updates to qc scripts/rules for remote zarrs

workflow/rules/qc.smk

@@ -47,11 +47,14 @@ rule generate_flatfield_qc:
 rule generate_whole_slice_qc:
     "Generates an html file to view whole slices from preprocessed images"
     input:
+        **get_storage_creds(),
         ome=get_input_ome_zarr_to_nii(),
     params:
         ws_s_start=config["report"]["whole_slice_viewer"]["slice_start"],
         ws_s_step=config["report"]["whole_slice_viewer"]["slice_step"],
         ws_cmap=config["report"]["whole_slice_viewer"]["colour_map"],
+        uri=get_output_ome_zarr_uri(),
+        storage_provider_settings=workflow.storage_provider_settings,
     output:
         html="qc/sub-{subject}_sample-{sample}_acq-{acq}/whole_slice_qc.html",
         images_dir=directory("qc/sub-{subject}_sample-{sample}_acq-{acq}/images/whole"),
@@ -71,7 +74,11 @@ rule generate_whole_slice_qc:
 rule generate_volume_qc:
     "Generates an html file to view the volume rendered image"
     input:
+        **get_storage_creds(),
         ome=get_input_ome_zarr_to_nii(),
+    params:
+        uri=get_output_ome_zarr_uri(),
+        storage_provider_settings=workflow.storage_provider_settings,
     output:
         resources=directory(
             "qc/sub-{subject}_sample-{sample}_acq-{acq}/volume_resources"

workflow/scripts/generate_volume_qc.py

@@ -3,7 +3,11 @@
 from pathlib import Path
 from distutils.dir_util import copy_tree
 from zarrnii import ZarrNii
+import dask.array as da
 import math
+from upath import UPath as Path
+from lib.cloud_io import get_fsspec, is_remote
+import zarr
 
 # directory containing the volume rendering files
 resource_dir = Path(snakemake.output.resources)
@@ -17,8 +21,18 @@
 copy_tree("qc/resources/volViewer", str(resource_dir))
 shutil.move(resource_dir / "volRender.html", html_dest)
 
+uri = snakemake.params.uri
+if is_remote(uri):
+    fs_args={'storage_provider_settings':snakemake.params.storage_provider_settings,'creds':snakemake.input.creds}
+else:
+    fs_args={}
+
+fs = get_fsspec(uri,**fs_args)
+store = zarr.storage.FSStore(Path(uri).path,fs=fs,dimension_separator='/',mode='r')
+darr = da.from_zarr(store,component='/5')
+
 # Get most downsampled ome-zarr image
-ds_z = ZarrNii.from_path(ome_data,level=5, channels=[0,1])
+ds_z = ZarrNii.from_darr(darr)
 z_length = ds_z.darr.shape[1]
 
 # downsample it so it has at most 100 slices and ast least 50 slices in z-direction

workflow/scripts/generate_whole_slice_qc.py

@@ -5,6 +5,9 @@
 import matplotlib.pyplot as plt
 import numpy as np
 from jinja2 import Environment, FileSystemLoader
+from upath import UPath as Path
+from lib.cloud_io import get_fsspec, is_remote
+import zarr
 
 # load jinja html template
 file_loader = FileSystemLoader(".")
@@ -23,8 +26,20 @@
 image_dir = snakemake.output.images_dir
 out_html = snakemake.output.html
 
+from ome_zarr.io import ZarrLocation
+
+uri = snakemake.params.uri
+if is_remote(uri):
+    fs_args={'storage_provider_settings':snakemake.params.storage_provider_settings,'creds':snakemake.input.creds}
+else:
+    fs_args={}
+
+fs = get_fsspec(uri,**fs_args)
+store = zarr.storage.FSStore(Path(uri).path,fs=fs,dimension_separator='/',mode='r')
+zarrloc = ZarrLocation(store)
+
 # read ome-zarr data and convert to list
-proc_reader= Reader(parse_url(ome))
+proc_reader= Reader(zarrloc)
 proc_data=list(proc_reader())[0].data
 
 os.makedirs(image_dir, exist_ok=True)