def ingest_data(self, data, modeled=True, length_fit=False):
self.BM = BodyModel(data)
if length_fit:
self.BM.fit()
self.data = data
if modeled:
n = normalize_data({'kin': self.BM.data})['kin']
X = np.column_stack((n[key] for key in KL))
self.model_data = normalize_rows(X)
times = self.data['Time']
newtimes = np.linspace(times[0], times[-1], 1000)
self.times = times
self.newtimes = newtimes
floor = self.floor if self.floor else 0.5 * (np.mean(self.data['FootRight'].T[2]) + np.mean(self.data['FootLeft'].T[2]))
#if (floor == None): floor = 0.5 * (np.mean(self.data['FootRight'].T[2]) + np.mean(self.data['FootLeft'].T[2]))
vl = VL(self.BM.data, floor=floor)
modeled_vl = vl + VLm(self.model_data) if modeled else vl
subsampled_vl = UnivariateSpline(times, modeled_vl, s=0)(newtimes)
self.smoothed_VL = UnivariateSpline(newtimes, sav_gol.savgol_filter(subsampled_vl, 31, 3), s=0) # smooth pretty hard to stop wobbling, overest?
hl = HL(self.BM.data)
modeled_hl = hl + HLm(self.model_data) if modeled else hl
subsampled_hl = UnivariateSpline(times, modeled_hl, s=0)(newtimes)
self.smoothed_HL = UnivariateSpline(newtimes, sav_gol.savgol_filter(subsampled_hl, 31, 3), s=0) # smooth pretty hard to stop wobbling, overest?
aa = AA(self.BM.data)
modeled_aa = aa + AAm(self.model_data) if modeled else aa
subsampled_aa = UnivariateSpline(times, modeled_aa, s=0)(newtimes)
self.smoothed_AA = UnivariateSpline(newtimes, sav_gol.savgol_filter(subsampled_aa, 31, 3), s=0) # smooth pretty hard to stop wobbling, overest?
self.find_F()
# self.find_origins()
# self.find_ends()
self.find_V()
self.find_H()
self.find_D()
self.find_A()
def sg_filter_data(self):
window = 33
degree = 3
times = self.data.raw['Time']
newtimes = np.linspace(times[0], times[-1], 33*(times[-1] - times[0]))
for label in KIN_LABELS:
# if not 'Hand' in label and not 'Foot' in label:
# self.dampen_jitter(label)
filtered = []
for i in range(3):
temp = UnivariateSpline(times, self.data.raw[label].T[i], s=0)(newtimes)
filt = sav_gol.savgol_filter(temp, window, degree)
filt_spline = UnivariateSpline(newtimes, filt, s=0)
filtered.append(filt_spline(times))
self.data.raw[label] = np.column_stack((filtered[i] for i in range(3)))
self.smoothing = "SG"
def sg_filter_data(point):
window = 33
degree = 3
if "Time" not in point:
raise Exception("Need time to filter!")
times = point["Time"]
newtimes = np.linspace(times[0], times[-1], 33 * (times[-1] - times[0]))
outpoint = {}
for label in point:
if not label in data_model.KIN_TREE:
outpoint[label] = point[label]
else:
filtered = []
for i in range(3):
temp = UnivariateSpline(times, point[label].T[i], s=0)(newtimes)
filt = sav_gol.savgol_filter(temp, window, degree)
filt_spline = UnivariateSpline(newtimes, filt, s=0)
filtered.append(filt_spline(times))
outpoint[label] = np.column_stack((filtered[i] for i in range(3)))
return outpoint
