standard-transform 1.1.0

Define and repeat basic affine transformation tasks for datasets

standard_transforms

Orient and scale points in EM datasets the same way!

When working with EM data, often the orientation of the dataset does not match the desired orientation in space. For example, in cortical data you might want "down" to correspond to the direction orthogonal to the pial surface. This package includes prebaked affine transforms for two datasets, Minnie65 and v1dd, to convert from voxel or nanometer coordinates to a consistent oriented frame in microns.

Install via pip install standard-transform.

Usage

At its simplest, we import the transform we want, initialize and object, and then are ready to rotate, scale, and translate away! Let's start with going from nanometer space in Minnie to the oriented space. We can make the pre-baked transform by importing one of the generating functions, in this case minnie_transform_nm.

from standard_transform import minnie_transform_nm

tform_nm = minnie_transform_nm()

There are three main useful functions, apply, apply_project, and apply_dataframe. All functions transform an n x 3 array or pandas series with 3-element vectors to points in a new space, with the y-axis oriented along axis from pia to white matter and the units in microns and the pial surface at approximately y=0. Using apply alone returns another n x 3 array, while apply_project takes both an axis and points and returns just the values along that axis. For example, if you have skeleton vertices in nm, you can produce transformed ones with:

new_vertices = tform_nm.apply(sk.vertices)

while if you just want the depth:

sk_depth = tform_nm.apply_project('y', sk.vertices)

These two functions can take either 3-element points, n x 3 arrays, or a column of a pandas dataframe with 3-element vectors in each row.

The third function is specifically for use with dataframes, but offers a bit more flexibility. Instead of passing the series, you pass the column name and the dataframe itself.

pts_out = tform_nm.apply_dataframe(column_name, df)

Why is this useful when you can just use tform.apply(df[column_name])? It is often handy to work with dataframes with split position columns, where x, y, and z coordinates are in three separate columns. If they are named {column_name}_x, {column_name}_y, and {column_name}_z, then the apply_dataframe function will autodetect this split position situation and act seamlessly to generate points out of them. To get the projection functionality with the apply_dataframe method, pass it as an additional argument. e.g.

pts_out_x = tform_nm.apply_dataframe(column_name, df, projection='x')

Available transforms

There are four transforms currently available, two for each dataset.

Minnie65

• minnie_transform_nm : Transform from nanometer units in the original Minnie65 space to microns in a space where the pial surface is flat in x and z along y=0.

• minnie_transform_vx : Transform from voxel units in the original Minnie65 space to microns in a space where the pial surface is flat in x and z along y=0. By default, minnie_transform_vx() assumes a voxel size of [4,4,40] nm/voxel, but specifying a voxel resolution (for example, with minnie_transform_vx(voxel_resolution=[8,8,40])) will use a different scale.

Both functions will also take dataframe columns, for example tform.apply(df['pt_position]).

V1dd

• v1dd_transform_nm : Transform from nanometer units in the original V1dd space to microns in a space where the pial surface is flat in x and z along y=0.

• v1dd_transform_vx : Transform from voxel units in the original Minnie65 space to microns in a space where the pial surface is flat in x and z along y=0. By default, v1dd_transform_vx() assumes a voxel size of [9,9,45] nm/voxel, but specifying a voxel resolution (for example, with v1dd_transform_vx(voxel_resolution=[1000, 1000, 1000])) will use a different scale.

Identity

• identity_transform : This transform returns the input data unchanged (although perhaps axis-projected), but can be useful for compatability purposes.