{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# [Jansen-Rit whole brain C++ implementation](https://github.com/Ziaeemehr/vbi_paper/blob/main/docs/examples/jansen_rit_cpp.ipynb)\n" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "import torch\n", "import pickle\n", "import numpy as np\n", "from tqdm import tqdm\n", "import networkx as nx\n", "import sbi.utils as utils\n", "import matplotlib.pyplot as plt\n", "from multiprocessing import Pool\n", "from sbi.analysis import pairplot\n", "from vbi.inference import Inference\n", "from vbi.models.cpp.jansen_rit import JR_sde\n", "from sklearn.preprocessing import StandardScaler" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "from vbi import report_cfg\n", "from vbi import extract_features_df\n", "from vbi import get_features_by_domain, get_features_by_given_names\n", "from helpers import *" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [], "source": [ "seed = 2\n", "np.random.seed(seed)\n", "torch.manual_seed(seed);" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [], "source": [ "LABESSIZE = 12\n", "plt.rcParams['axes.labelsize'] = LABESSIZE\n", "plt.rcParams['xtick.labelsize'] = LABESSIZE\n", "plt.rcParams['ytick.labelsize'] = LABESSIZE" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [], "source": [ "nn = 6\n", "SC = nx.to_numpy_array(nx.complete_graph(nn))" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [], "source": [ "par = {\n", " \"G\": 1.0,\n", " \"noise_mu\": 0.24,\n", " \"noise_std\": 0.1,\n", " \"dt\": 0.05,\n", " \"C0\": 135.0 * 1.0,\n", " \"C1\": 135.0 * 0.8,\n", " \"C2\": 135.0 * 0.25,\n", " \"C3\": 135.0 * 0.25,\n", " \"weights\": SC,\n", " \"t_transition\": 500.0, # ms\n", " \"t_end\": 2501.0, # ms\n", " \"output\": \"output\",\n", "}" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [], "source": [ "# g, c1[0], c1[2,3]\n", "theta_true = [1.0, 135, 155]" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Jansen-Rit sde model\n", "{'G': 1.0, 'A': 3.25, 'B': 22.0, 'a': 0.1, 'b': 0.05, 'noise_mu': 0.24, 'noise_std': 0.1, 'vmax': 0.005, 'v0': 6, 'r': 0.56, 'initial_state': None, 'weights': array([[0., 1., 1., 1., 1., 1.],\n", " [1., 0., 1., 1., 1., 1.],\n", " [1., 1., 0., 1., 1., 1.],\n", " [1., 1., 1., 0., 1., 1.],\n", " [1., 1., 1., 1., 0., 1.],\n", " [1., 1., 1., 1., 1., 0.]]), 'C0': 135.0, 'C1': 108.0, 'C2': 33.75, 'C3': 33.75, 'noise_seed': 0, 'seed': None, 'dt': 0.05, 'dim': 6, 'method': 'heun', 't_transition': 500.0, 't_end': 2501.0, 'output': 'output', 'RECORD_AVG': False}\n" ] } ], "source": [ "obj = JR_sde(par)\n", "print(obj())" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [], "source": [ "# C1 needs to be a vector of size nn\n", "C1 = np.ones(nn) * par['C1']\n", "C1[0] = theta_true[1]\n", "C1[[2,3]] = theta_true[2]\n", "theta_true_dict = {\"G\": 1.0, \"C1\":C1}\n", "data = obj.run(theta_true_dict)" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1, 2, figsize=(10, 2.5))\n", "plot_ts_pxx_jr(data, par, [ax[0], ax[1]], alpha=0.6, lw=1)\n", "ax[0].set_xlim(2000, 2500)\n", "plt.tight_layout()" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Selected features:\n", "------------------\n", "■ Domain: statistical\n", " ▢ Function: calc_std\n", " ▫ description: Computes the standard deviation of the signal.\n", " ▫ function : vbi.feature_extraction.features.calc_std\n", " ▫ parameters : {'indices': None, 'verbose': False}\n", " ▫ tag : all\n", " ▫ use : yes\n", " ▢ Function: calc_mean\n", " ▫ description: Computes the mean of the signal.