cryotypes 0.2.0

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 relevant metadata:

• PoseSet: a set of particle poses, compatible with 2D and 3D data
• ProjectionModel: a set of parameters for a projection model (tilt-series alignments)
• 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.

Image

An Image is a dataclass holding a simple data array and some metadata.

Image fields

Field	Semantics
data	image data (ZYX ordering)
experiment_id	identifier for micrograph/tilt-series
pixel_spacing	isotropic pixel/voxel spacing
source	the source file of this data
stack	whether the data represent a stack of 2D images

PoseSet

A PoseSet is a dataclass with a few fields describing positions, orientations and so on for a set of particles. It can be used for both 2D and 3D particle poses.

PoseSet fields

Field	Semantics
position	particle positions (x, y, z) in pixels
shift	particle shifts (x, y, z) in pixels
orientation	particle orientation
experiment_id	identifier for micrograph/tilt-series
pixel_spacing	isotropic pixel/voxel spacing for particle positions
source	the source file of this data

Positions

Particle positions are coordinates in 2D or 3D images (for 2D, z is simply set to 0). The center of the first pixel is taken to be the origin (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 (ordered xyz) of a reference such that they are correctly oriented in a tomogram.

Note: this yields rotated basis vectored ordered xyz whilst dimensions in an image are normally zyx!

ProjectionModel

A ProjectionModel is a pandas DataFrame with specific column headings for the parameters of a projection model. Together, this information constitues a 'tilt-series alignment'.

Heading	Python name	Semantics
rotation_x	ROTATION_X	specimen rotation around x-axis
rotation_y	ROTATION_Y	specimen rotation around y-axis
rotation_z	ROTATION_Z	specimen rotation around z-axis
dx	SHIFT_X	specimen shift in x-dimension of the camera plane
dy	SHIFT_Y	particle shift in y-dimension of the camera plane
experiment_id	EXPERIMENT_ID	identifier for micrograph/tilt-series
pixel_spacing	PIXEL_SPACING	isotropic pixel/voxel spacing for shifts
source	SOURCE	reference to the file from which data came

In the microsope reference frame, the z-axis is the beam direction. Extrinsic rotation of the tomogram around the x-axis, the y-axis, then the z-axis by rotation_x, rotation_y, rotation_z followed by projection along the z-axis (beam direction) then shifting the 2D image in the camera plane by dx and dy produces the experimental projection image.

A utility function is also provided for generating projection matrices from these data. These projection matrices can be used to calculate a 2D position in a tilt-image from a 3D position in the tomogram.

from cryotypes.projectionmodel import projection_model_to_projection_matrices

projection_matrices = projection_model_to_projection_matrices(
    df=projection_model,  # ProjectionModel dataframe
    tilt_image_center=(1919, 1355),  # tilt-image rotation center (xy)
    tomogram_dimensions=(3838, 3710, 2000)  # dimensions of tomogram (xyz)
)

Note: these projection matrices are only valid for positions in a tomogram of the dimensions provided in this function and must be recalculated for different tomogram dimensions.

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