
- 295
-
-
-
-
-
-
-
- 0.50
-
- 1000
-
-
-
-
- 295
-
-
-
diff --git a/examples/md_ipi/client_EANN.py b/examples/md_ipi/client_EANN.py
deleted file mode 100644
index caae2b00e..000000000
--- a/examples/md_ipi/client_EANN.py
+++ /dev/null
@@ -1,50 +0,0 @@
-#!/usr/bin/env python3
-import os
-import sys
-import driver
-import numpy as np
-import EANN
-#import time
-
-
-class EANNDriver(driver.BaseDriver):
-
- def __init__(self, addr, port, socktype):
- addr = addr + '_%s'%os.environ['SLURM_JOB_ID']
- driver.BaseDriver.__init__(self, port, addr, socktype)
-
- return
-
- def grad(self, crd, cell): # receive SI input, return SI values
- positions = np.array(crd*1e10) # convert to Angstrom
- length = np.size(positions,0)
- coor = np.zeros((3,length),dtype=np.float64,order="F")
- coor = positions.transpose()
- y = np.zeros(1,dtype=np.float64,order="F")
- eaforce = np.zeros(3*length,dtype=np.float64,order="F")
- table = 0
- start_force = 1
- #time1 = time.time()
- EANN.eann_out(table,start_force,coor,y,eaforce)
- #time2 = time.time()
- #print('calculate:',time2-time1)
- # finish calculating but wrong format & unit
- grad = np.zeros((length,3))
- grad = eaforce.reshape(length,3)
-
- # convert to SI
- energy = y * 1000 / 6.0221409e+23 # kj/mol to Joules
- grad = -(grad * 1000 / 6.0221409e+23 * 1e10) # convert kj/mol/A to joule/m
- #print(grad)
- return energy, grad
-
-if __name__ == '__main__':
- EANN.init_pes()
- addr = sys.argv[1]
- port = int(sys.argv[2])
- socktype = sys.argv[3]
- driver_eann = EANNDriver(addr, port, socktype)
- while True:
- driver_eann.parse()
- EANN.deallocate_all()
-
diff --git a/examples/md_ipi/client_dmff.py b/examples/md_ipi/client_dmff.py
deleted file mode 100755
index 3d8012ce0..000000000
--- a/examples/md_ipi/client_dmff.py
+++ /dev/null
@@ -1,141 +0,0 @@
-#!/usr/bin/env python3
-import os
-import sys
-import driver
-import numpy as np
-import jax
-import jax.numpy as jnp
-import dmff.admp.pme
-import openmm.app as app
-from model6 import compute_leading_terms
-import openmm.unit as unit
-from dmff.api import Hamiltonian
-from dmff.common import nblist
-from dmff.admp.parser import *
-from dmff.admp.pairwise import (
- generate_pairwise_interaction,
- TT_damping_qq_kernel
-)
-from intra import onebodyenergy
-from jax_md import space, partition
-from jax import jit, vmap
-# os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'
-# os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID" # see issue #152
-# os.environ["CUDA_VISIBLE_DEVICES"]="1"
-
-dmff.admp.pme.DEFAULT_THOLE_WIDTH = 2.6
-
-@vmap
-@jit
-def TT_damping_qq_disp_kernel(dr, m, ai, aj, bi, bj, qi, qj, c6i, c6j, c8i, c8j, c10i, c10j):
- a = jnp.sqrt(ai * aj)
- b = jnp.sqrt(bi * bj)
- c6 = c6i * c6j
- c8 = c8i * c8j
- c10 = c10i * c10j
- q = qi * qj
- r = dr * 1.889726878 # convert to bohr
- br = b * r
- br2 = br * br
- br3 = br2 * br
- br4 = br2 * br2
- br5 = br3 * br2
- br6 = br3 * br3
- br7 = br3 * br4
- br8 = br4 * br4
- br9 = br4 * br5
- br10 = br5 * br5
- exp_br = jnp.exp(-br)
- f = 2625.5 * a * exp_br \
- + (-2625.5) * exp_br * (1+br) * q / r \
- + exp_br*(1+br+br2/2+br3/6+br4/24+br5/120+br6/720) * c6 / dr**6 \
- + exp_br*(1+br+br2/2+br3/6+br4/24+br5/120+br6/720+br7/5040+br8/40320) * c8 / dr**8 \
- + exp_br*(1+br+br2/2+br3/6+br4/24+br5/120+br6/720+br7/5040+br8/40320+br9/362880+br10/3628800) * c10 / dr**10
-
- return f * m
-
-class DMFFDriver(driver.BaseDriver):
-
- def __init__(self, addr, port, pdb, ffxml, topo_xml, socktype, device='cpu'):
- addr = addr + '_%s' %os.environ['SLURM_JOB_ID']
- # set up the interface with ipi
- driver.BaseDriver.__init__(self, port, addr, socktype)
- # set up various force calculators
- #self.pdb = pdb
- H = Hamiltonian(ffxml)
- app.Topology.loadBondDefinitions(topo_xml)
- pdb = app.PDBFile(pdb)
- positions = jnp.array(pdb.positions._value) * 10
- a, b, c = pdb.topology.getPeriodicBoxVectors()
- box = jnp.array([a._value, b._value, c._value]) * 10
- #self.box = box
- rc = 4
- disp_generator, pme_generator = H.getGenerators()
- pots = H.createPotential(pdb.topology, nonbondedCutoff=rc*unit.angstrom, step_pol=5)
- pot_disp = pots.dmff_potentials['ADMPDispForce']
- pot_pme = pots.dmff_potentials['ADMPPmeForce']
- pot_sr = generate_pairwise_interaction(TT_damping_qq_disp_kernel, static_args={})
-
-
- self.nbl = nblist.NeighborList(box, rc, H.getGenerators()[0].covalent_map)
- self.nbl.allocate(positions)
- pairs = self.nbl.pairs
-
- def admp_calculator(positions, box, pairs):
- params_pme = pme_generator.paramtree['ADMPPmeForce']
- params_disp = disp_generator.paramtree['ADMPDispForce']
- c0, c6_list = compute_leading_terms(positions,box) # compute fluctuated leading terms
- Q_local = params_pme["Q_local"][pme_generator.map_atomtype]
- Q_local = Q_local.at[:,0].set(c0) # change fixed charge into fluctuated one
- pol = params_pme["pol"][pme_generator.map_atomtype]
- tholes = params_pme["tholes"][pme_generator.map_atomtype]
- c8_list = jnp.sqrt(params_disp["C8"][disp_generator.map_atomtype]*1e8)
- c10_list = jnp.sqrt(params_disp["C10"][disp_generator.map_atomtype]*1e10)
- c_list = jnp.vstack((c6_list, c8_list, c10_list))
- covalent_map = disp_generator.covalent_map
- a_list = (params_disp["A"][disp_generator.map_atomtype] / 2625.5)
- b_list = params_disp["B"][disp_generator.map_atomtype] * 0.0529177249
- q_list = c0
- E_pme = pme_generator.pme_force.get_energy(
- positions, box, pairs, Q_local, pol, tholes, params_pme["mScales"], params_pme["pScales"], params_pme["dScales"]
- )
- E_disp = disp_generator.disp_pme_force.get_energy(positions, box, pairs, c_list.T, params_disp["mScales"])
- E_sr = pot_sr(positions, box, pairs, params_disp["mScales"], a_list, b_list, q_list, c_list[0], c_list[1], c_list[2])
-
- E4 = onebodyenergy(positions, box)
-
- return E_pme - E_disp + E_sr + E4
- self.tot_force = jit(jax.value_and_grad(admp_calculator,argnums=(0)))
-
- # compile tot_force function
- E, F = self.tot_force(positions, box, pairs)
-
-
- def grad(self, crd, cell): # receive SI input, return SI values
- positions = jnp.array(crd*1e10) # convert to angstrom
- box = jnp.array(cell*1e10) # convert to angstrom
- # nb list
- nbl = self.nbl.allocate(positions)
- pairs = self.nbl.pairs
- energy, grad = self.tot_force(positions, box, pairs)
- energy = np.float64(energy)
- grad = np.float64(grad)
- energy = energy * 1000 / 6.0221409e+23 # kj/mol to Joules
- grad = grad * 1000 / 6.0221409e+23 * 1e10 # convert kj/mol/A to joule/m
-
- return energy, grad
-
-
-if __name__ == '__main__':
- # the forces are composed by three parts:
- # the long range part computed using openmm, parameters in xml
- # the short range part writen by hand, parameters in psr
- fn_pdb = sys.argv[1] # pdb file used to define openmm topology, this one should contain all virtual sites
- ff_xml = sys.argv[2] # xml file that defines the force field
- topo_xml = sys.argv[3]
- addr = sys.argv[4]
- port = int(sys.argv[5])
- socktype = sys.argv[6]
- driver_dmff = DMFFDriver(addr, port, fn_pdb, ff_xml, topo_xml, socktype)
- while True:
- driver_dmff.parse()
diff --git a/examples/md_ipi/density_0.03338.pdb b/examples/md_ipi/density_0.03338.pdb
deleted file mode 100644
index cf606d526..000000000
--- a/examples/md_ipi/density_0.03338.pdb
+++ /dev/null
@@ -1,3057 +0,0 @@
-CRYST1 31.24 31.24 31.24 90.00 90.00 90.00 P 1 1
-MODEL 1
-HETATM 1 O HOH A 1 25.175 22.305 16.545 1.00 0.00 O
-HETATM 2 H1 HOH A 1 24.749 22.864 17.195 1.00 0.00 H
-HETATM 3 H2 HOH A 1 25.475 22.910 15.866 1.00 0.00 H
-HETATM 4 O HOH A 2 23.436 24.025 18.158 1.00 0.00 O
-HETATM 5 H1 HOH A 2 22.664 23.459 18.191 1.00 0.00 H
-HETATM 6 H2 HOH A 2 23.149 24.800 17.676 1.00 0.00 H
-HETATM 7 O HOH A 3 8.666 17.980 15.216 1.00 0.00 O
-HETATM 8 H1 HOH A 3 9.352 18.623 15.036 1.00 0.00 H
-HETATM 9 H2 HOH A 3 9.137 17.208 15.531 1.00 0.00 H
-HETATM 10 O HOH A 4 10.852 19.637 14.187 1.00 0.00 O
-HETATM 11 H1 HOH A 4 10.489 19.836 13.324 1.00 0.00 H
-HETATM 12 H2 HOH A 4 11.639 19.124 14.007 1.00 0.00 H
-HETATM 13 O HOH A 5 12.463 22.545 14.108 1.00 0.00 O
-HETATM 14 H1 HOH A 5 13.157 22.267 14.706 1.00 0.00 H
-HETATM 15 H2 HOH A 5 12.379 21.822 13.486 1.00 0.00 H
-HETATM 16 O HOH A 6 14.939 21.843 15.510 1.00 0.00 O
-HETATM 17 H1 HOH A 6 15.392 22.683 15.430 1.00 0.00 H
-HETATM 18 H2 HOH A 6 15.475 21.230 15.008 1.00 0.00 H
-HETATM 19 O HOH A 7 28.388 4.837 10.557 1.00 0.00 O
-HETATM 20 H1 HOH A 7 28.901 5.392 9.970 1.00 0.00 H
-HETATM 21 H2 HOH A 7 28.756 3.961 10.442 1.00 0.00 H
-HETATM 22 O HOH A 8 29.631 6.303 8.346 1.00 0.00 O
-HETATM 23 H1 HOH A 8 28.842 6.702 7.976 1.00 0.00 H
-HETATM 24 H2 HOH A 8 29.950 5.718 7.660 1.00 0.00 H
-HETATM 25 O HOH A 9 1.627 18.594 7.581 1.00 0.00 O
-HETATM 26 H1 HOH A 9 1.533 17.842 8.166 1.00 0.00 H
-HETATM 27 H2 HOH A 9 1.127 19.292 8.005 1.00 0.00 H
-HETATM 28 O HOH A 10 0.814 16.247 9.136 1.00 0.00 O
-HETATM 29 H1 HOH A 10 0.582 15.667 8.410 1.00 0.00 H
-HETATM 30 H2 HOH A 10 -0.008 16.392 9.603 1.00 0.00 H
-HETATM 31 O HOH A 11 6.555 24.911 4.695 1.00 0.00 O
-HETATM 32 H1 HOH A 11 6.717 25.684 5.236 1.00 0.00 H
-HETATM 33 H2 HOH A 11 6.980 24.194 5.165 1.00 0.00 H
-HETATM 34 O HOH A 12 7.580 27.256 6.120 1.00 0.00 O
-HETATM 35 H1 HOH A 12 7.874 27.767 5.365 1.00 0.00 H
-HETATM 36 H2 HOH A 12 8.381 27.061 6.606 1.00 0.00 H
-HETATM 37 O HOH A 13 22.252 2.396 24.356 1.00 0.00 O
-HETATM 38 H1 HOH A 13 21.446 2.242 24.849 1.00 0.00 H
-HETATM 39 H2 HOH A 13 21.960 2.519 23.452 1.00 0.00 H
-HETATM 40 O HOH A 14 19.817 1.393 25.641 1.00 0.00 O
-HETATM 41 H1 HOH A 14 20.190 0.626 26.077 1.00 0.00 H
-HETATM 42 H2 HOH A 14 19.197 1.034 25.007 1.00 0.00 H
-HETATM 43 O HOH A 15 27.387 20.314 2.397 1.00 0.00 O
-HETATM 44 H1 HOH A 15 28.249 20.472 2.782 1.00 0.00 H
-HETATM 45 H2 HOH A 15 27.342 20.915 1.654 1.00 0.00 H
-HETATM 46 O HOH A 16 29.777 21.270 3.798 1.00 0.00 O
-HETATM 47 H1 HOH A 16 29.409 21.340 4.679 1.00 0.00 H
-HETATM 48 H2 HOH A 16 30.000 22.168 3.557 1.00 0.00 H
-HETATM 49 O HOH A 17 24.758 28.817 12.939 1.00 0.00 O
-HETATM 50 H1 HOH A 17 25.054 27.908 12.983 1.00 0.00 H
-HETATM 51 H2 HOH A 17 24.709 29.099 13.852 1.00 0.00 H
-HETATM 52 O HOH A 18 25.118 25.923 13.222 1.00 0.00 O
-HETATM 53 H1 HOH A 18 24.430 25.633 12.622 1.00 0.00 H
-HETATM 54 H2 HOH A 18 24.839 25.608 14.081 1.00 0.00 H
-HETATM 55 O HOH A 19 1.251 26.111 29.064 1.00 0.00 O
-HETATM 56 H1 HOH A 19 2.177 26.071 28.827 1.00 0.00 H
-HETATM 57 H2 HOH A 19 1.250 26.314 30.000 1.00 0.00 H
-HETATM 58 O HOH A 20 4.070 25.471 28.590 1.00 0.00 O
-HETATM 59 H1 HOH A 20 3.955 24.637 28.133 1.00 0.00 H
-HETATM 60 H2 HOH A 20 4.529 25.243 29.398 1.00 0.00 H
-HETATM 61 O HOH A 21 27.389 13.595 26.581 1.00 0.00 O
-HETATM 62 H1 HOH A 21 26.487 13.857 26.767 1.00 0.00 H
-HETATM 63 H2 HOH A 21 27.584 13.993 25.732 1.00 0.00 H
-HETATM 64 O HOH A 22 24.491 13.985 26.773 1.00 0.00 O
-HETATM 65 H1 HOH A 22 24.239 13.089 26.998 1.00 0.00 H
-HETATM 66 H2 HOH A 22 24.091 14.142 25.918 1.00 0.00 H
-HETATM 67 O HOH A 23 27.106 9.133 3.515 1.00 0.00 O
-HETATM 68 H1 HOH A 23 27.018 9.921 2.979 1.00 0.00 H
-HETATM 69 H2 HOH A 23 26.711 9.367 4.355 1.00 0.00 H
-HETATM 70 O HOH A 24 27.283 11.738 2.186 1.00 0.00 O
-HETATM 71 H1 HOH A 24 28.202 11.701 1.918 1.00 0.00 H
-HETATM 72 H2 HOH A 24 27.242 12.463 2.808 1.00 0.00 H
-HETATM 73 O HOH A 25 15.513 14.065 29.610 1.00 0.00 O
-HETATM 74 H1 HOH A 25 16.141 13.387 29.362 1.00 0.00 H
-HETATM 75 H2 HOH A 25 16.047 14.757 30.000 1.00 0.00 H
-HETATM 76 O HOH A 26 17.435 11.862 29.412 1.00 0.00 O
-HETATM 77 H1 HOH A 26 16.909 11.175 29.823 1.00 0.00 H
-HETATM 78 H2 HOH A 26 18.180 11.981 30.000 1.00 0.00 H
-HETATM 79 O HOH A 27 6.539 15.263 11.697 1.00 0.00 O
-HETATM 80 H1 HOH A 27 6.868 14.982 12.551 1.00 0.00 H
-HETATM 81 H2 HOH A 27 7.101 15.998 11.452 1.00 0.00 H
-HETATM 82 O HOH A 28 7.261 14.865 14.509 1.00 0.00 O
-HETATM 83 H1 HOH A 28 6.379 14.852 14.884 1.00 0.00 H
-HETATM 84 H2 HOH A 28 7.674 15.640 14.888 1.00 0.00 H
-HETATM 85 O HOH A 29 29.126 10.153 8.629 1.00 0.00 O
-HETATM 86 H1 HOH A 29 29.255 10.375 7.707 1.00 0.00 H
-HETATM 87 H2 HOH A 29 29.915 9.670 8.873 1.00 0.00 H
-HETATM 88 O HOH A 30 29.378 10.281 5.713 1.00 0.00 O
-HETATM 89 H1 HOH A 30 28.504 9.969 5.479 1.00 0.00 H
-HETATM 90 H2 HOH A 30 29.973 9.613 5.373 1.00 0.00 H
-HETATM 91 O HOH A 31 15.008 7.594 19.567 1.00 0.00 O
-HETATM 92 H1 HOH A 31 15.119 7.399 20.497 1.00 0.00 H
-HETATM 93 H2 HOH A 31 14.063 7.698 19.454 1.00 0.00 H
-HETATM 94 O HOH A 32 15.170 6.470 22.267 1.00 0.00 O
-HETATM 95 H1 HOH A 32 15.742 5.725 22.080 1.00 0.00 H
-HETATM 96 H2 HOH A 32 14.343 6.077 22.544 1.00 0.00 H
-HETATM 97 O HOH A 33 24.123 15.176 10.909 1.00 0.00 O
-HETATM 98 H1 HOH A 33 24.901 15.658 10.628 1.00 0.00 H
-HETATM 99 H2 HOH A 33 24.203 14.322 10.485 1.00 0.00 H
-HETATM 100 O HOH A 34 26.215 16.717 9.555 1.00 0.00 O
-HETATM 101 H1 HOH A 34 25.696 17.485 9.315 1.00 0.00 H
-HETATM 102 H2 HOH A 34 26.467 16.325 8.719 1.00 0.00 H
-HETATM 103 O HOH A 35 25.842 2.926 0.685 1.00 0.00 O
-HETATM 104 H1 HOH A 35 26.459 3.173 1.374 1.00 0.00 H
-HETATM 105 H2 HOH A 35 25.971 3.581 -0.000 1.00 0.00 H
-HETATM 106 O HOH A 36 27.340 4.088 2.918 1.00 0.00 O
-HETATM 107 H1 HOH A 36 26.667 4.020 3.596 1.00 0.00 H
-HETATM 108 H2 HOH A 36 27.478 5.029 2.809 1.00 0.00 H
-HETATM 109 O HOH A 37 26.934 22.974 29.101 1.00 0.00 O
-HETATM 110 H1 HOH A 37 27.133 23.762 28.595 1.00 0.00 H
-HETATM 111 H2 HOH A 37 26.829 23.286 30.000 1.00 0.00 H
-HETATM 112 O HOH A 38 28.069 25.329 27.778 1.00 0.00 O
-HETATM 113 H1 HOH A 38 28.814 24.908 27.348 1.00 0.00 H
-HETATM 114 H2 HOH A 38 28.463 25.938 28.402 1.00 0.00 H
-HETATM 115 O HOH A 39 21.401 21.808 8.377 1.00 0.00 O
-HETATM 116 H1 HOH A 39 20.915 21.320 7.713 1.00 0.00 H
-HETATM 117 H2 HOH A 39 21.403 22.711 8.059 1.00 0.00 H
-HETATM 118 O HOH A 40 20.402 20.350 6.040 1.00 0.00 O
-HETATM 119 H1 HOH A 40 21.077 19.672 5.993 1.00 0.00 H
-HETATM 120 H2 HOH A 40 20.506 20.852 5.231 1.00 0.00 H
-HETATM 121 O HOH A 41 17.146 7.120 23.040 1.00 0.00 O
-HETATM 122 H1 HOH A 41 17.808 7.642 22.587 1.00 0.00 H
-HETATM 123 H2 HOH A 41 17.192 7.412 23.951 1.00 0.00 H
-HETATM 124 O HOH A 42 19.546 8.349 21.894 1.00 0.00 O
-HETATM 125 H1 HOH A 42 19.915 7.575 21.468 1.00 0.00 H
-HETATM 126 H2 HOH A 42 20.185 8.585 22.565 1.00 0.00 H
-HETATM 127 O HOH A 43 21.518 29.905 1.168 1.00 0.00 O
-HETATM 128 H1 HOH A 43 20.603 29.910 1.447 1.00 0.00 H
-HETATM 129 H2 HOH A 43 21.659 29.024 0.819 1.00 0.00 H
-HETATM 130 O HOH A 44 18.913 29.624 2.478 1.00 0.00 O
-HETATM 131 H1 HOH A 44 19.138 30.000 3.329 1.00 0.00 H
-HETATM 132 H2 HOH A 44 18.701 28.710 2.666 1.00 0.00 H
-HETATM 133 O HOH A 45 28.600 28.373 12.620 1.00 0.00 O
-HETATM 134 H1 HOH A 45 28.490 27.995 13.493 1.00 0.00 H
-HETATM 135 H2 HOH A 45 28.749 27.619 12.049 1.00 0.00 H
-HETATM 136 O HOH A 46 28.839 27.167 15.280 1.00 0.00 O
-HETATM 137 H1 HOH A 46 29.457 27.789 15.665 1.00 0.00 H
-HETATM 138 H2 HOH A 46 29.314 26.337 15.257 1.00 0.00 H
-HETATM 139 O HOH A 47 0.133 7.561 21.579 1.00 0.00 O
-HETATM 140 H1 HOH A 47 0.402 6.783 22.067 1.00 0.00 H
-HETATM 141 H2 HOH A 47 -0.005 7.249 20.685 1.00 0.00 H
-HETATM 142 O HOH A 48 1.466 5.265 22.818 1.00 0.00 O
-HETATM 143 H1 HOH A 48 2.210 5.724 23.209 1.00 0.00 H
-HETATM 144 H2 HOH A 48 1.857 4.682 22.167 1.00 0.00 H
-HETATM 145 O HOH A 49 26.542 3.942 23.693 1.00 0.00 O
-HETATM 146 H1 HOH A 49 27.375 3.926 24.163 1.00 0.00 H
-HETATM 147 H2 HOH A 49 26.763 3.673 22.801 1.00 0.00 H
-HETATM 148 O HOH A 50 29.192 4.392 24.859 1.00 0.00 O
-HETATM 149 H1 HOH A 50 29.041 5.263 25.228 1.00 0.00 H
-HETATM 150 H2 HOH A 50 29.862 4.525 24.189 1.00 0.00 H
-HETATM 151 O HOH A 51 10.648 21.636 11.400 1.00 0.00 O
-HETATM 152 H1 HOH A 51 11.599 21.644 11.297 1.00 0.00 H
-HETATM 153 H2 HOH A 51 10.505 21.800 12.333 1.00 0.00 H
-HETATM 154 O HOH A 52 13.533 21.132 11.314 1.00 0.00 O
-HETATM 155 H1 HOH A 52 13.522 20.313 10.816 1.00 0.00 H
-HETATM 156 H2 HOH A 52 13.882 20.889 12.171 1.00 0.00 H
-HETATM 157 O HOH A 53 21.644 12.803 10.659 1.00 0.00 O
-HETATM 158 H1 HOH A 53 21.097 13.432 11.130 1.00 0.00 H
-HETATM 159 H2 HOH A 53 21.173 12.640 9.842 1.00 0.00 H
-HETATM 160 O HOH A 54 19.645 14.269 12.222 1.00 0.00 O
-HETATM 161 H1 HOH A 54 19.688 13.755 13.029 1.00 0.00 H
-HETATM 162 H2 HOH A 54 18.757 14.129 11.893 1.00 0.00 H
-HETATM 163 O HOH A 55 2.391 20.333 17.493 1.00 0.00 O
-HETATM 164 H1 HOH A 55 3.058 20.789 18.006 1.00 0.00 H
-HETATM 165 H2 HOH A 55 1.676 20.965 17.412 1.00 0.00 H
-HETATM 166 O HOH A 56 4.092 21.605 19.511 1.00 0.00 O
-HETATM 167 H1 HOH A 56 4.200 20.858 20.101 1.00 0.00 H
-HETATM 168 H2 HOH A 56 3.614 22.254 20.028 1.00 0.00 H
-HETATM 169 O HOH A 57 7.713 2.571 17.467 1.00 0.00 O
-HETATM 170 H1 HOH A 57 8.381 3.171 17.798 1.00 0.00 H
-HETATM 171 H2 HOH A 57 7.070 2.514 18.174 1.00 0.00 H
-HETATM 172 O HOH A 58 9.934 3.954 18.786 1.00 0.00 O
-HETATM 173 H1 HOH A 58 10.642 3.358 18.538 1.00 0.00 H
-HETATM 174 H2 HOH A 58 9.884 3.888 19.739 1.00 0.00 H
-HETATM 175 O HOH A 59 29.430 3.793 19.811 1.00 0.00 O
-HETATM 176 H1 HOH A 59 28.521 3.507 19.712 1.00 0.00 H
-HETATM 177 H2 HOH A 59 29.447 4.254 20.649 1.00 0.00 H
-HETATM 178 O HOH A 60 26.552 3.468 19.364 1.00 0.00 O
-HETATM 179 H1 HOH A 60 26.506 3.805 18.469 1.00 0.00 H
-HETATM 180 H2 HOH A 60 26.035 4.085 19.882 1.00 0.00 H
-HETATM 181 O HOH A 61 5.741 19.128 6.617 1.00 0.00 O
-HETATM 182 H1 HOH A 61 6.589 19.285 7.032 1.00 0.00 H
-HETATM 183 H2 HOH A 61 5.956 18.760 5.760 1.00 0.00 H
-HETATM 184 O HOH A 62 8.358 20.068 7.541 1.00 0.00 O
-HETATM 185 H1 HOH A 62 8.111 20.959 7.791 1.00 0.00 H
-HETATM 186 H2 HOH A 62 8.975 20.182 6.818 1.00 0.00 H
-HETATM 187 O HOH A 63 16.833 7.327 12.868 1.00 0.00 O
-HETATM 188 H1 HOH A 63 17.503 6.702 13.144 1.00 0.00 H
-HETATM 189 H2 HOH A 63 17.251 7.834 12.171 1.00 0.00 H
-HETATM 190 O HOH A 64 19.110 5.865 13.992 1.00 0.00 O
-HETATM 191 H1 HOH A 64 18.972 6.072 14.917 1.00 0.00 H
-HETATM 192 H2 HOH A 64 19.946 6.277 13.774 1.00 0.00 H
-HETATM 193 O HOH A 65 10.448 22.006 8.592 1.00 0.00 O
-HETATM 194 H1 HOH A 65 9.736 21.720 9.165 1.00 0.00 H
-HETATM 195 H2 HOH A 65 10.130 21.830 7.707 1.00 0.00 H
-HETATM 196 O HOH A 66 8.469 20.600 10.233 1.00 0.00 O
-HETATM 197 H1 HOH A 66 9.070 20.103 10.789 1.00 0.00 H
-HETATM 198 H2 HOH A 66 7.996 19.934 9.734 1.00 0.00 H
-HETATM 199 O HOH A 67 9.638 7.195 13.398 1.00 0.00 O
-HETATM 200 H1 HOH A 67 9.505 7.014 12.467 1.00 0.00 H
-HETATM 201 H2 HOH A 67 10.582 7.327 13.485 1.00 0.00 H
-HETATM 202 O HOH A 68 9.431 6.118 10.681 1.00 0.00 O
-HETATM 203 H1 HOH A 68 8.889 5.351 10.870 1.00 0.00 H
-HETATM 204 H2 HOH A 68 10.262 5.757 10.372 1.00 0.00 H
-HETATM 205 O HOH A 69 27.781 11.682 9.460 1.00 0.00 O
-HETATM 206 H1 HOH A 69 26.883 11.579 9.774 1.00 0.00 H
-HETATM 207 H2 HOH A 69 27.692 11.818 8.517 1.00 0.00 H
-HETATM 208 O HOH A 70 25.076 10.835 10.201 1.00 0.00 O
-HETATM 209 H1 HOH A 70 25.302 10.049 10.699 1.00 0.00 H
-HETATM 210 H2 HOH A 70 24.581 10.511 9.449 1.00 0.00 H
-HETATM 211 O HOH A 71 19.448 22.986 23.278 1.00 0.00 O
-HETATM 212 H1 HOH A 71 19.265 22.883 24.212 1.00 0.00 H
-HETATM 213 H2 HOH A 71 18.642 23.354 22.915 1.00 0.00 H
-HETATM 214 O HOH A 72 18.591 22.208 25.970 1.00 0.00 O
-HETATM 215 H1 HOH A 72 18.919 21.309 25.972 1.00 0.00 H
-HETATM 216 H2 HOH A 72 17.640 22.119 26.034 1.00 0.00 H
-HETATM 217 O HOH A 73 24.069 17.897 6.744 1.00 0.00 O
-HETATM 218 H1 HOH A 73 24.797 17.437 6.327 1.00 0.00 H
-HETATM 219 H2 HOH A 73 24.304 17.930 7.671 1.00 0.00 H
-HETATM 220 O HOH A 74 26.051 16.016 5.687 1.00 0.00 O
-HETATM 221 H1 HOH A 74 25.457 15.431 5.215 1.00 0.00 H
-HETATM 222 H2 HOH A 74 26.432 15.471 6.375 1.00 0.00 H
-HETATM 223 O HOH A 75 5.267 14.098 26.296 1.00 0.00 O
-HETATM 224 H1 HOH A 75 4.779 14.829 26.675 1.00 0.00 H
-HETATM 225 H2 HOH A 75 5.581 13.601 27.051 1.00 0.00 H
-HETATM 226 O HOH A 76 4.295 16.558 27.557 1.00 0.00 O
-HETATM 227 H1 HOH A 76 4.744 17.193 26.999 1.00 0.00 H
-HETATM 228 H2 HOH A 76 4.672 16.690 28.427 1.00 0.00 H
-HETATM 229 O HOH A 77 26.889 12.793 28.975 1.00 0.00 O
-HETATM 230 H1 HOH A 77 26.397 11.979 28.875 1.00 0.00 H
-HETATM 231 H2 HOH A 77 27.389 12.675 29.783 1.00 0.00 H
-HETATM 232 O HOH A 78 25.040 10.523 29.075 1.00 0.00 O
-HETATM 233 H1 HOH A 78 24.222 10.995 28.912 1.00 0.00 H
-HETATM 234 H2 HOH A 78 24.945 10.175 29.961 1.00 0.00 H
-HETATM 235 O HOH A 79 27.108 20.663 12.545 1.00 0.00 O
-HETATM 236 H1 HOH A 79 27.967 21.067 12.665 1.00 0.00 H
-HETATM 237 H2 HOH A 79 27.144 20.275 11.670 1.00 0.00 H
-HETATM 238 O HOH A 80 29.527 22.315 12.572 1.00 0.00 O
-HETATM 239 H1 HOH A 80 29.138 23.136 12.874 1.00 0.00 H
-HETATM 240 H2 HOH A 80 29.842 22.506 11.689 1.00 0.00 H
-HETATM 241 O HOH A 81 15.369 6.421 17.677 1.00 0.00 O
-HETATM 242 H1 HOH A 81 15.479 6.016 16.816 1.00 0.00 H
-HETATM 243 H2 HOH A 81 16.230 6.347 18.089 1.00 0.00 H
-HETATM 244 O HOH A 82 15.674 4.686 15.336 1.00 0.00 O
-HETATM 245 H1 HOH A 82 14.884 4.156 15.445 1.00 0.00 H
-HETATM 246 H2 HOH A 82 16.397 4.067 15.438 1.00 0.00 H
-HETATM 247 O HOH A 83 13.390 27.805 1.684 1.00 0.00 O
-HETATM 248 H1 HOH A 83 12.880 27.091 2.064 1.00 0.00 H
-HETATM 249 H2 HOH A 83 14.199 27.822 2.195 1.00 0.00 H
-HETATM 250 O HOH A 84 11.716 25.996 3.267 1.00 0.00 O
-HETATM 251 H1 HOH A 84 10.930 26.541 3.326 1.00 0.00 H
-HETATM 252 H2 HOH A 84 12.024 25.917 4.169 1.00 0.00 H
-HETATM 253 O HOH A 85 19.050 18.937 20.460 1.00 0.00 O
-HETATM 254 H1 HOH A 85 18.461 19.586 20.077 1.00 0.00 H
-HETATM 255 H2 HOH A 85 18.470 18.310 20.891 1.00 0.00 H
-HETATM 256 O HOH A 86 17.206 21.123 19.825 1.00 0.00 O
-HETATM 257 H1 HOH A 86 17.715 21.851 20.185 1.00 0.00 H
-HETATM 258 H2 HOH A 86 16.414 21.094 20.361 1.00 0.00 H
-HETATM 259 O HOH A 87 0.927 3.096 19.204 1.00 0.00 O
-HETATM 260 H1 HOH A 87 0.984 3.974 18.828 1.00 0.00 H
-HETATM 261 H2 HOH A 87 -0.000 2.867 19.143 1.00 0.00 H
-HETATM 262 O HOH A 88 0.886 5.953 18.559 1.00 0.00 O
-HETATM 263 H1 HOH A 88 1.400 6.276 19.300 1.00 0.00 H
-HETATM 264 H2 HOH A 88 0.018 6.338 18.681 1.00 0.00 H
-HETATM 265 O HOH A 89 27.009 10.380 18.760 1.00 0.00 O
-HETATM 266 H1 HOH A 89 27.698 9.809 19.099 1.00 0.00 H
-HETATM 267 H2 HOH A 89 26.870 10.083 17.861 1.00 0.00 H
-HETATM 268 O HOH A 90 29.471 8.967 19.486 1.00 0.00 O
-HETATM 269 H1 HOH A 90 29.969 9.711 19.826 1.00 0.00 H
-HETATM 270 H2 HOH A 90 29.961 8.679 18.716 1.00 0.00 H
-HETATM 271 O HOH A 91 27.639 8.241 6.809 1.00 0.00 O
-HETATM 272 H1 HOH A 91 27.261 8.569 7.625 1.00 0.00 H
-HETATM 273 H2 HOH A 91 27.210 7.397 6.668 1.00 0.00 H
-HETATM 274 O HOH A 92 26.840 8.867 9.557 1.00 0.00 O
-HETATM 275 H1 HOH A 92 27.704 9.075 9.915 1.00 0.00 H
-HETATM 276 H2 HOH A 92 26.560 8.088 10.037 1.00 0.00 H
-HETATM 277 O HOH A 93 11.165 29.212 7.517 1.00 0.00 O
-HETATM 278 H1 HOH A 93 12.067 29.318 7.818 1.00 0.00 H
-HETATM 279 H2 HOH A 93 10.715 30.000 7.825 1.00 0.00 H
-HETATM 280 O HOH A 94 13.715 29.379 8.949 1.00 0.00 O
-HETATM 281 H1 HOH A 94 13.736 28.492 9.311 1.00 0.00 H
-HETATM 282 H2 HOH A 94 13.662 29.951 9.715 1.00 0.00 H
-HETATM 283 O HOH A 95 3.036 20.745 12.672 1.00 0.00 O
-HETATM 284 H1 HOH A 95 2.823 20.245 11.885 1.00 0.00 H
-HETATM 285 H2 HOH A 95 3.991 20.711 12.728 1.00 0.00 H
-HETATM 286 O HOH A 96 2.485 18.754 10.595 1.00 0.00 O
-HETATM 287 H1 HOH A 96 1.816 18.246 11.056 1.00 0.00 H
-HETATM 288 H2 HOH A 96 3.213 18.144 10.479 1.00 0.00 H
-HETATM 289 O HOH A 97 10.755 18.042 12.079 1.00 0.00 O
-HETATM 290 H1 HOH A 97 10.520 17.714 11.211 1.00 0.00 H
-HETATM 291 H2 HOH A 97 11.711 18.084 12.069 1.00 0.00 H
-HETATM 292 O HOH A 98 10.196 16.527 9.635 1.00 0.00 O
-HETATM 293 H1 HOH A 98 9.605 15.870 10.004 1.00 0.00 H
-HETATM 294 H2 HOH A 98 10.957 16.028 9.338 1.00 0.00 H
-HETATM 295 O HOH A 99 21.620 21.445 25.219 1.00 0.00 O
-HETATM 296 H1 HOH A 99 22.188 21.129 24.518 1.00 0.00 H
-HETATM 297 H2 HOH A 99 22.083 22.201 25.581 1.00 0.00 H
-HETATM 298 O HOH A 100 23.671 20.252 23.501 1.00 0.00 O
-HETATM 299 H1 HOH A 100 23.614 19.348 23.814 1.00 0.00 H
-HETATM 300 H2 HOH A 100 24.553 20.536 23.742 1.00 0.00 H
-HETATM 301 O HOH A 101 16.449 27.071 26.096 1.00 0.00 O
-HETATM 302 H1 HOH A 101 16.758 27.663 26.781 1.00 0.00 H
-HETATM 303 H2 HOH A 101 17.042 27.223 25.360 1.00 0.00 H
-HETATM 304 O HOH A 102 17.128 29.291 27.883 1.00 0.00 O
-HETATM 305 H1 HOH A 102 16.244 29.619 28.049 1.00 0.00 H
-HETATM 306 H2 HOH A 102 17.569 30.003 27.421 1.00 0.00 H
-HETATM 307 O HOH A 103 23.163 0.135 10.057 1.00 0.00 O
-HETATM 308 H1 HOH A 103 23.826 0.824 10.036 1.00 0.00 H
-HETATM 309 H2 HOH A 103 22.929 -0.001 9.138 1.00 0.00 H
-HETATM 310 O HOH A 104 24.744 2.598 9.915 1.00 0.00 O
-HETATM 311 H1 HOH A 104 24.300 3.094 10.604 1.00 0.00 H
-HETATM 312 H2 HOH A 104 24.572 3.090 9.113 1.00 0.00 H
-HETATM 313 O HOH A 105 20.635 6.807 23.779 1.00 0.00 O
-HETATM 314 H1 HOH A 105 20.202 5.984 24.007 1.00 0.00 H
-HETATM 315 H2 HOH A 105 21.114 7.054 24.570 1.00 0.00 H
-HETATM 316 O HOH A 106 18.940 4.627 24.759 1.00 0.00 O
-HETATM 317 H1 HOH A 106 18.099 4.934 24.419 1.00 0.00 H
-HETATM 318 H2 HOH A 106 18.839 4.649 25.710 1.00 0.00 H
-HETATM 319 O HOH A 107 23.557 5.499 28.413 1.00 0.00 O
-HETATM 320 H1 HOH A 107 23.431 6.319 28.891 1.00 0.00 H
-HETATM 321 H2 HOH A 107 23.761 5.770 27.518 1.00 0.00 H
-HETATM 322 O HOH A 108 22.657 8.015 29.614 1.00 0.00 O
-HETATM 323 H1 HOH A 108 21.837 7.705 30.001 1.00 0.00 H
-HETATM 324 H2 HOH A 108 22.388 8.653 28.954 1.00 0.00 H
-HETATM 325 O HOH A 109 9.191 17.586 7.989 1.00 0.00 O
-HETATM 326 H1 HOH A 109 10.056 17.875 7.699 1.00 0.00 H
-HETATM 327 H2 HOH A 109 8.943 18.211 8.669 1.00 0.00 H
-HETATM 328 O HOH A 110 12.023 18.223 7.590 1.00 0.00 O
-HETATM 329 H1 HOH A 110 12.381 17.338 7.658 1.00 0.00 H
-HETATM 330 H2 HOH A 110 12.395 18.693 8.336 1.00 0.00 H
-HETATM 331 O HOH A 111 29.214 6.787 4.684 1.00 0.00 O
-HETATM 332 H1 HOH A 111 28.887 6.074 5.233 1.00 0.00 H
-HETATM 333 H2 HOH A 111 28.765 6.671 3.847 1.00 0.00 H
-HETATM 334 O HOH A 112 28.578 4.279 6.059 1.00 0.00 O
-HETATM 335 H1 HOH A 112 29.463 4.034 6.330 1.00 0.00 H
-HETATM 336 H2 HOH A 112 28.312 3.591 5.449 1.00 0.00 H
-HETATM 337 O HOH A 113 3.798 12.379 9.475 1.00 0.00 O
-HETATM 338 H1 HOH A 113 4.627 11.909 9.387 1.00 0.00 H
-HETATM 339 H2 HOH A 113 3.692 12.506 10.418 1.00 0.00 H
-HETATM 340 O HOH A 114 6.029 10.483 9.371 1.00 0.00 O
-HETATM 341 H1 HOH A 114 5.640 9.834 8.783 1.00 0.00 H
-HETATM 342 H2 HOH A 114 6.145 10.018 10.199 1.00 0.00 H
-HETATM 343 O HOH A 115 26.692 30.002 17.094 1.00 0.00 O
-HETATM 344 H1 HOH A 115 26.355 29.414 17.769 1.00 0.00 H
-HETATM 345 H2 HOH A 115 26.686 29.479 16.293 1.00 0.00 H
-HETATM 346 O HOH A 116 26.188 27.970 19.143 1.00 0.00 O
-HETATM 347 H1 HOH A 116 26.905 28.187 19.741 1.00 0.00 H
-HETATM 348 H2 HOH A 116 26.390 27.088 18.833 1.00 0.00 H
-HETATM 349 O HOH A 117 19.996 9.604 6.531 1.00 0.00 O
-HETATM 350 H1 HOH A 117 20.342 8.825 6.097 1.00 0.00 H
-HETATM 351 H2 HOH A 117 20.258 9.505 7.447 1.00 0.00 H
-HETATM 352 O HOH A 118 20.607 6.953 5.443 1.00 0.00 O
-HETATM 353 H1 HOH A 118 19.769 6.777 5.013 1.00 0.00 H
-HETATM 354 H2 HOH A 118 20.671 6.288 6.129 1.00 0.00 H
-HETATM 355 O HOH A 119 26.521 21.500 6.873 1.00 0.00 O
-HETATM 356 H1 HOH A 119 26.152 22.058 7.557 1.00 0.00 H
-HETATM 357 H2 HOH A 119 27.467 21.629 6.942 1.00 0.00 H
-HETATM 358 O HOH A 120 25.498 23.637 8.597 1.00 0.00 O
-HETATM 359 H1 HOH A 120 24.860 24.017 7.993 1.00 0.00 H
-HETATM 360 H2 HOH A 120 26.144 24.329 8.735 1.00 0.00 H
-HETATM 361 O HOH A 121 1.945 8.398 14.854 1.00 0.00 O
-HETATM 362 H1 HOH A 121 1.790 8.056 15.734 1.00 0.00 H
-HETATM 363 H2 HOH A 121 2.845 8.723 14.877 1.00 0.00 H
-HETATM 364 O HOH A 122 1.411 7.906 17.692 1.00 0.00 O
-HETATM 365 H1 HOH A 122 0.578 8.373 17.766 1.00 0.00 H
-HETATM 366 H2 HOH A 122 2.010 8.389 18.261 1.00 0.00 H
-HETATM 367 O HOH A 123 27.303 11.704 22.846 1.00 0.00 O
-HETATM 368 H1 HOH A 123 27.807 11.066 22.341 1.00 0.00 H
-HETATM 369 H2 HOH A 123 27.811 11.832 23.647 1.00 0.00 H
-HETATM 370 O HOH A 124 28.619 9.367 21.666 1.00 0.00 O
-HETATM 371 H1 HOH A 124 27.842 8.833 21.500 1.00 0.00 H
-HETATM 372 H2 HOH A 124 29.120 8.871 22.314 1.00 0.00 H
-HETATM 373 O HOH A 125 26.904 3.221 11.445 1.00 0.00 O
-HETATM 374 H1 HOH A 125 26.527 3.805 12.103 1.00 0.00 H
-HETATM 375 H2 HOH A 125 26.592 3.568 10.609 1.00 0.00 H
-HETATM 376 O HOH A 126 25.264 4.711 13.362 1.00 0.00 O
-HETATM 377 H1 HOH A 126 24.950 3.974 13.887 1.00 0.00 H
-HETATM 378 H2 HOH A 126 24.476 5.073 12.957 1.00 0.00 H
-HETATM 379 O HOH A 127 14.087 10.230 2.306 1.00 0.00 O
-HETATM 380 H1 HOH A 127 13.438 10.191 3.008 1.00 0.00 H
-HETATM 381 H2 HOH A 127 14.316 9.316 2.139 1.00 0.00 H
-HETATM 382 O HOH A 128 12.556 10.020 4.795 1.00 0.00 O
-HETATM 383 H1 HOH A 128 13.011 10.694 5.302 1.00 0.00 H
-HETATM 384 H2 HOH A 128 12.736 9.204 5.261 1.00 0.00 H
-HETATM 385 O HOH A 129 15.695 17.576 19.226 1.00 0.00 O
-HETATM 386 H1 HOH A 129 15.677 16.793 18.676 1.00 0.00 H
-HETATM 387 H2 HOH A 129 16.041 17.273 20.065 1.00 0.00 H
-HETATM 388 O HOH A 130 15.166 15.046 17.845 1.00 0.00 O
-HETATM 389 H1 HOH A 130 14.266 15.219 17.568 1.00 0.00 H
-HETATM 390 H2 HOH A 130 15.096 14.312 18.454 1.00 0.00 H
-HETATM 391 O HOH A 131 21.987 24.075 15.925 1.00 0.00 O
-HETATM 392 H1 HOH A 131 22.790 23.798 15.483 1.00 0.00 H
-HETATM 393 H2 HOH A 131 21.582 23.260 16.220 1.00 0.00 H
-HETATM 394 O HOH A 132 24.089 23.087 14.139 1.00 0.00 O
-HETATM 395 H1 HOH A 132 23.901 23.635 13.377 1.00 0.00 H
-HETATM 396 H2 HOH A 132 23.919 22.193 13.842 1.00 0.00 H
-HETATM 397 O HOH A 133 12.024 25.525 14.519 1.00 0.00 O
-HETATM 398 H1 HOH A 133 12.019 24.781 13.917 1.00 0.00 H
-HETATM 399 H2 HOH A 133 11.110 25.805 14.563 1.00 0.00 H
-HETATM 400 O HOH A 134 11.970 23.652 12.267 1.00 0.00 O
-HETATM 401 H1 HOH A 134 12.706 24.005 11.767 1.00 0.00 H
-HETATM 402 H2 HOH A 134 11.208 23.787 11.704 1.00 0.00 H
-HETATM 403 O HOH A 135 20.349 29.059 23.079 1.00 0.00 O
-HETATM 404 H1 HOH A 135 20.659 28.268 23.519 1.00 0.00 H
-HETATM 405 H2 HOH A 135 21.052 29.696 23.207 1.00 0.00 H
-HETATM 406 O HOH A 136 21.144 26.912 24.908 1.00 0.00 O
-HETATM 407 H1 HOH A 136 20.313 26.801 25.370 1.00 0.00 H
-HETATM 408 H2 HOH A 136 21.755 27.218 25.578 1.00 0.00 H
-HETATM 409 O HOH A 137 15.082 16.412 10.612 1.00 0.00 O
-HETATM 410 H1 HOH A 137 14.907 16.863 11.437 1.00 0.00 H
-HETATM 411 H2 HOH A 137 16.014 16.557 10.451 1.00 0.00 H
-HETATM 412 O HOH A 138 14.556 18.270 12.815 1.00 0.00 O
-HETATM 413 H1 HOH A 138 13.785 18.709 12.453 1.00 0.00 H
-HETATM 414 H2 HOH A 138 15.220 18.957 12.873 1.00 0.00 H
-HETATM 415 O HOH A 139 25.564 10.730 13.348 1.00 0.00 O
-HETATM 416 H1 HOH A 139 26.286 11.066 12.817 1.00 0.00 H
-HETATM 417 H2 HOH A 139 25.303 11.470 13.896 1.00 0.00 H
-HETATM 418 O HOH A 140 28.084 11.690 12.202 1.00 0.00 O
-HETATM 419 H1 HOH A 140 28.632 10.930 12.402 1.00 0.00 H
-HETATM 420 H2 HOH A 140 28.464 12.404 12.715 1.00 0.00 H
-HETATM 421 O HOH A 141 24.845 14.200 19.827 1.00 0.00 O
-HETATM 422 H1 HOH A 141 24.141 14.034 19.201 1.00 0.00 H
-HETATM 423 H2 HOH A 141 24.522 13.841 20.653 1.00 0.00 H
-HETATM 424 O HOH A 142 22.434 14.140 18.163 1.00 0.00 O
-HETATM 425 H1 HOH A 142 22.457 15.043 17.843 1.00 0.00 H
-HETATM 426 H2 HOH A 142 21.635 14.090 18.688 1.00 0.00 H
-HETATM 427 O HOH A 143 17.784 12.042 14.165 1.00 0.00 O
-HETATM 428 H1 HOH A 143 17.460 12.912 13.933 1.00 0.00 H
-HETATM 429 H2 HOH A 143 17.350 11.838 14.994 1.00 0.00 H
-HETATM 430 O HOH A 144 17.168 14.889 13.852 1.00 0.00 O
-HETATM 431 H1 HOH A 144 18.053 15.210 13.674 1.00 0.00 H
-HETATM 432 H2 HOH A 144 16.924 15.298 14.682 1.00 0.00 H
-HETATM 433 O HOH A 145 7.784 29.353 2.961 1.00 0.00 O
-HETATM 434 H1 HOH A 145 7.705 28.474 2.591 1.00 0.00 H
-HETATM 435 H2 HOH A 145 7.879 29.926 2.200 1.00 0.00 H
-HETATM 436 O HOH A 146 8.111 26.701 1.759 1.00 0.00 O
-HETATM 437 H1 HOH A 146 8.769 26.350 2.361 1.00 0.00 H
-HETATM 438 H2 HOH A 146 8.555 26.741 0.912 1.00 0.00 H
-HETATM 439 O HOH A 147 26.892 7.536 13.011 1.00 0.00 O
-HETATM 440 H1 HOH A 147 27.726 7.752 12.593 1.00 0.00 H
-HETATM 441 H2 HOH A 147 27.128 6.947 13.727 1.00 0.00 H
-HETATM 442 O HOH A 148 29.397 7.674 11.498 1.00 0.00 O
-HETATM 443 H1 HOH A 148 29.050 7.486 10.625 1.00 0.00 H
-HETATM 444 H2 HOH A 148 29.976 6.937 11.692 1.00 0.00 H
-HETATM 445 O HOH A 149 2.022 14.237 19.721 1.00 0.00 O
-HETATM 446 H1 HOH A 149 1.741 14.718 18.943 1.00 0.00 H
-HETATM 447 H2 HOH A 149 1.212 13.893 20.097 1.00 0.00 H
-HETATM 448 O HOH A 150 1.003 16.117 17.718 1.00 0.00 O
-HETATM 449 H1 HOH A 150 1.527 16.882 17.958 1.00 0.00 H
-HETATM 450 H2 HOH A 150 0.099 16.376 17.894 1.00 0.00 H
-HETATM 451 O HOH A 151 24.256 22.402 25.309 1.00 0.00 O
-HETATM 452 H1 HOH A 151 25.065 22.398 24.797 1.00 0.00 H
-HETATM 453 H2 HOH A 151 24.201 21.520 25.678 1.00 0.00 H
-HETATM 454 O HOH A 152 26.417 22.099 23.354 1.00 0.00 O
-HETATM 455 H1 HOH A 152 25.980 22.485 22.594 1.00 0.00 H
-HETATM 456 H2 HOH A 152 26.558 21.184 23.112 1.00 0.00 H
-HETATM 457 O HOH A 153 5.524 20.988 14.825 1.00 0.00 O
-HETATM 458 H1 HOH A 153 6.329 21.162 14.338 1.00 0.00 H
-HETATM 459 H2 HOH A 153 5.643 21.445 15.658 1.00 0.00 H
-HETATM 460 O HOH A 154 8.216 21.117 13.677 1.00 0.00 O
-HETATM 461 H1 HOH A 154 8.323 20.192 13.454 1.00 0.00 H
-HETATM 462 H2 HOH A 154 8.884 21.284 14.343 1.00 0.00 H
-HETATM 463 O HOH A 155 17.978 5.586 3.134 1.00 0.00 O
-HETATM 464 H1 HOH A 155 18.596 6.146 2.665 1.00 0.00 H
-HETATM 465 H2 HOH A 155 18.002 5.903 4.037 1.00 0.00 H
-HETATM 466 O HOH A 156 20.273 6.969 1.949 1.00 0.00 O
-HETATM 467 H1 HOH A 156 20.701 6.216 1.541 1.00 0.00 H
-HETATM 468 H2 HOH A 156 20.893 7.270 2.612 1.00 0.00 H
-HETATM 469 O HOH A 157 22.229 6.013 13.032 1.00 0.00 O
-HETATM 470 H1 HOH A 157 22.098 5.318 12.388 1.00 0.00 H
-HETATM 471 H2 HOH A 157 22.036 5.600 13.874 1.00 0.00 H
-HETATM 472 O HOH A 158 21.269 3.995 11.138 1.00 0.00 O
-HETATM 473 H1 HOH A 158 20.730 4.561 10.584 1.00 0.00 H
-HETATM 474 H2 HOH A 158 20.647 3.389 11.541 1.00 0.00 H
-HETATM 475 O HOH A 159 27.948 20.692 15.420 1.00 0.00 O
-HETATM 476 H1 HOH A 159 28.088 21.590 15.122 1.00 0.00 H
-HETATM 477 H2 HOH A 159 27.565 20.244 14.666 1.00 0.00 H
-HETATM 478 O HOH A 160 27.809 23.498 14.588 1.00 0.00 O
-HETATM 479 H1 HOH A 160 27.396 23.860 15.372 1.00 0.00 H
-HETATM 480 H2 HOH A 160 27.168 23.635 13.891 1.00 0.00 H
-HETATM 481 O HOH A 161 4.662 14.970 10.511 1.00 0.00 O
-HETATM 482 H1 HOH A 161 4.597 14.340 11.229 1.00 0.00 H
-HETATM 483 H2 HOH A 161 4.367 14.487 9.739 1.00 0.00 H
-HETATM 484 O HOH A 162 4.952 12.827 12.487 1.00 0.00 O
-HETATM 485 H1 HOH A 162 5.836 13.032 12.795 1.00 0.00 H
-HETATM 486 H2 HOH A 162 5.033 11.966 12.076 1.00 0.00 H
-HETATM 487 O HOH A 163 12.166 16.309 29.247 1.00 0.00 O
-HETATM 488 H1 HOH A 163 12.145 15.355 29.176 1.00 0.00 H
-HETATM 489 H2 HOH A 163 12.053 16.620 28.349 1.00 0.00 H
-HETATM 490 O HOH A 164 12.640 13.447 28.838 1.00 0.00 O
-HETATM 491 H1 HOH A 164 13.431 13.354 29.369 1.00 0.00 H
-HETATM 492 H2 HOH A 164 12.924 13.253 27.945 1.00 0.00 H
-HETATM 493 O HOH A 165 23.236 23.538 20.356 1.00 0.00 O
-HETATM 494 H1 HOH A 165 23.348 24.487 20.307 1.00 0.00 H
-HETATM 495 H2 HOH A 165 24.098 23.183 20.139 1.00 0.00 H
-HETATM 496 O HOH A 166 23.553 26.361 19.637 1.00 0.00 O
-HETATM 497 H1 HOH A 166 22.767 26.461 19.098 1.00 0.00 H
-HETATM 498 H2 HOH A 166 24.280 26.498 19.031 1.00 0.00 H
-HETATM 499 O HOH A 167 26.700 6.169 27.213 1.00 0.00 O
-HETATM 500 H1 HOH A 167 26.377 6.975 27.614 1.00 0.00 H
-HETATM 501 H2 HOH A 167 27.072 6.448 26.376 1.00 0.00 H
-HETATM 502 O HOH A 168 25.274 8.580 28.071 1.00 0.00 O
-HETATM 503 H1 HOH A 168 24.427 8.181 28.275 1.00 0.00 H
-HETATM 504 H2 HOH A 168 25.094 9.171 27.341 1.00 0.00 H
-HETATM 505 O HOH A 169 10.291 22.352 20.708 1.00 0.00 O
-HETATM 506 H1 HOH A 169 9.569 22.940 20.486 1.00 0.00 H
-HETATM 507 H2 HOH A 169 10.090 22.048 21.593 1.00 0.00 H
-HETATM 508 O HOH A 170 8.360 24.529 20.368 1.00 0.00 O
-HETATM 509 H1 HOH A 170 8.961 25.209 20.064 1.00 0.00 H
-HETATM 510 H2 HOH A 170 8.032 24.853 21.207 1.00 0.00 H
-HETATM 511 O HOH A 171 9.126 22.797 5.054 1.00 0.00 O
-HETATM 512 H1 HOH A 171 9.780 23.121 5.673 1.00 0.00 H
-HETATM 513 H2 HOH A 171 9.608 22.682 4.234 1.00 0.00 H
-HETATM 514 O HOH A 172 11.053 24.291 6.678 1.00 0.00 O
-HETATM 515 H1 HOH A 172 10.478 25.000 6.969 1.00 0.00 H
-HETATM 516 H2 HOH A 172 11.718 24.723 6.142 1.00 0.00 H
-HETATM 517 O HOH A 173 16.589 21.937 17.878 1.00 0.00 O
-HETATM 518 H1 HOH A 173 17.092 21.954 17.064 1.00 0.00 H
-HETATM 519 H2 HOH A 173 15.857 22.533 17.720 1.00 0.00 H
-HETATM 520 O HOH A 174 18.321 22.495 15.582 1.00 0.00 O
-HETATM 521 H1 HOH A 174 19.145 22.630 16.052 1.00 0.00 H
-HETATM 522 H2 HOH A 174 18.129 23.343 15.183 1.00 0.00 H
-HETATM 523 O HOH A 175 16.339 14.099 11.959 1.00 0.00 O
-HETATM 524 H1 HOH A 175 16.745 13.242 11.832 1.00 0.00 H
-HETATM 525 H2 HOH A 175 16.252 14.459 11.076 1.00 0.00 H
-HETATM 526 O HOH A 176 18.067 11.780 11.497 1.00 0.00 O
-HETATM 527 H1 HOH A 176 18.720 11.948 12.177 1.00 0.00 H
-HETATM 528 H2 HOH A 176 18.551 11.849 10.674 1.00 0.00 H
-HETATM 529 O HOH A 177 8.677 8.663 23.701 1.00 0.00 O
-HETATM 530 H1 HOH A 177 8.452 8.962 24.582 1.00 0.00 H
-HETATM 531 H2 HOH A 177 9.612 8.849 23.616 1.00 0.00 H
-HETATM 532 O HOH A 178 8.000 10.104 26.160 1.00 0.00 O
-HETATM 533 H1 HOH A 178 7.237 10.586 25.840 1.00 0.00 H
-HETATM 534 H2 HOH A 178 8.643 10.781 26.370 1.00 0.00 H
-HETATM 535 O HOH A 179 21.950 23.393 23.221 1.00 0.00 O
-HETATM 536 H1 HOH A 179 21.514 24.234 23.362 1.00 0.00 H
-HETATM 537 H2 HOH A 179 22.437 23.508 22.405 1.00 0.00 H
-HETATM 538 O HOH A 180 20.249 25.779 23.246 1.00 0.00 O
-HETATM 539 H1 HOH A 180 19.407 25.354 23.415 1.00 0.00 H
-HETATM 540 H2 HOH A 180 20.156 26.163 22.375 1.00 0.00 H
-HETATM 541 O HOH A 181 6.137 21.182 17.785 1.00 0.00 O
-HETATM 542 H1 HOH A 181 6.023 22.113 17.974 1.00 0.00 H
-HETATM 543 H2 HOH A 181 5.251 20.820 17.814 1.00 0.00 H
-HETATM 544 O HOH A 182 5.731 23.857 18.910 1.00 0.00 O
-HETATM 545 H1 HOH A 182 6.430 23.846 19.564 1.00 0.00 H
-HETATM 546 H2 HOH A 182 4.923 23.914 19.419 1.00 0.00 H
-HETATM 547 O HOH A 183 25.045 14.947 13.414 1.00 0.00 O
-HETATM 548 H1 HOH A 183 25.494 14.886 14.256 1.00 0.00 H
-HETATM 549 H2 HOH A 183 25.744 14.894 12.763 1.00 0.00 H
-HETATM 550 O HOH A 184 26.559 15.312 15.895 1.00 0.00 O
-HETATM 551 H1 HOH A 184 26.087 16.070 16.242 1.00 0.00 H
-HETATM 552 H2 HOH A 184 27.445 15.633 15.732 1.00 0.00 H
-HETATM 553 O HOH A 185 13.628 15.282 24.411 1.00 0.00 O
-HETATM 554 H1 HOH A 185 14.433 15.025 24.861 1.00 0.00 H
-HETATM 555 H2 HOH A 185 13.785 15.061 23.493 1.00 0.00 H
-HETATM 556 O HOH A 186 16.304 14.981 25.566 1.00 0.00 O
-HETATM 557 H1 HOH A 186 16.360 15.821 26.022 1.00 0.00 H
-HETATM 558 H2 HOH A 186 16.990 15.021 24.900 1.00 0.00 H
-HETATM 559 O HOH A 187 29.204 23.911 2.476 1.00 0.00 O
-HETATM 560 H1 HOH A 187 28.437 23.917 3.048 1.00 0.00 H
-HETATM 561 H2 HOH A 187 28.917 23.441 1.693 1.00 0.00 H
-HETATM 562 O HOH A 188 27.003 23.382 4.335 1.00 0.00 O
-HETATM 563 H1 HOH A 188 27.517 23.086 5.087 1.00 0.00 H
-HETATM 564 H2 HOH A 188 26.489 22.617 4.077 1.00 0.00 H
-HETATM 565 O HOH A 189 12.916 16.517 10.241 1.00 0.00 O
-HETATM 566 H1 HOH A 189 12.752 16.273 11.152 1.00 0.00 H
-HETATM 567 H2 HOH A 189 12.502 17.375 10.143 1.00 0.00 H
-HETATM 568 O HOH A 190 11.884 15.645 12.841 1.00 0.00 O
-HETATM 569 H1 HOH A 190 11.578 14.773 12.590 1.00 0.00 H
-HETATM 570 H2 HOH A 190 11.090 16.118 13.090 1.00 0.00 H
-HETATM 571 O HOH A 191 11.078 11.030 8.006 1.00 0.00 O
-HETATM 572 H1 HOH A 191 11.864 11.576 8.013 1.00 0.00 H
-HETATM 573 H2 HOH A 191 11.187 10.442 8.753 1.00 0.00 H
-HETATM 574 O HOH A 192 13.740 12.236 7.799 1.00 0.00 O
-HETATM 575 H1 HOH A 192 13.892 12.091 6.864 1.00 0.00 H
-HETATM 576 H2 HOH A 192 14.409 11.710 8.237 1.00 0.00 H
-HETATM 577 O HOH A 193 4.690 13.658 19.918 1.00 0.00 O
-HETATM 578 H1 HOH A 193 4.454 14.084 20.742 1.00 0.00 H
-HETATM 579 H2 HOH A 193 4.169 14.111 19.255 1.00 0.00 H
-HETATM 580 O HOH A 194 3.490 14.644 22.402 1.00 0.00 O
-HETATM 581 H1 HOH A 194 3.348 13.804 22.838 1.00 0.00 H
-HETATM 582 H2 HOH A 194 2.610 14.996 22.263 1.00 0.00 H
-HETATM 583 O HOH A 195 5.212 23.256 12.478 1.00 0.00 O
-HETATM 584 H1 HOH A 195 6.112 23.351 12.168 1.00 0.00 H
-HETATM 585 H2 HOH A 195 5.294 22.818 13.326 1.00 0.00 H
-HETATM 586 O HOH A 196 7.921 22.988 11.397 1.00 0.00 O
-HETATM 587 H1 HOH A 196 7.699 22.638 10.534 1.00 0.00 H
-HETATM 588 H2 HOH A 196 8.412 22.286 11.824 1.00 0.00 H
-HETATM 589 O HOH A 197 10.802 9.033 20.329 1.00 0.00 O
-HETATM 590 H1 HOH A 197 10.059 9.613 20.166 1.00 0.00 H
-HETATM 591 H2 HOH A 197 10.820 8.926 21.280 1.00 0.00 H
-HETATM 592 O HOH A 198 8.885 11.225 20.006 1.00 0.00 O
-HETATM 593 H1 HOH A 198 9.405 11.789 19.433 1.00 0.00 H
-HETATM 594 H2 HOH A 198 8.778 11.731 20.811 1.00 0.00 H
-HETATM 595 O HOH A 199 24.079 20.594 5.977 1.00 0.00 O
-HETATM 596 H1 HOH A 199 24.278 21.464 5.631 1.00 0.00 H
-HETATM 597 H2 HOH A 199 23.166 20.651 6.260 1.00 0.00 H
-HETATM 598 O HOH A 200 24.727 23.406 5.472 1.00 0.00 O
-HETATM 599 H1 HOH A 200 25.539 23.460 5.976 1.00 0.00 H
-HETATM 600 H2 HOH A 200 24.125 24.001 5.919 1.00 0.00 H
-HETATM 601 O HOH A 201 5.847 25.259 17.200 1.00 0.00 O
-HETATM 602 H1 HOH A 201 6.373 24.566 16.800 1.00 0.00 H
-HETATM 603 H2 HOH A 201 4.964 25.120 16.856 1.00 0.00 H
-HETATM 604 O HOH A 202 7.438 23.473 15.508 1.00 0.00 O
-HETATM 605 H1 HOH A 202 8.119 24.093 15.244 1.00 0.00 H
-HETATM 606 H2 HOH A 202 6.986 23.250 14.695 1.00 0.00 H
-HETATM 607 O HOH A 203 16.212 28.172 20.446 1.00 0.00 O
-HETATM 608 H1 HOH A 203 16.453 27.246 20.457 1.00 0.00 H
-HETATM 609 H2 HOH A 203 16.350 28.446 19.539 1.00 0.00 H
-HETATM 610 O HOH A 204 17.492 25.537 20.478 1.00 0.00 O
-HETATM 611 H1 HOH A 204 18.040 25.652 21.255 1.00 0.00 H
-HETATM 612 H2 HOH A 204 18.112 25.429 19.758 1.00 0.00 H
-HETATM 613 O HOH A 205 0.384 27.732 22.887 1.00 0.00 O
-HETATM 614 H1 HOH A 205 0.103 28.041 23.748 1.00 0.00 H
-HETATM 615 H2 HOH A 205 0.149 26.804 22.876 1.00 0.00 H
-HETATM 616 O HOH A 206 -0.000 28.415 25.710 1.00 0.00 O
-HETATM 617 H1 HOH A 206 0.842 28.842 25.871 1.00 0.00 H
-HETATM 618 H2 HOH A 206 0.010 27.641 26.273 1.00 0.00 H
-HETATM 619 O HOH A 207 25.791 9.863 23.635 1.00 0.00 O
-HETATM 620 H1 HOH A 207 24.942 9.618 23.270 1.00 0.00 H
-HETATM 621 H2 HOH A 207 26.430 9.409 23.085 1.00 0.00 H
-HETATM 622 O HOH A 208 23.278 8.614 22.795 1.00 0.00 O
-HETATM 623 H1 HOH A 208 22.931 8.393 23.659 1.00 0.00 H
-HETATM 624 H2 HOH A 208 23.421 7.769 22.368 1.00 0.00 H
-HETATM 625 O HOH A 209 11.466 22.975 3.032 1.00 0.00 O
-HETATM 626 H1 HOH A 209 11.403 23.072 2.082 1.00 0.00 H
-HETATM 627 H2 HOH A 209 11.266 23.845 3.376 1.00 0.00 H
-HETATM 628 O HOH A 210 11.788 23.521 0.171 1.00 0.00 O
-HETATM 629 H1 HOH A 210 12.629 23.084 0.034 1.00 0.00 H
-HETATM 630 H2 HOH A 210 11.965 24.446 -0.001 1.00 0.00 H
-HETATM 631 O HOH A 211 15.835 19.922 26.081 1.00 0.00 O
-HETATM 632 H1 HOH A 211 15.371 20.461 26.721 1.00 0.00 H
-HETATM 633 H2 HOH A 211 15.142 19.470 25.600 1.00 0.00 H
-HETATM 634 O HOH A 212 14.351 21.077 28.328 1.00 0.00 O
-HETATM 635 H1 HOH A 212 14.913 20.774 29.043 1.00 0.00 H
-HETATM 636 H2 HOH A 212 13.515 20.634 28.473 1.00 0.00 H
-HETATM 637 O HOH A 213 22.537 4.162 15.557 1.00 0.00 O
-HETATM 638 H1 HOH A 213 22.941 3.805 14.767 1.00 0.00 H
-HETATM 639 H2 HOH A 213 22.694 3.499 16.229 1.00 0.00 H
-HETATM 640 O HOH A 214 23.303 2.736 13.115 1.00 0.00 O
-HETATM 641 H1 HOH A 214 22.522 2.919 12.590 1.00 0.00 H
-HETATM 642 H2 HOH A 214 23.297 1.787 13.235 1.00 0.00 H
-HETATM 643 O HOH A 215 3.861 12.999 14.673 1.00 0.00 O
-HETATM 644 H1 HOH A 215 3.370 13.473 15.345 1.00 0.00 H
-HETATM 645 H2 HOH A 215 4.710 13.440 14.640 1.00 0.00 H
-HETATM 646 O HOH A 216 2.289 14.846 16.317 1.00 0.00 O
-HETATM 647 H1 HOH A 216 1.522 14.927 15.749 1.00 0.00 H
-HETATM 648 H2 HOH A 216 2.669 15.724 16.336 1.00 0.00 H
-HETATM 649 O HOH A 217 15.810 13.518 21.847 1.00 0.00 O
-HETATM 650 H1 HOH A 217 16.488 14.153 21.618 1.00 0.00 H
-HETATM 651 H2 HOH A 217 15.472 13.820 22.690 1.00 0.00 H
-HETATM 652 O HOH A 218 18.206 15.175 21.532 1.00 0.00 O
-HETATM 653 H1 HOH A 218 18.834 14.483 21.324 1.00 0.00 H
-HETATM 654 H2 HOH A 218 18.508 15.534 22.366 1.00 0.00 H
-HETATM 655 O HOH A 219 9.820 6.403 21.283 1.00 0.00 O
-HETATM 656 H1 HOH A 219 9.057 6.159 21.806 1.00 0.00 H
-HETATM 657 H2 HOH A 219 9.519 6.341 20.376 1.00 0.00 H
-HETATM 658 O HOH A 220 7.622 5.119 22.734 1.00 0.00 O
-HETATM 659 H1 HOH A 220 8.135 4.508 23.263 1.00 0.00 H
-HETATM 660 H2 HOH A 220 7.092 4.561 22.165 1.00 0.00 H
-HETATM 661 O HOH A 221 6.909 7.937 14.295 1.00 0.00 O
-HETATM 662 H1 HOH A 221 6.983 8.363 13.441 1.00 0.00 H
-HETATM 663 H2 HOH A 221 7.798 7.952 14.649 1.00 0.00 H
-HETATM 664 O HOH A 222 7.359 8.736 11.512 1.00 0.00 O
-HETATM 665 H1 HOH A 222 6.873 8.034 11.080 1.00 0.00 H
-HETATM 666 H2 HOH A 222 8.267 8.604 11.242 1.00 0.00 H
-HETATM 667 O HOH A 223 6.284 13.490 29.853 1.00 0.00 O
-HETATM 668 H1 HOH A 223 5.430 13.059 29.857 1.00 0.00 H
-HETATM 669 H2 HOH A 223 6.258 14.069 29.092 1.00 0.00 H
-HETATM 670 O HOH A 224 3.892 11.849 29.441 1.00 0.00 O
-HETATM 671 H1 HOH A 224 4.289 10.984 29.552 1.00 0.00 H
-HETATM 672 H2 HOH A 224 3.597 11.864 28.531 1.00 0.00 H
-HETATM 673 O HOH A 225 9.697 13.757 8.227 1.00 0.00 O
-HETATM 674 H1 HOH A 225 10.428 14.333 8.005 1.00 0.00 H
-HETATM 675 H2 HOH A 225 9.613 13.831 9.178 1.00 0.00 H
-HETATM 676 O HOH A 226 12.261 15.089 7.746 1.00 0.00 O
-HETATM 677 H1 HOH A 226 12.676 14.414 7.207 1.00 0.00 H
-HETATM 678 H2 HOH A 226 12.799 15.132 8.536 1.00 0.00 H
-HETATM 679 O HOH A 227 19.115 23.034 12.695 1.00 0.00 O
-HETATM 680 H1 HOH A 227 19.198 23.785 13.282 1.00 0.00 H
-HETATM 681 H2 HOH A 227 18.693 22.359 13.226 1.00 0.00 H
-HETATM 682 O HOH A 228 19.812 25.039 14.715 1.00 0.00 O
-HETATM 683 H1 HOH A 228 20.733 25.164 14.483 1.00 0.00 H
-HETATM 684 H2 HOH A 228 19.833 24.669 15.597 1.00 0.00 H
-HETATM 685 O HOH A 229 2.320 26.551 8.725 1.00 0.00 O
-HETATM 686 H1 HOH A 229 2.829 25.859 8.303 1.00 0.00 H
-HETATM 687 H2 HOH A 229 1.878 26.114 9.453 1.00 0.00 H
-HETATM 688 O HOH A 230 3.370 24.275 7.208 1.00 0.00 O
-HETATM 689 H1 HOH A 230 3.048 24.519 6.340 1.00 0.00 H
-HETATM 690 H2 HOH A 230 2.912 23.461 7.416 1.00 0.00 H
-HETATM 691 O HOH A 231 14.324 23.521 6.821 1.00 0.00 O
-HETATM 692 H1 HOH A 231 14.851 23.428 6.028 1.00 0.00 H
-HETATM 693 H2 HOH A 231 14.714 24.263 7.284 1.00 0.00 H
-HETATM 694 O HOH A 232 16.322 22.991 4.745 1.00 0.00 O
-HETATM 695 H1 HOH A 232 16.425 22.049 4.881 1.00 0.00 H
-HETATM 696 H2 HOH A 232 17.176 23.363 4.964 1.00 0.00 H
-HETATM 697 O HOH A 233 18.185 8.776 7.544 1.00 0.00 O
-HETATM 698 H1 HOH A 233 17.735 9.579 7.285 1.00 0.00 H
-HETATM 699 H2 HOH A 233 17.666 8.433 8.271 1.00 0.00 H
-HETATM 700 O HOH A 234 17.059 11.461 7.216 1.00 0.00 O
-HETATM 701 H1 HOH A 234 17.876 11.961 7.216 1.00 0.00 H
-HETATM 702 H2 HOH A 234 16.596 11.755 8.000 1.00 0.00 H
-HETATM 703 O HOH A 235 1.501 22.598 21.725 1.00 0.00 O
-HETATM 704 H1 HOH A 235 2.245 23.127 22.010 1.00 0.00 H
-HETATM 705 H2 HOH A 235 1.569 22.581 20.770 1.00 0.00 H
-HETATM 706 O HOH A 236 3.455 24.664 22.430 1.00 0.00 O
-HETATM 707 H1 HOH A 236 2.903 25.194 23.006 1.00 0.00 H
-HETATM 708 H2 HOH A 236 3.655 25.237 21.691 1.00 0.00 H
-HETATM 709 O HOH A 237 2.932 24.787 19.083 1.00 0.00 O
-HETATM 710 H1 HOH A 237 2.804 25.552 18.523 1.00 0.00 H
-HETATM 711 H2 HOH A 237 2.339 24.928 19.822 1.00 0.00 H
-HETATM 712 O HOH A 238 2.869 27.399 17.757 1.00 0.00 O
-HETATM 713 H1 HOH A 238 3.813 27.544 17.679 1.00 0.00 H
-HETATM 714 H2 HOH A 238 2.565 28.093 18.342 1.00 0.00 H
-HETATM 715 O HOH A 239 11.194 11.595 16.052 1.00 0.00 O
-HETATM 716 H1 HOH A 239 11.813 12.310 15.905 1.00 0.00 H
-HETATM 717 H2 HOH A 239 11.270 11.047 15.271 1.00 0.00 H
-HETATM 718 O HOH A 240 12.681 13.989 15.250 1.00 0.00 O
-HETATM 719 H1 HOH A 240 12.029 14.650 15.486 1.00 0.00 H
-HETATM 720 H2 HOH A 240 12.764 14.060 14.300 1.00 0.00 H
-HETATM 721 O HOH A 241 25.336 16.298 19.383 1.00 0.00 O
-HETATM 722 H1 HOH A 241 24.616 16.553 19.960 1.00 0.00 H
-HETATM 723 H2 HOH A 241 24.909 16.015 18.575 1.00 0.00 H
-HETATM 724 O HOH A 242 23.064 16.510 21.221 1.00 0.00 O
-HETATM 725 H1 HOH A 242 23.420 16.000 21.950 1.00 0.00 H
-HETATM 726 H2 HOH A 242 22.309 16.006 20.919 1.00 0.00 H
-HETATM 727 O HOH A 243 1.774 29.045 2.649 1.00 0.00 O
-HETATM 728 H1 HOH A 243 2.669 28.758 2.833 1.00 0.00 H
-HETATM 729 H2 HOH A 243 1.830 30.000 2.617 1.00 0.00 H
-HETATM 730 O HOH A 244 4.392 28.327 3.751 1.00 0.00 O
-HETATM 731 H1 HOH A 244 4.099 27.743 4.451 1.00 0.00 H
-HETATM 732 H2 HOH A 244 4.806 29.061 4.205 1.00 0.00 H
-HETATM 733 O HOH A 245 16.569 27.266 6.986 1.00 0.00 O
-HETATM 734 H1 HOH A 245 16.482 26.377 7.330 1.00 0.00 H
-HETATM 735 H2 HOH A 245 16.423 27.174 6.044 1.00 0.00 H
-HETATM 736 O HOH A 246 16.835 24.472 7.826 1.00 0.00 O
-HETATM 737 H1 HOH A 246 17.648 24.548 8.327 1.00 0.00 H
-HETATM 738 H2 HOH A 246 17.055 23.895 7.095 1.00 0.00 H
-HETATM 739 O HOH A 247 15.176 5.598 11.031 1.00 0.00 O
-HETATM 740 H1 HOH A 247 15.005 4.717 10.701 1.00 0.00 H
-HETATM 741 H2 HOH A 247 14.349 5.872 11.427 1.00 0.00 H
-HETATM 742 O HOH A 248 14.381 3.173 9.593 1.00 0.00 O
-HETATM 743 H1 HOH A 248 14.761 3.381 8.738 1.00 0.00 H
-HETATM 744 H2 HOH A 248 13.437 3.140 9.436 1.00 0.00 H
-HETATM 745 O HOH A 249 28.455 28.705 7.190 1.00 0.00 O
-HETATM 746 H1 HOH A 249 28.054 27.926 7.577 1.00 0.00 H
-HETATM 747 H2 HOH A 249 29.299 28.787 7.634 1.00 0.00 H
-HETATM 748 O HOH A 250 27.114 26.671 8.819 1.00 0.00 O
-HETATM 749 H1 HOH A 250 26.285 27.128 8.967 1.00 0.00 H
-HETATM 750 H2 HOH A 250 27.505 26.592 9.689 1.00 0.00 H
-HETATM 751 O HOH A 251 18.415 14.251 28.405 1.00 0.00 O
-HETATM 752 H1 HOH A 251 19.152 13.973 28.947 1.00 0.00 H
-HETATM 753 H2 HOH A 251 18.542 13.793 27.574 1.00 0.00 H
-HETATM 754 O HOH A 252 20.974 13.774 29.749 1.00 0.00 O
-HETATM 755 H1 HOH A 252 21.176 14.676 30.000 1.00 0.00 H
-HETATM 756 H2 HOH A 252 21.666 13.535 29.132 1.00 0.00 H
-HETATM 757 O HOH A 253 20.062 20.314 16.346 1.00 0.00 O
-HETATM 758 H1 HOH A 253 20.695 19.602 16.264 1.00 0.00 H
-HETATM 759 H2 HOH A 253 20.014 20.695 15.469 1.00 0.00 H
-HETATM 760 O HOH A 254 22.393 18.555 16.109 1.00 0.00 O
-HETATM 761 H1 HOH A 254 22.871 18.843 16.888 1.00 0.00 H
-HETATM 762 H2 HOH A 254 22.956 18.800 15.376 1.00 0.00 H
-HETATM 763 O HOH A 255 23.681 23.160 2.801 1.00 0.00 O
-HETATM 764 H1 HOH A 255 23.981 23.694 2.066 1.00 0.00 H
-HETATM 765 H2 HOH A 255 22.879 22.744 2.486 1.00 0.00 H
-HETATM 766 O HOH A 256 24.182 25.157 0.717 1.00 0.00 O
-HETATM 767 H1 HOH A 256 24.307 25.923 1.279 1.00 0.00 H
