Start legacy code cleanup

Former-commit-id: 6dd209d837466d33e1749114da672a2a08e09f0c

misc/iht-saving.py

@@ -31,5 +31,5 @@ k_0 = omega * math.sqrt(epsilon_b) / c
 output_prefix = '/tmp/diracpoints-newdata2/%d/' % maxlayer
 
 os.makedirs(output_prefix, exist_ok=True)
-qpms.hexlattice_precalc_AB_save3(file=output_prefix+str(omega_eV), lMax=lMax, k_hexside=k_0*hexside,
+qpms.hexlattice_precalc_AB_save(file=output_prefix+str(omega_eV), lMax=lMax, k_hexside=k_0*hexside,
         maxlayer=maxlayer, savepointinfo=True)

qpms/hexpoints.py

@@ -170,74 +170,9 @@ def generate_trianglepoints_hexcomplement(maxlayer, v3d = True, circular = True,
 
 
 from qpms_c import get_mn_y, trans_calculator
-from .qpms_p import Ã, B̃, cart2sph
+from .qpms_p import cart2sph
 
-def hexlattice_precalc_AB_save2(file, lMax, k_hexside, maxlayer, circular=True, savepointinfo = False, J_scat=3):
+def hexlattice_precalc_AB_save(file, lMax, k_hexside, maxlayer, circular=True, savepointinfo = False, J_scat=3):
-    params = {
-        'lMax' : lMax,
-        'k_hexside' : k_hexside,
-        'maxlayer' : maxlayer,
-        'circular' : circular,
-        'savepointinfo' : savepointinfo,
-        'J_scat' : J_scat
-    }
-    tpdict = generate_trianglepoints(maxlayer, v3d=True, circular=circular, sixthindices=True, mirrorindices=True)
-    tphcdict = generate_trianglepoints_hexcomplement(maxlayer, v3d=True, circular=circular, thirdindices=True, mirrorindices=True)
-    my, ny = get_mn_y(lMax)
-    nelem = len(my)
-    a_self_nm = np.empty((tpdict['nmi'].shape[0],nelem,nelem), dtype=complex)
-    b_self_nm = np.empty((tpdict['nmi'].shape[0],nelem,nelem), dtype=complex)
-    a_self_m0 = np.empty((tpdict['mi'].shape[1],nelem,nelem), dtype=complex)
-    b_self_m0 = np.empty((tpdict['mi'].shape[1],nelem,nelem), dtype=complex)
-    a_d2u_nm = np.empty((tphcdict['nmi'].shape[0],nelem,nelem), dtype=complex)
-    b_d2u_nm = np.empty((tphcdict['nmi'].shape[0],nelem,nelem), dtype=complex)
-    a_d2u_m0 = np.empty((tphcdict['mi'].shape[1],nelem,nelem), dtype=complex)
-    b_d2u_m0 = np.empty((tphcdict['mi'].shape[1],nelem,nelem), dtype=complex)
-    
-    k_0 = k_hexside*_s3 # not really a wave vector here because of the normalisation!
-    tc = trans_calculator(lMax)
-    
-    y = np.arange(nelem)
-
-    points = tpdict['points'][tpdict['nmi']]
-    d_i2j = cart2sph(points)
-    a_self_nm, b_self_nm = tc.get_AB(my[nx,:,nx],ny[nx,:,nx],my[nx,nx,:],ny[nx,nx,:],k_0*d_i2j[:,nx,nx,0],d_i2j[:,nx,nx,1],d_i2j[:,nx,nx,2],False,J_scat)
-
-    points = tpdict['points'][tpdict['mi'][0]]
-    d_i2j = cart2sph(points)
-    a_self_m0, b_self_m0 = tc.get_AB(my[nx,:,nx],ny[nx,:,nx],my[nx,nx,:],ny[nx,nx,:],k_0*d_i2j[:,nx,nx,0],d_i2j[:,nx,nx,1],d_i2j[:,nx,nx,2],False,J_scat)
-   
-    points = tphcdict['points'][tphcdict['nmi']]
-    d_i2j = cart2sph(points)
-    a_d2u_nm, b_d2u_nm = tc.get_AB(my[nx,:,nx],ny[nx,:,nx],my[nx,nx,:],ny[nx,nx,:],k_0*d_i2j[:,nx,nx,0],d_i2j[:,nx,nx,1],d_i2j[:,nx,nx,2],False,J_scat)
-  
-    points = tphcdict['points'][tphcdict['mi'][0]]
-    d_i2j = cart2sph(points)
-    a_d2u_m0, b_d2u_m0 = tc.get_AB(my[nx,:,nx],ny[nx,:,nx],my[nx,nx,:],ny[nx,nx,:],k_0*d_i2j[:,nx,nx,0],d_i2j[:,nx,nx,1],d_i2j[:,nx,nx,2],False,J_scat)
-  
-    tosave = {
-        'a_self_nm' : a_self_nm,
-        'a_self_m0' : a_self_m0,
-        'b_self_nm' : b_self_nm,
-        'b_self_m0' : b_self_m0,
-        'a_d2u_nm' : a_d2u_nm,
-        'a_d2u_m0' : a_d2u_m0,
-        'b_d2u_nm' : b_d2u_nm,
-        'b_d2u_m0' : b_d2u_m0,
-        'precalc_params' : params
-    }
-    if savepointinfo:
-        tosave['tp_points'] = tpdict['points'],
-        tosave['tp_si'] = tpdict['si'],
-        tosave['tp_mi'] = tpdict['mi'],
-        tosave['tp_nmi'] = tpdict['nmi']
-        tosave['tphc_points'] = tphcdict['points'],
-        tosave['tphc_ti'] = tphcdict['ti'],
-        tosave['tphc_mi'] = tphcdict['mi'],
-        tosave['tphc_nmi'] = tphcdict['nmi']
-    np.savez(file, **tosave)
-
-def hexlattice_precalc_AB_save3(file, lMax, k_hexside, maxlayer, circular=True, savepointinfo = False, J_scat=3):
     params = {
         'lMax' : lMax,
         'k_hexside' : k_hexside,
@@ -302,82 +237,6 @@ def hexlattice_precalc_AB_save3(file, lMax, k_hexside, maxlayer, circular=True,
         tosave['tphc_nmi'] = tphcdict['nmi']
     np.savez(file, **tosave)
 
