Difference between revisions of "Matplotlib DPL94 R1rho R2eff"

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 +
__TOC__
 +
 
== About ==
 
== About ==
 +
The production to these figures relates to the Suppport Request:<br>
 +
[https://gna.org/support/?3124 sr #3124: Grace graphs production for R1rho analysis with R2_eff as function of Omega_eff]
 +
 +
== References ==
 +
[http://www.nmr-relax.com/manual/Dispersion_model_summary.html Refer to the manual for parameter explanation]
 +
 +
* {{#lst:Citations|Evenäs01}}
 +
* {{#lst:Citations|KempfLoria04}}
 +
* {{#lst:Citations|Massi05}}
 +
* {{#lst:Citations|Palmer01}}
 +
* {{#lst:Citations|PalmerMassi06}}
 +
 +
=== Figures ===
 +
 +
Ref [1], Figure 1.b.
 +
'''The bell-curves''' As function of angle calculation.
 +
 +
Ref [1], Figure 1.c.
 +
The wanted graph. No clear "name" for the calculated parameter.
 +
 +
Ref [2], Equation 27.
 +
Here the calculated value is noted as: {{:Reff}} = {{:R1rho}} / sin<sup>2</sup>(θ) - {{:R1}} / tan<sup>2</sup>(θ) = {{:R2zero}} + {{:Rex}}, where {{:R2zero}} refers to {{:R1rhoprime}} as seen at [[DPL94]]
 +
 +
Ref [3], Equation 20.
 +
Here the calculated value is noted as: {{:R2}} = {{:R1rho}} / sin<sup>2</sup>(θ) - {{:R1}} / tan<sup>2</sup>(θ).  Figure 11+16, would be the reference.
 +
 +
Ref [4], Equation 43. {{:Reff}} = {{:R1rho}} / sin<sup>2</sup>(θ) - {{:R1}} / tan<sup>2</sup>(θ).
 +
 +
Ref [5], Material and Methods, page 740. Here the calculated value is noted as: {{:R2}}: {{:R2}} = {{:R2zero}} + {{:Rex}}.  Figure 4 would be the wished graphs.
 +
 +
 +
A little table of conversion then gives
 +
 +
<source lang="text">
 +
Relax equation    |  Relax store    | Articles
 +
---------------------------------------------------------------
 +
R1rho'                spin.r2            R^{0}_2 or Bar{R}_2
 +
Fitted pars          Not stored          R_ex
 +
R1rho                spin.r2eff          R1rho
 +
R_1                  spin.ri_data['R1']  R_1 or Bar{R}_1
 +
</source>
 +
 +
The parameter is called R_2 or R_eff in the articles.
 +
Since reff is not used in relax, this could be used?
 +
 +
A description could be:
 +
* The effective rate
 +
* The effective transverse relaxation rate constant
 +
* The effective relaxation rate constant.
 +
 +
== Make graphs ==
 +
 +
=== The outcome ===
 +
[[File:Matplotlib 52 N R1 rho theta sep.png|center|upright=2|Figure 1]]
 +
[[File:Matplotlib 52 N R1 rho R2eff w eff.png|center|upright=2|Figure 2]]
 +
[[File:Matplotlib 52 N R1 rho R2eff disp.png|center|upright=2|Figure 3]]
 +
 +
== To run ==
 +
<source lang="bash">
 +
relax -p r1rhor2eff.py
 +
</source>
  
−
== Code ==
+
=== Code ===
−
<source lang="python">
+
 
 +
{{Collapsible script
 +
| title  = r1rhor2eff.py Python script
 +
| lang   = python
 +
| script =
 
