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Optimization

minimize() scalar objective bounds in optimization constraints in minimize() least_squares() curve_fit() root() nonlinear equations linprog() linear programming minimize_scalar()

Integration

quad() single integral dblquad() double integral cumulative_trapezoid() solve_ivp() ODE ODE event detection dense ODE output simpson() sampled integration fixed_quad() Gaussian quadrature

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stats.norm distribution t-test one sample t-test independent samples Mann–Whitney U test chi-square test Pearson correlation Spearman correlation bootstrap confidence interval

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find_peaks() butter() filter design sosfilt() welch() power spectrum correlate() resample() signal savgol_filter() smoothing spectrogram()

Linear Algebra & Sparse

linalg.solve() LU factorization QR decomposition SVD sparse csr_matrix sparse linear solve eigs() sparse eigenvalues distance matrix

Spatial & Interpolation

KDTree nearest neighbors ConvexHull Delaunay triangulation interp1d() CubicSpline RegularGridInterpolator griddata() distance.cdist()

SciPy / Spatial & Interpolation

RegularGridInterpolator

Learn RegularGridInterpolator in SciPy with a dedicated explanation, a concept-specific example, practical cautions, and a mastery check.

12 min read • Advanced

scipy-046.py

python

import numpy as np
from scipy.interpolate import RegularGridInterpolator
x=np.array([0.,1.,2.]); y=np.array([0.,10.])
values=x[:,None]+y[None,:]
f=RegularGridInterpolator((x,y),values)
print(f([[1.5,5.0]]))

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RegularGridInterpolator Mastery Check →