{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting Started with scHopfield"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f36bd4fa",
   "metadata": {},
   "source": [
    "# Network Inference\n",
    "\n",
    "scHopfield infers gene regulatory networks (GRNs) from RNA velocity data using gradient descent optimization.\n",
    "\n",
    "## Theory\n",
    "\n",
    "The Hopfield network dynamics are:\n",
    "\n",
    "\n",
    "\n",
    "$$\n",
    "\\frac{dx}{dt} = W \\cdot \\sigma(x) - \\gamma \\cdot x + I\n",
    "$$\n",
    "\n",
    "Where RNA velocity approximates $dx/dt$. By fitting W (interactions) and I (biases) to minimize the difference between observed and predicted velocity, we infer the GRN structure.\n",
    "\n",
    "## Basic Usage\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfbb572f",
   "metadata": {},
   "source": [
    "# Preprocessing\n",
    "\n",
    "## Sigmoid Function Fitting\n",
    "\n",
    "scHopfield uses sigmoid activation functions to model gene expression:\n",
    "\n",
    "\n",
    "\n",
    "$$\n",
    "\\sigma(x; \\theta, \\alpha, \\beta) = \\frac{\\alpha}{1 + e^{-\\beta(x - \\theta)}} + \\text{offset}\n",
    "$$\n",
    "\n",
    "Where:\n",
    "\n",
    "- $\\theta$ is the threshold parameter\n",
    "- $\\alpha$ is the amplitude (typically 1)\n",
    "- $\\beta$ is the exponent (steepness)\n",
    "- offset is a baseline value\n",
    "\n",
    "### Fitting Process\n",
    "\n",
    "The `fit_all_sigmoids()` function fits these parameters to each gene's expression distribution:\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "scHopfield models gene regulatory networks (GRNs) as continuous Hopfield\n",
    "\n",
    "networks, where gene expression dynamics follow:\n",
    "\n",
    "\n",
    "\n",
    "$$\\frac{dx}{dt} = W \\cdot \\sigma(x) - \\gamma \\cdot x + I$$\n",
    "\n",
    "\n",
    "\n",
    "- **W**: Interaction matrix encoding gene-gene regulatory relationships\n",
    "\n",
    "- **σ(x)**: Sigmoid activation function fitted to expression data\n",
    "\n",
    "- **γ**: Degradation rates (mRNA decay)\n",
    "\n",
    "- **I**: Bias vector representing external inputs / basal expression\n",
    "\n",
    "\n",
    "\n",
    "This notebook walks through the full setup: data loading, sigmoid fitting,\n",
    "\n",
    "network inference (with and without a scaffold), and saving/loading the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 Setup & Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/bernaljp/miniconda3/envs/SCH/lib/python3.11/site-packages/louvain/__init__.py:54: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n",
      "  from pkg_resources import get_distribution, DistributionNotFound\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import scanpy as sc\n",
    "import scHopfield as sch\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Analysis parameters (update for your dataset)\n",
    "DATA_PATH = './scratch/Data/'\n",
    "DATASET_FILE = 'hematopoiesis.h5ad'\n",
    "\n",
    "CLUSTER_KEY = 'cell_type'\n",
    "VELOCITY_KEY = 'velocity_alpha_minus_gamma_s'\n",
    "SPLICED_KEY  = 'M_t'\n",
    "DEGRADATION_KEY = 'gamma'\n",
    "DYNAMIC_GENES_KEY = 'use_for_dynamics'\n",
    "\n",
    "CELL_TYPE_ORDER = ['Meg', 'Ery', 'MEP-like', 'HSC', 'GMP-like', 'Mon', 'Bas', 'Neu']\n",
    "\n",
    "# Network inference hyper-parameters\n",
    "N_EPOCHS              = 1000\n",
    "BATCH_SIZE            = 128\n",
    "W_THRESHOLD           = 1e-12\n",