-
-
-def hexlattice_precalc_AB_save(file, lMax, k_hexside, maxlayer, circular=True, savepointinfo = False, J_scat=3):
-    params = {
-        'lMax' : lMax,
-        'k_hexside' : k_hexside,
-        'maxlayer' : maxlayer,
-        'circular' : circular,
-        'savepointinfo' : savepointinfo,
-        'J_scat' : J_scat
-    }
-    tpdict = generate_trianglepoints(maxlayer, v3d=True, circular=circular, sixthindices=True, mirrorindices=True)
-    tphcdict = generate_trianglepoints_hexcomplement(maxlayer, v3d=True, circular=circular, thirdindices=True, mirrorindices=True)
-    my, ny = get_mn_y(lMax)
-    nelem = len(my)
-    a_self_nm = np.empty((tpdict['nmi'].shape[0],nelem,nelem), dtype=complex)
-    b_self_nm = np.empty((tpdict['nmi'].shape[0],nelem,nelem), dtype=complex)
-    a_self_m0 = np.empty((tpdict['mi'].shape[1],nelem,nelem), dtype=complex)
-    b_self_m0 = np.empty((tpdict['mi'].shape[1],nelem,nelem), dtype=complex)
-    a_d2u_nm = np.empty((tphcdict['nmi'].shape[0],nelem,nelem), dtype=complex)
-    b_d2u_nm = np.empty((tphcdict['nmi'].shape[0],nelem,nelem), dtype=complex)
-    a_d2u_m0 = np.empty((tphcdict['mi'].shape[1],nelem,nelem), dtype=complex)
-    b_d2u_m0 = np.empty((tphcdict['mi'].shape[1],nelem,nelem), dtype=complex)
-    
-    k_0 = k_hexside*_s3 # not really a wave vector here because of the normalisation!
-    
-    points = tpdict['points'][tpdict['nmi']]
-    for j in range(points.shape[0]):
-        d_i2j = cart2sph(points[j])
-        for yi in range(nelem):
-            for yj in range(nelem):
-                a_self_nm[j, yj, yi] = Ã(my[yj],ny[yj],my[yi],ny[yi],kdlj=d_i2j[0]*k_0,θlj=d_i2j[1],φlj=d_i2j[2],r_ge_d=False,J=J_scat)
-                b_self_nm[j, yj, yi] = B̃(my[yj],ny[yj],my[yi],ny[yi],kdlj=d_i2j[0]*k_0,θlj=d_i2j[1],φlj=d_i2j[2],r_ge_d=False,J=J_scat)
-    points = tpdict['points'][tpdict['mi'][0]]
-    for j in range(points.shape[0]):
-        d_i2j = cart2sph(points[j])
-        for yi in range(nelem):
-            for yj in range(nelem):
-                a_self_m0[j, yj, yi] = Ã(my[yj],ny[yj],my[yi],ny[yi],kdlj=d_i2j[0]*k_0,θlj=d_i2j[1],φlj=d_i2j[2],r_ge_d=False,J=J_scat)
-                b_self_m0[j, yj, yi] = B̃(my[yj],ny[yj],my[yi],ny[yi],kdlj=d_i2j[0]*k_0,θlj=d_i2j[1],φlj=d_i2j[2],r_ge_d=False,J=J_scat)
-    points = tphcdict['points'][tphcdict['nmi']]
-    for j in range(points.shape[0]):
-        d_i2j = cart2sph(points[j])
-        for yi in range(nelem):
-            for yj in range(nelem):
-                a_d2u_nm[j, yj, yi] = Ã(my[yj],ny[yj],my[yi],ny[yi],kdlj=d_i2j[0]*k_0,θlj=d_i2j[1],φlj=d_i2j[2],r_ge_d=False,J=J_scat)
-                b_d2u_nm[j, yj, yi] = B̃(my[yj],ny[yj],my[yi],ny[yi],kdlj=d_i2j[0]*k_0,θlj=d_i2j[1],φlj=d_i2j[2],r_ge_d=False,J=J_scat)
-    points = tphcdict['points'][tphcdict['mi'][0]]
-    for j in range(points.shape[0]):
-        d_i2j = cart2sph(points[j])
-        for yi in range(nelem):
-            for yj in range(nelem):
-                a_d2u_m0[j, yj, yi] = Ã(my[yj],ny[yj],my[yi],ny[yi],kdlj=d_i2j[0]*k_0,θlj=d_i2j[1],φlj=d_i2j[2],r_ge_d=False,J=J_scat)
-                b_d2u_m0[j, yj, yi] = B̃(my[yj],ny[yj],my[yi],ny[yi],kdlj=d_i2j[0]*k_0,θlj=d_i2j[1],φlj=d_i2j[2],r_ge_d=False,J=J_scat)
-    tosave = {
-        'a_self_nm' : a_self_nm,
-        'a_self_m0' : a_self_m0,
-        'b_self_nm' : b_self_nm,
-        'b_self_m0' : b_self_m0,
-        'a_d2u_nm' : a_d2u_nm,
-        'a_d2u_m0' : a_d2u_m0,
-        'b_d2u_nm' : b_d2u_nm,
-        'b_d2u_m0' : b_d2u_m0,
-        'precalc_params' : params
-    }
-    if savepointinfo:
-        tosave['tp_points'] = tpdict['points'],
-        tosave['tp_si'] = tpdict['si'],
-        tosave['tp_mi'] = tpdict['mi'],
-        tosave['tp_nmi'] = tpdict['nmi']
-        tosave['tphc_points'] = tphcdict['points'],
-        tosave['tphc_ti'] = tphcdict['ti'],
-        tosave['tphc_mi'] = tphcdict['mi'],
-        tosave['tphc_nmi'] = tphcdict['nmi']
-    np.savez(file, **tosave)
-
 def hexlattice_precalc_AB_loadunwrap(file, tpdict = None, tphcdict = None, return_points = False):
     npz = np.load(file)
     precalc_params = npz['precalc_params'][()]

qpms/qpms_p.py

@@ -11,6 +11,7 @@ import cmath
 import quaternion, spherical_functions as sf # because of the Wigner matrices
 import sys, time
 
+"""
 '''
 Try to import numba. Its pre-0.28.0 versions can not handle functions
 containing utf8 identifiers, so we keep track about that.
@@ -43,7 +44,7 @@ def ujit(f):
         return numba.jit(f)
     else:
         return f
-
+"""
 
 # Coordinate transforms for arrays of "arbitrary" shape
 #@ujit
@@ -473,274 +474,6 @@ def plane_pq_y(nmax, kdir_cart, E_cart):
     
 
 
