pysteps.utils¶

Implementation of miscellaneous utility functions.

pysteps.utils.interface¶

Interface for the utils module.

get_method(name, \*\*kwargs) Return a callable function for the utility method corresponding to the given name.

Utility methods for creating and processing arrays.

compute_centred_coord_array(M, N) Compute a 2D coordinate array, where the origin is at the center.

Data cleansing routines for pysteps.

`decluster`(coord, input_array, scale[, …])	Decluster a set of sparse data points by aggregating, that is, taking the median value of all values lying within a certain distance (i.e., a cluster).
`detect_outliers`(input_array, thr[, coord, …])	Detect outliers in a (multivariate and georeferenced) dataset.

Methods for converting physical units.

`to_rainrate`(R, metadata[, zr_a, zr_b])	Convert to rain rate [mm/h].
`to_raindepth`(R, metadata[, zr_a, zr_b])	Convert to rain depth [mm].
`to_reflectivity`(R, metadata[, zr_a, zr_b])	Convert to reflectivity [dBZ].

Functions to manipulate array dimensions.

`aggregate_fields`(data, window_size[, axis, …])	Aggregate fields along a given direction.
`aggregate_fields_time`(R, metadata, …[, …])	Aggregate fields in time.
`aggregate_fields_space`(R, metadata, space_window)	Upscale fields in space.
`clip_domain`(R, metadata[, extent])	Clip the field domain by geographical coordinates.
`square_domain`(R, metadata[, method, inverse])	Either pad or crop a field to obtain a square domain.

Interface module for different FFT methods.

`get_numpy`(shape[, fftn_shape])
`get_scipy`(shape[, fftn_shape])
`get_pyfftw`(shape[, fftn_shape, n_threads])

Image processing routines for pysteps.

`shitomasi_detection`(input_image[, …])	Interface to the OpenCV Shi-Tomasi features detection method to detect corners in an image.
`morph_opening`(input_image, thr, n)	Filter out small scale noise on the image by applying a binary morphological opening, that is, erosion followed by dilation.

Interpolation routines for pysteps.

rbfinterp2d(coord, input_array, xgrid, ygrid) Fast 2-D grid interpolation of a sparse (multivariate) array using a radial basis function.

Utility methods for processing and analyzing precipitation fields in the Fourier domain.

`corrcoef`(X, Y, shape[, use_full_fft])	Compute the correlation coefficient between two-dimensional arrays in the spectral domain.
`mean`(X, shape)	Compute the mean value of a two-dimensional array in the spectral domain.
`rapsd`(Z[, fft_method, return_freq, d, normalize])	Compute radially averaged power spectral density (RAPSD) from the given 2D input field.
`remove_rain_norain_discontinuity`(R)	Function to remove the rain/no-rain discontinuity.
`std`(X, shape[, use_full_fft])	Compute the standard deviation of a two-dimensional array in the spectral domain.

Implementations of window functions for computing of the FFT.

`compute_mask_window_function`(mask, func, …)	Compute window function for a two-dimensional area defined by a non-rectangular mask.
`compute_window_function`(m, n, func, \\kwargs)	Compute window function for a two-dimensional rectangular region.

Methods for transforming data values.

`boxcox_transform`(R[, metadata, Lambda, …])	The one-parameter Box-Cox transformation.
`dB_transform`(R[, metadata, threshold, …])	Methods to transform precipitation intensities to/from dB units.
`NQ_transform`(R[, metadata, inverse])	The normal quantile transformation as in Bogner et al (2012).
`sqrt_transform`(R[, metadata, inverse])	Square-root transform.