def __init__(self, **d):
"""
The constructor have two variants : you can either provide the mesh in
Matsubara frequencies yourself, or give the parameters to build it.
All parameters must be given with keyword arguments.
GfImFreq(indices, beta, statistic, n_points, data, tail, name)
* ``indices``: a list of indices names of the block
* ``beta``: Inverse Temperature
* ``statistic``: 'F' or 'B'
* ``positive_only``: True or False
* ``n_points``: Number of Matsubara frequencies
* ``data``: A numpy array of dimensions (len(indices),len(indices),n_points) representing the value of the Green function on the mesh.
* ``tail``: the tail
* ``name``: a name of the GF
GfImFreq(indices, mesh, data, tail, name)
* ``indices``: a list of indices names of the block
* ``mesh``: a MeshGf object, such that mesh.TypeGF== GF_Type.Imaginary_Frequency
* ``data``: A numpy array of dimensions (len(indices),len(indices),:) representing the value of the Green function on the mesh.
* ``tail``: the tail
* ``name``: a name of the GF
.. warning::
The Green function take a **view** of the array data, and a **reference** to the tail.
"""
mesh = d.pop('mesh', None)
if mesh is None:
if 'beta' not in d: raise ValueError, "beta not provided"
beta = float(d.pop('beta'))
n_points = d.pop('n_points', 1025)
stat = d.pop('statistic', 'F')
positive_only = d.pop('positive_only', True)
mesh = MeshImFreq(beta, stat, n_points, positive_only)
self.dtype = numpy.complex_
indices_pack = get_indices_in_dict(d)
indicesL, indicesR = indices_pack
N1, N2 = len(indicesL), len(indicesR)
data = d.pop('data') if 'data' in d else numpy.zeros(
(len(mesh), N1, N2), self.dtype)
tail = d.pop('tail') if 'tail' in d else TailGf(shape=(N1, N2))
symmetry = d.pop('symmetry', Nothing())
name = d.pop('name', 'g')
assert len(
d
) == 0, "Unknown parameters in GFBloc constructions %s" % d.keys()
GfGeneric.__init__(self, mesh, data, tail, symmetry, indices_pack,
name, GfImFreq)
GfImFreq_cython.__init__(self, mesh, data, tail)
def __init__(self, **d):
"""
The constructor have two variants : you can either provide the mesh in
Matsubara frequencies yourself, or give the parameters to build it.
All parameters must be given with keyword arguments.
GfImFreq(indices, beta, statistic, n_points, data, tail, name)
* ``indices``: a list of indices names of the block
* ``beta``: Inverse Temperature
* ``statistic``: 'F' or 'B'
* ``positive_only``: True or False
* ``n_points``: Number of Matsubara frequencies
* ``data``: A numpy array of dimensions (len(indices),len(indices),n_points) representing the value of the Green function on the mesh.
* ``tail``: the tail
* ``name``: a name of the GF
GfImFreq(indices, mesh, data, tail, name)
* ``indices``: a list of indices names of the block
* ``mesh``: a MeshGf object, such that mesh.TypeGF== GF_Type.Imaginary_Frequency
* ``data``: A numpy array of dimensions (len(indices),len(indices),:) representing the value of the Green function on the mesh.
* ``tail``: the tail
* ``name``: a name of the GF
.. warning::
The Green function take a **view** of the array data, and a **reference** to the tail.
"""
mesh = d.pop('mesh',None)
if mesh is None :
if 'beta' not in d : raise ValueError, "beta not provided"
beta = float(d.pop('beta'))
n_points = d.pop('n_points',1025)
stat = d.pop('statistic','F')
positive_only = d.pop('positive_only',True)
mesh = MeshImFreq(beta,stat,n_points, positive_only)
self.dtype = numpy.complex_
indices_pack = get_indices_in_dict(d)
indicesL, indicesR = indices_pack
N1, N2 = len(indicesL),len(indicesR)
data = d.pop('data') if 'data' in d else numpy.zeros((len(mesh),N1,N2), self.dtype )
tail = d.pop('tail') if 'tail' in d else TailGf(shape = (N1,N2))
symmetry = d.pop('symmetry', Nothing())
name = d.pop('name','g')
assert len(d) ==0, "Unknown parameters in GFBloc constructions %s"%d.keys()
GfGeneric.__init__(self, mesh, data, tail, symmetry, indices_pack, name, GfImFreq)
GfImFreq_cython.__init__(self, mesh, data, tail)
def __init__(self, **d):
"""
The constructor have two variants : you can either provide the mesh in
Matsubara frequencies yourself, or give the parameters to build it.
All parameters must be given with keyword arguments.
GfImFreq(indices, beta, statistic, n_matsubara, data, tail, name)
* ``indices``: a list of indices names of the block
* ``beta``: Inverse Temperature
* ``statistic``: 'F' or 'B'
* ``n_matsubara``: Number of Matsubara frequencies
* ``data``: A numpy array of dimensions (len(indices),len(indices),n_matsubara) representing the value of the Green function on the mesh.
* ``tail``: the tail
* ``name``: a name of the GF
If you already have the mesh, you can use a simpler version :
GfImFreq(indices, mesh, data, tail, name)
* ``indices``: a list of indices names of the block
* ``mesh``: a MeshGf object, such that mesh.TypeGF== GF_Type.Imaginary_Frequency
* ``data``: A numpy array of dimensions (len(indices),len(indices),n_matsubara) representing the value of the Green function on the mesh.
* ``tail``: the tail
* ``name``: a name of the GF
.. warning::
The Green function take a **view** of the array data, and a **reference** to the tail.
"""
mesh = d.pop('mesh',None)
if mesh is None :
if 'beta' not in d : raise ValueError, "beta not provided"
beta = float(d.pop('beta'))
n_max = d.pop('n_matsubara',1025)
stat = d.pop('statistic','F')
sh = 1 if stat== 'F' else 0
mesh = MeshImFreq(beta,'F',n_max)
self.dtype = numpy.complex_
indicesL, indicesR = get_indices_in_dict(d)
N1, N2 = len(indicesL),len(indicesR)
data = d.pop('data') if 'data' in d else numpy.zeros((N1,N2,len(mesh)), self.dtype )
tail= d.pop('tail') if 'tail' in d else TailGf(shape = (N1,N2), size=10, order_min=-1)
symmetry = d.pop('symmetry', None)
name = d.pop('name','g')
assert len(d) ==0, "Unknown parameters in GFBloc constructions %s"%d.keys()
GfImFreq_cython.__init__(self, mesh, data, tail, symmetry, (indicesL,indicesR), name)