def test_ecg_plot():
ecg = nk.ecg_simulate(duration=60, heart_rate=70, noise=0.05)
ecg_summary, _ = nk.ecg_process(ecg, sampling_rate=1000, method="neurokit")
# Plot data over samples.
nk.ecg_plot(ecg_summary)
# This will identify the latest figure.
fig = plt.gcf()
assert len(fig.axes) == 2
titles = ["Raw and Cleaned Signal", "Heart Rate"]
for (ax, title) in zip(fig.get_axes(), titles):
assert ax.get_title() == title
assert fig.get_axes()[1].get_xlabel() == "Samples"
np.testing.assert_array_equal(fig.axes[0].get_xticks(),
fig.axes[1].get_xticks())
plt.close(fig)
# Plot data over seconds.
nk.ecg_plot(ecg_summary, sampling_rate=1000)
# This will identify the latest figure.
fig = plt.gcf()
assert len(fig.axes) == 3
titles = ["Raw and Cleaned Signal", "Heart Rate", "Individual Heart Beats"]
for (ax, title) in zip(fig.get_axes(), titles):
assert ax.get_title() == title
assert fig.get_axes()[1].get_xlabel() == "Time (seconds)"
np.testing.assert_array_equal(fig.axes[0].get_xticks(),
fig.axes[1].get_xticks())
plt.close(fig)
def segment(yClean):
ECG_signal, info = nk.ecg_process(yClean, sampling_rate=1000)
# Visualise the processing
# plt.savefig('./images/signal_varios.png')
fig = Figure()
axis = fig.add_subplot(1, 1, 1)
nk.ecg_plot(ECG_signal, sampling_rate=1000)
return nk.ecg_plot(ECG_signal, sampling_rate=1000)
# =============================================================================
# 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)
# Process it
signals, info = nk.ecg_process(ecg, sampling_rate=250)
# Visualise the processing
nk.ecg_plot(signals, sampling_rate=250)
# Save it
plot = nk.ecg_plot(signals, sampling_rate=250)
plot.set_size_inches(10, 6, forward=True)
plot.savefig("README_ecg.png", dpi=300, h_pad=3)
# =============================================================================
# Respiration (RSP) processing
# =============================================================================
# Generate one minute of RSP signal (recorded at 250 samples / second)
rsp = nk.rsp_simulate(duration=60, sampling_rate=250, respiratory_rate=15)
# Process it
signals, info = nk.rsp_process(rsp, sampling_rate=250)
def test_ecg_process_func(signal,
start,
n_samples,
fs,
plot_builtin=False,
plot_events=True):
"""Runs neurokit.ecg_process() on specified section of data and plots builtin plot or plot that
shows P, R, and T waves, atrial/ventricular phases, and peaks.
:param signal: array of data
:param start: start index in signal
:param n_samples: how many samples to process
:param fs: sample rate, Hz
:param plot_builtin: Boolean; runs neurokit.ecg_plot()
:param plot_events: boolean: runs custom plot
:return: data objects generated in neurokit.ecg_process()
"""
s, i = nk.ecg_process(signal[start:start + n_samples], fs)
if plot_events:
fig, axes = plt.subplots(1, figsize=(10, 6))
axes.plot(np.arange(0, s.shape[0]) / fs,
s["ECG_Clean"],
label="Filtered",
color='black')
axes.plot(s.loc[s["ECG_R_Peaks"] == 1].index / fs,
s.loc[s["ECG_R_Peaks"] == 1]["ECG_Clean"],
marker="o",
linestyle="",
color='grey',
label="R Peak")
axes.plot(s.loc[s["ECG_T_Peaks"] == 1].index / fs,
s.loc[s["ECG_T_Peaks"] == 1]["ECG_Clean"],
marker="x",
linestyle="",
color='red',
label="T Peak")
axes.plot(s.loc[s["ECG_P_Peaks"] == 1].index / fs,
s.loc[s["ECG_P_Peaks"] == 1]["ECG_Clean"],
marker="x",
linestyle="",
color='green',
label="P Peak")
axes.legend(loc='lower left')
axes2 = axes.twinx()
axes2.set_yticks([])
axes2.set_ylim(0, 1)
axes2.fill_between(x=np.arange(0, s.shape[0]) / fs,
y1=.5,
y2=s["ECG_Phase_Atrial"].replace(to_replace=0,
value=.5),
color='green',
alpha=.25,
label="Atrial phase")
axes2.fill_between(x=np.arange(0, s.shape[0]) / fs,
y1=.5,
y2=s["ECG_Phase_Ventricular"].replace(to_replace=0,
value=.5),
color='purple',
alpha=.25,
label="Ventr. phase")
p_start = s.loc[s["ECG_P_Onsets"] == 1]
p_end = s.loc[s["ECG_P_Offsets"] == 1]
t_start = s.loc[s["ECG_T_Onsets"] == 1]
t_end = s.loc[s["ECG_T_Offsets"] == 1]
r_start = s.loc[s["ECG_R_Onsets"] == 1]
r_end = s.loc[s["ECG_R_Offsets"] == 1]
for i in range(p_start.shape[0]):
if i == range(p_start.shape[0])[-1]:
axes2.fill_between(
x=[p_start.index[i] / fs, p_end.index[i] / fs],
y1=0,
y2=.5,
color='dodgerblue',
alpha=.25,
label="P wave")
else:
axes2.fill_between(
x=[p_start.index[i] / fs, p_end.index[i] / fs],
y1=0,
y2=.5,
color='dodgerblue',
alpha=.25)
for i in range(t_start.shape[0]):
if i == range(t_start.shape[0])[-1]:
axes2.fill_between(
x=[t_start.index[i] / fs, t_end.index[i] / fs],
y1=0,
y2=.5,
color='red',
alpha=.25,
label="T wave")
else:
axes2.fill_between(
x=[t_start.index[i] / fs, t_end.index[i] / fs],
y1=0,
y2=.5,
color='red',
alpha=.25)
for i in range(r_start.shape[0]):
if i == range(r_start.shape[0])[-1]:
axes2.fill_between(
x=[r_start.index[i] / fs, r_end.index[i] / fs],
y1=0,
y2=.5,
color='grey',
alpha=.25,
label="R wave")
else:
axes2.fill_between(
x=[r_start.index[i] / fs, r_end.index[i] / fs],
y1=0,
y2=.5,
color='grey',
alpha=.25)
axes2.legend(loc='lower right')
axes.set_xlabel("Seconds")
if plot_builtin:
nk.ecg_plot(s, fs)
return s, i
