Cleanup of legacy files.
This commit is contained in:
Marek Nečada
parent
e0861a7d40
commit
7d92f4990b
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import math
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import numpy as np
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nx = None
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_s3 = math.sqrt(3)
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def generate_trianglepoints(maxlayer, include_origin = False, v3d = True, circular = True, sixthindices = False, mirrorindices = False):
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_e6 = np.array([[math.cos(2*math.pi*i/6),math.sin(2*math.pi*i/6),0] if v3d else [math.cos(2*math.pi*i/6),math.sin(2*math.pi*i/6)] for i in range(6)])
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points = np.empty((3*maxlayer*(maxlayer+1)+(1 if include_origin else 0), 3 if v3d else 2))
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point_i = 0
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if (include_origin):
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points[0] = np.array((0,0,0) if v3d else (0,0))
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point_i = 1
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if sixthindices:
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si = np.empty((6,(maxlayer*(maxlayer+1))//2), dtype=int)
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sii = [0,0,0,0,0,0]
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if mirrorindices:
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if(maxlayer < 3):
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mi = np.empty((2,0,), dtype=int)
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else:
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#layer indices start from one!
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ilayer = np.arange(1, maxlayer+1) # We need first to "copy" layer indices to correspond to the Muster count
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mustercount = (ilayer - 1)//2
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mustercum = np.cumsum(mustercount)
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layerstart = np.zeros((mustercum[maxlayer - 1]), dtype=int)
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layerstart[mustercum[:(maxlayer-1)]] = 1
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layer = np.cumsum(layerstart) + 2 # That's it
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lb = 3*layer*(layer-1) # layer base (lowest) index
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li = np.arange(len(layer)) - mustercum[layer-2] # muster indices for each layers
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mi = np.empty((2, len(layer)), dtype=int)
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mi[0] = lb + 1 + li + include_origin
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mi[1] = lb + layer - (1 + li) + include_origin
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# there are two non-musters in each even layer, one non-muster in each odd
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layer = (2*np.arange(((3*maxlayer)//2))+1)//3 + 1
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nmi = 3*layer*(layer-1)
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nmi[2::3] += layer[2::3] // 2 # second non-musters in even layers
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nmi += include_origin
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for layer in range(1,maxlayer+1):
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for i in range(6):
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base = _e6[i]*layer
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shift = _e6[(i+2)%6]
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ar = np.arange(layer)
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points[point_i:(point_i+layer)] = base[nx,:] + ar[:,nx] * shift[nx,:]
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if sixthindices:
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si[i, sii[i]:sii[i]+layer] = point_i + ar
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sii[i] += layer
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point_i += layer
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if (circular):
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mask = (np.sum(points * points, axis = -1) <= maxlayer * maxlayer * 3/ 4 + 0.1) # UGLY FIX OF ASYMMETRY BECAUSE OF ROUNDING ERROR
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points = points[mask]
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if sixthindices:
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cum = np.cumsum(mask) - 1
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mask0 = mask[si[0]]
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si_ = si[:,mask0]
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si = cum[si_]
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if mirrorindices:
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cum = np.cumsum(mask) - 1
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mask0 = mask[mi[0]]
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mi_ = mi[:,mask0]
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mi = cum[mi_]
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mask0 = mask[nmi]
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nmi_ = nmi[mask0]
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nmi = cum[nmi_]
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if not (mirrorindices or sixthindices):
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return points
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else:
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return {'points': points,
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'si' : si if sixthindices else None,
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'mi' : mi if mirrorindices else None,
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'nmi' : nmi if mirrorindices else None}
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def generate_trianglepoints_hexcomplement(maxlayer, v3d = True, circular = True, thirdindices = False, mirrorindices=False):
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_e6 = np.array([[math.cos(2*math.pi*i/6),math.sin(2*math.pi*i/6),0] if v3d else [math.cos(2*math.pi*i/6),math.sin(2*math.pi*i/6)] for i in range(6)])
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_f6 = np.array([[-math.sin(2*math.pi*i/6),math.cos(2*math.pi*i/6),0] if v3d else [math.sin(2*math.pi*i/6),-math.cos(2*math.pi*i/6)] for i in range(6)])
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points = np.empty((3*maxlayer*maxlayer, 3 if v3d else 2))
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point_i = 0
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# 3 * layer ** 2 is the basis index for a layer, a layer contains 3 * (2*layer + 1) points
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if thirdindices:
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ii = np.arange(maxlayer**2)
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layer = np.empty((maxlayer**2), dtype=int)
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layer = np.sqrt(ii, out=layer, casting='unsafe')
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#ti0 = 2*layer**2 + ii
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ti = np.arange(3)[:, nx] * (2*layer + 1)[nx, :] + (2*layer**2 + ii)[nx,:]
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if mirrorindices:
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ii = np.arange(((maxlayer-1)*maxlayer)//2)
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layer = np.empty((((maxlayer-1)*maxlayer)//2), dtype=int)
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layer = (np.sqrt(1+8*ii, out=layer, casting='unsafe')+1)//2
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li = ii - ((layer ) * (layer-1))//2# numbers indices in a each layer
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lb = 3*layer **2 # base index of a layer
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mi = np.empty((2,len(layer)), dtype=int)
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mi[0] = lb + li + layer % 2
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mi[1] = lb + 2*layer - li
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# indices of non-mirrored/self-mirrored
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layer = np.arange(maxlayer)
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lb = 3 * layer**2
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nmi = lb + ((layer + 1) % 2) * layer
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for layer in range(0,maxlayer):
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if (layer % 2): # odd layer
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for i in range(3):
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base = _f6[(2*i-1)%6] * ((0.5 + 1.5 * layer) / _s3)
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shift = _e6[(2*i+2)%6]
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count = (layer + 1) // 2
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ar = np.arange(count)
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points[point_i:point_i+count,:] = base + ar[:,nx]*shift[nx,:]
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point_i += count
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base = _e6[(2*i+1)%6]*layer + _f6[(2*i)%6] / _s3
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shift = _e6[(2*i+3)%6]
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count = layer
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ar = np.arange(count)
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points[point_i:point_i+count,:] = base + ar[:,nx]*shift[nx,:]
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point_i += count
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base = _e6[(2*i+2)%6]*layer + _f6[(2*i)%6] / _s3
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shift = _e6[(2*i+4)%6]
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count = (layer + 1) // 2
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ar = np.arange(count)
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points[point_i:point_i+count,:] = base + ar[:,nx]*shift[nx,:]
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point_i += count
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else: # even layer:
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for i in range(3):
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shift = _e6[(2*i+2)%6]
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base = _f6[(2*i-1)%6] * ((0.5 + 1.5 * layer) / _s3) + shift / 2
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count = layer // 2
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ar = np.arange(count)
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points[point_i:point_i+count,:] = base + ar[:,nx]*shift[nx,:]
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point_i += count
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base = _e6[(2*i+1)%6]*layer + _f6[(2*i)%6] / _s3
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shift = _e6[(2*i+3)%6]
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count = layer
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ar = np.arange(count)
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points[point_i:point_i+count,:] = base + ar[:,nx]*shift[nx,:]
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point_i += count
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base = _e6[(2*i+2)%6]*layer + _f6[(2*i)%6] / _s3
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shift = _e6[(2*i+4)%6]
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count = (layer + 2) // 2
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ar = np.arange(count)
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points[point_i:point_i+count,:] = base + ar[:,nx]*shift[nx,:]
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point_i += count
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#if (mirrorindices):
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if (circular):
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mask = (np.sum(points * points, axis = -1) <= maxlayer * maxlayer * 3/ 4 + 0.01) # UGLY FIX OF ASYMMETRY BECAUSE OF ROUNDING ERROR
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points = points[mask]
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if thirdindices:
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cum = np.cumsum(mask) - 1
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mask0 = mask[ti[0]]
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ti_ = ti[:,mask0]
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ti = cum[ti_]
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if mirrorindices:
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cum = np.cumsum(mask) - 1
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mask0 = mask[mi[0]]
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mi_ = mi[:,mask0]
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mi = cum[mi_]
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mask0 = mask[nmi]
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nmi_ = nmi[mask0]
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nmi = cum[nmi_]
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if not (mirrorindices or thirdindices):
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return points
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else:
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return {'points': points,
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'ti' : ti if thirdindices else None,
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'mi' : mi if mirrorindices else None,
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'nmi' : nmi if mirrorindices else None
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}
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from .cycommon import get_mn_y
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from .cytranslations import trans_calculator
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from .qpms_p import cart2sph
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def hexlattice_precalc_AB_save(file, lMax, k_hexside, maxlayer, circular=True, savepointinfo = False, J_scat=3):
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params = {
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'lMax' : lMax,
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'k_hexside' : k_hexside,
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'maxlayer' : maxlayer,
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'circular' : circular,
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'savepointinfo' : savepointinfo,
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'J_scat' : J_scat
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}
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tpdict = generate_trianglepoints(maxlayer, v3d=True, circular=circular, sixthindices=True, mirrorindices=True)
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tphcdict = generate_trianglepoints_hexcomplement(maxlayer, v3d=True, circular=circular, thirdindices=True, mirrorindices=True)
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my, ny = get_mn_y(lMax)
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nelem = len(my)
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a_self_nm = np.empty((tpdict['nmi'].shape[0],nelem,nelem), dtype=complex)
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b_self_nm = np.empty((tpdict['nmi'].shape[0],nelem,nelem), dtype=complex)
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a_self_m0 = np.empty((tpdict['mi'].shape[1],nelem,nelem), dtype=complex)
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b_self_m0 = np.empty((tpdict['mi'].shape[1],nelem,nelem), dtype=complex)
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a_d2u_nm = np.empty((tphcdict['nmi'].shape[0],nelem,nelem), dtype=complex)
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b_d2u_nm = np.empty((tphcdict['nmi'].shape[0],nelem,nelem), dtype=complex)
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a_d2u_m0 = np.empty((tphcdict['mi'].shape[1],nelem,nelem), dtype=complex)
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b_d2u_m0 = np.empty((tphcdict['mi'].shape[1],nelem,nelem), dtype=complex)
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k_0 = k_hexside*_s3 # not really a wave vector here because of the normalisation!