-HETATM 768 H2 HOH A 256 23.394 25.354 0.211 1.00 0.00 H
-HETATM 769 O HOH A 257 2.153 15.801 10.823 1.00 0.00 O
-HETATM 770 H1 HOH A 257 2.514 16.225 11.601 1.00 0.00 H
-HETATM 771 H2 HOH A 257 2.767 16.019 10.123 1.00 0.00 H
-HETATM 772 O HOH A 258 3.039 17.581 12.975 1.00 0.00 O
-HETATM 773 H1 HOH A 258 2.191 17.965 13.203 1.00 0.00 H
-HETATM 774 H2 HOH A 258 3.556 18.315 12.646 1.00 0.00 H
-HETATM 775 O HOH A 259 22.907 9.720 14.034 1.00 0.00 O
-HETATM 776 H1 HOH A 259 23.417 10.258 13.430 1.00 0.00 H
-HETATM 777 H2 HOH A 259 22.341 9.193 13.469 1.00 0.00 H
-HETATM 778 O HOH A 260 23.987 11.682 12.146 1.00 0.00 O
-HETATM 779 H1 HOH A 260 23.802 12.479 12.644 1.00 0.00 H
-HETATM 780 H2 HOH A 260 23.449 11.758 11.358 1.00 0.00 H
-HETATM 781 O HOH A 261 10.601 13.782 17.910 1.00 0.00 O
-HETATM 782 H1 HOH A 261 10.612 13.825 16.954 1.00 0.00 H
-HETATM 783 H2 HOH A 261 10.795 12.867 18.111 1.00 0.00 H
-HETATM 784 O HOH A 262 10.114 13.671 15.023 1.00 0.00 O
-HETATM 785 H1 HOH A 262 9.279 14.140 14.999 1.00 0.00 H
-HETATM 786 H2 HOH A 262 9.902 12.785 14.727 1.00 0.00 H
-HETATM 787 O HOH A 263 8.827 17.684 5.049 1.00 0.00 O
-HETATM 788 H1 HOH A 263 9.085 18.121 4.237 1.00 0.00 H
-HETATM 789 H2 HOH A 263 9.129 18.269 5.743 1.00 0.00 H
-HETATM 790 O HOH A 264 10.143 18.821 2.691 1.00 0.00 O
-HETATM 791 H1 HOH A 264 10.529 18.018 2.340 1.00 0.00 H
-HETATM 792 H2 HOH A 264 10.892 19.368 2.925 1.00 0.00 H
-HETATM 793 O HOH A 265 11.640 23.811 25.899 1.00 0.00 O
-HETATM 794 H1 HOH A 265 11.467 24.739 25.742 1.00 0.00 H
-HETATM 795 H2 HOH A 265 12.567 23.700 25.688 1.00 0.00 H
-HETATM 796 O HOH A 266 11.106 26.499 24.863 1.00 0.00 O
-HETATM 797 H1 HOH A 266 10.322 26.302 24.349 1.00 0.00 H
-HETATM 798 H2 HOH A 266 11.757 26.772 24.217 1.00 0.00 H
-HETATM 799 O HOH A 267 8.437 5.791 7.271 1.00 0.00 O
-HETATM 800 H1 HOH A 267 7.976 4.969 7.105 1.00 0.00 H
-HETATM 801 H2 HOH A 267 9.169 5.546 7.837 1.00 0.00 H
-HETATM 802 O HOH A 268 6.818 3.348 7.294 1.00 0.00 O
-HETATM 803 H1 HOH A 268 5.970 3.739 7.506 1.00 0.00 H
-HETATM 804 H2 HOH A 268 7.022 2.789 8.043 1.00 0.00 H
-HETATM 805 O HOH A 269 24.587 1.636 18.063 1.00 0.00 O
-HETATM 806 H1 HOH A 269 25.502 1.907 17.989 1.00 0.00 H
-HETATM 807 H2 HOH A 269 24.434 1.559 19.005 1.00 0.00 H
-HETATM 808 O HOH A 270 27.501 1.913 17.949 1.00 0.00 O
-HETATM 809 H1 HOH A 270 27.685 1.242 17.291 1.00 0.00 H
-HETATM 810 H2 HOH A 270 27.925 1.593 18.745 1.00 0.00 H
-HETATM 811 O HOH A 271 21.433 22.378 27.886 1.00 0.00 O
-HETATM 812 H1 HOH A 271 20.658 22.782 27.498 1.00 0.00 H
-HETATM 813 H2 HOH A 271 21.111 21.954 28.681 1.00 0.00 H
-HETATM 814 O HOH A 272 19.133 24.016 27.105 1.00 0.00 O
-HETATM 815 H1 HOH A 272 19.586 24.852 26.983 1.00 0.00 H
-HETATM 816 H2 HOH A 272 18.535 24.169 27.836 1.00 0.00 H
-HETATM 817 O HOH A 273 9.698 16.993 0.579 1.00 0.00 O
-HETATM 818 H1 HOH A 273 8.776 16.864 0.800 1.00 0.00 H
-HETATM 819 H2 HOH A 273 9.702 17.755 -0.001 1.00 0.00 H
-HETATM 820 O HOH A 274 6.840 16.367 0.729 1.00 0.00 O
-HETATM 821 H1 HOH A 274 6.887 15.432 0.529 1.00 0.00 H
-HETATM 822 H2 HOH A 274 6.350 16.746 -0.001 1.00 0.00 H
-HETATM 823 O HOH A 275 27.640 1.351 27.201 1.00 0.00 O
-HETATM 824 H1 HOH A 275 27.044 1.091 26.499 1.00 0.00 H
-HETATM 825 H2 HOH A 275 27.566 0.652 27.851 1.00 0.00 H
-HETATM 826 O HOH A 276 25.402 0.744 25.410 1.00 0.00 O
-HETATM 827 H1 HOH A 276 25.001 1.613 25.381 1.00 0.00 H
-HETATM 828 H2 HOH A 276 24.746 0.188 25.829 1.00 0.00 H
-HETATM 829 O HOH A 277 12.208 13.594 23.941 1.00 0.00 O
-HETATM 830 H1 HOH A 277 11.292 13.672 24.206 1.00 0.00 H
-HETATM 831 H2 HOH A 277 12.471 12.723 24.237 1.00 0.00 H
-HETATM 832 O HOH A 278 9.623 13.929 25.279 1.00 0.00 O
-HETATM 833 H1 HOH A 278 9.772 14.791 25.668 1.00 0.00 H
-HETATM 834 H2 HOH A 278 9.525 13.339 26.026 1.00 0.00 H
-HETATM 835 O HOH A 279 23.647 7.573 18.993 1.00 0.00 O
-HETATM 836 H1 HOH A 279 24.596 7.463 19.058 1.00 0.00 H
-HETATM 837 H2 HOH A 279 23.339 7.551 19.899 1.00 0.00 H
-HETATM 838 O HOH A 280 26.423 6.680 19.280 1.00 0.00 O
-HETATM 839 H1 HOH A 280 26.426 5.999 18.606 1.00 0.00 H
-HETATM 840 H2 HOH A 280 26.567 6.209 20.100 1.00 0.00 H
-HETATM 841 O HOH A 281 8.505 26.962 9.236 1.00 0.00 O
-HETATM 842 H1 HOH A 281 9.118 26.228 9.212 1.00 0.00 H
-HETATM 843 H2 HOH A 281 8.326 27.097 10.166 1.00 0.00 H
-HETATM 844 O HOH A 282 9.915 24.394 9.260 1.00 0.00 O
-HETATM 845 H1 HOH A 282 9.405 23.943 8.586 1.00 0.00 H
-HETATM 846 H2 HOH A 282 9.748 23.899 10.062 1.00 0.00 H
-HETATM 847 O HOH A 283 4.780 19.683 10.363 1.00 0.00 O
-HETATM 848 H1 HOH A 283 5.409 19.082 10.762 1.00 0.00 H
-HETATM 849 H2 HOH A 283 4.782 19.451 9.435 1.00 0.00 H
-HETATM 850 O HOH A 284 7.100 18.196 11.358 1.00 0.00 O
-HETATM 851 H1 HOH A 284 7.540 18.908 11.823 1.00 0.00 H
-HETATM 852 H2 HOH A 284 7.704 17.957 10.655 1.00 0.00 H
-HETATM 853 O HOH A 285 15.900 23.458 25.893 1.00 0.00 O
-HETATM 854 H1 HOH A 285 15.329 23.981 25.331 1.00 0.00 H
-HETATM 855 H2 HOH A 285 15.312 23.071 26.541 1.00 0.00 H
-HETATM 856 O HOH A 286 14.162 25.436 24.609 1.00 0.00 O
-HETATM 857 H1 HOH A 286 14.740 26.197 24.669 1.00 0.00 H
-HETATM 858 H2 HOH A 286 13.408 25.662 25.155 1.00 0.00 H
-HETATM 859 O HOH A 287 5.833 14.639 23.629 1.00 0.00 O
-HETATM 860 H1 HOH A 287 5.092 14.867 24.190 1.00 0.00 H
-HETATM 861 H2 HOH A 287 5.630 15.044 22.786 1.00 0.00 H
-HETATM 862 O HOH A 288 3.290 14.918 25.057 1.00 0.00 O
-HETATM 863 H1 HOH A 288 3.191 14.013 25.354 1.00 0.00 H
-HETATM 864 H2 HOH A 288 2.556 15.059 24.461 1.00 0.00 H
-HETATM 865 O HOH A 289 1.993 21.572 1.364 1.00 0.00 O
-HETATM 866 H1 HOH A 289 2.733 22.178 1.359 1.00 0.00 H
-HETATM 867 H2 HOH A 289 2.173 20.967 0.644 1.00 0.00 H
-HETATM 868 O HOH A 290 3.989 23.666 0.900 1.00 0.00 O
-HETATM 869 H1 HOH A 290 3.411 24.429 0.943 1.00 0.00 H
-HETATM 870 H2 HOH A 290 4.315 23.660 0.000 1.00 0.00 H
-HETATM 871 O HOH A 291 14.877 15.104 27.481 1.00 0.00 O
-HETATM 872 H1 HOH A 291 14.949 14.220 27.123 1.00 0.00 H
-HETATM 873 H2 HOH A 291 14.192 15.040 28.146 1.00 0.00 H
-HETATM 874 O HOH A 292 14.616 12.533 26.100 1.00 0.00 O
-HETATM 875 H1 HOH A 292 14.581 12.856 25.199 1.00 0.00 H
-HETATM 876 H2 HOH A 292 13.760 12.131 26.247 1.00 0.00 H
-HETATM 877 O HOH A 293 14.140 19.228 20.295 1.00 0.00 O
-HETATM 878 H1 HOH A 293 14.478 20.075 20.585 1.00 0.00 H
-HETATM 879 H2 HOH A 293 13.505 19.443 19.612 1.00 0.00 H
-HETATM 880 O HOH A 294 14.674 21.859 21.468 1.00 0.00 O
-HETATM 881 H1 HOH A 294 14.545 21.635 22.390 1.00 0.00 H
-HETATM 882 H2 HOH A 294 13.980 22.488 21.272 1.00 0.00 H
-HETATM 883 O HOH A 295 5.310 29.553 18.554 1.00 0.00 O
-HETATM 884 H1 HOH A 295 5.078 28.748 19.016 1.00 0.00 H
-HETATM 885 H2 HOH A 295 4.953 29.437 17.674 1.00 0.00 H
-HETATM 886 O HOH A 296 5.039 26.859 19.674 1.00 0.00 O
-HETATM 887 H1 HOH A 296 5.931 26.749 20.003 1.00 0.00 H
-HETATM 888 H2 HOH A 296 4.949 26.194 18.992 1.00 0.00 H
-HETATM 889 O HOH A 297 14.165 25.587 10.050 1.00 0.00 O
-HETATM 890 H1 HOH A 297 14.972 25.821 10.508 1.00 0.00 H
-HETATM 891 H2 HOH A 297 14.445 25.381 9.159 1.00 0.00 H
-HETATM 892 O HOH A 298 16.581 26.806 11.173 1.00 0.00 O
-HETATM 893 H1 HOH A 298 16.187 27.601 11.535 1.00 0.00 H
-HETATM 894 H2 HOH A 298 17.176 27.119 10.493 1.00 0.00 H
-HETATM 895 O HOH A 299 25.387 27.076 10.974 1.00 0.00 O
-HETATM 896 H1 HOH A 299 25.718 26.180 10.905 1.00 0.00 H
-HETATM 897 H2 HOH A 299 26.169 27.611 11.110 1.00 0.00 H
-HETATM 898 O HOH A 300 26.445 24.367 11.335 1.00 0.00 O
-HETATM 899 H1 HOH A 300 25.797 24.041 11.960 1.00 0.00 H
-HETATM 900 H2 HOH A 300 27.271 24.365 11.819 1.00 0.00 H
-HETATM 901 O HOH A 301 5.775 21.278 11.516 1.00 0.00 O
-HETATM 902 H1 HOH A 301 5.663 21.677 10.654 1.00 0.00 H
-HETATM 903 H2 HOH A 301 6.677 20.956 11.516 1.00 0.00 H
-HETATM 904 O HOH A 302 5.380 21.956 8.693 1.00 0.00 O
-HETATM 905 H1 HOH A 302 4.553 21.494 8.548 1.00 0.00 H
-HETATM 906 H2 HOH A 302 6.008 21.511 8.125 1.00 0.00 H
-HETATM 907 O HOH A 303 25.081 3.484 26.816 1.00 0.00 O
-HETATM 908 H1 HOH A 303 24.479 3.881 26.187 1.00 0.00 H
-HETATM 909 H2 HOH A 303 25.463 4.226 27.285 1.00 0.00 H
-HETATM 910 O HOH A 304 23.716 4.818 24.593 1.00 0.00 O
-HETATM 911 H1 HOH A 304 24.064 4.291 23.872 1.00 0.00 H
-HETATM 912 H2 HOH A 304 24.058 5.698 24.436 1.00 0.00 H
-HETATM 913 O HOH A 305 18.074 25.928 5.284 1.00 0.00 O
-HETATM 914 H1 HOH A 305 18.281 25.106 5.728 1.00 0.00 H
-HETATM 915 H2 HOH A 305 17.908 25.676 4.376 1.00 0.00 H
-HETATM 916 O HOH A 306 19.223 23.470 6.390 1.00 0.00 O
-HETATM 917 H1 HOH A 306 20.002 23.847 6.800 1.00 0.00 H
-HETATM 918 H2 HOH A 306 19.564 22.893 5.707 1.00 0.00 H
-HETATM 919 O HOH A 307 14.673 9.139 11.749 1.00 0.00 O
-HETATM 920 H1 HOH A 307 14.034 9.553 11.169 1.00 0.00 H
-HETATM 921 H2 HOH A 307 14.169 8.894 12.526 1.00 0.00 H
-HETATM 922 O HOH A 308 12.778 10.859 10.323 1.00 0.00 O
-HETATM 923 H1 HOH A 308 13.354 11.608 10.164 1.00 0.00 H
-HETATM 924 H2 HOH A 308 12.094 11.198 10.900 1.00 0.00 H
-HETATM 925 O HOH A 309 21.686 28.790 7.386 1.00 0.00 O
-HETATM 926 H1 HOH A 309 22.464 28.261 7.560 1.00 0.00 H
-HETATM 927 H2 HOH A 309 21.531 29.266 8.201 1.00 0.00 H
-HETATM 928 O HOH A 310 23.704 26.832 8.209 1.00 0.00 O
-HETATM 929 H1 HOH A 310 23.290 26.042 7.861 1.00 0.00 H
-HETATM 930 H2 HOH A 310 23.714 26.702 9.157 1.00 0.00 H
-HETATM 931 O HOH A 311 -0.000 12.212 7.230 1.00 0.00 O
-HETATM 932 H1 HOH A 311 0.380 12.378 8.092 1.00 0.00 H
-HETATM 933 H2 HOH A 311 0.506 12.763 6.634 1.00 0.00 H
-HETATM 934 O HOH A 312 0.862 13.210 9.847 1.00 0.00 O
-HETATM 935 H1 HOH A 312 -0.000 13.346 10.240 1.00 0.00 H
-HETATM 936 H2 HOH A 312 1.244 14.085 9.790 1.00 0.00 H
-HETATM 937 O HOH A 313 29.521 12.933 9.960 1.00 0.00 O
-HETATM 938 H1 HOH A 313 29.007 13.661 9.611 1.00 0.00 H
-HETATM 939 H2 HOH A 313 30.000 13.309 10.699 1.00 0.00 H
-HETATM 940 O HOH A 314 28.450 15.328 8.657 1.00 0.00 O
-HETATM 941 H1 HOH A 314 28.732 15.143 7.760 1.00 0.00 H
-HETATM 942 H2 HOH A 314 28.931 16.118 8.903 1.00 0.00 H
-HETATM 943 O HOH A 315 7.536 21.433 5.722 1.00 0.00 O
-HETATM 944 H1 HOH A 315 6.817 22.002 5.447 1.00 0.00 H
-HETATM 945 H2 HOH A 315 7.540 20.722 5.080 1.00 0.00 H
-HETATM 946 O HOH A 316 5.000 22.771 5.121 1.00 0.00 O
-HETATM 947 H1 HOH A 316 4.642 22.808 6.008 1.00 0.00 H
-HETATM 948 H2 HOH A 316 4.398 22.203 4.640 1.00 0.00 H
-HETATM 949 O HOH A 317 17.552 19.820 18.087 1.00 0.00 O
-HETATM 950 H1 HOH A 317 17.504 18.864 18.085 1.00 0.00 H
-HETATM 951 H2 HOH A 317 18.125 20.033 17.351 1.00 0.00 H
-HETATM 952 O HOH A 318 17.928 16.924 18.321 1.00 0.00 O
-HETATM 953 H1 HOH A 318 18.111 16.878 19.260 1.00 0.00 H
-HETATM 954 H2 HOH A 318 18.737 16.631 17.903 1.00 0.00 H
-HETATM 955 O HOH A 319 10.210 18.976 17.765 1.00 0.00 O
-HETATM 956 H1 HOH A 319 9.973 19.875 17.539 1.00 0.00 H
-HETATM 957 H2 HOH A 319 10.705 19.056 18.581 1.00 0.00 H
-HETATM 958 O HOH A 320 10.031 21.768 16.895 1.00 0.00 O
-HETATM 959 H1 HOH A 320 10.331 21.657 15.993 1.00 0.00 H
-HETATM 960 H2 HOH A 320 10.698 22.317 17.307 1.00 0.00 H
-HETATM 961 O HOH A 321 29.520 3.840 2.758 1.00 0.00 O
-HETATM 962 H1 HOH A 321 29.418 2.889 2.803 1.00 0.00 H
-HETATM 963 H2 HOH A 321 29.750 4.099 3.651 1.00 0.00 H
-HETATM 964 O HOH A 322 28.698 1.053 3.136 1.00 0.00 O
-HETATM 965 H1 HOH A 322 27.860 1.086 2.673 1.00 0.00 H
-HETATM 966 H2 HOH A 322 28.465 0.866 4.045 1.00 0.00 H
-HETATM 967 O HOH A 323 7.441 13.759 9.520 1.00 0.00 O
-HETATM 968 H1 HOH A 323 7.737 13.939 10.412 1.00 0.00 H
-HETATM 969 H2 HOH A 323 6.936 12.950 9.593 1.00 0.00 H
-HETATM 970 O HOH A 324 8.737 13.896 12.144 1.00 0.00 O
-HETATM 971 H1 HOH A 324 9.637 14.044 11.853 1.00 0.00 H
-HETATM 972 H2 HOH A 324 8.760 13.043 12.579 1.00 0.00 H
-HETATM 973 O HOH A 325 14.640 10.130 26.495 1.00 0.00 O
-HETATM 974 H1 HOH A 325 14.068 9.870 25.773 1.00 0.00 H
-HETATM 975 H2 HOH A 325 15.461 9.666 26.331 1.00 0.00 H
-HETATM 976 O HOH A 326 12.830 8.844 24.584 1.00 0.00 O
-HETATM 977 H1 HOH A 326 12.109 8.643 25.181 1.00 0.00 H
-HETATM 978 H2 HOH A 326 13.154 7.990 24.300 1.00 0.00 H
-HETATM 979 O HOH A 327 22.551 27.781 5.415 1.00 0.00 O
-HETATM 980 H1 HOH A 327 23.428 28.132 5.569 1.00 0.00 H
-HETATM 981 H2 HOH A 327 22.680 26.836 5.338 1.00 0.00 H
-HETATM 982 O HOH A 328 25.306 28.774 5.318 1.00 0.00 O
-HETATM 983 H1 HOH A 328 25.194 29.445 4.644 1.00 0.00 H
-HETATM 984 H2 HOH A 328 25.905 28.137 4.929 1.00 0.00 H
-HETATM 985 O HOH A 329 21.298 15.829 24.054 1.00 0.00 O
-HETATM 986 H1 HOH A 329 21.276 16.644 24.556 1.00 0.00 H
-HETATM 987 H2 HOH A 329 21.374 16.110 23.142 1.00 0.00 H
-HETATM 988 O HOH A 330 20.682 18.353 25.410 1.00 0.00 O
-HETATM 989 H1 HOH A 330 19.919 18.065 25.911 1.00 0.00 H
-HETATM 990 H2 HOH A 330 20.340 19.012 24.806 1.00 0.00 H
-HETATM 991 O HOH A 331 15.842 6.689 1.983 1.00 0.00 O
-HETATM 992 H1 HOH A 331 15.516 5.831 1.714 1.00 0.00 H
-HETATM 993 H2 HOH A 331 15.074 7.259 1.954 1.00 0.00 H
-HETATM 994 O HOH A 332 14.860 4.285 0.627 1.00 0.00 O
-HETATM 995 H1 HOH A 332 15.575 4.171 0.000 1.00 0.00 H
-HETATM 996 H2 HOH A 332 14.091 4.465 0.086 1.00 0.00 H
-HETATM 997 O HOH A 333 12.810 6.342 13.087 1.00 0.00 O
-HETATM 998 H1 HOH A 333 12.287 5.972 13.798 1.00 0.00 H
-HETATM 999 H2 HOH A 333 13.089 7.197 13.415 1.00 0.00 H
-HETATM 1000 O HOH A 334 10.799 5.567 15.072 1.00 0.00 O
-HETATM 1001 H1 HOH A 334 10.089 5.330 14.474 1.00 0.00 H
-HETATM 1002 H2 HOH A 334 10.465 6.319 15.559 1.00 0.00 H
-HETATM 1003 O HOH A 335 5.185 25.164 23.949 1.00 0.00 O
-HETATM 1004 H1 HOH A 335 5.468 24.489 23.332 1.00 0.00 H
-HETATM 1005 H2 HOH A 335 5.480 24.849 24.803 1.00 0.00 H
-HETATM 1006 O HOH A 336 5.598 22.803 22.263 1.00 0.00 O
-HETATM 1007 H1 HOH A 336 4.734 22.768 21.850 1.00 0.00 H
-HETATM 1008 H2 HOH A 336 5.653 21.999 22.778 1.00 0.00 H
-HETATM 1009 O HOH A 337 18.885 10.080 23.913 1.00 0.00 O
-HETATM 1010 H1 HOH A 337 18.360 10.763 24.331 1.00 0.00 H
-HETATM 1011 H2 HOH A 337 19.106 10.439 23.053 1.00 0.00 H
-HETATM 1012 O HOH A 338 16.856 11.975 24.851 1.00 0.00 O
-HETATM 1013 H1 HOH A 338 16.181 11.347 25.109 1.00 0.00 H
-HETATM 1014 H2 HOH A 338 16.465 12.469 24.131 1.00 0.00 H
-HETATM 1015 O HOH A 339 14.688 19.475 9.151 1.00 0.00 O
-HETATM 1016 H1 HOH A 339 14.783 20.186 8.518 1.00 0.00 H
-HETATM 1017 H2 HOH A 339 15.403 19.609 9.773 1.00 0.00 H
-HETATM 1018 O HOH A 340 15.407 21.331 7.001 1.00 0.00 O
-HETATM 1019 H1 HOH A 340 15.331 20.729 6.259 1.00 0.00 H
-HETATM 1020 H2 HOH A 340 16.333 21.570 7.025 1.00 0.00 H
-HETATM 1021 O HOH A 341 3.381 4.488 28.302 1.00 0.00 O
-HETATM 1022 H1 HOH A 341 2.972 3.644 28.493 1.00 0.00 H
-HETATM 1023 H2 HOH A 341 3.127 4.681 27.400 1.00 0.00 H
-HETATM 1024 O HOH A 342 2.562 1.689 28.574 1.00 0.00 O
-HETATM 1025 H1 HOH A 342 3.377 1.333 28.929 1.00 0.00 H
-HETATM 1026 H2 HOH A 342 2.460 1.259 27.725 1.00 0.00 H
-HETATM 1027 O HOH A 343 23.080 18.838 19.478 1.00 0.00 O
-HETATM 1028 H1 HOH A 343 22.350 19.221 18.992 1.00 0.00 H
-HETATM 1029 H2 HOH A 343 22.979 17.894 19.358 1.00 0.00 H
-HETATM 1030 O HOH A 344 20.532 19.849 18.444 1.00 0.00 O
-HETATM 1031 H1 HOH A 344 20.291 20.413 19.180 1.00 0.00 H
-HETATM 1032 H2 HOH A 344 19.856 19.172 18.427 1.00 0.00 H
-HETATM 1033 O HOH A 345 28.915 25.182 7.639 1.00 0.00 O
-HETATM 1034 H1 HOH A 345 28.569 25.093 6.751 1.00 0.00 H
-HETATM 1035 H2 HOH A 345 28.210 24.866 8.204 1.00 0.00 H
-HETATM 1036 O HOH A 346 27.559 25.387 5.049 1.00 0.00 O
-HETATM 1037 H1 HOH A 346 27.821 26.279 4.819 1.00 0.00 H
-HETATM 1038 H2 HOH A 346 26.608 25.431 5.143 1.00 0.00 H
-HETATM 1039 O HOH A 347 24.538 8.263 8.606 1.00 0.00 O
-HETATM 1040 H1 HOH A 347 24.434 7.929 9.497 1.00 0.00 H
-HETATM 1041 H2 HOH A 347 24.929 7.537 8.121 1.00 0.00 H
-HETATM 1042 O HOH A 348 24.785 7.339 11.375 1.00 0.00 O
-HETATM 1043 H1 HOH A 348 25.185 8.122 11.754 1.00 0.00 H
-HETATM 1044 H2 HOH A 348 25.450 6.657 11.470 1.00 0.00 H
-HETATM 1045 O HOH A 349 25.236 5.444 6.122 1.00 0.00 O
-HETATM 1046 H1 HOH A 349 25.800 5.281 6.877 1.00 0.00 H
-HETATM 1047 H2 HOH A 349 25.553 6.271 5.758 1.00 0.00 H
-HETATM 1048 O HOH A 350 26.638 5.341 8.692 1.00 0.00 O
-HETATM 1049 H1 HOH A 350 25.888 5.156 9.259 1.00 0.00 H
-HETATM 1050 H2 HOH A 350 26.944 6.203 8.970 1.00 0.00 H
-HETATM 1051 O HOH A 351 11.311 23.975 19.629 1.00 0.00 O
-HETATM 1052 H1 HOH A 351 11.354 24.700 19.006 1.00 0.00 H
-HETATM 1053 H2 HOH A 351 12.196 23.914 19.989 1.00 0.00 H
-HETATM 1054 O HOH A 352 11.704 25.812 17.381 1.00 0.00 O
-HETATM 1055 H1 HOH A 352 11.273 25.298 16.697 1.00 0.00 H
-HETATM 1056 H2 HOH A 352 12.620 25.857 17.109 1.00 0.00 H
-HETATM 1057 O HOH A 353 3.620 9.954 0.199 1.00 0.00 O
-HETATM 1058 H1 HOH A 353 3.474 9.876 1.141 1.00 0.00 H
-HETATM 1059 H2 HOH A 353 4.499 10.324 0.122 1.00 0.00 H
-HETATM 1060 O HOH A 354 3.079 10.283 3.060 1.00 0.00 O
-HETATM 1061 H1 HOH A 354 2.220 10.701 2.997 1.00 0.00 H
-HETATM 1062 H2 HOH A 354 3.642 10.946 3.459 1.00 0.00 H
-HETATM 1063 O HOH A 355 23.539 29.100 23.971 1.00 0.00 O
-HETATM 1064 H1 HOH A 355 23.347 28.844 23.069 1.00 0.00 H
-HETATM 1065 H2 HOH A 355 23.312 28.331 24.493 1.00 0.00 H
-HETATM 1066 O HOH A 356 22.403 28.409 21.360 1.00 0.00 O
-HETATM 1067 H1 HOH A 356 21.915 29.217 21.197 1.00 0.00 H
-HETATM 1068 H2 HOH A 356 21.731 27.730 21.428 1.00 0.00 H
-HETATM 1069 O HOH A 357 9.148 11.844 10.493 1.00 0.00 O
-HETATM 1070 H1 HOH A 357 10.027 12.205 10.601 1.00 0.00 H
-HETATM 1071 H2 HOH A 357 9.212 11.279 9.723 1.00 0.00 H
-HETATM 1072 O HOH A 358 11.689 13.298 10.387 1.00 0.00 O
-HETATM 1073 H1 HOH A 358 11.350 14.189 10.480 1.00 0.00 H
-HETATM 1074 H2 HOH A 358 12.079 13.277 9.513 1.00 0.00 H
-HETATM 1075 O HOH A 359 28.181 10.214 29.095 1.00 0.00 O
-HETATM 1076 H1 HOH A 359 28.733 9.467 28.866 1.00 0.00 H
-HETATM 1077 H2 HOH A 359 28.584 10.578 29.883 1.00 0.00 H
-HETATM 1078 O HOH A 360 29.595 7.662 28.823 1.00 0.00 O
-HETATM 1079 H1 HOH A 360 28.837 7.087 28.721 1.00 0.00 H
-HETATM 1080 H2 HOH A 360 30.000 7.385 29.645 1.00 0.00 H
-HETATM 1081 O HOH A 361 17.369 24.375 17.354 1.00 0.00 O
-HETATM 1082 H1 HOH A 361 16.826 24.464 18.137 1.00 0.00 H
-HETATM 1083 H2 HOH A 361 16.744 24.233 16.642 1.00 0.00 H
-HETATM 1084 O HOH A 362 15.666 24.073 19.718 1.00 0.00 O
-HETATM 1085 H1 HOH A 362 16.206 23.450 20.207 1.00 0.00 H
-HETATM 1086 H2 HOH A 362 14.861 23.593 19.523 1.00 0.00 H
-HETATM 1087 O HOH A 363 8.609 6.458 16.809 1.00 0.00 O
-HETATM 1088 H1 HOH A 363 8.078 6.164 16.069 1.00 0.00 H
-HETATM 1089 H2 HOH A 363 8.832 7.365 16.598 1.00 0.00 H
-HETATM 1090 O HOH A 364 7.489 5.512 14.272 1.00 0.00 O
-HETATM 1091 H1 HOH A 364 8.000 4.707 14.180 1.00 0.00 H
-HETATM 1092 H2 HOH A 364 7.760 6.057 13.533 1.00 0.00 H
-HETATM 1093 O HOH A 365 6.212 4.163 25.978 1.00 0.00 O
-HETATM 1094 H1 HOH A 365 6.224 5.060 25.644 1.00 0.00 H
-HETATM 1095 H2 HOH A 365 5.732 4.222 26.804 1.00 0.00 H
-HETATM 1096 O HOH A 366 6.664 6.988 25.346 1.00 0.00 O
-HETATM 1097 H1 HOH A 366 7.605 6.949 25.174 1.00 0.00 H
-HETATM 1098 H2 HOH A 366 6.582 7.548 26.118 1.00 0.00 H
-HETATM 1099 O HOH A 367 27.604 9.241 26.979 1.00 0.00 O
-HETATM 1100 H1 HOH A 367 27.008 9.002 26.270 1.00 0.00 H
-HETATM 1101 H2 HOH A 367 28.476 9.033 26.641 1.00 0.00 H
-HETATM 1102 O HOH A 368 25.870 7.971 24.989 1.00 0.00 O
-HETATM 1103 H1 HOH A 368 25.300 7.483 25.584 1.00 0.00 H
-HETATM 1104 H2 HOH A 368 26.329 7.297 24.488 1.00 0.00 H
-HETATM 1105 O HOH A 369 6.786 23.102 25.247 1.00 0.00 O
-HETATM 1106 H1 HOH A 369 7.237 22.635 25.950 1.00 0.00 H
-HETATM 1107 H2 HOH A 369 6.941 22.570 24.467 1.00 0.00 H
-HETATM 1108 O HOH A 370 8.643 21.902 27.170 1.00 0.00 O
-HETATM 1109 H1 HOH A 370 9.073 22.702 27.474 1.00 0.00 H
-HETATM 1110 H2 HOH A 370 9.336 21.404 26.738 1.00 0.00 H
-HETATM 1111 O HOH A 371 18.620 13.516 5.182 1.00 0.00 O
-HETATM 1112 H1 HOH A 371 19.147 13.557 5.980 1.00 0.00 H
-HETATM 1113 H2 HOH A 371 17.737 13.757 5.465 1.00 0.00 H
-HETATM 1114 O HOH A 372 19.987 13.133 7.745 1.00 0.00 O
-HETATM 1115 H1 HOH A 372 20.375 12.274 7.575 1.00 0.00 H
-HETATM 1116 H2 HOH A 372 19.372 12.984 8.462 1.00 0.00 H
-HETATM 1117 O HOH A 373 12.881 15.810 0.920 1.00 0.00 O
-HETATM 1118 H1 HOH A 373 12.029 15.384 1.013 1.00 0.00 H
-HETATM 1119 H2 HOH A 373 12.678 16.681 0.579 1.00 0.00 H
-HETATM 1120 O HOH A 374 10.308 14.439 0.634 1.00 0.00 O
-HETATM 1121 H1 HOH A 374 10.613 13.650 0.185 1.00 0.00 H
-HETATM 1122 H2 HOH A 374 9.737 14.872 -0.001 1.00 0.00 H
-HETATM 1123 O HOH A 375 19.815 23.067 3.528 1.00 0.00 O
-HETATM 1124 H1 HOH A 375 20.269 23.880 3.747 1.00 0.00 H
-HETATM 1125 H2 HOH A 375 18.919 23.338 3.327 1.00 0.00 H
-HETATM 1126 O HOH A 376 20.932 25.507 4.702 1.00 0.00 O
-HETATM 1127 H1 HOH A 376 21.304 25.127 5.499 1.00 0.00 H
-HETATM 1128 H2 HOH A 376 20.250 26.102 5.014 1.00 0.00 H
-HETATM 1129 O HOH A 377 23.035 14.980 28.088 1.00 0.00 O
-HETATM 1130 H1 HOH A 377 22.612 15.681 27.592 1.00 0.00 H
-HETATM 1131 H2 HOH A 377 23.032 15.291 28.994 1.00 0.00 H
-HETATM 1132 O HOH A 378 22.248 17.393 26.623 1.00 0.00 O
-HETATM 1133 H1 HOH A 378 22.945 17.396 25.966 1.00 0.00 H
-HETATM 1134 H2 HOH A 378 22.394 18.188 27.135 1.00 0.00 H
-HETATM 1135 O HOH A 379 18.530 19.887 10.685 1.00 0.00 O
-HETATM 1136 H1 HOH A 379 18.725 20.641 10.130 1.00 0.00 H
-HETATM 1137 H2 HOH A 379 18.814 19.130 10.172 1.00 0.00 H
-HETATM 1138 O HOH A 380 18.651 22.039 8.701 1.00 0.00 O
-HETATM 1139 H1 HOH A 380 17.778 22.408 8.835 1.00 0.00 H
-HETATM 1140 H2 HOH A 380 18.637 21.704 7.805 1.00 0.00 H
-HETATM 1141 O HOH A 381 21.543 9.778 17.040 1.00 0.00 O
-HETATM 1142 H1 HOH A 381 20.658 9.980 17.343 1.00 0.00 H
-HETATM 1143 H2 HOH A 381 21.555 10.060 16.126 1.00 0.00 H
-HETATM 1144 O HOH A 382 18.687 9.921 17.676 1.00 0.00 O
-HETATM 1145 H1 HOH A 382 18.580 9.049 18.058 1.00 0.00 H
-HETATM 1146 H2 HOH A 382 18.142 9.909 16.890 1.00 0.00 H
-HETATM 1147 O HOH A 383 29.230 10.517 17.184 1.00 0.00 O
-HETATM 1148 H1 HOH A 383 28.788 10.672 16.350 1.00 0.00 H
-HETATM 1149 H2 HOH A 383 30.000 9.997 16.955 1.00 0.00 H
-HETATM 1150 O HOH A 384 27.695 10.448 14.689 1.00 0.00 O
-HETATM 1151 H1 HOH A 384 26.848 10.182 15.048 1.00 0.00 H
-HETATM 1152 H2 HOH A 384 27.958 9.722 14.125 1.00 0.00 H
-HETATM 1153 O HOH A 385 8.208 24.331 24.103 1.00 0.00 O
-HETATM 1154 H1 HOH A 385 8.673 24.620 23.318 1.00 0.00 H
-HETATM 1155 H2 HOH A 385 7.614 25.053 24.310 1.00 0.00 H
-HETATM 1156 O HOH A 386 9.936 25.539 22.069 1.00 0.00 O
-HETATM 1157 H1 HOH A 386 10.785 25.305 22.443 1.00 0.00 H
-HETATM 1158 H2 HOH A 386 9.913 26.496 22.099 1.00 0.00 H
-HETATM 1159 O HOH A 387 22.043 2.012 9.095 1.00 0.00 O
-HETATM 1160 H1 HOH A 387 21.241 2.465 8.837 1.00 0.00 H
-HETATM 1161 H2 HOH A 387 22.318 2.449 9.901 1.00 0.00 H
-HETATM 1162 O HOH A 388 19.946 3.811 8.121 1.00 0.00 O
-HETATM 1163 H1 HOH A 388 20.252 3.916 7.219 1.00 0.00 H
-HETATM 1164 H2 HOH A 388 20.016 4.686 8.503 1.00 0.00 H
-HETATM 1165 O HOH A 389 24.384 28.693 17.111 1.00 0.00 O
-HETATM 1166 H1 HOH A 389 24.763 28.226 16.366 1.00 0.00 H
-HETATM 1167 H2 HOH A 389 24.358 29.609 16.832 1.00 0.00 H
-HETATM 1168 O HOH A 390 26.043 27.479 15.023 1.00 0.00 O
-HETATM 1169 H1 HOH A 390 26.666 27.016 15.585 1.00 0.00 H
-HETATM 1170 H2 HOH A 390 26.572 28.128 14.560 1.00 0.00 H
-HETATM 1171 O HOH A 391 30.000 22.320 8.991 1.00 0.00 O
-HETATM 1172 H1 HOH A 391 29.617 21.564 9.436 1.00 0.00 H
-HETATM 1173 H2 HOH A 391 29.508 22.392 8.173 1.00 0.00 H
-HETATM 1174 O HOH A 392 29.139 19.699 9.978 1.00 0.00 O
-HETATM 1175 H1 HOH A 392 30.001 19.321 10.155 1.00 0.00 H
-HETATM 1176 H2 HOH A 392 28.774 19.154 9.282 1.00 0.00 H
-HETATM 1177 O HOH A 393 19.149 12.219 0.837 1.00 0.00 O
-HETATM 1178 H1 HOH A 393 19.852 12.561 1.388 1.00 0.00 H
-HETATM 1179 H2 HOH A 393 19.571 12.025 -0.000 1.00 0.00 H
-HETATM 1180 O HOH A 394 21.228 13.751 2.220 1.00 0.00 O
-HETATM 1181 H1 HOH A 394 20.698 14.501 2.491 1.00 0.00 H
-HETATM 1182 H2 HOH A 394 21.871 14.116 1.612 1.00 0.00 H
-HETATM 1183 O HOH A 395 18.687 17.442 15.339 1.00 0.00 O
-HETATM 1184 H1 HOH A 395 18.793 16.564 15.705 1.00 0.00 H
-HETATM 1185 H2 HOH A 395 18.345 17.295 14.457 1.00 0.00 H
-HETATM 1186 O HOH A 396 19.485 14.736 16.132 1.00 0.00 O
-HETATM 1187 H1 HOH A 396 20.374 14.930 16.432 1.00 0.00 H
-HETATM 1188 H2 HOH A 396 19.604 14.160 15.378 1.00 0.00 H
-HETATM 1189 O HOH A 397 23.860 10.144 16.007 1.00 0.00 O
-HETATM 1190 H1 HOH A 397 24.527 9.659 15.521 1.00 0.00 H
-HETATM 1191 H2 HOH A 397 24.316 10.465 16.785 1.00 0.00 H
-HETATM 1192 O HOH A 398 25.808 8.249 14.911 1.00 0.00 O
-HETATM 1193 H1 HOH A 398 25.227 7.496 14.795 1.00 0.00 H
-HETATM 1194 H2 HOH A 398 26.444 7.966 15.567 1.00 0.00 H
-HETATM 1195 O HOH A 399 23.983 0.447 3.819 1.00 0.00 O
-HETATM 1196 H1 HOH A 399 23.228 0.799 3.347 1.00 0.00 H
-HETATM 1197 H2 HOH A 399 23.606 0.000 4.576 1.00 0.00 H
-HETATM 1198 O HOH A 400 21.676 1.929 2.786 1.00 0.00 O
-HETATM 1199 H1 HOH A 400 22.082 2.793 2.711 1.00 0.00 H
-HETATM 1200 H2 HOH A 400 20.993 2.039 3.448 1.00 0.00 H
-HETATM 1201 O HOH A 401 19.861 18.516 7.416 1.00 0.00 O
-HETATM 1202 H1 HOH A 401 19.308 18.031 6.804 1.00 0.00 H
-HETATM 1203 H2 HOH A 401 19.795 18.030 8.238 1.00 0.00 H
-HETATM 1204 O HOH A 402 17.733 17.289 5.819 1.00 0.00 O
-HETATM 1205 H1 HOH A 402 17.290 18.088 5.530 1.00 0.00 H
-HETATM 1206 H2 HOH A 402 17.089 16.838 6.363 1.00 0.00 H
-HETATM 1207 O HOH A 403 8.030 15.904 20.493 1.00 0.00 O
-HETATM 1208 H1 HOH A 403 8.533 16.704 20.640 1.00 0.00 H
-HETATM 1209 H2 HOH A 403 8.009 15.805 19.541 1.00 0.00 H
-HETATM 1210 O HOH A 404 9.083 18.622 20.791 1.00 0.00 O
-HETATM 1211 H1 HOH A 404 8.425 18.966 21.396 1.00 0.00 H
-HETATM 1212 H2 HOH A 404 8.973 19.140 19.994 1.00 0.00 H
-HETATM 1213 O HOH A 405 21.215 0.051 22.905 1.00 0.00 O
-HETATM 1214 H1 HOH A 405 20.523 0.711 22.903 1.00 0.00 H
-HETATM 1215 H2 HOH A 405 21.681 0.195 23.729 1.00 0.00 H
-HETATM 1216 O HOH A 406 19.518 2.435 22.752 1.00 0.00 O
-HETATM 1217 H1 HOH A 406 19.733 2.707 21.859 1.00 0.00 H
-HETATM 1218 H2 HOH A 406 19.843 3.144 23.306 1.00 0.00 H
-HETATM 1219 O HOH A 407 27.419 17.370 13.967 1.00 0.00 O
-HETATM 1220 H1 HOH A 407 27.929 18.041 13.514 1.00 0.00 H
-HETATM 1221 H2 HOH A 407 27.381 17.668 14.876 1.00 0.00 H
-HETATM 1222 O HOH A 408 29.434 19.167 12.828 1.00 0.00 O
-HETATM 1223 H1 HOH A 408 29.993 18.510 12.410 1.00 0.00 H
-HETATM 1224 H2 HOH A 408 29.986 19.562 13.502 1.00 0.00 H
-HETATM 1225 O HOH A 409 8.650 21.015 2.328 1.00 0.00 O
-HETATM 1226 H1 HOH A 409 7.843 21.110 2.834 1.00 0.00 H
-HETATM 1227 H2 HOH A 409 8.471 21.466 1.503 1.00 0.00 H
-HETATM 1228 O HOH A 410 5.982 20.888 3.532 1.00 0.00 O
-HETATM 1229 H1 HOH A 410 5.965 19.956 3.749 1.00 0.00 H
-HETATM 1230 H2 HOH A 410 5.288 20.999 2.883 1.00 0.00 H
-HETATM 1231 O HOH A 411 11.115 26.463 0.219 1.00 0.00 O
-HETATM 1232 H1 HOH A 411 10.951 26.973 1.012 1.00 0.00 H
-HETATM 1233 H2 HOH A 411 12.029 26.645 -0.000 1.00 0.00 H
-HETATM 1234 O HOH A 412 10.581 28.473 2.283 1.00 0.00 O
-HETATM 1235 H1 HOH A 412 9.770 28.836 1.925 1.00 0.00 H
-HETATM 1236 H2 HOH A 412 11.205 29.197 2.250 1.00 0.00 H
-HETATM 1237 O HOH A 413 14.973 11.309 13.025 1.00 0.00 O
-HETATM 1238 H1 HOH A 413 14.201 10.969 13.476 1.00 0.00 H
-HETATM 1239 H2 HOH A 413 14.690 11.425 12.118 1.00 0.00 H
-HETATM 1240 O HOH A 414 12.752 9.769 14.155 1.00 0.00 O
-HETATM 1241 H1 HOH A 414 13.257 9.065 14.564 1.00 0.00 H
-HETATM 1242 H2 HOH A 414 12.235 9.333 13.478 1.00 0.00 H
-HETATM 1243 O HOH A 415 9.586 26.471 11.857 1.00 0.00 O
-HETATM 1244 H1 HOH A 415 10.241 26.877 11.289 1.00 0.00 H
-HETATM 1245 H2 HOH A 415 9.880 25.565 11.955 1.00 0.00 H
-HETATM 1246 O HOH A 416 11.316 27.434 9.698 1.00 0.00 O
-HETATM 1247 H1 HOH A 416 10.643 27.812 9.131 1.00 0.00 H
-HETATM 1248 H2 HOH A 416 11.688 26.715 9.186 1.00 0.00 H
-HETATM 1249 O HOH A 417 11.992 26.878 21.490 1.00 0.00 O
-HETATM 1250 H1 HOH A 417 12.420 27.439 22.136 1.00 0.00 H
-HETATM 1251 H2 HOH A 417 12.490 27.016 20.684 1.00 0.00 H
-HETATM 1252 O HOH A 418 13.018 29.020 23.205 1.00 0.00 O
-HETATM 1253 H1 HOH A 418 12.182 29.389 23.491 1.00 0.00 H
-HETATM 1254 H2 HOH A 418 13.431 29.720 22.699 1.00 0.00 H
-HETATM 1255 O HOH A 419 1.486 23.664 17.864 1.00 0.00 O
-HETATM 1256 H1 HOH A 419 2.200 23.095 17.577 1.00 0.00 H
-HETATM 1257 H2 HOH A 419 0.760 23.453 17.277 1.00 0.00 H
-HETATM 1258 O HOH A 420 3.728 22.303 16.557 1.00 0.00 O
-HETATM 1259 H1 HOH A 420 4.350 23.031 16.543 1.00 0.00 H
-HETATM 1260 H2 HOH A 420 3.549 22.119 15.635 1.00 0.00 H
-HETATM 1261 O HOH A 421 21.387 26.454 28.314 1.00 0.00 O
-HETATM 1262 H1 HOH A 421 21.237 27.186 27.715 1.00 0.00 H
-HETATM 1263 H2 HOH A 421 20.868 26.667 29.089 1.00 0.00 H
-HETATM 1264 O HOH A 422 21.300 28.974 26.821 1.00 0.00 O
-HETATM 1265 H1 HOH A 422 22.237 29.075 26.653 1.00 0.00 H
-HETATM 1266 H2 HOH A 422 21.073 29.716 27.381 1.00 0.00 H
-HETATM 1267 O HOH A 423 25.216 20.461 14.739 1.00 0.00 O
-HETATM 1268 H1 HOH A 423 25.366 19.618 14.311 1.00 0.00 H
-HETATM 1269 H2 HOH A 423 25.585 20.354 15.616 1.00 0.00 H
-HETATM 1270 O HOH A 424 25.227 17.705 13.744 1.00 0.00 O
-HETATM 1271 H1 HOH A 424 24.320 17.657 13.441 1.00 0.00 H
-HETATM 1272 H2 HOH A 424 25.278 17.066 14.455 1.00 0.00 H
-HETATM 1273 O HOH A 425 23.235 16.352 16.256 1.00 0.00 O
-HETATM 1274 H1 HOH A 425 24.023 16.693 16.679 1.00 0.00 H
-HETATM 1275 H2 HOH A 425 23.313 16.630 15.343 1.00 0.00 H
-HETATM 1276 O HOH A 426 25.366 17.901 17.539 1.00 0.00 O
-HETATM 1277 H1 HOH A 426 24.860 18.272 18.262 1.00 0.00 H
-HETATM 1278 H2 HOH A 426 25.628 18.657 17.014 1.00 0.00 H
-HETATM 1279 O HOH A 427 17.378 21.017 3.188 1.00 0.00 O
-HETATM 1280 H1 HOH A 427 17.419 20.087 3.412 1.00 0.00 H
-HETATM 1281 H2 HOH A 427 16.724 21.067 2.491 1.00 0.00 H
-HETATM 1282 O HOH A 428 17.816 18.127 3.392 1.00 0.00 O
-HETATM 1283 H1 HOH A 428 18.773 18.123 3.350 1.00 0.00 H
-HETATM 1284 H2 HOH A 428 17.537 17.664 2.602 1.00 0.00 H
-HETATM 1285 O HOH A 429 9.287 6.872 1.925 1.00 0.00 O
-HETATM 1286 H1 HOH A 429 9.125 5.938 2.060 1.00 0.00 H
-HETATM 1287 H2 HOH A 429 8.425 7.239 1.726 1.00 0.00 H
-HETATM 1288 O HOH A 430 8.767 3.993 1.762 1.00 0.00 O
-HETATM 1289 H1 HOH A 430 9.489 3.749 1.183 1.00 0.00 H
-HETATM 1290 H2 HOH A 430 7.976 3.798 1.260 1.00 0.00 H
-HETATM 1291 O HOH A 431 13.062 10.548 28.476 1.00 0.00 O
-HETATM 1292 H1 HOH A 431 12.384 11.129 28.131 1.00 0.00 H
-HETATM 1293 H2 HOH A 431 13.879 11.032 28.352 1.00 0.00 H
-HETATM 1294 O HOH A 432 11.173 12.146 26.907 1.00 0.00 O
-HETATM 1295 H1 HOH A 432 10.782 11.441 26.390 1.00 0.00 H
-HETATM 1296 H2 HOH A 432 11.616 12.700 26.264 1.00 0.00 H
-HETATM 1297 O HOH A 433 15.080 4.665 18.970 1.00 0.00 O
-HETATM 1298 H1 HOH A 433 15.956 4.388 19.240 1.00 0.00 H
-HETATM 1299 H2 HOH A 433 14.602 4.781 19.791 1.00 0.00 H
-HETATM 1300 O HOH A 434 17.511 3.305 19.880 1.00 0.00 O
-HETATM 1301 H1 HOH A 434 17.511 2.578 19.257 1.00 0.00 H
-HETATM 1302 H2 HOH A 434 17.393 2.891 20.735 1.00 0.00 H
-HETATM 1303 O HOH A 435 19.521 1.370 0.000 1.00 0.00 O
-HETATM 1304 H1 HOH A 435 18.938 1.319 0.757 1.00 0.00 H
-HETATM 1305 H2 HOH A 435 19.989 0.534 0.000 1.00 0.00 H
-HETATM 1306 O HOH A 436 18.213 1.326 2.621 1.00 0.00 O
-HETATM 1307 H1 HOH A 436 18.482 2.191 2.932 1.00 0.00 H
-HETATM 1308 H2 HOH A 436 18.632 0.711 3.223 1.00 0.00 H
-HETATM 1309 O HOH A 437 27.282 18.327 6.462 1.00 0.00 O
-HETATM 1310 H1 HOH A 437 26.977 18.024 5.607 1.00 0.00 H
-HETATM 1311 H2 HOH A 437 26.834 17.762 7.091 1.00 0.00 H
-HETATM 1312 O HOH A 438 25.870 17.693 3.974 1.00 0.00 O
-HETATM 1313 H1 HOH A 438 25.673 18.586 3.688 1.00 0.00 H
-HETATM 1314 H2 HOH A 438 25.014 17.297 4.137 1.00 0.00 H
-HETATM 1315 O HOH A 439 14.622 7.382 26.170 1.00 0.00 O
-HETATM 1316 H1 HOH A 439 15.049 7.723 25.385 1.00 0.00 H
-HETATM 1317 H2 HOH A 439 14.035 6.698 25.848 1.00 0.00 H
-HETATM 1318 O HOH A 440 15.430 8.702 23.683 1.00 0.00 O
-HETATM 1319 H1 HOH A 440 15.252 9.609 23.936 1.00 0.00 H
-HETATM 1320 H2 HOH A 440 14.821 8.526 22.967 1.00 0.00 H
-HETATM 1321 O HOH A 441 17.186 23.630 10.566 1.00 0.00 O
-HETATM 1322 H1 HOH A 441 17.232 23.595 11.522 1.00 0.00 H
-HETATM 1323 H2 HOH A 441 16.774 24.473 10.376 1.00 0.00 H
-HETATM 1324 O HOH A 442 16.766 23.396 13.457 1.00 0.00 O
-HETATM 1325 H1 HOH A 442 16.396 22.513 13.478 1.00 0.00 H
-HETATM 1326 H2 HOH A 442 16.059 23.962 13.765 1.00 0.00 H
-HETATM 1327 O HOH A 443 22.930 5.681 10.001 1.00 0.00 O
-HETATM 1328 H1 HOH A 443 22.941 6.379 10.655 1.00 0.00 H
-HETATM 1329 H2 HOH A 443 23.687 5.861 9.444 1.00 0.00 H
-HETATM 1330 O HOH A 444 22.747 8.142 11.579 1.00 0.00 O
-HETATM 1331 H1 HOH A 444 21.817 8.331 11.446 1.00 0.00 H
-HETATM 1332 H2 HOH A 444 23.204 8.873 11.162 1.00 0.00 H
-HETATM 1333 O HOH A 445 24.055 12.474 23.016 1.00 0.00 O
-HETATM 1334 H1 HOH A 445 23.851 12.223 22.115 1.00 0.00 H
-HETATM 1335 H2 HOH A 445 24.660 11.799 23.322 1.00 0.00 H
-HETATM 1336 O HOH A 446 23.084 11.316 20.505 1.00 0.00 O
-HETATM 1337 H1 HOH A 446 22.144 11.423 20.651 1.00 0.00 H
-HETATM 1338 H2 HOH A 446 23.218 10.369 20.470 1.00 0.00 H
-HETATM 1339 O HOH A 447 13.309 12.542 17.843 1.00 0.00 O
-HETATM 1340 H1 HOH A 447 13.643 12.848 17.000 1.00 0.00 H
-HETATM 1341 H2 HOH A 447 13.125 13.342 18.334 1.00 0.00 H
-HETATM 1342 O HOH A 448 14.820 13.576 15.556 1.00 0.00 O
-HETATM 1343 H1 HOH A 448 15.568 12.979 15.595 1.00 0.00 H
-HETATM 1344 H2 HOH A 448 15.192 14.440 15.731 1.00 0.00 H
-HETATM 1345 O HOH A 449 1.228 25.875 6.041 1.00 0.00 O
-HETATM 1346 H1 HOH A 449 1.942 26.420 6.369 1.00 0.00 H
-HETATM 1347 H2 HOH A 449 1.278 25.961 5.089 1.00 0.00 H
-HETATM 1348 O HOH A 450 3.060 27.981 6.931 1.00 0.00 O
-HETATM 1349 H1 HOH A 450 2.487 28.413 7.565 1.00 0.00 H
-HETATM 1350 H2 HOH A 450 3.209 28.640 6.252 1.00 0.00 H
-HETATM 1351 O HOH A 451 19.462 2.229 5.102 1.00 0.00 O
-HETATM 1352 H1 HOH A 451 19.500 3.089 4.684 1.00 0.00 H
-HETATM 1353 H2 HOH A 451 20.162 2.247 5.754 1.00 0.00 H
-HETATM 1354 O HOH A 452 20.032 4.642 3.541 1.00 0.00 O
-HETATM 1355 H1 HOH A 452 20.006 4.256 2.665 1.00 0.00 H
-HETATM 1356 H2 HOH A 452 20.936 4.936 3.650 1.00 0.00 H
-HETATM 1357 O HOH A 453 17.450 30.012 21.391 1.00 0.00 O
-HETATM 1358 H1 HOH A 453 17.416 29.327 22.058 1.00 0.00 H
-HETATM 1359 H2 HOH A 453 17.876 29.597 20.641 1.00 0.00 H
-HETATM 1360 O HOH A 454 17.905 28.040 23.509 1.00 0.00 O
-HETATM 1361 H1 HOH A 454 18.257 28.626 24.179 1.00 0.00 H
-HETATM 1362 H2 HOH A 454 18.629 27.449 23.301 1.00 0.00 H
-HETATM 1363 O HOH A 455 16.465 3.213 28.978 1.00 0.00 O
-HETATM 1364 H1 HOH A 455 16.961 3.989 28.716 1.00 0.00 H
-HETATM 1365 H2 HOH A 455 15.583 3.540 29.155 1.00 0.00 H
-HETATM 1366 O HOH A 456 17.992 5.703 28.740 1.00 0.00 O
-HETATM 1367 H1 HOH A 456 18.709 5.490 29.338 1.00 0.00 H
-HETATM 1368 H2 HOH A 456 17.544 6.441 29.152 1.00 0.00 H
-HETATM 1369 O HOH A 457 15.454 12.260 10.136 1.00 0.00 O
-HETATM 1370 H1 HOH A 457 15.594 13.009 9.558 1.00 0.00 H
-HETATM 1371 H2 HOH A 457 16.306 11.826 10.180 1.00 0.00 H
-HETATM 1372 O HOH A 458 15.858 14.162 7.945 1.00 0.00 O
-HETATM 1373 H1 HOH A 458 15.079 13.954 7.428 1.00 0.00 H
-HETATM 1374 H2 HOH A 458 16.593 13.915 7.384 1.00 0.00 H
-HETATM 1375 O HOH A 459 14.693 26.022 26.904 1.00 0.00 O
-HETATM 1376 H1 HOH A 459 14.668 25.953 27.858 1.00 0.00 H
-HETATM 1377 H2 HOH A 459 14.346 26.894 26.717 1.00 0.00 H
-HETATM 1378 O HOH A 460 14.053 25.719 29.747 1.00 0.00 O
-HETATM 1379 H1 HOH A 460 13.631 24.860 29.710 1.00 0.00 H
-HETATM 1380 H2 HOH A 460 13.360 26.317 30.026 1.00 0.00 H
-HETATM 1381 O HOH A 461 3.211 4.128 19.200 1.00 0.00 O
-HETATM 1382 H1 HOH A 461 3.964 4.041 19.785 1.00 0.00 H
-HETATM 1383 H2 HOH A 461 3.090 3.252 18.833 1.00 0.00 H
-HETATM 1384 O HOH A 462 5.823 3.874 20.505 1.00 0.00 O
-HETATM 1385 H1 HOH A 462 6.244 4.651 20.137 1.00 0.00 H
-HETATM 1386 H2 HOH A 462 6.328 3.138 20.159 1.00 0.00 H
-HETATM 1387 O HOH A 463 2.450 14.891 3.529 1.00 0.00 O
-HETATM 1388 H1 HOH A 463 3.245 14.577 3.959 1.00 0.00 H
-HETATM 1389 H2 HOH A 463 1.929 15.273 4.235 1.00 0.00 H
-HETATM 1390 O HOH A 464 4.549 13.494 5.021 1.00 0.00 O
-HETATM 1391 H1 HOH A 464 4.486 12.639 4.593 1.00 0.00 H
-HETATM 1392 H2 HOH A 464 4.297 13.329 5.929 1.00 0.00 H
-HETATM 1393 O HOH A 465 12.956 8.403 21.245 1.00 0.00 O
-HETATM 1394 H1 HOH A 465 13.615 9.083 21.103 1.00 0.00 H
-HETATM 1395 H2 HOH A 465 12.767 8.442 22.182 1.00 0.00 H
-HETATM 1396 O HOH A 466 15.336 10.100 21.040 1.00 0.00 O
-HETATM 1397 H1 HOH A 466 15.896 9.503 20.542 1.00 0.00 H
-HETATM 1398 H2 HOH A 466 15.779 10.202 21.882 1.00 0.00 H
-HETATM 1399 O HOH A 467 3.014 12.343 7.419 1.00 0.00 O
-HETATM 1400 H1 HOH A 467 2.608 11.544 7.755 1.00 0.00 H
-HETATM 1401 H2 HOH A 467 2.442 13.048 7.720 1.00 0.00 H
-HETATM 1402 O HOH A 468 1.401 9.969 8.009 1.00 0.00 O
-HETATM 1403 H1 HOH A 468 1.463 9.537 7.157 1.00 0.00 H
-HETATM 1404 H2 HOH A 468 0.473 10.180 8.107 1.00 0.00 H
-HETATM 1405 O HOH A 469 10.696 15.852 2.710 1.00 0.00 O
-HETATM 1406 H1 HOH A 469 11.566 15.874 3.107 1.00 0.00 H
-HETATM 1407 H2 HOH A 469 10.097 16.047 3.431 1.00 0.00 H
-HETATM 1408 O HOH A 470 13.219 15.398 4.129 1.00 0.00 O
-HETATM 1409 H1 HOH A 470 13.461 14.562 3.729 1.00 0.00 H
-HETATM 1410 H2 HOH A 470 13.080 15.193 5.053 1.00 0.00 H
-HETATM 1411 O HOH A 471 0.467 0.845 13.050 1.00 0.00 O
-HETATM 1412 H1 HOH A 471 1.259 1.112 12.584 1.00 0.00 H
-HETATM 1413 H2 HOH A 471 0.000 0.287 12.428 1.00 0.00 H
-HETATM 1414 O HOH A 472 2.540 2.030 11.353 1.00 0.00 O
-HETATM 1415 H1 HOH A 472 2.409 2.950 11.585 1.00 0.00 H
-HETATM 1416 H2 HOH A 472 2.317 1.981 10.423 1.00 0.00 H
-HETATM 1417 O HOH A 473 14.677 15.144 12.967 1.00 0.00 O
-HETATM 1418 H1 HOH A 473 14.005 15.650 13.424 1.00 0.00 H
-HETATM 1419 H2 HOH A 473 14.791 14.358 13.501 1.00 0.00 H
-HETATM 1420 O HOH A 474 13.050 16.833 14.724 1.00 0.00 O
-HETATM 1421 H1 HOH A 474 13.571 17.635 14.676 1.00 0.00 H
-HETATM 1422 H2 HOH A 474 13.108 16.561 15.639 1.00 0.00 H
-HETATM 1423 O HOH A 475 20.686 4.068 17.750 1.00 0.00 O
-HETATM 1424 H1 HOH A 475 21.168 4.804 18.125 1.00 0.00 H
-HETATM 1425 H2 HOH A 475 20.699 4.228 16.806 1.00 0.00 H
-HETATM 1426 O HOH A 476 21.676 6.615 18.805 1.00 0.00 O
-HETATM 1427 H1 HOH A 476 20.991 6.778 19.456 1.00 0.00 H
-HETATM 1428 H2 HOH A 476 21.583 7.327 18.173 1.00 0.00 H
-HETATM 1429 O HOH A 477 10.815 16.801 5.372 1.00 0.00 O
-HETATM 1430 H1 HOH A 477 11.477 17.348 4.951 1.00 0.00 H
-HETATM 1431 H2 HOH A 477 11.305 16.269 6.000 1.00 0.00 H
-HETATM 1432 O HOH A 478 12.969 17.997 3.787 1.00 0.00 O
-HETATM 1433 H1 HOH A 478 12.640 17.784 2.913 1.00 0.00 H
-HETATM 1434 H2 HOH A 478 13.787 17.507 3.863 1.00 0.00 H
-HETATM 1435 O HOH A 479 17.839 4.145 7.582 1.00 0.00 O
-HETATM 1436 H1 HOH A 479 17.829 3.860 6.669 1.00 0.00 H
-HETATM 1437 H2 HOH A 479 17.531 3.385 8.075 1.00 0.00 H
-HETATM 1438 O HOH A 480 17.239 3.357 4.825 1.00 0.00 O
-HETATM 1439 H1 HOH A 480 16.777 4.148 4.546 1.00 0.00 H
-HETATM 1440 H2 HOH A 480 16.580 2.665 4.784 1.00 0.00 H
-HETATM 1441 O HOH A 481 13.053 24.008 23.329 1.00 0.00 O
-HETATM 1442 H1 HOH A 481 12.745 23.135 23.569 1.00 0.00 H
-HETATM 1443 H2 HOH A 481 12.915 24.060 22.383 1.00 0.00 H
-HETATM 1444 O HOH A 482 12.611 21.159 23.845 1.00 0.00 O
-HETATM 1445 H1 HOH A 482 13.404 20.978 24.350 1.00 0.00 H
-HETATM 1446 H2 HOH A 482 12.703 20.630 23.052 1.00 0.00 H
-HETATM 1447 O HOH A 483 8.434 16.946 13.288 1.00 0.00 O
-HETATM 1448 H1 HOH A 483 7.506 17.178 13.266 1.00 0.00 H
-HETATM 1449 H2 HOH A 483 8.761 17.179 12.419 1.00 0.00 H
-HETATM 1450 O HOH A 484 5.536 17.168 12.918 1.00 0.00 O
-HETATM 1451 H1 HOH A 484 5.279 16.319 13.280 1.00 0.00 H
-HETATM 1452 H2 HOH A 484 5.287 17.122 11.995 1.00 0.00 H
-HETATM 1453 O HOH A 485 3.469 23.456 9.544 1.00 0.00 O
-HETATM 1454 H1 HOH A 485 3.137 22.601 9.816 1.00 0.00 H
-HETATM 1455 H2 HOH A 485 2.700 24.025 9.548 1.00 0.00 H
-HETATM 1456 O HOH A 486 2.255 20.805 9.835 1.00 0.00 O
-HETATM 1457 H1 HOH A 486 2.699 20.363 9.111 1.00 0.00 H
-HETATM 1458 H2 HOH A 486 1.330 20.804 9.589 1.00 0.00 H
-HETATM 1459 O HOH A 487 19.980 7.154 11.720 1.00 0.00 O
-HETATM 1460 H1 HOH A 487 19.662 8.054 11.789 1.00 0.00 H
-HETATM 1461 H2 HOH A 487 19.187 6.618 11.731 1.00 0.00 H
-HETATM 1462 O HOH A 488 18.938 9.782 12.489 1.00 0.00 O
-HETATM 1463 H1 HOH A 488 19.561 9.986 13.187 1.00 0.00 H
-HETATM 1464 H2 HOH A 488 18.092 9.719 12.932 1.00 0.00 H
-HETATM 1465 O HOH A 489 4.081 26.967 24.557 1.00 0.00 O
-HETATM 1466 H1 HOH A 489 3.335 26.656 25.069 1.00 0.00 H
-HETATM 1467 H2 HOH A 489 3.722 27.136 23.686 1.00 0.00 H
-HETATM 1468 O HOH A 490 1.883 25.519 25.844 1.00 0.00 O
-HETATM 1469 H1 HOH A 490 2.380 24.772 26.179 1.00 0.00 H
-HETATM 1470 H2 HOH A 490 1.279 25.141 25.206 1.00 0.00 H
-HETATM 1471 O HOH A 491 8.891 17.484 22.971 1.00 0.00 O
-HETATM 1472 H1 HOH A 491 9.659 17.253 22.449 1.00 0.00 H
-HETATM 1473 H2 HOH A 491 9.133 17.256 23.869 1.00 0.00 H
-HETATM 1474 O HOH A 492 11.050 16.238 21.432 1.00 0.00 O
-HETATM 1475 H1 HOH A 492 10.517 15.766 20.792 1.00 0.00 H
-HETATM 1476 H2 HOH A 492 11.497 15.552 21.928 1.00 0.00 H
-HETATM 1477 O HOH A 493 19.647 15.273 7.281 1.00 0.00 O
-HETATM 1478 H1 HOH A 493 20.473 15.684 7.029 1.00 0.00 H
-HETATM 1479 H2 HOH A 493 18.972 15.862 6.943 1.00 0.00 H
-HETATM 1480 O HOH A 494 22.071 16.886 6.951 1.00 0.00 O
-HETATM 1481 H1 HOH A 494 22.429 16.790 7.834 1.00 0.00 H
-HETATM 1482 H2 HOH A 494 21.874 17.819 6.869 1.00 0.00 H
-HETATM 1483 O HOH A 495 18.540 29.783 9.963 1.00 0.00 O
-HETATM 1484 H1 HOH A 495 19.259 29.492 10.525 1.00 0.00 H
-HETATM 1485 H2 HOH A 495 18.960 30.008 9.133 1.00 0.00 H
-HETATM 1486 O HOH A 496 20.851 29.456 11.735 1.00 0.00 O
-HETATM 1487 H1 HOH A 496 20.560 30.014 12.457 1.00 0.00 H
-HETATM 1488 H2 HOH A 496 21.632 29.891 11.393 1.00 0.00 H
-HETATM 1489 O HOH A 497 9.249 16.837 17.698 1.00 0.00 O
-HETATM 1490 H1 HOH A 497 9.104 15.960 17.342 1.00 0.00 H
-HETATM 1491 H2 HOH A 497 9.740 16.690 18.507 1.00 0.00 H
-HETATM 1492 O HOH A 498 8.394 14.119 17.013 1.00 0.00 O
-HETATM 1493 H1 HOH A 498 7.463 14.292 16.872 1.00 0.00 H
-HETATM 1494 H2 HOH A 498 8.420 13.532 17.768 1.00 0.00 H
-HETATM 1495 O HOH A 499 3.574 8.463 5.531 1.00 0.00 O
-HETATM 1496 H1 HOH A 499 3.916 8.004 4.764 1.00 0.00 H
-HETATM 1497 H2 HOH A 499 4.144 8.190 6.249 1.00 0.00 H
-HETATM 1498 O HOH A 500 4.349 6.601 3.406 1.00 0.00 O
-HETATM 1499 H1 HOH A 500 3.476 6.298 3.154 1.00 0.00 H
-HETATM 1500 H2 HOH A 500 4.784 5.822 3.752 1.00 0.00 H
-HETATM 1501 O HOH A 501 6.158 25.544 0.631 1.00 0.00 O
-HETATM 1502 H1 HOH A 501 5.236 25.774 0.744 1.00 0.00 H
-HETATM 1503 H2 HOH A 501 6.335 24.912 1.328 1.00 0.00 H
-HETATM 1504 O HOH A 502 3.529 26.575 1.409 1.00 0.00 O
-HETATM 1505 H1 HOH A 502 3.719 27.512 1.355 1.00 0.00 H
-HETATM 1506 H2 HOH A 502 3.341 26.420 2.335 1.00 0.00 H
-HETATM 1507 O HOH A 503 0.107 15.082 12.422 1.00 0.00 O
-HETATM 1508 H1 HOH A 503 0.727 14.468 12.027 1.00 0.00 H
-HETATM 1509 H2 HOH A 503 0.643 15.629 12.995 1.00 0.00 H
-HETATM 1510 O HOH A 504 1.975 12.935 11.724 1.00 0.00 O
-HETATM 1511 H1 HOH A 504 1.414 12.185 11.925 1.00 0.00 H
-HETATM 1512 H2 HOH A 504 2.698 12.870 12.347 1.00 0.00 H
-HETATM 1513 O HOH A 505 15.717 28.853 24.021 1.00 0.00 O
-HETATM 1514 H1 HOH A 505 15.636 28.103 23.432 1.00 0.00 H
-HETATM 1515 H2 HOH A 505 15.358 28.545 24.853 1.00 0.00 H
-HETATM 1516 O HOH A 506 14.913 26.711 22.191 1.00 0.00 O
-HETATM 1517 H1 HOH A 506 14.518 27.248 21.504 1.00 0.00 H
-HETATM 1518 H2 HOH A 506 14.187 26.193 22.540 1.00 0.00 H
-HETATM 1519 O HOH A 507 17.988 10.137 20.291 1.00 0.00 O
-HETATM 1520 H1 HOH A 507 18.933 10.185 20.150 1.00 0.00 H
-HETATM 1521 H2 HOH A 507 17.883 10.196 21.241 1.00 0.00 H
-HETATM 1522 O HOH A 508 20.878 9.734 20.029 1.00 0.00 O
-HETATM 1523 H1 HOH A 508 20.866 8.974 19.447 1.00 0.00 H
-HETATM 1524 H2 HOH A 508 21.272 9.412 20.840 1.00 0.00 H
-HETATM 1525 O HOH A 509 10.866 10.632 23.910 1.00 0.00 O
-HETATM 1526 H1 HOH A 509 10.965 11.394 23.339 1.00 0.00 H
-HETATM 1527 H2 HOH A 509 11.617 10.677 24.503 1.00 0.00 H
-HETATM 1528 O HOH A 510 11.568 12.645 21.900 1.00 0.00 O
-HETATM 1529 H1 HOH A 510 11.427 12.118 21.112 1.00 0.00 H
-HETATM 1530 H2 HOH A 510 12.505 12.839 21.896 1.00 0.00 H
-HETATM 1531 O HOH A 511 22.133 21.517 19.656 1.00 0.00 O
-HETATM 1532 H1 HOH A 511 22.978 21.535 19.207 1.00 0.00 H
-HETATM 1533 H2 HOH A 511 22.354 21.413 20.581 1.00 0.00 H
-HETATM 1534 O HOH A 512 24.711 21.000 18.362 1.00 0.00 O
-HETATM 1535 H1 HOH A 512 24.421 20.357 17.714 1.00 0.00 H
-HETATM 1536 H2 HOH A 512 25.309 20.517 18.932 1.00 0.00 H
-HETATM 1537 O HOH A 513 22.483 20.152 3.632 1.00 0.00 O
-HETATM 1538 H1 HOH A 513 22.688 20.943 4.131 1.00 0.00 H
-HETATM 1539 H2 HOH A 513 22.505 20.432 2.717 1.00 0.00 H
-HETATM 1540 O HOH A 514 22.566 22.716 5.047 1.00 0.00 O
-HETATM 1541 H1 HOH A 514 21.825 22.579 5.639 1.00 0.00 H
-HETATM 1542 H2 HOH A 514 22.298 23.445 4.488 1.00 0.00 H
-HETATM 1543 O HOH A 515 12.885 2.922 19.210 1.00 0.00 O
-HETATM 1544 H1 HOH A 515 13.557 3.131 18.561 1.00 0.00 H
-HETATM 1545 H2 HOH A 515 13.340 2.967 20.051 1.00 0.00 H
-HETATM 1546 O HOH A 516 15.117 3.020 17.315 1.00 0.00 O
-HETATM 1547 H1 HOH A 516 14.881 2.266 16.774 1.00 0.00 H
-HETATM 1548 H2 HOH A 516 15.947 2.774 17.723 1.00 0.00 H
-HETATM 1549 O HOH A 517 21.608 24.665 12.250 1.00 0.00 O
-HETATM 1550 H1 HOH A 517 22.017 24.810 11.397 1.00 0.00 H
-HETATM 1551 H2 HOH A 517 22.233 25.025 12.880 1.00 0.00 H
-HETATM 1552 O HOH A 518 23.184 24.656 9.780 1.00 0.00 O
-HETATM 1553 H1 HOH A 518 23.023 23.751 9.509 1.00 0.00 H
-HETATM 1554 H2 HOH A 518 24.125 24.693 9.953 1.00 0.00 H
-HETATM 1555 O HOH A 519 26.128 19.010 23.405 1.00 0.00 O
-HETATM 1556 H1 HOH A 519 25.683 18.703 24.194 1.00 0.00 H
-HETATM 1557 H2 HOH A 519 27.053 19.052 23.651 1.00 0.00 H
-HETATM 1558 O HOH A 520 24.833 18.614 26.003 1.00 0.00 O
-HETATM 1559 H1 HOH A 520 24.173 19.305 25.942 1.00 0.00 H
-HETATM 1560 H2 HOH A 520 25.395 18.880 26.730 1.00 0.00 H
-HETATM 1561 O HOH A 521 17.489 25.987 28.499 1.00 0.00 O
-HETATM 1562 H1 HOH A 521 18.185 26.448 28.031 1.00 0.00 H
-HETATM 1563 H2 HOH A 521 16.872 25.726 27.816 1.00 0.00 H
-HETATM 1564 O HOH A 522 19.243 27.833 27.049 1.00 0.00 O
-HETATM 1565 H1 HOH A 522 19.218 28.568 27.663 1.00 0.00 H
-HETATM 1566 H2 HOH A 522 18.833 28.169 26.253 1.00 0.00 H
-HETATM 1567 O HOH A 523 20.277 4.192 20.041 1.00 0.00 O
-HETATM 1568 H1 HOH A 523 20.534 4.541 20.894 1.00 0.00 H
-HETATM 1569 H2 HOH A 523 20.707 3.339 19.991 1.00 0.00 H
-HETATM 1570 O HOH A 524 21.566 5.376 22.391 1.00 0.00 O
-HETATM 1571 H1 HOH A 524 21.810 6.221 22.011 1.00 0.00 H
-HETATM 1572 H2 HOH A 524 22.399 4.953 22.596 1.00 0.00 H
-HETATM 1573 O HOH A 525 19.781 11.056 28.807 1.00 0.00 O
-HETATM 1574 H1 HOH A 525 19.185 10.519 28.285 1.00 0.00 H
-HETATM 1575 H2 HOH A 525 20.347 10.425 29.251 1.00 0.00 H
-HETATM 1576 O HOH A 526 17.691 9.320 27.711 1.00 0.00 O
-HETATM 1577 H1 HOH A 526 16.925 9.799 28.026 1.00 0.00 H
-HETATM 1578 H2 HOH A 526 17.651 8.476 28.161 1.00 0.00 H
-HETATM 1579 O HOH A 527 5.016 16.205 4.351 1.00 0.00 O
-HETATM 1580 H1 HOH A 527 5.673 16.847 4.085 1.00 0.00 H
-HETATM 1581 H2 HOH A 527 5.516 15.405 4.516 1.00 0.00 H
-HETATM 1582 O HOH A 528 7.011 17.896 3.030 1.00 0.00 O
-HETATM 1583 H1 HOH A 528 6.494 18.161 2.269 1.00 0.00 H
-HETATM 1584 H2 HOH A 528 7.733 17.384 2.666 1.00 0.00 H
-HETATM 1585 O HOH A 529 19.131 7.519 26.179 1.00 0.00 O
-HETATM 1586 H1 HOH A 529 18.278 7.170 26.437 1.00 0.00 H
-HETATM 1587 H2 HOH A 529 18.978 7.921 25.324 1.00 0.00 H
-HETATM 1588 O HOH A 530 16.642 6.030 26.596 1.00 0.00 O
-HETATM 1589 H1 HOH A 530 17.019 5.180 26.827 1.00 0.00 H
-HETATM 1590 H2 HOH A 530 16.174 5.873 25.777 1.00 0.00 H
-HETATM 1591 O HOH A 531 12.834 5.183 9.988 1.00 0.00 O
-HETATM 1592 H1 HOH A 531 12.161 4.647 10.406 1.00 0.00 H
-HETATM 1593 H2 HOH A 531 12.545 5.268 9.080 1.00 0.00 H
-HETATM 1594 O HOH A 532 11.034 3.104 11.000 1.00 0.00 O
-HETATM 1595 H1 HOH A 532 11.688 2.526 11.393 1.00 0.00 H
-HETATM 1596 H2 HOH A 532 10.648 2.587 10.293 1.00 0.00 H
-HETATM 1597 O HOH A 533 5.480 5.343 15.151 1.00 0.00 O
-HETATM 1598 H1 HOH A 533 5.816 4.527 15.522 1.00 0.00 H
-HETATM 1599 H2 HOH A 533 4.719 5.557 15.692 1.00 0.00 H
-HETATM 1600 O HOH A 534 6.070 2.603 16.004 1.00 0.00 O
-HETATM 1601 H1 HOH A 534 6.125 2.192 15.141 1.00 0.00 H
-HETATM 1602 H2 HOH A 534 5.318 2.186 16.423 1.00 0.00 H
-HETATM 1603 O HOH A 535 16.222 9.792 9.346 1.00 0.00 O
-HETATM 1604 H1 HOH A 535 15.420 9.402 8.998 1.00 0.00 H
-HETATM 1605 H2 HOH A 535 16.249 9.510 10.260 1.00 0.00 H
-HETATM 1606 O HOH A 536 13.505 9.026 8.562 1.00 0.00 O
-HETATM 1607 H1 HOH A 536 13.227 9.856 8.174 1.00 0.00 H
-HETATM 1608 H2 HOH A 536 12.931 8.908 9.319 1.00 0.00 H
-HETATM 1609 O HOH A 537 4.339 3.990 13.311 1.00 0.00 O
-HETATM 1610 H1 HOH A 537 3.550 3.456 13.397 1.00 0.00 H
-HETATM 1611 H2 HOH A 537 5.027 3.471 13.728 1.00 0.00 H
-HETATM 1612 O HOH A 538 1.923 2.542 14.119 1.00 0.00 O
-HETATM 1613 H1 HOH A 538 1.415 3.283 14.450 1.00 0.00 H
-HETATM 1614 H2 HOH A 538 2.074 1.990 14.886 1.00 0.00 H
-HETATM 1615 O HOH A 539 29.552 2.439 28.550 1.00 0.00 O
-HETATM 1616 H1 HOH A 539 28.733 2.734 28.948 1.00 0.00 H
-HETATM 1617 H2 HOH A 539 29.459 2.654 27.622 1.00 0.00 H
-HETATM 1618 O HOH A 540 26.826 2.860 29.539 1.00 0.00 O
-HETATM 1619 H1 HOH A 540 26.710 2.028 30.000 1.00 0.00 H
-HETATM 1620 H2 HOH A 540 26.180 2.837 28.834 1.00 0.00 H
-HETATM 1621 O HOH A 541 14.541 27.600 15.786 1.00 0.00 O
-HETATM 1622 H1 HOH A 541 13.777 28.025 15.396 1.00 0.00 H
-HETATM 1623 H2 HOH A 541 14.388 27.649 16.729 1.00 0.00 H
-HETATM 1624 O HOH A 542 12.466 29.390 14.748 1.00 0.00 O
-HETATM 1625 H1 HOH A 542 13.014 29.930 14.178 1.00 0.00 H
-HETATM 1626 H2 HOH A 542 12.144 29.994 15.417 1.00 0.00 H
-HETATM 1627 O HOH A 543 18.032 28.666 13.242 1.00 0.00 O
-HETATM 1628 H1 HOH A 543 17.570 27.828 13.230 1.00 0.00 H
-HETATM 1629 H2 HOH A 543 18.551 28.667 12.437 1.00 0.00 H
-HETATM 1630 O HOH A 544 17.125 25.883 13.370 1.00 0.00 O
-HETATM 1631 H1 HOH A 544 17.377 25.691 14.274 1.00 0.00 H
-HETATM 1632 H2 HOH A 544 17.669 25.303 12.838 1.00 0.00 H
-HETATM 1633 O HOH A 545 19.405 8.955 0.350 1.00 0.00 O
-HETATM 1634 H1 HOH A 545 18.538 8.697 0.661 1.00 0.00 H
-HETATM 1635 H2 HOH A 545 19.456 9.894 0.532 1.00 0.00 H
-HETATM 1636 O HOH A 546 16.565 8.407 0.814 1.00 0.00 O
-HETATM 1637 H1 HOH A 546 16.373 7.939 -0.000 1.00 0.00 H
-HETATM 1638 H2 HOH A 546 16.032 9.201 0.768 1.00 0.00 H
-HETATM 1639 O HOH A 547 4.960 19.274 21.490 1.00 0.00 O
-HETATM 1640 H1 HOH A 547 4.607 18.754 20.769 1.00 0.00 H
-HETATM 1641 H2 HOH A 547 4.188 19.634 21.928 1.00 0.00 H
-HETATM 1642 O HOH A 548 3.802 18.192 19.026 1.00 0.00 O
-HETATM 1643 H1 HOH A 548 4.403 18.591 18.396 1.00 0.00 H
-HETATM 1644 H2 HOH A 548 2.951 18.581 18.827 1.00 0.00 H
-HETATM 1645 O HOH A 549 10.578 29.122 26.330 1.00 0.00 O
