def add_zeeman(h,zeeman=[0.0,0.0,0.0]):
""" Add Zeeman to the hamiltonian """
from scipy.sparse import coo_matrix as coo
from scipy.sparse import bmat
if h.has_spin: # only if the system has spin
sx = coo([[0.0,1.0],[1.0,0.0]]) # sigma x
sy = coo([[0.0,-1j],[1j,0.0]]) # sigma y
sz = coo([[1.0,0.0],[0.0,-1.0]]) # sigma z
no = h.num_orbitals # number of orbitals (without spin)
# create matrix to add to the hamiltonian
bzee = [[None for i in range(no)] for j in range(no)]
# assign diagonal terms
if not callable(zeeman): # if it is a number
for i in range(no):
bzee[i][i] = zeeman[0]*sx+zeeman[1]*sy+zeeman[2]*sz
elif callable(zeeman): # if it is a function
x = h.geometry.x # x position
y = h.geometry.y # y position
for i in range(no):
z = zeeman(x[i],y[i]) # get the value of the zeeman
bzee[i][i] = z[0]*sx+z[1]*sy+z[2]*sz
else:
raise
bzee = bmat(bzee) # create matrix
if h.has_eh: # if electron hole, double the dimension
bzee = bmat([[bzee,None],[None,-bzee]])
bzee = bzee.todense() # create dense matrix
h.intra = h.intra + bzee
if not h.has_spin: # still have to implement this...
raise
def kron(self, B):
fp = self.fparity # def fp just for convenience
ss = self.sym_sum # def ss just for convenience
if self.sym!=B.sym:
raise Exception('A, B symmetries do not match.')
sym = B.sym
dim = sym * self.dim * B.dim
if not np.array_equal(self.fparity, B.fparity):
raise Exception('A, B fermion types do not match.')
datatype = self.datatype
if np.dtype(datatype) < np.dtype(B.datatype): datatype = B.datatype
ret = quantum_operator(sym, dim, datatype, fp, ss)
for row in range(sym):
for col in range(sym):
for row1 in range(sym):
for col1 in range(sym):
row2 = ss(row, -row1)
col2 = ss(col, -col1)
if not self._empty_(row1, col1) and not B._empty_(row2, col2):
sign = 1 - 2*( fp[col1] * fp[ss(col2, -row2)] )
block = self.dim * B.dim
temp = coo( skron(self.val[row1][col1], B.val[row2][col2]) * sign )
add = coo((temp.data, (temp.row + row1*block, temp.col + col1*block)), (dim, dim), dtype=datatype)
ret.val[row][col] += add
return ret
def print_hamiltonian(h): """ Print the hamilotnian on screen """ from scipy.sparse import coo_matrix as coo # import sparse matrix intra = coo(h.intra) # intracell inter = coo(h.inter) # intracell print "Intracell matrix" print intra print "Intercell matrix" print inter return
def print_hamiltonian(h):
""" Print the hamilotnian on screen """
from scipy.sparse import coo_matrix as coo # import sparse matrix
intra = coo(h.intra) # intracell
inter = coo(h.inter) # intracell
print("Intracell matrix")
print(intra)
print("Intercell matrix")
print(inter)
return
def add_swave(intra,delta=0.0,mu=0.0,phi=0.0):
""" Adds swave pairing """
intra_sc = 0.0*intra # intracell pairing potential
from scipy.sparse import coo_matrix as coo
from scipy.sparse import bmat
# intracell contribution
deltac = delta*np.exp(1j*phi*np.pi) # complex SC order parameter
for i in range(len(intra)/2): # loop over intracell pairs
intra_sc[2*i,2*i+1] = deltac # up down electron hole
intra_sc[2*i+1,2*i] = -deltac # down up
intra_eh = nambu(coo(intra),coupling=coo(intra_sc))
return intra_eh
def test_isotope_images_are_stored(search_results, spark_context):
mask = np.array([[1, 1], [1, 0]])
IMG_ID = "iso_image_id"
img_store_mock = MagicMock(spec=ImageStoreServiceWrapper)
img_store_mock.post_image.return_value = IMG_ID
img_store_mock.reset_mock()
ion_iso_images = spark_context.parallelize([
(0, [ coo([[0, 0], [0, 1]]), None, coo([[2, 3], [1, 0]]), None ]),
(1, [ coo([[1, 1], [0, 1]]), None, None, None])
])
ids = search_results.post_images_to_image_store(ion_iso_images, mask, img_store_mock, 'fs')
assert ids == { 0: {'iso_image_ids': [IMG_ID, None, IMG_ID, None]}, 1: {'iso_image_ids': [IMG_ID, None, None, None]} }
assert img_store_mock.post_image.call_count == 3
def __init__(self, factorList=None, copy=True, isLog=False):
"""Take in a list of factors and convert & store them in the internal format
Can also accept a matrix of Ising parameters
"""
if factorList is None:
self.h = np.zeros(0); self.L=csr((0,0)); return;
if not isinstance(factorList[0], Factor): # not a factor list => matrix?