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tc = trans_calculator(lMax)
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y = np.arange(nelem)
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points = tpdict['points'][tpdict['nmi']]
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d_i2j = cart2sph(points)
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a_self_nm, b_self_nm = tc.get_AB_arrays(k_0*d_i2j[:,0],d_i2j[:,1],d_i2j[:,2],np.array([False]),J_scat)
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points = tpdict['points'][tpdict['mi'][0]]
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d_i2j = cart2sph(points)
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a_self_m0, b_self_m0 = tc.get_AB_arrays(k_0*d_i2j[:,0],d_i2j[:,1],d_i2j[:,2],np.array([False]),J_scat)
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points = tphcdict['points'][tphcdict['nmi']]
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d_i2j = cart2sph(points)
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a_d2u_nm, b_d2u_nm = tc.get_AB_arrays(k_0*d_i2j[:,0],d_i2j[:,1],d_i2j[:,2],np.array([False]),J_scat)
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points = tphcdict['points'][tphcdict['mi'][0]]
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d_i2j = cart2sph(points)
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a_d2u_m0, b_d2u_m0 = tc.get_AB_arrays(k_0*d_i2j[:,0],d_i2j[:,1],d_i2j[:,2],np.array([False]),J_scat)
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tosave = {
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'a_self_nm' : a_self_nm,
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'a_self_m0' : a_self_m0,
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'b_self_nm' : b_self_nm,
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'b_self_m0' : b_self_m0,
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'a_d2u_nm' : a_d2u_nm,
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'a_d2u_m0' : a_d2u_m0,
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'b_d2u_nm' : b_d2u_nm,
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'b_d2u_m0' : b_d2u_m0,
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'precalc_params' : params
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}
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if savepointinfo:
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tosave['tp_points'] = tpdict['points'],
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tosave['tp_si'] = tpdict['si'],
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tosave['tp_mi'] = tpdict['mi'],
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tosave['tp_nmi'] = tpdict['nmi']
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tosave['tphc_points'] = tphcdict['points'],
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tosave['tphc_ti'] = tphcdict['ti'],
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tosave['tphc_mi'] = tphcdict['mi'],
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tosave['tphc_nmi'] = tphcdict['nmi']
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np.savez(file, **tosave)
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def hexlattice_precalc_AB_loadunwrap(file, tpdict = None, tphcdict = None, return_points = False):
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npz = np.load(file)
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precalc_params = npz['precalc_params'][()]
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my, ny = get_mn_y(precalc_params['lMax'])
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nelem = len(my)
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# this I should have made more universal...
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if precalc_params['savepointinfo']:
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if not tpdict:
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tpdict = {
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'points' : npz['tp_points'],
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'si' : npz['tp_si'],
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'mi' : npz['tp_mi'],
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'nmi' : npz['tp_nmi'],
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}
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if not tphcdict:
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tphcdict = {
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'points' : npz['tphc_points'],
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'ti' : npz['tphc_ti'],
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'mi' : npz['tphc_mi'],
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'nmi' : npz['tphc_nmi']
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}
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else:
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if not tpdict:
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tpdict = generate_trianglepoints(maxlayer = precalc_params['maxlayer'], v3d=True,
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circular=precalc_params['circular'], sixthindices=True, mirrorindices=True)
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if not tphcdict:
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tphcdict = generate_trianglepoints_hexcomplement(maxlayer=precalc_params['maxlayer'], v3d=True,
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circular=precalc_params['circular'], thirdindices=True, mirrorindices=True)
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# For some obscure reason, I keep getting trailing single-dimension in the beginning for these arrays
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for a in (tpdict['points'], tphcdict['points'], tpdict['si'], tpdict['mi'],
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tphcdict['ti'], tphcdict['mi']):
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if len(a.shape) > 2:
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a.shape = a.shape[1::]
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self_tr = tpdict['points']
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d2u_tr = tphcdict['points']
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if len(self_tr.shape)>2:
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self_tr = np.reshape(self_tr, self_tr.shape[1::])
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if len(d2u_tr.shape)>2:
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d2u_tr = np.reshape(d2u_tr, d2u_tr.shape[1::])
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u2d_tr = -d2u_tr
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a_self = np.empty((self_tr.shape[0],nelem,nelem), dtype=complex)
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b_self = np.empty((self_tr.shape[0],nelem,nelem), dtype=complex)
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a_d2u = np.empty(( d2u_tr.shape[0],nelem,nelem), dtype=complex)
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b_d2u = np.empty(( d2u_tr.shape[0],nelem,nelem), dtype=complex)
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a_self[tpdict['nmi']]=npz['a_self_nm']
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a_self[tpdict['mi'][0]]=npz['a_self_m0']
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b_self[tpdict['nmi']]=npz['b_self_nm']
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b_self[tpdict['mi'][0]]=npz['b_self_m0']
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mirrorangles = cart2sph(self_tr[tpdict['mi'][1]])[:,2] - cart2sph(self_tr[tpdict['mi'][0]])[:,2]
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a_self[tpdict['mi'][1],:,:] = a_self[tpdict['mi'][0],:,:] * np.exp(1j*mirrorangles[:,nx,nx]*(my[nx,nx,:]-my[nx,:,nx]))
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||||||
b_self[tpdict['mi'][1],:,:] = b_self[tpdict['mi'][0],:,:] * np.exp(1j*mirrorangles[:,nx,nx]*(my[nx,nx,:]-my[nx,:,nx]))
|
|
||||||
for i in range(1,6):
|
|
||||||
a_self[tpdict['si'][i],:,:] = a_self[tpdict['si'][0],:,:] * np.exp(1j*math.pi/3*i*(my[nx,:]-my[:,nx]))
|
|
||||||
b_self[tpdict['si'][i],:,:] = b_self[tpdict['si'][0],:,:] * np.exp(1j*math.pi/3*i*(my[nx,:]-my[:,nx]))
|
|
||||||
a_d2u[tphcdict['nmi']]=npz['a_d2u_nm']
|
|
||||||
a_d2u[tphcdict['mi'][0]]=npz['a_d2u_m0']
|
|
||||||
b_d2u[tphcdict['nmi']]=npz['b_d2u_nm']
|
|
||||||
b_d2u[tphcdict['mi'][0]]=npz['b_d2u_m0']
|
|
||||||
mirrorangles = cart2sph(self_tr[tphcdict['mi'][1]])[:,2] - cart2sph(self_tr[tphcdict['mi'][0]])[:,2]
|
|
||||||
a_d2u[tphcdict['mi'][1],:,:] = a_d2u[tphcdict['mi'][0],:,:] * np.exp(1j*mirrorangles[:,nx,nx]*(my[nx,nx,:]-my[nx,:,nx]))
|
|
||||||
b_d2u[tphcdict['mi'][1],:,:] = b_d2u[tphcdict['mi'][0],:,:] * np.exp(1j*mirrorangles[:,nx,nx]*(my[nx,nx,:]-my[nx,:,nx]))
|
|
||||||
for i in (1,-1):
|
|
||||||
a_d2u[tphcdict['ti'][i],:,:] = a_d2u[tphcdict['ti'][0],:,:] * np.exp(i*2j*math.pi/3*(my[nx,:]-my[:,nx]))
|
|
||||||
b_d2u[tphcdict['ti'][i],:,:] = b_d2u[tphcdict['ti'][0],:,:] * np.exp(i*2j*math.pi/3*(my[nx,:]-my[:,nx]))
|
|
||||||
a_u2d = a_d2u * (-1)**(my[nx,:]-my[:,nx])
|
|
||||||
b_u2d = b_d2u * (-1)**(my[nx,:]-my[:,nx])
|
|
||||||
d = {
|
|
||||||
'a_self' : a_self,
|
|
||||||
'b_self' : b_self,
|
|
||||||
'a_d2u' : a_d2u,
|
|
||||||
'b_d2u' : b_d2u,
|
|
||||||
'a_u2d' : a_u2d,
|
|
||||||
'b_u2d' : b_u2d,
|
|
||||||
}
|
|
||||||
for k in precalc_params.keys():
|
|
||||||
d[k] = precalc_params[k]
|
|
||||||
if return_points:
|
|
||||||
d['d2u_tr'] = tphcdict['points']
|
|
||||||
d['u2d_tr'] = -tphcdict['points']
|
|
||||||
d['self_tr'] = tpdict['points']
|
|
||||||
return d
|
|
||||||
|
|
||||||
def hexlattice_get_AB(lMax, k_hexside, maxlayer, circular=True, return_points = True, J_scat=3):
|
|
||||||
params = {
|
|
||||||
'lMax' : lMax,
|
|
||||||
'k_hexside' : k_hexside,
|
|
||||||
'maxlayer' : maxlayer,
|
|
||||||
'circular' : circular,
|
|
||||||
'savepointinfo' : return_points, # should I delete this key?
