repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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dmrgpy | dmrgpy-master/src/dmrgpy/algebra/parallel.py | # routines to call a function in parallel
from __future__ import print_function
import scipy.linalg as lg
from . import algebra
try:
from multiprocess import Pool
except:
print("Multiprocess not working")
def Pool(n=1): # workaround
class mpool():
def map(self,f,xs):
... | 1,791 | 22.578947 | 71 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/pyfermion/mbfermion.py | from . import states
from ..pychain.spectrum import ground_state
import numpy as np
from scipy.sparse import csc_matrix,identity
import scipy.sparse.linalg as slg
from ..algebra import algebra
from .. import operatornames
from .. import multioperator
from .. import funtk
from ..edtk import edchain
nmax = 20 # maximum ... | 8,728 | 32.964981 | 85 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/pyfermion/__init__.py | 0 | 0 | 0 | py | |
dmrgpy | dmrgpy-master/src/dmrgpy/pyfermion/states.py | from __future__ import print_function
import numpy as np
from scipy.sparse import csc_matrix
dimmax = 50000 # maximum dimension of the matrix
def constrain_nelectrons(ne=1):
"""Return a function that will constrain the total number of
electrons to ne"""
def f(v):
if np.sum(v)==ne: return True
else: ret... | 7,437 | 33.276498 | 80 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/mpscpp2/extra/ampotk/main.py |
n = 100 # maximum number of operators
f = open("ampotk.h","w")
for ni in range(1,n): # number of operators
if ni==1: f.write("if (numprod==1) {\n")
else: f.write("else if (numprod=="+str(ni)+") {\n")
f.write(" string ") # define the strings
for i in range(ni): # loop over number
f.write("op... | 921 | 26.939394 | 57 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/pyzn/zn.py | # library to solve zn models using ED
import numpy as np
from ..edtk.one2many import one2many
class znchain():
def __init__(self,ns):
"""Initialize"""
self.nsites = len(ns) # number of sites
self.ns = ns # list with the integers of the Zn model
self.create_operators() # initialize ... | 1,056 | 33.096774 | 75 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/pyzn/__init__.py | 0 | 0 | 0 | py | |
dmrgpy | dmrgpy-master/src/dmrgpy/pychain/checking.py | from __future__ import print_function
import numpy as np
def angular(x,y,z):
xy = x*y - y*x
xy = xy - 1j*z
data = xy.data
if len(data)>0:
if np.max(np.abs(data))>0.00001:
raise
def zero(x,y):
xy = x*y - y*x
if np.max(np.abs(xy.data))>0.00001:
raise
| 280 | 13.789474 | 37 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/pychain/main.py | from __future__ import print_function
import build
import read
import pyximport; pyximport.install()
import matplotlib.pyplot as plt
import numpy as np
import spectrum
import os
import examples
import entanglement
n = 5 # number of spins
#spins = [.5,.5,1,1.5,2.5,1.5,1,.5,.5]
spins = [.5 for i in range(n)]
#spin:w
... | 2,649 | 26.894737 | 76 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/pychain/dmrgio.py | from __future__ import print_function,division
import numpy as np
import os
def savedict(indict,name="chaindict"):
"""Save dictionary in a folder"""
os.system("rm -rf "+name) # remove folder
for key in indict: # loop over keys
obj = indict[key] # get the object
if type(obj) is np.matrix:
np.save(na... | 366 | 23.466667 | 58 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/pychain/jit.py |
from numba import jit
def jsum(n):
a = 0.0
for i in range(n):
for j in range(n):
a += i - j
return a
print(jsum(10000))
| 142 | 8.533333 | 22 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/pychain/inout.py | import numpy as np
from scipy.sparse import csr_matrix
def save_sparse_csr(filename,array):
np.savez(filename,data = array.data ,indices=array.indices,
indptr =array.indptr, shape=array.shape )
def load_sparse_csr(filename):
loader = np.load(filename)
return csr_matrix(( loader['data'], load... | 2,026 | 33.355932 | 78 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/pychain/examples.py | import numpy as np
def linear_chain(n=5,s=.5):
"""Linear chain"""
spins = [.5 for i in range(n)]
return spins
def kitaev_coupling():
"""Return a list of matrices with the kitaev coupling"""
m0 = np.matrix([[0. for i in range(3)] for j in range(3)]) # zero matrix
ts = [m0.copy() for i in range(3)] # coupl... | 395 | 21 | 74 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/pychain/tensorial.py | from __future__ import print_function
from scipy.sparse.linalg import LinearOperator
from scipy.sparse import coo_matrix,kron
import numpy as np
def tensorial_LO(op1,op2,sparse=True,fortran=True,adapted=True):
"""Perform the tensorial product, returning a LinearOperator"""
op1 = coo_matrix(op1)
op2 = coo_matrix(... | 2,245 | 28.552632 | 76 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/pychain/evolution.py | import numpy as np
