SAXS/WAXS Analysis Workflows

Data analysis workflows for SAXS/WAXS

Clone repository and setup Python environment

git clone git@github.com:als-computing/SAXSWAXS_workflows.git
cd SAXSWAXS_workflows
python -m venv saxswaxs-workflows-env
source saxswaxs-workflows-env/bin/activate
pip install -r requirements.txt

The command source saxswaxs-workflows-env/bin/activate may need to be adapted for the specific operating system, see the venv documentation

Set up environment file with relevant paths

Create a file .env with the following content

TILED_URI="http://127.0.0.1:8888"
PREFECT_API_URL="http://127.0.0.1:4200/api"
TILED_API_KEY="<randomly generated key>"
PATH_TO_DATA="<path to folder that contains raw data>"
PATH_TO_PROCESSED_DATA="<path to folder where processed data can be written>"
PREFECT_WORK_DIR="<path to folder where this code resides>"

Prefect Server

In one terminal that has the environment activated start a prefect server

prefect server start

As instructed in the Prefect server startup prompt, make sure prefect is configured with the correct PREFECT_API_URL:

prefect config set PREFECT_API_URL=http://127.0.0.1:4200/api

Tiled server

Within the interface folder (the other repository /workflow-viz), follow the instructions to start a Tiled server, and a Tiled watch process that observes changes in a directory

First test

Adapt the parameters in the example in reduction.py to point to a dataset that is contained in the folder and run it

python saxswaxs-workflows/flows/reduction.py

Setup for beamtime

(Once we confirmed that the first part runs)

In another terminal, create work-pools:, the reduction-pool for reducing data, the fitting-pool for feature extraction, and the gpcam-pool for running gpCAM, and deploy all flows that are defined within prefect.yaml. For convinience, these steps are summarized in the script

./create_deployments.sh

Finally, start the workers with

prefect worker start --pool 'reduction-pool'

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