def test_eda_plot():
sampling_rate = 1000
eda = nk.eda_simulate(duration=30, sampling_rate=sampling_rate,
scr_number=6, noise=0, drift=0.01, random_state=42)
eda_summary, _ =nk.eda_process(eda, sampling_rate=sampling_rate)
# Plot data over samples.
nk.eda_plot(eda_summary)
# This will identify the latest figure.
fig = plt.gcf()
assert len(fig.axes) == 3
titles = ["Raw and Cleaned Signal",
"Skin Conductance Response (SCR)",
"Skin Conductance Level (SCL)"]
for (ax, title) in zip(fig.get_axes(), titles):
assert ax.get_title() == title
assert fig.get_axes()[2].get_xlabel() == "Samples"
np.testing.assert_array_equal(fig.axes[0].get_xticks(),
fig.axes[1].get_xticks(),
fig.axes[2].get_xticks())
plt.close(fig)
# Plot data over seconds.
nk.eda_plot(eda_summary, sampling_rate=sampling_rate)
# This will identify the latest figure.
fig = plt.gcf()
assert fig.get_axes()[2].get_xlabel() == "Seconds"
def load_csv_file():
file = open( "EDA3.csv", "r")
reader = csv.reader(file)
data = []
i =0
for line in reader:
if i!=0:
value = float(line[0])
data.append(value) #TODO: se podria usar el promedio (D[i-1] + D[i])/2.0
data.append(value)
i+=1
data_eda = np.array(data)
# Preprocess the data (filter, find peaks, etc.)
#signals, info = nk.bio_process(eda=data_eda, sampling_rate=f_muestreo)
signals, info = nk.eda_process(data_eda, sampling_rate=f_muestreo)
#signals, info = nk.bio_process(ecg=data["ECG"], rsp=data["RSP"], eda=data["EDA"], sampling_rate=100)
# Compute relevant features
#results = nk.bio_analyze(signals, sampling_rate=f_muestreo)
#print(results)
# Extract clean EDA and SCR features
cleaned = signals["EDA_Clean"]
features = [info["SCR_Onsets"], info["SCR_Peaks"], info["SCR_Recovery"]]
#print(features)
#print(cleaned[0])
plt.plot(cleaned)
plt.ylabel('some numbers')
plt.show()
# Visualise the processing
nk.eda_plot(signals, sampling_rate=f_muestreo)
def generarDataSintetica():
#generando datos
# Generate 10 seconds of EDA signal (recorded at 250 samples / second) with 2 SCR peaks
eda = nk.eda_simulate(duration=20, sampling_rate=250, scr_number=3, drift=0.01)
# Process
signals, info = nk.eda_process(eda, sampling_rate=250)
# Visualise the processing
nk.eda_plot(signals, sampling_rate=250)
fig.set_size_inches(10, 6, forward=True)
[ax.legend(loc=1) for ax in plt.gcf().axes]
fig.savefig("README_simulation.png", dpi=300, h_pad=3)
# =============================================================================
# Electrodermal Activity (EDA) processing
# =============================================================================
# Generate 10 seconds of EDA signal (recorded at 250 samples / second) with 2 SCR peaks
eda = nk.eda_simulate(duration=10, sampling_rate=250, scr_number=2, drift=0.1)
# Process it
signals, info = nk.eda_process(eda, sampling_rate=250)
# Visualise the processing
nk.eda_plot(signals, sampling_rate=None)
# Save it
plot = nk.eda_plot(signals, sampling_rate=None)
plot.set_size_inches(10, 6, forward=True)
plot.savefig("README_eda.png", dpi=300, h_pad=3)
# =============================================================================
# Cardiac activity (ECG) processing
# =============================================================================
# Generate 15 seconds of ECG signal (recorded at 250 samples / second)
ecg = nk.ecg_simulate(duration=15,
sampling_rate=250,
heart_rate=70,
random_state=333)
