def localize_pysf(rel_sounds,
r_locs,
invert_alg='gps',
center_array=True,
use_sos_selection=False,
temperature=20.0):
'''
Use PySoundFinder to localize many sounds
Inputs:
rel_sounds (pandas DF): relative TOAs of sounds
columns are recorder names
rows are sound indices
r_locs (pandas DF): recorder locations
columns are x, y, and optionally, z
rows are recorder names as in rel_sounds
invert_alg: algorithm to invert matrix (see pysoundfinder)
center_array (bool): whether to center array coordinates
for computational stability prior to localizing sounds
use_sos_selection (bool): whether to select solution
based on sum of squares (original Sound Finder behavior)
temperature (float): temperature in Celsius
'''
# Localize sounds in rel_sounds
localized = []
for sound_num in rel_sounds.index:
delays = rel_sounds.T[sound_num]
localized.append(
pysf.localize_sound(positions=r_locs,
times=delays,
temp=temperature,
invert_alg='gps',
center=center_array,
pseudo=not use_sos_selection))
# Return dataframe in desired format
# TODO: Currently, reshape is necessary in 3D case to bring results
# into an (A, B) shaped matrix instead of an (A, B, 1) shaped matrix.
# This doesn't happen in 2D case. Worth inspecting. This try/except is a quick patch.
try:
df = pd.DataFrame(np.array(localized).reshape(rel_sounds.shape),
index=rel_sounds.index)
except ValueError:
return localized
if df.shape[1] == 4:
df.columns = ['x', 'y', 'z', 'r']
elif df.shape[1] == 3:
df.columns = ['x', 'y', 'r']
else:
print('Warning: results are not expected dimension (3 or 4 columns)')
return df
def test_localize_3d_grid(simulated_times_df):
'''
Test pysf.localize_sound for a 3d grid of points
on a 30m^2 x 16m high plot. Asserts accuracy to
4 decimals in each coordinate.
Note: sounds with one component exactly at 0
aren't localized well for 4 recorders perfectly
at the corners of a 30x30 plot. This is a highly
unlikely situation, though; even a 0.01m
offset is enough for near perfect localization.
Also note that if all of the recorders are on
the same plane, 3D localization fails frequently.
Even a +- 0.2m difference in recorder heights is
enough for highly accurate localization.
'''
recorder_list = [(0, 0, 3), (0, 30, 3.2), (30, 0, 3.3), (30, 30, 3.4)]
temp_c = 20.0
grid_points = [(x, y, z) for x in np.arange(0.01, 30, 2)
for y in np.arange(0.01, 30, 2)
for z in np.arange(0.01, 15, 2)]
failed_list = []
for point in grid_points:
true_position = point
positions, times, temps = simulated_times_df(sound=true_position,
recorders=recorder_list,
temp=temp_c)
[x, y, z, s] = pysf.localize_sound(positions, times, temps.temp)
est_position = (x[0], y[0], z[0])
try:
npt.assert_almost_equal(est_position, true_position, decimal=4)
except AssertionError:
failed_list.append((true_position, est_position))
# Print information about the failures if the failed_list contains anything
for i, failure in enumerate(failed_list):
pass
print("Failure {}:".format(i + 1))
print(" True position: {} ".format(failure[0]))
print(" Estimated position: {} ".format(failure[1]))
print("Number failed: {}".format(len(failed_list)))
assert (len(failed_list) == 0)
def test_localize_3d(simulated_times_df):
'''
Test pysf.localize_sound() for sound at (4, 2, 20).
Asserts accuracy to 6 decimals in each coordinate.
'''
recorder_list = [(0, 0, 2), (0, 30, 3), (30, 0, 3), (30, 30, 3.0)]
true_position = (4, 2, 20)
temp_c = 20.0
positions, times, temps = simulated_times_df(sound=true_position,
recorders=recorder_list,
temp=temp_c)
[x, y, z, s] = pysf.localize_sound(positions, times, temps.temp)
est_position = [x[0], y[0], z[0]]
npt.assert_almost_equal(est_position, true_position, decimal=6)
def test_localizing_grid_with_6_recorders(simulated_times_df):
'''
Ensure that pysf can localize a grid of points
using 6 delays. Asserts accuracy to 6 decimals in each coordinate.
'''
recorder_list = [(0, 0), (0, 30), (30, 0), (30, 30), (40, 40), (-10, 5)]
temp_c = 18
grid_points = [(x, y) for x in range(-10, 40, 2)
for y in range(-10, 40, 2)]
failed_list = []
# Simulate localization of each point in the grid
for true_position in grid_points:
print(true_position)
positions, times, temps = simulated_times_df(sound=true_position,
recorders=recorder_list,
temp=temp_c)
[x, y, s] = pysf.localize_sound(positions, times, temps.temp)
est_position = [x[0], y[0]]
try:
npt.assert_almost_equal(est_position, true_position, decimal=6)
except AssertionError:
failed_list.append((true_position, est_position))
# Print information about the failures if the failed_list contains anything
for i, failure in enumerate(failed_list):
print("Failure {}:".format(i + 1))
print(" True position: {} ".format(failure[0]))
print(" Estimated position: {} ".format(failure[1]))
assert (len(failed_list) == 0)
def test_localize_2d_grid(simulated_times_df):
'''
Test pysf.localize_sound for a 2d grid of points
on and around a 30m^2 plot. Asserts accuracy to
6 decimals in each coordinate.
'''
recorder_list = [(0, 0), (0, 30), (30, 0), (30, 30)]
temp_c = 20.0
grid_points = [(x, y) for x in range(-10, 40, 2)
for y in range(-10, 40, 2)]
failed_list = []
for point in grid_points:
true_position = point
positions, times, temps = simulated_times_df(sound=true_position,
recorders=recorder_list,
temp=temp_c)
[x, y, s] = pysf.localize_sound(positions, times, temps.temp)
est_position = [x[0], y[0]]
try:
npt.assert_almost_equal(est_position, true_position, decimal=6)
except AssertionError:
failed_list.append((true_position, est_position))
# Print information about the failures if the failed_list contains anything
for i, failure in enumerate(failed_list):
print("Failure {}:".format(i + 1))
print(" True position: {} ".format(failure[0]))
print(" Estimated position: {} ".format(failure[1]))
assert (len(failed_list) == 0)