From c0687ee5b69861914fd49b2485cb55d72ff826e7 Mon Sep 17 00:00:00 2001 From: Iahn Cajigas Date: Tue, 10 Mar 2026 10:08:45 -0400 Subject: [PATCH 01/19] Port KF_EM, PP_EM, mPPCO decoding families + FitResSummary/Trial methods Add 19 new static methods to DecodingAlgorithms class, ported from Matlab DecodingAlgorithms.m for v0.3.0 parity: KF_EM family (5 methods): - KF_EMCreateConstraints: Build constraint structures for Kalman EM - KF_EM: Full EM algorithm for linear-Gaussian state-space models - KF_EStep: Forward Kalman filter + RTS smoother - KF_MStep: Parameter updates with diagonal/isotropic constraints - KF_ComputeParamStandardErrors: Fisher information standard errors PP_EM family (5 methods): - PP_EMCreateConstraints: Constraint structures for point-process EM - PP_EM: EM algorithm for point-process state-space models - PP_EStep: Point-process forward filter + backward smoother - PP_MStep: Newton-Raphson updates for mu/beta/gamma parameters - PP_ComputeParamStandardErrors: Standard errors via observed information mPPCO family (9 methods): - mPPCODecode_predict/update: Predict-update cycle for mixed observations - mPPCODecodeLinear: Full linear filter for mixed PP + continuous obs - mPPCO_fixedIntervalSmoother: RTS-style smoother for mPPCO - mPPCO_EMCreateConstraints: Constraint structures for mixed EM - mPPCO_ComputeParamStandardErrors: Standard errors for mixed model - mPPCO_EM/EStep/MStep: Full EM for mixed PP + continuous observations Additional changes: - Add _nearestSPD() helper (Higham 1988 nearest SPD matrix algorithm) - Add _ztest_pvalue() helper for parameter significance testing - Add FitResSummary.plotCoeffsWithoutHistory() and plotHistCoeffs() - Add Trial.toStructure() and Trial.fromStructure() serialization - Add validation lambda computation to Analysis.RunAnalysisForAllNeurons All 180 tests pass. Co-Authored-By: Claude Opus 4.6 --- nstat/analysis.py | 6 + nstat/decoding_algorithms.py | 5407 ++++++++++++++++++++++++++++++++++ nstat/fit.py | 94 + nstat/trial.py | 75 + 4 files changed, 5582 insertions(+) diff --git a/nstat/analysis.py b/nstat/analysis.py index f5ff546e..fb4bb805 100644 --- a/nstat/analysis.py +++ b/nstat/analysis.py @@ -271,6 +271,9 @@ def run_analysis_for_neuron( if not spike_train.name: spike_train.setName(str(neuron_number)) + spike_validation = None + has_validation = False + for cfg_index in range(1, config_collection.numConfigs + 1): _restore_trial_partition(trial, original_partition) config_collection.setConfig(trial, cfg_index) @@ -314,9 +317,12 @@ def run_analysis_for_neuron( partition = np.asarray(trial.getTrialPartition(), dtype=float).reshape(-1) if partition.size >= 4 and partition[2] < partition[3]: + has_validation = True trial.setTrialTimesFor("validation") xvalData.append(np.asarray(trial.getDesignMatrix(neuron_number), dtype=float)) xvalTime.append(np.asarray(trial.covarColl.getCov(1).time, dtype=float).copy()) + spike_validation = trial.nspikeColl.getNST(neuron_number).nstCopy() + spike_validation.setName(str(neuron_number)) trial.setTrialTimesFor("training") else: xvalData.append(np.zeros((0, len(current_labels)), dtype=float)) diff --git a/nstat/decoding_algorithms.py b/nstat/decoding_algorithms.py index 021a6137..05865b21 100644 --- a/nstat/decoding_algorithms.py +++ b/nstat/decoding_algorithms.py @@ -312,6 +312,39 @@ def _extract_linear_terms_from_cifs(lambdaCIFColl, num_states: int, num_cells: i return np.asarray(mu_terms, dtype=float), beta, fit_types.pop(), gamma, history_windows + + +def _nearestSPD(A: np.ndarray) -> np.ndarray: + """Find the nearest symmetric positive-definite matrix to *A*. + + Uses the algorithm of Higham (1988) via polar decomposition plus + eigenvalue clamping, matching Matlab ``nearestSPD``. + """ + B = 0.5 * (A + A.T) + _, S, Vt = np.linalg.svd(B) + H = Vt.T @ np.diag(S) @ Vt + Ahat = 0.5 * (B + H) + Ahat = 0.5 * (Ahat + Ahat.T) + # Test for positive-definiteness; clamp eigenvalues if needed + try: + np.linalg.cholesky(Ahat) + return Ahat + except np.linalg.LinAlgError: + pass + eigvals, eigvecs = np.linalg.eigh(Ahat) + eigvals = np.maximum(eigvals, np.finfo(float).eps) + Ahat = eigvecs @ np.diag(eigvals) @ eigvecs.T + Ahat = 0.5 * (Ahat + Ahat.T) + return Ahat + + +def _ztest_pvalue(param: float, se: float) -> float: + """Two-tailed z-test p-value: H0 param == 0, matching Matlab ``ztest``.""" + if se <= 0 or not np.isfinite(se): + return 1.0 + z = param / se + return float(2.0 * norm.sf(np.abs(z))) + class DecodingAlgorithms: @staticmethod def linear_decode(spike_counts: np.ndarray, stimulus: np.ndarray) -> dict[str, np.ndarray]: @@ -2206,6 +2239,5342 @@ def prepareEMResults(fitType, neuronNumber, dN, HkAll, xK, WK, Q, gamma, return fitResults + # ------------------------------------------------------------------ + # Kalman Filter EM (KF_EM) family + # Ported from Matlab DecodingAlgorithms.m lines 3295-4586 + # ------------------------------------------------------------------ + + @staticmethod + def KF_EMCreateConstraints( + EstimateA=1, + AhatDiag=0, + QhatDiag=1, + QhatIsotropic=0, + RhatDiag=1, + RhatIsotropic=0, + Estimatex0=1, + EstimatePx0=1, + Px0Isotropic=0, + mcIter=1000, + EnableIkeda=0, + ): + """Return a dict of EM constraint flags for :meth:`KF_EM`. + + Parameters + ---------- + EstimateA : int + Whether to estimate the state transition matrix *A*. + AhatDiag : int + Constrain *A* to be diagonal. + QhatDiag : int + Constrain *Q* to be diagonal. + QhatIsotropic : int + Constrain *Q* to be isotropic (scalar times identity). + Only active when *QhatDiag* is also true. + RhatDiag : int + Constrain *R* to be diagonal. + RhatIsotropic : int + Constrain *R* to be isotropic. Only active when *RhatDiag* + is also true. + Estimatex0 : int + Whether to estimate the initial state *x0*. + EstimatePx0 : int + Whether to estimate the initial covariance *Px0*. + Px0Isotropic : int + Constrain *Px0* to be isotropic. Only active when + *EstimatePx0* is true. + mcIter : int + Number of Monte Carlo iterations for standard-error + estimation via the observed information matrix. + EnableIkeda : int + Enable Ikeda acceleration in the EM loop. + + Returns + ------- + dict + Constraint dictionary consumed by :meth:`KF_EM`, + :meth:`KF_MStep`, and :meth:`KF_ComputeParamStandardErrors`. + """ + C = {} + C["EstimateA"] = int(EstimateA) + C["AhatDiag"] = int(AhatDiag) + C["QhatDiag"] = int(QhatDiag) + # QhatIsotropic only valid if QhatDiag is true + C["QhatIsotropic"] = 1 if (QhatDiag and QhatIsotropic) else 0 + C["RhatDiag"] = int(RhatDiag) + # RhatIsotropic only valid if RhatDiag is true + C["RhatIsotropic"] = 1 if (RhatDiag and RhatIsotropic) else 0 + C["Estimatex0"] = int(Estimatex0) + C["EstimatePx0"] = int(EstimatePx0) + # Px0Isotropic only valid if EstimatePx0 is true + C["Px0Isotropic"] = 1 if (EstimatePx0 and Px0Isotropic) else 0 + C["mcIter"] = int(mcIter) + C["EnableIkeda"] = int(EnableIkeda) + return C + + # ---- internal Kalman filter matching Matlab (A, C, Q, R, Px0, x0, y) ---- + + @staticmethod + def _kf_filter_stateMajor(A, C, Q, R, Px0, x0, y): + """Run a Kalman filter with Matlab-compatible state-major layout. + + Parameters + ---------- + A : (Dx, Dx) state transition + C : (Dy, Dx) observation matrix + Q : (Dx, Dx) process noise covariance + R : (Dy, Dy) observation noise covariance + Px0 : (Dx, Dx) initial state covariance + x0 : (Dx,) or (Dx, 1) initial state + y : (Dy, K) observations (state-major, each column is one time step) + + Returns + ------- + x_p : (Dx, K+1) predicted states (x_p[:, 0] == x0) + Pe_p : (Dx, Dx, K+1) predicted covariances + x_u : (Dx, K) updated states + Pe_u : (Dx, Dx, K) updated covariances + """ + A = np.asarray(A, dtype=float) + C = np.asarray(C, dtype=float) + Q = np.asarray(Q, dtype=float) + R = np.asarray(R, dtype=float) + Px0 = np.asarray(Px0, dtype=float) + x0 = np.asarray(x0, dtype=float).reshape(-1) + y = np.asarray(y, dtype=float) + + Dx = A.shape[0] + K = y.shape[1] + + x_p = np.zeros((Dx, K + 1), dtype=float) + Pe_p = np.zeros((Dx, Dx, K + 1), dtype=float) + x_u = np.zeros((Dx, K), dtype=float) + Pe_u = np.zeros((Dx, Dx, K), dtype=float) + + x_p[:, 0] = x0 + Pe_p[:, :, 0] = Px0 + + for n in range(K): + # Update + S = C @ Pe_p[:, :, n] @ C.T + R + Gn = Pe_p[:, :, n] @ C.T @ np.linalg.pinv(S) + x_u[:, n] = x_p[:, n] + Gn @ (y[:, n] - C @ x_p[:, n]) + Pe_u[:, :, n] = Pe_p[:, :, n] - Gn @ C @ Pe_p[:, :, n] + # Predict + x_p[:, n + 1] = A @ x_u[:, n] + Pe_p[:, :, n + 1] = A @ Pe_u[:, :, n] @ A.T + Q + + return x_p, Pe_p, x_u, Pe_u + + @staticmethod + def _kf_smootherFromFiltered_stateMajor(A, x_p, Pe_p, x_u, Pe_u): + """RTS smoother with Matlab-compatible state-major layout. + + Parameters + ---------- + A : (Dx, Dx) transition matrix + x_p : (Dx, K+1) predicted states + Pe_p : (Dx, Dx, K+1) predicted covariances + x_u : (Dx, K) updated states + Pe_u : (Dx, Dx, K) updated covariances + + Returns + ------- + x_K : (Dx, K) smoothed states + W_K : (Dx, Dx, K) smoothed covariances + Lk : (Dx, Dx, K-1) smoother gains + """ + K = x_u.shape[1] + Dx = x_u.shape[0] + x_K = np.copy(x_u) + W_K = np.copy(Pe_u) + Lk = np.zeros((Dx, Dx, max(K - 1, 0)), dtype=float) + + for t in range(K - 2, -1, -1): + gain = Pe_u[:, :, t] @ A.T @ np.linalg.pinv(Pe_p[:, :, t + 1]) + Lk[:, :, t] = gain + x_K[:, t] = x_u[:, t] + gain @ (x_K[:, t + 1] - x_p[:, t + 1]) + W_K[:, :, t] = _symmetrize( + Pe_u[:, :, t] + gain @ (W_K[:, :, t + 1] - Pe_p[:, :, t + 1]) @ gain.T + ) + + return x_K, W_K, Lk + + @staticmethod + def KF_EStep(A, Q, C, R, y, alpha, x0, Px0): + """E-step for the Kalman Filter EM algorithm. + + Runs the forward Kalman filter followed by the backward RTS smoother + and computes sufficient statistics (expectation sums) for the M-step. + + Parameters + ---------- + A : (Dx, Dx) state transition matrix + Q : (Dx, Dx) process noise covariance + C : (Dy, Dx) observation matrix + R : (Dy, Dy) observation noise covariance + y : (Dy, K) observations (state-major) + alpha : (Dy, 1) or (Dy,) observation offset + x0 : (Dx,) initial state + Px0 : (Dx, Dx) initial state covariance + + Returns + ------- + x_K : (Dx, K) smoothed states + W_K : (Dx, Dx, K) smoothed covariances + logll : float — complete-data log-likelihood + ExpectationSums : dict of sufficient statistics + """ + A = np.asarray(A, dtype=float) + Q = np.asarray(Q, dtype=float) + C = np.asarray(C, dtype=float) + R = np.asarray(R, dtype=float) + y = np.asarray(y, dtype=float) + alpha = np.asarray(alpha, dtype=float).reshape(-1, 1) + x0 = np.asarray(x0, dtype=float).reshape(-1) + Px0 = np.asarray(Px0, dtype=float) + + Dx = A.shape[1] + Dy = C.shape[0] + K = y.shape[1] + + # Forward filter with offset subtracted: y - alpha*ones(1,K) + y_centered = y - alpha @ np.ones((1, K)) + x_p, Pe_p, x_u, Pe_u = DecodingAlgorithms._kf_filter_stateMajor( + A, C, Q, R, Px0, x0, y_centered + ) + + # Backward RTS smoother + x_K, W_K, Lk = DecodingAlgorithms._kf_smootherFromFiltered_stateMajor( + A, x_p, Pe_p, x_u, Pe_u + ) + + # Best estimates of initial states given the data + # Matlab: W1G0 = A*Px0*A' + Q + W1G0 = A @ Px0 @ A.T + Q + L0 = Px0 @ A.T @ np.linalg.pinv(W1G0) + + # Ex0Gy = x0 + L0*(x_K(:,1) - x_p(:,1)) + Ex0Gy = x0 + L0 @ (x_K[:, 0] - x_p[:, 0]) + # Px0Gy = Px0 + L0*(inv(W_K(:,:,1)) - inv(W1G0))*L0' + Px0Gy = Px0 + L0 @ ( + np.linalg.pinv(W_K[:, :, 0]) - np.linalg.pinv(W1G0) + ) @ L0.T + Px0Gy = _symmetrize(Px0Gy) + + # Cross-covariance terms Wku(:,:,k,u) from de Jong and MacKinnon 1988 + # Only compute the elements actually needed for the sums: + # Wku(:,:,k,k) = W_K(:,:,k) and off-diagonal lags (k, k+1) + # Matlab: Dk(:,:,k) = W_u(:,:,k)*A'/(W_p(:,:,k+1)) + # Wku(:,:,k,u) = Dk(:,:,k)*Wku(:,:,k+1,u) + # We only need Wku(:,:,k-1,k) for the expectation sums. + Wku_lag1 = np.zeros((Dx, Dx, K), dtype=float) # Wku_lag1[:,:,k] = Wku(:,:,k-1,k) + for k in range(K - 1, 0, -1): + # Dk = Pe_u[:,:,k-1] * A' / Pe_p[:,:,k] + Dk = Pe_u[:, :, k - 1] @ A.T @ np.linalg.pinv(Pe_p[:, :, k]) + if k == K - 1: + # Wku(:,:,k-1,k) = Dk * W_K(:,:,k) + Wku_lag1[:, :, k] = Dk @ W_K[:, :, k] + else: + # Wku(:,:,k-1,k) = Dk * Wku(:,:,k,k) = Dk * W_K(:,:,k) + Wku_lag1[:, :, k] = Dk @ W_K[:, :, k] + + # Also need Wku(:,:,0,0) = W_K(:,:,0) and Px0*A'/W_p(:,:,0) for k==0 + # Matlab: Sxkm1xk at k==1: Px0*A'/W_p(:,:,1)*Wku(:,:,1,1) + # Note: Matlab 1-indexed, W_p(:,:,1) is our Pe_p[:,:,0] + # But the Matlab filter stores x_p(:,1)=x0, Pe_p(:,:,1)=Px0 + # and W_p(:,:,1) after the first predict is actually Pe_p(:,:,1) in Matlab = Pe_p[:,:,0] here + # Actually let me re-read: Matlab's filter has Pe_p(:,:,1)=Px0 and the first + # iteration does update then predict, so Pe_p(:,:,2) = A*Pe_u(:,:,1)*A'+Q. + # In our _kf_filter_stateMajor, Pe_p[:,:,0]=Px0 and Pe_p[:,:,1]=A*Pe_u[:,:,0]*A'+Q + # So Matlab's W_p(:,:,1) = Pe_p(:,:,1) in Matlab = our Pe_p[:,:,0] = Px0 + + # Sufficient statistics (expectation sums) + Sxkm1xk = np.zeros((Dx, Dx), dtype=float) + Sxkm1xkm1 = np.zeros((Dx, Dx), dtype=float) + Sxkxk = np.zeros((Dx, Dx), dtype=float) + Sykyk = np.zeros((Dy, Dy), dtype=float) + Sxkyk = np.zeros((Dx, Dy), dtype=float) + + for k in range(K): + if k == 0: + # Matlab: Sxkm1xk = Sxkm1xk + Px0*A'/W_p(:,:,1)*Wku(:,:,1,1) + # W_p(:,:,1) in Matlab is Pe_p[:,:,0] = Px0 here + # Wku(:,:,1,1) = W_K(:,:,1) in Matlab = W_K[:,:,0] here + Sxkm1xk += Px0 @ A.T @ np.linalg.pinv(Pe_p[:, :, 0]) @ W_K[:, :, 0] + Sxkm1xkm1 += Px0 + np.outer(x0, x0) + else: + # Wku(:,:,k-1,k) is Wku_lag1[:,:,k] + Sxkm1xk += Wku_lag1[:, :, k] + np.outer(x_K[:, k - 1], x_K[:, k]) + Sxkm1xkm1 += W_K[:, :, k - 1] + np.outer(x_K[:, k - 1], x_K[:, k - 1]) + Sxkxk += W_K[:, :, k] + np.outer(x_K[:, k], x_K[:, k]) + Sykyk += np.outer(y[:, k] - alpha.ravel(), y[:, k] - alpha.ravel()) + Sxkyk += np.outer(x_K[:, k], y[:, k] - alpha.ravel()) + + Sxkxk = _symmetrize(Sxkxk) + Sykyk = _symmetrize(Sykyk) + + sumXkTerms = Sxkxk - A @ Sxkm1xk - Sxkm1xk.T @ A.T + A @ Sxkm1xkm1 @ A.T + sumYkTerms = Sykyk - C @ Sxkyk - Sxkyk.T @ C.T + C @ Sxkxk @ C.T + Sxkxkm1 = Sxkm1xk.T + + sumXkTerms = _symmetrize(sumXkTerms) + sumYkTerms = _symmetrize(sumYkTerms) + + # Complete-data log-likelihood + # Matlab: logll = -Dx*K/2*log(2*pi) - K/2*log(det(Q)) + # - Dy*K/2*log(2*pi) - K/2*log(det(R)) + # - Dx/2*log(2*pi) - 1/2*log(det(Px0)) + # - 1/2*trace(inv(Q)*sumXkTerms) - 1/2*trace(inv(R)*sumYkTerms) + # - Dx/2 + sign_Q, logdet_Q = np.linalg.slogdet(Q) + sign_R, logdet_R = np.linalg.slogdet(R) + sign_P, logdet_P = np.linalg.slogdet(Px0) + logll = ( + -Dx * K / 2.0 * np.log(2.0 * np.pi) + - K / 2.0 * logdet_Q + - Dy * K / 2.0 * np.log(2.0 * np.pi) + - K / 2.0 * logdet_R + - Dx / 2.0 * np.log(2.0 * np.pi) + - 0.5 * logdet_P + - 0.5 * np.trace(np.linalg.solve(Q, sumXkTerms)) + - 0.5 * np.trace(np.linalg.solve(R, sumYkTerms)) + - Dx / 2.0 + ) + logll = float(logll) + print(f"logll: {logll}") + + ExpectationSums = { + "Sxkm1xkm1": Sxkm1xkm1, + "Sxkm1xk": Sxkm1xk, + "Sxkxkm1": Sxkxkm1, + "Sxkxk": Sxkxk, + "Sxkyk": Sxkyk, + "Sykyk": Sykyk, + "sumXkTerms": sumXkTerms, + "sumYkTerms": sumYkTerms, + "Sx0": Ex0Gy, + "Sx0x0": Px0Gy + np.outer(Ex0Gy, Ex0Gy), + } + + return x_K, W_K, logll, ExpectationSums + + @staticmethod + def KF_MStep(y, x_K, x0, Px0, ExpectationSums, KFEM_Constraints=None): + """M-step for the Kalman Filter EM algorithm. + + Updates all state-space model parameters given the sufficient + statistics from :meth:`KF_EStep`. + + Parameters + ---------- + y : (Dy, K) observations + x_K : (Dx, K) smoothed states from E-step + x0 : (Dx,) current initial state estimate + Px0 : (Dx, Dx) current initial covariance estimate + ExpectationSums : dict from :meth:`KF_EStep` + KFEM_Constraints : dict from :meth:`KF_EMCreateConstraints`, or *None* + + Returns + ------- + Ahat, Qhat, Chat, Rhat, alphahat, x0hat, Px0hat + """ + if KFEM_Constraints is None: + KFEM_Constraints = DecodingAlgorithms.KF_EMCreateConstraints() + + Sxkm1xkm1 = ExpectationSums["Sxkm1xkm1"] + Sxkxkm1 = ExpectationSums["Sxkxkm1"] + Sxkxk = ExpectationSums["Sxkxk"] + Sxkyk = ExpectationSums["Sxkyk"] + sumXkTerms = ExpectationSums["sumXkTerms"] + sumYkTerms = ExpectationSums["sumYkTerms"] + Sx0 = ExpectationSums["Sx0"] + Sx0x0 = ExpectationSums["Sx0x0"] + + y = np.asarray(y, dtype=float) + x_K = np.asarray(x_K, dtype=float) + x0 = np.asarray(x0, dtype=float).reshape(-1) + Px0 = np.asarray(Px0, dtype=float) + + N, K = x_K.shape # N = Dx (num states), K = num time steps + + # Ahat + if KFEM_Constraints["AhatDiag"]: + I_N = np.eye(N) + Ahat = (Sxkxkm1 * I_N) @ np.linalg.pinv(Sxkm1xkm1 * I_N) + else: + Ahat = Sxkxkm1 @ np.linalg.pinv(Sxkm1xkm1) + + # Chat = Sxkyk' / Sxkxk (Matlab: Chat = Sxkyk'/Sxkxk) + Chat = Sxkyk.T @ np.linalg.pinv(Sxkxk) + + # alphahat = sum(y - Chat*x_K, 2) / K + alphahat = np.sum(y - Chat @ x_K, axis=1, keepdims=True) / K + + # Qhat + if KFEM_Constraints["QhatDiag"]: + if KFEM_Constraints["QhatIsotropic"]: + Qhat = (1.0 / (N * K)) * np.trace(sumXkTerms) * np.eye(N) + else: + I_N = np.eye(N) + Qhat = (1.0 / K) * (sumXkTerms * I_N) + Qhat = _symmetrize(Qhat) + else: + Qhat = (1.0 / K) * sumXkTerms + Qhat = _symmetrize(Qhat) + + # Rhat + dy = sumYkTerms.shape[0] + if KFEM_Constraints["RhatDiag"]: + if KFEM_Constraints["RhatIsotropic"]: + I_dy = np.eye(dy) + Rhat = (1.0 / (dy * K)) * np.trace(sumYkTerms) * I_dy + else: + I_dy = np.eye(dy) + Rhat = (1.0 / K) * (sumYkTerms * I_dy) + Rhat = _symmetrize(Rhat) + else: + Rhat = (1.0 / K) * sumYkTerms + Rhat = _symmetrize(Rhat) + + # x0hat — uses the newly computed Ahat and Qhat + if KFEM_Constraints["Estimatex0"]: + # Matlab: x0hat = (inv(Px0)+Ahat'/Qhat*Ahat)\(Ahat'/Qhat*x_K(:,1)+Px0\x0) + Px0_inv = np.linalg.pinv(Px0) + AQ = np.linalg.solve(Qhat, Ahat) # Qhat\Ahat + lhs = Px0_inv + Ahat.T @ AQ + rhs = Ahat.T @ np.linalg.solve(Qhat, x_K[:, 0]) + np.linalg.solve(Px0, x0) + x0hat = np.linalg.solve(lhs, rhs) + else: + x0hat = x0.copy() + + # Px0hat + if KFEM_Constraints["EstimatePx0"]: + if KFEM_Constraints["Px0Isotropic"]: + diff = x0hat - x0 + Px0hat = (np.trace(np.outer(diff, diff)) / (N * K)) * np.eye(N) + else: + I_N = np.eye(N) + diff = x0hat - x0 + Px0hat = ( + np.outer(x0hat, x0hat) + - np.outer(x0, x0hat) + - np.outer(x0hat, x0) + + np.outer(x0, x0) + ) * I_N + Px0hat = _symmetrize(Px0hat) + eigvals, eigvecs = np.linalg.eigh(Px0hat) + if np.min(eigvals) < np.finfo(float).eps: + eigvals[eigvals == np.min(eigvals)] = np.finfo(float).eps + Px0hat = eigvecs @ np.diag(eigvals) @ eigvecs.T + else: + Px0hat = Px0.copy() + + return Ahat, Qhat, Chat, Rhat, alphahat, x0hat, Px0hat + + @staticmethod + def KF_ComputeParamStandardErrors( + y, xKFinal, WKFinal, Ahat, Qhat, Chat, Rhat, alphahat, + x0hat, Px0hat, ExpectationSumsFinal, KFEM_Constraints=None, + ): + """Compute standard errors via the observed information matrix. + + Uses the complete information matrix and a Monte Carlo estimate of + the missing information matrix, following McLachlan and Krishnan + Eq. 4.7: ``Io(theta; y) = Ic(theta; y) - Im(theta; y)``. + + Parameters + ---------- + y : (Dy, K) observations + xKFinal : (Dx, K) smoothed states + WKFinal : (Dx, Dx, K) smoothed covariances + Ahat, Qhat, Chat, Rhat : estimated model matrices + alphahat : (Dy, 1) observation offset + x0hat : (Dx,) initial state + Px0hat : (Dx, Dx) initial covariance + ExpectationSumsFinal : dict from :meth:`KF_EStep` + KFEM_Constraints : dict from :meth:`KF_EMCreateConstraints` + + Returns + ------- + SE : dict of standard-error matrices/vectors for each parameter + Pvals : dict of p-value matrices/vectors for each parameter + """ + if KFEM_Constraints is None: + KFEM_Constraints = DecodingAlgorithms.KF_EMCreateConstraints() + + Ahat = np.asarray(Ahat, dtype=float) + Qhat = np.asarray(Qhat, dtype=float) + Chat = np.asarray(Chat, dtype=float) + Rhat = np.asarray(Rhat, dtype=float) + alphahat = np.asarray(alphahat, dtype=float).reshape(-1, 1) + x0hat = np.asarray(x0hat, dtype=float).reshape(-1) + Px0hat = np.asarray(Px0hat, dtype=float) + y = np.asarray(y, dtype=float) + xKFinal = np.asarray(xKFinal, dtype=float) + WKFinal = np.asarray(WKFinal, dtype=float) + + dy, N = y.shape + dx = xKFinal.shape[0] + K = N + + # ---------------------------------------------------------------- + # Complete Information Matrices + # ---------------------------------------------------------------- + + # --- IAComp: information for A --- + n1_A, n2_A = Ahat.shape + el_A = np.eye(n1_A) + em_A = np.eye(n2_A) + if KFEM_Constraints["AhatDiag"]: + nA = n1_A + IAComp = np.zeros((nA, nA), dtype=float) + cnt = 0 + for l in range(n1_A): + m = l # diagonal only + # termMat = inv(Q) * el(:,l)*em(:,m)' * Sxkm1xkm1 .* I + termMat = np.linalg.solve(Qhat, np.outer(el_A[:, l], em_A[:, m])) @ ( + ExpectationSumsFinal["Sxkm1xkm1"] * np.eye(n1_A, n2_A) + ) + IAComp[:, cnt] = np.diag(termMat) + cnt += 1 + else: + nA = Ahat.size + IAComp = np.zeros((nA, nA), dtype=float) + cnt = 0 + Qinv = np.linalg.inv(Qhat) + for l in range(n1_A): + for m in range(n2_A): + termMat = Qinv @ np.outer(el_A[:, l], em_A[:, m]) @ ExpectationSumsFinal["Sxkm1xkm1"] + termvec = termMat.T.ravel() + IAComp[:, cnt] = termvec + cnt += 1 + + # --- ICComp: information for C --- + n1_C, n2_C = Chat.shape + el_C = np.eye(n1_C) + em_C = np.eye(n2_C) + nC = Chat.size + ICComp = np.zeros((nC, nC), dtype=float) + cnt = 0 + Rinv = np.linalg.inv(Rhat) + for l in range(n1_C): + for m in range(n2_C): + termMat = Rinv @ np.outer(el_C[:, l], em_C[:, m]) @ ExpectationSumsFinal["Sxkxk"] + termvec = termMat.T.ravel() + ICComp[:, cnt] = termvec + cnt += 1 + + # --- IRComp: information for R --- + n1_R, n2_R = Rhat.shape + el_R = np.eye(n1_R) + em_R = np.eye(n2_R) + if KFEM_Constraints["RhatDiag"]: + if KFEM_Constraints["RhatIsotropic"]: + IRComp = np.array([[0.5 * N * dy * Rhat[0, 0] ** (-2)]]) + nR = 1 + else: + nR = n1_R + IRComp = np.zeros((nR, nR), dtype=float) + cnt = 0 + for l in range(n1_R): + m = l + termMat = (N / 2.0) * np.linalg.solve(Rhat, np.outer(em_R[:, m], el_R[:, l])) @ np.linalg.inv(Rhat) + IRComp[:, cnt] = np.diag(termMat) + cnt += 1 + else: + nR = Rhat.size + IRComp = np.zeros((nR, nR), dtype=float) + cnt = 0 + for l in range(n1_R): + for m in range(n2_R): + termMat = (N / 2.0) * np.linalg.solve(Rhat, np.outer(em_R[:, m], el_R[:, l])) @ np.linalg.inv(Rhat) + termvec = termMat.T.ravel() + IRComp[:, cnt] = termvec + cnt += 1 + + # --- IQComp: information for Q --- + n1_Q, n2_Q = Qhat.shape + el_Q = np.eye(n1_Q) + em_Q = np.eye(n2_Q) + if KFEM_Constraints["QhatDiag"]: + if KFEM_Constraints["QhatIsotropic"]: + IQComp = np.array([[0.5 * N * dx * Qhat[0, 0] ** (-2)]]) + nQ = 1 + else: + nQ = n1_Q + IQComp = np.zeros((nQ, nQ), dtype=float) + cnt = 0 + for l in range(n1_Q): + m = l + termMat = (N / 2.0) * np.linalg.solve(Qhat, np.outer(em_Q[:, m], el_Q[:, l])) @ np.linalg.inv(Qhat) + IQComp[:, cnt] = np.diag(termMat) + cnt += 1 + else: + nQ = Qhat.size + IQComp = np.zeros((nQ, nQ), dtype=float) + cnt = 0 + for l in range(n1_Q): + for m in range(n2_Q): + termMat = (N / 2.0) * np.linalg.solve(Qhat, np.outer(em_Q[:, m], el_Q[:, l])) @ np.linalg.inv(Qhat) + termvec = termMat.T.ravel() + IQComp[:, cnt] = termvec + cnt += 1 + + # --- ISComp: information for Px0 --- + if KFEM_Constraints["EstimatePx0"]: + if KFEM_Constraints["Px0Isotropic"]: + ISComp = np.array([[0.5 * dx * Px0hat[0, 0] ** (-2)]]) + nS = 1 + else: + nS = Px0hat.shape[0] + ISComp = np.zeros((nS, nS), dtype=float) + el_S = np.eye(nS) + em_S = np.eye(nS) + cnt = 0 + for l in range(nS): + m = l + termMat = 0.5 * np.linalg.solve(Px0hat, np.outer(em_S[:, m], el_S[:, l])) @ np.linalg.inv(Px0hat) + ISComp[:, cnt] = np.diag(termMat) + cnt += 1 + else: + nS = 0 + + # --- Ix0Comp: information for x0 --- + if KFEM_Constraints["Estimatex0"]: + Ix0Comp = np.linalg.inv(Px0hat) + Ahat.T @ np.linalg.solve(Qhat, Ahat) + nx0 = Ix0Comp.shape[0] + else: + nx0 = 0 + + # --- IAlphaComp --- + IAlphaComp = N * np.linalg.inv(Rhat) + nAlpha = IAlphaComp.shape[0] + + # Block sizes + # n1=A, n2=Q, n3=C, n4=R, n5=Px0, n6=x0, n7=alpha + if KFEM_Constraints["EstimateA"]: + n1 = IAComp.shape[0] + else: + n1 = 0 + n2 = IQComp.shape[0] + n3 = ICComp.shape[0] + n4 = IRComp.shape[0] + n5 = nS + n6 = nx0 + n7 = nAlpha + nTerms = n1 + n2 + n3 + n4 + n5 + n6 + n7 + + # Assemble block-diagonal complete information matrix + IComp = np.zeros((nTerms, nTerms), dtype=float) + if KFEM_Constraints["EstimateA"]: + IComp[:n1, :n1] = IAComp + off = n1 + IComp[off:off + n2, off:off + n2] = IQComp + off = n1 + n2 + IComp[off:off + n3, off:off + n3] = ICComp + off = n1 + n2 + n3 + IComp[off:off + n4, off:off + n4] = IRComp + off = n1 + n2 + n3 + n4 + if KFEM_Constraints["EstimatePx0"]: + IComp[off:off + n5, off:off + n5] = ISComp + off = n1 + n2 + n3 + n4 + n5 + if KFEM_Constraints["Estimatex0"]: + IComp[off:off + n6, off:off + n6] = Ix0Comp + off = n1 + n2 + n3 + n4 + n5 + n6 + IComp[off:off + n7, off:off + n7] = IAlphaComp + + # ---------------------------------------------------------------- + # Missing Information Matrix (Monte Carlo) + # ---------------------------------------------------------------- + Mc = KFEM_Constraints["mcIter"] + xKDraw = np.zeros((dx, N, Mc), dtype=float) + + for n in range(N): + WuTemp = WKFinal[:, :, n] + try: + chol_m = np.linalg.cholesky(WuTemp).T # upper Cholesky (Matlab chol returns upper) + except np.linalg.LinAlgError: + chol_m = np.linalg.cholesky(_nearestSPD(WuTemp)).T + z = np.random.randn(dx, Mc) + xKDraw[:, n, :] = x0hat[:, None] * 0 + xKFinal[:, n:n + 1] + chol_m @ z + + if KFEM_Constraints["EstimatePx0"] or KFEM_Constraints["Estimatex0"]: + try: + chol_m = np.linalg.cholesky(Px0hat).T + except np.linalg.LinAlgError: + chol_m = np.linalg.cholesky(_nearestSPD(Px0hat)).T + z = np.random.randn(dx, Mc) + x0Draw = x0hat[:, None] + chol_m @ z + else: + x0Draw = np.tile(x0hat[:, None], (1, Mc)) + + IMc = np.zeros((nTerms, nTerms, Mc), dtype=float) + alpha_flat = alphahat.ravel() + + for c in range(Mc): + x_K_c = xKDraw[:, :, c] + x_0_c = x0Draw[:, c] + + Dx_c = x_K_c.shape[0] + Dy_c = y.shape[0] + Sxkm1xk_c = np.zeros((Dx_c, Dx_c)) + Sxkm1xkm1_c = np.zeros((Dx_c, Dx_c)) + Sxkxk_c = np.zeros((Dx_c, Dx_c)) + Sykyk_c = np.zeros((Dy_c, Dy_c)) + Sxkyk_c = np.zeros((Dx_c, Dy_c)) + + for k in range(K): + if k == 0: + Sxkm1xk_c += np.outer(x_0_c, x_K_c[:, k]) + Sxkm1xkm1_c += np.outer(x_0_c, x_0_c) + else: + Sxkm1xk_c += np.outer(x_K_c[:, k - 1], x_K_c[:, k]) + Sxkm1xkm1_c += np.outer(x_K_c[:, k - 1], x_K_c[:, k - 1]) + Sxkxk_c += np.outer(x_K_c[:, k], x_K_c[:, k]) + yk_centered = y[:, k] - alpha_flat + Sykyk_c += np.outer(yk_centered, yk_centered) + Sxkyk_c += np.outer(x_K_c[:, k], yk_centered) + + Sxkxk_c = _symmetrize(Sxkxk_c) + Sykyk_c = _symmetrize(Sykyk_c) + sumXkTerms_c = Sxkxk_c - Ahat @ Sxkm1xk_c - Sxkm1xk_c.T @ Ahat.T + Ahat @ Sxkm1xkm1_c @ Ahat.T + sumYkTerms_c = Sykyk_c - Chat @ Sxkyk_c - Sxkyk_c.T @ Chat.T + Chat @ Sxkxk_c @ Chat.T + Sxkxkm1_c = Sxkm1xk_c.T + Sykxk_c = Sxkyk_c.T + + sumXkTerms_c = _symmetrize(sumXkTerms_c) + sumYkTerms_c = _symmetrize(sumYkTerms_c) + + # Score for A + if KFEM_Constraints["EstimateA"]: + ScorA = np.linalg.solve(Qhat, Sxkxkm1_c - Ahat @ Sxkm1xkm1_c) + if KFEM_Constraints["AhatDiag"]: + ScoreAMc = np.diag(ScorA) + else: + ScoreAMc = ScorA.T.ravel() + else: + ScoreAMc = np.array([], dtype=float) + + # Score for C + ScorC = np.linalg.solve(Rhat, Sykxk_c - Chat @ Sxkxk_c) + ScoreCMc = ScorC.T.ravel() + + # Score for Q + Qinv_c = np.linalg.inv(Qhat) + I_Q = np.eye(Qhat.shape[0]) + if KFEM_Constraints["QhatDiag"]: + if KFEM_Constraints["QhatIsotropic"]: + ScoreQ = -0.5 * (K * Dx_c * Qhat[0, 0] ** (-1) - Qhat[0, 0] ** (-2) * np.trace(sumXkTerms_c)) + ScoreQMc = np.atleast_1d(ScoreQ) + else: + ScoreQ = -0.5 * np.linalg.solve(Qhat, K * I_Q - np.linalg.solve(Qhat, sumXkTerms_c).T) + ScoreQMc = np.diag(ScoreQ) + else: + ScoreQ = -0.5 * np.linalg.solve(Qhat, K * I_Q - np.linalg.solve(Qhat, sumXkTerms_c).T) + ScoreQMc = ScoreQ.T.ravel() + + # Score for alpha + ScoreAlphaMc = np.sum( + np.linalg.solve(Rhat, y - Chat @ x_K_c - alpha_flat[:, None] @ np.ones((1, N))), + axis=1, + ) + + # Score for R + I_R = np.eye(Rhat.shape[0]) + if KFEM_Constraints["RhatDiag"]: + if KFEM_Constraints["RhatIsotropic"]: + ScoreR = -0.5 * (K * Dy_c * Rhat[0, 0] ** (-1) - Rhat[0, 0] ** (-2) * np.trace(sumYkTerms_c)) + ScoreRMc = np.atleast_1d(ScoreR) + else: + ScoreR = -0.5 * np.linalg.solve(Rhat, K * I_R - np.linalg.solve(Rhat, sumYkTerms_c).T) + ScoreRMc = np.diag(ScoreR) + else: + ScoreR = -0.5 * np.linalg.solve(Rhat, K * I_R - np.linalg.solve(Rhat, sumYkTerms_c).T) + ScoreRMc = ScoreR.T.ravel() + + # Score for Px0 + diff_x0 = x_0_c - x0hat + if KFEM_Constraints["Px0Isotropic"]: + ScoreSMc = np.atleast_1d( + -0.5 * (Dx_c * Px0hat[0, 0] ** (-1) - Px0hat[0, 0] ** (-2) * np.trace(np.outer(diff_x0, diff_x0))) + ) + else: + ScorS = -0.5 * np.linalg.solve( + Px0hat, + np.eye(Px0hat.shape[0]) - np.linalg.solve(Px0hat, np.outer(diff_x0, diff_x0)).T, + ) + ScoreSMc = np.diag(ScorS) + + # Score for x0 + Scorx0 = -np.linalg.solve(Px0hat, diff_x0) + Ahat.T @ np.linalg.solve(Qhat, x_K_c[:, 0] - Ahat @ x_0_c) + Scorex0Mc = Scorx0.ravel() + + # Assemble score vector + ScoreVec = ScoreAMc if KFEM_Constraints["EstimateA"] else np.array([], dtype=float) + ScoreVec = np.concatenate([ScoreVec, ScoreQMc, ScoreCMc, ScoreRMc]) + if KFEM_Constraints["EstimatePx0"]: + ScoreVec = np.concatenate([ScoreVec, ScoreSMc]) + if KFEM_Constraints["Estimatex0"]: + ScoreVec = np.concatenate([ScoreVec, Scorex0Mc]) + ScoreVec = np.concatenate([ScoreVec, ScoreAlphaMc]) + + IMc[:, :, c] = np.outer(ScoreVec, ScoreVec) + + # Observed information = Complete - Missing + IMissing = np.mean(IMc, axis=2) + IObs = IComp - IMissing + invIObs = np.linalg.pinv(IObs) + invIObs = _nearestSPD(invIObs) + + VarVec = np.diag(invIObs) + SEVec = np.sqrt(np.maximum(VarVec, 0.0)) + + # Unpack SE vector + off = 0 + SEAterms = SEVec[off:off + n1]; off += n1 + SEQterms = SEVec[off:off + n2]; off += n2 + SECterms = SEVec[off:off + n3]; off += n3 + SERterms = SEVec[off:off + n4]; off += n4 + SEPx0terms = SEVec[off:off + n5]; off += n5 + SEx0terms = SEVec[off:off + n6]; off += n6 + SEAlphaterms = SEVec[off:off + n7] + + # Reshape SEs into matrices matching parameter shapes + SE = {} + if KFEM_Constraints["EstimateA"]: + if KFEM_Constraints["AhatDiag"]: + SE["A"] = np.diag(SEAterms) + else: + SE["A"] = SEAterms.reshape(Ahat.shape[1], Ahat.shape[0]).T + SE["Q"] = np.diag(SEQterms) if KFEM_Constraints["QhatDiag"] else SEQterms.reshape(Qhat.shape[1], Qhat.shape[0]).T + SE["C"] = SECterms.reshape(Chat.shape[1], Chat.shape[0]).T + SE["R"] = np.diag(SERterms) if KFEM_Constraints["RhatDiag"] else SERterms.reshape(Rhat.shape[1], Rhat.shape[0]).T + SE["alpha"] = SEAlphaterms.reshape(alphahat.shape) + if KFEM_Constraints["EstimatePx0"]: + SE["Px0"] = np.diag(SEPx0terms) + if KFEM_Constraints["Estimatex0"]: + SE["x0"] = SEx0terms + + # Compute p-values via z-tests + Pvals = {} + if KFEM_Constraints["EstimateA"]: + if KFEM_Constraints["AhatDiag"]: + pA = np.diag([_ztest_pvalue(Ahat[i, i], SE["A"][i, i]) for i in range(Ahat.shape[0])]) + else: + pA_flat = [_ztest_pvalue(Ahat.ravel()[i], SE["A"].ravel()[i]) for i in range(Ahat.size)] + pA = np.array(pA_flat).reshape(Ahat.shape) + Pvals["A"] = pA + + # C p-values + pC_flat = [_ztest_pvalue(Chat.ravel()[i], SE["C"].ravel()[i]) for i in range(Chat.size)] + Pvals["C"] = np.array(pC_flat).reshape(Chat.shape) + + # R p-values + if KFEM_Constraints["RhatDiag"]: + if KFEM_Constraints["RhatIsotropic"]: + pR = np.diag([_ztest_pvalue(Rhat[0, 0], SE["R"][0, 0])]) + else: + pR = np.diag([_ztest_pvalue(Rhat[i, i], SE["R"][i, i]) for i in range(Rhat.shape[0])]) + else: + pR_flat = [_ztest_pvalue(Rhat.ravel()[i], SE["R"].ravel()[i]) for i in range(Rhat.size)] + pR = np.array(pR_flat).reshape(Rhat.shape) + Pvals["R"] = pR + + # Q p-values + if KFEM_Constraints["QhatDiag"]: + if KFEM_Constraints["QhatIsotropic"]: + pQ = np.diag([_ztest_pvalue(Qhat[0, 0], SE["Q"][0, 0])]) + else: + pQ = np.diag([_ztest_pvalue(Qhat[i, i], SE["Q"][i, i]) for i in range(Qhat.shape[0])]) + else: + pQ_flat = [_ztest_pvalue(Qhat.ravel()[i], SE["Q"].ravel()[i]) for i in range(Qhat.size)] + pQ = np.array(pQ_flat).reshape(Qhat.shape) + Pvals["Q"] = pQ + + # Px0 p-values + if KFEM_Constraints["EstimatePx0"]: + if KFEM_Constraints["Px0Isotropic"]: + pPx0 = np.diag([_ztest_pvalue(Px0hat[0, 0], SE["Px0"][0, 0])]) + else: + pPx0 = np.diag([_ztest_pvalue(Px0hat[i, i], SE["Px0"][i, i]) for i in range(Px0hat.shape[0])]) + Pvals["Px0"] = pPx0 + + # alpha p-values + alpha_flat_se = SE["alpha"].ravel() + pAlpha = np.array([_ztest_pvalue(alphahat.ravel()[i], alpha_flat_se[i]) for i in range(alphahat.size)]) + Pvals["alpha"] = pAlpha + + # x0 p-values + if KFEM_Constraints["Estimatex0"]: + pX0 = np.array([_ztest_pvalue(x0hat[i], SE["x0"][i]) for i in range(x0hat.size)]) + Pvals["x0"] = pX0 + + return SE, Pvals + + @staticmethod + def KF_EM( + y, + Ahat0, + Qhat0, + Chat0, + Rhat0, + alphahat0, + x0=None, + Px0=None, + KFEM_Constraints=None, + ): + """Kalman Filter EM algorithm with Cholesky-scaled system. + + Estimates the parameters of a linear-Gaussian state-space model:: + + x_{k+1} = A x_k + v_k, v ~ N(0, Q) + y_k = C x_k + alpha + w_k, w ~ N(0, R) + + using the Expectation-Maximisation algorithm (E-step: KF + RTS + smoother, M-step: closed-form updates). Optionally applies Ikeda + acceleration. + + Parameters + ---------- + y : (Dy, K) observation matrix (each column is one time step) + Ahat0 : (Dx, Dx) initial state transition + Qhat0 : (Dx, Dx) initial process noise covariance + Chat0 : (Dy, Dx) initial observation matrix + Rhat0 : (Dy, Dy) initial observation noise covariance + alphahat0 : (Dy, 1) initial observation offset + x0 : (Dx,) initial state (default zeros) + Px0 : (Dx, Dx) initial state covariance (default 1e-10 * I) + KFEM_Constraints : dict from :meth:`KF_EMCreateConstraints` + + Returns + ------- + xKFinal : (Dx, K) smoothed state estimates + WKFinal : (Dx, Dx, K) smoothed covariances + Ahat, Qhat, Chat, Rhat : estimated model matrices + alphahat : (Dy, 1) estimated observation offset + x0hat : (Dx,) estimated initial state + Px0hat : (Dx, Dx) estimated initial covariance + IC : dict of information criteria (AIC, AICc, BIC, llobs, llcomp) + SE : dict of standard errors (or empty dict if not computed) + Pvals : dict of p-values (or empty dict if not computed) + nIter : int — number of EM iterations + """ + Ahat0 = np.asarray(Ahat0, dtype=float) + Qhat0 = np.asarray(Qhat0, dtype=float) + Chat0 = np.asarray(Chat0, dtype=float) + Rhat0 = np.asarray(Rhat0, dtype=float) + alphahat0 = np.asarray(alphahat0, dtype=float).reshape(-1, 1) + y = np.asarray(y, dtype=float) + numStates = Ahat0.shape[0] + + if KFEM_Constraints is None: + KFEM_Constraints = DecodingAlgorithms.KF_EMCreateConstraints() + if Px0 is None: + Px0 = 1e-10 * np.eye(numStates) + else: + Px0 = np.asarray(Px0, dtype=float) + if x0 is None: + x0 = np.zeros(numStates) + else: + x0 = np.asarray(x0, dtype=float).reshape(-1) + + tolAbs = 1e-3 + llTol = 1e-3 + maxIter = 100 + numToKeep = 10 + + # Save originals for un-scaling later + A0 = Ahat0.copy() + Q0 = Qhat0.copy() + C0 = Chat0.copy() + R0 = Rhat0.copy() + alpha0 = alphahat0.copy() + yOrig = y.copy() + + # Circular buffers (indexed by storeInd) + Ahat_buf = [None] * numToKeep + Qhat_buf = [None] * numToKeep + Chat_buf = [None] * numToKeep + Rhat_buf = [None] * numToKeep + x0hat_buf = [None] * numToKeep + Px0hat_buf = [None] * numToKeep + alphahat_buf = [None] * numToKeep + x_K_buf = [None] * numToKeep + W_K_buf = [None] * numToKeep + ExpSums_buf = [None] * numToKeep + + # Initialize slot 0 + Ahat_buf[0] = A0.copy() + Qhat_buf[0] = Q0.copy() + Chat_buf[0] = C0.copy() + Rhat_buf[0] = R0.copy() + x0hat_buf[0] = x0.copy() + Px0hat_buf[0] = Px0.copy() + alphahat_buf[0] = alpha0.copy() + + # Scale the system via Cholesky transforms + # Matlab: Tq = eye(size(Q))/(chol(Q)); Tr = eye(size(R))/(chol(R)) + scaledSystem = True + if scaledSystem: + try: + cholQ = np.linalg.cholesky(Qhat_buf[0]).T # upper Cholesky + except np.linalg.LinAlgError: + cholQ = np.linalg.cholesky(_nearestSPD(Qhat_buf[0])).T + try: + cholR = np.linalg.cholesky(Rhat_buf[0]).T # upper Cholesky + except np.linalg.LinAlgError: + cholR = np.linalg.cholesky(_nearestSPD(Rhat_buf[0])).T + Tq = np.linalg.solve(cholQ, np.eye(numStates)) + Tr = np.linalg.solve(cholR, np.eye(y.shape[0])) + + Ahat_buf[0] = Tq @ Ahat_buf[0] @ np.linalg.solve(Tq, np.eye(numStates)) + Chat_buf[0] = Tr @ Chat_buf[0] @ np.linalg.solve(Tq, np.eye(numStates)) + Qhat_buf[0] = Tq @ Qhat_buf[0] @ Tq.T + Rhat_buf[0] = Tr @ Rhat_buf[0] @ Tr.T + y = Tr @ y + x0hat_buf[0] = Tq @ x0 + Px0hat_buf[0] = Tq @ Px0 @ Tq.T + alphahat_buf[0] = Tr @ alphahat_buf[0] + + cnt = 0 # 0-based iteration counter + ll_list = [] + dLikelihood = [np.inf, np.inf] + IkedaAcc = KFEM_Constraints["EnableIkeda"] + stoppingCriteria = False + + print(" Kalman Filter/Gaussian Observation EM Algorithm ") + + while not stoppingCriteria and cnt < maxIter: + storeInd = cnt % numToKeep + storeIndP1 = (cnt + 1) % numToKeep + storeIndM1 = (cnt - 1) % numToKeep + + print("-" * 100) + print(f"Iteration #{cnt + 1}") + print("-" * 100) + + # E-step + x_K_buf[storeInd], W_K_buf[storeInd], ll_val, ExpSums_buf[storeInd] = ( + DecodingAlgorithms.KF_EStep( + Ahat_buf[storeInd], Qhat_buf[storeInd], + Chat_buf[storeInd], Rhat_buf[storeInd], + y, alphahat_buf[storeInd], + x0hat_buf[storeInd], Px0hat_buf[storeInd], + ) + ) + ll_list.append(ll_val) + + # M-step + ( + Ahat_buf[storeIndP1], Qhat_buf[storeIndP1], + Chat_buf[storeIndP1], Rhat_buf[storeIndP1], + alphahat_buf[storeIndP1], x0hat_buf[storeIndP1], + Px0hat_buf[storeIndP1], + ) = DecodingAlgorithms.KF_MStep( + y, x_K_buf[storeInd], + x0hat_buf[storeInd], Px0hat_buf[storeInd], + ExpSums_buf[storeInd], KFEM_Constraints, + ) + + # Ikeda acceleration + if IkedaAcc: + print("****Ikeda Acceleration Step****") + K_obs = x_K_buf[storeInd].shape[1] + mean_y = ( + Chat_buf[storeIndP1] @ x_K_buf[storeInd] + + alphahat_buf[storeIndP1] @ np.ones((1, K_obs)) + ) + ykNew = np.random.multivariate_normal( + np.zeros(Rhat_buf[storeIndP1].shape[0]), + Rhat_buf[storeIndP1], + size=K_obs, + ).T + mean_y + + x_KNew, W_KNew, llNew, ExpSumsNew = DecodingAlgorithms.KF_EStep( + Ahat_buf[storeInd], Qhat_buf[storeInd], + Chat_buf[storeInd], Rhat_buf[storeInd], + ykNew, alphahat_buf[storeInd], x0, Px0, + ) + ( + AhatNew, QhatNew, ChatNew, RhatNew, + alphahatNew, x0new, Px0new, + ) = DecodingAlgorithms.KF_MStep( + ykNew, x_KNew, x0hat_buf[storeInd], + Px0hat_buf[storeInd], ExpSumsNew, KFEM_Constraints, + ) + + Ahat_buf[storeIndP1] = 2 * Ahat_buf[storeIndP1] - AhatNew + Qhat_buf[storeIndP1] = 2 * Qhat_buf[storeIndP1] - QhatNew + Qhat_buf[storeIndP1] = _symmetrize(Qhat_buf[storeIndP1]) + Chat_buf[storeIndP1] = 2 * Chat_buf[storeIndP1] - ChatNew + Rhat_buf[storeIndP1] = 2 * Rhat_buf[storeIndP1] - RhatNew + Rhat_buf[storeIndP1] = _symmetrize(Rhat_buf[storeIndP1]) + alphahat_buf[storeIndP1] = 2 * alphahat_buf[storeIndP1] - alphahatNew + + # Override A if not estimating + if not KFEM_Constraints["EstimateA"]: + Ahat_buf[storeIndP1] = Ahat_buf[storeInd] + + # Likelihood change + if cnt == 0: + dLikelihood_val = np.inf + else: + dLikelihood_val = ll_list[cnt] - ll_list[cnt - 1] + + # Convergence check: max parameter change + if cnt == 0: + dMax = np.inf + else: + prev = storeIndM1 + dQvals = np.max(np.abs( + np.sqrt(np.abs(np.diag(Qhat_buf[storeInd]))) + - np.sqrt(np.abs(np.diag(Qhat_buf[prev]))) + )) + dRvals = np.max(np.abs( + np.sqrt(np.abs(np.diag(Rhat_buf[storeInd]))) + - np.sqrt(np.abs(np.diag(Rhat_buf[prev]))) + )) + dAvals = np.max(np.abs(Ahat_buf[storeInd] - Ahat_buf[prev])) + dCvals = np.max(np.abs(Chat_buf[storeInd] - Chat_buf[prev])) + dAlphavals = np.max(np.abs(alphahat_buf[storeInd] - alphahat_buf[prev])) + dMax = max(dQvals, dRvals, dAvals, dCvals, dAlphavals) + + if cnt == 0: + print("Max Parameter Change: N/A") + else: + print(f"Max Parameter Change: {dMax}") + + cnt += 1 + + if dMax < tolAbs: + stoppingCriteria = True + print(f" EM converged at iteration# {cnt} b/c change in params was within criteria") + + if abs(dLikelihood_val) < llTol or dLikelihood_val < 0: + stoppingCriteria = True + print(f" EM stopped at iteration# {cnt} b/c change in likelihood was negative") + + print("-" * 100) + + # Select best iteration by max log-likelihood + ll_arr = np.array(ll_list) + maxLLIndex = int(np.argmax(ll_arr)) + maxLLIndMod = maxLLIndex % numToKeep + nIter = cnt + + xKFinal = x_K_buf[maxLLIndMod] + WKFinal = W_K_buf[maxLLIndMod] + Ahat_final = Ahat_buf[maxLLIndMod] + Qhat_final = Qhat_buf[maxLLIndMod] + Chat_final = Chat_buf[maxLLIndMod] + Rhat_final = Rhat_buf[maxLLIndMod] + alphahat_final = alphahat_buf[maxLLIndMod] + x0hat_final = x0hat_buf[maxLLIndMod] + Px0hat_final = Px0hat_buf[maxLLIndMod] + + # Un-scale the system + if scaledSystem: + # Reconstruct Tq, Tr from original Q0, R0 + try: + cholQ0 = np.linalg.cholesky(Q0).T + except np.linalg.LinAlgError: + cholQ0 = np.linalg.cholesky(_nearestSPD(Q0)).T + try: + cholR0 = np.linalg.cholesky(R0).T + except np.linalg.LinAlgError: + cholR0 = np.linalg.cholesky(_nearestSPD(R0)).T + Tq = np.linalg.solve(cholQ0, np.eye(numStates)) + Tr = np.linalg.solve(cholR0, np.eye(y.shape[0])) + + # Matlab: Ahat = Tq\Ahat*Tq + Tq_inv = np.linalg.inv(Tq) + Tr_inv = np.linalg.inv(Tr) + Ahat_final = Tq_inv @ Ahat_final @ Tq + Qhat_final = Tq_inv @ Qhat_final @ Tq_inv.T + Chat_final = Tr_inv @ Chat_final @ Tq + Rhat_final = Tr_inv @ Rhat_final @ Tr_inv.T + alphahat_final = Tr_inv @ alphahat_final + xKFinal = Tq_inv @ xKFinal + x0hat_final = Tq_inv @ x0hat_final + Px0hat_final = Tq_inv @ Px0hat_final @ Tq_inv.T + K_steps = WKFinal.shape[2] + tempWK = np.zeros_like(WKFinal) + for kk in range(K_steps): + tempWK[:, :, kk] = Tq_inv @ WKFinal[:, :, kk] @ Tq_inv.T + WKFinal = tempWK + + ll_best = ll_list[maxLLIndex] + ExpectationSumsFinal = ExpSums_buf[maxLLIndMod] + + # Compute standard errors + SE, Pvals = DecodingAlgorithms.KF_ComputeParamStandardErrors( + yOrig, xKFinal, WKFinal, Ahat_final, Qhat_final, + Chat_final, Rhat_final, alphahat_final, x0hat_final, + Px0hat_final, ExpectationSumsFinal, KFEM_Constraints, + ) + + # Compute information criteria + # Count number of estimated parameters (matches Matlab lines 3600-3640) + if KFEM_Constraints["EstimateA"] and KFEM_Constraints["AhatDiag"]: + np1 = Ahat_final.shape[0] + elif KFEM_Constraints["EstimateA"] and not KFEM_Constraints["AhatDiag"]: + np1 = Ahat_final.size + else: + np1 = 0 + + if KFEM_Constraints["QhatDiag"] and KFEM_Constraints["QhatIsotropic"]: + np2 = 1 + elif KFEM_Constraints["QhatDiag"] and not KFEM_Constraints["QhatIsotropic"]: + np2 = Qhat_final.shape[0] + else: + np2 = Qhat_final.size + + np3 = Chat_final.size + + if KFEM_Constraints["RhatDiag"] and KFEM_Constraints["RhatIsotropic"]: + np4 = 1 + elif KFEM_Constraints["QhatDiag"] and not KFEM_Constraints["QhatIsotropic"]: + # Note: Matlab line 3618 checks QhatDiag here (likely a bug, but we match it) + np4 = Rhat_final.shape[0] + else: + np4 = Rhat_final.size + + if KFEM_Constraints["EstimatePx0"] and KFEM_Constraints["Px0Isotropic"]: + np5 = 1 + elif KFEM_Constraints["EstimatePx0"] and not KFEM_Constraints["Px0Isotropic"]: + np5 = Px0hat_final.shape[0] + else: + np5 = 0 + + np6 = x0hat_final.shape[0] if KFEM_Constraints["Estimatex0"] else 0 + np7 = alphahat_final.shape[0] + nTerms_ic = np1 + np2 + np3 + np4 + np5 + np6 + np7 + + K_steps = yOrig.shape[1] + Dx = Ahat_final.shape[1] + sumXkTerms_final = ExpectationSumsFinal["sumXkTerms"] + + # Matlab: llobs = ll + Dx*K/2*log(2*pi) + K/2*log(det(Qhat)) + # + 1/2*trace(Qhat\sumXkTerms) + Dx/2*log(2*pi) + # + 1/2*log(det(Px0hat)) + 1/2*Dx + _, logdet_Q_final = np.linalg.slogdet(Qhat_final) + _, logdet_Px0_final = np.linalg.slogdet(Px0hat_final) + llobs = ( + ll_best + + Dx * K_steps / 2.0 * np.log(2.0 * np.pi) + + K_steps / 2.0 * logdet_Q_final + + 0.5 * np.trace(np.linalg.solve(Qhat_final, sumXkTerms_final)) + + Dx / 2.0 * np.log(2.0 * np.pi) + + 0.5 * logdet_Px0_final + + 0.5 * Dx + ) + + AIC = 2.0 * nTerms_ic - 2.0 * llobs + AICc = AIC + 2.0 * nTerms_ic * (nTerms_ic + 1) / max(K_steps - nTerms_ic - 1, 1) + BIC = -2.0 * llobs + nTerms_ic * np.log(K_steps) + + IC = { + "AIC": float(AIC), + "AICc": float(AICc), + "BIC": float(BIC), + "llobs": float(llobs), + "llcomp": float(ll_best), + } + + return ( + xKFinal, WKFinal, Ahat_final, Qhat_final, + Chat_final, Rhat_final, alphahat_final, + x0hat_final, Px0hat_final, IC, SE, Pvals, nIter, + ) + + # ------------------------------------------------------------------ + # State-Space GLM (SSGLM) via EM Forward-Backward + # Ported from Matlab DecodingAlgorithms.m (PPSS_EMFB and helpers) + # ------------------------------------------------------------------ + + @staticmethod + def _ssglm_build_basis(numBasis, minTime, maxTime, delta): + """Build unit-impulse basis matrix for SSGLM.""" + from .trial import SpikeTrainCollection + + sampleRate = 1.0 / delta + basisWidth = (maxTime - minTime) / numBasis + basis_cov = SpikeTrainCollection.generateUnitImpulseBasis( + basisWidth, minTime, maxTime, sampleRate + ) + return np.asarray(basis_cov.data, dtype=float) + + @staticmethod + def _ssglm_build_history(dN, windowTimes, delta): + """Build history design matrices for each trial from spike observations.""" + from .history import History + + K, N = dN.shape + minTime = 0.0 + maxTime = (N - 1) * delta + + if windowTimes is not None and len(windowTimes) > 0: + histObj = History(windowTimes, minTime, maxTime) + HkAll = [] + for k in range(K): + spike_indices = np.where(dN[k, :] > 0.5)[0] + spike_times = spike_indices.astype(float) * delta + nst = nspikeTrain(spike_times, makePlots=-1) + nst.setMinTime(minTime) + nst.setMaxTime(maxTime) + hist_cov = histObj._compute_single_history(nst) + HkAll.append(np.asarray(hist_cov.data, dtype=float)) + return HkAll + else: + return [np.zeros((N, 0), dtype=float) for _ in range(K)] + + @staticmethod + def PPSS_EStep(A, Q, x0, dN, HkAll, fitType, delta, gamma, numBasis): + """E-step: Forward Kalman filter + backward RTS smoother + cross-covariance. + + Parameters + ---------- + A : (R, R) state transition matrix + Q : (R,) or (R, R) state noise covariance (diagonal vector or matrix) + x0 : (R,) initial state + dN : (K, N) binary spike observations (K trials, N time bins) + HkAll : list of K arrays, each (N, J) history design matrices + fitType : 'poisson' or 'binomial' + delta : time bin width + gamma : (J,) history coefficients + numBasis : number of basis functions R + + Returns + ------- + x_K, W_K, Wku, logll, sumXkTerms, sumPPll + """ + K, N = dN.shape + minTime = 0.0 + maxTime = (N - 1) * delta + + basisMat = DecodingAlgorithms._ssglm_build_basis(numBasis, minTime, maxTime, delta) + # Ensure basisMat has N rows matching dN columns + if basisMat.shape[0] != N: + basisMat = basisMat[:N, :] if basisMat.shape[0] > N else np.vstack( + [basisMat, np.zeros((N - basisMat.shape[0], basisMat.shape[1]))] + ) + + Q_diag = np.asarray(Q, dtype=float).reshape(-1) + if Q_diag.size == numBasis * numBasis: + Q_mat = Q_diag.reshape(numBasis, numBasis) + Q_diag = np.diag(Q_mat) + Q_mat = np.diag(Q_diag) + + A_mat = np.asarray(A, dtype=float) + if A_mat.ndim < 2: + A_mat = np.eye(numBasis, dtype=float) * A_mat + x0_vec = np.asarray(x0, dtype=float).reshape(-1) + gamma_vec = np.asarray(gamma, dtype=float).reshape(-1) + R = numBasis + fitType = str(fitType).lower() + + # Forward Kalman filter + x_p = np.zeros((R, K), dtype=float) + x_u = np.zeros((R, K), dtype=float) + W_p = np.zeros((R, R, K), dtype=float) + W_u = np.zeros((R, R, K), dtype=float) + + for k in range(K): + if k == 0: + x_p[:, k] = A_mat @ x0_vec + W_p[:, :, k] = Q_mat.copy() + else: + x_p[:, k] = A_mat @ x_u[:, k - 1] + W_p[:, :, k] = A_mat @ W_u[:, :, k - 1] @ A_mat.T + Q_mat + + Hk = HkAll[k] + stimK = basisMat @ x_p[:, k] + + if fitType == "poisson": + histEffect = np.exp(np.clip(Hk @ gamma_vec, -30, 30)) if gamma_vec.size > 0 and Hk.shape[1] > 0 else np.ones(N) + stimEffect = np.exp(np.clip(stimK, -30, 30)) + lambdaDelta = stimEffect * histEffect + + GradLogLD = basisMat # (N, R) + GradLD = basisMat * lambdaDelta[:, None] # (N, R) + + sumValVec = GradLogLD.T @ dN[k, :] - np.diag(GradLD.T @ basisMat) + sumValMat = GradLD.T @ basisMat + + elif fitType == "binomial": + Hk = HkAll[k] + stimK = basisMat @ x_p[:, k] + linpred = stimK + (Hk @ gamma_vec if gamma_vec.size > 0 and Hk.shape[1] > 0 else 0.0) + linpred = np.clip(linpred, -30, 30) + lambdaDelta = 1.0 / (1.0 + np.exp(-linpred)) + + GradLogLD = basisMat * (1.0 - lambdaDelta)[:, None] + JacobianLogLD = basisMat * (lambdaDelta * (-1.0 + lambdaDelta))[:, None] + GradLD = basisMat * (lambdaDelta * (1.0 - lambdaDelta))[:, None] + JacobianLD = basisMat * (lambdaDelta * (1.0 - lambdaDelta) * (1.0 - 2.0 * lambdaDelta ** 2))[:, None] + + sumValVec = GradLogLD.T @ dN[k, :] - np.diag(GradLD.T @ basisMat) + sumValMat = -np.diag(JacobianLogLD.T @ dN[k, :]) + JacobianLD.T @ basisMat + else: + raise ValueError(f"Unsupported fitType: {fitType}") + + # Kalman update + W_p_inv = np.linalg.inv(W_p[:, :, k] + 1e-12 * np.eye(R)) + invW_u = W_p_inv + sumValMat + W_u[:, :, k] = np.linalg.inv(invW_u + 1e-12 * np.eye(R)) + + # Ensure positive definiteness + eigvals, eigvecs = np.linalg.eigh(W_u[:, :, k]) + eigvals = np.maximum(eigvals, np.finfo(float).eps) + W_u[:, :, k] = eigvecs @ np.diag(eigvals) @ eigvecs.T + + x_u[:, k] = x_p[:, k] + W_u[:, :, k] @ sumValVec + + # Backward RTS smoother + x_K = np.zeros((R, K), dtype=float) + W_K = np.zeros((R, R, K), dtype=float) + Lk = np.zeros((R, R, K), dtype=float) + + x_K[:, K - 1] = x_u[:, K - 1] + W_K[:, :, K - 1] = W_u[:, :, K - 1] + + for k in range(K - 2, -1, -1): + Lk[:, :, k] = W_u[:, :, k] @ A_mat.T @ np.linalg.inv(W_p[:, :, k + 1] + 1e-12 * np.eye(R)) + x_K[:, k] = x_u[:, k] + Lk[:, :, k] @ (x_K[:, k + 1] - x_p[:, k + 1]) + W_K[:, :, k] = W_u[:, :, k] + Lk[:, :, k] @ (W_K[:, :, k + 1] - W_p[:, :, k + 1]) @ Lk[:, :, k].T + W_K[:, :, k] = 0.5 * (W_K[:, :, k] + W_K[:, :, k].T) + + # Cross-trial covariance Wku (R, R, K, K) + Wku = np.zeros((R, R, K, K), dtype=float) + for k in range(K): + Wku[:, :, k, k] = W_K[:, :, k] + + Dk = np.zeros((R, R, K), dtype=float) + for u in range(K - 1, 0, -1): + for k in range(u - 1, -1, -1): + Dk[:, :, k] = W_u[:, :, k] @ A_mat.T @ np.linalg.inv(W_p[:, :, k + 1] + 1e-12 * np.eye(R)) + Wku[:, :, k, u] = Dk[:, :, k] @ Wku[:, :, k + 1, u] + Wku[:, :, u, k] = Wku[:, :, k, u] + + # Sufficient statistics for M-step + Sxkxkp1 = np.zeros((R, R), dtype=float) + Sxkp1xkp1 = np.zeros((R, R), dtype=float) + Sxkxk = np.zeros((R, R), dtype=float) + for k in range(K - 1): + Sxkxkp1 += Wku[:, :, k, k + 1] + np.outer(x_K[:, k], x_K[:, k + 1]) + Sxkp1xkp1 += W_K[:, :, k + 1] + np.outer(x_K[:, k + 1], x_K[:, k + 1]) + Sxkxk += W_K[:, :, k] + np.outer(x_K[:, k], x_K[:, k]) + + sumXkTerms = ( + Sxkp1xkp1 - A_mat @ Sxkxkp1 - Sxkxkp1.T @ A_mat.T + A_mat @ Sxkxk @ A_mat.T + + W_K[:, :, 0] + np.outer(x_K[:, 0], x_K[:, 0]) + - A_mat @ np.outer(x0_vec, x_K[:, 0]) - np.outer(x_K[:, 0], x0_vec) @ A_mat.T + + A_mat @ np.outer(x0_vec, x0_vec) @ A_mat.T + ) + + # Point process log-likelihood + sumPPll = 0.0 + for k in range(K): + Hk = HkAll[k] + Wk = basisMat @ np.diag(W_K[:, :, k]) + stimK = basisMat @ x_K[:, k] + + if fitType == "poisson": + hist_term = Hk @ gamma_vec if gamma_vec.size > 0 and Hk.shape[1] > 0 else np.zeros(N) + histEffect = np.exp(np.clip(hist_term, -30, 30)) + stimK_clipped = np.clip(stimK, -30, 30) + stimEffect = np.exp(stimK_clipped) + np.exp(stimK_clipped) / 2.0 * Wk + ExplambdaDelta = stimEffect * histEffect + ExplogLD = stimK + hist_term + sumPPll += float(np.sum(dN[k, :] * ExplogLD - ExplambdaDelta)) + + elif fitType == "binomial": + hist_term = Hk @ gamma_vec if gamma_vec.size > 0 and Hk.shape[1] > 0 else np.zeros(N) + linpred = np.clip(stimK + hist_term, -30, 30) + lambdaDelta = 1.0 / (1.0 + np.exp(-linpred)) + ExplambdaDelta = lambdaDelta + Wk * (lambdaDelta * (1.0 - lambdaDelta) * (1.0 - 2.0 * lambdaDelta)) / 2.0 + ExplogLD = linpred - np.log(1.0 + np.exp(linpred)) - Wk * lambdaDelta * (1.0 - lambdaDelta) * 0.5 + sumPPll += float(np.sum(dN[k, :] * ExplogLD - ExplambdaDelta)) + + det_Q = float(np.prod(np.maximum(Q_diag, np.finfo(float).eps))) + logll = ( + -R * K * np.log(2.0 * np.pi) + - K / 2.0 * np.log(det_Q) + + sumPPll + - 0.5 * float(np.trace(np.linalg.pinv(Q_mat) @ sumXkTerms)) + ) + + return x_K, W_K, Wku, logll, sumXkTerms, sumPPll + + @staticmethod + def PPSS_MStep(dN, HkAll, fitType, x_K, W_K, gamma, delta, sumXkTerms, windowTimes): + """M-step: Update Q via closed form, gamma via Newton-Raphson. + + Parameters + ---------- + dN : (K, N) + HkAll : list of K arrays (N, J) + fitType : 'poisson' or 'binomial' + x_K : (R, K) smoothed states + W_K : (R, R, K) smoothed covariances + gamma : (J,) current history coefficients + delta : time bin width + sumXkTerms : (R, R) sufficient statistics from E-step + windowTimes : array of history window boundaries + + Returns + ------- + Qhat : (R,) updated state noise variance (diagonal) + gamma_new : (J,) updated history coefficients + """ + K, N = dN.shape + R = x_K.shape[0] + fitType = str(fitType).lower() + + # Q update (closed form) + sumQ = np.diag(np.diag(sumXkTerms)) + Qhat = sumQ / K + eigvals, eigvecs = np.linalg.eigh(Qhat) + eigvals = np.maximum(eigvals, 1e-8) + Qhat = eigvecs @ np.diag(eigvals) @ eigvecs.T + Qhat = np.diag(Qhat) # Return as vector + + # Build basis matrix for gamma update + minTime = 0.0 + maxTime = (N - 1) * delta + basisMat = DecodingAlgorithms._ssglm_build_basis(R, minTime, maxTime, delta) + if basisMat.shape[0] != N: + basisMat = basisMat[:N, :] if basisMat.shape[0] > N else np.vstack( + [basisMat, np.zeros((N - basisMat.shape[0], basisMat.shape[1]))] + ) + + gamma_vec = np.asarray(gamma, dtype=float).reshape(-1) + gamma_new = gamma_vec.copy() + J = gamma_new.size + + # Newton-Raphson for gamma (history coefficients) + if windowTimes is not None and len(windowTimes) > 0 and J > 0 and np.any(gamma_new != 0): + converged = False + max_iter = 300 + for iteration in range(max_iter): + gradQ = np.zeros(J, dtype=float) + jacQ = np.zeros((J, J), dtype=float) + + for k in range(K): + Hk = HkAll[k] + if Hk.shape[1] == 0: + continue + Wk = basisMat @ np.diag(W_K[:, :, k]) + stimK = basisMat @ x_K[:, k] + + if fitType == "poisson": + hist_term = np.clip(gamma_new @ Hk.T, -30, 30) + histEffect = np.exp(hist_term) + stimK_clipped = np.clip(stimK, -30, 30) + stimEffect = np.exp(stimK_clipped) + np.exp(stimK_clipped) / 2.0 * Wk + lambdaDelta = stimEffect * histEffect + + gradQ += Hk.T @ dN[k, :] - Hk.T @ lambdaDelta + jacQ -= (Hk * lambdaDelta[:, None]).T @ Hk + + elif fitType == "binomial": + linpred = np.clip(stimK + Hk @ gamma_new, -30, 30) + lambdaDelta = 1.0 / (1.0 + np.exp(-linpred)) + histEffect = np.exp(np.clip(gamma_new @ Hk.T, -30, 30)) + stimEffect = np.exp(np.clip(stimK, -30, 30)) + C = stimEffect * histEffect + M = np.where(C > 1e-30, 1.0 / C, 1e30) + ExpLambdaDelta = lambdaDelta + Wk * (lambdaDelta * (1.0 - lambdaDelta) * (1.0 - 2.0 * lambdaDelta)) / 2.0 + ExpLDSquaredTimesInvExp = lambdaDelta ** 2 * M + ExpLDCubedTimesInvExpSquared = ( + lambdaDelta ** 3 * M ** 2 + + Wk / 2.0 * (3.0 * M ** 4 * lambdaDelta ** 3 + + 12.0 * lambdaDelta ** 3 * M ** 3 + - 12.0 * M ** 4 * lambdaDelta ** 4) + ) + + gradQ += (Hk * (1.0 - ExpLambdaDelta)[:, None]).T @ dN[k, :] \ + - (Hk * (ExpLDSquaredTimesInvExp / np.maximum(lambdaDelta, 1e-30))[:, None]).T @ lambdaDelta + jacQ -= (Hk * (ExpLDSquaredTimesInvExp * dN[k, :])[:, None]).T @ Hk \ + + (Hk * ExpLDSquaredTimesInvExp[:, None]).T @ Hk \ + + (Hk * (2.0 * ExpLDCubedTimesInvExpSquared)[:, None]).T @ Hk + + # Newton-Raphson update + try: + gamma_temp = gamma_new - np.linalg.pinv(jacQ) @ gradQ + except np.linalg.LinAlgError: + gamma_temp = gamma_new + + if np.any(np.isnan(gamma_temp)): + gamma_temp = gamma_new + + mabsDiff = float(np.max(np.abs(gamma_temp - gamma_new))) + gamma_new = gamma_temp + if mabsDiff < 1e-2: + converged = True + break + + # Clamp gamma + gamma_new = np.clip(gamma_new, -1e2, 1e2) + + return Qhat, gamma_new + + @staticmethod + def PPSS_EM(A, Q0, x0, dN, fitType, delta, gamma0, windowTimes, numBasis, HkAll): + """Inner EM loop for state-space GLM. + + Parameters + ---------- + A : (R, R) state transition + Q0 : (R,) initial state noise variance + x0 : (R,) initial state + dN : (K, N) observations + fitType : 'poisson' or 'binomial' + delta : time bin width + gamma0 : (J,) initial history coefficients + windowTimes : history window boundaries + numBasis : number of basis functions + HkAll : precomputed history matrices + + Returns + ------- + xKFinal, WKFinal, WkuFinal, Qhat, gammahat, logll, QhatAll, gammahatAll, nIter, negLL + """ + if numBasis is None: + numBasis = 20 + if delta is None or delta == 0: + delta = 0.001 + fitType = str(fitType or "poisson").lower() + + Q0_vec = np.asarray(Q0, dtype=float).reshape(-1) + if Q0_vec.size == numBasis * numBasis: + Q0_vec = np.diag(Q0_vec.reshape(numBasis, numBasis)) + + gamma0_vec = np.asarray(gamma0, dtype=float).reshape(-1) if gamma0 is not None else np.array([], dtype=float) + + tolAbs = 1e-3 + tolRel = 1e-3 + llTol = 1e-3 + maxIter = 100 + numToKeep = 10 + + # Circular buffer storage + Qhat = np.zeros((Q0_vec.size, numToKeep), dtype=float) + Qhat[:, 0] = Q0_vec + gammahat = np.zeros((numToKeep, gamma0_vec.size), dtype=float) + gammahat[0, :] = gamma0_vec + + xK_buf = [None] * numToKeep + WK_buf = [None] * numToKeep + Wku_buf = [None] * numToKeep + + x0hat = np.asarray(x0, dtype=float).reshape(-1) + logll_list = [] + dLikelihood = [np.inf] + negLL = False + stoppingCriteria = False + cnt = 0 + + while not stoppingCriteria and cnt < maxIter: + si = cnt % numToKeep + si_p1 = (cnt + 1) % numToKeep + si_m1 = (cnt - 1) % numToKeep + + xK_cur, WK_cur, Wku_cur, ll, SumXkTerms, _ = DecodingAlgorithms.PPSS_EStep( + A, Qhat[:, si], x0hat, dN, HkAll, fitType, delta, gammahat[si, :], numBasis + ) + xK_buf[si] = xK_cur + WK_buf[si] = WK_cur + Wku_buf[si] = Wku_cur + logll_list.append(ll) + + Qnew, gnew = DecodingAlgorithms.PPSS_MStep( + dN, HkAll, fitType, xK_cur, WK_cur, gammahat[si, :], delta, SumXkTerms, windowTimes + ) + Qhat[:, si_p1] = Qnew + gammahat[si_p1, :] = gnew + + if cnt == 0: + dLikelihood.append(np.inf) + else: + dLikelihood.append(logll_list[cnt] - logll_list[cnt - 1]) + + # Check convergence + if cnt > 0: + dQvals = np.abs(np.sqrt(np.maximum(Qhat[:, si], 0)) - np.sqrt(np.maximum(Qhat[:, si_m1], 0))) + dGamma = np.abs(gammahat[si, :] - gammahat[si_m1, :]) + dMax = max(np.max(dQvals), np.max(dGamma)) if dGamma.size > 0 else float(np.max(dQvals)) + + Q_prev = np.sqrt(np.maximum(Qhat[:, si_m1], 1e-30)) + dQRel = float(np.max(np.abs(dQvals / Q_prev))) + if dGamma.size > 0: + g_prev = np.maximum(np.abs(gammahat[si_m1, :]), 1e-30) + dGammaRel = float(np.max(np.abs(dGamma / g_prev))) + dMaxRel = max(dQRel, dGammaRel) + else: + dMaxRel = dQRel + + if dMax < tolAbs and dMaxRel < tolRel: + stoppingCriteria = True + negLL = False + + if abs(dLikelihood[-1]) < llTol or dLikelihood[-1] < 0: + stoppingCriteria = True + negLL = True + + cnt += 1 + + # Select best iteration by log-likelihood + logll_arr = np.array(logll_list) + if logll_arr.size > 0: + maxLLIndex = int(np.argmax(logll_arr)) + else: + maxLLIndex = 0 + + maxLLIndMod = maxLLIndex % numToKeep + nIter = cnt + + xKFinal = xK_buf[maxLLIndMod] if xK_buf[maxLLIndMod] is not None else np.zeros((numBasis, dN.shape[0])) + WKFinal = WK_buf[maxLLIndMod] if WK_buf[maxLLIndMod] is not None else np.zeros((numBasis, numBasis, dN.shape[0])) + WkuFinal = Wku_buf[maxLLIndMod] if Wku_buf[maxLLIndMod] is not None else np.zeros((numBasis, numBasis, dN.shape[0], dN.shape[0])) + + QhatFinal = Qhat[:, maxLLIndMod] + gammahatFinal = gammahat[maxLLIndMod, :] + logllFinal = float(logll_arr[maxLLIndex]) if logll_arr.size > 0 else -np.inf + + QhatAll = Qhat[:, : min(cnt + 1, numToKeep)] + gammahatAll = gammahat[: min(cnt + 1, numToKeep), :] + + return xKFinal, WKFinal, WkuFinal, QhatFinal, gammahatFinal, logllFinal, QhatAll, gammahatAll, nIter, negLL + + @staticmethod + def PPSS_EMFB(A, Q0, x0, dN, fitType, delta, gamma0, windowTimes, numBasis, neuronName=None): + """EM Forward-Backward algorithm for state-space GLM. + + Wraps PPSS_EM in a forward-backward-forward cycle for improved convergence. + + Parameters + ---------- + A : (R, R) state transition matrix (typically identity for random walk) + Q0 : (R,) initial state noise variance + x0 : (R,) initial state coefficients + dN : (K, N) binary spike observations (K trials, N time bins) + fitType : 'poisson' or 'binomial' + delta : time bin width in seconds + gamma0 : (J,) initial history coefficients + windowTimes : array of history window boundaries + numBasis : number of basis functions R + neuronName : identifier for the neuron (for labeling) + + Returns + ------- + xKFinal : (R, K) estimated state trajectories + WKFinal : (R, R, K) estimated state covariances + WkuFinal : (R, R, K, K) cross-trial covariances + Qhat : (R,) estimated state noise variance + gammahat : (J,) estimated history coefficients + fitResults : FitResult object with goodness-of-fit diagnostics + stimulus : (R, K) estimated stimulus effect + stimCIs : (R, K, 2) stimulus confidence intervals + logll : float, log-likelihood at convergence + QhatAll : parameter history + gammahatAll : parameter history + nIter : total EM iterations + """ + K, N = dN.shape + fitType = str(fitType or "poisson").lower() + + Q0_vec = np.asarray(Q0, dtype=float).reshape(-1) + if Q0_vec.size == numBasis * numBasis: + Q0_vec = np.diag(Q0_vec.reshape(numBasis, numBasis)) + + gamma0_vec = np.asarray(gamma0, dtype=float).reshape(-1) if gamma0 is not None else np.array([], dtype=float) + + Qhat_cur = Q0_vec.copy() + gammahat_cur = gamma0_vec.copy() + xK0 = np.asarray(x0, dtype=float).reshape(-1) + + # Build history matrices + HkAll = DecodingAlgorithms._ssglm_build_history(dN, windowTimes, delta) + HkAllR = list(reversed(HkAll)) + + tolAbs = 1e-3 + tolRel = 1e-3 + llTol = 1e-3 + maxIter = 2000 + + Qhat_history = [Qhat_cur.copy()] + gammahat_history = [gammahat_cur.copy()] + logll_list = [] + stoppingCriteria = False + cnt = 0 + + xK = None + WK = None + Wku = None + + while not stoppingCriteria and cnt < maxIter: + # Forward EM + xK, WK, Wku, Qnew, gnew, ll, _, _, _, negLL = DecodingAlgorithms.PPSS_EM( + A, Qhat_cur, xK0, dN, fitType, delta, gammahat_cur, windowTimes, numBasis, HkAll + ) + + if not negLL: + # Backward EM + _, _, _, QnewR, gnewR, _, _, _, _, negLLR = DecodingAlgorithms.PPSS_EM( + A, Qnew, xK[:, -1], np.flipud(dN), fitType, delta, gnew, windowTimes, numBasis, HkAllR + ) + + if not negLLR: + # Forward EM again with backward-updated parameters + # Matlab: PPSS_EM(A, QhatR(:,cnt+1), xKR(:,end), dN, ...) + xK2, WK2, Wku2, Qnew2, gnew2, ll2, _, _, _, negLL2 = DecodingAlgorithms.PPSS_EM( + A, QnewR, xK[:, -1], dN, fitType, delta, gnewR, + windowTimes, numBasis, HkAll + ) + + if not negLL2: + xK = xK2 + WK = WK2 + Wku = Wku2 + Qnew = Qnew2 + gnew = gnew2 + ll = ll2 + + Qhat_cur = Qnew + gammahat_cur = gnew + Qhat_history.append(Qnew.copy()) + gammahat_history.append(gnew.copy()) + logll_list.append(ll) + + xK0 = xK[:, 0] + + # Check convergence + if cnt > 0: + dLikelihood = logll_list[cnt] - logll_list[cnt - 1] + else: + dLikelihood = np.inf + + if len(Qhat_history) >= 2: + Q_prev = Qhat_history[-2] + Q_cur = Qhat_history[-1] + dQvals = np.abs(np.sqrt(np.maximum(Q_cur, 0)) - np.sqrt(np.maximum(Q_prev, 0))) + g_prev = gammahat_history[-2] + g_cur = gammahat_history[-1] + dGamma = np.abs(g_cur - g_prev) if g_cur.size > 0 else np.array([0.0]) + + dMax = max(float(np.max(dQvals)), float(np.max(dGamma))) + + Q_denom = np.sqrt(np.maximum(Q_prev, 1e-30)) + dQRel = float(np.max(np.abs(dQvals / Q_denom))) + if g_prev.size > 0 and np.any(g_prev != 0): + g_denom = np.maximum(np.abs(g_prev), 1e-30) + dGammaRel = float(np.max(np.abs(dGamma / g_denom))) + dMaxRel = max(dQRel, dGammaRel) + else: + dMaxRel = dQRel + + if dMax < tolAbs and dMaxRel < tolRel: + stoppingCriteria = True + + if abs(dLikelihood) < llTol or dLikelihood < 0: + stoppingCriteria = True + + cnt += 1 + + # Select best iteration + logll_arr = np.array(logll_list) + if logll_arr.size > 0: + maxLLIndex = int(np.argmax(logll_arr)) + else: + maxLLIndex = 0 + + xKFinal = xK + WKFinal = WK + WkuFinal = Wku + Qhat = Qhat_history[min(maxLLIndex + 1, len(Qhat_history) - 1)] + gammahat = gammahat_history[min(maxLLIndex + 1, len(gammahat_history) - 1)] + logll = float(logll_arr[maxLLIndex]) if logll_arr.size > 0 else -np.inf + + QhatAll = np.column_stack(Qhat_history) if Qhat_history else Q0_vec.reshape(-1, 1) + gammahatAll = np.row_stack(gammahat_history) if gammahat_history and gammahat_history[0].size > 0 else np.array([[]]) + + R = numBasis + x0Final = xK[:, 0] if xK is not None else np.zeros(R) + SumXkTermsFinal = np.diag(Qhat) * K + McInfo = 100 + McCI = 3000 + + # Observed log-likelihood + logllobs = logll + R * K * np.log(2 * np.pi) + K / 2.0 * np.log( + max(float(np.prod(np.maximum(Qhat, np.finfo(float).eps))), np.finfo(float).eps) + ) + 0.5 * float(np.trace(np.linalg.pinv(np.diag(Qhat)) @ SumXkTermsFinal)) + + nIter = cnt + + # Information matrix and result packaging + InfoMat = DecodingAlgorithms.estimateInfoMat( + fitType, dN, HkAll, A, x0Final, xKFinal, WKFinal, WkuFinal, + Qhat, gammahat, windowTimes, SumXkTermsFinal, delta, McInfo + ) + fitResults = DecodingAlgorithms.prepareEMResults( + fitType, neuronName, dN, HkAll, xKFinal, WKFinal, + Qhat, gammahat, windowTimes, delta, InfoMat, logllobs + ) + + stimCIs, stimulus = DecodingAlgorithms._ComputeStimulusCIs_MC( + fitType, xKFinal, WkuFinal, delta, McCI + ) + + return (xKFinal, WKFinal, WkuFinal, Qhat, gammahat, fitResults, + stimulus, stimCIs, logll, QhatAll, gammahatAll, nIter) + + @staticmethod + def _ComputeStimulusCIs_MC(fitType, xK, Wku, delta, Mc=3000, alphaVal=0.05): + """Monte Carlo confidence intervals for SSGLM stimulus estimate. + + Uses Cholesky decomposition of the cross-trial covariance to generate + draws of the state trajectory, then computes empirical CIs. + """ + fitType = str(fitType).lower() + numBasis, K = xK.shape + + CIs = np.zeros((numBasis, K, 2), dtype=float) + + for r in range(numBasis): + WkuTemp = Wku[r, r, :, :] # (K, K) cross-trial covariance for basis r + try: + chol_m = np.linalg.cholesky(WkuTemp + 1e-10 * np.eye(K)) + except np.linalg.LinAlgError: + eigvals, eigvecs = np.linalg.eigh(WkuTemp) + eigvals = np.maximum(eigvals, 1e-10) + chol_m = eigvecs @ np.diag(np.sqrt(eigvals)) + + stimulusDraw = np.zeros((Mc, K), dtype=float) + for c in range(Mc): + z = np.random.randn(K) + xKDraw = xK[r, :] + chol_m.T @ z + if fitType == "poisson": + stimulusDraw[c, :] = np.exp(np.clip(xKDraw, -30, 30)) / delta + elif fitType == "binomial": + xKDraw_clip = np.clip(xKDraw, -30, 30) + stimulusDraw[c, :] = (np.exp(xKDraw_clip) / (1.0 + np.exp(xKDraw_clip))) / delta + else: + stimulusDraw[c, :] = xKDraw / delta + + for k in range(K): + CIs[r, k, 0] = float(np.percentile(stimulusDraw[:, k], 100.0 * alphaVal / 2.0)) + CIs[r, k, 1] = float(np.percentile(stimulusDraw[:, k], 100.0 * (1.0 - alphaVal / 2.0))) + + if fitType == "poisson": + stimulus = np.exp(np.clip(xK, -30, 30)) / delta + elif fitType == "binomial": + xK_clip = np.clip(xK, -30, 30) + stimulus = (np.exp(xK_clip) / (1.0 + np.exp(xK_clip))) / delta + else: + stimulus = xK / delta + + return CIs, stimulus + + @staticmethod + def estimateInfoMat(fitType, dN, HkAll, A, x0, xK, WK, Wku, Q, gamma, + windowTimes, SumXkTerms, delta, Mc=500): + """Observed information matrix via Louis' identity with Monte Carlo. + + Computes I_obs = I_complete - I_missing where I_missing is estimated + by MC sampling from the smoothing distribution. + """ + fitType = str(fitType).lower() + K, N = dN.shape + gamma_vec = np.asarray(gamma, dtype=float).reshape(-1) + J = gamma_vec.size if (windowTimes is not None and len(windowTimes) > 0) else 0 + + Q_vec = np.asarray(Q, dtype=float).reshape(-1) + R = Q_vec.size + Q_mat = np.diag(Q_vec) + numBasis = R + + # Build basis matrix + minTime = 0.0 + maxTime = (N - 1) * delta + basisMat = DecodingAlgorithms._ssglm_build_basis(numBasis, minTime, maxTime, delta) + if basisMat.shape[0] != N: + basisMat = basisMat[:N, :] if basisMat.shape[0] > N else np.vstack( + [basisMat, np.zeros((N - basisMat.shape[0], basisMat.shape[1]))] + ) + + # Complete data information matrix + Ic = np.zeros((R + J, R + J), dtype=float) + Q_mat_safe = np.diag(np.maximum(Q_vec, np.finfo(float).eps)) + Q2 = Q_mat_safe @ Q_mat_safe + Q3 = Q2 @ Q_mat_safe + + Ic[:R, :R] = K / 2.0 * np.linalg.inv(Q2) + np.linalg.inv(Q3) @ SumXkTerms + + # History portion of information matrix + jacQ = np.zeros((J, J), dtype=float) if J > 0 else np.zeros((0, 0)) + if fitType == "poisson" and J > 0: + for k in range(K): + Hk = HkAll[k] + if Hk.shape[1] == 0: + continue + Wk = basisMat @ np.diag(WK[:, :, k]) + stimK = basisMat @ xK[:, k] + stimK_clip = np.clip(stimK, -30, 30) + hist_term = np.clip(gamma_vec @ Hk.T, -30, 30) + histEffect = np.exp(hist_term) + stimEffect = np.exp(stimK_clip) + np.exp(stimK_clip) / 2.0 * Wk + lambdaDelta = stimEffect * histEffect + jacQ -= (Hk * lambdaDelta[:, None]).T @ Hk + elif fitType == "binomial" and J > 0: + for k in range(K): + Hk = HkAll[k] + if Hk.shape[1] == 0: + continue + Wk = basisMat @ np.diag(WK[:, :, k]) + stimK = basisMat @ xK[:, k] + linpred = np.clip(stimK + Hk @ gamma_vec, -30, 30) + histEffect = np.exp(np.clip(gamma_vec @ Hk.T, -30, 30)) + stimEffect = np.exp(np.clip(stimK, -30, 30)) + C = stimEffect * histEffect + M = np.where(C > 1e-30, 1.0 / C, 1e30) + lambdaDelta = 1.0 / (1.0 + np.exp(-linpred)) + ExpLDSquaredTimesInvExp = lambdaDelta ** 2 * M + ExpLDCubedTimesInvExpSquared = ( + lambdaDelta ** 3 * M ** 2 + + Wk / 2.0 * (3.0 * M ** 4 * lambdaDelta ** 3 + + 12.0 * lambdaDelta ** 3 * M ** 3 + - 12.0 * M ** 4 * lambdaDelta ** 4) + ) + jacQ -= (Hk * (ExpLDSquaredTimesInvExp * dN[k, :])[:, None]).T @ Hk \ + + (Hk * ExpLDSquaredTimesInvExp[:, None]).T @ Hk \ + + (Hk * (2.0 * ExpLDCubedTimesInvExpSquared)[:, None]).T @ Hk + + Ic[:R, :R] = K * np.linalg.inv(2.0 * Q2) + np.linalg.inv(Q3) @ SumXkTerms + if J > 0: + Ic[R:R + J, R:R + J] = -jacQ + + # MC estimation of missing information + xKDraw = np.zeros((numBasis, K, Mc), dtype=float) + for r in range(numBasis): + WkuTemp = Wku[r, r, :, :] + try: + chol_m = np.linalg.cholesky(WkuTemp + 1e-10 * np.eye(K)) + except np.linalg.LinAlgError: + eigvals, eigvecs = np.linalg.eigh(WkuTemp) + eigvals = np.maximum(eigvals, 1e-10) + chol_m = eigvecs @ np.diag(np.sqrt(eigvals)) + + for c in range(Mc): + z = np.random.randn(K) + xKDraw[r, :, c] = xK[r, :] + chol_m.T @ z + + ImMC = np.zeros((R + J, R + J), dtype=float) + A_mat = np.asarray(A, dtype=float) + if A_mat.ndim < 2: + A_mat = np.eye(R) * A_mat + x0_vec = np.asarray(x0, dtype=float).reshape(-1) + Q_inv = np.linalg.inv(Q_mat_safe) + + for c in range(Mc): + gradQGammahat = np.zeros(J, dtype=float) if J > 0 else np.array([], dtype=float) + gradQQhat = np.zeros(R, dtype=float) + + for k in range(K): + Hk = HkAll[k] + stimK = basisMat @ xKDraw[:, k, c] + + if fitType == "poisson": + hist_term = np.clip(gamma_vec @ Hk.T, -30, 30) if J > 0 and Hk.shape[1] > 0 else np.zeros(N) + histEffect = np.exp(hist_term) + stimK_clip = np.clip(stimK, -30, 30) + stimEffect = np.exp(stimK_clip) + lambdaDelta = stimEffect * histEffect + if J > 0 and Hk.shape[1] > 0: + gradQGammahat += Hk.T @ dN[k, :] - Hk.T @ lambdaDelta + elif fitType == "binomial": + Wk = basisMat @ np.diag(WK[:, :, k]) + linpred = np.clip(stimK + (Hk @ gamma_vec if J > 0 and Hk.shape[1] > 0 else 0.0), -30, 30) + histEffect = np.exp(np.clip(gamma_vec @ Hk.T, -30, 30)) if J > 0 and Hk.shape[1] > 0 else np.ones(N) + stimEffect = np.exp(np.clip(stimK, -30, 30)) + C = stimEffect * histEffect + M = np.where(C > 1e-30, 1.0 / C, 1e30) + lambdaDelta = 1.0 / (1.0 + np.exp(-linpred)) + ExpLambdaDelta = lambdaDelta + Wk * (lambdaDelta * (1.0 - lambdaDelta) * (1.0 - 2.0 * lambdaDelta)) / 2.0 + ExpLDSquaredTimesInvExp = lambdaDelta ** 2 * M + if J > 0 and Hk.shape[1] > 0: + gradQGammahat += (Hk * (1.0 - ExpLambdaDelta)[:, None]).T @ dN[k, :] \ + - (Hk * (ExpLDSquaredTimesInvExp / np.maximum(lambdaDelta, 1e-30))[:, None]).T @ lambdaDelta + + if k == 0: + diff = xKDraw[:, k, c] - A_mat @ x0_vec + else: + diff = xKDraw[:, k, c] - A_mat @ xKDraw[:, k - 1, c] + gradQQhat += diff * diff + + gradQQhat_scaled = 0.5 * Q_inv @ gradQQhat - np.diag(K / 2.0 * np.linalg.inv(Q2)) + ImMC[:R, :R] += np.outer(gradQQhat_scaled, gradQQhat_scaled) + if J > 0: + ImMC[R:R + J, R:R + J] += np.diag(gradQGammahat ** 2) + + Im = ImMC / Mc + InfoMatrix = Ic - Im + + return InfoMatrix + + @staticmethod + def prepareEMResults(fitType, neuronNumber, dN, HkAll, xK, WK, Q, gamma, + windowTimes, delta, informationMatrix, logll): + """Package SSGLM EM results into a FitResult object.""" + from .core import Covariate + from .fit import FitResult + from .history import History + from .trial import ( + ConfigCollection, + SpikeTrainCollection, + TrialConfig, + ) + from .analysis import Analysis + + fitType = str(fitType).lower() + numBasis, K = xK.shape + R = numBasis + N = dN.shape[1] + minTime = 0.0 + maxTime = (N - 1) * delta + sampleRate = 1.0 / delta + gamma_vec = np.asarray(gamma, dtype=float).reshape(-1) + + # Build basis matrix + basisMat = DecodingAlgorithms._ssglm_build_basis(numBasis, minTime, maxTime, delta) + if basisMat.shape[0] != N: + basisMat = basisMat[:N, :] if basisMat.shape[0] > N else np.vstack( + [basisMat, np.zeros((N - basisMat.shape[0], basisMat.shape[1]))] + ) + + # Standard errors from information matrix + try: + SE = np.sqrt(np.abs(np.diag(np.linalg.inv(informationMatrix)))) + except np.linalg.LinAlgError: + SE = np.zeros(informationMatrix.shape[0], dtype=float) + + # Build per-trial standard errors + xKbeta = xK.T.reshape(-1) # (K*R,) + seXK = np.zeros(K * R, dtype=float) + for k in range(K): + seXK[k * R:(k + 1) * R] = np.sqrt(np.maximum(np.diag(WK[:, :, k]), 0.0)) + + # Neuron name + if neuronNumber is None: + name = "N01" + elif isinstance(neuronNumber, (int, float)): + n = int(neuronNumber) + name = f"N{n:02d}" if 0 < n < 10 else f"N{n}" + else: + name = str(neuronNumber) + + # Create spike trains from dN + nst_list = [] + for k in range(K): + spike_indices = np.where(dN[k, :] > 0.5)[0] + spike_times = spike_indices.astype(float) * delta + nst_k = nspikeTrain(spike_times, name=name, makePlots=-1) + nst_k.setMinTime(minTime) + nst_k.setMaxTime(maxTime) + nst_list.append(nst_k) + + nCopy = SpikeTrainCollection(nst_list) + nCopy = nCopy.toSpikeTrain() + + # Compute lambda (conditional intensity) + lambdaData = [] + otherLabels = [] + cnt = 0 + for k in range(K): + Hk = HkAll[k] + stimK = basisMat @ xK[:, k] + + if fitType == "poisson": + hist_term = gamma_vec @ Hk.T if gamma_vec.size > 0 and Hk.shape[1] > 0 else np.zeros(N) + histEffect = np.exp(np.clip(hist_term, -30, 30)) + stimEffect = np.exp(np.clip(stimK, -30, 30)) + lambdaDelta = histEffect * stimEffect / delta + elif fitType == "binomial": + linpred = np.clip(stimK + (Hk @ gamma_vec if gamma_vec.size > 0 and Hk.shape[1] > 0 else 0.0), -30, 30) + hist_term = np.clip(gamma_vec @ Hk.T, -30, 30) if gamma_vec.size > 0 and Hk.shape[1] > 0 else np.zeros(N) + histEffect = np.exp(hist_term) + stimEffect = np.exp(np.clip(stimK, -30, 30)) + C = histEffect * stimEffect + lambdaDelta = C / (1.0 + C) / delta + else: + lambdaDelta = np.zeros(N) + + lambdaData.append(lambdaDelta) + + for r in range(R): + label = f"b{r + 1:02d}_{{{k + 1}}}" if r + 1 < 10 else f"b{r + 1}_{{{k + 1}}}" + otherLabels.append(label) + cnt += 1 + + lambdaData = np.concatenate(lambdaData) + lambdaTime = np.arange(len(lambdaData)) * delta + minTime + + nCopy.setMaxTime(float(np.max(lambdaTime))) + nCopy.setMinTime(float(np.min(lambdaTime))) + + # Covariance labels + covarianceLabels = [f"Q{r + 1:02d}" if r + 1 < 10 else f"Q{r + 1}" for r in range(R)] + + # History labels + histLabels = [] + if windowTimes is not None and len(windowTimes) > 0: + wt = np.asarray(windowTimes, dtype=float) + for i in range(len(wt) - 1): + histLabels.append(f"[{wt[i]:.3g},{wt[i + 1]:.3g}]") + + allLabels = otherLabels + covarianceLabels + histLabels + + # History objects + if windowTimes is not None and len(windowTimes) > 0: + histObj = [History(windowTimes, minTime, maxTime)] + else: + histObj = [None] + + # Trial configuration + numBasisStr = str(numBasis) + numHistStr = str(len(windowTimes) - 1) if windowTimes is not None and len(windowTimes) > 1 else "0" + if histObj[0] is not None: + cfg_name = f"SSGLM(N_{{b}}={numBasisStr})+Hist(N_{{h}}={numHistStr})" + else: + cfg_name = f"SSGLM(N_{{b}}={numBasisStr})" + + tc = TrialConfig([allLabels], sampleRate, histObj, []) + tc.setName(cfg_name) + configColl = ConfigCollection([tc]) + + # Lambda covariate + lambda_cov = Covariate( + lambdaTime, lambdaData, + r"\Lambda(t)", "time", "s", "Hz", + [r"\lambda_{1}"] + ) + + # Model selection criteria + AIC = 2.0 * len(allLabels) - 2.0 * logll + BIC = -2.0 * logll + len(allLabels) * np.log(max(len(lambdaData), 1)) + dev = -2.0 * logll + + # Stats structure + statsStruct = { + "beta": np.concatenate([xKbeta, np.asarray(Q, dtype=float).reshape(-1), gamma_vec]), + "se": np.concatenate([seXK, SE]), + } + + # Coefficients + b = [statsStruct["beta"]] + stats = [statsStruct] + distrib = [fitType] + + # Spike trains for FitResult + spikeTraining = [nst.nstCopy() for nst in nst_list] + for st in spikeTraining: + st.setName(name) + + XvalData = [None] + XvalTime = [None] + numHist = [len(windowTimes) - 1] if windowTimes is not None and len(windowTimes) > 1 else [0] + ensHistObj = [None] + + fitResults = FitResult( + nCopy, [allLabels], numHist, histObj, ensHistObj, + lambda_cov, b, dev, stats, AIC, BIC, logll, + configColl, XvalData, XvalTime, distrib + ) + + # Goodness-of-fit (silent) + try: + Analysis.KSPlot(fitResults, DTCorrection=1, makePlot=0) + except Exception: + pass + try: + Analysis.plotInvGausTrans(fitResults, makePlot=0) + except Exception: + pass + try: + Analysis.plotFitResidual(fitResults, makePlot=0) + except Exception: + pass + + return fitResults + + + # PP_EM family: Point-Process state-space EM (without basis functions) + # ------------------------------------------------------------------ + + @staticmethod + def PP_EMCreateConstraints( + EstimateA=1, + AhatDiag=0, + QhatDiag=1, + QhatIsotropic=0, + Estimatex0=1, + EstimatePx0=1, + Px0Isotropic=0, + mcIter=1000, + EnableIkeda=0, + ): + """Build a constraints dict for PP_EM. + + Parameters + ---------- + EstimateA : int + Whether to estimate the state transition matrix A. + AhatDiag : int + Constrain A to be diagonal. + QhatDiag : int + Constrain Q to be diagonal. + QhatIsotropic : int + Constrain Q to be isotropic (scalar * I). + Estimatex0 : int + Whether to estimate the initial state x0. + EstimatePx0 : int + Whether to estimate the initial state covariance Px0. + Px0Isotropic : int + Constrain Px0 to be isotropic. + mcIter : int + Number of Monte Carlo iterations for standard error estimation. + EnableIkeda : int + Enable Ikeda acceleration. + + Returns + ------- + dict + Constraints dictionary with all fields. + """ + C = {} + C["EstimateA"] = int(EstimateA) + C["AhatDiag"] = int(AhatDiag) + C["QhatDiag"] = int(QhatDiag) + C["QhatIsotropic"] = 1 if (QhatDiag and QhatIsotropic) else 0 + C["Estimatex0"] = int(Estimatex0) + C["EstimatePx0"] = int(EstimatePx0) + C["Px0Isotropic"] = 1 if (EstimatePx0 and Px0Isotropic) else 0 + C["mcIter"] = int(mcIter) + C["EnableIkeda"] = int(EnableIkeda) + return C + + @staticmethod + def _nearestSPD(A): + """Compute the nearest symmetric positive semi-definite matrix. + + Uses the algorithm of Higham (1988). + """ + B = 0.5 * (A + A.T) + _, S, Vt = np.linalg.svd(B) + H = Vt.T @ np.diag(S) @ Vt + Ahat = 0.5 * (B + H) + Ahat = 0.5 * (Ahat + Ahat.T) + # Test positive definiteness and fix if needed + try: + np.linalg.cholesky(Ahat) + return Ahat + except np.linalg.LinAlgError: + pass + spacing = np.spacing(np.linalg.norm(A)) + I = np.eye(A.shape[0]) + k = 1 + while True: + try: + np.linalg.cholesky(Ahat) + return Ahat + except np.linalg.LinAlgError: + mineig = np.min(np.real(np.linalg.eigvalsh(Ahat))) + Ahat += I * (-mineig * k ** 2 + spacing) + k += 1 + if k > 100: + return Ahat + + @staticmethod + def _ztest_pvalue(param, se): + """Two-sided z-test p-value for H0: param == 0.""" + se_safe = np.where(se > 0, se, 1.0) + z = np.abs(param / se_safe) + p = 2.0 * (1.0 - norm.cdf(z)) + # Where se was 0, return 1.0 + p = np.where(se > 0, p, 1.0) + return p + + @staticmethod + def PP_ComputeParamStandardErrors( + dN, + xKFinal, + WKFinal, + Ahat, + Qhat, + x0hat, + Px0hat, + ExpectationSumsFinal, + fitType, + muhat, + betahat, + gammahat, + windowTimes, + HkAll, + PPEM_Constraints=None, + ): + """Compute standard errors via the observed information matrix. + + Uses a Monte-Carlo approximation of the missing information matrix + (McLachlan & Krishnan, Eq. 4.7). + + Parameters + ---------- + dN : (C, N) spike observations + xKFinal : (dx, N) smoothed states + WKFinal : (dx, dx, N) smoothed state covariances + Ahat : (dx, dx) estimated state transition + Qhat : (dx, dx) estimated state noise covariance + x0hat : (dx,) estimated initial state + Px0hat : (dx, dx) estimated initial state covariance + ExpectationSumsFinal : dict with sufficient statistics + fitType : 'poisson' or 'binomial' + muhat : (C,) estimated baseline rates + betahat : (dx, C) estimated stimulus coefficients + gammahat : (nW, C) or scalar estimated history coefficients + windowTimes : history window boundaries or None + HkAll : (N, nW, C) history design tensor + PPEM_Constraints : dict from PP_EMCreateConstraints + + Returns + ------- + SE : dict of standard errors for each parameter group + Pvals : dict of p-values for each parameter group + nTerms : int, total number of estimated parameters + """ + if PPEM_Constraints is None: + PPEM_Constraints = DecodingAlgorithms.PP_EMCreateConstraints() + + Ahat = np.atleast_2d(Ahat) + Qhat = np.atleast_2d(Qhat) + Px0hat = np.atleast_2d(Px0hat) + x0hat = np.asarray(x0hat, dtype=float).reshape(-1) + muhat = np.asarray(muhat, dtype=float).reshape(-1) + betahat = np.atleast_2d(betahat) + gammahat = np.asarray(gammahat, dtype=float) + dN = np.atleast_2d(dN) + + dx = Ahat.shape[0] + N = xKFinal.shape[1] + K = N + numCells = betahat.shape[1] + fitType = str(fitType).lower() + + # ---- Complete Information Matrices ---- + + # A information + if PPEM_Constraints["EstimateA"]: + n1_A, n2_A = Ahat.shape + Qinv = np.linalg.inv(Qhat) + if PPEM_Constraints["AhatDiag"]: + IAComp = np.zeros((n1_A, n1_A)) + for l in range(n1_A): + el = np.zeros(n1_A) + el[l] = 1.0 + em = np.zeros(n2_A) + em[l] = 1.0 + termMat = Qinv @ np.outer(el, em) @ (ExpectationSumsFinal["Sxkm1xkm1"] * np.eye(n1_A)) + IAComp[:, l] = np.diag(termMat) + else: + nA = Ahat.size + IAComp = np.zeros((nA, nA)) + cnt = 0 + for l in range(n1_A): + el = np.zeros(n1_A) + el[l] = 1.0 + for m in range(n2_A): + em = np.zeros(n2_A) + em[m] = 1.0 + termMat = Qinv @ np.outer(el, em) @ ExpectationSumsFinal["Sxkm1xkm1"] + IAComp[:, cnt] = termMat.T.ravel() + cnt += 1 + else: + IAComp = np.zeros((0, 0)) + + # Q information + n1_Q, n2_Q = Qhat.shape + Qinv = np.linalg.inv(Qhat) + if PPEM_Constraints["QhatDiag"]: + if PPEM_Constraints["QhatIsotropic"]: + IQComp = np.array([[0.5 * N * dx * Qhat[0, 0] ** (-2)]]) + else: + IQComp = np.zeros((n1_Q, n1_Q)) + cnt = 0 + for l in range(n1_Q): + el = np.zeros(n1_Q) + el[l] = 1.0 + termMat = N / 2.0 * Qinv @ np.outer(el, el) @ Qinv + IQComp[:, cnt] = np.diag(termMat) + cnt += 1 + else: + nQ = Qhat.size + IQComp = np.zeros((nQ, nQ)) + cnt = 0 + for l in range(n1_Q): + el = np.zeros(n1_Q) + el[l] = 1.0 + for m in range(n2_Q): + em = np.zeros(n2_Q) + em[m] = 1.0 + termMat = N / 2.0 * Qinv @ np.outer(em, el) @ Qinv + IQComp[:, cnt] = termMat.T.ravel() + cnt += 1 + + # Px0 information + if PPEM_Constraints["EstimatePx0"]: + Px0inv = np.linalg.inv(Px0hat) + if PPEM_Constraints["Px0Isotropic"]: + ISComp = np.array([[0.5 * dx * Px0hat[0, 0] ** (-2)]]) + else: + n1_S, n2_S = Px0hat.shape + ISComp = np.zeros((n1_S, n1_S)) + cnt = 0 + for l in range(n1_S): + el = np.zeros(n1_S) + el[l] = 1.0 + termMat = 0.5 * Px0inv @ np.outer(el, el) @ Px0inv + ISComp[:, cnt] = np.diag(termMat) + cnt += 1 + else: + ISComp = np.zeros((0, 0)) + + # x0 information + if PPEM_Constraints["Estimatex0"]: + Qinv = np.linalg.inv(Qhat) + Px0inv = np.linalg.inv(Px0hat) + Ix0Comp = Px0inv + Ahat.T @ Qinv @ Ahat + else: + Ix0Comp = np.zeros((0, 0)) + + # Monte Carlo draws for expectation approximation + McExp = PPEM_Constraints["mcIter"] + xKDrawExp = np.zeros((dx, K, McExp)) + for k in range(K): + WuTemp = WKFinal[:, :, k] + try: + chol_m = np.linalg.cholesky(WuTemp).T # upper triangular + except np.linalg.LinAlgError: + eigv, eigvec = np.linalg.eigh(WuTemp) + eigv = np.maximum(eigv, 1e-12) + chol_m = np.linalg.cholesky(eigvec @ np.diag(eigv) @ eigvec.T).T + z = np.random.randn(dx, McExp) + xKDrawExp[:, k, :] = xKFinal[:, k:k + 1] + chol_m @ z + + # Beta information (Hessian approximation via MC) + IBetaComp = np.zeros((dx * numCells, dx * numCells)) + # xkPerm: (dx, McExp, K) + xkPerm = np.transpose(xKDrawExp, (0, 2, 1)) + + for c in range(numCells): + HessianTerm = np.zeros((dx, dx, K)) + for k in range(K): + Hk = HkAll[k, :, c] if HkAll.ndim == 3 else np.zeros(0) + xk = xkPerm[:, :, k] # (dx, McExp) + + gammaC = gammahat if gammahat.ndim == 0 or gammahat.size == 1 else gammahat[:, c] + gammaC = np.atleast_1d(gammaC) + + Hk_vec = np.atleast_1d(Hk) + hist_term = float(gammaC @ Hk_vec) if Hk_vec.size == gammaC.size and gammaC.size > 0 else 0.0 + + terms = muhat[c] + betahat[:, c] @ xk + hist_term + if fitType == "poisson": + ld = np.exp(np.clip(terms, -30, 30)) + HessianTerm[:, :, k] = -1.0 / McExp * (ld[None, :] * xk) @ xk.T + else: # binomial + ld = 1.0 / (1.0 + np.exp(-np.clip(terms, -30, 30))) + ExplambdaDeltaXkXk = 1.0 / McExp * (ld[None, :] * xk) @ xk.T + ExplambdaDeltaSqXkXkT = 1.0 / McExp * (ld[None, :] ** 2 * xk) @ xk.T + ExplambdaDeltaCubeXkXkT = 1.0 / McExp * (ld[None, :] ** 3 * xk) @ xk.T + HessianTerm[:, :, k] = ExplambdaDeltaXkXk + ExplambdaDeltaSqXkXkT - 2 * ExplambdaDeltaCubeXkXkT + + startInd = dx * c + endInd = dx * (c + 1) + IBetaComp[startInd:endInd, startInd:endInd] = -np.sum(HessianTerm, axis=2) + + # Mu information + IMuComp = np.zeros((numCells, numCells)) + for c in range(numCells): + HessianTerm = 0.0 + for k in range(K): + Hk_full = HkAll[:, :, c] if HkAll.ndim == 3 else np.zeros((K, 0)) + Hk_vec = Hk_full[k, :] if Hk_full.ndim == 2 and Hk_full.shape[0] > k else np.zeros(0) + xk = xkPerm[:, :, k] + + gammaC = gammahat if gammahat.ndim == 0 or gammahat.size == 1 else gammahat[:, c] + gammaC = np.atleast_1d(gammaC) + Hk_vec = np.atleast_1d(Hk_vec) + hist_term = float(gammaC @ Hk_vec) if Hk_vec.size == gammaC.size and gammaC.size > 0 else 0.0 + + terms = muhat[c] + betahat[:, c] @ xk + hist_term + if fitType == "poisson": + ld = np.exp(np.clip(terms, -30, 30)) + HessianTerm -= 1.0 / McExp * np.sum(ld) + else: + ld = 1.0 / (1.0 + np.exp(-np.clip(terms, -30, 30))) + ExplambdaDelta = 1.0 / McExp * np.sum(ld) + ExplambdaDeltaSq = 1.0 / McExp * np.sum(ld ** 2) + ExplambdaDeltaCubed = 1.0 / McExp * np.sum(ld ** 3) + HessianTerm += -(dN[c, k] + 1) * ExplambdaDelta + (dN[c, k] + 3) * ExplambdaDeltaSq - 3 * ExplambdaDeltaCubed + IMuComp[c, c] = -HessianTerm + + # Gamma information + gammahat_flat = gammahat.ravel() + has_gamma = gammahat_flat.size > 1 or (gammahat_flat.size == 1 and gammahat_flat[0] != 0) + if windowTimes is not None and len(windowTimes) > 0 and has_gamma: + nHist = HkAll.shape[1] if HkAll.ndim == 3 else 0 + IGammaComp = np.zeros((nHist * numCells, nHist * numCells)) + for c in range(numCells): + HessianTerm = np.zeros((nHist, nHist)) + Hk_full = HkAll[:, :, c] if HkAll.ndim == 3 else np.zeros((K, nHist)) + for k in range(K): + Hk_vec = Hk_full[k, :] + xk = xkPerm[:, :, k] + gammaC = gammahat if gammahat.ndim == 0 or gammahat.size == 1 else gammahat[:, c] + gammaC = np.atleast_1d(gammaC) + hist_term = float(gammaC @ Hk_vec) if Hk_vec.size == gammaC.size else 0.0 + terms = muhat[c] + betahat[:, c] @ xk + hist_term + if fitType == "poisson": + ld = np.exp(np.clip(terms, -30, 30)) + ExplambdaDelta = 1.0 / McExp * np.sum(ld) + HessianTerm -= np.outer(Hk_vec, Hk_vec) * ExplambdaDelta + else: + ld = 1.0 / (1.0 + np.exp(-np.clip(terms, -30, 30))) + ExplambdaDelta = 1.0 / McExp * np.sum(ld) + ExplambdaDeltaSq = 1.0 / McExp * np.sum(ld ** 2) + ExplambdaDeltaCubed = 1.0 / McExp * np.sum(ld ** 2) # Matlab uses ld.^2 here + HessianTerm += (-ExplambdaDelta * (dN[c, k] + 1) + + ExplambdaDeltaSq * (dN[c, k] + 3) + - 2 * ExplambdaDeltaCubed) * np.outer(Hk_vec, Hk_vec) + startInd = nHist * c + endInd = nHist * (c + 1) + IGammaComp[startInd:endInd, startInd:endInd] = -HessianTerm + else: + IGammaComp = np.zeros((0, 0)) + + # Assemble complete information matrix + n1 = IAComp.shape[0] if PPEM_Constraints["EstimateA"] else 0 + n2 = IQComp.shape[0] + n3 = ISComp.shape[0] if PPEM_Constraints["EstimatePx0"] else 0 + n4 = Ix0Comp.shape[0] if PPEM_Constraints["Estimatex0"] else 0 + n5 = IMuComp.shape[0] + n6 = IBetaComp.shape[0] + n7 = IGammaComp.shape[0] if has_gamma else 0 + nTerms = n1 + n2 + n3 + n4 + n5 + n6 + n7 + + IComp = np.zeros((nTerms, nTerms)) + off = 0 + if PPEM_Constraints["EstimateA"] and n1 > 0: + IComp[off:off + n1, off:off + n1] = IAComp + off = n1 + IComp[off:off + n2, off:off + n2] = IQComp + off = n1 + n2 + if PPEM_Constraints["EstimatePx0"] and n3 > 0: + IComp[off:off + n3, off:off + n3] = ISComp + off = n1 + n2 + n3 + if PPEM_Constraints["Estimatex0"] and n4 > 0: + IComp[off:off + n4, off:off + n4] = Ix0Comp + off = n1 + n2 + n3 + n4 + IComp[off:off + n5, off:off + n5] = IMuComp + off = n1 + n2 + n3 + n4 + n5 + IComp[off:off + n6, off:off + n6] = IBetaComp + off = n1 + n2 + n3 + n4 + n5 + n6 + if n7 > 0: + IComp[off:off + n7, off:off + n7] = IGammaComp + + # ---- Missing Information Matrix (Monte Carlo) ---- + Mc = PPEM_Constraints["mcIter"] + xKDraw = np.zeros((dx, N, Mc)) + for n_idx in range(N): + WuTemp = WKFinal[:, :, n_idx] + try: + chol_m = np.linalg.cholesky(WuTemp).T + except np.linalg.LinAlgError: + eigv, eigvec = np.linalg.eigh(WuTemp) + eigv = np.maximum(eigv, 1e-12) + chol_m = np.linalg.cholesky(eigvec @ np.diag(eigv) @ eigvec.T).T + z = np.random.randn(dx, Mc) + xKDraw[:, n_idx, :] = xKFinal[:, n_idx:n_idx + 1] + chol_m @ z + + if PPEM_Constraints["EstimatePx0"] or PPEM_Constraints["Estimatex0"]: + try: + chol_m = np.linalg.cholesky(Px0hat).T + except np.linalg.LinAlgError: + eigv, eigvec = np.linalg.eigh(Px0hat) + eigv = np.maximum(eigv, 1e-12) + chol_m = np.linalg.cholesky(eigvec @ np.diag(eigv) @ eigvec.T).T + z = np.random.randn(dx, Mc) + x0Draw = x0hat[:, None] + chol_m @ z + else: + x0Draw = np.tile(x0hat[:, None], (1, Mc)) + + Qinv = np.linalg.inv(Qhat) + Px0inv = np.linalg.inv(Px0hat) + IMc = np.zeros((nTerms, nTerms, Mc)) + + for c_mc in range(Mc): + x_K = xKDraw[:, :, c_mc] + x_0 = x0Draw[:, c_mc] + Dx = x_K.shape[0] + + Sxkm1xk = np.zeros((Dx, Dx)) + Sxkm1xkm1 = np.zeros((Dx, Dx)) + Sxkxk = np.zeros((Dx, Dx)) + + for k in range(K): + if k == 0: + Sxkm1xk += np.outer(x_0, x_K[:, k]) + Sxkm1xkm1 += np.outer(x_0, x_0) + else: + Sxkm1xk += np.outer(x_K[:, k - 1], x_K[:, k]) + Sxkm1xkm1 += np.outer(x_K[:, k - 1], x_K[:, k - 1]) + Sxkxk += np.outer(x_K[:, k], x_K[:, k]) + + Sxkxk = 0.5 * (Sxkxk + Sxkxk.T) + sumXkTerms_mc = Sxkxk - Ahat @ Sxkm1xk - Sxkm1xk.T @ Ahat.T + Ahat @ Sxkm1xkm1 @ Ahat.T + Sxkxkm1 = Sxkm1xk.T + sumXkTerms_mc = 0.5 * (sumXkTerms_mc + sumXkTerms_mc.T) + + # Score for A + if PPEM_Constraints["EstimateA"]: + ScorA = np.linalg.solve(Qhat, Sxkxkm1 - Ahat @ Sxkm1xkm1) + if PPEM_Constraints["AhatDiag"]: + ScoreAMc = np.diag(ScorA) + else: + ScoreAMc = ScorA.T.ravel() + else: + ScoreAMc = np.array([]) + + # Score for Q + if PPEM_Constraints["QhatDiag"]: + if PPEM_Constraints["QhatIsotropic"]: + ScoreQ = -0.5 * (K * Dx * Qhat[0, 0] ** (-1) - Qhat[0, 0] ** (-2) * np.trace(sumXkTerms_mc)) + ScoreQMc = np.atleast_1d(ScoreQ) + else: + ScoreQ = -0.5 * np.linalg.solve(Qhat, K * np.eye(dx) - np.linalg.solve(Qhat, sumXkTerms_mc).T) + ScoreQMc = np.diag(ScoreQ) + else: + ScoreQ = -0.5 * np.linalg.solve(Qhat, K * np.eye(dx) - np.linalg.solve(Qhat, sumXkTerms_mc).T) + ScoreQMc = ScoreQ.T.ravel() + + # Score for Px0 + if PPEM_Constraints["Px0Isotropic"]: + diff = x_0 - x0hat + ScoreSMc = np.atleast_1d(-0.5 * (Dx * Px0hat[0, 0] ** (-1) + - Px0hat[0, 0] ** (-2) * np.dot(diff, diff))) + else: + diff = x_0 - x0hat + ScorS = -0.5 * np.linalg.solve(Px0hat, np.eye(dx) - np.linalg.solve(Px0hat, np.outer(diff, diff)).T) + ScoreSMc = np.diag(ScorS) + + # Score for x0 + Scorx0 = -np.linalg.solve(Px0hat, x_0 - x0hat) + Ahat.T @ Qinv @ (x_K[:, 0] - Ahat @ x_0) + Scorex0Mc = Scorx0.ravel() + + # Cell scores + ScoreMuMc = np.zeros(numCells) + ScoreBetaMc = np.array([], dtype=float) + ScoreGammaMc = np.array([], dtype=float) + + for nc in range(numCells): + Hk_full = HkAll[:, :, nc] if HkAll.ndim == 3 else np.zeros((K, 0)) + nHist_c = Hk_full.shape[1] + gammaC = gammahat if gammahat.ndim == 0 or gammahat.size == 1 else gammahat[:, nc] + gammaC = np.atleast_1d(gammaC) + + hist_terms = Hk_full @ gammaC if gammaC.size == nHist_c and nHist_c > 0 else np.zeros(K) + terms = muhat[nc] + betahat[:, nc] @ x_K + hist_terms + + if fitType == "poisson": + ld = np.exp(np.clip(terms, -30, 30)) + ScoreMuMc[nc] = np.sum(dN[nc, :] - ld) + ScoreBetaMc = np.concatenate([ScoreBetaMc, + np.sum((dN[nc, :] - ld)[None, :] * x_K, axis=1)]) + if nHist_c > 0: + ScoreGammaMc = np.concatenate([ScoreGammaMc, + np.sum((dN[nc, :] - ld)[None, :] * Hk_full.T, axis=1)]) + else: # binomial + ld = 1.0 / (1.0 + np.exp(-np.clip(terms, -30, 30))) + ScoreMuMc[nc] = np.sum(dN[nc, :] - (dN[nc, :] + 1) * ld + ld ** 2) + ScoreBetaMc = np.concatenate([ScoreBetaMc, + np.sum((dN[nc, :] * (1 - ld) - ld * (1 - ld))[None, :] * x_K, axis=1)]) + if nHist_c > 0: + ScoreGammaMc = np.concatenate([ScoreGammaMc, + np.sum((dN[nc, :] - (dN[nc, :] + 1) * ld + ld ** 2)[None, :] * Hk_full.T, axis=1)]) + + # Assemble score vector + ScoreVec = np.concatenate([ScoreAMc, ScoreQMc]) + if PPEM_Constraints["EstimatePx0"]: + ScoreVec = np.concatenate([ScoreVec, ScoreSMc]) + if PPEM_Constraints["Estimatex0"]: + ScoreVec = np.concatenate([ScoreVec, Scorex0Mc]) + ScoreVec = np.concatenate([ScoreVec, ScoreMuMc, ScoreBetaMc]) + if has_gamma and ScoreGammaMc.size > 0: + ScoreVec = np.concatenate([ScoreVec, ScoreGammaMc]) + + IMc[:, :, c_mc] = np.outer(ScoreVec, ScoreVec) + + IMissing = np.mean(IMc, axis=2) + IObs = IComp - IMissing + try: + invIObs = np.linalg.inv(IObs) + except np.linalg.LinAlgError: + invIObs = np.linalg.pinv(IObs) + invIObs = DecodingAlgorithms._nearestSPD(invIObs) + + VarVec = np.diag(invIObs) + SEVec = np.sqrt(np.maximum(VarVec, 0.0)) + + # Unpack SE vector + off = 0 + SEAterms = SEVec[off:off + n1]; off += n1 + SEQterms = SEVec[off:off + n2]; off += n2 + SEPx0terms = SEVec[off:off + n3]; off += n3 + SEx0terms = SEVec[off:off + n4]; off += n4 + SEMuTerms = SEVec[off:off + n5]; off += n5 + SEBetaTerms = SEVec[off:off + n6]; off += n6 + SEGammaTerms = SEVec[off:off + n7] + + SE = {} + Pvals = {} + + # A + if PPEM_Constraints["EstimateA"]: + if PPEM_Constraints["AhatDiag"]: + SEA = np.diag(SEAterms) + pA = np.diag(DecodingAlgorithms._ztest_pvalue(np.diag(Ahat), np.diag(SEA))) + else: + SEA = SEAterms.reshape(Ahat.shape[1], Ahat.shape[0]).T + pA = DecodingAlgorithms._ztest_pvalue(Ahat.ravel(), SEA.ravel()).reshape(Ahat.shape) + SE["A"] = SEA + Pvals["A"] = pA + + # Q + if PPEM_Constraints["QhatDiag"]: + SEQ = np.diag(SEQterms) + if PPEM_Constraints["QhatIsotropic"]: + pQ = np.diag(DecodingAlgorithms._ztest_pvalue(np.atleast_1d(Qhat[0, 0]), np.atleast_1d(SEQ[0, 0]))) + else: + pQ = np.diag(DecodingAlgorithms._ztest_pvalue(np.diag(Qhat), np.diag(SEQ))) + else: + SEQ = SEQterms.reshape(Qhat.shape[1], Qhat.shape[0]).T + pQ = DecodingAlgorithms._ztest_pvalue(Qhat.ravel(), SEQ.ravel()).reshape(Qhat.shape) + SE["Q"] = SEQ + Pvals["Q"] = pQ + + # Px0 + if PPEM_Constraints["EstimatePx0"]: + SES = np.diag(SEPx0terms) + if PPEM_Constraints["Px0Isotropic"]: + pPx0 = np.diag(DecodingAlgorithms._ztest_pvalue(np.atleast_1d(Px0hat[0, 0]), np.atleast_1d(SES[0, 0]))) + else: + pPx0 = np.diag(DecodingAlgorithms._ztest_pvalue(np.diag(Px0hat), np.diag(SES))) + SE["Px0"] = SES + Pvals["Px0"] = pPx0 + + # x0 + if PPEM_Constraints["Estimatex0"]: + SEx0 = SEx0terms + pX0 = DecodingAlgorithms._ztest_pvalue(x0hat, SEx0) + SE["x0"] = SEx0 + Pvals["x0"] = pX0 + + # Mu + SEMu = SEMuTerms + pMu = DecodingAlgorithms._ztest_pvalue(muhat, SEMu) + SE["mu"] = SEMu + Pvals["mu"] = pMu + + # Beta + SEBeta = SEBetaTerms.reshape(betahat.shape[1], betahat.shape[0]).T + pBeta = DecodingAlgorithms._ztest_pvalue(betahat.ravel(), SEBeta.ravel()).reshape(betahat.shape) + SE["beta"] = SEBeta + Pvals["beta"] = pBeta + + # Gamma + if has_gamma and n7 > 0: + SEGamma = SEGammaTerms.reshape(gammahat.shape[1], gammahat.shape[0]).T if gammahat.ndim == 2 else SEGammaTerms + pGamma = DecodingAlgorithms._ztest_pvalue(gammahat.ravel(), SEGammaTerms).reshape(gammahat.shape) if gammahat.ndim == 2 else DecodingAlgorithms._ztest_pvalue(gammahat.ravel(), SEGammaTerms) + SE["gamma"] = SEGamma + Pvals["gamma"] = pGamma + + return SE, Pvals, nTerms + + @staticmethod + def PP_EStep(A, Q, dN, mu, beta, fitType, gamma, HkAll, x0, Px0): + """E-step for PP EM: forward filter + RTS smoother + cross-covariance. + + Parameters + ---------- + A : (dx, dx) state transition matrix + Q : (dx, dx) state noise covariance + dN : (C, N) binary spike observations + mu : (C,) baseline log-rate + beta : (dx, C) stimulus coefficients + fitType : 'poisson' or 'binomial' + gamma : (nW, C) or scalar history coefficients + HkAll : (N, nW, C) history design tensor + x0 : (dx,) initial state + Px0 : (dx, dx) initial state covariance + + Returns + ------- + x_K : (dx, N) smoothed states + W_K : (dx, dx, N) smoothed covariances + logll : float, log-likelihood + ExpectationSums : dict of sufficient statistics + """ + A = np.atleast_2d(A).astype(float) + Q = np.atleast_2d(Q).astype(float) + dN = np.atleast_2d(dN).astype(float) + mu = np.asarray(mu, dtype=float).reshape(-1) + beta = np.atleast_2d(beta).astype(float) + gamma = np.asarray(gamma, dtype=float) + x0 = np.asarray(x0, dtype=float).reshape(-1) + Px0 = np.atleast_2d(Px0).astype(float) + fitType = str(fitType).lower() + + numCells, K = dN.shape + Dx = A.shape[1] + + # Forward filter + x_p = np.zeros((Dx, K + 1)) + x_u = np.zeros((Dx, K)) + W_p = np.zeros((Dx, Dx, K + 1)) + W_u = np.zeros((Dx, Dx, K)) + x_p[:, 0] = A @ x0 + W_p[:, :, 0] = A @ Px0 @ A.T + Q + + # Permute HkAll for PPDecode_updateLinear: (nW, C, N) + HkPerm = np.transpose(HkAll, (1, 2, 0)) if HkAll.ndim == 3 else HkAll + + for k in range(K): + x_u[:, k], W_u[:, :, k], _ = DecodingAlgorithms.PPDecode_updateLinear( + x_p[:, k], W_p[:, :, k], dN, mu, beta, fitType, gamma, HkPerm, k + 1, None + ) + A_k = A[:, :, min(k, A.shape[2] - 1)] if A.ndim == 3 else A + Q_k = Q[:, :, min(k, Q.shape[2] - 1)] if Q.ndim == 3 else Q + x_p[:, k + 1], W_p[:, :, k + 1] = DecodingAlgorithms.PPDecode_predict( + x_u[:, k], W_u[:, :, k], A_k, Q_k + ) + + # RTS smoother using kalman_smootherFromFiltered + # Convert state-major (dx, K+1/K) to time-major (K+1/K, dx) for + # the smoother, which uses _state_history_time_major internally. + x_p_tm = x_p.T # (K+1, dx) + W_p_tm = np.transpose(W_p, (2, 0, 1)) # (K+1, dx, dx) + x_u_tm = x_u.T # (K, dx) + W_u_tm = np.transpose(W_u, (2, 0, 1)) # (K, dx, dx) + + x_K_tm, W_K_tm, Lk = DecodingAlgorithms.kalman_smootherFromFiltered( + A, x_p_tm, W_p_tm, x_u_tm, W_u_tm + ) + + # Convert back to state-major: x_K (dx, K), W_K (dx, dx, K) + x_K = x_K_tm.T if x_K_tm.ndim == 2 else x_K_tm + W_K = np.transpose(W_K_tm, (1, 2, 0)) if W_K_tm.ndim == 3 else W_K_tm + + numStates = x_K.shape[0] + + # Cross-covariance Wku + Wku = np.zeros((numStates, numStates, K, K)) + for k in range(K): + Wku[:, :, k, k] = W_K[:, :, k] + + # W_u and W_p remain in state-major (dx, dx, K) format + W_u_sm = W_u + W_p_sm = W_p + + Dk = np.zeros((numStates, numStates, K)) + for u in range(K - 1, 0, -1): + k = u - 1 + Dk[:, :, k] = W_u_sm[:, :, k] @ A.T @ np.linalg.inv(W_p_sm[:, :, k + 1]) + Wku[:, :, k, u] = Dk[:, :, k] @ Wku[:, :, k + 1, u] + Wku[:, :, u, k] = Wku[:, :, k, u].T + + # Sufficient statistics + Sxkm1xk = np.zeros((Dx, Dx)) + Sxkm1xkm1 = np.zeros((Dx, Dx)) + Sxkxk = np.zeros((Dx, Dx)) + + for k in range(K): + if k == 0: + Sxkm1xk += Px0 @ A.T @ np.linalg.inv(W_p_sm[:, :, 0]) @ Wku[:, :, 0, 0] + Sxkm1xkm1 += Px0 + np.outer(x0, x0) + else: + Sxkm1xk += Wku[:, :, k - 1, k] + np.outer(x_K[:, k - 1], x_K[:, k]) + Sxkm1xkm1 += Wku[:, :, k - 1, k - 1] + np.outer(x_K[:, k - 1], x_K[:, k - 1]) + Sxkxk += Wku[:, :, k, k] + np.outer(x_K[:, k], x_K[:, k]) + + Sxkxk = 0.5 * (Sxkxk + Sxkxk.T) + sumXkTerms = Sxkxk - A @ Sxkm1xk - Sxkm1xk.T @ A.T + A @ Sxkm1xkm1 @ A.T + Sxkxkm1 = Sxkm1xk.T + + # Point process log-likelihood + sumPPll = 0.0 + + if fitType == "poisson": + for k in range(K): + if HkAll.ndim == 3: + Hk = HkAll[k, :, :] # (nW, C) — need to handle orientation + if Hk.shape[0] == numCells: + Hk = Hk.T + else: + Hk = np.zeros((0, numCells)) + + xk = x_K[:, k] + gammaC = np.tile(gamma, numCells) if gamma.ndim == 0 or gamma.size == 1 else gamma + gammaC = np.atleast_2d(gammaC) + if gammaC.shape[0] == 1 and gammaC.shape[1] == 1: + gammaC = np.full((max(Hk.shape[0], 1), numCells), float(gamma.ravel()[0]) if gamma.size > 0 else 0.0) + + if Hk.ndim == 2 and Hk.shape[0] > 0 and gammaC.shape[0] == Hk.shape[0]: + hist_diag = np.diag(gammaC.T @ Hk) if Hk.shape[0] > 0 else np.zeros(numCells) + else: + hist_diag = np.zeros(numCells) + + terms = mu + beta.T @ xk + hist_diag + Wk = W_K[:, :, k] + ld = np.exp(np.clip(terms, -30, 30)) + bt = beta + ExplambdaDelta = ld + 0.5 * (ld * np.diag(bt.T @ Wk @ bt)) + ExplogLD = terms + sumPPll += float(np.sum(dN[:, k] * ExplogLD - ExplambdaDelta)) + + elif fitType == "binomial": + for k in range(K): + if HkAll.ndim == 3: + Hk = HkAll[k, :, :] + if Hk.shape[0] == numCells: + Hk = Hk.T + else: + Hk = np.zeros((0, numCells)) + + xk = x_K[:, k] + gammaC = np.tile(gamma, numCells) if gamma.ndim == 0 or gamma.size == 1 else gamma + gammaC = np.atleast_2d(gammaC) + if gammaC.shape[0] == 1 and gammaC.shape[1] == 1: + gammaC = np.full((max(Hk.shape[0], 1), numCells), float(gamma.ravel()[0]) if gamma.size > 0 else 0.0) + + if Hk.ndim == 2 and Hk.shape[0] > 0 and gammaC.shape[0] == Hk.shape[0]: + hist_diag = np.diag(gammaC.T @ Hk) if Hk.shape[0] > 0 else np.zeros(numCells) + else: + hist_diag = np.zeros(numCells) + + terms = mu + beta.T @ xk + hist_diag + Wk = W_K[:, :, k] + ld_raw = np.clip(terms, -30, 30) + ld = 1.0 / (1.0 + np.exp(-ld_raw)) + bt = beta + btWbt_diag = np.diag(bt.T @ Wk @ bt) + ExplambdaDelta = ld + 0.5 * (ld * (1 - ld) * (1 - 2 * ld)) * btWbt_diag + ExplogLD = np.log(np.maximum(ld, 1e-30)) + 0.5 * (-ld * (1 - ld)) * btWbt_diag + sumPPll += float(np.sum(dN[:, k] * ExplogLD - ExplambdaDelta)) + + det_Q = max(float(np.linalg.det(Q)), np.finfo(float).tiny) + det_Px0 = max(float(np.linalg.det(Px0)), np.finfo(float).tiny) + logll = ( + -Dx * K / 2.0 * np.log(2.0 * np.pi) + - K / 2.0 * np.log(det_Q) + - Dx / 2.0 * np.log(2.0 * np.pi) + - 0.5 * np.log(det_Px0) + + sumPPll + - 0.5 * np.trace(np.linalg.solve(Q, sumXkTerms)) + - Dx / 2.0 + ) + + ExpectationSums = { + "Sxkm1xkm1": Sxkm1xkm1, + "Sxkm1xk": Sxkm1xk, + "Sxkxkm1": Sxkxkm1, + "Sxkxk": Sxkxk, + "sumXkTerms": sumXkTerms, + "sumPPll": sumPPll, + } + + return x_K, W_K, logll, ExpectationSums + + @staticmethod + def PP_MStep( + dN, x_K, W_K, x0, Px0, ExpectationSums, fitType, + muhat, betahat, gammahat, windowTimes, HkAll, + PPEM_Constraints=None, MstepMethod="NewtonRaphson", + ): + """M-step for PP EM: update all model parameters. + + Parameters + ---------- + dN : (C, N) spike observations + x_K : (dx, N) smoothed states + W_K : (dx, dx, N) smoothed covariances + x0 : (dx,) current initial state estimate + Px0 : (dx, dx) current initial covariance estimate + ExpectationSums : dict from E-step + fitType : 'poisson' or 'binomial' + muhat : (C,) current baseline rates + betahat : (dx, C) current stimulus coefficients + gammahat : scalar or (nW, C) current history coefficients + windowTimes : history window boundaries or None + HkAll : (N, nW, C) history tensor + PPEM_Constraints : dict from PP_EMCreateConstraints + MstepMethod : 'NewtonRaphson' (default) or 'GLM' + + Returns + ------- + Ahat, Qhat, muhat_new, betahat_new, gammahat_new, x0hat, Px0hat + """ + if PPEM_Constraints is None: + PPEM_Constraints = DecodingAlgorithms.PP_EMCreateConstraints() + + Sxkm1xkm1 = ExpectationSums["Sxkm1xkm1"] + Sxkxkm1 = ExpectationSums["Sxkxkm1"] + sumXkTerms = ExpectationSums["sumXkTerms"] + + dx, K = x_K.shape + numCells = dN.shape[0] + fitType = str(fitType).lower() + + x0 = np.asarray(x0, dtype=float).reshape(-1) + Px0 = np.atleast_2d(Px0).astype(float) + muhat = np.asarray(muhat, dtype=float).reshape(-1) + betahat = np.atleast_2d(betahat).astype(float) + gammahat = np.asarray(gammahat, dtype=float) + + # --- A update --- + I_dx = np.eye(dx) + if PPEM_Constraints["AhatDiag"]: + Ahat = (Sxkxkm1 * I_dx) @ np.linalg.inv(Sxkm1xkm1 * I_dx + 1e-12 * I_dx) + else: + Ahat = np.linalg.solve(Sxkm1xkm1.T + 1e-12 * I_dx, Sxkxkm1.T).T + + # --- Q update --- + if PPEM_Constraints["QhatDiag"]: + if PPEM_Constraints["QhatIsotropic"]: + Qhat = (1.0 / (dx * K)) * np.trace(sumXkTerms) * I_dx + else: + Qhat = (1.0 / K) * (sumXkTerms * I_dx) + Qhat = 0.5 * (Qhat + Qhat.T) + else: + Qhat = (1.0 / K) * sumXkTerms + Qhat = 0.5 * (Qhat + Qhat.T) + + # Ensure positive definiteness + eigvals, eigvecs = np.linalg.eigh(Qhat) + eigvals = np.maximum(eigvals, 1e-10) + Qhat = eigvecs @ np.diag(eigvals) @ eigvecs.T + Qhat = 0.5 * (Qhat + Qhat.T) + + # --- x0 update --- + if PPEM_Constraints["Estimatex0"]: + Px0inv = np.linalg.inv(Px0 + 1e-12 * I_dx) + Qinv = np.linalg.inv(Qhat + 1e-12 * I_dx) + x0hat = np.linalg.solve(Px0inv + Ahat.T @ Qinv @ Ahat, + Ahat.T @ Qinv @ x_K[:, 0] + Px0inv @ x0) + else: + x0hat = x0.copy() + + # --- Px0 update --- + if PPEM_Constraints["EstimatePx0"]: + if PPEM_Constraints["Px0Isotropic"]: + diff = x0hat - x0 + Px0hat = (np.dot(diff, diff) / (dx * K)) * I_dx + else: + diff = x0hat - x0 + Px0hat = np.outer(diff, diff) * I_dx + Px0hat = 0.5 * (Px0hat + Px0hat.T) + # Ensure positive definiteness + eigvals, eigvecs = np.linalg.eigh(Px0hat) + eigvals = np.maximum(eigvals, 1e-10) + Px0hat = eigvecs @ np.diag(eigvals) @ eigvecs.T + else: + Px0hat = Px0.copy() + + betahat_new = betahat.copy() + gammahat_new = gammahat.copy() if gammahat.ndim > 0 else np.atleast_1d(gammahat).copy() + muhat_new = muhat.copy() + + # --- Newton-Raphson for beta, mu, gamma --- + McExp = 50 + xKDrawExp = np.zeros((dx, K, McExp)) + diffTol = 1e-5 + + for k in range(K): + WuTemp = W_K[:, :, k] + try: + chol_m = np.linalg.cholesky(WuTemp).T + except np.linalg.LinAlgError: + eigv, eigvec = np.linalg.eigh(WuTemp) + eigv = np.maximum(eigv, 1e-12) + chol_m = np.linalg.cholesky(eigvec @ np.diag(eigv) @ eigvec.T).T + z = np.random.randn(dx, McExp) + xKDrawExp[:, k, :] = x_K[:, k:k + 1] + chol_m @ z + + # xkPerm: (dx, McExp, K) + xkPerm = np.transpose(xKDrawExp, (0, 2, 1)) + + # --- Beta Newton-Raphson --- + for c in range(numCells): + converged = False + maxIter_nr = 100 + for iteration in range(maxIter_nr): + HessianTerm = np.zeros((dx, dx)) + GradTerm = np.zeros(dx) + + for k in range(K): + Hk_full = HkAll[:, :, c] if HkAll.ndim == 3 else np.zeros((K, 0)) + Hk_vec = Hk_full[k, :] if Hk_full.ndim == 2 and Hk_full.shape[0] > k else np.zeros(0) + xk = xkPerm[:, :, k] # (dx, McExp) + + gammaC = gammahat if gammahat.ndim == 0 or gammahat.size == 1 else gammahat[:, c] + gammaC = np.atleast_1d(gammaC) + Hk_vec = np.atleast_1d(Hk_vec) + hist_term = float(gammaC @ Hk_vec) if Hk_vec.size == gammaC.size and gammaC.size > 0 else 0.0 + + terms = muhat[c] + betahat_new[:, c] @ xk + hist_term + + if fitType == "poisson": + ld = np.exp(np.clip(terms, -30, 30)) + ExpLambdaXk = (1.0 / McExp) * np.sum(ld[None, :] * xk, axis=1) + ExpLambdaXkXkT = (1.0 / McExp) * (ld[None, :] * xk) @ xk.T + GradTerm += dN[c, k] * x_K[:, k] - ExpLambdaXk + HessianTerm -= ExpLambdaXkXkT + else: # binomial + ld = 1.0 / (1.0 + np.exp(-np.clip(terms, -30, 30))) + ExplambdaDeltaXkXk = (1.0 / McExp) * (ld[None, :] * xk) @ xk.T + ExplambdaDeltaSqXkXkT = (1.0 / McExp) * ((ld ** 2)[None, :] * xk) @ xk.T + ExplambdaDeltaCubeXkXkT = (1.0 / McExp) * ((ld ** 3)[None, :] * xk) @ xk.T + ExpLambdaXk = (1.0 / McExp) * np.sum(ld[None, :] * xk, axis=1) + ExpLambdaSquaredXk = (1.0 / McExp) * np.sum((ld ** 2)[None, :] * xk, axis=1) + GradTerm += dN[c, k] * x_K[:, k] - (dN[c, k] + 1) * ExpLambdaXk + ExpLambdaSquaredXk + HessianTerm += ExplambdaDeltaXkXk + ExplambdaDeltaSqXkXkT - 2 * ExplambdaDeltaCubeXkXkT + + if np.any(np.isnan(HessianTerm)) or np.any(np.isinf(HessianTerm)): + betahat_newTemp = betahat_new[:, c] + else: + try: + betahat_newTemp = betahat_new[:, c] - np.linalg.solve(HessianTerm, GradTerm) + except np.linalg.LinAlgError: + betahat_newTemp = betahat_new[:, c] + if np.any(np.isnan(betahat_newTemp)): + betahat_newTemp = betahat_new[:, c] + + mabsDiff = float(np.max(np.abs(betahat_newTemp - betahat_new[:, c]))) + if mabsDiff < diffTol: + converged = True + betahat_new[:, c] = betahat_newTemp + if converged: + break + + # --- Mu Newton-Raphson --- + for c in range(numCells): + converged = False + maxIter_nr = 100 + for iteration in range(maxIter_nr): + HessianTerm = 0.0 + GradTerm = 0.0 + + for k in range(K): + Hk_full = HkAll[:, :, c] if HkAll.ndim == 3 else np.zeros((K, 0)) + Hk_vec = Hk_full[k, :] if Hk_full.ndim == 2 and Hk_full.shape[0] > k else np.zeros(0) + xk = xkPerm[:, :, k] + + gammaC = gammahat if gammahat.ndim == 0 or gammahat.size == 1 else gammahat[:, c] + gammaC = np.atleast_1d(gammaC) + Hk_vec = np.atleast_1d(Hk_vec) + hist_term = float(gammaC @ Hk_vec) if Hk_vec.size == gammaC.size and gammaC.size > 0 else 0.0 + + terms = muhat_new[c] + betahat[:, c] @ xk + hist_term + + if fitType == "poisson": + ld = np.exp(np.clip(terms, -30, 30)) + ExpLambdaDelta = (1.0 / McExp) * np.sum(ld) + GradTerm += dN[c, k] - ExpLambdaDelta + HessianTerm -= ExpLambdaDelta + else: # binomial + ld = 1.0 / (1.0 + np.exp(-np.clip(terms, -30, 30))) + ExpLambdaDelta = (1.0 / McExp) * np.sum(ld) + ExpLambdaDeltaSq = (1.0 / McExp) * np.sum(ld ** 2) + ExpLambdaDeltaCubed = (1.0 / McExp) * np.sum(ld ** 3) + GradTerm += dN[c, k] - (dN[c, k] + 1) * ExpLambdaDelta + ExpLambdaDeltaSq + HessianTerm += -(dN[c, k] + 1) * ExpLambdaDelta + (dN[c, k] + 3) * ExpLambdaDeltaSq - 2 * ExpLambdaDeltaCubed + + if np.isnan(HessianTerm) or np.isinf(HessianTerm) or abs(HessianTerm) < 1e-30: + muhat_newTemp = muhat_new[c] + else: + muhat_newTemp = muhat_new[c] - GradTerm / HessianTerm + if np.isnan(muhat_newTemp): + muhat_newTemp = muhat_new[c] + + mabsDiff = abs(muhat_newTemp - muhat_new[c]) + if mabsDiff < diffTol: + converged = True + muhat_new[c] = muhat_newTemp + if converged: + break + + # --- Gamma Newton-Raphson --- + gammahat_flat = gammahat_new.ravel() + has_gamma = (windowTimes is not None and len(windowTimes) > 0 + and (gammahat_flat.size > 1 or (gammahat_flat.size == 1 and gammahat_flat[0] != 0))) + + if has_gamma and gammahat_new.ndim >= 1: + nGamma = gammahat_new.shape[0] if gammahat_new.ndim == 1 else gammahat_new.shape[0] + for c in range(numCells): + converged = False + maxIter_nr = 100 + gammaC = gammahat_new if gammahat_new.ndim == 0 or gammahat_new.size == 1 else gammahat_new[:, c] if gammahat_new.ndim == 2 else gammahat_new + gammaC = np.atleast_1d(gammaC).copy() + + for iteration in range(maxIter_nr): + HessianTerm = np.zeros((nGamma, nGamma)) + GradTerm = np.zeros(nGamma) + + for k in range(K): + Hk_full = HkAll[:, :, c] if HkAll.ndim == 3 else np.zeros((K, 0)) + Hk_vec = Hk_full[k, :] if Hk_full.ndim == 2 and Hk_full.shape[0] > k else np.zeros(0) + Hk_vec = np.atleast_1d(Hk_vec) + xk = xkPerm[:, :, k] + + hist_term = float(gammaC @ Hk_vec) if Hk_vec.size == gammaC.size and gammaC.size > 0 else 0.0 + terms = muhat[c] + betahat[:, c] @ xk + hist_term + + if fitType == "poisson": + ld = np.exp(np.clip(terms, -30, 30)) + ExpLambdaDelta = (1.0 / McExp) * np.sum(ld) + GradTerm += (dN[c, k] - ExpLambdaDelta) * Hk_vec + HessianTerm -= ExpLambdaDelta * np.outer(Hk_vec, Hk_vec) + else: # binomial + ld = 1.0 / (1.0 + np.exp(-np.clip(terms, -30, 30))) + ExpLambdaDelta = (1.0 / McExp) * np.sum(ld) + ExpLambdaDeltaSq = (1.0 / McExp) * np.sum(ld ** 2) + ExpLambdaDeltaCubed = (1.0 / McExp) * np.sum(ld ** 3) + GradTerm += (dN[c, k] - (dN[c, k] + 1) * ExpLambdaDelta + ExpLambdaDeltaSq) * Hk_vec + HessianTerm += (-(dN[c, k] + 1) * ExpLambdaDelta + (dN[c, k] + 3) * ExpLambdaDeltaSq - 2 * ExpLambdaDeltaCubed) * np.outer(Hk_vec, Hk_vec) + + if np.any(np.isnan(HessianTerm)) or np.any(np.isinf(HessianTerm)): + gammahat_newTemp = gammaC + else: + try: + gammahat_newTemp = gammaC - np.linalg.solve(HessianTerm, GradTerm) + except np.linalg.LinAlgError: + gammahat_newTemp = gammaC + if np.any(np.isnan(gammahat_newTemp)): + gammahat_newTemp = gammaC + + mabsDiff = float(np.max(np.abs(gammahat_newTemp - gammaC))) + if mabsDiff < diffTol: + converged = True + gammaC = gammahat_newTemp + if converged: + break + + if gammahat_new.ndim == 2: + gammahat_new[:, c] = gammaC + else: + gammahat_new = gammaC + + return Ahat, Qhat, muhat_new, betahat_new, gammahat_new, x0hat, Px0hat + + @staticmethod + def PP_EM( + dN, + Ahat0, + Qhat0, + mu, + beta, + fitType="poisson", + delta=0.001, + gamma=None, + windowTimes=None, + x0=None, + Px0=None, + PPEM_Constraints=None, + MstepMethod="NewtonRaphson", + ): + """Full Point-Process state-space EM algorithm. + + Estimates state-space model parameters (A, Q, mu, beta, gamma) via EM + for point-process observations. Unlike PPSS_EM, this operates on raw + spike observations with explicit beta/mu/gamma parameters (no basis + functions). + + Parameters + ---------- + dN : (C, N) binary spike observations + Ahat0 : (dx, dx) initial state transition matrix + Qhat0 : (dx, dx) initial state noise covariance + mu : (C,) initial baseline log-rates + beta : (dx, C) initial stimulus coefficients + fitType : 'poisson' or 'binomial' + delta : float, time bin width + gamma : (nW, C) or scalar, initial history coefficients + windowTimes : history window boundaries + x0 : (dx,) initial state (default zeros) + Px0 : (dx, dx) initial state covariance + PPEM_Constraints : dict from PP_EMCreateConstraints + MstepMethod : 'NewtonRaphson' or 'GLM' + + Returns + ------- + xKFinal, WKFinal, Ahat, Qhat, muhat, betahat, gammahat, + x0hat, Px0hat, IC, SE, Pvals, nIter + """ + from .history import History # local import to avoid circular dependency + + Ahat0 = np.atleast_2d(Ahat0).astype(float) + Qhat0 = np.atleast_2d(Qhat0).astype(float) + numStates = Ahat0.shape[0] + dN = np.atleast_2d(dN).astype(float) + + if PPEM_Constraints is None: + PPEM_Constraints = DecodingAlgorithms.PP_EMCreateConstraints() + if Px0 is None: + Px0 = 1e-9 * np.eye(numStates) + else: + Px0 = np.atleast_2d(Px0).astype(float) + if x0 is None: + x0 = np.zeros(numStates) + else: + x0 = np.asarray(x0, dtype=float).reshape(-1) + if gamma is None: + gamma = np.zeros(0) + gamma = np.asarray(gamma, dtype=float) + + if delta is None or delta == 0: + delta = 0.001 + + if windowTimes is None: + gamma_flat = gamma.ravel() + if gamma_flat.size == 0 or (gamma_flat.size == 1 and gamma_flat[0] == 0): + windowTimes = [] + else: + windowTimes = np.arange(0, (gamma.shape[0] + 2) * delta, delta).tolist() + + mu = np.asarray(mu, dtype=float).reshape(-1) + beta = np.atleast_2d(beta).astype(float) + + # Build HkAll from spike trains and history windows + K_cells = dN.shape[0] + N_time = dN.shape[1] + minTime = 0.0 + maxTime = (N_time - 1) * delta + + if len(windowTimes) > 0: + histObj = History(windowTimes, minTime, maxTime) + HkAll_list = [] + for k in range(K_cells): + spike_indices = np.where(dN[k, :] == 1)[0] + spike_times = (spike_indices) * delta + nst = nspikeTrain(spike_times) + nst.setMinTime(minTime) + nst.setMaxTime(maxTime) + hmat = histObj.computeHistory(nst).dataToMatrix() + HkAll_list.append(hmat) + # Stack: (N_time, nW, K_cells) + HkAll = np.stack(HkAll_list, axis=2) + else: + HkAll = np.zeros((N_time, 0, K_cells)) + gamma = np.zeros(1) + gamma[0] = 0.0 + + # EM setup + tolAbs = 1e-3 + llTol = 1e-3 + maxIter = 100 + numToKeep = 10 + + # Circular buffer storage + A_buf = [None] * numToKeep + Q_buf = [None] * numToKeep + x0_buf = [None] * numToKeep + Px0_buf = [None] * numToKeep + mu_buf = [None] * numToKeep + beta_buf = [None] * numToKeep + gamma_buf = [None] * numToKeep + x_K_buf = [None] * numToKeep + W_K_buf = [None] * numToKeep + ExpSums_buf = [None] * numToKeep + + # Scaled system initialization + A0 = Ahat0.copy() + Q0 = Qhat0.copy() + + A_buf[0] = A0.copy() + Q_buf[0] = Q0.copy() + x0_buf[0] = x0.copy() + Px0_buf[0] = Px0.copy() + mu_buf[0] = mu.copy() + beta_buf[0] = beta.copy() + gamma_buf[0] = gamma.copy() + + # Apply scaling + try: + Tq = np.linalg.solve(np.linalg.cholesky(Q_buf[0]).T, np.eye(numStates)) + except np.linalg.LinAlgError: + Tq = np.eye(numStates) + TqInv = np.linalg.inv(Tq) + + A_buf[0] = Tq @ A_buf[0] @ TqInv + Q_buf[0] = Tq @ Q_buf[0] @ Tq.T + x0_buf[0] = Tq @ x0 + Px0_buf[0] = Tq @ Px0 @ Tq.T + beta_buf[0] = np.linalg.solve(Tq.T, beta_buf[0]) + + ll_list = [] + dLikelihood = [np.inf] + stoppingCriteria = False + cnt = 0 + + print(" Point-Process Observation EM Algorithm ") + while not stoppingCriteria and cnt < maxIter: + si = cnt % numToKeep + si_p1 = (cnt + 1) % numToKeep + si_m1 = (cnt - 1) % numToKeep + + print("-" * 80) + print(f"Iteration #{cnt + 1}") + print("-" * 80) + + # E-step + x_K_cur, W_K_cur, ll, ExpSums = DecodingAlgorithms.PP_EStep( + A_buf[si], Q_buf[si], dN, mu_buf[si], beta_buf[si], + fitType, gamma_buf[si], HkAll, x0_buf[si], Px0_buf[si] + ) + x_K_buf[si] = x_K_cur + W_K_buf[si] = W_K_cur + ExpSums_buf[si] = ExpSums + ll_list.append(ll) + + # M-step + Anew, Qnew, munew, bnew, gnew, x0new, Px0new = DecodingAlgorithms.PP_MStep( + dN, x_K_cur, W_K_cur, x0_buf[si], Px0_buf[si], ExpSums, + fitType, mu_buf[si], beta_buf[si], gamma_buf[si], + windowTimes, HkAll, PPEM_Constraints, MstepMethod + ) + A_buf[si_p1] = Anew + Q_buf[si_p1] = Qnew + mu_buf[si_p1] = munew + beta_buf[si_p1] = bnew + gamma_buf[si_p1] = gnew + x0_buf[si_p1] = x0new + Px0_buf[si_p1] = Px0new + + if not PPEM_Constraints["EstimateA"]: + A_buf[si_p1] = A_buf[si] + + # Convergence check + if cnt == 0: + dLikelihood.append(np.inf) + dMax = np.inf + else: + dLikelihood.append(ll_list[cnt] - ll_list[cnt - 1]) + dQvals = float(np.max(np.abs(np.sqrt(np.maximum(np.abs(Q_buf[si]), 0)) - np.sqrt(np.maximum(np.abs(Q_buf[si_m1]), 0))))) + dAvals = float(np.max(np.abs(A_buf[si] - A_buf[si_m1]))) + dMuvals = float(np.max(np.abs(mu_buf[si] - mu_buf[si_m1]))) + dBetavals = float(np.max(np.abs(beta_buf[si] - beta_buf[si_m1]))) + gam_cur = gamma_buf[si].ravel() if gamma_buf[si] is not None else np.zeros(1) + gam_prev = gamma_buf[si_m1].ravel() if gamma_buf[si_m1] is not None else np.zeros(1) + dGammavals = float(np.max(np.abs(gam_cur[:min(len(gam_cur), len(gam_prev))] - gam_prev[:min(len(gam_cur), len(gam_prev))]))) if gam_cur.size > 0 else 0.0 + dMax = max(dQvals, dAvals, dMuvals, dBetavals, dGammavals) + + if cnt == 0: + print("Max Parameter Change: N/A") + else: + print(f"Max Parameter Change: {dMax:.6f}") + + cnt += 1 + if dMax < tolAbs: + stoppingCriteria = True + print(f" EM converged at iteration# {cnt} b/c change in params was within criteria") + + if abs(dLikelihood[-1]) < llTol or dLikelihood[-1] < 0: + stoppingCriteria = True + print(f" EM stopped at iteration# {cnt} b/c change in likelihood was negative") + + print("-" * 80) + + # Select best iteration + ll_arr = np.array(ll_list) + if ll_arr.size > 0: + maxLLIndex = int(np.argmax(ll_arr)) + else: + maxLLIndex = 0 + maxLLIndMod = maxLLIndex % numToKeep + nIter = cnt + + xKFinal = x_K_buf[maxLLIndMod] if x_K_buf[maxLLIndMod] is not None else np.zeros((numStates, N_time)) + WKFinal = W_K_buf[maxLLIndMod] if W_K_buf[maxLLIndMod] is not None else np.zeros((numStates, numStates, N_time)) + Ahat = A_buf[maxLLIndMod] if A_buf[maxLLIndMod] is not None else A0 + Qhat = Q_buf[maxLLIndMod] if Q_buf[maxLLIndMod] is not None else Q0 + muhat = mu_buf[maxLLIndMod] if mu_buf[maxLLIndMod] is not None else mu + betahat = beta_buf[maxLLIndMod] if beta_buf[maxLLIndMod] is not None else beta + gammahat = gamma_buf[maxLLIndMod] if gamma_buf[maxLLIndMod] is not None else gamma + x0hat = x0_buf[maxLLIndMod] if x0_buf[maxLLIndMod] is not None else x0 + Px0hat = Px0_buf[maxLLIndMod] if Px0_buf[maxLLIndMod] is not None else Px0 + ExpSumsFinal = ExpSums_buf[maxLLIndMod] if ExpSums_buf[maxLLIndMod] is not None else {} + + # Unscale system + try: + Tq_unscale = np.linalg.solve(np.linalg.cholesky(Q0).T, np.eye(numStates)) + except np.linalg.LinAlgError: + Tq_unscale = np.eye(numStates) + TqInv_unscale = np.linalg.inv(Tq_unscale) + + Ahat = TqInv_unscale @ Ahat @ Tq_unscale + Qhat = TqInv_unscale @ Qhat @ TqInv_unscale.T + xKFinal = TqInv_unscale @ xKFinal + x0hat = TqInv_unscale @ x0hat + Px0hat = TqInv_unscale @ Px0hat @ TqInv_unscale.T + if WKFinal.ndim == 3: + for kk in range(WKFinal.shape[2]): + WKFinal[:, :, kk] = TqInv_unscale @ WKFinal[:, :, kk] @ TqInv_unscale.T + betahat = (betahat.T @ Tq_unscale).T + + # Compute standard errors + SE = {} + Pvals = {} + if ExpSumsFinal: + try: + SE, Pvals, _ = DecodingAlgorithms.PP_ComputeParamStandardErrors( + dN, xKFinal, WKFinal, Ahat, Qhat, x0hat, Px0hat, + ExpSumsFinal, fitType, muhat, betahat, gammahat, + windowTimes, HkAll, PPEM_Constraints + ) + except Exception: + pass + + # Information criteria + K_total = xKFinal.shape[1] + Dx = Ahat.shape[1] + + # Count parameters + if PPEM_Constraints["EstimateA"] and PPEM_Constraints["AhatDiag"]: + n1_ic = Ahat.shape[0] + elif PPEM_Constraints["EstimateA"]: + n1_ic = Ahat.size + else: + n1_ic = 0 + if PPEM_Constraints["QhatDiag"] and PPEM_Constraints["QhatIsotropic"]: + n2_ic = 1 + elif PPEM_Constraints["QhatDiag"]: + n2_ic = Qhat.shape[0] + else: + n2_ic = Qhat.size + if PPEM_Constraints["EstimatePx0"] and PPEM_Constraints["Px0Isotropic"]: + n3_ic = 1 + elif PPEM_Constraints["EstimatePx0"]: + n3_ic = Px0hat.shape[0] + else: + n3_ic = 0 + n4_ic = x0hat.size if PPEM_Constraints["Estimatex0"] else 0 + n5_ic = muhat.size + n6_ic = betahat.size + gammahat_flat = gammahat.ravel() + if gammahat_flat.size == 1 and gammahat_flat[0] == 0: + n7_ic = 0 + else: + n7_ic = gammahat.size + nTerms_ic = n1_ic + n2_ic + n3_ic + n4_ic + n5_ic + n6_ic + n7_ic + + sumXkTerms_ic = ExpSumsFinal.get("sumXkTerms", np.zeros((Dx, Dx))) + ll_best = ll_list[maxLLIndex] if ll_list else 0.0 + det_Q = max(float(np.linalg.det(Qhat)), np.finfo(float).tiny) + det_Px0 = max(float(np.linalg.det(Px0hat)), np.finfo(float).tiny) + + llobs = (ll_best + + Dx * K_total / 2.0 * np.log(2.0 * np.pi) + + K_total / 2.0 * np.log(det_Q) + + 0.5 * np.trace(np.linalg.solve(Qhat, sumXkTerms_ic)) + + Dx / 2.0 * np.log(2.0 * np.pi) + + 0.5 * np.log(det_Px0) + + 0.5 * Dx) + AIC = 2 * nTerms_ic - 2 * llobs + AICc = AIC + 2 * nTerms_ic * (nTerms_ic + 1) / max(K_total - nTerms_ic - 1, 1) + BIC = -2 * llobs + nTerms_ic * np.log(K_total) + + IC = { + "AIC": AIC, + "AICc": AICc, + "BIC": BIC, + "llobs": llobs, + "llcomp": ll_best, + } + + return (xKFinal, WKFinal, Ahat, Qhat, muhat, betahat, gammahat, + x0hat, Px0hat, IC, SE, Pvals, nIter) + + + # mPPCO family -- mixed Point-Process & Continuous Observation + # ------------------------------------------------------------------ + + @staticmethod + def mPPCODecode_predict(x_u, W_u, A, Q): + """Predict step for the mPPCO filter. + + Matlab: ``DecodingAlgorithms.mPPCODecode_predict`` (lines 4846-4854) + + Parameters + ---------- + x_u : array (ns,) -- updated state + W_u : array (ns,ns) -- updated covariance + A : array (ns,ns) -- state transition + Q : array (ns,ns) -- process noise + + Returns + ------- + x_p : array (ns,) + W_p : array (ns,ns) + """ + x_u = np.asarray(x_u, dtype=float).reshape(-1) + ns = x_u.size + A = np.asarray(A, dtype=float).reshape(ns, ns) + Q = np.asarray(Q, dtype=float).reshape(ns, ns) + W_u = np.asarray(W_u, dtype=float).reshape(ns, ns) + x_p = A @ x_u + W_p = A @ W_u @ A.T + Q + W_p = _symmetrize(W_p) + return x_p, W_p + + @staticmethod + def mPPCODecode_update(x_p, W_p, C, R, y, alpha, dN, mu, beta, + fitType='poisson', gamma=None, HkAll=None, + time_index=1, WuConv=None): + """Update step for the mPPCO filter (PP + continuous observation). + + Matlab: ``DecodingAlgorithms.mPPCODecode_update`` (lines 4855-4944) + + This combines both the point-process update terms (sumValVec/sumValMat) + AND the Kalman/continuous-observation terms C'*R^{-1}*C and C'*R^{-1}*(y-Cx-alpha). + + Parameters + ---------- + x_p : (ns,) -- predicted state + W_p : (ns,ns) -- predicted covariance + C : (nObs,ns) -- observation matrix + R : (nObs,nObs) -- observation noise covariance + y : (nObs,) -- continuous observation at this time step + alpha : (nObs,) -- observation offset + dN : (numCells,N) -- spike matrix (full) + mu : (numCells,) -- CIF baseline + beta : (ns,numCells) -- CIF state coefficients + fitType : 'poisson' or 'binomial' + gamma : (numWindows,numCells) or scalar -- history coefficients + HkAll : (numWindows,numCells,N) -- permuted history tensor with time on 3rd axis + time_index : int -- 1-based time index + WuConv : converged covariance or None + + Returns + ------- + x_u : (ns,) + W_u : (ns,ns) + lambdaDeltaMat : (numCells,1) + """ + x_p = np.asarray(x_p, dtype=float).reshape(-1) + ns = x_p.size + W_p = np.asarray(W_p, dtype=float).reshape(ns, ns) + obs = _as_observation_matrix(dN) + numCells = obs.shape[0] + C = np.asarray(C, dtype=float) + R = np.asarray(R, dtype=float) + y = np.asarray(y, dtype=float).reshape(-1) + alpha = np.asarray(alpha, dtype=float).reshape(-1) + mu_vec = np.asarray(mu, dtype=float).reshape(-1) + beta_mat = np.asarray(beta, dtype=float) + if beta_mat.ndim == 1: + beta_mat = beta_mat.reshape(-1, 1) + + # Default gamma + if gamma is None or (np.isscalar(gamma) and gamma == 0): + gamma_mat = np.zeros((1, numCells), dtype=float) + else: + gamma_mat = np.asarray(gamma, dtype=float) + if gamma_mat.ndim == 1: + gamma_mat = gamma_mat.reshape(-1, 1) + + # Default HkAll -- expects (numWindows, numCells, N) orientation + if HkAll is None: + HkAll_arr = np.zeros((1, numCells, 1), dtype=float) + else: + HkAll_arr = np.asarray(HkAll, dtype=float) + + sumValVec = np.zeros(ns, dtype=float) + sumValMat = np.zeros((ns, ns), dtype=float) + lambdaDeltaMat = np.zeros(numCells, dtype=float) + + # If gamma is scalar zero, expand + if gamma_mat.size == 1 and gamma_mat.flat[0] == 0: + gamma_mat = np.zeros_like(mu_vec).reshape(-1, 1) + + # Ensure gamma_mat is (numWindows, numCells) + if gamma_mat.shape[1] != numCells: + if gamma_mat.shape[0] == numCells: + gamma_mat = gamma_mat.T + + # Replicate gamma for all cells if needed + if gamma_mat.ndim == 2 and gamma_mat.shape[1] != numCells: + gamma_mat = np.tile(gamma_mat, (1, numCells)) + + # time_index is 1-based; extract history at this time + tidx = int(time_index) - 1 # zero-based + if HkAll_arr.ndim == 3 and HkAll_arr.shape[2] > tidx: + Histterm = HkAll_arr[:, :, tidx] # (numWindows, numCells) + else: + Histterm = np.zeros((gamma_mat.shape[0], numCells), dtype=float) + + if Histterm.shape[0] != numCells: + pass # already (numWindows, numCells) orientation + else: + if Histterm.shape[0] == numCells and Histterm.shape[1] != numCells: + Histterm = Histterm.T + + if str(fitType) == 'binomial': + # linTerm = mu + beta'*x_p + diag(gamma'*Histterm') + linTerm = mu_vec + beta_mat.T @ x_p + np.diag(gamma_mat.T @ Histterm) + exp_linTerm = np.exp(np.clip(linTerm, -500, 500)) + lambdaDeltaMat = exp_linTerm / (1.0 + exp_linTerm) + lambdaDeltaMat = np.where(np.isnan(lambdaDeltaMat) | np.isinf(lambdaDeltaMat), 1.0, lambdaDeltaMat) + + dN_t = obs[:, int(time_index) - 1] + factor = (dN_t - lambdaDeltaMat) * (1.0 - lambdaDeltaMat) + sumValVec = np.sum(beta_mat * factor[None, :], axis=1) + tempVec = (dN_t + (1.0 - 2.0 * lambdaDeltaMat)) * (1.0 - lambdaDeltaMat) * lambdaDeltaMat + sumValMat = (beta_mat * tempVec[None, :]) @ beta_mat.T + + elif str(fitType) == 'poisson': + linTerm = mu_vec + beta_mat.T @ x_p + np.diag(gamma_mat.T @ Histterm) + lambdaDeltaMat = np.exp(np.clip(linTerm, -500, 500)) + lambdaDeltaMat = np.where(np.isnan(lambdaDeltaMat) | np.isinf(lambdaDeltaMat), 1.0, lambdaDeltaMat) + + dN_t = obs[:, int(time_index) - 1] + sumValVec = np.sum(beta_mat * (dN_t - lambdaDeltaMat)[None, :], axis=1) + sumValMat = (beta_mat * lambdaDeltaMat[None, :]) @ beta_mat.T + + if WuConv is None or _is_empty_value(WuConv): + # sumValMat += C' * R^{-1} * C (continuous observation term) + sumValMat = sumValMat + C.T @ np.linalg.solve(R, C) + I = np.eye(ns, dtype=float) + try: + Wu = W_p @ (I - np.linalg.solve(I + sumValMat @ W_p, sumValMat @ W_p)) + except np.linalg.LinAlgError: + Wu = W_p.copy() + if np.any(np.isnan(Wu)) or np.any(np.isinf(Wu)): + Wu = W_p.copy() + W_u = _symmetrize(Wu) + else: + W_u = np.asarray(WuConv, dtype=float).reshape(ns, ns) + + # x_u = x_p + W_u*sumValVec + (W_u*C'/R)*(y - C*x_p - alpha) + x_u = x_p + W_u @ sumValVec + W_u @ C.T @ np.linalg.solve(R, y - C @ x_p - alpha) + + return x_u, W_u, lambdaDeltaMat.reshape(-1, 1) + + @staticmethod + def mPPCODecodeLinear(A, Q, C, R, y, alpha, dN, mu, beta, + fitType='poisson', delta=0.001, gamma=None, + windowTimes=None, x0=None, Px0=None, HkAll=None): + """Full mPPCO decode filter (linear CIF version). + + Matlab: ``DecodingAlgorithms.mPPCODecodeLinear`` (lines 4689-4845) + + Returns + ------- + x_p, W_p, x_u, W_u -- predicted / updated states & covariances + x_p : (ns, N+1), W_p : (ns, ns, N+1) + x_u : (ns, N), W_u : (ns, ns, N) + """ + obs = _as_observation_matrix(dN) + numCells, N = obs.shape + A_arr = np.asarray(A, dtype=float) + ns = A_arr.shape[0] + + # Defaults + if Px0 is None or _is_empty_value(Px0): + Px0 = np.zeros((ns, ns), dtype=float) + else: + Px0 = np.asarray(Px0, dtype=float).reshape(ns, ns) + if x0 is None or _is_empty_value(x0): + x0 = np.zeros(ns, dtype=float) + else: + x0 = np.asarray(x0, dtype=float).reshape(-1) + if gamma is None: + gamma = 0 + if delta is None: + delta = 0.001 + + minTime = 0.0 + maxTime = (N - 1) * delta + + # Build history tensor if not provided + if HkAll is None or _is_empty_value(HkAll): + if windowTimes is not None and not _is_empty_value(windowTimes): + wt = np.asarray(windowTimes, dtype=float).reshape(-1) + HkAll = _compute_history_terms(dN, delta, wt) # (N, numWindows, numCells) + gamma_arr = np.asarray(gamma, dtype=float) + if gamma_arr.ndim <= 1 and gamma_arr.size == 1 and numCells > 1: + gamma = np.tile(gamma_arr.reshape(-1, 1), (1, numCells)) + else: + HkAll = np.zeros((N, 1, numCells), dtype=float) + gamma = np.zeros(numCells, dtype=float) + else: + HkAll = np.asarray(HkAll, dtype=float) + + gamma_arr = np.asarray(gamma, dtype=float) + if gamma_arr.ndim == 2 and gamma_arr.shape[1] != numCells: + gamma = gamma_arr.T + + # Permute HkAll from (N, numWindows, numCells) to (numWindows, numCells, N) + # This is Matlab: permute(HkAll, [2 3 1]) + if HkAll.ndim == 3 and HkAll.shape[0] == N: + Histtermperm = np.transpose(HkAll, (1, 2, 0)) + else: + Histtermperm = HkAll + + mu_vec = np.asarray(mu, dtype=float).reshape(-1) + beta_mat = np.asarray(beta, dtype=float) + if beta_mat.ndim == 1: + beta_mat = beta_mat.reshape(-1, 1) + + # Allocate outputs + x_p = np.zeros((ns, N + 1), dtype=float) + x_u = np.zeros((ns, N), dtype=float) + W_p = np.zeros((ns, ns, N + 1), dtype=float) + W_u = np.zeros((ns, ns, N), dtype=float) + + # Time-varying or static matrices: pick slice for time 0 + def _sel_A(n): + if A_arr.ndim == 3: + return A_arr[:, :, min(n, A_arr.shape[2] - 1)] + return A_arr.reshape(ns, ns) + + def _sel_Q(n): + Q_arr = np.asarray(Q, dtype=float) + if Q_arr.ndim == 3: + return Q_arr[:, :, min(n, Q_arr.shape[2] - 1)] + return Q_arr.reshape(ns, ns) + + def _sel_C(n): + C_arr = np.asarray(C, dtype=float) + if C_arr.ndim == 3: + return C_arr[:, :, min(n, C_arr.shape[2] - 1)] + return C_arr + + def _sel_R(n): + R_arr = np.asarray(R, dtype=float) + if R_arr.ndim == 3: + return R_arr[:, :, min(n, R_arr.shape[2] - 1)] + return R_arr + + def _sel_alpha(n): + alpha_arr = np.asarray(alpha, dtype=float) + if alpha_arr.ndim >= 2 and alpha_arr.shape[-1] > 1: + return alpha_arr[:, min(n, alpha_arr.shape[-1] - 1)] + return alpha_arr.reshape(-1) + + # Initial prediction + A1 = _sel_A(0) + Q1 = _sel_Q(0) + x_p[:, 0] = A1 @ x0 + W_p[:, :, 0] = A1 @ Px0 @ A1.T + Q1 + + y_arr = np.asarray(y, dtype=float) + + for n in range(N): + # 1-based time_index for mPPCODecode_update + x_u[:, n], W_u[:, :, n], _ = DecodingAlgorithms.mPPCODecode_update( + x_p[:, n], W_p[:, :, n], + _sel_C(n), _sel_R(n), + y_arr[:, n] if y_arr.ndim == 2 else y_arr, + _sel_alpha(n), + dN, mu_vec, beta_mat, fitType, + gamma, Histtermperm, n + 1, None) + if n < N - 1: + x_p[:, n + 1], W_p[:, :, n + 1] = DecodingAlgorithms.mPPCODecode_predict( + x_u[:, n], W_u[:, :, n], _sel_A(n), _sel_Q(n)) + + return x_p, W_p, x_u, W_u + + @staticmethod + def mPPCO_fixedIntervalSmoother(A, Q, C, R, y, alpha, dN, lags, mu, beta, + fitType, delta=0.001, gamma=None, + windowTimes=None, x0=None, Px0=None, HkAll=None): + """State-augmentation smoother for the mPPCO filter. + + Matlab: ``DecodingAlgorithms.mPPCO_fixedIntervalSmoother`` (lines 4587-4688) + + Returns + ------- + x_pLag, W_pLag, x_uLag, W_uLag -- lagged state estimates + """ + obs = _as_observation_matrix(dN) + numCells, N = obs.shape + A_arr = np.asarray(A, dtype=float) + ns = A_arr.shape[0] + nObs = np.asarray(C, dtype=float).shape[0] + + if Px0 is None or _is_empty_value(Px0): + Px0 = np.zeros((ns, ns), dtype=float) + else: + Px0 = np.asarray(Px0, dtype=float).reshape(ns, ns) + if x0 is None or _is_empty_value(x0): + x0 = np.zeros(ns, dtype=float) + else: + x0 = np.asarray(x0, dtype=float).reshape(-1) + if gamma is None: + gamma = 0 + if delta is None: + delta = 0.001 + + minTime = 0.0 + maxTime = (N - 1) * delta + + # Build history if needed + if HkAll is None or _is_empty_value(HkAll): + if windowTimes is not None and not _is_empty_value(windowTimes): + wt = np.asarray(windowTimes, dtype=float).reshape(-1) + HkAll = _compute_history_terms(dN, delta, wt) + gamma_arr = np.asarray(gamma, dtype=float) + if gamma_arr.ndim <= 1 and gamma_arr.size == 1 and numCells > 1: + gamma = np.tile(gamma_arr.reshape(-1, 1), (1, numCells)) + else: + HkAll = np.zeros((N, 1, numCells), dtype=float) + gamma = np.zeros(numCells, dtype=float) + + gamma_arr = np.asarray(gamma, dtype=float) + if gamma_arr.ndim == 2 and gamma_arr.shape[1] != numCells: + gamma = gamma_arr.T + + lags = int(lags) + nStates = ns + + # Build augmented system + aug_dim = (lags + 1) * nStates + + def _sel_A(n): + if A_arr.ndim == 3: + return A_arr[:, :, min(n, A_arr.shape[2] - 1)] + return A_arr.reshape(ns, ns) + + def _sel_Q(n): + Q_arr = np.asarray(Q, dtype=float) + if Q_arr.ndim == 3: + return Q_arr[:, :, min(n, Q_arr.shape[2] - 1)] + return Q_arr.reshape(ns, ns) + + def _sel_C(n): + C_arr = np.asarray(C, dtype=float) + if C_arr.ndim == 3: + return C_arr[:, :, min(n, C_arr.shape[2] - 1)] + return C_arr + + def _sel_R(n): + R_arr = np.asarray(R, dtype=float) + if R_arr.ndim == 3: + return R_arr[:, :, min(n, R_arr.shape[2] - 1)] + return R_arr + + Alag = np.zeros((aug_dim, aug_dim, N), dtype=float) + Qlag = np.zeros((aug_dim, aug_dim, N), dtype=float) + Clag = np.zeros((nObs, aug_dim, N), dtype=float) + Rlag = np.zeros((nObs, nObs, N), dtype=float) + x0lag = np.zeros(aug_dim, dtype=float) + Px0lag = np.zeros((aug_dim, aug_dim), dtype=float) + Px0lag[:nStates, :nStates] = Px0 + x0lag[:nStates] = x0 + + for n in range(N): + offset = 0 + for i in range(lags + 1): + if i == 0: + Alag[offset:offset + nStates, offset:offset + nStates, n] = _sel_A(n) + Qlag[offset:offset + nStates, offset:offset + nStates, n] = _sel_Q(n) + Clag[:nObs, offset:offset + nStates, n] = _sel_C(n) + Rlag[:nObs, :nObs, n] = _sel_R(n) + else: + Alag[offset:offset + nStates, offset - nStates:offset, n] = np.eye(nStates) + # Qlag block remains zeros + # Clag block remains zeros + offset += nStates + + betaLag = np.zeros((aug_dim, numCells), dtype=float) + beta_mat = np.asarray(beta, dtype=float) + if beta_mat.ndim == 1: + beta_mat = beta_mat.reshape(-1, 1) + betaLag[:nStates, :numCells] = beta_mat + + x_p, W_p, x_u, W_u = DecodingAlgorithms.mPPCODecodeLinear( + Alag, Qlag, Clag, Rlag, y, alpha, dN, + mu, betaLag, fitType, delta, gamma, windowTimes, + x0lag, Px0lag, HkAll) + + # Extract lagged portion + lag_start = lags * nStates + lag_end = (lags + 1) * nStates + x_pLag = x_p[lag_start:lag_end, :] + W_pLag = W_p[lag_start:lag_end, lag_start:lag_end, :] + x_uLag = x_u[lag_start:lag_end, :] + W_uLag = W_u[lag_start:lag_end, lag_start:lag_end, :] + + return x_pLag, W_pLag, x_uLag, W_uLag + + @staticmethod + def mPPCO_EMCreateConstraints(EstimateA=1, AhatDiag=0, QhatDiag=1, + QhatIsotropic=0, RhatDiag=1, + RhatIsotropic=0, Estimatex0=1, + EstimatePx0=1, Px0Isotropic=0, + mcIter=1000, EnableIkeda=0): + """Create constraint dictionary for mPPCO EM. + + Matlab: ``DecodingAlgorithms.mPPCO_EMCreateConstraints`` (lines 4945-5005) + """ + C = {} + C['EstimateA'] = int(EstimateA) + C['AhatDiag'] = int(AhatDiag) + C['QhatDiag'] = int(QhatDiag) + C['QhatIsotropic'] = 1 if (QhatDiag and QhatIsotropic) else 0 + C['RhatDiag'] = int(RhatDiag) + C['RhatIsotropic'] = 1 if (RhatDiag and RhatIsotropic) else 0 + C['Estimatex0'] = int(Estimatex0) + C['EstimatePx0'] = int(EstimatePx0) + C['Px0Isotropic'] = 1 if (EstimatePx0 and Px0Isotropic) else 0 + C['mcIter'] = int(mcIter) + C['EnableIkeda'] = int(EnableIkeda) + return C + + @staticmethod + def mPPCO_ComputeParamStandardErrors(y, dN, xKFinal, WKFinal, Ahat, Qhat, + Chat, Rhat, alphahat, x0hat, Px0hat, + ExpectationSumsFinal, fitType, + muhat, betahat, gammahat, + windowTimes, HkAll, + mPPCOEM_Constraints=None): + """Compute standard errors for mPPCO EM parameters. + + Matlab: ``DecodingAlgorithms.mPPCO_ComputeParamStandardErrors`` (lines 5006-6138) + + Uses the observed information matrix approach: Io = Ic - Im (McLachlan & Krishnan Eq 4.7). + """ + if mPPCOEM_Constraints is None: + mPPCOEM_Constraints = DecodingAlgorithms.mPPCO_EMCreateConstraints() + + y = np.asarray(y, dtype=float) + obs = _as_observation_matrix(dN) + xKFinal = np.asarray(xKFinal, dtype=float) + Ahat = np.asarray(Ahat, dtype=float) + Qhat = np.asarray(Qhat, dtype=float) + Chat = np.asarray(Chat, dtype=float) + Rhat = np.asarray(Rhat, dtype=float) + alphahat = np.asarray(alphahat, dtype=float).reshape(-1) + x0hat = np.asarray(x0hat, dtype=float).reshape(-1) + Px0hat = np.asarray(Px0hat, dtype=float) + muhat = np.asarray(muhat, dtype=float).reshape(-1) + betahat = np.asarray(betahat, dtype=float) + if betahat.ndim == 1: + betahat = betahat.reshape(-1, 1) + gammahat = np.asarray(gammahat, dtype=float) + HkAll = np.asarray(HkAll, dtype=float) + + dy, N = y.shape if y.ndim == 2 else (1, y.shape[0]) + K = N + dx = xKFinal.shape[0] + numCells = betahat.shape[1] + McExp = mPPCOEM_Constraints['mcIter'] + + Qhat_inv = np.linalg.inv(Qhat) + Rhat_inv = np.linalg.inv(Rhat) + Px0hat_inv = np.linalg.inv(Px0hat + np.eye(Px0hat.shape[0]) * 1e-12) + + # ---- Complete Information Matrices ---- + + # IAComp - A parameter + if mPPCOEM_Constraints['EstimateA']: + n1A, n2A = Ahat.shape + el = np.eye(n1A) + em = np.eye(n2A) + if mPPCOEM_Constraints['AhatDiag']: + IAComp = np.zeros((n1A, n1A)) + for l in range(n1A): + termMat = Qhat_inv @ np.outer(el[:, l], em[:, l]) @ ExpectationSumsFinal['Sxkm1xkm1'] * np.eye(n1A) + IAComp[:, l] = np.diag(termMat) + else: + nA = Ahat.size + IAComp = np.zeros((nA, nA)) + cnt = 0 + for l in range(n1A): + for m in range(n2A): + termMat = Qhat_inv @ np.outer(el[:, l], em[:, m]) @ ExpectationSumsFinal['Sxkm1xkm1'] + IAComp[:, cnt] = termMat.T.reshape(-1) + cnt += 1 + + # ICComp - C parameter + n1C, n2C = Chat.shape + nC = Chat.size + ICComp = np.zeros((nC, nC)) + el = np.eye(n1C) + em = np.eye(n2C) + cnt = 0 + for l in range(n1C): + for m in range(n2C): + termMat = Rhat_inv @ np.outer(el[:, l], em[:, m]) @ ExpectationSumsFinal['Sxkxk'] + ICComp[:, cnt] = termMat.T.reshape(-1) + cnt += 1 + + # IRComp - R parameter + n1R, n2R = Rhat.shape + el = np.eye(n1R) + em = np.eye(n2R) + if mPPCOEM_Constraints['RhatDiag']: + if mPPCOEM_Constraints['RhatIsotropic']: + IRComp = np.array([[0.5 * N * dy * Rhat[0, 0] ** (-2)]]) + else: + IRComp = np.zeros((n1R, n1R)) + for l in range(n1R): + termMat = N / 2.0 * Rhat_inv @ np.outer(em[:, l], el[:, l]) @ Rhat_inv + IRComp[:, l] = np.diag(termMat) + else: + nR = Rhat.size + IRComp = np.zeros((nR, nR)) + cnt = 0 + for l in range(n1R): + for m in range(n2R): + termMat = N / 2.0 * Rhat_inv @ np.outer(em[:, m], el[:, l]) @ Rhat_inv + IRComp[:, cnt] = termMat.T.reshape(-1) + cnt += 1 + + # IQComp - Q parameter + n1Q, n2Q = Qhat.shape + el = np.eye(n1Q) + em = np.eye(n2Q) + if mPPCOEM_Constraints['QhatDiag']: + if mPPCOEM_Constraints['QhatIsotropic']: + IQComp = np.array([[0.5 * N * dx * Qhat[0, 0] ** (-2)]]) + else: + IQComp = np.zeros((n1Q, n1Q)) + for l in range(n1Q): + termMat = N / 2.0 * Qhat_inv @ np.outer(em[:, l], el[:, l]) @ Qhat_inv + IQComp[:, l] = np.diag(termMat) + else: + nQ = Qhat.size + IQComp = np.zeros((nQ, nQ)) + cnt = 0 + for l in range(n1Q): + for m in range(n2Q): + termMat = N / 2.0 * Qhat_inv @ np.outer(em[:, m], el[:, l]) @ Qhat_inv + IQComp[:, cnt] = termMat.T.reshape(-1) + cnt += 1 + + # ISComp - Px0 parameter + if mPPCOEM_Constraints['EstimatePx0']: + if mPPCOEM_Constraints['Px0Isotropic']: + ISComp = np.array([[0.5 * dx * Px0hat[0, 0] ** (-2)]]) + else: + n1S, n2S = Px0hat.shape + ISComp = np.zeros((n1S, n1S)) + el = np.eye(n1S) + em = np.eye(n2S) + for l in range(n1S): + termMat = 0.5 * Px0hat_inv @ np.outer(em[:, l], el[:, l]) @ Px0hat_inv + ISComp[:, l] = np.diag(termMat) + + # Ix0Comp + if mPPCOEM_Constraints['Estimatex0']: + Ix0Comp = Px0hat_inv + Ahat.T @ Qhat_inv @ Ahat + + # IAlphaComp + IAlphaComp = N * Rhat_inv + + # IBetaComp - Monte Carlo + xKDrawExp = np.zeros((dx, K, McExp), dtype=float) + for k in range(K): + WuTemp = WKFinal[:, :, k] + try: + chol_m = np.linalg.cholesky(WuTemp) + except np.linalg.LinAlgError: + chol_m = np.linalg.cholesky(nearestSPD(WuTemp)) + z = np.random.randn(dx, McExp) + xKDrawExp[:, k, :] = xKFinal[:, k:k + 1] + chol_m @ z + + IBetaComp = np.zeros((dx * numCells, dx * numCells), dtype=float) + xkPerm = np.transpose(xKDrawExp, (0, 2, 1)) # (dx, McExp, K) + + for c in range(numCells): + HessianTerm = np.zeros((dx, dx), dtype=float) + for k in range(K): + Hk = HkAll[k, :, c] if HkAll.ndim == 3 else np.zeros(1) + xk = xkPerm[:, :, k] + gammaC = gammahat if gammahat.size == 1 else (gammahat[:, c] if gammahat.ndim == 2 else gammahat) + terms = muhat[c] + betahat[:, c] @ xk + float(np.dot(gammaC.reshape(-1), Hk.reshape(-1))) + if fitType == 'poisson': + ld = np.exp(np.clip(terms, -500, 500)) + HessianTerm -= (1.0 / McExp) * (np.tile(ld, (dx, 1)) * xk) @ xk.T + else: + ld = np.exp(np.clip(terms, -500, 500)) + ld = ld / (1.0 + ld) + EldXkXk = (1.0 / McExp) * (np.tile(ld, (dx, 1)) * xk) @ xk.T + EldSqXkXk = (1.0 / McExp) * (np.tile(ld ** 2, (dx, 1)) * xk) @ xk.T + EldCubeXkXk = (1.0 / McExp) * (np.tile(ld ** 3, (dx, 1)) * xk) @ xk.T + HessianTerm += EldXkXk + EldSqXkXk - 2.0 * EldCubeXkXk + si = dx * c + ei = dx * (c + 1) + IBetaComp[si:ei, si:ei] = -HessianTerm + + # IMuComp + IMuComp = np.zeros((numCells, numCells), dtype=float) + for c in range(numCells): + HessianTerm = 0.0 + for k in range(K): + Hk_full = HkAll[:, :, c] if HkAll.ndim == 3 else np.zeros((K, 1)) + Hk = Hk_full[k, :] + xk = xkPerm[:, :, k] + gammaC = gammahat if gammahat.size == 1 else (gammahat[:, c] if gammahat.ndim == 2 else gammahat) + terms = muhat[c] + betahat[:, c] @ xk + float(np.dot(gammaC.reshape(-1), Hk.reshape(-1))) + if fitType == 'poisson': + ld = np.exp(np.clip(terms, -500, 500)) + HessianTerm -= (1.0 / McExp) * float(np.sum(ld)) + else: + ld = np.exp(np.clip(terms, -500, 500)) / (1.0 + np.exp(np.clip(terms, -500, 500))) + Eld = (1.0 / McExp) * float(np.sum(ld)) + EldSq = (1.0 / McExp) * float(np.sum(ld ** 2)) + EldCube = (1.0 / McExp) * float(np.sum(ld ** 3)) + HessianTerm += -(obs[c, k] + 1) * Eld + (obs[c, k] + 3) * EldSq - 3 * EldCube + IMuComp[c, c] = -HessianTerm + + # IGammaComp + nHist = HkAll.shape[1] if HkAll.ndim == 3 else 1 + IGammaComp = np.zeros((nHist * numCells, nHist * numCells), dtype=float) + has_gamma = (windowTimes is not None and not _is_empty_value(windowTimes) + and np.any(gammahat != 0)) + if has_gamma: + for c in range(numCells): + HessianTerm = np.zeros((nHist, nHist), dtype=float) + for k in range(K): + Hk_full = HkAll[:, :, c] if HkAll.ndim == 3 else np.zeros((K, 1)) + Hk = Hk_full[k, :] + xk = xkPerm[:, :, k] + gammaC = gammahat if gammahat.size == 1 else (gammahat[:, c] if gammahat.ndim == 2 else gammahat) + terms = muhat[c] + betahat[:, c] @ xk + float(np.dot(gammaC.reshape(-1), Hk.reshape(-1))) + if fitType == 'poisson': + ld = np.exp(np.clip(terms, -500, 500)) + Eld = (1.0 / McExp) * float(np.sum(ld)) + HessianTerm -= np.outer(Hk, Hk) * Eld + else: + ld_raw = np.exp(np.clip(terms, -500, 500)) + ld = ld_raw / (1.0 + ld_raw) + Eld = (1.0 / McExp) * float(np.sum(ld)) + EldSq = (1.0 / McExp) * float(np.sum(ld ** 2)) + EldCube = (1.0 / McExp) * float(np.sum(ld ** 2)) # matches Matlab typo (ld.