L = coo(factorList)
LL = csr(factorList)
n = L.shape[0]
self.h = np.array([LL[i,i] for i in range(n)]); # extract diagonal
self.dims = np.array([2 for i in range(n)], dtype=int); # all variables binary
keep = (L.row != L.col)
data,row,col = L.data[keep],L.row[keep],L.col[keep]
#for j in np.where(L.row > L.col): row[j],col[j] = col[j],row[j]
self.L = csr((data,(row,col)),shape=(n,n)) # keep in csr format
self.L = .5*(self.L+self.L.T) # force symmetric if not (TODO: detect zeros & overwrite?)
else:
n = np.max([np.max(f.vars.labels) for f in factorList if len(f.vars)])+1
assert np.max([np.max(f.vars.dims()) for f in factorList]) <= 2, "Variables must be binary"
assert np.max([f.nvar for f in factorList]) <= 2, "Factors must be pairwise"
self.dims = np.zeros((n,), dtype=int)
for f in factorList:
for v in f.vars: self.dims[v] = v.states
self.h = np.zeros(n);
self.L = csr(([],([],[])),shape=(n,n));
self.addFactors(factorList, isLog=isLog)
def spm(m):
"""Non vanishing elements"""
from scipy.sparse import coo_matrix as coo
mc = coo(m) # to coo_matrixi
data = mc.data # get data
col = mc.col # get column index
row = mc.row # get row index
return (row, col, data.real, data.imag) # return things to print
def kron_s(A, B, sec): fp = A.fparity ss = A.sym_sum sym = A.sym dim = sym * A.dim * B.dim ret = csr((dim, dim), dtype=np.complex) for row1 in range(sym): for col1 in range(sym): row2 = ss(sec, -row1) col2 = ss(sec, -col1) if not A._empty_(row1, col1) and not B._empty_(row2, col2): sign = 1 - 2*( fp[col1] * fp[ss(col2, -row2)] ) temp = coo( sparse.kron(A.val[row1][col1], B.val[row2][col2]) * sign ) block = A.dim * B.dim add = coo((temp.data, (temp.row + row1*block, temp.col + col1*block)),(dim, dim), dtype=np.complex) ret += add return ret
def factors(self): """Return a list of factors (converted to full tables)""" X = [Var(i,2) for i in range(self.nvar)] factors = [Factor([],np.exp(self.c))] # TODO: exclude if zero? or exclude if inf/-inf, or if in "assigned", or? factors = factors + [Factor([X[i]],[-th,th]).exp() for i,th in enumerate(self.h) if self.dims[i]>1] L = coo(self.L) factors = factors + [Factor([X[i],X[j]],[[th,-th],[-th,th]]).exp() for i,j,th in zip(L.row,L.col,L.data) if i<j] return factors
def single_chrom_ABC(pregs, prna, eregs, ek27ac, llinks, contact_path, binsize,
normalisation, gamma, distance_thresh, chrom):
print("loading contact data, chromosome {}".format(chrom))
contacts = straw_extract_chr_matrix(contact_path,
chrom,
binsize=binsize,
normalisation=normalisation)
prom_bins = np.mean(pregs[chrom], axis=1).astype('int')
enh_bins = np.mean(eregs[chrom], axis=1).astype('int')
ep_dists = abs(prom_bins[:, None] - enh_bins[None, :])
distance_mask = ep_dists < distance_thresh
prom_bins = (prom_bins / binsize).astype('int')
enh_bins = (enh_bins / binsize).astype('int')
enh_act = ek27ac[chrom]
if contact_path is not None:
clipped_contacts, logs = clip_contacts(prom_bins, enh_bins, contacts,
gamma, chrom)
print("Clipped contact data for chromosome {}".format(chrom))
else:
clipped_contacts, logs = theoretical_contact_strengths(
prom_bins, enh_bins, gamma, chrom)
print("Using theoretical contact strengths for chromosome {}".format(
chrom))
ABC_scores = np.multiply(
llinks[chrom].todense(),
np.repeat(enh_act[None, :], prom_bins.shape[0], axis=0))
ABC_scores = np.multiply(ABC_scores, clipped_contacts)
ABC_scores[~prna[chrom], :] = 0
ABC_scores[distance_mask] = 0
ABC_score_sums = np.array(np.sum(ABC_scores, axis=1))[:, 0]
if np.sum(ABC_score_sums) > 0:
ABC_score_sums[ABC_score_sums == 0] = np.percentile(
ABC_score_sums[ABC_score_sums > 0], 1)
ABC_scores[ABC_score_sums > 0, :] = np.divide(
ABC_scores[ABC_score_sums > 0, :],
np.repeat(ABC_score_sums[ABC_score_sums > 0, None],
ABC_scores.shape[1],
axis=1))
ABC_scores = coo(ABC_scores)
print("Got scores for chromosome {}".format(chrom))
return ABC_scores.data, np.append(ABC_scores.row[:, None],
ABC_scores.col[:, None],
axis=1), chrom, logs
else:
return np.zeros(1), np.append(np.zeros(1)[:, None],
np.zeros(1)[:, None],
axis=1), chrom, logs
def write_mean_field(m,output_file="mean_field.in"):
