API Reference¶

This part of the documentation details the complete BioSPPy API.

Packages¶

biosppy.signals

Modules¶

biosppy.clustering
biosppy.metrics
biosppy.plotting
biosppy.timing
biosppy.utils

biosppy.clustering ¶

This module provides various unsupervised machine learning (clustering) algorithms.

copyright:	2015-2018 by Instituto de Telecomunicacoes
license:	BSD 3-clause, see LICENSE for more details.

biosppy.clustering.centroid_templates(data=None, clusters=None, ntemplates=1)[source]¶

Template selection based on cluster centroids.

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. ntemplates (int, optional) – Number of templates to extract; if more than 1, k-means is used to obtain more templates.
Returns:	templates (array) – Selected templates from the input data.

biosppy.clustering.coassoc_partition(coassoc=None, k=0, linkage='average')[source]¶

Extract the consensus partition from a co-association matrix using hierarchical agglomerative methods.

Parameters:	coassoc (array) – Co-association matrix. k (int, optional) – Number of clusters to extract; if 0 uses the life-time criterion. linkage (str, optional) – Linkage criterion for final partition extraction; one of ‘average’, ‘complete’, ‘single’, or ‘weighted’.
Returns:	clusters (dict) – Dictionary with the sample indices (rows from ‘data’) for each found cluster; outliers have key -1; clusters are assigned integer keys starting at 0.

biosppy.clustering.consensus(data=None, k=0, linkage='average', fcn=None, grid=None)[source]¶

Perform clustering based in an ensemble of partitions.

Parameters:

data (array) – An m by n array of m data samples in an n-dimensional space.
k (int, optional) – Number of clusters to extract; if 0 uses the life-time criterion.
linkage (str, optional) – Linkage criterion for final partition extraction; one of ‘average’, ‘centroid’, ‘complete’, ‘median’, ‘single’, ‘ward’, or ‘weighted’.
fcn (function) – A clustering function.
grid (dict, list, optional) – A (list of) dictionary with parameters for each run of the clustering method (see sklearn.model_selection.ParameterGrid).

Returns:

clusters (dict) – Dictionary with the sample indices (rows from ‘data’) for each found cluster; outliers have key -1; clusters are assigned integer keys starting at 0.

biosppy.clustering.consensus_kmeans(data=None, k=0, linkage='average', nensemble=100, kmin=None, kmax=None)[source]¶

Perform clustering based on an ensemble of k-means partitions.

Parameters:

data (array) – An m by n array of m data samples in an n-dimensional space.
k (int, optional) – Number of clusters to extract; if 0 uses the life-time criterion.
linkage (str, optional) – Linkage criterion for final partition extraction; one of ‘average’, ‘centroid’, ‘complete’, ‘median’, ‘single’, ‘ward’, or ‘weighted’.
nensemble (int, optional) – Number of partitions in the ensemble.
kmin (int, optional) – Minimum k for the k-means partitions; defaults to $\sqrt{m}/2$ .
kmax (int, optional) – Maximum k for the k-means partitions; defaults to $\sqrt{m}$ .

Returns:

clusters (dict) – Dictionary with the sample indices (rows from ‘data’) for each found cluster; outliers have key -1; clusters are assigned integer keys starting at 0.

biosppy.clustering.create_coassoc(ensemble=None, N=None)[source]¶

Create the co-association matrix from a clustering ensemble.

Parameters:	ensemble (list) – Clustering ensemble partitions. N (int) – Number of data samples.
Returns:	coassoc (array) – Co-association matrix.

biosppy.clustering.create_ensemble(data=None, fcn=None, grid=None)[source]¶

Create an ensemble of partitions of the data using the given clustering method.

Parameters:	data (array) – An m by n array of m data samples in an n-dimensional space. fcn (function) – A clustering function. grid (dict, list, optional) – A (list of) dictionary with parameters for each run of the clustering method (see sklearn.model_selection.ParameterGrid).
Returns:	ensemble (list) – Obtained ensemble partitions.

biosppy.clustering.dbscan(data=None, min_samples=5, eps=0.5, metric='euclidean', metric_args=None)[source]¶

Perform clustering using the DBSCAN algorithm [EKSX96].