import scipy.linalg as lg
import scipy.sparse.linalg as slg
from scipy.sparse import csc_matrix
from scipy.sparse import identity
#from numba import jit
name_sx = "SX.OUT"
name_sy = "SY.OUT"
name_sz = "SZ.OUT"
from scipy.integrate import solve_ivp
def evolve(waves,h,t=0.0,mode="scipy",dt=0.... | 5,295 | 29.262857 | 77 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/pychain/test.py | # this script checks that everythong is fine, and
# that all the libraries are present
try:
import entanglement
except:
print("entanglement library not properly compiled")
exit()
| 188 | 16.181818 | 53 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/pychain/plotting.py | import matplotlib.pyplot as plt
import numpy as np
import matplotlib.patches as patches
def get(name):
return np.genfromtxt(name).transpose()
##################
# create figures #
##################
# energy
def get_energy():
fig_energy = plt.figure()
fig_energy.set_facecolor("white")
ax_energy = fig_energy.a... | 2,382 | 22.83 | 68 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/pychain/dos.py |
import numpy as np
import scipy.sparse.linalg as lg
import scipy.sparse.linalg as slg
def dos_kpm(h0,delta=1e-1):
"""Compute full DOS"""
e0,wf0 = slg.eigsh(-h0,k=1,ncv=20,which="LA")
emax,wfmax = slg.eigsh(h0,k=1,ncv=20,which="LA")
e0,wf0 = -e0[0],np.transpose(wf0)[0]
emax = emax[0]
scale = (emax-e0)*1.2 ... | 454 | 24.277778 | 50 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/pychain/dmrgmethods.py |
def dmrg_BooB(indict,integrate_right=True):
"""Perform a single DMRG step"""
outdict = deepcopy(indict) # output dictionary
rs = indict["right_site_coupling"] # coupling in the right site
ls = indict["left_site_coupling"] # coupling in the left site
crs = indict["right_block_coupling"] # coupling t... | 4,027 | 49.35 | 78 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/pychain/correlator.py | from __future__ import print_function
import numpy as np
from .spectrum import viAvj
from . import spectrum
from scipy.sparse import csc_matrix as csc
from scipy.sparse import identity
import scipy.sparse.linalg as lg
import scipy.sparse.linalg as slg
from ..algebra import algebra
from ..algebra import kpm
from ..edtk ... | 4,176 | 33.808333 | 76 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/pychain/sctex.py |
# routines to write latex formula
| 41 | 3.666667 | 33 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/pychain/dmrgtk.py | from __future__ import print_function,division
from .tensorial import tensorial_LO
import numpy as np
from . import traceoverf90
from . import tensorial
from scipy.sparse import csc_matrix,kron
from scipy.sparse import coo_matrix
from . import tensorialf90
import time
from scipy.sparse import linalg as slg # linear alg... | 7,547 | 35.114833 | 101 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/pychain/dmrg.py | from __future__ import print_function
from __future__ import division
from . import tensorialf90 # fortran90 library
from . import traceoverf90
from . import spectrum
from scipy.sparse import linalg as slg # linear algebra library
from scipy import linalg as lg # linear algebra library
import scipy.sparse as sp
from sc... | 33,083 | 41.634021 | 94 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/pychain/otoc.py |
from ..algebra import algebra
from .evolution import discrete_evolution
def otoc(w0,h,A,B,t,dt=1e-4):
"""Compute the out of time ordered correlator"""
def evolt(w,t): # evolve a wavefunction
return discrete_evolution(w,h,t,dt)
# compute all the expectation values
| 289 | 21.307692 | 52 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/pychain/spectrum.py | from __future__ import print_function
import scipy.sparse.linalg as slg
import scipy.sparse as sp
from scipy.sparse import csc_matrix as csc
import scipy.linalg as lg
import numpy as np
#from numba import jit
nfull = 2000 # dimension for using full diagonalizetion
maxfull = 10000 # hard limit using full diagonalizet... | 5,277 | 29.865497 | 78 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/pychain/dmrgcheck.py | import build
import spectrum
import rlc
import numpy as np
import dmrg
# library to compare CI with DMRG
def heisenberg_ci(spins=[.5,.5]):
"""Model for a Heisenberg chain"""
sc = build.Spin_chain() # create class
sc.build(spins) # create the object
h = sc.template(name="open_chain")
(e,w) = spectrum.ground_... | 794 | 24.645161 | 70 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/pychain/templates.py | from __future__ import print_function
import numpy as np
import build
def sc_template(sc=None,spins=None,name="open_chain",j=1.0):
"""Generate the Hamiltonian of a certain spin model, using
the input spin chain"""
if sc is None:
if spins is None: raise
sc = build.Spin_chain() # create class
sc.build... | 1,334 | 26.244898 | 66 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/pychain/__init__.py | 0 | 0 | 0 | py | |