### python imports
 
### python imports
 
import sys
 
import sys
 
import os
 
import os
−
from math import cos, sin
+
from math import cos, sin, sqrt, pi
−
 
+
from numpy import array, float64
 +
 
### plotting facility.
 
### plotting facility.
 
import matplotlib.pyplot as plt
 
import matplotlib.pyplot as plt
−
 
+
 +
# Ordered dictionary
 +
import collections
 +
 
### relax modules
 
### relax modules
 
# Import some tools to loop over the spins.
 
# Import some tools to loop over the spins.
 
from pipe_control.mol_res_spin import return_spin, spin_loop
 
from pipe_control.mol_res_spin import return_spin, spin_loop
−
# Import method to calculate the R1rho offset data
+
# Import method to calculate the R1_rho offset data
−
from specific_analyses.relax_disp.disp_data import calc_rotating_frame_params, generate_r20_key, loop_exp_frq
+
from specific_analyses.relax_disp.disp_data import calc_rotating_frame_params, generate_r20_key, loop_exp_frq, loop_exp_frq_offset, loop_point, return_param_key_from_data, return_spin_lock_nu1
−
 
+
from specific_analyses.relax_disp import optimisation
 +
from lib.nmr import frequency_to_Hz, frequency_to_ppm, frequency_to_rad_per_s
 +
 
###############
 
###############
−
 
+
 
# You have to provide a DPL94 results state file
 
# You have to provide a DPL94 results state file
 
res_folder = "resultsR1"
 
res_folder = "resultsR1"
 +
#res_folder = "results_clustering"
 
res_state = os.path.join(res_folder, "DPL94", "results")
 
res_state = os.path.join(res_folder, "DPL94", "results")
−
spin_inte = ":52@N"
+
spin_inte = ":44@N"
 +
# Make a fake spin, from the spin of interest
 +
fake_spin_inte = spin_inte.replace("N","X")
 +
 +
# Interpolate graph settings
 +
#num_points=1000, extend=500.0
 +
num_points=100
 +
extend=5000.0
 +
 +
################
 
spin_inte_rep = spin_inte.replace('#', '_').replace(':', '_').replace('@', '_')
 
spin_inte_rep = spin_inte.replace('#', '_').replace(':', '_').replace('@', '_')
−
 
+
 
# Load the state
 
# Load the state
 
state.load(res_state, force=True)
 
state.load(res_state, force=True)
  
 +
# Get the dictionary key
 +
for exp_type, frq in loop_exp_frq():
 +
    r20_key = generate_r20_key(exp_type=exp_type, frq=frq)
 +
 
# Show pipes
 
# Show pipes
 
pipe.display()
 
pipe.display()
 
pipe.current()
 
pipe.current()
−
 
+
 
# Get the spin of interest and save it in cdp, to access it after execution of script.
 
# Get the spin of interest and save it in cdp, to access it after execution of script.
 
cdp.myspin = return_spin(spin_inte)
 
cdp.myspin = return_spin(spin_inte)
 +
 +
# Copy the parameters from spin of interest to a fake spin to be modified.
 +
spin.copy(spin_from=spin_inte, spin_to=fake_spin_inte)
 +
# Returnspin
 +
cdp.fakespin = return_spin(fake_spin_inte)
 +
 +
# Modify data
 +
if spin_inte == ":52@N":
 +
    # Set reference data
 +
    cdp.fakespin.r2[r20_key] = 6.51945
 +
    cdp.fakespin.kex = 13193.82986
 +
    cdp.fakespin.kex_err = 2307.09152
 +
    phi_ex_rad2_s2 = 93499.92172
 +
    phi_ex_err_rad2_s2 = 33233.23039
 +
    scaling_rad2_s2 = frequency_to_ppm(frq=1/(2*pi), B0=cdp.spectrometer_frq_list[0], isotope='15N')**2
 +
    print scaling_rad2_s2
 +
 +
    cdp.fakespin.phi_ex = phi_ex_rad2_s2*scaling_rad2_s2
 +
    cdp.fakespin.phi_ex_err = phi_ex_err_rad2_s2*scaling_rad2_s2
 +
 +
    print cdp.myspin.ri_data['R1'], cdp.myspin.ri_data_err['R1'], cdp.myspin.r2[r20_key], cdp.myspin.kex, cdp.myspin.phi_ex
 +
    print cdp.fakespin.ri_data['R1'], cdp.fakespin.ri_data_err['R1'], cdp.fakespin.r2[r20_key], cdp.fakespin.kex, cdp.fakespin.phi_ex
 +
  
