{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "68b945c0-3537-486c-8aec-2fd005f6f0f7",
   "metadata": {},
   "source": [
    "(categorical_hgf)=\n",
    "# The categorical Hierarchical Gaussian Filter"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5af600c9-eb11-48fb-ba83-0a2c2d6e824e",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ComputationalPsychiatry/pyhgf/blob/master/docs/source/notebooks/1.3-CAtegorical_HGF.ipynb)\n",
    "\n",
    "```{warning}\n",
    "The categorical state node and the categorical state-transition nodes are still work in progress. The examples provided here are given for illustration. Things may change or not work until the official publication.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e9143ab5-131e-4a1c-9130-326c3538f888",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-01-10T13:54:39.961150Z",
     "iopub.status.busy": "2025-01-10T13:54:39.960587Z",
     "iopub.status.idle": "2025-01-10T13:54:39.964462Z",
     "shell.execute_reply": "2025-01-10T13:54:39.963900Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "from IPython.utils import io\n",
    "if 'google.colab' in sys.modules:\n",
    "\n",
    "  with io.capture_output() as captured:\n",
    "      ! pip install pyhgf watermark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d2c1e257-91ab-455d-a3ea-0ac0136797ed",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-01-10T13:54:39.966276Z",
     "iopub.status.busy": "2025-01-10T13:54:39.965942Z",
     "iopub.status.idle": "2025-01-10T13:54:42.378005Z",
     "shell.execute_reply": "2025-01-10T13:54:42.377244Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n"
     ]
    }
   ],
   "source": [
    "import jax.numpy as jnp\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pytensor.tensor as pt\n",
    "import seaborn as sns\n",
    "from jax import jit, vjp\n",
    "from jax.tree_util import Partial\n",
    "from pyhgf.model import Network\n",
    "from pytensor.graph import Apply, Op"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbb1fadf-bc75-4695-b62c-0db2d3d8689b",
   "metadata": {},
   "source": [
    "The binary state nodes that we introduced in the previous section are useful to encode information about stochastic boolean variables that are common in reinforcement learning and decision-making design. However, situations may occur where discrete variables can have more than two categories and therefore need to be encoded by a categorical distribution. Here, we introduce two probabilistic nodes tailored to handle this kind of variable: the **categorical state node** and the **categorical state-transition node**.\n",
    "\n",
    "Both nodes are a generalisation of the binary HGFs (in the sense that they internally represent a collection of binary state nodes). We refer to **categorical HGF** in a broad sense for HGFs that can handle categorical distributions, but as we will illustrate below, there are many ways to do that and a more precise terminology is to refer to the kind of node used internally (the **categorical state node** and the **categorical state-transition node**)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66a3991f-2170-43d6-8bb9-2541e80bf3d2",
   "metadata": {},
   "source": [
    "## Simulating a dataset\n",
    "We start by simulating a dataset on which we can apply the categorical HGFs. The dataset consists of a categorical input where the number of categories $K=10$. The underlying contingencies are generated by three Dirichlet distributions on which we sample 150 observations sequentially."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dba7fabb-e5d9-48d6-8c8e-2fecc7df28be",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-10T13:54:42.380496Z",
     "iopub.status.busy": "2025-01-10T13:54:42.380198Z",
     "iopub.status.idle": "2025-01-10T13:54:42.385700Z",
     "shell.execute_reply": "2025-01-10T13:54:42.385198Z"
    }
   },
   "outputs": [],
   "source": [
