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Update chunking for fusion #51

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Update chunking for fusion #51

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0457384
Update chunking for fusion
akhanf Sep 14, 2024
8943f63
fix type casting
akhanf Sep 15, 2024
3dedae2
use int16 throughout
akhanf Sep 15, 2024
068d422
revert back to 2d output chunks
akhanf Sep 15, 2024
2357f4c
updates to get bigstitcher-spark fusion working
akhanf Sep 24, 2024
c3586de
updates for fuse-spark
akhanf Sep 25, 2024
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20 changes: 19 additions & 1 deletion workflow/rules/bigstitcher.smk
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Original file line number Diff line number Diff line change
Expand Up @@ -269,6 +269,21 @@ rule fuse_dataset_spark:
suffix="bigstitcher.xml",
),
ijm=Path(workflow.basedir) / "macros" / "FuseImageMacroZarr.ijm",
params:
channel=lambda wildcards: "--channelId={channel}".format(
channel=get_stains(wildcards).index(wildcards.stain)
),
block_size="--blockSize={bsx},{bsy},{bsz}".format(
bsx=config["bigstitcher"]["fuse_dataset"]["block_size_x"],
bsy=config["bigstitcher"]["fuse_dataset"]["block_size_y"],
bsz=config["bigstitcher"]["fuse_dataset"]["block_size_z"],
),
block_size_factor="--blockScale={bsfx},{bsfy},{bsfz}".format(
bsfx=config["bigstitcher"]["fuse_dataset"]["block_size_factor_x"],
bsfy=config["bigstitcher"]["fuse_dataset"]["block_size_factor_y"],
bsfz=config["bigstitcher"]["fuse_dataset"]["block_size_factor_z"],
),
mem_gb=lambda wikdcards, resources: '{mem_gb}'.format(mem_gb=int(resources.mem_mb/1000))
output:
zarr=temp(
directory(
Expand Down Expand Up @@ -315,4 +330,7 @@ rule fuse_dataset_spark:
group:
"preproc"
shell:
"affine-fusion ..."
"affine-fusion {params.mem_gb} {threads} --preserveAnisotropy -x {input.dataset_xml} "
" -o {output.zarr} -d /fused/s0 -s ZARR "
" --UINT16 --minIntensity 0 --maxIntensity 65535 "
"{params.block_size} {params.block_size_factor} {params.channel}"
2 changes: 1 addition & 1 deletion workflow/scripts/zarr_to_n5_bdv.py
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Expand Up @@ -99,7 +99,7 @@ def update_xml_h5_to_n5(in_xml,out_xml,in_n5):
ds_list=[] #for setup-level attrs
for ds in range(max_downsampling_layers):
step=2**ds #1,2,4,8..
zstack = da.squeeze(darr[tile_i,chan_i,:,::step,::step]).astype(np.int16)
zstack = da.squeeze(darr[tile_i,chan_i,:,::step,::step])
print(f'writing to setup{setup_i}/timepoint0/s{ds}')
with ProgressBar():
zstack.to_zarr(n5_store,component=f'setup{setup_i}/timepoint0/s{ds}',overwrite=True,compute=True)
Expand Down
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