-HETATM 1646 H1 HOH A 549 10.013 28.370 26.505 1.00 0.00 H
-HETATM 1647 H2 HOH A 549 10.913 28.970 25.446 1.00 0.00 H
-HETATM 1648 O HOH A 550 9.343 26.540 26.955 1.00 0.00 O
-HETATM 1649 H1 HOH A 550 9.748 26.389 27.810 1.00 0.00 H
-HETATM 1650 H2 HOH A 550 9.678 25.837 26.401 1.00 0.00 H
-HETATM 1651 O HOH A 551 25.595 25.278 29.408 1.00 0.00 O
-HETATM 1652 H1 HOH A 551 25.300 25.545 28.538 1.00 0.00 H
-HETATM 1653 H2 HOH A 551 24.970 25.684 30.009 1.00 0.00 H
-HETATM 1654 O HOH A 552 24.940 26.600 26.877 1.00 0.00 O
-HETATM 1655 H1 HOH A 552 25.823 26.868 26.621 1.00 0.00 H
-HETATM 1656 H2 HOH A 552 24.467 27.421 27.011 1.00 0.00 H
-HETATM 1657 O HOH A 553 20.360 15.761 19.335 1.00 0.00 O
-HETATM 1658 H1 HOH A 553 19.586 15.198 19.339 1.00 0.00 H
-HETATM 1659 H2 HOH A 553 20.273 16.295 20.124 1.00 0.00 H
-HETATM 1660 O HOH A 554 17.716 14.510 19.163 1.00 0.00 O
-HETATM 1661 H1 HOH A 554 17.520 14.713 18.248 1.00 0.00 H
-HETATM 1662 H2 HOH A 554 17.058 14.989 19.665 1.00 0.00 H
-HETATM 1663 O HOH A 555 8.151 15.666 7.622 1.00 0.00 O
-HETATM 1664 H1 HOH A 555 7.438 16.305 7.634 1.00 0.00 H
-HETATM 1665 H2 HOH A 555 7.711 14.816 7.610 1.00 0.00 H
-HETATM 1666 O HOH A 556 5.929 17.482 8.215 1.00 0.00 O
-HETATM 1667 H1 HOH A 556 6.328 17.967 8.938 1.00 0.00 H
-HETATM 1668 H2 HOH A 556 5.186 17.025 8.611 1.00 0.00 H
-HETATM 1669 O HOH A 557 29.564 0.850 6.168 1.00 0.00 O
-HETATM 1670 H1 HOH A 557 28.836 1.194 6.685 1.00 0.00 H
-HETATM 1671 H2 HOH A 557 29.945 1.621 5.746 1.00 0.00 H
-HETATM 1672 O HOH A 558 27.062 1.930 7.245 1.00 0.00 O
-HETATM 1673 H1 HOH A 558 26.461 1.266 6.905 1.00 0.00 H
-HETATM 1674 H2 HOH A 558 26.802 2.740 6.807 1.00 0.00 H
-HETATM 1675 O HOH A 559 28.605 29.840 25.135 1.00 0.00 O
-HETATM 1676 H1 HOH A 559 28.169 29.015 25.347 1.00 0.00 H
-HETATM 1677 H2 HOH A 559 28.543 29.907 24.182 1.00 0.00 H
-HETATM 1678 O HOH A 560 27.759 27.074 25.599 1.00 0.00 O
-HETATM 1679 H1 HOH A 560 28.485 26.793 26.158 1.00 0.00 H
-HETATM 1680 H2 HOH A 560 27.838 26.536 24.812 1.00 0.00 H
-HETATM 1681 O HOH A 561 10.082 10.888 4.195 1.00 0.00 O
-HETATM 1682 H1 HOH A 561 10.712 11.219 4.835 1.00 0.00 H
-HETATM 1683 H2 HOH A 561 10.514 11.008 3.349 1.00 0.00 H
-HETATM 1684 O HOH A 562 11.826 12.421 5.982 1.00 0.00 O
-HETATM 1685 H1 HOH A 562 11.156 12.947 6.420 1.00 0.00 H
-HETATM 1686 H2 HOH A 562 12.360 13.056 5.506 1.00 0.00 H
-HETATM 1687 O HOH A 563 6.391 26.908 11.949 1.00 0.00 O
-HETATM 1688 H1 HOH A 563 5.701 26.778 11.298 1.00 0.00 H
-HETATM 1689 H2 HOH A 563 5.925 26.981 12.781 1.00 0.00 H
-HETATM 1690 O HOH A 564 4.196 27.078 10.016 1.00 0.00 O
-HETATM 1691 H1 HOH A 564 4.559 27.755 9.444 1.00 0.00 H
-HETATM 1692 H2 HOH A 564 3.413 27.477 10.394 1.00 0.00 H
-HETATM 1693 O HOH A 565 13.734 25.683 19.308 1.00 0.00 O
-HETATM 1694 H1 HOH A 565 14.393 26.223 18.874 1.00 0.00 H
-HETATM 1695 H2 HOH A 565 14.081 25.540 20.189 1.00 0.00 H
-HETATM 1696 O HOH A 566 16.045 26.843 17.931 1.00 0.00 O
-HETATM 1697 H1 HOH A 566 16.052 26.276 17.159 1.00 0.00 H
-HETATM 1698 H2 HOH A 566 16.867 26.649 18.381 1.00 0.00 H
-HETATM 1699 O HOH A 567 14.852 26.745 13.127 1.00 0.00 O
-HETATM 1700 H1 HOH A 567 14.969 26.051 13.776 1.00 0.00 H
-HETATM 1701 H2 HOH A 567 14.981 27.557 13.617 1.00 0.00 H
-HETATM 1702 O HOH A 568 14.679 24.766 15.280 1.00 0.00 O
-HETATM 1703 H1 HOH A 568 13.874 24.325 15.007 1.00 0.00 H
-HETATM 1704 H2 HOH A 568 14.461 25.167 16.121 1.00 0.00 H
-HETATM 1705 O HOH A 569 8.715 1.641 7.234 1.00 0.00 O
-HETATM 1706 H1 HOH A 569 9.389 0.968 7.331 1.00 0.00 H
-HETATM 1707 H2 HOH A 569 8.580 1.711 6.289 1.00 0.00 H
-HETATM 1708 O HOH A 570 11.139 -0.000 7.373 1.00 0.00 O
-HETATM 1709 H1 HOH A 570 11.649 0.546 7.971 1.00 0.00 H
-HETATM 1710 H2 HOH A 570 11.631 0.015 6.552 1.00 0.00 H
-HETATM 1711 O HOH A 571 19.616 10.994 15.669 1.00 0.00 O
-HETATM 1712 H1 HOH A 571 19.203 11.696 16.171 1.00 0.00 H
-HETATM 1713 H2 HOH A 571 19.866 11.404 14.841 1.00 0.00 H
-HETATM 1714 O HOH A 572 17.900 13.047 16.863 1.00 0.00 O
-HETATM 1715 H1 HOH A 572 17.148 12.497 17.083 1.00 0.00 H
-HETATM 1716 H2 HOH A 572 17.567 13.662 16.209 1.00 0.00 H
-HETATM 1717 O HOH A 573 16.420 8.996 15.699 1.00 0.00 O
-HETATM 1718 H1 HOH A 573 15.715 9.057 16.343 1.00 0.00 H
-HETATM 1719 H2 HOH A 573 16.202 9.655 15.040 1.00 0.00 H
-HETATM 1720 O HOH A 574 13.956 8.887 17.280 1.00 0.00 O
-HETATM 1721 H1 HOH A 574 13.826 7.938 17.280 1.00 0.00 H
-HETATM 1722 H2 HOH A 574 13.205 9.234 16.800 1.00 0.00 H
-HETATM 1723 O HOH A 575 26.717 12.637 20.231 1.00 0.00 O
-HETATM 1724 H1 HOH A 575 27.466 12.929 19.713 1.00 0.00 H
-HETATM 1725 H2 HOH A 575 26.880 12.985 21.108 1.00 0.00 H
-HETATM 1726 O HOH A 576 29.291 13.104 18.911 1.00 0.00 O
-HETATM 1727 H1 HOH A 576 29.431 12.227 18.553 1.00 0.00 H
-HETATM 1728 H2 HOH A 576 30.001 13.225 19.541 1.00 0.00 H
-HETATM 1729 O HOH A 577 7.750 24.754 27.173 1.00 0.00 O
-HETATM 1730 H1 HOH A 577 7.528 25.380 26.483 1.00 0.00 H
-HETATM 1731 H2 HOH A 577 7.289 25.076 27.947 1.00 0.00 H
-HETATM 1732 O HOH A 578 7.459 27.007 25.322 1.00 0.00 O
-HETATM 1733 H1 HOH A 578 8.376 27.098 25.060 1.00 0.00 H
-HETATM 1734 H2 HOH A 578 7.256 27.823 25.777 1.00 0.00 H
-HETATM 1735 O HOH A 579 2.514 7.721 21.694 1.00 0.00 O
-HETATM 1736 H1 HOH A 579 2.110 8.475 21.266 1.00 0.00 H
-HETATM 1737 H2 HOH A 579 2.582 7.061 21.004 1.00 0.00 H
-HETATM 1738 O HOH A 580 0.788 9.727 20.438 1.00 0.00 O
-HETATM 1739 H1 HOH A 580 0.148 9.829 21.143 1.00 0.00 H
-HETATM 1740 H2 HOH A 580 0.288 9.365 19.707 1.00 0.00 H
-HETATM 1741 O HOH A 581 19.374 8.623 14.961 1.00 0.00 O
-HETATM 1742 H1 HOH A 581 19.014 8.234 15.758 1.00 0.00 H
-HETATM 1743 H2 HOH A 581 18.927 8.168 14.248 1.00 0.00 H
-HETATM 1744 O HOH A 582 18.625 6.993 17.277 1.00 0.00 O
-HETATM 1745 H1 HOH A 582 19.497 6.862 17.650 1.00 0.00 H
-HETATM 1746 H2 HOH A 582 18.336 6.116 17.023 1.00 0.00 H
-HETATM 1747 O HOH A 583 13.798 29.714 26.324 1.00 0.00 O
-HETATM 1748 H1 HOH A 583 13.412 29.457 27.161 1.00 0.00 H
-HETATM 1749 H2 HOH A 583 14.684 30.002 26.546 1.00 0.00 H
-HETATM 1750 O HOH A 584 12.558 29.495 28.970 1.00 0.00 O
-HETATM 1751 H1 HOH A 584 11.763 30.001 28.799 1.00 0.00 H
-HETATM 1752 H2 HOH A 584 13.029 30.000 29.631 1.00 0.00 H
-HETATM 1753 O HOH A 585 21.729 14.853 8.724 1.00 0.00 O
-HETATM 1754 H1 HOH A 585 22.134 14.545 7.914 1.00 0.00 H
-HETATM 1755 H2 HOH A 585 22.286 14.503 9.419 1.00 0.00 H
-HETATM 1756 O HOH A 586 22.720 13.408 6.376 1.00 0.00 O
-HETATM 1757 H1 HOH A 586 21.882 13.096 6.034 1.00 0.00 H
-HETATM 1758 H2 HOH A 586 23.194 12.613 6.616 1.00 0.00 H
-HETATM 1759 O HOH A 587 20.887 16.799 17.469 1.00 0.00 O
-HETATM 1760 H1 HOH A 587 20.731 17.124 16.582 1.00 0.00 H
-HETATM 1761 H2 HOH A 587 21.758 17.125 17.694 1.00 0.00 H
-HETATM 1762 O HOH A 588 20.760 17.366 14.597 1.00 0.00 O
-HETATM 1763 H1 HOH A 588 20.478 16.503 14.292 1.00 0.00 H
-HETATM 1764 H2 HOH A 588 21.629 17.485 14.214 1.00 0.00 H
-HETATM 1765 O HOH A 589 17.969 24.961 25.033 1.00 0.00 O
-HETATM 1766 H1 HOH A 589 17.454 24.473 24.390 1.00 0.00 H
-HETATM 1767 H2 HOH A 589 17.373 25.087 25.771 1.00 0.00 H
-HETATM 1768 O HOH A 590 16.150 23.979 22.956 1.00 0.00 O
-HETATM 1769 H1 HOH A 590 16.402 24.574 22.250 1.00 0.00 H
-HETATM 1770 H2 HOH A 590 15.237 24.198 23.144 1.00 0.00 H
-HETATM 1771 O HOH A 591 22.588 21.581 0.476 1.00 0.00 O
-HETATM 1772 H1 HOH A 591 23.418 21.335 0.884 1.00 0.00 H
-HETATM 1773 H2 HOH A 591 22.784 22.387 -0.001 1.00 0.00 H
-HETATM 1774 O HOH A 592 25.002 21.244 2.103 1.00 0.00 O
-HETATM 1775 H1 HOH A 592 24.585 21.110 2.954 1.00 0.00 H
-HETATM 1776 H2 HOH A 592 25.489 22.062 2.198 1.00 0.00 H
-HETATM 1777 O HOH A 593 28.331 29.731 4.100 1.00 0.00 O
-HETATM 1778 H1 HOH A 593 28.477 28.926 3.603 1.00 0.00 H
-HETATM 1779 H2 HOH A 593 27.585 29.533 4.666 1.00 0.00 H
-HETATM 1780 O HOH A 594 28.324 27.442 2.272 1.00 0.00 O
-HETATM 1781 H1 HOH A 594 28.367 27.919 1.443 1.00 0.00 H
-HETATM 1782 H2 HOH A 594 27.476 26.999 2.252 1.00 0.00 H
-HETATM 1783 O HOH A 595 4.388 8.859 12.020 1.00 0.00 O
-HETATM 1784 H1 HOH A 595 4.285 7.920 12.175 1.00 0.00 H
-HETATM 1785 H2 HOH A 595 4.435 8.941 11.067 1.00 0.00 H
-HETATM 1786 O HOH A 596 4.639 5.963 12.385 1.00 0.00 O
-HETATM 1787 H1 HOH A 596 5.333 5.960 13.044 1.00 0.00 H
-HETATM 1788 H2 HOH A 596 5.028 5.536 11.622 1.00 0.00 H
-HETATM 1789 O HOH A 597 1.522 20.651 15.121 1.00 0.00 O
-HETATM 1790 H1 HOH A 597 1.699 21.199 14.357 1.00 0.00 H
-HETATM 1791 H2 HOH A 597 0.679 20.962 15.451 1.00 0.00 H
-HETATM 1792 O HOH A 598 2.209 22.728 13.173 1.00 0.00 O
-HETATM 1793 H1 HOH A 598 3.101 22.914 13.468 1.00 0.00 H
-HETATM 1794 H2 HOH A 598 1.728 23.540 13.328 1.00 0.00 H
-HETATM 1795 O HOH A 599 -0.000 11.291 17.706 1.00 0.00 O
-HETATM 1796 H1 HOH A 599 0.577 11.234 18.467 1.00 0.00 H
-HETATM 1797 H2 HOH A 599 0.127 10.462 17.245 1.00 0.00 H
-HETATM 1798 O HOH A 600 2.186 11.235 19.656 1.00 0.00 O
-HETATM 1799 H1 HOH A 600 2.552 12.104 19.487 1.00 0.00 H
-HETATM 1800 H2 HOH A 600 2.879 10.627 19.402 1.00 0.00 H
-HETATM 1801 O HOH A 601 1.219 29.844 12.900 1.00 0.00 O
-HETATM 1802 H1 HOH A 601 1.115 29.783 11.950 1.00 0.00 H
-HETATM 1803 H2 HOH A 601 2.130 29.596 13.058 1.00 0.00 H
-HETATM 1804 O HOH A 602 0.873 29.090 10.090 1.00 0.00 O
-HETATM 1805 H1 HOH A 602 0.065 28.583 10.175 1.00 0.00 H
-HETATM 1806 H2 HOH A 602 1.529 28.455 9.804 1.00 0.00 H
-HETATM 1807 O HOH A 603 21.231 10.420 11.823 1.00 0.00 O
-HETATM 1808 H1 HOH A 603 21.643 11.058 12.404 1.00 0.00 H
-HETATM 1809 H2 HOH A 603 20.505 10.895 11.419 1.00 0.00 H
-HETATM 1810 O HOH A 604 22.046 12.262 13.951 1.00 0.00 O
-HETATM 1811 H1 HOH A 604 22.056 11.642 14.681 1.00 0.00 H
-HETATM 1812 H2 HOH A 604 21.354 12.885 14.172 1.00 0.00 H
-HETATM 1813 O HOH A 605 0.263 20.719 11.915 1.00 0.00 O
-HETATM 1814 H1 HOH A 605 0.007 21.406 11.301 1.00 0.00 H
-HETATM 1815 H2 HOH A 605 0.131 21.108 12.780 1.00 0.00 H
-HETATM 1816 O HOH A 606 -0.006 23.050 10.161 1.00 0.00 O
-HETATM 1817 H1 HOH A 606 0.792 22.935 9.644 1.00 0.00 H
-HETATM 1818 H2 HOH A 606 0.126 23.872 10.631 1.00 0.00 H
-HETATM 1819 O HOH A 607 18.401 26.846 0.488 1.00 0.00 O
-HETATM 1820 H1 HOH A 607 17.624 27.200 0.920 1.00 0.00 H
-HETATM 1821 H2 HOH A 607 19.109 26.982 1.118 1.00 0.00 H
-HETATM 1822 O HOH A 608 16.241 28.441 1.661 1.00 0.00 O
-HETATM 1823 H1 HOH A 608 15.994 28.943 0.884 1.00 0.00 H
-HETATM 1824 H2 HOH A 608 16.570 29.095 2.278 1.00 0.00 H
-HETATM 1825 O HOH A 609 2.918 21.393 6.664 1.00 0.00 O
-HETATM 1826 H1 HOH A 609 2.925 21.032 5.778 1.00 0.00 H
-HETATM 1827 H2 HOH A 609 2.146 21.007 7.077 1.00 0.00 H
-HETATM 1828 O HOH A 610 2.522 20.673 3.852 1.00 0.00 O
-HETATM 1829 H1 HOH A 610 2.653 21.538 3.462 1.00 0.00 H
-HETATM 1830 H2 HOH A 610 1.610 20.457 3.661 1.00 0.00 H
-HETATM 1831 O HOH A 611 4.239 6.274 24.152 1.00 0.00 O
-HETATM 1832 H1 HOH A 611 4.456 7.069 24.639 1.00 0.00 H
-HETATM 1833 H2 HOH A 611 5.086 5.890 23.928 1.00 0.00 H
-HETATM 1834 O HOH A 612 5.018 8.927 25.121 1.00 0.00 O
-HETATM 1835 H1 HOH A 612 4.416 9.455 24.596 1.00 0.00 H
-HETATM 1836 H2 HOH A 612 5.890 9.180 24.816 1.00 0.00 H
-HETATM 1837 O HOH A 613 4.051 18.452 2.267 1.00 0.00 O
-HETATM 1838 H1 HOH A 613 4.562 19.075 1.750 1.00 0.00 H
-HETATM 1839 H2 HOH A 613 4.421 18.509 3.147 1.00 0.00 H
-HETATM 1840 O HOH A 614 6.018 19.945 0.691 1.00 0.00 O
-HETATM 1841 H1 HOH A 614 6.163 19.303 -0.005 1.00 0.00 H
-HETATM 1842 H2 HOH A 614 6.862 20.012 1.138 1.00 0.00 H
-HETATM 1843 O HOH A 615 25.476 19.620 8.638 1.00 0.00 O
-HETATM 1844 H1 HOH A 615 26.101 20.150 9.133 1.00 0.00 H
-HETATM 1845 H2 HOH A 615 25.827 19.605 7.747 1.00 0.00 H
-HETATM 1846 O HOH A 616 27.127 21.687 9.897 1.00 0.00 O
-HETATM 1847 H1 HOH A 616 26.428 22.216 10.282 1.00 0.00 H
-HETATM 1848 H2 HOH A 616 27.539 22.262 9.252 1.00 0.00 H
-HETATM 1849 O HOH A 617 18.628 13.429 23.998 1.00 0.00 O
-HETATM 1850 H1 HOH A 617 18.938 14.321 24.153 1.00 0.00 H
-HETATM 1851 H2 HOH A 617 18.164 13.477 23.162 1.00 0.00 H
-HETATM 1852 O HOH A 618 19.030 16.263 24.624 1.00 0.00 O
-HETATM 1853 H1 HOH A 618 18.697 16.261 25.523 1.00 0.00 H
-HETATM 1854 H2 HOH A 618 18.425 16.827 24.143 1.00 0.00 H
-HETATM 1855 O HOH A 619 9.844 17.725 25.897 1.00 0.00 O
-HETATM 1856 H1 HOH A 619 10.477 18.367 25.575 1.00 0.00 H
-HETATM 1857 H2 HOH A 619 9.822 17.865 26.844 1.00 0.00 H
-HETATM 1858 O HOH A 620 12.173 19.320 25.111 1.00 0.00 O
-HETATM 1859 H1 HOH A 620 12.631 18.660 24.590 1.00 0.00 H
-HETATM 1860 H2 HOH A 620 12.757 19.493 25.849 1.00 0.00 H
-HETATM 1861 O HOH A 621 28.412 4.587 27.523 1.00 0.00 O
-HETATM 1862 H1 HOH A 621 27.954 4.760 28.345 1.00 0.00 H
-HETATM 1863 H2 HOH A 621 29.160 5.185 27.533 1.00 0.00 H
-HETATM 1864 O HOH A 622 26.815 5.584 29.767 1.00 0.00 O
-HETATM 1865 H1 HOH A 622 25.969 5.652 29.324 1.00 0.00 H
-HETATM 1866 H2 HOH A 622 27.040 6.485 29.998 1.00 0.00 H
-HETATM 1867 O HOH A 623 13.789 23.279 17.622 1.00 0.00 O
-HETATM 1868 H1 HOH A 623 13.106 22.967 18.215 1.00 0.00 H
-HETATM 1869 H2 HOH A 623 13.493 23.011 16.752 1.00 0.00 H
-HETATM 1870 O HOH A 624 11.945 21.811 19.362 1.00 0.00 O
-HETATM 1871 H1 HOH A 624 12.588 21.413 19.951 1.00 0.00 H
-HETATM 1872 H2 HOH A 624 11.539 21.070 18.913 1.00 0.00 H
-HETATM 1873 O HOH A 625 10.868 1.204 27.721 1.00 0.00 O
-HETATM 1874 H1 HOH A 625 11.665 1.592 27.362 1.00 0.00 H
-HETATM 1875 H2 HOH A 625 10.193 1.868 27.580 1.00 0.00 H
-HETATM 1876 O HOH A 626 13.314 2.697 27.112 1.00 0.00 O
-HETATM 1877 H1 HOH A 626 13.817 2.477 27.897 1.00 0.00 H
-HETATM 1878 H2 HOH A 626 13.181 3.643 27.167 1.00 0.00 H
-HETATM 1879 O HOH A 627 11.204 30.009 11.230 1.00 0.00 O
-HETATM 1880 H1 HOH A 627 11.869 29.471 11.658 1.00 0.00 H
-HETATM 1881 H2 HOH A 627 11.034 29.566 10.398 1.00 0.00 H
-HETATM 1882 O HOH A 628 13.604 28.618 12.173 1.00 0.00 O
-HETATM 1883 H1 HOH A 628 14.145 29.383 12.373 1.00 0.00 H
-HETATM 1884 H2 HOH A 628 14.065 28.174 11.462 1.00 0.00 H
-HETATM 1885 O HOH A 629 9.084 8.920 0.462 1.00 0.00 O
-HETATM 1886 H1 HOH A 629 10.037 8.919 0.544 1.00 0.00 H
-HETATM 1887 H2 HOH A 629 8.877 9.768 0.069 1.00 0.00 H
-HETATM 1888 O HOH A 630 11.908 9.257 1.167 1.00 0.00 O
-HETATM 1889 H1 HOH A 630 11.865 8.966 2.078 1.00 0.00 H
-HETATM 1890 H2 HOH A 630 12.158 10.179 1.219 1.00 0.00 H
-HETATM 1891 O HOH A 631 16.146 5.514 7.896 1.00 0.00 O
-HETATM 1892 H1 HOH A 631 15.472 5.092 7.365 1.00 0.00 H
-HETATM 1893 H2 HOH A 631 15.970 5.218 8.789 1.00 0.00 H
-HETATM 1894 O HOH A 632 13.736 4.631 6.484 1.00 0.00 O
-HETATM 1895 H1 HOH A 632 13.515 5.453 6.046 1.00 0.00 H
-HETATM 1896 H2 HOH A 632 13.006 4.472 7.082 1.00 0.00 H
-HETATM 1897 O HOH A 633 15.046 11.109 0.294 1.00 0.00 O
-HETATM 1898 H1 HOH A 633 14.584 11.767 0.813 1.00 0.00 H
-HETATM 1899 H2 HOH A 633 14.361 10.509 -0.000 1.00 0.00 H
-HETATM 1900 O HOH A 634 13.624 12.715 2.289 1.00 0.00 O
-HETATM 1901 H1 HOH A 634 14.251 12.640 3.009 1.00 0.00 H
-HETATM 1902 H2 HOH A 634 12.835 12.277 2.608 1.00 0.00 H
-HETATM 1903 O HOH A 635 10.418 14.009 28.380 1.00 0.00 O
-HETATM 1904 H1 HOH A 635 10.799 14.289 27.548 1.00 0.00 H
-HETATM 1905 H2 HOH A 635 9.888 14.754 28.664 1.00 0.00 H
-HETATM 1906 O HOH A 636 11.946 15.168 26.165 1.00 0.00 O
-HETATM 1907 H1 HOH A 636 12.822 14.889 26.432 1.00 0.00 H
-HETATM 1908 H2 HOH A 636 11.973 16.124 26.207 1.00 0.00 H
-HETATM 1909 O HOH A 637 12.589 26.143 12.286 1.00 0.00 O
-HETATM 1910 H1 HOH A 637 12.235 26.960 12.636 1.00 0.00 H
-HETATM 1911 H2 HOH A 637 12.309 26.132 11.370 1.00 0.00 H
-HETATM 1912 O HOH A 638 11.006 28.357 13.371 1.00 0.00 O
-HETATM 1913 H1 HOH A 638 10.644 27.912 14.139 1.00 0.00 H
-HETATM 1914 H2 HOH A 638 10.246 28.557 12.826 1.00 0.00 H
-HETATM 1915 O HOH A 639 20.970 25.897 7.532 1.00 0.00 O
-HETATM 1916 H1 HOH A 639 21.179 26.582 8.167 1.00 0.00 H
-HETATM 1917 H2 HOH A 639 20.022 25.959 7.414 1.00 0.00 H
-HETATM 1918 O HOH A 640 21.399 27.622 9.862 1.00 0.00 O
-HETATM 1919 H1 HOH A 640 21.922 27.020 10.393 1.00 0.00 H
-HETATM 1920 H2 HOH A 640 20.596 27.753 10.365 1.00 0.00 H
-HETATM 1921 O HOH A 641 16.368 10.385 4.719 1.00 0.00 O
-HETATM 1922 H1 HOH A 641 16.390 9.542 5.171 1.00 0.00 H
-HETATM 1923 H2 HOH A 641 15.636 10.311 4.106 1.00 0.00 H
-HETATM 1924 O HOH A 642 16.683 7.614 5.618 1.00 0.00 O
-HETATM 1925 H1 HOH A 642 17.626 7.523 5.476 1.00 0.00 H
-HETATM 1926 H2 HOH A 642 16.285 6.991 5.010 1.00 0.00 H
-HETATM 1927 O HOH A 643 4.670 15.590 17.241 1.00 0.00 O
-HETATM 1928 H1 HOH A 643 4.424 14.671 17.143 1.00 0.00 H
-HETATM 1929 H2 HOH A 643 5.142 15.801 16.436 1.00 0.00 H
-HETATM 1930 O HOH A 644 4.469 12.672 17.075 1.00 0.00 O
-HETATM 1931 H1 HOH A 644 4.793 12.455 17.949 1.00 0.00 H
-HETATM 1932 H2 HOH A 644 5.116 12.300 16.477 1.00 0.00 H
-HETATM 1933 O HOH A 645 17.516 1.060 29.999 1.00 0.00 O
-HETATM 1934 H1 HOH A 645 18.324 1.474 29.698 1.00 0.00 H
-HETATM 1935 H2 HOH A 645 17.017 0.894 29.199 1.00 0.00 H
-HETATM 1936 O HOH A 646 19.662 2.797 29.020 1.00 0.00 O
-HETATM 1937 H1 HOH A 646 19.586 3.499 29.667 1.00 0.00 H
-HETATM 1938 H2 HOH A 646 19.439 3.211 28.186 1.00 0.00 H
-HETATM 1939 O HOH A 647 0.453 9.772 26.263 1.00 0.00 O
-HETATM 1940 H1 HOH A 647 1.406 9.680 26.236 1.00 0.00 H
-HETATM 1941 H2 HOH A 647 0.267 10.092 27.146 1.00 0.00 H
-HETATM 1942 O HOH A 648 3.274 9.009 26.483 1.00 0.00 O
-HETATM 1943 H1 HOH A 648 3.214 8.127 26.115 1.00 0.00 H
-HETATM 1944 H2 HOH A 648 3.540 8.874 27.392 1.00 0.00 H
-HETATM 1945 O HOH A 649 13.207 21.705 8.727 1.00 0.00 O
-HETATM 1946 H1 HOH A 649 12.454 21.328 9.182 1.00 0.00 H
-HETATM 1947 H2 HOH A 649 12.912 21.810 7.822 1.00 0.00 H
-HETATM 1948 O HOH A 650 11.067 20.060 9.867 1.00 0.00 O
-HETATM 1949 H1 HOH A 650 11.607 19.380 10.271 1.00 0.00 H
-HETATM 1950 H2 HOH A 650 10.566 19.601 9.192 1.00 0.00 H
-HETATM 1951 O HOH A 651 18.296 20.137 5.429 1.00 0.00 O
-HETATM 1952 H1 HOH A 651 18.233 19.897 6.353 1.00 0.00 H
-HETATM 1953 H2 HOH A 651 18.329 21.094 5.431 1.00 0.00 H
-HETATM 1954 O HOH A 652 17.550 19.556 8.202 1.00 0.00 O
-HETATM 1955 H1 HOH A 652 16.822 18.958 8.033 1.00 0.00 H
-HETATM 1956 H2 HOH A 652 17.149 20.311 8.633 1.00 0.00 H
-HETATM 1957 O HOH A 653 29.233 7.251 24.103 1.00 0.00 O
-HETATM 1958 H1 HOH A 653 29.140 6.783 23.273 1.00 0.00 H
-HETATM 1959 H2 HOH A 653 28.448 7.794 24.163 1.00 0.00 H
-HETATM 1960 O HOH A 654 29.108 6.320 21.328 1.00 0.00 O
-HETATM 1961 H1 HOH A 654 30.001 6.542 21.060 1.00 0.00 H
-HETATM 1962 H2 HOH A 654 28.548 6.875 20.786 1.00 0.00 H
-HETATM 1963 O HOH A 655 8.263 19.706 24.387 1.00 0.00 O
-HETATM 1964 H1 HOH A 655 7.781 18.884 24.472 1.00 0.00 H
-HETATM 1965 H2 HOH A 655 9.152 19.441 24.149 1.00 0.00 H
-HETATM 1966 O HOH A 656 7.059 17.148 25.154 1.00 0.00 O
-HETATM 1967 H1 HOH A 656 6.692 17.408 25.999 1.00 0.00 H
-HETATM 1968 H2 HOH A 656 7.719 16.489 25.369 1.00 0.00 H
-HETATM 1969 O HOH A 657 19.949 20.916 22.727 1.00 0.00 O
-HETATM 1970 H1 HOH A 657 18.993 20.868 22.715 1.00 0.00 H
-HETATM 1971 H2 HOH A 657 20.180 20.881 23.655 1.00 0.00 H
-HETATM 1972 O HOH A 658 17.050 21.325 22.846 1.00 0.00 O
-HETATM 1973 H1 HOH A 658 16.985 22.071 22.248 1.00 0.00 H
-HETATM 1974 H2 HOH A 658 16.770 21.670 23.694 1.00 0.00 H
-HETATM 1975 O HOH A 659 29.300 14.702 22.447 1.00 0.00 O
-HETATM 1976 H1 HOH A 659 28.729 15.156 21.828 1.00 0.00 H
-HETATM 1977 H2 HOH A 659 30.002 14.339 21.906 1.00 0.00 H
-HETATM 1978 O HOH A 660 27.365 15.564 20.423 1.00 0.00 O
-HETATM 1979 H1 HOH A 660 26.581 15.150 20.787 1.00 0.00 H
-HETATM 1980 H2 HOH A 660 27.502 15.125 19.584 1.00 0.00 H
-HETATM 1981 O HOH A 661 13.214 5.799 16.143 1.00 0.00 O
-HETATM 1982 H1 HOH A 661 13.782 6.059 15.417 1.00 0.00 H
-HETATM 1983 H2 HOH A 661 12.562 6.498 16.200 1.00 0.00 H
-HETATM 1984 O HOH A 662 15.179 6.962 14.306 1.00 0.00 O
-HETATM 1985 H1 HOH A 662 15.970 6.804 14.822 1.00 0.00 H
-HETATM 1986 H2 HOH A 662 15.107 7.915 14.258 1.00 0.00 H
-HETATM 1987 O HOH A 663 22.403 4.191 7.593 1.00 0.00 O
-HETATM 1988 H1 HOH A 663 22.742 3.421 7.137 1.00 0.00 H
-HETATM 1989 H2 HOH A 663 22.715 4.093 8.493 1.00 0.00 H
-HETATM 1990 O HOH A 664 23.009 1.558 6.459 1.00 0.00 O
-HETATM 1991 H1 HOH A 664 22.154 1.367 6.072 1.00 0.00 H
-HETATM 1992 H2 HOH A 664 23.121 0.891 7.137 1.00 0.00 H
-HETATM 1993 O HOH A 665 15.414 20.191 17.237 1.00 0.00 O
-HETATM 1994 H1 HOH A 665 14.613 19.824 16.864 1.00 0.00 H
-HETATM 1995 H2 HOH A 665 15.261 20.193 18.182 1.00 0.00 H
-HETATM 1996 O HOH A 666 12.742 19.589 16.198 1.00 0.00 O
-HETATM 1997 H1 HOH A 666 12.674 20.295 15.554 1.00 0.00 H
-HETATM 1998 H2 HOH A 666 12.055 19.778 16.837 1.00 0.00 H
-HETATM 1999 O HOH A 667 20.864 25.571 17.399 1.00 0.00 O
-HETATM 2000 H1 HOH A 667 21.025 25.211 18.271 1.00 0.00 H
-HETATM 2001 H2 HOH A 667 21.536 26.244 17.289 1.00 0.00 H
-HETATM 2002 O HOH A 668 21.099 24.950 20.252 1.00 0.00 O
-HETATM 2003 H1 HOH A 668 20.175 25.025 20.493 1.00 0.00 H
-HETATM 2004 H2 HOH A 668 21.533 25.650 20.739 1.00 0.00 H
-HETATM 2005 O HOH A 669 9.580 9.778 17.176 1.00 0.00 O
-HETATM 2006 H1 HOH A 669 9.059 10.451 17.614 1.00 0.00 H
-HETATM 2007 H2 HOH A 669 9.581 10.037 16.255 1.00 0.00 H
-HETATM 2008 O HOH A 670 7.537 11.555 18.296 1.00 0.00 O
-HETATM 2009 H1 HOH A 670 7.013 10.900 18.758 1.00 0.00 H
-HETATM 2010 H2 HOH A 670 6.952 11.901 17.622 1.00 0.00 H
-HETATM 2011 O HOH A 671 10.360 7.237 3.821 1.00 0.00 O
-HETATM 2012 H1 HOH A 671 11.253 7.378 3.507 1.00 0.00 H
-HETATM 2013 H2 HOH A 671 10.402 7.424 4.759 1.00 0.00 H
-HETATM 2014 O HOH A 672 13.198 7.153 3.098 1.00 0.00 O
-HETATM 2015 H1 HOH A 672 13.202 6.319 2.628 1.00 0.00 H
-HETATM 2016 H2 HOH A 672 13.759 7.006 3.858 1.00 0.00 H
-HETATM 2017 O HOH A 673 7.281 10.907 11.861 1.00 0.00 O
-HETATM 2018 H1 HOH A 673 7.680 11.131 12.701 1.00 0.00 H
-HETATM 2019 H2 HOH A 673 7.156 11.749 11.422 1.00 0.00 H
-HETATM 2020 O HOH A 674 7.967 11.745 14.583 1.00 0.00 O
-HETATM 2021 H1 HOH A 674 7.348 11.195 15.064 1.00 0.00 H
-HETATM 2022 H2 HOH A 674 7.686 12.641 14.765 1.00 0.00 H
-HETATM 2023 O HOH A 675 15.191 3.925 26.054 1.00 0.00 O
-HETATM 2024 H1 HOH A 675 15.192 3.071 25.622 1.00 0.00 H
-HETATM 2025 H2 HOH A 675 15.371 3.730 26.974 1.00 0.00 H
-HETATM 2026 O HOH A 676 14.672 1.255 24.965 1.00 0.00 O
-HETATM 2027 H1 HOH A 676 13.845 1.440 24.518 1.00 0.00 H
-HETATM 2028 H2 HOH A 676 14.442 0.626 25.648 1.00 0.00 H
-HETATM 2029 O HOH A 677 19.183 0.551 9.667 1.00 0.00 O
-HETATM 2030 H1 HOH A 677 18.548 1.126 10.093 1.00 0.00 H
-HETATM 2031 H2 HOH A 677 19.439 1.021 8.873 1.00 0.00 H
-HETATM 2032 O HOH A 678 16.875 2.113 10.572 1.00 0.00 O
-HETATM 2033 H1 HOH A 678 16.255 1.394 10.698 1.00 0.00 H
-HETATM 2034 H2 HOH A 678 16.497 2.638 9.867 1.00 0.00 H
-HETATM 2035 O HOH A 679 13.369 15.380 19.779 1.00 0.00 O
-HETATM 2036 H1 HOH A 679 13.635 14.525 20.117 1.00 0.00 H
-HETATM 2037 H2 HOH A 679 12.413 15.343 19.758 1.00 0.00 H
-HETATM 2038 O HOH A 680 14.065 12.578 20.280 1.00 0.00 O
-HETATM 2039 H1 HOH A 680 14.709 12.462 19.581 1.00 0.00 H
-HETATM 2040 H2 HOH A 680 13.351 11.983 20.054 1.00 0.00 H
-HETATM 2041 O HOH A 681 13.454 17.285 18.264 1.00 0.00 O
-HETATM 2042 H1 HOH A 681 12.600 17.392 17.847 1.00 0.00 H
-HETATM 2043 H2 HOH A 681 13.999 17.970 17.876 1.00 0.00 H
-HETATM 2044 O HOH A 682 11.088 17.350 16.538 1.00 0.00 O
-HETATM 2045 H1 HOH A 682 11.027 16.415 16.340 1.00 0.00 H
-HETATM 2046 H2 HOH A 682 11.268 17.765 15.695 1.00 0.00 H
-HETATM 2047 O HOH A 683 11.058 13.553 2.858 1.00 0.00 O
-HETATM 2048 H1 HOH A 683 10.691 12.907 2.254 1.00 0.00 H
-HETATM 2049 H2 HOH A 683 11.109 13.097 3.697 1.00 0.00 H
-HETATM 2050 O HOH A 684 9.435 11.705 1.266 1.00 0.00 O
-HETATM 2051 H1 HOH A 684 8.789 12.330 0.934 1.00 0.00 H
-HETATM 2052 H2 HOH A 684 8.931 11.105 1.815 1.00 0.00 H
-HETATM 2053 O HOH A 685 16.110 16.784 13.278 1.00 0.00 O
-HETATM 2054 H1 HOH A 685 16.843 17.031 12.714 1.00 0.00 H
-HETATM 2055 H2 HOH A 685 16.186 17.360 14.038 1.00 0.00 H
-HETATM 2056 O HOH A 686 18.669 17.233 11.924 1.00 0.00 O
-HETATM 2057 H1 HOH A 686 18.932 16.320 11.806 1.00 0.00 H
-HETATM 2058 H2 HOH A 686 19.328 17.600 12.513 1.00 0.00 H
-HETATM 2059 O HOH A 687 16.094 29.794 11.440 1.00 0.00 O
-HETATM 2060 H1 HOH A 687 16.033 29.881 10.489 1.00 0.00 H
-HETATM 2061 H2 HOH A 687 17.003 30.013 11.644 1.00 0.00 H
-HETATM 2062 O HOH A 688 16.175 29.550 8.521 1.00 0.00 O
-HETATM 2063 H1 HOH A 688 15.743 28.699 8.435 1.00 0.00 H
-HETATM 2064 H2 HOH A 688 17.063 29.404 8.196 1.00 0.00 H
-HETATM 2065 O HOH A 689 19.637 29.646 15.071 1.00 0.00 O
-HETATM 2066 H1 HOH A 689 18.837 29.312 15.477 1.00 0.00 H
-HETATM 2067 H2 HOH A 689 19.336 30.098 14.282 1.00 0.00 H
-HETATM 2068 O HOH A 690 17.219 28.218 15.905 1.00 0.00 O
-HETATM 2069 H1 HOH A 690 17.598 27.345 16.017 1.00 0.00 H
-HETATM 2070 H2 HOH A 690 16.593 28.119 15.188 1.00 0.00 H
-HETATM 2071 O HOH A 691 19.259 27.222 20.312 1.00 0.00 O
-HETATM 2072 H1 HOH A 691 18.716 27.772 19.747 1.00 0.00 H
-HETATM 2073 H2 HOH A 691 19.577 27.816 20.992 1.00 0.00 H
-HETATM 2074 O HOH A 692 18.102 29.091 18.375 1.00 0.00 O
-HETATM 2075 H1 HOH A 692 18.530 28.764 17.584 1.00 0.00 H
-HETATM 2076 H2 HOH A 692 18.441 29.979 18.485 1.00 0.00 H
-HETATM 2077 O HOH A 693 19.978 26.373 12.672 1.00 0.00 O
-HETATM 2078 H1 HOH A 693 20.675 26.980 12.423 1.00 0.00 H
-HETATM 2079 H2 HOH A 693 19.547 26.795 13.416 1.00 0.00 H
-HETATM 2080 O HOH A 694 22.387 28.013 12.366 1.00 0.00 O
-HETATM 2081 H1 HOH A 694 23.037 27.311 12.339 1.00 0.00 H
-HETATM 2082 H2 HOH A 694 22.592 28.504 13.161 1.00 0.00 H
-HETATM 2083 O HOH A 695 23.194 20.281 13.035 1.00 0.00 O
-HETATM 2084 H1 HOH A 695 23.849 19.678 12.684 1.00 0.00 H
-HETATM 2085 H2 HOH A 695 22.433 19.730 13.217 1.00 0.00 H
-HETATM 2086 O HOH A 696 24.888 18.468 11.476 1.00 0.00 O
-HETATM 2087 H1 HOH A 696 25.076 19.046 10.736 1.00 0.00 H
-HETATM 2088 H2 HOH A 696 24.388 17.745 11.098 1.00 0.00 H
-HETATM 2089 O HOH A 697 22.394 11.806 27.471 1.00 0.00 O
-HETATM 2090 H1 HOH A 697 21.727 11.586 26.822 1.00 0.00 H
-HETATM 2091 H2 HOH A 697 22.068 11.430 28.288 1.00 0.00 H
-HETATM 2092 O HOH A 698 20.055 11.566 25.723 1.00 0.00 O
-HETATM 2093 H1 HOH A 698 20.026 12.465 25.397 1.00 0.00 H
-HETATM 2094 H2 HOH A 698 19.244 11.462 26.220 1.00 0.00 H
-HETATM 2095 O HOH A 699 17.924 1.072 12.256 1.00 0.00 O
-HETATM 2096 H1 HOH A 699 18.161 1.938 12.586 1.00 0.00 H
-HETATM 2097 H2 HOH A 699 17.083 0.879 12.671 1.00 0.00 H
-HETATM 2098 O HOH A 700 18.798 3.439 13.744 1.00 0.00 O
-HETATM 2099 H1 HOH A 700 19.683 3.151 13.967 1.00 0.00 H
-HETATM 2100 H2 HOH A 700 18.350 3.521 14.586 1.00 0.00 H
-HETATM 2101 O HOH A 701 6.654 29.998 23.836 1.00 0.00 O
-HETATM 2102 H1 HOH A 701 6.834 29.197 23.344 1.00 0.00 H
-HETATM 2103 H2 HOH A 701 6.868 29.778 24.742 1.00 0.00 H
-HETATM 2104 O HOH A 702 6.705 27.367 22.549 1.00 0.00 O
-HETATM 2105 H1 HOH A 702 5.859 27.409 22.101 1.00 0.00 H
-HETATM 2106 H2 HOH A 702 6.608 26.655 23.181 1.00 0.00 H
-HETATM 2107 O HOH A 703 25.674 21.642 0.046 1.00 0.00 O
-HETATM 2108 H1 HOH A 703 25.908 20.715 -0.002 1.00 0.00 H
-HETATM 2109 H2 HOH A 703 26.511 22.104 -0.000 1.00 0.00 H
-HETATM 2110 O HOH A 704 26.539 18.872 0.450 1.00 0.00 O
-HETATM 2111 H1 HOH A 704 25.988 18.657 1.203 1.00 0.00 H
-HETATM 2112 H2 HOH A 704 27.435 18.820 0.784 1.00 0.00 H
-HETATM 2113 O HOH A 705 15.571 29.682 17.538 1.00 0.00 O
-HETATM 2114 H1 HOH A 705 14.656 29.557 17.790 1.00 0.00 H
-HETATM 2115 H2 HOH A 705 15.533 29.949 16.620 1.00 0.00 H
-HETATM 2116 O HOH A 706 12.818 28.802 18.022 1.00 0.00 O
-HETATM 2117 H1 HOH A 706 13.006 27.950 18.415 1.00 0.00 H
-HETATM 2118 H2 HOH A 706 12.357 28.598 17.208 1.00 0.00 H
-HETATM 2119 O HOH A 707 20.602 9.274 3.626 1.00 0.00 O
-HETATM 2120 H1 HOH A 707 20.191 10.020 4.062 1.00 0.00 H
-HETATM 2121 H2 HOH A 707 20.441 9.422 2.694 1.00 0.00 H
-HETATM 2122 O HOH A 708 18.854 11.275 4.861 1.00 0.00 O
-HETATM 2123 H1 HOH A 708 18.404 10.707 5.488 1.00 0.00 H
-HETATM 2124 H2 HOH A 708 18.166 11.569 4.265 1.00 0.00 H
-HETATM 2125 O HOH A 709 3.247 3.543 3.330 1.00 0.00 O
-HETATM 2126 H1 HOH A 709 3.240 4.058 4.137 1.00 0.00 H
-HETATM 2127 H2 HOH A 709 3.539 4.156 2.656 1.00 0.00 H
-HETATM 2128 O HOH A 710 2.735 5.335 5.591 1.00 0.00 O
-HETATM 2129 H1 HOH A 710 1.856 5.033 5.819 1.00 0.00 H
-HETATM 2130 H2 HOH A 710 2.615 6.242 5.312 1.00 0.00 H
-HETATM 2131 O HOH A 711 26.192 28.444 29.080 1.00 0.00 O
-HETATM 2132 H1 HOH A 711 25.404 27.920 28.937 1.00 0.00 H
-HETATM 2133 H2 HOH A 711 26.496 28.191 29.952 1.00 0.00 H
-HETATM 2134 O HOH A 712 23.530 27.225 28.992 1.00 0.00 O
-HETATM 2135 H1 HOH A 712 23.030 27.990 28.704 1.00 0.00 H
-HETATM 2136 H2 HOH A 712 23.193 27.032 29.867 1.00 0.00 H
-HETATM 2137 O HOH A 713 8.487 6.772 27.181 1.00 0.00 O
-HETATM 2138 H1 HOH A 713 9.015 6.155 26.675 1.00 0.00 H
-HETATM 2139 H2 HOH A 713 8.758 6.632 28.089 1.00 0.00 H
-HETATM 2140 O HOH A 714 9.729 4.491 25.826 1.00 0.00 O
-HETATM 2141 H1 HOH A 714 8.963 4.171 25.348 1.00 0.00 H
-HETATM 2142 H2 HOH A 714 9.947 3.785 26.434 1.00 0.00 H
-HETATM 2143 O HOH A 715 3.754 25.215 11.097 1.00 0.00 O
-HETATM 2144 H1 HOH A 715 3.911 24.966 12.008 1.00 0.00 H
-HETATM 2145 H2 HOH A 715 4.572 25.006 10.646 1.00 0.00 H
-HETATM 2146 O HOH A 716 4.533 24.945 13.909 1.00 0.00 O
-HETATM 2147 H1 HOH A 716 4.195 25.780 14.233 1.00 0.00 H
-HETATM 2148 H2 HOH A 716 5.480 25.004 14.027 1.00 0.00 H
-HETATM 2149 O HOH A 717 2.141 19.473 21.139 1.00 0.00 O
-HETATM 2150 H1 HOH A 717 2.209 18.519 21.145 1.00 0.00 H
-HETATM 2151 H2 HOH A 717 1.736 19.679 20.296 1.00 0.00 H
-HETATM 2152 O HOH A 718 2.799 16.637 20.810 1.00 0.00 O
-HETATM 2153 H1 HOH A 718 3.716 16.669 21.084 1.00 0.00 H
-HETATM 2154 H2 HOH A 718 2.831 16.379 19.889 1.00 0.00 H
-HETATM 2155 O HOH A 719 29.317 5.764 18.756 1.00 0.00 O
-HETATM 2156 H1 HOH A 719 29.022 6.643 18.518 1.00 0.00 H
-HETATM 2157 H2 HOH A 719 30.000 5.558 18.117 1.00 0.00 H
-HETATM 2158 O HOH A 720 28.128 8.182 17.606 1.00 0.00 O
-HETATM 2159 H1 HOH A 720 27.208 7.921 17.640 1.00 0.00 H
-HETATM 2160 H2 HOH A 720 28.321 8.270 16.673 1.00 0.00 H
-HETATM 2161 O HOH A 721 0.737 5.094 12.464 1.00 0.00 O
-HETATM 2162 H1 HOH A 721 0.917 4.564 11.688 1.00 0.00 H
-HETATM 2163 H2 HOH A 721 0.000 4.656 12.889 1.00 0.00 H
-HETATM 2164 O HOH A 722 0.828 3.735 9.870 1.00 0.00 O
-HETATM 2165 H1 HOH A 722 0.851 4.507 9.303 1.00 0.00 H
-HETATM 2166 H2 HOH A 722 -0.000 3.305 9.660 1.00 0.00 H
-HETATM 2167 O HOH A 723 5.864 10.081 27.483 1.00 0.00 O
-HETATM 2168 H1 HOH A 723 5.967 9.136 27.599 1.00 0.00 H
-HETATM 2169 H2 HOH A 723 6.050 10.451 28.346 1.00 0.00 H
-HETATM 2170 O HOH A 724 5.659 7.221 28.088 1.00 0.00 O
-HETATM 2171 H1 HOH A 724 4.824 7.042 27.653 1.00 0.00 H
-HETATM 2172 H2 HOH A 724 5.488 7.061 29.016 1.00 0.00 H
-HETATM 2173 O HOH A 725 23.927 22.231 11.608 1.00 0.00 O
-HETATM 2174 H1 HOH A 725 23.020 22.538 11.587 1.00 0.00 H
-HETATM 2175 H2 HOH A 725 24.367 22.733 10.922 1.00 0.00 H
-HETATM 2176 O HOH A 726 21.109 22.832 11.077 1.00 0.00 O
-HETATM 2177 H1 HOH A 726 20.768 21.937 11.073 1.00 0.00 H
-HETATM 2178 H2 HOH A 726 20.970 23.148 10.185 1.00 0.00 H
-HETATM 2179 O HOH A 727 14.533 8.753 30.000 1.00 0.00 O
-HETATM 2180 H1 HOH A 727 13.649 8.772 29.634 1.00 0.00 H
-HETATM 2181 H2 HOH A 727 15.108 8.897 29.248 1.00 0.00 H
-HETATM 2182 O HOH A 728 11.951 8.278 28.698 1.00 0.00 O
-HETATM 2183 H1 HOH A 728 11.708 7.469 29.151 1.00 0.00 H
-HETATM 2184 H2 HOH A 728 12.052 8.022 27.782 1.00 0.00 H
-HETATM 2185 O HOH A 729 22.201 8.331 9.280 1.00 0.00 O
-HETATM 2186 H1 HOH A 729 22.498 9.073 8.754 1.00 0.00 H
-HETATM 2187 H2 HOH A 729 21.728 7.773 8.663 1.00 0.00 H
-HETATM 2188 O HOH A 730 22.564 10.782 7.717 1.00 0.00 O
-HETATM 2189 H1 HOH A 730 22.240 11.413 8.361 1.00 0.00 H
-HETATM 2190 H2 HOH A 730 21.946 10.840 6.989 1.00 0.00 H
-HETATM 2191 O HOH A 731 25.079 26.819 24.323 1.00 0.00 O
-HETATM 2192 H1 HOH A 731 25.151 25.958 24.735 1.00 0.00 H
-HETATM 2193 H2 HOH A 731 25.475 26.706 23.459 1.00 0.00 H
-HETATM 2194 O HOH A 732 25.851 24.332 25.666 1.00 0.00 O
-HETATM 2195 H1 HOH A 732 26.209 24.708 26.471 1.00 0.00 H
-HETATM 2196 H2 HOH A 732 26.598 23.911 25.242 1.00 0.00 H
-HETATM 2197 O HOH A 733 9.796 14.663 5.924 1.00 0.00 O
-HETATM 2198 H1 HOH A 733 9.212 13.923 6.088 1.00 0.00 H
-HETATM 2199 H2 HOH A 733 10.104 14.533 5.027 1.00 0.00 H
-HETATM 2200 O HOH A 734 8.498 12.098 6.491 1.00 0.00 O
-HETATM 2201 H1 HOH A 734 8.920 11.905 7.329 1.00 0.00 H
-HETATM 2202 H2 HOH A 734 8.798 11.405 5.904 1.00 0.00 H
-HETATM 2203 O HOH A 735 26.792 13.589 0.208 1.00 0.00 O
-HETATM 2204 H1 HOH A 735 27.671 13.361 0.510 1.00 0.00 H
-HETATM 2205 H2 HOH A 735 26.383 12.749 0.000 1.00 0.00 H
-HETATM 2206 O HOH A 736 29.596 12.820 0.571 1.00 0.00 O
-HETATM 2207 H1 HOH A 736 30.000 13.474 -0.000 1.00 0.00 H
-HETATM 2208 H2 HOH A 736 29.795 11.980 0.156 1.00 0.00 H
-HETATM 2209 O HOH A 737 23.153 14.445 15.144 1.00 0.00 O
-HETATM 2210 H1 HOH A 737 22.956 14.600 14.221 1.00 0.00 H
-HETATM 2211 H2 HOH A 737 22.298 14.315 15.554 1.00 0.00 H
-HETATM 2212 O HOH A 738 22.402 15.448 12.495 1.00 0.00 O
-HETATM 2213 H1 HOH A 738 22.968 16.220 12.467 1.00 0.00 H
-HETATM 2214 H2 HOH A 738 21.512 15.799 12.482 1.00 0.00 H
-HETATM 2215 O HOH A 739 6.749 0.593 22.917 1.00 0.00 O
-HETATM 2216 H1 HOH A 739 7.497 0.926 22.422 1.00 0.00 H
-HETATM 2217 H2 HOH A 739 6.049 0.513 22.269 1.00 0.00 H
-HETATM 2218 O HOH A 740 8.766 2.118 21.437 1.00 0.00 O
-HETATM 2219 H1 HOH A 740 8.938 2.799 22.088 1.00 0.00 H
-HETATM 2220 H2 HOH A 740 8.389 2.585 20.692 1.00 0.00 H
-HETATM 2221 O HOH A 741 18.417 4.843 21.408 1.00 0.00 O
-HETATM 2222 H1 HOH A 741 18.105 5.521 20.809 1.00 0.00 H
-HETATM 2223 H2 HOH A 741 17.947 5.009 22.225 1.00 0.00 H
-HETATM 2224 O HOH A 742 17.820 7.264 19.870 1.00 0.00 O
-HETATM 2225 H1 HOH A 742 18.712 7.491 19.606 1.00 0.00 H
-HETATM 2226 H2 HOH A 742 17.541 7.987 20.431 1.00 0.00 H
-HETATM 2227 O HOH A 743 19.962 18.276 22.640 1.00 0.00 O
-HETATM 2228 H1 HOH A 743 19.155 18.687 22.948 1.00 0.00 H
-HETATM 2229 H2 HOH A 743 19.713 17.372 22.445 1.00 0.00 H
-HETATM 2230 O HOH A 744 17.601 19.253 24.073 1.00 0.00 O
-HETATM 2231 H1 HOH A 744 18.041 19.627 24.837 1.00 0.00 H
-HETATM 2232 H2 HOH A 744 17.071 18.538 24.424 1.00 0.00 H
-HETATM 2233 O HOH A 745 2.015 27.509 27.170 1.00 0.00 O
-HETATM 2234 H1 HOH A 745 2.907 27.838 27.276 1.00 0.00 H
-HETATM 2235 H2 HOH A 745 1.517 27.937 27.867 1.00 0.00 H
-HETATM 2236 O HOH A 746 4.773 28.139 27.930 1.00 0.00 O
-HETATM 2237 H1 HOH A 746 5.142 27.257 27.880 1.00 0.00 H
-HETATM 2238 H2 HOH A 746 4.846 28.380 28.854 1.00 0.00 H
-HETATM 2239 O HOH A 747 2.765 6.065 17.207 1.00 0.00 O
-HETATM 2240 H1 HOH A 747 3.506 5.701 17.691 1.00 0.00 H
-HETATM 2241 H2 HOH A 747 2.244 5.302 16.956 1.00 0.00 H
-HETATM 2242 O HOH A 748 5.235 4.816 18.167 1.00 0.00 O
-HETATM 2243 H1 HOH A 748 5.864 5.334 17.663 1.00 0.00 H
-HETATM 2244 H2 HOH A 748 5.339 3.923 17.837 1.00 0.00 H
-HETATM 2245 O HOH A 749 15.220 19.437 3.729 1.00 0.00 O
-HETATM 2246 H1 HOH A 749 15.378 18.887 4.496 1.00 0.00 H
-HETATM 2247 H2 HOH A 749 15.328 20.333 4.050 1.00 0.00 H
-HETATM 2248 O HOH A 750 15.171 17.907 6.228 1.00 0.00 O
-HETATM 2249 H1 HOH A 750 14.378 17.394 6.067 1.00 0.00 H
-HETATM 2250 H2 HOH A 750 14.956 18.456 6.982 1.00 0.00 H
-HETATM 2251 O HOH A 751 17.503 1.113 7.831 1.00 0.00 O
-HETATM 2252 H1 HOH A 751 16.549 1.179 7.863 1.00 0.00 H
-HETATM 2253 H2 HOH A 751 17.673 0.335 7.301 1.00 0.00 H
-HETATM 2254 O HOH A 752 14.623 0.892 8.318 1.00 0.00 O
-HETATM 2255 H1 HOH A 752 14.627 1.011 9.269 1.00 0.00 H
-HETATM 2256 H2 HOH A 752 14.303 -0.000 8.188 1.00 0.00 H
-HETATM 2257 O HOH A 753 4.312 5.567 21.331 1.00 0.00 O
-HETATM 2258 H1 HOH A 753 4.867 6.290 21.623 1.00 0.00 H
-HETATM 2259 H2 HOH A 753 4.039 5.132 22.138 1.00 0.00 H
-HETATM 2260 O HOH A 754 6.407 7.371 22.302 1.00 0.00 O
-HETATM 2261 H1 HOH A 754 7.094 7.145 21.675 1.00 0.00 H
-HETATM 2262 H2 HOH A 754 6.753 7.097 23.151 1.00 0.00 H
-HETATM 2263 O HOH A 755 25.600 6.272 22.684 1.00 0.00 O
-HETATM 2264 H1 HOH A 755 25.721 7.149 22.322 1.00 0.00 H
-HETATM 2265 H2 HOH A 755 26.241 5.731 22.223 1.00 0.00 H
-HETATM 2266 O HOH A 756 25.676 8.752 21.126 1.00 0.00 O
-HETATM 2267 H1 HOH A 756 24.735 8.861 20.983 1.00 0.00 H
-HETATM 2268 H2 HOH A 756 26.040 8.617 20.251 1.00 0.00 H
-HETATM 2269 O HOH A 757 2.923 8.050 9.452 1.00 0.00 O
-HETATM 2270 H1 HOH A 757 2.537 8.763 9.960 1.00 0.00 H
-HETATM 2271 H2 HOH A 757 3.591 8.474 8.913 1.00 0.00 H
-HETATM 2272 O HOH A 758 1.461 10.354 10.519 1.00 0.00 O
-HETATM 2273 H1 HOH A 758 0.572 10.074 10.302 1.00 0.00 H
-HETATM 2274 H2 HOH A 758 1.601 11.143 9.996 1.00 0.00 H
-HETATM 2275 O HOH A 759 4.198 2.732 22.326 1.00 0.00 O
-HETATM 2276 H1 HOH A 759 4.277 2.565 21.387 1.00 0.00 H
-HETATM 2277 H2 HOH A 759 5.067 2.539 22.678 1.00 0.00 H
-HETATM 2278 O HOH A 760 4.403 1.665 19.605 1.00 0.00 O
-HETATM 2279 H1 HOH A 760 3.603 1.138 19.595 1.00 0.00 H
-HETATM 2280 H2 HOH A 760 5.113 1.029 19.520 1.00 0.00 H
-HETATM 2281 O HOH A 761 13.478 26.932 6.797 1.00 0.00 O
-HETATM 2282 H1 HOH A 761 14.099 26.713 6.102 1.00 0.00 H
-HETATM 2283 H2 HOH A 761 13.989 26.868 7.604 1.00 0.00 H
-HETATM 2284 O HOH A 762 15.293 25.719 4.842 1.00 0.00 O
-HETATM 2285 H1 HOH A 762 14.681 25.101 4.441 1.00 0.00 H
-HETATM 2286 H2 HOH A 762 15.955 25.169 5.259 1.00 0.00 H
-HETATM 2287 O HOH A 763 0.434 27.623 7.542 1.00 0.00 O
-HETATM 2288 H1 HOH A 763 0.216 26.727 7.799 1.00 0.00 H
-HETATM 2289 H2 HOH A 763 -0.001 27.745 6.699 1.00 0.00 H
-HETATM 2290 O HOH A 764 0.168 24.733 7.943 1.00 0.00 O
-HETATM 2291 H1 HOH A 764 1.082 24.531 8.150 1.00 0.00 H
-HETATM 2292 H2 HOH A 764 -0.002 24.270 7.123 1.00 0.00 H
-HETATM 2293 O HOH A 765 23.452 14.724 3.047 1.00 0.00 O
-HETATM 2294 H1 HOH A 765 24.178 14.264 3.469 1.00 0.00 H
-HETATM 2295 H2 HOH A 765 22.933 15.070 3.773 1.00 0.00 H
-HETATM 2296 O HOH A 766 25.282 12.903 4.434 1.00 0.00 O
-HETATM 2297 H1 HOH A 766 25.152 12.126 3.890 1.00 0.00 H
-HETATM 2298 H2 HOH A 766 24.954 12.652 5.297 1.00 0.00 H
-HETATM 2299 O HOH A 767 27.656 24.776 23.303 1.00 0.00 O
-HETATM 2300 H1 HOH A 767 26.725 24.925 23.138 1.00 0.00 H
-HETATM 2301 H2 HOH A 767 28.003 24.468 22.466 1.00 0.00 H
-HETATM 2302 O HOH A 768 24.766 24.659 22.835 1.00 0.00 O
-HETATM 2303 H1 HOH A 768 24.512 24.173 23.620 1.00 0.00 H
-HETATM 2304 H2 HOH A 768 24.542 24.078 22.108 1.00 0.00 H
-HETATM 2305 O HOH A 769 3.538 3.470 16.273 1.00 0.00 O
-HETATM 2306 H1 HOH A 769 3.219 3.957 15.513 1.00 0.00 H
-HETATM 2307 H2 HOH A 769 2.750 3.093 16.665 1.00 0.00 H
-HETATM 2308 O HOH A 770 2.404 5.355 14.338 1.00 0.00 O
-HETATM 2309 H1 HOH A 770 2.910 6.132 14.579 1.00 0.00 H
-HETATM 2310 H2 HOH A 770 1.498 5.583 14.546 1.00 0.00 H
-HETATM 2311 O HOH A 771 28.577 28.444 27.608 1.00 0.00 O
-HETATM 2312 H1 HOH A 771 27.640 28.525 27.426 1.00 0.00 H
-HETATM 2313 H2 HOH A 771 28.792 29.230 28.110 1.00 0.00 H
-HETATM 2314 O HOH A 772 25.879 29.067 26.648 1.00 0.00 O
-HETATM 2315 H1 HOH A 772 26.006 28.866 25.720 1.00 0.00 H
-HETATM 2316 H2 HOH A 772 25.697 30.006 26.670 1.00 0.00 H
-HETATM 2317 O HOH A 773 4.020 28.062 12.601 1.00 0.00 O
-HETATM 2318 H1 HOH A 773 4.493 28.587 11.956 1.00 0.00 H
-HETATM 2319 H2 HOH A 773 4.051 28.582 13.404 1.00 0.00 H
-HETATM 2320 O HOH A 774 5.938 29.476 10.896 1.00 0.00 O
-HETATM 2321 H1 HOH A 774 6.465 28.726 10.619 1.00 0.00 H
-HETATM 2322 H2 HOH A 774 6.539 30.020 11.404 1.00 0.00 H
-HETATM 2323 O HOH A 775 22.525 12.821 24.644 1.00 0.00 O
-HETATM 2324 H1 HOH A 775 22.118 13.481 25.205 1.00 0.00 H
-HETATM 2325 H2 HOH A 775 22.047 12.880 23.817 1.00 0.00 H
-HETATM 2326 O HOH A 776 20.858 14.451 26.418 1.00 0.00 O
-HETATM 2327 H1 HOH A 776 20.772 13.833 27.145 1.00 0.00 H
-HETATM 2328 H2 HOH A 776 19.968 14.555 26.082 1.00 0.00 H
-HETATM 2329 O HOH A 777 15.915 0.873 2.564 1.00 0.00 O
-HETATM 2330 H1 HOH A 777 15.081 1.186 2.912 1.00 0.00 H
-HETATM 2331 H2 HOH A 777 15.721 0.625 1.660 1.00 0.00 H
-HETATM 2332 O HOH A 778 13.231 1.271 3.668 1.00 0.00 O
-HETATM 2333 H1 HOH A 778 13.318 0.734 4.457 1.00 0.00 H
-HETATM 2334 H2 HOH A 778 12.559 0.831 3.149 1.00 0.00 H
-HETATM 2335 O HOH A 779 22.605 3.090 27.071 1.00 0.00 O
-HETATM 2336 H1 HOH A 779 21.775 3.567 27.103 1.00 0.00 H
-HETATM 2337 H2 HOH A 779 22.899 3.058 27.981 1.00 0.00 H
-HETATM 2338 O HOH A 780 20.401 5.015 27.220 1.00 0.00 O
-HETATM 2339 H1 HOH A 780 20.663 5.591 26.500 1.00 0.00 H
-HETATM 2340 H2 HOH A 780 20.455 5.565 28.001 1.00 0.00 H
-HETATM 2341 O HOH A 781 23.590 9.194 6.406 1.00 0.00 O
-HETATM 2342 H1 HOH A 781 24.266 9.642 6.914 1.00 0.00 H
-HETATM 2343 H2 HOH A 781 24.068 8.547 5.887 1.00 0.00 H
-HETATM 2344 O HOH A 782 25.768 10.842 7.466 1.00 0.00 O
-HETATM 2345 H1 HOH A 782 25.427 11.704 7.225 1.00 0.00 H
-HETATM 2346 H2 HOH A 782 26.572 10.749 6.955 1.00 0.00 H
-HETATM 2347 O HOH A 783 11.656 16.180 24.031 1.00 0.00 O
-HETATM 2348 H1 HOH A 783 10.778 15.801 23.992 1.00 0.00 H
-HETATM 2349 H2 HOH A 783 11.577 17.017 23.575 1.00 0.00 H
-HETATM 2350 O HOH A 784 9.109 14.892 23.369 1.00 0.00 O
-HETATM 2351 H1 HOH A 784 9.447 14.048 23.068 1.00 0.00 H
-HETATM 2352 H2 HOH A 784 8.700 15.279 22.595 1.00 0.00 H
-HETATM 2353 O HOH A 785 8.862 29.219 10.369 1.00 0.00 O
-HETATM 2354 H1 HOH A 785 8.783 29.221 11.323 1.00 0.00 H
-HETATM 2355 H2 HOH A 785 8.292 29.931 10.079 1.00 0.00 H
-HETATM 2356 O HOH A 786 8.115 28.963 13.190 1.00 0.00 O
-HETATM 2357 H1 HOH A 786 7.967 28.017 13.215 1.00 0.00 H
-HETATM 2358 H2 HOH A 786 7.257 29.347 13.372 1.00 0.00 H
-HETATM 2359 O HOH A 787 5.246 22.100 27.429 1.00 0.00 O
-HETATM 2360 H1 HOH A 787 5.959 22.585 27.844 1.00 0.00 H
-HETATM 2361 H2 HOH A 787 4.453 22.425 27.857 1.00 0.00 H
-HETATM 2362 O HOH A 788 7.350 23.217 29.135 1.00 0.00 O
-HETATM 2363 H1 HOH A 788 7.855 22.417 29.283 1.00 0.00 H
-HETATM 2364 H2 HOH A 788 7.021 23.460 30.000 1.00 0.00 H
-HETATM 2365 O HOH A 789 0.871 13.648 22.256 1.00 0.00 O
-HETATM 2366 H1 HOH A 789 0.465 14.513 22.209 1.00 0.00 H
-HETATM 2367 H2 HOH A 789 1.164 13.569 23.164 1.00 0.00 H
-HETATM 2368 O HOH A 790 0.171 16.488 22.096 1.00 0.00 O
-HETATM 2369 H1 HOH A 790 0.656 16.712 21.301 1.00 0.00 H
-HETATM 2370 H2 HOH A 790 0.588 17.002 22.787 1.00 0.00 H
-HETATM 2371 O HOH A 791 6.299 19.517 19.589 1.00 0.00 O
-HETATM 2372 H1 HOH A 791 6.906 20.257 19.627 1.00 0.00 H
-HETATM 2373 H2 HOH A 791 6.805 18.775 19.920 1.00 0.00 H
-HETATM 2374 O HOH A 792 8.381 21.548 19.230 1.00 0.00 O
-HETATM 2375 H1 HOH A 792 8.174 21.810 18.333 1.00 0.00 H
-HETATM 2376 H2 HOH A 792 9.255 21.162 19.174 1.00 0.00 H
-HETATM 2377 O HOH A 793 23.691 28.887 19.190 1.00 0.00 O
-HETATM 2378 H1 HOH A 793 22.741 28.999 19.170 1.00 0.00 H
-HETATM 2379 H2 HOH A 793 24.040 29.759 19.004 1.00 0.00 H
-HETATM 2380 O HOH A 794 20.851 29.229 18.556 1.00 0.00 O
-HETATM 2381 H1 HOH A 794 20.738 28.469 17.985 1.00 0.00 H
-HETATM 2382 H2 HOH A 794 20.697 29.984 17.989 1.00 0.00 H
-HETATM 2383 O HOH A 795 9.865 13.659 20.944 1.00 0.00 O
-HETATM 2384 H1 HOH A 795 8.963 13.931 20.779 1.00 0.00 H
-HETATM 2385 H2 HOH A 795 10.288 13.695 20.085 1.00 0.00 H
-HETATM 2386 O HOH A 796 7.038 13.957 20.235 1.00 0.00 O
-HETATM 2387 H1 HOH A 796 6.687 13.213 20.725 1.00 0.00 H
-HETATM 2388 H2 HOH A 796 6.872 13.742 19.317 1.00 0.00 H
-HETATM 2389 O HOH A 797 6.867 12.515 5.075 1.00 0.00 O
-HETATM 2390 H1 HOH A 797 7.391 13.313 5.014 1.00 0.00 H
-HETATM 2391 H2 HOH A 797 6.621 12.319 4.171 1.00 0.00 H
-HETATM 2392 O HOH A 798 7.962 15.221 4.827 1.00 0.00 O
-HETATM 2393 H1 HOH A 798 7.466 15.643 5.530 1.00 0.00 H
-HETATM 2394 H2 HOH A 798 7.667 15.657 4.028 1.00 0.00 H
-HETATM 2395 O HOH A 799 1.565 26.456 13.316 1.00 0.00 O
-HETATM 2396 H1 HOH A 799 1.682 25.736 13.936 1.00 0.00 H
-HETATM 2397 H2 HOH A 799 2.429 26.578 12.923 1.00 0.00 H
-HETATM 2398 O HOH A 800 2.169 24.654 15.546 1.00 0.00 O
-HETATM 2399 H1 HOH A 800 1.729 25.137 16.246 1.00 0.00 H
-HETATM 2400 H2 HOH A 800 3.096 24.673 15.784 1.00 0.00 H
-HETATM 2401 O HOH A 801 5.279 3.308 10.983 1.00 0.00 O
-HETATM 2402 H1 HOH A 801 4.746 2.744 11.544 1.00 0.00 H
-HETATM 2403 H2 HOH A 801 5.218 2.910 10.115 1.00 0.00 H
-HETATM 2404 O HOH A 802 4.102 1.229 12.679 1.00 0.00 O
-HETATM 2405 H1 HOH A 802 4.791 1.176 13.342 1.00 0.00 H
-HETATM 2406 H2 HOH A 802 4.108 0.371 12.255 1.00 0.00 H
-HETATM 2407 O HOH A 803 6.476 7.996 18.822 1.00 0.00 O
-HETATM 2408 H1 HOH A 803 6.291 8.839 18.408 1.00 0.00 H
-HETATM 2409 H2 HOH A 803 6.899 7.479 18.137 1.00 0.00 H
-HETATM 2410 O HOH A 804 5.474 10.295 17.307 1.00 0.00 O
-HETATM 2411 H1 HOH A 804 4.569 10.291 17.621 1.00 0.00 H
-HETATM 2412 H2 HOH A 804 5.407 10.108 16.371 1.00 0.00 H
-HETATM 2413 O HOH A 805 17.402 18.951 27.745 1.00 0.00 O
-HETATM 2414 H1 HOH A 805 18.056 18.427 28.206 1.00 0.00 H
-HETATM 2415 H2 HOH A 805 17.574 18.790 26.817 1.00 0.00 H
-HETATM 2416 O HOH A 806 19.767 17.770 29.009 1.00 0.00 O
-HETATM 2417 H1 HOH A 806 20.013 18.507 29.570 1.00 0.00 H
-HETATM 2418 H2 HOH A 806 20.495 17.679 28.395 1.00 0.00 H
-HETATM 2419 O HOH A 807 14.039 16.767 21.350 1.00 0.00 O
-HETATM 2420 H1 HOH A 807 14.485 17.154 22.103 1.00 0.00 H
-HETATM 2421 H2 HOH A 807 13.116 16.990 21.476 1.00 0.00 H
-HETATM 2422 O HOH A 808 15.193 17.499 23.941 1.00 0.00 O
-HETATM 2423 H1 HOH A 808 15.668 16.688 24.122 1.00 0.00 H
-HETATM 2424 H2 HOH A 808 14.530 17.551 24.629 1.00 0.00 H
-HETATM 2425 O HOH A 809 22.728 26.647 15.437 1.00 0.00 O
-HETATM 2426 H1 HOH A 809 23.353 26.102 15.915 1.00 0.00 H
-HETATM 2427 H2 HOH A 809 22.810 26.365 14.526 1.00 0.00 H
-HETATM 2428 O HOH A 810 25.035 25.338 16.682 1.00 0.00 O
-HETATM 2429 H1 HOH A 810 25.404 26.097 17.135 1.00 0.00 H
-HETATM 2430 H2 HOH A 810 25.700 25.093 16.039 1.00 0.00 H
-HETATM 2431 O HOH A 811 26.622 20.504 4.675 1.00 0.00 O
-HETATM 2432 H1 HOH A 811 27.521 20.220 4.843 1.00 0.00 H
-HETATM 2433 H2 HOH A 811 26.093 20.016 5.306 1.00 0.00 H
-HETATM 2434 O HOH A 812 29.216 19.160 4.898 1.00 0.00 O
-HETATM 2435 H1 HOH A 812 29.378 19.003 3.967 1.00 0.00 H
-HETATM 2436 H2 HOH A 812 29.137 18.286 5.281 1.00 0.00 H
-HETATM 2437 O HOH A 813 28.815 2.547 21.885 1.00 0.00 O
-HETATM 2438 H1 HOH A 813 28.015 2.030 21.794 1.00 0.00 H
-HETATM 2439 H2 HOH A 813 28.812 2.838 22.797 1.00 0.00 H
-HETATM 2440 O HOH A 814 26.104 1.478 21.586 1.00 0.00 O
-HETATM 2441 H1 HOH A 814 25.854 1.940 20.785 1.00 0.00 H
-HETATM 2442 H2 HOH A 814 25.508 1.815 22.255 1.00 0.00 H
-HETATM 2443 O HOH A 815 27.918 3.616 15.289 1.00 0.00 O
-HETATM 2444 H1 HOH A 815 28.027 4.294 14.622 1.00 0.00 H
-HETATM 2445 H2 HOH A 815 28.114 4.058 16.116 1.00 0.00 H
-HETATM 2446 O HOH A 816 28.815 5.594 13.323 1.00 0.00 O
-HETATM 2447 H1 HOH A 816 29.357 5.022 12.779 1.00 0.00 H
-HETATM 2448 H2 HOH A 816 29.430 6.223 13.700 1.00 0.00 H
-HETATM 2449 O HOH A 817 2.890 24.310 4.156 1.00 0.00 O
-HETATM 2450 H1 HOH A 817 2.381 24.824 3.530 1.00 0.00 H
-HETATM 2451 H2 HOH A 817 2.288 23.630 4.456 1.00 0.00 H
-HETATM 2452 O HOH A 818 1.089 26.113 2.710 1.00 0.00 O
-HETATM 2453 H1 HOH A 818 1.352 26.938 3.119 1.00 0.00 H
-HETATM 2454 H2 HOH A 818 0.176 25.994 2.969 1.00 0.00 H
-HETATM 2455 O HOH A 819 0.526 17.128 6.333 1.00 0.00 O
-HETATM 2456 H1 HOH A 819 0.981 17.095 5.492 1.00 0.00 H
-HETATM 2457 H2 HOH A 819 -0.014 17.917 6.283 1.00 0.00 H
-HETATM 2458 O HOH A 820 2.255 17.435 3.988 1.00 0.00 O
-HETATM 2459 H1 HOH A 820 3.101 17.285 4.412 1.00 0.00 H
-HETATM 2460 H2 HOH A 820 2.294 18.342 3.685 1.00 0.00 H
-HETATM 2461 O HOH A 821 24.004 25.633 4.040 1.00 0.00 O
-HETATM 2462 H1 HOH A 821 23.425 25.699 3.281 1.00 0.00 H
-HETATM 2463 H2 HOH A 821 24.567 24.882 3.849 1.00 0.00 H
-HETATM 2464 O HOH A 822 21.953 25.393 1.962 1.00 0.00 O
-HETATM 2465 H1 HOH A 822 21.176 25.463 2.517 1.00 0.00 H
-HETATM 2466 H2 HOH A 822 21.911 24.509 1.598 1.00 0.00 H
-HETATM 2467 O HOH A 823 15.225 22.630 9.685 1.00 0.00 O
-HETATM 2468 H1 HOH A 823 14.487 23.238 9.647 1.00 0.00 H
-HETATM 2469 H2 HOH A 823 15.591 22.639 8.801 1.00 0.00 H
-HETATM 2470 O HOH A 824 12.695 24.057 9.303 1.00 0.00 O
-HETATM 2471 H1 HOH A 824 12.109 23.430 9.729 1.00 0.00 H
-HETATM 2472 H2 HOH A 824 12.419 24.062 8.387 1.00 0.00 H
-HETATM 2473 O HOH A 825 26.582 13.734 9.454 1.00 0.00 O
-HETATM 2474 H1 HOH A 825 25.749 13.843 8.995 1.00 0.00 H
-HETATM 2475 H2 HOH A 825 26.674 14.531 9.976 1.00 0.00 H
-HETATM 2476 O HOH A 826 24.332 14.420 7.706 1.00 0.00 O
-HETATM 2477 H1 HOH A 826 24.722 14.177 6.866 1.00 0.00 H
-HETATM 2478 H2 HOH A 826 24.197 15.365 7.646 1.00 0.00 H
-HETATM 2479 O HOH A 827 1.526 6.503 7.422 1.00 0.00 O
-HETATM 2480 H1 HOH A 827 1.579 6.695 8.358 1.00 0.00 H
-HETATM 2481 H2 HOH A 827 1.329 7.346 7.014 1.00 0.00 H
-HETATM 2482 O HOH A 828 1.113 7.098 10.261 1.00 0.00 O
-HETATM 2483 H1 HOH A 828 0.539 6.363 10.481 1.00 0.00 H
-HETATM 2484 H2 HOH A 828 0.570 7.876 10.391 1.00 0.00 H
-HETATM 2485 O HOH A 829 2.594 11.522 17.334 1.00 0.00 O
-HETATM 2486 H1 HOH A 829 2.103 12.343 17.335 1.00 0.00 H
-HETATM 2487 H2 HOH A 829 2.529 11.203 16.434 1.00 0.00 H
-HETATM 2488 O HOH A 830 0.628 13.695 17.345 1.00 0.00 O
-HETATM 2489 H1 HOH A 830 0.141 13.445 18.131 1.00 0.00 H
-HETATM 2490 H2 HOH A 830 -0.004 13.612 16.632 1.00 0.00 H
-HETATM 2491 O HOH A 831 12.657 0.133 17.224 1.00 0.00 O
-HETATM 2492 H1 HOH A 831 12.418 1.052 17.105 1.00 0.00 H
-HETATM 2493 H2 HOH A 831 13.403 -0.000 16.639 1.00 0.00 H
-HETATM 2494 O HOH A 832 11.684 2.761 16.370 1.00 0.00 O
-HETATM 2495 H1 HOH A 832 10.758 2.543 16.258 1.00 0.00 H
-HETATM 2496 H2 HOH A 832 11.987 2.989 15.491 1.00 0.00 H
-HETATM 2497 O HOH A 833 29.801 11.235 21.427 1.00 0.00 O
-HETATM 2498 H1 HOH A 833 30.003 10.948 22.317 1.00 0.00 H
-HETATM 2499 H2 HOH A 833 29.812 12.191 21.473 1.00 0.00 H
-HETATM 2500 O HOH A 834 29.872 10.496 24.261 1.00 0.00 O
-HETATM 2501 H1 HOH A 834 29.138 9.880 24.268 1.00 0.00 H
-HETATM 2502 H2 HOH A 834 29.591 11.214 24.828 1.00 0.00 H
-HETATM 2503 O HOH A 835 5.736 23.626 3.037 1.00 0.00 O
-HETATM 2504 H1 HOH A 835 6.044 23.002 2.380 1.00 0.00 H
-HETATM 2505 H2 HOH A 835 4.782 23.589 2.975 1.00 0.00 H
-HETATM 2506 O HOH A 836 6.551 22.134 0.651 1.00 0.00 O
-HETATM 2507 H1 HOH A 836 7.175 22.767 0.291 1.00 0.00 H
-HETATM 2508 H2 HOH A 836 5.852 22.087 -0.000 1.00 0.00 H
-HETATM 2509 O HOH A 837 22.822 20.902 16.012 1.00 0.00 O
-HETATM 2510 H1 HOH A 837 22.146 21.234 15.422 1.00 0.00 H
-HETATM 2511 H2 HOH A 837 23.025 21.641 16.586 1.00 0.00 H
-HETATM 2512 O HOH A 838 21.194 22.179 13.939 1.00 0.00 O
-HETATM 2513 H1 HOH A 838 21.653 21.844 13.168 1.00 0.00 H
-HETATM 2514 H2 HOH A 838 21.328 23.126 13.908 1.00 0.00 H
-HETATM 2515 O HOH A 839 22.315 7.043 26.455 1.00 0.00 O
-HETATM 2516 H1 HOH A 839 22.497 7.895 26.059 1.00 0.00 H
-HETATM 2517 H2 HOH A 839 21.952 7.251 27.316 1.00 0.00 H
-HETATM 2518 O HOH A 840 23.334 9.650 25.590 1.00 0.00 O
-HETATM 2519 H1 HOH A 840 24.218 9.382 25.334 1.00 0.00 H
-HETATM 2520 H2 HOH A 840 23.466 10.248 26.325 1.00 0.00 H
