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PredictionWrapper

Wraps KFLSTMModel for both the dynamic evaluation loop and the fixed-length ONNX-compatible circuit forwards. Source: src/PredictionWrapper.py.

PredictionWrapper 

PredictionWrapper(model, SD, MD, ht_test, D)

Bases: Module

Wraps a KFLSTMModel to run the EKF prediction loop over a DVAL interval.

Provides multiple forward methods corresponding to fixed-length unrolled EKF passes (forward1, forward2, forward4, ...) and a dynamic loop variant (forward_Dloop). The fixed-length variants are compiled into ONNX and used as the prediction zkSNARK circuit. All fixed-length forwards append one column of ezkl nonce values to each output tensor to maintain consistent ONNX shapes across different DVAL configurations.

Parameters:

Name Type Description Default
model KFLSTMModel

The underlying EKF-LSTM model.

 required
SD int

State space dimension.

 required
MD int

Measurement space dimension.

 required
ht_test Tensor

Full test dataset tensor; rows are indexed by time step.

 required
D int

DVAL interval length (number of EKF steps per proof interval).

 required
Source code in src/PredictionWrapper.py 
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def __init__(self, model,  SD, MD, ht_test,D):
    super(PredictionWrapper, self).__init__()
    self.model = model
    self.period_start = 0
    self.SD = SD
    self.MD = MD
    self.ht_test = ht_test
    self.jac_fh = []
    self.jac_Ch = []
    self.D = D

forward1 

forward1(h, P, delta_y, CPCT_cov, y, jac_fh, jac_Ch, Kt, KCR_status, nonce_h, nonce_P, nonce_dy, nonce_CPCT_cov, nonce_KCR_status, nonce_kalman_diff, nonce_kalman_threshold)

ONNX-compatible forward pass for exactly 1 EKF timestep. Compiled into the prediction zkSNARK circuit when DVAL=1.

Accepts padded inputs (extra trailing columns) to match the fixed ONNX tensor shapes used by ezkl, strips the padding before computation, runs one EKF step via model.forward(), then appends nonce columns to each output for ezkl's Poseidon hash commitment scheme.

Parameters:

Name Type Description Default
h Tensor

Padded initial hidden state (1, 1, SD+1).

 required
P Tensor

Padded initial covariance (1, SD, SD+1).

 required
delta_y Tensor

Padded initial residual accumulator (1, 1, MD+1).

 required
CPCT_cov Tensor

Padded initial innovation covariance (1, MD, MD+1).

 required
y Tensor

Observation for 1 timestep (1, 1, MD+1).

 required
jac_fh Tensor

State transition Jacobian (1, SD, SD+1).

 required
jac_Ch Tensor

Observation Jacobian (1, MD, MD+1).

 required
Kt Tensor

Transposed Kalman gain (1, MD, SD+1).

 required
KCR_status Tensor

Kalman check status carry-in (1, 1, 2).

 required
nonce_h Tensor

Nonce column for h output (1, 1, 1).

 required
nonce_P Tensor

Nonce column for P output (1, SD, 1).

 required
nonce_dy Tensor

Nonce column for delta_y output (1, 1, 1).

 required
nonce_CPCT_cov Tensor

Nonce column for CPCT_cov output (1, MD, 1).

 required
nonce_KCR_status Tensor

Nonce column for KCR_status output (1, 1, 1).

 required
nonce_kalman_diff Tensor

Nonce row for kalman_diff output (1, 1, SD).

 required
nonce_kalman_threshold Tensor

Nonce row for kalman_threshold output (1, 1, SD).

 required

Returns:

Type Description

Tuple[torch.Tensor, ...]: - h_current: Updated hidden state with nonce appended (1, 1, SD+1). - P_current: Updated covariance with nonce appended (1, SD, SD+1). - delta_y_current: Accumulated residual with nonce appended (1, 1, MD+1). - CPCT_cov_current: Accumulated innovation covariance with nonce appended (1, MD, MD+1). - KCR_status: Kalman check status with nonce appended (1, 1, 2). - kalman_diff: Kalman gain check diff with nonce row appended (1, 2, SD). - kalman_threshold: Kalman gain check threshold with nonce row appended (1, 2, SD).
Source code in src/PredictionWrapper.py 
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def forward1(self,h,P,delta_y,CPCT_cov,y,jac_fh,jac_Ch,Kt,KCR_status,
             nonce_h,nonce_P,nonce_dy,nonce_CPCT_cov,nonce_KCR_status,nonce_kalman_diff,nonce_kalman_threshold,): #nonce_P,nonce_delta_y,nonce_CPCT_cov,nonce_y,
    """
    ONNX-compatible forward pass for exactly 1 EKF timestep. Compiled into the
    prediction zkSNARK circuit when DVAL=1.

    Accepts padded inputs (extra trailing columns) to match the fixed ONNX tensor
    shapes used by ezkl, strips the padding before computation, runs one EKF step
    via model.forward(), then appends nonce columns to each output for ezkl's
    Poseidon hash commitment scheme.

    Args:
        h (torch.Tensor): Padded initial hidden state (1, 1, SD+1).
        P (torch.Tensor): Padded initial covariance (1, SD, SD+1).
        delta_y (torch.Tensor): Padded initial residual accumulator (1, 1, MD+1).
        CPCT_cov (torch.Tensor): Padded initial innovation covariance (1, MD, MD+1).
        y (torch.Tensor): Observation for 1 timestep (1, 1, MD+1).
        jac_fh (torch.Tensor): State transition Jacobian (1, SD, SD+1).
        jac_Ch (torch.Tensor): Observation Jacobian (1, MD, MD+1).
        Kt (torch.Tensor): Transposed Kalman gain (1, MD, SD+1).
        KCR_status (torch.Tensor): Kalman check status carry-in (1, 1, 2).
        nonce_h (torch.Tensor): Nonce column for h output (1, 1, 1).
        nonce_P (torch.Tensor): Nonce column for P output (1, SD, 1).
        nonce_dy (torch.Tensor): Nonce column for delta_y output (1, 1, 1).
        nonce_CPCT_cov (torch.Tensor): Nonce column for CPCT_cov output (1, MD, 1).
        nonce_KCR_status (torch.Tensor): Nonce column for KCR_status output (1, 1, 1).
        nonce_kalman_diff (torch.Tensor): Nonce row for kalman_diff output (1, 1, SD).
        nonce_kalman_threshold (torch.Tensor): Nonce row for kalman_threshold output (1, 1, SD).

    Returns:
        Tuple[torch.Tensor, ...]:
            - h_current: Updated hidden state with nonce appended (1, 1, SD+1).
            - P_current: Updated covariance with nonce appended (1, SD, SD+1).
            - delta_y_current: Accumulated residual with nonce appended (1, 1, MD+1).
            - CPCT_cov_current: Accumulated innovation covariance with nonce appended (1, MD, MD+1).
            - KCR_status: Kalman check status with nonce appended (1, 1, 2).
            - kalman_diff: Kalman gain check diff with nonce row appended (1, 2, SD).
            - kalman_threshold: Kalman gain check threshold with nonce row appended (1, 2, SD).
    """
    h_current = h[0:1, :, :self.SD].reshape(1, 1, self.SD)
    P_current = P[0:1, :, :self.SD].reshape(1, self.SD, self.SD)

    delta_y_current = delta_y.reshape(1, 1, self.MD + 1)
    CPCT_cov_current = CPCT_cov.reshape(1, self.MD, self.MD + 1)

    delta_y_current = delta_y_current[0:1, :, :self.MD].reshape(1, 1, self.MD)
    CPCT_cov_current = CPCT_cov_current[0:1, :, :self.MD].reshape(1, self.MD, self.MD)
    y = y[:, :, :self.MD]
    jac_fh = jac_fh[:, :, :self.SD]
    jac_Ch = jac_Ch[:, :, :self.MD]
    Kt = Kt[:, :, :self.SD]
    KCR_status = KCR_status[:, :, 0:1].reshape(1, 1, 1)

    nonce_h = nonce_h.reshape((1, 1, 1))
    nonce_dy = nonce_dy.reshape((1, 1, 1))
    nonce_KCR_status = nonce_KCR_status.reshape((1, 1, 1))
    nonce_P = nonce_P.reshape((1, self.SD, 1))
    nonce_CPCT_cov = nonce_CPCT_cov.reshape((1, self.MD, 1))
    nonce_kalman_diff = nonce_kalman_diff.reshape((1, 1, self.SD))
    nonce_kalman_threshold = nonce_kalman_threshold.reshape((1, 1, self.SD))

    assert h_current.shape[2] == self.SD
    assert nonce_h.shape == (1, 1, 1)
    assert nonce_KCR_status.shape == (1, 1, 1)
    assert nonce_dy.shape == (1, 1, 1)
    assert nonce_P.shape == (1, self.SD, 1)
    assert nonce_CPCT_cov.shape == (1, self.MD, 1)
    assert nonce_kalman_diff.shape == (1, 1, self.SD)
    assert nonce_kalman_threshold.shape == (1, 1, self.SD)


    outputs = self.model.forward(h_current,
                                 y[0:1,:,:],
                                 jac_fh[0:1,:,:].reshape(1,self.SD,self.SD),
                                 jac_Ch[0:1,:,:].reshape(1,self.MD,self.SD),
                                 P_current,
                                 Kt[0:1,:,:].reshape(1,self.MD,self.SD))

    h_current = outputs[0].reshape(1,1,self.SD)
    delta_y_current = delta_y_current + outputs[2]
    CPCT_cov_current = CPCT_cov_current + outputs[5]
    P_current = outputs[6].reshape(1,self.SD,self.SD)
    KCR_status = KCR_status*outputs[7]
    kalman_diff = outputs[8]
    kalman_threshold = outputs[9]

    h_current = torch.cat([h_current,nonce_h],dim=2)
    P_current = torch.cat([P_current,nonce_P],dim=2)
    delta_y_current = torch.cat([delta_y_current,nonce_dy],dim=2)
    CPCT_cov_current = torch.cat([CPCT_cov_current, nonce_CPCT_cov],dim=2)
    KCR_status = torch.cat([KCR_status, nonce_KCR_status], dim=2)
    kalman_diff = torch.cat([kalman_diff, nonce_kalman_diff], dim=1)
    kalman_threshold = torch.cat([kalman_threshold, nonce_kalman_threshold], dim=1)

    return h_current, P_current, delta_y_current, CPCT_cov_current, KCR_status, kalman_diff, kalman_threshold

forward16 

forward16(h, P, delta_y, CPCT_cov, y, jac_fh, jac_Ch, Kt, KCR_status, nonce_h, nonce_P, nonce_dy, nonce_CPCT_cov, nonce_KCR_status, nonce_kalman_diff, nonce_kalman_threshold)

Forward pass with exactly 16 timesteps

Parameters:

Name Type Description Default
h

Initial hidden state [1, 1, SD]

 required
P

Initial covariance matrix [1, SD, SD]

 required
delta_y

Initial residual [1, 1, is_r]

 required
CPCT_cov

Initial residual covariance [1, is_r, is_r]

 required
y

Observations for 16 timesteps [16, obs_dim]

 required
jac_fh

Jacobian fh for 16 timesteps [16, ...]

 required
jac_Ch

Jacobian Ch for 16 timesteps [16, ...]

 required
Kt

Kalman gain for 16 timesteps [16, ...]

 required
Source code in src/PredictionWrapper.py 
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def forward16(self, h, P, delta_y, CPCT_cov, y, jac_fh, jac_Ch, Kt, KCR_status,
             nonce_h,nonce_P,nonce_dy,nonce_CPCT_cov,nonce_KCR_status,nonce_kalman_diff,nonce_kalman_threshold,):
    """
    Forward pass with exactly 16 timesteps

    Args:
        h: Initial hidden state [1, 1, SD]
        P: Initial covariance matrix [1, SD, SD]
        delta_y: Initial residual [1, 1, is_r]
        CPCT_cov: Initial residual covariance [1, is_r, is_r]
        y: Observations for 16 timesteps [16, obs_dim]
        jac_fh: Jacobian fh for 16 timesteps [16, ...]
        jac_Ch: Jacobian Ch for 16 timesteps [16, ...]
        Kt: Kalman gain for 16 timesteps [16, ...]
    """

    h_current = h[0:1, :, :self.SD].reshape(1, 1, self.SD)
    P_current = P[0:1, :, :self.SD].reshape(1, self.SD, self.SD)

    delta_y_current = delta_y.reshape(1, 1, self.MD+1)
    CPCT_cov_current = CPCT_cov.reshape(1, self.MD, self.MD+1)

    delta_y_current = delta_y_current[0:1, :, :self.MD].reshape(1, 1, self.MD)
    CPCT_cov_current = CPCT_cov_current[0:1, :, :self.MD].reshape(1, self.MD, self.MD)
    y = y[:, :, :self.MD]
    jac_fh = jac_fh[:, :, :self.SD]
    jac_Ch = jac_Ch[:, :, :self.MD]
    Kt = Kt[:, :, :self.SD]
    KCR_status = KCR_status[:,:,0:1].reshape(1,1,1)

    nonce_h = nonce_h.reshape((1, 1, 1))
    nonce_dy = nonce_dy.reshape((1, 1, 1))
    nonce_KCR_status = nonce_KCR_status.reshape((1, 1, 1))
    nonce_P = nonce_P.reshape((1, self.SD, 1))
    nonce_CPCT_cov = nonce_CPCT_cov.reshape((1, self.MD, 1))
    nonce_kalman_diff = nonce_kalman_diff.reshape((1, 1, self.SD))
    nonce_kalman_threshold = nonce_kalman_threshold.reshape((1, 1, self.SD))

    assert h_current.shape[2] == self.SD
    assert nonce_h.shape == (1, 1, 1)
    assert nonce_KCR_status.shape == (1, 1, 1)
    assert nonce_dy.shape == (1, 1, 1)
    assert nonce_P.shape == (1, self.SD, 1)
    assert nonce_CPCT_cov.shape == (1, self.MD, 1)
    assert nonce_kalman_diff.shape == (1, 1, self.SD)
    assert nonce_kalman_threshold.shape == (1, 1, self.SD)