"""Reads the file input_file and returns a traceless matrix"""
f = open(output_file,"w") # open file
mf = coo(m) # sparse matrix
f.write("# number of non vanishing elements\n"+str(len(mf.col))+"\n")
f.write("# i j real imaginary\n")
for (i,j,d) in zip(mf.row,mf.col,mf.data):
f.write(str(i+1)+" "+str(j+1)+" "+str(d.real)+" "+str(d.imag)+"\n")
f.close() # close file
def spm(m):
"""Non vanishing elements"""
from scipy.sparse import coo_matrix as coo
mc = coo(m) # to coo_matrixi
data = mc.data # get data
col = mc.col # get column index
row = mc.row # get row index
return (row, col, data.real, data.imag) # return things to print
def write_mean_field(m, output_file="mean_field.in"):
"""Reads the file input_file and returns a traceless matrix"""
f = open(output_file, "w") # open file
mf = coo(m) # sparse matrix
f.write("# number of non vanishing elements\n" + str(len(mf.col)) + "\n")
f.write("# i j real imaginary\n")
for (i, j, d) in zip(mf.row, mf.col, mf.data):
f.write(
str(i + 1) + " " + str(j + 1) + " " + str(d.real) + " " +
str(d.imag) + "\n")
f.close() # close file
def add_zeeman(h,zeeman=[0.0,0.0,0.0]):
""" Add Zeeman to the hamiltonian """
# convert the input into a list
def convert(z):
try: # try to get the first element
a = z[0]
except: # convert to list
z = [0.,0.,z]
return z
from scipy.sparse import coo_matrix as coo
from scipy.sparse import bmat
if h.has_spin: # only if the system has spin
sx = coo([[0.0,1.0],[1.0,0.0]]) # sigma x
sy = coo([[0.0,-1j],[1j,0.0]]) # sigma y
sz = coo([[1.0,0.0],[0.0,-1.0]]) # sigma z
# no = h.num_orbitals # number of orbitals (without spin)
no = h.intra.shape[0]//2 # number of orbitals (without spin)
# create matrix to add to the hamiltonian
bzee = [[None for i in range(no)] for j in range(no)]
# assign diagonal terms
if not callable(zeeman): # if it is a number
for i in range(no):
zeeman = convert(zeeman) # convert to list
bzee[i][i] = zeeman[0]*sx+zeeman[1]*sy+zeeman[2]*sz
elif callable(zeeman): # if it is a function
x = h.geometry.x # x position
y = h.geometry.y # y position
z = h.geometry.z # z position
for i in range(no):
mm = zeeman(x[i],y[i],z[i]) # get the value of the zeeman
mm = convert(mm) # convert to list
bzee[i][i] = mm[0]*sx+mm[1]*sy+mm[2]*sz
else:
raise
bzee = bmat(bzee) # create matrix
if h.has_eh: # if electron hole, double the dimension
bzee = bmat([[bzee,None],[None,-bzee]])
if not h.is_sparse: bzee = bzee.todense() # create dense matrix
h.intra = h.intra + bzee
if not h.has_spin: # still have to implement this...
raise
def nv_el(m): """ get the non vanishing elments of a matrix""" from scipy.sparse import coo_matrix as coo mc = coo(m) # to coo_matrixi data = mc.data # get data col = mc.col # get column index row = mc.row # get row index nv = [] nt=len(data) for i in range(nt): # save the nonvanishing values nv.append([row[i]+1,col[i]+1,data[i].real,data[i].imag]) return nv
def nv_el(m): """ get the non vanishing elments of a matrix""" from scipy.sparse import coo_matrix as coo mc = coo(m) # to coo_matrixi data = mc.data # get data col = mc.col # get column index row = mc.row # get row index nv = [] nt=len(data) for i in range(nt): # save the nonvanishing values nv.append([row[i]+1,col[i]+1,data[i].real,data[i].imag]) return nv
def test_isotope_images_are_stored(search_results, spark_context):
mask = np.array([[1, 1], [1, 0]])
IMG_ID = "iso_image_id"
img_store_mock = MagicMock(spec=ImageStoreServiceWrapper)
img_store_mock.post_image.return_value = IMG_ID
img_store_mock.reset_mock()
ion_iso_images = spark_context.parallelize([
(0, [coo([[0, 0], [0, 1]]), None,
coo([[2, 3], [1, 0]]), None]),
(1, [coo([[1, 1], [0, 1]]), None, None, None])
])
ids = search_results.post_images_to_image_store(ion_iso_images, mask,
img_store_mock, 'fs')
assert ids == {
0: {
'iso_image_ids': [IMG_ID, None, IMG_ID, None]
},
1: {
'iso_image_ids': [IMG_ID, None, None, None]
}
}
assert img_store_mock.post_image.call_count == 3
def kron_s(A, B, sec):
fp = A.fparity
ss = A.sym_sum
sym = A.sym
dim = sym * A.dim * B.dim
ret = csr((dim, dim), dtype=np.complex)
for row1 in range(sym):