|
|
||||||
'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_arrays(k_0*d_i2j[:,0],d_i2j[:,1],d_i2j[:,2],np.array([False]),J_scat)
|
|
||||||
|
|
||||||
points = tpdict['points'][tpdict['mi'][0]]
|
|
||||||
d_i2j = cart2sph(points)
|
|
||||||
a_self_m0, b_self_m0 = tc.get_AB_arrays(k_0*d_i2j[:,0],d_i2j[:,1],d_i2j[:,2],np.array([False]),J_scat)
|
|
||||||
|
|
||||||
points = tphcdict['points'][tphcdict['nmi']]
|
|
||||||
d_i2j = cart2sph(points)
|
|
||||||
a_d2u_nm, b_d2u_nm = tc.get_AB_arrays(k_0*d_i2j[:,0],d_i2j[:,1],d_i2j[:,2],np.array([False]),J_scat)
|
|
||||||
|
|
||||||
points = tphcdict['points'][tphcdict['mi'][0]]
|
|
||||||
d_i2j = cart2sph(points)
|
|
||||||
a_d2u_m0, b_d2u_m0 = tc.get_AB_arrays(k_0*d_i2j[:,0],d_i2j[:,1],d_i2j[:,2],np.array([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)
|
|
||||||
'''
|
|
||||||
self_tr = tpdict['points']
|
|
||||||
d2u_tr = tphcdict['points']
|
|
||||||
if len(self_tr.shape)>2:
|
|
||||||
self_tr = np.reshape(self_tr, self_tr.shape[1::])
|
|
||||||
if len(d2u_tr.shape)>2:
|
|
||||||
d2u_tr = np.reshape(d2u_tr, d2u_tr.shape[1::])
|
|
||||||
u2d_tr = -d2u_tr
|
|
||||||
a_self = np.empty((self_tr.shape[0],nelem,nelem), dtype=complex)
|
|
||||||
b_self = np.empty((self_tr.shape[0],nelem,nelem), dtype=complex)
|
|
||||||
a_d2u = np.empty(( d2u_tr.shape[0],nelem,nelem), dtype=complex)
|
|
||||||
b_d2u = np.empty(( d2u_tr.shape[0],nelem,nelem), dtype=complex)
|
|
||||||
a_self[tpdict['nmi']]=a_self_nm
|
|
||||||
a_self[tpdict['mi'][0]]=a_self_m0
|
|
||||||
b_self[tpdict['nmi']]=b_self_nm
|
|
||||||
b_self[tpdict['mi'][0]]=b_self_m0
|
|
||||||
mirrorangles = cart2sph(self_tr[tpdict['mi'][1]])[:,2] - cart2sph(self_tr[tpdict['mi'][0]])[:,2]
|
|
||||||
a_self[tpdict['mi'][1],:,:] = a_self[tpdict['mi'][0],:,:] * np.exp(1j*mirrorangles[:,nx,nx]*(my[nx,nx,:]-my[nx,:,nx]))
|
|
||||||
b_self[tpdict['mi'][1],:,:] = b_self[tpdict['mi'][0],:,:] * np.exp(1j*mirrorangles[:,nx,nx]*(my[nx,nx,:]-my[nx,:,nx]))
|
|
||||||
for i in range(1,6):
|
|
||||||
a_self[tpdict['si'][i],:,:] = a_self[tpdict['si'][0],:,:] * np.exp(1j*math.pi/3*i*(my[nx,:]-my[:,nx]))
|
|
||||||
b_self[tpdict['si'][i],:,:] = b_self[tpdict['si'][0],:,:] * np.exp(1j*math.pi/3*i*(my[nx,:]-my[:,nx]))
|
|
||||||
a_d2u[tphcdict['nmi']]=a_d2u_nm
|
|
||||||
a_d2u[tphcdict['mi'][0]]=a_d2u_m0
|
|
||||||
b_d2u[tphcdict['nmi']]=b_d2u_nm
|
|
||||||
b_d2u[tphcdict['mi'][0]]=b_d2u_m0
|
|
||||||
mirrorangles = cart2sph(self_tr[tphcdict['mi'][1]])[:,2] - cart2sph(self_tr[tphcdict['mi'][0]])[:,2]
|
|
||||||
a_d2u[tphcdict['mi'][1],:,:] = a_d2u[tphcdict['mi'][0],:,:] * np.exp(1j*mirrorangles[:,nx,nx]*(my[nx,nx,:]-my[nx,:,nx]))
|
|
||||||
b_d2u[tphcdict['mi'][1],:,:] = b_d2u[tphcdict['mi'][0],:,:] * np.exp(1j*mirrorangles[:,nx,nx]*(my[nx,nx,:]-my[nx,:,nx]))
|
|
||||||
for i in (1,-1):
|
|
||||||
a_d2u[tphcdict['ti'][i],:,:] = a_d2u[tphcdict['ti'][0],:,:] * np.exp(i*2j*math.pi/3*(my[nx,:]-my[:,nx]))
|
|
||||||
b_d2u[tphcdict['ti'][i],:,:] = b_d2u[tphcdict['ti'][0],:,:] * np.exp(i*2j*math.pi/3*(my[nx,:]-my[:,nx]))
|
|
||||||
a_u2d = a_d2u * (-1)**(my[nx,:]-my[:,nx])
|
|
||||||
b_u2d = b_d2u * (-1)**(my[nx,:]-my[:,nx])
|
|
||||||
d = {
|
|
||||||
'a_self' : a_self,
|
|
||||||
'b_self' : b_self,
|
|
||||||
'a_d2u' : a_d2u,
|
|
||||||
'b_d2u' : b_d2u,
|
|
||||||
'a_u2d' : a_u2d,
|
|
||||||
'b_u2d' : b_u2d,
|
|
||||||
}
|
|
||||||
for k in params.keys():
|
|
||||||
d[k] = params[k]
|
|
||||||
if return_points:
|
|
||||||
d['d2u_tr'] = tphcdict['points']
|
|
||||||
d['u2d_tr'] = -tphcdict['points']
|
|
||||||
d['self_tr'] = tpdict['points']
|
|
||||||
return d
|
|
||||||
|
|
||||||
from scipy.constants import c
|
|
||||||
from .timetrack import _time_b, _time_e
|
|
||||||
from .tmatrices import symz_indexarrays
|
|
||||||
|
|
||||||
def hexlattice_zsym_getSVD(lMax, TMatrices_om, epsilon_b, hexside, maxlayer, omega, klist, gaussianSigma=False, onlyNmin=0, verbose=False):
|
|
||||||
btime = _time_b(verbose)
|
|
||||||
nelem = lMax * (lMax + 2)
|
|
||||||
n2id = np.identity(2*nelem)
|
|
||||||
n2id.shape = (2,nelem,2,nelem)
|
|
||||||
nan = float('nan')
|
|
||||||
k_0 = omega * math.sqrt(epsilon_b) / c
|
|
||||||
tdic = hexlattice_get_AB(lMax,k_0*hexside,maxlayer)
|
|
||||||
a_self = tdic['a_self'][:,:nelem,:nelem]
|
|
||||||
b_self = tdic['b_self'][:,:nelem,:nelem]
|
|
||||||
a_u2d = tdic['a_u2d'][:,:nelem,:nelem]
|
|
||||||
b_u2d = tdic['b_u2d'][:,:nelem,:nelem]
|
|
||||||
a_d2u = tdic['a_d2u'][:,:nelem,:nelem]
|
|
||||||
b_d2u = tdic['b_d2u'][:,:nelem,:nelem]
|
|
||||||
unitcell_translations = tdic['self_tr']*hexside*_s3
|
|
||||||
u2d_translations = tdic['u2d_tr']*hexside*_s3
|
|
||||||
d2u_translations = tdic['d2u_tr']*hexside*_s3
|
|
||||||
|
|
||||||
if gaussianSigma:
|
|
||||||
sbtime = _time_b(verbose, step='Calculating gaussian envelope')
|
|
||||||
unitcell_envelope = np.exp(-np.sum(tdic['self_tr']**2,axis=-1)/(2*gaussianSigma**2))
|
|
||||||
u2d_envelope = np.exp(-np.sum(tdic['u2d_tr']**2,axis=-1)/(2*gaussianSigma**2))
|
|
||||||
d2u_envelope = np.exp(-np.sum(tdic['d2u_tr']**2,axis=-1)/(2*gaussianSigma**2))
|
|
||||||
_time_e(sbtime, verbose, step='Calculating gaussian envelope')
|
|
||||||
|
|
||||||
#TMatrices_om = TMatrices_interp(omega)
|
|
||||||
if(not onlyNmin):
|
|
||||||
svUfullTElist = np.full((klist.shape[0], 2*nelem, 2*nelem), np.nan, dtype=complex)
|
|
||||||
svVfullTElist = np.full((klist.shape[0], 2*nelem, 2*nelem), np.nan, dtype=complex)
|
|
||||||
svSfullTElist = np.full((klist.shape[0], 2*nelem), np.nan, dtype=complex)
|
|
||||||
svUfullTMlist = np.full((klist.shape[0], 2*nelem, 2*nelem), np.nan, dtype=complex)
|
|
||||||
svVfullTMlist = np.full((klist.shape[0], 2*nelem, 2*nelem), np.nan, dtype=complex)
|
|
||||||
svSfullTMlist = np.full((klist.shape[0], 2*nelem), np.nan, dtype=complex)
|
|
||||||
else:
|
|
||||||
minsvTElist = np.full((klist.shape[0], onlyNmin),np.nan)
|
|
||||||
minsvTMlist = np.full((klist.shape[0], onlyNmin),np.nan)
|
|
||||||
|
|
||||||
leftmatrixlist = np.full((klist.shape[0],2,2,nelem,2,2,nelem),np.nan,dtype=complex)
|
|
||||||
#isNaNlist = np.zeros((klist.shape[0]), dtype=bool)
|
|
||||||
isNaNlist = (k_0*k_0 - klist[:,0]**2 - klist[:,1]**2 < 0)
|
|
||||||
nnlist = np.logical_not(isNaNlist)
|
|
||||||
|
|
||||||