dmrgpy | dmrgpy-master/src/dmrgpy/pychain/analyze.py | from __future__ import print_function
import os
import numpy as np
import spectrum
fform = "{0:.5f}".format
def spins(h,sc,k=1):
"""Calculate the expectation value of spin operators in the ground state"""
(es,vs) = spectrum.eigenstates(h,evals=True,k=k)
es = es-min(es)
fo = open("SPIN_VALUE.OUT","w")
for ... | 676 | 23.178571 | 78 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/pychain/states.py | import numpy as np
def select_state(sc,indexes):
"""Return the wavefunction which has certain indexes"""
v = np.array(indexes) # vector
print("Select state",v)
vout = np.array([0. for i in range(sc.size)]) # zero vector
for i in range(sc.size): # loop over basis
d = v - sc.basis[i] # difference
if np... | 393 | 25.266667 | 61 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/pychain/read.py | from __future__ import print_function
import os
import numpy as np
from .inout import save_sparse_csr
from .inout import load_sparse_csr
from scipy.sparse import csr_matrix
from scipy.sparse import csc_matrix
check = True
def read_couplings(cs):
"""Read matrices associated to this couplings"""
ms = []
for c in... | 3,952 | 27.644928 | 88 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/pychain/build.py |
from __future__ import print_function
import os
from scipy.sparse import csc_matrix as csc
import scipy.sparse as sparse
from . import spectrum
import numpy as np
from . import read
from . import states
from ..algebra import algebra
from .. import multioperator
from ..edtk import edchain
usecpp = False # use c++ libr... | 9,773 | 34.413043 | 84 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/pychain/rlc.py | from __future__ import print_function
from . import build
import numpy as np
import scipy.linalg as lg
from . import tensorial
from . import dmrg
def biladder(s1,s2,j1=1.0,j2=1.0,j12=1.0):
"""Create a ladder"""
spins = [s1,s2]
sc = build.Spin_chain()
sc.build(spins)
c1 = np.sqrt(j1+0j) # coupling
c2 = np... | 5,703 | 31.971098 | 90 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/pychain/entanglement.py | from __future__ import print_function
import numpy as np
import scipy.linalg as lg
import scipy.optimize as optimize
try:
import density_matrixf90 as dm90
use_fortran = True
except:
print("PROBLEM WITH FORTRAN COMPILATION, ENTROPY IS NOT CALCULATED")
use_fortran = False
def reduced_density_matrix(wave,basis... | 3,211 | 27.936937 | 72 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/pychain/ppdmrg.py | from __future__ import print_function
import numpy as np
import scipy.sparse.linalg as slg
import scipy.sparse as sp
from . import tensorial
# functions to perform posprocessing on the DMRG results
def correlator(indict,outfile="CORRELATORS.OUT"):
"""Calculates the correlation function between different
operators ... | 2,590 | 34.013514 | 88 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/pychain/chain.py | from __future__ import print_function
import numpy as np
#from numba import jit
from scipy.sparse import csc_matrix
from .read import write_matrix
def generate_basis(spins):
"""Generate the basis for a spin chain"""
basis = [] # list with the basis
ns = len(spins) # number of spins
v = np.array([0 for s in... | 5,975 | 32.954545 | 75 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/pyspin/__init__.py | 0 | 0 | 0 | py | |
dmrgpy | dmrgpy-master/src/dmrgpy/pyspin/spin.py | from ..edtk import edchain
import numpy as np
class Spin_Chain(edchain.EDchain):
"""Z3 parafermion chain"""
def __init__(self,MBO):
n = MBO.ns # number of sites
super().__init__() # super initializetion
self.localdim = [2 for i in range(n)]
self.hamiltonian = MBO.hamiltonian
| 319 | 23.615385 | 49 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/nonhermitian/__init__.py | 0 | 0 | 0 | py | |
dmrgpy | dmrgpy-master/src/dmrgpy/nonhermitian/dynamics.py |
import numpy as np
def dynamical_correlator_cvm_explicit(self,name=None,
delta=1e-1,es=np.linspace(0.,5.0,300)):
"""
Compute the dynamical correlator using analytic continuation
"""
print("Non Hermitian mode in dynamical correlator")
### So far this just works for onsite correlators
... | 1,067 | 24.428571 | 74 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/fermionchaintk/mop.py | from .. import multioperator
def get_zero(name="multioperator"):
return multioperator.MultiOperator(name,c=0.0) # generate the MO object
def get_si(j=0,**kwargs):
if j==0: return get_sx(**kwargs)
elif j==1: return get_sy(**kwargs)
elif j==2: return get_sz(**kwargs)
else: raise
def get_sx(name="... | 3,203 | 31.04 | 75 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/fermionchaintk/hamiltonian.py | import numpy as np
from . import mop
def set_swave_pairing_spinful(self,fun):
"""
Add onsite swave pairing to a spinful Hamiltonian
The pairing term is of the form
Delta_i c_{i,up} c_{i,down} + h.c.
