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minor modification on sydney's model #5

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May 17, 2024
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minor modification on sydney's model #5

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6a7e34a
minor modification on sydney's model
moetomg May 16, 2024
8ede57f
Merge branch 'main' into main
wkirgsn May 17, 2024
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Binary file modified src_py/magnethub/models/sydney/3C92.pt
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154 changes: 80 additions & 74 deletions src_py/magnethub/sydney.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,5 @@
"""
File contains the model according to the Paderborn University approach for the magnet challenge.
File contains the model according to the Sydney University approach for the magnet challenge.

Source: https://github.com/moetomg/magnet-engine
"""
Expand All @@ -24,83 +24,82 @@
"N87": "N87.pt",
"ML95S": "ML95S.pt",
}

# Material normalization data (1.B 2.H 3.F 4.T 5.dB/dt)
normsDict ={"77": [[-2.63253458e-19, 7.47821754e-02],
[-7.60950004e-18, 1.10664739e+01],
[5.24678898e+00, 2.89351404e-01],
[5.87473793e+01, 2.40667381e+01],
[6.16727829e+00, 3.83645439e+01]],
"78": [[5.67033925e-19, 7.22424510e-02],
[ 5.24678898e+00, 2.89351404e-01],
[ 5.87473793e+01, 2.40667381e+01],
[ 6.16727829e+00, 3.83645439e+01]],
"78": [[ 5.67033925e-19, 7.22424510e-02],
[-1.54283684e-16, 1.15338828e+01],
[5.23810768e+00, 2.89979160e-01],
[5.87434082e+01, 2.40685291e+01],
[6.09561586e+00, 3.81356049e+01]],
"79": [[1.70344847e-13, 9.41321492e-02],
[ 5.23810768e+00, 2.89979160e-01],
[ 5.87434082e+01, 2.40685291e+01],
[ 6.09561586e+00, 3.81356049e+01]],
"79": [[ 1.70344847e-13, 9.41321492e-02],
[-4.54025068e-02, 3.20463941e+01],
[5.21954346e+00, 2.66715437e-01],
[5.52068787e+01, 2.37196522e+01],
[6.77422905e+00, 3.90895233e+01]],
"N27": [[7.52738469e-19, 7.48951129e-02],
[ 5.21954346e+00, 2.66715437e-01],
[ 5.52068787e+01, 2.37196522e+01],
[ 6.77422905e+00, 3.90895233e+01]],
"N27": [[ 7.52738469e-19, 7.48951129e-02],
[-8.97477366e-17, 1.47606605e+01],
[5.24649334e+00, 2.89964765e-01],
[5.87355194e+01, 2.40766029e+01],
[6.17841434e+00, 3.84738274e+01]],
"N30": [[1.43320465e-19, 6.56044649e-02],
[ 5.24649334e+00, 2.89964765e-01],
[ 5.87355194e+01, 2.40766029e+01],
[ 6.17841434e+00, 3.84738274e+01]],
"N30": [[ 1.43320465e-19, 6.56044649e-02],
[-1.57874135e-16, 1.09083332e+01],
[5.31786680e+00, 2.78960317e-01],
[5.86466904e+01, 2.40616817e+01],
[7.01255989e+00, 4.09709969e+01]],
[ 5.31786680e+00, 2.78960317e-01],
[ 5.86466904e+01, 2.40616817e+01],
[ 7.01255989e+00, 4.09709969e+01]],
"N49": [[-8.99073580e-19, 8.94479227e-02],
[4.15423721e-16, 3.70622618e+01],
[5.25545311e+00, 3.00384015e-01],
[5.94716339e+01, 2.44349327e+01],
[6.75209475e+00, 3.91901703e+01]],
"N87": [[1.72051200e-13, 6.26231476e-02],
[4.02299992e-02, 7.61060358e+00],
[5.26309967e+00, 2.87137657e-01],
[5.83059006e+01, 2.40639057e+01],
