def test_kdp_maesaka_linear_psidp(slope=0.002, maxiter=100):
radar = _make_linear_psidp_radar(slope=slope)
kdp_dict, phidpf_dict, phidpr_dict = kdp_proc.kdp_maesaka(
radar, maxiter=maxiter, check_outliers=False)
assert np.allclose(np.diff(kdp_dict['data'][0]), 0.0, atol=0.1)
assert np.allclose(kdp_dict['data'], 1000.0 * slope / 2.0, atol=0.1)
return
def test_kdp_maesaka_all_excluded(first_guess=0.01, maxiter=100):
radar = _make_linear_psidp_radar()
gatefilter = GateFilter(radar)
gatefilter.exclude_all()
kdp_dict, phidpf_dict, phidpr_dict = kdp_proc.kdp_maesaka(
radar, gatefilter=gatefilter, first_guess=first_guess, maxiter=maxiter,
check_outliers=False)
assert np.allclose(kdp_dict['data'][0], 0.0, atol=first_guess)
return
def test_compare_kdp_estimation_methods():
# Get profile of noisy psidp
prof_psidp = _make_real_psidp_radar()
# Maesaka method
kdp_mae, phidpf_mae, phidp_mae = kdp_maesaka(prof_psidp,
maxiter=1000,
check_outliers=False,
parallel=False)
# Vulpiani method (note windsize is just a guess here..)
kdp_vulp, phidp_vulp = kdp_vulpiani(prof_psidp,
windsize=2,
n_iter=20,
parallel=False,
band='X')
# Kalman filter method
kdp_schnee, kdp_std_schnee, phidp_schnee = kdp_schneebeli(prof_psidp,
parallel=False,
band='X')
# Create figure
fig = plt.figure(figsize=(10, 10))
plt.subplot(2, 1, 1)
plt.grid(True)
plt.title('Kdp estimation')
ranges = prof_psidp.range['data']
plt.plot(ranges, kdp_mae['data'][0])
plt.plot(ranges, kdp_vulp['data'][0])
plt.plot(ranges, kdp_schnee['data'][0])
plt.xlabel('Range [m]')
plt.ylabel('Kdp [deg/km]')
plt.legend(['Maesaka', 'Vulpiani', 'Schneebeli'], loc=0)
plt.subplot(2, 1, 2)
plt.grid(True)
plt.title('Reconstructed Phidp')
ranges = prof_psidp.range['data']
phidp_mae = 0.5 * (phidp_mae['data'][0] + phidp_mae['data'][0])
plt.plot(ranges, phidp_mae)
plt.plot(ranges, phidp_vulp['data'][0])
plt.plot(ranges, phidp_schnee['data'][0])
plt.plot(ranges, prof_psidp.fields['differential_phase']['data'][0])
plt.xlabel('Range [m]')
plt.ylabel('Diff. phase [deg]')
plt.legend(['Maesaka', 'Vulpiani', 'Schneebeli', 'Real Psidp'], loc=0)
try:
return fig
finally:
plt.close(fig)
def compare_kdp_estimation_methods():
# Get profile of noisy psidp
prof_psidp = _make_real_psidp_radar()
# Maesaka method
kdp_mae, phidpf_mae, phidp_mae = kdp_maesaka(prof_psidp, maxiter=1000,
check_outliers=False)
# Vulpiani method (note windsize is just a guess here..)
kdp_vulp, phidp_vulp = kdp_vulpiani(prof_psidp, windsize=30, n_iter=20,
band='X')
# Kalman filter method
kdp_schnee, kdp_std_schnee, phidp_schnee = kdp_schneebeli(prof_psidp,
band='X')
# Create figure
plt.figure(figsize=(10, 10))
plt.subplot(2, 1, 1)
plt.grid(True)
plt.title('Kdp estimation')
plt.hold(True)
ranges = prof_psidp.range['data']
plt.plot(ranges, kdp_mae['data'][0])
plt.plot(ranges, kdp_vulp['data'][0])
plt.plot(ranges, kdp_schnee['data'][0])
plt.xlabel('Range [m]')
plt.ylabel(r'Kdp [deg/km]')
plt.legend(['Maesaka', 'Vulpiani', 'Schneebeli'], loc=0)
plt.subplot(2, 1, 2)
plt.grid(True)
plt.title('Reconstructed Phidp')
plt.hold(True)
ranges = prof_psidp.range['data']
phidp_mae = 0.5 * (phidp_mae['data'][0] + phidp_mae['data'][0])
plt.plot(ranges, phidp_mae)
plt.plot(ranges, phidp_vulp['data'][0])
plt.plot(ranges, phidp_schnee['data'][0])
plt.plot(ranges, prof_psidp.fields['differential_phase']['data'][0])
plt.xlabel('Range [m]')
plt.ylabel(r'Diff. phase [deg]')
plt.legend(['Maesaka', 'Vulpiani', 'Schneebeli', 'Real Psidp'], loc=0)
# Display plot
plt.show()
try:
if not os.path.exists(FOLDER_RADAR + 'ZH_' + str(i) + '.npy'):
time = pycosmo.get_time_from_COSMO_filename(f)
# Radar
files = rad_db.query(date=[str(time)],
radar=RADAR,
res='L',
angle=1)
rad = pyart_wrapper.PyradCH(files[0][0].rstrip(), True)
rad.snr_threshold(5)
# rad.average(n_gates=6)
rad.correct_velocity()
KDP_rad, b, a = kdp_proc.kdp_maesaka(rad,
kdp_field='KDP',
psidp_field='PHIDP')
KDP_rad['data'][np.isnan(rad.get_field(0, 'PHIDP'))] = np.nan
KDP_rad = ma.filled(KDP_rad['data'], np.nan)
ZH_rad = ma.filled(rad.get_field(0, 'Z'), np.nan)
ZDR_rad = ma.filled(rad.get_field(0, 'ZDR'), np.nan)
RHOHV_rad = ma.filled(rad.get_field(0, 'RHO'), np.nan)
RVEL_rad = ma.filled(rad.get_field(0, 'V'), np.nan)
np.save(FOLDER_RADAR + 'ZH_' + str(i) + '.npy', ZH_rad)
np.save(FOLDER_RADAR + 'ZDR_' + str(i) + '.npy', ZDR_rad)
np.save(FOLDER_RADAR + 'KDP_' + str(i) + '.npy', KDP_rad)
np.save(FOLDER_RADAR + 'RHOHV_' + str(i) + '.npy', RHOHV_rad)
np.save(FOLDER_RADAR + 'RVEL_' + str(i) + '.npy', RVEL_rad)