    # === TIMESTEP 0 ===
    outputs_0 = self.model.forward(h_current,
                                   y[0:1, :, :],
                                   jac_fh[0:1, :, :].reshape(1, self.SD, self.SD),
                                   jac_Ch[0:1, :, :].reshape(1, self.MD, self.SD),
                                   P_current,
                                   Kt[0:1, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_0[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_0[2]
    CPCT_cov_current = CPCT_cov_current + outputs_0[5]
    P_current = outputs_0[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status*outputs_0[7]
    kalman_diff = outputs_0[8]
    kalman_threshold = outputs_0[9]

    # === TIMESTEP 1 ===
    outputs_1 = self.model.forward(h_current,
                                   y[1:2, :, :],
                                   jac_fh[1:2, :, :].reshape(1, self.SD, self.SD),
                                   jac_Ch[1:2, :, :].reshape(1, self.MD, self.SD),
                                   P_current,
                                   Kt[1:2, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_1[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_1[2]
    CPCT_cov_current = CPCT_cov_current + outputs_1[5]
    P_current = outputs_1[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_1[7]
    kalman_diff = kalman_diff + outputs_1[8]
    kalman_threshold = kalman_threshold + outputs_1[9]

    # === TIMESTEP 2 ===
    outputs_2 = self.model.forward(h_current,
                                   y[2:3, :, :],
                                   jac_fh[2:3, :, :].reshape(1, self.SD, self.SD),
                                   jac_Ch[2:3, :, :].reshape(1, self.MD, self.SD),
                                   P_current,
                                   Kt[2:3, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_2[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_2[2]
    CPCT_cov_current = CPCT_cov_current + outputs_2[5]
    P_current = outputs_2[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_2[7]
    kalman_diff = kalman_diff + outputs_2[8]
    kalman_threshold = kalman_threshold + outputs_2[9]

    # === TIMESTEP 3 ===
    outputs_3 = self.model.forward(h_current,
                                   y[3:4, :, :],
                                   jac_fh[3:4, :, :].reshape(1, self.SD, self.SD),
                                   jac_Ch[3:4, :, :].reshape(1, self.MD, self.SD),
                                   P_current,
                                   Kt[3:4, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_3[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_3[2]
    CPCT_cov_current = CPCT_cov_current + outputs_3[5]
    P_current = outputs_3[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_3[7]
    kalman_diff = kalman_diff + outputs_3[8]
    kalman_threshold = kalman_threshold + outputs_3[9]

    # === TIMESTEP 4 ===
    outputs_4 = self.model.forward(h_current,
                                   y[4:5, :, :],
                                   jac_fh[4:5, :, :].reshape(1, self.SD, self.SD),
                                   jac_Ch[4:5, :, :].reshape(1, self.MD, self.SD),
                                   P_current,
                                   Kt[4:5, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_4[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_4[2]
    CPCT_cov_current = CPCT_cov_current + outputs_4[5]
    P_current = outputs_4[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_4[7]
    kalman_diff = kalman_diff + outputs_4[8]
    kalman_threshold = kalman_threshold + outputs_4[9]

    # === TIMESTEP 5 ===
    outputs_5 = self.model.forward(h_current,
                                   y[5:6, :, :],
                                   jac_fh[5:6, :, :].reshape(1, self.SD, self.SD),
                                   jac_Ch[5:6, :, :].reshape(1, self.MD, self.SD),
                                   P_current,
                                   Kt[5:6, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_5[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_5[2]
    CPCT_cov_current = CPCT_cov_current + outputs_5[5]
    P_current = outputs_5[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_5[7]
    kalman_diff = kalman_diff + outputs_5[8]
    kalman_threshold = kalman_threshold + outputs_5[9]

    # === TIMESTEP 6 ===
    outputs_6 = self.model.forward(h_current,
                                   y[6:7, :, :],
                                   jac_fh[6:7, :, :].reshape(1, self.SD, self.SD),
                                   jac_Ch[6:7, :, :].reshape(1, self.MD, self.SD),
                                   P_current,
                                   Kt[6:7, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_6[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_6[2]
    CPCT_cov_current = CPCT_cov_current + outputs_6[5]
    P_current = outputs_6[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_6[7]
    kalman_diff = kalman_diff + outputs_6[8]
    kalman_threshold = kalman_threshold + outputs_6[9]

    # === TIMESTEP 7 ===
    outputs_7 = self.model.forward(h_current,
                                   y[7:8, :, :],
                                   jac_fh[7:8, :, :].reshape(1, self.SD, self.SD),
                                   jac_Ch[7:8, :, :].reshape(1, self.MD, self.SD),
                                   P_current,
                                   Kt[7:8, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_7[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_7[2]
    CPCT_cov_current = CPCT_cov_current + outputs_7[5]
    P_current = outputs_7[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_7[7]
    kalman_diff = kalman_diff + outputs_7[8]
    kalman_threshold = kalman_threshold + outputs_7[9]

    # === TIMESTEP 8 ===
    outputs_8 = self.model.forward(h_current,
                                   y[8:9, :, :],
                                   jac_fh[8:9, :, :].reshape(1, self.SD, self.SD),
                                   jac_Ch[8:9, :, :].reshape(1, self.MD, self.SD),
                                   P_current,
                                   Kt[8:9, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_8[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_8[2]
    CPCT_cov_current = CPCT_cov_current + outputs_8[5]
    P_current = outputs_8[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_8[7]
    kalman_diff = kalman_diff + outputs_8[8]
    kalman_threshold = kalman_threshold + outputs_8[9]

    # === TIMESTEP 9 ===
    outputs_9 = self.model.forward(h_current,
                                   y[9:10, :, :],
                                   jac_fh[9:10, :, :].reshape(1, self.SD, self.SD),
                                   jac_Ch[9:10, :, :].reshape(1, self.MD, self.SD),
                                   P_current,
                                   Kt[9:10, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_9[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_9[2]
    CPCT_cov_current = CPCT_cov_current + outputs_9[5]
    P_current = outputs_9[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_9[7]
    kalman_diff = kalman_diff + outputs_9[8]
    kalman_threshold = kalman_threshold + outputs_9[9]

    # === TIMESTEP 10 ===
    outputs_10 = self.model.forward(h_current,
                                    y[10:11, :, :],
                                    jac_fh[10:11, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[10:11, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[10:11, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_10[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_10[2]
    CPCT_cov_current = CPCT_cov_current + outputs_10[5]
    P_current = outputs_10[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_10[7]
    kalman_diff = kalman_diff + outputs_10[8]
    kalman_threshold = kalman_threshold + outputs_10[9]

    # === TIMESTEP 11 ===
    outputs_11 = self.model.forward(h_current,
                                    y[11:12, :, :],
                                    jac_fh[11:12, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[11:12, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[11:12, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_11[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_11[2]
    CPCT_cov_current = CPCT_cov_current + outputs_11[5]
    P_current = outputs_11[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_11[7]
    kalman_diff = kalman_diff + outputs_11[8]
    kalman_threshold = kalman_threshold + outputs_11[9]

    # === TIMESTEP 12 ===
    outputs_12 = self.model.forward(h_current,
                                    y[12:13, :, :],
                                    jac_fh[12:13, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[12:13, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[12:13, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_12[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_12[2]
    CPCT_cov_current = CPCT_cov_current + outputs_12[5]
    P_current = outputs_12[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_12[7]
    kalman_diff = kalman_diff + outputs_12[8]
    kalman_threshold = kalman_threshold + outputs_12[9]

    # === TIMESTEP 13 ===
    outputs_13 = self.model.forward(h_current,
                                    y[13:14, :, :],
                                    jac_fh[13:14, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[13:14, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[13:14, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_13[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_13[2]
    CPCT_cov_current = CPCT_cov_current + outputs_13[5]
    P_current = outputs_13[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_13[7]
    kalman_diff = kalman_diff + outputs_13[8]
    kalman_threshold = kalman_threshold + outputs_13[9]

    # === TIMESTEP 14 ===
    outputs_14 = self.model.forward(h_current,
                                    y[14:15, :, :],
                                    jac_fh[14:15, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[14:15, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[14:15, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_14[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_14[2]
    CPCT_cov_current = CPCT_cov_current + outputs_14[5]
    P_current = outputs_14[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_14[7]
    kalman_diff = kalman_diff + outputs_14[8]
    kalman_threshold = kalman_threshold + outputs_14[9]

    # === TIMESTEP 15 ===
    outputs_15 = self.model.forward(h_current,
                                    y[15:16, :, :],
                                    jac_fh[15:16, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[15:16, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[15:16, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_15[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_15[2]
    CPCT_cov_current = CPCT_cov_current + outputs_15[5]
    P_current = outputs_15[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_15[7]
    kalman_diff = kalman_diff + outputs_15[8]
    kalman_threshold = kalman_threshold + outputs_15[9]

    h_current = torch.cat([h_current, nonce_h], dim=2)
    P_current = torch.cat([P_current, nonce_P], dim=2)
    delta_y_current = torch.cat([delta_y_current, nonce_dy], dim=2)
    CPCT_cov_current = torch.cat([CPCT_cov_current, nonce_CPCT_cov], dim=2)
    KCR_status = torch.cat([KCR_status, nonce_KCR_status], dim=2)
    kalman_diff = torch.cat([kalman_diff, nonce_kalman_diff], dim=1)
    kalman_threshold = torch.cat([kalman_threshold, nonce_kalman_threshold], dim=1)

    return h_current, P_current, delta_y_current, CPCT_cov_current, KCR_status, kalman_diff, kalman_threshold

forward2 

forward2(h, P, delta_y, CPCT_cov, y, jac_fh, jac_Ch, Kt, KCR_status, nonce_h, nonce_P, nonce_dy, nonce_CPCT_cov, nonce_KCR_status, nonce_kalman_diff, nonce_kalman_threshold)

Forward pass with exactly 2 timesteps

Parameters:

Name Type Description Default
h

Initial hidden state [1, 1, SD]

 required
P

Initial covariance matrix [1, SD, SD]

 required
delta_y

Initial residual [1, 1, is_r]

 required
CPCT_cov

Initial residual covariance [1, is_r, is_r]

 required
y

Observations for 2 timesteps [2, obs_dim]

 required
jac_fh

Jacobian fh for 2 timesteps [2, ...]

 required
jac_Ch

Jacobian Ch for 2 timesteps [2, ...]

 required
Kt

Kalman gain for 2 timesteps [2, ...]

 required
Source code in src/PredictionWrapper.py 
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def forward2(self, h, P, delta_y, CPCT_cov, y, jac_fh, jac_Ch, Kt, KCR_status,
             nonce_h,nonce_P,nonce_dy,nonce_CPCT_cov,nonce_KCR_status,nonce_kalman_diff,nonce_kalman_threshold,):
    """
    Forward pass with exactly 2 timesteps

    Args:
        h: Initial hidden state [1, 1, SD]
        P: Initial covariance matrix [1, SD, SD]
        delta_y: Initial residual [1, 1, is_r]
        CPCT_cov: Initial residual covariance [1, is_r, is_r]
        y: Observations for 2 timesteps [2, obs_dim]
        jac_fh: Jacobian fh for 2 timesteps [2, ...]
        jac_Ch: Jacobian Ch for 2 timesteps [2, ...]
        Kt: Kalman gain for 2 timesteps [2, ...]
    """

    h_current = h[0:1, :, :self.SD].reshape(1, 1, self.SD)
    P_current = P[0:1, :, :self.SD].reshape(1, self.SD, self.SD)

    delta_y_current = delta_y.reshape(1, 1, self.MD+1)
    CPCT_cov_current = CPCT_cov.reshape(1, self.MD, self.MD+1)

    delta_y_current = delta_y_current[0:1, :, :self.MD].reshape(1, 1, self.MD)
    CPCT_cov_current = CPCT_cov_current[0:1, :, :self.MD].reshape(1, self.MD, self.MD)
    y = y[:,:,:self.MD]
    jac_fh = jac_fh[:,:,:self.SD]
    jac_Ch = jac_Ch[:, :, :self.MD]
    Kt = Kt[:, :, :self.SD]
    KCR_status = KCR_status[:,:,0:1].reshape(1,1,1)

    nonce_h = nonce_h.reshape((1, 1, 1))
    nonce_dy = nonce_dy.reshape((1, 1, 1))
    nonce_KCR_status = nonce_KCR_status.reshape((1, 1, 1))
    nonce_P = nonce_P.reshape((1, self.SD, 1))
    nonce_CPCT_cov = nonce_CPCT_cov.reshape((1, self.MD, 1))
    nonce_kalman_diff = nonce_kalman_diff.reshape((1, 1, self.SD))
    nonce_kalman_threshold = nonce_kalman_threshold.reshape((1, 1, self.SD))

    assert h_current.shape[2] == self.SD
    assert nonce_h.shape == (1, 1, 1)
    assert nonce_KCR_status.shape == (1, 1, 1)
    assert nonce_dy.shape == (1, 1, 1)
    assert nonce_P.shape == (1, self.SD, 1)
    assert nonce_CPCT_cov.shape == (1, self.MD, 1)
    assert nonce_kalman_diff.shape == (1, 1, self.SD)
    assert nonce_kalman_threshold.shape == (1, 1, self.SD)