sbtime = _time_b(verbose, step='Initialization of matrices for SVD for a given list of k\'s')
|
|
||||||
|
|
||||||
|
|
||||||
#ki = np.arange(klist.shape[0])[k_0*k_0 - klist[:,0]**2 - klist[:,1]**2 >= 0]
|
|
||||||
k = klist[nnlist]
|
|
||||||
|
|
||||||
phases_self = np.exp(1j*np.tensordot(k,unitcell_translations,axes=(-1,-1)))
|
|
||||||
phases_u2d = np.exp(1j*np.tensordot(k,u2d_translations,axes=(-1,-1)))
|
|
||||||
phases_d2u = np.exp(1j*np.tensordot(k,d2u_translations,axes=(-1,-1)))
|
|
||||||
if gaussianSigma:
|
|
||||||
phases_self *= unitcell_envelope
|
|
||||||
phases_u2d *= u2d_envelope
|
|
||||||
phases_d2u *= d2u_envelope
|
|
||||||
leftmatrix = np.zeros((k.shape[0],2,2,nelem, 2,2,nelem), dtype=complex)
|
|
||||||
# 0:[u,E<--u,E ]
|
|
||||||
# 1:[d,M<--d,M ]
|
|
||||||
leftmatrix[:,0,0,:,0,0,:] = np.tensordot(phases_self,a_self, axes=(-1,0)) # u2u, E2E
|
|
||||||
leftmatrix[:,1,0,:,1,0,:] = leftmatrix[:,0,0,:,0,0,:] # d2d, E2E
|
|
||||||
leftmatrix[:,0,1,:,0,1,:] = leftmatrix[:,0,0,:,0,0,:] # u2u, M2M
|
|
||||||
leftmatrix[:,1,1,:,1,1,:] = leftmatrix[:,0,0,:,0,0,:] # d2d, M2M
|
|
||||||
leftmatrix[:,0,0,:,0,1,:] = np.tensordot(phases_self,b_self, axes=(-1,0)) # u2u, M2E
|
|
||||||
leftmatrix[:,0,1,:,0,0,:] = leftmatrix[:,0,0,:,0,1,:] # u2u, E2M
|
|
||||||
leftmatrix[:,1,1,:,1,0,:] = leftmatrix[:,0,0,:,0,1,:] # d2d, E2M
|
|
||||||
leftmatrix[:,1,0,:,1,1,:] = leftmatrix[:,0,0,:,0,1,:] # d2d, M2E
|
|
||||||
leftmatrix[:,0,0,:,1,0,:] = np.tensordot(phases_d2u, a_d2u,axes=(-1,0)) #d2u,E2E
|
|
||||||
leftmatrix[:,0,1,:,1,1,:] = leftmatrix[:,0,0,:,1,0,:] #d2u, M2M
|
|
||||||
leftmatrix[:,1,0,:,0,0,:] = np.tensordot(phases_u2d, a_u2d,axes=(-1,0)) #u2d,E2E
|
|
||||||
leftmatrix[:,1,1,:,0,1,:] = leftmatrix[:,1,0,:,0,0,:] #u2d, M2M
|
|
||||||
leftmatrix[:,0,0,:,1,1,:] = np.tensordot(phases_d2u, b_d2u,axes=(-1,0)) #d2u,M2E
|
|
||||||
leftmatrix[:,0,1,:,1,0,:] = leftmatrix[:,0,0,:,1,1,:] #d2u, E2M
|
|
||||||
leftmatrix[:,1,0,:,0,1,:] = np.tensordot(phases_u2d, b_u2d,axes=(-1,0)) #u2d,M2E
|
|
||||||
leftmatrix[:,1,1,:,0,0,:] = leftmatrix[:,1,0,:,0,1,:] #u2d, E2M
|
|
||||||
#leftmatrix is now the translation matrix T
|
|
||||||
for j in range(2):
|
|
||||||
leftmatrix[:,j] = np.rollaxis(-np.tensordot(TMatrices_om[j], leftmatrix[:,j], axes=([-2,-1],[1,2])),2)
|
|
||||||
# at this point, jth row of leftmatrix is that of -MT
|
|
||||||
leftmatrix[:,j,:,:,j,:,:] += n2id
|
|
||||||
#now we are done, 1-MT
|
|
||||||
|
|
||||||
leftmatrixlist[nnlist] = leftmatrix
|
|
||||||
|
|
||||||
'''
|
|
||||||
# sem nějaká rozumná smyčka
|
|
||||||
for ki in range(klist.shape[0]):
|
|
||||||
k = klist[ki]
|
|
||||||
if (k_0*k_0 - k[0]*k[0] - k[1]*k[1] < 0):
|
|
||||||
isNaNlist[ki] = True
|
|
||||||
continue
|
|
||||||
|
|
||||||
phases_self = np.exp(1j*np.tensordot(k,unitcell_translations,axes=(0,-1)))
|
|
||||||
phases_u2d = np.exp(1j*np.tensordot(k,u2d_translations,axes=(0,-1)))
|
|
||||||
phases_d2u = np.exp(1j*np.tensordot(k,d2u_translations,axes=(0,-1)))
|
|
||||||
if gaussianSigma:
|
|
||||||
phases_self *= unitcell_envelope
|
|
||||||
phases_u2d *= u2d_envelope
|
|
||||||
phases_d2u *= d2u_envelope
|
|
||||||
leftmatrix = np.zeros((2,2,nelem, 2,2,nelem), dtype=complex)
|
|
||||||
# 0:[u,E<--u,E ]
|
|
||||||
# 1:[d,M<--d,M ]
|
|
||||||
leftmatrix[0,0,:,0,0,:] = np.tensordot(a_self,phases_self, axes=(0,-1)) # u2u, E2E
|
|
||||||
leftmatrix[1,0,:,1,0,:] = leftmatrix[0,0,:,0,0,:] # d2d, E2E
|
|
||||||
leftmatrix[0,1,:,0,1,:] = leftmatrix[0,0,:,0,0,:] # u2u, M2M
|
|
||||||
leftmatrix[1,1,:,1,1,:] = leftmatrix[0,0,:,0,0,:] # d2d, M2M
|
|
||||||
leftmatrix[0,0,:,0,1,:] = np.tensordot(b_self,phases_self, axes=(0,-1)) # u2u, M2E
|
|
||||||
leftmatrix[0,1,:,0,0,:] = leftmatrix[0,0,:,0,1,:] # u2u, E2M
|
|
||||||
leftmatrix[1,1,:,1,0,:] = leftmatrix[0,0,:,0,1,:] # d2d, E2M
|
|
||||||
leftmatrix[1,0,:,1,1,:] = leftmatrix[0,0,:,0,1,:] # d2d, M2E
|
|
||||||
leftmatrix[0,0,:,1,0,:] = np.tensordot(a_d2u, phases_d2u,axes=(0,-1)) #d2u,E2E
|
|
||||||
leftmatrix[0,1,:,1,1,:] = leftmatrix[0,0,:,1,0,:] #d2u, M2M
|
|
||||||
leftmatrix[1,0,:,0,0,:] = np.tensordot(a_u2d, phases_u2d,axes=(0,-1)) #u2d,E2E
|
|
||||||
leftmatrix[1,1,:,0,1,:] = leftmatrix[1,0,:,0,0,:] #u2d, M2M
|
|
||||||
leftmatrix[0,0,:,1,1,:] = np.tensordot(b_d2u, phases_d2u,axes=(0,-1)) #d2u,M2E
|
|
||||||
leftmatrix[0,1,:,1,0,:] = leftmatrix[0,0,:,1,1,:] #d2u, E2M
|
|
||||||
leftmatrix[1,0,:,0,1,:] = np.tensordot(b_u2d, phases_u2d,axes=(0,-1)) #u2d,M2E
|
|
||||||
leftmatrix[1,1,:,0,0,:] = leftmatrix[1,0,:,0,1,:] #u2d, E2M
|
|
||||||
#leftmatrix is now the translation matrix T
|
|
||||||
for j in range(2):
|
|
||||||
leftmatrix[j] = -np.tensordot(TMatrices_om[j], leftmatrix[j], axes=([-2,-1],[0,1]))
|
|
||||||
# at this point, jth row of leftmatrix is that of -MT
|
|
||||||
leftmatrix[j,:,:,j,:,:] += n2id
|
|
||||||
#now we are done, 1-MT
|
|
||||||
|
|
||||||
leftmatrixlist[ki] = leftmatrix
|
|
||||||
'''
|
|
||||||
|
|
||||||
leftmatrixlist_s = np.reshape(leftmatrixlist,(klist.shape[0], 2*2*nelem,2*2*nelem))[nnlist]
|
|
||||||
TEč, TMč = symz_indexarrays(lMax, 2)
|
|
||||||
leftmatrixlist_TE = leftmatrixlist_s[np.ix_(np.arange(leftmatrixlist_s.shape[0]),TEč,TEč)]
|
|
||||||
leftmatrixlist_TM = leftmatrixlist_s[np.ix_(np.arange(leftmatrixlist_s.shape[0]),TMč,TMč)]
|
|
||||||
_time_e(sbtime, verbose, step='Initializing matrices for SVD for a given list of k\'s')
|
|
||||||
|
|
||||||
sbtime = _time_b(verbose, step='Calculating SVDs for a given list of k\'s.')
|
|
||||||
if(not onlyNmin):
|
|
||||||
svUfullTElist[nnlist], svSfullTElist[nnlist], svVfullTElist[nnlist] = np.linalg.svd(leftmatrixlist_TE, compute_uv=True)
|
|
||||||
svUfullTMlist[nnlist], svSfullTMlist[nnlist], svVfullTMlist[nnlist] = np.linalg.svd(leftmatrixlist_TM, compute_uv=True)
|
|
||||||
_time_e(sbtime, verbose, step='Calculating SVDs for a given list of k\'s.')
|
|
||||||
_time_e(btime, verbose)
|
|
||||||
return ((svUfullTElist, svSfullTElist, svVfullTElist), (svUfullTMlist, svSfullTMlist, svVfullTMlist))
|
|
||||||
else:
|
|
||||||
minsvTElist[nnlist] = np.linalg.svd(leftmatrixlist_TE, compute_uv=False)[...,-onlyNmin:]
|
|
||||||
minsvTMlist[nnlist] = np.linalg.svd(leftmatrixlist_TM, compute_uv=False)[...,-onlyNmin:]
|
|
||||||
_time_e(sbtime, verbose, step='Calculating SVDs for a given list of k\'s.')