^2) + HessianTerm += (-Eld * (obs[c, k] + 1) + EldSq * (obs[c, k] + 3) - 2 * EldCube) * np.outer(Hk, Hk) + si = nHist * c + ei = nHist * (c + 1) + IGammaComp[si:ei, si:ei] = -HessianTerm + + # Assemble IComp + n1 = IAComp.shape[0] if mPPCOEM_Constraints['EstimateA'] else 0 + n2 = IQComp.shape[0] + n3 = ICComp.shape[0] + n4 = IRComp.shape[0] + n5 = ISComp.shape[0] if mPPCOEM_Constraints['EstimatePx0'] else 0 + n6 = Ix0Comp.shape[0] if mPPCOEM_Constraints['Estimatex0'] else 0 + n7 = IAlphaComp.shape[0] + n8 = IMuComp.shape[0] + n9 = IBetaComp.shape[0] + if gammahat.size == 1 and float(gammahat.flat[0]) == 0: + n10 = 0 + else: + n10 = IGammaComp.shape[0] + nTerms = n1 + n2 + n3 + n4 + n5 + n6 + n7 + n8 + n9 + n10 + IComp = np.zeros((nTerms, nTerms), dtype=float) + + offset = 0 + if mPPCOEM_Constraints['EstimateA']: + IComp[offset:offset + n1, offset:offset + n1] = IAComp + offset += n1 + IComp[offset:offset + n2, offset:offset + n2] = IQComp + offset += n2 + IComp[offset:offset + n3, offset:offset + n3] = ICComp + offset += n3 + IComp[offset:offset + n4, offset:offset + n4] = IRComp + offset += n4 + if mPPCOEM_Constraints['EstimatePx0']: + IComp[offset:offset + n5, offset:offset + n5] = ISComp + offset += n5 + if mPPCOEM_Constraints['Estimatex0']: + IComp[offset:offset + n6, offset:offset + n6] = Ix0Comp + offset += n6 + IComp[offset:offset + n7, offset:offset + n7] = IAlphaComp + offset += n7 + IComp[offset:offset + n8, offset:offset + n8] = IMuComp + offset += n8 + IComp[offset:offset + n9, offset:offset + n9] = IBetaComp + offset += n9 + if n10 > 0: + IComp[offset:offset + n10, offset:offset + n10] = IGammaComp + + # ---- Missing Information Matrix (Monte Carlo) ---- + Mc = McExp + xKDraw = np.zeros((dx, N, Mc), dtype=float) + for n in range(N): + WuTemp = WKFinal[:, :, n] + try: + chol_m = np.linalg.cholesky(WuTemp) + except np.linalg.LinAlgError: + chol_m = np.linalg.cholesky(nearestSPD(WuTemp)) + z = np.random.randn(dx, Mc) + xKDraw[:, n, :] = xKFinal[:, n:n + 1] + chol_m @ z + + if mPPCOEM_Constraints['EstimatePx0'] or mPPCOEM_Constraints['Estimatex0']: + try: + chol_m = np.linalg.cholesky(Px0hat) + except np.linalg.LinAlgError: + chol_m = np.linalg.cholesky(nearestSPD(Px0hat)) + z = np.random.randn(dx, Mc) + x0Draw = x0hat.reshape(-1, 1) + chol_m @ z + else: + x0Draw = np.tile(x0hat.reshape(-1, 1), (1, Mc)) + + IMc = np.zeros((nTerms, nTerms, Mc), dtype=float) + Dx = dx + Dy = dy + + for c_mc in range(Mc): + x_K = xKDraw[:, :, c_mc] + x_0 = x0Draw[:, c_mc] + + Sxkm1xk = np.zeros((Dx, Dx)) + Sxkm1xkm1 = np.zeros((Dx, Dx)) + Sxkxk = np.zeros((Dx, Dx)) + Sykyk = np.zeros((Dy, Dy)) + Sxkyk = np.zeros((Dx, Dy)) + + for k in range(K): + if k == 0: + Sxkm1xk += np.outer(x_0, x_K[:, k]) + Sxkm1xkm1 += np.outer(x_0, x_0) + else: + Sxkm1xk += np.outer(x_K[:, k - 1], x_K[:, k]) + Sxkm1xkm1 += np.outer(x_K[:, k - 1], x_K[:, k - 1]) + Sxkxk += np.outer(x_K[:, k], x_K[:, k]) + yk_alpha = y[:, k] - alphahat if y.ndim == 2 else y - alphahat + Sykyk += np.outer(yk_alpha, yk_alpha) + Sxkyk += np.outer(x_K[:, k], yk_alpha) + + Sxkxk = _symmetrize(Sxkxk) + Sykyk = _symmetrize(Sykyk) + sumXkTerms_mc = Sxkxk - Ahat @ Sxkm1xk - Sxkm1xk.T @ Ahat.T + Ahat @ Sxkm1xkm1 @ Ahat.T + sumYkTerms_mc = Sykyk - Chat @ Sxkyk - Sxkyk.T @ Chat.T + Chat @ Sxkxk @ Chat.T + Sxkxkm1 = Sxkm1xk.T + sumXkTerms_mc = _symmetrize(sumXkTerms_mc) + sumYkTerms_mc = _symmetrize(sumYkTerms_mc) + + # Score: A + if mPPCOEM_Constraints['EstimateA']: + ScorA = np.linalg.solve(Qhat, Sxkxkm1 - Ahat @ Sxkm1xkm1) + if mPPCOEM_Constraints['AhatDiag']: + ScoreAMc = np.diag(ScorA) + else: + ScoreAMc = ScorA.T.reshape(-1) + else: + ScoreAMc = np.array([], dtype=float) + + # Score: C + ScorC = np.linalg.solve(Rhat, Sxkyk.T - Chat @ Sxkxk) + ScoreCMc = ScorC.T.reshape(-1) + + # Score: Q + if mPPCOEM_Constraints['QhatDiag']: + if mPPCOEM_Constraints['QhatIsotropic']: + ScoreQ = -0.5 * (K * Dx * Qhat[0, 0] ** (-1) - Qhat[0, 0] ** (-2) * np.trace(sumXkTerms_mc)) + ScoreQMc = np.array([ScoreQ]) + else: + ScoreQ = -0.5 * np.linalg.solve(Qhat, K * np.eye(Dx) - np.linalg.solve(Qhat, sumXkTerms_mc).T) + ScoreQMc = np.diag(ScoreQ) + else: + ScoreQ = -0.5 * np.linalg.solve(Qhat, K * np.eye(Dx) - np.linalg.solve(Qhat, sumXkTerms_mc).T) + ScoreQMc = ScoreQ.T.reshape(-1) + + # Score: alpha + resid = y - Chat @ x_K - alphahat.reshape(-1, 1) @ np.ones((1, N)) if y.ndim == 2 else y - Chat @ x_K - alphahat.reshape(-1, 1) + ScoreAlphaMc = np.sum(np.linalg.solve(Rhat, resid), axis=1) + + # Score: R + if mPPCOEM_Constraints['RhatDiag']: + if mPPCOEM_Constraints['RhatIsotropic']: + ScoreR = -0.5 * (K * Dy * Rhat[0, 0] ** (-1) - Rhat[0, 0] ** (-2) * np.trace(sumYkTerms_mc)) + ScoreRMc = np.array([ScoreR]) + else: + ScoreR = -0.5 * np.linalg.solve(Rhat, K * np.eye(Dy) - np.linalg.solve(Rhat, sumYkTerms_mc).T) + ScoreRMc = np.diag(ScoreR) + else: + ScoreR = -0.5 * np.linalg.solve(Rhat, K * np.eye(Dy) - np.linalg.solve(Rhat, sumYkTerms_mc).T) + ScoreRMc = ScoreR.T.reshape(-1) + + # Score: Px0 + if mPPCOEM_Constraints['Px0Isotropic']: + diff0 = x_0 - x0hat + ScoreSMc = np.array([-0.5 * (Dx * Px0hat[0, 0] ** (-1) - Px0hat[0, 0] ** (-2) * np.trace(np.outer(diff0, diff0)))]) + else: + diff0 = x_0 - x0hat + ScorS = -0.5 * np.linalg.solve(Px0hat, np.eye(Dx) - np.linalg.solve(Px0hat, np.outer(diff0, diff0)).T) + ScoreSMc = np.diag(ScorS) + + # Score: x0 + Scorx0 = -np.linalg.solve(Px0hat, x_0 - x0hat) + Ahat.T @ np.linalg.solve(Qhat, x_K[:, 0] - Ahat @ x_0) + Scorex0Mc = Scorx0.reshape(-1) + + # Score: mu, beta, gamma per cell + ScoreMuMc = np.zeros(numCells) + ScoreBetaMc = np.array([], dtype=float) + ScoreGammaMc = np.array([], dtype=float) + for nc in range(numCells): + Hk_full = HkAll[:, :, nc] if HkAll.ndim == 3 else np.zeros((K, 1)) + nHistC = Hk_full.shape[1] + gammaC = gammahat if gammahat.size == 1 else (gammahat[:, nc] if gammahat.ndim == 2 else gammahat) + terms = muhat[nc] + betahat[:, nc] @ x_K + gammaC.reshape(-1) @ Hk_full.T + if fitType == 'poisson': + ld = np.exp(np.clip(terms, -500, 500)) + ScoreMuMc[nc] = float(np.sum(obs[nc, :] - ld)) + ScoreBetaMc = np.concatenate([ScoreBetaMc, np.sum(np.tile(obs[nc, :] - ld, (Dx, 1)) * x_K, axis=1)]) + ScoreGammaMc = np.concatenate([ScoreGammaMc, np.sum(np.tile(obs[nc, :] - ld, (nHistC, 1)) * Hk_full.T, axis=1)]) + else: + ld_raw = np.exp(np.clip(terms, -500, 500)) + ld = ld_raw / (1.0 + ld_raw) + ScoreMuMc[nc] = float(np.sum(obs[nc, :] - (obs[nc, :] + 1) * ld + ld ** 2)) + ScoreBetaMc = np.concatenate([ScoreBetaMc, np.sum(np.tile(obs[nc, :] * (1 - ld) - ld * (1 - ld), (Dx, 1)) * x_K, axis=1)]) + ScoreGammaMc = np.concatenate([ScoreGammaMc, np.sum(np.tile(obs[nc, :] - (obs[nc, :] + 1) * ld + ld ** 2, (nHistC, 1)) * Hk_full.T, axis=1)]) + + ScoreVec = np.concatenate([ScoreAMc, ScoreQMc, ScoreCMc, ScoreRMc]) + if mPPCOEM_Constraints['EstimatePx0']: + ScoreVec = np.concatenate([ScoreVec, ScoreSMc]) + if mPPCOEM_Constraints['Estimatex0']: + ScoreVec = np.concatenate([ScoreVec, Scorex0Mc]) + ScoreVec = np.concatenate([ScoreVec, ScoreAlphaMc, ScoreMuMc, ScoreBetaMc]) + if n10 > 0: + ScoreVec = np.concatenate([ScoreVec, ScoreGammaMc]) + + IMc[:, :, c_mc] = np.outer(ScoreVec, ScoreVec) + + IMissing = np.mean(IMc, axis=2) + IObs = IComp - IMissing + try: + invIObs = np.linalg.inv(IObs) + except np.linalg.LinAlgError: + invIObs = np.linalg.pinv(IObs) + invIObs = nearestSPD(invIObs) + VarVec = np.diag(invIObs) + SEVec = np.sqrt(np.maximum(VarVec, 0.0)) + + # Partition SE vector + off = 0 + SEAterms = SEVec[off:off + n1]; off += n1 + SEQterms = SEVec[off:off + n2]; off += n2 + SECterms = SEVec[off:off + n3]; off += n3 + SERterms = SEVec[off:off + n4]; off += n4 + SEPx0terms = SEVec[off:off + n5]; off += n5 + SEx0terms = SEVec[off:off + n6]; off += n6 + SEAlphaterms = SEVec[off:off + n7]; off += n7 + SEMuTerms = SEVec[off:off + n8]; off += n8 + SEBetaTerms = SEVec[off:off + n9]; off += n9 + SEGammaTerms = SEVec[off:off + n10]; off += n10 + + SE = {} + if mPPCOEM_Constraints['EstimateA']: + if mPPCOEM_Constraints['AhatDiag']: + SE['A'] = np.diag(SEAterms) + else: + SE['A'] = SEAterms.reshape(Ahat.shape[1], Ahat.shape[0]).T + SE['Q'] = np.diag(SEQterms) if mPPCOEM_Constraints['QhatDiag'] else SEQterms.reshape(Qhat.shape[1], Qhat.shape[0]).T + SE['C'] = SECterms.reshape(Chat.shape[1], Chat.shape[0]).T + SE['R'] = np.diag(SERterms) if mPPCOEM_Constraints['RhatDiag'] else SERterms.reshape(Rhat.shape[1], Rhat.shape[0]).T + SE['alpha'] = SEAlphaterms.reshape(alphahat.shape) + if mPPCOEM_Constraints['EstimatePx0']: + SE['Px0'] = np.diag(SEPx0terms) + if mPPCOEM_Constraints['Estimatex0']: + SE['x0'] = SEx0terms + SE['mu'] = SEMuTerms + SE['beta'] = SEBetaTerms.reshape(betahat.shape[1], betahat.shape[0]).T + if n10 > 0: + SE['gamma'] = SEGammaTerms.reshape(gammahat.shape[1], gammahat.shape[0]).T if gammahat.ndim == 2 else SEGammaTerms + + # P-values (two-sided z-test) + Pvals = {} + if mPPCOEM_Constraints['EstimateA']: + pA_flat = np.array([_ztest_pvalue(p, s) for p, s in zip(Ahat.reshape(-1) if not mPPCOEM_Constraints['AhatDiag'] else np.diag(Ahat), SE['A'].reshape(-1) if not mPPCOEM_Constraints['AhatDiag'] else np.diag(SE['A']))]) + Pvals['A'] = np.diag(pA_flat) if mPPCOEM_Constraints['AhatDiag'] else pA_flat.reshape(Ahat.shape) + pC_flat = np.array([_ztest_pvalue(p, s) for p, s in zip(Chat.reshape(-1), SE['C'].reshape(-1))]) + Pvals['C'] = pC_flat.reshape(Chat.shape) + if mPPCOEM_Constraints['RhatDiag']: + pR_flat = np.array([_ztest_pvalue(p, s) for p, s in zip(np.diag(Rhat), np.diag(SE['R']))]) + Pvals['R'] = np.diag(pR_flat) + else: + pR_flat = np.array([_ztest_pvalue(p, s) for p, s in zip(Rhat.reshape(-1), SE['R'].reshape(-1))]) + Pvals['R'] = pR_flat.reshape(Rhat.shape) + if mPPCOEM_Constraints['QhatDiag']: + pQ_flat = np.array([_ztest_pvalue(p, s) for p, s in zip(np.diag(Qhat), np.diag(SE['Q']))]) + Pvals['Q'] = np.diag(pQ_flat) + else: + pQ_flat = np.array([_ztest_pvalue(p, s) for p, s in zip(Qhat.reshape(-1), SE['Q'].reshape(-1))]) + Pvals['Q'] = pQ_flat.reshape(Qhat.shape) + if mPPCOEM_Constraints['EstimatePx0']: + pPx0_flat = np.array([_ztest_pvalue(p, s) for p, s in zip(np.diag(Px0hat), np.diag(SE['Px0']))]) + Pvals['Px0'] = np.diag(pPx0_flat) + if mPPCOEM_Constraints['Estimatex0']: + Pvals['x0'] = np.array([_ztest_pvalue(p, s) for p, s in zip(x0hat, SE['x0'])]) + Pvals['alpha'] = np.array([_ztest_pvalue(p, s) for p, s in zip(alphahat.reshape(-1), SE['alpha'].reshape(-1))]) + Pvals['mu'] = np.array([_ztest_pvalue(p, s) for p, s in zip(muhat, SE['mu'])]) + pBeta_flat = np.array([_ztest_pvalue(p, s) for p, s in zip(betahat.reshape(-1), SE['beta'].reshape(-1))]) + Pvals['beta'] = pBeta_flat.reshape(betahat.shape) + if n10 > 0: + pGamma_flat = np.array([_ztest_pvalue(p, s) for p, s in zip(gammahat.reshape(-1), SE['gamma'].reshape(-1))]) + Pvals['gamma'] = pGamma_flat.reshape(gammahat.shape) if gammahat.ndim == 2 else pGamma_flat + + return SE, Pvals, nTerms + + @staticmethod + def mPPCO_EStep(A, Q, C, R, y, alpha, dN, mu, beta, fitType='poisson', + delta=0.001, gamma=None, HkAll=None, x0=None, Px0=None): + """E-step for the mPPCO EM algorithm. + + Matlab: ``DecodingAlgorithms.mPPCO_EStep`` (lines 6555-6772) + + Returns + ------- + x_K : (dx, K) -- smoothed states + W_K : (dx, dx, K) -- smoothed covariances + logll : float -- log-likelihood + ExpectationSums : dict + """ + A = np.asarray(A, dtype=float) + Q = np.asarray(Q, dtype=float) + C = np.asarray(C, dtype=float) + R = np.asarray(R, dtype=float) + y = np.asarray(y, dtype=float) + alpha = np.asarray(alpha, dtype=float) + obs = _as_observation_matrix(dN) + numCells, K = obs.shape + Dx = A.shape[1] if A.ndim >= 2 else A.shape[0] + Dy = C.shape[0] if C.ndim >= 2 else 1 + mu_vec = np.asarray(mu, dtype=float).reshape(-1) + beta_mat = np.asarray(beta, dtype=float) + if beta_mat.ndim == 1: + beta_mat = beta_mat.reshape(-1, 1) + if gamma is None: + gamma = 0 + gamma_arr = np.asarray(gamma, dtype=float) + if x0 is None or _is_empty_value(x0): + x0 = np.zeros(Dx, dtype=float) + else: + x0 = np.asarray(x0, dtype=float).reshape(-1) + if Px0 is None or _is_empty_value(Px0): + Px0 = np.zeros((Dx, Dx), dtype=float) + else: + Px0 = np.asarray(Px0, dtype=float).reshape(Dx, Dx) + + if HkAll is None or _is_empty_value(HkAll): + HkAll_arr = np.zeros((K, 1, numCells), dtype=float) + else: + HkAll_arr = np.asarray(HkAll, dtype=float) + + # Forward filter + x_p, W_p, x_u, W_u = DecodingAlgorithms.mPPCODecodeLinear( + A, Q, C, R, y, alpha, dN, mu_vec, beta_mat, fitType, + delta, gamma, None, x0, Px0, HkAll_arr) + + # Smoother -- x_p has N+1 columns, x_u has N columns + # kalman_smootherFromFiltered expects matching shapes + # Trim x_p and W_p to first N entries for smoother input + x_K, W_K, Lk = DecodingAlgorithms.kalman_smootherFromFiltered( + A, x_p[:, :N], W_p[:, :, :N], x_u, W_u) + + # Handle Matlab-style output -- ensure x_K is (dx, K) + if x_K.ndim == 2 and x_K.shape[0] == K and x_K.shape[1] == Dx: + x_K = x_K.T + if W_K.ndim == 3 and W_K.shape[0] == K: + W_K = np.transpose(W_K, (1, 2, 0)) + + # Best estimates of initial state given data + W1G0 = A @ Px0 @ A.T + Q if A.ndim == 2 else A.reshape(Dx, Dx) @ Px0 @ A.reshape(Dx, Dx).T + Q.reshape(Dx, Dx) + A_2d = A.reshape(Dx, Dx) if A.ndim != 2 else A + L0 = Px0 @ A_2d.T @ np.linalg.pinv(W1G0) + Ex0Gy = x0 + L0 @ (x_K[:, 0] - x_p[:, 0]) + Px0Gy = Px0 + L0 @ (np.linalg.pinv(W_K[:, :, 0]) - np.linalg.pinv(W1G0)) @ L0.T + Px0Gy = _symmetrize(Px0Gy) + + # Cross-covariance matrices Wku + numStates = Dx + Wku = np.zeros((numStates, numStates, K, K), dtype=float) + for k in range(K): + Wku[:, :, k, k] = W_K[:, :, k] + + for u in range(K - 1, 0, -1): + k = u - 1 + Dk = W_u[:, :, k] @ A_2d.T @ np.linalg.pinv(W_p[:, :, k + 1]) + Wku[:, :, k, u] = Dk @ Wku[:, :, k + 1, u] + Wku[:, :, u, k] = Wku[:, :, k, u].T + + # Sufficient statistics + Sxkm1xk = np.zeros((Dx, Dx)) + Sxkm1xkm1 = np.zeros((Dx, Dx)) + Sxkxk = np.zeros((Dx, Dx)) + Sykyk = np.zeros((Dy, Dy)) + Sxkyk = np.zeros((Dx, Dy)) + + alpha_vec = alpha.reshape(-1) + for k in range(K): + if k == 0: + Sxkm1xk += Px0 @ A_2d.T @ np.linalg.pinv(W_p[:, :, 0]) @ Wku[:, :, 0, 0] + Sxkm1xkm1 += Px0 + np.outer(x0, x0) + else: + Sxkm1xk += Wku[:, :, k - 1, k] + np.outer(x_K[:, k - 1], x_K[:, k]) + Sxkm1xkm1 += Wku[:, :, k - 1, k - 1] + np.outer(x_K[:, k - 1], x_K[:, k - 1]) + Sxkxk += Wku[:, :, k, k] + np.outer(x_K[:, k], x_K[:, k]) + yk = y[:, k] if y.ndim == 2 else y + Sykyk += np.outer(yk - alpha_vec, yk - alpha_vec) + Sxkyk += np.outer(x_K[:, k], yk - alpha_vec) + + Sxkxk = _symmetrize(Sxkxk) + Sykyk = _symmetrize(Sykyk) + sumXkTerms = Sxkxk - A_2d @ Sxkm1xk - Sxkm1xk.T @ A_2d.T + A_2d @ Sxkm1xkm1 @ A_2d.T + sumYkTerms = Sykyk - C @ Sxkyk - Sxkyk.T @ C.T + C @ Sxkxk @ C.T + Sxkxkm1 = Sxkm1xk.T + + # Log-likelihood with PP term + if str(fitType) == 'poisson': + sumPPll = 0.0 + HkPerm = np.transpose(HkAll_arr, (1, 2, 0)) if HkAll_arr.ndim == 3 and HkAll_arr.shape[0] == K else HkAll_arr + for k in range(K): + Hk = HkPerm[:, :, k] if HkPerm.ndim == 3 else np.zeros((1, numCells)) + if Hk.shape[0] == numCells and Hk.shape[1] != numCells: + Hk = Hk.T + xk = x_K[:, k] + gammaC_mat = np.tile(gamma_arr.reshape(-1, 1), (1, numCells)) if gamma_arr.size == 1 else gamma_arr + if gammaC_mat.ndim == 2 and gammaC_mat.shape[1] != numCells: + gammaC_mat = np.tile(gammaC_mat, (1, numCells)) + terms = mu_vec + beta_mat.T @ xk + np.diag(gammaC_mat.T @ Hk) if Hk.size > 0 and gammaC_mat.size > 0 else mu_vec + beta_mat.T @ xk + Wk = W_K[:, :, k] + ld = np.exp(np.clip(terms, -500, 500)) + bt = beta_mat + ExplambdaDelta = ld + 0.5 * (ld * np.diag(bt.T @ Wk @ bt)) + ExplogLD = terms + sumPPll += float(np.sum(obs[:, k] * ExplogLD - ExplambdaDelta)) + else: # binomial + sumPPll = 0.0 + HkPerm = np.transpose(HkAll_arr, (1, 2, 0)) if HkAll_arr.ndim == 3 and HkAll_arr.shape[0] == K else HkAll_arr + for k in range(K): + Hk = HkPerm[:, :, k] if HkPerm.ndim == 3 else np.zeros((1, numCells)) + if Hk.shape[0] == numCells and Hk.shape[1] != numCells: + Hk = Hk.T + xk = x_K[:, k] + gammaC_mat = np.tile(gamma_arr.reshape(-1, 1), (1, numCells)) if gamma_arr.size == 1 else gamma_arr + if gammaC_mat.ndim == 2 and gammaC_mat.shape[1] != numCells: + gammaC_mat = np.tile(gammaC_mat, (1, numCells)) + terms = mu_vec + beta_mat.T @ xk + np.diag(gammaC_mat.T @ Hk) if Hk.size > 0 and gammaC_mat.size > 0 else mu_vec + beta_mat.T @ xk + Wk = W_K[:, :, k] + ld_raw = np.exp(np.clip(terms, -500, 500)) + ld = ld_raw / (1.0 + ld_raw) + bt = beta_mat + ExplambdaDelta = ld + 0.5 * (ld * (1 - ld) * (1 - 2 * ld)) * np.diag(bt.T @ Wk @ bt) + ExplogLD = np.log(np.clip(ld, 1e-300, None)) + 0.5 * (-ld * (1 - ld)) * np.diag(bt.T @ Wk @ bt) + sumPPll += float(np.sum(obs[:, k] * ExplogLD - ExplambdaDelta)) + + Q_2d = Q.reshape(Dx, Dx) if Q.ndim != 2 else Q + R_2d = R.reshape(Dy, Dy) if R.ndim != 2 else R + logll = (-Dx * K / 2.0 * np.log(2 * np.pi) + - K / 2.0 * np.log(max(np.linalg.det(Q_2d), 1e-300)) + - Dy * K / 2.0 * np.log(2 * np.pi) + - K / 2.0 * np.log(max(np.linalg.det(R_2d), 1e-300)) + - Dx / 2.0 * np.log(2 * np.pi) + - 0.5 * np.log(max(np.linalg.det(Px0), 1e-300)) + + sumPPll + - 0.5 * np.trace(np.linalg.solve(Q_2d, sumXkTerms)) + - 0.5 * np.trace(np.linalg.solve(R_2d, sumYkTerms)) + - Dx / 2.0) + + ExpectationSums = { + 'Sxkm1xkm1': Sxkm1xkm1, + 'Sxkm1xk': Sxkm1xk, + 'Sxkxkm1': Sxkxkm1, + 'Sxkxk': Sxkxk, + 'Sxkyk': Sxkyk, + 'Sykyk': Sykyk, + 'sumXkTerms': sumXkTerms, + 'sumYkTerms': sumYkTerms, + 'sumPPll': sumPPll, + 'Sx0': Ex0Gy, + 'Sx0x0': Px0Gy + np.outer(Ex0Gy, Ex0Gy), + } + + return x_K, W_K, float(logll), ExpectationSums + + @staticmethod + def mPPCO_MStep(dN, y, x_K, W_K, x0, Px0, ExpectationSums, fitType='poisson', + muhat=None, betahat=None, gammahat=None, windowTimes=None, + HkAll=None, mPPCOEM_Constraints=None, MstepMethod='GLM'): + """M-step for the mPPCO EM algorithm. + + Matlab: ``DecodingAlgorithms.mPPCO_MStep`` (lines 6773-7662) + + Returns + ------- + Ahat, Qhat, Chat, Rhat, alphahat, muhat_new, betahat_new, gammahat_new, x0hat, Px0hat + """ + if mPPCOEM_Constraints is None: + mPPCOEM_Constraints = DecodingAlgorithms.mPPCO_EMCreateConstraints() + + obs = _as_observation_matrix(dN) + numCells = obs.shape[0] + x_K = np.asarray(x_K, dtype=float) + y = np.asarray(y, dtype=float) + x0 = np.asarray(x0, dtype=float).reshape(-1) + Px0 = np.asarray(Px0, dtype=float) + muhat = np.asarray(muhat, dtype=float).reshape(-1) + betahat = np.asarray(betahat, dtype=float) + if betahat.ndim == 1: + betahat = betahat.reshape(-1, 1) + gammahat = np.asarray(gammahat, dtype=float) + if HkAll is None or _is_empty_value(HkAll): + HkAll = np.zeros((obs.shape[1], 1, numCells), dtype=float) + else: + HkAll = np.asarray(HkAll, dtype=float) + + Sxkm1xkm1 = ExpectationSums['Sxkm1xkm1'] + Sxkm1xk = ExpectationSums['Sxkm1xk'] + Sxkxkm1 = ExpectationSums['Sxkxkm1'] + Sxkxk = ExpectationSums['Sxkxk'] + Sxkyk = ExpectationSums['Sxkyk'] + Sykyk = ExpectationSums['Sykyk'] + sumXkTerms = ExpectationSums['sumXkTerms'] + sumYkTerms = ExpectationSums['sumYkTerms'] + Sx0 = ExpectationSums['Sx0'] + Sx0x0 = ExpectationSums['Sx0x0'] + + dx, K = x_K.shape + dy = y.shape[0] if y.ndim == 2 else 1 + I_dx = np.eye(dx) + + # A estimate + if mPPCOEM_Constraints['AhatDiag']: + Ahat = (Sxkxkm1 * I_dx) @ np.linalg.inv(Sxkm1xkm1 * I_dx) + else: + Ahat = Sxkxkm1 @ np.linalg.inv(Sxkm1xkm1) + + # C estimate + Chat = Sxkyk.T @ np.linalg.inv(Sxkxk) + + # alpha estimate + alphahat = np.sum(y - Chat @ x_K, axis=1) / K if y.ndim == 2 else (y - Chat @ x_K) / K + + # Q estimate + if mPPCOEM_Constraints['QhatDiag']: + if mPPCOEM_Constraints['QhatIsotropic']: + Qhat = (1.0 / (dx * K)) * np.trace(sumXkTerms) * I_dx + else: + Qhat = (1.0 / K) * (sumXkTerms * I_dx) + Qhat = _symmetrize(Qhat) + else: + Qhat = (1.0 / K) * sumXkTerms + Qhat = _symmetrize(Qhat) + + # R estimate + I_dy = np.eye(dy) + if mPPCOEM_Constraints['RhatDiag']: + if mPPCOEM_Constraints['RhatIsotropic']: + Rhat = (1.0 / (dy * K)) * np.trace(sumYkTerms) * I_dy + else: + Rhat = (1.0 / K) * (sumYkTerms * I_dy) + Rhat = _symmetrize(Rhat) + else: + Rhat = (1.0 / K) * sumYkTerms + Rhat = _symmetrize(Rhat) + + # x0 estimate + if mPPCOEM_Constraints['Estimatex0']: + x0hat = np.linalg.solve( + np.linalg.inv(Px0) + Ahat.T @ np.linalg.solve(Qhat, Ahat), + Ahat.T @ np.linalg.solve(Qhat, x_K[:, 0]) + np.linalg.solve(Px0, x0)) + else: + x0hat = x0.copy() + + # Px0 estimate + if mPPCOEM_Constraints['EstimatePx0']: + if mPPCOEM_Constraints['Px0Isotropic']: + Px0hat = (np.trace(np.outer(x0hat, x0hat) - np.outer(x0, x0hat) - np.outer(x0hat, x0) + np.outer(x0, x0)) / (dx * K)) * I_dx + else: + Px0hat = (np.outer(x0hat, x0hat) - np.outer(x0, x0hat) - np.outer(x0hat, x0) + np.outer(x0, x0)) * I_dx + Px0hat = _symmetrize(Px0hat) + else: + Px0hat = Px0.copy() + + # CIF parameter updates via Newton-Raphson + betahat_new = betahat.copy() + gammahat_new = gammahat.copy() + muhat_new = muhat.copy() + + # Newton-Raphson for beta, mu, gamma + McExp = 50 + diffTol = 1e-5 + maxIter_nr = 100 + + xKDrawExp = np.zeros((dx, K, McExp), dtype=float) + for k in range(K): + WuTemp = W_K[:, :, k] + try: + chol_m = np.linalg.cholesky(WuTemp) + except np.linalg.LinAlgError: + chol_m = np.linalg.cholesky(nearestSPD(WuTemp)) + z = np.random.randn(dx, McExp) + xKDrawExp[:, k, :] = x_K[:, k:k + 1] + chol_m @ z + + xkPerm = np.transpose(xKDrawExp, (0, 2, 1)) # (dx, McExp, K) + + # -- beta update -- + for c in range(numCells): + converged = False + iterNR = 0 + while not converged and iterNR < maxIter_nr: + HessianTerm = np.zeros((dx, dx)) + GradTerm = np.zeros(dx) + for k in range(K): + Hk_full = HkAll[:, :, c] if HkAll.ndim == 3 else np.zeros((K, 1)) + Hk = Hk_full[k, :] + xk = xkPerm[:, :, k] + gammaC = gammahat if gammahat.size == 1 else (gammahat[:, c] if gammahat.ndim == 2 else gammahat) + terms = muhat[c] + betahat_new[:, c] @ xk + float(np.dot(gammaC.reshape(-1), Hk.reshape(-1))) + if fitType == 'poisson': + ld = np.exp(np.clip(terms, -500, 500)) + ExpLambdaXk = (1.0 / McExp) * np.sum(np.tile(ld, (dx, 1)) * xk, axis=1) + ExpLambdaXkXkT = (1.0 / McExp) * (np.tile(ld, (dx, 1)) * xk) @ xk.T + GradTerm += obs[c, k] * x_K[:, k] - ExpLambdaXk + HessianTerm -= ExpLambdaXkXkT + else: + ld_raw = np.exp(np.clip(terms, -500, 500)) + ld = ld_raw / (1.0 + ld_raw) + EldXkXk = (1.0 / McExp) * (np.tile(ld, (dx, 1)) * xk) @ xk.T + EldSqXkXk = (1.0 / McExp) * (np.tile(ld ** 2, (dx, 1)) * xk) @ xk.T + EldCubeXkXk = (1.0 / McExp) * (np.tile(ld ** 3, (dx, 1)) * xk) @ xk.T + ExpLambdaXk = (1.0 / McExp) * np.sum(np.tile(ld, (dx, 1)) * xk, axis=1) + ExpLambdaSquaredXk = (1.0 / McExp) * np.sum(np.tile(ld ** 2, (dx, 1)) * xk, axis=1) + GradTerm += obs[c, k] * x_K[:, k] - (obs[c, k] + 1) * ExpLambdaXk + ExpLambdaSquaredXk + HessianTerm += EldXkXk + EldSqXkXk - 2 * EldCubeXkXk + + if np.any(np.isnan(HessianTerm)) or np.any(np.isinf(HessianTerm)): + betahat_newTemp = betahat_new[:, c] + else: + try: + betahat_newTemp = betahat_new[:, c] - np.linalg.solve(HessianTerm, GradTerm) + except np.linalg.LinAlgError: + betahat_newTemp = betahat_new[:, c] + if np.any(np.isnan(betahat_newTemp)): + betahat_newTemp = betahat_new[:, c] + + mabsDiff = float(np.max(np.abs(betahat_newTemp - betahat_new[:, c]))) + if mabsDiff < diffTol: + converged = True + betahat_new[:, c] = betahat_newTemp + iterNR += 1 + + # -- mu update -- + for c in range(numCells): + converged = False + iterNR = 0 + while not converged and iterNR < maxIter_nr: + HessianTerm_mu = 0.0 + GradTerm_mu = 0.0 + for k in range(K): + Hk_full = HkAll[:, :, c] if HkAll.ndim == 3 else np.zeros((K, 1)) + Hk = Hk_full[k, :] + xk = xkPerm[:, :, k] + gammaC = gammahat if gammahat.size == 1 else (gammahat[:, c] if gammahat.ndim == 2 else gammahat) + terms = muhat_new[c] + betahat[:, c] @ xk + float(np.dot(gammaC.reshape(-1), Hk.reshape(-1))) + if fitType == 'poisson': + ld = np.exp(np.clip(terms, -500, 500)) + ExpLD = (1.0 / McExp) * float(np.sum(ld)) + GradTerm_mu += obs[c, k] - ExpLD + HessianTerm_mu -= ExpLD + else: + ld_raw = np.exp(np.clip(terms, -500, 500)) + ld = ld_raw / (1.0 + ld_raw) + ExpLD = (1.0 / McExp) * float(np.sum(ld)) + ExpLDSq = (1.0 / McExp) * float(np.sum(ld ** 2)) + ExpLDCube = (1.0 / McExp) * float(np.sum(ld ** 3)) + GradTerm_mu += obs[c, k] - (obs[c, k] + 1) * ExpLD + ExpLDSq + HessianTerm_mu += -ExpLD * (obs[c, k] + 1) + ExpLDSq * (obs[c, k] + 3) - 2 * ExpLDCube + + if np.isnan(HessianTerm_mu) or np.isinf(HessianTerm_mu) or abs(HessianTerm_mu) < 1e-300: + muhat_newTemp = muhat_new[c] + else: + muhat_newTemp = muhat_new[c] - GradTerm_mu / HessianTerm_mu + if np.isnan(muhat_newTemp): + muhat_newTemp = muhat_new[c] + + mabsDiff = abs(muhat_newTemp - muhat_new[c]) + if mabsDiff < diffTol: + converged = True + muhat_new[c] = muhat_newTemp + iterNR += 1 + + # -- gamma update -- + if (windowTimes is not None and not _is_empty_value(windowTimes) + and np.any(gammahat_new != 0)): + nGamma = gammahat.shape[0] if gammahat.ndim >= 1 else 1 + for c in range(numCells): + converged = False + iterNR = 0 + gammaC = gammahat_new.copy() if gammahat_new.size == 1 else (gammahat_new[:, c].copy() if gammahat_new.ndim == 2 else gammahat_new.copy()) + while not converged and iterNR < maxIter_nr: + HessianTerm_g = np.zeros((nGamma, nGamma)) + GradTerm_g = np.zeros(nGamma) + for k in range(K): + Hk_full = HkAll[:, :, c] if HkAll.ndim == 3 else np.zeros((K, 1)) + Hk = Hk_full[k, :] + xk = xkPerm[:, :, k] + terms = muhat[c] + betahat[:, c] @ xk + float(np.dot(gammaC.reshape(-1), Hk.reshape(-1))) + if fitType == 'poisson': + ld = np.exp(np.clip(terms, -500, 500)) + ExpLD = (1.0 / McExp) * float(np.sum(ld)) + GradTerm_g += (obs[c, k] - ExpLD) * Hk + HessianTerm_g -= ExpLD * np.outer(Hk, Hk) + else: + ld_raw = np.exp(np.clip(terms, -500, 500)) + ld = ld_raw / (1.0 + ld_raw) + ExpLD = (1.0 / McExp) * float(np.sum(ld)) + ExpLDSq = (1.0 / McExp) * float(np.sum(ld ** 2)) + ExpLDCube = (1.0 / McExp) * float(np.sum(ld ** 3)) + GradTerm_g += (obs[c, k] - (obs[c, k] + 1) * ExpLD + ExpLDSq) * Hk + HessianTerm_g += (-ExpLD * (obs[c, k] + 1) + ExpLDSq * (obs[c, k] + 3) - 2 * ExpLDCube) * np.outer(Hk, Hk) + + if np.any(np.isnan(HessianTerm_g)) or np.any(np.isinf(HessianTerm_g)): + gammahat_newTemp = gammaC.copy() + else: + try: + gammahat_newTemp = gammaC - np.linalg.solve(HessianTerm_g, GradTerm_g) + except np.linalg.LinAlgError: + gammahat_newTemp = gammaC.copy() + if np.any(np.isnan(gammahat_newTemp)): + gammahat_newTemp = gammaC.copy() + + mabsDiff = float(np.max(np.abs(gammahat_newTemp - gammaC))) + if mabsDiff < diffTol: + converged = True + gammaC = gammahat_newTemp + iterNR += 1 + + if gammahat_new.ndim == 2: + gammahat_new[:, c] = gammaC + else: + gammahat_new = gammaC + + return Ahat, Qhat, Chat, Rhat, alphahat, muhat_new, betahat_new, gammahat_new, x0hat, Px0hat + + @staticmethod + def mPPCO_EM(y, dN, Ahat0, Qhat0, Chat0, Rhat0, alphahat0, mu, beta, + fitType='poisson', delta=0.001, gamma=None, windowTimes=None, + x0=None, Px0=None, mPPCOEM_Constraints=None, MstepMethod='GLM'): + """Full EM algorithm for the mixed Point-Process / Continuous Observation model. + + Matlab: ``DecodingAlgorithms.mPPCO_EM`` (lines 6139-6554) + + Returns + ------- + xKFinal, WKFinal, Ahat, Qhat, Chat, Rhat, alphahat, + muhat, betahat, gammahat, x0hat, Px0hat, IC, SE, Pvals + """ + Ahat0 = np.asarray(Ahat0, dtype=float) + Qhat0 = np.asarray(Qhat0, dtype=float) + Chat0 = np.asarray(Chat0, dtype=float) + Rhat0 = np.asarray(Rhat0, dtype=float) + alphahat0 = np.asarray(alphahat0, dtype=float).reshape(-1) + mu = np.asarray(mu, dtype=float).reshape(-1) + beta = np.asarray(beta, dtype=float) + if beta.ndim == 1: + beta = beta.reshape(-1, 1) + numStates = Ahat0.shape[0] + obs = _as_observation_matrix(dN) + numCells_K, N = obs.shape + + if mPPCOEM_Constraints is None: + mPPCOEM_Constraints = DecodingAlgorithms.mPPCO_EMCreateConstraints() + if Px0 is None or _is_empty_value(Px0): + Px0 = 1e-9 * np.eye(numStates) + else: + Px0 = np.asarray(Px0, dtype=float).reshape(numStates, numStates) + if x0 is None or _is_empty_value(x0): + x0 = np.zeros(numStates, dtype=float) + else: + x0 = np.asarray(x0, dtype=float).reshape(-1) + if gamma is None: + gamma = np.array(0.0) + else: + gamma = np.asarray(gamma, dtype=float) + if delta is None: + delta = 0.001 + if windowTimes is None or _is_empty_value(windowTimes): + if gamma is not None and np.any(gamma != 0): + windowTimes = np.arange(gamma.size + 2, dtype=float) * delta + else: + windowTimes = None + + minTime = 0.0 + maxTime = (N - 1) * delta + K_cells = numCells_K + + # Build history + if windowTimes is not None and not _is_empty_value(windowTimes): + wt = np.asarray(windowTimes, dtype=float).reshape(-1) + HkAll = _compute_history_terms(dN, delta, wt) + else: + HkAll = np.zeros((N, 1, K_cells), dtype=float) + gamma = np.array(0.0) + + y_arr = np.asarray(y, dtype=float) + yOrig = y_arr.copy() + + tolAbs = 1e-3 + llTol = 1e-3 + maxIter = 100 + numToKeep = 10 + + # Circular buffers + Ahat_buf = [None] * numToKeep + Qhat_buf = [None] * numToKeep + Chat_buf = [None] * numToKeep + Rhat_buf = [None] * numToKeep + alphahat_buf = [None] * numToKeep + muhat_buf = [None] * numToKeep + betahat_buf = [None] * numToKeep + gammahat_buf = [None] * numToKeep + x0hat_buf = [None] * numToKeep + Px0hat_buf = [None] * numToKeep + x_K_buf = [None] * numToKeep + W_K_buf = [None] * numToKeep + ExpSums_buf = [None] * numToKeep + ll_list = [] + + # Initialize (scaled system) + A0 = Ahat0.copy() + Q0 = Qhat0.copy() + C0 = Chat0.copy() + R0 = Rhat0.copy() + + Tq = np.linalg.solve(np.linalg.cholesky(Q0), np.eye(numStates)) + Tr = np.linalg.solve(np.linalg.cholesky(R0), np.eye(R0.shape[0])) + + Ahat_buf[0] = Tq @ A0 @ np.linalg.inv(Tq) + Chat_buf[0] = Tr @ C0 @ np.linalg.inv(Tq) + Qhat_buf[0] = Tq @ Q0 @ Tq.T + Rhat_buf[0] = Tr @ R0 @ Tr.T + y_arr = Tr @ y_arr + x0hat_buf[0] = Tq @ x0 + Px0hat_buf[0] = Tq @ Px0 @ Tq.T + alphahat_buf[0] = Tr @ alphahat0 + betahat_buf[0] = np.linalg.solve(Tq.T, beta) + muhat_buf[0] = mu.copy() + gammahat_buf[0] = gamma.copy() + + cnt = 0 + stoppingCriteria = False + + print(" Joint Point-Process/Gaussian Observation EM Algorithm ") + + while not stoppingCriteria and cnt < maxIter: + si = cnt % numToKeep + si_p1 = (cnt + 1) % numToKeep + si_m1 = (cnt - 1) % numToKeep + + print("-" * 80) + print(f"Iteration #{cnt + 1}") + print("-" * 80) + + # E-step + x_K_buf[si], W_K_buf[si], ll_val, ExpSums_buf[si] = DecodingAlgorithms.mPPCO_EStep( + Ahat_buf[si], Qhat_buf[si], Chat_buf[si], Rhat_buf[si], + y_arr, alphahat_buf[si], dN, + muhat_buf[si], betahat_buf[si], fitType, delta, + gammahat_buf[si], HkAll, x0hat_buf[si], Px0hat_buf[si]) + ll_list.append(ll_val) + + # M-step + (Ahat_buf[si_p1], Qhat_buf[si_p1], Chat_buf[si_p1], Rhat_buf[si_p1], + alphahat_buf[si_p1], muhat_buf[si_p1], betahat_buf[si_p1], + gammahat_buf[si_p1], x0hat_buf[si_p1], Px0hat_buf[si_p1]) = \ + DecodingAlgorithms.mPPCO_MStep( + dN, y_arr, x_K_buf[si], W_K_buf[si], + x0hat_buf[si], Px0hat_buf[si], ExpSums_buf[si], + fitType, muhat_buf[si], betahat_buf[si], + gammahat_buf[si], windowTimes, HkAll, + mPPCOEM_Constraints, MstepMethod) + + if not mPPCOEM_Constraints['EstimateA']: + Ahat_buf[si_p1] = Ahat_buf[si].copy() + + # Convergence check + if cnt == 0: + dMax = np.inf + else: + diffs = [] + for arr_curr, arr_prev in [ + (Qhat_buf[si], Qhat_buf[si_m1]), + (Rhat_buf[si], Rhat_buf[si_m1]), + (Ahat_buf[si], Ahat_buf[si_m1]), + (Chat_buf[si], Chat_buf[si_m1]), + ]: + if arr_curr is not None and arr_prev is not None: + diffs.append(float(np.max(np.abs(np.sqrt(np.abs(arr_curr)) - np.sqrt(np.abs(arr_prev))))) if 'Q' in str(id(arr_curr)) else float(np.max(np.abs(arr_curr - arr_prev)))) + for arr_curr, arr_prev in [ + (muhat_buf[si], muhat_buf[si_m1]), + (alphahat_buf[si], alphahat_buf[si_m1]), + (betahat_buf[si], betahat_buf[si_m1]), + (gammahat_buf[si], gammahat_buf[si_m1]), + ]: + if arr_curr is not None and arr_prev is not None: + diffs.append(float(np.max(np.abs(np.asarray(arr_curr) - np.asarray(arr_prev))))) + dMax = max(diffs) if diffs else np.inf + + if cnt == 0: + print("Max Parameter Change: N/A") + else: + print(f"Max Parameter Change: {dMax}") + + cnt += 1 + + if dMax < tolAbs: + stoppingCriteria = True + print(f" EM converged at iteration# {cnt} b/c change in params was within criteria") + + if cnt >= 2: + dll = ll_list[-1] - ll_list[-2] + if abs(dll) < llTol or dll < 0: + stoppingCriteria = True + print(f" EM stopped at iteration# {cnt} b/c change in likelihood was negative or small") + + print("-" * 80) + + # Select best iteration + ll_arr = np.array(ll_list) + maxLLIndex = int(np.argmax(ll_arr)) + maxLLIndMod = maxLLIndex % numToKeep + + xKFinal = x_K_buf[maxLLIndMod] + WKFinal = W_K_buf[maxLLIndMod] + Ahat_out = Ahat_buf[maxLLIndMod] + Qhat_out = Qhat_buf[maxLLIndMod] + Chat_out = Chat_buf[maxLLIndMod] + Rhat_out = Rhat_buf[maxLLIndMod] + alphahat_out = alphahat_buf[maxLLIndMod] + muhat_out = muhat_buf[maxLLIndMod] + betahat_out = betahat_buf[maxLLIndMod] + gammahat_out = gammahat_buf[maxLLIndMod] + x0hat_out = x0hat_buf[maxLLIndMod] + Px0hat_out = Px0hat_buf[maxLLIndMod] + ExpectationSumsFinal = ExpSums_buf[maxLLIndMod] + + # Unscale + Tq = np.linalg.solve(np.linalg.cholesky(Q0), np.eye(numStates)) + Tr = np.linalg.solve(np.linalg.cholesky(R0), np.eye(R0.shape[0])) + Tq_inv = np.linalg.inv(Tq) + Tr_inv = np.linalg.inv(Tr) + + Ahat_out = Tq_inv @ Ahat_out @ Tq + Qhat_out = Tq_inv @ Qhat_out @ np.linalg.inv(Tq.T) + Chat_out = Tr_inv @ Chat_out @ Tq + Rhat_out = Tr_inv @ Rhat_out @ np.linalg.inv(Tr.T) + alphahat_out = Tr_inv @ alphahat_out + xKFinal = Tq_inv @ xKFinal + x0hat_out = Tq_inv @ x0hat_out + Px0hat_out = Tq_inv @ Px0hat_out @ np.linalg.inv(Tq.T) + for kk in range(WKFinal.shape[2]): + WKFinal[:, :, kk] = Tq_inv @ WKFinal[:, :, kk] @ np.linalg.inv(Tq.T) + betahat_out = (betahat_out.T @ Tq).T + + # Information criteria + ll_best = ll_arr[maxLLIndex] + # Count parameters + if mPPCOEM_Constraints['EstimateA'] and mPPCOEM_Constraints['AhatDiag']: + n1 = Ahat_out.shape[0] + elif mPPCOEM_Constraints['EstimateA']: + n1 = Ahat_out.size + else: + n1 = 0 + + if mPPCOEM_Constraints['QhatDiag'] and mPPCOEM_Constraints['QhatIsotropic']: + n2 = 1 + elif mPPCOEM_Constraints['QhatDiag']: + n2 = Qhat_out.shape[0] + else: + n2 = Qhat_out.size + + n3 = Chat_out.size + + if mPPCOEM_Constraints['RhatDiag'] and mPPCOEM_Constraints['RhatIsotropic']: + n4 = 1 + elif mPPCOEM_Constraints['RhatDiag']: + n4 = Rhat_out.shape[0] + else: + n4 = Rhat_out.size + + if mPPCOEM_Constraints['EstimatePx0'] and mPPCOEM_Constraints['Px0Isotropic']: + n5 = 1 + elif mPPCOEM_Constraints['EstimatePx0']: + n5 = Px0hat_out.shape[0] + else: + n5 = 0 + + n6 = x0hat_out.size if mPPCOEM_Constraints['Estimatex0'] else 0 + n7 = alphahat_out.size + n8 = muhat_out.size + n9 = betahat_out.size + if gammahat_out.size == 1 and float(gammahat_out.flat[0]) == 0: + n10 = 0 + else: + n10 = gammahat_out.size + nTerms = n1 + n2 + n3 + n4 + n5 + n6 + n7 + n8 + n9 + n10 + + Dx = Ahat_out.shape[1] + sumXkTerms = ExpectationSumsFinal['sumXkTerms'] + llobs = (ll_best + Dx * N / 2.0 * np.log(2 * np.pi) + + N / 2.0 * np.log(max(np.linalg.det(Qhat_out), 1e-300)) + + 0.5 * np.trace(np.linalg.solve(Qhat_out, sumXkTerms)) + + Dx / 2.0 * np.log(2 * np.pi) + + 0.5 * np.log(max(np.linalg.det(Px0hat_out), 1e-300)) + + 0.5 * Dx) + + AIC = 2 * nTerms - 2 * llobs + AICc = AIC + 2 * nTerms * (nTerms + 1) / max(N - nTerms - 1, 1) + BIC = -2 * llobs + nTerms * np.log(max(N, 1)) + + IC = { + 'AIC': AIC, 'AICc': AICc, 'BIC': BIC, + 'llobs': llobs, 'llcomp': ll_best, + } + + # Standard errors + SE = {} + Pvals = {} + try: + SE, Pvals, _ = DecodingAlgorithms.mPPCO_ComputeParamStandardErrors( + yOrig, dN, xKFinal, WKFinal, Ahat_out, Qhat_out, + Chat_out, Rhat_out, alphahat_out, x0hat_out, Px0hat_out, + ExpectationSumsFinal, fitType, muhat_out, betahat_out, + gammahat_out, windowTimes, HkAll, mPPCOEM_Constraints) + except Exception: + pass + + return (xKFinal, WKFinal, Ahat_out, Qhat_out, Chat_out, Rhat_out, + alphahat_out, muhat_out, betahat_out, gammahat_out, + x0hat_out, Px0hat_out, IC, SE, Pvals) + + + + +# Module-level aliases for backward compatibility PP_fixedIntervalSmoother = DecodingAlgorithms.PP_fixedIntervalSmoother PPDecodeFilter = DecodingAlgorithms.PPDecodeFilter PPDecodeFilterLinear = DecodingAlgorithms.PPDecodeFilterLinear @@ -2228,11 +7597,40 @@ def prepareEMResults(fitType, neuronNumber, dN, HkAll, xK, WK, Q, gamma, ukf = DecodingAlgorithms.ukf ukf_ut = DecodingAlgorithms.ukf_ut ukf_sigmas = DecodingAlgorithms.ukf_sigmas +KF_EM = DecodingAlgorithms.KF_EM +KF_EMCreateConstraints = DecodingAlgorithms.KF_EMCreateConstraints +KF_EStep = DecodingAlgorithms.KF_EStep +KF_MStep = DecodingAlgorithms.KF_MStep +KF_ComputeParamStandardErrors = DecodingAlgorithms.KF_ComputeParamStandardErrors +PP_EM = DecodingAlgorithms.PP_EM +PP_EMCreateConstraints = DecodingAlgorithms.PP_EMCreateConstraints +PP_ComputeParamStandardErrors = DecodingAlgorithms.PP_ComputeParamStandardErrors +PP_EStep = DecodingAlgorithms.PP_EStep +PP_MStep = DecodingAlgorithms.PP_MStep +mPPCODecode_predict = DecodingAlgorithms.mPPCODecode_predict +mPPCODecode_update = DecodingAlgorithms.mPPCODecode_update +mPPCODecodeLinear = DecodingAlgorithms.mPPCODecodeLinear +mPPCO_fixedIntervalSmoother = DecodingAlgorithms.mPPCO_fixedIntervalSmoother +mPPCO_EMCreateConstraints = DecodingAlgorithms.mPPCO_EMCreateConstraints +mPPCO_ComputeParamStandardErrors = DecodingAlgorithms.mPPCO_ComputeParamStandardErrors +mPPCO_EM = DecodingAlgorithms.mPPCO_EM +mPPCO_EStep = DecodingAlgorithms.mPPCO_EStep +mPPCO_MStep = DecodingAlgorithms.mPPCO_MStep __all__ = [ "ComputeStimulusCIs", "DecodingAlgorithms", + "KF_ComputeParamStandardErrors", + "KF_EM", + "KF_EMCreateConstraints", + "KF_EStep", + "KF_MStep", + "PP_ComputeParamStandardErrors", + "PP_EM", + "PP_EMCreateConstraints", + "PP_EStep", + "PP_MStep", "PPDecodeFilter", "PPDecodeFilterLinear", "PPDecode_predict", @@ -2251,6 +7649,15 @@ def prepareEMResults(fitType, neuronNumber, dN, HkAll, xK, WK, Q, gamma, "kalman_smoother", "kalman_smootherFromFiltered", "kalman_update", + "mPPCODecode_predict", + "mPPCODecode_update", + "mPPCODecodeLinear", + "mPPCO_ComputeParamStandardErrors", + "mPPCO_EM", + "mPPCO_EMCreateConstraints", + "mPPCO_EStep", + "mPPCO_MStep", + "mPPCO_fixedIntervalSmoother", "ukf", "ukf_sigmas", "ukf_ut", diff --git a/nstat/fit.py b/nstat/fit.py index 5c306290..5f01ee16 100644 --- a/nstat/fit.py +++ b/nstat/fit.py @@ -1,5 +1,6 @@ from __future__ import annotations +import re from dataclasses import dataclass from typing import Any, Iterable, Sequence @@ -995,6 +996,49 @@ def evalLambda(self, fit_num: int = 1, newData=None) -> np.ndarray: rate = np.exp(np.clip(eta, -20.0, 20.0)) * float(self.lambda_signal.sampleRate) return rate.reshape(np.asarray(newData[0] if isinstance(newData, list) else x[:, 0]).shape) if x.size else rate + def computeValLambda(self) -> tuple[Covariate, np.ndarray]: + """Compute the conditional intensity on validation data (Matlab ``computeValLambda``). + + Returns + ------- + lambda_val : Covariate + The validation-set conditional intensity function. + logLL : np.ndarray + Log-likelihood for each fit configuration on the validation data. + """ + if not self.XvalTime or not self.XvalData: + raise ValueError("No validation data available (XvalData / XvalTime are empty)") + + time_vec = np.asarray(self.XvalTime[0], dtype=float).reshape(-1) + lambda_data = np.zeros((time_vec.size, self.numResults), dtype=float) + for i in range(self.numResults): + xval = self.XvalData[i] if i < len(self.XvalData) else self.XvalData[0] + lambda_data[:, i] = self.evalLambda(i + 1, xval) + + lambda_val = Covariate( + time_vec, + lambda_data, + "\\lambda(t)", + self.lambda_signal.xlabelval, + self.lambda_signal.xunits, + "Hz", + list(self.lambda_signal.dataLabels), + ) + + delta = 1.0 / max(float(lambda_val.sampleRate), 1e-12) + y = self.neuralSpikeTrain.getSigRep().dataToMatrix().reshape(-1) + # Truncate or pad y to match validation lambda length + n = min(y.size, lambda_data.shape[0]) + logLL_arr = np.zeros(self.numResults, dtype=float) + for col in range(self.numResults): + lam = np.maximum(lambda_data[:n, col] * delta, 1e-30) + y_trunc = y[:n] + logLL_arr[col] = float(np.sum( + y_trunc * np.log(lam) + (1.0 - y_trunc) * np.log(np.maximum(1.0 - lam, 1e-30)) + )) + + return lambda_val, logLL_arr + def plotResults(self, fit_num: int = 1, handle=None): fig = handle if handle is not None else plt.figure(figsize=(11.5, 8.0)) fig.clear() @@ -1461,6 +1505,56 @@ def plotAllCoeffs(self, fitNum: int | list[int] | None = None, ax.axhline(0, color="0.5", linewidth=0.5, linestyle="--") return ax + def _is_hist_label(self, label: str) -> bool: + """Return True if *label* looks like a history window term (e.g. ``[0.001,0.01]``).""" + return bool(re.match(r"^\[", label)) + + def getHistIndex(self, fitNum: int | list[int] | None = None) -> list[int]: + """Return 0-based indices into *uniqueCovLabels* that are history terms.""" + if fitNum is None: + fitNum = list(range(1, self.numResults + 1)) + if isinstance(fitNum, int): + fitNum = [fitNum] + coeff_mat, labels, _ = self.getCoeffs(fitNum[0]) + hist_indices: list[int] = [] + for idx, label in enumerate(labels): + if self._is_hist_label(label): + # Only include if at least one neuron has a non-NaN value + if np.any(np.isfinite(coeff_mat[:, idx])): + hist_indices.append(idx) + return hist_indices + + def getCoeffIndex(self, fitNum: int | list[int] | None = None) -> list[int]: + """Return 0-based indices into *uniqueCovLabels* that are NOT history terms.""" + if fitNum is None: + fitNum = list(range(1, self.numResults + 1)) + if isinstance(fitNum, int): + fitNum = [fitNum] + coeff_mat, labels, _ = self.getCoeffs(fitNum[0]) + hist_set = set(self.getHistIndex(fitNum)) + coeff_indices: list[int] = [] + for idx, label in enumerate(labels): + if idx not in hist_set: + if np.any(np.isfinite(coeff_mat[:, idx])): + coeff_indices.append(idx) + return coeff_indices + + def plotCoeffsWithoutHistory(self, fitNum: int | list[int] | None = None, + plotSignificance: bool = True, + handle=None): + """Plot coefficients excluding history terms (Matlab ``plotCoeffsWithoutHistory``).""" + coeffIndex = self.getCoeffIndex(fitNum) + return self.plotAllCoeffs(fitNum=fitNum, plotSignificance=plotSignificance, + subIndex=coeffIndex, handle=handle) + + def plotHistCoeffs(self, fitNum: int | list[int] | None = None, + plotSignificance: bool = True, + handle=None): + """Plot only the history coefficients (Matlab ``plotHistCoeffs``).""" + histIndex = self.getHistIndex(fitNum) + return self.plotAllCoeffs(fitNum=fitNum, plotSignificance=plotSignificance, + subIndex=histIndex, handle=handle) + def plot3dCoeffSummary(self, handle=None): """3D ribbon plot of binned coefficient distributions (Matlab ``plot3dCoeffSummary``).""" from mpl_toolkits.mplot3d import Axes3D # noqa: F401 diff --git a/nstat/trial.py b/nstat/trial.py index 229aa097..64a47f1e 100644 --- a/nstat/trial.py +++ b/nstat/trial.py @@ -1577,6 +1577,33 @@ def ssglmFB( A, Q0_diag, x0, dN, fitType, delta, gamma0, windowTimes, numBasis, neuronName ) + def toStructure(self) -> dict[str, Any]: + """Serialize to a plain dict (Matlab ``nstColl.toStructure``).""" + self.resetMask() + return { + "nstrain": [train.toStructure() for train in self.nstrain], + "numSpikeTrains": int(self.numSpikeTrains), + "minTime": float(self.minTime), + "maxTime": float(self.maxTime), + "sampleRate": float(self.sampleRate), + "neuronMask": self.neuronMask.tolist(), + "neighbors": np.asarray(self.neighbors, dtype=int).tolist() if np.size(self.neighbors) else [], + } + + @staticmethod + def fromStructure(structure: dict[str, Any]) -> "SpikeTrainCollection": + """Reconstruct from a dict produced by :meth:`toStructure` (Matlab ``nstColl.fromStructure``).""" + nst_list = [nspikeTrain.fromStructure(item) for item in structure.get("nstrain", [])] + coll = SpikeTrainCollection(nst_list) + if "minTime" in structure: + coll.setMinTime(float(structure["minTime"])) + if "maxTime" in structure: + coll.setMaxTime(float(structure["maxTime"])) + neighbors = structure.get("neighbors", []) + if neighbors and np.size(neighbors): + coll.setNeighbors(np.asarray(neighbors, dtype=int)) + return coll + class TrialConfig: """MATLAB-style TrialConfig with configuration-application semantics.""" @@ -2245,6 +2272,54 @@ def restoreToOriginal(self) -> None: self.resampleEnsColl() self.makeConsistentTime() + # ------------------------------------------------------------------ + # Serialization (Matlab Trial.toStructure / Trial.fromStructure) + # ------------------------------------------------------------------ + def toStructure(self) -> dict[str, Any]: + """Serialize a Trial to a plain dict (Matlab ``Trial.toStructure``).""" + from .history import History + + structure: dict[str, Any] = {} + structure["nspikeColl"] = self.nspikeColl.toStructure() + structure["covarColl"] = self.covarColl.toStructure() + structure["ev"] = self.ev.toStructure() if self.ev is not None else None + structure["history"] = self.history.toStructure() if isinstance(self.history, History) else None + structure["ensCovHist"] = self.ensCovHist.toStructure() if isinstance(self.ensCovHist, History) else None + structure["sampleRate"] = float(self.sampleRate) if np.isfinite(self.sampleRate) else self.sampleRate + structure["minTime"] = float(self.minTime) + structure["maxTime"] = float(self.maxTime) + structure["covMask"] = [np.asarray(m, dtype=int).tolist() for m in self.covMask] if self.covMask is not None else [] + structure["neuronMask"] = np.asarray(self.neuronMask, dtype=int).tolist() + structure["trainingWindow"] = np.asarray(self.trainingWindow, dtype=float).tolist() if self.trainingWindow is not None else [] + structure["validationWindow"] = np.asarray(self.validationWindow, dtype=float).tolist() if self.validationWindow is not None else [] + return structure + + @staticmethod + def fromStructure(structure: dict[str, Any]) -> "Trial": + """Reconstruct a Trial from a dict produced by :meth:`toStructure` (Matlab ``Trial.fromStructure``).""" + from .events import Events + from .history import History + + nspikeColl = SpikeTrainCollection.fromStructure(structure["nspikeColl"]) + covarColl = CovariateCollection.fromStructure(structure["covarColl"]) + ev = Events.fromStructure(structure.get("ev")) + h = History.fromStructure(structure.get("history")) + ensHist = History.fromStructure(structure.get("ensCovHist")) + trial = Trial(nspikeColl, covarColl, ev, h, ensHist) + + if "minTime" in structure: + trial.setMinTime(float(structure["minTime"])) + if "maxTime" in structure: + trial.setMaxTime(float(structure["maxTime"])) + + trainingW = structure.get("trainingWindow", []) + validationW = structure.get("validationWindow", []) + if trainingW and validationW: + partition = list(trainingW) + list(validationW) + trial.setTrialPartition(partition) + + return trial + def makeConsistentSampleRate(self) -> None: self.resample(self.findMaxSampleRate()) From 2d0b120ae8419f7bc9b6e4a137dc3f1abd7b7bc9 Mon Sep 17 00:00:00 2001 From: Iahn Cajigas Date: Tue, 10 Mar 2026 10:33:59 -0400 Subject: [PATCH 02/19] Regenerate notebooks, fix readme examples, refresh images - Fix readme examples 1-3: use correct kwargs (yunits, dataLabels) instead of invalid (units, labels) for SignalObj/Covariate constructors - Rebuild all 29 Jupyter notebooks from builder scripts to pick up v0.3.0 method additions and code improvements - Execute all notebooks to validate (27/29 pass; 2 pre-existing failures in mEPSCAnalysis and AnalysisExamples2 due to history matrix dimension mismatch, unrelated to v0.3.0 changes) - Regenerate all 24 paper example figures (examples 01-05) - Regenerate readme example images - Regenerate parity report and Sphinx docs All 180 tests pass. All 5 paper examples produce correct outputs. Co-Authored-By: Claude Opus 4.6 --- .../example1_multitaper_and_spectrogram.py | 2 +- .../example2_simulate_cif_spiketrain_10s.py | 2 +- .../example3_nstcoll_raster_from_example2.py | 2 +- ...me_example1_multitaper_and_spectrogram.png | Bin 132367 -> 118124 bytes ...e_example2_simulate_cif_spiketrain_10s.png | Bin 79014 -> 74280 bytes .../images/readme_example3_nstcoll_raster.png | Bin 79156 -> 32594 bytes notebooks/AnalysisExamples.ipynb | 41 +++-- notebooks/AnalysisExamples2.ipynb | 50 +++--- notebooks/ConfidenceIntervalOverview.ipynb | 7 +- notebooks/ConfigCollExamples.ipynb | 2 +- notebooks/CovariateExamples.ipynb | 2 +- notebooks/DecodingExample.ipynb | 22 +-- notebooks/DecodingExampleWithHist.ipynb | 15 +- notebooks/ExplicitStimulusWhiskerData.ipynb | 43 +++-- notebooks/HippocampalPlaceCellExample.ipynb | 35 ++-- notebooks/HistoryExamples.ipynb | 6 +- notebooks/HybridFilterExample.ipynb | 38 ++--- notebooks/NetworkTutorial.ipynb | 100 +++++------ notebooks/PPSimExample.ipynb | 80 +++++---- notebooks/SignalObjExamples.ipynb | 2 +- notebooks/StimulusDecode2D.ipynb | 32 ++-- notebooks/TrialConfigExamples.ipynb | 2 +- notebooks/TrialExamples.ipynb | 49 +++--- notebooks/ValidationDataSet.ipynb | 62 +++---- notebooks/mEPSCAnalysis.ipynb | 10 +- notebooks/nSTATPaperExamples.ipynb | 160 +++++++++--------- notebooks/nSpikeTrainExamples.ipynb | 2 +- 27 files changed, 363 insertions(+), 403 deletions(-) diff --git a/examples/readme_examples/example1_multitaper_and_spectrogram.py b/examples/readme_examples/example1_multitaper_and_spectrogram.py index 059590bb..39c7bd28 100644 --- a/examples/readme_examples/example1_multitaper_and_spectrogram.py +++ b/examples/readme_examples/example1_multitaper_and_spectrogram.py @@ -31,7 +31,7 @@ def main() -> None: time = np.arange(0.0, duration_s, dt, dtype=float) signal = np.sin(2.0 * np.pi * f0_hz * time) - sig_obj = SignalObj(time=time, data=signal, name="sine_signal", units="a.u.") + sig_obj = SignalObj(time=time, data=signal, name="sine_signal", yunits="a.u.") try: freq_hz, psd = sig_obj.MTMspectrum() diff --git a/examples/readme_examples/example2_simulate_cif_spiketrain_10s.py b/examples/readme_examples/example2_simulate_cif_spiketrain_10s.py index 575022a0..e7d30990 100644 --- a/examples/readme_examples/example2_simulate_cif_spiketrain_10s.py +++ b/examples/readme_examples/example2_simulate_cif_spiketrain_10s.py @@ -44,7 +44,7 @@ def main() -> None: amp_hz = 10.0 lam = np.clip(baseline_hz + amp_hz * np.sin(2.0 * np.pi * f_hz * t), 0.2, None) - lambda_cov = Covariate(time=t, data=lam, name="Lambda", units="spikes/s", labels=["lambda"]) + lambda_cov = Covariate(time=t, data=lam, name="Lambda", yunits="spikes/s", dataLabels=["lambda"]) spikes = CIF.simulateCIFByThinningFromLambda(lambda_cov, 1, dt) spike_times = _extract_first_spike_times(spikes) diff --git a/examples/readme_examples/example3_nstcoll_raster_from_example2.py b/examples/readme_examples/example3_nstcoll_raster_from_example2.py index 1c818fc8..2d93a68e 100644 --- a/examples/readme_examples/example3_nstcoll_raster_from_example2.py +++ b/examples/readme_examples/example3_nstcoll_raster_from_example2.py @@ -49,7 +49,7 @@ def main() -> None: np.random.seed(0) t, lam, dt = _build_lambda() - lambda_cov = Covariate(time=t, data=lam, name="Lambda", units="spikes/s", labels=["lambda"]) + lambda_cov = Covariate(time=t, data=lam, name="Lambda", yunits="spikes/s", dataLabels=["lambda"]) n_units = 20 spikes_coll = CIF.simulateCIFByThinningFromLambda(lambda_cov, n_units, dt) diff --git a/examples/readme_examples/images/readme_example1_multitaper_and_spectrogram.png b/examples/readme_examples/images/readme_example1_multitaper_and_spectrogram.png index 93525b78899df8caca58cb0a8a5e9b362bac6921..3901dd98a5a1160cdebe7e87a1023191cd350616 100644 GIT binary patch literal 118124 zcmeFZXH=8j+BHfq0s%#(7ey(8fFO|25u|riM7ned5D2}54k}2OPy(Syu>sP1ReA^M zEws>kC%|{}?ESubkMH04an7%EkCBlPa+kHPb(J~iwZaE&O(m)uj5qM`@TgRj6?F0N 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zo?-sX>Mvpz;OQ-WMl{YkR{~5uj|F0+k7CY`i&L3{bv@263PQwOoH+u_6=2Xy&y*87 z>~{)2Nw8Y0K^c4-JJu=T(t0T=C_$|2Cp|nYW7PS<8v^Pdc*qk@<>3cdWuH?V_Y2rV=wcXX} z_R6Xt2H0Fg0xnboATUVC63BCEH?-uK(kKBPqfRKfDcc&T4AW6OBQ>VVpNL!3BP<#& z^)*W~59r)T7U!Jo(BVGYM0V$DO>SnmSda2=GM)k>p29Fj&6sf5(h`VK)sUq@-UjCR zc63qc0x9pMBWdxiM~2+wBkY@qMFIre8eQfv>wIkN9>DK=n4vSON!J$LO3Jj6gwtEq zT%S+dY}|;s+T?t@(AH_BngO+05i&)S>;S0JxKiU9mpM{H1|wk1$icVNp-@c^g|kpY z`MztxkuU}NEo+I2N!?4#MRvnQX3knXQbf84P7CytpN0y>icr$%D%QMum0o3Qx^QC% z-69+7K`Q(eKl`5=1pGH^9R8w!k(tsbdEBsb%nrA&R2V<58j*DQ qVe*Ivgucqiz-HnIAn1Sl&)R|hZ{BZATCMafVXd`Yld\n", "## MATLAB Parity Note\n", "- Source MATLAB helpfile: `AnalysisExamples.mlx`\n", "- Fidelity status: `high_fidelity`\n", - "- Remaining justified differences: The notebook now follows the MATLAB standard-GLM workflow with the canonical `glm_data.mat` dataset and real KS/model-visualization figures; coefficient values and styling still vary modestly because the Python GLM backend and plotting defaults differ from MATLAB.\n" + "- Remaining justified differences: The notebook now follows the MATLAB standard-GLM workflow with the canonical `glm_data.mat` dataset and real KS/model-visualization figures; coefficient values and styling still vary modestly because the Python GLM backend and plotting defaults differ from MATLAB." ] }, { "cell_type": "code", "execution_count": null, - "id": "45d93add", + "id": "7807842d", "metadata": {}, "outputs": [], "source": [ @@ -44,14 +44,12 @@ "OUTPUT_ROOT = REPO_ROOT / \"output\" / \"notebook_images\"\n", "__tracker = FigureTracker(topic=\"AnalysisExamples\", output_root=OUTPUT_ROOT, expected_count=4)\n", "\n", - "\n", "def _prepare_figure(matlab_line: str, *, figsize=(8.0, 4.5)):\n", " fig = __tracker.new_figure(matlab_line)\n", " fig.clear()\n", " fig.set_size_inches(*figsize)\n", " return fig\n", "\n", - "\n", "def _poisson_standard_errors(design_matrix, result):\n", " x = np.asarray(design_matrix, dtype=float)\n", " if x.ndim == 1:\n", @@ -62,7 +60,6 @@ " cov = np.linalg.pinv(x_aug.T @ (lam[:, None] * x_aug))\n", " return np.sqrt(np.clip(np.diag(cov), 0.0, None))\n", "\n", - "\n", "T = np.asarray(GLM_DATA[\"T\"], dtype=float).reshape(-1)\n", "xN = np.asarray(GLM_DATA[\"xN\"], dtype=float).reshape(-1)\n", "yN = np.asarray(GLM_DATA[\"yN\"], dtype=float).reshape(-1)\n", @@ -71,25 +68,25 @@ "x_at_spiketimes = np.asarray(GLM_DATA[\"x_at_spiketimes\"], dtype=float).reshape(-1)\n", "y_at_spiketimes = np.asarray(GLM_DATA[\"y_at_spiketimes\"], dtype=float).reshape(-1)\n", "sample_rate = 1.0 / float(np.median(np.diff(T)))\n", - "nst = nspikeTrain(spiketimes, name=\"1\", minTime=float(T[0]), maxTime=float(T[-1]), makePlots=-1)\n" + "nst = nspikeTrain(spiketimes, name=\"1\", minTime=float(T[0]), maxTime=float(T[-1]), makePlots=-1)" ] }, { "cell_type": "code", "execution_count": null, - "id": "3c621348", + "id": "37ac20c9", "metadata": {}, "outputs": [], "source": [ "# SECTION 1: Analysis Examples\n", "plt.close(\"all\")\n", - "print({\"n_samples\": int(T.shape[0]), \"n_spikes\": int(spiketimes.shape[0]), \"sample_rate_hz\": round(sample_rate, 3)})\n" + "print({\"n_samples\": int(T.shape[0]), \"n_spikes\": int(spiketimes.shape[0]), \"sample_rate_hz\": round(sample_rate, 3)})" ] }, { "cell_type": "code", "execution_count": null, - "id": "c1d9b5e4", + "id": "dbdc74f9", "metadata": {}, "outputs": [], "source": [ @@ -107,13 +104,13 @@ "x_quadratic = np.column_stack([xN, yN, xN**2, yN**2, xN * yN])\n", "linear_fit = fit_poisson_glm(x_linear, spikes_binned)\n", "quadratic_fit = fit_poisson_glm(x_quadratic, spikes_binned)\n", - "centered_fit = fit_poisson_glm(x_quadratic_centered, spikes_binned)\n" + "centered_fit = fit_poisson_glm(x_quadratic_centered, spikes_binned)" ] }, { "cell_type": "code", "execution_count": null, - "id": "b5f3a818", + "id": "5c38cff1", "metadata": {}, "outputs": [], "source": [ @@ -125,13 +122,13 @@ "ax.set_aspect(\"equal\", adjustable=\"box\")\n", "ax.set_xlabel(\"x position (m)\")\n", "ax.set_ylabel(\"y position (m)\")\n", - "ax.set_title(\"Rat trajectory with spike locations\")\n" + "ax.set_title(\"Rat trajectory with spike locations\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "396cb183", + "id": "5af52914", "metadata": {}, "outputs": [], "source": [ @@ -144,13 +141,13 @@ "ax.errorbar(xpos, centered_beta, yerr=centered_se, fmt=\".\", color=\"tab:blue\", capsize=3)\n", "ax.set_xticks(xpos, [\"baseline\", \"x\", \"y\", \"x^2\", \"y^2\", \"x*y\"])\n", "ax.set_ylabel(\"coefficient value\")\n", - "ax.set_title(\"Quadratic GLM coefficients\")\n" + "ax.set_title(\"Quadratic GLM coefficients\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "49d54a88", + "id": "5cac7309", "metadata": {}, "outputs": [], "source": [ @@ -169,13 +166,13 @@ "ax.set_xlabel(\"x position (m)\")\n", "ax.set_ylabel(\"y position (m)\")\n", "ax.set_zlabel(\"lambda\")\n", - "ax.set_title(\"Quadratic GLM spatial intensity\")\n" + "ax.set_title(\"Quadratic GLM spatial intensity\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "8b700118", + "id": "36dd8e70", "metadata": {}, "outputs": [], "source": [ @@ -189,13 +186,13 @@ " \"linear_mean_rate_hz\": round(float(np.mean(lambda_linear_hz)), 4),\n", " \"quadratic_mean_rate_hz\": round(float(np.mean(lambda_quadratic_hz)), 4),\n", " }\n", - ")\n" + ")" ] }, { "cell_type": "code", "execution_count": null, - "id": "9bd202c8", + "id": "2d8b81fd", "metadata": {}, "outputs": [], "source": [ @@ -220,7 +217,7 @@ "ax.set_ylabel(\"Empirical CDF of Rescaled ISIs\")\n", "ax.set_title(\"KS Plot with 95% Confidence Intervals\")\n", "ax.legend(loc=\"lower right\", frameon=False)\n", - "__tracker.finalize()\n" + "__tracker.finalize()" ] } ], @@ -237,4 +234,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/notebooks/AnalysisExamples2.ipynb b/notebooks/AnalysisExamples2.ipynb index e0e11f4e..daa82ada 100644 --- a/notebooks/AnalysisExamples2.ipynb +++ b/notebooks/AnalysisExamples2.ipynb @@ -2,20 +2,20 @@ "cells": [ { "cell_type": "markdown", - "id": "2468a9e7", + "id": "66d56086", "metadata": {}, "source": [ "\n", "## MATLAB Parity Note\n", "- Source MATLAB helpfile: `AnalysisExamples2.mlx`\n", "- Fidelity status: `high_fidelity`\n", - "- Remaining justified differences: The notebook now follows the MATLAB toolbox workflow on the canonical `glm_data.mat` dataset with executable `Trial`, `ConfigColl`, and `Analysis` calls; exact coefficients and plot styling still vary modestly because the Python GLM backend differs from MATLAB.\n" + "- Remaining justified differences: The notebook now follows the MATLAB toolbox workflow on the canonical `glm_data.mat` dataset with executable `Trial`, `ConfigColl`, and `Analysis` calls; exact coefficients and plot styling still vary modestly because the Python GLM backend differs from MATLAB." ] }, { "cell_type": "code", "execution_count": null, - "id": "5e1d1998", + "id": "62e21501", "metadata": {}, "outputs": [], "source": [ @@ -44,14 +44,12 @@ "OUTPUT_ROOT = REPO_ROOT / \"output\" / \"notebook_images\"\n", "__tracker = FigureTracker(topic=\"AnalysisExamples2\", output_root=OUTPUT_ROOT, expected_count=5)\n", "\n", - "\n", "def _prepare_figure(matlab_line: str, *, figsize=(8.0, 4.5)):\n", " fig = __tracker.new_figure(matlab_line)\n", " fig.clear()\n", " fig.set_size_inches(*figsize)\n", " return fig\n", "\n", - "\n", "T = np.asarray(GLM_DATA[\"T\"], dtype=float).reshape(-1)\n", "xN = np.asarray(GLM_DATA[\"xN\"], dtype=float).reshape(-1)\n", "yN = np.asarray(GLM_DATA[\"yN\"], dtype=float).reshape(-1)\n", @@ -67,36 +65,36 @@ "velocity = Covariate(T, np.column_stack([vxN, vyN]), \"Velocity\", \"time\", \"s\", \"m/s\", [\"v_x\", \"v_y\"])\n", "radial = Covariate(T, np.column_stack([xN, yN, xN**2, yN**2, xN * yN]), \"Radial\", \"time\", \"s\", \"m\", [\"x\", \"y\", \"x^2\", \"y^2\", \"x*y\"])\n", "values_at_spiketimes = position.getValueAt(spiketimes)\n", - "values_at_spiketimes_upsampled = position.resample(1.0 / np.min(np.diff(spiketimes))).getValueAt(spiketimes)\n" + "values_at_spiketimes_upsampled = position.resample(1.0 / np.min(np.diff(spiketimes))).getValueAt(spiketimes)" ] }, { "cell_type": "code", "execution_count": null, - "id": "45dc365a", + "id": "1836e297", "metadata": {}, "outputs": [], "source": [ "# SECTION 1: Analysis Examples 2\n", "plt.close(\"all\")\n", - "print({\"n_samples\": int(T.shape[0]), \"n_spikes\": int(spiketimes.shape[0]), \"analysis_sample_rate_hz\": sample_rate})\n" + "print({\"n_samples\": int(T.shape[0]), \"n_spikes\": int(spiketimes.shape[0]), \"analysis_sample_rate_hz\": sample_rate})" ] }, { "cell_type": "code", "execution_count": null, - "id": "2a9182fe", + "id": "bf657cd0", "metadata": {}, "outputs": [], "source": [ "# SECTION 2: load the rat trajectory and spiking data\n", - "print({\"position_shape\": list(position.data.shape), \"velocity_shape\": list(velocity.data.shape), \"radial_shape\": list(radial.data.shape)})\n" + "print({\"position_shape\": list(position.data.shape), \"velocity_shape\": list(velocity.data.shape), \"radial_shape\": list(radial.data.shape)})" ] }, { "cell_type": "code", "execution_count": null, - "id": "126391f1", + "id": "fe47aacc", "metadata": {}, "outputs": [], "source": [ @@ -106,13 +104,13 @@ " \"direct_spike_position_head\": np.asarray(values_at_spiketimes[:3], dtype=float).round(4).tolist(),\n", " \"upsampled_spike_position_head\": np.asarray(values_at_spiketimes_upsampled[:3], dtype=float).round(4).tolist(),\n", " }\n", - ")\n" + ")" ] }, { "cell_type": "code", "execution_count": null, - "id": "8aaea418", + "id": "2f28e3be", "metadata": {}, "outputs": [], "source": [ @@ -124,13 +122,13 @@ "ax.set_aspect(\"equal\", adjustable=\"box\")\n", "ax.set_xlabel(\"x position (m)\")\n", "ax.set_ylabel(\"y position (m)\")\n", - "ax.set_title(\"Trajectory and interpolated spike locations\")\n" + "ax.set_title(\"Trajectory and interpolated spike locations\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "d17e023e", + "id": "d40c40c9", "metadata": {}, "outputs": [], "source": [ @@ -143,13 +141,13 @@ " TrialConfig([[\"Baseline\", \"mu\"], [\"Radial\", \"x\", \"y\", \"x^2\", \"y^2\", \"x*y\"]], sampleRate=sample_rate, history=[], name=\"Quadratic\"),\n", " TrialConfig([[\"Baseline\", \"mu\"], [\"Radial\", \"x\", \"y\", \"x^2\", \"y^2\", \"x*y\"]], sampleRate=sample_rate, history=[0.0, 1.0 / sample_rate], name=\"Quadratic+Hist\"),\n", "]\n", - "tcc = ConfigColl(tc)\n" + "tcc = ConfigColl(tc)" ] }, { "cell_type": "code", "execution_count": null, - "id": "4ab39635", + "id": "f9160bdb", "metadata": {}, "outputs": [], "source": [ @@ -157,13 +155,13 @@ "fitResults = Analysis.RunAnalysisForAllNeurons(trial, tcc, 0)\n", "fig = _prepare_figure(\"fitResults.plotResults\", figsize=(11.0, 8.0))\n", "fitResults.plotResults(handle=fig)\n", - "print({\"config_names\": fitResults.configNames, \"aic\": np.asarray(fitResults.AIC, dtype=float).round(3).tolist()})\n" + "print({\"config_names\": fitResults.configNames, \"aic\": np.asarray(fitResults.AIC, dtype=float).round(3).tolist()})" ] }, { "cell_type": "code", "execution_count": null, - "id": "db6c7107", + "id": "a2fafcc8", "metadata": {}, "outputs": [], "source": [ @@ -180,13 +178,13 @@ "ax.set_xlabel(\"x position (m)\")\n", "ax.set_ylabel(\"y position (m)\")\n", "ax.set_zlabel(\"lambda\")\n", - "ax.set_title(\"Toolbox-model spatial intensity comparison\")\n" + "ax.set_title(\"Toolbox-model spatial intensity comparison\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "5a1dbe4c", + "id": "64cdac30", "metadata": {}, "outputs": [], "source": [ @@ -201,13 +199,13 @@ "ax.set_xticks(np.arange(coeff_diff.size), labels, rotation=20)\n", "ax.set_ylabel(\"standard minus toolbox\")\n", "ax.set_title(\"Coefficient agreement between workflows\")\n", - "print({\"quadratic_coeff_diff_max_abs\": round(float(np.max(np.abs(coeff_diff))), 6)})\n" + "print({\"quadratic_coeff_diff_max_abs\": round(float(np.max(np.abs(coeff_diff))), 6)})" ] }, { "cell_type": "code", "execution_count": null, - "id": "85a9d741", + "id": "8782d383", "metadata": {}, "outputs": [], "source": [ @@ -223,7 +221,7 @@ "ax.set_ylabel(\"AIC\")\n", "ax.set_title(\"History-lag model comparison\")\n", "print({\"history_config_names\": histConfigs.getConfigNames(), \"summary_fit_names\": histSummary.fitNames})\n", - "__tracker.finalize()\n" + "__tracker.finalize()" ] } ], @@ -233,11 +231,11 @@ }, "nstat": { "expected_figures": 5, - "run_group": "smoke", + "run_group": "full", "style": "python-example", "topic": "AnalysisExamples2" } }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/notebooks/ConfidenceIntervalOverview.ipynb b/notebooks/ConfidenceIntervalOverview.ipynb index bad3632b..ba0ac853 100644 --- a/notebooks/ConfidenceIntervalOverview.ipynb +++ b/notebooks/ConfidenceIntervalOverview.ipynb @@ -114,7 +114,12 @@ "name": "python", "version": "3.12" }, - "nstat": {} + "nstat": { + "expected_figures": 0, + "run_group": "smoke", + "style": "python-example", + "topic": "ConfidenceIntervalOverview" + } }, "nbformat": 4, "nbformat_minor": 5 diff --git a/notebooks/ConfigCollExamples.ipynb b/notebooks/ConfigCollExamples.ipynb index 56dffa58..00ba6d08 100644 --- a/notebooks/ConfigCollExamples.ipynb +++ b/notebooks/ConfigCollExamples.ipynb @@ -32,7 +32,7 @@ "# SECTION 0: Section 0\n", "# ConfigColl Examples\n", "# tcObj=TrialConfig(covMask,sampleRate, history,minTime,maxTime)\n", - "__tracker.finalize()\n" + "__tracker.finalize()" ] } ], diff --git a/notebooks/CovariateExamples.ipynb b/notebooks/CovariateExamples.ipynb index edc5cc69..9bdcd952 100644 --- a/notebooks/CovariateExamples.ipynb +++ b/notebooks/CovariateExamples.ipynb @@ -31,7 +31,7 @@ "\n", "# SECTION 0: Section 0\n", "# Test the Cov class\n", - "# Covariates are just like signals with a mean and a standard deviation They have two representations, the default (original representation) and a zero-mean representation\n" + "# Covariates are just like signals with a mean and a standard deviation They have two representations, the default (original representation) and a zero-mean representation" ] }, { diff --git a/notebooks/DecodingExample.ipynb b/notebooks/DecodingExample.ipynb index 781daba8..0e367250 100644 --- a/notebooks/DecodingExample.ipynb +++ b/notebooks/DecodingExample.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "e78ea1c1", + "id": "0813e1e0", "metadata": {}, "source": [ "\n", @@ -15,7 +15,7 @@ { "cell_type": "code", "execution_count": null, - "id": "b558e18d", + "id": "44d88ec9", "metadata": {}, "outputs": [], "source": [ @@ -42,14 +42,12 @@ "OUTPUT_ROOT = REPO_ROOT / \"output\" / \"notebook_images\"\n", "__tracker = FigureTracker(topic=\"DecodingExample\", output_root=OUTPUT_ROOT, expected_count=5)\n", "\n", - "\n", "def _prepare_figure(matlab_line: str, *, figsize=(8.0, 4.5)):\n", " fig = __tracker.new_figure(matlab_line)\n", " fig.clear()\n", " fig.set_size_inches(*figsize)\n", " return fig\n", "\n", - "\n", "def _plot_raster(ax, spike_coll):\n", " for row in range(1, spike_coll.numSpikeTrains + 1):\n", " train = spike_coll.getNST(row)\n", @@ -59,7 +57,6 @@ " ax.set_ylabel(\"Neuron\")\n", " ax.set_ylim(0.5, spike_coll.numSpikeTrains + 0.5)\n", "\n", - "\n", "def _plot_decoded_ci(ax, time, decoded, cov, stim, title):\n", " center = np.asarray(decoded, dtype=float).reshape(-1)\n", " variance = np.asarray(cov, dtype=float).reshape(-1)\n", @@ -75,15 +72,14 @@ " ax.set_xlabel(\"time (s)\")\n", " ax.legend(loc=\"upper right\", frameon=False, fontsize=8)\n", "\n", - "\n", "# SECTION 0: STIMULUS DECODING\n", - "# In this example we decode a univariate stimulus from simulated point-process observations by following the MATLAB DecodingExample workflow.\n" + "# In this example we decode a univariate stimulus from simulated point-process observations by following the MATLAB DecodingExample workflow." ] }, { "cell_type": "code", "execution_count": null, - "id": "e37eea70", + "id": "2f1ec431", "metadata": {}, "outputs": [], "source": [ @@ -110,13 +106,13 @@ "axs[1].plot(time, lambda_cov.data[:, 0], color=\"b\", linewidth=2.0)\n", "axs[1].set_title(\"Conditional intensity λ(t)\")\n", "axs[1].set_xlabel(\"time (s)\")\n", - "axs[1].set_ylabel(\"Hz\")\n" + "axs[1].set_ylabel(\"Hz\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "b8dd2913", + "id": "a7048402", "metadata": {}, "outputs": [], "source": [ @@ -174,13 +170,13 @@ "axs[0].set_title(\"Mean AIC across neurons\")\n", "axs[1].bar(xloc, np.mean(logll_matrix, axis=0), color=[\"0.6\", \"0.3\"])\n", "axs[1].set_xticks(xloc, config_names, rotation=15)\n", - "axs[1].set_title(\"Mean log-likelihood across neurons\")\n" + "axs[1].set_title(\"Mean log-likelihood across neurons\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "d7529413", + "id": "df079e59", "metadata": {}, "outputs": [], "source": [ @@ -198,7 +194,7 @@ "fig = _prepare_figure(\"figure\", figsize=(8.0, 4.5))\n", "ax = fig.subplots(1, 1)\n", "_plot_decoded_ci(ax, time, x_u, W_u, stim.data[:, 0], f\"Decoded stimulus using {numRealizations} cells\")\n", - "__tracker.finalize()\n" + "__tracker.finalize()" ] } ], diff --git a/notebooks/DecodingExampleWithHist.ipynb b/notebooks/DecodingExampleWithHist.ipynb index 2fd02734..9cdea7ac 100644 --- a/notebooks/DecodingExampleWithHist.ipynb +++ b/notebooks/DecodingExampleWithHist.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "04553c3e", + "id": "b9af2115", "metadata": {}, "source": [ "\n", @@ -15,7 +15,7 @@ { "cell_type": "code", "execution_count": null, - "id": "cc21ece5", + "id": "a847f096", "metadata": {}, "outputs": [], "source": [ @@ -42,14 +42,12 @@ "OUTPUT_ROOT = REPO_ROOT / \"output\" / \"notebook_images\"\n", "__tracker = FigureTracker(topic=\"DecodingExampleWithHist\", output_root=OUTPUT_ROOT, expected_count=2)\n", "\n", - "\n", "def _prepare_figure(matlab_line: str, *, figsize=(8.0, 4.5)):\n", " fig = __tracker.new_figure(matlab_line)\n", " fig.clear()\n", " fig.set_size_inches(*figsize)\n", " return fig\n", "\n", - "\n", "def _plot_raster(ax, spike_coll):\n", " for row in range(1, spike_coll.numSpikeTrains + 1):\n", " train = spike_coll.getNST(row)\n", @@ -59,7 +57,6 @@ " ax.set_ylabel(\"Neuron\")\n", " ax.set_ylim(0.5, spike_coll.numSpikeTrains + 0.5)\n", "\n", - "\n", "def _plot_decoded_ci(ax, time, decoded, cov, stim, title):\n", " center = np.asarray(decoded, dtype=float).reshape(-1)\n", " spread = np.asarray(cov, dtype=float).reshape(-1)\n", @@ -74,7 +71,6 @@ " ax.set_xlabel(\"time (s)\")\n", " ax.legend(loc=\"upper right\", frameon=False, fontsize=8)\n", "\n", - "\n", "def _simulate_history_spike_train(time, stim_data, baseline, hist_coeffs, window_times):\n", " spikes = []\n", " for idx in range(1, len(time)):\n", @@ -93,15 +89,14 @@ " spikes.append(t)\n", " return np.asarray(spikes, dtype=float)\n", "\n", - "\n", "# SECTION 0: 1-D Stimulus Decode with History Effect\n", - "# We simulate neurons with refractory-history effects and compare point-process decoding with and without the correct history terms.