    # === TIMESTEP 0 ===
    outputs_0 = self.model.forward(h_current,
                                 y[0:1, :, :],
                                 jac_fh[0:1, :, :].reshape(1, self.SD, self.SD),
                                 jac_Ch[0:1, :, :].reshape(1, self.MD, self.SD),
                                 P_current,
                                 Kt[0:1, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_0[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_0[2]
    CPCT_cov_current = CPCT_cov_current + outputs_0[5]
    P_current = outputs_0[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status*outputs_0[7]
    kalman_diff = outputs_0[8]
    kalman_threshold = outputs_0[9]

    # === TIMESTEP 1 ===
    outputs_1 = self.model.forward(h_current,
                                   y[1:2, :, :],
                                   jac_fh[1:2, :, :].reshape(1, self.SD, self.SD),
                                   jac_Ch[1:2, :, :].reshape(1, self.MD, self.SD),
                                   P_current,
                                   Kt[1:2, :, :].reshape(1, self.MD, self.SD))

    h_current = outputs_1[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_1[2]
    CPCT_cov_current = CPCT_cov_current + outputs_1[5]
    P_current = outputs_1[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status*outputs_1[7]
    kalman_diff = kalman_diff + outputs_1[8]
    kalman_threshold = kalman_threshold + outputs_1[9]

    h_current = torch.cat([h_current, nonce_h], dim=2)
    P_current = torch.cat([P_current, nonce_P], dim=2)
    delta_y_current = torch.cat([delta_y_current, nonce_dy], dim=2)
    CPCT_cov_current = torch.cat([CPCT_cov_current, nonce_CPCT_cov], dim=2)
    KCR_status = torch.cat([KCR_status, nonce_KCR_status], dim=2)
    kalman_diff = torch.cat([kalman_diff, nonce_kalman_diff], dim=1)
    kalman_threshold = torch.cat([kalman_threshold, nonce_kalman_threshold], dim=1)

    return h_current, P_current, delta_y_current, CPCT_cov_current, KCR_status, kalman_diff, kalman_threshold

forward32 

forward32(h, P, delta_y, CPCT_cov, y, jac_fh, jac_Ch, Kt, KCR_status, nonce_h, nonce_P, nonce_dy, nonce_CPCT_cov, nonce_KCR_status, nonce_kalman_diff, nonce_kalman_threshold)

Forward pass with exactly 16 timesteps

Parameters:

Name Type Description Default
h

Initial hidden state [1, 1, SD]

 required
P

Initial covariance matrix [1, SD, SD]

 required
delta_y

Initial residual [1, 1, is_r]

 required
CPCT_cov

Initial residual covariance [1, is_r, is_r]

 required
y

Observations for 16 timesteps [16, obs_dim]

 required
jac_fh

Jacobian fh for 16 timesteps [16, ...]

 required
jac_Ch

Jacobian Ch for 16 timesteps [16, ...]

 required
Kt

Kalman gain for 16 timesteps [16, ...]

 required
Source code in src/PredictionWrapper.py 
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def forward32(self, h, P, delta_y, CPCT_cov, y, jac_fh, jac_Ch, Kt, KCR_status,
             nonce_h,nonce_P,nonce_dy,nonce_CPCT_cov,nonce_KCR_status,nonce_kalman_diff,nonce_kalman_threshold,):
    """
    Forward pass with exactly 16 timesteps

    Args:
        h: Initial hidden state [1, 1, SD]
        P: Initial covariance matrix [1, SD, SD]
        delta_y: Initial residual [1, 1, is_r]
        CPCT_cov: Initial residual covariance [1, is_r, is_r]
        y: Observations for 16 timesteps [16, obs_dim]
        jac_fh: Jacobian fh for 16 timesteps [16, ...]
        jac_Ch: Jacobian Ch for 16 timesteps [16, ...]
        Kt: Kalman gain for 16 timesteps [16, ...]
    """

    h_current = h[0:1, :, :self.SD].reshape(1, 1, self.SD)
    P_current = P[0:1, :, :self.SD].reshape(1, self.SD, self.SD)

    delta_y_current = delta_y.reshape(1, 1, self.MD+1)
    CPCT_cov_current = CPCT_cov.reshape(1, self.MD, self.MD+1)

    delta_y_current = delta_y_current[0:1, :, :self.MD].reshape(1, 1, self.MD)
    CPCT_cov_current = CPCT_cov_current[0:1, :, :self.MD].reshape(1, self.MD, self.MD)
    y = y[:, :, :self.MD]
    jac_fh = jac_fh[:, :, :self.SD]
    jac_Ch = jac_Ch[:, :, :self.MD]
    Kt = Kt[:, :, :self.SD]
    KCR_status = KCR_status[:,:,0:1].reshape(1,1,1)

    nonce_h = nonce_h.reshape((1, 1, 1))
    nonce_dy = nonce_dy.reshape((1, 1, 1))
    nonce_KCR_status = nonce_KCR_status.reshape((1, 1, 1))
    nonce_P = nonce_P.reshape((1, self.SD, 1))
    nonce_CPCT_cov = nonce_CPCT_cov.reshape((1, self.MD, 1))
    nonce_kalman_diff = nonce_kalman_diff.reshape((1, 1, self.SD))
    nonce_kalman_threshold = nonce_kalman_threshold.reshape((1, 1, self.SD))

    assert h_current.shape[2] == self.SD
    assert nonce_h.shape == (1, 1, 1)
    assert nonce_KCR_status.shape == (1, 1, 1)
    assert nonce_dy.shape == (1, 1, 1)
    assert nonce_P.shape == (1, self.SD, 1)
    assert nonce_CPCT_cov.shape == (1, self.MD, 1)
    assert nonce_kalman_diff.shape == (1, 1, self.SD)
    assert nonce_kalman_threshold.shape == (1, 1, self.SD)

    # === TIMESTEP 0 ===
    outputs_0 = self.model.forward(h_current,
                                   y[0:1, :, :],
                                   jac_fh[0:1, :, :].reshape(1, self.SD, self.SD),
                                   jac_Ch[0:1, :, :].reshape(1, self.MD, self.SD),
                                   P_current,
                                   Kt[0:1, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_0[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_0[2]
    CPCT_cov_current = CPCT_cov_current + outputs_0[5]
    P_current = outputs_0[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status*outputs_0[7]
    kalman_diff = outputs_0[8]
    kalman_threshold = outputs_0[9]

    # === TIMESTEP 1 ===
    outputs_1 = self.model.forward(h_current,
                                   y[1:2, :, :],
                                   jac_fh[1:2, :, :].reshape(1, self.SD, self.SD),
                                   jac_Ch[1:2, :, :].reshape(1, self.MD, self.SD),
                                   P_current,
                                   Kt[1:2, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_1[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_1[2]
    CPCT_cov_current = CPCT_cov_current + outputs_1[5]
    P_current = outputs_1[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_1[7]
    kalman_diff = kalman_diff + outputs_1[8]
    kalman_threshold = kalman_threshold + outputs_1[9]

    # === TIMESTEP 2 ===
    outputs_2 = self.model.forward(h_current,
                                   y[2:3, :, :],
                                   jac_fh[2:3, :, :].reshape(1, self.SD, self.SD),
                                   jac_Ch[2:3, :, :].reshape(1, self.MD, self.SD),
                                   P_current,
                                   Kt[2:3, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_2[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_2[2]
    CPCT_cov_current = CPCT_cov_current + outputs_2[5]
    P_current = outputs_2[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_2[7]
    kalman_diff = kalman_diff + outputs_2[8]
    kalman_threshold = kalman_threshold + outputs_2[9]

    # === TIMESTEP 3 ===
    outputs_3 = self.model.forward(h_current,
                                   y[3:4, :, :],
                                   jac_fh[3:4, :, :].reshape(1, self.SD, self.SD),
                                   jac_Ch[3:4, :, :].reshape(1, self.MD, self.SD),
                                   P_current,
                                   Kt[3:4, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_3[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_3[2]
    CPCT_cov_current = CPCT_cov_current + outputs_3[5]
    P_current = outputs_3[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_3[7]
    kalman_diff = kalman_diff + outputs_3[8]
    kalman_threshold = kalman_threshold + outputs_3[9]

    # === TIMESTEP 4 ===
    outputs_4 = self.model.forward(h_current,
                                   y[4:5, :, :],
                                   jac_fh[4:5, :, :].reshape(1, self.SD, self.SD),
                                   jac_Ch[4:5, :, :].reshape(1, self.MD, self.SD),
                                   P_current,
                                   Kt[4:5, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_4[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_4[2]
    CPCT_cov_current = CPCT_cov_current + outputs_4[5]
    P_current = outputs_4[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_4[7]
    kalman_diff = kalman_diff + outputs_4[8]
    kalman_threshold = kalman_threshold + outputs_4[9]

    # === TIMESTEP 5 ===
    outputs_5 = self.model.forward(h_current,
                                   y[5:6, :, :],
                                   jac_fh[5:6, :, :].reshape(1, self.SD, self.SD),
                                   jac_Ch[5:6, :, :].reshape(1, self.MD, self.SD),
                                   P_current,
                                   Kt[5:6, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_5[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_5[2]
    CPCT_cov_current = CPCT_cov_current + outputs_5[5]
    P_current = outputs_5[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_5[7]
    kalman_diff = kalman_diff + outputs_5[8]
    kalman_threshold = kalman_threshold + outputs_5[9]

    # === TIMESTEP 6 ===
    outputs_6 = self.model.forward(h_current,
                                   y[6:7, :, :],
                                   jac_fh[6:7, :, :].reshape(1, self.SD, self.SD),
                                   jac_Ch[6:7, :, :].reshape(1, self.MD, self.SD),
                                   P_current,
                                   Kt[6:7, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_6[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_6[2]
    CPCT_cov_current = CPCT_cov_current + outputs_6[5]
    P_current = outputs_6[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_6[7]
    kalman_diff = kalman_diff + outputs_6[8]
    kalman_threshold = kalman_threshold + outputs_6[9]

    # === TIMESTEP 7 ===
    outputs_7 = self.model.forward(h_current,
                                   y[7:8, :, :],
                                   jac_fh[7:8, :, :].reshape(1, self.SD, self.SD),
                                   jac_Ch[7:8, :, :].reshape(1, self.MD, self.SD),
                                   P_current,
                                   Kt[7:8, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_7[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_7[2]
    CPCT_cov_current = CPCT_cov_current + outputs_7[5]
    P_current = outputs_7[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_7[7]
    kalman_diff = kalman_diff + outputs_7[8]
    kalman_threshold = kalman_threshold + outputs_7[9]

    # === TIMESTEP 8 ===
    outputs_8 = self.model.forward(h_current,
                                   y[8:9, :, :],
                                   jac_fh[8:9, :, :].reshape(1, self.SD, self.SD),
                                   jac_Ch[8:9, :, :].reshape(1, self.MD, self.SD),
                                   P_current,
                                   Kt[8:9, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_8[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_8[2]
    CPCT_cov_current = CPCT_cov_current + outputs_8[5]
    P_current = outputs_8[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_8[7]
    kalman_diff = kalman_diff + outputs_8[8]
    kalman_threshold = kalman_threshold + outputs_8[9]

    # === TIMESTEP 9 ===
    outputs_9 = self.model.forward(h_current,
                                   y[9:10, :, :],
                                   jac_fh[9:10, :, :].reshape(1, self.SD, self.SD),
                                   jac_Ch[9:10, :, :].reshape(1, self.MD, self.SD),
                                   P_current,
                                   Kt[9:10, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_9[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_9[2]
    CPCT_cov_current = CPCT_cov_current + outputs_9[5]
    P_current = outputs_9[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_9[7]
    kalman_diff = kalman_diff + outputs_9[8]
    kalman_threshold = kalman_threshold + outputs_9[9]

    # === TIMESTEP 10 ===
    outputs_10 = self.model.forward(h_current,
                                    y[10:11, :, :],
                                    jac_fh[10:11, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[10:11, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[10:11, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_10[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_10[2]
    CPCT_cov_current = CPCT_cov_current + outputs_10[5]
    P_current = outputs_10[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_10[7]
    kalman_diff = kalman_diff + outputs_10[8]
    kalman_threshold = kalman_threshold + outputs_10[9]

    # === TIMESTEP 11 ===
    outputs_11 = self.model.forward(h_current,
                                    y[11:12, :, :],
                                    jac_fh[11:12, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[11:12, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[11:12, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_11[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_11[2]
    CPCT_cov_current = CPCT_cov_current + outputs_11[5]
    P_current = outputs_11[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_11[7]
    kalman_diff = kalman_diff + outputs_11[8]
    kalman_threshold = kalman_threshold + outputs_11[9]

