updated tests and optimization code to import from the new locations, changed name of the general finite_difference function to be finitediff so it doesn't collide with the module name, updated the basic usage notebook to be more in line with the new structure, reran everything

Author: pavelkomarov

examples/1_basic_tutorial.ipynb

@@ -1,12 +1,10 @@
 """Import useful functions from all modules
 """
-from pynumdiff._version import __version__
-from pynumdiff.finite_difference import finite_difference, first_order, second_order, fourth_order
-from pynumdiff.smooth_finite_difference import mediandiff, meandiff, gaussiandiff,\
-    friedrichsdiff, butterdiff
-from pynumdiff.polynomial_fit import splinediff, polydiff, savgoldiff
-from pynumdiff.total_variation_regularization import tvrdiff, velocity, acceleration, jerk,\
-    iterative_velocity, smooth_acceleration, jerk_sliding
-from pynumdiff.linear_model import lineardiff, spectraldiff
-from pynumdiff.kalman_smooth import rts_const_deriv, constant_velocity, constant_acceleration, constant_jerk,\
-    known_dynamics
+from ._version import __version__
+
+from .finite_difference import finitediff, first_order, second_order, fourth_order
+from .smooth_finite_difference import meandiff, mediandiff, gaussiandiff, friedrichsdiff, butterdiff
+from .polynomial_fit import splinediff, polydiff, savgoldiff
+from .total_variation_regularization import tvrdiff, velocity, acceleration, jerk, iterative_velocity, smooth_acceleration, jerk_sliding
+from .kalman_smooth import rts_const_deriv, constant_velocity, constant_acceleration, constant_jerk, known_dynamics
+from .linear_model import spectraldiff, lineardiff

examples/2a_optimizing_parameters_with_dxdt_known.ipynb

@@ -1,3 +1,3 @@
 """This module implements some common finite difference schemes
 """
-from ._finite_difference import finite_difference, first_order, second_order, fourth_order
+from ._finite_difference import finitediff, first_order, second_order, fourth_order

examples/2b_optimizing_parameters_with_dxdt_unknown.ipynb

@@ -4,7 +4,7 @@
 from warnings import warn
 
 
-def finite_difference(x, dt, num_iterations, order):
+def finitediff(x, dt, num_iterations, order):
     """Perform iterated finite difference of a given order. This serves as the common backing function for
     all other methods in this module.
     
@@ -85,7 +85,7 @@ def first_order(x, dt, params=None, options={}, num_iterations=1):
         warn("`params` and `options` parameters will be removed in a future version. Use `num_iterations` instead.", DeprecationWarning)
         num_iterations = params[0] if isinstance(params, list) else params
 
-    return finite_difference(x, dt, num_iterations, 1)
+    return finitediff(x, dt, num_iterations, 1)
 
 
 def second_order(x, dt, num_iterations=1):
@@ -100,7 +100,7 @@ def second_order(x, dt, num_iterations=1):
              - **x_hat** -- original x if :code:`num_iterations=1`, else smoothed x that yielded dxdt_hat
              - **dxdt_hat** -- estimated derivative of x
     """
-    return finite_difference(x, dt, num_iterations, 2)
+    return finitediff(x, dt, num_iterations, 2)
 
 
 def fourth_order(x, dt, num_iterations=1):
@@ -115,4 +115,4 @@ def fourth_order(x, dt, num_iterations=1):
              - **x_hat** -- original x if :code:`num_iterations=1`, else smoothed x that yielded dxdt_hat
              - **dxdt_hat** -- estimated derivative of x
     """
-    return finite_difference(x, dt, num_iterations, 4)
+    return finitediff(x, dt, num_iterations, 4)

pynumdiff/__init__.py

@@ -8,9 +8,10 @@
 from tqdm import tqdm
 
 from ..utils import evaluate
-from ..finite_difference import finite_difference, first_order, second_order, fourth_order
-from ..smooth_finite_difference import mediandiff, meandiff, gaussiandiff, friedrichsdiff, butterdiff, splinediff
-from ..linear_model import spectraldiff, polydiff, savgoldiff, lineardiff
+from ..finite_difference import finitediff, first_order, second_order, fourth_order
+from ..smooth_finite_difference import mediandiff, meandiff, gaussiandiff, friedrichsdiff, butterdiff
+from ..linear_model import spectraldiff, lineardiff
+from ..polynomial_fit import polydiff, savgoldiff, splinediff
 from ..total_variation_regularization import tvrdiff, velocity, acceleration, jerk, iterative_velocity, smooth_acceleration, jerk_sliding
 from ..kalman_smooth import rts_const_deriv, constant_velocity, constant_acceleration, constant_jerk
 
