{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Gaussian filtering and smoothing\n",
    "\n",
    "Provided are two examples of linear state-space models on which one can perform Bayesian filtering and smoothing in order to obtain\n",
    "a posterior distribution over a latent state trajectory based on noisy observations.\n",
    "In order to understand the theory behind these methods in detail we refer to [1] and [2].\n",
    "\n",
    "We provide examples for two different types of state-space model:\n",
    "1. [Linear, Discrete State-Space Model](#1.-Linear-Discrete-State-Space-Model:-Car-Tracking): Car Tracking\n",
    "2. [Linear, Continuous-Discrete State-Space Model](#2.-Linear-Continuous-Discrete-State-Space-Model:-Ornstein-Uhlenbeck-Process): The Ornstein-Uhlenbeck Process\n",
    "\n",
    "\n",
    "**References**:\n",
    "> [1] Särkkä, Simo, and Solin, Arno. Applied Stochastic Differential Equations. Cambridge University Press, 2019.  \n",
    ">\n",
    "> [2] Särkkä, Simo. Bayesian Filtering and Smoothing. Cambridge University Press, 2013."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "import probnum as pn\n",
    "from probnum import filtsmooth, randvars, statespace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(12345)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make inline plots vector graphics instead of raster graphics\n",
    "%matplotlib inline\n",
    "from IPython.display import set_matplotlib_formats\n",
    "set_matplotlib_formats('pdf', 'svg')\n",
    "\n",
    "# Plotting\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "plt.style.use('../../probnum.mplstyle')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. **Linear Discrete** State-Space Model: Car Tracking"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We begin showcasing the arguably most simple case in which we consider the following state-space model. Consider matrices $A \\in \\mathbb{R}^{d \\times d}$ and $H \\in \\mathbb{R}^{m \\times d}$ where $d$ is the state dimension and $m$ is the dimension of the measurements. Then we define the dynamics and the measurement model as follows:\n",
    "\n",
    "For $k = 1, \\dots, K$ and $x_0 \\sim \\mathcal{N}(\\mu_0, \\Sigma_0)$:\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "    \\boldsymbol{x}_k &\\sim \\mathcal{N}(\\boldsymbol{A} \\, \\boldsymbol{x}_{k-1}, \\boldsymbol{Q}) \\\\\n",
    "    \\boldsymbol{y}_k &\\sim \\mathcal{N}(\\boldsymbol{H} \\, \\boldsymbol{x}_k, \\boldsymbol{R})\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "This defines a dynamics model that assumes a state $\\boldsymbol{x}_k$ in a **discrete** sequence of states arising from a linear projection of the previous state $x_{k-1}$ corrupted with additive Gaussian noise under a **process noise** covariance matrix $Q$.  \n",
    "Similarly, the measurements $\\boldsymbol{y}_k$ are assumed to be linear projections of the latent state under additive Gaussian noise according to a **measurement noise** covariance $R$.\n",
    "In the following example we consider projections and covariances that are constant over the state and measurement trajectories (linear time invariant, or **LTI**). Note that this can be generalized to a linear time-varying state-space model, as well. Then $A$ is a function $A: \\mathbb{T} \\rightarrow \\mathbb{R}^{d \\times d}$ and $H$ is a function $H: \\mathbb{T} \\rightarrow \\mathbb{R}^{m \\times d}$ where $\\mathbb{T}$ is the \"time dimension\".\n",
    "\n",
    "In other words, here, every relationship is linear and every distribution is a Gaussian distribution.\n",
    "Under these simplifying assumptions it is possible to obtain a filtering posterior distribution over the state trajectory $(\\boldsymbol{x}_k)_{k=1}^{K}$ by using a \n",
    "**Kalman Filter**. The example is taken from Example 3.6 in [2]."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define State-Space Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### I. Discrete Dynamics Model: Linear, Time-Invariant, Gaussian Transitions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "state_dim = 4\n",
    "observation_dim = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "delta_t = 0.2\n",
    "# Define linear transition operator\n",
    "dynamics_transition_matrix = np.eye(state_dim) + delta_t * np.diag(np.ones(2), 2)\n",
    "# Define process noise (covariance) matrix\n",
    "process_noise_matrix = (\n",
    "    np.diag(np.array([delta_t ** 3 / 3, delta_t ** 3 / 3, delta_t, delta_t]))\n",
    "    + np.diag(np.array([delta_t ** 2 / 2, delta_t ** 2 / 2]), 2)\n",
    "    + np.diag(np.array([delta_t ** 2 / 2, delta_t ** 2 / 2]), -2)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To create a discrete, LTI Gaussian dynamics model, `probnum` provides the `DiscreteLTIGaussian` class that takes\n",
    "- `state_trans_mat` : the linear transition matrix (above: $A$)\n",