    # === TIMESTEP 12 ===
    outputs_12 = self.model.forward(h_current,
                                    y[12:13, :, :],
                                    jac_fh[12:13, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[12:13, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[12:13, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_12[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_12[2]
    CPCT_cov_current = CPCT_cov_current + outputs_12[5]
    P_current = outputs_12[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_12[7]
    kalman_diff = kalman_diff + outputs_12[8]
    kalman_threshold = kalman_threshold + outputs_12[9]

    # === TIMESTEP 13 ===
    outputs_13 = self.model.forward(h_current,
                                    y[13:14, :, :],
                                    jac_fh[13:14, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[13:14, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[13:14, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_13[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_13[2]
    CPCT_cov_current = CPCT_cov_current + outputs_13[5]
    P_current = outputs_13[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_13[7]
    kalman_diff = kalman_diff + outputs_13[8]
    kalman_threshold = kalman_threshold + outputs_13[9]

    # === TIMESTEP 14 ===
    outputs_14 = self.model.forward(h_current,
                                    y[14:15, :, :],
                                    jac_fh[14:15, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[14:15, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[14:15, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_14[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_14[2]
    CPCT_cov_current = CPCT_cov_current + outputs_14[5]
    P_current = outputs_14[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_14[7]
    kalman_diff = kalman_diff + outputs_14[8]
    kalman_threshold = kalman_threshold + outputs_14[9]

    # === TIMESTEP 15 ===
    outputs_15 = self.model.forward(h_current,
                                    y[15:16, :, :],
                                    jac_fh[15:16, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[15:16, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[15:16, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_15[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_15[2]
    CPCT_cov_current = CPCT_cov_current + outputs_15[5]
    P_current = outputs_15[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_15[7]
    kalman_diff = kalman_diff + outputs_15[8]
    kalman_threshold = kalman_threshold + outputs_15[9]

    # === TIMESTEP 16 ===
    outputs_16 = self.model.forward(h_current,
                                    y[16:17, :, :],
                                    jac_fh[16:17, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[16:17, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[16:17, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_16[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_16[2]
    CPCT_cov_current = CPCT_cov_current + outputs_16[5]
    P_current = outputs_16[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_16[7]
    kalman_diff = kalman_diff + outputs_16[8]
    kalman_threshold = kalman_threshold + outputs_16[9]

    # === TIMESTEP 17 ===
    outputs_17 = self.model.forward(h_current,
                                    y[17:18, :, :],
                                    jac_fh[17:18, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[17:18, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[17:18, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_17[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_17[2]
    CPCT_cov_current = CPCT_cov_current + outputs_17[5]
    P_current = outputs_17[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_17[7]
    kalman_diff = kalman_diff + outputs_17[8]
    kalman_threshold = kalman_threshold + outputs_17[9]

    # === TIMESTEP 18 ===
    outputs_18 = self.model.forward(h_current,
                                    y[18:19, :, :],
                                    jac_fh[18:19, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[18:19, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[18:19, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_18[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_18[2]
    CPCT_cov_current = CPCT_cov_current + outputs_18[5]
    P_current = outputs_18[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_18[7]
    kalman_diff = kalman_diff + outputs_18[8]
    kalman_threshold = kalman_threshold + outputs_18[9]

    # === TIMESTEP 19 ===
    outputs_19 = self.model.forward(h_current,
                                    y[19:20, :, :],
                                    jac_fh[19:20, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[19:20, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[19:20, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_19[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_19[2]
    CPCT_cov_current = CPCT_cov_current + outputs_19[5]
    P_current = outputs_19[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_19[7]
    kalman_diff = kalman_diff + outputs_19[8]
    kalman_threshold = kalman_threshold + outputs_19[9]

    # === TIMESTEP 20 ===
    outputs_20 = self.model.forward(h_current,
                                    y[20:21, :, :],
                                    jac_fh[20:21, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[20:21, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[20:21, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_20[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_20[2]
    CPCT_cov_current = CPCT_cov_current + outputs_20[5]
    P_current = outputs_20[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_20[7]
    kalman_diff = kalman_diff + outputs_20[8]
    kalman_threshold = kalman_threshold + outputs_20[9]

    # === TIMESTEP 21 ===
    outputs_21 = self.model.forward(h_current,
                                    y[21:22, :, :],
                                    jac_fh[21:22, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[21:22, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[21:22, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_21[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_21[2]
    CPCT_cov_current = CPCT_cov_current + outputs_21[5]
    P_current = outputs_21[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_21[7]
    kalman_diff = kalman_diff + outputs_21[8]
    kalman_threshold = kalman_threshold + outputs_21[9]

    # === TIMESTEP 22 ===
    outputs_22 = self.model.forward(h_current,
                                    y[22:23, :, :],
                                    jac_fh[22:23, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[22:23, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[22:23, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_22[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_22[2]
    CPCT_cov_current = CPCT_cov_current + outputs_22[5]
    P_current = outputs_22[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_22[7]
    kalman_diff = kalman_diff + outputs_22[8]
    kalman_threshold = kalman_threshold + outputs_22[9]

    # === TIMESTEP 23 ===
    outputs_23 = self.model.forward(h_current,
                                    y[23:24, :, :],
                                    jac_fh[23:24, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[23:24, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[23:24, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_23[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_23[2]
    CPCT_cov_current = CPCT_cov_current + outputs_23[5]
    P_current = outputs_23[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_23[7]
    kalman_diff = kalman_diff + outputs_23[8]
    kalman_threshold = kalman_threshold + outputs_23[9]

    # === TIMESTEP 24 ===
    outputs_24 = self.model.forward(h_current,
                                    y[24:25, :, :],
                                    jac_fh[24:25, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[24:25, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[24:25, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_24[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_24[2]
    CPCT_cov_current = CPCT_cov_current + outputs_24[5]
    P_current = outputs_24[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_24[7]
    kalman_diff = kalman_diff + outputs_24[8]
    kalman_threshold = kalman_threshold + outputs_24[9]

    # === TIMESTEP 25 ===
    outputs_25 = self.model.forward(h_current,
                                    y[25:26, :, :],
                                    jac_fh[25:26, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[25:26, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[25:26, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_25[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_25[2]
    CPCT_cov_current = CPCT_cov_current + outputs_25[5]
    P_current = outputs_25[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_25[7]
    kalman_diff = kalman_diff + outputs_25[8]
    kalman_threshold = kalman_threshold + outputs_25[9]

    # === TIMESTEP 26 ===
    outputs_26 = self.model.forward(h_current,
                                    y[26:27, :, :],
                                    jac_fh[26:27, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[26:27, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[26:27, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_26[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_26[2]
    CPCT_cov_current = CPCT_cov_current + outputs_26[5]
    P_current = outputs_26[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_26[7]
    kalman_diff = kalman_diff + outputs_26[8]
    kalman_threshold = kalman_threshold + outputs_26[9]

    # === TIMESTEP 27 ===
    outputs_27 = self.model.forward(h_current,
                                    y[27:28, :, :],
                                    jac_fh[27:28, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[27:28, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[27:28, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_27[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_27[2]
    CPCT_cov_current = CPCT_cov_current + outputs_27[5]
    P_current = outputs_27[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_27[7]
    kalman_diff = kalman_diff + outputs_27[8]
    kalman_threshold = kalman_threshold + outputs_27[9]

    # === TIMESTEP 28 ===
    outputs_28 = self.model.forward(h_current,
                                    y[28:29, :, :],
                                    jac_fh[28:29, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[28:29, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[28:29, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_28[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_28[2]
    CPCT_cov_current = CPCT_cov_current + outputs_28[5]
    P_current = outputs_28[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_28[7]
    kalman_diff = kalman_diff + outputs_28[8]
    kalman_threshold = kalman_threshold + outputs_28[9]

    # === TIMESTEP 29 ===
    outputs_29 = self.model.forward(h_current,
                                    y[29:30, :, :],
                                    jac_fh[29:30, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[29:30, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[29:30, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_29[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_29[2]
    CPCT_cov_current = CPCT_cov_current + outputs_29[5]
    P_current = outputs_29[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_29[7]
    kalman_diff = kalman_diff + outputs_29[8]
    kalman_threshold = kalman_threshold + outputs_29[9]

    # === TIMESTEP 30 ===
    outputs_30 = self.model.forward(h_current,
                                    y[30:31, :, :],
                                    jac_fh[30:31, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[30:31, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[30:31, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_30[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_30[2]
    CPCT_cov_current = CPCT_cov_current + outputs_30[5]
    P_current = outputs_30[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_30[7]
    kalman_diff = kalman_diff + outputs_30[8]
    kalman_threshold = kalman_threshold + outputs_30[9]

    # === TIMESTEP 31 ===
    outputs_31 = self.model.forward(h_current,
                                    y[31:32, :, :],
                                    jac_fh[31:32, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[31:32, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[31:32, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_31[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_31[2]
    CPCT_cov_current = CPCT_cov_current + outputs_31[5]
    P_current = outputs_31[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_31[7]
    kalman_diff = kalman_diff + outputs_31[8]
    kalman_threshold = kalman_threshold + outputs_31[9]

    h_current = torch.cat([h_current, nonce_h], dim=2)
    P_current = torch.cat([P_current, nonce_P], dim=2)
    delta_y_current = torch.cat([delta_y_current, nonce_dy], dim=2)
    CPCT_cov_current = torch.cat([CPCT_cov_current, nonce_CPCT_cov], dim=2)
    KCR_status = torch.cat([KCR_status, nonce_KCR_status], dim=2)
    kalman_diff = torch.cat([kalman_diff, nonce_kalman_diff], dim=1)
    kalman_threshold = torch.cat([kalman_threshold, nonce_kalman_threshold], dim=1)

    return h_current, P_current, delta_y_current, CPCT_cov_current, KCR_status, kalman_diff, kalman_threshold

forward4 

forward4(h, P, delta_y, CPCT_cov, y, jac_fh, jac_Ch, Kt, KCR_status, nonce_h, nonce_P, nonce_dy, nonce_CPCT_cov, nonce_KCR_status, nonce_kalman_diff, nonce_kalman_threshold)

Forward pass with exactly 4 timesteps (EZKL-compatible)

Parameters:

Name Type Description Default
h

Initial hidden state [1, 1, SD]

 required
P

Initial covariance matrix [1, SD, SD]

 required
delta_y

Initial residual [1, 1, is_r]

 required
CPCT_cov

Initial residual covariance [1, is_r, is_r]

 required
y

Observations for 4 timesteps [4, obs_dim]

 required
jac_fh

Jacobian fh for 4 timesteps [4, ...]

 required
jac_Ch

Jacobian Ch for 4 timesteps [4, ...]

 required
Kt

Kalman gain for 4 timesteps [4, ...]

 required
Source code in src/PredictionWrapper.py 
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def forward4(self, h, P, delta_y, CPCT_cov, y, jac_fh, jac_Ch, Kt, KCR_status,
             nonce_h,nonce_P,nonce_dy,nonce_CPCT_cov,nonce_KCR_status,nonce_kalman_diff,nonce_kalman_threshold,):
    """
    Forward pass with exactly 4 timesteps (EZKL-compatible)

    Args:
        h: Initial hidden state [1, 1, SD]
        P: Initial covariance matrix [1, SD, SD]
        delta_y: Initial residual [1, 1, is_r]
        CPCT_cov: Initial residual covariance [1, is_r, is_r]
        y: Observations for 4 timesteps [4, obs_dim]
        jac_fh: Jacobian fh for 4 timesteps [4, ...]
        jac_Ch: Jacobian Ch for 4 timesteps [4, ...]
        Kt: Kalman gain for 4 timesteps [4, ...]
    """

    h_current = h[0:1, :, :self.SD].reshape(1, 1, self.SD)
    P_current = P[0:1, :, :self.SD].reshape(1, self.SD, self.SD)

    delta_y_current = delta_y.reshape(1, 1, self.MD+1)
    CPCT_cov_current = CPCT_cov.reshape(1, self.MD, self.MD+1)

    delta_y_current = delta_y_current[0:1, :, :self.MD].reshape(1, 1, self.MD)
    CPCT_cov_current = CPCT_cov_current[0:1, :, :self.MD].reshape(1, self.MD, self.MD)
    y = y[:, :, :self.MD]
    jac_fh = jac_fh[:, :, :self.SD]
    jac_Ch = jac_Ch[:, :, :self.MD]
    Kt = Kt[:, :, :self.SD]
    KCR_status = KCR_status[:,:,0:1].reshape(1,1,1)

    nonce_h = nonce_h.reshape((1, 1, 1))
    nonce_dy = nonce_dy.reshape((1, 1, 1))
    nonce_KCR_status = nonce_KCR_status.reshape((1, 1, 1))
    nonce_P = nonce_P.reshape((1, self.SD, 1))
    nonce_CPCT_cov = nonce_CPCT_cov.reshape((1, self.MD, 1))
    nonce_kalman_diff = nonce_kalman_diff.reshape((1, 1, self.SD))
    nonce_kalman_threshold = nonce_kalman_threshold.reshape((1, 1, self.SD))

    assert h_current.shape[2] == self.SD
    assert nonce_h.shape == (1, 1, 1)
    assert nonce_KCR_status.shape == (1, 1, 1)
    assert nonce_dy.shape == (1, 1, 1)
    assert nonce_P.shape == (1, self.SD, 1)
    assert nonce_CPCT_cov.shape == (1, self.MD, 1)
    assert nonce_kalman_diff.shape == (1, 1, self.SD)
    assert nonce_kalman_threshold.shape == (1, 1, self.SD)


    outputs_0 = self.model.forward(h_current,
                                 y[0:1,:,:],
                                 jac_fh[0:1,:,:].reshape(1,self.SD,self.SD),
                                 jac_Ch[0:1,:,:].reshape(1,self.MD,self.SD),
                                 P_current,
                                 Kt[0:1,:,:].reshape(1,self.MD,self.SD))
    h_current = outputs_0[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_0[2]
    CPCT_cov_current = CPCT_cov_current + outputs_0[5]
    P_current = outputs_0[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status*outputs_0[7]
    kalman_diff = outputs_0[8]
    kalman_threshold = outputs_0[9]

