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
def test_nacomplex_workaround():
vec = robjects.ComplexVector((1 + 1j, 2 + 2j, 3 + 3j))
vec[0] = complex(robjects.NA_Complex.real, robjects.NA_Complex.imag)
assert robjects.baseenv['is.na'](vec)[0] is True
def test_nacomplex():
vec = robjects.ComplexVector((1 + 1j, 2 + 2j, 3 + 3j))
vec[0] = robjects.NA_Complex
assert robjects.baseenv['is.na'](vec)[0] is True
def testNAComplex(self):
vec = robjects.ComplexVector((1 + 1j, 2 + 2j, 3 + 3j))
vec[0] = robjects.NA_Complex
self.assertTrue(robjects.baseenv['is.na'](vec)[0])
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