Skip to main content

Numerical differentiation in python.

Project description

Documentation Status MIT License

Numerical differentiation of noisy time series data in python

Numerical differentiation methods for noisy time series data in python includes:

from derivative import dxdt
import numpy as np

t = np.linspace(0,2*np.pi,50)
x = np.sin(x)

# 1. Finite differences with central differencing using 3 points.
result1 = dxdt(x, t, kind="finite_difference", k=1)

# 2. Savitzky-Golay using cubic polynomials to fit in a centered window of length 1
result2 = dxdt(x, t, kind="savitzky_golay", left=.5, right=.5, order=3)

# 3. Spectral derivative
result3 = dxdt(x, t, kind="spectral")

# 4. Spline derivative with smoothing set to 0.01
result4 = dxdt(x, t, kind="spline", s=1e-2)

# 5. Total variational derivative with regularization set to 0.01
result5 = dxdt(x, t, kind="trend_filtered", order=0, alpha=1e-2)
  1. Symmetric finite difference schemes using arbitrary window size.
  2. Savitzky-Galoy derivatives of any polynomial order with independent left and right window parameters.
  3. Spectral derivatives with optional filter.
  4. Spline derivatives of any order.
  5. Polynomial-trend-filtered derivatives generalizing methods like total variational derivatives.

The goal of this package is to provide some common numerical differentiation techniques that showcase improvements that can be made on finite differences when data is noisy.

This package binds these common differentiation methods to a single easily implemented differentiation interface to encourage user adaptation.

References:

[1] Numerical differentiation of experimental data: local versus global methods- K. Ahnert and M. Abel

[2] Numerical Differentiation of Noisy, Nonsmooth Data- Rick Chartrand

[3] The Solution Path of the Generalized LASSO- R.J. Tibshirani and J. Taylor

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for derivative, version 0.3.1
Filename, size File type Python version Upload date Hashes
Filename, size derivative-0.3.1-py3-none-any.whl (10.6 kB) File type Wheel Python version py3 Upload date Hashes View
Filename, size derivative-0.3.1.tar.gz (10.1 kB) File type Source Python version None Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring DigiCert DigiCert EV certificate Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page