-# Functions copied from scattering_xu, additionaly normalized
-from py_gmm.gmm import vec_trans as vc
-
-#@ujit
-def q_max(m,n,μ,ν):
-    return min(n,ν,(n+ν-abs(m+μ))/2)
-
-q_max_v = np.vectorize(q_max)
-
-# returns array with indices corresponding to q
-# argument q does nothing for now
-#@ujit
-def a_q(m,n,μ,ν,q = None):
-    qm=q_max(m,n,μ,ν)
-    res, err= vc.gaunt_xu(m,n,μ,ν,qm)
-    if(err):
-        print("m,n,μ,ν,qm = ",m,n,μ,ν,qm)
-        raise ValueError('Something bad in the fortran subroutine gaunt_xu happened')
-    return res
-
-a_q_v = np.vectorize(a_q)
-
-
-# All arguments are single numbers (for now)
-# ZDE VYCHÁZEJÍ DIVNÁ ZNAMÉNKA
-#@ujit
-def Ã(m,n,μ,ν,kdlj,θlj,φlj,r_ge_d,J):
-    """
-    The à translation coefficient for spherical vector waves.
-    
-    Parameters
-    ----------
-    m, n: int
-        The indices (degree and order) of the destination basis.
-    μ, ν: int
-        The indices of the source basis wave.
-    kdlj, θlj, φlj: float
-        The spherical coordinates of the relative position of
-        the new center vs. the old one (R_new - R_old);
-        the distance has to be already multiplied by the wavenumber!
-    r_ge_d: TODO
-    J: 1, 2, 3 or 4
-        Type of the wave in the old center.
-
-    Returns
-    -------
-    TODO
-
-    Bugs
-    ----
-    gevero's gaunt coefficient implementation fails for large m, n (the unsafe territory
-    is somewhere around -72, 80)
-
-    """
-    exponent=(math.lgamma(2*n+1)-math.lgamma(n+2)+math.lgamma(2*ν+3)-math.lgamma(ν+2) 
-                +math.lgamma(n+ν+m-μ+1)-math.lgamma(n-m+1)-math.lgamma(ν+μ+1)
-                +math.lgamma(n+ν+1) - math.lgamma(2*(n+ν)+1))
-    presum = math.exp(exponent)
-    presum = presum * np.exp(1j*(μ-m)*φlj) * (-1)**m * 1j**(ν+n) / (4*n)
-    qmax = math.floor(q_max(-m,n,μ,ν)) #nemá tu být +m?
-    q = np.arange(qmax+1, dtype=int)
-    # N.B. -m !!!!!!
-    a1q = a_q(-m,n,μ,ν) # there is redundant calc. of qmax
-    ã1q = a1q / a1q[0]
-    p = n+ν-2*q
-    if(r_ge_d):
-        J = 1
-    zp = zJn(n+ν,kdlj,J)[0][p]
-    Pp = lpmv(μ-m,p,math.cos(θlj))
-    summandq = (n*(n+1) + ν*(ν+1) - p*(p+1)) * (-1)**q * ã1q * zp * Pp
-  
-    # Taylor normalisation v2, proven to be equivalent (NS which is better)
-    prenormratio = 1j**(ν-n) * math.sqrt(((2*ν+1)/(2*n+1))* math.exp(
-        math.lgamma(n+m+1)-math.lgamma(n-m+1)+math.lgamma(ν-μ+1)-math.lgamma(ν+μ+1)))
-    presum = presum / prenormratio
-    
-    # Taylor normalisation
-    #prenormmn =  math.sqrt((2*n + 1)*math.factorial(n-m)/(4*π*factorial(n+m)))
-    #prenormμν =  math.sqrt((2*ν + 1)*math.factorial(ν-μ)/(4*π*factorial(ν+μ)))
-    #presum = presum * prenormμν / prenormmn
-    
-    return presum * np.sum(summandq)
-    
-# ZDE OPĚT JINAK ZNAMÉNKA než v Xu (J. comp. phys 127, 285)
-#@ujit
-def B̃(m,n,μ,ν,kdlj,θlj,φlj,r_ge_d,J):
-    """
-    The B̃ translation coefficient for spherical vector waves.
-    
-    Parameters
-    ----------
-    m, n: int
-        The indices (degree and order) of the destination basis.
-    μ, ν: int
-        The indices of the source basis wave.
-    kdlj, θlj, φlj: float
-        The spherical coordinates of the relative position of
-        the new center vs. the old one (R_new - R_old);
-        the distance has to be already multiplied by the wavenumber!
-    r_ge_d: TODO
-    J: 1, 2, 3 or 4
-        Type of the wave in the old center.
-
-    Returns:
-    --------
-    TODO
-    """
-    exponent=(math.lgamma(2*n+3)-math.lgamma(n+2)+math.lgamma(2*ν+3)-math.lgamma(ν+2) 
-                +math.lgamma(n+ν+m-μ+2)-math.lgamma(n-m+1)-math.lgamma(ν+μ+1)
-                +math.lgamma(n+ν+2) - math.lgamma(2*(n+ν)+3))
-    presum = math.exp(exponent)
-    presum = presum * np.exp(1j*(μ-m)*φlj) * (-1)**m * 1j**(ν+n+1) / (
-        (4*n)*(n+1)*(n+m+1))
-    Qmax = math.floor(q_max(-m,n+1,μ,ν))
-    q = np.arange(Qmax+1, dtype=int)
-    if (μ == ν): # it would disappear in the sum because of the factor (ν-μ) anyway
-        ã2q = 0
-    else:
-        a2q = a_q(-m-1,n+1,μ+1,ν)
-        ã2q = a2q / a2q[0]
-    a3q = a_q(-m,n+1,μ,ν)
-    ã3q = a3q / a3q[0]
-    #print(len(a2q),len(a3q))
-    p = n+ν-2*q
-    if(r_ge_d):
-        J = 1
-    zp_ = zJn(n+1+ν,kdlj,J)[0][p+1] # je ta +1 správně?
-    Pp_ = lpmv(μ-m,p+1,math.cos(θlj))
-    summandq = ((2*(n+1)*(ν-μ)*ã2q
-                 -(-ν*(ν+1) - n*(n+3) - 2*μ*(n+1)+p*(p+3))* ã3q)
-                *(-1)**q * zp_ * Pp_)
-    
-    # Taylor normalisation v2, proven to be equivalent
-    prenormratio = 1j**(ν-n) * math.sqrt(((2*ν+1)/(2*n+1))* math.exp(
-        math.lgamma(n+m+1)-math.lgamma(n-m+1)+math.lgamma(ν-μ+1)-math.lgamma(ν+μ+1)))
