property-manager3 2.3.1

Description:

propertymanager3 2.3.1

The property-manager3 package defines several custom property variants for
Python programming including required properties, writable properties, cached
properties, etc. It’s currently tested on Python 2.6, 2.7, 3.4, 3.5, 3.6, 3.7, 3.8 and
PyPy. For usage instructions please refer to the documentation.


Status
Installation
Usage

Writable properties
Required properties
Cached properties
Properties based on environment variables
Support for setters and deleters
The PropertyManager class


Similar projects

Distinguishing features


Contact
License



Status
The property-manager3 package came into existence as a submodule of my
executor package where I wanted to define classes with a lot of properties
that had a default value which was computed on demand but also needed to
support assignment to easily override the default value.
Since I created that module I’d wanted to re-use it in a couple of other
projects I was working on, but adding an executor dependency just for the
property_manager3 submodule felt kind of ugly.
This is when I decided that it was time for the property-manager3 package to
be created. When I extracted the submodule from executor I significantly
changed its implementation (making the code more robust and flexible) and
improved the tests, documentation and coverage in the process.


Installation
The property-manager3 package is available on PyPI which means installation
should be as simple as:
$ pip install property-manager3
There’s actually a multitude of ways to install Python packages (e.g. the per
user site-packages directory, virtual environments or just installing
system wide) and I have no intention of getting into that discussion here, so
if this intimidates you then read up on your options before returning to these
instructions ;-).


Usage
This section shows how to use the most useful property subclasses. Please refer
to the documentation for more detailed information.


Writable properties
Required properties
Cached properties
Properties based on environment variables
Support for setters and deleters
The PropertyManager class



Writable properties
Writable properties with a computed default value are easy to create using the
writable_property decorator:
from random import random
from property_manager3 import writable_property

class WritablePropertyDemo(object):

@writable_property
def change_me(self):
return random()
First let’s see how the computed default value behaves:
>>> instance = WritablePropertyDemo()
>>> print(instance.change_me)
0.13692489329941815
>>> print(instance.change_me)
0.8664002331885933

As you can see the value is recomputed each time. Now we’ll assign it a value:
>>> instance.change_me = 42
>>> print(instance.change_me)
42

From this point onwards change_me will be the number 42 and it’s impossible
to revert back to the computed value:
>>> delattr(instance, 'change_me')
Traceback (most recent call last):
File "property_manager3/__init__.py", line 584, in __delete__
raise AttributeError(msg % (obj.__class__.__name__, self.__name__))
AttributeError: 'WritablePropertyDemo' object attribute 'change_me' is read-only

If you’re looking for a property that supports both assignment and deletion
(clearing the assigned value) you can use mutable_property.


Required properties
The required_property decorator can be used to create required properties:
from property_manager3 import PropertyManager, required_property

class RequiredPropertyDemo(PropertyManager):

@required_property
def important(self):
"""A very important attribute."""
What does it mean for a property to be required? Let’s create an instance of
the class and find out:
>>> instance = RequiredPropertyDemo()
Traceback (most recent call last):
File "property_manager3/__init__.py", line 131, in __init__
raise TypeError("%s (%s)" % (msg, concatenate(missing_properties)))
TypeError: missing 1 required argument (important)

So the constructor of the class raises an exception when the property hasn’t
been given a value. We can give the property a value by providing keyword
arguments to the constructor:
>>> instance = RequiredPropertyDemo(important=42)
>>> print(instance)
RequiredPropertyDemo(important=42)

We can also assign a new value to the property:
>>> instance.important = 13
>>> print(instance)
RequiredPropertyDemo(important=13)



Cached properties
Two kinds of cached properties are supported, we’ll show both here:
from random import random
from property_manager3 import cached_property, lazy_property

class CachedPropertyDemo(object):

@cached_property
def expensive(self):
print("Calculating expensive property ..")
return random()

@lazy_property
def non_idempotent(self):
print("Calculating non-idempotent property ..")
return random()
The properties created by the cached_property decorator compute the
property’s value on demand and cache the result:
>>> instance = CachedPropertyDemo()
>>> print(instance.expensive)
Calculating expensive property ..
0.763863180683
>>> print(instance.expensive)
0.763863180683

The property’s cached value can be invalidated in order to recompute its value:
>>> del instance.expensive
>>> print(instance.expensive)
Calculating expensive property ..
0.396322737214
>>> print(instance.expensive)
0.396322737214

Now that you understand cached_property, explaining lazy_property is very
simple: It simply doesn’t support invalidation of cached values! Here’s how
that works in practice:
>>> instance.non_idempotent
Calculating non-idempotent property ..
0.27632566561900895
>>> instance.non_idempotent
0.27632566561900895
>>> del instance.non_idempotent
Traceback (most recent call last):
File "property_manager3/__init__.py", line 499, in __delete__
raise AttributeError(msg % (obj.__class__.__name__, self.__name__))
AttributeError: 'CachedPropertyDemo' object attribute 'non_idempotent' is read-only
>>> instance.non_idempotent
0.27632566561900895



