def test_beam_height_ft(self):
self.assertTrue(np.allclose(qual.beam_height_ft(np.array([100, 200]),
np.array([2.0])),
np.array([3.49053756, 6.98225089])))
self.assertTrue(np.allclose(qual.beam_height_ft(np.array([100, 200]),
np.deg2rad([2.0]), degrees=False),
np.array([3.49053756, 6.98225089])))
def ex_compose_by_height():
# just making sure that the plots immediately pop up
# pl.interactive(True)
# ---------------------------------------------------------------------------
# load the data for the first radar
# ---------------------------------------------------------------------------
path = os.path.dirname(__file__) + '/'
rad1 = np.loadtxt(path + 'data/polar_dBZ_tur.gz').ravel()
rad1coords = np.loadtxt(path + 'data/bin_coords_tur.gz')
center1 = rad1coords.mean(axis=0)
radius1 = 128000.
# ---------------------------------------------------------------------------
# calculate height field for the first radar
# ---------------------------------------------------------------------------
ranges1 = np.arange(128)[None, :] * 1000.
# for the time being assuming a sinusoidal elevation pattern
elevs1 = np.sin(np.deg2rad(np.arange(360)[:, None])) * 0.2 + 0.3
heights1 = qual.beam_height_ft(ranges1, elevs1).ravel()
# ---------------------------------------------------------------------------
# load the data for the second radar
# ---------------------------------------------------------------------------
rad2 = np.loadtxt(path + 'data/polar_dBZ_fbg.gz').ravel()
rad2coords = np.loadtxt(path + 'data/bin_coords_fbg.gz')
center2 = rad2coords.mean(axis=0)
radius2 = 128000.
# ---------------------------------------------------------------------------
# calculate the height field for the second radar
# ---------------------------------------------------------------------------
# here assuming it to be the same as for radar 1
heights2 = qual.beam_height_ft(ranges1, elevs1).ravel()
# ---------------------------------------------------------------------------
# set up the common grid
# ---------------------------------------------------------------------------
xc = np.concatenate((rad1coords[:, 0], rad2coords[:, 0]))
yc = np.concatenate((rad1coords[:, 1], rad2coords[:, 1]))
xmin = np.min(xc) - 5000
xmax = np.max(xc) + 5000
ymin = np.min(yc) - 5000
ymax = np.max(yc) + 5000
gridshape = (100, 100)
coords = np.meshgrid(np.linspace(xmin, xmax, gridshape[0]), np.linspace(ymin, ymax, gridshape[1]))
coords = np.vstack((coords[0].ravel(), coords[1].ravel())).transpose()
# ---------------------------------------------------------------------------
# transfer the first radar data to the grid
# ---------------------------------------------------------------------------
rad1_gridded = comp.togrid(rad1coords, coords, radius1, center1, rad1, ipol.Nearest)
heights1_gridded = comp.togrid(rad1coords, coords, radius1, center1, heights1, ipol.Nearest)
# ---------------------------------------------------------------------------
# transfer the second radar data to the grid
# ---------------------------------------------------------------------------
rad2_gridded = comp.togrid(rad2coords, coords, radius2, center2, rad2, ipol.Nearest)
heights2_gridded = comp.togrid(rad2coords, coords, radius2, center2, heights2, ipol.Nearest)
# ---------------------------------------------------------------------------
# combine radar data according to height (lower bin wins)
# ---------------------------------------------------------------------------
## radinfo = np.hstack((np.empty_like(rad1_gridded)*np.nan, rad1_gridded, rad2_gridded))
## heightinfo = np.hstack((np.ones_like(heights1_gridded)*1e10, heights1_gridded, heights2_gridded))
## select = np.nanargmin(heightinfo, axis=1)
## composite = radinfo[np.arange(select.shape[0]),select]
# second approach using the function
## composite = comp.compose_ko([rad1_gridded, rad2_gridded],[1./(heights1_gridded+0.001), 1./(heights2_gridded+0.001)])
# third approach using weighted averaging
composite = comp.compose_weighted([rad1_gridded, rad2_gridded],
[1. / (heights1_gridded + 0.001), 1. / (heights2_gridded + 0.001)])
# ---------------------------------------------------------------------------
# visualize the results
# ---------------------------------------------------------------------------
pl.figure()
pl.imshow(rad1_gridded.reshape(gridshape), interpolation='nearest', origin='lower')
pl.figure()
pl.imshow(rad2_gridded.reshape(gridshape), interpolation='nearest', origin='lower')
pl.figure()
pl.imshow(composite.reshape(gridshape), interpolation='nearest', origin='lower')
pl.figure()
pl.imshow(heights1_gridded.reshape(gridshape), interpolation='nearest', origin='lower')
pl.figure()
pl.imshow(heights2_gridded.reshape(gridshape), interpolation='nearest', origin='lower')
pl.show()
def ex_compose_by_height():
# just making sure that the plots immediately pop up
# pl.interactive(True)
# ---------------------------------------------------------------------------
# load the data for the first radar
# ---------------------------------------------------------------------------
path = os.path.dirname(__file__) + '/'
rad1 = np.loadtxt(path + 'data/polar_dBZ_tur.gz').ravel()
rad1coords = np.loadtxt(path + 'data/bin_coords_tur.gz')
center1 = rad1coords.mean(axis=0)
radius1 = 128000.