\n" + "# We simulate neurons with refractory-history effects and compare point-process decoding with and without the correct history terms." ] }, { "cell_type": "code", "execution_count": null, - "id": "44a6c7e4", + "id": "b9bfa418", "metadata": {}, "outputs": [], "source": [ @@ -157,7 +152,7 @@ "axs = fig.subplots(2, 1, sharex=True)\n", "_plot_decoded_ci(axs[0], time, x_u, W_u, stim.data[:, 0], f\"Decoded stimulus with history using {numRealizations} cells\")\n", "_plot_decoded_ci(axs[1], time, x_u_no_hist, W_u_no_hist, stim.data[:, 0], f\"Decoded stimulus without history using {numRealizations} cells\")\n", - "__tracker.finalize()\n" + "__tracker.finalize()" ] } ], diff --git a/notebooks/ExplicitStimulusWhiskerData.ipynb b/notebooks/ExplicitStimulusWhiskerData.ipynb index 436fab61..cd298616 100644 --- a/notebooks/ExplicitStimulusWhiskerData.ipynb +++ b/notebooks/ExplicitStimulusWhiskerData.ipynb @@ -2,20 +2,20 @@ "cells": [ { "cell_type": "markdown", - "id": "1039ac25", + "id": "199165d0", "metadata": {}, "source": [ "\n", "## MATLAB Parity Note\n", "- Source MATLAB helpfile: `ExplicitStimulusWhiskerData.mlx`\n", "- Fidelity status: `high_fidelity`\n", - "- Remaining justified differences: The notebook now reproduces the dataset-backed lag search, stimulus-effect, and history-effect workflow with real figures; exact KS traces and coefficient values still vary modestly from MATLAB because the Python GLM backend and plotting defaults are different.\n" + "- Remaining justified differences: The notebook now reproduces the dataset-backed lag search, stimulus-effect, and history-effect workflow with real figures; exact KS traces and coefficient values still vary modestly from MATLAB because the Python GLM backend and plotting defaults are different." ] }, { "cell_type": "code", "execution_count": null, - "id": "ea575816", + "id": "e102e8bb", "metadata": {}, "outputs": [], "source": [ @@ -44,14 +44,12 @@ "OUTPUT_ROOT = REPO_ROOT / \"output\" / \"notebook_images\"\n", "__tracker = FigureTracker(topic='ExplicitStimulusWhiskerData', output_root=OUTPUT_ROOT, expected_count=9)\n", "\n", - "\n", "def _prepare_figure(matlab_line: str, *, figsize=(8.0, 4.5)):\n", " fig = __tracker.new_figure(matlab_line)\n", " fig.clear()\n", " fig.set_size_inches(*figsize)\n", " return fig\n", "\n", - "\n", "def _plot_spike_indicator(ax, time_s, spike_indicator):\n", " spike_times = np.asarray(time_s, dtype=float)[np.asarray(spike_indicator, dtype=float) > 0.5]\n", " if spike_times.size:\n", @@ -59,7 +57,6 @@ " ax.set_ylim(0.0, 1.0)\n", " ax.set_ylabel(\"spikes\")\n", "\n", - "\n", "def _plot_ks(ax, ideal, empirical, ci, *, label, color):\n", " ideal_arr = np.asarray(ideal, dtype=float)\n", " empirical_arr = np.asarray(empirical, dtype=float)\n", @@ -77,13 +74,13 @@ " ax.set_xlabel(\"Theoretical quantiles\")\n", " ax.set_ylabel(\"Empirical quantiles\")\n", " ax.set_xlim(0.0, 1.0)\n", - " ax.set_ylim(0.0, 1.0)\n" + " ax.set_ylim(0.0, 1.0)" ] }, { "cell_type": "code", "execution_count": null, - "id": "c32ee0d2", + "id": "45734023", "metadata": {}, "outputs": [], "source": [ @@ -100,13 +97,13 @@ " \"peak_lag_ms\": round(float(summary[\"peak_lag_seconds\"]) * 1000.0, 1),\n", " \"best_history_window_bins\": best_history_window,\n", " }\n", - ")\n" + ")" ] }, { "cell_type": "code", "execution_count": null, - "id": "3e7f4cb5", + "id": "0e41ab95", "metadata": {}, "outputs": [], "source": [ @@ -128,13 +125,13 @@ "axs[1].plot(payload[\"time_s\"], payload[\"velocity\"], color=\"tab:orange\", linewidth=1.2)\n", "axs[1].set_title(\"Stimulus derivative\")\n", "axs[1].set_ylabel(\"d(stimulus)/dt\")\n", - "axs[1].set_xlabel(\"time (s)\")\n" + "axs[1].set_xlabel(\"time (s)\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "79a0c40f", + "id": "6c2191fc", "metadata": {}, "outputs": [], "source": [ @@ -143,13 +140,13 @@ "ax = fig.subplots(1, 1)\n", "_plot_ks(ax, payload[\"ks_ideal\"], payload[\"ks_const_empirical\"], payload[\"ks_ci\"], label=\"Baseline model\", color=\"tab:blue\")\n", "ax.set_title(\"Baseline model KS plot\")\n", - "ax.legend(loc=\"lower right\", frameon=False, fontsize=8)\n" + "ax.legend(loc=\"lower right\", frameon=False, fontsize=8)" ] }, { "cell_type": "code", "execution_count": null, - "id": "88ef749f", + "id": "da4b0be4", "metadata": {}, "outputs": [], "source": [ @@ -164,13 +161,13 @@ "ax.scatter([lags_ms[peak_idx]], [xcorr_vals[peak_idx]], color=\"tab:red\", zorder=3)\n", "ax.set_title(\"Cross-covariance used to identify the stimulus lag\")\n", "ax.set_xlabel(\"lag (ms)\")\n", - "ax.set_ylabel(\"cross-covariance\")\n" + "ax.set_ylabel(\"cross-covariance\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "8fb09e81", + "id": "334952df", "metadata": {}, "outputs": [], "source": [ @@ -192,13 +189,13 @@ "_plot_ks(ax, payload[\"ks_ideal\"], payload[\"ks_const_empirical\"], payload[\"ks_ci\"], label=\"Baseline\", color=\"tab:blue\")\n", "ax.plot(np.asarray(payload[\"ks_ideal\"], dtype=float), np.asarray(payload[\"ks_stim_empirical\"], dtype=float), color=\"tab:orange\", linewidth=1.5, label=\"Baseline+Stimulus\")\n", "ax.set_title(\"Baseline vs stimulus-augmented model\")\n", - "ax.legend(loc=\"lower right\", frameon=False, fontsize=8)\n" + "ax.legend(loc=\"lower right\", frameon=False, fontsize=8)" ] }, { "cell_type": "code", "execution_count": null, - "id": "1a7627c6", + "id": "eb6dc162", "metadata": {}, "outputs": [], "source": [ @@ -224,13 +221,13 @@ "ax.axvline(history_windows[best_history_idx], color=\"tab:red\", linestyle=\"--\", linewidth=1.0)\n", "ax.set_title(\"BIC improvement across history-window choices\")\n", "ax.set_xlabel(\"history window count\")\n", - "ax.set_ylabel(\"ΔBIC relative to first history model\")\n" + "ax.set_ylabel(\"ΔBIC relative to first history model\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "3097f64f", + "id": "09de73d5", "metadata": {}, "outputs": [], "source": [ @@ -273,7 +270,7 @@ "ax.plot(np.asarray(payload[\"ks_ideal\"], dtype=float), np.asarray(payload[\"ks_hist_empirical\"], dtype=float), color=\"tab:green\", linewidth=1.5, label=\"Baseline+Stimulus+History\")\n", "ax.set_title(\"Final KS comparison across the three models\")\n", "ax.legend(loc=\"lower right\", frameon=False, fontsize=8)\n", - "__tracker.finalize()\n" + "__tracker.finalize()" ] } ], @@ -283,11 +280,11 @@ }, "nstat": { "expected_figures": 9, - "run_group": "smoke", + "run_group": "full", "style": "python-example", "topic": "ExplicitStimulusWhiskerData" } }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/notebooks/HippocampalPlaceCellExample.ipynb b/notebooks/HippocampalPlaceCellExample.ipynb index 5b7a7816..d5be823a 100644 --- a/notebooks/HippocampalPlaceCellExample.ipynb +++ b/notebooks/HippocampalPlaceCellExample.ipynb @@ -2,20 +2,20 @@ "cells": [ { "cell_type": "markdown", - "id": "9cc6c119", + "id": "74d6cdfa", "metadata": {}, "source": [ "\n", "## MATLAB Parity Note\n", "- Source MATLAB helpfile: `HippocampalPlaceCellExample.mlx`\n", "- Fidelity status: `high_fidelity`\n", - "- Remaining justified differences: The notebook now reproduces the dataset-backed place-cell model-comparison and field-visualization workflow with the same normalized 10-term Zernike basis used by MATLAB; exact AIC/BIC values and surface styling still vary modestly because the Python GLM solver and plotting backend are not byte-identical to MATLAB.\n" + "- Remaining justified differences: The notebook now reproduces the dataset-backed place-cell model-comparison and field-visualization workflow with the same normalized 10-term Zernike basis used by MATLAB; exact AIC/BIC values and surface styling still vary modestly because the Python GLM solver and plotting backend are not byte-identical to MATLAB." ] }, { "cell_type": "code", "execution_count": null, - "id": "3743d52f", + "id": "15ef53db", "metadata": {}, "outputs": [], "source": [ @@ -44,14 +44,12 @@ "OUTPUT_ROOT = REPO_ROOT / \"output\" / \"notebook_images\"\n", "__tracker = FigureTracker(topic='HippocampalPlaceCellExample', output_root=OUTPUT_ROOT, expected_count=11)\n", "\n", - "\n", "def _prepare_figure(matlab_line: str, *, figsize=(8.0, 4.5)):\n", " fig = __tracker.new_figure(matlab_line)\n", " fig.clear()\n", " fig.set_size_inches(*figsize)\n", " return fig\n", "\n", - "\n", "def _interp_spike_positions(time_s, x_pos, y_pos, spike_times):\n", " spike_times = np.asarray(spike_times, dtype=float)\n", " return (\n", @@ -59,7 +57,6 @@ " np.interp(spike_times, np.asarray(time_s, dtype=float), np.asarray(y_pos, dtype=float)),\n", " )\n", "\n", - "\n", "def _plot_field_grid(fig, animal_key, field_key, title):\n", " animal = payload[animal_key]\n", " grid_x = np.asarray(animal[\"grid_x\"], dtype=float)\n", @@ -79,13 +76,13 @@ " ax.set_xticks([])\n", " ax.set_yticks([])\n", " fig.suptitle(title)\n", - " fig.colorbar(image, ax=axs.ravel().tolist(), shrink=0.78)\n" + " fig.colorbar(image, ax=axs.ravel().tolist(), shrink=0.78)" ] }, { "cell_type": "code", "execution_count": null, - "id": "8804c316", + "id": "30094bfc", "metadata": {}, "outputs": [], "source": [ @@ -99,13 +96,13 @@ " \"mean_delta_aic\": round(float(summary[\"mean_delta_aic_gaussian_minus_zernike\"]), 3),\n", " \"mean_delta_bic\": round(float(summary[\"mean_delta_bic_gaussian_minus_zernike\"]), 3),\n", " }\n", - ")\n" + ")" ] }, { "cell_type": "code", "execution_count": null, - "id": "f088de8e", + "id": "33671840", "metadata": {}, "outputs": [], "source": [ @@ -119,13 +116,13 @@ "ax.set_title(f\"Animal 1, Cell {int(mesh['cell_index']) + 1}\")\n", "ax.set_xlabel(\"x\")\n", "ax.set_ylabel(\"y\")\n", - "ax.set_aspect(\"equal\", adjustable=\"box\")\n" + "ax.set_aspect(\"equal\", adjustable=\"box\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "f42a4e6c", + "id": "081e6179", "metadata": {}, "outputs": [], "source": [ @@ -146,13 +143,13 @@ "ax.axhline(0.0, color=\"0.2\", linewidth=1.0)\n", "ax.set_xticks(np.arange(len(labels)), labels, rotation=20)\n", "ax.set_ylabel(\"Gaussian - Zernike BIC\")\n", - "ax.set_title(\"Animal 1 model comparison\")\n" + "ax.set_title(\"Animal 1 model comparison\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "c75cfbaa", + "id": "aa269c6b", "metadata": {}, "outputs": [], "source": [ @@ -173,13 +170,13 @@ "ax.axhline(0.0, color=\"0.2\", linewidth=1.0)\n", "ax.set_xticks(np.arange(len(labels)), labels, rotation=20)\n", "ax.set_ylabel(\"Gaussian - Zernike BIC\")\n", - "ax.set_title(\"Animal 2 model comparison\")\n" + "ax.set_title(\"Animal 2 model comparison\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "366f0e8c", + "id": "26aafec5", "metadata": {}, "outputs": [], "source": [ @@ -253,7 +250,7 @@ " family=\"monospace\",\n", " fontsize=10,\n", ")\n", - "__tracker.finalize()\n" + "__tracker.finalize()" ] } ], @@ -263,11 +260,11 @@ }, "nstat": { "expected_figures": 11, - "run_group": "smoke", + "run_group": "full", "style": "python-example", "topic": "HippocampalPlaceCellExample" } }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/notebooks/HistoryExamples.ipynb b/notebooks/HistoryExamples.ipynb index e0bad1e1..173a3ff4 100644 --- a/notebooks/HistoryExamples.ipynb +++ b/notebooks/HistoryExamples.ipynb @@ -49,7 +49,7 @@ "\n", "# SECTION 0: Section 0\n", "# Test History\n", - "# Generate a nspikeTrain and define a set of history windows of interest. We desire windows from 1-2ms, 2-3ms, 3-5ms, and 5-10ms, then compute the corresponding history covariates.\n" + "# Generate a nspikeTrain and define a set of history windows of interest. We desire windows from 1-2ms, 2-3ms, 3-5ms, and 5-10ms, then compute the corresponding history covariates." ] }, { @@ -76,7 +76,7 @@ "ax1.set_title(\"History windows\")\n", "ax2.set_title(\"History covariate for Neuron1\")\n", "ax3.set_title(\"Neuron1 spike raster\")\n", - "fig.tight_layout()\n" + "fig.tight_layout()" ] }, { @@ -108,7 +108,7 @@ "ax = fig.subplots(1, 1)\n", "coll.plot(handle=ax)\n", "fig.tight_layout()\n", - "__tracker.finalize()\n" + "__tracker.finalize()" ] } ], diff --git a/notebooks/HybridFilterExample.ipynb b/notebooks/HybridFilterExample.ipynb index 72346edf..e0a9614c 100644 --- a/notebooks/HybridFilterExample.ipynb +++ b/notebooks/HybridFilterExample.ipynb @@ -2,20 +2,20 @@ "cells": [ { "cell_type": "markdown", - "id": "51ab8762", + "id": "d60b95a7", "metadata": {}, "source": [ "\n", "## MATLAB Parity Note\n", "- Source MATLAB helpfile: `HybridFilterExample.mlx`\n", "- Fidelity status: `high_fidelity`\n", - "- Remaining justified differences: The notebook now reproduces the hybrid-filter simulation, single-run decoding, and averaged summary figures with real outputs; the Python port still uses the current hybrid-filter implementation instead of every MATLAB-specific reporting branch.\n" + "- Remaining justified differences: The notebook now reproduces the hybrid-filter simulation, single-run decoding, and averaged summary figures with real outputs; the Python port still uses the current hybrid-filter implementation instead of every MATLAB-specific reporting branch." ] }, { "cell_type": "code", "execution_count": null, - "id": "b9c53e65", + "id": "d22dd216", "metadata": {}, "outputs": [], "source": [ @@ -42,14 +42,12 @@ "OUTPUT_ROOT = REPO_ROOT / \"output\" / \"notebook_images\"\n", "__tracker = FigureTracker(topic='HybridFilterExample', output_root=OUTPUT_ROOT, expected_count=3)\n", "\n", - "\n", "def _prepare_figure(matlab_line: str, *, figsize=(8.0, 4.5)):\n", " fig = __tracker.new_figure(matlab_line)\n", " fig.clear()\n", " fig.set_size_inches(*figsize)\n", " return fig\n", "\n", - "\n", "def _plot_raster(ax, time_s, spikes, *, max_cells=18):\n", " n_cells = min(int(spikes.shape[1]), max_cells)\n", " for row in range(n_cells):\n", @@ -57,13 +55,13 @@ " if spike_times.size:\n", " ax.vlines(spike_times, row + 0.6, row + 1.4, color=\"k\", linewidth=0.35)\n", " ax.set_ylim(0.5, n_cells + 0.5)\n", - " ax.set_ylabel(\"cell\")\n" + " ax.set_ylabel(\"cell\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "2aeada10", + "id": "cacac4c3", "metadata": {}, "outputs": [], "source": [ @@ -81,35 +79,35 @@ " \"num_cells\": int(summary[\"num_cells\"]),\n", " \"state_accuracy\": round(float(summary[\"state_accuracy\"]), 3),\n", " }\n", - ")\n" + ")" ] }, { "cell_type": "code", "execution_count": null, - "id": "1ca4154b", + "id": "b031dd85", "metadata": {}, "outputs": [], "source": [ "# SECTION 1: Problem Statement\n", - "# We infer both a discrete movement state and a continuous reach trajectory from point-process observations.\n" + "# We infer both a discrete movement state and a continuous reach trajectory from point-process observations." ] }, { "cell_type": "code", "execution_count": null, - "id": "bc311d6b", + "id": "fd11f1d4", "metadata": {}, "outputs": [], "source": [ "# SECTION 2: Hybrid state-space setup\n", - "# The Python port keeps the same two-state problem structure as MATLAB: a low-motion state and a movement state.\n" + "# The Python port keeps the same two-state problem structure as MATLAB: a low-motion state and a movement state." ] }, { "cell_type": "code", "execution_count": null, - "id": "9a25e97a", + "id": "fd690f04", "metadata": {}, "outputs": [], "source": [ @@ -155,24 +153,24 @@ " va=\"top\",\n", " family=\"monospace\",\n", " fontsize=9,\n", - ")\n" + ")" ] }, { "cell_type": "code", "execution_count": null, - "id": "78a4e6b5", + "id": "f9e4eb9d", "metadata": {}, "outputs": [], "source": [ "# SECTION 4: Simulate Neural Firing\n", - "# The simulated spike population depends on the latent state and the movement dynamics.\n" + "# The simulated spike population depends on the latent state and the movement dynamics." ] }, { "cell_type": "code", "execution_count": null, - "id": "90fd8d80", + "id": "57220fb5", "metadata": {}, "outputs": [], "source": [ @@ -231,7 +229,7 @@ " color=[\"tab:blue\", \"tab:orange\"],\n", ")\n", "axs[1, 1].set_title(\"Single-run decoding RMSE\")\n", - "__tracker.finalize()\n" + "__tracker.finalize()" ] } ], @@ -241,11 +239,11 @@ }, "nstat": { "expected_figures": 3, - "run_group": "smoke", + "run_group": "full", "style": "python-example", "topic": "HybridFilterExample" } }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/notebooks/NetworkTutorial.ipynb b/notebooks/NetworkTutorial.ipynb index 0f6a4622..3a206f5e 100644 --- a/notebooks/NetworkTutorial.ipynb +++ b/notebooks/NetworkTutorial.ipynb @@ -2,20 +2,20 @@ "cells": [ { "cell_type": "markdown", - "id": "81a6687d", + "id": "7203c2c5", "metadata": {}, "source": [ "\n", "## MATLAB Parity Note\n", "- Source MATLAB helpfile: `NetworkTutorial.mlx`\n", "- Fidelity status: `high_fidelity`\n", - "- Remaining justified differences: The notebook now mirrors the MATLAB helpfile section order and published figure inventory with a native Python network simulator and MATLAB-style `Analysis` workflow; exact spike realizations still vary modestly because NumPy and Simulink do not share identical random streams.\n" + "- Remaining justified differences: The notebook now mirrors the MATLAB helpfile section order and published figure inventory with a native Python network simulator and MATLAB-style `Analysis` workflow; exact spike realizations still vary modestly because NumPy and Simulink do not share identical random streams." ] }, { "cell_type": "code", "execution_count": null, - "id": "1a559c47", + "id": "f9506f0f", "metadata": {}, "outputs": [], "source": [ @@ -46,14 +46,12 @@ "OUTPUT_ROOT = REPO_ROOT / \"output\" / \"notebook_images\"\n", "__tracker = FigureTracker(topic='NetworkTutorial', output_root=OUTPUT_ROOT, expected_count=14)\n", "\n", - "\n", "def _figure(label: str, *, figsize=(8.5, 4.5)):\n", " fig = __tracker.new_figure(label)\n", " fig.clear()\n", " fig.set_size_inches(*figsize)\n", " return fig\n", "\n", - "\n", "def _text_panel(fig, title: str, lines):\n", " ax = fig.subplots(1, 1)\n", " ax.axis(\"off\")\n", @@ -70,7 +68,6 @@ " )\n", " return ax\n", "\n", - "\n", "def _stem_kernel(ax, coeffs, title: str, xlabel: str, color: str):\n", " coeffs = np.asarray(coeffs, dtype=float).reshape(-1)\n", " x = np.arange(1, coeffs.size + 1, dtype=float)\n", @@ -85,7 +82,6 @@ " ax.set_title(title)\n", " ax.grid(axis=\"y\", alpha=0.2)\n", "\n", - "\n", "def _draw_network(ax, actual_network):\n", " ax.set_title(\"Two-neuron connectivity diagram\")\n", " ax.axis(\"off\")\n", @@ -121,7 +117,6 @@ " ax.text(0.25, 0.14, \"$S_1=+1 \\cdot u_{stim}$\", ha=\"center\", fontsize=10)\n", " ax.text(0.75, 0.14, \"$S_2=-1 \\cdot u_{stim}$\", ha=\"center\", fontsize=10)\n", "\n", - "\n", "def _draw_block_diagram(ax):\n", " ax.axis(\"off\")\n", " ax.set_title(\"Conditional-intensity block diagram\")\n", @@ -143,7 +138,6 @@ " ax.add_patch(FancyArrowPatch((0.66, 0.43), (0.72, 0.43), arrowstyle=\"-|>\", mutation_scale=15, linewidth=1.8, color=\"0.25\"))\n", " ax.add_patch(FancyArrowPatch((0.90, 0.43), (0.98, 0.43), arrowstyle=\"-|>\", mutation_scale=15, linewidth=1.8, color=\"0.25\"))\n", "\n", - "\n", "def _estimate_network(results):\n", " estimated = np.zeros((2, 2), dtype=float)\n", " for neuron_idx, fit in enumerate(results, start=1):\n", @@ -154,51 +148,51 @@ " estimated[0, 1] = float(coeff)\n", " elif neuron_idx == 2 and label_str.startswith(\"1:\"):\n", " estimated[1, 0] = float(coeff)\n", - " return estimated\n" + " return estimated" ] }, { "cell_type": "code", "execution_count": null, - "id": "1bb3afdf", + "id": "ea4ee423", "metadata": {}, "outputs": [], "source": [ "# SECTION 1: Point Process Network Simulation\n", "# In order to understand how the point process GLM framework can be used to estimate the network connectivity within a population of neurons, we simulate a network of 2 neurons.\n", - "plt.close(\"all\")\n" + "plt.close(\"all\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "60c517c7", + "id": "28900ba3", "metadata": {}, "outputs": [], "source": [ "# SECTION 2: Published network diagram\n", "fig = _figure(\"SimulatedNetwork2.png\", figsize=(8.0, 4.5))\n", "ax = fig.subplots(1, 1)\n", - "_draw_network(ax, np.array([[0.0, 1.0], [-4.0, 0.0]], dtype=float))\n" + "_draw_network(ax, np.array([[0.0, 1.0], [-4.0, 0.0]], dtype=float))" ] }, { "cell_type": "code", "execution_count": null, - "id": "2e75a150", + "id": "789cad35", "metadata": {}, "outputs": [], "source": [ "# SECTION 3: Published block diagram\n", "fig = _figure(\"PPSimExample-BlockDiagram.png\", figsize=(10.0, 4.5))\n", "ax = fig.subplots(1, 1)\n", - "_draw_block_diagram(ax)\n" + "_draw_block_diagram(ax)" ] }, { "cell_type": "code", "execution_count": null, - "id": "c1a5a339", + "id": "38f509ef", "metadata": {}, "outputs": [], "source": [ @@ -211,35 +205,35 @@ " \"lambda_i * Delta = logistic(mu_i + H * DeltaN_i[n]\",\n", " \" + S * u_stim[n] + E * DeltaN_k[n])\",\n", " ],\n", - ")\n" + ")" ] }, { "cell_type": "code", "execution_count": null, - "id": "d8518832", + "id": "956f11ed", "metadata": {}, "outputs": [], "source": [ "# SECTION 5: Logistic nonlinearity\n", - "# logistic(x) = exp(x) / (1 + exp(x)). Note that * is the convolution operator.\n" + "# logistic(x) = exp(x) / (1 + exp(x)). Note that * is the convolution operator." ] }, { "cell_type": "code", "execution_count": null, - "id": "b58aea0f", + "id": "378207bd", "metadata": {}, "outputs": [], "source": [ "# SECTION 6: Convolution operator note\n", - "# The MATLAB helpfile presents the recursive history, stimulus, and ensemble filters separately below.\n" + "# The MATLAB helpfile presents the recursive history, stimulus, and ensemble filters separately below." ] }, { "cell_type": "code", "execution_count": null, - "id": "db9ba65f", + "id": "fc5899c7", "metadata": {}, "outputs": [], "source": [ @@ -257,35 +251,35 @@ "history_kernel = np.asarray(network.history_kernel, dtype=float)\n", "stim_kernel = np.asarray(network.stimulus_kernel, dtype=float)\n", "ensemble_kernel = np.asarray(network.ensemble_kernel, dtype=float)\n", - "actual_network = np.asarray(network.actual_network, dtype=float)\n" + "actual_network = np.asarray(network.actual_network, dtype=float)" ] }, { "cell_type": "code", "execution_count": null, - "id": "6c7098b3", + "id": "1b5c6fd9", "metadata": {}, "outputs": [], "source": [ "# SECTION 8: Baseline firing rate of the neurons being modeled\n", - "print({\"mu1\": float(baseline_mu[0]), \"mu2\": float(baseline_mu[1]), \"sample_rate_hz\": sampleRate})\n" + "print({\"mu1\": float(baseline_mu[0]), \"mu2\": float(baseline_mu[1]), \"sample_rate_hz\": sampleRate})" ] }, { "cell_type": "code", "execution_count": null, - "id": "a805e8c4", + "id": "163000ec", "metadata": {}, "outputs": [], "source": [ "# SECTION 9: History Effect\n", - "# Captures how the firing of a neuron modulates its own probability of firing, including the refractory period and short-term history dependence.\n" + "# Captures how the firing of a neuron modulates its own probability of firing, including the refractory period and short-term history dependence." ] }, { "cell_type": "code", "execution_count": null, - "id": "a1559df4", + "id": "df2d7f8f", "metadata": {}, "outputs": [], "source": [ @@ -293,87 +287,87 @@ "fig = _figure(\"1*h[n]=-4*DeltaN[n-1]-2*DeltaN[n-2]-1*DeltaN[n-3]\", figsize=(8.0, 4.5))\n", "ax = fig.subplots(1, 1)\n", "_stem_kernel(ax, history_kernel, \"Self-history kernel\", \"lag (ms)\", \"tab:red\")\n", - "ax.set_xticklabels([f\"{int(k)}\" for k in [1, 2, 3]])\n" + "ax.set_xticklabels([f\"{int(k)}\" for k in [1, 2, 3]])" ] }, { "cell_type": "code", "execution_count": null, - "id": "b472fd0e", + "id": "56f686ad", "metadata": {}, "outputs": [], "source": [ "# SECTION 11: Stimulus Effect\n", - "# Neuron 1 is positively modulated by the stimulus and neuron 2 is negatively modulated by the same drive.\n" + "# Neuron 1 is positively modulated by the stimulus and neuron 2 is negatively modulated by the same drive." ] }, { "cell_type": "code", "execution_count": null, - "id": "8d71c27d", + "id": "3d5c9380", "metadata": {}, "outputs": [], "source": [ "# SECTION 12: Stimulus filter for neuron 1\n", "fig = _figure(\"1*s_1[n]=1*u_stim[n]\", figsize=(7.5, 4.5))\n", "ax = fig.subplots(1, 1)\n", - "_stem_kernel(ax, [stim_kernel[0]], \"Stimulus filter for neuron 1\", \"lag (samples)\", \"tab:blue\")\n" + "_stem_kernel(ax, [stim_kernel[0]], \"Stimulus filter for neuron 1\", \"lag (samples)\", \"tab:blue\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "406603d4", + "id": "50f617ae", "metadata": {}, "outputs": [], "source": [ "# SECTION 13: Stimulus filter for neuron 2\n", "fig = _figure(\"1*s_2[n]=-1*u_stim[n]\", figsize=(7.5, 4.5))\n", "ax = fig.subplots(1, 1)\n", - "_stem_kernel(ax, [stim_kernel[1]], \"Stimulus filter for neuron 2\", \"lag (samples)\", \"tab:orange\")\n" + "_stem_kernel(ax, [stim_kernel[1]], \"Stimulus filter for neuron 2\", \"lag (samples)\", \"tab:orange\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "f9878dce", + "id": "49818eae", "metadata": {}, "outputs": [], "source": [ "# SECTION 14: Ensemble Effect\n", - "# Captures how neighboring neuron firing modulates the firing probability of a given neuron, with a one-sample delay included in the Simulink model.\n" + "# Captures how neighboring neuron firing modulates the firing probability of a given neuron, with a one-sample delay included in the Simulink model." ] }, { "cell_type": "code", "execution_count": null, - "id": "7ddb2550", + "id": "337ec866", "metadata": {}, "outputs": [], "source": [ "# SECTION 15: Ensemble filter for neuron 1\n", "fig = _figure(\"1*e_1[n]=1*DeltaN_2[n-1]\", figsize=(7.5, 4.5))\n", "ax = fig.subplots(1, 1)\n", - "_stem_kernel(ax, [ensemble_kernel[0]], \"Ensemble filter for neuron 1\", \"lag (samples)\", \"tab:green\")\n" + "_stem_kernel(ax, [ensemble_kernel[0]], \"Ensemble filter for neuron 1\", \"lag (samples)\", \"tab:green\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "1554e895", + "id": "58c23f04", "metadata": {}, "outputs": [], "source": [ "# SECTION 16: Ensemble filter for neuron 2\n", "fig = _figure(\"1*e_2[n]=-4*DeltaN_1[n-1]\", figsize=(7.5, 4.5))\n", "ax = fig.subplots(1, 1)\n", - "_stem_kernel(ax, [ensemble_kernel[1]], \"Ensemble filter for neuron 2\", \"lag (samples)\", \"tab:purple\")\n" + "_stem_kernel(ax, [ensemble_kernel[1]], \"Ensemble filter for neuron 2\", \"lag (samples)\", \"tab:purple\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "fcb5f672", + "id": "39aa44a9", "metadata": {}, "outputs": [], "source": [ @@ -388,13 +382,13 @@ "ax.set_xlim(0.0, 5.0)\n", "ax.set_xlabel(\"time (s)\")\n", "ax.set_ylabel(\"stimulus\")\n", - "ax.set_title(\"Sine-wave stimulus used by the network tutorial\")\n" + "ax.set_title(\"Sine-wave stimulus used by the network tutorial\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "37804e6b", + "id": "a0ede261", "metadata": {}, "outputs": [], "source": [ @@ -422,13 +416,13 @@ "ax.set_xlabel(\"time (s)\")\n", "ax.set_ylabel(\"lambda * Delta\")\n", "ax.set_title(\"Conditional-intensity trajectories\")\n", - "ax.legend(loc=\"upper right\")\n" + "ax.legend(loc=\"upper right\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "3455475c", + "id": "1e6a1f2d", "metadata": {}, "outputs": [], "source": [ @@ -436,13 +430,13 @@ "c1 = TrialConfig([[\"Baseline\", \"mu\"]], sampleRate, [], [], [], name=\"Baseline\")\n", "c2 = TrialConfig([[\"Baseline\", \"mu\"]], sampleRate, [], ensHist, [], name=\"Baseline+EnsHist\")\n", "c3 = TrialConfig([[\"Baseline\", \"mu\"], [\"Stimulus\", \"sin\"]], sampleRate, selfHist, ensHist, [], name=\"Stim+Hist+EnsHist\")\n", - "cfgColl = ConfigColl([c1, c2, c3])\n" + "cfgColl = ConfigColl([c1, c2, c3])" ] }, { "cell_type": "code", "execution_count": null, - "id": "84eb9f17", + "id": "7b583e33", "metadata": {}, "outputs": [], "source": [ @@ -473,13 +467,13 @@ " ax.set_title(title)\n", " for (row, col), value in np.ndenumerate(matrix):\n", " ax.text(col, row, f\"{value:.2f}\", ha=\"center\", va=\"center\", color=\"white\", fontsize=10)\n", - "fig.colorbar(im, ax=axs, shrink=0.8)\n" + "fig.colorbar(im, ax=axs, shrink=0.8)" ] }, { "cell_type": "code", "execution_count": null, - "id": "165afc2b", + "id": "d43be2ce", "metadata": {}, "outputs": [], "source": [ @@ -492,7 +486,7 @@ " \"estimated_network\": np.round(estimated_network, 3).tolist(),\n", " }\n", ")\n", - "__tracker.finalize()\n" + "__tracker.finalize()" ] } ], @@ -509,4 +503,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/notebooks/PPSimExample.ipynb b/notebooks/PPSimExample.ipynb index 820a2183..cd5e02cf 100644 --- a/notebooks/PPSimExample.ipynb +++ b/notebooks/PPSimExample.ipynb @@ -2,20 +2,20 @@ "cells": [ { "cell_type": "markdown", - "id": "d32212af", + "id": "2eba515a", "metadata": {}, "source": [ "\n", "## MATLAB Parity Note\n", "- Source MATLAB helpfile: `PPSimExample.mlx`\n", "- Fidelity status: `high_fidelity`\n", - "- Remaining justified differences: The notebook now follows the MATLAB recursive-CIF workflow with the native Python `CIF.simulateCIF` path; exact Simulink block timing and solver semantics are still not fixture-matched one-for-one against MATLAB.\n" + "- Remaining justified differences: The notebook now follows the MATLAB recursive-CIF workflow with the native Python `CIF.simulateCIF` path; exact Simulink block timing and solver semantics are still not fixture-matched one-for-one against MATLAB." ] }, { "cell_type": "code", "execution_count": null, - "id": "10c55dde", + "id": "a6759a0c", "metadata": {}, "outputs": [], "source": [ @@ -42,14 +42,12 @@ "OUTPUT_ROOT = REPO_ROOT / \"output\" / \"notebook_images\"\n", "__tracker = FigureTracker(topic='PPSimExample', output_root=OUTPUT_ROOT, expected_count=8)\n", "\n", - "\n", "def _figure(label: str, *, figsize=(8.5, 4.5)):\n", " fig = __tracker.new_figure(label)\n", " fig.clear()\n", " fig.set_size_inches(*figsize)\n", " return fig\n", "\n", - "\n", "Ts = 0.001\n", "tMin = 0.0\n", "tMax = 50.0\n", @@ -65,69 +63,69 @@ "sC, lambda_cov = CIF.simulateCIF(mu, H, S, E, stim, ens, 5, \"binomial\", seed=5, return_lambda=True)\n", "cc = CovColl([stim, baseline])\n", "trial = Trial(sC, cc)\n", - "print({\"duration_s\": tMax, \"num_realizations\": sC.numSpikeTrains, \"mean_rate_hz\": round(float(np.mean(lambda_cov.data[:, 0])), 3)})\n" + "print({\"duration_s\": tMax, \"num_realizations\": sC.numSpikeTrains, \"mean_rate_hz\": round(float(np.mean(lambda_cov.data[:, 0])), 3)})" ] }, { "cell_type": "code", "execution_count": null, - "id": "82e2e6d2", + "id": "fb22ed56", "metadata": {}, "outputs": [], "source": [ "# SECTION 1: General Point Process Simulation\n", - "plt.close(\"all\")\n" + "plt.close(\"all\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "bb8a8dca", + "id": "aefbf353", "metadata": {}, "outputs": [], "source": [ "# SECTION 2: Point Process Sample Path Generation\n", - "print(\"Using native Python CIF.simulateCIF to mirror the MATLAB recursive-CIF workflow.\")\n" + "print(\"Using native Python CIF.simulateCIF to mirror the MATLAB recursive-CIF workflow.\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "7aa83848", + "id": "ae867985", "metadata": {}, "outputs": [], "source": [ "# SECTION 3: History Effect\n", "selfHist = [0.0, 0.001, 0.002, 0.003]\n", - "print({\"history_windows_s\": selfHist})\n" + "print({\"history_windows_s\": selfHist})" ] }, { "cell_type": "code", "execution_count": null, - "id": "e8660e03", + "id": "9076f004", "metadata": {}, "outputs": [], "source": [ "# SECTION 4: Stimulus Effect\n", - "print({\"stimulus_frequency_hz\": 1.0, \"stimulus_amplitude\": 1.0})\n" + "print({\"stimulus_frequency_hz\": 1.0, \"stimulus_amplitude\": 1.0})" ] }, { "cell_type": "code", "execution_count": null, - "id": "7b3aa59d", + "id": "fa8120b8", "metadata": {}, "outputs": [], "source": [ "# SECTION 5: Ensemble Effect\n", - "print({\"ensemble_effect\": 0.0})\n" + "print({\"ensemble_effect\": 0.0})" ] }, { "cell_type": "code", "execution_count": null, - "id": "a2f76513", + "id": "c58f3108", "metadata": {}, "outputs": [], "source": [ @@ -137,13 +135,13 @@ "sC.plot(handle=axs[0])\n", "axs[0].set_xlim(0.0, tMax / 5.0)\n", "stim.plot(handle=axs[1])\n", - "axs[1].set_xlim(0.0, tMax / 5.0)\n" + "axs[1].set_xlim(0.0, tMax / 5.0)" ] }, { "cell_type": "code", "execution_count": null, - "id": "5dd18704", + "id": "a7b37585", "metadata": {}, "outputs": [], "source": [ @@ -151,13 +149,13 @@ "fig = _figure(\"figure; lambda.plot\", figsize=(10.0, 4.0))\n", "ax = fig.subplots(1, 1)\n", "lambda_cov.getSubSignal(1).plot(handle=ax)\n", - "ax.set_xlim(0.0, tMax / 5.0)\n" + "ax.set_xlim(0.0, tMax / 5.0)" ] }, { "cell_type": "code", "execution_count": null, - "id": "d1867d92", + "id": "bac3e6f1", "metadata": {}, "outputs": [], "source": [ @@ -167,84 +165,84 @@ " TrialConfig([[\"Baseline\", \"mu\"], [\"Stimulus\", \"sin\"]], sampleRate=1.0 / Ts, name=\"Stim\"),\n", " TrialConfig([[\"Baseline\", \"mu\"], [\"Stimulus\", \"sin\"]], sampleRate=1.0 / Ts, history=selfHist, name=\"Stim+Hist\"),\n", "]\n", - "cfgColl = ConfigColl(cfg)\n" + "cfgColl = ConfigColl(cfg)" ] }, { "cell_type": "code", "execution_count": null, - "id": "393f2a17", + "id": "278b16e6", "metadata": {}, "outputs": [], "source": [ "# SECTION 9: Choose the MATLAB-style fitting algorithm\n", "Algorithm = \"BNLRCG\"\n", - "print({\"algorithm\": Algorithm, \"binary_representation\": bool(sC.getNST(1).isSigRepBinary())})\n" + "print({\"algorithm\": Algorithm, \"binary_representation\": bool(sC.getNST(1).isSigRepBinary())})" ] }, { "cell_type": "code", "execution_count": null, - "id": "59d8878a", + "id": "1c9f83d1", "metadata": {}, "outputs": [], "source": [ "# SECTION 10: GLM Model Fitting and Results\n", - "results = Analysis.RunAnalysisForAllNeurons(trial, cfgColl)\n" + "results = Analysis.RunAnalysisForAllNeurons(trial, cfgColl)" ] }, { "cell_type": "code", "execution_count": null, - "id": "e13231cb", + "id": "c939f67c", "metadata": {}, "outputs": [], "source": [ "# SECTION 11: Results for sample neuron\n", "fig = _figure(\"results{1}.plotResults\", figsize=(11.0, 8.0))\n", - "results[0].plotResults(handle=fig)\n" + "results[0].plotResults(handle=fig)" ] }, { "cell_type": "code", "execution_count": null, - "id": "9298a78b", + "id": "21f39091", "metadata": {}, "outputs": [], "source": [ "# SECTION 12: Baseline-only diagnostic view\n", "fig = _figure(\"results{1}.plotResults baseline\", figsize=(11.0, 8.0))\n", - "results[0].plotResults(fit_num=1, handle=fig)\n" + "results[0].plotResults(fit_num=1, handle=fig)" ] }, { "cell_type": "code", "execution_count": null, - "id": "1bebdc33", + "id": "6faa0468", "metadata": {}, "outputs": [], "source": [ "# SECTION 13: Stimulus model diagnostic view\n", "fig = _figure(\"results{2}.plotResults stim\", figsize=(11.0, 8.0))\n", - "results[0].plotResults(fit_num=2, handle=fig)\n" + "results[0].plotResults(fit_num=2, handle=fig)" ] }, { "cell_type": "code", "execution_count": null, - "id": "37097000", + "id": "27d1e5af", "metadata": {}, "outputs": [], "source": [ "# SECTION 14: Stimulus-plus-history diagnostic view\n", "fig = _figure(\"results{3}.plotResults hist\", figsize=(11.0, 8.0))\n", - "results[0].plotResults(fit_num=3, handle=fig)\n" + "results[0].plotResults(fit_num=3, handle=fig)" ] }, { "cell_type": "code", "execution_count": null, - "id": "a6cdb847", + "id": "bd5e5ca2", "metadata": {}, "outputs": [], "source": [ @@ -252,13 +250,13 @@ "fig = _figure(\"results.lambda.plot\", figsize=(9.5, 4.5))\n", "ax = fig.subplots(1, 1)\n", "results[0].lambdaSignal.getSubSignal(3).plot(handle=ax)\n", - "ax.set_xlim(0.0, tMax / 5.0)\n" + "ax.set_xlim(0.0, tMax / 5.0)" ] }, { "cell_type": "code", "execution_count": null, - "id": "78d2200b", + "id": "9d323187", "metadata": {}, "outputs": [], "source": [ @@ -266,13 +264,13 @@ "summary = FitResSummary(results)\n", "fig = _figure(\"Summary.plotSummary\", figsize=(10.0, 4.5))\n", "summary.plotSummary(handle=fig)\n", - "print({\"fit_names\": summary.fitNames, \"mean_AIC\": np.asarray(summary.meanAIC, dtype=float).round(3).tolist()})\n" + "print({\"fit_names\": summary.fitNames, \"mean_AIC\": np.asarray(summary.meanAIC, dtype=float).round(3).tolist()})" ] }, { "cell_type": "code", "execution_count": null, - "id": "b920b8cb", + "id": "0c7da4fb", "metadata": {}, "outputs": [], "source": [ @@ -283,7 +281,7 @@ "ax.set_xticks(np.arange(len(summary.fitNames)), summary.fitNames, rotation=20)\n", "ax.set_ylabel(\"mean AIC\")\n", "ax.set_title(\"Model comparison across realizations\")\n", - "__tracker.finalize()\n" + "__tracker.finalize()" ] } ], @@ -300,4 +298,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/notebooks/SignalObjExamples.ipynb b/notebooks/SignalObjExamples.ipynb index 76e0a497..5666e428 100644 --- a/notebooks/SignalObjExamples.ipynb +++ b/notebooks/SignalObjExamples.ipynb @@ -31,7 +31,7 @@ "\n", "# SECTION 0: Section 0\n", "# Using the SignalObj Class\n", - "# In this file we will give several examples of how the SignalObj can be used. A description of all of the properties of SignalObj can be found at: SignalObj Class Definition\n" + "# In this file we will give several examples of how the SignalObj can be used. A description of all of the properties of SignalObj can be found at: SignalObj Class Definition" ] }, { diff --git a/notebooks/StimulusDecode2D.ipynb b/notebooks/StimulusDecode2D.ipynb index e90ff61a..e4c5aa29 100644 --- a/notebooks/StimulusDecode2D.ipynb +++ b/notebooks/StimulusDecode2D.ipynb @@ -2,20 +2,20 @@ "cells": [ { "cell_type": "markdown", - "id": "4f37c1b6", + "id": "4599bf89", "metadata": {}, "source": [ "\n", "## MATLAB Parity Note\n", "- Source MATLAB helpfile: `StimulusDecode2D.mlx`\n", "- Fidelity status: `high_fidelity`\n", - "- Remaining justified differences: The notebook now follows the MATLAB nonlinear-CIF decoding workflow and uses `DecodingAlgorithms.PPDecodeFilter` before the same documented linear fallback branch as MATLAB. Exact decoded traces and figure styling can still vary modestly because Python's symbolic/numeric stack and random streams are not byte-identical to MATLAB.\n" + "- Remaining justified differences: The notebook now follows the MATLAB nonlinear-CIF decoding workflow and uses `DecodingAlgorithms.PPDecodeFilter` before the same documented linear fallback branch as MATLAB. Exact decoded traces and figure styling can still vary modestly because Python's symbolic/numeric stack and random streams are not byte-identical to MATLAB." ] }, { "cell_type": "code", "execution_count": null, - "id": "f94aa3da", + "id": "6f7a27a5", "metadata": {}, "outputs": [], "source": [ @@ -42,20 +42,17 @@ "OUTPUT_ROOT = REPO_ROOT / \"output\" / \"notebook_images\"\n", "__tracker = FigureTracker(topic='StimulusDecode2D', output_root=OUTPUT_ROOT, expected_count=6)\n", "\n", - "\n", "def _prepare_figure(matlab_line: str, *, figsize=(8.0, 4.5)):\n", " fig = __tracker.new_figure(matlab_line)\n", " fig.clear()\n", " fig.set_size_inches(*figsize)\n", " return fig\n", "\n", - "\n", "def _subplot_grid(count):\n", " rows = max(int(np.floor(np.sqrt(count))), 1)\n", " cols = int(np.ceil(count / rows))\n", " return rows, cols\n", "\n", - "\n", "def _simulate_decode(seed=0, *, num_realizations=80, delta=0.001, tmax=1.0):\n", " rng = np.random.default_rng(seed)\n", " time = np.arange(0.0, tmax + delta, delta)\n", @@ -149,7 +146,6 @@ " \"num_cells\": num_realizations,\n", " }\n", "\n", - "\n", "def _plot_raster(ax, time_s, spikes, *, max_cells=20):\n", " n_cells = min(int(spikes.shape[1]), max_cells)\n", " for row in range(n_cells):\n", @@ -157,13 +153,13 @@ " if spike_times.size:\n", " ax.vlines(spike_times, row + 0.6, row + 1.4, color=\"k\", linewidth=0.35)\n", " ax.set_ylim(0.5, n_cells + 0.5)\n", - " ax.set_ylabel(\"cell\")\n" + " ax.set_ylabel(\"cell\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "4c32de3a", + "id": "8809d377", "metadata": {}, "outputs": [], "source": [ @@ -178,13 +174,13 @@ " \"decode_rmse\": round(float(payload[\"decode_rmse\"]), 4),\n", " \"fallback_error\": payload[\"decode_error\"] or \"\",\n", " }\n", - ")\n" + ")" ] }, { "cell_type": "code", "execution_count": null, - "id": "25960ea2", + "id": "d87abf0b", "metadata": {}, "outputs": [], "source": [ @@ -224,13 +220,13 @@ " ax.set_xticks([])\n", " ax.set_yticks([])\n", "if image is not None:\n", - " fig.colorbar(image, ax=axs.ravel().tolist(), shrink=0.78)\n" + " fig.colorbar(image, ax=axs.ravel().tolist(), shrink=0.78)" ] }, { "cell_type": "code", "execution_count": null, - "id": "b0784e40", + "id": "ab3bc9c7", "metadata": {}, "outputs": [], "source": [ @@ -242,13 +238,13 @@ "axs[1].plot(payload[\"time_s\"], np.mean(payload[\"spikes\"], axis=1), color=\"tab:green\", linewidth=1.2)\n", "axs[1].set_title(\"Population firing fraction\")\n", "axs[1].set_xlabel(\"time (s)\")\n", - "axs[1].set_ylabel(\"mean spike/bin\")\n" + "axs[1].set_ylabel(\"mean spike/bin\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "714372bb", + "id": "5eddb87b", "metadata": {}, "outputs": [], "source": [ @@ -286,7 +282,7 @@ "ax.set_xlabel(\"time (s)\")\n", "ax.set_ylabel(\"Euclidean error\")\n", "ax.legend(loc=\"best\", frameon=False, fontsize=8)\n", - "__tracker.finalize()\n" + "__tracker.finalize()" ] } ], @@ -296,11 +292,11 @@ }, "nstat": { "expected_figures": 6, - "run_group": "smoke", + "run_group": "full", "style": "python-example", "topic": "StimulusDecode2D" } }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/notebooks/TrialConfigExamples.ipynb b/notebooks/TrialConfigExamples.ipynb index 525a79cc..41cae891 100644 --- a/notebooks/TrialConfigExamples.ipynb +++ b/notebooks/TrialConfigExamples.ipynb @@ -32,7 +32,7 @@ "# SECTION 0: Section 0\n", "# TrialConfig Examples\n", "# tcObj=TrialConfig(covMask,sampleRate, history,minTime,maxTime)\n", - "__tracker.finalize()\n" + "__tracker.finalize()" ] } ], diff --git a/notebooks/TrialExamples.ipynb b/notebooks/TrialExamples.ipynb index ffe918b2..c56a6d48 100644 --- a/notebooks/TrialExamples.ipynb +++ b/notebooks/TrialExamples.ipynb @@ -2,20 +2,20 @@ "cells": [ { "cell_type": "markdown", - "id": "914238fa", + "id": "d48a2677", "metadata": {}, "source": [ "\n", "## MATLAB Parity Note\n", "- Source MATLAB helpfile: `TrialExamples.mlx`\n", "- Fidelity status: `high_fidelity`\n", - "- Remaining justified differences: The notebook now mirrors the MATLAB Trial workflow with executable object construction, masking, history extraction, and plotting; only minor Python plotting defaults differ from the published MATLAB help output.\n" + "- Remaining justified differences: The notebook now mirrors the MATLAB Trial workflow with executable object construction, masking, history extraction, and plotting; only minor Python plotting defaults differ from the published MATLAB help output." ] }, { "cell_type": "code", "execution_count": null, - "id": "8ffbef9e", + "id": "f23e6389", "metadata": {}, "outputs": [], "source": [ @@ -42,14 +42,12 @@ "OUTPUT_ROOT = REPO_ROOT / \"output\" / \"notebook_images\"\n", "__tracker = FigureTracker(topic='TrialExamples', output_root=OUTPUT_ROOT, expected_count=6)\n", "\n", - "\n", "def _figure(label: str, *, figsize=(8.5, 3.5)):\n", " fig = __tracker.new_figure(label)\n", " fig.clear()\n", " fig.set_size_inches(*figsize)\n", " return fig\n", "\n", - "\n", "def _build_trial():\n", " length_trial = 1.0\n", " sample_rate = 1000.0\n", @@ -109,7 +107,6 @@ " \"trial\": trial,\n", " }\n", "\n", - "\n", "ctx = _build_trial()\n", "print(\n", " {\n", @@ -118,13 +115,13 @@ " \"covariates\": ctx[\"cov_coll\"].names,\n", " \"history_windows\": ctx[\"history\"].windowTimes.tolist(),\n", " }\n", - ")\n" + ")" ] }, { "cell_type": "code", "execution_count": null, - "id": "3ada46f2", + "id": "5f01a6dd", "metadata": {}, "outputs": [], "source": [ @@ -134,76 +131,76 @@ "spikeColl = ctx[\"spike_coll\"]\n", "cc = ctx[\"cov_coll\"]\n", "e = ctx[\"events\"]\n", - "h = ctx[\"history\"]\n" + "h = ctx[\"history\"]" ] }, { "cell_type": "code", "execution_count": null, - "id": "c5ad1d59", + "id": "947fce2d", "metadata": {}, "outputs": [], "source": [ "# SECTION 2: Create History windows of interest\n", "fig = _figure(\"figure; h.plot\", figsize=(8.0, 2.5))\n", "ax = fig.subplots(1, 1)\n", - "h.plot(handle=ax)\n" + "h.plot(handle=ax)" ] }, { "cell_type": "code", "execution_count": null, - "id": "87bae7f2", + "id": "948c1c5e", "metadata": {}, "outputs": [], "source": [ "# SECTION 3: Load Covariates\n", "fig = _figure(\"figure; cc.plot\", figsize=(8.5, 5.0))\n", - "cc.plot(handle=fig)\n" + "cc.plot(handle=fig)" ] }, { "cell_type": "code", "execution_count": null, - "id": "2bb06d98", + "id": "addc115e", "metadata": {}, "outputs": [], "source": [ "# SECTION 4: Create trial events\n", "fig = _figure(\"figure; e.plot\", figsize=(8.0, 2.3))\n", "ax = fig.subplots(1, 1)\n", - "e.plot(handle=ax)\n" + "e.plot(handle=ax)" ] }, { "cell_type": "code", "execution_count": null, - "id": "9ecba539", + "id": "8160b030", "metadata": {}, "outputs": [], "source": [ "# SECTION 5: Create neural Spike Train Data\n", "fig = _figure(\"figure; spikeColl.plot\", figsize=(8.5, 3.5))\n", "ax = fig.subplots(1, 1)\n", - "spikeColl.plot(handle=ax)\n" + "spikeColl.plot(handle=ax)" ] }, { "cell_type": "code", "execution_count": null, - "id": "828b38f9", + "id": "f15709a2", "metadata": {}, "outputs": [], "source": [ "# SECTION 6: Finally we have everything we need to create a Trial object.\n", "fig = _figure(\"figure; trial1.plot\", figsize=(9.0, 8.0))\n", - "trial1.plot(handle=fig)\n" + "trial1.plot(handle=fig)" ] }, { "cell_type": "code", "execution_count": null, - "id": "24ada846", + "id": "d25ae0c3", "metadata": {}, "outputs": [], "source": [ @@ -213,30 +210,30 @@ "trial1.plot(handle=fig)\n", "hist_cov = trial1.getHistForNeurons([1, 2])\n", "print({\"masked_labels\": trial1.getLabelsFromMask(1), \"history_covariates\": hist_cov.getAllCovLabels()[:4]})\n", - "trial1.resetCovMask()\n" + "trial1.resetCovMask()" ] }, { "cell_type": "code", "execution_count": null, - "id": "406f0e5e", + "id": "48633477", "metadata": {}, "outputs": [], "source": [ "# SECTION 8: Example 2: Analyzing Trial Data\n", - "print(\"Examples of neural spike analysis using AnalysisExamples2 (Neural Spike Analysis Toolbox) or AnalysisExamples (standard methods).\")\n" + "print(\"Examples of neural spike analysis using AnalysisExamples2 (Neural Spike Analysis Toolbox) or AnalysisExamples (standard methods).\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "2a99d085", + "id": "ebaa9bdc", "metadata": {}, "outputs": [], "source": [ "# SECTION 9: Related analysis workflows\n", "print({\"recommended_next_notebooks\": [\"AnalysisExamples2\", \"AnalysisExamples\"]})\n", - "__tracker.finalize()\n" + "__tracker.finalize()" ] } ], @@ -253,4 +250,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/notebooks/ValidationDataSet.ipynb b/notebooks/ValidationDataSet.ipynb index 120e63a0..5512e73c 100644 --- a/notebooks/ValidationDataSet.ipynb +++ b/notebooks/ValidationDataSet.ipynb @@ -2,20 +2,20 @@ "cells": [ { "cell_type": "markdown", - "id": "d15a297a", + "id": "ff1426a4", "metadata": {}, "source": [ "\n", "## MATLAB Parity Note\n", "- Source MATLAB helpfile: `ValidationDataSet.mlx`\n", "- Fidelity status: `high_fidelity`\n", - "- Remaining justified differences: The notebook now reproduces the constant-rate and piecewise-rate validation workflows with real `Trial`/`Analysis` objects and figure outputs; local execution uses the MATLAB-scale simulation sizes, while CI switches to a documented shorter deterministic fast path for stability.\n" + "- Remaining justified differences: The notebook now reproduces the constant-rate and piecewise-rate validation workflows with real `Trial`/`Analysis` objects and figure outputs; local execution uses the MATLAB-scale simulation sizes, while CI switches to a documented shorter deterministic fast path for stability." ] }, { "cell_type": "code", "execution_count": null, - "id": "f2698163", + "id": "5a19211e", "metadata": {}, "outputs": [], "source": [ @@ -44,14 +44,12 @@ "OUTPUT_ROOT = REPO_ROOT / \"output\" / \"notebook_images\"\n", "__tracker = FigureTracker(topic='ValidationDataSet', output_root=OUTPUT_ROOT, expected_count=10)\n", "\n", - "\n", "def _prepare_figure(matlab_line: str, *, figsize=(8.0, 4.5)):\n", " fig = __tracker.new_figure(matlab_line)\n", " fig.clear()\n", " fig.set_size_inches(*figsize)\n", " return fig\n", "\n", - "\n", "def _lambda_columns(fit_result):\n", " time = np.asarray(fit_result.lambda_signal.time, dtype=float)\n", " data = np.asarray(fit_result.lambda_signal.data, dtype=float)\n", @@ -59,10 +57,8 @@ " data = data[:, None]\n", " return time, data\n", "\n", - "\n", "CI_FAST_PATH = os.environ.get(\"CI\", \"\").strip().lower() in {\"1\", \"true\", \"yes\"}\n", "\n", - "\n", "def _simulate_constant_case(seed=0, *, p=0.01, n_samples=None, delta=0.001):\n", " if n_samples is None:\n", " n_samples = 20001 if CI_FAST_PATH else 100001\n", @@ -91,7 +87,6 @@ " \"trains\": trains,\n", " }\n", "\n", - "\n", "def _simulate_piecewise_case(seed=1, *, p1=0.001, p2=0.01, n1=None, n2=None, delta=0.001):\n", " if n1 is None:\n", " n1 = 20000 if CI_FAST_PATH else 100000\n", @@ -139,7 +134,6 @@ " \"trains\": trains,\n", " }\n", "\n", - "\n", "def _plot_isi_hist(ax, train, lambda_hz, *, title):\n", " isi = np.asarray(train.getISIs(), dtype=float)\n", " if isi.size:\n", @@ -148,13 +142,13 @@ " ax.plot(x, lambda_hz * np.exp(-lambda_hz * x), color=\"tab:red\", linewidth=1.5)\n", " ax.set_title(title)\n", " ax.set_xlabel(\"ISI (s)\")\n", - " ax.set_ylabel(\"density\")\n" + " ax.set_ylabel(\"density\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "702ec99c", + "id": "de07a751", "metadata": {}, "outputs": [], "source": [ @@ -170,36 +164,36 @@ " \"piecewise_lambda1_hz\": round(float(piecewise_case[\"lambda1_hz\"]), 4),\n", " \"piecewise_lambda2_hz\": round(float(piecewise_case[\"lambda2_hz\"]), 4),\n", " }\n", - ")\n" + ")" ] }, { "cell_type": "code", "execution_count": null, - "id": "a65040f3", + "id": "4c326ba4", "metadata": {}, "outputs": [], "source": [ "# SECTION 1: Case #1: Constant Rate Poisson Process\n", - "# First we verify that the analysis recovers a constant Poisson rate from simulated spike trains.\n" + "# First we verify that the analysis recovers a constant Poisson rate from simulated spike trains." ] }, { "cell_type": "code", "execution_count": null, - "id": "1f04cbfd", + "id": "0348ff8b", "metadata": {}, "outputs": [], "source": [ "# SECTION 2: Generate constant-rate neural firing activity\n", "constant_time = np.asarray(constant_case[\"time_s\"], dtype=float)\n", - "constant_trains = list(constant_case[\"trains\"])\n" + "constant_trains = list(constant_case[\"trains\"])" ] }, { "cell_type": "code", "execution_count": null, - "id": "1a464c1b", + "id": "127d7e94", "metadata": {}, "outputs": [], "source": [ @@ -207,13 +201,13 @@ "fig = _prepare_figure(\"nst{1}.plotISIHistogram\", figsize=(10.0, 4.0))\n", "axs = fig.subplots(1, 2)\n", "_plot_isi_hist(axs[0], constant_trains[0], constant_case[\"lambda_hz\"], title=\"Neuron 1 ISI histogram\")\n", - "_plot_isi_hist(axs[1], constant_trains[1], constant_case[\"lambda_hz\"], title=\"Neuron 2 ISI histogram\")\n" + "_plot_isi_hist(axs[1], constant_trains[1], constant_case[\"lambda_hz\"], title=\"Neuron 2 ISI histogram\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "fb4f5ef8", + "id": "ce2f5297", "metadata": {}, "outputs": [], "source": [ @@ -229,13 +223,13 @@ "ax.set_xticks(xloc, [f\"Neuron {idx}\" for idx in xloc])\n", "ax.set_ylabel(\"μ coefficient\")\n", "ax.set_title(\"Estimated constant-rate coefficient\")\n", - "ax.legend(loc=\"best\", frameon=False)\n" + "ax.legend(loc=\"best\", frameon=False)" ] }, { "cell_type": "code", "execution_count": null, - "id": "c286b8c0", + "id": "e4a199c4", "metadata": {}, "outputs": [], "source": [ @@ -251,26 +245,26 @@ " ax.set_xlabel(\"time (s)\")\n", " ax.grid(alpha=0.25)\n", "axs[0].set_ylabel(\"rate (Hz)\")\n", - "axs[1].legend(loc=\"best\", frameon=False, fontsize=8)\n" + "axs[1].legend(loc=\"best\", frameon=False, fontsize=8)" ] }, { "cell_type": "code", "execution_count": null, - "id": "879b1951", + "id": "26380744", "metadata": {}, "outputs": [], "source": [ "# SECTION 6: Case #2: Piece-wise Constant Rate Poisson Process\n", "# Next we compare a single-rate model against a two-epoch rate model.\n", "piecewise_time = np.asarray(piecewise_case[\"time_s\"], dtype=float)\n", - "piecewise_trains = list(piecewise_case[\"trains\"])\n" + "piecewise_trains = list(piecewise_case[\"trains\"])" ] }, { "cell_type": "code", "execution_count": null, - "id": "58f09d75", + "id": "cf2c6402", "metadata": {}, "outputs": [], "source": [ @@ -294,24 +288,24 @@ "ax.set_title(\"Ground-truth rates for the two-epoch simulation\")\n", "ax.set_xlabel(\"time (s)\")\n", "ax.set_ylabel(\"rate (Hz)\")\n", - "ax.legend(loc=\"best\", frameon=False, fontsize=8)\n" + "ax.legend(loc=\"best\", frameon=False, fontsize=8)" ] }, { "cell_type": "code", "execution_count": null, - "id": "6928e1f4", + "id": "25dfb2e5", "metadata": {}, "outputs": [], "source": [ "# SECTION 8: Setup the piecewise-rate analysis\n", - "piecewise_results = Analysis.RunAnalysisForAllNeurons(piecewise_case[\"trial\"], piecewise_case[\"cfg\"], 0)\n" + "piecewise_results = Analysis.RunAnalysisForAllNeurons(piecewise_case[\"trial\"], piecewise_case[\"cfg\"], 0)" ] }, { "cell_type": "code", "execution_count": null, - "id": "3522b3b9", + "id": "113d3272", "metadata": {}, "outputs": [], "source": [ @@ -340,13 +334,13 @@ " ax.set_xlabel(\"time (s)\")\n", " ax.grid(alpha=0.25)\n", "axs[0].set_ylabel(\"rate (Hz)\")\n", - "axs[1].legend(loc=\"best\", frameon=False, fontsize=8)\n" + "axs[1].legend(loc=\"best\", frameon=False, fontsize=8)" ] }, { "cell_type": "code", "execution_count": null, - "id": "f576be75", + "id": "1d9d4cce", "metadata": {}, "outputs": [], "source": [ @@ -373,7 +367,7 @@ "ax.set_ylabel(\"log-likelihood\")\n", "ax.set_title(\"Per-neuron log-likelihood comparison\")\n", "ax.legend(loc=\"best\", frameon=False, fontsize=8)\n", - "__tracker.finalize()\n" + "__tracker.finalize()" ] } ], @@ -383,11 +377,11 @@ }, "nstat": { "expected_figures": 10, - "run_group": "smoke", + "run_group": "full", "style": "python-example", "topic": "ValidationDataSet" } }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/notebooks/mEPSCAnalysis.ipynb b/notebooks/mEPSCAnalysis.ipynb index 4f1a9241..9dc5dec0 100644 --- a/notebooks/mEPSCAnalysis.ipynb +++ b/notebooks/mEPSCAnalysis.ipynb @@ -59,7 +59,7 @@ "\n", "# SECTION 0: Section 0\n", "# MINIATURE EXCITATORY POST-SYNAPTIC CURRENTS (mEPSCs)\n", - "# Data from Marnie Phillips; this notebook keeps the original analysis narrative but replaces the old placeholder cells with executable Python workflows.\n" + "# Data from Marnie Phillips; this notebook keeps the original analysis narrative but replaces the old placeholder cells with executable Python workflows." ] }, { @@ -128,7 +128,7 @@ "\n", "fig = __tracker.new_figure(\"constant-magnesium-results\")\n", "const_results.plotResults(handle=fig)\n", - "print({\"constant_events\": int(const_spike_times.size), \"AIC\": const_results.AIC.tolist()})\n" + "print({\"constant_events\": int(const_spike_times.size), \"AIC\": const_results.AIC.tolist()})" ] }, { @@ -196,7 +196,7 @@ "ax.set_title(\"Washout event raster with selected segments\")\n", "for marker in (260.0, 400.0, 745.0):\n", " ax.axvline(marker, color=\"tab:red\", linestyle=\"--\", linewidth=1.0)\n", - "fig.tight_layout()\n" + "fig.tight_layout()" ] }, { @@ -253,7 +253,7 @@ "])\n", "results = Analysis.RunAnalysisForNeuron(washout_trial, 1, configs, 0)\n", "summary = FitResSummary([results])\n", - "print({\"washout_events\": int(washout_spikes.size), \"config_names\": results.configNames})\n" + "print({\"washout_events\": int(washout_spikes.size), \"config_names\": results.configNames})" ] }, { @@ -276,7 +276,7 @@ "\n", "fig = __tracker.new_figure(\"washout-summary\")\n", "summary.plotSummary(handle=fig)\n", - "__tracker.finalize()\n" + "__tracker.finalize()" ] }, { diff --git a/notebooks/nSTATPaperExamples.ipynb b/notebooks/nSTATPaperExamples.ipynb index 093de5b2..4ee710a6 100644 --- a/notebooks/nSTATPaperExamples.ipynb +++ b/notebooks/nSTATPaperExamples.ipynb @@ -2,20 +2,20 @@ "cells": [ { "cell_type": "markdown", - "id": "989e2794", + "id": "e7957a0e", "metadata": {}, "source": [ "\n", "## MATLAB Parity Note\n", "- Source MATLAB helpfile: `nSTATPaperExamples.mlx`\n", "- Fidelity status: `high_fidelity`\n", - "- Remaining justified differences: The notebook now executes the canonical paper-example workflows through the standalone Python implementations and real figshare-backed datasets; exact numerical traces and figure styling still vary modestly because the Python GLM/decoder stack and plotting defaults are not byte-identical to MATLAB.\n" + "- Remaining justified differences: The notebook now executes the canonical paper-example workflows through the standalone Python implementations and real figshare-backed datasets; exact numerical traces and figure styling still vary modestly because the Python GLM/decoder stack and plotting defaults are not byte-identical to MATLAB." ] }, { "cell_type": "code", "execution_count": null, - "id": "e3ae0b3f", + "id": "64f99156", "metadata": {}, "outputs": [], "source": [ @@ -52,14 +52,12 @@ "OUTPUT_ROOT = REPO_ROOT / \"output\" / \"notebook_images\"\n", "__tracker = FigureTracker(topic=\"nSTATPaperExamples\", output_root=OUTPUT_ROOT, expected_count=26)\n", "\n", - "\n", "def _fig(label: str, *, figsize=(8.5, 4.5)):\n", " fig = __tracker.new_figure(label)\n", " fig.clear()\n", " fig.set_size_inches(*figsize)\n", " return fig\n", "\n", - "\n", "plt.close(\"all\")\n", "exp1_summary, exp1 = run_experiment1(DATA_DIR, return_payload=True)\n", "exp2_summary, exp2 = run_experiment2(DATA_DIR, return_payload=True)\n", @@ -69,24 +67,24 @@ "exp5_summary, exp5 = run_experiment5(return_payload=True)\n", "exp5b_summary, exp5b = run_experiment5b(return_payload=True)\n", "exp6_summary, exp6 = run_experiment6(REPO_ROOT, return_payload=True)\n", - "print({\"dataset_root\": str(DATA_DIR), \"paper_examples_loaded\": 8})\n" + "print({\"dataset_root\": str(DATA_DIR), \"paper_examples_loaded\": 8})" ] }, { "cell_type": "code", "execution_count": null, - "id": "ad04d480", + "id": "cf03a39b", "metadata": {}, "outputs": [], "source": [ "# SECTION 1: Experiment 1\n", - "print(exp1_summary)\n" + "print(exp1_summary)" ] }, { "cell_type": "code", "execution_count": null, - "id": "f5fd7ff0", + "id": "facb48a1", "metadata": {}, "outputs": [], "source": [ @@ -96,24 +94,24 @@ "ax.plot(exp1[\"constant_time_s\"], exp1[\"constant_rate_hz\"], color=\"tab:blue\", linewidth=1.4)\n", "ax.set_xlabel(\"time (s)\")\n", "ax.set_ylabel(\"rate (Hz)\")\n", - "ax.set_title(\"Constant Mg condition: homogeneous Poisson fit\")\n" + "ax.set_title(\"Constant Mg condition: homogeneous Poisson fit\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "2b8b5cc6", + "id": "b3453ddd", "metadata": {}, "outputs": [], "source": [ "# SECTION 3: Varying Magnesium Concentration - Piecewise Constant rate poisson\n", - "print({\"decreasing_condition_spikes\": exp1_summary[\"decreasing_condition_spikes\"], \"piecewise_model_aic\": round(float(exp1_summary[\"piecewise_model_aic\"]), 3)})\n" + "print({\"decreasing_condition_spikes\": exp1_summary[\"decreasing_condition_spikes\"], \"piecewise_model_aic\": round(float(exp1_summary[\"piecewise_model_aic\"]), 3)})" ] }, { "cell_type": "code", "execution_count": null, - "id": "36d369c6", + "id": "6a1a4315", "metadata": {}, "outputs": [], "source": [ @@ -132,13 +130,13 @@ " axs[1].axvline(edge, color=\"tab:red\", linestyle=\"--\", linewidth=0.9)\n", "axs[1].set_xlabel(\"time (s)\")\n", "axs[1].set_ylabel(\"rate (Hz)\")\n", - "axs[1].legend(loc=\"upper left\", frameon=False, fontsize=8)\n" + "axs[1].legend(loc=\"upper left\", frameon=False, fontsize=8)" ] }, { "cell_type": "code", "execution_count": null, - "id": "780da944", + "id": "9bf5cb26", "metadata": {}, "outputs": [], "source": [ @@ -152,13 +150,13 @@ "ax.set_ylim(0.0, 1.0)\n", "ax.set_xlabel(\"theoretical CDF\")\n", "ax.set_ylabel(\"empirical CDF\")\n", - "ax.set_title(\"Constant-condition KS plot\")\n" + "ax.set_title(\"Constant-condition KS plot\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "3c220adb", + "id": "197eb8cd", "metadata": {}, "outputs": [], "source": [ @@ -170,13 +168,13 @@ "ax.axhline(-exp1_summary[\"constant_acf_ci\"], color=\"tab:red\", linewidth=1.0)\n", "ax.set_xlabel(\"lag\")\n", "ax.set_ylabel(\"autocorrelation\")\n", - "ax.set_title(\"Sequential correlation under constant Mg\")\n" + "ax.set_title(\"Sequential correlation under constant Mg\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "78a302f9", + "id": "53c12b4c", "metadata": {}, "outputs": [], "source": [ @@ -188,24 +186,24 @@ "ax.bar(np.arange(3), aics, color=[\"0.6\", \"tab:green\", \"tab:red\"])\n", "ax.set_xticks(np.arange(3), names)\n", "ax.set_ylabel(\"AIC\")\n", - "ax.set_title(\"Experiment 1 model comparison\")\n" + "ax.set_title(\"Experiment 1 model comparison\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "2b09c5e3", + "id": "b6751400", "metadata": {}, "outputs": [], "source": [ "# SECTION 8: Experiment 2\n", - "print(exp2_summary)\n" + "print(exp2_summary)" ] }, { "cell_type": "code", "execution_count": null, - "id": "03f25c28", + "id": "be05f22d", "metadata": {}, "outputs": [], "source": [ @@ -218,13 +216,13 @@ "axs[0].set_ylabel(\"spikes\")\n", "axs[1].plot(exp2[\"time_s\"], exp2[\"stimulus\"], color=\"tab:blue\", linewidth=1.2)\n", "axs[1].set_ylabel(\"stimulus\")\n", - "axs[1].set_xlabel(\"time (s)\")\n" + "axs[1].set_xlabel(\"time (s)\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "03ee9c55", + "id": "f704736c", "metadata": {}, "outputs": [], "source": [ @@ -234,13 +232,13 @@ "ax.plot(1000.0 * np.asarray(exp2[\"xcorr_lags_s\"], dtype=float), exp2[\"xcorr_values\"], color=\"tab:purple\", linewidth=1.3)\n", "ax.set_xlabel(\"lag (ms)\")\n", "ax.set_ylabel(\"cross-covariance\")\n", - "ax.set_title(\"Stimulus lag search\")\n" + "ax.set_title(\"Stimulus lag search\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "09f0d4f1", + "id": "620d4f28", "metadata": {}, "outputs": [], "source": [ @@ -253,13 +251,13 @@ "axs[0].set_title(\"AIC\")\n", "axs[1].bar(np.arange(3), [exp2_summary[\"model1_bic\"], exp2_summary[\"model2_bic\"], exp2_summary[\"model3_bic\"]], color=[\"0.65\", \"tab:blue\", \"tab:green\"])\n", "axs[1].set_xticks(np.arange(3), model_names, rotation=15)\n", - "axs[1].set_title(\"BIC\")\n" + "axs[1].set_title(\"BIC\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "b32115d9", + "id": "0803cc01", "metadata": {}, "outputs": [], "source": [ @@ -275,13 +273,13 @@ "ax.set_xlim(0.0, 1.0)\n", "ax.set_ylim(0.0, 1.0)\n", "ax.legend(loc=\"lower right\", frameon=False, fontsize=8)\n", - "ax.set_title(\"Experiment 2 KS diagnostics\")\n" + "ax.set_title(\"Experiment 2 KS diagnostics\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "d209b1e8", + "id": "e8e2cecc", "metadata": {}, "outputs": [], "source": [ @@ -295,13 +293,13 @@ "axs[1].set_ylabel(\"Delta AIC\")\n", "axs[2].plot(windows, exp2[\"delta_bic\"], marker=\"o\", color=\"tab:brown\", linewidth=1.2)\n", "axs[2].set_ylabel(\"Delta BIC\")\n", - "axs[2].set_xlabel(\"history windows\")\n" + "axs[2].set_xlabel(\"history windows\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "570fa694", + "id": "86253034", "metadata": {}, "outputs": [], "source": [ @@ -315,24 +313,24 @@ "ax.errorbar(xpos, coef_values, yerr=np.vstack([coef_values - lower, upper - coef_values]), fmt=\"o\", color=\"tab:blue\", capsize=3)\n", "ax.set_xticks(xpos, exp2[\"coef_names\"], rotation=30)\n", "ax.set_ylabel(\"coefficient value\")\n", - "ax.set_title(\"Experiment 2 coefficient intervals\")\n" + "ax.set_title(\"Experiment 2 coefficient intervals\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "2e5c472a", + "id": "214e5be7", "metadata": {}, "outputs": [], "source": [ "# SECTION 15: Experiment 3\n", - "print(exp3_summary)\n" + "print(exp3_summary)" ] }, { "cell_type": "code", "execution_count": null, - "id": "ad2e93f8", + "id": "f39efc64", "metadata": {}, "outputs": [], "source": [ @@ -342,13 +340,13 @@ "ax.plot(exp3[\"time_s\"], exp3[\"true_rate_hz\"], color=\"tab:blue\", linewidth=1.3)\n", "ax.set_xlabel(\"time (s)\")\n", "ax.set_ylabel(\"rate (Hz)\")\n", - "ax.set_title(\"Experiment 3 true conditional intensity\")\n" + "ax.set_title(\"Experiment 3 true conditional intensity\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "03fe133c", + "id": "39340a3b", "metadata": {}, "outputs": [], "source": [ @@ -360,24 +358,24 @@ "axs[0].set_ylabel(\"trial\")\n", "axs[1].plot(exp3[\"psth_bin_centers_s\"], exp3[\"psth_rate_hz\"], color=\"tab:red\", linewidth=1.4)\n", "axs[1].set_ylabel(\"PSTH (Hz)\")\n", - "axs[1].set_xlabel(\"time (s)\")\n" + "axs[1].set_xlabel(\"time (s)\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "2be08d3f", + "id": "e80bdf17", "metadata": {}, "outputs": [], "source": [ "# SECTION 18: Experiment 3b\n", - "print(exp3b_summary)\n" + "print(exp3b_summary)" ] }, { "cell_type": "code", "execution_count": null, - "id": "4470cf1f", + "id": "d3f429ac", "metadata": {}, "outputs": [], "source": [ @@ -388,13 +386,13 @@ "axs[0].set_title(\"True stimulus\")\n", "axs[1].imshow(exp3b[\"xk\"], aspect=\"auto\", cmap=\"viridis\")\n", "axs[1].set_title(\"Decoded state\")\n", - "axs[1].set_xlabel(\"time bin\")\n" + "axs[1].set_xlabel(\"time bin\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "2c42e63b", + "id": "9e327384", "metadata": {}, "outputs": [], "source": [ @@ -404,13 +402,13 @@ "axs[0].plot(np.mean(exp3b[\"ci_width\"], axis=0), color=\"tab:orange\", linewidth=1.3)\n", "axs[0].set_title(\"Mean CI width over time\")\n", "axs[1].plot(np.mean(exp3b[\"qhat_all\"], axis=0), marker=\"o\", color=\"tab:blue\", linewidth=1.2)\n", - "axs[1].set_title(\"Mean Qhat across models\")\n" + "axs[1].set_title(\"Mean Qhat across models\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "e193a719", + "id": "2fd1d209", "metadata": {}, "outputs": [], "source": [ @@ -420,24 +418,24 @@ "axs[0].bar(np.arange(len(exp3b[\"gammahat\"])), exp3b[\"gammahat\"], color=\"tab:green\")\n", "axs[0].set_title(\"gammahat\")\n", "axs[1].plot(np.asarray(exp3b[\"gammahat_all\"], dtype=float), marker=\"o\", color=\"tab:red\", linewidth=1.2)\n", - "axs[1].set_title(\"gammahatAll\")\n" + "axs[1].set_title(\"gammahatAll\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "3718c5d4", + "id": "8150177d", "metadata": {}, "outputs": [], "source": [ "# SECTION 22: Experiment 4\n", - "print(exp4_summary)\n" + "print(exp4_summary)" ] }, { "cell_type": "code", "execution_count": null, - "id": "96e2af7c", + "id": "19dabcf1", "metadata": {}, "outputs": [], "source": [ @@ -447,13 +445,13 @@ "ax.bar(np.arange(len(exp4[\"animal1\"][\"selected_indices\"])), exp4[\"animal1\"][\"delta_aic\"], color=\"tab:blue\")\n", "ax.set_xticks(np.arange(len(exp4[\"animal1\"][\"selected_indices\"])), [str(int(v) + 1) for v in exp4[\"animal1\"][\"selected_indices\"]])\n", "ax.set_ylabel(\"Gaussian - Zernike AIC\")\n", - "ax.set_title(\"Animal 1 place-cell comparison\")\n" + "ax.set_title(\"Animal 1 place-cell comparison\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "ccd871af", + "id": "21ef2693", "metadata": {}, "outputs": [], "source": [ @@ -463,13 +461,13 @@ "ax.bar(np.arange(len(exp4[\"animal2\"][\"selected_indices\"])), exp4[\"animal2\"][\"delta_bic\"], color=\"tab:green\")\n", "ax.set_xticks(np.arange(len(exp4[\"animal2\"][\"selected_indices\"])), [str(int(v) + 1) for v in exp4[\"animal2\"][\"selected_indices\"]])\n", "ax.set_ylabel(\"Gaussian - Zernike BIC\")\n", - "ax.set_title(\"Animal 2 place-cell comparison\")\n" + "ax.set_title(\"Animal 2 place-cell comparison\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "113c1903", + "id": "549f0f6b", "metadata": {}, "outputs": [], "source": [ @@ -477,13 +475,13 @@ "fig = _fig(\"experiment4 gaussian mesh\", figsize=(9.0, 6.5))\n", "ax = fig.add_subplot(111, projection=\"3d\")\n", "ax.plot_surface(exp4[\"mesh\"][\"grid_x\"], exp4[\"mesh\"][\"grid_y\"], exp4[\"mesh\"][\"gaussian_field\"], cmap=\"Blues\", linewidth=0.0, antialiased=True)\n", - "ax.set_title(\"Gaussian place-field estimate\")\n" + "ax.set_title(\"Gaussian place-field estimate\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "64654717", + "id": "3e3bb9b7", "metadata": {}, "outputs": [], "source": [ @@ -491,24 +489,24 @@ "fig = _fig(\"experiment4 zernike mesh\", figsize=(9.0, 6.5))\n", "ax = fig.add_subplot(111, projection=\"3d\")\n", "ax.plot_surface(exp4[\"mesh\"][\"grid_x\"], exp4[\"mesh\"][\"grid_y\"], exp4[\"mesh\"][\"zernike_field\"], cmap=\"Greens\", linewidth=0.0, antialiased=True)\n", - "ax.set_title(\"Zernike place-field estimate\")\n" + "ax.set_title(\"Zernike place-field estimate\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "115929b8", + "id": "96c64e62", "metadata": {}, "outputs": [], "source": [ "# SECTION 27: Experiment 5\n", - "print(exp5_summary)\n" + "print(exp5_summary)" ] }, { "cell_type": "code", "execution_count": null, - "id": "26af3ac2", + "id": "b88f5c9e", "metadata": {}, "outputs": [], "source": [ @@ -520,24 +518,24 @@ "ax.fill_between(exp5[\"time_s\"], exp5[\"ci_low\"], exp5[\"ci_high\"], color=\"0.85\")\n", "ax.legend(loc=\"upper right\", frameon=False, fontsize=8)\n", "ax.set_xlabel(\"time (s)\")\n", - "ax.set_title(\"Experiment 5 adaptive decoding\")\n" + "ax.set_title(\"Experiment 5 adaptive decoding\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "22ee7cab", + "id": "b751e28e", "metadata": {}, "outputs": [], "source": [ "# SECTION 29: Experiment 5b\n", - "print(exp5b_summary)\n" + "print(exp5b_summary)" ] }, { "cell_type": "code", "execution_count": null, - "id": "f0312bf9", + "id": "48493368", "metadata": {}, "outputs": [], "source": [ @@ -550,13 +548,13 @@ "axs[1].plot(exp5b[\"time_s\"], exp5b[\"y_true\"], color=\"0.3\", linewidth=1.0, label=\"True y\")\n", "axs[1].plot(exp5b[\"time_s\"], exp5b[\"dy_goal\"], color=\"tab:orange\", linewidth=1.2, label=\"Decoded y\")\n", "axs[1].legend(loc=\"upper right\", frameon=False, fontsize=8)\n", - "axs[1].set_xlabel(\"time (s)\")\n" + "axs[1].set_xlabel(\"time (s)\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "216a748d", + "id": "fdeed99c", "metadata": {}, "outputs": [], "source": [ @@ -569,24 +567,24 @@ "axs[1].plot(exp5b[\"time_s\"], exp5b[\"y_true\"], color=\"0.3\", linewidth=1.0, label=\"True y\")\n", "axs[1].plot(exp5b[\"time_s\"], exp5b[\"dy_free\"], color=\"tab:red\", linewidth=1.2, label=\"Decoded y\")\n", "axs[1].legend(loc=\"upper right\", frameon=False, fontsize=8)\n", - "axs[1].set_xlabel(\"time (s)\")\n" + "axs[1].set_xlabel(\"time (s)\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "358d0d12", + "id": "98b8c650", "metadata": {}, "outputs": [], "source": [ "# SECTION 32: Experiment 6\n", - "print(exp6_summary)\n" + "print(exp6_summary)" ] }, { "cell_type": "code", "execution_count": null, - "id": "be288a10", + "id": "01efa943", "metadata": {}, "outputs": [], "source": [ @@ -598,13 +596,13 @@ "axs[1].plot(exp6[\"time_s\"], exp6[\"state_prob_1\"], color=\"tab:blue\", linewidth=1.2, label=\"P(state=1)\")\n", "axs[1].plot(exp6[\"time_s\"], exp6[\"state_prob_2\"], color=\"tab:orange\", linewidth=1.2, label=\"P(state=2)\")\n", "axs[1].legend(loc=\"upper right\", frameon=False, fontsize=8)\n", - "axs[1].set_xlabel(\"time (s)\")\n" + "axs[1].set_xlabel(\"time (s)\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "7e158b30", + "id": "fbcd95b6", "metadata": {}, "outputs": [], "source": [ @@ -617,13 +615,13 @@ "axs[1].plot(exp6[\"time_s\"], exp6[\"y_pos\"], color=\"0.3\", linewidth=1.0, label=\"True y\")\n", "axs[1].plot(exp6[\"time_s\"], exp6[\"decoded_y\"], color=\"tab:orange\", linewidth=1.2, label=\"Decoded y\")\n", "axs[1].legend(loc=\"upper right\", frameon=False, fontsize=8)\n", - "axs[1].set_xlabel(\"time (s)\")\n" + "axs[1].set_xlabel(\"time (s)\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "2b0b149c", + "id": "2c1cf92e", "metadata": {}, "outputs": [], "source": [ @@ -635,13 +633,13 @@ "ax.bar(np.arange(len(labels)), rmses, color=[\"tab:blue\", \"tab:green\", \"tab:red\", \"tab:purple\", \"tab:orange\"])\n", "ax.set_xticks(np.arange(len(labels)), labels, rotation=20)\n", "ax.set_ylabel(\"RMSE\")\n", - "ax.set_title(\"Decoding summary across paper examples\")\n" + "ax.set_title(\"Decoding summary across paper examples\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "74cb3002", + "id": "bb79988e", "metadata": {}, "outputs": [], "source": [ @@ -658,13 +656,13 @@ "labels = [\"Exp1 spikes\", \"Exp2 samples\", \"Exp3 trials\", \"Exp4 cells\", \"Exp6 cells\"]\n", "ax.bar(np.arange(len(labels)), counts, color=\"0.65\")\n", "ax.set_xticks(np.arange(len(labels)), labels, rotation=20)\n", - "ax.set_title(\"Paper-example dataset scale\")\n" + "ax.set_title(\"Paper-example dataset scale\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "a52f8e5c", + "id": "9aa50b57", "metadata": {}, "outputs": [], "source": [ @@ -677,7 +675,7 @@ " \"experiment6_state_accuracy\": round(float(exp6_summary[\"state_accuracy\"]), 3),\n", " }\n", ")\n", - "__tracker.finalize()\n" + "__tracker.finalize()" ] } ], @@ -694,4 +692,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/notebooks/nSpikeTrainExamples.ipynb b/notebooks/nSpikeTrainExamples.ipynb index 71075a0e..f6b44cae 100644 --- a/notebooks/nSpikeTrainExamples.ipynb +++ b/notebooks/nSpikeTrainExamples.ipynb @@ -30,7 +30,7 @@ "__tracker = FigureTracker(topic='nSpikeTrainExamples', output_root=OUTPUT_ROOT, expected_count=4)\n", "\n", "# SECTION 0: Section 0\n", - "# Test the nspikeTrain Class\n" + "# Test the nspikeTrain Class" ] }, { From 44550834aba9586f4c8a118133e80e2abfb2eba4 Mon Sep 17 00:00:00 2001 From: Iahn Cajigas Date: Tue, 10 Mar 2026 14:11:02 -0400 Subject: [PATCH 03/19] Port remaining missing methods + fix getDesignMatrix dimension bug DecodingAlgorithms: - Add computeSpikeRateCIs: Monte Carlo spike-rate CIs over time window - Add computeSpikeRateDiffCIs: MC CIs for spike-rate difference between windows - Remove 1018-line duplicate SSGLM block (byte-for-byte copy) - Fix nspikeTrain constructor calls (positional arg, not keyword) Trial: - Add getNumHist(): return number of history window coefficients - Add findMinSampleRate(): minimum sample rate across components - Fix getDesignMatrix() dimension mismatch: truncate to min row count when covariate matrix and history matrix have off-by-one time grids SignalObj (15 new methods): - Label utilities: areDataLabelsEmpty, isLabelPresent, convertNamesToIndices - Operators: __matmul__ (mtimes), ldivide, T property (transpose) - Plot props: clearPlotProps, plotPropsSet - Alignment: alignToMax, windowedSignal, normWindowedSignal - Statistics: getSubSignalsWithinNStd - Plotting: plotVariability, plotAllVariability SpikeTrainCollection: - Add reverseOrder parameter to plot() Events: - Add colorString parameter to plot() All 179 tests pass, 2 skipped. Co-Authored-By: Claude Opus 4.6 --- nstat/core.py | 257 +++++++ nstat/decoding_algorithms.py | 1399 +++++++++------------------------- nstat/events.py | 15 +- nstat/trial.py | 78 +- 4 files changed, 724 insertions(+), 1025 deletions(-) diff --git a/nstat/core.py b/nstat/core.py index 2035151f..c7f537ac 100644 --- a/nstat/core.py +++ b/nstat/core.py @@ -368,6 +368,52 @@ def getIndicesFromLabels(self, label: Sequence[str] | str): return [item[0] for item in out] return out + def areDataLabelsEmpty(self) -> bool: + """Return ``True`` if all data labels are empty strings. + + Matches Matlab ``SignalObj.areDataLabelsEmpty()``. + """ + return all(not str(label) for label in self.dataLabels) + + def isLabelPresent(self, label: str) -> bool: + """Return ``True`` if *label* matches any data label or equals ``'all'``. + + Matches Matlab ``SignalObj.isLabelPresent()``. + """ + if str(label).lower() == "all": + return True + try: + self.getIndexFromLabel(label) + return True + except ValueError: + return False + + def convertNamesToIndices(self, selectorArray) -> list[int] | np.ndarray: + """Convert label names (or mixed) to 1-based indices. + + Matches Matlab ``SignalObj.convertNamesToIndices()``. + """ + if isinstance(selectorArray, str): + if selectorArray == "all": + return list(range(1, self.dimension + 1)) + if self.isLabelPresent(selectorArray): + return self.getIndexFromLabel(selectorArray) + raise ValueError(f"Specified label '{selectorArray}' does not match data label") + if isinstance(selectorArray, (int, float, np.integer)): + return [int(selectorArray)] + if isinstance(selectorArray, np.ndarray): + return selectorArray.astype(int).ravel().tolist() + if isinstance(selectorArray, (list, tuple)): + result: list[int] = [] + for item in selectorArray: + if isinstance(item, str): + if self.isLabelPresent(item): + result.extend(self.getIndexFromLabel(item)) + else: + result.append(int(item)) + return result + return list(range(1, self.dimension + 1)) + def getValueAt(self, x: Sequence[float] | float) -> np.ndarray: query = np.asarray(x, dtype=float).reshape(-1) out = np.zeros((query.size, self.dimension), dtype=float) @@ -557,6 +603,48 @@ def __rtruediv__(self, other) -> "SignalObj": right = np.repeat(right, left.shape[1], axis=1) return self._spawn(self.time, np.divide(left, right), data_labels=labels) + def __matmul__(self, other) -> "SignalObj": + """Matrix multiply (``@`` operator). Matches Matlab ``mtimes``.""" + if isinstance(other, SignalObj): + return self._spawn(self.time, self.data * other.data, data_labels=list(self.dataLabels)) + other_arr = np.asarray(other, dtype=float) + result = (self.data.T @ other_arr).T if other_arr.ndim <= 1 else self.data @ other_arr + return self._spawn(self.time[:result.shape[0]] if result.ndim == 2 else self.time, result) + + def ldivide(self, other) -> "SignalObj": + r"""Element-wise left division (Matlab ``.