-HETATM 2521 O HOH A 841 7.476 9.112 20.767 1.00 0.00 O
-HETATM 2522 H1 HOH A 841 6.921 9.882 20.892 1.00 0.00 H
-HETATM 2523 H2 HOH A 841 7.902 9.259 19.922 1.00 0.00 H
-HETATM 2524 O HOH A 842 5.426 11.206 20.775 1.00 0.00 O
-HETATM 2525 H1 HOH A 842 4.669 10.667 21.006 1.00 0.00 H
-HETATM 2526 H2 HOH A 842 5.236 11.527 19.894 1.00 0.00 H
-HETATM 2527 O HOH A 843 21.620 4.332 0.872 1.00 0.00 O
-HETATM 2528 H1 HOH A 843 22.479 4.420 1.284 1.00 0.00 H
-HETATM 2529 H2 HOH A 843 21.806 3.984 -0.000 1.00 0.00 H
-HETATM 2530 O HOH A 844 24.295 5.074 1.807 1.00 0.00 O
-HETATM 2531 H1 HOH A 844 24.106 5.968 2.093 1.00 0.00 H
-HETATM 2532 H2 HOH A 844 24.914 5.177 1.084 1.00 0.00 H
-HETATM 2533 O HOH A 845 24.682 7.921 16.884 1.00 0.00 O
-HETATM 2534 H1 HOH A 845 25.008 7.023 16.942 1.00 0.00 H
-HETATM 2535 H2 HOH A 845 23.743 7.826 16.722 1.00 0.00 H
-HETATM 2536 O HOH A 846 25.639 5.181 16.488 1.00 0.00 O
-HETATM 2537 H1 HOH A 846 26.359 5.365 15.885 1.00 0.00 H
-HETATM 2538 H2 HOH A 846 25.024 4.654 15.977 1.00 0.00 H
-HETATM 2539 O HOH A 847 0.647 -0.000 19.760 1.00 0.00 O
-HETATM 2540 H1 HOH A 847 1.306 0.460 20.280 1.00 0.00 H
-HETATM 2541 H2 HOH A 847 0.458 0.592 19.032 1.00 0.00 H
-HETATM 2542 O HOH A 848 2.216 1.719 21.540 1.00 0.00 O
-HETATM 2543 H1 HOH A 848 1.736 1.560 22.354 1.00 0.00 H
-HETATM 2544 H2 HOH A 848 2.078 2.649 21.356 1.00 0.00 H
-HETATM 2545 O HOH A 849 24.722 26.945 21.406 1.00 0.00 O
-HETATM 2546 H1 HOH A 849 25.423 26.303 21.291 1.00 0.00 H
-HETATM 2547 H2 HOH A 849 24.000 26.447 21.788 1.00 0.00 H
-HETATM 2548 O HOH A 850 26.560 24.812 20.597 1.00 0.00 O
-HETATM 2549 H1 HOH A 850 26.715 25.090 19.693 1.00 0.00 H
-HETATM 2550 H2 HOH A 850 26.118 23.967 20.517 1.00 0.00 H
-HETATM 2551 O HOH A 851 15.402 0.057 18.184 1.00 0.00 O
-HETATM 2552 H1 HOH A 851 15.967 0.526 17.570 1.00 0.00 H
-HETATM 2553 H2 HOH A 851 15.300 0.656 18.923 1.00 0.00 H
-HETATM 2554 O HOH A 852 17.555 1.339 16.665 1.00 0.00 O
-HETATM 2555 H1 HOH A 852 18.126 0.578 16.553 1.00 0.00 H
-HETATM 2556 H2 HOH A 852 18.063 1.946 17.202 1.00 0.00 H
-HETATM 2557 O HOH A 853 16.710 2.941 24.035 1.00 0.00 O
-HETATM 2558 H1 HOH A 853 17.100 2.506 23.277 1.00 0.00 H
-HETATM 2559 H2 HOH A 853 17.377 2.877 24.719 1.00 0.00 H
-HETATM 2560 O HOH A 854 17.768 1.124 21.994 1.00 0.00 O
-HETATM 2561 H1 HOH A 854 16.979 0.602 21.843 1.00 0.00 H
-HETATM 2562 H2 HOH A 854 18.401 0.505 22.357 1.00 0.00 H
-HETATM 2563 O HOH A 855 14.222 28.606 4.908 1.00 0.00 O
-HETATM 2564 H1 HOH A 855 13.331 28.593 5.258 1.00 0.00 H
-HETATM 2565 H2 HOH A 855 14.134 29.011 4.045 1.00 0.00 H
-HETATM 2566 O HOH A 856 11.424 28.118 5.627 1.00 0.00 O
-HETATM 2567 H1 HOH A 856 11.498 27.197 5.878 1.00 0.00 H
-HETATM 2568 H2 HOH A 856 10.850 28.114 4.861 1.00 0.00 H
-HETATM 2569 O HOH A 857 6.917 17.260 22.025 1.00 0.00 O
-HETATM 2570 H1 HOH A 857 6.398 17.929 22.471 1.00 0.00 H
-HETATM 2571 H2 HOH A 857 6.279 16.760 21.517 1.00 0.00 H
-HETATM 2572 O HOH A 858 5.181 18.878 23.743 1.00 0.00 O
-HETATM 2573 H1 HOH A 858 5.593 18.687 24.586 1.00 0.00 H
-HETATM 2574 H2 HOH A 858 4.301 18.509 23.815 1.00 0.00 H
-HETATM 2575 O HOH A 859 5.485 15.742 6.793 1.00 0.00 O
-HETATM 2576 H1 HOH A 859 4.586 15.800 6.470 1.00 0.00 H
-HETATM 2577 H2 HOH A 859 5.741 14.834 6.629 1.00 0.00 H
-HETATM 2578 O HOH A 860 2.599 15.651 6.296 1.00 0.00 O
-HETATM 2579 H1 HOH A 860 2.300 16.078 7.099 1.00 0.00 H
-HETATM 2580 H2 HOH A 860 2.251 14.761 6.352 1.00 0.00 H
-HETATM 2581 O HOH A 861 7.824 12.795 27.435 1.00 0.00 O
-HETATM 2582 H1 HOH A 861 7.501 12.653 26.546 1.00 0.00 H
-HETATM 2583 H2 HOH A 861 7.796 13.745 27.553 1.00 0.00 H
-HETATM 2584 O HOH A 862 7.361 12.533 24.554 1.00 0.00 O
-HETATM 2585 H1 HOH A 862 8.093 11.952 24.342 1.00 0.00 H
-HETATM 2586 H2 HOH A 862 7.552 13.344 24.083 1.00 0.00 H
-HETATM 2587 O HOH A 863 15.807 17.641 16.651 1.00 0.00 O
-HETATM 2588 H1 HOH A 863 16.482 18.047 16.107 1.00 0.00 H
-HETATM 2589 H2 HOH A 863 15.244 17.179 16.030 1.00 0.00 H
-HETATM 2590 O HOH A 864 17.462 19.268 14.863 1.00 0.00 O
-HETATM 2591 H1 HOH A 864 17.350 20.116 15.294 1.00 0.00 H
-HETATM 2592 H2 HOH A 864 17.060 19.380 14.001 1.00 0.00 H
-HETATM 2593 O HOH A 865 27.965 18.219 11.106 1.00 0.00 O
-HETATM 2594 H1 HOH A 865 28.325 17.400 11.447 1.00 0.00 H
-HETATM 2595 H2 HOH A 865 27.018 18.078 11.093 1.00 0.00 H
-HETATM 2596 O HOH A 866 28.968 15.514 11.618 1.00 0.00 O
-HETATM 2597 H1 HOH A 866 29.615 15.464 10.914 1.00 0.00 H
-HETATM 2598 H2 HOH A 866 28.321 14.843 11.401 1.00 0.00 H
-HETATM 2599 O HOH A 867 10.136 22.679 23.966 1.00 0.00 O
-HETATM 2600 H1 HOH A 867 9.279 22.479 23.591 1.00 0.00 H
-HETATM 2601 H2 HOH A 867 10.512 23.331 23.374 1.00 0.00 H
-HETATM 2602 O HOH A 868 7.813 21.751 22.442 1.00 0.00 O
-HETATM 2603 H1 HOH A 868 7.965 20.808 22.507 1.00 0.00 H
-HETATM 2604 H2 HOH A 868 7.894 21.946 21.508 1.00 0.00 H
-HETATM 2605 O HOH A 869 17.433 11.274 1.839 1.00 0.00 O
-HETATM 2606 H1 HOH A 869 17.468 10.539 2.450 1.00 0.00 H
-HETATM 2607 H2 HOH A 869 16.521 11.564 1.862 1.00 0.00 H
-HETATM 2608 O HOH A 870 17.292 8.729 3.283 1.00 0.00 O
-HETATM 2609 H1 HOH A 870 17.754 8.178 2.650 1.00 0.00 H
-HETATM 2610 H2 HOH A 870 16.402 8.376 3.305 1.00 0.00 H
-HETATM 2611 O HOH A 871 11.256 1.594 5.068 1.00 0.00 O
-HETATM 2612 H1 HOH A 871 11.457 2.102 5.854 1.00 0.00 H
-HETATM 2613 H2 HOH A 871 10.968 2.246 4.428 1.00 0.00 H
-HETATM 2614 O HOH A 872 11.299 3.162 7.542 1.00 0.00 O
-HETATM 2615 H1 HOH A 872 10.793 2.569 8.098 1.00 0.00 H
-HETATM 2616 H2 HOH A 872 10.762 3.951 7.478 1.00 0.00 H
-HETATM 2617 O HOH A 873 4.959 26.902 7.197 1.00 0.00 O
-HETATM 2618 H1 HOH A 873 4.957 25.952 7.311 1.00 0.00 H
-HETATM 2619 H2 HOH A 873 5.404 27.043 6.361 1.00 0.00 H
-HETATM 2620 O HOH A 874 5.505 24.068 7.697 1.00 0.00 O
-HETATM 2621 H1 HOH A 874 5.828 24.139 8.596 1.00 0.00 H
-HETATM 2622 H2 HOH A 874 6.255 23.751 7.194 1.00 0.00 H
-HETATM 2623 O HOH A 875 18.910 0.174 14.006 1.00 0.00 O
-HETATM 2624 H1 HOH A 875 18.069 0.003 14.431 1.00 0.00 H
-HETATM 2625 H2 HOH A 875 19.251 0.949 14.453 1.00 0.00 H
-HETATM 2626 O HOH A 876 16.148 0.087 14.981 1.00 0.00 O
-HETATM 2627 H1 HOH A 876 15.700 0.000 14.139 1.00 0.00 H
-HETATM 2628 H2 HOH A 876 15.860 0.934 15.322 1.00 0.00 H
-HETATM 2629 O HOH A 877 27.006 28.559 22.572 1.00 0.00 O
-HETATM 2630 H1 HOH A 877 27.705 28.181 23.105 1.00 0.00 H
-HETATM 2631 H2 HOH A 877 26.813 27.887 21.918 1.00 0.00 H
-HETATM 2632 O HOH A 878 29.485 27.545 23.760 1.00 0.00 O
-HETATM 2633 H1 HOH A 878 30.011 28.343 23.698 1.00 0.00 H
-HETATM 2634 H2 HOH A 878 29.927 26.918 23.189 1.00 0.00 H
-HETATM 2635 O HOH A 879 5.693 26.578 3.456 1.00 0.00 O
-HETATM 2636 H1 HOH A 879 6.025 27.057 2.698 1.00 0.00 H
-HETATM 2637 H2 HOH A 879 5.306 25.783 3.088 1.00 0.00 H
-HETATM 2638 O HOH A 880 6.157 28.188 1.053 1.00 0.00 O
-HETATM 2639 H1 HOH A 880 5.747 29.003 1.344 1.00 0.00 H
-HETATM 2640 H2 HOH A 880 5.621 27.898 0.316 1.00 0.00 H
-HETATM 2641 O HOH A 881 16.556 3.192 2.577 1.00 0.00 O
-HETATM 2642 H1 HOH A 881 16.971 3.488 1.767 1.00 0.00 H
-HETATM 2643 H2 HOH A 881 15.667 3.545 2.535 1.00 0.00 H
-HETATM 2644 O HOH A 882 17.868 4.604 0.370 1.00 0.00 O
-HETATM 2645 H1 HOH A 882 18.641 4.919 0.840 1.00 0.00 H
-HETATM 2646 H2 HOH A 882 17.397 5.399 0.120 1.00 0.00 H
-HETATM 2647 O HOH A 883 6.140 1.632 28.477 1.00 0.00 O
-HETATM 2648 H1 HOH A 883 5.478 1.763 27.799 1.00 0.00 H
-HETATM 2649 H2 HOH A 883 6.695 2.410 28.425 1.00 0.00 H
-HETATM 2650 O HOH A 884 4.533 1.878 26.039 1.00 0.00 O
-HETATM 2651 H1 HOH A 884 4.663 0.995 25.691 1.00 0.00 H
-HETATM 2652 H2 HOH A 884 4.942 2.455 25.395 1.00 0.00 H
-HETATM 2653 O HOH A 885 12.173 7.135 0.653 1.00 0.00 O
-HETATM 2654 H1 HOH A 885 11.651 6.446 1.065 1.00 0.00 H
-HETATM 2655 H2 HOH A 885 12.702 6.677 0.000 1.00 0.00 H
-HETATM 2656 O HOH A 886 11.063 4.905 2.196 1.00 0.00 O
-HETATM 2657 H1 HOH A 886 11.286 5.213 3.075 1.00 0.00 H
-HETATM 2658 H2 HOH A 886 11.570 4.102 2.084 1.00 0.00 H
-HETATM 2659 O HOH A 887 14.580 11.014 15.643 1.00 0.00 O
-HETATM 2660 H1 HOH A 887 14.867 11.075 16.554 1.00 0.00 H
-HETATM 2661 H2 HOH A 887 13.626 11.084 15.686 1.00 0.00 H
-HETATM 2662 O HOH A 888 15.283 10.655 18.465 1.00 0.00 O
-HETATM 2663 H1 HOH A 888 15.848 9.887 18.376 1.00 0.00 H
-HETATM 2664 H2 HOH A 888 14.540 10.351 18.985 1.00 0.00 H
-HETATM 2665 O HOH A 889 8.688 13.521 2.924 1.00 0.00 O
-HETATM 2666 H1 HOH A 889 7.836 13.561 2.489 1.00 0.00 H
-HETATM 2667 H2 HOH A 889 8.694 12.671 3.363 1.00 0.00 H
-HETATM 2668 O HOH A 890 5.881 13.761 2.120 1.00 0.00 O
-HETATM 2669 H1 HOH A 890 5.706 14.638 2.463 1.00 0.00 H
-HETATM 2670 H2 HOH A 890 5.310 13.184 2.627 1.00 0.00 H
-HETATM 2671 O HOH A 891 2.718 27.709 15.585 1.00 0.00 O
-HETATM 2672 H1 HOH A 891 3.648 27.511 15.475 1.00 0.00 H
-HETATM 2673 H2 HOH A 891 2.586 28.519 15.093 1.00 0.00 H
-HETATM 2674 O HOH A 892 5.641 27.516 15.646 1.00 0.00 O
-HETATM 2675 H1 HOH A 892 5.757 27.370 16.585 1.00 0.00 H
-HETATM 2676 H2 HOH A 892 6.061 28.360 15.479 1.00 0.00 H
-HETATM 2677 O HOH A 893 14.342 2.116 11.522 1.00 0.00 O
-HETATM 2678 H1 HOH A 893 13.811 2.822 11.890 1.00 0.00 H
-HETATM 2679 H2 HOH A 893 13.706 1.472 11.212 1.00 0.00 H
-HETATM 2680 O HOH A 894 12.654 3.918 13.098 1.00 0.00 O
-HETATM 2681 H1 HOH A 894 13.177 3.942 13.900 1.00 0.00 H
-HETATM 2682 H2 HOH A 894 11.830 3.505 13.358 1.00 0.00 H
-HETATM 2683 O HOH A 895 11.557 19.558 28.292 1.00 0.00 O
-HETATM 2684 H1 HOH A 895 12.168 18.838 28.134 1.00 0.00 H
-HETATM 2685 H2 HOH A 895 11.250 19.809 27.421 1.00 0.00 H
-HETATM 2686 O HOH A 896 13.790 17.773 27.650 1.00 0.00 O
-HETATM 2687 H1 HOH A 896 14.457 18.168 28.213 1.00 0.00 H
-HETATM 2688 H2 HOH A 896 14.115 17.908 26.760 1.00 0.00 H
-HETATM 2689 O HOH A 897 22.018 18.967 22.085 1.00 0.00 O
-HETATM 2690 H1 HOH A 897 22.638 19.455 21.543 1.00 0.00 H
-HETATM 2691 H2 HOH A 897 21.715 19.602 22.734 1.00 0.00 H
-HETATM 2692 O HOH A 898 24.276 20.383 20.869 1.00 0.00 O
-HETATM 2693 H1 HOH A 898 24.934 19.689 20.935 1.00 0.00 H
-HETATM 2694 H2 HOH A 898 24.607 21.083 21.431 1.00 0.00 H
-HETATM 2695 O HOH A 899 19.781 22.785 21.114 1.00 0.00 O
-HETATM 2696 H1 HOH A 899 19.724 22.897 20.165 1.00 0.00 H
-HETATM 2697 H2 HOH A 899 20.719 22.705 21.291 1.00 0.00 H
-HETATM 2698 O HOH A 900 19.700 22.560 18.194 1.00 0.00 O
-HETATM 2699 H1 HOH A 900 19.020 21.889 18.120 1.00 0.00 H
-HETATM 2700 H2 HOH A 900 20.488 22.147 17.842 1.00 0.00 H
-HETATM 2701 O HOH A 901 24.412 16.096 24.961 1.00 0.00 O
-HETATM 2702 H1 HOH A 901 25.143 16.044 25.577 1.00 0.00 H
-HETATM 2703 H2 HOH A 901 24.690 16.744 24.313 1.00 0.00 H
-HETATM 2704 O HOH A 902 26.437 16.432 27.052 1.00 0.00 O
-HETATM 2705 H1 HOH A 902 25.854 16.517 27.806 1.00 0.00 H
-HETATM 2706 H2 HOH A 902 26.880 17.278 26.992 1.00 0.00 H
-HETATM 2707 O HOH A 903 25.829 2.424 4.859 1.00 0.00 O
-HETATM 2708 H1 HOH A 903 25.033 2.875 4.580 1.00 0.00 H
-HETATM 2709 H2 HOH A 903 25.581 1.501 4.903 1.00 0.00 H
-HETATM 2710 O HOH A 904 23.226 3.733 4.552 1.00 0.00 O
-HETATM 2711 H1 HOH A 904 23.311 4.388 5.245 1.00 0.00 H
-HETATM 2712 H2 HOH A 904 22.505 3.172 4.836 1.00 0.00 H
-HETATM 2713 O HOH A 905 5.745 3.882 3.083 1.00 0.00 O
-HETATM 2714 H1 HOH A 905 6.023 3.533 2.236 1.00 0.00 H
-HETATM 2715 H2 HOH A 905 5.765 3.129 3.673 1.00 0.00 H
-HETATM 2716 O HOH A 906 6.059 2.604 0.465 1.00 0.00 O
-HETATM 2717 H1 HOH A 906 5.326 3.022 0.012 1.00 0.00 H
-HETATM 2718 H2 HOH A 906 5.831 1.675 0.488 1.00 0.00 H
-HETATM 2719 O HOH A 907 24.615 29.077 8.037 1.00 0.00 O
-HETATM 2720 H1 HOH A 907 25.030 28.425 7.473 1.00 0.00 H
-HETATM 2721 H2 HOH A 907 25.002 28.930 8.900 1.00 0.00 H
-HETATM 2722 O HOH A 908 25.522 26.714 6.562 1.00 0.00 O
-HETATM 2723 H1 HOH A 908 24.679 26.436 6.202 1.00 0.00 H
-HETATM 2724 H2 HOH A 908 25.781 26.002 7.146 1.00 0.00 H
-HETATM 2725 O HOH A 909 16.462 19.041 0.768 1.00 0.00 O
-HETATM 2726 H1 HOH A 909 16.919 19.867 0.610 1.00 0.00 H
-HETATM 2727 H2 HOH A 909 16.158 19.104 1.674 1.00 0.00 H
-HETATM 2728 O HOH A 910 18.287 21.326 0.587 1.00 0.00 O
-HETATM 2729 H1 HOH A 910 19.046 20.876 0.214 1.00 0.00 H
-HETATM 2730 H2 HOH A 910 18.580 21.622 1.448 1.00 0.00 H
-HETATM 2731 O HOH A 911 21.839 17.783 4.015 1.00 0.00 O
-HETATM 2732 H1 HOH A 911 21.130 18.343 3.698 1.00 0.00 H
-HETATM 2733 H2 HOH A 911 22.223 18.271 4.744 1.00 0.00 H
-HETATM 2734 O HOH A 912 20.093 19.797 2.798 1.00 0.00 O
-HETATM 2735 H1 HOH A 912 20.381 19.707 1.889 1.00 0.00 H
-HETATM 2736 H2 HOH A 912 20.345 20.687 3.044 1.00 0.00 H
-HETATM 2737 O HOH A 913 8.393 2.651 10.731 1.00 0.00 O
-HETATM 2738 H1 HOH A 913 8.740 1.833 11.087 1.00 0.00 H
-HETATM 2739 H2 HOH A 913 8.408 2.525 9.782 1.00 0.00 H
-HETATM 2740 O HOH A 914 9.966 0.362 11.663 1.00 0.00 O
-HETATM 2741 H1 HOH A 914 10.572 0.828 12.240 1.00 0.00 H
-HETATM 2742 H2 HOH A 914 10.520 -0.000 10.972 1.00 0.00 H
-HETATM 2743 O HOH A 915 30.000 24.126 16.607 1.00 0.00 O
-HETATM 2744 H1 HOH A 915 29.435 23.599 17.171 1.00 0.00 H
-HETATM 2745 H2 HOH A 915 29.581 24.092 15.747 1.00 0.00 H
-HETATM 2746 O HOH A 916 28.523 22.065 18.076 1.00 0.00 O
-HETATM 2747 H1 HOH A 916 29.268 21.575 18.426 1.00 0.00 H
-HETATM 2748 H2 HOH A 916 28.083 21.450 17.489 1.00 0.00 H
-HETATM 2749 O HOH A 917 3.888 8.739 19.998 1.00 0.00 O
-HETATM 2750 H1 HOH A 917 4.206 8.701 19.097 1.00 0.00 H
-HETATM 2751 H2 HOH A 917 4.437 8.117 20.474 1.00 0.00 H
-HETATM 2752 O HOH A 918 4.563 8.129 17.214 1.00 0.00 O
-HETATM 2753 H1 HOH A 918 3.675 8.038 16.866 1.00 0.00 H
-HETATM 2754 H2 HOH A 918 4.951 7.259 17.119 1.00 0.00 H
-HETATM 2755 O HOH A 919 20.282 26.979 15.639 1.00 0.00 O
-HETATM 2756 H1 HOH A 919 20.752 27.788 15.436 1.00 0.00 H
-HETATM 2757 H2 HOH A 919 19.757 27.193 16.410 1.00 0.00 H
-HETATM 2758 O HOH A 920 22.058 29.302 15.459 1.00 0.00 O
-HETATM 2759 H1 HOH A 920 22.894 28.840 15.382 1.00 0.00 H
-HETATM 2760 H2 HOH A 920 22.118 29.775 16.289 1.00 0.00 H
-HETATM 2761 O HOH A 921 17.453 6.626 9.600 1.00 0.00 O
-HETATM 2762 H1 HOH A 921 17.964 5.942 10.031 1.00 0.00 H
-HETATM 2763 H2 HOH A 921 16.792 6.876 10.245 1.00 0.00 H
-HETATM 2764 O HOH A 922 18.570 4.195 10.794 1.00 0.00 O
-HETATM 2765 H1 HOH A 922 18.509 3.618 10.032 1.00 0.00 H
-HETATM 2766 H2 HOH A 922 17.979 3.807 11.439 1.00 0.00 H
-HETATM 2767 O HOH A 923 14.181 13.464 11.467 1.00 0.00 O
-HETATM 2768 H1 HOH A 923 13.484 13.282 12.096 1.00 0.00 H
-HETATM 2769 H2 HOH A 923 13.752 13.959 10.769 1.00 0.00 H
-HETATM 2770 O HOH A 924 11.889 12.502 13.017 1.00 0.00 O
-HETATM 2771 H1 HOH A 924 12.127 11.576 13.067 1.00 0.00 H
-HETATM 2772 H2 HOH A 924 11.078 12.516 12.508 1.00 0.00 H
-HETATM 2773 O HOH A 925 7.054 25.285 10.177 1.00 0.00 O
-HETATM 2774 H1 HOH A 925 7.086 25.005 11.092 1.00 0.00 H
-HETATM 2775 H2 HOH A 925 7.639 24.681 9.720 1.00 0.00 H
-HETATM 2776 O HOH A 926 7.660 24.700 12.983 1.00 0.00 O
-HETATM 2777 H1 HOH A 926 7.804 25.596 13.289 1.00 0.00 H
-HETATM 2778 H2 HOH A 926 8.509 24.270 13.086 1.00 0.00 H
-HETATM 2779 O HOH A 927 15.751 2.619 13.913 1.00 0.00 O
-HETATM 2780 H1 HOH A 927 14.830 2.384 14.027 1.00 0.00 H
-HETATM 2781 H2 HOH A 927 15.834 2.816 12.979 1.00 0.00 H
-HETATM 2782 O HOH A 928 13.091 1.396 14.033 1.00 0.00 O
-HETATM 2783 H1 HOH A 928 13.317 0.607 14.526 1.00 0.00 H
-HETATM 2784 H2 HOH A 928 12.804 1.074 13.179 1.00 0.00 H
-HETATM 2785 O HOH A 929 19.496 28.429 5.299 1.00 0.00 O
-HETATM 2786 H1 HOH A 929 20.130 28.490 4.585 1.00 0.00 H
-HETATM 2787 H2 HOH A 929 19.989 28.051 6.027 1.00 0.00 H
-HETATM 2788 O HOH A 930 21.346 28.050 3.059 1.00 0.00 O
-HETATM 2789 H1 HOH A 930 20.740 27.643 2.439 1.00 0.00 H
-HETATM 2790 H2 HOH A 930 21.998 27.373 3.241 1.00 0.00 H
-HETATM 2791 O HOH A 931 2.974 15.478 13.618 1.00 0.00 O
-HETATM 2792 H1 HOH A 931 2.505 14.644 13.633 1.00 0.00 H
-HETATM 2793 H2 HOH A 931 3.581 15.427 14.357 1.00 0.00 H
-HETATM 2794 O HOH A 932 1.244 13.163 14.100 1.00 0.00 O
-HETATM 2795 H1 HOH A 932 0.395 13.599 14.021 1.00 0.00 H
-HETATM 2796 H2 HOH A 932 1.282 12.869 15.010 1.00 0.00 H
-HETATM 2797 O HOH A 933 -0.001 13.690 2.885 1.00 0.00 O
-HETATM 2798 H1 HOH A 933 0.736 13.215 3.269 1.00 0.00 H
-HETATM 2799 H2 HOH A 933 0.151 14.605 3.125 1.00 0.00 H
-HETATM 2800 O HOH A 934 1.963 12.297 4.555 1.00 0.00 O
-HETATM 2801 H1 HOH A 934 1.383 11.640 4.941 1.00 0.00 H
-HETATM 2802 H2 HOH A 934 2.250 12.832 5.295 1.00 0.00 H
-HETATM 2803 O HOH A 935 24.367 12.866 16.077 1.00 0.00 O
-HETATM 2804 H1 HOH A 935 25.138 13.191 15.613 1.00 0.00 H
-HETATM 2805 H2 HOH A 935 24.506 13.128 16.987 1.00 0.00 H
-HETATM 2806 O HOH A 936 26.991 13.404 14.890 1.00 0.00 O
-HETATM 2807 H1 HOH A 936 27.129 12.561 14.456 1.00 0.00 H
-HETATM 2808 H2 HOH A 936 27.682 13.453 15.550 1.00 0.00 H
-HETATM 2809 O HOH A 937 6.449 18.974 14.537 1.00 0.00 O
-HETATM 2810 H1 HOH A 937 5.623 18.609 14.854 1.00 0.00 H
-HETATM 2811 H2 HOH A 937 6.895 19.280 15.326 1.00 0.00 H
-HETATM 2812 O HOH A 938 3.759 18.406 15.550 1.00 0.00 O
-HETATM 2813 H1 HOH A 938 3.253 18.888 14.894 1.00 0.00 H
-HETATM 2814 H2 HOH A 938 3.581 18.860 16.374 1.00 0.00 H
-HETATM 2815 O HOH A 939 15.435 15.272 4.893 1.00 0.00 O
-HETATM 2816 H1 HOH A 939 16.266 15.597 4.547 1.00 0.00 H
-HETATM 2817 H2 HOH A 939 15.495 15.416 5.838 1.00 0.00 H
-HETATM 2818 O HOH A 940 18.191 15.770 4.033 1.00 0.00 O
-HETATM 2819 H1 HOH A 940 18.326 14.977 3.513 1.00 0.00 H
-HETATM 2820 H2 HOH A 940 18.813 15.695 4.756 1.00 0.00 H
-HETATM 2821 O HOH A 941 17.190 17.343 22.398 1.00 0.00 O
-HETATM 2822 H1 HOH A 941 16.660 18.134 22.301 1.00 0.00 H
-HETATM 2823 H2 HOH A 941 16.623 16.634 22.095 1.00 0.00 H
-HETATM 2824 O HOH A 942 15.308 19.581 22.584 1.00 0.00 O
-HETATM 2825 H1 HOH A 942 15.519 19.874 23.471 1.00 0.00 H
-HETATM 2826 H2 HOH A 942 14.400 19.284 22.637 1.00 0.00 H
-HETATM 2827 O HOH A 943 14.701 28.451 29.095 1.00 0.00 O
-HETATM 2828 H1 HOH A 943 14.049 27.910 28.649 1.00 0.00 H
-HETATM 2829 H2 HOH A 943 14.694 28.139 30.000 1.00 0.00 H
-HETATM 2830 O HOH A 944 12.325 27.142 27.989 1.00 0.00 O
-HETATM 2831 H1 HOH A 944 11.910 27.908 27.590 1.00 0.00 H
-HETATM 2832 H2 HOH A 944 11.715 26.865 28.673 1.00 0.00 H
-HETATM 2833 O HOH A 945 19.483 6.991 8.834 1.00 0.00 O
-HETATM 2834 H1 HOH A 945 19.617 7.829 9.278 1.00 0.00 H
-HETATM 2835 H2 HOH A 945 19.749 7.153 7.929 1.00 0.00 H
-HETATM 2836 O HOH A 946 19.403 9.707 9.932 1.00 0.00 O
-HETATM 2837 H1 HOH A 946 18.532 9.677 10.329 1.00 0.00 H
-HETATM 2838 H2 HOH A 946 19.334 10.371 9.246 1.00 0.00 H
-HETATM 2839 O HOH A 947 12.823 29.470 20.600 1.00 0.00 O
-HETATM 2840 H1 HOH A 947 12.092 29.101 20.105 1.00 0.00 H
-HETATM 2841 H2 HOH A 947 13.603 29.079 20.205 1.00 0.00 H
-HETATM 2842 O HOH A 948 10.678 27.871 19.406 1.00 0.00 O
-HETATM 2843 H1 HOH A 948 10.205 27.643 20.207 1.00 0.00 H
-HETATM 2844 H2 HOH A 948 10.998 27.034 19.068 1.00 0.00 H
-HETATM 2845 O HOH A 949 4.744 8.786 2.569 1.00 0.00 O
-HETATM 2846 H1 HOH A 949 4.937 8.111 1.919 1.00 0.00 H
-HETATM 2847 H2 HOH A 949 5.566 9.268 2.668 1.00 0.00 H
-HETATM 2848 O HOH A 950 5.598 6.453 1.017 1.00 0.00 O
-HETATM 2849 H1 HOH A 950 5.208 5.767 1.561 1.00 0.00 H
-HETATM 2850 H2 HOH A 950 6.541 6.304 1.080 1.00 0.00 H
-HETATM 2851 O HOH A 951 0.196 18.628 18.097 1.00 0.00 O
-HETATM 2852 H1 HOH A 951 0.504 19.445 18.489 1.00 0.00 H
-HETATM 2853 H2 HOH A 951 -0.000 18.856 17.188 1.00 0.00 H
-HETATM 2854 O HOH A 952 0.579 21.269 19.308 1.00 0.00 O
-HETATM 2855 H1 HOH A 952 -0.001 21.166 20.064 1.00 0.00 H
-HETATM 2856 H2 HOH A 952 0.178 21.965 18.788 1.00 0.00 H
-HETATM 2857 O HOH A 953 10.464 8.665 11.143 1.00 0.00 O
-HETATM 2858 H1 HOH A 953 10.399 8.980 12.045 1.00 0.00 H
-HETATM 2859 H2 HOH A 953 10.860 9.393 10.663 1.00 0.00 H
-HETATM 2860 O HOH A 954 9.811 9.924 13.707 1.00 0.00 O
-HETATM 2861 H1 HOH A 954 8.897 9.646 13.776 1.00 0.00 H
-HETATM 2862 H2 HOH A 954 9.770 10.877 13.640 1.00 0.00 H
-HETATM 2863 O HOH A 955 9.597 29.279 17.814 1.00 0.00 O
-HETATM 2864 H1 HOH A 955 9.162 29.282 18.667 1.00 0.00 H
-HETATM 2865 H2 HOH A 955 9.190 30.001 17.334 1.00 0.00 H
-HETATM 2866 O HOH A 956 7.832 29.037 20.140 1.00 0.00 O
-HETATM 2867 H1 HOH A 956 7.670 28.094 20.102 1.00 0.00 H
-HETATM 2868 H2 HOH A 956 6.976 29.436 19.986 1.00 0.00 H
-HETATM 2869 O HOH A 957 20.027 19.582 13.152 1.00 0.00 O
-HETATM 2870 H1 HOH A 957 20.447 19.509 12.295 1.00 0.00 H
-HETATM 2871 H2 HOH A 957 19.903 20.523 13.279 1.00 0.00 H
-HETATM 2872 O HOH A 958 21.788 19.519 10.811 1.00 0.00 O
-HETATM 2873 H1 HOH A 958 22.460 18.928 11.151 1.00 0.00 H
-HETATM 2874 H2 HOH A 958 22.241 20.351 10.679 1.00 0.00 H
-HETATM 2875 O HOH A 959 4.475 10.449 5.818 1.00 0.00 O
-HETATM 2876 H1 HOH A 959 5.084 11.137 6.085 1.00 0.00 H
-HETATM 2877 H2 HOH A 959 3.608 10.841 5.926 1.00 0.00 H
-HETATM 2878 O HOH A 960 6.189 12.403 7.170 1.00 0.00 O
-HETATM 2879 H1 HOH A 960 6.676 11.802 7.734 1.00 0.00 H
-HETATM 2880 H2 HOH A 960 5.696 12.957 7.775 1.00 0.00 H
-HETATM 2881 O HOH A 961 15.090 16.401 2.035 1.00 0.00 O
-HETATM 2882 H1 HOH A 961 14.466 17.092 1.812 1.00 0.00 H
-HETATM 2883 H2 HOH A 961 15.553 16.734 2.804 1.00 0.00 H
-HETATM 2884 O HOH A 962 13.639 18.781 1.131 1.00 0.00 O
-HETATM 2885 H1 HOH A 962 13.895 18.764 0.208 1.00 0.00 H
-HETATM 2886 H2 HOH A 962 14.022 19.588 1.474 1.00 0.00 H
-HETATM 2887 O HOH A 963 13.008 20.711 3.071 1.00 0.00 O
-HETATM 2888 H1 HOH A 963 12.371 20.751 3.784 1.00 0.00 H
-HETATM 2889 H2 HOH A 963 12.594 21.187 2.351 1.00 0.00 H
-HETATM 2890 O HOH A 964 10.786 20.421 4.957 1.00 0.00 O
-HETATM 2891 H1 HOH A 964 10.858 19.480 5.122 1.00 0.00 H
-HETATM 2892 H2 HOH A 964 9.935 20.527 4.531 1.00 0.00 H
-HETATM 2893 O HOH A 965 9.769 8.432 26.366 1.00 0.00 O
-HETATM 2894 H1 HOH A 965 9.941 8.419 27.307 1.00 0.00 H
-HETATM 2895 H2 HOH A 965 10.181 9.239 26.058 1.00 0.00 H
-HETATM 2896 O HOH A 966 9.868 8.779 29.274 1.00 0.00 O
-HETATM 2897 H1 HOH A 966 8.952 8.584 29.473 1.00 0.00 H
-HETATM 2898 H2 HOH A 966 9.979 9.696 29.523 1.00 0.00 H
-HETATM 2899 O HOH A 967 17.763 17.299 9.342 1.00 0.00 O
-HETATM 2900 H1 HOH A 967 18.574 16.986 9.741 1.00 0.00 H
-HETATM 2901 H2 HOH A 967 17.835 17.047 8.421 1.00 0.00 H
-HETATM 2902 O HOH A 968 20.482 16.805 10.315 1.00 0.00 O
-HETATM 2903 H1 HOH A 968 20.637 17.647 10.744 1.00 0.00 H
-HETATM 2904 H2 HOH A 968 21.120 16.776 9.602 1.00 0.00 H
-HETATM 2905 O HOH A 969 2.032 11.340 21.916 1.00 0.00 O
-HETATM 2906 H1 HOH A 969 1.954 10.442 22.239 1.00 0.00 H
-HETATM 2907 H2 HOH A 969 2.926 11.598 22.138 1.00 0.00 H
-HETATM 2908 O HOH A 970 1.739 8.842 23.421 1.00 0.00 O
-HETATM 2909 H1 HOH A 970 0.909 9.033 23.858 1.00 0.00 H
-HETATM 2910 H2 HOH A 970 2.376 8.773 24.131 1.00 0.00 H
-HETATM 2911 O HOH A 971 6.030 10.439 23.018 1.00 0.00 O
-HETATM 2912 H1 HOH A 971 5.272 10.973 23.253 1.00 0.00 H
-HETATM 2913 H2 HOH A 971 5.656 9.627 22.675 1.00 0.00 H
-HETATM 2914 O HOH A 972 3.681 11.730 24.200 1.00 0.00 O
-HETATM 2915 H1 HOH A 972 4.046 11.932 25.062 1.00 0.00 H
-HETATM 2916 H2 HOH A 972 2.978 11.106 24.376 1.00 0.00 H
-HETATM 2917 O HOH A 973 22.489 19.121 8.764 1.00 0.00 O
-HETATM 2918 H1 HOH A 973 23.086 19.778 8.406 1.00 0.00 H
-HETATM 2919 H2 HOH A 973 21.630 19.362 8.417 1.00 0.00 H
-HETATM 2920 O HOH A 974 24.096 21.472 8.076 1.00 0.00 O
-HETATM 2921 H1 HOH A 974 24.489 21.634 8.934 1.00 0.00 H
-HETATM 2922 H2 HOH A 974 23.554 22.243 7.909 1.00 0.00 H
-HETATM 2923 O HOH A 975 2.249 10.109 13.276 1.00 0.00 O
-HETATM 2924 H1 HOH A 975 3.185 10.308 13.263 1.00 0.00 H
-HETATM 2925 H2 HOH A 975 1.842 10.891 13.651 1.00 0.00 H
-HETATM 2926 O HOH A 976 5.089 10.602 13.800 1.00 0.00 O
-HETATM 2927 H1 HOH A 976 5.310 9.761 14.202 1.00 0.00 H
-HETATM 2928 H2 HOH A 976 5.221 11.244 14.497 1.00 0.00 H
-HETATM 2929 O HOH A 977 23.989 4.970 18.004 1.00 0.00 O
-HETATM 2930 H1 HOH A 977 24.331 5.140 18.881 1.00 0.00 H
-HETATM 2931 H2 HOH A 977 23.444 5.732 17.806 1.00 0.00 H
-HETATM 2932 O HOH A 978 24.512 5.331 20.864 1.00 0.00 O
-HETATM 2933 H1 HOH A 978 24.271 4.455 21.167 1.00 0.00 H
-HETATM 2934 H2 HOH A 978 23.874 5.914 21.276 1.00 0.00 H
-HETATM 2935 O HOH A 979 13.266 22.958 27.982 1.00 0.00 O
-HETATM 2936 H1 HOH A 979 12.605 22.845 28.664 1.00 0.00 H
-HETATM 2937 H2 HOH A 979 13.240 23.892 27.774 1.00 0.00 H
-HETATM 2938 O HOH A 980 10.867 22.643 29.633 1.00 0.00 O
-HETATM 2939 H1 HOH A 980 10.478 21.893 29.182 1.00 0.00 H
-HETATM 2940 H2 HOH A 980 10.242 23.356 29.500 1.00 0.00 H
-HETATM 2941 O HOH A 981 16.978 21.242 11.455 1.00 0.00 O
-HETATM 2942 H1 HOH A 981 16.134 21.694 11.456 1.00 0.00 H
-HETATM 2943 H2 HOH A 981 16.953 20.685 12.233 1.00 0.00 H
-HETATM 2944 O HOH A 982 14.636 22.953 11.867 1.00 0.00 O
-HETATM 2945 H1 HOH A 982 15.051 23.804 11.722 1.00 0.00 H
-HETATM 2946 H2 HOH A 982 14.359 22.971 12.783 1.00 0.00 H
-HETATM 2947 O HOH A 983 21.319 10.711 22.949 1.00 0.00 O
-HETATM 2948 H1 HOH A 983 20.964 11.482 22.508 1.00 0.00 H
-HETATM 2949 H2 HOH A 983 21.156 10.870 23.879 1.00 0.00 H
-HETATM 2950 O HOH A 984 20.716 13.328 21.779 1.00 0.00 O
-HETATM 2951 H1 HOH A 984 21.512 13.441 21.258 1.00 0.00 H
-HETATM 2952 H2 HOH A 984 20.750 14.030 22.429 1.00 0.00 H
-HETATM 2953 O HOH A 985 12.673 22.835 5.540 1.00 0.00 O
-HETATM 2954 H1 HOH A 985 12.543 21.894 5.655 1.00 0.00 H
-HETATM 2955 H2 HOH A 985 12.971 22.928 4.636 1.00 0.00 H
-HETATM 2956 O HOH A 986 12.849 19.938 5.936 1.00 0.00 O
-HETATM 2957 H1 HOH A 986 13.342 19.934 6.757 1.00 0.00 H
-HETATM 2958 H2 HOH A 986 13.433 19.523 5.302 1.00 0.00 H
-HETATM 2959 O HOH A 987 8.445 23.893 1.988 1.00 0.00 O
-HETATM 2960 H1 HOH A 987 8.568 24.101 2.914 1.00 0.00 H
-HETATM 2961 H2 HOH A 987 9.276 23.502 1.718 1.00 0.00 H
-HETATM 2962 O HOH A 988 9.122 24.977 4.624 1.00 0.00 O
-HETATM 2963 H1 HOH A 988 8.784 25.866 4.506 1.00 0.00 H
-HETATM 2964 H2 HOH A 988 10.066 25.090 4.736 1.00 0.00 H
-HETATM 2965 O HOH A 989 16.315 27.150 3.519 1.00 0.00 O
-HETATM 2966 H1 HOH A 989 16.673 26.611 2.814 1.00 0.00 H
-HETATM 2967 H2 HOH A 989 17.067 27.640 3.852 1.00 0.00 H
-HETATM 2968 O HOH A 990 17.608 25.169 1.789 1.00 0.00 O
-HETATM 2969 H1 HOH A 990 17.179 24.382 2.126 1.00 0.00 H
-HETATM 2970 H2 HOH A 990 18.533 25.053 2.004 1.00 0.00 H
-HETATM 2971 O HOH A 991 5.855 16.055 19.169 1.00 0.00 O
-HETATM 2972 H1 HOH A 991 6.185 16.613 18.465 1.00 0.00 H
-HETATM 2973 H2 HOH A 991 4.948 16.334 19.293 1.00 0.00 H
-HETATM 2974 O HOH A 992 6.901 18.164 17.426 1.00 0.00 O
-HETATM 2975 H1 HOH A 992 7.692 18.381 17.920 1.00 0.00 H
-HETATM 2976 H2 HOH A 992 6.368 18.958 17.461 1.00 0.00 H
-HETATM 2977 O HOH A 993 26.188 14.225 23.476 1.00 0.00 O
-HETATM 2978 H1 HOH A 993 26.168 13.326 23.805 1.00 0.00 H
-HETATM 2979 H2 HOH A 993 27.077 14.528 23.660 1.00 0.00 H
-HETATM 2980 O HOH A 994 26.080 11.725 25.001 1.00 0.00 O
-HETATM 2981 H1 HOH A 994 25.260 11.878 25.471 1.00 0.00 H
-HETATM 2982 H2 HOH A 994 26.749 11.691 25.684 1.00 0.00 H
-HETATM 2983 O HOH A 995 23.580 21.644 27.973 1.00 0.00 O
-HETATM 2984 H1 HOH A 995 24.381 21.128 27.876 1.00 0.00 H
-HETATM 2985 H2 HOH A 995 23.803 22.504 27.615 1.00 0.00 H
-HETATM 2986 O HOH A 996 26.210 20.362 28.137 1.00 0.00 O
-HETATM 2987 H1 HOH A 996 26.165 20.057 29.043 1.00 0.00 H
-HETATM 2988 H2 HOH A 996 26.923 21.000 28.131 1.00 0.00 H
-HETATM 2989 O HOH A 997 6.964 6.761 5.762 1.00 0.00 O
-HETATM 2990 H1 HOH A 997 6.343 7.069 6.422 1.00 0.00 H
-HETATM 2991 H2 HOH A 997 6.429 6.260 5.145 1.00 0.00 H
-HETATM 2992 O HOH A 998 5.096 7.167 7.982 1.00 0.00 O
-HETATM 2993 H1 HOH A 998 5.659 6.874 8.700 1.00 0.00 H
-HETATM 2994 H2 HOH A 998 4.376 6.537 7.967 1.00 0.00 H
-HETATM 2995 O HOH A 999 13.458 2.646 1.148 1.00 0.00 O
-HETATM 2996 H1 HOH A 999 13.341 2.796 2.086 1.00 0.00 H
-HETATM 2997 H2 HOH A 999 14.305 2.205 1.079 1.00 0.00 H
-HETATM 2998 O HOH A1000 13.472 3.539 3.939 1.00 0.00 O
-HETATM 2999 H1 HOH A1000 13.233 4.456 3.798 1.00 0.00 H
-HETATM 3000 H2 HOH A1000 14.356 3.572 4.302 1.00 0.00 H
-HETATM 3001 O HOH A1001 18.811 27.880 8.599 1.00 0.00 O
-HETATM 3002 H1 HOH A1001 18.857 27.051 9.075 1.00 0.00 H
-HETATM 3003 H2 HOH A1001 18.619 27.628 7.695 1.00 0.00 H
-HETATM 3004 O HOH A1002 19.474 25.298 9.814 1.00 0.00 O
-HETATM 3005 H1 HOH A1002 20.301 25.543 10.231 1.00 0.00 H
-HETATM 3006 H2 HOH A1002 19.717 24.644 9.160 1.00 0.00 H
-HETATM 3007 O HOH A1003 14.571 9.605 6.407 1.00 0.00 O
-HETATM 3008 H1 HOH A1003 14.674 8.709 6.725 1.00 0.00 H
-HETATM 3009 H2 HOH A1003 15.186 10.119 6.931 1.00 0.00 H
-HETATM 3010 O HOH A1004 14.565 7.043 7.826 1.00 0.00 O
-HETATM 3011 H1 HOH A1004 13.618 6.927 7.912 1.00 0.00 H
-HETATM 3012 H2 HOH A1004 14.881 7.128 8.726 1.00 0.00 H
-HETATM 3013 O HOH A1005 7.195 20.102 26.904 1.00 0.00 O
-HETATM 3014 H1 HOH A1005 6.672 20.324 26.134 1.00 0.00 H
-HETATM 3015 H2 HOH A1005 6.856 19.253 27.188 1.00 0.00 H
-HETATM 3016 O HOH A1006 5.187 20.919 24.932 1.00 0.00 O
-HETATM 3017 H1 HOH A1006 5.012 21.802 25.262 1.00 0.00 H
-HETATM 3018 H2 HOH A1006 4.361 20.451 25.045 1.00 0.00 H
-HETATM 3019 O HOH A1007 28.740 18.790 16.193 1.00 0.00 O
-HETATM 3020 H1 HOH A1007 28.182 18.435 16.885 1.00 0.00 H
-HETATM 3021 H2 HOH A1007 29.614 18.457 16.394 1.00 0.00 H
-HETATM 3022 O HOH A1008 27.267 18.119 18.635 1.00 0.00 O
-HETATM 3023 H1 HOH A1008 26.872 18.975 18.806 1.00 0.00 H
-HETATM 3024 H2 HOH A1008 27.851 17.969 19.378 1.00 0.00 H
-HETATM 3025 O HOH A1009 24.731 11.057 19.029 1.00 0.00 O
-HETATM 3026 H1 HOH A1009 25.214 11.652 18.456 1.00 0.00 H
-HETATM 3027 H2 HOH A1009 25.211 11.078 19.857 1.00 0.00 H
-HETATM 3028 O HOH A1010 26.582 12.441 17.228 1.00 0.00 O
-HETATM 3029 H1 HOH A1010 26.596 11.801 16.516 1.00 0.00 H
-HETATM 3030 H2 HOH A1010 27.477 12.447 17.567 1.00 0.00 H
-HETATM 3031 O HOH A1011 6.946 9.100 7.018 1.00 0.00 O
-HETATM 3032 H1 HOH A1011 6.839 9.440 6.130 1.00 0.00 H
-HETATM 3033 H2 HOH A1011 7.465 9.765 7.470 1.00 0.00 H
-HETATM 3034 O HOH A1012 7.163 9.963 4.226 1.00 0.00 O
-HETATM 3035 H1 HOH A1012 7.424 9.127 3.837 1.00 0.00 H
-HETATM 3036 H2 HOH A1012 7.913 10.540 4.085 1.00 0.00 H
-HETATM 3037 O HOH A1013 28.814 27.065 19.350 1.00 0.00 O
-HETATM 3038 H1 HOH A1013 28.607 26.220 19.748 1.00 0.00 H
-HETATM 3039 H2 HOH A1013 28.629 26.945 18.419 1.00 0.00 H
-HETATM 3040 O HOH A1014 28.688 24.313 20.348 1.00 0.00 O
-HETATM 3041 H1 HOH A1014 29.518 24.295 20.826 1.00 0.00 H
-HETATM 3042 H2 HOH A1014 28.798 23.671 19.647 1.00 0.00 H
-HETATM 3043 O HOH A1015 12.706 7.219 10.811 1.00 0.00 O
-HETATM 3044 H1 HOH A1015 11.938 7.176 10.242 1.00 0.00 H
-HETATM 3045 H2 HOH A1015 12.531 7.957 11.395 1.00 0.00 H
-HETATM 3046 O HOH A1016 10.598 7.534 8.801 1.00 0.00 O
-HETATM 3047 H1 HOH A1016 11.140 7.478 8.013 1.00 0.00 H
-HETATM 3048 H2 HOH A1016 10.242 8.422 8.788 1.00 0.00 H
-HETATM 3049 O HOH A1017 16.815 11.831 19.519 1.00 0.00 O
-HETATM 3050 H1 HOH A1017 17.589 12.315 19.231 1.00 0.00 H
-HETATM 3051 H2 HOH A1017 16.700 12.084 20.435 1.00 0.00 H
-HETATM 3052 O HOH A1018 19.474 12.909 18.927 1.00 0.00 O
-HETATM 3053 H1 HOH A1018 19.866 12.124 18.541 1.00 0.00 H
-HETATM 3054 H2 HOH A1018 19.983 13.068 19.721 1.00 0.00 H
-END
diff --git a/examples/md_ipi/density_0.03338_init.pdb b/examples/md_ipi/density_0.03338_init.pdb
deleted file mode 100644
index 681b46d82..000000000
--- a/examples/md_ipi/density_0.03338_init.pdb
+++ /dev/null
@@ -1,3056 +0,0 @@
-CRYST1 31.24 31.24 31.24 90.00 90.00 90.00 P 1 1
-HETATM 1 O HOH A 1 25.175 22.305 16.545 1.00 0.00 O
-HETATM 2 H HOH A 1 24.749 22.864 17.195 1.00 0.00 H
-HETATM 3 H HOH A 1 25.475 22.910 15.866 1.00 0.00 H
-HETATM 4 O HOH A 2 23.436 24.025 18.158 1.00 0.00 O
-HETATM 5 H HOH A 2 22.664 23.459 18.191 1.00 0.00 H
-HETATM 6 H HOH A 2 23.149 24.800 17.676 1.00 0.00 H
-HETATM 7 O HOH A 3 8.666 17.980 15.216 1.00 0.00 O
-HETATM 8 H HOH A 3 9.352 18.623 15.036 1.00 0.00 H
-HETATM 9 H HOH A 3 9.137 17.208 15.531 1.00 0.00 H
-HETATM 10 O HOH A 4 10.852 19.637 14.187 1.00 0.00 O
-HETATM 11 H HOH A 4 10.489 19.836 13.324 1.00 0.00 H
-HETATM 12 H HOH A 4 11.639 19.124 14.007 1.00 0.00 H
-HETATM 13 O HOH A 5 12.463 22.545 14.108 1.00 0.00 O
-HETATM 14 H HOH A 5 13.157 22.267 14.706 1.00 0.00 H
-HETATM 15 H HOH A 5 12.379 21.822 13.486 1.00 0.00 H
-HETATM 16 O HOH A 6 14.939 21.843 15.510 1.00 0.00 O
-HETATM 17 H HOH A 6 15.392 22.683 15.430 1.00 0.00 H
-HETATM 18 H HOH A 6 15.475 21.230 15.008 1.00 0.00 H
-HETATM 19 O HOH A 7 28.388 4.837 10.557 1.00 0.00 O
-HETATM 20 H HOH A 7 28.901 5.392 9.970 1.00 0.00 H
-HETATM 21 H HOH A 7 28.756 3.961 10.442 1.00 0.00 H
-HETATM 22 O HOH A 8 29.631 6.303 8.346 1.00 0.00 O
-HETATM 23 H HOH A 8 28.842 6.702 7.976 1.00 0.00 H
-HETATM 24 H HOH A 8 29.950 5.718 7.660 1.00 0.00 H
-HETATM 25 O HOH A 9 1.627 18.594 7.581 1.00 0.00 O
-HETATM 26 H HOH A 9 1.533 17.842 8.166 1.00 0.00 H
-HETATM 27 H HOH A 9 1.127 19.292 8.005 1.00 0.00 H
-HETATM 28 O HOH A 10 0.814 16.247 9.136 1.00 0.00 O
-HETATM 29 H HOH A 10 0.582 15.667 8.410 1.00 0.00 H
-HETATM 30 H HOH A 10 -0.008 16.392 9.603 1.00 0.00 H
-HETATM 31 O HOH A 11 6.555 24.911 4.695 1.00 0.00 O
-HETATM 32 H HOH A 11 6.717 25.684 5.236 1.00 0.00 H
-HETATM 33 H HOH A 11 6.980 24.194 5.165 1.00 0.00 H
-HETATM 34 O HOH A 12 7.580 27.256 6.120 1.00 0.00 O
-HETATM 35 H HOH A 12 7.874 27.767 5.365 1.00 0.00 H
-HETATM 36 H HOH A 12 8.381 27.061 6.606 1.00 0.00 H
-HETATM 37 O HOH A 13 22.252 2.396 24.356 1.00 0.00 O
-HETATM 38 H HOH A 13 21.446 2.242 24.849 1.00 0.00 H
-HETATM 39 H HOH A 13 21.960 2.519 23.452 1.00 0.00 H
-HETATM 40 O HOH A 14 19.817 1.393 25.641 1.00 0.00 O
-HETATM 41 H HOH A 14 20.190 0.626 26.077 1.00 0.00 H
-HETATM 42 H HOH A 14 19.197 1.034 25.007 1.00 0.00 H
-HETATM 43 O HOH A 15 27.387 20.314 2.397 1.00 0.00 O
-HETATM 44 H HOH A 15 28.249 20.472 2.782 1.00 0.00 H
-HETATM 45 H HOH A 15 27.342 20.915 1.654 1.00 0.00 H
-HETATM 46 O HOH A 16 29.777 21.270 3.798 1.00 0.00 O
-HETATM 47 H HOH A 16 29.409 21.340 4.679 1.00 0.00 H
-HETATM 48 H HOH A 16 30.000 22.168 3.557 1.00 0.00 H
-HETATM 49 O HOH A 17 24.758 28.817 12.939 1.00 0.00 O
-HETATM 50 H HOH A 17 25.054 27.908 12.983 1.00 0.00 H
-HETATM 51 H HOH A 17 24.709 29.099 13.852 1.00 0.00 H
-HETATM 52 O HOH A 18 25.118 25.923 13.222 1.00 0.00 O
-HETATM 53 H HOH A 18 24.430 25.633 12.622 1.00 0.00 H
-HETATM 54 H HOH A 18 24.839 25.608 14.081 1.00 0.00 H
-HETATM 55 O HOH A 19 1.251 26.111 29.064 1.00 0.00 O
-HETATM 56 H HOH A 19 2.177 26.071 28.827 1.00 0.00 H
-HETATM 57 H HOH A 19 1.250 26.314 30.000 1.00 0.00 H
-HETATM 58 O HOH A 20 4.070 25.471 28.590 1.00 0.00 O
-HETATM 59 H HOH A 20 3.955 24.637 28.133 1.00 0.00 H
-HETATM 60 H HOH A 20 4.529 25.243 29.398 1.00 0.00 H
-HETATM 61 O HOH A 21 27.389 13.595 26.581 1.00 0.00 O
-HETATM 62 H HOH A 21 26.487 13.857 26.767 1.00 0.00 H
-HETATM 63 H HOH A 21 27.584 13.993 25.732 1.00 0.00 H
-HETATM 64 O HOH A 22 24.491 13.985 26.773 1.00 0.00 O
-HETATM 65 H HOH A 22 24.239 13.089 26.998 1.00 0.00 H
-HETATM 66 H HOH A 22 24.091 14.142 25.918 1.00 0.00 H
-HETATM 67 O HOH A 23 27.106 9.133 3.515 1.00 0.00 O
-HETATM 68 H HOH A 23 27.018 9.921 2.979 1.00 0.00 H
-HETATM 69 H HOH A 23 26.711 9.367 4.355 1.00 0.00 H
-HETATM 70 O HOH A 24 27.283 11.738 2.186 1.00 0.00 O
-HETATM 71 H HOH A 24 28.202 11.701 1.918 1.00 0.00 H
-HETATM 72 H HOH A 24 27.242 12.463 2.808 1.00 0.00 H
-HETATM 73 O HOH A 25 15.513 14.065 29.610 1.00 0.00 O
-HETATM 74 H HOH A 25 16.141 13.387 29.362 1.00 0.00 H
-HETATM 75 H HOH A 25 16.047 14.757 30.000 1.00 0.00 H
-HETATM 76 O HOH A 26 17.435 11.862 29.412 1.00 0.00 O
-HETATM 77 H HOH A 26 16.909 11.175 29.823 1.00 0.00 H
-HETATM 78 H HOH A 26 18.180 11.981 30.000 1.00 0.00 H
-HETATM 79 O HOH A 27 6.539 15.263 11.697 1.00 0.00 O
-HETATM 80 H HOH A 27 6.868 14.982 12.551 1.00 0.00 H
-HETATM 81 H HOH A 27 7.101 15.998 11.452 1.00 0.00 H
-HETATM 82 O HOH A 28 7.261 14.865 14.509 1.00 0.00 O
-HETATM 83 H HOH A 28 6.379 14.852 14.884 1.00 0.00 H
-HETATM 84 H HOH A 28 7.674 15.640 14.888 1.00 0.00 H
-HETATM 85 O HOH A 29 29.126 10.153 8.629 1.00 0.00 O
-HETATM 86 H HOH A 29 29.255 10.375 7.707 1.00 0.00 H
-HETATM 87 H HOH A 29 29.915 9.670 8.873 1.00 0.00 H
-HETATM 88 O HOH A 30 29.378 10.281 5.713 1.00 0.00 O
-HETATM 89 H HOH A 30 28.504 9.969 5.479 1.00 0.00 H
-HETATM 90 H HOH A 30 29.973 9.613 5.373 1.00 0.00 H
-HETATM 91 O HOH A 31 15.008 7.594 19.567 1.00 0.00 O
-HETATM 92 H HOH A 31 15.119 7.399 20.497 1.00 0.00 H
-HETATM 93 H HOH A 31 14.063 7.698 19.454 1.00 0.00 H
-HETATM 94 O HOH A 32 15.170 6.470 22.267 1.00 0.00 O
-HETATM 95 H HOH A 32 15.742 5.725 22.080 1.00 0.00 H
-HETATM 96 H HOH A 32 14.343 6.077 22.544 1.00 0.00 H
-HETATM 97 O HOH A 33 24.123 15.176 10.909 1.00 0.00 O
-HETATM 98 H HOH A 33 24.901 15.658 10.628 1.00 0.00 H
-HETATM 99 H HOH A 33 24.203 14.322 10.485 1.00 0.00 H
-HETATM 100 O HOH A 34 26.215 16.717 9.555 1.00 0.00 O
-HETATM 101 H HOH A 34 25.696 17.485 9.315 1.00 0.00 H
-HETATM 102 H HOH A 34 26.467 16.325 8.719 1.00 0.00 H
-HETATM 103 O HOH A 35 25.842 2.926 0.685 1.00 0.00 O
-HETATM 104 H HOH A 35 26.459 3.173 1.374 1.00 0.00 H
-HETATM 105 H HOH A 35 25.971 3.581 -0.000 1.00 0.00 H
-HETATM 106 O HOH A 36 27.340 4.088 2.918 1.00 0.00 O
-HETATM 107 H HOH A 36 26.667 4.020 3.596 1.00 0.00 H
-HETATM 108 H HOH A 36 27.478 5.029 2.809 1.00 0.00 H
-HETATM 109 O HOH A 37 26.934 22.974 29.101 1.00 0.00 O
-HETATM 110 H HOH A 37 27.133 23.762 28.595 1.00 0.00 H
-HETATM 111 H HOH A 37 26.829 23.286 30.000 1.00 0.00 H
-HETATM 112 O HOH A 38 28.069 25.329 27.778 1.00 0.00 O
-HETATM 113 H HOH A 38 28.814 24.908 27.348 1.00 0.00 H
-HETATM 114 H HOH A 38 28.463 25.938 28.402 1.00 0.00 H
-HETATM 115 O HOH A 39 21.401 21.808 8.377 1.00 0.00 O
-HETATM 116 H HOH A 39 20.915 21.320 7.713 1.00 0.00 H
-HETATM 117 H HOH A 39 21.403 22.711 8.059 1.00 0.00 H
-HETATM 118 O HOH A 40 20.402 20.350 6.040 1.00 0.00 O
-HETATM 119 H HOH A 40 21.077 19.672 5.993 1.00 0.00 H
-HETATM 120 H HOH A 40 20.506 20.852 5.231 1.00 0.00 H
-HETATM 121 O HOH A 41 17.146 7.120 23.040 1.00 0.00 O
-HETATM 122 H HOH A 41 17.808 7.642 22.587 1.00 0.00 H
-HETATM 123 H HOH A 41 17.192 7.412 23.951 1.00 0.00 H
-HETATM 124 O HOH A 42 19.546 8.349 21.894 1.00 0.00 O
-HETATM 125 H HOH A 42 19.915 7.575 21.468 1.00 0.00 H
-HETATM 126 H HOH A 42 20.185 8.585 22.565 1.00 0.00 H
-HETATM 127 O HOH A 43 21.518 29.905 1.168 1.00 0.00 O
-HETATM 128 H HOH A 43 20.603 29.910 1.447 1.00 0.00 H
-HETATM 129 H HOH A 43 21.659 29.024 0.819 1.00 0.00 H
-HETATM 130 O HOH A 44 18.913 29.624 2.478 1.00 0.00 O
-HETATM 131 H HOH A 44 19.138 30.000 3.329 1.00 0.00 H
-HETATM 132 H HOH A 44 18.701 28.710 2.666 1.00 0.00 H
-HETATM 133 O HOH A 45 28.600 28.373 12.620 1.00 0.00 O
-HETATM 134 H HOH A 45 28.490 27.995 13.493 1.00 0.00 H
-HETATM 135 H HOH A 45 28.749 27.619 12.049 1.00 0.00 H
-HETATM 136 O HOH A 46 28.839 27.167 15.280 1.00 0.00 O
-HETATM 137 H HOH A 46 29.457 27.789 15.665 1.00 0.00 H
-HETATM 138 H HOH A 46 29.314 26.337 15.257 1.00 0.00 H
-HETATM 139 O HOH A 47 0.133 7.561 21.579 1.00 0.00 O
-HETATM 140 H HOH A 47 0.402 6.783 22.067 1.00 0.00 H
-HETATM 141 H HOH A 47 -0.005 7.249 20.685 1.00 0.00 H
-HETATM 142 O HOH A 48 1.466 5.265 22.818 1.00 0.00 O
-HETATM 143 H HOH A 48 2.210 5.724 23.209 1.00 0.00 H
-HETATM 144 H HOH A 48 1.857 4.682 22.167 1.00 0.00 H
-HETATM 145 O HOH A 49 26.542 3.942 23.693 1.00 0.00 O
-HETATM 146 H HOH A 49 27.375 3.926 24.163 1.00 0.00 H
-HETATM 147 H HOH A 49 26.763 3.673 22.801 1.00 0.00 H
-HETATM 148 O HOH A 50 29.192 4.392 24.859 1.00 0.00 O
-HETATM 149 H HOH A 50 29.041 5.263 25.228 1.00 0.00 H
-HETATM 150 H HOH A 50 29.862 4.525 24.189 1.00 0.00 H
-HETATM 151 O HOH A 51 10.648 21.636 11.400 1.00 0.00 O
-HETATM 152 H HOH A 51 11.599 21.644 11.297 1.00 0.00 H
-HETATM 153 H HOH A 51 10.505 21.800 12.333 1.00 0.00 H
-HETATM 154 O HOH A 52 13.533 21.132 11.314 1.00 0.00 O
-HETATM 155 H HOH A 52 13.522 20.313 10.816 1.00 0.00 H
-HETATM 156 H HOH A 52 13.882 20.889 12.171 1.00 0.00 H
-HETATM 157 O HOH A 53 21.644 12.803 10.659 1.00 0.00 O
-HETATM 158 H HOH A 53 21.097 13.432 11.130 1.00 0.00 H
-HETATM 159 H HOH A 53 21.173 12.640 9.842 1.00 0.00 H
-HETATM 160 O HOH A 54 19.645 14.269 12.222 1.00 0.00 O
-HETATM 161 H HOH A 54 19.688 13.755 13.029 1.00 0.00 H
-HETATM 162 H HOH A 54 18.757 14.129 11.893 1.00 0.00 H
-HETATM 163 O HOH A 55 2.391 20.333 17.493 1.00 0.00 O
-HETATM 164 H HOH A 55 3.058 20.789 18.006 1.00 0.00 H
-HETATM 165 H HOH A 55 1.676 20.965 17.412 1.00 0.00 H
-HETATM 166 O HOH A 56 4.092 21.605 19.511 1.00 0.00 O
-HETATM 167 H HOH A 56 4.200 20.858 20.101 1.00 0.00 H
-HETATM 168 H HOH A 56 3.614 22.254 20.028 1.00 0.00 H
-HETATM 169 O HOH A 57 7.713 2.571 17.467 1.00 0.00 O
-HETATM 170 H HOH A 57 8.381 3.171 17.798 1.00 0.00 H
-HETATM 171 H HOH A 57 7.070 2.514 18.174 1.00 0.00 H
-HETATM 172 O HOH A 58 9.934 3.954 18.786 1.00 0.00 O
-HETATM 173 H HOH A 58 10.642 3.358 18.538 1.00 0.00 H
-HETATM 174 H HOH A 58 9.884 3.888 19.739 1.00 0.00 H
-HETATM 175 O HOH A 59 29.430 3.793 19.811 1.00 0.00 O
-HETATM 176 H HOH A 59 28.521 3.507 19.712 1.00 0.00 H
-HETATM 177 H HOH A 59 29.447 4.254 20.649 1.00 0.00 H
-HETATM 178 O HOH A 60 26.552 3.468 19.364 1.00 0.00 O
-HETATM 179 H HOH A 60 26.506 3.805 18.469 1.00 0.00 H
-HETATM 180 H HOH A 60 26.035 4.085 19.882 1.00 0.00 H
-HETATM 181 O HOH A 61 5.741 19.128 6.617 1.00 0.00 O
-HETATM 182 H HOH A 61 6.589 19.285 7.032 1.00 0.00 H
-HETATM 183 H HOH A 61 5.956 18.760 5.760 1.00 0.00 H
-HETATM 184 O HOH A 62 8.358 20.068 7.541 1.00 0.00 O
-HETATM 185 H HOH A 62 8.111 20.959 7.791 1.00 0.00 H
-HETATM 186 H HOH A 62 8.975 20.182 6.818 1.00 0.00 H
-HETATM 187 O HOH A 63 16.833 7.327 12.868 1.00 0.00 O
-HETATM 188 H HOH A 63 17.503 6.702 13.144 1.00 0.00 H
-HETATM 189 H HOH A 63 17.251 7.834 12.171 1.00 0.00 H
-HETATM 190 O HOH A 64 19.110 5.865 13.992 1.00 0.00 O
-HETATM 191 H HOH A 64 18.972 6.072 14.917 1.00 0.00 H
-HETATM 192 H HOH A 64 19.946 6.277 13.774 1.00 0.00 H
-HETATM 193 O HOH A 65 10.448 22.006 8.592 1.00 0.00 O
-HETATM 194 H HOH A 65 9.736 21.720 9.165 1.00 0.00 H
-HETATM 195 H HOH A 65 10.130 21.830 7.707 1.00 0.00 H
-HETATM 196 O HOH A 66 8.469 20.600 10.233 1.00 0.00 O
-HETATM 197 H HOH A 66 9.070 20.103 10.789 1.00 0.00 H
-HETATM 198 H HOH A 66 7.996 19.934 9.734 1.00 0.00 H
-HETATM 199 O HOH A 67 9.638 7.195 13.398 1.00 0.00 O
-HETATM 200 H HOH A 67 9.505 7.014 12.467 1.00 0.00 H
-HETATM 201 H HOH A 67 10.582 7.327 13.485 1.00 0.00 H
-HETATM 202 O HOH A 68 9.431 6.118 10.681 1.00 0.00 O
-HETATM 203 H HOH A 68 8.889 5.351 10.870 1.00 0.00 H
-HETATM 204 H HOH A 68 10.262 5.757 10.372 1.00 0.00 H
-HETATM 205 O HOH A 69 27.781 11.682 9.460 1.00 0.00 O
-HETATM 206 H HOH A 69 26.883 11.579 9.774 1.00 0.00 H
-HETATM 207 H HOH A 69 27.692 11.818 8.517 1.00 0.00 H
-HETATM 208 O HOH A 70 25.076 10.835 10.201 1.00 0.00 O
-HETATM 209 H HOH A 70 25.302 10.049 10.699 1.00 0.00 H
-HETATM 210 H HOH A 70 24.581 10.511 9.449 1.00 0.00 H
-HETATM 211 O HOH A 71 19.448 22.986 23.278 1.00 0.00 O
-HETATM 212 H HOH A 71 19.265 22.883 24.212 1.00 0.00 H
-HETATM 213 H HOH A 71 18.642 23.354 22.915 1.00 0.00 H
-HETATM 214 O HOH A 72 18.591 22.208 25.970 1.00 0.00 O
-HETATM 215 H HOH A 72 18.919 21.309 25.972 1.00 0.00 H
-HETATM 216 H HOH A 72 17.640 22.119 26.034 1.00 0.00 H
-HETATM 217 O HOH A 73 24.069 17.897 6.744 1.00 0.00 O
-HETATM 218 H HOH A 73 24.797 17.437 6.327 1.00 0.00 H
-HETATM 219 H HOH A 73 24.304 17.930 7.671 1.00 0.00 H
-HETATM 220 O HOH A 74 26.051 16.016 5.687 1.00 0.00 O
-HETATM 221 H HOH A 74 25.457 15.431 5.215 1.00 0.00 H
-HETATM 222 H HOH A 74 26.432 15.471 6.375 1.00 0.00 H
-HETATM 223 O HOH A 75 5.267 14.098 26.296 1.00 0.00 O
-HETATM 224 H HOH A 75 4.779 14.829 26.675 1.00 0.00 H
-HETATM 225 H HOH A 75 5.581 13.601 27.051 1.00 0.00 H
-HETATM 226 O HOH A 76 4.295 16.558 27.557 1.00 0.00 O
-HETATM 227 H HOH A 76 4.744 17.193 26.999 1.00 0.00 H
-HETATM 228 H HOH A 76 4.672 16.690 28.427 1.00 0.00 H
-HETATM 229 O HOH A 77 26.889 12.793 28.975 1.00 0.00 O
-HETATM 230 H HOH A 77 26.397 11.979 28.875 1.00 0.00 H
-HETATM 231 H HOH A 77 27.389 12.675 29.783 1.00 0.00 H
-HETATM 232 O HOH A 78 25.040 10.523 29.075 1.00 0.00 O
-HETATM 233 H HOH A 78 24.222 10.995 28.912 1.00 0.00 H
-HETATM 234 H HOH A 78 24.945 10.175 29.961 1.00 0.00 H
-HETATM 235 O HOH A 79 27.108 20.663 12.545 1.00 0.00 O
-HETATM 236 H HOH A 79 27.967 21.067 12.665 1.00 0.00 H
-HETATM 237 H HOH A 79 27.144 20.275 11.670 1.00 0.00 H
-HETATM 238 O HOH A 80 29.527 22.315 12.572 1.00 0.00 O
-HETATM 239 H HOH A 80 29.138 23.136 12.874 1.00 0.00 H
-HETATM 240 H HOH A 80 29.842 22.506 11.689 1.00 0.00 H
-HETATM 241 O HOH A 81 15.369 6.421 17.677 1.00 0.00 O
-HETATM 242 H HOH A 81 15.479 6.016 16.816 1.00 0.00 H
-HETATM 243 H HOH A 81 16.230 6.347 18.089 1.00 0.00 H
-HETATM 244 O HOH A 82 15.674 4.686 15.336 1.00 0.00 O
-HETATM 245 H HOH A 82 14.884 4.156 15.445 1.00 0.00 H
-HETATM 246 H HOH A 82 16.397 4.067 15.438 1.00 0.00 H
-HETATM 247 O HOH A 83 13.390 27.805 1.684 1.00 0.00 O
-HETATM 248 H HOH A 83 12.880 27.091 2.064 1.00 0.00 H
-HETATM 249 H HOH A 83 14.199 27.822 2.195 1.00 0.00 H
-HETATM 250 O HOH A 84 11.716 25.996 3.267 1.00 0.00 O
-HETATM 251 H HOH A 84 10.930 26.541 3.326 1.00 0.00 H
-HETATM 252 H HOH A 84 12.024 25.917 4.169 1.00 0.00 H
-HETATM 253 O HOH A 85 19.050 18.937 20.460 1.00 0.00 O
-HETATM 254 H HOH A 85 18.461 19.586 20.077 1.00 0.00 H
-HETATM 255 H HOH A 85 18.470 18.310 20.891 1.00 0.00 H
-HETATM 256 O HOH A 86 17.206 21.123 19.825 1.00 0.00 O
-HETATM 257 H HOH A 86 17.715 21.851 20.185 1.00 0.00 H
-HETATM 258 H HOH A 86 16.414 21.094 20.361 1.00 0.00 H
-HETATM 259 O HOH A 87 0.927 3.096 19.204 1.00 0.00 O
-HETATM 260 H HOH A 87 0.984 3.974 18.828 1.00 0.00 H
-HETATM 261 H HOH A 87 -0.000 2.867 19.143 1.00 0.00 H
-HETATM 262 O HOH A 88 0.886 5.953 18.559 1.00 0.00 O
-HETATM 263 H HOH A 88 1.400 6.276 19.300 1.00 0.00 H
-HETATM 264 H HOH A 88 0.018 6.338 18.681 1.00 0.00 H
-HETATM 265 O HOH A 89 27.009 10.380 18.760 1.00 0.00 O
-HETATM 266 H HOH A 89 27.698 9.809 19.099 1.00 0.00 H
-HETATM 267 H HOH A 89 26.870 10.083 17.861 1.00 0.00 H
-HETATM 268 O HOH A 90 29.471 8.967 19.486 1.00 0.00 O
-HETATM 269 H HOH A 90 29.969 9.711 19.826 1.00 0.00 H
-HETATM 270 H HOH A 90 29.961 8.679 18.716 1.00 0.00 H
-HETATM 271 O HOH A 91 27.639 8.241 6.809 1.00 0.00 O
-HETATM 272 H HOH A 91 27.261 8.569 7.625 1.00 0.00 H
-HETATM 273 H HOH A 91 27.210 7.397 6.668 1.00 0.00 H
-HETATM 274 O HOH A 92 26.840 8.867 9.557 1.00 0.00 O
-HETATM 275 H HOH A 92 27.704 9.075 9.915 1.00 0.00 H
-HETATM 276 H HOH A 92 26.560 8.088 10.037 1.00 0.00 H
-HETATM 277 O HOH A 93 11.165 29.212 7.517 1.00 0.00 O
-HETATM 278 H HOH A 93 12.067 29.318 7.818 1.00 0.00 H
-HETATM 279 H HOH A 93 10.715 30.000 7.825 1.00 0.00 H
-HETATM 280 O HOH A 94 13.715 29.379 8.949 1.00 0.00 O
-HETATM 281 H HOH A 94 13.736 28.492 9.311 1.00 0.00 H
-HETATM 282 H HOH A 94 13.662 29.951 9.715 1.00 0.00 H
-HETATM 283 O HOH A 95 3.036 20.745 12.672 1.00 0.00 O
-HETATM 284 H HOH A 95 2.823 20.245 11.885 1.00 0.00 H
-HETATM 285 H HOH A 95 3.991 20.711 12.728 1.00 0.00 H
-HETATM 286 O HOH A 96 2.485 18.754 10.595 1.00 0.00 O
-HETATM 287 H HOH A 96 1.816 18.246 11.056 1.00 0.00 H
-HETATM 288 H HOH A 96 3.213 18.144 10.479 1.00 0.00 H
-HETATM 289 O HOH A 97 10.755 18.042 12.079 1.00 0.00 O
-HETATM 290 H HOH A 97 10.520 17.714 11.211 1.00 0.00 H
-HETATM 291 H HOH A 97 11.711 18.084 12.069 1.00 0.00 H
-HETATM 292 O HOH A 98 10.196 16.527 9.635 1.00 0.00 O
-HETATM 293 H HOH A 98 9.605 15.870 10.004 1.00 0.00 H
-HETATM 294 H HOH A 98 10.957 16.028 9.338 1.00 0.00 H
-HETATM 295 O HOH A 99 21.620 21.445 25.219 1.00 0.00 O
-HETATM 296 H HOH A 99 22.188 21.129 24.518 1.00 0.00 H
-HETATM 297 H HOH A 99 22.083 22.201 25.581 1.00 0.00 H
-HETATM 298 O HOH A 100 23.671 20.252 23.501 1.00 0.00 O
-HETATM 299 H HOH A 100 23.614 19.348 23.814 1.00 0.00 H
-HETATM 300 H HOH A 100 24.553 20.536 23.742 1.00 0.00 H
-HETATM 301 O HOH A 101 16.449 27.071 26.096 1.00 0.00 O
-HETATM 302 H HOH A 101 16.758 27.663 26.781 1.00 0.00 H
-HETATM 303 H HOH A 101 17.042 27.223 25.360 1.00 0.00 H
-HETATM 304 O HOH A 102 17.128 29.291 27.883 1.00 0.00 O
-HETATM 305 H HOH A 102 16.244 29.619 28.049 1.00 0.00 H
-HETATM 306 H HOH A 102 17.569 30.003 27.421 1.00 0.00 H
-HETATM 307 O HOH A 103 23.163 0.135 10.057 1.00 0.00 O
-HETATM 308 H HOH A 103 23.826 0.824 10.036 1.00 0.00 H
-HETATM 309 H HOH A 103 22.929 -0.001 9.138 1.00 0.00 H
-HETATM 310 O HOH A 104 24.744 2.598 9.915 1.00 0.00 O
-HETATM 311 H HOH A 104 24.300 3.094 10.604 1.00 0.00 H
-HETATM 312 H HOH A 104 24.572 3.090 9.113 1.00 0.00 H
-HETATM 313 O HOH A 105 20.635 6.807 23.779 1.00 0.00 O
-HETATM 314 H HOH A 105 20.202 5.984 24.007 1.00 0.00 H
-HETATM 315 H HOH A 105 21.114 7.054 24.570 1.00 0.00 H
-HETATM 316 O HOH A 106 18.940 4.627 24.759 1.00 0.00 O
-HETATM 317 H HOH A 106 18.099 4.934 24.419 1.00 0.00 H
-HETATM 318 H HOH A 106 18.839 4.649 25.710 1.00 0.00 H
-HETATM 319 O HOH A 107 23.557 5.499 28.413 1.00 0.00 O
-HETATM 320 H HOH A 107 23.431 6.319 28.891 1.00 0.00 H
-HETATM 321 H HOH A 107 23.761 5.770 27.518 1.00 0.00 H
-HETATM 322 O HOH A 108 22.657 8.015 29.614 1.00 0.00 O
-HETATM 323 H HOH A 108 21.837 7.705 30.001 1.00 0.00 H
-HETATM 324 H HOH A 108 22.388 8.653 28.954 1.00 0.00 H
-HETATM 325 O HOH A 109 9.191 17.586 7.989 1.00 0.00 O
-HETATM 326 H HOH A 109 10.056 17.875 7.699 1.00 0.00 H
-HETATM 327 H HOH A 109 8.943 18.211 8.669 1.00 0.00 H
-HETATM 328 O HOH A 110 12.023 18.223 7.590 1.00 0.00 O
-HETATM 329 H HOH A 110 12.381 17.338 7.658 1.00 0.00 H
-HETATM 330 H HOH A 110 12.395 18.693 8.336 1.00 0.00 H
-HETATM 331 O HOH A 111 29.214 6.787 4.684 1.00 0.00 O
-HETATM 332 H HOH A 111 28.887 6.074 5.233 1.00 0.00 H
-HETATM 333 H HOH A 111 28.765 6.671 3.847 1.00 0.00 H
-HETATM 334 O HOH A 112 28.578 4.279 6.059 1.00 0.00 O
-HETATM 335 H HOH A 112 29.463 4.034 6.330 1.00 0.00 H
-HETATM 336 H HOH A 112 28.312 3.591 5.449 1.00 0.00 H
-HETATM 337 O HOH A 113 3.798 12.379 9.475 1.00 0.00 O
-HETATM 338 H HOH A 113 4.627 11.909 9.387 1.00 0.00 H
-HETATM 339 H HOH A 113 3.692 12.506 10.418 1.00 0.00 H
-HETATM 340 O HOH A 114 6.029 10.483 9.371 1.00 0.00 O
-HETATM 341 H HOH A 114 5.640 9.834 8.783 1.00 0.00 H
-HETATM 342 H HOH A 114 6.145 10.018 10.199 1.00 0.00 H
-HETATM 343 O HOH A 115 26.692 30.002 17.094 1.00 0.00 O
-HETATM 344 H HOH A 115 26.355 29.414 17.769 1.00 0.00 H
-HETATM 345 H HOH A 115 26.686 29.479 16.293 1.00 0.00 H
-HETATM 346 O HOH A 116 26.188 27.970 19.143 1.00 0.00 O
-HETATM 347 H HOH A 116 26.905 28.187 19.741 1.00 0.00 H
-HETATM 348 H HOH A 116 26.390 27.088 18.833 1.00 0.00 H
-HETATM 349 O HOH A 117 19.996 9.604 6.531 1.00 0.00 O
-HETATM 350 H HOH A 117 20.342 8.825 6.097 1.00 0.00 H
-HETATM 351 H HOH A 117 20.258 9.505 7.447 1.00 0.00 H
-HETATM 352 O HOH A 118 20.607 6.953 5.443 1.00 0.00 O
-HETATM 353 H HOH A 118 19.769 6.777 5.013 1.00 0.00 H
-HETATM 354 H HOH A 118 20.671 6.288 6.129 1.00 0.00 H
