API Reference

This part of the documentation details the complete BioSPPy API.

Packages

biosppy.signals

Modules

biosppy.biometrics 

This module provides classifier interfaces for identity recognition (biometrics) applications. The core API methods are: * enroll: add a new subject; * dismiss: remove an existing subject; * identify: determine the identity of collected biometric dataset; * authenticate: verify the identity of collected biometric dataset.

copyright:

2015-2018 by Instituto de Telecomunicacoes

license:

BSD 3-clause, see LICENSE for more details.

class biosppy.biometrics.BaseClassifier[source]

Bases: object

Base biometric classifier class.

This class is a skeleton for actual classifier classes. The following methods must be overridden or adapted to build a new classifier:

__init__
_authenticate
_get_thresholds
_identify
_prepare
_train
_update

EER_IDX

Reference index for the Equal Error Rate.

Type:: int

EER_IDX = 0

authenticate(data, subject, threshold=None)[source]

Authenticate a set of feature vectors, allegedly belonging to the given subject.

Parameters:

data (array) – Input test data.
subject (hashable) – Subject identity.
threshold (int, float, optional) – Authentication threshold.

Returns:

decision (array) – Authentication decision for each input sample.

batch_train(data=None)[source]

Train the classifier in batch mode.

Parameters:: data (dict) – Dictionary holding training data for each subject; if the object for a subject is None, performs a dismiss.

check_subject(subject)[source]

Check if a subject is enrolled.

Parameters:: subject (hashable) – Subject identity.
Returns:: check (bool) – If True, the subject is enrolled.

classmethod cross_validation(data, labels, cv, thresholds=None, **kwargs)[source]

Perform Cross Validation (CV) on a data set.

Parameters:

data (array) – An m by n array of m data samples in an n-dimensional space.
labels (list, array) – A list of m class labels.
cv (CV iterator) – A sklearn.model_selection iterator.
thresholds (array, optional) – Classifier thresholds to use.
**kwargs (dict, optional) – Classifier parameters.

Returns:

runs (list) – Evaluation results for each CV run.
assessment (dict) – Final CV biometric statistics.

dismiss(subject=None, deferred=False)[source]

Remove a subject.

Parameters:

subject (hashable) – Subject identity.
deferred (bool, optional) – If True, computations are delayed until flush is called.

Raises:

SubjectError – If the subject to remove is not enrolled.

Notes

When using deferred calls, a dismiss overrides a previous enroll for the same subject.

enroll(data=None, subject=None, deferred=False)[source]

Enroll new data for a subject.

If the subject is already enrolled, new data is combined with existing data.

Parameters:

data (array) – Data to enroll.
subject (hashable) – Subject identity.
deferred (bool, optional) – If True, computations are delayed until flush is called.

Notes

When using deferred calls, an enroll overrides a previous dismiss for the same subject.

evaluate(data, thresholds=None, path=None, show=False)[source]

Assess the performance of the classifier in both authentication and identification scenarios.

Parameters:

data (dict) – Dictionary holding test data for each subject.
thresholds (array, optional) – Classifier thresholds to use.
path (str, optional) – If provided, the plot will be saved to the specified file.
show (bool, optional) – If True, show a summary plot.

Returns:

classification (dict) – Classification results.
assessment (dict) – Biometric statistics.

flush()[source]: Flush deferred computations.

get_auth_thr(subject, ready=False)[source]

Get the authentication threshold of a subject.

Parameters:

subject (hashable) – Subject identity.
ready (bool, optional) – If True, subject is the internal classifier label.

Returns:

threshold (int, float) – Threshold value.

get_id_thr(subject, ready=False)[source]

Get the identification threshold of a subject.

Parameters:

subject (hashable) – Subject identity.
ready (bool, optional) – If True, subject is the internal classifier label.

Returns:

threshold (int, float) – Threshold value.

get_thresholds(force=False)[source]

Get an array of reasonable thresholds.

Parameters:: force (bool, optional) – If True, forces generation of thresholds.
Returns:: ths (array) – Generated thresholds.

identify(data, threshold=None)[source]

Identify a set of feature vectors.

Parameters:

data (array) – Input test data.
threshold (int, float, optional) – Identification threshold.

Returns:

subjects (list) – Identity of each input sample.

io_del(label)[source]

Delete subject data.

Parameters:: label (str) – Internal classifier subject label.

io_load(label)[source]

Load enrolled subject data.

