def test_stair_freeyaw(self):
"""
"""
fname = '/home/dave/Repositories/public/0_davidovitch/'
fname += 'freeyaw-ojf-wt-tests/data/calibrated/DataFrame/'
fname += '0212_run_064_9.0ms_dc1_freeyawplaying_stiffblades'
fname += '_coning_pwm1000_highrpm.h5'
res = pd.read_hdf(fname, 'table')
time = res.time.values
sps = 1.0 / np.diff(time).mean()
ff = Filters()
cutoff_hz = 1.0
order = 2
Wn = cutoff_hz*2.0/sps
B, A = sp.signal.butter(order, Wn, output='ba')
yawf = sp.signal.filtfilt(B, A, res.yaw_angle.values)
# YAW
plt.figure('yaw')
plt.plot(res.time, res.yaw_angle, 'r-')
plt.plot(res.time, yawf, 'b-')
B, A = sp.signal.butter(order, 1.0*2.0/sps, output='ba')
yawf2 = sp.signal.filtfilt(B, A, res.yaw_angle.values)
plt.plot(res.time, yawf2, 'k--')
# RPM
data = res.rpm.values
data_f = ff.butter_lowpass(sps, data, order=2, cutoff_hz=1.0)
plt.figure('rpm')
plt.plot(res.time, data, 'r-')
plt.plot(res.time, data_f, 'b-')
# filtered_x, N, delay = ff.fir(time, res.rpm, cutoff_hz=1.0,
# freq_trans_width=1.0, ripple_db=50.0)
# plt.plot(res.time, filtered_x, 'k--')
smooth_window = 2.0
ws = int(smooth_window*sps)
data_s = ff.smooth(res.rpm, window_len=ws, window='hanning')
NN = len(data_s) - len(time)
data_s = data_s[NN:]
# time_s = time[NN:]
plt.plot(time+(smooth_window/2.0), data_s, 'k--')
# and up again in order not to brake the plotting further down
time_down = np.arange(time[0], time[-1], 0.1)
data_f_down = sp.interpolate.griddata(time, data_f, time_down)
plt.plot(time_down, data_f_down, 'm-', alpha=0.7)
# # and upsampling again
# data = sp.interpolate.griddata(time_down, data_down, time)
slope, intercept, r_value, p_value, std_err \
= sp.stats.linregress(data_f_down, y=time_down)
def test_linregress(self):
"""
"""
fname = '/home/dave/Repositories/public/0_davidovitch/'
fname += 'freeyaw-ojf-wt-tests/data/calibrated/DataFrame/'
fname += '0212_run_064_9.0ms_dc1_freeyawplaying_stiffblades'
fname += '_coning_pwm1000_highrpm.h5'
res = pd.read_hdf(fname, 'table')
time = res.time.values
sps = 1.0 / np.diff(time).mean()
freq_down = 0.1
window = 4.0
ff = Filters()
data = res.rpm.values
data_f = ff.butter_lowpass(sps, data, order=2, cutoff_hz=1.0)
time_down = np.arange(time[0], time[-1], freq_down)
data_f_down = sp.interpolate.griddata(time, data_f, time_down)
regress = ff.linregress(time_down, data_f_down, int(window/freq_down))
diff = np.diff(data_f_down) / freq_down
plt.figure('rpm')
plt.plot(time, data, 'r-')
plt.plot(time_down, data_f_down, 'k--')
plt.twinx()
plt.plot(time_down[:-int(window/freq_down)], np.abs(regress[:,0]), 'b--')
plt.plot(time_down[:-1], np.abs(diff), 'g--')
plt.ylim([0, 5])
plt.grid()
data = res.yaw_angle.values
data_f = ff.butter_lowpass(sps, data, order=2, cutoff_hz=1.0)
data_f_down = sp.interpolate.griddata(time, data_f, time_down)
regress = ff.linregress(time_down, data_f_down, int(window/freq_down))
diff = np.diff(data_f_down) / freq_down
plt.figure('yaw')
plt.plot(time, data, 'r-')
plt.plot(time_down, data_f_down, 'k--')
plt.twinx()
plt.plot(time_down[:-int(window/freq_down)], np.abs(regress[:,0]), 'b--')
plt.plot(time_down[:-1], np.abs(diff), 'g--')
plt.ylim([0, 5])
plt.grid()
