def plot_spectrum(signal=None, sampling_rate=1000., path=None, show=True):
"""Plot the power spectrum of a signal (one-sided).
Parameters
----------
signal : array
Input signal.
sampling_rate : int, float, optional
Sampling frequency (Hz).
path : str, optional
If provided, the plot will be saved to the specified file.
show : bool, optional
If True, show the plot immediately.
"""
freqs, power = st.power_spectrum(signal,
sampling_rate,
pad=0,
pow2=False,
decibel=True)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(freqs, power, linewidth=MAJOR_LW)
ax.set_xlabel('Frequency (Hz)')
ax.set_ylabel('Power (dB)')
ax.grid()
# make layout tight
fig.tight_layout()
# save to file
if path is not None:
path = utils.normpath(path)
root, ext = os.path.splitext(path)
ext = ext.lower()
if ext not in ['png', 'jpg']:
path = root + '.png'
fig.savefig(path, dpi=200, bbox_inches='tight')
# show
if show:
plt.show()
else:
# close
plt.close(fig)
def plot_ecg(ts=None,
raw=None,
filtered=None,
rpeaks=None,
templates_ts=None,
templates=None,
heart_rate_ts=None,
heart_rate=None,
path=None,
show=False):
"""Create a summary plot from the output of signals.ecg.ecg.
Parameters
----------
ts : array
Signal time axis reference (seconds).
raw : array
Raw ECG signal.
filtered : array
Filtered ECG signal.
rpeaks : array
R-peak location indices.
templates_ts : array
Templates time axis reference (seconds).
templates : array
Extracted heartbeat templates.
heart_rate_ts : array
Heart rate time axis reference (seconds).
heart_rate : array
Instantaneous heart rate (bpm).
path : str, optional
If provided, the plot will be saved to the specified file.
show : bool, optional
If True, show the plot immediately.
"""
fig = plt.figure()
fig.suptitle('ECG Summary')
gs = gridspec.GridSpec(6, 2)
# raw signal
ax1 = fig.add_subplot(gs[:2, 0])
ax1.plot(ts, raw, linewidth=MAJOR_LW, label='Raw')
ax1.set_ylabel('Amplitude')
ax1.legend()
ax1.grid()
# filtered signal with rpeaks
ax2 = fig.add_subplot(gs[2:4, 0], sharex=ax1)
ymin = np.min(filtered)
ymax = np.max(filtered)
alpha = 0.1 * (ymax - ymin)
ymax += alpha
ymin -= alpha
ax2.plot(ts, filtered, linewidth=MAJOR_LW, label='Filtered')
ax2.vlines(ts[rpeaks],
ymin,
ymax,
color='m',
linewidth=MINOR_LW,
label='R-peaks')
ax2.set_ylabel('Amplitude')
ax2.legend()
ax2.grid()
# heart rate
ax3 = fig.add_subplot(gs[4:, 0], sharex=ax1)
ax3.plot(heart_rate_ts, heart_rate, linewidth=MAJOR_LW, label='Heart Rate')
ax3.set_xlabel('Time (s)')
ax3.set_ylabel('Heart Rate (bpm)')
ax3.legend()
ax3.grid()
# templates
ax4 = fig.add_subplot(gs[1:5, 1])
ax4.plot(templates_ts, templates.T, 'm', linewidth=MINOR_LW, alpha=0.7)
ax4.set_xlabel('Time (s)')
ax4.set_ylabel('Amplitude')
ax4.set_title('Templates')
ax4.grid()
# make layout tight
gs.tight_layout(fig)
# save to file
if path is not None:
path = utils.normpath(path)
root, ext = os.path.splitext(path)
ext = ext.lower()
if ext not in ['png', 'jpg']:
path = root + '.png'
fig.savefig(path, dpi=200, bbox_inches='tight')
# show
if show:
plt.show()
else:
# close
plt.close(fig)
def plot_filter(ftype='FIR',
band='lowpass',
order=None,
frequency=None,
sampling_rate=1000.,
path=None,
show=True,
**kwargs):
"""Plot the frequency response of the filter specified with the given
parameters.
