updates for compatibility with input zstacks

README.md

@@ -18,6 +18,7 @@ Takes TIF images (tiled or prestitched) and outputs a validated BIDS Microscopy
  - Python >= 3.11
  - Lightsheet data:
     - Raw Ultramicroscope Blaze OME TIFF files (include `blaze` in the acquisition tag)
+        - can be 2D or 3D TIFF files
     - Prestitched TIFF files (include `prestitched` in the acquisition tag)
 
 
@@ -63,10 +64,8 @@ or for snakemake<8.0, use:
 snakemake -c all --use-singularity 
 ```
 
-Note: if you run the workflow on a system with large memory, you will need to set the heap size for the stitching and fusion rules. This can be done with e.g.: `--set-resources bigstitcher_spark_stitching:mem_mb=60000 bigstitcher_spark_fusion:mem_mb=100000`
+Note: if you run the workflow on a system with large memory, you will need to set the heap size for the stitching and fusion rules. This can be done with e.g.: `--set-resources bigstitcher_stitching:mem_mb=60000 bigstitcher_fusion:mem_mb=100000`
 
 7. If you want to run the workflow using a batch job submission server, please see the executor plugins here: https://snakemake.github.io/snakemake-plugin-catalog/
 
-
-Alternate usage of this workflow (making use of conda) is described in the [Snakemake Workflow Catalog](https://snakemake.github.io/snakemake-workflow-catalog?repo=khanlab/SPIMprep).
 <!--intro-end-->

config/config.yml

@@ -1,6 +1,5 @@
 datasets: 'config/datasets.tsv'
 
-
 root: 'bids' # can use a s3:// or gcs:// prefix to write output to cloud storage
 work: 'work' 
 
@@ -13,6 +12,7 @@ cores_per_rule: 32
 #import wildcards:  tilex, tiley, channel, zslice (and prefix - unused)
 import_blaze:
   raw_tif_pattern: "{prefix}_Blaze[{tilex} x {tiley}]_C{channel}_xyz-Table Z{zslice}.ome.tif"
+  raw_tif_pattern_zstack:  "{prefix}_Blaze[{tilex} x {tiley}]_C{channel}.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_prestitched:
@@ -36,12 +36,10 @@ bigstitcher:
     downsample_in_x: 4
     downsample_in_y: 4
     downsample_in_z: 1
-    method: "phase_corr" #unused
-    methods: #unused
+    methods: #unused, only for reference
       phase_corr: "Phase Correlation" 
       optical_flow: "Lucas-Kanade"  
   filter_pairwise_shifts:
-    enabled: 1 #unused
     min_r: 0.7
     max_shift_total: 50
   global_optimization:
@@ -64,7 +62,7 @@ bigstitcher:
     block_size_factor_z: 32
 
 ome_zarr:
-  desc: sparkstitchedflatcorr
+  desc: stitchedflatcorr
   max_downsampling_layers: 5 # e.g. 4 levels: { 0: orig, 1: ds2, 2: ds4, 3: ds8, 4: ds16}
   rechunk_size: #z, y, x 
     - 1 
@@ -155,5 +153,5 @@ report:
 
 
 containers:
-  spimprep: 'docker://khanlab/spimprep-deps:main'
+  spimprep: 'docker://khanlab/spimprep-deps:v0.1.0'
 

resources/qc/ff_html_temp.html

@@ -43,13 +43,13 @@ <h2>Chunk - {{ chunk_num }} Channel - {{ loop.index0 }}</h2>
         <tr>
     {%- for image in channel %}
             <td>
-                <img src="{{ image.img_corr }}"></img>
-                <h3>Corrected</h3>
+                <img src="{{ image.img_uncorr }}"></img>
+                <h3>Uncorrected</h3>
                 <p>Slice-{{ image.slice }}</p>
             </td>
             <td>
-                <img src="{{ image.img_uncorr }}"></img>
-                <h3>Uncorrected</h3>
+                <img src="{{ image.img_corr }}"></img>
+                <h3>Corrected</h3>
                 <p>Slice-{{ image.slice }}</p>
             </td>
     {%- endfor %}
@@ -117,4 +117,4 @@ <h3>Uncorrected</h3>
             }
         })
     </script>
-</body>
+</body>

workflow/Snakefile

@@ -33,7 +33,7 @@ rule all:
     input:
         get_all_targets(),
         get_bids_toplevel_targets(),
-        get_qc_targets(),  #need to skip this if using prestitched
+#        get_qc_targets(),  #need to skip this if using prestitched
     localrule: True
 
 

workflow/lib/dask_image.py

@@ -0,0 +1,45 @@
+from __future__ import annotations
+
+import os
+from glob import glob
+
+try:
+    import tifffile
+except (AttributeError, ImportError):
+    pass
+
+from dask.array.core import Array
+from dask.base import tokenize
+
+
+def add_leading_dimension(x):
+    return x[None, ...]
+
+def imread(fn, page):
+    return tifffile.imread(fn,key=page)
+
+
+def imread_pages(filename, preprocess=None):
+
+
+    tif = tifffile.TiffFile(filename)
+    pages = [i for i in range(len(tif.pages))]
+    name = "imread-%s" % tokenize(filename, os.path.getmtime(filename))
+
+    sample = tif.pages[0].asarray()
+
+    if preprocess:
+        sample = preprocess(sample)
+
+    keys = [(name, i) + (0,) * len(sample.shape) for i in pages]
+    if preprocess:
+        values = [
+            (add_leading_dimension, (preprocess, (imread, filename,i))) for i in pages
+        ]
+    else:
+        values = [(add_leading_dimension, (imread, filename,i)) for i in pages]
+    dsk = dict(zip(keys, values))
+
+    chunks = ((1,) * len(pages),) + tuple((d,) for d in sample.shape)
+
+    return Array(dsk, name, chunks, sample.dtype)