Param

Param is a library providing Parameters: Python attributes extended to have features such as type and range checking, dynamically generated values, documentation strings, default values, etc., each of which is inherited from parent classes if not specified in a subclass. Param lets you program declaratively in Python, by just stating facts about each of your parameters, and then using them throughout your code. With Parameters, error checking will be automatic, which eliminates huge amounts of boilerplate code that would otherwise be required to verify or test user-supplied values.

Param-based programs tend to contain much less code than other Python programs, instead just having easily readable and maintainable manifests of Parameters for each object or function. This way your remaining code can be much simpler and clearer, while users can also easily see how to use it properly.

What is a Parameter?

A Parameter is a special type of Python attribute extended to have features such as type and range checking, dynamically generated values, documentation strings, default values, etc., each of which is inherited from parent classes if not specified in a subclass.

>>> import param,random
>>> class A(param.Parameterized):
...    a = param.Number(0.5,bounds=(0,1),doc="Probability that...")
...    b = param.Boolean(False,doc="Enable feature...")

>>> class B(A):
...    b = param.Boolean(True)

>>> x = B(a=lambda: random.uniform(0,1))

>>> x.a
0.37053399325641945

>>> x.a
0.64907392300071842

Parameters provide optional range and type checking

>>> x.a=5
[...]
ValueError: Parameter 'a' must be at most 1

>>> x.a="0.5"
[...]
ValueError: Parameter 'a' only takes numeric values

Parameters have docstrings

>>> help(x)
[...]
class B(A)
[...]
   Data descriptors defined here:
   b
       Enable feature...
[...]
   Data descriptors inherited from A:
   a
       Probability that...

Param is lightweight

Param consists of two required BSD-licensed Python files, with no dependencies outside of the standard library, and so it can easily be included as part of larger projects without adding external dependencies.

Parameters make GUI programming simpler

Parameters make it simple to generate GUIs by separating your semantic information (what is this parameter? what type can it have? does it have bounds?) from anything to do with a particular GUI library. To use Parameters in a particular GUI toolkit, you just need to write a simple set of interfaces that indicate how a given Parameter type should be displayed, and what widgets to generate for it. Currently, interfaces are provided for use in Jupyter Notebooks ([ParamNB] ( https://github.com/ioam/paramnb )) or in Tk ( ParamTk ), both of which make it simple to provide a property sheet that automatically generates a set of widgets for viewing and editing an object's Parameters.

Optional dynamic parameter values using numbergen

Providing random or other types of varying values for parameters can be tricky, because unnamed ("lambda") functions as used above cannot easily be pickled, causing problems for people who wish to store Parameterized objects containing random state. To avoid users having to write a separate function for each random value, Param includes an optional set of value-generating objects that are easily configured and support pickling. These objects are available if you import the optional numbergen module. If you wish to use numbergen, the above example can be rewritten as:

>>> import param,numbergen
>>> class A(param.Parameterized):
...    a = param.Number(0.5,bounds=(0,1),doc="Probability that...")
...    b = param.Boolean(False,doc="Enable feature...")

>>> class B(A):
...    b = param.Boolean(True)

>>> x = B(a=numbergen.UniformRandom())

Numbergen objects support the usual arithmetic operations like + , - , * , / , // , % , ** , and abs() , and so they can be freely combined with each other or with mathematical constants:

>>> y = B(a=2.0*numbergen.UniformRandom()/(numbergen.NormalRandom()+1.5))

Note that unlike the lambda-function approach, all varying numbergen objects respect param.Dynamic.time_fn , e.g. to ensure that new values will be generated only when Param's time has changed.
Parameterized programs can define a time function to maintain a logical/simulated time, such as the state of a simulator, which allows all Parameter values to be kept synchronized without any special coordination code.

Installation

Param has no required dependencies outside of Python's standard library.

Official releases of Param are available on Anaconda and PyPI , and can be installed via conda install -c ioam param , pip install --user param , or pip install param .