Properties based on environment variables
The constructor of the custom_property class (and its subclasses) accepts the
keyword argument environment_variable which can be provided to get the
property’s value from the environment:
from random import random
from property_manager3 import mutable_property

class EnvironmentPropertyDemo(object):

@mutable_property(environment_variable='WHATEVER_YOU_WANT')
def environment_based(self):
return 'some-default-value'
By default the property’s value is computed as expected:
>>> instance = EnvironmentPropertyDemo()
>>> print(instance.environment_based)
some-default-value

When the environment variable is set it overrides the computed value:
>>> os.environ['WHATEVER_YOU_WANT'] = '42'
>>> print(instance.environment_based)
42

Assigning a value to the property overrides the value from the environment:
>>> instance.environment_based = '13'
>>> print(instance.environment_based)
13

Deleting the property clears the assigned value so that the property falls back
to the environment:
>>> delattr(instance, 'environment_based')
>>> print(instance.environment_based)
42

If we now clear the environment variable as well then the property falls back
to the computed value:
>>> os.environ.pop('WHATEVER_YOU_WANT')
'42'
>>> print(instance.environment_based)
some-default-value



Support for setters and deleters
All of the custom property classes support setters and deleters just like
Python’s property decorator does.


The PropertyManager class
When you define a class that inherits from the PropertyManager class the
following behavior is made available to your class:

Required properties raise an exception if they’re not set.
The values of writable properties can be set by passing
keyword arguments to the constructor of your class.
The repr() of your objects will render the name of the class and the names
and values of all properties. Individual properties can easily be excluded
from the repr() output.
The clear_cached_properties() method can be used to invalidate the cached
values of all cached properties at once.

Additionally you can use the property_manager3.sphinx module as a Sphinx
extension to automatically generate boilerplate documentation that provides an
overview of base classes, properties, public methods and special methods.



Similar projects
The Python Package Index contains quite a few packages that provide custom
properties with similar semantics:

cached-property
My personal favorite until I wrote my own :-). This package provides several
cached property variants. It supports threading and time based cache
invalidation which property-manager3 doesn’t support.

lazy-property
This package provides two cached property variants: a read only property and
a writable property. Both variants cache computed values indefinitely.

memoized-property
This package provides a single property variant which simply caches computed
values indefinitely.

property-caching
This package provides several cached property variants supporting class
properties, object properties and cache invalidation.

propertylib
This package uses metaclasses to implement an alternative syntax for defining
computed properties. It defines several property variants with semantics that
are similar to those defined by the property-manager3 package.

rwproperty
This package implements computed, writable properties using an alternative
syntax to define the properties.



Distinguishing features
Despite all of the existing Python packages discussed above I decided to create
and publish the property-manager3 package because it was fun to get to know
Python’s descriptor protocol and I had several features in mind I couldn’t
find anywhere else:

A superclass that sets writable properties based on constructor arguments.
A superclass that understands required properties and raises a clear
exception if a required property is not properly initialized.
Clear disambiguation between lazy properties (whose computed value is cached
but cannot be invalidated because it would compromise internal state) and
cached properties (whose computed value is cached but can be invalidated to
compute a fresh value).
An easy way to quickly invalidate all cached properties of an object.
An easy way to change the semantics of custom properties, e.g. what if the
user wants a writable cached property? With property-manager3 it is trivial
to define new property variants by combining existing semantics:
from property_manager3 import cached_property

class WritableCachedPropertyDemo(object):

@cached_property(writable=True)
def expensive_overridable_attribute(self):
"""Expensive calculations go here."""
The example above creates a new anonymous class and then immediately uses
that to decorate the method. We could have given the class a name though:
from property_manager3 import cached_property

writable_cached_property = cached_property(writable=True)

class WritableCachedPropertyDemo(object):

@writable_cached_property
def expensive_overridable_attribute(self):
"""Expensive calculations go here."""
By giving the new property variant a name it can be reused. We can go one
step further and properly document the new property variant:
from property_manager3 import cached_property

class writable_cached_property(cached_property):

"""A cached property that supports assignment."""

writable = True

class WritableCachedPropertyDemo(object):

@writable_cached_property
def expensive_overridable_attribute(self):
"""Expensive calculations go here."""
I’ve used computed properties for years in Python and over those years I’ve
learned that different Python projects have different requirements from
custom property variants. Defining every possible permutation up front is
madness, but I think that the flexibility with which the property-manager3
package enables adaptation gets a long way. This was the one thing that
bothered me the most about all of the other Python packages that implement
property variants: They are not easily adapted to unanticipated use cases.





Contact
The latest version of property-manager3 is available on PyPI and GitHub. The
documentation is hosted on Read the Docs and includes a changelog. For bug
reports please create an issue on GitHub. If you have questions, suggestions,
etc. feel free to send me an e-mail at [email protected].


License
This software is licensed under the MIT license.
© 2018 Peter Odding.