Tutorial for sorting data stored as numpy to on-resonance R1rho analysis

Data background

This is data recorded at 600 and 950 MHz.

For each spectrometer frequency, the data is saved in np.arrays

1. one for the residue number,
2. one for the rates,
3. one for the errorbars,
4. 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:

1. For 600 MHz
   1. residues (1, 60)
   2. rates (60, 10)
   3. errorbars_rate (60, 10)
   4. RFfields (1, 10)

1. For 950 Mhz
   1. residues (1, 61)
   2. rates (61, 19)
   3. errorbars_rate (61, 19)
   4. RFfields (1, 19)

Try to reproduce Figure 1.

950 MHz

600 MHz

• 

  600 MHz

• 

  950 MHz

Create data files for relax

File: mat_example.py