added publish script for PIP

README.md

 # Akvo 
- Akvo provides processing of surface NMR data. It aims to be simple to use yet flexible for accommodating changes to processing flow. Akvo is written primarily in Python 3 with a small amount of R as well. The application is written around a Qt GUI with plotting provided by Matplotlib.
+
+Akvo provides processing of surface NMR data. It aims to be simple to use yet flexible for accommodating changes to processing flow. Akvo is written primarily in Python 3 with a small amount of R as well. The application is written around a Qt GUI with plotting provided by Matplotlib.
 
 The bleeding-edge code may be accesed using the git client 
 ```
 ```
 
 ## Installation 
-Installation is straightforward. The only prerequisite that is sometimes not properly handled is PyQt5 which needs to be manually installed. 
 
+Installation is straightforward. The only prerequisite that is sometimes not properly handled is PyQt5 which sometimes needs to be manually installed. 
 ```
 python3 setup.py build 
 python3 setup.py install
 ```
 
+Alternatively, release versions can be installed via pip
+```
+pip install akvo
+```
+
+
 ## Team 
+
 Akvo is developed by several teams including the University of Utah.
 
 ## Capabilities 
+
 Akvo currently has preprocessing capabilities for VistaClara GMR data. 
 
 ## Benefits 
+
 Processing steps are retained and logged in the processed file header, which is written in YAML. 
 This allows data processing to be repeatible, which is a major benefit. 
 
 ## Languages
-Akvo is written primarily in Python 3, but has components written in R as well. The graphical user unterface is written in PyQt5.  An interface to modelling software written in C++ (Lemma and Merlin) is in development. 
+
+Akvo is written primarily in Python 3. The graphical user unterface is written in PyQt5.  An interface to modelling software written in C++ (Lemma and Merlin) is in development. 

akvo/gui/main.ui

               <rect>
                <x>0</x>
                <y>0</y>
-               <width>100</width>
-               <height>30</height>
+               <width>96</width>
+               <height>26</height>
               </rect>
              </property>
              <attribute name="label">
               <rect>
                <x>0</x>
                <y>0</y>
-               <width>411</width>
-               <height>77</height>
+               <width>96</width>
+               <height>26</height>
               </rect>
              </property>
              <attribute name="label">

akvo/tressel/decay.py

 from matplotlib import pyplot as plt
 import numpy as np
 from scipy.optimize import least_squares
-from rpy2.robjects.packages import importr
 
-import rpy2.robjects as robjects
-import rpy2.robjects.numpy2ri
+#from rpy2.robjects.packages import importr
+#import rpy2.robjects as robjects
+#import rpy2.robjects.numpy2ri
 
