{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "

Mini-Project 0: Installation Test

\n", "

MCHE513: Intermediate Dynamics

\n", "

Dr. Joshua Vaughan
\n", "joshua.vaughan@louisiana.edu
\n", "http://www.ucs.louisiana.edu/~jev9637/

" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this notebook, we'll simply be testing that all the tools that we'll be using this semester have been installed correctly, while hopefully providing a simple first exercise with the Jupyter Notebook." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll first place a \"watermark\" that will display our system information. You should have followed the instructions on the lab handout to install this package. You can see more information about this package at:\n", "\n", "[https://github.com/rasbt/watermark](https://github.com/rasbt/watermark)\n", "\n", "If you haven't yet installed it, you can by issuing the command below from a terminal window:\n", "\n", "```bash\n", "conda install -c conda-forge watermark \n", "```\n", "\n", "Many more examples of its use are included in [this example Jupyter Notebook](http://nbviewer.jupyter.org/github/rasbt/watermark/blob/master/docs/watermark.ipynb)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# We first need to tell the Jupyter Notebook to load the extension\n", "%load_ext watermark" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2018-08-15T17:58:41-05:00\n", "\n", "CPython 3.6.2\n", "IPython 6.1.0\n", "\n", "compiler : GCC 4.2.1 Compatible Apple LLVM 6.0 (clang-600.0.57)\n", "system : Darwin\n", "release : 17.7.0\n", "machine : x86_64\n", "processor : i386\n", "CPU cores : 8\n", "interpreter: 64bit\n" ] } ], "source": [ "# The base usage will tell us information about our machine and the version of Python we're using\n", "%watermark " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also use the options in the package to tell us about versions of specific python packages. In this case, we'll check the versions of the NumPy, SciPy, matplotlib, Jupyter Notebook, and Control packages." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPython 3.6.2\n", "IPython 6.1.0\n", "\n", "numpy 1.13.3\n", "scipy 1.0.0\n", "control 0.7.0\n", "notebook 5.0.0\n", "matplotlib 2.0.2\n", "\n", "compiler : GCC 4.2.1 Compatible Apple LLVM 6.0 (clang-600.0.57)\n", "system : Darwin\n", "release : 17.7.0\n", "machine : x86_64\n", "processor : i386\n", "CPU cores : 8\n", "interpreter: 64bit\n" ] } ], "source": [ "%watermark -v -m -p numpy,scipy,control,notebook,matplotlib" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The output of that command look similar to the first, but with the versions of the NumPy, SciPy, matplotlib, Jupyter Notebook, and Control packages indicated as well. Now, let's run some simple commands to double-check that everything is working.\n", "\n", "We'll first import NumPy, the set up the notebook for plotting using the matplotlib library." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Grab all of the NumPy functions with namespace np\n", "import numpy as np " ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "# Import the plotting functions \n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Let's plot an array of random numbers. We'll use the numeric portion of our CLID's as a \"seed\" for the random number generator. This will make the string of numbers *i.)* repeatable and *ii.)* unique to the seed value, meaning your plots will be unique to your CLID numerals. \n", "\n", "In the first code block below, change 1234 to be the numeric portion of your CLID. This creates a random number generator with that as the seed. Eliminate the leading zeros from the number, so that if your ULID is `C00123456`, you would change the seed to be `123456`." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Change 1234 to match the numeric portion of your ULID\n", "my_random_generator = np.random.RandomState(seed=123456) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we'll use this generator to generate an array of 500 elements and plot them." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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3Ttn5x45f9PxNQFSUzr/RU0p3576/HcA6TzEsqjcfwBIAywEsBXAhON2DDIsW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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "my_random_numbers = my_random_generator.rand(500) # Generate 500 random numbers\n", "\n", "\n", "# Now, we'll plot them. Here, we'll just use the matplotlib defaults. \n", "# By default, no axis labels, etc are included.\n", "# Later, we'll generate nicer plots by modifying some of those defaults.\n", "plt.plot(my_random_numbers)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, let's import the scipy, sympy, and Control modules, just to check that they will load. If there were no errors from the:\n", "\n", "```%watermark -v -m -p numpy,scipy,control,notebook,matplotlib``` \n", "\n", "command above, then they should load without any issue." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import control # This will import the control library. We'll need to preface any commands from it with control." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import scipy # This will import the scipy libary. We'll almost never import this entire library again." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import sympy # This will import the sympy libary. We'll almost never import this entire library again." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "#### Licenses\n", "Code is licensed under a 3-clause BSD style license. See the licenses/LICENSE.md file.\n", "\n", "Other content is provided under a [Creative Commons Attribution-NonCommercial 4.0 International License](http://creativecommons.org/licenses/by-nc/4.0/), CC-BY-NC 4.0." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# This cell will just improve the styling of the notebook\n", "from IPython.core.display import HTML\n", "import urllib.request\n", "response = urllib.request.urlopen(\"https://cl.ly/1B1y452Z1d35\")\n", "HTML(response.read().decode(\"utf-8\"))" ] } ], "metadata": { "anaconda-cloud": {}, "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": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 1 }