def test_detectpeak2(filename, node):
""" Test detectpeak implemented in Python
Returns
-------
status: str
"OK" or "ERROR"
"""
data = plot_imu.imu_load(filename)
datanode = plot_imu.imu_extract(data, node, sensor_type=TYP_SENS)
timea = np.transpose([datanode['time'], datanode['time'], datanode['time']])
sens = np.transpose([datanode['X'], datanode['Y'], datanode['Z']])
print len(timea), len(sens), len(datanode['X'])
sens_norm = algo.compute_norm(sens)
sens_filt = algo.moving_average(sens_norm, WINDOW_SIZE)
#index_peak = algo.search_maxpeak(sens_filt)
[time_peak, sens_peak] = algo.threshold_under_signal(timea,
sens_filt, THRESHOLD)
print "Step numbers=", len(sens_peak)
plt.figure()
plt.plot(timea[:, 0], sens[:, 0:3])
plt.title(TYP_SENS + node)
plt.legend(('x', 'y', 'z'), bbox_to_anchor=(0, 1, 1, 0),
ncol=2, loc=3, borderaxespad=0.)
plt.figure()
plt.title("Peak detection " + node)
plt.plot(timea[:, 0], sens_norm)
plt.plot(timea[:, 0], sens_filt)
plt.plot(time_peak, sens_peak, "o")
return "OK"
def test3():
""" Test3
"""
from sensbiotk.io import iofox_deprec as fox
import pylab as py
print "Test Calibration"
[_, acc, _, _] = fox.load_foximu_csvfile(FILETEST)
norm_acc = algo.compute_norm(acc)
py.plot(norm_acc)
py.show()
return
def test_pedometer():
""" Test pedometer implemented in Python
Returns
-------
status: str
"OK" or "ERROR"
"""
from sensbiotk.io import iofox
from sensbiotk.algorithms import basic as algo
import pylab as py
[timea, acc] = iofox.load_foxacc_csvfile("./data/walk1_acc.csv")
[timea, acc] = algo.cut_signal(timea, acc, 12.0, 29.0)
acc_norm = algo.compute_norm(acc)
#acc_filt = algo.lowpass_filter(acc_norm, 2.0, 100.0)
#acc_filt = algo.lowpass_filter2(acc_norm, 2.0, 200.0)
acc_filt = algo.moving_average(acc_norm, 30)
#acc_filt = algo.moving_average2(acc_norm, 50)
index_peak = algo.search_maxpeak(acc_filt)
[time_peak, acc_peak] = algo.threshold_signal(timea[index_peak],
acc_filt[index_peak], 11.0)
print "Step numbers=", len(acc_peak)
py.figure()
py.plot(timea[:, 0], acc[:, 0:3])
py.title("Walking 1 Accelerations")
py.legend(('x', 'y', 'z'),
bbox_to_anchor=(0, 1, 1, 0),
ncol=2,
loc=3,
borderaxespad=0.)
py.figure()
py.title("Walking 1 Results")
py.plot(timea[:, 0], acc_norm)
py.plot(timea[:, 0], acc_filt)
py.plot(time_peak, acc_peak, "o")
py.figure()
py.plot(np.diff(acc_filt))
py.plot(np.diff(np.sign(np.diff(acc_filt)) < 0))
py.show()
return "OK"
def test_detectpeak(filename, node):
""" Test detectpeak implemented in Python
Returns
-------
status: str
"OK" or "ERROR"
"""
data = plot_imu.imu_load(filename)
datanode = plot_imu.imu_extract(data, node, sensor_type=TYP_SENS)
timea = np.transpose(
[datanode['time'], datanode['time'], datanode['time']])
sens = np.transpose([datanode['X'], datanode['Y'], datanode['Z']])
print len(timea), len(sens), len(datanode['X'])
sens_norm = algo.compute_norm(sens)
sens_filt = algo.moving_average(sens_norm, WINDOW_SIZE)
index_peak = algo.search_maxpeak(sens_filt)
[time_peak,
sens_peak] = algo.threshold_signal(timea[index_peak],
sens_filt[index_peak], THRESHOLD)
print "Step numbers=", len(sens_peak)
plt.figure()
plt.plot(timea[:, 0], sens[:, 0:3])
plt.title(TYP_SENS + node)
plt.legend(('x', 'y', 'z'),
bbox_to_anchor=(0, 1, 1, 0),
ncol=2,
loc=3,
borderaxespad=0.)
plt.figure()
plt.title("Peak detection " + node)
plt.plot(timea[:, 0], sens_norm)
plt.plot(timea[:, 0], sens_filt)
plt.plot(time_peak, sens_peak, "o")
#plt.figure()
#plt.plot(np.diff(sens_filt))
#plt.plot(np.diff(np.sign(np.diff(sens_filt)) < 0))
return "OK"
def compute_simple(data):
""" Compute IMU accelerometer calibration parameters
Parameters :
------------
data : numpy array of float
data sensor in motion containing all the acquisition
on three axis
Returns
-------
offset : numpy array of float (dim=3)
offset parameter
scale : numpy array of float (dim=3)
scale parameters
"""
offset = [0, 0, 0]
norm_acc = algo.compute_norm(data)
val_scale = scale_fit_norm(norm_acc, 9.81)
scale = [val_scale, val_scale, val_scale]
return scale, offset
def test_freefall():
""" Test freefall detector implemented in Python
Returns
-------
status: str
"OK" or "ERROR"
"""
from sensbiotk.io import iofox
from sensbiotk.algorithms import basic as algo
[timea, acc] = iofox.load_foxacc_csvfile("./data/freefall1_acc.csv")
acc_norm = algo.compute_norm(acc)
[state_freefall] = threshold_inf(acc_norm, 1.5)
[state_freefall] = threshold_time(timea[:, 0], state_freefall, 0.1)
[state_inactive] = search_state(timea[:, 0], acc_norm, 10.0, 5.0)
[state_inactive] = threshold_time(timea[:, 0], state_inactive, 0.1)
py.figure()
py.plot(timea[:, 0], acc[:, 0:3])
py.title("FreeFall Accelerations")
py.legend(('x', 'y', 'z'),
bbox_to_anchor=(0, 1, 1, 0),
ncol=2,
loc=3,
borderaxespad=0.)
py.figure()
py.title("FreeFall detection Results (Python)")
py.plot(acc_norm)
py.plot(state_freefall)
py.plot(state_inactive * 5)
return "OK"