additional replacements: str ->Path

deruyter92 · Jul 15, 2026 · May 29, 2026 · May 29, 2026 · May 29, 2026 · May 29, 2026
commit f2bb35d3d9ba32a0405bc7438243023b63cd27c0
diff --git a/deeplabcut/benchmark/__init__.py b/deeplabcut/benchmark/__init__.py
@@ -13,12 +13,13 @@
 import json
 import os
 from collections.abc import Container
+from pathlib import Path
 from typing import Literal
 
 from deeplabcut.benchmark.base import Benchmark, Result, ResultCollection
 
-DATA_ROOT = os.path.join(os.getcwd(), "data")
-CACHE = os.path.join(os.getcwd(), ".results")
+DATA_ROOT = Path.cwd() / "data"
+CACHE = Path.cwd() / ".results"
 
 __registry = []
 
@@ -99,20 +100,20 @@ def evaluate(
 
 
 def get_filepath(basename: str):
-    return os.path.join(DATA_ROOT, basename)
+    return DATA_ROOT / basename
 
 
 def savecache(results: ResultCollection):
-    with open(CACHE, "w") as fh:
+    with Path(CACHE).open("w") as fh:
         json.dump(results.todicts(), fh, indent=2)
 
 
 def loadcache(cache=CACHE, on_missing: Literal["raise", "ignore"] = "ignore") -> ResultCollection:
-    if not os.path.exists(cache):
+    if not Path(cache).exists():
         if on_missing == "raise":
             raise FileNotFoundError(cache)
         return ResultCollection()
-    with open(cache) as fh:
+    with Path(cache).open() as fh:
         try:
             data = json.load(fh)
         except json.decoder.JSONDecodeError as e:

diff --git a/deeplabcut/benchmark/metrics.py b/deeplabcut/benchmark/metrics.py
@@ -11,9 +11,9 @@
 
 """Evaluation metrics for the DeepLabCut benchmark."""
 
-import os
 import pickle
 from collections import defaultdict
+from pathlib import Path
 
 import numpy as np
 import pandas as pd
@@ -33,7 +33,7 @@ def _format_gt_data(h5file: str, test_indices: list[int] | None = None):
     except KeyError:
         n_unique = 0
     guarantee_multiindex_rows(df)
-    file_paths = [os.path.join(*row) for row in df.index.to_list()]
+    file_paths = [Path(*row) for row in df.index.to_list()]
     temp = (
         df.stack("individuals", dropna=False)
         .reindex(animals, level="individuals")
@@ -248,13 +248,13 @@ def load_test_images(h5file: str, metadata: str) -> list[str]:
     test_images = []
     for img_path in df_test.index:
         if not isinstance(img_path, str):
-            img_path = os.path.join(*img_path)
+            img_path = str(Path(*img_path))
         test_images.append(img_path)
     return test_images
 
 
 def _load_test_indices(shuffle_metadata_path: str) -> list[int]:
     """Returns the indices of test images in the training dataset dataframe."""
-    with open(shuffle_metadata_path, "rb") as f:
+    with Path(shuffle_metadata_path).open("rb") as f:
         test_indices = set([int(i) for i in pickle.load(f)[2]])
     return list(sorted(test_indices))
diff --git a/deeplabcut/benchmark/utils.py b/deeplabcut/benchmark/utils.py
@@ -18,6 +18,7 @@
 import os
 import pkgutil
 import sys
+from pathlib import Path
 
 
 class RedirectStdStreams:
@@ -46,7 +47,7 @@ def __exit__(self, exc_type, exc_value, traceback):
 
 class DisableOutput(RedirectStdStreams):
     def __init__(self):
-        devnull = open(os.devnull, "w")
+        devnull = Path(os.devnull).open("w")
         super().__init__(stdout=devnull, stderr=devnull)
 
 

diff --git a/deeplabcut/compat.py b/deeplabcut/compat.py
@@ -2000,7 +2000,7 @@ def _load_config(config: str) -> dict:
     if not config_path.exists():
         raise FileNotFoundError(f"Config {config} is not found. Please make sure that the file exists.")
 
-    with open(config) as f:
+    with config_path.open() as f:
         project_config = YAML(typ="safe", pure=True).load(f)
 
     return project_config
diff --git a/deeplabcut/core/config.py b/deeplabcut/core/config.py
@@ -26,7 +26,7 @@ def read_config_as_dict(config_path: str | Path) -> dict:
     Returns:
         The configuration file with pure Python classes
     """
-    with open(config_path) as f:
+    with Path(config_path).open() as f:
         cfg = YAML(typ="safe", pure=True).load(f)
 
     return cfg
@@ -46,7 +46,7 @@ def write_config(config_path: str | Path, config: dict, overwrite: bool = True)
     if not overwrite and Path(config_path).exists():
         raise FileExistsError(f"Cannot write to {config_path} - set overwrite=True to force")
 
-    with open(config_path, "w") as file:
+    with Path(config_path).open("w") as file:
         YAML().dump(config, file)
 
