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brainglobe_atlasapi/atlas_generation/atlas_scripts/kim_devccf_mouse.py

@@ -1,12 +1,10 @@
 import json
-import multiprocessing as mp
 import time
 from pathlib import Path
-from os import listdir, path
-import pooch
 
 import numpy as np
 import pandas as pd
+import pooch
 from brainglobe_utils.IO.image import load_nii
 from rich.progress import track
 
@@ -16,7 +14,6 @@
     create_region_mesh,
 )
 from brainglobe_atlasapi.atlas_generation.wrapup import wrapup_atlas_from_data
-
 from brainglobe_atlasapi.config import DEFAULT_WORKDIR
 from brainglobe_atlasapi.structure_tree_util import get_structures_tree
 
@@ -31,84 +28,93 @@
 PACKAGER = "Carlo Castoldi <castoldi[at]ipmc.cnrs.fr>"
 ATLAS_FILE_URL = "https://doi.org/10.6084/m9.figshare.26377171.v1"
 
-TIMEPOINTS = (
-    "E11.5",
-    "E13.5",
-    "E15.5",
-    "E18.5",
-    "P04",
-    "P14",
-    "P56"
-)
+TIMEPOINTS = ("E11.5", "E13.5", "E15.5", "E18.5", "P04", "P14", "P56")
 RESOLUTIONS = (20, 50)
 MODALITIES = (
-    "LSFM",     # Light Sheet Fluorescence Microscopy
+    "LSFM",  # Light Sheet Fluorescence Microscopy
     "MRI-adc",  # MRI Apparent Diffusion Coefficient
     "MRI-dwi",  # MRI Difusion Weighted Imaging
-    "MRI-fa",   # MRI Fractional Anisotropy
+    "MRI-fa",  # MRI Fractional Anisotropy
     "MRI-MTR",  # MRI Magnetization Transfer Ratio
-    "MRI-T2"    # MRI T2-weighted
+    "MRI-T2",  # MRI T2-weighted
 )
 
+
 def pooch_init(download_dir_path: Path):
     utils.check_internet_connection()
 
     hash = None
-    registry = {a+".zip": hash for a in TIMEPOINTS}
+    registry = {a + ".zip": hash for a in TIMEPOINTS}
     registry["DevCCFv1_OntologyStructure.xlsx"] = hash
     return pooch.create(
-        path=download_dir_path, #/ATLAS_NAME,
+        path=download_dir_path,  # /ATLAS_NAME,
         base_url="doi:10.6084/m9.figshare.26377171.v1/",
-        registry=registry
+        registry=registry,
     )
 
+
 def fetch_animal(pooch_: pooch.Pooch, age: str):
     assert age in TIMEPOINTS, f"Unknown age timepoint: '{age}'"
-    archive = age+".zip"
+    archive = age + ".zip"
     members = [
         f"{age}/{age.replace('.','-')}_DevCCF_Annotations_20um.nii.gz",
-        f"{age}/{age.replace('.','-')}_LSFM_20um.nii.gz"
+        f"{age}/{age.replace('.','-')}_LSFM_20um.nii.gz",
     ]
-    annotations_path, reference_path = pooch_.fetch(archive,
-                        progressbar=True,
-                        processor=pooch.Unzip(extract_dir=".", members=members)
-                        )
+    annotations_path, reference_path = pooch_.fetch(
+        archive,
+        progressbar=True,
+        processor=pooch.Unzip(extract_dir=".", members=members),
+    )
     # archive_path: Path = (pooch_.path/archive)
     # archive_path.unlink()
     annotations = load_nii(annotations_path, as_array=True)
     reference = load_nii(reference_path, as_array=True)
     return annotations, reference
 