 #import notch
 from numpy.fft import fft, fftfreq
         indices           
 
 
-#################################################
-# Regress for T2 using rpy2 interface
-def regressCurve(peaks,times,sigma2=1,intercept=True):
-
-    # TODO, if regression fails, it might be because there is no exponential
-    # term, maybe do a second regression then on a linear model. 
-    b1  = 0                  # Bias
-    b2  = 0                  # Linear 
-    rT2 = 0.3                # T2 regressed
-    r   = robjects.r         
-
-    # Variable shared between R and Python
-    robjects.globalenv['b1'] = b1
-    robjects.globalenv['b2'] = b2
-    robjects.globalenv['rT2'] = rT2
-    robjects.globalenv['sigma2'] = sigma2
-    value = robjects.FloatVector(peaks)
-    times = robjects.FloatVector(numpy.array(times))
-    
-#    my_weights = robjects.RVector(value/sigma2)
-#    robjects.globalenv['my_weigts'] = my_weights
-
-#    if sigma2 != 0:
-#        print "weighting"
-#        tw = numpy.array(peaks)/sigma2 
-#        my_weights = robjects.RVector( tw/numpy.max(tw) )
-#    else:
-#        my_weights = robjects.RVector(numpy.ones(len(peaks))) 
-
-#    robjects.globalenv['my_weights'] = my_weights
-    
-    if (intercept):
-        my_list = robjects.r('list(b1=50, b2=1e2, rT2=0.03)')
-        my_lower = robjects.r('list(b1=0, b2=0, rT2=.005)')
-        my_upper = robjects.r('list(b1=20000, b2=2000, rT2=.700)')
-    else:
-        my_list = robjects.r('list(b2=1e2, rT2=0.3)')
-        my_lower = robjects.r('list(b2=0, rT2=.005)')
-        my_upper = robjects.r('list(b2=2000, rT2=.700)')
-
-    my_cont = robjects.r('nls.control(maxiter=1000, warnOnly=TRUE, printEval=FALSE)')
-
-    
-    if (intercept):
-        #fmla = robjects.RFormula('value ~ b1 + exp(-times/rT2)')
-        fmla = robjects.Formula('value ~ b1 + b2*exp(-times/rT2)')
-        #fmla = robjects.RFormula('value ~ b1 + b2*times + exp(-times/rT2)')
-    else:
-        fmla = robjects.Formula('value ~ b2*exp(-times/rT2)')
-
-    env = fmla.getenvironment()
-    env['value'] = value
-    env['times'] = times
-    
-    # ugly, but I get errors with everything else I've tried
-    my_weights = robjects.r('rep(1,length(value))')
-    for ii in range(len(my_weights)):
-        my_weights[ii] *= peaks[ii]/sigma2
-    Error = False
-    #fit = robjects.r.nls(fmla,start=my_list,control=my_cont,weights=my_weights)
-    if (sigma2 != 1):
-        print("SIGMA 2")
-        #fit = robjects.r.tryCatch(robjects.r.suppressWarnings(robjects.r.nls(fmla,start=my_list,control=my_cont,algorithm="port", \
-        #                     weights=my_weights)), 'silent=TRUE')
-        fit = robjects.r.tryCatch(robjects.r.nls(fmla,start=my_list,control=my_cont))#, \
-                            # weights=my_weights))
-    else:
-        try:
-            fit = robjects.r.tryCatch(robjects.r.nls(fmla,start=my_list,control=my_cont,algorithm="port"))#,lower=my_lower,upper=my_upper))
-        except:
-            print("regression issue pass")
-            Error = True
-    # If failure fall back on zero regression values   
-    if not Error:
-        #Error = fit[3][0]
-        report =  r.summary(fit)
-    b1 = 0
-    b2 = 0 
-    rT2 = 1
-    if (intercept):
-        if not Error:
-            b1  =  r['$'](report,'par')[0]
-            b2  =  r['$'](report,'par')[1]
-            rT2 =  r['$'](report,'par')[2]
-            #print  report
-            #print  r['$'](report,'convergence')
-            #print  r['convergence'] #(report,'convergence')
-            #print  r['$'](report,'par')[13]
-            #print  r['$'](report,'par')[14]
-        else:
-            print("ERROR DETECTED, regressed values set to default")
-            b1 = 1e1
-            b2 = 1e-2
-            rT2 = 1e-2
-            #print r['$'](report,'par')[0]
-            #print r['$'](report,'par')[1]
-            #print r['$'](report,'par')[2]
-        return [b1,b2,rT2] 
-    else:
-        if not Error:
-            rT2 =  r['$'](report,'par')[1]
-            b2  =  r['$'](report,'par')[0]
-        else:
-            print("ERROR DETECTED, regressed values set to default")
-        return [b2, rT2] 
-
-#################################################
-# Regress for T2 using rpy2 interface
