Run data transformation jobs for transmart.
First make virtual environment to install dependencies using Python3.6+
pip install -r requirements
packer/config.py)From root dir run:
redis-server
celery -A packer.tasks worker --loglevel=info
and
python -m packer
Alternatively, you could build and run the stack from code using docker-compose. This has only been tested using Docker for Mac, but should work regardless.
# Downloads redis image and creates image with project dependencies.
docker-compose build
# After build is complete, start via:
docker-compose up
On code change the webserver will automatically restart, but the Celery workers will not. After updating the Celery task logic, you will need to restart the Docker container.
To run the test suite, we have to start redis-server and celery workers with the commands above. Then you can run:
python -m unittest discover -s ./tests
Available handlers:
/jobs
/jobs/create
job_type and job_parameters, creates the job and returns a task_id./jobs/status/<task_id>
/jobs/cancel/<task_id>
/jobs/data/<task_id>
/jobs/subscribe
To start the toy job "add" on the localhost machine
make call to http://localhost:8999/jobs/create?job_type=add&job_parameters={%22x%22:500,%22y%22:1501}.
New jobs can be added by adding a new Celery function to the jobs folder and adding the function to the jobs registry. See the ./packer/jobs/example.py to learn how.
Export transmart api client observation dataframe to tsv file
{
"job_type":"basic_export",
"job_parameters": {
"constraint": {
"type":"study_name",
"studyId":"CSR"
},
"custom_name":"name of the export"
}
}
Exports patient > diagnosis > biosource > biomaterial hierarchy as first 4 columns of the file. The rest of the columns are concepts. Higher level concepts (e.g Age that is specific to Patient level) get distributed to all rows specific to lower levels (e.g. Diagnosis)
See the CSR test study as an example.
{
"job_type":"patient_diagnosis_biosource_biomaterial_export",
"job_parameters": {
"constraint": {
"type":"study_name",
"studyId":"CSR"
},
"custom_name":"name of the export",
"row_filter": {
"type":"patient_set",
"subjectIds": ["P2", "P6"]
}
}
}
where:
job_parameters.constraint - any transmart v2 api constraint
or composition of them that used to get data from transmart.
job_parameters.custom_name(optional) - name of the export job and the output tsv file.
job_parameters.row_filter (optional) - any transmart v2 api constraint
or composition of them to fetch keys ([[[[patient], diagnosis], biosource], biomaterial]) that will make it to the end result.
e.g. Given the CSR study and query above only rows specific to P2 and P6 patients will end up to the result table such as P2, D2, BS2, BM2, ... row.
Please note that keys do not have to be equals in length. A row gets selected if only part of keys matches. e.g. P1 vs P1, D1
Copyright © 2019 The Hyve B.V.
This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License along with this program. If not, see https://www.gnu.org/licenses/.
Content type
Image
Digest
Size
128.3 MB
Last updated
about 6 years ago
docker pull thehyve/transmart-packer