"""
def fp(i,j):
if i//2==j//2 and i!=j: # same site, different spins
if i... | 2,128 | 27.013158 | 74 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/fermionchaintk/staticcorrelator.py | import numpy as np
from . import mop # multioperator for spinful fermions
def get_correlator_spinless(self,name="cdc",mode="DMRG",**kwargs):
"""
Wrapper for static correlator
"""
if mode=="DMRG": # using DMRG
return self.get_correlator_MB(name=name,**kwargs)
elif mode=="ED": # us... | 5,139 | 29.595238 | 83 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/fermionchaintk/__init__.py | 0 | 0 | 0 | py | |
dmrgpy | dmrgpy-master/src/dmrgpy/fermionchaintk/dynamicalcorrelator.py | import numpy as np
from .. import multioperator
from .. import operatornames
from . import mop
def get_dynamical_correlator_spinless(self,name="densitydensity",
mode="DMRG",**kwargs):
"""
Compute a dynamical correlator for a spinless chain
"""
if mode=="DMRG":
return self.get_dynamical... | 3,243 | 31.767677 | 81 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/multioperatortk/sympymultioperator.py | from sympy import *
import numbers
from .. import multioperator as classicMO
class MultiOperator(Symbol):
"""Multioperator class"""
def __init__(self,name=None,c=1.0,O="Id_1"):
super().__init__(O,commutative=False) # initialize
if name is None:
self.name = "ampo_operator_"+str(ampo_... | 2,282 | 25.241379 | 68 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/multioperatortk/staticoperator.py | # library for immutable operators,
# they can act over a wavefunction, but they do not have
# algebra
from ..mps import MPS
import numpy as np
class StaticOperator():
def __init__(self,MO,MBO):
"""Init, takes as input a multioperator and the MBO"""
self.MBO = MBO # store the many-body object
... | 4,879 | 35.969697 | 89 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/multioperatortk/__init__.py | 0 | 0 | 0 | py | |
dmrgpy | dmrgpy-master/src/dmrgpy/multioperatortk/jordanwigner.py | from .. import multioperator
def obj2MO(a): return multioperator.obj2MO([a])
def CdagC(i,j):
if i==j: return obj2MO(["Adag",i])*obj2MO(["A",j])
elif i<j:
m = obj2MO(["Adag",i])
for k in range(i,j-1):
m = m*obj2MO(["F",k+1])
return m*obj2MO(["A",j])
elif j<i: return -1*C... | 2,014 | 22.988095 | 76 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/pyboson/__init__.py | 0 | 0 | 0 | py | |
dmrgpy | dmrgpy-master/src/dmrgpy/pyboson/boson.py | # library to solve zn models using ED
import numpy as np
from ..edtk.one2many import one2many
from ..edtk.edchain import EDchain
class BosonChain(EDchain):
def __init__(self,maxnb):
"""Initialize"""
self.nsites = len(maxnb) # number of sites
self.maxnb = maxnb # list with the maximum numbe... | 2,991 | 38.893333 | 80 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/dmrgpy2pychain/correlator.py | from .. import pychainwrapper
from .. import operatornames
from ..pychain import correlator as pychaincorrelator
import numpy as np
def correlator(sc,pairs=[[]],name="SS",**kwargs):
"""Compute a static correlator"""
if name=="SS": # total correlator
f = lambda n: correlator(sc,pairs=pairs,name=n,**kwar... | 892 | 37.826087 | 71 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/dmrgpy2pychain/measure.py | from .. import pychainwrapper
import numpy as np
from ..algebra import algebra
def get_magnetization(sc):
"""Compute a static correlator"""
scp = sc.get_pychain() # get pychain spinchain object
h = pychainwrapper.get_full_hamiltonian(sc) # get Hamiltonian
wf = algebra.ground_state(h)[1] # get GS wavefu... | 659 | 33.736842 | 65 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/dmrgpy2pychain/__init__.py | 0 | 0 | 0 | py | |
dmrgpy | dmrgpy-master/src/dmrgpy/dmrgpy2pychain/timedependent.py | from __future__ import print_function
