Introduction to Epipebomb

Epipebomb is a robust toolkit designed for efficient data streaming and manipulation. This article aims to provide an in-depth guide to its versatile APIs through multiple examples to help you get started quickly. Whether you’re looking to process streams of data or handle complex transformations, epipebomb offers a wide range of functionalities to simplify these tasks.

Getting Started

First, install epipebomb:

pip install epipebomb

Core APIs

Pipeline

The Pipeline API allows for complex transformations to be applied to data streams.

  from epipebomb import Pipeline

  def transform_uppercase(data):
      return data.upper()

  pipeline = Pipeline()
  pipeline.add_step(transform_uppercase)
  
  for output in pipeline.run(['hello', 'world']):
      print(output)

Filter

The Filter API filters data based on given conditions.

  from epipebomb import Filter

  def filter_vowels(data):
      return data in 'AEIOUaeiou'

  filter = Filter(filter_vowels)
  
  for output in filter.run(['a', 'b', 'c', 'e']):
      print(output)

Map

The Map API maps a function to each data element.

  from epipebomb import Map

  def square(number):
      return number * number

  mapper = Map(square)
  
  for output in mapper.run([1, 2, 3, 4]):
      print(output)

Reduce

The Reduce API applies a reduction function cumulatively to the data stream.

  from epipebomb import Reduce

  def add(x, y):
      return x + y

  reducer = Reduce(add)
  
  result = reducer.run([1, 2, 3, 4])
  print(result)

App Example

Below is an application example that uses all of the above APIs to process a stream of data.

  from epipebomb import Pipeline, Filter, Map, Reduce

  def transform_uppercase(data):
      return data.upper()

  def filter_vowels(data):
      return data in 'AEIOU'

  def square(number):
      return number * number

  def add(x, y):
      return x + y

  def main():
      pipeline = Pipeline()
      pipeline.add_step(transform_uppercase)

      filter = Filter(filter_vowels)
      mapper = Map(square)
      reducer = Reduce(add)

      data = ['a', 'b', 'c', 'e']
      transformed_data = list(pipeline.run(data))
      filtered_data = list(filter.run(transformed_data))
      mapped_data = list(mapper.run(mapped_data))
      result = reducer.run(mapped_data)
      print("Result:", result)

  if __name__ == "__main__":
      main()

By following this guide, you should be able to utilize epipebomb’s powerful APIs to streamline your data processing tasks efficiently. Keep exploring its extensive features for more advanced use cases!

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