Python modules for common tasks in the Pincus lab.
Functions for computations over plane curves, approximated as series of points (polylines) or parametric splines.
- curve.geometry: basic algorithms for polyline curves.
- curve.interpolate: methods for resampling polylines and fitting them to smoothing splines (using scipy.interpolate.fitpack).
Functions for basic image processing.
- image.colorize: color-map and color-tint grayscale images to RGB color, and combine color images. Also convert wavelengths to colors.
- image.fft: construct simple Butterworth filters for filtering image frequencies by FFT.
- image.fast_fft: use pyfftw to speed up FFT image filtering.
- image.mask: basic mask-processing functions to demonstrate the uses of scipy.ndimage's binary morphology operators.
- image.polyfit: fit image intensities to a low-order polynomial as a way of estimating the image background.
- image.resample: transform an image into the frame of reference of an aribrtrary rectangle or spline (defined using curve.interpolate).
- image.sample_texture: sample image patches for use in texture-based classification
Functions for analysis of 1-dimensional data.
- scalar_stats.hmm: estimate paths through a HMM with the Viterbi algorithm and estimate HMM parameters from observed data.
- scalar_stats.kde: estimate the distribution or survival function of a set of data using scipy.stats.kde
- scalar_stats.mcd: calculate the robust mean and standard deviation of data with the univarite MCD estimator of outliers vs. inliers.
- scalar_stats.moving_mean_std: estimate smooth trendlines for mean and standard deviation from scattered data in x and y.
- scalar_stats.smoothing: calculate weighted means of data, or smooth scattered data with Savitzky-Golay, LOWESS, or robust polynomial fitting.