from .. import operatornames
import numpy as np
from scipy.sparse import csc_matrix
import scipy.sparse.linalg as slg
from scipy.sparse import identity
def evolution(self,name="XX",i=0,j=0,nt=100,dt=0.01):
"""Perform time evolution exactly"""
h = self.get_full_hamiltonian(... | 1,400 | 35.868421 | 70 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/edtk/tdtk.py | import numpy as np
import scipy.linalg as lg
import scipy.sparse.linalg as slg
from scipy.sparse import csc_matrix
from scipy.sparse import identity
#from numba import jit
from scipy.integrate import solve_ivp
def evolve(w,h,t=0.0,mode="scipy",dt=0.01,de=0.0,dp=0.0):
"""Evolve the wavefunctions using the Schrod... | 2,894 | 26.571429 | 76 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/edtk/edchain.py | from ..algebra import algebra
from .. import multioperator
import scipy.sparse.linalg as slg
from .one2many import one2many
import numpy as np
class EDchain():
"""Generic class for an ED chain"""
def __init__(self):
self.operators = dict() # empty dictionary
self.localdim = [] # empty list
... | 8,560 | 35.275424 | 78 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/edtk/distribution.py | import scipy.sparse.linalg as slg
from ..algebra import kpm
from ..algebra import algebra
import numpy as np
from scipy.interpolate import interp1d
from .edchain import State
def get_distribution(self,X=None,wf=None,method="KPM",**kwargs):
"""Get a certain distribution"""
if wf is None: wf = self.get_gs_array(... | 2,357 | 37.032258 | 76 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/edtk/finitetemperature.py | # routines for exact diagonalization at finite temperature
from ..algebra import algebra
import numpy as np
#from numba import jit
def thermal_rho(h,beta=1.0):
"""Return the thermal density matrix"""
out = algebra.expm(-beta*h) # return density matrix
out = out/np.trace(out) # normlize
return out
def... | 1,951 | 33.857143 | 77 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/edtk/__init__.py | 0 | 0 | 0 | py | |
dmrgpy | dmrgpy-master/src/dmrgpy/edtk/timedependent.py | import numpy as np
from scipy.sparse import linalg as slg
from scipy.sparse import identity
from .tdtk import evolve # evolve the wavefunction
from .. import multioperator
from .edchain import State
def evolution_ABC(self,h,A=None,B=None,C=None,wf=None,nt=100,dt=0.01):
"""Aply operator C, evolve, apply operator B,... | 2,503 | 34.267606 | 70 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/edtk/dynamics.py | from ..algebra import algebra
from .. import multioperator
import scipy.sparse.linalg as slg
from ..algebra import kpm
import numpy as np
#from numba import jit
is_hermitian = algebra.is_hermitian
def get_dynamical_correlator(self,name=None,submode="KPM",**kwargs):
"""
Compute the dynamical correlator
""... | 4,778 | 35.761538 | 78 | py |
dmrgpy | dmrgpy-master/src/dmrgpy/edtk/one2many.py | import numpy as np
from scipy.sparse import csc_matrix
def one2many(ids,op=None,i=-1):
"""Function to transform to many body basis given identity operators"""
tmp = np.zeros((1,1),dtype=np.complex) # initialize
tmp[0,0] = 1.0
for j in range(len(ids)): # loop over sites
if i!=j: op2 = ids[j] # identi... | 487 | 27.705882 | 74 | py |
spektral | spektral-master/setup.py | import setuptools
if __name__ == "__main__":
setuptools.setup()
| 69 | 13 | 26 | py |
spektral | spektral-master/spektral/__init__.py | from . import datasets, layers, utils
__version__ = "1.2.0"
| 61 | 14.5 | 37 | py |
spektral | spektral-master/spektral/models/gnn_explainer.py | import networkx as nx
import numpy as np
import tensorflow as tf
from scipy.sparse import csr_matrix
from spektral.layers import MessagePassing
from spektral.layers.convolutional.conv import Conv
from spektral.layers.ops import dot