    # === TIMESTEP 1 ===
    outputs_1 = self.model.forward(h_current,
                                   y[1:2, :, :],
                                   jac_fh[1:2, :, :].reshape(1, self.SD, self.SD),
                                   jac_Ch[1:2, :, :].reshape(1, self.MD, self.SD),
                                   P_current,
                                   Kt[1:2, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_1[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_1[2]
    CPCT_cov_current = CPCT_cov_current + outputs_1[5]
    P_current = outputs_1[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status*outputs_1[7]
    kalman_diff = kalman_diff + outputs_1[8]
    kalman_threshold = kalman_threshold + outputs_1[9]

    # === TIMESTEP 2 ===
    outputs_2 = self.model.forward(h_current,
                                   y[2:3, :, :],
                                   jac_fh[2:3, :, :].reshape(1, self.SD, self.SD),
                                   jac_Ch[2:3, :, :].reshape(1, self.MD, self.SD),
                                   P_current,
                                   Kt[2:3, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_2[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_2[2]
    CPCT_cov_current = CPCT_cov_current + outputs_2[5]
    P_current = outputs_2[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status*outputs_2[7]
    kalman_diff = kalman_diff + outputs_2[8]
    kalman_threshold = kalman_threshold + outputs_2[9]

    # === TIMESTEP 2 ===
    outputs_3 = self.model.forward(h_current,
                                   y[3:4, :, :],
                                   jac_fh[3:4, :, :].reshape(1, self.SD, self.SD),
                                   jac_Ch[3:4, :, :].reshape(1, self.MD, self.SD),
                                   P_current,
                                   Kt[3:4, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_3[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_3[2]
    CPCT_cov_current = CPCT_cov_current + outputs_3[5]
    P_current = outputs_3[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status*outputs_3[7]
    kalman_diff = kalman_diff + outputs_3[8]
    kalman_threshold = kalman_threshold + outputs_3[9]

    h_current = torch.cat([h_current, nonce_h], dim=2)
    P_current = torch.cat([P_current, nonce_P], dim=2)
    delta_y_current = torch.cat([delta_y_current, nonce_dy], dim=2)
    CPCT_cov_current = torch.cat([CPCT_cov_current, nonce_CPCT_cov], dim=2)
    KCR_status = torch.cat([KCR_status, nonce_KCR_status], dim=2)
    kalman_diff = torch.cat([kalman_diff, nonce_kalman_diff], dim=1)
    kalman_threshold = torch.cat([kalman_threshold, nonce_kalman_threshold], dim=1)

    return h_current, P_current, delta_y_current, CPCT_cov_current, KCR_status, kalman_diff, kalman_threshold

forward64 

forward64(h, P, delta_y, CPCT_cov, y, jac_fh, jac_Ch, Kt, KCR_status, nonce_h, nonce_P, nonce_dy, nonce_CPCT_cov, nonce_KCR_status, nonce_kalman_diff, nonce_kalman_threshold)

Forward pass with exactly 64 timesteps

Parameters:

Name Type Description Default
h

Initial hidden state [1, 1, SD]

 required
P

Initial covariance matrix [1, SD, SD]

 required
delta_y

Initial residual [1, 1, is_r]

 required
CPCT_cov

Initial residual covariance [1, is_r, is_r]

 required
y

Observations for 16 timesteps [16, obs_dim]

 required
jac_fh

Jacobian fh for 16 timesteps [16, ...]

 required
jac_Ch

Jacobian Ch for 16 timesteps [16, ...]

 required
Kt

Kalman gain for 16 timesteps [16, ...]

 required
Source code in src/PredictionWrapper.py 
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def forward64(self, h, P, delta_y, CPCT_cov, y, jac_fh, jac_Ch, Kt, KCR_status,
             nonce_h,nonce_P,nonce_dy,nonce_CPCT_cov,nonce_KCR_status,nonce_kalman_diff,nonce_kalman_threshold,):
    """
    Forward pass with exactly 64 timesteps

    Args:
        h: Initial hidden state [1, 1, SD]
        P: Initial covariance matrix [1, SD, SD]
        delta_y: Initial residual [1, 1, is_r]
        CPCT_cov: Initial residual covariance [1, is_r, is_r]
        y: Observations for 16 timesteps [16, obs_dim]
        jac_fh: Jacobian fh for 16 timesteps [16, ...]
        jac_Ch: Jacobian Ch for 16 timesteps [16, ...]
        Kt: Kalman gain for 16 timesteps [16, ...]
    """

    h_current = h[0:1, :, :self.SD].reshape(1, 1, self.SD)
    P_current = P[0:1, :, :self.SD].reshape(1, self.SD, self.SD)

    delta_y_current = delta_y.reshape(1, 1, self.MD+1)
    CPCT_cov_current = CPCT_cov.reshape(1, self.MD, self.MD+1)

    delta_y_current = delta_y_current[0:1, :, :self.MD].reshape(1, 1, self.MD)
    CPCT_cov_current = CPCT_cov_current[0:1, :, :self.MD].reshape(1, self.MD, self.MD)
    y = y[:, :, :self.MD]
    jac_fh = jac_fh[:, :, :self.SD]
    jac_Ch = jac_Ch[:, :, :self.MD]
    Kt = Kt[:, :, :self.SD]
    KCR_status = KCR_status[:,:,0:1].reshape(1,1,1)

    nonce_h = nonce_h.reshape((1, 1, 1))
    nonce_dy = nonce_dy.reshape((1, 1, 1))
    nonce_KCR_status = nonce_KCR_status.reshape((1, 1, 1))
    nonce_P = nonce_P.reshape((1, self.SD, 1))
    nonce_CPCT_cov = nonce_CPCT_cov.reshape((1, self.MD, 1))
    nonce_kalman_diff = nonce_kalman_diff.reshape((1, 1, self.SD))
    nonce_kalman_threshold = nonce_kalman_threshold.reshape((1, 1, self.SD))

    assert h_current.shape[2] == self.SD
    assert nonce_h.shape == (1, 1, 1)
    assert nonce_KCR_status.shape == (1, 1, 1)
    assert nonce_dy.shape == (1, 1, 1)
    assert nonce_P.shape == (1, self.SD, 1)
    assert nonce_CPCT_cov.shape == (1, self.MD, 1)
    assert nonce_kalman_diff.shape == (1, 1, self.SD)
    assert nonce_kalman_threshold.shape == (1, 1, self.SD)


    # === TIMESTEP 0 ===
    outputs_0 = self.model.forward(h_current,
                                   y[0:1, :, :],
                                   jac_fh[0:1, :, :].reshape(1, self.SD, self.SD),
                                   jac_Ch[0:1, :, :].reshape(1, self.MD, self.SD),
                                   P_current,
                                   Kt[0:1, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_0[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_0[2]
    CPCT_cov_current = CPCT_cov_current + outputs_0[5]
    P_current = outputs_0[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status*outputs_0[7]
    kalman_diff = outputs_0[8]
    kalman_threshold = outputs_0[9]

    # === TIMESTEP 1 ===
    outputs_1 = self.model.forward(h_current,
                                   y[1:2, :, :],
                                   jac_fh[1:2, :, :].reshape(1, self.SD, self.SD),
                                   jac_Ch[1:2, :, :].reshape(1, self.MD, self.SD),
                                   P_current,
                                   Kt[1:2, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_1[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_1[2]
    CPCT_cov_current = CPCT_cov_current + outputs_1[5]
    P_current = outputs_1[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_1[7]
    kalman_diff = kalman_diff + outputs_1[8]
    kalman_threshold = kalman_threshold + outputs_1[9]

    # === TIMESTEP 2 ===
    outputs_2 = self.model.forward(h_current,
                                   y[2:3, :, :],
                                   jac_fh[2:3, :, :].reshape(1, self.SD, self.SD),
                                   jac_Ch[2:3, :, :].reshape(1, self.MD, self.SD),
                                   P_current,
                                   Kt[2:3, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_2[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_2[2]
    CPCT_cov_current = CPCT_cov_current + outputs_2[5]
    P_current = outputs_2[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_2[7]
    kalman_diff = kalman_diff + outputs_2[8]
    kalman_threshold = kalman_threshold + outputs_2[9]

    # === TIMESTEP 3 ===
    outputs_3 = self.model.forward(h_current,
                                   y[3:4, :, :],
                                   jac_fh[3:4, :, :].reshape(1, self.SD, self.SD),
                                   jac_Ch[3:4, :, :].reshape(1, self.MD, self.SD),
                                   P_current,
                                   Kt[3:4, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_3[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_3[2]
    CPCT_cov_current = CPCT_cov_current + outputs_3[5]
    P_current = outputs_3[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_3[7]
    kalman_diff = kalman_diff + outputs_3[8]
    kalman_threshold = kalman_threshold + outputs_3[9]

    # === TIMESTEP 4 ===
    outputs_4 = self.model.forward(h_current,
                                   y[4:5, :, :],
                                   jac_fh[4:5, :, :].reshape(1, self.SD, self.SD),
                                   jac_Ch[4:5, :, :].reshape(1, self.MD, self.SD),
                                   P_current,
                                   Kt[4:5, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_4[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_4[2]
    CPCT_cov_current = CPCT_cov_current + outputs_4[5]
    P_current = outputs_4[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_4[7]
    kalman_diff = kalman_diff + outputs_4[8]
    kalman_threshold = kalman_threshold + outputs_4[9]

    # === TIMESTEP 5 ===
    outputs_5 = self.model.forward(h_current,
                                   y[5:6, :, :],
                                   jac_fh[5:6, :, :].reshape(1, self.SD, self.SD),
                                   jac_Ch[5:6, :, :].reshape(1, self.MD, self.SD),
                                   P_current,
                                   Kt[5:6, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_5[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_5[2]
    CPCT_cov_current = CPCT_cov_current + outputs_5[5]
    P_current = outputs_5[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_5[7]
    kalman_diff = kalman_diff + outputs_5[8]
    kalman_threshold = kalman_threshold + outputs_5[9]

    # === TIMESTEP 6 ===
    outputs_6 = self.model.forward(h_current,
                                   y[6:7, :, :],
                                   jac_fh[6:7, :, :].reshape(1, self.SD, self.SD),
                                   jac_Ch[6:7, :, :].reshape(1, self.MD, self.SD),
                                   P_current,
                                   Kt[6:7, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_6[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_6[2]
    CPCT_cov_current = CPCT_cov_current + outputs_6[5]
    P_current = outputs_6[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_6[7]
    kalman_diff = kalman_diff + outputs_6[8]
    kalman_threshold = kalman_threshold + outputs_6[9]

    # === TIMESTEP 7 ===
    outputs_7 = self.model.forward(h_current,
                                   y[7:8, :, :],
                                   jac_fh[7:8, :, :].reshape(1, self.SD, self.SD),
                                   jac_Ch[7:8, :, :].reshape(1, self.MD, self.SD),
                                   P_current,
                                   Kt[7:8, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_7[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_7[2]
    CPCT_cov_current = CPCT_cov_current + outputs_7[5]
    P_current = outputs_7[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_7[7]
    kalman_diff = kalman_diff + outputs_7[8]
    kalman_threshold = kalman_threshold + outputs_7[9]

    # === TIMESTEP 8 ===
    outputs_8 = self.model.forward(h_current,
                                   y[8:9, :, :],
                                   jac_fh[8:9, :, :].reshape(1, self.SD, self.SD),
                                   jac_Ch[8:9, :, :].reshape(1, self.MD, self.SD),
                                   P_current,
                                   Kt[8:9, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_8[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_8[2]
    CPCT_cov_current = CPCT_cov_current + outputs_8[5]
    P_current = outputs_8[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_8[7]
    kalman_diff = kalman_diff + outputs_8[8]
    kalman_threshold = kalman_threshold + outputs_8[9]

    # === TIMESTEP 9 ===
    outputs_9 = self.model.forward(h_current,
                                   y[9:10, :, :],
                                   jac_fh[9:10, :, :].reshape(1, self.SD, self.SD),
                                   jac_Ch[9:10, :, :].reshape(1, self.MD, self.SD),
                                   P_current,
                                   Kt[9:10, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_9[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_9[2]
    CPCT_cov_current = CPCT_cov_current + outputs_9[5]
    P_current = outputs_9[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_9[7]
    kalman_diff = kalman_diff + outputs_9[8]
    kalman_threshold = kalman_threshold + outputs_9[9]

    # === TIMESTEP 10 ===
    outputs_10 = self.model.forward(h_current,
                                    y[10:11, :, :],
                                    jac_fh[10:11, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[10:11, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[10:11, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_10[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_10[2]
    CPCT_cov_current = CPCT_cov_current + outputs_10[5]
    P_current = outputs_10[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_10[7]
    kalman_diff = kalman_diff + outputs_10[8]
    kalman_threshold = kalman_threshold + outputs_10[9]

