interpScalarVals = tf.gather_nd(avgScalarField, inputIndices)
interpGaugeVals = tf.gather_nd(avgGaugeField, inputIndices)
outputIndexVectorsEven = inputIndexVectors.copy()
outputIndexVectorsEven[axis] = 2*inputIndexVectors[axis]
outputIndicesEven = tf.stack(tf.meshgrid(outputIndexVectorsEven[0], outputIndexVectorsEven[1], outputIndexVectorsEven[2], indexing="ij"), -1)
outputScalarField = tf.tensor_scatter_nd_update(outputScalarField, outputIndicesEven, interpScalarVals)
outputGaugeField = tf.tensor_scatter_nd_update(outputGaugeField, outputIndicesEven, interpGaugeVals)
return outputScalarField, outputGaugeField
B = 9
smallMagneticField = FieldTools.constantMagneticField(
inputR, inputY, inputZ, 0, -B
)
inputGaugeField = FieldTools.linearSuperpose(
inputGaugeField, smallMagneticField
)
outputScalarField, outputGaugeField = interp1d(inputScalarField, inputGaugeField, 0, theory)
outputScalarField, outputGaugeField = interp1d(outputScalarField, outputGaugeField, 1, theory)
outputScalarField, outputGaugeField = interp1d(outputScalarField, outputGaugeField, 2, theory)
outputLatShape = tf.shape(outputScalarField)[0:3]
outputParams = inputParams
outputParams["vev"] /= 2
outputParams["latShape"] = outputLatShape
r = tf.cast(
tf.linspace(
1/2, tf.cast(outputLatShape[0], tf.float32) - 1/2, outputLatShape[0]
), tf.float64
# Shift the poles around, obeying the BCs
leftGaugeField = inputGaugeField
leftScalarField = inputScalarField
rightGaugeField = inputGaugeField
rightScalarField = inputScalarField
for ii in range(shiftNumLeft):
leftGaugeField = singlePoleTheory.shiftGaugeField(leftGaugeField, 0, +1)
leftScalarField = singlePoleTheory.shiftScalarField(leftScalarField, 0, +1)
# Because shiftNumRight > monopoleXCoord, the monopole ends up conjugated
for ii in range(shiftNumRight):
rightGaugeField = singlePoleTheory.shiftGaugeField(rightGaugeField, 0, +1)
rightScalarField = singlePoleTheory.shiftScalarField(
rightScalarField, 0, +1)
gaugeField = FieldTools.linearSuperpose(leftGaugeField, rightGaugeField)
scalarField = 0.5 * (leftScalarField + rightScalarField)
# Shift once more to centre the pair
gaugeField = tf.roll(gaugeField, -1, 0)
scalarField = tf.roll(scalarField, -1, 0)
numFluxQuanta = args.externalField
# Negative flux quanta results in lowering of energy (dipole points along
# negative x axis)
magField = FieldTools.constantMagneticField(X, Y, Z, 0, -numFluxQuanta)
gaugeField = FieldTools.linearSuperpose(gaugeField, magField)
gaugeFieldVar = tf.Variable(gaugeField, trainable=True)
scalarFieldVar = tf.Variable(scalarField, trainable=True)
inputParams = np.load(inputPath + "/params.npy", allow_pickle=True).item()
# Halve the lattice and field size
inputShape = tf.shape(inputX)
R = inputX[int(inputShape[0]) // 2:, ...]
Y = inputY[int(inputShape[0]) // 2:, ...]
Z = inputZ[int(inputShape[0]) // 2:, ...]
latShape = tf.shape(R)
scalarField = inputScalarField[int(inputShape[0]) // 2:, ...]
gaugeField = inputGaugeField[int(inputShape[0]) // 2:, ...]
# Add magnetic field if required
numFluxQuanta = args.externalField
magField = FieldTools.constantMagneticField(R, Y, Z, 0, numFluxQuanta)
gaugeField = FieldTools.linearSuperpose(gaugeField, magField)
# Theory parameters
params = {
"vev": args.vev,
"selfCoupling": args.selfCoupling,
"gaugeCoupling": args.gaugeCoupling,
"latShape": latShape
}
theory = GeorgiGlashowRadialTheory(params)
scalarFieldVar = tf.Variable(scalarField)
gaugeFieldVar = tf.Variable(gaugeField)