phasorpy.cluster#

Cluster phasor coordinates.

The phasorpy.cluster module provides functions to:

  • fit elliptic clusters to phasor coordinates using Gaussian Mixture Model (GMM):

phasorpy.cluster.phasor_cluster_gmm(real, imag, /, *, sigma=2.0, clusters=1, **kwargs)[source]#

Return elliptic clusters in phasor coordinates using GMM.

Fit a Gaussian Mixture Model (GMM) to the provided phasor coordinates and extract the parameters of ellipses that represent each cluster according to [1].

Parameters:

  • real (array_like) – Real component of phasor coordinates.

  • imag (array_like) – Imaginary component of phasor coordinates.

  • sigma (float, default = 2.0) – Scaling factor for radii of major and minor axes. Defaults to 2, which corresponds to the scaling of eigenvalues for a 95% confidence ellipse.

  • clusters (int, optional) – Number of Gaussian distributions to fit to phasor coordinates. Defaults to 1.

  • **kwargs –

    Additional keyword arguments passed to sklearn.mixture.GaussianMixture.

    Common options include:

    • covariance_type : {‘full’, ‘tied’, ‘diag’, ‘spherical’}

    • max_iter : int, maximum number of EM iterations

    • random_state : int, for reproducible results

Returns:

  • center_real (tuple of float) – Real component of ellipse centers.

  • center_imag (tuple of float) – Imaginary component of ellipse centers.

  • radius_major (tuple of float) – Major radii of ellipses.

  • radius_minor (tuple of float) – Minor radii of ellipses.

  • angle (tuple of float) – Rotation angles of major axes in radians, within range [0, pi].

Raises:

ValueError – If the array shapes of real and imag do not match. If clusters is not a positive integer.

References

Examples

Recover the clusters from a synthetic distribution of phasor coordinates with two clusters:

>>> real1, imag1 = numpy.random.multivariate_normal(
...     [0.2, 0.3], [[3e-3, 1e-3], [1e-3, 2e-3]], 100
... ).T
>>> real2, imag2 = numpy.random.multivariate_normal(
...     [0.4, 0.5], [[2e-3, -1e-3], [-1e-3, 3e-3]], 100
... ).T
>>> real = numpy.concatenate([real1, real2])
>>> imag = numpy.concatenate([imag1, imag2])
>>> center_real, center_imag, radius_major, radius_minor, angle = (
...     phasor_cluster_gmm(real, imag, clusters=2)
... )
>>> centers_real
(0.2, 0.4)
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