    "SCAFFOLD_REGULARIZATION = 1e-2\n",
    "DEVICE = 'cuda'   # or 'cpu'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 Data Loading\n",
    "\n",
    "The hematopoiesis dataset can be loaded from two sources:\n",
    "\n",
    "| Option | Command | Notes |\n",
    "|--------|---------|-------|\n",
    "| **Local file** | `sc.read_h5ad(DATA_PATH + DATASET_FILE)` | Fastest; requires the file on disk |\n",
    "| **dynamo** | `dyn.sample_data.hematopoiesis()` | Downloads on first use; `pip install dynamo-release` |\n",
    "\n",
    "The cell below tries the local file first and falls back to dynamo automatically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading data...\n",
      "  Loaded: 1947 cells × 1956 genes\n",
      "AnnData object with n_obs × n_vars = 1947 × 1956\n",
      "    obs: 'batch', 'time', 'cell_type', 'nGenes', 'nCounts', 'pMito', 'pass_basic_filter', 'new_Size_Factor', 'initial_new_cell_size', 'total_Size_Factor', 'initial_total_cell_size', 'spliced_Size_Factor', 'initial_spliced_cell_size', 'unspliced_Size_Factor', 'initial_unspliced_cell_size', 'Size_Factor', 'initial_cell_size', 'ntr', 'cell_cycle_phase', 'leiden', 'control_point_pca', 'inlier_prob_pca', 'obs_vf_angle_pca', 'pca_ddhodge_div', 'pca_ddhodge_potential', 'acceleration_pca', 'curvature_pca', 'n_counts', 'mt_frac', 'jacobian_det_pca', 'manual_selection', 'divergence_pca', 'curv_leiden', 'curv_louvain', 'SPI1->GATA1_jacobian', 'jacobian', 'umap_ori_leiden', 'umap_ori_louvain', 'umap_ddhodge_div', 'umap_ddhodge_potential', 'curl_umap', 'divergence_umap', 'acceleration_umap', 'control_point_umap_ori', 'inlier_prob_umap_ori', 'obs_vf_angle_umap_ori', 'curvature_umap_ori'\n",
      "    var: 'gene_name', 'gene_id', 'nCells', 'nCounts', 'pass_basic_filter', 'use_for_pca', 'frac', 'ntr', 'time_3_alpha', 'time_3_beta', 'time_3_gamma', 'time_3_half_life', 'time_3_alpha_b', 'time_3_alpha_r2', 'time_3_gamma_b', 'time_3_gamma_r2', 'time_3_gamma_logLL', 'time_3_delta_b', 'time_3_delta_r2', 'time_3_bs', 'time_3_bf', 'time_3_uu0', 'time_3_ul0', 'time_3_su0', 'time_3_sl0', 'time_3_U0', 'time_3_S0', 'time_3_total0', 'time_3_beta_k', 'time_3_gamma_k', 'time_5_alpha', 'time_5_beta', 'time_5_gamma', 'time_5_half_life', 'time_5_alpha_b', 'time_5_alpha_r2', 'time_5_gamma_b', 'time_5_gamma_r2', 'time_5_gamma_logLL', 'time_5_bs', 'time_5_bf', 'time_5_uu0', 'time_5_ul0', 'time_5_su0', 'time_5_sl0', 'time_5_U0', 'time_5_S0', 'time_5_total0', 'time_5_beta_k', 'time_5_gamma_k', 'use_for_dynamics', 'gamma', 'gamma_r2', 'use_for_transition', 'gamma_k', 'gamma_b'\n",
      "    uns: 'PCs', 'VecFld_pca', 'VecFld_umap', 'X_umap_neighbors', 'cell_phase_genes', 'cell_type_colors', 'dynamics', 'explained_variance_ratio_', 'feature_selection', 'grid_velocity_pca', 'grid_velocity_umap', 'grid_velocity_umap_ori_perturbation', 'grid_velocity_umap_test', 'jacobian_pca', 'leiden', 'neighbors', 'pca_mean', 'pp', 'response'\n",
      "    obsm: 'X', 'X_pca', 'X_pca_SparseVFC', 'X_umap', 'X_umap_SparseVFC', 'X_umap_ori_perturbation', 'X_umap_test', 'acceleration_pca', 'acceleration_umap', 'cell_cycle_scores', 'curvature_pca', 'curvature_umap', 'j_delta_x_perturbation', 'velocity_pca', 'velocity_pca_SparseVFC', 'velocity_umap', 'velocity_umap_SparseVFC', 'velocity_umap_ori_perturbation', 'velocity_umap_test'\n",
      "    layers: 'M_n', 'M_nn', 'M_t', 'M_tn', 'M_tt', 'X_new', 'X_total', 'velocity_alpha_minus_gamma_s'\n",