 
# Calculate the offset data
 
# Calculate the offset data
−
theta_spin_dic, Domega_spin_dic, w_eff_spin_dic, dic_key_list = calc_rotating_frame_params(spin=cdp.myspin, spin_id=spin_inte, verbosity=1)
+
theta_spin_dic, Domega_spin_dic, w_eff_spin_dic, dic_key_list = calc_rotating_frame_params(spin=cdp.myspin, spin_id=spin_inte, verbosity=0)
 
# Save the data in cdp to access it after execution of script.
 
# Save the data in cdp to access it after execution of script.
 
cdp.myspin.theta_spin_dic = theta_spin_dic
 
cdp.myspin.theta_spin_dic = theta_spin_dic
 
cdp.myspin.w_eff_spin_dic = w_eff_spin_dic
 
cdp.myspin.w_eff_spin_dic = w_eff_spin_dic
 
cdp.myspin.dic_key_list = dic_key_list
 
cdp.myspin.dic_key_list = dic_key_list
 +
 +
############################
 +
# First creacte back calculated R2eff data for interpolated plots.
 +
############################
 +
 +
 +
# Return the original structure for frq, offset
 +
spin_lock_nu1 = return_spin_lock_nu1(ref_flag=False)
  
−
# Create spin.r2 keys
+
# Back calculate R2eff data for the set parameters.
−
r2keys = []
+
cdp.fakespin.back_calc = optimisation.back_calc_r2eff(spin=cdp.fakespin, spin_id=fake_spin_inte, spin_lock_nu1=spin_lock_nu1)
−
for exp_type, frq in loop_exp_frq():
 
−
    r2keys.append(generate_r20_key(exp_type=exp_type, frq=frq) )
 
−
# Save the keys
 
−
cdp.r2keys = r2keys
 
−
 
 
−
x_w_eff = []
 
−
x_theta = []
 
−
y = []
 
  
−
for dic_key in cdp.myspin.dic_key_list:
+
# Prepare list to hold new data
−
     R1_rho_prime = cdp.myspin.r2[cdp.r2keys[0]]
+
spin_lock_nu1_new = []
 +
# Loop over the structures to generate data
 +
for ei in range(len(spin_lock_nu1)):
 +
    # Add a new dimension.
 +
    spin_lock_nu1_new.append([])
 +
 +
    # Then loop over the spectrometer frequencies.
 +
    for mi in range(len(spin_lock_nu1[ei])):
 +
        # Add a new dimension.
 +
        spin_lock_nu1_new[ei].append([])
 +
 +
        # Finally the offsets.
 +
        for oi in range(len(spin_lock_nu1[ei][mi])):
 +
            # Add a new dimension.
 +
            spin_lock_nu1_new[ei][mi].append([])
 +
 +
            # No data.
 +
            if not len(spin_lock_nu1[ei][mi][oi]):
 +
                continue
 +
 +
            # Interpolate (adding the extended amount to the end).
 +
            for di in range(num_points):
 +
                point = (di + 1) * (max(spin_lock_nu1[ei][mi][oi])+extend) / num_points
 +
                spin_lock_nu1_new[ei][mi][oi].append(point)
 +
            # Intersert field 0
 +
            #spin_lock_nu1_new[ei][mi][oi][0] = 0.0
 +
 +
            # Convert to a numpy array.
 +
            spin_lock_nu1_new[ei][mi][oi] = array(spin_lock_nu1_new[ei][mi][oi], float64)
 +
 +
# Then back calculate R2eff data for the interpolated points.
 +
cdp.myspin.back_calc = optimisation.back_calc_r2eff(spin=cdp.myspin, spin_id=spin_inte, spin_lock_nu1=spin_lock_nu1_new)
 +
 +
# Calculate the offset data, interpolated
 +
theta_spin_dic_inter, Domega_spin_dic_inter, w_eff_spin_dic_inter, dic_key_list_inter = calc_rotating_frame_params(spin=cdp.myspin, spin_id=spin_inte, fields = spin_lock_nu1_new, verbosity=0)
 +
 +
###### Store the data before plotting
 +
# Create a dictionary to hold data
 +
cdp.mydic = collections.OrderedDict()
 +
 +
# Loop over the data structures and save to dictionary
 +
for exp_type, frq, offset, ei, mi, oi in loop_exp_frq_offset(return_indices=True):
 +
    # This is not used, but could be used to get Rex.
 +
     R1_rho_prime = cdp.myspin.r2[r20_key]
 +
    #print R1_rho_prime
 +
 +
    # Get R1
 