\``): ``other ./ self``. + + Matches Matlab ``SignalObj.ldivide()``. + """ + return self._binary_op(other, lambda a, b: np.divide(b, a)) + + @property + def T(self) -> "SignalObj": + """Transpose the data matrix. Matches Matlab ``ctranspose`` / ``transpose``.""" + new_data = self.data.T + new_time = self.time[:new_data.shape[0]] if new_data.shape[0] != self.time.size else self.time + return self._spawn(new_time, new_data) + + def clearPlotProps(self, index=None) -> None: + """Clear plot properties. Matches Matlab ``clearPlotProps``.""" + if index is None: + index = list(range(self.dimension)) + else: + index = [i - 1 for i in np.atleast_1d(index)] + for i in index: + if i < len(self.plotProps): + self.plotProps[i] = None + + def plotPropsSet(self) -> bool: + """Return ``True`` if any plot property is non-empty. + + Matches Matlab ``SignalObj.plotPropsSet()``. + """ + for prop in self.plotProps: + if prop is not None and str(prop) != "": + return True + return False + def getSigInTimeWindow( self, wMin: Sequence[float] | float | None = None, @@ -1072,6 +1160,175 @@ def findGlobalPeak( values = data[idx, np.arange(data.shape[1])] return np.atleast_1d(times), np.atleast_1d(values) + # ------------------------------------------------------------------ + # Alignment / windowing (match Matlab SignalObj) + # ------------------------------------------------------------------ + def alignToMax(self) -> tuple["SignalObj", float]: + """Align all dimensions so their peaks coincide at the mean peak time. + + Returns ``(aligned_signal, mean_peak_time)``. + Matches Matlab ``SignalObj.alignToMax()``. + """ + peak_times, _ = self.findGlobalPeak("maxima") + mean_time = float(np.mean(peak_times)) + delta_t = -(peak_times - mean_time) + aligned = self.getSubSignal(1).shift(float(delta_t[0])) + for i in range(1, self.dimension): + aligned = aligned.merge(self.getSubSignal(i + 1).shift(float(delta_t[i]))) + return aligned, mean_time + + def windowedSignal(self, windowTimes) -> "SignalObj": + """Extract and concatenate windowed segments. + + Matches Matlab ``SignalObj.windowedSignal()``. + """ + windowTimes = np.asarray(windowTimes, dtype=float).ravel() + result = None + for i in range(len(windowTimes) - 1): + seg = self.getSigInTimeWindow(float(windowTimes[i]), float(windowTimes[i + 1])) + if i == 0: + result = seg + else: + seg = seg.shift(-float(windowTimes[i])) + result = result.merge(seg) + return result if result is not None else self.copySignal() + + def normWindowedSignal( + self, + windowTimes, + numPoints: int = 100, + lbound: float | None = None, + ubound: float | None = None, + ) -> "SignalObj": + """Normalize windowed signal segments to a common time axis. + + Matches Matlab ``SignalObj.normWindowedSignal()``. + """ + windowTimes = np.asarray(windowTimes, dtype=float).ravel() + columns: list[np.ndarray] = [] + for i in range(len(windowTimes) - 1): + minT = float(windowTimes[i]) + maxT = float(windowTimes[i + 1]) + dur = abs(maxT - minT) + if lbound is not None and ubound is not None: + if dur > ubound or dur < lbound: + continue + seg = self.getSigInTimeWindow(minT, maxT) + norm_time = np.linspace(minT, maxT, numPoints) + interp_data = np.interp(norm_time, seg.time, seg.data[:, 0], left=0.0, right=0.0) + columns.append(interp_data) + + if not columns: + return self.copySignal() + data = np.column_stack(columns) + act_time = np.arange(numPoints, dtype=float) / float(numPoints) + labels = list(self.dataLabels[:1]) * data.shape[1] + return self.__class__(act_time, data, self.name, self.xlabelval, "%", self.yunits, labels) + + def getSubSignalsWithinNStd(self, nStd: float = 2.0) -> tuple["SignalObj", np.ndarray]: + """Return sub-signals within *nStd* standard deviations of the mean. + + Returns ``(filtered_signal, selected_indices)``. + Matches Matlab ``SignalObj.getSubSignalsWithinNStd()``. + """ + mean_sig = np.mean(self.data, axis=1) + std_sig = np.std(self.data, axis=1, ddof=1) + min_val = mean_sig - nStd * std_sig + max_val = mean_sig + nStd * std_sig + # A column passes if ALL rows are within [minVal, maxVal] + above_min = np.all(self.data >= min_val[:, None], axis=0) + below_max = np.all(self.data <= max_val[:, None], axis=0) + sig_index = np.flatnonzero(above_min & below_max) + if sig_index.size == 0: + return self.copySignal(), sig_index + # 1-based indices for getSubSignal + return self.getSubSignal((sig_index + 1).tolist()), sig_index + + # ------------------------------------------------------------------ + # Variability plots (match Matlab SignalObj) + # ------------------------------------------------------------------ + def plotAllVariability( + self, + faceColor=None, + linewidth: float = 3.0, + ciUpper: float | np.ndarray = 1.96, + ciLower: float | np.ndarray | None = None, + ax=None, + ): + """Plot mean ± CI shaded area. Matches Matlab ``plotAllVariability``. + + Parameters + ---------- + faceColor : color, optional + Fill colour (default: tab:blue). + linewidth : float + Width of mean line. + ciUpper, ciLower : float or array + Number of std-devs (scalar) or explicit bounds (array). + ax : matplotlib Axes, optional + """ + import matplotlib.pyplot as plt + + if faceColor is None: + faceColor = "tab:blue" + if ciLower is None: + ciLower = ciUpper + if ax is None: + ax = plt.gca() + + mean_sig = np.mean(self.data, axis=1) + std_sig = np.std(self.data, axis=1, ddof=1) + + ciUpper_arr = np.atleast_1d(ciUpper).ravel() + ciLower_arr = np.atleast_1d(ciLower).ravel() + if ciUpper_arr.size == 1: + ci_top = mean_sig + float(ciUpper_arr[0]) * std_sig + else: + ci_top = mean_sig + ciUpper_arr[:len(mean_sig)] + if ciLower_arr.size == 1: + ci_bottom = mean_sig - float(ciLower_arr[0]) * std_sig + else: + ci_bottom = mean_sig - ciLower_arr[:len(mean_sig)] + + ax.fill_between(self.time, ci_bottom, ci_top, color=faceColor, alpha=0.5, edgecolor="none") + (h,) = ax.plot(self.time, mean_sig, "k-", linewidth=linewidth) + return h + + def plotVariability(self, selectorArray=None, ax=None): + """Plot mean ± CI for each label group. Matches Matlab ``plotVariability``. + + Parameters + ---------- + selectorArray : list of list[int] or list[int], optional + ax : matplotlib Axes, optional + """ + import matplotlib.pyplot as plt + + if ax is None: + ax = plt.gca() + if selectorArray is None: + if not self.areDataLabelsEmpty(): + unique_labels = list(dict.fromkeys(self.dataLabels)) + selectorArray = [self.getIndexFromLabel(lbl) for lbl in unique_labels] + else: + selectorArray = list(range(1, self.dimension + 1)) + + _TAB_COLORS = [ + "tab:blue", "tab:orange", "tab:green", "tab:red", + "tab:purple", "tab:brown", "tab:pink", "tab:gray", + ] + handles = [] + if isinstance(selectorArray, list) and selectorArray and isinstance(selectorArray[0], (list, tuple, np.ndarray)): + for i, sel in enumerate(selectorArray): + h = self.getSubSignal(sel).plotAllVariability( + faceColor=_TAB_COLORS[i % len(_TAB_COLORS)], ax=ax + ) + handles.append(h) + else: + h = self.getSubSignal(selectorArray).plotAllVariability(ax=ax) + handles.append(h) + return handles + # ------------------------------------------------------------------ # Cross-covariance (match Matlab SignalObj.xcov) # ------------------------------------------------------------------ diff --git a/nstat/decoding_algorithms.py b/nstat/decoding_algorithms.py index 05865b21..dddda97c 100644 --- a/nstat/decoding_algorithms.py +++ b/nstat/decoding_algorithms.py @@ -553,6 +553,381 @@ def ComputeStimulusCIs(fitType, xK, Wku, delta, Mc=None, alphaVal=0.05): return np.transpose(ci, (1, 0, 2)), stimulus.T return ci, stimulus + @staticmethod + def computeSpikeRateCIs( + xK, + Wku, + dN, + t0: float, + tf: float, + fitType: str = "poisson", + delta: float = 0.001, + gamma=None, + windowTimes=None, + Mc: int = 500, + alphaVal: float = 0.05, + ): + """Monte Carlo spike-rate confidence intervals. + + Computes the average firing rate over ``[t0, tf]`` for each trial + by drawing ``Mc`` samples from the smoothing distribution and + evaluating the conditional intensity. + + Parameters + ---------- + xK : array, shape (numBasis, K) + Smoothed state estimates (basis coefficients × trials). + Wku : array, shape (numBasis, numBasis, K, K) or compatible + Smoothed state covariance. + dN : array, shape (C, N) + Observation (spike indicator) matrix. + t0, tf : float + Time window over which to compute the average rate. + fitType : str + ``'poisson'`` or ``'binomial'``. + delta : float + Time-step size in seconds. + gamma : array or None + History-effect coefficients. + windowTimes : array or None + History window boundaries. + Mc : int + Number of Monte Carlo draws. + alphaVal : float + Significance level for CIs (one-sided). + + Returns + ------- + spikeRateSig : Covariate + Mean spike rate per trial with attached ConfidenceInterval. + ProbMat : ndarray, shape (K, K) + ``ProbMat[k, m]`` = P(rate_m > rate_k) estimated from MC draws. + sigMat : ndarray, shape (K, K) + Binary significance matrix at level ``1 - alphaVal``. + """ + from .confidence_interval import ConfidenceInterval + from .core import Covariate + from .history import History + from .nspikeTrain import nspikeTrain + from .trial import SpikeTrainCollection + + xK = np.asarray(xK, dtype=float) + dN = np.asarray(dN, dtype=float) + if dN.ndim == 1: + dN = dN.reshape(1, -1) + numBasis, K = xK.shape + minTime = 0.0 + maxTime = (dN.shape[1] - 1) * delta + + # Build unit-impulse basis matrix + basisWidth = (maxTime - minTime) / numBasis + sampleRate = 1.0 / delta + unitPulseBasis = SpikeTrainCollection.generateUnitImpulseBasis( + basisWidth, minTime, maxTime, sampleRate + ) + basisMat = unitPulseBasis.data # shape (T, numBasis) + + # Build history matrices if windowTimes provided + Hk = {} + if windowTimes is not None and len(windowTimes) > 0: + histObj = History(windowTimes, minTime, maxTime) + for k in range(K): + spike_idx = np.flatnonzero(dN[k, :] == 1) + spike_times = (spike_idx) * delta + nst_k = nspikeTrain(spike_times) + nst_k.setMinTime(minTime) + nst_k.setMaxTime(maxTime) + hist_cov = histObj.computeHistory(nst_k) + Hk[k] = hist_cov.dataToMatrix() + else: + for k in range(K): + Hk[k] = 0.0 + gamma = 0.0 + + if gamma is None: + gamma = 0.0 + gamma = np.asarray(gamma, dtype=float) + + # Monte Carlo draws from smoothing distribution + Wku = np.asarray(Wku, dtype=float) + xKDraw = np.zeros((numBasis, K, Mc), dtype=float) + for r in range(numBasis): + WkuTemp = Wku[r, r, :, :].squeeze() if Wku.ndim == 4 else Wku[r, r] + WkuTemp = np.atleast_2d(WkuTemp) + if WkuTemp.shape[0] != K: + WkuTemp = np.diag(np.full(K, float(WkuTemp.flat[0]))) + try: + chol_m = np.linalg.cholesky(WkuTemp) + except np.linalg.LinAlgError: + eigvals = np.linalg.eigvalsh(WkuTemp) + WkuTemp += np.eye(K) * (abs(min(eigvals.min(), 0.0)) + 1e-10) + chol_m = np.linalg.cholesky(WkuTemp) + for c in range(Mc): + z = np.random.randn(K) + xKDraw[r, :, c] = xK[r, :] + chol_m.T @ z + + # Compute lambda for each MC draw and each trial + time_vec = np.arange(minTime, maxTime + delta, delta) + T = basisMat.shape[0] + fit_type = str(fitType).lower() + spikeRate = np.zeros((Mc, K), dtype=float) + + for c in range(Mc): + for k in range(K): + stimK = basisMat @ xKDraw[:, k, c] + if fit_type == "poisson": + histEffect = np.exp(gamma @ Hk[k].T).ravel() if not np.isscalar(Hk[k]) else np.ones(T) + stimEffect = np.exp(np.clip(stimK, -20.0, 20.0)) + lambdaDelta_kc = stimEffect * histEffect[:T] + elif fit_type == "binomial": + if np.isscalar(Hk[k]): + eta = stimK + else: + eta = stimK + (gamma @ Hk[k].T).ravel()[:T] + eta = np.clip(eta, -20.0, 20.0) + lambdaDelta_kc = np.exp(eta) / (1.0 + np.exp(eta)) + else: + lambdaDelta_kc = np.exp(np.clip(stimK, -20.0, 20.0)) + + # Integrate via cumulative trapezoid + rate_per_sec = lambdaDelta_kc / delta + time_k = time_vec[:len(rate_per_sec)] + cum_integral = np.zeros(len(rate_per_sec)) + cum_integral[1:] = np.cumsum(rate_per_sec[:-1] * delta + 0.5 * np.diff(rate_per_sec) * delta) + + # Interpolate integral at t0 and tf + val_t0 = np.interp(t0, time_k, cum_integral) + val_tf = np.interp(tf, time_k, cum_integral) + spikeRate[c, k] = (1.0 / (tf - t0)) * (val_tf - val_t0) + + # Compute CIs from ECDF (one-sided) + CIs = np.zeros((K, 2), dtype=float) + for k in range(K): + sorted_rates = np.sort(spikeRate[:, k]) + ecdf = np.arange(1, Mc + 1, dtype=float) / float(Mc) + lower_idx = np.flatnonzero(ecdf < alphaVal) + upper_idx = np.flatnonzero(ecdf > (1.0 - alphaVal)) + CIs[k, 0] = sorted_rates[lower_idx[-1]] if lower_idx.size else sorted_rates[0] + CIs[k, 1] = sorted_rates[upper_idx[0]] if upper_idx.size else sorted_rates[-1] + + trial_axis = np.arange(1, K + 1, dtype=float) + mean_rate = np.mean(spikeRate, axis=0) + spikeRateSig = Covariate( + trial_axis, + mean_rate, + f"({tf:g}-{t0:g})^{{-1}} * \\Lambda({tf:g}-{t0:g})", + "Trial", + "k", + "Hz", + ) + ciSpikeRate = ConfidenceInterval( + trial_axis, CIs, "CI_{spikeRate}", "Trial", "k", "Hz" + ) + spikeRateSig.setConfInterval(ciSpikeRate) + + # Pairwise probability matrix + ProbMat = np.zeros((K, K), dtype=float) + for k in range(K): + for m in range(k + 1, K): + ProbMat[k, m] = np.sum(spikeRate[:, m] > spikeRate[:, k]) / float(Mc) + + sigMat = (ProbMat > (1.0 - alphaVal)).astype(float) + + return spikeRateSig, ProbMat, sigMat + + @staticmethod + def computeSpikeRateDiffCIs( + xK, + Wku, + dN, + time1, + time2, + fitType: str = "poisson", + delta: float = 0.001, + gamma=None, + windowTimes=None, + Mc: int = 500, + alphaVal: float = 0.05, + ): + """Monte Carlo CIs for the difference in spike rates between two time windows. + + Computes the difference of average firing rates + ``rate(time1) - rate(time2)`` for each trial by drawing ``Mc`` + samples from the smoothing distribution. + + Parameters + ---------- + xK : array, shape (numBasis, K) + Smoothed state estimates (basis coefficients × trials). + Wku : array, shape (numBasis, numBasis, K, K) or compatible + Smoothed state covariance. + dN : array, shape (C, N) + Observation (spike indicator) matrix. + time1 : array-like, length 2 + ``[t0_1, tf_1]`` — first time window. + time2 : array-like, length 2 + ``[t0_2, tf_2]`` — second time window. + fitType : str + ``'poisson'`` or ``'binomial'``. + delta : float + Time-step size in seconds. + gamma : array or None + History-effect coefficients. + windowTimes : array or None + History window boundaries. + Mc : int + Number of Monte Carlo draws. + alphaVal : float + Significance level for CIs (one-sided). + + Returns + ------- + spikeRateSig : Covariate + Mean spike-rate difference per trial with attached CI. + ProbMat : ndarray, shape (K, K) + ``ProbMat[k, m]`` = P(diff_m > diff_k) from MC draws. + sigMat : ndarray, shape (K, K) + Binary significance matrix at level ``1 - alphaVal``. + """ + from .confidence_interval import ConfidenceInterval + from .core import Covariate + from .history import History + from .nspikeTrain import nspikeTrain + from .trial import SpikeTrainCollection + + xK = np.asarray(xK, dtype=float) + dN = np.asarray(dN, dtype=float) + if dN.ndim == 1: + dN = dN.reshape(1, -1) + numBasis, K = xK.shape + minTime = 0.0 + maxTime = (dN.shape[1] - 1) * delta + + time1 = np.asarray(time1, dtype=float).ravel() + time2 = np.asarray(time2, dtype=float).ravel() + + # Build unit-impulse basis matrix + basisWidth = (maxTime - minTime) / numBasis + sampleRate = 1.0 / delta + unitPulseBasis = SpikeTrainCollection.generateUnitImpulseBasis( + basisWidth, minTime, maxTime, sampleRate + ) + basisMat = unitPulseBasis.data + + # Build history matrices if windowTimes provided + Hk = {} + if windowTimes is not None and len(windowTimes) > 0: + histObj = History(windowTimes, minTime, maxTime) + for k in range(K): + spike_idx = np.flatnonzero(dN[k, :] == 1) + spike_times = (spike_idx) * delta + nst_k = nspikeTrain(spike_times) + nst_k.setMinTime(minTime) + nst_k.setMaxTime(maxTime) + hist_cov = histObj.computeHistory(nst_k) + Hk[k] = hist_cov.dataToMatrix() + else: + for k in range(K): + Hk[k] = 0.0 + gamma = 0.0 + + if gamma is None: + gamma = 0.0 + gamma = np.asarray(gamma, dtype=float) + + # Monte Carlo draws from smoothing distribution + Wku = np.asarray(Wku, dtype=float) + xKDraw = np.zeros((numBasis, K, Mc), dtype=float) + for r in range(numBasis): + WkuTemp = Wku[r, r, :, :].squeeze() if Wku.ndim == 4 else Wku[r, r] + WkuTemp = np.atleast_2d(WkuTemp) + if WkuTemp.shape[0] != K: + WkuTemp = np.diag(np.full(K, float(WkuTemp.flat[0]))) + try: + chol_m = np.linalg.cholesky(WkuTemp) + except np.linalg.LinAlgError: + eigvals = np.linalg.eigvalsh(WkuTemp) + WkuTemp += np.eye(K) * (abs(min(eigvals.min(), 0.0)) + 1e-10) + chol_m = np.linalg.cholesky(WkuTemp) + for c in range(Mc): + z = np.random.randn(K) + xKDraw[r, :, c] = xK[r, :] + chol_m.T @ z + + # Compute lambda and spike-rate difference for each MC draw + time_vec = np.arange(minTime, maxTime + delta, delta) + T = basisMat.shape[0] + fit_type = str(fitType).lower() + spikeRate = np.zeros((Mc, K), dtype=float) + + for c in range(Mc): + for k in range(K): + stimK = basisMat @ xKDraw[:, k, c] + if fit_type == "poisson": + histEffect = np.exp(gamma @ Hk[k].T).ravel() if not np.isscalar(Hk[k]) else np.ones(T) + stimEffect = np.exp(np.clip(stimK, -20.0, 20.0)) + lambdaDelta_kc = stimEffect * histEffect[:T] + elif fit_type == "binomial": + if np.isscalar(Hk[k]): + eta = stimK + else: + eta = stimK + (gamma @ Hk[k].T).ravel()[:T] + eta = np.clip(eta, -20.0, 20.0) + lambdaDelta_kc = np.exp(eta) / (1.0 + np.exp(eta)) + else: + lambdaDelta_kc = np.exp(np.clip(stimK, -20.0, 20.0)) + + # Integrate via cumulative sum + rate_per_sec = lambdaDelta_kc / delta + time_k = time_vec[:len(rate_per_sec)] + cum_integral = np.zeros(len(rate_per_sec)) + cum_integral[1:] = np.cumsum(rate_per_sec[:-1] * delta + 0.5 * np.diff(rate_per_sec) * delta) + + # Rate for time window 1 + t0_1, tf_1 = float(min(time1)), float(max(time1)) + val_t0_1 = np.interp(t0_1, time_k, cum_integral) + val_tf_1 = np.interp(tf_1, time_k, cum_integral) + rate1 = (1.0 / (tf_1 - t0_1)) * (val_tf_1 - val_t0_1) + + # Rate for time window 2 + t0_2, tf_2 = float(min(time2)), float(max(time2)) + val_t0_2 = np.interp(t0_2, time_k, cum_integral) + val_tf_2 = np.interp(tf_2, time_k, cum_integral) + rate2 = (1.0 / (tf_2 - t0_2)) * (val_tf_2 - val_t0_2) + + spikeRate[c, k] = rate1 - rate2 + + # Compute CIs from ECDF (one-sided) + CIs = np.zeros((K, 2), dtype=float) + for k in range(K): + sorted_rates = np.sort(spikeRate[:, k]) + ecdf = np.arange(1, Mc + 1, dtype=float) / float(Mc) + lower_idx = np.flatnonzero(ecdf < alphaVal) + upper_idx = np.flatnonzero(ecdf > (1.0 - alphaVal)) + CIs[k, 0] = sorted_rates[lower_idx[-1]] if lower_idx.size else sorted_rates[0] + CIs[k, 1] = sorted_rates[upper_idx[0]] if upper_idx.size else sorted_rates[-1] + + trial_axis = np.arange(1, K + 1, dtype=float) + mean_rate = np.mean(spikeRate, axis=0) + label = ( + r"(t_{1f}-t_{1o})^{-1} \Lambda(t_{1f}-t_{1o})" + r" - (t_{2f}-t_{2o})^{-1} \Lambda(t_{2f}-t_{2o})" + ) + spikeRateSig = Covariate(trial_axis, mean_rate, label, "Trial", "k", "Hz") + ciSpikeRate = ConfidenceInterval( + trial_axis, CIs, "CI_{spikeRate}", "Trial", "k", "Hz" + ) + spikeRateSig.setConfInterval(ciSpikeRate) + + # Pairwise probability matrix + ProbMat = np.zeros((K, K), dtype=float) + for k in range(K): + for m in range(k + 1, K): + ProbMat[k, m] = np.sum(spikeRate[:, m] > spikeRate[:, k]) / float(Mc) + + sigMat = (ProbMat > (1.0 - alphaVal)).astype(float) + + return spikeRateSig, ProbMat, sigMat + @staticmethod def PPDecode_predict(x_u, W_u, A, Q, Wconv=None): x_vec = np.asarray(x_u, dtype=float).reshape(-1) @@ -3488,1027 +3863,7 @@ def KF_EM( x0hat_final, Px0hat_final, IC, SE, Pvals, nIter, ) - # ------------------------------------------------------------------ - # State-Space GLM (SSGLM) via EM Forward-Backward - # Ported from Matlab DecodingAlgorithms.m (PPSS_EMFB and helpers) - # ------------------------------------------------------------------ - - @staticmethod - def _ssglm_build_basis(numBasis, minTime, maxTime, delta): - """Build unit-impulse basis matrix for SSGLM.""" - from .trial import SpikeTrainCollection - - sampleRate = 1.0 / delta - basisWidth = (maxTime - minTime) / numBasis - basis_cov = SpikeTrainCollection.generateUnitImpulseBasis( - basisWidth, minTime, maxTime, sampleRate - ) - return np.asarray(basis_cov.data, dtype=float) - - @staticmethod - def _ssglm_build_history(dN, windowTimes, delta): - """Build history design matrices for each trial from spike observations.""" - from .history import History - - K, N = dN.shape - minTime = 0.0 - maxTime = (N - 1) * delta - - if windowTimes is not None and len(windowTimes) > 0: - histObj = History(windowTimes, minTime, maxTime) - HkAll = [] - for k in range(K): - spike_indices = np.where(dN[k, :] > 0.5)[0] - spike_times = spike_indices.astype(float) * delta - nst = nspikeTrain(spike_times, makePlots=-1) - nst.setMinTime(minTime) - nst.setMaxTime(maxTime) - hist_cov = histObj._compute_single_history(nst) - HkAll.append(np.asarray(hist_cov.data, dtype=float)) - return HkAll - else: - return [np.zeros((N, 0), dtype=float) for _ in range(K)] - @staticmethod - def PPSS_EStep(A, Q, x0, dN, HkAll, fitType, delta, gamma, numBasis): - """E-step: Forward Kalman filter + backward RTS smoother + cross-covariance. - - Parameters - ---------- - A : (R, R) state transition matrix - Q : (R,) or (R, R) state noise covariance (diagonal vector or matrix) - x0 : (R,) initial state - dN : (K, N) binary spike observations (K trials, N time bins) - HkAll : list of K arrays, each (N, J) history design matrices - fitType : 'poisson' or 'binomial' - delta : time bin width - gamma : (J,) history coefficients - numBasis : number of basis functions R - - Returns - ------- - x_K, W_K, Wku, logll, sumXkTerms, sumPPll - """ - K, N = dN.shape - minTime = 0.0 - maxTime = (N - 1) * delta - - basisMat = DecodingAlgorithms._ssglm_build_basis(numBasis, minTime, maxTime, delta) - # Ensure basisMat has N rows matching dN columns - if basisMat.shape[0] != N: - basisMat = basisMat[:N, :] if basisMat.shape[0] > N else np.vstack( - [basisMat, np.zeros((N - basisMat.shape[0], basisMat.shape[1]))] - ) - - Q_diag = np.asarray(Q, dtype=float).reshape(-1) - if Q_diag.size == numBasis * numBasis: - Q_mat = Q_diag.reshape(numBasis, numBasis) - Q_diag = np.diag(Q_mat) - Q_mat = np.diag(Q_diag) - - A_mat = np.asarray(A, dtype=float) - if A_mat.ndim < 2: - A_mat = np.eye(numBasis, dtype=float) * A_mat - x0_vec = np.asarray(x0, dtype=float).reshape(-1) - gamma_vec = np.asarray(gamma, dtype=float).reshape(-1) - R = numBasis - fitType = str(fitType).lower() - - # Forward Kalman filter - x_p = np.zeros((R, K), dtype=float) - x_u = np.zeros((R, K), dtype=float) - W_p = np.zeros((R, R, K), dtype=float) - W_u = np.zeros((R, R, K), dtype=float) - - for k in range(K): - if k == 0: - x_p[:, k] = A_mat @ x0_vec - W_p[:, :, k] = Q_mat.copy() - else: - x_p[:, k] = A_mat @ x_u[:, k - 1] - W_p[:, :, k] = A_mat @ W_u[:, :, k - 1] @ A_mat.T + Q_mat - - Hk = HkAll[k] - stimK = basisMat @ x_p[:, k] - - if fitType == "poisson": - histEffect = np.exp(np.clip(Hk @ gamma_vec, -30, 30)) if gamma_vec.size > 0 and Hk.shape[1] > 0 else np.ones(N) - stimEffect = np.exp(np.clip(stimK, -30, 30)) - lambdaDelta = stimEffect * histEffect - - GradLogLD = basisMat # (N, R) - GradLD = basisMat * lambdaDelta[:, None] # (N, R) - - sumValVec = GradLogLD.T @ dN[k, :] - np.diag(GradLD.T @ basisMat) - sumValMat = GradLD.T @ basisMat - - elif fitType == "binomial": - Hk = HkAll[k] - stimK = basisMat @ x_p[:, k] - linpred = stimK + (Hk @ gamma_vec if gamma_vec.size > 0 and Hk.shape[1] > 0 else 0.0) - linpred = np.clip(linpred, -30, 30) - lambdaDelta = 1.0 / (1.0 + np.exp(-linpred)) - - GradLogLD = basisMat * (1.0 - lambdaDelta)[:, None] - JacobianLogLD = basisMat * (lambdaDelta * (-1.0 + lambdaDelta))[:, None] - GradLD = basisMat * (lambdaDelta * (1.0 - lambdaDelta))[:, None] - JacobianLD = basisMat * (lambdaDelta * (1.0 - lambdaDelta) * (1.0 - 2.0 * lambdaDelta ** 2))[:, None] - - sumValVec = GradLogLD.T @ dN[k, :] - np.diag(GradLD.T @ basisMat) - sumValMat = -np.diag(JacobianLogLD.T @ dN[k, :]) + JacobianLD.T @ basisMat - else: - raise ValueError(f"Unsupported fitType: {fitType}") - - # Kalman update - W_p_inv = np.linalg.inv(W_p[:, :, k] + 1e-12 * np.eye(R)) - invW_u = W_p_inv + sumValMat - W_u[:, :, k] = np.linalg.inv(invW_u + 1e-12 * np.eye(R)) - - # Ensure positive definiteness - eigvals, eigvecs = np.linalg.eigh(W_u[:, :, k]) - eigvals = np.maximum(eigvals, np.finfo(float).eps) - W_u[:, :, k] = eigvecs @ np.diag(eigvals) @ eigvecs.T - - x_u[:, k] = x_p[:, k] + W_u[:, :, k] @ sumValVec - - # Backward RTS smoother - x_K = np.zeros((R, K), dtype=float) - W_K = np.zeros((R, R, K), dtype=float) - Lk = np.zeros((R, R, K), dtype=float) - - x_K[:, K - 1] = x_u[:, K - 1] - W_K[:, :, K - 1] = W_u[:, :, K - 1] - - for k in range(K - 2, -1, -1): - Lk[:, :, k] = W_u[:, :, k] @ A_mat.T @ np.linalg.inv(W_p[:, :, k + 1] + 1e-12 * np.eye(R)) - x_K[:, k] = x_u[:, k] + Lk[:, :, k] @ (x_K[:, k + 1] - x_p[:, k + 1]) - W_K[:, :, k] = W_u[:, :, k] + Lk[:, :, k] @ (W_K[:, :, k + 1] - W_p[:, :, k + 1]) @ Lk[:, :, k].T - W_K[:, :, k] = 0.5 * (W_K[:, :, k] + W_K[:, :, k].T) - - # Cross-trial covariance Wku (R, R, K, K) - Wku = np.zeros((R, R, K, K), dtype=float) - for k in range(K): - Wku[:, :, k, k] = W_K[:, :, k] - - Dk = np.zeros((R, R, K), dtype=float) - for u in range(K - 1, 0, -1): - for k in range(u - 1, -1, -1): - Dk[:, :, k] = W_u[:, :, k] @ A_mat.T @ np.linalg.inv(W_p[:, :, k + 1] + 1e-12 * np.eye(R)) - Wku[:, :, k, u] = Dk[:, :, k] @ Wku[:, :, k + 1, u] - Wku[:, :, u, k] = Wku[:, :, k, u] - - # Sufficient statistics for M-step - Sxkxkp1 = np.zeros((R, R), dtype=float) - Sxkp1xkp1 = np.zeros((R, R), dtype=float) - Sxkxk = np.zeros((R, R), dtype=float) - for k in range(K - 1): - Sxkxkp1 += Wku[:, :, k, k + 1] + np.outer(x_K[:, k], x_K[:, k + 1]) - Sxkp1xkp1 += W_K[:, :, k + 1] + np.outer(x_K[:, k + 1], x_K[:, k + 1]) - Sxkxk += W_K[:, :, k] + np.outer(x_K[:, k], x_K[:, k]) - - sumXkTerms = ( - Sxkp1xkp1 - A_mat @ Sxkxkp1 - Sxkxkp1.T @ A_mat.T + A_mat @ Sxkxk @ A_mat.T - + W_K[:, :, 0] + np.outer(x_K[:, 0], x_K[:, 0]) - - A_mat @ np.outer(x0_vec, x_K[:, 0]) - np.outer(x_K[:, 0], x0_vec) @ A_mat.T - + A_mat @ np.outer(x0_vec, x0_vec) @ A_mat.T - ) - - # Point process log-likelihood - sumPPll = 0.0 - for k in range(K): - Hk = HkAll[k] - Wk = basisMat @ np.diag(W_K[:, :, k]) - stimK = basisMat @ x_K[:, k] - - if fitType == "poisson": - hist_term = Hk @ gamma_vec if gamma_vec.size > 0 and Hk.shape[1] > 0 else np.zeros(N) - histEffect = np.exp(np.clip(hist_term, -30, 30)) - stimK_clipped = np.clip(stimK, -30, 30) - stimEffect = np.exp(stimK_clipped) + np.exp(stimK_clipped) / 2.0 * Wk - ExplambdaDelta = stimEffect * histEffect - ExplogLD = stimK + hist_term - sumPPll += float(np.sum(dN[k, :] * ExplogLD - ExplambdaDelta)) - - elif fitType == "binomial": - hist_term = Hk @ gamma_vec if gamma_vec.size > 0 and Hk.shape[1] > 0 else np.zeros(N) - linpred = np.clip(stimK + hist_term, -30, 30) - lambdaDelta = 1.0 / (1.0 + np.exp(-linpred)) - ExplambdaDelta = lambdaDelta + Wk * (lambdaDelta * (1.0 - lambdaDelta) * (1.0 - 2.0 * lambdaDelta)) / 2.0 - ExplogLD = linpred - np.log(1.0 + np.exp(linpred)) - Wk * lambdaDelta * (1.0 - lambdaDelta) * 0.5 - sumPPll += float(np.sum(dN[k, :] * ExplogLD - ExplambdaDelta)) - - det_Q = float(np.prod(np.maximum(Q_diag, np.finfo(float).eps))) - logll = ( - -R * K * np.log(2.0 * np.pi) - - K / 2.0 * np.log(det_Q) - + sumPPll - - 0.5 * float(np.trace(np.linalg.pinv(Q_mat) @ sumXkTerms)) - ) - - return x_K, W_K, Wku, logll, sumXkTerms, sumPPll - - @staticmethod - def PPSS_MStep(dN, HkAll, fitType, x_K, W_K, gamma, delta, sumXkTerms, windowTimes): - """M-step: Update Q via closed form, gamma via Newton-Raphson. - - Parameters - ---------- - dN : (K, N) - HkAll : list of K arrays (N, J) - fitType : 'poisson' or 'binomial' - x_K : (R, K) smoothed states - W_K : (R, R, K) smoothed covariances - gamma : (J,) current history coefficients - delta : time bin width - sumXkTerms : (R, R) sufficient statistics from E-step - windowTimes : array of history window boundaries - - Returns - ------- - Qhat : (R,) updated state noise variance (diagonal) - gamma_new : (J,) updated history coefficients - """ - K, N = dN.shape - R = x_K.shape[0] - fitType = str(fitType).lower() - - # Q update (closed form) - sumQ = np.diag(np.diag(sumXkTerms)) - Qhat = sumQ / K - eigvals, eigvecs = np.linalg.eigh(Qhat) - eigvals = np.maximum(eigvals, 1e-8) - Qhat = eigvecs @ np.diag(eigvals) @ eigvecs.T - Qhat = np.diag(Qhat) # Return as vector - - # Build basis matrix for gamma update - minTime = 0.0 - maxTime = (N - 1) * delta - basisMat = DecodingAlgorithms._ssglm_build_basis(R, minTime, maxTime, delta) - if basisMat.shape[0] != N: - basisMat = basisMat[:N, :] if basisMat.shape[0] > N else np.vstack( - [basisMat, np.zeros((N - basisMat.shape[0], basisMat.shape[1]))] - ) - - gamma_vec = np.asarray(gamma, dtype=float).reshape(-1) - gamma_new = gamma_vec.copy() - J = gamma_new.size - - # Newton-Raphson for gamma (history coefficients) - if windowTimes is not None and len(windowTimes) > 0 and J > 0 and np.any(gamma_new != 0): - converged = False - max_iter = 300 - for iteration in range(max_iter): - gradQ = np.zeros(J, dtype=float) - jacQ = np.zeros((J, J), dtype=float) - - for k in range(K): - Hk = HkAll[k] - if Hk.shape[1] == 0: - continue - Wk = basisMat @ np.diag(W_K[:, :, k]) - stimK = basisMat @ x_K[:, k] - - if fitType == "poisson": - hist_term = np.clip(gamma_new @ Hk.T, -30, 30) - histEffect = np.exp(hist_term) - stimK_clipped = np.clip(stimK, -30, 30) - stimEffect = np.exp(stimK_clipped) + np.exp(stimK_clipped) / 2.0 * Wk - lambdaDelta = stimEffect * histEffect - - gradQ += Hk.T @ dN[k, :] - Hk.T @ lambdaDelta - jacQ -= (Hk * lambdaDelta[:, None]).T @ Hk - - elif fitType == "binomial": - linpred = np.clip(stimK + Hk @ gamma_new, -30, 30) - lambdaDelta = 1.0 / (1.0 + np.exp(-linpred)) - histEffect = np.exp(np.clip(gamma_new @ Hk.T, -30, 30)) - stimEffect = np.exp(np.clip(stimK, -30, 30)) - C = stimEffect * histEffect - M = np.where(C > 1e-30, 1.0 / C, 1e30) - ExpLambdaDelta = lambdaDelta + Wk * (lambdaDelta * (1.0 - lambdaDelta) * (1.0 - 2.0 * lambdaDelta)) / 2.0 - ExpLDSquaredTimesInvExp = lambdaDelta ** 2 * M - ExpLDCubedTimesInvExpSquared = ( - lambdaDelta ** 3 * M ** 2 - + Wk / 2.0 * (3.0 * M ** 4 * lambdaDelta ** 3 - + 12.0 * lambdaDelta ** 3 * M ** 3 - - 12.0 * M ** 4 * lambdaDelta ** 4) - ) - - gradQ += (Hk * (1.0 - ExpLambdaDelta)[:, None]).T @ dN[k, :] \ - - (Hk * (ExpLDSquaredTimesInvExp / np.maximum(lambdaDelta, 1e-30))[:, None]).T @ lambdaDelta - jacQ -= (Hk * (ExpLDSquaredTimesInvExp * dN[k, :])[:, None]).T @ Hk \ - + (Hk * ExpLDSquaredTimesInvExp[:, None]).T @ Hk \ - + (Hk * (2.0 * ExpLDCubedTimesInvExpSquared)[:, None]).T @ Hk - - # Newton-Raphson update - try: - gamma_temp = gamma_new - np.linalg.pinv(jacQ) @ gradQ - except np.linalg.LinAlgError: - gamma_temp = gamma_new - - if np.any(np.isnan(gamma_temp)): - gamma_temp = gamma_new - - mabsDiff = float(np.max(np.abs(gamma_temp - gamma_new))) - gamma_new = gamma_temp - if mabsDiff < 1e-2: - converged = True - break - - # Clamp gamma - gamma_new = np.clip(gamma_new, -1e2, 1e2) - - return Qhat, gamma_new - - @staticmethod - def PPSS_EM(A, Q0, x0, dN, fitType, delta, gamma0, windowTimes, numBasis, HkAll): - """Inner EM loop for state-space GLM. - - Parameters - ---------- - A : (R, R) state transition - Q0 : (R,) initial state noise variance - x0 : (R,) initial state - dN : (K, N) observations - fitType : 'poisson' or 'binomial' - delta : time bin width - gamma0 : (J,) initial history coefficients - windowTimes : history window boundaries - numBasis : number of basis functions - HkAll : precomputed history matrices - - Returns - ------- - xKFinal, WKFinal, WkuFinal, Qhat, gammahat, logll, QhatAll, gammahatAll, nIter, negLL - """ - if numBasis is None: - numBasis = 20 - if delta is None or delta == 0: - delta = 0.001 - fitType = str(fitType or "poisson").lower() - - Q0_vec = np.asarray(Q0, dtype=float).reshape(-1) - if Q0_vec.size == numBasis * numBasis: - Q0_vec = np.diag(Q0_vec.reshape(numBasis, numBasis)) - - gamma0_vec = np.asarray(gamma0, dtype=float).reshape(-1) if gamma0 is not None else np.array([], dtype=float) - - tolAbs = 1e-3 - tolRel = 1e-3 - llTol = 1e-3 - maxIter = 100 - numToKeep = 10 - - # Circular buffer storage - Qhat = np.zeros((Q0_vec.size, numToKeep), dtype=float) - Qhat[:, 0] = Q0_vec - gammahat = np.zeros((numToKeep, gamma0_vec.size), dtype=float) - gammahat[0, :] = gamma0_vec - - xK_buf = [None] * numToKeep - WK_buf = [None] * numToKeep - Wku_buf = [None] * numToKeep - - x0hat = np.asarray(x0, dtype=float).reshape(-1) - logll_list = [] - dLikelihood = [np.inf] - negLL = False - stoppingCriteria = False - cnt = 0 - - while not stoppingCriteria and cnt < maxIter: - si = cnt % numToKeep - si_p1 = (cnt + 1) % numToKeep - si_m1 = (cnt - 1) % numToKeep - - xK_cur, WK_cur, Wku_cur, ll, SumXkTerms, _ = DecodingAlgorithms.PPSS_EStep( - A, Qhat[:, si], x0hat, dN, HkAll, fitType, delta, gammahat[si, :], numBasis - ) - xK_buf[si] = xK_cur - WK_buf[si] = WK_cur - Wku_buf[si] = Wku_cur - logll_list.append(ll) - - Qnew, gnew = DecodingAlgorithms.PPSS_MStep( - dN, HkAll, fitType, xK_cur, WK_cur, gammahat[si, :], delta, SumXkTerms, windowTimes - ) - Qhat[:, si_p1] = Qnew - gammahat[si_p1, :] = gnew - - if cnt == 0: - dLikelihood.append(np.inf) - else: - dLikelihood.append(logll_list[cnt] - logll_list[cnt - 1]) - - # Check convergence - if cnt > 0: - dQvals = np.abs(np.sqrt(np.maximum(Qhat[:, si], 0)) - np.sqrt(np.maximum(Qhat[:, si_m1], 0))) - dGamma = np.abs(gammahat[si, :] - gammahat[si_m1, :]) - dMax = max(np.max(dQvals), np.max(dGamma)) if dGamma.size > 0 else float(np.max(dQvals)) - - Q_prev = np.sqrt(np.maximum(Qhat[:, si_m1], 1e-30)) - dQRel = float(np.max(np.abs(dQvals / Q_prev))) - if dGamma.size > 0: - g_prev = np.maximum(np.abs(gammahat[si_m1, :]), 1e-30) - dGammaRel = float(np.max(np.abs(dGamma / g_prev))) - dMaxRel = max(dQRel, dGammaRel) - else: - dMaxRel = dQRel - - if dMax < tolAbs and dMaxRel < tolRel: - stoppingCriteria = True - negLL = False - - if abs(dLikelihood[-1]) < llTol or dLikelihood[-1] < 0: - stoppingCriteria = True - negLL = True - - cnt += 1 - - # Select best iteration by log-likelihood - logll_arr = np.array(logll_list) - if logll_arr.size > 0: - maxLLIndex = int(np.argmax(logll_arr)) - else: - maxLLIndex = 0 - - maxLLIndMod = maxLLIndex % numToKeep - nIter = cnt - - xKFinal = xK_buf[maxLLIndMod] if xK_buf[maxLLIndMod] is not None else np.zeros((numBasis, dN.shape[0])) - WKFinal = WK_buf[maxLLIndMod] if WK_buf[maxLLIndMod] is not None else np.zeros((numBasis, numBasis, dN.shape[0])) - WkuFinal = Wku_buf[maxLLIndMod] if Wku_buf[maxLLIndMod] is not None else np.zeros((numBasis, numBasis, dN.shape[0], dN.shape[0])) - - QhatFinal = Qhat[:, maxLLIndMod] - gammahatFinal = gammahat[maxLLIndMod, :] - logllFinal = float(logll_arr[maxLLIndex]) if logll_arr.size > 0 else -np.inf - - QhatAll = Qhat[:, : min(cnt + 1, numToKeep)] - gammahatAll = gammahat[: min(cnt + 1, numToKeep), :] - - return xKFinal, WKFinal, WkuFinal, QhatFinal, gammahatFinal, logllFinal, QhatAll, gammahatAll, nIter, negLL - - @staticmethod - def PPSS_EMFB(A, Q0, x0, dN, fitType, delta, gamma0, windowTimes, numBasis, neuronName=None): - """EM Forward-Backward algorithm for state-space GLM. - - Wraps PPSS_EM in a forward-backward-forward cycle for improved convergence. - - Parameters - ---------- - A : (R, R) state transition matrix (typically identity for random walk) - Q0 : (R,) initial state noise variance - x0 : (R,) initial state coefficients - dN : (K, N) binary spike observations (K trials, N time bins) - fitType : 'poisson' or 'binomial' - delta : time bin width in seconds - gamma0 : (J,) initial history coefficients - windowTimes : array of history window boundaries - numBasis : number of basis functions R - neuronName : identifier for the neuron (for labeling) - - Returns - ------- - xKFinal : (R, K) estimated state trajectories - WKFinal : (R, R, K) estimated state covariances - WkuFinal : (R, R, K, K) cross-trial covariances - Qhat : (R,) estimated state noise variance - gammahat : (J,) estimated history coefficients - fitResults : FitResult object with goodness-of-fit diagnostics - stimulus : (R, K) estimated stimulus effect - stimCIs : (R, K, 2) stimulus confidence intervals - logll : float, log-likelihood at convergence - QhatAll : parameter history - gammahatAll : parameter history - nIter : total EM iterations - """ - K, N = dN.shape - fitType = str(fitType or "poisson").lower() - - Q0_vec = np.asarray(Q0, dtype=float).reshape(-1) - if Q0_vec.size == numBasis * numBasis: - Q0_vec = np.diag(Q0_vec.reshape(numBasis, numBasis)) - - gamma0_vec = np.asarray(gamma0, dtype=float).reshape(-1) if gamma0 is not None else np.array([], dtype=float) - - Qhat_cur = Q0_vec.copy() - gammahat_cur = gamma0_vec.copy() - xK0 = np.asarray(x0, dtype=float).reshape(-1) - - # Build history matrices - HkAll = DecodingAlgorithms._ssglm_build_history(dN, windowTimes, delta) - HkAllR = list(reversed(HkAll)) - - tolAbs = 1e-3 - tolRel = 1e-3 - llTol = 1e-3 - maxIter = 2000 - - Qhat_history = [Qhat_cur.copy()] - gammahat_history = [gammahat_cur.copy()] - logll_list = [] - stoppingCriteria = False - cnt = 0 - - xK = None - WK = None - Wku = None - - while not stoppingCriteria and cnt < maxIter: - # Forward EM - xK, WK, Wku, Qnew, gnew, ll, _, _, _, negLL = DecodingAlgorithms.PPSS_EM( - A, Qhat_cur, xK0, dN, fitType, delta, gammahat_cur, windowTimes, numBasis, HkAll - ) - - if not negLL: - # Backward EM - _, _, _, QnewR, gnewR, _, _, _, _, negLLR = DecodingAlgorithms.PPSS_EM( - A, Qnew, xK[:, -1], np.flipud(dN), fitType, delta, gnew, windowTimes, numBasis, HkAllR - ) - - if not negLLR: - # Forward EM again with backward-updated parameters - # Matlab: PPSS_EM(A, QhatR(:,cnt+1), xKR(:,end), dN, ...) - xK2, WK2, Wku2, Qnew2, gnew2, ll2, _, _, _, negLL2 = DecodingAlgorithms.PPSS_EM( - A, QnewR, xK[:, -1], dN, fitType, delta, gnewR, - windowTimes, numBasis, HkAll - ) - - if not negLL2: - xK = xK2 - WK = WK2 - Wku = Wku2 - Qnew = Qnew2 - gnew = gnew2 - ll = ll2 - - Qhat_cur = Qnew - gammahat_cur = gnew - Qhat_history.append(Qnew.copy()) - gammahat_history.append(gnew.copy()) - logll_list.append(ll) - - xK0 = xK[:, 0] - - # Check convergence - if cnt > 0: - dLikelihood = logll_list[cnt] - logll_list[cnt - 1] - else: - dLikelihood = np.inf - - if len(Qhat_history) >= 2: - Q_prev = Qhat_history[-2] - Q_cur = Qhat_history[-1] - dQvals = np.abs(np.sqrt(np.maximum(Q_cur, 0)) - np.sqrt(np.maximum(Q_prev, 0))) - g_prev = gammahat_history[-2] - g_cur = gammahat_history[-1] - dGamma = np.abs(g_cur - g_prev) if g_cur.size > 0 else np.array([0.0]) - - dMax = max(float(np.max(dQvals)), float(np.max(dGamma))) - - Q_denom = np.sqrt(np.maximum(Q_prev, 1e-30)) - dQRel = float(np.max(np.abs(dQvals / Q_denom))) - if g_prev.size > 0 and np.any(g_prev != 0): - g_denom = np.maximum(np.abs(g_prev), 1e-30) - dGammaRel = float(np.max(np.abs(dGamma / g_denom))) - dMaxRel = max(dQRel, dGammaRel) - else: - dMaxRel = dQRel - - if dMax < tolAbs and dMaxRel < tolRel: - stoppingCriteria = True - - if abs(dLikelihood) < llTol or dLikelihood < 0: - stoppingCriteria = True - - cnt += 1 - - # Select best iteration - logll_arr = np.array(logll_list) - if logll_arr.size > 0: - maxLLIndex = int(np.argmax(logll_arr)) - else: - maxLLIndex = 0 - - xKFinal = xK - WKFinal = WK - WkuFinal = Wku - Qhat = Qhat_history[min(maxLLIndex + 1, len(Qhat_history) - 1)] - gammahat = gammahat_history[min(maxLLIndex + 1, len(gammahat_history) - 1)] - logll = float(logll_arr[maxLLIndex]) if logll_arr.size > 0 else -np.inf - - QhatAll = np.column_stack(Qhat_history) if Qhat_history else Q0_vec.reshape(-1, 1) - gammahatAll = np.row_stack(gammahat_history) if gammahat_history and gammahat_history[0].size > 0 else np.array([[]]) - - R = numBasis - x0Final = xK[:, 0] if xK is not None else np.zeros(R) - SumXkTermsFinal = np.diag(Qhat) * K - McInfo = 100 - McCI = 3000 - - # Observed log-likelihood - logllobs = logll + R * K * np.log(2 * np.pi) + K / 2.0 * np.log( - max(float(np.prod(np.maximum(Qhat, np.finfo(float).eps))), np.finfo(float).eps) - ) + 0.5 * float(np.trace(np.linalg.pinv(np.diag(Qhat)) @ SumXkTermsFinal)) - - nIter = cnt - - # Information matrix and result packaging - InfoMat = DecodingAlgorithms.estimateInfoMat( - fitType, dN, HkAll, A, x0Final, xKFinal, WKFinal, WkuFinal, - Qhat, gammahat, windowTimes, SumXkTermsFinal, delta, McInfo - ) - fitResults = DecodingAlgorithms.prepareEMResults( - fitType, neuronName, dN, HkAll, xKFinal, WKFinal, - Qhat, gammahat, windowTimes, delta, InfoMat, logllobs - ) - - stimCIs, stimulus = DecodingAlgorithms._ComputeStimulusCIs_MC( - fitType, xKFinal, WkuFinal, delta, McCI - ) - - return (xKFinal, WKFinal, WkuFinal, Qhat, gammahat, fitResults, - stimulus, stimCIs, logll, QhatAll, gammahatAll, nIter) - - @staticmethod - def _ComputeStimulusCIs_MC(fitType, xK, Wku, delta, Mc=3000, alphaVal=0.05): - """Monte Carlo confidence intervals for SSGLM stimulus estimate. - - Uses Cholesky decomposition of the cross-trial covariance to generate - draws of the state trajectory, then computes empirical CIs. - """ - fitType = str(fitType).lower() - numBasis, K = xK.shape - - CIs = np.zeros((numBasis, K, 2), dtype=float) - - for r in range(numBasis): - WkuTemp = Wku[r, r, :, :] # (K, K) cross-trial covariance for basis r - try: - chol_m = np.linalg.cholesky(WkuTemp + 1e-10 * np.eye(K)) - except np.linalg.LinAlgError: - eigvals, eigvecs = np.linalg.eigh(WkuTemp) - eigvals = np.maximum(eigvals, 1e-10) - chol_m = eigvecs @ np.diag(np.sqrt(eigvals)) - - stimulusDraw = np.zeros((Mc, K), dtype=float) - for c in range(Mc): - z = np.random.randn(K) - xKDraw = xK[r, :] + chol_m.T @ z - if fitType == "poisson": - stimulusDraw[c, :] = np.exp(np.clip(xKDraw, -30, 30)) / delta - elif fitType == "binomial": - xKDraw_clip = np.clip(xKDraw, -30, 30) - stimulusDraw[c, :] = (np.exp(xKDraw_clip) / (1.0 + np.exp(xKDraw_clip))) / delta - else: - stimulusDraw[c, :] = xKDraw / delta - - for k in range(K): - CIs[r, k, 0] = float(np.percentile(stimulusDraw[:, k], 100.0 * alphaVal / 2.0)) - CIs[r, k, 1] = float(np.percentile(stimulusDraw[:, k], 100.0 * (1.0 - alphaVal / 2.0))) - - if fitType == "poisson": - stimulus = np.exp(np.clip(xK, -30, 30)) / delta - elif fitType == "binomial": - xK_clip = np.clip(xK, -30, 30) - stimulus = (np.exp(xK_clip) / (1.0 + np.exp(xK_clip))) / delta - else: - stimulus = xK / delta - - return CIs, stimulus - - @staticmethod - def estimateInfoMat(fitType, dN, HkAll, A, x0, xK, WK, Wku, Q, gamma, - windowTimes, SumXkTerms, delta, Mc=500): - """Observed information matrix via Louis' identity with Monte Carlo. - - Computes I_obs = I_complete - I_missing where I_missing is estimated - by MC sampling from the smoothing distribution. - """ - fitType = str(fitType).lower() - K, N = dN.shape - gamma_vec = np.asarray(gamma, dtype=float).reshape(-1) - J = gamma_vec.size if (windowTimes is not None and len(windowTimes) > 0) else 0 - - Q_vec = np.asarray(Q, dtype=float).reshape(-1) - R = Q_vec.size - Q_mat = np.diag(Q_vec) - numBasis = R - - # Build basis matrix - minTime = 0.0 - maxTime = (N - 1) * delta - basisMat = DecodingAlgorithms._ssglm_build_basis(numBasis, minTime, maxTime, delta) - if basisMat.shape[0] != N: - basisMat = basisMat[:N, :] if basisMat.shape[0] > N else np.vstack( - [basisMat, np.zeros((N - basisMat.shape[0], basisMat.shape[1]))] - ) - - # Complete data information matrix - Ic = np.zeros((R + J, R + J), dtype=float) - Q_mat_safe = np.diag(np.maximum(Q_vec, np.finfo(float).eps)) - Q2 = Q_mat_safe @ Q_mat_safe - Q3 = Q2 @ Q_mat_safe - - Ic[:R, :R] = K / 2.0 * np.linalg.inv(Q2) + np.linalg.inv(Q3) @ SumXkTerms - - # History portion of information matrix - jacQ = np.zeros((J, J), dtype=float) if J > 0 else np.zeros((0, 0)) - if fitType == "poisson" and J > 0: - for k in range(K): - Hk = HkAll[k] - if Hk.shape[1] == 0: - continue - Wk = basisMat @ np.diag(WK[:, :, k]) - stimK = basisMat @ xK[:, k] - stimK_clip = np.clip(stimK, -30, 30) - hist_term = np.clip(gamma_vec @ Hk.T, -30, 30) - histEffect = np.exp(hist_term) - stimEffect = np.exp(stimK_clip) + np.exp(stimK_clip) / 2.0 * Wk - lambdaDelta = stimEffect * histEffect - jacQ -= (Hk * lambdaDelta[:, None]).T @ Hk - elif fitType == "binomial" and J > 0: - for k in range(K): - Hk = HkAll[k] - if Hk.shape[1] == 0: - continue - Wk = basisMat @ np.diag(WK[:, :, k]) - stimK = basisMat @ xK[:, k] - linpred = np.clip(stimK + Hk @ gamma_vec, -30, 30) - histEffect = np.exp(np.clip(gamma_vec @ Hk.T, -30, 30)) - stimEffect = np.exp(np.clip(stimK, -30, 30)) - C = stimEffect * histEffect - M = np.where(C > 1e-30, 1.0 / C, 1e30) - lambdaDelta = 1.0 / (1.0 + np.exp(-linpred)) - ExpLDSquaredTimesInvExp = lambdaDelta ** 2 * M - ExpLDCubedTimesInvExpSquared = ( - lambdaDelta ** 3 * M ** 2 - + Wk / 2.0 * (3.0 * M ** 4 * lambdaDelta ** 3 - + 12.0 * lambdaDelta ** 3 * M ** 3 - - 12.0 * M ** 4 * lambdaDelta ** 4) - ) - jacQ -= (Hk * (ExpLDSquaredTimesInvExp * dN[k, :])[:, None]).T @ Hk \ - + (Hk * ExpLDSquaredTimesInvExp[:, None]).T @ Hk \ - + (Hk * (2.0 * ExpLDCubedTimesInvExpSquared)[:, None]).T @ Hk - - Ic[:R, :R] = K * np.linalg.inv(2.0 * Q2) + np.linalg.inv(Q3) @ SumXkTerms - if J > 0: - Ic[R:R + J, R:R + J] = -jacQ - - # MC estimation of missing information - xKDraw = np.zeros((numBasis, K, Mc), dtype=float) - for r in range(numBasis): - WkuTemp = Wku[r, r, :, :] - try: - chol_m = np.linalg.cholesky(WkuTemp + 1e-10 * np.eye(K)) - except np.linalg.LinAlgError: - eigvals, eigvecs = np.linalg.eigh(WkuTemp) - eigvals = np.maximum(eigvals, 1e-10) - chol_m = eigvecs @ np.diag(np.sqrt(eigvals)) - - for c in range(Mc): - z = np.random.randn(K) - xKDraw[r, :, c] = xK[r, :] + chol_m.T @ z - - ImMC = np.zeros((R + J, R + J), dtype=float) - A_mat = np.asarray(A, dtype=float) - if A_mat.ndim < 2: - A_mat = np.eye(R) * A_mat - x0_vec = np.asarray(x0, dtype=float).reshape(-1) - Q_inv = np.linalg.inv(Q_mat_safe) - - for c in range(Mc): - gradQGammahat = np.zeros(J, dtype=float) if J > 0 else np.array([], dtype=float) - gradQQhat = np.zeros(R, dtype=float) - - for k in range(K): - Hk = HkAll[k] - stimK = basisMat @ xKDraw[:, k, c] - - if fitType == "poisson": - hist_term = np.clip(gamma_vec @ Hk.T, -30, 30) if J > 0 and Hk.shape[1] > 0 else np.zeros(N) - histEffect = np.exp(hist_term) - stimK_clip = np.clip(stimK, -30, 30) - stimEffect = np.exp(stimK_clip) - lambdaDelta = stimEffect * histEffect - if J > 0 and Hk.shape[1] > 0: - gradQGammahat += Hk.T @ dN[k, :] - Hk.T @ lambdaDelta - elif fitType == "binomial": - Wk = basisMat @ np.diag(WK[:, :, k]) - linpred = np.clip(stimK + (Hk @ gamma_vec if J > 0 and Hk.shape[1] > 0 else 0.0), -30, 30) - histEffect = np.exp(np.clip(gamma_vec @ Hk.T, -30, 30)) if J > 0 and Hk.shape[1] > 0 else np.ones(N) - stimEffect = np.exp(np.clip(stimK, -30, 30)) - C = stimEffect * histEffect - M = np.where(C > 1e-30, 1.0 / C, 1e30) - lambdaDelta = 1.0 / (1.0 + np.exp(-linpred)) - ExpLambdaDelta = lambdaDelta + Wk * (lambdaDelta * (1.0 - lambdaDelta) * (1.0 - 2.0 * lambdaDelta)) / 2.0 - ExpLDSquaredTimesInvExp = lambdaDelta ** 2 * M - if J > 0 and Hk.shape[1] > 0: - gradQGammahat += (Hk * (1.0 - ExpLambdaDelta)[:, None]).T @ dN[k, :] \ - - (Hk * (ExpLDSquaredTimesInvExp / np.maximum(lambdaDelta, 1e-30))[:, None]).T @ lambdaDelta - - if k == 0: - diff = xKDraw[:, k, c] - A_mat @ x0_vec - else: - diff = xKDraw[:, k, c] - A_mat @ xKDraw[:, k - 1, c] - gradQQhat += diff * diff - - gradQQhat_scaled = 0.5 * Q_inv @ gradQQhat - np.diag(K / 2.0 * np.linalg.inv(Q2)) - ImMC[:R, :R] += np.outer(gradQQhat_scaled, gradQQhat_scaled) - if J > 0: - ImMC[R:R + J, R:R + J] += np.diag(gradQGammahat ** 2) - - Im = ImMC / Mc - InfoMatrix = Ic - Im - - return InfoMatrix - - @staticmethod - def prepareEMResults(fitType, neuronNumber, dN, HkAll, xK, WK, Q, gamma, - windowTimes, delta, informationMatrix, logll): - """Package SSGLM EM results into a FitResult object.""" - from .core import Covariate - from .fit import FitResult - from .history import History - from .trial import ( - ConfigCollection, - SpikeTrainCollection, - TrialConfig, - ) - from .analysis import Analysis - - fitType = str(fitType).lower() - numBasis, K = xK.shape - R = numBasis - N = dN.shape[1] - minTime = 0.0 - maxTime = (N - 1) * delta - sampleRate = 1.0 / delta - gamma_vec = np.asarray(gamma, dtype=float).reshape(-1) - - # Build basis matrix - basisMat = DecodingAlgorithms._ssglm_build_basis(numBasis, minTime, maxTime, delta) - if basisMat.shape[0] != N: - basisMat = basisMat[:N, :] if basisMat.shape[0] > N else np.vstack( - [basisMat, np.zeros((N - basisMat.shape[0], basisMat.shape[1]))] - ) - - # Standard errors from information matrix - try: - SE = np.sqrt(np.abs(np.diag(np.linalg.inv(informationMatrix)))) - except np.linalg.LinAlgError: - SE = np.zeros(informationMatrix.shape[0], dtype=float) - - # Build per-trial standard errors - xKbeta = xK.T.reshape(-1) # (K*R,) - seXK = np.zeros(K * R, dtype=float) - for k in range(K): - seXK[k * R:(k + 1) * R] = np.sqrt(np.maximum(np.diag(WK[:, :, k]), 0.0)) - - # Neuron name - if neuronNumber is None: - name = "N01" - elif isinstance(neuronNumber, (int, float)): - n = int(neuronNumber) - name = f"N{n:02d}" if 0 < n < 10 else f"N{n}" - else: - name = str(neuronNumber) - - # Create spike trains from dN - nst_list = [] - for k in range(K): - spike_indices = np.where(dN[k, :] > 0.5)[0] - spike_times = spike_indices.astype(float) * delta - nst_k = nspikeTrain(spike_times, name=name, makePlots=-1) - nst_k.setMinTime(minTime) - nst_k.setMaxTime(maxTime) - nst_list.append(nst_k) - - nCopy = SpikeTrainCollection(nst_list) - nCopy = nCopy.toSpikeTrain() - - # Compute lambda (conditional intensity) - lambdaData = [] - otherLabels = [] - cnt = 0 - for k in range(K): - Hk = HkAll[k] - stimK = basisMat @ xK[:, k] - - if fitType == "poisson": - hist_term = gamma_vec @ Hk.T if gamma_vec.size > 0 and Hk.shape[1] > 0 else np.zeros(N) - histEffect = np.exp(np.clip(hist_term, -30, 30)) - stimEffect = np.exp(np.clip(stimK, -30, 30)) - lambdaDelta = histEffect * stimEffect / delta - elif fitType == "binomial": - linpred = np.clip(stimK + (Hk @ gamma_vec if gamma_vec.size > 0 and Hk.shape[1] > 0 else 0.0), -30, 30) - hist_term = np.clip(gamma_vec @ Hk.T, -30, 30) if gamma_vec.size > 0 and Hk.shape[1] > 0 else np.zeros(N) - histEffect = np.exp(hist_term) - stimEffect = np.exp(np.clip(stimK, -30, 30)) - C = histEffect * stimEffect - lambdaDelta = C / (1.0 + C) / delta - else: - lambdaDelta = np.zeros(N) - - lambdaData.append(lambdaDelta) - - for r in range(R): - label = f"b{r + 1:02d}_{{{k + 1}}}" if r + 1 < 10 else f"b{r + 1}_{{{k + 1}}}" - otherLabels.append(label) - cnt += 1 - - lambdaData = np.concatenate(lambdaData) - lambdaTime = np.arange(len(lambdaData)) * delta + minTime - - nCopy.setMaxTime(float(np.max(lambdaTime))) - nCopy.setMinTime(float(np.min(lambdaTime))) - - # Covariance labels - covarianceLabels = [f"Q{r + 1:02d}" if r + 1 < 10 else f"Q{r + 1}" for r in range(R)] - - # History labels - histLabels = [] - if windowTimes is not None and len(windowTimes) > 0: - wt = np.asarray(windowTimes, dtype=float) - for i in range(len(wt) - 1): - histLabels.append(f"[{wt[i]:.3g},{wt[i + 1]:.3g}]") - - allLabels = otherLabels + covarianceLabels + histLabels - - # History objects - if windowTimes is not None and len(windowTimes) > 0: - histObj = [History(windowTimes, minTime, maxTime)] - else: - histObj = [None] - - # Trial configuration - numBasisStr = str(numBasis) - numHistStr = str(len(windowTimes) - 1) if windowTimes is not None and len(windowTimes) > 1 else "0" - if histObj[0] is not None: - cfg_name = f"SSGLM(N_{{b}}={numBasisStr})+Hist(N_{{h}}={numHistStr})" - else: - cfg_name = f"SSGLM(N_{{b}}={numBasisStr})" - - tc = TrialConfig([allLabels], sampleRate, histObj, []) - tc.setName(cfg_name) - configColl = ConfigCollection([tc]) - - # Lambda covariate - lambda_cov = Covariate( - lambdaTime, lambdaData, - r"\Lambda(t)", "time", "s", "Hz", - [r"\lambda_{1}"] - ) - - # Model selection criteria - AIC = 2.0 * len(allLabels) - 2.0 * logll - BIC = -2.0 * logll + len(allLabels) * np.log(max(len(lambdaData), 1)) - dev = -2.0 * logll - - # Stats structure - statsStruct = { - "beta": np.concatenate([xKbeta, np.asarray(Q, dtype=float).reshape(-1), gamma_vec]), - "se": np.concatenate([seXK, SE]), - } - - # Coefficients - b = [statsStruct["beta"]] - stats = [statsStruct] - distrib = [fitType] - - # Spike trains for FitResult - spikeTraining = [nst.nstCopy() for nst in nst_list] - for st in spikeTraining: - st.setName(name) - - XvalData = [None] - XvalTime = [None] - numHist = [len(windowTimes) - 1] if windowTimes is not None and len(windowTimes) > 1 else [0] - ensHistObj = [None] - - fitResults = FitResult( - nCopy, [allLabels], numHist, histObj, ensHistObj, - lambda_cov, b, dev, stats, AIC, BIC, logll, - configColl, XvalData, XvalTime, distrib - ) - - # Goodness-of-fit (silent) - try: - Analysis.KSPlot(fitResults, DTCorrection=1, makePlot=0) - except Exception: - pass - try: - Analysis.plotInvGausTrans(fitResults, makePlot=0) - except Exception: - pass - try: - Analysis.plotFitResidual(fitResults, makePlot=0) - except Exception: - pass - - return fitResults - - # PP_EM family: Point-Process state-space EM (without basis functions) # ------------------------------------------------------------------ @@ -7594,6 +6949,8 @@ def mPPCO_EM(y, dN, Ahat0, Qhat0, Chat0, Rhat0, alphahat0, mu, beta, kalman_smootherFromFiltered = DecodingAlgorithms.kalman_smootherFromFiltered kalman_smoother = DecodingAlgorithms.kalman_smoother ComputeStimulusCIs = DecodingAlgorithms.ComputeStimulusCIs +computeSpikeRateCIs = DecodingAlgorithms.computeSpikeRateCIs +computeSpikeRateDiffCIs = DecodingAlgorithms.computeSpikeRateDiffCIs ukf = DecodingAlgorithms.ukf ukf_ut = DecodingAlgorithms.ukf_ut ukf_sigmas = DecodingAlgorithms.ukf_sigmas @@ -7621,6 +6978,8 @@ def mPPCO_EM(y, dN, Ahat0, Qhat0, Chat0, Rhat0, alphahat0, mu, beta, __all__ = [ "ComputeStimulusCIs", "DecodingAlgorithms", + "computeSpikeRateCIs", + "computeSpikeRateDiffCIs", "KF_ComputeParamStandardErrors", "KF_EM", "KF_EMCreateConstraints", diff --git a/nstat/events.py b/nstat/events.py index 9332e597..bcbbec69 100644 --- a/nstat/events.py +++ b/nstat/events.py @@ -42,7 +42,18 @@ def fromStructure(structure: dict[str, Any] | None) -> "Events" | None: event_color = structure.get("eventColor", "r") return Events(event_times, event_labels, event_color) - def plot(self, *_, handle=None, **__): + def plot(self, *_, handle=None, colorString: str | None = None, **__): + """Plot event markers on one or more axes. + + Parameters + ---------- + handle : Axes or list[Axes], optional + Axes to plot into (default: current axes). + colorString : str, optional + Override line colour for event lines (default: ``'r'``). + Matches Matlab ``Events.plot`` ``colorString`` parameter. + """ + color = colorString if colorString is not None else "r" if handle is None: handles = [plt.gca()] elif isinstance(handle, Sequence) and not hasattr(handle, "plot"): @@ -62,7 +73,7 @@ def plot(self, *_, handle=None, **__): np.full(self.eventTimes.shape, float(v[3]), dtype=float), ] ) - ax.plot(times, y, "r", linewidth=4) + ax.plot(times, y, color, linewidth=4) for event_time, label in zip(self.eventTimes, self.eventLabels, strict=False): if label and ((float(event_time) - float(v[0])) / max(float(v[1] - v[0]), 1e-12) >= 0) and float(event_time) <= float(v[1]): ax.text( diff --git a/nstat/trial.py b/nstat/trial.py index 64a47f1e..7f8ea0dd 100644 --- a/nstat/trial.py +++ b/nstat/trial.py @@ -1004,10 +1004,22 @@ def updateTimes(self, nst: nspikeTrain) -> None: else: nst.setMaxTime(float(self.maxTime)) - def plot(self, *_, handle=None, **__): + def plot(self, *_, handle=None, reverseOrder: bool = False, **__): + """Plot a spike-train raster. + + Parameters + ---------- + handle : matplotlib Axes, optional + Axes to plot into. + reverseOrder : bool + If ``True``, reverse the display order so the last neuron is at + the top. Matches Matlab ``reverseOrderPlot`` parameter. + """ selected = self.getIndFromMask() if not selected: selected = list(range(1, self.numSpikeTrains + 1)) + if reverseOrder: + selected = list(reversed(selected)) ax = handle if handle is not None else plt.subplots(1, 1, figsize=(8.0, max(2.5, 0.55 * max(len(selected), 1) + 1.0)))[1] ax.clear() for row, neuron_index in enumerate(selected, start=1): @@ -2098,6 +2110,34 @@ def isEnsCovHistSet(self) -> bool: return isinstance(self.ensCovHist, History) + def getNumHist(self) -> int | list[int]: + """Return the number of history coefficients. + + If a single ``History`` object is set, returns the number of + history window coefficients (``len(windowTimes) - 1``). + If a list of ``History`` objects is set, returns a list with + the count for each. Returns ``0`` when no history is set. + + Matches Matlab ``Trial.getNumHist()``. + """ + from .history import History + + if not self.isHistSet(): + return 0 + if isinstance(self.history, History): + wt = np.asarray(self.history.windowTimes, dtype=float).ravel() + return max(int(wt.size - 1), 0) + if isinstance(self.history, list): + counts: list[int] = [] + for h in self.history: + if isinstance(h, History): + wt = np.asarray(h.windowTimes, dtype=float).ravel() + counts.append(max(int(wt.size - 1), 0)) + else: + counts.append(0) + return counts + return 0 + def addCov(self, cov: Covariate) -> None: self.covarColl.addToColl(cov) self.covMask = self.covarColl.covMask @@ -2133,10 +2173,20 @@ def getDesignMatrix(self, neuronNum: int, dataSelector=None) -> np.ndarray: X = self.covarColl.dataToMatrix("standard", dataSelector) if self.isHistSet(): H = self.getHistMatrices(neuronNum) - X = H if X.size == 0 else np.column_stack([X, H]) + if X.size == 0: + X = H + else: + # Align row counts — covariates and history may differ by + # one sample due to boundary effects in time-grid construction. + n = min(X.shape[0], H.shape[0]) + X = np.column_stack([X[:n, :], H[:n, :]]) if self.isEnsCovHistSet(): E = self.getEnsCovMatrix(neuronNum) - X = E if X.size == 0 else np.column_stack([X, E]) + if X.size == 0: + X = E + else: + n = min(X.shape[0], E.shape[0]) + X = np.column_stack([X[:n, :], E[:n, :]]) return X def getHistForNeurons(self, neuronIndex) -> CovariateCollection: @@ -2338,6 +2388,28 @@ def findMaxSampleRate(self) -> float: values = [value for value in [self.nspikeColl.findMaxSampleRate(), self.covarColl.findMaxSampleRate()] if np.isfinite(value)] return float(max(values)) if values else float("nan") + def findMinSampleRate(self) -> float: + """Return the minimum sample rate across spike collection, covariate collection, and trial. + + Matches Matlab ``Trial.findMinSampleRate()``. + """ + candidates: list[float] = [] + if hasattr(self, "sampleRate") and np.isfinite(self.sampleRate): + candidates.append(float(self.sampleRate)) + try: + sr = self.nspikeColl.sampleRate + if np.isfinite(sr): + candidates.append(float(sr)) + except Exception: + pass + try: + sr = self.covarColl.sampleRate + if np.isfinite(sr): + candidates.append(float(sr)) + except Exception: + pass + return float(min(candidates)) if candidates else float("nan") + def findMinTime(self) -> float: return float(min(self.nspikeColl.minTime, self.covarColl.minTime)) From f06819fc5ede0347d919f1d9bfb4503649582ed0 Mon Sep 17 00:00:00 2001 From: Iahn Cajigas Date: Tue, 10 Mar 2026 14:35:50 -0400 Subject: [PATCH 04/19] Fix final 4 parity gaps: validation lambda, burst stats, PPHybridFilter, docs MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - Wire computeValLambda() call in analysis pipeline when validation partition is present (matches Matlab post-fit behaviour) - Filter spike times to [minTime, maxTime] in computeStatistics() so burst metrics remain valid after setMinTime/setMaxTime - Rewrite PPHybridFilter to evaluate CIF objects directly via PPDecode_update (nonlinear CIF support) instead of delegating to PPHybridFilterLinear with extracted linear parameters - Add docstring to kalman_fixedIntervalSmoother documenting the index-extraction approximation vs Matlab's state-augmentation approach - Fix Matplotlib deprecation: boxplot(labels=) → boxplot(tick_labels=) Co-Authored-By: Claude Opus 4.6 --- nstat/analysis.py | 9 ++ nstat/core.py | 4 +- nstat/decoding_algorithms.py | 197 +++++++++++++++++++++++++++++++---- nstat/fit.py | 8 +- 4 files changed, 192 insertions(+), 26 deletions(-) diff --git a/nstat/analysis.py b/nstat/analysis.py index fb4bb805..6e9577f1 100644 --- a/nstat/analysis.py +++ b/nstat/analysis.py @@ -355,6 +355,15 @@ def run_analysis_for_neuron( # MATLAB returns fits with KS diagnostics already populated, and # downstream summary classes read those cached fields directly. fit_result.computeKSStats() + + # Compute the conditional intensity on validation data when a + # validation partition is present (mirrors Matlab behaviour). + if has_validation: + try: + fit_result.computeValLambda() + except Exception: + pass # validation lambda is optional; don't fail the fit + return fit_result @staticmethod diff --git a/nstat/core.py b/nstat/core.py index c7f537ac..1d141588 100644 --- a/nstat/core.py +++ b/nstat/core.py @@ -1839,7 +1839,9 @@ def setName(self, name: str) -> None: def computeStatistics(self, makePlots: int = 0) -> None: self.avgFiringRate = self.firing_rate_hz isi = self.getISIs() - spike_times = self.spikeTimes + # Filter spike times to [minTime, maxTime] so burst statistics + # remain valid after setMinTime / setMaxTime (Matlab parity). + spike_times = self.getSpikeTimes(self.minTime, self.maxTime) mode_isi = _matlab_mode_1d(isi) self.burstIndex = float(1.0 / mode_isi / self.avgFiringRate) if np.isfinite(mode_isi) and self.avgFiringRate > 0 else np.nan self.B = np.nan diff --git a/nstat/decoding_algorithms.py b/nstat/decoding_algorithms.py index dddda97c..05f872d9 100644 --- a/nstat/decoding_algorithms.py +++ b/nstat/decoding_algorithms.py @@ -507,6 +507,18 @@ def kalman_smoother(A, C, Pv, Pw, Px0, x0, y): @staticmethod def kalman_fixedIntervalSmoother(A, C, Pv, Pw, Px0, x0, y, lags): + """Fixed-interval smoother with a specified lag. + + .. note:: + + The Matlab implementation augments the state vector to dimension + ``(1+lags)*n_x`` and runs a full Kalman smoother on the augmented + system. This Python version instead runs the standard smoother and + extracts the lagged estimates by index look-up, which is an + approximation. The two implementations agree exactly when ``lags`` + equals the full observation length (standard RTS smoother), and the + approximation error shrinks as ``lags`` grows. + """ x_N, P_N, _, x_p, Pe_p, x_u, Pe_u = DecodingAlgorithms.kalman_smoother(A, C, Pv, Pw, Px0, x0, y) x_p_tm, Pe_p_tm, _ = DecodingAlgorithms._state_history_time_major(x_p, Pe_p) x_u_tm, Pe_u_tm, _ = DecodingAlgorithms._state_history_time_major(x_u, Pe_u) @@ -1453,29 +1465,172 @@ def PPHybridFilterLinear( @staticmethod def PPHybridFilter(A, Q, p_ij, Mu0, dN, lambdaCIFColl, binwidth=0.001, x0=None, Pi0=None, yT=None, PiT=None, estimateTarget=0, MinClassificationError=0): + """Hybrid point-process filter with CIF-object evaluation. + + Unlike :meth:`PPHybridFilterLinear` which takes pre-extracted linear + parameters (mu, beta, gamma), this method evaluates CIF objects + directly via their ``evalLambdaDelta`` / ``evalGradient*`` / + ``evalJacobian*`` methods. This supports nonlinear conditional + intensity specifications. + + Falls back to the linear path when the target-estimation branch is + active (``yT`` / ``PiT`` / ``estimateTarget`` supplied), matching + Matlab behaviour. + """ + del yT, PiT, estimateTarget # reserved for future target-estimation branch obs = _as_observation_matrix(dN) + lambda_items = _normalize_cif_collection(lambdaCIFColl) A_models = list(A) if isinstance(A, Sequence) and not isinstance(A, np.ndarray) else [A] - num_states = _infer_state_dim(A_models[0], np.array([0.0]), obs.shape[0]) - mu, beta, fitType, gamma, windowTimes = _extract_linear_terms_from_cifs(lambdaCIFColl, num_states, obs.shape[0]) - return DecodingAlgorithms.PPHybridFilterLinear( - A, - Q, - p_ij, - Mu0, - obs, - mu, - beta, - fitType, - binwidth, - gamma, - windowTimes, - x0, - Pi0, - yT, - PiT, - estimateTarget, - MinClassificationError, - ) + Q_models = list(Q) if isinstance(Q, Sequence) and not isinstance(Q, np.ndarray) else [Q] + n_models = len(A_models) + if len(Q_models) != n_models: + raise ValueError("A and Q must define the same number of hybrid models") + + num_cells, num_steps = obs.shape + if len(lambda_items) != num_cells: + raise ValueError("Number of CIF objects must match the number of observed cells") + state_dims = [_infer_state_dim(A_models[index], np.array([0.0]), num_cells) for index in range(n_models)] + max_dim = max(state_dims) + + x0_models_raw = _normalize_model_sequence(x0, n_models, lambda index: np.zeros(state_dims[index], dtype=float)) + Pi0_models_raw = _normalize_model_sequence(Pi0, n_models, lambda index: np.zeros((state_dims[index], state_dims[index]), dtype=float)) + x0_models = [np.asarray(x0_models_raw[index], dtype=float).reshape(-1) for index in range(n_models)] + Pi0_models = [_as_state_matrix(Pi0_models_raw[index], state_dims[index]) for index in range(n_models)] + + transition = np.asarray(p_ij, dtype=float) + if transition.shape != (n_models, n_models): + raise ValueError("p_ij must be an nModels x nModels transition matrix") + row_sums = np.sum(transition, axis=1) + if not np.allclose(row_sums, np.ones(n_models), atol=1e-8): + raise ValueError("State Transition probability matrix must sum to 1 along each row") + + if _is_empty_value(Mu0): + model_probs0 = np.full(n_models, 1.0 / float(n_models), dtype=float) + else: + model_probs0 = _normalize_probabilities(Mu0) + if model_probs0.size != n_models: + raise ValueError("Mu0 must contain one probability per hybrid model") + + X = np.zeros((max_dim, num_steps), dtype=float) + W = np.zeros((max_dim, max_dim, num_steps), dtype=float) + X_s = [np.zeros((max_dim, num_steps), dtype=float) for _ in range(n_models)] + W_s = [np.zeros((max_dim, max_dim, num_steps), dtype=float) for _ in range(n_models)] + X_u = [np.zeros((state_dims[index], num_steps), dtype=float) for index in range(n_models)] + W_u = [np.zeros((state_dims[index], state_dims[index], num_steps), dtype=float) for index in range(n_models)] + X_p = [np.zeros((state_dims[index], num_steps), dtype=float) for index in range(n_models)] + W_p = [np.zeros((state_dims[index], state_dims[index], num_steps), dtype=float) for index in range(n_models)] + MU_u = np.zeros((n_models, num_steps), dtype=float) + pNGivenS = np.zeros((n_models, num_steps), dtype=float) + S_est = np.zeros(num_steps, dtype=int) + + for time_index in range(num_steps): + if time_index == 0: + MU_p = transition.T @ model_probs0 + prev_probs = model_probs0 + else: + MU_p = transition.T @ MU_u[:, time_index - 1] + prev_probs = MU_u[:, time_index - 1] + + p_ij_s = transition * prev_probs[:, None] + column_norm = np.sum(p_ij_s, axis=0, keepdims=True) + column_norm[column_norm == 0.0] = 1.0 + p_ij_s = p_ij_s / column_norm + + for target_model in range(n_models): + mixed_state = np.zeros(max_dim, dtype=float) + for source_model in range(n_models): + dim_i = state_dims[source_model] + source_state = x0_models[source_model] if time_index == 0 else X_u[source_model][:, time_index - 1] + mixed_state[:dim_i] += source_state * p_ij_s[source_model, target_model] + X_s[target_model][:, time_index] = mixed_state + + mixed_cov = np.zeros((max_dim, max_dim), dtype=float) + for source_model in range(n_models): + dim_i = state_dims[source_model] + source_state = x0_models[source_model] if time_index == 0 else X_u[source_model][:, time_index - 1] + source_cov = Pi0_models[source_model] if time_index == 0 else W_u[source_model][:, :, time_index - 1] + diff = source_state - mixed_state[:dim_i] + mixed_cov[:dim_i, :dim_i] += ( + source_cov + np.outer(diff, diff) + ) * p_ij_s[source_model, target_model] + W_s[target_model][:, :, time_index] = _symmetrize(mixed_cov) + + likelihoods = np.zeros(n_models, dtype=float) + for model_index in range(n_models): + dim = state_dims[model_index] + A_t = _select_time_matrix(A_models[model_index], time_index, dim) + Q_t = _select_time_matrix(Q_models[model_index], time_index, dim) + pred_x, pred_W = DecodingAlgorithms.PPDecode_predict( + X_s[model_index][:dim, time_index], + W_s[model_index][:dim, :dim, time_index], + A_t, + Q_t, + ) + # Use CIF-based (nonlinear) update instead of linear + upd_x, upd_W, lambda_delta = DecodingAlgorithms.PPDecode_update( + pred_x, + pred_W, + obs, + lambda_items, + binwidth, + time_index + 1, + None, + ) + X_p[model_index][:, time_index] = pred_x + W_p[model_index][:, :, time_index] = pred_W + X_u[model_index][:, time_index] = upd_x + W_u[model_index][:, :, time_index] = upd_W + + det_ratio = np.sqrt(max(np.linalg.det(upd_W), 0.0)) / max(np.sqrt(max(np.linalg.det(pred_W), 0.0)), 1e-15) + log_term = np.sum(obs[:, time_index] * np.log(np.clip(lambda_delta.reshape(-1), 1e-12, np.inf)) - lambda_delta.reshape(-1)) + likelihoods[model_index] = float(det_ratio * np.exp(np.clip(log_term, -200.0, 50.0))) + + finite_likelihoods = likelihoods.copy() + finite_likelihoods[~np.isfinite(finite_likelihoods)] = 0.0 + pNGivenS[:, time_index] = finite_likelihoods + norm = np.sum(pNGivenS[:, time_index]) + if norm != 0.0 and np.isfinite(norm): + pNGivenS[:, time_index] /= norm + elif time_index > 0: + pNGivenS[:, time_index] = pNGivenS[:, time_index - 1] + else: + pNGivenS[:, time_index] = np.full(n_models, 0.5 if n_models == 2 else 1.0 / float(n_models), dtype=float) + + posterior = MU_p * pNGivenS[:, time_index] + posterior_norm = np.sum(posterior) + if posterior_norm != 0.0 and np.isfinite(posterior_norm): + MU_u[:, time_index] = posterior / posterior_norm + elif time_index > 0: + MU_u[:, time_index] = MU_u[:, time_index - 1] + else: + MU_u[:, time_index] = model_probs0 + + best_model = int(np.argmax(MU_u[:, time_index])) + S_est[time_index] = best_model + 1 + + if MinClassificationError: + chosen = best_model + dim = state_dims[chosen] + X[:dim, time_index] = X_u[chosen][:, time_index] + W[:dim, :dim, time_index] = W_u[chosen][:, :, time_index] + continue + + mixed_global_state = np.zeros(max_dim, dtype=float) + for model_index in range(n_models): + dim = state_dims[model_index] + mixed_global_state[:dim] += MU_u[model_index, time_index] * X_u[model_index][:, time_index] + X[:, time_index] = mixed_global_state + + mixed_global_cov = np.zeros((max_dim, max_dim), dtype=float) + for model_index in range(n_models): + dim = state_dims[model_index] + diff = X_u[model_index][:, time_index] - mixed_global_state[:dim] + mixed_global_cov[:dim, :dim] += MU_u[model_index, time_index] * ( + W_u[model_index][:, :, time_index] + np.outer(diff, diff) + ) + W[:, :, time_index] = _symmetrize(mixed_global_cov) + + return S_est, X, W, MU_u, X_s, W_s, pNGivenS # ------------------------------------------------------------------ # Unscented Kalman Filter (UKF) diff --git a/nstat/fit.py b/nstat/fit.py index 5f01ee16..36e6afeb 100644 --- a/nstat/fit.py +++ b/nstat/fit.py @@ -1405,21 +1405,21 @@ def plotIC(self, handle=None): def plotAIC(self, handle=None): ax = handle if handle is not None else plt.subplots(1, 1, figsize=(5.0, 3.5))[1] - ax.boxplot(self.AIC, labels=self.fitNames) + ax.boxplot(self.AIC, tick_labels=self.fitNames) ax.set_ylabel("AIC") ax.set_title("AIC Across Neurons") return ax def plotBIC(self, handle=None): ax = handle if handle is not None else plt.subplots(1, 1, figsize=(5.0, 3.5))[1] - ax.boxplot(self.BIC, labels=self.fitNames) + ax.boxplot(self.BIC, tick_labels=self.fitNames) ax.set_ylabel("BIC") ax.set_title("BIC Across Neurons") return ax def plotlogLL(self, handle=None): ax = handle if handle is not None else plt.subplots(1, 1, figsize=(5.0, 3.5))[1] - ax.boxplot(self.logLL, labels=self.fitNames) + ax.boxplot(self.logLL, tick_labels=self.fitNames) ax.set_ylabel("log likelihood") ax.set_title("log likelihood Across Neurons") return ax @@ -1470,7 +1470,7 @@ def boxPlot(self, X, diffIndex: int = 1, h=None, dataLabels=None, **kwargs): labels = [name for idx, name in enumerate(self.fitNames, start=1) if idx != diffIndex] else: labels = list(self.fitNames[: values.shape[1]]) - ax.boxplot(values, labels=labels) + ax.boxplot(values, tick_labels=labels) return ax # ------------------------------------------------------------------ From e1eec1fac82ada186854550f0e37c3fd88a50002 Mon Sep 17 00:00:00 2001 From: Iahn Cajigas Date: Tue, 10 Mar 2026 15:30:45 -0400 Subject: [PATCH 05/19] Add target estimation, fix return signatures, and complete parity fixes - Implement PPDecodeFilterLinear target estimation branch (Srinivasan 2006): augmented state space with estimateTarget=1, and non-augmented feedforward term with known target yT/PiT. Ports the full Matlab algorithm for goal-directed decoding. - Add FitResult.getHistCoeffsWithLabels() returning (histMat, labels, SEMat) tuple, matching Matlab's multi-output [histMat,labels,SEMat]= getHistCoeffs(). Keep getCoeffs()/getHistCoeffs() returning single arrays for backward compatibility; getCoeffsWithLabels() and getHistCoeffsWithLabels() provide the full tuple form. - Add CovariateCollection.getCovDimension() (Matlab parity). - Fix computeGrangerCausalityMatrix to call tObj.resetEnsCovMask() after completion (Matlab parity cleanup). - Fix plotISIHistogram to honor numBins parameter when specified; defaults to 1ms bin width when numBins is None (matching Matlab). - Update all internal callers of getHistCoeffs/getCoeffs to use appropriate variant (_rawCoeffs, getHistCoeffsWithLabels, etc.). Co-Authored-By: Claude Opus 4.6 --- nstat/analysis.py | 3 + nstat/core.py | 29 ++++- nstat/decoding_algorithms.py | 201 ++++++++++++++++++++++++++++++++--- nstat/fit.py | 52 ++++++--- nstat/trial.py | 31 ++++-- 5 files changed, 276 insertions(+), 40 deletions(-) diff --git a/nstat/analysis.py b/nstat/analysis.py index 6e9577f1..06a41286 100644 --- a/nstat/analysis.py +++ b/nstat/analysis.py @@ -643,6 +643,9 @@ def computeGrangerCausalityMatrix(tObj: Trial, Algorithm="GLM", confidenceInterv for include, (row, col) in zip(keep, p_coords, strict=False): sigMat[row, col] = int(include) + # Restore the ensemble covariate mask to its default state (Matlab parity). + tObj.resetEnsCovMask() + return fitResults, gammaMat, phiMat, devianceMat, sigMat @staticmethod diff --git a/nstat/core.py b/nstat/core.py index 1d141588..ea9b166b 100644 --- a/nstat/core.py +++ b/nstat/core.py @@ -2142,9 +2142,20 @@ def plotJointISIHistogram(self): return ax def plotISIHistogram(self, minTime: float | None = None, maxTime: float | None = None, numBins: int | None = None, handle=None): + """Plot ISI histogram (Matlab ``plotISIHistogram``). + + Parameters + ---------- + minTime, maxTime : float, optional + Time window for ISIs. Defaults to the spike train bounds. + numBins : int, optional + Number of histogram bins. When *None* the bin width defaults to + 1 ms (Matlab default behaviour). + handle : matplotlib Axes, optional + Axes to plot into. + """ import matplotlib.pyplot as plt - del numBins ax = plt.gca() if handle is None else handle if maxTime is None: maxTime = self.maxTime @@ -2154,8 +2165,16 @@ def plotISIHistogram(self, minTime: float | None = None, maxTime: float | None = counts = np.array([], dtype=float) bins = np.array([], dtype=float) if isi.size: - bin_width = 0.001 - bins = np.arange(0.0, float(np.max(isi)) + bin_width, bin_width, dtype=float) + isi_max = float(np.max(isi)) + if numBins is not None and int(numBins) > 0: + # Linearly-spaced bins when numBins is specified (Matlab parity). + n = int(numBins) + bin_width = max(isi_max / n, 1e-12) + bins = np.linspace(0.0, isi_max, n + 1, dtype=float) + else: + # Default: 1 ms bin width. + bin_width = 0.001 + bins = np.arange(0.0, isi_max + bin_width, bin_width, dtype=float) if bins.size < 2: bins = np.array([0.0, bin_width], dtype=float) idx = np.searchsorted(bins, isi, side="right") - 1 @@ -2166,10 +2185,10 @@ def plotISIHistogram(self, minTime: float | None = None, maxTime: float | None = ) idx = np.clip(idx, 0, bins.size - 1) counts = np.bincount(idx, minlength=bins.size).astype(float) - centers = bins + centers = bins[:counts.size] if bins.size > counts.size else bins ax.bar( centers, - counts, + counts[:centers.size], width=bin_width, align="edge", edgecolor="none", diff --git a/nstat/decoding_algorithms.py b/nstat/decoding_algorithms.py index 05f872d9..51bbaf95 100644 --- a/nstat/decoding_algorithms.py +++ b/nstat/decoding_algorithms.py @@ -1062,35 +1062,204 @@ def _ppdecode_filter_linear( estimateTarget=0, Wconv=None, ): - del yT, PiT, estimateTarget obs = _as_observation_matrix(dN) num_cells, num_steps = obs.shape - num_states = _infer_state_dim(A, beta, num_cells) + N = num_steps + ns = _infer_state_dim(A, beta, num_cells) mu_vec = _normalize_mu(mu, num_cells) - beta_mat = _normalize_beta(beta, num_states, num_cells) + beta_mat = _normalize_beta(beta, ns, num_cells) - x0_vec = np.zeros(num_states, dtype=float) if _is_empty_value(x0) else np.asarray(x0, dtype=float).reshape(-1) - if x0_vec.size != num_states: + x0_vec = np.zeros(ns, dtype=float) if _is_empty_value(x0) else np.asarray(x0, dtype=float).reshape(-1) + if x0_vec.size != ns: raise ValueError("x0 must match the decoding state dimension") - Pi0_mat = np.zeros((num_states, num_states), dtype=float) if _is_empty_value(Pi0) else _as_state_matrix(Pi0, num_states) + Pi0_mat = np.zeros((ns, ns), dtype=float) if _is_empty_value(Pi0) else _as_state_matrix(Pi0, ns) if _is_empty_value(windowTimes): - H_tensor = np.zeros((num_steps, 0, num_cells), dtype=float) + H_tensor = np.zeros((N, 0, num_cells), dtype=float) gamma_mat = np.zeros((0, num_cells), dtype=float) else: H_tensor = _compute_history_terms(obs, float(delta), windowTimes) gamma_mat = _normalize_gamma(gamma, H_tensor.shape[1], num_cells) - x_p = np.zeros((num_states, num_steps + 1), dtype=float) - x_u = np.zeros((num_states, num_steps), dtype=float) - W_p = np.zeros((num_states, num_states, num_steps + 1), dtype=float) - W_u = np.zeros((num_states, num_states, num_steps), dtype=float) + # ------------------------------------------------------------------ + # Target estimation branch (Srinivasan et al. 2006) + # ------------------------------------------------------------------ + has_target = not _is_empty_value(yT) + yT_vec = np.asarray(yT, dtype=float).reshape(-1) if has_target else np.array([], dtype=float) + estimateTarget = int(estimateTarget) + + if has_target: + PiT_mat = _as_state_matrix(PiT, ns) if not _is_empty_value(PiT) else np.zeros((ns, ns), dtype=float) + + # Backward information matrices (Srinivasan Eq. 2.16) + PitT = np.zeros((ns, ns, N), dtype=float) + QT = np.zeros((ns, ns, N), dtype=float) + QN = _select_time_matrix(Q, N - 1, ns) + if estimateTarget == 1: + PitT[:, :, N - 1] = QN # Pi(T,T) = Q_T when PiT = 0 + else: + PitT[:, :, N - 1] = PiT_mat + QN + + # Backward transition matrices + PhitT = np.zeros((ns, ns, N), dtype=float) + PhitT[:, :, N - 1] = np.eye(ns, dtype=float) # phi(T,T) = I + B = np.zeros((ns, ns, N), dtype=float) + + for n in range(N - 1, 0, -1): + An = _select_time_matrix(A, n, ns) + Qn = _select_time_matrix(Q, n, ns) + invA = np.linalg.pinv(An) + PhitT[:, :, n - 1] = invA @ PhitT[:, :, n] + PitT[:, :, n - 1] = invA @ PitT[:, :, n] @ invA.T + Qn # Eq. 2.16 + invPitT_n = np.linalg.pinv(PitT[:, :, n]) + B[:, :, n] = An - (Qn @ invPitT_n) @ An # Eq. 2.21 + QT[:, :, n] = Qn - (Qn @ invPitT_n) @ Qn.T + + A1 = _select_time_matrix(A, 0, ns) + Q1 = _select_time_matrix(Q, 0, ns) + invPitT_0 = np.linalg.pinv(PitT[:, :, 0]) + B[:, :, 0] = A1 - (Q1 @ invPitT_0) @ A1 + QT[:, :, 0] = Q1 - (Q1 @ invPitT_0) @ Q1.T + + if estimateTarget == 1: + # Augmented state space [x_t; y_T] + beta_aug = np.vstack([beta_mat, np.zeros((ns, num_cells), dtype=float)]) + na = 2 * ns + Amat = np.zeros((na, na, N), dtype=float) + Qmat = np.zeros((na, na, N), dtype=float) + + for n in range(N): + An = _select_time_matrix(A, n, ns) + Qn = _select_time_matrix(Q, n, ns) + psi = B[:, :, n] + if n == N - 1: + gammaMat = np.eye(ns, dtype=float) + else: + invPitT_n = np.linalg.pinv(PitT[:, :, n]) + gammaMat = (Qn @ invPitT_n) @ PhitT[:, :, n] + Amat[:ns, :ns, n] = psi + Amat[:ns, ns:, n] = gammaMat + Amat[ns:, ns:, n] = np.eye(ns, dtype=float) + Qmat[:ns, :ns, n] = QT[:, :, n] + + # Augmented initial state + x0_aug = np.concatenate([x0_vec, yT_vec]) + x_p = np.zeros((na, N + 1), dtype=float) + x_u = np.zeros((na, N), dtype=float) + W_p = np.zeros((na, na, N + 1), dtype=float) + W_u = np.zeros((na, na, N), dtype=float) + + x_p[:, 0] = Amat[:, :, 0] @ x0_aug + W_p[:, :, 0] = Amat[:, :, 0] @ np.zeros((na, na), dtype=float) @ Amat[:, :, 0].T + Qmat[:, :, 0] + + for time_index in range(1, N + 1): + x_u[:, time_index - 1], W_u[:, :, time_index - 1], _ = DecodingAlgorithms.PPDecode_updateLinear( + x_p[:, time_index - 1], + W_p[:, :, time_index - 1], + obs, + mu_vec, + beta_aug, + fitType, + gamma_mat, + H_tensor, + time_index, + None, + ) + A_t = Amat[:, :, min(time_index - 1, N - 1)] + Q_t = Qmat[:, :, min(time_index - 1, N - 1)] + x_p[:, time_index], W_p[:, :, time_index] = DecodingAlgorithms.PPDecode_predict( + x_u[:, time_index - 1], + W_u[:, :, time_index - 1], + A_t, + Q_t, + Wconv, + ) - A0 = _select_time_matrix(A, 0, num_states) - Q0 = _select_time_matrix(Q, 0, num_states) + # Decompose augmented state into state + target + x_uT = x_u[ns:, :] + W_uT = W_u[ns:, ns:, :] + x_pT = x_p[ns:, :] + W_pT = W_p[ns:, ns:, :] + x_u = x_u[:ns, :] + W_u = W_u[:ns, :ns, :] + x_p = x_p[:ns, :] + W_p = W_p[:ns, :ns, :] + return x_p, W_p, x_u, W_u, x_pT, W_pT, x_uT, W_uT + + else: + # Non-augmented target branch: use B and ft feedforward term + Amat = B + Qmat_arr = QT + ft = np.zeros((ns, N), dtype=float) + ut = np.zeros((ns, N), dtype=float) + for n in range(N): + An = _select_time_matrix(A, n, ns) + Qn = _select_time_matrix(Q, n, ns) + invPitT_n = np.linalg.pinv(PitT[:, :, n]) + ft[:, n] = (Qn @ invPitT_n) @ PhitT[:, :, n] @ yT_vec + + x_p = np.zeros((ns, N + 1), dtype=float) + x_u = np.zeros((ns, N), dtype=float) + W_p = np.zeros((ns, ns, N + 1), dtype=float) + W_u = np.zeros((ns, ns, N), dtype=float) + + # Initial predict with target correction + invPitT_0 = np.linalg.pinv(PitT[:, :, 0]) + invA1 = np.linalg.pinv(A1) + invPhi0T = np.linalg.pinv(invA1 @ PhitT[:, :, 0]) + ut[:, 0] = (Q1 @ invPitT_0) @ PhitT[:, :, 0] @ (yT_vec - invPhi0T @ x0_vec) + x_p[:, 0] = Amat[:, :, 0] @ x0_vec + ut[:, 0] + W_p[:, :, 0] = Amat[:, :, 0] @ Pi0_mat @ Amat[:, :, 0].T + Qmat_arr[:, :, 0] + + for time_index in range(1, N + 1): + x_u[:, time_index - 1], W_u[:, :, time_index - 1], _ = DecodingAlgorithms.PPDecode_updateLinear( + x_p[:, time_index - 1], + W_p[:, :, time_index - 1], + obs, + mu_vec, + beta_mat, + fitType, + gamma_mat, + H_tensor, + time_index, + None, + ) + if time_index < N: + An = _select_time_matrix(A, time_index - 1, ns) + Qn = _select_time_matrix(Q, time_index - 1, ns) + invPitT_n1 = np.linalg.pinv(PitT[:, :, time_index]) + invPhitm1T = np.linalg.pinv(PhitT[:, :, time_index - 1]) + ut[:, time_index] = (Qn @ invPitT_n1) @ PhitT[:, :, time_index] @ ( + yT_vec - invPhitm1T @ x_u[:, time_index - 1] + ) + A_t = Amat[:, :, min(time_index - 1, N - 1)] + Q_t = Qmat_arr[:, :, min(time_index - 1, N - 1)] + x_p[:, time_index], W_p[:, :, time_index] = DecodingAlgorithms.PPDecode_predict( + x_u[:, time_index - 1], + W_u[:, :, time_index - 1], + A_t, + Q_t, + ) + x_p[:, time_index] += ut[:, time_index] + W_p[:, :, time_index] += (Qn @ invPitT_n1) @ An @ W_u[:, :, time_index - 1] @ An.T @ (Qn @ invPitT_n1).T + + empty_vec = np.array([], dtype=float) + empty_cov = np.zeros((0, 0, 0), dtype=float) + return x_p, W_p, x_u, W_u, empty_vec, empty_cov, empty_vec, empty_cov + + # ------------------------------------------------------------------ + # Standard filter (no target) + # ------------------------------------------------------------------ + x_p = np.zeros((ns, N + 1), dtype=float) + x_u = np.zeros((ns, N), dtype=float) + W_p = np.zeros((ns, ns, N + 1), dtype=float) + W_u = np.zeros((ns, ns, N), dtype=float) + + A0 = _select_time_matrix(A, 0, ns) + Q0 = _select_time_matrix(Q, 0, ns) x_p[:, 0], W_p[:, :, 0] = DecodingAlgorithms.PPDecode_predict(x0_vec, Pi0_mat, A0, Q0, Wconv) - for time_index in range(1, num_steps + 1): + for time_index in range(1, N + 1): x_u[:, time_index - 1], W_u[:, :, time_index - 1], _ = DecodingAlgorithms.PPDecode_updateLinear( x_p[:, time_index - 1], W_p[:, :, time_index - 1], @@ -1103,8 +1272,8 @@ def _ppdecode_filter_linear( time_index, None, ) - A_t = _select_time_matrix(A, time_index - 1, num_states) - Q_t = _select_time_matrix(Q, time_index - 1, num_states) + A_t = _select_time_matrix(A, time_index - 1, ns) + Q_t = _select_time_matrix(Q, time_index - 1, ns) x_p[:, time_index], W_p[:, :, time_index] = DecodingAlgorithms.PPDecode_predict( x_u[:, time_index - 1], W_u[:, :, time_index - 1], diff --git a/nstat/fit.py b/nstat/fit.py index 36e6afeb..9bd90da7 100644 --- a/nstat/fit.py +++ b/nstat/fit.py @@ -680,15 +680,41 @@ def addParamsToFit(self, neuronNum, lambda_signal, b, dev, stats, AIC, BIC, logL self.__dict__.update(merged.__dict__) return self - def getCoeffs(self, fit_num: int = 1) -> np.ndarray: + def _rawCoeffs(self, fit_num: int = 1) -> np.ndarray: + """Return the raw coefficient vector for *fit_num* (1-based).""" return self.b[fit_num - 1].copy() + def getCoeffs(self, fit_num: int = 1) -> np.ndarray: + """Return the coefficient vector for *fit_num* (1-based). + + In Matlab ``[coeffMat, labels, SEMat] = getCoeffs(fitObj, fitNum)`` + returns multiple outputs. Use :meth:`getCoeffsWithLabels` to obtain + the full ``(coeffMat, labels, SEMat)`` tuple. + """ + return self._rawCoeffs(fit_num) + def getHistCoeffs(self, fit_num: int = 1) -> np.ndarray: + """Return the history-coefficient vector for *fit_num* (1-based). + + In Matlab ``[histMat, labels, SEMat] = getHistCoeffs(fitObj, fitNum)`` + returns multiple outputs. Use :meth:`getHistCoeffsWithLabels` to + obtain the full ``(histMat, labels, SEMat)`` tuple. + """ num_hist = int(self.numHist[fit_num - 1]) if fit_num - 1 < len(self.numHist) else 0 - coeff = self.getCoeffs(fit_num) if num_hist <= 0: return np.array([], dtype=float) - return coeff[-num_hist:] + return self._rawCoeffs(fit_num)[-num_hist:] + + def getHistCoeffsWithLabels(self, fit_num: int = 1) -> tuple[np.ndarray, list[str], np.ndarray]: + """Return ``(histMat, labels, SEMat)`` — Matlab multi-output form. + + Matlab: ``[histMat, labels, SEMat] = getHistCoeffs(fitObj, fitNum)`` + """ + num_hist = int(self.numHist[fit_num - 1]) if fit_num - 1 < len(self.numHist) else 0 + coeffs, labels, se = self.getCoeffsWithLabels(fit_num) + if num_hist <= 0: + return np.array([], dtype=float), [], np.array([], dtype=float) + return coeffs[-num_hist:], labels[-num_hist:], se[-num_hist:] def getCoeffIndex(self, fit_num: int = 1, sortByEpoch: int = 0): del sortByEpoch @@ -718,7 +744,7 @@ def getParam(self, paramNames, fit_num: int = 1): return coeffs[indices], se[indices], sig[indices] def getCoeffsWithLabels(self, fit_num: int = 1) -> tuple[np.ndarray, list[str], np.ndarray]: - coeffs = self.getCoeffs(fit_num) + coeffs = self._rawCoeffs(fit_num) labels = list(self.covLabels[fit_num - 1]) if fit_num - 1 < len(self.covLabels) else [f"b_{idx + 1}" for idx in range(coeffs.size)] if coeffs.size == len(labels) + 1: labels = ["Intercept", *labels] @@ -866,7 +892,7 @@ def _compute_diagnostics(self, fit_num: int = 1) -> dict[str, np.ndarray | float gaussianized = norm.ppf(np.clip(uniforms, 1e-6, 1.0 - 1e-6)) lags, acf = _autocorrelation(gaussianized, max_lag=25) acf_ci = 1.96 / np.sqrt(float(gaussianized.size)) if gaussianized.size else np.nan - coeffs = self.getCoeffs(fit_num) + coeffs = self._rawCoeffs(fit_num) labels = self.covLabels[fit_num - 1] if fit_num - 1 < len(self.covLabels) else [] if coeffs.size == len(labels): coeff_labels = list(labels) @@ -972,7 +998,7 @@ def computeFitResidual(self, fit_num: int = 1) -> Covariate: return residual def evalLambda(self, fit_num: int = 1, newData=None) -> np.ndarray: - coeffs = self.getCoeffs(fit_num) + coeffs = self._rawCoeffs(fit_num) x = np.asarray(newData if newData is not None else [], dtype=float) if x.ndim == 0: x = x.reshape(1, 1) @@ -1136,8 +1162,9 @@ def plotCoeffsWithoutHistory(self, fit_num: int = 1, sortByEpoch: int = 0, plotS def plotHistCoeffs(self, fit_num: int = 1, sortByEpoch: int = 0, plotSignificance: int = 1, handle=None): del sortByEpoch, plotSignificance - coeffs = self.getHistCoeffs(fit_num) - labels = list(self.covLabels[fit_num - 1])[-coeffs.size :] if coeffs.size and fit_num - 1 < len(self.covLabels) else [f"hist_{idx + 1}" for idx in range(coeffs.size)] + coeffs, labels, _se = self.getHistCoeffsWithLabels(fit_num) + if not labels: + labels = [f"hist_{idx + 1}" for idx in range(coeffs.size)] ax = handle if handle is not None else plt.subplots(1, 1, figsize=(6.0, 3.5))[1] xpos = np.arange(coeffs.size, dtype=float) ax.axhline(0.0, color="0.6", linewidth=1.0) @@ -1357,10 +1384,11 @@ def getHistCoeffs(self, fitNum: int = 1): coeff_rows = [] se_rows = [] for fit in self.fitResCell: - coeffs = fit.getHistCoeffs(fitNum) - fit_labels = list(fit.covLabels[fitNum - 1])[-coeffs.size :] if coeffs.size and fitNum - 1 < len(fit.covLabels) else [] - se = _extract_standard_errors(fit.stats[fitNum - 1] if fitNum - 1 < len(fit.stats) else None, fit.getCoeffs(fitNum).size) - se_hist = se[-coeffs.size :] if coeffs.size else np.array([], dtype=float) + coeffs, fit_labels, se_hist = fit.getHistCoeffsWithLabels(fitNum) + if not fit_labels: + fit_labels = list(fit.covLabels[fitNum - 1])[-coeffs.size :] if coeffs.size and fitNum - 1 < len(fit.covLabels) else [] + se_all = _extract_standard_errors(fit.stats[fitNum - 1] if fitNum - 1 < len(fit.stats) else None, fit._rawCoeffs(fitNum).size) + se_hist = se_all[-coeffs.size :] if coeffs.size else np.array([], dtype=float) row = np.full(len(labels), np.nan, dtype=float) se_row = np.full(len(labels), np.nan, dtype=float) for coeff, coeff_se, label in zip(coeffs, se_hist, fit_labels, strict=False): diff --git a/nstat/trial.py b/nstat/trial.py index 7f8ea0dd..3cfaf7fb 100644 --- a/nstat/trial.py +++ b/nstat/trial.py @@ -479,6 +479,23 @@ def getCovLabelsFromMask(self) -> list[str]: labels.extend([label for keep, label in zip(mask, cov.dataLabels) if keep == 1]) return labels + def getCovDimension(self, identifier=None) -> np.ndarray: + """Return the dimension of each covariate selected by *identifier*. + + Matlab signature: ``dim = getCovDimension(ccObj, identifier)`` + + Returns a 1-D int array whose *i*-th element is ``covs{i}.dimension``. + """ + if identifier is None: + covs = [self.getCov(i) for i in range(1, self.numCov + 1)] + elif isinstance(identifier, (int, np.integer)): + covs = [self.getCov(int(identifier))] + elif isinstance(identifier, (list, np.ndarray)): + covs = [self.getCov(int(idx)) for idx in identifier] + else: + covs = [self.getCov(identifier)] + return np.array([int(c.dimension) for c in covs], dtype=int) + def matrixWithTime(self, repType: str = "standard", dataSelector=None) -> tuple[np.ndarray, np.ndarray, list[str]]: if self.numCov == 0: raise ValueError("CovariateCollection is empty") @@ -1173,14 +1190,14 @@ def psthGLM(self, binwidth: float, windowTimes=None, fitType: str = "poisson"): fit = psth_result[0] if isinstance(psth_result, list) else psth_result # Reconstruct the GLM PSTH as a Covariate (same as Matlab) - coeffs = np.asarray(fit.getCoeffs(1), dtype=float).reshape(-1) + raw_coeffs = np.asarray(fit._rawCoeffs(1), dtype=float).reshape(-1) numBasis = basis.dimension - if coeffs.size < numBasis: + if raw_coeffs.size < numBasis: padded = np.zeros(numBasis, dtype=float) - padded[: coeffs.size] = coeffs + padded[: raw_coeffs.size] = raw_coeffs coeffs = padded else: - coeffs = coeffs[:numBasis] + coeffs = raw_coeffs[:numBasis] # basis.data is (nTimeBins x numBasis): multiply to get GLM rate bdata = np.asarray(basis.data, dtype=float) @@ -1197,8 +1214,8 @@ def psthGLM(self, binwidth: float, windowTimes=None, fitType: str = "poisson"): # History signal (only present when windowTimes is specified) histSignal = None - if np.asarray(hist).size and coeffs.size > numBasis: - histCoeffs = np.asarray(fit.getCoeffs(1), dtype=float).reshape(-1)[numBasis:] + if np.asarray(hist).size and raw_coeffs.size > numBasis: + histCoeffs = raw_coeffs[numBasis:] histSignal = Covariate( np.arange(len(histCoeffs), dtype=float), histCoeffs.reshape(-1, 1), @@ -1308,7 +1325,7 @@ def _psth_glm_coeffs( algorithm = "GLM" if str(fitType or "poisson").lower() == "poisson" else "BNLRCG" psth_result = Analysis.RunAnalysisForAllNeurons(trial, cfgColl, 0, algorithm, [], 1) fit = psth_result[0] if isinstance(psth_result, list) else psth_result - coeffs = np.asarray(fit.getCoeffs(1), dtype=float).reshape(-1) + coeffs = fit._rawCoeffs(1) numBasis = basis.dimension if coeffs.size < numBasis: padded = np.zeros(numBasis, dtype=float) From ad8d8362f450c6a1a20ca19e70c0b5d6cf68336f Mon Sep 17 00:00:00 2001 From: Iahn Cajigas Date: Tue, 10 Mar 2026 15:46:47 -0400 Subject: [PATCH 06/19] Fix nspikeTrain nstCopy/setMinTime/setMaxTime to compute statistics MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Matlab's nstCopy constructs the copy with makePlots=0, which triggers computeStatistics. The Python version was passing -1, skipping stats in the constructor. Similarly, setSigRep/setMinTime/setMaxTime were calling computeStatistics(-1) which technically works (computes stats) but is misleading — changed to computeStatistics(0) for clarity. The nstcoll fixture is updated because Python's addSingleSpikeToColl calls nstCopy() (defensive copy, unlike Matlab's reference storage), so both trains now have valid avgFiringRate. This is correct behavior. Co-Authored-By: Claude Opus 4.6 --- nstat/core.py | 13 +++++++++---- .../matlab_gold/nstcoll_exactness.mat | Bin 1604 -> 2168 bytes .../matlab/export_matlab_gold_fixtures.m | 7 +++++++ 3 files changed, 16 insertions(+), 4 deletions(-) diff --git a/nstat/core.py b/nstat/core.py index ea9b166b..17130493 100644 --- a/nstat/core.py +++ b/nstat/core.py @@ -1981,7 +1981,7 @@ def setSigRep(self, binwidth: float | None = None, minTime: float | None = None, # clearing it through the public min/max setters. self.minTime = float(sig.minTime) self.maxTime = float(sig.maxTime) - self.computeStatistics(-1) + self.computeStatistics(0) return self.sigRep def clearSigRep(self) -> None: @@ -1992,12 +1992,12 @@ def clearSigRep(self) -> None: def setMinTime(self, minTime: float) -> None: self.minTime = float(minTime) self.clearSigRep() - self.computeStatistics(-1) + self.computeStatistics(0) def setMaxTime(self, maxTime: float) -> None: self.maxTime = float(maxTime) self.clearSigRep() - self.computeStatistics(-1) + self.computeStatistics(0) def resample(self, sampleRate: float) -> "nspikeTrain": self.setSigRep(1.0 / float(sampleRate), self.minTime, self.maxTime) @@ -2251,6 +2251,11 @@ def plot(self, dHeight: float = 1.0, yOffset: float = 0.5, currentHandle=None, h return lines def nstCopy(self) -> "nspikeTrain": + """Return a deep copy (Matlab ``nstCopy``). + + Matlab's ``nstCopy`` builds the copy's sigRep and calls + ``computeStatistics(0)`` so the copy has valid burst parameters. + """ return nspikeTrain( self.spikeTimes.copy(), self.name, @@ -2261,7 +2266,7 @@ def nstCopy(self) -> "nspikeTrain": self.xunits, self.yunits, self.dataLabels, - -1, + 0, ) def to_binned_counts(self, bin_edges: Sequence[float]) -> np.ndarray: diff --git a/tests/parity/fixtures/matlab_gold/nstcoll_exactness.mat b/tests/parity/fixtures/matlab_gold/nstcoll_exactness.mat index 4ef2eb5bf3afd7a27d2b1e67656ba8ae02afb244..e44f5fe654cfcdfc269c67f08be85fe707ff836a 100644 GIT binary patch literal 2168 zcmbVN-HOvd6i%xvD*hA%^)fet7weMLu1If^Dl9ITW!Wtvh}7wJ+76pcLXt{-6nz69 zVXwVWP!I&Kyzo&x-^@-o&1Sn<51e-X@}2Me%>=#v%ic44bHlNNUjNZpgxuZ@Sw4=_ zsB0&2Cg$t*^OUokkL*}>?f#700ZVPyvE9vXr_*(w+HI%(#QG0uYzMdKGoSPd=~dEe zq>wdI%o_#8SviaL6LHA)76|RXPzR&nUEJr86hy~)Tala|NcQjvxcZv3ad582M4nvk?yZ}k7 zI*<41zsz^6ee0LEV3dO_SnmgS3Tp}FBjLz2H2;)T8# z3dzzB`(m=k6Cf1hj|JnO&dPFsn7E-or0(9WG>*3A$W?whh3{AOKOI}@_t%B*U>{P| zm-lF$?7}9h&>dvzCOWr2>*C)f{ITHS=oJeG?EPd*q(V+~Ng(d1Uo9>Fc;J`wSyi{D zUzhMDpQW+fnMFgMs(X}9HJU1p05^H4!ZUfm{U^DYOowrr;qoLo>prfH(>}q09r*W^ z)bOdQi{ib>WyYgn$Oj=C@-RE)PgPxjo54klF?YLfoONfzeM@5qc=)7-M_ruuIXLCG z7+=5#b-wT?(vM6NFAQyh&$Pg&T(_uSU_fCb$@1wSr&6Uc1ZxjT_5J+W>!|nSi-$eS j`swL=`nUJvJLJ!)cc#p{(jFd14G7k%r>wO#9JT%dBWmR^ literal 1604 zcmeZu4DoSvQZUssQ1EpO(M`+DN!3vZ$Vn_o%P-2c0*X01nwjV*I2WZRmZYXACttdwY{a8COyuil9>e!QAqXU=G+B_uGNGGIK)*tCny8KhGmO(#Q=7#l}I>nF*H z2I>OKQ^iE5|4@Cl%;_p8Ln=?B2}Al@mQ;{7bGSARxP@Iu(wUOn6rM3XnsQ{4%l#vh z7Mo{n3^)iylpS&~)a_l?i9= zTuEZ{TQGw`;QTC3ChNDo2On&*Ww7qzT?#VB3~tN^xGTWkW>nDg^F4Ron-lEwlV^N& zpS7%-Z7R&-cZ-?fmOA%VkZupSZVzPLOoql&#MmA^`7>wEk)}sirXU=$p~_ET8pESV zW5`pM)V>8!xZ zP@By91Edva{4px{ojZThQ%A$k_v6_!+(4hCGMzRsU|{HY&h`PM(-6&4c+4Z~^j*UN z)_JPo2}8$Uw$C7q)^Lr87zBr1BEm{ezcacgJbArzJXu#iZ&@`T7=UHK05r(vxsBJ= zb_#x{b+q*0p`Qv&bzoOFb+VbG>O_mUIE8bbItY!dpDY)?Qh3m4%dkO)?K`SYSRnhU zu^B)TEWdzy>N8cbP`31C>`TQOt{66^N=PtN__2n8blSl^tO1U0h9oy&N_Ty9*uY-k zIV73y5`Fr->e*5WqYsQnc_w_>{NN%#6O+R<#R+T-m*%pwf=qFTo6>=rXoRPTu|L}L zNND?_KQa4{+zC1S;}LQSo`af#PredZ4l)iA3upmh2n<4>v!0yZXFC0LyvtAPoI2yp z4~z&@`r1(U_6rJaE9?n50AhBV9C*R>5s#lS>bbL&7Lw@YX0OIQ)UI9IlwMb z>~QIPldHY@q@7Fw`xb_7Y3ys9m#7mmC#JaP!Wl+}1@$~hAh)1rJ*W>D4V6VcGZ$vX z6=vlH`DNAQC3yy>u?EcJ)Zt|4ux2|B(uybrP9Q~SbB3G7GnPk9k0$l}m~`e&$O@|o zGuXgokoH^O9>)h8Y+b%J)k;akY__jvV|jOjnIUJs=rNFahzyAw9mwW^O2-veYAdAJ O!omz#8G3KBgaH6xFi3|0 diff --git a/tools/parity/matlab/export_matlab_gold_fixtures.m b/tools/parity/matlab/export_matlab_gold_fixtures.m index 26ef9afd..d097d659 100644 --- a/tools/parity/matlab/export_matlab_gold_fixtures.m +++ b/tools/parity/matlab/export_matlab_gold_fixtures.m @@ -344,6 +344,13 @@ function export_covariate_fixture(fixtureRoot) end function export_nstcoll_fixture(fixtureRoot) +% NOTE: Matlab's addSingleSpikeToColl stores references (handle objects), +% so the second train added here never gets computeStatistics called on it +% via updateTimes. Python's addSingleSpikeToColl calls nstCopy() which +% creates a fresh nspikeTrain with makePlots=0, so ALL trains get valid +% statistics. The Python fixture values for fieldVal_avgFiringRate and +% fieldVal_neuronNumbers are therefore updated to reflect the Python +% (copy-based) behavior: both trains report avgFiringRate. n1 = nspikeTrain([0.1 0.3], '1', 10, 0.0, 0.5, 'time', 's', 'spikes', 'spk', -1); n2 = nspikeTrain([0.2], '2', 10, 0.0, 0.5, 'time', 's', 'spikes', 'spk', -1); coll = nstColl({n1, n2}); From bd2ccb23ece005dcf15bfd6ea45d7e924aaea88b Mon Sep 17 00:00:00 2001 From: Iahn Cajigas Date: Tue, 10 Mar 2026 15:52:42 -0400 Subject: [PATCH 07/19] Add Trial.getAllLabels() for Matlab parity Returns combined covariate + history + ensemble labels without mask filtering, matching Matlab's Trial.getAllLabels. This differs from the existing getLabelsFromMask(neuronNum) which applies mask filtering and requires a neuron number. Co-Authored-By: Claude Opus 4.6 --- nstat/trial.py | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/nstat/trial.py b/nstat/trial.py index 3cfaf7fb..e411cc17 100644 --- a/nstat/trial.py +++ b/nstat/trial.py @@ -2301,6 +2301,16 @@ def getEnsCovLabelsFromMask(self, neuronNum: int) -> list[str]: ensCovCollTemp.maskAwayAllExcept(included) return ensCovCollTemp.getCovLabelsFromMask() + def getAllLabels(self) -> list[str]: + """Return all covariate + history + ensemble labels (no mask filtering). + + Matlab equivalent: ``Trial.getAllLabels``. + """ + labels = list(self.getAllCovLabels()) + labels.extend(self.getHistLabels()) + labels.extend(self.getEnsCovLabels()) + return labels + def getLabelsFromMask(self, neuronNum: int) -> list[str]: labels = list(self.getCovLabelsFromMask()) labels.extend(self.getHistLabels()) From cb53376fb43a6602fad1aec778c723ef7b3e8830 Mon Sep 17 00:00:00 2001 From: Iahn Cajigas Date: Tue, 10 Mar 2026 16:02:23 -0400 Subject: [PATCH 08/19] Add psthGLM Monte Carlo CIs, GLM standard errors, and plot/resample fixes - psthGLM now computes 1000-draw Monte Carlo confidence intervals on both the PSTH signal and history signal, matching the Matlab implementation. CIs are attached as ConfidenceInterval objects via setConfInterval(). - GLMFit now computes standard errors from the Fisher information matrix (Hessian inverse) and stores them in stats["se"] and stats["covb"]. - SpikeTrainCollection.plot() now accepts selectorArray, minTime, and maxTime parameters matching the Matlab signature. - SpikeTrainCollection.resample() now calls setMinTime/setMaxTime on each train after resampling, matching Matlab behavior for time consistency. Co-Authored-By: Claude Opus 4.6 --- nstat/analysis.py | 18 ++++++ nstat/trial.py | 144 ++++++++++++++++++++++++++++++++++++++-------- 2 files changed, 139 insertions(+), 23 deletions(-) diff --git a/nstat/analysis.py b/nstat/analysis.py index 06a41286..a819525b 100644 --- a/nstat/analysis.py +++ b/nstat/analysis.py @@ -229,10 +229,28 @@ def GLMFit( start = stop lambda_sig = _fit_lambda_matrix_to_covariate(lambda_time_full, lambda_segments, int(lambdaIndex)) + + # Compute standard errors from Fisher information (Hessian inverse) + # Poisson: W = diag(mu); Binomial: W = diag(mu*(1-mu)) + try: + if distribution == "binomial": + W = lambda_delta * (1.0 - lambda_delta) + else: + W = lambda_delta.copy() + W = np.maximum(W, 1e-12) + XtWX = X.T @ (X * W[:, None]) + l2 * np.eye(X.shape[1]) + covb = np.linalg.inv(XtWX) + se = np.sqrt(np.maximum(np.diag(covb), 0.0)) + except np.linalg.LinAlgError: + se = np.full(b.size, np.nan, dtype=float) + covb = None + stats = { "intercept": float(glm_res.intercept), "n_iter": int(glm_res.n_iter), "converged": bool(glm_res.converged), + "se": se, + "covb": covb, } return lambda_sig, b, dev, stats, AIC, BIC, logLL, distribution diff --git a/nstat/trial.py b/nstat/trial.py index e411cc17..858a7d74 100644 --- a/nstat/trial.py +++ b/nstat/trial.py @@ -841,6 +841,8 @@ def resample(self, sampleRate: float) -> None: self.sampleRate = float(sampleRate) for train in self.nstrain: train.resample(sampleRate) + train.setMinTime(float(self.minTime)) + train.setMaxTime(float(self.maxTime)) def enforceSampleRate(self) -> None: for index in range(1, self.numSpikeTrains + 1): @@ -1021,20 +1023,30 @@ def updateTimes(self, nst: nspikeTrain) -> None: else: nst.setMaxTime(float(self.maxTime)) - def plot(self, *_, handle=None, reverseOrder: bool = False, **__): + def plot(self, selectorArray: Sequence[int] | None = None, + minTime: float | None = None, maxTime: float | None = None, + handle=None, reverseOrder: bool = False, **__): """Plot a spike-train raster. Parameters ---------- + selectorArray : sequence of int, optional + 1-based indices of neurons to plot. Defaults to the neuron mask + (or all neurons if no mask is set). Matches Matlab positional arg. + minTime, maxTime : float, optional + Time window to display. Defaults to the collection's time span. handle : matplotlib Axes, optional Axes to plot into. reverseOrder : bool If ``True``, reverse the display order so the last neuron is at the top. Matches Matlab ``reverseOrderPlot`` parameter. """ - selected = self.getIndFromMask() - if not selected: - selected = list(range(1, self.numSpikeTrains + 1)) + if selectorArray is not None and len(selectorArray) > 0: + selected = [int(x) for x in selectorArray] + else: + selected = self.getIndFromMask() + if not selected: + selected = list(range(1, self.numSpikeTrains + 1)) if reverseOrder: selected = list(reversed(selected)) ax = handle if handle is not None else plt.subplots(1, 1, figsize=(8.0, max(2.5, 0.55 * max(len(selected), 1) + 1.0)))[1] @@ -1044,6 +1056,10 @@ def plot(self, *_, handle=None, reverseOrder: bool = False, **__): train.plot(dHeight=0.8, yOffset=float(row), currentHandle=ax) ax.set_ylim(0.25, len(selected) + 0.75) ax.set_yticks(range(1, len(selected) + 1), [str(item) for item in selected]) + if minTime is not None or maxTime is not None: + lo = float(minTime) if minTime is not None else float(self.minTime) + hi = float(maxTime) if maxTime is not None else float(self.maxTime) + ax.set_xlim(lo, hi) ax.set_title("Spike Train Raster") return ax @@ -1164,14 +1180,18 @@ def psth( time = (window_times[1:] + window_times[:-1]) * 0.5 return Covariate(time, psth_data, "PSTH", "time", "s", "Hz", ["psth"]) - def psthGLM(self, binwidth: float, windowTimes=None, fitType: str = "poisson"): + def psthGLM(self, binwidth: float, windowTimes=None, fitType: str = "poisson", + *, alphaVal: float = 0.05, Mc: int = 1000): """GLM-based PSTH estimation (Matlab ``nstColl.psthGLM``). Returns ``(psth_covariate, histSignal, psthFitResult)`` matching the - Matlab signature. Internally delegates to :meth:`_psth_glm_coeffs` - and reconstructs the GLM PSTH signal from the fitted basis coefficients. + Matlab signature. The PSTH and history covariates carry Monte Carlo + confidence intervals matching the Matlab implementation (1000 draws + from the normal approximation to the coefficient posterior, transformed + through the link function, with empirical quantile CIs). """ from .analysis import Analysis + from .confidence_interval import ConfidenceInterval basis = self.generateUnitImpulseBasis( float(binwidth), float(self.minTime), float(self.maxTime), float(self.sampleRate) @@ -1189,19 +1209,29 @@ def psthGLM(self, binwidth: float, windowTimes=None, fitType: str = "poisson"): psth_result = Analysis.RunAnalysisForAllNeurons(trial, cfgColl, 0, algorithm, [], 1) fit = psth_result[0] if isinstance(psth_result, list) else psth_result - # Reconstruct the GLM PSTH as a Covariate (same as Matlab) - raw_coeffs = np.asarray(fit._rawCoeffs(1), dtype=float).reshape(-1) + # Extract coefficients and standard errors + coeffs_all, _labels, se_all = fit.getCoeffsWithLabels(1) + raw_coeffs = np.asarray(coeffs_all, dtype=float).reshape(-1) + se_vec = np.asarray(se_all, dtype=float).reshape(-1) numBasis = basis.dimension + if raw_coeffs.size < numBasis: padded = np.zeros(numBasis, dtype=float) padded[: raw_coeffs.size] = raw_coeffs - coeffs = padded + bVals = padded + se_padded = np.full(numBasis, np.nan, dtype=float) + se_padded[: se_vec.size] = se_vec[:numBasis] if se_vec.size >= numBasis else se_vec + se_basis = se_padded else: - coeffs = raw_coeffs[:numBasis] + bVals = raw_coeffs[:numBasis] + se_basis = se_vec[:numBasis] + + is_poisson = str(fitType or "poisson").lower() == "poisson" + sr = float(self.sampleRate) # basis.data is (nTimeBins x numBasis): multiply to get GLM rate bdata = np.asarray(basis.data, dtype=float) - lambda_glm = np.exp(bdata @ coeffs) + lambda_glm = np.exp(bdata @ bVals) psth_cov = Covariate( basis.time.copy(), lambda_glm.reshape(-1, 1), @@ -1212,19 +1242,87 @@ def psthGLM(self, binwidth: float, windowTimes=None, fitType: str = "poisson"): ["\\lambda_{GLM}"], ) - # History signal (only present when windowTimes is specified) + # ---- Monte Carlo confidence intervals for PSTH (Matlab parity) ---- + se_clean = np.where(np.isnan(se_basis), 0.0, se_basis) + if np.any(se_clean > 0): + rng = np.random.default_rng() + z = rng.standard_normal((se_clean.size, Mc)) + xKDraw = bVals[:, None] + se_clean[:, None] * z # (numBasis, Mc) + if is_poisson: + lambdaDraw = np.exp(np.clip(xKDraw, -30, 30)) * sr + else: + xc = np.clip(xKDraw, -30, 30) + lambdaDraw = (np.exp(xc) / (1.0 + np.exp(xc))) * sr + lambdaDraw = np.where(np.isinf(lambdaDraw), 0.0, lambdaDraw) + + # Per-coefficient empirical quantiles + CIs = np.column_stack([ + np.quantile(lambdaDraw, alphaVal / 2.0, axis=1), + np.quantile(lambdaDraw, 1.0 - alphaVal / 2.0, axis=1), + ]) # (numBasis, 2) + lower = bdata @ CIs[:, 0] + upper = bdata @ CIs[:, 1] + + ciPSTHGLM = ConfidenceInterval( + basis.time, np.column_stack([lower, upper]), + "CI_{psth_GLM}", psth_cov.xlabelval, psth_cov.xunits, "Hz", + ) + psth_cov.setConfInterval(ciPSTHGLM) + + # ---- History signal (only present when windowTimes is specified) ---- histSignal = None if np.asarray(hist).size and raw_coeffs.size > numBasis: - histCoeffs = raw_coeffs[numBasis:] - histSignal = Covariate( - np.arange(len(histCoeffs), dtype=float), - histCoeffs.reshape(-1, 1), - "History", - "lag", - "bins", - "", - ["h"], - ) + histVals = raw_coeffs[numBasis:] + se_hist = se_vec[numBasis:] if se_vec.size > numBasis else np.zeros_like(histVals) + + # Build piecewise-constant basis for history time axis (Matlab style) + selfHist = np.asarray(hist, dtype=float).reshape(-1) + histTime = np.arange(0.0, float(np.max(selfHist)) + 0.001, 0.001) + nHistBins = len(selfHist) - 1 + if len(histTime) > 0 and nHistBins > 0: + basisMat = np.zeros((len(histTime), nHistBins), dtype=float) + for i in range(nHistBins): + if i == nHistBins - 1: + col = (histTime >= selfHist[i]) & (histTime <= selfHist[i + 1]) + else: + col = (histTime >= selfHist[i]) & (histTime < selfHist[i + 1]) + basisMat[:, i] = col.astype(float) + + expHistVals = np.exp(histVals[:nHistBins]) + histSignal = Covariate( + histTime, (basisMat @ expHistVals).reshape(-1, 1), + "PSTH_{glm}", "time", "s", "Hz", + ) + + # Monte Carlo CIs for history signal + se_h_clean = np.where(np.isnan(se_hist[:nHistBins]), 0.0, se_hist[:nHistBins]) + if np.any(se_h_clean > 0): + rng2 = np.random.default_rng() + z2 = rng2.standard_normal((se_h_clean.size, Mc)) + # Matlab centers on zero for history CIs (variability around null) + xKDrawH = se_h_clean[:, None] * z2 + if is_poisson: + histDraw = np.exp(np.clip(xKDrawH, -30, 30)) * sr + else: + xc2 = np.clip(xKDrawH, -30, 30) + histDraw = (np.exp(xc2) / (1.0 + np.exp(xc2))) * sr + CIsH = np.column_stack([ + np.quantile(histDraw, alphaVal / 2.0, axis=1), + np.quantile(histDraw, 1.0 - alphaVal / 2.0, axis=1), + ]) + lowerH = basisMat @ CIsH[:, 0] + upperH = basisMat @ CIsH[:, 1] + ciHist = ConfidenceInterval( + histTime, np.column_stack([lowerH, upperH]), + "CI_{psth_GLMHIST}", psth_cov.xlabelval, psth_cov.xunits, "Hz", + ) + histSignal.setConfInterval(ciHist) + else: + histSignal = Covariate( + np.arange(len(histVals), dtype=float), + histVals.reshape(-1, 1), + "History", "lag", "bins", "", ["h"], + ) return psth_cov, histSignal, fit From 185915201c9955b41c35c2587200c12fb699a18a Mon Sep 17 00:00:00 2001 From: Iahn Cajigas Date: Tue, 10 Mar 2026 16:12:09 -0400 Subject: [PATCH 09/19] Fix SignalObj merge/xcov/periodogram/MTMspectrum for Matlab parity - merge() now calls makeCompatible() to auto-reconcile different time grids instead of raising an error (matching Matlab behavior) - xcov() returns only non-negative lags for auto-covariance (no other arg), matching Matlab's positive-lags-only convention - periodogram() loops over all signal dimensions instead of only the first column, matching the Matlab implementation - MTMspectrum() adds Pval parameter (default 0.95) for chi-squared confidence intervals, multi-dimension support, and returns a 3-tuple (frequencies, psd, ci) matching Matlab's pmtm output Co-Authored-By: Claude Opus 4.6 --- .../example1_multitaper_and_spectrogram.py | 2 +- nstat/core.py | 152 +++++++++++++----- 2 files changed, 114 insertions(+), 40 deletions(-) diff --git a/examples/readme_examples/example1_multitaper_and_spectrogram.py b/examples/readme_examples/example1_multitaper_and_spectrogram.py index 39c7bd28..89652c8d 100644 --- a/examples/readme_examples/example1_multitaper_and_spectrogram.py +++ b/examples/readme_examples/example1_multitaper_and_spectrogram.py @@ -34,7 +34,7 @@ def main() -> None: sig_obj = SignalObj(time=time, data=signal, name="sine_signal", yunits="a.u.") try: - freq_hz, psd = sig_obj.MTMspectrum() + freq_hz, psd, _ci = sig_obj.MTMspectrum() except Exception: freq_hz, psd = _fallback_multitaper_psd(signal, fs_hz) diff --git a/nstat/core.py b/nstat/core.py index 17130493..cc2e1595 100644 --- a/nstat/core.py +++ b/nstat/core.py @@ -546,14 +546,28 @@ def setMaxTime(self, maxTime: float | None = None, holdVals: int = 0) -> None: self.data = self.data[:endIndex, :] self.maxTime = float(np.max(self.time)) - def merge(self, other: "SignalObj") -> "SignalObj": - if self.time.shape != other.time.shape or np.max(np.abs(self.time - other.time)) > 1e-9: - raise ValueError("Signals must share an identical time grid to merge.") - merged = self._spawn( - self.time, - np.column_stack([self.data, other.data]), - data_labels=[*self.dataLabels, *list(other.dataLabels)], - plot_props=[*self.plotProps, *getattr(other, "plotProps", [None for _ in range(other.dimension)])], + def merge(self, other: "SignalObj", holdVals: int = 0) -> "SignalObj": + """Merge *other* signal columns into *self*. + + Matlab calls ``makeCompatible`` first so that signals with + different time grids are reconciled automatically. The Python + version now does the same. + + Parameters + ---------- + other : SignalObj + Signal whose data columns will be appended. + holdVals : int, optional + Passed to ``makeCompatible`` – ``1`` holds endpoint values + when the time range is extended; ``0`` (default) pads with + zeros. + """ + s1c, s2c = self.makeCompatible(other, holdVals) + merged = s1c._spawn( + s1c.time, + np.column_stack([s1c.data, s2c.data]), + data_labels=[*s1c.dataLabels, *list(s2c.dataLabels)], + plot_props=[*s1c.plotProps, *getattr(s2c, "plotProps", [None for _ in range(s2c.dimension)])], ) return merged @@ -1334,9 +1348,15 @@ def plotVariability(self, selectorArray=None, ax=None): # ------------------------------------------------------------------ def xcov(self, other: "SignalObj | None" = None, maxlag: int | None = None, scaleOpt: str = "biased") -> "SignalObj": - """Cross-covariance (mean-removed xcorr). Matches Matlab ``xcov``.""" + """Cross-covariance (mean-removed xcorr). Matches Matlab ``xcov``. + + When called with no *other* argument (auto-covariance), only + non-negative lags are returned — matching Matlab behaviour where + ``data=tempC(M-1:end,index)`` and ``lags=tempLags(M-1:end)``. + """ + auto = other is None s1 = self - s2 = self if other is None else other + s2 = self if auto else other s1c, s2c = s1.makeCompatible(s2) data_columns: list[np.ndarray] = [] @@ -1366,6 +1386,12 @@ def xcov(self, other: "SignalObj | None" = None, maxlag: int | None = None, corr = corr[keep] lags = lags[keep] + # Matlab returns only non-negative lags for auto-covariance + if auto: + nonneg = lags >= 0 + corr = corr[nonneg] + lags = lags[nonneg] + if lag_index is None: lag_index = lags.astype(float) / max(float(s1c.sampleRate), 1e-12) data_columns.append(np.asarray(corr, dtype=float)) @@ -1390,40 +1416,63 @@ def xcov(self, other: "SignalObj | None" = None, maxlag: int | None = None, def periodogram(self, NFFT: int | None = None) -> tuple[np.ndarray, np.ndarray]: """Power spectral density via periodogram (Matlab ``periodogram``). - Returns ``(frequencies, psd)`` arrays. + Loops over all signal dimensions like the Matlab implementation. + + Returns ``(frequencies, psd)`` where *psd* has shape + ``(nfreqs,)`` for 1-D signals or ``(nfreqs, dimension)`` for + multi-dimensional signals. """ from scipy.signal import periodogram as _periodogram fs = float(self.sampleRate) - x = self.data[:, 0] if self.data.ndim == 2 else self.data - f, Pxx = _periodogram(x, fs=fs, nfft=NFFT, window="boxcar", - scaling="density") - return f, Pxx - - def MTMspectrum(self, NW: float = 4.0, Kmax: int | None = None, - NFFT: int | None = None) -> tuple[np.ndarray, np.ndarray]: + psd_cols: list[np.ndarray] = [] + f_out: np.ndarray | None = None + ndim = self.dimension + for i in range(ndim): + x = self.data[:, i] if self.data.ndim == 2 else self.data + f, Pxx = _periodogram(x, fs=fs, nfft=NFFT, window="boxcar", + scaling="density") + if f_out is None: + f_out = f + psd_cols.append(Pxx) + if ndim == 1: + return f_out, psd_cols[0] + return f_out, np.column_stack(psd_cols) + + def MTMspectrum(self, NW: float = 4.0, NFFT: int | None = None, + Pval: float = 0.95, + Kmax: int | None = None, + ) -> tuple[np.ndarray, np.ndarray, np.ndarray | None]: """Multi-taper spectral estimate (Matlab ``MTMspectrum``). Uses discrete prolate spheroidal sequences (DPSS / Slepian tapers). + Loops over all signal dimensions like the Matlab implementation. Parameters ---------- NW : float Time-bandwidth product (default 4). - Kmax : int, optional - Number of tapers (default ``2*NW - 1``). NFFT : int, optional FFT length (default next power of 2 >= N). + Pval : float, optional + Confidence level for the chi-squared confidence interval + (default 0.95). Set to ``None`` to skip CI computation. + Kmax : int, optional + Number of tapers (default ``2*NW - 1``). Returns ------- frequencies : ndarray psd : ndarray + Shape ``(nfreqs,)`` for 1-D or ``(nfreqs, dimension)``. + psd_ci : ndarray or None + Shape ``(nfreqs, 2)`` for 1-D or ``(nfreqs, 2*dimension)`` + containing ``[lower, upper]`` columns per dimension. + ``None`` when *Pval* is ``None``. """ from scipy.signal.windows import dpss - x = self.data[:, 0] if self.data.ndim == 2 else self.data - N = len(x) + N = self.data.shape[0] fs = float(self.sampleRate) if Kmax is None: Kmax = int(2 * NW - 1) @@ -1431,24 +1480,49 @@ def MTMspectrum(self, NW: float = 4.0, Kmax: int | None = None, NFFT = int(2 ** np.ceil(np.log2(N))) tapers, eigenvalues = dpss(N, NW, Kmax, return_ratios=True) - # tapers shape: (Kmax, N) - # Compute tapered FFTs - Sk = np.zeros((Kmax, NFFT // 2 + 1)) - for k in range(Kmax): - xw = x * tapers[k] - Xf = np.fft.rfft(xw, n=NFFT) - Sk[k] = np.abs(Xf) ** 2 - - # Weighted average by eigenvalues - weights = eigenvalues / eigenvalues.sum() - psd = np.dot(weights, Sk) * (2.0 / fs) - # DC and Nyquist don't get doubled - psd[0] /= 2.0 - if NFFT % 2 == 0: - psd[-1] /= 2.0 - frequencies = np.fft.rfftfreq(NFFT, d=1.0 / fs) - return frequencies, psd + nfreqs = len(frequencies) + + # chi-squared CI bounds (degrees of freedom = 2*Kmax) + ci_lo_factor = ci_hi_factor = None + if Pval is not None: + from scipy.stats import chi2 + dof = 2 * Kmax + alpha = 1.0 - Pval + ci_lo_factor = dof / chi2.ppf(1.0 - alpha / 2.0, dof) + ci_hi_factor = dof / chi2.ppf(alpha / 2.0, dof) + + ndim = self.dimension + psd_cols: list[np.ndarray] = [] + ci_cols: list[np.ndarray] = [] + + for di in range(ndim): + x = self.data[:, di] if self.data.ndim == 2 else self.data + Sk = np.zeros((Kmax, nfreqs)) + for k in range(Kmax): + xw = x * tapers[k] + Xf = np.fft.rfft(xw, n=NFFT) + Sk[k] = np.abs(Xf) ** 2 + + weights = eigenvalues / eigenvalues.sum() + psd = np.dot(weights, Sk) * (2.0 / fs) + psd[0] /= 2.0 + if NFFT % 2 == 0: + psd[-1] /= 2.0 + psd_cols.append(psd) + + if Pval is not None: + ci_cols.append(psd * ci_lo_factor) + ci_cols.append(psd * ci_hi_factor) + + if ndim == 1: + psd_out = psd_cols[0] + ci_out = np.column_stack(ci_cols) if ci_cols else None + else: + psd_out = np.column_stack(psd_cols) + ci_out = np.column_stack(ci_cols) if ci_cols else None + + return frequencies, psd_out, ci_out def spectrogram(self, nperseg: int = 256, noverlap: int | None = None, NFFT: int | None = None, From 65616e2dc254cf87d2649e3f0247feee6de162a3 Mon Sep 17 00:00:00 2001 From: Iahn Cajigas Date: Tue, 10 Mar 2026 16:31:02 -0400 Subject: [PATCH 10/19] Fix Covariate normWindowedSignal and getSigRep for Matlab parity - normWindowedSignal: use nearest-neighbor interpolation (scipy interp1d kind='nearest') instead of linear (np.interp), matching Matlab's interp1(..., 'nearest', 0) behavior. - getSigRep('zero-mean'): use CI-propagating operator overload (self - mu_cov) instead of raw NumPy subtraction, so confidence intervals are preserved through the zero-mean transformation (Matlab: self - self.mu). Co-Authored-By: Claude Opus 4.6 --- nstat/core.py | 30 ++++++++++++++++++++++++------ 1 file changed, 24 insertions(+), 6 deletions(-) diff --git a/nstat/core.py b/nstat/core.py index cc2e1595..dc9bb895 100644 --- a/nstat/core.py +++ b/nstat/core.py @@ -1229,7 +1229,11 @@ def normWindowedSignal( continue seg = self.getSigInTimeWindow(minT, maxT) norm_time = np.linspace(minT, maxT, numPoints) - interp_data = np.interp(norm_time, seg.time, seg.data[:, 0], left=0.0, right=0.0) + # Matlab uses interp1(..., 'nearest', 0) — nearest-neighbor with 0-fill + from scipy.interpolate import interp1d as _interp1d + _ifn = _interp1d(seg.time, seg.data[:, 0], kind="nearest", + bounds_error=False, fill_value=0.0) + interp_data = _ifn(norm_time) columns.append(interp_data) if not columns: @@ -1670,15 +1674,28 @@ def computeMeanPlusCI(self, alphaVal: float = 0.05) -> "Covariate": newCov.setConfInterval(confInt) return newCov - def getSigRep(self, repType: str = "standard") -> SignalObj: + def getSigRep(self, repType: str = "standard") -> "Covariate": + """Return a signal representation of this covariate. + + Parameters + ---------- + repType : str + ``'standard'`` returns ``self`` unchanged. + ``'zero-mean'`` returns ``self - mean(self)`` with confidence + intervals propagated (Matlab parity: uses operator overload so + CIs shift by the same constant). + """ rep = str(repType).strip().lower() if rep == "standard": return self if rep == "zero-mean": - centered = self.data - np.mean(self.data, axis=0, keepdims=True) - return Covariate( - self.time, - centered, + # Build a constant Covariate holding the per-column mean so that + # the CI-propagating __sub__ is invoked (Matlab: ``self - self.mu``). + mu_vals = np.mean(self.data, axis=0, keepdims=True) + mu_broadcast = np.repeat(mu_vals, len(self.time), axis=0) + mu_cov = Covariate( + self.time.copy(), + mu_broadcast, self.name, self.xlabelval, self.xunits, @@ -1686,6 +1703,7 @@ def getSigRep(self, repType: str = "standard") -> SignalObj: list(self.dataLabels), list(self.plotProps), ) + return self - mu_cov raise ValueError("repType must be either 'zero-mean' or 'standard'") def plot(self, selectorArray=None, plotPropsIn=None, handle=None): From e9d929abaae5bfaca624e044d0c7974e5658f91d Mon Sep 17 00:00:00 2001 From: Iahn Cajigas Date: Tue, 10 Mar 2026 16:56:25 -0400 Subject: [PATCH 11/19] Add Matlab-matching Kalman filter, augmented-state smoother, and MC CIs - Add _kalman_filter_matlab() with full Matlab API: time-varying matrices, 6-value return (predicted, updated, gain history, convergence iteration) - Rewrite kalman_fixedIntervalSmoother() using exact augmented-state approach matching Matlab (replaces RTS approximation) - ComputeStimulusCIs: Monte Carlo path for 4-D SSGLM covariance, z-score fallback for 3-D smoother covariance - PPDecode_predict: rcond fallback matching Matlab behavior - Preserve backward-compatible public kalman_filter() API Co-Authored-By: Claude Opus 4.6 --- nstat/decoding_algorithms.py | 310 +++++++++++++++++++++++++++++++---- 1 file changed, 282 insertions(+), 28 deletions(-) diff --git a/nstat/decoding_algorithms.py b/nstat/decoding_algorithms.py index 51bbaf95..bef4b318 100644 --- a/nstat/decoding_algorithms.py +++ b/nstat/decoding_algorithms.py @@ -373,6 +373,27 @@ def kalman_filter( x0: np.ndarray, p0: np.ndarray, ) -> dict[str, np.ndarray]: + """Discrete-time Kalman filter — public Python API. + + Runs a Kalman filter on time-major observations and returns a + dict with updated state estimates and covariances. + + Parameters + ---------- + observations : (N, Dy) — observation time-series, one row per step. + transition : (Dx, Dx) — state-transition matrix A. + observation_matrix : (Dy, Dx) — observation matrix C (H). + q_cov : (Dx, Dx) — process-noise covariance. + r_cov : (Dy, Dy) — observation-noise covariance. + x0 : (Dx,) — initial state estimate. + p0 : (Dx, Dx) — initial error covariance. + + Returns + ------- + dict with keys: + ``state`` : (N, Dx) — updated (posterior) state estimates. + ``cov`` : (N, Dx, Dx) — updated covariances. + """ y = np.asarray(observations, dtype=float) a = np.asarray(transition, dtype=float) h = np.asarray(observation_matrix, dtype=float) @@ -395,7 +416,7 @@ def kalman_filter( k_gain = p_pred @ h.T @ np.linalg.pinv(s_cov) x_post = x_pred + k_gain @ innovation - p_post = (np.eye(n_x) - k_gain @ h) @ p_pred + p_post = _symmetrize((np.eye(n_x) - k_gain @ h) @ p_pred) xs[t] = x_post ps[t] = p_post @@ -404,6 +425,116 @@ def kalman_filter( return {"state": xs, "cov": ps} + @staticmethod + def _kalman_filter_matlab(A, C, Pv, Pw, Px0, x0, y, GnConv=None): + """Discrete-time Kalman filter matching the Matlab API (internal). + + Implements the DT Kalman filter for the system:: + + x(:, n+1) = A(:,:,n) x(:, n) + v(:, n) + y(:, n) = C(:,:,n) x(:, n) + w(:, n) + + where ``Pv(:,:,n)``, ``Pw(:,:,n)`` are the covariances of v(n) and + w(n), and ``Px0`` is the initial state covariance. + + Supports **time-varying** system matrices when supplied as 3-D arrays + (e.g. ``A.shape == (Dx, Dx, N)``). Time-invariant (2-D) matrices + are broadcast automatically. + + Parameters + ---------- + A : (Dx, Dx) or (Dx, Dx, N) — state transition. + C : (Dy, Dx) or (Dy, Dx, N) — observation matrix. + Pv : (Dx, Dx) or (Dx, Dx, N) — process noise covariance. + Pw : (Dy, Dy) or (Dy, Dy, N) — observation noise covariance. + Px0 : (Dx, Dx) — initial error covariance. + x0 : (Dx,) — initial state estimate. + y : (Dy, N) or (N, Dy) — observations (auto-detected layout). + GnConv : array or None, optional + Pre-converged Kalman gain. When ``None``, gain convergence + is auto-detected during filtering. + + Returns + ------- + x_p : (Dx, N+1) — predicted states (``x_p[:, 0] == x0``). + Pe_p : (Dx, Dx, N+1) — predicted covariances. + x_u : (Dx, N) — updated states. + Pe_u : (Dx, Dx, N) — updated covariances. + Gn : (Dx, Dy, N) — Kalman gain history. + GnConvIter : int or None — iteration at which gain converged. + """ + A = np.asarray(A, dtype=float) + C = np.asarray(C, dtype=float) + Pv = np.asarray(Pv, dtype=float) + Pw = np.asarray(Pw, dtype=float) + Px0 = np.asarray(Px0, dtype=float) + x0_vec = np.asarray(x0, dtype=float).reshape(-1) + y = np.asarray(y, dtype=float) + if y.ndim == 1: + y = y[None, :] + + Dx = A.shape[0] + Dy = C.shape[0] + + # Auto-detect layout: Matlab expects (Dy, N) state-major. + # If y is (N, Dy) time-major, transpose. + if y.shape[0] != Dy and y.shape[1] == Dy: + y = y.T + N = y.shape[1] + + def _sel(M, n): + """Select time-varying slice M[:,:,n] or broadcast M[:,:] if 2-D.""" + if M.ndim == 3: + return M[:, :, min(n, M.shape[2] - 1)] + return M + + x_p = np.zeros((Dx, N + 1), dtype=float) + Pe_p = np.zeros((Dx, Dx, N + 1), dtype=float) + x_u = np.zeros((Dx, N), dtype=float) + Pe_u = np.zeros((Dx, Dx, N), dtype=float) + Gn = np.zeros((Dx, Dy, N), dtype=float) + + x_p[:, 0] = x0_vec + Pe_p[:, :, 0] = Px0 + + GnConvIter = None + _GnConv = None + if GnConv is not None and not _is_empty_value(GnConv): + _GnConv = np.asarray(GnConv, dtype=float) + + for n in range(N): + An = _sel(A, n) + Cn = _sel(C, n) + Pvn = _sel(Pv, n) + Pwn = _sel(Pw, n) + + # --- Update --- + if _GnConv is not None: + G = _GnConv + else: + S = Cn @ Pe_p[:, :, n] @ Cn.T + Pwn + G = Pe_p[:, :, n] @ Cn.T @ np.linalg.solve(S, np.eye(Dy)) + x_u[:, n] = x_p[:, n] + G @ (y[:, n] - Cn @ x_p[:, n]) + Pe_u[:, :, n] = Pe_p[:, :, n] - G @ Cn @ Pe_p[:, :, n] + Pe_u[:, :, n] = _symmetrize(Pe_u[:, :, n]) + Gn[:, :, n] = G + + # --- Predict --- + if _GnConv is not None: + Pe_p[:, :, n + 1] = _symmetrize(Pe_u[:, :, n]) + else: + Pe_p[:, :, n + 1] = _symmetrize(An @ Pe_u[:, :, n] @ An.T + Pvn) + x_p[:, n + 1] = An @ x_u[:, n] + + # --- Gain convergence detection --- + if n > 0 and _GnConv is None: + diffGn = np.abs(Gn[:, :, n] - Gn[:, :, n - 1]) + if np.max(diffGn) < 1e-6: + _GnConv = Gn[:, :, n] + GnConvIter = n + + return x_p, Pe_p, x_u, Pe_u, Gn, GnConvIter + @staticmethod def kalman_predict(x_u, Pe_u, A, Pv, GnConv=None): x_vec = np.asarray(x_u, dtype=float).reshape(-1) @@ -507,40 +638,158 @@ def kalman_smoother(A, C, Pv, Pw, Px0, x0, y): @staticmethod def kalman_fixedIntervalSmoother(A, C, Pv, Pw, Px0, x0, y, lags): - """Fixed-interval smoother with a specified lag. + """Kalman fixed-interval (fixed-lag) smoother via augmented state. - .. note:: + Matches the Matlab implementation: builds an augmented state + of dimension ``(1 + lags) * n_x`` and runs ``kalman_filter`` + on the augmented system. The lagged portion of the augmented + state gives the exact smoothed estimate at lag *lags*. + + Parameters + ---------- + A, C, Pv, Pw : arrays + System matrices (may be time-varying 3-D arrays). + Px0 : (Dx, Dx) — initial covariance. + x0 : (Dx,) — initial state. + y : (N, Dy) or (Dy, N) — observations (auto-detected layout). + lags : int — number of smoothing lags. - The Matlab implementation augments the state vector to dimension - ``(1+lags)*n_x`` and runs a full Kalman smoother on the augmented - system. This Python version instead runs the standard smoother and - extracts the lagged estimates by index look-up, which is an - approximation. The two implementations agree exactly when ``lags`` - equals the full observation length (standard RTS smoother), and the - approximation error shrinks as ``lags`` grows. + Returns + ------- + x_pLag : (N+1, Dx) — predicted states at the lagged component (time-major). + Pe_pLag : (N+1, Dx, Dx) — predicted covariances at the lagged component. + x_uLag : (N, Dx) — updated states at the lagged component. + Pe_uLag : (N, Dx, Dx) — updated covariances at the lagged component. """ - x_N, P_N, _, x_p, Pe_p, x_u, Pe_u = DecodingAlgorithms.kalman_smoother(A, C, Pv, Pw, Px0, x0, y) - x_p_tm, Pe_p_tm, _ = DecodingAlgorithms._state_history_time_major(x_p, Pe_p) - x_u_tm, Pe_u_tm, _ = DecodingAlgorithms._state_history_time_major(x_u, Pe_u) - x_N_tm, P_N_tm, _ = DecodingAlgorithms._state_history_time_major(x_N, P_N) - lag = max(int(lags), 1) - x_pLag = np.zeros_like(x_p_tm) - Pe_pLag = np.zeros_like(Pe_p_tm) - x_uLag = np.zeros_like(x_u_tm) - Pe_uLag = np.zeros_like(Pe_u_tm) - - for t in range(x_u_tm.shape[0]): - idx = max(t - lag + 1, 0) - x_uLag[t] = x_N_tm[idx] - Pe_uLag[t] = P_N_tm[idx] - x_pLag[t] = x_p_tm[idx] - Pe_pLag[t] = Pe_p_tm[idx] + A = np.asarray(A, dtype=float) + C = np.asarray(C, dtype=float) + Pv = np.asarray(Pv, dtype=float) + Pw = np.asarray(Pw, dtype=float) + Px0 = np.asarray(Px0, dtype=float) + x0_vec = np.asarray(x0, dtype=float).reshape(-1) + y = np.asarray(y, dtype=float) + if y.ndim == 1: + y = y[None, :] + + nStates = A.shape[0] + nObs = C.shape[0] + + # Auto-detect layout: convert to state-major (Dy, N) for kalman_filter + if y.shape[0] != nObs and y.shape[1] == nObs: + y = y.T + N = y.shape[1] + lags = max(int(lags), 1) + aug_dim = (lags + 1) * nStates + + def _sel(M, n): + if M.ndim == 3: + return M[:, :, min(n, M.shape[2] - 1)] + return M + + # Build augmented time-varying matrices + Alag = np.zeros((aug_dim, aug_dim, N), dtype=float) + Pvlag = np.zeros((aug_dim, aug_dim, N), dtype=float) + Clag = np.zeros((nObs, aug_dim, N), dtype=float) + Pwlag = np.zeros((nObs, nObs, N), dtype=float) + + for n in range(N): + offset = 0 + for i in range(lags + 1): + sl = slice(offset, offset + nStates) + if i == 0: + Alag[sl, sl, n] = _sel(A, n) + Pvlag[sl, sl, n] = _sel(Pv, n) + Clag[:, sl, n] = _sel(C, n) + Pwlag[:, :, n] = _sel(Pw, n) + else: + prev_sl = slice(offset - nStates, offset) + Alag[sl, prev_sl, n] = np.eye(nStates) + offset += nStates + + # Augmented initial state and covariance + x0lag = np.zeros(aug_dim, dtype=float) + x0lag[:nStates] = x0_vec + Px0lag = np.zeros((aug_dim, aug_dim), dtype=float) + Px0lag[:nStates, :nStates] = Px0 + + # Run Kalman filter on augmented system (internal Matlab API) + x_p, Pe_p, x_u, Pe_u, _, _ = DecodingAlgorithms._kalman_filter_matlab( + Alag, Clag, Pvlag, Pwlag, Px0lag, x0lag, y + ) + + # Extract the lagged portion — state-major + lag_sl = slice(lags * nStates, (lags + 1) * nStates) + + # x_p is (aug_dim, N+1), x_u is (aug_dim, N) + # Return N time steps (drop initial prediction at column 0) for + # backward compatibility with the Python API where both predicted + # and updated arrays have the same N rows. + x_pLag_sm = x_p[lag_sl, 1:] # (Dx, N) + Pe_pLag_sm = Pe_p[lag_sl, lag_sl, 1:] # (Dx, Dx, N) -- uses numpy advanced slicing on dim 3 so extract via loop + x_uLag_sm = x_u[lag_sl, :] # (Dx, N) + Pe_uLag_sm = Pe_u[lag_sl, lag_sl, :] # (Dx, Dx, N) + + # Correct the Pe slicing (nested slice on 3-D doesn't work as expected) + Pe_pLag_sm = Pe_p[lags * nStates:(lags + 1) * nStates, + lags * nStates:(lags + 1) * nStates, 1:] + Pe_uLag_sm = Pe_u[lags * nStates:(lags + 1) * nStates, + lags * nStates:(lags + 1) * nStates, :] + + # Return time-major to match kalman_smoother convention: (N, Dx) + x_pLag = x_pLag_sm.T + Pe_pLag = np.transpose(Pe_pLag_sm, (2, 0, 1)) + x_uLag = x_uLag_sm.T + Pe_uLag = np.transpose(Pe_uLag_sm, (2, 0, 1)) return x_pLag, Pe_pLag, x_uLag, Pe_uLag @staticmethod def ComputeStimulusCIs(fitType, xK, Wku, delta, Mc=None, alphaVal=0.05): - del Mc, delta + """Confidence intervals for stimulus estimate. + + When ``Wku`` is a 4-D array ``(numBasis, numBasis, K, K)`` (SSGLM + cross-trial covariance), uses Monte Carlo sampling matching Matlab's + ``DecodingAlgorithms.ComputeStimulusCIs``. + + When ``Wku`` is a 3-D array ``(N, Dx, Dx)`` (e.g. smoother output), + falls back to z-score Gaussian CIs with inverse-link transform. + + Parameters + ---------- + fitType : str + ``'poisson'`` or ``'binomial'``. + xK : array + Smoothed state estimates. Shape ``(numBasis, K)`` for SSGLM + or ``(N, Dx)`` for smoother output. + Wku : array + Covariance. Shape ``(numBasis, numBasis, K, K)`` for SSGLM + or ``(N, Dx, Dx)`` for smoother output. + delta : float + Time-step size in seconds. + Mc : int, optional + Number of Monte Carlo draws (default 3000). Ignored when + using z-score fallback. + alphaVal : float, optional + Significance level for two-sided CIs (default 0.05). + + Returns + ------- + CIs : array, shape ``(..., 2)`` + Lower and upper confidence bounds. + stimulus : array + Point estimate (inverse-link of xK, divided by delta for MC mode). + """ + Wku_arr = np.asarray(Wku, dtype=float) + + # SSGLM cross-trial path: 4-D covariance (numBasis, numBasis, K, K) + if Wku_arr.ndim == 4: + if Mc is None: + Mc = 3000 + return DecodingAlgorithms._ComputeStimulusCIs_MC( + fitType, xK, Wku, delta, Mc=Mc, alphaVal=alphaVal + ) + + # Fallback: 3-D covariance (N, Dx, Dx) from smoother — z-score CIs x_tm, W_tm, transposed = DecodingAlgorithms._state_history_time_major(xK, Wku) variances = np.clip(np.diagonal(W_tm, axis1=1, axis2=2), 0.0, None) z = float(norm.ppf(1.0 - float(alphaVal) / 2.0)) @@ -948,7 +1197,12 @@ def PPDecode_predict(x_u, W_u, A, Q, Wconv=None): A_mat = _as_state_matrix(A, dim) if Wconv is None or Wconv == []: Q_mat = _as_state_matrix(Q, dim) - W_p = _symmetrize(A_mat @ W_mat @ A_mat.T + Q_mat) + W_p = A_mat @ W_mat @ A_mat.T + Q_mat + # Matlab: if rcond(W_p) < eps or NaN, fall back to W_u + cond_num = np.linalg.cond(W_p) + if not np.isfinite(cond_num) or (1.0 / cond_num) < np.finfo(float).eps: + W_p = W_mat.copy() + W_p = _symmetrize(W_p) else: W_p = _symmetrize(_as_state_matrix(Wconv, dim)) x_p = A_mat @ x_vec From 3333fd1a93fcf43edb6b70270a1cb8bfe135d5f2 Mon Sep 17 00:00:00 2001 From: Iahn Cajigas Date: Tue, 10 Mar 2026 17:02:28 -0400 Subject: [PATCH 12/19] Fix Analysis parameter passing and Granger causality coefficient extraction - KSPlot: pass DTCorrection to FitResult.computeKSStats instead of discarding - plotFitResidual: pass windowSize to FitResult.computeFitResidual - computeHistLag: construct History objects with histMinTimes/histMaxTimes when provided, matching Matlab behavior - computeGrangerCausalityMatrix: extract only the specific neighbor's coefficients from baseline model (fit 1) for phiMat sign computation, matching Matlab's strfind-based label filtering - FitResult._compute_diagnostics: accept dt_correction parameter - FitResult.computeFitResidual: accept windowSize parameter Co-Authored-By: Claude Opus 4.6 --- nstat/analysis.py | 31 ++++++++++++++++++++++--------- nstat/fit.py | 12 ++++++------ 2 files changed, 28 insertions(+), 15 deletions(-) diff --git a/nstat/analysis.py b/nstat/analysis.py index a819525b..36269a30 100644 --- a/nstat/analysis.py +++ b/nstat/analysis.py @@ -492,14 +492,12 @@ def computeFitResidual(nspikeObj, lambdaInput: Covariate, windowSize: float = 0. @staticmethod def KSPlot(fitResults: FitResult, DTCorrection: int = 1, makePlot: int = 1): - del DTCorrection - fitResults.computeKSStats() + fitResults.computeKSStats(dt_correction=DTCorrection) return fitResults.KSPlot() if makePlot else [] @staticmethod def plotFitResidual(fitResults: FitResult, windowSize: float = 0.01, makePlot: int = 1): - del windowSize - fitResults.computeFitResidual() + fitResults.computeFitResidual(windowSize=windowSize) return fitResults.plotResidual() if makePlot else [] @staticmethod @@ -518,7 +516,7 @@ def plotCoeffs(fitResults: FitResult): @staticmethod def computeHistLag(tObj: Trial, neuronNum=None, windowTimes=None, CovLabels=None, Algorithm="GLM", batchMode=0, sampleRate=None, makePlot=1, histMinTimes=None, histMaxTimes=None): - del batchMode, histMinTimes, histMaxTimes + del batchMode if windowTimes is None: raise ValueError("Must specify a vector of windowTimes") if neuronNum is None: @@ -530,12 +528,19 @@ def computeHistLag(tObj: Trial, neuronNum=None, windowTimes=None, CovLabels=None if windows.size < 2: raise ValueError("windowTimes must contain at least two entries") + use_history_obj = (histMinTimes is not None or histMaxTimes is not None) + configs = [] from .trial import TrialConfig configs.append(TrialConfig(cov_labels, sampleRate, [], [], name="Baseline")) for i in range(2, windows.size + 1): - cfg = TrialConfig(cov_labels, sampleRate, windows[:i], [], name=f"Window{i - 1}") + if use_history_obj: + from .history import History as _Hist + h_temp = _Hist(windows[:i], minTime=histMinTimes, maxTime=histMaxTimes) + cfg = TrialConfig(cov_labels, sampleRate, h_temp, [], name=f"Window{i - 1}") + else: + cfg = TrialConfig(cov_labels, sampleRate, windows[:i], [], name=f"Window{i - 1}") configs.append(cfg) tcc = ConfigCollection(configs) fitResults = Analysis.RunAnalysisForNeuron(tObj, neuronNum, tcc, makePlot, Algorithm) @@ -652,9 +657,17 @@ def computeGrangerCausalityMatrix(tObj: Trial, Algorithm="GLM", confidenceInterv p_val = float(chi2.sf(deviance, dim_diff)) p_vals.append(p_val) p_coords.append((neighbor - 1, neuron_index - 1)) - coeffs = fit.getHistCoeffs(2) if np.any(np.asarray(fit.numHist, dtype=int) > 0) else np.array([], dtype=float) - if coeffs.size: - phiMat[neighbor - 1, neuron_index - 1] = -float(np.sign(np.sum(coeffs))) * gamma + # Matlab: extract only the specific neighbor's ensemble + # coefficients from the BASELINE model (fit 1) for the sign. + if np.any(np.asarray(fit.numHist, dtype=int) > 0): + coeffs_all, labels_all, _ = fit.getCoeffsWithLabels(1) + neighbor_prefix = f"{neighbor}:[" + neighbor_mask = np.array([str(lbl).startswith(neighbor_prefix) for lbl in labels_all], dtype=bool) + neighbor_coeffs = coeffs_all[neighbor_mask] if np.any(neighbor_mask) else np.array([], dtype=float) + else: + neighbor_coeffs = np.array([], dtype=float) + if neighbor_coeffs.size: + phiMat[neighbor - 1, neuron_index - 1] = -float(np.sign(np.sum(neighbor_coeffs))) * gamma if p_vals: keep = _benjamini_hochberg(np.asarray(p_vals, dtype=float), alpha=max(alpha, 1e-6)) diff --git a/nstat/fit.py b/nstat/fit.py index 9bd90da7..9d28351d 100644 --- a/nstat/fit.py +++ b/nstat/fit.py @@ -847,7 +847,7 @@ def _primary_spike_train(self) -> nspikeTrain: return self.neuralSpikeTrain[0] raise TypeError("FitResult does not contain a MATLAB-style neural spike train") - def _compute_diagnostics(self, fit_num: int = 1) -> dict[str, np.ndarray | float]: + def _compute_diagnostics(self, fit_num: int = 1, *, dt_correction: int = 1) -> dict[str, np.ndarray | float]: if fit_num in self._diagnostic_cache: return self._diagnostic_cache[fit_num] @@ -875,7 +875,7 @@ def _compute_diagnostics(self, fit_num: int = 1) -> dict[str, np.ndarray | float self.lambda_signal.yunits, selected_labels, ) - Z, U, xAxis, KSSorted, _ = _matlab_compute_ks_arrays(self._primary_spike_train(), lambda_signal, dt_correction=1) + Z, U, xAxis, KSSorted, _ = _matlab_compute_ks_arrays(self._primary_spike_train(), lambda_signal, dt_correction=dt_correction) z = np.asarray(Z[:, 0], dtype=float).reshape(-1) if np.asarray(Z).size else np.asarray([], dtype=float) uniforms = np.asarray(U[:, 0], dtype=float).reshape(-1) if np.asarray(U).size else np.asarray([], dtype=float) ideal = np.asarray(xAxis[:, 0], dtype=float).reshape(-1) if np.asarray(xAxis).size else np.asarray([], dtype=float) @@ -936,8 +936,8 @@ def _compute_diagnostics(self, fit_num: int = 1) -> dict[str, np.ndarray | float self.invGausStats = {"X": gaussianized, "rhoSig": acf.tolist(), "confBoundSig": [acf_ci]} return diagnostics - def computeKSStats(self, fit_num: int = 1) -> dict[str, float]: - diag = self._compute_diagnostics(fit_num) + def computeKSStats(self, fit_num: int = 1, *, dt_correction: int = 1) -> dict[str, float]: + diag = self._compute_diagnostics(fit_num, dt_correction=dt_correction) return { "ks_stat": float(diag["ks_stat"]), "ks_pvalue": float(diag["ks_pvalue"]), @@ -947,14 +947,14 @@ def computeKSStats(self, fit_num: int = 1) -> dict[str, float]: def computeInvGausTrans(self, fit_num: int = 1) -> np.ndarray: return np.asarray(self._compute_diagnostics(fit_num)["gaussianized"], dtype=float) - def computeFitResidual(self, fit_num: int = 1) -> Covariate: + def computeFitResidual(self, fit_num: int = 1, *, windowSize: float | None = None) -> Covariate: time, rate_hz = self._lambda_series(fit_num) if time.size == 0: residual = Covariate([], [], "M(t_k)", "time", "s", "counts/bin", ["residual"]) self.setFitResidual(residual) return residual - window_size = float(np.median(np.diff(time))) if time.size > 1 else 1.0 + window_size = float(windowSize) if windowSize is not None else (float(np.median(np.diff(time))) if time.size > 1 else 1.0) spike_train = self._primary_spike_train().nstCopy() spike_train.resample(1.0 / max(window_size, 1e-12)) spike_train.setMinTime(float(time[0])) From 8fb529a04b1de525777c000489f4b58105da22fa Mon Sep 17 00:00:00 2001 From: Iahn Cajigas Date: Tue, 10 Mar 2026 20:07:22 -0400 Subject: [PATCH 13/19] Fix 6 behavioral parity gaps found by comprehensive Matlab audit MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - psthGLM: add missing sampleRate multiplier to GLM lambda (trial.py) - PPSS_EMFB: use backward EM final state xKR[:,-1] for second forward pass - PPSS_MStep: match Matlab gamma clamping (only >1e2 reduced to 1e1) - plotSeqCorr/plotInvGausTrans: fix swap — SeqCorr shows U_j vs U_{j+1} scatter, InvGausTrans shows ACF of gaussianized rescaled ISIs - plotCoeffs: add error bars (±1 SE) and significance markers - binCoeffs: per-covariate histograms matching Matlab (returns 2-D array) Co-Authored-By: Claude Opus 4.6 --- nstat/decoding_algorithms.py | 12 +-- nstat/fit.py | 118 ++++++++++++++++++++++------ nstat/trial.py | 2 +- tests/test_fitresult_diagnostics.py | 5 +- 4 files changed, 107 insertions(+), 30 deletions(-) diff --git a/nstat/decoding_algorithms.py b/nstat/decoding_algorithms.py index bef4b318..3fd66675 100644 --- a/nstat/decoding_algorithms.py +++ b/nstat/decoding_algorithms.py @@ -2498,8 +2498,9 @@ def PPSS_MStep(dN, HkAll, fitType, x_K, W_K, gamma, delta, sumXkTerms, windowTim converged = True break - # Clamp gamma - gamma_new = np.clip(gamma_new, -1e2, 1e2) + # Clamp gamma — Matlab: gamma_new(gamma_new>1e2)=1e1 + # Only reduce excessively large positive values to 10 + gamma_new[gamma_new > 1e2] = 1e1 return Qhat, gamma_new @@ -2704,16 +2705,17 @@ def PPSS_EMFB(A, Q0, x0, dN, fitType, delta, gamma0, windowTimes, numBasis, neur ) if not negLL: - # Backward EM - _, _, _, QnewR, gnewR, _, _, _, _, negLLR = DecodingAlgorithms.PPSS_EM( + # Backward EM (reversed trial order) + xKR, _, _, QnewR, gnewR, _, _, _, _, negLLR = DecodingAlgorithms.PPSS_EM( A, Qnew, xK[:, -1], np.flipud(dN), fitType, delta, gnew, windowTimes, numBasis, HkAllR ) if not negLLR: # Forward EM again with backward-updated parameters # Matlab: PPSS_EM(A, QhatR(:,cnt+1), xKR(:,end), dN, ...) + # Use backward EM's final state as initial state for forward pass xK2, WK2, Wku2, Qnew2, gnew2, ll2, _, _, _, negLL2 = DecodingAlgorithms.PPSS_EM( - A, QnewR, xK[:, -1], dN, fitType, delta, gnewR, + A, QnewR, xKR[:, -1], dN, fitType, delta, gnewR, windowTimes, numBasis, HkAll ) diff --git a/nstat/fit.py b/nstat/fit.py index 9d28351d..1f38b9e2 100644 --- a/nstat/fit.py +++ b/nstat/fit.py @@ -893,6 +893,8 @@ def _compute_diagnostics(self, fit_num: int = 1, *, dt_correction: int = 1) -> d lags, acf = _autocorrelation(gaussianized, max_lag=25) acf_ci = 1.96 / np.sqrt(float(gaussianized.size)) if gaussianized.size else np.nan coeffs = self._rawCoeffs(fit_num) + se = _extract_standard_errors(self.stats[fit_num - 1] if fit_num - 1 < len(self.stats) else None, coeffs.size) + sig_mask = _extract_significance_mask(self.stats[fit_num - 1] if fit_num - 1 < len(self.stats) else None, coeffs, se) labels = self.covLabels[fit_num - 1] if fit_num - 1 < len(self.covLabels) else [] if coeffs.size == len(labels): coeff_labels = list(labels) @@ -921,6 +923,8 @@ def _compute_diagnostics(self, fit_num: int = 1, *, dt_correction: int = 1) -> d "acf_ci": acf_ci, "gaussianized": gaussianized, "coefficients": coeffs, + "coeff_se": se, + "coeff_sig": sig_mask, "coeff_labels": np.asarray(coeff_labels, dtype=object), } self._diagnostic_cache[fit_num] = diagnostics @@ -1105,18 +1109,11 @@ def plotResidual(self, fit_num: int = 1, handle=None): return ax def plotInvGausTrans(self, fit_num: int = 1, handle=None): - diag = self._compute_diagnostics(fit_num) - ax = handle if handle is not None else plt.subplots(1, 1, figsize=(6.0, 3.5))[1] - x = np.asarray(diag["gaussianized"], dtype=float) - if x.size: - ax.plot(np.arange(1, x.size + 1), x, color="tab:green", linewidth=1.0) - ax.axhline(0.0, color="0.4", linewidth=1.0, linestyle="--") - ax.set_xlabel("event index") - ax.set_ylabel("\\Phi^{-1}(u_i)") - ax.set_title("Inverse-Gaussian/Uniform Transform") - return ax + """Plot ACF of gaussianized rescaled ISIs with 95% CIs. - def plotSeqCorr(self, fit_num: int = 1, handle=None): + Matlab: plotInvGausTrans computes X_j = Φ⁻¹(U_j) and plots the + autocorrelation function of X_j with 95% confidence bounds. + """ diag = self._compute_diagnostics(fit_num) ax = handle if handle is not None else plt.subplots(1, 1, figsize=(6.0, 3.5))[1] lags = np.asarray(diag["acf_lags"], dtype=float) @@ -1128,19 +1125,64 @@ def plotSeqCorr(self, fit_num: int = 1, handle=None): ax.axhline(0.0, color="0.4", linewidth=1.0) ax.set_xlabel("lag") ax.set_ylabel("autocorrelation") - ax.set_title("Sequential Correlation of Rescaled ISIs") + ax.set_title("Autocorrelation Function\nof Rescaled ISIs\nwith 95% CIs") return ax - def plotCoeffs(self, fit_num: int = 1, handle=None): + def plotSeqCorr(self, fit_num: int = 1, handle=None): + """Plot U_j vs U_{j+1} scatter with correlation coefficient. + + Matlab: plotSeqCorr plots the sequential correlation scatter of + U_j (uniform-transformed rescaled ISIs) to detect serial dependence. + """ + diag = self._compute_diagnostics(fit_num) + ax = handle if handle is not None else plt.subplots(1, 1, figsize=(6.0, 3.5))[1] + u = np.asarray(diag.get("uniforms", []), dtype=float) + if u.size > 1: + uj = u[:-1] + uj1 = u[1:] + ax.plot(uj, uj1, ".", color="tab:blue", markersize=4.0) + # Compute correlation coefficient (guard against constant series) + if uj.size > 2 and np.std(uj) > 0 and np.std(uj1) > 0: + with np.errstate(invalid="ignore"): + rho_mat = np.corrcoef(uj, uj1) + rho = rho_mat[0, 1] if rho_mat.shape[0] > 1 else float("nan") + ax.set_title(f"Sequential Correlation ($\\rho$ = {rho:.2g})") + else: + ax.set_title("Sequential Correlation") + else: + ax.set_title("Sequential Correlation") + ax.set_xlabel("$U_j$") + ax.set_ylabel("$U_{j+1}$") + ax.set_xlim(0, 1) + ax.set_ylim(0, 1) + return ax + + def plotCoeffs(self, fit_num: int = 1, handle=None, plotSignificance: int = 1): + """Plot GLM coefficients with error bars and significance markers. + + Matches Matlab FitResult.plotCoeffs: errorbar plot with ±1 SE, + and asterisks (*) above significant coefficients (p < 0.05). + """ diag = self._compute_diagnostics(fit_num) ax = handle if handle is not None else plt.subplots(1, 1, figsize=(6.0, 3.5))[1] coeffs = np.asarray(diag["coefficients"], dtype=float) + se = np.asarray(diag["coeff_se"], dtype=float) + sig = np.asarray(diag["coeff_sig"], dtype=float) labels = list(np.asarray(diag["coeff_labels"], dtype=object)) - xpos = np.arange(coeffs.size, dtype=float) + xpos = np.arange(1, coeffs.size + 1, dtype=float) ax.axhline(0.0, color="0.6", linewidth=1.0) - ax.plot(xpos, coeffs, "o-", color="tab:blue", linewidth=1.0) - ax.set_xticks(xpos, labels, rotation=45, ha="right") - ax.set_ylabel("coefficient value") + # Errorbar plot like Matlab (dot markers with SE whiskers) + valid_se = np.where(np.isfinite(se), se, 0.0) + ax.errorbar(xpos, coeffs, yerr=valid_se, fmt=".", color="tab:blue", + linewidth=1.0, markersize=8.0, capsize=3.0) + if plotSignificance and np.any(sig > 0): + ylims = ax.get_ylim() + y_star = 0.8 * ylims[1] + sig_idx = xpos[sig.astype(bool)] + ax.plot(sig_idx, np.full(sig_idx.size, y_star), "*", color="tab:blue", markersize=10.0) + ax.set_xticks(xpos) + ax.set_xticklabels(labels, rotation=45, ha="right", fontsize=6) + ax.set_ylabel("GLM Fit Coefficients") ax.set_title("GLM Coefficients") return ax @@ -1411,14 +1453,46 @@ def getSigCoeffs(self, fitNum: int = 1): sig[row_idx, labels.index(label)] = value return sig - def binCoeffs(self, minVal, maxVal, binSize): - coeff_mat, _, _ = self.getCoeffs(1) - values = coeff_mat[np.isfinite(coeff_mat)] + def binCoeffs(self, minVal=-12.0, maxVal=12.0, binSize=0.1): + """Histogram of regression coefficients per covariate. + + Matches Matlab FitResSummary.binCoeffs: for each unique covariate, + bins the significant coefficient values across all neurons/fits, + normalizes to a PDF, and computes the fraction of times each + covariate was significant. + + Returns + ------- + N : (nBins, nCovariates) per-covariate normalized histograms (PDFs) + edges : (nBins + 1,) bin edges + percentSig : (nCovariates,) fraction of times each covariate was significant + """ edges = np.arange(float(minVal), float(maxVal) + float(binSize), float(binSize), dtype=float) if edges.size < 2: edges = np.array([float(minVal), float(maxVal)], dtype=float) - N, edges = np.histogram(values, bins=edges) - percentSig = float(np.mean(self.getSigCoeffs(1))) if coeff_mat.size else 0.0 + + # Build per-covariate data across all fits + # bAct: (nNeurons, nCov), sigIdx: (nNeurons, nCov) + coeff_mat, labels, se_mat = self.getCoeffs(1) # (nNeurons, nCov) + sig_mat = self.getSigCoeffs(1) # (nNeurons, nCov) boolean + + nCov = len(labels) + N = np.zeros((edges.size - 1, nCov), dtype=float) + percentSig = np.zeros(nCov, dtype=float) + + for i in range(nCov): + vals = coeff_mat[:, i] + sig = sig_mat[:, i].astype(bool) + valid = np.isfinite(vals) + numPresent = float(np.sum(valid)) + # Take only significant values + sig_vals = vals[sig & valid] + Ntemp, _ = np.histogram(sig_vals, bins=edges) + numSig = float(Ntemp.sum()) + percentSig[i] = numSig / max(numPresent, 1.0) + if numSig > 0: + N[:, i] = Ntemp.astype(float) / numSig # normalize to PDF + return N, edges, percentSig def plotIC(self, handle=None): diff --git a/nstat/trial.py b/nstat/trial.py index 858a7d74..558b6c77 100644 --- a/nstat/trial.py +++ b/nstat/trial.py @@ -1231,7 +1231,7 @@ def psthGLM(self, binwidth: float, windowTimes=None, fitType: str = "poisson", # basis.data is (nTimeBins x numBasis): multiply to get GLM rate bdata = np.asarray(basis.data, dtype=float) - lambda_glm = np.exp(bdata @ bVals) + lambda_glm = np.exp(bdata @ bVals) * sr psth_cov = Covariate( basis.time.copy(), lambda_glm.reshape(-1, 1), diff --git a/tests/test_fitresult_diagnostics.py b/tests/test_fitresult_diagnostics.py index 0b9b2a26..5c4784a5 100644 --- a/tests/test_fitresult_diagnostics.py +++ b/tests/test_fitresult_diagnostics.py @@ -101,9 +101,10 @@ def test_fitsummary_matlab_style_helpers_cover_ic_and_coeff_views() -> None: assert coeff_mat.shape[0] == summary.numNeurons assert sig.shape == coeff_mat.shape assert len(labels) == coeff_mat.shape[1] - assert bins.ndim == 1 + assert bins.ndim == 2 # (nBins, nCovariates) — per-covariate histograms assert edges.ndim == 1 - assert 0.0 <= percent_sig <= 1.0 + assert percent_sig.ndim == 1 # one value per covariate + assert np.all((0.0 <= percent_sig) & (percent_sig <= 1.0)) assert summary.coeffMin == -2.0 assert summary.coeffMax == 2.0 From 162a718e775399f865f8076d9d9693b31c968d36 Mon Sep 17 00:00:00 2001 From: Iahn Cajigas Date: Tue, 10 Mar 2026 21:27:22 -0400 Subject: [PATCH 14/19] Rewrite paper examples as self-contained scripts with proper decoding APIs - Rewrite example05 to use PPDecodeFilterLinear and PPHybridFilterLinear instead of OLS linear_decode, with 3 self-contained experiments: univariate sinusoidal decode, 4-D reach PPAF (free vs goal), and hybrid filter with discrete/continuous state estimation - Expand examples 02-04 as self-contained documented scripts matching Matlab counterparts (full workflow, comments, CLI args) - Add goal-directed backward information filter to PPHybridFilterLinear (was previously stubbed out) - Upgrade FitResult.plotResults to Matlab-matching 2x4 subplot layout with 5 diagnostic panels (KS, InvGaus, SeqCorr, Coeffs, Residual) - Fix plotResidual label indexing for multi-fit results - Align all figure filenames to manifest.yml for test compatibility - Add --repo-root CLI arg to examples 03, 04, 05 Co-Authored-By: Claude Opus 4.6 --- .../example02_whisker_stimulus_thalamus.py | 416 ++++++++++-- examples/paper/example03_psth_and_ssglm.py | 640 ++++++++++++++++-- ...ample04_place_cells_continuous_stimulus.py | 423 +++++++++++- .../paper/example05_decoding_ppaf_pphf.py | 614 +++++++++++++++-- nstat/decoding_algorithms.py | 111 ++- nstat/fit.py | 40 +- tests/test_fitresult_diagnostics.py | 2 +- 7 files changed, 2047 insertions(+), 199 deletions(-) diff --git a/examples/paper/example02_whisker_stimulus_thalamus.py b/examples/paper/example02_whisker_stimulus_thalamus.py index 8b4f1abd..7747037a 100644 --- a/examples/paper/example02_whisker_stimulus_thalamus.py +++ b/examples/paper/example02_whisker_stimulus_thalamus.py @@ -12,74 +12,406 @@ (whisker displacement ``t``, binary spike indicator ``y``, 1000 Hz). Expected outputs: - - Figure 1: Data overview (raster, stimulus, velocity). + - Figure 1: Data overview (raster, stimulus displacement, velocity). - Figure 2: Lag selection (CCF), history diagnostics, KS plot, coefficients. Paper mapping: - Section 2.3.2 (thalamic whisker-stimulus analysis). + Section 2.3.2 (thalamic whisker-stimulus analysis); Figs. 4 and 11. """ from __future__ import annotations import argparse -import json import sys from pathlib import Path +import matplotlib +import matplotlib.pyplot as plt +import numpy as np +from scipy.io import loadmat + +# --------------------------------------------------------------------------- +# Ensure nstat is importable when running from the examples/paper directory. +# --------------------------------------------------------------------------- THIS_DIR = Path(__file__).resolve().parent REPO_ROOT = THIS_DIR.parents[1] if str(REPO_ROOT) not in sys.path: sys.path.insert(0, str(REPO_ROOT)) +import nstat # noqa: E402 +from nstat import ( # noqa: E402 + Analysis, + ConfigColl, + CovColl, + nspikeTrain, + nstColl, + Trial, + TrialConfig, +) +from nstat.signal import Covariate # noqa: E402 from nstat.data_manager import ensure_example_data # noqa: E402 -from nstat.paper_examples_full import run_experiment2 # noqa: E402 -from nstat.paper_figures import export_named_paper_figures # noqa: E402 - - -def run_example02(*, export_figures: bool = False, export_dir: Path | None = None): - """Run Example 02: Whisker stimulus GLM. - - Analysis workflow (mirrors Matlab example02_whisker_stimulus_thalamus.m): - 1. Load trngdataBis.mat — stimulus displacement and spike indicator. - 2. Compute cross-covariance between residual spikes and stimulus. - 3. Identify peak lag; shift stimulus by optimal lag. - 4. Fit 3 nested GLMs: - (a) baseline only, - (b) baseline + stimulus + velocity, - (c) baseline + stimulus + velocity + spike history. - 5. Sweep history orders 1..28 via AIC/BIC to select optimal lag. - 6. Generate figures comparing models. + + +# ========================================================================= +# Helper: export figure +# ========================================================================= +def _maybe_export(fig, export_dir: Path | None, name: str, dpi: int = 250): + """Save figure to disk if export_dir is set.""" + saved = [] + if export_dir is not None: + export_dir.mkdir(parents=True, exist_ok=True) + png_path = export_dir / f"{name}.png" + fig.savefig(png_path, dpi=dpi, bbox_inches="tight") + saved.append(png_path) + print(f" Saved {png_path}") + return saved + + +# ========================================================================= +# Main example function +# ========================================================================= +def run_example02(*, export_figures: bool = False, export_dir: Path | None = None, + visible: bool = True): + """Run Example 02: Whisker stimulus GLM with lag and history selection. + + Mirrors Matlab example02_whisker_stimulus_thalamus.m exactly: + 1. Load trngdataBis.mat (struct with fields t=stimulus, y=spike indicator). + 2. Construct nSTAT objects (nspikeTrain, Covariate, Trial). + 3. Fit baseline-only GLM; compute residual cross-covariance with stimulus. + 4. Identify optimal lag from peak xcov; shift stimulus by that lag. + 5. Sweep history windows via Analysis.computeHistLagForAll with logspace grid. + 6. Select optimal history order from min(AIC_idx, BIC_idx). + 7. Fit 3 nested models: baseline, baseline+stim, baseline+stim+hist. + 8. Generate 2 figures with Matlab-matching subplot layouts. """ + if not visible: + matplotlib.use("Agg") + data_dir = ensure_example_data(download=True) + figure_files: list[Path] = [] + + sampleRate = 1000 # Hz + + # ================================================================== + # Load data from trngdataBis.mat + # ================================================================== + print("=== Example 02: Whisker Stimulus GLM ===") + + mat_path = (data_dir / "Explicit Stimulus" / "Dir3" / "Neuron1" + / "Stim2" / "trngdataBis.mat") + d = loadmat(mat_path, squeeze_me=True, struct_as_record=False) + + # Extract stimulus signal and spike indicator from struct + # Matlab: data.t is stimulus, data.y is binary spike indicator + if hasattr(d.get("data", None), "t"): + stimData = np.asarray(d["data"].t, dtype=float).reshape(-1) + yData = np.asarray(d["data"].y, dtype=float).reshape(-1) + else: + # Fallback: try direct keys + stimData = np.asarray(d["t"], dtype=float).reshape(-1) + yData = np.asarray(d["y"], dtype=float).reshape(-1) + + # Construct time vector at 1 ms resolution + time = np.arange(0, len(stimData)) * (1.0 / sampleRate) + + # Extract spike times from binary indicator + spikeTimes = time[yData == 1] + print(f" Data length: {len(stimData)} samples ({time[-1]:.1f} s)") + print(f" Total spikes: {len(spikeTimes)}") + + # ================================================================== + # Create nSTAT objects + # ================================================================== + # Stimulus covariate (divided by 10, matching Matlab: stimData ./ 10) + stim = Covariate( + time, stimData / 10.0, + "Stimulus", "time", "s", "mm", + dataLabels=["stim"], + ) + # Constant baseline covariate + baseline = Covariate( + time, np.ones((len(time), 1)), + "Baseline", "time", "s", "", + dataLabels=["constant"], + ) + + nst = nspikeTrain(spikeTimes) + spikeColl = nstColl(nst) + trial = Trial(spikeColl, CovColl([stim, baseline])) + + # ================================================================== + # Figure 1: Data overview — raster, stimulus, velocity (3x1 layout) + # ================================================================== + fig1, axes1 = plt.subplots(3, 1, figsize=(14, 9)) + viewWindow = 21.0 # First 21 seconds, matching Matlab + + # Subplot 1: Neural raster (first 21 s) + ax = axes1[0] + nstView = nspikeTrain(spikeTimes) + nstView.setMaxTime(viewWindow) + nstView.plot(handle=ax) + ax.set_yticks([0, 1]) + ax.set_title("Neural Raster", fontweight="bold", fontsize=12) + ax.set_xlabel("time [s]", fontsize=12, fontweight="bold") + ax.set_ylabel("Spikes", fontsize=12, fontweight="bold") + + # Subplot 2: Stimulus displacement (first 21 s) + ax = axes1[1] + stimView = stim.getSigInTimeWindow(0, viewWindow) + stimView.plot(handle=ax) + ax.set_ylabel("Displacement [mm]", fontsize=12, fontweight="bold") + ax.set_xlabel("time [s]", fontsize=12, fontweight="bold") + + # Subplot 3: Stimulus velocity (derivative, first 21 s) + ax = axes1[2] + stimDeriv = stim.derivative + stimDerivView = stimDeriv.getSigInTimeWindow(0, viewWindow) + stimDerivView.plot(handle=ax) + ax.set_ylim(-80, 80) + ax.set_ylabel("Velocity", fontsize=12, fontweight="bold") + ax.set_xlabel("time [s]", fontsize=12, fontweight="bold") + + fig1.suptitle("Example 02 — Figure 1: Data Overview", + fontsize=14, fontweight="bold") + fig1.tight_layout() + figure_files.extend(_maybe_export( + fig1, export_dir, "fig01_data_overview")) + + # ================================================================== + # Fit baseline-only model + # ================================================================== + print("\n--- Fitting baseline-only model ---") + cfgBase = TrialConfig([("Baseline", "constant")], sampleRate, [], []) + cfgBase.setName("Baseline") + baselineResults = Analysis.RunAnalysisForAllNeurons( + trial, ConfigColl([cfgBase]), 0) - # Run analysis (returns summary statistics and figure payload) - summary, payload = run_experiment2(data_dir, return_payload=True) + # ================================================================== + # Compute residual cross-covariance with stimulus to find optimal lag + # ================================================================== + print("--- Computing residual cross-covariance ---") + residual = baselineResults.computeFitResidual() + xcovSig = residual.xcov(stim) - print(json.dumps(summary, indent=2)) + # Window to positive lags [0, 1] s (matching Matlab) + xcovWindowed = xcovSig.windowedSignal([0, 1]) + + # Find peak lag — findGlobalPeak returns (times, values) + peakTimes, peakVals = xcovWindowed.findGlobalPeak("maxima") + shiftTime = float(peakTimes[0]) + peakVal = float(peakVals[0]) + print(f" Peak xcov at lag = {shiftTime:.4f} s (value = {peakVal:.4f})") + + # ================================================================== + # Shift stimulus by optimal lag and build new Trial + # ================================================================== + # Matlab: stimShifted = Covariate(time, stimData, ...).shift(shiftTime) + # Note: Matlab uses raw stimData (not /10) with units 'V' for the shifted version + stimShifted = Covariate( + time, stimData, + "Stimulus", "time", "s", "V", + dataLabels=["stim"], + ) + stimShifted = stimShifted.shift(shiftTime) + + baselineMu = Covariate( + time, np.ones((len(time), 1)), + "Baseline", "time", "s", "", + dataLabels=["\\mu"], + ) + + trialShifted = Trial( + nstColl(nspikeTrain(spikeTimes)), + CovColl([stimShifted, baselineMu]), + ) - if export_figures: - if export_dir is None: - export_dir = THIS_DIR / "figures" / "example02" - saved = export_named_paper_figures( - "example02", summary=summary, payload=payload, export_dir=export_dir - ) - print(f"\nGenerated {len(saved)} figure(s):") - for p in saved: - print(f" {p}") + # ================================================================== + # History model-order search via computeHistLagForAll + # ================================================================== + print("\n--- Sweeping history windows ---") + delta = 1.0 / sampleRate + maxWindow = 1.0 + numWindows = 32 - return summary + # Construct log-spaced history window boundaries (matching Matlab) + logVals = np.logspace(np.log10(delta), np.log10(maxWindow), numWindows) + windowTimes = np.concatenate([[0.0], logVals]) + # Round to nearest ms and remove duplicates + windowTimes = np.unique(np.round(windowTimes * sampleRate) / sampleRate) + print(f" Window boundaries: {len(windowTimes)} unique values") + print(f" Range: [{windowTimes[0]:.4f}, {windowTimes[-1]:.4f}] s") + historySweep = Analysis.computeHistLagForAll( + trialShifted, windowTimes, + CovLabels=[("Baseline", "\\mu"), ("Stimulus", "stim")], + Algorithm="GLM", + batchMode=0, + sampleRate=sampleRate, + makePlot=0, + ) + + # ================================================================== + # Select optimal history order + # ================================================================== + # historySweep is a list of FitResult objects (one per neuron) + sweep = historySweep[0] + aicArr = np.asarray(sweep.AIC, dtype=float) + bicArr = np.asarray(sweep.BIC, dtype=float) + ksArr = np.asarray(sweep.KSStats, dtype=float).ravel() + + # Delta AIC/BIC relative to no-history model (index 0) + dAIC = aicArr[1:] - aicArr[0] + dBIC = bicArr[1:] - bicArr[0] + + # Find index of minimum delta (offset by +1 since we skipped index 0) + aicIdx = int(np.argmin(dAIC)) + 1 if dAIC.size > 0 else None + bicIdx = int(np.argmin(dBIC)) + 1 if dBIC.size > 0 else None + ksIdx = int(np.argmin(ksArr)) if ksArr.size > 0 else 0 + + # Take minimum of AIC and BIC optimal indices + candidates = [] + if aicIdx is not None and aicIdx > 0: + candidates.append(aicIdx) + if bicIdx is not None and bicIdx > 0: + candidates.append(bicIdx) + windowIndex = min(candidates) if candidates else ksIdx + + if windowIndex > len(windowTimes): + windowIndex = ksIdx + + # Extract selected history windows + if windowIndex > 1: + selectedHistory = list(windowTimes[:windowIndex]) + else: + selectedHistory = [] + + print(f" AIC optimal index: {aicIdx}") + print(f" BIC optimal index: {bicIdx}") + print(f" KS optimal index: {ksIdx}") + print(f" Selected window index: {windowIndex}") + print(f" Selected history: {len(selectedHistory)} windows") + + # ================================================================== + # Final 3-model comparison + # ================================================================== + print("\n--- Fitting 3 nested models ---") + + cfg1 = TrialConfig([("Baseline", "\\mu")], sampleRate, [], []) + cfg1.setName("Baseline") + + cfg2 = TrialConfig( + [("Baseline", "\\mu"), ("Stimulus", "stim")], + sampleRate, [], [], + ) + cfg2.setName("Baseline+Stimulus") + + cfg3 = TrialConfig( + [("Baseline", "\\mu"), ("Stimulus", "stim")], + sampleRate, selectedHistory, [], + ) + cfg3.setName("Baseline+Stimulus+Hist") + + modelCompare = Analysis.RunAnalysisForAllNeurons( + trialShifted, ConfigColl([cfg1, cfg2, cfg3]), 0) + modelCompare.lambda_signal.setDataLabels([ + "\\lambda_{const}", + "\\lambda_{const+stim}", + "\\lambda_{const+stim+hist}", + ]) + + print(f" AIC: {modelCompare.AIC}") + print(f" BIC: {modelCompare.BIC}") + + # ================================================================== + # Figure 2: Lag selection, history diagnostics, KS, coefficients + # (Matlab uses subplot(7,2,...) layout) + # ================================================================== + fig2 = plt.figure(figsize=(14, 12)) + import matplotlib.gridspec as gridspec + gs = gridspec.GridSpec(7, 2, figure=fig2, hspace=0.5, wspace=0.3) + + # --- Left column, rows 1-3: Cross-correlation function --- + ax_xcov = fig2.add_subplot(gs[0:3, 0]) + xcovWindowed.plot(handle=ax_xcov) + ax_xcov.plot(shiftTime, peakVal, "ro", markersize=8, + markerfacecolor="r", markeredgecolor="r", linewidth=3) + ax_xcov.set_title("Residual Cross-Covariance", fontweight="bold") + ax_xcov.set_xlabel("Lag [s]") + ax_xcov.set_ylabel("Cross-covariance") + + # --- Right column, row 1: KS statistic vs Q --- + ax_ks_sweep = fig2.add_subplot(gs[0, 1]) + xvals = np.arange(len(ksArr)) + ax_ks_sweep.plot(xvals, ksArr, ".-") + if windowIndex < len(ksArr): + ax_ks_sweep.plot(xvals[windowIndex], ksArr[windowIndex], "r*", + markersize=10) + ax_ks_sweep.set_title("KS Statistic vs Q", fontweight="bold") + ax_ks_sweep.set_xlabel("Number of History Windows") + ax_ks_sweep.set_ylabel("KS Stat") + + # --- Right column, row 2: Delta AIC vs Q --- + ax_daic = fig2.add_subplot(gs[1, 1]) + dAIC_full = aicArr - aicArr[0] + ax_daic.plot(np.arange(len(dAIC_full)), dAIC_full, ".-") + if windowIndex < len(dAIC_full): + ax_daic.plot(windowIndex, dAIC_full[windowIndex], "r*", markersize=10) + ax_daic.set_title("$\\Delta$AIC vs Q", fontweight="bold") + ax_daic.set_xlabel("Number of History Windows") + ax_daic.set_ylabel("$\\Delta$AIC") + + # --- Right column, row 3: Delta BIC vs Q --- + ax_dbic = fig2.add_subplot(gs[2, 1]) + dBIC_full = bicArr - bicArr[0] + ax_dbic.plot(np.arange(len(dBIC_full)), dBIC_full, ".-") + if windowIndex < len(dBIC_full): + ax_dbic.plot(windowIndex, dBIC_full[windowIndex], "r*", markersize=10) + ax_dbic.set_title("$\\Delta$BIC vs Q", fontweight="bold") + ax_dbic.set_xlabel("Number of History Windows") + ax_dbic.set_ylabel("$\\Delta$BIC") + + # --- Left column, rows 5-7: KS plot (3 models) --- + ax_ks = fig2.add_subplot(gs[4:7, 0]) + modelCompare.KSPlot(handle=ax_ks) + + # --- Right column, rows 5-7: Coefficient comparison --- + ax_coeff = fig2.add_subplot(gs[4:7, 1]) + modelCompare.plotCoeffs(handle=ax_coeff) + + fig2.suptitle("Example 02 — Figure 2: Lag & History Selection", + fontsize=14, fontweight="bold") + figure_files.extend(_maybe_export( + fig2, export_dir, "fig02_lag_and_model_comparison")) + + if visible: + plt.show() + + print(f"\nExample 02 complete. Generated {len(figure_files)} figure(s).") + return figure_files + + +# ========================================================================= +# CLI entry point +# ========================================================================= if __name__ == "__main__": - parser = argparse.ArgumentParser(description="Example 02: Whisker Stimulus GLM") - parser.add_argument("--repo-root", type=Path, default=REPO_ROOT) - parser.add_argument("--export-figures", action="store_true") - parser.add_argument("--export-dir", type=Path, default=None) - parser.add_argument("--output-json", type=Path, default=None) + parser = argparse.ArgumentParser( + description="Example 02: Whisker Stimulus GLM") + parser.add_argument("--repo-root", type=Path, default=REPO_ROOT, + help="Repository root (default: auto-detected).") + parser.add_argument("--export-figures", action="store_true", + help="Export figures to disk.") + parser.add_argument("--export-dir", type=Path, default=None, + help="Directory for exported figures.") + parser.add_argument("--no-display", action="store_true", + help="Run without displaying figures (headless).") args = parser.parse_args() - result = run_example02( + export_dir = args.export_dir + if args.export_figures and export_dir is None: + export_dir = THIS_DIR / "figures" / "example02" + + run_example02( export_figures=args.export_figures, - export_dir=args.export_dir, + export_dir=export_dir if args.export_figures else None, + visible=not args.no_display, ) - if args.output_json: - args.output_json.write_text(json.dumps(result, indent=2), encoding="utf-8") diff --git a/examples/paper/example03_psth_and_ssglm.py b/examples/paper/example03_psth_and_ssglm.py index a5f66c72..c05a74b3 100644 --- a/examples/paper/example03_psth_and_ssglm.py +++ b/examples/paper/example03_psth_and_ssglm.py @@ -8,20 +8,21 @@ 4) Across-trial learning dynamics and stimulus-effect surfaces. The example has two parts: - Part A (experiment3): PSTH analysis — simulate 20 trials from sinusoidal - CIF, load real data from ``data/PSTH/Results.mat``, compare histogram + Part A (PSTH): Simulate 20 trials from a sinusoidal CIF, + load real data from ``data/PSTH/Results.mat``, compare histogram PSTH vs GLM-PSTH. - Part B (experiment3b): SSGLM analysis — simulate 50-trial dataset with - across-trial gain modulation, fit SSGLM via EM, visualise learning - dynamics and 3-D stimulus-effect surfaces. + Part B (SSGLM): Simulate 50-trial dataset with across-trial gain + modulation, load precomputed SSGLM fit from + ``data/SSGLMExampleData.mat``, visualise learning dynamics and + 3-D stimulus-effect surfaces. Expected outputs: - - Figure 1: Simulated and real rasters. + - Figure 1: Simulated CIF + simulated/real raster examples. - Figure 2: PSTH comparison (histogram vs GLM). - Figure 3: SSGLM simulation summary. - - Figure 4: SSGLM fit diagnostics. + - Figure 4: SSGLM vs PSTH model diagnostics. - Figure 5: Stimulus-effect surfaces (3-D). - - Figure 6: Learning-trial comparison. + - Figure 6: Learning-trial comparison and significance matrix. Paper mapping: Section 2.3.3 (PSTH) and Section 2.4 (SSGLM). @@ -29,71 +30,601 @@ from __future__ import annotations import argparse -import json import sys from pathlib import Path +import matplotlib.pyplot as plt +import numpy as np +from scipy.io import loadmat + THIS_DIR = Path(__file__).resolve().parent REPO_ROOT = THIS_DIR.parents[1] if str(REPO_ROOT) not in sys.path: sys.path.insert(0, str(REPO_ROOT)) +from nstat import ( # noqa: E402 + Analysis, + Covariate, + CovariateCollection, + FitResult, + Trial, + TrialConfig, + ConfigCollection, +) +from nstat.cif import CIF # noqa: E402 +from nstat.confidence_interval import ConfidenceInterval # noqa: E402 +from nstat.core import nspikeTrain # noqa: E402 from nstat.data_manager import ensure_example_data # noqa: E402 -from nstat.paper_examples_full import run_experiment3, run_experiment3b # noqa: E402 -from nstat.paper_figures import export_named_paper_figures # noqa: E402 +from nstat.decoding_algorithms import DecodingAlgorithms # noqa: E402 +from nstat.trial import SpikeTrainCollection # noqa: E402 -def run_example03(*, export_figures: bool = False, export_dir: Path | None = None): - """Run Example 03: PSTH and SSGLM dynamics. +# ===================================================================== +# Helper: load Matlab FitResult struct into Python FitResult +# ===================================================================== +def _load_matlab_fitresult(mat_struct, spike_trains): + """Convert a Matlab FitResult structured array to a Python FitResult. + + Parameters + ---------- + mat_struct : numpy structured array + The `fR` or `psthR` field from the .mat file. + spike_trains : list[nspikeTrain] + The spike trains corresponding to this FitResult (since Matlab + MCOS objects cannot be deserialized by scipy). + """ + # Extract lambda signal + lam = mat_struct["lambda"].item() + lam_time = np.asarray(lam["time"].item(), dtype=float).ravel() + lam_data = np.asarray(lam["data"].item(), dtype=float) + lam_name = str(lam["name"].item()) if lam["name"].size else "\\lambda" - Analysis workflow (mirrors Matlab example03_psth_and_ssglm.m): + lambda_cov = Covariate( + lam_time, lam_data, lam_name, "time", "s", "spikes/sec", + ) - Part A — PSTH: - 1. Define sinusoidal CIF: lambda(t) = exp(b0 + b1*cos(2*pi*f*t)). - 2. Simulate 20 spike trains via CIF thinning. - 3. Load real multi-trial data from PSTH/Results.mat. - 4. Compute histogram PSTH and GLM-PSTH; compare. + # Extract scalar statistics + b_raw = np.asarray(mat_struct["b"].item(), dtype=float).reshape(-1) + AIC_val = float(np.asarray(mat_struct["AIC"].item(), dtype=float).ravel()[0]) + BIC_val = float(np.asarray(mat_struct["BIC"].item(), dtype=float).ravel()[0]) + logLL_val = float(np.asarray(mat_struct["logLL"].item(), dtype=float).ravel()[0]) + config_name = str(mat_struct["configNames"].item()) - Part B — SSGLM: - 5. Simulate 50-trial population with across-trial stimulus gain. - 6. Fit SSGLM via EM (forward-backward Kalman + Newton M-step). - 7. Plot per-trial coefficient trajectories and confidence bands. - 8. Generate 3-D stimulus-effect surface and learning-trial figure. - """ + # Extract covariate labels + cov_labels_raw = mat_struct["covLabels"].item() + if isinstance(cov_labels_raw, np.ndarray): + cov_labels = [str(x) for x in cov_labels_raw.ravel()] + elif isinstance(cov_labels_raw, str): + cov_labels = [cov_labels_raw] + else: + cov_labels = list(cov_labels_raw) if cov_labels_raw is not None else [] + + num_hist_raw = mat_struct["numHist"].item() + num_hist = [int(num_hist_raw)] if np.isscalar(num_hist_raw) else [int(x) for x in np.asarray(num_hist_raw).ravel()] + + cfgs = ConfigCollection([TrialConfig(name=config_name)]) + + return FitResult( + spike_trains, + [cov_labels], # covLabels (list of lists) + num_hist, # numHist + [], # histObjects + [], # ensHistObjects + lambda_cov, # lambda_signal + [b_raw], # b + [0.0], # dev + [None], # stats + [AIC_val], # AIC + [BIC_val], # BIC + [logLL_val], # logLL + cfgs, # configColl + [], # XvalData + [], # XvalTime + "poisson", # distribution + ) + + +# ===================================================================== +# Part A: PSTH Analysis +# ===================================================================== +def run_part_a(data_dir, export_dir=None): + """Simulate and real PSTH + GLM-PSTH analysis.""" + print("=== Part A: PSTH Analysis ===") + + # ------------------------------------------------------------------ + # 1. Define sinusoidal CIF: lambda(t) = sigmoid(sin(2*pi*f*t) + mu) / dt + # ------------------------------------------------------------------ + delta = 0.001 + tmax = 1.0 + time = np.arange(0.0, tmax + delta, delta) + f = 2 + mu = -3 + + lambdaRaw = np.sin(2 * np.pi * f * time) + mu + lambdaData = np.exp(lambdaRaw) / (1 + np.exp(lambdaRaw)) * (1 / delta) + lambdaCov = Covariate( + time, lambdaData, "\\lambda(t)", "time", "s", "spikes/sec", + ["\\lambda_{1}"], + ) + + # ------------------------------------------------------------------ + # 2. Simulate 20 spike trains via CIF thinning + # ------------------------------------------------------------------ + numRealizations = 20 + spikeCollSim = CIF.simulateCIFByThinningFromLambda( + lambdaCov, numRealizations, seed=0, + ) + spikeCollSim.setMinTime(0.0) + spikeCollSim.setMaxTime(tmax) + print(f" Simulated {numRealizations} spike trains") + + # ------------------------------------------------------------------ + # 3. Load real PSTH data from Results.mat + # ------------------------------------------------------------------ + psth_path = data_dir / "PSTH" / "Results.mat" + psthData = loadmat(str(psth_path), squeeze_me=False) + Results = psthData["Results"][0, 0] + Data = Results["Data"][0, 0] + STC = Data["Spike_times_STC"][0, 0] + SUA = STC["balanced_SUA"][0, 0] + numTrials = int(SUA["Nr_trials"][0, 0]) + spikeTimesArr = SUA["spike_times"] # shape (16, numTrials, 8) + + # Cell 6 (Matlab 1-indexed) + trains6 = [] + for iTrial in range(numTrials): + st = spikeTimesArr[0, iTrial, 5].ravel() # cell index 5 = cell 6 + nst = nspikeTrain(st, name="6", minTime=0.0, maxTime=2.0, makePlots=-1) + trains6.append(nst) + spikeCollReal1 = SpikeTrainCollection(trains6) + spikeCollReal1.setMinTime(0.0) + spikeCollReal1.setMaxTime(2.0) + + # Cell 1 (Matlab 1-indexed) + trains1 = [] + for iTrial in range(numTrials): + st = spikeTimesArr[0, iTrial, 0].ravel() # cell index 0 = cell 1 + nst = nspikeTrain(st, name="1", minTime=0.0, maxTime=2.0, makePlots=-1) + trains1.append(nst) + spikeCollReal2 = SpikeTrainCollection(trains1) + spikeCollReal2.setMinTime(0.0) + spikeCollReal2.setMaxTime(2.0) + print(f" Loaded real data: {numTrials} trials, cells 6 and 1") + + # ------------------------------------------------------------------ + # Figure 1: Simulated CIF + simulated/real rasters (2x2) + # ------------------------------------------------------------------ + fig1, axes1 = plt.subplots(2, 2, figsize=(14, 9)) + + # Top-left: CIF + ax = axes1[0, 0] + ax.plot(time, lambdaData, "b", linewidth=2) + ax.set_title("Simulated CIF", fontweight="bold", fontsize=14) + ax.set_xlabel("time [s]") + ax.set_ylabel("spikes/sec") + + # Bottom-left: simulated raster + ax = axes1[1, 0] + spikeCollSim.plot(handle=ax) + ax.set_yticks(range(0, numRealizations + 1, 5)) + ax.set_title(f"{numRealizations} Simulated Sample Paths", + fontweight="bold", fontsize=14) + ax.set_xlabel("time [s]") + ax.set_ylabel("Trial [k]") + + # Top-right: real cell 6 raster + ax = axes1[0, 1] + spikeCollReal1.plot(handle=ax) + ax.set_yticks(range(0, numTrials + 1, 2)) + ax.set_title("Response to Moving Visual Stimulus (Neuron 6)", + fontweight="bold", fontsize=14) + ax.set_xlabel("time [s]") + ax.set_ylabel("Trial [k]") + + # Bottom-right: real cell 1 raster + ax = axes1[1, 1] + spikeCollReal2.plot(handle=ax) + ax.set_yticks(range(0, numTrials + 1, 2)) + ax.set_title("Response to Moving Visual Stimulus (Neuron 1)", + fontweight="bold", fontsize=14) + ax.set_xlabel("time [s]") + ax.set_ylabel("Trial [k]") + + fig1.tight_layout() + + # ------------------------------------------------------------------ + # 4. Compute PSTH and GLM-PSTH + # ------------------------------------------------------------------ + binsize = 0.05 + psthSim = spikeCollSim.psth(binsize) + psthGLMSim, _, _ = spikeCollSim.psthGLM(binsize) + + psthReal1 = spikeCollReal1.psth(binsize) + psthGLMReal1, _, _ = spikeCollReal1.psthGLM(binsize) + + psthReal2 = spikeCollReal2.psth(binsize) + psthGLMReal2, _, _ = spikeCollReal2.psthGLM(binsize) + print(" PSTH and GLM-PSTH computed for all 3 collections") + + # ------------------------------------------------------------------ + # Figure 2: PSTH comparison (2x3) + # ------------------------------------------------------------------ + fig2, axes2 = plt.subplots(2, 3, figsize=(14, 9)) + + # Top row: rasters + spikeCollSim.plot(handle=axes2[0, 0]) + axes2[0, 0].set_yticks(range(0, numRealizations + 1, 2)) + axes2[0, 0].set_xlabel("time [s]") + axes2[0, 0].set_ylabel("Trial [k]") + + spikeCollReal1.plot(handle=axes2[0, 1]) + axes2[0, 1].set_yticks(range(0, numTrials + 1, 2)) + axes2[0, 1].set_xlabel("time [s]") + axes2[0, 1].set_ylabel("Trial [k]") + + spikeCollReal2.plot(handle=axes2[0, 2]) + axes2[0, 2].set_yticks(range(0, numTrials + 1, 2)) + axes2[0, 2].set_xlabel("time [s]") + axes2[0, 2].set_ylabel("Trial [k]") + + # Bottom row: PSTH comparisons + ax = axes2[1, 0] + h_true, = ax.plot(time, lambdaData, "b", linewidth=4, label="true") + psth_time = np.asarray(psthSim.time, dtype=float).ravel() + psth_data = np.asarray(psthSim.data, dtype=float).ravel() + h_psth, = ax.plot(psth_time, psth_data, "rx", linewidth=4, label="PSTH") + glm_time = np.asarray(psthGLMSim.time, dtype=float).ravel() + glm_data = np.asarray(psthGLMSim.data, dtype=float).ravel() + h_glm, = ax.plot(glm_time, glm_data, "k", linewidth=4, label="PSTH_{glm}") + ax.legend(handles=[h_true, h_psth, h_glm]) + ax.set_xlabel("time [s]") + ax.set_ylabel("[spikes/sec]") + + ax = axes2[1, 1] + psth_t1 = np.asarray(psthReal1.time, dtype=float).ravel() + psth_d1 = np.asarray(psthReal1.data, dtype=float).ravel() + glm_t1 = np.asarray(psthGLMReal1.time, dtype=float).ravel() + glm_d1 = np.asarray(psthGLMReal1.data, dtype=float).ravel() + h2, = ax.plot(psth_t1, psth_d1, "rx", linewidth=4, label="PSTH") + h3, = ax.plot(glm_t1, glm_d1, "k", linewidth=4, label="PSTH_{glm}") + ax.legend(handles=[h2, h3]) + ax.set_xlabel("time [s]") + ax.set_ylabel("[spikes/sec]") + + ax = axes2[1, 2] + psth_t2 = np.asarray(psthReal2.time, dtype=float).ravel() + psth_d2 = np.asarray(psthReal2.data, dtype=float).ravel() + glm_t2 = np.asarray(psthGLMReal2.time, dtype=float).ravel() + glm_d2 = np.asarray(psthGLMReal2.data, dtype=float).ravel() + h2, = ax.plot(psth_t2, psth_d2, "rx", linewidth=4, label="PSTH") + h3, = ax.plot(glm_t2, glm_d2, "k", linewidth=4, label="PSTH_{glm}") + ax.legend(handles=[h2, h3]) + ax.set_xlabel("time [s]") + ax.set_ylabel("[spikes/sec]") + + fig2.tight_layout() + + figures = {"fig01_simulated_and_real_rasters": fig1, "fig02_psth_comparison": fig2} + return figures, spikeCollSim, lambdaCov + + +# ===================================================================== +# Part B: SSGLM Analysis +# ===================================================================== +def run_part_b(data_dir, export_dir=None): + """SSGLM simulation, diagnostics, stimulus surfaces, learning trial.""" + print("\n=== Part B: SSGLM Analysis ===") + + # ------------------------------------------------------------------ + # 1. Simulate 50-trial CIF with across-trial stimulus gain + # ------------------------------------------------------------------ + delta = 0.001 + tmax = 1.0 + time = np.arange(0.0, tmax + delta, delta) + f = 2 + numRealizations = 50 + b0 = -3 + + # Linearly increasing stimulus gain across trials + b1 = 3 * np.arange(1, numRealizations + 1) / numRealizations + + # Simulate each trial using CIF.simulateCIF + trains = [] + for iTrial in range(numRealizations): + u = np.sin(2 * np.pi * f * time) + stim = Covariate(time, u, "Stimulus", "time", "s", "V", ["sin"]) + ens = Covariate(time, np.zeros_like(time), "Ensemble", "time", "s", + "Spikes", ["n1"]) + + # Transfer function coefficients (z-domain) + histCoeffs = [-4, -1, -0.5] + from scipy.signal import TransferFunction, dlti + htf = dlti([1], np.concatenate([[1], -np.array(histCoeffs)]), dt=delta) + stf_num = [b1[iTrial]] + stf = dlti(stf_num, [1], dt=delta) + etf = dlti([0], [1], dt=delta) + + sC, lambdaTemp = CIF.simulateCIF( + b0, histCoeffs, [b1[iTrial]], [0], + stim, ens, 1, "binomial", + seed=iTrial, return_lambda=True, + ) + nst = sC.getNST(1) + nst = nst.nstCopy() + nst.resample(1 / delta) + trains.append(nst) + + spikeColl = SpikeTrainCollection(trains) + spikeColl.setMinTime(0.0) + spikeColl.setMaxTime(tmax) + print(f" Simulated {numRealizations} spike trains with CIF.simulateCIF") + + # Compute true CIF surface: sigma(b0 + b1[k]*u(t)) / delta + u = np.sin(2 * np.pi * f * time) + stimDataEta = np.outer(u, b1) # (T, K) + stimData = np.exp(stimDataEta + b0) + stimData = stimData / (1 + stimData) / delta # binomial link + + # ------------------------------------------------------------------ + # Figure 3: SSGLM simulation summary (3x2) + # ------------------------------------------------------------------ + fig3, axes3 = plt.subplots(3, 2, figsize=(14, 9)) + + # (1,1): Within-trial stimulus + ax = axes3[0, 0] + ax.plot(time, u, "k", linewidth=3) + ax.set_xlabel("time [s]") + ax.set_ylabel("Stimulus") + ax.set_title("Within Trial Stimulus", fontweight="bold", fontsize=14) + + # (1,2): Across-trial gain + ax = axes3[0, 1] + ax.plot(np.arange(1, numRealizations + 1), b1, "k", linewidth=3) + ax.set_xlabel("Trial [k]") + ax.set_ylabel("Stimulus Gain") + ax.set_title("Across Trial Stimulus Gain", fontweight="bold", fontsize=14) + + # (2,1)+(2,2): Raster spanning both columns + axes3[1, 1].remove() + ax = axes3[1, 0] + ax.set_position( + [axes3[1, 0].get_position().x0, + axes3[1, 0].get_position().y0, + axes3[1, 1].get_position().x1 - axes3[1, 0].get_position().x0, + axes3[1, 0].get_position().height] + ) + spikeColl.plot(handle=ax) + ax.set_yticks(range(0, numRealizations + 1, 10)) + ax.set_xlabel("time [s]") + ax.set_ylabel("Trial [k]") + ax.set_title("Simulated Neural Raster", fontweight="bold", fontsize=14) + + # (3,1)+(3,2): True CIF heatmap spanning both columns + axes3[2, 1].remove() + ax = axes3[2, 0] + ax.set_position( + [axes3[2, 0].get_position().x0, + axes3[2, 0].get_position().y0, + ax.get_position().width * 2.1, + axes3[2, 0].get_position().height] + ) + ax.imshow(stimData.T, aspect="auto", origin="lower", + extent=[time[0], time[-1], 1, numRealizations]) + ax.set_xlabel("time [s]") + ax.set_ylabel("Trial [k]") + ax.set_title("True Conditional Intensity Function", fontweight="bold", + fontsize=14) + ax.set_yticks(range(0, numRealizations + 1, 10)) + + fig3.tight_layout() + + # ------------------------------------------------------------------ + # 2. Load precomputed SSGLM data + # ------------------------------------------------------------------ + ssglm_path = data_dir / "SSGLMExampleData.mat" + ssglm = loadmat(str(ssglm_path), squeeze_me=True) + + xK = np.asarray(ssglm["xK"], dtype=float) # (25, 50) + WkuFinal = np.asarray(ssglm["WkuFinal"], dtype=float) # (25, 25, 50, 50) + stimulus_true = np.asarray(ssglm["stimulus"], dtype=float) # (25, 50) + stimCIs = np.asarray(ssglm["stimCIs"], dtype=float) # (25, 50, 2) + gammahat = np.asarray(ssglm["gammahat"], dtype=float) # (3,) + numBasis = xK.shape[0] + K = xK.shape[1] + print(f" Loaded precomputed SSGLM: {numBasis} basis x {K} trials") + + # ------------------------------------------------------------------ + # 3. Reconstruct FitResult objects from loaded data + # ------------------------------------------------------------------ + # Construct FitResult objects from Matlab structs, using our simulated + # spike trains as proxies (since Matlab MCOS objects can't be deserialized). + ssglm_fit = _load_matlab_fitresult(ssglm["fR"], trains) + psth_fit = _load_matlab_fitresult(ssglm["psthR"], trains) + + # Merge PSTH and SSGLM results for comparison diagnostics + tCompare = psth_fit.mergeResults(ssglm_fit) + tCompare.lambda_signal.setDataLabels( + ["\\lambda_{PSTH}", "\\lambda_{SSGLM}"] + ) + + # ------------------------------------------------------------------ + # Figure 4: SSGLM vs PSTH diagnostics (2x2) + # ------------------------------------------------------------------ + fig4, axes4 = plt.subplots(2, 2, figsize=(14, 9)) + tCompare.KSPlot(handle=axes4[0, 0]) + tCompare.plotResidual(handle=axes4[0, 1]) + tCompare.plotInvGausTrans(handle=axes4[1, 0]) + tCompare.plotSeqCorr(handle=axes4[1, 1]) + fig4.tight_layout() + print(" Figure 4: SSGLM vs PSTH diagnostics") + + # ------------------------------------------------------------------ + # 4. Compute stimulus effect surfaces from basis coefficients + # ------------------------------------------------------------------ + minTime = 0.0 + maxTime = tmax + sampleRate = 1 / delta + basisWidth = (maxTime - minTime) / numBasis + + unitPulseBasis = SpikeTrainCollection.generateUnitImpulseBasis( + basisWidth, minTime, maxTime, sampleRate, + ) + basisMat = np.asarray(unitPulseBasis.data, dtype=float) # (T, numBasis) + + # Estimated CIF from SSGLM: exp(basisMat @ xK) / delta + estStimEffect = np.exp(basisMat @ xK) / delta # (T, K) + + # PSTH coefficients from psth_fit + psth_b = np.asarray(psth_fit.b[0], dtype=float).reshape(-1) + # The PSTH model has numBasis + history coefficients; extract first numBasis + psth_basis_coeffs = psth_b[:numBasis] if psth_b.size >= numBasis else np.pad( + psth_b, (0, numBasis - psth_b.size)) + psthSurface = np.exp(basisMat @ psth_basis_coeffs) / delta # (T,) constant across trials + psthSurface2D = np.tile(psthSurface[:, None], (1, K)) # (T, K) + + # True surface from our simulation params + actStimEffect = stimData[:basisMat.shape[0], :K] + + basis_time = np.asarray(unitPulseBasis.time, dtype=float).ravel() + + # ------------------------------------------------------------------ + # Figure 5: True/PSTH/SSGLM stimulus effect surfaces (3x1) + # ------------------------------------------------------------------ + fig5, axes5 = plt.subplots(3, 1, figsize=(10, 12)) + trial_axis = np.arange(1, K + 1) + + ax = axes5[0] + T_mesh, K_mesh = np.meshgrid(basis_time[:actStimEffect.shape[0]], + trial_axis, indexing="ij") + ax.pcolormesh(T_mesh, K_mesh, actStimEffect, shading="auto") + ax.set_title("True Stimulus Effect", fontweight="bold", fontsize=14) + ax.set_xlabel("time [s]") + ax.set_ylabel("Trial [k]") + + ax = axes5[1] + ax.pcolormesh(T_mesh, K_mesh, psthSurface2D[:actStimEffect.shape[0], :], + shading="auto") + ax.set_title("PSTH Estimated Stimulus Effect", fontweight="bold", + fontsize=14) + ax.set_xlabel("time [s]") + ax.set_ylabel("Trial [k]") + + ax = axes5[2] + ax.pcolormesh(T_mesh, K_mesh, estStimEffect[:actStimEffect.shape[0], :], + shading="auto") + ax.set_title("SSGLM Estimated Stimulus Effect", fontweight="bold", + fontsize=14) + ax.set_xlabel("time [s]") + ax.set_ylabel("Trial [k]") + + fig5.tight_layout() + print(" Figure 5: Stimulus effect surfaces") + + # ------------------------------------------------------------------ + # 5. Learning-trial analysis: spike rate CIs + # ------------------------------------------------------------------ + # Get spike matrix from our simulated data + dN = spikeColl.dataToMatrix() # shape (numRealizations, T) + if dN.ndim == 1: + dN = dN.reshape(1, -1) + dN = np.asarray(dN, dtype=float) + dN[dN > 1] = 1 + + windowTimes = np.arange(0.0, 0.004, delta) + + tRate, probMat, sigMat = DecodingAlgorithms.computeSpikeRateCIs( + xK, WkuFinal, dN, 0, tmax, "poisson", delta, gammahat, windowTimes, + ) + + # Find first learning trial (first column where significance appears) + sig_cols = np.where(sigMat[0, :] == 1)[0] + lt = int(sig_cols[0]) if sig_cols.size > 0 else 2 + if lt < 2: + lt = 2 + + # ------------------------------------------------------------------ + # Figure 6: Learning trial comparison + significance matrix (2x3) + # ------------------------------------------------------------------ + fig6 = plt.figure(figsize=(14, 9)) + + # (1,1): average spike rate with learning trial marker + ax1 = fig6.add_subplot(2, 3, 1) + rate_time = np.asarray(tRate.time, dtype=float).ravel() + rate_data = np.asarray(tRate.data, dtype=float).ravel() + ax1.plot(rate_time, rate_data, "k", linewidth=4) + ylims = ax1.get_ylim() + ax1.plot([lt, lt], ylims, "r", linewidth=2) + ax1.set_xlabel("Trial [k]") + ax1.set_ylabel("Average Firing Rate [spikes/sec]") + ax1.set_title(f"Learning Trial: {lt}", fontweight="bold", fontsize=12) + + # (1,2)+(1,3)+(2,2)+(2,3): significance matrix + ax2 = fig6.add_subplot(2, 3, (2, 6)) + ax2.imshow(probMat, cmap="gray_r", aspect="auto") + kTrials = sigMat.shape[0] + for k in range(kTrials): + for m in range(k + 1, kTrials): + if sigMat[k, m] == 1: + ax2.plot(m, k, "r*", markersize=3) + ax2.xaxis.set_ticks_position("top") + ax2.yaxis.set_ticks_position("right") + ax2.set_xlabel("Trial Number") + ax2.set_ylabel("Trial Number") + + # (2,1): CIF comparison for trial 1 vs learning trial + ax3 = fig6.add_subplot(2, 3, 4) + stim1_data = basisMat @ stimulus_true[:, 0] + stimlt_data = basisMat @ stimulus_true[:, lt - 1] + # CIs + ci1_lo = basisMat @ stimCIs[:, 0, 0] + ci1_hi = basisMat @ stimCIs[:, 0, 1] + cilt_lo = basisMat @ stimCIs[:, lt - 1, 0] + cilt_hi = basisMat @ stimCIs[:, lt - 1, 1] + + ax3.fill_between(basis_time, ci1_lo, ci1_hi, alpha=0.3, color="gray") + ax3.fill_between(basis_time, cilt_lo, cilt_hi, alpha=0.3, color="red") + h1, = ax3.plot(basis_time, stim1_data, "k", linewidth=4, + label="\\lambda_1(t)") + h2, = ax3.plot(basis_time, stimlt_data, "r", linewidth=4, + label=f"\\lambda_{{{lt}}}(t)") + ax3.legend(handles=[h1, h2]) + ax3.set_xlabel("time [s]") + ax3.set_ylabel("Firing Rate [spikes/sec]") + ax3.set_title("Learning Trial Vs. Baseline Trial\nwith 95% CIs", + fontweight="bold", fontsize=12) + + fig6.tight_layout() + print(f" Figure 6: Learning trial = {lt}") + + figures = {"fig03_ssglm_simulation_summary": fig3, "fig04_ssglm_fit_diagnostics": fig4, "fig05_stimulus_effect_surfaces": fig5, "fig06_learning_trial_comparison": fig6} + return figures + + +# ===================================================================== +# Main +# ===================================================================== +def run_example03(*, export_figures: bool = False, export_dir: Path | None = None): + """Run Example 03: PSTH and SSGLM dynamics.""" data_dir = ensure_example_data(download=True) - # --- Part A: PSTH analysis --- - summary3, payload3 = run_experiment3(return_payload=True) + if export_dir is None: + export_dir = THIS_DIR / "figures" / "example03" - # --- Part B: SSGLM analysis --- - summary3b, payload3b = run_experiment3b(data_dir, return_payload=True) + figs_a, _, _ = run_part_a(data_dir, export_dir) + figs_b = run_part_b(data_dir, export_dir) - # Merge summaries for JSON output - combined_summary = { - "experiment3": summary3, - "experiment3b": summary3b, - } - print(json.dumps(combined_summary, indent=2)) + all_figs = {**figs_a, **figs_b} if export_figures: - if export_dir is None: - export_dir = THIS_DIR / "figures" / "example03" - # Figure generation needs the combined dicts (multi-section example) - combined_payload = { - "experiment3": payload3, - "experiment3b": payload3b, - } - saved = export_named_paper_figures( - "example03", - summary=combined_summary, - payload=combined_payload, - export_dir=export_dir, - ) - print(f"\nGenerated {len(saved)} figure(s):") - for p in saved: - print(f" {p}") + export_dir.mkdir(parents=True, exist_ok=True) + for name, fig in all_figs.items(): + path = export_dir / f"{name}.png" + fig.savefig(str(path), dpi=150, bbox_inches="tight") + print(f" Saved {path}") - return combined_summary + plt.show() + print(f"\nExample 03 complete. Generated {len(all_figs)} figure(s).") + return all_figs if __name__ == "__main__": @@ -103,12 +634,9 @@ def run_example03(*, export_figures: bool = False, export_dir: Path | None = Non parser.add_argument("--repo-root", type=Path, default=REPO_ROOT) parser.add_argument("--export-figures", action="store_true") parser.add_argument("--export-dir", type=Path, default=None) - parser.add_argument("--output-json", type=Path, default=None) args = parser.parse_args() - result = run_example03( + run_example03( export_figures=args.export_figures, export_dir=args.export_dir, ) - if args.output_json: - args.output_json.write_text(json.dumps(result, indent=2), encoding="utf-8") diff --git a/examples/paper/example04_place_cells_continuous_stimulus.py b/examples/paper/example04_place_cells_continuous_stimulus.py index f5a3cae0..5414590b 100644 --- a/examples/paper/example04_place_cells_continuous_stimulus.py +++ b/examples/paper/example04_place_cells_continuous_stimulus.py @@ -4,17 +4,18 @@ This example demonstrates: 1) Loading hippocampal place-cell data from two animals. 2) Visualising spike locations overlaid on the animal's path. - 3) Fitting Gaussian and Zernike polynomial receptive-field models. - 4) Comparing model families via KS, AIC, and BIC statistics. - 5) Generating 2-D heatmaps and 3-D mesh plots of place fields. + 3) Loading precomputed Gaussian and Zernike polynomial receptive-field fits. + 4) Comparing model families via KS, AIC, and BIC statistics using FitSummary. + 5) Generating 2-D heatmaps of place fields for all neurons. + 6) Generating 3-D mesh comparison for selected example cells. Data provenance: - Uses ``data/PlaceCellDataAnimal1.mat`` and ``data/PlaceCellDataAnimal2.mat`` - (position trajectories + multi-neuron spike times). + Uses ``data/PlaceCellDataAnimal{1,2}.mat`` (trajectories + spike times) + and ``PlaceCellAnimal{1,2}Results.mat`` (precomputed FitResult structures). Expected outputs: - Figure 1: Example cells — spike locations over path (4 cells per animal). - - Figure 2: Population model-comparison statistics (Delta-KS, Delta-AIC, Delta-BIC). + - Figure 2: Population model-comparison statistics (dKS, dAIC, dBIC). - Figure 3: Gaussian receptive-field heatmaps (Animal 1). - Figure 4: Zernike receptive-field heatmaps (Animal 1). - Figure 5: Gaussian receptive-field heatmaps (Animal 2). @@ -22,55 +23,406 @@ - Figure 7: 3-D mesh comparison for selected example cells. Paper mapping: - Section 2.3.4 (place-cell continuous-stimulus analysis). + Section 2.3.5 (place-cell continuous-stimulus analysis). """ from __future__ import annotations import argparse -import json +import math import sys from pathlib import Path +import matplotlib.pyplot as plt +import numpy as np +from scipy.io import loadmat + THIS_DIR = Path(__file__).resolve().parent REPO_ROOT = THIS_DIR.parents[1] if str(REPO_ROOT) not in sys.path: sys.path.insert(0, str(REPO_ROOT)) +from nstat import ( # noqa: E402 + Covariate, + FitResult, + FitSummary, + TrialConfig, + ConfigCollection, +) +from nstat.core import nspikeTrain # noqa: E402 from nstat.data_manager import ensure_example_data # noqa: E402 -from nstat.paper_examples_full import run_experiment4 # noqa: E402 -from nstat.paper_figures import export_named_paper_figures # noqa: E402 +from nstat.zernike import zernike_basis_from_cartesian # noqa: E402 + + +# ===================================================================== +# Helpers +# ===================================================================== +def _load_animal_data(path): + """Load place-cell trajectory and spike data from a .mat file.""" + d = loadmat(str(path), squeeze_me=True) + x = np.asarray(d["x"], dtype=float).ravel() + y = np.asarray(d["y"], dtype=float).ravel() + time = np.asarray(d["time"], dtype=float).ravel() + neurons = np.asarray(d["neuron"], dtype=object).ravel() + return x, y, time, neurons + + +def _load_animal_results(path, x, y, time, neurons): + """Load precomputed FitResult structures and reconstruct Python FitResults.""" + d = loadmat(str(path), squeeze_me=True) + res_structs = np.asarray(d["resStruct"], dtype=object).ravel() + fit_results = [] + + for i, rs in enumerate(res_structs): + # Extract lambda signal + lam = rs["lambda"].item() + lam_time = np.asarray(lam["time"].item(), dtype=float).ravel() + lam_data = np.asarray(lam["data"].item(), dtype=float) + lam_name = str(lam["name"].item()) if lam["name"].size else "\\lambda" + + lambda_cov = Covariate( + lam_time, lam_data, lam_name, "time", "s", "spikes/sec", + ) + + # Extract coefficients + b_raw = rs["b"].item() + if b_raw.dtype == object: + b_list = [np.asarray(b_raw[j], dtype=float).ravel() for j in range(b_raw.size)] + else: + b_list = [np.asarray(b_raw, dtype=float).ravel()] + + numResults = int(np.asarray(rs["numResults"].item()).ravel()[0]) + + # Extract AIC/BIC/logLL + AIC = np.asarray(rs["AIC"].item(), dtype=float).ravel() + BIC = np.asarray(rs["BIC"].item(), dtype=float).ravel() + logLL = np.asarray(rs["logLL"].item(), dtype=float).ravel() + + # Config names + cn_raw = rs["configNames"].item() + if isinstance(cn_raw, np.ndarray): + config_names = [str(c) for c in cn_raw.ravel()] + else: + config_names = [str(cn_raw)] + + # Covariate labels + if "covLabels" in rs.dtype.names: + cl_raw = rs["covLabels"].item() + cl = cl_raw + else: + cl = [] + if isinstance(cl, np.ndarray) and cl.dtype == object: + cov_labels = [] + for j in range(cl.size): + item = cl[j] + if isinstance(item, np.ndarray): + cov_labels.append([str(x) for x in item.ravel()]) + else: + cov_labels.append([str(item)]) + elif isinstance(cl, str): + cov_labels = [[cl]] * numResults + else: + cov_labels = [config_names] * numResults + + # Create spike train for this neuron + st = np.asarray(neurons[i]["spikeTimes"].item(), dtype=float).ravel() + nst = nspikeTrain(st, name=str(i + 1), + minTime=float(time[0]), maxTime=float(time[-1]), + makePlots=-1) + # numHist + if "numHist" in rs.dtype.names: + nh = rs["numHist"].item() + num_hist = list(np.asarray(nh, dtype=int).ravel()) + else: + num_hist = [0] * numResults + cfgs = ConfigCollection([TrialConfig(name=n) for n in config_names]) + + fr = FitResult( + nst, + cov_labels, + num_hist, + [], # histObjects + [], # ensHistObjects + lambda_cov, + b_list, + [0.0] * numResults, # dev + [None] * numResults, # stats + AIC, + BIC, + logLL, + cfgs, + [], # XvalData + [], # XvalTime + "poisson", + ) + + # Load KS statistics if available + if "KSStats" in rs.dtype.names: + ks_struct = rs["KSStats"].item() + if hasattr(ks_struct, "dtype") and ks_struct.dtype.names: + ks_stat = np.asarray(ks_struct["ks_stat"].item(), dtype=float).ravel() + pval = np.asarray(ks_struct["pValue"].item(), dtype=float).ravel() + within = np.asarray(ks_struct["withinConfInt"].item(), dtype=float).ravel() + if ks_stat.size >= numResults: + fr.KSStats = ks_stat[:numResults].reshape(numResults, 1) + fr.KSPvalues = pval[:numResults] + fr.withinConfInt = within[:numResults] + + fit_results.append(fr) + + return fit_results + + +def _compute_place_field(coeffs, grid_design, grid_shape): + """Compute predicted firing rate on a spatial grid.""" + eta = grid_design @ coeffs + rate = np.exp(eta) + return rate.reshape(grid_shape) + + +# ===================================================================== +# Main example +# ===================================================================== def run_example04(*, export_figures: bool = False, export_dir: Path | None = None): - """Run Example 04: Place-cell receptive fields. - - Analysis workflow (mirrors Matlab example04_place_cells_continuous_stimulus.m): - 1. Load PlaceCellDataAnimal1.mat and PlaceCellDataAnimal2.mat. - 2. For each animal, visualise 4 example neurons (spike locations on path). - 3. Load or compute precomputed fit results for all neurons. - 4. Compute per-neuron Delta-KS, Delta-AIC, Delta-BIC (Gaussian vs Zernike). - 5. Generate Gaussian receptive-field heatmaps for all neurons (both animals). - 6. Generate Zernike polynomial receptive-field heatmaps. - 7. Generate 3-D mesh comparison for selected example cells. - """ + """Run Example 04: Place-cell receptive fields.""" + print("=== Example 04: Place-Cell Receptive Fields ===") + data_dir = ensure_example_data(download=True) + if export_dir is None: + export_dir = THIS_DIR / "figures" / "example04" + + # ================================================================== + # 1. Load data for both animals + # ================================================================== + x1, y1, t1, neurons1 = _load_animal_data( + data_dir / "Place Cells" / "PlaceCellDataAnimal1.mat") + x2, y2, t2, neurons2 = _load_animal_data( + data_dir / "Place Cells" / "PlaceCellDataAnimal2.mat") + nCells1 = len(neurons1) + nCells2 = len(neurons2) + print(f" Animal 1: {nCells1} cells, {len(t1)} time points") + print(f" Animal 2: {nCells2} cells, {len(t2)} time points") + + # ================================================================== + # 2. Load precomputed FitResults + # ================================================================== + fitResults1 = _load_animal_results( + data_dir / "PlaceCellAnimal1Results.mat", x1, y1, t1, neurons1) + fitResults2 = _load_animal_results( + data_dir / "PlaceCellAnimal2Results.mat", x2, y2, t2, neurons2) + print(f" Loaded {len(fitResults1)} + {len(fitResults2)} FitResult objects") + + # ================================================================== + # 3. Build FitSummary for each animal + # ================================================================== + summary1 = FitSummary(fitResults1) + summary2 = FitSummary(fitResults2) - # Run analysis (returns summary statistics and figure payload) - summary, payload = run_experiment4(data_dir, return_payload=True) + # Delta statistics: Gaussian (index 0) minus Zernike (index 1) + dAIC1 = summary1.AIC[:, 0] - summary1.AIC[:, 1] + dBIC1 = summary1.BIC[:, 0] - summary1.BIC[:, 1] + dKS1 = summary1.KSStats[:, 0] - summary1.KSStats[:, 1] - print(json.dumps(summary, indent=2)) + dAIC2 = summary2.AIC[:, 0] - summary2.AIC[:, 1] + dBIC2 = summary2.BIC[:, 0] - summary2.BIC[:, 1] + dKS2 = summary2.KSStats[:, 0] - summary2.KSStats[:, 1] + + dAIC_all = np.concatenate([dAIC1, dAIC2]) + dBIC_all = np.concatenate([dBIC1, dBIC2]) + dKS_all = np.concatenate([dKS1, dKS2]) + + print(f" Mean dAIC (Gauss-Zern): {np.nanmean(dAIC_all):.2f}") + print(f" Mean dBIC (Gauss-Zern): {np.nanmean(dBIC_all):.2f}") + print(f" Mean dKS (Gauss-Zern): {np.nanmean(dKS_all):.4f}") + + # ================================================================== + # Figure 1: Example cells — spike locations over path (2x2) + # ================================================================== + exampleCells = [1, 20, 24, 48] # 0-indexed + fig1, axes1 = plt.subplots(2, 2, figsize=(12, 10)) + for i, cidx in enumerate(exampleCells): + ax = axes1.flat[i] + ax.plot(x1, y1, "b-", linewidth=0.5, alpha=0.5) + n = neurons1[min(cidx, nCells1 - 1)] + xn = np.asarray(n["xN"].item(), dtype=float).ravel() + yn = np.asarray(n["yN"].item(), dtype=float).ravel() + ax.plot(xn, yn, "r.", markersize=7) + ax.set_title(f"Cell {cidx + 1}", fontweight="bold", fontsize=12) + ax.set_aspect("equal") + fig1.suptitle("Animal 1 — Example Place Cells", fontweight="bold", + fontsize=14) + fig1.tight_layout() + + # ================================================================== + # Figure 2: Population statistics (1x3 box plots) + # ================================================================== + fig2, axes2 = plt.subplots(1, 3, figsize=(14, 5)) + + axes2[0].boxplot([dKS1[np.isfinite(dKS1)], dKS2[np.isfinite(dKS2)]], + tick_labels=["Animal 1", "Animal 2"]) + axes2[0].set_ylabel(r"$\Delta$KS (Gaussian - Zernike)") + axes2[0].set_title("KS Statistic Difference") + axes2[0].axhline(0, color="gray", linestyle="--", linewidth=0.5) + + axes2[1].boxplot([dAIC1[np.isfinite(dAIC1)], dAIC2[np.isfinite(dAIC2)]], + tick_labels=["Animal 1", "Animal 2"]) + axes2[1].set_ylabel(r"$\Delta$AIC (Gaussian - Zernike)") + axes2[1].set_title("AIC Difference") + axes2[1].axhline(0, color="gray", linestyle="--", linewidth=0.5) + + axes2[2].boxplot([dBIC1[np.isfinite(dBIC1)], dBIC2[np.isfinite(dBIC2)]], + tick_labels=["Animal 1", "Animal 2"]) + axes2[2].set_ylabel(r"$\Delta$BIC (Gaussian - Zernike)") + axes2[2].set_title("BIC Difference") + axes2[2].axhline(0, color="gray", linestyle="--", linewidth=0.5) + + fig2.tight_layout() + + # ================================================================== + # 4. Build spatial grids and design matrices for heatmaps + # ================================================================== + grid_res = 100 + xGrid = np.linspace(-1, 1, grid_res) + yGrid = np.linspace(-1, 1, grid_res) + xx, yy = np.meshgrid(xGrid, yGrid) + xf, yf = xx.ravel(), yy.ravel() + + # Gaussian design: [1, x, y, x^2, y^2, xy] (intercept prepended) + gridDesignGauss = np.column_stack([ + np.ones(xf.size), xf, yf, xf**2, yf**2, xf * yf + ]) + + # Zernike design: [1, z1, z2, ..., z9] (intercept prepended) + zBasis = zernike_basis_from_cartesian(xf, yf, fill_value=0.0) + gridDesignZern = np.column_stack([np.ones(xf.size), zBasis]) + + # ================================================================== + # Figures 3-6: Place field heatmaps + # ================================================================== + def _plot_heatmaps(fit_results, nCells, title_prefix, design_gauss, + design_zern, grid_shape): + nRows = math.ceil(nCells / 7) + nCols = 7 + + figG, axesG = plt.subplots(nRows, nCols, figsize=(14, 2 * nRows)) + figZ, axesZ = plt.subplots(nRows, nCols, figsize=(14, 2 * nRows)) + if nRows == 1: + axesG = axesG[np.newaxis, :] + axesZ = axesZ[np.newaxis, :] + + for i in range(nCells): + row, col = divmod(i, nCols) + fr = fit_results[i] + coeffs_g = np.asarray(fr.b[0], dtype=float).ravel() + coeffs_z = np.asarray(fr.b[1], dtype=float).ravel() if fr.numResults > 1 else coeffs_g + + # Gaussian field + ax = axesG[row, col] + try: + field_g = _compute_place_field(coeffs_g, design_gauss[:, :coeffs_g.size], grid_shape) + ax.pcolormesh(xx, yy, field_g, shading="auto", cmap="jet") + except Exception: + pass + ax.set_aspect("equal") + ax.set_xticks([]) + ax.set_yticks([]) + ax.set_title(f"{i + 1}", fontsize=8) + + # Zernike field + ax = axesZ[row, col] + try: + field_z = _compute_place_field(coeffs_z, design_zern[:, :coeffs_z.size], grid_shape) + ax.pcolormesh(xx, yy, field_z, shading="auto", cmap="jet") + except Exception: + pass + ax.set_aspect("equal") + ax.set_xticks([]) + ax.set_yticks([]) + ax.set_title(f"{i + 1}", fontsize=8) + + # Hide unused subplots + for i in range(nCells, nRows * nCols): + row, col = divmod(i, nCols) + axesG[row, col].set_visible(False) + axesZ[row, col].set_visible(False) + + figG.suptitle(f"{title_prefix} — Gaussian Place Fields", + fontweight="bold", fontsize=14) + figZ.suptitle(f"{title_prefix} — Zernike Place Fields", + fontweight="bold", fontsize=14) + figG.tight_layout() + figZ.tight_layout() + return figG, figZ + + figG1, figZ1 = _plot_heatmaps( + fitResults1, nCells1, "Animal 1", + gridDesignGauss, gridDesignZern, xx.shape, + ) + figG2, figZ2 = _plot_heatmaps( + fitResults2, nCells2, "Animal 2", + gridDesignGauss, gridDesignZern, xx.shape, + ) + print(" Figures 3-6: Place field heatmaps") + + # ================================================================== + # Figure 7: 3-D mesh comparison for an example cell + # ================================================================== + exampleCell = min(24, nCells1 - 1) # 0-indexed → cell 25 in Matlab + fr_ex = fitResults1[exampleCell] + coeffs_g = np.asarray(fr_ex.b[0], dtype=float).ravel() + coeffs_z = np.asarray(fr_ex.b[1], dtype=float).ravel() + + field_g = _compute_place_field( + coeffs_g, gridDesignGauss[:, :coeffs_g.size], xx.shape) + field_z = _compute_place_field( + coeffs_z, gridDesignZern[:, :coeffs_z.size], xx.shape) + + fig7 = plt.figure(figsize=(12, 8)) + ax3d = fig7.add_subplot(111, projection="3d") + ax3d.plot_surface(xx, yy, field_g, alpha=0.3, color="blue", + label="Gaussian") + ax3d.plot_surface(xx, yy, field_z, alpha=0.3, color="green", + label="Zernike") + # Overlay animal path at z=0 + ax3d.plot(x1, y1, np.zeros_like(x1), "b-", linewidth=0.3, alpha=0.3) + # Overlay spike locations + n_ex = neurons1[exampleCell] + xn_ex = np.asarray(n_ex["xN"].item(), dtype=float).ravel() + yn_ex = np.asarray(n_ex["yN"].item(), dtype=float).ravel() + ax3d.scatter(xn_ex, yn_ex, np.zeros_like(xn_ex), c="r", s=5, + alpha=0.5) + ax3d.set_xlabel("x") + ax3d.set_ylabel("y") + ax3d.set_zlabel("Firing Rate") + ax3d.set_title(f"Cell {exampleCell + 1}: Gaussian (blue) vs Zernike (green)", + fontweight="bold", fontsize=14) + + print(f" Figure 7: 3D mesh for cell {exampleCell + 1}") + + # ================================================================== + # Save figures + # ================================================================== + all_figs = { + "fig01_example_cells_path_overlay": fig1, + "fig02_model_summary_statistics": fig2, + "fig03_gaussian_place_fields_animal1": figG1, + "fig04_zernike_place_fields_animal1": figZ1, + "fig05_gaussian_place_fields_animal2": figG2, + "fig06_zernike_place_fields_animal2": figZ2, + "fig07_example_cell_mesh_comparison": fig7, + } if export_figures: - if export_dir is None: - export_dir = THIS_DIR / "figures" / "example04" - saved = export_named_paper_figures( - "example04", summary=summary, payload=payload, export_dir=export_dir - ) - print(f"\nGenerated {len(saved)} figure(s):") - for p in saved: - print(f" {p}") + export_dir.mkdir(parents=True, exist_ok=True) + for name, fig in all_figs.items(): + path = export_dir / f"{name}.png" + fig.savefig(str(path), dpi=150, bbox_inches="tight") + print(f" Saved {path}") - return summary + plt.show() + print(f"\nExample 04 complete. Generated {len(all_figs)} figure(s).") + return all_figs if __name__ == "__main__": @@ -80,12 +432,9 @@ def run_example04(*, export_figures: bool = False, export_dir: Path | None = Non parser.add_argument("--repo-root", type=Path, default=REPO_ROOT) parser.add_argument("--export-figures", action="store_true") parser.add_argument("--export-dir", type=Path, default=None) - parser.add_argument("--output-json", type=Path, default=None) args = parser.parse_args() - result = run_example04( + run_example04( export_figures=args.export_figures, export_dir=args.export_dir, ) - if args.output_json: - args.output_json.write_text(json.dumps(result, indent=2), encoding="utf-8") diff --git a/examples/paper/example05_decoding_ppaf_pphf.py b/examples/paper/example05_decoding_ppaf_pphf.py index 60a37fad..89e41a67 100644 --- a/examples/paper/example05_decoding_ppaf_pphf.py +++ b/examples/paper/example05_decoding_ppaf_pphf.py @@ -1,31 +1,37 @@ #!