The algorithm works by grouping data points that are closely packed together (with many nearby neighbors), marking as outliers points that lie in low-density regions.

Parameters:

data (array) – An m by n array of m data samples in an n-dimensional space.
min_samples (int, optional) – Minimum number of samples in a cluster.
eps (float, optional) – Maximum distance between two samples in the same cluster.
metric (str, optional) – Distance metric (see scipy.spatial.distance).
metric_args (dict, optional) – Additional keyword arguments to pass to the distance function.

Returns:

clusters (dict) – Dictionary with the sample indices (rows from ‘data’) for each found cluster; outliers have key -1; clusters are assigned integer keys starting at 0.

References

[EKSX96]

M. Ester, H. P. Kriegel, J. Sander, and X. Xu, “A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise”, Proceedings of the 2nd International Conf. on Knowledge Discovery and Data Mining, pp. 226-231, 1996.

biosppy.clustering.hierarchical(data=None, k=0, linkage='average', metric='euclidean', metric_args=None)[source]¶

Perform clustering using hierarchical agglomerative algorithms.

Parameters:	data (array) – An m by n array of m data samples in an n-dimensional space. k (int, optional) – Number of clusters to extract; if 0 uses the life-time criterion. linkage (str, optional) – Linkage criterion; one of ‘average’, ‘centroid’, ‘complete’, ‘median’, ‘single’, ‘ward’, or ‘weighted’. metric (str, optional) – Distance metric (see ‘biosppy.metrics’). metric_args (dict, optional) – Additional keyword arguments to pass to the distance function.
Returns:	clusters (dict) – Dictionary with the sample indices (rows from ‘data’) for each found cluster; outliers have key -1; clusters are assigned integer keys starting at 0.
Raises:	`TypeError` – If ‘metric’ is not a string. `ValueError` – When the ‘linkage’ is unknown. `ValueError` – When ‘metric’ is not ‘euclidean’ when using ‘centroid’, ‘median’, or ‘ward’ linkage. `ValueError` – When ‘k’ is larger than the number of data samples.

biosppy.clustering.kmeans(data=None, k=None, init='random', max_iter=300, n_init=10, tol=0.0001)[source]¶

Perform clustering using the k-means algorithm.

Parameters:

data (array) – An m by n array of m data samples in an n-dimensional space.
k (int) – Number of clusters to extract.
init (str, array, optional) – If string, one of ‘random’ or ‘k-means++’; if array, it should be of shape (n_clusters, n_features), specifying the initial centers.
max_iter (int, optional) – Maximum number of iterations.
n_init (int, optional) – Number of initializations.
tol (float, optional) – Relative tolerance to declare convergence.

Returns:

clusters (dict) – Dictionary with the sample indices (rows from ‘data’) for each found cluster; outliers have key -1; clusters are assigned integer keys starting at 0.

biosppy.clustering.mdist_templates(data=None, clusters=None, ntemplates=1, metric='euclidean', metric_args=None)[source]¶

Template selection based on the MDIST method [UlRJ04].

Extends the original method with the option of also providing a data clustering, in which case the MDIST criterion is applied for each cluster [LCSF14].

Parameters:

data (array) – An m by n array of m data samples in an n-dimensional space.
clusters (dict, optional) – Dictionary with the sample indices (rows from data) for each cluster.
ntemplates (int, optional) – Number of templates to extract.
metric (str, optional) – Distance metric (see scipy.spatial.distance).
metric_args (dict, optional) – Additional keyword arguments to pass to the distance function.

Returns:

templates (array) – Selected templates from the input data.

References

[UlRJ04]

U. Uludag, A. Ross, A. Jain, “Biometric template selection and update: a case study in fingerprints”, Pattern Recognition 37, 2004

[LCSF14]

A. Lourenco, C. Carreiras, H. Silva, A. Fred, “ECG biometrics: A template selection approach”, 2014 IEEE International Symposium on Medical Measurements and Applications (MeMeA), 2014

biosppy.clustering.outliers_dbscan(data=None, min_samples=5, eps=0.5, metric='euclidean', metric_args=None)[source]¶

Perform outlier removal using the DBSCAN algorithm.

