def filter_data(self, low_f=.05, high_f=15, filter_type="bandpass"):
print("\nFiltering data...")
# Left ankle
self.la.x_filt = filter_signal(data=self.la.x,
filter_type=filter_type,
low_f=low_f,
high_f=high_f,
sample_f=self.la.sample_rate)
self.la.y_filt = filter_signal(data=self.la.y,
filter_type=filter_type,
low_f=low_f,
high_f=high_f,
sample_f=self.la.sample_rate)
self.la.z_filt = filter_signal(data=self.la.z,
filter_type=filter_type,
low_f=low_f,
high_f=high_f,
sample_f=self.la.sample_rate)
# Left wrist
self.lw.x_filt = filter_signal(data=self.lw.x,
filter_type=filter_type,
low_f=low_f,
high_f=high_f,
sample_f=self.lw.sample_rate)
self.lw.y_filt = filter_signal(data=self.lw.y,
filter_type=filter_type,
low_f=low_f,
high_f=high_f,
sample_f=self.lw.sample_rate)
self.lw.z_filt = filter_signal(data=self.lw.z,
filter_type=filter_type,
low_f=low_f,
high_f=high_f,
sample_f=self.lw.sample_rate)
# Right wrist
self.rw.x_filt = filter_signal(data=self.rw.x,
filter_type=filter_type,
low_f=low_f,
high_f=high_f,
sample_f=self.rw.sample_rate)
self.rw.y_filt = filter_signal(data=self.rw.y,
filter_type=filter_type,
low_f=low_f,
high_f=high_f,
sample_f=self.rw.sample_rate)
self.rw.z_filt = filter_signal(data=self.rw.z,
filter_type=filter_type,
low_f=low_f,
high_f=high_f,
sample_f=self.rw.sample_rate)
print("Complete.")
def filter_triaxial(x, y, z, fs=75, cutoff=10, epoch_len=1):
print(
"\nFiltering data with {}Hz lowpass filtering and epoching it into {}-s epochs..."
.format(cutoff, epoch_len))
x_filt = filter_signal(data=x,
filter_type='lowpass',
sample_f=fs,
low_f=cutoff,
filter_order=3)
y_filt = filter_signal(data=y,
filter_type='lowpass',
sample_f=fs,
low_f=cutoff,
filter_order=3)
z_filt = filter_signal(data=z,
filter_type='lowpass',
sample_f=fs,
low_f=cutoff,
filter_order=3)
vm = np.sqrt(
np.square(np.array([x_filt, y_filt, z_filt])).sum(axis=0)) - 1
vm[vm < 0] = 0
avm = [
np.mean(vm[i:i + int(fs * epoch_len)])
for i in range(0, len(vm), int(epoch_len * fs))
]
svm = [
np.sum(vm[i:i + int(fs * epoch_len)])
for i in range(0, len(vm), int(epoch_len * fs))
]
return vm, avm, svm
def remove_gravity(self):
if self.rem_gravity:
print("-Filtering data to remove gravity...")
self.lw["x_filt"] = filter_signal(data=self.lw["x"], filter_type="highpass", high_f=0.1, filter_order=2,
sample_f=self.sample_rate)
self.lw["y_filt"] = filter_signal(data=self.lw["y"], filter_type="highpass", high_f=0.1, filter_order=2,
sample_f=self.sample_rate)
self.lw["z_filt"] = filter_signal(data=self.lw["z"], filter_type="highpass", high_f=0.1, filter_order=2,
sample_f=self.sample_rate)
self.la["x_filt"] = filter_signal(data=self.la["x"], filter_type="highpass", high_f=0.1, filter_order=2,
sample_f=self.sample_rate)
self.la["y_filt"] = filter_signal(data=self.la["y"], filter_type="highpass", high_f=0.1, filter_order=2,
sample_f=self.sample_rate)
self.la["z_filt"] = filter_signal(data=self.la["z"], filter_type="highpass", high_f=0.1, filter_order=2,
sample_f=self.sample_rate)
def remove_high_freq(self):
if self.rem_highf:
print("\nFiltering data to remove high-frequency noise...")
