Welcome to Connectors’ documentation!

The Connectors package facilitates the writing of block-diagram-like processing networks. For this it provides decorators for the methods of processing classes, so they can be connected to each other. When a parameter in such a processing network is changed, the result values will also be updated automatically. This is similar to a pipes and filters architecture, the observer pattern or streams.

This short example demonstrates the core functionality of the Connectors package by implementing a processing network of two sequential blocks, which double their input value:

>>> import connectors
>>>
>>> class TimesTwo:
...     def __init__(self, value=0):
...         self.__value = value
...
...     @connectors.Input("get_double")
...     def set_value(self, value):
...         self.__value = value
...
...     @connectors.Output()
...     def get_double(self):
...          return 2 * self.__value
>>>
>>> d1 = TimesTwo()                                     # create an instance that doubles its input value
>>> d2 = TimesTwo().set_value.connect(d1.get_double)    # create a second instance and connect it to the first
>>> d2.get_double()
0
>>> d1.set_value(2)
>>> d2.get_double()                                     # causes the new input value 2 to be processed by d1 and d2
8

Contents

• 1. Reference
  • 1.1. Decorators
  • 1.2. Connectors
  • 1.3. Configuration options
  • 1.4. Helper functionalities
  • 1.5. Macro connectors for encapsulating processing networks in a class
  • 1.6. Processing blocks for common tasks
  • 1.7. Internal features
• 2. Organisation
  • 2.1. Installation
  • 2.2. Dependencies
  • 2.3. Makefile targets
  • 2.4. Licenses
• 3. Information
  • 3.1. Lazy execution
  • 3.2. Caching
  • 3.3. Automated parallelization
  • 3.4. Implementation details
• 4. Tutorials
  • 4.1. Measuring a transfer function (demonstrates the core functionalities)
  • 4.2. Impementing a multiplexer (demonstrates the use of a multi-input connector as arbitrarily many single-input connectors)
  • 4.3. Improving the multiplexer (demonstrates avoiding unneccessary computations with conditional input connectors)
  • 4.4. Implementing a polynomial (demonstrates the encapsulation of a processing network in a single class with macro connectors)
  • 4.5. Improving the polynomial implementation (demonstrates memory saving techniques)

Indices and tables

• Index
• Search Page