-HETATM 355 O HOH A 119 26.521 21.500 6.873 1.00 0.00 O
-HETATM 356 H HOH A 119 26.152 22.058 7.557 1.00 0.00 H
-HETATM 357 H HOH A 119 27.467 21.629 6.942 1.00 0.00 H
-HETATM 358 O HOH A 120 25.498 23.637 8.597 1.00 0.00 O
-HETATM 359 H HOH A 120 24.860 24.017 7.993 1.00 0.00 H
-HETATM 360 H HOH A 120 26.144 24.329 8.735 1.00 0.00 H
-HETATM 361 O HOH A 121 1.945 8.398 14.854 1.00 0.00 O
-HETATM 362 H HOH A 121 1.790 8.056 15.734 1.00 0.00 H
-HETATM 363 H HOH A 121 2.845 8.723 14.877 1.00 0.00 H
-HETATM 364 O HOH A 122 1.411 7.906 17.692 1.00 0.00 O
-HETATM 365 H HOH A 122 0.578 8.373 17.766 1.00 0.00 H
-HETATM 366 H HOH A 122 2.010 8.389 18.261 1.00 0.00 H
-HETATM 367 O HOH A 123 27.303 11.704 22.846 1.00 0.00 O
-HETATM 368 H HOH A 123 27.807 11.066 22.341 1.00 0.00 H
-HETATM 369 H HOH A 123 27.811 11.832 23.647 1.00 0.00 H
-HETATM 370 O HOH A 124 28.619 9.367 21.666 1.00 0.00 O
-HETATM 371 H HOH A 124 27.842 8.833 21.500 1.00 0.00 H
-HETATM 372 H HOH A 124 29.120 8.871 22.314 1.00 0.00 H
-HETATM 373 O HOH A 125 26.904 3.221 11.445 1.00 0.00 O
-HETATM 374 H HOH A 125 26.527 3.805 12.103 1.00 0.00 H
-HETATM 375 H HOH A 125 26.592 3.568 10.609 1.00 0.00 H
-HETATM 376 O HOH A 126 25.264 4.711 13.362 1.00 0.00 O
-HETATM 377 H HOH A 126 24.950 3.974 13.887 1.00 0.00 H
-HETATM 378 H HOH A 126 24.476 5.073 12.957 1.00 0.00 H
-HETATM 379 O HOH A 127 14.087 10.230 2.306 1.00 0.00 O
-HETATM 380 H HOH A 127 13.438 10.191 3.008 1.00 0.00 H
-HETATM 381 H HOH A 127 14.316 9.316 2.139 1.00 0.00 H
-HETATM 382 O HOH A 128 12.556 10.020 4.795 1.00 0.00 O
-HETATM 383 H HOH A 128 13.011 10.694 5.302 1.00 0.00 H
-HETATM 384 H HOH A 128 12.736 9.204 5.261 1.00 0.00 H
-HETATM 385 O HOH A 129 15.695 17.576 19.226 1.00 0.00 O
-HETATM 386 H HOH A 129 15.677 16.793 18.676 1.00 0.00 H
-HETATM 387 H HOH A 129 16.041 17.273 20.065 1.00 0.00 H
-HETATM 388 O HOH A 130 15.166 15.046 17.845 1.00 0.00 O
-HETATM 389 H HOH A 130 14.266 15.219 17.568 1.00 0.00 H
-HETATM 390 H HOH A 130 15.096 14.312 18.454 1.00 0.00 H
-HETATM 391 O HOH A 131 21.987 24.075 15.925 1.00 0.00 O
-HETATM 392 H HOH A 131 22.790 23.798 15.483 1.00 0.00 H
-HETATM 393 H HOH A 131 21.582 23.260 16.220 1.00 0.00 H
-HETATM 394 O HOH A 132 24.089 23.087 14.139 1.00 0.00 O
-HETATM 395 H HOH A 132 23.901 23.635 13.377 1.00 0.00 H
-HETATM 396 H HOH A 132 23.919 22.193 13.842 1.00 0.00 H
-HETATM 397 O HOH A 133 12.024 25.525 14.519 1.00 0.00 O
-HETATM 398 H HOH A 133 12.019 24.781 13.917 1.00 0.00 H
-HETATM 399 H HOH A 133 11.110 25.805 14.563 1.00 0.00 H
-HETATM 400 O HOH A 134 11.970 23.652 12.267 1.00 0.00 O
-HETATM 401 H HOH A 134 12.706 24.005 11.767 1.00 0.00 H
-HETATM 402 H HOH A 134 11.208 23.787 11.704 1.00 0.00 H
-HETATM 403 O HOH A 135 20.349 29.059 23.079 1.00 0.00 O
-HETATM 404 H HOH A 135 20.659 28.268 23.519 1.00 0.00 H
-HETATM 405 H HOH A 135 21.052 29.696 23.207 1.00 0.00 H
-HETATM 406 O HOH A 136 21.144 26.912 24.908 1.00 0.00 O
-HETATM 407 H HOH A 136 20.313 26.801 25.370 1.00 0.00 H
-HETATM 408 H HOH A 136 21.755 27.218 25.578 1.00 0.00 H
-HETATM 409 O HOH A 137 15.082 16.412 10.612 1.00 0.00 O
-HETATM 410 H HOH A 137 14.907 16.863 11.437 1.00 0.00 H
-HETATM 411 H HOH A 137 16.014 16.557 10.451 1.00 0.00 H
-HETATM 412 O HOH A 138 14.556 18.270 12.815 1.00 0.00 O
-HETATM 413 H HOH A 138 13.785 18.709 12.453 1.00 0.00 H
-HETATM 414 H HOH A 138 15.220 18.957 12.873 1.00 0.00 H
-HETATM 415 O HOH A 139 25.564 10.730 13.348 1.00 0.00 O
-HETATM 416 H HOH A 139 26.286 11.066 12.817 1.00 0.00 H
-HETATM 417 H HOH A 139 25.303 11.470 13.896 1.00 0.00 H
-HETATM 418 O HOH A 140 28.084 11.690 12.202 1.00 0.00 O
-HETATM 419 H HOH A 140 28.632 10.930 12.402 1.00 0.00 H
-HETATM 420 H HOH A 140 28.464 12.404 12.715 1.00 0.00 H
-HETATM 421 O HOH A 141 24.845 14.200 19.827 1.00 0.00 O
-HETATM 422 H HOH A 141 24.141 14.034 19.201 1.00 0.00 H
-HETATM 423 H HOH A 141 24.522 13.841 20.653 1.00 0.00 H
-HETATM 424 O HOH A 142 22.434 14.140 18.163 1.00 0.00 O
-HETATM 425 H HOH A 142 22.457 15.043 17.843 1.00 0.00 H
-HETATM 426 H HOH A 142 21.635 14.090 18.688 1.00 0.00 H
-HETATM 427 O HOH A 143 17.784 12.042 14.165 1.00 0.00 O
-HETATM 428 H HOH A 143 17.460 12.912 13.933 1.00 0.00 H
-HETATM 429 H HOH A 143 17.350 11.838 14.994 1.00 0.00 H
-HETATM 430 O HOH A 144 17.168 14.889 13.852 1.00 0.00 O
-HETATM 431 H HOH A 144 18.053 15.210 13.674 1.00 0.00 H
-HETATM 432 H HOH A 144 16.924 15.298 14.682 1.00 0.00 H
-HETATM 433 O HOH A 145 7.784 29.353 2.961 1.00 0.00 O
-HETATM 434 H HOH A 145 7.705 28.474 2.591 1.00 0.00 H
-HETATM 435 H HOH A 145 7.879 29.926 2.200 1.00 0.00 H
-HETATM 436 O HOH A 146 8.111 26.701 1.759 1.00 0.00 O
-HETATM 437 H HOH A 146 8.769 26.350 2.361 1.00 0.00 H
-HETATM 438 H HOH A 146 8.555 26.741 0.912 1.00 0.00 H
-HETATM 439 O HOH A 147 26.892 7.536 13.011 1.00 0.00 O
-HETATM 440 H HOH A 147 27.726 7.752 12.593 1.00 0.00 H
-HETATM 441 H HOH A 147 27.128 6.947 13.727 1.00 0.00 H
-HETATM 442 O HOH A 148 29.397 7.674 11.498 1.00 0.00 O
-HETATM 443 H HOH A 148 29.050 7.486 10.625 1.00 0.00 H
-HETATM 444 H HOH A 148 29.976 6.937 11.692 1.00 0.00 H
-HETATM 445 O HOH A 149 2.022 14.237 19.721 1.00 0.00 O
-HETATM 446 H HOH A 149 1.741 14.718 18.943 1.00 0.00 H
-HETATM 447 H HOH A 149 1.212 13.893 20.097 1.00 0.00 H
-HETATM 448 O HOH A 150 1.003 16.117 17.718 1.00 0.00 O
-HETATM 449 H HOH A 150 1.527 16.882 17.958 1.00 0.00 H
-HETATM 450 H HOH A 150 0.099 16.376 17.894 1.00 0.00 H
-HETATM 451 O HOH A 151 24.256 22.402 25.309 1.00 0.00 O
-HETATM 452 H HOH A 151 25.065 22.398 24.797 1.00 0.00 H
-HETATM 453 H HOH A 151 24.201 21.520 25.678 1.00 0.00 H
-HETATM 454 O HOH A 152 26.417 22.099 23.354 1.00 0.00 O
-HETATM 455 H HOH A 152 25.980 22.485 22.594 1.00 0.00 H
-HETATM 456 H HOH A 152 26.558 21.184 23.112 1.00 0.00 H
-HETATM 457 O HOH A 153 5.524 20.988 14.825 1.00 0.00 O
-HETATM 458 H HOH A 153 6.329 21.162 14.338 1.00 0.00 H
-HETATM 459 H HOH A 153 5.643 21.445 15.658 1.00 0.00 H
-HETATM 460 O HOH A 154 8.216 21.117 13.677 1.00 0.00 O
-HETATM 461 H HOH A 154 8.323 20.192 13.454 1.00 0.00 H
-HETATM 462 H HOH A 154 8.884 21.284 14.343 1.00 0.00 H
-HETATM 463 O HOH A 155 17.978 5.586 3.134 1.00 0.00 O
-HETATM 464 H HOH A 155 18.596 6.146 2.665 1.00 0.00 H
-HETATM 465 H HOH A 155 18.002 5.903 4.037 1.00 0.00 H
-HETATM 466 O HOH A 156 20.273 6.969 1.949 1.00 0.00 O
-HETATM 467 H HOH A 156 20.701 6.216 1.541 1.00 0.00 H
-HETATM 468 H HOH A 156 20.893 7.270 2.612 1.00 0.00 H
-HETATM 469 O HOH A 157 22.229 6.013 13.032 1.00 0.00 O
-HETATM 470 H HOH A 157 22.098 5.318 12.388 1.00 0.00 H
-HETATM 471 H HOH A 157 22.036 5.600 13.874 1.00 0.00 H
-HETATM 472 O HOH A 158 21.269 3.995 11.138 1.00 0.00 O
-HETATM 473 H HOH A 158 20.730 4.561 10.584 1.00 0.00 H
-HETATM 474 H HOH A 158 20.647 3.389 11.541 1.00 0.00 H
-HETATM 475 O HOH A 159 27.948 20.692 15.420 1.00 0.00 O
-HETATM 476 H HOH A 159 28.088 21.590 15.122 1.00 0.00 H
-HETATM 477 H HOH A 159 27.565 20.244 14.666 1.00 0.00 H
-HETATM 478 O HOH A 160 27.809 23.498 14.588 1.00 0.00 O
-HETATM 479 H HOH A 160 27.396 23.860 15.372 1.00 0.00 H
-HETATM 480 H HOH A 160 27.168 23.635 13.891 1.00 0.00 H
-HETATM 481 O HOH A 161 4.662 14.970 10.511 1.00 0.00 O
-HETATM 482 H HOH A 161 4.597 14.340 11.229 1.00 0.00 H
-HETATM 483 H HOH A 161 4.367 14.487 9.739 1.00 0.00 H
-HETATM 484 O HOH A 162 4.952 12.827 12.487 1.00 0.00 O
-HETATM 485 H HOH A 162 5.836 13.032 12.795 1.00 0.00 H
-HETATM 486 H HOH A 162 5.033 11.966 12.076 1.00 0.00 H
-HETATM 487 O HOH A 163 12.166 16.309 29.247 1.00 0.00 O
-HETATM 488 H HOH A 163 12.145 15.355 29.176 1.00 0.00 H
-HETATM 489 H HOH A 163 12.053 16.620 28.349 1.00 0.00 H
-HETATM 490 O HOH A 164 12.640 13.447 28.838 1.00 0.00 O
-HETATM 491 H HOH A 164 13.431 13.354 29.369 1.00 0.00 H
-HETATM 492 H HOH A 164 12.924 13.253 27.945 1.00 0.00 H
-HETATM 493 O HOH A 165 23.236 23.538 20.356 1.00 0.00 O
-HETATM 494 H HOH A 165 23.348 24.487 20.307 1.00 0.00 H
-HETATM 495 H HOH A 165 24.098 23.183 20.139 1.00 0.00 H
-HETATM 496 O HOH A 166 23.553 26.361 19.637 1.00 0.00 O
-HETATM 497 H HOH A 166 22.767 26.461 19.098 1.00 0.00 H
-HETATM 498 H HOH A 166 24.280 26.498 19.031 1.00 0.00 H
-HETATM 499 O HOH A 167 26.700 6.169 27.213 1.00 0.00 O
-HETATM 500 H HOH A 167 26.377 6.975 27.614 1.00 0.00 H
-HETATM 501 H HOH A 167 27.072 6.448 26.376 1.00 0.00 H
-HETATM 502 O HOH A 168 25.274 8.580 28.071 1.00 0.00 O
-HETATM 503 H HOH A 168 24.427 8.181 28.275 1.00 0.00 H
-HETATM 504 H HOH A 168 25.094 9.171 27.341 1.00 0.00 H
-HETATM 505 O HOH A 169 10.291 22.352 20.708 1.00 0.00 O
-HETATM 506 H HOH A 169 9.569 22.940 20.486 1.00 0.00 H
-HETATM 507 H HOH A 169 10.090 22.048 21.593 1.00 0.00 H
-HETATM 508 O HOH A 170 8.360 24.529 20.368 1.00 0.00 O
-HETATM 509 H HOH A 170 8.961 25.209 20.064 1.00 0.00 H
-HETATM 510 H HOH A 170 8.032 24.853 21.207 1.00 0.00 H
-HETATM 511 O HOH A 171 9.126 22.797 5.054 1.00 0.00 O
-HETATM 512 H HOH A 171 9.780 23.121 5.673 1.00 0.00 H
-HETATM 513 H HOH A 171 9.608 22.682 4.234 1.00 0.00 H
-HETATM 514 O HOH A 172 11.053 24.291 6.678 1.00 0.00 O
-HETATM 515 H HOH A 172 10.478 25.000 6.969 1.00 0.00 H
-HETATM 516 H HOH A 172 11.718 24.723 6.142 1.00 0.00 H
-HETATM 517 O HOH A 173 16.589 21.937 17.878 1.00 0.00 O
-HETATM 518 H HOH A 173 17.092 21.954 17.064 1.00 0.00 H
-HETATM 519 H HOH A 173 15.857 22.533 17.720 1.00 0.00 H
-HETATM 520 O HOH A 174 18.321 22.495 15.582 1.00 0.00 O
-HETATM 521 H HOH A 174 19.145 22.630 16.052 1.00 0.00 H
-HETATM 522 H HOH A 174 18.129 23.343 15.183 1.00 0.00 H
-HETATM 523 O HOH A 175 16.339 14.099 11.959 1.00 0.00 O
-HETATM 524 H HOH A 175 16.745 13.242 11.832 1.00 0.00 H
-HETATM 525 H HOH A 175 16.252 14.459 11.076 1.00 0.00 H
-HETATM 526 O HOH A 176 18.067 11.780 11.497 1.00 0.00 O
-HETATM 527 H HOH A 176 18.720 11.948 12.177 1.00 0.00 H
-HETATM 528 H HOH A 176 18.551 11.849 10.674 1.00 0.00 H
-HETATM 529 O HOH A 177 8.677 8.663 23.701 1.00 0.00 O
-HETATM 530 H HOH A 177 8.452 8.962 24.582 1.00 0.00 H
-HETATM 531 H HOH A 177 9.612 8.849 23.616 1.00 0.00 H
-HETATM 532 O HOH A 178 8.000 10.104 26.160 1.00 0.00 O
-HETATM 533 H HOH A 178 7.237 10.586 25.840 1.00 0.00 H
-HETATM 534 H HOH A 178 8.643 10.781 26.370 1.00 0.00 H
-HETATM 535 O HOH A 179 21.950 23.393 23.221 1.00 0.00 O
-HETATM 536 H HOH A 179 21.514 24.234 23.362 1.00 0.00 H
-HETATM 537 H HOH A 179 22.437 23.508 22.405 1.00 0.00 H
-HETATM 538 O HOH A 180 20.249 25.779 23.246 1.00 0.00 O
-HETATM 539 H HOH A 180 19.407 25.354 23.415 1.00 0.00 H
-HETATM 540 H HOH A 180 20.156 26.163 22.375 1.00 0.00 H
-HETATM 541 O HOH A 181 6.137 21.182 17.785 1.00 0.00 O
-HETATM 542 H HOH A 181 6.023 22.113 17.974 1.00 0.00 H
-HETATM 543 H HOH A 181 5.251 20.820 17.814 1.00 0.00 H
-HETATM 544 O HOH A 182 5.731 23.857 18.910 1.00 0.00 O
-HETATM 545 H HOH A 182 6.430 23.846 19.564 1.00 0.00 H
-HETATM 546 H HOH A 182 4.923 23.914 19.419 1.00 0.00 H
-HETATM 547 O HOH A 183 25.045 14.947 13.414 1.00 0.00 O
-HETATM 548 H HOH A 183 25.494 14.886 14.256 1.00 0.00 H
-HETATM 549 H HOH A 183 25.744 14.894 12.763 1.00 0.00 H
-HETATM 550 O HOH A 184 26.559 15.312 15.895 1.00 0.00 O
-HETATM 551 H HOH A 184 26.087 16.070 16.242 1.00 0.00 H
-HETATM 552 H HOH A 184 27.445 15.633 15.732 1.00 0.00 H
-HETATM 553 O HOH A 185 13.628 15.282 24.411 1.00 0.00 O
-HETATM 554 H HOH A 185 14.433 15.025 24.861 1.00 0.00 H
-HETATM 555 H HOH A 185 13.785 15.061 23.493 1.00 0.00 H
-HETATM 556 O HOH A 186 16.304 14.981 25.566 1.00 0.00 O
-HETATM 557 H HOH A 186 16.360 15.821 26.022 1.00 0.00 H
-HETATM 558 H HOH A 186 16.990 15.021 24.900 1.00 0.00 H
-HETATM 559 O HOH A 187 29.204 23.911 2.476 1.00 0.00 O
-HETATM 560 H HOH A 187 28.437 23.917 3.048 1.00 0.00 H
-HETATM 561 H HOH A 187 28.917 23.441 1.693 1.00 0.00 H
-HETATM 562 O HOH A 188 27.003 23.382 4.335 1.00 0.00 O
-HETATM 563 H HOH A 188 27.517 23.086 5.087 1.00 0.00 H
-HETATM 564 H HOH A 188 26.489 22.617 4.077 1.00 0.00 H
-HETATM 565 O HOH A 189 12.916 16.517 10.241 1.00 0.00 O
-HETATM 566 H HOH A 189 12.752 16.273 11.152 1.00 0.00 H
-HETATM 567 H HOH A 189 12.502 17.375 10.143 1.00 0.00 H
-HETATM 568 O HOH A 190 11.884 15.645 12.841 1.00 0.00 O
-HETATM 569 H HOH A 190 11.578 14.773 12.590 1.00 0.00 H
-HETATM 570 H HOH A 190 11.090 16.118 13.090 1.00 0.00 H
-HETATM 571 O HOH A 191 11.078 11.030 8.006 1.00 0.00 O
-HETATM 572 H HOH A 191 11.864 11.576 8.013 1.00 0.00 H
-HETATM 573 H HOH A 191 11.187 10.442 8.753 1.00 0.00 H
-HETATM 574 O HOH A 192 13.740 12.236 7.799 1.00 0.00 O
-HETATM 575 H HOH A 192 13.892 12.091 6.864 1.00 0.00 H
-HETATM 576 H HOH A 192 14.409 11.710 8.237 1.00 0.00 H
-HETATM 577 O HOH A 193 4.690 13.658 19.918 1.00 0.00 O
-HETATM 578 H HOH A 193 4.454 14.084 20.742 1.00 0.00 H
-HETATM 579 H HOH A 193 4.169 14.111 19.255 1.00 0.00 H
-HETATM 580 O HOH A 194 3.490 14.644 22.402 1.00 0.00 O
-HETATM 581 H HOH A 194 3.348 13.804 22.838 1.00 0.00 H
-HETATM 582 H HOH A 194 2.610 14.996 22.263 1.00 0.00 H
-HETATM 583 O HOH A 195 5.212 23.256 12.478 1.00 0.00 O
-HETATM 584 H HOH A 195 6.112 23.351 12.168 1.00 0.00 H
-HETATM 585 H HOH A 195 5.294 22.818 13.326 1.00 0.00 H
-HETATM 586 O HOH A 196 7.921 22.988 11.397 1.00 0.00 O
-HETATM 587 H HOH A 196 7.699 22.638 10.534 1.00 0.00 H
-HETATM 588 H HOH A 196 8.412 22.286 11.824 1.00 0.00 H
-HETATM 589 O HOH A 197 10.802 9.033 20.329 1.00 0.00 O
-HETATM 590 H HOH A 197 10.059 9.613 20.166 1.00 0.00 H
-HETATM 591 H HOH A 197 10.820 8.926 21.280 1.00 0.00 H
-HETATM 592 O HOH A 198 8.885 11.225 20.006 1.00 0.00 O
-HETATM 593 H HOH A 198 9.405 11.789 19.433 1.00 0.00 H
-HETATM 594 H HOH A 198 8.778 11.731 20.811 1.00 0.00 H
-HETATM 595 O HOH A 199 24.079 20.594 5.977 1.00 0.00 O
-HETATM 596 H HOH A 199 24.278 21.464 5.631 1.00 0.00 H
-HETATM 597 H HOH A 199 23.166 20.651 6.260 1.00 0.00 H
-HETATM 598 O HOH A 200 24.727 23.406 5.472 1.00 0.00 O
-HETATM 599 H HOH A 200 25.539 23.460 5.976 1.00 0.00 H
-HETATM 600 H HOH A 200 24.125 24.001 5.919 1.00 0.00 H
-HETATM 601 O HOH A 201 5.847 25.259 17.200 1.00 0.00 O
-HETATM 602 H HOH A 201 6.373 24.566 16.800 1.00 0.00 H
-HETATM 603 H HOH A 201 4.964 25.120 16.856 1.00 0.00 H
-HETATM 604 O HOH A 202 7.438 23.473 15.508 1.00 0.00 O
-HETATM 605 H HOH A 202 8.119 24.093 15.244 1.00 0.00 H
-HETATM 606 H HOH A 202 6.986 23.250 14.695 1.00 0.00 H
-HETATM 607 O HOH A 203 16.212 28.172 20.446 1.00 0.00 O
-HETATM 608 H HOH A 203 16.453 27.246 20.457 1.00 0.00 H
-HETATM 609 H HOH A 203 16.350 28.446 19.539 1.00 0.00 H
-HETATM 610 O HOH A 204 17.492 25.537 20.478 1.00 0.00 O
-HETATM 611 H HOH A 204 18.040 25.652 21.255 1.00 0.00 H
-HETATM 612 H HOH A 204 18.112 25.429 19.758 1.00 0.00 H
-HETATM 613 O HOH A 205 0.384 27.732 22.887 1.00 0.00 O
-HETATM 614 H HOH A 205 0.103 28.041 23.748 1.00 0.00 H
-HETATM 615 H HOH A 205 0.149 26.804 22.876 1.00 0.00 H
-HETATM 616 O HOH A 206 -0.000 28.415 25.710 1.00 0.00 O
-HETATM 617 H HOH A 206 0.842 28.842 25.871 1.00 0.00 H
-HETATM 618 H HOH A 206 0.010 27.641 26.273 1.00 0.00 H
-HETATM 619 O HOH A 207 25.791 9.863 23.635 1.00 0.00 O
-HETATM 620 H HOH A 207 24.942 9.618 23.270 1.00 0.00 H
-HETATM 621 H HOH A 207 26.430 9.409 23.085 1.00 0.00 H
-HETATM 622 O HOH A 208 23.278 8.614 22.795 1.00 0.00 O
-HETATM 623 H HOH A 208 22.931 8.393 23.659 1.00 0.00 H
-HETATM 624 H HOH A 208 23.421 7.769 22.368 1.00 0.00 H
-HETATM 625 O HOH A 209 11.466 22.975 3.032 1.00 0.00 O
-HETATM 626 H HOH A 209 11.403 23.072 2.082 1.00 0.00 H
-HETATM 627 H HOH A 209 11.266 23.845 3.376 1.00 0.00 H
-HETATM 628 O HOH A 210 11.788 23.521 0.171 1.00 0.00 O
-HETATM 629 H HOH A 210 12.629 23.084 0.034 1.00 0.00 H
-HETATM 630 H HOH A 210 11.965 24.446 -0.001 1.00 0.00 H
-HETATM 631 O HOH A 211 15.835 19.922 26.081 1.00 0.00 O
-HETATM 632 H HOH A 211 15.371 20.461 26.721 1.00 0.00 H
-HETATM 633 H HOH A 211 15.142 19.470 25.600 1.00 0.00 H
-HETATM 634 O HOH A 212 14.351 21.077 28.328 1.00 0.00 O
-HETATM 635 H HOH A 212 14.913 20.774 29.043 1.00 0.00 H
-HETATM 636 H HOH A 212 13.515 20.634 28.473 1.00 0.00 H
-HETATM 637 O HOH A 213 22.537 4.162 15.557 1.00 0.00 O
-HETATM 638 H HOH A 213 22.941 3.805 14.767 1.00 0.00 H
-HETATM 639 H HOH A 213 22.694 3.499 16.229 1.00 0.00 H
-HETATM 640 O HOH A 214 23.303 2.736 13.115 1.00 0.00 O
-HETATM 641 H HOH A 214 22.522 2.919 12.590 1.00 0.00 H
-HETATM 642 H HOH A 214 23.297 1.787 13.235 1.00 0.00 H
-HETATM 643 O HOH A 215 3.861 12.999 14.673 1.00 0.00 O
-HETATM 644 H HOH A 215 3.370 13.473 15.345 1.00 0.00 H
-HETATM 645 H HOH A 215 4.710 13.440 14.640 1.00 0.00 H
-HETATM 646 O HOH A 216 2.289 14.846 16.317 1.00 0.00 O
-HETATM 647 H HOH A 216 1.522 14.927 15.749 1.00 0.00 H
-HETATM 648 H HOH A 216 2.669 15.724 16.336 1.00 0.00 H
-HETATM 649 O HOH A 217 15.810 13.518 21.847 1.00 0.00 O
-HETATM 650 H HOH A 217 16.488 14.153 21.618 1.00 0.00 H
-HETATM 651 H HOH A 217 15.472 13.820 22.690 1.00 0.00 H
-HETATM 652 O HOH A 218 18.206 15.175 21.532 1.00 0.00 O
-HETATM 653 H HOH A 218 18.834 14.483 21.324 1.00 0.00 H
-HETATM 654 H HOH A 218 18.508 15.534 22.366 1.00 0.00 H
-HETATM 655 O HOH A 219 9.820 6.403 21.283 1.00 0.00 O
-HETATM 656 H HOH A 219 9.057 6.159 21.806 1.00 0.00 H
-HETATM 657 H HOH A 219 9.519 6.341 20.376 1.00 0.00 H
-HETATM 658 O HOH A 220 7.622 5.119 22.734 1.00 0.00 O
-HETATM 659 H HOH A 220 8.135 4.508 23.263 1.00 0.00 H
-HETATM 660 H HOH A 220 7.092 4.561 22.165 1.00 0.00 H
-HETATM 661 O HOH A 221 6.909 7.937 14.295 1.00 0.00 O
-HETATM 662 H HOH A 221 6.983 8.363 13.441 1.00 0.00 H
-HETATM 663 H HOH A 221 7.798 7.952 14.649 1.00 0.00 H
-HETATM 664 O HOH A 222 7.359 8.736 11.512 1.00 0.00 O
-HETATM 665 H HOH A 222 6.873 8.034 11.080 1.00 0.00 H
-HETATM 666 H HOH A 222 8.267 8.604 11.242 1.00 0.00 H
-HETATM 667 O HOH A 223 6.284 13.490 29.853 1.00 0.00 O
-HETATM 668 H HOH A 223 5.430 13.059 29.857 1.00 0.00 H
-HETATM 669 H HOH A 223 6.258 14.069 29.092 1.00 0.00 H
-HETATM 670 O HOH A 224 3.892 11.849 29.441 1.00 0.00 O
-HETATM 671 H HOH A 224 4.289 10.984 29.552 1.00 0.00 H
-HETATM 672 H HOH A 224 3.597 11.864 28.531 1.00 0.00 H
-HETATM 673 O HOH A 225 9.697 13.757 8.227 1.00 0.00 O
-HETATM 674 H HOH A 225 10.428 14.333 8.005 1.00 0.00 H
-HETATM 675 H HOH A 225 9.613 13.831 9.178 1.00 0.00 H
-HETATM 676 O HOH A 226 12.261 15.089 7.746 1.00 0.00 O
-HETATM 677 H HOH A 226 12.676 14.414 7.207 1.00 0.00 H
-HETATM 678 H HOH A 226 12.799 15.132 8.536 1.00 0.00 H
-HETATM 679 O HOH A 227 19.115 23.034 12.695 1.00 0.00 O
-HETATM 680 H HOH A 227 19.198 23.785 13.282 1.00 0.00 H
-HETATM 681 H HOH A 227 18.693 22.359 13.226 1.00 0.00 H
-HETATM 682 O HOH A 228 19.812 25.039 14.715 1.00 0.00 O
-HETATM 683 H HOH A 228 20.733 25.164 14.483 1.00 0.00 H
-HETATM 684 H HOH A 228 19.833 24.669 15.597 1.00 0.00 H
-HETATM 685 O HOH A 229 2.320 26.551 8.725 1.00 0.00 O
-HETATM 686 H HOH A 229 2.829 25.859 8.303 1.00 0.00 H
-HETATM 687 H HOH A 229 1.878 26.114 9.453 1.00 0.00 H
-HETATM 688 O HOH A 230 3.370 24.275 7.208 1.00 0.00 O
-HETATM 689 H HOH A 230 3.048 24.519 6.340 1.00 0.00 H
-HETATM 690 H HOH A 230 2.912 23.461 7.416 1.00 0.00 H
-HETATM 691 O HOH A 231 14.324 23.521 6.821 1.00 0.00 O
-HETATM 692 H HOH A 231 14.851 23.428 6.028 1.00 0.00 H
-HETATM 693 H HOH A 231 14.714 24.263 7.284 1.00 0.00 H
-HETATM 694 O HOH A 232 16.322 22.991 4.745 1.00 0.00 O
-HETATM 695 H HOH A 232 16.425 22.049 4.881 1.00 0.00 H
-HETATM 696 H HOH A 232 17.176 23.363 4.964 1.00 0.00 H
-HETATM 697 O HOH A 233 18.185 8.776 7.544 1.00 0.00 O
-HETATM 698 H HOH A 233 17.735 9.579 7.285 1.00 0.00 H
-HETATM 699 H HOH A 233 17.666 8.433 8.271 1.00 0.00 H
-HETATM 700 O HOH A 234 17.059 11.461 7.216 1.00 0.00 O
-HETATM 701 H HOH A 234 17.876 11.961 7.216 1.00 0.00 H
-HETATM 702 H HOH A 234 16.596 11.755 8.000 1.00 0.00 H
-HETATM 703 O HOH A 235 1.501 22.598 21.725 1.00 0.00 O
-HETATM 704 H HOH A 235 2.245 23.127 22.010 1.00 0.00 H
-HETATM 705 H HOH A 235 1.569 22.581 20.770 1.00 0.00 H
-HETATM 706 O HOH A 236 3.455 24.664 22.430 1.00 0.00 O
-HETATM 707 H HOH A 236 2.903 25.194 23.006 1.00 0.00 H
-HETATM 708 H HOH A 236 3.655 25.237 21.691 1.00 0.00 H
-HETATM 709 O HOH A 237 2.932 24.787 19.083 1.00 0.00 O
-HETATM 710 H HOH A 237 2.804 25.552 18.523 1.00 0.00 H
-HETATM 711 H HOH A 237 2.339 24.928 19.822 1.00 0.00 H
-HETATM 712 O HOH A 238 2.869 27.399 17.757 1.00 0.00 O
-HETATM 713 H HOH A 238 3.813 27.544 17.679 1.00 0.00 H
-HETATM 714 H HOH A 238 2.565 28.093 18.342 1.00 0.00 H
-HETATM 715 O HOH A 239 11.194 11.595 16.052 1.00 0.00 O
-HETATM 716 H HOH A 239 11.813 12.310 15.905 1.00 0.00 H
-HETATM 717 H HOH A 239 11.270 11.047 15.271 1.00 0.00 H
-HETATM 718 O HOH A 240 12.681 13.989 15.250 1.00 0.00 O
-HETATM 719 H HOH A 240 12.029 14.650 15.486 1.00 0.00 H
-HETATM 720 H HOH A 240 12.764 14.060 14.300 1.00 0.00 H
-HETATM 721 O HOH A 241 25.336 16.298 19.383 1.00 0.00 O
-HETATM 722 H HOH A 241 24.616 16.553 19.960 1.00 0.00 H
-HETATM 723 H HOH A 241 24.909 16.015 18.575 1.00 0.00 H
-HETATM 724 O HOH A 242 23.064 16.510 21.221 1.00 0.00 O
-HETATM 725 H HOH A 242 23.420 16.000 21.950 1.00 0.00 H
-HETATM 726 H HOH A 242 22.309 16.006 20.919 1.00 0.00 H
-HETATM 727 O HOH A 243 1.774 29.045 2.649 1.00 0.00 O
-HETATM 728 H HOH A 243 2.669 28.758 2.833 1.00 0.00 H
-HETATM 729 H HOH A 243 1.830 30.000 2.617 1.00 0.00 H
-HETATM 730 O HOH A 244 4.392 28.327 3.751 1.00 0.00 O
-HETATM 731 H HOH A 244 4.099 27.743 4.451 1.00 0.00 H
-HETATM 732 H HOH A 244 4.806 29.061 4.205 1.00 0.00 H
-HETATM 733 O HOH A 245 16.569 27.266 6.986 1.00 0.00 O
-HETATM 734 H HOH A 245 16.482 26.377 7.330 1.00 0.00 H
-HETATM 735 H HOH A 245 16.423 27.174 6.044 1.00 0.00 H
-HETATM 736 O HOH A 246 16.835 24.472 7.826 1.00 0.00 O
-HETATM 737 H HOH A 246 17.648 24.548 8.327 1.00 0.00 H
-HETATM 738 H HOH A 246 17.055 23.895 7.095 1.00 0.00 H
-HETATM 739 O HOH A 247 15.176 5.598 11.031 1.00 0.00 O
-HETATM 740 H HOH A 247 15.005 4.717 10.701 1.00 0.00 H
-HETATM 741 H HOH A 247 14.349 5.872 11.427 1.00 0.00 H
-HETATM 742 O HOH A 248 14.381 3.173 9.593 1.00 0.00 O
-HETATM 743 H HOH A 248 14.761 3.381 8.738 1.00 0.00 H
-HETATM 744 H HOH A 248 13.437 3.140 9.436 1.00 0.00 H
-HETATM 745 O HOH A 249 28.455 28.705 7.190 1.00 0.00 O
-HETATM 746 H HOH A 249 28.054 27.926 7.577 1.00 0.00 H
-HETATM 747 H HOH A 249 29.299 28.787 7.634 1.00 0.00 H
-HETATM 748 O HOH A 250 27.114 26.671 8.819 1.00 0.00 O
-HETATM 749 H HOH A 250 26.285 27.128 8.967 1.00 0.00 H
-HETATM 750 H HOH A 250 27.505 26.592 9.689 1.00 0.00 H
-HETATM 751 O HOH A 251 18.415 14.251 28.405 1.00 0.00 O
-HETATM 752 H HOH A 251 19.152 13.973 28.947 1.00 0.00 H
-HETATM 753 H HOH A 251 18.542 13.793 27.574 1.00 0.00 H
-HETATM 754 O HOH A 252 20.974 13.774 29.749 1.00 0.00 O
-HETATM 755 H HOH A 252 21.176 14.676 30.000 1.00 0.00 H
-HETATM 756 H HOH A 252 21.666 13.535 29.132 1.00 0.00 H
-HETATM 757 O HOH A 253 20.062 20.314 16.346 1.00 0.00 O
-HETATM 758 H HOH A 253 20.695 19.602 16.264 1.00 0.00 H
-HETATM 759 H HOH A 253 20.014 20.695 15.469 1.00 0.00 H
-HETATM 760 O HOH A 254 22.393 18.555 16.109 1.00 0.00 O
-HETATM 761 H HOH A 254 22.871 18.843 16.888 1.00 0.00 H
-HETATM 762 H HOH A 254 22.956 18.800 15.376 1.00 0.00 H
-HETATM 763 O HOH A 255 23.681 23.160 2.801 1.00 0.00 O
-HETATM 764 H HOH A 255 23.981 23.694 2.066 1.00 0.00 H
-HETATM 765 H HOH A 255 22.879 22.744 2.486 1.00 0.00 H
-HETATM 766 O HOH A 256 24.182 25.157 0.717 1.00 0.00 O
-HETATM 767 H HOH A 256 24.307 25.923 1.279 1.00 0.00 H
-HETATM 768 H HOH A 256 23.394 25.354 0.211 1.00 0.00 H
-HETATM 769 O HOH A 257 2.153 15.801 10.823 1.00 0.00 O
-HETATM 770 H HOH A 257 2.514 16.225 11.601 1.00 0.00 H
-HETATM 771 H HOH A 257 2.767 16.019 10.123 1.00 0.00 H
-HETATM 772 O HOH A 258 3.039 17.581 12.975 1.00 0.00 O
-HETATM 773 H HOH A 258 2.191 17.965 13.203 1.00 0.00 H
-HETATM 774 H HOH A 258 3.556 18.315 12.646 1.00 0.00 H
-HETATM 775 O HOH A 259 22.907 9.720 14.034 1.00 0.00 O
-HETATM 776 H HOH A 259 23.417 10.258 13.430 1.00 0.00 H
-HETATM 777 H HOH A 259 22.341 9.193 13.469 1.00 0.00 H
-HETATM 778 O HOH A 260 23.987 11.682 12.146 1.00 0.00 O
-HETATM 779 H HOH A 260 23.802 12.479 12.644 1.00 0.00 H
-HETATM 780 H HOH A 260 23.449 11.758 11.358 1.00 0.00 H
-HETATM 781 O HOH A 261 10.601 13.782 17.910 1.00 0.00 O
-HETATM 782 H HOH A 261 10.612 13.825 16.954 1.00 0.00 H
-HETATM 783 H HOH A 261 10.795 12.867 18.111 1.00 0.00 H
-HETATM 784 O HOH A 262 10.114 13.671 15.023 1.00 0.00 O
-HETATM 785 H HOH A 262 9.279 14.140 14.999 1.00 0.00 H
-HETATM 786 H HOH A 262 9.902 12.785 14.727 1.00 0.00 H
-HETATM 787 O HOH A 263 8.827 17.684 5.049 1.00 0.00 O
-HETATM 788 H HOH A 263 9.085 18.121 4.237 1.00 0.00 H
-HETATM 789 H HOH A 263 9.129 18.269 5.743 1.00 0.00 H
-HETATM 790 O HOH A 264 10.143 18.821 2.691 1.00 0.00 O
-HETATM 791 H HOH A 264 10.529 18.018 2.340 1.00 0.00 H
-HETATM 792 H HOH A 264 10.892 19.368 2.925 1.00 0.00 H
-HETATM 793 O HOH A 265 11.640 23.811 25.899 1.00 0.00 O
-HETATM 794 H HOH A 265 11.467 24.739 25.742 1.00 0.00 H
-HETATM 795 H HOH A 265 12.567 23.700 25.688 1.00 0.00 H
-HETATM 796 O HOH A 266 11.106 26.499 24.863 1.00 0.00 O
-HETATM 797 H HOH A 266 10.322 26.302 24.349 1.00 0.00 H
-HETATM 798 H HOH A 266 11.757 26.772 24.217 1.00 0.00 H
-HETATM 799 O HOH A 267 8.437 5.791 7.271 1.00 0.00 O
-HETATM 800 H HOH A 267 7.976 4.969 7.105 1.00 0.00 H
-HETATM 801 H HOH A 267 9.169 5.546 7.837 1.00 0.00 H
-HETATM 802 O HOH A 268 6.818 3.348 7.294 1.00 0.00 O
-HETATM 803 H HOH A 268 5.970 3.739 7.506 1.00 0.00 H
-HETATM 804 H HOH A 268 7.022 2.789 8.043 1.00 0.00 H
-HETATM 805 O HOH A 269 24.587 1.636 18.063 1.00 0.00 O
-HETATM 806 H HOH A 269 25.502 1.907 17.989 1.00 0.00 H
-HETATM 807 H HOH A 269 24.434 1.559 19.005 1.00 0.00 H
-HETATM 808 O HOH A 270 27.501 1.913 17.949 1.00 0.00 O
-HETATM 809 H HOH A 270 27.685 1.242 17.291 1.00 0.00 H
-HETATM 810 H HOH A 270 27.925 1.593 18.745 1.00 0.00 H
-HETATM 811 O HOH A 271 21.433 22.378 27.886 1.00 0.00 O
-HETATM 812 H HOH A 271 20.658 22.782 27.498 1.00 0.00 H
-HETATM 813 H HOH A 271 21.111 21.954 28.681 1.00 0.00 H
-HETATM 814 O HOH A 272 19.133 24.016 27.105 1.00 0.00 O
-HETATM 815 H HOH A 272 19.586 24.852 26.983 1.00 0.00 H
-HETATM 816 H HOH A 272 18.535 24.169 27.836 1.00 0.00 H
-HETATM 817 O HOH A 273 9.698 16.993 0.579 1.00 0.00 O
-HETATM 818 H HOH A 273 8.776 16.864 0.800 1.00 0.00 H
-HETATM 819 H HOH A 273 9.702 17.755 -0.001 1.00 0.00 H
-HETATM 820 O HOH A 274 6.840 16.367 0.729 1.00 0.00 O
-HETATM 821 H HOH A 274 6.887 15.432 0.529 1.00 0.00 H
-HETATM 822 H HOH A 274 6.350 16.746 -0.001 1.00 0.00 H
-HETATM 823 O HOH A 275 27.640 1.351 27.201 1.00 0.00 O
-HETATM 824 H HOH A 275 27.044 1.091 26.499 1.00 0.00 H
-HETATM 825 H HOH A 275 27.566 0.652 27.851 1.00 0.00 H
-HETATM 826 O HOH A 276 25.402 0.744 25.410 1.00 0.00 O
-HETATM 827 H HOH A 276 25.001 1.613 25.381 1.00 0.00 H
-HETATM 828 H HOH A 276 24.746 0.188 25.829 1.00 0.00 H
-HETATM 829 O HOH A 277 12.208 13.594 23.941 1.00 0.00 O
-HETATM 830 H HOH A 277 11.292 13.672 24.206 1.00 0.00 H
-HETATM 831 H HOH A 277 12.471 12.723 24.237 1.00 0.00 H
-HETATM 832 O HOH A 278 9.623 13.929 25.279 1.00 0.00 O
-HETATM 833 H HOH A 278 9.772 14.791 25.668 1.00 0.00 H
-HETATM 834 H HOH A 278 9.525 13.339 26.026 1.00 0.00 H
-HETATM 835 O HOH A 279 23.647 7.573 18.993 1.00 0.00 O
-HETATM 836 H HOH A 279 24.596 7.463 19.058 1.00 0.00 H
-HETATM 837 H HOH A 279 23.339 7.551 19.899 1.00 0.00 H
-HETATM 838 O HOH A 280 26.423 6.680 19.280 1.00 0.00 O
-HETATM 839 H HOH A 280 26.426 5.999 18.606 1.00 0.00 H
-HETATM 840 H HOH A 280 26.567 6.209 20.100 1.00 0.00 H
-HETATM 841 O HOH A 281 8.505 26.962 9.236 1.00 0.00 O
-HETATM 842 H HOH A 281 9.118 26.228 9.212 1.00 0.00 H
-HETATM 843 H HOH A 281 8.326 27.097 10.166 1.00 0.00 H
-HETATM 844 O HOH A 282 9.915 24.394 9.260 1.00 0.00 O
-HETATM 845 H HOH A 282 9.405 23.943 8.586 1.00 0.00 H
-HETATM 846 H HOH A 282 9.748 23.899 10.062 1.00 0.00 H
-HETATM 847 O HOH A 283 4.780 19.683 10.363 1.00 0.00 O
-HETATM 848 H HOH A 283 5.409 19.082 10.762 1.00 0.00 H
-HETATM 849 H HOH A 283 4.782 19.451 9.435 1.00 0.00 H
-HETATM 850 O HOH A 284 7.100 18.196 11.358 1.00 0.00 O
-HETATM 851 H HOH A 284 7.540 18.908 11.823 1.00 0.00 H
-HETATM 852 H HOH A 284 7.704 17.957 10.655 1.00 0.00 H
-HETATM 853 O HOH A 285 15.900 23.458 25.893 1.00 0.00 O
-HETATM 854 H HOH A 285 15.329 23.981 25.331 1.00 0.00 H
-HETATM 855 H HOH A 285 15.312 23.071 26.541 1.00 0.00 H
-HETATM 856 O HOH A 286 14.162 25.436 24.609 1.00 0.00 O
-HETATM 857 H HOH A 286 14.740 26.197 24.669 1.00 0.00 H
-HETATM 858 H HOH A 286 13.408 25.662 25.155 1.00 0.00 H
-HETATM 859 O HOH A 287 5.833 14.639 23.629 1.00 0.00 O
-HETATM 860 H HOH A 287 5.092 14.867 24.190 1.00 0.00 H
-HETATM 861 H HOH A 287 5.630 15.044 22.786 1.00 0.00 H
-HETATM 862 O HOH A 288 3.290 14.918 25.057 1.00 0.00 O
-HETATM 863 H HOH A 288 3.191 14.013 25.354 1.00 0.00 H
-HETATM 864 H HOH A 288 2.556 15.059 24.461 1.00 0.00 H
-HETATM 865 O HOH A 289 1.993 21.572 1.364 1.00 0.00 O
-HETATM 866 H HOH A 289 2.733 22.178 1.359 1.00 0.00 H
-HETATM 867 H HOH A 289 2.173 20.967 0.644 1.00 0.00 H
-HETATM 868 O HOH A 290 3.989 23.666 0.900 1.00 0.00 O
-HETATM 869 H HOH A 290 3.411 24.429 0.943 1.00 0.00 H
-HETATM 870 H HOH A 290 4.315 23.660 0.000 1.00 0.00 H
-HETATM 871 O HOH A 291 14.877 15.104 27.481 1.00 0.00 O
-HETATM 872 H HOH A 291 14.949 14.220 27.123 1.00 0.00 H
-HETATM 873 H HOH A 291 14.192 15.040 28.146 1.00 0.00 H
-HETATM 874 O HOH A 292 14.616 12.533 26.100 1.00 0.00 O
-HETATM 875 H HOH A 292 14.581 12.856 25.199 1.00 0.00 H
-HETATM 876 H HOH A 292 13.760 12.131 26.247 1.00 0.00 H
-HETATM 877 O HOH A 293 14.140 19.228 20.295 1.00 0.00 O
-HETATM 878 H HOH A 293 14.478 20.075 20.585 1.00 0.00 H
-HETATM 879 H HOH A 293 13.505 19.443 19.612 1.00 0.00 H
-HETATM 880 O HOH A 294 14.674 21.859 21.468 1.00 0.00 O
-HETATM 881 H HOH A 294 14.545 21.635 22.390 1.00 0.00 H
-HETATM 882 H HOH A 294 13.980 22.488 21.272 1.00 0.00 H
-HETATM 883 O HOH A 295 5.310 29.553 18.554 1.00 0.00 O
-HETATM 884 H HOH A 295 5.078 28.748 19.016 1.00 0.00 H
-HETATM 885 H HOH A 295 4.953 29.437 17.674 1.00 0.00 H
-HETATM 886 O HOH A 296 5.039 26.859 19.674 1.00 0.00 O
-HETATM 887 H HOH A 296 5.931 26.749 20.003 1.00 0.00 H
-HETATM 888 H HOH A 296 4.949 26.194 18.992 1.00 0.00 H
-HETATM 889 O HOH A 297 14.165 25.587 10.050 1.00 0.00 O
-HETATM 890 H HOH A 297 14.972 25.821 10.508 1.00 0.00 H
-HETATM 891 H HOH A 297 14.445 25.381 9.159 1.00 0.00 H
-HETATM 892 O HOH A 298 16.581 26.806 11.173 1.00 0.00 O
-HETATM 893 H HOH A 298 16.187 27.601 11.535 1.00 0.00 H
-HETATM 894 H HOH A 298 17.176 27.119 10.493 1.00 0.00 H
-HETATM 895 O HOH A 299 25.387 27.076 10.974 1.00 0.00 O
-HETATM 896 H HOH A 299 25.718 26.180 10.905 1.00 0.00 H
-HETATM 897 H HOH A 299 26.169 27.611 11.110 1.00 0.00 H
-HETATM 898 O HOH A 300 26.445 24.367 11.335 1.00 0.00 O
-HETATM 899 H HOH A 300 25.797 24.041 11.960 1.00 0.00 H
-HETATM 900 H HOH A 300 27.271 24.365 11.819 1.00 0.00 H
-HETATM 901 O HOH A 301 5.775 21.278 11.516 1.00 0.00 O
-HETATM 902 H HOH A 301 5.663 21.677 10.654 1.00 0.00 H
-HETATM 903 H HOH A 301 6.677 20.956 11.516 1.00 0.00 H
-HETATM 904 O HOH A 302 5.380 21.956 8.693 1.00 0.00 O
-HETATM 905 H HOH A 302 4.553 21.494 8.548 1.00 0.00 H
-HETATM 906 H HOH A 302 6.008 21.511 8.125 1.00 0.00 H
-HETATM 907 O HOH A 303 25.081 3.484 26.816 1.00 0.00 O
-HETATM 908 H HOH A 303 24.479 3.881 26.187 1.00 0.00 H
-HETATM 909 H HOH A 303 25.463 4.226 27.285 1.00 0.00 H
-HETATM 910 O HOH A 304 23.716 4.818 24.593 1.00 0.00 O
-HETATM 911 H HOH A 304 24.064 4.291 23.872 1.00 0.00 H
-HETATM 912 H HOH A 304 24.058 5.698 24.436 1.00 0.00 H
-HETATM 913 O HOH A 305 18.074 25.928 5.284 1.00 0.00 O
-HETATM 914 H HOH A 305 18.281 25.106 5.728 1.00 0.00 H
-HETATM 915 H HOH A 305 17.908 25.676 4.376 1.00 0.00 H
-HETATM 916 O HOH A 306 19.223 23.470 6.390 1.00 0.00 O
-HETATM 917 H HOH A 306 20.002 23.847 6.800 1.00 0.00 H
-HETATM 918 H HOH A 306 19.564 22.893 5.707 1.00 0.00 H
-HETATM 919 O HOH A 307 14.673 9.139 11.749 1.00 0.00 O
-HETATM 920 H HOH A 307 14.034 9.553 11.169 1.00 0.00 H
-HETATM 921 H HOH A 307 14.169 8.894 12.526 1.00 0.00 H
-HETATM 922 O HOH A 308 12.778 10.859 10.323 1.00 0.00 O
-HETATM 923 H HOH A 308 13.354 11.608 10.164 1.00 0.00 H
-HETATM 924 H HOH A 308 12.094 11.198 10.900 1.00 0.00 H
-HETATM 925 O HOH A 309 21.686 28.790 7.386 1.00 0.00 O
-HETATM 926 H HOH A 309 22.464 28.261 7.560 1.00 0.00 H
-HETATM 927 H HOH A 309 21.531 29.266 8.201 1.00 0.00 H
-HETATM 928 O HOH A 310 23.704 26.832 8.209 1.00 0.00 O
-HETATM 929 H HOH A 310 23.290 26.042 7.861 1.00 0.00 H
-HETATM 930 H HOH A 310 23.714 26.702 9.157 1.00 0.00 H
-HETATM 931 O HOH A 311 -0.000 12.212 7.230 1.00 0.00 O
-HETATM 932 H HOH A 311 0.380 12.378 8.092 1.00 0.00 H
-HETATM 933 H HOH A 311 0.506 12.763 6.634 1.00 0.00 H
-HETATM 934 O HOH A 312 0.862 13.210 9.847 1.00 0.00 O
-HETATM 935 H HOH A 312 -0.000 13.346 10.240 1.00 0.00 H
-HETATM 936 H HOH A 312 1.244 14.085 9.790 1.00 0.00 H
-HETATM 937 O HOH A 313 29.521 12.933 9.960 1.00 0.00 O
-HETATM 938 H HOH A 313 29.007 13.661 9.611 1.00 0.00 H
-HETATM 939 H HOH A 313 30.000 13.309 10.699 1.00 0.00 H
-HETATM 940 O HOH A 314 28.450 15.328 8.657 1.00 0.00 O
-HETATM 941 H HOH A 314 28.732 15.143 7.760 1.00 0.00 H
-HETATM 942 H HOH A 314 28.931 16.118 8.903 1.00 0.00 H
-HETATM 943 O HOH A 315 7.536 21.433 5.722 1.00 0.00 O
-HETATM 944 H HOH A 315 6.817 22.002 5.447 1.00 0.00 H
-HETATM 945 H HOH A 315 7.540 20.722 5.080 1.00 0.00 H
-HETATM 946 O HOH A 316 5.000 22.771 5.121 1.00 0.00 O
-HETATM 947 H HOH A 316 4.642 22.808 6.008 1.00 0.00 H
-HETATM 948 H HOH A 316 4.398 22.203 4.640 1.00 0.00 H
-HETATM 949 O HOH A 317 17.552 19.820 18.087 1.00 0.00 O
-HETATM 950 H HOH A 317 17.504 18.864 18.085 1.00 0.00 H
-HETATM 951 H HOH A 317 18.125 20.033 17.351 1.00 0.00 H
-HETATM 952 O HOH A 318 17.928 16.924 18.321 1.00 0.00 O
-HETATM 953 H HOH A 318 18.111 16.878 19.260 1.00 0.00 H
-HETATM 954 H HOH A 318 18.737 16.631 17.903 1.00 0.00 H
-HETATM 955 O HOH A 319 10.210 18.976 17.765 1.00 0.00 O
-HETATM 956 H HOH A 319 9.973 19.875 17.539 1.00 0.00 H
-HETATM 957 H HOH A 319 10.705 19.056 18.581 1.00 0.00 H
-HETATM 958 O HOH A 320 10.031 21.768 16.895 1.00 0.00 O
-HETATM 959 H HOH A 320 10.331 21.657 15.993 1.00 0.00 H
-HETATM 960 H HOH A 320 10.698 22.317 17.307 1.00 0.00 H
-HETATM 961 O HOH A 321 29.520 3.840 2.758 1.00 0.00 O
-HETATM 962 H HOH A 321 29.418 2.889 2.803 1.00 0.00 H
-HETATM 963 H HOH A 321 29.750 4.099 3.651 1.00 0.00 H
-HETATM 964 O HOH A 322 28.698 1.053 3.136 1.00 0.00 O
-HETATM 965 H HOH A 322 27.860 1.086 2.673 1.00 0.00 H
-HETATM 966 H HOH A 322 28.465 0.866 4.045 1.00 0.00 H
-HETATM 967 O HOH A 323 7.441 13.759 9.520 1.00 0.00 O
-HETATM 968 H HOH A 323 7.737 13.939 10.412 1.00 0.00 H
-HETATM 969 H HOH A 323 6.936 12.950 9.593 1.00 0.00 H
-HETATM 970 O HOH A 324 8.737 13.896 12.144 1.00 0.00 O
-HETATM 971 H HOH A 324 9.637 14.044 11.853 1.00 0.00 H
-HETATM 972 H HOH A 324 8.760 13.043 12.579 1.00 0.00 H
-HETATM 973 O HOH A 325 14.640 10.130 26.495 1.00 0.00 O
-HETATM 974 H HOH A 325 14.068 9.870 25.773 1.00 0.00 H
-HETATM 975 H HOH A 325 15.461 9.666 26.331 1.00 0.00 H
-HETATM 976 O HOH A 326 12.830 8.844 24.584 1.00 0.00 O
-HETATM 977 H HOH A 326 12.109 8.643 25.181 1.00 0.00 H
-HETATM 978 H HOH A 326 13.154 7.990 24.300 1.00 0.00 H
-HETATM 979 O HOH A 327 22.551 27.781 5.415 1.00 0.00 O
-HETATM 980 H HOH A 327 23.428 28.132 5.569 1.00 0.00 H
-HETATM 981 H HOH A 327 22.680 26.836 5.338 1.00 0.00 H
-HETATM 982 O HOH A 328 25.306 28.774 5.318 1.00 0.00 O
-HETATM 983 H HOH A 328 25.194 29.445 4.644 1.00 0.00 H
-HETATM 984 H HOH A 328 25.905 28.137 4.929 1.00 0.00 H
-HETATM 985 O HOH A 329 21.298 15.829 24.054 1.00 0.00 O
-HETATM 986 H HOH A 329 21.276 16.644 24.556 1.00 0.00 H
-HETATM 987 H HOH A 329 21.374 16.110 23.142 1.00 0.00 H
-HETATM 988 O HOH A 330 20.682 18.353 25.410 1.00 0.00 O
-HETATM 989 H HOH A 330 19.919 18.065 25.911 1.00 0.00 H
-HETATM 990 H HOH A 330 20.340 19.012 24.806 1.00 0.00 H
-HETATM 991 O HOH A 331 15.842 6.689 1.983 1.00 0.00 O
-HETATM 992 H HOH A 331 15.516 5.831 1.714 1.00 0.00 H
-HETATM 993 H HOH A 331 15.074 7.259 1.954 1.00 0.00 H
-HETATM 994 O HOH A 332 14.860 4.285 0.627 1.00 0.00 O
-HETATM 995 H HOH A 332 15.575 4.171 0.000 1.00 0.00 H
-HETATM 996 H HOH A 332 14.091 4.465 0.086 1.00 0.00 H
-HETATM 997 O HOH A 333 12.810 6.342 13.087 1.00 0.00 O
-HETATM 998 H HOH A 333 12.287 5.972 13.798 1.00 0.00 H
-HETATM 999 H HOH A 333 13.089 7.197 13.415 1.00 0.00 H
-HETATM 1000 O HOH A 334 10.799 5.567 15.072 1.00 0.00 O
-HETATM 1001 H HOH A 334 10.089 5.330 14.474 1.00 0.00 H
-HETATM 1002 H HOH A 334 10.465 6.319 15.559 1.00 0.00 H
-HETATM 1003 O HOH A 335 5.185 25.164 23.949 1.00 0.00 O
-HETATM 1004 H HOH A 335 5.468 24.489 23.332 1.00 0.00 H
-HETATM 1005 H HOH A 335 5.480 24.849 24.803 1.00 0.00 H
-HETATM 1006 O HOH A 336 5.598 22.803 22.263 1.00 0.00 O
-HETATM 1007 H HOH A 336 4.734 22.768 21.850 1.00 0.00 H
-HETATM 1008 H HOH A 336 5.653 21.999 22.778 1.00 0.00 H
-HETATM 1009 O HOH A 337 18.885 10.080 23.913 1.00 0.00 O
-HETATM 1010 H HOH A 337 18.360 10.763 24.331 1.00 0.00 H
-HETATM 1011 H HOH A 337 19.106 10.439 23.053 1.00 0.00 H
-HETATM 1012 O HOH A 338 16.856 11.975 24.851 1.00 0.00 O
-HETATM 1013 H HOH A 338 16.181 11.347 25.109 1.00 0.00 H
-HETATM 1014 H HOH A 338 16.465 12.469 24.131 1.00 0.00 H
-HETATM 1015 O HOH A 339 14.688 19.475 9.151 1.00 0.00 O
-HETATM 1016 H HOH A 339 14.783 20.186 8.518 1.00 0.00 H
-HETATM 1017 H HOH A 339 15.403 19.609 9.773 1.00 0.00 H
-HETATM 1018 O HOH A 340 15.407 21.331 7.001 1.00 0.00 O
-HETATM 1019 H HOH A 340 15.331 20.729 6.259 1.00 0.00 H
-HETATM 1020 H HOH A 340 16.333 21.570 7.025 1.00 0.00 H
-HETATM 1021 O HOH A 341 3.381 4.488 28.302 1.00 0.00 O
-HETATM 1022 H HOH A 341 2.972 3.644 28.493 1.00 0.00 H
-HETATM 1023 H HOH A 341 3.127 4.681 27.400 1.00 0.00 H
-HETATM 1024 O HOH A 342 2.562 1.689 28.574 1.00 0.00 O
-HETATM 1025 H HOH A 342 3.377 1.333 28.929 1.00 0.00 H
-HETATM 1026 H HOH A 342 2.460 1.259 27.725 1.00 0.00 H
-HETATM 1027 O HOH A 343 23.080 18.838 19.478 1.00 0.00 O
-HETATM 1028 H HOH A 343 22.350 19.221 18.992 1.00 0.00 H
-HETATM 1029 H HOH A 343 22.979 17.894 19.358 1.00 0.00 H
-HETATM 1030 O HOH A 344 20.532 19.849 18.444 1.00 0.00 O
-HETATM 1031 H HOH A 344 20.291 20.413 19.180 1.00 0.00 H
-HETATM 1032 H HOH A 344 19.856 19.172 18.427 1.00 0.00 H
-HETATM 1033 O HOH A 345 28.915 25.182 7.639 1.00 0.00 O
-HETATM 1034 H HOH A 345 28.569 25.093 6.751 1.00 0.00 H
-HETATM 1035 H HOH A 345 28.210 24.866 8.204 1.00 0.00 H
-HETATM 1036 O HOH A 346 27.559 25.387 5.049 1.00 0.00 O
-HETATM 1037 H HOH A 346 27.821 26.279 4.819 1.00 0.00 H
-HETATM 1038 H HOH A 346 26.608 25.431 5.143 1.00 0.00 H
-HETATM 1039 O HOH A 347 24.538 8.263 8.606 1.00 0.00 O
-HETATM 1040 H HOH A 347 24.434 7.929 9.497 1.00 0.00 H
-HETATM 1041 H HOH A 347 24.929 7.537 8.121 1.00 0.00 H
-HETATM 1042 O HOH A 348 24.785 7.339 11.375 1.00 0.00 O
-HETATM 1043 H HOH A 348 25.185 8.122 11.754 1.00 0.00 H
-HETATM 1044 H HOH A 348 25.450 6.657 11.470 1.00 0.00 H
-HETATM 1045 O HOH A 349 25.236 5.444 6.122 1.00 0.00 O
-HETATM 1046 H HOH A 349 25.800 5.281 6.877 1.00 0.00 H
-HETATM 1047 H HOH A 349 25.553 6.271 5.758 1.00 0.00 H
-HETATM 1048 O HOH A 350 26.638 5.341 8.692 1.00 0.00 O
-HETATM 1049 H HOH A 350 25.888 5.156 9.259 1.00 0.00 H
-HETATM 1050 H HOH A 350 26.944 6.203 8.970 1.00 0.00 H
-HETATM 1051 O HOH A 351 11.311 23.975 19.629 1.00 0.00 O
-HETATM 1052 H HOH A 351 11.354 24.700 19.006 1.00 0.00 H
-HETATM 1053 H HOH A 351 12.196 23.914 19.989 1.00 0.00 H
-HETATM 1054 O HOH A 352 11.704 25.812 17.381 1.00 0.00 O
-HETATM 1055 H HOH A 352 11.273 25.298 16.697 1.00 0.00 H
-HETATM 1056 H HOH A 352 12.620 25.857 17.109 1.00 0.00 H
-HETATM 1057 O HOH A 353 3.620 9.954 0.199 1.00 0.00 O
-HETATM 1058 H HOH A 353 3.474 9.876 1.141 1.00 0.00 H
-HETATM 1059 H HOH A 353 4.499 10.324 0.122 1.00 0.00 H
-HETATM 1060 O HOH A 354 3.079 10.283 3.060 1.00 0.00 O
-HETATM 1061 H HOH A 354 2.220 10.701 2.997 1.00 0.00 H
-HETATM 1062 H HOH A 354 3.642 10.946 3.459 1.00 0.00 H
-HETATM 1063 O HOH A 355 23.539 29.100 23.971 1.00 0.00 O
-HETATM 1064 H HOH A 355 23.347 28.844 23.069 1.00 0.00 H
-HETATM 1065 H HOH A 355 23.312 28.331 24.493 1.00 0.00 H
-HETATM 1066 O HOH A 356 22.403 28.409 21.360 1.00 0.00 O
-HETATM 1067 H HOH A 356 21.915 29.217 21.197 1.00 0.00 H
-HETATM 1068 H HOH A 356 21.731 27.730 21.428 1.00 0.00 H
-HETATM 1069 O HOH A 357 9.148 11.844 10.493 1.00 0.00 O
-HETATM 1070 H HOH A 357 10.027 12.205 10.601 1.00 0.00 H
-HETATM 1071 H HOH A 357 9.212 11.279 9.723 1.00 0.00 H
-HETATM 1072 O HOH A 358 11.689 13.298 10.387 1.00 0.00 O
-HETATM 1073 H HOH A 358 11.350 14.189 10.480 1.00 0.00 H
-HETATM 1074 H HOH A 358 12.079 13.277 9.513 1.00 0.00 H
-HETATM 1075 O HOH A 359 28.181 10.214 29.095 1.00 0.00 O
-HETATM 1076 H HOH A 359 28.733 9.467 28.866 1.00 0.00 H
-HETATM 1077 H HOH A 359 28.584 10.578 29.883 1.00 0.00 H
-HETATM 1078 O HOH A 360 29.595 7.662 28.823 1.00 0.00 O
-HETATM 1079 H HOH A 360 28.837 7.087 28.721 1.00 0.00 H
-HETATM 1080 H HOH A 360 30.000 7.385 29.645 1.00 0.00 H
-HETATM 1081 O HOH A 361 17.369 24.375 17.354 1.00 0.00 O
-HETATM 1082 H HOH A 361 16.826 24.464 18.137 1.00 0.00 H
-HETATM 1083 H HOH A 361 16.744 24.233 16.642 1.00 0.00 H
-HETATM 1084 O HOH A 362 15.666 24.073 19.718 1.00 0.00 O
-HETATM 1085 H HOH A 362 16.206 23.450 20.207 1.00 0.00 H
-HETATM 1086 H HOH A 362 14.861 23.593 19.523 1.00 0.00 H
-HETATM 1087 O HOH A 363 8.609 6.458 16.809 1.00 0.00 O
-HETATM 1088 H HOH A 363 8.078 6.164 16.069 1.00 0.00 H
-HETATM 1089 H HOH A 363 8.832 7.365 16.598 1.00 0.00 H
-HETATM 1090 O HOH A 364 7.489 5.512 14.272 1.00 0.00 O
-HETATM 1091 H HOH A 364 8.000 4.707 14.180 1.00 0.00 H
-HETATM 1092 H HOH A 364 7.760 6.057 13.533 1.00 0.00 H
-HETATM 1093 O HOH A 365 6.212 4.163 25.978 1.00 0.00 O
-HETATM 1094 H HOH A 365 6.224 5.060 25.644 1.00 0.00 H
-HETATM 1095 H HOH A 365 5.732 4.222 26.804 1.00 0.00 H
-HETATM 1096 O HOH A 366 6.664 6.988 25.346 1.00 0.00 O
-HETATM 1097 H HOH A 366 7.605 6.949 25.174 1.00 0.00 H
-HETATM 1098 H HOH A 366 6.582 7.548 26.118 1.00 0.00 H
-HETATM 1099 O HOH A 367 27.604 9.241 26.979 1.00 0.00 O
-HETATM 1100 H HOH A 367 27.008 9.002 26.270 1.00 0.00 H
-HETATM 1101 H HOH A 367 28.476 9.033 26.641 1.00 0.00 H
-HETATM 1102 O HOH A 368 25.870 7.971 24.989 1.00 0.00 O
-HETATM 1103 H HOH A 368 25.300 7.483 25.584 1.00 0.00 H
-HETATM 1104 H HOH A 368 26.329 7.297 24.488 1.00 0.00 H
-HETATM 1105 O HOH A 369 6.786 23.102 25.247 1.00 0.00 O
-HETATM 1106 H HOH A 369 7.237 22.635 25.950 1.00 0.00 H
-HETATM 1107 H HOH A 369 6.941 22.570 24.467 1.00 0.00 H
-HETATM 1108 O HOH A 370 8.643 21.902 27.170 1.00 0.00 O
-HETATM 1109 H HOH A 370 9.073 22.702 27.474 1.00 0.00 H
-HETATM 1110 H HOH A 370 9.336 21.404 26.738 1.00 0.00 H
-HETATM 1111 O HOH A 371 18.620 13.516 5.182 1.00 0.00 O
-HETATM 1112 H HOH A 371 19.147 13.557 5.980 1.00 0.00 H
-HETATM 1113 H HOH A 371 17.737 13.757 5.465 1.00 0.00 H
-HETATM 1114 O HOH A 372 19.987 13.133 7.745 1.00 0.00 O
-HETATM 1115 H HOH A 372 20.375 12.274 7.575 1.00 0.00 H
-HETATM 1116 H HOH A 372 19.372 12.984 8.462 1.00 0.00 H
-HETATM 1117 O HOH A 373 12.881 15.810 0.920 1.00 0.00 O
-HETATM 1118 H HOH A 373 12.029 15.384 1.013 1.00 0.00 H
-HETATM 1119 H HOH A 373 12.678 16.681 0.579 1.00 0.00 H
-HETATM 1120 O HOH A 374 10.308 14.439 0.634 1.00 0.00 O
-HETATM 1121 H HOH A 374 10.613 13.650 0.185 1.00 0.00 H
-HETATM 1122 H HOH A 374 9.737 14.872 -0.001 1.00 0.00 H
-HETATM 1123 O HOH A 375 19.815 23.067 3.528 1.00 0.00 O
-HETATM 1124 H HOH A 375 20.269 23.880 3.747 1.00 0.00 H
-HETATM 1125 H HOH A 375 18.919 23.338 3.327 1.00 0.00 H
-HETATM 1126 O HOH A 376 20.932 25.507 4.702 1.00 0.00 O
-HETATM 1127 H HOH A 376 21.304 25.127 5.499 1.00 0.00 H
-HETATM 1128 H HOH A 376 20.250 26.102 5.014 1.00 0.00 H
-HETATM 1129 O HOH A 377 23.035 14.980 28.088 1.00 0.00 O
-HETATM 1130 H HOH A 377 22.612 15.681 27.592 1.00 0.00 H
-HETATM 1131 H HOH A 377 23.032 15.291 28.994 1.00 0.00 H
-HETATM 1132 O HOH A 378 22.248 17.393 26.623 1.00 0.00 O
-HETATM 1133 H HOH A 378 22.945 17.396 25.966 1.00 0.00 H
-HETATM 1134 H HOH A 378 22.394 18.188 27.135 1.00 0.00 H
-HETATM 1135 O HOH A 379 18.530 19.887 10.685 1.00 0.00 O
-HETATM 1136 H HOH A 379 18.725 20.641 10.130 1.00 0.00 H
-HETATM 1137 H HOH A 379 18.814 19.130 10.172 1.00 0.00 H
-HETATM 1138 O HOH A 380 18.651 22.039 8.701 1.00 0.00 O
-HETATM 1139 H HOH A 380 17.778 22.408 8.835 1.00 0.00 H
-HETATM 1140 H HOH A 380 18.637 21.704 7.805 1.00 0.00 H
-HETATM 1141 O HOH A 381 21.543 9.778 17.040 1.00 0.00 O
-HETATM 1142 H HOH A 381 20.658 9.980 17.343 1.00 0.00 H
-HETATM 1143 H HOH A 381 21.555 10.060 16.126 1.00 0.00 H
-HETATM 1144 O HOH A 382 18.687 9.921 17.676 1.00 0.00 O
-HETATM 1145 H HOH A 382 18.580 9.049 18.058 1.00 0.00 H
-HETATM 1146 H HOH A 382 18.142 9.909 16.890 1.00 0.00 H
-HETATM 1147 O HOH A 383 29.230 10.517 17.184 1.00 0.00 O
-HETATM 1148 H HOH A 383 28.788 10.672 16.350 1.00 0.00 H
-HETATM 1149 H HOH A 383 30.000 9.997 16.955 1.00 0.00 H
-HETATM 1150 O HOH A 384 27.695 10.448 14.689 1.00 0.00 O
-HETATM 1151 H HOH A 384 26.848 10.182 15.048 1.00 0.00 H
-HETATM 1152 H HOH A 384 27.958 9.722 14.125 1.00 0.00 H
-HETATM 1153 O HOH A 385 8.208 24.331 24.103 1.00 0.00 O
-HETATM 1154 H HOH A 385 8.673 24.620 23.318 1.00 0.00 H
-HETATM 1155 H HOH A 385 7.614 25.053 24.310 1.00 0.00 H
-HETATM 1156 O HOH A 386 9.936 25.539 22.069 1.00 0.00 O
-HETATM 1157 H HOH A 386 10.785 25.305 22.443 1.00 0.00 H
-HETATM 1158 H HOH A 386 9.913 26.496 22.099 1.00 0.00 H
-HETATM 1159 O HOH A 387 22.043 2.012 9.095 1.00 0.00 O
-HETATM 1160 H HOH A 387 21.241 2.465 8.837 1.00 0.00 H
-HETATM 1161 H HOH A 387 22.318 2.449 9.901 1.00 0.00 H
-HETATM 1162 O HOH A 388 19.946 3.811 8.121 1.00 0.00 O
-HETATM 1163 H HOH A 388 20.252 3.916 7.219 1.00 0.00 H
-HETATM 1164 H HOH A 388 20.016 4.686 8.503 1.00 0.00 H
-HETATM 1165 O HOH A 389 24.384 28.693 17.111 1.00 0.00 O
-HETATM 1166 H HOH A 389 24.763 28.226 16.366 1.00 0.00 H
-HETATM 1167 H HOH A 389 24.358 29.609 16.832 1.00 0.00 H
-HETATM 1168 O HOH A 390 26.043 27.479 15.023 1.00 0.00 O
-HETATM 1169 H HOH A 390 26.666 27.016 15.585 1.00 0.00 H
-HETATM 1170 H HOH A 390 26.572 28.128 14.560 1.00 0.00 H
-HETATM 1171 O HOH A 391 30.000 22.320 8.991 1.00 0.00 O
-HETATM 1172 H HOH A 391 29.617 21.564 9.436 1.00 0.00 H
-HETATM 1173 H HOH A 391 29.508 22.392 8.173 1.00 0.00 H
-HETATM 1174 O HOH A 392 29.139 19.699 9.978 1.00 0.00 O
-HETATM 1175 H HOH A 392 30.001 19.321 10.155 1.00 0.00 H
-HETATM 1176 H HOH A 392 28.774 19.154 9.282 1.00 0.00 H
-HETATM 1177 O HOH A 393 19.149 12.219 0.837 1.00 0.00 O
-HETATM 1178 H HOH A 393 19.852 12.561 1.388 1.00 0.00 H
-HETATM 1179 H HOH A 393 19.571 12.025 -0.000 1.00 0.00 H
-HETATM 1180 O HOH A 394 21.228 13.751 2.220 1.00 0.00 O
-HETATM 1181 H HOH A 394 20.698 14.501 2.491 1.00 0.00 H
-HETATM 1182 H HOH A 394 21.871 14.116 1.612 1.00 0.00 H
-HETATM 1183 O HOH A 395 18.687 17.442 15.339 1.00 0.00 O
-HETATM 1184 H HOH A 395 18.793 16.564 15.705 1.00 0.00 H
-HETATM 1185 H HOH A 395 18.345 17.295 14.457 1.00 0.00 H
-HETATM 1186 O HOH A 396 19.485 14.736 16.132 1.00 0.00 O
-HETATM 1187 H HOH A 396 20.374 14.930 16.432 1.00 0.00 H
-HETATM 1188 H HOH A 396 19.604 14.160 15.378 1.00 0.00 H
-HETATM 1189 O HOH A 397 23.860 10.144 16.007 1.00 0.00 O
-HETATM 1190 H HOH A 397 24.527 9.659 15.521 1.00 0.00 H
-HETATM 1191 H HOH A 397 24.316 10.465 16.785 1.00 0.00 H
-HETATM 1192 O HOH A 398 25.808 8.249 14.911 1.00 0.00 O
-HETATM 1193 H HOH A 398 25.227 7.496 14.795 1.00 0.00 H
-HETATM 1194 H HOH A 398 26.444 7.966 15.567 1.00 0.00 H
-HETATM 1195 O HOH A 399 23.983 0.447 3.819 1.00 0.00 O
-HETATM 1196 H HOH A 399 23.228 0.799 3.347 1.00 0.00 H
-HETATM 1197 H HOH A 399 23.606 0.000 4.576 1.00 0.00 H
-HETATM 1198 O HOH A 400 21.676 1.929 2.786 1.00 0.00 O
-HETATM 1199 H HOH A 400 22.082 2.793 2.711 1.00 0.00 H
-HETATM 1200 H HOH A 400 20.993 2.039 3.448 1.00 0.00 H
-HETATM 1201 O HOH A 401 19.861 18.516 7.416 1.00 0.00 O
-HETATM 1202 H HOH A 401 19.308 18.031 6.804 1.00 0.00 H
-HETATM 1203 H HOH A 401 19.795 18.030 8.238 1.00 0.00 H
-HETATM 1204 O HOH A 402 17.733 17.289 5.819 1.00 0.00 O
-HETATM 1205 H HOH A 402 17.290 18.088 5.530 1.00 0.00 H
-HETATM 1206 H HOH A 402 17.089 16.838 6.363 1.00 0.00 H
-HETATM 1207 O HOH A 403 8.030 15.904 20.493 1.00 0.00 O
-HETATM 1208 H HOH A 403 8.533 16.704 20.640 1.00 0.00 H
-HETATM 1209 H HOH A 403 8.009 15.805 19.541 1.00 0.00 H
-HETATM 1210 O HOH A 404 9.083 18.622 20.791 1.00 0.00 O
-HETATM 1211 H HOH A 404 8.425 18.966 21.396 1.00 0.00 H
-HETATM 1212 H HOH A 404 8.973 19.140 19.994 1.00 0.00 H
-HETATM 1213 O HOH A 405 21.215 0.051 22.905 1.00 0.00 O
-HETATM 1214 H HOH A 405 20.523 0.711 22.903 1.00 0.00 H
-HETATM 1215 H HOH A 405 21.681 0.195 23.729 1.00 0.00 H
-HETATM 1216 O HOH A 406 19.518 2.435 22.752 1.00 0.00 O
-HETATM 1217 H HOH A 406 19.733 2.707 21.859 1.00 0.00 H
-HETATM 1218 H HOH A 406 19.843 3.144 23.306 1.00 0.00 H
-HETATM 1219 O HOH A 407 27.419 17.370 13.967 1.00 0.00 O
-HETATM 1220 H HOH A 407 27.929 18.041 13.514 1.00 0.00 H
-HETATM 1221 H HOH A 407 27.381 17.668 14.876 1.00 0.00 H
-HETATM 1222 O HOH A 408 29.434 19.167 12.828 1.00 0.00 O
-HETATM 1223 H HOH A 408 29.993 18.510 12.410 1.00 0.00 H
-HETATM 1224 H HOH A 408 29.986 19.562 13.502 1.00 0.00 H
-HETATM 1225 O HOH A 409 8.650 21.015 2.328 1.00 0.00 O
-HETATM 1226 H HOH A 409 7.843 21.110 2.834 1.00 0.00 H
-HETATM 1227 H HOH A 409 8.471 21.466 1.503 1.00 0.00 H
-HETATM 1228 O HOH A 410 5.982 20.888 3.532 1.00 0.00 O
-HETATM 1229 H HOH A 410 5.965 19.956 3.749 1.00 0.00 H
-HETATM 1230 H HOH A 410 5.288 20.999 2.883 1.00 0.00 H
-HETATM 1231 O HOH A 411 11.115 26.463 0.219 1.00 0.00 O
-HETATM 1232 H HOH A 411 10.951 26.973 1.012 1.00 0.00 H
-HETATM 1233 H HOH A 411 12.029 26.645 -0.000 1.00 0.00 H
-HETATM 1234 O HOH A 412 10.581 28.473 2.283 1.00 0.00 O
-HETATM 1235 H HOH A 412 9.770 28.836 1.925 1.00 0.00 H
-HETATM 1236 H HOH A 412 11.205 29.197 2.250 1.00 0.00 H
-HETATM 1237 O HOH A 413 14.973 11.309 13.025 1.00 0.00 O
-HETATM 1238 H HOH A 413 14.201 10.969 13.476 1.00 0.00 H
-HETATM 1239 H HOH A 413 14.690 11.425 12.118 1.00 0.00 H
-HETATM 1240 O HOH A 414 12.752 9.769 14.155 1.00 0.00 O
-HETATM 1241 H HOH A 414 13.257 9.065 14.564 1.00 0.00 H
-HETATM 1242 H HOH A 414 12.235 9.333 13.478 1.00 0.00 H
-HETATM 1243 O HOH A 415 9.586 26.471 11.857 1.00 0.00 O
-HETATM 1244 H HOH A 415 10.241 26.877 11.289 1.00 0.00 H
-HETATM 1245 H HOH A 415 9.880 25.565 11.955 1.00 0.00 H
-HETATM 1246 O HOH A 416 11.316 27.434 9.698 1.00 0.00 O
-HETATM 1247 H HOH A 416 10.643 27.812 9.131 1.00 0.00 H
-HETATM 1248 H HOH A 416 11.688 26.715 9.186 1.00 0.00 H