/usr/bin/env python3 """Example 05 — Stimulus Decoding With PPAF and PPHF. -This example demonstrates: - 1) Univariate sinusoidal stimulus encoding and decoding via PPDecodeFilterLinear. - 2) 4-state arm-reach simulation with 20-cell population encoding. - 3) PPAF (Point-Process Adaptive Filter) decoding: free vs goal-informed. - 4) Hybrid filter (PPHybridFilterLinear) for joint discrete/continuous states. +This example demonstrates neural decoding using point-process adaptive filters +(PPAF) and point-process hybrid filters (PPHF) from the nSTAT toolbox. The example has three parts: - Part A (experiment5): Univariate sinusoidal stimulus — encode with 20 - neurons, decode with PPDecodeFilterLinear. - Part B (experiment5b): 4-state arm reaching — simulate 20-cell population, - compare PPAF vs PPAF+Goal across 20 simulations. - Part C (experiment6): Hybrid filter — simulate 40-cell population with - discrete reach states and continuous kinematics, decode with - PPHybridFilterLinear. -Expected outputs: - - Figure 1: Univariate stimulus setup (CIF tuning curves, simulated spikes). - - Figure 2: Univariate decoding results (decoded stimulus vs true). - - Figure 3: Reach setup and population encoding. - - Figure 4: PPAF comparison (free vs goal-informed). - - Figure 5: Hybrid filter setup. - - Figure 6: Hybrid decoding summary. +Part A — Univariate Sinusoidal Stimulus (Figures 1–2): + 1. Define 20-cell population with logistic (binomial) tuning to a 1-D + sinusoidal stimulus. + 2. Simulate spike observations from the binomial CIF. + 3. Decode the stimulus using ``PPDecodeFilterLinear`` (PPAF). + +Part B — 4-State Arm Reach with PPAF (Figures 3–4): + 4. Simulate reaching trajectories (position + velocity, 4-D state). + 5. Encode with 20-cell cosine-tuning population (binomial CIF). + 6. Decode with PPAF (free) and PPAF + Goal; compare across 20 simulations. + +Part C — Hybrid Filter (Figures 5–6): + 7. Simulate 40-cell population with 2 discrete reach-states (rest / reach) + that modulate baseline firing rate, plus velocity-tuned continuous state. + 8. Decode joint discrete + continuous state via ``PPHybridFilterLinear``. Paper mapping: Section 2.5 (point-process adaptive filter) and Section 2.6 (hybrid filter). + +Expected outputs: + - Figure 1: CIF tuning curves and simulated spike raster. + - Figure 2: Decoded stimulus vs true (with ±2σ confidence band). + - Figure 3: Reach trajectory and population spike raster. + - Figure 4: PPAF comparison (free vs goal-informed, 20 runs box plot). + - Figure 5: Hybrid filter setup (state sequence, spike raster). + - Figure 6: Hybrid decoding results (state probabilities, decoded kinematics). """ from __future__ import annotations @@ -34,27 +40,490 @@ import sys from pathlib import Path +import matplotlib.pyplot as plt +import numpy as np + THIS_DIR = Path(__file__).resolve().parent REPO_ROOT = THIS_DIR.parents[1] if str(REPO_ROOT) not in sys.path: sys.path.insert(0, str(REPO_ROOT)) -from nstat.paper_examples_full import ( # noqa: E402 - run_experiment5, - run_experiment5b, - run_experiment6, -) -from nstat.paper_figures import export_named_paper_figures # noqa: E402 +from nstat import DecodingAlgorithms # noqa: E402 + + +# ────────────────────────────────────────────────────────────────────────────── +# Helper: simulate binomial spikes from linear-logistic CIF +# ────────────────────────────────────────────────────────────────────────────── + + +def _simulate_binomial_spikes(x, mu, beta, rng): + """Simulate spikes from binomial CIF: p_c = sigmoid(mu_c + beta_c @ x). + + Parameters + ---------- + x : (ns, T) array — stimulus/state trajectory + mu : (C,) array — baseline log-odds per cell + beta : (ns, C) array — tuning coefficients + rng : numpy Generator + + Returns + ------- + dN : (C, T) array — binary spike indicators + """ + ns, T = x.shape + C = mu.size + dN = np.zeros((C, T), dtype=float) + for t in range(T): + eta = mu + beta.T @ x[:, t] # (C,) + p = 1.0 / (1.0 + np.exp(-np.clip(eta, -20.0, 20.0))) + dN[:, t] = (rng.random(C) < p).astype(float) + return dN + + +# ────────────────────────────────────────────────────────────────────────────── +# Part A — Univariate sinusoidal stimulus +# ────────────────────────────────────────────────────────────────────────────── + + +def _run_part_a(seed=11, n_cells=20): + """Encode/decode a 1-D sinusoidal stimulus with 20-cell binomial CIF.""" + rng = np.random.default_rng(seed) + delta = 0.001 # 1 ms bins + time = np.arange(0.0, 1.0 + delta, delta) + T = len(time) + + # True stimulus: sinusoidal + x_true = np.sin(2.0 * np.pi * 2.0 * time) # (T,) + + # ── Encoding model: logistic CIF ── + # mu_c ~ log(10*delta) + N(0, 0.3) — baseline firing probability + # beta_c ~ N(1.0, 0.5) — stimulus gain + b0 = np.log(10.0 * delta) * np.ones(n_cells) + rng.normal(0.0, 0.3, n_cells) + b1 = rng.normal(1.0, 0.5, n_cells) + + # Simulate spikes + x_2d = x_true.reshape(1, -1) # (1, T) — scalar state + beta = b1.reshape(1, -1) # (1, C) — stimulus coefficients + dN = _simulate_binomial_spikes(x_2d, b0, beta, rng) + + # ── State-space model ── + # x(t+1) = A * x(t) + w, w ~ N(0, Q) + A = np.array([[1.0]]) + Q = np.array([[0.001]]) + x0 = np.array([0.0]) + Pi0 = 0.5 * np.eye(1) + + # ── Decode with PPDecodeFilterLinear ── + # dN is (C, T) — the API expects (num_cells, num_steps) + x_p, W_p, x_u, W_u, _, _, _, _ = DecodingAlgorithms.PPDecodeFilterLinear( + A, Q, dN, b0, beta, "binomial", delta, None, None, x0, Pi0 + ) + + # Extract decoded signal and ±2σ confidence band + x_decoded = x_u[0, :] # (T,) + sigma = np.sqrt(np.maximum(W_u[0, 0, :], 0.0)) + ci_low = x_decoded - 2.0 * sigma + ci_high = x_decoded + 2.0 * sigma + rmse = float(np.sqrt(np.mean((x_decoded - x_true) ** 2))) + + return { + "time": time, + "x_true": x_true, + "x_decoded": x_decoded, + "ci_low": ci_low, + "ci_high": ci_high, + "dN": dN, + "b0": b0, + "b1": b1, + "rmse": rmse, + "n_cells": n_cells, + } + + +# ────────────────────────────────────────────────────────────────────────────── +# Part B — 4-state arm reach with PPAF +# ────────────────────────────────────────────────────────────────────────────── + + +def _simulate_reach(delta, T_total, rng): + """Simulate a 2-D reaching trajectory with 4-D state [x, y, vx, vy]. + + Uses a simple sinusoidal trajectory to mimic a reaching task. + """ + time = np.arange(0.0, T_total + delta, delta) + T = len(time) + + # Smooth trajectory + x_pos = 0.25 * np.sin(2.0 * np.pi * 0.15 * time) + y_pos = 0.20 * np.cos(2.0 * np.pi * 0.10 * time) + vx = np.gradient(x_pos, delta) + vy = np.gradient(y_pos, delta) + + state = np.vstack([x_pos, y_pos, vx, vy]) # (4, T) + return time, state + + +def _run_part_b(seed=19, n_cells=20, n_sims=20): + """Compare PPAF free vs goal-directed decoding for arm reach.""" + rng = np.random.default_rng(seed) + delta = 0.01 # 10 ms bins + ns = 4 # state dimension + + # State-space model (constant-velocity kinematic model) + A = np.array([ + [1, 0, delta, 0], + [0, 1, 0, delta], + [0, 0, 1, 0], + [0, 0, 0, 1], + ], dtype=float) + Q = 0.001 * np.eye(ns, dtype=float) + + # Encoding model: cosine tuning to velocity + # mu_c ~ N(-3.0, 0.2) + # beta_c = [0, 0, w_vx, w_vy] — velocity tuned + b0 = rng.normal(-3.0, 0.2, n_cells) + beta = np.zeros((ns, n_cells), dtype=float) + for c in range(n_cells): + beta[2, c] = 3.0 * rng.normal(0.0, 1.0) # vx weight + beta[3, c] = 3.0 * rng.normal(0.0, 1.0) # vy weight + + # Run multiple simulations to compare free vs goal-directed + rmse_free = np.zeros((n_sims, ns), dtype=float) + rmse_goal = np.zeros((n_sims, ns), dtype=float) + + # Store one example run for plotting + example_run = None + + for sim_idx in range(n_sims): + sim_rng = np.random.default_rng(seed + sim_idx + 100) + time, state = _simulate_reach(delta, 10.0, sim_rng) + T = state.shape[1] + + # Simulate spikes + dN = _simulate_binomial_spikes(state, b0, beta, sim_rng) + + # Initial conditions + x0 = state[:, 0] + Pi0 = 0.1 * np.eye(ns) + + # --- Free decode (no goal) --- + x_p_free, _, x_u_free, W_u_free, _, _, _, _ = ( + DecodingAlgorithms.PPDecodeFilterLinear( + A, Q, dN, b0, beta, "binomial", delta, + None, None, x0, Pi0 + ) + ) + + # --- Goal-directed decode --- + yT = state[:, -1] # target = final state + PiT = 0.01 * np.eye(ns) # tight target uncertainty + x_p_goal, _, x_u_goal, W_u_goal, _, _, _, _ = ( + DecodingAlgorithms.PPDecodeFilterLinear( + A, Q, dN, b0, beta, "binomial", delta, + None, None, x0, Pi0, yT, PiT, 0 + ) + ) + + # Compute RMSE per state dimension + for d in range(ns): + rmse_free[sim_idx, d] = np.sqrt(np.mean((x_u_free[d, :] - state[d, :]) ** 2)) + rmse_goal[sim_idx, d] = np.sqrt(np.mean((x_u_goal[d, :] - state[d, :]) ** 2)) + + if sim_idx == 0: + example_run = { + "time": time, + "state": state, + "dN": dN, + "x_u_free": x_u_free, + "x_u_goal": x_u_goal, + "W_u_free": W_u_free, + "W_u_goal": W_u_goal, + } + + return { + "rmse_free": rmse_free, + "rmse_goal": rmse_goal, + "example": example_run, + "n_cells": n_cells, + "n_sims": n_sims, + "state_labels": ["x", "y", "vx", "vy"], + } + + +# ────────────────────────────────────────────────────────────────────────────── +# Part C — Hybrid filter +# ────────────────────────────────────────────────────────────────────────────── + +def _run_part_c(seed=37, n_cells=40): + """PPHybridFilterLinear: joint discrete/continuous state decoding.""" + rng = np.random.default_rng(seed) + delta = 0.01 # 10 ms bins + ns = 4 # continuous state dimension (x, y, vx, vy) -def run_example05(*, export_figures: bool = False, export_dir: Path | None = None): + # ── Simulate trajectory ── + time = np.arange(0.0, 10.0, delta, dtype=float) + T = len(time) + x_pos = 0.3 * np.sin(2.0 * np.pi * 0.15 * time) + y_pos = 0.25 * np.cos(2.0 * np.pi * 0.10 * time) + vx = np.gradient(x_pos, delta) + vy = np.gradient(y_pos, delta) + state = np.vstack([x_pos, y_pos, vx, vy]) # (4, T) + + # Discrete state: alternating reach / hold (period ~6s) + true_mode = np.where(np.sin(2.0 * np.pi * time / 6.0) > 0.0, 1, 2).astype(int) + # Add stochastic flips + flip = rng.random(T) < 0.01 + true_mode[flip] = 3 - true_mode[flip] + + # ── State-space models (one per mode) ── + A_reach = np.array([ + [1, 0, delta, 0], + [0, 1, 0, delta], + [0, 0, 1, 0], + [0, 0, 0, 1], + ], dtype=float) + Q_reach = 0.001 * np.eye(ns) + + # Hold state: damped velocity + A_hold = np.array([ + [1, 0, delta, 0], + [0, 1, 0, delta], + [0, 0, 0.95, 0], + [0, 0, 0, 0.95], + ], dtype=float) + Q_hold = 0.0005 * np.eye(ns) + + # ── Encoding model ── + # Neurons tuned to ALL state dimensions (position + velocity). + # Mode-dependent baseline: mode 1 (reach) has different rate than mode 2 (hold). + b0_mode1 = rng.normal(-3.5, 0.2, n_cells) # reach baseline + b0_mode2 = rng.normal(-2.5, 0.2, n_cells) # hold baseline + + # Full state tuning: position + velocity + beta_mat = np.zeros((ns, n_cells), dtype=float) + beta_mat[0, :] = rng.normal(0.0, 2.0, n_cells) # x position + beta_mat[1, :] = rng.normal(0.0, 2.0, n_cells) # y position + beta_mat[2, :] = rng.normal(0.0, 3.0, n_cells) # vx + beta_mat[3, :] = rng.normal(0.0, 3.0, n_cells) # vy + + # Simulate spikes with mode-dependent baseline (binomial) + dN = np.zeros((n_cells, T), dtype=float) + for t in range(T): + b0 = b0_mode1 if true_mode[t] == 1 else b0_mode2 + eta = b0 + beta_mat.T @ state[:, t] + p = 1.0 / (1.0 + np.exp(-np.clip(eta, -20.0, 20.0))) + dN[:, t] = (rng.random(n_cells) < p).astype(float) + + # ── Transition matrix ── + p_ij = np.array([[0.985, 0.015], [0.02, 0.98]], dtype=float) + + # ── Decode with PPHybridFilterLinear ── + Mu0 = np.array([0.5, 0.5]) + x0 = [state[:, 0], state[:, 0]] + Pi0 = [0.5 * np.eye(ns), 0.5 * np.eye(ns)] + + S_est, X_est, W_est, MU_u, _, _, _ = DecodingAlgorithms.PPHybridFilterLinear( + [A_reach, A_hold], + [Q_reach, Q_hold], + p_ij, + Mu0, + dN, + [b0_mode1, b0_mode2], + [beta_mat, beta_mat], + "binomial", + delta, + None, # gamma + None, # windowTimes + x0, + Pi0, + ) + + # Classification accuracy + state_acc = float(np.mean(S_est == true_mode)) + + # Position RMSE + rmse_x = float(np.sqrt(np.mean((X_est[0, :] - x_pos) ** 2))) + rmse_y = float(np.sqrt(np.mean((X_est[1, :] - y_pos) ** 2))) + + return { + "time": time, + "state": state, + "true_mode": true_mode, + "S_est": S_est, + "X_est": X_est, + "MU_u": MU_u, + "dN": dN, + "state_acc": state_acc, + "rmse_x": rmse_x, + "rmse_y": rmse_y, + "n_cells": n_cells, + } + + +# ────────────────────────────────────────────────────────────────────────────── +# Plotting +# ────────────────────────────────────────────────────────────────────────────── + + +def _plot_part_a(result): + """Figure 1: CIF setup & raster. Figure 2: Decoded vs true stimulus.""" + time = result["time"] + x_true = result["x_true"] + dN = result["dN"] + + # ── Figure 1: CIF tuning and spike raster ── + fig1, axes1 = plt.subplots(2, 1, figsize=(10, 6), sharex=True) + + # Top: true stimulus + axes1[0].plot(time, x_true, "k-", linewidth=1.5) + axes1[0].set_ylabel("Stimulus x(t)") + axes1[0].set_title("Part A: Sinusoidal Stimulus Encoding") + + # Bottom: spike raster + n_cells = dN.shape[0] + for c in range(n_cells): + spike_times = time[dN[c, :] > 0] + axes1[1].plot(spike_times, np.full_like(spike_times, c + 1), "|", color="k", markersize=2) + axes1[1].set_ylabel("Neuron") + axes1[1].set_xlabel("Time (s)") + axes1[1].set_ylim(0.5, n_cells + 0.5) + fig1.tight_layout() + + # ── Figure 2: Decoding results ── + fig2, ax2 = plt.subplots(1, 1, figsize=(10, 4)) + ax2.plot(time, x_true, "k-", linewidth=1.5, label="True stimulus") + ax2.plot(time, result["x_decoded"], "r-", linewidth=1.0, label="PPAF decoded") + ax2.fill_between( + time, result["ci_low"], result["ci_high"], + color="red", alpha=0.15, label="±2σ CI" + ) + ax2.set_xlabel("Time (s)") + ax2.set_ylabel("x(t)") + ax2.set_title(f"PPDecodeFilterLinear — Decoded Stimulus (RMSE = {result['rmse']:.4f})") + ax2.legend(loc="upper right") + fig2.tight_layout() + + return fig1, fig2 + + +def _plot_part_b(result): + """Figure 3: Reach trajectory & encoding. Figure 4: RMSE comparison.""" + ex = result["example"] + time = ex["time"] + state = ex["state"] + + # ── Figure 3: Example reach with decoded trajectories ── + fig3, axes3 = plt.subplots(2, 2, figsize=(12, 8)) + labels = result["state_labels"] + ylabels = ["x (m)", "y (m)", "vx (m/s)", "vy (m/s)"] + + for d, (ax, lab, ylab) in enumerate(zip(axes3.ravel(), labels, ylabels)): + ax.plot(time, state[d, :], "k-", linewidth=1.0, label="True") + ax.plot(time, ex["x_u_free"][d, :], "b-", linewidth=0.7, alpha=0.8, label="PPAF free") + ax.plot(time, ex["x_u_goal"][d, :], "r-", linewidth=0.7, alpha=0.8, label="PPAF+Goal") + ax.set_ylabel(ylab) + ax.set_title(f"State: {lab}") + if d >= 2: + ax.set_xlabel("Time (s)") + if d == 0: + ax.legend(loc="upper right", fontsize=8) + + fig3.suptitle("Part B: Arm Reach — PPAF Decoding (Example Run)", fontsize=12) + fig3.tight_layout() + + # ── Figure 4: RMSE box plot (free vs goal) ── + fig4, axes4 = plt.subplots(1, 4, figsize=(14, 4)) + for d, (ax, lab) in enumerate(zip(axes4, labels)): + data = [result["rmse_free"][:, d], result["rmse_goal"][:, d]] + bp = ax.boxplot(data, tick_labels=["Free", "Goal"]) + ax.set_title(f"RMSE: {lab}") + ax.set_ylabel("RMSE") + + fig4.suptitle( + f"Part B: PPAF Free vs Goal ({result['n_sims']} simulations, {result['n_cells']} cells)", + fontsize=12, + ) + fig4.tight_layout() + + return fig3, fig4 + + +def _plot_part_c(result): + """Figure 5: Hybrid setup. Figure 6: Hybrid decoding results.""" + time = result["time"] + + # ── Figure 5: Setup — state sequence + raster ── + fig5, axes5 = plt.subplots(2, 1, figsize=(12, 5), sharex=True) + + # Top: discrete state + axes5[0].plot(time, result["true_mode"], "k-", linewidth=1.0, label="True mode") + axes5[0].set_ylabel("Discrete State") + axes5[0].set_yticks([1, 2]) + axes5[0].set_yticklabels(["Reach", "Hold"]) + axes5[0].set_title("Part C: Hybrid Filter Setup") + axes5[0].legend() + + # Bottom: spike raster (first 20 cells) + dN = result["dN"] + n_show = min(20, dN.shape[0]) + for c in range(n_show): + idx = np.where(dN[c, :] > 0)[0] + spike_t = time[idx] + axes5[1].plot(spike_t, np.full_like(spike_t, c + 1), "|", color="k", markersize=2) + axes5[1].set_ylabel("Neuron") + axes5[1].set_xlabel("Time (s)") + axes5[1].set_ylim(0.5, n_show + 0.5) + fig5.tight_layout() + + # ── Figure 6: Decoding results ── + fig6, axes6 = plt.subplots(3, 1, figsize=(12, 8), sharex=True) + + # Top: model probabilities + axes6[0].plot(time, result["MU_u"][0, :], "b-", linewidth=0.5, label="P(Reach)") + axes6[0].plot(time, result["MU_u"][1, :], "r-", linewidth=0.5, label="P(Hold)") + axes6[0].axhline(0.5, color="gray", linestyle="--", linewidth=0.5) + axes6[0].set_ylabel("Model Prob") + axes6[0].set_title( + f"PPHybridFilterLinear — State Accuracy: {result['state_acc']:.1%}" + ) + axes6[0].legend(loc="upper right", fontsize=8) + + # Middle: decoded x-position + axes6[1].plot(time, result["state"][0, :], "k-", linewidth=1.0, label="True") + axes6[1].plot(time, result["X_est"][0, :], "b-", linewidth=0.7, alpha=0.8, label="Decoded") + axes6[1].set_ylabel("x (m)") + axes6[1].legend(loc="upper right", fontsize=8) + + # Bottom: decoded y-position + axes6[2].plot(time, result["state"][1, :], "k-", linewidth=1.0, label="True") + axes6[2].plot(time, result["X_est"][1, :], "r-", linewidth=0.7, alpha=0.8, label="Decoded") + axes6[2].set_ylabel("y (m)") + axes6[2].set_xlabel("Time (s)") + axes6[2].legend(loc="upper right", fontsize=8) + + fig6.suptitle( + f"Hybrid Decoding (RMSE: x={result['rmse_x']:.4f}, y={result['rmse_y']:.4f})", + fontsize=12, + ) + fig6.tight_layout() + + return fig5, fig6 + + +# ────────────────────────────────────────────────────────────────────────────── +# Main entry point +# ────────────────────────────────────────────────────────────────────────────── + + +def run_example05(*, export_figures=False, export_dir=None, show=False): """Run Example 05: PPAF and PPHF decoding. - Analysis workflow (mirrors Matlab example05_decoding_ppaf_pphf.m): + Analysis workflow (mirrors Matlab ``example05_decoding_ppaf_pphf.m``): Part A — Univariate stimulus decoding: 1. Define 20-cell population with sinusoidal tuning. - 2. Simulate spikes from sinusoidal stimulus CIF. + 2. Simulate spikes from binomial CIF. 3. Decode stimulus via PPDecodeFilterLinear. Part B — Arm-reach PPAF: @@ -63,45 +532,82 @@ def run_example05(*, export_figures: bool = False, export_dir: Path | None = Non 6. Decode with PPAF (free) and PPAF+Goal; compare across 20 runs. Part C — Hybrid filter: - 7. Simulate 40-cell population with discrete reach-state modulation. + 7. Simulate 40-cell population with discrete state modulation. 8. Decode joint discrete/continuous state via PPHybridFilterLinear. """ + print("=" * 70) + print("Example 05: Stimulus Decoding with PPAF and PPHF") + print("=" * 70) + # --- Part A: Univariate sinusoidal stimulus --- - summary5, payload5 = run_experiment5(return_payload=True) + print("\n--- Part A: Univariate Sinusoidal Stimulus ---") + result_a = _run_part_a() + print(f" {result_a['n_cells']} cells, decode RMSE = {result_a['rmse']:.4f}") # --- Part B: Arm-reach PPAF --- - summary5b, payload5b = run_experiment5b(return_payload=True) + print("\n--- Part B: Arm Reach PPAF (20 simulations) ---") + result_b = _run_part_b() + mean_free = result_b["rmse_free"].mean(axis=0) + mean_goal = result_b["rmse_goal"].mean(axis=0) + print(f" Mean RMSE (free): x={mean_free[0]:.4f}, y={mean_free[1]:.4f}, " + f"vx={mean_free[2]:.4f}, vy={mean_free[3]:.4f}") + print(f" Mean RMSE (goal): x={mean_goal[0]:.4f}, y={mean_goal[1]:.4f}, " + f"vx={mean_goal[2]:.4f}, vy={mean_goal[3]:.4f}") # --- Part C: Hybrid filter --- - summary6, payload6 = run_experiment6(REPO_ROOT, return_payload=True) + print("\n--- Part C: Hybrid Filter ---") + result_c = _run_part_c() + print(f" {result_c['n_cells']} cells, state accuracy = {result_c['state_acc']:.1%}") + print(f" Position RMSE: x={result_c['rmse_x']:.4f}, y={result_c['rmse_y']:.4f}") - # Merge summaries for JSON output - combined_summary = { - "experiment5": summary5, - "experiment5b": summary5b, - "experiment6": summary6, + # Summary + summary = { + "experiment5": { + "num_cells": float(result_a["n_cells"]), + "decode_rmse": result_a["rmse"], + }, + "experiment5b": { + "num_cells": float(result_b["n_cells"]), + "n_sims": float(result_b["n_sims"]), + "mean_rmse_free_x": float(mean_free[0]), + "mean_rmse_goal_x": float(mean_goal[0]), + }, + "experiment6": { + "num_cells": float(result_c["n_cells"]), + "state_accuracy": result_c["state_acc"], + "decode_rmse_x": result_c["rmse_x"], + "decode_rmse_y": result_c["rmse_y"], + }, } - print(json.dumps(combined_summary, indent=2)) + print("\n" + json.dumps(summary, indent=2)) + + # --- Figures --- + fig1, fig2 = _plot_part_a(result_a) + fig3, fig4 = _plot_part_b(result_b) + fig5, fig6 = _plot_part_c(result_c) + figures = [fig1, fig2, fig3, fig4, fig5, fig6] if export_figures: if export_dir is None: export_dir = THIS_DIR / "figures" / "example05" - combined_payload = { - "experiment5": payload5, - "experiment5b": payload5b, - "experiment6": payload6, - } - saved = export_named_paper_figures( - "example05", - summary=combined_summary, - payload=combined_payload, - export_dir=export_dir, - ) - print(f"\nGenerated {len(saved)} figure(s):") - for p in saved: - print(f" {p}") + export_dir = Path(export_dir) + export_dir.mkdir(parents=True, exist_ok=True) + for i, fig in enumerate(figures, 1): + fig_names = [ + "fig01_univariate_setup", "fig02_univariate_decoding", + "fig03_reach_and_population_setup", "fig04_ppaf_goal_vs_free", + "fig05_hybrid_setup", "fig06_hybrid_decoding_summary", + ] + path = export_dir / f"{fig_names[i - 1]}.png" + fig.savefig(path, dpi=150, bbox_inches="tight") + print(f" Saved: {path}") + + if show: + plt.show() + else: + plt.close("all") - return combined_summary + return summary if __name__ == "__main__": @@ -112,11 +618,13 @@ def run_example05(*, export_figures: bool = False, export_dir: Path | None = Non parser.add_argument("--export-figures", action="store_true") parser.add_argument("--export-dir", type=Path, default=None) parser.add_argument("--output-json", type=Path, default=None) + parser.add_argument("--show", action="store_true", help="Display figures interactively") args = parser.parse_args() result = run_example05( export_figures=args.export_figures, export_dir=args.export_dir, + show=args.show, ) if args.output_json: args.output_json.write_text(json.dumps(result, indent=2), encoding="utf-8") diff --git a/nstat/decoding_algorithms.py b/nstat/decoding_algorithms.py index 3fd66675..c8544f26 100644 --- a/nstat/decoding_algorithms.py +++ b/nstat/decoding_algorithms.py @@ -1722,7 +1722,6 @@ def PPHybridFilterLinear( estimateTarget=0, MinClassificationError=0, ): - del yT, PiT, estimateTarget obs = _as_observation_matrix(dN) A_models = list(A) if isinstance(A, Sequence) and not isinstance(A, np.ndarray) else [A] Q_models = list(Q) if isinstance(Q, Sequence) and not isinstance(Q, np.ndarray) else [Q] @@ -1761,6 +1760,85 @@ def PPHybridFilterLinear( H_tensor = _compute_history_terms(obs, float(binwidth), windowTimes) gamma_mat = _normalize_gamma(gamma, H_tensor.shape[1], num_cells) + # ------------------------------------------------------------------ + # Goal-directed target branch (Srinivasan et al. 2006) + # ------------------------------------------------------------------ + estimateTarget = int(estimateTarget) + + # Normalize yT, PiT as per-model lists + if _is_empty_value(yT): + yT_models = [None] * n_models + elif isinstance(yT, (list, tuple)) and not isinstance(yT, np.ndarray): + yT_models = [ + np.asarray(y, dtype=float).reshape(-1) if not _is_empty_value(y) else None + for y in yT + ] + else: + yT_vec = np.asarray(yT, dtype=float).reshape(-1) + yT_models = [yT_vec] * n_models + + if _is_empty_value(PiT): + PiT_models = [None] * n_models + elif isinstance(PiT, (list, tuple)) and not isinstance(PiT, np.ndarray): + PiT_models = [ + _as_state_matrix(p, state_dims[i]) if not _is_empty_value(p) else None + for i, p in enumerate(PiT) + ] + else: + PiT_models = [_as_state_matrix(PiT, state_dims[i]) for i in range(n_models)] + + _has_target = [yT_models[s] is not None for s in range(n_models)] + _any_target = any(_has_target) + + if estimateTarget == 1 and _any_target: + raise NotImplementedError( + "Augmented state-space target estimation (estimateTarget=1) is not yet " + "supported for PPHybridFilterLinear. Use estimateTarget=0 with fixed target, " + "or use PPDecodeFilterLinear which supports both modes." + ) + + # Backward information filter for each target model + PhitT_m = [None] * n_models + PitT_m = [None] * n_models + B_m = [None] * n_models + QT_m = [None] * n_models + + for s in range(n_models): + if not _has_target[s]: + continue + dim = state_dims[s] + PiT_s = PiT_models[s] if PiT_models[s] is not None else np.zeros((dim, dim), dtype=float) + + PitT = np.zeros((dim, dim, num_steps), dtype=float) + PhitT = np.zeros((dim, dim, num_steps), dtype=float) + B_arr = np.zeros((dim, dim, num_steps), dtype=float) + QT_arr = np.zeros((dim, dim, num_steps), dtype=float) + + QN = _select_time_matrix(Q_models[s], num_steps - 1, dim) + PitT[:, :, num_steps - 1] = PiT_s + QN + PhitT[:, :, num_steps - 1] = np.eye(dim, dtype=float) + + for n in range(num_steps - 1, 0, -1): + An = _select_time_matrix(A_models[s], n, dim) + Qn = _select_time_matrix(Q_models[s], n, dim) + invA = np.linalg.pinv(An) + PhitT[:, :, n - 1] = invA @ PhitT[:, :, n] + PitT[:, :, n - 1] = invA @ PitT[:, :, n] @ invA.T + Qn + invPitT_n = np.linalg.pinv(PitT[:, :, n]) + B_arr[:, :, n] = An - (Qn @ invPitT_n) @ An + QT_arr[:, :, n] = Qn - (Qn @ invPitT_n) @ Qn.T + + A1 = _select_time_matrix(A_models[s], 0, dim) + Q1 = _select_time_matrix(Q_models[s], 0, dim) + invPitT_0 = np.linalg.pinv(PitT[:, :, 0]) + B_arr[:, :, 0] = A1 - (Q1 @ invPitT_0) @ A1 + QT_arr[:, :, 0] = Q1 - (Q1 @ invPitT_0) @ Q1.T + + PhitT_m[s] = PhitT + PitT_m[s] = PitT + B_m[s] = B_arr + QT_m[s] = QT_arr + X = np.zeros((max_dim, num_steps), dtype=float) W = np.zeros((max_dim, max_dim, num_steps), dtype=float) X_s = [np.zeros((max_dim, num_steps), dtype=float) for _ in range(n_models)] @@ -1810,14 +1888,41 @@ def PPHybridFilterLinear( likelihoods = np.zeros(n_models, dtype=float) for model_index in range(n_models): dim = state_dims[model_index] - A_t = _select_time_matrix(A_models[model_index], time_index, dim) - Q_t = _select_time_matrix(Q_models[model_index], time_index, dim) + if _has_target[model_index]: + A_t = B_m[model_index][:, :, time_index] + Q_t = QT_m[model_index][:, :, time_index] + else: + A_t = _select_time_matrix(A_models[model_index], time_index, dim) + Q_t = _select_time_matrix(Q_models[model_index], time_index, dim) pred_x, pred_W = DecodingAlgorithms.PPDecode_predict( X_s[model_index][:dim, time_index], W_s[model_index][:dim, :dim, time_index], A_t, Q_t, ) + # Goal-directed offset for fixed target (Srinivasan Eq. 2.21) + if _has_target[model_index] and estimateTarget == 0: + Qn_orig = _select_time_matrix(Q_models[model_index], time_index, dim) + invPitT_n = np.linalg.pinv(PitT_m[model_index][:, :, time_index]) + if time_index > 0: + invPhitm1 = np.linalg.pinv(PhitT_m[model_index][:, :, time_index - 1]) + ut = (Qn_orig @ invPitT_n) @ PhitT_m[model_index][:, :, time_index] @ ( + yT_models[model_index] - invPhitm1 @ X_s[model_index][:dim, time_index] + ) + else: + A1_orig = _select_time_matrix(A_models[model_index], 0, dim) + invA1 = np.linalg.pinv(A1_orig) + invPhi0 = np.linalg.pinv(invA1 @ PhitT_m[model_index][:, :, 0]) + ut = (Qn_orig @ invPitT_n) @ PhitT_m[model_index][:, :, time_index] @ ( + yT_models[model_index] - invPhi0 @ X_s[model_index][:dim, time_index] + ) + pred_x = pred_x + ut + An_orig = _select_time_matrix(A_models[model_index], time_index, dim) + pred_W = pred_W + ( + (Qn_orig @ invPitT_n) @ An_orig + @ W_s[model_index][:dim, :dim, time_index] + @ An_orig.T @ (Qn_orig @ invPitT_n).T + ) upd_x, upd_W, lambda_delta = DecodingAlgorithms.PPDecode_updateLinear( pred_x, pred_W, diff --git a/nstat/fit.py b/nstat/fit.py index 1f38b9e2..535d9dd7 100644 --- a/nstat/fit.py +++ b/nstat/fit.py @@ -966,6 +966,10 @@ def computeFitResidual(self, fit_num: int = 1, *, windowSize: float | None = Non sum_spikes = spike_train.getSigRep(window_size, float(time[0]), float(time[-1])) window_times = np.linspace(float(time[0]), float(time[-1]), sum_spikes.time.size, dtype=float) + # Use the label for the specific fit_num, not all labels + all_labels = self.lambda_signal.dataLabels if getattr(self.lambda_signal, "dataLabels", None) else ["\\lambda"] + idx = min(max(fit_num - 1, 0), len(all_labels) - 1) + fit_label = [all_labels[idx]] lambda_signal = Covariate( time, rate_hz, @@ -973,7 +977,7 @@ def computeFitResidual(self, fit_num: int = 1, *, windowSize: float | None = Non self.lambda_signal.xlabelval, self.lambda_signal.xunits, self.lambda_signal.yunits, - self.lambda_signal.dataLabels if getattr(self.lambda_signal, "dataLabels", None) else ["\\lambda"], + fit_label, ) lambda_int = lambda_signal.integral() lambda_int_vals = ( @@ -1070,13 +1074,35 @@ def computeValLambda(self) -> tuple[Covariate, np.ndarray]: return lambda_val, logLL_arr def plotResults(self, fit_num: int = 1, handle=None): - fig = handle if handle is not None else plt.figure(figsize=(11.5, 8.0)) + """Matlab-matching 2x4 subplot layout with 5 diagnostic panels. + + Layout (matching Matlab ``subplot(2,4,...)``): + [1,2] KSPlot (double-wide) [3] InvGausTrans [4] SeqCorr + [5,6] plotCoeffs (double-wide) [7,8] plotResidual (double-wide) + """ + import matplotlib.gridspec as gridspec + + fig = handle if handle is not None else plt.figure(figsize=(14.0, 8.0)) fig.clear() - axes = fig.subplots(2, 2) - self.KSPlot(fit_num=fit_num, handle=axes[0, 0]) - self.plotInvGausTrans(fit_num=fit_num, handle=axes[0, 1]) - self.plotSeqCorr(fit_num=fit_num, handle=axes[1, 0]) - self.plotCoeffs(fit_num=fit_num, handle=axes[1, 1]) + gs = gridspec.GridSpec(2, 4, figure=fig) + + ax_ks = fig.add_subplot(gs[0, 0:2]) + ax_ig = fig.add_subplot(gs[0, 2]) + ax_sc = fig.add_subplot(gs[0, 3]) + ax_co = fig.add_subplot(gs[1, 0:2]) + ax_re = fig.add_subplot(gs[1, 2:4]) + + self.KSPlot(fit_num=fit_num, handle=ax_ks) + # Add neuron number label (matching Matlab) + ax_ks.text( + 0.45, 0.95, f"Neuron: {self.neuronNumber}", + transform=ax_ks.transAxes, fontweight="bold", fontsize=10, + verticalalignment="top", + ) + self.plotInvGausTrans(fit_num=fit_num, handle=ax_ig) + self.plotSeqCorr(fit_num=fit_num, handle=ax_sc) + self.plotCoeffs(fit_num=fit_num, handle=ax_co) + self.plotResidual(fit_num=fit_num, handle=ax_re) fig.tight_layout() return fig diff --git a/tests/test_fitresult_diagnostics.py b/tests/test_fitresult_diagnostics.py index 5c4784a5..8b0d6862 100644 --- a/tests/test_fitresult_diagnostics.py +++ b/tests/test_fitresult_diagnostics.py @@ -45,7 +45,7 @@ def test_fitresult_plotting_methods_return_matplotlib_objects() -> None: ax4 = fit.plotSeqCorr() ax5 = fit.plotCoeffs() - assert len(fig.axes) == 4 + assert len(fig.axes) == 5 # 2x4 layout: KS, InvGaus, SeqCorr, Coeffs, Residual for ax in (ax1, ax2, ax3, ax4, ax5): assert hasattr(ax, "plot") plt.close("all") From 932985b1414746c242bdee08f1332ea16a7ae041 Mon Sep 17 00:00:00 2001 From: Iahn Cajigas Date: Tue, 10 Mar 2026 21:33:14 -0400 Subject: [PATCH 15/19] Fix example01 figure layout and epoch boundary to match Matlab - Replace manual 2x2 subplot grids with plotResults() calls for Figures 1 and 3, giving the Matlab-matching 2x4 layout with all 5 diagnostic panels (KS, InvGaus, SeqCorr, Coeffs, Residual) - Fix epoch boundary off-by-one: use searchsorted(side='right') to match Matlab's find(time<495,1,'last') inclusive behavior Co-Authored-By: Claude Opus 4.6 --- examples/paper/example01_mepsc_poisson.py | 77 ++++++----------------- 1 file changed, 20 insertions(+), 57 deletions(-) diff --git a/examples/paper/example01_mepsc_poisson.py b/examples/paper/example01_mepsc_poisson.py index e93c7263..3e104660 100644 --- a/examples/paper/example01_mepsc_poisson.py +++ b/examples/paper/example01_mepsc_poisson.py @@ -128,33 +128,14 @@ def run_example01(*, export_figures: bool = False, export_dir: Path | None = Non print(f" AIC: {resultConst.AIC}") print(f" BIC: {resultConst.BIC}") - # --- Figure 1: Constant Mg2+ raster + diagnostics + lambda --- - fig1, axes1 = plt.subplots(2, 2, figsize=(14, 9)) - - # Subplot 1: Neural raster - ax = axes1[0, 0] - spikeCollConst.plot(handle=ax) - ax.set_title("Neural Raster with constant Mg$^{2+}$ Concentration", - fontweight="bold", fontsize=12) - ax.set_xlabel("time [s]", fontsize=12, fontweight="bold") - ax.set_ylabel("mEPSCs", fontsize=12, fontweight="bold") - ax.set_yticks([0, 1]) - - # Subplot 2: Inverse Gaussian Transform (autocorrelation of rescaled residuals) - ax = axes1[0, 1] - resultConst.plotInvGausTrans(handle=ax) - - # Subplot 3: KS plot - ax = axes1[1, 0] - resultConst.KSPlot(handle=ax) - - # Subplot 4: Lambda estimate - ax = axes1[1, 1] - resultConst.lambda_signal.plot(handle=ax) - ax.set_xlabel("time [s]", fontsize=12, fontweight="bold") - ax.legend(["$\\lambda_{const}$"], loc="upper right") - - fig1.suptitle("Example 01 — Figure 1: Constant Mg$^{2+}$ Summary", fontsize=14, fontweight="bold") + # --- Figure 1: Constant Mg2+ diagnostics (Matlab-matching plotResults) --- + # Matlab calls resultConst.plotResults which creates a 2x4 grid: + # [KSPlot (2-wide)] [InvGausTrans] [SeqCorr] + # [plotCoeffs (2-wide)] [plotResidual (2-wide)] + fig1 = plt.figure(figsize=(14, 9)) + resultConst.plotResults(handle=fig1) + fig1.suptitle("Example 01 — Figure 1: Constant Mg$^{2+}$ Summary", + fontsize=14, fontweight="bold") fig1.tight_layout() figure_files.extend(_maybe_export(fig1, export_dir, "fig01_constant_mg_summary")) @@ -198,8 +179,12 @@ def run_example01(*, export_figures: bool = False, export_dir: Path | None = Non print("\n=== Part 3: Piecewise Baseline Model Comparison ===") # Build piecewise indicator covariates - timeInd1 = np.searchsorted(timeWashout, 495.0) - timeInd2 = np.searchsorted(timeWashout, 765.0) + # Matlab: find(time<495,1,'last') — last index strictly before 495 + # np.searchsorted gives first index >= 495, so subtract 1 isn't needed + # because Python slice [:idx] is exclusive. But Matlab's 1:timeInd1 is + # inclusive, so we need searchsorted(..., side='right') to include 495. + timeInd1 = np.searchsorted(timeWashout, 495.0, side="right") + timeInd2 = np.searchsorted(timeWashout, 765.0, side="right") N = len(timeWashout) constantRate = np.ones((N, 1)) @@ -233,34 +218,12 @@ def run_example01(*, export_figures: bool = False, export_dir: Path | None = Non print(f" AIC: {resultWashout.AIC}") print(f" BIC: {resultWashout.BIC}") - # --- Figure 3: Piecewise model diagnostics + lambda comparison --- - fig3, axes3 = plt.subplots(2, 2, figsize=(14, 9)) - - # Subplot 1: Raster with epoch boundaries - ax = axes3[0, 0] - spikeCollWashout.plot(handle=ax) - ax.set_title("Neural Raster with decreasing Mg$^{2+}$ Concentration", - fontweight="bold", fontsize=12) - ax.set_xlabel("time [s]", fontsize=12, fontweight="bold") - ax.axvline(495.0, color="r", linewidth=4) - ax.axvline(765.0, color="r", linewidth=4) - - # Subplot 2: Inverse Gaussian Transform - ax = axes3[0, 1] - resultWashout.plotInvGausTrans(handle=ax) - - # Subplot 3: KS plot - ax = axes3[1, 0] - resultWashout.KSPlot(handle=ax) - - # Subplot 4: Lambda comparison - ax = axes3[1, 1] - resultWashout.lambda_signal.plot(handle=ax) - ax.set_ylim(0, 5) - ax.set_xlabel("time [s]", fontsize=12, fontweight="bold") - ax.legend(["$\\lambda_{const}$", "$\\lambda_{const-epoch}$"], loc="upper right") - - fig3.suptitle("Example 01 — Figure 3: Piecewise Baseline Comparison", fontsize=14, fontweight="bold") + # --- Figure 3: Piecewise model diagnostics (Matlab-matching plotResults) --- + # Matlab calls resultWashout.plotResults which creates the same 2x4 grid. + fig3 = plt.figure(figsize=(14, 9)) + resultWashout.plotResults(handle=fig3) + fig3.suptitle("Example 01 — Figure 3: Piecewise Baseline Comparison", + fontsize=14, fontweight="bold") fig3.tight_layout() figure_files.extend(_maybe_export(fig3, export_dir, "fig03_piecewise_baseline_comparison")) From e11b6ffda7977bf8ac03c9242bab9e084eb35a0e Mon Sep 17 00:00:00 2001 From: Iahn Cajigas Date: Tue, 10 Mar 2026 21:42:28 -0400 Subject: [PATCH 16/19] Update help files and README for v0.3.0 parity - Update examples/README.md with current paper example paths and table - Add missing v0.3.0 methods to ClassDefinitions.md: nstColl PSTH/data methods, FitResSummary plotting, DecodingAlgorithms helpers, Trial.getAllLabels - Document goal-directed decoding support in PaperOverview.md for both PPDecodeFilterLinear and PPHybridFilterLinear (Srinivasan et al. 2006) - Add computeSpikeRateCIs and Monte Carlo CI details to PaperOverview.md - Update README.md overview to mention PPAF and PPHF by name Co-Authored-By: Claude Opus 4.6 --- README.md | 5 +++-- docs/ClassDefinitions.md | 20 ++++++++++++++++--- docs/PaperOverview.md | 12 +++++++++-- examples/README.md | 43 ++++++++++++++++++++++++++++++++-------- 4 files changed, 65 insertions(+), 15 deletions(-) diff --git a/README.md b/README.md index ba1ddbd7..ab29d068 100644 --- a/README.md +++ b/README.md @@ -12,8 +12,9 @@ One of nSTAT's key strengths is point-process generalized linear models for spik train signals that provide a formal statistical framework for processing signals recorded from ensembles of single neurons. It also has extensive support for model fitting, model-order analysis, and adaptive decoding — including state-space GLM -(SSGLM) estimation via EM, unscented Kalman filtering (UKF), and hybrid -discrete/continuous point-process filters. +(SSGLM) estimation via EM, unscented Kalman filtering (UKF), goal-directed +point-process adaptive filters (PPAF), and hybrid discrete/continuous +point-process filters (PPHF). Although created with neural signal processing in mind, nSTAT can be used as a generic tool for analyzing any types of discrete and continuous signals, and thus diff --git a/docs/ClassDefinitions.md b/docs/ClassDefinitions.md index 7da9db12..14ab03ff 100644 --- a/docs/ClassDefinitions.md +++ b/docs/ClassDefinitions.md @@ -102,12 +102,21 @@ Primary notebook: `notebooks/nstCollExamples.ipynb` **Time operations**: `shiftTime`, `setMinTime`, `setMaxTime` +**Data export**: +`dataToMatrix`, `resample` + +**PSTH**: +`psth`, `psthGLM`, `estimateVarianceAcrossTrials`, `psthBars` + **SSGLM (state-space GLM)**: `ssglm`, `ssglmFB` **Basis generation**: `generateUnitImpulseBasis` +**Plotting**: +`plot` + ### `History` (`nstat.History`) Primary notebook: `notebooks/HistoryExamples.ipynb` @@ -149,7 +158,8 @@ Primary notebook: `notebooks/TrialExamples.ipynb` `flattenMask` **Utilities**: -`shiftCovariates`, `resampleEnsColl`, `restoreToOriginal`, `plot` +`shiftCovariates`, `resampleEnsColl`, `restoreToOriginal`, `getAllLabels`, +`plot` ### `TrialConfig` (`nstat.TrialConfig`) @@ -230,7 +240,8 @@ Alias of `FitSummary`. Aggregates multiple `FitResult` objects. **Plotting**: `plotIC`, `plotAIC`, `plotBIC`, `plotlogLL`, `plotResidualSummary`, -`plotSummary`, `boxPlot` +`plotSummary`, `boxPlot`, `plotAllCoeffs`, `plot3dCoeffSummary`, +`plot2dCoeffSummary`, `plotKSSummary` ### `DecodingAlgorithms` (`nstat.DecodingAlgorithms`) @@ -238,11 +249,14 @@ Primary notebook: `notebooks/DecodingExample.ipynb` **Point-process decode filters**: `PPDecodeFilterLinear`, `PPDecodeFilter`, `PPHybridFilterLinear`, -`ComputeStimulusCIs` +`ComputeStimulusCIs`, `computeSpikeRateCIs` **Kalman and unscented Kalman filters**: `kalman_filter`, `PP_fixedIntervalSmoother`, `ukf` +**Helper methods**: +`PPDecode_predict`, `PPDecode_updateLinear` + **State-space GLM (SSGLM) — EM algorithm**: `PPSS_EStep`, `PPSS_MStep`, `PPSS_EM`, `PPSS_EMFB` diff --git a/docs/PaperOverview.md b/docs/PaperOverview.md index 92423dc9..9ecde011 100644 --- a/docs/PaperOverview.md +++ b/docs/PaperOverview.md @@ -88,10 +88,16 @@ map to: **Point-process adaptive filters (Section 2.5)**: - `DecodingAlgorithms.PPDecodeFilterLinear` — linear-CIF point-process - adaptive filter for continuous stimulus decoding. + adaptive filter for continuous stimulus decoding. Supports goal-directed + decoding via backward information filter (Srinivasan et al. 2006) when + `yT` and `PiT` target parameters are provided. - `DecodingAlgorithms.PPDecodeFilter` — general CIF version using symbolic gradients/Jacobians. -- `DecodingAlgorithms.ComputeStimulusCIs` — stimulus confidence intervals. +- `DecodingAlgorithms.ComputeStimulusCIs` — stimulus confidence intervals + via Monte Carlo sampling (dual-path: 4-D SSGLM cross-trial + 3-D smoother + z-score). +- `DecodingAlgorithms.computeSpikeRateCIs` — spike rate confidence intervals + and pairwise significance testing across trials. - `DecodingAlgorithms.PP_fixedIntervalSmoother` — fixed-interval smoother for off-line smoothing of decode estimates. @@ -100,6 +106,8 @@ map to: - `DecodingAlgorithms.PPHybridFilterLinear` — joint discrete/continuous state estimation combining point-process observations with a hidden Markov model over discrete states and Kalman filtering over continuous kinematics. + Supports goal-directed decoding via per-model backward information filters + when `yT` and `PiT` are provided. **Kalman and UKF filters**: diff --git a/examples/README.md b/examples/README.md index cfffe48b..648eb9ce 100644 --- a/examples/README.md +++ b/examples/README.md @@ -1,17 +1,44 @@ -# Python nSTAT Examples +# nSTAT Python Examples -## Basic examples +## Paper Examples (Self-Contained) + +Five self-contained scripts mirroring the MATLAB paper examples. Each +generates publication-quality figures and supports `--export-figures`. ```bash -python3 examples/basic_data_workflow.py -python3 examples/fit_poisson_glm.py -python3 examples/simulate_population_psth.py +python examples/paper/example01_mepsc_poisson.py --export-figures +python examples/paper/example02_whisker_stimulus_thalamus.py --export-figures +python examples/paper/example03_psth_and_ssglm.py --export-figures +python examples/paper/example04_place_cells_continuous_stimulus.py --export-figures +python examples/paper/example05_decoding_ppaf_pphf.py --export-figures ``` -## Paper-style example workflow +| Example | Focus | Paper Section | +|---|---|---| +| 01 | mEPSC Poisson models (constant vs piecewise baseline) | 2.3.1 | +| 02 | Whisker stimulus GLM with lag and history selection | 2.3.2 | +| 03 | PSTH and SSGLM across-trial dynamics | 2.3.3-2.3.4 | +| 04 | Place-cell receptive fields (Gaussian vs Zernike) | 2.3.5 | +| 05 | PPAF and hybrid filter decoding | 2.5-2.6 | + +## Basic Examples ```bash -python3 examples/nstat_paper_examples.py --repo-root .. +python examples/basic_data_workflow.py +python examples/fit_poisson_glm.py +python examples/simulate_population_psth.py ``` -This mirrors key analyses described in the nSTAT paper using the bundled Python APIs. +## README Examples (Quick Checks) + +```bash +python examples/readme_examples/example1_multitaper_and_spectrogram.py +python examples/readme_examples/example2_simulate_cif_spiketrain_10s.py +python examples/readme_examples/example3_nstcoll_raster_from_example2.py +``` + +## Jupyter Notebooks + +All 29 class-tutorial and data-analysis notebooks are in `notebooks/`. +They mirror the MATLAB helpfile examples one-to-one. See +[docs/Examples.md](../docs/Examples.md) for the full index. From 40104287b93523d6ba2045bcca8c387245b2a013 Mon Sep 17 00:00:00 2001 From: Iahn Cajigas Date: Tue, 10 Mar 2026 22:00:10 -0400 Subject: [PATCH 17/19] Fix example03 Figure 5 to match Matlab: 3D mesh, Poisson link, fresh psthGLM - Replace 2D pcolormesh with 3D plot_surface for stimulus effect surfaces - Use Poisson link (exp(x)/delta) for Figure 5 true CIF surface, matching Matlab's fitType='poisson' (binomial link still used for Figure 3) - Add fresh psthGLM() call on 50-trial spikeColl for PSTH surface, matching Matlab flow (precomputed data only used for Figure 4 diagnostics) - Remove unused scipy.signal dlti/TransferFunction imports from sim loop Co-Authored-By: Claude Opus 4.6 --- examples/paper/example03_psth_and_ssglm.py | 133 +++++++++++---------- 1 file changed, 70 insertions(+), 63 deletions(-) diff --git a/examples/paper/example03_psth_and_ssglm.py b/examples/paper/example03_psth_and_ssglm.py index c05a74b3..377d6e4b 100644 --- a/examples/paper/example03_psth_and_ssglm.py +++ b/examples/paper/example03_psth_and_ssglm.py @@ -336,13 +336,7 @@ def run_part_b(data_dir, export_dir=None): ens = Covariate(time, np.zeros_like(time), "Ensemble", "time", "s", "Spikes", ["n1"]) - # Transfer function coefficients (z-domain) histCoeffs = [-4, -1, -0.5] - from scipy.signal import TransferFunction, dlti - htf = dlti([1], np.concatenate([[1], -np.array(histCoeffs)]), dt=delta) - stf_num = [b1[iTrial]] - stf = dlti(stf_num, [1], dt=delta) - etf = dlti([0], [1], dt=delta) sC, lambdaTemp = CIF.simulateCIF( b0, histCoeffs, [b1[iTrial]], [0], @@ -419,7 +413,28 @@ def run_part_b(data_dir, export_dir=None): fig3.tight_layout() # ------------------------------------------------------------------ - # 2. Load precomputed SSGLM data + # 2. Compute PSTH-GLM and prepare data matrices + # (Matlab: psthGLM + dN before loading precomputed SSGLM) + # ------------------------------------------------------------------ + numBasis = 25 + basisWidth = (tmax - 0.0) / numBasis + windowTimes = np.arange(0.0, 0.004, delta) + fitType = "poisson" + + spikeColl.resample(1 / delta) + spikeColl.setMaxTime(tmax) + + dN = spikeColl.dataToMatrix() + if dN.ndim == 1: + dN = dN.reshape(1, -1) + dN = np.asarray(dN, dtype=float) + dN[dN > 1] = 1 + + psthSig, _, _ = spikeColl.psthGLM(basisWidth, windowTimes, fitType) + print(" Computed psthGLM on 50-trial collection") + + # ------------------------------------------------------------------ + # 3. Load precomputed SSGLM data # ------------------------------------------------------------------ ssglm_path = data_dir / "SSGLMExampleData.mat" ssglm = loadmat(str(ssglm_path), squeeze_me=True) @@ -429,19 +444,15 @@ def run_part_b(data_dir, export_dir=None): stimulus_true = np.asarray(ssglm["stimulus"], dtype=float) # (25, 50) stimCIs = np.asarray(ssglm["stimCIs"], dtype=float) # (25, 50, 2) gammahat = np.asarray(ssglm["gammahat"], dtype=float) # (3,) - numBasis = xK.shape[0] K = xK.shape[1] print(f" Loaded precomputed SSGLM: {numBasis} basis x {K} trials") # ------------------------------------------------------------------ - # 3. Reconstruct FitResult objects from loaded data + # 4. Reconstruct FitResult objects from loaded data # ------------------------------------------------------------------ - # Construct FitResult objects from Matlab structs, using our simulated - # spike trains as proxies (since Matlab MCOS objects can't be deserialized). ssglm_fit = _load_matlab_fitresult(ssglm["fR"], trains) psth_fit = _load_matlab_fitresult(ssglm["psthR"], trains) - # Merge PSTH and SSGLM results for comparison diagnostics tCompare = psth_fit.mergeResults(ssglm_fit) tCompare.lambda_signal.setDataLabels( ["\\lambda_{PSTH}", "\\lambda_{SSGLM}"] @@ -459,81 +470,73 @@ def run_part_b(data_dir, export_dir=None): print(" Figure 4: SSGLM vs PSTH diagnostics") # ------------------------------------------------------------------ - # 4. Compute stimulus effect surfaces from basis coefficients + # 5. Compute stimulus effect surfaces # ------------------------------------------------------------------ - minTime = 0.0 - maxTime = tmax sampleRate = 1 / delta - basisWidth = (maxTime - minTime) / numBasis unitPulseBasis = SpikeTrainCollection.generateUnitImpulseBasis( - basisWidth, minTime, maxTime, sampleRate, + basisWidth, 0.0, tmax, sampleRate, ) basisMat = np.asarray(unitPulseBasis.data, dtype=float) # (T, numBasis) + basis_time = np.asarray(unitPulseBasis.time, dtype=float).ravel() - # Estimated CIF from SSGLM: exp(basisMat @ xK) / delta - estStimEffect = np.exp(basisMat @ xK) / delta # (T, K) - - # PSTH coefficients from psth_fit - psth_b = np.asarray(psth_fit.b[0], dtype=float).reshape(-1) - # The PSTH model has numBasis + history coefficients; extract first numBasis - psth_basis_coeffs = psth_b[:numBasis] if psth_b.size >= numBasis else np.pad( - psth_b, (0, numBasis - psth_b.size)) - psthSurface = np.exp(basisMat @ psth_basis_coeffs) / delta # (T,) constant across trials - psthSurface2D = np.tile(psthSurface[:, None], (1, K)) # (T, K) + # True stimulus effect (Poisson link, matching fitType for analysis) + u_basis = np.sin(2 * np.pi * f * basis_time) + actStimEffect = np.exp(np.outer(u_basis, b1) + b0) / delta # (T, K) - # True surface from our simulation params - actStimEffect = stimData[:basisMat.shape[0], :K] + # PSTH surface (constant across trials — replicate fresh psthGLM output) + psthSig_data = np.asarray(psthSig.data, dtype=float).ravel() + psthSurface2D = np.tile(psthSig_data[:, None], (1, numRealizations)) - basis_time = np.asarray(unitPulseBasis.time, dtype=float).ravel() + # SSGLM estimated CIF from basis coefficients + estStimEffect = np.exp(basisMat @ xK) / delta # (T, K) # ------------------------------------------------------------------ - # Figure 5: True/PSTH/SSGLM stimulus effect surfaces (3x1) + # Figure 5: True/PSTH/SSGLM stimulus effect surfaces (3D mesh) # ------------------------------------------------------------------ - fig5, axes5 = plt.subplots(3, 1, figsize=(10, 12)) - trial_axis = np.arange(1, K + 1) + from mpl_toolkits.mplot3d import Axes3D # noqa: F401 + + fig5 = plt.figure(figsize=(10, 12)) + trial_axis = np.arange(1, numRealizations + 1) + T_act = min(actStimEffect.shape[0], len(basis_time)) + T_mesh, K_mesh = np.meshgrid( + basis_time[:T_act], trial_axis, indexing="ij" + ) - ax = axes5[0] - T_mesh, K_mesh = np.meshgrid(basis_time[:actStimEffect.shape[0]], - trial_axis, indexing="ij") - ax.pcolormesh(T_mesh, K_mesh, actStimEffect, shading="auto") + ax = fig5.add_subplot(3, 1, 1, projection="3d") + ax.plot_surface(K_mesh, T_mesh, actStimEffect[:T_act, :], + cmap="viridis", edgecolor="none") + ax.view_init(elev=-90, azim=90) + ax.set_xticks([]) + ax.set_yticks([]) ax.set_title("True Stimulus Effect", fontweight="bold", fontsize=14) - ax.set_xlabel("time [s]") - ax.set_ylabel("Trial [k]") - ax = axes5[1] - ax.pcolormesh(T_mesh, K_mesh, psthSurface2D[:actStimEffect.shape[0], :], - shading="auto") + ax = fig5.add_subplot(3, 1, 2, projection="3d") + ax.plot_surface(K_mesh, T_mesh, psthSurface2D[:T_act, :], + cmap="viridis", edgecolor="none") + ax.view_init(elev=-90, azim=90) + ax.set_xticks([]) + ax.set_yticks([]) ax.set_title("PSTH Estimated Stimulus Effect", fontweight="bold", fontsize=14) - ax.set_xlabel("time [s]") - ax.set_ylabel("Trial [k]") - ax = axes5[2] - ax.pcolormesh(T_mesh, K_mesh, estStimEffect[:actStimEffect.shape[0], :], - shading="auto") + ax = fig5.add_subplot(3, 1, 3, projection="3d") + ax.plot_surface(K_mesh, T_mesh, estStimEffect[:T_act, :], + cmap="viridis", edgecolor="none") + ax.view_init(elev=-90, azim=90) + ax.set_xticks([]) + ax.set_yticks([]) ax.set_title("SSGLM Estimated Stimulus Effect", fontweight="bold", fontsize=14) - ax.set_xlabel("time [s]") - ax.set_ylabel("Trial [k]") fig5.tight_layout() - print(" Figure 5: Stimulus effect surfaces") + print(" Figure 5: Stimulus effect surfaces (3D mesh)") # ------------------------------------------------------------------ - # 5. Learning-trial analysis: spike rate CIs + # 6. Learning-trial analysis: spike rate CIs # ------------------------------------------------------------------ - # Get spike matrix from our simulated data - dN = spikeColl.dataToMatrix() # shape (numRealizations, T) - if dN.ndim == 1: - dN = dN.reshape(1, -1) - dN = np.asarray(dN, dtype=float) - dN[dN > 1] = 1 - - windowTimes = np.arange(0.0, 0.004, delta) - tRate, probMat, sigMat = DecodingAlgorithms.computeSpikeRateCIs( - xK, WkuFinal, dN, 0, tmax, "poisson", delta, gammahat, windowTimes, + xK, WkuFinal, dN, 0, tmax, fitType, delta, gammahat, windowTimes, ) # Find first learning trial (first column where significance appears) @@ -575,7 +578,6 @@ def run_part_b(data_dir, export_dir=None): ax3 = fig6.add_subplot(2, 3, 4) stim1_data = basisMat @ stimulus_true[:, 0] stimlt_data = basisMat @ stimulus_true[:, lt - 1] - # CIs ci1_lo = basisMat @ stimCIs[:, 0, 0] ci1_hi = basisMat @ stimCIs[:, 0, 1] cilt_lo = basisMat @ stimCIs[:, lt - 1, 0] @@ -596,7 +598,12 @@ def run_part_b(data_dir, export_dir=None): fig6.tight_layout() print(f" Figure 6: Learning trial = {lt}") - figures = {"fig03_ssglm_simulation_summary": fig3, "fig04_ssglm_fit_diagnostics": fig4, "fig05_stimulus_effect_surfaces": fig5, "fig06_learning_trial_comparison": fig6} + figures = { + "fig03_ssglm_simulation_summary": fig3, + "fig04_ssglm_fit_diagnostics": fig4, + "fig05_stimulus_effect_surfaces": fig5, + "fig06_learning_trial_comparison": fig6, + } return figures From 79895af686781afb1784bfa560dd563d7f0bef44 Mon Sep 17 00:00:00 2001 From: Iahn Cajigas Date: Tue, 10 Mar 2026 22:08:11 -0400 Subject: [PATCH 18/19] Fix example04 grid resolution and orientation to match Matlab - Change grid from 100x100 to 201x201 (Matlab: meshgrid(-1:0.01:1)) - Add flipud(yy)/fliplr(xx) for Matlab coordinate convention - Use shading='gouraud' to match Matlab's shading interp Co-Authored-By: Claude Opus 4.6 --- .../paper/example04_place_cells_continuous_stimulus.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/examples/paper/example04_place_cells_continuous_stimulus.py b/examples/paper/example04_place_cells_continuous_stimulus.py index 5414590b..96013b6d 100644 --- a/examples/paper/example04_place_cells_continuous_stimulus.py +++ b/examples/paper/example04_place_cells_continuous_stimulus.py @@ -282,10 +282,12 @@ def run_example04(*, export_figures: bool = False, export_dir: Path | None = Non # ================================================================== # 4. Build spatial grids and design matrices for heatmaps # ================================================================== - grid_res = 100 + grid_res = 201 # Matlab: meshgrid(-1:0.01:1) → 201 points xGrid = np.linspace(-1, 1, grid_res) yGrid = np.linspace(-1, 1, grid_res) xx, yy = np.meshgrid(xGrid, yGrid) + yy = np.flipud(yy) # Matlab: y increases bottom-to-top + xx = np.fliplr(xx) # Matlab: x increases right-to-left xf, yf = xx.ravel(), yy.ravel() # Gaussian design: [1, x, y, x^2, y^2, xy] (intercept prepended) @@ -321,7 +323,7 @@ def _plot_heatmaps(fit_results, nCells, title_prefix, design_gauss, ax = axesG[row, col] try: field_g = _compute_place_field(coeffs_g, design_gauss[:, :coeffs_g.size], grid_shape) - ax.pcolormesh(xx, yy, field_g, shading="auto", cmap="jet") + ax.pcolormesh(xx, yy, field_g, shading="gouraud", cmap="jet") except Exception: pass ax.set_aspect("equal") @@ -333,7 +335,7 @@ def _plot_heatmaps(fit_results, nCells, title_prefix, design_gauss, ax = axesZ[row, col] try: field_z = _compute_place_field(coeffs_z, design_zern[:, :coeffs_z.size], grid_shape) - ax.pcolormesh(xx, yy, field_z, shading="auto", cmap="jet") + ax.pcolormesh(xx, yy, field_z, shading="gouraud", cmap="jet") except Exception: pass ax.set_aspect("equal") From 41db8b5df18111d2558c9b1d81bb387afa86321e Mon Sep 17 00:00:00 2001 From: Iahn Cajigas Date: Tue, 10 Mar 2026 22:32:14 -0400 Subject: [PATCH 19/19] Regenerate NetworkTutorial.ipynb from builder to fix test The committed notebook had drifted from the builder output (missing trailing newlines in cells, whitespace differences). Regenerated to match, fixing test_network_tutorial_builder. Co-Authored-By: Claude Opus 4.6 --- notebooks/NetworkTutorial.ipynb | 98 +++++++++++++++++---------------- 1 file changed, 52 insertions(+), 46 deletions(-) diff --git a/notebooks/NetworkTutorial.ipynb b/notebooks/NetworkTutorial.ipynb index 3a206f5e..cc460728 100644 --- a/notebooks/NetworkTutorial.ipynb +++ b/notebooks/NetworkTutorial.ipynb @@ -2,20 +2,20 @@ "cells": [ { "cell_type": "markdown", - "id": "7203c2c5", + "id": "5f17a36a", "metadata": {}, "source": [ "\n", "## MATLAB Parity Note\n", "- Source MATLAB helpfile: `NetworkTutorial.mlx`\n", "- Fidelity status: `high_fidelity`\n", - "- Remaining justified differences: The notebook now mirrors the MATLAB helpfile section order and published figure inventory with a native Python network simulator and MATLAB-style `Analysis` workflow; exact spike realizations still vary modestly because NumPy and Simulink do not share identical random streams." + "- Remaining justified differences: The notebook now mirrors the MATLAB helpfile section order and published figure inventory with a native Python network simulator and MATLAB-style `Analysis` workflow; exact spike realizations still vary modestly because NumPy and Simulink do not share identical random streams.\n" ] }, { "cell_type": "code", "execution_count": null, - "id": "f9506f0f", + "id": "5f466245", "metadata": {}, "outputs": [], "source": [ @@ -46,12 +46,14 @@ "OUTPUT_ROOT = REPO_ROOT / \"output\" / \"notebook_images\"\n", "__tracker = FigureTracker(topic='NetworkTutorial', output_root=OUTPUT_ROOT, expected_count=14)\n", "\n", + "\n", "def _figure(label: str, *, figsize=(8.5, 4.5)):\n", " fig = __tracker.new_figure(label)\n", " fig.clear()\n", " fig.set_size_inches(*figsize)\n", " return fig\n", "\n", + "\n", "def _text_panel(fig, title: str, lines):\n", " ax = fig.subplots(1, 1)\n", " ax.axis(\"off\")\n", @@ -68,6 +70,7 @@ " )\n", " return ax\n", "\n", + "\n", "def _stem_kernel(ax, coeffs, title: str, xlabel: str, color: str):\n", " coeffs = np.asarray(coeffs, dtype=float).reshape(-1)\n", " x = np.arange(1, coeffs.size + 1, dtype=float)\n", @@ -82,6 +85,7 @@ " ax.set_title(title)\n", " ax.grid(axis=\"y\", alpha=0.2)\n", "\n", + "\n", "def _draw_network(ax, actual_network):\n", " ax.set_title(\"Two-neuron connectivity diagram\")\n", " ax.axis(\"off\")\n", @@ -117,6 +121,7 @@ " ax.text(0.25, 0.14, \"$S_1=+1 \\cdot u_{stim}$\", ha=\"center\", fontsize=10)\n", " ax.text(0.75, 0.14, \"$S_2=-1 \\cdot u_{stim}$\", ha=\"center\", fontsize=10)\n", "\n", + "\n", "def _draw_block_diagram(ax):\n", " ax.axis(\"off\")\n", " ax.set_title(\"Conditional-intensity block diagram\")\n", @@ -138,6 +143,7 @@ " ax.add_patch(FancyArrowPatch((0.66, 0.43), (0.72, 0.43), arrowstyle=\"-|>\", mutation_scale=15, linewidth=1.8, color=\"0.25\"))\n", " ax.add_patch(FancyArrowPatch((0.90, 0.43), (0.98, 0.43), arrowstyle=\"-|>\", mutation_scale=15, linewidth=1.8, color=\"0.25\"))\n", "\n", + "\n", "def _estimate_network(results):\n", " estimated = np.zeros((2, 2), dtype=float)\n", " for neuron_idx, fit in enumerate(results, start=1):\n", @@ -148,51 +154,51 @@ " estimated[0, 1] = float(coeff)\n", " elif neuron_idx == 2 and label_str.startswith(\"1:\"):\n", " estimated[1, 0] = float(coeff)\n", - " return estimated" + " return estimated\n" ] }, { "cell_type": "code", "execution_count": null, - "id": "ea4ee423", + "id": "4a15a6be", "metadata": {}, "outputs": [], "source": [ "# SECTION 1: Point Process Network Simulation\n", "# In order to understand how the point process GLM framework can be used to estimate the network connectivity within a population of neurons, we simulate a network of 2 neurons.\n", - "plt.close(\"all\")" + "plt.close(\"all\")\n" ] }, { "cell_type": "code", "execution_count": null, - "id": "28900ba3", + "id": "2c6a36bd", "metadata": {}, "outputs": [], "source": [ "# SECTION 2: Published network diagram\n", "fig = _figure(\"SimulatedNetwork2.png\", figsize=(8.0, 4.5))\n", "ax = fig.subplots(1, 1)\n", - "_draw_network(ax, np.array([[0.0, 1.0], [-4.0, 0.0]], dtype=float))" + "_draw_network(ax, np.array([[0.0, 1.0], [-4.0, 0.0]], dtype=float))\n" ] }, { "cell_type": "code", "execution_count": null, - "id": "789cad35", + "id": "8a5a2fc7", "metadata": {}, "outputs": [], "source": [ "# SECTION 3: Published block diagram\n", "fig = _figure(\"PPSimExample-BlockDiagram.png\", figsize=(10.0, 4.5))\n", "ax = fig.subplots(1, 1)\n", - "_draw_block_diagram(ax)" + "_draw_block_diagram(ax)\n" ] }, { "cell_type": "code", "execution_count": null, - "id": "38f509ef", + "id": "f7bdd433", "metadata": {}, "outputs": [], "source": [ @@ -205,35 +211,35 @@ " \"lambda_i * Delta = logistic(mu_i + H * DeltaN_i[n]\",\n", " \" + S * u_stim[n] + E * DeltaN_k[n])\",\n", " ],\n", - ")" + ")\n" ] }, { "cell_type": "code", "execution_count": null, - "id": "956f11ed", + "id": "65b83184", "metadata": {}, "outputs": [], "source": [ "# SECTION 5: Logistic nonlinearity\n", - "# logistic(x) = exp(x) / (1 + exp(x)). Note that * is the convolution operator." + "# logistic(x) = exp(x) / (1 + exp(x)). Note that * is the convolution operator.\n" ] }, { "cell_type": "code", "execution_count": null, - "id": "378207bd", + "id": "079aaae7", "metadata": {}, "outputs": [], "source": [ "# SECTION 6: Convolution operator note\n", - "# The MATLAB helpfile presents the recursive history, stimulus, and ensemble filters separately below." + "# The MATLAB helpfile presents the recursive history, stimulus, and ensemble filters separately below.\n" ] }, { "cell_type": "code", "execution_count": null, - "id": "fc5899c7", + "id": "f2fab951", "metadata": {}, "outputs": [], "source": [ @@ -251,35 +257,35 @@ "history_kernel = np.asarray(network.history_kernel, dtype=float)\n", "stim_kernel = np.asarray(network.stimulus_kernel, dtype=float)\n", "ensemble_kernel = np.asarray(network.ensemble_kernel, dtype=float)\n", - "actual_network = np.asarray(network.actual_network, dtype=float)" + "actual_network = np.asarray(network.actual_network, dtype=float)\n" ] }, { "cell_type": "code", "execution_count": null, - "id": "1b5c6fd9", + "id": "e50a0d22", "metadata": {}, "outputs": [], "source": [ "# SECTION 8: Baseline firing rate of the neurons being modeled\n", - "print({\"mu1\": float(baseline_mu[0]), \"mu2\": float(baseline_mu[1]), \"sample_rate_hz\": sampleRate})" + "print({\"mu1\": float(baseline_mu[0]), \"mu2\": float(baseline_mu[1]), \"sample_rate_hz\": sampleRate})\n" ] }, { "cell_type": "code", "execution_count": null, - "id": "163000ec", + "id": "b9331965", "metadata": {}, "outputs": [], "source": [ "# SECTION 9: History Effect\n", - "# Captures how the firing of a neuron modulates its own probability of firing, including the refractory period and short-term history dependence." + "# Captures how the firing of a neuron modulates its own probability of firing, including the refractory period and short-term history dependence.\n" ] }, { "cell_type": "code", "execution_count": null, - "id": "df2d7f8f", + "id": "e086cad1", "metadata": {}, "outputs": [], "source": [ @@ -287,87 +293,87 @@ "fig = _figure(\"1*h[n]=-4*DeltaN[n-1]-2*DeltaN[n-2]-1*DeltaN[n-3]\", figsize=(8.0, 4.5))\n", "ax = fig.subplots(1, 1)\n", "_stem_kernel(ax, history_kernel, \"Self-history kernel\", \"lag (ms)\", \"tab:red\")\n", - "ax.set_xticklabels([f\"{int(k)}\" for k in [1, 2, 3]])" + "ax.set_xticklabels([f\"{int(k)}\" for k in [1, 2, 3]])\n" ] }, { "cell_type": "code", "execution_count": null, - "id": "56f686ad", + "id": "ed5a15eb", "metadata": {}, "outputs": [], "source": [ "# SECTION 11: Stimulus Effect\n", - "# Neuron 1 is positively modulated by the stimulus and neuron 2 is negatively modulated by the same drive." + "# Neuron 1 is positively modulated by the stimulus and neuron 2 is negatively modulated by the same drive.\n" ] }, { "cell_type": "code", "execution_count": null, - "id": "3d5c9380", + "id": "41f7ea4d", "metadata": {}, "outputs": [], "source": [ "# SECTION 12: Stimulus filter for neuron 1\n", "fig = _figure(\"1*s_1[n]=1*u_stim[n]\", figsize=(7.5, 4.5))\n", "ax = fig.subplots(1, 1)\n", - "_stem_kernel(ax, [stim_kernel[0]], \"Stimulus filter for neuron 1\", \"lag (samples)\", \"tab:blue\")" + "_stem_kernel(ax, [stim_kernel[0]], \"Stimulus filter for neuron 1\", \"lag (samples)\", \"tab:blue\")\n" ] }, { "cell_type": "code", "execution_count": null, - "id": "50f617ae", + "id": "de315fea", "metadata": {}, "outputs": [], "source": [ "# SECTION 13: Stimulus filter for neuron 2\n", "fig = _figure(\"1*s_2[n]=-1*u_stim[n]\", figsize=(7.5, 4.5))\n", "ax = fig.subplots(1, 1)\n", - "_stem_kernel(ax, [stim_kernel[1]], \"Stimulus filter for neuron 2\", \"lag (samples)\", \"tab:orange\")" + "_stem_kernel(ax, [stim_kernel[1]], \"Stimulus filter for neuron 2\", \"lag (samples)\", \"tab:orange\")\n" ] }, { "cell_type": "code", "execution_count": null, - "id": "49818eae", + "id": "d5901b54", "metadata": {}, "outputs": [], "source": [ "# SECTION 14: Ensemble Effect\n", - "# Captures how neighboring neuron firing modulates the firing probability of a given neuron, with a one-sample delay included in the Simulink model." + "# Captures how neighboring neuron firing modulates the firing probability of a given neuron, with a one-sample delay included in the Simulink model.\n" ] }, { "cell_type": "code", "execution_count": null, - "id": "337ec866", + "id": "96ebc713", "metadata": {}, "outputs": [], "source": [ "# SECTION 15: Ensemble filter for neuron 1\n", "fig = _figure(\"1*e_1[n]=1*DeltaN_2[n-1]\", figsize=(7.5, 4.5))\n", "ax = fig.subplots(1, 1)\n", - "_stem_kernel(ax, [ensemble_kernel[0]], \"Ensemble filter for neuron 1\", \"lag (samples)\", \"tab:green\")" + "_stem_kernel(ax, [ensemble_kernel[0]], \"Ensemble filter for neuron 1\", \"lag (samples)\", \"tab:green\")\n" ] }, { "cell_type": "code", "execution_count": null, - "id": "58c23f04", + "id": "2a0ee92c", "metadata": {}, "outputs": [], "source": [ "# SECTION 16: Ensemble filter for neuron 2\n", "fig = _figure(\"1*e_2[n]=-4*DeltaN_1[n-1]\", figsize=(7.5, 4.5))\n", "ax = fig.subplots(1, 1)\n", - "_stem_kernel(ax, [ensemble_kernel[1]], \"Ensemble filter for neuron 2\", \"lag (samples)\", \"tab:purple\")" + "_stem_kernel(ax, [ensemble_kernel[1]], \"Ensemble filter for neuron 2\", \"lag (samples)\", \"tab:purple\")\n" ] }, { "cell_type": "code", "execution_count": null, - "id": "39aa44a9", + "id": "76c17655", "metadata": {}, "outputs": [], "source": [ @@ -382,13 +388,13 @@ "ax.set_xlim(0.0, 5.0)\n", "ax.set_xlabel(\"time (s)\")\n", "ax.set_ylabel(\"stimulus\")\n", - "ax.set_title(\"Sine-wave stimulus used by the network tutorial\")" + "ax.set_title(\"Sine-wave stimulus used by the network tutorial\")\n" ] }, { "cell_type": "code", "execution_count": null, - "id": "a0ede261", + "id": "50c4e444", "metadata": {}, "outputs": [], "source": [ @@ -416,13 +422,13 @@ "ax.set_xlabel(\"time (s)\")\n", "ax.set_ylabel(\"lambda * Delta\")\n", "ax.set_title(\"Conditional-intensity trajectories\")\n", - "ax.legend(loc=\"upper right\")" + "ax.legend(loc=\"upper right\")\n" ] }, { "cell_type": "code", "execution_count": null, - "id": "1e6a1f2d", + "id": "37dc5453", "metadata": {}, "outputs": [], "source": [ @@ -430,13 +436,13 @@ "c1 = TrialConfig([[\"Baseline\", \"mu\"]], sampleRate, [], [], [], name=\"Baseline\")\n", "c2 = TrialConfig([[\"Baseline\", \"mu\"]], sampleRate, [], ensHist, [], name=\"Baseline+EnsHist\")\n", "c3 = TrialConfig([[\"Baseline\", \"mu\"], [\"Stimulus\", \"sin\"]], sampleRate, selfHist, ensHist, [], name=\"Stim+Hist+EnsHist\")\n", - "cfgColl = ConfigColl([c1, c2, c3])" + "cfgColl = ConfigColl([c1, c2, c3])\n" ] }, { "cell_type": "code", "execution_count": null, - "id": "7b583e33", + "id": "18abcf5f", "metadata": {}, "outputs": [], "source": [ @@ -467,13 +473,13 @@ " ax.set_title(title)\n", " for (row, col), value in np.ndenumerate(matrix):\n", " ax.text(col, row, f\"{value:.2f}\", ha=\"center\", va=\"center\", color=\"white\", fontsize=10)\n", - "fig.colorbar(im, ax=axs, shrink=0.8)" + "fig.colorbar(im, ax=axs, shrink=0.8)\n" ] }, { "cell_type": "code", "execution_count": null, - "id": "d43be2ce", + "id": "acffc016", "metadata": {}, "outputs": [], "source": [ @@ -486,7 +492,7 @@ " \"estimated_network\": np.round(estimated_network, 3).tolist(),\n", " }\n", ")\n", - "__tracker.finalize()" + "__tracker.finalize()\n" ] } ],