Parameters:: label (str) – Internal classifier subject label.
Returns:: data (array) – Subject data.

io_save(label, data)[source]

Save subject data.

Parameters:

label (str) – Internal classifier subject label.
data (array) – Subject data.

list_subjects()[source]

List all the enrolled subjects.

Returns:: subjects (list) – Enrolled subjects.

classmethod load(path)[source]

Load classifier instance from a file.

Parameters:: path (str) – Source file path.
Returns:: clf (object) – Loaded classifier instance.

save(path)[source]

Save classifier instance to a file.

Parameters:: path (str) – Destination file path.

set_auth_thr(subject, threshold, ready=False)[source]

Set the authentication threshold of a subject.

Parameters:

subject (hashable) – Subject identity.
threshold (int, float) – Threshold value.
ready (bool, optional) – If True, subject is the internal classifier label.

set_id_thr(subject, threshold, ready=False)[source]

Set the identification threshold of a subject.

Parameters:

subject (hashable) – Subject identity.
threshold (int, float) – Threshold value.
ready (bool, optional) – If True, subject is the internal classifier label.

update_thresholds(fraction=1.0)[source]

Update subject-specific thresholds based on the enrolled data.

Parameters:: fraction (float, optional) – Fraction of samples to select from training data.

exception biosppy.biometrics.CombinationError[source]

Bases: Exception

Exception raised when the combination method fails.

class biosppy.biometrics.KNN(k=3, metric='euclidean', metric_args=None)[source]

Bases: BaseClassifier

K Nearest Neighbors (k-NN) biometric classifier.

Parameters:

k (int, optional) – Number of neighbors.
metric (str, optional) – Distance metric.
metric_args (dict, optional) – Additional keyword arguments are passed to the distance function.

EER_IDX

Reference index for the Equal Error Rate.

Type:: int

EER_IDX = 0

class biosppy.biometrics.SVM(C=1.0, kernel='linear', degree=3, gamma='auto', coef0=0.0, shrinking=True, tol=0.001, cache_size=200, max_iter=-1, random_state=None)[source]

Bases: BaseClassifier

Support Vector Machines (SVM) biometric classifier.

Wraps the ‘OneClassSVM’ and ‘SVC’ classes from ‘scikit-learn’.

Parameters:

C (float, optional) – Penalty parameter C of the error term.
kernel (str, optional) – Specifies the kernel type to be used in the algorithm. It must be one of ‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’ or a callable. If none is given, ‘rbf’ will be used. If a callable is given it is used to precompute the kernel matrix.
degree (int, optional) – Degree of the polynomial kernel function (‘poly’). Ignored by all other kernels.
gamma (float, optional) – Kernel coefficient for ‘rbf’, ‘poly’ and ‘sigmoid’. If gamma is ‘auto’ then 1/n_features will be used instead.
coef0 (float, optional) – Independent term in kernel function. It is only significant in ‘poly’ and ‘sigmoid’.
shrinking (bool, optional) – Whether to use the shrinking heuristic.
tol (float, optional) – Tolerance for stopping criterion.
cache_size (float, optional) – Specify the size of the kernel cache (in MB).
max_iter (int, optional) – Hard limit on iterations within solver, or -1 for no limit.
random_state (int, RandomState, optional) – The seed of the pseudo random number generator to use when shuffling the data for probability estimation.

EER_IDX

Reference index for the Equal Error Rate.

Type:: int

EER_IDX = -1

exception biosppy.biometrics.SubjectError(subject=None)[source]

Bases: Exception

Exception raised when the subject is unknown.

exception biosppy.biometrics.UntrainedError[source]

Bases: Exception

Exception raised when classifier is not trained.

biosppy.biometrics.assess_classification(results=None, thresholds=None)[source]

Assess the performance of a biometric classification test.

Parameters:

results (dict) – Classification results.
thresholds (array) – Classifier thresholds.

Returns:

assessment (dict) – Classification assessment.

biosppy.biometrics.assess_runs(results=None, subjects=None)[source]

Assess the performance of multiple biometric classification runs.

Parameters:

results (list) – Classification assessment for each run.
subjects (list) – Common target subject classes.

Returns:

assessment (dict) – Global classification assessment.

biosppy.biometrics.combination(results=None, weights=None)[source]

Combine results from multiple classifiers.

Parameters:

results (dict) – Results for each classifier.
weights (dict, optional) – Weight for each classifier.