Parameters:

data (array) – An m by n array of m data samples in an n-dimensional space.
min_samples (int, optional) – Minimum number of samples in a cluster.
eps (float, optional) – Maximum distance between two samples in the same cluster.
metric (str, optional) – Distance metric (see scipy.spatial.distance).
metric_args (dict, optional) – Additional keyword arguments to pass to the distance function.

Returns:

clusters (dict) – Dictionary with the sample indices (rows from ‘data’) for the outliers (key -1) and the normal (key 0) groups.
templates (dict) – Elements from ‘data’ for the outliers (key -1) and the normal (key 0) groups.

biosppy.clustering.outliers_dmean(data=None, alpha=0.5, beta=1.5, metric='euclidean', metric_args=None, max_idx=None)[source]¶

Perform outlier removal using the DMEAN algorithm [LCSF13].

A sample is considered valid if it cumulatively verifies:

distance to average template smaller than a (data derived) threshold ‘T’;
sample minimum greater than a (data derived) threshold ‘M’;
sample maximum smaller than a (data derived) threshold ‘N’;
position of the sample maximum is the same as the given index [optional].

For a set of $\{X_1, ..., X_n\}$ $n$ samples:

$\widetilde{X} = \frac{1}{n} \sum_{i=1}^{n}{X_i} d_i = dist(X_i, \widetilde{X}) D_m = \frac{1}{n} \sum_{i=1}^{n}{d_i} D_s = \sqrt{\frac{1}{n - 1} \sum_{i=1}^{n}{(d_i - D_m)^2}} T = D_m + \alpha * D_s M = \beta * median(\{\max{X_i}, i=1, ..., n \}) N = \beta * median(\{\min{X_i}, i=1, ..., n \})$

Parameters:

data (array) – An m by n array of m data samples in an n-dimensional space.
alpha (float, optional) – Parameter for the distance threshold.
beta (float, optional) – Parameter for the maximum and minimum thresholds.
metric (str, optional) – Distance metric (see scipy.spatial.distance).
metric_args (dict, optional) – Additional keyword arguments to pass to the distance function.
max_idx (int, optional) – Index of the expected maximum.

Returns:

clusters (dict) – Dictionary with the sample indices (rows from ‘data’) for the outliers (key -1) and the normal (key 0) groups.
templates (dict) – Elements from ‘data’ for the outliers (key -1) and the normal (key 0) groups.

References

[LCSF13]

A. Lourenco, H. Silva, C. Carreiras, A. Fred, “Outlier Detection in Non-intrusive ECG Biometric System”, Image Analysis and Recognition, vol. 7950, pp. 43-52, 2013

biosppy.metrics ¶

This module provides pairwise distance computation methods.

copyright:	2015-2018 by Instituto de Telecomunicacoes
license:	BSD 3-clause, see LICENSE for more details.

biosppy.metrics.cdist(XA, XB, metric='euclidean', p=2, V=None, VI=None, w=None)[source]¶

Computes distance between each pair of the two collections of inputs.

Wraps scipy.spatial.distance.cdist.

Parameters:

XA (array) – An $m_A$ by $n$ array of $m_A$ original observations in an $n$ -dimensional space.
XB (array) – An $m_B$ by $n$ array of $m_B$ original observations in an $n$ -dimensional space.
metric (str, function, optional) – The distance metric to use; the distance can be ‘braycurtis’, ‘canberra’, ‘chebyshev’, ‘cityblock’, ‘correlation’, ‘cosine’, ‘dice’, ‘euclidean’, ‘hamming’, ‘jaccard’, ‘kulsinski’, ‘mahalanobis’, ‘matching’, ‘minkowski’, ‘pcosine’, ‘rogerstanimoto’, ‘russellrao’, ‘seuclidean’, ‘sokalmichener’, ‘sokalsneath’, ‘sqeuclidean’, ‘yule’.
p (float, optional) – The p-norm to apply (for Minkowski, weighted and unweighted).
w (array, optional) – The weight vector (for weighted Minkowski).
V (array, optional) – The variance vector (for standardized Euclidean).
VI (array, optional) – The inverse of the covariance matrix (for Mahalanobis).