    # === TIMESTEP 11 ===
    outputs_11 = self.model.forward(h_current,
                                    y[11:12, :, :],
                                    jac_fh[11:12, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[11:12, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[11:12, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_11[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_11[2]
    CPCT_cov_current = CPCT_cov_current + outputs_11[5]
    P_current = outputs_11[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_11[7]
    kalman_diff = kalman_diff + outputs_11[8]
    kalman_threshold = kalman_threshold + outputs_11[9]

    # === TIMESTEP 12 ===
    outputs_12 = self.model.forward(h_current,
                                    y[12:13, :, :],
                                    jac_fh[12:13, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[12:13, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[12:13, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_12[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_12[2]
    CPCT_cov_current = CPCT_cov_current + outputs_12[5]
    P_current = outputs_12[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_12[7]
    kalman_diff = kalman_diff + outputs_12[8]
    kalman_threshold = kalman_threshold + outputs_12[9]

    # === TIMESTEP 13 ===
    outputs_13 = self.model.forward(h_current,
                                    y[13:14, :, :],
                                    jac_fh[13:14, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[13:14, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[13:14, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_13[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_13[2]
    CPCT_cov_current = CPCT_cov_current + outputs_13[5]
    P_current = outputs_13[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_13[7]
    kalman_diff = kalman_diff + outputs_13[8]
    kalman_threshold = kalman_threshold + outputs_13[9]

    # === TIMESTEP 14 ===
    outputs_14 = self.model.forward(h_current,
                                    y[14:15, :, :],
                                    jac_fh[14:15, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[14:15, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[14:15, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_14[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_14[2]
    CPCT_cov_current = CPCT_cov_current + outputs_14[5]
    P_current = outputs_14[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_14[7]
    kalman_diff = kalman_diff + outputs_14[8]
    kalman_threshold = kalman_threshold + outputs_14[9]

    # === TIMESTEP 15 ===
    outputs_15 = self.model.forward(h_current,
                                    y[15:16, :, :],
                                    jac_fh[15:16, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[15:16, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[15:16, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_15[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_15[2]
    CPCT_cov_current = CPCT_cov_current + outputs_15[5]
    P_current = outputs_15[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_15[7]
    kalman_diff = kalman_diff + outputs_15[8]
    kalman_threshold = kalman_threshold + outputs_15[9]

    # === TIMESTEP 16 ===
    outputs_16 = self.model.forward(h_current,
                                    y[16:17, :, :],
                                    jac_fh[16:17, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[16:17, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[16:17, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_16[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_16[2]
    CPCT_cov_current = CPCT_cov_current + outputs_16[5]
    P_current = outputs_16[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_16[7]
    kalman_diff = kalman_diff + outputs_16[8]
    kalman_threshold = kalman_threshold + outputs_16[9]

    # === TIMESTEP 17 ===
    outputs_17 = self.model.forward(h_current,
                                    y[17:18, :, :],
                                    jac_fh[17:18, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[17:18, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[17:18, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_17[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_17[2]
    CPCT_cov_current = CPCT_cov_current + outputs_17[5]
    P_current = outputs_17[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_17[7]
    kalman_diff = kalman_diff + outputs_17[8]
    kalman_threshold = kalman_threshold + outputs_17[9]

    # === TIMESTEP 18 ===
    outputs_18 = self.model.forward(h_current,
                                    y[18:19, :, :],
                                    jac_fh[18:19, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[18:19, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[18:19, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_18[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_18[2]
    CPCT_cov_current = CPCT_cov_current + outputs_18[5]
    P_current = outputs_18[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_18[7]
    kalman_diff = kalman_diff + outputs_18[8]
    kalman_threshold = kalman_threshold + outputs_18[9]

    # === TIMESTEP 19 ===
    outputs_19 = self.model.forward(h_current,
                                    y[19:20, :, :],
                                    jac_fh[19:20, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[19:20, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[19:20, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_19[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_19[2]
    CPCT_cov_current = CPCT_cov_current + outputs_19[5]
    P_current = outputs_19[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_19[7]
    kalman_diff = kalman_diff + outputs_19[8]
    kalman_threshold = kalman_threshold + outputs_19[9]

    # === TIMESTEP 20 ===
    outputs_20 = self.model.forward(h_current,
                                    y[20:21, :, :],
                                    jac_fh[20:21, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[20:21, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[20:21, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_20[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_20[2]
    CPCT_cov_current = CPCT_cov_current + outputs_20[5]
    P_current = outputs_20[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_20[7]
    kalman_diff = kalman_diff + outputs_20[8]
    kalman_threshold = kalman_threshold + outputs_20[9]

    # === TIMESTEP 21 ===
    outputs_21 = self.model.forward(h_current,
                                    y[21:22, :, :],
                                    jac_fh[21:22, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[21:22, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[21:22, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_21[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_21[2]
    CPCT_cov_current = CPCT_cov_current + outputs_21[5]
    P_current = outputs_21[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_21[7]
    kalman_diff = kalman_diff + outputs_21[8]
    kalman_threshold = kalman_threshold + outputs_21[9]

    # === TIMESTEP 22 ===
    outputs_22 = self.model.forward(h_current,
                                    y[22:23, :, :],
                                    jac_fh[22:23, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[22:23, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[22:23, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_22[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_22[2]
    CPCT_cov_current = CPCT_cov_current + outputs_22[5]
    P_current = outputs_22[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_22[7]
    kalman_diff = kalman_diff + outputs_22[8]
    kalman_threshold = kalman_threshold + outputs_22[9]

    # === TIMESTEP 23 ===
    outputs_23 = self.model.forward(h_current,
                                    y[23:24, :, :],
                                    jac_fh[23:24, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[23:24, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[23:24, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_23[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_23[2]
    CPCT_cov_current = CPCT_cov_current + outputs_23[5]
    P_current = outputs_23[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_23[7]
    kalman_diff = kalman_diff + outputs_23[8]
    kalman_threshold = kalman_threshold + outputs_23[9]

    # === TIMESTEP 24 ===
    outputs_24 = self.model.forward(h_current,
                                    y[24:25, :, :],
                                    jac_fh[24:25, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[24:25, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[24:25, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_24[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_24[2]
    CPCT_cov_current = CPCT_cov_current + outputs_24[5]
    P_current = outputs_24[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_24[7]
    kalman_diff = kalman_diff + outputs_24[8]
    kalman_threshold = kalman_threshold + outputs_24[9]

    # === TIMESTEP 25 ===
    outputs_25 = self.model.forward(h_current,
                                    y[25:26, :, :],
                                    jac_fh[25:26, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[25:26, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[25:26, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_25[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_25[2]
    CPCT_cov_current = CPCT_cov_current + outputs_25[5]
    P_current = outputs_25[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_25[7]
    kalman_diff = kalman_diff + outputs_25[8]
    kalman_threshold = kalman_threshold + outputs_25[9]

    # === TIMESTEP 26 ===
    outputs_26 = self.model.forward(h_current,
                                    y[26:27, :, :],
                                    jac_fh[26:27, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[26:27, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[26:27, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_26[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_26[2]
    CPCT_cov_current = CPCT_cov_current + outputs_26[5]
    P_current = outputs_26[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_26[7]
    kalman_diff = kalman_diff + outputs_26[8]
    kalman_threshold = kalman_threshold + outputs_26[9]

    # === TIMESTEP 27 ===
    outputs_27 = self.model.forward(h_current,
                                    y[27:28, :, :],
                                    jac_fh[27:28, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[27:28, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[27:28, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_27[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_27[2]
    CPCT_cov_current = CPCT_cov_current + outputs_27[5]
    P_current = outputs_27[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_27[7]
    kalman_diff = kalman_diff + outputs_27[8]
    kalman_threshold = kalman_threshold + outputs_27[9]

    # === TIMESTEP 28 ===
    outputs_28 = self.model.forward(h_current,
                                    y[28:29, :, :],
                                    jac_fh[28:29, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[28:29, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[28:29, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_28[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_28[2]
    CPCT_cov_current = CPCT_cov_current + outputs_28[5]
    P_current = outputs_28[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_28[7]
    kalman_diff = kalman_diff + outputs_28[8]
    kalman_threshold = kalman_threshold + outputs_28[9]

    # === TIMESTEP 29 ===
    outputs_29 = self.model.forward(h_current,
                                    y[29:30, :, :],
                                    jac_fh[29:30, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[29:30, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[29:30, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_29[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_29[2]
    CPCT_cov_current = CPCT_cov_current + outputs_29[5]
    P_current = outputs_29[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_29[7]
    kalman_diff = kalman_diff + outputs_29[8]
    kalman_threshold = kalman_threshold + outputs_29[9]

    # === TIMESTEP 30 ===
    outputs_30 = self.model.forward(h_current,
                                    y[30:31, :, :],
                                    jac_fh[30:31, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[30:31, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[30:31, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_30[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_30[2]
    CPCT_cov_current = CPCT_cov_current + outputs_30[5]
    P_current = outputs_30[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_30[7]
    kalman_diff = kalman_diff + outputs_30[8]
    kalman_threshold = kalman_threshold + outputs_30[9]

    # === TIMESTEP 31 ===
    outputs_31 = self.model.forward(h_current,
                                    y[31:32, :, :],
                                    jac_fh[31:32, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[31:32, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[31:32, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_31[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_31[2]
    CPCT_cov_current = CPCT_cov_current + outputs_31[5]
    P_current = outputs_31[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_31[7]
    kalman_diff = kalman_diff + outputs_31[8]
    kalman_threshold = kalman_threshold + outputs_31[9]

    # === TIMESTEP 32 ===
    outputs_32 = self.model.forward(h_current,
                                    y[32:33, :, :],
                                    jac_fh[32:33, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[32:33, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[32:33, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_32[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_32[2]
    CPCT_cov_current = CPCT_cov_current + outputs_32[5]
    P_current = outputs_32[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_32[7]
    kalman_diff = kalman_diff + outputs_32[8]
    kalman_threshold = kalman_threshold + outputs_32[9]

    # === TIMESTEP 33 ===
    outputs_33 = self.model.forward(h_current,
                                    y[33:34, :, :],
                                    jac_fh[33:34, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[33:34, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[33:34, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_33[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_33[2]
    CPCT_cov_current = CPCT_cov_current + outputs_33[5]
    P_current = outputs_33[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_33[7]
    kalman_diff = kalman_diff + outputs_33[8]
    kalman_threshold = kalman_threshold + outputs_33[9]

    # === TIMESTEP 34 ===
    outputs_34 = self.model.forward(h_current,
                                    y[34:35, :, :],
                                    jac_fh[34:35, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[34:35, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[34:35, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_34[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_34[2]
    CPCT_cov_current = CPCT_cov_current + outputs_34[5]
    P_current = outputs_34[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_34[7]
    kalman_diff = kalman_diff + outputs_34[8]
    kalman_threshold = kalman_threshold + outputs_34[9]

    # === TIMESTEP 35 ===
    outputs_35 = self.model.forward(h_current,
                                    y[35:36, :, :],
                                    jac_fh[35:36, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[35:36, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[35:36, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_35[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_35[2]
    CPCT_cov_current = CPCT_cov_current + outputs_35[5]
    P_current = outputs_35[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_35[7]
    kalman_diff = kalman_diff + outputs_35[8]
    kalman_threshold = kalman_threshold + outputs_35[9]

    # === TIMESTEP 36 ===
    outputs_36 = self.model.forward(h_current,
                                    y[36:37, :, :],
                                    jac_fh[36:37, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[36:37, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[36:37, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_36[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_36[2]
    CPCT_cov_current = CPCT_cov_current + outputs_36[5]
    P_current = outputs_36[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_36[7]
    kalman_diff = kalman_diff + outputs_36[8]
    kalman_threshold = kalman_threshold + outputs_36[9]

    # === TIMESTEP 37 ===
    outputs_37 = self.model.forward(h_current,
                                    y[37:38, :, :],
                                    jac_fh[37:38, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[37:38, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[37:38, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_37[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_37[2]
    CPCT_cov_current = CPCT_cov_current + outputs_37[5]
    P_current = outputs_37[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_37[7]
    kalman_diff = kalman_diff + outputs_37[8]
    kalman_threshold = kalman_threshold + outputs_37[9]

    # === TIMESTEP 38 ===
    outputs_38 = self.model.forward(h_current,
                                    y[38:39, :, :],
                                    jac_fh[38:39, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[38:39, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[38:39, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_38[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_38[2]
    CPCT_cov_current = CPCT_cov_current + outputs_38[5]
    P_current = outputs_38[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_38[7]
    kalman_diff = kalman_diff + outputs_38[8]
    kalman_threshold = kalman_threshold + outputs_38[9]

    # === TIMESTEP 39 ===
    outputs_39 = self.model.forward(h_current,
                                    y[39:40, :, :],
                                    jac_fh[39:40, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[39:40, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[39:40, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_39[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_39[2]
    CPCT_cov_current = CPCT_cov_current + outputs_39[5]
    P_current = outputs_39[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_39[7]
    kalman_diff = kalman_diff + outputs_39[8]
    kalman_threshold = kalman_threshold + outputs_39[9]

    # === TIMESTEP 40 ===
    outputs_40 = self.model.forward(h_current,
                                    y[40:41, :, :],
                                    jac_fh[40:41, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[40:41, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[40:41, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_40[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_40[2]
    CPCT_cov_current = CPCT_cov_current + outputs_40[5]
    P_current = outputs_40[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_40[7]
    kalman_diff = kalman_diff + outputs_40[8]
    kalman_threshold = kalman_threshold + outputs_40[9]