for col1 in range(sym):
row2 = ss(sec, -row1)
col2 = ss(sec, -col1)
if not A._empty_(row1, col1) and not B._empty_(row2, col2):
sign = 1 - 2 * (fp[col1] * fp[ss(col2, -row2)])
temp = coo(
sparse.kron(A.val[row1][col1], B.val[row2][col2]) *
sign)
block = A.dim * B.dim
add = coo(
(temp.data,
(temp.row + row1 * block, temp.col + col1 * block)),
(dim, dim),
dtype=np.complex)
ret += add
return ret
def add_pwave_electron_hole_pairing(h,delta=0.0,mu=0.0,phi=0.0):
""" Adds an pwave (spin conserving) electron-hole pairing
potential to first neighbors,
delta is pairing potential
mu is chemical potential
phi is superconducting phase"""
h.has_eh = True # has electron hole pairs
intra = h.intra # intra cell potential
intra_sc = 0.0*intra # intracell pairing potential
x = h.geometry.x # x position
y = h.geometry.y # y position
from scipy.sparse import coo_matrix as coo
from scipy.sparse import bmat
deltac = delta*np.exp(1j*phi*np.pi) # complex SC order parameter
# intracell contribution
pairs = find_first_neighbor(x,y,x,y) # get intra pairs of first neighbors
for p in pairs: # loop over intracell pairs
# geometric phase, dependent on bonding orientation
gp = np.arctan2(x[p[0]]-x[p[1]],y[p[0]]-y[p[1]])
gp = np.exp(1j*gp) # get complex phase
intra_sc[2*p[0],2*p[1]] = deltac*gp # up up, times phase
intra_sc[2*p[0]+1,2*p[1]+1] = deltac*gp # down down, times phase
# intracell eh matrix
intra_eh = [[coo(intra),coo(intra_sc-intra_sc.H)],
[coo(intra_sc.H-intra_sc),-coo(intra)]]
intra_eh = bmat(intra_eh).todense()
if h.dimensionality == 0: # if 0 dimensional return
return (intra_eh,None)
# intercell contribution
if h.dimensionality == 1: # if one dimensional calculate neighbor contribution
celldis = h.geometry.celldis # distance to neighboring cell
inter = h.inter # inter cell potential
inter_sc = 0.0*inter # intercell pairing potential
pairs = find_first_neighbor(x,y,x+celldis,y) # get inter pairs of first neighbors
for p in pairs: # loop over intercell pairs
gp = np.arctan2(x[p[0]]-x[p[1]-celldis],y[p[0]]-y[p[1]])
gp = np.exp(1j*gp) # get complex phase
inter_sc[2*p[0],2*p[1]] = deltac*gp # up up, times phase
inter_sc[2*p[0]+1,2*p[1]+1] = deltac*gp # down down, times phase
# intercell eh matrix
inter_eh = [[coo(inter),coo(inter_sc)],[-coo(inter_sc.H),-coo(inter)]]
inter_eh = bmat(inter_eh).todense()
return (intra_eh,inter_eh)
def straw_extract_chr_matrix(hic_file,
chrom,
binsize = 5000,
normalisation = 'KR',
trim = False
):
result = straw.straw(normalisation,hic_file,str(chrom), str(chrom),'BP',binsize)
data = np.array(result[2] + result[2])
goodidxs = ~np.isnan(data)
if np.sum(goodidxs) == 0:
print("Unable to get good data (no valid contact pairs) for requested normalisation on chromosome {}, using SQRT_VC instead".format(chrom))
result = straw.straw('VC_SQRT',hic_file,str(chrom), str(chrom),'BP',binsize)
data = np.array(result[2] + result[2])
goodidxs = ~np.isnan(data)
print("\t{} pairs covered with SQRT_VC normalisation".format(np.sum(goodidxs)))
row = np.array(result[0] + result[1])
row = row[goodidxs]
rowmin = np.min(row)
rowmax = np.max(row)
if trim:
row = (row - rowmin)/binsize
else:
row = row/binsize
row = row.astype('int32')
col = np.array(result[1] + result[0])
col = col[goodidxs]
if trim:
col = (col - rowmin)/binsize
else:
col = col/binsize
col = col.astype('int32')
mycoo = coo((data[goodidxs], (row, col)), shape=(np.max(row)+1, np.max(row)+1))
return mycoo
def FK(L=8, sec=0, t1=1., t2=1.0, f0=1., f1=1., pbc=False):
''' see the paper for Hamiltonian
hopping t: i c^d c
f0: -(2n-1)(2n-1)
f1: -4(n+n-1)(n+n-1) + 8(cccc+h.c.) '''
if L not in [1,2,4,8]:
raise Exception("Illegal L value")
print "\n--> Construct the Hamiltonian for the FK model of size L=%d ..." % (L)
n_up = op.fermion_up4().hermitian().mult( op.fermion_up4() )
n_down = op.fermion_down4().hermitian().mult( op.fermion_down4() )
pp = op.fermion_up4().mult( op.fermion_down4() ) # cc
ide = n_up.identity()
n2_up = n_up.copy().times(-2).plus( ide ) # 1-2n
n2_down = n_down.copy().times(-2).plus( ide ) # 1-2n
nn = n2_up.copy().plus(n2_down) # 2(1-n-n)
mH = n2_up.mult( n2_down ).times(-f0/2.)
