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_build/html/_sources/docs/tutorials/full_quick_start_guide.ipynb
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{ | ||
"cells": [ | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"# Quick start guide\n", | ||
"\n", | ||
"This tutorial covers some basic usage patterns and best practices to help you get started with Matplotlib.\n", | ||
"\n", | ||
"Matplotlib is a versatile library for creating static, animated, and interactive visualizations in Python." | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"import matplotlib.pyplot as plt\n", | ||
"import numpy as np" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"## A simple example\n", | ||
"\n", | ||
"Matplotlib graphs your data on [Figure](https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure)s, each containing one or more [Axes](https://matplotlib.org/stable/api/axes_api.html#matplotlib.axes.Axes), where data is plotted." | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"fig, ax = plt.subplots() # Create a figure containing a single Axes.\n", | ||
"ax.plot([1, 2, 3, 4], [1, 4, 2, 3]) # Plot some data on the Axes.\n", | ||
"plt.show()" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"## Parts of a Figure\n", | ||
"\n", | ||
"Here are the components of a Matplotlib Figure:\n", | ||
"\n", | ||
"- The **Figure** keeps track of all the child [Axes](https://matplotlib.org/stable/api/axes_api.html).\n", | ||
"- An **Axes** is a region where data is plotted. Each `Axes` has an x-label, y-label, and title." | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"## Types of inputs to plotting functions\n", | ||
"\n", | ||
"Matplotlib plotting functions expect `numpy.array` or objects that can be converted to `numpy.asarray`.\n", | ||
"For example, to convert a `numpy.matrix`:" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"b = np.matrix([[1, 2], [3, 4]])\n", | ||
"b_asarray = np.asarray(b)\n", | ||
"b_asarray" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"## Coding styles\n", | ||
"\n", | ||
"Matplotlib supports both the object-oriented (OO) style and the pyplot style.\n", | ||
"See the [Matplotlib documentation](https://matplotlib.org/stable/tutorials/introductory/usage.html) for more details." | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"x = np.linspace(0, 2, 100)\n", | ||
"fig, ax = plt.subplots()\n", | ||
"ax.plot(x, x, label='linear')\n", | ||
"ax.plot(x, x**2, label='quadratic')\n", | ||
"ax.plot(x, x**3, label='cubic')\n", | ||
"ax.set_xlabel('x label')\n", | ||
"ax.set_ylabel('y label')\n", | ||
"ax.set_title('Simple Plot')\n", | ||
"ax.legend()\n", | ||
"plt.show()" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"x = np.linspace(0, 2, 100)\n", | ||
"plt.figure()\n", | ||
"plt.plot(x, x, label='linear')\n", | ||
"plt.plot(x, x**2, label='quadratic')\n", | ||
"plt.plot(x, x**3, label='cubic')\n", | ||
"plt.xlabel('x label')\n", | ||
"plt.ylabel('y label')\n", | ||
"plt.title('Simple Plot')\n", | ||
"plt.legend()\n", | ||
"plt.show()" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"## Styling Artists\n", | ||
"\n", | ||
"Most plotting methods have styling options for the Artists, accessible either when a plotting method is called or from a \"setter\" on the Artist." | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"fig, ax = plt.subplots()\n", | ||
"x = np.arange(0, 10, 0.1)\n", | ||
"y = np.sin(x)\n", | ||
"ax.plot(x, y, color='blue', linewidth=2, linestyle='--')\n", | ||
"plt.show()" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"## Working with multiple Figures and Axes\n", | ||
"\n", | ||
"You can open multiple Figures with multiple calls to `fig = plt.figure()` or `fig, ax = plt.subplots()`." | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"fig, ax = plt.subplots()\n", | ||
"x = np.linspace(0, 2 * np.pi, 400)\n", | ||
"y = np.sin(x ** 2)\n", | ||
"ax.plot(x, y)\n", | ||
"ax.set_title('Simple Sine Wave')\n", | ||
"plt.show()" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"## Using mathematical expressions in text\n", | ||
"\n", | ||
"Matplotlib accepts TeX equation expressions in any text expression. For example, to write the expression $\\sigma_i=15$ in the title:" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"fig, ax = plt.subplots()\n", | ||
"ax.set_title(r'$\\sigma_i=15$')\n", | ||
"ax.plot([1, 2, 3], [1, 4, 2])\n", | ||
"plt.show()" | ||
] | ||
} | ||
], | ||
"metadata": { | ||
"kernelspec": { | ||
"display_name": "Python 3", | ||
"language": "python", | ||
"name": "python3" | ||
}, | ||
"language_info": { | ||
"name": "python", | ||
"version": "3.x" | ||
} | ||
}, | ||
"nbformat": 4, | ||
"nbformat_minor": 2 | ||
} |
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