Changes

 A little table of conversion then gives <source lang="text">Relax equation | Relax store | Articles---------------------------------------------------------------R1rho' spin.r2 R^{0}_2 or Bar{R}_2Fitted pars Not stored R_exR1rho spin.r2eff R1rhoR_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 == Code  === 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 === {{Collapsible script| title = r1rhor2eff.py Python script| lang ="python">| script =

from math import cos, sin, sqrt, pifrom numpy import array, float64

# Import method to calculate the R1rho R1_rho offset datafrom 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_nu1from specific_analyses.relax_disp import optimisationfrom lib.nmr import frequency_to_Hz, frequency_to_ppm, frequency_to_rad_per_s

spin_inte = ":5244@N"# Make a fake spin, from the spin of interestfake_spin_inte = spin_inte.replace("N","X") # Interpolate graph settings#num_points=1000, extend=500.0num_points=100extend=5000.0 ################

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=10)

# Create spinBack calculate R2eff data for the set parameters.r2 keysr2keys cdp.fakespin.back_calc = []for exp_type, frq in loop_exp_frqoptimisation.back_calc_r2eff(): r2keysspin=cdp.append(generate_r20_key(exp_typefakespin, spin_id=exp_typefake_spin_inte, frqspin_lock_nu1=frq) spin_lock_nu1)# Save the keyscdp.r2keys = r2keys

x_w_eff # Prepare list to hold new dataspin_lock_nu1_new = []x_theta # Loop over the structures to generate datafor 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_pointsx_disp_point 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]y = array(spin_lock_nu1_new[ei][mi][oi], float64) # Then back calculate R2eff data for dic_key in 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, interpolatedtheta_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 datacdp.mydic = collections.dic_key_listOrderedDict() # Loop over the data structures and save to dictionaryfor 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[cdp.r2keys[0]r20_key] #print R1_rho_prime # Get R1

R1_rho R1_err = cdp.myspin.r2effri_data_err[dic_key'R1'] # Add to dic printif exp_type not in cdp.mydic: cdp.mydic[exp_type] = collections.OrderedDict(dic_key) if frq not in cdp.mydic[exp_type]: cdp.mydic[exp_type][frq] = collections.splitOrderedDict("_") 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 disp_point, the Spin-lock field strengthY val fake x_disp_point cdp.append(float(dic_key.split("_")mydic[exp_type][frq][offset]['fake_R1_rho'] = [-1]))

# Y2 val cdp.mydic[exp_type][frq][offset]['R1_rho_R2eff'] = [] 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'] = [] # Get 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.w_eff_spin_dicr2eff_err[param_key] cdp.mydic[exp_type][frq][offset]['R1_rho_err'].append(R1_rho_err) R1_rho_bc = cdp.myspin.r2eff_bc[dic_keyparam_key] x_w_eff cdp.mydic[exp_type][frq][offset]['R1_rho_bc'].append(w_effR1_rho_bc)