      "    obsp: 'X_umap_connectivities', 'X_umap_distances', 'connectivities', 'cosine_transition_matrix', 'distances', 'fp_transition_rate', 'moments_con', 'pca_ddhodge', 'perturbation_transition_matrix', 'umap_ddhodge'\n"
     ]
    }
   ],
   "source": [
    "from fileinput import filename\n",
    "import os\n",
    "\n",
    "print(\"Loading data...\")\n",
    "_local_path = DATA_PATH + DATASET_FILE\n",
    "\n",
    "if os.path.exists(_local_path):\n",
    "    print(f\"  Source: local file ({_local_path})\")\n",
    "    adata = sc.read_h5ad(_local_path)\n",
    "else:\n",
    "    # Requires: pip install dynamo-release\n",
    "    print(\"  Local file not found – downloading from dynamo (pip install dynamo-release)...\")\n",
    "    import dynamo as dyn\n",
    "    adata = dyn.sample_data.hematopoiesis(filename=_local_path)\n",
    "    # Optionally cache locally to skip the download next time:\n",
    "    # os.makedirs(DATA_PATH, exist_ok=True)\n",
    "    # adata.write_h5ad(_local_path)\n",
    "\n",
    "print(f\"  Loaded: {adata.n_obs} cells × {adata.n_vars} genes\")\n",
    "print(adata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.3 Preprocessing\n",
    "\n",
    "\n",
    "\n",
    "Remove genes with NaN velocities by subsetting adata, then identify the\n",
    "\n",
    "dynamic genes that will be passed to the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removing genes with NaN velocities...\n",
      "  After filtering: 1947 cells × 1728 genes\n",
      "  Dynamic genes for modelling: 1728\n"
     ]
    }
   ],
   "source": [
    "# Remove genes that have any NaN velocity entry (hematopoiesis dataset)\n",
    "print(\"Removing genes with NaN velocities...\")\n",
    "velocity_layer = adata.layers[VELOCITY_KEY]\n",
    "if hasattr(velocity_layer, 'toarray'):\n",
    "    velocity_layer = velocity_layer.toarray()\n",
    "bad_gene_idx = np.unique(np.where(np.isnan(velocity_layer))[1])\n",
    "keep_mask = np.ones(adata.n_vars, dtype=bool)\n",
    "keep_mask[bad_gene_idx] = False\n",
    "adata = adata[:, keep_mask].copy()\n",
    "print(f\"  After filtering: {adata.n_obs} cells × {adata.n_vars} genes\")\n",
    "\n",
    "# Boolean mask of dynamic genes used for modelling\n",
    "genes_to_use = adata.var[DYNAMIC_GENES_KEY].values\n",
    "n_genes = genes_to_use.sum()\n",
    "print(f\"  Dynamic genes for modelling: {n_genes}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.4 Sigmoid Fitting\n",
    "\n",
    "\n",
    "\n",
    "Fit a sigmoid function to each gene's expression distribution.\n",
    "$$\\varphi_i(x_i) = \\frac{x_i^{n_i}}{x_i^{n_i}+k_i^{n_i}}$$\n",
    "The fitted parameters are stored in `adata.var`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/bernaljp/packages/scHopfield/scHopfield/_utils/math.py:93: RuntimeWarning: divide by zero encountered in divide\n",
      "  ty = np.log(y / (1 - y))\n",
      "/home/bernaljp/packages/scHopfield/scHopfield/_utils/math.py:93: RuntimeWarning: divide by zero encountered in log\n",
      "  ty = np.log(y / (1 - y))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigmoid fitting complete.\n",
      "AnnData object with n_obs × n_vars = 1947 × 1728\n",
