Seaflowpy
A Python package for SeaFlow flow cytometer data.
Table of Contents
- Install
- Read EVT/OPP/VCT Files
- Command-line Interface
- Configuration
- Integration with R
- Testing
- Development
Install
This package is compatible with Python 3.7 and 3.8.
Source
This will clone the repo and create a new virtual environment seaflowpy. venv can be replaced with virtualenv, conda, etc.
git clone https://github.com/armbrustlab/seaflowpy
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
pytest
# Leave the new virtual environment
deactivate
PyPI
pip3 install seaflowpy
Docker
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 /build.sh.
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
.
The basic worfkflow should be
-
If starting with an SDS file, first convert to SFL with
seaflowpy sds2sfl -
If the SFL file is output from
sds2sflor is a raw SeaFlow SFL file, convert it to a normalized format withseaflowpy sfl print. This command can be used to concatenate multiple SFL files, e.g. merge all SFL files in day-of-year directories. -
Check for potential errors or warnings with
seaflowpy sfl validate. -
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 withseaflowpy sfl detect-gga. Other errors or missing values may need to be fixed manually. -
(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 withseaflowpy evt count. If filtering has already been performed then event counts can be pulled from theall_countcolumn of the opp table in the SQLITE3 database. e.g.sqlite3 -separator $'\t' SCOPE_14.db 'SELECT file, all_count ORDER BY file' -
(Optional) As a check for dataset completeness, the list of files in an SFL file can be compared to the actual EVT files present with
seaflowpy sfl manifest. It's normal for a few files to differ, especially near midnight. If a large number of files are missing it may be a sign that the data transfer was incomplete or the SFL file is missing some days. -
Once all errors or warnings have been fixed, do a final
seaflowpy validatebefore adding the SFL file to the appropriate repository.
Configuration
To use seaflowpy sfl manifest AWS credentials need to be configured. The easiest way to do this is to install the awscli Python package and go through configuration.
pip3 install awscli
aws configure
This will store AWS configuration in ~/.aws which seaflowpy will use to access Seaflow data in S3 storage.
Integration with R
To call seaflowpy from R, update the PATH environment variable in ~/.Renviron. For example:
PATH=${PATH}:${HOME}/venvs/seaflowpy/bin
Testing
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.
Development
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.
Build
To build source tarball, wheel, and Docker image, run ./build.sh. This will
-
create
seaflowpy-distwith source tarball and wheel file (created during Docker build) -
Docker image named
seaflowpy:
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
pytest
pip3 freeze | grep -v seaflowpy >requirements-test.txt
Then install dev dependencies, test, and freeze
pip3 install pylint twine
pytest
pip3 freeze | grep -v seaflowpy >requirements-dev.txt
Leave the virtual environment
deactivate
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