# Path: wradlib/dp.py
# def process_raw_phidp_vulpiani(
#     phidp, dr, ndespeckle=5, winlen=7, niter=2, copy=False, **kwargs
# ):
# def unfold_phi_vulpiani(phidp, kdp, th=-20, winlen=7):
# def _fill_sweep(dat, kind="nan_to_num", fill_value=0.0):
# def kdp_from_phidp(
#     phidp, winlen=7, dr=1.0, method="lanczos_conv", skipna=True, **kwargs
# ):
# def unfold_phi(phidp, rho, width=5, copy=False):
# def unfold_phi_naive(phidp, rho, width=5, copy=False):
# def linear_despeckle(data, ndespeckle=3, copy=False):
# def texture(data):
# def depolarization(zdr, rho):
# 
# Path: wradlib/util.py
# def maximum_intensity_projection(*args, **kwargs):
#     def __init__(self, name):
#     def __getattr__(self, name):
# def import_optional(module):
# def _shape_to_size(shape):
# def from_to(tstart, tend, tdelta):
# def _idvalid(data, isinvalid=None, minval=None, maxval=None):
# def meshgrid_n(*arrs):
# def gridaspoints(*arrs):
# def issequence(x):
# def trapezoid(data, x1, x2, x3, x4):
# def filter_window_polar(img, wsize, fun, rscale, random=False):
# def prob_round(x, prec=0):
# def filter_window_cartesian(img, wsize, fun, scale, **kwargs):
# def roll2d_polar(img, shift=1, axis=0):
#     def __repr__(self):
#     def utcoffset(self, dtime):
#     def tzname(self, dtime):
#     def dst(self, dtime):
# def half_power_radius(r, bwhalf):
# def get_raster_origin(coords):
# def find_bbox_indices(coords, bbox):
# def has_geos():
# def get_wradlib_data_path():
# def get_wradlib_data_file(relfile):
# def calculate_polynomial(data, w):
# def medfilt_along_axis(x, n, axis=-1):
# def gradient_along_axis(x):
# def gradient_from_smoothed(x, n=5):
# def center_to_edge(centers):
# def _pad_array(data, pad, mode="reflect", **kwargs):
# def _rolling_dim(data, window):
# def _linregress_1d(rhs, method="lstsq"):
# def _nan_lstsq(y, x):
# def _nan_matrix_inv(y, x):
# def _matrix_inv(x, y):
# def _nan_cov(x, y):
# def _cov(x, y):
# def _lanczos_differentiator(winlen):
# def derivate(data, winlen=7, method="lanczos_conv", skipna=False, **kwargs):
# def despeckle(data, n=3, copy=False):
# def show_versions(file=None):
# def _open_file(name):
# def has_import(module):
# class OptionalModuleStub:
# class UTC(dt.tzinfo):


import numpy as np
from scipy import ndimage
from wradlib import dp, util
        an array with the same shape as ``img``, containing the
        filter's results.

    See Also
    --------
    :func:`~wradlib.clutter.filter_gabella_a` - Original version of the filter

    :func:`~wradlib.clutter.filter_gabella_b` - filter using a echo area
    """
    ascale = 2 * np.pi / img.shape[0]
    count = np.ones(img.shape, dtype=int)
    similar = np.zeros(img.shape, dtype=float)
    good = np.ones(img.shape, dtype=float)
    valid = ~np.isnan(img)
    hole = np.sum(~valid) > 0
    nr = int(round(fsize / rscale))
    range_shift = range(-nr, nr + 1)
    r = np.arange(img.shape[1]) * rscale + rscale / 2
    adist = r * ascale
    na = np.around(fsize / adist).astype(int)
    max_na = img.shape[0] / 10
    sa = 0
    while sa < max_na:
        imax = np.where(na >= sa)[0][-1] + 1
        refa1 = util.roll2d_polar(img, sa, axis=0)
        refa2 = util.roll2d_polar(img, -sa, axis=0)
        for sr in range_shift:
            refr1 = util.roll2d_polar(refa1, sr, axis=1)
            similar[:, 0:imax] += img[:, 0:imax] - refr1[:, 0:imax] < tr1
            if sa > 0:

# Path: wradlib/dp.py
# def process_raw_phidp_vulpiani(
#     phidp, dr, ndespeckle=5, winlen=7, niter=2, copy=False, **kwargs
# ):
# def unfold_phi_vulpiani(phidp, kdp, th=-20, winlen=7):
# def _fill_sweep(dat, kind="nan_to_num", fill_value=0.0):
# def kdp_from_phidp(
#     phidp, winlen=7, dr=1.0, method="lanczos_conv", skipna=True, **kwargs
# ):
# def unfold_phi(phidp, rho, width=5, copy=False):
# def unfold_phi_naive(phidp, rho, width=5, copy=False):
# def linear_despeckle(data, ndespeckle=3, copy=False):
# def texture(data):
# def depolarization(zdr, rho):
# 
# Path: wradlib/util.py
# def maximum_intensity_projection(*args, **kwargs):
#     def __init__(self, name):
#     def __getattr__(self, name):
# def import_optional(module):
# def _shape_to_size(shape):
# def from_to(tstart, tend, tdelta):
# def _idvalid(data, isinvalid=None, minval=None, maxval=None):
# def meshgrid_n(*arrs):
# def gridaspoints(*arrs):
# def issequence(x):
# def trapezoid(data, x1, x2, x3, x4):
# def filter_window_polar(img, wsize, fun, rscale, random=False):
# def prob_round(x, prec=0):
# def filter_window_cartesian(img, wsize, fun, scale, **kwargs):
# def roll2d_polar(img, shift=1, axis=0):
#     def __repr__(self):
#     def utcoffset(self, dtime):
#     def tzname(self, dtime):
#     def dst(self, dtime):
# def half_power_radius(r, bwhalf):
# def get_raster_origin(coords):
# def find_bbox_indices(coords, bbox):
# def has_geos():
# def get_wradlib_data_path():
# def get_wradlib_data_file(relfile):
# def calculate_polynomial(data, w):
# def medfilt_along_axis(x, n, axis=-1):
# def gradient_along_axis(x):
# def gradient_from_smoothed(x, n=5):
# def center_to_edge(centers):
# def _pad_array(data, pad, mode="reflect", **kwargs):
# def _rolling_dim(data, window):
# def _linregress_1d(rhs, method="lstsq"):
# def _nan_lstsq(y, x):
# def _nan_matrix_inv(y, x):
# def _matrix_inv(x, y):
# def _nan_cov(x, y):
# def _cov(x, y):
# def _lanczos_differentiator(winlen):
# def derivate(data, winlen=7, method="lanczos_conv", skipna=False, **kwargs):
# def despeckle(data, n=3, copy=False):
# def show_versions(file=None):
# def _open_file(name):
# def has_import(module):
# class OptionalModuleStub:
# class UTC(dt.tzinfo):


# Path: wradlib/clutter.py
import numpy as np
from scipy import ndimage
from wradlib import dp, util

        an array with the same shape as ``img``, containing the
        filter's results.

    See Also
    --------
    :func:`~wradlib.clutter.filter_gabella_a` - Original version of the filter

    :func:`~wradlib.clutter.filter_gabella_b` - filter using a echo area
    """
    ascale = 2 * np.pi / img.shape[0]
    count = np.ones(img.shape, dtype=int)
    similar = np.zeros(img.shape, dtype=float)
    good = np.ones(img.shape, dtype=float)
    valid = ~np.isnan(img)
    hole = np.sum(~valid) > 0
    nr = int(round(fsize / rscale))
    range_shift = range(-nr, nr + 1)
    r = np.arange(img.shape[1]) * rscale + rscale / 2
    adist = r * ascale
    na = np.around(fsize / adist).astype(int)
    max_na = img.shape[0] / 10
    sa = 0
    while sa < max_na:
        imax = np.where(na >= sa)[0][-1] + 1
        refa1 = util.roll2d_polar(img, sa, axis=0)
        refa2 = util.roll2d_polar(img, -sa, axis=0)
        for sr in range_shift:
            refr1 = util.roll2d_polar(refa1, sr, axis=1)
            similar[:, 0:imax] += img[:, 0:imax] - refr1[:, 0:imax] < tr1
            if sa > 0:

                refr2 = util.roll2d_polar(refa2, sr, axis=1)