API

API highlights

Quick API reference

Core modules:
- enb.atable: definition of the core ATable functionality
  
  enb.atable.ATable: core class with automatic parallelization and persistence management.
  
  enb.atable.ATable.get_df(): method to produce a pandas.DataFrame with the table contents.
  
  See Basic workflow: enb.atable.ATable for extra information on enb.atable.ATable
- enb.sets: Definition of ATable subclasses defining datasets
  
  enb.sets.FilePropertiesTable
- enb.isets: Definition of image datasets based on enb.sets
  
  enb.isets.load_array_bsq(): load a raw image as a 3D numpy array indexed as [x,y,z]
  
  enb.isets.dump_array_bsq(): dump a 3D numpy array indexed as [x,y,z] as a BSQ raw image.
  
  enb.isets.ImagePropertiesTable: ATable subclass for image datasets, including relevant image properties.
- enb.experiment: Experiments apply a set of tasks to each of the elements of a dataset (see enb.sets and enb.isets)
  
  enb.experiment.Experiment: subclass of ATable, base for experiment subclass definition. Run with enb.experiment.Experiment.get_df()
  
  enb.experiment.ExperimentTask: defines one task to be applied to each element of the dataset
  
  See Experiments = data + tasks for extra information on experiments.
- enb.icompression: Tools for easy definition of lossless and lossy compression experiments.
  
  enb.icompression.CompressionExperiment
  
  enb.icompression.LosslessCompressionExperiment
  
  enb.icompression.LossyCompressionExperiment
  
  See Image compression and manipulation for extra information on image compression experiments.
- enb.aanalysis: Tools for automatic analysis and plotting of the pandas.DataFrame instances obtained
  
  with enb.atable.ATable.get_df() or its subclass implementations, e.g., in enb.experiment.Experiment.
  
  enb.aanalysis.Analyzer: subclasses implement different types of automatic analysis
  
  enb.aanalysis.ScalarNumericAnalyzer : for individual columns of numeric data
  
  enb.aanalysis.TwoNumericAnalyzer : for comparing pairs of columns with numeric data
  
  enb.aanalysis.DictNumericAnalyzer : for analyzing columns with cells containing dictionaries with numeric values
  
  enb.aanalysis.ScalarNumeric2DAnalyzer : for spatially analyzing numeric data whose x and y position is given in other columns
  
  enb.aanalysis.ScalarNumericJointAnalyzer : for splitting a data column into two categories, one giving multiple columns, the other givin multiple rows.
  
  class:enb.experiment.TaskFamily: class to group list of tasks under the same name.
  
  See Result analysis and plotting with enb for extra documentation on data analysis and plotting.
Utility modules
- enb.plotdata and enb.render: module with the implementation of the low-level plotting routines.
  
  enb.render.render_plds_by_group(): used to generate plots by all enb.aanalysis.Analyzer subclasses.
- enb.log: Logging utilities for enb.
- Parallelization
  
  enb.parallel: Implementation of the multiprocess parallelization functionality using pathos (default)
  
  enb.parallel_ray: Implementation of the multiprocess parallelization functionality using ray.
- enb.tarlite: module with tools that implement a lightweight tar-like format (a concatenation of files).
  
  enb.tarlite.tarlite_files()
  
  enb.tarlite.untarlite_files()
- enb.pgm, enb.jpg, enb.png: manipulation (load/save) of PGM, PPM, PNG, JPG images
  to and from 2D or 3D numpy arrays

Full API

Full, automatically generated API is provided next.

enb
- enb package