Visualizing_neuronal_activity.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import napari\n",
    "import skimage.io as skio\n",
    "import numpy as np\n",
    "\n",
    "from napari_animation import Animation\n",
    "from napari.utils.colormaps import Colormap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Load L and S\n",
    "\n",
    "path = \"./S_img.tif\"\n",
    "S = skio.imread(path).astype(float)\n",
    "\n",
    "path = \"./L_img.tif\"\n",
    "L = skio.imread(path).astype(float)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2. Generate white box\n",
    "\n",
    "white_box = np.zeros(L.shape)  # \n",
    "max_data = 1\n",
    "width = 1\n",
    "white_box[:, 0:width, :] = max_data\n",
    "white_box[:, -width:, :] = max_data\n",
    "white_box[:, :, -width:] = max_data\n",
    "white_box[:, :, 0:width] = max_data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assign viewer and show image\n",
    "viewer = napari.Viewer()\n",
    "\n",
    "L_layer = viewer.add_image(L, colormap='gray', opacity=0.32)\n",
    "S_layer = viewer.add_image(S, colormap='green', blending='additive')\n",
    "white_box_layer = viewer.add_image(white_box, blending='additive')\n",
    "\n",
    "\n",
    "from matplotlib import cm\n",
    "color_hot = cm.hot(range(256))\n",
    "\n",
    "import numpy as np\n",
    "from scipy.io import loadmat\n",
    "\n",
    "\n",
    "\n",
    "custom_cmap = Colormap(colors=color_hot, interpolation='linear', name='custom_hot')\n",
    "S_layer.colormap = custom_cmap\n",
    "\n",
    "\n",
    "viewer.dims.ndisplay = 2\n",
    "viewer.reset_view()\n"
   ]
  }
 ],
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