In this lesson, we will briefly discuss the imports and configurations required throughout the rest of this course.

Setting Matplotlib Charts to Display Within Jupyter Notebooks

Apr 02, 2020 How to Visualize Decision Trees using Matplotlib; How to Visualize Decision Trees using Graphviz (what is Graphviz, how to install it on Mac and Windows, and how to use it to visualize decision trees) How to Visualize Individual Decision Trees from Bagged Trees or Random Forests; As always, the co d e used in this tutorial is available on my. It's cross-platform and portable: Matplotlib can run on Linux, Windows, Mac OS X, and Sun Solaris (and Python can run on almost every architecture available). In short, Python became very common in the scientific field, and this success is reflected even on this book, where we'll find some mathematical formulas. But don't be concerned about.

Every lesson in this course will assume that you have run the following code before creating any Python visualizations:

You probably have two questions:

• What is the meaning of the % syntax?
• What does this command do?

First, let's discuss the % syntax.

The % operator is used in iPython notebooks (of which the Jupyter Notebook is an example) to reference magic functions. These are special functions that only work in iPython notebooks (like Jupyter). Here is more information on magic functions from the iPython documentation:

'IPython has a set of predefined ‘magic functions’ that you can call with a command line style syntax. There are two kinds of magics, line-oriented and cell-oriented. Line magics are prefixed with the % character and work much like OS command-line calls: they get as an argument the rest of the line, where arguments are passed without parentheses or quotes. Lines magics can return results and can be used in the right hand side of an assignment. Cell magics are prefixed with a double %%, and they are functions that get as an argument not only the rest of the line, but also the lines below it in a separate argument.'

So what is the purpose of %matplotlib inline command? Let's again refer to iPython's documentation:

'With this backend, the output of plotting commands is displayed inline within frontends like the Jupyter notebook, directly below the code cell that produced it. The resulting plots will then also be stored in the notebook document.'

This means that the visualizations we produce will show directly in our Jupyter Notebook instead of opening in a separate window.

How To Import Matplotlib

Matplotlib has a variety of modules available for import. To begin this course, we will be using the pyplot module, which is typically imported under the alias plt.

The full command for importing this is below:

How To Test That Matplotlib Has Installed Correctly

Let's test that matplotlib has been installed correctly by creating a very basic line plot. First, create a basic dataset with the range function:

Next, plot this data using pyplot's plot method:

If your output looks like this, then you can proceed!

A Note For Mac Users

If you are working through this course on a Mac that has a Retina display, you may notice that the chart we just created is pretty blurry.

IPython has a built-in solution for this. You can run the following code to change your Jupyter Notebook to 'Retina Mode', which significantly improves the resolution of your visualizations:

Moving On

In this lesson, we learned how to import matplotlib's pyplot module and how to set visualizations to display within a Jupyter Notebook using %matplotlib inline. Let's move on to creating some real charts in the next section!

I. What is MatplotLib and BaseMaps

Matplotlib and BaseMaps are python libraries used to display and manipulate data. MatplotLib is the basic package which allows the programmer to create graphs and plots in 2D and 3D (includining animation). The BaseMaps extension adds the additional capabilities to display data overlayed onto a 2D map or 3D globe. The combination of the two allows anyone with familiarity with python a quick and easy way to create analysis tools oor even just some quick visualizations.

II. Easiest Install

The easiest way to install both MatPlotLib and BaseMaps is to use DarwinPorts (http://darwinports.com/) or Fink(http://www.finkproject.org/).These applications are 'package managers' that will allow you to be able to download all sorts of free software packages for the Mac. You can install either or both applications (the available packages overlap quite a bit).

For Fink, you can install matplotlib and basemaps by running:

For DarwinPorts you can install by using this command:

In either case, this should install everything needed in order to use the python libraries. To test it, use the example presented below in this document.

This is by far the simplest method of installation. Manual installation isn't difficult but certainly takes a little longer.

