Leginon

h1. Smart-leginon multi-grid autoscreening

Automated square and hole finders are added to leginon to use Ptolemy (https://arxiv.org/abs/2112.01534) to determine optimal targets using computer vision algorithm and Convolutional Neural Network trained on user target selections accumulated at Simons Electron Microscopy Center at New York Biology Center (SEMC@NYSBC.

This implementation requires Ptolemy which has a separate license. For this release, the communication is through command line interface and rely on shared file system.

* At SEMC@NYSBC, Ptolemy, which was tested on python 3.9, runs in anaconda environment on the same CentOS7 computer where the main leginon process is.

h2. Square finding (localization and classification)

* Within Leginon MosaicExternalScoreFinder instance defines, for each incoming gr image:

** the shell script to run Ptolemy (user set it in settings)

** the mrc file path of the gr image leginon is currently processing as ptolemy input (leginon decides this for you)

** the output json file path leginon will later look for the result. (leginon decision)

Here is an example how we write this shell script sq_finding.sh

<pre>

#!/bin/sh -f

# Local runs

source /usr/local/anaconda3/etc/profile.d/conda.sh

conda activate /opt/condaenvs/ptolemy_env

# variable 1 is the outputname without json extension.

# variable 2 is the input mrc path

echo $1

echo $2

mrc_path=$2

python /home/your_name/site-packages/ptolemy/lowmag_cli.py -f json $2 > $1

conda deactivate

# mv the result to leginon session rawdata directory

mv $1 ${mrc_path%%rawdata/*}rawdata/$1.json

echo ${mrc_path%%rawdata/*}rawdata/$1.json

</pre>