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expylain is a Python package for Jupyter notebooks that enables rapid interactive exploration of random processes. It is designed for ease-of-use in learning contexts.

Project description

# `expylain`

`expylain` is a Python package for Jupyter notebooks that enables rapid
interactive exploration of random processes. It is designed for ease-of-use in
learning contexts.

## Example

`expylain` works by taking `Data` and defining `Step`s that work on the data.
Each `Step` is just a Python function that takes in data from the previous
`Data` or `Step`. `Data` and its `Steps` are grouped in a `Process`.

For example, to flip a coin that lands heads with probability `p`:

```python
from random import random

def flip(coin, p=0.5):
return coin[0] if random() < p else coin[1]

Process([
Data(['H', 'T']),
Step(flip),
])
```

**Output:**

```
Data: ['H', 'T']
|
flip |
v
'H'

[Rerun]
```

The real power of `expylain` results when using its built-in support for
interactive functions:

```python
Process([
Data(['H', 'T']),
# Specify a (start, end, step) for the arg p
Step(flip, p=(0, 1, 0.1)),
])
```

**Output:**

```
Data: ['H', 'T']
|
flip | p: 0 --- | ---- 1 [0.5]
|
v
'H'

[Rerun]
```

This allows you to interact and visualize random processes:

```python
from random import choices
from bqplot import pyplot as plt

def flip_n(coin, p=0.5, n=100):
return choices(coin, weights=[p, 1-p], k=n)

def plot_flips(flips):
heads, tails = flips.count('H'), flips.count('T')
plt.bar(['H', 'T'], [heads, tails])

Process([
Data(['H', 'T']),
Step(flip_n, p=(0, 1, 0.1), n=(50, 500, 50)),
Step(plot_flips),
])
```

**Output:**

```
Data: ['H', 'T']
|
flip_n | p: 0 --- | --- 1 [0.5]
| n: 50 --- | --- 500 [100]
|
v
['H', 'T', ..., 'H']
|
plot_flips|
v

<bar_chart>

[Rerun]
```

Processes can be composed, giving high expressivity. The `Repeat` constructor
allows the output of multiple runs of a process to become an input to a next
step:

```python
def count_heads(flips):
return flips.count('H')

heads_in_n_flips = Process([
Data(['H', 'T']),
Step(flip_n, p=(0, 1, 0.1), n=(50, 500, 50))
Step(count_heads)
],
name='heads_in_n_flips',
)

distribution_of_heads = Process([
Repeat(heads_in_n_flips, times=1000),
Step(plt.hist),
])
```

**Output:**

```
+-------------------------------------------+
| Data: ['H', 'T'] |
| | |
| flip_n | p: 0 --- | --- 1 [0.5] |
| | n: 50 --- | --- 500 [100] |
| | |
| v | heads_in_n_flips
| ['H', 'T', ..., 'H'] |
| | |
| count_heads | |
| v |
| 48 |
+-------------------------------------------+
|
Repeat(1000) |
v
[48, 49, ..., 50]
|
|
v
<histogram>

[Rerun]
```

## Getting Started

Run these commands in your terminal to install `expylain` and its Jupyter
extension:

```
pip install expylain
jupyter nbextension enable --py --sys-prefix expylain
```


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