require 'blaze' or 'prestitched' as part of the acq wildcard

now uses this to determine how to load data and metadata

e.g. previously acq-4x now could be acq-blaze4x

config/config.yml

@@ -9,8 +9,12 @@ import:
   raw_tif_pattern: "{prefix}_Blaze[{tilex} x {tiley}]_C{channel}_xyz-Table Z{zslice}.ome.tif"
   intensity_rescaling: 0.5 #raw images seem to be at the upper end of uint16 (over-saturated) -- causes wrapping issues when adjusting with flatfield correction etc. this rescales the raw data as it imports it..
 
-import_stitched:
+import_prestitched:
   stitched_tif_glob: "*nm_{stain}/*.tif"
+  physical_size_x_um: 1.8
+  physical_size_y_um: 1.8
+  physical_size_z_um: 4
+
 
 basic_flatfield_corr:
   max_n_images: 500

config/datasets.tsv

@@ -1,3 +1,3 @@
 subject	sample	acq	stain_0	stain_1	dataset_path
-mouse1	brain	1x	abeta	autof	.test/dryrun/data
-onuska21	brain	lifecanvas	PI	BetaAmyloid	/cifs/prado/Kate/AK_Pipeline_LSM/Onuska_21_PI-BetaAmyloid
+mouse1	brain	blaze1x	abeta	autof	.test/dryrun/data
+onuska21	brain	prestitched	PI	BetaAmyloid	/cifs/prado/Kate/AK_Pipeline_LSM/Onuska_21_PI-BetaAmyloid

workflow/rules/ome_zarr.smk

@@ -31,7 +31,7 @@ rule zarr_to_ome_zarr:
                     subject="{subject}",
                     datatype="micr",
                     sample="{sample}",
-                    acq="{acq,^(?!.*lifecanvas)}", #anything but lifecanvas
+                    acq="{acq,blaze[a-zA-Z0-9]*}",
                     suffix="SPIM.ome.zarr",
                 )
             )
@@ -60,7 +60,7 @@ rule tif_stacks_to_ome_zarr:
     params:
         in_tif_glob=lambda wildcards, input: os.path.join(
             input.tif_dir,
-            config["import_stitched"]["stitched_tif_glob"],
+            config["import_prestitched"]["stitched_tif_glob"],
         ),
         max_downsampling_layers=config["ome_zarr"]["max_downsampling_layers"],
         rechunk_size=config["ome_zarr"]["rechunk_size"],
@@ -75,7 +75,7 @@ rule tif_stacks_to_ome_zarr:
                     subject="{subject}",
                     datatype="micr",
                     sample="{sample}",
-                    acq="{acq,lifecanvas}",
+                    acq="{acq,prestitched[a-zA-Z0-9]*}",
                     suffix="SPIM.ome.zarr",
                 )
             )

workflow/scripts/raw_to_metadata.py

@@ -5,23 +5,19 @@
 from itertools import product
 from snakemake.io import glob_wildcards
 
-if snakemake.wildcards.acq == 'lifecanvas':
-    physical_size_x=1.8
-    physical_size_y=1.8
-    physical_size_z=4 
-
+if 'prestitched' in snakemake.wildcards.acq:
     metadata={}
-    metadata['physical_size_x'] = physical_size_x
-    metadata['physical_size_y'] = physical_size_y
-    metadata['physical_size_z'] = physical_size_z
-    metadata['PixelSize'] = [ float(physical_size_z/1000.0), float(physical_size_y/1000.0), float(physical_size_x/1000.0) ] #zyx since OME-Zarr is ZYX
+    metadata['physical_size_x'] = config['import_prestitched']['physical_size_x_um']
+    metadata['physical_size_y'] = config['import_prestitched']['physical_size_y_um']
+    metadata['physical_size_z'] = config['import_prestitched']['physical_size_z_um']
+    metadata['PixelSize'] = [ float(metadata['physical_size_z']/1000.0), float(metadata['physical_size_y']/1000.0), float(metadata['physical_size_x']/1000.0) ] #zyx since OME-Zarr is ZYX
     metadata['PixelSizeUnits'] = 'mm' 
 
     #write metadata to json
     with open(snakemake.output.metadata_json, 'w') as fp:
         json.dump(metadata, fp,indent=4)
 
-else:
+elif 'blaze' in snakemake.wildcards.acq:
 
     in_tif_pattern = snakemake.params.in_tif_pattern
 
@@ -111,3 +107,5 @@
     #write metadata to json
     with open(snakemake.output.metadata_json, 'w') as fp:
         json.dump(metadata, fp,indent=4)
+else:
+    print('ERROR: unsupported input acquisition')