      "    obs: 'batch', 'time', 'cell_type', 'nGenes', 'nCounts', 'pMito', 'pass_basic_filter', 'new_Size_Factor', 'initial_new_cell_size', 'total_Size_Factor', 'initial_total_cell_size', 'spliced_Size_Factor', 'initial_spliced_cell_size', 'unspliced_Size_Factor', 'initial_unspliced_cell_size', 'Size_Factor', 'initial_cell_size', 'ntr', 'cell_cycle_phase', 'leiden', 'control_point_pca', 'inlier_prob_pca', 'obs_vf_angle_pca', 'pca_ddhodge_div', 'pca_ddhodge_potential', 'acceleration_pca', 'curvature_pca', 'n_counts', 'mt_frac', 'jacobian_det_pca', 'manual_selection', 'divergence_pca', 'curv_leiden', 'curv_louvain', 'SPI1->GATA1_jacobian', 'jacobian', 'umap_ori_leiden', 'umap_ori_louvain', 'umap_ddhodge_div', 'umap_ddhodge_potential', 'curl_umap', 'divergence_umap', 'acceleration_umap', 'control_point_umap_ori', 'inlier_prob_umap_ori', 'obs_vf_angle_umap_ori', 'curvature_umap_ori'\n",
      "    var: 'gene_name', 'gene_id', 'nCells', 'nCounts', 'pass_basic_filter', 'use_for_pca', 'frac', 'ntr', 'time_3_alpha', 'time_3_beta', 'time_3_gamma', 'time_3_half_life', 'time_3_alpha_b', 'time_3_alpha_r2', 'time_3_gamma_b', 'time_3_gamma_r2', 'time_3_gamma_logLL', 'time_3_delta_b', 'time_3_delta_r2', 'time_3_bs', 'time_3_bf', 'time_3_uu0', 'time_3_ul0', 'time_3_su0', 'time_3_sl0', 'time_3_U0', 'time_3_S0', 'time_3_total0', 'time_3_beta_k', 'time_3_gamma_k', 'time_5_alpha', 'time_5_beta', 'time_5_gamma', 'time_5_half_life', 'time_5_alpha_b', 'time_5_alpha_r2', 'time_5_gamma_b', 'time_5_gamma_r2', 'time_5_gamma_logLL', 'time_5_bs', 'time_5_bf', 'time_5_uu0', 'time_5_ul0', 'time_5_su0', 'time_5_sl0', 'time_5_U0', 'time_5_S0', 'time_5_total0', 'time_5_beta_k', 'time_5_gamma_k', 'use_for_dynamics', 'gamma', 'gamma_r2', 'use_for_transition', 'gamma_k', 'gamma_b', 'scHopfield_used', 'sigmoid_threshold', 'sigmoid_exponent', 'sigmoid_offset', 'sigmoid_mse'\n",
      "    uns: 'PCs', 'VecFld_pca', 'VecFld_umap', 'X_umap_neighbors', 'cell_phase_genes', 'cell_type_colors', 'dynamics', 'explained_variance_ratio_', 'feature_selection', 'grid_velocity_pca', 'grid_velocity_umap', 'grid_velocity_umap_ori_perturbation', 'grid_velocity_umap_test', 'jacobian_pca', 'leiden', 'neighbors', 'pca_mean', 'pp', 'response', 'scHopfield'\n",
      "    obsm: 'X', 'X_pca', 'X_pca_SparseVFC', 'X_umap', 'X_umap_SparseVFC', 'X_umap_ori_perturbation', 'X_umap_test', 'acceleration_pca', 'acceleration_umap', 'cell_cycle_scores', 'curvature_pca', 'curvature_umap', 'j_delta_x_perturbation', 'velocity_pca', 'velocity_pca_SparseVFC', 'velocity_umap', 'velocity_umap_SparseVFC', 'velocity_umap_ori_perturbation', 'velocity_umap_test'\n",
      "    layers: 'M_n', 'M_nn', 'M_t', 'M_tn', 'M_tt', 'X_new', 'X_total', 'velocity_alpha_minus_gamma_s', 'sigmoid'\n",
      "    obsp: 'X_umap_connectivities', 'X_umap_distances', 'connectivities', 'cosine_transition_matrix', 'distances', 'fp_transition_rate', 'moments_con', 'pca_ddhodge', 'perturbation_transition_matrix', 'umap_ddhodge'\n"
     ]
    }
   ],
   "source": [
    "sch.pp.fit_all_sigmoids(\n",
    "    adata,\n",
    "    spliced_key=SPLICED_KEY,\n",
    "    genes=adata.var[DYNAMIC_GENES_KEY].values\n",
    ")\n",
    "\n",
    "sch.pp.compute_sigmoid(adata, spliced_key=SPLICED_KEY, copy=False)\n",
    "\n",
    "print(\"Sigmoid fitting complete.\")\n",
    "print(adata)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x800 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize sigmoid fits for key genes\n",
    "genes_to_plot = ['Gata1', 'Klf1', 'Gata2', 'Spi1', 'Cebpa', 'FLI1']\n",
    "genes_to_plot = [g.upper() for g in genes_to_plot if g.upper() in adata.var_names]\n",
    "\n",
    "if genes_to_plot:\n",
    "    n_cols = min(3, len(genes_to_plot))\n",
    "    n_rows = (len(genes_to_plot) + n_cols - 1) // n_cols\n",
    "    fig, axes = plt.subplots(n_rows, n_cols, figsize=[5*n_cols, 4*n_rows])\n",
    "    axes = np.atleast_2d(axes).flatten()\n",