-def regressCurve2(peaks,times,sigma2=[None],intercept=True):
-
-    if sigma2[0] != None:
-        my_weights = robjects.FloatVector( sigma2 )
-
-    # TODO, if regression fails, it might be because there is no exponential
-    # term, maybe do a second regression then on a linear model. 
-    b1  = 0                  # Bias
-    b2  = 0                  # Linear 
-    bb2  = 0                 # Linear 
-    rT2 = 0.3                # T2 regressed
-    rrT2 = 1.3               # T2 regressed
-    r   = robjects.r         
-
-    # Variable shared between R and Python
-    robjects.globalenv['b1'] = b1
-    robjects.globalenv['b2'] = b2
-    robjects.globalenv['rT2'] = rT2
-    
-    robjects.globalenv['bb2'] = b2
-    robjects.globalenv['rrT2'] = rT2
-    
-    #robjects.globalenv['sigma2'] = sigma2
-    value = robjects.FloatVector(peaks)
-    times = robjects.FloatVector(numpy.array(times))
-    
-    
-    if (intercept):
-        my_list = robjects.r('list(b1=.50, b2=1e2, rT2=0.03, bb2=1e1, rrT2=1.3)')
-        my_lower = robjects.r('list(b1=0, b2=0, rT2=.005, bb2=0, rrT2=.005 )')
-        my_upper = robjects.r('list(b1=2000, b2=2000, rT2=.700, bb2=2000, rrT2=1.3 )')
-    else:
-        my_list  = robjects.r('list(b2=.5, rT2=0.3,  bb2=.5, rrT2=1.3)')
-        my_lower = robjects.r('list(b2=0,  rT2=.005, bb2=0,  rrT2=.005)')
-        my_upper = robjects.r('list(b2=1,  rT2=2.6,    bb2=1,  rrT2=2.6)')
-
-    my_cont = robjects.r('nls.control(maxiter=1000, warnOnly=TRUE, printEval=FALSE)')
-
-    
-    if (intercept):
-        #fmla = robjects.RFormula('value ~ b1 + exp(-times/rT2)')
-        fmla = robjects.Formula('value ~ b1 + b2*exp(-times/rT2) + bb2*exp(-times/rrT2)')
-        #fmla = robjects.RFormula('value ~ b1 + b2*times + exp(-times/rT2)')
-    else:
-        fmla = robjects.Formula('value ~ b2*exp(-times/rT2) + bb2*exp(-times/rrT2)')
-
-    env = fmla.getenvironment()
-    env['value'] = value
-    env['times'] = times
-    
-    # ugly, but I get errors with everything else I've tried
-    Error = False
-    #fit = robjects.r.nls(fmla,start=my_list,control=my_cont,weights=my_weights)
-    if (sigma2[0] != None):
-        #print("SIGMA 2")
-        #fit = robjects.r.tryCatch(robjects.r.suppressWarnings(robjects.r.nls(fmla,start=my_list,control=my_cont,algorithm="port", \
-        #                     weights=my_weights)), 'silent=TRUE')
-        fit = robjects.r.tryCatch(robjects.r.nls(fmla,start=my_list,control=my_cont,algorithm='port',weights=my_weights,lower=my_lower,upper=my_upper))#, \
-                            # weights=my_weights))
-    else:
-        try:
-            fit = robjects.r.tryCatch(robjects.r.nls(fmla,start=my_list,control=my_cont,algorithm="port"))#,lower=my_lower,upper=my_upper))
-        except:
-            print("regression issue pass")
-            Error = True
-    # If failure fall back on zero regression values   
-    if not Error:
-        #Error = fit[3][0]
-        report =  r.summary(fit)
-    b1 = 0
-    b2 = 0 
-    rT2 = 1
-    if (intercept):
-        if not Error:
-            b1  =  r['$'](report,'par')[0]
-            b2  =  r['$'](report,'par')[1]
-            rT2 =  r['$'](report,'par')[2]
-            #print  report
-            #print  r['$'](report,'convergence')
-            #print  r['convergence'] #(report,'convergence')
-            #print  r['$'](report,'par')[13]
-            #print  r['$'](report,'par')[14]
-        else:
-            print("ERROR DETECTED, regressed values set to default")
-            b1 = 1e1
-            b2 = 1e-2
-            rT2 = 1e-2
-            #print r['$'](report,'par')[0]
-            #print r['$'](report,'par')[1]
-            #print r['$'](report,'par')[2]
-        return [b1,b2,rT2, bb2, rrT2] 
-    else:
-        if not Error:
-            rT2 =  r['$'](report,'par')[1]
-            b2  =  r['$'](report,'par')[0]
-            rrT2 =  r['$'](report,'par')[3]
-            bb2  =  r['$'](report,'par')[2]
-        else:
-            print("ERROR DETECTED, regressed values set to default")
-        return [b2, rT2, bb2, rrT2] 
-
 def fun(x, t, y):
     """ Cost function for regression, single exponential, no DC term 
         x[0] = A0
     #return conv, E0,df,phi,T2
     #return res_lsq.success, x[0], 0, x[1], x[2]
 