    "# generate some categorical inputs data using three underlying distributions\n",
    "p1 = np.random.dirichlet(alpha=[1, 1, 1, 2, 2, 5, 20, 20])\n",
    "p2 = np.random.dirichlet(alpha=[1, 1, 2, 20, 20, 2, 1, 1])\n",
    "p3 = np.random.dirichlet(alpha=[20, 20, 5, 2, 2, 1, 1, 1])\n",
    "input_data = np.array(\n",
    "    [np.random.multinomial(n=1, pvals=p) for p in [p1, p2, p3] for _ in range(250)], dtype=float\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cf4027a-be50-42cf-88d7-e0847c1f9930",
   "metadata": {},
   "source": [
    "The Dirichlet distributions are parametrized in such a way that it goes from a \"skewed\" distribution to a centred distribution to another \"skewed\" distribution. The resulting sequence of categorical observations then looks like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f37e6acb-ae8e-4aea-8914-5333cbe46921",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-10T13:54:42.388289Z",
     "iopub.status.busy": "2025-01-10T13:54:42.387711Z",
     "iopub.status.idle": "2025-01-10T13:54:42.526104Z",
     "shell.execute_reply": "2025-01-10T13:54:42.525371Z"
    }
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 3))\n",
    "plt.imshow(input_data.T, interpolation=\"none\", aspect=\"auto\", cmap=\"binary\")\n",
    "plt.ylabel(\"Categories\")\n",
    "plt.xlabel(\"Time\")\n",
    "plt.title(\"Categorical observations\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15a23310-1d29-4a84-9cd2-a30c0e0b64e3",
   "metadata": {},
   "source": [
    "```{note}\n",
    "The lower part of the figure represent the surprise associated with the categorical node. Here, we use the [Kullback-Leibler divergence between two Dirichlet distributions](https://statproofbook.github.io/P/dir-kl.html) as a measure of Bayesian surprise. The Kullback-Leibler divergence of the Dirichlet distribution $P$ from the Dirichlet distribution $Q$ is given by the following equation:\n",
    "\n",
    "$$\n",
    "KL[P||Q] = \\ln{\\frac{\\Gamma(\\sum_{i=1}^k\\alpha_{1i})}{\\Gamma(\\sum_{i=1}^k\\alpha_{2i})}} + \\sum_{i=1}^k \\ln{\\frac{\\Gamma(\\alpha_{2i})}{\\Gamma(\\alpha_{1i})}} + \\sum_{i=1}^k(\\alpha_{1i} - \\alpha_{2i}) \\left[ \\psi(\\alpha_{1i}) - \\psi(\\sum_{i=1}^k \\alpha_{1i}) \\right]\n",
    "$$\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6511fd24-48a0-4913-9cb2-2ed3dba57f4f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-10T13:54:42.528151Z",
     "iopub.status.busy": "2025-01-10T13:54:42.527950Z",
     "iopub.status.idle": "2025-01-10T13:54:42.531877Z",
     "shell.execute_reply": "2025-01-10T13:54:42.531171Z"
    }
   },
   "outputs": [],
   "source": [
    "# adding a blank input time series for the categorical state node\n",
    "# this is because the categorical state node does not receive anything\n",
    "# only binary nodes are the actual inputs of the network\n",
    "input_data = np.vstack([[0.0] * input_data.shape[1], input_data])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc71d527-2f88-4c4f-b81e-5ededd64d678",
   "metadata": {},
   "source": [
    "## The categorical state node"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19c97cda-64b8-4e1e-b90a-7bf538c033e1",
   "metadata": {},
   "source": [
    "### Creating the probabilistic network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1ae2e67a-0b7b-462b-89b7-0c1ec1fb4262",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-01-10T13:54:42.533925Z",
     "iopub.status.busy": "2025-01-10T13:54:42.533737Z",
     "iopub.status.idle": "2025-01-10T13:54:42.605056Z",
     "shell.execute_reply": "2025-01-10T13:54:42.604102Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "categorical_hgf = Network().add_nodes(\n",
    "    kind=\"categorical-state\", n_categories=8, binary_parameters={\"tonic_volatility_2\": -2.0},\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2806c2d9-e6a9-4113-829f-1f0616a31b4b",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-01-10T13:54:42.607272Z",
     "iopub.status.busy": "2025-01-10T13:54:42.607082Z",