|
|
||||||
_time_e(btime, verbose)
|
|
||||||
return (minsvTElist, minsvTMlist)
|
|
||||||
|
|
@ -1,130 +0,0 @@
|
||||||
import math
|
|
||||||
import numpy as np
|
|
||||||
cimport numpy as np
|
|
||||||
nx = None
|
|
||||||
|
|
||||||
cdef double _s3 = math.sqrt(3)
|
|
||||||
|
|
||||||
from scipy.constants import c
|
|
||||||
from .timetrack import _time_b, _time_e
|
|
||||||
from .tmatrices import symz_indexarrays
|
|
||||||
from .hexpoints import hexlattice_get_AB
|
|
||||||
|
|
||||||
cpdef hexlattice_zsym_getSVD(int lMax, TMatrices_om, double epsilon_b, double hexside, size_t maxlayer, double omega, klist, gaussianSigma=False, int onlyNmin=0, verbose=False):
|
|
||||||
cdef np.ndarray[np.npy_double, ndim = 2] klist_c = klist
|
|
||||||
btime = _time_b(verbose)
|
|
||||||
cdef size_t nelem = lMax * (lMax + 2)
|
|
||||||
_n2id = np.identity(2*nelem)
|
|
||||||
_n2id.shape = (2,nelem,2,nelem)
|
|
||||||
cdef np.ndarray[np.npy_double, ndim = 4] n2id = _n2id
|
|
||||||
cdef double nan = float('nan')
|
|
||||||
k_0 = omega * math.sqrt(epsilon_b) / c
|
|
||||||
tdic = hexlattice_get_AB(lMax,k_0*hexside,maxlayer)
|
|
||||||
cdef np.ndarray[np.npy_cdouble, ndim = 3] a_self = tdic['a_self'][:,:nelem,:nelem]
|
|
||||||
cdef np.ndarray[np.npy_cdouble, ndim = 3] b_self = tdic['b_self'][:,:nelem,:nelem]
|
|
||||||
cdef np.ndarray[np.npy_cdouble, ndim = 3] a_u2d = tdic['a_u2d'][:,:nelem,:nelem]
|
|
||||||
cdef np.ndarray[np.npy_cdouble, ndim = 3] b_u2d = tdic['b_u2d'][:,:nelem,:nelem]
|
|
||||||
cdef np.ndarray[np.npy_cdouble, ndim = 3] a_d2u = tdic['a_d2u'][:,:nelem,:nelem]
|
|
||||||
cdef np.ndarray[np.npy_cdouble, ndim = 3] b_d2u = tdic['b_d2u'][:,:nelem,:nelem]
|
|
||||||
cdef np.ndarray[np.npy_double, ndim = 2] unitcell_translations = tdic['self_tr']*hexside*_s3
|
|
||||||
cdef np.ndarray[np.npy_double, ndim = 2] u2d_translations = tdic['u2d_tr']*hexside*_s3
|
|
||||||
cdef np.ndarray[np.npy_double, ndim = 2] d2u_translations = tdic['d2u_tr']*hexside*_s3
|
|
||||||
|
|
||||||
cdef np.ndarray[np.npy_double, ndim = 1] unitcell_envelope, u2d_envelope, d2u_envelope
|
|
||||||
if gaussianSigma:
|
|
||||||
sbtime = _time_b(verbose, step='Calculating gaussian envelope')
|
|
||||||
unitcell_envelope = np.exp(-np.sum(tdic['self_tr']**2,axis=-1)/(2*gaussianSigma**2))
|
|
||||||
u2d_envelope = np.exp(-np.sum(tdic['u2d_tr']**2,axis=-1)/(2*gaussianSigma**2))
|
|
||||||
d2u_envelope = np.exp(-np.sum(tdic['d2u_tr']**2,axis=-1)/(2*gaussianSigma**2))
|
|
||||||
_time_e(sbtime, verbose, step='Calculating gaussian envelope')
|
|
||||||
|
|
||||||
cdef np.ndarray[np.npy_cdouble, ndim = 3] svUfullTElist, svVfullTElist, svUfullTMlist, svVfullTMlist
|
|
||||||
cdef np.ndarray[np.npy_cdouble, ndim = 2] svSfullTElist, svSfullTMlist
|
|
||||||
cdef np.ndarray[np.npy_double, ndim = 2] minsvTElist, minsvTMlist
|
|
||||||
|
|
||||||
#TMatrices_om = TMatrices_interp(omega)
|
|
||||||
if(not onlyNmin):
|
|
||||||
svUfullTElist = np.full((klist_c.shape[0], 2*nelem, 2*nelem), np.nan, dtype=complex)
|
|
||||||
svVfullTElist = np.full((klist_c.shape[0], 2*nelem, 2*nelem), np.nan, dtype=complex)
|
|
||||||
svSfullTElist = np.full((klist_c.shape[0], 2*nelem), np.nan, dtype=complex)
|
|
||||||
svUfullTMlist = np.full((klist_c.shape[0], 2*nelem, 2*nelem), np.nan, dtype=complex)
|
|
||||||
svVfullTMlist = np.full((klist_c.shape[0], 2*nelem, 2*nelem), np.nan, dtype=complex)
|
|
||||||
svSfullTMlist = np.full((klist_c.shape[0], 2*nelem), np.nan, dtype=complex)
|
|
||||||
else:
|
|
||||||
minsvTElist = np.full((klist_c.shape[0], onlyNmin),np.nan)
|
|
||||||
minsvTMlist = np.full((klist_c.shape[0], onlyNmin),np.nan)
|
|
||||||
|
|
||||||
cdef np.ndarray[np.npy_cdouble] leftmatrixlist = np.full((klist_c.shape[0],2,2,nelem,2,2,nelem),np.nan,dtype=complex)
|
|
||||||
cdef np.ndarray[np.npy_bool, ndim=1] isNaNlist = np.zeros((klist_c.shape[0]), dtype=bool)
|
|
||||||
|
|
||||||
sbtime = _time_b(verbose, step='Initialization of matrices for SVD for a given list of k\'s')
|
|
||||||
# sem nějaká rozumná smyčka
|
|
||||||
|
|
||||||
# declarations for the ki loop:
|
|
||||||
cdef size_t ki
|
|
||||||
cdef np.ndarray[np.npy_cdouble, ndim = 1] phases_self
|
|
||||||
cdef np.ndarray[np.npy_cdouble, ndim = 1] phases_u2d
|
|
||||||
cdef np.ndarray[np.npy_cdouble, ndim = 1] phases_d2u
|
|
||||||
cdef np.ndarray[np.npy_cdouble, ndim=6] leftmatrix
|
|
||||||
cdef np.ndarray[np.npy_double, ndim=1] k
|
|
||||||
cdef int j
|
|
||||||
|
|
||||||
for ki in range(klist_c.shape[0]):
|
|
||||||
k = klist_c[ki]
|
|
||||||
if (k_0*k_0 - k[0]*k[0] - k[1]*k[1] < 0):
|
|
||||||
isNaNlist[ki] = True
|
|
||||||
continue
|
|
||||||
|
|
||||||
phases_self = np.exp(1j*np.tensordot(k,unitcell_translations,axes=(0,-1)))
|
|
||||||
phases_u2d = np.exp(1j*np.tensordot(k,u2d_translations,axes=(0,-1)))
|
|
||||||
phases_d2u = np.exp(1j*np.tensordot(k,d2u_translations,axes=(0,-1)))
|
|
||||||
if gaussianSigma:
|
|
||||||
phases_self *= unitcell_envelope
|
|
||||||
phases_u2d *= u2d_envelope
|
|
||||||
phases_d2u *= d2u_envelope
|
|
||||||
leftmatrix = np.zeros((2,2,nelem, 2,2,nelem), dtype=complex)
|
|
||||||
# 0:[u,E<--u,E ]
|
|
||||||
# 1:[d,M<--d,M ]
|
|
||||||
leftmatrix[0,0,:,0,0,:] = np.tensordot(a_self,phases_self, axes=(0,-1)) # u2u, E2E
|
|
||||||
leftmatrix[1,0,:,1,0,:] = leftmatrix[0,0,:,0,0,:] # d2d, E2E
|
|
||||||
leftmatrix[0,1,:,0,1,:] = leftmatrix[0,0,:,0,0,:] # u2u, M2M
|
|
||||||
leftmatrix[1,1,:,1,1,:] = leftmatrix[0,0,:,0,0,:] # d2d, M2M
|
|
||||||
leftmatrix[0,0,:,0,1,:] = np.tensordot(b_self,phases_self, axes=(0,-1)) # u2u, M2E
|
|
||||||
leftmatrix[0,1,:,0,0,:] = leftmatrix[0,0,:,0,1,:] # u2u, E2M
|
|
||||||
leftmatrix[1,1,:,1,0,:] = leftmatrix[0,0,:,0,1,:] # d2d, E2M
|
|
||||||
leftmatrix[1,0,:,1,1,:] = leftmatrix[0,0,:,0,1,:] # d2d, M2E
|
|
||||||
leftmatrix[0,0,:,1,0,:] = np.tensordot(a_d2u, phases_d2u,axes=(0,-1)) #d2u,E2E
|
|
||||||
leftmatrix[0,1,:,1,1,:] = leftmatrix[0,0,:,1,0,:] #d2u, M2M
|
|
||||||
leftmatrix[1,0,:,0,0,:] = np.tensordot(a_u2d, phases_u2d,axes=(0,-1)) #u2d,E2E
|
|
||||||
leftmatrix[1,1,:,0,1,:] = leftmatrix[1,0,:,0,0,:] #u2d, M2M
|
|
||||||
leftmatrix[0,0,:,1,1,:] = np.tensordot(b_d2u, phases_d2u,axes=(0,-1)) #d2u,M2E
|
|
||||||
leftmatrix[0,1,:,1,0,:] = leftmatrix[0,0,:,1,1,:] #d2u, E2M
|
|
||||||
leftmatrix[1,0,:,0,1,:] = np.tensordot(b_u2d, phases_u2d,axes=(0,-1)) #u2d,M2E
|
|
||||||
leftmatrix[1,1,:,0,0,:] = leftmatrix[1,0,:,0,1,:] #u2d, E2M
|
|
||||||
#leftmatrix is now the translation matrix T
|
|
||||||
for j in range(2):
|
|
||||||
leftmatrix[j] = -np.tensordot(TMatrices_om[j], leftmatrix[j], axes=([-2,-1],[0,1]))
|
|
||||||
# at this point, jth row of leftmatrix is that of -MT
|
|
||||||
leftmatrix[j,:,:,j,:,:] += n2id
|
|
||||||
#now we are done, 1-MT
|
|
||||||
|
|
||||||
leftmatrixlist[ki] = leftmatrix
|
|
||||||
|
|
||||||
nnlist = np.logical_not(isNaNlist)
|
|
||||||
leftmatrixlist_s = np.reshape(leftmatrixlist,(klist_c.shape[0], 2*2*nelem,2*2*nelem))[nnlist]
|
|
||||||
TEc, TMc = symz_indexarrays(lMax, 2)
|
|
||||||
leftmatrixlist_TE = leftmatrixlist_s[np.ix_(np.arange(leftmatrixlist_s.shape[0]),TEc,TEc)]
|
|
||||||
leftmatrixlist_TM = leftmatrixlist_s[np.ix_(np.arange(leftmatrixlist_s.shape[0]),TMc,TMc)]
|
|
||||||
_time_e(sbtime, verbose, step='Initializing matrices for SVD for a given list of k\'s')
|
|
||||||
|
|
||||||
sbtime = _time_b(verbose, step='Calculating SVDs for a given list of k\'s.')
|
|
||||||
if(not onlyNmin):
|
|
||||||
svUfullTElist[nnlist], svSfullTElist[nnlist], svVfullTElist[nnlist] = np.linalg.svd(leftmatrixlist_TE, compute_uv=True)
|
|
||||||
svUfullTMlist[nnlist], svSfullTMlist[nnlist], svVfullTMlist[nnlist] = np.linalg.svd(leftmatrixlist_TM, compute_uv=True)
|
|
||||||
_time_e(sbtime, verbose, step='Calculating SVDs for a given list of k\'s.')
|
|
||||||
return ((svUfullTElist, svSfullTElist, svVfullTElist), (svUfullTMlist, svSfullTMlist, svVfullTMlist))
|
|
||||||
else:
|
|
||||||
minsvTElist[nnlist] = np.linalg.svd(leftmatrixlist_TE, compute_uv=False)[...,-onlyNmin:]
|
|
||||||
minsvTMlist[nnlist] = np.linalg.svd(leftmatrixlist_TM, compute_uv=False)[...,-onlyNmin:]
|
|
||||||
_time_e(sbtime, verbose, step='Calculating SVDs for a given list of k\'s.')