if L==1:
H = mH.hermitian().plus(mH)
H.L = 1
H.basis = op.basis(4)
return H
# the 2-stie ham with the hopping t1 and onsite-interaction f0
H2 = op.fermion_up4().hermitian().kron( op.fermion_up4() )
H2.plus( op.fermion_down4().hermitian().kron( op.fermion_down4() ) )
H2.times( 2.j*float(t1) )
H2.plus( mH.kron(ide).plus( ide.kron(mH) ) )
if L==2:
H = H2.hermitian().plus(H2)
H.L = 1
H.basis = op.basis(4)
return H
# the 4 site ham with interaction f1 and hopping t2
H4 = ham.operator(4, [op.fermion_up4().hermitian(), op.fermion_up4()], [1,3])
H4.plus( ham.operator(4, [op.fermion_down4().hermitian(), op.fermion_down4()], [1,3]) )
H4.times( 2.j*float(t2) )
ide = H2.identity()
H4.plus( H2.kron(ide).plus( ide.kron(H2) ) )
H4.plus( ham.operator(4, [nn, nn], [1,2]).times(-f1/2.) )
H4.plus( ham.operator(4, [pp, pp], [1,2]).times(f1*8.) )
if L==4:
H = H4.hermitian().plus(H4)
H.L = 1
H.basis = op.basis(4)
return H
def kron_s(A, B, sec):
fp = A.fparity
ss = A.sym_sum
sym = A.sym
dim = sym * A.dim * B.dim
ret = csr((dim, dim), dtype=np.complex)
for row1 in range(sym):
for col1 in range(sym):
row2 = ss(sec, -row1)
col2 = ss(sec, -col1)
if not A._empty_(row1, col1) and not B._empty_(row2, col2):
sign = 1 - 2*( fp[col1] * fp[ss(col2, -row2)] )
temp = coo( sparse.kron(A.val[row1][col1], B.val[row2][col2]) * sign )
block = A.dim * B.dim
add = coo((temp.data, (temp.row + row1*block, temp.col + col1*block)),(dim, dim), dtype=np.complex)
ret += add
return ret
# the 8 site ham
lup = ham.operator(4, [op.fermion_up4().hermitian()], [2])
rup = ham.operator(4, [op.fermion_up4()], [0])
ldown = ham.operator(4, [op.fermion_down4().hermitian()], [2])
rdown = ham.operator(4, [op.fermion_down4()], [0])
ide = H4.identity()
ln = ham.operator(4, [nn], [3])
rn = ham.operator(4, [nn], [0])
lp = ham.operator(4, [pp], [3])
rp = ham.operator(4, [pp], [0])
Hs = coo((16384, 16384), dtype=np.complex)
Hs += kron_s(lup, rup, sec)
Hs += kron_s(ldown, rdown, sec)
Hs *= 2.j*float(t2)
Hs += kron_s(H4, ide, sec)
Hs += kron_s(ide, H4, sec)
Hs += kron_s(ln, rn, sec)*(-f1/2.)
Hs += kron_s(lp, rp, sec)*(f1*8.)
if pbc:
edge = kron_s(rup,lup, sec)
edge += kron_s(rdown, ldown, sec)
edge *= -2.j*float(t2)
Hs += edge
Hs += kron_s(rn, ln, sec)*(-f1/2.)
Hs += kron_s(rp, lp, sec)*(f1*8.)
Hs += Hs.conjugate().transpose()
print "--> Hamiltonian constructed."
return Hs
def FK(L=8, sec=0, t1=1., t2=1.0, f0=1., f1=1., pbc=False):
''' see the paper for Hamiltonian
hopping t: i c^d c
f0: -(2n-1)(2n-1)
f1: -4(n+n-1)(n+n-1) + 8(cccc+h.c.) '''
if L not in [1, 2, 4, 8]:
raise Exception("Illegal L value")
print "\n--> Construct the Hamiltonian for the FK model of size L=%d ..." % (
L)
n_up = op.fermion_up4().hermitian().mult(op.fermion_up4())
n_down = op.fermion_down4().hermitian().mult(op.fermion_down4())
pp = op.fermion_up4().mult(op.fermion_down4()) # cc
ide = n_up.identity()
n2_up = n_up.copy().times(-2).plus(ide) # 1-2n
n2_down = n_down.copy().times(-2).plus(ide) # 1-2n
nn = n2_up.copy().plus(n2_down) # 2(1-n-n)
mH = n2_up.mult(n2_down).times(-f0 / 2.)
