import numpy import scipy from scipy.interpolate import UnivariateSpline, splrep class PeriodicUnivariateSpline(UnivariateSpline): def __init__(self, x, y, w=None, bbox=[None]*2, k=3, s=0): #_data == x,y,w,xb,xe,k,s,n,t,c,fp,fpint,nrdata,ier tck, fp, ier, msg = splrep(x, y, k=k, w=w, xb=bbox[0], xe=bbox[1], s=0, per=1, full_output=1) self._data = (x,y,w,bbox[0],bbox[1],k,s,len(tck[0]),tck[0],tck[1], fp,None,None,ier) self._reset_class() x = array([ 0.003 , 0.006 , 0.008 , 0.00800001, 0.012 , 0.014 , 0.016 , 0.02 , 0.02000001, 0.024 , 0.026 , 0.028 , 0.032 , 0.037 , 0.039 , 0.03900001, 0.042 , 0.044 , 0.046 , 0.04600001, 0.052 , 0.05200001, 0.055 , 0.056 , 0.061 , 0.06100001, 0.062 , 0.067 , 0.073 , 0.075 , 0.081 , 0.084 , 0.086 , 0.08600001, 0.092 , 0.093 , 0.094 , 0.09400001, 0.095 , 0.097 , 0.098 , 0.099 , 0.101 , 0.10100001, 0.102 , 0.103 , 0.104 , 0.107 , 0.10700001, 0.10700002, 0.112 , 0.117 , 0.118 , 0.119 , 0.12 , 0.126 , 0.127 , 0.12700001, 0.128 , 0.129 , 0.131 , 0.133 , 0.134 , 0.136 , 0.14 , 0.149 , 0.151 , 0.153 , 0.157 , 0.158 , 0.16 , 0.163 , 0.166 , 0.169 , 0.173 , 0.174 , 0.179 , 0.181 , 0.186 , 0.189 , 0.192 , 0.19200001, 0.193 , 0.199 , 0.204 , 0.207 , 0.208 , 0.21 , 0.211 , 0.214 , 0.21400001, 0.215 , 0.217 , 0.21700001, 0.221 , 0.222 , 0.225 , 0.226 , 0.228 , 0.24 , 0.242 , 0.24200001, 0.243 , 0.246 , 0.249 , 0.252 , 0.257 , 0.25700001, 0.25700002, 0.258 , 0.261 , 0.272 , 0.279 , 0.289 , 0.28900001, 0.29 , 0.292 , 0.299 , 0.304 , 0.308 , 0.30800001, 0.311 , 0.316 , 0.318 , 0.319 , 0.326 , 0.328 , 0.335 , 0.33500001, 0.346 , 0.348 , 0.349 , 0.353 , 0.354 , 0.356 , 0.35600001, 0.357 , 0.361 , 0.365 , 0.366 , 0.373 , 0.37300001, 0.377 , 0.382 , 0.383 , 0.38300001, 0.389 , 0.39 , 0.391 , 0.394 , 0.396 , 0.402 , 0.40200001, 0.406 , 0.413 , 0.417 , 0.425 , 0.433 , 0.435 , 0.439 , 0.43900001, 0.43900002, 0.441 , 0.444 , 0.453 , 0.46 , 0.46000001, 0.467 , 0.469 , 0.474 , 0.476 , 0.478 , 0.48 , 0.481 , 0.482 , 0.486 , 0.487 , 0.488 , 0.48800001, 0.491 , 0.49100001, 0.497 , 0.5 , 0.501 , 0.50100001, 0.50100002, 0.508 , 0.514 , 0.51400001, 0.515 , 0.517 , 0.519 , 0.531 , 0.535 , 0.538 , 0.546 , 0.547 , 0.548 , 0.55 , 0.55000001, 0.55000002, 0.552 , 0.555 , 0.558 , 0.562 , 0.56200001, 0.566 , 0.56600001, 0.571 , 0.579 , 0.584 , 0.6 , 0.602 , 0.608 , 0.612 , 0.614 , 0.616 , 0.61600001, 0.618 , 0.62 , 0.625 , 0.626 , 0.63 , 0.631 , 0.635 , 0.637 , 0.64 , 0.643 , 0.647 , 0.649 , 0.654 , 0.656 , 0.659 , 0.65900001, 0.663 , 0.666 , 0.669 , 0.67 , 0.67000001, 0.672 , 0.673 , 0.675 , 0.67500001, 0.679 , 0.681 , 0.689 , 0.691 , 0.697 , 0.69700001, 0.7 , 0.70000001, 0.703 , 0.705 , 0.708 , 0.70800001, 0.71 , 0.712 , 0.713 , 0.716 , 0.71600001, 0.718 , 0.733 , 0.735 , 0.736 , 0.738 , 0.747 , 0.75 , 0.75000001, 0.755 , 0.76 , 0.761 , 0.763 , 0.766 , 0.776 , 0.777 , 0.778 , 0.783 , 0.784 , 0.78400001, 0.786 , 0.788 , 0.791 , 0.793 , 0.794 , 0.798 , 0.8 , 0.802 , 0.806 , 0.809 , 0.813 , 0.819 , 0.82 , 0.824 , 0.828 , 0.829 , 0.831 , 0.83100001, 0.834 , 0.844 , 0.846 , 0.847 , 0.85 , 0.852 , 0.854 , 0.857 , 0.861 , 0.862 , 0.863 , 0.865 , 0.866 , 0.871 , 0.876 , 0.87600001, 0.879 , 0.897 , 0.899 , 0.902 , 0.903 , 0.90300001, 0.906 , 0.909 , 0.911 , 