scriptconfig 0.7.14

Goal

The idea is we want to be able to start writing a simple program with a simple configuration and allow it to evolve with minimal refactoring. In the early stages we will insist that there be little-to-no boilerplate, but as a program evolves we will add boilerplate to enhance the featurefull-ness of our program.

When we start coding we should aim for something like this:

def my_function():

    config = {
        'simple_option1': 1,
        'simple_option2': 2,
    }

    # Early algorithmic and debugging logic
    ...

As we evolve our code, we can plug scriptconfig in like this:

def my_function():

    default_config = {
        'simple_option1': 1,
        'simple_option2': 2,
    }

    import scriptconfig
    class MyConfig(scriptconfig.DataConfig):
        __default__ = default_config

    config = MyConfig()

    # Transition algorithmic and debugging logic
    ...

It’s not pretty, but it gives us the ability to a fairly advanced CLI right away (i.e by calling the .cli classmethod) without any major sacrifice to code simplicity. However, as a project evolves we may eventually want to refactor our CLI to gain full control over the metadata in our configuration an CLI. Scriptconfig has a tool to help with this too. Given this janky definition, we can port to a more ellegant style. We can run print(config.port_to_dataconf()) which prints:

import ubelt as ub
import scriptconfig as scfg

class MyConfig(scfg.DataConfig):
    """
    argparse CLI generated by scriptconfig 0.7.12
    """
    simple_option1 = scfg.Value(1, help=None)
    simple_option2 = scfg.Value(2, help=None)

And then use that to make the refactor much easier. The final state of a scriptconfig program might look something like this:

import ubelt as ub
import scriptconfig as scfg

class MyConfig(scfg.DataConfig):
    """
    This is my CLI description
    """
    simple_option1 = scfg.Value(1, help=ub.paragraph(
        '''
        A reasonably detailed but concise description of an argument.
        About one paragraph is reasonable.
        ''')
    simple_option2 = scfg.Value(2, help='more help is better')

    @classmethod
    def main(cls, cmdline=1, **kwargs):
        config = cls.cli(cmdline=cmdline, data=kwargs)
        my_function(config)

def my_function(config):
    # Continued algorithmic and debugging logic
    ...

Note that the fundamental impact on the ... – i.e. the intereting part of the function – remain completely unchanged! From it’s point of view, you never did anything to the original config dictionary, because scriptconfig duck-typed it at every stage.

Installation

The scriptconfig package can be installed via pip:

pip install scriptconfig

To install with argcomplete and rich-argparse support, either install these packages separately or use:

pip install scriptconfig[optional]

Features

• Serializes to JSON

• Dict-like interface. By default a Config object operates independent of config files or the command line.

• Can create command line interfaces

  • Can directly create an independent argparse object

  • Can use special command line loading using self.load(cmdline=True). This extends the basic argparse interface with:

    • Can specify options as either --option value or --option=value

    • Default config options allow for “smartcasting” values like lists and paths

    • Automatically add --config, --dumps, and --dump CLI options when reading cmdline via load.

• Fuzzy hyphen matching: e.g. --foo-bar=2 and --foo_bar=2 are treated the same for argparse options (note: modal commands do not have this option yet)

• Inheritance unions configs.

• Modal configs (see scriptconfig.modal)

• Integration with argcomplete for shell autocomplete.

• Integration with rich_argparse for colorful CLI help pages.

Example Script

Scriptconfig is used to define a flat configuration dictionary with values that can be specified via Python keyword arguments, command line parameters, or a YAML config file. Consider the following script that prints its config, opens a file, computes its hash, and then prints it to stdout.

import scriptconfig as scfg
import hashlib


class FileHashConfig(scfg.DataConfig):
    """
    The docstring will be the description in the CLI help
    """
    fpath = scfg.Value(None, position=1, help='a path to a file to hash')
    hasher = scfg.Value('sha1', choices=['sha1', 'sha512'], help='a name of a hashlib hasher')


def main(**kwargs):
    config = FileHashConfig.cli(data=kwargs)
    print('config = {!r}'.format(config))
    fpath = config['fpath']
    hasher = getattr(hashlib, config['hasher'])()

    with open(fpath, 'rb') as file:
        hasher.update(file.read())

    hashstr = hasher.hexdigest()
    print('The {hasher} hash of {fpath} is {hashstr}'.format(
        hashstr=hashstr, **config))


if __name__ == '__main__':
    main()

If this script is in a module hash_demo.py (e.g. in the examples folder of this repo), it can be invoked in these following ways.