from spektral.utils.sparse import sp_matrix_to_sp_tensor
class GNNExplainer:
"""
... | 13,348 | 34.981132 | 109 | py |
spektral | spektral-master/spektral/models/general_gnn.py | from tensorflow.keras import Model, Sequential
from tensorflow.keras.layers import (
Activation,
Add,
BatchNormalization,
Concatenate,
Dense,
Dropout,
PReLU,
)
from spektral.layers import GeneralConv
from spektral.layers.pooling import global_pool
def get_act(identifier):
if identifie... | 6,997 | 29.426087 | 85 | py |
spektral | spektral-master/spektral/models/gcn.py | import tensorflow as tf
from spektral.layers.convolutional import gcn_conv
class GCN(tf.keras.Model):
"""
This model, with its default hyperparameters, implements the architecture
from the paper:
> [Semi-Supervised Classification with Graph Convolutional Networks](https://arxiv.org/abs/1609.02907)<b... | 2,705 | 29.75 | 110 | py |
spektral | spektral-master/spektral/models/__init__.py | from .gcn import GCN
from .general_gnn import GeneralGNN
from .gnn_explainer import GNNExplainer
| 97 | 23.5 | 39 | py |
spektral | spektral-master/spektral/datasets/graphsage.py | import json
import os
import os.path as osp
import shutil
import numpy as np
import scipy.sparse as sp
from networkx.readwrite import json_graph
from spektral.data import Dataset, Graph
from spektral.data.dataset import DATASET_FOLDER
from spektral.datasets.utils import download_file
class GraphSage(Dataset):
"... | 6,927 | 31.223256 | 95 | py |
spektral | spektral-master/spektral/datasets/tudataset.py | import glob
import os
import shutil
from os import path as osp
from urllib.error import URLError
import numpy as np
import pandas as pd
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from spektral.data import Dataset, Graph
from spektral.datasets.utils import download_file
from spektral.utils import ... | 8,089 | 34.955556 | 88 | py |
spektral | spektral-master/spektral/datasets/qm9.py | import os
import os.path as osp
import numpy as np
from joblib import Parallel, delayed
from tensorflow.keras.utils import get_file
from tqdm import tqdm
from spektral.data import Dataset, Graph
from spektral.utils import label_to_one_hot, sparse
from spektral.utils.io import load_csv, load_sdf
ATOM_TYPES = [1, 6, 7... | 3,361 | 28.491228 | 80 | py |
spektral | spektral-master/spektral/datasets/qm7.py | import os.path as osp
import numpy as np
import scipy.sparse as sp
from scipy.io import loadmat
from tensorflow.keras.utils import get_file
from spektral.data import Dataset, Graph
from spektral.utils import sparse
class QM7(Dataset):
"""
The QM7b dataset of molecules from the paper:
> [MoleculeNet: A ... | 1,562 | 25.948276 | 101 | py |
spektral | spektral-master/spektral/datasets/citation.py | import os
import os.path as osp
import networkx as nx
import numpy as np
import requests
import scipy.sparse as sp
from sklearn.model_selection import train_test_split
from spektral.data import Dataset, Graph
from spektral.datasets.utils import DATASET_FOLDER
from spektral.utils.io import load_binary
class Citation... | 6,590 | 32.120603 | 89 | py |
spektral | spektral-master/spektral/datasets/utils.py | import os
import os.path as osp
import zipfile
import requests
from tqdm import tqdm
_dataset_folder = os.path.join("~", "spektral", "datasets")
_config_path = osp.expanduser(os.path.join("~", "spektral", "config.json"))
if osp.isfile(_config_path):
import json
with open(_config_path) as fh:
_config ... | 1,405 | 30.954545 | 75 | py |
spektral | spektral-master/spektral/datasets/ogb.py | import numpy as np
from spektral.data import Dataset, Graph
from spektral.utils import sparse
class OGB(Dataset):
"""
Wrapper for datasets from the [Open Graph Benchmark (OGB)](https://ogb.stanford.edu/).
**Arguments**
- `dataset`: an OGB library-agnostic dataset.
