nattrs

Nested attributes utility functions for python. Allows getting/setting of object attributes and dict members interchangeably. Useful to populate nested dicts for storing outputs of a loop.

Alpha stage. Subject to change.

https://pypi.python.org/pypi/nattrs/

Installation

Install from PyPI:

pip install nattrs

Install from GitHub:

python -m pip install git+https://github.com/ludvigolsen/nattrs

Main functions

Class/Function	Description
`nested_getattr`	Get object attributes/dict members recursively, given by dot-separated names
`nested_setattr`	Set object attribute/dict member by recursive lookup, given by dot-separated names.
`nested_mutattr`	Apply function (mutate) to object attribute/dict member by recursive lookup, given by dot-separated names.
`nested_hasattr`	Check whether recursive object attributes/dict members exist.
`populate_product`	Create and populate nested dicts with specified layers and the same leaf value.

Examples

Create class B with a dict c with the member d:

class B:
    def __init__(self):
        self.c = {
            "d": 1
        }

Add to a dict a:

a = {"b": B()}

nested_getattr

Get the value of d:

nested_getattr(a, "b.c.d")
>> 1

Get default value when not finding an attribute:

nested_getattr(a, "b.o.p", default="not found")
>> "not found"

nested_setattr

Set the value of d:

nested_setattr(a, "b.c.d", 2)

Check new value of d:

nested_getattr(a, "b.c.d")
>> 2

nested_mutattr

Mutate d with an anonymous function (lambda):

nested_mutattr(a, "b.c.d", lambda x: x * 5)

Check new value of d:

nested_getattr(a, "b.c.d")
>> 10

Note: If your function performs the assignment in-place, remember to enable the is_inplace_fn argument.

nested_hasattr

Check presence of the member 'd':

nested_hasattr(a, "b.c.d")
>> True

Fail to find member 'o':

nested_hasattr(a, "b.o.p")
>> False

populate_product

In this example, we wish to pre-populate nested dicts with empty lists to allow appending within a for loop. First, we go through the manual approach of doing this. Second, we show how easy it is to do with populate_product().

Say we have 3 variables that can each hold 2 values. We want to compute something for each combination of these values. Let's first define these variables and their options:

animal = ["cat", "dog"]
food = ["strawberry", "cucumber"]
temperature = ["cold", "warm"]

Let's generate the product of these options:

import itertools

combinations = list(itertools.product(*[animal, food, temperature]))
combinations
>> [('cat', 'strawberry', 'cold'),
>>  ('cat', 'strawberry', 'warm'),
>>  ('cat', 'cucumber', 'cold'),
>>  ('cat', 'cucumber', 'warm'),
>>  ('dog', 'strawberry', 'cold'),
>>  ('dog', 'strawberry', 'warm'),
>>  ('dog', 'cucumber', 'cold'),
>>  ('dog', 'cucumber', 'warm')]

Now we can create a nested dict structure with a list in the leaf element:

# Initialize empty dict
nested_dict = {}

for leaf in combinations:
    # Join each string with dot-separation:
    attr = ".".join(list(leaf))

    # Assign empty list to the leafs
    # `make_missing` creates dicts for each 
    # missing attribute/dict member
    nattrs.nested_setattr(
        obj=nested_dict,
        attr=attr,
        value=[],
        make_missing=True
    )

nested_dict
>> {'cat': {'strawberry': {'cold': [], 'warm': []},
>>          'cucumber':   {'cold': [], 'warm': []}},
>>  'dog': {'strawberry': {'cold': [], 'warm': []},
>>          'cucumber':   {'cold': [], 'warm': []}}}

This dict population is actually provided by populate_product(). Instead of an empty list, let's set the value to an "edibility" score that could be changed by a later function:

layers = [animal, food, temperature]
populate_product(
    layers=layers,
    val=False
)
>> {'cat': {'strawberry': {'cold': False, 'warm': False},
>>          'cucumber':   {'cold': False, 'warm': False}},
>>  'dog': {'strawberry': {'cold': False, 'warm': False},
>>          'cucumber':   {'cold': False, 'warm': False}}}

LudvigOlsen / nattrs