Returns:

decision (object) – Consensus decision.
confidence (float) – Confidence estimate of the decision.
counts (array) – Weight for each possible decision outcome.
classes (array) – List of possible decision outcomes.

biosppy.biometrics.cross_validation(labels, n_iter=10, test_size=0.1, train_size=None, random_state=None)[source]

Return a Cross Validation (CV) iterator.

Wraps the StratifiedShuffleSplit iterator from sklearn.model_selection. This iterator returns stratified randomized folds, which preserve the percentage of samples for each class.

Parameters:

labels (list, array) – List of class labels for each data sample.
n_iter (int, optional) – Number of splitting iterations.
test_size (float, int, optional) – If float, represents the proportion of the dataset to include in the test split; if int, represents the absolute number of test samples.
train_size (float, int, optional) – If float, represents the proportion of the dataset to include in the train split; if int, represents the absolute number of train samples.
random_state (int, RandomState, optional) – The seed of the pseudo random number generator to use when shuffling the data.

Returns:

cv (CV iterator) – Cross Validation iterator.

biosppy.biometrics.get_auth_rates(TP=None, FP=None, TN=None, FN=None, thresholds=None)[source]

Compute authentication rates from the confusion matrix.

Parameters:

TP (array) – True Positive counts for each classifier threshold.
FP (array) – False Positive counts for each classifier threshold.
TN (array) – True Negative counts for each classifier threshold.
FN (array) – False Negative counts for each classifier threshold.
thresholds (array) – Classifier thresholds.

Returns:

Acc (array) – Accuracy at each classifier threshold.
TAR (array) – True Accept Rate at each classifier threshold.
FAR (array) – False Accept Rate at each classifier threshold.
FRR (array) – False Reject Rate at each classifier threshold.
TRR (array) – True Reject Rate at each classifier threshold.
EER (array) – Equal Error Rate points, with format (threshold, rate).
Err (array) – Error rate at each classifier threshold.
PPV (array) – Positive Predictive Value at each classifier threshold.
FDR (array) – False Discovery Rate at each classifier threshold.
NPV (array) – Negative Predictive Value at each classifier threshold.
FOR (array) – False Omission Rate at each classifier threshold.
MCC (array) – Matthrews Correlation Coefficient at each classifier threshold.

biosppy.biometrics.get_id_rates(H=None, M=None, R=None, N=None, thresholds=None)[source]

Compute identification rates from the confusion matrix.

Parameters:

H (array) – Hit counts for each classifier threshold.
M (array) – Miss counts for each classifier threshold.
R (array) – Reject counts for each classifier threshold.
N (int) – Number of test samples.
thresholds (array) – Classifier thresholds.

Returns:

Acc (array) – Accuracy at each classifier threshold.
Err (array) – Error rate at each classifier threshold.
MR (array) – Miss Rate at each classifier threshold.
RR (array) – Reject Rate at each classifier threshold.
EID (array) – Error of Identification points, with format (threshold, rate).
EER (array) – Equal Error Rate points, with format (threshold, rate).

biosppy.biometrics.get_subject_results(results=None, subject=None, thresholds=None, subjects=None, subject_dict=None, subject_idx=None)[source]

Compute authentication and identification performance metrics for a given subject.

Parameters:

results (dict) – Classification results.
subject (hashable) – True subject label.
thresholds (array) – Classifier thresholds.
subjects (list) – Target subject classes.
subject_dict (bidict) – Subject-label conversion dictionary.
subject_idx (list) – Subject index.

Returns:

assessment (dict) – Authentication and identification results.

biosppy.biometrics.majority_rule(labels=None, random=True)[source]

Determine the most frequent class label.

Parameters:

labels (array, list) – List of clas labels.
random (bool, optional) – If True, will choose randomly in case of tied classes, otherwise the first element is chosen.

Returns:

decision (object) – Consensus decision.
count (int) – Number of elements of the consensus decision.

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', **kwargs)[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’.
kwargs (Possible) –

pfloat
The p-norm to apply (for Minkowski, weighted and unweighted).

warray
The weight vector (for weighted Minkowski).

Varray
The variance vector (for standardized Euclidean).

VIarray
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', **kwargs)[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’.
kwargs (Possible) –

pfloat
The p-norm to apply (for Minkowski, weighted and unweighted).

warray
The weight vector (for weighted Minkowski).

Varray
The variance vector (for standardized Euclidean).

