Source code for WrightTools.kit._interpolate

"""Interpolation tools."""

# --- import --------------------------------------------------------------------------------------

import numpy as np

from scipy import ndimage
from scipy.interpolate import UnivariateSpline

from ._array import remove_nans_1D

# --- define --------------------------------------------------------------------------------------

__all__ = ["zoom2D", "Spline"]

# --- functions -----------------------------------------------------------------------------------

[docs] def zoom2D(xi, yi, zi, xi_zoom=3.0, yi_zoom=3.0, order=3, mode="nearest", cval=0.0): """Zoom a 2D array, with axes. Parameters ---------- xi : 1D array x axis points. yi : 1D array y axis points. zi : 2D array array values. Shape of (x, y). xi_zoom : float (optional) Zoom factor along x axis. Default is 3. yi_zoom : float (optional) Zoom factor along y axis. Default is 3. order : int (optional) The order of the spline interpolation, between 0 and 5. Default is 3. mode : {'constant', 'nearest', 'reflect', or 'wrap'} Points outside the boundaries of the input are filled according to the given mode. Default is nearest. cval : scalar (optional) Value used for constant mode. Default is 0.0. """ xi = ndimage.zoom(xi, xi_zoom, order=order, mode="nearest") yi = ndimage.zoom(yi, yi_zoom, order=order, mode="nearest") zi = ndimage.zoom(zi, (xi_zoom, yi_zoom), order=order, mode=mode, cval=cval) return xi, yi, zi
# --- classes -------------------------------------------------------------------------------------
[docs] class Spline: """Spline.""" def __call__(self, *args, **kwargs): """Evaluate.""" return self.true_spline(*args, **kwargs)
[docs] def __init__(self, xi, yi, k=3, s=1000, ignore_nans=True): """Initialize. Parameters ---------- xi : 1D array x points. yi : 1D array y points. k : integer (optional) Degree of smoothing. Must be between 1 and 5 (inclusive). Default is 3. s : integer (optional) Positive smoothing factor used to choose the number of knots. Number of knots will be increased until the smoothing condition is satisfied:: ``sum((w[i] * (y[i]-spl(x[i])))**2, axis=0) <= s`` If 0, spline will interpolate through all data points. Default is 1000. ignore_nans : boolean (optional) Toggle removle of nans. Default is True. .. note:: Use k=1 and s=0 for a linear interplation. """ # import xi_internal = np.array(xi).copy() yi_internal = np.array(yi).copy() # nans if ignore_nans: lis = [xi_internal, yi_internal] xi_internal, yi_internal = remove_nans_1D(*lis) # UnivariateSpline needs ascending xi sort = np.argsort(xi_internal) xi_internal = xi_internal[sort] yi_internal = yi_internal[sort] # create true spline self.true_spline = UnivariateSpline(xi_internal, yi_internal, k=k, s=s)