-def quadratureDetect(X, Y, tt, CorrectFreq=False, BiExp=False, CorrectDC=False):
- 
-    r   = robjects.r        
-
-    if CorrectDC:
-        robjects.r(''' 
-             Xc1 <- function(E01, df, tt, phi, T2_1, DC) {
-	                DC + E01*cos(2*pi*df*tt + phi) * exp(-tt/T2_1)
-            }
-    
-            Yc1 <- function(E01, df, tt, phi, T2_1, DC) {
-	                DC - E01*sin(2*pi*df*tt + phi) * exp(-tt/T2_1)
-            } 
-            ''')
-    else:   
-        robjects.r(''' 
-             Xc1 <- function(E01, df, tt, phi, T2_1) {
-	                E01*cos(2*pi*df*tt + phi) * exp(-tt/T2_1)
-            }
-    
-            Yc1 <- function(E01, df, tt, phi, T2_1) {
-	                -E01*sin(2*pi*df*tt + phi) * exp(-tt/T2_1)
-            } 
-            ''')
-
-    # bi-exponential 
-    if CorrectDC:
-        robjects.r(''' 
-             Xc2 <- function(E01, E02, df, tt, phi, T2_1, T2_2, DC) {
-	               DC + E01*cos(2*pi*df*tt + phi) * exp(-tt/T2_1) + 
-	                DC + E02*cos(2*pi*df*tt + phi) * exp(-tt/T2_2)
-            }
-
-            Yc2 <- function(E01, E02, df, tt, phi, T2_1, T2_2, DC) {
-	                DC - E01*sin(2*pi*df*tt + phi) * exp(-tt/T2_1) + 
-	                DC - E02*sin(2*pi*df*tt + phi) * exp(-tt/T2_2)
-            } 
-            ''')
-    else:   
-        robjects.r(''' 
-             Xc2 <- function(E01, E02, df, tt, phi, T2_1, T2_2) {
-	               E01*cos(2*pi*df*tt + phi) * exp(-tt/T2_1) + 
-	               E02*cos(2*pi*df*tt + phi) * exp(-tt/T2_2)
-            }
-
-            Yc2 <- function(E01, E02, df, tt, phi, T2_1, T2_2) {
-	                -E01*sin(2*pi*df*tt + phi) * exp(-tt/T2_1) + 
-	                -E02*sin(2*pi*df*tt + phi) * exp(-tt/T2_2)
-            } 
-            ''')
-
-    # Make 0 vector 
-    Zero = robjects.FloatVector(numpy.zeros(len(X)))
-    
-    # Fitted Parameters
-    E01 = 0.
-    E02 = 0.
-    df = 0.
-    phi = 0.
-    T2_1 = 0.
-    T2_2 = 0.
-    DC = 0.
-    robjects.globalenv['DC'] = DC
-    robjects.globalenv['E01'] = E01
-    robjects.globalenv['E02'] = E02
-    robjects.globalenv['df'] = df
-    robjects.globalenv['phi'] = phi
-    robjects.globalenv['T2_1'] = T2_1
-    robjects.globalenv['T2_2'] = T2_2
-    XY = robjects.FloatVector(numpy.concatenate((X,Y)))
-    
-    # Arrays
-    tt = robjects.FloatVector(numpy.array(tt))
-    X = robjects.FloatVector(numpy.array(X))
-    Y = robjects.FloatVector(numpy.array(Y))
-    Zero = robjects.FloatVector(numpy.array(Zero))
-
-    
-
-    if BiExp:
-        if CorrectDC:
-            fmla = robjects.Formula('XY ~ c(Xc2( E01, E02, df, tt, phi, T2_1, T2_2, DC ), Yc2( E01, E02, df, tt, phi, T2_1, T2_2, DC ))')
-            if CorrectFreq:    
-                start = robjects.r('list(E01=.100, E02=.01,   df=0,    phi=0.    ,  T2_1=.100, T2_2=.01, DC=0.0)')
-                lower = robjects.r('list(E01=1e-6, E02=1e-6,  df=-50,  phi=-3.14 ,  T2_1=.001, T2_2=.001, DC=0.0)')
-                upper = robjects.r('list(E01=1.00, E02=1.0,   df=50,   phi=3.14  ,  T2_1=.800, T2_2=.8, DC=0.5)')
-            else:
-                start = robjects.r('list(E01=.100, E02=.01,   phi=0.9   ,  T2_1=.100, T2_2=.01,  DC=0.0)')
-                lower = robjects.r('list(E01=1e-6, E02=1e-6,  phi=-3.14 ,  T2_1=.001, T2_2=.001, DC=0.0)')
-                upper = robjects.r('list(E01=1.00, E02=1.0,   phi=3.14  ,  T2_1=.800, T2_2=.8,   DC=0.5)')
-        else:
-            fmla = robjects.Formula('XY ~ c(Xc2( E01, E02, df, tt, phi, T2_1, T2_2 ), Yc2( E01, E02, df, tt, phi, T2_1, T2_2))')
-            if CorrectFreq:    
-                start = robjects.r('list(E01=.100, E02=.01,   df=0,    phi=0.    ,  T2_1=.100, T2_2=.01)')
