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Posted to commits@airavata.apache.org by la...@apache.org on 2025/03/22 10:35:54 UTC
(airavata) 05/21: add AF2Complex notebook
This is an automated email from the ASF dual-hosted git repository.
lahirujayathilake pushed a commit to branch cybershuttle-dev
in repository https://gitbox.apache.org/repos/asf/airavata.git
commit 56f28c3639e1921d246625374dc655375e325f68
Author: yasith <ya...@gatech.edu>
AuthorDate: Sun Mar 16 03:47:37 2025 +0000
add AF2Complex notebook
---
.../data/af2complex/AF2Complex_notebook.ipynb | 1021 ++++++++++++++++++++
1 file changed, 1021 insertions(+)
diff --git a/modules/agent-framework/deployments/jupyterhub/data/af2complex/AF2Complex_notebook.ipynb b/modules/agent-framework/deployments/jupyterhub/data/af2complex/AF2Complex_notebook.ipynb
new file mode 100644
index 0000000000..5841c5f093
--- /dev/null
+++ b/modules/agent-framework/deployments/jupyterhub/data/af2complex/AF2Complex_notebook.ipynb
@@ -0,0 +1,1021 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "sl1IbeCnW4zg"
+ },
+ "source": [
+ "# Workflow\n",
+ "\n",
+ "Step 1: Setup AF2Complex by running through the setup module\n",
+ "\n",
+ "Step 2: Pick one of three Target Run modules and follow the steps in each module\n",
+ "\n",
+ "Step 3: Download your predictions\n",
+ "- After running AF2Complex on a target the prediction location will be printed out Such as the screenshot below. \n",
+ "\n",
+ "![Screen Shot 2022-08-30 at 3.28.40 PM.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAACvAAAAByCAYAAACPggTFAAAMa2lDQ1BJQ0MgUHJvZmlsZQAASImVVwdYU8kWnluSkJDQAhGQEnoTRHqREkILICBVsBGSQEKJMSGo2NFFBdeKiGJFV0UUXQsgi4rYy6LY+2JBRVkXC4qC8iYkoOu+8r3zfXPnv2fO/KfcmXvvAKDZw5VIclAtAHLFedK4sCDmuJRUJukZIAIU0IA30OfyZBJWbGwUgDLY/10+3ASIor/mqOD65/h/FR2+QMYDAJkAcTpfxsuFuAkAfANPIs0DgKjQW0zLkyjwPIh1pTBAiEsVOFOJdylwuhI3DtgkxLEhvgKAGpXLlWYCoHEf6pn5vEzIo9EHsbOYLxIDoDkCYn+ekMuHWBH7iNzcKQpcDrEttJdADOMBX [...]
+ "\n",
+ "- You can then download these predictions by using Google Colab's file explorer like the GIF below demonstrates:\n",
+ "\n",
+ "![download_final.gif](data:image/gif;base64,R0lGODlhvwHlAPUAAAAAAAAAVSQkACQkVUgkAEgkVWwkAGwkVZAkACRIAEhIAEhIVUhIqmxIAGxIVZBIAJBIVbRIAGxsVWxsqpBsAJBsVbRsALRsVdhsANhsVZCQVZCQqrS0VbS0qrS0/9jYqtjY//z8qv///wAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACH+IENyb3BwZWQgd2l0aCBlemdpZi5jb20gR0lGIG1ha2VyACH5BAUCACMALAAAAAC/AeUAAAb/QMVAKBgUi0SjMnkcKpvM5VPaRE6tVWc2ip1ytd4rGCr+msvobTqsbrPfZHccTqV35/h6/q7v8/9jfoGAZ3KChXRCigM [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "iyrTK1-HJ6Gs"
+ },
+ "source": [
+ "#Setup AF2Complex\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "DuR67XCbLKod"
+ },
+ "outputs": [],
+ "source": [
+ "#@title 1. Download deep learning model parameters of AlphaFold 2\n",
+ "\n",
+ "#@markdown Please execute this cell by pressing the *Play* button on \n",
+ "#@markdown the left.\n",
+ "\n",
+ "# from google.colab import output\n",
+ "# output.enable_custom_widget_manager()\n",
+ "from IPython.utils import io\n",
+ "import os\n",
+ "import subprocess\n",
+ "import tqdm.notebook\n",
+ "from google.colab import output\n",
+ "import os\n",
+ "\n",
+ "output.enable_custom_widget_manager()\n",
+ "os.environ['TF_FORCE_UNIFIED_MEMORY'] = '1'\n",
+ "os.environ['XLA_PYTHON_CLIENT_MEM_FRACTION'] = '2.0'\n",
+ "\n",
+ "#SOURCE_URL = \"https://storage.googleapis.com/alphafold/alphafold_params_2022-03-02.tar\"\n",
+ "SOURCE_URL = \"https://storage.googleapis.com/alphafold/alphafold_params_2022-12-06.tar\"\n",
+ "PARAMS_DIR = '/content/afold/data/params'\n",
+ "\n",
+ "PARAMS_PATH = os.path.join(PARAMS_DIR, os.path.basename(SOURCE_URL))\n",
+ "\n",
+ "TQDM_BAR_FORMAT = '{l_bar}{bar}| {n_fmt}/{total_fmt} [elapsed: {elapsed} remaining: {remaining}]'\n",
+ "try:\n",
+ " with tqdm.notebook.tqdm(total=100, bar_format=TQDM_BAR_FORMAT) as pbar:\n",
+ " with io.capture_output() as captured:\n",
+ "\n",
+ " if not os.path.exists(PARAMS_DIR):\n",
+ " %shell mkdir --parents \"{PARAMS_DIR}\"\n",
+ " %shell wget -O \"{PARAMS_PATH}\" \"{SOURCE_URL}\"\n",
+ " pbar.update(40)\n",
+ " \n",
+ " %shell tar --extract --verbose --file=\"{PARAMS_PATH}\" \\\n",
+ " --directory=\"{PARAMS_DIR}\" --preserve-permissions\n",
+ " %shell rm \"{PARAMS_PATH}\"\n",
+ " pbar.update(60)\n",
+ " else:\n",
+ " pbar.update(100)\n",
+ "except subprocess.CalledProcessError:\n",
+ " print(captured)\n",
+ " raise\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "zo7qqlklKQmd"
+ },
+ "outputs": [],
+ "source": [
+ "#@title 2. Install AF2Complex\n",
+ "\n",
+ "#@markdown Please execute this cell by pressing the _Play_ button \n",
+ "#@markdown \n",
+ "#@markdown This installs AF2Complex and the python packages it uses\n",
