Constraints¶

Overview¶

Terminology

In this section, the use of words “parameter” vs “option” is not accidental. In Click, a Parameter can be an Option or an Argument. Constraints works with both. Most importantly, when I write “option group”, I’m referring to an instance of OptionGroup, i.e. objects implicitly created with @option_group, not just a collection of options specified by name.

A Constraint is essentially a validator for groups of parameters that:

has a textual description (Constraint.help())
when unsatisfied (or unsatisfiable), raises an exception with an appropriate error message
it’s easily composable with other constraints using logical operators and you can easy change its description and/or error message (see Combining and rephrasing).

Even though immutability is not enforced (full immutability is not even possible in Python), constraints should be treated as immutable objects. In fact, methods like rephrased() and hidden() don’t work by mutating but by wrapping the constraint.

Constraints are well-integrated with OptionGroup’s but decoupled from them. Thus, you can use them to check any group of parameters (for example, options belonging to different option groups); you can do that by providing the parameter (destination) names (see Usage with @constraint and Usage inside functions).

In order to support constraints, a Command must inherit from ConstraintMixin. Of course, cloup.Command satisfies this requirement.

Implemented constraints¶

cloup uses the following convention:

parametric constraints are subclasses of Constraint and so they are camel-cased;
non-parametric constraints are instances of Constraint and so they are snake-cased.

Parametric constraints¶

`RequireExactly`(n)	Requires an exact number of parameters to be set.
`RequireAtLeast`(n)	Satisfied if the number of set parameters is >= n.
`AcceptAtMost`(n)	Satisfied if the number of set parameters is <= n.
`AcceptBetween`(min, max)	Satisfied if the number of set parameters is between `min` and `max` (included).

Non-parametric constraints¶

`require_all`	Requires all parameters to be set.
`accept_none`	Requires all parameters to be unset.
`all_or_none`	Satisfied if either all or none of the parameters are set.
`mutually_exclusive`	A rephrased version of `AcceptAtMost(1)`.

Yeah… but what does it mean “set”?¶

Cloup validates the values available in ctx.params after argument parsing; this is a dictionary created by Click which contains the keyword arguments that will be provided to the command callback, meaning that the source of an option value is not necessarily the command line: it may be a default value or eventually an environment variable (if enabled).

That said, Cloup uses the following policy:

if the value is None, the parameter is unset;
a parameter that takes multiple values is set if at least one value is provided, since the default value is an empty tuple;
a boolean flag is set only if True, since the default value is False;
a boolean non-flag option is set if not None, even if it’s False.

Conditional constraints¶

If allows to define conditional constraints:

If(condition, then, [else_])

condition is either a concrete instance of Predicate or the name of a parameter; passing the name is a shortcut for IsSet(name)
then is an instance of Constraint
else_ is another instance of Constraint or None.

Available predicates can be imported from cloup.constraints and are:

`IsSet`(param_name)	True if the parameter is set.
`Equal`(param_name, value)	True if the parameter value equals `value`.

For example:

from cloup.constraints import (
    If, RequireAtLeast, require_all, accept_none,
    IsSet, Equal
)

# If parameter with name "param" is set,
# then require all parameters, else forbid them all
If('param', then=require_all, else_=accept_none)

# Equivalent to:
If(IsSet('param'), then=require_all, else_=accept_none)

# Another example... of course the else branch is optional
If(Equal('param', 'value'), then=RequireAtLeast(1))

Predicates have an associated description and can be composed with the logical operators

~ (not),
& (and),
| (or).

For example:

predicate = ~IsSet('foo') & Equal('bar', 'value')
# --foo is not set and --bar="value"

Usage with @option_group¶

As you have probably seen in the Option groups section, you can easily apply a constraint to an option group by setting the constraint (keyword-only) argument or @option_group (or OptionGroup):

@option_group(
    'Output options',
    option('--four / --no-four', help='1st output option'),
    option('--five', help='2nd output option'),
    option('--six', help='3rd output option'),
    constraint=RequireAtLeast(1),
)

This code produces the following help section, whose title contains the constraint description between square brackets:

Output options [at least 1 required]:
  --four / --no-four    1st output option
  --five TEXT           2nd output option
  --six TEXT            3rd output option

If you don’t want to show the constraint description, you can use the method hidden():

@option_group(
    ...
    constraint=RequireAtLeast(1).hidden(),
)

Specifying parameters by name¶

If you need to define a constraint that involves parameters that don’t constitute an OptionGroup, you have two options:

use the constraint as a function inside the command callback (a Constraint is indeed callable)
use the @cloup.constraint decorator.

In both cases, the involved parameters must be specified providing their destination names, i.e. the names of the function arguments they are mapped to (by Click). For example, by default, Click maps an option named --input-file to the function argument input_file. Remember that you can always override the default destination name by providing it after the option “command-line name(s)”:

# map this option to 'output_path' rather than 'out_path'
@option('-o', '--out-path', 'output_path')

Usage with @constraint¶

In essence, @cloup.constraint allows to include the constraint as part of the command “metadata”, which opens new possibilities as opposed to using the constraint as a function:

it becomes possible to document the constraints in a section of the help page; note that this is disabled by default and can be enabled passing show_constraints=True to @command() (or cloup.Command);
the sanity checks performed to detect your mistakes can be performed before parsing, just after the constraints applied to option groups are checked (see Checking protocol); this is not of huge importance but a nice-to-have.