Parameters
----------
ftype : str
Filter type:
* Finite Impulse Response filter ('FIR');
* Butterworth filter ('butter');
* Chebyshev filters ('cheby1', 'cheby2');
* Elliptic filter ('ellip');
* Bessel filter ('bessel').
band : str
Band type:
* Low-pass filter ('lowpass');
* High-pass filter ('highpass');
* Band-pass filter ('bandpass');
* Band-stop filter ('bandstop').
order : int
Order of the filter.
frequency : int, float, list, array
Cutoff frequencies; format depends on type of band:
* 'lowpass' or 'bandpass': single frequency;
* 'bandpass' or 'bandstop': pair of frequencies.
sampling_rate : int, float, optional
Sampling frequency (Hz).
path : str, optional
If provided, the plot will be saved to the specified file.
show : bool, optional
If True, show the plot immediately.
``**kwargs`` : dict, optional
Additional keyword arguments are passed to the underlying
scipy.signal function.
"""
# get filter
b, a = st.get_filter(ftype=ftype,
band=band,
order=order,
frequency=frequency,
sampling_rate=sampling_rate,
**kwargs)
# plot
fig = _plot_filter(b, a, sampling_rate)
# make layout tight
fig.tight_layout()
# save to file
if path is not None:
path = utils.normpath(path)
root, ext = os.path.splitext(path)
ext = ext.lower()
if ext not in ['png', 'jpg']:
path = root + '.png'
fig.savefig(path, dpi=200, bbox_inches='tight')
# show
if show:
plt.show()
else:
# close
plt.close(fig)
def plot_eeg(ts=None,
raw=None,
filtered=None,
labels=None,
features_ts=None,
theta=None,
alpha_low=None,
alpha_high=None,
beta=None,
gamma=None,
plf_pairs=None,
plf=None,
path=None,
show=False):
"""Create a summary plot from the output of signals.eeg.eeg.
Parameters
----------
ts : array
Signal time axis reference (seconds).
raw : array
Raw EEG signal.
filtered : array
Filtered EEG signal.
labels : list
Channel labels.
features_ts : array
Features time axis reference (seconds).
theta : array
Average power in the 4 to 8 Hz frequency band; each column is one
EEG channel.
alpha_low : array
Average power in the 8 to 10 Hz frequency band; each column is one
EEG channel.
alpha_high : array
Average power in the 10 to 13 Hz frequency band; each column is one
EEG channel.
beta : array
Average power in the 13 to 25 Hz frequency band; each column is one
EEG channel.
gamma : array
Average power in the 25 to 40 Hz frequency band; each column is one
EEG channel.
plf_pairs : list
PLF pair indices.
plf : array
PLF matrix; each column is a channel pair.
path : str, optional
If provided, the plot will be saved to the specified file.
show : bool, optional
If True, show the plot immediately.
"""
nrows = MAX_ROWS
alpha = 2.
figs = []
# raw
fig = _plot_multichannel(ts=ts,
signal=raw,
labels=labels,
nrows=nrows,
alpha=alpha,
title='EEG Summary - Raw',
xlabel='Time (s)',
ylabel='Amplitude')
figs.append(('_Raw', fig))
# filtered
fig = _plot_multichannel(ts=ts,
signal=filtered,
labels=labels,
nrows=nrows,
alpha=alpha,
title='EEG Summary - Filtered',
xlabel='Time (s)',
ylabel='Amplitude')
figs.append(('_Filtered', fig))
# band-power
names = ('Theta Band', 'Lower Alpha Band', 'Higher Alpha Band',
'Beta Band', 'Gamma Band')
args = (theta, alpha_low, alpha_high, beta, gamma)
for n, a in zip(names, args):
fig = _plot_multichannel(ts=features_ts,
signal=a,
labels=labels,
nrows=nrows,
alpha=alpha,
title='EEG Summary - %s' % n,
xlabel='Time (s)',
ylabel='Power')
figs.append(('_' + n.replace(' ', '_'), fig))
# PLF
plf_labels = ['%s vs %s' % (labels[p[0]], labels[p[1]]) for p in plf_pairs]
fig = _plot_multichannel(ts=features_ts,
signal=plf,
labels=plf_labels,
nrows=nrows,
alpha=alpha,
title='EEG Summary - Phase-Locking Factor',
xlabel='Time (s)',
ylabel='PLF')
figs.append(('_PLF', fig))
# save to file
if path is not None:
path = utils.normpath(path)
root, ext = os.path.splitext(path)
ext = ext.lower()
if ext not in ['png', 'jpg']:
ext = '.png'
for n, fig in figs:
path = root + n + ext
fig.savefig(path, dpi=200, bbox_inches='tight')
# show
if show:
plt.show()
else:
# close
for _, fig in figs:
plt.close(fig)
def plot_resp(ts=None,
raw=None,
filtered=None,
zeros=None,
resp_rate_ts=None,
resp_rate=None,
path=None,
show=False):
"""Create a summary plot from the output of signals.bvp.bvp.