    "- `shift_vec` : a force vector for _affine_ transformations of the state (here: zero)\n",
    "- `proc_noise_cov_mat` : the covariance matrix for the Gaussian process noise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create discrete, Linear Time-Invariant Gaussian dynamics model\n",
    "dynamics_model = statespace.DiscreteLTIGaussian(\n",
    "    state_trans_mat=dynamics_transition_matrix,\n",
    "    shift_vec=np.zeros(state_dim),\n",
    "    proc_noise_cov_mat=process_noise_matrix,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### II. Discrete Measurement Model: Linear, Time-Invariant, Gaussian Measurements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "measurement_marginal_variance = 0.5\n",
    "measurement_matrix = np.eye(observation_dim, state_dim)\n",
    "measurement_noise_matrix = measurement_marginal_variance * np.eye(observation_dim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "measurement_model = statespace.DiscreteLTIGaussian(\n",
    "    state_trans_mat=measurement_matrix,\n",
    "    shift_vec=np.zeros(observation_dim),\n",
    "    proc_noise_cov_mat=measurement_noise_matrix,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### III. Initial State Random Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "mu_0 = np.zeros(state_dim)\n",
    "sigma_0 = 0.5 * measurement_marginal_variance * np.eye(state_dim)\n",
    "initial_state_rv = randvars.Normal(mean=mu_0, cov=sigma_0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate Data for the State-Space Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`statespace.generate_samples()` is used to sample both latent states and noisy observations from the specified state space model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_grid = np.arange(0., 10., step=delta_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "latent_states, observations = statespace.generate_samples(\n",
    "    dynmod=dynamics_model,\n",
    "    measmod=measurement_model,\n",
    "    initrv=initial_state_rv,\n",
    "    times=time_grid,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Kalman Filtering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### I. Kalman Filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "kalman_filter = filtsmooth.Kalman(\n",
    "    dynamics_model=dynamics_model,\n",
    "    measurement_model=measurement_model,\n",
    "    initrv=initial_state_rv\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### II. Perform Kalman Filtering + Rauch-Tung-Striebel Smoothing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "state_posterior = kalman_filter.filtsmooth(\n",
    "    dataset=observations,\n",
    "    times=time_grid,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The method `filtsmooth` returns a `KalmanPosterior` object which provides convenience functions for e.g. sampling and interpolation.\n",
    "We can also extract the just computed posterior smoothing state variables by querying the `.state_rvs` property.  \n",
    "This yields a list of Gaussian Random Variables from which we can extract the statistics in order to visualize them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = state_posterior.locations    \n",
    "posterior_state_rvs = state_posterior.states          # List of <num_time_points> Normal Random Variables\n",
    "posterior_state_means = posterior_state_rvs.mean      # Shape: (num_time_points, state_dim)\n",
    "posterior_state_covs = posterior_state_rvs.cov        # Shape: (num_time_points, state_dim, state_dim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "<Figure size 800x450 with 4 Axes>"
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     "metadata": {
      "needs_background": "light"
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     "output_type": "display_data"
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   ],
   "source": [
    "state_fig = plt.figure()\n",
    "state_fig_gs = gridspec.GridSpec(ncols=2, nrows=2, figure=state_fig)\n",
    "\n",
    "ax_00 = state_fig.add_subplot(state_fig_gs[0, 0])\n",
    "ax_01 = state_fig.add_subplot(state_fig_gs[0, 1])\n",
    "ax_10 = state_fig.add_subplot(state_fig_gs[1, 0])\n",
    "ax_11 = state_fig.add_subplot(state_fig_gs[1, 1])\n",
    "\n",
    "# Plot means\n",
    "mu_x_1, mu_x_2, mu_x_3, mu_x_4 = [posterior_state_means[:, i] for i in range(state_dim)]\n",
    "\n",
    "ax_00.plot(grid, mu_x_1, label=\"posterior mean\");\n",
    "ax_01.plot(grid, mu_x_2);\n",
    "ax_10.plot(grid, mu_x_3);\n",
    "ax_11.plot(grid, mu_x_4);\n",
    "\n",
    "# Plot marginal standard deviations\n",
    "std_x_1, std_x_2, std_x_3, std_x_4 = [np.sqrt(posterior_state_covs[:, i, i]) for i in range(state_dim)]\n",
    "\n",
    "ax_00.fill_between(grid, mu_x_1 - 1.96 * std_x_1,  mu_x_1 + 1.96 * std_x_1, alpha=0.2, label=\"1.96 marginal stddev\");\n",
