Tutorial for sorting data stored as numpy to on-resonance R1rho analysis
Revision as of 22:49, 14 November 2015 by Troels Emtekær Linnet (talk | contribs) (→Data background)
Data background
This is data recorded at 600 and 950 MHz.
For each spectrometer frequency, the data is saved in np.arrays
- one for the residue number,
- one for the rates,
- one for the errorbars,
- one for the RF field strength.
They can be retrieved also with scipy's loadmat command.
The experiments are on-resonance R1rho, and the rates are already corrected for the (small) offset effect, using the experimentally determined R1.
Specifically, the numpy shapes of the data is:
- For 600 MHz
- residues (1, 60)
- rates (60, 10)
- errorbars_rate (60, 10)
- RFfields (1, 10)
- For 950 Mhz
- residues (1, 61)
- rates (61, 19)
- errorbars_rate (61, 19)
- RFfields (1, 19)
An example of the data at the 2 fields is:
Create data files for relax
File: mat_example.py