Add more inner ear analysis code

Author: constantinpape

scripts/inner_ear/analysis/analyze_vesicle_diameters.py

@@ -0,0 +1,132 @@
+import os
+import sys
+
+from glob import glob
+
+import mrcfile
+import pandas as pd
+from tqdm import tqdm
+
+from synaptic_reconstruction.imod.export import load_points_from_imodinfo
+from synaptic_reconstruction.file_utils import get_data_path
+
+sys.path.append("../processing")
+
+
+def aggregate_radii(data_root, table, save_path, get_tab):
+    if os.path.exists(save_path):
+        return
+
+    radius_table = []
+    for _, row in tqdm(table.iterrows(), total=len(table), desc="Collect tomo information"):
+        folder = row["Local Path"]
+        if folder == "":
+            continue
+
+        tomo_name = os.path.relpath(folder, os.path.join(data_root, "Electron-Microscopy-Susi/Analyse"))
+        tab_path = get_tab(folder)
+        if tab_path is None:
+            continue
+
+        tab = pd.read_excel(tab_path)
+        this_tab = tab[["pool", "radius [nm]"]]
+        this_tab.insert(0, "tomogram", [tomo_name] * len(this_tab))
+        radius_table.append(this_tab)
+
+    radius_table = pd.concat(radius_table)
+    print("Saving table for", len(radius_table), "vesicles to", save_path)
+    radius_table.to_excel(save_path, index=False)
+
+
+def aggregate_radii_imod(data_root, table, save_path):
+    if os.path.exists(save_path):
+        return
+
+    radius_table = []
+    for _, row in tqdm(table.iterrows(), total=len(table), desc="Collect tomo information"):
+        folder = row["Local Path"]
+        if folder == "":
+            continue
+
+        tomo_name = os.path.relpath(folder, os.path.join(data_root, "Electron-Microscopy-Susi/Analyse"))
+        annotation_folder = os.path.join(folder, "manuell")
+        if not os.path.exists(annotation_folder):
+            annotation_folder = os.path.join(folder, "Manuell")
+        if not os.path.exists(annotation_folder):
+            continue
+
+        annotations = glob(os.path.join(annotation_folder, "*.mod"))
+        annotation_file = [ann for ann in annotations if ("vesikel" in ann.lower()) or ("vesicle" in ann.lower())]
+        if len(annotation_file) != 1:
+            continue
+        annotation_file = annotation_file[0]
+
+        tomo_file = get_data_path(folder)
+        with mrcfile.open(tomo_file) as f:
+            shape = f.data.shape
+            resolution = list(f.voxel_size.item())
+            resolution = [res / 10 for res in resolution][0]
+
+        try:
+            _, radii, labels, label_names = load_points_from_imodinfo(annotation_file, shape, resolution=resolution)
+        except AssertionError:
+            continue
+
+        this_tab = pd.DataFrame({
+            "tomogram": [tomo_name] * len(radii),
+            "pool": [label_names[label_id] for label_id in labels],
+            "radius [nm]": radii,
+        })
+        radius_table.append(this_tab)
+
+    radius_table = pd.concat(radius_table)
+    print("Saving table for", len(radius_table), "vesicles to", save_path)
+    radius_table.to_excel(save_path, index=False)
+
+
+def get_tab_automatic(folder):
+    tab_name = "measurements_uncorrected_assignments.xlsx"
+    res_path = os.path.join(folder, "korrektur", tab_name)
+    if not os.path.exists(res_path):
+        res_path = os.path.join(folder, "Korrektur", tab_name)
+    if not os.path.exists(res_path):
+        res_path = None
+    return res_path
+
+
+def get_tab_semi_automatic(folder):
+    tab_name = "measurements.xlsx"
+    res_path = os.path.join(folder, "korrektur", tab_name)
+    if not os.path.exists(res_path):
+        res_path = os.path.join(folder, "Korrektur", tab_name)
+    if not os.path.exists(res_path):
+        res_path = None
+    return res_path
+
+
+def get_tab_manual(folder):
+    tab_name = "measurements.xlsx"
+    res_path = os.path.join(folder, "manuell", tab_name)
+    if not os.path.exists(res_path):
+        res_path = os.path.join(folder, "Manuell", tab_name)
+    if not os.path.exists(res_path):
+        res_path = None
+    return res_path
+
+
+def main():
+    from parse_table import parse_table, get_data_root
+
+    data_root = get_data_root()
+    table_path = os.path.join(data_root, "Electron-Microscopy-Susi", "Übersicht.xlsx")
+    table = parse_table(table_path, data_root)
+
+    # TODO get the radii from imod
+    aggregate_radii(data_root, table, save_path="./results/vesicle_radii_automatic.xlsx", get_tab=get_tab_automatic)
+    aggregate_radii(data_root, table, save_path="./results/vesicle_radii_semi_automatic.xlsx", get_tab=get_tab_semi_automatic)  # noqa
+    aggregate_radii(data_root, table, save_path="./results/vesicle_radii_manual.xlsx", get_tab=get_tab_manual)
+    aggregate_radii_imod(data_root, table, save_path="./results/vesicle_radii_imod.xlsx")
+
+
+if __name__ == "__main__":
+    main()

scripts/inner_ear/analysis/extract_ribbon_stats.py

@@ -0,0 +1,36 @@
+import numpy as np
+import pandas as pd
+
+
+def main():
+    man_path = "../results/20240917_1/fully_manual_analysis_results.xlsx"
+    auto_path = "../results/20240917_1/automatic_analysis_results.xlsx"
+
+    man_measurements = pd.read_excel(man_path, sheet_name="morphology")
+    man_measurements = man_measurements[man_measurements.structure == "ribbon"][
+        ["tomogram", "surface [nm^2]", "volume [nm^3]"]
+    ]
+
+    auto_measurements = pd.read_excel(auto_path, sheet_name="morphology")
+    auto_measurements = auto_measurements[auto_measurements.structure == "ribbon"][
+        ["tomogram", "surface [nm^2]", "volume [nm^3]"]
+    ]
+
+    # save all the automatic measurements
+    auto_measurements.to_excel("./results/ribbon_morphology_auto.xlsx", index=False)
+
+    man_tomograms = pd.unique(man_measurements["tomogram"])
+    auto_tomograms = pd.unique(auto_measurements["tomogram"])
+    tomos = np.intersect1d(man_tomograms, auto_tomograms)
+
+    man_measurements = man_measurements[man_measurements.tomogram.isin(tomos)]
+    auto_measurements = auto_measurements[auto_measurements.tomogram.isin(tomos)]
+
+    save_path = "./results/ribbon_morphology_man-v-auto.xlsx"
+    man_measurements.to_excel(save_path, sheet_name="manual", index=False)
+    with pd.ExcelWriter(save_path, engine="openpyxl", mode="a") as writer:
+        auto_measurements.to_excel(writer, sheet_name="auto", index=False)
+
+
+if __name__ == "__main__":
+    main()