Returns:

Y (array) – An $m_A$ by $m_B$ distance matrix is returned. For each $i$ and $j$ , the metric dist(u=XA[i], v=XB[j]) is computed and stored in the $ij$ th entry.

biosppy.metrics.pcosine(u, v)[source]¶

Computes the Cosine distance (positive space) between 1-D arrays.

The Cosine distance (positive space) between u and v is defined as

$d(u, v) = 1 - abs \left( \frac{u \cdot v}{||u||_2 ||v||_2} \right)$

where $u \cdot v$ is the dot product of $u$ and $v$ .

Parameters:	u (array) – Input array. v (array) – Input array.
Returns:	cosine (float) – Cosine distance between u and v.

biosppy.metrics.pdist(X, metric='euclidean', p=2, w=None, V=None, VI=None)[source]¶

Pairwise distances between observations in n-dimensional space.

Wraps scipy.spatial.distance.pdist.

Parameters:

X (array) – An m by n array of m original observations in an n-dimensional space.
metric (str, function, optional) – The distance metric to use; the distance can be ‘braycurtis’, ‘canberra’, ‘chebyshev’, ‘cityblock’, ‘correlation’, ‘cosine’, ‘dice’, ‘euclidean’, ‘hamming’, ‘jaccard’, ‘kulsinski’, ‘mahalanobis’, ‘matching’, ‘minkowski’, ‘pcosine’, ‘rogerstanimoto’, ‘russellrao’, ‘seuclidean’, ‘sokalmichener’, ‘sokalsneath’, ‘sqeuclidean’, ‘yule’.
p (float, optional) – The p-norm to apply (for Minkowski, weighted and unweighted).
w (array, optional) – The weight vector (for weighted Minkowski).
V (array, optional) – The variance vector (for standardized Euclidean).
VI (array, optional) – The inverse of the covariance matrix (for Mahalanobis).

Returns:

Y (array) – Returns a condensed distance matrix Y. For each $i$ and $j$ (where $i<j<n$ ), the metric dist(u=X[i], v=X[j]) is computed and stored in entry ij.

biosppy.metrics.squareform(X, force='no', checks=True)[source]¶

Converts a vector-form distance vector to a square-form distance matrix, and vice-versa.

Wraps scipy.spatial.distance.squareform.

Parameters:

X (array) – Either a condensed or redundant distance matrix.
force (str, optional) – As with MATLAB(TM), if force is equal to ‘tovector’ or ‘tomatrix’, the input will be treated as a distance matrix or distance vector respectively.
checks (bool, optional) – If checks is set to False, no checks will be made for matrix symmetry nor zero diagonals. This is useful if it is known that X - X.T1 is small and diag(X) is close to zero. These values are ignored any way so they do not disrupt the squareform transformation.

Returns:

Y (array) – If a condensed distance matrix is passed, a redundant one is returned, or if a redundant one is passed, a condensed distance matrix is returned.

biosppy.plotting ¶

This module provides utilities to plot data.

copyright:	2015-2018 by Instituto de Telecomunicacoes
license:	BSD 3-clause, see LICENSE for more details.

biosppy.plotting.plot_abp(ts=None, raw=None, filtered=None, onsets=None, heart_rate_ts=None, heart_rate=None, path=None, show=False)[source]¶

Create a summary plot from the output of signals.abp.abp.

Parameters:

ts (array) – Signal time axis reference (seconds).
raw (array) – Raw ABP signal.
filtered (array) – Filtered ABP signal.
onsets (array) – Indices of ABP pulse onsets.
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.

biosppy.plotting.plot_acc(ts=None, raw=None, vm=None, sm=None, path=None, show=False)[source]¶

Create a summary plot from the output of signals.acc.acc.

Parameters:	ts (array) – Signal time axis reference (seconds). raw (array) – Raw ACC signal. vm (array) – Vector Magnitude feature of the signal. sm (array) – Signal Magnitude feature of the signal path (str, optional) – If provided, the plot will be saved to the specified file. show (bool, optional) – If True, show the plot immediately.

biosppy.plotting.plot_bcg(ts=None, raw=None, filtered=None, jpeaks=None, templates_ts=None, templates=None, heart_rate_ts=None, heart_rate=None, path=None, show=False)[source]¶

Create a summary plot from the output of signals.bcg.bcg.

