sidis 0.0.2

Simple Data Interfaces in Python

sidis : simple data interfaces

is a Python module that aims to reduce common programming tasks to a single line of code, such as typecasting and data conversion, sorting and mapping, accessing and assigning values through loops, etc. The name is a tribute to the early American polymath, William James Sidis. Documentation can be found on the respective pages in the docs website at https://noeloikeau.github.io/sidis/. Here we give a brief overview of some of the main utilities.

Install

pip install sidis

How to use

import sidis
from sidis import *

Typecasting

Sidis supports extensible typecasting between iterables and non-iterables, letting you do intuitive conversions such as:

cast(0,list)

[0]

cast([0.9,1.1],int)

[0, 1]

or define your own rules:

mycast = Caster()
mycast[list][float] = lambda t: sum(t)
mycast([0.9,1.1],float)

2.0

Sorting and converting complex datastructures

Sidis lets you sort python objects by the result of maps over those objects, and provides convenient conversion functions.

from sidis import sort, convert

Convert the elements of a list to binary arrays, and sort by the length of the array:

sort([0,10,3,5],by=convert,key=lambda t:len(t[-1]))

[(10, [1, 0, 1, 0]), (5, [1, 0, 1]), (3, [1, 1]), (0, [0])]

By default, convert converts to a binary array. If we want to change the arguments of the map without yet evaluating it, we can use pipe:

from sidis import pipe #like functools.partial, but also allows for typecasting of input and output data

Now let's convert to hex:

sort([0.9,10.5,3.1,5.5],by=pipe(convert,to=hex,otype=int)) #convert the elements `otype` into integers, then hex 

[(10.5, '0xa'), (5.5, '0x5'), (3.1, '0x3'), (0.9, '0x0')]

Arbitrary access to data structures

We can access and change arbitrary datastructures with get and give:

a=[1]
get(a,0) #get 0th level or attribute of a

1

[give(a,0,a[0]+1) for i in range(10)] #and can assign arbitrary objects without writing entire loops!
a

[11]

and can apply this level of control to more complex datastructures:

import networkx as nx
g=nx.DiGraph() #create a ring with a loop, 0->1->2->0->0
g.add_edges_from([(0,1),(1,2),(2,0)])
g.add_edges_from([(0,0)])
g.in_degree()

InDegreeView({0: 2, 1: 1, 2: 1})

from sidis import get
get(g,'in_degree',0) #get in-degree of node 0

2

and use this to sort with more abstract methods:

sort(g.nodes,by=g.in_degree)

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

sort(g.nodes,by=g.edges,key=lambda t:len(list(t[-1])))

[(0, OutEdgeDataView([(0, 1), (0, 0)])),
 (1, OutEdgeDataView([(1, 2)])),
 (2, OutEdgeDataView([(2, 0)]))]

or even give attributes to objects:

from sidis import give
give(g,'nodes',0,newattr='for fun')
g.nodes[0]['newattr']

'for fun'

[give(g,'edges',e,weight=np.random.rand(1)) for e in g.edges]
sort(g.edges,'weight')

[array([0.99698538]),
 array([0.90294296]),
 array([0.47634498]),
 array([0.1643804])]

Arbitrary mapping

sidis also extends mapping using maps, which lets you pass in an object and a series of functions and evaluate those functions independently or sequentially over the input to the desired depth:

f1=lambda t:t
f2=lambda t:t+1
f3=lambda t:t+2
maps(0,f1,f2,f3) #apply them individually

[0, 1, 2]

maps(0,f1,f2,f3,depth=-1) #apply them sequentially

3

maps([0,1],f1,f2,f3,depth=1) #apply f1 to 0, then return f2(f1(0)) and f2(f1(0)), then repeat for 1

[[1, 2], [2, 3]]

... and more!

from sidis import depth,flatten,unflatten,fill,Template,RNG

depth([[0]])

2

depth({'a':0,'b':{'c':1,'d':3}})

2

flatten({'a':0,'b':{'c':1,'d':3}})

{'a': 0, 'b,c': 1, 'b,d': 3}

unflatten({'a': 0, 'b,c': 1, 'b,d': 3})

{'a': 0, 'b': {'c': 1, 'd': 3}}

fill([[1],[2,3]],fillwith=1000,mask=False)

array([[   1, 1000],
       [   2,    3]])

Template('''
fill out your _name
and provide {0} ZIP _iter, lambda t: 'embedded iterable functions!'
''',_name='name and information',_iter=range(1))

fill out your name and information
and provide embedded iterable functions! 

rng=RNG(0) #globally stable
rng.random(0,1,shape=(2,2))

array([[0.63696169, 0.26978671],
       [0.04097352, 0.01652764]])