@@ -41,9 +42,9 @@
                  {'order': (1, 5),
                   'gamma': (1e-3, 1000),
                   'window_size': (15, 1000)}),
-    finite_difference: ({'num_iterations': [5, 10, 30, 50],
-                         'order': {2, 4}}, # order is categorical here, because it can't be 3
-                        {'num_iterations': (1, 1000)}),
+    finitediff: ({'num_iterations': [5, 10, 30, 50],
+                  'order': {2, 4}}, # order is categorical here, because it can't be 3
+                 {'num_iterations': (1, 1000)}),
     first_order: ({'num_iterations': [5, 10, 30, 50]},
                   {'num_iterations': (1, 1000)}),
     mediandiff: ({'window_size': [5, 15, 30, 50],
@@ -236,7 +237,7 @@ def suggest_method(x, dt, dxdt_truth=None, cutoff_frequency=None):
             raise ValueError('Either dxdt_truth or cutoff_frequency must be provided.')
         tvgamma = np.exp(-1.6*np.log(cutoff_frequency) -0.71*np.log(dt) - 5.1) # See https://ieeexplore.ieee.org/document/9241009
 
-    methods = [finite_difference, mediandiff, meandiff, gaussiandiff, friedrichsdiff, butterdiff,
+    methods = [finitediff, mediandiff, meandiff, gaussiandiff, friedrichsdiff, butterdiff,
         splinediff, spectraldiff, polydiff, savgoldiff, rts_const_deriv]
     try: # optionally skip some methods
         import cvxpy

pynumdiff/finite_difference/__init__.py

@@ -3,10 +3,11 @@
 from warnings import warn
 
 from ..finite_difference import first_order, second_order, fourth_order
-from ..linear_model import lineardiff, polydiff, savgoldiff, spectraldiff
+from ..linear_model import lineardiff, spectraldiff
+from ..polynomial_fit import polydiff, savgoldiff, splinediff
 from ..total_variation_regularization import velocity, acceleration, jerk, iterative_velocity, smooth_acceleration, jerk_sliding
 from ..kalman_smooth import constant_velocity, constant_acceleration, constant_jerk
-from ..smooth_finite_difference import mediandiff, meandiff, gaussiandiff, friedrichsdiff, butterdiff, splinediff
+from ..smooth_finite_difference import mediandiff, meandiff, gaussiandiff, friedrichsdiff, butterdiff
 # Function alias for testing a case where parameters change the behavior in a big way
 def iterated_second_order(*args, **kwargs): return second_order(*args, **kwargs)
 def iterated_fourth_order(*args, **kwargs): return fourth_order(*args, **kwargs)

pynumdiff/finite_difference/_finite_difference.py

@@ -1,9 +1,10 @@
 import numpy as np
 from pytest import skip
 
-from ..finite_difference import first_order as iterated_finite_difference # actually second order
-from ..smooth_finite_difference import mediandiff, meandiff, gaussiandiff, friedrichsdiff, butterdiff, splinediff
-from ..linear_model import spectraldiff, polydiff, savgoldiff
+from ..finite_difference import first_order as iterated_finite_difference
+from ..smooth_finite_difference import mediandiff, meandiff, gaussiandiff, friedrichsdiff, butterdiff
+from ..linear_model import spectraldiff
+from ..polynomial_fit import polydiff, savgoldiff, splinediff
 from ..total_variation_regularization import velocity, acceleration, iterative_velocity
 from ..optimize import optimize
 from ..utils.simulate import pi_cruise_control

pynumdiff/optimize/_optimize.py

@@ -228,7 +228,8 @@ def smooth_acceleration(x, dt, params=None, options=None, gamma=None, window_siz
 
 
 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.
+    """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