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A Python library for SeaFlow data.

Project description


A Python package for SeaFlow flow cytometer data.

Table of Contents

  1. Install
  2. Read EVT/OPP/VCT Files
  3. Command-line Interface
  4. Configuration
  5. Integration with R
  6. Testing
  7. Development


This package is compatible with Python 3.7 and 3.8.


This will clone the repo and create a new virtual environment seaflowpy. venv can be replaced with virtualenv, conda, etc.

git clone
cd seaflowpy
[[ -d ~/venvs ]] || mkdir ~/venvs
python3 -m venv ~/venvs/seaflowpy
source ~/venvs/seaflowpy/bin/activate
pip3 install -U pip setuptools wheel
pip3 install -r requirements-test.txt
pip3 install .
# Confirm the seaflowpy command-line tool is accessible
seaflowpy version
# Make sure basic tests pass
# Leave the new virtual environment


pip3 install seaflowpy


Docker images are available from Docker Hub at ctberthiaume/seaflowpy.

docker pull ctberthiaume/seaflowpy
docker run -it ctberthiaume/seaflowpy seaflowpy version

The Docker build file is in this repo at /Dockerfile. The build process for the Docker image is detailed in /

Read EVT/OPP/VCT Files

All file reading functions will return a pandas.DataFrame of particle data. Gzipped EVT, OPP, or VCT files can be read if they end with a ".gz" extension. For these code examples assume seaflowpy has been imported as sfp and pandas has been imported as pd, e.g.

import pandas as pd
import seaflowpy as sfp

and *_filepath has been set to the correct data file.

Read an EVT file

evt = sfp.fileio.read_evt_labview(evt_filepath)

Read an OPP file as an Apache Arrow Parquet file, select the 50% quantile, and subset columns. VCT files created with popcycle are also standard Parquet files and can be read in a similar fashion.

opp = pd.read_parquet(opp_filepath)
opp50 = opp[opp["q50"]]
opp50 = opp50[['fsc_small', 'chl_small', 'pe']]

Command-line interface

All seaflowpy CLI tools are accessible from the seaflowpy executable. Run seaflowpy --help to begin exploring the CLI usage documentation.

SFL validation workflow

SFL validation sub-commands are available under the seaflowpy sfl command. The usage details for each command can be accessed as seaflowpy sfl <cmd> -h.

The basic worfkflow should be

  1. If starting with an SDS file, first convert to SFL with seaflowpy sds2sfl

  2. If the SFL file is output from sds2sfl or is a raw SeaFlow SFL file, convert it to a normalized format with seaflowpy sfl print. This command can be used to concatenate multiple SFL files, e.g. merge all SFL files in day-of-year directories.

  3. Check for potential errors or warnings with seaflowpy sfl validate.

  4. Fix errors and warnings. Duplicate file errors can be fixed with seaflowpy sfl dedup. Bad lat/lon errors may be fixed withseaflowpy sfl convert-gga, assuming the bad coordinates are GGA to begin with. This can be checked with with seaflowpy sfl detect-gga. Other errors or missing values may need to be fixed manually.

  5. (Optional) Update event rates based on true event counts and file duration with seaflowpy sfl fix-event-rate. True event counts for raw EVT files can be determined with seaflowpy evt count. If filtering has already been performed then event counts can be pulled from the all_count column of the opp table in the SQLITE3 database. e.g. sqlite3 -separator $'\t' SCOPE_14.db 'SELECT file, all_count ORDER BY file'

  6. (Optional) As a check for dataset completeness, the list of files in an SFL file can be compared to the actual EVT files present with seaflowpy sfl manifest. It's normal for a few files to differ, especially near midnight. If a large number of files are missing it may be a sign that the data transfer was incomplete or the SFL file is missing some days.

  7. Once all errors or warnings have been fixed, do a final seaflowpy validate before adding the SFL file to the appropriate repository.


To use seaflowpy sfl manifest AWS credentials need to be configured. The easiest way to do this is to install the awscli Python package and go through configuration.

pip3 install awscli
aws configure

This will store AWS configuration in ~/.aws which seaflowpy will use to access Seaflow data in S3 storage.

Integration with R

To call seaflowpy from R, update the PATH environment variable in ~/.Renviron. For example:



Seaflowpy uses pytest for testing. Tests can be run from this directory as pytest to test the installed version of the package, or run tox to install the source into a temporary virtual environment for testing.


Source code structure

This project follows the Git feature branch workflow. Active development happens on the develop branch and on feature branches which are eventually merged into develop.


To build source tarball, wheel, and Docker image, run ./ This will

  • create seaflowpy-dist with source tarball and wheel file (created during Docker build)

  • Docker image named seaflowpy:<version>

To remove all build files, run rm -rf ./seaflowpy-dist.

Updating requirements files

Create a new virtual environment

python3 -m venv newenv
source newenv/bin/activate

Update pip, wheel, setuptools

pip3 install -U pip wheel setuptools

And install seaflowpy

pip3 install .

Then freeze the requirements

pip3 freeze | grep -v seaflowpy >requirements.txt

Then install test dependencies, test, and freeze

pip3 install pytest pytest-benchmark
pip3 freeze | grep -v seaflowpy >requirements-test.txt

Then install dev dependencies, test, and freeze

pip3 install pylint twine
pip3 freeze | grep -v seaflowpy >requirements-dev.txt

Leave the virtual environment


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