    "ax_01.fill_between(grid, mu_x_2 - 1.96 * std_x_2,  mu_x_2 + 1.96 * std_x_2, alpha=0.2);\n",
    "ax_10.fill_between(grid, mu_x_3 - 1.96 * std_x_3,  mu_x_3 + 1.96 * std_x_3, alpha=0.2);\n",
    "ax_11.fill_between(grid, mu_x_4 - 1.96 * std_x_4,  mu_x_4 + 1.96 * std_x_4, alpha=0.2);\n",
    "\n",
    "# Plot groundtruth\n",
    "obs_x_1, obs_x_2 = [observations[:, i] for i in range(observation_dim)]\n",
    "\n",
    "ax_00.scatter(time_grid, obs_x_1, marker=\".\", label=\"measurements\");\n",
    "ax_01.scatter(time_grid, obs_x_2, marker=\".\");\n",
    "\n",
    "# Add labels etc.\n",
    "ax_00.set_xlabel(\"t\")\n",
    "ax_01.set_xlabel(\"t\")\n",
    "ax_10.set_xlabel(\"t\")\n",
    "ax_11.set_xlabel(\"t\")\n",
    "\n",
    "ax_00.set_title(r\"$x_1$\")\n",
    "ax_01.set_title(r\"$x_2$\")\n",
    "ax_10.set_title(r\"$x_3$\")\n",
    "ax_11.set_title(r\"$x_4$\")\n",
    "handles, labels = ax_00.get_legend_handles_labels()\n",
    "state_fig.legend(handles, labels, loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "\n",
    "state_fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. **Linear Continuous-Discrete** State-Space Model: Ornstein-Uhlenbeck Process"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, consider we have a look at **continuous** dynamics. We assume that there is a continuous process that defines the dynamics of our latent space from which we collect discrete linear-Gaussian measurements (as above). Only the dynamics model becomes continuous. In particular, we formulate the dynamics as a stochastic process in terms of a linear time-invariant stochastic differential equation (LTISDE). We refer to [1] for more details.\n",
    "Consider matrices $\\boldsymbol{F} \\in \\mathbb{R}^{d \\times d}$, $\\boldsymbol{L} \\in \\mathbb{R}^{s \\times d}$ and $H \\in \\mathbb{R}^{m \\times d}$ where $d$ is the state dimension and $m$ is the dimension of the measurements.\n",
    "We define the following **continuous-discrete** state-space model:\n",
    "\n",
    "Let $x(t_0) \\sim \\mathcal{N}(\\mu_0, \\Sigma_0)$.\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "    d\\boldsymbol{x} &= \\boldsymbol{F} \\, \\boldsymbol{x} \\, dt + \\boldsymbol{L} \\, d \\boldsymbol{\\omega} \\\\\n",
    "    \\boldsymbol{y}_k &\\sim \\mathcal{N}(\\boldsymbol{H} \\, \\boldsymbol{x}(t_k), \\boldsymbol{R}), \\qquad k = 1, \\dots, K\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "where $\\boldsymbol{\\omega} \\in \\mathbb{R}^s$ denotes a vector of driving forces (often Brownian Motion).\n",
    "\n",
    "Note that this can be generalized to a linear time-varying state-space model, as well. Then $\\boldsymbol{F}$ is a function $\\mathbb{T} \\rightarrow \\mathbb{R}^{d \\times d}$,\n",
    "$\\boldsymbol{L}$ is a function $\\mathbb{T} \\rightarrow \\mathbb{R}^{s \\times d}$, and $H$ is a function $\\mathbb{T} \\rightarrow \\mathbb{R}^{m \\times d}$ where $\\mathbb{T}$ is the \"time dimension\". In the following example, however, we consider a LTI SDE, namely, the Ornstein-Uhlenbeck Process from which we observe discrete linear Gaussian measurements."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define State-Space Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### I. Continuous Dynamics Model: Linear, Time-Invariant Stochastic Differential Equation (LTISDE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "state_dim = 1\n",
    "observation_dim = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "delta_t = 0.2\n",
    "# Define Linear, time-invariant Stochastic Differential Equation that models\n",
    "# the (scalar) Ornstein-Uhlenbeck Process\n",
    "drift_constant = 0.21\n",
    "dispersion_constant = np.sqrt(0.5)\n",
    "drift = -drift_constant * np.eye(state_dim)\n",
    "force = np.zeros(state_dim)\n",
    "dispersion = dispersion_constant * np.eye(state_dim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The _continuous_ counterpart to the discrete LTI Gaussian model from above is provided via the `LTISDE` class.\n",
    "It is initialized by the state space components\n",
    "- `driftmat` : the drift matrix $\\boldsymbol{F}$\n",
    "- `forcevec` : a force vector that is added to the state (note that this is **not** $\\boldsymbol{\\omega}$.) Here: zero.\n",
    "- `dispmat` : the dispersion matrix $\\boldsymbol{L}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create dynamics model\n",
    "dynamics_model = statespace.LTISDE(\n",
    "    driftmat=drift, \n",
    "    forcevec=force,\n",
    "    dispmat=dispersion,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### II. Discrete Measurement Model: Linear, Time-Invariant Gaussian Measurements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "measurement_marginal_variance = 0.1\n",
    "measurement_matrix = np.eye(observation_dim, state_dim)\n",
