Problem-solving strategies for interpolation in numerical methods
Use this skill when working on interpolation problems in numerical methods.
Assess Data Characteristics
Select Interpolation Method
Implement with SciPy
scipy.interpolate.CubicSpline(x, y) - natural cubic splinescipy.interpolate.make_interp_spline(x, y, k=3) - B-splinescipy.interpolate.interp1d(x, y, kind='cubic') - 1D interpolationValidate Results
sympy_compute.py limit "interp_error" --at boundariesHigh-Dimensional Considerations
uv run python -c "from scipy.interpolate import CubicSpline; import numpy as np; x = np.array([0,1,2,3]); y = np.array([0,1,4,9]); cs = CubicSpline(x, y); print(cs(1.5))"
uv run python -c "from scipy.interpolate import make_interp_spline; import numpy as np; x = np.array([0,1,2,3]); y = np.array([0,1,4,9]); bspl = make_interp_spline(x, y, k=3); print(bspl(1.5))"
uv run python -m runtime.harness scripts/sympy_compute.py interpolate "[(0,0),(1,1),(2,4)]" --var x
From indexed textbooks:
See .claude/skills/math-mode/SKILL.md for full tool documentation.