import numpy, array #,rpy2 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 #import notch from numpy.fft import fft, fftfreq # We know/can calculate frequency peak, use this to guess where picks will be. # maybe have a sliding window that reports peak values. def peakPicker(data, omega, dt): # compute window based on omega and dt # make sure you are not aliased, grab every other peak window = (2*numpy.pi) / (omega*dt) data = numpy.array(data) peaks = [] troughs = [] times = [] times2 = [] indices = [] ws = 0 we = window ii = 0 for i in range((int)(len(data)/window)): # initially was just returning this I think avg is better #times.append( (ws + numpy.abs(data[ws:we]).argmax()) * dt ) peaks.append(numpy.max(data[ws:we])) times.append( (ws + data[ws:we].argmax()) * dt ) indices.append( ii + data[ws:we].argmax() ) troughs.append(numpy.min(data[ws:we])) times2.append( (ws + (data[ws:we]).argmin()) * dt ) indices.append( ii + data[ws:we].argmin() ) ws += window we += window ii += (int)(we-ws) #return numpy.array(peaks), numpy.array(times) # Averaging peaks does a good job of removing bias in noise return (numpy.array(peaks)-numpy.array(troughs))/2., \ (numpy.array(times)+numpy.array(times2))/2., \ 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 x[1] = zeta x[2] = df x[3] = T2 """ # concatenated real and imaginary parts pre = np.concatenate((x[0]*np.cos(2.*np.pi*x[2]*t + x[1])*np.exp(-t/x[3]), \ -1.*x[0]*np.sin(2.*np.pi*x[2]*t + x[1])*np.exp(-t/x[3]))) return y-pre def fun2(x, t, y): """ Cost function for regression, single exponential, no DC term x[0] = A0 x[1] = zeta x[2] = T2 """ # concatenated real and imaginary parts pre = np.concatenate((x[0]*np.cos(x[1])*np.exp(-t/x[2]), \ -1.*x[0]*np.sin(x[1])*np.exp(-t/x[2]))) return y-pre def quadratureDetect2(X, Y, tt): """ Pure python quadrature detection using Scipy. X = real part of NMR signal Y = imaginary component of NMR signal tt = time """ # df x = np.array( [1., 0., 0., .2] ) res_lsq = least_squares(fun, x, args=(tt, np.concatenate((X, Y))), loss='soft_l1', f_scale=0.1,\ bounds=( [0., -np.pi, -10, .0] , [1., np.pi, 10, .6] )) x = res_lsq.x return res_lsq.success, x[0], x[2], x[1], x[3] # no df #x = np.array( [1., 0., 0.2] ) #res_lsq = least_squares(fun2, x, args=(tt, np.concatenate((X, Y))), loss='soft_l1', f_scale=0.1) #x = res_lsq.x #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 ################################################################### ################################################################### ################################################################### if __name__ == "__main__": dt = .0001 T2 = .1 omega = 2000.*2*numpy.pi phi = .0 T = 8.*T2 t = numpy.arange(0, T, dt) # Synthetic data, simple single decaying sinusoid # with a single decay parameter and gaussian noise added data = numpy.exp(-t/T2) * numpy.sin(omega * t + phi) + numpy.random.normal(0,.05,len(t)) \ + numpy.random.randint(-1,2,len(t))*numpy.random.exponential(.2,len(t)) cdata = numpy.exp(-t/T2) * numpy.sin(omega * t + phi) #+ numpy.random.normal(0,.25,len(t)) #data = numpy.random.normal(0,.25,len(t)) sigma2 = numpy.std(data[::-len(data)/4]) #sigma2 = numpy.var(data[::-len(data)/4]) print("sigma2", sigma2) [peaks,times,indices] = peakPicker(data, omega, dt) [b1,b2,rT2] = regressCurve(peaks,times) print("rT2 nonweighted", rT2) [b1,b2,rT2] = regressCurve(peaks,times,sigma2) print("rT2 weighted", rT2) envelope = numpy.exp(-t/T2) renvelope = numpy.exp(-t/rT2) #outf = file('regress.txt','w') #for i in range(len(times)): # outf.write(str(times[i]) + " " + str(peaks[i]) + "\n") #outf.close() plt.plot(t,data, 'b') plt.plot(t,cdata, 'g', linewidth=1) plt.plot(t,envelope, color='violet', linewidth=4) plt.plot(t,renvelope, 'r', linewidth=4) plt.plot(times, numpy.array(peaks), 'bo', markersize=8, alpha=.25) plt.legend(['noisy data','clean data','real envelope','regressed env','picks']) plt.savefig("regression.pdf") # FFT check fourier = fft(data) plt.figure() freq = fftfreq(len(data), d=dt) plt.plot(freq, (fourier.real)) plt.show() # TODO do a bunch in batch mode to see if T2 estimate is better with or without # weighting and which model is best. # TODO try with real data # TODO test filters (median, FFT, notch) # It looks like weighting is good for relatively low sigma, but for noisy data # it hurts us. Check