practice_napari.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Based on: https://napari.org/tutorials/fundamentals/viewer.html and https://github.com/alisterburt/napari-workshops/tree/main/napari-workshops/notebooks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import napari\n",
    "from skimage import data\n",
    "from napari.utils import nbscreenshot\n",
    "import numpy as np\n",
    "\n",
    "# create the viewer and display the image\n",
    "viewer = napari.view_image(data.astronaut(), rgb=True, name='astronaut')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nbscreenshot(viewer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "viewer.add_image(data.moon(), name='moon')\n",
    "viewer.add_image(data.camera(), name='camera')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nbscreenshot(viewer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "viewer.layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "viewer.layers[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "points = np.array([[100, 100], [200, 200], [300, 100]])\n",
    "viewer.add_points(points, size=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "viewer.layers[0].opacity = 0.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nbscreenshot(viewer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "viewer.layers.pop(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nbscreenshot(viewer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "blobs = np.stack(\n",
    "    [\n",
    "        data.binary_blobs(\n",
    "            length=512, blob_size_fraction=0.05, n_dim=2, volume_fraction=f\n",
    "        )\n",
    "        for f in np.linspace(0.05, 0.5, 10)\n",
    "    ],\n",
    "    axis=0,\n",
    ").astype(float)\n",
    "viewer.add_image(blobs, name='blobs', opacity=0.5, colormap='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "viewer.layers.pop('points')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nbscreenshot(viewer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "viewer.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from scipy import ndimage as ndi\n",
    "\n",
    "blobs = data.binary_blobs(length=128, volume_fraction=0.1, n_dim=3)\n",
    "viewer = napari.view_image(blobs.astype(float), name='blobs')\n",
    "labeled = ndi.label(blobs)[0]\n",
    "viewer.add_labels(labeled, name='blob ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# change the viewer theme\n",
    "viewer.theme = 'light'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
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  "kernelspec": {
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