Parameters
----------
ts : array
Signal time axis reference (seconds).
raw : array
Raw Resp signal.
filtered : array
Filtered Resp signal.
zeros : array
Indices of Respiration zero crossings.
resp_rate_ts : array
Respiration rate time axis reference (seconds).
resp_rate : array
Instantaneous respiration rate (Hz).
path : str, optional
If provided, the plot will be saved to the specified file.
show : bool, optional
If True, show the plot immediately.
"""
fig = plt.figure()
fig.suptitle('Respiration Summary')
# raw signal
ax1 = fig.add_subplot(311)
ax1.plot(ts, raw, linewidth=MAJOR_LW, label='Raw')
ax1.set_ylabel('Amplitude')
ax1.legend()
ax1.grid()
# filtered signal with zeros
ax2 = fig.add_subplot(312, sharex=ax1)
ymin = np.min(filtered)
ymax = np.max(filtered)
alpha = 0.1 * (ymax - ymin)
ymax += alpha
ymin -= alpha
ax2.plot(ts, filtered, linewidth=MAJOR_LW, label='Filtered')
ax2.vlines(ts[zeros],
ymin,
ymax,
color='m',
linewidth=MINOR_LW,
label='Zero crossings')
ax2.set_ylabel('Amplitude')
ax2.legend()
ax2.grid()
# heart rate
ax3 = fig.add_subplot(313, sharex=ax1)
ax3.plot(resp_rate_ts,
resp_rate,
linewidth=MAJOR_LW,
label='Respiration Rate')
ax3.set_xlabel('Time (s)')
ax3.set_ylabel('Respiration Rate (Hz)')
ax3.legend()
ax3.grid()
# make layout tight
fig.tight_layout()
# save to file
if path is not None:
path = utils.normpath(path)
root, ext = os.path.splitext(path)
ext = ext.lower()
if ext not in ['png', 'jpg']:
path = root + '.png'
fig.savefig(path, dpi=200, bbox_inches='tight')
# show
if show:
plt.show()
else:
# close
plt.close(fig)
def plot_emg(ts=None,
sampling_rate=None,
raw=None,
filtered=None,
onsets=None,
processed=None,
path=None,
show=False):
"""Create a summary plot from the output of signals.emg.emg.
Parameters
----------
ts : array
Signal time axis reference (seconds).
sampling_rate : int, float
Sampling frequency (Hz).
raw : array
Raw EMG signal.
filtered : array
Filtered EMG signal.
onsets : array
Indices of EMG pulse onsets.
processed : array, optional
Processed EMG signal according to the chosen onset detector.
path : str, optional
If provided, the plot will be saved to the specified file.
show : bool, optional
If True, show the plot immediately.