    # === TIMESTEP 41 ===
    outputs_41 = self.model.forward(h_current,
                                    y[41:42, :, :],
                                    jac_fh[41:42, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[41:42, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[41:42, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_41[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_41[2]
    CPCT_cov_current = CPCT_cov_current + outputs_41[5]
    P_current = outputs_41[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_41[7]
    kalman_diff = kalman_diff + outputs_41[8]
    kalman_threshold = kalman_threshold + outputs_41[9]

    # === TIMESTEP 42 ===
    outputs_42 = self.model.forward(h_current,
                                    y[42:43, :, :],
                                    jac_fh[42:43, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[42:43, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[42:43, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_42[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_42[2]
    CPCT_cov_current = CPCT_cov_current + outputs_42[5]
    P_current = outputs_42[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_42[7]
    kalman_diff = kalman_diff + outputs_42[8]
    kalman_threshold = kalman_threshold + outputs_42[9]

    # === TIMESTEP 43 ===
    outputs_43 = self.model.forward(h_current,
                                    y[43:44, :, :],
                                    jac_fh[43:44, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[43:44, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[43:44, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_43[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_43[2]
    CPCT_cov_current = CPCT_cov_current + outputs_43[5]
    P_current = outputs_43[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_43[7]
    kalman_diff = kalman_diff + outputs_43[8]
    kalman_threshold = kalman_threshold + outputs_43[9]

    # === TIMESTEP 44 ===
    outputs_44 = self.model.forward(h_current,
                                    y[44:45, :, :],
                                    jac_fh[44:45, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[44:45, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[44:45, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_44[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_44[2]
    CPCT_cov_current = CPCT_cov_current + outputs_44[5]
    P_current = outputs_44[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_44[7]
    kalman_diff = kalman_diff + outputs_43[8]
    kalman_threshold = kalman_threshold + outputs_43[9]

    # === TIMESTEP 45 ===
    outputs_45 = self.model.forward(h_current,
                                    y[45:46, :, :],
                                    jac_fh[45:46, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[45:46, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[45:46, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_45[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_45[2]
    CPCT_cov_current = CPCT_cov_current + outputs_45[5]
    P_current = outputs_45[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_45[7]
    kalman_diff = kalman_diff + outputs_45[8]
    kalman_threshold = kalman_threshold + outputs_45[9]

    # === TIMESTEP 46 ===
    outputs_46 = self.model.forward(h_current,
                                    y[46:47, :, :],
                                    jac_fh[46:47, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[46:47, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[46:47, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_46[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_46[2]
    CPCT_cov_current = CPCT_cov_current + outputs_46[5]
    P_current = outputs_46[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_46[7]
    kalman_diff = kalman_diff + outputs_46[8]
    kalman_threshold = kalman_threshold + outputs_46[9]

    # === TIMESTEP 47 ===
    outputs_47 = self.model.forward(h_current,
                                    y[47:48, :, :],
                                    jac_fh[47:48, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[47:48, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[47:48, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_47[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_47[2]
    CPCT_cov_current = CPCT_cov_current + outputs_47[5]
    P_current = outputs_47[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_47[7]
    kalman_diff = kalman_diff + outputs_47[8]
    kalman_threshold = kalman_threshold + outputs_47[9]

    # === TIMESTEP 48 ===
    outputs_48 = self.model.forward(h_current,
                                    y[48:49, :, :],
                                    jac_fh[48:49, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[48:49, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[48:49, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_48[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_48[2]
    CPCT_cov_current = CPCT_cov_current + outputs_48[5]
    P_current = outputs_48[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_48[7]
    kalman_diff = kalman_diff + outputs_48[8]
    kalman_threshold = kalman_threshold + outputs_48[9]

    # === TIMESTEP 49 ===
    outputs_49 = self.model.forward(h_current,
                                    y[49:50, :, :],
                                    jac_fh[49:50, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[49:50, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[49:50, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_49[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_49[2]
    CPCT_cov_current = CPCT_cov_current + outputs_49[5]
    P_current = outputs_49[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_49[7]
    kalman_diff = kalman_diff + outputs_49[8]
    kalman_threshold = kalman_threshold + outputs_49[9]

    # === TIMESTEP 50 ===
    outputs_50 = self.model.forward(h_current,
                                    y[50:51, :, :],
                                    jac_fh[50:51, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[50:51, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[50:51, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_50[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_50[2]
    CPCT_cov_current = CPCT_cov_current + outputs_50[5]
    P_current = outputs_50[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_50[7]
    kalman_diff = kalman_diff + outputs_50[8]
    kalman_threshold = kalman_threshold + outputs_50[9]

    # === TIMESTEP 51 ===
    outputs_51 = self.model.forward(h_current,
                                    y[51:52, :, :],
                                    jac_fh[51:52, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[51:52, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[51:52, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_51[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_51[2]
    CPCT_cov_current = CPCT_cov_current + outputs_51[5]
    P_current = outputs_51[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_51[7]
    kalman_diff = kalman_diff + outputs_51[8]
    kalman_threshold = kalman_threshold + outputs_51[9]

    # === TIMESTEP 52 ===
    outputs_52 = self.model.forward(h_current,
                                    y[52:53, :, :],
                                    jac_fh[52:53, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[52:53, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[52:53, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_52[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_52[2]
    CPCT_cov_current = CPCT_cov_current + outputs_52[5]
    P_current = outputs_52[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_52[7]
    kalman_diff = kalman_diff + outputs_52[8]
    kalman_threshold = kalman_threshold + outputs_52[9]

    # === TIMESTEP 53 ===
    outputs_53 = self.model.forward(h_current,
                                    y[53:54, :, :],
                                    jac_fh[53:54, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[53:54, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[53:54, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_53[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_53[2]
    CPCT_cov_current = CPCT_cov_current + outputs_53[5]
    P_current = outputs_53[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_53[7]
    kalman_diff = kalman_diff + outputs_53[8]
    kalman_threshold = kalman_threshold + outputs_53[9]

    # === TIMESTEP 54 ===
    outputs_54 = self.model.forward(h_current,
                                    y[54:55, :, :],
                                    jac_fh[54:55, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[54:55, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[54:55, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_54[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_54[2]
    CPCT_cov_current = CPCT_cov_current + outputs_54[5]
    P_current = outputs_54[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_54[7]
    kalman_diff = kalman_diff + outputs_54[8]
    kalman_threshold = kalman_threshold + outputs_54[9]

    # === TIMESTEP 55 ===
    outputs_55 = self.model.forward(h_current,
                                    y[55:56, :, :],
                                    jac_fh[55:56, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[55:56, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[55:56, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_55[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_55[2]
    CPCT_cov_current = CPCT_cov_current + outputs_55[5]
    P_current = outputs_55[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_55[7]
    kalman_diff = kalman_diff + outputs_55[8]
    kalman_threshold = kalman_threshold + outputs_55[9]

    # === TIMESTEP 56 ===
    outputs_56 = self.model.forward(h_current,
                                    y[56:57, :, :],
                                    jac_fh[56:57, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[56:57, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[56:57, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_56[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_56[2]
    CPCT_cov_current = CPCT_cov_current + outputs_56[5]
    P_current = outputs_56[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_56[7]
    kalman_diff = kalman_diff + outputs_56[8]
    kalman_threshold = kalman_threshold + outputs_56[9]

    # === TIMESTEP 57 ===
    outputs_57 = self.model.forward(h_current,
                                    y[57:58, :, :],
                                    jac_fh[57:58, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[57:58, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[57:58, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_57[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_57[2]
    CPCT_cov_current = CPCT_cov_current + outputs_57[5]
    P_current = outputs_57[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_57[7]
    kalman_diff = kalman_diff + outputs_57[8]
    kalman_threshold = kalman_threshold + outputs_57[9]

    # === TIMESTEP 58 ===
    outputs_58 = self.model.forward(h_current,
                                    y[58:59, :, :],
                                    jac_fh[58:59, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[58:59, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[58:59, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_58[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_58[2]
    CPCT_cov_current = CPCT_cov_current + outputs_58[5]
    P_current = outputs_58[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_58[7]
    kalman_diff = kalman_diff + outputs_58[8]
    kalman_threshold = kalman_threshold + outputs_58[9]

    # === TIMESTEP 59 ===
    outputs_59 = self.model.forward(h_current,
                                    y[59:60, :, :],
                                    jac_fh[59:60, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[59:60, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[59:60, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_59[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_59[2]
    CPCT_cov_current = CPCT_cov_current + outputs_59[5]
    P_current = outputs_59[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_59[7]
    kalman_diff = kalman_diff + outputs_59[8]
    kalman_threshold = kalman_threshold + outputs_59[9]

    # === TIMESTEP 60 ===
    outputs_60 = self.model.forward(h_current,
                                    y[60:61, :, :],
                                    jac_fh[60:61, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[60:61, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[60:61, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_60[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_60[2]
    CPCT_cov_current = CPCT_cov_current + outputs_60[5]
    P_current = outputs_60[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_60[7]
    kalman_diff = kalman_diff + outputs_60[8]
    kalman_threshold = kalman_threshold + outputs_60[9]

    # === TIMESTEP 61 ===
    outputs_61 = self.model.forward(h_current,
                                    y[61:62, :, :],
                                    jac_fh[61:62, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[61:62, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[61:62, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_61[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_61[2]
    CPCT_cov_current = CPCT_cov_current + outputs_61[5]
    P_current = outputs_61[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_61[7]
    kalman_diff = kalman_diff + outputs_61[8]
    kalman_threshold = kalman_threshold + outputs_61[9]

    # === TIMESTEP 62 ===
    outputs_62 = self.model.forward(h_current,
                                    y[62:63, :, :],
                                    jac_fh[62:63, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[62:63, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[62:63, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_62[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_62[2]
    CPCT_cov_current = CPCT_cov_current + outputs_62[5]
    P_current = outputs_62[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_62[7]
    kalman_diff = kalman_diff + outputs_62[8]
    kalman_threshold = kalman_threshold + outputs_62[9]

    # === TIMESTEP 63 ===
    outputs_63 = self.model.forward(h_current,
                                    y[63:64, :, :],
                                    jac_fh[63:64, :, :].reshape(1, self.SD, self.SD),
                                    jac_Ch[63:64, :, :].reshape(1, self.MD, self.SD),
                                    P_current,
                                    Kt[63:64, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_63[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_63[2]
    CPCT_cov_current = CPCT_cov_current + outputs_63[5]
    P_current = outputs_63[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_63[7]
    kalman_diff = kalman_diff + outputs_63[8]
    kalman_threshold = kalman_threshold + outputs_63[9]

    h_current = torch.cat([h_current, nonce_h], dim=2)
    P_current = torch.cat([P_current, nonce_P], dim=2)
    delta_y_current = torch.cat([delta_y_current, nonce_dy], dim=2)
    CPCT_cov_current = torch.cat([CPCT_cov_current, nonce_CPCT_cov], dim=2)
    KCR_status = torch.cat([KCR_status, nonce_KCR_status], dim=2)
    kalman_diff = torch.cat([kalman_diff, nonce_kalman_diff], dim=1)
    kalman_threshold = torch.cat([kalman_threshold, nonce_kalman_threshold], dim=1)

    return h_current, P_current, delta_y_current, CPCT_cov_current, KCR_status, kalman_diff, kalman_threshold

forward8 

forward8(h, P, delta_y, CPCT_cov, y, jac_fh, jac_Ch, Kt, KCR_status, nonce_h, nonce_P, nonce_dy, nonce_CPCT_cov, nonce_KCR_status, nonce_kalman_diff, nonce_kalman_threshold)

Forward pass with exactly 8 timesteps (EZKL-compatible)

Parameters:

Name Type Description Default
h

Initial hidden state [1, 1, SD]

 required
P

Initial covariance matrix [1, SD, SD]

 required
delta_y

Initial residual [1, 1, is_r]

 required
CPCT_cov

Initial residual covariance [1, is_r, is_r]

 required
y

Observations for 8 timesteps [8, obs_dim]

 required
jac_fh

Jacobian fh for 8 timesteps [8, ...]

 required
jac_Ch

Jacobian Ch for 8 timesteps [8, ...]

 required
Kt

Kalman gain for 8 timesteps [8, ...]

 required
Source code in src/PredictionWrapper.py 
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def forward8(self, h, P, delta_y, CPCT_cov, y, jac_fh, jac_Ch, Kt, KCR_status,
             nonce_h,nonce_P,nonce_dy,nonce_CPCT_cov,nonce_KCR_status,nonce_kalman_diff,nonce_kalman_threshold,):
    """
    Forward pass with exactly 8 timesteps (EZKL-compatible)

