Changes

== Code = 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}} / sin²(θ) - {{:R1}} / tan²(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}} / sin²(θ) - {{:R1}} / tan²(θ). Figure 11+16, would be the reference. Ref [4], Equation 43. {{:Reff}} = {{:R1rho}} / sin²(θ) - {{:R1}} / tan²(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}_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 == === 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="pythonbash">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_pointsy 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 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.dic_key_listmydic = collections.OrderedDict() # 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_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.myspinmydic[exp_type][frq][offset]['R1_rho_bc'] = [] cdp.r2effmydic[exp_type][frq][offset]['R1_rho_inter'] = [dic_key]

# Get w_effY val fake w_eff = cdp.myspin.w_eff_spin_dicmydic[exp_type][frq][offset]['fake_R1_rho'] = [dic_key] x_w_eff.append(w_eff)

# 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.theta_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_theta cdp.mydic[exp_type][frq][offset]['R1_rho_bc'].append(thetaR1_rho_bc)

# Calculate y valueY val, fake R1rho_R2eff fake_R1_rho = (R1_rho - R1*cos(theta)*cos(theta)) / (sin(theta) * sin(theta))cdp.fakespin.back_calc[ei][0][mi][oi][di] y cdp.mydic[exp_type][frq][offset]['fake_R1_rho'].append(R1rho_R2efffake_R1_rho)

# Modify dataY2 valw_eff_div # 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 = 10(R1_rho_err - R1_err*cos(theta)*cos(theta)) / (sin(theta) *4sin(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) ## Loop over the new dispersion points. 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)rem_points w_eff = 2w_eff_spin_dic_inter[param_key] cdp.mydic[exp_type][frq][offset]['w_eff_inter'].append(w_eff) # Y valx_w_eff_mod R1_rho = cdp.myspin.back_calc[xei][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)) /w_eff_div (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 x plottingfilesave_R1_rho_R2eff = 'R1_rho_R2eff'filesave_R1_rho = 'R1_rho' # For writing math in x_w_effmatplotlib, 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$^{-rem_points1}$]' xlabel_theta = 'Rotating frame tilt angle [rad]'y_mod xlabel_w_eff = yr'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 / 1.6legend_size = 6 # Plot values in dicfor 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'], '-rem_points', 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 R1rho_R2eff 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)plotlabel plt.errorbar(val_dics['w_eff'], val_dics['R1_rho_R2eff'], yerr= val_dics['R1rho_R2effR1_rho_R2eff_err'], fmt='o', label=graphlabel, color=line.get_color()) plt.plot(x_w_eff_modval_dics['w_eff'], y_modval_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['R1rho_R2effpoint'], 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= {'Effective field in rotating frame size':legend_size})plt.grid(True)#plt.ylim([0,16])plt.title("%s rad.\n %s as function of %s^-1]'"%(strspin_inte, ylabel_R1_rho, xlabel_theta))fig1.set_size_inches(img_inch_x, img_inch_y)plt.savefig(w_eff_div"matplotlib_%s_%s_theta_sep.png"%(spin_inte_rep, filesave_R1_rho) ) ## Plot 2: R1_rho_R2eff as function of w_efffig2 = plt.figure(2)plt.xlabel(xlabelxlabel_w_eff)plt.ylabel (ylabel_R1_rho_R2eff)plt.legend(loc='best', prop= {'size'R1rho_R2eff :legend_size})plt.grid(True)#plt.ylim([rad0,16])#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-1]'lock field strengthfig3 = plt.figure(3)plt.xlabel(xlabel_lock)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_lock))fig3.set_size_inches(img_inch_x, img_inch_y)plt.savefig("matplotlib_%s_%s_w_effs_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]]