-    presum = presum / prenormratio
-    
-    ## Taylor normalisation
-    #prenormmn =  math.sqrt((2*n + 1)*math.factorial(n-m)/(4*π*factorial(n+m)))
-    #prenormμν =  math.sqrt((2*ν + 1)*math.factorial(ν-μ)/(4*π*factorial(ν+μ)))
-    #presum = presum * prenormμν / prenormmn
-    
-    return presum * np.sum(summandq)
- 
-# vectorised versions - conservative
-# ZDE VYCHÁZEJÍ DIVNÁ ZNAMÉNKA
-#@ujit
-def Ã_v0(m,n,μ,ν,kdlj,θlj,φlj,r_ge_d,J):
-    """
-    The à translation coefficient for spherical vector waves.
-    
-    Parameters
-    ----------
-    m, n: int
-        The indices (degree and order) of the destination basis.
-    μ, ν: int
-        The indices of the source basis wave.
-    kdlj, θlj, φlj: float
-        The spherical coordinates of the relative position of
-        the new center vs. the old one (R_new - R_old);
-        the distance has to be already multiplied by the wavenumber!
-    r_ge_d: TODO
-    J: 1, 2, 3 or 4
-        Type of the wave in the old center.
-
-    Returns
-    -------
-    TODO
-
-    Bugs
-    ----
-    gevero's gaunt coefficient implementation fails for large m, n (the unsafe territory
-    is somewhere around -72, 80)
-
-    """
-    lMax = max(np.amax(n),np.amax(ν))
-    exponent=(gammaln(2*n+1)-gammaln(n+2)+gammaln(2*ν+3)-gammaln(ν+2) 
-                +gammaln(n+ν+m-μ+1)-gammaln(n-m+1)-gammaln(ν+μ+1)
-                +gammaln(n+ν+1) - gammaln(2*(n+ν)+1))
-    presum = np.exp(exponent)
-    presum = presum * np.exp(1j*(μ-m)*φlj) * (-1)**m * 1j**(ν+n) / (4*n)
-    qmax = np.floor(q_max(-m,n,μ,ν)) #nemá tu být +m?
-    q = np.arange(qmax+1, dtype=int)
-    # N.B. -m !!!!!!
-    a1q = a_q_v(-m,n,μ,ν) # there is redundant calc. of qmax
-    ã1q = a1q / a1q[0]
-    p = n+ν-2*q
-    if(r_ge_d):
-        J = 1
-    zp = zJn(n+ν,kdlj,J)[0][p]
-    Pp = lpmv(μ-m,p,np.cos(θlj))
-    summandq = (n*(n+1) + ν*(ν+1) - p*(p+1)) * (-1)**q * ã1q * zp * Pp
-  
-    # Taylor normalisation v2, proven to be equivalent (NS which is better)
-    prenormratio = 1j**(ν-n) * np.sqrt(((2*ν+1)/(2*n+1))* np.exp(
-        gammaln(n+m+1)-gammaln(n-m+1)+gammaln(ν-μ+1)-gammaln(ν+μ+1)))
-    presum = presum / prenormratio
-    
-    # Taylor normalisation
-    #prenormmn =  math.sqrt((2*n + 1)*math.factorial(n-m)/(4*π*factorial(n+m)))
-    #prenormμν =  math.sqrt((2*ν + 1)*math.factorial(ν-μ)/(4*π*factorial(ν+μ)))
-    #presum = presum * prenormμν / prenormmn
-    
-    return presum * np.sum(summandq)
-    
-# ZDE OPĚT JINAK ZNAMÉNKA než v Xu (J. comp. phys 127, 285)
-#@ujit
-def B̃_v(m,n,μ,ν,kdlj,θlj,φlj,r_ge_d,J):
-    """
-    The B̃ translation coefficient for spherical vector waves.
-    
-    Parameters
-    ----------
-    m, n: int
-        The indices (degree and order) of the destination basis.
-    μ, ν: int
-        The indices of the source basis wave.
-    kdlj, θlj, φlj: float
-        The spherical coordinates of the relative position of
-        the new center vs. the old one (R_new - R_old);
-        the distance has to be already multiplied by the wavenumber!
-    r_ge_d: TODO
-    J: 1, 2, 3 or 4
-        Type of the wave in the old center.
-
-    Returns:
-    --------
-    TODO
-    """
-    exponent=(gammaln(2*n+3)-gammaln(n+2)+gammaln(2*ν+3)-gammaln(ν+2) 
-                +gammaln(n+ν+m-μ+2)-gammaln(n-m+1)-gammaln(ν+μ+1)
-                +gammaln(n+ν+2) - gammaln(2*(n+ν)+3))
-    presum = math.exp(exponent)
-    presum = presum * np.exp(1j*(μ-m)*φlj) * (-1)**m * 1j**(ν+n+1) / (
-        (4*n)*(n+1)*(n+m+1))
-    Qmax = math.floor(q_max(-m,n+1,μ,ν))
-    q = np.arange(Qmax+1, dtype=int)
-    if (μ == ν): # it would disappear in the sum because of the factor (ν-μ) anyway
-        ã2q = 0
-    else:
-        a2q = a_q(-m-1,n+1,μ+1,ν)
-        ã2q = a2q / a2q[0]
-    a3q = a_q(-m,n+1,μ,ν)
-    ã3q = a3q / a3q[0]
-    #print(len(a2q),len(a3q))
-    p = n+ν-2*q
-    if(r_ge_d):
-        J = 1
-    zp_ = zJn(n+1+ν,kdlj,J)[0][p+1] # je ta +1 správně?
-    Pp_ = lpmv(μ-m,p+1,math.cos(θlj))
-    summandq = ((2*(n+1)*(ν-μ)*ã2q
-                 -(-ν*(ν+1) - n*(n+3) - 2*μ*(n+1)+p*(p+3))* ã3q)
-                *(-1)**q * zp_ * Pp_)
-    
-    # Taylor normalisation v2, proven to be equivalent
-    prenormratio = 1j**(ν-n) * math.sqrt(((2*ν+1)/(2*n+1))* math.exp(
-        gammaln(n+m+1)-gammaln(n-m+1)+gammaln(ν-μ+1)-gammaln(ν+μ+1)))
-    presum = presum / prenormratio
-    
-    ## Taylor normalisation
-    #prenormmn =  math.sqrt((2*n + 1)*math.factorial(n-m)/(4*π*factorial(n+m)))
-    #prenormμν =  math.sqrt((2*ν + 1)*math.factorial(ν-μ)/(4*π*factorial(ν+μ)))
-    #presum = presum * prenormμν / prenormmn
-    
-    return presum * np.sum(summandq)
- 
-
-# In[7]:
 
