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splines already have a derivative implemented, so there isn't necessarily a need to FD on top of them #137

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Merged
pavelkomarov merged 4 commits into from
Aug 8, 2025
Merged

splines already have a derivative implemented, so there isn't necessarily a need to FD on top of them #137

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2b1514b
splines already have a derivative implemented, so there isn't necessa…
pavelkomarov Aug 1, 2025
ef49978
merged with master
pavelkomarov Aug 7, 2025
bf327f5
merged in master
pavelkomarov Aug 8, 2025
bd280b3
updated docstrings
pavelkomarov Aug 8, 2025
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2 changes: 1 addition & 1 deletion examples/1_basic_tutorial.ipynb
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4 changes: 2 additions & 2 deletions examples/2a_optimizing_parameters_with_dxdt_known.ipynb
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4 changes: 2 additions & 2 deletions examples/2b_optimizing_parameters_with_dxdt_unknown.ipynb
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16 changes: 9 additions & 7 deletions pynumdiff/smooth_finite_difference/_smooth_finite_difference.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,7 +11,7 @@
# Smoothing finite differences #
################################
def mediandiff(x, dt, params=None, options={}, window_size=5, num_iterations=1):
"""Perform median smoothing using scipy.signal.medfilt followed by first order finite difference
"""Perform median smoothing using scipy.signal.medfilt followed by second order finite difference

:param np.array[float] x: data to differentiate
:param float dt: step size
Expand Down Expand Up @@ -44,7 +44,7 @@ def mediandiff(x, dt, params=None, options={}, window_size=5, num_iterations=1):


def meandiff(x, dt, params=None, options={}, window_size=5, num_iterations=1):
"""Perform mean smoothing by convolving mean kernel with x followed by first order finite difference
"""Perform mean smoothing by convolving mean kernel with x followed by second order finite difference

:param np.ndarray[float] x: data to differentiate
:param float dt: step size
Expand Down Expand Up @@ -74,7 +74,7 @@ def meandiff(x, dt, params=None, options={}, window_size=5, num_iterations=1):


def gaussiandiff(x, dt, params=None, options={}, window_size=5, num_iterations=1):
"""Perform gaussian smoothing by convolving gaussian kernel with x followed by first order finite difference
"""Perform gaussian smoothing by convolving gaussian kernel with x followed by second order finite difference

:param np.array[float] x: data to differentiate
:param float dt: step size
Expand Down Expand Up @@ -103,7 +103,7 @@ def gaussiandiff(x, dt, params=None, options={}, window_size=5, num_iterations=1


def friedrichsdiff(x, dt, params=None, options={}, window_size=5, num_iterations=1):
"""Perform friedrichs smoothing by convolving friedrichs kernel with x followed by first order finite difference
"""Perform friedrichs smoothing by convolving friedrichs kernel with x followed by second order finite difference

:param np.array[float] x: data to differentiate
:param float dt: step size
Expand Down Expand Up @@ -132,7 +132,7 @@ def friedrichsdiff(x, dt, params=None, options={}, window_size=5, num_iterations


def butterdiff(x, dt, params=None, options={}, filter_order=2, cutoff_freq=0.5, num_iterations=1):
"""Perform butterworth smoothing on x with scipy.signal.filtfilt followed by first order finite difference
"""Perform butterworth smoothing on x with scipy.signal.filtfilt followed by second order finite difference

:param np.array[float] x: data to differentiate
:param float dt: step size
Expand Down Expand Up @@ -171,7 +171,8 @@ def butterdiff(x, dt, params=None, options={}, filter_order=2, cutoff_freq=0.5,


def splinediff(x, dt, params=None, options={}, order=3, s=None, num_iterations=1):
"""Perform spline smoothing on x with scipy.interpolate.UnivariateSpline followed by first order finite difference
"""Find smoothed data and derivative estimates by fitting a smoothing spline to the data with
scipy.interpolate.UnivariateSpline.

:param np.array[float] x: data to differentiate
:param float dt: step size
Expand Down Expand Up @@ -200,6 +201,7 @@ def splinediff(x, dt, params=None, options={}, order=3, s=None, num_iterations=1
spline = scipy.interpolate.UnivariateSpline(t, x_hat, k=order, s=s)
x_hat = spline(t)

x_hat, dxdt_hat = finite_difference(x_hat, dt)
dspline = spline.derivative()
dxdt_hat = dspline(t)

return x_hat, dxdt_hat
2 changes: 1 addition & 1 deletion pynumdiff/tests/test_diff_methods.py
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Original file line number Diff line number Diff line change
Expand Up @@ -137,7 +137,7 @@ def iterated_first_order(*args, **kwargs): return first_order(*args, **kwargs)
[(1, 0), (1, 1), (1, 0), (1, 1)],
[(2, 2), (3, 2), (2, 2), (3, 2)],
[(2, 1), (3, 3), (2, 1), (3, 3)]],
splinediff: [[(-14, -15), (-14, -14), (-1, -1), (0, 0)],
splinediff: [[(-14, -15), (-14, -15), (-1, -1), (0, 0)],
[(-14, -14), (-13, -14), (-1, -1), (0, 0)],
[(-14, -14), (-13, -13), (-1, -1), (0, 0)],
[(0, 0), (1, 1), (0, 0), (1, 1)],
Expand Down