+ "\n",
+ "import os\n",
+ "import subprocess\n",
+ "\n",
+ "AF2C_examples = '/content/af2complex/example'\n",
+ "AF2C_src = '/content/af2complex/src'\n",
+ "AF_LIB_DIR = os.path.join(AF2C_src, 'alphafold')\n",
+ "UPLOAD_DIR = '/content/uploaded_feats/'\n",
+ "os.chdir('/content/')\n",
+ "\n",
+ "try:\n",
+ " with tqdm.notebook.tqdm(total=100, bar_format=TQDM_BAR_FORMAT) as pbar:\n",
+ " with io.capture_output() as captured:\n",
+ " if not os.path.exists('/content/af2complex'):\n",
+ " %shell git clone https://github.com/FreshAirTonight/af2complex.git\n",
+ " pbar.update(15)\n",
+ "\n",
+ " #Install third-party software\n",
+ " %shell pip uninstall -y tensorflow keras\n",
+ " pbar.update(5)\n",
+ " # Install py3dmol.\n",
+ " %shell pip install py3dmol\n",
+ " pbar.update(5)\n",
+ " %shell cd af2complex && pip install -r requirements.txt\n",
+ " pbar.update(50)\n",
+ "\n",
+ " if not os.path.exists('/content/uploaded_feats/'):\n",
+ " %shell mkdir /content/uploaded_feats/\n",
+ " pbar.update(25)\n",
+ "except subprocess.CalledProcessError:\n",
+ " print(captured)\n",
+ " raise\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cellView": "form",
+ "id": "-dkd1Qcz4254"
+ },
+ "outputs": [],
+ "source": [
+ "#@title 3. Define the configuration of your structure prediction run\n",
+ "#@markdown **Note**: Please re-run this cell if any variable below is changed\n",
+ "\n",
+ "#@markdown Choose preset model configuration: <deepmind> standard settings according to DeepMind, \n",
+ "#@markdown i.e., 3 recycles and 1 ensemble; \n",
+ "#@markdown - **economy**: no ensemble, up to 256 MSA clusters, recycling up to 3 rounds; \n",
+ "#@markdown - **super/super2**: 1 or 2 ensembles, up to 512 MSA clusters, recycling up to 20 rounds; \n",
+ "#@markdown - **genome/genome2**: 1 or 2 ensembles, up to 512 MSA clusters, max number \n",
+ "#@markdown of recycles and ensembles adjusted according to input sequence length; \n",
+ "#@markdown - **expert**: similar to super but maintain the same recycle number regardless target size; \n",
+ "#@markdown - **casp14**: 8 model ensemblings used by DeepMind in CASP14.')\n",
+ "import numpy as np\n",
+ "DATA_DIR = '/content/afold/data/'\n",
+ "preset = 'economy' #@param ['deepmind', 'casp14', 'economy', 'super', 'expert', 'super2', 'genome', 'genome2']\n",
+ "\n",
+ "#@markdown Choose between multimer_v2 or ptm AF parameter sets:\n",
+ "model_type = 'multimer_v3' #@param ['multimer_v3', 'monomer_ptm']\n",
+ "model_preset = {\n",
+ " 'multimer_v3': 'multimer_np',\n",
+ " 'monomer_ptm': 'monomer_ptm',\n",
+ " }[model_type]\n",
+ "if model_type == 'monomer_ptm':\n",
+ " model_type = 'ptm'\n",
+ "\n",
+ "#@markdown There are five different models you can choose from, check the ones you want to run (please check at least one) \n",
+ "param_set_1 = True #@param {type:\"boolean\"}\n",
+ "param_set_2 = False #@param {type:\"boolean\"}\n",
+ "param_set_3 = False #@param {type:\"boolean\"}\n",
+ "param_set_4 = False #@param {type:\"boolean\"}\n",
+ "param_set_5 = False #@param {type:\"boolean\"}\n",
+ "\n",
+ "param_set_nums = [param_set_1,param_set_2,param_set_3,param_set_4,param_set_5]\n",
+ "assert np.any(param_set_nums), 'Please check one of the param_sets '\n",
+ "models = []\n",
+ "for i, param_set in enumerate(param_set_nums):\n",
+ " if param_set:\n",
+ " models.append(f\"model_{i+1}_{model_type}\")\n",
+ "\n",
+ "#@markdown Choose your recycling setting:\n",
+ "#@markdown 0. no recycle info saving \n",
+ "#@markdown 1. print metrics of intermediate recycles\n",
+ "#@markdown 2. additionally saving pdb structures of all recycles, \n",
+ "#@markdown 3. additionally save all results in pickle\n",
+ "recycling_setting=\"1\" #@param [0, 1, 2, 3]\n",
+ "\n",
+ "#@markdown Input below how many predictions (each with a different random seed) will be \n",
+ "#@markdown generated per model. \n",
+ "\n",
+ "#@markdown E.g. if this is 2 and there are 5\n",
+ "#@markdown models then there will be 10 predictions per input. \n",
+ "num_predictions_per_model=1 #@param {type:\"integer\"}\n",
+ "\n",
+ "#@markdown Input below the maximum number of recycles. Leave as -1 if you don't want to limit the number of recycles.\n",
+ "max_recycles = 4 #@param {type: \"integer\"}\n",
+ "\n",
+ "\n",
+ "class dotdict(dict):\n",
+ " \"\"\"dot.notation access to dictionary attributes\"\"\"\n",
+ " __getattr__ = dict.get\n",
+ " __setattr__ = dict.__setitem__\n",
+ " __delattr__ = dict.__delitem__\n",
+ "def make_default_flags():\n",
+ " return dotdict({\n",
+ " 'target_lst_path':None,\n",
+ " 'output_dir':'/content/af2complex/example/af2c_mod',\n",
+ " 'feature_dir':'/content/af2complex/example/af2c_fea',\n",
+ " 'model_names':None,\n",