\n", " ▫ function : vbi.feature_extraction.features.calc_mean\n", " ▫ parameters : {'indices': None, 'verbose': False}\n", " ▫ tag : all\n", " ▫ use : yes\n" ] } ], "source": [ "cfg = get_features_by_domain(domain=\"statistical\")\n", "cfg = get_features_by_given_names(cfg, names=['calc_std', 'calc_mean'])\n", "report_cfg(cfg)" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [], "source": [ "from copy import deepcopy\n", "\n", "def wrapper(par, control, cfg, verbose=False):\n", " g, x1, x2 = control\n", " par1 = deepcopy(par)\n", " C1 = np.ones(nn) * par['C1']\n", " par1['G'] = g\n", " par1['C1'] = C1\n", " par1['C1'][0] = x1\n", " par1['C1'][[2,3]] = x2\n", " \n", " ode = JR_sde(par1)\n", " sol = ode.run()\n", "\n", " # extract features\n", " fs = 1.0 / par['dt'] * 1000 # [Hz]\n", " stat_vec = extract_features_df(ts=[sol['x']],\n", " cfg=cfg,\n", " fs=fs,\n", " n_workers=1,\n", " verbose=verbose).values\n", " return stat_vec[0]" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [], "source": [ "def batch_run(par, control_list, cfg, n_workers=1):\n", " n = len(control_list)\n", " def update_bar(_):\n", " pbar.update()\n", " with Pool(processes=n_workers) as pool:\n", " with tqdm(total=n) as pbar:\n", " async_results = [pool.apply_async(wrapper,\n", " args=(\n", " par, control_list[i], cfg, False),\n", " callback=update_bar)\n", " for i in range(n)]\n", " stat_vec = [res.get() for res in async_results]\n", " return stat_vec" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[2.0830188 3.294864 2.4442172 2.181784 2.2507007 2.2812603 9.048109\n", " 7.727638 9.848883 9.870417 7.8413916 8.090325 ]\n" ] } ], "source": [ "x_ = wrapper(par, theta_true, cfg)\n", "print(x_)" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [], "source": [ "num_sim = 2000\n", "num_workers = 10\n", "C11_min, C11_max = 130.0, 300.0\n", "C12_min, C12_max = 130.0, 300.0\n", "G_min, G_max = 0.0, 5.0\n", "prior_min = [G_min, C11_min, C12_min]\n", "prior_max = [G_max, C11_max, C12_max]\n", "prior = utils.BoxUniform(low=torch.tensor(prior_min),\n", " high=torch.tensor(prior_max))" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [], "source": [ "obj = Inference()\n", "theta = obj.sample_prior(prior, num_sim)\n", "theta_np = theta.numpy().astype(float)" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 2000/2000 [00:36<00:00, 55.03it/s]\n" ] } ], "source": [ "stat_vec = batch_run(par, theta_np, cfg, num_workers)" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [], "source": [ "scaler = StandardScaler()\n", "stat_vec_st = scaler.fit_transform(np.array(stat_vec))\n", "stat_vec_st = torch.tensor(stat_vec_st, dtype=torch.float32)\n", "torch.save(theta, 'output/theta.pt')\n", "torch.save(stat_vec, 'output/stat_vec.pt')" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2000, 3]) torch.Size([2000, 12])\n" ] } ], "source": [ "print(theta.shape, stat_vec_st.shape)" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Neural network successfully converged after 163 epochs.train Done in 0 hours 0 minutes 21.653859 seconds\n" ] } ], "source": [ "posterior = obj.train(theta, stat_vec_st, prior, method=\"SNPE\", density_estimator=\"maf\")" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [], "source": [ "with open('output/posterior.pkl', 'wb') as f:\n", " pickle.dump(posterior, f)" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [], "source": [ "xo = wrapper(par, theta_true, cfg)\n", "xo_st = scaler.transform(xo.reshape(1, -1))" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "fb85e6a58b844c70967d4fad5e9609d1", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Drawing 10000 posterior samples: 0%| | 0/10000 [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "limits = [[i, j] for i, j in zip(prior_min, prior_max)]\n", "points = [theta_true]\n", "fig, ax = pairplot(\n", " samples,\n", " limits=limits,\n", " figsize=(5, 5),\n", " points=points,\n", " labels=[\"G\", \"C11\", \"C12\"],\n", " upper=\"kde\",\n", " diag=\"kde\",\n", " fig_kwargs=dict(\n", " points_offdiag=dict(marker=\"*\", markersize=10),\n", " points_colors=[\"g\"],\n", " ),\n", ")\n", "ax[0, 0].tick_params(labelsize=14)\n", "ax[0, 0].margins(y=0)\n", "fig.savefig(\"output/jr_sde_cpp.jpeg\", dpi=300)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "language_info": { "name": "python" } }, "nbformat": 4, "nbformat_minor": 2 }