{ "cells": [ { "cell_type": "markdown", "id": "4d9e97b7", "metadata": {}, "source": [ "## Example 2: Epidemic model" ] }, { "cell_type": "markdown", "id": "098e3327", "metadata": {}, "source": [ "The DynGPT package can be applied to solve a given state transition network and infer the associated model parameters. In this section, we will demonstrate the basic use of DynGPT using the epidemic model as an illustrative example." ] }, { "cell_type": "markdown", "id": "84dff04a", "metadata": {}, "source": [ "\"Schematic" ] }, { "cell_type": "markdown", "id": "3ee9e6a6", "metadata": {}, "source": [ "The STN of epidemic model can be described as:
\n", "(1)\tThe system state is $s={{\\left( {{s}_{\\text{S}}},{{s}_{\\text{I}}},{{s}_{\\text{R}}} \\right)}^{\\text{T}}}\\in {{\\mathbb{N}}^{3}},$ where ${{s}_{\\text{S}}},\\text{ }{{s}_{\\text{I}}}$ and ${{s}_{\\text{R}}}$ represent susceptible, infected, and recovered individuals, respectively.
\n", "(2) Event sets are $\\mathcal{R}=\\left\\{ \\left. {{\\mathcal{R}}_{1}},\\ldots ,{{\\mathcal{R}}_{7}} \\right\\} \\right.$, ${{\\mathcal{R}}_{1}}$ represents susceptible individual infection, ${{\\mathcal{R}}_{2}}$ represents infected individual recovery, ${{\\mathcal{R}}_{3}}$ represents recovered individual lose immunity, ${{\\mathcal{R}}_{4}}$ represents susceptible individual generation, ${{\\mathcal{R}}_{5}},\\text{ }{{\\mathcal{R}}_{6}}$ and ${{\\mathcal{R}}_{7}}$ represent removal of susceptible, infected, and recovered individuals.
\n", "(3)\tState change vectors of events are: $\\left\\{ {{\\Delta }^{\\left( k \\right)}} \\right\\},k=1,2,\\ldots ,7,$ where ${{\\Delta }^{\\left( k \\right)}}=\\left( \\Delta _{\\text{S}}^{\\left( k \\right)},\\Delta _{\\text{I}}^{\\left( k \\right)},\\Delta _{\\text{R}}^{\\left( k \\right)} \\right)$ represents the state change vector corresponding to $k\\text{-th}$ event with \n", "$$\\begin{align}\n", " & \\Delta _{i}^{\\left( 1 \\right)}=-{{\\delta }_{i,\\text{S}}}+{{\\delta }_{i,\\text{I}}},\\text{ } \\\\ \n", " & \\Delta _{i}^{\\left( 2 \\right)}=-{{\\delta }_{i,\\text{I}}}+{{\\delta }_{i,\\text{R}}}, \\\\ \n", " & \\Delta _{i}^{\\left( 3 \\right)}=-{{\\delta }_{i,\\text{R}}}+{{\\delta }_{i,\\text{S}}}, \\\\ \n", " & \\Delta _{i}^{\\left( 4 \\right)}={{\\delta }_{i,\\text{S}}},\\text{ }\\Delta _{i}^{\\left( 5 \\right)}=-{{\\delta }_{i,\\text{S}}}, \\\\ \n", " & \\Delta _{i}^{\\left( 6 \\right)}=-{{\\delta }_{i,\\text{I}}},\\text{ }\\Delta _{i}^{\\left( 7 \\right)}=-{{\\delta }_{i,\\text{R}}},\\text{ } \\\\ \n", "\\end{align}$$\n", "where $i\\in \\left\\{ \\text{OFF},\\text{ON},\\text{M},\\text{P} \\right\\}$ and ${{\\delta }_{i,j}}$ is the Kronecker delta.
