apply x2

brainlit/BrainLine/apply_ilastik.py

@@ -823,7 +823,7 @@ def collect_axon_results(self, brain_id: str, ng_layer_name: str):
         ]
         for layer in ngl_json["layers"]:
             if layer["name"] == ng_layer_name:
-                source_pieces = layer["source"].split("/")
+                source_pieces = layer["source"]["url"].split("/")
                 source = ""
                 for piece in source_pieces[:-1]:
                     source += piece

brainlit/BrainLine/tests/test_apply_ilastik.py

@@ -5,6 +5,7 @@
     examine_threshold,
     ApplyIlastik,
     ApplyIlastik_LargeImage,
+    downsample_mask,
 )
 import os
 import shutil
@@ -14,6 +15,16 @@
 from datetime import date
 
 
+soma_data_file = (
+    Path(os.path.abspath(__file__)).parents[3]
+    / "docs"
+    / "notebooks"
+    / "pipelines"
+    / "BrainLine"
+    / "soma_data.json"
+)
+
+
 @pytest.fixture(scope="session")
 def axon_data_dir(tmp_path_factory):
     data_dir = tmp_path_factory.mktemp("data")
@@ -207,10 +218,17 @@ def test_examine_threshold_soma(soma_data_dir):
     )
 
 
+def test_downsample_mask_somafile():
+    with pytest.raises(ValueError) as e_info:
+        downsample_mask(brain="placeholder", data_file=soma_data_file)
+    assert e_info.value.args[0] == f"Entered non-axon data file"
+
+
 # ApplyIlastik_LargeImage
 
 
-def test_ApplyIlastik_LargeImage():
+def test_ApplyIlastik_LargeImage(soma_data_dir):
+    # Axon data
     data_file = (
         Path(os.path.abspath(__file__)).parents[3]
         / "docs"
@@ -222,16 +240,24 @@ def test_ApplyIlastik_LargeImage():
     aili = ApplyIlastik_LargeImage(
         ilastik_path="", ilastik_project="", ncpu=1, data_file=data_file
     )
+    aili.collect_axon_results(brain_id="pytest", ng_layer_name="average_10um")
     # Sample data is there but file path in data json is specific to thomastathey
 
-    data_file = (
-        Path(os.path.abspath(__file__)).parents[3]
-        / "docs"
-        / "notebooks"
-        / "pipelines"
-        / "BrainLine"
-        / "soma_data.json"
-    )
+    # Soma data
+    data_dir = soma_data_dir
     aili = ApplyIlastik_LargeImage(
-        ilastik_path="", ilastik_project="", ncpu=1, data_file=data_file
+        ilastik_path="",
+        ilastik_project="",
+        ncpu=1,
+        data_file=soma_data_file,
+        results_dir=str(data_dir),
     )
+
+    somas_path = aili.results_dir / "somas.txt"
+    print(somas_path)
+    with open(somas_path, "w") as f:
+        f.write("[-1, 1, 1]\n")
+        f.write("[1, 1, 1]\n")
+        f.write("[2, 1, 1]\n")
+
+    aili.collect_soma_results(brain_id="pytest_download")

experiments/BrainLine/soma_analysis.ipynb

@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -87,9 +87,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Path to brainlit: /Users/thomasathey/Documents/mimlab/mouselight/brainlit_parent/brainlit\n"
+     ]
+    }
+   ],
    "source": [
     "brainlit_path = Path(os.path.abspath(\"\"))\n",
     "brainlit_path = brainlit_path.parents[1]\n",
@@ -568,6 +576,7 @@
     "\n",
     "```\n",
     "from cloudreg.scripts.registration import get_affine_matrix\n",
+    "\n",
     "get_affine_matrix([1,1,1], [0,0,0], \"PIR\", \"RPI\", 1.15, \"precomputed://https://open-neurodata.s3.amazonaws.com/ara_2016/sagittal_10um/annotation_10um_2017\")\n",
     "```"
    ]
@@ -586,7 +595,7 @@
    "metadata": {},
    "source": [
     "```\n",
-    "python -m cloudreg.scripts.registration -input_s3_path precomputed://s3://smartspim-precomputed-volumes/2023_12_08/MS25/Ch_561 --output_s3_path precomputed://s3://smartspim-precomputed-volumes/2023_12_08/MS25/atlas_to_target --atlas_s3_path https://open-neurodata.s3.amazonaws.com/ara_2016/sagittal_50um/average_50um --parcellation_s3_path https://open-neurodata.s3.amazonaws.com/ara_2016/sagittal_10um/annotation_10um_2017 --atlas_orientation PIR -orientation RPI --rotation -5 0 0 --translation -500 -2000 0 --fixed_scale 1.05 -log_s3_path precomputed://s3://smartspim-precomputed-volumes/2023_12_08/MS25/atlas_to_target --missing_data_correction True --grid_correction False --bias_correction True --regularization 5000.0 --iterations 3000 --registration_resolution 100\n",
+    "python -m cloudreg.scripts.registration -input_s3_path precomputed://s3://smartspim-precomputed-volumes/2023_12_20/1252B/Ch_561 --output_s3_path precomputed://s3://smartspim-precomputed-volumes/2023_12_20/1252B/atlas_to_target --atlas_s3_path https://open-neurodata.s3.amazonaws.com/ara_2016/sagittal_50um/average_50um --parcellation_s3_path https://open-neurodata.s3.amazonaws.com/ara_2016/sagittal_10um/annotation_10um_2017 --atlas_orientation PIR -orientation RPI --rotation 0 -10 -5 --translation 0 0 0 --fixed_scale 1.1 -log_s3_path precomputed://s3://smartspim-precomputed-volumes/2023_12_20/1252B/atlas_to_target --missing_data_correction True --grid_correction False --bias_correction True --regularization 5000.0 --iterations 3000 --registration_resolution 100\n",
     "```"
    ]
   },