-                lower = robjects.r('list(E01=1e-6, E02=1e-6,  df=-50,  phi=-3.14 ,  T2_1=.001, T2_2=.001)')
-                upper = robjects.r('list(E01=1.00, E02=1.0,   df=50,   phi=3.14  ,  T2_1=.800, T2_2=.8)')
-            else:
-                start = robjects.r('list(E01=.100, E02=.01,   phi=0.9   ,  T2_1=.100, T2_2=.01)')
-                lower = robjects.r('list(E01=1e-6, E02=1e-6,  phi=-3.14 ,  T2_1=.001, T2_2=.001)')
-                upper = robjects.r('list(E01=1.00, E02=1.0,   phi=3.14  ,  T2_1=.800, T2_2=.8)')
-    else: 
-        if CorrectDC:
-            fmla = robjects.Formula('XY ~ c(Xc1( E01, df, tt, phi, T2_1, DC), Yc1( E01, df, tt, phi, T2_1,DC))')
-            if CorrectFreq:    
-                start = robjects.r('list(E01=.100, df=0   , phi=0.   , T2_1=.100, DC=0.0)')
-                lower = robjects.r('list(E01=1e-6, df=-50., phi=-3.14, T2_1=.001, DC=0.0)')
-                upper = robjects.r('list(E01=1.00, df=50. , phi=3.14 , T2_1=.800, DC=0.5)')
-            else:
-                start = robjects.r('list(E01=.100, phi= 0.  , T2_1=.100, DC=0.0)')
-                lower = robjects.r('list(E01=1e-6, phi=-3.13, T2_1=.001, DC=0.0)')
-                upper = robjects.r('list(E01=1.00, phi= 3.13, T2_1=.800, DC=0.5)')
-        else:
-            fmla = robjects.Formula('XY ~ c(Xc1( E01, df, tt, phi, T2_1), Yc1( E01, df, tt, phi, T2_1))')
-            if CorrectFreq:    
-                start = robjects.r('list(E01=.100, df=0     , phi=0.   ,  T2_1=.100)')
-                lower = robjects.r('list(E01=1e-6, df=-50. , phi=-3.14 ,  T2_1=.001)')
-                upper = robjects.r('list(E01=1.00, df=50.  , phi=3.14  ,  T2_1=.800)')
-            else:
-                start = robjects.r('list(E01=.100, phi= 0.  , T2_1=.100)')
-                lower = robjects.r('list(E01=1e-6, phi=-3.13, T2_1=.001)')
-                upper = robjects.r('list(E01=1.00, phi= 3.13, T2_1=.800)')
-
-    env = fmla.getenvironment()
-    env['Zero'] = Zero
-    env['X'] = X
-    env['Y'] = Y
-    env['XY'] = XY 
-    env['tt'] = tt
-
-    cont = robjects.r('nls.control(maxiter=10000, warnOnly=TRUE, printEval=FALSE)')
-    
-    fit = robjects.r.tryCatch(robjects.r.nls(fmla, start=start, control=cont, lower=lower, upper=upper, algorithm='port')) #, \
-    #fit = robjects.r.tryCatch(robjects.r.nls(fmla, start=start, control=cont)) #, \
-    report =  r.summary(fit)
-
-    conv = r['$'](fit,'convergence')[0]
-    #if conv:
-    #    print (report)
-    #    print ("conv", conv)
-    print ("Conv",  r['$'](fit,'convergence'))  # T2
-    print (report)
-    
-    if BiExp:
-        if CorrectFreq:    
-            E0   =  r['$'](report,'par')[0]   # E01
-            E0  +=  r['$'](report,'par')[1]   # E02
-            df  =  r['$'](report,'par')[2]   # offset
-            phi =  r['$'](report,'par')[3]   # phase 
-            T2  =  r['$'](report,'par')[4]   # T2
-        else:
-            E0   =  r['$'](report,'par')[0]   # E01
-            E0  +=  r['$'](report,'par')[1]   # E02
-            phi =  r['$'](report,'par')[2]   # phase 
-            T2  =  r['$'](report,'par')[3]   # T2
-    else:
-        if CorrectFreq:    
-            E0   =  r['$'](report,'par')[0]   # E01
-            df  =  r['$'](report,'par')[1]   # offset
-            phi =  r['$'](report,'par')[2]   # phase 
-            T2  =  r['$'](report,'par')[3]   # T2
-        else:
-            E0   =  r['$'](report,'par')[0]   # E01
-            phi =  r['$'](report,'par')[1]   # phase 
-            T2  =  r['$'](report,'par')[2]   # T2
-    #phi = 0.907655876627
-    #phi = 0
-    #print ("df", df)# = 0
