6.3.1. Hdf5 module and classes

Summary of the classes in the h5module

backends.H5Backend([backend])	Attributes
saving.H5Saver(*args, **kwargs)	status_sig: Signal
browsing.H5Browser(parent[, h5file, ...])	UI used to explore h5 files, plot and export subdatas

6.3.1.1. Interacting with hdf5 files

The H5Backend is a wrapper around three hdf5 python packages: pytables, h5py and h5pyd. It allows seamless integration of any of these with PyMoDAQ features.

6.3.1.2. Saving Data

H5SaverBase and H5Saver classes are a help to save data in a hierachical hdf5 binary file through the H5Backend object and allowing integration in the PyMoDAQ Framework. Using the H5Saver object will make sure you can explore your datas with the H5Browser. The object can be used to: punctually save one set of data such as with the DAQ_Viewer (see Single Datas), save multiple acquisition such as with the DAQ_Scan (see From DAQ_Scan) or save on the fly with enlargeable arrays such as the Continuous Saving mode of the DAQ_Viewer.

The saving functionalities are divided in two objects: H5SaverBase and H5Saver. H5SaverBase contains everything needed for saving, while H5Saver, inheriting H5SaverBase, add Qt functionality such as emitted signals.

Punctual use (adapted from the DAQ_Viewer):

# Datas to be saved are contained within a dictionary on the form
"""
datas = OrderedDict(data1D=[OrderedDict(data=ndarray, xaxis=dict(data=..., units=..., labels=...),
                            OrderedDict(...),...])
"""

path = '/test.h5'
h5saver = H5Saver(save_type='detector')
h5saver.init_file(update_h5=True, custom_naming=False, addhoc_file_path=path)

settings_str = 'various information as a string. Best used to convert Parameter object as xml string, so the ' + \
               'H5Browser can render the settings as a Tree'

#creates an incremental detector group under the base raw_group adding detector metadata
det_group = h5saver.add_det_group(h5saver.raw_group, "Data", settings_str) #creates a Det00i group

try:
    self.channel_arrays = OrderedDict([])

    data_types = ['data1D'] #we don't record 0D data in this mode (only in continuous)
    if h5saver.settings.child(('save_2D')).value():
        data_types.append('data2D')

    for data_type in data_types:
        if datas[data_type] is not None:
            if data_type in datas.keys() and len(datas[data_type]) != 0:
                if not h5saver.is_node_in_group(det_group, data_type):

                    data_group = h5saver.add_data_group(det_group, data_type) #for a given detector, creates one
                    #group by dimensionality of data

                    for ind_channel, channel in enumerate(datas[data_type]):  # list of OrderedDict

                        #creates a channel 00i group to store each set of data together with its axis (if relevant)
                        channel_group = h5saver.add_CH_group(data_group, title=channel)

                        #store data in the created channel group
                        h5saver.add_data(channel_group, datas[data_type][channel], scan_type='', enlargeable=False)

6.3.1.3. Browsing Data

using the H5Backend it is possible to write scripts to easily access a hdf5 file content. However, PyMoDAQ includes a dedicated hdf5 viewer understanding dedicated metadata and therefore displaying nicely the content of the file, see H5Browser. Two objects can be used to browse data: H5BrowserUtil and H5Browser. H5BrowserUtil gives you methods to quickly (in a script) get info and data from your file while the H5Browser adds a UI to interact with the hdf5 file.