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nextflow.config
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77 lines (71 loc) · 2.17 KB
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manifest {
name = 'FrancisCrickInstitute/Segment-Flow'
contributors = [
[
name: "Cameron Shand",
email: "[email protected]"
],
[
name: "Numan Ahmed",
email: "[email protected]"
],
[
name: "Fiona Young",
email: "[email protected]"
],
[
name: "Qin Yu",
email: "[email protected]"
],
[
name: "Jon Smith",
email: "[email protected]"
]
]
description = 'Nextflow pipeline for deep learning segmentation models'
homePage = 'https://github.com/FrancisCrickInstitute/Segment-Flow'
docsUrl = 'https://franciscrickinstitute.github.io/aiod_docs/'
mainScript = 'main.nf'
defaultBranch = 'master'
version = '0.1.0'
}
process.container = 'quay.io/nextflow/bash'
nextflow.enable.moduleBinaries = true
conda.enabled = true
params {
// General parameters
help = false
root_dir = "${System.getProperty('user.home')}/.nextflow/aiod"
// Input csv specifying image filepaths and some basic dim info
img_dir = null
// General hashing param, used in mask saving in AIoD cache (root_dir)
// If null, calcs a hash using params block
// NOTE: This will be different from the hash that Napari calculates!!
param_hash = null
// Preprocessing parameters
preprocess = null
// Substack parameters
num_substacks = "auto,auto,auto"
overlap = "0.0,0.0,0.0"
// Model parameters
// NOTE: Defaults here just so that a user can "hello world"-style run the pipeline
model = "cellpose"
model_type = "cyto3"
task = "cyto"
model_config = null
// Postprocessing parameters
postprocess = false
iou_threshold = 0.8
// Output parameters
output_format = "rle"
output_mask_type = "auto"
}
profiles {
crick { includeConfig 'profiles/crick.conf' }
crick_dev { includeConfig 'profiles/crick_dev.conf' }
local { includeConfig 'profiles/local.conf' }
rosalind { includeConfig 'profiles/rosalind.conf' }
}
env {
PYTHONNOUSERSITE = 'true'
}