The API reference documentation is available here:  REST API for Partek Flow

The public key and Python libraries are available here:  PartekFlow-REST.zip

Upload and analyze a group of samples.

Get the name of a pipeline from the GUI or from the API

http://localhost:8080/flow/api/v1/pipelines/list

Get the list of required inputs for the pipeline from the API

http://localhost:8080/flow/api/v1/pipelines/inputs?project=iDEA&pipeline=AlignAndQuantify

Get the IDs for the library files that match the required inputs

http://localhost:8080/flow/api/v1/library_files/list?assembly=hg19

Use UploadSamples.py to upload the samples and launch the pipeline

>python UploadSamples.py -v --server http://localhost:8080 --user admin --files ~/sample1.fastq.gz ~/sample2.fastq.gz --project ProjectName --pipeline AlignAndQuantify --inputs 28061,145855

Generate an authentication token

An access token can be generated from the System information section of the settings page.

Alternatively, GetToken.py will generate a token.

>python GetToken.py --server localhost:8080 --user admin

Example output:

TOKEN: cUOWY0VvkSFagrDUANVtM7A8SPal8Gx0cf0ee24bfa9fe68e2b5564dab2b6a27e1fb525e5...

This token can be specified as the --password parameter for the Python API.

If the token is not supplied, then the Python API will prompt for the password and encrypt it.

When accessing the API directly, the encrypt parameter must be specified with the RSA value to use the token.

For example:

curl --form username=admin --form encrypt=RSA --form password=cUOWY0VvkSFagr... http://localhost:8080/flow/api/v1/users/list

Add a collaborator to a project

curl -X PUT "http://localhost:8080/flow/api/v1/projects?project=ProjectName&collaborator=user1&role=Collaborator&username=admin

&encrypt=RSA&password=[url encoded token]"

Monitor a folder and upload files as they are created

#!/bin/bash

inotifywait -m $PATH_TO_MONITOR -e create -e moved_to |

  while read path action file; do

      if [[ $file == *.fastq.gz ]]; then

              echo "Uploading $file"

              python UploadSamples.py -v --server $SERVER --user $USER --password $TOKEN --files $path/$file --project "$PROJECT" --pipeline $PIPELINE --inputs $INPUTS

      fi

  done

Monitor the queue and send a notification if there are too many waiting tasks

#!/bin/bash

while true; do

      result=`python QueueStatistics.py --server $SERVER --user $USER --password $TOKEN --max_waiting $MAX_WAITING`

      if [ $? -eq 1 ]; then

              /usr/bin/notify-send $result

              exit 1

      fi

      sleep $INTERVAL

done

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