III. Installing MatplotLib

If you want to do this a little more hands on, it's not much more difficult. First, you'll need to download four dmg files:

1. Python 2.7.3:
   
   • http://www.python.org/ftp/python/2.7.3/python-2.7.3-macosx10.3.dmg
2. Numpy:
   • http://sourceforge.net/projects/numpy/files/NumPy/1.6.2/numpy-1.6.2-py2.7-python .org-macosx10.3.dmg/download
3. SciPy:
   • http://sourceforge.net/projects/scipy/files/scipy/0.11.0/scipy-0.11.0-py2.7-pyth on.org-macosx10.3.dmg/download
4. Matplotlib:
   

Install Python first. Then install Numpy and SciPy. Lastly, install Matplotlib. These are all dmg’s so they should automatically install everything. The installation will be done in the directory:

You will then need to set up your environment variables (.cshrc file for instance):

Once everything is installed, create a new python script, cut and paste one of their examples into it, then try to run it. Here is the one I used:
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure()
ax = Axes3D(fig)
for c, z in zip(['r', 'g', 'b', 'y'], [30, 20, 10, 0]):
xs = np.arange(20)
ys = np.random.rand(20)
ax.bar(xs, ys, zs=z, zdir='y', color=c, alpha=0.8)
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
plt.show()

This should display a 3D bar graph using transparency and multiple colors.

IV. Installing BaseMaps From Source

This gets a little trickier. There is no pre-built dmg file for installation, so this has to be downloaded and built.

First, get the source from here: http://sourceforge.net/projects/matplotlib/files/matplotlib-toolkits/basemap-0.9 9.4/basemap-0.99.4.tar.gz/download

The size is about 100MB, but almost all of that is taken up by map data. Extract the source, then open a temrinal and navigate to the directory.

Before BaseMaps can be built, the GEOS library (nothing to do with the GEOS model) needs to be built. To do so, navigate to the GEOS directory and run configure like so: ./configure --prefix=some/path. After that, you run make and make install. This will create the GEOS libraries.

When the libraries are created, you will need to create a GEOS_DIR environment variable. The GEOS_DIR environemnt variable should match whatever path you used for the prefix when you ran configure. After the environment variable has been created, do a source or whatever action is required to make sure the variable is there.

Now you can go back to the root BaseMap directory and install BaseMaps. To do so, use the command: python setup.py install. This will finish the build process and install the necessary files and libraries to use BaseMaps. To test the installation, you can navigate to the exampless directory and run a few of them.

V. Installing BaseMaps From Binary Distribution

Matplotlib For Mac Download

The setup.py script in BaseMaps gives an option to create a binary distribution. While this is not a self-installing dmg file, it creates a tarball with all the correct folder heirarchy and files for distribution. If you have a binary distribution, installation is easy. Extract the archive and then use cp -R to copy the folder to the root directory.

VI. Installing Python Image Library (PIL)

This is an optional package but may be necessary depending on whether or not you use certain image sources (see the bluemarble example in BaseMaps).

Again, no binary distribution is available for the Mac so you'll need to build from source. Worse, the actual build fails on the Mac (but it can be worked around).

You can get the source here: http://effbot.org/downloads/Imaging-1.1.7.tar.gz

Extract the source. In a terminal window, navigate to the image library directory. Run python setup.py install. This will most likely fail. The failure arises from the script, which tries to build libraries for both PPC and Intel based machines. Unless you've explicitly installed PPC versions of the required libraries, the python script will fail.

The workaround is simple. There is only one file that this fails on. Copy the failed buid lines beginning with gcc, then paste them on the commandline. After that, go back and delete the -arch ppc part off the command. Once the file builds, re-run the the setup.py script. This should successfully build and instal the library.

Matplotlib For Mac Torrent

To ensure that it installed correctly, you can go to the BaseMaps example directory and try running the bluemarble or warpimage examples.

Matplotlib For Mac Online