+ " 'data_dir':DATA_DIR,\n",
+ " 'preset':'economy',\n",
+ " 'random_seed':None,\n",
+ " 'max_recycles':None,\n",
+ " 'num_ensemble':None,\n",
+ " 'max_msa_clusters':None,\n",
+ " 'max_extra_msa':None,\n",
+ " 'write_complex_features':False,\n",
+ " 'no_template':False,\n",
+ " 'output_pickle':True,\n",
+ " 'save_recycled':0,\n",
+ " 'checkpoint_tag':None,\n",
+ " 'max_mono_msa_depth':10000,\n",
+ " 'mono_msa_crop_size':5000,\n",
+ " 'max_template_hits':4,\n",
+ " 'model_preset':'monomer_ptm',\n",
+ " 'num_predictions_per_model':1,\n",
+ " 'msa_pairing':None,\n",
+ " 'do_cluster_analysis':False,\n",
+ " 'cluster_edge_thres':10,\n",
+ " })\n",
+ "FLAGS = make_default_flags()\n",
+ "FLAGS['preset'] = preset\n",
+ "FLAGS['model_preset'] = model_preset\n",
+ "FLAGS['model_names'] = models\n",
+ "FLAGS['save_recycled'] = recycling_setting\n",
+ "FLAGS['num_predictions_per_model'] = num_predictions_per_model\n",
+ "FLAGS['max_recycles'] = max_recycles\n",
+ "\n",
+ "def make_mod_params():\n",
+ " preset = FLAGS['preset'] \n",
+ " model_preset = FLAGS['model_preset'] \n",
+ " models = FLAGS['model_names'] \n",
+ " recycling_setting = FLAGS['save_recycled'] \n",
+ " target_lst_file = FLAGS['target_lst_file'] \n",
+ " msa_pairing = FLAGS['msa_pairing'] \n",
+ " out_dir = FLAGS['output_dir']\n",
+ " fea_dir = FLAGS['feature_dir']\n",
+ " num_predictions_per_model = FLAGS['num_predictions_per_model']\n",
+ " max_recycles = FLAGS['max_recycles']\n",
+ "\n",
+ " parameters = [\n",
+ " f'--target_lst_path={target_lst_file}',\n",
+ " f'--data_dir={DATA_DIR}',\n",
+ " f'--output_dir={out_dir}',\n",
+ " f'--feature_dir={fea_dir}',\n",
+ " f'--model_names={\",\".join(models)}',\n",
+ " f'--preset={preset}',\n",
+ " f'--model_preset={model_preset}',\n",
+ " f'--num_predictions_per_model={num_predictions_per_model}',\n",
+ " f'--save_recycled={recycling_setting}']\n",
+ " \n",
+ " if msa_pairing != 'none':\n",
+ " parameters.append(f'--msa_pairing={msa_pairing}')\n",
+ " if max_recycles > 0:\n",
+ " parameters.append(f'--max_recycles={max_recycles}')\n",
+ "\n",
+ " return ' '.join(parameters)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cellView": "form",
+ "id": "7ZKu8W-rTzJ3"
+ },
+ "outputs": [],
+ "source": [
+ "#@title 4. Define relevant methods for visualization\n",
+ "\n",
+ "os.chdir(AF2C_src)\n",
+ "import py3Dmol\n",
+ "from alphafold.data.complex import make_complex_features\n",
+ "from alphafold.model import config\n",
+ "from alphafold.common import confidence\n",
+ "from alphafold.data.complex import initialize_template_feats\n",
+ "\n",
+ "import alphafold.data.complex as af2c\n",
+ "from run_af2c_mod import get_asymid2chain_name\n",
+ "import pickle\n",
+ "\n",
+ "import numpy as np\n",
+ "import re\n",
+ "import pandas as pd\n",
+ "from ipywidgets import interact, Dropdown\n",
+ "from google.colab import widgets\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import ipywidgets\n",
+ "from IPython.display import display\n",
+ "import pandas as pd\n",
+ "\n",
+ "\n",
+ "def show_pdb(pred_output_path, show_sidechains=False, show_mainchains=False):\n",
+ " view = py3Dmol.view(js='https://3dmol.org/build/3Dmol.js',)\n",
+ " view.addModel(open(pred_output_path,'r').read(),'pdb')\n",
+ " view.setStyle({'cartoon': {'colorscheme': 'chain'}})\n",
+ " if show_sidechains:\n",
+ " BB = ['C','O','N']\n",
+ " view.addStyle({'and':[{'resn':[\"GLY\",\"PRO\"],'invert':True},{'atom':BB,'invert':True}]},\n",
+ " {'stick':{'colorscheme':f\"WhiteCarbon\",'radius':0.3}})\n",
+ " view.addStyle({'and':[{'resn':\"GLY\"},{'atom':'CA'}]},\n",
+ " {'sphere':{'colorscheme':f\"WhiteCarbon\",'radius':0.3}})\n",
+ " view.addStyle({'and':[{'resn':\"PRO\"},{'atom':['C','O'],'invert':True}]},\n",
+ " {'stick':{'colorscheme':f\"WhiteCarbon\",'radius':0.3}}) \n",
+ " if show_mainchains:\n",
+ " BB = ['C','O','N','CA']\n",
+ " view.addStyle({'atom':BB},{'stick':{'colorscheme':f\"WhiteCarbon\",'radius':0.3}})\n",
+ "\n",
+ " view.zoomTo()\n",
+ " return view\n",
+ "\n",
+ "def get_asym_id(target, flags):\n",
+ " \"\"\"Defines the sequence of preprocessing steps to get the asym_id feature\n",
+ " Args:\n",
+ " target: dictionary with the items:\n",
+ " name: name of the multimer,\n",
+ " split: information about each monomer composing the multimer,\n",
+ " full: a string denoting all stoichiometry and domains of all monomers\n",
+ " composing the multimer to be modeled,\n",
+ " flags: variable containing inference configuration\n",
+ " Returns:\n",
+ " asym_id\n",
+ " \"\"\"\n",
+ " monomers = af2c.load_monomer_feature(target, flags)\n",
+ "\n",
+ " if flags.msa_pairing is not None:\n",
+ " for i in range(len(monomers)):\n",
+ " if 'deletion_matrix' in monomers[i]['feature_dict']:\n",