The very latest changes can be obtained via conda install -c pyviz/label/dev param or pip install https://github.com/ioam/param/archive/master.zip .

For development, the git repository can be cloned and then 'develop installed' ( pip install -e . or python setup.py develop ). Tests can be run via [tox] ( https://tox.readthedocs.io/en/latest/ ): tox for all tests, or e.g. tox -e coverage to run unit tests with coverage for the currently active python. Alternatively, unit tests can be run via nosetests (after installing nose .

Comparison to other packages

Param was first developed in 2003, in the context of the Topographica brain simulator project, and was made into a separate package in 2012. In the interim other parameter libraries were developed, including Traits and Traitlets . These libraries have broadly similar goals, but each differs in important ways:

Dependencies : Traits is a much more heavyweight solution, requiring installation of a large suite of tools, including C code, which makes it difficult to include in separate projects. In contrast, Param and Traitlets are both pure Python projects, with minimal dependencies.

GUI toolkits : Although any of the packages could in principle add support for any GUI toolkit, the toolkits actually provided differ: Traits (via the separate TraitsUI package) supports wxWidgets and QT, while Param supports Tkinter (via the separate ParamTk package) and browser-based IPython widgets (via the separate ParamNB package), while Traitlets only supports IPython widgets.

>>> from time import time
     >>> import traitlets as tr
     >>> class A(tr.HasTraits):
     ...     instantiation_time = tr.Float()
     ...     @tr.default('instantiation_time')
     ...     def _look_up_time(self):
     ...         return time()
     ... 
     >>> a=A()
     >>> a.instantiation_time
     1475587151.967874
     >>> a.instantiation_time
     1475587151.967874
     >>> b=A()
     >>> b.instantiation_time
     1475587164.750875

Dynamic values : Param, Traits, and Traitlets all allow any Python expression to be supplied for initializing parameters, allowing parameter default values to be computed at the time a module is first loaded. Traits and Traitlets also allow a class author to add code for a given parameter to compute a default value on first access. Param does not provide any special support for programmatic default values, instead allowing fully dynamic values for any numeric Parameter instance:

>>> from time import time
  >>> import param
  >>> class A(param.Parameterized):
  ...     val=param.Number(0)
  ... 
  >>> a=A()
  >>> a.val
  0
  >>> a.val=lambda:time()
  >>> a.val
  1475587455.437027
  >>> a.val
  1475587456.501314

Note that here it is the user of a Parameterized class, not the author of the class, that decides whether any particular value is dynamic, without writing any new methods or other code. All the usual type checking, etc. is done on dynamic values when they are computed, and so the rest of the code does not need to know or care whether the user has set a particular parameter to a dynamic value. This approach provides an enormous amount of power to the user, without making the code more complex.

On_change callbacks Traitlets and Traits allow the author of a HasTraits-derived class to specify code to run when a specific parameter used in that class instance is modified. Param supports similar capabilities, but not at the Parameterized class level, only at the Parameter class level or as part of ParamNB. I.e., a class author needs to first write a new Parameter class, adding methods to implement checking on changes, and then add it to a Parameterized class, or else such functionality can be added as callbacks at the whole-object level, using ParamNB. Each approach has advantages and disadvantages, and per-parameter on_change callbacks could be added in the future if there are clear use cases.

All of these packages also overlap in functionality with Python properties, which were added to the language after Traits and Param were developed. Like parameters and traits, properties act like attributes with possible method-like actions, and so they can all be used to provide the same user-visible functionality. However, implementing Param/Traits-like functionality using properties would require vastly more code (multiple method definitions for every parameter in a class), and so in practice Parameters and Traits are much more practical for the use cases that they cover.

Release notes

Recent release notes are available on GitHub .

For older releases, see our historical release notes .

Support

Questions and comments are welcome at https://github.com/ioam/param/issues .