"""
fig = plt.figure()
fig.suptitle('EMG Summary')
if processed is not None:
ax1 = fig.add_subplot(311)
ax2 = fig.add_subplot(312, sharex=ax1)
ax3 = fig.add_subplot(313)
# processed signal
L = len(processed)
T = (L - 1) / sampling_rate
ts_processed = np.linspace(0, T, L, endpoint=True)
ax3.plot(ts_processed,
processed,
linewidth=MAJOR_LW,
label='Processed')
ax3.set_xlabel('Time (s)')
ax3.set_ylabel('Amplitude')
ax3.legend()
ax3.grid()
else:
ax1 = fig.add_subplot(211)
ax2 = fig.add_subplot(212, sharex=ax1)
# raw signal
ax1.plot(ts, raw, linewidth=MAJOR_LW, label='Raw')
ax1.set_ylabel('Amplitude')
ax1.legend()
ax1.grid()
# filtered signal with onsets
ymin = np.min(filtered)
ymax = np.max(filtered)
alpha = 0.1 * (ymax - ymin)
ymax += alpha
ymin -= alpha
ax2.plot(ts, filtered, linewidth=MAJOR_LW, label='Filtered')
ax2.vlines(ts[onsets],
ymin,
ymax,
color='m',
linewidth=MINOR_LW,
label='Onsets')
ax2.set_xlabel('Time (s)')
ax2.set_ylabel('Amplitude')
ax2.legend()
ax2.grid()
# make layout tight
fig.tight_layout()
# save to file
if path is not None:
path = utils.normpath(path)
root, ext = os.path.splitext(path)
ext = ext.lower()
if ext not in ['png', 'jpg']:
path = root + '.png'
fig.savefig(path, dpi=200, bbox_inches='tight')
# show
if show:
plt.show()
else:
# close
plt.close(fig)
def plot_eda(ts=None,
raw=None,
filtered=None,
onsets=None,
peaks=None,
amplitudes=None,
path=None,
show=False):
"""Create a summary plot from the output of signals.eda.eda.
Parameters
----------
ts : array
Signal time axis reference (seconds).
raw : array
Raw EDA signal.
filtered : array
Filtered EDA signal.
onsets : array
Indices of SCR pulse onsets.
peaks : array
Indices of the SCR peaks.
amplitudes : array
SCR pulse amplitudes.
path : str, optional
If provided, the plot will be saved to the specified file.
show : bool, optional
If True, show the plot immediately.
"""
fig = plt.figure()
fig.suptitle('EDA Summary')
# raw signal
ax1 = fig.add_subplot(311)
ax1.plot(ts, raw, linewidth=MAJOR_LW, label='raw')
ax1.set_ylabel('Amplitude')
ax1.legend()
ax1.grid()
# filtered signal with onsets, peaks
ax2 = fig.add_subplot(312, sharex=ax1)
ymin = np.min(filtered)
ymax = np.max(filtered)
alpha = 0.1 * (ymax - ymin)
ymax += alpha
ymin -= alpha
ax2.plot(ts, filtered, linewidth=MAJOR_LW, label='Filtered')
ax2.vlines(ts[onsets],
ymin,
ymax,
color='m',
linewidth=MINOR_LW,
label='Onsets')
ax2.vlines(ts[peaks],
ymin,
ymax,
color='g',
linewidth=MINOR_LW,
label='Peaks')
ax2.set_ylabel('Amplitude')
ax2.legend()
ax2.grid()
# amplitudes
ax3 = fig.add_subplot(313, sharex=ax1)
ax3.plot(ts[onsets], amplitudes, linewidth=MAJOR_LW, label='Amplitudes')
ax3.set_xlabel('Time (s)')
ax3.set_ylabel('Amplitude')
ax3.legend()
ax3.grid()
# make layout tight
fig.tight_layout()
# save to file
if path is not None:
path = utils.normpath(path)
root, ext = os.path.splitext(path)
ext = ext.lower()
if ext not in ['png', 'jpg']:
path = root + '.png'
fig.savefig(path, dpi=200, bbox_inches='tight')
# show
if show:
plt.show()
else:
# close
plt.close(fig)
def plot_clustering(data=None, clusters=None, path=None, show=False):
"""Create a summary plot of a data clustering.
Parameters
----------
data : array
An m by n array of m data samples in an n-dimensional space.
clusters : dict
Dictionary with the sample indices (rows from `data`) for each cluster.
path : str, optional
If provided, the plot will be saved to the specified file.
show : bool, optional
If True, show the plot immediately.