    "measurement_noise_matrix = measurement_marginal_variance * np.eye(observation_dim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As above, the measurement model is discrete, LTI Gaussian. Only the dymanics are continuous (i.e. continuous-discrete)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "measurement_model = statespace.DiscreteLTIGaussian(\n",
    "    state_trans_mat=measurement_matrix,\n",
    "    shift_vec=np.zeros(observation_dim),\n",
    "    proc_noise_cov_mat=measurement_noise_matrix,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### III. Initial State Random Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "mu_0 = 10. * np.ones(state_dim)\n",
    "sigma_0 = np.eye(state_dim)\n",
    "initial_state_rv = randvars.Normal(mean=mu_0, cov=sigma_0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate Data for the State-Space Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`statespace.generate_samples()` is used to sample both latent states and noisy observations from the specified state space model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_grid = np.arange(0., 10., step=delta_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "latent_states, observations = statespace.generate_samples(\n",
    "    dynmod=dynamics_model,\n",
    "    measmod=measurement_model,\n",
    "    initrv=initial_state_rv,\n",
    "    times=time_grid,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Kalman Filtering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In fact, since we still consider a **linear** model, we can apply Kalman Filtering in this case again.\n",
    "According to Section 10 in [1], the moments of the filtering posterior in the continuous-discrete case are solutions to linear differential equations, which `probnum` solves for us when invoking the `<Kalman_object>.filtsmooth(...)` method."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### I. Kalman Filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "kalman_filter = filtsmooth.Kalman(\n",
    "    dynamics_model=dynamics_model,\n",
    "    measurement_model=measurement_model,\n",
    "    initrv=initial_state_rv,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### II. Perform Kalman Filtering + Rauch-Tung-Striebel Smoothing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "state_posterior = kalman_filter.filtsmooth(\n",
    "    dataset=observations,\n",
    "    times=time_grid,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The method `filtsmooth` returns a `KalmanPosterior` object which provides convenience functions for e.g. sampling and prediction.\n",
    "We can also extract the just computed posterior smoothing state variables by querying the `.state_rvs` property.  \n",
    "This yields a list of Gaussian Random Variables from which we can extract the statistics in order to visualize them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = np.linspace(0, 11, 500)\n",
    "\n",
    "posterior_state_rvs = state_posterior(grid)                 # List of <num_time_points> Normal Random Variables\n",
    "posterior_state_means = posterior_state_rvs.mean.squeeze()  # Shape: (num_time_points, )\n",
    "posterior_state_covs = posterior_state_rvs.cov              # Shape: (num_time_points, )\n",
    "\n",
    "samples = state_posterior.sample(size=3, t=grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "tags": [
     "nbsphinx-thumbnail"
    ]
   },
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 800x450 with 1 Axes>"
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     },
     "metadata": {
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     "output_type": "display_data"
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   ],
   "source": [
    "state_fig = plt.figure()\n",
    "\n",
    "ax = state_fig.add_subplot()\n",
    "\n",
    "# Plot means\n",
    "ax.plot(grid, posterior_state_means, label=\"posterior mean\")\n",
    "\n",
    "# Plot samples\n",
    "for smp in samples:\n",
    "    ax.plot(grid, smp[:, 0], color=\"gray\", alpha=0.75, linewidth=1, linestyle=\"dashed\", label=\"sample\")\n",
    "    \n",
    "\n",
    "# Plot marginal standard deviations\n",
    "std_x = np.sqrt(np.abs(posterior_state_covs)).squeeze()\n",
    "ax.fill_between(\n",
    "    grid,\n",
    "    posterior_state_means - 1.96 * std_x,\n",
    "    posterior_state_means + 1.96 * std_x,\n",
    "    alpha=0.2,\n",
    "    label=\"1.96 marginal stddev\",\n",
    ")\n",
    "ax.scatter(time_grid, observations, marker=\".\", label=\"measurements\")\n",
    "# Add labels etc.\n",
    "ax.set_xlabel(\"t\")\n",
    "ax.set_title(r\"$x$\")\n",
    "\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "by_label = dict(zip(labels, handles))\n",
    "\n",
    "ax.legend(\n",
    "    by_label.values(), by_label.keys(), loc=\"center left\", bbox_to_anchor=(1, 0.5)\n",
    ")\n",
    "\n",
    "\n",
    "state_fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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