    "\n",
    "    for i, gene in enumerate(genes_to_plot):\n",
    "        sch.pl.plot_sigmoid_fit(adata, gene=gene, ax=axes[i],\n",
    "                                spliced_key='M_t', show_zeros=False)\n",
    "\n",
    "    # Hide empty axes\n",
    "    for j in range(i+1, len(axes)):\n",
    "        axes[j].axis('off')\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.5 Network Inference (no scaffold)\n",
    "\n",
    "\n",
    "\n",
    "Learn the interaction matrix **W** for each cell cluster by minimizing the\n",
    "\n",
    "velocity reconstruction error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inferring interaction matrix W and bias vector I for cluster Mon\n",
      "Inferring interaction matrix W and bias vector I for cluster Meg\n",
      "Inferring interaction matrix W and bias vector I for cluster MEP-like\n",
      "Inferring interaction matrix W and bias vector I for cluster Ery\n",
      "Inferring interaction matrix W and bias vector I for cluster Bas\n",
      "Inferring interaction matrix W and bias vector I for cluster GMP-like\n",
      "Inferring interaction matrix W and bias vector I for cluster HSC\n",
      "Inferring interaction matrix W and bias vector I for cluster Neu\n",
      "Inferring interaction matrix W and bias vector I for cluster all\n",
      "Network inference complete (no scaffold).\n"
     ]
    }
   ],
   "source": [
    "sch.inf.fit_interactions(\n",
    "    adata,\n",
    "    cluster_key=CLUSTER_KEY,\n",
    "    spliced_key=SPLICED_KEY,\n",
    "    velocity_key=VELOCITY_KEY,\n",
    "    degradation_key=DEGRADATION_KEY,\n",
    "    w_threshold=W_THRESHOLD,\n",
    "    infer_I=True,\n",
    "    refit_gamma=False,\n",
    "    n_epochs=N_EPOCHS,\n",
    "    criterion='MSE',\n",
    "    batch_size=BATCH_SIZE,\n",
    "    use_scheduler=True,\n",
    "    get_plots=False,\n",
    "    device=DEVICE,\n",
    ")\n",
    "\n",
    "print(\"Network inference complete (no scaffold).\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.6 Scaffold-Constrained Inference\n",
    "\n",
    "\n",
    "\n",
    "Load a prior gene regulatory network (GRN) from CellOracle's mouse scATAC-seq\n",
    "\n",
    "atlas and use it as a scaffold to regularise the learned **W** matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "which: no R in (/opt/slurm/puppet/bin:/opt/slurm/cluster/ibex/install-v2/RedHat-9/bin:/opt/slurm/scripts/bin:/usr/lpp/mmfs/bin:/home/bernaljp/miniconda3/envs/SCH/bin:/opt/slurm/puppet/bin:/opt/slurm/cluster/ibex/install-v2/RedHat-9/bin:/opt/slurm/scripts/bin:/usr/lpp/mmfs/bin:/home/bernaljp/miniconda3/condabin:/opt/slurm/puppet/bin:/usr/share/Modules/bin:/opt/slurm/cluster/ibex/install-v2/RedHat-9/bin:/opt/slurm/scripts/bin:/usr/lpp/mmfs/bin:/usr/local/bin:/usr/bin:/usr/local/sbin:/usr/sbin:/opt/slurm/scripts/bin:/opt/puppetlabs/bin:/home/bernaljp/.local/bin:/home/bernaljp/bin:/opt/slurm/scripts/bin:/home/bernaljp/.local/bin:/home/bernaljp/bin:/opt/slurm/scripts/bin:/home/bernaljp/.local/bin:/home/bernaljp/bin)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading CellOracle scaffold...\n",
      "  Scaffold: 41693 potential connections\n",
      "  TFs: 73, Target genes: 1148\n"
     ]
    }
   ],
   "source": [
    "import celloracle as co\n",
    "import pandas as pd\n",
    "\n",
    "print(\"Loading CellOracle scaffold...\")\n",
    "base_GRN = co.data.load_mouse_scATAC_atlas_base_GRN()\n",
    "base_GRN.drop(['peak_id'], axis=1, inplace=True)\n",
