{ "cells": [ { "cell_type": "markdown", "id": "4d9e97b7", "metadata": {}, "source": [ "## Example 4: Non-Markovian RNA splicing model" ] }, { "cell_type": "markdown", "id": "098e3327", "metadata": {}, "source": [ "The DynGPT package can be applied to solve a given non-Markovian state transition network and infer the associated model parameters. In this section, we will demonstrate the basic use of DynGPT using the Non-Markovian RNA splicing model as an illustrative example." ] }, { "cell_type": "markdown", "id": "84dff04a", "metadata": {}, "source": [ "\"Schematic" ] }, { "cell_type": "markdown", "id": "f3ebc819", "metadata": {}, "source": [ "The STN of non-Markovian RNA splicing model can be described as:
\n", "(1)\tThe system state is $s={{\\left({{s}_{\\text{OFF}}},{{s}_{\\text{ON}}},{{s}_{\\text{nRNA}}},{{s}_{\\text{mRNA}}}\\right)}^{\\text{T}}}\\in {{\\left\\{ 0,1 \\right\\}}^{2}}\\times{{\\mathbb{N}}^{2}},$ where ${{s}_{\\text{OFF}}}$ and ${{s}_{\\text{ON}}}$ indicate whether the promoter is in the inactive or active state, ${{s}_{\\text{nRNA}}}$ and ${{s}_{\\text{mRNA}}}$ are the number of nascent RNA and mature RNA molecules, respectively.
\n", "(2)\tEvent sets are $\\mathcal{R}=\\left\\{ \\left. {{\\mathcal{R}}_{1}},\\ldots ,{{\\mathcal{R}}_{5}} \\right\\} \\right.,$ where ${{\\mathcal{R}}_{1}}$ represents promoter binding, ${{\\mathcal{R}}_{2}}$ represents promoter unbinding, ${{\\mathcal{R}}_{3}}$ represents transcription of nascent RNA, ${{\\mathcal{R}}_{4}}$ represents splicing of nascent RNA into mature mRNA , ${{\\mathcal{R}}_{5}}$ represents degradation of mature RNA.
\n", "(3)\tState change vectors are: $\\left\\{{{\\Delta }^{\\left(k\\right)}} \\right\\},k=1,2,\\ldots ,5,$ where ${{\\Delta }^{\\left(k \\right)}}=\\left(\\Delta_{\\text{OFF}}^{\\left(k\\right)},\\Delta_{\\text{ON}}^{\\left( k \\right)},\\Delta_{\\text{nRNA}}^{\\left(k\\right)},\\Delta_{\\text{mRNA}}^{\\left(k\\right)} \\right)$ represents the state change vector corresponding to $k\\text{-th}$ event with \n", "$$\\begin{align}\n", " & \\Delta _{i}^{\\left( 1 \\right)}=-{{\\delta }_{i,\\text{OFF}}}+{{\\delta }_{i,\\text{ON}}}, \\\\ \n", " & \\Delta _{i}^{\\left( 2 \\right)}={{\\delta }_{i,\\text{OFF}}}-{{\\delta }_{i,\\text{ON}}}, \\\\ \n", " & \\Delta _{i}^{\\left( 3 \\right)}={{\\delta }_{i,\\text{nRNA}}}, \\\\ \n", " & \\Delta _{i}^{\\left( 4 \\right)}=-{{\\delta }_{i,\\text{nRNA}}}+{{\\delta }_{i,\\text{mRNA}}}, \\\\ \n", " & \\Delta _{i}^{\\left( 5 \\right)}=-{{\\delta }_{i,\\text{mRNA}}}, \\\\ \n", "\\end{align}$$\n", "where $i\\in \\left\\{ \\text{OFF},\\text{ON},\\text{nRNA},\\text{mRNA} \\right\\}$ and ${{\\delta }_{i,j}}$ is the Kronecker delta.
