Update analyses

scripts/cooper/analysis/.gitignore

@@ -0,0 +1,8 @@
+screenshots/
+20241108_3D_Imig_DATA_2014/
+*az*/
+mrc_files/
+imig_data/
+results/
+*.xlsx
+*.tsv

scripts/cooper/analysis/export_az_to_imod.py

@@ -0,0 +1,136 @@
+import os
+import tempfile
+from glob import glob
+from subprocess import run
+from shutil import copyfile
+
+import h5py
+import pandas as pd
+
+from synaptic_reconstruction.imod.to_imod import write_segmentation_to_imod
+from scipy.ndimage import binary_dilation, binary_closing
+
+
+def check_imod(tomo_path, mod_path):
+    run(["imod", tomo_path, mod_path])
+
+
+def export_all_to_imod(check_input=True, check_export=True):
+    files = sorted(glob("./az_segmentation/**/*.h5", recursive=True))
+    mrc_root = "./mrc_files"
+    output_folder = "./az_export/initial_model"
+
+    for ff in files:
+        ds, fname = os.path.split(ff)
+        ds = os.path.basename(ds)
+        out_folder = os.path.join(output_folder, ds)
+        out_path = os.path.join(out_folder, fname.replace(".h5", ".mod"))
+        if os.path.exists(out_path):
+            continue
+
+        os.makedirs(out_folder, exist_ok=True)
+        mrc_path = os.path.join(mrc_root, ds, fname.replace(".h5", ".rec"))
+        assert os.path.exists(mrc_path), mrc_path
+
+        with h5py.File(ff, "r") as f:
+            seg = f["thin_az"][:]
+
+        seg = binary_dilation(seg, iterations=2)
+        seg = binary_closing(seg, iterations=2)
+
+        write_segmentation_to_imod(mrc_path, seg, out_path)
+
+        if check_input:
+            import napari
+            from elf.io import open_file
+            with open_file(mrc_path, "r") as f:
+                raw = f["data"][:]
+            v = napari.Viewer()
+            v.add_image(raw)
+            v.add_labels(seg)
+            napari.run()
+
+        if check_export:
+            check_imod(mrc_path, out_path)
+
+
+# https://bio3d.colorado.edu/imod/doc/man/reducecont.html
+def reduce_all_contours():
+    pass
+
+
+# https://bio3d.colorado.edu/imod/doc/man/smoothsurf.html#TOP
+def smooth_all_surfaces(check_output=True):
+    input_files = sorted(glob("./az_export/initial_model/**/*.mod", recursive=True))
+
+    mrc_root = "./mrc_files"
+    output_folder = "./az_export/smoothed_model"
+    for ff in input_files:
+        ds, fname = os.path.split(ff)
+        ds = os.path.basename(ds)
+        out_folder = os.path.join(output_folder, ds)
+        out_file = os.path.join(out_folder, fname)
+        if os.path.exists(out_file):
+            continue
+
+        os.makedirs(out_folder, exist_ok=True)
+        run(["smoothsurf", ff, out_file])
+        if check_output:
+            mrc_path = os.path.join(mrc_root, ds, fname.replace(".mod", ".rec"))
+            assert os.path.exists(mrc_path), mrc_path
+            check_imod(mrc_path, out_file)
+
+
+def measure_surfaces():
+    input_files = sorted(glob("./az_export/smoothed_model/**/*.mod", recursive=True))
+
+    result = {
+        "Dataset": [],
+        "Tomogram": [],
+        "AZ Surface": [],
+    }
+    for ff in input_files:
+        ds, fname = os.path.split(ff)
+        ds = os.path.basename(ds)
+        fname = os.path.splitext(fname)[0]
+
+        with tempfile.NamedTemporaryFile() as f_mesh, tempfile.NamedTemporaryFile() as f_mod:
+            tmp_path_mesh = f_mesh.name
+            tmp_path_mod = f_mod.name
+            copyfile(ff, tmp_path_mesh)
+            run(["imodmesh", tmp_path_mesh])
+            run(["imodinfo", "-f", tmp_path_mod, tmp_path_mesh])
+            area = None
+            with open(tmp_path_mod, "r") as f:
+                for line in f.readlines():
+                    line = line.strip()
+                    if line.startswith("Total mesh surface area"):
+                        area = float(line.split(" ")[-1])
+            assert area is not None
+            area /= 2
+
+            result["Dataset"].append(ds)
+            result["Tomogram"].append(fname)
+            result["AZ Surface"].append(area)
+
+    result = pd.DataFrame(result)
+    result.to_excel("./az_measurements_all.xlsx", index=False)
+
+
+def filter_surfaces():
+    all_results = pd.read_excel("./az_measurements_all.xlsx")
+    man_tomos = pd.read_csv("./man_tomos.tsv")
+
+    man_results = all_results.merge(man_tomos[["Dataset", "Tomogram"]], on=["Dataset", "Tomogram"], how="inner")
+    man_results.to_excel("./az_measuerements_manual.xlsx", index=False)
+
+
+def main():
+    export_all_to_imod(False, False)
+    smooth_all_surfaces(False)
+    # measure_surfaces()
+    filter_surfaces()
+
+
+if __name__ == "__main__":
+    main()