     "iopub.status.idle": "2025-01-10T13:54:42.653222Z",
     "shell.execute_reply": "2025-01-10T13:54:42.652355Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
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     "execution_count": 7,
     "metadata": {},
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   "source": [
    "categorical_hgf.plot_network()"
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  {
   "cell_type": "markdown",
   "id": "ea060dd5-e679-4793-bfe2-78f3116f1b38",
   "metadata": {},
   "source": [
    "### Fitting the model forwards"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6e27b3d9-1b10-4cf9-9074-725a9638345d",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-01-10T13:54:42.655278Z",
     "iopub.status.busy": "2025-01-10T13:54:42.655094Z",
     "iopub.status.idle": "2025-01-10T13:54:48.769903Z",
     "shell.execute_reply": "2025-01-10T13:54:48.768756Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "categorical_hgf.input_data(input_data=input_data);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3e76f37e-36b0-49d4-8169-86f08b309e0d",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-01-10T13:54:48.772235Z",
     "iopub.status.busy": "2025-01-10T13:54:48.772013Z",
     "iopub.status.idle": "2025-01-10T13:54:49.746157Z",
     "shell.execute_reply": "2025-01-10T13:54:49.745305Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x900 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(nrows=5, figsize=(12, 9), sharex=True)\n",
    "categorical_hgf.plot_nodes(node_idxs=17, axs=axs[0])\n",
    "axs[1].imshow(\n",
    "    categorical_hgf.node_trajectories[0][\"mean\"].T, interpolation=\"none\", aspect=\"auto\"\n",
    ")\n",
    "axs[1].set_title(\"Mean of the implied Dirichlet distribution\", loc=\"left\")\n",
    "axs[1].set_ylabel(\"Categories\")\n",
    "\n",
    "# observations\n",
    "axs[2].imshow(input_data.T, interpolation=\"none\", aspect=\"auto\", cmap=\"binary\")\n",
    "axs[2].set_title(\"Observations\", loc=\"left\")\n",
    "axs[2].set_ylabel(\"Categories\")\n",
    "\n",
    "# KL divergences\n",
    "axs[3].plot(\n",
    "    np.arange(2, len(categorical_hgf.node_trajectories[0][\"kl_divergence\"])),\n",
    "    categorical_hgf.node_trajectories[0][\"kl_divergence\"][2:],\n",
    "    color=\"#2a2a2a\",\n",
    "    linewidth=0.5,\n",
    "    zorder=-1,\n",
    "    label=\"Surprise\",\n",
    ")\n",
    "axs[3].fill_between(\n",
    "    x=np.arange(2, len(categorical_hgf.node_trajectories[0][\"kl_divergence\"])),\n",
    "    y1=categorical_hgf.node_trajectories[0][\"kl_divergence\"][2:],\n",
    "    y2=0.0,\n",
    "    color=\"#7f7f7f\",\n",
    "    alpha=0.1,\n",
    "    zorder=-1,\n",
    ")\n",
    "axs[3].set_title(\"Kullback-Leibler divergences\", loc=\"left\")\n",
    "axs[3].set_ylabel(\"Inputs\")\n",
    "\n",
    "axs[4].plot(\n",
    "    np.arange(len(categorical_hgf.node_trajectories[0][\"surprise\"])),\n",
    "    categorical_hgf.node_trajectories[0][\"surprise\"],\n",
    "    color=\"#2a2a2a\",\n",
    "    linewidth=0.5,\n",
    "    zorder=-1,\n",
    "    label=\"Surprise\",\n",
    ")\n",
    "axs[4].fill_between(\n",
    "    x=np.arange(len(categorical_hgf.node_trajectories[0][\"surprise\"])),\n",
    "    y1=categorical_hgf.node_trajectories[0][\"surprise\"],\n",
    "    y2=categorical_hgf.node_trajectories[0][\"surprise\"].min(),\n",
    "    color=\"#7f7f7f\",\n",
    "    alpha=0.1,\n",
    "    zorder=-1,\n",
    ")\n",
    "axs[4].set_title(\"Sum of binary surprises\", loc=\"left\")\n",
    "axs[4].set_ylabel(\"Inputs\")\n",
    "\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d915da54-5396-467c-827b-583a77ff8391",
   "metadata": {},
   "source": [
    "### Inference using MCMC sampling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aea0e621-26f2-436e-b958-f0e9caa50992",
   "metadata": {},
   "source": [