\n", "(4) The propensity functions of events are: ${{\\lambda }_{1}}={{k}_{\\text{on}}}{{s}_{\\text{OFF}}}$, ${{\\lambda }_{2}}={{k}_{\\text{off}}}{{s}_{\\text{ON}}}$, ${{\\lambda }_{3}}={{k}_{\\text{syn}}}{{s}_{\\text{ON}}}$, ${{\\lambda }_{4}}={{k}_{\\text{spl}}}{{s}_{\\text{nRNA}}}$ and ${{\\lambda }_{5}}={{k}_{\\deg }}{{s}_{\\text{mRNA}}}$.
\n", "\n", "We consider that the following four events involve delays introduced by molecular memory and change the state only upon events’ completion (delays finish): promoter state transitions (activation and inactivation), splicing of nascent mRNAs, and degradation of mature mRNAs. That is, $\\mathcal{R}{{}_{k}}\\in \\mathcal{R}{{}^{\\left( \\text{CD} \\right)}}$ with $k=1,2,4,5$ and $\\mathcal{R}{{}_{3}}\\in \\mathcal{R}{{}^{\\left( \\text{ND} \\right)}}.$ We denote the corresponding delays follow Gamma distributions, represented as ${{f}_{\\text{on}}}\\left( {{k}_{\\text{on}}},{{r}_{\\text{on}}} \\right),\\text{ }{{f}_{\\text{off}}}\\left( {{k}_{\\text{off}}},{{r}_{\\text{off}}} \\right),\\text{ }{{f}_{\\text{spl}}}\\left( {{k}_{\\text{spl}}},{{r}_{\\text{spl}}} \\right)$ and ${{f}_{\\text{deg}}}\\left( {{k}_{\\text{deg}}},{{r}_{\\text{deg}}} \\right),$ respectively. Note that Gamma distributions can characterize multi-step behaviors introduced by molecular memory. In this STN, the input of the DynGPT-Solver is the parameters ${{\\theta }_{\\text{dyn}}}=\\left\\{ {{k}_{\\text{on}}},{{r}_{\\text{on}}},{{k}_{\\text{off}}},{{r}_{\\text{off}}},{{k}_{\\text{syn}}},{{k}_{\\text{spl}}},{{r}_{\\text{spl}}},{{k}_{\\deg }},{{r}_{\\deg }} \\right\\}.$\n" ] }, { "cell_type": "markdown", "id": "b99c7072", "metadata": {}, "source": [ "### 1. Generate 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 theNon-Markovian RNA splicing 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 Non-Markovian RNA splicing model, the state names (`state_names`) are: (\"ON\", \"OFF\", \"nRNA\", \"mRNA\").
\n", "(2) Event-related configuration: The occurrence rate functions governing event dynamics, and state transition vectors associated with each event's occurrence. In Non-Markovian RNA splicing model, the state transition vectors (`state_transition`) are: ${{\\lambda }_{1}}={{k}_{\\text{on}}}{{s}_{\\text{OFF}}}$, ${{\\lambda }_{2}}={{k}_{\\text{off}}}{{s}_{\\text{ON}}}$,${{\\lambda }_{3}}={{k}_{\\text{syn}}}{{s}_{\\text{ON}}}$,${{\\lambda }_{4}}={{k}_{\\text{spl}}}{{s}_{\\text{nRNA}}}$ and ${{\\lambda }_{5}}={{k}_{\\deg }}{{s}_{\\text{mRNA}}}$. Specifically, in certain events, specific states actively participate and drive the occurrence of the event without changing their own state. Such a state is referred to as an `drive state` in the context of the event. In ${{R}_{3}}$,ON can be described as active state, as its presence is essential for initiating the mRNA transciption while remaining unchanged in the event .
\n", "(3) Parameter-related configuration: The name of parameter values and the range of parameters. In Non-Markovian RNA splicing model,the parameter names (`param_names`) are: [\"kon\", \"ron\",\"koff\",\"roff\", \"ksyn\", \"kspl\", \"rspl\", \"kdeg\", \"rdeg\"]. Upper bound value of parameters (`upper_bound_val`): [5, 5, 5, 5, 30, 5, 5, 5, 5]. Lower bound value of parameters (`lower_bound_val`): [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01].