     R1 = cdp.myspin.ri_data['R1']
 
     R1 = cdp.myspin.ri_data['R1']
−
     R1_rho = cdp.myspin.r2eff[dic_key]
+
     R1_err = cdp.myspin.ri_data_err['R1']
 +
 +
    # Add to dic
 +
    if exp_type not in cdp.mydic:
 +
        cdp.mydic[exp_type] = collections.OrderedDict()
 +
    if frq not in cdp.mydic[exp_type]:
 +
        cdp.mydic[exp_type][frq] = collections.OrderedDict()
 +
    if offset not in cdp.mydic[exp_type][frq]:
 +
        cdp.mydic[exp_type][frq][offset] = collections.OrderedDict()
 +
        # X val
 +
        cdp.mydic[exp_type][frq][offset]['point'] = []
 +
        cdp.mydic[exp_type][frq][offset]['point_inter'] = []
 +
        cdp.mydic[exp_type][frq][offset]['theta'] = []
 +
        cdp.mydic[exp_type][frq][offset]['theta_inter'] = []
 +
        cdp.mydic[exp_type][frq][offset]['w_eff'] = []
 +
        cdp.mydic[exp_type][frq][offset]['w_eff_inter'] = []
 +
        # Y val
 +
        cdp.mydic[exp_type][frq][offset]['R1_rho'] = []
 +
        cdp.mydic[exp_type][frq][offset]['R1_rho_err'] = []
 +
        cdp.mydic[exp_type][frq][offset]['R1_rho_bc'] = []
 +
        cdp.mydic[exp_type][frq][offset]['R1_rho_inter'] = []
  
−
    # Get w_eff
+
        # Y val fake
−
    w_eff = cdp.myspin.w_eff_spin_dic[dic_key]
+
        cdp.mydic[exp_type][frq][offset]['fake_R1_rho'] = []
−
    x_w_eff.append(w_eff)
 
  
−
     # Get theta
+
        # Y2 val
−
    theta = cdp.myspin.theta_spin_dic[dic_key]
+
        cdp.mydic[exp_type][frq][offset]['R1_rho_R2eff'] = []
−
    x_theta.append(theta)
+
        cdp.mydic[exp_type][frq][offset]['R1_rho_R2eff_err'] = []
 +
        cdp.mydic[exp_type][frq][offset]['R1_rho_R2eff_bc'] = []
 +
        cdp.mydic[exp_type][frq][offset]['R1_rho_R2eff_inter'] = []
 +
 +
     # Loop over the original dispersion points.
 +
    for point, di in loop_point(exp_type=exp_type, frq=frq, offset=offset, return_indices=True):
 +
        param_key = return_param_key_from_data(exp_type=exp_type, frq=frq, offset=offset, point=point)
 +
 +
        # X val
 +
        cdp.mydic[exp_type][frq][offset]['point'].append(point)
 +
        theta = theta_spin_dic[param_key]
 +
        cdp.mydic[exp_type][frq][offset]['theta'].append(theta)
 +
        w_eff = w_eff_spin_dic[param_key]
 +
        cdp.mydic[exp_type][frq][offset]['w_eff'].append(w_eff)
 +
 +
        # Average resonance spin_lock_offset
 +
        #print Domega_spin_dic[param_key]
 +
 +
        # Y val
 +
        R1_rho = cdp.myspin.r2eff[param_key]
 +
        cdp.mydic[exp_type][frq][offset]['R1_rho'].append(R1_rho)
 +
        R1_rho_err = cdp.myspin.r2eff_err[param_key]
 +
        cdp.mydic[exp_type][frq][offset]['R1_rho_err'].append(R1_rho_err)
 +
        R1_rho_bc = cdp.myspin.r2eff_bc[param_key]
 +
        cdp.mydic[exp_type][frq][offset]['R1_rho_bc'].append(R1_rho_bc)
  
