This is a complete api reference to the openpiv python module.
The openpiv.tools module is a collection of utilities and tools often used.
| imread(filename) | Read an image file into a numpy array |
| save(x, y, u, v, filename[, fmt, delimiter]) | Save flow field to an ascii file. |
| display(message) | Display a message to standard output. |
This module contains a pure python implementation of the cross-correlation algorithm for PIV image processing. It also contains some useful helper functions.
| normalize_intensity(window) | Remove mean value from window and masks negative, dark pixels |
| correlate_windows(window_a, window_b[, ...]) | Compute correlation function between two interrogation windows. |
| get_coordinates(image_size, window_size, overlap) | Compute the x, y coordinates of the centers of the interrogation windows. |
| get_field_shape(image_size, window_size, overlap) | Compute the shape of the resulting flow field. |
| moving_window_array(array, window_size, overlap) | This is a nice numpy trick. |
| find_first_peak(corr) | Find row and column indices of the first correlation peak. |
| find_second_peak(corr[, i, j]) | Find the value of the second largest peak |
| find_pixel_peak_position(corr[, ...]) | Find pixel approximation of the correlation peak. |
| find_subpixel_peak_position(corr, peak_indices) | Find subpixel approximation of the correlation peak. |
| piv(frame_a, frame_b[, window_size, ...]) | Basic python implementation of the PIV cross-correlation |
A cython module for fast advanced algorithms for PIV image analysis.
| extended_search_area_piv | The implementation of the one-step direct correlation with different size of the interrogation window and the search area. |
The openpiv.filters module contains some filtering/smoothing routines.
| gaussian(u, v, size) | Smooths the velocity field with a Gaussian kernel. |
| _gaussian_kernel(size) | A normalized 2D Gaussian kernel array |
| replace_outliers(u, v[, method, n_iter, ...]) | Replace nans in an velocity field using an iterative image inpainting algorithm. |
A module for spurious vector detection.
| global_val(u, v, u_thresholds, v_thresholds) | Eliminate spurious vectors with a global threshold. |
| sig2noise_val(u, v, sig2noise[, threshold]) | Eliminate spurious vectors from cross-correlation signal to noise ratio. |
| global_std(u, v[, std_threshold]) | Eliminate spurious vectors with a global threshold defined by the standard deviation |