-    return conv, E0,df,phi,T2
-    
-
-#################################################
-# Regress for T2 using rpy2 interface
-def regressSpec(w, wL, X): #,sigma2=1,intercept=True):
-
-    # compute s
-    s = -1j*w
-
-    # TODO, if regression fails, it might be because there is no exponential
-    # term, maybe do a second regression then on a linear model. 
-    a   = 0                  # Linear 
-    rT2 = 0.1                # T2 regressed
-    r   = robjects.r         
-
-    # Variable shared between R and Python
-    robjects.globalenv['a'] = a
-    robjects.globalenv['rT2'] = rT2
-    robjects.globalenv['wL'] = wL
-    robjects.globalenv['nb'] = 0
-
-    s = robjects.ComplexVector(numpy.array(s))
-    XX = robjects.ComplexVector(X)
-    Xr = robjects.FloatVector(numpy.real(X))
-    Xi = robjects.FloatVector(numpy.imag(X))
-    Xa = robjects.FloatVector(numpy.abs(X))
-    Xri = robjects.FloatVector(numpy.concatenate((Xr,Xi)))
-    
-    #my_lower = robjects.r('list(a=.001, rT2=.001, nb=.0001)')
-    my_lower = robjects.r('list(a=.001, rT2=.001)')
-    #my_upper = robjects.r('list(a=1.5, rT2=.300, nb =100.)')
-    my_upper = robjects.r('list(a=1.5, rT2=.300)')
-     
-    #my_list = robjects.r('list(a=.2, rT2=0.03, nb=.1)')
-    my_list = robjects.r('list(a=.2, rT2=0.03)')
-    my_cont = robjects.r('nls.control(maxiter=5000, warnOnly=TRUE, printEval=FALSE)')
-    
-    #fmla = robjects.Formula('Xri ~ c(a*Re((wL) / (wL^2+(s+1/rT2)^2 )), a*Im((wL)/(wL^2 + (s+1/rT2)^2 )))') # envelope
-    ##fmla = robjects.Formula('Xri ~ c(a*Re((wL) / (wL^2+(s+1/rT2)^2 )), a*Im((wL)/(wL^2 + (s+1/rT2)^2 )))') # envelope
-    #fmla = robjects.Formula('XX ~ a*(wL) / (wL^2 + (s+1/rT2)^2 )') # complex
-    #fmla = robjects.Formula('Xa ~ abs(a*(wL) / (wL^2 + (s+1/rT2)^2 )) + nb') # complex
-    fmla = robjects.Formula('Xa ~ abs(a*(wL) / (wL^2 + (s+1/rT2)^2 ))') # complex
- 
-    env = fmla.getenvironment()
-    env['s'] = s
-    env['Xr'] = Xr
-    env['Xa'] = Xa
-    env['Xi'] = Xi
-    env['Xri'] = Xri
-    env['XX'] = XX
-     
-    #fit = robjects.r.tryCatch(robjects.r.nls(fmla,start=my_list, control=my_cont)) #, lower=my_lower, algorithm='port')) #, \
-    fit = robjects.r.tryCatch(robjects.r.nls(fmla, start=my_list, control=my_cont, lower=my_lower, upper=my_upper, algorithm='port')) #, \
-    report =  r.summary(fit)
-    #print report 
-    #print  r.warnings()
- 
-    a  =  r['$'](report,'par')[0]
-    rT2 =  r['$'](report,'par')[1]
-    nb =  r['$'](report,'par')[2]
-    
-    return a, rT2, nb
-
-#################################################
-# Regress for T2 using rpy2 interface
-def regressSpecComplex(w, wL, X): #,sigma2=1,intercept=True):
-
-    # compute s
-    s = -1j*w
-
-    # TODO, if regression fails, it might be because there is no exponential
-    # term, maybe do a second regression then on a linear model. 
-    a   = 1                  # Linear 
-    rT2 = 0.1                # T2 regressed
-    r   = robjects.r         
-    phi2 = 0                 # phase
-    wL2 = wL
-
-    # Variable shared between R and Python
-    robjects.globalenv['a'] = a
-    robjects.globalenv['rT2'] = rT2
-    robjects.globalenv['wL'] = wL
-    robjects.globalenv['wL2'] = 0
-    robjects.globalenv['nb'] = 0
-    robjects.globalenv['phi2'] = phi2
-
-    s = robjects.ComplexVector(numpy.array(s))
-    XX = robjects.ComplexVector(X)
-    Xr = robjects.FloatVector(numpy.real(X))
-    Xi = robjects.FloatVector(numpy.imag(X))
-    Xa = robjects.FloatVector(numpy.abs(X))
-    Xri = robjects.FloatVector(numpy.concatenate((X.real,X.imag)))
-
-    robjects.r(''' 
-        source('kernel.r')
-    ''')   
-    #Kw = robjects.globalenv['Kwri']