 # Material parameters
 #@ujit
@@ -868,79 +601,6 @@ def G_Mie_scat_precalc_cart_new(source_cart, dest_cart, RH, RV, a, nmax, k_i, k_
            RV[ny][:,ň,ň] * Ñlo_cart_y[:,:,ň].conj() * Ñhi_cart_y[:,ň,:]) / (ny * (ny+1))[:,ň,ň]
     return 1j* k_e*np.sum(G_y,axis=0)
     
-#@ujit
-def G_Mie_scat_precalc_cart(source_cart, dest_cart, RH, RV, a, nmax, k_i, k_e, μ_i=1, μ_e=1, J_ext=1, J_scat=3):
-    """
-    r1_cart (destination), r2_cart (source) and the result are in cartesian coordinates
-    the result indices are in the source-destination order
-    TODO
-    """
-    my, ny = get_mn_y(nmax)
-    nelem = len(my)
-    #source to origin
-    so_sph = cart2sph(-source_cart)
-    kd_so = k_e * so_sph[0]
-    θ_so = so_sph[1]
-    φ_so = so_sph[2]
-    # Decomposition of the source N_0,1, N_-1,1, and N_1,1 in the nanoparticle center
-    p_0 = np.empty((nelem), dtype=np.complex_)
-    q_0 = np.empty((nelem), dtype=np.complex_)
-    p_minus = np.empty((nelem), dtype=np.complex_)
-    q_minus = np.empty((nelem), dtype=np.complex_)
-    p_plus = np.empty((nelem), dtype=np.complex_)
-    q_plus = np.empty((nelem), dtype=np.complex_)
-    for y in range(nelem):
-        m = my[y]
-        n = ny[y]
-        p_0[y]     = Ã(m,n, 0,1,kd_so,θ_so,φ_so,False,J=J_scat)
-        q_0[y]     = B̃(m,n, 0,1,kd_so,θ_so,φ_so,False,J=J_scat)
-        p_minus[y] = Ã(m,n,-1,1,kd_so,θ_so,φ_so,False,J=J_scat)
-        q_minus[y] = B̃(m,n,-1,1,kd_so,θ_so,φ_so,False,J=J_scat)
-        p_plus[y]  = Ã(m,n, 1,1,kd_so,θ_so,φ_so,False,J=J_scat)
-        q_plus[y]  = B̃(m,n, 1,1,kd_so,θ_so,φ_so,False,J=J_scat)
-    a_0 = RV[ny] * p_0
-    b_0 = RH[ny] * q_0
-    a_plus = RV[ny] * p_plus
-    b_plus = RH[ny] * q_plus
-    a_minus = RV[ny] * p_minus
-    b_minus = RH[ny] * q_minus
-    orig2dest_sph = cart2sph(dest_cart)
-    orig2dest_sph[0] = k_e*orig2dest_sph[0]
-    M_dest_y, N_dest_y = vswf_yr1(orig2dest_sph,nmax,J=J_scat)
-    # N.B. these are in the local cartesian coordinates (r̂,θ̂,φ̂)
-    N_dest_0     = np.sum(a_0[:,ň]    * N_dest_y, axis=-2)
-    M_dest_0     = np.sum(b_0[:,ň]    * M_dest_y, axis=-2)
-    N_dest_plus  = np.sum(a_plus[:,ň] * N_dest_y, axis=-2)
-    M_dest_plus  = np.sum(b_plus[:,ň] * M_dest_y, axis=-2)
-    N_dest_minus = np.sum(a_minus[:,ň]* N_dest_y, axis=-2)
-    M_dest_minus = np.sum(b_minus[:,ň]* M_dest_y, axis=-2)
-    prefac = math.sqrt(1/(4*3*π))#/ε_0
-    G_sourcez_dest = prefac * (N_dest_0+M_dest_0)
-    G_sourcex_dest = prefac * (N_dest_minus+M_dest_minus-N_dest_plus-M_dest_plus)/math.sqrt(2)
-    G_sourcey_dest = prefac * (N_dest_minus+M_dest_minus+N_dest_plus+M_dest_plus)/(1j*math.sqrt(2))
-    G_source_dest = np.array([G_sourcex_dest, G_sourcey_dest, G_sourcez_dest])
-    # To global cartesian coordinates:
-    G_source_dest = sph_loccart2cart(G_source_dest, sph=orig2dest_sph, axis=-1)
-    return G_source_dest
-    
-#@ujit
-def G_Mie_scat_cart(source_cart, dest_cart, a, nmax, k_i, k_e, μ_i=1, μ_e=1, J_ext=1, J_scat=3):
-    """
-    TODO
-    """
-    RH, RV, TH, TV = mie_coefficients(a=a, nmax=nmax, k_i=k_i, k_e=k_e, μ_i=μ_i, μ_e=μ_e, J_ext=J_ext, J_scat=J_scat)
-    return G_Mie_scat_precalc_cart_new(source_cart, dest_cart, RH, RV, a, nmax, k_i, k_e, μ_i, μ_e, J_ext, J_scat)
-
-
-#TODO
-def cross_section_Mie_precalc():
-    pass
-
-def cross_section_Mie(a, nmax, k_i, k_e, μ_i, μ_e,):
-    pass
-
-
-# In[9]:
 
