spellops

Developer Guide

Instead of writing this:

L(f_c(L(...).map(f_a).filter(f_b))).map(f_d)[0]

With spellops’s pipe operator, you can visually maintain the order of execution (f_a → filter(f_b) → f_c → f_d) by writing this:

L(...).map(f_a).filter(f_b).pipe(f_c).map(f_d)[0]

And with tee operator, you can inspect and document your pipeline like this:

(L(...)             .tee('input data')
    .map(f_a)       .tee('apply f_a to individual elements')
    .filter(f_b)    .tee('filter by f_b')
    .pipe(f_c)      .tee('apply f_c to the whole list')
    .map(f_d)[0]    .tee('finally apply f_d to the resulting elements and returning first value')
)

And once you’re done, you can keep all of this in your production code:

def process(data,verbose=False):
    s = dict(show=verbose)
    return (L(data)     .tee('input data', **s)
        .map(f_a)       .tee('apply f_a to individual elements', **s)
        .filter(f_b)    .tee('filter by f_b', **s)
        .pipe(f_c)      .tee('apply f_c to the whole list', **s)
        .map(f_d)       .tee('finally apply f_d to the resulting elements and returning first value', **s)
        .unwrap()       # Extract the element out of the list
    )

Here are some examples:

Import L and spellops

NOTE: order of import does not matters.

import spellops
from fastcore.foundation import L

Fluent string manipulaiton

L(['a', 'b', 'c']).map(str.upper).pipe('->'.join, wrap=False)

'A->B->C'

Or using convenience mehtod pipen that stands for pipe with wrap=False (no wrap):

L(['a', 'b', 'c']).map(str.upper).pipen('->'.join)

'A->B->C'

Count how many elements are present in both sublists

(L([[1,2,3,2],[2,4,1]])    .tee('input')
     .map(set)             .tee('to set')
     .starpipe(set.intersection)  .tee('common elements')
     .pipe(len)            .tee('count elements')
     .unwrap()
)

input
[[1, 2, 3, 2], [2, 4, 1]]
to set
[{1, 2, 3}, {1, 2, 4}]
common elements
[1, 2]
count elements
[2]

2

An alternative approach can be to pipe with wrap=False:

NOTE: In this case, you can’t use tee after the final pipe operator because it no longer returns an L instance.

(L([[1,2,3,2],[2,4,1]])    .tee('input')
     .map(set)             .tee('to set')
     .starpipe(set.intersection)  .tee('common elements')
     .pipe(len, wrap=False) # count elements
)

input
[[1, 2, 3, 2], [2, 4, 1]]
to set
[{1, 2, 3}, {1, 2, 4}]
common elements
[1, 2]

2

Solve an AoC task

SPOILER ALERT: the following is a speLL to solve AoC 2024 part B.

GOAL: For each number in the left list, we need to count how many times it appears in the right list, multiply these together, and sum all results. This creates a “similarity score”, counting occurrences and multiplying, that measures how frequently numbers from the left list appear in the right list.

sample = '''3   4
4   3
2   5
1   3
3   9
3   3
'''

# This function will be "mapped" to individual elements
def to_int_tuple(a,b): return (int(a),int(b))

# this function acts on the whole list
def count_instances(As,Bs): return [(o,len([t for t in Bs if t==o])) for  o in As]

from math import prod
(L(sample.splitlines())
                .map(str.split)         .tee('input data')
                .starmap(to_int_tuple)  .tee('int to tuples')
                .zip(cycled=True)       .tee('tuple of lists')
                .starpipe(count_instances)  .tee('apply count_instances to the "whole list"')
                .map(prod)              .tee('multiply tuple elements')
                .sum()
    )

input data
[['3', '4'], ['4', '3'], ['2', '5'], ['1', '3'], ['3', '9'], ['3', '3']]
int to tuples
[(3, 4), (4, 3), (2, 5), (1, 3), (3, 9), (3, 3)]
tuple of lists
[(3, 4, 2, 1, 3, 3), (4, 3, 5, 3, 9, 3)]
apply count_instances to the "whole list"
[(3, 3), (4, 1), (2, 0), (1, 0), (3, 3), (3, 3)]
multiply tuple elements
[9, 4, 0, 0, 9, 9]

31

Visually inspect images transformations:

import matplotlib.pyplot as plt
import numpy as np

def plot_images(x:L, msg):
    N = len(x)
    plt.figure(figsize=(4*N,4))
    for i,o in enumerate(x):
        plt.subplot(1,N,i+1)
        plt.imshow(o)
        plt.title(f'Image: {i}')
    plt.suptitle(msg)

(L([[[0,0,0],[1,1,1],[0,0,0]],[[1,0,0],[0,1,0],[0,0,1]]])  .tee('Input images',f=plot_images)
    .map(lambda x: np.rot90(x,k=1)) .tee('Rotate 90 degree',f=plot_images)
)

(#2) [array([[0, 1, 0],
       [0, 1, 0],
       [0, 1, 0]]),array([[0, 0, 1],
       [0, 1, 0],
       [1, 0, 0]])]

Install spellops in Development mode

# make sure spellops package is installed in development mode
$ pip install -e .

# make changes under nbs/ directory
# ...

# compile to have changes apply to spellops
$ nbdev_prepare

Usage

Installation

Install latest from the GitHub repository:

$ pip install git+https://github.com/artste/spellops.git

or from pypi

$ pip install spellops