-     
-    #print (numpy.shape(X))
-    
-    #my_lower = robjects.r('list(a=.001, rT2=.001, nb=.0001)')
-    #my_lower = robjects.r('list(a=.001, rT2=.001)') # Working
-    my_lower = robjects.r('list(a=.001, rT2=.001, phi2=-3.14, wL2=wL-5)')
-    #my_upper = robjects.r('list(a=1.5, rT2=.300, nb =100.)')
-    my_upper = robjects.r('list(a=3.5, rT2=.300, phi2=3.14, wL2=wL+5)')
-     
-    #my_list = robjects.r('list(a=.2, rT2=0.03, nb=.1)')
-    my_list = robjects.r('list(a=.2, rT2=0.03, phi2=0, wL2=wL)')
-    my_cont = robjects.r('nls.control(maxiter=5000, warnOnly=TRUE, printEval=FALSE)')
-    
-    #fmla = robjects.Formula('Xri ~ c(a*Re((wL) / (wL^2+(s+1/rT2)^2 )), a*Im((wL)/(wL^2 + (s+1/rT2)^2 )))') # envelope
-    #fmla = robjects.Formula('Xi   ~   Im(a*(sin(phi2)*s + ((1/rT2)*sin(phi2)) + wL*cos(phi2)) / (wL^2+(s+1/rT2)^2 ))') # envelope
-    #fmla = robjects.Formula('Xri ~ c(Re(a*(sin(phi2)*s + ((1/rT2)*sin(phi2)) + wL*cos(phi2)) / (wL^2+(s+1/rT2)^2 )), Im(a*(sin(phi2)*s + ((1/rT2)*sin(phi2)) + wL*cos(phi2)) / (wL^2+(s+1/rT2)^2 )))') # envelope
-    
-    #fmlar = robjects.Formula('Xr ~ (Kwr(a, phi2, s, rT2, wL)) ') # envelope
-    #fmlai = robjects.Formula('Xi ~ (Kwi(a, phi2, s, rT2, wL)) ') # envelope
-    fmla = robjects.Formula('Xri ~ c(Kwr(a, phi2, s, rT2, wL2), Kwi(a, phi2, s, rT2, wL2) ) ') # envelope
-    #fmla = robjects.Formula('Xri ~ (Kwri(a, phi2, s, rT2, wL)) ') # envelope
-    
-    #fmla = robjects.Formula('Xa ~ (abs(a*(sin(phi2)*s + ((1/rT2)*sin(phi2)) + wL*cos(phi2)) / (wL^2+(s+1/rT2)^2 )))') # envelope
-    #fmla = robjects.Formula('XX ~ a*(wL) / (wL^2 + (s+1/rT2)^2 )') # complex
-    #fmla = robjects.Formula('Xa ~ abs(a*(wL) / (wL^2 + (s+1/rT2)^2 )) + nb') # complex
-    
-    #fmla = robjects.Formula('Xri ~ c(a*Re((wL) / (wL^2+(s+1/rT2)^2 )), a*Im((wL)/(wL^2 + (s+1/rT2)^2 )))') # envelope
-    
-    #        self.Gw[iw, iT2] = ((np.sin(phi2) *  (alpha + 1j*self.w[iw]) + self.wL*np.cos(phi2)) / \
-    #                               (self.wL**2 + (alpha+1.j*self.w[iw])**2 ))
-    #        self.Gw[iw, iT2] = ds * self.sc*((np.sin(phi2)*( alpha + 1j*self.w[iw]) + self.wL*np.cos(phi2)) / \
-    #                               (self.wL**2 + (alpha+1.j*self.w[iw])**2 ))
-    
-    # Works Amplitude Only!
-    #fmla = robjects.Formula('Xa ~ abs(a*(wL) / (wL^2 + (s+1/rT2)^2 ))') # complex
- 
-    env = fmla.getenvironment()
-    env['s'] = s
-    env['Xr'] = Xr
-    env['Xa'] = Xa
-    env['Xi'] = Xi
-    env['Xri'] = Xri
-    env['XX'] = XX
-     
-    fit = robjects.r.tryCatch(robjects.r.nls(fmla,start=my_list, control=my_cont)) #, lower=my_lower, algorithm='port')) #, \
-    #fitr = robjects.r.tryCatch(robjects.r.nls(fmlar, start=my_list, control=my_cont, lower=my_lower, upper=my_upper, algorithm='port')) #, \
-    
-    #env = fmlai.getenvironment()
-    #fiti = robjects.r.tryCatch(robjects.r.nls(fmlai, start=my_list, control=my_cont, lower=my_lower, upper=my_upper, algorithm='port')) #, \
-    
-    #reportr =  r.summary(fitr)
-    #reporti =  r.summary(fiti)
-    report =  r.summary(fit)
-    #print( report )
-    #exit()
-    #print( reportr )
-    #print( reporti  )
-    #exit()
-    #print  r.warnings()
- 
-    #a   =  (r['$'](reportr,'par')[0] + r['$'](reporti,'par')[0]) / 2.
-    #rT2 =  (r['$'](reportr,'par')[1] + r['$'](reporti,'par')[1]) / 2.
-    #nb  =  (r['$'](reportr,'par')[2] + r['$'](reporti,'par')[2]) / 2.
-    a   =  r['$'](report,'par')[0] 
-    rT2 =  r['$'](report,'par')[1] 
-    nb  =  r['$'](report,'par')[2] #phi2 
-
-    print ("Python wL2", r['$'](report,'par')[3] )   
-    print ("Python zeta", r['$'](report,'par')[2] )   
- 
-    return a, rT2, nb
-
 
