mirror of
https://github.com/triqs/dft_tools
synced 2024-11-01 03:33:50 +01:00
142 lines
6.1 KiB
Python
142 lines
6.1 KiB
Python
from gf import GfImFreq_cython, MeshImFreq, TailGf
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from gf_generic import GfGeneric
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import numpy
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from scipy.optimize import leastsq
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from tools import get_indices_in_dict
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from nothing import Nothing
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import impl_plot
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class GfImFreq ( GfGeneric, GfImFreq_cython ) :
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def __init__(self, **d):
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"""
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The constructor have two variants : you can either provide the mesh in
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Matsubara frequencies yourself, or give the parameters to build it.
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All parameters must be given with keyword arguments.
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GfImFreq(indices, beta, statistic, n_points, data, tail, name)
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* ``indices``: a list of indices names of the block
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* ``beta``: Inverse Temperature
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* ``statistic``: 'F' or 'B'
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* ``n_points``: Number of Matsubara frequencies
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* ``data``: A numpy array of dimensions (len(indices),len(indices),n_points) representing the value of the Green function on the mesh.
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* ``tail``: the tail
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* ``name``: a name of the GF
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GfImFreq(indices, mesh, data, tail, name)
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* ``indices``: a list of indices names of the block
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* ``mesh``: a MeshGf object, such that mesh.TypeGF== GF_Type.Imaginary_Frequency
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* ``data``: A numpy array of dimensions (len(indices),len(indices),:) representing the value of the Green function on the mesh.
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* ``tail``: the tail
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* ``name``: a name of the GF
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.. warning::
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The Green function take a **view** of the array data, and a **reference** to the tail.
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"""
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mesh = d.pop('mesh',None)
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if mesh is None :
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if 'beta' not in d : raise ValueError, "beta not provided"
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beta = float(d.pop('beta'))
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n_max = d.pop('n_points',1025)
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stat = d.pop('statistic','F')
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mesh = MeshImFreq(beta,stat,n_max)
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self.dtype = numpy.complex_
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indices_pack = get_indices_in_dict(d)
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indicesL, indicesR = indices_pack
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N1, N2 = len(indicesL),len(indicesR)
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data = d.pop('data') if 'data' in d else numpy.zeros((len(mesh),N1,N2), self.dtype )
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tail = d.pop('tail') if 'tail' in d else TailGf(shape = (N1,N2))
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symmetry = d.pop('symmetry', Nothing())
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name = d.pop('name','g')
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assert len(d) ==0, "Unknown parameters in GFBloc constructions %s"%d.keys()
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GfGeneric.__init__(self, mesh, data, tail, symmetry, indices_pack, name, GfImFreq)
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GfImFreq_cython.__init__(self, mesh, data, tail)
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#-------------- PLOT ---------------------------------------
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def _plot_(self, opt_dict):
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""" Plot protocol. opt_dict can contain :
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* :param RIS: 'R', 'I', 'S', 'RI' [ default]
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* :param x_window: (xmin,xmax) or None [default]
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* :param name: a string [default ='']. If not '', it remplaces the name of the function just for this plot.
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"""
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return impl_plot.plot_base( self, opt_dict, r'$\omega_n$',
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lambda name : r'%s$(i\omega_n)$'%name, True, [x.imag for x in self.mesh] )
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#-------------- OTHER OPERATIONS -----------------------------------------------------
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def replace_by_tail(self,start) :
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d = self.data
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t = self.tail
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for n, om in enumerate(self.mesh) :
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if n >= start : d[n,:,:] = t(om)
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def fit_tail(self, fixed_coef, order_max, fit_start, fit_stop, replace_tail = True):
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"""
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Fit the tail of the Green's function
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Input:
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- fixed_coef: a 3-dim array of known coefficients for the fit starting from the order -1
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- order_max: highest order in the fit
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- fit_start, fit_stop: fit the data between fit_start and fit_stop
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Output:
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On output all the data above fit_start is replaced by the fitted tail
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and the new moments are included in the Green's function
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"""
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# Turn known_coefs into a numpy array if ever it is not already the case
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known_coef = fixed_coef
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# Change the order_max
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# It is assumed that any known_coef will start at order -1
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self.tail.zero()
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self.tail.mask.fill(order_max)
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# Fill up two arrays with the frequencies and values over the range of interest
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ninit, nstop = 0, -1
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x = []
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for om in self.mesh:
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if (om.imag < fit_start): ninit = ninit+1
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if (om.imag <= fit_stop): nstop = nstop+1
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if (om.imag <= fit_stop and om.imag >= fit_start): x += [om]
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omegas = numpy.array(x)
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values = self.data[ninit:nstop+1,:,:]
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# Loop over the indices of the Green's function
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for n1,indR in enumerate(self.indicesR):
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for n2,indL in enumerate(self.indicesL):
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# Construct the part of the fitting functions which is known
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f_known = numpy.zeros((len(omegas)),numpy.complex)
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for order in range(len(known_coef[n1][n2])):
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f_known += known_coef[n1][n2][order]*omegas**(1-order)
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# Compute how many free parameters we have and give an initial guess
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len_param = order_max-len(known_coef[n1][n2])+2
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p0 = len_param*[1.0]
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# This is the function to be minimized, the diff between the original
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# data in values and the fitting function
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def fct(p):
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y_fct = 1.0*f_known
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for order in range(len_param):
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y_fct += p[order]*omegas**(1-len(known_coef[n1][n2])-order)
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y_fct -= values[:,n1,n2]
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return abs(y_fct)
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# Now call the minimizing function
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sol = leastsq(fct, p0, maxfev=1000*len_param)
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# Put the known and the new found moments in the tail
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for order in range(len(known_coef[n1][n2])):
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self.tail[order-1][n1,n2] = numpy.array([[ known_coef[n1][n2][order] ]])
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for order, moment in enumerate(sol[0]):
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self.tail[len(known_coef[n1][n2])+order-1][n1,n2] = numpy.array([[ moment ]])
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self.tail.mask.fill(order_max)
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# Replace then end of the Green's function by the tail
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if replace_tail: self.replace_by_tail(ninit);
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