resource-manager 0.6.9

A small resource manager for config files

This repository contains an interpreter for config files written in a subset of Python’s grammar. It aims to combine Python’s beautiful syntax, lazy initialization and a much simpler import machinery.

Syntax overview

Statements

Only two Python statements are supported: variable definition and import.

Variable definition

Variables are declared using the = operator. Because of lazy initialization a variable cannot be redeclared, which is similar to the final behaviour in Java.

Here are some examples:

file_path = "/some/path"
num_folds = 5
kernel_sizes = [3, 3, 5]
some_dict = {
  'a': 'my string',
  'b': ["some", "list", 3]
}

Imports

You can import from other libraries inside config files just like in regular Python:

from numpy import prod as product
from numpy import sum, random
import pandas as pd
import tqdm
import math.radians

You can also import from config files, located relative to the main config:

# import from ./some_config.config and ./folder/dataset.config
from .some_config import *
from .folder.dataset import DataSet as D

You can also import configs relative to a shortcut:

# another.config content
from assets.core import *

Provided, that resource manager knows the assets shortcut:

from resource_manager import read_config

rm = read_config('another.config', {'assets': '/path/to/assets'})

Expressions

A large subset of Python’s expressions are supported:

# you can use other variables declared or imported in the config
x = another_variable

# lists, dictionaries
l = [x, y, z]
d = {'a': l}

# literals
literals = [True, False, None]

# numbers
nums = [1, -1, 3e-5, 0x11, 0b1001, 10_000]

# strings
s = 'some string'
others = [b'one', u'two', r'three\n']
multiline = """
    multiline
    string
"""

# calling functions
a = f(1, t=10, *z, y=2)

# getting attributes
value = x.shape

# getting items
item = d['a']

Comments

You can also use comments inside you configs:

# comment
x = 1 # another comment

There is also one special comment, that actually has syntactical meaning. Consider the following example:

import numpy as np
from functools import partial

random_triplets = partial(np.random.uniform, size=3)

This is a common case, when you need to pass a callable with certain parameters being fixed. Inside configs you can achieve the same effect with the following syntax:

import numpy as np

random_triplets = np.random.uniform(
    # lazy
    size=3
)

Using the comment # lazy inside a function call compiles to a corresponding functools.partial.