def test(datasetname, N, et, rt, ur):
dataset = sio.loadmat(datasetname)
fs =float(dataset['fs'])
h = float(dataset['h'])
l = float(dataset['l'])
w = float(dataset['w'])
r = dataset['receivers']
s = dataset['sources']
data = dataset['data'].T[:N]
c = float(dataset['c'])
room = np.array([[0,0],[0,l],[w,l],[w,0]])
maxsize = np.sqrt(w**2+l**2+h**2) #m
max_delay = maxsize / float(c)
maxlength = int(2 * max_delay * fs)
t0 = time.time()
measurements = [measurement.MeasurementData(data=np.hstack(source_data).T,
receivers=r,
sources=s[i],
room_dimensions=(w,l,h),
c=c,
fs=fs)
for i,source_data in enumerate(data)]
echo_data = [echo.EchoData(m.find_echoes(crop=maxlength, interpolate=ur)) for m in measurements]
D = measurement.squared_distance_matrix(r, augmented=True)
S, E = zip(*[e.find_labels(D,threshold=et, parallel=True, verbose=args.verbose) for e in echo_data[:N]])
E = [e for e in E if len(e) > 0]
S = np.vstack(S)
distancedata = distance.DistanceData(S,E)
results = distancedata.find_images(r)
t1 = time.time()
if len(results) > 0:
imagedata = image.ImageSourceData(results, N, r, (w,l,h))
wall_points,vertices = imagedata.find_walls(threshold=rt, bestN=10)
if len(vertices) == 4:
i = find_matching(room, vertices)
error = np.sqrt(np.mean((vertices[i] - room)**2))
return (w*l*h, error, fs, t1-t0)
return (w*l*h, -1, fs, t1-t0)
maxsize = np.sqrt(w**2+l**2+h**2) #m
max_delay = maxsize / float(c)
maxlength = int(2 * max_delay * fs)
measurements = [measurement.MeasurementData(data=np.hstack(source_data).T,
receivers=r,
sources=s[i],
room_dimensions=(w,l,h),
c=c,
fs=fs)
for i,source_data in enumerate(data)]
echo_data = [echo.EchoData(m.find_echoes(crop=maxlength, interpolate=10)) for m in measurements]
D = measurement.squared_distance_matrix(r, augmented=True)
S, E = zip(*[e.find_labels(D,threshold=et, parallel=True, verbose=args.verbose) for e in echo_data[:N]])
E = [e for e in E if len(e) > 0]
S = np.vstack(S)
distancedata = distance.DistanceData(S,E)
results = distancedata.find_images(r)
if len(results) > 0:
imagedata = image.ImageSourceData(results, N, r, (w,l,h))
wall_points,vertices = imagedata.find_walls(threshold=rt, bestN=10)
im = np.vstack(imagedata.images)
plt.scatter(im[:,0], im[:,1])
wp = np.vstack(wall_points)
plt.scatter(wp[:,0], wp[:,1], color=(1,0,0,1))
plt.scatter(vertices[:,0], vertices[:,1], color=(0,1,0,1))