\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`), the maximum simulation time (`t_end`), the number of parameter sets to simulate for the training (`train_data_size`) and validation datasets (`valid_data_size`), and the directory for storing the resulting simulation data (`dataset_dir`). Specifically, in the Non-Markovian RNA splicing model, ON and OFF are mutually exclusive, such that their sum is always equal to 1. Consequently, it suffices to record the state value of ON, mRNA, and Protein. Accordingly, we define a subset of state indices corresponding to the state names to extract, referred to as the `minimal_state_indices`." ] }, { "cell_type": "markdown", "id": "cb61a2a5", "metadata": {}, "source": [ "Hence, the following configuration files can be defined for Non-Markovian RNA splicing 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\": [\"ON\", \"OFF\", \"nRNA\", \"mRNA\"],\n", " \"events\": [\n", " {\"state_transition\": {\"OFF\": -1, \"ON\": 1}, \"event_rates\": \"kon*OFF\"},\n", " {\"state_transition\": {\"ON\": -1, \"OFF\": 1}, \"event_rates\": \"koff*ON\"},\n", " {\"state_transition\": {\"nRNA\": 1}, \"event_rates\": \"ksynon*ON\", \"drive_state\": \"ON\"},\n", " {\"state_transition\": {\"nRNA\": -1, \"mRNA\": 1}, \"event_rates\": \"kspl*nRNA\"},\n", " {\"state_transition\": {\"mRNA\": -1}, \"event_rates\": \"kdeg*mRNA\"},\n", " ],\n", " \"parameters\": {\n", " \"param_names\": [\"kon\", \"ron\", \"koff\", \"roff\", \"ksyn\", \"kspl\", \"rspl\", \"kdeg\", \"rdeg\"],\n", " \"upper_bound_val\": [5, 5, 5, 5, 30, 5, 5, 5, 5],\n", " \"lower_bound_val\": [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01],\n", " },\n", " \"model_name\": \"NM_RNA_splice\",\n", " \"initial_val\": [1, 0, 0, 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/NM_RNA_splice/\",\n", " \"minimal_state_indices\": [1, 3, 4],\n", " \"state_upper_bound\": [2, 251, 250],\n", " \"observed_data_indices\": [3, 4]\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, we construct and simulate a state transition network for the gene expression central dogma model to obtain the dataset required for training the neural network addressing the model. The simulation is implemented in Julia, which provides higher efficiency for stochastic simulation algorithms (SSA) compared to Python. To generate the training data, execute the following command in the terminal:" ] }, { "cell_type": "code", "execution_count": null, "id": "f1e1f555", "metadata": {}, "outputs": [], "source": [ "julia nm_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 Non-Markovian RNA splicing model and inferring its parameters, a basic configuration file must first be defined. This file includes four key components: (1) the information of the Non-Markovian RNA splicing 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": "7c6ce4a1", "metadata": {}, "source": [ "\"Schematic" ] }, { "cell_type": "markdown", "id": "57d7c49b", "metadata": {}, "source": [ "Firstly, the basic configuration file must include detailed information about the underlying dynamic model." ] }, { "cell_type": "code", "execution_count": 2, "id": "d07c9342", "metadata": {}, "outputs": [], "source": [ "config_basic = {}\n", "config_path_dynmodel = \"your_path_config\"\n", "model_name =\"NM_RNA_splice\"\n", "config_path_dynmodel = \"config/{}_config.json\".format(model_name)\n", "with open(config_path_dynmodel, 'r') as file:\n", " dynmodel_config = json.load(file)\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, the user must designate a directory for saving output files and visualizations." ] }, { "cell_type": "code", "execution_count": 4, "id": "f91f5cfd", "metadata": {}, "outputs": [], "source": [ "config_basic[\"dataset_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 configuration file, we proceed to train the neural network designed to solve the Non-Markovian RNA splicing 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 will visualize the corresponding loss function curves to evaluate the training progress." ] }, { "cell_type": "markdown", "id": "6e8bd71b", "metadata": {}, "source": [ "For model pretraining, the configuration file is extended from the basic configuration. The user 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": "0a78f33d", "metadata": {}, "outputs": [], "source": [ "config_pretrain = config_basic\n", "config_pretrain.epochs_pretrain = 1000\n", "config_pretrain.train_dataset_path = \"your/path/to/train_dataset\"\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/NM_RNA_splice/train_loss/NM_RNA_splice_epoch1000.