\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"### Questions\n",
"1. How are line plots created using Matplotlib?\n",
"1. What methods exist to customize the look of these plots?\n",
"\n",
"### Objectives\n",
"1. Create a basic line plot.\n",
"1. Add labels and grid lines to the plot.\n",
"1. Plot multiple series of data.\n",
"1. Plot imshow, contour, and filled contour plots."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plotting with Matplotlib"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Matplotlib is a python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The first step is to set up our notebook environment so that matplotlib plots appear inline as images:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next we import the matplotlib library's `pyplot` interface; this interface is the simplest way to create new Matplotlib figures. To shorten this long name, we import it as `plt` to keep things short but clear."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we generate some data to use while experimenting with plotting:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"times = np.array([ 93., 96., 99., 102., 105., 108., 111., 114., 117.,\n",
" 120., 123., 126., 129., 132., 135., 138., 141., 144.,\n",
" 147., 150., 153., 156., 159., 162.])\n",
"temps = np.array([310.7, 308.0, 296.4, 289.5, 288.5, 287.1, 301.1, 308.3,\n",
" 311.5, 305.1, 295.6, 292.4, 290.4, 289.1, 299.4, 307.9,\n",
" 316.6, 293.9, 291.2, 289.8, 287.1, 285.8, 303.3, 310.])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we come to two quick lines to create a plot. Matplotlib has two core objects: the `Figure` and the `Axes`. The `Axes` is an individual plot with an x-axis, a y-axis, labels, etc; it has all of the various plotting methods we use. A `Figure` holds one or more `Axes` on which we draw; think of the `Figure` as the level at which things are saved to files (e.g. PNG, SVG)\n",
"\n",
"![anatomy of a figure](anatomy-of-a-figure.png)\n",
"\n",
"Below the first line asks for a `Figure` 10 inches by 6 inches. We then ask for an `Axes` or subplot on the `Figure`. After that, we call `plot`, with `times` as the data along the x-axis (independant values) and `temps` as the data along the y-axis (the dependant values)."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
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