 # From PRL 112, 253601 (1)
 #@ujit
@@ -1255,476 +915,3 @@ def apply_ndmatrix_left(matrix,tensor,axes):
     matrix = np.tensordot(matrix, tensor, axes=([-N+axn for axn in range(N)],axes))
     matrix = np.moveaxis(matrix, range(N), axes)
     return matrix
-
-####################
-# Array simulations
-####################
-
-#@jit
-def nelem2lMax(nelem):
-    """
-    Auxiliary inverse function to nelem(lMax) = (lMax + 2) * lMax. Returns 0 if
-    it nelem does not come from positive integer lMax.
-    """
-    lMax = round(math.sqrt(1+nelem) - 1)
-    if ((lMax < 1) or ((lMax + 2) * lMax != nelem)):
-        return 0
-    else:
-        return lMax
-
-def scatter_plane_wave(omega, epsilon_b, positions, Tmatrices, k_dirs, E_0s, #saveto = None
-                      ):
-    """
-    Solves the plane wave linear scattering problem for a structure of "non-touching" particles
-    for one frequency and arbitrary number K of incoming plane waves.
-    
-    Parameters
-    ----------
-    omega : positive number
-        The frequency of the field.
-    epsilon_b : complex number
-        Permittivity of the background medium (which has to be isotropic).
-    positions : (N,3)-shaped real array
-        Cartesian positions of the particles.
-    TMatrices : (N,2,nelem,2,nelem) or compatible
-        The T-matrices in the "Taylor convention" describing the scattering on a single nanoparticle.
-        If all the particles are identical and equally oriented, only one T-matrix can be given.
-        nelems = (lMax + 2) * lMax, where lMax is the highest multipole order to which the scattering
-        is calculated.
-    k_dirs : (K,3)-shaped real array or compatible
-        The direction of the incident field wave vector, normalized to one.
-    E_0s : (K,3)-shaped complex array or compatible
-        The electric intensity amplitude of the incident field.
-        
-    Returns
-    -------
-    ab : (K, N, 2, nelem)-shaped complex array
-        The a (electric wave), b (magnetic wave) coefficients of the outgoing field for each particle
-    # Fuck this, it will be wiser to make separate function to calculate those from ab:
-    # sigma_xxx : TODO (K, 2, nelem)
-    #    TODO partial (TODO which?) cross-section for each type of outgoing waves, summed over all
-    #    nanoparticles (total cross section is given by the sum of this.)
-    """
-    nelem = TMatrices.shape[-1]
-    if ((nelem != TMatrices.shape[-3]) or (2 != TMatrices.shape[-2]) or (2 != TMatrices.shape[-4])):
-        raise ValueError('The T-matrices must be of shape (N, 2, nelem, 2, nelem) but are of shape %s' % (str(TMatrices.shape),))
-    lMax = nelem2lMax(nelem)
-    if not lMax:
-        raise ValueError('The "nelem" dimension of T-matrix has invalid value (%d).' % nelem)
-    # TODO perhaps more checks.
-    raise Error('Not implemented.')
-    pass
-
-import warnings
-#@ujit
-def scatter_plane_wave_rectarray(omega, epsilon_b, xN, yN, xd, yd, TMatrices, k_dirs, E_0s, 
-        return_pq_0 = False, return_pq= False, return_xy = False, watch_time = False):
-    """
-    Solves the plane wave linear scattering problem for a rectangular array of particles
-    for one frequency and arbitrary number K of incoming plane waves.
-    
-    Parameters
-    ----------
-    omega : positive number
-        The frequency of the field.
-    epsilon_b : complex number
-        Permittivity of the background medium (which has to be isotropic).
-    xN, yN : positive integers
-        Particle numbers in the x and y dimensions
-    xd, yd : positive numbers
-        Periodicities in the x and y direction
-    TMatrices : (xN, yN,2,nelem,2,nelem) or compatible or (2,nelem,2,nelem)
-        The T-matrices in the "Taylor convention" describing the scattering on a single nanoparticle.
-        If all the particles are identical and equally oriented, only one T-matrix can be given.
-        nelems = (lMax + 2) * lMax, where lMax is the highest multipole order to which the scattering
-        is calculated.
-        Electric wave index is 0, magnetic wave index is 1.
-    k_dirs : (K,3)-shaped real array or compatible
-        The direction of the incident field wave vector, normalized to one.
-    E_0s : (K,3)-shaped complex array or compatible
-        The electric intensity amplitude of the incident field.
-    return_pq_0 : bool
-        Return also the multipole decomposition coefficients of the incoming plane wave.
-    return_pq : bool NOT IMPLEMENTED
-        Return also the multipole decomposition coefficients of the field incoming to each
-        particle (inc. the field scattered from other particles.
-    return_xy : bool
-        Return also the cartesian x, y positions of the particles. 
-    watch_time : bool
-        Inform about the progress on stderr
-        
-    Returns
-    -------
-    ab : (K, xN, yN, 2, nelem)-shaped complex array
-        The a (electric wave), b (magnetic wave) coefficients of the outgoing field for each particle.
-        If none of return_pq or return_xy is set, the array is not enclosed in a tuple.
-    pq_0 : (K, xN, yn, 2, nelem)-shaped complex array
-        The p_0 (electric wave), b_0 (magnetic wave) coefficients of the incoming plane wave for each particle.
-    pq : (K, xN, yN, 2, nelem)-shaped complex array NOT IMPLEMENTED
-        The p (electric wave), q (magnetic wave) coefficients of the total exciting field
-        for each particle (including the field scattered from other particles)
-    x, y : (xN, yN)-shaped real array
-        The x,y positions of the nanoparticles.
-    """
-    if (watch_time):
-        timec = time.time()
-        print('%.4f: running scatter_plane_wave_rectarray' % timec, file = sys.stderr)
-        sys.stderr.flush()
-    nelem = TMatrices.shape[-1]
-    if ((nelem != TMatrices.shape[-3]) or (2 != TMatrices.shape[-2]) or (2 != TMatrices.shape[-4])):
-        raise ValueError('The T-matrices must be of shape (N, 2, nelem, 2, nelem) but are of shape %s' % (str(TMatrices.shape),))
-    lMax = nelem2lMax(nelem)
-    if not lMax:
-        raise ValueError('The "nelem" dimension of T-matrix has invalid value (%d).' % nelem)
-    if (watch_time):
-        print('xN = %d, yN = %d, lMax = %d' % (xN, yN, lMax), file = sys.stderr)
-        sys.stderr.flush()
-    # TODO perhaps more checks.
-    k_out = omega * math.sqrt(epsilon_b) / c # wave number
-    my, ny = get_mn_y(lMax)
-    N = yN * xN
-    
-    J_scat=3
-    J_ext=1
-    
-    # Do something with this ugly indexing crap
-    xind, yind = np.meshgrid(np.arange(xN),np.arange(yN), indexing='ij')
-    xind = xind.flatten()
-    yind = yind.flatten()
-    xyind = np.stack((xind, yind, np.zeros((xind.shape),dtype=int)),axis=-1)
-    cart_lattice=xyind * np.array([xd, yd, 0])
-    x=cart_lattice[:,0]
-    y=cart_lattice[:,1]
-    xyind = xyind[:,0:2]
-    
-    # Lattice speedup
-    if (watch_time):
-        timec = time.time()
-        print('%.4f: calculating the %d translation matrix elements' % (timec, 8*nelem*nelem*xN*yN), file = sys.stderr)
-        sys.stderr.flush()
-    Agrid = np.zeros((nelem, 2*xN, 2*yN, nelem),dtype=np.complex_)
-    Bgrid = np.zeros((nelem, 2*xN, 2*yN, nelem),dtype=np.complex_)
-    for yl in range(nelem): # source
-        for xij in range(2*xN):
-            for yij in range(2*yN):
-                for yj in range(nelem): #dest
