cryotypes 0.1.0

Generalized python types and validators for cryoEM data.

cryotypes

cryotypes defines a set of super-simple, extensible data structures for the fundamental types of cryoEM data and their relative metadata:

• PoseSet: a set of particle poses, compatible with 2D and 3D data
• Tomogram: a 3D image
• Micrograph: a 2D image

Each cryotype defines an experiment_id attribute which is intended as a unique identifier for individual experiments. This can be used, for example, to match particles to the correct tilt series and tomogram.

PoseSet

A PoseSet is a pandas DataFrame with specific column headings for particle positions and orientations, experiment identifiers and pixel/voxel spacing. This data-structure can be used for both 2D and 3D particle poses.

DataFrame column headings

Heading	Python name	Semantics
x	POSITION_X	particle position in x-dimension
y	POSITION_Y	particle position in y-dimension
z	POSITION_Z	particle position in z-dimension
dx	SHIFT_X	particle shift in x-dimension
dy	SHIFT_Y	particle shift in y-dimension
dz	SHIFT_Z	particle shift in z-dimension
orientation	ORIENTATION	particle orientation
experiment_id	EXPERIMENT_ID	experimental identifier for micrograph/tilt-series
pixel_spacing	PIXEL_SPACING	isotropic pixel/voxel spacing for particle positions

The labels can be conveniently accessed from Python should you need them.

from cryotypes import PoseSetDataLabels as CPDL

pose_dataframe[PSDL.POSITION_X] = xyz[:, 0]

Positions

Particle positions are coordinates in 2D or 3D images. The center of the first pixel is taken to be the origin (0, 0) or (0, 0, 0) and the units of particle positions are pixels.

Shifts

Particle shifts are in image pixels and are additive to positions, such that POSITION + SHIFT is the position of the particle in the tomogram.

Orientations

Particle orientations are stored as scipy.spatial.transform.Rotation objects. These transformations should rotate the basis vectors of a reference such that they are correctly oriented in a tomogram.

Tomogram

A Tomogram is an object that follows a specific python Protocol for tomogram data. The protocol specifies the following attributes:

• data: an array-like 3D image (numpy, dask, ...)
• experiment_id: experimental identifier
• pixel_spacing: isotropic pixel/voxel spacing