|
|
||||||
return (minsvTElist, minsvTMlist)
|
|
||||||
|
|
@ -1,3 +1,7 @@
|
||||||
|
"""
|
||||||
|
These functions are mostly deprecated by the C counterparts from lattices.h.
|
||||||
|
This file is still kept for reference, but might be removed in the future.
|
||||||
|
"""
|
||||||
import numpy as np
|
import numpy as np
|
||||||
from enum import Enum
|
from enum import Enum
|
||||||
from math import floor
|
from math import floor
|
||||||
|
|
|
||||||
1058
qpms/legacy.py
1058
qpms/legacy.py
File diff suppressed because it is too large
Load Diff
|
|
@ -1,7 +1,5 @@
|
||||||
# Kde co je
|
# Kde co je
|
||||||
## Staré věci
|
## Staré věci
|
||||||
hexpoints.py
|
|
||||||
legacy.py
|
|
||||||
qpms_p.py (až na změny souřadnic???)
|
qpms_p.py (až na změny souřadnic???)
|
||||||
|
|
||||||
## Nové věci
|
## Nové věci
|
||||||
|
|
@ -12,31 +10,8 @@ tmatrices.py
|
||||||
types.py
|
types.py
|
||||||
svwf.c
|
svwf.c
|
||||||
|
|
||||||
## Smíšené / v přepisu
|
|
||||||
scattering.py
|
|
||||||
qpms_c.pyx
|
qpms_c.pyx
|
||||||
|
|
||||||
## ???
|
|
||||||
hexpoints_c.pyx
|
|
||||||
|
|
||||||
# hexpoints.py
|
|
||||||
Asi hlavně starý kód pro vytváření trojúhelníkových a hexagonálních mřížek
|
|
||||||
a počítání (a ukládání) interakčních matic
|
|
||||||
|
|
||||||
## funkce
|
|
||||||
generate_trianglepoints
|
|
||||||
generate_trianglepoints_hexcomplement
|
|
||||||
hexlattice_precalc_AB_save
|
|
||||||
hexlattice_precalc_AB_loadunwrap
|
|
||||||
hexlattice_get_AB
|
|
||||||
hexlattice_zsym_getSVD
|
|
||||||
|
|
||||||
# hexpoints_c.pyx
|
|
||||||
Obsahuje pouze jedinou funkci (která je i v hexpoints.py).
|
|
||||||
Používá se tohle vůbec někde?
|
|
||||||
## funkce
|
|
||||||
hexlattice_zsym_getSVD
|
|
||||||
|
|
||||||
# lattices2d.py
|
# lattices2d.py
|
||||||
Nový kód, manipulace s basemi, vytváření mřížek atd.
|
Nový kód, manipulace s basemi, vytváření mřížek atd.
|
||||||
|
|
||||||
|
|
@ -58,23 +33,6 @@ filledWS
|
||||||
filledWS2
|
filledWS2
|
||||||
change_basis
|
change_basis
|
||||||
|
|
||||||
# legacy.py
|
|
||||||
Stařičký kód
|
|
||||||
|
|
||||||
## funkce
|
|
||||||
q_max
|
|
||||||
a_q
|
|
||||||
Ã
|
|
||||||
B̃
|
|
||||||
G_Mie_scat_precalc_cart
|
|
||||||
G_Mie_scat_cart
|
|
||||||
scatter_plane_wave
|
|
||||||
scatter_plane_wave_rectarray
|
|
||||||
scatter_constmultipole_rectarray
|
|
||||||
hexlattice_precalc_AB_save2
|
|
||||||
hexlattice_precalc_AB_save_purepy
|
|
||||||
hexlattice_precalc_AB_loadunwrap
|
|
||||||
hexlattice_get_AB
|
|
||||||
|
|
||||||
# qpms_p.py
|
# qpms_p.py
|
||||||
## funkce
|
## funkce
|
||||||
|
|
@ -111,12 +69,6 @@ G0L_analytical
|
||||||
G0T_analytical
|
G0T_analytical
|
||||||
G0_sum_1_slow
|
G0_sum_1_slow
|
||||||
|
|
||||||
# scattering.py
|
|
||||||
## třídy
|
|
||||||
Scattering
|
|
||||||
LatticeScattering (neimplementováno nic, asi zrovna rozepsáno)
|
|
||||||
Scattering_2D_zsym
|
|
||||||
|
|
||||||
# scripts_common.py
|
# scripts_common.py
|
||||||
## funkce
|
## funkce
|
||||||
make_action_sharedlist
|
make_action_sharedlist
|
||||||
|
|
|
||||||
|
|
@ -1,3 +1,7 @@
|
||||||
|
"""
|
||||||
|
This file contains mostly legacy code, but is still kept for reference. Avoid using this.
|
||||||
|
"""
|
||||||
|
|
||||||
import numpy as np
|
import numpy as np
|
||||||
from .qpms_c import *
|
from .qpms_c import *
|
||||||
ň = np.newaxis
|
ň = np.newaxis
|
||||||
|
|
|
||||||
|
|
@ -1,386 +0,0 @@
|
||||||
'''
|
|
||||||
Object oriented approach for the classical multiple scattering problem.
|
|
||||||
'''
|
|
||||||
|
|
||||||
__TODO__ = '''
|
|
||||||
- Implement per-scatterer lMax
|
|
||||||
- This means that Scattering.TMatrices either can not be a single array with a fixed
|
|
||||||
(N, 2, nelem, 2, nelem) shape but rather list of (2, nelem, 2, nelem) with nelem varying
|
|
||||||
per particle or some of its elements have to be unused. Anyways, there has to be some kind of
|
|
||||||
list with the lMaxes.
|
|
||||||
'''
|
|
||||||
|
|
||||||
import numpy as np
|
|
||||||
nx = np.newaxis
|
|
||||||
import time
|
|
||||||
import scipy
|
|
||||||
import sys
|
|
||||||
import warnings
|
|
||||||
import math
|
|
||||||
from qpms_c import get_mn_y, trans_calculator # TODO be explicit about what is imported
|
|
||||||
from .qpms_p import cart2sph, nelem2lMax # TODO be explicit about what is imported
|
|
||||||
from .timetrack import _time_b, _time_e
|
|
||||||
|
|
||||||
class Scattering(object):
|
|
||||||
'''
|
|
||||||
|
|
||||||
This is the most general class for a system of scatterers
|
|
||||||
in a non-lossy homogeneous background
|
|
||||||
to be solved with the multiple_scattering method. The scatterers,
|
|
||||||
as long as they comply with the disjoint circumscribed sphere
|
|
||||||
hypothesis, can each have any position in the 3D space and
|
|
||||||
any T-matrix.
|
|
||||||
|
|
||||||
Note that this object describes the scattering problem only for
|
|
||||||
a single given frequency, as the T-matrices and wavelenght
|
|
||||||
otherwise differ and all the computationally demanding
|
|
||||||
parts have to be done for each frequency. However,
|
|
||||||
the object can be recycled for many incident field shapes
|
|
||||||
at the given frequency.
|
|
||||||
|
|
||||||
Attributes should be perhaps later redefined to be read-only
|
|
||||||
(or make descriptors for them).
|
|
||||||
|
|
||||||
Args:
|
|
||||||
positions: (N,3)-shaped real array
|
|
||||||
TMatrices: (N,2,nelem,2,nelem)-shaped array
|
|
||||||
k_0 (float): Wave number for the space between scatterers.
|
|
||||||
|
|
||||||
Attributes:
|
|
||||||
positions:
|
|
||||||
TMatrices:
|
|
||||||
k_0 (float): Wave number for the space between scatterers.
|
|
||||||
lMax (int): Absolute maximum l for all scatterers. Depending on implementation,
|
|
||||||
lMax can be smaller for some individual scatterers in certain subclasses.
|
|
||||||
FIXME: here it is still implemented as constant lMax for all sites, see #!
|
|
||||||
prepared (bool): Keeps information whether the interaction matrix has
|
|
||||||
already been built and factorized.
|
|
||||||
|
|
||||||
|
|
||||||
'''
|
|
||||||
|
|
||||||
def __init__(self, positions, TMatrices, k_0, lMax = None, verbose=False, J_scat=3):
|
|
||||||
self.J_scat = J_scat
|
|
||||||
self.positions = positions.reshape((-1, positions.shape[-1]))
|
|
||||||
self.interaction_matrix = None
|
|
||||||
self.N = self.positions.shape[0]
|
|
||||||
self.k_0 = k_0
|
|
||||||
self.lMax = lMax if lMax else nelem2lMax(TMatrices.shape[-1])
|
|
||||||
self.tc = trans_calculator(self.lMax)
|
|
||||||
nelem = self.lMax * (self.lMax + 2) #!
|
|
||||||
self.nelem = nelem #!
