loess

library2.1.2pypypi✓ verified 85d ago

LOESS (Locally Estimated Scatterplot Smoothing) is a non-parametric regression method that fits simple models to localized subsets of data to build up a function that describes the deterministic part of the variation. This Python library provides robust implementations for 1D and 2D LOESS smoothing. The current version is 2.1.2, and it typically sees releases for bug fixes and minor improvements, with a stable API.

data

pip install loess

package ↗homepage ↗

INSTALL

IMPORT

SIG · LOESS

loess

datapythonv2.1.2

Install

11.6s avg

Import

250ms

Disk

322MB

Pass rate

10/ 10

Env Coverage10 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v2.1.2 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.920 runs

installs and imports cleanly · install 0.0s · import 0.246s · 319.1MB

glibc

py 3.10–3.920 runs

installs and imports cleanly · install 11.6s · import 0.255s · 306MB

322MB installed

● package 322MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

loess_1d

✓ from loess.loess_1d import loess_1d

loess_2d

✓ from loess.loess_2d import loess_2d

This quickstart demonstrates how to use `loess_1d` to smooth a one-dimensional dataset. It generates noisy sinusoidal data and then applies LOESS, printing the first few original and smoothed values. Ensure your `x` array is sorted.

import numpy as np from loess.loess_1d import loess_1d # Generate some sample data with noise x = np.linspace(0, 10, 100) y = np.sin(x) + np.random.normal(0, 0.5, 100) # Apply LOESS smoothing # x must be sorted for loess_1d y_smoothed, w_out = loess_1d(x, y, xnew=x, span=0.5, degree=1) print(f"Original x (first 5): {x[:5]}") print(f"Original y (first 5): {y[:5]}") print(f"Smoothed y (first 5): {y_smoothed[:5]}") print(f"Shape of smoothed y: {y_smoothed.shape}")

Debug

Known issues

gotchaThe input array `x` for `loess_1d` must be sorted in ascending order. If `x` is not sorted, the algorithm will not work correctly and will raise a `ValueError`.

fix

Ensure `x` is sorted before passing it to `loess_1d`. For example: `x_sorted, y_sorted = x[x.argsort()], y[x.argsort()]` if `y` needs to be reordered along with `x`.

affects: All versions

gotchaInput data (`x`, `y` for 1D; `x`, `y`, `z` for 2D) must not contain `inf` or `NaN` values. The library performs checks and will raise a `ValueError` if these are present.

fix

Pre-process your data to handle missing values (e.g., imputation, removal) or infinite values before passing them to the LOESS functions.

affects: All versions

gotchaThe `span` parameter (also known as `alpha` or `bandwidth`) is expected to be a float between 0.0 and 1.0 (exclusive of 0.0, inclusive of 1.0), representing the fraction of data points used for each local regression. Providing a value outside this range will result in a `ValueError`.

fix

Set `span` to a value between 0.0 and 1.0 (e.g., 0.5 for half the data points). Experiment with different `span` values to find the optimal smoothing for your data.

affects: All versions

Errors

Common errors & fixes

ValueError: array must not contain infs or NaNs

One or more of the input arrays (`x`, `y`, `z`) contains infinite (`inf`) or Not-a-Number (`NaN`) values.

fix

Filter out or impute `NaN` and `inf` values from your data before passing them to `loess_1d` or `loess_2d`. Example: `x = x[~np.isnan(x)]` and similarly for other arrays.

ValueError: x must be sorted for this algorithm to work

The input array `x` for `loess_1d` was not provided in ascending sorted order.

fix

Sort your `x` array. If `y` data corresponds to `x`, ensure `y` is reordered accordingly: `sort_idx = np.argsort(x); x_sorted = x[sort_idx]; y_sorted = y[sort_idx]`.

ValueError: span must be between 0.0 and 1.0

The `span` parameter was set to a value outside the valid range (0.0, 1.0].

fix

Adjust the `span` parameter to be a float value greater than 0.0 and less than or equal to 1.0. For example, `span=0.5` is a common starting point.

Upgrade

Version history

2.1.2latest on PyPI · released May 20, 2024

Audit

Dependencies

numpyrequiredRequired for numerical operations and array handling.

scipyrequiredRequired for statistical functions and interpolation.

Agent activity

10 hits · last 30 days

node

Resources

packagehttps://pypi.org/project/loess/ ↗

homepagehttps://purl.org/cappellari/software ↗