"""
fig = plt.figure()
fig.suptitle('Clustering Summary')
ymin, ymax = _yscaling(data, alpha=1.2)
# determine number of clusters
keys = list(clusters)
nc = len(keys)
if nc <= 4:
nrows = 2
ncols = 4
else:
area = nc + 4
# try to fit to a square
nrows = int(np.ceil(np.sqrt(area)))
if nrows > MAX_ROWS:
# prefer to increase number of columns
nrows = MAX_ROWS
ncols = int(np.ceil(area / float(nrows)))
# plot grid
gs = gridspec.GridSpec(nrows, ncols, hspace=0.2, wspace=0.2)
# global axes
ax_global = fig.add_subplot(gs[:2, :2])
# cluster axes
c_grid = np.ones((nrows, ncols), dtype='bool')
c_grid[:2, :2] = False
c_rows, c_cols = np.nonzero(c_grid)
# generate color map
x = np.linspace(0., 1., nc)
cmap = plt.get_cmap('rainbow')
for i, k in enumerate(keys):
aux = data[clusters[k]]
color = cmap(x[i])
label = 'Cluster %s' % k
ax = fig.add_subplot(gs[c_rows[i], c_cols[i]], sharex=ax_global)
ax.set_ylim([ymin, ymax])
ax.set_title(label)
ax.grid()
if len(aux) > 0:
ax_global.plot(aux.T, color=color, lw=MINOR_LW, alpha=0.7)
ax.plot(aux.T, color=color, lw=MAJOR_LW)
ax_global.set_title('All Clusters')
ax_global.set_ylim([ymin, ymax])
ax_global.grid()
# make layout tight
gs.tight_layout(fig)
# save to file
if path is not None:
path = utils.normpath(path)
root, ext = os.path.splitext(path)
ext = ext.lower()
if ext not in ['png', 'jpg']:
path = root + '.png'
fig.savefig(path, dpi=200, bbox_inches='tight')
# show
if show:
plt.show()
else:
# close
plt.close(fig)
def plot_biometrics(assessment=None, eer_idx=None, path=None, show=False):
"""Create a summary plot of a biometrics test run.
Parameters
----------
assessment : dict
Classification assessment results.
eer_idx : int, optional
Classifier reference index for the Equal Error Rate.
path : str, optional
If provided, the plot will be saved to the specified file.
show : bool, optional
If True, show the plot immediately.
"""
fig = plt.figure()
fig.suptitle('Biometrics Summary')
c_sub = ['#008bff', '#8dd000']
c_global = ['#0037ff', 'g']
ths = assessment['thresholds']
auth_ax = fig.add_subplot(121)
id_ax = fig.add_subplot(122)
# subject results
for sub in six.iterkeys(assessment['subject']):
auth_rates = assessment['subject'][sub]['authentication']['rates']
_ = _plot_rates(ths,
auth_rates, ['FAR', 'FRR'],
lw=MINOR_LW,
colors=c_sub,
alpha=0.4,
eer_idx=None,
labels=False,
ax=auth_ax)
id_rates = assessment['subject'][sub]['identification']['rates']
_ = _plot_rates(ths,
id_rates, ['MR', 'RR'],
lw=MINOR_LW,
colors=c_sub,
alpha=0.4,
eer_idx=None,
labels=False,
ax=id_ax)
# global results
auth_rates = assessment['global']['authentication']['rates']
_ = _plot_rates(ths,
auth_rates, ['FAR', 'FRR'],
lw=MAJOR_LW,
colors=c_global,
alpha=1,
eer_idx=eer_idx,
labels=True,
ax=auth_ax)
id_rates = assessment['global']['identification']['rates']
_ = _plot_rates(ths,
id_rates, ['MR', 'RR'],
lw=MAJOR_LW,
colors=c_global,
alpha=1,
eer_idx=eer_idx,
labels=True,
ax=id_ax)
# set labels and grids
auth_ax.set_xlabel('Threshold')
auth_ax.set_ylabel('Authentication')
auth_ax.grid()
auth_ax.legend()
id_ax.set_xlabel('Threshold')
id_ax.set_ylabel('Identification')
id_ax.grid()
id_ax.legend()
# make layout tight
fig.tight_layout()
# save to file
if path is not None:
path = utils.normpath(path)
root, ext = os.path.splitext(path)
ext = ext.lower()
if ext not in ['png', 'jpg']:
path = root + '.png'
fig.savefig(path, dpi=200, bbox_inches='tight')
# show
if show:
plt.show()
else:
# close
plt.close(fig)
