from pytriqs.Base.Plot.MatplotlibInterface import oplot
from pytriqs.Base.GF_Local import GFBloc_ImFreq, Omega, inverse
g = GFBloc_ImFreq(Indices=[1], Beta=300, NFreqMatsubara=1000, Name="g")
g <<= inverse(Omega + 0.5)
# the data we want to fit...
# The green function for omega \in [0,0.2]
X, Y = g.x_data_view(x_window=(0, 0.2), flatten_y=True)
from pytriqs.Base.Fit.fit import Fit, linear, quadratic
fitl = Fit(X, Y.imag, linear)
fitq = Fit(X, Y.imag, quadratic)
oplot(g, '-o', x_window=(0, 5))
oplot(fitl, '-x', x_window=(0, 0.5))
oplot(fitq, '-x', x_window=(0, 1))
# a bit more complex, we want to fit with a one fermion level ....
# Cf the definition of linear and quadratic in the lib
one_fermion_level = lambda X, a, b: 1 / (a * X * 1j + b
), r"${1}/(%f x + %f)$", (1, 1)
fit1 = Fit(X, Y, one_fermion_level)
oplot(fit1, '-x', x_window=(0, 3))
from pytriqs.Base.Plot.MatplotlibInterface import oplot
from pytriqs.Base.GF_Local import GFBloc_ImFreq, Omega, inverse
g = GFBloc_ImFreq(Indices = [1], Beta = 300, NFreqMatsubara = 1000, Name = "g")
g <<= inverse( Omega + 0.5 )
# the data we want to fit...
# The green function for omega \in [0,0.2]
X,Y = g.x_data_view (x_window = (0,0.2), flatten_y = True )
from pytriqs.Base.Fit.fit import Fit, linear, quadratic
fitl = Fit ( X,Y.imag, linear )
fitq = Fit ( X,Y.imag, quadratic )
oplot (g, '-o', x_window = (0,5) )
oplot (fitl , '-x', x_window = (0,0.5) )
oplot (fitq , '-x', x_window = (0,1) )
# a bit more complex, we want to fit with a one fermion level ....
# Cf the definition of linear and quadratic in the lib
one_fermion_level = lambda X, a,b : 1/(a * X *1j + b), r"${1}/(%f x + %f)$" , (1,1)
fit1 = Fit ( X,Y, one_fermion_level )
oplot (fit1 , '-x', x_window = (0,3) )