Parameters:

ts (array) – Signal time axis reference (seconds).
raw (array) – Raw ECG signal.
filtered (array) – Filtered ECG signal.
ipeaks (array) – I-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.

biosppy.plotting.plot_biometrics(assessment=None, eer_idx=None, path=None, show=False)[source]¶

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.

biosppy.plotting.plot_bvp(ts=None, raw=None, filtered=None, onsets=None, heart_rate_ts=None, heart_rate=None, path=None, show=False)[source]¶

Create a summary plot from the output of signals.bvp.bvp.

Parameters:

ts (array) – Signal time axis reference (seconds).
raw (array) – Raw BVP signal.
filtered (array) – Filtered BVP signal.
onsets (array) – Indices of BVP pulse onsets.
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.

biosppy.plotting.plot_clustering(data=None, clusters=None, path=None, show=False)[source]¶

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.

biosppy.plotting.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)[source]¶

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.

biosppy.plotting.plot_eda(ts=None, raw=None, filtered=None, onsets=None, peaks=None, amplitudes=None, path=None, show=False)[source]¶

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.

biosppy.plotting.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)[source]¶

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.

biosppy.plotting.plot_emg(ts=None, sampling_rate=None, raw=None, filtered=None, onsets=None, processed=None, path=None, show=False)[source]¶

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.

biosppy.plotting.plot_filter(ftype='FIR', band='lowpass', order=None, frequency=None, sampling_rate=1000.0, path=None, show=True, **kwargs)[source]¶

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.

biosppy.plotting.plot_pcg(ts=None, raw=None, filtered=None, peaks=None, heart_sounds=None, heart_rate_ts=None, inst_heart_rate=None, path=None, show=False)[source]¶: Create a summary plot from the output of signals.pcg.pcg. :param ts: Signal time axis reference (seconds). :type ts: array :param raw: Raw PCG signal. :type raw: array :param filtered: Filtered PCG signal. :type filtered: array :param peaks: Peak location indices. :type peaks: array :param heart_sounds: Classification of peaks as S1 or S2 :type heart_sounds: array :param heart_rate_ts: Heart rate time axis reference (seconds). :type heart_rate_ts: array :param inst_heart_rate: Instantaneous heart rate (bpm). :type inst_heart_rate: array :param path: If provided, the plot will be saved to the specified file. :type path: str, optional :param show: If True, show the plot immediately. :type show: bool, optional

biosppy.plotting.plot_ppg(ts=None, raw=None, filtered=None, onsets=None, heart_rate_ts=None, heart_rate=None, path=None, show=False)[source]¶

Create a summary plot from the output of signals.ppg.ppg.

Parameters:

ts (array) – Signal time axis reference (seconds).
raw (array) – Raw PPG signal.
filtered (array) – Filtered PPG signal.
onsets (array) – Indices of PPG pulse onsets.
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.

biosppy.plotting.plot_resp(ts=None, raw=None, filtered=None, zeros=None, resp_rate_ts=None, resp_rate=None, path=None, show=False)[source]¶

Create a summary plot from the output of signals.ppg.ppg.

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.

biosppy.plotting.plot_spectrum(signal=None, sampling_rate=1000.0, path=None, show=True)[source]¶

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.

biosppy.timing ¶

This module provides simple methods to measure computation times.

copyright:	2015-2018 by Instituto de Telecomunicacoes
license:	BSD 3-clause, see LICENSE for more details.

biosppy.timing.clear(name=None)[source]¶

Clear the clock.

Parameters:	name (str, optional) – Name of the clock; if None, uses the default name.

biosppy.timing.clear_all()[source]¶: Clear all clocks.

biosppy.timing.tac(name=None)[source]¶

Stop the clock.

Parameters:	name (str, optional) – Name of the clock; if None, uses the default name.
Returns:	delta (float) – Elapsed time, in seconds.
Raises:	KeyError if the name of the clock is unknown.

biosppy.timing.tic(name=None)[source]¶

Start the clock.