    "\n",
    "# Build scaffold matrix (genes × genes; 1 = edge allowed by prior)\n",
    "scaffold = pd.DataFrame(\n",
    "    0,\n",
    "    index=adata.var.index[genes_to_use],\n",
    "    columns=adata.var.index[genes_to_use]\n",
    ")\n",
    "\n",
    "tfs = list(set(base_GRN.columns.str.lower()) & set(scaffold.index.str.lower()))\n",
    "target_genes = list(\n",
    "    set(base_GRN['gene_short_name'].str.lower().values) & set(scaffold.columns.str.lower())\n",
    ")\n",
    "\n",
    "index_map = {gene.lower(): gene for gene in scaffold.index}\n",
    "col_map   = {gene.lower(): gene for gene in scaffold.columns}\n",
    "\n",
    "for tf in tfs:\n",
    "    tf_original = index_map[tf]\n",
    "    tf_col = [c for c in base_GRN.columns if c.lower() == tf][0]\n",
    "    for target in base_GRN[base_GRN[tf_col] == 1]['gene_short_name']:\n",
    "        if target.lower() in target_genes:\n",
    "            scaffold.loc[tf_original, col_map[target.lower()]] = 1\n",
    "\n",
    "print(f\"  Scaffold: {scaffold.sum().sum()} potential connections\")\n",
    "print(f\"  TFs: {len(tfs)}, Target genes: {len(target_genes)}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inferring interaction matrix W and bias vector I for cluster Mon\n"
     ]
    },
    {
     "name": "stderr",
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     "text": [
      "/home/bernaljp/packages/scHopfield/scHopfield/inference/optimizer.py:18: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.detach().clone() or sourceTensor.detach().clone().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  self.register_buffer('mask', torch.tensor(mask, dtype=torch.float32, device=device))\n",
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      "[Epoch 201/1000] Total Loss: 3.396171, Reconstruction Loss: 0.001726, LR: 1.60e-02, Batch size: 128\n"
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      "[Epoch 401/1000] Total Loss: 0.448385, Reconstruction Loss: 0.000669, LR: 2.56e-03, Batch size: 128\n"
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      "[Epoch 501/1000] Total Loss: 0.181946, Reconstruction Loss: 0.000663, LR: 1.02e-03, Batch size: 128\n"
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      "[Epoch 801/1000] Total Loss: 0.011939, Reconstruction Loss: 0.000655, LR: 6.55e-05, Batch size: 128\n"
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      "[Epoch 901/1000] Total Loss: 0.005090, Reconstruction Loss: 0.000657, LR: 2.62e-05, Batch size: 128\n"
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      "/home/bernaljp/packages/scHopfield/scHopfield/inference/optimizer.py:18: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.detach().clone() or sourceTensor.detach().clone().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  self.register_buffer('mask', torch.tensor(mask, dtype=torch.float32, device=device))\n"
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    },
    {
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     "text": [
      "[Epoch 1000/1000] Total Loss: 0.002839, Reconstruction Loss: 0.000648, LR: 1.05e-05, Batch size: 128\n",
      "Inferring interaction matrix W and bias vector I for cluster Meg\n"
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      "Inferring interaction matrix W and bias vector I for cluster MEP-like\n"
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      "Inferring interaction matrix W and bias vector I for cluster Ery\n"
