wradlib.classify.ClassifyMethods

class wradlib.classify.ClassifyMethods(obj)

Bases: XarrayMethods

wradlib xarray SubAccessor methods for Classify.

filter_gabella(**kwargs)

Clutter identification filter developed by [Gabella et al., 2002].

This is a two-part identification algorithm using echo continuity and minimum echo area to distinguish between meteorological (rain) and non- meteorological echos (ground clutter etc.)

Parameters:

obj (xarray.DataArray)

Keyword Arguments:

• wsize (int) – Size of the window surrounding the central pixel

• thrsnorain (float) –

• tr1 (float) –

• n_p (int) –

• tr2 (float) –

• rm_nans (bool) – True replaces nans with Inf False takes nans into acount

• radial (bool) – True to use radial information only in filter_gabella_a.

• cartesian (bool) – True if cartesian data are used, polar assumed if False.

Returns:

output (xarray.DataArray) – boolean array with pixels identified as clutter set to True.

See also

filter_gabella_a, filter_gabella_b

Examples

See Clutter detection using the Gabella approach.

filter_gabella_a(**kwargs)

First part of the Gabella filter looking for large reflectivity gradients.

This function checks for each pixel in img how many pixels surrounding it in a window of wsize are by tr1 smaller than the central pixel.

Parameters:

obj (xarray.DataArray) – radar image to which the filter is to be applied

Keyword Arguments:

• wsize (int) – Size of the window surrounding the central pixel

• tr1 (float) – Threshold value

• cartesian (bool) – Specify if the input grid is Cartesian or polar

• radial (bool) – Specify if only radial information should be used

Returns:

out (xarray.DataArray) – an array with the same shape as img, containing the filter’s results.

See also

filter_gabella, filter_gabella_b

Examples

See Clutter detection using the Gabella approach.

filter_gabella_b(**kwargs)

Second part of the Gabella filter comparing area to circumference of contiguous echo regions.

Parameters:

obj (xarray.DataArray)

Keyword Arguments:

thrs (float) – Threshold below which the field values will be considered as no rain

Returns:

out (xarray.DataArray) – contains in each pixel the ratio between area and circumference of the meteorological echo it is assigned to or 0 for non precipitation pixels.

See also

filter_gabella, filter_gabella_a

Examples

See Clutter detection using the Gabella approach.

histo_cut(**kwargs)

Histogram based clutter identification.

This identification algorithm uses the histogram of temporal accumulated rainfall. It iteratively detects classes whose frequency falls below a specified percentage (1% by default) of the frequency of the class with the biggest frequency and remove the values from the dataset until the changes from iteration to iteration falls below a threshold. This algorithm is able to detect static clutter as well as shadings.

The tresholds for the upper frequency (clutter) and the lower frequency (shading) can be parameterized by the respective kwargs, upper_frequency/lower_frequency.

It is suggested to choose a representative time periode for the input precipitation accumulation. The recommended time period should cover one year.

Parameters:

obj (xarray.DataArray) – spatial array containing rain accumulation

Keyword Arguments:

• upper_frequency (float) – Upper frequency percentage for clutter detection, defaults to 0.01.

• lower_frequency (float) – Lower frequency percentage for shading detection, defaults to 0.01.

Returns:

output (xarray.DataArray) – uint8 array with pixels identified as clutter set to 1 and shadings set to 2. Remaining pixels set to 0. Users strictly relying on a boolean mask might have to explicitely cast to boolean (adding .astype(np.bool) on the return).

Examples

See Heuristic clutter detection based on distribution properties ("histo cut").

classify_echo_fuzzy(dat, **kwargs)

Fuzzy echo classification and clutter identification based on polarimetric moments.

The implementation is based on [Vulpiani et al., 2012]. At the moment, it only distinguishes between meteorological and non-meteorological echos.

For Clutter Phase Alignment (CPA) see [Hubbert et al., 2009a] and [Hubbert et al., 2009b]

For each decision variable and radar bin, the algorithm uses trapezoidal functions in order to define the membership to the non-meteorological echo class. Based on pre-defined weights, a linear combination of the different degrees of membership is computed. The echo is assumed to be non-meteorological in case the linear combination exceeds a threshold.