+ " monomers[i]['feature_dict']['deletion_matrix_int'] = monomers[i]['feature_dict']['deletion_matrix']\n",
+ " curr_input = {'monomers': monomers, 'target': target, 'flags': flags}\n",
+ "\n",
+ " curr_input = af2c.targeted_domain_cropping_mono(curr_input)\n",
+ " curr_input = af2c.add_asym_id_monomer_ptm(curr_input)\n",
+ " asym_id = curr_input['asym_id_mono_ptm']\n",
+ "\n",
+ " return asym_id\n",
+ "\n",
+ "def get_interface_score(\n",
+ " model_name, target_name, full_name, asym_id, idx2chain_name, out_dir, asym_id_list):\n",
+ " metric = []\n",
+ " value = []\n",
+ " pdb_path = os.path.join(out_dir, target_name, f'{model_name}.pdb')\n",
+ " pkl_path = os.path.join(out_dir, target_name, f'{model_name}.pkl')\n",
+ "\n",
+ " model_config = config.model_config(model_name[:7])\n",
+ " breaks = np.linspace(\n",
+ " 0., model_config.model.heads.predicted_aligned_error.max_error_bin,\n",
+ " model_config.model.heads.predicted_aligned_error.num_bins - 1)\n",
+ " try:\n",
+ " result = pickle.load(open(pkl_path, \"rb\"))\n",
+ " except (EOFError,IOError) as error:\n",
+ " print(f\"Warning: {target_name} {error} encountered, check the pickle file\")\n",
+ " raise\n",
+ "\n",
+ " super_asym_id, superid2chainids = confidence.join_superchains_asym_id(asym_id, asym_id_list)\n",
+ "\n",
+ " res = confidence.interface_score(\n",
+ " result['aligned_confidence_probs'],\n",
+ " breaks,\n",
+ " result['structure_module']['final_atom_positions'],\n",
+ " result['structure_module']['final_atom_mask'],\n",
+ " super_asym_id,\n",
+ " is_probs=True)\n",
+ "\n",
+ " ptm = result['ptm'].tolist()\n",
+ " pitm = result['pitm']['score'].tolist()\n",
+ "\n",
+ " inter_sc = res['score'].tolist()\n",
+ " inter_residues = res['num_residues'].tolist()\n",
+ " inter_contacts = res['num_contacts'].tolist()\n",
+ " metric.append('MODEL NAME')\n",
+ " value.append(model_name)\n",
+ " metric.append('TARGET CHAINS')\n",
+ " value.append(full_name)\n",
+ " metric.append('===========')\n",
+ " value.append('===========')\n",
+ " metric.append('pTM-score')\n",
+ " value.append(ptm)\n",
+ " metric.append('piTM-score')\n",
+ " value.append(pitm)\n",
+ " metric.append('iRes')\n",
+ " value.append(inter_residues)\n",
+ " metric.append('iCnt')\n",
+ " value.append(inter_contacts)\n",
+ " metric.append('interface-score')\n",
+ " value.append(inter_sc)\n",
+ "\n",
+ " if FLAGS.do_cluster_analysis:\n",
+ " clus_res = confidence.cluster_analysis(\n",
+ " super_asym_id,\n",
+ " result['structure_module']['final_atom_positions'],\n",
+ " result['structure_module']['final_atom_mask'],\n",
+ " edge_contacts_thres=FLAGS.cluster_edge_thres,\n",
+ " superid2chainids=superid2chainids,\n",
+ " )\n",
+ " cluster_identities = []\n",
+ " for cluster in clus_res['clusters']:\n",
+ " cluster_identities.append([idx2chain_name[c] for c in cluster])\n",
+ "\n",
+ " metric.append('num_clusters')\n",
+ " value.append(clus_res['num_clusters'])\n",
+ " metric.append('cluster_sizes')\n",
+ " value.append(clus_res['cluster_size'])\n",
+ " metric.append('clusters')\n",
+ " value.append(cluster_identities)\n",
+ " return pd.DataFrame({'Metric Name':metric, 'Value':value})\n",
+ "\n",
+ "DATA_DIR = '/content/afold/data' \n",
+ "def display_metrics(target_lst_path, model_path, support, show_sidechains_=True, show_mainchains_=True, ):\n",
+ " with io.capture_output() as captured:\n",
+ " target_lst = af2c.read_af2c_target_file(target_lst_path)\n",
+ " full_name = support[\"full_name\"] \n",
+ " target_name= support[\"target_name\"] \n",
+ " idx2chain_name= support[\"idx2chain_name\"]\n",
+ " asym_id_list= support[\"asym_id_list\"]\n",
+ " asym_id= support[\"asym_id\"] \n",
+ " model_name = os.path.basename(model_path)\n",
+ " pdb_path = os.path.join(FLAGS.output_dir, target_name, f'{model_name}.pdb')\n",
+ "\n",
+ " metrics = get_interface_score(\n",
+ " model_name, target_name, full_name, asym_id, idx2chain_name, FLAGS.output_dir, asym_id_list\n",
+ " )\n",
+ "\n",
+ " print(metrics.to_markdown())\n",
+ " view = show_pdb(pdb_path, \n",
+ " show_sidechains=show_sidechains_,\n",
+ " show_mainchains=show_mainchains_)\n",
+ " view.show()\n",
+ "\n",
+ "def visualize(show_sidechains, show_mainchains):\n",
+ " is_pdb = lambda x: '.pdb' in x \n",
+ " if not os.path.exists(FLAGS.target_lst_file):\n",
+ " raise f'{FLAGS.target_lst_file} does not exist!'\n",
+ " target_lst = af2c.read_af2c_target_file(FLAGS.target_lst_file)\n",
+ " files = []\n",
+ " model2support = {}\n",
+ " for target in target_lst:\n",
+ " target_name = target['name']\n",
+ " target_name = re.sub(\":\", \"_x\", target_name)\n",
+ " target_name = re.sub(\"/\", \"+\", target_name)\n",
+ " target_dir = os.path.join(FLAGS.output_dir, target_name)\n",