 
 ###################################################################

publish.sh

+rm -rf dist
+python setup.py build
+python setup.py sdist bdist_wheel
+twine upload dist/*

setup.py

         self.run_command('build_ui')
         build_py.run(self)
 
+
+with open("README.md", "r") as fh:
+    long_description = fh.read()
+
 setup(name='Akvo',
-      version='1.0.7',
+      version='1.0.12',
       description='Surface nuclear magnetic resonance workbench',
+      long_description=long_description,
       author='Trevor P. Irons',
       author_email='Trevor.Irons@lemmasoftware.org',
-      url='https://svn.lemmasofware.org/akvo',
+      url='https://akvo.lemmasoftware.org/',
       #setup_requires=['PyQt5'],
       setup_requires=[
         # Setuptools 18.0 properly handles Cython extensions.
 #      ext_modules = cythonise("akvo/tressel/*.pyx"), 
 #      build_requires=['cython'],
       install_requires=[
-#          'cython',
-          'rpy2',
+#         'cython',
+#          'rpy2',
           'matplotlib',
           'scipy',
           'numpy',
-          'PyQt5',
+          'pyqt5',
           'pyyaml',
           'pandas',
           'pyqt-distutils',
       package_data={
         'akvo.gui': ['*.png']  #All .r files 
       },
+      classifiers=[
+        "Programming Language :: Python :: 3",
+        "License :: OSI Approved :: GNU Lesser General Public License v3 or later (LGPLv3+)",
+        "Operating System :: OS Independent",
+      ],
     )