loucerac/bps-workflow

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Overview

Latest release: v0.1.2, Last update: 2026-11-08

Share link: https://snakemake.github.io/snakemake-workflow-catalog?wf=loucerac/bps-workflow

Quality control: linting: failed formatting: passed

Workflow Rule Graph

This visualization of the workflow’s rule graph was automatically generated using Snakevision

Deployment

Step 1: Install Snakemake and Snakedeploy

Snakemake and Snakedeploy are best installed via the Conda package manager. It is recommended to install conda via Miniforge. Run

conda create -c conda-forge -c bioconda -c nodefaults --name snakemake snakemake snakedeploy

to install both Snakemake and Snakedeploy in an isolated environment. For all following commands ensure that this environment is activated via

conda activate snakemake

For other installation methods, refer to the Snakemake and Snakedeploy documentation.

Step 2: Deploy workflow

With Snakemake and Snakedeploy installed, the workflow can be deployed as follows. First, create an appropriate project working directory on your system and enter it:

mkdir -p path/to/project-workdir
cd path/to/project-workdir

In all following steps, we will assume that you are inside of that directory. Then run

snakedeploy deploy-workflow https://github.com/loucerac/bps-workflow . --tag v0.1.2

Snakedeploy will create two folders, workflow and config. The former contains the deployment of the chosen workflow as a Snakemake module, the latter contains configuration files which will be modified in the next step in order to configure the workflow to your needs.

Step 4: Run workflow

The deployment method is controlled using the --software-deployment-method (short --sdm) argument.

To run the workflow with automatic deployment of all required software via conda/mamba, use

snakemake --cores all --sdm conda

Snakemake will automatically detect the main Snakefile in the workflow subfolder and execute the workflow module that has been defined by the deployment in step 2.

For further options such as cluster and cloud execution, see the docs.

Step 5: Generate report

After finalizing your data analysis, you can automatically generate an interactive visual HTML report for inspection of results together with parameters and code inside of the browser using

snakemake --report report.zip

Configuration

The following section is imported from the workflow’s config/README.md.

Configuration

Everything is in config/config.yaml, with a comment on each key, checked at start-up against workflow/schemas/config.schema.yaml. An analysis workflow importing this one as a module passes its own config, so these keys go in that config too.

1. Point it at the delivery

delivery_root is the folder holding the delivery. It is a property of the machine, so set it in a workflow profile, which Snakemake reads automatically:

mkdir -p profiles/default
cp profiles/default.example/config.yaml profiles/default/config.yaml
# edit:  delivery_root=/abs/path/to/the/delivery

profiles/default/ is gitignored. The run refuses to start if delivery_root does not exist.

2. Describe the delivery

key	what to set
`universe`	the people file: id, sex (and its labels in `sex_values`), birth and death columns, date format
`sources.<name>`	one entry per diagnosis file: id, date, ICD code, date format; optionally `position` / `primary_position` (principal diagnosis), `episode_flags` (admission types to record, e.g. obstetric) and `icd9_changeover`
`sentinels`, `mid_month_day`	the delivery’s codes for “unknown”, and where month-precision dates land
`map_icd9`	map ICD-9 records to ICD-10 with the CMS GEM
`scale`	the CSV encoding and memory bounds; these never change a result

Set icd9_changeover only for a file where the ICD-9 → ICD-10 changeover was measured: before it, V/E-led codes are read as ICD-9.

3. What to ingest

key	what it decides
`records.from` / `records.to`	records dated outside this window are not ingested
`buckets.n` / `buckets.seed`	optional: random person groups for a grouped bootstrap

The test case in .test/config/config.yaml has the same keys, on the synthetic delivery in .test/data.

Workflow parameters

The following table is automatically parsed from the workflow’s config.schema.y(a)ml file.

Parameter	Type	Required
delivery_root	string	yes
universe		yes
. sex_values		yes
sources		yes
sentinels	array	yes
mid_month_day	integer	yes
map_icd9	boolean	yes
scale		yes
. csv_block_mb	number	yes
. batch_rows	integer	yes
. person_batch	integer	yes
. pair_chunk	integer	yes
records		yes
. from	string	yes
. to	string	yes
buckets
. n	integer	yes
. seed	integer	yes

Linting and formatting

Linting results

/home/runner/work/snakemake-workflow-catalog/snakemake-workflow-catalog/.pixi/envs/default/lib/python3.13/site-packages/google/auth/transport/grpc.py:43: FutureWarning: grpcio < 1.83.0 does not support Post-Quantum Cryptography (PQC). Support for non-PQC environments is deprecated. In April 2027, google-auth will raise its minimum requirements to enforce grpcio >= 1.83.0. For more details on Google Cloud's post-quantum security migration, visit: https://cloud.google.com/security/resources/post-quantum-cryptography
  warnings.warn(
WorkflowError in file "/tmp/tmp0q3r2_yn/loucerac-bps-workflow-b4fffc4/workflow/rules/common.smk", line 11:
delivery_root 'data/raw/20260903' does not exist (resolved from /tmp/tmp0q3r2_yn/loucerac-bps-workflow-b4fffc4). Set it in config/config.yaml or, per machine, in profiles/default/config.yaml (copy profiles/default.example/).
  File "/tmp/tmp0q3r2_yn/loucerac-bps-workflow-b4fffc4/workflow/rules/common.smk", line 11, in <module>