scripts/inner_ear/analysis/analyze_distances.py

@@ -7,6 +7,8 @@
 
 from common import get_all_measurements, get_measurements_with_annotation
 
+POOL_DICT = {"Docked-V": "MP-V", "MP-V": "MP-V", "RA-V": "RA-V"}
+
 
 def _plot_all(distances):
     pools = pd.unique(distances["pool"])
@@ -45,7 +47,7 @@ def _plot_selected(distances, save_path=None):
 
     def _plot(pool_name, distance_col, structure_name, ax):
 
-        this_distances = distances[distances["pool"] == pool_name][["tomogram", "approach", distance_col]]
+        this_distances = distances[distances["combined_pool"] == pool_name][["tomogram", "approach", distance_col]]
 
         ax.set_title(f"{pool_name} to {structure_name}")
         sns.histplot(
@@ -88,8 +90,8 @@ def _plot(pool_name, distance_col, structure_name, ax):
     # NOTE: we over-ride a plot here, should not do this in the actual version
     _plot("MP-V", "pd_distance [nm]", "PD", axes[0, 0])
     _plot("MP-V", "boundary_distance [nm]", "AZ Membrane", axes[0, 1])
-    _plot("Docked-V", "pd_distance [nm]", "PD", axes[1, 0])
-    _plot("Docked-V", "boundary_distance [nm]", "AZ Membrane", axes[1, 0])
+    # _plot("Docked-V", "pd_distance [nm]", "PD", axes[1, 0])
+    # _plot("Docked-V", "boundary_distance [nm]", "AZ Membrane", axes[1, 0])
     _plot("RA-V", "ribbon_distance [nm]", "Ribbon", axes[1, 1])
 
     fig.tight_layout()
@@ -103,16 +105,19 @@ def for_tomos_with_annotation(plot_all=True):
         ["tomogram", "pool", "ribbon_distance [nm]", "pd_distance [nm]", "boundary_distance [nm]"]
     ]
     manual_distances["approach"] = ["manual"] * len(manual_distances)
+    manual_distances.insert(1, "combined_pool", manual_distances["pool"].replace(POOL_DICT))
 
     semi_automatic_distances = semi_automatic_assignments[
         ["tomogram", "pool", "ribbon_distance [nm]", "pd_distance [nm]", "boundary_distance [nm]"]
     ]
     semi_automatic_distances["approach"] = ["semi_automatic"] * len(semi_automatic_distances)
+    semi_automatic_distances.insert(1, "combined_pool", semi_automatic_distances["pool"].replace(POOL_DICT))
 