−
    # Calculate y value
+
        # Y val, fake
−
    R1rho_R2eff = (R1_rho - R1*cos(theta)*cos(theta)) / (sin(theta) * sin(theta))
+
        fake_R1_rho = cdp.fakespin.back_calc[ei][0][mi][oi][di]
−
    y.append(R1rho_R2eff)
+
        cdp.mydic[exp_type][frq][offset]['fake_R1_rho'].append(fake_R1_rho)
  
−
# Modify data
+
        # Y2 val
−
w_eff_div = 10**4
+
        # Calc R1_rho_R2eff
−
rem_points = 2
+
        R1_rho_R2eff = (R1_rho - R1*cos(theta)*cos(theta)) / (sin(theta) * sin(theta))
−
x_w_eff_mod = [x/w_eff_div for x in x_w_eff[:-rem_points]]
+
        cdp.mydic[exp_type][frq][offset]['R1_rho_R2eff'].append(R1_rho_R2eff)
−
y_mod = y[:-rem_points]
+
−
 
+
        R1_rho_R2eff_err = (R1_rho_err - R1_err*cos(theta)*cos(theta)) / (sin(theta) * sin(theta))
−
 
+
        cdp.mydic[exp_type][frq][offset]['R1_rho_R2eff_err'].append(R1_rho_R2eff_err)
−
# Plot R1rho_R2eff as function of w_eff
+
−
plt.figure()
+
        R1_rho_R2eff_bc = (R1_rho_bc - R1*cos(theta)*cos(theta)) / (sin(theta) * sin(theta))
−
plotlabel = 'R1rho_R2eff'
+
        cdp.mydic[exp_type][frq][offset]['R1_rho_R2eff_bc'].append(R1_rho_R2eff_bc)
−
plt.plot(x_w_eff_mod, y_mod, 'o', label='R1rho_R2eff')
+
−
xlabel = 'Effective field in rotating frame [%s rad.s^-1]'%(str(w_eff_div))
+
    ## Loop over the new dispersion points.
−
plt.xlabel(xlabel)
+
    for di in range(len(cdp.myspin.back_calc[ei][0][mi][oi])):
−
ylabel = 'R1rho_R2eff [rad.s^-1]'
+
        point = spin_lock_nu1_new[ei][mi][oi][di]
−
plt.ylabel(ylabel)
+
        param_key = return_param_key_from_data(exp_type=exp_type, frq=frq, offset=offset, point=point)
−
plt.legend(loc='best')
+
 +
        # X val
 +
        cdp.mydic[exp_type][frq][offset]['point_inter'].append(point)
 +
        theta = theta_spin_dic_inter[param_key]
 +
        cdp.mydic[exp_type][frq][offset]['theta_inter'].append(theta)
 +
        w_eff = w_eff_spin_dic_inter[param_key]
 +
        cdp.mydic[exp_type][frq][offset]['w_eff_inter'].append(w_eff)
 +
 +
        # Y val
 +
        R1_rho = cdp.myspin.back_calc[ei][0][mi][oi][di]
 +
        cdp.mydic[exp_type][frq][offset]['R1_rho_inter'].append(R1_rho)
 +
 +
        # Y2 val
 +
        # Calc R1_rho_R2eff
 +
        R1_rho_R2eff = (R1_rho - R1*cos(theta)*cos(theta)) / (sin(theta) * sin(theta))
 +
        cdp.mydic[exp_type][frq][offset]['R1_rho_R2eff_inter'].append(R1_rho_R2eff)
 +
 +
        #if oi == 0:
 +
            #print exp_type, frq, offset, point, theta, w_eff
 +
 +
####### PLOT ####
 +
 +
## Define labels for plotting
 +
filesave_R1_rho_R2eff = 'R1_rho_R2eff'
 +
filesave_R1_rho = 'R1_rho'
 +
 +
# For writing math in matplotlib, see
 +
# http://matplotlib.org/1.3.1/users/mathtext.html
 +
 +
ylabel_R1_rho = r'R$_{1\rho}$ [rad s$^{-1}$]'
 +
ylabel_R1_rho_R2eff = r'R$_{1\rho}$, R$_{2,eff}$ [rad s$^{-1}$]'
 +
 +
xlabel_theta = 'Rotating frame tilt angle [rad]'
 +
xlabel_w_eff = r'Effective field in rotating frame [rad s$^{-1}$]'
 +
xlabel_lock = 'Spin-lock field strength [Hz]'
 +
 +
# Set image inches size
 +
img_inch_x = 12
 +
img_inch_y = img_inch_x / 1.6
 +
legend_size = 6
 +
 +
 +
# Plot values in dic
 +
for exptype, frq_dic in cdp.mydic.items():
 +
    for frq, offset_dic in frq_dic.items():
 +
        for offset, val_dics in offset_dic.items():
 +
            # General plot label
 +
            graphlabel = "%3.1f_%3.3f_meas"%(frq/1E6, offset)
 +
            graphlabel_bc = "%3.1f_%3.3f_bc"%(frq/1E6, offset)
