Documentation fixes for 0.12.1

README.md

@@ -32,11 +32,16 @@ More details on the installation of DeerLab can be found [here](https://jeschkel
 
 ### Citation
 
-A paper about DeerLab is currently submitted for publication. When you use DeerLab in your work, for now, please cite the preprint
-
-> Fábregas Ibáñez, L., Jeschke, G., and Stoll, S.: a comprehensive software package for analyzing dipolar electron paramagnetic resonance spectroscopy data, Magn. Reson., 1, 209–224, 2020, doi.org/10.5194/mr-1-209-2020
-
-Please check back frequently for updated publication information.
+When you use DeerLab in your work, please cite the following publication:
+
+<div style="height:6%px; width:100%; display:flex; flex-wrap:wrap; align-items:center; border-style:solid">
+    <div style="margin-left:2%; margin-bottom:2%; font-size:14px">
+        <h3 style="font-size:110%">  DeerLab: a comprehensive software package for analyzing dipolar electron paramagnetic resonance spectroscopy data </h3> 
+        Luis Fábregas Ibáñez, Gunnar Jeschke, Stefan Stoll <br>
+        Magn. Reson., 1, 209–224, 2020 <br>
+        <a href="https://doi.org/10.5194/mr-1-209-2020"> doi.org/10.5194/mr-1-209-2020</a>
+    </div>
+</div>
 
 ### License
 

docsrc/source/funding.rst

@@ -11,7 +11,7 @@ DeerLab is a project that has been funded by the following sources:
             Grant ETH-35 18-2
         </div>
         <div style="height:70%; width=30%">
-            <img src="./_images/eth_logo.png", style="margin-top:1%; margin-bottom:1%;height:96%;">
+            <img src="https://raw.githubusercontent.com/JeschkeLab/DeerLab/main/docsrc/source/images/eth_logo.png", style="margin-top:1%; margin-bottom:1%;height:96%;">
         </div>
     </div>    
 
@@ -25,7 +25,7 @@ DeerLab is a project that has been funded by the following sources:
             Grant GM127325
         </div>
         <div style="height:70%; width=30%">
-            <img src="./_images/nih_logo.png", style="margin-top:1%; margin-bottom:1%;height:96%;">
+            <img src="https://raw.githubusercontent.com/JeschkeLab/DeerLab/main/docsrc/source/images/nih_logo.png", style="margin-top:1%; margin-bottom:1%;height:96%;">
         </div>
     </div>    
 
@@ -38,7 +38,7 @@ DeerLab is a project that has been funded by the following sources:
             Grant CHE-1452967
         </div>
         <div style="height:70%; width=30%">
-            <img src="./_images/nsf_logo.png", style="margin-top:1%; margin-bottom:1%;height:96%;">
+            <img src="https://raw.githubusercontent.com/JeschkeLab/DeerLab/main/docsrc/source/images/nsf_logo.png", style="margin-top:1%; margin-bottom:1%;height:96%;">
         </div>
     </div>    
 

examples/plot_analyzing_pake_pattern.py

@@ -27,11 +27,11 @@
 
 # Plot
 plt.plot(t,V,'k.')
-plt.tight_layout()
-plt.grid()
+plt.grid(alpha=0.3)
 plt.xlabel('Time [$\\mu s$]')
 plt.ylabel('V(t)')
-
+plt.tight_layout()
+plt.show()
 # %% [markdown]
 # Prepare the signal
 # ------------------
@@ -71,11 +71,11 @@ def Bmodel(par):
 Vcorr = (V/Bfit - (1 - lam))/lam
 
 plt.plot(t,Vcorr,'k.')
-plt.tight_layout()
 plt.grid(alpha=0.3)
 plt.xlabel('Time [$\\mu s$]')
 plt.ylabel('V(t)')
-
+plt.tight_layout()
+plt.show()
 # %% [markdown]
 # Computing the dipolar spectrum
 # --------------------------------
@@ -99,13 +99,13 @@ def Bmodel(par):
 