\n", "(4) The propensity functions are: ${{\\lambda }_{1}}={{k}_{\\text{S}}}{{s}_{\\text{S}}}{{s}_{\\text{I}}},$ ${{\\lambda }_{2}}={{k}_{\\text{I}}}{{s}_{\\text{I}}},$ ${{\\lambda }_{3}}={{k}_{\\text{R}}}{{s}_{\\text{R}}},$ ${{\\lambda }_{4}}=\\rho ,$ ${{\\lambda }_{5}}={{\\delta }_{\\text{S}}}{{s}_{\\text{S}}},$ ${{\\lambda }_{6}}={{\\delta }_{\\text{I}}}{{s}_{\\text{I}}}$ and ${{\\lambda }_{7}}={{\\delta }_{\\text{R}}}{{s}_{\\text{R}}}.$
\n", "In this STN, the input of the DynGPT-Solver is the parameters ${{\\theta }_{\\text{dyn}}}=\\left\\{ {{k}_{\\text{S}}},{{k}_{\\text{I}}},{{k}_{\\text{R}}},\\rho ,{{\\delta }_{\\text{S}}},{{\\delta }_{\\text{I}}},{{\\delta }_{\\text{R}}} \\right\\}$." ] }, { "cell_type": "markdown", "id": "b99c7072", "metadata": {}, "source": [ "### 1. Construct configuration file" ] }, { "cell_type": "markdown", "id": "fbcb707b", "metadata": {}, "source": [ "To facilitate data generation and subsequent neural network training, we need create a configuration file for the epidemic model that encapsulates its key properties. The key propertie of STN primarily consists of the following components:
\n", "(1) State-related configuration: This includes the names of the states. In epidemic model, the state names (`state_names`) are: (\"S\", \"I\", \"R\").
\n", "(2) Event-related configuration: The occurrence rate functions governing event dynamics, and state transition vectors associated with each event's occurrence. In epidemic model, the state transition vectors (`state_transition`) are: $\\Delta _{i}^{\\left( 1 \\right)}$, $\\Delta _{i}^{\\left( 2 \\right)}$, $\\Delta _{i}^{\\left( 3 \\right)}$, $\\Delta _{i}^{\\left( 4 \\right)}$, $\\Delta _{i}^{\\left( 5 \\right)}$, $\\Delta _{i}^{\\left( 6 \\right)}$ and $\\Delta _{i}^{\\left( 7 \\right)}$. Occurrence rates for each event (`event_rates`) are: ${{\\lambda }_{1}}={{k}_{\\text{S}}}{{s}_{\\text{S}}}{{s}_{\\text{I}}}$, ${{\\lambda }_{2}}={{k}_{\\text{I}}}{{s}_{\\text{I}}}$, ${{\\lambda }_{3}}={{k}_{\\text{R}}}{{s}_{\\text{R}}}$, ${{\\lambda }_{4}}=\\rho$, ${{\\lambda }_{5}}={{\\delta }_{\\text{S}}}{{s}_{\\text{S}}}$, ${{\\lambda }_{6}}={{\\delta }_{\\text{I}}}{{s}_{\\text{I}}}$, ${{\\lambda }_{7}}={{\\delta }_{\\text{R}}}{{s}_{\\text{R}}}$. Specifically, in certain events, specific states actively participate and drive the occurrence of the event without changing their own identity. Such a state is referred to as an `drive state` in the context of the event. In ${{R}_{2}}$, \"I\" can be described as a driving state, as its presence is essential for the transition of \"S\" to \"I\". However, the identity of \"I\" in the event remains unchanged as \"I\".
\n", "(3) Parameter-related configuration: The parameter names and the parameter ranges. In epidemic model, the parameter names (`param_names`) are: [\"ρ\", \"kS\", \"kI\", \"kR\", \"δS\", \"δI\",\"δR\"]. Upper bounds of parameters (`upper_bound_val`) are: [10.0, 0.2, 0.1, 0.4, 0.2, 0.2, 0.2]. Lower bounds of parameters (`lower_bound_val`) are: [0.05, 0.1, 0.05, 0.05, 0.05, 0.05, 0.05].