VIarray
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:

2015-2023 by Instituto de Telecomunicacoes

license:

BSD 3-clause, see LICENSE for more details.

biosppy.plotting.add_logo(fig)[source]

biosppy.plotting.color_palette(idx)[source]

Color palette to use throughout the biosppy package

Parameters:: idx (str or int) – identifier of color to use
Returns:: color_id (str) – hexadecimal color code chosen

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, units=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
units (str, optional) – Units of the vertical axis. If provided, the plot title will include the units information. Default is None.
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, units=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).
units (str, optional) – Units of the vertical axis. If provided, the plot title will include the units information. Default is None.
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, edr=None, edl=None, onsets=None, peaks=None, amplitudes=None, units=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.
edr (array) – Electrodermal response signal.
edl (array) – Electrodermal level signal.
onsets (array) – Events onsets indices.
peaks (array) – Events peaks indices.
amplitudes (array) – Amplitudes location indices.
units (str, optional) – Units of the vertical axis. If provided, the plot title will include the units information. Default is None.
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, units=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.
units (str, optional) – Units of the vertical axis. If provided, the plot title will include the units information. Default is None.
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, log_xscale=True, 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).
log_xscale (bool, optional) – Whether to use log scale for x-axis.
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_hrv(rri, rri_trend=None, td_out=None, nl_out=None, fd_out=None, show=False)[source]

Create a summary plot of a HRV analysis.

Parameters:

rri (array) – RR-intervals (ms).
rri_trend (array, optional) – RR-intervals trend (ms).
td_out (dict) – Output of signals.hrv.timedomain.
nl_out (dict) – Output of signals.hrv.nonlinear.
fd_out (dict) – Output of signals.hrv.frequencyomain.
show (bool, optional) – If True, show the plot immediately.

biosppy.plotting.plot_hrv_fbands(frequencies=None, powers=None, fbands=None, method_name=None, legends=None, ax=None, show=False)[source]

Plots the power spectrum and highlights the defined frequency bands from the output of signals.hrv.compute_fbands.

Parameters:

frequencies (array) – Frequency axis.
powers (array) – Power spectrum values for the frequency axis.
fbands (dict, optional) – Dictionary containing the limits of the frequency bands.
method_name (str, optional) – Method that was used to compute the power spectrum.
legends (dict, optional) – Additional legend elements.
ax (axis, optional) – Plot Axis to use.
show (bool, optional) – If True, show the plot immediately.

biosppy.plotting.plot_hrv_hist(rri=None, bins=None, q_hist=None, hti=None, tinn=None, ax=None, show=False)[source]

Plots the RRI histogram with the corresponding geometrical HRV features from the output of signals.hrv.compute_geometrical.

Parameters:

rri (array) – RR-intervals (ms).
bins (array) – Histogram bins.
q_hist (array) – Multilinear function fitted to the histogram.
hti (float) – HTI - HRV triangular index - Integral of the density of the RR interval histogram divided by its height.
tinn (float) – TINN - Baseline width of RR interval histogram (ms).
ax (axis, optional) – Plot Axis to use.
show (bool, optional) – If True, show the plot immediately.

biosppy.plotting.plot_pcg(ts=None, raw=None, filtered=None, peaks=None, heart_sounds=None, heart_rate_ts=None, inst_heart_rate=None, units=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 units: Units of the vertical axis. If provided, the plot title will include

the units information. Default is None.

Parameters:

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_poincare(rri=None, s=None, sd1=None, sd2=None, legends=None, ax=None, show=False)[source]

Plot a Poincaré plot of a series of RR intervals (RRI[i+1] vs. RRI[i]) from the output of signals.hrv.compute_poincare.

Parameters:

rri (array) – RR-intervals (ms).
s (float) – S - Area of the ellipse of the Poincaré plot (ms^2).
sd1 (float) – SD1 - Poincaré plot standard deviation perpendicular to the identity line (ms).
sd2 (float) – SD2 - Poincaré plot standard deviation along the identity line (ms).
legends (dict, optional) – Dictionary of features to add to the plot legend.
ax (axis, optional) – Plot Axis to use.
show (bool, optional) – If True, show the plot immediately.

biosppy.plotting.plot_ppg(ts=None, raw=None, filtered=None, peaks=None, templates_ts=None, templates=None, heart_rate_ts=None, heart_rate=None, units=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.
peaks (array) – Indices of PPG pulse peaks.
templates_ts (array) – Templates time axis reference (seconds).
templates (array) – Extracted PPG templates.
heart_rate_ts (array) – Heart rate time axis reference (seconds).
heart_rate (array) – Instantaneous heart rate (bpm).
units (str, optional) – Units of the vertical axis. If provided, the plot title will include the units information. Default is None.
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, units=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).
units (str, optional) – Units of the vertical axis. If provided, the plot title will include the units information. Default is None.
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_rri(rri, rri_trend=None, legends=None, ax=None, show=False)[source]

Plot a series of RR intervals.

