Core

The Core module of the Quantalyze package provides essential utilities for scientific data analysis. These utilities include functions for fitting, symmetrization, smoothing, differentiation, and Fourier transforms. All of the more specific modules, such as transport, derive much of their functionality from this foundational module.

All core submodules are importable from quantalyze

Differentiation

The differentiation module provides tools for calculating numerical derivatives of data in pandas.DataFrame objects. It includes methods forward_difference and backward_difference for approximating derivatives using one-sided differences, and central_difference for a more accurate two-sided approach. The derivative function simply wraps central_difference.

Interpolation

The interpolation module provides tools for interpolating data in pandas DataFrames. It allows users to map data onto new x-values using methods like linear, quadratic, or cubic interpolation. The interpolate function works seamlessly with pandas objects, making it easy to handle missing data or resample datasets for analysis.

Smoothing

The smoothing module provides practical methods for reducing noise in data. It includes bin for grouping data into bins, window for rolling window smoothing, and savgol_filter for preserving features like peaks during smoothing.

Fitting

The fitting module provides a simple wrapper for scipy.curve_fit() that facilitates the rapid fitting to data held in a pandas.DataFrame and subsequent evaluation and plotting of these fits. The expected work flow is to call quantalyze.fit(...) and either retrieve the result of the fit from the attributes of the returned Fit object or call one of the Fit object's utility methods (e.g. evaluate(...) or plot(...)).

Symmetrization

The symmetrization module provides tools for processing data to enforce symmetry or antisymmetry. The symmetrize function averages values with their reversed counterparts to create symmetric datasets, while the antisymmetrize function computes the difference between values and their reversed counterparts to create antisymmetric datasets.