self.lw["x_filt"] = filter_signal(data=self.lw["x"], filter_type="lowpass", low_f=5, filter_order=2,
sample_f=self.sample_rate)
self.lw["y_filt"] = filter_signal(data=self.lw["y"], filter_type="lowpass", low_f=5, filter_order=2,
sample_f=self.sample_rate)
self.lw["z_filt"] = filter_signal(data=self.lw["z"], filter_type="lowpass", low_f=5, filter_order=2,
sample_f=self.sample_rate)
self.la["x_filt"] = filter_signal(data=self.la["x"], filter_type="lowpass", low_f=5, filter_order=2,
sample_f=self.sample_rate)
self.la["y_filt"] = filter_signal(data=self.la["y"], filter_type="lowpass", low_f=5, filter_order=2,
sample_f=self.sample_rate)
self.la["z_filt"] = filter_signal(data=self.la["z"], filter_type="lowpass", low_f=5, filter_order=2,
sample_f=self.sample_rate)
def filter_accels(self, low_f=0.05, high_f=15, filter_type="lowpass"):
print("\nFiltering data...")
# Left ankle
if self.df_la is not None:
print("-Left ankle")
self.df_la["x_filt"] = filter_signal(data=self.df_la["x"], filter_type=filter_type, filter_order=3,
low_f=low_f, high_f=high_f, sample_f=self.fs)
self.df_la["y_filt"] = filter_signal(data=self.df_la["y"], filter_type=filter_type, filter_order=3,
low_f=low_f, high_f=high_f, sample_f=self.fs)
self.df_la["z_filt"] = filter_signal(data=self.df_la["z"], filter_type=filter_type, filter_order=3,
low_f=low_f, high_f=high_f, sample_f=self.fs)
# Right ankle
if self.df_ra is not None:
print("-Right ankle")
self.df_ra["x_filt"] = filter_signal(data=self.df_ra["x"], filter_type=filter_type, filter_order=3,
low_f=low_f, high_f=high_f, sample_f=self.fs)
self.df_ra["y_filt"] = filter_signal(data=self.df_ra["y"], filter_type=filter_type, filter_order=3,
low_f=low_f, high_f=high_f, sample_f=self.fs)
self.df_ra["z_filt"] = filter_signal(data=self.df_ra["z"], filter_type=filter_type, filter_order=3,
low_f=low_f, high_f=high_f, sample_f=self.fs)
# Left wrist
if self.df_lw is not None:
print("-Left wrist")
self.df_lw["x_filt"] = filter_signal(data=self.df_lw["x"], filter_type=filter_type, filter_order=3,
low_f=low_f, high_f=high_f, sample_f=self.fs)
self.df_lw["y_filt"] = filter_signal(data=self.df_lw["y"], filter_type=filter_type, filter_order=3,
low_f=low_f, high_f=high_f, sample_f=self.fs)
self.df_lw["z_filt"] = filter_signal(data=self.df_lw["z"], filter_type=filter_type, filter_order=3,
low_f=low_f, high_f=high_f, sample_f=self.fs)
# Right wrist 1
if self.df_rw_d is not None:
print("-Right wrist #1")
self.df_rw_d["x_filt"] = filter_signal(data=self.df_rw_d["x"], filter_type=filter_type, filter_order=3,
low_f=low_f, high_f=high_f, sample_f=self.fs)
self.df_rw_d["y_filt"] = filter_signal(data=self.df_rw_d["y"], filter_type=filter_type, filter_order=3,
low_f=low_f, high_f=high_f, sample_f=self.fs)
self.df_rw_d["z_filt"] = filter_signal(data=self.df_rw_d["z"], filter_type=filter_type, filter_order=3,
low_f=low_f, high_f=high_f, sample_f=self.fs)
# Right wrist 2
if self.df_rw_p is not None:
print("-Right wrist #2")
self.df_rw_p["x_filt"] = filter_signal(data=self.df_rw_p["x"], filter_type=filter_type, filter_order=3,
low_f=low_f, high_f=high_f, sample_f=self.fs)
self.df_rw_p["y_filt"] = filter_signal(data=self.df_rw_p["y"], filter_type=filter_type, filter_order=3,
low_f=low_f, high_f=high_f, sample_f=self.fs)
self.df_rw_p["z_filt"] = filter_signal(data=self.df_rw_p["z"], filter_type=filter_type, filter_order=3,
low_f=low_f, high_f=high_f, sample_f=self.fs)
# Bittium Faros
if self.df_bf is not None:
print("-Bittium Faros")
self.df_bf["x_filt"] = filter_signal(data=self.df_bf["x"], filter_type=filter_type, filter_order=3,
low_f=low_f, high_f=high_f, sample_f=self.bf_fs)
self.df_bf["y_filt"] = filter_signal(data=self.df_bf["y"], filter_type=filter_type, filter_order=3,
low_f=low_f, high_f=high_f, sample_f=self.bf_fs)
self.df_bf["z_filt"] = filter_signal(data=self.df_bf["z"], filter_type=filter_type, filter_order=3,
low_f=low_f, high_f=high_f, sample_f=self.bf_fs)
print("Complete.")
