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reproduction_effort/functions/get_experiments.py

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+import torch
+import os
+import glob
+
+
+@torch.no_grad()
+def get_experiments(path: str) -> torch.Tensor:
+    filename_np: str = os.path.join(
+        path,
+        "Exp*_Part001.npy",
+    )
+
+    list_str = glob.glob(filename_np)
+    list_int: list[int] = []
+    for i in range(0, len(list_str)):
+        list_int.append(int(list_str[i].split("Exp")[-1].split("_Trial")[0]))
+    list_int = sorted(list_int)
+
+    return torch.tensor(list_int).unique()

reproduction_effort/functions/get_parts.py

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+import torch
+import os
+import glob
+
+
+@torch.no_grad()
+def get_parts(path: str, experiment_id: int, trial_id: int) -> torch.Tensor:
+    filename_np: str = os.path.join(
+        path,
+        f"Exp{experiment_id:03d}_Trial{trial_id:03d}_Part*.npy",
+    )
+
+    list_str = glob.glob(filename_np)
+    list_int: list[int] = []
+    for i in range(0, len(list_str)):
+        list_int.append(int(list_str[i].split("_Part")[-1].split(".npy")[0]))
+    list_int = sorted(list_int)
+    return torch.tensor(list_int).unique()

reproduction_effort/functions/get_trials.py

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+import torch
+import os
+import glob
+
+
+@torch.no_grad()
+def get_trials(path: str, experiment_id: int) -> torch.Tensor:
+    filename_np: str = os.path.join(
+        path,
+        f"Exp{experiment_id:03d}_Trial*_Part001.npy",
+    )
+
+    list_str = glob.glob(filename_np)
+    list_int: list[int] = []
+    for i in range(0, len(list_str)):
+        list_int.append(int(list_str[i].split("_Trial")[-1].split("_Part")[0]))
+    list_int = sorted(list_int)
+    return torch.tensor(list_int).unique()