# ---------------------------------------------------------------------------
# calculate height field for the first radar
# ---------------------------------------------------------------------------
ranges1 = np.arange(128)[None, :] * 1000.
# for the time being assuming a sinusoidal elevation pattern
elevs1 = np.sin(np.deg2rad(np.arange(360)[:, None])) * 0.2 + 0.3
heights1 = qual.beam_height_ft(ranges1, elevs1).ravel()
# ---------------------------------------------------------------------------
# load the data for the second radar
# ---------------------------------------------------------------------------
rad2 = np.loadtxt(path + 'data/polar_dBZ_fbg.gz').ravel()
rad2coords = np.loadtxt(path + 'data/bin_coords_fbg.gz')
center2 = rad2coords.mean(axis=0)
radius2 = 128000.
# ---------------------------------------------------------------------------
# calculate the height field for the second radar
# ---------------------------------------------------------------------------
# here assuming it to be the same as for radar 1
heights2 = qual.beam_height_ft(ranges1, elevs1).ravel()
# ---------------------------------------------------------------------------
# set up the common grid
# ---------------------------------------------------------------------------
xc = np.concatenate((rad1coords[:, 0], rad2coords[:, 0]))
yc = np.concatenate((rad1coords[:, 1], rad2coords[:, 1]))
xmin = np.min(xc) - 5000
xmax = np.max(xc) + 5000
ymin = np.min(yc) - 5000
ymax = np.max(yc) + 5000
gridshape = (100, 100)
coords = np.meshgrid(np.linspace(xmin, xmax, gridshape[0]),
np.linspace(ymin, ymax, gridshape[1]))
coords = np.vstack((coords[0].ravel(), coords[1].ravel())).transpose()
# ---------------------------------------------------------------------------
# transfer the first radar data to the grid
# ---------------------------------------------------------------------------
rad1_gridded = comp.togrid(rad1coords, coords, radius1, center1, rad1,
ipol.Nearest)
heights1_gridded = comp.togrid(rad1coords, coords, radius1, center1,
heights1, ipol.Nearest)
# ---------------------------------------------------------------------------
# transfer the second radar data to the grid
# ---------------------------------------------------------------------------
rad2_gridded = comp.togrid(rad2coords, coords, radius2, center2, rad2,
ipol.Nearest)
heights2_gridded = comp.togrid(rad2coords, coords, radius2, center2,
heights2, ipol.Nearest)
# ---------------------------------------------------------------------------
# combine radar data according to height (lower bin wins)
# ---------------------------------------------------------------------------
## radinfo = np.hstack((np.empty_like(rad1_gridded)*np.nan, rad1_gridded, rad2_gridded))
## heightinfo = np.hstack((np.ones_like(heights1_gridded)*1e10, heights1_gridded, heights2_gridded))
## select = np.nanargmin(heightinfo, axis=1)
## composite = radinfo[np.arange(select.shape[0]),select]
# second approach using the function
## composite = comp.compose_ko([rad1_gridded, rad2_gridded],[1./(heights1_gridded+0.001), 1./(heights2_gridded+0.001)])
# third approach using weighted averaging
composite = comp.compose_weighted(
[rad1_gridded, rad2_gridded],
[1. / (heights1_gridded + 0.001), 1. / (heights2_gridded + 0.001)])
# ---------------------------------------------------------------------------
# visualize the results
# ---------------------------------------------------------------------------
pl.figure()
pl.imshow(rad1_gridded.reshape(gridshape),
interpolation='nearest',
origin='lower')
pl.figure()
pl.imshow(rad2_gridded.reshape(gridshape),
interpolation='nearest',
origin='lower')
pl.figure()
pl.imshow(composite.reshape(gridshape),
interpolation='nearest',
origin='lower')
pl.figure()
pl.imshow(heights1_gridded.reshape(gridshape),
interpolation='nearest',
origin='lower')
pl.figure()
pl.imshow(heights2_gridded.reshape(gridshape),
interpolation='nearest',
origin='lower')
pl.show()