[6.53078842e+00, 3.93127785e+01]],
"3E6": [[1.01579639e-18, 7.04261607e-02],
[2.34374135e-16, 7.21573964e+00],
[5.34307003e+00, 2.66708523e-01],
[5.86578026e+01, 2.40552864e+01],
[7.23155785e+00, 4.15975838e+01]],
[ 4.15423721e-16, 3.70622618e+01],
[ 5.25545311e+00, 3.00384015e-01],
[ 5.94716339e+01, 2.44349327e+01],
[ 6.75209475e+00, 3.91901703e+01]],
"N87": [[ 1.72051200e-13, 6.26231476e-02],
[ 4.02299992e-02, 7.61060358e+00],
[ 5.26309967e+00, 2.87137657e-01],
[ 5.83059006e+01, 2.40639057e+01],
[ 6.53078842e+00, 3.93127785e+01]],
"3E6": [[ 1.01579639e-18, 7.04261607e-02],
[ 2.34374135e-16, 7.21573964e+00],
[ 5.34307003e+00, 2.66708523e-01],
[ 5.86578026e+01, 2.40552864e+01],
[ 7.23155785e+00, 4.15975838e+01]],
"3F4": [[-1.75200068e-19, 5.98892952e-02],
[-9.48865199e-18, 4.74414811e+01],
[5.14398336e+00, 3.04210454e-01],
[5.76523476e+01, 2.43824081e+01],
[6.23030663e+00, 3.64991379e+01]],
"T37": [[1.72051200e-13, 6.26231476e-02],
[4.02299992e-02, 7.61060358e+00],
[5.26309967e+00, 2.87137657e-01],
[5.83059006e+01, 2.40639057e+01],
[6.53078842e+00, 3.93127785e+01]],
[ 5.14398336e+00, 3.04210454e-01],
[ 5.76523476e+01, 2.43824081e+01],
[ 6.23030663e+00, 3.64991379e+01]],
"T37": [[ 1.72051200e-13, 6.26231476e-02],
[ 4.02299992e-02, 7.61060358e+00],
[ 5.26309967e+00, 2.87137657e-01],
[ 5.83059006e+01, 2.40639057e+01],
[ 6.53078842e+00, 3.93127785e+01]],
"3C90":[[-3.27923689e-19, 6.56109348e-02],
[6.99196716e-17, 1.26583787e+01],
[5.19875193e+00, 2.68499136e-01],
[5.86049919e+01, 2.40574703e+01],
[6.29652929e+00, 3.84585190e+01]],
[ 6.99196716e-17, 1.26583787e+01],
[ 5.19875193e+00, 2.68499136e-01],
[ 5.86049919e+01, 2.40574703e+01],
[ 6.29652929e+00, 3.84585190e+01]],
"3C92":[[-2.35520104e-13, 6.53518693e-02],
[1.18689366e-01, 1.23585692e+01],
[5.16579533e+00, 2.73998171e-01],
[5.84305267e+01, 2.40970516e+01],
[5.88209248e+00, 3.69935722e+01]],
"3C94":[[1.21232679e-19, 7.44383659e-02],
[ 1.18689366e-01, 1.23585692e+01],
[ 5.16579533e+00, 2.73998171e-01],
[ 5.84305267e+01, 2.40970516e+01],
[ 5.88209248e+00, 3.69935722e+01]],
"3C94":[[ 1.21232679e-19, 7.44383659e-02],
[-2.19613879e-17, 1.18042579e+01],
[5.22766781e+00, 2.68348873e-01],
[5.87128143e+01, 2.40769634e+01],
[6.53718996e+00, 3.91955910e+01]],
"3C95":[[5.64116728e-14, 7.90115297e-02],
[1.11898437e-01, 1.29696641e+01],
[5.18842697e+00, 2.69014776e-01],
[5.86223640e+01, 2.40957470e+01],
[6.25767517e+00, 3.84026108e+01]],
[ 5.22766781e+00, 2.68348873e-01],
[ 5.87128143e+01, 2.40769634e+01],
[ 6.53718996e+00, 3.91955910e+01]],
"3C95":[[ 5.64116728e-14, 7.90115297e-02],
[ 1.11898437e-01, 1.29696641e+01],
[ 5.18842697e+00, 2.69014776e-01],
[ 5.86223640e+01, 2.40957470e+01],
[ 6.25767517e+00, 3.84026108e+01]],
"ML95S":[[-1.53185180e-13, 1.15827541e-01],
[3.84426934e-01, 4.45061606e+01],
[5.21606445e+00, 2.65364528e-01],
[5.70770302e+01, 2.44398289e+01],
[7.30377579e+00, 4.04136391e+01]],
[ 3.84426934e-01, 4.45061606e+01],
[ 5.21606445e+00, 2.65364528e-01],
[ 5.70770302e+01, 2.44398289e+01],
[ 7.30377579e+00, 4.04136391e+01]],
}