Parameters:

rri (array) – RR-intervals (ms).
rri_trend (array, optional) – RR-intervals trend (ms).
legends (dict, optional) – Dictionary of features to add to the plot legend.
ax (axis, optional) – Plot Axis to use.
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.storage 

This module provides several data storage methods.

copyright:

2015-2018 by Instituto de Telecomunicacoes

license:

BSD 3-clause, see LICENSE for more details.

class biosppy.storage.HDF(path=None, mode='a')[source]

Bases: object

Wrapper class to operate on BioSPPy HDF5 files.

Parameters:

path (str) – Path to the HDF5 file.
mode (str, optional) –
File mode; one of:
- ’a’: read/write, creates file if it does not exist;
- ’r+’: read/write, file must exist;
- ’r’: read only, file must exist;
- ’w’: create file, truncate if it already exists;
- ’w-’: create file, fails if it already esists.

add_event(ts=None, values=None, mdata=None, group='', name=None, compress=False)[source]

Add an event to the file.

Parameters:

ts (array) – Array of time stamps.
values (array, optional) – Array with data for each time stamp.
mdata (dict, optional) – Event metadata.
group (str, optional) – Destination event group.
name (str, optional) – Name of the dataset to create.
compress (bool, optional) – If True, the data will be compressed with gzip.

Returns:

group (str) – Destination group.
name (str) – Name of the created event dataset.

add_header(header=None)[source]

Add header metadata.

Parameters:: header (dict) – Header metadata.

add_signal(signal=None, mdata=None, group='', name=None, compress=False)[source]

Add a signal to the file.

Parameters:

signal (array) – Signal to add.
mdata (dict, optional) – Signal metadata.
group (str, optional) – Destination signal group.
name (str, optional) – Name of the dataset to create.
compress (bool, optional) – If True, the signal will be compressed with gzip.

Returns:

group (str) – Destination group.
name (str) – Name of the created signal dataset.

close()[source]: Close file descriptor.

del_event(group='', name=None)[source]

Delete an event from the file.

Parameters:

group (str, optional) – Event group.
name (str) – Name of the event dataset.

del_event_group(group='')[source]

Delete all events in a file group.

Parameters:

str (group) – Event group.
optional – Event group.

del_signal(group='', name=None)[source]

Delete a signal from the file.

Parameters:

group (str, optional) – Signal group.
name (str) – Name of the dataset.

del_signal_group(group='')[source]

Delete all signals in a file group.

Parameters:: group (str, optional) – Signal group.

get_event(group='', name=None)[source]

Retrieve an event from the file.

Parameters:

group (str, optional) – Event group.
name (str) – Name of the event dataset.

Returns:

ts (array) – Array of time stamps.
values (array) – Array with data for each time stamp.
mdata (dict) – Event metadata.

Notes

Loads the entire event data into memory.

get_header()[source]

Retrieve header metadata.

Returns:: header (dict) – Header metadata.

get_signal(group='', name=None)[source]

Retrieve a signal from the file.

Parameters:

group (str, optional) – Signal group.
name (str) – Name of the signal dataset.

Returns:

signal (array) – Retrieved signal.
mdata (dict) – Signal metadata.

Notes

Loads the entire signal data into memory.

list_events(group='', recursive=False)[source]

List events in the file.

Parameters:

group (str, optional) – Event group.
recursive (bool, optional) – If True, also lists events in sub-groups.

Returns:

events (list) – List of (group, name) tuples of the found events.

list_signals(group='', recursive=False)[source]

List signals in the file.

Parameters:

group (str, optional) – Signal group.
recursive (bool, optional) – If True, also lists signals in sub-groups.

Returns:

signals (list) – List of (group, name) tuples of the found signals.

biosppy.storage.alloc_h5(path)[source]

Prepare an HDF5 file.