0.91100001, 0.914 , 0.92 , 0.929 , 0.931 , 0.934 , 0.94 , 0.942 , 0.944 , 0.946 , 0.948 , 0.953 , 0.955 , 0.95500001, 0.959 , 0.96 , 0.965 , 0.967 , 0.968 , 0.971 , 0.978 , 0.983 , 0.985 , 0.988 , 0.99 , 0.993 , 0.99300001, 0.994 , 0.99400001, 0.996 , 1. ]) y = numpy.array([ 14.878, 14.864, 14.853, 14.857, 14.896, 14.855, 14.847, 14.874, 14.837, 14.874, 14.879, 14.875, 14.86 , 14.873, 14.873, 14.871, 14.853, 14.878, 14.853, 14.857, 14.849, 14.837, 14.841, 14.869, 14.883, 14.864, 14.86 , 14.844, 14.871, 14.826, 14.836, 14.811, 14.873, 14.8 , 14.849, 14.877, 14.831, 14.817, 14.829, 14.836, 14.872, 14.802, 14.849, 14.828, 14.827, 14.848, 14.842, 14.855, 14.823, 14.773, 14.791, 14.866, 14.804, 14.833, 14.822, 14.793, 14.81 , 14.777, 14.783, 14.795, 14.822, 14.77 , 14.811, 14.83 , 14.852, 14.835, 14.799, 14.751, 14.78 , 14.757, 14.759, 14.736, 14.685, 14.74 , 14.766, 14.688, 14.793, 14.68 , 14.798, 14.717, 14.758, 14.726, 14.694, 14.739, 14.768, 14.697, 14.646, 14.674, 14.756, 14.774, 14.733, 14.614, 14.66 , 14.7 , 14.64 , 14.669, 14.638, 14.647, 14.629, 14.645, 14.639, 14.609, 14.609, 14.589, 14.615, 14.703, 14.599, 14.646, 14.637, 14.594, 14.595, 14.622, 14.585, 14.583, 14.612, 14.602, 14.584, 14.597, 14.588, 14.58 , 14.6 , 14.595, 14.589, 14.578, 14.561, 14.578, 14.603, 14.584, 14.593, 14.573, 14.593, 14.588, 14.598, 14.583, 14.581, 14.593, 14.573, 14.591, 14.593, 14.583, 14.602, 14.578, 14.601, 14.574, 14.564, 14.607, 14.592, 14.595, 14.587, 14.58 , 14.584, 14.585, 14.606, 14.594, 14.596, 14.608, 14.609, 14.603, 14.596, 14.616, 14.582, 14.626, 14.626, 14.617, 14.624, 14.626, 14.618, 14.618, 14.632, 14.634, 14.631, 14.615, 14.634, 14.617, 14.622, 14.621, 14.645, 14.638, 14.626, 14.636, 14.661, 14.637, 14.625, 14.613, 14.628, 14.641, 14.632, 14.658, 14.649, 14.623, 14.632, 14.633, 14.637, 14.675, 14.646, 14.633, 14.656, 14.652, 14.662, 14.688, 14.668, 14.64 , 14.685, 14.644, 14.671, 14.678, 14.675, 14.676, 14.665, 14.7 , 14.668, 14.696, 14.705, 14.671, 14.692, 14.696, 14.712, 14.705, 14.716, 14.734, 14.696, 14.727, 14.715, 14.716, 14.719, 14.727, 14.736, 14.716, 14.732, 14.692, 14.721, 14.784, 14.737, 14.724, 14.739, 14.739, 14.714, 14.76 , 14.747, 14.735, 14.71 , 14.766, 14.715, 14.734, 14.737, 14.764, 14.75 , 14.753, 14.751, 14.734, 14.775, 14.738, 14.77 , 14.759, 14.775, 14.79 , 14.756, 14.758, 14.787, 14.786, 14.758, 14.763, 14.76 , 14.784, 14.759, 14.783, 14.755, 14.783, 14.781, 14.797, 14.767, 14.79 , 14.807, 14.803, 14.834, 14.762, 14.819, 14.797, 14.809, 14.816, 14.824, 14.836, 14.821, 14.775, 14.827, 14.802, 14.799, 14.804, 14.837, 14.847, 14.797, 14.812, 14.834, 14.848, 14.825, 14.826, 14.849, 14.844, 14.825, 14.827, 14.852, 14.827, 14.843, 14.852, 14.854, 14.863, 14.859, 14.855, 14.875, 14.849, 14.836, 14.86 , 14.858, 14.853, 14.851, 14.873, 14.886, 14.882, 14.881, 14.869, 14.868, 14.876, 14.87 , 14.869, 14.872, 14.874, 14.871, 14.876, 14.888, 14.873, 14.881, 14.873, 14.881, 14.877, 14.875, 14.9 , 14.882, 14.883, 14.88 , 14.863, 14.892, 14.89 , 14.871, 14.873, 14.857, 14.881, 14.875, 14.873, 14.875, 14.856, 14.843, 14.87 , 14.901]) pus = PeriodicUnivariateSpline(x,y) pus_points = pus(numpy.linspace(0, 0.99, 100)) # Gives a bizarre answer