nanshe_learner: Multiprocessing from beginning to end. This cleverly slices the given image into blocks without having to read the image into memory or copy it. Uses subprocess.Popen to launch each learner in a separate process (required in order to ensure SPAMS remained sandboxed). Also, it creates a config file and HDF5 files for each block, which it places in a directory below the output file. This makes it easy to use a cluster scheduler to call each job. In addition, it aids in debugging in that one can inspect the results of each block. Also, it allows the user to run different configurations on a single block before applying them to all blocks. This fixes issue #9.
nanshe_learner: Allowed generate_neurons_blocks to extract and reappend the debug flag from parameters.
HDF5_recorder: Changed generic Exception to ValueError in __call__ methods of the classes EmptyArrayRecorder and HDF5ArrayRecorder.
HDF5_serializers: Renamed write_numpy_structured_array_to_HDF5 to create_numpy_structured_array_in_HDF5. Create more accurately reflects the operation that write does not.
HDF5_recorder: Renamed HDF5_serializers.write_numpy_structured_array_to_HDF5 to HDF5_serializers.create_numpy_structured_array_in_HDF5.
nanshe_learner: Renamed HDF5_serializers.write_numpy_structured_array_to_HDF5 to HDF5_serializers.create_numpy_structured_array_in_HDF5.
advanced_image_processing: Added casting to numpy.uint64 to resolve DeprecationWarning raised by NumPy.
additional_generators: Added doctests to reformat_slices and len_slices. This closes #57 and closes #58.
HDF5_serializers: Removed file handle portion as this does not work and sometimes closes the wrong file.
HDF5_serializers: Fixed so that it can now read single numbers from HDF5 Datasets.
expanded_numpy: Added doctest for tagging_reorder_array. This fixes #55.
debugging_tools: Specified stream to be sys.stderr.
generic_decorators: Created to provide common decorators and expand on functools.
generic_decorators: Added the functions update_wrapper, and wraps. These expand on the equivalent functions in functools by adding the __wrapped__ attribute to show what the previously unwrapped function looked like.
generic_decorators: Added the function static_variables, which modifies the given callable to have the static variables and values specified.
generic_decorators: Added the decorator metaclass, which applies a metaclass to a class given after it has already been defined. Stores a __wrapped__ attribute on the wrapped class to refer back to the previously unwrapped class.
generic_decorators: Added the decorator metaclasses, which applies the given metaclasses to a class.
generic_decorators: Added the decorator class_static_variables, which uses a metaclass to add static variables to a class provided.
generic_decorators: Added class_decorate_all_methods, which will decorate all class methods of a given class with the given decorators.
generic_decorators: Added class_decorate_methods, which decorates selected methods that are specifed. If the method does not exist, it fails silently.
debugging_tools: Changed to use tools from generic_decorators make code shorter and more flexible.
debugging_tools: Added the decorator log_class, which will decorate all methods of a class using log_call and the arguments provided.