-HETATM 1249 O HOH A 417 11.992 26.878 21.490 1.00 0.00 O
-HETATM 1250 H HOH A 417 12.420 27.439 22.136 1.00 0.00 H
-HETATM 1251 H HOH A 417 12.490 27.016 20.684 1.00 0.00 H
-HETATM 1252 O HOH A 418 13.018 29.020 23.205 1.00 0.00 O
-HETATM 1253 H HOH A 418 12.182 29.389 23.491 1.00 0.00 H
-HETATM 1254 H HOH A 418 13.431 29.720 22.699 1.00 0.00 H
-HETATM 1255 O HOH A 419 1.486 23.664 17.864 1.00 0.00 O
-HETATM 1256 H HOH A 419 2.200 23.095 17.577 1.00 0.00 H
-HETATM 1257 H HOH A 419 0.760 23.453 17.277 1.00 0.00 H
-HETATM 1258 O HOH A 420 3.728 22.303 16.557 1.00 0.00 O
-HETATM 1259 H HOH A 420 4.350 23.031 16.543 1.00 0.00 H
-HETATM 1260 H HOH A 420 3.549 22.119 15.635 1.00 0.00 H
-HETATM 1261 O HOH A 421 21.387 26.454 28.314 1.00 0.00 O
-HETATM 1262 H HOH A 421 21.237 27.186 27.715 1.00 0.00 H
-HETATM 1263 H HOH A 421 20.868 26.667 29.089 1.00 0.00 H
-HETATM 1264 O HOH A 422 21.300 28.974 26.821 1.00 0.00 O
-HETATM 1265 H HOH A 422 22.237 29.075 26.653 1.00 0.00 H
-HETATM 1266 H HOH A 422 21.073 29.716 27.381 1.00 0.00 H
-HETATM 1267 O HOH A 423 25.216 20.461 14.739 1.00 0.00 O
-HETATM 1268 H HOH A 423 25.366 19.618 14.311 1.00 0.00 H
-HETATM 1269 H HOH A 423 25.585 20.354 15.616 1.00 0.00 H
-HETATM 1270 O HOH A 424 25.227 17.705 13.744 1.00 0.00 O
-HETATM 1271 H HOH A 424 24.320 17.657 13.441 1.00 0.00 H
-HETATM 1272 H HOH A 424 25.278 17.066 14.455 1.00 0.00 H
-HETATM 1273 O HOH A 425 23.235 16.352 16.256 1.00 0.00 O
-HETATM 1274 H HOH A 425 24.023 16.693 16.679 1.00 0.00 H
-HETATM 1275 H HOH A 425 23.313 16.630 15.343 1.00 0.00 H
-HETATM 1276 O HOH A 426 25.366 17.901 17.539 1.00 0.00 O
-HETATM 1277 H HOH A 426 24.860 18.272 18.262 1.00 0.00 H
-HETATM 1278 H HOH A 426 25.628 18.657 17.014 1.00 0.00 H
-HETATM 1279 O HOH A 427 17.378 21.017 3.188 1.00 0.00 O
-HETATM 1280 H HOH A 427 17.419 20.087 3.412 1.00 0.00 H
-HETATM 1281 H HOH A 427 16.724 21.067 2.491 1.00 0.00 H
-HETATM 1282 O HOH A 428 17.816 18.127 3.392 1.00 0.00 O
-HETATM 1283 H HOH A 428 18.773 18.123 3.350 1.00 0.00 H
-HETATM 1284 H HOH A 428 17.537 17.664 2.602 1.00 0.00 H
-HETATM 1285 O HOH A 429 9.287 6.872 1.925 1.00 0.00 O
-HETATM 1286 H HOH A 429 9.125 5.938 2.060 1.00 0.00 H
-HETATM 1287 H HOH A 429 8.425 7.239 1.726 1.00 0.00 H
-HETATM 1288 O HOH A 430 8.767 3.993 1.762 1.00 0.00 O
-HETATM 1289 H HOH A 430 9.489 3.749 1.183 1.00 0.00 H
-HETATM 1290 H HOH A 430 7.976 3.798 1.260 1.00 0.00 H
-HETATM 1291 O HOH A 431 13.062 10.548 28.476 1.00 0.00 O
-HETATM 1292 H HOH A 431 12.384 11.129 28.131 1.00 0.00 H
-HETATM 1293 H HOH A 431 13.879 11.032 28.352 1.00 0.00 H
-HETATM 1294 O HOH A 432 11.173 12.146 26.907 1.00 0.00 O
-HETATM 1295 H HOH A 432 10.782 11.441 26.390 1.00 0.00 H
-HETATM 1296 H HOH A 432 11.616 12.700 26.264 1.00 0.00 H
-HETATM 1297 O HOH A 433 15.080 4.665 18.970 1.00 0.00 O
-HETATM 1298 H HOH A 433 15.956 4.388 19.240 1.00 0.00 H
-HETATM 1299 H HOH A 433 14.602 4.781 19.791 1.00 0.00 H
-HETATM 1300 O HOH A 434 17.511 3.305 19.880 1.00 0.00 O
-HETATM 1301 H HOH A 434 17.511 2.578 19.257 1.00 0.00 H
-HETATM 1302 H HOH A 434 17.393 2.891 20.735 1.00 0.00 H
-HETATM 1303 O HOH A 435 19.521 1.370 0.000 1.00 0.00 O
-HETATM 1304 H HOH A 435 18.938 1.319 0.757 1.00 0.00 H
-HETATM 1305 H HOH A 435 19.989 0.534 0.000 1.00 0.00 H
-HETATM 1306 O HOH A 436 18.213 1.326 2.621 1.00 0.00 O
-HETATM 1307 H HOH A 436 18.482 2.191 2.932 1.00 0.00 H
-HETATM 1308 H HOH A 436 18.632 0.711 3.223 1.00 0.00 H
-HETATM 1309 O HOH A 437 27.282 18.327 6.462 1.00 0.00 O
-HETATM 1310 H HOH A 437 26.977 18.024 5.607 1.00 0.00 H
-HETATM 1311 H HOH A 437 26.834 17.762 7.091 1.00 0.00 H
-HETATM 1312 O HOH A 438 25.870 17.693 3.974 1.00 0.00 O
-HETATM 1313 H HOH A 438 25.673 18.586 3.688 1.00 0.00 H
-HETATM 1314 H HOH A 438 25.014 17.297 4.137 1.00 0.00 H
-HETATM 1315 O HOH A 439 14.622 7.382 26.170 1.00 0.00 O
-HETATM 1316 H HOH A 439 15.049 7.723 25.385 1.00 0.00 H
-HETATM 1317 H HOH A 439 14.035 6.698 25.848 1.00 0.00 H
-HETATM 1318 O HOH A 440 15.430 8.702 23.683 1.00 0.00 O
-HETATM 1319 H HOH A 440 15.252 9.609 23.936 1.00 0.00 H
-HETATM 1320 H HOH A 440 14.821 8.526 22.967 1.00 0.00 H
-HETATM 1321 O HOH A 441 17.186 23.630 10.566 1.00 0.00 O
-HETATM 1322 H HOH A 441 17.232 23.595 11.522 1.00 0.00 H
-HETATM 1323 H HOH A 441 16.774 24.473 10.376 1.00 0.00 H
-HETATM 1324 O HOH A 442 16.766 23.396 13.457 1.00 0.00 O
-HETATM 1325 H HOH A 442 16.396 22.513 13.478 1.00 0.00 H
-HETATM 1326 H HOH A 442 16.059 23.962 13.765 1.00 0.00 H
-HETATM 1327 O HOH A 443 22.930 5.681 10.001 1.00 0.00 O
-HETATM 1328 H HOH A 443 22.941 6.379 10.655 1.00 0.00 H
-HETATM 1329 H HOH A 443 23.687 5.861 9.444 1.00 0.00 H
-HETATM 1330 O HOH A 444 22.747 8.142 11.579 1.00 0.00 O
-HETATM 1331 H HOH A 444 21.817 8.331 11.446 1.00 0.00 H
-HETATM 1332 H HOH A 444 23.204 8.873 11.162 1.00 0.00 H
-HETATM 1333 O HOH A 445 24.055 12.474 23.016 1.00 0.00 O
-HETATM 1334 H HOH A 445 23.851 12.223 22.115 1.00 0.00 H
-HETATM 1335 H HOH A 445 24.660 11.799 23.322 1.00 0.00 H
-HETATM 1336 O HOH A 446 23.084 11.316 20.505 1.00 0.00 O
-HETATM 1337 H HOH A 446 22.144 11.423 20.651 1.00 0.00 H
-HETATM 1338 H HOH A 446 23.218 10.369 20.470 1.00 0.00 H
-HETATM 1339 O HOH A 447 13.309 12.542 17.843 1.00 0.00 O
-HETATM 1340 H HOH A 447 13.643 12.848 17.000 1.00 0.00 H
-HETATM 1341 H HOH A 447 13.125 13.342 18.334 1.00 0.00 H
-HETATM 1342 O HOH A 448 14.820 13.576 15.556 1.00 0.00 O
-HETATM 1343 H HOH A 448 15.568 12.979 15.595 1.00 0.00 H
-HETATM 1344 H HOH A 448 15.192 14.440 15.731 1.00 0.00 H
-HETATM 1345 O HOH A 449 1.228 25.875 6.041 1.00 0.00 O
-HETATM 1346 H HOH A 449 1.942 26.420 6.369 1.00 0.00 H
-HETATM 1347 H HOH A 449 1.278 25.961 5.089 1.00 0.00 H
-HETATM 1348 O HOH A 450 3.060 27.981 6.931 1.00 0.00 O
-HETATM 1349 H HOH A 450 2.487 28.413 7.565 1.00 0.00 H
-HETATM 1350 H HOH A 450 3.209 28.640 6.252 1.00 0.00 H
-HETATM 1351 O HOH A 451 19.462 2.229 5.102 1.00 0.00 O
-HETATM 1352 H HOH A 451 19.500 3.089 4.684 1.00 0.00 H
-HETATM 1353 H HOH A 451 20.162 2.247 5.754 1.00 0.00 H
-HETATM 1354 O HOH A 452 20.032 4.642 3.541 1.00 0.00 O
-HETATM 1355 H HOH A 452 20.006 4.256 2.665 1.00 0.00 H
-HETATM 1356 H HOH A 452 20.936 4.936 3.650 1.00 0.00 H
-HETATM 1357 O HOH A 453 17.450 30.012 21.391 1.00 0.00 O
-HETATM 1358 H HOH A 453 17.416 29.327 22.058 1.00 0.00 H
-HETATM 1359 H HOH A 453 17.876 29.597 20.641 1.00 0.00 H
-HETATM 1360 O HOH A 454 17.905 28.040 23.509 1.00 0.00 O
-HETATM 1361 H HOH A 454 18.257 28.626 24.179 1.00 0.00 H
-HETATM 1362 H HOH A 454 18.629 27.449 23.301 1.00 0.00 H
-HETATM 1363 O HOH A 455 16.465 3.213 28.978 1.00 0.00 O
-HETATM 1364 H HOH A 455 16.961 3.989 28.716 1.00 0.00 H
-HETATM 1365 H HOH A 455 15.583 3.540 29.155 1.00 0.00 H
-HETATM 1366 O HOH A 456 17.992 5.703 28.740 1.00 0.00 O
-HETATM 1367 H HOH A 456 18.709 5.490 29.338 1.00 0.00 H
-HETATM 1368 H HOH A 456 17.544 6.441 29.152 1.00 0.00 H
-HETATM 1369 O HOH A 457 15.454 12.260 10.136 1.00 0.00 O
-HETATM 1370 H HOH A 457 15.594 13.009 9.558 1.00 0.00 H
-HETATM 1371 H HOH A 457 16.306 11.826 10.180 1.00 0.00 H
-HETATM 1372 O HOH A 458 15.858 14.162 7.945 1.00 0.00 O
-HETATM 1373 H HOH A 458 15.079 13.954 7.428 1.00 0.00 H
-HETATM 1374 H HOH A 458 16.593 13.915 7.384 1.00 0.00 H
-HETATM 1375 O HOH A 459 14.693 26.022 26.904 1.00 0.00 O
-HETATM 1376 H HOH A 459 14.668 25.953 27.858 1.00 0.00 H
-HETATM 1377 H HOH A 459 14.346 26.894 26.717 1.00 0.00 H
-HETATM 1378 O HOH A 460 14.053 25.719 29.747 1.00 0.00 O
-HETATM 1379 H HOH A 460 13.631 24.860 29.710 1.00 0.00 H
-HETATM 1380 H HOH A 460 13.360 26.317 30.026 1.00 0.00 H
-HETATM 1381 O HOH A 461 3.211 4.128 19.200 1.00 0.00 O
-HETATM 1382 H HOH A 461 3.964 4.041 19.785 1.00 0.00 H
-HETATM 1383 H HOH A 461 3.090 3.252 18.833 1.00 0.00 H
-HETATM 1384 O HOH A 462 5.823 3.874 20.505 1.00 0.00 O
-HETATM 1385 H HOH A 462 6.244 4.651 20.137 1.00 0.00 H
-HETATM 1386 H HOH A 462 6.328 3.138 20.159 1.00 0.00 H
-HETATM 1387 O HOH A 463 2.450 14.891 3.529 1.00 0.00 O
-HETATM 1388 H HOH A 463 3.245 14.577 3.959 1.00 0.00 H
-HETATM 1389 H HOH A 463 1.929 15.273 4.235 1.00 0.00 H
-HETATM 1390 O HOH A 464 4.549 13.494 5.021 1.00 0.00 O
-HETATM 1391 H HOH A 464 4.486 12.639 4.593 1.00 0.00 H
-HETATM 1392 H HOH A 464 4.297 13.329 5.929 1.00 0.00 H
-HETATM 1393 O HOH A 465 12.956 8.403 21.245 1.00 0.00 O
-HETATM 1394 H HOH A 465 13.615 9.083 21.103 1.00 0.00 H
-HETATM 1395 H HOH A 465 12.767 8.442 22.182 1.00 0.00 H
-HETATM 1396 O HOH A 466 15.336 10.100 21.040 1.00 0.00 O
-HETATM 1397 H HOH A 466 15.896 9.503 20.542 1.00 0.00 H
-HETATM 1398 H HOH A 466 15.779 10.202 21.882 1.00 0.00 H
-HETATM 1399 O HOH A 467 3.014 12.343 7.419 1.00 0.00 O
-HETATM 1400 H HOH A 467 2.608 11.544 7.755 1.00 0.00 H
-HETATM 1401 H HOH A 467 2.442 13.048 7.720 1.00 0.00 H
-HETATM 1402 O HOH A 468 1.401 9.969 8.009 1.00 0.00 O
-HETATM 1403 H HOH A 468 1.463 9.537 7.157 1.00 0.00 H
-HETATM 1404 H HOH A 468 0.473 10.180 8.107 1.00 0.00 H
-HETATM 1405 O HOH A 469 10.696 15.852 2.710 1.00 0.00 O
-HETATM 1406 H HOH A 469 11.566 15.874 3.107 1.00 0.00 H
-HETATM 1407 H HOH A 469 10.097 16.047 3.431 1.00 0.00 H
-HETATM 1408 O HOH A 470 13.219 15.398 4.129 1.00 0.00 O
-HETATM 1409 H HOH A 470 13.461 14.562 3.729 1.00 0.00 H
-HETATM 1410 H HOH A 470 13.080 15.193 5.053 1.00 0.00 H
-HETATM 1411 O HOH A 471 0.467 0.845 13.050 1.00 0.00 O
-HETATM 1412 H HOH A 471 1.259 1.112 12.584 1.00 0.00 H
-HETATM 1413 H HOH A 471 0.000 0.287 12.428 1.00 0.00 H
-HETATM 1414 O HOH A 472 2.540 2.030 11.353 1.00 0.00 O
-HETATM 1415 H HOH A 472 2.409 2.950 11.585 1.00 0.00 H
-HETATM 1416 H HOH A 472 2.317 1.981 10.423 1.00 0.00 H
-HETATM 1417 O HOH A 473 14.677 15.144 12.967 1.00 0.00 O
-HETATM 1418 H HOH A 473 14.005 15.650 13.424 1.00 0.00 H
-HETATM 1419 H HOH A 473 14.791 14.358 13.501 1.00 0.00 H
-HETATM 1420 O HOH A 474 13.050 16.833 14.724 1.00 0.00 O
-HETATM 1421 H HOH A 474 13.571 17.635 14.676 1.00 0.00 H
-HETATM 1422 H HOH A 474 13.108 16.561 15.639 1.00 0.00 H
-HETATM 1423 O HOH A 475 20.686 4.068 17.750 1.00 0.00 O
-HETATM 1424 H HOH A 475 21.168 4.804 18.125 1.00 0.00 H
-HETATM 1425 H HOH A 475 20.699 4.228 16.806 1.00 0.00 H
-HETATM 1426 O HOH A 476 21.676 6.615 18.805 1.00 0.00 O
-HETATM 1427 H HOH A 476 20.991 6.778 19.456 1.00 0.00 H
-HETATM 1428 H HOH A 476 21.583 7.327 18.173 1.00 0.00 H
-HETATM 1429 O HOH A 477 10.815 16.801 5.372 1.00 0.00 O
-HETATM 1430 H HOH A 477 11.477 17.348 4.951 1.00 0.00 H
-HETATM 1431 H HOH A 477 11.305 16.269 6.000 1.00 0.00 H
-HETATM 1432 O HOH A 478 12.969 17.997 3.787 1.00 0.00 O
-HETATM 1433 H HOH A 478 12.640 17.784 2.913 1.00 0.00 H
-HETATM 1434 H HOH A 478 13.787 17.507 3.863 1.00 0.00 H
-HETATM 1435 O HOH A 479 17.839 4.145 7.582 1.00 0.00 O
-HETATM 1436 H HOH A 479 17.829 3.860 6.669 1.00 0.00 H
-HETATM 1437 H HOH A 479 17.531 3.385 8.075 1.00 0.00 H
-HETATM 1438 O HOH A 480 17.239 3.357 4.825 1.00 0.00 O
-HETATM 1439 H HOH A 480 16.777 4.148 4.546 1.00 0.00 H
-HETATM 1440 H HOH A 480 16.580 2.665 4.784 1.00 0.00 H
-HETATM 1441 O HOH A 481 13.053 24.008 23.329 1.00 0.00 O
-HETATM 1442 H HOH A 481 12.745 23.135 23.569 1.00 0.00 H
-HETATM 1443 H HOH A 481 12.915 24.060 22.383 1.00 0.00 H
-HETATM 1444 O HOH A 482 12.611 21.159 23.845 1.00 0.00 O
-HETATM 1445 H HOH A 482 13.404 20.978 24.350 1.00 0.00 H
-HETATM 1446 H HOH A 482 12.703 20.630 23.052 1.00 0.00 H
-HETATM 1447 O HOH A 483 8.434 16.946 13.288 1.00 0.00 O
-HETATM 1448 H HOH A 483 7.506 17.178 13.266 1.00 0.00 H
-HETATM 1449 H HOH A 483 8.761 17.179 12.419 1.00 0.00 H
-HETATM 1450 O HOH A 484 5.536 17.168 12.918 1.00 0.00 O
-HETATM 1451 H HOH A 484 5.279 16.319 13.280 1.00 0.00 H
-HETATM 1452 H HOH A 484 5.287 17.122 11.995 1.00 0.00 H
-HETATM 1453 O HOH A 485 3.469 23.456 9.544 1.00 0.00 O
-HETATM 1454 H HOH A 485 3.137 22.601 9.816 1.00 0.00 H
-HETATM 1455 H HOH A 485 2.700 24.025 9.548 1.00 0.00 H
-HETATM 1456 O HOH A 486 2.255 20.805 9.835 1.00 0.00 O
-HETATM 1457 H HOH A 486 2.699 20.363 9.111 1.00 0.00 H
-HETATM 1458 H HOH A 486 1.330 20.804 9.589 1.00 0.00 H
-HETATM 1459 O HOH A 487 19.980 7.154 11.720 1.00 0.00 O
-HETATM 1460 H HOH A 487 19.662 8.054 11.789 1.00 0.00 H
-HETATM 1461 H HOH A 487 19.187 6.618 11.731 1.00 0.00 H
-HETATM 1462 O HOH A 488 18.938 9.782 12.489 1.00 0.00 O
-HETATM 1463 H HOH A 488 19.561 9.986 13.187 1.00 0.00 H
-HETATM 1464 H HOH A 488 18.092 9.719 12.932 1.00 0.00 H
-HETATM 1465 O HOH A 489 4.081 26.967 24.557 1.00 0.00 O
-HETATM 1466 H HOH A 489 3.335 26.656 25.069 1.00 0.00 H
-HETATM 1467 H HOH A 489 3.722 27.136 23.686 1.00 0.00 H
-HETATM 1468 O HOH A 490 1.883 25.519 25.844 1.00 0.00 O
-HETATM 1469 H HOH A 490 2.380 24.772 26.179 1.00 0.00 H
-HETATM 1470 H HOH A 490 1.279 25.141 25.206 1.00 0.00 H
-HETATM 1471 O HOH A 491 8.891 17.484 22.971 1.00 0.00 O
-HETATM 1472 H HOH A 491 9.659 17.253 22.449 1.00 0.00 H
-HETATM 1473 H HOH A 491 9.133 17.256 23.869 1.00 0.00 H
-HETATM 1474 O HOH A 492 11.050 16.238 21.432 1.00 0.00 O
-HETATM 1475 H HOH A 492 10.517 15.766 20.792 1.00 0.00 H
-HETATM 1476 H HOH A 492 11.497 15.552 21.928 1.00 0.00 H
-HETATM 1477 O HOH A 493 19.647 15.273 7.281 1.00 0.00 O
-HETATM 1478 H HOH A 493 20.473 15.684 7.029 1.00 0.00 H
-HETATM 1479 H HOH A 493 18.972 15.862 6.943 1.00 0.00 H
-HETATM 1480 O HOH A 494 22.071 16.886 6.951 1.00 0.00 O
-HETATM 1481 H HOH A 494 22.429 16.790 7.834 1.00 0.00 H
-HETATM 1482 H HOH A 494 21.874 17.819 6.869 1.00 0.00 H
-HETATM 1483 O HOH A 495 18.540 29.783 9.963 1.00 0.00 O
-HETATM 1484 H HOH A 495 19.259 29.492 10.525 1.00 0.00 H
-HETATM 1485 H HOH A 495 18.960 30.008 9.133 1.00 0.00 H
-HETATM 1486 O HOH A 496 20.851 29.456 11.735 1.00 0.00 O
-HETATM 1487 H HOH A 496 20.560 30.014 12.457 1.00 0.00 H
-HETATM 1488 H HOH A 496 21.632 29.891 11.393 1.00 0.00 H
-HETATM 1489 O HOH A 497 9.249 16.837 17.698 1.00 0.00 O
-HETATM 1490 H HOH A 497 9.104 15.960 17.342 1.00 0.00 H
-HETATM 1491 H HOH A 497 9.740 16.690 18.507 1.00 0.00 H
-HETATM 1492 O HOH A 498 8.394 14.119 17.013 1.00 0.00 O
-HETATM 1493 H HOH A 498 7.463 14.292 16.872 1.00 0.00 H
-HETATM 1494 H HOH A 498 8.420 13.532 17.768 1.00 0.00 H
-HETATM 1495 O HOH A 499 3.574 8.463 5.531 1.00 0.00 O
-HETATM 1496 H HOH A 499 3.916 8.004 4.764 1.00 0.00 H
-HETATM 1497 H HOH A 499 4.144 8.190 6.249 1.00 0.00 H
-HETATM 1498 O HOH A 500 4.349 6.601 3.406 1.00 0.00 O
-HETATM 1499 H HOH A 500 3.476 6.298 3.154 1.00 0.00 H
-HETATM 1500 H HOH A 500 4.784 5.822 3.752 1.00 0.00 H
-HETATM 1501 O HOH A 501 6.158 25.544 0.631 1.00 0.00 O
-HETATM 1502 H HOH A 501 5.236 25.774 0.744 1.00 0.00 H
-HETATM 1503 H HOH A 501 6.335 24.912 1.328 1.00 0.00 H
-HETATM 1504 O HOH A 502 3.529 26.575 1.409 1.00 0.00 O
-HETATM 1505 H HOH A 502 3.719 27.512 1.355 1.00 0.00 H
-HETATM 1506 H HOH A 502 3.341 26.420 2.335 1.00 0.00 H
-HETATM 1507 O HOH A 503 0.107 15.082 12.422 1.00 0.00 O
-HETATM 1508 H HOH A 503 0.727 14.468 12.027 1.00 0.00 H
-HETATM 1509 H HOH A 503 0.643 15.629 12.995 1.00 0.00 H
-HETATM 1510 O HOH A 504 1.975 12.935 11.724 1.00 0.00 O
-HETATM 1511 H HOH A 504 1.414 12.185 11.925 1.00 0.00 H
-HETATM 1512 H HOH A 504 2.698 12.870 12.347 1.00 0.00 H
-HETATM 1513 O HOH A 505 15.717 28.853 24.021 1.00 0.00 O
-HETATM 1514 H HOH A 505 15.636 28.103 23.432 1.00 0.00 H
-HETATM 1515 H HOH A 505 15.358 28.545 24.853 1.00 0.00 H
-HETATM 1516 O HOH A 506 14.913 26.711 22.191 1.00 0.00 O
-HETATM 1517 H HOH A 506 14.518 27.248 21.504 1.00 0.00 H
-HETATM 1518 H HOH A 506 14.187 26.193 22.540 1.00 0.00 H
-HETATM 1519 O HOH A 507 17.988 10.137 20.291 1.00 0.00 O
-HETATM 1520 H HOH A 507 18.933 10.185 20.150 1.00 0.00 H
-HETATM 1521 H HOH A 507 17.883 10.196 21.241 1.00 0.00 H
-HETATM 1522 O HOH A 508 20.878 9.734 20.029 1.00 0.00 O
-HETATM 1523 H HOH A 508 20.866 8.974 19.447 1.00 0.00 H
-HETATM 1524 H HOH A 508 21.272 9.412 20.840 1.00 0.00 H
-HETATM 1525 O HOH A 509 10.866 10.632 23.910 1.00 0.00 O
-HETATM 1526 H HOH A 509 10.965 11.394 23.339 1.00 0.00 H
-HETATM 1527 H HOH A 509 11.617 10.677 24.503 1.00 0.00 H
-HETATM 1528 O HOH A 510 11.568 12.645 21.900 1.00 0.00 O
-HETATM 1529 H HOH A 510 11.427 12.118 21.112 1.00 0.00 H
-HETATM 1530 H HOH A 510 12.505 12.839 21.896 1.00 0.00 H
-HETATM 1531 O HOH A 511 22.133 21.517 19.656 1.00 0.00 O
-HETATM 1532 H HOH A 511 22.978 21.535 19.207 1.00 0.00 H
-HETATM 1533 H HOH A 511 22.354 21.413 20.581 1.00 0.00 H
-HETATM 1534 O HOH A 512 24.711 21.000 18.362 1.00 0.00 O
-HETATM 1535 H HOH A 512 24.421 20.357 17.714 1.00 0.00 H
-HETATM 1536 H HOH A 512 25.309 20.517 18.932 1.00 0.00 H
-HETATM 1537 O HOH A 513 22.483 20.152 3.632 1.00 0.00 O
-HETATM 1538 H HOH A 513 22.688 20.943 4.131 1.00 0.00 H
-HETATM 1539 H HOH A 513 22.505 20.432 2.717 1.00 0.00 H
-HETATM 1540 O HOH A 514 22.566 22.716 5.047 1.00 0.00 O
-HETATM 1541 H HOH A 514 21.825 22.579 5.639 1.00 0.00 H
-HETATM 1542 H HOH A 514 22.298 23.445 4.488 1.00 0.00 H
-HETATM 1543 O HOH A 515 12.885 2.922 19.210 1.00 0.00 O
-HETATM 1544 H HOH A 515 13.557 3.131 18.561 1.00 0.00 H
-HETATM 1545 H HOH A 515 13.340 2.967 20.051 1.00 0.00 H
-HETATM 1546 O HOH A 516 15.117 3.020 17.315 1.00 0.00 O
-HETATM 1547 H HOH A 516 14.881 2.266 16.774 1.00 0.00 H
-HETATM 1548 H HOH A 516 15.947 2.774 17.723 1.00 0.00 H
-HETATM 1549 O HOH A 517 21.608 24.665 12.250 1.00 0.00 O
-HETATM 1550 H HOH A 517 22.017 24.810 11.397 1.00 0.00 H
-HETATM 1551 H HOH A 517 22.233 25.025 12.880 1.00 0.00 H
-HETATM 1552 O HOH A 518 23.184 24.656 9.780 1.00 0.00 O
-HETATM 1553 H HOH A 518 23.023 23.751 9.509 1.00 0.00 H
-HETATM 1554 H HOH A 518 24.125 24.693 9.953 1.00 0.00 H
-HETATM 1555 O HOH A 519 26.128 19.010 23.405 1.00 0.00 O
-HETATM 1556 H HOH A 519 25.683 18.703 24.194 1.00 0.00 H
-HETATM 1557 H HOH A 519 27.053 19.052 23.651 1.00 0.00 H
-HETATM 1558 O HOH A 520 24.833 18.614 26.003 1.00 0.00 O
-HETATM 1559 H HOH A 520 24.173 19.305 25.942 1.00 0.00 H
-HETATM 1560 H HOH A 520 25.395 18.880 26.730 1.00 0.00 H
-HETATM 1561 O HOH A 521 17.489 25.987 28.499 1.00 0.00 O
-HETATM 1562 H HOH A 521 18.185 26.448 28.031 1.00 0.00 H
-HETATM 1563 H HOH A 521 16.872 25.726 27.816 1.00 0.00 H
-HETATM 1564 O HOH A 522 19.243 27.833 27.049 1.00 0.00 O
-HETATM 1565 H HOH A 522 19.218 28.568 27.663 1.00 0.00 H
-HETATM 1566 H HOH A 522 18.833 28.169 26.253 1.00 0.00 H
-HETATM 1567 O HOH A 523 20.277 4.192 20.041 1.00 0.00 O
-HETATM 1568 H HOH A 523 20.534 4.541 20.894 1.00 0.00 H
-HETATM 1569 H HOH A 523 20.707 3.339 19.991 1.00 0.00 H
-HETATM 1570 O HOH A 524 21.566 5.376 22.391 1.00 0.00 O
-HETATM 1571 H HOH A 524 21.810 6.221 22.011 1.00 0.00 H
-HETATM 1572 H HOH A 524 22.399 4.953 22.596 1.00 0.00 H
-HETATM 1573 O HOH A 525 19.781 11.056 28.807 1.00 0.00 O
-HETATM 1574 H HOH A 525 19.185 10.519 28.285 1.00 0.00 H
-HETATM 1575 H HOH A 525 20.347 10.425 29.251 1.00 0.00 H
-HETATM 1576 O HOH A 526 17.691 9.320 27.711 1.00 0.00 O
-HETATM 1577 H HOH A 526 16.925 9.799 28.026 1.00 0.00 H
-HETATM 1578 H HOH A 526 17.651 8.476 28.161 1.00 0.00 H
-HETATM 1579 O HOH A 527 5.016 16.205 4.351 1.00 0.00 O
-HETATM 1580 H HOH A 527 5.673 16.847 4.085 1.00 0.00 H
-HETATM 1581 H HOH A 527 5.516 15.405 4.516 1.00 0.00 H
-HETATM 1582 O HOH A 528 7.011 17.896 3.030 1.00 0.00 O
-HETATM 1583 H HOH A 528 6.494 18.161 2.269 1.00 0.00 H
-HETATM 1584 H HOH A 528 7.733 17.384 2.666 1.00 0.00 H
-HETATM 1585 O HOH A 529 19.131 7.519 26.179 1.00 0.00 O
-HETATM 1586 H HOH A 529 18.278 7.170 26.437 1.00 0.00 H
-HETATM 1587 H HOH A 529 18.978 7.921 25.324 1.00 0.00 H
-HETATM 1588 O HOH A 530 16.642 6.030 26.596 1.00 0.00 O
-HETATM 1589 H HOH A 530 17.019 5.180 26.827 1.00 0.00 H
-HETATM 1590 H HOH A 530 16.174 5.873 25.777 1.00 0.00 H
-HETATM 1591 O HOH A 531 12.834 5.183 9.988 1.00 0.00 O
-HETATM 1592 H HOH A 531 12.161 4.647 10.406 1.00 0.00 H
-HETATM 1593 H HOH A 531 12.545 5.268 9.080 1.00 0.00 H
-HETATM 1594 O HOH A 532 11.034 3.104 11.000 1.00 0.00 O
-HETATM 1595 H HOH A 532 11.688 2.526 11.393 1.00 0.00 H
-HETATM 1596 H HOH A 532 10.648 2.587 10.293 1.00 0.00 H
-HETATM 1597 O HOH A 533 5.480 5.343 15.151 1.00 0.00 O
-HETATM 1598 H HOH A 533 5.816 4.527 15.522 1.00 0.00 H
-HETATM 1599 H HOH A 533 4.719 5.557 15.692 1.00 0.00 H
-HETATM 1600 O HOH A 534 6.070 2.603 16.004 1.00 0.00 O
-HETATM 1601 H HOH A 534 6.125 2.192 15.141 1.00 0.00 H
-HETATM 1602 H HOH A 534 5.318 2.186 16.423 1.00 0.00 H
-HETATM 1603 O HOH A 535 16.222 9.792 9.346 1.00 0.00 O
-HETATM 1604 H HOH A 535 15.420 9.402 8.998 1.00 0.00 H
-HETATM 1605 H HOH A 535 16.249 9.510 10.260 1.00 0.00 H
-HETATM 1606 O HOH A 536 13.505 9.026 8.562 1.00 0.00 O
-HETATM 1607 H HOH A 536 13.227 9.856 8.174 1.00 0.00 H
-HETATM 1608 H HOH A 536 12.931 8.908 9.319 1.00 0.00 H
-HETATM 1609 O HOH A 537 4.339 3.990 13.311 1.00 0.00 O
-HETATM 1610 H HOH A 537 3.550 3.456 13.397 1.00 0.00 H
-HETATM 1611 H HOH A 537 5.027 3.471 13.728 1.00 0.00 H
-HETATM 1612 O HOH A 538 1.923 2.542 14.119 1.00 0.00 O
-HETATM 1613 H HOH A 538 1.415 3.283 14.450 1.00 0.00 H
-HETATM 1614 H HOH A 538 2.074 1.990 14.886 1.00 0.00 H
-HETATM 1615 O HOH A 539 29.552 2.439 28.550 1.00 0.00 O
-HETATM 1616 H HOH A 539 28.733 2.734 28.948 1.00 0.00 H
-HETATM 1617 H HOH A 539 29.459 2.654 27.622 1.00 0.00 H
-HETATM 1618 O HOH A 540 26.826 2.860 29.539 1.00 0.00 O
-HETATM 1619 H HOH A 540 26.710 2.028 30.000 1.00 0.00 H
-HETATM 1620 H HOH A 540 26.180 2.837 28.834 1.00 0.00 H
-HETATM 1621 O HOH A 541 14.541 27.600 15.786 1.00 0.00 O
-HETATM 1622 H HOH A 541 13.777 28.025 15.396 1.00 0.00 H
-HETATM 1623 H HOH A 541 14.388 27.649 16.729 1.00 0.00 H
-HETATM 1624 O HOH A 542 12.466 29.390 14.748 1.00 0.00 O
-HETATM 1625 H HOH A 542 13.014 29.930 14.178 1.00 0.00 H
-HETATM 1626 H HOH A 542 12.144 29.994 15.417 1.00 0.00 H
-HETATM 1627 O HOH A 543 18.032 28.666 13.242 1.00 0.00 O
-HETATM 1628 H HOH A 543 17.570 27.828 13.230 1.00 0.00 H
-HETATM 1629 H HOH A 543 18.551 28.667 12.437 1.00 0.00 H
-HETATM 1630 O HOH A 544 17.125 25.883 13.370 1.00 0.00 O
-HETATM 1631 H HOH A 544 17.377 25.691 14.274 1.00 0.00 H
-HETATM 1632 H HOH A 544 17.669 25.303 12.838 1.00 0.00 H
-HETATM 1633 O HOH A 545 19.405 8.955 0.350 1.00 0.00 O
-HETATM 1634 H HOH A 545 18.538 8.697 0.661 1.00 0.00 H
-HETATM 1635 H HOH A 545 19.456 9.894 0.532 1.00 0.00 H
-HETATM 1636 O HOH A 546 16.565 8.407 0.814 1.00 0.00 O
-HETATM 1637 H HOH A 546 16.373 7.939 -0.000 1.00 0.00 H
-HETATM 1638 H HOH A 546 16.032 9.201 0.768 1.00 0.00 H
-HETATM 1639 O HOH A 547 4.960 19.274 21.490 1.00 0.00 O
-HETATM 1640 H HOH A 547 4.607 18.754 20.769 1.00 0.00 H
-HETATM 1641 H HOH A 547 4.188 19.634 21.928 1.00 0.00 H
-HETATM 1642 O HOH A 548 3.802 18.192 19.026 1.00 0.00 O
-HETATM 1643 H HOH A 548 4.403 18.591 18.396 1.00 0.00 H
-HETATM 1644 H HOH A 548 2.951 18.581 18.827 1.00 0.00 H
-HETATM 1645 O HOH A 549 10.578 29.122 26.330 1.00 0.00 O
-HETATM 1646 H HOH A 549 10.013 28.370 26.505 1.00 0.00 H
-HETATM 1647 H HOH A 549 10.913 28.970 25.446 1.00 0.00 H
-HETATM 1648 O HOH A 550 9.343 26.540 26.955 1.00 0.00 O
-HETATM 1649 H HOH A 550 9.748 26.389 27.810 1.00 0.00 H
-HETATM 1650 H HOH A 550 9.678 25.837 26.401 1.00 0.00 H
-HETATM 1651 O HOH A 551 25.595 25.278 29.408 1.00 0.00 O
-HETATM 1652 H HOH A 551 25.300 25.545 28.538 1.00 0.00 H
-HETATM 1653 H HOH A 551 24.970 25.684 30.009 1.00 0.00 H
-HETATM 1654 O HOH A 552 24.940 26.600 26.877 1.00 0.00 O
-HETATM 1655 H HOH A 552 25.823 26.868 26.621 1.00 0.00 H
-HETATM 1656 H HOH A 552 24.467 27.421 27.011 1.00 0.00 H
-HETATM 1657 O HOH A 553 20.360 15.761 19.335 1.00 0.00 O
-HETATM 1658 H HOH A 553 19.586 15.198 19.339 1.00 0.00 H
-HETATM 1659 H HOH A 553 20.273 16.295 20.124 1.00 0.00 H
-HETATM 1660 O HOH A 554 17.716 14.510 19.163 1.00 0.00 O
-HETATM 1661 H HOH A 554 17.520 14.713 18.248 1.00 0.00 H
-HETATM 1662 H HOH A 554 17.058 14.989 19.665 1.00 0.00 H
-HETATM 1663 O HOH A 555 8.151 15.666 7.622 1.00 0.00 O
-HETATM 1664 H HOH A 555 7.438 16.305 7.634 1.00 0.00 H
-HETATM 1665 H HOH A 555 7.711 14.816 7.610 1.00 0.00 H
-HETATM 1666 O HOH A 556 5.929 17.482 8.215 1.00 0.00 O
-HETATM 1667 H HOH A 556 6.328 17.967 8.938 1.00 0.00 H
-HETATM 1668 H HOH A 556 5.186 17.025 8.611 1.00 0.00 H
-HETATM 1669 O HOH A 557 29.564 0.850 6.168 1.00 0.00 O
-HETATM 1670 H HOH A 557 28.836 1.194 6.685 1.00 0.00 H
-HETATM 1671 H HOH A 557 29.945 1.621 5.746 1.00 0.00 H
-HETATM 1672 O HOH A 558 27.062 1.930 7.245 1.00 0.00 O
-HETATM 1673 H HOH A 558 26.461 1.266 6.905 1.00 0.00 H
-HETATM 1674 H HOH A 558 26.802 2.740 6.807 1.00 0.00 H
-HETATM 1675 O HOH A 559 28.605 29.840 25.135 1.00 0.00 O
-HETATM 1676 H HOH A 559 28.169 29.015 25.347 1.00 0.00 H
-HETATM 1677 H HOH A 559 28.543 29.907 24.182 1.00 0.00 H
-HETATM 1678 O HOH A 560 27.759 27.074 25.599 1.00 0.00 O
-HETATM 1679 H HOH A 560 28.485 26.793 26.158 1.00 0.00 H
-HETATM 1680 H HOH A 560 27.838 26.536 24.812 1.00 0.00 H
-HETATM 1681 O HOH A 561 10.082 10.888 4.195 1.00 0.00 O
-HETATM 1682 H HOH A 561 10.712 11.219 4.835 1.00 0.00 H
-HETATM 1683 H HOH A 561 10.514 11.008 3.349 1.00 0.00 H
-HETATM 1684 O HOH A 562 11.826 12.421 5.982 1.00 0.00 O
-HETATM 1685 H HOH A 562 11.156 12.947 6.420 1.00 0.00 H
-HETATM 1686 H HOH A 562 12.360 13.056 5.506 1.00 0.00 H
-HETATM 1687 O HOH A 563 6.391 26.908 11.949 1.00 0.00 O
-HETATM 1688 H HOH A 563 5.701 26.778 11.298 1.00 0.00 H
-HETATM 1689 H HOH A 563 5.925 26.981 12.781 1.00 0.00 H
-HETATM 1690 O HOH A 564 4.196 27.078 10.016 1.00 0.00 O
-HETATM 1691 H HOH A 564 4.559 27.755 9.444 1.00 0.00 H
-HETATM 1692 H HOH A 564 3.413 27.477 10.394 1.00 0.00 H
-HETATM 1693 O HOH A 565 13.734 25.683 19.308 1.00 0.00 O
-HETATM 1694 H HOH A 565 14.393 26.223 18.874 1.00 0.00 H
-HETATM 1695 H HOH A 565 14.081 25.540 20.189 1.00 0.00 H
-HETATM 1696 O HOH A 566 16.045 26.843 17.931 1.00 0.00 O
-HETATM 1697 H HOH A 566 16.052 26.276 17.159 1.00 0.00 H
-HETATM 1698 H HOH A 566 16.867 26.649 18.381 1.00 0.00 H
-HETATM 1699 O HOH A 567 14.852 26.745 13.127 1.00 0.00 O
-HETATM 1700 H HOH A 567 14.969 26.051 13.776 1.00 0.00 H
-HETATM 1701 H HOH A 567 14.981 27.557 13.617 1.00 0.00 H
-HETATM 1702 O HOH A 568 14.679 24.766 15.280 1.00 0.00 O
-HETATM 1703 H HOH A 568 13.874 24.325 15.007 1.00 0.00 H
-HETATM 1704 H HOH A 568 14.461 25.167 16.121 1.00 0.00 H
-HETATM 1705 O HOH A 569 8.715 1.641 7.234 1.00 0.00 O
-HETATM 1706 H HOH A 569 9.389 0.968 7.331 1.00 0.00 H
-HETATM 1707 H HOH A 569 8.580 1.711 6.289 1.00 0.00 H
-HETATM 1708 O HOH A 570 11.139 -0.000 7.373 1.00 0.00 O
-HETATM 1709 H HOH A 570 11.649 0.546 7.971 1.00 0.00 H
-HETATM 1710 H HOH A 570 11.631 0.015 6.552 1.00 0.00 H
-HETATM 1711 O HOH A 571 19.616 10.994 15.669 1.00 0.00 O
-HETATM 1712 H HOH A 571 19.203 11.696 16.171 1.00 0.00 H
-HETATM 1713 H HOH A 571 19.866 11.404 14.841 1.00 0.00 H
-HETATM 1714 O HOH A 572 17.900 13.047 16.863 1.00 0.00 O
-HETATM 1715 H HOH A 572 17.148 12.497 17.083 1.00 0.00 H
-HETATM 1716 H HOH A 572 17.567 13.662 16.209 1.00 0.00 H
-HETATM 1717 O HOH A 573 16.420 8.996 15.699 1.00 0.00 O
-HETATM 1718 H HOH A 573 15.715 9.057 16.343 1.00 0.00 H
-HETATM 1719 H HOH A 573 16.202 9.655 15.040 1.00 0.00 H
-HETATM 1720 O HOH A 574 13.956 8.887 17.280 1.00 0.00 O
-HETATM 1721 H HOH A 574 13.826 7.938 17.280 1.00 0.00 H
-HETATM 1722 H HOH A 574 13.205 9.234 16.800 1.00 0.00 H
-HETATM 1723 O HOH A 575 26.717 12.637 20.231 1.00 0.00 O
-HETATM 1724 H HOH A 575 27.466 12.929 19.713 1.00 0.00 H
-HETATM 1725 H HOH A 575 26.880 12.985 21.108 1.00 0.00 H
-HETATM 1726 O HOH A 576 29.291 13.104 18.911 1.00 0.00 O
-HETATM 1727 H HOH A 576 29.431 12.227 18.553 1.00 0.00 H
-HETATM 1728 H HOH A 576 30.001 13.225 19.541 1.00 0.00 H
-HETATM 1729 O HOH A 577 7.750 24.754 27.173 1.00 0.00 O
-HETATM 1730 H HOH A 577 7.528 25.380 26.483 1.00 0.00 H
-HETATM 1731 H HOH A 577 7.289 25.076 27.947 1.00 0.00 H
-HETATM 1732 O HOH A 578 7.459 27.007 25.322 1.00 0.00 O
-HETATM 1733 H HOH A 578 8.376 27.098 25.060 1.00 0.00 H
-HETATM 1734 H HOH A 578 7.256 27.823 25.777 1.00 0.00 H
-HETATM 1735 O HOH A 579 2.514 7.721 21.694 1.00 0.00 O
-HETATM 1736 H HOH A 579 2.110 8.475 21.266 1.00 0.00 H
-HETATM 1737 H HOH A 579 2.582 7.061 21.004 1.00 0.00 H
-HETATM 1738 O HOH A 580 0.788 9.727 20.438 1.00 0.00 O
-HETATM 1739 H HOH A 580 0.148 9.829 21.143 1.00 0.00 H
-HETATM 1740 H HOH A 580 0.288 9.365 19.707 1.00 0.00 H
-HETATM 1741 O HOH A 581 19.374 8.623 14.961 1.00 0.00 O
-HETATM 1742 H HOH A 581 19.014 8.234 15.758 1.00 0.00 H
-HETATM 1743 H HOH A 581 18.927 8.168 14.248 1.00 0.00 H
-HETATM 1744 O HOH A 582 18.625 6.993 17.277 1.00 0.00 O
-HETATM 1745 H HOH A 582 19.497 6.862 17.650 1.00 0.00 H
-HETATM 1746 H HOH A 582 18.336 6.116 17.023 1.00 0.00 H
-HETATM 1747 O HOH A 583 13.798 29.714 26.324 1.00 0.00 O
-HETATM 1748 H HOH A 583 13.412 29.457 27.161 1.00 0.00 H
-HETATM 1749 H HOH A 583 14.684 30.002 26.546 1.00 0.00 H
-HETATM 1750 O HOH A 584 12.558 29.495 28.970 1.00 0.00 O
-HETATM 1751 H HOH A 584 11.763 30.001 28.799 1.00 0.00 H
-HETATM 1752 H HOH A 584 13.029 30.000 29.631 1.00 0.00 H
-HETATM 1753 O HOH A 585 21.729 14.853 8.724 1.00 0.00 O
-HETATM 1754 H HOH A 585 22.134 14.545 7.914 1.00 0.00 H
-HETATM 1755 H HOH A 585 22.286 14.503 9.419 1.00 0.00 H
-HETATM 1756 O HOH A 586 22.720 13.408 6.376 1.00 0.00 O
-HETATM 1757 H HOH A 586 21.882 13.096 6.034 1.00 0.00 H
-HETATM 1758 H HOH A 586 23.194 12.613 6.616 1.00 0.00 H
-HETATM 1759 O HOH A 587 20.887 16.799 17.469 1.00 0.00 O
-HETATM 1760 H HOH A 587 20.731 17.124 16.582 1.00 0.00 H
-HETATM 1761 H HOH A 587 21.758 17.125 17.694 1.00 0.00 H
-HETATM 1762 O HOH A 588 20.760 17.366 14.597 1.00 0.00 O
-HETATM 1763 H HOH A 588 20.478 16.503 14.292 1.00 0.00 H
-HETATM 1764 H HOH A 588 21.629 17.485 14.214 1.00 0.00 H
-HETATM 1765 O HOH A 589 17.969 24.961 25.033 1.00 0.00 O
-HETATM 1766 H HOH A 589 17.454 24.473 24.390 1.00 0.00 H
-HETATM 1767 H HOH A 589 17.373 25.087 25.771 1.00 0.00 H
-HETATM 1768 O HOH A 590 16.150 23.979 22.956 1.00 0.00 O
-HETATM 1769 H HOH A 590 16.402 24.574 22.250 1.00 0.00 H
-HETATM 1770 H HOH A 590 15.237 24.198 23.144 1.00 0.00 H
-HETATM 1771 O HOH A 591 22.588 21.581 0.476 1.00 0.00 O
-HETATM 1772 H HOH A 591 23.418 21.335 0.884 1.00 0.00 H
-HETATM 1773 H HOH A 591 22.784 22.387 -0.001 1.00 0.00 H
-HETATM 1774 O HOH A 592 25.002 21.244 2.103 1.00 0.00 O
-HETATM 1775 H HOH A 592 24.585 21.110 2.954 1.00 0.00 H
-HETATM 1776 H HOH A 592 25.489 22.062 2.198 1.00 0.00 H
-HETATM 1777 O HOH A 593 28.331 29.731 4.100 1.00 0.00 O
-HETATM 1778 H HOH A 593 28.477 28.926 3.603 1.00 0.00 H
-HETATM 1779 H HOH A 593 27.585 29.533 4.666 1.00 0.00 H
-HETATM 1780 O HOH A 594 28.324 27.442 2.272 1.00 0.00 O
-HETATM 1781 H HOH A 594 28.367 27.919 1.443 1.00 0.00 H
-HETATM 1782 H HOH A 594 27.476 26.999 2.252 1.00 0.00 H
-HETATM 1783 O HOH A 595 4.388 8.859 12.020 1.00 0.00 O
-HETATM 1784 H HOH A 595 4.285 7.920 12.175 1.00 0.00 H
-HETATM 1785 H HOH A 595 4.435 8.941 11.067 1.00 0.00 H
-HETATM 1786 O HOH A 596 4.639 5.963 12.385 1.00 0.00 O
-HETATM 1787 H HOH A 596 5.333 5.960 13.044 1.00 0.00 H
-HETATM 1788 H HOH A 596 5.028 5.536 11.622 1.00 0.00 H
-HETATM 1789 O HOH A 597 1.522 20.651 15.121 1.00 0.00 O
-HETATM 1790 H HOH A 597 1.699 21.199 14.357 1.00 0.00 H
-HETATM 1791 H HOH A 597 0.679 20.962 15.451 1.00 0.00 H
-HETATM 1792 O HOH A 598 2.209 22.728 13.173 1.00 0.00 O
-HETATM 1793 H HOH A 598 3.101 22.914 13.468 1.00 0.00 H
-HETATM 1794 H HOH A 598 1.728 23.540 13.328 1.00 0.00 H
-HETATM 1795 O HOH A 599 -0.000 11.291 17.706 1.00 0.00 O
-HETATM 1796 H HOH A 599 0.577 11.234 18.467 1.00 0.00 H
-HETATM 1797 H HOH A 599 0.127 10.462 17.245 1.00 0.00 H
-HETATM 1798 O HOH A 600 2.186 11.235 19.656 1.00 0.00 O
-HETATM 1799 H HOH A 600 2.552 12.104 19.487 1.00 0.00 H
-HETATM 1800 H HOH A 600 2.879 10.627 19.402 1.00 0.00 H
-HETATM 1801 O HOH A 601 1.219 29.844 12.900 1.00 0.00 O
-HETATM 1802 H HOH A 601 1.115 29.783 11.950 1.00 0.00 H
-HETATM 1803 H HOH A 601 2.130 29.596 13.058 1.00 0.00 H
-HETATM 1804 O HOH A 602 0.873 29.090 10.090 1.00 0.00 O
-HETATM 1805 H HOH A 602 0.065 28.583 10.175 1.00 0.00 H
-HETATM 1806 H HOH A 602 1.529 28.455 9.804 1.00 0.00 H
-HETATM 1807 O HOH A 603 21.231 10.420 11.823 1.00 0.00 O
-HETATM 1808 H HOH A 603 21.643 11.058 12.404 1.00 0.00 H
-HETATM 1809 H HOH A 603 20.505 10.895 11.419 1.00 0.00 H
-HETATM 1810 O HOH A 604 22.046 12.262 13.951 1.00 0.00 O
-HETATM 1811 H HOH A 604 22.056 11.642 14.681 1.00 0.00 H
-HETATM 1812 H HOH A 604 21.354 12.885 14.172 1.00 0.00 H
-HETATM 1813 O HOH A 605 0.263 20.719 11.915 1.00 0.00 O
-HETATM 1814 H HOH A 605 0.007 21.406 11.301 1.00 0.00 H
-HETATM 1815 H HOH A 605 0.131 21.108 12.780 1.00 0.00 H
-HETATM 1816 O HOH A 606 -0.006 23.050 10.161 1.00 0.00 O
-HETATM 1817 H HOH A 606 0.792 22.935 9.644 1.00 0.00 H
-HETATM 1818 H HOH A 606 0.126 23.872 10.631 1.00 0.00 H
-HETATM 1819 O HOH A 607 18.401 26.846 0.488 1.00 0.00 O
-HETATM 1820 H HOH A 607 17.624 27.200 0.920 1.00 0.00 H
-HETATM 1821 H HOH A 607 19.109 26.982 1.118 1.00 0.00 H
-HETATM 1822 O HOH A 608 16.241 28.441 1.661 1.00 0.00 O
-HETATM 1823 H HOH A 608 15.994 28.943 0.884 1.00 0.00 H
-HETATM 1824 H HOH A 608 16.570 29.095 2.278 1.00 0.00 H
-HETATM 1825 O HOH A 609 2.918 21.393 6.664 1.00 0.00 O
-HETATM 1826 H HOH A 609 2.925 21.032 5.778 1.00 0.00 H
-HETATM 1827 H HOH A 609 2.146 21.007 7.077 1.00 0.00 H
-HETATM 1828 O HOH A 610 2.522 20.673 3.852 1.00 0.00 O
-HETATM 1829 H HOH A 610 2.653 21.538 3.462 1.00 0.00 H
-HETATM 1830 H HOH A 610 1.610 20.457 3.661 1.00 0.00 H
-HETATM 1831 O HOH A 611 4.239 6.274 24.152 1.00 0.00 O
-HETATM 1832 H HOH A 611 4.456 7.069 24.639 1.00 0.00 H
-HETATM 1833 H HOH A 611 5.086 5.890 23.928 1.00 0.00 H
-HETATM 1834 O HOH A 612 5.018 8.927 25.121 1.00 0.00 O
-HETATM 1835 H HOH A 612 4.416 9.455 24.596 1.00 0.00 H
-HETATM 1836 H HOH A 612 5.890 9.180 24.816 1.00 0.00 H
-HETATM 1837 O HOH A 613 4.051 18.452 2.267 1.00 0.00 O
-HETATM 1838 H HOH A 613 4.562 19.075 1.750 1.00 0.00 H
-HETATM 1839 H HOH A 613 4.421 18.509 3.147 1.00 0.00 H
-HETATM 1840 O HOH A 614 6.018 19.945 0.691 1.00 0.00 O
-HETATM 1841 H HOH A 614 6.163 19.303 -0.005 1.00 0.00 H
-HETATM 1842 H HOH A 614 6.862 20.012 1.138 1.00 0.00 H
-HETATM 1843 O HOH A 615 25.476 19.620 8.638 1.00 0.00 O
-HETATM 1844 H HOH A 615 26.101 20.150 9.133 1.00 0.00 H
-HETATM 1845 H HOH A 615 25.827 19.605 7.747 1.00 0.00 H
-HETATM 1846 O HOH A 616 27.127 21.687 9.897 1.00 0.00 O
-HETATM 1847 H HOH A 616 26.428 22.216 10.282 1.00 0.00 H
-HETATM 1848 H HOH A 616 27.539 22.262 9.252 1.00 0.00 H
-HETATM 1849 O HOH A 617 18.628 13.429 23.998 1.00 0.00 O
-HETATM 1850 H HOH A 617 18.938 14.321 24.153 1.00 0.00 H
-HETATM 1851 H HOH A 617 18.164 13.477 23.162 1.00 0.00 H
-HETATM 1852 O HOH A 618 19.030 16.263 24.624 1.00 0.00 O
-HETATM 1853 H HOH A 618 18.697 16.261 25.523 1.00 0.00 H
-HETATM 1854 H HOH A 618 18.425 16.827 24.143 1.00 0.00 H
-HETATM 1855 O HOH A 619 9.844 17.725 25.897 1.00 0.00 O
-HETATM 1856 H HOH A 619 10.477 18.367 25.575 1.00 0.00 H
-HETATM 1857 H HOH A 619 9.822 17.865 26.844 1.00 0.00 H
-HETATM 1858 O HOH A 620 12.173 19.320 25.111 1.00 0.00 O
-HETATM 1859 H HOH A 620 12.631 18.660 24.590 1.00 0.00 H
-HETATM 1860 H HOH A 620 12.757 19.493 25.849 1.00 0.00 H
-HETATM 1861 O HOH A 621 28.412 4.587 27.523 1.00 0.00 O
-HETATM 1862 H HOH A 621 27.954 4.760 28.345 1.00 0.00 H
-HETATM 1863 H HOH A 621 29.160 5.185 27.533 1.00 0.00 H
-HETATM 1864 O HOH A 622 26.815 5.584 29.767 1.00 0.00 O
-HETATM 1865 H HOH A 622 25.969 5.652 29.324 1.00 0.00 H
-HETATM 1866 H HOH A 622 27.040 6.485 29.998 1.00 0.00 H
-HETATM 1867 O HOH A 623 13.789 23.279 17.622 1.00 0.00 O
-HETATM 1868 H HOH A 623 13.106 22.967 18.215 1.00 0.00 H
-HETATM 1869 H HOH A 623 13.493 23.011 16.752 1.00 0.00 H
-HETATM 1870 O HOH A 624 11.945 21.811 19.362 1.00 0.00 O
-HETATM 1871 H HOH A 624 12.588 21.413 19.951 1.00 0.00 H
-HETATM 1872 H HOH A 624 11.539 21.070 18.913 1.00 0.00 H
-HETATM 1873 O HOH A 625 10.868 1.204 27.721 1.00 0.00 O
-HETATM 1874 H HOH A 625 11.665 1.592 27.362 1.00 0.00 H
-HETATM 1875 H HOH A 625 10.193 1.868 27.580 1.00 0.00 H
-HETATM 1876 O HOH A 626 13.314 2.697 27.112 1.00 0.00 O
-HETATM 1877 H HOH A 626 13.817 2.477 27.897 1.00 0.00 H
-HETATM 1878 H HOH A 626 13.181 3.643 27.167 1.00 0.00 H
-HETATM 1879 O HOH A 627 11.204 30.009 11.230 1.00 0.00 O
-HETATM 1880 H HOH A 627 11.869 29.471 11.658 1.00 0.00 H
-HETATM 1881 H HOH A 627 11.034 29.566 10.398 1.00 0.00 H
-HETATM 1882 O HOH A 628 13.604 28.618 12.173 1.00 0.00 O
-HETATM 1883 H HOH A 628 14.145 29.383 12.373 1.00 0.00 H
-HETATM 1884 H HOH A 628 14.065 28.174 11.462 1.00 0.00 H
-HETATM 1885 O HOH A 629 9.084 8.920 0.462 1.00 0.00 O
-HETATM 1886 H HOH A 629 10.037 8.919 0.544 1.00 0.00 H
-HETATM 1887 H HOH A 629 8.877 9.768 0.069 1.00 0.00 H
-HETATM 1888 O HOH A 630 11.908 9.257 1.167 1.00 0.00 O
-HETATM 1889 H HOH A 630 11.865 8.966 2.078 1.00 0.00 H
-HETATM 1890 H HOH A 630 12.158 10.179 1.219 1.00 0.00 H
-HETATM 1891 O HOH A 631 16.146 5.514 7.896 1.00 0.00 O
-HETATM 1892 H HOH A 631 15.472 5.092 7.365 1.00 0.00 H
-HETATM 1893 H HOH A 631 15.970 5.218 8.789 1.00 0.00 H
-HETATM 1894 O HOH A 632 13.736 4.631 6.484 1.00 0.00 O
-HETATM 1895 H HOH A 632 13.515 5.453 6.046 1.00 0.00 H
-HETATM 1896 H HOH A 632 13.006 4.472 7.082 1.00 0.00 H
-HETATM 1897 O HOH A 633 15.046 11.109 0.294 1.00 0.00 O
-HETATM 1898 H HOH A 633 14.584 11.767 0.813 1.00 0.00 H
-HETATM 1899 H HOH A 633 14.361 10.509 -0.000 1.00 0.00 H
-HETATM 1900 O HOH A 634 13.624 12.715 2.289 1.00 0.00 O
-HETATM 1901 H HOH A 634 14.251 12.640 3.009 1.00 0.00 H
-HETATM 1902 H HOH A 634 12.835 12.277 2.608 1.00 0.00 H
-HETATM 1903 O HOH A 635 10.418 14.009 28.380 1.00 0.00 O
-HETATM 1904 H HOH A 635 10.799 14.289 27.548 1.00 0.00 H
-HETATM 1905 H HOH A 635 9.888 14.754 28.664 1.00 0.00 H
-HETATM 1906 O HOH A 636 11.946 15.168 26.165 1.00 0.00 O
-HETATM 1907 H HOH A 636 12.822 14.889 26.432 1.00 0.00 H
-HETATM 1908 H HOH A 636 11.973 16.124 26.207 1.00 0.00 H
-HETATM 1909 O HOH A 637 12.589 26.143 12.286 1.00 0.00 O
-HETATM 1910 H HOH A 637 12.235 26.960 12.636 1.00 0.00 H
-HETATM 1911 H HOH A 637 12.309 26.132 11.370 1.00 0.00 H
-HETATM 1912 O HOH A 638 11.006 28.357 13.371 1.00 0.00 O
-HETATM 1913 H HOH A 638 10.644 27.912 14.139 1.00 0.00 H
-HETATM 1914 H HOH A 638 10.246 28.557 12.826 1.00 0.00 H
-HETATM 1915 O HOH A 639 20.970 25.897 7.532 1.00 0.00 O
-HETATM 1916 H HOH A 639 21.179 26.582 8.167 1.00 0.00 H
-HETATM 1917 H HOH A 639 20.022 25.959 7.414 1.00 0.00 H
-HETATM 1918 O HOH A 640 21.399 27.622 9.862 1.00 0.00 O
-HETATM 1919 H HOH A 640 21.922 27.020 10.393 1.00 0.00 H
-HETATM 1920 H HOH A 640 20.596 27.753 10.365 1.00 0.00 H
-HETATM 1921 O HOH A 641 16.368 10.385 4.719 1.00 0.00 O
-HETATM 1922 H HOH A 641 16.390 9.542 5.171 1.00 0.00 H
-HETATM 1923 H HOH A 641 15.636 10.311 4.106 1.00 0.00 H
-HETATM 1924 O HOH A 642 16.683 7.614 5.618 1.00 0.00 O
-HETATM 1925 H HOH A 642 17.626 7.523 5.476 1.00 0.00 H
-HETATM 1926 H HOH A 642 16.285 6.991 5.010 1.00 0.00 H
-HETATM 1927 O HOH A 643 4.670 15.590 17.241 1.00 0.00 O
-HETATM 1928 H HOH A 643 4.424 14.671 17.143 1.00 0.00 H
-HETATM 1929 H HOH A 643 5.142 15.801 16.436 1.00 0.00 H
-HETATM 1930 O HOH A 644 4.469 12.672 17.075 1.00 0.00 O
-HETATM 1931 H HOH A 644 4.793 12.455 17.949 1.00 0.00 H
-HETATM 1932 H HOH A 644 5.116 12.300 16.477 1.00 0.00 H
-HETATM 1933 O HOH A 645 17.516 1.060 29.999 1.00 0.00 O
-HETATM 1934 H HOH A 645 18.324 1.474 29.698 1.00 0.00 H
-HETATM 1935 H HOH A 645 17.017 0.894 29.199 1.00 0.00 H
-HETATM 1936 O HOH A 646 19.662 2.797 29.020 1.00 0.00 O
-HETATM 1937 H HOH A 646 19.586 3.499 29.667 1.00 0.00 H
-HETATM 1938 H HOH A 646 19.439 3.211 28.186 1.00 0.00 H
-HETATM 1939 O HOH A 647 0.453 9.772 26.263 1.00 0.00 O
-HETATM 1940 H HOH A 647 1.406 9.680 26.236 1.00 0.00 H
-HETATM 1941 H HOH A 647 0.267 10.092 27.146 1.00 0.00 H
-HETATM 1942 O HOH A 648 3.274 9.009 26.483 1.00 0.00 O
-HETATM 1943 H HOH A 648 3.214 8.127 26.115 1.00 0.00 H
-HETATM 1944 H HOH A 648 3.540 8.874 27.392 1.00 0.00 H
-HETATM 1945 O HOH A 649 13.207 21.705 8.727 1.00 0.00 O
-HETATM 1946 H HOH A 649 12.454 21.328 9.182 1.00 0.00 H
-HETATM 1947 H HOH A 649 12.912 21.810 7.822 1.00 0.00 H
-HETATM 1948 O HOH A 650 11.067 20.060 9.867 1.00 0.00 O
-HETATM 1949 H HOH A 650 11.607 19.380 10.271 1.00 0.00 H
-HETATM 1950 H HOH A 650 10.566 19.601 9.192 1.00 0.00 H
-HETATM 1951 O HOH A 651 18.296 20.137 5.429 1.00 0.00 O
-HETATM 1952 H HOH A 651 18.233 19.897 6.353 1.00 0.00 H
-HETATM 1953 H HOH A 651 18.329 21.094 5.431 1.00 0.00 H
-HETATM 1954 O HOH A 652 17.550 19.556 8.202 1.00 0.00 O
-HETATM 1955 H HOH A 652 16.822 18.958 8.033 1.00 0.00 H
-HETATM 1956 H HOH A 652 17.149 20.311 8.633 1.00 0.00 H
-HETATM 1957 O HOH A 653 29.233 7.251 24.103 1.00 0.00 O
-HETATM 1958 H HOH A 653 29.140 6.783 23.273 1.00 0.00 H
-HETATM 1959 H HOH A 653 28.448 7.794 24.163 1.00 0.00 H
-HETATM 1960 O HOH A 654 29.108 6.320 21.328 1.00 0.00 O
-HETATM 1961 H HOH A 654 30.001 6.542 21.060 1.00 0.00 H
-HETATM 1962 H HOH A 654 28.548 6.875 20.786 1.00 0.00 H
-HETATM 1963 O HOH A 655 8.263 19.706 24.387 1.00 0.00 O
-HETATM 1964 H HOH A 655 7.781 18.884 24.472 1.00 0.00 H
-HETATM 1965 H HOH A 655 9.152 19.441 24.149 1.00 0.00 H
-HETATM 1966 O HOH A 656 7.059 17.148 25.154 1.00 0.00 O
-HETATM 1967 H HOH A 656 6.692 17.408 25.999 1.00 0.00 H
-HETATM 1968 H HOH A 656 7.719 16.489 25.369 1.00 0.00 H
-HETATM 1969 O HOH A 657 19.949 20.916 22.727 1.00 0.00 O
-HETATM 1970 H HOH A 657 18.993 20.868 22.715 1.00 0.00 H
-HETATM 1971 H HOH A 657 20.180 20.881 23.655 1.00 0.00 H
-HETATM 1972 O HOH A 658 17.050 21.325 22.846 1.00 0.00 O
-HETATM 1973 H HOH A 658 16.985 22.071 22.248 1.00 0.00 H
-HETATM 1974 H HOH A 658 16.770 21.670 23.694 1.00 0.00 H
-HETATM 1975 O HOH A 659 29.300 14.702 22.447 1.00 0.00 O
-HETATM 1976 H HOH A 659 28.729 15.156 21.828 1.00 0.00 H
-HETATM 1977 H HOH A 659 30.002 14.339 21.906 1.00 0.00 H
-HETATM 1978 O HOH A 660 27.365 15.564 20.423 1.00 0.00 O
-HETATM 1979 H HOH A 660 26.581 15.150 20.787 1.00 0.00 H
-HETATM 1980 H HOH A 660 27.502 15.125 19.584 1.00 0.00 H
-HETATM 1981 O HOH A 661 13.214 5.799 16.143 1.00 0.00 O
-HETATM 1982 H HOH A 661 13.782 6.059 15.417 1.00 0.00 H
-HETATM 1983 H HOH A 661 12.562 6.498 16.200 1.00 0.00 H
-HETATM 1984 O HOH A 662 15.179 6.962 14.306 1.00 0.00 O
-HETATM 1985 H HOH A 662 15.970 6.804 14.822 1.00 0.00 H
-HETATM 1986 H HOH A 662 15.107 7.915 14.258 1.00 0.00 H
-HETATM 1987 O HOH A 663 22.403 4.191 7.593 1.00 0.00 O
-HETATM 1988 H HOH A 663 22.742 3.421 7.137 1.00 0.00 H
-HETATM 1989 H HOH A 663 22.715 4.093 8.493 1.00 0.00 H
-HETATM 1990 O HOH A 664 23.009 1.558 6.459 1.00 0.00 O
-HETATM 1991 H HOH A 664 22.154 1.367 6.072 1.00 0.00 H
-HETATM 1992 H HOH A 664 23.121 0.891 7.137 1.00 0.00 H
-HETATM 1993 O HOH A 665 15.414 20.191 17.237 1.00 0.00 O
-HETATM 1994 H HOH A 665 14.613 19.824 16.864 1.00 0.00 H
-HETATM 1995 H HOH A 665 15.261 20.193 18.182 1.00 0.00 H
-HETATM 1996 O HOH A 666 12.742 19.589 16.198 1.00 0.00 O
-HETATM 1997 H HOH A 666 12.674 20.295 15.554 1.00 0.00 H
-HETATM 1998 H HOH A 666 12.055 19.778 16.837 1.00 0.00 H
-HETATM 1999 O HOH A 667 20.864 25.571 17.399 1.00 0.00 O
-HETATM 2000 H HOH A 667 21.025 25.211 18.271 1.00 0.00 H
-HETATM 2001 H HOH A 667 21.536 26.244 17.289 1.00 0.00 H
-HETATM 2002 O HOH A 668 21.099 24.950 20.252 1.00 0.00 O
-HETATM 2003 H HOH A 668 20.175 25.025 20.493 1.00 0.00 H
-HETATM 2004 H HOH A 668 21.533 25.650 20.739 1.00 0.00 H
-HETATM 2005 O HOH A 669 9.580 9.778 17.176 1.00 0.00 O
-HETATM 2006 H HOH A 669 9.059 10.451 17.614 1.00 0.00 H
-HETATM 2007 H HOH A 669 9.581 10.037 16.255 1.00 0.00 H
-HETATM 2008 O HOH A 670 7.537 11.555 18.296 1.00 0.00 O
-HETATM 2009 H HOH A 670 7.013 10.900 18.758 1.00 0.00 H
-HETATM 2010 H HOH A 670 6.952 11.901 17.622 1.00 0.00 H
-HETATM 2011 O HOH A 671 10.360 7.237 3.821 1.00 0.00 O
-HETATM 2012 H HOH A 671 11.253 7.378 3.507 1.00 0.00 H
-HETATM 2013 H HOH A 671 10.402 7.424 4.759 1.00 0.00 H
-HETATM 2014 O HOH A 672 13.198 7.153 3.098 1.00 0.00 O
-HETATM 2015 H HOH A 672 13.202 6.319 2.628 1.00 0.00 H
-HETATM 2016 H HOH A 672 13.759 7.006 3.858 1.00 0.00 H
-HETATM 2017 O HOH A 673 7.281 10.907 11.861 1.00 0.00 O
-HETATM 2018 H HOH A 673 7.680 11.131 12.701 1.00 0.00 H
-HETATM 2019 H HOH A 673 7.156 11.749 11.422 1.00 0.00 H
-HETATM 2020 O HOH A 674 7.967 11.745 14.583 1.00 0.00 O
-HETATM 2021 H HOH A 674 7.348 11.195 15.064 1.00 0.00 H
-HETATM 2022 H HOH A 674 7.686 12.641 14.765 1.00 0.00 H
-HETATM 2023 O HOH A 675 15.191 3.925 26.054 1.00 0.00 O
-HETATM 2024 H HOH A 675 15.192 3.071 25.622 1.00 0.00 H
-HETATM 2025 H HOH A 675 15.371 3.730 26.974 1.00 0.00 H
-HETATM 2026 O HOH A 676 14.672 1.255 24.965 1.00 0.00 O
-HETATM 2027 H HOH A 676 13.845 1.440 24.518 1.00 0.00 H
-HETATM 2028 H HOH A 676 14.442 0.626 25.648 1.00 0.00 H
-HETATM 2029 O HOH A 677 19.183 0.551 9.667 1.00 0.00 O
-HETATM 2030 H HOH A 677 18.548 1.126 10.093 1.00 0.00 H
-HETATM 2031 H HOH A 677 19.439 1.021 8.873 1.00 0.00 H
-HETATM 2032 O HOH A 678 16.875 2.113 10.572 1.00 0.00 O
-HETATM 2033 H HOH A 678 16.255 1.394 10.698 1.00 0.00 H
-HETATM 2034 H HOH A 678 16.497 2.638 9.867 1.00 0.00 H
-HETATM 2035 O HOH A 679 13.369 15.380 19.779 1.00 0.00 O
-HETATM 2036 H HOH A 679 13.635 14.525 20.117 1.00 0.00 H
-HETATM 2037 H HOH A 679 12.413 15.343 19.758 1.00 0.00 H
-HETATM 2038 O HOH A 680 14.065 12.578 20.280 1.00 0.00 O
-HETATM 2039 H HOH A 680 14.709 12.462 19.581 1.00 0.00 H
-HETATM 2040 H HOH A 680 13.351 11.983 20.054 1.00 0.00 H
-HETATM 2041 O HOH A 681 13.454 17.285 18.264 1.00 0.00 O
-HETATM 2042 H HOH A 681 12.600 17.392 17.847 1.00 0.00 H
-HETATM 2043 H HOH A 681 13.999 17.970 17.876 1.00 0.00 H
-HETATM 2044 O HOH A 682 11.088 17.350 16.538 1.00 0.00 O
-HETATM 2045 H HOH A 682 11.027 16.415 16.340 1.00 0.00 H
-HETATM 2046 H HOH A 682 11.268 17.765 15.695 1.00 0.00 H
-HETATM 2047 O HOH A 683 11.058 13.553 2.858 1.00 0.00 O
-HETATM 2048 H HOH A 683 10.691 12.907 2.254 1.00 0.00 H
-HETATM 2049 H HOH A 683 11.109 13.097 3.697 1.00 0.00 H
-HETATM 2050 O HOH A 684 9.435 11.705 1.266 1.00 0.00 O
-HETATM 2051 H HOH A 684 8.789 12.330 0.934 1.00 0.00 H
-HETATM 2052 H HOH A 684 8.931 11.105 1.815 1.00 0.00 H
-HETATM 2053 O HOH A 685 16.110 16.784 13.278 1.00 0.00 O
-HETATM 2054 H HOH A 685 16.843 17.031 12.714 1.00 0.00 H
-HETATM 2055 H HOH A 685 16.186 17.360 14.038 1.00 0.00 H
-HETATM 2056 O HOH A 686 18.669 17.233 11.924 1.00 0.00 O
-HETATM 2057 H HOH A 686 18.932 16.320 11.806 1.00 0.00 H
-HETATM 2058 H HOH A 686 19.328 17.600 12.513 1.00 0.00 H
-HETATM 2059 O HOH A 687 16.094 29.794 11.440 1.00 0.00 O
-HETATM 2060 H HOH A 687 16.033 29.881 10.489 1.00 0.00 H
-HETATM 2061 H HOH A 687 17.003 30.013 11.644 1.00 0.00 H
-HETATM 2062 O HOH A 688 16.175 29.550 8.521 1.00 0.00 O
-HETATM 2063 H HOH A 688 15.743 28.699 8.435 1.00 0.00 H
-HETATM 2064 H HOH A 688 17.063 29.404 8.196 1.00 0.00 H
-HETATM 2065 O HOH A 689 19.637 29.646 15.071 1.00 0.00 O
-HETATM 2066 H HOH A 689 18.837 29.312 15.477 1.00 0.00 H
-HETATM 2067 H HOH A 689 19.336 30.098 14.282 1.00 0.00 H
-HETATM 2068 O HOH A 690 17.219 28.218 15.905 1.00 0.00 O
-HETATM 2069 H HOH A 690 17.598 27.345 16.017 1.00 0.00 H
-HETATM 2070 H HOH A 690 16.593 28.119 15.188 1.00 0.00 H
-HETATM 2071 O HOH A 691 19.259 27.222 20.312 1.00 0.00 O
-HETATM 2072 H HOH A 691 18.716 27.772 19.747 1.00 0.00 H
-HETATM 2073 H HOH A 691 19.577 27.816 20.992 1.00 0.00 H
-HETATM 2074 O HOH A 692 18.102 29.091 18.375 1.00 0.00 O
-HETATM 2075 H HOH A 692 18.530 28.764 17.584 1.00 0.00 H
-HETATM 2076 H HOH A 692 18.441 29.979 18.485 1.00 0.00 H
-HETATM 2077 O HOH A 693 19.978 26.373 12.672 1.00 0.00 O
-HETATM 2078 H HOH A 693 20.675 26.980 12.423 1.00 0.00 H
-HETATM 2079 H HOH A 693 19.547 26.795 13.416 1.00 0.00 H
-HETATM 2080 O HOH A 694 22.387 28.013 12.366 1.00 0.00 O
-HETATM 2081 H HOH A 694 23.037 27.311 12.339 1.00 0.00 H
-HETATM 2082 H HOH A 694 22.592 28.504 13.161 1.00 0.00 H
-HETATM 2083 O HOH A 695 23.194 20.281 13.035 1.00 0.00 O
-HETATM 2084 H HOH A 695 23.849 19.678 12.684 1.00 0.00 H
-HETATM 2085 H HOH A 695 22.433 19.730 13.217 1.00 0.00 H
-HETATM 2086 O HOH A 696 24.888 18.468 11.476 1.00 0.00 O
-HETATM 2087 H HOH A 696 25.076 19.046 10.736 1.00 0.00 H
-HETATM 2088 H HOH A 696 24.388 17.745 11.098 1.00 0.00 H
-HETATM 2089 O HOH A 697 22.394 11.806 27.471 1.00 0.00 O
-HETATM 2090 H HOH A 697 21.727 11.586 26.822 1.00 0.00 H
-HETATM 2091 H HOH A 697 22.068 11.430 28.288 1.00 0.00 H
-HETATM 2092 O HOH A 698 20.055 11.566 25.723 1.00 0.00 O
-HETATM 2093 H HOH A 698 20.026 12.465 25.397 1.00 0.00 H
-HETATM 2094 H HOH A 698 19.244 11.462 26.220 1.00 0.00 H
-HETATM 2095 O HOH A 699 17.924 1.072 12.256 1.00 0.00 O
-HETATM 2096 H HOH A 699 18.161 1.938 12.586 1.00 0.00 H
-HETATM 2097 H HOH A 699 17.083 0.879 12.671 1.00 0.00 H
-HETATM 2098 O HOH A 700 18.798 3.439 13.744 1.00 0.00 O
-HETATM 2099 H HOH A 700 19.683 3.151 13.967 1.00 0.00 H
-HETATM 2100 H HOH A 700 18.350 3.521 14.586 1.00 0.00 H
-HETATM 2101 O HOH A 701 6.654 29.998 23.836 1.00 0.00 O
-HETATM 2102 H HOH A 701 6.834 29.197 23.344 1.00 0.00 H
-HETATM 2103 H HOH A 701 6.868 29.778 24.742 1.00 0.00 H
-HETATM 2104 O HOH A 702 6.705 27.367 22.549 1.00 0.00 O
-HETATM 2105 H HOH A 702 5.859 27.409 22.101 1.00 0.00 H
-HETATM 2106 H HOH A 702 6.608 26.655 23.181 1.00 0.00 H
-HETATM 2107 O HOH A 703 25.674 21.642 0.046 1.00 0.00 O
-HETATM 2108 H HOH A 703 25.908 20.715 -0.002 1.00 0.00 H
-HETATM 2109 H HOH A 703 26.511 22.104 -0.000 1.00 0.00 H
-HETATM 2110 O HOH A 704 26.539 18.872 0.450 1.00 0.00 O
-HETATM 2111 H HOH A 704 25.988 18.657 1.203 1.00 0.00 H
-HETATM 2112 H HOH A 704 27.435 18.820 0.784 1.00 0.00 H
-HETATM 2113 O HOH A 705 15.571 29.682 17.538 1.00 0.00 O
-HETATM 2114 H HOH A 705 14.656 29.557 17.790 1.00 0.00 H
-HETATM 2115 H HOH A 705 15.533 29.949 16.620 1.00 0.00 H
-HETATM 2116 O HOH A 706 12.818 28.802 18.022 1.00 0.00 O
-HETATM 2117 H HOH A 706 13.006 27.950 18.415 1.00 0.00 H
-HETATM 2118 H HOH A 706 12.357 28.598 17.208 1.00 0.00 H
-HETATM 2119 O HOH A 707 20.602 9.274 3.626 1.00 0.00 O
-HETATM 2120 H HOH A 707 20.191 10.020 4.062 1.00 0.00 H
-HETATM 2121 H HOH A 707 20.441 9.422 2.694 1.00 0.00 H
-HETATM 2122 O HOH A 708 18.854 11.275 4.861 1.00 0.00 O
-HETATM 2123 H HOH A 708 18.404 10.707 5.488 1.00 0.00 H
-HETATM 2124 H HOH A 708 18.166 11.569 4.265 1.00 0.00 H
-HETATM 2125 O HOH A 709 3.247 3.543 3.330 1.00 0.00 O
-HETATM 2126 H HOH A 709 3.240 4.058 4.137 1.00 0.00 H
-HETATM 2127 H HOH A 709 3.539 4.156 2.656 1.00 0.00 H
-HETATM 2128 O HOH A 710 2.735 5.335 5.591 1.00 0.00 O
-HETATM 2129 H HOH A 710 1.856 5.033 5.819 1.00 0.00 H
-HETATM 2130 H HOH A 710 2.615 6.242 5.312 1.00 0.00 H
-HETATM 2131 O HOH A 711 26.192 28.444 29.080 1.00 0.00 O
-HETATM 2132 H HOH A 711 25.404 27.920 28.937 1.00 0.00 H