 

diff --git a/deeplabcut/core/crossvalutils.py b/deeplabcut/core/crossvalutils.py
@@ -10,11 +10,11 @@
 #
 
 
-import os
 import pickle
 import shutil
 from collections import defaultdict
 from copy import deepcopy
+from pathlib import Path
 
 import networkx as nx
 import numpy as np
@@ -44,9 +44,10 @@ def _set_up_evaluation(data):
 
 
 def _form_original_path(path):
-    root, filename = os.path.split(path)
-    base, ext = os.path.splitext(filename)
-    return os.path.join(root, filename.split("c")[0] + ext)
+    p = Path(path)
+    ext = p.suffix
+    filename = p.name
+    return str(p.parent / (filename.split("c")[0] + ext))
 
 
 def _unsorted_unique(array):
@@ -395,9 +396,9 @@ def cross_validate_paf_graphs(
     inf_cfg_temp = inf_cfg.copy()
     inf_cfg_temp["pcutoff"] = pcutoff
 
-    with open(full_data_file, "rb") as file:
+    with Path(full_data_file).open("rb") as file:
         data = pickle.load(file)
-    with open(metadata_file, "rb") as file:
+    with Path(metadata_file).open("rb") as file:
         metadata = pickle.load(file)
 
     params = _set_up_evaluation(data)
@@ -415,10 +416,8 @@ def cross_validate_paf_graphs(
 
     if calibrate:
         trainingsetfolder = auxiliaryfunctions.get_training_set_folder(cfg)
-        calibration_file = os.path.join(
-            cfg["project_path"],
-            str(trainingsetfolder),
-            "CollectedData_" + cfg["scorer"] + ".h5",
+        calibration_file = str(
+            Path(cfg["project_path"]) / str(trainingsetfolder) / ("CollectedData_" + cfg["scorer"] + ".h5")
         )
     else:
         calibration_file = ""
@@ -448,7 +447,7 @@ def cross_validate_paf_graphs(
     inds = list(paf_inds[size_opt])
     auxiliaryfunctions.edit_config(pose_config, {"paf_best": [int(ind) for ind in inds]})
     if output_name:
-        with open(output_name, "wb") as file:
+        with Path(output_name).open("wb") as file:
             pickle.dump([results], file)
     return results[:3], paf_scores, results[3][size_opt]
 

diff --git a/deeplabcut/core/inferenceutils.py b/deeplabcut/core/inferenceutils.py
@@ -20,6 +20,7 @@
 from collections.abc import Iterable
 from dataclasses import dataclass
 from math import erf, sqrt
+from pathlib import Path
 from typing import Any
 
 import networkx as nx
@@ -828,7 +829,7 @@ def wrapped(i):
                         pbar.update()
 
     def from_pickle(self, pickle_path):
-        with open(pickle_path, "rb") as file:
+        with Path(pickle_path).open("rb") as file:
             data = pickle.load(file)
         self.unique = data.pop("single", {})
         self.assemblies = data
@@ -876,7 +877,7 @@ def to_pickle(self, output_name):
             data[ind] = [ass.data for ass in assemblies]
         if self.unique:
             data["single"] = self.unique
-        with open(output_name, "wb") as file:
+        with Path(output_name).open("wb") as file:
             pickle.dump(data, file, pickle.HIGHEST_PROTOCOL)
 
 

diff --git a/deeplabcut/create_project/add.py b/deeplabcut/create_project/add.py
@@ -95,7 +95,7 @@ def add_new_videos(config, videos, copy_videos=False, coords=None, extract_frame
             try:
                 src = str(src)
                 dst = str(dst)
-                os.symlink(src, dst)
+                Path(dst).symlink_to(src)
                 print(f"Created the symlink of {src} to {dst}")
             except OSError:
                 try:
@@ -117,7 +117,7 @@ def add_new_videos(config, videos, copy_videos=False, coords=None, extract_frame
             video_path = str(Path.resolve(Path(video)))
         #           video_path = os.path.realpath(video)
         except Exception:
-            video_path = os.readlink(video)
+            video_path = str(Path(video).readlink())
 
         vid = VideoReader(video_path)
         if coords is not None:

diff --git a/deeplabcut/create_project/demo_data.py b/deeplabcut/create_project/demo_data.py
@@ -9,7 +9,6 @@
 # Licensed under GNU Lesser General Public License v3.0
 #
 
-import os
 from pathlib import Path
 
 import deeplabcut
@@ -61,9 +60,9 @@ def transform_data(config):
 
     cfg["project_path"] = project_path
     if "Reaching" in project_path:
-        video_file = os.path.join(project_path, "videos", "reachingvideo1.avi")
+        video_file = Path(project_path) / "videos" / "reachingvideo1.avi"
     elif "openfield" in project_path:
-        video_file = os.path.join(project_path, "videos", "m4s1.mp4")
+        video_file = Path(project_path) / "videos" / "m4s1.mp4"
     else:
         print("This is not an official demo dataset.")
 

