From 1807b436bc0e835e4e0e43a1ddd8e27c1b59cd6c Mon Sep 17 00:00:00 2001
From: Ulrich <ulrich.kerzel@rwth-aachen.de>
Date: Wed, 16 Nov 2022 17:10:54 +0100
Subject: [PATCH] Numba example with 2D random walk
---
pythonintro/solutions/Solution_Numba.ipynb | 149 +++++++++++++++++++++
1 file changed, 149 insertions(+)
create mode 100644 pythonintro/solutions/Solution_Numba.ipynb
diff --git a/pythonintro/solutions/Solution_Numba.ipynb b/pythonintro/solutions/Solution_Numba.ipynb
new file mode 100644
index 0000000..7dd4058
--- /dev/null
+++ b/pythonintro/solutions/Solution_Numba.ipynb
@@ -0,0 +1,149 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Fast Python with Numba\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 153,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# all imports\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib as mpl\n",
+ "import seaborn as sns\n",
+ "import numba\n",
+ "from numba import jit\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 154,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.56.4\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(numba.__version__)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 168,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "@jit(nopython=True)\n",
+ "def random_walk(n_steps = 5000, step_size = 1):\n",
+ "\n",
+ " # we always start at (0,0)\n",
+ " x_points = [0]\n",
+ " y_points = [0]\n",
+ "\n",
+ " # do the random walk:\n",
+ " for i in range(0, n_steps):\n",
+ " # choose direction:\n",
+ " # the following is the same as np.random.choice([-1,1]) but this cannot be optimized with Numba\n",
+ " x_dir = np.round(2*(np.random.randint(0,2)-0.5)) \n",
+ " y_dir = np.round(2*(np.random.randint(0,2)-0.5))\n",
+ "\n",
+ " # calculate new positions: last position + step_size * direction\n",
+ " new_x = x_points[-1] + step_size * x_dir\n",
+ " new_y = y_points[-1] + step_size * y_dir\n",
+ "\n",
+ " # append to arrays\n",
+ " x_points.append(new_x)\n",
+ " y_points.append(new_y)\n",
+ "\n",
+ " # calculate distance between start and end as Eucledian distance\n",
+ " # bit explicit as numba does not work with the one line we have used before\n",
+ " x_start = x_points[0]\n",
+ " y_start = y_points[0] \n",
+ " x_stop = x_points[-1] \n",
+ " y_stop = y_points[-1]\n",
+ " distance2 = (x_stop - x_start )**2 + ( y_stop - y_start )**2\n",
+ " distance = np.sqrt( distance2)\n",
+ " \n",
+ "\n",
+ " return x_points, y_points, distance\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we can compare the timings with and without the ```@jit``` decorator. \\\n",
+ "Remember that decorators change the behaviour of the function - but we do not have to change the function itself.\n",
+ "\n",
+ "In this case, Numba is a specialised package that optimises a function \"behind the scenes\".\n",
+ "\n",
+ "Note that the first call includes the optimisation / compile time. If we want to measure the time the optimised function takes, we need to discard the timing from the first call.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 170,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 33.4 ms, sys: 0 ns, total: 33.4 ms\n",
+ "Wall time: 33 ms\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%time\n",
+ "distances = []\n",
+ "\n",
+ "for i in range(0,200):\n",
+ " _, _, distance = random_walk()\n",
+ " distances.append(distance)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3.10.6 ('pythonintro-A08DqnGu-py3.10')",
+ "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": "3.10.6"
+ },
+ "orig_nbformat": 4,
+ "vscode": {
+ "interpreter": {
+ "hash": "eb3a944512c48027e49906d9e47d21cfb9b2c1ddd33f9b79c72df4b5e3a553dc"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
--
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