|
|
||||||
self.prepared = False
|
|
||||||
self.TMatrices = np.broadcast_to(TMatrices, (self.N,2,nelem,2,nelem))
|
|
||||||
if np.isnan(np.min(TMatrices)):
|
|
||||||
warnings.warn("Some TMatrices contain NaNs. Expect invalid results")
|
|
||||||
if np.isnan(np.min(positions)):
|
|
||||||
warnings.warn("positions contain NaNs. Expect invalid results")
|
|
||||||
if math.isnan(k_0):
|
|
||||||
warnings.warn("k_0 is NaN. Expect invalid results")
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
def prepare(self, keep_interaction_matrix = False, verbose=False):
|
|
||||||
btime = _time_b(verbose)
|
|
||||||
if not self.prepared:
|
|
||||||
if self.interaction_matrix is None:
|
|
||||||
self.build_interaction_matrix(verbose=verbose)
|
|
||||||
self.lupiv = scipy.linalg.lu_factor(self.interaction_matrix,overwrite_a = not keep_interaction_matrix)
|
|
||||||
if not keep_interaction_matrix:
|
|
||||||
self.interaction_matrix = None
|
|
||||||
self.prepared = True
|
|
||||||
_time_e(btime, verbose)
|
|
||||||
|
|
||||||
def build_interaction_matrix(self,verbose = False):
|
|
||||||
btime = _time_b(verbose)
|
|
||||||
N = self.N
|
|
||||||
my, ny = get_mn_y(self.lMax)
|
|
||||||
nelem = len(my)
|
|
||||||
leftmatrix = np.zeros((N,2,nelem,N,2,nelem), dtype=complex)
|
|
||||||
sbtime = _time_b(verbose, step = 'Calculating interparticle translation coefficients')
|
|
||||||
"""
|
|
||||||
for i in range(N):
|
|
||||||
for j in range(N):
|
|
||||||
for yi in range(nelem):
|
|
||||||
for yj in range(nelem):
|
|
||||||
if(i != j):
|
|
||||||
d_i2j = cart2sph(self.positions[j]-self.positions[i])
|
|
||||||
a = Ã(my[yj],ny[yj],my[yi],ny[yi],kdlj=d_i2j[0]*self.k_0,θlj=d_i2j[1],φlj=d_i2j[2],r_ge_d=False,J=self.J_scat)
|
|
||||||
b = B̃(my[yj],ny[yj],my[yi],ny[yi],kdlj=d_i2j[0]*self.k_0,θlj=d_i2j[1],φlj=d_i2j[2],r_ge_d=False,J=self.J_scat)
|
|
||||||
leftmatrix[j,0,yj,i,0,yi] = a
|
|
||||||
leftmatrix[j,1,yj,i,1,yi] = a
|
|
||||||
leftmatrix[j,0,yj,i,1,yi] = b
|
|
||||||
leftmatrix[j,1,yj,i,0,yi] = b
|
|
||||||
"""
|
|
||||||
kdji = cart2sph(self.positions[:,nx,:] - self.positions[nx,:,:])
|
|
||||||
kdji[:,:,0] *= self.k_0
|
|
||||||
# get_AB array structure: [j,yj,i,yi]
|
|
||||||
a, b = self.tc.get_AB(my[nx,:,nx,nx],ny[nx,:,nx,nx],my[nx,nx,nx,:],ny[nx,nx,nx,:],
|
|
||||||
(kdji[:,:,0])[:,nx,:,nx], (kdji[:,:,1])[:,nx,:,nx], (kdji[:,:,2])[:,nx,:,nx],
|
|
||||||
False,self.J_scat)
|
|
||||||
mask = np.broadcast_to(np.eye(N,dtype=bool)[:,nx,:,nx],(N,nelem,N,nelem))
|
|
||||||
a[mask] = 0 # no self-translations
|
|
||||||
b[mask] = 0
|
|
||||||
leftmatrix[:,0,:,:,0,:] = a
|
|
||||||
leftmatrix[:,1,:,:,1,:] = a
|
|
||||||
leftmatrix[:,0,:,:,1,:] = b
|
|
||||||
leftmatrix[:,1,:,:,0,:] = b
|
|
||||||
_time_e(sbtime, verbose, step = 'Calculating interparticle translation coefficients')
|
|
||||||
# at this point, leftmatrix is the translation matrix
|
|
||||||
n2id = np.identity(2*nelem)
|
|
||||||
n2id.shape = (2,nelem,2,nelem)
|
|
||||||
for j in range(N):
|
|
||||||
leftmatrix[j] = - np.tensordot(self.TMatrices[j],leftmatrix[j],axes=([-2,-1],[0,1]))
|
|
||||||
# at this point, jth row of leftmatrix is that of -MT
|
|
||||||
leftmatrix[j,:,:,j,:,:] += n2id
|
|
||||||
# now we are done, 1-MT
|
|
||||||
leftmatrix.shape=(N*2*nelem,N*2*nelem)
|
|
||||||
self.interaction_matrix = leftmatrix
|
|
||||||
_time_e(btime, verbose)
|
|
||||||
|
|
||||||
def scatter(self, pq_0, verbose = False):
|
|
||||||
'''
|
|
||||||
pq_0 is (N, nelem, 2)-shaped array
|
|
||||||
'''
|
|
||||||
btime = _time_b(verbose)
|
|
||||||
if math.isnan(np.min(pq_0)):
|
|
||||||
warnings.warn("The incident field expansion contains NaNs. Expect invalid results.")
|
|
||||||
self.prepare(verbose=verbose)
|
|
||||||
pq_0 = np.broadcast_to(pq_0, (self.N,2,self.nelem))
|
|
||||||
MP_0 = np.empty((self.N,2,self.nelem),dtype=np.complex_)
|
|
||||||
for j in range(self.N):
|
|
||||||
MP_0[j] = np.tensordot(self.TMatrices[j], pq_0[j],axes=([-2,-1],[-2,-1]))
|
|
||||||
MP_0.shape = (self.N*2*self.nelem,)
|
|
||||||
solvebtime = _time_b(verbose,step='Solving the linear equation')
|
|
||||||
ab = scipy.linalg.lu_solve(self.lupiv, MP_0)
|
|
||||||
if math.isnan(np.min(ab)):
|
|
||||||
warnings.warn("Got NaN in the scattering result. Damn.")
|
|
||||||
raise
|
|
||||||
_time_e(solvebtime, verbose, step='Solving the linear equation')
|
|
||||||
ab.shape = (self.N,2,self.nelem)
|
|
||||||
_time_e(btime, verbose)
|
|
||||||
return ab
|
|
||||||
|
|
||||||
def scatter_constmultipole(self, pq_0_c, verbose = False):
|
|
||||||
btime = _time_b(verbose)
|
|
||||||
N = self.N
|
|
||||||
self.prepare(verbose=verbose)
|
|
||||||
nelem = self.nelem
|
|
||||||
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):
|
|
||||||
MP_0[j] = np.tensordot(self.TMatrices[j], pq_0[j],axes=([-2,-1],[-2,-1]))
|
|
||||||
MP_0.shape = (N*2*nelem,)
|
|
||||||
ab[N_or_M,yy] = scipy.linalg.lu_solve(self.lupiv,MP_0)
|
|
||||||
ab.shape = (2,nelem,N,2,nelem)
|
|
||||||
_time_e(btime, verbose)
|
|
||||||
return ab
|
|
||||||
|
|
||||||
class LatticeScattering(Scattering):
|
|
||||||
def __init__(self, lattice_spec, k_0, zSym = False):
|
|
||||||
pass
|
|
||||||
|
|
||||||
|
|
||||||
"""
|
|
||||||
class Scattering_2D_lattice_rectcells(Scattering):
|
|
||||||
def __init__(self, rectcell_dims, rectcell_elem_positions, cellspec, k_0, rectcell_TMatrices = None, TMatrices = None, lMax = None, verbose=False, J_scat=3):
|
|
||||||
'''
|
|
||||||
cellspec: dvojice ve tvaru (seznam_zaplněnosti, seznam_pozic)
|
|
||||||
'''
|
|
||||||
if (rectcell_TMatrices is None) == (TMatrices is None):
|
|
||||||
raise ValueError('Either rectcell_TMatrices or TMatrices has to be given')
|
|
||||||
###self.positions = ZDE JSEM SKONČIL
|
|
||||||
self.J_scat = J_scat
|
|
||||||
self.positions = positions
|
|
||||||
self.interaction_matrix = None
|
|
||||||
self.N = positions.shape[0]
|
|
||||||
self.k_0 = k_0
|
|
||||||
self.lMax = lMax if lMax else nelem2lMax(TMatrices.shape[-1])
|
|
||||||
nelem = lMax * (lMax + 2) #!
|
|
||||||
self.nelem = nelem #!
|
|
||||||
self.prepared = False
|
|
||||||
self.TMatrices = np.broadcast_to(TMatrices, (self.N,2,nelem,2,nelem))
|
|
||||||
"""
|
|
||||||
|
|
||||||
class Scattering_2D_zsym(Scattering):
|
|
||||||
def __init__(self, positions, TMatrices, k_0, lMax = None, verbose=False, J_scat=3):
|
|
||||||
Scattering.__init__(self, positions, TMatrices, k_0, lMax, verbose, J_scat)
|
|
||||||
#TODO some checks on TMatrices symmetry
|
|
||||||
self.TE_yz = np.arange(self.nelem)
|
|
||||||
self.TM_yz = self.TE_yz
|
|
||||||
self.my, self.ny = get_mn_y(self.lMax)
|
|
||||||
self.TE_NMz = (self.my + self.ny) % 2
|
|
||||||
self.TM_NMz = 1 - self.TE_NMz
|
|
||||||
self.tc = trans_calculator(self.lMax)
|
|
||||||
# TODO možnost zadávat T-matice rovnou ve zhuštěné podobě
|
|
||||||
TMatrices_TE = TMatrices[...,self.TE_NMz[:,nx],self.TE_yz[:,nx],self.TE_NMz[nx,:],self.TE_yz[nx,:]]
|
|
||||||
TMatrices_TM = TMatrices[...,self.TM_NMz[:,nx],self.TM_yz[:,nx],self.TM_NMz[nx,:],self.TM_yz[nx,:]]
|
|
||||||
self.TMatrices_TE = np.broadcast_to(TMatrices_TE, (self.N, self.nelem, self.nelem))
|
|
||||||
self.TMatrices_TM = np.broadcast_to(TMatrices_TM, (self.N, self.nelem, self.nelem))
|
|
||||||
self.prepared_TE = False
|
|
||||||
self.prepared_TM = False
|
|
||||||
self.interaction_matrix_TE = None
|
|
||||||
self.interaction_matrix_TM= None
|
|
||||||
|
|
||||||
def prepare_partial(self, TE_or_TM, keep_interaction_matrix = False, verbose=False):
|
|
||||||
'''
|
|
||||||
TE is 0, TM is 1.
|
|
||||||
'''
|
|
||||||
btime = _time_b(verbose)
|
|
||||||
if (TE_or_TM == 0): #TE
|
|
||||||
if not self.prepared_TE:
|
|
||||||
if self.interaction_matrix_TE is None:
|
|
||||||
self.build_interaction_matrix(0, verbose)
|
|
||||||
sbtime = _time_b(verbose, step = 'Calculating LU decomposition of the interaction matrix, TE part')
|
|
||||||
self.lupiv_TE = scipy.linalg.lu_factor(self.interaction_matrix_TE, overwrite_a = not keep_interaction_matrix)
|
|
||||||
_time_e(sbtime, verbose, step = 'Calculating LU decomposition of the interaction matrix, TE part')
|
|
||||||
if(np.isnan(np.min(self.lupiv_TE[0])) or np.isnan(np.min(self.lupiv_TE[1]))):
|
|
||||||
warnings.warn("LU factorisation of interaction matrix contains NaNs. Expect invalid results.")