 +
            graphlabel_inter = "%3.1f_%3.3f_inter"%(frq/1E6, offset)
 +
            graphlabel_fake = "%3.1f_%3.3f_fake"%(frq/1E6, offset)
 +
 +
            # Plot 1: R1_rho as function of theta.
 +
            plt.figure(1)
 +
            line, = plt.plot(val_dics['theta_inter'], val_dics['R1_rho_inter'], '-', label=graphlabel_inter)
 +
            plt.errorbar(val_dics['theta'], val_dics['R1_rho'], yerr=val_dics['R1_rho_err'], fmt='o', label=graphlabel, color=line.get_color())
 +
            plt.plot(val_dics['theta'], val_dics['R1_rho_bc'], 'D', label=graphlabel_bc, color=line.get_color())
 +
 +
            # Plot 2: R1_rho_R2eff as function of w_eff
 +
            plt.figure(2)
 +
            w_eff2_inter = [x*x for x in val_dics['w_eff_inter']]
 +
            w_eff2 = [x*x for x in val_dics['w_eff']]
 +
            #line, = plt.plot(w_eff2_inter, val_dics['R1_rho_R2eff_inter'], '-', label=graphlabel_inter)
 +
            #plt.errorbar(w_eff2, val_dics['R1_rho_R2eff'], yerr=val_dics['R1_rho_R2eff_err'], fmt='o', label=graphlabel, color=line.get_color())
 +
            #plt.plot(w_eff2, val_dics['R1_rho_R2eff_bc'], 'D', label=graphlabel_bc, color=line.get_color())
 +
            line, = plt.plot(val_dics['w_eff_inter'], val_dics['R1_rho_R2eff_inter'], '-', label=graphlabel_inter)
 +
            plt.errorbar(val_dics['w_eff'], val_dics['R1_rho_R2eff'], yerr=val_dics['R1_rho_R2eff_err'], fmt='o', label=graphlabel, color=line.get_color())
 +
            plt.plot(val_dics['w_eff'], val_dics['R1_rho_R2eff_bc'], 'D', label=graphlabel_bc, color=line.get_color())
 +
 +
            # Plot 3: R1_rho as function of as function of disp_point, the Spin-lock field strength
 +
            plt.figure(3)
 +
            line, = plt.plot(val_dics['point_inter'], val_dics['R1_rho_inter'], '-', label=graphlabel_inter)
 +
            plt.errorbar(val_dics['point'], val_dics['R1_rho'], yerr=val_dics['R1_rho_err'], fmt='o', label=graphlabel, color=line.get_color())
 +
            plt.plot(val_dics['point'], val_dics['R1_rho_bc'], 'D', label=graphlabel_bc, color=line.get_color())
 +
            plt.plot(val_dics['point'], val_dics['fake_R1_rho'], '*', label=graphlabel_fake, color=line.get_color())
 +
 +
 +
# Define settings for each graph
 +
# Plot 1: R1_rho as function of theta.
 +
fig1 = plt.figure(1)
 +
plt.xlabel(xlabel_theta)
 +
plt.ylabel(ylabel_R1_rho)
 +
plt.legend(loc='best', prop={'size':legend_size})
 +
plt.grid(True)
 +
#plt.ylim([0,16])
 +
plt.title("%s \n %s as function of %s"%(spin_inte, ylabel_R1_rho, xlabel_theta))
 +
fig1.set_size_inches(img_inch_x, img_inch_y)
 +
plt.savefig("matplotlib_%s_%s_theta_sep.png"%(spin_inte_rep, filesave_R1_rho) )
 +
 +
## Plot 2: R1_rho_R2eff as function of w_eff
 +
fig2 = plt.figure(2)
 +
plt.xlabel(xlabel_w_eff)
 +
plt.ylabel(ylabel_R1_rho_R2eff)
 +
plt.legend(loc='best', prop={'size':legend_size})
 +
plt.grid(True)
 +
#plt.ylim([0,16])
 +
#plt.xlim([0,20000*20000])
 +
plt.xlim([0,20000])
 +
plt.title("%s \n %s as function of %s"%(spin_inte, ylabel_R1_rho_R2eff, xlabel_w_eff))
 +
fig2.set_size_inches(img_inch_x, img_inch_y)
 +
plt.savefig("matplotlib_%s_%s_w_eff.png"%(spin_inte_rep, filesave_R1_rho_R2eff) )
 +
 +
## Plot 3: R1_rho as function of as function of disp_point, the Spin-lock field strength
 +
fig3 = plt.figure(3)
 +
plt.xlabel(xlabel_lock)
 +
plt.ylabel(ylabel_R1_rho)
 +
plt.legend(loc='best', prop={'size':legend_size})
 
