map_array example mapping measurements to labels

map_array_example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "satellite-momentum",
   "metadata": {},
   "outputs": [],
   "source": [
    "import napari\n",
    "from napari.utils import nbscreenshot\n",
    "from skimage import data\n",
    "import scipy.ndimage as ndi\n",
    "import skimage.measure\n",
    "import pandas as pd\n",
    "from skimage.util import map_array\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "second-credit",
   "metadata": {},
   "outputs": [],
   "source": [
    "%gui qt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "later-arrival",
   "metadata": {},
   "outputs": [],
   "source": [
    "time.sleep(2) # this is necessary for the %qui qt magic to do its job if we run all cells automatically"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "independent-riding",
   "metadata": {},
   "source": [
    "Create some artifical test data and label it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "sweet-cross",
   "metadata": {},
   "outputs": [],
   "source": [
    "blobs = data.binary_blobs(length=128, volume_fraction=0.1, n_dim=3)\n",
    "labeled = ndi.label(blobs)[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "beautiful-electric",
   "metadata": {},
   "source": [
    "Measure area of each label. Note that we could also measure properties that return float values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "sudden-archive",
   "metadata": {},
   "outputs": [],
   "source": [
    "props = skimage.measure.regionprops_table(labeled, properties=('label','area'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "deadly-berkeley",
   "metadata": {},
   "source": [
    "Look at the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "expensive-supervisor",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>346</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>347</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>348</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>349</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>350</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>350 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     label  area\n",
       "0        1  1102\n",
       "1        2   417\n",
       "2        3    83\n",
       "3        4   820\n",
       "4        5   725\n",
       "..     ...   ...\n",
       "345    346   365\n",
       "346    347   103\n",
       "347    348    89\n",
       "348    349    80\n",
       "349    350    80\n",
       "\n",
       "[350 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(props)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "indirect-controversy",
   "metadata": {},
   "source": [
    "Map labels to their values.\n",
    "\n",
    "Note that it is also worth taking a look at the underlying `ArrayMap` class in scikit image. It can do quite some magic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "incident-berry",
   "metadata": {},
   "outputs": [],
   "source": [
    "value_map = map_array(labeled, props[\"label\"], props[\"area\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "phantom-dimension",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Image layer 'area heatmap' at 0x7fa9a2a57d50>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "viewer = napari.view_image(blobs.astype(float), name='blobs')\n",
    "viewer.add_labels(labeled, name='labels')\n",
    "viewer.add_image(value_map, name=\"area heatmap\", colormap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "statewide-chick",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<napari.utils.notebook_display.NotebookScreenshot at 0x7fa902744ad0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nbscreenshot(viewer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "hawaiian-recognition",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}