-                  if((yij != yN) or (xij != xN)):
-                    d_l2j = cart2sph(np.array([(xij-xN)*xd, (yij-yN)*yd, 0]))
-                    Agrid[yj, xij, yij, yl] = Ã(my[yj],ny[yj],my[yl],ny[yl],kdlj=d_l2j[0]*k_out,θlj=d_l2j[1],φlj=d_l2j[2],r_ge_d=False,J=J_scat)
-                    Bgrid[yj, xij, yij, yl] = B̃(my[yj],ny[yj],my[yl],ny[yl],kdlj=d_l2j[0]*k_out,θlj=d_l2j[1],φlj=d_l2j[2],r_ge_d=False,J=J_scat)
-
-    # Translation coefficient matrix T
-    if (watch_time):
-        timecold = timec
-        timec = time.time()
-        print('%4f: translation matrix elements calculated (elapsed %.2f s), filling the matrix' 
-                % (timec, timec-timecold), file = sys.stderr)
-        sys.stderr.flush()
-    transmat = np.zeros((xN* yN, 2, nelem, xN* yN, 2, nelem),dtype=np.complex_)
-    for l in range(N):
-        xil, yil = xyind[l]
-        for j in range(N):
-            xij, yij = xyind[j]
-            if (l!=j):
-                        transmat[j,0,:,l,0,:] = Agrid[:, xij - xil + xN, yij - yil + yN, :]
-                        transmat[j,0,:,l,1,:] = Bgrid[:, xij - xil + xN, yij - yil + yN, :]
-                        transmat[j,1,:,l,0,:] = Bgrid[:, xij - xil + xN, yij - yil + yN, :]
-                        transmat[j,1,:,l,1,:] = Agrid[:, xij - xil + xN, yij - yil + yN, :]
-    Agrid = None
-    Bgrid = None
-    if (watch_time):
-        timecold = timec
-        timec = time.time()
-        print('%4f: translation matrix filled (elapsed %.2f s), building the interaction matrix' 
-                % (timec, timec-timecold), file=sys.stderr)
-        sys.stderr.flush()
-
-    # Now we solve a linear problem (1 - M T) A = M P_0 where M is the T-matrix :-)
-    MT = np.empty((N,2,nelem,N,2,nelem),dtype=np.complex_)
-    
-    TMatrices = np.broadcast_to(TMatrices, (xN, yN, 2, nelem, 2, nelem))
-    for j in range(N): # I wonder how this can be done without this loop...
-        xij, yij = xyind[j]
-        MT[j] = np.tensordot(TMatrices[xij, yij],transmat[j],axes=([-2,-1],[0,1]))
-    MT.shape = (N*2*nelem, N*2*nelem)
-    leftmatrix = np.identity(N*2*nelem) - MT
-    MT = None
-    if (watch_time):
-        timecold = timec
-        timec = time.time()
-        print('%.4f: interaction matrix complete (elapsed %.2f s)' % (timec, timec-timecold),
-                file=sys.stderr)
-        sys.stderr.flush()
-
-    if ((1 == k_dirs.ndim) and (1 == E_0s.ndim)):
-        k_cart = k_dirs * k_out # wave vector of the incident plane wave
-        pq_0 = np.zeros((N,2,nelem), dtype=np.complex_)
-        p_y0, q_y0 = plane_pq_y(lMax, k_cart, E_0s)
-        pq_0[:,0,:] = np.exp(1j*np.sum(k_cart[ň,:]*cart_lattice,axis=-1))[:, ň] * p_y0[ň, :]
-        pq_0[:,1,:] = np.exp(1j*np.sum(k_cart[ň,:]*cart_lattice,axis=-1))[:, ň] * q_y0[ň, :]
-        if (return_pq_0):
-            pq_0_arr = pq_0
-        MP_0 = np.empty((N,2,nelem),dtype=np.complex_)
-        #if (watch_time):
-        #    print('%4f: building the interaction matrix' % time.time(), file=sys.stderr)
-
-        for j in range(N): # I wonder how this can be done without this loop...
-            MP_0[j] = np.tensordot(TMatrices[xij, yij],pq_0[j],axes=([-2,-1],[-2,-1]))
-        MP_0.shape = (N*2*nelem,)
-        
-        if (watch_time):
-            timecold = time.time()
-            print('%4f: solving the scattering problem for single incoming wave' % timecold,
-                    file = sys.stderr)
-            sys.stderr.flush()
-        ab = np.linalg.solve(leftmatrix, MP_0)
-        if watch_time:
-            timec = time.time()
-            print('%4f: solved (elapsed %.2f s)' % (timec, timec-timecold), file=sys.stderr)
-            sys.stderr.flush()
-
-        ab.shape = (xN, yN, 2, nelem)
-    else:
-        # handle "broadcasting" for k, E
-        if 1 == k_dirs.ndim:
-            k_dirs = k_dirs[ň,:]
-        if 1 == E_0s.ndim:
-            E_0s = E_0s[ň,:]
-        K = max(E_0s.shape[-2], k_dirs.shape[-2])
-        k_dirs = np.broadcast_to(k_dirs,(K,3))
-        E_0s = np.broadcast_to(E_0s, (K,3))
-        
-        # А ну, чики-брики и в дамки!
-        if watch_time:
-            timecold = time.time()
-            print('%.4f: factorizing the interaction matrix' % timecold, file=sys.stderr)
-            sys.stderr.flush()
-        lupiv = scipy.linalg.lu_factor(leftmatrix, overwrite_a=True)
-        leftmatrix = None
-        if watch_time:
-            timec = time.time()
-            print('%.4f: factorization complete (elapsed %.2f s)' % (timec, timec-timecold),
-                    file = sys.stderr)
-            print('%.4f: solving the scattering problem for %d incoming waves' % (timec, K),
-                    file=sys.stderr)
-            sys.stderr.flush()
-            timecold = timec
-        
-        if (return_pq_0):
-            pq_0_arr = np.zeros((K,N,2,nelem), dtype=np.complex_)
-        ab = np.empty((K,N*2*nelem), dtype=complex)
-        for ki in range(K):
-            k_cart = k_dirs[ki] * k_out
-            pq_0 = np.zeros((N,2,nelem), dtype=np.complex_)
-            p_y0, q_y0 = plane_pq_y(lMax, k_cart, E_0s[ki])
-            pq_0[:,0,:] = np.exp(1j*np.sum(k_cart[ň,:]*cart_lattice,axis=-1))[:, ň] * p_y0[ň, :]
-            pq_0[:,1,:] = np.exp(1j*np.sum(k_cart[ň,:]*cart_lattice,axis=-1))[:, ň] * q_y0[ň, :]
-            if (return_pq_0):
-                pq_0_arr[ki] = pq_0
-            MP_0 = np.empty((N,2,nelem),dtype=np.complex_)
-            for j in range(N): # I wonder how this can be done without this loop...
-                MP_0[j] = np.tensordot(TMatrices[xij, yij],pq_0[j],axes=([-2,-1],[-2,-1]))
-            MP_0.shape = (N*2*nelem,)
-
-            ab[ki] = scipy.linalg.lu_solve(lupiv, MP_0)
-        ab.shape = (K, xN, yN, 2, nelem)
-        if watch_time:
-            timec = time.time()
-            print('%.4f: done (elapsed %.2f s)' % (timec, timec-timecold),file = sys.stderr)
-            sys.stderr.flush()
-    if not (return_pq_0 + return_pq + return_xy):
-        return ab
-    returnlist = [ab]
-    if (return_pq_0):
-        pq_0_arr.shape = ab.shape
-        returnlist.append(pq_0_arr)
-    if (return_pq):
-        warnings.warn("return_pq not implemented, ignoring")
-        # returnlist.append(pq_arr)
-    if (return_xy):
-        returnlist.append(x)
-        returnlist.append(y)
-    return tuple(returnlist) 
-
-
-import warnings
-#@ujit
-def scatter_constmultipole_rectarray(omega, epsilon_b, xN, yN, xd, yd, TMatrices, pq_0_c = 1, 
-        return_pq= False, return_xy = False, watch_time = False):
-    """
-    Solves the plane wave linear scattering problem for a rectangular array of particles
-    for one frequency and constant exciting spherical waves throughout the array.
-    
-    Parameters
-    ----------
-    omega : positive number
-        The frequency of the field.
-    epsilon_b : complex number
-        Permittivity of the background medium (which has to be isotropic).
-    xN, yN : positive integers
-        Particle numbers in the x and y dimensions
-    xd, yd : positive numbers
-        Periodicities in the x and y direction
-    TMatrices : (xN, yN,2,nelem,2,nelem) or compatible or (2,nelem,2,nelem)
-        The T-matrices in the "Taylor convention" describing the scattering on a single nanoparticle.
-        If all the particles are identical and equally oriented, only one T-matrix can be given.
