# Development Information

The source code can be found on Github at https://github.com/frejanordsiek/hdf5storage

## Package Overview

The package is currently a pure Python package; using no Cython, C/C++, or other languages.

Also, pickling is not used at all and should not be added. It is a security risk since pickled data is read through the interpreter allowing arbitrary code (which could be malicious) to be executed in the interpreter. One wants to be able to read possibly HDF5 and MAT files from untrusted sources, so pickling is avoided in this package.

The `hdf5storage` module contains the high level reading and writing functions, as well as the `Options` class for encapsulating all the various options governing how data is read and written. The high level reading and writing functions can either be given an `Options` object, or be given the keyword arguments that its constructur takes (they will make one from those arguments). There is also the `MarshallerCollection` which holds all the Marshallers (more below) and provides functions to find the appropriate Marshaller given the `type` of a Python object, the type string used for the ‘Python.Type’ Attribute, or the MATLAB class string (contained in the ‘MATLAB_class’ Attribute). One can give the collection additional user provided Marshallers.

`hdf5storage.lowlevel` contains the low level reading and writing functions `lowlevel.read_data()` and `lowlevel.write_data()`. They can only work on already opened HDF5 files (the high level ones handle file creation/opening), can only be given options using a `Options` object, and read/write individual Groups/Datasets and Python objects. Any Marshaller (more below) that needs to read or write a nested object within a Group or Python object must call these functions.

`hdf5storage.Marshallers` contains all the Marshallers for the different Python data types that can be read from or written to an HDF5 file. They are all automitically added to any `MarshallerCollection` which inspects this module and grabs all classes within it (if a class other than a Marshaller is added to this module, `MarshallerCollection` will need to be modified). All Marshallers need to provide the same interface as `Marshallers.TypeMarshaller`, which is the base class for all Marshallers in this module, and should probably be inherited from by any custom Marshallers that one would write (while it can’t marshall any types, it does have some useful built in functionality). The main Marshaller in the module is `Marshallers.NumpyScalarArrayMarshaller`, which can marshall most Numpy types. All the other built in Marshallers other than `Marshallers.PythonDictMarshaller` inherit from it since they convert their types to and from Numpy types and use the inherited functions to do the actual work with the HDF5 file.

`hdf5storage.utilities` contains many functions that are used throughout the pacakge, especially by the Marshallers. There are several functions to get, set, and delete different kinds of HDF5 Attributes (handle things such as them already existing, not existing, etc). Then there functions to convert between different string representations, as well as encode for writing and decode after reading complex types. And then there is the function `utilities.next_unused_name_in_group()` which produces a random unused name in a Group.

## TODO

There are several features that need to be added, bugs that need to be fixed, etc.

### Standing Bugs

- Structured `np.ndarray` with no elements, when `Options.structured_numpy_ndarray_as_struct` is set, are not written in a way that the dtypes for the fields can be restored when it is read back from file.

- The Attribute ‘MATLAB_fields’ is supported for h5py version `2.3` and newer. But for older versions, it is not currently set when writing data that should be imported into MATLAB as structures, and is ignored when reading data from file. This is because the h5py package cannot work with its format in older versions. If a structure with fields ‘a’ and ‘cd’ are saved, the Attribute looks like the following when using the `h5dump` utility:

      ATTRIBUTE "MATLAB_fields" {
         DATATYPE  H5T_VLEN { H5T_STRING {
            STRSIZE 1;
            STRPAD H5T_STR_NULLTERM;
            CSET H5T_CSET_ASCII;
            CTYPE H5T_C_S1;
         }}
         DATASPACE  SIMPLE { ( 1 ) / ( 1 ) }
         DATA {
         (0): ("a"), ("c", "d")
         }
      }

  In h5py version `2.3`, the Attribute is an array of variable length arrays of single character ASCII numpy strings (vlen of `'S1'`). It is created like so:

      fields = ['a', 'cd']
      dt = h5py.special_dtype(vlen=np.dtype('S1'))
      fs = np.empty(shape=(len(fields),), dtype=dt)
      for i, s in enumerate(fields):
          fs[i] = np.array([c.encode('ascii') for c in s],
                           dtype='S1')

  Then `fs` looks like:

      array([array([b'a'], dtype='|S1'),
             array([b'c', b'd'], dtype='|S1']), dtype=object)

  MATLAB doesn’t strictly require this field, but supporting it will help with reading/writing empty MATLAB structs and not losing the fields. Adding support for older verions of h5py would probably require writing a custom Cython or C function, or porting some h5py code.

### Features to Add

- Marshallers for more Python types.
- Marshallers to be able to read the following MATLAB types
  - Categorical Arrays
  - Tables
  - Maps
  - Time Series
  - Classes (could be hard if they don’t look like a struct in file)
  - Function Handles (wouldn’t be able run in Python, but could at least manipulate)
- A `whosmat` function like the SciPy one `scipy.io.whosmat()`.
- A function to find and delete Datasets and Groups inside the Group `Options.group_for_references` that are not referenced by other Datasets in the file.