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Clutter detection using the Gabella approach

Clutter detection using the Gabella approach#

[1]:

import matplotlib.pyplot as plt
import numpy as np
import wradlib as wrl
import warnings

warnings.filterwarnings("ignore")
try:
    get_ipython().run_line_magic("matplotlib inline")
except:
    plt.ion()
import numpy as np

/home/runner/micromamba/envs/wradlib-tests/lib/python3.11/site-packages/h5py/__init__.py:36: UserWarning: h5py is running against HDF5 1.14.3 when it was built against 1.14.2, this may cause problems
  _warn(("h5py is running against HDF5 {0} when it was built against {1}, "

Read the data#

[2]:

filename = wrl.util.get_wradlib_data_file("misc/polar_dBZ_fbg.gz")
data = np.loadtxt(filename)
data = wrl.georef.create_xarray_dataarray(data, rf=0.001).wrl.georef.georeference()
data

Downloading file 'misc/polar_dBZ_fbg.gz' from 'https://github.com/wradlib/wradlib-data/raw/pooch/data/misc/polar_dBZ_fbg.gz' to '/home/runner/work/wradlib/wradlib/wradlib-data'.

[2]:

<xarray.DataArray (azimuth: 360, range: 128)>
array([[  7.01,   8.28,   5.46, ..., -10.  , -10.  , -10.  ],
       [  7.01,   7.01,   7.01, ..., -10.  , -10.  , -10.  ],
       [  7.01,   7.67,   5.46, ..., -10.  , -10.  , -10.  ],
       ...,
       [  7.01,   8.28,   5.46, ...,  14.87,  17.76,  15.51],
       [  7.67,   7.67,   5.46, ...,   0.64,   8.83,   4.53],
       [  8.83,   7.67,   4.53, ...,  -8.99,  -4.17,  -8.99]])
Coordinates: (12/14)
  * range       (range) float64 500.0 1.5e+03 2.5e+03 ... 1.265e+05 1.275e+05
  * azimuth     (azimuth) float64 0.5 1.5 2.5 3.5 ... 356.5 357.5 358.5 359.5
    elevation   (azimuth) float64 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0
    longitude   float64 0.0
    latitude    float64 0.0
    altitude    float64 0.0
    ...          ...
    y           (azimuth, range) float64 500.0 1.5e+03 ... 1.265e+05 1.275e+05
    z           (azimuth, range) float64 0.0147 0.1323 0.3675 ... 940.8 955.7
    gr          (azimuth, range) float64 500.0 1.5e+03 ... 1.265e+05 1.275e+05
    rays        (azimuth, range) float64 0.5 0.5 0.5 0.5 ... 359.5 359.5 359.5
    bins        (azimuth, range) float64 500.0 1.5e+03 ... 1.265e+05 1.275e+05
    crs_wkt     int64 0

Apply filter#

[3]:

clmap = data.wrl.classify.filter_gabella(
    wsize=5, thrsnorain=0.0, tr1=6.0, n_p=8, tr2=1.3
)
clmap

[3]:

<xarray.DataArray 'filter_gabella' (azimuth: 360, range: 128)>
array([[False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       ...,
       [False, False, False, ...,  True,  True,  True],
       [False, False, False, ...,  True,  True,  True],
       [False, False, False, ..., False, False, False]])
Coordinates: (12/14)
  * range       (range) float64 500.0 1.5e+03 2.5e+03 ... 1.265e+05 1.275e+05
  * azimuth     (azimuth) float64 0.5 1.5 2.5 3.5 ... 356.5 357.5 358.5 359.5
    elevation   (azimuth) float64 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0
    longitude   float64 0.0
    latitude    float64 0.0
    altitude    float64 0.0
    ...          ...
    y           (azimuth, range) float64 500.0 1.5e+03 ... 1.265e+05 1.275e+05
    z           (azimuth, range) float64 0.0147 0.1323 0.3675 ... 940.8 955.7
    gr          (azimuth, range) float64 500.0 1.5e+03 ... 1.265e+05 1.275e+05
    rays        (azimuth, range) float64 0.5 0.5 0.5 0.5 ... 359.5 359.5 359.5
    bins        (azimuth, range) float64 500.0 1.5e+03 ... 1.265e+05 1.275e+05
    crs_wkt     int64 0

Plot results#

[4]:

fig = plt.figure(figsize=(12, 8))
ax1 = fig.add_subplot(121)
pm = data.wrl.vis.plot(ax=ax1)
ax1.set_title("Reflectivity")
ax2 = fig.add_subplot(122)
pm = clmap.wrl.vis.plot(ax=ax2)
ax2.set_title("Cluttermap")

[4]:

Text(0.5, 1.0, 'Cluttermap')
../../_images/notebooks_classify_clutter_gabella_8_1.png
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