Datalens.ai
← All algorithms

INTERPOLATION

InterpolatedUnivariateSpline

1-D interpolating spline for a given set of data points.

Example of usage

Python API reference included; executable data demonstration pending review.

Usage / limitations

+Use when: Estimate values between samples.

−Limitations: Check input requirements, mathematical assumptions and version-specific behaviour before using.

Mathematical formula

See method documentation: implementation and assumptions vary.
Sample dataShow 
{
  "variable": "observations",
  "domain": "Environmental monitoring — hourly weather station measurements",
  "feature_names": [
    "hour",
    "temperature_c"
  ],
  "records": [
    {
      "hour": 0,
      "temperature_c": 14.0
    },
    {
      "hour": 1,
      "temperature_c": 15.2
    },
    {
      "hour": 2,
      "temperature_c": 17.1
    },
    {
      "hour": 3,
      "temperature_c": 18.0
    },
    {
      "hour": 4,
      "temperature_c": 19.4
    },
    {
      "hour": 5,
      "temperature_c": 20.1
    },
    {
      "hour": 6,
      "temperature_c": 21.6
    },
    {
      "hour": 7,
      "temperature_c": 22.0
    }
  ]
}

Code example (Python)

# DataLens.ai example — InterpolatedUnivariateSpline
# Domain: environmental temperature monitoring.
# Input observations is supplied by the Sample Data section.
import numpy as np
from scipy.interpolate import InterpolatedUnivariateSpline

# Read recorded measurements with explicit, meaningful feature names.
x = np.array([row["hour"] for row in observations["records"]], dtype=float)
y = np.array([row["temperature_c"] for row in observations["records"]], dtype=float)
# Perform the specified interpolation and validate expected properties.
curve = InterpolatedUnivariateSpline(x, y, k=3)
result = curve(x)
assert np.allclose(result, y, atol=1e-8)

# Self-test completed without raising an assertion error.
print("Expected output verified")