sem tomo flow

Sem Tomo pipeline overview:

  • Single directory is used to contain a set of gifs, which make up a single stack.

  • tifs compiled into a single mrc file (source.mrc) in convert_tif_to_mrc()

  • two metadata files (align xf, and align xg, are generated to allow creation of aligned mrc file (align.mrc)

  • another mrc file is created, which tries to correct for stage tilt (needs stretch file).

This file is called corrected.mrc. Note, if no correction is needed, a correction.mrc is still created, but without any actual correction of angle.

The corrected mrc file is then contrast adjusted with mean std dev magic numbers “150,40” in gen_newstack_norm_command() this is referred to as the base mrc - A movie is created using the base.mrc file. - the midpoint of that file is computed, and snapshots are created using this midpoint. - We now want to create the pyramid assets, for neuroglancer / viewer. - Firstly create nifti file using the base mrc, then convert this to ng format. - To conclude, send callback stating the location of the various outputs.

em_workflows.sem_tomo.flow.gen_xfalign_comand(fp_in: FilePath) → None

eg:

xfalign -pa -1 -pr source.mrc align.xf
em_workflows.sem_tomo.flow.gen_align_xg(fp_in: FilePath) → None

eg:

xftoxg -ro -mi 2 {WORKDIR}/align.xf {WORKDIR}/align.xg
em_workflows.sem_tomo.flow.convert_tif_to_mrc(file_path: FilePath) → int
Generates source.mrc
assumes there’s tifs in input dir, uses all the tifs in dir
eg::

tif2mrc {DATAPATH}/*.tif {WORKDIR}/Source.mrc

em_workflows.sem_tomo.flow.create_stretch_file(tilt: float, fp_in: FilePath) → None
Creates stretch.xf used to gen corrected.mrc
File looks like:

1 0 0 {TILT_PARAMETER} 0 0

where TILT_PARAMETER is calculated as 1/cos({TILT_ANGLE}).
Note that tilt angle is input in degrees, however cos method expects radians
em_workflows.sem_tomo.flow.gen_newstack_mid_mrc_command(fp_in: FilePath) → None

Generates mid.mrc. eg:

newstack -secs {MIDZ}-{MIDZ} {WORKDIR}/{BASENAME}.mrc {WORKDIR}/mid.mrc
em_workflows.sem_tomo.flow.gen_keyimg(fp_in: FilePath) → Dict

Generates keyimg (large thumb), eg:

mrc2tif -j -C 0,255 mid.mrc {WORKDIR}/keyimg_{BASENAME}.jpg
em_workflows.sem_tomo.flow.gen_keyimg_small(fp_in: FilePath) → Dict

eg:

convert -size 300x300 {WORKDIR}/hedwig/keyimg_{BASENAME}.jpg                 -resize 300x300 -sharpen 2 -quality 70 {WORKDIR}/hedwig/keyimg_{BASENAME}_s.jpg
em_workflows.sem_tomo.flow.gen_ng_metadata(fp_in: FilePath) → Dict
Initialize HedwigZarrImages instace from .zarr group array
For each HedwigZarrImage obtain their metadata, consisting of:

  • shader_type

  • dimension

  • neuroglancer shader parameters

Note; the seemingly redundancy of working and asset fp here.
However asset fp is in the network file system and is deployed for access to the users
Working fp is actually used for getting the metadata