    "In the binary and continuous HGF example, we have been using the {py:class}`pyhgf.distribution.HGFDistribution` class to create a PyMC-compatible distribution of the HGF. This was possible when using the most standard models as we can easily write a pre-defined distribution that fits exactly the network specification. However, when using more exotic network structures, as this is the case here with the categorical state nodes where the number of nodes in the network grows with the number of categories, we need a more flexible approach that can let us wrap a PyMC distribution for every kind of network we can have. \n",
    "\n",
    "This is what we are doing below (see [this blog post](https://www.pymc-labs.io/blog-posts/jax-functions-in-pymc-3-quick-examples/) and the [PyMC documentation](https://www.pymc.io/projects/examples/en/latest/case_studies/wrapping_jax_function.html) on how to do that). First, we start by creating a function that computes the surprise of the model, here using the Kullback-Leibler divergences of the implied Dirichlet distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "895b59fc-9e49-4848-bc07-59325e34b5d4",
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   "source": [
    "def categorical_surprise(omega_2, hgf, input_data):\n",
    "\n",
    "    # replace with a new omega in the model\n",
    "    for idx in np.arange(21, 31):\n",
    "        hgf.attributes[idx][\"tonic_volatility\"] = omega_2\n",
    "\n",
    "    # run the model forward again\n",
    "    hgf.input_data(input_data=input_data.T)\n",
    "\n",
    "    # compute the surprises using KL divergences\n",
    "    surprise = hgf.node_trajectories[0][\"kl_divergence\"][2:].sum()\n",
    "\n",
    "    # return an infinite surprise if the model could not fit at any point\n",
    "    surprise = jnp.where(\n",
    "        jnp.any(jnp.isnan(hgf.node_trajectories[0][\"xi\"])), jnp.inf, surprise\n",
    "    )\n",
    "\n",
    "    return surprise\n",
    "\n",
    "\n",
    "surprise_fn = Partial(categorical_surprise, hgf=categorical_hgf, input_data=input_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5ebba47-0e0e-47ca-9c8f-91dc84422394",
   "metadata": {},
   "source": [
    "We create both jitted and the vector-jacobian product requiered for a custom Op in PyTensor:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "05bcec35-1b57-4593-9285-b773f0f9e5c9",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "jitted_custom_op_jax = jit(surprise_fn)\n",
    "\n",
    "\n",
    "def vjp_custom_op_jax(x, gz):\n",
    "    _, vjp_fn = vjp(surprise_fn, x)\n",
    "    return vjp_fn(gz)[0]\n",
    "\n",
    "\n",
    "jitted_vjp_custom_op_jax = jit(vjp_custom_op_jax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "191f65ab-6b82-4de6-9a3a-940f5d6c7400",
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    "editable": true,
    "execution": {
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     "slide_type": ""
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    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": [
    "# The CustomOp needs `make_node`, `perform` and `grad`.\n",
    "class CustomOp(Op):\n",
    "    def make_node(self, x):\n",
    "        # Create a PyTensor node specifying the number and type of inputs and outputs\n",
    "\n",
    "        # We convert the input into a PyTensor tensor variable\n",
    "        inputs = [pt.as_tensor_variable(x)]\n",
    "        # Output has the same type and shape as `x`\n",
    "        outputs = [inputs[0].type()]\n",
    "        return Apply(self, inputs, outputs)\n",
    "\n",
    "    def perform(self, node, inputs, outputs):\n",
    "        # Evaluate the Op result for a specific numerical input\n",
    "\n",
    "        # The inputs are always wrapped in a list\n",
    "        (x,) = inputs\n",
    "        result = jitted_custom_op_jax(x)\n",
    "        # The results should be assigned inplace to the nested list\n",
    "        # of outputs provided by PyTensor. If you have multiple\n",
