clima 0.6.1

Simple boilerplate for cli scripts

clima - command line interface with a schema

Create a command line interface with minimal setup and less maintenance. Clima handles loading and parsing command line arguments complimenting them with definitions found in optional configuration files, env files, env variables and secrets stored with pass.

Example: to setup a configuration and a command line interface ready to go:

from clima import c, Schema

class C(Schema):
   a = 1
 
@c
class Cli:
    def foo(self):
        # using configuration
        print(c.a)

Command line usage in a terminal would then be e.g.:

./script.py foo
./script.py foo --a 42

Or as packaged with for example poetry

my_tool foo
my_tool foo --a 42

See the example scripts and other sections for more examples.

Long description

The subcommands are written as a class encapsulating the "business logic". The class' methods are handled as subcommands. You can define a simple schema of the configuration which will be populated by clima with the aforementioned command line arguments and other definitions.

In other words, you can use this to wrap your scripts as command line commands without resorting to bash or maintaining argument parsing in python. This removes the need of duplicating comments in order --help to remember what the arguments were and what they did. Sprinkling some decorator magic offers a typical use experience of a cli program (e.g. argument parsing and validation, --help, subcommands, ...).

The implementation is focused on a premise that for a simple script there's usually a script wide global configuration which would be used through out the user code i.e. a context for the program that is refered to in different parts of the code. That configuration is populated with given arguments falling back on defaults in the code and some further complimentary options. Those are then made accessible via a global c variable that can be tossed around the code base with very little additional effort. With a small adjustment this can made to autocomplete in IDEs (as attributes). This helps when the schema of the configuration grows larger as the autocomplete kicks in after typing c. offering those fields in your "schema" as attributes.

Installing

pip install --user clima

or with pipx to use dedicated virtualenv:

pipx install --user clima

Installing from source

Choose your favourite flavour of build system. Check their documentation if puzzled (poetry, flit, pipx, pipx, pipenv..)

The tooling here has been exported with DepHell from the poetry declarations. In case your favourite build tool flavour files are not up to date, see the publish.py for its convert subcommand, which should convert to all the possible alternatives once dephell is installed.

Usage

See the example file in examples/script_example.py. Here's a run down of the individual parts in such a script (adapted from another example at module example).

First import the required components:

from clima import c, Schema

In your code define the Schema subclass:

class Configuration(Schema):
    a: str = 'A'  # a description
    x: int = 1  # x description

Here "Configuration" is an arbitrary name, no magic there. The inherited Schema class defines the attributes (i.e. a and x in this example).

Note the specific formatting of the Schema subclass:

    # attribute[: type] = default value  [# Description for the --help]
    a: str = 'A'  # a description

a is the attribute which can be called in the code later with c.a. In this example, it has a type of 'str' and a default value of 'A'. The comment after is not redundant in the sense that it is parsed for the command line help. The values in square brackets [] are optional.

All of these parts will be parsed for the '--help' for the subcommands of the cli, for example:

./script.py foo -h

Will now produce:

 Usage:       script.py foo [ARGS]
 
 Description: Args:
     --a (str): a description (Default is 'A')
     --x (int): x description (Default is 1)    

The example in the readme can be found at examples/readme_example.py.

The subcommands - or commands of the script - should be defined somewhat as follows:

@c
class Cli:
    def subcommand_foo(self):
        """This will be shown in --help for subcommand-foo"""
        print('foo')
        print(c.a)
        print(c.x)

    def subcommand_bar(self):
        """This will be shown in --help for subcommand-bar"""
        print('bar')

The methods are parsed as subcommands and their respective doc strings will show in the subcommands' help printout. Note the double usage of the c - first as a decorated and later as the parsed configuration inside the method:

...
    ...
    print(c.a)
    print(c.x)

Examples and platforms

Tried and used on linux, macos and windows. However, python packaging and dependency management is sometimes hairy and your mileage may vary.

More examples in the examples directory with printouts of the defined subcommands and helps.

Testing the examples

The examples can be tried out by cloning the repo and running from repo directory root (on linux and the like):

git clone https://github.com/d3rp/clima.git 
cd clima
PYTHONPATH=$PWD python ./examples/readme_example.py foo -h

Running the examples that wrap a module:

PYTHONPATH=$PWD python ./examples/module_example/__main__.py -h
PYTHONPATH=$PWD python ./examples/module_example/__main__.py subcommand-foo -h
PYTHONPATH=$PWD python ./examples/module_example/__main__.py subcommand-bar
...