 # Plot results
 plt.plot(nu,pake,'k',nuapo,pakeapo,'b',linewidth=1.5)
-plt.tight_layout()
 plt.grid(alpha=0.3)
 plt.xlim([-10, 10])
 plt.xlabel('Frequency [MHz]')
 plt.ylabel('Intensity [a.u.]')
 plt.legend(['Raw','Apodized'])
-
+plt.tight_layout()
+plt.show()
 # %% [markdown]
 # We do not need to worry about the zero-filling since ``fftspec`` takes care 
 # of setting it to twice the amount of points in the signal, to preserve all information. 

examples/plot_comparing_uncertainties_for_regularization.py

@@ -22,7 +22,7 @@
 r = np.linspace(2,5,160)                # distance axis, nm
 P = dl.dd_gauss2(r,[3, 0.1, 0.6, 3.5, 0.2, 0.4]) # model distribution
 lam = 0.32                              # modulatio depth
-B = dl.bg_strexp(t,[0.04,1],lam)        # background decay
+B = dl.bg_strexp(t,[0.04,1])        # background decay
 K = dl.dipolarkernel(t,r,lam,B)         # dipolar kernel matrix
 V = K@P + dl.whitegaussnoise(t,0.01)    # signal with added noise
 
@@ -66,7 +66,7 @@ def mybootfcn(V):
 # matrices. This is because bootstrapping takes the nonnegativity constraint of P into
 # account, whereas the curvature matrix CIs do not. 
 
-fig, ax = plt.subplots(1,2,sharey=True)
+fig, ax = plt.subplots(2,1,sharey=True)
 ax[0].plot(r,P,'k',r,Pfit,'r',linewidth=1)
 ax[0].fill_between(r,Pci50_cm[:,0],Pci50_cm[:,1],color='r',linestyle='None',alpha=0.45)
 ax[0].fill_between(r,Pci95_cm[:,0],Pci95_cm[:,1],color='r',linestyle='None',alpha=0.25)
@@ -85,5 +85,6 @@ def mybootfcn(V):
 ax[1].set_xlabel('r [nm]')
 ax[1].set_title('Bootstrapped CI')
 ax[1].legend(['Truth','Fit','50%-CI','95%-CI'])
-
+plt.tight_layout()
+plt.show()
 # %%

examples/plot_emulating_deeranalysis_workflow.py

@@ -39,7 +39,8 @@
 plt.ylabel('V(t)')
 plt.grid(alpha=0.3)
 plt.legend(['real','imag'])
-
+plt.tight_layout()
+plt.show()
 # %% [markdown]
 # DeerAnalysis workflow
 # ---------------------
@@ -58,17 +59,17 @@
 mask = t>tstart
 def Bmodel(par):
     lam,kappa,d = par # unpack parameters
-    B = (1-lam)*dl.bg_strexp(t[mask],[kappa,d],lam)
+    B = (1-lam)*dl.bg_strexp(t[mask],[kappa,d])
     return B
 
 #       lam     k   d
 par0 = [0.5,   0.5, 3]
-lb   = [0.1,   0.1, 1]
+lb   = [0.1,    0,  1]
 ub   = [1,      5,  6]
 fit = dl.fitparamodel(V[mask],Bmodel,par0,lb,ub,rescale=False)
 
 lamfit,kappa,d = fit.param
-Bfit = dl.bg_strexp(t,[kappa,d],lamfit)
+Bfit = dl.bg_strexp(t,[kappa,d])
 
 # Background "correction" by division
 Vcorr = (V/Bfit - 1 + lamfit)/lamfit
@@ -99,6 +100,7 @@ def Bmodel(par):
 plt.xlabel('r [nm]')
 plt.ylabel('P [nm^{-1}]')
 plt.legend(['truth','fit'])
-
+plt.tight_layout()
+plt.show()
 
 # %%

examples/plot_multigauss_fitting_4pdeer.py

@@ -96,7 +96,6 @@ def K4pdeer(par,t,r):
 plt.plot(t,V,'k.')
 plt.plot(t,Vfit,'b',linewidth=1.5)
 plt.plot(t,(1-Kparfit[0])*dl.bg_hom3d(t,Kparfit[1],Kparfit[0]),'b--',linewidth=1.5)
-plt.tight_layout()
 plt.grid(alpha=0.3)
 plt.legend(['data','Vfit','Bfit'])
 plt.xlabel('t [µs]')
@@ -107,34 +106,32 @@ def K4pdeer(par,t,r):
 plt.plot(r,Pfit,'b',linewidth=1.5)
 plt.fill_between(r,Pci50[:,0],Pci50[:,1],color='b',linestyle='None',alpha=0.45)
 plt.fill_between(r,Pci95[:,0],Pci95[:,1],color='b',linestyle='None',alpha=0.25)
-plt.tight_layout()
 plt.grid(alpha=0.3)
 plt.legend(['truth','optimal fit','95%-CI'])
 plt.xlabel('r [nm]')
 plt.ylabel('P(r)')
 