|
|
||||||
self.prepared_TE = True
|
|
||||||
if (TE_or_TM == 1): #TM
|
|
||||||
if not self.prepared_TM:
|
|
||||||
if self.interaction_matrix_TM is None:
|
|
||||||
self.build_interaction_matrix(1, verbose)
|
|
||||||
sbtime = _time_b(verbose, step = 'Calculating LU decomposition of the interaction matrix, TM part')
|
|
||||||
self.lupiv_TM = scipy.linalg.lu_factor(self.interaction_matrix_TM, overwrite_a = not keep_interaction_matrix)
|
|
||||||
_time_e(sbtime, verbose, step = 'Calculating LU decomposition of the interaction matrix, TM part')
|
|
||||||
if(np.isnan(np.min(self.lupiv_TM[0])) or np.isnan(np.min(self.lupiv_TM[1]))):
|
|
||||||
warnings.warn("LU factorisation of interaction matrix contains NaNs. Expect invalid results.")
|
|
||||||
self.prepared_TM = True
|
|
||||||
_time_e(btime, verbose)
|
|
||||||
|
|
||||||
def prepare(self, keep_interaction_matrix = False, verbose=False):
|
|
||||||
btime = _time_b(verbose)
|
|
||||||
if not self.prepared:
|
|
||||||
self.prepare_partial(0, keep_interaction_matrix, verbose)
|
|
||||||
self.prepare_partial(1, keep_interaction_matrix, verbose)
|
|
||||||
self.prepared = True
|
|
||||||
_time_e(btime, verbose)
|
|
||||||
|
|
||||||
def build_interaction_matrix(self,TE_or_TM = None, verbose = False):
|
|
||||||
#None means both
|
|
||||||
btime = _time_b(verbose)
|
|
||||||
N = self.N
|
|
||||||
my, ny = get_mn_y(self.lMax)
|
|
||||||
nelem = len(my)
|
|
||||||
idm = np.identity(nelem)
|
|
||||||
if (TE_or_TM == 0):
|
|
||||||
EoMl = (0,)
|
|
||||||
elif (TE_or_TM == 1):
|
|
||||||
EoMl = (1,)
|
|
||||||
elif (TE_or_TM is None):
|
|
||||||
EoMl = (0,1)
|
|
||||||
sbtime = _time_b(verbose, step = 'Calculating interparticle translation coefficients')
|
|
||||||
kdji = cart2sph(self.positions[:,nx,:] - self.positions[nx,:,:], allow2d=True)
|
|
||||||
kdji[:,:,0] *= self.k_0
|
|
||||||
# get_AB array structure: [j,yj,i,yi]
|
|
||||||
# FIXME I could save some memory by calculating only half of these coefficients
|
|
||||||
a, b = self.tc.get_AB(my[nx,:,nx,nx],ny[nx,:,nx,nx],my[nx,nx,nx,:],ny[nx,nx,nx,:],
|
|
||||||
(kdji[:,:,0])[:,nx,:,nx], (kdji[:,:,1])[:,nx,:,nx], (kdji[:,:,2])[:,nx,:,nx],
|
|
||||||
False,self.J_scat)
|
|
||||||
mask = np.broadcast_to(np.eye(N,dtype=bool)[:,nx,:,nx],(N,nelem,N,nelem))
|
|
||||||
a[mask] = 0 # no self-translations
|
|
||||||
b[mask] = 0
|
|
||||||
if np.isnan(np.min(a)) or np.isnan(np.min(b)):
|
|
||||||
warnings.warn("Some of the translation coefficients is a NaN. Expect invalid results.")
|
|
||||||
_time_e(sbtime, verbose, step = 'Calculating interparticle translation coefficients')
|
|
||||||
for EoM in EoMl:
|
|
||||||
leftmatrix = np.zeros((N,nelem,N,nelem), dtype=complex)
|
|
||||||
y = np.arange(nelem)
|
|
||||||
yi = y[nx,nx,nx,:]
|
|
||||||
yj = y[nx,:,nx,nx]
|
|
||||||
mask = np.broadcast_to((((yi - yj) % 2) == 0),(N,nelem,N,nelem))
|
|
||||||
leftmatrix[mask] = a[mask]
|
|
||||||
mask = np.broadcast_to((((yi - yj) % 2) != 0),(N,nelem,N,nelem))
|
|
||||||
leftmatrix[mask] = b[mask]
|
|
||||||
""" # we use to calculate the AB coefficients here
|
|
||||||
for i in range(N):
|
|
||||||
for j in range(i):
|
|
||||||
for yi in range(nelem):
|
|
||||||
for yj in range(nelem):
|
|
||||||
d_i2j = cart2sph(self.positions[j]-self.positions[i])
|
|
||||||
if ((yi - yj) % 2) == 0:
|
|
||||||
tr = Ã(my[yj],ny[yj],my[yi],ny[yi],kdlj=d_i2j[0]*self.k_0,θlj=d_i2j[1],φlj=d_i2j[2],r_ge_d=False,J=self.J_scat)
|
|
||||||
else:
|
|
||||||
tr = B̃(my[yj],ny[yj],my[yi],ny[yi],kdlj=d_i2j[0]*self.k_0,θlj=d_i2j[1],φlj=d_i2j[2],r_ge_d=False,J=self.J_scat)
|
|
||||||
leftmatrix[j,yj,i,yi] = tr
|
|
||||||
leftmatrix[i,yi,j,yj] = tr if (0 == (my[yj]+my[yi]) % 2) else -tr
|
|
||||||
_time_e(sbtime, verbose, step = 'Calculating interparticle translation coefficients, T%s part' % ('M' if EoM else 'E'))
|
|
||||||
"""
|
|
||||||
for j in range(N):
|
|
||||||
leftmatrix[j] = - np.tensordot(self.TMatrices_TM[j] if EoM else self.TMatrices_TE[j],leftmatrix[j],
|
|
||||||
axes = ([-1],[0]))
|
|
||||||
leftmatrix[j,:,j,:] += idm
|
|
||||||
leftmatrix.shape = (self.N*self.nelem, self.N*self.nelem)
|
|
||||||
if np.isnan(np.min(leftmatrix)):
|
|
||||||
warnings.warn("Interaction matrix contains some NaNs. Expect invalid results.")
|
|
||||||
if EoM == 0:
|
|
||||||
self.interaction_matrix_TE = leftmatrix
|
|
||||||
if EoM == 1:
|
|
||||||
self.interaction_matrix_TM = leftmatrix
|
|
||||||
a = None
|
|
||||||
b = None
|
|
||||||
_time_e(btime, verbose)
|
|
||||||
|
|
||||||
def scatter_partial(self, TE_or_TM, pq_0, verbose = False):
|
|
||||||
'''
|
|
||||||
pq_0 is (N, nelem)-shaped array
|
|
||||||
'''
|
|
||||||
btime = _time_b(verbose)
|
|
||||||
self.prepare_partial(TE_or_TM, verbose = verbose)
|
|
||||||
pq_0 = np.broadcast_to(pq_0, (self.N, self.nelem))
|
|
||||||
MP_0 = np.empty((self.N,self.nelem),dtype=np.complex_)
|
|
||||||
for j in range(self.N):
|
|
||||||
if TE_or_TM: #TM
|
|
||||||
MP_0[j] = np.tensordot(self.TMatrices_TM[j], pq_0[j], axes=([-1],[-1]))
|
|
||||||
else: #TE
|
|
||||||
MP_0[j] = np.tensordot(self.TMatrices_TE[j], pq_0[j], axes=([-1],[-1]))
|
|
||||||
MP_0.shape = (self.N*self.nelem,)
|
|
||||||
solvebtime = _time_b(verbose,step='Solving the linear equation')
|
|
||||||
ab = scipy.linalg.lu_solve(self.lupiv_TM if TE_or_TM else self.lupiv_TE, MP_0)
|
|
||||||
_time_e(solvebtime, verbose, step='Solving the linear equation')
|
|
||||||
ab.shape = (self.N, self.nelem)
|
|
||||||
_time_e(btime,verbose)
|
|
||||||
return ab
|
|
||||||
|
|
||||||
def scatter(self, pq_0, verbose = False):
|
|
||||||
'''
|
|
||||||
FI7ME
|
|
||||||
pq_0 is (N, nelem, 2)-shaped array
|
|
||||||
'''
|
|
||||||
btime = _time_b(verbose)
|
|
||||||
raise Exception('Not yet implemented')
|
|
||||||
self.prepare(verbose=verbose)
|
|
||||||
pq_0 = np.broadcast_to(pq_0, (self.N,2,self.nelem))
|
|
||||||
MP_0 = np.empty((N,2,nelem),dtype=np.complex_)
|
|
||||||
for j in range(self.N):
|
|
||||||
MP_0[j] = np.tensordot(self.TMatrices[j], pq_0[j],axes=([-2,-1],[-2,-1]))
|
|
||||||
MP_0.shape = (N*2*self.nelem,)
|
|
||||||
solvebtime = _time_b(verbose,step='Solving the linear equation')
|
|
||||||
ab = scipy.linalg.lu_solve(self.lupiv, MP_0)
|
|
||||||
_time_e(solvebtime, verbose, step='Solving the linear equation')
|
|
||||||
ab.shape = (N,2,nelem)
|
|
||||||
_time_e(btime, verbose)
|
|
||||||
return ab
|
|
||||||
|
|
||||||
def forget_matrices(self):
|
|
||||||
'''
|
|
||||||
Free interaction matrices and set the respective flags
|
|
||||||
(useful when memory is a bottleneck).
|
|
||||||
'''
|
|
||||||
self.interaction_matrix_TE = None
|
|
||||||
self.interaction_matrix_TM = None
|
|
||||||
self.lupiv_TE = None
|
|
||||||
self.lupiv_TM = None
|
|
||||||
self.prepared_TE = False
|
|
||||||
self.prepared_TM = False
|
|
||||||
self.prepared = False
|
|
||||||
|
|
||||||
|
|
||||||
|
|
@ -1,3 +1,6 @@
|
||||||
|
"""
|
||||||
|
Legacy code, used only in scripts_common.py. To be removed in future versions.
|
||||||
|
"""
|
||||||
import time
|
import time
|
||||||
import sys
|
import sys
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -1,3 +1,8 @@
|
||||||
|
"""
|
||||||
|
Mostly legacy code, but still kept for reference.
|
||||||
|
Might be removed in future versions.
|
||||||
|
"""
|
||||||
|
|
||||||
import numpy as np
|
import numpy as np
|
||||||
use_moble_quaternion = False
|
use_moble_quaternion = False
|
||||||
try:
|
try:
|
||||||
|
|
|
||||||
Loading…
Reference in New Issue