     proofread_distances = proofread_assignments[
         ["tomogram", "pool", "ribbon_distance [nm]", "pd_distance [nm]", "boundary_distance [nm]"]
     ]
     proofread_distances["approach"] = ["proofread"] * len(proofread_distances)
+    proofread_distances.insert(1, "combined_pool", proofread_distances["pool"].replace(POOL_DICT))
 
     distances = pd.concat([manual_distances, semi_automatic_distances, proofread_distances])
     if plot_all:
@@ -129,11 +134,13 @@ def for_all_tomos(plot_all=True):
         ["tomogram", "pool", "ribbon_distance [nm]", "pd_distance [nm]", "boundary_distance [nm]"]
     ]
     semi_automatic_distances["approach"] = ["semi_automatic"] * len(semi_automatic_distances)
+    semi_automatic_distances.insert(1, "combined_pool", semi_automatic_distances["pool"].replace(POOL_DICT))
 
     proofread_distances = proofread_assignments[
         ["tomogram", "pool", "ribbon_distance [nm]", "pd_distance [nm]", "boundary_distance [nm]"]
     ]
     proofread_distances["approach"] = ["proofread"] * len(proofread_distances)
+    proofread_distances.insert(1, "combined_pool", proofread_distances["pool"].replace(POOL_DICT))
 
     distances = pd.concat([semi_automatic_distances, proofread_distances])
     if plot_all:

scripts/inner_ear/analysis/analyze_vesicle_diameters.py

@@ -12,6 +12,8 @@
 
 from common import get_finished_tomos
 
+POOL_DICT = {"Docked-V": "MP-V", "MP-V": "MP-V", "RA-V": "RA-V"}
+
 sys.path.append("../processing")
 
 
@@ -42,6 +44,7 @@ def aggregate_diameters(data_root, table, save_path, get_tab, include_names, she
         radius_table.append(this_tab)
 
     radius_table = pd.concat(radius_table)
+    radius_table.insert(1, "combined_pool", radius_table["pool"].replace(POOL_DICT))
 
     print("Saving table for", len(radius_table), "vesicles to", save_path, sheet_name)
     if os.path.exists(save_path):
@@ -91,11 +94,13 @@ def aggregate_diameters_imod(data_root, table, save_path, include_names, sheet_n
         except AssertionError:
             continue
 
+        # Determined from matching the size of vesicles in IMOD.
+        radius_factor = 0.85
         this_tab = pd.DataFrame({
             "tomogram": [tomo_name] * len(radii),
             "pool": [label_names[label_id] for label_id in labels],
-            "radius [nm]": radii,
-            "diameter [nm]": 2 * radii,
+            "radius [nm]": radii * radius_factor,
+            "diameter [nm]": 2 * radii * radius_factor,
         })
         radius_table.append(this_tab)
 

synaptic_reconstruction/imod/to_imod.py

@@ -182,7 +182,7 @@ def _pad(inp, n=3):
 
 def write_segmentation_to_imod_as_points(
     mrc_path: str,
-    segmentation_path: str,
+    segmentation: Union[str, np.ndarray],
     output_path: str,
     min_radius: Union[int, float],
     radius_factor: float = 1.0,
@@ -194,7 +194,7 @@ def write_segmentation_to_imod_as_points(
 
     Args:
         mrc_path: Filepath to the mrc volume that was segmented.
-        segmentation_path: Filepath to the segmentation stored as .tif.
+        segmentation: The segmentation (either as numpy array or filepath to a .tif file).
         output_path: Where to save the .mod file.
         min_radius: Minimum radius for export.
         radius_factor: Factor for increasing the radius to account for too small exported spheres.
@@ -211,7 +211,8 @@ def write_segmentation_to_imod_as_points(
     resolution = [res / 10 for res in resolution]
 
     # Extract the center coordinates and radii from the segmentation.
-    segmentation = imageio.imread(segmentation_path)
+    if isinstance(segmentation, str):
+        segmentation = imageio.imread(segmentation)
     coordinates, radii = convert_segmentation_to_spheres(
         segmentation, resolution=resolution, radius_factor=radius_factor, estimate_radius_2d=estimate_radius_2d
     )