if L == 1:
H = mH.hermitian().plus(mH)
H.L = 1
H.basis = op.basis(4)
return H
# the 2-stie ham with the hopping t1 and onsite-interaction f0
H2 = op.fermion_up4().hermitian().kron(op.fermion_up4())
H2.plus(op.fermion_down4().hermitian().kron(op.fermion_down4()))
H2.times(2.j * float(t1))
H2.plus(mH.kron(ide).plus(ide.kron(mH)))
if L == 2:
H = H2.hermitian().plus(H2)
H.L = 1
H.basis = op.basis(4)
return H
# the 4 site ham with interaction f1 and hopping t2
H4 = ham.operator(4, [op.fermion_up4().hermitian(),
op.fermion_up4()], [1, 3])
H4.plus(
ham.operator(4, [op.fermion_down4().hermitian(),
op.fermion_down4()], [1, 3]))
H4.times(2.j * float(t2))
ide = H2.identity()
H4.plus(H2.kron(ide).plus(ide.kron(H2)))
H4.plus(ham.operator(4, [nn, nn], [1, 2]).times(-f1 / 2.))
H4.plus(ham.operator(4, [pp, pp], [1, 2]).times(f1 * 8.))
if L == 4:
H = H4.hermitian().plus(H4)
H.L = 1
H.basis = op.basis(4)
return H
def kron_s(A, B, sec):
fp = A.fparity
ss = A.sym_sum
sym = A.sym
dim = sym * A.dim * B.dim
ret = csr((dim, dim), dtype=np.complex)
for row1 in range(sym):
for col1 in range(sym):
row2 = ss(sec, -row1)
col2 = ss(sec, -col1)
if not A._empty_(row1, col1) and not B._empty_(row2, col2):
sign = 1 - 2 * (fp[col1] * fp[ss(col2, -row2)])
temp = coo(
sparse.kron(A.val[row1][col1], B.val[row2][col2]) *
sign)
block = A.dim * B.dim
add = coo(
(temp.data,
(temp.row + row1 * block, temp.col + col1 * block)),
(dim, dim),
dtype=np.complex)
ret += add
return ret
# the 8 site ham
lup = ham.operator(4, [op.fermion_up4().hermitian()], [2])
rup = ham.operator(4, [op.fermion_up4()], [0])
ldown = ham.operator(4, [op.fermion_down4().hermitian()], [2])
rdown = ham.operator(4, [op.fermion_down4()], [0])
ide = H4.identity()
ln = ham.operator(4, [nn], [3])
rn = ham.operator(4, [nn], [0])
lp = ham.operator(4, [pp], [3])
rp = ham.operator(4, [pp], [0])
Hs = coo((16384, 16384), dtype=np.complex)
Hs += kron_s(lup, rup, sec)
Hs += kron_s(ldown, rdown, sec)
Hs *= 2.j * float(t2)
Hs += kron_s(H4, ide, sec)
Hs += kron_s(ide, H4, sec)
Hs += kron_s(ln, rn, sec) * (-f1 / 2.)
Hs += kron_s(lp, rp, sec) * (f1 * 8.)
if pbc:
edge = kron_s(rup, lup, sec)
edge += kron_s(rdown, ldown, sec)
edge *= -2.j * float(t2)
Hs += edge
Hs += kron_s(rn, ln, sec) * (-f1 / 2.)
Hs += kron_s(rp, lp, sec) * (f1 * 8.)
Hs += Hs.conjugate().transpose()
print "--> Hamiltonian constructed."
return Hs
def add_swave_electron_hole_pairing(h,delta=0.0,mu=0.0,phi=0.0):
""" Adds an swave electron-hole pairing
potential to first neighbors,
delta is pairing potential
mu is chemical potential
phi is superconducting phase,
delta and phi can be functions!!!!!"""
