From 0ee9657b021b9528369a678eaac406883eb32554 Mon Sep 17 00:00:00 2001
From: Robrecht Cannoodt <rcannood@gmail.com>
Date: Wed, 28 Aug 2024 13:09:13 +0200
Subject: [PATCH] reformat code

---
 src/data_processors/crop_region/script.py | 93 ++++++++++++-----------
 1 file changed, 48 insertions(+), 45 deletions(-)

diff --git a/src/data_processors/crop_region/script.py b/src/data_processors/crop_region/script.py
index 665ecefb..b847a463 100644
--- a/src/data_processors/crop_region/script.py
+++ b/src/data_processors/crop_region/script.py
@@ -4,13 +4,13 @@
 
 ## VIASH START
 par = {
-  "input": "temp/datasets/10x_xenium/2023_10x_mouse_brain_xenium/full_dataset.zarr",
-  "output": "resources_test/common/2023_10x_mouse_brain_xenium/dataset.zarr",
-  "replicate_id": ["rep1", "rep2", "rep3"],
-  "min_x": [10000, 10000, 10000],
-  "max_x": [12000, 12000, 12000],
-  "min_y": [10000, 10000, 10000],
-  "max_y": [12000, 12000, 12000],
+    "input": "temp/datasets/10x_xenium/2023_10x_mouse_brain_xenium/full_dataset.zarr",
+    "output": "resources_test/common/2023_10x_mouse_brain_xenium/dataset.zarr",
+    "replicate_id": ["rep1", "rep2", "rep3"],
+    "min_x": [10000, 10000, 10000],
+    "max_x": [12000, 12000, 12000],
+    "min_y": [10000, 10000, 10000],
+    "max_y": [12000, 12000, 12000],
 }
 ## VIASH END
 
@@ -28,44 +28,47 @@
 sdata_out = []
 
 for i, replicate_id in enumerate(par["replicate_id"]):
-  print(f"Processing replicate '{replicate_id}'", flush=True)
-
-  min_x = par["min_x"][i]
-  max_x = par["max_x"][i]
-  min_y = par["min_y"][i]
-  max_y = par["max_y"][i]
-
-  print(f"  Cropping to region: {min_x}, {min_y}, {max_x}, {max_y}", flush=True)
-  sdata_query = sdata.query.bounding_box(
-    axes=["x", "y"],
-    min_coordinate=[min_x, min_y],
-    max_coordinate=[max_x, max_y],
-    target_coordinate_system=f"{replicate_id}_global",
-    filter_table=True
-  )
-
-  # process the segmentations
-  for segmentation_id in segmentation_ids:
-    print(f"  Processing replicate '{replicate_id}' segmentation '{segmentation_id}'", flush=True)
-    shape_name = f"{replicate_id}_{segmentation_id}_boundaries"
-    table_name = f"{replicate_id}_{segmentation_id}_table"
-
-    if shape_name not in sdata_query.shapes:
-      print(f"    Shape '{shape_name}' not found in sdata, skipping", flush=True)
-      continue
-
-    if table_name not in sdata.tables:
-      print(f"    Table '{table_name}' not found in sdata, skipping", flush=True)
-      continue
-
-    obs_index = sdata_query.shapes[shape_name].index
-    table_query = sdata.tables[table_name][obs_index]
-
-    # add filtered table to sdata_query
-    sdata_query.tables[table_name] = table_query
-    
-  # add filtered shapes to sdata_query
-  sdata_out.append(sdata_query)
+    print(f"Processing replicate '{replicate_id}'", flush=True)
+
+    min_x = par["min_x"][i]
+    max_x = par["max_x"][i]
+    min_y = par["min_y"][i]
+    max_y = par["max_y"][i]
+
+    print(f"  Cropping to region: {min_x}, {min_y}, {max_x}, {max_y}", flush=True)
+    sdata_query = sdata.query.bounding_box(
+        axes=["x", "y"],
+        min_coordinate=[min_x, min_y],
+        max_coordinate=[max_x, max_y],
+        target_coordinate_system=f"{replicate_id}_global",
+        filter_table=True,
+    )
+
+    # process the segmentations
+    for segmentation_id in segmentation_ids:
+        print(
+            f"  Processing replicate '{replicate_id}' segmentation '{segmentation_id}'",
+            flush=True,
+        )
+        shape_name = f"{replicate_id}_{segmentation_id}_boundaries"
+        table_name = f"{replicate_id}_{segmentation_id}_table"
+
+        if shape_name not in sdata_query.shapes:
+            print(f"    Shape '{shape_name}' not found in sdata, skipping", flush=True)
+            continue
+
+        if table_name not in sdata.tables:
+            print(f"    Table '{table_name}' not found in sdata, skipping", flush=True)
+            continue
+
+        obs_index = sdata_query.shapes[shape_name].index
+        table_query = sdata.tables[table_name][obs_index]
+
+        # add filtered table to sdata_query
+        sdata_query.tables[table_name] = table_query
+
+    # add filtered shapes to sdata_query
+    sdata_out.append(sdata_query)
 
 # concatenate the sdata objects
 print("Concatenating sdata objects", flush=True)