Gain insights into Python for scientific computing. Explore arrays, plotting, linear equations, and algorithms using NumPy, Matplotlib, SciPy. Delve into applying tools with practical exercises.

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Python3

If you're a scientist or an engineer interested in learning scientific computing, this is the place to start.

In this course, you'll learn to write your own useful code to perform impactful scientific computations. Along the way, your understanding will be tested with periodic quizzes and exercises.

Topics covered in this course include arrays, plotting, linear equations, symbolic computation, scientific algorithms, and random variables. You’ll also be exposed to popular Python packages like NumPy, Matplotlib, SciPy, and others. In the last part of the course, the application section will test your ability to recall and apply the tools you have studied into newly learned scientific concepts.

At the end of this course, you'll be equipped with the tools necessary for everyday scientific computation.

Python for Scientists and Engineers

In this course, we will be using the object-oriented approach when working with `matplotlib`.The main idea with object-oriented programming is to have objects that one can apply functions and actions to, and no object or program states should be global. The real advantage of the object-oriented approach in plotting becomes apparent when more than one figure is created, or when a figure contains more than one plot.

# Multiple curves on the same graph
We can draw multiple curves on the same graph by repeatedly using the `plot` command.

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0, 2 * np.pi , 100)
y1 = np.sin(x)
y2 = np.cos(x)

plt.plot(x, y1)     # curve 1
plt.plot(x, y2)     # curve 2 
plt.plot(x, y1 + y2)       # curve 3


> <img src="/api/collection/10370001/6066104977850368/page/4580102618742784/image/6441996354846720" width="35"> If you don't specify a color for a plotting statement, `matplotlib` will use its default colors. The first three default colors are special shades of blue, orange and green. 

You can find more colors using the command `help("matplotlib.pyplot.plot") below:

import matplotlib.pyplot as plt

help("matplotlib.pyplot.plot")

## Adding legends
When plotting multiple curves on the same graph, it is imperative to add a legend to make the graph more readable. There are two ways to add legends:
1. Use the `legend()` method of the axis object and pass a list of legend texts for the curves plotted previously.
```python 3
plt.plot(x1, y1)
plt.plot(x2, y2)
plt.legend(["curve 1", "curve 2"]) 
```

2. Use the `label = "curve 1"` keyword argument when plots are added to the figure, and then use the `legend()` method without arguments to add the legend to the figure.

```python 3
plt.plot(x1, y1, label="curve 1")
plt.plot(x2, y2, label="curve 2")
legend()
```
While method 1 makes the code look cleaner, method 2 removes the hassle of separately changing the legend when making changes to the graph. It is entirely up to the user which method they choose.

The legend function takes an optional keyword argument `loc` that can be used to specify where in the figure the legend is to be drawn. 

```python 3
plt.legend(loc=0) # let matplotlib decide the optimal location 
plt.legend(loc=1) # upper right corner
plt.legend(loc=2) # upper left corner
plt.legend(loc=3) # lower left corner
plt.legend(loc=4) # lower right corner 
```

The example below uses method 1 for implementing legends.

In this lesson, we will learn some different ways of plotting multiple graphs and how to label them.

Introduction

Python Refresher

Arrays

Plotting

Systems of Linear Equations

Symbolic Computation

Scientific Algorithms

Random Variables

Applications

Conclusion

Appendix

Python for Scientists and Engineers Exam

Plotting Multiple Curves

Multiple curves on the same graph #