def plot_sensors_cwt(data_seg, n_periods, wavelet, figsize=(12, 32)):
f, axs = plt.subplots(10,
2,
figsize=figsize,
constrained_layout=True,
sharex=True)
periods = np.arange(1, n_periods)
scales = scaleogram.periods2scales(periods)
time = data_seg.reset_index()['index'].values
for ax, case in zip(axs, data_seg.columns):
sensor_number = case.split("_")[-1]
ax[0].set_title(f'Time series: sensor:{sensor_number}')
ax[0].plot(data_seg[case].values,
marker='o',
ls='-',
ms=0.05,
alpha=0.5)
ax[1] = scaleogram.cws(time=time,
wavelet=wavelet,
scales=scales,
signal=data_seg[case].values,
coikw={"alpha": 0.9},
ax=ax[1])
ax[1].set_title(f'CWT: sensor:{sensor_number}: wavelet: {wavelet}')
plt.show()
def generate_spectral_analysis(ecg_data: list,
start: int,
end: int,
classif: str = 'NA',
spectrum_max_hz: int = 40,
fs: int = 1_000):
fig, (ax0, ax1, ax2) = plt.subplots(3, 1, figsize=(8, 4), sharex=True)
fig.subplots_adjust(hspace=.01)
# ax0
ax0.plot(np.arange(0, len(ecg_data)), ecg_data, linewidth=1)
ax0.axis('off')
# ax2
f, t, Sxx = signal.spectrogram(
ecg_data,
fs,
# window=('tukey', 0.1),
# nperseg=150)
window=('tukey', 0.1),
nperseg=int(round(fs / 4, 0)))
ax1.pcolormesh(t * fs, -f, Sxx, shading='flat')
ax1.set_ylim(-spectrum_max_hz)
ax1.set_ylabel('Hz (inv)')
# ax1
scg.set_default_wavelet('morl')
signal_length = spectrum_max_hz
# range of scales to perform the transform
scales = scg.periods2scales(np.arange(1, signal_length + 1))
# the scaleogram
# sampling_ratio = 4
# sample_ecg = []
# for element in range(int(round(len(ecg_data)/sampling_ratio, 0))):
# sample_ecg.append(np.mean(ecg_data[sampling_ratio * element:
# sampling_ratio * element +
# sampling_ratio]))
scg.cws(ecg_data,
scales=scales,
figsize=(10, 2.0),
coi=False,
ylabel="Hz",
xlabel='Frame',
ax=ax2,
cbar=None,
title='')
fig.suptitle(
'spectrum frequency from frame {} to {} - classif : {}'.format(
start, end, classif),
fontsize=10)
st.pyplot(fig)
def wavelets(time, flux):
"""Using morlet wavelet from scaleogram package to determine period.
Values are plotted on a 2-D contour map, and then transformed into
a 1-D plot.
Args:
time (List): Time values from processed data file.
flux (List): Flux values from processed data file.
"""
flux = flux / np.median(flux) - 1
flux = flux / np.std(np.diff(flux))
# Convert time to np array for scaleogram.
time = np.asarray(time)
# Spacing in time values for computing transform
dt = time[1] - time[0]
scales = scg.periods2scales(np.arange(1, len(time)))
scg.set_default_wavelet('cmor2-2.0')
wavelet = scg.get_default_wavelet
ax2 = scg.cws(time,
flux,
scales=scales,
coikw={
'alpha': 0.5,
'hatch': '/'
})
plt.show()
# Same code in scaleogram package, used to visualize 1-D version of the data.
coeff, scales_freq = scg.fastcwt(flux, scales, 'cmor2-2.0', dt)
# Sum all of the x values pertaining to each period value.
period_sum = []
for idx, arr in enumerate(coeff):
period_sum.append(np.sum(np.abs(arr)))
# Period values from the fastcwt function.
transformed_time = 1. / scales_freq
output.plot_graph(transformed_time, period_sum, "Period", "Sum per Period",
"Wavelet Transformation - 1-D")
# Wavelet attempt
wl_rise_data = rolling_rise_mean.dropna()
wl_rise_time = rise_panda.time[wl_rise_data.index].to_numpy()
wl_rise_data = wl_rise_data.to_numpy()
wl_rise_data_norm = wl_rise_data-wl_rise_data.mean()
# choose default wavelet function for the entire notebook
scg.set_default_wavelet('cmor1.5-1.0')
if testing == True:
fig1, axs = plt.subplots(2, figsize=(20,12));
lines = axs[0].plot(wl_rise_time, wl_rise_data);
axs[0].set(xlabel='time',
ylabel='detrended and norm width of jet [km]')
scales = scg.periods2scales(np.arange(1, 120))
scg.cws(wl_rise_time, wl_rise_data_norm, scales=scales, ax=axs[1])
scg.cws(wl_rise_time, wl_rise_data_norm, ax=axs[1],
yscale='linear', cbar='horizontal');
plt.tight_layout()
plt.show()
wl_fall_data = rolling_fall_mean.dropna()
wl_fall_time = fall_panda.time[wl_fall_data.index].to_numpy()
wl_fall_time = wl_fall_time-wl_fall_time[0]
wl_fall_data = wl_fall_data.to_numpy()
wl_fall_data_norm = wl_fall_data-wl_fall_data.mean()
# choose default wavelet function forlen() the entire notebook
if testing == True:
fig1, axs = plt.subplots(2, figsize=(20,12));
if st.checkbox('Show raw data'):
st.subheader('Raw data')
st.write(data)
st.subheader(f"ECG record : {chosen_record}")
st.line_chart(data)
if samptovalue - sampfromvalue > 2000:
st.text('No scaleogram range to long')
else:
# choose default wavelet function
scg.set_default_wavelet('morl')
signal_length = samptovalue - sampfromvalue
# range of scales to perform the transform
scales = scg.periods2scales(np.arange(1, signal_length + 1))
x_values_wvt_arr = range(0, len(data), 1)
# the scaleogram
fig = scg.cws(data,
scales=scales,
figsize=(10, 4.0),
coi=False,
ylabel="Period",
xlabel="Time")
#st.plotly_chart(scal)
#coeff, freq = pywt.cwt(data, 500 , 'morl', 1)
st.pyplot(fig.figure)