update figures script

figures/01_connectivity-matrix.ipynb

@@ -0,0 +1,116 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "import os.path as op\n",
+    "\n",
+    "import numpy as np\n",
+    "import nibabel as nib\n",
+    "from nilearn import plotting\n",
+    "\n",
+    "sys.path.append(op.abspath(\"../\"))\n",
+    "import utils"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_dir = op.abspath(op.join(\"..\", \"data\"))\n",
+    "template_dir = op.join(data_dir, \"templates\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subcortical_fn = op.join(template_dir, \"rois-subcortical_mni152_mask.nii.gz\")\n",
+    "subcort_img = nib.load(subcortical_fn)\n",
+    "subcort_dat = subcort_img.get_fdata()\n",
+    "subcort_mask = subcort_dat != 0\n",
+    "n_subcort_vox = np.where(subcort_mask)[0].shape[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dcon_img = nib.load(\n",
+    "            op.join(\n",
+    "                data_dir, \"hcp\", \"HCP_S1200_1003_rfMRI_MSMAll_groupPCA_d4500ROW_zcorr.dconn.nii\"\n",
+    "            )\n",
+    "        )\n",
+    "dcon_mtx = np.tanh(dcon_img.get_fdata())  # 91,282 X 91,282 grayordinates\n",
+    "\n",
+    "del dcon_img"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cort_mtx = dcon_mtx[: dcon_mtx.shape[0] - n_subcort_vox, : dcon_mtx.shape[0] - n_subcort_vox] # 59,412 X 59,412 vertex\n",
+    "del dcon_mtx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plotting.plot_matrix(cort_mtx, colorbar=True, vmax=1, vmin=-1, cmap=\"spectral\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Calculate affinity matrix\n",
+    "affin_mtx = utils.affinity(dcon_mtx, 90)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.12 ('gradec_env')",
+   "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": "3.9.16"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "32eae8b81dca7564140c4bb02978cc1f5cb3ca50d75c604cb4f67ffb4db99fb5"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

figures/02_gradient.ipynb

@@ -0,0 +1,188 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"Example for plotting gradient data\"\"\"\n",
+    "import os.path as op\n",
+    "from glob import glob\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from utils import plot_gradient, plot_subcortical_gradient"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Visualize Gradients"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Margulies 2016 (HCP S900)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "from neuromaps.datasets import fetch_annotation\n",
+    "\n",
+    "data_dir = \"../data\"\n",
+    "neuromaps_dir = op.join(data_dir, \"neuromaps-data\")\n",
+    "\n",
+    "marg_grad_out_path = \"../figures/Fig/gradient\"\n",
+    "grad_marg_lh, grad_marg_rh = fetch_annotation(\n",
+    "    source=\"margulies2016\",\n",
+    "    desc=\"fcgradient01\",\n",
+    "    space=\"fsLR\",\n",
+    "    den=\"32k\",\n",
+    "    data_dir=neuromaps_dir,\n",
+    ")\n",
+    "\n",
+    "plot_gradient(data_dir, [(grad_marg_lh, grad_marg_rh)], cbar=True, out_dir=marg_grad_out_path)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Peraza 2022 (HCP S1200)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "jperaza_grad_path = op.abspath(\"../results/gradient\")\n",
+    "jperaza_grad_out_path = \"../figures/Fig/gradient\"\n",
+    "jperaza_grad_lh_fnames = sorted(glob(op.join(jperaza_grad_path, \"*hemi-L_feature.func.gii\")))\n",
+    "jperaza_grad_rh_fnames = sorted(glob(op.join(jperaza_grad_path, \"*hemi-R_feature.func.gii\")))\n",
+    "jperaza_subcort_grad_fnames = sorted(glob(op.join(jperaza_grad_path, \"*_feature.nii.gz\")))\n",
+    "jperaza_grad_fnames = zip(jperaza_grad_lh_fnames, jperaza_grad_rh_fnames)\n",
+    "\n",
+    "plot_gradient(\"../data\", jperaza_grad_fnames, cbar=True, out_dir=jperaza_grad_out_path)\n",
+    "# plot_subcortical_gradient(jperaza_subcort_grad_fnames, threshold_=0.01)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lambdas = np.load(\"../results/gradient/lambdas.npy\")\n",
+    "lambdas = lambdas[:9]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "sns.set_style(\"ticks\",{'axes.grid' : True})\n",
+    "# sns.set(style=\"white\")\n",
+    "\n",
+    "_, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, figsize=(3.5, 6))\n",
+    "#ax1.set_xlabel(\"Component Nb\")\n",
+    "\n",
+    "\n",
+    "ax1.set_ylabel(\"Explained Variance Ratio\")\n",
+    "ax2.set_ylabel(\"Difference in Explained\\nVariance Ratio\")\n",
+    "x1 = range(1, lambdas.size + 1, 1)\n",
+    "x2 = range(1, lambdas.size, 1)\n",
+    "y1 = (100 * lambdas) / lambdas.sum()\n",
+    "y2 = 100 * (lambdas[:-1] - lambdas[1:]) / lambdas.sum()\n",
+    "\n",
+    "sns.lineplot(\n",
+    "    x=x1,\n",
+    "    y=y1,\n",
+    "    marker=\"o\",\n",
+    "    color=\"#1D5B79\",\n",
+    "    ax=ax1,\n",
+    ")\n",
+    "sns.lineplot(\n",
+    "    x=x2,\n",
+    "    y=y2,\n",
+    "    marker=\"o\",\n",
+    "    color=\"#1D5B79\",\n",
+    "    ax=ax2,\n",
+    ")\n",
+    "ax1.set_xticks(x1)\n",
+    "ax1.set_xticklabels(x1)\n",
+    "ax1.set_xlabel(\"Component\")\n",
+    "\n",
+    "x2_lbs = [f\"{x}-{x+1}\" for x in x2]\n",
+    "ax2.set_xticks(x2)\n",
+    "ax2.set_xticklabels(x2_lbs) \n",
+    "ax2.set_xlabel(\"Component Difference ($C_{i} - C_{i+1}$)\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(op.join(\"./\", \"Fig\", \"Fig-S2.png\"), bbox_inches=\"tight\", dpi=500)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.12 ('gradec_env')",
+   "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": "3.9.16"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "32eae8b81dca7564140c4bb02978cc1f5cb3ca50d75c604cb4f67ffb4db99fb5"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}