h.has_eh = True # has electron hole pairs
intra = h.intra # intra cell potential
intra_sc = 0.0*intra # intracell pairing potential
x = h.geometry.x # x position
y = h.geometry.y # y position
from scipy.sparse import coo_matrix as coo
from scipy.sparse import bmat
# intracell contribution
pairs = find_first_neighbor(x,y,x,y) # get intra pairs of first neighbors
for i in range(len(x)): # loop over intracell pairs
if is_number(delta) and is_number(phi): # if both are numbers
deltac = delta*np.exp(1j*phi*np.pi) # complex SC order parameter
elif callable(delta) and callable(phi): # if both are functions
deltac = delta(x[i],y[i])*np.exp(1j*phi(x[i],y[i])*np.pi)
elif callable(phi) and is_number(delta): # if phi is function
deltac = delta*np.exp(1j*phi(x[i],y[i])*np.pi)
elif callable(delta) and is_number(phi): # if delta is function
deltac = delta(x[i],y[i])*np.exp(1j*phi*np.pi)
else: # error if unrecognized
print type(delta),type(phi)
raise
# notation is
# psi_up
# psi_dn
# psi_dn_dag
# psi_up_dag
if h.has_spin:
# couples electron in even orbital with hole in odd orbital
# in the curren notation even corresponds to up
# and odd corresponds to down, so this term couples
# at the same atom, up electrons with down holes
# index 0 is first atom up
# index 1 is first atom down
intra_sc[2*i,2*i+1] = deltac # up down electron hole
intra_sc[2*i+1,2*i] = -deltac # down up
# intra_sc[2*i,2*i] = deltac # up down electron hole
# intra_sc[2*i+1,2*i+1] = -deltac # down up
if not h.has_spin:
raise
# intracell eh matrix
# intra_eh = [[coo(intra),coo(intra_sc)],[coo(intra_sc.H)
# ,-coo(np.conjugate(intra))]]
# ,-coo(intra)]]
# ,-coo(intra.H)]]
# intra_eh = bmat(intra_eh).todense()
intra_eh = build_eh(intra,coupling=intra_sc)
if h.dimensionality == 0: # if 0 dimensional return
return (intra_eh,None)
# intercell contribution
if h.dimensionality == 1: # if one dimensional calculate neighbor contribution
inter = h.inter # inter cell potential
# intercell eh matrix, no mixing terms
inter_eh = [[coo(inter),None],[None,-coo(np.conjugate(inter))]]
inter_eh = bmat(inter_eh).todense()
inter_eh = build_eh(inter) # create electron hole matrix
return (intra_eh,inter_eh)
def _single_clr_edge_and_node_info_from_sites(c: cooler.Cooler,
sites: Dict[str, np.ndarray],
balance: Optional[bool] = True,
join: Optional[bool] = False):
'''
Given some cooler and a dictionary of sites (with chromosomes as keys), return the submatrices retrieved from these slices within the Hi-C map. Submatrices are returned in sparse COO format with an edge_idxs dictionary, an edge_attrs dictionary and a node_info dictionary. Optionally users can balance the Hi-C matrix before retrieval of matrix information. SInce multiple chromosomes and slices per chromosome can be supplied, user may optionally join regions into one larger region consisting of the given slices concatenated together. This function does not actually do the joining procedure since the passed slices may not be disjoint.
:param cooler: Cooler file object
:type edge_index: cooler.Cooler
:param slices: Dictionary with chromosomes as keys and lists of sites as values. Multiple sites are allowed per chromosome.
:type slices: Dict[str,List[np.ndarray]]
:param balance: Whether to perform matrix balancing on the Hi-C matrix before retrieving individual slices.
:type balance: Optional[bool]
:param join: Boolean determining whether to retrieve Hi-C martrix information corresponding to the interface between slices. This is only recommended if slices are disjoint since the interface isn't well defined if slices aren't disjoint.
:type join: Optional[bool]
'''
# Iterate through slices, adding in edge indexes and edge attributes
edge_idxs = {}
edge_attrs = {}
sub_graph_nodes = {}
chroms = list(sites.keys())
for idx, chrom1 in enumerate(chroms):
edge_idxs[chrom1] = {}
edge_attrs[chrom1] = {}
sub_graph_nodes[chrom1] = {}
for chrom2 in chroms[idx:]:
if chrom1 != chrom2 and not join:
continue
mat = c.matrix(balance=balance).fetch(chrom1, chrom2)
mat = mat[sites[chrom1], :]
mat = mat[:, sites[chrom2]]
mat = coo(mat)
b1 = c.bins().fetch(chrom1).index.values[sites[chrom1]]
b2 = c.bins().fetch(chrom2).index.values[sites[chrom2]]
edge_index = np.concatenate(
[b1[mat.row][None, :], b2[mat.col][None, :]],
axis=0,
)
edge_data = mat.data[:, None]
if chrom1 != chrom2:
edge_index = np.append(edge_index, edge_index[::-1, :], axis=1)
edge_data = np.append(edge_data, edge_data, axis=0)
edge_data[np.isnan(edge_data)] = 0
ind = np.lexsort((edge_index[0, :], edge_index[1, :]))
edge_index = edge_index[:, ind]
edge_data = edge_data[ind, :]
edge_idxs[chrom1][chrom2] = [edge_index]