def create_plot_gif(wrist_file=None, ankle_file=None, start_time=None, stop_time=None,
sample_rate=75, plot_period_ms=100, wrist_obj=None, ankle_obj=None,
output_dir=None,
slide_window=False, remove_gravity=False, remove_high_f=False, remove_dc=True):
print("\nImporting data...")
if wrist_obj is None:
if "csv" in wrist_file:
lw = pd.read_csv(wrist_file, skiprows=100)
if "edf" in wrist_file:
d = ImportEDF.GENEActiv(filepath=wrist_file, load_raw=True)
d.sample_rate = 50
lw = pd.DataFrame(list(zip(d.timestamps, d.x, d.y, d.z, [None for i in range(len(d.timestamps))],
[None for i in range(len(d.timestamps))], [None for i in range(len(d.timestamps))])))
lw.columns = ["Timestamp", "x", "y", "z", "light", 'button', 'temperature']
lw["Timestamp"] = pd.to_datetime(lw["Timestamp"], format="%Y-%m-%d %H:%M:%S:%f")
if start_time is not None and stop_time is not None:
lw = lw.loc[(lw["Timestamp"] >= pd.to_datetime(start_time)) &
(lw["Timestamp"] < pd.to_datetime(stop_time))]
if ankle_obj is None:
if "csv" in ankle_file:
la = pd.read_csv(ankle_file, skiprows=100)
if "edf" in ankle_file:
d = ImportEDF.GENEActiv(filepath=ankle_file, load_raw=True)
d.sample_rate = 50
la = pd.DataFrame(list(zip(d.timestamps, d.x, d.y, d.z, [None for i in range(len(d.timestamps))],
[None for i in range(len(d.timestamps))],
[None for i in range(len(d.timestamps))])))
la.columns = ["Timestamp", "x", "y", "z", "light", 'button', 'temperature']
la["Timestamp"] = pd.to_datetime(la["Timestamp"], format="%Y-%m-%d %H:%M:%S:%f")
if start_time is not None and stop_time is not None:
la = la.loc[(la["Timestamp"] >= pd.to_datetime(start_time)) &
(la["Timestamp"] < pd.to_datetime(stop_time))]
filenames = []
# Converts cropped data to list
time = [i / sample_rate for i in range(lw.shape[0])]
lw_x = [i for i in lw["x"]]
lw_y = [i for i in lw["y"]]
lw_z = [i for i in lw["z"]]
la_x = [i for i in la["x"]]
la_y = [i for i in la["y"]]
la_z = [i for i in la["z"]]
if remove_gravity:
print("-Filtering data to remove gravity...")
lw_x = filter_signal(data=lw_x, filter_type="highpass", high_f=0.1, filter_order=2, sample_f=sample_rate)
lw_y = filter_signal(data=lw_y, filter_type="highpass", high_f=0.1, filter_order=2, sample_f=sample_rate)
lw_z = filter_signal(data=lw_z, filter_type="highpass", high_f=0.1, filter_order=2, sample_f=sample_rate)
la_x = filter_signal(data=la_x, filter_type="highpass", high_f=0.1, filter_order=2, sample_f=sample_rate)
la_y = filter_signal(data=la_y, filter_type="highpass", high_f=0.1, filter_order=2, sample_f=sample_rate)
la_z = filter_signal(data=la_z, filter_type="highpass", high_f=0.1, filter_order=2, sample_f=sample_rate)
if remove_high_f:
print("-Filtering data to remove high frequency...")