"""
def __init__(self, d... | 1,116 | 22.765957 | 90 | py |
spektral | spektral-master/spektral/datasets/flickr.py | import json
import os
import os.path as osp
import numpy as np
import scipy.sparse as sp
from spektral.data import Dataset, Graph
from spektral.datasets.citation import _preprocess_features
from spektral.datasets.dblp import _download_url
from spektral.utils import label_to_one_hot
class Flickr(Dataset):
"""
... | 2,891 | 31.863636 | 93 | py |
spektral | spektral-master/spektral/datasets/__init__.py | from .citation import Citation, Citeseer, Cora, Pubmed
from .dblp import DBLP
from .flickr import Flickr
from .graphsage import PPI, GraphSage, Reddit
from .mnist import MNIST
from .modelnet import ModelNet
from .ogb import OGB
from .qm7 import QM7
from .qm9 import QM9
from .tudataset import TUDataset
| 303 | 26.636364 | 54 | py |
spektral | spektral-master/spektral/datasets/dblp.py | import errno
import os
import os.path as osp
import ssl
import sys
import urllib
import numpy as np
import scipy.sparse as sp
from spektral.data import Dataset, Graph
from spektral.datasets.citation import _preprocess_features
from spektral.datasets.utils import DATASET_FOLDER
from spektral.utils import label_to_one_... | 2,869 | 26.075472 | 82 | py |
spektral | spektral-master/spektral/datasets/modelnet.py | import os
import os.path as osp
import shutil
from glob import glob
from joblib import Parallel, delayed
from tqdm import tqdm
from spektral.data import Dataset
from spektral.datasets.utils import download_file
from spektral.utils import load_off, one_hot
class ModelNet(Dataset):
"""
The ModelNet10 and Mode... | 3,257 | 32.244898 | 102 | py |
spektral | spektral-master/spektral/datasets/mnist.py | import numpy as np
import scipy.sparse as sp
from sklearn.neighbors import kneighbors_graph
from tensorflow.keras.datasets import mnist as m
from spektral.data import Dataset, Graph
MNIST_SIZE = 28
class MNIST(Dataset):
"""
The MNIST images used as node features for a grid graph, as described by
[Deffer... | 3,018 | 28.598039 | 80 | py |
spektral | spektral-master/spektral/layers/base.py | import numpy as np
import tensorflow as tf
from tensorflow.keras import activations
from tensorflow.keras import backend as K
from tensorflow.keras import constraints, initializers, regularizers
from tensorflow.keras.layers import Layer
from tensorflow.python.framework import smart_cond
from spektral.layers import ops... | 8,948 | 31.075269 | 88 | py |
spektral | spektral-master/spektral/layers/__init__.py | from . import ops
from .base import *
from .convolutional import *
from .pooling import *
| 90 | 17.2 | 28 | py |
spektral | spektral-master/spektral/layers/pooling/global_pool.py | import tensorflow as tf
from tensorflow.keras import backend as K
from tensorflow.keras import constraints, initializers, regularizers
from tensorflow.keras.layers import Dense, Layer
from spektral.layers import ops
class GlobalPool(Layer):
def __init__(self, **kwargs):
super().__init__(**kwargs)
... | 14,218 | 29.91087 | 93 | py |
spektral | spektral-master/spektral/layers/pooling/asym_cheeger_cut_pool.py | import tensorflow as tf
import tensorflow.keras.backend as K
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense
from spektral.layers import ops
from spektral.layers.pooling.src import SRCPool
class AsymCheegerCutPool(SRCPool):
r"""
An Asymmetric Cheeger Cut Pooling layer from t... | 8,006 | 32.642857 | 128 | py |
spektral | spektral-master/spektral/layers/pooling/dmon_pool.py | import tensorflow as tf
from tensorflow.keras import Sequential
from tensorflow.keras import backend as K
from tensorflow.keras.layers import Dense
from spektral.layers import ops
from spektral.layers.pooling.src import SRCPool
class DMoNPool(SRCPool):
r"""
The DMoN pooling layer from the paper
> [Graph... | 6,994 | 32.151659 | 104 | py |
spektral | spektral-master/spektral/layers/pooling/diff_pool.py | import tensorflow as tf
from tensorflow.keras import activations
from tensorflow.keras import backend as K
from spektral.layers import ops
from spektral.layers.pooling.src import SRCPool
class DiffPool(SRCPool):
r"""
A DiffPool layer from the paper
> [Hierarchical Graph Representation Learning with Diff... | 5,706 | 30.357143 | 116 | py |
spektral | spektral-master/spektral/layers/pooling/sag_pool.py | import tensorflow as tf
from tensorflow.keras import backend as K
from spektral.layers import ops
from spektral.layers.pooling.topk_pool import TopKPool
class SAGPool(TopKPool):
r"""
A self-attention graph pooling layer from the paper
> [Self-Attention Graph Pooling](https://arxiv.org/abs/1904.08082)<br... | 3,287 | 31.554455 | 88 | py |
spektral | spektral-master/spektral/layers/pooling/topk_pool.py | import tensorflow as tf
from tensorflow.keras import backend as K
from spektral.layers import ops
from spektral.layers.pooling.src import SRCPool
class TopKPool(SRCPool):
r"""
A gPool/Top-K layer from the papers
> [Graph U-Nets](https://arxiv.org/abs/1905.05178)<br>
> Hongyang Gao and Shuiwang Ji
... | 6,197 | 32.868852 | 92 | py |
spektral | spektral-master/spektral/layers/pooling/la_pool.py | import tensorflow as tf
from scipy import sparse
from tensorflow.keras import backend as K
from spektral.layers import ops
from spektral.layers.pooling.src import SRCPool
class LaPool(SRCPool):
r"""
A Laplacian pooling (LaPool) layer from the paper
> [Towards Interpretable Sparse Graph Representation Le... | 6,279 | 32.404255 | 127 | py |
spektral | spektral-master/spektral/layers/pooling/__init__.py | from .asym_cheeger_cut_pool import AsymCheegerCutPool
from .diff_pool import DiffPool
from .dmon_pool import DMoNPool
from .global_pool import (
GlobalAttentionPool,
GlobalAttnSumPool,
GlobalAvgPool,
GlobalMaxPool,
GlobalSumPool,
SortPool,
)
from .just_balance_pool import JustBalancePool
from .l... | 464 | 24.833333 | 53 | py |
spektral | spektral-master/spektral/layers/pooling/mincut_pool.py | import tensorflow as tf
from tensorflow.keras import Sequential
from tensorflow.keras import backend as K
from tensorflow.keras.layers import Dense
from spektral.layers import ops
from spektral.layers.pooling.src import SRCPool
class MinCutPool(SRCPool):
r"""
A MinCut pooling layer from the paper
> [Spe... | 6,106 | 31.142105 | 110 | py |
spektral | spektral-master/spektral/layers/pooling/just_balance_pool.py | import tensorflow as tf
from tensorflow.keras import Sequential
from tensorflow.keras import backend as K
from tensorflow.keras.layers import Dense
from spektral.layers import ops
from spektral.layers.pooling.src import SRCPool
class JustBalancePool(SRCPool):
r"""
The Just Balance pooling layer from the pape... | 5,777 | 31.829545 | 95 | py |
spektral | spektral-master/spektral/layers/pooling/src.py | import inspect
import tensorflow as tf
from tensorflow.keras import backend as K
from tensorflow.keras.layers import Layer
from spektral.utils.keras import (
deserialize_kwarg,
is_keras_kwarg,
is_layer_kwarg,
serialize_kwarg,
)
class SRCPool(Layer):
r"""
A general class for graph pooling lay... | 12,671 | 39.101266 | 112 | py |
spektral | spektral-master/spektral/layers/convolutional/diffusion_conv.py | import tensorflow as tf
import tensorflow.keras.layers as layers
from spektral.layers.convolutional.conv import Conv
from spektral.utils import normalized_adjacency
class DiffuseFeatures(layers.Layer):
r"""
Utility layer calculating a single channel of the diffusional convolution.
The procedure is based... | 5,944 | 31.664835 | 98 | py |
spektral | spektral-master/spektral/layers/convolutional/xenet_conv.py | from collections.abc import Iterable
import tensorflow as tf
from tensorflow.keras.layers import Concatenate, Dense, Multiply, PReLU, ReLU
from tensorflow.python.ops import gen_sparse_ops
from spektral.layers.convolutional.conv import Conv
from spektral.layers.convolutional.message_passing import MessagePassing
cla... | 13,762 | 36.603825 | 178 | py |
spektral | spektral-master/spektral/layers/convolutional/cheb_conv.py | from tensorflow.keras import backend as KB
from spektral.layers import ops
from spektral.layers.convolutional.conv import Conv
from spektral.utils import normalized_laplacian, rescale_laplacian
class ChebConv(Conv):
r"""
A Chebyshev convolutional layer from the paper
> [Convolutional Neural Networks on ... | 4,462 | 29.993056 | 91 | py |
spektral | spektral-master/spektral/layers/convolutional/appnp_conv.py | from tensorflow.keras import activations
from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.models import Sequential
from spektral.layers import ops
from spektral.layers.convolutional.conv import Conv
from spektral.utils import gcn_filter
class APPNPConv(Conv):
r"""
The APPNP operator f... | 5,098 | 33.452703 | 118 | py |
spektral | spektral-master/spektral/layers/convolutional/agnn_conv.py | import tensorflow as tf
from tensorflow.keras import backend as K
from spektral.layers import ops
from spektral.layers.convolutional.message_passing import MessagePassing
class AGNNConv(MessagePassing):
r"""
An Attention-based Graph Neural Network (AGNN) from the paper
> [Attention-based Graph Neural Ne... | 2,666 | 28.633333 | 111 | py |
spektral | spektral-master/spektral/layers/convolutional/arma_conv.py | from tensorflow.keras import activations
from tensorflow.keras import backend as K
from tensorflow.keras.layers import Dropout
from spektral.layers import ops
from spektral.layers.convolutional.conv import Conv
from spektral.utils import normalized_adjacency
class ARMAConv(Conv):
r"""
An Auto-Regressive Movi... | 8,048 | 34.148472 | 99 | py |
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