Measurement.h5 file

To enable clean archiving of the processing and analysis of raw data, a measurement_{ms_id}.h5 file is created for each measurement. The file has the following structure:

measurement_{ms_id}.h5
├── raw_data
│   ├── data
│   ├── data_imag
│   ├── data_real
│   └── xaxis
├── corrected_data
│   └── <can be added>
└── evaluations
    └── <can be added>

When a new measurement is created using the data_management-package, the file is automatically created and the raw data are added to the raw_data-group. When you work with the data and correct them or do analysis, like fitting, it is advised to add the results to the groups of the measurement.h5-file, so that they are systematically archived.

Hint

As the file is a HDF5-file, it can be inspected by any reader for HDF5-files e.g. the online tool myHDF5. The data can be loaded to python by the h5py package and to matlab using the HDF5 Functions.

For easy interactions with the HDF5-file in python specatalog provides the HDF5Object. In the following, the usage of this class is explained.

HDF5Object

open

It is possible to load the data to an HDF5Object by using the function load_from_id. All datasets are attributes to the data-object and can be easilly called as shown in the example below. Set the optional argument mode to "a" if you want to change the contens of the file and to "r" if you only want to read the file:

from specatalog.data_management.hdf5_reader import load_from_id
import matplotlib.pyplot as plt

dat, file = load_from_id(1, mode="a")
x = dat.raw_data.xaxis
intensity = dat.raw_data.data

plt.plot(x, np.real(intensity))

update

If you have new data and want to add them or want to update existing attributes you can use the set_attr method for attributes (= numbers / strings) and the set_dataset method for datasets (= arrays):

offset = 2.3
corrected = x - offset
fit = -2*(x-12000)**2 + 2500

dat.corrected_data.set_attr("x-offset", 2.3)
dat.corrected_data.set_dataset("xaxis", corrected)
dat.evaluations.set_dataset("fit1", fit)

delete

If you want to delete attributes you can use the delete_attr method, for datasets you can use the delete_dataset:

dat.raw_data.delete_dataset("data_imag")

sync and close

All changes are written to the file using the sync method. Call this method ath the end of your script and close the file:

dat.sync()
file.close()