PYME.Analysis.points.coordinate_tools module

PYME.Analysis.points.coordinate_tools.cart2sph(x, y, z)
PYME.Analysis.points.coordinate_tools.cartesian_to_spherical(x, y, z, azimuth_0=0, zenith_0=0)
PYME.Analysis.points.coordinate_tools.direction_to_nearest_n_points(x, y, z, x0, y0, z0, n, subsample_fraction=1.0)
Parameters
x
y
z
x0
y0
z0
n
subsample_fraction: float

fraction of points to randomly subsample before querying. This can be helpful / necessary when len(x) >= 100,000

Returns
azimuthndarray

azimuth (angle in x,y plane)

zenithndarray

elevation (angle from z axis)

rndarray

distance to center of mass for n nearest points for each point queried

cartesian_vector: ndarray

Unit vectors pointing to nearest n points for each point queried

PYME.Analysis.points.coordinate_tools.distance_to_image_mask(mask, points)

Calculate the distance from point positions to the edge of an image mask.

Parameters
maskPYME.IO.ImageStack

Binary mask where True denotes inside of the mask. Can be integers too, e.g. from an image of labels where 0 denotes unlabeled, but this will be compressed into a single mask of inside object(s) and outside. Edge of the mask is considered the first pixel which is False.

pointsPYME.IO.tabular.TabularBase

points to query distance with respect to mask

Returns
distancendarray

Distance from each point to the edge of the mask in units of nanometers. Negative values denote being inside of the mask.

PYME.Analysis.points.coordinate_tools.find_points_within_cone(x, y, z, x0, y0, z0, azimuth, zenith, d_omega=0.15, cutoff_r=1500)
PYME.Analysis.points.coordinate_tools.find_points_within_cylinder(x, y, z, x0, y0, z0, radius, length, v0, v1, v2)
Parameters
x
y
z
x0
y0
z0
v0ndarray

cartesian vector defining the ‘axial axis’ of the cylinder

v1ndarray

cartesian vector orthogonal to the axial axis

v2ndarray

cartesian vector othogonal to the previous two

Returns
PYME.Analysis.points.coordinate_tools.find_principle_axes(x, y, z, sample_fraction=None)
Parameters
x: list-like

x positions

y: list-like

y positions

z: list-like

z positions

sample_fraction: float

[optional] fraction of points to choose randomly and use for principal axes calculations, reducing computation. Default of None uses all points.

Returns
standard_deviations: ndarray

standard deviations of the input positions along the principle axes

eigen_vectors: ndarray

principle axes

PYME.Analysis.points.coordinate_tools.pixel_index_of_points_in_image(image, points)

Map positions into indices of an image

Parameters
image: PYME.IO.image.ImageStack

image with complete metadata

points: PYME.IO.tabular.TabularBase
Returns
x_index: ndarray

x pixel index in image for each point

y_index: ndarray

y pixel index in image for each point

z_index: ndarray

z pixel index in image for each point

PYME.Analysis.points.coordinate_tools.scaled_projection(x, y, z, scaling_factors, scaling_axes)
PYME.Analysis.points.coordinate_tools.spherical_to_cartesian(az, el, r)

Convert spherical coordinates into cartesian

Parameters
azndarray

azimuth (angle in x,y plane)

elndarray

elevation (angle from z axis)

rndarray

radius

Returns
x, y, z