Purely from the command line:

# Get help
python hash_demo.py --help

# Using key-val pairs
python hash_demo.py --fpath=$HOME/.bashrc --hasher=sha1

# Using a positional arguments and other defaults
python hash_demo.py $HOME/.bashrc

From the command line using a YAML config:

# Write out a config file
echo '{"fpath": "hashconfig.json", "hasher": "sha512"}' > hashconfig.json

# Use the special `--config` cli arg provided by scriptconfig
python hash_demo.py --config=hashconfig.json

# You can also mix and match, this overrides the hasher in the config with sha1
python hash_demo.py --config=hashconfig.json --hasher=sha1

Lastly you can call it from good ol’ Python.

import hash_demo
hash_demo.main(fpath=hash_demo.__file__, hasher='sha512')

Modal CLIs

A ModalCLI defines a way to group several smaller scriptconfig CLIs into a single parent CLI that chooses between them “modally”. E.g. if we define two configs: do_foo and do_bar, we use ModalCLI to define a parent program that can run one or the other. Let’s make this more concrete.

Consider the code in examples/demo_modal.py:

import scriptconfig as scfg

class DoFooCLI(scfg.DataConfig):
    __command__ = 'do_foo'
    option1 = scfg.Value(None, help='option1')

    @classmethod
    def main(cls, cmdline=1, **kwargs):
        self = cls.cli(cmdline=cmdline, data=kwargs)
        print('Called Foo with: ' + str(self))

class DoBarCLI(scfg.DataConfig):
    __command__ = 'do_bar'
    option1 = scfg.Value(None, help='option1')

    @classmethod
    def main(cls, cmdline=1, **kwargs):
        self = cls.cli(cmdline=cmdline, data=kwargs)
        print('Called Bar with: ' + str(self))

class MyModalCLI(scfg.ModalCLI):
    __version__ = '1.2.3'
    foo = DoFooCLI
    bar = DoBarCLI

if __name__ == '__main__':
    MyModalCLI().main()

Running: python examples/demo_modal.py --help, results in:

usage: demo_modal.py [-h] [--version] {do_foo,do_bar} ...

options:
  -h, --help       show this help message and exit
  --version        show version number and exit (default: False)

commands:
  {do_foo,do_bar}  specify a command to run
    do_foo         argparse CLI generated by scriptconfig 0.7.12
    do_bar         argparse CLI generated by scriptconfig 0.7.12

And if you specify a command, python examples/demo_modal.py do_bar --help, you get the help for that subcommand:

usage: DoBarCLI [-h] [--option1 OPTION1]

argparse CLI generated by scriptconfig 0.7.12

options:
  -h, --help         show this help message and exit
  --option1 OPTION1  option1 (default: None)

Autocomplete

If you installed the optional argcomplete package you will find that pressing tab will autocomplete registered arguments for scriptconfig CLIs. See project instructions for details, but on standard Linux distributions you can enable global completion via:

pip install argcomplete
mkdir -p ~/.bash_completion.d
activate-global-python-argcomplete --dest ~/.bash_completion.d
source ~/.bash_completion.d/python-argcomplete

And then add these lines to your .bashrc:

if [ -f "$HOME/.bash_completion.d/python-argcomplete" ]; then
    source ~/.bash_completion.d/python-argcomplete
fi

Lastly, ensure your Python script has the following two comments at the top:

#!/usr/bin/env python
# PYTHON_ARGCOMPLETE_OK

Project Design Goals

• Write Python programs that can be invoked either through the commandline or via Python itself.

• Drop in replacement for any dictionary-based configuration system.

• Intuitive parsing (currently working on this), ideally improve on argparse if possible. This means being able to easily specify simple lists, numbers, strings, and paths.

To get started lets consider some example usage:

>>> import scriptconfig as scfg
>>> # In its simplest incarnation, the config class specifies default values.
>>> # For each configuration parameter.
>>> class ExampleConfig(scfg.DataConfig):
>>>     num = 1
>>>     mode = 'bar'
>>>     ignore = ['baz', 'biz']
>>> # Creating an instance, starts using the defaults
>>> config = ExampleConfig()
>>> assert config['num'] == 1
>>> # Or pass in known data. (load as shown in the original example still works)
>>> kwargs = {'num': 2}
>>> config = ExampleConfig.cli(default=kwargs, cmdline=False)
>>> assert config['num'] == 2
>>> # The `load` method can also be passed a JSON/YAML file/path.
>>> config_fpath = '/tmp/foo'
>>> open(config_fpath, 'w').write('{"mode": "foo"}')
>>> config.load(config_fpath, cmdline=False)
>>> assert config['num'] == 2
>>> assert config['mode'] == "foo"
>>> # It is possbile to load only from CLI by setting cmdline=True
>>> # or by setting it to a custom sys.argv
>>> config = ExampleConfig.cli(argv=['--num=4'])
>>> assert config['num'] == 4
>>> # Note that using `config.load(cmdline=True)` will just use the
>>> # contents of sys.argv

Notice in the above example the keys in your default dictionary are command line arguments and values are their defaults. You can augment default values by wrapping them in scriptconfig.Value objects to encapsulate information like help documentation or type information.