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 network weights (`weight_nn_pretrain_path`)." ] }, { "cell_type": "code", "execution_count": null, "id": "090e8113", "metadata": {}, "outputs": [], "source": [ "config_fine_tune = config_basic\n", "config_fine_tune.epochs_fine_tune = 1000\n", "config_pretrain.train_dataset_path = \"your/path/to/train_dataset\"\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": 8, "id": "f0231303", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "result_fine_tune = np.load(\"result/NM_RNA_splice/train_loss/fine_tune_NM_RNA_splice_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 Non-Markovian RNA splicing 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": "231ddd68", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "the indices length is 200\n" ] } ], "source": [ "config_sampling = config_basic\n", "valid_dataset_path = \"your/valid/datasetpath/\"\n", "valid_dataset_path = \"data/nm_nm/nm_nm_valid_stable.json\"\n", "config_sampling.weight_nn_fine_tune_path = result_fine_tune[\"weight_nn_fine_tune_path\"]\n", "config_sampling.weight_nn_fine_tune_path = \"/GPUFS/sysu_jjzhang_1/hzw/academicCode/DynGPTTest/DynGPT/dyngpt/_weights/nm_nm_final.pt\"\n", "config_sampling.weight_nn_fine_tune_path = \"/GPUFS/sysu_jjzhang_1/hzw/academicCode/DynGPTTest/DynGPT/dyngpt/_weights/nm_nm_epoch11000.pt\"\n", "config_sampling.valid_dataset_path = valid_dataset_path\n", "result_valid_set = dg.tl.compute_sampling_stats(config_sampling,param_index = range(200))" ] }, { "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": 20, "id": "e81c72bd", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "dg.pl.plot_distribution_comparison_2d(result_valid_set,data_index = [1,15,16],width_mm=180)" ] }, { "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": 7, "id": "677263da", "metadata": {}, "outputs": [], "source": [ "valid_dataset_path = \"data/nm_nm/nm_nm_valid_stable.json\"\n", "observed_data,true_params = dg.tl.load_synthetic_data(valid_dataset_path) " ] }, { "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": null, "id": "1b8dcd3a", "metadata": {}, "outputs": [], "source": [ "config_sampling = config_basic\n", "config_infer.weight_nn_fine_tune_path = result_fine_tune[\"weight_nn_fine_tune_path\"]\n", "config_sampling.weight_nn_fine_tune_path = \"/GPUFS/sysu_jjzhang_1/hzw/academicCode/DynGPTTest/DynGPT/dyngpt/_weights/nm_nm_epoch11000.pt\"\n", "config_infer.inference_loss_func = \"Likelihood\"\n", "config_infer.inference_lr = 0.0001\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)\n", "result_infer = dg.inferrer.inferring_dynamics(observed_data,config_infer,new_model=True,synthetic_flag=True,test_params = 5)" ] }, { "cell_type": "markdown", "id": "d8cdcde7", "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": 16, "id": "e6ce61f1", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dg.pl.plot_distribution_comparison_nd(result_infer,species_index=[1,2],data_index=[3],width_mm=120)" ] }, { "cell_type": "markdown", "id": "b6edc600", "metadata": {}, "source": [ "We visualize the posterior distribution of the inferred dynamical model parameters." ] }, { "cell_type": "code", "execution_count": 18, "id": "4be0b288", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "result_infer[\"param_names\"] = np.array([\"$k_{on}$\", \"$r_{on}$\", \"$k_{off}$\", \"$r_{off}$\", \"$k_{syn}$\", \"$k_{spl}$\", \"$r_{spl}$\", \"$k_{deg}$\", \"$r_{deg}$\"])\n", "dg.pl.plot_param_posterior_dist(result_infer,excluded_indexes=[0],true_values=[])" ] } ], "metadata": { "celltoolbar": "原始单元格格式", "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": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 5 }