Output should resemble this (fire v0.1.3 prints out Args, fire v0.2.1 doesn't (though looks much nicer))

$ tester subcommand-foo -- -h

Type:        method
String form: <bound method Cli.subcommand_foo of <__main__.Cli object at 0x000002995AD74BE0>>
File:        C:\Users\foobar\code\py\clima\tester\__main__.py
Line:        18
Docstring:   This will be shown in --help for subcommand-foo
Args:
    --a (str): a description (Default is 'A')
    --x (int): x description (Default is 1)

Usage:       __main__.py subcommand-foo [--X ...]

Version printing

Version printing works via the version subcommand. This is intended for scripts that are packaged as command line tools with poetry. Thus with bumping the version with poetry, clima will handle parsing the current version of your tool so it can be queried with:

my_tool version

The actual version is parsed into the c so overwrite it with post_init or something if you want control over it.

Autocompletion in IDEs (wip)

Also, to enable autocompletion in IDEs, this hack suffices:

c: Configuration = c

Put it in the "global space" e.g. just after defining the template. See the examples/script_example.py for a specific example.

When all is complete, the imported c variable should have all the bits and pieces for the configuration. It can be used inside the Cli class as well as imported around the codebase thus encapsulating all the configurations into one container with quick access with attributes c.a, c.x, etc...

Post init hook

In some occasions it's useful to deduce specific defaults from the given parameters e.g. in a cross platform build allowing only minimal cli arguments. For those cases there's an post_init hook in which the fields can be refered to as in a typical class, but that still allows validation and type casting etc.:

class SoAdvanced(Schema):
    platform: str = 'win'  # a description
    bin_path: pathlib.Path = ''  # x description
    
    def post_init(self, *args):
        if self.platform = 'win':
            self.bin_path = 'c:/Users/foo/bar'
        else:
            self.bin_path = '/Users/mac/sth'

Configuration file and environment variables

The c decorator/configuration chains multiple configuration options together in order of priority (lower number overrides higher number):

1. command line arguments
2. Environment variables
3. configuration file definitions
4. defaults in the schema/template/namedtuple class

The configuration file should be named with postfix .cfg e.g. foo.cfg and have an ini type formatting with a 'Default' section:

# foo.cfg
[Default]
x = 2

The keys are the same as what you define in the schema. You can define all, some or none of the attributes. Same applies for the env variables.

# linux example
X=2 tester subcommand-foo

Additional features via Fire

See the Python Fire's Flags documentation for nice additional features such as:

# e.g. tester.py is our cli program
tester.py subcommand-foo -- --trace
tester.py -- --interactive
tester.py -- --completion

Password unwrapping/decryption with pass

Note: Currently this works only for gpg-keys without password. It's not ideal, but it's better than plain text .env files ;)

pass can be used to store passwords as gpg encrypted files under the home directory. Clima uses the default path of ~/.password-store and the files found within. It will then match the arguments with the stored passwords, for example:

tree -A ~/.password-store                                                                                                                                                                                                                                                                             ✔ | 41s | anaconda3 
/Users/me/.password-store
├── work
│   ├── ci
│   │   ├── sign_id.gpg
│   │   ├── sign_pw.gpg
... ... ...

And an according Schema definition:

class Conf(Schema):
    sign_id: str = ''  # signing id for the CI
    sign_pw: str = ''  # signing pw for the CI

Would accept those arguments as cli arguments, or if omitted, would traverse through the .password-store and decrypt the found sign_id.gpg and sign_pw.gpg placing the values found in the configuration object c.

Truncated error printing

Even though I've used python for a few years professionally, I'm still not satisfied with its error printing. Clima truncates the error lists and tries to provide a more readable version of the "first" point of failure. The whole traceback is written into a logfile exception_traceback.log so it can be examined when the truncated output is not enough.

Type casting with configuration definition

The Schema definition can have type annotations, which are used to cast the given arguments. For example

class C(Schema):
    p: Path = ''  # Path to something

Results in c.p's type cast as Path.

Note: Currently type casting to anything other than Path seems broken, though the tests are passing.. Run the example/type_casting_example.py to validate the current state of things:

# cloned repo
poetry run bash -c 'cd examples; python type_casting_example.py run'

# pip installed clima
cd examples; python type_casting_example.py run

The exception_traceback.log file will be written inside the examples directory

Why another cli framework?

This is just a tool to slap together a cli program in python instead that grew out of the need of having a build automation system and an entrypoint script to build various flavours of C++ projects. The intention is to get something reasonably configurable and generic up and running as fast as possible while still having the "power" of python. I can't bother to memorize argparses syntax, even though it's a very good package. Also click works nice for more elaborate things though fire is my personal favourite for the time being. Often times when I kick off a bash script for this it ends up too elaborate very quick and then I miss python.

Also docopt looks very nice, but it doesn't provide autocompletion and all the configuration chaining magic I was after.

Other options for full cli experience:

• docopt
• fire
• click

Dependencies

• fire - python-fire from google does the cli wrapping