plt.grid(True)
 
plt.grid(True)
−
plt.ylim([0,16])
+
#plt.ylim([0,16])
−
plt.title("%s \n %s as function of %s"%(spin_inte,ylabel, xlabel))
+
plt.title("%s \n %s as function of %s"%(spin_inte, ylabel_R1_rho, xlabel_lock))
−
plt.savefig("matplotlib_%s_%s_w_eff.png"%(spin_inte_rep, plotlabel) )
+
fig3.set_size_inches(img_inch_x, img_inch_y)
 +
plt.savefig("matplotlib_%s_%s_disp.png"%(spin_inte_rep, filesave_R1_rho_R2eff) )
  
−
# Plot R1rho_R2eff as function of w_eff
 
−
plt.figure()
 
−
plotlabel = 'R1rho_R2eff'
 
−
plt.plot(x_theta, y, 'o', label='R1rho_R2eff')
 
−
xlabel = 'Rotating frame tilt angle [rad]'
 
−
plt.xlabel(xlabel)
 
−
ylabel = 'R1rho_R2eff [rad.s^-1]'
 
−
plt.ylabel(ylabel)
 
−
plt.legend(loc='best')
 
−
plt.grid(True)
 
−
plt.ylim([0,16])
 
−
plt.title("%s \n %s as function of %s"%(spin_inte,ylabel, xlabel))
 