Parameters:: path (str) – Path to file.

biosppy.storage.deserialize(path)[source]

Deserialize data from a file using sklearn’s joblib.

Parameters:: path (str) – Source path.
Returns:: data (object) – Deserialized object.

biosppy.storage.dumpJSON(data, path)[source]

Save JSON data to a file.

Parameters:

data (dict) – The JSON data to dump.
path (str) – Destination path.

biosppy.storage.loadJSON(path)[source]

Load JSON data from a file.

Parameters:: path (str) – Source path.
Returns:: data (dict) – The loaded JSON data.

biosppy.storage.load_h5(path, label)[source]

Load data from an HDF5 file.

Parameters:

path (str) – Path to file.
label (hashable) – Data label.

Returns:

data (array) – Loaded data.

biosppy.storage.load_txt(path)[source]

Load data from a text file.

Parameters:

path (str) – Path to file.

Returns:

data (array) – Loaded data.
mdata (dict) – Metadata.

biosppy.storage.pack_zip(files, path, recursive=True, forceExt=True)[source]

Pack files into a zip archive.

Parameters:

files (iterable) – List of files or directories to pack.
path (str) – Destination path.
recursive (bool, optional) – If True, sub-directories and sub-folders are also written to the archive.
forceExt (bool, optional) – Append default extension.

Returns:

zip_path (str) – Full path to created zip archive.

biosppy.storage.serialize(data, path, compress=3)[source]

Serialize data and save to a file using sklearn’s joblib.

Parameters:

data (object) – Object to serialize.
path (str) – Destination path.
compress (int, optional) – Compression level; from 0 to 9 (highest compression).

biosppy.storage.store_h5(path, label, data)[source]

Store data to HDF5 file.

Parameters:

path (str) – Path to file.
label (hashable) – Data label.
data (array) – Data to store.

biosppy.storage.store_txt(path, data, sampling_rate=1000.0, resolution=None, date=None, labels=None, precision=6)[source]

Store data to a simple text file.

Parameters:

path (str) – Path to file.
data (array) – Data to store (up to 2 dimensions).
sampling_rate (int, float, optional) – Sampling frequency (Hz).
resolution (int, optional) – Sampling resolution.
date (datetime, str, optional) – Datetime object, or an ISO 8601 formatted date-time string.
labels (list, optional) – Labels for each column of data.
precision (int, optional) – Precision for string conversion.

Raises:

ValueError – If the number of data dimensions is greater than 2.
ValueError – If the number of labels is inconsistent with the data.

biosppy.storage.unpack_zip(zip_path, path)[source]

Unpack a zip archive.

Parameters:

zip_path (str) – Path to zip archive.
path (str) – Destination path (directory).

biosppy.storage.zip_write(fid, files, recursive=True, root=None)[source]

Write files to zip archive.

Parameters:

fid (file-like object) – The zip file to write into.
files (iterable) – List of files or directories to pack.
recursive (bool, optional) – If True, sub-directories and sub-folders are also written to the archive.
root (str, optional) – Relative folder path.

Notes

Ignores non-existent files and directories.

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.

append(new_values: (<class 'int'>, <class 'float'>, <class 'complex'>, <class 'str'>, typing.Collection, <Mock id='139734269714496'>), new_keys=None)[source]

Returns a new ReturnTuple with the new values and keys appended to the original object.

Parameters:

new_values (int, float, list, tuple, dict, array) – Values to append. If given a dict and new_keys is None, the correspondent keys and values will be used.
new_keys (str, list, tuple, optional) – Keys to append.

Returns:

object (ReturnTuple) – A ReturnTuple with the values and keys appended.

as_dict()[source]

Convert to an ordered dictionary.

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

delete(key)[source]

Returns a ReturnTuple without the specified key.

Parameters:: key (str) – ReturnTuple key to be deleted.
Returns:: object (ReturnTuple) – The ReturnTuple with the key removed.

join(new_tuple)[source]

Returns a ReturnTuple with the new ReturnTuple appended.

Parameters:: new_tuple (ReturnTuple) – ReturnTuple to be joined.
Returns:: object (ReturnTuple) – The joined ReturnTuple.

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.biometrics

biosppy.clustering

biosppy.metrics

biosppy.plotting

biosppy.storage

biosppy.timing

biosppy.utils

biosppy.biometrics 

biosppy.clustering 

biosppy.metrics 

biosppy.plotting 

biosppy.storage 

biosppy.timing 

biosppy.utils 