\n", "      Additionally, we need to specify several hyperparameters for performing SSA simulations on the STN, including the model name (`model_name`), the initial values (`initial_val`), maximum simulation time (`t_end`), the number of parameter sets to simulate for training (`train_data_size`) and validation datasets (`valid_data_size`), and the directory for storing the simulation results (`dataset_dir`). A subset of state indices corresponding to the state names to extract is defined as `minimal_state_indices`." ] }, { "cell_type": "markdown", "id": "cb61a2a5", "metadata": {}, "source": [ "Hence, the following configuration files can be defined for epidemic model. And we use Python's json package to store this configuration for subsequent simulation and neural network training." ] }, { "cell_type": "code", "execution_count": null, "id": "926c8b00", "metadata": {}, "outputs": [], "source": [ "config_file = {\n", " \"state_names\": [\"S\", \"I\", \"R\"],\n", " \"events\": [\n", " {\"state_transition\": {\"S\": 1}, \"event_rates\": \"ρ\"},\n", " {\"state_transition\": {\"S\": -1, \"I\": 1}, \"event_rates\": \"kS*S*I\", \"drive_state\": \"I\"},\n", " {\"state_transition\": {\"I\": -1, \"R\": 1}, \"event_rates\": \"kI*I\"},\n", " {\"state_transition\": {\"R\": -1, \"S\": 1}, \"event_rates\": \"kR*R\"},\n", " {\"state_transition\": {\"S\": -1}, \"event_rates\": \"δS\"},\n", " {\"state_transition\": {\"I\": -1}, \"event_rates\": \"δI\"},\n", " {\"state_transition\": {\"R\": -1}, \"event_rates\": \"δR\"}\n", " ],\n", " \"parameters\": {\n", " \"param_names\": [\"ρ\", \"kS\", \"kI\", \"kR\", \"δS\", \"δI\", \"δR\"],\n", " \"upper_bound_val\": [10.0, 0.2, 0.1, 0.4, 0.2, 0.2, 0.2],\n", " \"lower_bound_val\": [0.05, 0.1, 0.05, 0.05, 0.05, 0.05, 0.05]\n", " },\n", " \"model_name\": \"epidemic_model\",\n", " \"initial_val\": [10, 5, 0],\n", " \"t_end\": 6000,\n", " \"train_data_size\": 10000,\n", " \"valid_data_size\": 1000,\n", " \"dataset_dir\": \"/GPUFS/sysu_jjzhang_1/hzw/academicCode/DynGPTTest/DynGPT/data/epidemic_model/\",\n", " \"minimal_state_indices\": [1, 2, 3],\n", " \"state_upper_bound\": [242, 190, 130],\n", " \"observed_data_indices\": [1, 2, 3]\n", "}\n", "\n", "with open(\"config/{}_config.json\".format(config_file[\"model_name\"]), \"w\") as file:\n", " json.dump(config_file, file, indent=4)" ] }, { "cell_type": "markdown", "id": "b7ca67e6", "metadata": {}, "source": [ "### 2. Generate datasets" ] }, { "cell_type": "markdown", "id": "ebd6f06c", "metadata": {}, "source": [ "After generating the configuration file, a state transition network for the epidemic model is constructed and simulated to produce the dataset required for training the neural network that solves the model. To generate the training dataset, execute the following command in the terminal:" ] }, { "cell_type": "code", "execution_count": null, "id": "f1e1f555", "metadata": {}, "outputs": [], "source": [ "julia simulation.jl /path/to/config.json" ] }, { "cell_type": "markdown", "id": "597a08ee", "metadata": {}, "source": [ "### 3. Train DynGPT-Solver" ] }, { "cell_type": "code", "execution_count": 1, "id": "88774620", "metadata": {}, "outputs": [], "source": [ "# import packages\n", "import sys\n", "import os\n", "import numpy as np\n", "import json\n", "import logging\n", "import warnings\n", "root_dir = \"/GPUFS/sysu_jjzhang_1/hzw/academicCode/DynGPTTest/DynGPT/\"\n", "sys.path.append(root_dir)\n", "os.chdir(root_dir)\n", "import dyngpt as dg\n", "logging.getLogger().setLevel(logging.ERROR)\n", "warnings.filterwarnings(\"ignore\")" ] }, { "cell_type": "markdown", "id": "dc47acbc", "metadata": {}, "source": [ "To train a neural network for addressing the epidemic model and