From 00508c1bc9c8ce1d803a1cb49fcf522d67b1acc8 Mon Sep 17 00:00:00 2001 From: Evgeny Prilepin Date: Tue, 5 Oct 2021 11:37:28 +0100 Subject: [PATCH] update docs for normalizedsmooth option --- csaps/_shortcut.py | 2 ++ csaps/_sspndg.py | 2 ++ csaps/_sspumv.py | 3 +++ docs/tutorial.rst | 34 ++++++++++++++++++++++++++++++++++ 4 files changed, 41 insertions(+) diff --git a/csaps/_shortcut.py b/csaps/_shortcut.py index a1ed192..31e66db 100644 --- a/csaps/_shortcut.py +++ b/csaps/_shortcut.py @@ -150,6 +150,8 @@ def csaps(xdata: Union[UnivariateDataType, NdGridDataType], If True, the smooth parameter is normalized such that results are invariant to xdata range and less sensitive to nonuniformity of weights and xdata clumping + .. versionadded:: 1.1.0 + Returns ------- diff --git a/csaps/_sspndg.py b/csaps/_sspndg.py index d14003f..8143cb3 100644 --- a/csaps/_sspndg.py +++ b/csaps/_sspndg.py @@ -197,6 +197,8 @@ class NdGridCubicSmoothingSpline(ISmoothingSpline[ If True, the smooth parameter is normalized such that results are invariant to xdata range and less sensitive to nonuniformity of weights and xdata clumping + .. versionadded:: 1.1.0 + """ __module__ = 'csaps' diff --git a/csaps/_sspumv.py b/csaps/_sspumv.py index e59205e..d7f3568 100644 --- a/csaps/_sspumv.py +++ b/csaps/_sspumv.py @@ -118,6 +118,9 @@ class CubicSmoothingSpline(ISmoothingSpline[ normalizedsmooth : [*Optional*] bool If True, the smooth parameter is normalized such that results are invariant to xdata range and less sensitive to nonuniformity of weights and xdata clumping + + .. versionadded:: 1.1.0 + """ __module__ = 'csaps' diff --git a/docs/tutorial.rst b/docs/tutorial.rst index 202d53e..0aa33cb 100644 --- a/docs/tutorial.rst +++ b/docs/tutorial.rst @@ -344,6 +344,40 @@ For example, the following code will raise ``ValueError`` without ``axis`` param ``axis`` parameter is ignored in ND-gridded data cases. +.. _tutorial-normalizedsmooth: + +Smooth normalization +~~~~~~~~~~~~~~~~~~~~ + +.. versionadded:: 1.1.0 + +We can use ``normalizedsmooth`` option to normalize smooth parameter. +If ``normalizedsmooth`` is True, the smooth parameter is normalized such that results are invariant to xdata range +and less sensitive to nonuniformity of weights and xdata clumping. + +Let's show it on a simple example. + +.. plot:: + + x1, y = univariate_data(seed=1327) + x2 = x1 * 10 + + xi1 = np.linspace(x1[0], x1[-1], 150) + xi2 = np.linspace(x2[0], x2[-1], 150) + + yi1 = csaps(x1, y, xi1, smooth=0.8) + yi2 = csaps(x2, y, xi2, smooth=0.8) + + yi1_n = csaps(x1, y, xi1, smooth=0.8, normalizedsmooth=True) + yi2_n = csaps(x2, y, xi2, smooth=0.8, normalizedsmooth=True) + + f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(8, 6)) + ax1.plot(x1, y, 'o', xi1, yi1, '-') + ax2.plot(x2, y, 'o', xi2, yi2, '-') + ax3.plot(x1, y, 'o', xi1, yi1_n, '-') + ax4.plot(x2, y, 'o', xi2, yi2_n, '-') + + Computing Spline Without Evaluating ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~