{
"cells": [
{
"cell_type": "markdown",
"id": "dca63629",
"metadata": {},
"source": [
"# Interactive functions in Jupyter Lab\n",
"\n",
"In this notebook we demonstate interactive functions using [stackview](https://github.com/haesleinhuepf/stackview/) in combination with [pyclesperanto_prototype](https://github.com/clEsperanto/pyclesperanto_prototype). Interactive image stack viewing in jupyter notebooks based on \n",
"[ipycanvas](https://ipycanvas.readthedocs.io/) and \n",
"[ipywidgets](https://ipywidgets.readthedocs.io/en/latest/). \n",
"\n",
"Note: This notebook is not fully rendered in the Jupyter book and on github. You need to run it locally to see how it works."
]
},
{
"cell_type": "markdown",
"id": "79a8abbc",
"metadata": {},
"source": [
"## Usage\n",
"First we test the installation by importing the two libraries and scikit-image for loading image data."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "ca154076",
"metadata": {},
"outputs": [],
"source": [
"import stackview\n",
"import pyclesperanto_prototype as cle\n",
"from skimage.io import imread"
]
},
{
"cell_type": "markdown",
"id": "b9f91fcf-243b-4b8d-af0b-29c4649907c4",
"metadata": {},
"source": [
"Starting point is a 3D image dataset provided as numpy array. "
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "2f21ee1d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(100, 256, 256)"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"image_stack = cle.imread('../../data/Lund_000500_resampled-cropped.tif')\n",
"\n",
"image_stack.shape"
]
},
{
"cell_type": "markdown",
"id": "684f3268-6388-40fa-b633-ca65e4763e57",
"metadata": {},
"source": [
"`cle`-images are visualized in jupyer notebooks with a static view. In case the dataset is 3D, we see a maximum-intensity projection."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "cd9be4e3-89b1-4105-9b66-54af398b4811",
"metadata": {},
"outputs": [
{
"data": {
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