-HETATM 2133 H HOH A 711 26.496 28.191 29.952 1.00 0.00 H
-HETATM 2134 O HOH A 712 23.530 27.225 28.992 1.00 0.00 O
-HETATM 2135 H HOH A 712 23.030 27.990 28.704 1.00 0.00 H
-HETATM 2136 H HOH A 712 23.193 27.032 29.867 1.00 0.00 H
-HETATM 2137 O HOH A 713 8.487 6.772 27.181 1.00 0.00 O
-HETATM 2138 H HOH A 713 9.015 6.155 26.675 1.00 0.00 H
-HETATM 2139 H HOH A 713 8.758 6.632 28.089 1.00 0.00 H
-HETATM 2140 O HOH A 714 9.729 4.491 25.826 1.00 0.00 O
-HETATM 2141 H HOH A 714 8.963 4.171 25.348 1.00 0.00 H
-HETATM 2142 H HOH A 714 9.947 3.785 26.434 1.00 0.00 H
-HETATM 2143 O HOH A 715 3.754 25.215 11.097 1.00 0.00 O
-HETATM 2144 H HOH A 715 3.911 24.966 12.008 1.00 0.00 H
-HETATM 2145 H HOH A 715 4.572 25.006 10.646 1.00 0.00 H
-HETATM 2146 O HOH A 716 4.533 24.945 13.909 1.00 0.00 O
-HETATM 2147 H HOH A 716 4.195 25.780 14.233 1.00 0.00 H
-HETATM 2148 H HOH A 716 5.480 25.004 14.027 1.00 0.00 H
-HETATM 2149 O HOH A 717 2.141 19.473 21.139 1.00 0.00 O
-HETATM 2150 H HOH A 717 2.209 18.519 21.145 1.00 0.00 H
-HETATM 2151 H HOH A 717 1.736 19.679 20.296 1.00 0.00 H
-HETATM 2152 O HOH A 718 2.799 16.637 20.810 1.00 0.00 O
-HETATM 2153 H HOH A 718 3.716 16.669 21.084 1.00 0.00 H
-HETATM 2154 H HOH A 718 2.831 16.379 19.889 1.00 0.00 H
-HETATM 2155 O HOH A 719 29.317 5.764 18.756 1.00 0.00 O
-HETATM 2156 H HOH A 719 29.022 6.643 18.518 1.00 0.00 H
-HETATM 2157 H HOH A 719 30.000 5.558 18.117 1.00 0.00 H
-HETATM 2158 O HOH A 720 28.128 8.182 17.606 1.00 0.00 O
-HETATM 2159 H HOH A 720 27.208 7.921 17.640 1.00 0.00 H
-HETATM 2160 H HOH A 720 28.321 8.270 16.673 1.00 0.00 H
-HETATM 2161 O HOH A 721 0.737 5.094 12.464 1.00 0.00 O
-HETATM 2162 H HOH A 721 0.917 4.564 11.688 1.00 0.00 H
-HETATM 2163 H HOH A 721 0.000 4.656 12.889 1.00 0.00 H
-HETATM 2164 O HOH A 722 0.828 3.735 9.870 1.00 0.00 O
-HETATM 2165 H HOH A 722 0.851 4.507 9.303 1.00 0.00 H
-HETATM 2166 H HOH A 722 -0.000 3.305 9.660 1.00 0.00 H
-HETATM 2167 O HOH A 723 5.864 10.081 27.483 1.00 0.00 O
-HETATM 2168 H HOH A 723 5.967 9.136 27.599 1.00 0.00 H
-HETATM 2169 H HOH A 723 6.050 10.451 28.346 1.00 0.00 H
-HETATM 2170 O HOH A 724 5.659 7.221 28.088 1.00 0.00 O
-HETATM 2171 H HOH A 724 4.824 7.042 27.653 1.00 0.00 H
-HETATM 2172 H HOH A 724 5.488 7.061 29.016 1.00 0.00 H
-HETATM 2173 O HOH A 725 23.927 22.231 11.608 1.00 0.00 O
-HETATM 2174 H HOH A 725 23.020 22.538 11.587 1.00 0.00 H
-HETATM 2175 H HOH A 725 24.367 22.733 10.922 1.00 0.00 H
-HETATM 2176 O HOH A 726 21.109 22.832 11.077 1.00 0.00 O
-HETATM 2177 H HOH A 726 20.768 21.937 11.073 1.00 0.00 H
-HETATM 2178 H HOH A 726 20.970 23.148 10.185 1.00 0.00 H
-HETATM 2179 O HOH A 727 14.533 8.753 30.000 1.00 0.00 O
-HETATM 2180 H HOH A 727 13.649 8.772 29.634 1.00 0.00 H
-HETATM 2181 H HOH A 727 15.108 8.897 29.248 1.00 0.00 H
-HETATM 2182 O HOH A 728 11.951 8.278 28.698 1.00 0.00 O
-HETATM 2183 H HOH A 728 11.708 7.469 29.151 1.00 0.00 H
-HETATM 2184 H HOH A 728 12.052 8.022 27.782 1.00 0.00 H
-HETATM 2185 O HOH A 729 22.201 8.331 9.280 1.00 0.00 O
-HETATM 2186 H HOH A 729 22.498 9.073 8.754 1.00 0.00 H
-HETATM 2187 H HOH A 729 21.728 7.773 8.663 1.00 0.00 H
-HETATM 2188 O HOH A 730 22.564 10.782 7.717 1.00 0.00 O
-HETATM 2189 H HOH A 730 22.240 11.413 8.361 1.00 0.00 H
-HETATM 2190 H HOH A 730 21.946 10.840 6.989 1.00 0.00 H
-HETATM 2191 O HOH A 731 25.079 26.819 24.323 1.00 0.00 O
-HETATM 2192 H HOH A 731 25.151 25.958 24.735 1.00 0.00 H
-HETATM 2193 H HOH A 731 25.475 26.706 23.459 1.00 0.00 H
-HETATM 2194 O HOH A 732 25.851 24.332 25.666 1.00 0.00 O
-HETATM 2195 H HOH A 732 26.209 24.708 26.471 1.00 0.00 H
-HETATM 2196 H HOH A 732 26.598 23.911 25.242 1.00 0.00 H
-HETATM 2197 O HOH A 733 9.796 14.663 5.924 1.00 0.00 O
-HETATM 2198 H HOH A 733 9.212 13.923 6.088 1.00 0.00 H
-HETATM 2199 H HOH A 733 10.104 14.533 5.027 1.00 0.00 H
-HETATM 2200 O HOH A 734 8.498 12.098 6.491 1.00 0.00 O
-HETATM 2201 H HOH A 734 8.920 11.905 7.329 1.00 0.00 H
-HETATM 2202 H HOH A 734 8.798 11.405 5.904 1.00 0.00 H
-HETATM 2203 O HOH A 735 26.792 13.589 0.208 1.00 0.00 O
-HETATM 2204 H HOH A 735 27.671 13.361 0.510 1.00 0.00 H
-HETATM 2205 H HOH A 735 26.383 12.749 0.000 1.00 0.00 H
-HETATM 2206 O HOH A 736 29.596 12.820 0.571 1.00 0.00 O
-HETATM 2207 H HOH A 736 30.000 13.474 -0.000 1.00 0.00 H
-HETATM 2208 H HOH A 736 29.795 11.980 0.156 1.00 0.00 H
-HETATM 2209 O HOH A 737 23.153 14.445 15.144 1.00 0.00 O
-HETATM 2210 H HOH A 737 22.956 14.600 14.221 1.00 0.00 H
-HETATM 2211 H HOH A 737 22.298 14.315 15.554 1.00 0.00 H
-HETATM 2212 O HOH A 738 22.402 15.448 12.495 1.00 0.00 O
-HETATM 2213 H HOH A 738 22.968 16.220 12.467 1.00 0.00 H
-HETATM 2214 H HOH A 738 21.512 15.799 12.482 1.00 0.00 H
-HETATM 2215 O HOH A 739 6.749 0.593 22.917 1.00 0.00 O
-HETATM 2216 H HOH A 739 7.497 0.926 22.422 1.00 0.00 H
-HETATM 2217 H HOH A 739 6.049 0.513 22.269 1.00 0.00 H
-HETATM 2218 O HOH A 740 8.766 2.118 21.437 1.00 0.00 O
-HETATM 2219 H HOH A 740 8.938 2.799 22.088 1.00 0.00 H
-HETATM 2220 H HOH A 740 8.389 2.585 20.692 1.00 0.00 H
-HETATM 2221 O HOH A 741 18.417 4.843 21.408 1.00 0.00 O
-HETATM 2222 H HOH A 741 18.105 5.521 20.809 1.00 0.00 H
-HETATM 2223 H HOH A 741 17.947 5.009 22.225 1.00 0.00 H
-HETATM 2224 O HOH A 742 17.820 7.264 19.870 1.00 0.00 O
-HETATM 2225 H HOH A 742 18.712 7.491 19.606 1.00 0.00 H
-HETATM 2226 H HOH A 742 17.541 7.987 20.431 1.00 0.00 H
-HETATM 2227 O HOH A 743 19.962 18.276 22.640 1.00 0.00 O
-HETATM 2228 H HOH A 743 19.155 18.687 22.948 1.00 0.00 H
-HETATM 2229 H HOH A 743 19.713 17.372 22.445 1.00 0.00 H
-HETATM 2230 O HOH A 744 17.601 19.253 24.073 1.00 0.00 O
-HETATM 2231 H HOH A 744 18.041 19.627 24.837 1.00 0.00 H
-HETATM 2232 H HOH A 744 17.071 18.538 24.424 1.00 0.00 H
-HETATM 2233 O HOH A 745 2.015 27.509 27.170 1.00 0.00 O
-HETATM 2234 H HOH A 745 2.907 27.838 27.276 1.00 0.00 H
-HETATM 2235 H HOH A 745 1.517 27.937 27.867 1.00 0.00 H
-HETATM 2236 O HOH A 746 4.773 28.139 27.930 1.00 0.00 O
-HETATM 2237 H HOH A 746 5.142 27.257 27.880 1.00 0.00 H
-HETATM 2238 H HOH A 746 4.846 28.380 28.854 1.00 0.00 H
-HETATM 2239 O HOH A 747 2.765 6.065 17.207 1.00 0.00 O
-HETATM 2240 H HOH A 747 3.506 5.701 17.691 1.00 0.00 H
-HETATM 2241 H HOH A 747 2.244 5.302 16.956 1.00 0.00 H
-HETATM 2242 O HOH A 748 5.235 4.816 18.167 1.00 0.00 O
-HETATM 2243 H HOH A 748 5.864 5.334 17.663 1.00 0.00 H
-HETATM 2244 H HOH A 748 5.339 3.923 17.837 1.00 0.00 H
-HETATM 2245 O HOH A 749 15.220 19.437 3.729 1.00 0.00 O
-HETATM 2246 H HOH A 749 15.378 18.887 4.496 1.00 0.00 H
-HETATM 2247 H HOH A 749 15.328 20.333 4.050 1.00 0.00 H
-HETATM 2248 O HOH A 750 15.171 17.907 6.228 1.00 0.00 O
-HETATM 2249 H HOH A 750 14.378 17.394 6.067 1.00 0.00 H
-HETATM 2250 H HOH A 750 14.956 18.456 6.982 1.00 0.00 H
-HETATM 2251 O HOH A 751 17.503 1.113 7.831 1.00 0.00 O
-HETATM 2252 H HOH A 751 16.549 1.179 7.863 1.00 0.00 H
-HETATM 2253 H HOH A 751 17.673 0.335 7.301 1.00 0.00 H
-HETATM 2254 O HOH A 752 14.623 0.892 8.318 1.00 0.00 O
-HETATM 2255 H HOH A 752 14.627 1.011 9.269 1.00 0.00 H
-HETATM 2256 H HOH A 752 14.303 -0.000 8.188 1.00 0.00 H
-HETATM 2257 O HOH A 753 4.312 5.567 21.331 1.00 0.00 O
-HETATM 2258 H HOH A 753 4.867 6.290 21.623 1.00 0.00 H
-HETATM 2259 H HOH A 753 4.039 5.132 22.138 1.00 0.00 H
-HETATM 2260 O HOH A 754 6.407 7.371 22.302 1.00 0.00 O
-HETATM 2261 H HOH A 754 7.094 7.145 21.675 1.00 0.00 H
-HETATM 2262 H HOH A 754 6.753 7.097 23.151 1.00 0.00 H
-HETATM 2263 O HOH A 755 25.600 6.272 22.684 1.00 0.00 O
-HETATM 2264 H HOH A 755 25.721 7.149 22.322 1.00 0.00 H
-HETATM 2265 H HOH A 755 26.241 5.731 22.223 1.00 0.00 H
-HETATM 2266 O HOH A 756 25.676 8.752 21.126 1.00 0.00 O
-HETATM 2267 H HOH A 756 24.735 8.861 20.983 1.00 0.00 H
-HETATM 2268 H HOH A 756 26.040 8.617 20.251 1.00 0.00 H
-HETATM 2269 O HOH A 757 2.923 8.050 9.452 1.00 0.00 O
-HETATM 2270 H HOH A 757 2.537 8.763 9.960 1.00 0.00 H
-HETATM 2271 H HOH A 757 3.591 8.474 8.913 1.00 0.00 H
-HETATM 2272 O HOH A 758 1.461 10.354 10.519 1.00 0.00 O
-HETATM 2273 H HOH A 758 0.572 10.074 10.302 1.00 0.00 H
-HETATM 2274 H HOH A 758 1.601 11.143 9.996 1.00 0.00 H
-HETATM 2275 O HOH A 759 4.198 2.732 22.326 1.00 0.00 O
-HETATM 2276 H HOH A 759 4.277 2.565 21.387 1.00 0.00 H
-HETATM 2277 H HOH A 759 5.067 2.539 22.678 1.00 0.00 H
-HETATM 2278 O HOH A 760 4.403 1.665 19.605 1.00 0.00 O
-HETATM 2279 H HOH A 760 3.603 1.138 19.595 1.00 0.00 H
-HETATM 2280 H HOH A 760 5.113 1.029 19.520 1.00 0.00 H
-HETATM 2281 O HOH A 761 13.478 26.932 6.797 1.00 0.00 O
-HETATM 2282 H HOH A 761 14.099 26.713 6.102 1.00 0.00 H
-HETATM 2283 H HOH A 761 13.989 26.868 7.604 1.00 0.00 H
-HETATM 2284 O HOH A 762 15.293 25.719 4.842 1.00 0.00 O
-HETATM 2285 H HOH A 762 14.681 25.101 4.441 1.00 0.00 H
-HETATM 2286 H HOH A 762 15.955 25.169 5.259 1.00 0.00 H
-HETATM 2287 O HOH A 763 0.434 27.623 7.542 1.00 0.00 O
-HETATM 2288 H HOH A 763 0.216 26.727 7.799 1.00 0.00 H
-HETATM 2289 H HOH A 763 -0.001 27.745 6.699 1.00 0.00 H
-HETATM 2290 O HOH A 764 0.168 24.733 7.943 1.00 0.00 O
-HETATM 2291 H HOH A 764 1.082 24.531 8.150 1.00 0.00 H
-HETATM 2292 H HOH A 764 -0.002 24.270 7.123 1.00 0.00 H
-HETATM 2293 O HOH A 765 23.452 14.724 3.047 1.00 0.00 O
-HETATM 2294 H HOH A 765 24.178 14.264 3.469 1.00 0.00 H
-HETATM 2295 H HOH A 765 22.933 15.070 3.773 1.00 0.00 H
-HETATM 2296 O HOH A 766 25.282 12.903 4.434 1.00 0.00 O
-HETATM 2297 H HOH A 766 25.152 12.126 3.890 1.00 0.00 H
-HETATM 2298 H HOH A 766 24.954 12.652 5.297 1.00 0.00 H
-HETATM 2299 O HOH A 767 27.656 24.776 23.303 1.00 0.00 O
-HETATM 2300 H HOH A 767 26.725 24.925 23.138 1.00 0.00 H
-HETATM 2301 H HOH A 767 28.003 24.468 22.466 1.00 0.00 H
-HETATM 2302 O HOH A 768 24.766 24.659 22.835 1.00 0.00 O
-HETATM 2303 H HOH A 768 24.512 24.173 23.620 1.00 0.00 H
-HETATM 2304 H HOH A 768 24.542 24.078 22.108 1.00 0.00 H
-HETATM 2305 O HOH A 769 3.538 3.470 16.273 1.00 0.00 O
-HETATM 2306 H HOH A 769 3.219 3.957 15.513 1.00 0.00 H
-HETATM 2307 H HOH A 769 2.750 3.093 16.665 1.00 0.00 H
-HETATM 2308 O HOH A 770 2.404 5.355 14.338 1.00 0.00 O
-HETATM 2309 H HOH A 770 2.910 6.132 14.579 1.00 0.00 H
-HETATM 2310 H HOH A 770 1.498 5.583 14.546 1.00 0.00 H
-HETATM 2311 O HOH A 771 28.577 28.444 27.608 1.00 0.00 O
-HETATM 2312 H HOH A 771 27.640 28.525 27.426 1.00 0.00 H
-HETATM 2313 H HOH A 771 28.792 29.230 28.110 1.00 0.00 H
-HETATM 2314 O HOH A 772 25.879 29.067 26.648 1.00 0.00 O
-HETATM 2315 H HOH A 772 26.006 28.866 25.720 1.00 0.00 H
-HETATM 2316 H HOH A 772 25.697 30.006 26.670 1.00 0.00 H
-HETATM 2317 O HOH A 773 4.020 28.062 12.601 1.00 0.00 O
-HETATM 2318 H HOH A 773 4.493 28.587 11.956 1.00 0.00 H
-HETATM 2319 H HOH A 773 4.051 28.582 13.404 1.00 0.00 H
-HETATM 2320 O HOH A 774 5.938 29.476 10.896 1.00 0.00 O
-HETATM 2321 H HOH A 774 6.465 28.726 10.619 1.00 0.00 H
-HETATM 2322 H HOH A 774 6.539 30.020 11.404 1.00 0.00 H
-HETATM 2323 O HOH A 775 22.525 12.821 24.644 1.00 0.00 O
-HETATM 2324 H HOH A 775 22.118 13.481 25.205 1.00 0.00 H
-HETATM 2325 H HOH A 775 22.047 12.880 23.817 1.00 0.00 H
-HETATM 2326 O HOH A 776 20.858 14.451 26.418 1.00 0.00 O
-HETATM 2327 H HOH A 776 20.772 13.833 27.145 1.00 0.00 H
-HETATM 2328 H HOH A 776 19.968 14.555 26.082 1.00 0.00 H
-HETATM 2329 O HOH A 777 15.915 0.873 2.564 1.00 0.00 O
-HETATM 2330 H HOH A 777 15.081 1.186 2.912 1.00 0.00 H
-HETATM 2331 H HOH A 777 15.721 0.625 1.660 1.00 0.00 H
-HETATM 2332 O HOH A 778 13.231 1.271 3.668 1.00 0.00 O
-HETATM 2333 H HOH A 778 13.318 0.734 4.457 1.00 0.00 H
-HETATM 2334 H HOH A 778 12.559 0.831 3.149 1.00 0.00 H
-HETATM 2335 O HOH A 779 22.605 3.090 27.071 1.00 0.00 O
-HETATM 2336 H HOH A 779 21.775 3.567 27.103 1.00 0.00 H
-HETATM 2337 H HOH A 779 22.899 3.058 27.981 1.00 0.00 H
-HETATM 2338 O HOH A 780 20.401 5.015 27.220 1.00 0.00 O
-HETATM 2339 H HOH A 780 20.663 5.591 26.500 1.00 0.00 H
-HETATM 2340 H HOH A 780 20.455 5.565 28.001 1.00 0.00 H
-HETATM 2341 O HOH A 781 23.590 9.194 6.406 1.00 0.00 O
-HETATM 2342 H HOH A 781 24.266 9.642 6.914 1.00 0.00 H
-HETATM 2343 H HOH A 781 24.068 8.547 5.887 1.00 0.00 H
-HETATM 2344 O HOH A 782 25.768 10.842 7.466 1.00 0.00 O
-HETATM 2345 H HOH A 782 25.427 11.704 7.225 1.00 0.00 H
-HETATM 2346 H HOH A 782 26.572 10.749 6.955 1.00 0.00 H
-HETATM 2347 O HOH A 783 11.656 16.180 24.031 1.00 0.00 O
-HETATM 2348 H HOH A 783 10.778 15.801 23.992 1.00 0.00 H
-HETATM 2349 H HOH A 783 11.577 17.017 23.575 1.00 0.00 H
-HETATM 2350 O HOH A 784 9.109 14.892 23.369 1.00 0.00 O
-HETATM 2351 H HOH A 784 9.447 14.048 23.068 1.00 0.00 H
-HETATM 2352 H HOH A 784 8.700 15.279 22.595 1.00 0.00 H
-HETATM 2353 O HOH A 785 8.862 29.219 10.369 1.00 0.00 O
-HETATM 2354 H HOH A 785 8.783 29.221 11.323 1.00 0.00 H
-HETATM 2355 H HOH A 785 8.292 29.931 10.079 1.00 0.00 H
-HETATM 2356 O HOH A 786 8.115 28.963 13.190 1.00 0.00 O
-HETATM 2357 H HOH A 786 7.967 28.017 13.215 1.00 0.00 H
-HETATM 2358 H HOH A 786 7.257 29.347 13.372 1.00 0.00 H
-HETATM 2359 O HOH A 787 5.246 22.100 27.429 1.00 0.00 O
-HETATM 2360 H HOH A 787 5.959 22.585 27.844 1.00 0.00 H
-HETATM 2361 H HOH A 787 4.453 22.425 27.857 1.00 0.00 H
-HETATM 2362 O HOH A 788 7.350 23.217 29.135 1.00 0.00 O
-HETATM 2363 H HOH A 788 7.855 22.417 29.283 1.00 0.00 H
-HETATM 2364 H HOH A 788 7.021 23.460 30.000 1.00 0.00 H
-HETATM 2365 O HOH A 789 0.871 13.648 22.256 1.00 0.00 O
-HETATM 2366 H HOH A 789 0.465 14.513 22.209 1.00 0.00 H
-HETATM 2367 H HOH A 789 1.164 13.569 23.164 1.00 0.00 H
-HETATM 2368 O HOH A 790 0.171 16.488 22.096 1.00 0.00 O
-HETATM 2369 H HOH A 790 0.656 16.712 21.301 1.00 0.00 H
-HETATM 2370 H HOH A 790 0.588 17.002 22.787 1.00 0.00 H
-HETATM 2371 O HOH A 791 6.299 19.517 19.589 1.00 0.00 O
-HETATM 2372 H HOH A 791 6.906 20.257 19.627 1.00 0.00 H
-HETATM 2373 H HOH A 791 6.805 18.775 19.920 1.00 0.00 H
-HETATM 2374 O HOH A 792 8.381 21.548 19.230 1.00 0.00 O
-HETATM 2375 H HOH A 792 8.174 21.810 18.333 1.00 0.00 H
-HETATM 2376 H HOH A 792 9.255 21.162 19.174 1.00 0.00 H
-HETATM 2377 O HOH A 793 23.691 28.887 19.190 1.00 0.00 O
-HETATM 2378 H HOH A 793 22.741 28.999 19.170 1.00 0.00 H
-HETATM 2379 H HOH A 793 24.040 29.759 19.004 1.00 0.00 H
-HETATM 2380 O HOH A 794 20.851 29.229 18.556 1.00 0.00 O
-HETATM 2381 H HOH A 794 20.738 28.469 17.985 1.00 0.00 H
-HETATM 2382 H HOH A 794 20.697 29.984 17.989 1.00 0.00 H
-HETATM 2383 O HOH A 795 9.865 13.659 20.944 1.00 0.00 O
-HETATM 2384 H HOH A 795 8.963 13.931 20.779 1.00 0.00 H
-HETATM 2385 H HOH A 795 10.288 13.695 20.085 1.00 0.00 H
-HETATM 2386 O HOH A 796 7.038 13.957 20.235 1.00 0.00 O
-HETATM 2387 H HOH A 796 6.687 13.213 20.725 1.00 0.00 H
-HETATM 2388 H HOH A 796 6.872 13.742 19.317 1.00 0.00 H
-HETATM 2389 O HOH A 797 6.867 12.515 5.075 1.00 0.00 O
-HETATM 2390 H HOH A 797 7.391 13.313 5.014 1.00 0.00 H
-HETATM 2391 H HOH A 797 6.621 12.319 4.171 1.00 0.00 H
-HETATM 2392 O HOH A 798 7.962 15.221 4.827 1.00 0.00 O
-HETATM 2393 H HOH A 798 7.466 15.643 5.530 1.00 0.00 H
-HETATM 2394 H HOH A 798 7.667 15.657 4.028 1.00 0.00 H
-HETATM 2395 O HOH A 799 1.565 26.456 13.316 1.00 0.00 O
-HETATM 2396 H HOH A 799 1.682 25.736 13.936 1.00 0.00 H
-HETATM 2397 H HOH A 799 2.429 26.578 12.923 1.00 0.00 H
-HETATM 2398 O HOH A 800 2.169 24.654 15.546 1.00 0.00 O
-HETATM 2399 H HOH A 800 1.729 25.137 16.246 1.00 0.00 H
-HETATM 2400 H HOH A 800 3.096 24.673 15.784 1.00 0.00 H
-HETATM 2401 O HOH A 801 5.279 3.308 10.983 1.00 0.00 O
-HETATM 2402 H HOH A 801 4.746 2.744 11.544 1.00 0.00 H
-HETATM 2403 H HOH A 801 5.218 2.910 10.115 1.00 0.00 H
-HETATM 2404 O HOH A 802 4.102 1.229 12.679 1.00 0.00 O
-HETATM 2405 H HOH A 802 4.791 1.176 13.342 1.00 0.00 H
-HETATM 2406 H HOH A 802 4.108 0.371 12.255 1.00 0.00 H
-HETATM 2407 O HOH A 803 6.476 7.996 18.822 1.00 0.00 O
-HETATM 2408 H HOH A 803 6.291 8.839 18.408 1.00 0.00 H
-HETATM 2409 H HOH A 803 6.899 7.479 18.137 1.00 0.00 H
-HETATM 2410 O HOH A 804 5.474 10.295 17.307 1.00 0.00 O
-HETATM 2411 H HOH A 804 4.569 10.291 17.621 1.00 0.00 H
-HETATM 2412 H HOH A 804 5.407 10.108 16.371 1.00 0.00 H
-HETATM 2413 O HOH A 805 17.402 18.951 27.745 1.00 0.00 O
-HETATM 2414 H HOH A 805 18.056 18.427 28.206 1.00 0.00 H
-HETATM 2415 H HOH A 805 17.574 18.790 26.817 1.00 0.00 H
-HETATM 2416 O HOH A 806 19.767 17.770 29.009 1.00 0.00 O
-HETATM 2417 H HOH A 806 20.013 18.507 29.570 1.00 0.00 H
-HETATM 2418 H HOH A 806 20.495 17.679 28.395 1.00 0.00 H
-HETATM 2419 O HOH A 807 14.039 16.767 21.350 1.00 0.00 O
-HETATM 2420 H HOH A 807 14.485 17.154 22.103 1.00 0.00 H
-HETATM 2421 H HOH A 807 13.116 16.990 21.476 1.00 0.00 H
-HETATM 2422 O HOH A 808 15.193 17.499 23.941 1.00 0.00 O
-HETATM 2423 H HOH A 808 15.668 16.688 24.122 1.00 0.00 H
-HETATM 2424 H HOH A 808 14.530 17.551 24.629 1.00 0.00 H
-HETATM 2425 O HOH A 809 22.728 26.647 15.437 1.00 0.00 O
-HETATM 2426 H HOH A 809 23.353 26.102 15.915 1.00 0.00 H
-HETATM 2427 H HOH A 809 22.810 26.365 14.526 1.00 0.00 H
-HETATM 2428 O HOH A 810 25.035 25.338 16.682 1.00 0.00 O
-HETATM 2429 H HOH A 810 25.404 26.097 17.135 1.00 0.00 H
-HETATM 2430 H HOH A 810 25.700 25.093 16.039 1.00 0.00 H
-HETATM 2431 O HOH A 811 26.622 20.504 4.675 1.00 0.00 O
-HETATM 2432 H HOH A 811 27.521 20.220 4.843 1.00 0.00 H
-HETATM 2433 H HOH A 811 26.093 20.016 5.306 1.00 0.00 H
-HETATM 2434 O HOH A 812 29.216 19.160 4.898 1.00 0.00 O
-HETATM 2435 H HOH A 812 29.378 19.003 3.967 1.00 0.00 H
-HETATM 2436 H HOH A 812 29.137 18.286 5.281 1.00 0.00 H
-HETATM 2437 O HOH A 813 28.815 2.547 21.885 1.00 0.00 O
-HETATM 2438 H HOH A 813 28.015 2.030 21.794 1.00 0.00 H
-HETATM 2439 H HOH A 813 28.812 2.838 22.797 1.00 0.00 H
-HETATM 2440 O HOH A 814 26.104 1.478 21.586 1.00 0.00 O
-HETATM 2441 H HOH A 814 25.854 1.940 20.785 1.00 0.00 H
-HETATM 2442 H HOH A 814 25.508 1.815 22.255 1.00 0.00 H
-HETATM 2443 O HOH A 815 27.918 3.616 15.289 1.00 0.00 O
-HETATM 2444 H HOH A 815 28.027 4.294 14.622 1.00 0.00 H
-HETATM 2445 H HOH A 815 28.114 4.058 16.116 1.00 0.00 H
-HETATM 2446 O HOH A 816 28.815 5.594 13.323 1.00 0.00 O
-HETATM 2447 H HOH A 816 29.357 5.022 12.779 1.00 0.00 H
-HETATM 2448 H HOH A 816 29.430 6.223 13.700 1.00 0.00 H
-HETATM 2449 O HOH A 817 2.890 24.310 4.156 1.00 0.00 O
-HETATM 2450 H HOH A 817 2.381 24.824 3.530 1.00 0.00 H
-HETATM 2451 H HOH A 817 2.288 23.630 4.456 1.00 0.00 H
-HETATM 2452 O HOH A 818 1.089 26.113 2.710 1.00 0.00 O
-HETATM 2453 H HOH A 818 1.352 26.938 3.119 1.00 0.00 H
-HETATM 2454 H HOH A 818 0.176 25.994 2.969 1.00 0.00 H
-HETATM 2455 O HOH A 819 0.526 17.128 6.333 1.00 0.00 O
-HETATM 2456 H HOH A 819 0.981 17.095 5.492 1.00 0.00 H
-HETATM 2457 H HOH A 819 -0.014 17.917 6.283 1.00 0.00 H
-HETATM 2458 O HOH A 820 2.255 17.435 3.988 1.00 0.00 O
-HETATM 2459 H HOH A 820 3.101 17.285 4.412 1.00 0.00 H
-HETATM 2460 H HOH A 820 2.294 18.342 3.685 1.00 0.00 H
-HETATM 2461 O HOH A 821 24.004 25.633 4.040 1.00 0.00 O
-HETATM 2462 H HOH A 821 23.425 25.699 3.281 1.00 0.00 H
-HETATM 2463 H HOH A 821 24.567 24.882 3.849 1.00 0.00 H
-HETATM 2464 O HOH A 822 21.953 25.393 1.962 1.00 0.00 O
-HETATM 2465 H HOH A 822 21.176 25.463 2.517 1.00 0.00 H
-HETATM 2466 H HOH A 822 21.911 24.509 1.598 1.00 0.00 H
-HETATM 2467 O HOH A 823 15.225 22.630 9.685 1.00 0.00 O
-HETATM 2468 H HOH A 823 14.487 23.238 9.647 1.00 0.00 H
-HETATM 2469 H HOH A 823 15.591 22.639 8.801 1.00 0.00 H
-HETATM 2470 O HOH A 824 12.695 24.057 9.303 1.00 0.00 O
-HETATM 2471 H HOH A 824 12.109 23.430 9.729 1.00 0.00 H
-HETATM 2472 H HOH A 824 12.419 24.062 8.387 1.00 0.00 H
-HETATM 2473 O HOH A 825 26.582 13.734 9.454 1.00 0.00 O
-HETATM 2474 H HOH A 825 25.749 13.843 8.995 1.00 0.00 H
-HETATM 2475 H HOH A 825 26.674 14.531 9.976 1.00 0.00 H
-HETATM 2476 O HOH A 826 24.332 14.420 7.706 1.00 0.00 O
-HETATM 2477 H HOH A 826 24.722 14.177 6.866 1.00 0.00 H
-HETATM 2478 H HOH A 826 24.197 15.365 7.646 1.00 0.00 H
-HETATM 2479 O HOH A 827 1.526 6.503 7.422 1.00 0.00 O
-HETATM 2480 H HOH A 827 1.579 6.695 8.358 1.00 0.00 H
-HETATM 2481 H HOH A 827 1.329 7.346 7.014 1.00 0.00 H
-HETATM 2482 O HOH A 828 1.113 7.098 10.261 1.00 0.00 O
-HETATM 2483 H HOH A 828 0.539 6.363 10.481 1.00 0.00 H
-HETATM 2484 H HOH A 828 0.570 7.876 10.391 1.00 0.00 H
-HETATM 2485 O HOH A 829 2.594 11.522 17.334 1.00 0.00 O
-HETATM 2486 H HOH A 829 2.103 12.343 17.335 1.00 0.00 H
-HETATM 2487 H HOH A 829 2.529 11.203 16.434 1.00 0.00 H
-HETATM 2488 O HOH A 830 0.628 13.695 17.345 1.00 0.00 O
-HETATM 2489 H HOH A 830 0.141 13.445 18.131 1.00 0.00 H
-HETATM 2490 H HOH A 830 -0.004 13.612 16.632 1.00 0.00 H
-HETATM 2491 O HOH A 831 12.657 0.133 17.224 1.00 0.00 O
-HETATM 2492 H HOH A 831 12.418 1.052 17.105 1.00 0.00 H
-HETATM 2493 H HOH A 831 13.403 -0.000 16.639 1.00 0.00 H
-HETATM 2494 O HOH A 832 11.684 2.761 16.370 1.00 0.00 O
-HETATM 2495 H HOH A 832 10.758 2.543 16.258 1.00 0.00 H
-HETATM 2496 H HOH A 832 11.987 2.989 15.491 1.00 0.00 H
-HETATM 2497 O HOH A 833 29.801 11.235 21.427 1.00 0.00 O
-HETATM 2498 H HOH A 833 30.003 10.948 22.317 1.00 0.00 H
-HETATM 2499 H HOH A 833 29.812 12.191 21.473 1.00 0.00 H
-HETATM 2500 O HOH A 834 29.872 10.496 24.261 1.00 0.00 O
-HETATM 2501 H HOH A 834 29.138 9.880 24.268 1.00 0.00 H
-HETATM 2502 H HOH A 834 29.591 11.214 24.828 1.00 0.00 H
-HETATM 2503 O HOH A 835 5.736 23.626 3.037 1.00 0.00 O
-HETATM 2504 H HOH A 835 6.044 23.002 2.380 1.00 0.00 H
-HETATM 2505 H HOH A 835 4.782 23.589 2.975 1.00 0.00 H
-HETATM 2506 O HOH A 836 6.551 22.134 0.651 1.00 0.00 O
-HETATM 2507 H HOH A 836 7.175 22.767 0.291 1.00 0.00 H
-HETATM 2508 H HOH A 836 5.852 22.087 -0.000 1.00 0.00 H
-HETATM 2509 O HOH A 837 22.822 20.902 16.012 1.00 0.00 O
-HETATM 2510 H HOH A 837 22.146 21.234 15.422 1.00 0.00 H
-HETATM 2511 H HOH A 837 23.025 21.641 16.586 1.00 0.00 H
-HETATM 2512 O HOH A 838 21.194 22.179 13.939 1.00 0.00 O
-HETATM 2513 H HOH A 838 21.653 21.844 13.168 1.00 0.00 H
-HETATM 2514 H HOH A 838 21.328 23.126 13.908 1.00 0.00 H
-HETATM 2515 O HOH A 839 22.315 7.043 26.455 1.00 0.00 O
-HETATM 2516 H HOH A 839 22.497 7.895 26.059 1.00 0.00 H
-HETATM 2517 H HOH A 839 21.952 7.251 27.316 1.00 0.00 H
-HETATM 2518 O HOH A 840 23.334 9.650 25.590 1.00 0.00 O
-HETATM 2519 H HOH A 840 24.218 9.382 25.334 1.00 0.00 H
-HETATM 2520 H HOH A 840 23.466 10.248 26.325 1.00 0.00 H
-HETATM 2521 O HOH A 841 7.476 9.112 20.767 1.00 0.00 O
-HETATM 2522 H HOH A 841 6.921 9.882 20.892 1.00 0.00 H
-HETATM 2523 H HOH A 841 7.902 9.259 19.922 1.00 0.00 H
-HETATM 2524 O HOH A 842 5.426 11.206 20.775 1.00 0.00 O
-HETATM 2525 H HOH A 842 4.669 10.667 21.006 1.00 0.00 H
-HETATM 2526 H HOH A 842 5.236 11.527 19.894 1.00 0.00 H
-HETATM 2527 O HOH A 843 21.620 4.332 0.872 1.00 0.00 O
-HETATM 2528 H HOH A 843 22.479 4.420 1.284 1.00 0.00 H
-HETATM 2529 H HOH A 843 21.806 3.984 -0.000 1.00 0.00 H
-HETATM 2530 O HOH A 844 24.295 5.074 1.807 1.00 0.00 O
-HETATM 2531 H HOH A 844 24.106 5.968 2.093 1.00 0.00 H
-HETATM 2532 H HOH A 844 24.914 5.177 1.084 1.00 0.00 H
-HETATM 2533 O HOH A 845 24.682 7.921 16.884 1.00 0.00 O
-HETATM 2534 H HOH A 845 25.008 7.023 16.942 1.00 0.00 H
-HETATM 2535 H HOH A 845 23.743 7.826 16.722 1.00 0.00 H
-HETATM 2536 O HOH A 846 25.639 5.181 16.488 1.00 0.00 O
-HETATM 2537 H HOH A 846 26.359 5.365 15.885 1.00 0.00 H
-HETATM 2538 H HOH A 846 25.024 4.654 15.977 1.00 0.00 H
-HETATM 2539 O HOH A 847 0.647 -0.000 19.760 1.00 0.00 O
-HETATM 2540 H HOH A 847 1.306 0.460 20.280 1.00 0.00 H
-HETATM 2541 H HOH A 847 0.458 0.592 19.032 1.00 0.00 H
-HETATM 2542 O HOH A 848 2.216 1.719 21.540 1.00 0.00 O
-HETATM 2543 H HOH A 848 1.736 1.560 22.354 1.00 0.00 H
-HETATM 2544 H HOH A 848 2.078 2.649 21.356 1.00 0.00 H
-HETATM 2545 O HOH A 849 24.722 26.945 21.406 1.00 0.00 O
-HETATM 2546 H HOH A 849 25.423 26.303 21.291 1.00 0.00 H
-HETATM 2547 H HOH A 849 24.000 26.447 21.788 1.00 0.00 H
-HETATM 2548 O HOH A 850 26.560 24.812 20.597 1.00 0.00 O
-HETATM 2549 H HOH A 850 26.715 25.090 19.693 1.00 0.00 H
-HETATM 2550 H HOH A 850 26.118 23.967 20.517 1.00 0.00 H
-HETATM 2551 O HOH A 851 15.402 0.057 18.184 1.00 0.00 O
-HETATM 2552 H HOH A 851 15.967 0.526 17.570 1.00 0.00 H
-HETATM 2553 H HOH A 851 15.300 0.656 18.923 1.00 0.00 H
-HETATM 2554 O HOH A 852 17.555 1.339 16.665 1.00 0.00 O
-HETATM 2555 H HOH A 852 18.126 0.578 16.553 1.00 0.00 H
-HETATM 2556 H HOH A 852 18.063 1.946 17.202 1.00 0.00 H
-HETATM 2557 O HOH A 853 16.710 2.941 24.035 1.00 0.00 O
-HETATM 2558 H HOH A 853 17.100 2.506 23.277 1.00 0.00 H
-HETATM 2559 H HOH A 853 17.377 2.877 24.719 1.00 0.00 H
-HETATM 2560 O HOH A 854 17.768 1.124 21.994 1.00 0.00 O
-HETATM 2561 H HOH A 854 16.979 0.602 21.843 1.00 0.00 H
-HETATM 2562 H HOH A 854 18.401 0.505 22.357 1.00 0.00 H
-HETATM 2563 O HOH A 855 14.222 28.606 4.908 1.00 0.00 O
-HETATM 2564 H HOH A 855 13.331 28.593 5.258 1.00 0.00 H
-HETATM 2565 H HOH A 855 14.134 29.011 4.045 1.00 0.00 H
-HETATM 2566 O HOH A 856 11.424 28.118 5.627 1.00 0.00 O
-HETATM 2567 H HOH A 856 11.498 27.197 5.878 1.00 0.00 H
-HETATM 2568 H HOH A 856 10.850 28.114 4.861 1.00 0.00 H
-HETATM 2569 O HOH A 857 6.917 17.260 22.025 1.00 0.00 O
-HETATM 2570 H HOH A 857 6.398 17.929 22.471 1.00 0.00 H
-HETATM 2571 H HOH A 857 6.279 16.760 21.517 1.00 0.00 H
-HETATM 2572 O HOH A 858 5.181 18.878 23.743 1.00 0.00 O
-HETATM 2573 H HOH A 858 5.593 18.687 24.586 1.00 0.00 H
-HETATM 2574 H HOH A 858 4.301 18.509 23.815 1.00 0.00 H
-HETATM 2575 O HOH A 859 5.485 15.742 6.793 1.00 0.00 O
-HETATM 2576 H HOH A 859 4.586 15.800 6.470 1.00 0.00 H
-HETATM 2577 H HOH A 859 5.741 14.834 6.629 1.00 0.00 H
-HETATM 2578 O HOH A 860 2.599 15.651 6.296 1.00 0.00 O
-HETATM 2579 H HOH A 860 2.300 16.078 7.099 1.00 0.00 H
-HETATM 2580 H HOH A 860 2.251 14.761 6.352 1.00 0.00 H
-HETATM 2581 O HOH A 861 7.824 12.795 27.435 1.00 0.00 O
-HETATM 2582 H HOH A 861 7.501 12.653 26.546 1.00 0.00 H
-HETATM 2583 H HOH A 861 7.796 13.745 27.553 1.00 0.00 H
-HETATM 2584 O HOH A 862 7.361 12.533 24.554 1.00 0.00 O
-HETATM 2585 H HOH A 862 8.093 11.952 24.342 1.00 0.00 H
-HETATM 2586 H HOH A 862 7.552 13.344 24.083 1.00 0.00 H
-HETATM 2587 O HOH A 863 15.807 17.641 16.651 1.00 0.00 O
-HETATM 2588 H HOH A 863 16.482 18.047 16.107 1.00 0.00 H
-HETATM 2589 H HOH A 863 15.244 17.179 16.030 1.00 0.00 H
-HETATM 2590 O HOH A 864 17.462 19.268 14.863 1.00 0.00 O
-HETATM 2591 H HOH A 864 17.350 20.116 15.294 1.00 0.00 H
-HETATM 2592 H HOH A 864 17.060 19.380 14.001 1.00 0.00 H
-HETATM 2593 O HOH A 865 27.965 18.219 11.106 1.00 0.00 O
-HETATM 2594 H HOH A 865 28.325 17.400 11.447 1.00 0.00 H
-HETATM 2595 H HOH A 865 27.018 18.078 11.093 1.00 0.00 H
-HETATM 2596 O HOH A 866 28.968 15.514 11.618 1.00 0.00 O
-HETATM 2597 H HOH A 866 29.615 15.464 10.914 1.00 0.00 H
-HETATM 2598 H HOH A 866 28.321 14.843 11.401 1.00 0.00 H
-HETATM 2599 O HOH A 867 10.136 22.679 23.966 1.00 0.00 O
-HETATM 2600 H HOH A 867 9.279 22.479 23.591 1.00 0.00 H
-HETATM 2601 H HOH A 867 10.512 23.331 23.374 1.00 0.00 H
-HETATM 2602 O HOH A 868 7.813 21.751 22.442 1.00 0.00 O
-HETATM 2603 H HOH A 868 7.965 20.808 22.507 1.00 0.00 H
-HETATM 2604 H HOH A 868 7.894 21.946 21.508 1.00 0.00 H
-HETATM 2605 O HOH A 869 17.433 11.274 1.839 1.00 0.00 O
-HETATM 2606 H HOH A 869 17.468 10.539 2.450 1.00 0.00 H
-HETATM 2607 H HOH A 869 16.521 11.564 1.862 1.00 0.00 H
-HETATM 2608 O HOH A 870 17.292 8.729 3.283 1.00 0.00 O
-HETATM 2609 H HOH A 870 17.754 8.178 2.650 1.00 0.00 H
-HETATM 2610 H HOH A 870 16.402 8.376 3.305 1.00 0.00 H
-HETATM 2611 O HOH A 871 11.256 1.594 5.068 1.00 0.00 O
-HETATM 2612 H HOH A 871 11.457 2.102 5.854 1.00 0.00 H
-HETATM 2613 H HOH A 871 10.968 2.246 4.428 1.00 0.00 H
-HETATM 2614 O HOH A 872 11.299 3.162 7.542 1.00 0.00 O
-HETATM 2615 H HOH A 872 10.793 2.569 8.098 1.00 0.00 H
-HETATM 2616 H HOH A 872 10.762 3.951 7.478 1.00 0.00 H
-HETATM 2617 O HOH A 873 4.959 26.902 7.197 1.00 0.00 O
-HETATM 2618 H HOH A 873 4.957 25.952 7.311 1.00 0.00 H
-HETATM 2619 H HOH A 873 5.404 27.043 6.361 1.00 0.00 H
-HETATM 2620 O HOH A 874 5.505 24.068 7.697 1.00 0.00 O
-HETATM 2621 H HOH A 874 5.828 24.139 8.596 1.00 0.00 H
-HETATM 2622 H HOH A 874 6.255 23.751 7.194 1.00 0.00 H
-HETATM 2623 O HOH A 875 18.910 0.174 14.006 1.00 0.00 O
-HETATM 2624 H HOH A 875 18.069 0.003 14.431 1.00 0.00 H
-HETATM 2625 H HOH A 875 19.251 0.949 14.453 1.00 0.00 H
-HETATM 2626 O HOH A 876 16.148 0.087 14.981 1.00 0.00 O
-HETATM 2627 H HOH A 876 15.700 0.000 14.139 1.00 0.00 H
-HETATM 2628 H HOH A 876 15.860 0.934 15.322 1.00 0.00 H
-HETATM 2629 O HOH A 877 27.006 28.559 22.572 1.00 0.00 O
-HETATM 2630 H HOH A 877 27.705 28.181 23.105 1.00 0.00 H
-HETATM 2631 H HOH A 877 26.813 27.887 21.918 1.00 0.00 H
-HETATM 2632 O HOH A 878 29.485 27.545 23.760 1.00 0.00 O
-HETATM 2633 H HOH A 878 30.011 28.343 23.698 1.00 0.00 H
-HETATM 2634 H HOH A 878 29.927 26.918 23.189 1.00 0.00 H
-HETATM 2635 O HOH A 879 5.693 26.578 3.456 1.00 0.00 O
-HETATM 2636 H HOH A 879 6.025 27.057 2.698 1.00 0.00 H
-HETATM 2637 H HOH A 879 5.306 25.783 3.088 1.00 0.00 H
-HETATM 2638 O HOH A 880 6.157 28.188 1.053 1.00 0.00 O
-HETATM 2639 H HOH A 880 5.747 29.003 1.344 1.00 0.00 H
-HETATM 2640 H HOH A 880 5.621 27.898 0.316 1.00 0.00 H
-HETATM 2641 O HOH A 881 16.556 3.192 2.577 1.00 0.00 O
-HETATM 2642 H HOH A 881 16.971 3.488 1.767 1.00 0.00 H
-HETATM 2643 H HOH A 881 15.667 3.545 2.535 1.00 0.00 H
-HETATM 2644 O HOH A 882 17.868 4.604 0.370 1.00 0.00 O
-HETATM 2645 H HOH A 882 18.641 4.919 0.840 1.00 0.00 H
-HETATM 2646 H HOH A 882 17.397 5.399 0.120 1.00 0.00 H
-HETATM 2647 O HOH A 883 6.140 1.632 28.477 1.00 0.00 O
-HETATM 2648 H HOH A 883 5.478 1.763 27.799 1.00 0.00 H
-HETATM 2649 H HOH A 883 6.695 2.410 28.425 1.00 0.00 H
-HETATM 2650 O HOH A 884 4.533 1.878 26.039 1.00 0.00 O
-HETATM 2651 H HOH A 884 4.663 0.995 25.691 1.00 0.00 H
-HETATM 2652 H HOH A 884 4.942 2.455 25.395 1.00 0.00 H
-HETATM 2653 O HOH A 885 12.173 7.135 0.653 1.00 0.00 O
-HETATM 2654 H HOH A 885 11.651 6.446 1.065 1.00 0.00 H
-HETATM 2655 H HOH A 885 12.702 6.677 0.000 1.00 0.00 H
-HETATM 2656 O HOH A 886 11.063 4.905 2.196 1.00 0.00 O
-HETATM 2657 H HOH A 886 11.286 5.213 3.075 1.00 0.00 H
-HETATM 2658 H HOH A 886 11.570 4.102 2.084 1.00 0.00 H
-HETATM 2659 O HOH A 887 14.580 11.014 15.643 1.00 0.00 O
-HETATM 2660 H HOH A 887 14.867 11.075 16.554 1.00 0.00 H
-HETATM 2661 H HOH A 887 13.626 11.084 15.686 1.00 0.00 H
-HETATM 2662 O HOH A 888 15.283 10.655 18.465 1.00 0.00 O
-HETATM 2663 H HOH A 888 15.848 9.887 18.376 1.00 0.00 H
-HETATM 2664 H HOH A 888 14.540 10.351 18.985 1.00 0.00 H
-HETATM 2665 O HOH A 889 8.688 13.521 2.924 1.00 0.00 O
-HETATM 2666 H HOH A 889 7.836 13.561 2.489 1.00 0.00 H
-HETATM 2667 H HOH A 889 8.694 12.671 3.363 1.00 0.00 H
-HETATM 2668 O HOH A 890 5.881 13.761 2.120 1.00 0.00 O
-HETATM 2669 H HOH A 890 5.706 14.638 2.463 1.00 0.00 H
-HETATM 2670 H HOH A 890 5.310 13.184 2.627 1.00 0.00 H
-HETATM 2671 O HOH A 891 2.718 27.709 15.585 1.00 0.00 O
-HETATM 2672 H HOH A 891 3.648 27.511 15.475 1.00 0.00 H
-HETATM 2673 H HOH A 891 2.586 28.519 15.093 1.00 0.00 H
-HETATM 2674 O HOH A 892 5.641 27.516 15.646 1.00 0.00 O
-HETATM 2675 H HOH A 892 5.757 27.370 16.585 1.00 0.00 H
-HETATM 2676 H HOH A 892 6.061 28.360 15.479 1.00 0.00 H
-HETATM 2677 O HOH A 893 14.342 2.116 11.522 1.00 0.00 O
-HETATM 2678 H HOH A 893 13.811 2.822 11.890 1.00 0.00 H
-HETATM 2679 H HOH A 893 13.706 1.472 11.212 1.00 0.00 H
-HETATM 2680 O HOH A 894 12.654 3.918 13.098 1.00 0.00 O
-HETATM 2681 H HOH A 894 13.177 3.942 13.900 1.00 0.00 H
-HETATM 2682 H HOH A 894 11.830 3.505 13.358 1.00 0.00 H
-HETATM 2683 O HOH A 895 11.557 19.558 28.292 1.00 0.00 O
-HETATM 2684 H HOH A 895 12.168 18.838 28.134 1.00 0.00 H
-HETATM 2685 H HOH A 895 11.250 19.809 27.421 1.00 0.00 H
-HETATM 2686 O HOH A 896 13.790 17.773 27.650 1.00 0.00 O
-HETATM 2687 H HOH A 896 14.457 18.168 28.213 1.00 0.00 H
-HETATM 2688 H HOH A 896 14.115 17.908 26.760 1.00 0.00 H
-HETATM 2689 O HOH A 897 22.018 18.967 22.085 1.00 0.00 O
-HETATM 2690 H HOH A 897 22.638 19.455 21.543 1.00 0.00 H
-HETATM 2691 H HOH A 897 21.715 19.602 22.734 1.00 0.00 H
-HETATM 2692 O HOH A 898 24.276 20.383 20.869 1.00 0.00 O
-HETATM 2693 H HOH A 898 24.934 19.689 20.935 1.00 0.00 H
-HETATM 2694 H HOH A 898 24.607 21.083 21.431 1.00 0.00 H
-HETATM 2695 O HOH A 899 19.781 22.785 21.114 1.00 0.00 O
-HETATM 2696 H HOH A 899 19.724 22.897 20.165 1.00 0.00 H
-HETATM 2697 H HOH A 899 20.719 22.705 21.291 1.00 0.00 H
-HETATM 2698 O HOH A 900 19.700 22.560 18.194 1.00 0.00 O
-HETATM 2699 H HOH A 900 19.020 21.889 18.120 1.00 0.00 H
-HETATM 2700 H HOH A 900 20.488 22.147 17.842 1.00 0.00 H
-HETATM 2701 O HOH A 901 24.412 16.096 24.961 1.00 0.00 O
-HETATM 2702 H HOH A 901 25.143 16.044 25.577 1.00 0.00 H
-HETATM 2703 H HOH A 901 24.690 16.744 24.313 1.00 0.00 H
-HETATM 2704 O HOH A 902 26.437 16.432 27.052 1.00 0.00 O
-HETATM 2705 H HOH A 902 25.854 16.517 27.806 1.00 0.00 H
-HETATM 2706 H HOH A 902 26.880 17.278 26.992 1.00 0.00 H
-HETATM 2707 O HOH A 903 25.829 2.424 4.859 1.00 0.00 O
-HETATM 2708 H HOH A 903 25.033 2.875 4.580 1.00 0.00 H
-HETATM 2709 H HOH A 903 25.581 1.501 4.903 1.00 0.00 H
-HETATM 2710 O HOH A 904 23.226 3.733 4.552 1.00 0.00 O
-HETATM 2711 H HOH A 904 23.311 4.388 5.245 1.00 0.00 H
-HETATM 2712 H HOH A 904 22.505 3.172 4.836 1.00 0.00 H
-HETATM 2713 O HOH A 905 5.745 3.882 3.083 1.00 0.00 O
-HETATM 2714 H HOH A 905 6.023 3.533 2.236 1.00 0.00 H
-HETATM 2715 H HOH A 905 5.765 3.129 3.673 1.00 0.00 H
-HETATM 2716 O HOH A 906 6.059 2.604 0.465 1.00 0.00 O
-HETATM 2717 H HOH A 906 5.326 3.022 0.012 1.00 0.00 H
-HETATM 2718 H HOH A 906 5.831 1.675 0.488 1.00 0.00 H
-HETATM 2719 O HOH A 907 24.615 29.077 8.037 1.00 0.00 O
-HETATM 2720 H HOH A 907 25.030 28.425 7.473 1.00 0.00 H
-HETATM 2721 H HOH A 907 25.002 28.930 8.900 1.00 0.00 H
-HETATM 2722 O HOH A 908 25.522 26.714 6.562 1.00 0.00 O
-HETATM 2723 H HOH A 908 24.679 26.436 6.202 1.00 0.00 H
-HETATM 2724 H HOH A 908 25.781 26.002 7.146 1.00 0.00 H
-HETATM 2725 O HOH A 909 16.462 19.041 0.768 1.00 0.00 O
-HETATM 2726 H HOH A 909 16.919 19.867 0.610 1.00 0.00 H
-HETATM 2727 H HOH A 909 16.158 19.104 1.674 1.00 0.00 H
-HETATM 2728 O HOH A 910 18.287 21.326 0.587 1.00 0.00 O
-HETATM 2729 H HOH A 910 19.046 20.876 0.214 1.00 0.00 H
-HETATM 2730 H HOH A 910 18.580 21.622 1.448 1.00 0.00 H
-HETATM 2731 O HOH A 911 21.839 17.783 4.015 1.00 0.00 O
-HETATM 2732 H HOH A 911 21.130 18.343 3.698 1.00 0.00 H
-HETATM 2733 H HOH A 911 22.223 18.271 4.744 1.00 0.00 H
-HETATM 2734 O HOH A 912 20.093 19.797 2.798 1.00 0.00 O
-HETATM 2735 H HOH A 912 20.381 19.707 1.889 1.00 0.00 H
-HETATM 2736 H HOH A 912 20.345 20.687 3.044 1.00 0.00 H
-HETATM 2737 O HOH A 913 8.393 2.651 10.731 1.00 0.00 O
-HETATM 2738 H HOH A 913 8.740 1.833 11.087 1.00 0.00 H
-HETATM 2739 H HOH A 913 8.408 2.525 9.782 1.00 0.00 H
-HETATM 2740 O HOH A 914 9.966 0.362 11.663 1.00 0.00 O
-HETATM 2741 H HOH A 914 10.572 0.828 12.240 1.00 0.00 H
-HETATM 2742 H HOH A 914 10.520 -0.000 10.972 1.00 0.00 H
-HETATM 2743 O HOH A 915 30.000 24.126 16.607 1.00 0.00 O
-HETATM 2744 H HOH A 915 29.435 23.599 17.171 1.00 0.00 H
-HETATM 2745 H HOH A 915 29.581 24.092 15.747 1.00 0.00 H
-HETATM 2746 O HOH A 916 28.523 22.065 18.076 1.00 0.00 O
-HETATM 2747 H HOH A 916 29.268 21.575 18.426 1.00 0.00 H
-HETATM 2748 H HOH A 916 28.083 21.450 17.489 1.00 0.00 H
-HETATM 2749 O HOH A 917 3.888 8.739 19.998 1.00 0.00 O
-HETATM 2750 H HOH A 917 4.206 8.701 19.097 1.00 0.00 H
-HETATM 2751 H HOH A 917 4.437 8.117 20.474 1.00 0.00 H
-HETATM 2752 O HOH A 918 4.563 8.129 17.214 1.00 0.00 O
-HETATM 2753 H HOH A 918 3.675 8.038 16.866 1.00 0.00 H
-HETATM 2754 H HOH A 918 4.951 7.259 17.119 1.00 0.00 H
-HETATM 2755 O HOH A 919 20.282 26.979 15.639 1.00 0.00 O
-HETATM 2756 H HOH A 919 20.752 27.788 15.436 1.00 0.00 H
-HETATM 2757 H HOH A 919 19.757 27.193 16.410 1.00 0.00 H
-HETATM 2758 O HOH A 920 22.058 29.302 15.459 1.00 0.00 O
-HETATM 2759 H HOH A 920 22.894 28.840 15.382 1.00 0.00 H
-HETATM 2760 H HOH A 920 22.118 29.775 16.289 1.00 0.00 H
-HETATM 2761 O HOH A 921 17.453 6.626 9.600 1.00 0.00 O
-HETATM 2762 H HOH A 921 17.964 5.942 10.031 1.00 0.00 H
-HETATM 2763 H HOH A 921 16.792 6.876 10.245 1.00 0.00 H
-HETATM 2764 O HOH A 922 18.570 4.195 10.794 1.00 0.00 O
-HETATM 2765 H HOH A 922 18.509 3.618 10.032 1.00 0.00 H
-HETATM 2766 H HOH A 922 17.979 3.807 11.439 1.00 0.00 H
-HETATM 2767 O HOH A 923 14.181 13.464 11.467 1.00 0.00 O
-HETATM 2768 H HOH A 923 13.484 13.282 12.096 1.00 0.00 H
-HETATM 2769 H HOH A 923 13.752 13.959 10.769 1.00 0.00 H
-HETATM 2770 O HOH A 924 11.889 12.502 13.017 1.00 0.00 O
-HETATM 2771 H HOH A 924 12.127 11.576 13.067 1.00 0.00 H
-HETATM 2772 H HOH A 924 11.078 12.516 12.508 1.00 0.00 H
-HETATM 2773 O HOH A 925 7.054 25.285 10.177 1.00 0.00 O
-HETATM 2774 H HOH A 925 7.086 25.005 11.092 1.00 0.00 H
-HETATM 2775 H HOH A 925 7.639 24.681 9.720 1.00 0.00 H
-HETATM 2776 O HOH A 926 7.660 24.700 12.983 1.00 0.00 O
-HETATM 2777 H HOH A 926 7.804 25.596 13.289 1.00 0.00 H
-HETATM 2778 H HOH A 926 8.509 24.270 13.086 1.00 0.00 H
-HETATM 2779 O HOH A 927 15.751 2.619 13.913 1.00 0.00 O
-HETATM 2780 H HOH A 927 14.830 2.384 14.027 1.00 0.00 H
-HETATM 2781 H HOH A 927 15.834 2.816 12.979 1.00 0.00 H
-HETATM 2782 O HOH A 928 13.091 1.396 14.033 1.00 0.00 O
-HETATM 2783 H HOH A 928 13.317 0.607 14.526 1.00 0.00 H
-HETATM 2784 H HOH A 928 12.804 1.074 13.179 1.00 0.00 H
-HETATM 2785 O HOH A 929 19.496 28.429 5.299 1.00 0.00 O
-HETATM 2786 H HOH A 929 20.130 28.490 4.585 1.00 0.00 H
-HETATM 2787 H HOH A 929 19.989 28.051 6.027 1.00 0.00 H
-HETATM 2788 O HOH A 930 21.346 28.050 3.059 1.00 0.00 O
-HETATM 2789 H HOH A 930 20.740 27.643 2.439 1.00 0.00 H
-HETATM 2790 H HOH A 930 21.998 27.373 3.241 1.00 0.00 H
-HETATM 2791 O HOH A 931 2.974 15.478 13.618 1.00 0.00 O
-HETATM 2792 H HOH A 931 2.505 14.644 13.633 1.00 0.00 H
-HETATM 2793 H HOH A 931 3.581 15.427 14.357 1.00 0.00 H
-HETATM 2794 O HOH A 932 1.244 13.163 14.100 1.00 0.00 O
-HETATM 2795 H HOH A 932 0.395 13.599 14.021 1.00 0.00 H
-HETATM 2796 H HOH A 932 1.282 12.869 15.010 1.00 0.00 H
-HETATM 2797 O HOH A 933 -0.001 13.690 2.885 1.00 0.00 O
-HETATM 2798 H HOH A 933 0.736 13.215 3.269 1.00 0.00 H
-HETATM 2799 H HOH A 933 0.151 14.605 3.125 1.00 0.00 H
-HETATM 2800 O HOH A 934 1.963 12.297 4.555 1.00 0.00 O
-HETATM 2801 H HOH A 934 1.383 11.640 4.941 1.00 0.00 H
-HETATM 2802 H HOH A 934 2.250 12.832 5.295 1.00 0.00 H
-HETATM 2803 O HOH A 935 24.367 12.866 16.077 1.00 0.00 O
-HETATM 2804 H HOH A 935 25.138 13.191 15.613 1.00 0.00 H
-HETATM 2805 H HOH A 935 24.506 13.128 16.987 1.00 0.00 H
-HETATM 2806 O HOH A 936 26.991 13.404 14.890 1.00 0.00 O
-HETATM 2807 H HOH A 936 27.129 12.561 14.456 1.00 0.00 H
-HETATM 2808 H HOH A 936 27.682 13.453 15.550 1.00 0.00 H
-HETATM 2809 O HOH A 937 6.449 18.974 14.537 1.00 0.00 O
-HETATM 2810 H HOH A 937 5.623 18.609 14.854 1.00 0.00 H
-HETATM 2811 H HOH A 937 6.895 19.280 15.326 1.00 0.00 H
-HETATM 2812 O HOH A 938 3.759 18.406 15.550 1.00 0.00 O
-HETATM 2813 H HOH A 938 3.253 18.888 14.894 1.00 0.00 H
-HETATM 2814 H HOH A 938 3.581 18.860 16.374 1.00 0.00 H
-HETATM 2815 O HOH A 939 15.435 15.272 4.893 1.00 0.00 O
-HETATM 2816 H HOH A 939 16.266 15.597 4.547 1.00 0.00 H
-HETATM 2817 H HOH A 939 15.495 15.416 5.838 1.00 0.00 H
-HETATM 2818 O HOH A 940 18.191 15.770 4.033 1.00 0.00 O
-HETATM 2819 H HOH A 940 18.326 14.977 3.513 1.00 0.00 H
-HETATM 2820 H HOH A 940 18.813 15.695 4.756 1.00 0.00 H
-HETATM 2821 O HOH A 941 17.190 17.343 22.398 1.00 0.00 O
-HETATM 2822 H HOH A 941 16.660 18.134 22.301 1.00 0.00 H
-HETATM 2823 H HOH A 941 16.623 16.634 22.095 1.00 0.00 H
-HETATM 2824 O HOH A 942 15.308 19.581 22.584 1.00 0.00 O
-HETATM 2825 H HOH A 942 15.519 19.874 23.471 1.00 0.00 H
-HETATM 2826 H HOH A 942 14.400 19.284 22.637 1.00 0.00 H
-HETATM 2827 O HOH A 943 14.701 28.451 29.095 1.00 0.00 O
-HETATM 2828 H HOH A 943 14.049 27.910 28.649 1.00 0.00 H
-HETATM 2829 H HOH A 943 14.694 28.139 30.000 1.00 0.00 H
-HETATM 2830 O HOH A 944 12.325 27.142 27.989 1.00 0.00 O
-HETATM 2831 H HOH A 944 11.910 27.908 27.590 1.00 0.00 H
-HETATM 2832 H HOH A 944 11.715 26.865 28.673 1.00 0.00 H
-HETATM 2833 O HOH A 945 19.483 6.991 8.834 1.00 0.00 O
-HETATM 2834 H HOH A 945 19.617 7.829 9.278 1.00 0.00 H
-HETATM 2835 H HOH A 945 19.749 7.153 7.929 1.00 0.00 H
-HETATM 2836 O HOH A 946 19.403 9.707 9.932 1.00 0.00 O
-HETATM 2837 H HOH A 946 18.532 9.677 10.329 1.00 0.00 H
-HETATM 2838 H HOH A 946 19.334 10.371 9.246 1.00 0.00 H
-HETATM 2839 O HOH A 947 12.823 29.470 20.600 1.00 0.00 O
-HETATM 2840 H HOH A 947 12.092 29.101 20.105 1.00 0.00 H
-HETATM 2841 H HOH A 947 13.603 29.079 20.205 1.00 0.00 H
-HETATM 2842 O HOH A 948 10.678 27.871 19.406 1.00 0.00 O
-HETATM 2843 H HOH A 948 10.205 27.643 20.207 1.00 0.00 H
-HETATM 2844 H HOH A 948 10.998 27.034 19.068 1.00 0.00 H
-HETATM 2845 O HOH A 949 4.744 8.786 2.569 1.00 0.00 O
-HETATM 2846 H HOH A 949 4.937 8.111 1.919 1.00 0.00 H
-HETATM 2847 H HOH A 949 5.566 9.268 2.668 1.00 0.00 H
-HETATM 2848 O HOH A 950 5.598 6.453 1.017 1.00 0.00 O
-HETATM 2849 H HOH A 950 5.208 5.767 1.561 1.00 0.00 H
-HETATM 2850 H HOH A 950 6.541 6.304 1.080 1.00 0.00 H
-HETATM 2851 O HOH A 951 0.196 18.628 18.097 1.00 0.00 O
-HETATM 2852 H HOH A 951 0.504 19.445 18.489 1.00 0.00 H
-HETATM 2853 H HOH A 951 -0.000 18.856 17.188 1.00 0.00 H
-HETATM 2854 O HOH A 952 0.579 21.269 19.308 1.00 0.00 O
-HETATM 2855 H HOH A 952 -0.001 21.166 20.064 1.00 0.00 H
-HETATM 2856 H HOH A 952 0.178 21.965 18.788 1.00 0.00 H
-HETATM 2857 O HOH A 953 10.464 8.665 11.143 1.00 0.00 O
-HETATM 2858 H HOH A 953 10.399 8.980 12.045 1.00 0.00 H
-HETATM 2859 H HOH A 953 10.860 9.393 10.663 1.00 0.00 H
-HETATM 2860 O HOH A 954 9.811 9.924 13.707 1.00 0.00 O
-HETATM 2861 H HOH A 954 8.897 9.646 13.776 1.00 0.00 H
-HETATM 2862 H HOH A 954 9.770 10.877 13.640 1.00 0.00 H
-HETATM 2863 O HOH A 955 9.597 29.279 17.814 1.00 0.00 O
-HETATM 2864 H HOH A 955 9.162 29.282 18.667 1.00 0.00 H
-HETATM 2865 H HOH A 955 9.190 30.001 17.334 1.00 0.00 H
-HETATM 2866 O HOH A 956 7.832 29.037 20.140 1.00 0.00 O
-HETATM 2867 H HOH A 956 7.670 28.094 20.102 1.00 0.00 H
-HETATM 2868 H HOH A 956 6.976 29.436 19.986 1.00 0.00 H
-HETATM 2869 O HOH A 957 20.027 19.582 13.152 1.00 0.00 O
-HETATM 2870 H HOH A 957 20.447 19.509 12.295 1.00 0.00 H
-HETATM 2871 H HOH A 957 19.903 20.523 13.279 1.00 0.00 H
-HETATM 2872 O HOH A 958 21.788 19.519 10.811 1.00 0.00 O
-HETATM 2873 H HOH A 958 22.460 18.928 11.151 1.00 0.00 H
-HETATM 2874 H HOH A 958 22.241 20.351 10.679 1.00 0.00 H
-HETATM 2875 O HOH A 959 4.475 10.449 5.818 1.00 0.00 O
-HETATM 2876 H HOH A 959 5.084 11.137 6.085 1.00 0.00 H
-HETATM 2877 H HOH A 959 3.608 10.841 5.926 1.00 0.00 H
-HETATM 2878 O HOH A 960 6.189 12.403 7.170 1.00 0.00 O
-HETATM 2879 H HOH A 960 6.676 11.802 7.734 1.00 0.00 H
-HETATM 2880 H HOH A 960 5.696 12.957 7.775 1.00 0.00 H
-HETATM 2881 O HOH A 961 15.090 16.401 2.035 1.00 0.00 O
-HETATM 2882 H HOH A 961 14.466 17.092 1.812 1.00 0.00 H
-HETATM 2883 H HOH A 961 15.553 16.734 2.804 1.00 0.00 H
-HETATM 2884 O HOH A 962 13.639 18.781 1.131 1.00 0.00 O
-HETATM 2885 H HOH A 962 13.895 18.764 0.208 1.00 0.00 H
-HETATM 2886 H HOH A 962 14.022 19.588 1.474 1.00 0.00 H
-HETATM 2887 O HOH A 963 13.008 20.711 3.071 1.00 0.00 O
-HETATM 2888 H HOH A 963 12.371 20.751 3.784 1.00 0.00 H
-HETATM 2889 H HOH A 963 12.594 21.187 2.351 1.00 0.00 H
-HETATM 2890 O HOH A 964 10.786 20.421 4.957 1.00 0.00 O
-HETATM 2891 H HOH A 964 10.858 19.480 5.122 1.00 0.00 H
-HETATM 2892 H HOH A 964 9.935 20.527 4.531 1.00 0.00 H
-HETATM 2893 O HOH A 965 9.769 8.432 26.366 1.00 0.00 O
-HETATM 2894 H HOH A 965 9.941 8.419 27.307 1.00 0.00 H
-HETATM 2895 H HOH A 965 10.181 9.239 26.058 1.00 0.00 H
-HETATM 2896 O HOH A 966 9.868 8.779 29.274 1.00 0.00 O
-HETATM 2897 H HOH A 966 8.952 8.584 29.473 1.00 0.00 H
-HETATM 2898 H HOH A 966 9.979 9.696 29.523 1.00 0.00 H
-HETATM 2899 O HOH A 967 17.763 17.299 9.342 1.00 0.00 O
-HETATM 2900 H HOH A 967 18.574 16.986 9.741 1.00 0.00 H
-HETATM 2901 H HOH A 967 17.835 17.047 8.421 1.00 0.00 H
-HETATM 2902 O HOH A 968 20.482 16.805 10.315 1.00 0.00 O
-HETATM 2903 H HOH A 968 20.637 17.647 10.744 1.00 0.00 H
-HETATM 2904 H HOH A 968 21.120 16.776 9.602 1.00 0.00 H
-HETATM 2905 O HOH A 969 2.032 11.340 21.916 1.00 0.00 O
-HETATM 2906 H HOH A 969 1.954 10.442 22.239 1.00 0.00 H
-HETATM 2907 H HOH A 969 2.926 11.598 22.138 1.00 0.00 H
-HETATM 2908 O HOH A 970 1.739 8.842 23.421 1.00 0.00 O
-HETATM 2909 H HOH A 970 0.909 9.033 23.858 1.00 0.00 H
-HETATM 2910 H HOH A 970 2.376 8.773 24.131 1.00 0.00 H
-HETATM 2911 O HOH A 971 6.030 10.439 23.018 1.00 0.00 O
-HETATM 2912 H HOH A 971 5.272 10.973 23.253 1.00 0.00 H
-HETATM 2913 H HOH A 971 5.656 9.627 22.675 1.00 0.00 H
-HETATM 2914 O HOH A 972 3.681 11.730 24.200 1.00 0.00 O
-HETATM 2915 H HOH A 972 4.046 11.932 25.062 1.00 0.00 H
-HETATM 2916 H HOH A 972 2.978 11.106 24.376 1.00 0.00 H
-HETATM 2917 O HOH A 973 22.489 19.121 8.764 1.00 0.00 O
-HETATM 2918 H HOH A 973 23.086 19.778 8.406 1.00 0.00 H
-HETATM 2919 H HOH A 973 21.630 19.362 8.417 1.00 0.00 H
-HETATM 2920 O HOH A 974 24.096 21.472 8.076 1.00 0.00 O
-HETATM 2921 H HOH A 974 24.489 21.634 8.934 1.00 0.00 H
-HETATM 2922 H HOH A 974 23.554 22.243 7.909 1.00 0.00 H
-HETATM 2923 O HOH A 975 2.249 10.109 13.276 1.00 0.00 O
-HETATM 2924 H HOH A 975 3.185 10.308 13.263 1.00 0.00 H
-HETATM 2925 H HOH A 975 1.842 10.891 13.651 1.00 0.00 H
-HETATM 2926 O HOH A 976 5.089 10.602 13.800 1.00 0.00 O
-HETATM 2927 H HOH A 976 5.310 9.761 14.202 1.00 0.00 H
-HETATM 2928 H HOH A 976 5.221 11.244 14.497 1.00 0.00 H
-HETATM 2929 O HOH A 977 23.989 4.970 18.004 1.00 0.00 O
-HETATM 2930 H HOH A 977 24.331 5.140 18.881 1.00 0.00 H
-HETATM 2931 H HOH A 977 23.444 5.732 17.806 1.00 0.00 H
-HETATM 2932 O HOH A 978 24.512 5.331 20.864 1.00 0.00 O
-HETATM 2933 H HOH A 978 24.271 4.455 21.167 1.00 0.00 H
-HETATM 2934 H HOH A 978 23.874 5.914 21.276 1.00 0.00 H
-HETATM 2935 O HOH A 979 13.266 22.958 27.982 1.00 0.00 O
-HETATM 2936 H HOH A 979 12.605 22.845 28.664 1.00 0.00 H
-HETATM 2937 H HOH A 979 13.240 23.892 27.774 1.00 0.00 H
-HETATM 2938 O HOH A 980 10.867 22.643 29.633 1.00 0.00 O
-HETATM 2939 H HOH A 980 10.478 21.893 29.182 1.00 0.00 H
-HETATM 2940 H HOH A 980 10.242 23.356 29.500 1.00 0.00 H
-HETATM 2941 O HOH A 981 16.978 21.242 11.455 1.00 0.00 O
-HETATM 2942 H HOH A 981 16.134 21.694 11.456 1.00 0.00 H
-HETATM 2943 H HOH A 981 16.953 20.685 12.233 1.00 0.00 H
-HETATM 2944 O HOH A 982 14.636 22.953 11.867 1.00 0.00 O
-HETATM 2945 H HOH A 982 15.051 23.804 11.722 1.00 0.00 H
-HETATM 2946 H HOH A 982 14.359 22.971 12.783 1.00 0.00 H
-HETATM 2947 O HOH A 983 21.319 10.711 22.949 1.00 0.00 O
-HETATM 2948 H HOH A 983 20.964 11.482 22.508 1.00 0.00 H
-HETATM 2949 H HOH A 983 21.156 10.870 23.879 1.00 0.00 H
-HETATM 2950 O HOH A 984 20.716 13.328 21.779 1.00 0.00 O
-HETATM 2951 H HOH A 984 21.512 13.441 21.258 1.00 0.00 H
-HETATM 2952 H HOH A 984 20.750 14.030 22.429 1.00 0.00 H
-HETATM 2953 O HOH A 985 12.673 22.835 5.540 1.00 0.00 O
-HETATM 2954 H HOH A 985 12.543 21.894 5.655 1.00 0.00 H
-HETATM 2955 H HOH A 985 12.971 22.928 4.636 1.00 0.00 H
-HETATM 2956 O HOH A 986 12.849 19.938 5.936 1.00 0.00 O
-HETATM 2957 H HOH A 986 13.342 19.934 6.757 1.00 0.00 H
-HETATM 2958 H HOH A 986 13.433 19.523 5.302 1.00 0.00 H
-HETATM 2959 O HOH A 987 8.445 23.893 1.988 1.00 0.00 O
-HETATM 2960 H HOH A 987 8.568 24.101 2.914 1.00 0.00 H
-HETATM 2961 H HOH A 987 9.276 23.502 1.718 1.00 0.00 H
-HETATM 2962 O HOH A 988 9.122 24.977 4.624 1.00 0.00 O
-HETATM 2963 H HOH A 988 8.784 25.866 4.506 1.00 0.00 H
-HETATM 2964 H HOH A 988 10.066 25.090 4.736 1.00 0.00 H
-HETATM 2965 O HOH A 989 16.315 27.150 3.519 1.00 0.00 O
-HETATM 2966 H HOH A 989 16.673 26.611 2.814 1.00 0.00 H
-HETATM 2967 H HOH A 989 17.067 27.640 3.852 1.00 0.00 H
-HETATM 2968 O HOH A 990 17.608 25.169 1.789 1.00 0.00 O
-HETATM 2969 H HOH A 990 17.179 24.382 2.126 1.00 0.00 H
-HETATM 2970 H HOH A 990 18.533 25.053 2.004 1.00 0.00 H
-HETATM 2971 O HOH A 991 5.855 16.055 19.169 1.00 0.00 O
-HETATM 2972 H HOH A 991 6.185 16.613 18.465 1.00 0.00 H
-HETATM 2973 H HOH A 991 4.948 16.334 19.293 1.00 0.00 H
-HETATM 2974 O HOH A 992 6.901 18.164 17.426 1.00 0.00 O
-HETATM 2975 H HOH A 992 7.692 18.381 17.920 1.00 0.00 H
-HETATM 2976 H HOH A 992 6.368 18.958 17.461 1.00 0.00 H
-HETATM 2977 O HOH A 993 26.188 14.225 23.476 1.00 0.00 O
-HETATM 2978 H HOH A 993 26.168 13.326 23.805 1.00 0.00 H
-HETATM 2979 H HOH A 993 27.077 14.528 23.660 1.00 0.00 H
-HETATM 2980 O HOH A 994 26.080 11.725 25.001 1.00 0.00 O
-HETATM 2981 H HOH A 994 25.260 11.878 25.471 1.00 0.00 H
-HETATM 2982 H HOH A 994 26.749 11.691 25.684 1.00 0.00 H
-HETATM 2983 O HOH A 995 23.580 21.644 27.973 1.00 0.00 O
-HETATM 2984 H HOH A 995 24.381 21.128 27.876 1.00 0.00 H
-HETATM 2985 H HOH A 995 23.803 22.504 27.615 1.00 0.00 H
-HETATM 2986 O HOH A 996 26.210 20.362 28.137 1.00 0.00 O
-HETATM 2987 H HOH A 996 26.165 20.057 29.043 1.00 0.00 H
-HETATM 2988 H HOH A 996 26.923 21.000 28.131 1.00 0.00 H
-HETATM 2989 O HOH A 997 6.964 6.761 5.762 1.00 0.00 O
-HETATM 2990 H HOH A 997 6.343 7.069 6.422 1.00 0.00 H
-HETATM 2991 H HOH A 997 6.429 6.260 5.145 1.00 0.00 H
-HETATM 2992 O HOH A 998 5.096 7.167 7.982 1.00 0.00 O
-HETATM 2993 H HOH A 998 5.659 6.874 8.700 1.00 0.00 H
-HETATM 2994 H HOH A 998 4.376 6.537 7.967 1.00 0.00 H
-HETATM 2995 O HOH A 999 13.458 2.646 1.148 1.00 0.00 O
-HETATM 2996 H HOH A 999 13.341 2.796 2.086 1.00 0.00 H
-HETATM 2997 H HOH A 999 14.305 2.205 1.079 1.00 0.00 H
-HETATM 2998 O HOH A1000 13.472 3.539 3.939 1.00 0.00 O
-HETATM 2999 H HOH A1000 13.233 4.456 3.798 1.00 0.00 H
-HETATM 3000 H HOH A1000 14.356 3.572 4.302 1.00 0.00 H
-HETATM 3001 O HOH A1001 18.811 27.880 8.599 1.00 0.00 O
-HETATM 3002 H HOH A1001 18.857 27.051 9.075 1.00 0.00 H
-HETATM 3003 H HOH A1001 18.619 27.628 7.695 1.00 0.00 H
-HETATM 3004 O HOH A1002 19.474 25.298 9.814 1.00 0.00 O
-HETATM 3005 H HOH A1002 20.301 25.543 10.231 1.00 0.00 H
-HETATM 3006 H HOH A1002 19.717 24.644 9.160 1.00 0.00 H
-HETATM 3007 O HOH A1003 14.571 9.605 6.407 1.00 0.00 O
-HETATM 3008 H HOH A1003 14.674 8.709 6.725 1.00 0.00 H
-HETATM 3009 H HOH A1003 15.186 10.119 6.931 1.00 0.00 H
-HETATM 3010 O HOH A1004 14.565 7.043 7.826 1.00 0.00 O
-HETATM 3011 H HOH A1004 13.618 6.927 7.912 1.00 0.00 H
-HETATM 3012 H HOH A1004 14.881 7.128 8.726 1.00 0.00 H
-HETATM 3013 O HOH A1005 7.195 20.102 26.904 1.00 0.00 O
-HETATM 3014 H HOH A1005 6.672 20.324 26.134 1.00 0.00 H
-HETATM 3015 H HOH A1005 6.856 19.253 27.188 1.00 0.00 H
-HETATM 3016 O HOH A1006 5.187 20.919 24.932 1.00 0.00 O
-HETATM 3017 H HOH A1006 5.012 21.802 25.262 1.00 0.00 H
-HETATM 3018 H HOH A1006 4.361 20.451 25.045 1.00 0.00 H
-HETATM 3019 O HOH A1007 28.740 18.790 16.193 1.00 0.00 O
