def test_if_pointcare_plot_features_features_are_correct(self):
nn_intervals = load_test_data(TEST_DATA_FILENAME)
function_pointcare_plot_features = get_poincare_plot_features(
nn_intervals)
real_pointcare_plot_features = {
'sd1': 13.80919037557993,
'sd2': 59.38670497373513,
'ratio_sd2_sd1': 4.300520404060338
}
self.assertAlmostEqual(function_pointcare_plot_features,
real_pointcare_plot_features)
temp_RR, 'welch', 360)
# Frequency domain components
time_series_total_power.append(frequency_domain['total_power'])
time_series_hf.append(frequency_domain['hf'])
# Non linear features require minimum 3 RR intervals
if (i == 1):
time_series_sd1.append(0)
time_series_sample_entropy.append(0)
else:
poincare = get_poincare_plot_features(temp_RR)
# Get non linear features
time_series_sd1.append(poincare['sd1'])
RR_entropy = entropy(temp_RR)
time_series_sample_entropy.append(RR_entropy)
mean_nni.append(time_series_mean_nni)
sdnn.append(time_series_sdnn)
sdsd.append(time_series_sdsd)
pnni_20.append(time_series_pnni_20)
pnni_50.append(time_series_pnni_50)
range_nni.append(time_series_range_nni)
total_power.append(time_series_total_power)
def plot_poincare(nn_intervals: List[float], plot_sd_features: bool = True):
"""
Pointcare / Lorentz Plot of the NN Intervals
Arguments
---------
nn_intervals : list
list of NN intervals
plot_sd_features : bool
Option to show or not SD1 and SD2 features on plot. By default, set to True.
Notes
---------
The transverse axis (T) reflects beat-to-beat variation
the longitudinal axis (L) reflects the overall fluctuation
"""
# For Lorentz / poincaré Plot
ax1 = nn_intervals[:-1]
ax2 = nn_intervals[1:]
# compute features for ellipse's height, width and center
dict_sd1_sd2 = get_poincare_plot_features(nn_intervals)
sd1 = dict_sd1_sd2["sd1"]
sd2 = dict_sd1_sd2["sd2"]
mean_nni = np.mean(nn_intervals)
# Plot options and settings
style.use("seaborn-darkgrid")
fig = plt.figure(figsize=(12, 12))
ax = fig.add_subplot(111)
plt.title("Poincaré / Lorentz Plot", fontsize=20)
plt.xlabel('NN_n (s)', fontsize=15)
plt.ylabel('NN_n+1 (s)', fontsize=15)
plt.xlim(min(nn_intervals) - 10, max(nn_intervals) + 10)
plt.ylim(min(nn_intervals) - 10, max(nn_intervals) + 10)
# Poincaré Plot
ax.scatter(ax1, ax2, c='b', s=2)
if plot_sd_features:
# Ellipse plot settings
ells = Ellipse(xy=(mean_nni, mean_nni),
width=2 * sd2 + 1,
height=2 * sd1 + 1,
angle=45,
linewidth=2,
fill=False)
ax.add_patch(ells)
ells = Ellipse(xy=(mean_nni, mean_nni),
width=2 * sd2,
height=2 * sd1,
angle=45)
ells.set_alpha(0.05)
ells.set_facecolor("blue")
ax.add_patch(ells)
# Arrow plot settings
sd1_arrow = ax.arrow(mean_nni,
mean_nni,
-sd1 * np.sqrt(2) / 2,
sd1 * np.sqrt(2) / 2,
linewidth=3,
ec='r',
fc="r",
label="SD1")
sd2_arrow = ax.arrow(mean_nni,
mean_nni,
sd2 * np.sqrt(2) / 2,
sd2 * np.sqrt(2) / 2,
linewidth=3,
ec='g',
fc="g",
label="SD2")
plt.legend(handles=[sd1_arrow, sd2_arrow], fontsize=12, loc="best")
plt.show()