+ " if not os.path.exists(target_dir):\n",
+ " raise Exception(\n",
+ " f'No predictions for {target_name}. Predictions available are for {os.listdir(FLAGS.output_dir)}. Please make sure the inference cell was run correctly.'\n",
+ " )\n",
+ " target_files = os.listdir(target_dir)\n",
+ " if len(target_files) == 0:\n",
+ " raise Exception(\n",
+ " f'No predictions for {target_name}. Predictions available are for {os.listdir(FLAGS.output_dir)}. Please make sure the inference cell was run correctly.'\n",
+ " )\n",
+ " for f in target_files:\n",
+ " full_name = target['full']\n",
+ " idx2chain_name = get_asymid2chain_name(target)\n",
+ " asym_id_list = target['asym_id_list']\n",
+ " if not FLAGS.write_complex_features:\n",
+ " with io.capture_output() as captured:\n",
+ " asym_id = get_asym_id(target, FLAGS)\n",
+ " else:\n",
+ " feat_path = os.path.join(target_name, 'features_comp.pkl')\n",
+ " try:\n",
+ " feature_dict = np.load(open(feat_path, 'rb'))\n",
+ " except FileNotFoundError:\n",
+ " print('Did not find feature_comp.pkl file. ',\n",
+ " 'To rebuild complex features, run without ',\n",
+ " '--write_complex_features flag.')\n",
+ " asym_id = feature_dict['asym_id']\n",
+ " if is_pdb(f):\n",
+ " model_name = os.path.join(target_dir, target_name, f)[:-4]\n",
+ " files.append(model_name)\n",
+ " model2support[model_name] = {\n",
+ " 'full_name': full_name, \n",
+ " 'target_name': target_name, \n",
+ " 'idx2chain_name': idx2chain_name, \n",
+ " 'asym_id_list': asym_id_list, \n",
+ " 'asym_id': asym_id, \n",
+ " }\n",
+ "\n",
+ " tabs = widgets.TabBar(files)\n",
+ "\n",
+ " for i, model in enumerate(files): \n",
+ " with tabs.output_to(i):\n",
+ " display_metrics(\n",
+ " FLAGS.target_lst_file,\n",
+ " model,\n",
+ " model2support[model],\n",
+ " show_sidechains,\n",
+ " show_mainchains, \n",
+ " )\n",
+ "\n",
+ "def get_dataset_desc(file_path):\n",
+ " from google.colab import data_table\n",
+ " data_table.enable_dataframe_formatter()\n",
+ " with open(file_path, 'r') as f:\n",
+ " ecoli_txt = f.readlines()\n",
+ "\n",
+ " ids = []\n",
+ " genes = []\n",
+ " acs = []\n",
+ " fulllen = []\n",
+ " ranges = []\n",
+ " length = []\n",
+ " desc = []\n",
+ " for line in ecoli_txt:\n",
+ " if line.startswith('#'):\n",
+ " continue\n",
+ " line = line.split('\\t')\n",
+ " ids.append(line[0])\n",
+ " genes.append(line[1])\n",
+ " acs.append(line[2])\n",
+ " fulllen.append(line[3])\n",
+ " ranges.append(line[4])\n",
+ " length.append(line[5])\n",
+ " desc.append(line[6])\n",
+ "\n",
+ " df = pd.DataFrame({\n",
+ " 'ID': ids[1:],\n",
+ " 'Gene': genes[1:],\n",
+ " 'AC': acs[1:],\n",
+ " 'Full Length': fulllen[1:],\n",
+ " 'Range': ranges[1:],\n",
+ " 'Length': length[1:],\n",
+ " 'Description': desc[1:],\n",
+ " })\n",
+ " return df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "EUsieqM-OsSJ"
+ },
+ "source": [
+ "# Target Run (AF2Complex Examples)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cellView": "form",
+ "id": "1QFJ32_zOqJU"
+ },
+ "outputs": [],
+ "source": [
+ "import subprocess\n",
+ "import numpy as np\n",
+ "os.chdir(AF_LIB_DIR)\n",
+ "#@markdown #1. Choose one of the AF2Complex examples to run below! \n",
+ "#@markdown Note: After choosing your parameters below, press the play button to run the example chosen:\n",
+ "FLAGS['feature_dir'] = '/content/af2complex/example/af2c_fea' \n",
+ "FLAGS['output_dir'] = '/content/af2complex/example/af2c_mod'\n",
+ "\n",
+ "example = 'H1065' #@param ['H1065', 'H1072', 'H1072_H1065', 'H1060v4']\n",
+ "\n",
+ "target_lst_file = {\n",
+ " 'H1065': '/content/af2complex/example/targets/example1.lst',\n",
+ " 'H1072': '/content/af2complex/example/targets/example2.lst',\n",
+ " 'H1072_H1065': '/content/af2complex/example/targets/example3.lst',\n",
+ " 'H1060v4': '/content/af2complex/example/targets/example4.lst',\n",
+ "}[example]\n",
+ "\n",
+ "#@markdown Choose the type of msa pairing you want to use (Note: 'none' will do no msa_pairing, 'all' will do every possible species pairing as was done in AF-Multimer):\n",
+ "msa_pairing = 'none' #@param ['none', 'all', 'custom', 'cyclic', 'linear']\n",
+ "\n",
+ "FLAGS['target_lst_file'] = target_lst_file\n",
+ "FLAGS['msa_pairing'] = msa_pairing\n",
+ "\n",
+ "pred_params = make_mod_params()\n",
+ "\n",
+ "# with io.capture_output() as captured:\n",
+ "%shell python -u ../run_af2c_mod.py {pred_params}\n",
+ "print(f'DONE! (predictions available on {FLAGS.output_dir}' )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cellView": "form",
+ "id": "sd1bBQgTJLEA"
+ },
+ "outputs": [],
+ "source": [
+ "# %matplotlib inline\n",
+ "\n",
+ "#@markdown #2. Visualize your results below by pressing the *Play* button on the left\n",
