Clutter detection using the Gabella approach

[1]:
import matplotlib.pyplot as pl
import numpy as np
import wradlib.vis as vis
import wradlib.clutter as clutter
import wradlib.util as util
import warnings
warnings.filterwarnings('ignore')
try:
    get_ipython().magic("matplotlib inline")
except:
    pl.ion()
import numpy as np

Read the data

[2]:
filename = util.get_wradlib_data_file('misc/polar_dBZ_fbg.gz')
data = np.loadtxt(filename)

Apply filter

[3]:
clmap = clutter.filter_gabella(data,
                               wsize=5,
                               thrsnorain=0.,
                               tr1=6.,
                               n_p=8,
                               tr2=1.3)

Plot results

[4]:
fig = pl.figure(figsize=(12,8))
ax = fig.add_subplot(121)
ax, pm = vis.plot_ppi(data, ax=ax)
ax.set_title('Reflectivity')
ax = fig.add_subplot(122)
ax, pm = vis.plot_ppi(clmap, ax=ax)
ax.set_title('Cluttermap')
[4]:
Text(0.5, 1.0, 'Cluttermap')
../../_images/notebooks_classify_wradlib_clutter_gabella_example_8_1.png
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