DTAT: sources.rng (#287)

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Author: Pwhsky

tutorials/3-advanced-topics/DTAT391C_sources.rng.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# deeptrack.sources.rng\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/DeepTrackAI/DeepTrack2/blob/develop/tutorials/3-advanced-topics/DTAT391C_sources.rng.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 sources.rng module."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. What is `rng`?\n",
+    "\n",
+    "The `rng` module is an extension of both Numpy and Python random number generator objects. It lets the user instance several generators with different seeds, returned as lists.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Instance Python random number generator objects.\n",
+    "Generate a list of Python rng's and sample some numbers from them, followed by resetting the states and sampling once more."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-06-29T20:33:47.187180Z",
+     "iopub.status.busy": "2022-06-29T20:33:47.186679Z",
+     "iopub.status.idle": "2022-06-29T20:33:50.691576Z",
+     "shell.execute_reply": "2022-06-29T20:33:50.691075Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python rng #0 yields a Random Number -> 36\n",
+      "Python rng #1 yields a Random Number -> 83\n",
+      "Python rng #2 yields a Random Number -> 28\n",
+      "Python rng #0 yields a Random Number -> 36\n",
+      "Python rng #1 yields a Random Number -> 83\n",
+      "Python rng #2 yields a Random Number -> 28\n"
+     ]
+    }
+   ],
+   "source": [
+    "from deeptrack.sources.rng import PythonRNG\n",
+    "\n",
+    "\n",
+    "python_rng = PythonRNG(n_states=3, seed=123)\n",
+    "states = python_rng._generate_states()\n",
+    "\n",
+    "for i, rng in enumerate(states):\n",
+    "    print(f\"Python rng #{i} yields a Random Number: {rng.randint(0, 100)}\")\n",
+    "\n",
+    "# Reset states to obtain the same numbers.\n",
+    "python_rng.reset()\n",
+    "new_states = python_rng._generate_states()\n",
+    "\n",
+    "for i, rng in enumerate(new_states):\n",
+    "    print(f\"Python rng #{i} yields a Random Number: {rng.randint(0, 100)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Instance Numpy random number generator objects.\n",
+    "In the same way, we do it for Numpy rng's."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Numpy rng #0 yields a Random Number -> 4\n",
+      "Numpy rng #1 yields a Random Number -> 88\n",
+      "Numpy rng #2 yields a Random Number -> 55\n",
+      "Numpy rng #0 yields a Random Number -> 4\n",
+      "Numpy rng #1 yields a Random Number -> 88\n",
+      "Numpy rng #2 yields a Random Number -> 55\n"
+     ]
+    }
+   ],
+   "source": [
+    "from deeptrack.sources.rng import NumpyRNG\n",
+    "\n",
+    "\n",
+    "numpy_rng = NumpyRNG(n_states=3, seed=123)\n",
+    "states = numpy_rng._generate_states()\n",
+    "\n",
+    "for i, rng in enumerate(states):\n",
+    "    print(f\"Numpy rng #{i} yields a Random Number: {rng.randint(0, 100)}\")\n",
+    "\n",
+    "# Reset states to obtain the same numbers.\n",
+    "numpy_rng.reset()\n",
+    "new_states = numpy_rng._generate_states()\n",
+    "\n",
+    "for i, rng in enumerate(new_states):\n",
+    "    print(f\"Numpy rng #{i} yields a Random Number: {rng.randint(0, 100)}\")"
+   ]
+  }
+ ],
+ "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
+}