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      "[Epoch 401/1000] Total Loss: 0.632268, Reconstruction Loss: 0.000978, LR: 2.56e-03, Batch size: 128\n"
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      "[Epoch 501/1000] Total Loss: 0.264172, Reconstruction Loss: 0.000935, LR: 1.02e-03, Batch size: 128\n"
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      "[Epoch 601/1000] Total Loss: 0.106983, Reconstruction Loss: 0.000956, LR: 4.10e-04, Batch size: 128\n"
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      "[Epoch 701/1000] Total Loss: 0.044257, Reconstruction Loss: 0.000955, LR: 1.64e-04, Batch size: 128\n"
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      "[Epoch 801/1000] Total Loss: 0.018226, Reconstruction Loss: 0.000959, LR: 6.55e-05, Batch size: 128\n"
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      "[Epoch 901/1000] Total Loss: 0.007662, Reconstruction Loss: 0.000973, LR: 2.62e-05, Batch size: 128\n"
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      "[Epoch 1000/1000] Total Loss: 0.003573, Reconstruction Loss: 0.000967, LR: 1.05e-05, Batch size: 128\n",
      "Inferring interaction matrix W and bias vector I for cluster GMP-like\n"
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      "Inferring interaction matrix W and bias vector I for cluster HSC\n"
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     "output_type": "stream",
     "text": [
      "[Epoch 1000/1000] Total Loss: 0.003123, Reconstruction Loss: 0.000836, LR: 1.05e-05, Batch size: 128\n",
      "Scaffold-constrained network inference complete.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "sch.inf.fit_interactions(\n",
    "    adata,\n",
    "    cluster_key=CLUSTER_KEY,\n",
    "    spliced_key=SPLICED_KEY,\n",
    "    velocity_key=VELOCITY_KEY,\n",
    "    degradation_key=DEGRADATION_KEY,\n",
    "    w_threshold=W_THRESHOLD,\n",
    "    w_scaffold=scaffold.values.T,\n",
    "    scaffold_regularization=SCAFFOLD_REGULARIZATION,\n",
    "    only_TFs=True,\n",
    "    infer_I=True,\n",
    "    refit_gamma=False,\n",
    "    n_epochs=N_EPOCHS,\n",
    "    criterion='MSE',\n",
    "    batch_size=BATCH_SIZE,\n",
    "    use_scheduler=True,\n",
    "    get_plots=False,\n",
    "    device=DEVICE,\n",
    ")\n",
    "\n",
    "print(\"Scaffold-constrained network inference complete.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.7 Save / Load Model\n",
    "\n",
    "\n",
    "\n",
    "Models can be serialised to an HDF5 file for later use.  This is especially\n",
    "\n",
    "useful for large datasets where re-inference is expensive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model saved to 'model.h5sch'  |  clusters=['Bas', 'Ery', 'GMP-like', 'HSC', 'MEP-like', 'Meg', 'Mon', 'Neu', 'all']  |  genes=1728\n",
      "Model saved to 'model.h5sch'\n"
     ]
    }
   ],
   "source": [
    "# Save the fitted model\n",
    "MODEL_FILE = 'model.h5sch'\n",
    "sch.tl.save_model(adata, MODEL_FILE, overwrite=True)\n",
    "print(f\"Model saved to '{MODEL_FILE}'\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model loaded from 'model.h5sch'  |  clusters=['Bas', 'Ery', 'GMP-like', 'HSC', 'MEP-like', 'Meg', 'Mon', 'Neu', 'all']  |  genes=1728\n",
      "Model loaded successfully.\n",
      "AnnData object with n_obs × n_vars = 1947 × 1728\n",
