PYME.Analysis.points.cluster_morphology module

PYME.Analysis.points.cluster_morphology.get_labels_from_image(label_image, points, minimum_localizations=1)

Function to extract labels from a segmented image (2D or 3D) at given locations.

TODO - Move this somewhere more sensible and drop minimum_localizations in favour of post-filtering.

Parameters

label_image: PYME.IO.image.ImageStack instance

an image containing object labels

points: tabular-like (PYME.IO.tabular, np.recarray, pandas DataFrame) containing ‘x’, ‘y’ & ‘z’ columns

locations at which to extract labels

Returns

ids: Label number from image, mapped to each localization within that label

numPerObject: Number of localizations within the label that a given localization belongs to

PYME.Analysis.points.cluster_morphology.measure_3d(x, y, z, output=None)

Calculates various metrics for a single cluster, whose 3D coordinates are input. ‘output’, an optional input argument, allows one to use a single structured array to contain measures for multiple clusters. See PYME.recipes.localisations.MeasureClusters3D

Parameters

xndarray

x-positions, typically in nanometers

yndarray

y-positions, typically in nanometers

zndarray

z-positions, typically in nanometers

outputdict-like

If present, output will have measurements written into it, otherwise a structured array will be created and returned

Returns

outputdict-like, structured ndarray

dict-like object, typically a structured ndarray containing the following measurements: count : int

Number of localizations (points) in the cluster

xfloat

x center of mass

yfloat

y center of mass

zfloat

z center of mass

gyrationRadiusfloat

root mean square displacement to center of cluster, a measure of compaction or spatial extent see also supplemental text of DOI: 10.1038/nature16496

median_abs_deviationfloat

median absolute deviation, i.e. median of the radius from the median (x, y, z) position of the cluster

axis0ndarray, shape (3,)

principle axis which accounts for the largest variance of the cluster, i.e. corresponds to the largest eigenvalue

axis1ndarray, shape (3,)

next principle axis

axis2ndarray, shape (3,)

principle axis corresponding to the smallest eigenvalue

sigma0float

standard deviation along axis0

sigma1float

standard deviation along axis1

sigma2float

standard deviation along axis2

anisotropyfloat

metric of anisotropy based on the spread along principle axes. Standard deviations of alpha * [1, 0, 0], where alpha is a scalar, will result in an ‘anisotropy’ value of 1, i.e. maximally anisotropic. Completely isotropic clusters will have equal standard deviations, i.e. alpha * [1, 1, 1], which corresponds to an ‘anisotropy’ value of 0. Intermediate cases result in values between 0 and 1.

thetafloat

Azimuthal angle, in radians, along which the principle axis (axis0) points

phifloat

Zenith angle, in radians, along which the principle axis (axis0) points