class: big, middle

# Engineering 1020: Introduction to Programming

.title[
  .lecture[Lecture \19\:]
  .title[Packages and plotting]
  
  
]

.footer[[/lecture/19/](/lecture/19/)]

---

# Recall: modules

```python
import central

x = central.mean([1.0, 4.2, 2.3, 9.9], geometric=True)
```

---

# Also recall: iteration

.floatleft[
```python
for c in "hello world":
    print(c > 'a')
```
]

.floatleft[
```python
for n in range(10):
    print(n*n)
```
]

.floatleft[
```python
for temp in [-2, -20, 6]:
    print(temp)
```
]

```python
for each_thing in a_bunch_of_things:
    # statements that do something with each_thing
```

---

# Today

### Let's get visual!

* installing Python packages
* plotting with `matplotlib` and `numpy`
* also, arrays

---

# Packages

### How can you share your module with others?

### One common answer: [PyPI](https://pypi.org)

* Python Package Index
* e.g., https://pypi.org/search/?q=engi1020

### Includes software _license_ information

---

# Licensing

### Copyright

* what does it mean?
???

Definition from
[CIPO](https://ised-isde.canada.ca/site/canadian-intellectual-property-office/en/learn-copyright-canadian-intellectual-property-office):

* what does it apply to?
???

**Copyright** is the sole right to produce, reproduce, publish or perform a work (or a substantial part of it) that belongs to one of the following categories:

- **literary** (e.g. books, pamphlets, computer programs and other works consisting of text)
 - **dramatic** (e.g. motion picture films, plays, screenplays and scripts)
 - **musical** (e.g. musical compositions, with or without words)
 - **artistic** (e.g. paintings, drawings, maps, photographs, sculptures and plans)

* how do you get it?

???

> Copyright automatically protects your work as soon as you create it.
>
> It lasts for the life of the creator plus 70 years after their death.

### Licensing

Can be _proprietary_ or
--
 _open source_ ([choosealicense.com](https://choosealicense.com))

---

# Installing packages

**From the command line&ast;:**

.footnote[
&ast; Windows Command Prompt, macOS Terminal, Linux terminal emulator...
]

```python
pip install engi1020
Collecting engi1020
  Downloading engi1020-0.1.15-py3-none-any.whl (11 kB)
[...]
Successfully installed engi1020-0.1.15 pyserial-3.5
```

**Within a Python interpreter:**

```python
>>> import pip
>>> pip.main(['install', 'engi1020'])
```

---

# Install some packages

### Let's install some packages!

```sh
$ pip install engi1020 matplotlib numpy
```

Now we're ready for...

---

# Mathematical operations

### We have seen:

```python
x = 1 + 2
r = 1 % 2
```

```python
from math import *
y = sin(x)
z = log(x)
```

### ... but what about lists?

```python
from math import *
sin([0, pi/2, pi])
```

---

# Numerical Python

### `numpy` module provides:

* more math functions
* ability to work with _arrays_

### What do you see when we run this example?

```python
import numpy
from math import *

numpy.sin([0, pi/2, pi, 3*pi/2, 2*pi])
```

---

# `numpy` arrays

### Like lists, but:

* all elements have the same type
--

* can do math with them
--
:

.floatleft[
```python
l1 = [1,2,3]
l2 = [4,5,6]

l1 + l2
l1 * l2
```
]

.floatleft[
```python
a1 = array([1,2,3])
a2 = array([4,5,6])

a1 + a2
a1 * a2
```
]

---

# More numerical Python

### `numpy` provides some other useful functions:

* `linspace` — a bit like `range()`, but can do `float`
--

* `dot` and `cross` — $a \cdot b$ and $a \times b$ (á la MATH 2050)
--

* `cumsum` (cumulative sum), `gcd`, `lcm`...
--

* `convolve` — you can worry about that in Term 4 :)

---

# Plotting with `matplotlib`

### Can `plot` iterable things with `matplotlib`:

```python
plot(x_values, y_values)
```

```python
from matplotlib.pyplot import *
from numpy import *

# Our x values will be spread out over the range [0, 2pi]
x = [0, pi/2, pi, 3*pi/2, 2*pi]

# Each y value will be the sin of a corresponding x value
y = sin(x)     # note: numpy.sin, not math.sin

# Plot our y values against our x values
plot(x, y)
```

---

# More detailed plots

```python
import matplotlib.pyplot as plt
import numpy as np

x = [0, pi/2, pi, 3*pi/2, 2*pi]
y = np.sin(x)

plt.style.use('seaborn-colorblind')
plt.plot(x, y, 'r--')
plt.title('A simple plot')
plt.xlabel('theta')
plt.xlabel('sin(Θ)')
plt.show()
```

* and bar graphs, and histograms, and...
--

* see
[the `pyplot` tutorial](https://matplotlib.org/tutorials/introductory/pyplot.html)
on the `matplotlib` website

---

# A better `sin` plot?

---

# Summary

### Numerical Python

* arrays
* mathematical functions

### Plotting with `matplotlib`

* much more to explore in
  [the `pyplot` tutorial](https://matplotlib.org/tutorials/introductory/pyplot.html)!

---
class: big, middle

(here endeth the lesson)