+ "#@markdown Choose one of the AF2Complex examples to visualize below! \n",
+ "FLAGS['feature_dir'] = '/content/af2complex/example/af2c_fea' \n",
+ "FLAGS['output_dir'] = '/content/af2complex/example/af2c_mod'\n",
+ "\n",
+ "example = 'H1065' #@param ['H1065', 'H1072', 'H1072_H1065', 'H1060v4']\n",
+ "\n",
+ "target_lst_file = {\n",
+ " 'H1065': '/content/af2complex/example/targets/example1.lst',\n",
+ " 'H1072': '/content/af2complex/example/targets/example2.lst',\n",
+ " 'H1072_H1065': '/content/af2complex/example/targets/example3.lst',\n",
+ " 'H1060v4': '/content/af2complex/example/targets/example4.lst',\n",
+ "}[example]\n",
+ "\n",
+ "FLAGS['target_lst_file'] = target_lst_file\n",
+ "FLAGS['msa_pairing'] = msa_pairing\n",
+ "\n",
+ "pred_params = make_mod_params()\n",
+ "\n",
+ "show_sidechains = False #@param {type: 'boolean'}\n",
+ "show_mainchains = False #@param {type: 'boolean'}\n",
+ "\n",
+ "\n",
+ "AF2C_examples = '/content/af2complex/example'\n",
+ "AF2C_egtargets = os.path.join(AF2C_examples, 'targets')\n",
+ "\n",
+ "visualize(show_sidechains, show_mainchains)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "1s0RID2AYdpk"
+ },
+ "source": [
+ "# Target Run (within the *E. coli* by using pre-generated features for the proteome!)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cellView": "form",
+ "id": "ClAAecbPYrr0"
+ },
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "#@title 1. Download the dataset from [Zenodo](https://zenodo.org/record/7008599#.YwFWR3bMJaQ)\n",
+ "#@markdown Note: Usually takes less than 20 minutes.\n",
+ "\n",
+ "AF2C_examples = '/content/af2complex/example'\n",
+ "os.chdir(AF2C_examples)\n",
+ "AF2C_ecoli = os.path.join(AF2C_examples, 'ecoli')\n",
+ "zenodo_link = 'https://zenodo.org/record/7008599/files/af2c_fea_ecoli_220331_msa10ktem10.tar?download=1'\n",
+ "\n",
+ "AF2C_ecoli_path = os.path.join(AF2C_ecoli, os.path.basename(zenodo_link))\n",
+ "\n",
+ "if not os.path.exists(AF2C_ecoli):\n",
+ " os.mkdir(AF2C_ecoli)\n",
+ "\n",
+ "%shell wget -O {AF2C_ecoli_path} {zenodo_link}\n",
+ " \n",
+ "with io.capture_output() as captured:\n",
+ " %shell tar --extract --verbose --file={AF2C_ecoli_path} \\\n",
+ " --directory={AF2C_ecoli} --preserve-permissions\n",
+ " \n",
+ "AF2C_ecoli_feas = AF2C_ecoli_path.split('.tar')[0]\n",
+ "txt_zenodo_link = \"https://zenodo.org/record/7008599/files/ecoli_af2c_fea.txt?download=1\"\n",
+ "AF2C_ecoli_txt_path = os.path.join(AF2C_ecoli, os.path.basename(txt_zenodo_link))\n",
+ "\n",
+ "%shell wget -O {AF2C_ecoli_txt_path} {txt_zenodo_link}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cellView": "form",
+ "id": "IQuqyTcMMaS7"
+ },
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "\n",
+ "# @markdown # Find the genes in *E. coli* with pre-generated input features:\n",
+ "\n",
+ "ecoli_df = get_dataset_desc(AF2C_ecoli_txt_path)\n",
+ "ecoli_df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cellView": "form",
+ "id": "yNYa7QFWQPZF"
+ },
+ "outputs": [],
+ "source": [
+ "import subprocess\n",
+ "import numpy as np\n",
+ "# from run_af2c_mod import FLAGS\n",
+ "os.chdir(AF_LIB_DIR)\n",
+ "# print(FLAGS.fea_dir, AF2C_ecoli_feas)\n",
+ "FLAGS.feature_dir = AF2C_ecoli_feas\n",
+ "# print(FLAGS.fea_dir, AF2C_ecoli_feas)\n",
+ "FLAGS.output_dir = '/content/af2complex/example/ecoli/af2c_mod'\n",
+ "if not os.path.exists(FLAGS.output_dir):\n",
+ " os.mkdir(FLAGS.output_dir)\n",
+ "\n",
+ "#@markdown #2. Define your protein complex target using the UniProt IDs you found in the table above, then run AF2Complex using the *Play* button on the left\n",
+ "#@markdown Define how the chains compose the target,\n",
+ "#@markdown e.g.: \n",
+ "# #@markdown - T1065s1/T1065s2 *(Explanation on [example1](https://github.gatech.edu/gmu3/af2complex/tree/master/example#example-1) for more)*\n",
+ "# #@markdown - T1072s1:2/T1072s2:2 *(Explanation on [example2](https://github.gatech.edu/gmu3/af2complex/tree/master/example#example-2) for more)\n",
+ "# #@markdown - T1065s1/T1065s2+T1072s1:2/T1072s2:2 *(Explanation on [example3](https://github.gatech.edu/gmu3/af2complex/tree/master/example#example-3) for more)*\n",
+ "# #@markdown - T1060s3:12 *(Explanation on [example4](https://github.gatech.edu/gmu3/af2complex/tree/master/example#example-4) for more)*\n",
+ "\n",
+ "#@markdown - SECE/SECG/SECY *(SecYEG translocon, a hetero-trimer composed of SecE, SecG, and SecY, 680 AAs)*\n",
+ "#@markdown - PPID|265-359/DSBA|20-208 *(PpiD parvulin domain and DsbA, each has a residue ID range, 285 AAs)*\n",
+ "#@markdown - SURA|21-428/BAMA|21-420 *(surA and BamA, both have signal peptide removed, 808 AAs)*\n",
+ "#@markdown - PPID/YFGM *(chaperon proteins PpiD and YfgM, 829 AAs)*\n",