diff --git a/deeplabcut/create_project/modelzoo.py b/deeplabcut/create_project/modelzoo.py
@@ -46,7 +46,7 @@
 
 
 def MakeTrain_pose_yaml(itemstochange, saveasconfigfile, defaultconfigfile):
-    raw = open(defaultconfigfile).read()
+    raw = Path(defaultconfigfile).open().read()
     docs = []
     for raw_doc in raw.split("\n---"):
         try:
@@ -57,7 +57,7 @@ def MakeTrain_pose_yaml(itemstochange, saveasconfigfile, defaultconfigfile):
     for key in itemstochange.keys():
         docs[0][key] = itemstochange[key]
     docs[0]["max_input_size"] = 1500
-    with open(saveasconfigfile, "w") as f:
+    with Path(saveasconfigfile).open("w") as f:
         yaml.dump(docs[0], f)
     return docs[0]
 
@@ -561,7 +561,7 @@ def create_pretrained_project_tensorflow(
         model = "full_human"
 
     if model in MODELOPTIONS:
-        cwd = os.getcwd()
+        cwd = Path.cwd()
 
         cfg = deeplabcut.create_new_project(
             project, experimenter, videos, working_directory, copy_videos, video_extensions=video_extensions
@@ -610,31 +610,27 @@ def create_pretrained_project_tensorflow(
         auxiliaryfunctions.write_config(cfg, config)
         config = auxiliaryfunctions.read_config(cfg)
 
-        train_dir = Path(
-            os.path.join(
-                config["project_path"],
-                str(
-                    auxiliaryfunctions.get_model_folder(
-                        trainFraction=config["TrainingFraction"][0],
-                        shuffle=1,
-                        cfg=config,
-                    )
-                ),
-                "train",
+        train_dir = (
+            Path(config["project_path"])
+            / str(
+                auxiliaryfunctions.get_model_folder(
+                    trainFraction=config["TrainingFraction"][0],
+                    shuffle=1,
+                    cfg=config,
+                )
             )
+            / "train"
         )
-        test_dir = Path(
-            os.path.join(
-                config["project_path"],
-                str(
-                    auxiliaryfunctions.get_model_folder(
-                        trainFraction=config["TrainingFraction"][0],
-                        shuffle=1,
-                        cfg=config,
-                    )
-                ),
-                "test",
+        test_dir = (
+            Path(config["project_path"])
+            / str(
+                auxiliaryfunctions.get_model_folder(
+                    trainFraction=config["TrainingFraction"][0],
+                    shuffle=1,
+                    cfg=config,
+                )
             )
+            / "test"
         )
 
         # Create the model directory
@@ -644,8 +640,8 @@ def create_pretrained_project_tensorflow(
         modelfoldername = auxiliaryfunctions.get_model_folder(
             trainFraction=config["TrainingFraction"][0], shuffle=1, cfg=config
         )
-        path_train_config = str(os.path.join(config["project_path"], Path(modelfoldername), "train", "pose_cfg.yaml"))
-        path_test_config = str(os.path.join(config["project_path"], Path(modelfoldername), "test", "pose_cfg.yaml"))
+        path_train_config = str(Path(config["project_path"]) / Path(modelfoldername) / "train" / "pose_cfg.yaml")
+        path_test_config = str(Path(config["project_path"]) / Path(modelfoldername) / "test" / "pose_cfg.yaml")
 
         # Download the weights and put then in appropriate directory
         print("Downloading weights...")
@@ -668,9 +664,9 @@ def create_pretrained_project_tensorflow(
         # model_path = auxfun_models.check_for_weights(pose_cfg['net_type'], parent_path)
 
         # Updating training and test pose_cfg:
-        snapshotname = [fn for fn in os.listdir(train_dir) if ".meta" in fn][0].split(".meta")[0]
+        snapshotname = [p.name for p in Path(train_dir).iterdir() if ".meta" in p.name][0].split(".meta")[0]
         dict2change = {
-            "init_weights": str(os.path.join(train_dir, snapshotname)),
+            "init_weights": str(Path(train_dir) / snapshotname),
             "project_path": str(config["project_path"]),
         }
 

diff --git a/deeplabcut/create_project/new.py b/deeplabcut/create_project/new.py
@@ -198,7 +198,7 @@ def create_new_project(
             try:
                 src = str(src)
                 dst = str(dst)
-                os.symlink(src, dst)
+                Path(dst).symlink_to(src)
                 print(f"Created the symlink of {src} to {dst}")
             except OSError:
                 try:
@@ -223,14 +223,14 @@ def create_new_project(
             # video. [old: rel_video_path = os.path.realpath(video)]
             rel_video_path = str(Path.resolve(Path(video)))
         except Exception:
-            rel_video_path = os.readlink(str(video))
+            rel_video_path = str(Path(str(video)).readlink())
 
         try:
             vid = VideoReader(rel_video_path)
             video_sets[rel_video_path] = {"crop": ", ".join(map(str, vid.get_bbox()))}
         except OSError:
             warnings.warn("Cannot open the video file! Skipping to the next one...", stacklevel=2)
-            os.remove(video)  # Removing the video or link from the project
+            Path(video).unlink()  # Removing the video or link from the project
 
     if not len(video_sets):
         # Silently sweep the files that were already written.