# Get thetaY val, fake theta fake_R1_rho = cdp.myspinfakespin.theta_spin_dicback_calc[dic_keyei][0][mi][oi][di] x_theta cdp.mydic[exp_type][frq][offset]['fake_R1_rho'].append(thetafake_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'].append(R1_rho_R2eff) 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) R1_rho_R2eff_bc = (R1_rho_bc - R1*cos(theta)*cos(theta)) / (sin(theta) * sin(theta)) cdp.mydic[exp_type][frq][offset]['R1_rho_R2eff_bc'].append(R1_rho_R2eff_bc) # Calculate y value# Loop over the new dispersion points. R1rho_R2eff for di in range(len(cdp.myspin.back_calc[ei][0][mi][oi])): point = spin_lock_nu1_new[ei][mi][oi][di] 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_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)) y cdp.mydic[exp_type][frq][offset]['R1_rho_R2eff_inter'].append(R1rho_R2effR1_rho_R2eff) #if oi == 0: #print x_disp_pointexp_type, frq, offset, point, theta, w_eff ####### PLOT #### ## Define labels for plottingfilesave_R1_rho_R2eff = 'R1_rho_R2eff'filesave_R1_rho = 'R1_rho' # Modify dataFor writing math in matplotlib, see# http://matplotlib.org/1.3.1/users/mathtext.html w_eff_div ylabel_R1_rho = 10**4r'R$_{1\rho}$ [rad s$^{-1}$]'rem_points ylabel_R1_rho_R2eff = r'R$_{1\rho}$, R$_{2,eff}$ [rad s$^{-1}$]' x_w_eff_mod xlabel_theta = 'Rotating frame tilt angle [xrad]'xlabel_w_eff = r'Effective field in rotating frame [rad s$^{-1}$]'xlabel_lock = 'Spin-lock field strength [Hz]' # Set image inches sizeimg_inch_x = 12img_inch_y = img_inch_x /w_eff_div 1.6legend_size = 6 # Plot values in dicfor exptype, frq_dic in cdp.mydic.items(): for frq, offset_dic in frq_dic.items(): for x offset, val_dics in x_w_effoffset_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'], '-rem_points', label=graphlabel_inter) plt.errorbar(val_dics['theta'], val_dics['R1_rho']y_mod , yerr= yval_dics[:-rem_points'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 R1rho_R2eff 2: R1_rho_R2eff as function of w_eff plt.figure(2)plotlabel w_eff2_inter = [x*x for x in val_dics['R1rho_R2effw_eff_inter']] w_eff2 = [x*x for x in val_dics['w_eff']] #line, = plt.plot(x_w_eff_modw_eff2_inter, y_modval_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'], 'R1rho_R2effD', label=graphlabel_bc, color=line.get_color())xlabel line, = plt.plot(val_dics['Effective field in rotating frame w_eff_inter'], val_dics[%s rad'R1_rho_R2eff_inter'], '-', label=graphlabel_inter) plt.s^-1errorbar(val_dics['w_eff'], val_dics['R1_rho_R2eff'], yerr=val_dics['R1_rho_R2eff_err'], fmt='o'%, label=graphlabel, color=line.get_color(str)) plt.plot(val_dics['w_eff'], val_dics['R1_rho_R2eff_bc'], 'D', label=graphlabel_bc, color=line.get_color(w_eff_div)) # Plot 3: R1_rho as function of as function of disp_point, the Spin-lock field strength plt.figure(3) line, = plt.xlabelplot(xlabelval_dics['point_inter'], val_dics['R1_rho_inter'], '-', label=graphlabel_inter)ylabel plt.errorbar(val_dics['point'], val_dics['R1_rho'], yerr=val_dics['R1_rho_err'], fmt= 'R1rho_R2eff o', label=graphlabel, color=line.get_color()) plt.plot(val_dics['point'], val_dics[rad'R1_rho_bc'], 'D', label=graphlabel_bc, color=line.get_color()) plt.s^-1plot(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(ylabelylabel_R1_rho)plt.legend(loc='best', prop={'size':legend_size})

#plt.ylim([0,16])plt.title("%s \n %s as function of %s"%(spin_inte,ylabelylabel_R1_rho, xlabelxlabel_theta))#fig1.set_size_inches(img_inch_x, img_inch_y)plt.savefig("matplotlib_%s_%s_w_effs_theta_sep.png"%(spin_inte_rep, plotlabelfilesave_R1_rho) ) ## Plot R1rho_R2eff 2: R1_rho_R2eff as function of w_efffig2 = plt.figure(2)plotlabel = 'R1rho_R2eff'plt.plot(x_theta, y, 'o', label='R1rho_R2eff')xlabel = 'Rotating frame tilt angle [rad]'plt.xlabel(xlabelxlabel_w_eff)ylabel = 'R1rho_R2eff [rad.s^-1]'plt.ylabel(ylabelylabel_R1_rho_R2eff)plt.legend(loc='best', prop={'size':legend_size})

#plt.ylim([0,16])#plt.xlim([0,20000*20000])plt.xlim([0,20000])plt.title("%s \n %s as function of %s"%(spin_inte,ylabelylabel_R1_rho_R2eff, xlabelxlabel_w_eff))fig2.set_size_inches(img_inch_x, img_inch_y)#plt.savefig("matplotlib_%s_%s_thetas_w_eff.png"%(spin_inte_rep, plotlabelfilesave_R1_rho_R2eff) ) ## Plot R1rho_R2eff 3: R1_rho as function of as function of disp_point, the Spin-lock field strengthfig3 = plt.figure(3)plotlabel = 'R1rho_R2eff'plt.plot(x_disp_point, y, 'o', label='R1rho_R2eff')xlabel = 'Spin-lock field strength [Hz]'plt.xlabel(xlabelxlabel_lock)ylabel = 'R1rho_R2eff [rad.s^-1]'plt.ylabel(ylabelylabel_R1_rho)plt.legend(loc='best', prop={'size':legend_size})

plt.title("%s \n %s as function of %s"%(spin_inte,ylabelylabel_R1_rho, xlabelxlabel_lock))fig3.set_size_inches(img_inch_x, img_inch_y)plt.savefig("matplotlib_%s_%s_disp.png"%(spin_inte_rep, plotlabelfilesave_R1_rho_R2eff) ) 

== To run Bugs ? ==<source lang="bash">relax -p r1rhor2eff.py</source>Do you get an error with matplotlib about dateutil? Then see [[Matplotlib_dateutil_bug]]