# %% Initialize model
Expand All @@ -122,9 +121,10 @@ def __init__(self, mdl_path, material):
def __call__(self, data_B, data_F, data_T):
"""Call method."""
# ----------------------------------------------------------- batch execution
# if isinstance(data_F, np.ndarray):

# 1.Get dataloader
if data_B.ndim == 1:
data_B = np.array(data_B).reshape(1, -1)

loader = get_dataloader(data_B,data_F,data_T,self.mdl.norm)

# 2.Validate the models
Expand All @@ -138,12 +138,17 @@ def __call__(self, data_B, data_F, data_T):

data_P = torch.cat((data_P,Pv.to(self.device)),dim=0)
data_P, h_series = data_P.cpu().numpy(), h_series.cpu().numpy()

# 3.Return results
if data_P.size == 1:
data_P = data_P.item()
if h_series.ndim == 1:
h_series = h_series.reshape(1, -1)

return data_P, h_series



class MMINet(torch.nn.Module):
"""
Magnetization mechanism-determined neural network.
Expand Down Expand Up @@ -235,7 +240,7 @@ def forward(self, x, var):
H = torch.from_numpy(H).view(batch_size,-1,1)
real_H = torch.cat((H[:, -self.n_init:, :],H[:, :-self.n_init, :]), dim=1)
return torch.flatten(Pv).cpu(), real_H[:, :, 0].cpu()

class StopOperatorCell():
"""
MMINN Sub-layer: Static hysteresis prediction using stop operators.
Expand All @@ -257,8 +262,7 @@ def __call__(self, dB, state):
r = self.operator_thre.to(dB.device)
output = self.sslu((dB + state)/r)*r
return output.float()



class EddyCell(torch.nn.Module):
"""
MMINN subsubnetwork: Dynamic hysteresis prediction.
Expand Down Expand Up @@ -312,12 +316,14 @@ def get_dataloader(data_B, data_F, data_T, norm, n_init=32):
n_init : int
Additional points for computing the history magnetization
"""

# Data pre-process
# 1. Down-sample to 128 points
seq_length = 128
cols = np.array(range(0,1023,8))
data_B = data_B[:, cols]

cols = np.array(range(0,data_B.shape[1],round(data_B.shape[1]/seq_length)))
data_B = data_B[:, cols]

# 2. Add extra points for initial magnetization calculation
data_length = seq_length + n_init
data_B = np.hstack((data_B,data_B[:,1:1+n_init]))
Expand All @@ -335,7 +341,7 @@ def get_dataloader(data_B, data_F, data_T, norm, n_init=32):
in_B = (B-norm[0][0])/norm[0][1]
in_F = (F-norm[2][0])/norm[2][1]
in_T = (T-norm[3][0])/norm[3][1]

# 5. Extra features
in_dB = torch.diff(in_B,dim=1) # Flux density change
in_dB = torch.cat((in_dB[:, 0:1, :], in_dB), dim=1)
Expand Down Expand Up @@ -371,9 +377,9 @@ def get_operator_init(B0, dB, Bmax, Bmin, operator_size=30, max_out_H=1):
Bmin: torch_like (batch)
Min flux density of each cycle
operator_size: int
TBD
The number of operators
max_out_H:
TBD
The maximum output of field strength
"""
# 1. Parameter setting
batch = dB.shape[0]
Expand Down
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