lowering tolerance for what is considered uniform spacing

We ran into an issue of the following "high res" data not being interpolated correctly (which was a scaled version of an enthalpy defect). Lowering the uniform spacing tolerance addressed the issue.

""" import numpy as np from pytabprops import LagrangeInterpolant1D, StateTable import matplotlib.pyplot as plt

x = np.array([1. , 2.02321341, 3.04642683, 4.06964024]) x2 = np.array([1.000000000000000e+00, 2.023213414585707e+00, 3.046426829171402e+00, 4.069640243757108e+00]) y = np.array([950.29194127, 945.06973705, 939.99250336, 935.04420355]) - 934. y += 4.*(x-1)**2

kind = 1 interp = LagrangeInterpolant1D(kind, x, y, True) interp2 = LagrangeInterpolant1D(kind, x2, y, True)

tbl = StateTable() tbl.add_entry("val", interp, ['x']) tbl.add_entry("val2", interp2, ['x']) interpolant = lambda x: tbl.query("val", x) interpolant2 = lambda x: tbl.query("val2", x)

query = np.linspace(x[0],x[-1],100) plt.plot(x,y,'k*') plt.plot(query, interpolant(query), label='low res') plt.plot(query, interpolant2(query), label='high res') plt.grid() plt.legend() plt.show()

print('low res',np.max(np.abs(y-interpolant(x)))) print('high res',np.max(np.abs(y-interpolant2(x2)))) """