matplotlib

1 matplotlib #matplotlibTable of ContentsAbout1 Chapter 1: Getting started with matplotlib2 Remarks2 Overview2 Versions2 Examples2 Installation and Setup2 Windows2OS X2 Linux3 Debian/Ubuntu3 Fedora/Red Hat3 Troubleshooting3 Customizing a matplotlib plot3 Imperative vs. Object-oriented Syntax5 Two dimensional (2D) arrays6 Chapter 2: Animations and interactive plotting8 Introduction8 Examples8 Basic animation with FuncAnimation8 Save animation to gif9 Interactive controls with live data from pipe with matplotlib11 Chapter 3: Basic Plots14 Examples14 Scatter Plots14A simple scatter plot14A Scatterplot with Labelled Points15 Shaded Plots16 Shaded region below a line16 Shaded Region between two lines17 Line plots18 Simple line plot18 Data plot20 Data and line21 Heatmap22 Chapter 4: Boxplots26 Examples26 Basic Boxplots26 Chapter 5.

2 Boxplots28 Examples28 Boxplot function28 Chapter 6: Closing a figure window35 Syntax35 Examples35 Closing the current active figure using pyplot35 Closing a specific figure using ()35 Chapter 7: Colormaps36 Examples36 Basic usage36 Using custom colormaps38 Perceptually uniform colormaps40 Custom discrete colormap42 Chapter 8: Contour Maps44 Examples44 Simple filled contour plotting44 Simple contour plotting45 Chapter 9: Coordinates Systems46 Remarks46 Examples47 Coordinate systems and text47 Chapter 10: Figures and Axes Objects50 Examples50 Creating a figure50 Creating an axes50 Chapter 11: Grid Lines and Tick Marks52 Examples52 Plot With Gridlines52 Plot With Grid Lines52 Plot With Major and Minor Grid Lines53 Chapter 12: Histogram55 Examples55 Simple histogram55 Chapter 13: Image manipulation56 Examples56 Opening images56 Chapter 14: integration with TeX/LaTeX58 Remarks58 Examples58 Inserting TeX formulae in plots58 Saving and exporting plots that use TeX60 Chapter 15: Legends62 Examples62 Simple Legend62 Legend Placed Outside of Plot64 Single Legend Shared Across Multiple Subplots66 Multiple Legends on the Same Axes67 Chapter 16.

3 LogLog Graphing71 Introduction71 Examples71 LogLog graphing71 Chapter 17: Multiple Plots74 Syntax74 Examples74 Grid of Subplots using subplot74 Multiple Lines/Curves in the Same Plot75 Multiple Plots with gridspec77A plot of 2 functions on shared Plots and Multiple Plot Features79 Chapter 18: Three-dimensional plots87 Remarks87 Examples90 Creating three-dimensional axes90 Credits92 AboutYou can share this PDF with anyone you feel could benefit from it, downloaded the latest version from: matplotlibIt is an unofficial and free matplotlib ebook created for educational purposes. All the content is extracted from Stack Overflow Documentation, which is written by many hardworking individuals at Stack Overflow.

4 It is neither affiliated with Stack Overflow nor official content is released under Creative Commons BY-SA, and the list of contributors to each chapter are provided in the credits section at the end of this book. Images may be copyright of their respective owners unless otherwise specified. All trademarks and registered trademarks are the property of their respective company the content presented in this book at your own risk; it is not guaranteed to be correct nor accurate, please send your feedback and corrections to 1: Getting started with matplotlibRemarksOverviewmatplotlib is a plotting library for Python.

5 It provides object-oriented APIs for embedding plots into applications. It is similar to MATLAB in capacity and was originally written by and is actively being developed. It is distributed under a BSD-Style Versions SupportedRemarksRelease , , Stable , , Stable , Stable , Development Version2016-07-25 ExamplesInstallation and SetupThere are several ways to go about installing matplotlib , some of which will depend on the system you are using. If you are lucky, you will be able to use a package manager to easily install the matplotlib module and its Windows machines you can try to use the pip package manager to install matplotlib .

6 See here for information on setting up pip in a Windows XIt is recommended that you use the pip package manager to install matplotlib . If you need to install some of the non-Python libraries on your system ( libfreetype) then consider using you cannot use pip for whatever reason, then try to install from , the system package manager or pip should be used to install matplotlib , either by installing the python- matplotlib package or by running pip install this is not possible ( you do not have sudo privileges on the machine you are using), then you can install from source using the --user option: python install --user.

7 Typically, this will install matplotlib into ~/. apt-get install python-matplotlibFedora/Red Hatsudo yum install python-matplotlibTroubleshootingSee the matplotlib website for advice on how to fix a broken a matplotlib plotimport pylab as plt import numpy as np ('ggplot') fig = (1) ax = () # make some testing data x = ( 0, , 1000 ) test_f = lambda x: (x)*3 + (2*x) # plot the test data ( x, test_f(x) , lw = 2) # set the axis labels (r'$x$', fontsize=14, labelpad=10) (r'$f(x)$', fontsize=14, labelpad=25, rotation=0) # set axis limits (0, ) () # Customize the plot (1, ls='--', color='#777777', alpha= , lw=1) (labelsize=12, length=0) ('w') # add a legend leg = ( ['text'], loc=1 ) fr = () ('w') (.)

8 7) () vs. Object-oriented SyntaxMatplotlib supports both object-oriented and imperative syntax for plotting. The imperative syntax is intentionally designed to be very close to Matlab imperative syntax (sometimes called 'state-machine' syntax) issues a string of commands all of which act on the most recent figure or axis (like Matlab). The object-oriented syntax, on the other hand, explicitly acts on the objects (figure, axis, etc.) of interest. A key point in the zen of Python states that explicit is better than implicit so the object-oriented syntax is more pythonic. However, the imperative syntax is convenient for new converts from Matlab and for writing small, "throwaway" plot scripts.

9 Below is an example of the two different as plt import numpy as np t = (0, 2, ) y = (4 * * t) # Imperative syntax (1) () (t, y) ('Time (s)') ('Amplitude (V)') ('Sine Wave') (True) # Object oriented syntax fig = (2) () ax = (1,1,1) (t, y) ('Time (s)') ('Amplitude (V)') ('Sine Wave') (True)Both examples produce the same plot which is shown dimensional (2D) a two dimensional (2D) array on the numpy as np from import imshow, show, colorbar image = (4,4) imshow(image) colorbar() show()Read Getting started with matplotlib online: 2: Animations and interactive plottingIntroductionWith python matplotlib you can properly make animated animation with FuncAnimationThe package offer some classes for creating animations.

10 FuncAnimation creates animations by repeatedly calling a function. Here we use a function animate() that changes the coordinates of a point on the graph of a sine numpy as np import as plt import as animation TWOPI = 2* fig, ax = () t = ( , TWOPI, ) s = (t) l = (t, s) ax = ([0,TWOPI,-1,1]) redDot, = ([0], [ (0)], 'ro') def animate(i): (i, (i)) return redDot, # create animation using the animate() function myAnimation = (fig, animate, frames= ( , TWOPI, ), \ interval=10, blit=True, repeat=True) () animation to gifIn this example we use the save method to save an Animation object using numpy as np import as plt import as animation from matplotlib import rcParams # make sure the full paths for ImageMagick and ffmpeg are configured rcParams[' '] = r'C:\Program Files\ImageMagick\convert' rcParams[' '] = r'C.