Add code for synapse model export and for initial synapse analysis

Author: constantinpape

scripts/measurements/measure_synapses.py

@@ -0,0 +1,52 @@
+import os
+import json
+
+import numpy as np
+import pandas as pd
+
+from flamingo_tools.s3_utils import BUCKET_NAME, create_s3_target
+
+
+def check_project():
+    s3 = create_s3_target()
+    cochleae = ['M_LR_000226_L', 'M_LR_000226_R', 'M_LR_000227_L', 'M_LR_000227_R']
+
+    for cochlea in cochleae:
+        content = s3.open(f"{BUCKET_NAME}/{cochlea}/dataset.json", mode="r", encoding="utf-8")
+        info = json.loads(content.read())
+        sources = info["sources"]
+
+        # Load the synapse table.
+        syn = sources["synapse_v3"]["spots"]
+        rel_path = syn["tableData"]["tsv"]["relativePath"]
+        table_content = s3.open(os.path.join(BUCKET_NAME, cochlea, rel_path, "default.tsv"), mode="rb")
+        syn_table = pd.read_csv(table_content, sep="\t")
+
+        # Load the corresponding ihc table.
+        ihc = sources["IHC_v2"]["segmentation"]
+        rel_path = ihc["tableData"]["tsv"]["relativePath"]
+        table_content = s3.open(os.path.join(BUCKET_NAME, cochlea, rel_path, "default.tsv"), mode="rb")
+        ihc_table = pd.read_csv(table_content, sep="\t")
+
+        # Keep only the synapses that were matched to a valid IHC.
+        component_id = 2 if cochlea == "M_LR_000226_R" else 1
+        valid_ihcs = ihc_table.label_id[ihc_table.component_labels == component_id].values
+
+        valid_syn_table = syn_table[syn_table.matched_ihc.isin(valid_ihcs)]
+        n_synapses = len(valid_syn_table)
+
+        _, syn_per_ihc = np.unique(valid_syn_table.matched_ihc.values, return_counts=True)
+
+        print("Cochlea:", cochlea)
+        print("N-Synapses:", n_synapses)
+        print("Average Syn per IHC:", np.mean(syn_per_ihc))
+        print("STDEV Syn per IHC:", np.std(syn_per_ihc))
+        print()
+
+
+def main():
+    check_project()
+
+
+if __name__ == "__main__":
+    main()

scripts/synapse_marker_detection/export_model.py

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+import argparse
+import sys
+
+import torch
+from torch_em.util import load_model
+
+sys.path.append("/home/pape/Work/my_projects/czii-protein-challenge")
+sys.path.append("/user/pape41/u12086/Work/my_projects/czii-protein-challenge")
+
+
+def export_model(input_, output):
+    model = load_model(input_, device="cpu")
+    torch.save(model, output)
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-i", "--input", required=True)
+    parser.add_argument("-o", "--output", required=True)
+    args = parser.parse_args()
+    export_model(args.input, args.output)
+
+
+if __name__ == "__main__":
+    main()