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)

An example of the data at the 2 fields is:

• 

  600 MHz

• 

  950 MHz

Create data files for relax

First prepare data, by running in python.

python 1_prepare_data.py

File: 1_prepare_data.py

import os
import scipy as sc
import scipy.io
import numpy as np

# Set path
cwd = os.getcwd()

fields = [600, 950]
file_names = ['residues', 'rates', 'errorbars_rate', 'RFfields']

# Store data in dictionary
all_data = {}
all_data['fields'] = fields
all_data['file_names'] = file_names

# Make list of residues and make unique
all_res = []

# Loop over the experiments, collect all data
for field in fields:
    print "\n", field

    # Make a dic inside
    all_data['%s'%field] = {}

    # Construct the path to the data
    path = cwd + os.sep + "Archive" + os.sep + "exp_%s"%field + os.sep + "matrices" + os.sep
    all_data['%s'%field]['path'] = path

    # Collect all filename paths
    field_file_name_paths = []
    for file_name in file_names:
        # Create path name
        file_name_path = path + "%s.mat"%file_name
        field_file_name_paths.append(file_name_path)

        # Load the data
        file_name_path_data = sc.io.loadmat(file_name_path)
        # Extract as numpy
        file_name_path_data_np = file_name_path_data[file_name]
        # And store
        all_data['%s'%field]['%s'%file_name] = file_name_path_data
        all_data['%s'%field]['np_%s'%file_name] = file_name_path_data_np

        print file_name, file_name_path_data_np.shape

        # Collect residues
        if file_name == "residues":
            all_res += list(file_name_path_data_np.flatten())

    # Store
    all_data['%s'%field]['field_file_name_paths'] = field_file_name_paths


# Make list of residues and make unique
all_res_uniq = sorted(list(set(all_res)))
all_data['all_res_uniq'] = all_res_uniq

# Write a sequence file for relax
f = open("residues.txt", "w")
f.write("# Residue_i\n")
for res in all_res_uniq:
    f.write("%s\n"%res)
f.close()

f_exp = open("exp_settings.txt", "w")
f_exp.write("# sfrq_MHz RFfield_kHz file_name\n")

# Then write the files for the rates
for field in all_data['fields']:
    resis = all_data['%s'%field]['np_residues'][0]
    rates = all_data['%s'%field]['np_rates']
    errorbars_rate = all_data['%s'%field]['np_errorbars_rate']
    RFfields = all_data['%s'%field]['np_RFfields'][0]

    print "\nfield: %3.3f"%field
    for i, RF_field_strength_kHz in enumerate(RFfields):
        #print "RF_field_strength_kHz: %3.3f"%RF_field_strength_kHz
        # Generate file name
        f_name = "sfrq_%i_MHz_RFfield_%1.3f_kHz.in"%(field, RF_field_strength_kHz)
        cur_file = open(f_name, "w")
        cur_file.write("# resi rate        rate_err\n")

        exp_string = "%11.7f %11.7f %s\n"%(field, RF_field_strength_kHz, f_name)
        print exp_string,
        f_exp.write(exp_string)

        for j, resi in enumerate(resis):
            rate = rates[j, i]
            error = errorbars_rate[j, i]
            string = "%4d %11.7f %11.7f\n"%(resi, rate, error)
            cur_file.write(string)

        cur_file.close()

f_exp.close()