    Args:
        h: Initial hidden state [1, 1, SD]
        P: Initial covariance matrix [1, SD, SD]
        delta_y: Initial residual [1, 1, is_r]
        CPCT_cov: Initial residual covariance [1, is_r, is_r]
        y: Observations for 8 timesteps [8, obs_dim]
        jac_fh: Jacobian fh for 8 timesteps [8, ...]
        jac_Ch: Jacobian Ch for 8 timesteps [8, ...]
        Kt: Kalman gain for 8 timesteps [8, ...]
    """

    h_current = h[0:1, :, :self.SD].reshape(1, 1, self.SD)
    P_current = P[0:1, :, :self.SD].reshape(1, self.SD, self.SD)

    delta_y_current = delta_y.reshape(1, 1, self.MD+1)
    CPCT_cov_current = CPCT_cov.reshape(1, self.MD, self.MD+1)

    delta_y_current = delta_y_current[0:1, :, :self.MD].reshape(1, 1, self.MD)
    CPCT_cov_current = CPCT_cov_current[0:1, :, :self.MD].reshape(1, self.MD, self.MD)
    y = y[:, :, :self.MD]
    jac_fh = jac_fh[:, :, :self.SD]
    jac_Ch = jac_Ch[:, :, :self.MD]
    Kt = Kt[:, :, :self.SD]
    KCR_status = KCR_status[:,:,0:1].reshape(1,1,1)

    nonce_h = nonce_h.reshape((1, 1, 1))
    nonce_dy = nonce_dy.reshape((1, 1, 1))
    nonce_KCR_status = nonce_KCR_status.reshape((1, 1, 1))
    nonce_P = nonce_P.reshape((1, self.SD, 1))
    nonce_CPCT_cov = nonce_CPCT_cov.reshape((1, self.MD, 1))
    nonce_kalman_diff = nonce_kalman_diff.reshape((1, 1, self.SD))
    nonce_kalman_threshold = nonce_kalman_threshold.reshape((1, 1, self.SD))

    assert h_current.shape[2] == self.SD
    assert nonce_h.shape == (1, 1, 1)
    assert nonce_KCR_status.shape == (1, 1, 1)
    assert nonce_dy.shape == (1, 1, 1)
    assert nonce_P.shape == (1, self.SD, 1)
    assert nonce_CPCT_cov.shape == (1, self.MD, 1)
    assert nonce_kalman_diff.shape == (1, 1, self.SD)
    assert nonce_kalman_threshold.shape == (1, 1, self.SD)

    outputs_0 = self.model.forward(h_current,
                                   y[0:1, :, :],
                                   jac_fh[0:1, :, :].reshape(1, self.SD, self.SD),
                                   jac_Ch[0:1, :, :].reshape(1, self.MD, self.SD),
                                   P_current,
                                   Kt[0:1, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_0[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_0[2]
    CPCT_cov_current = CPCT_cov_current + outputs_0[5]
    P_current = outputs_0[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status*outputs_0[7]
    kalman_diff = outputs_0[8]
    kalman_threshold = outputs_0[9]

    # === TIMESTEP 1 ===
    outputs_1 = self.model.forward(h_current,
                                   y[1:2, :, :],
                                   jac_fh[1:2, :, :].reshape(1, self.SD, self.SD),
                                   jac_Ch[1:2, :, :].reshape(1, self.MD, self.SD),
                                   P_current,
                                   Kt[1:2, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_1[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_1[2]
    CPCT_cov_current = CPCT_cov_current + outputs_1[5]
    P_current = outputs_1[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_1[7]
    kalman_diff = kalman_diff + outputs_1[8]
    kalman_threshold = kalman_threshold + outputs_1[9]

    # === TIMESTEP 2 ===
    outputs_2 = self.model.forward(h_current,
                                   y[2:3, :, :],
                                   jac_fh[2:3, :, :].reshape(1, self.SD, self.SD),
                                   jac_Ch[2:3, :, :].reshape(1, self.MD, self.SD),
                                   P_current,
                                   Kt[2:3, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_2[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_2[2]
    CPCT_cov_current = CPCT_cov_current + outputs_2[5]
    P_current = outputs_2[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_2[7]
    kalman_diff = kalman_diff + outputs_2[8]
    kalman_threshold = kalman_threshold + outputs_2[9]

    # === TIMESTEP 2 ===
    outputs_3 = self.model.forward(h_current,
                                   y[3:4, :, :],
                                   jac_fh[3:4, :, :].reshape(1, self.SD, self.SD),
                                   jac_Ch[3:4, :, :].reshape(1, self.MD, self.SD),
                                   P_current,
                                   Kt[3:4, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_3[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_3[2]
    CPCT_cov_current = CPCT_cov_current + outputs_3[5]
    P_current = outputs_3[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status * outputs_3[7]
    kalman_diff = kalman_diff + outputs_3[8]
    kalman_threshold = kalman_threshold + outputs_3[9]

    # === TIMESTEP 4 ===
    outputs_4 = self.model.forward(h_current,
                                   y[4:5, :, :],
                                   jac_fh[4:5, :, :].reshape(1, self.SD, self.SD),
                                   jac_Ch[4:5, :, :].reshape(1, self.MD, self.SD),
                                   P_current,
                                   Kt[4:5, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_4[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_4[2]
    CPCT_cov_current = CPCT_cov_current + outputs_4[5]
    P_current = outputs_4[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status*outputs_4[7]
    kalman_diff = kalman_diff + outputs_4[8]
    kalman_threshold = kalman_threshold + outputs_4[9]

    # === TIMESTEP 5 ===
    outputs_5 = self.model.forward(h_current,
                                   y[5:6, :, :],
                                   jac_fh[5:6, :, :].reshape(1, self.SD, self.SD),
                                   jac_Ch[5:6, :, :].reshape(1, self.MD, self.SD),
                                   P_current,
                                   Kt[5:6, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_5[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_5[2]
    CPCT_cov_current = CPCT_cov_current + outputs_5[5]
    P_current = outputs_5[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status*outputs_5[7]
    kalman_diff = kalman_diff + outputs_5[8]
    kalman_threshold = kalman_threshold + outputs_5[9]

    # === TIMESTEP 6 ===
    outputs_6 = self.model.forward(h_current,
                                   y[6:7, :, :],
                                   jac_fh[6:7, :, :].reshape(1, self.SD, self.SD),
                                   jac_Ch[6:7, :, :].reshape(1, self.MD, self.SD),
                                   P_current,
                                   Kt[6:7, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_6[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_6[2]
    CPCT_cov_current = CPCT_cov_current + outputs_6[5]
    P_current = outputs_6[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status*outputs_6[7]
    kalman_diff = kalman_diff + outputs_6[8]
    kalman_threshold = kalman_threshold + outputs_6[9]

    # === TIMESTEP 7 ===
    outputs_7 = self.model.forward(h_current,
                                   y[7:8, :, :],
                                   jac_fh[7:8, :, :].reshape(1, self.SD, self.SD),
                                   jac_Ch[7:8, :, :].reshape(1, self.MD, self.SD),
                                   P_current,
                                   Kt[7:8, :, :].reshape(1, self.MD, self.SD))
    h_current = outputs_7[0].reshape(1, 1, self.SD)
    delta_y_current = delta_y_current + outputs_7[2]
    CPCT_cov_current = CPCT_cov_current + outputs_7[5]
    P_current = outputs_7[6].reshape(1, self.SD, self.SD)
    KCR_status = KCR_status*outputs_7[7]
    kalman_diff = kalman_diff + outputs_6[8]
    kalman_threshold = kalman_threshold + outputs_6[9]

    h_current = torch.cat([h_current, nonce_h], dim=2)
    P_current = torch.cat([P_current, nonce_P], dim=2)
    delta_y_current = torch.cat([delta_y_current, nonce_dy], dim=2)
    CPCT_cov_current = torch.cat([CPCT_cov_current, nonce_CPCT_cov], dim=2)
    KCR_status = torch.cat([KCR_status, nonce_KCR_status], dim=2)
    kalman_diff = torch.cat([kalman_diff, nonce_kalman_diff], dim=1)
    kalman_threshold = torch.cat([kalman_threshold, nonce_kalman_threshold], dim=1)

    return h_current, P_current, delta_y_current, CPCT_cov_current, KCR_status, kalman_diff, kalman_threshold

forward_Dloop 

forward_Dloop(h, P, delta_y, CPCT_cov)

Dynamically unrolled EKF loop over D timesteps. Used during non-ZKP training and evaluation; not compiled into ONNX.

Iterates from period_start to period_start + D, accumulating the DVAL-summed residual (delta_y) and innovation covariance (CPCT_cov) across all timesteps. The Jacobians and Kalman gains must be precomputed via forward_yield_jacobians before calling this method.

Parameters:

Name Type Description Default
h Tensor

Initial hidden state (1, 1, SD).

 required
P Tensor

Initial state error covariance (1, SD, SD).

 required
delta_y Tensor

Initial (zero) residual accumulator (1, 1, MD).

 required
CPCT_cov Tensor

Initial (zero) covariance accumulator (1, MD, MD).

 required

Returns:

Type Description

Tuple[torch.Tensor, ...]: - h_current: Updated hidden state after D steps (1, 1, SD). - P: Updated state covariance after D steps (1, SD, SD). - delta_y_tensor: DVAL-summed innovation vector (1, 1, MD). - CPCT_cov_tensor: DVAL-summed innovation covariance (1, MD, MD). - KCR_status_check: Product of per-step Kalman check results (scalar).
Source code in src/PredictionWrapper.py 
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def forward_Dloop(self, h, P, delta_y, CPCT_cov, ):
    """
    Dynamically unrolled EKF loop over D timesteps. Used during non-ZKP training
    and evaluation; not compiled into ONNX.

    Iterates from period_start to period_start + D, accumulating the DVAL-summed
    residual (delta_y) and innovation covariance (CPCT_cov) across all timesteps.
    The Jacobians and Kalman gains must be precomputed via forward_yield_jacobians
    before calling this method.

    Args:
        h (torch.Tensor): Initial hidden state (1, 1, SD).
        P (torch.Tensor): Initial state error covariance (1, SD, SD).
        delta_y (torch.Tensor): Initial (zero) residual accumulator (1, 1, MD).
        CPCT_cov (torch.Tensor): Initial (zero) covariance accumulator (1, MD, MD).

    Returns:
        Tuple[torch.Tensor, ...]:
            - h_current: Updated hidden state after D steps (1, 1, SD).
            - P: Updated state covariance after D steps (1, SD, SD).
            - delta_y_tensor: DVAL-summed innovation vector (1, 1, MD).
            - CPCT_cov_tensor: DVAL-summed innovation covariance (1, MD, MD).
            - KCR_status_check: Product of per-step Kalman check results (scalar).
    """
    h_current = h.clone()

    delta_y_tensor = delta_y.reshape(1, 1, self.model.is_r)  # torch.zeros(1, 1, self.model.is_r, device=h.device)
    CPCT_cov_tensor = CPCT_cov.reshape(1, self.model.is_r,self.model.is_r)  # torch.zeros(1, 1, self.model.is_r, self.model.is_r, device=h.device)
    cov_h_tensor = torch.zeros(1, self.D, self.model.is_f, self.model.is_f, device=h.device)
    cov_r_tensor = torch.zeros(1, self.D, self.model.is_r, self.model.is_r, device=h.device)
    KCR_status_check = torch.tensor([1.0])

    for i in range(self.D):
        start = i + self.period_start
        end = start + 1

        y = self.ht_test[start:end, :, self.SD:self.SD + self.MD].reshape(1, 1, self.MD)

        outputs = self.model.forward(h_current, y, self.jac_fh[i], self.jac_Ch[i], P, self.Kt[i])
        # outputs = self.model.forward(h, y, self.jac_fh[i], self.jac_Ch[i], self.P[i], self.Kt[i])

        h_current = outputs[0].reshape(1, 1, self.SD)  # Update hidden state
        delta_y_tensor = delta_y_tensor + outputs[2]
        cov_h_tensor = cov_h_tensor + outputs[3]
        cov_r_tensor = cov_r_tensor + outputs[4]
        CPCT_cov_tensor = CPCT_cov_tensor + outputs[5]
        P = outputs[6].reshape(1, self.SD, self.SD)
        KCR_status_check = KCR_status_check * outputs[7]

    return h_current, P, delta_y_tensor, CPCT_cov_tensor, KCR_status_check  # , CPCT_cov_corr_tensor

forward_yield_jacobians 

forward_yield_jacobians(h)

Runs the EKF linearization pass over D timesteps to populate per-step Jacobians and Kalman gains stored on the instance.

Calls model.forward_yield_jacobians at each step starting from period_start, building lists self.jac_fh, self.jac_Ch, self.P, and self.Kt. These are consumed by forward_Dloop and the fixed-length forward methods.

Parameters:

Name Type Description Default
h Tensor

Initial hidden state (1, 1, SD).

 required
Source code in src/PredictionWrapper.py 
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def forward_yield_jacobians(self,h,):
    """
    Runs the EKF linearization pass over D timesteps to populate per-step
    Jacobians and Kalman gains stored on the instance.

    Calls model.forward_yield_jacobians at each step starting from period_start,
    building lists self.jac_fh, self.jac_Ch, self.P, and self.Kt. These are
    consumed by forward_Dloop and the fixed-length forward methods.

    Args:
        h (torch.Tensor): Initial hidden state (1, 1, SD).
    """
    self.jac_Ch = []
    self.jac_fh = []
    self.P = []
    self.Kt = []

    forecast_horizon = self.D
    for i in range(forecast_horizon):
        start = i + self.period_start
        end = start + 1

        y = self.ht_test[start:end, :, self.SD:self.SD + self.MD].reshape(1, 1, self.MD)

        h,jac_fh,jac_Ch,P,Kt = self.model.forward_yield_jacobians(h, y)
        self.jac_fh.append(jac_fh)
        self.jac_Ch.append(jac_Ch)
        self.P.append(P)
        # print(start,end,torch.linalg.norm(Kt),torch.linalg.norm(jac_Ch),)
        self.Kt.append(Kt)