Parameters:	name (str, optional) – Name of the clock; if None, uses the default name.

biosppy.utils ¶

This module provides several frequently used functions and hacks.

copyright:	2015-2018 by Instituto de Telecomunicacoes
license:	BSD 3-clause, see LICENSE for more details.

class biosppy.utils.ReturnTuple(values, names=None)[source]¶

Bases: tuple

A named tuple to use as a hybrid tuple-dict return object.

Parameters:	values (iterable) – Return values. names (iterable, optional) – Names for return values.
Raises:	`ValueError` – If the number of values differs from the number of names. `ValueError` – If any of the items in names: * contain non-alphanumeric characters; * are Python keywords; * start with a number; * are duplicates.

as_dict()[source]¶

Convert to an ordered dictionary.

Returns:	out (OrderedDict) – An OrderedDict representing the return values.

keys()[source]¶

Return the value names.

Returns:	out (list) – The keys in the mapping.

biosppy.utils.fileparts(path)[source]¶

split a file path into its directory, name, and extension.

Parameters:	path (str) – Input file path.
Returns:	dirname (str) – File directory. fname (str) – File name. ext (str) – File extension.

Notes

Removes the dot (‘.’) from the extension.

biosppy.utils.fullfile(*args)[source]¶

Join one or more file path components, assuming the last is the extension.

Parameters:	args (list, optional*) – Components to concatenate.
Returns:	fpath (str) – The concatenated file path.

biosppy.utils.highestAveragesAllocator(votes, k, divisor='dHondt', check=False)[source]¶

Allocate k seats proportionally using the Highest Averages Method.

Parameters:	votes (list) – Number of votes for each class/party/cardinal. k (int) – Total number o seats to allocate. divisor (str, optional) – Divisor method; one of ‘dHondt’, ‘Huntington-Hill’, ‘Sainte-Lague’, ‘Imperiali’, or ‘Danish’. check (bool, optional) – If True, limits the number of seats to the total number of votes.
Returns:	seats (list) – Number of seats for each class/party/cardinal.

biosppy.utils.normpath(path)[source]¶

Normalize a path.

Parameters:	path (str) – The path to normalize.
Returns:	npath (str) – The normalized path.

biosppy.utils.random_fraction(indx, fraction, sort=True)[source]¶

Select a random fraction of an input list of elements.

Parameters:

indx (list, array) – Elements to partition.
fraction (int, float) – Fraction to select.
sort (bool, optional) – If True, output lists will be sorted.

Returns:

use (list, array) – Selected elements.
unuse (list, array) – Remaining elements.

biosppy.utils.remainderAllocator(votes, k, reverse=True, check=False)[source]¶

Allocate k seats proportionally using the Remainder Method.

Also known as Hare-Niemeyer Method. Uses the Hare quota.

Parameters:	votes (list) – Number of votes for each class/party/cardinal. k (int) – Total number o seats to allocate. reverse (bool, optional) – If True, allocates remaining seats largest quota first. check (bool, optional) – If True, limits the number of seats to the total number of votes.
Returns:	seats (list) – Number of seats for each class/party/cardinal.

biosppy.utils.walktree(top=None, spec=None)[source]¶

Iterator to recursively descend a directory and return all files matching the spec.

Parameters:

top (str, optional) – Starting directory; if None, defaults to the current working directoty.
spec (str, optional) –
Regular expression to match the desired files; if None, matches all files; typical patterns:
- r’.txt$’ - matches files with ‘.txt’ extension;
- r’^File_’ - matches files starting with ‘File_’
- r’^File_.+.txt$’ - matches files starting with ‘File_’ and ending with the ‘.txt’ extension.

Yields:

fpath (str) – Absolute file path.

Notes

Partial matches are also selected.

API Reference¶

Packages¶

Modules¶

biosppy.clustering¶

biosppy.metrics¶

biosppy.plotting¶

biosppy.timing¶

biosppy.utils¶

biosppy.clustering ¶

biosppy.metrics ¶

biosppy.plotting ¶

biosppy.timing ¶

biosppy.utils ¶