At the moment, the following decision variables are considered:

• Texture of differential reflectivity (zdr) (mandatory)

• Texture of correlation coefficient (rho) (mandatory)

• Texture of differential propagation phase (phidp) (mandatory)

• Doppler velocity (dop) (mandatory)

• Static clutter map (map) (mandatory)

• Correlation coefficient (rho2) (additional)

• Depolarization Ratio (dr), computed from correlation coefficient & differential reflectivity (additional)

• clutter phase alignment (cpa) (additional)

Parameters:

• obj (xarray.Dataset)

• dat (dict) – Mapping of moment names.

Keyword Arguments:

• weights (dict) – dictionary of floats. Defines the weights of the decision variables. Default is: zdr: 0.4, rho: 0.4, phi: 0.1, dop: 0.1, map: 0.5, rho2: 0.4, dr: 0.4, cpa: 0.4.

• trpz (dict) – dictionary of lists of floats. Contains the arguments of the trapezoidal membership functions for each decision variable. Default is: zdr: [0.7, 1.0, 9999, 9999], rho: [0.1, 0.15, 9999, 9999], phi: [15, 20, 10000, 10000], dop: [-0.2, -0.1, 0.1, 0.2], map: [1, 1, 9999, 9999], rho2: [-9999, -9999, 0.95, 0.98], dr: [-20, -12, 9999, 9999], cpa: [0.6, 0.9, 9999, 9999].

Returns:

• prob (xarray.DataArray) – DataArray indicating probability of meteorological echos based on the fuzzy classification.

• mask (xarray.DataArray) – DataArray indicating where all the polarimetric moments had missing values which could be used as an additional information criterion.

See also

texture, depolarization

filter_window_distance(**kwargs)

2d filter looking for large reflectivity gradients.

This function counts for each bin in img the percentage of surrounding bins in a window of half size fsize which are not tr1 smaller than the central bin. The window is defined using geometrical distance.

Parameters:

obj (xarray.DataArray | xarray.Dataset) – 2d polar data to which the filter is to be applied

Keyword Arguments:

• fsize (int) – Half-size [m] of the square window surrounding the central pixel

• tr1 (float) – Threshold value

Returns:

output (xarray.DataArray | xarray.Dataset) – an array with the same shape as img, containing the filter’s results.

See also

filter_gabella_a, filter_gabella_b

msf_index_indep(obs)

fuzzyfi(hmc_ds, msf_obs_mapping)

probability(weights)

Calculate probability of hmc-class for every data bin.

Parameters:

• data (xarray.Dataset) – Dataset containing the membership probability values.

• weights (numpy.ndarray) – Array of length (observables) containing the weights for each observable.

Returns:

out (xarray.DataArray) – Array containing weighted hmc-membership probabilities.

classify(threshold=0.0)

Calculate probability of hmc-class for every data bin.

Parameters:

data (numpy.ndarray) – Array which is of size (hmc-class, data.shape), containing the weighted hmc-membership probability values.

Keyword Arguments:

threshold (float) – Threshold value where probability is considered no precip, defaults to 0

Returns:

out (xarray.DataArray) – DataArray containing probability scores. No precip is added on the top.

classify([threshold])	Calculate probability of hmc-class for every data bin.
classify_echo_fuzzy(dat, **kwargs)	Fuzzy echo classification and clutter identification based on polarimetric moments.
filter_gabella(**kwargs)	Clutter identification filter developed by [Gabella et al., 2002].
filter_gabella_a(**kwargs)	First part of the Gabella filter looking for large reflectivity gradients.
filter_gabella_b(**kwargs)	Second part of the Gabella filter comparing area to circumference of contiguous echo regions.
filter_window_distance(**kwargs)	2d filter looking for large reflectivity gradients.
fuzzyfi(hmc_ds, msf_obs_mapping)
histo_cut(**kwargs)	Histogram based clutter identification.
msf_index_indep(obs)
probability(weights)	Calculate probability of hmc-class for every data bin.