Skip to content

Docs updates #54

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Draft
wants to merge 1 commit into
base: main
Choose a base branch
from
Draft

Docs updates #54

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
25 changes: 25 additions & 0 deletions docs/workflow.md
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,7 +1,32 @@
# Workflow

Under construction!

## Overview

SPIMprep is implemented in Python using the [Snakemake](https://snakemake.github.io/) workflow management system. It performs metadata extraction, flatfield correction (BaSiC), and stitching (BigStitcher), followed by the creation of a final validated BIDS dataset.

## Inputs to SPIMprep




## Installation

The workflow is installed via pip, and the required container dependency is downloaded automatically by Snakemake.

## Configuration

Input datasets are configured using a TSV file, specifying subject identifiers and paths to folders/archives containing the raw TIF files, and a YAML file is used to customize workflow configuration.

## Running Parallelization

The workflow can be executed in parallel on any local, cluster or cloud resources, and each step is also internally parallelized (Dask), taking advantage of the parallelization afforded by the chunked file format.

## Cloud support

The workflow can optionally write directly to cloud storage, facilitating data sharing and interoperability with existing web-based viewers.

## Outputs of SPIMprep