+
 def fetch_ontology(pooch_: pooch.Pooch):
-    devccfv1_path = pooch_.fetch("DevCCFv1_OntologyStructure.xlsx", progressbar=True)
+    devccfv1_path = pooch_.fetch(
+        "DevCCFv1_OntologyStructure.xlsx", progressbar=True
+    )
     xl = pd.ExcelFile(devccfv1_path)
     # xl.sheet_names # it has two excel sheets
-                     # 'DevCCFv1_Ontology', 'README'
+    # 'DevCCFv1_Ontology', 'README'
     df = xl.parse("DevCCFv1_Ontology", header=1)
     df = df[["Acronym", "ID16", "Name", "Structure ID Path16", "R", "G", "B"]]
-    df.rename(columns={
-        "Acronym": "acronym",
-        "ID16": "id",
-        "Name": "name",
-        "Structure ID Path16": "structure_id_path",
-        "R": "r",
-        "G": "g",
-        "B": "b"
-    }, inplace=True)
+    df.rename(
+        columns={
+            "Acronym": "acronym",
+            "ID16": "id",
+            "Name": "name",
+            "Structure ID Path16": "structure_id_path",
+            "R": "r",
+            "G": "g",
+            "B": "b",
+        },
+        inplace=True,
+    )
     structures = list(df.to_dict(orient="index").values())
     for structure in structures:
         if structure["acronym"] == "mouse":
             structure["acronym"] = "root"
         structure_path = structure["structure_id_path"]
-        structure["structure_id_path"] = [int(id) for id in structure_path.strip("/").split("/")]
-        structure["rgb_triplet"] = [structure["r"], structure["g"], structure["b"]]
+        structure["structure_id_path"] = [
+            int(id) for id in structure_path.strip("/").split("/")
+        ]
+        structure["rgb_triplet"] = [
+            structure["r"],
+            structure["g"],
+            structure["b"],
+        ]
         del structure["r"]
         del structure["g"]
         del structure["b"]
     return structures
 
-def create_meshes(output_path: str|Path,
-                  structures, annotation_volume, root_id):
+
+def create_meshes(
+    output_path: str | Path, structures, annotation_volume, root_id
+):
     if not isinstance(output_path, Path):
         output_path = Path(output_path)
     output_path.mkdir(exist_ok=True)
@@ -126,7 +132,7 @@ def create_meshes(output_path: str|Path,
 
     # Mesh creation
     closing_n_iters = 2
-    decimate_fraction = 0.2 # 0.04
+    decimate_fraction = 0.2  # 0.04
     # What fraction of the original number of vertices is to be kept.
     smooth = False  # smooth meshes after creation
     start = time.time()
@@ -160,6 +166,7 @@ def create_meshes(output_path: str|Path,
     )
     return output_path
 
+
 def create_mesh_dict(structures, meshes_dir_path):
     meshes_dict = dict()
     structures_with_mesh = []
@@ -182,30 +189,26 @@ def create_mesh_dict(structures, meshes_dir_path):
     )
     return meshes_dict, structures_with_mesh
 
+
 if __name__ == "__main__":
-    bg_root_dir = DEFAULT_WORKDIR/ATLAS_NAME
-    download_dir_path = bg_root_dir/"downloads"
+    bg_root_dir = DEFAULT_WORKDIR / ATLAS_NAME
+    download_dir_path = bg_root_dir / "downloads"
     download_dir_path.mkdir(exist_ok=True, parents=True)
     pooch_ = pooch_init(download_dir_path)
     structures = fetch_ontology(pooch_)
     # save regions list json:
-    with open(bg_root_dir/"structures.json", "w") as f:
+    with open(bg_root_dir / "structures.json", "w") as f:
         json.dump(structures, f)
 
     for age in TIMEPOINTS:
         atlas_name = f"{ATLAS_NAME}_{age.replace('.', '-')}"
         annotation_volume, reference_volume = fetch_animal(pooch_, age)
-        atlas_dir = bg_root_dir/atlas_name
+        atlas_dir = bg_root_dir / atlas_name
         atlas_dir.mkdir(exist_ok=True)
         print(f"Saving atlas data at {atlas_dir}")
         # Create meshes:
-        meshes_dir_path = atlas_dir/"meshes"
-        create_meshes(
-            meshes_dir_path,
-            structures,
-            annotation_volume,
-            ROOT_ID
-        )
+        meshes_dir_path = atlas_dir / "meshes"
+        create_meshes(meshes_dir_path, structures, annotation_volume, ROOT_ID)
         meshes_dict, structures_with_mesh = create_mesh_dict(
             structures, meshes_dir_path
         )
@@ -217,7 +220,7 @@ def create_mesh_dict(structures, meshes_dir_path):
             citation=CITATION,
             atlas_link=ATLAS_LINK,
             species=SPECIES,
-            resolution=(RESOLUTION_UM,)*3,
+            resolution=(RESOLUTION_UM,) * 3,
             orientation=ORIENTATION,
             root_id=ROOT_ID,
             reference_stack=reference_volume,
@@ -226,10 +229,10 @@ def create_mesh_dict(structures, meshes_dir_path):
             meshes_dict=meshes_dict,
             working_dir=atlas_dir,
             atlas_packager=PACKAGER,
-            hemispheres_stack=None, # it is symmetric
+            hemispheres_stack=None,  # it is symmetric
             cleanup_files=False,
             compress=True,
             scale_meshes=True,
             # resolution_mapping=[2, 1, 0],
         )
-        print("Done. Atlas generated at: ", output_filename)
+        print("Done. Atlas generated at: ", output_filename)