Versions Compared

Key

  • This line was added.
  • This line was removed.
  • Formatting was changed.

...

Code Block
languagebash
twolevel_model_building.py --num-executors=600 
--init-model=/hpf/largeprojects/MICe/tools/initial-models/Pydpiper-MEMRI-90micron-saddle-july-2015/p65_MEMRI_mouse_brain.mnc
--pipeline-name=09feb17 
--lsq12-protocol=/hpf/largeprojects/MICe/tools/protocols/linear/Pydpiper_testing_default_lsq12.csv
--nlin-protocol=/hpf/largeprojects/MICe/tools/protocols/nonlinear/Pydpiper_mincANTS_standard_90_micron.pl
--registration-method=mincANTS 
--no-run-maget 
--maget-no-mask
--csv-file=/hpf/largeprojects/MICe/vousdend/CREB_EE/documents/creb_ee_images_to_reg_09feb17.csv
--output-dir=/hpf/largeprojects/MICe/vousdend/CREB_EE/registration/09feb17/


Example: In vivo images on hpf using registration chain


At time of writing, I used 4G of vmem, but this could likely be reduced to 2G.

  1. Login to hpf
  2. Start cluster and load modules
Code Block
languagebash
qlogin -l vmem=4G,walltime=96:00:00
Please refer to the Pydpiper on the SickKids HPF page for details on which modules to load:

Pydpiper on the SickKids HPF

Note: Right now you also need to specify an lsq12 protocol, in addition to an nlin-protocol. Additionally, your csv file needs to have at least the following columns: group (aka how you want scans to be grouped, which is typically by mouse ID) and file).

Code Block
languagebash
registration_chain.py --pipeline-name=arghef_group2 --chain-csv-file Group2_LongitudinalFiles_cleaned_with_id_new.csv --num-executors 250 --lsq12-protocol /hpf/largeprojects/MICe/tools/protocols/linear/Pydpiper_testing_default_lsq12.csv --chain-common-time-point 12 --pride-of-models /hpf/largeprojects/MICe/tools/initial-models/pride_of_models_mapping.csv --latency-tolerance 1800

 

--chain-csv-file tells the pipeline how to group data. You need to have atleast three columns: timepoint, subject_id, filename (relative of full paths of the images being registered).
--chain-common-time-point is the timepoint to be registered together
--pride-of-models csvfile with at least two columns: time_point, and model_file. Each row is an init model so you can have a unique init model for each age.



At MICe

Example: Basic registration pipeline at MICe on 20 56um brains

Note

Please ensure you are using the latest quarantine. For a list of all quarantines see this page: Registration Quarantines (deprecated)

Code Block
languagebash
MBM.py \
--num-executors 20 \
--queue-type sge \
--init-model /axiom2/projects/software/initial-models/Pydpiper-init-model-basket-may-2014/basket_mouse_brain.mnc \
--pipeline-name test_registration \
input_files_*.mnc

...