DTAT389

* added elementwise tutorial

* Removed extra lines

Author: Pwhsky

tutorials/3-advanced-topics/DTAT389_elementwise.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# deeptrack.elementwise\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT389_elementwise.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !pip install deeptrack  # Uncomment if running on Colab/Kaggle."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This advanced tutorial introduces the elementwise module."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. What is elementwise?\n",
+    "\n",
+    "The elementwise module introduces utility functions which lets the user apply Numpy functions to `Feature` objects elementwise.\n",
+    "Some functions included in elementwise are:\n",
+    "\n",
+    "- Trigonometric\n",
+    "- Hyperbolic\n",
+    "- Rounding \n",
+    "- Exponents and Logarithms\n",
+    "- Complex "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Initialize a `Feature`\n",
+    "\n",
+    "We create a feature that subtracts values from an image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-9 -8 -7]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from deeptrack.features import Feature\n",
+    "\n",
+    "\n",
+    "class Subtract(Feature):\n",
+    "    def get(self, image, value, **kwargs):\n",
+    "        return image - value\n",
+    "\n",
+    "subtract_10 = Subtract(value=10)\n",
+    "\n",
+    "input_image = np.array([1, 2, 3])\n",
+    "output_image = subtract_10(input_image)\n",
+    "print(output_image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Compute the Absolute value of the feature (In sequence)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[9 8 7]\n",
+      "[9 8 7]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from deeptrack.elementwise import Abs\n",
+    "\n",
+    "\n",
+    "# Sequentially take the absolute value after subtraction.\n",
+    "pipeline = Abs(subtract_10)\n",
+    "output_image = pipeline(input_image)\n",
+    "print(output_image)\n",
+    "\n",
+    "# Or equivalently:\n",
+    "pipeline = subtract_10 >> Abs()\n",
+    "output_image = pipeline(input_image)\n",
+    "print(output_image)"
+   ]
+  }
+ ],
+ "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": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}