−
plt.savefig("matplotlib_%s_%s_theta.png"%(spin_inte_rep, plotlabel) )
 
 
plt.show()
 
plt.show()
−
</source>
+
}}
  
−
== To run ==
+
== Bugs ? ==
−
<source lang="bash">
+
Do you get an error with matplotlib about dateutil? Then see [[Matplotlib_dateutil_bug]]
−
</source>
 
  
 
== See also ==
 
== See also ==
 
[[Category:Matplotlib]]
 
[[Category:Matplotlib]]
 +
[[Category:Relaxation_dispersion analysis]]

Latest revision as of 15:53, 6 November 2015

About

The production to these figures relates to the Suppport Request:
sr #3124: Grace graphs production for R1rho analysis with R2_eff as function of Omega_eff

References

Refer to the manual for parameter explanation

  • Evenäs, J., Malmendal, A. and Akke, M. (2001). Dynamics of the transition between open and closed conformations in a calmodulin C-terminal domain mutant. Structure, 9(3), 185-195. (DOI: 10.1016/S0969-2126(01)00575-5)
  • Kempf, J. G. and Loria, J. P. (2004). Measurement of intermediate exchange phenomena. Methods Mol. Biol., 278, 185-231. (DOI: 10.1385/1-59259-809-9:185)
  • Massi, F., Grey, M. J., Palmer, 3rd, A. G. (2005). Microsecond timescale backbone conformational dynamics in ubiquitin studied with NMR R1ρ relaxation experiments Protein science, 14(3), 735-742. (DOI: 10.1110/ps.041139505)
  • Palmer, 3rd, A. G., Kroenke, C. D., and Loria, J. P. (2001). Nuclear magnetic resonance methods for quantifying microsecond-to-millisecond motions in biological macromolecules. Methods Enzymol., 339, 204-238. (DOI: 10.1016/S0076-6879(01)39315-1)
  • Palmer, 3rd, A. G. and Massi, F. (2006). Characterization of the dynamics of biomacromolecules using rotating-frame spin relaxation NMR spectroscopy. Chem. Rev., 106(5), 1700-1719. (DOI: 10.1021/cr0404287)

Figures

Ref [1], Figure 1.b. The bell-curves As function of angle calculation.

Ref [1], Figure 1.c. The wanted graph. No clear "name" for the calculated parameter.

Ref [2], Equation 27. Here the calculated value is noted as: Reff = R1ρ / sin2(θ) - R1 / tan2(θ) = R20 + Rex, where R20 refers to R1ρ' as seen at DPL94

Ref [3], Equation 20. Here the calculated value is noted as: R2 = R1ρ / sin2(θ) - R1 / tan2(θ). Figure 11+16, would be the reference.

Ref [4], Equation 43. Reff = R1ρ / sin2(θ) - R1 / tan2(θ).

Ref [5], Material and Methods, page 740. Here the calculated value is noted as: R2: R2 = R20 + Rex. Figure 4 would be the wished graphs.


A little table of conversion then gives 

Relax equation    |   Relax store    | Articles
---------------------------------------------------------------
R1rho'                spin.r2             R^{0}_2 or Bar{R}_2
Fitted pars           Not stored          R_ex
R1rho                 spin.r2eff          R1rho
R_1                   spin.ri_data['R1']  R_1 or Bar{R}_1

The parameter is called R_2 or R_eff in the articles. Since reff is not used in relax, this could be used?

A description could be:

  • The effective rate
  • The effective transverse relaxation rate constant
  • The effective relaxation rate constant.

Make graphs

The outcome

Figure 1
Figure 2
Figure 3

To run

relax -p r1rhor2eff.py

Code

Code: r1rhor2eff.py Python script

Bugs ?

Do you get an error with matplotlib about dateutil? Then see Matplotlib_dateutil_bug

See also

Retrieved from "http://wiki.nmr-relax.com/wiki/index.php?title=Matplotlib_DPL94_R1rho_R2eff&oldid=6053"