-        nelems = (lMax + 2) * lMax, where lMax is the highest multipole order to which the scattering
-        is calculated.
-        Electric wave index is 0, magnetic wave index is 1.
-    pq_0_c : (nelem)-shaped complex array or compatible
-        The initial excitation coefficients for the ("complex") multipole waves, in Taylor's convention.
-    return_pq : bool NOT IMPLEMENTED
-        Return also the multipole decomposition coefficients of the field incoming to each
-        particle (inc. the field scattered from other particles.
-    return_xy : bool
-        Return also the cartesian x, y positions of the particles. 
-    watch_time : bool
-        Inform about the progress on stderr
-        
-    Returns
-    -------
-    ab : (nelem, xN, yN, 2, nelem)-shaped complex array
-        The a (electric wave), b (magnetic wave) coefficients of the outgoing field for each particle.
-        If none of return_pq or return_xy is set, the array is not enclosed in a tuple.
-    pq : (nelem, xN, yN, 2, nelem)-shaped complex array NOT IMPLEMENTED
-        The p (electric wave), q (magnetic wave) coefficients of the total exciting field
-        for each particle (including the field scattered from other particles)
-    x, y : (xN, yN)-shaped real array
-        The x,y positions of the nanoparticles.
-    """
-    if (watch_time):
-        timec = time.time()
-        print('%.4f: running scatter_plane_wave_rectarray' % timec, file = sys.stderr)
-        sys.stderr.flush()
-    nelem = TMatrices.shape[-1]
-    if ((nelem != TMatrices.shape[-3]) or (2 != TMatrices.shape[-2]) or (2 != TMatrices.shape[-4])):
-        raise ValueError('The T-matrices must be of shape (N, 2, nelem, 2, nelem) but are of shape %s' % (str(TMatrices.shape),))
-    lMax = nelem2lMax(nelem)
-    if not lMax:
-        raise ValueError('The "nelem" dimension of T-matrix has invalid value (%d).' % nelem)
-    if (watch_time):
-        print('xN = %d, yN = %d, lMax = %d' % (xN, yN, lMax), file = sys.stderr)
-        sys.stderr.flush()
-    # TODO perhaps more checks.
-    k_out = omega * math.sqrt(epsilon_b) / c # wave number
-    my, ny = get_mn_y(lMax)
-    N = yN * xN
-    
-    J_scat=3
-    J_ext=1
-    
-    # Do something with this ugly indexing crap
-    xind, yind = np.meshgrid(np.arange(xN),np.arange(yN), indexing='ij')
-    xind = xind.flatten()
-    yind = yind.flatten()
-    xyind = np.stack((xind, yind, np.zeros((xind.shape),dtype=int)),axis=-1)
-    cart_lattice=xyind * np.array([xd, yd, 0])
-    x=cart_lattice[:,0]
-    y=cart_lattice[:,1]
-    xyind = xyind[:,0:2]
-    
-    # Lattice speedup
-    if (watch_time):
-        timec = time.time()
-        print('%.4f: calculating the %d translation matrix elements' % (timec, 8*nelem*nelem*xN*yN), file = sys.stderr)
-        sys.stderr.flush()
-    Agrid = np.zeros((nelem, 2*xN, 2*yN, nelem),dtype=np.complex_)
-    Bgrid = np.zeros((nelem, 2*xN, 2*yN, nelem),dtype=np.complex_)
-    for yl in range(nelem): # source
-        for xij in range(2*xN):
-            for yij in range(2*yN):
-                for yj in range(nelem): #dest
-                  if((yij != yN) or (xij != xN)):
-                    d_l2j = cart2sph(np.array([(xij-xN)*xd, (yij-yN)*yd, 0]))
-                    Agrid[yj, xij, yij, yl] = Ã(my[yj],ny[yj],my[yl],ny[yl],kdlj=d_l2j[0]*k_out,θlj=d_l2j[1],φlj=d_l2j[2],r_ge_d=False,J=J_scat)
-                    Bgrid[yj, xij, yij, yl] = B̃(my[yj],ny[yj],my[yl],ny[yl],kdlj=d_l2j[0]*k_out,θlj=d_l2j[1],φlj=d_l2j[2],r_ge_d=False,J=J_scat)
-
-    # Translation coefficient matrix T
-    if (watch_time):
-        timecold = timec
-        timec = time.time()
-        print('%4f: translation matrix elements calculated (elapsed %.2f s), filling the matrix' 
-                % (timec, timec-timecold), file = sys.stderr)
-        sys.stderr.flush()
-    transmat = np.zeros((xN* yN, 2, nelem, xN* yN, 2, nelem),dtype=np.complex_)
-    for l in range(N):
-        xil, yil = xyind[l]
-        for j in range(N):
-            xij, yij = xyind[j]
-            if (l!=j):
-                        transmat[j,0,:,l,0,:] = Agrid[:, xij - xil + xN, yij - yil + yN, :]
-                        transmat[j,0,:,l,1,:] = Bgrid[:, xij - xil + xN, yij - yil + yN, :]
-                        transmat[j,1,:,l,0,:] = Bgrid[:, xij - xil + xN, yij - yil + yN, :]
-                        transmat[j,1,:,l,1,:] = Agrid[:, xij - xil + xN, yij - yil + yN, :]
-    Agrid = None
-    Bgrid = None
-    if (watch_time):
-        timecold = timec
-        timec = time.time()
-        print('%4f: translation matrix filled (elapsed %.2f s), building the interaction matrix' 
-                % (timec, timec-timecold), file=sys.stderr)
-        sys.stderr.flush()
-
-    # Now we solve a linear problem (1 - M T) A = M P_0 where M is the T-matrix :-)
-    MT = np.empty((N,2,nelem,N,2,nelem),dtype=np.complex_)
-    
-    TMatrices = np.broadcast_to(TMatrices, (xN, yN, 2, nelem, 2, nelem))
-    for j in range(N): # I wonder how this can be done without this loop...
-        xij, yij = xyind[j]
-        MT[j] = np.tensordot(TMatrices[xij, yij],transmat[j],axes=([-2,-1],[0,1]))
-    MT.shape = (N*2*nelem, N*2*nelem)
-    leftmatrix = np.identity(N*2*nelem) - MT
-    MT = None
-    if (watch_time):
-        timecold = timec
-        timec = time.time()
-        print('%.4f: interaction matrix complete (elapsed %.2f s)' % (timec, timec-timecold),
-                file=sys.stderr)
-        sys.stderr.flush()
-
-    # А ну, чики-брики и в дамки!
-    if watch_time:
-        timecold = time.time()
-        print('%.4f: factorizing the interaction matrix' % timecold, file=sys.stderr)
-        sys.stderr.flush()
-    lupiv = scipy.linalg.lu_factor(leftmatrix, overwrite_a=True)
-    leftmatrix = None
-    if watch_time:
-        timec = time.time()
-        print('%.4f: factorization complete (elapsed %.2f s)' % (timec, timec-timecold),
-                file = sys.stderr)
-        print('%.4f: solving the scattering problem for %d incoming multipoles' % (timec, nelem*2),
-                file=sys.stderr)
-        sys.stderr.flush()
-        timecold = timec
-    
-    if(pq_0_c == 1):
-        pq_0_c = np.full((2,nelem),1)
-    ab = np.empty((2,nelem,N*2*nelem), dtype=complex)
-    for N_or_M in range(2):
-      for yy in range(nelem):
-        pq_0 = np.zeros((2,nelem), dtype=np.complex_)
-        pq_0[N_or_M,yy] = pq_0_c[N_or_M,yy]
-        pq_0 = np.broadcast_to(pq_0, (N, 2, nelem))
-        MP_0 = np.empty((N,2,nelem),dtype=np.complex_)
-        for j in range(N): # I wonder how this can be done without this loop...
-            xij, yij = xyind[j]
-            MP_0[j] = np.tensordot(TMatrices[xij, yij],pq_0[j],axes=([-2,-1],[-2,-1]))
-        MP_0.shape = (N*2*nelem,)
-
-        ab[N_or_M, yy] = scipy.linalg.lu_solve(lupiv, MP_0)
-    ab.shape = (2,nelem, xN, yN, 2, nelem)
-    if watch_time:
-        timec = time.time()
-        print('%.4f: done (elapsed %.2f s)' % (timec, timec-timecold),file = sys.stderr)
-        sys.stderr.flush()
-    if not (return_pq + return_xy):
-        return ab
-    returnlist = [ab]
-    if (return_pq):
-        warnings.warn("return_pq not implemented, ignoring")
-        # returnlist.append(pq_arr)
-    if (return_xy):
-        returnlist.append(x)
-        returnlist.append(y)
-    return tuple(returnlist)