+ "#@markdown - CCMA:2/CCMB:2/CCMC/CCMD/CCME *(CcmI system, 1327 AAs)*\n",
+ "#@markdown - YAJC:3 *(YajC, a membrane protein chaperon? 330 AAs)*\n",
+ "\n",
+ "#@markdown Note that a large target may require resources beyond the free-tier.\n",
+ "\n",
+ "# chains = 'e.g. T1065s1/T1065s2' #@param {type:'string'}\n",
+ "chains = 'SECE/SECG/SECY' #@param {type:'string'}\n",
+ "\n",
+ "#@markdown Name your target\n",
+ "# target = 'e.g. H1065' #@param {type: 'string'}\n",
+ "target = 'SecYEG' #@param {type: 'string'}\n",
+ "\n",
+ "#@markdown Put down the total number of AA of the target (does not need to be exact as this number will be parsed but not used in the code)\n",
+ "num_AA = 680 #@param{type: 'integer'}\n",
+ "\n",
+ "#@markdown Choose the type of msa pairing you want to use (Note: 'none' will do no msa_pairing, 'all' will do species pairing as in AF-Multimer):\n",
+ "msa_pairing = 'all' #@param ['none', 'all', 'custom', 'cyclic', 'linear']\n",
+ "FLAGS.msa_pairing = msa_pairing\n",
+ "\n",
+ "target_lst = f'{chains} {num_AA} {target}'\n",
+ "\n",
+ "target_lst_file = os.path.join(AF2C_ecoli, f'{target}.lst')\n",
+ "with open(target_lst_file, 'w') as f:\n",
+ " f.write(target_lst)\n",
+ " f.close()\n",
+ "FLAGS.target_lst_file = target_lst_file\n",
+ "\n",
+ "pred_params = make_mod_params()\n",
+ "\n",
+ "# with io.capture_output() as captured:\n",
+ "%shell python -u ../run_af2c_mod.py {pred_params}\n",
+ "print(f'DONE! (predictions available on {FLAGS.output_dir}' )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cellView": "form",
+ "id": "MewHtxVmBxNS"
+ },
+ "outputs": [],
+ "source": [
+ "#@markdown # Press **Play** button to the left to see which targets you have predictions for so far\n",
+ "from ipywidgets import interact\n",
+ "pd.DataFrame({\n",
+ " 'Target Name': os.listdir(FLAGS.output_dir),\n",
+ " 'Number of Predictions': [\n",
+ " len(list(filter(lambda x: '.pdb' in x, os.listdir(os.path.join(FLAGS.output_dir,f )))))\n",
+ " for f in os.listdir(FLAGS.output_dir)],\n",
+ "})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cellView": "form",
+ "id": "9G2i6I84ZJql"
+ },
+ "outputs": [],
+ "source": [
+ "#@markdown #3. Visualize your results below by pressing the *Play* button on the left\n",
+ "\n",
+ "#@markdown Check out the cell above to see which targets have predictions. Imput the target name below to visualize the proteins. \n",
+ "target_name = 'SecYEG' #@param {type: 'string'}\n",
+ "FLAGS.target_lst_file = os.path.join(AF2C_ecoli, f'{target_name}.lst')\n",
+ "if not os.path.exists(FLAGS.target_lst_file):\n",
+ " raise Exception(f' Target: predictions for {target_name} do not exist, run the cell above to see which targets have predictions')\n",
+ "\n",
+ "show_sidechains = False #@param {type: 'boolean'}\n",
+ "show_mainchains = False #@param {type: 'boolean'}\n",
+ "\n",
+ "visualize(show_sidechains, show_mainchains)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "tct8Uw1TW8RQ"
+ },
+ "source": [
+ "#Target Run (Upload your own features)\n",
+ "\n",
+ "First create a folder with your features with the following file structure: \n",
+ "```\n",
+ "dataset_name\n",
+ "│ \n",
+ "└───chain_1\n",
+ "│ │ \n",
+ "│ └───features.pkl\n",
+ "└───chain_2\n",
+ "│ │ \n",
+ "│ └───features.pkl\n",
+ "...\n",
+ "```\n",
+ "Then, upload a .tar (or .tgz) file of this folder below (Section 1a). You can create a .tar file with the following unix terminal command (Please keep the folder name and the .tar file name the same): \n",
+ "\n",
+ "\n",
+ "```\n",
+ "tar -czf af2c_fea.tgz af2c_fea\n",
+ "```\n",
+ "\n",
+ "\n",
+ "Optionally, you can also upload a txt file describing the dataset to easily search through the dataset. It should look like the following (all tab separated):\n",
+ "```\n",
+ "### ID -- UniProt ID\n",
+ "### Gene -- Recommended gene name\n",
+ "### AC -- Accession ID\n",
+ "### Fulllen -- Full sequence length\n",
+ "### Range -- Residue range of the longest mature chain\n",
+ "### Len -- Seuence length\n",
+ "### Description -- description of the gene\n",
+ "ID\tGene\tAC\tFullLen\tRange\tLen\tDescription\n",
+ "3MG1\ttag\tP05100\t187\t1-187\t187\tDNA-3-methyladenine glycosylase 1\n",
+ "...\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cellView": "form",
+ "id": "nnj1Vwg9KW3A"
+ },
+ "outputs": [],
+ "source": [
+ "#@markdown #1a. Upload your dataset here (press *Play* button to the left)\n",
+ "#@markdown Please upload only one dataset at a time\n",
+ "\n",
+ "from google.colab import files\n",
+ "os.chdir(UPLOAD_DIR)\n",
+ "print('Upload the .tar (or .tgz) file')\n",
+ "\n",
+ "uploaded = files.upload()\n",
+ "dset_file = list(uploaded.keys())[0]\n",
+ "dset_name = dset_file.split('.')[0]\n",
+ "dset_dir = os.path.join(UPLOAD_DIR, dset_name)\n",
