properties: an organizational aid and wrapper for validation and tab completion of class properties
Project description
Overview Video
An overview of Properties, November 2016.
Why
Properties provides structure to aid development in an interactive programming environment while allowing for an easy transition to production code. It emphasizes usability and reproducibility for developers and users at every stage of the code life cycle.
Scope
The properties
package enables the creation of strongly typed objects in a
consistent, declarative way. This allows validation of developer expectations and hooks
into notifications and other libraries. It provides documentation with
no extra work, and serialization for portability and reproducibility.
Goals
Keep a clean namespace for easy interactive programming
Prioritize documentation
Provide built-in serialization/deserialization
Connect to other libraries for GUIs and visualizations
Documentation
API Documentation is available at ReadTheDocs.
Alternatives
traitlets (Jupyter project) and traits (Enthought) - These libraries are driven by GUI development (much of the Jupyter environment is built on traitlets; traits has automatic GUI generation) which leads to many similar features as properties such as strong typing, validation, and notifications. Also, some Properties features and aspects of the API take heavy inspiration from traitlets.
However, There are a few key areas where properties differs:
properties has a clean namespace - this (in addition to ? docstrings) allows for very easy discovery in an interactive programming environment.
properties prioritizes documentation - this is not explicitly implemented yet in traits or traitlets, but works out-of-the-box in properties.
properties prioritizes serialization - this is present in traits with pickling (but difficult to customize) and in traitlets with configuration files (which require extra work beyond standard class definition); in properties, serialization works out of the box but is also highly customizable.
properties allows invalid object states - the GUI focus of traits/traitlets means an invalid object state at any time is never ok; without that constraint, properties allows interactive object building and experimentation. Validation then occurs when the user is ready and calls
validate
Significant advantages of traitlets and traits over properties are GUI interaction and larger suites of existing property types. Besides numerous types built-in to these libraries, some other examples are trait types that support unit conversion and NumPy/SciPy trait types (note: properties has a NumPy array property type).
param - This library also provides type-checking, validation, and notification. It has a few unique features and parameter types (possibly of note is the ability to provide dynamic values for parameters at any time, not just as the default). This was first introduced before built-in Python properties, and current development is very slow.
mypy and PEP0484 - This provides static typing for Python without coersion, notifications, etc. It has a very different scope and implementation than traits-like libraries.
built-in Python property - properties/traits-like behavior can be mostly recreated using
@property
. This requires significantly more work by the programmer, and results in not-declarative, difficult-to-read code.
Connections
casingSimulations - Research repository for electromagnetic simulations in the presence of well casing
OMF - Open Mining Format API and file serialization
SimPEG - Simulation and Parameter Estimation in Geophysics
Steno3D - Python client for building and uploading 3D models
Installation
To install the repository, ensure that you have pip installed and run:
pip install properties
For the development version:
git clone https://github.com/aranzgeo/properties.git
cd properties
pip install -e .
Examples
Lets start by making a class to organize your coffee habits.
import properties
class CoffeeProfile(properties.HasProperties):
name = properties.String('What should I call you?')
count = properties.Integer(
'How many coffees have you had today?',
default=0
)
had_enough_coffee = properties.Bool(
'Have you had enough coffee today?',
default=False
)
caffeine_choice = properties.StringChoice(
'How do you take your caffeine?' ,
choices=['coffee', 'tea', 'latte', 'cappuccino', 'something fancy'],
required=False
)
The CoffeeProfile
class has 4 properties, all of which are documented!
These can be set on class instantiation:
profile = CoffeeProfile(name='Bob')
print(profile.name)
Out [1]: Bob
Since a default value was provided for had_enough_coffee
, the response is (naturally)
print(profile.had_enough_coffee)
Out [2]: False
We can set Bob’s caffeine_choice
to one of the available choices; he likes coffee
profile.caffeine_choice = 'coffee'
Also, Bob is half way through his fourth cup of coffee today:
profile.count = 3.5
Out [3]: ValueError: The 'count' property of a CoffeeProfile instance must
be an integer.
Ok, Bob, chug that coffee:
profile.count = 4
Now that Bob’s CoffeeProfile
is established, properties
can
check that it is valid:
profile.validate()
Out [4]: True
Property Classes are auto-documented in Sphinx-style reStructuredText!
When you ask for the doc string of CoffeeProfile
, you get
**Required Properties:**
* **count** (:class:`Integer <properties.basic.Integer>`): How many coffees have you had today?, an integer, Default: 0
* **had_enough_coffee** (:class:`Bool <properties.basic.Bool>`): Have you had enough coffee today?, a boolean, Default: False
* **name** (:class:`String <properties.basic.String>`): What should I call you?, a unicode string
**Optional Properties:**
* **caffeine_choice** (:class:`StringChoice <properties.basic.StringChoice>`): How do you take your caffeine?, any of "coffee", "tea", "latte", "cappuccino", "something fancy"
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