inferring its parameters, a basic configuration file must first be defined. This file includes four key components: (1) the information of the epidemic model, (2) the neural network architecture, (3) the directory for saving outputs and visualizations, and (4) the default parameter settings for the neural network. Subsequent configurations for training, sampling, and inference build upon this foundation by incorporating additional task-specific parameters as needed. The relationships between the configurations of each subsequent stage are presented as follows:" ] }, { "cell_type": "markdown", "id": "70bed2d9", "metadata": {}, "source": [ "\"Schematic" ] }, { "cell_type": "markdown", "id": "fdd7effd", "metadata": {}, "source": [ "Firstly, the basic configuration file must include detailed information about the dynamic model, which has already been stored in the previous configuration file." ] }, { "cell_type": "code", "execution_count": 2, "id": "d07c9342", "metadata": {}, "outputs": [], "source": [ "config_basic = {}\n", "config_path_dynmodel = \"your_path_config\"\n", "model_name =\"epidemic_model\"\n", "config_path_dynmodel = \"config/{}_config.json\".format(model_name)\n", "config_basic = dg.tl.update_dynmodel_config(config_basic,config_path_dynmodel)" ] }, { "cell_type": "markdown", "id": "8fb1d480", "metadata": {}, "source": [ "Next, the architecture of the neural network is defined by specifying the relevant structural details within the configuration." ] }, { "cell_type": "code", "execution_count": 3, "id": "fd5a6fb3", "metadata": {}, "outputs": [], "source": [ "config_basic[\"num_encoder_layers\"] = 1 # transformer n_layer\n", "config_basic[\"n_head\"] = 8 # transformer n_head\n", "config_basic[\"feed_forward_dimension\"] = 1024 # transformer feed_forward_dimension" ] }, { "cell_type": "markdown", "id": "523716a2", "metadata": {}, "source": [ "Additionally, we must designate a directory for saving output files and visualizations." ] }, { "cell_type": "code", "execution_count": 4, "id": "f91f5cfd", "metadata": {}, "outputs": [], "source": [ "config_basic[\"result_dir\"]=\"your/path/to/saveresult/\"\n", "config_basic[\"figure_dir\"]=\"your/path/to/savefigure/\"\n", "config_basic[\"result_dir\"]=\"result/{}/\".format(config_basic[\"model\"])\n", "config_basic[\"figure_dir\"]=\"figure/{}/\".format(config_basic[\"model\"])" ] }, { "cell_type": "markdown", "id": "382ce42e", "metadata": {}, "source": [ "Finally, execute the provided code to append default configuration parameters." ] }, { "cell_type": "code", "execution_count": 5, "id": "8e1954d8", "metadata": {}, "outputs": [], "source": [ "config_basic = dg.tl.update_default_config(config_basic)" ] }, { "cell_type": "markdown", "id": "36396aaf", "metadata": {}, "source": [ "After defining the basic configuration file, we proceed to train the neural network designed to solve the epidemic model. The training process is divided into two stages: the first stage involves pretraining the neural network, while the second stage focuses on fine-tuning it. Additionally, for each stage, we visualize the corresponding loss curves to assess the training progress." ] }, { "cell_type": "markdown", "id": "6e8bd71b", "metadata": {}, "source": [ "For model pretraining, the configuration file is extended from the basic configuration. We should specify the number of epochs (`epochs_pretrain`) and the training dataset path (`train_dataset_path`) in the configuration file." ] }, { "cell_type": "code", "execution_count": null, "id": "bb48237d", "metadata": {}, "outputs": [], "source": [ "config_pretrain = config_basic\n", "config_pretrain.epochs_pretrain = 1\n", "config_pretrain.train_dataset_path = \"your/path/to/train_dataset\"\n", "config_pretrain.train_dataset_path = \"data/sirs/sirs_train_stable.json\"\n", "result_pre_train = dg.solver.pre_training(config_pretrain)" ] }, { "cell_type": "markdown", "id": "c433755d", "metadata": {}, "source": [ "Visualize the loss values during the pretraining phase of the model." ] }, { "cell_type": "code", "execution_count": 6, "id": "52e1ceab", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "result_pre_train = np.load(\"result/epidemic_model/train_loss/epidemic_model_epoch500.npz\")\n", "dg.pl.plot_loss(result_pre_train[\"loss_mean\"])" ] }, { "cell_type": "markdown", "id": "5b87a25f", "metadata": {}, "source": [ "During the fine-tuning phase, the configuration file is also extended from the basic configuration, specifying the number of epochs (`epochs_fine_tune`), the training dataset path (`train_dataset_path`), and the pretrained neural network weights (`weight_nn_pretrain_path`)." ] }, { "cell_type": "code", "execution_count": 15, "id": "090e8113", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "|epoch 0|mean loss: 24.1329, std loss: 31.7089, ed: 0.0000, kd: 0.0003, lr: 0.00000000\n" ] } ], "source": [ "config_fine_tune = config_basic\n", "config_fine_tune.epochs_fine_tune = 1 # 1000\n", "config_fine_tune.train_dataset_path = \"your/path/to/train_dataset\"\n", "config_fine_tune.train_dataset_path = \"data/sirs/sirs_train_stable.json\"\n", "config_fine_tune.weight_nn_pretrain_path = result_pre_train[\"weight_nn_pretrain_path\"]\n", "result_fine_tune = dg.solver.fine_tune_training(config_fine_tune)" ] }, { "cell_type": "markdown", "id": "633a8d3a", "metadata": {}, "source": [ "Visualize the loss values during the fine tunning phase of the model." ] }, { "cell_type": "code", "execution_count": 7, "id": "f0231303", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "result_fine_tune = np.load(\"result/epidemic_model/train_loss/fine_tune_epidemic_model_epoch1500.npz\")\n", "dg.pl.plot_loss(result_fine_tune[\"loss_mean\"])" ] }, { "cell_type": "markdown", "id": "9b0e45db", "metadata": {}, "source": [ "After the neural network training converges, we valid the performance of the neural network model in solving epidemic model from comparative analysis of the samples generated by the neural network and those obtained through simulations in terms of their mean, standard deviation, and overall distribution. \n", "When using the trained model for sampling, the configuration file is also extended from the basic configuration, specifying the validation dataset path (`valid_dataset_path`) and incorporating the fine-tuned network weights (`weight_nn_fine_tune_path`)." ] }, { "cell_type": "code", "execution_count": null, "id": "414726f1", "metadata": {}, "outputs": [], "source": [ "config_sampling = config_basic\n", "valid_dataset_path = \"your/valid/datasetpath/\"\n", "config_sampling.weight_nn_fine_tune_path = result_fine_tune[\"weight_nn_fine_tune_path\"]\n", "valid_dataset_path = \"data/sirs/sirs_valid_stable.json\"\n", "config_sampling.weight_nn_fine_tune_path = \"result/epidemic_model/weights/fine_tune_epidemic_model_epoch1600.pt\"\n", "config_sampling.valid_dataset_path = valid_dataset_path\n", "result_valid_set = dg.tl.compute_sampling_stats(config_sampling)" ] }, { "cell_type": "markdown", "id": "67973826", "metadata": {}, "source": [ "We visualize the performance of the neural network model on the validation set. This includes a comparative analysis of the samples generated by the neural network and those obtained through simulations in terms of their mean, standard deviation, and overall distribution." ] }, { "cell_type": "code", "execution_count": 7, "id": "e81c72bd", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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", 