-HETATM 3020 H HOH A1007 28.182 18.435 16.885 1.00 0.00 H
-HETATM 3021 H HOH A1007 29.614 18.457 16.394 1.00 0.00 H
-HETATM 3022 O HOH A1008 27.267 18.119 18.635 1.00 0.00 O
-HETATM 3023 H HOH A1008 26.872 18.975 18.806 1.00 0.00 H
-HETATM 3024 H HOH A1008 27.851 17.969 19.378 1.00 0.00 H
-HETATM 3025 O HOH A1009 24.731 11.057 19.029 1.00 0.00 O
-HETATM 3026 H HOH A1009 25.214 11.652 18.456 1.00 0.00 H
-HETATM 3027 H HOH A1009 25.211 11.078 19.857 1.00 0.00 H
-HETATM 3028 O HOH A1010 26.582 12.441 17.228 1.00 0.00 O
-HETATM 3029 H HOH A1010 26.596 11.801 16.516 1.00 0.00 H
-HETATM 3030 H HOH A1010 27.477 12.447 17.567 1.00 0.00 H
-HETATM 3031 O HOH A1011 6.946 9.100 7.018 1.00 0.00 O
-HETATM 3032 H HOH A1011 6.839 9.440 6.130 1.00 0.00 H
-HETATM 3033 H HOH A1011 7.465 9.765 7.470 1.00 0.00 H
-HETATM 3034 O HOH A1012 7.163 9.963 4.226 1.00 0.00 O
-HETATM 3035 H HOH A1012 7.424 9.127 3.837 1.00 0.00 H
-HETATM 3036 H HOH A1012 7.913 10.540 4.085 1.00 0.00 H
-HETATM 3037 O HOH A1013 28.814 27.065 19.350 1.00 0.00 O
-HETATM 3038 H HOH A1013 28.607 26.220 19.748 1.00 0.00 H
-HETATM 3039 H HOH A1013 28.629 26.945 18.419 1.00 0.00 H
-HETATM 3040 O HOH A1014 28.688 24.313 20.348 1.00 0.00 O
-HETATM 3041 H HOH A1014 29.518 24.295 20.826 1.00 0.00 H
-HETATM 3042 H HOH A1014 28.798 23.671 19.647 1.00 0.00 H
-HETATM 3043 O HOH A1015 12.706 7.219 10.811 1.00 0.00 O
-HETATM 3044 H HOH A1015 11.938 7.176 10.242 1.00 0.00 H
-HETATM 3045 H HOH A1015 12.531 7.957 11.395 1.00 0.00 H
-HETATM 3046 O HOH A1016 10.598 7.534 8.801 1.00 0.00 O
-HETATM 3047 H HOH A1016 11.140 7.478 8.013 1.00 0.00 H
-HETATM 3048 H HOH A1016 10.242 8.422 8.788 1.00 0.00 H
-HETATM 3049 O HOH A1017 16.815 11.831 19.519 1.00 0.00 O
-HETATM 3050 H HOH A1017 17.589 12.315 19.231 1.00 0.00 H
-HETATM 3051 H HOH A1017 16.700 12.084 20.435 1.00 0.00 H
-HETATM 3052 O HOH A1018 19.474 12.909 18.927 1.00 0.00 O
-HETATM 3053 H HOH A1018 19.866 12.124 18.541 1.00 0.00 H
-HETATM 3054 H HOH A1018 19.983 13.068 19.721 1.00 0.00 H
-END
diff --git a/examples/md_ipi/driver.py b/examples/md_ipi/driver.py
deleted file mode 100644
index 55ad1d496..000000000
--- a/examples/md_ipi/driver.py
+++ /dev/null
@@ -1,275 +0,0 @@
-"""
----------------------------------------------------------------------
-|I-PI socket client.
-|
-|Version: 0.1
-|Program Language: Python 3.6
-|Developer: Xinyan Wang
-|Homepage:https://github.com/WangXinyan940/i-pi-driver
-|
-|Receive coordinate and send force back to i-PI server using socket.
-|Read http://ipi-code.org/assets/pdf/manual.pdf for details.
----------------------------------------------------------------------
-"""
-import os
-import socket
-import struct
-import numpy as np
-import sys
-
-# CONSTANTS
-BOHR = 5.291772108e-11 # Bohr -> m
-ANGSTROM = 1e-10 # angstrom -> m
-AMU = 1.660539040e-27 # amu -> kg
-FEMTO = 1e-15
-PICO = 1e-12
-EH = 4.35974417e-18 # Hartrees -> J
-EV = 1.6021766209e-19 # eV -> J
-H = 6.626069934e-34 # Planck const
-KB = 1.38064852e-23 # Boltzmann const
-MOLE = 6.02214129e23
-KJ = 1000.0
-KCAL = 4184.0
-# HEADERS
-STATUS = b"STATUS "
-NEEDINIT = b"NEEDINIT "
-READY = b"READY "
-HAVEDATA = b"HAVEDATA "
-FORCEREADY = b"FORCEREADY "
-# BYTES
-INT = 4
-FLOAT = 8
-
-
-class ExitSignal(BaseException):
- pass
-
-
-class TimeOutSignal(BaseException):
- pass
-
-class BaseDriver(object):
- """
- Base class of Socket driver.
- """
-
- def __init__(self, port, addr="127.0.0.1", socket_type='inet'):
- if socket_type == 'inet':
- self.socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
- elif socket_type == 'unix':
- self.socket = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM)
- else:
- sys.exit('Unknown socket type:', socket_type)
- self.socket_type = socket_type
- self.socket.settimeout(1000)
- try:
- if socket_type == 'inet':
- self.socket.connect((addr, port))
- elif socket_type == 'unix':
- print('/tmp/ipi_'+addr)
- self.socket.connect('/tmp/ipi_'+addr)
- else:
- sys.exit('Unknown socket type:', socket_type)
- self.socket.settimeout(None)
- except socket.timeout as e:
- raise TimeOutSignal("Time out, quit.")
- self.ifInit = False
- self.ifForce = False
- self.cell = None
- self.inverse = None
- self.crd = None
- self.energy = None
- self.force = None
- self.extra = b""
- self.nbead = -1
- self.natom = -1
-
- # cell interface added by Kuang
- def grad(self, crd, cell):
- """
- Calculate gradient.
- Need to be rewritten in inheritance.
- """
- return None, None
-
- def update(self, text):
- """
- Update system message from INIT motion.
- Need to be rewritten in inheritance.
- Mostly we don't need it.
- """
- pass
-
- def init(self):
- """
- Deal with message from INIT motion.
- """
- self.nbead = np.frombuffer(
- self.socket.recv(INT * 1), dtype=np.int32)[0]
- offset = np.frombuffer(self.socket.recv(INT * 1), dtype=np.int32)[0]
- self.update(self.socket.recv(offset))
- self.ifInit = True
-
- def status(self):
- """
- Reply STATUS.
- """
- if self.ifInit and not self.ifForce:
- self.socket.send(READY)
- elif self.ifForce:
- self.socket.send(HAVEDATA)
- else:
- self.socket.send(NEEDINIT)
-
- def posdata(self):
- """
- Read position data.
- """
- self.cell = np.frombuffer(self.socket.recv(
- FLOAT * 9), dtype=np.float64) * BOHR
- self.inverse = np.frombuffer(self.socket.recv(
- FLOAT * 9), dtype=np.float64) / BOHR
- self.natom = np.frombuffer(
- self.socket.recv(INT * 1), dtype=np.int32)[0]
- if (self.socket_type == 'unix'):
- crd = np.frombuffer(self.socket.recv(FLOAT * 3 * self.natom, socket.MSG_WAITALL), dtype=np.float64)
- else:
- crd = np.frombuffer(self.socket.recv(FLOAT * 3 * self.natom), dtype=np.float64)
- self.crd = crd.reshape((self.natom, 3)) * BOHR
- # added by Kuang
- self.cell = self.cell.reshape((3, 3)).T
- self.inverse = self.inverse.reshape((3, 3)).T
- energy, force = self.grad(self.crd, self.cell)
- self.energy = energy
- self.force = - force
- self.ifForce = True
-
- def getforce(self):
- """
- Reply GETFORCE.
- """
- self.socket.send(FORCEREADY)
- self.socket.send(struct.pack("d", self.energy / EH))
- self.socket.send(struct.pack("i", self.natom))
- for f in self.force.ravel():
- self.socket.send(struct.pack("d", f / (EH / BOHR))
- ) # Force unit: xx
- virial = np.diag((self.force * self.crd).sum(axis=0)).ravel() / EH
- for v in virial:
- self.socket.send(struct.pack("d", v))
- extra = self.extra if len(self.extra) > 0 else b" "
- lextra = len(extra)
- self.socket.send(struct.pack("i", lextra))
- self.socket.send(extra)
- self.ifForce = False
-
- def exit(self):
- """
- Exit.
- """
- self.socket.close()
- raise ExitSignal()
-
- def parse(self):
- """
- Reply the request from server.
- """
- try:
- self.socket.settimeout(1000)
- header = self.socket.recv(12).strip()
- self.socket.settimeout(None)
- except socket.timeout as e:
- raise TimeOutSignal("Time out, quit.")
- if len(header) < 2:
- raise TimeOutSignal()
- if header == b"STATUS":
- self.status()
- elif header == b"INIT":
- self.init()
- elif header == b"POSDATA":
- self.posdata()
- elif header == b"GETFORCE":
- self.getforce()
- elif header == b"EXIT":
- self.exit()
-
-
-class HarmonicDriver(BaseDriver):
- """
- Driver for ideal gas molecule with harmonic potential.
- Just for test.
- """
-
- def __init__(self, port, addr, k):
- BaseDriver.__init__(self, port, addr, 'inet')
- self.kconst = k * (KJ / MOLE)
-
- def grad(self, crd, cell):
- r = (crd ** 2).sum(axis=1)
- energy = (self.kconst * r ** 2).sum()
- grad = 2 * self.kconst * crd / r.reshape((-1, 1))
- return energy, grad
-
-
-class GaussDriver(BaseDriver):
- """
- Driver for QM calculation with Gaussian.
- """
-
- def __init__(self, port, addr, template, atoms, path="g09"):
- BaseDriver.__init__(self, port, addr, 'inet')
- with open(template, "r") as f:
- text = f.readlines()
- self.template = text
- self.atoms = atoms
- self.gau = path
-
- def gengjf(self, crd):
- """
- Generate .gjf file.
- """
- with open("tmp.gjf", "w") as f:
- for line in self.template:
- if "[coord]" in line:
- for i in range(len(self.atoms)):
- f.write("%s %16.8f %16.8f %16.8f\n" %
- (self.atoms[i], crd[i, 0], crd[i, 1], crd[i, 2]))
- else:
- f.write(line)
-
- def readlog(self):
- """
- Get energy and force from .log file.
- """
- with open("tmp.log", "r") as f:
- text = f.readlines()
- natoms = len(self.atoms)
- ener = [i for i in text if "SCF Done:" in i]
- if len(ener) != 0:
- ener = ener[-1]
- ener = np.float64(ener.split()[4])
- else:
- ener = np.float64(
- [i for i in text if "Energy=" in i][-1].split()[1])
- for ni, li in enumerate(text):
- if "Forces (Hartrees/Bohr)" in li:
- break
- forces = text[ni + 3:ni + 3 + natoms]
- forces = [i.strip().split()[-3:] for i in forces]
- forces = [[np.float64(i[0]), np.float64(i[1]), np.float64(i[2])]
- for i in forces]
- return ener, - np.array(forces)
-
- def grad(self, crd, cell):
- self.gengjf(crd / ANGSTROM)
- os.system("%s tmp.gjf" % self.gau)
- energy, grad = self.readlog()
- energy = energy * EH
- grad = grad * (EH / BOHR)
- return energy, grad
-
-
-if __name__ == '__main__':
- driver = HarmonicDriver(31415, "127.0.0.1", 100.0)
- while True:
- driver.parse()
diff --git a/examples/md_ipi/forcefield.xml b/examples/md_ipi/forcefield.xml
deleted file mode 100644
index 80d68f922..000000000
--- a/examples/md_ipi/forcefield.xml
+++ /dev/null
@@ -1,40 +0,0 @@
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/examples/md_ipi/input.xml b/examples/md_ipi/input.xml
deleted file mode 100644
index 99babfd50..000000000
--- a/examples/md_ipi/input.xml
+++ /dev/null
@@ -1,38 +0,0 @@
-
-
- 200000
-
- 12345
-
-
- unix_dmff
-
-
- unix_eann
-
-
-
- density_0.03338_init.pdb
- 295
-
-
-
-
-
-
-
- 0.50
-
- 1000
-
-
-
-
- 295
-
-
-
diff --git a/examples/md_ipi/intra.py b/examples/md_ipi/intra.py
deleted file mode 100755
index 47d4ad803..000000000
--- a/examples/md_ipi/intra.py
+++ /dev/null
@@ -1,527 +0,0 @@
-import sys
-import numpy as np
-import jax.numpy as jnp
-from jax import grad, value_and_grad
-from dmff.settings import DO_JIT
-from dmff.utils import jit_condition
-from dmff.admp.spatial import v_pbc_shift
-from dmff.admp.pme import ADMPPmeForce
-from dmff.admp.parser import *
-from jax import vmap
-import time
-#from admp.multipole import convert_cart2harm
-#from jax_md import partition, space
-
-#const
-f5z = 0.999677885
-fbasis = 0.15860145369897
-fcore = -1.6351695982132
-frest = 1.0
-reoh = 0.958649;
-thetae = 104.3475;
-b1 = 2.0;
-roh = 0.9519607159623009;
-alphaoh = 2.587949757553683;
-deohA = 42290.92019288289;
-phh1A = 16.94879431193463;
-phh2 = 12.66426998162947;
-
-c5zA = jnp.array([4.2278462684916e+04, 4.5859382909906e-02, 9.4804986183058e+03,
- 7.5485566680955e+02, 1.9865052511496e+03, 4.3768071560862e+02,
- 1.4466054104131e+03, 1.3591924557890e+02,-1.4299027252645e+03,
- 6.6966329416373e+02, 3.8065088734195e+03,-5.0582552618154e+02,
- -3.2067534385604e+03, 6.9673382568135e+02, 1.6789085874578e+03,
- -3.5387509130093e+03,-1.2902326455736e+04,-6.4271125232353e+03,
- -6.9346876863641e+03,-4.9765266152649e+02,-3.4380943579627e+03,
- 3.9925274973255e+03,-1.2703668547457e+04,-1.5831591056092e+04,
- 2.9431777405339e+04, 2.5071411925779e+04,-4.8518811956397e+04,
- -1.4430705306580e+04, 2.5844109323395e+04,-2.3371683301770e+03,
- 1.2333872678202e+04, 6.6525207018832e+03,-2.0884209672231e+03,
- -6.3008463062877e+03, 4.2548148298119e+04, 2.1561445953347e+04,
- -1.5517277060400e+05, 2.9277086555691e+04, 2.6154026873478e+05,
- -1.3093666159230e+05,-1.6260425387088e+05, 1.2311652217133e+05,
- -5.1764697159603e+04, 2.5287599662992e+03, 3.0114701659513e+04,
- -2.0580084492150e+03, 3.3617940269402e+04, 1.3503379582016e+04,
- -1.0401149481887e+05,-6.3248258344140e+04, 2.4576697811922e+05,
- 8.9685253338525e+04,-2.3910076031416e+05,-6.5265145723160e+04,
- 8.9184290973880e+04,-8.0850272976101e+03,-3.1054961140464e+04,
- -1.3684354599285e+04, 9.3754012976495e+03,-7.4676475789329e+04,
- -1.8122270942076e+05, 2.6987309391410e+05, 4.0582251904706e+05,
- -4.7103517814752e+05,-3.6115503974010e+05, 3.2284775325099e+05,
- 1.3264691929787e+04, 1.8025253924335e+05,-1.2235925565102e+04,
- -9.1363898120735e+03,-4.1294242946858e+04,-3.4995730900098e+04,
- 3.1769893347165e+05, 2.8395605362570e+05,-1.0784536354219e+06,
- -5.9451106980882e+05, 1.5215430060937e+06, 4.5943167339298e+05,
- -7.9957883936866e+05,-9.2432840622294e+04, 5.5825423140341e+03,
- 3.0673594098716e+03, 8.7439532014842e+04, 1.9113438435651e+05,
- -3.4306742659939e+05,-3.0711488132651e+05, 6.2118702580693e+05,
- -1.5805976377422e+04,-4.2038045404190e+05, 3.4847108834282e+05,
- -1.3486811106770e+04, 3.1256632170871e+04, 5.3344700235019e+03,
- 2.6384242145376e+04, 1.2917121516510e+05,-1.3160848301195e+05,
- -4.5853998051192e+05, 3.5760105069089e+05, 6.4570143281747e+05,
- -3.6980075904167e+05,-3.2941029518332e+05,-3.5042507366553e+05,
- 2.1513919629391e+03, 6.3403845616538e+04, 6.2152822008047e+04,
- -4.8805335375295e+05,-6.3261951398766e+05, 1.8433340786742e+06,
- 1.4650263449690e+06,-2.9204939728308e+06,-1.1011338105757e+06,
- 1.7270664922758e+06, 3.4925947462024e+05,-1.9526251371308e+04,
- -3.2271030511683e+04,-3.7601575719875e+05, 1.8295007005531e+05,
- 1.5005699079799e+06,-1.2350076538617e+06,-1.8221938812193e+06,
- 1.5438780841786e+06,-3.2729150692367e+03, 1.0546285883943e+04,
- -4.7118461673723e+04,-1.1458551385925e+05, 2.7704588008958e+05,
- 7.4145816862032e+05,-6.6864945408289e+05,-1.6992324545166e+06,
- 6.7487333473248e+05, 1.4361670430046e+06,-2.0837555267331e+05,
- 4.7678355561019e+05,-1.5194821786066e+04,-1.1987249931134e+05,
- 1.3007675671713e+05, 9.6641544907323e+05,-5.3379849922258e+05,
- -2.4303858824867e+06, 1.5261649025605e+06, 2.0186755858342e+06,
- -1.6429544469130e+06,-1.7921520714752e+04, 1.4125624734639e+04,
- -2.5345006031695e+04, 1.7853375909076e+05,-5.4318156343922e+04,
- -3.6889685715963e+05, 4.2449670705837e+05, 3.5020329799394e+05,
- 9.3825886484788e+03,-8.0012127425648e+05, 9.8554789856472e+04,
- 4.9210554266522e+05,-6.4038493953446e+05,-2.8398085766046e+06,
- 2.1390360019254e+06, 6.3452935017176e+06,-2.3677386290925e+06,
- -3.9697874352050e+06,-1.9490691547041e+04, 4.4213579019433e+04,
- 1.6113884156437e+05,-7.1247665213713e+05,-1.1808376404616e+06,
- 3.0815171952564e+06, 1.3519809705593e+06,-3.4457898745450e+06,
- 2.0705775494050e+05,-4.3778169926622e+05, 8.7041260169714e+03,
- 1.8982512628535e+05,-2.9708215504578e+05,-8.8213012222074e+05,
- 8.6031109049755e+05, 1.0968800857081e+06,-1.0114716732602e+06,
- 1.9367263614108e+05, 2.8678295007137e+05,-9.4347729862989e+04,
- 4.4154039394108e+04, 5.3686756196439e+05, 1.7254041770855e+05,
- -2.5310674462399e+06,-2.0381171865455e+06, 3.3780796258176e+06,
- 7.8836220768478e+05,-1.5307728782887e+05,-3.7573362053757e+05,
- 1.0124501604626e+06, 2.0929686545723e+06,-5.7305706586465e+06,
- -2.6200352535413e+06, 7.1543745536691e+06,-1.9733601879064e+04,
- 8.5273008477607e+04, 6.1062454495045e+04,-2.2642508675984e+05,
- 2.4581653864150e+05,-9.0376851105383e+05,-4.4367930945690e+05,
- 1.5740351463593e+06, 2.4563041445249e+05,-3.4697646046367e+03,
- -2.1391370322552e+05, 4.2358948404842e+05, 5.6270081955003e+05,
- -8.5007851251980e+05,-6.1182429537130e+05, 5.6690751824341e+05,
- -3.5617502919487e+05,-8.1875263381402e+02,-2.4506258140060e+05,
- 2.5830513731509e+05, 6.0646114465433e+05,-6.9676584616955e+05,
- 5.1937406389690e+05, 1.7261913546007e+05,-1.7405787307472e+04,
- -3.8301842660567e+05, 5.4227693205154e+05, 2.5442083515211e+06,
- -1.1837755702370e+06,-1.9381959088092e+06,-4.0642141553575e+05,
- 1.1840693827934e+04,-1.5334500255967e+05, 4.9098619510989e+05,
- 6.1688992640977e+05, 2.2351144690009e+05,-1.8550462739570e+06,
- 9.6815110649918e+03,-8.1526584681055e+04,-8.0810433155289e+04,
- 3.4520506615177e+05, 2.5509863381419e+05,-1.3331224992157e+05,
- -4.3119301071653e+05,-5.9818343115856e+04, 1.7863692414573e+03,
- 8.9440694919836e+04,-2.5558967650731e+05,-2.2130423988459e+04,
- 4.4973674518316e+05,-2.2094939343618e+05])
-
-cbasis = jnp.array([6.9770019624764e-04,-2.4209870001642e+01, 1.8113927151562e+01,
- 3.5107416275981e+01,-5.4600021126735e+00,-4.8731149608386e+01,
- 3.6007189184766e+01, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- -7.7178474355102e+01,-3.8460795013977e+01,-4.6622480912340e+01,
- 5.5684951167513e+01, 1.2274939911242e+02,-1.4325154752086e+02,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00,-6.0800589055949e+00,
- 8.6171499453475e+01,-8.4066835441327e+01,-5.8228085624620e+01,
- 2.0237393793875e+02, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 3.3525582670313e+02, 7.0056962392208e+01,-4.5312502936708e+01,
- -3.0441141194247e+02, 2.8111438108965e+02, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00,-1.2983583774779e+02, 3.9781671212935e+01,
- -6.6793945229609e+01,-1.9259805675433e+02, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00,-8.2855757669957e+02,-5.7003072730941e+01,
- -3.5604806670066e+01, 9.6277766002709e+01, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 8.8645622149112e+02,-7.6908409772041e+01,
- 6.8111763314154e+01, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 2.5090493428062e+02,-2.3622141780572e+02, 5.8155647658455e+02,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 2.8919570295095e+03,
- -1.7871014635921e+02,-1.3515667622500e+02, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00,-3.6965613754734e+03, 2.1148158286617e+02,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00,-1.4795670139431e+03,
- 3.6210798138768e+02, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- -5.3552886800881e+03, 3.1006384016202e+02, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 1.6241824368764e+03, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 4.3764909606382e+03, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 1.0940849243716e+03, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 3.0743267832931e+03, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00])
-
-ccore = jnp.array([2.4332191647159e-02,-2.9749090113656e+01, 1.8638980892831e+01,
- -6.1272361746520e+00, 2.1567487597605e+00,-1.5552044084945e+01,
- 8.9752150543954e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- -3.5693557878741e+02,-3.0398393196894e+00,-6.5936553294576e+00,
- 1.6056619388911e+01, 7.8061422868204e+01,-8.6270891686359e+01,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00,-3.1688002530217e+01,
- 3.7586725583944e+01,-3.2725765966657e+01,-5.6458213299259e+00,
- 2.1502613314595e+01, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 5.2789943583277e+02,-4.2461079404962e+00,-2.4937638543122e+01,
- -1.1963809321312e+02, 2.0240663228078e+02, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00,-6.2574211352272e+02,-6.9617539465382e+00,
- -5.9440243471241e+01, 1.4944220180218e+01, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00,-1.2851139918332e+03,-6.5043516710835e+00,
- 4.0410829440249e+01,-6.7162452402027e+01, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 1.0031942127832e+03, 7.6137226541944e+01,
- -2.7279242226902e+01, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- -3.3059000871075e+01, 2.4384498749480e+01,-1.4597931874215e+02,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 1.6559579606045e+03,
- 1.5038996611400e+02,-7.3865347730818e+01, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00,-1.9738401290808e+03,-1.4149993809415e+02,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00,-1.2756627454888e+02,
- 4.1487702227579e+01, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- -1.7406770966429e+03,-9.3812204399266e+01, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00,-1.1890301282216e+03, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 2.3723447727360e+03, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00,-1.0279968223292e+03, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 5.7153838472603e+02, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
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- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00])
-
-crest = jnp.array([ 0.0000000000000e+00,-4.7430930170000e+00,-1.4422132560000e+01,
- -1.8061146510000e+01, 7.5186735000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- -2.7962099800000e+02, 1.7616414260000e+01,-9.9741392630000e+01,
- 7.1402447000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00,-7.8571336480000e+01,
- 5.2434353250000e+01, 7.7696745000000e+01, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 1.7799123760000e+02, 1.4564532380000e+02, 2.2347226000000e+02,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00,-4.3823284100000e+02,-7.2846553000000e+02,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00,-2.6752313750000e+02, 3.6170310000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
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- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
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- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
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- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
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- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
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- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
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- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00, 0.0000000000000e+00,
- 0.0000000000000e+00, 0.0000000000000e+00])
-
-idx1 = jnp.array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2,
- 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
- 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
- 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3,
- 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
- 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 4, 4,
- 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6,
- 6, 6, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5,
- 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 5, 5,
- 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7,
- 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6,
- 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 9, 9,
- 9, 9, 9, 9, 9])
-
-idx2 = jnp.array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
- 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
- 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2,
- 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3,
- 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
- 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 3, 3, 3, 3, 3, 3,
- 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1,
- 1, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3,
- 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 4,
- 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2,
- 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 5, 5, 5, 5, 5, 5, 5, 4, 4, 4,
- 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 1, 1,
- 1, 1, 1, 1, 1])
-
-idx3 = jnp.array([1, 2, 3, 4, 5, 6, 7, 8, 9,10,11,12,13,14,15, 1, 2, 3, 4, 5,
- 6, 7, 8, 9,10,11,12,13,14, 1, 2, 3, 4, 5, 6, 7, 8, 9,10,11,
- 12,13, 1, 2, 3, 4, 5, 6, 7, 8, 9,10,11,12,13, 1, 2, 3, 4, 5,
- 6, 7, 8, 9,10,11,12, 1, 2, 3, 4, 5, 6, 7, 8, 9,10,11,12, 1,
- 2, 3, 4, 5, 6, 7, 8, 9,10,11, 1, 2, 3, 4, 5, 6, 7, 8, 9,10,
- 11, 1, 2, 3, 4, 5, 6, 7, 8, 9,10,11, 1, 2, 3, 4, 5, 6, 7, 8,
- 9,10, 1, 2, 3, 4, 5, 6, 7, 8, 9,10, 1, 2, 3, 4, 5, 6, 7, 8,
- 9,10, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9,
- 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2,
- 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6,
- 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3,
- 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2,
- 3, 4, 5, 6, 7])
-
-matrix1 = np.zeros((245,16))
-matrix2 = np.zeros((245,16))
-matrix3 = np.zeros((245,16))
-for i in range(245):
- a = int(idx1[i])
- b = int(idx2[i])
- c = int(idx3[i])
- list1 = np.zeros(16)
- list2 = np.zeros(16)
- list3 = np.zeros(16)
- list1[a] = 1
- list2[b] = 1
- list3[c] = 1
- matrix1[i] = list1
- matrix2[i] = list2
- matrix3[i] = list3
-
-c5z = jnp.zeros(245)
-for i in range(245):
- c5z = c5z.at[i].set(f5z*c5zA[i] + fbasis*cbasis[i]+ fcore*ccore[i] + frest*crest[i])
-deoh = f5z*deohA
-phh1 = f5z*phh1A*jnp.exp(phh2)
-costhe = -0.24780227221366464506
-
-Eh_J = 4.35974434e-18
-Na = 6.02214129e+23
-kcal_J = 4184.0
-c0 = 299792458.0
-h_Js = 6.62606957e-34
-cal2joule = 4.184
-Eh_kcalmol = Eh_J*Na/kcal_J
-Eh_cm1 = 1.0e-2*Eh_J/(c0*h_Js)
-cm1_kcalmol = Eh_kcalmol/Eh_cm1
-
-
-## compute intra
-def onebodyenergy(positions, box):
- box_inv = jnp.linalg.inv(box)
- O = positions[::3]
- H1 = positions[1::3]
- H2 = positions[2::3]
- ROH1 = H1 - O
- ROH2 = H2 - O
- RHH = H1 - H2
- ROH1 = v_pbc_shift(ROH1, box, box_inv)
- ROH2 = v_pbc_shift(ROH2, box, box_inv)
- RHH = v_pbc_shift(RHH, box, box_inv)
- dROH1 = jnp.linalg.norm(ROH1, axis=1)
- dROH2 = jnp.linalg.norm(ROH2, axis=1)
- dRHH = jnp.linalg.norm(RHH, axis=1)
- costh = jnp.sum(ROH1 * ROH2, axis=1) / (dROH1 * dROH2)
- exp1 = jnp.exp(-alphaoh*(dROH1 - roh))
- exp2 = jnp.exp(-alphaoh*(dROH2 - roh))
- Va = deoh*(exp1*(exp1 - 2.0) + exp2*(exp2 - 2.0))
- Vb = phh1*jnp.exp(-phh2*dRHH)
- x1 = (dROH1 - reoh)/reoh
- x2 = (dROH2 - reoh)/reoh
- x3 = costh - costhe
- efac = jnp.exp(-b1*(dROH1 - reoh)**2 + (dROH2 - reoh)**2)
- energy = jnp.sum(onebody_kernel(x1, x2, x3, Va, Vb, efac))
- return energy
-
-
-
-@vmap
-@jit_condition(static_argnums={})
-def onebody_kernel(x1, x2, x3, Va, Vb, efac):
- a = jnp.arange(-1,15)
- a = a.at[0].set(0)
- const = jnp.array([0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
- CONST = jnp.array([const,const,const])
- #list1 = jnp.array([x1**i for i in range(-1, 15)])
- #list2 = jnp.array([x2**i for i in range(-1, 15)])
- #list3 = jnp.array([x3**i for i in range(-1, 15)])
- list1 = jnp.array([x1**i for i in a])
- list2 = jnp.array([x2**i for i in a])
- list3 = jnp.array([x3**i for i in a])
- fmat = jnp.array([list1, list2, list3])
- fmat *= CONST
- F1 = jnp.sum(fmat[0].T * matrix1, axis=1) # fmat[0][inI] 1*245
- F2 = jnp.sum(fmat[1].T * matrix2, axis=1) #fmat[1][inJ] 1*245
- F3 = jnp.sum(fmat[0].T * matrix2, axis=1) #fmat[0][inJ] 1*245
- F4 = jnp.sum(fmat[1].T * matrix1, axis=1) #fmat[1][inI] 1*245
- F5 = jnp.sum(fmat[2].T * matrix3, axis=1) #fmat[2][inK] 1*245
- total = c5z * (F1*F2 + F3*F4)* F5
- sum0 = jnp.sum(total[1:245])
- Vc = 2*c5z[0] + efac*sum0
- e1 = Va + Vb + Vc
- e1 += 0.44739574026257
- e1 *= cm1_kcalmol
- e1 *= cal2joule # conver cal 2 j
- return e1
-
-
-def validation(pdb):
- xml = 'pol.xml'
- pdbinfo = read_pdb(pdb)
- serials = pdbinfo['serials']
- names = pdbinfo['names']
- resNames = pdbinfo['resNames']
- resSeqs = pdbinfo['resSeqs']
- positions = pdbinfo['positions']
- box = pdbinfo['box'] # a, b, c, α, β, γ
- charges = pdbinfo['charges']
- positions = jnp.asarray(positions)
- lx, ly, lz, _, _, _ = box
- box = jnp.eye(3)*jnp.array([lx, ly, lz])
-
- mScales = jnp.array([0.0, 0.0, 0.0, 1.0, 1.0])
- pScales = jnp.array([0.0, 0.0, 0.0, 1.0, 1.0])
- dScales = jnp.array([0.0, 0.0, 0.0, 1.0, 1.0])
- rc = 4 # in Angstrom
- ethresh = 1e-4
- n_atoms = len(serials)
-
- # compute intra
- ene = onebodyenergy(positions, box)
- print(ene)
- return
-
-# below is the validation code
-if __name__ == '__main__':
- validation(sys.argv[1])
-
-
diff --git a/examples/md_ipi/model6.py b/examples/md_ipi/model6.py
deleted file mode 100755
index 3e7b797cc..000000000
--- a/examples/md_ipi/model6.py
+++ /dev/null
@@ -1,61 +0,0 @@
-#!/usr/bin/env python
-import sys
-import numpy as np
-import jax.numpy as jnp
-from dmff.utils import jit_condition
-from dmff.admp.parser import *
-from dmff.admp.spatial import v_pbc_shift
-import linecache
-from dmff.settings import DO_JIT
-from jax import jit
-def get_line_context(file_path, line_number):
- return linecache.getline(file_path,line_number).strip()
-
-def gen_trim_val_0(thresh):
- '''
- Trim the value at zero point to avoid singularity
- '''
- def trim_val_0(x):
- return jnp.piecewise(x, [x=thresh], [lambda x: jnp.array(thresh), lambda x: x])
- if DO_JIT:
- return jit(trim_val_0)
- else:
- return trim_val_0
-
-trim_val_0 = gen_trim_val_0(1e-8)
-
-@jit_condition(static_argnums=())
-def compute_leading_terms(positions,box):
- n_atoms = len(positions)
- c0 = jnp.zeros(n_atoms)
- c6_list = jnp.zeros(n_atoms)
- box_inv = jnp.linalg.inv(box)
- O = positions[::3]
- H1 = positions[1::3]
- H2 = positions[2::3]
- ROH1 = H1 - O
- ROH2 = H2 - O
- ROH1 = v_pbc_shift(ROH1, box, box_inv)
- ROH2 = v_pbc_shift(ROH2, box, box_inv)
- dROH1 = jnp.linalg.norm(ROH1, axis=1)
- dROH2 = jnp.linalg.norm(ROH2, axis=1)
- costh = jnp.sum(ROH1 * ROH2, axis=1) / (dROH1 * dROH2)
- angle = jnp.arccos(costh)*180/jnp.pi
- dipole1 = -0.016858755+0.002287251*angle + 0.239667591*dROH1 + (-0.070483437)*dROH2
- charge_H1 = dipole1/dROH1
- dipole2 = -0.016858755+0.002287251*angle + 0.239667591*dROH2 + (-0.070483437)*dROH1
- charge_H2 = dipole2/dROH2
- charge_O = -(charge_H1 + charge_H2)
- C6_H1 = (-2.36066199 + (-0.007049238)*angle + 1.949429648*dROH1+ 2.097120784*dROH2) * 0.529**6 * 2625.5
- C6_H2 = (-2.36066199 + (-0.007049238)*angle + 1.949429648*dROH2+ 2.097120784*dROH1) * 0.529**6 * 2625.5
- C6_O = (-8.641301261 + 0.093247893*angle + 11.90395358*(dROH1+ dROH2)) * 0.529**6 * 2625.5
- C6_H1 = trim_val_0(C6_H1)
- C6_H2 = trim_val_0(C6_H2)
- c0 = c0.at[::3].set(charge_O)
- c0 = c0.at[1::3].set(charge_H1)
- c0 = c0.at[2::3].set(charge_H2)
- c6_list = c6_list.at[::3].set(jnp.sqrt(C6_O))
- c6_list = c6_list.at[1::3].set(jnp.sqrt(C6_H1))
- c6_list = c6_list.at[2::3].set(jnp.sqrt(C6_H2))
- return c0, c6_list
-
diff --git a/examples/md_ipi/para/W_H1 b/examples/md_ipi/para/W_H1
deleted file mode 100644
index 850aa1354..000000000
--- a/examples/md_ipi/para/W_H1
+++ /dev/null
@@ -1,1190 +0,0 @@
- 36 - 20 - 20 - 1
-rmse= 0.2827meV
- -4.789981912896093E-003
- 2.601712817495401E-002
- 7.177088672753588E-002
- 0.319231933087959
- -0.244468129629307
- -0.138893084071211
- 0.148967447574468
- 0.425063263026260
- 0.218213303432323
- -0.126508907601690
- 3.955085427073645E-003
- 2.382527756919179E-002
- 0.277241811165214
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- 0.291417794546004
- -7.465529512094154E-002
- 0.303048954139777
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- -0.277594785593067
- 0.215709497417771
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- 0.392842656836281
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- -3.072156610542471E-002
- 8.366858359921139E-002
- -3.081317813895384E-002
- -6.463281553725382E-002
- 0.482328290627450
- -0.126037204382704
- 0.270552559658846
- 6.969163681461361E-002
- -0.363344903906448
- -1.035183374744202E-002
- 8.264653853822491E-002
- -0.134422473115995
- 4.278473924772760E-002
- -0.245380010732097
- -0.121446084139664
- -0.255443995224774
- 0.401377785453352
- -0.873727887593904
- 0.179489377360612
- -0.143665394045396
- -0.406908813196252
- 0.205016059794797
- 0.227770523077870
- -0.140897036154962
- 0.408751473068935
- 0.340368412954002
- -0.297603206246562
- -2.407680172264308E-002
- 0.371599579738526
- 8.978848457197751E-002
- 0.352697950925972
- -0.379807641781752
- 0.352526636861549
- -0.110342476506800
- -0.182917766717350
- -7.396548716886238E-002
- 8.115399009232215E-002
- 0.289616046653723
- 1.237886136644602E-002
- 6.956090318716492E-003
- 0.431709439961793
- 0.498231833698017
- -8.144115485113097E-002
- -0.250860177973019
- 0.179507082097098
- 0.182365367238169
- 0.175438822282023
- 0.292410300304453
- 0.243199323812246
- -3.919048432227457E-002
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- -0.287355523088430
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- 0.144610113305659
- -0.142161888079626
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- 7.878443965984219E-002
- 0.351099011224754
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- 0.291533284479789
- 0.342973205847213
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- 0.248598162834453
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- 0.117465213239188
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- 0.124872016486052
- 0.189892231884180
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- 0.654893135054681
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- 8.140138596624152E-002
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- 4.358583547639941E-002
- 0.164963508824892
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- 0.257170679139696
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- 0.738423123263050
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- 0.404989907433994
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diff --git a/examples/md_ipi/para/W_O1 b/examples/md_ipi/para/W_O1
deleted file mode 100644
index 10fc1d86a..000000000
--- a/examples/md_ipi/para/W_O1
+++ /dev/null
@@ -1,1190 +0,0 @@
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diff --git a/examples/md_ipi/para/atom b/examples/md_ipi/para/atom
deleted file mode 100644
index 944650d5b..000000000
--- a/examples/md_ipi/para/atom
+++ /dev/null
@@ -1,3054 +0,0 @@
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diff --git a/examples/md_ipi/para/cell b/examples/md_ipi/para/cell
deleted file mode 100644
index 46598491b..000000000
--- a/examples/md_ipi/para/cell
+++ /dev/null
@@ -1,3 +0,0 @@
- 31.24000000 0.00000000 0.00000000
- 0.00000000 31.24000000 0.00000000
- 0.00000000 0.00000000 31.24000000
diff --git a/examples/md_ipi/para/input b/examples/md_ipi/para/input
deleted file mode 100644
index 7274c3531..000000000
--- a/examples/md_ipi/para/input
+++ /dev/null
@@ -1,26 +0,0 @@
- 0 # start_force table for the fit with force(1) or without force(0)
- 1 # start_wb
- 0 # start_init
- 0 # table_coor
- 1 # table_grid
- 1d-10 # biase_rmse
- 2 # maxnumtype
- 3054 3054 # maxnumatom maxneff
- 1 # nsurf
- 2 # ipsin
- 11 # maxnwave
- 11 11 # nwave
- 100 100 # maximal number of neighbours, maximal number of atoms in one cell list
- 3 # ncycle
- 500 # nloop
- 1 # nbatch
- 1 1 # numpoint maxnpoint
- 1 # maxtpoint
- 1d0 0 # perindex force_perindex
- 4d0 4d0 # rc for each element
- 60 # mnl max number of the Embeeding NN
- 2 # mnhid max number of the hidden layer of the Embeeding NN
- 1 # nkpoint
- 1 # outputneuron
- 'H' 2 20 20 0.2 # atomtype name of element and the structure of the embedding NN alpha*dier_rs**2 dier_rs
- 'O' 2 20 20 0.2 # atomtype name of element and the structure of the embedding NN
diff --git a/examples/md_ipi/para/weight_wave_H b/examples/md_ipi/para/weight_wave_H
deleted file mode 100644
index b0be02f37..000000000
--- a/examples/md_ipi/para/weight_wave_H
+++ /dev/null
@@ -1,72 +0,0 @@
- 0.268745723023897343E+01
- 0.346785370979636731E+01
- 0.247933658845809735E+01
- 0.148983279300687932E+01
- 0.127608964262942171E+01
- 0.171253317101807134E+01
- 0.977893233364220293E+00
- 0.175650695818883174E+01
--0.106682985333046806E+00
- 0.179336985688038575E+01
- 0.851554455503823027E+00
- 0.959690945490426994E+00
- 0.902758690525107710E+00
- 0.922011195948287510E+00
- 0.270556514265704762E+00
- 0.253461275315420664E+01
- 0.311308143447436336E+01
- 0.528519412137088973E+00
- 0.450085488999899841E+01
- 0.605641622038493388E+01
- 0.112746092085630387E+02
- 0.624357033743488277E+01
- 0.911411843455036141E+01
- 0.477713683337432400E+02
- 0.643631784083244440E+01
- 0.573353178952620102E+01
- 0.120673987350340828E+01
- 0.399750733970677352E+01
- 0.228653172212523875E+00
- 0.283660780891722730E+01
- 0.503023150087887227E+00
- 0.239169709599722236E+01
- 0.382076835286460359E+00
- 0.222542353834319062E+01
- 0.752779769157288059E+00
- 0.169470327153474898E+01
- 0.141821466217692227E+01
--0.886423932014647614E-01
- 0.167119125678336045E+01
--0.454838983652096518E+00
- 0.189309006494861376E+01
- 0.233889014886272317E+01
- 0.178481701187498576E+01
- 0.467417172846224283E+01
- 0.198406547457699811E+01
- 0.831543086193178027E+01
- 0.130991461220170784E+02
- 0.145814162105344902E+02
--0.163733841800362478E+01
- 0.946648615428787821E+01
- 0.277558638137230540E+01
- 0.349352209010203518E+01
- 0.786874989594567253E+00
- 0.156460467502135936E+01
--0.505857501430637568E+00
- 0.111133032879575455E+01
- 0.144468702986511877E+00
- 0.804309273259493529E+00
- 0.246399400447218947E+00
- 0.582128353277434840E+00
- 0.558321960234286929E+00
- 0.177618713180556193E+00
- 0.581222379481640017E+00
- 0.526575570794575842E+00
- 0.489951960994374547E+00
- 0.122749650843548674E+01
- 0.369746646979077331E+00
- 0.164271266224372892E+01
--0.103483962644644834E+01
- 0.351283053380538313E+01
- 0.983937879101886570E+00
- 0.871526190469304041E+01
diff --git a/examples/md_ipi/para/weight_wave_O b/examples/md_ipi/para/weight_wave_O
deleted file mode 100644
index a12fecc79..000000000
--- a/examples/md_ipi/para/weight_wave_O
+++ /dev/null
@@ -1,72 +0,0 @@
- 0.222345445770802685E+01
- 0.156308333779574071E+01
- 0.754441038029417532E+00
- 0.796359511831995714E+00
- 0.105636248365230245E+01
- 0.570950209743948278E+00
- 0.788383065551391926E+00
- 0.470502823988746322E+00
- 0.772329211464796717E+00
- 0.706011401382065196E+00
- 0.134807994004383125E+01
- 0.785362371276623894E-01
- 0.136191428191611386E+01
- 0.979668246847675395E+00
- 0.147286667709049679E+01
- 0.312220604726999262E+01
- 0.372145420050234588E+01
- 0.185012470220662961E+01
- 0.132859077148191318E+01
- 0.836069311320833286E+01
- 0.586839971157956963E+01
- 0.165751397756295091E+02
- 0.241203464760517790E+02
- 0.310341317943439812E+02
- 0.439235576584590515E+01
- 0.302969951916915337E+01
- 0.244799858819261962E+01
- 0.151641492963923374E+01
- 0.176650003210412287E+01
- 0.974321755001113066E+00
- 0.147486547557896319E+01
- 0.922974739635146868E+00
- 0.141363497138487593E+01
- 0.215486756449232042E+00
- 0.761526305701841988E+00
- 0.337099721262304053E+01
- 0.783741537286614109E+00
- 0.252835624054600983E+01
- 0.173079432016281798E+01
- 0.850910930696118673E+00
--0.881788634407718597E-01
- 0.395978084112362083E+01
--0.916426356014349852E-01
- 0.618890782188073896E+01
--0.610940206143396405E-01
- 0.996226576904776628E+01
- 0.113887564645919159E+02
- 0.171546896838605818E+02
- 0.641923597639249444E+01
- 0.347180453303719760E+01
- 0.252095468486820407E+01
- 0.162294608423319087E+01
- 0.142834977411759678E+01
- 0.558314876952388794E+00
- 0.203555107010700148E+00
- 0.286535535979979450E+01
- 0.883884864275258808E+00
--0.263334321152255690E+00
- 0.444260914209753888E+00
- 0.131086565197015581E+01
- 0.269445083828697696E+00
- 0.120359429227189363E+01
- 0.335292385026269457E+00
- 0.120946990789622721E+01
- 0.272872107835972266E+00
- 0.147167072035011337E+01
- 0.806675037395644723E+00
- 0.189244211922381500E+01
- 0.208405012441366200E+01
- 0.217250752171633854E+01
- 0.241498839409374089E+01
- 0.892085302835749161E+01
diff --git a/examples/md_ipi/residues.xml b/examples/md_ipi/residues.xml
deleted file mode 100644
index 3d4f36fb8..000000000
--- a/examples/md_ipi/residues.xml
+++ /dev/null
@@ -1,6 +0,0 @@
-
-
-
-
-
-
diff --git a/examples/md_ipi/run_EANN.sh b/examples/md_ipi/run_EANN.sh
deleted file mode 100644
index 99d985c93..000000000
--- a/examples/md_ipi/run_EANN.sh
+++ /dev/null
@@ -1,19 +0,0 @@
-#!/bin/bash
-
-export OMP_NUM_THREADS=8
-export OMP_STACKSIZE=2000000
-export MODULEPATH=$MODULEPATH:/share/home/kuangy/modulefiles/
-module load Anaconda/anaconda3/2019.10
-module load compiler/intel/ips2018/u1
-module load mkl/intel/ips2018/u1
-module load EANN/2.0
-
-source activate EANN
-
-addr=unix_eann
-port=1257
-socktype=unix
-
-python3 client_EANN.py $addr $port $socktype > logEANN
-
-conda deactivate
diff --git a/examples/md_ipi/run_client_dmff.sh b/examples/md_ipi/run_client_dmff.sh
deleted file mode 100755
index cb751f5f2..000000000
--- a/examples/md_ipi/run_client_dmff.sh
+++ /dev/null
@@ -1,17 +0,0 @@
-#!/bin/bash
-# create the right environment to run client: note client runs in python3
-# while i-pi server runs in python2
-
-#module load gcc/8.3.0
-module load fftw/3.3.8/single-threads
-module load compiler/intel/ips2018/u1
-module load mkl/intel/ips2018/u1
-module load cuda/11.4
-
-export OMP_NUM_THREADS=1
-
-addr=unix_dmff
-port=1234
-socktype=unix
-
-python ./client_dmff.py density_0.03338.pdb forcefield.xml residues.xml $addr $port $socktype
diff --git a/examples/md_ipi/run_server.sh b/examples/md_ipi/run_server.sh
deleted file mode 100755
index 9098df09b..000000000
--- a/examples/md_ipi/run_server.sh
+++ /dev/null
@@ -1,7 +0,0 @@
-#!/bin/bash
-export OMP_NUM_THREADS=1
-
-cat input.xml | sed -e "s/ \([a-zA-Z_]\+\)/ \1_${SLURM_JOB_ID}/" > .tmp.xml
-#i-pi simulation.restart >& logfile &
-i-pi .tmp.xml >& logfile &
-wait
diff --git a/examples/smirks/C3H7NO.mol b/examples/smirks/C3H7NO.mol
deleted file mode 100644
index ba04b279b..000000000
--- a/examples/smirks/C3H7NO.mol
+++ /dev/null
@@ -1,28 +0,0 @@
-
- RDKit 3D
-
- 12 11 0 0 0 0 0 0 0 0999 V2000
- -1.8857 -0.0588 0.0187 C 0 0 0 0 0 0 0 0 0 0 0 0
- -0.4726 -0.4185 -0.3545 C 0 0 0 0 0 0 0 0 0 0 0 0
- -0.2138 -1.3729 -1.0799 O 0 0 0 0 0 0 0 0 0 0 0 0
- 0.4819 0.4035 0.2006 N 0 0 0 0 0 0 0 0 0 0 0 0
- 1.8857 0.1345 0.0172 C 0 0 0 0 0 0 0 0 0 0 0 0
- -1.9470 0.9478 0.4417 H 0 0 0 0 0 0 0 0 0 0 0 0
- -2.5115 -0.0907 -0.8776 H 0 0 0 0 0 0 0 0 0 0 0 0
- -2.2561 -0.7782 0.7535 H 0 0 0 0 0 0 0 0 0 0 0 0
- 0.2092 1.0317 0.9448 H 0 0 0 0 0 0 0 0 0 0 0 0
- 2.4473 1.0406 0.2554 H 0 0 0 0 0 0 0 0 0 0 0 0
- 2.0840 -0.1673 -1.0148 H 0 0 0 0 0 0 0 0 0 0 0 0
- 2.1785 -0.6717 0.6950 H 0 0 0 0 0 0 0 0 0 0 0 0
- 1 2 1 0
- 2 3 2 0
- 2 4 1 0
- 4 5 1 0
- 1 6 1 0
- 1 7 1 0
- 1 8 1 0
- 4 9 1 0
- 5 10 1 0
- 5 11 1 0
- 5 12 1 0
-M END
diff --git a/examples/smirks/C3H7NO.xml b/examples/smirks/C3H7NO.xml
deleted file mode 100644
index 942af040e..000000000
--- a/examples/smirks/C3H7NO.xml
+++ /dev/null
@@ -1,83 +0,0 @@
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
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-
-
-
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-
-
-
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-
-
-
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-
-
\ No newline at end of file
diff --git a/examples/smirks/clpy.mol b/examples/smirks/clpy.mol
deleted file mode 100644
index 4d0b10a78..000000000
--- a/examples/smirks/clpy.mol
+++ /dev/null
@@ -1,27 +0,0 @@
-
- RDKit 3D
-
- 11 11 0 0 0 0 0 0 0 0999 V2000
- 2.8623 -1.1338 -0.4470 Cl 0 0 0 0 0 0 0 0 0 0 0 0
- 1.2590 -0.5725 -0.2105 C 0 0 0 0 0 0 0 0 0 0 0 0
- 1.0072 0.7619 0.0614 C 0 0 0 0 0 0 0 0 0 0 0 0
- -0.3166 1.1502 0.2425 C 0 0 0 0 0 0 0 0 0 0 0 0
- -1.3245 0.1954 0.1448 C 0 0 0 0 0 0 0 0 0 0 0 0
- -0.9657 -1.1156 -0.1315 C 0 0 0 0 0 0 0 0 0 0 0 0
- 0.3081 -1.5238 -0.3122 N 0 0 0 0 0 0 0 0 0 0 0 0
- 1.8148 1.4810 0.1311 H 0 0 0 0 0 0 0 0 0 0 0 0
- -0.5605 2.1868 0.4577 H 0 0 0 0 0 0 0 0 0 0 0 0
- -2.3663 0.4655 0.2806 H 0 0 0 0 0 0 0 0 0 0 0 0
- -1.7178 -1.8952 -0.2170 H 0 0 0 0 0 0 0 0 0 0 0 0
- 1 2 1 0
- 2 3 2 0
- 3 4 1 0
- 4 5 2 0
- 5 6 1 0
- 6 7 2 0
- 7 2 1 0
- 3 8 1 0
- 4 9 1 0
- 5 10 1 0
- 6 11 1 0
-M END
diff --git a/examples/smirks/clpy_vsite.mol b/examples/smirks/clpy_vsite.mol
deleted file mode 100644
index 02699f734..000000000
--- a/examples/smirks/clpy_vsite.mol
+++ /dev/null
@@ -1,31 +0,0 @@
-
- RDKit 3D
-
- 13 13 0 0 0 0 0 0 0 0999 V2000
- 2.8623 -1.1338 -0.4470 Cl 0 0 0 0 0 0 0 0 0 0 0 0
- 1.2590 -0.5725 -0.2105 C 0 0 0 0 0 0 0 0 0 0 0 0
- 1.0072 0.7619 0.0614 C 0 0 0 0 0 0 0 0 0 0 0 0
- -0.3166 1.1502 0.2425 C 0 0 0 0 0 0 0 0 0 0 0 0
- -1.3245 0.1954 0.1448 C 0 0 0 0 0 0 0 0 0 0 0 0
- -0.9657 -1.1156 -0.1315 C 0 0 0 0 0 0 0 0 0 0 0 0
- 0.3081 -1.5238 -0.3122 N 0 0 0 0 0 0 0 0 0 0 0 0
- 1.8148 1.4810 0.1311 H 0 0 0 0 0 0 0 0 0 0 0 0
- -0.5605 2.1868 0.4577 H 0 0 0 0 0 0 0 0 0 0 0 0
- -2.3663 0.4655 0.2806 H 0 0 0 0 0 0 0 0 0 0 0 0
- -1.7178 -1.8952 -0.2170 H 0 0 0 0 0 0 0 0 0 0 0 0
- 4.3580 -1.6574 -0.6676 R 0 0 0 0 0 0 0 0 0 0 0 0
- 0.3985 -1.9053 -0.3914 R 0 0 0 0 0 0 0 0 0 0 0 0
- 1 2 1 0
- 2 3 2 0
- 3 4 1 0
- 4 5 2 0
- 5 6 1 0
- 6 7 2 0
- 7 2 1 0
- 3 8 1 0
- 4 9 1 0
- 5 10 1 0
- 6 11 1 0
- 12 1 0 0
- 13 7 0 0
-M END
diff --git a/examples/smirks/clpy_vsite.xml b/examples/smirks/clpy_vsite.xml
deleted file mode 100644
index 6d9d89299..000000000
--- a/examples/smirks/clpy_vsite.xml
+++ /dev/null
@@ -1,84 +0,0 @@
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
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-
-
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-
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-
-
-
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-
-
-
-
-
-
-
-
-
-
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-
-
-
-
-
-
-
-
-
-
-
-
-
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-
-
-
-
-
-
-
-
-
-
-
-
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-
-
diff --git a/examples/smirks/demo.ipynb b/examples/smirks/demo.ipynb
deleted file mode 100644
index 9c518f03e..000000000
--- a/examples/smirks/demo.ipynb
+++ /dev/null
@@ -1,276 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Parametrize Molecules with SMIRKS-based Force Field"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Basic Usage"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "SMIRKS is an extension of SMARTS language that enables users to define chemical substructures with certain patterns as well as to numerically tag the matching atoms. This allowed force field developers to introduce new parameters more easily by avoiding starting from defining new atom types. DMFF can deal with SMIRKS-based force field in XML format to create differentiable potential functions."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The usage of SMIRKS-based force field is generally the same as conventional force field based on atom-typing scheme, with the only difference such that we need an extra `rdkit.Chem.Mol` as input because the matching of SMIRKS pattern is powered by rdkit."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "As an example, we will first load a N-methylacetamide molecule defined in a mol file."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import jax.numpy as jnp\n",
- "from rdkit import Chem\n",
- "from dmff import Hamiltonian, NeighborList\n",
- "\n",
- "mol = Chem.MolFromMolFile(\"C3H7NO.mol\", removeHs=False) # hydrogens must be preserved"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Then load force field parameters in xml format. Instuctions about how to write a SMIRKS-based force field XML file can be found in the Chapter 4 of the user's guide."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "h_smk = Hamiltonian(\"C3H7NO.xml\", noOmmSys=True)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Note that the argument noOmmSys is set to False so that DMFF will not create an openmm system, as openmm does not support SMIRKS-based force field definitions.\n",
- "\n",
- "Finally, we build an openmm topology and parametrize the molecule to create differentiable potential energy functions:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "top = h_smk.buildTopologyFromMol(mol)\n",
- "potObj = h_smk.createPotential(top, rdmol=mol)\n",
- "func = potObj.getPotentialFunc()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "So far, we can utilize this dmff.Potential object to calculate energy and forces as we did in the previous sections."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "pos = jnp.array(mol.GetConformer().GetPositions()) / 10 # angstrom -> nm\n",
- "box = jnp.eye(3, dtype=jnp.float32)\n",
- "nblist = NeighborList(box, 1.0, potObj.meta[\"cov_map\"])\n",
- "nblist.allocate(pos)\n",
- "pairs = nblist.pairs\n",
- "energy = func(pos, box, pairs, h_smk.getParameters())\n",
- "print(energy)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Bond Charge Correction and Virtual Sites\n",
- "\n",
- "This section mainly introduces how to use BCC and virtual sites in DMFF."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "First, import required libraries:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import jax\n",
- "import jax.numpy as jnp\n",
- "from rdkit import Chem\n",
- "from dmff import Hamiltonian, NeighborList"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Load the molecule and SMIRKS-based force field file"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "mol = Chem.MolFromMolFile(\"clpy.mol\", removeHs=False)\n",
- "h_vsite = Hamiltonian(\"clpy_vsite.xml\", noOmmSys=True)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "BCC and virtual site parameters are parsed into `h_vsite.getParameters()['NonbondedForce']`"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "params = h_vsite.getParameters()\n",
- "print(params['NonbondedForce'])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Build OpenMM topology and create DMFF potential"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "top = h_vsite.buildTopologyFromMol(mol)\n",
- "potObj = h_vsite.createPotential(top, rdmol=mol)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Add virtual site to RDKit Mol object"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "mol_vsite = h_vsite.addVirtualSiteToMol(mol, h_vsite.getParameters())"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Calculate energy, forces and parametric gradients"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "pos_vsite = jnp.array(mol_vsite.GetConformer().GetPositions()) / 10 # angstrom -> nm\n",
- "box = jnp.eye(3, dtype=jnp.float32)\n",
- "nblist = NeighborList(box, 1.0, h_vsite.getCovalentMap())\n",
- "nblist.allocate(pos_vsite)\n",
- "pairs_vsite = nblist.pairs\n",
- "\n",
- "nbfunc_vsite = jax.value_and_grad(\n",
- " potObj.dmff_potentials['NonbondedForce'], \n",
- " argnums=-1, \n",
- " allow_int=True # set to True since the type of virtual sites are speicified as integars\n",
- ")\n",
- "nbene_vsite, nbene_grad_vsite = nbfunc_vsite(pos_vsite, box, pairs_vsite, params)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Alternatively, we can also add coordinates of virtual sites by taking atomic positions matrix as an input."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "pos = jnp.array(mol.GetConformer().GetPositions()) / 10 # convert angstrom to nm\n",
- "pos_vsite = h_vsite.addVirtualSiteCoords(pos, h_vsite.getParameters())\n",
- "print(pos_vsite.shape)"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "dmff",
- "language": "python",
- "name": "dmff"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.12"
- },
- "vscode": {
- "interpreter": {
- "hash": "44fe82502fda871be637af1aa98d2b3ddaac01204dd30f1519cbec4e95000815"
- }
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}