lw_x = filter_signal(data=lw_x, filter_type="lowpass", low_f=5, filter_order=2, sample_f=sample_rate)
lw_y = filter_signal(data=lw_y, filter_type="lowpass", low_f=5, filter_order=2, sample_f=sample_rate)
lw_z = filter_signal(data=lw_z, filter_type="lowpass", low_f=5, filter_order=2, sample_f=sample_rate)
la_x = filter_signal(data=la_x, filter_type="lowpass", low_f=5, filter_order=2, sample_f=sample_rate)
la_y = filter_signal(data=la_y, filter_type="lowpass", low_f=5, filter_order=2, sample_f=sample_rate)
la_z = filter_signal(data=la_z, filter_type="lowpass", low_f=5, filter_order=2, sample_f=sample_rate)
if remove_dc:
print("\n-Removing DC component from signal...")
lw_x = [i - np.mean(lw_x) for i in lw_x]
lw_y = [i - np.mean(lw_y) for i in lw_y]
lw_z = [i - np.mean(lw_z) for i in lw_z]
la_x = [i - np.mean(la_x) for i in la_x]
la_y = [i - np.mean(la_y) for i in la_y]
la_z = [i - np.mean(la_z) for i in la_z]
min_x = min([min(lw_x), min(la_x)])
min_y = min([min(lw_y), min(la_y)])
min_z = min([min(lw_z), min(la_z)])
max_x = max([max(lw_x), max(la_x)])
max_y = max([max(lw_y), max(la_y)])
max_z = max([max(lw_z), max(la_z)])
min_all = min([min_x, min_y, min_z])
max_all = max([max_x, max_y, max_z])
plot_rate = int(np.ceil(plot_period_ms / (1000 / sample_rate)))
if plot_rate == 0:
plot_rate = 1
print("\n-Data will be plotted in {}ms increments...\n".format(plot_period_ms))
for i in range(0, lw.shape[0], plot_rate):
print("-Generating plot {} of {}...".format(int((i/plot_rate))+1, int(len(range(0, lw.shape[0], plot_rate)))))
fig, (ax1, ax2) = plt.subplots(2, figsize=(10, 6))
plt.subplots_adjust(right=.75, left=.07, hspace=.3)
ax1.plot(time[:i], lw_x[:i], color='black')
ax1.plot(time[:i], lw_y[:i], color='red')
ax1.plot(time[:i], lw_z[:i], color='dodgerblue')
ax1.axvline(time[i], color='limegreen')
ax2.plot(time[:i], la_x[:i], color='black')
ax2.plot(time[:i], la_y[:i], color='red')
ax2.plot(time[:i], la_z[:i], color='dodgerblue')
ax2.axvline(time[i], color='limegreen')
ax1.set_ylim(min_all - .5, max_all + .5)
ax2.set_ylim(min_all - .5, max_all + .5)
if not slide_window:
ax1.set_xlim(0, len(lw_x)/sample_rate)
ax2.set_xlim(0, len(la_x)/sample_rate)
if slide_window:
if time[i] <= 12.5:
ax1.set_xlim(0, 15)
ax2.set_xlim(0, 15)
if time[i] > 12.5:
ax1.set_xlim(time[i]-7.5, time[i]+7.5)
ax2.set_xlim(time[i]-7.5, time[i]+7.5)
ax2.set_xlabel("Time (seconds)")
ax1.set_ylabel("Acceleration")
ax2.set_ylabel("Acceleration")
ax1.set_title("Left Wrist")
ax2.set_title("Left Ankle")
ax1.set_ylabel("Acceleration")
# create file name and append it to a list
filename = f'{i}.png'
filenames.append(filename)
plt.savefig(output_dir + filename)
plt.close()
# build gif
print("\nCombining images into gif...")
with imageio.get_writer(output_dir + "Output.gif", mode='I') as writer:
for filename in filenames:
image = imageio.imread(output_dir + filename)
writer.append_data(image)
# Remove files
for filename in set(filenames):
os.remove(output_dir + filename)
print("\nComplete.")
return lw, la