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "result_valid_set[\"state_name\"] = [\"S\",\"I\",\"R\"]\n", "result_valid_set['observed_datas'],result_valid_set['nn_sample_datas'] = result_valid_set[\"ssa_sample\"],result_valid_set[\"nn_sample\"]\n", "dg.pl.plot_distribution_comparison_nd(result_valid_set)" ] }, { "cell_type": "markdown", "id": "0f09266c", "metadata": {}, "source": [ "### 4. Infer the parameters with DynGPT-Inferrer" ] }, { "cell_type": "markdown", "id": "2b5e257f", "metadata": {}, "source": [ "In this section, we employ the trained model to infer the corresponding stochastic dynamics from the observed data." ] }, { "cell_type": "code", "execution_count": 6, "id": "677263da", "metadata": {}, "outputs": [], "source": [ "valid_dataset_path = \"your/valid/datasetpath/\"\n", "valid_dataset_path = \"data/sirs/sirs_valid_stable.json\"\n", "observed_data,true_params = dg.tl.load_synthetic_data(valid_dataset_path,sample_number=4000) " ] }, { "cell_type": "markdown", "id": "6cd00cbf", "metadata": {}, "source": [ "For the inference process, the configuration file is also extended from the basic configuration, defining the following hyperparameters: the trained neural network parameters path (`weight_nn_fine_tune_path`), the loss function (`inference_loss_func`), the learning rate (`inference_lr`), the maximum number of iterations (`iteration_steps`), the initial number of parameters sampled from the prior distribution (`random_r0_number`), and the threshold for parameter acceptance (`param_acceptance_threshold`)." ] }, { "cell_type": "code", "execution_count": 7, "id": "cfbedddf", "metadata": {}, "outputs": [], "source": [ "config_infer = config_basic\n", "# config_infer.weight_nn_fine_tune_path = result_fine_tune[\"weight_nn_fine_tune_path\"]\n", "config_infer.weight_nn_fine_tune_path = \"result/epidemic_model/weights/fine_tune_epidemic_model_epoch1600.pt\"\n", "config_infer.inference_loss_func = \"Likelihood\"\n", "config_infer.inference_lr = 0.001\n", "config_infer.iteration_steps = 80\n", "config_infer.random_r0_number = 2\n", "config_infer.param_acceptance_threshold = 0.5 \n", "result_infer = dg.inferrer.inferring_dynamics(observed_data,config_infer,new_model=True,synthetic_flag=True)" ] }, { "cell_type": "markdown", "id": "5584b8e7", "metadata": {}, "source": [ "We visualize the distribution obtained from the observational data with the distribution corresponding to the inferred dynamical parameters." ] }, { "cell_type": "code", "execution_count": 14, "id": "2f97eba1", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dg.pl.plot_distribution_comparison_nd(result_infer,data_index = [2],species_index=[0,1],width_mm = 120)" ] }, { "cell_type": "markdown", "id": "23247894", "metadata": {}, "source": [ "\n", "We visualize the posterior distribution of the inferred dynamical model parameters." ] }, { "cell_type": "code", "execution_count": 18, "id": "c303108d", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# result_infer[\"param_names\"] = np.array([\"$ρ$\", \"$k_{S}$\", \"$k_{I}$\", \"$k_{R}$\", \"$δ_{S}$\", \"$δ_{I}$\",\"$δ_{R}$\"])\n", "result_infer[\"param_names\"] = np.array([\"$k_{S}$\", \"$k_{I}$\", \"$k_{R}$\", \"$δ_{S}$\", \"$δ_{I}$\",\"$δ_{R}$\"])\n", "result_infer[\"es_params\"] = result_infer[\"es_params\"][:,:,1:]\n", "dg.pl.plot_param_posterior_dist(result_infer,param_indexes=[0,2])" ] } ], "metadata": { "celltoolbar": "原始单元格格式", "kernelspec": { "display_name": "Python [conda env:.conda-hzw_dyngpt_dev]", "language": "python", "name": "conda-env-.conda-hzw_dyngpt_dev-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.13" } }, "nbformat": 4, "nbformat_minor": 5 }