Lecture 26 Plotting

1. Introduction
Now, we arrived my most favorite part - plotting.

You work hard on procssing and saving your data, the most exciting part is to visualize your data and observe the trend!! We need figures to present our rearch to other people. We did extensive amount of work in MatLab to plot our data in the 1D, 2D, and 3D manner. Now, it is the turn of Python!!

The pip command is a tool for installing and managing Python packages, such as those found in the Python Package Index. It's a replacement for easy_install.

The function package for plotting in Python.

If you are using Spyder from Anaconda, you should have matplotlib and pip already in your system.
You can check it in this way:

If you have these two packages installed already, you can skip all the steps showed in the Python Crash Course textbook.
Now, let's directly dive into it.

2. Plotting simple graphs
Let's plot a simple 1D vector:

Change the label type and graph thickness and font size: It looks much better after this modification.

But now that we can read the chart better, we see that the data is not plotted correctly. Notice at the end of the graph that the square of 4.0 is shown as 25! Let’s fx that.

3. Plotting and Styling Individual Points with scatter()
Sometimes it’s useful to be able to plot and style individual points based on certain characteristics. For example, you might plot small values in oneGenerating Data 327 color and larger values in a different color. You could also plot a large data set with one set of styling options and then emphasize individual points by replotting them with different options.
To plot a single point, use the scatter() function. Pass the single (x, y) values of the point of interest to scatter(), and it should plot those values:

Let’s style the output to make it more interesting. We’ll add a title, label the axes, and make sure all the text is large enough to read:

4. Plotting a series of points with scatter()

I just showed you two different plots above. If you have 'plt.scatter(x_value, y_value)' then you will get the figure at the bottom, if you use 'plt.scatter(x_value, y_value, s=100)' you will get the one on the top. So 's=100' defines the size of the scatter points.

This is amazing that you can create a series of y values by 'x**2 for x in x_value'.

You can remove the edge of the scatter points :
plt.scatter(x_value, y_value, edgecolor='none', s=40)

Defining custom colors:

This is even accepting RGB color schemes.

Change the marker style:

The marker styles can be used in Python:
A snapshot of the table:

5. Plot multiple curves in one figure.

Add a legend to it:

Plot the scatter points and the line in the same figure:

6. Subplots:

Here is the example of a sin() function applied to x*2. Let's play around with this figure using the subplot function in Python:
Let's plot it in the regular way first:

Then apply 'plt.subplots()'. This returns two arguments: fig, and axes. They are:
1. fig : Figure
2. axes: Axes object or array of Axes objects. axes can be either a single Axes object or an array of Axes objects if more than one subplot was created. The dimensions of the resulting array can be controlled with the squeeze keyword, see above.

Having two subplots and share the Y axis:
When have 'sharey = True' included, the two subplots will share the Y axis.
The '1, 2' in 'plt.subplots(1,2,sharey=True)' means 1 row and 2 columns of figures will be plotted.

Accesses them through the returned array

All the subplots are accessible. This enables so many capabilities. You can change any property you like in each subplot:
Modify titles:

Let's modify the sharing axis thing step by step:
1) Share X axis with all figures in the same column:

2) At the same time, share Y axis with all figures in the same row.

3) To make it simple, you can also use 'all':

It is the same as:

I'd use 'sharex=Ture, sharey=True' more often. The subplots always have the same Y and X axis. We usually put subfigures in one window to compare the trend, so it is making sense to unify the X and Y axis.

One more thing:
Add a legend to it:


Plot cos(x) and sin(x) in the same figure. The range of x is 0:2*pi and there are 40 dots are being plotted. Your plot should have: 
• Two different markers for the two functions 
• Two different colors for the two lines 
• Have appropriate labels for both X and Y axis 
• A modified line width
• Have legends to indicate the two curves are cos(x) and sin(x)

2. Use the subplot function in matplotlib to plot the following four functions in the same figure but different subplots.
sin(x+pi/2), 2*sin(2*x+pi/4), 0.5*cos(x+pi/4), and 0.8*sin(x+pi/2).

Modify the axis, labels, fonts, line width, etc to make your figure presentable.
Points will be taken off if the plotted figure is in bad quality.