 plt.subplot(323)
 plt.bar(np.arange(NGauss)+1,metrics + abs(min(metrics)),facecolor='b',alpha=0.6)
-plt.tight_layout()
 plt.grid(alpha=0.3)
 plt.ylabel('$\Delta AIC$')
 plt.xlabel('Number of Gaussians')
 
 plt.subplot(325)
 plt.bar(np.arange(NGauss)+1,Akaikeweights,facecolor='b',alpha=0.6)
-plt.tight_layout()
 plt.grid(alpha=0.3)
 plt.ylabel('Akaike Weight [%]')
 plt.xlabel('Number of Gaussians')
 
 plt.subplot(3,2,(4,6))
 for i in range(len(Peval)):
     plt.plot(r,P + 2*i,'k',r,Peval[i] + 2*i,'b-',linewidth=1.5)
-plt.tight_layout()
 plt.grid(alpha=0.3)
 plt.xlabel('r [nm]')
 plt.ylabel('Number of Gaussians')
 plt.legend(['truth','fit'])
 
+plt.tight_layout()
+plt.show()
 
 # %%

examples/plot_pseudotitration_parameter_free.py

@@ -63,7 +63,7 @@ def Kmodel(par,ts,rA,rB,L):
     Ks = [[]]*Nsignals
     # General the dipolar kernels
     for i in range(Nsignals):
-        B = dl.bg_exp(ts[i],k,lam)
+        B = dl.bg_exp(ts[i],k)
         # Kernel for fraction A
         KstateA = dl.dipolarkernel(ts[i],rA,lam,B)
         # Kernel for fraction B
@@ -153,7 +153,7 @@ def Kmodel(par,ts,rA,rB,L):
 plt.subplot(3,2,(1,3))
 for i in range(Nsignals):
     Vsfit.append(Ksfit[i]@Pfit)
-    plt.plot(ts[i],Vs[i]+i/9,'k.',ts[i],Vsfit[i]+i/9,'r',linewidth=1.5)
+    plt.plot(ts[i],Vs[i]+i/9,'k.',ts[i],Vsfit[i]+i/9,'tab:blue',linewidth=1.5)
 plt.grid(alpha =0.3)
 plt.xlabel('t [$\mu s$]')
 plt.ylabel('V(t) [a.u.]')
@@ -164,7 +164,7 @@ def Kmodel(par,ts,rA,rB,L):
 for i in range(Nsignals):
     PAfit = xAfit[i]*Pfit[0:len(rA)]
     PBfit = xBfit[i]*Pfit[len(rA):len(rB)+len(rA)]
-    plt.plot(rA,PAfit+2*i,'r',rB,PBfit+2*i,'b',linewidth=1.5)
+    plt.plot(rA,PAfit+1.2*i,'tab:red',rB,PBfit+1.2*i,'tab:blue',linewidth=1.5)
 
 plt.grid(alpha =0.3)
 plt.xlabel('r [nm]')
@@ -173,10 +173,15 @@ def Kmodel(par,ts,rA,rB,L):
 plt.xlim([2,7])
 
 plt.subplot(325)
-plt.plot(np.log10(L),xA,'r-',np.log10(L),xB,'b-')
-plt.plot(np.log10(L),xAfit,'ro',np.log10(L),xBfit,'bo',linewidth=1.5)
+plt.plot(np.log10(L),xA,'tab:red',np.log10(L),xB,'tab:blue')
+plt.plot(np.log10(L),xAfit,'o',color='tab:red')
+plt.plot(np.log10(L),xBfit,'o',color='tab:blue')
 plt.grid(alpha =0.3)
 plt.xlabel('log$_{10}$([L])')
 plt.ylabel('Fractions')
 plt.legend(['state A','state B'])
 plt.ylim([0,1])
+
+plt.tight_layout()
+plt.show()
+# %%