Simple boilerplate for cli scripts
Project description
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):
- command line arguments
- Environment variables
- configuration file definitions
- 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:
Dependencies
- fire - python-fire from google does the cli wrapping
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