The signature is simple:

@constraint(constr: Constraint, params: Iterable[str])

where params is a list of parameter destination names; for example, an option --input-file is mapped (by Click) to the name input_file by default.

Here’s a meaningless example just to show how to use the API:

@command('cmd', show_constraints=True)
@option('--opt-1')
@option('--opt-2')
@option('--opt-3')
@option('--opt-4')
@option('--opt-5')
@constraint(mutually_exclusive, ['opt_1', 'opt_2'])
@constraint(If('opt_1', then=RequireExactly(1)), ['opt_3', 'opt_5'])
def cmd(opt_1, opt_2, opt_3, opt_4, opt_5):
    print('ciao')

Passing show_constraints=True as above will produce the following section at the bottom of the command help:

Constraints:
  {--opt-1, --opt-2}  mutually exclusive
  {--opt-3, --opt-4}  exactly 1 required if --opt-1 is set

Even in this case, you can still hide a specific constraint by calling the method hidden() on it.

Usage inside functions¶

You may consider this option if you are not interested in documenting constraints in the help page and you find it more readable than @constraint.

from cloup.constraint import If, RequireExactly, mutually_exclusive

# ...
def cmd(opt_1, opt_2, opt_3, opt_4, opt_5):

    mutually_exclusive(['opt_1', 'opt_4'])

    If(Equal('opt_1', 'value'), then=RequireExactly(1))([
        'opt_2', 'opt_3'
    ])

Calling a constraint is equivalent to call its check() method.

Important

The check() method needs a click.Context to work. Nonetheless, you are not required to provide it explicitly because the current context is automatically obtained using click.get_current_context(). This works as long as you call check() (or the constraint itself) in places where an a Click context is guaranteed to be defined.

Why do you need to pass the parameter names and not their values? That’s because values are not enough to generate an error message explaining what parameters don’t satisfy the constraint. Knowing the names, Cloup can reference both the Parameter instances and their values in the current click.Context.

Combining and rephrasing¶

The available constraints should cover 99% of use cases but if you want to combine them or even just change their description and/or the error message, you can do that with very little code:

to combine constraints you can use the logical operators & and |
to edit the description and/or the error message of a constraint, you can use the method rephrased(), which wraps the original constraint with a Rephraser
to define a new constraint type wrapping another constraint with minimal boilerplate, you can extend WrapperConstraint.

Some examples from Cloup itself:

# {param_list} is a comma-separated list of parameters
all_or_none = (require_all | accept_none).rephrased(
    help='provide all or none',
    error='either all or none of the following parameters must be set:\n{param_list}',
)

# WrapperConstraint is useful for defining a new constraint type
# It delegates all methods to the wrapped constraint so you can
# override only the methods you need to override.
class AcceptBetween(WrapperConstraint):
    def __init__(self, min: int, max: int):
        # [...]
        self._min = min
        self._max = max
        # the min and max kwargs will be used for the __repr__
        super().__init__(RequireAtLeast(min) & AcceptAtMost(max), min=min, max=max)

    def help(self, ctx: Context) -> str:
        return f'at least {self._min} required, at most {self._max} accepted'

# An alternative could be the following. Using WrapperConstraint has
# some advantage: it creates a new type and has a prettier __repr__
def accept_between(min, max):
   return (RequireAtLeast(min) & AcceptAtMost(max)).rephrased(
       help=f'at least {min} required, at most {max} accepted'
   )

>>> AcceptBetween(1, 3)
AcceptBetween(1, 3)

>>> accept_between(1, 3)
Rephraser(help='at least 1 required, at most 3 accepted')

If all this is not useful, just extend Constraint. Look at the code of existing constraints if you have any doubt.

Checking protocol¶

A constraint performs two types of checks and there’s a method for each type:

check_consistency() – performs sanity checks meant to detect mistakes of the developer; as such, they are performed before argument parsing (when possible); for example, if you try to apply a mutually_exclusive constraint to an option group containing multiple required options, this method will raise UnsatisfiableConstraint
check_values() – performs user input validation and, when unsatisfied, raises a ConstraintViolated error with an appropriate message; ConstrainedViolated is a subclass of click.UsageError and, as such, is handled by Click itself by showing the command usage and the error message.

Using a constraint as a function is equivalent to call the method check(), which performs (by default) both kind of checks, unless consistency checks are disabled (see below).

When you add constraints through @option_group, OptionGroup and @constraint, this is what happens:

constraints are checked for consistency before parsing
input is parsed and processed; all values are stored by Click in the Context object, precisely in ctx.params
constraints validate the parameter values.

In all cases, constraints applied to option groups are checked before those added through @constraint.

If you use a constraint inside a callback, of course, consistency checks can’t be performed before parsing. All checks are performed together after parsing.

Disabling consistency checks¶

Current consistency checks should not have any relevant impact on performance, so they are enabled by default. Nonetheless, they are completely useless in production and can be turned off (globally) using the class method Constraint.toggle_consistency_checks(). To disable them only in production, you can set an environment variable in the system you use for development, say PYTHON_ENV="dev"; then you can put the following code in the entry-point of your program:

import os

Constraint.toggle_consistency_checks(
    os.getenv('PYTHON_ENV') == 'dev'
)

Cloup v0.5.0