API Reference¶
This part of the documentation details the complete BioSPPy
API.
Packages¶
Modules¶
biosppy.clustering¶
This module provides various unsupervised machine learning (clustering) algorithms.
copyright: 


license:  BSD 3clause, 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 ndimensional 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, kmeans 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 coassociation matrix using hierarchical agglomerative methods.
Parameters:  coassoc (array) – Coassociation matrix.
 k (int, optional) – Number of clusters to extract; if 0 uses the lifetime 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 ndimensional space.
 k (int, optional) – Number of clusters to extract; if 0 uses the lifetime 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 kmeans partitions.
Parameters:  data (array) – An m by n array of m data samples in an ndimensional space.
 k (int, optional) – Number of clusters to extract; if 0 uses the lifetime 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 kmeans partitions; defaults to .
 kmax (int, optional) – Maximum k for the kmeans partitions; defaults to .
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 coassociation matrix from a clustering ensemble.
Parameters:  ensemble (list) – Clustering ensemble partitions.
 N (int) – Number of data samples.
Returns: coassoc (array) – Coassociation 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 ndimensional 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 lowdensity regions.
Parameters:  data (array) – An m by n array of m data samples in an ndimensional 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 DensityBased Algorithm for Discovering Clusters in Large Spatial Databases with Noise”, Proceedings of the 2nd International Conf. on Knowledge Discovery and Data Mining, pp. 226231, 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 ndimensional space.
 k (int, optional) – Number of clusters to extract; if 0 uses the lifetime 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 kmeans algorithm.
Parameters:  data (array) – An m by n array of m data samples in an ndimensional space.
 k (int) – Number of clusters to extract.
 init (str, array, optional) – If string, one of ‘random’ or ‘kmeans++’; 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 ndimensional 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 ndimensional 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 samples:
Parameters:  data (array) – An m by n array of m data samples in an ndimensional 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 Nonintrusive ECG Biometric System”, Image Analysis and Recognition, vol. 7950, pp. 4352, 2013
biosppy.metrics¶
This module provides pairwise distance computation methods.
copyright: 


license:  BSD 3clause, 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 by array of original observations in an dimensional space.
 XB (array) – An by array of original observations in an 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 pnorm 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 by distance matrix is returned. For each and , the metric
dist(u=XA[i], v=XB[j])
is computed and stored in the th entry.

biosppy.metrics.
pcosine
(u, v)[source]¶ Computes the Cosine distance (positive space) between 1D arrays.
The Cosine distance (positive space) between u and v is defined as
where is the dot product of and .
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 ndimensional space.
Wraps scipy.spatial.distance.pdist.
Parameters:  X (array) – An m by n array of m original observations in an ndimensional 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 pnorm 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 and (where ), the metric
dist(u=X[i], v=X[j])
is computed and stored in entryij
.

biosppy.metrics.
squareform
(X, force='no', checks=True)[source]¶ Converts a vectorform distance vector to a squareform distance matrix, and viceversa.
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 anddiag(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: 


license:  BSD 3clause, 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) – Ipeak 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 ndimensional 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) – Rpeak 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:
 Lowpass filter (‘lowpass’);
 Highpass filter (‘highpass’);
 Bandpass filter (‘bandpass’);
 Bandstop 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.
 ftype (str) –

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 (onesided).
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: 


license:  BSD 3clause, 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.utils¶
This module provides several frequently used functions and hacks.
copyright: 


license:  BSD 3clause, see LICENSE for more details. 

class
biosppy.utils.
ReturnTuple
(values, names=None)[source]¶ Bases:
tuple
A named tuple to use as a hybrid tupledict 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 nonalphanumeric characters; * are Python keywords; * start with a number; * are duplicates.

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’, ‘HuntingtonHill’, ‘SainteLague’, ‘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 HareNiemeyer 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.