removed jerk_sliding

pynumdiff/__init__.py

@@ -8,7 +8,7 @@
     from warnings import warn
     warn("tvrdiff, robustdiff, and lineardiff not available due to lack of convex solver. To use those, install CVXPY.")
 else: # executes if try is successful
-    from .total_variation_regularization import tvrdiff, velocity, acceleration, jerk, smooth_acceleration, jerk_sliding
+    from .total_variation_regularization import tvrdiff, velocity, acceleration, jerk, smooth_acceleration
     from .kalman_smooth import robustdiff, convex_smooth
     from .linear_model import lineardiff
 

pynumdiff/optimize/_optimize.py

@@ -13,7 +13,7 @@
 from ..smooth_finite_difference import kerneldiff, mediandiff, meandiff, gaussiandiff, friedrichsdiff, butterdiff
 from ..polynomial_fit import polydiff, savgoldiff, splinediff
 from ..basis_fit import spectraldiff, rbfdiff
-from ..total_variation_regularization import tvrdiff, velocity, acceleration, jerk, iterative_velocity, smooth_acceleration, jerk_sliding
+from ..total_variation_regularization import tvrdiff, velocity, acceleration, jerk, iterative_velocity, smooth_acceleration
 from ..kalman_smooth import rtsdiff, constant_velocity, constant_acceleration, constant_jerk, robustdiff
 from ..linear_model import lineardiff
 
@@ -110,7 +110,6 @@
     method_params_and_bounds[method] = method_params_and_bounds[meandiff]
 for method in [acceleration, jerk]: # Deprecated, redundant methods
     method_params_and_bounds[method] = method_params_and_bounds[velocity]
-method_params_and_bounds[jerk_sliding] = method_params_and_bounds[smooth_acceleration]
 for method in [constant_acceleration, constant_jerk]: # Deprecated, redundant methods
     method_params_and_bounds[method] = method_params_and_bounds[constant_velocity]
 

pynumdiff/tests/test_diff_methods.py

@@ -6,7 +6,7 @@
 from ..linear_model import lineardiff
 from ..basis_fit import spectraldiff, rbfdiff
 from ..polynomial_fit import polydiff, savgoldiff, splinediff
-from ..total_variation_regularization import velocity, acceleration, jerk, iterative_velocity, smooth_acceleration, jerk_sliding
+from ..total_variation_regularization import velocity, acceleration, jerk, iterative_velocity, smooth_acceleration
 from ..kalman_smooth import rtsdiff, constant_velocity, constant_acceleration, constant_jerk, robustdiff
 from ..smooth_finite_difference import mediandiff, meandiff, gaussiandiff, friedrichsdiff, butterdiff
 # Function aliases for testing cases where parameters change the behavior in a big way, so error limits can be indexed in dict
@@ -57,7 +57,6 @@ def spline_irreg_step(*args, **kwargs): return splinediff(*args, **kwargs)
     (jerk, {'gamma':10}), (jerk, [10]),
     (iterative_velocity, {'num_iterations':5, 'gamma':0.05}), (iterative_velocity, [5, 0.05]),
     (smooth_acceleration, {'gamma':2, 'window_size':5}), (smooth_acceleration, [2, 5]),
-    (jerk_sliding, {'gamma':1, 'window_size':15}), (jerk_sliding, [1], {'window_size':15}),
     (lineardiff, {'order':3, 'gamma':5, 'window_size':11, 'solver':'CLARABEL'}), (lineardiff, [3, 5, 11], {'solver':'CLARABEL'})
     ]
 
@@ -193,12 +192,6 @@ def spline_irreg_step(*args, **kwargs): return splinediff(*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: [[(-25, -25), (-16, -17), (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, 0), (2, 2), (1, 0), (2, 2)],
-                   [(1, 1), (3, 3), (1, 1), (3, 3)]],
     constant_velocity: [[(-25, -25), (-25, -25), (0, -1), (1, 1)],
                         [(-4, -5), (-3, -3), (0, -1), (1, 1)],
                         [(-3, -3), (0, 0), (0, -1), (1, 1)],

pynumdiff/total_variation_regularization/__init__.py

@@ -1,3 +1,3 @@
 """This module implements some common total variation regularization methods
 """
-from ._total_variation_regularization import tvrdiff, velocity, acceleration, jerk, jerk_sliding, smooth_acceleration, iterative_velocity
+from ._total_variation_regularization import tvrdiff, velocity, acceleration, jerk, smooth_acceleration, iterative_velocity

pynumdiff/total_variation_regularization/_total_variation_regularization.py

@@ -225,41 +225,3 @@ def smooth_acceleration(x, dt, params=None, options=None, gamma=None, window_siz
     x_hat = x_hat + x0
 
     return x_hat, dxdt_hat
-
-
-def jerk_sliding(x, dt, params=None, options=None, gamma=None, solver=None, window_size=101):
-    """Use convex optimization (cvxpy) to solve for the jerk total variation regularized derivative in a
-    sliding window.
-
-    :param np.array[float] x: data to differentiate
-    :param float dt: step size
-    :param params: (**deprecated**, prefer :code:`gamma`)
-    :param dict options: (**deprecated**, prefer :code:`solver`) a dictionary consisting of {'solver': (str)}
-    :param float gamma: the regularization parameter
-    :param str solver: the solver CVXPY should use, 'MOSEK', 'CVXOPT', 'CLARABEL', 'ECOS', etc.
-                In testing, 'MOSEK' was the most robust. If not given, fall back to CVXPY's default.
-    :param int window_size: how wide to make the kernel
-
-    :return: - **x_hat** (np.array) -- estimated (smoothed) x
-             - **dxdt_hat** (np.array) -- estimated derivative of x
-    """
-    if params != None: # Warning to support old interface for a while. Remove these lines along with params in a future release.
-        warn("`params` and `options` parameters will be removed in a future version. Use `gamma` " +
-            "and `solver` instead.", DeprecationWarning)
-        gamma = params[0] if isinstance(params, list) else params
-        if options != None:
-            if 'solver' in options: solver = options['solver']
-            if 'window_size' in options: window_size = options['window_size']
-    elif gamma == None:
-        raise ValueError("`gamma` must be given.")
-
-    if len(x) < window_size or window_size < 15:
-        warn("len(x) should be > window_size >= 15, calling standard jerk() without sliding")
-        return tvrdiff(x, dt, 3, gamma, solver=solver)
-
-    if window_size % 2 == 0:
-        window_size += 1 # has to be odd
-        warn("Kernel window size should be odd. Added 1 to length.")
-    ramp = window_size//5
-    kernel = np.hstack((np.arange(1, ramp+1)/ramp, np.ones(window_size - 2*ramp), np.arange(ramp, 0, -1)/ramp))
-    return utility.slide_function(tvrdiff, x, dt, kernel, 3, gamma, stride=ramp, solver=solver)