investigated #116 and added some comments to help clarify the code

pynumdiff/kalman_smooth/_kalman_smooth.py

@@ -36,7 +36,7 @@ def _kalman_forward_filter(xhat0, P0, y, A, C, Q, R, u=None, B=None):
         xhat_pre.append(xhat_) # the [n]th index holds _{n|n-1} info
         P_pre.append(P_)
 
-        xhat = xhat_.copy()
+        xhat = xhat_.copy() # copies very important here. See #122
         P = P_.copy()
         if not np.isnan(y[n]): # handle missing data
             K = P_ @ C.T @ np.linalg.inv(C @ P_ @ C.T + R)

pynumdiff/tests/test_diff_methods.py

@@ -185,10 +185,10 @@ def iterated_first_order(*args, **kwargs): return first_order(*args, **kwargs)
                           [(0, 0), (1, 0), (0, -1), (1, 0)],
                           [(1, 1), (2, 2), (1, 1), (2, 2)],
                           [(1, 1), (3, 3), (1, 1), (3, 3)]],
-    jerk_sliding: [[(-15, -15), (-16, -17), (0, -1), (1, 0)],
+    jerk_sliding: [[(-15, -15), (-16, -16), (0, -1), (1, 0)],
                    [(-14, -14), (-14, -14), (0, -1), (0, 0)],
                    [(-14, -14), (-14, -14), (0, -1), (0, 0)],
-                   [(-1, -1), (0, 0), (0, -1), (0, 0)],
+                   [(-1, -1), (0, 0), (0, -1), (1, 0)],
                    [(0, 0), (2, 2), (0, 0), (2, 2)],
                    [(1, 1), (3, 3), (1, 1), (3, 3)]]
 }

pynumdiff/total_variation_regularization/_total_variation_regularization.py

@@ -69,7 +69,7 @@ def _total_variation_regularized_derivative(x, dt, order, gamma, solver=None):
              - **x_hat** -- estimated (smoothed) x
              - **dxdt_hat** -- estimated derivative of x
     """
-    # Normalize
+    # Normalize for numerical consistency with convex solver
     mean = np.mean(x)
     std = np.std(x)
     if std == 0: std = 1 # safety guard
@@ -79,15 +79,16 @@ def _total_variation_regularized_derivative(x, dt, order, gamma, solver=None):
     deriv_values = cvxpy.Variable(len(x)) # values of the order^th derivative, in which we're penalizing variation
     integration_constants = cvxpy.Variable(order) # constants of integration that help get us back to x
 
-    # Recursively integrate the highest order derivative to get back to the position
+    # Recursively integrate the highest order derivative to get back to the position. This is a first-
+    # order scheme, but it's very fast and tends to not do markedly worse than 2nd order. See #116
     y = deriv_values
     for i in range(order):
         y = cvxpy.cumsum(y) + integration_constants[i]
 
     # Set up and solve the optimization problem
     prob = cvxpy.Problem(cvxpy.Minimize(
         # Compare the recursively integrated position to the noisy position, and add TVR penalty
-        cvxpy.sum_squares(y - x) + cvxpy.sum(gamma*cvxpy.tv(deriv_values)) ))
+        cvxpy.sum_squares(y - x) + gamma*cvxpy.sum(cvxpy.tv(deriv_values)) ))
     prob.solve(solver=solver)
 
     # Recursively integrate the final derivative values to get back to the function and derivative values
@@ -97,14 +98,10 @@ def _total_variation_regularized_derivative(x, dt, order, gamma, solver=None):
     dxdt_hat = y/dt # y only holds the dx values; to get deriv scale by dt
     x_hat = np.cumsum(y) + integration_constants.value[order-1] # smoothed data
 
-    dxdt_hat = (dxdt_hat[:-1] + dxdt_hat[1:])/2 # take first order FD to smooth a touch
-    ddxdt_hat_f = dxdt_hat[-1] - dxdt_hat[-2]
-    dxdt_hat_f = dxdt_hat[-1] + ddxdt_hat_f # What is this doing? Could we use a 2nd order FD above natively?
-    dxdt_hat = np.hstack((dxdt_hat, dxdt_hat_f))
-
-    # fix first point
-    d = dxdt_hat[2] - dxdt_hat[1]
-    dxdt_hat[0] = dxdt_hat[1] - d
+    # Due to the first-order nature of the derivative, it has a slight lag. Average together every two values
+    # to better center the answer. But this leaves us one-short, so devise a good last value.
+    dxdt_hat = (dxdt_hat[:-1] + dxdt_hat[1:])/2
+    dxdt_hat = np.hstack((dxdt_hat, 2*dxdt_hat[-1] - dxdt_hat[-2])) # last value = penultimate value [-1] + diff between [-1] and [-2]
 
     return x_hat*std+mean, dxdt_hat*std # derivative is linear, so scale derivative by std
 
@@ -262,5 +259,3 @@ def jerk_sliding(x, dt, params=None, options=None, gamma=None, solver=None, wind
     ramp = window_size//5
     kernel = np.hstack((np.arange(1, ramp+1)/ramp, np.ones(window_size - 2*ramp), (np.arange(1, ramp+1)/ramp)[::-1]))
     return utility.slide_function(_total_variation_regularized_derivative, x, dt, kernel, 3, gamma, stride=ramp, solver=solver)
-
-