>>> import scriptconfig as scfg
>>> class ExampleConfig(scfg.Config):
>>>     __default__ = {
>>>         'num': scfg.Value(1, help='a number'),
>>>         'mode': scfg.Value('bar', help='mode1 help'),
>>>         'mode2': scfg.Value('bar', type=str, help='mode2 help'),
>>>         'ignore': scfg.Value(['baz', 'biz'], help='list of ignore vals'),
>>>     }
>>> config = ExampleConfig()
>>> # smartcast can handle lists as long as there are no spaces
>>> config.load(cmdline=['--ignore=spam,eggs'])
>>> assert config['ignore'] == ['spam', 'eggs']
>>> # Note that the Value type can influence how data is parsed
>>> config.load(cmdline=['--mode=spam,eggs', '--mode2=spam,eggs'])

(Note the above example uses the older Config usage pattern where attributes are members of a __default__ dictionary. The DataConfig class should be favored moving forward past version 0.6.2. However, the __default__ attribute is always available if you have an existing dictionary you want to wrap with scriptconfig.

Gotchas

CLI Values with commas:

When using scriptconfig to generate a command line interface, it uses a function called smartcast to try to determine input type when it is not explicitly given. If you’ve ever used a program that tries to be “smart” you’ll know this can end up with some weird behavior. The case where that happens here is when you pass a value that contains commas on the command line. If you don’t specify the default value as a scriptconfig.Value with a specified type, if will interpret your input as a list of values. In the future we may change the behavior of smartcast, or prevent it from being used as a default.

Boolean flags and positional arguments:

scriptconfig always provides a key/value way to express arguments. However, it also recognizes that sometimes you want to just type --flag and not --flag=1. We allow for this for Values with isflag=1, but this causes a corner-case ambituity with positional arguments. For the following example:

class MyConfig(scfg.DataConfig):
    arg1 = scfg.Value(None, position=1)
    flag1 = scfg.Value(False, isflag=True, position=1)

For --flag 1 We cannot determine if you wanted {'arg1': 1, 'flag1': False} or {'arg1': None, 'flag1': True}.

This is fixable by either using strict key/value arguments, expressing all positional arguments before using flag arguments, or using the `` – `` construct and putting all positional arguments at the end. In the future we may raise an AmbiguityError when specifying arguments like this, but for now we leave the behavior undefined.

FAQ

Question: How do I override the default values for a scriptconfig object using JSON file?

Answer: This depends if you want to pass the path to that JSON file via the command line or if you have that file in memory already. There are ways to do either. In the first case you can pass --config=<path-to-your-file> (assuming you have set the cmdline=True keyword arg when creating your config object e.g.: config = MyConfig(cmdline=True). In the second case when you create an instance of the scriptconfig object pass the default=<your dict> when creating the object: e.g. config = MyConfig(default=json.load(open(fpath, 'r'))). But the special --config --dump and --dumps CLI arg is baked into script config to make this easier.

Related Software

I’ve never been completely happy with existing config / argument parser software. I prefer to not use decorators, so click and to some extend hydra are no-gos. Fire is nice when you want a really quick CLI, but is not so nice if you ever go to deploy the program in the real world.

The builtin argparse in Python is pretty good, but I with it was easier to do things like allowing arguments to be flags or key/value pairs. This library uses argparse under the hood because of its stable and standard backend, but that does mean we inherit some of its quirks.

The configargparse library - like this one - augments argparse with the ability to read defaults from config files, but it has some major usage limitations due to its implementation and there are better options (like jsonargparse). It also does not support the use case of calling the CLI as a Python function very well.

The jsonargparse library is newer than this one, and looks very compelling. I feel like the definition of CLIs in this library are complementary and I’m considering adding support in this library for jsonargparse because it solves the problem of nested configurations and I would like to inherit from that. Keep an eye out for this feature in future work.

Hydra - https://hydra.cc/docs/intro/

OmegaConf - https://omegaconf.readthedocs.io/en/latest/index.html

Argparse - https://docs.python.org/3/library/argparse.html

JsonArgparse - https://jsonargparse.readthedocs.io/en/stable/index.html

Fire - https://pypi-hypernode.com/project/fire/

Click - https://pypi-hypernode.com/project/click/

ConfigArgparse - https://pypi-hypernode.com/project/ConfigArgParse/

TODO

• [ ] Nested Modal CLI’s

• [ ] Fuzzy hyphens in ModelCLIs

• [X] Policy on nested heirachies (currently disallowed) - jsonargparse will be the solution here.

  • [ ] How to best integrate with jsonargparse

• [ ] Policy on smartcast (currently enabled)

  • [ ] Find a way to gracefully way to make smartcast do less. (e.g. no list parsing, but int is ok, we may think about accepting YAML)

• [X] Policy on positional arguments (currently experimental) - we have implemented them permissively with one undefined corner case.

  • [X] Fixed length - nope

  • [X] Variable length

  • [X] Can argparse be modified to always allow for them to appear at the beginning or end? - Probably not.

  • [x] Can we get argparse to allow a positional arg change the value of a prefixed arg and still have a sane help menu?

• [x] Policy on boolean flags - See the isflag argument of scriptconfig.Value

• [x] Improve over argparse’s default autogenerated help docs (needs exploration on what is possible with argparse and where extensions are feasible)