    "        # outputs and results, you should assign each at outputs[i][0]\n",
    "        outputs[0][0] = np.asarray(result, dtype=\"float64\")\n",
    "\n",
    "    def grad(self, inputs, output_gradients):\n",
    "        # Create a PyTensor expression of the gradient\n",
    "        (x,) = inputs\n",
    "        (gz,) = output_gradients\n",
    "        # We reference the VJP Op created below, which encapsulates\n",
    "        # the gradient operation\n",
    "        return [vjp_custom_op(x, gz)]\n",
    "\n",
    "\n",
    "class VJPCustomOp(Op):\n",
    "    def make_node(self, x, gz):\n",
    "        # Make sure the two inputs are tensor variables\n",
    "        inputs = [pt.as_tensor_variable(x), pt.as_tensor_variable(gz)]\n",
    "        # Output has the shape type and shape as the first input\n",
    "        outputs = [inputs[0].type()]\n",
    "        return Apply(self, inputs, outputs)\n",
    "\n",
    "    def perform(self, node, inputs, outputs):\n",
    "        (x, gz) = inputs\n",
    "        result = jitted_vjp_custom_op_jax(x, gz)\n",
    "        outputs[0][0] = np.asarray(result, dtype=\"float64\")\n",
    "\n",
    "\n",
    "# Instantiate the Ops\n",
    "custom_op = CustomOp()\n",
    "vjp_custom_op = VJPCustomOp()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ccf51208-980b-4ccd-9b2a-cae1525da3f0",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-01-10T13:54:49.766788Z",
     "iopub.status.busy": "2025-01-10T13:54:49.766487Z",
     "iopub.status.idle": "2025-01-10T13:54:49.769342Z",
     "shell.execute_reply": "2025-01-10T13:54:49.768779Z"
    },
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     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# with pm.Model() as model:\n",
    "#    omega_2 = pm.Normal(\"omega_2\", -2.0, 2)\n",
    "#    pm.Potential(\"hgf\", custom_op(omega_2))\n",
    "#    categorical_idata = pm.sample(chains=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bce093e9-1ab0-4ea1-99e6-a83b68331b9c",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-01-10T13:54:49.771424Z",
     "iopub.status.busy": "2025-01-10T13:54:49.770945Z",
     "iopub.status.idle": "2025-01-10T13:54:49.773974Z",
     "shell.execute_reply": "2025-01-10T13:54:49.773439Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# az.plot_trace(categorical_idata)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44abf936-9aba-487c-bbb0-96ed9af3de73",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## The categorical state-transition node\n",
    "\n",
    "```{warning}\n",
    "This is work in progress.\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3c8e69f-b9f6-4c42-ac1e-f31bfb6b1bf3",
   "metadata": {},
   "source": [
    "# System configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "25f8522f-9d42-47fd-a6bc-87ffc0dfd866",
   "metadata": {
    "editable": true,
    "execution": {
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     "shell.execute_reply": "2025-01-10T13:54:49.835887Z"
    },
    "scrolled": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last updated: Fri Jan 10 2025\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.12.3\n",
      "IPython version      : 8.31.0\n",
      "\n",
      "pyhgf : 0.2.1.post4.dev0+d49aafe9\n",
      "jax   : 0.4.31\n",
      "jaxlib: 0.4.31\n",
      "\n",
      "matplotlib: 3.10.0\n",
      "jax       : 0.4.31\n",
      "IPython   : 8.31.0\n",
      "numpy     : 1.26.0\n",
      "sys       : 3.12.3 | packaged by conda-forge | (main, Apr 15 2024, 18:38:13) [GCC 12.3.0]\n",
      "pyhgf     : 0.2.1.post4.dev0+d49aafe9\n",
      "seaborn   : 0.13.2\n",
      "pytensor  : 2.26.4\n",
      "\n",
      "Watermark: 2.5.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -n -u -v -iv -w -p pyhgf,jax,jaxlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f5351f55-8c0a-4e45-b5d5-32853b4b3e2e",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
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