expylain is a Python package for Jupyter notebooks that enables rapid interactive exploration of random processes. It is designed for easeofuse 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 easeofuse 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 builtin 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, 1p], 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 sysprefix expylain
```
`expylain` is a Python package for Jupyter notebooks that enables rapid
interactive exploration of random processes. It is designed for easeofuse 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 builtin 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, 1p], 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 sysprefix expylain
```
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