{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "*This notebook contains material for CBE 20258 Numerical and Statistical Analysis taught at the University of Notre Dame. (c) Professors Alexander Dowling, Ryan McClarren, and Yamil Colón. This collection of notebooks [cbe-xx258](https://ndcbe.github.io/cbe-xx258) is available [on Github](https://github.com/ndcbe/cbe-xx258).*\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "< [1.6 Linear Algebra with Numpy and Scipy](https://ndcbe.github.io/cbe-xx258/01.06-NumPy.html) | [Contents](toc.html) | [1.8 Manipulating Data with Pandas](https://ndcbe.github.io/cbe-xx258/01.08-Pandas.html) >
"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "MElKNIbuqnWJ",
"nbpages": {
"level": 1,
"link": "[1.7 Visualization with matplotlib](https://ndcbe.github.io/cbe-xx258/01.07-Matplotlib.html#1.7-Visualization-with-matplotlib)",
"section": "1.7 Visualization with matplotlib"
}
},
"source": [
"# 1.7 Visualization with matplotlib"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpages": {
"level": 1,
"link": "[1.7 Visualization with matplotlib](https://ndcbe.github.io/cbe-xx258/01.07-Matplotlib.html#1.7-Visualization-with-matplotlib)",
"section": "1.7 Visualization with matplotlib"
}
},
"source": [
"**Reference**: Chapter 1 of *Computational Nuclear Engineering and Radiological Science Using Python*, R. McClarren (2018)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "OS9TuD7EhSZQ",
"nbpages": {
"level": 2,
"link": "[1.7.1 Learning Objectives](https://ndcbe.github.io/cbe-xx258/01.07-Matplotlib.html#1.7.1-Learning-Objectives)",
"section": "1.7.1 Learning Objectives"
}
},
"source": [
"## 1.7.1 Learning Objectives\n",
"After studying this notebook, completing the activities, and asking questions in class, you should be able to:\n",
"* Add multiple lines to a single plot\n",
"* Specify the color, style, width, and other properties of a line\n",
"* Add title, legend, and axes labels\n",
"* Save figure to a PDF, download from Vocareum\n",
"* Add grid lines\n",
"* Look up advanced formatting options in matplotlib documentation and examples"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "esDQO0J4qnX7",
"nbpages": {
"level": 2,
"link": "[1.7.2 Matplotlib Basics](https://ndcbe.github.io/cbe-xx258/01.07-Matplotlib.html#1.7.2-Matplotlib-Basics)",
"section": "1.7.2 Matplotlib Basics"
}
},
"source": [
"## 1.7.2 Matplotlib Basics"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "2Qts69sjqnX8",
"nbpages": {
"level": 2,
"link": "[1.7.2 Matplotlib Basics](https://ndcbe.github.io/cbe-xx258/01.07-Matplotlib.html#1.7.2-Matplotlib-Basics)",
"section": "1.7.2 Matplotlib Basics"
}
},
"source": [
"Matplotlib is a simple plotting tool that allows you to plot the arrays from NumPy. We already saw an example above. Matplotlib is designed to be intuitive, easy to use, and mimic MATLAB syntax.\n",
"\n",
"As an example, let's plot the cardinal sine function: $$\\mathrm{sinc}(x) = \\frac{\\sin(x)}{x}$$"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 294
},
"colab_type": "code",
"executionInfo": {
"elapsed": 1067,
"status": "ok",
"timestamp": 1548774025086,
"user": {
"displayName": "Alexander Dowling",
"photoUrl": "https://lh3.googleusercontent.com/-LChdQ2m5OQE/AAAAAAAAAAI/AAAAAAAAAA0/JeXJe4vQJ7M/s64/photo.jpg",
"userId": "00988067626794866502"
},
"user_tz": 300
},
"id": "_l35x8ggqnX8",
"nbpages": {
"level": 2,
"link": "[1.7.2 Matplotlib Basics](https://ndcbe.github.io/cbe-xx258/01.07-Matplotlib.html#1.7.2-Matplotlib-Basics)",
"section": "1.7.2 Matplotlib Basics"
},
"outputId": "64af8290-1800-4439-b8dd-9a1a893cca55"
},
"outputs": [
{
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\n",
"text/plain": [
"
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]
}
],
"metadata": {
"colab": {
"collapsed_sections": [
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],
"name": "L4-NumPy-Matplotlib.ipynb",
"provenance": [],
"version": "0.3.2"
},
"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": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 1
}