      "    obs: 'batch', 'time', 'cell_type', 'nGenes', 'nCounts', 'pMito', 'pass_basic_filter', 'new_Size_Factor', 'initial_new_cell_size', 'total_Size_Factor', 'initial_total_cell_size', 'spliced_Size_Factor', 'initial_spliced_cell_size', 'unspliced_Size_Factor', 'initial_unspliced_cell_size', 'Size_Factor', 'initial_cell_size', 'ntr', 'cell_cycle_phase', 'leiden', 'control_point_pca', 'inlier_prob_pca', 'obs_vf_angle_pca', 'pca_ddhodge_div', 'pca_ddhodge_potential', 'acceleration_pca', 'curvature_pca', 'n_counts', 'mt_frac', 'jacobian_det_pca', 'manual_selection', 'divergence_pca', 'curv_leiden', 'curv_louvain', 'SPI1->GATA1_jacobian', 'jacobian', 'umap_ori_leiden', 'umap_ori_louvain', 'umap_ddhodge_div', 'umap_ddhodge_potential', 'curl_umap', 'divergence_umap', 'acceleration_umap', 'control_point_umap_ori', 'inlier_prob_umap_ori', 'obs_vf_angle_umap_ori', 'curvature_umap_ori'\n",
      "    var: 'gene_name', 'gene_id', 'nCells', 'nCounts', 'pass_basic_filter', 'use_for_pca', 'frac', 'ntr', 'time_3_alpha', 'time_3_beta', 'time_3_gamma', 'time_3_half_life', 'time_3_alpha_b', 'time_3_alpha_r2', 'time_3_gamma_b', 'time_3_gamma_r2', 'time_3_gamma_logLL', 'time_3_delta_b', 'time_3_delta_r2', 'time_3_bs', 'time_3_bf', 'time_3_uu0', 'time_3_ul0', 'time_3_su0', 'time_3_sl0', 'time_3_U0', 'time_3_S0', 'time_3_total0', 'time_3_beta_k', 'time_3_gamma_k', 'time_5_alpha', 'time_5_beta', 'time_5_gamma', 'time_5_half_life', 'time_5_alpha_b', 'time_5_alpha_r2', 'time_5_gamma_b', 'time_5_gamma_r2', 'time_5_gamma_logLL', 'time_5_bs', 'time_5_bf', 'time_5_uu0', 'time_5_ul0', 'time_5_su0', 'time_5_sl0', 'time_5_U0', 'time_5_S0', 'time_5_total0', 'time_5_beta_k', 'time_5_gamma_k', 'use_for_dynamics', 'gamma', 'gamma_r2', 'use_for_transition', 'gamma_k', 'gamma_b', 'I_Bas', 'I_Ery', 'I_GMP-like', 'I_HSC', 'I_MEP-like', 'I_Meg', 'I_Mon', 'I_Neu', 'I_all', 'scHopfield_used', 'sigmoid_exponent', 'sigmoid_mse', 'sigmoid_offset', 'sigmoid_threshold'\n",
      "    uns: 'PCs', 'VecFld_pca', 'VecFld_umap', 'X_umap_neighbors', 'cell_phase_genes', 'cell_type_colors', 'dynamics', 'explained_variance_ratio_', 'feature_selection', 'grid_velocity_pca', 'grid_velocity_umap', 'grid_velocity_umap_ori_perturbation', 'grid_velocity_umap_test', 'jacobian_pca', 'leiden', 'neighbors', 'pca_mean', 'pp', 'response', 'scHopfield'\n",
      "    obsm: 'X', 'X_pca', 'X_pca_SparseVFC', 'X_umap', 'X_umap_SparseVFC', 'X_umap_ori_perturbation', 'X_umap_test', 'acceleration_pca', 'acceleration_umap', 'cell_cycle_scores', 'curvature_pca', 'curvature_umap', 'j_delta_x_perturbation', 'velocity_pca', 'velocity_pca_SparseVFC', 'velocity_umap', 'velocity_umap_SparseVFC', 'velocity_umap_ori_perturbation', 'velocity_umap_test'\n",
      "    layers: 'M_n', 'M_nn', 'M_t', 'M_tn', 'M_tt', 'X_new', 'X_total', 'velocity_alpha_minus_gamma_s'\n",
      "    obsp: 'X_umap_connectivities', 'X_umap_distances', 'connectivities', 'cosine_transition_matrix', 'distances', 'fp_transition_rate', 'moments_con', 'pca_ddhodge', 'perturbation_transition_matrix', 'umap_ddhodge'\n",
      "    varp: 'W_Bas', 'W_Ery', 'W_GMP-like', 'W_HSC', 'W_MEP-like', 'W_Meg', 'W_Mon', 'W_Neu', 'W_all'\n"
     ]
    }
   ],
   "source": [
    "# Load the model back into a fresh AnnData object.\n",
    "# If adata_loaded has more genes than the saved model (e.g. the full dataset\n",
    "# before NaN filtering), load_model subsets it in-place to the model gene set.\n",
    "adata_loaded = sc.read_h5ad(DATA_PATH + DATASET_FILE)\n",
    "adata_loaded = sch.tl.load_model(adata_loaded, MODEL_FILE)\n",
    "print(\"Model loaded successfully.\")\n",
    "print(adata_loaded)\n"
   ]
  }
 ],
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