edge_attrs[chrom1][chrom2] = [edge_data]
if chrom1 == chrom2:
sub_graph_nodes[chrom1][chrom2] = [b1]
else:
sub_graph_nodes[chrom1][chrom2] = [np.append(b1, b2)]
return edge_idxs, edge_attrs, sub_graph_nodes
def super_normal_super(g,f_sc,f_n,replicas = [1,1,1],phi=0.0,delta=0.1):
"""Creates a junction superconductor-normal-superconductor,
- g is the geometry of the unit cell of the system
- f_sc is the function which generates the superconductor
which takes three arguments, geometry, SC order and phase
- f_n generates the hamiltonian of the central part,
result has to have electron-hole dimension
- replicas are the replicas of the SC-normal_SC of the cell
- phi is the infinitesimal phase difference
- delta is the superconducting parameter """
h_left = f_sc(g,delta=delta,phi=phi) # create hamiltonian of the left part
h_right = f_sc(g,delta=delta,phi=-phi) # create hamiltonian of the right part
h_central = f_n(g) # central part
from scipy.sparse import coo_matrix as coo
from scipy.sparse import bmat
from hamiltonians import hamiltonian
hj = hamiltonian(g) # create hamiltonian of the junction
tc = replicas[0] + replicas[1] + replicas[2] # total number of cells
intra = [[None for i in range(tc)] for j in range(tc)] # create intracell
# transfor to coo matrix
h_left.intra = coo(h_left.intra)
h_left.inter = coo(h_left.inter)
h_central.intra = coo(h_central.intra)
h_central.inter = coo(h_central.inter)
h_right.intra = coo(h_right.intra)
h_right.inter = coo(h_right.inter)
# intracell contributions
for i in range(replicas[0]):
intra[i][i] = h_left.intra # intracell of the left lead
init = replicas[0] # inital index
for i in range(replicas[1]):
intra[init+i][init+i] = h_central.intra # intracell of the left lead
init = replicas[0]+replicas[1] # inital index
for i in range(replicas[2]):
intra[init+i][init+i] = h_right.intra # intracell of the left lead
# intercell contributions
for i in range(replicas[0]-1):
intra[i][i+1] = h_left.inter # intercell of the left lead
intra[i+1][i] = h_left.inter.H # intercell of the left lead
init = replicas[0] # inital index
for i in range(replicas[1]-1):
intra[init+i][init+i+1] = h_central.inter # intercell of the central lead
intra[init+i+1][init+i] = h_central.inter.H # intercell of the central lead
init = replicas[0]+replicas[1] # inital index
for i in range(replicas[2]-1):
intra[init+i][init+i+1] = h_right.inter # intercell of the right lead
intra[init+i+1][init+i] = h_right.inter.H # intercell of the right lead
# coupling between SC and central part, take couapling of the central
il = replicas[0]
intra[il-1][il] = h_central.inter # intracell of the left lead
intra[il][il-1] = h_central.inter.H # intracell of the left lead
il = replicas[0]+replicas[1]
intra[il-1][il] = h_central.inter # intracell of the left lead
intra[il][il-1] = h_central.inter.H # intracell of the left lead
# create matrix
intra = bmat(intra).todense()
hj.intra = intra # add to hamiltonian junction
hj = hj.finite_system() # convert in finite system
return hj
for i in xrange(len(T0)):
for j in T0[i]:
T[i][j - 1] = 1.0
print set(R.keys()) & set([1])
print np.random.normal(size=(2, 3))
x = defaultdict(dict)
a = [1, 2, 3, 4]
print a[-4:], a[-2]
row = np.array([0, 0, 1, 2, 2, 5])
col = np.array([0, 2, 2, 0, 1, 5])
data = np.array([1, 2, 3, 4, 5, 6])
m2 = csr((data, (row, col)))
a = [1, 2, 3, 4, 5]
b = [1, 2, 3, 4, 5]
c = [1, 2, 3, 4, 5]
m1 = coo((a, (b, c)))
print m2.toarray()
print m1.toarray()
print m2.todok
x = np.array([[1], [2], [3], [4], [5], [6]]).dot(np.array([[1, 2, 3, 4, 5,
6]]))
print x
kx = m2.todok().keys()
print kx
print "np", np.array(m2.todok().keys())
x1 = np.sum(kx, axis=1)
print x1
print np.take(x, x1)
y = coo((np.take(x, x1), kx))
print y.toarray()
for chrom in CHROMS:
if chrom not in eregs:
eregs[chrom] = np.zeros((1, 2)).astype('int32')
if chrom not in pregs:
pregs[chrom] = np.zeros((1, 2)).astype('int32')
if chrom not in estrength:
estrength[chrom] = np.zeros((1)).astype('int32')
if chrom not in expressed_proms:
expressed_proms[chrom] = np.zeros((1)).astype('int32')
print("Parsing E-P links...")
llinks = np.load(args.linearlinks, allow_pickle=True)
llinks = {
key: coo((np.ones(llinks[key].shape[0]),
(llinks[key][:, 0], llinks[key][:, 1])),
shape=(pregs[key].shape[0], eregs[key].shape[0]))
for key in llinks
}
if args.gamma is None:
if args.contacts is not None:
print(
"No value for gamma provided. Inferring power law relationship from contacts..."
)
args.gamma = infer_gamma(args.contacts,
binsize=5e3,
thresh=1e6,
normalisation=args.normalisation)
print("Inferred gamma: {}".format(args.gamma))
else: