def loadMultiFrequencyFiles(currentPatient, freqAxis, isExample):
#load mat files for multi frequency recon
matFileBase = currentPatient.mfr
if isExample:
matFileBase = 'exampleData/freqReconstructions/' + matFileBase
else:
matFileBase = 'matFilesIntermediate/' + matFileBase
sampleFile = matFileBase + '_f' + str(1) + '.mat'
array = loadmat(sampleFile)['bb']
array = np.squeeze(np.cdouble(array))
arrayShape = array.shape
nx = arrayShape[0]
ny = arrayShape[1]
ntime = arrayShape[2]
nmet = arrayShape[3]
nf = len(freqAxis)
#for storing abs images
mfr = np.zeros((nx, ny, ntime, nmet, nf), dtype=np.cdouble)
#for storing complex images
if currentPatient.multichannel:
ncoils = arrayShape[4]
mfrc = np.zeros((nx, ny, ntime, nmet, ncoils, nf), dtype=np.cdouble)
else:
mfrc = np.zeros((nx, ny, ntime, nmet, nf), dtype=np.cdouble)
print("image array shape")
print(mfr.shape)
for f in range(len(freqAxis)):
#print('on frequency '+str(f+1)+' of ' + str(len(freqAxis)))
currentFile = matFileBase + '_f' + str(f + 1) + '.mat'
if currentPatient.multichannel:
pixelArrayComplex = loadmat(currentFile)['bb']
pixelArrayComplex = np.squeeze(np.cdouble(pixelArrayComplex))
mfrc[:, :, :, :, :, f] = pixelArrayComplex
# do the SOS here in the magnitude channel
for coil in range(ncoils):
if coil in channelList:
mfr[:, :, :, :, f] = mfr[:, :, :, :, f] + np.abs(
pixelArrayComplex[:, :, :, :, coil])
mfr[:, :, :, :, f] = np.sqrt(mfr[:, :, :, :, f] / len(channelList))
else:
#pixelArray = loadmat(currentFile)['bbabs']
pixelArrayComplex = loadmat(currentFile)['bb']
#mfr[:,:,:,:,f] = pixelArray
mfr[:, :, :, :, f] = pixelArrayComplex
mfrc[:, :, :, :, f] = pixelArrayComplex
return mfr, mfrc
def test_precisions_consistent(self):
z = 1 + 1j
for f in self.funcs:
fcf = f(np.csingle(z))
fcd = f(np.cdouble(z))
fcl = f(np.clongdouble(z))
assert_almost_equal(fcf, fcd, decimal=6, err_msg="fch-fcd %s" % f)
assert_almost_equal(fcl, fcd, decimal=15, err_msg="fch-fcl %s" % f)
def test_precisions_consistent(self):
z = 1 + 1j
for f in self.funcs:
fcf = f(np.csingle(z))
fcd = f(np.cdouble(z))
fcl = f(np.clongdouble(z))
assert_almost_equal(fcf, fcd, decimal=6, err_msg='fch-fcd %s' % f)
assert_almost_equal(fcl, fcd, decimal=15, err_msg='fch-fcl %s' % f)
def ExpectationValue(fsoperarray_, fockspace_, mat_idx_, evec_):
ans = np.cdouble(0.e0 + 0.e0j)
for fsoper in fsoperarray_:
for isnum in fockspace_:
ifsstate = copy.deepcopy(fockspace_[isnum])
i = mat_idx_[isnum]
factor = fsoper.act_on(ifsstate)
if not ifsstate.isZero:
jsnum = ifsstate.fock_state_number()
j = mat_idx_[jsnum]
mat_elem = factor * fsoper.coeff * np.conj(evec_[j]) * evec_[i]
if fsoper.hermitian:
mat_elem += np.conj(mat_elem)
ans += mat_elem
del ifsstate
return ans
def __init__(self, M, d=np.sqrt(2)):
"""
@parameters:
- M: integer, number of constellations. Should be a power of 2
- d: coordinate distance between adjacent constellations
"""
assert (M != 0) and (M & (M - 1) == 0) # M must be a power of 2
self.M = M
self.name = 'QAM'
n = int(np.sqrt(M))
self.n = n
self.constellations = np.zeros((n, n), dtype=np.cdouble)
self.symbols = []
for p, q in itertools.product(np.arange(n), np.arange(n)):
self.constellations[p][q] = np.cdouble(
complex((-n / 2 + 0.5) + p, (-n / 2 + 0.5) + q))
self.constellations[p][q] *= d
self.symbols = np.reshape(self.constellations, -1)
def test_np_sanitization():
class CustomParamsLogger(CustomLogger):
def __init__(self):
super().__init__()
self.logged_params = None
@rank_zero_only
def log_hyperparams(self, params):
params = self._convert_params(params)
params = self._sanitize_params(params)
self.logged_params = params
logger = CustomParamsLogger()
np_params = {
"np.bool_": np.bool_(1),
"np.byte": np.byte(2),
"np.intc": np.intc(3),
"np.int_": np.int_(4),
"np.longlong": np.longlong(5),
"np.single": np.single(6.0),
"np.double": np.double(8.9),
"np.csingle": np.csingle(7 + 2j),
"np.cdouble": np.cdouble(9 + 4j),
}
sanitized_params = {
"np.bool_": True,
"np.byte": 2,
"np.intc": 3,
"np.int_": 4,
"np.longlong": 5,
"np.single": 6.0,
"np.double": 8.9,
"np.csingle": "(7+2j)",
"np.cdouble": "(9+4j)",
}
logger.log_hyperparams(Namespace(**np_params))
assert logger.logged_params == sanitized_params
reveal_type(np.short()) # E: {short}
reveal_type(np.intc()) # E: {intc}
reveal_type(np.intp()) # E: {intp}
reveal_type(np.int0()) # E: {intp}
reveal_type(np.int_()) # E: {int_}
reveal_type(np.longlong()) # E: {longlong}
reveal_type(np.ubyte()) # E: {ubyte}
reveal_type(np.ushort()) # E: {ushort}
reveal_type(np.uintc()) # E: {uintc}
reveal_type(np.uintp()) # E: {uintp}
reveal_type(np.uint0()) # E: {uintp}
reveal_type(np.uint()) # E: {uint}
reveal_type(np.ulonglong()) # E: {ulonglong}
reveal_type(np.half()) # E: {half}
reveal_type(np.single()) # E: {single}
reveal_type(np.double()) # E: {double}
reveal_type(np.float_()) # E: {double}
reveal_type(np.longdouble()) # E: {longdouble}
reveal_type(np.longfloat()) # E: {longdouble}
reveal_type(np.csingle()) # E: {csingle}
reveal_type(np.singlecomplex()) # E: {csingle}
reveal_type(np.cdouble()) # E: {cdouble}
reveal_type(np.complex_()) # E: {cdouble}
reveal_type(np.cfloat()) # E: {cdouble}
reveal_type(np.clongdouble()) # E: {clongdouble}
reveal_type(np.clongfloat()) # E: {clongdouble}
reveal_type(np.longcomplex()) # E: {clongdouble}
np.uintc() np.uintp() np.uint0() np.uint() np.ulonglong() np.half() np.single() np.double() np.float_() np.longdouble() np.longfloat() np.csingle() np.singlecomplex() np.cdouble() np.complex_() np.cfloat() np.clongdouble() np.clongfloat() np.longcomplex() np.bool_().item() np.int_().item() np.uint64().item() np.float32().item() np.complex128().item() np.str_().item() np.bytes_().item() np.bool_().tolist()
reveal_type(np.short()) # E: numpy.signedinteger[numpy.typing._ reveal_type(np.intc()) # E: numpy.signedinteger[numpy.typing._ reveal_type(np.intp()) # E: numpy.signedinteger[numpy.typing._ reveal_type(np.int0()) # E: numpy.signedinteger[numpy.typing._ reveal_type(np.int_()) # E: numpy.signedinteger[numpy.typing._ reveal_type(np.longlong()) # E: numpy.signedinteger[numpy.typing._ reveal_type(np.ubyte()) # E: numpy.unsignedinteger[numpy.typing._ reveal_type(np.ushort()) # E: numpy.unsignedinteger[numpy.typing._ reveal_type(np.uintc()) # E: numpy.unsignedinteger[numpy.typing._ reveal_type(np.uintp()) # E: numpy.unsignedinteger[numpy.typing._ reveal_type(np.uint0()) # E: numpy.unsignedinteger[numpy.typing._ reveal_type(np.uint()) # E: numpy.unsignedinteger[numpy.typing._ reveal_type(np.ulonglong()) # E: numpy.unsignedinteger[numpy.typing._ reveal_type(np.half()) # E: numpy.floating[numpy.typing._ reveal_type(np.single()) # E: numpy.floating[numpy.typing._ reveal_type(np.double()) # E: numpy.floating[numpy.typing._ reveal_type(np.float_()) # E: numpy.floating[numpy.typing._ reveal_type(np.longdouble()) # E: numpy.floating[numpy.typing._ reveal_type(np.longfloat()) # E: numpy.floating[numpy.typing._ reveal_type(np.csingle()) # E: numpy.complexfloating[numpy.typing._ reveal_type(np.singlecomplex()) # E: numpy.complexfloating[numpy.typing._ reveal_type(np.cdouble()) # E: numpy.complexfloating[numpy.typing._ reveal_type(np.complex_()) # E: numpy.complexfloating[numpy.typing._ reveal_type(np.cfloat()) # E: numpy.complexfloating[numpy.typing._ reveal_type(np.clongdouble()) # E: numpy.complexfloating[numpy.typing._ reveal_type(np.clongfloat()) # E: numpy.complexfloating[numpy.typing._ reveal_type(np.longcomplex()) # E: numpy.complexfloating[numpy.typing._
'float_5': 1.23, 'float_6': np.half(1234.0), 'float_7': np.single(1234.0), 'float_8': np.double(1234.0), 'float_9': np.longdouble(1234.0), 'float_10': np.float32(1234.0), 'float_11': np.float64(1234.0), # Needs typeshed sync 'complex_1': complex(0.0j), # type: ignore # Needs typeshed sync 'complex_2': complex(0.0 + 0.0j), # type: ignore 'complex_3': 1.0j, 'complex_4': 1.0 + 1.0j, 'complex_5': 1.0 - 1.0j, 'complex_7': np.csingle(1234 + 1234j), 'complex_8': np.cdouble(1234 + 1234j), 'complex_9': np.clongdouble(1234 + 1234j), # needs newer numpy 'complex_10': np.complex64(1234 + 1234j), # type: ignore # needs newer numpy 'complex_11': np.complex128(1234 + 1234j), # type: ignore 'bool_1': False, 'bool_2': True, 'bool_3': np.bool_(False), 'bool_4': np.bool_(True), 'seq-str_1': [], 'seq-str_2': ['0'], 'seq-str_3': ['0, 1', 'abc', '@#!$^%&(#'], 'seq-str_4': ['A'] * 10, 'seq-int_1': [], 'seq-int_2': [0],
class TestNumpy:
@staticmethod
def test_get_numpy() -> None:
"""
Test get_numpy when module is present
"""
# Arrange
# Act
result = Numpy.get_numpy()
# Assert
assert result is np
@staticmethod
def test_get_numpy_missing(mocker: MockFixture) -> None:
"""
Test get_numpy when module is missing
"""
# Arrange
mocker.patch.dict("sys.modules", {"numpy": None})
# Act
result = Numpy.get_numpy()
# Assert
assert result is None
@staticmethod
def test_get_numpy_missing_error(mocker: MockFixture) -> None:
"""
Test get_numpy when module is missing raises error
"""
# Arrange
mocker.patch.dict("sys.modules", {"numpy": None})
# Act / assert
with pytest.raises(ImportError, match="foo"):
Numpy.get_numpy(raise_error=True, custom_error_message="foo")
@staticmethod
@pytest.mark.parametrize("value, expected", [(np.array([1, 2, 3]), True),
([1, 2, 3], False)])
def test_is_numpy_object(value, expected) -> None:
"""
Test is_numpy_object
"""
# Arrange
# Act
result = Numpy.is_numpy_object(value)
# Assert
assert result == expected
@staticmethod
def test_get_numpy_primatives() -> None:
"""
Test _get_numpy_primatives
"""
# Arrange
# Act
result = Numpy._get_numpy_primatives(np)
# Assert
assert len(result) == 33 # Expected number of types
for thing in result:
assert "numpy" in getattr(thing, "__module__", "").split(
".") # Check that type is from numpy
assert type(thing) is type # Check that each type is a type
@staticmethod
def test_encode_numpy_error():
""" Test that the encode_numpy raises an error if no encoding is defined. """
# Arrange
value = "not a numpy"
# Act & Assert
with pytest.raises(NotImplementedError):
Numpy.encode_numpy(value)
@staticmethod
@pytest.mark.parametrize(
"value, expected",
[
# fmt: off
(np.array([['balloons'], ['are'], ['awesome']
]), [['balloons'], ['are'], ['awesome']]),
(np.bool_(1), True),
(np.byte(4), 4),
(np.ubyte(4), 4),
(np.short(4), 4),
(np.ushort(4), 4),
(np.intc(4), 4),
(np.uintc(4), 4),
(np.int_(4), 4),
(np.uint(4), 4),
(np.longlong(4), 4),
(np.ulonglong(4), 4),
(np.float16(4), 4),
(np.single(4), 4),
(np.double(4), 4),
(np.longdouble(4), 4),
(np.csingle(4), 4),
(np.cdouble(4), 4),
(np.clongdouble(4), 4),
(np.int8(4), 4),
(np.int16(4), 4),
(np.int32(4), 4),
(np.int64(4), 4),
(np.uint8(4), 4),
(np.uint16(4), 4),
(np.uint32(4), 4),
(np.uint64(4), 4),
(np.intp(4), 4),
(np.uintp(4), 4),
(np.float32(4), 4),
(np.float64(4), 4),
(np.complex64(4), 4 + 0j),
(np.complex128(4), 4 + 0j),
(np.complex_(4), 4 + 0j),
# fmt: on
],
)
def test_encode_numpy(value, expected) -> None:
"""
Test encode_numpy
"""
# Arrange
# Act
result = Numpy.encode_numpy(value)
# Assert
assert result == expected
def test_table_typing_numpy():
# Pulled from https://numpy.org/devdocs/user/basics.types.html
# Numerics
table = wandb.Table(columns=["A"], dtype=[NumberType])
table.add_data(None)
table.add_data(42)
table.add_data(np.byte(1))
table.add_data(np.short(42))
table.add_data(np.ushort(42))
table.add_data(np.intc(42))
table.add_data(np.uintc(42))
table.add_data(np.int_(42))
table.add_data(np.uint(42))
table.add_data(np.longlong(42))
table.add_data(np.ulonglong(42))
table.add_data(np.half(42))
table.add_data(np.float16(42))
table.add_data(np.single(42))
table.add_data(np.double(42))
table.add_data(np.longdouble(42))
table.add_data(np.csingle(42))
table.add_data(np.cdouble(42))
table.add_data(np.clongdouble(42))
table.add_data(np.int8(42))
table.add_data(np.int16(42))
table.add_data(np.int32(42))
table.add_data(np.int64(42))
table.add_data(np.uint8(42))
table.add_data(np.uint16(42))
table.add_data(np.uint32(42))
table.add_data(np.uint64(42))
table.add_data(np.intp(42))
table.add_data(np.uintp(42))
table.add_data(np.float32(42))
table.add_data(np.float64(42))
table.add_data(np.float_(42))
table.add_data(np.complex64(42))
table.add_data(np.complex128(42))
table.add_data(np.complex_(42))
# Booleans
table = wandb.Table(columns=["A"], dtype=[BooleanType])
table.add_data(None)
table.add_data(True)
table.add_data(False)
table.add_data(np.bool_(True))
# Array of Numerics
table = wandb.Table(columns=["A"], dtype=[[NumberType]])
table.add_data(None)
table.add_data([42])
table.add_data(np.array([1, 0], dtype=np.byte))
table.add_data(np.array([42, 42], dtype=np.short))
table.add_data(np.array([42, 42], dtype=np.ushort))
table.add_data(np.array([42, 42], dtype=np.intc))
table.add_data(np.array([42, 42], dtype=np.uintc))
table.add_data(np.array([42, 42], dtype=np.int_))
table.add_data(np.array([42, 42], dtype=np.uint))
table.add_data(np.array([42, 42], dtype=np.longlong))
table.add_data(np.array([42, 42], dtype=np.ulonglong))
table.add_data(np.array([42, 42], dtype=np.half))
table.add_data(np.array([42, 42], dtype=np.float16))
table.add_data(np.array([42, 42], dtype=np.single))
table.add_data(np.array([42, 42], dtype=np.double))
table.add_data(np.array([42, 42], dtype=np.longdouble))
table.add_data(np.array([42, 42], dtype=np.csingle))
table.add_data(np.array([42, 42], dtype=np.cdouble))
table.add_data(np.array([42, 42], dtype=np.clongdouble))
table.add_data(np.array([42, 42], dtype=np.int8))
table.add_data(np.array([42, 42], dtype=np.int16))
table.add_data(np.array([42, 42], dtype=np.int32))
table.add_data(np.array([42, 42], dtype=np.int64))
table.add_data(np.array([42, 42], dtype=np.uint8))
table.add_data(np.array([42, 42], dtype=np.uint16))
table.add_data(np.array([42, 42], dtype=np.uint32))
table.add_data(np.array([42, 42], dtype=np.uint64))
table.add_data(np.array([42, 42], dtype=np.intp))
table.add_data(np.array([42, 42], dtype=np.uintp))
table.add_data(np.array([42, 42], dtype=np.float32))
table.add_data(np.array([42, 42], dtype=np.float64))
table.add_data(np.array([42, 42], dtype=np.float_))
table.add_data(np.array([42, 42], dtype=np.complex64))
table.add_data(np.array([42, 42], dtype=np.complex128))
table.add_data(np.array([42, 42], dtype=np.complex_))
# Array of Booleans
table = wandb.Table(columns=["A"], dtype=[[BooleanType]])
table.add_data(None)
table.add_data([True])
table.add_data([False])
table.add_data(np.array([True, False], dtype=np.bool_))
# Nested arrays
table = wandb.Table(columns=["A"])
table.add_data([[[[1, 2, 3]]]])
table.add_data(np.array([[[[1, 2, 3]]]]))
np.ubyte(valor) # entero sin signo almacenado como un byte (definido por la plataforma) np.short(valor) # entero corto con signo (definido por la plataforma) np.ushort(valor) # entero corto sin signo (definido por la plataforma) np.intc(valor) # entero medio con signo (definido por la plataforma) np.uintc(valor) # entero medio sin signo (definido por la plataforma) np.int_(valor) # entero largo con signo (definido por la plataforma) np.uint(valor) # entero largo sin signo (definido por la plataforma) np.longlong(valor) # entero largo largo con signo (definido por la plataforma) np.ulonglong(valor) # entero largo largo sin signo (definido por la plataforma) np.half(valor) # Flotante de precisión media (signo de 1 bit, exponente de 5 bits, mantisa de 10 bits) np.float16(valor) # Flotante de precisión media (signo de 1 bit, exponente de 5 bits, mantisa de 10 bits) np.single(valor) # Flotante de precisión simple (signo de 1 bit, exponente de 8 bits, mantisa de 23 bits) np.double(valor) # Flotante de precisión doble (signo de 1 bit, exponente de 11 bits, mantisa de 52 bits) np.longdouble(valor) # Flotante de precisión extendida (definido por la plataforma) np.csingle(valor) # Número complejo representado por dos flotantes de precisión simple (componente real e imaginario) np.cdouble(valor) # Número complejo representado por dos flotantes de precisión doble (componente real e imaginario) np.clongdouble(valor) # Número complejo representado por dos flotantes de precisión extendida (componente real e imaginario) # Tipos de datos con Alias de tamaño (se convierte el valor al tipo especificado) np.int8(valor) # entero de 1 byte con signo (-128 a 127) np.uint8(valor) # entero de 1 byte sin signo (0 a 255) np.int16(valor) # entero de 2 bytes con signo (-32768 a 32767) np.uint16(valor) # entero de 2 bytes sin signo (0 a 65535) np.int32(valor) # entero de 4 bytes con signo (-2147483648 a 2147483647) np.uint32(valor) # entero de 4 bytes sin signo (0 a 4294967295) np.int64(valor) # entero de 8 bytes con signo (-9223372036854775808 a 9223372036854775807) np.uint64(valor) # entero de 8 bytes sin signo (0 a 18446744073709551615) np.intp(valor) intptr_t # entero utilizado para indexar np.uintp(valor) uintptr_t # entero lo suficientemente grande como para contener un puntero np.float32(valor) # Flotante de precisión simple (signo de 1 bit, exponente de 8 bits, mantisa de 23 bits) np.float64(valor) # Flotante de precisión doble (signo de 1 bit, exponente de 11 bits, mantisa de 52 bits)
qin, pin = Wigner_dist.WignerSampling()
# As I am doing everything mass-weighted, also applied to the widths
dt = dyn.dt
tr = Traj.traj()
# tr.n = 1
tr.iprop = 0
tr.q = qin
tr.p = pin
tr.d = np.zeros(dyn.nstates, dtype=np.cdouble)
tr.d[dyn.inipes - 1] = np.cdouble(1)
tr.s = np.zeros(dyn.nstates)
tr.a = tr.d * np.exp(1j * tr.s)
with open('initialqp.dat', 'r') as f:
f.readline()
for i in range(geo.ndf):
N, M = f.readline().strip().split()
tr.q[i] = np.double(float(N.replace('D', 'E')))
tr.p[i] = np.double(float(M.replace('D', 'E')))
# f.readline()
# f.readline()
# idf=0
# for i in range(geo.natoms):
# for j in range(3):
# N = f.readline().strip().split()
def __init__(self, file_name, wls=None, nk=None, eps=None):
r"""
Parameters:
file_name:
1. complex value, written in numpy format or as string;
2. one of the predefined strings (air, water, glass);
3. filename with optical constants.
File header should state `lambda`, `n` and `k` columns
If either `nk= n + 1j*k` or `eps = re + 1j*im` arrays are
specified, then the data from one of them will be used
and filename content will be ignored.
wls: float array
array of wavelengths (in nm) used for data interpolation.
If None then ``np.linspace(300, 800, 500)`` will be used.
"""
if isinstance(file_name, str):
self.__name__ = 'Mat_%s' % os.path.basename(file_name)
else:
self.__name__ = 'Mat_%.3f' % file_name
if wls is None:
wl_min = 200 # 149.9
wl_max = 1200 # 950.1
wls = np.array([wl_min, wl_max])
k = np.array([0.0, 0.0])
if nk is not None:
n = np.real(nk)
k = np.imag(nk)
elif eps is not None:
mod = np.absolute(eps)
n = np.sqrt((mod + np.real(eps)) / 2)
k = np.sqrt((mod - np.real(eps)) / 2)
else:
try:
np.cdouble(file_name)
is_complex = True
except ValueError:
is_complex = False
if is_complex:
nk = np.cdouble(file_name)
n = np.array([np.real(nk), np.real(nk)])
k = np.array([np.imag(nk), np.imag(nk)])
else:
if file_name.lower() == 'air':
n = np.array([1.0, 1.0])
elif file_name.lower() == 'water':
n = np.array([1.33, 1.33])
elif file_name.lower() == 'glass':
n = np.array([1.66, 1.66])
else:
optical_constants = np.genfromtxt(file_name, names=True)
wls = optical_constants['lambda']
if np.max(wls) < 100: # wavelengths are in micrometers
wls = wls * 1000 # convert to nm
n = optical_constants['n']
k = optical_constants['k']
if wls[0] > wls[1]: # form bigger to smaller
wls = np.flipud(wls) # reverse order
n = np.flipud(n)
k = np.flipud(k)
n = n[wls > wl_min]
k = k[wls > wl_min]
wls = wls[wls > wl_min]
n = n[wls < wl_max]
k = k[wls < wl_max]
wls = wls[wls < wl_max]
wl_step = np.abs(wls[1] - wls[0])
if (wl_step > 1.1) and (wl_step < 500):
interp_kind = 'cubic' # cubic interpolation
else: # too dense or too sparse mesh, linear interpolation is needed
interp_kind = 'linear'
# print('Interpolation kind : %s'%interp_kind)
self._get_n_interp = interpolate.interp1d(wls, n, kind=interp_kind)
self._get_k_interp = interpolate.interp1d(wls, k, kind=interp_kind)
print("Create tensor operations for actual tensor construction")
create_tensor0 = op_factory.createTensorOpShared(exatn.TensorOpCode.CREATE)
create_tensor0.setTensorOperand(tensor0)
create_tensor1 = op_factory.createTensorOpShared(exatn.TensorOpCode.CREATE)
create_tensor1.setTensorOperand(tensor1)
create_tensor2 = op_factory.createTensorOpShared(exatn.TensorOpCode.CREATE)
create_tensor2.setTensorOperand(tensor2)
print("Create tensor operation for contracting tensors")
contract_tensors = op_factory.createTensorOpShared(exatn.TensorOpCode.CONTRACT)
contract_tensors.setTensorOperand(tensor0)
contract_tensors.setTensorOperand(tensor1)
contract_tensors.setTensorOperand(tensor2)
contract_tensors.setScalar(0, np.cdouble(0.5j + 0))
contract_tensors.setIndexPattern("D(a,b,c,d)+=L(c,a,k,l)*R(d,l,k,b)")
destroy_tensor2 = op_factory.createTensorOpShared(exatn.TensorOpCode.DESTROY)
destroy_tensor2.setTensorOperand(tensor2)
destroy_tensor1 = op_factory.createTensorOpShared(exatn.TensorOpCode.DESTROY)
destroy_tensor1.setTensorOperand(tensor1)
destroy_tensor0 = op_factory.createTensorOpShared(exatn.TensorOpCode.DESTROY)
destroy_tensor0.setTensorOperand(tensor0)
numserver = exatn.getNumServer()
numserver.submit(create_tensor0)
numserver.submit(create_tensor1)
numserver.submit(create_tensor2)