+ "print(f\"INFO: Uploaded dataset with name: {dset_name}\")\n",
+ " \n",
+ "with io.capture_output() as captured:\n",
+ " %shell tar --extract --verbose --file=/content/uploaded_feats/{dset_file} \\\n",
+ " --directory={UPLOAD_DIR} --preserve-permissions\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cellView": "form",
+ "id": "XgDHn7W2vEWX"
+ },
+ "outputs": [],
+ "source": [
+ "#@markdown #1b. Check out the dataset (Optional)\n",
+ "#@markdown Upload the dataset description file\n",
+ "\n",
+ "os.chdir(UPLOAD_DIR)\n",
+ "print('Upload the .txt description file')\n",
+ "uploaded = files.upload()\n",
+ "desc_file = list(uploaded.keys())[0]\n",
+ "dset_df = get_dataset_desc(os.path.join(UPLOAD_DIR, desc_file))\n",
+ "dset_df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cellView": "form",
+ "id": "76o6KEjsy6R7"
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "# from run_af2c_mod import FLAGS\n",
+ "os.chdir(AF_LIB_DIR)\n",
+ "# print(FLAGS.fea_dir, AF2C_ecoli_feas)\n",
+ "FLAGS.feature_dir = os.path.join(UPLOAD_DIR, dset_name)\n",
+ "# print(FLAGS.fea_dir, AF2C_ecoli_feas)\n",
+ "FLAGS.output_dir = os.path.join(UPLOAD_DIR, dset_name, 'af2c_mod')\n",
+ "if not os.path.exists(FLAGS.output_dir):\n",
+ " os.mkdir(FLAGS.output_dir)\n",
+ "\n",
+ "#@markdown #2. Define your protein complex target and Run AF2Complex on it using the *Play* button on the left\n",
+ "#@markdown Define how the chains compose the target, look at sections above for more information (Section 2 of *E. coli* target run)\n",
+ "\n",
+ "#@markdown Note that a large target may require resources beyond the free-tier.\n",
+ "\n",
+ "# chains = 'e.g. T1065s1/T1065s2' #@param {type:'string'}\n",
+ "chains = 'HgcA/HgcB' #@param {type:'string'}\n",
+ "\n",
+ "#@markdown Name your target\n",
+ "# target = 'e.g. H1065' #@param {type: 'string'}\n",
+ "target = 'HgcAB' #@param {type: 'string'}\n",
+ "\n",
+ "#@markdown Put down the total number of AA of the target (does not need to be exact as this number will be parsed but not used in the code)\n",
+ "num_AA = 433 #@param{type: 'integer'}\n",
+ "\n",
+ "#@markdown Choose the type of msa pairing you want to use (Note: 'none' will do no msa_pairing, 'all' will do species pairing as in AF-Multimer):\n",
+ "msa_pairing = 'all' #@param ['none', 'all', 'custom', 'cyclic', 'linear']\n",
+ "FLAGS.msa_pairing = msa_pairing\n",
+ "\n",
+ "target_lst = f'{chains} {num_AA} {target}'\n",
+ "\n",
+ "target_lst_file = os.path.join(dset_dir, f'{target}.lst')\n",
+ "with open(target_lst_file, 'w') as f:\n",
+ " f.write(target_lst)\n",
+ " f.close()\n",
+ "FLAGS.target_lst_file = target_lst_file\n",
+ "\n",
+ "pred_params = make_mod_params()\n",
+ "\n",
+ "# with io.capture_output() as captured:\n",
+ "%shell python -u ../run_af2c_mod.py {pred_params}\n",
+ "print(f'DONE! (predictions available on {FLAGS.output_dir}' )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cellView": "form",
+ "id": "01DWp6Sjk6pP"
+ },
+ "outputs": [],
+ "source": [
+ "#@markdown # Press **Play** button to the left to see which targets you have predictions for so far\n",
+ "from ipywidgets import interact\n",
+ "pd.DataFrame({\n",
+ " 'Target Name': os.listdir(FLAGS.output_dir),\n",
+ " 'Number of Predictions': [\n",
+ " len(list(filter(lambda x: '.pdb' in x, os.listdir(os.path.join(FLAGS.output_dir,f )))))\n",
+ " for f in os.listdir(FLAGS.output_dir)],\n",
+ "})\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cellView": "form",
+ "id": "1rjkGMLYyVcU"
+ },
+ "outputs": [],
+ "source": [
+ "# %matplotlib inline\n",
+ "\n",
+ "#@markdown #3. Visualize your results below by pressing the *Play* button on the left\n",
+ "\n",
+ "#@markdown Place the target name you want to visualize below\n",
+ "# target = 'e.g. H1065' #@param {type: 'string'}\n",
+ "target_name = 'HgcAB' #@param {type: 'string'}\n",
+ "FLAGS.target_lst_file = os.path.join(dset_dir, f'{target_name}.lst')\n",
+ "if not os.path.exists(FLAGS.target_lst_file):\n",
+ " raise Exception(f' Target: predictions for {target_name} do not exist, run the cell above to see which targets have predictions')\n",
+ "\n",
+ "show_sidechains = False #@param {type: 'boolean'}\n",
+ "show_mainchains = False #@param {type: 'boolean'}\n",
+ "\n",
+ "visualize(show_sidechains, show_mainchains)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "7PN-BhHoTtCr"
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "private_outputs": true,
+ "provenance": [],
+ "toc_visible": true
+ },
+ "gpuClass": "standard",
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "name": "python",
+ "version": "3.10.6 (main, Nov 14 2022, 16:10:14) [GCC 11.3.0]"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}